define hypothesis of etiology and intervention hypothesis

Mental Health and Social Work pp 3–21 Cite as

Theories on Mental Health, Illness and Intervention

• Rosaleen Ow 4 &
• Abner Weng Cheong Poon 5  
• Reference work entry
• First Online: 25 June 2020

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Part of the book series: Social Work ((SOWO))

The chapter attempts to provide an overview of thoughts and theories that influenced the understanding of etiology, manifestations, and management of mental health and mental illnesses from multiple perspectives. The perspectives of professional service providers and academia representing the social sciences, medicine and neuroscience, and those of service users and the implications for assessment and management of mental health and illness are discussed. The acknowledgment that the view of a human being must be holistic to be complete has put spiritual and cultural beliefs into the assessment and management of mental health. Hence, the biopsychosocial-spiritual framework may be the most consensual framework upon which social workers in multidisciplinary teams base the assessment and formulation of service plans for persons experiencing mental health problems and working toward recovery. The progression from control by external systems such as religious institutions and the state historically to the present-day movement of gaining control of individuals’ own recovery from mental illness has been a long journey of discourse. Research has also resulted in changes in policy, service provision, and the societal interpretation of abnormal behaviors that are outside the social norm. Emerging issues that may affect future social work practice in mental health such as the genetics of mental health leading to the targeting of specific therapies to individual patients or the relationship between structural issues such as economic and social inequalities with mental health are discussed.

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Ow, R., Poon, A.W.C. (2020). Theories on Mental Health, Illness and Intervention. In: Ow, R., Poon, A. (eds) Mental Health and Social Work. Social Work. Springer, Singapore. https://doi.org/10.1007/978-981-13-6975-9_1

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From intervention to interventional system: towards greater theorization in population health intervention research

• Linda Cambon   ORCID: orcid.org/0000-0001-6040-9826 1 , 2 ,
• Philippe Terral 3 &
• François Alla 2  

BMC Public Health volume  19 , Article number:  339 ( 2019 ) Cite this article

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Population health intervention research raises major conceptual and methodological issues. These require us to clarify what an intervention is and how best to address it.

This paper aims to clarify the concepts of intervention and context and to propose a way to consider their interactions in evaluation studies, especially by addressing the mechanisms and using the theory-driven evaluation methodology.

This article synthesizes the notions of intervention and context. It suggests that we consider an “interventional system”, defined as a set of interrelated human and non-human contextual agents within spatial and temporal boundaries generating mechanistic configurations – mechanisms – which are prerequisites for change in health. The evaluation focal point is no longer the interventional ingredients taken separately from the context, but rather mechanisms that punctuate the process of change. It encourages a move towards theorization in evaluation designs, in order to analyze the interventional system more effectively. More particularly, it promotes theory-driven evaluation, either alone or combined with experimental designs.

Considering the intervention system, hybridizing paradigms in a process of theorization within evaluation designs, including different scientific disciplines, practitioners and intervention beneficiaries, may allow researchers a better understanding of what is being investigated and enable them to design the most appropriate methods and modalities for characterizing the interventional system. Evaluation methodologies should therefore be repositioned in relation to one another with regard to a new definition of “evidence”, repositioning practitioners’ expertise, qualitative paradigms and experimental questions in order to address the intervention system more profoundly.

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Population health intervention research has been defined as “the use of scientific methods to produce knowledge about policy and program interventions that operate within or outside of the health sector and have the potential to impact health at the population level” [ 1 ] (see Table  1 ). This research raises a number of conceptual and methodological issues concerning, among other things, the interaction between context and intervention. This paper therefore aims to synthesize these issues, to clarify the concepts of intervention and context and to propose a way of considering their interactions in evaluation studies, especially by addressing the mechanisms and using the theory-driven evaluation methodology.

To clarify the notions of intervention, context and system

What is an intervention.

According to the International Classification of Health Interventions (ICHI), “a health intervention is an act performed for, with or on behalf of a person or population whose purpose is to assess, improve, maintain, promote or modify health, functioning or health conditions” [ 2 ]. Behind this simple definition lurks genuine complexity, creating a number of challenges for the investigators circumscribing, evaluating and transferring these interventions. This complexity arises in particular from the strong influence of what is called the context [ 3 ], defined as a “spatial and temporal conjunction of events, individuals and social interactions generating causal mechanisms that interact with the intervention and possibly modifying its outcomes” [ 4 ]. Acknowledgement of the influence of context has led to increased interest in process evaluation, such as that described in the Medical Research Council (MRC) guideline [ 5 ]. It defines the complexity of intervention by pinpointing its constituent parts. It also stresses the need for evaluations “to consider the influence of context insofar as it affects how we understand the problem and the system, informs intervention design, shapes implementation, interacts with interventions and moderates outcomes”.

Intervention components

How should intervention and context be defined when assessing their specificities and interactions? The components of the interventions have been addressed in different ways. Some authors have introduced the concept of “intervention components” [ 6 ] and others that of “active ingredients” [ 7 , 8 ] as a way to characterize interventions more effectively and distinguish them from context. For Hawe [ 9 ], certain basic elements of an intervention should be examined as a priority because they are “key” to producing an effect. She distinguishes an intervention’s theoretical processes (“key functions”) that must remain intact and transferable, from the aspects of the intervention that are structural and contingent on context. Further, she and her colleagues introduced a more systemic approach to intervention [ 10 , 11 ]. Intervention could be defined as “a series of inter-related events occurring within a system where the change in outcome (attenuated or amplified) is not proportional to change in input. Interventions are thus considered as ongoing social processes rather than fixed and bounded entities” [ 11 ]. Both intervention and context are thus defined as being dynamic over time, and interact with each other.

The notion of mechanisms

To understand these interactions between context and intervention, we can use the work by Pawson and Tilley [ 12 ] on realistic evaluation. This involves analyzing the configurations between contextual parameters, mechanisms and outcomes (CMO). As such, we can consider the process of change as being marked by various intermediate states illustrated by mechanisms.

Mechanisms may be the result of a combination of factors which can be human (knowledge, attitudes, representations, psychosocial and technical skills, etc.) or material (called “non-human” by Akrich et al. [ 13 ]). The notion of mechanism has various definitions. Some authors, such as Machamer et al. [ 14 ] , define them as “entities and activities organized such that they are productive of regular changes from start or set-up to finish or termination of conditions”. Others define them more as prerequisites to outcomes, as in the realistic approach: a mechanism is “an element of reasoning and reaction of an agent with regard to an intervention productive of an outcome in a given context” [ 15 , 16 ]. They can be defined in health psychology as “the processes by which a behavior change technique regulates behavior” [ 8 ]. This could include, for instance, how practitioners perceive an intervention’s usefulness, or how individuals perceive their ability to change their behavior.

Due to the combinations of contextual and interventional components, the process of change therefore produces mechanisms, which in turn produce effects (final and intermediate outcomes). For instance, we could consider that a motivational interview for smoking cessation could produce different psychosocial mechanisms, such as motivation, perception of the usefulness of cessation and self-efficacy. These mechanisms influence smoking cessation. This constitutes causal chains, defined here as the way in which an ordered sequence of events in the chain causes the next event. These mechanisms may also affect their own contextual or interventional components as a system. For example, the feeling of self-efficacy could influence the choice of smoking cessation supports.

From the intervention to the interventional system

Because the mechanism is the result of the interaction between the intervention and its context, the line between intervention and context becomes blurred [ 17 ]. Thus, rather than intervention, we suggest using “interventional system”, which includes interventional and contextual components. An interventional system is produced by successive changes over a given period in a given setting.

In this case, mechanisms become key to understanding the interventional system and could generally be defined as “what characterizes and punctuates the process of change and hence, the production of outcomes”. As an illustration, they could be psychological (motivation, self-efficacy, self-control, skills, etc) in behavioral intervention or social (values shared in a community, power sharing perception, etc.) in socio-ecological intervention.

In light of the above, we propose to define the interventional system in population health intervention research as: A set of interrelated human and non-human contextual agents within spatial and temporal boundaries generating mechanistic configurations – mechanisms – which are prerequisites for change in health . In the same way, we could also consider that the intervention could in fact be an arrangement of pre-existing contextual parameters influencing their own change over time. Figure  1 illustrates this interventional system.

The interventional system

Combining methods to explore the system’s key mechanisms

Attribution versus contribution: a need for theorization.

The dynamic nature of interventional systems raises the question of how best to address them in evaluation processes. Public health has historically favored research designs with strong internal validity [ 18 ], based on experimental designs. Individual randomized controlled trials are the gold standard for achieving causal attribution by counterfactual comparison in an experimental situation. Beyond the ethical, technical or legal constraints known in population health intervention research [ 19 ], trials in this field have a major drawback: they are “blind” to the contextual elements which do influence outcomes, however. Their theoretical efficacy may well be demonstrated, but their transferability is weak, which becomes an issue as intervention research is supposed to inform policy and practice [ 20 ]. Breslow [ 22 ] made the following statement: “Counterfactual causality with its paradigm, randomization, is the ultimate black box.” However, the black box has to be opened in order to understand how an intervention is effective and how it may be transferred elsewhere.

More in line with the notion of the interventional system, other models depart completely from causal attribution by counterfactual methods. They use a contributive understanding of an intervention through mechanistic interpretation, focusing on the exploration of causal chains [ 23 ]. In other words, instead of “does the intervention work? ” the question becomes “given the number of parameters influencing the result (including the intervention components), how did the intervention meaningfully contribute to the result observed?” This new paradigm promotes theory-driven evaluations (TDE) [ 24 , 25 ], which could clarify intervention-contextual configurations and mechanisms. In TDEs, the configurations and mechanisms are hypothesized by combining scientific evidence and the expertise of practitioners and researchers. The hypothetical system is then tested empirically. If this is conclusive, evidence therefore exists of contribution, and causal inferences can be made. Two main categories of TDEs can be distinguished [ 24 , 26 ]: realist evaluation and theories of change.

Realistic evaluation

In the first one, developed by Pawson and Tilley [ 12 ], intervention effectiveness depends on the underlying mechanisms at play within a given context. The evaluation consists in identifying context-mechanism-outcome configurations (CMOs), and their recurrences are observed in successive case studies or in mixed protocols, such a realist trials [ 27 ]. The aim is to understand how and under what circumstances an intervention works. In this approach, context is studied with and as a part of the intervention. This moves us towards the idea of an interventional system. For example, we applied this approach to the “Transfert de Connaissances en REGion” project (TC-REG project), an evaluation of a knowledge transfer scheme to improve policy making and practices in a health promotion and disease prevention setting in French regions [ 28 ]. This protocol describes the way in which we combined evidence and stakeholders’ expertise in order to define an explanatory theory. This explanatory theory (itself based on a combination of sociological and psychological classic theories) hypothesizes mechanism-context configurations for evidence-based decision-making. The three steps to build the theory in the TC-REG project [ 28 ] are: step 1/ a literature review of evidence-based strategies of knowledge transfer and mechanisms to enhance evidence-based decision making (e.g. the perceived usefulness of scientific evidence); step 2 / a seminar with decision makers and practitioners to choose the strategies to be implemented and hypothesize the mechanisms potentially activated by them, along with any contextual factors potentially influencing them (e.g. the availability of scientific data.) 3/ a seminar with the same stakeholders to elaborate the theory combining strategies, contextual factors and mechanisms to be activated. The theory is the interpretative framework for defining strategies, their implementation, the expected outcomes and all the investigation methods.

Theory of change

In theory of change [ 25 , 29 , 30 ], the intervention components or ingredients mentioned earlier are fleshed out and examined separately from those of context, as a way to study how they contribute to producing outcomes. As with realistic evaluation, the initial hypothesis (the theory) is based on empirical assumptions (i.e. from earlier evaluations) or theoretical assumptions (i.e. from social or psychosocial theories). What is validated (or not) is the extent to which the explanatory theory, including implementation parameters (unlike realist evaluation), corresponds to observations: expected change (i.e. 30 mins of daily physical activity); presence of individual or socio-ecological prerequisites for success (i.e. access to appropriate facilities, sufficient physical ability, knowledge about the meaning of physical activity, etc.) based on psychosocial or organizational theories (e.g. social cognitive theory, health belief model) called classic theories [ 31 ]; effectivity of actions to achieve the prerequisites for change (i.e. types of intervention or necessary environmental modifications and their effects) based on implementation theories [ 31 ] (e.g COM-B model: Capacity-Opportunity-Motvation – Behaviour Model).; effectivity of actions conducive to these prerequisites (i.e. use of the necessary intellectual, human, financial and organizational (…) resources). This can all be mapped out in a chart for checking [ 30 ]. Then, the contribution of the external factors of the intervention to the outcomes can be evaluated. For an interventional system, in both categories, the core elements to be characterized in TDE would be the mechanisms as prerequisites to outcome. The identification of these mechanisms should confirm the causal inference, rather than demonstrating causal attribution by comparison. By replicating these mechanisms, the interventions can be transferred [ 21 , 32 ]. In the case of TDEs, interventional research can be developed by natural experiment [ 33 ], allowing mechanisms to be explored, in order to explain the causal inferences, in a system which is outside the control of investigators. The GoveRnance for Equity ENvironment and Health in the City (GREENH-City) project illustrates this. It aims to address the conditions in which green areas could contribute to reducing health inequality by intervening on individual, political, organizational or geographical factors [ 34 ]. The researchers combined evidence, theories, frameworks and multidisciplinary expertise to hypothesize the potential action mechanisms of green areas on health inequalities. The investigation plans to verify these mechanisms by a retrospective study via qualitative interviews. The final goal is to determine recurring mechanisms and conditions for success by cross-sectional analysis and make recommendations for towns wishing to use green areas to help reduce health inequality.

In addition, new statistical models are emerging in epidemiology. They encourage researchers to devote more attention to causal modelling. [ 35 ].

The intervention theory

For both methods, before intervention and evaluation designs are elaborated, sources of scientific, theoretical and empirical knowledge should be combined to produce the explanatory theory (with varying numbers of implementation parameters). We call this explanatory theory the “intervention theory” to distinguish it from classic generalist psychosocial, organizational or social implementation theories, determinant frameworks or action models [ 31 ], which can fuel the intervention theory. The intervention theory would link activities, mechanisms (prerequisites of outcomes), outcomes and contextual parameters in causal hypotheses.

Note that to establish the theory, the contribution of social and human sciences (e.g. sociology, psychology, history, anthropology) is necessary. For example, the psychosocial, social and organizational theories enable investigators to hypothesize and confirm many components, mechanisms and their relationships involved in behavioral or organizational interventions. In this respect, intervention research becomes subordinate to the hybridization of different disciplines.

Combination of theory-based approaches and counterfactual designs

Notwithstanding the epistemic debates [ 36 ], counterfactual designs and theory-based approaches are not opposed, but complementary. They answer different questions and can be used successively or combined during an evaluation process. More particularly, TDEs could be used in experimental design, as some authors suggest [ 27 , 36 , 37 , 38 ]. This combination provides a way of comparing data across evaluations; in sites which have employed both an experimental design (true control group) and theory-based evaluation, an evaluator might, for example, look at the extent to which the success of the experimental group hinged upon the manipulation of components identified by the theory as relevant to learning.

On this basis, both intervention and evaluation could be designed better. For example, the “Évaluation de l’Efficacité de l’application Tabac Info service” (EE-TIS) project [ 39 ] combines a randomized trial with a theory-based analysis of mechanisms (motivation, self-efficacy, self-regulation, etc.) which are brought about through behavioral techniques used in an application for smoking cessation. The aim is to figure out how the application works, which techniques are used by users, which mechanisms are activated and for whom. Indeed in EE-TIS project [ 39 ], we attributed one or several behavioral change techniques [ 8 ] to each feature of the “TIS” application (messages, activities, questionnaires) and identified three mechanisms– potentially activated by them and supporting smoking cessation (i.e. motivation, self-efficacy, knowledge). This was carried out by a multidisciplinary committee in 3 steps: step 1/ two groups of researchers attributed behavior change techniques to each feature, step 2/ both groups compared their results and drew a consensus and step 3/ researchers presented their results to the committee which will in turn draw a consensus. To validate these hypotheses, a multivariate analysis embedded into the randomized control trial will make it possible to figure out which techniques influence which mechanisms and which contextual factors could moderate these links.

Other examples exist which combine a realist approach and trial designs [ 27 , 38 ].

Interdisciplinarity and stakeholder involvement

A focal point in theorizing evaluation designs is the interdisciplinary dimension, especially drawing on the expertise of social and human sciences and of practitioners and intervention beneficiaries [ 40 ]. As an intervention forms part of and influences contextual elements to produce an outcome, the expertise and feedback of stakeholders, including direct beneficiaries, offers valuable insights into how the intervention may be bringing about change. In addition, this empowers stakeholders and promotes a democratic process, which is to be upheld in population health [ 40 ]. The theorization could be done through specific workshops, including researchers, practitioners and beneficiaries on an equal basis. For example, the TC-REG project [ 28 ] has held a seminar involving both prevention practitioners and researchers, the aim being to discuss literature results and different theories/frameworks in order to define the explanatory theory (with context-mechanism configurations) and intervention strategies to be planned to test it.

Population health intervention research raises major conceptual and methodological issues. These imply clarifying what an intervention is and how best to address it. This involves a paradigm shift in order to consider that in intervention research, intervention is not a separate entity from context, but rather that there is an interventional system that is different from the sum of its parts, even though each part does need to be studied in itself. This gives rise to two challenges. The first is to integrate the notion of the interventional system, which underlines the fact that the boundaries between intervention and context are blurred. The evaluation focal point is no longer the interventional ingredients taken separately from their context, but rather mechanisms punctuating the process of change, considered as key factors in the intervention system. The second challenge, resulting from the first, is to move towards a theorization within evaluation designs, in order to analyze the interventional system more effectively. This would allow researchers a better understanding of what is being investigated and enable them to design the most appropriate methods and modalities for characterizing the interventional system. Evaluation methodologies should therefore be repositioned in relation to one another with regard to a new definition of “evidence”, including the points of view of various disciplines, and repositioning the expertise of the practitioners and beneficiaries, qualitative paradigms and experimental questions in order to address the interventional system more profoundly.

Abbreviations

Context-mechanism-outcome configurations

Capacity-Opportunity-Motvation – Behaviour Model

Évaluation de l’Efficacité de l’application Tabac Info Service

GoveRnance for Equity ENvironment and Health in the City

Classification of Health Interventions (ICHI)

Medical Research Council

Transfert de Connaissances en REGion

Theory-driven evaluation.

Tabac Info service

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Cambon, L., Terral, P. & Alla, F. From intervention to interventional system: towards greater theorization in population health intervention research. BMC Public Health 19 , 339 (2019). https://doi.org/10.1186/s12889-019-6663-y

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History and progress of hypotheses and clinical trials for Alzheimer’s disease

• Pei-Pei Liu 1 ,
• Xiao-Yan Meng 1 &
• Jian-Sheng Kang   ORCID: orcid.org/0000-0002-2603-9718 1  

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Alzheimer’s disease (AD) is a neurodegenerative disease characterized by progressive memory loss along with neuropsychiatric symptoms and a decline in activities of daily life. Its main pathological features are cerebral atrophy, amyloid plaques, and neurofibrillary tangles in the brains of patients. There are various descriptive hypotheses regarding the causes of AD, including the cholinergic hypothesis, amyloid hypothesis, tau propagation hypothesis, mitochondrial cascade hypothesis, calcium homeostasis hypothesis, neurovascular hypothesis, inflammatory hypothesis, metal ion hypothesis, and lymphatic system hypothesis. However, the ultimate etiology of AD remains obscure. In this review, we discuss the main hypotheses of AD and related clinical trials. Wealthy puzzles and lessons have made it possible to develop explanatory theories and identify potential strategies for therapeutic interventions for AD. The combination of hypometabolism and autophagy deficiency is likely to be a causative factor for AD. We further propose that fluoxetine, a selective serotonin reuptake inhibitor, has the potential to treat AD.

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Introduction

Alzheimer’s disease (AD) is an irreversible progressive neurological disorder that is characterized by memory loss, the retardation of thinking and reasoning, and changes in personality and behaviors. 1 , 2 AD seriously endangers the physical and mental health of the elderly. Aging is the biggest risk factor for the disease, the incidence of which doubles every 5 years after the age of 65. 3 Approximately 40 million people over the age of 60 worldwide suffer from AD, and the number of patients is increasing, doubling every 20 years. 4 , 5 , 6 , 7

In 1906, Alois Alzheimer presented his first signature case and the pathological features of the disease at the 37th convention of Southwestern German Psychiatrists. Later, in 1910, his coworker Emil Kraepelin named the disease in honor of his achievements. In the following years (from 1910 to 1963), researchers and physicians did not pay much attention to the disease until Robert Terry and Michael Kidd revived interest by performing electron microscopy of neuropathological lesions in 1963. Electron microscopy analysis showed that neurofibrillary tangles (NFTs) were present in brain biopsies from two patients with advanced AD. 8 , 9 Since then, studies on the pathological features and mechanisms of AD and drug treatments for the disease have been conducted for more than half a century (from 1963 to present). 10

Clinically, AD is divided into sporadic AD (SAD) and familial AD (FD). FD accounts for 1–5% of all AD cases. 11 , 12 , 13 , 14 , 15 In the early 1990s, linkage analyses of early-onset FD determined that mutations in three genes, namely, amyloid-beta A4 precursor protein ( APP ), presenilin 1 ( PSEN1 ), and presenilin 2 ( PSEN2 ), are involved in FD. PSEN1 mutations account for ~81% of FD cases, APP accounts for ~14%, and PSEN2 accounts for ~6%. 11 In addition to these three genes ( APP , PSEN1 , and PSEN2 ), more than 20 genetic risk loci for AD have been identified. 16 , 17 The strongest genetic risk factor for AD is the ε4 allele of apolipoprotein E ( APOE ). 18 , 19 , 20 , 21 APOE is a class of proteins involved in lipid metabolism and is immunochemically colocalized to senile plaques, vascular amyloid deposits, and NFTs in AD. The APOE gene is located on chromosome 19q13.2 and is associated with late-onset FD. The APOE gene has three alleles, namely, ε2 , ε3 , and ε4 , with frequencies of 8.4%, 77.9%, and 13.7%, respectively. The differences in APOE2 (Cys112, Cys158), APOE3 (Cys112, Arg158), and APOE4 (Arg112, Arg158) are limited to amino acid residues 112 and 158. 22 , 23 , 24 , 25 Analyses of the frequencies of these APOE alleles among human populations have revealed that there is a significant association between APOE4 and late-onset FD (with an ε4 allele frequency of ~40% in AD), suggesting that ApoE4 may be an important susceptibility factor for the etiopathology of AD. 25 , 26 , 27 Moreover, APOE4 can increase the neurotoxicity of β-amyloid (Aβ) and promote filament formation. 28 The APOE4 genotype influences the timing and amount of amyloid deposition in the human brain. 29 Reelin signaling protects synapses against toxic Aβ through APOE receptors, which suggests that APOE is a potential target for AD therapy. 30

The incidence of SAD accounts for more than 95% of all AD cases. Therefore, in this review, we focus our attention on recent SAD research and clinical trials. There are various descriptive hypotheses regarding the causes of SAD, including the cholinergic hypothesis, 31 amyloid hypothesis, 32 , 33 tau propagation hypothesis, 34 mitochondrial cascade hypothesis, 35 calcium homeostasis hypothesis, 36 inflammatory hypothesis, 37 neurovascular hypothesis, 38 metal ion hypothesis, 39 and lymphatic system hypothesis. 40 In addition, there are many other factors that increase the risk for SAD, including family history, 41 midlife hypertension, 42 sleep disorders, 43 midlife obesity, 44 and oxidative stress. 45 , 46 Interestingly, according to the latest evaluation of single-nucleotide polymorphisms (SNPs), Mukherjee et al. found 33 SNPs associated with AD and assigned people to six cognitively defined subgroups. 47

At present, clinical drug treatments are mainly divided into two categories: acetylcholinesterase inhibitors (AChEIs), represented by donepezil, and the antagonist of N-methyl-D-aspartic acid (NMDA) receptor, represented by memantine (Table 1 ). 48 As neurotransmitter regulators, these drugs can only relieve symptoms for a short time but cannot delay the progression of AD. Recent failures and the limited progress of therapeutics in phase III clinical trials suggest that it is time to consider alternative strategies for AD treatment. 49

In this review, we discuss the hypotheses of the molecular mechanisms of AD and related clinical trials (Fig. 1 ) and hope that these discussions will be helpful for developing explanatory theories and potential effective strategies for AD treatment.

Percentage of clinical trials in which each of the various hypotheses for AD were tested up to 2019. The amyloid hypothesis was the most heavily tested (22.3% of trials); the neurotransmitter hypothesis was the second most tested (19.0% of trials); the percentage of trials that tested the tau propagation hypothesis was ~12.7%; 17.0% of trials tested the mitochondrial cascade hypothesis and related hypotheses; 7.9% of trials tested the neurovascular hypothesis; 6.6% of trials tested the exercise hypothesis; 4.6% of trials tested the inflammatory hypothesis; 0.5% of trials tested the virus hypothesis; and the other uncatalogued trials made up approximately 8.4% of all trials

Cholinergic hypothesis

The cholinergic hypothesis was proposed by Peter Davies and A. J. F. Maloney in 1976 31 . They studied and compared the activities of the key enzymes involved in the synthesis of neurotransmitters, including acetylcholine, γ-aminobutyric acid, dopamine, noradrenaline, and 5-hydroxytryptamine, in 20 regions of AD and control brains. The activity of choline acetyltransferase in the AD brains was greatly reduced in the amygdala, hippocampus, and cortex, in which the concentration of acetylcholine was decreased at synapses. 50 , 51 , 52 The activity of glutamic acid decarboxylase, tyrosine hydroxylase, aromatic amino acid decarboxylase, dopamine-β-hydroxylase, and monoamine oxidase in all the areas of the AD brains studied appeared to be well within the normal range. Choline acetyltransferase is a key enzyme in the synthesis of acetylcholine, and its catalytic activity requires these substrates: choline, acetyl-CoA, and adenosine triphosphate (ATP). This was the first time that the concept of AD was noted as a cholinergic system failure. 31 , 53 This finding has also been reported in other neurological and psychiatric disorders, such as Parkinson’s disease (PD) and depression. 54 , 55

AChEIs can alleviate cognitive impairment in AD patients by inhibiting the degradation of acetylcholine. 56 , 57 , 58 , 59 Therefore, AChEIs have been used for more than 20 years since the FDA approved tacrine, the first drug for the treatment of AD, in 1995. 60 Tacrine is a reversible AChEI. Because of its liver toxicity, the number of tacrine prescriptions dropped after other AChEIs were introduced, and the usage of tacrine has been largely discontinued. The second generations of AChEI drugs that are widely used at present include donepezil, rivastigmine, and galantamine. 61 , 62 These drugs show fewer side effects and higher central selectivity and improve the cognition level of patients with mild to moderate AD. The daily living ability and overall function of patients treated with rivastigmine and galantamine are better than those treated with donepezil. 61 , 62 , 63 According to the latest meta-analysis on the efficacy of AChEIs for treating the cognitive symptoms of dementia, AChEIs have modest effects on dementia in AD, 64 but the effect is not continuous. 65 , 66

In conclusion, the current clinical drugs used for the treatment of AD improve the quality of life of AD patients, but have no significant effect on the occurrence or progression of AD. In 2012, the French Pharmacoeconomic Committee assessed the medical benefit of these drugs and downgraded its rating of the medical benefit provided by AChEIs in AD from "major" to "low." 67

Amyloid hypothesis

The amyloid hypothesis was first proposed in 1991 by John Hardy and David Allsop. 32 , 33 They found a pathogenic mutation in the Aβ precursor protein (APP) gene on chromosome 21, which suggested that APP mismetabolism and Aβ deposition were the primary events in AD. They thought that the pathological cascades in AD were Aβ deposition, tau phosphorylation, NFT formation, and neuronal death. The presence of Aβ deposits in an APP mutant (APP751) transgenic model supported the hypothesis and further contributed to shifting the amyloid hypothesis from a descriptive to a mechanistic hypothesis. 68 , 69 Positron emission tomography (PET) imaging studies have suggested that ~30% of clinically normal older individuals have signs of Aβ accumulation. 70 , 71 , 72 , 73

Aβ was first isolated by Glenner and Wong in 1984. 74 Aβ may provide a strategy for diagnostic testing for AD and for understanding its pathogenesis. 74 APP was first cloned and sequenced in 1987; APP consists of 695 amino acid residues and a glycosylated receptor located on the cell surface. 75 , 76 Aβ is composed of 39–43 residues derived from multiple proteolytic cleavages of APP. APP is cleaved in two ways (Fig. 2 ). The first method is through the α pathway. APP is hydrolyzed by α-secretase and then by γ-secretase; this process does not produce insoluble Aβ. The second method is through the β pathway. APP is hydrolyzed by β-secretase (BACE1) and then by γ-secretase to produce insoluble Aβ. Under normal conditions, the Aβ protein is not produced since APP hydrolysis is mainly based on the α pathway. A small amount of APP is hydrolyzed via the second method, and the Aβ that produced is eliminated by the immune system. However, when some mutations, such as the Lys670Asn/Met671Leu (Swedish) and Ala673Val mutations near the BACE1 cleavage site, are present, 77 , 78 APP is prone to hydrolysis by the β pathway, resulting in an excessive accumulation of insoluble Aβ and eventually the development of AD. 79 , 80 However, the Ala673Thr mutation has been suggested to be protective. 81

Schematic of the amyloid hypothesis and tau hypothesis. Upper: The transmembrane APP protein can be cleaved by two pathways. Under normal processing, APP is hydrolyzed by α-secretase and then by γ-secretase, which does not produce insoluble Aβ; under abnormal processing, APP is hydrolyzed by β secretase (BACE1) and then by γ secretase, which produces insoluble Aβ. Phase III clinical trials of solanezumab (Eli Lilly), crenezumab (Roche/Genentech/AC Immune), aducanumab (Biogen Idec), and umibecestat (Novartis/Amgen), which target the amyloid hypothesis, have all been terminated thus far. Lower: The tau protein can be hyperphosphorylated at amino residues Ser202, Thr205, Ser396, and Ser404 (which are responsible for tubulin binding), thereby leading to the release of tau from microtubules and the destabilization of microtubules. Hyperphosphorylated tau monomers aggregate to form complex oligomers and eventually neurofibrillary tangles, which may cause cell death

High concentrations of Aβ protein are neurotoxic to mature neurons because they cause dendritic and axonal atrophy followed by neuronal death. 82 The levels of insoluble Aβ are correlated with the decline of cognition. 83 In addition, Aβ inhibits hippocampal long-term potentiation (LTP) in vivo. 84 Neurofibrillary degeneration is enhanced in tau and APP mutant transgenic mice. 85 Transgenic mice that highly express human APP in the brain exhibit spontaneous seizures, which may be due to enhanced synaptic GABAergic inhibition and deficits in synaptic plasticity. 86 Individuals with Aβ are prone to cognitive decline 87 , 88 , 89 and symptomatic AD phenotypes. 90 , 91

The current strategies for AD treatment based on the Aβ hypothesis are mainly divided into the following categories: β- and γ-secretase inhibitors, which are used to inhibit Aβ production; antiaggregation drugs (including metal chelators), which are used to inhibit Aβ aggregation; protease activity-regulating drugs, which are used to clear Aβ; and immunotherapy. 92 We will discuss recent progress regarding immunotherapy and BACE1 inhibitors.

Aβ-targeting monoclonal antibodies (mAbs) are the major passive immunotherapy treatments for AD. For example, solanezumab (Eli Lilly), which can bind monomeric and soluble Aβ, failed to show curative effects in AD patients in phase III, although solanezumab effectively reduced free plasma Aβ concentrations by more than 90%. 93 Gantenerumab (Roche/Genentech) is a mAb that binds oligomeric and fibrillar Aβ and can activate the microglia-mediated phagocytic clearance of plaques. However, it also failed in phase III. 94 Crenezumab (Roche/Genentech/AC Immune) is a mAb that can bind to various Aβ, including monomers, oligomers, and fibrils. On January 30, 2019, Roche announced the termination of two phase III trials of crenezumab in AD patients. Aducanumab (Biogen Idec) is a mAb that targets aggregated forms of Aβ. Although aducanumab can significantly reduce Aβ deposition, Biogen and Eisai announced the discontinuation of trials of aducanumab on March 21, 2019. Together, the failure of these trials strongly suggests that it is better to treat Aβ deposits as a pathological feature rather than as part of a major mechanistic hypothesis.

BACE1 inhibitors aim to reduce Aβ and have been tested for years. However, no BACE1 inhibitors have passed clinical trials. Verubecestat (MK-8931, Merck & Co.) reduced Aβ levels by up to 90% in the cerebrospinal fluid (CSF) in AD. However, Merck no longer listed verubecestat in its research pipeline since verubecestat did not improve cognitive decline in AD patients and was associated with unfavorable side effects. 95 Lanabecestat (AZD3293, AstraZeneca/Eli Lilly) is another BACE1 inhibitor that can lower CSF Aβ levels by up to 75%. However, on June 12, 2018, phase II/III trials of lanabecestat were discontinued due to a lack of efficacy. The BACE1 inhibitor atabecestat (JNJ-54861911, Janssen) induced a robust reduction in Aβ levels by up to 95% in a phase I trial. However, Janssen announced the discontinuation of this program on May 17, 2018. The latest news regarding the BACE inhibitor umibecestat (Novartis/Amgen) was released on July 11, 2019; it was announced that the evaluation of umibecestat was discontinued in phase II/III trials since an assessment demonstrated a worsening of cognitive function. Elenbecestat (E2609, Eisai) is another BACE1 inhibitor that can reduce CSF Aβ levels by up to 80% 96 , 97 and is now in phase III trials (shown in Table 2 ). Although all BACE1 inhibitors seem to reduce CSF Aβ levels, the failure of trials of solanezumab, which can reduce free plasma Aβ concentrations by more than 90%, 93 may be sufficient to lead us to pessimistic expectations, especially considering that the treatment worsened cognition and induced side effects.

Tau propagation hypothesis

Intracellular tau-containing NFTs are an important pathological feature of AD. 98 , 99 NFTs are mainly formed by the aggregation of paired helical filaments (Fig. 2 ). Pathological NFTs are mainly composed of tau proteins, which are hyperphosphorylated. 100 , 101 , 102 , 103 Tau proteins belong to a family of microtubule-binding proteins, and are heterogeneous in molecular weight. A main function of tau is to stabilize microtubules, which is particularly important for neurons since microtubules serve as highways for transporting cargo in dendrites and axons. 34 , 104 Tau cDNA, which encodes a protein of 352 residues, was cloned and sequenced in 1988. RNA blot analysis has identified two major transcripts that are 6 and 2 kilobases long and are widely distributed in the brain. 105 , 106 The alternative splicing of exons 2, 3, and 10 of the tau gene produces six tau isoforms in humans; the differential splicing of exon 10 leads to tau species that contain various microtubule-binding carboxyl terminals with repeats of three arginines (3R) or four arginines (4R). 107 , 108 An equimolar ratio of 3R and 4R may be important for preventing tau from forming aggregates. 109

The tau propagation hypothesis was introduced in 2009. 34 The pathology of tau usually first appears in discrete and specific areas and later spreads to more regions of the brain. Aggregates of fibrillar and misfolded tau may propagate in a prion-like way through cells, eventually spreading through the brains of AD patients (Fig. 2 ). Clavaguera et al. demonstrated that tau can act as an endopathogen in vivo and in culture studies in vitro with a tau fragment. 104 In their study, brain extracts isolated from P301S tau transgenic mice 110 were injected into the brains (the hippocampus and cortical areas) of young ALZ17 mice, a tau transgenic mouse line that only develops late tau pathology. 111 After the injection, the ALZ17 mice developed tau pathology quickly, whereas the brain extracts from wild-type mice or immunodepleted P301S mice, which were used as controls, had no effect. The causes of tau aggregation in sporadic tauopathies are not fully understood. Tau can be phosphorylated at multiple serine and threonine residues (Fig. 2 ). 112 , 113 The gain- and loss-of-function of tau phosphorylation may be due to alterations in the activities of kinases or phosphatases that target tau, and thus, the toxicity of tau can be augmented as a result. Other posttranslational modifications can decrease tau phosphorylation or enhance the harmful states of tau. For example, serine–threonine modifications by O-glycosylation can reduce the extent of tau phosphorylation. 114 , 115 Thus, tau hyperphosphorylation may partially result from a decrease in tau O-glycosylation. In addition, tau can also be phosphorylated at tyrosine residues, 116 sumoylated and nitrated, 117 but the exact roles of these tau modifications remain elusive.

According to the tau propagation hypothesis, abnormally phosphorylated tau proteins depolymerize microtubules and affect signal transmission within and between neurons. 101 , 103 , 118 In addition, mutant forms of human tau cause enhanced neurotoxicity in Drosophila melanogaster . 119 There may be cross-talk between the tau propagation hypothesis and the amyloid hypothesis. As mentioned earlier, among the risk loci for AD, APOE is the most robust factor for AD pathogenesis. 120 Unlike other isoforms, APOE4 may increase Aβ by decreasing its clearance 121 , 122 , 123 and enhancing tau hyperphosphorylation. 124 , 125 , 126 GSK3 is one of the upstream factors that jointly regulates Aβ and tau. Increased GSK3 activity leads to the hyperphosphorylation of the tau protein. 126 GSK3 overactivity may also affect the enzymatic processing of APP and thus increase the Aβ level. 127 , 128 In addition, tau is essential for Aβ-induced neurotoxicity, and dendritic tau can mediate Aβ-induced synaptic toxicity and circuit abnormalities. 129 Moreover, APP and tau act together to regulate iron homeostasis. APP can interact with ferroportin-1 to regulate the efflux of ferrous ions. 130 , 131 As an intracellular microtubule-associated protein, tau can increase iron output by enhancing the transport of APP to the cell surface. 132 Decreased APP trafficking to the cell surface accounts for iron accumulation in tau knockout neurons. 133 , 134

As one of the most important hypotheses of AD, the tau propagation hypothesis has a wide range of impacts. Drugs that target the tau protein are divided into the following categories: tau assembly inhibitors, tau kinase inhibitors, O-GlcNAcase inhibitors, microtubule stabilizers, and immunotherapy drugs. 92 Only a few agents have undergone proof-of-principle tests as tau kinase inhibitors, microtubule-stabilizing agents, and inhibitors of heat shock protein 90 (Hsp90), which stabilize GSK3β. 135 , 136 In addition, some inhibitors of tau aggregation, such as TRx0237 (TauRx Therapeutics), are in clinical trials. The results of TRx 237–005 phase III clinical trials showed that the agent may be effective as a monotherapy since the brain atrophy rate of AD patients declined after 9 months of treatment. 137 ACI-35 (AC Immune/Janssen) and AADvac1 (Axon Neuroscience SE) are vaccines that target the hyperphosphorylated tau protein, and the vaccines are still being evaluated in clinical trials 138 (Table 2 ). Tau-directed therapies will inevitably face challenges similar to those presently encountered in Aβ-targeted trials. Overall, the effectiveness of tau-directed therapies remains to be tested in the future.

Mitochondrial cascade hypothesis and related hypotheses (Fig. 3 )

In 2004, Swerdlow and Khan first introduced the mitochondrial cascade hypothesis 35 and stated that mitochondrial function may affect the expression and processing of APP and the accumulation of Aβ in SAD. The hypothesis includes three main parts. First, an individual's baseline mitochondrial function is defined by genetic inheritance. Second, the rate of age-associated mitochondrial changes is determined by inherited and environmental factors. Moreover, a decline in mitochondrial function or efficiency drives aging phenotypes. 139 , 140 , 141 Third, the rate of change of mitochondrial function in individuals influences AD chronology.

Mitochondrial cascade and related hypotheses. Mitochondria are the main contributors to ROS production, which is significantly increased in AD. The metabolites of mitochondrial TCA, such as pyruvate, fumarate, malate, OAA, and α-KG, not only directly regulate energy production but also play an important role in the epigenetic regulation of neurons and longevity. 164 , 173 , 187 , 188 , 189 For example, SAM provides methyl groups for histone and DNA methyltransferases (HMTs and DNMTs). 165 , 166 α-KG is a necessary cofactor for TET DNA methylases, histone demethylases (HDMs), and lysine demethylases KDMs/JMJDs. 167 , 168 Mitochondria also regulate the levels and redox state of FAD, a cofactor of the histone demethylase LSD1. 175 Dysfunctional mitochondria can be removed by mitophagy, which is also very important in the progression of AD. BNIP3L interacts with LC3 or GABARAP and regulates the recruitment of damaged mitochondria to phagophores. In addition, Beclin 1 is released from its interaction with Bcl-2 to activate autophagy after BNIP3L competes with it. PINK1 promotes autophagy by recruiting the E3 ligase PARK2. Then, VDAC1 is ubiquitinated and then binds to SQSTM1. SQSTM1 can interact with LC3 and target this complex to the autophagosome. 445 L. monocytogenes can promote the aggregation of NLRX1 and the binding of LC3, thus activating mitophagy. 446 The MARCH5-FUNDC1 axis mediates hypoxia-induced mitophagy. 447 The mitochondrial proteins NIPSNAP1 and NIPSNAP2 can recruit autophagy receptors and bind to autophagy-related proteins. 448 ROS: reactive oxygen species; TCA: tricarboxylic acid cycle; OAA: oxaloacetate; α-KG: α-ketoglutarate; SAM: S-adenosyl methionine; TET: ten–eleven translocation methylcytosine dioxygenase; FAD: flavin adenine dinucleotide

Oxidative stress is defined as “an imbalance in pro-oxidants and antioxidants with associated disruption of redox circuitry and macromolecular damage.” 142 Oxidative stress is mainly caused by increased levels of reactive oxygen species (ROS) and/or reactive nitrogen species, including superoxide radical anions (O 2− ), hydrogen peroxide (H 2 O 2 ), hydroxyl radicals (HO − ), nitric oxide (NO), and peroxynitrite (ONOO − ). In intact cells, ROS can be produced from multiple sources, including mitochondria, ER, peroxisomes, NADPH oxidases, and monoamine oxidases. 143 , 144 In AD, neurons exhibit significantly increased oxidative damage and a reduced number of mitochondria, 145 which are the main contributors to ROS generation among these ROS sources. 146 , 147 The overproduction of ROS and/or an insufficient antioxidant defense can lead to oxidative stress. 148 Before the onset of the clinical symptoms of AD and the appearance of Aβ pathology, there is evidence that the production of ROS increases due to mitochondrial damage. 148 Both mtDNA and cytochrome oxidase levels increase in AD, and the number of intact mitochondria is significantly reduced in AD. 145 Several key enzymes involved in oxidative metabolism, including dehydrogenase complexes for α-ketoglutarate (α-KG) and pyruvate, and cytochrome oxidase also show reduced expression or activity in AD. 149 , 150 , 151 , 152 , 153 , 154 In addition, there is evidence in vitro and in vivo for a direct relationship between oxidative stress and neuronal dysfunction in AD. 155 , 156 Aβ-dependent endocytosis is involved in reducing the number of NMDA receptors on the cell surface and synaptic plasticity in neurons and brain tissue in AD mice. 157 Excessive Aβ may also trigger excitotoxicity and stress-related signaling pathways by increasing Ca 2+ influx, increasing oxidative stress, and impairing energy metabolism. 158

Although the majority of efforts have been focused on genetic variations and their roles in disease etiology, it has been postulated that epigenetic dysfunction may also be involved in AD. 159 , 160 Indeed, there is growing evidence that epigenetic dysregulation is linked to AD. 161 , 162 , 163 Mitochondrial metabolites are required for epigenetic modifications, such as the methylation of DNA and the methylation and acetylation of histones. 164 AD brains exhibited a global reduction in DNA modifications, including 5-methylcytosine and 5-hydroxymethylcytosine. 165 , 166 , 167 , 168 S-adenosyl methionine (SAM) provides a methyl group for histones and DNA methyltransferases in the nucleus. SAM is generated and maintained by coupling one-carbon metabolism and mitochondrial energy metabolism. 169 , 170 α-KG, which is generated by the tricarboxylic acid cycle (TCA) cycle in mitochondria and the cytosol, is a cofactor of ten–eleven translocation methylcytosine dioxygenase DNA methylases, histone demethylases (HDMs) and the lysine demethylases KDMs/JMJDs. 171 , 172 However, the activities of KDMs/JMJDs and TETs can be inhibited by fumarate, succinate, and 2-hydroxyglutarate. 173 Mutations that affect the succinate dehydrogenase complex and fumarate hydratase can induce the accumulation of succinate and fumarate, respectively. 174 Oxidized flavin adenine dinucleotide (FAD) is an essential cofactor of the HDM LSD1, a member of the KDM family. 175 In addition, acetyl-CoA, the source of acetyl groups that are consumed by histone acetyltransferases, is generated by ATP citrate lyase and pyruvate dehydrogenase in the cytosol and mitochondria, respectively. 176 In addition, oxidized nicotinamide adenine dinucleotide (NAD + ) is a cofactor for sirtuins (SIRTs), a family of deacetylases that includes nuclear-localized SIRT1, SIRT6, and SIRT7, cytosolic SIRT2, and three mitochondrial SIRTs (SIRT3, SIRT4, and SIRT5) (Fig. 3 ). Therefore, the activities of SIRTs are sensitive and are regulated by cellular NAD + pools. 177 As summarized by Fang, NAD + replenishment can enhance autophagy/mitophagy mainly through SIRT1 or SIRT3; meanwhile, SIRT6 and SIRT7 induce autophagy through the inhibition of mTOR; NAD + may also inhibit autophagy/mitophagy through SIRT2, SIRT4, SIRT5, and poly(ADP-ribose) polymerases. 178 In short, mitochondrial dysfunction can partially explain the epigenetic dysregulation in aging and AD.

Dysfunctional mitochondria can be removed by mitophagy, a term that was first coined by Dr Lemasters in 2005. 179 Since then, mitophagy has been linked to various diseases, including neurodegenerative disorders such as PD 180 and Huntington's disease (HD), 181 as well as normal physiological aging. 182 Mitophagosomes can effectively degrade their internalized cargo by fusing with lysosomes during axonal retrotransport. 183 Fang et al. demonstrated that neuronal mitophagy is impaired in AD. 184 Mitophagy stimulation can reverse memory impairment, diminish insoluble Aβ 1–42 and Aβ 1–40 through the microglial phagocytosis of extracellular Aβ plaques, and abolish AD-related tau hyperphosphorylation. 184 Therefore, deficiencies in mitophagy may have a pivotal role in AD etiology and may be a potential therapeutic target. 178 , 184 , 185 , 186

The metabolites of mitochondrial TCA, such as pyruvate, fumarate, malate, oxaloacetate (OAA), and α-KG, have been demonstrated to extend lifespan when fed to C. elegans . 173 , 187 , 188 , 189 Wilkins et al. found that OAA enhances the energy metabolism of neuronal cells. 190 Moreover, OAA can also activate mitochondrial biogenesis in the brain, reduce inflammation, and stimulate neurogenesis. 191 The application of OAA in AD was also investigated by Swerdlow et al., and the results showed that 100-mg OAA capsules did not result in an elevation of OAA in the blood 192 ; higher doses up to 2 g per day were also evaluated in clinical studies, but no results have been posted or published yet.

Clinical trials related to the mitochondrial cascade hypothesis and related hypotheses account for 17.0% of all clinical trials (Fig. 1 ). Based on the above, the mitochondrial cascade hypothesis and related hypotheses (Fig. 3 ) may link other hypotheses, including the cholinergic hypothesis, amyloid hypothesis, and tau propagation hypothesis.

Calcium homeostasis and NMDA hypotheses

The calcium homeostasis hypothesis was proposed in 1992 by Mattson et al. They found that Aβ can elevate intracellular calcium levels and render neurons more vulnerable to environmental stimuli. 36 The involvement of calcium in AD was first suggested long ago by Khachaturian, 193 and since then, there are many efforts to clarify this hypothesis. 194 , 195 , 196 Calcineurin can trigger reactive/inflammatory processes in astrocytes, which are upregulated in AD models. 197 In addition, calcium homeostasis is closely related to learning and memory. Rapid autopsies of the postmortem human brain have suggested that calcineurin/nuclear factor of activated T-cells signaling is selectively altered in AD and is involved in driving Aβ-mediated cognitive decline. 198 The evidence indicates that calcium homeostasis may be associated with the development of AD. 193 , 199

Memantine, a noncompetitive antagonist of NMDA glutamate receptors in the brain was approved for marketing in Europe in 2002 and received US FDA approval in 2003. 200 , 201 Memantine is not an AChEI. The functional mechanism of memantine likely involves blocking current flow (especial calcium currents) through NMDA receptors and reducing the excitotoxic effects of glutamate. 202 Memantine is also an antagonist of type 3 serotonergic (5-HT 3 ) receptors and nicotinic acetylcholine receptors, but it does not bind other receptors, such as adrenergic, dopamine, and GABA receptors. The inhibition of NMDA receptors can also reduce the inhibition of α-secretase and thus inhibit the production of Aβ. 203 However, the French Pharmacoeconomic Committee downgraded its rating of the medical benefit provided by memantine in AD from "major" to "low," 67 which was also supported by a recent meta-analysis. 64

Neurovascular hypothesis

The homeostasis of the microenvironment and metabolism in the brain relies on substrate delivery and the drainage of waste through the blood; neurons, astrocytes, and vascular cells form a delicate functional unit that supports the integrity of brain structure and function. 204 , 205 , 206 Vascular dysregulation leads to brain dysfunction and disease. Alterations in cerebrovascular function are features of both cerebrovascular pathologies and neurodegenerative diseases, including AD. 38 In 1994, it was demonstrated that the cerebral microvasculature is damaged in AD. 207 Aβ can induce the constriction of the cerebral arteries. 208 In an AD mouse model, neocortical microcirculation is impaired before Aβ accumulation. 209 , 210 Neuroimaging studies in AD patients have demonstrated that neurovascular dysfunction is found before the onset of neurodegeneration. 211 , 212 , 213 , 214 In addition to aberrant angiogenesis and the senescence of the cerebrovascular system, the faulty clearance of Aβ across the blood–brain barrier (BBB) can initiate neurovascular uncoupling and vessel regression and consequently cause brain hypoperfusion, brain hypoxia, and neurovascular inflammation. Eventually, BBB compromise and a chemical imbalance in the neuronal environment lead to neuronal dysfunction and loss. 215 In mice that overexpress APP, impairment in the neocortical microcirculation is observed. The cerebrovascular effects of Aβ in dementia may involve alterations in cerebral blood flow and neuronal dysfunction. 209 Moreover, neurovascular dysfunction may also play a role in the etiology of AD.

Many factors can lead to changes in the neurovasculature, which in turn affect the occurrence and progression of AD. Of these factors, hyperlipidemia is one of the most important. During the last two decades, growing evidence has shown that a high cholesterol level may increase the risk of AD. In one test, higher levels of low-density lipoprotein (LDL) or total cholesterol were correlated with lower scores on the MMSE (modified mini mental state exam) in nondemented patients. High total cholesterol levels in midlife increase the risk of AD nearly threefold: the odds ratio (OR) is 2.8 (95% confidence interval, CI: 1.2–6.7). 216 Midlife obesity is also a risk factor for AD, 217 and midlife adiposity may predict an earlier onset of dementia and Aβ accumulation. 218 Adipose tissue secretes some inflammation factors, such as tumor necrosis factor (TNF-α), interleukin-1 (IL-1), and interleukin-6, in obesity, 219 and these factors may induce insulin resistance, produce Aβ deposits, and stimulate excessive tau phosphorylation. 220

A hyperglycemic state is another risk factor. Type 2 diabetic patients (T2D) have an increased risk of dementia, 221 both vascular dementia (VD) and AD. In the largest and latest meta-analysis of T2D and dementia risk, data from 6184 individuals with diabetes and 38,350 without diabetes were pooled and analyzed. 222 The relative risk (RR) for dementia was 1.51 (95%CI: 1.31–1.74). The results of the analyses further suggested that there are two common subtypes of dementia: AD and VD. The results suggested that T2D conferred an RR of 2.48 (95%CI: 2.08–2.96) for VD and 1.46 (95%CI: 1.20–1.77) for AD. 222 Insulin resistance is a common feature of T2D and SAD. Accumulating evidence supports the involvement of impaired insulin signaling in AD progression. Insulin levels and insulin receptor expression are reduced in AD brains. 223 However, plasma insulin and Aβ levels are both increased in AD patients, suggesting that a decrease in insulin clearance may increase plasma Aβ levels. Blocking insulin signaling in the brain through the intracerebroventricular administration of STZ (the diabetogenic drug streptozotocin) resulted in various pathological features that resemble those found in human SAD, while the administration of insulin and glucose enhances learning and memory in AD patients. 224 , 225

Many institutions have conducted clinical trials of statins, drugs that are used to lower blood cholesterol, for the treatment of AD. However, in a phase IV clinical trial, simvastatin failed to reduce Aβ-42 and tau levels in the CSF. The results suggested that the use of statins for the treatment of AD requires more evidence. 226 To test the hyperglycemic hypothesis, rosiglitazone (RSG), a drug used for the treatment for type II diabetes mellitus, was evaluated. RSG XR had no effect in a phase III trial. 227 In addition, hypertension has also been linked to worse cognition and hypometabolism in AD. AD patients with hypertension exhibit worse cognitive function (on the AD assessment scale-cognitive subscale, P  = 0.038) and a higher burden of neuropsychiatric symptoms (on the neuropsychiatric inventory questionnaire, P  = 0.016) than those without hypertension. 228 As an antihypertensive medication, ramipril is a specific angiotensin-converting enzyme inhibitor; however, ramipril was tested and failed in a pilot clinical trial. 229

Therefore, trial failures of treatments related to the neurovascular hypothesis and related hypotheses suggest that these hypotheses alone may not be sufficient to explain the etiology of AD.

Inflammatory hypothesis

The inflammatory responses of microglia and astrocytes in the central nervous system (CNS) also play important roles in the development of AD. 230 , 231 , 232 Microglial cells are brain-specific macrophages in the CNS, and they make up 10–15% all brain cells. 233 Microglia cells exhibit higher activity in AD patients than in the control group. 234 The concentration of aggregated microglial cells near senile plaques and neurons with NFTs in AD patients is usually 2–5 times higher than that in normal individuals. Inflammatory factors that are expressed by microglia and histocompatibility complexes also cause inflammation. 235 In vitro studies have linked Aβ pathology in AD to neuroinflammation. It has been shown that Aβ possesses a synergistic effect on the cytokine-induced activation of microglia. 236 Two studies have confirmed that Aβ can induce glial activation in vivo. 237 , 238 The fibrillar conformation of Aβ seems to be crucial for such activation. 239 In AD patients, Aβ can bind to microglia cells through the CD36-TLR4-TLR6 receptor complex and the NLRP3 inflammatory complex, destroy cells, release inflammation-inducing factors, such as TNF-α, and cause immune responses. In addition to increased levels of TNF-α, increased levels of the inflammatory cytokines IL-1β, TGF-β, IL-12, and IL-18 in the CNS are also correlated with AD progression and increase damage in the brains of AD patients. 240 Interestingly, CD22 is a B-cell receptor that functions as a negative regulator of phagocytosis. The functional decline of aged microglia may result from the upregulation of CD22; thus, the inhibition of CD22 can enhance the clearance of debris and fibrils, including Aβ oligomers, in vivo, and this process may be potentially beneficial for the treatment of AD. 241

Considerable evidence suggests that the use of anti-inflammatory drugs may be linked with a reduced occurrence of AD. The ability of naproxen and celecoxib to delay or prevent the onset of AD and cognitive decline was evaluated in phase III clinical trials. However, therapeutic efficacy analysis indicated that naproxen and celecoxib do not exert a greater benefit compared with that of placebo. In addition, the naproxen and celecoxib groups experienced more adverse events, including hypertension, gastrointestinal, and vascular or cardiac problems, so these phase III clinical trials were discontinued. 242 A clinical trial of lornoxicam in AD patients was also terminated due to a lack of efficacy. These failures suggest that the clinical application of anti-inflammatory drugs for AD treatment needs to be further validated (Table 2 ).

Metal ion hypothesis

Metal ions that play functional roles in organisms are classified as biometals, while other metal ions are inert or toxic. 243 , 244 The dyshomeostasis of any metal ion in the body usually leads to disease. In the CNS, biometals, such as copper, zinc, and iron, are required to act as cofactors for enzymatic activity, mitochondrial function, and neuronal function. 245 , 246 In healthy brains, free metal ions are stringently regulated and kept at a very low level. 247

Biometal ions are involved in Aβ aggregation and toxicity. In the first study that evaluated biometals and Aβ, which was published by Bush et al. in 1994, zinc was linked to Aβ. The potential link between biometals and AD has been intensively studied. 39 , 248 , 249 , 250 There is evidence of the dyshomeostasis of biometals in AD brains. Biometals, especially zinc and copper, are directly coordinated by Aβ, and biometals such as iron can reach a high concentration (~1 mM) in plaques. 251 , 252 In the serum, the levels of copper, which are not associated with ceruloplasmin, are elevated in AD patients. Moreover, a higher copper content in the serum is associated with lower MMSE scores. 253 , 254 In the serum of AD patients, the levels of Zn 2+ ions are decreased compared with those in age-matched controls, whereas the concentration of Zn 2+ is elevated in the CSF. 255

The important role of biometals in Aβ formation has been reported in various animal models. For example, the role of Cu 2+ in Aβ formation was demonstrated in a cholesterol-fed rabbit model of AD. 256 Administering trace amounts of Cu 2+ in drinking water was sufficient to induce Aβ accumulation, the consequent formation of plaques, and deficits in learning. 256 On the other hand, Cu 2+ also plays a beneficial role. For example, transgenic mice that overexpress mutant human APP and are treated with Cu 2+ show a reduction in Aβ and do not exhibit a lethal phenotype. 257 In contrast, in Drosophila that specifically express human Aβ in the eye, dietary zinc and copper increase Aβ-associated damage, while different chelators of biometals demonstrate favorable effects. 258

During normal aging, the gradual accumulation of iron is observed in some brain areas, such as the substantia nigra, putamen, globus pallidus, and caudate nucleus. 259 , 260 , 261 , 262 , 263 An increase in the level of iron in AD brains was first demonstrated in 1953. 264 More recently, through the use of magnetic resonance imaging (MRI), iron accumulation was found in AD and was shown to be mainly localized to certain brain areas, such as the parietal cortex, motor cortex, and hippocampus. 265 , 266 , 267 , 268 , 269 , 270 , 271 , 272 Studies of gene mutations that affect the metabolism of iron have suggested that the dyshomeostasis of iron plays a role in neuronal death, such as the neuronal death that occurs in neurodegenerative disorders such as AD. 273 , 274 , 275 , 276 , 277 Iron overload accelerates neuronal Aβ production and consequently worsens cognitive decline in a transgenic AD mice. 278 There is evidence that the levels of labile iron can directly affect APP production via iron regulatory element. 279 As a potent source of highly toxic hydroxyl radicals, redox-active iron is actively associated with senile plaques and NFTs. 280

As the most common nutrient deficiency in the world, iron deficiency is also frequently observed and reported in AD. 281 Iron is present in polynuclear iron–sulfur (Fe/S) centers and hemoproteins. Mitochondrial complexes I–III require Fe/S clusters, and complexes II–IV need hemoproteins for electron transfer and the oxidative phosphorylation of the respiratory chain. 282 Thus, iron deficiency may partially account for hypometabolism in AD since women with iron deficiency anemia have a higher prevalence of dementia. 283 Interestingly, iron deficiency and iron accumulation in AD seem paradoxical. One potential explanation is that tau differentially regulates the motor proteins dynein and kinesin; specifically, tau may preferentially inhibit kinesin, which transports cargo toward the cell periphery. 284 Tau is distributed in a proximal-to-distal gradient with a low concentration in the cell body. 284 , 285 , 286 , 287 When tau is hyperphosphorylated, it is released from the distal microtubules into the neuronal axon and soma, and thus inhibits kinesin activity and prevents the transport of iron-containing cargo and other cargo (including mitochondria) to the neuronal periphery; this may result in the accumulation of mtDNA and iron accumulations in the soma of neurons in AD 145 , 280 and deficiencies in mitochondria and iron homeostasis in the white matter of the brain. Iron-targeted therapies were recently updated and reviewed. 288 Similar to the amyloid hypothesis, the conjecture that the therapeutic chelation of iron ions is an effective approach for treating AD remains widespread despite a lack of evidence of any clinical benefits. 288

Aluminum (Al), the most abundant metal in the earth’s crust, is a nonessential metal ion in organisms. The role of Al in AD needs to be further elucidated. Exley et al. hypothesized that Al is associated with Aβ in AD brains and Al can precipitate Aβ in vitro into fibrillar structures; in addition, Al is known to increase the Aβ burden in the brains of treated animals, which may be due to a direct or indirect effect on Aβ anabolism and catabolism. 289 , 290

Biometals may play various roles in AD and may influence the pathogenesis directly or indirectly. For example, biometals indirectly influence energy metabolism and APP processing, 249 while cellular iron levels can directly regulate APP through IREs identified in the 5′ -UTR of mRNA. 291 , 292

Lymphatic system hypothesis

The lymphatic network and the blood vasculature are essential for fluid balance in the body. 293 , 294 Below the human skull, the meninges, a three-layer membrane that envelopes the brain, contains a network of lymphatic vessels. This meningeal lymphatic system was first discovered in 1787, and interest in this system has been revived recently. 295 , 296 , 297 Proteins, metabolites, and waste produced by the brain flow through the interstitial fluid (ISF) and reach the CSF, which circulates through the ventricles and brain meninges. 298 In the classical form of transvascular removal, metabolic waste and other molecules in these fluids are drained from the brain, are transported across capillary walls, and cross the BBB. 298 , 299 Thrane et al.’s found that, in addition to transvascular removal, perivascular removal, in which the blood vasculature allows the CSF to flow into or exit the brain along the para-arterial space or via paravenous routes, occurs and that aquaporin-4 water channels that are expressed in astrocytes are essential for CSF–ISF exchange along the perivascular pathway. 300 , 301 This perivascular route is called the glymphatic system. 302 , 303

During aging, impairments in the transvascular/perivascular removal of waste may result in Aβ accumulation in the brain. 40 , 304 Animals that lack aquaporin-4 channels show a 70% decrease in the ability to remove large solutes, such as Aβ. 305 , 306 Da Masquita et al.’s investigated the importance of meningeal lymphatics for Aβ production in AD mouse models. They found that ablating meningeal lymphatics leads to Aβ accumulation in the meninges, accelerates Aβ deposition, and induces cognitive deficits. These findings are consistent with Aβ accumulation observed in the meninges of AD patients. Strategies for promoting the growth of meningeal lymphatic vessels may have the potential to enhance the clearance of Aβ and lessen the deposition of Aβ, 307 , 308 but this remains to be further validated.

Other hypotheses

In addition to the above hypotheses, there are many other factors that can affect the occurrence of AD. For a long time (at least 60 years), investigators have suspected that microbes may be involved in the onset and progression of AD, this was hypothesized by Sjogren et al. beginning in 1952. 309 In addition to McLachlan et al.’s proposal in 1980, 310 several investigators have proposed that AD may be caused by a viral form of herpes simplex. 311 , 312 , 313 , 314 There have been intensive reports suggesting that AD may be associated with various bacterial and viral pathogens, 315 , 316 , 317 especially herpesviridae (including HSV-1, 318 , 319 EBV, HCMV, HHV-6A, and HHV-7 314 , 320 ). However, these studies did not determine the underlying mechanisms or identify a robust association with a specific viral species. Recent reports have suggested that Aβ aggregation and deposition may be stimulated by different classes of microbes as a part of the innate immune response. Microbes trigger amyloidosis, and newly generated Aβ acts as an antimicrobial peptide to coat microbial particles to fight the infection. 321 , 322 , 323 Valaciclovir, an antiviral drug that is used for the management of herpes simplex and herpes zoster, is now in a phase II trial for AD (Table 2 ).

MicroRNAs (miRNAs) are involved in posttranscriptional gene regulation. 324 , 325 , 326 , 327 The decreased expression of miRNA-107 (miR-107) in AD may accelerate disease progression by regulating the expression of BACE1. 328 In SAD patients, the expression of miR-29a/b-1 is inversely correlated with BACE1 expression. 329 Only one clinical trial related to miRNAs is underway. Gregory Jicha launched a phase I trial to assess the safety and efficacy of gemfibrozil in modulating miR-107 levels for the prevention of AD in subjects with cognitive impairment (Table 2 ).

Mannose oligosaccharide diacid (GV-971) was developed by researchers at the Shanghai Institute of Medicine, the Chinese Academy of Sciences, the Ocean University of China, and the Shanghai Green Valley Pharmaceutical Co., Ltd. GV-971 is an oceanic oligosaccharide molecule extracted from seaweed. GV-971 may capture multiple fragments of Aβ in multiple sites and multiple states, inhibit the formation of Aβ filaments, and depolymerize filaments into nontoxic monomers 330 , 331 ; however, an understanding of the exact mechanism is still lacking. GV-971 has been reported to improve learning and memory in Aβ-treated mice. 332 In phase II trials, GV-971 improved cognition in AD patients. 333 In addition, a phase III clinical trial of GV-971 finished with positive results, and it is on its way to the market in China (Table 2 ).

Interestingly, a pilot clinical trial that included 120 nondemented elderly Chinese individuals (ages 60–79) living in Shanghai compared the effects of interventions (such as walking, Tai Chi, and social interaction) on cognition and whole brain volume, as determined by a neuropsychological battery and MRI scans. 334 The results showed that Tai Chi and social interaction were beneficial, but walking had no effect. Therefore, in addition to promising drugs, a healthy lifestyle can delay the progression of AD.

The whole brain atrophy rate is −0.67 to −0.8% per year in adulthood. 335 Freeman et al.’s results demonstrated that, although the frontal and temporal regions of the cortex undergoing thinning, the total number of neurons remains relatively constant from age 56 to age 103. However, there is a reduction in the number of hippocampal neurons in AD but not in normal aging. The loss of neuronal structural complexity may contribute to the thinning that occurs with aging. 336 The integrity of neurons and dendritic structures is the basis for maintaining the normal function of neurons. 337 , 338 , 339 Brain atrophy affects the function of neurons, which in turn impairs signal transmission and causes movement disorders, cognitive disorders etc. 340 , 341 , 342 , 343 Brain atrophy has been shown to be a key pathological change in AD. 344 , 345 , 346 , 347 In particular, the annual atrophy rate of the hippocampus in AD patients (−3.98 ± 1.92%) is two to four times that of the atrophy rate in healthy individuals (−1.55 ± 1.38%). At the same time, the annual increase in the temporal lobe volume of the lateral ventricle in AD patients (14.16 ± 8.47%) is significantly greater than that in healthy individuals (6.15 ± 7.69%). 348 The ratio of the volume of the lateral ventricle to the volume of the hippocampus may be a reliable measurement for evaluating AD since the ratio can minimize variances and fluctuations in clinical data and may be a more objective and sensitive method for diagnosis and evaluating AD. In 1975, brain atrophy and a reduction in perfusion were detected in AD patients. 349 In 1980, atrophy of hippocampal neurons and abnormal brain metabolism were first discovered in AD patients with PET. 350 Brain volume reduction in patients with AD is significantly associated with dementia severity and cognitive disturbances as well as neuropsychiatric symptoms. 351 The development of broad-spectrum drugs that target brain atrophy, a common feature of neurodegenerative diseases, is still ongoing. In our previous work, RAS–RAF–MEK signaling was demonstrated to protect hippocampal neurons from atrophy caused by dynein dysfunction and mitochondrial hypometabolism (tetramethylrhodamine ethyl ester mediated mitochondrial inhibition), suggesting the feasibility of interventions for neuronal atrophy. 352

The MAPK pathway protects neurons against dendritic atrophy and relies on MEK-dependent autophagy. 352 Autophagy is the principal cellular pathway by which degraded proteins and organelles are recycled, and it plays an essential role in cell fate in response to stress. 353 , 354 , 355 , 356 , 357 Aged organelles and protein aggregates are cleared by the autophagosome–lysosome pathway, which is particularly important in neurons. 358 , 359 , 360 Growing evidence has implicated defective autophagy in neurodegenerative diseases, including AD, PD, amyotrophic lateral sclerosis and HD. 358 , 361 , 362 , 363 , 364 Recent work using live-cell imaging determined that autophagosomes preferentially form at the axon tip and undergo retrograde transport to the cell body. 365 As a key protein in autophagy, Beclin 1 is decreased in the early stage of AD. 357 , 366 , 367 Moreover, a decrease in autophagy induced by the genetic ablation of Beclin 1 increases intracellular Aβ accumulation, extracellular Aβ deposition, and neurodegeneration. 368 Autophagy decline also causes microglial impairments and neuronal ultrastructural abnormalities. 368 On the other hand, transcriptome evidence has revealed enhanced autophagy–lysosome function in centenarians. 369 PPARA-mediated autophagy can reduce AD-like pathology and cognitive decline. 370 These results suggest that autophagy is a potential therapeutic target for AD. MEK-dependent autophagy is protective in neuronal cells. 352 The activation of the MEK–ERK signaling pathway can reduce the production of toxic amyloid Aβ by inhibiting γ-secretase activity. 371 , 372 , 373 , 374 , 375 Thus, MEK-dependent autophagy may provide a potential way to enhance Aβ and NFT clearance and may also be a new potential target for AD therapy (Fig. 4 ).

Schematic representation of autophagy. Yellow box: mTOR-dependent autophagy pathways. Growth factors can inhibit autophagy via activating the PI3K/Akt/mTORC1 pathway; under nutrient-rich conditions, mTORC1 is activated, whereas under starvation and oxidative stress, mTORC1 is inhibited. AMPK-dependent autophagy activation can be induced by starvation and hypoxia. 449 Ras can also activate autophagy via activating PI3K, 352 while p300 can inhibit autophagy. 450 p38 promotes autophagy by phosphorylating and inactivating Rheb and then inhibiting mTOR under stress. 451 Green boxes: mTOR-independent autophagy pathways. The PI3KCIII complex (also called the Beclin 1–Vps34–Vps15 complex) is essential for the induction of autophagy and is regulated by interacting proteins, such as the negative regulators Rubicon, Mcl-1, and Bcl-XL/Bcl-2, while proteins including UVRAG, Atg14, Bif-1, VMP-1, and Ambra-1 induce autophagy by binding Beclin 1 and Vps34 and promoting the activity of the PI3KCIII complex. 357 In addition, various kinases also regulate autophagy. ERK and JNK-1 can phosphorylate Bcl-2, release its inhibition, and consequently induce autophagy; the phosphorylation of Beclin 1 by Akt inhibits autophagy, whereas the phosphorylation of Beclin 1 by DAPK promotes autophagy. 452 Autophagy can be inhibited by the action of PKA and PKC on LC3. Finally, Atg4, Atg3, Atg7, and Atg10 are autophagy-related proteins that mediate the formation of the Atg12–Atg5–Atg16L1 complex and LC3-II. 453 RAS and p300 can also regulate autophagy via the mTOR-independent pathway 454

Hypometabolism is sufficient to cause neuronal atrophy in vitro and in vivo. 352 , 376 , 377 Hypometabolism may be a potential therapeutic target for AD. 378 Regional hypometabolism is another characteristic of AD brains (Fig. 5 ). The human brain makes up ~2% of the body weight but consumes up to ~20% of the oxygen supply; the brain is energy demanding and relies on the efficiency of the mitochondrial TCA cycle and oxidative phosphorylation for ATP generation. 379 , 380 , 381 , 382 However, glucose metabolism in the brain in AD and mild cognitive impairment is significantly impaired compared with that in the brain upon normal aging, and the decline in cerebral glucose metabolism occurs before pathology and symptoms manifest and gradually worsens as symptoms progress. 383 , 384 , 385 In 1983, de Leon et al. examined aged patients with senile dementia and found a 17–24% decline in the cerebral glucose metabolic rate. 386 Inefficient glucose utilization, impaired ATP production, and oxidative damage are closely correlated, and these deficiencies have profound consequences in AD. 387 , 388 For example, ATP deficiency causes the loss of the neuronal membrane potential since Na + /K + ATPase fails to maintain proper intracellular and extracellular gradients of Na + and K + ions. In addition, the propagation of action potentials and the production of neurotransmission is hindered by energy insufficiency. Moreover, after membrane depolarization (mainly due to the dissipation of Na + and K + ion gradients), Ca 2+ flows down the steep gradient (~1.2 mM of extracellular Ca 2+ to ~0.1 μM of intracellular Ca 2+ ) into the cell to raise intracellular Ca 2+ levels and stimulates the activities of various Ca 2+ -dependent enzymes (including endonucleases, phospholipases, and proteinases), eventually contributing to neuronal dysfunction and death. 158 Mitochondria are the most energetically and metabolically active organelles in the cell. 389 , 390 Mitochondria are also dynamic organelles; they experiences changes in their functional capacities, morphologies, and positions 391 , 392 , 393 so that they can be transported, and they respond to physiological signals to meet the energy and metabolic demands of cellular activities. 394 , 395 , 396 In addition to neuronal atrophy, mitochondrial dysfunction leads to hypometabolism, which in turn contributes to the progression of AD. 397 , 398 , 399 Indeed, there is evidence that hypometabolism and neuronal atrophy coexists in patients with amyloid-negative AD. 400 In addition to mitochondrial dysfunction, hypoperfusion and hypoxia in vascular diseases may also cause hypometabolism in the brain and thus contribute to the progression of AD (Fig. 5 ). Meanwhile, as the synthesis of acetylcholine requires the involvement of acetyl-CoA and ATP, hypometabolism leads to a decrease in acetylcholine synthesis in neurons, which suggests that hypometabolism may be an underlying explanation for the acetylcholine hypothesis (Fig. 5 ).

In addition to mitochondrial dysfunction, hypometabolism may underlie the cholinergic hypothesis, metal ion hypothesis, and neurovascular hypothesis. a Glucose is enzymatically catalyzed to produce pyruvate. Pyruvate is converted to acetyl-CoA and then enters the TCA cycle or is used in the cytoplasm to synthesize acetylcholine. However, in AD patients, because of hypometabolism, the production of acetyl-CoA and ATP is insufficient, which leads to a reduction in acetylcholine synthesis. b Mitochondrial complexes I–III require Fe/S clusters, and complexes II–IV need hemoproteins for electron transfer and the oxidative phosphorylation of the respiratory chain. When iron deficiency occurs, the production of Fe/S and hemoproteins decreases, thereby affecting mitochondrial function and resulting in hypometabolism. In addition, copper is essential for the function of complex IV. Clearly, Cu–Zn superoxide dismutase (SOD1) requires copper and zinc. 455 , 456 c Hypoperfusion and hypoxia in vascular diseases leads to insufficient oxygen supply, which in turn leads to insufficient ATP synthesis, resulting in hypometabolism in AD patients. TCA: tricarboxylic acid cycle; SOD1: superoxide dismutase 1

The relationship between hypometabolism and autophagy in neurons is still unknown, 352 but calorie restriction (CR) is known to enhance autophagy. CR-induced autophagy can recycle intracellular degraded components and aggregates to maintain mitochondrial function. 401 Hypometabolism and a simultaneous decrease in autophagy can worsen the situation and lead to the dysfunction and atrophy of neurons. Hypometabolism and a simultaneous decrease in autophagy may be causative factors of brain atrophy and AD (Fig. 6 ).

Hypometabolism and autophagy decline are likely to be causative factors of neuronal atrophy. Normal neurons vs. atrophic neurons. Upper: Normal levels of autophagy and metabolism exist in neurons to maintain their morphology and function. Lower: Hypometabolism and a reduction in autophagy are found in atrophic neurons

Perspective

AD, like the aging population, has increasingly become a medical and social concern. There are currently four clinically used drugs (a total of five therapies, the fifth one of which is a combination of two drugs) that have been approved by the FDA, but they only treat the symptoms and have no significant effect on the progression of AD. Based on this retrospective review of AD and the lessons learned, we propose that fluoxetine, 402 a selective serotonin reuptake inhibitor (SSRI), may have strong potential for the treatment of AD (Fig. 7 ).

The potential mechanisms of fluoxetine in the remission of AD. As a selective 5-HT reuptake inhibitor, fluoxetine can increase the extraneuronal concentration of 5-HT. 5-HT binds to the 5-HT 4 A receptor to promote neuronal dematuration through a Gs-mediated pathway. 5-HT binds to the 5-HT 1 A receptor, which is involved in BDNF-dependent neurogenesis through the Gi-mediated signaling pathway. After 5-HT stimulation, MeCP2 is phosphorylated at Ser421 through CaMKII-dependent signaling, and this promotes the dissociation of CREB from HDAC and then increases the expression of BDNF. BDNF activates downstream signaling pathways, including the MEK-ERK pathway, which might promote the activity of α-secretase, inhibit γ-secretase, and reduce the production of toxic amyloid Aβ. Moreover, the serotonylation of histone H3 at glutamine 5 (Q5) enhances the binding of H3K4me3 and TFIID and allows gene expression. Fluoxetine has been reported to bind and inhibit NMDA receptors directly, which can reduce the inhibition of α-secretase and thus prevent the production of Aβ. In addition, fluoxetine can bind to the endoplasmic reticulum protein sigma-1 receptor, which induces the dissociation of Bip from the sigma-1 receptor and promotes neuroprotection. 5-HT: serotonin; ER: endoplasmic reticulum

Based on functional brain imaging with PET, there is evidence that serotonin plays an important role in aging, late-life depression, and AD. 403 Short-term treatment with the antidepressant fluoxetine can trigger pyramidal dendritic spine synapse formation in the rat hippocampus. 404 In an MRI study of fluoxetine for the treatment of major depression, Vakili et al. found that female responders had a statistically significant higher mean right hippocampal volume than that of nonresponders. 405 Long-term treatment with fluoxetine can promote the neurogenesis and proliferation of hippocampal neurons in mice through the 5-HT 1 A receptor, and this can relieve anxiety phenotypes in mice 406 and enhance mitochondrial motility. 407 5-HT 4 A receptors that are expressed by mature neurons in the hippocampal dentate gyrus are also important for promoting neurogenesis and dematuration. 408 , 409 , 410 Fluoxetine can promote neurogenesis not only in the hippocampus but also in the anterior cortex and hypothalamus. 411 This action depends on BDNF, as fluoxetine can enhance the phosphorylation of methyl-CpG binding protein 2 (MeCP2) at serine 421 to relieve its transcriptional inhibition and thereby promote the expression of BDNF. 412 , 413 In addition to promoting neurite outgrowth and neurogenesis, enhanced BDNF signaling can rearrange the subcellular distribution of α-secretase, which increases its binding to APP peptides; in addition, the activity of β-secretase is inhibited after BDNF treatment. 414 Moreover, the serotonylation of glutamine (at position 5) in histone H3 by a transglutaminase 2-mediated manner is a sign of permissive gene expression. 415

Furthermore, fluoxetine has been reported to bind and inhibit NMDA receptors directly in the CNS, 416 and this can reduce the inhibition of α-secretase and thus prevent the production of Aβ. 203 , 417 Fluoxetine also inhibits γ-secretase activity and reduces the production of toxic amyloid Aβ by activating MEK-ERK signaling. 371 , 372 In addition, fluoxetine can bind to the endoplasmic reticulum protein sigma-1 receptor. 418 Sigma-1 receptor ligands can enhance acetylcholine secretion. 419 , 420 The sigma-1 receptor activator Anavex 2–73 has entered a phase III clinical trial after it was granted fast-track status by the FDA because of the promising results in phase II. The sigma-1 receptor is located in the mitochondrion-associated ER membrane so that the activation of the sigma-1 receptor can prolong Ca 2+ signaling in mitochondria. 421 Consequently, the local and specific elevation of [Ca 2+ ] in the mitochondrial matrix can enhance ATP synthesis, 422 , 423 which ameliorates hypometabolism.

In addition, our group examined the effect of SSRIs on cognitive function in AD by conducting a meta-analysis of randomized controlled studies. Of the 854 articles identified, 14 articles that involved 1091 participants were eligible for inclusion. We compared changes in MMSE scores between SSRI treatment groups and the placebo group, and we found that SSRIs may contribute to improved cognitive function, with a mean difference (MD) of 0.84 (95%CI: 0.32–1.37, P   =  0.002) compared with the control. Further subgroup analysis exploring the effect of fluoxetine and other SSRIs revealed a beneficial effect of fluoxetine (MD = 1.16, 95%CI: 0.41–1.90, P   =  0.002) but no benefit of other SSRIs (MD = 0.58, 95%CI: −0.17–1.33, P   =  0.13) on cognitive function. 424 Consequently, all of the above evidence indicates that fluoxetine has strong potential for the treatment of AD. In addition, because of above wealthy supporting documentation and the weak role of other SSRIs such as escitalopram in promoting BDNF release, 425 fluoxetine was singled out as a potential therapy for the treatment of AD, not just as a complementary treatment. 426 As summarized and illustrated in Fig. 7 , the exact mechanisms of the effects of fluoxetine remain to be further clarified.

Finally, to summarize this review of the history and progress of hypotheses and clinical trials for AD, the most perplexing question is in regards to amyloid hypothesis and its failed clinical trials, which account for 22.3% of all clinical trials (Fig. 1 ). Although mutations in APP , PSEN1 , or PSEN2 only account for ~0.5% of all AD cases, 11 mutations in PSEN1, which is the most common known genetic cause of FD and functions as the catalytic subunit of γ-secretase, 427 , 428 may cast light upon Aβ and its paradox. In 2017, Sun et al. analyzed the effect of 138 pathogenic mutations in PSEN1 on the production of Aβ−42 and Aβ−40 peptides by γ-secretase in vitro; they found that 90% of these mutations led to a decrease in the production of Aβ−42 and Aβ−40 and that 10% of these mutations result in decreased Aβ−42/Aβ−40 ratios. 429 This comprehensive assessment of the impact of FD mutations on γ-secretase activity and Aβ production does not support the amyloid hypothesis and suggests an alternative therapeutic strategy aimed at restoring γ-secretase activity 430 ; this is also supported by the fact that the functional loss of both PSEN1 and PSEN2 in the mouse postnatal forebrain causes memory impairment in an age-dependent manner. 431 Considering that the activation of Notch signaling by the cleavage of γ-secretase 432 is not involved in age-related neurodegeneration, 433 other signaling pathways mediated by Aβ and/or other products of γ-secretase substrates, such as ErbB4, 434 E-cadherin, 435 N-cadherin, 436 ephrin-B2, 437 CD44, 438 and LDL receptor-related protein, 439 may play active roles in neuronal survival in the adult brain.

The most interesting and challenging phenomena regarding fluoxetine is that fluoxetine is clinically more effective in women than in men 440 and that the prevalence of AD and other dementias is higher in women than in men 441 ; meanwhile, women live significantly longer than men. 442 These phenomena suggest that there are interplays or trade-offs between AD and longevity. In particular, APOE is the strongest genetic risk factor for AD 18 , 19 , 20 , 21 and is the only gene associated with longevity that achieves genome-wide significance ( P  < 5 × 10 –8 ). 443 APOE4 is associated with a risk of AD that declines after the age of 70; the OR for APOE4 heterozygotes remains above unity at almost all ages; surprisingly, however, the OR for APOE4 homozygotes dips below unity after the age of 89. 444 There may be genetic and nongenetic factors that interact with APOE4 , lead to shorter survival in more aggressive form of AD, or promote longevity in an age-dependent manner. 11 Uncovering the puzzle of APOE4 and the mystery of longevity may provide insights for AD prevention.

Change history

23 september 2019.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Acknowledgements

We thank all researchers for their work in the AD field, as well as all institutes and companies for their efforts in clinical trials. We are also grateful to the many authors for their papers that were uncited due to limited space and time. We are grateful for Wei Liu’s discussion about autophagy. We appreciate funding by the National Natural Science Foundation of China (Grant No. 31171369), the National Basic Research Program (973 Program) (Nos 2011CB910903 and 2010CB912001), the Chinese Academy of Sciences (Hundred Talents Program and 2009OHTP10), the Joint Construction Project of Henan Province (No. 2018020088 and No. 2018020114), and the First Affiliated Hospital of Zhengzhou University.

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Liu, PP., Xie, Y., Meng, XY. et al. History and progress of hypotheses and clinical trials for Alzheimer’s disease. Sig Transduct Target Ther 4 , 29 (2019). https://doi.org/10.1038/s41392-019-0063-8

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Module 2 Chapter 1: The Nature of Social Work Research Questions

The search for empirical evidence typically begins with a question or hypothesis. The nature of the questions asked determine many features of the studies that lead to answers: the study approach, design, measurement, participant selection, data collection, data analysis, and reporting of results. Not just any type of question will do, however:

“When the question is poorly formulated, the design, analysis, sample size calculations, and presentation of results may not be optimal. The gap between research and clinical practice could be bridged by a clear, complete, and informative research question” (Mayo, Asano, & Barbic, 2013, 513).

The topic concerning the nature of social work research questions has two parts: what constitutes a research question, and what makes it a social work question. We begin this chapter by examining a general model for understanding where different types of questions fit into the larger picture of knowledge building explored in Module 1. We then look at research questions and social work questions separately. Finally, we reassemble them to identify strong social work research questions.

In this chapter, you will learn:

• 4 types of social work research for knowledge building,
• characteristics of research questions,
• characteristics of social work research questions.

Translational Science

The concept of translational science addresses the application of basic science discoveries and knowledge to routine professional practice. In medicine, the concept is sometimes described as “bench to trench,” meaning that it takes what is learned at the laboratory “bench” to practitioners’ work in the real-world, or “in the trenches.” This way of thinking is about applied science—research aimed at eventual applications to create or support change. Figure 1-1 assembles the various pieces of the translational science knowledge building enterprise:

Figure 1-1. Overview of translational science elements

Basic Research .   Federal policy defines basic research  as systematic study that is directed toward understanding the fundamental aspects of phenomena without specific applications in mind (adapted from 32 CFR 272.3). Basic research efforts are those designed to describe something or answer questions about its nature. Basic research in social and behavioral science addresses questions of at least two major types: epidemiology  and  etiology  questions.

Epidemiology questions. Questions about the nature of a population, problem, or social phenomenon are often answered through epidemiological methods. Epidemiology is the branch of science (common in public health) for understanding how a problem or phenomenon is distributed in a population. Epidemiologists also ask and address questions related to the nature of relationships between problems or phenomena—such as the relationship between opioid misuse and infectious disease epidemics (NAS, 2018). One feature offered by epidemiological research is a picture of trends over time. Consider, for example, epidemiology data from the Centers for Disease Control and Prevention (the CDC) regarding trends in suicide rates in the state of Ohio over a four-year period (see Figure 1-2, created from data presented by CDC WONDER database).

Figure 1-2. Graph reflecting Ohio trend in suicide rate, 2012-2016

Since the upward trend is of concern, social workers might pursue additional questions to examine possible causes of the observed increases, as well as what the increase might mean to the expanded need for supportive services to families and friends of these individuals. The epidemiological data can help tease out some of these more nuanced answers. For example, epidemiology also tells us that firearms were the recorded cause in 46.9% of known suicide deaths among individuals aged 15-24 years across the nation during 2016 (CDC, WONDER database). Not only do we now know the numbers of suicide deaths in this age group, we know something about a relevant factor that might be addressed through preventive intervention and policy responses.

Epidemiology also addresses questions about the size and characteristics of a population being impacted by a problem or the scope of a problem. For example, a social worker might have a question about the “shape” of a problem defined as sexual violence victimization. Data from the United States’ 2010-2012 National Intimate Partner and Sexual Violence Survey (NISVS) indicated that over 36% of woman (1 in 3) and 17% of men (1 in 6) have experienced sexual violence involving physical contact at some point in their lives; the numbers vary by state, from 29.5% to 47.5% for women and 10.4% to 29.3% for men (Smith et al., 2017).

In developing informed responses to a problem, it helps to know for whom it is a problem. Practitioners, program administrators, and policy decision makers may not be aware that the problem of sexual violence is so prevalent, or that men are victimized at worrisome rates, as well as women. It is also helpful to know how the problem of interest might interface with other problems. For example, the interface between perpetrating sexual assault and alcohol use was examined in a study of college men (Testa & Cleveland, 2017). The study investigators determined that frequently attending parties and bars was associated with a greater probability of perpetrating sexual assault. Thus, epidemiological research helps answer questions about the scope and magnitude of a problem, as well as how it relates to other issues or factors, which can then inform next steps in research to address the problem.

Etiology questions.  Etiology research tests theories and hypotheses about the origins and natural course of a problem or phenomenon. This includes answering questions about factors that influence the appearance or course of a problem—these may be factors that mediate or moderate the phenomenon’s development or progression (e.g., demographic characteristics, co-occurring problems, or other environmental processes). To continue with our intimate partner violence example, multiple theories are presented in the literature concerning the etiology of intimate partner violence perpetration—theories also exist concerning the etiology of being the target of intimate partner violence (Begun, 2003). Perpetration theories include:

• personality/character traits
• biological/hereditary/genetic predisposition
• social learning/behavior modeling
• social skills
• self-esteem
• cultural norms (Begun, 2003, p. 642).

Evidence supporting each of these theories exists, to some degree; each theory leads to the development of a different type of prevention or intervention response. The “best” interventions will be informed by theories with the strongest evidence or will integrate elements from multiple evidence-supported theories.

Etiology research is often about understanding the mechanisms underlying the phenomena of interest. The questions are “how” questions—how does this happen (or not)? For example, scientists asked the question: how do opioid medications (used to manage pain) act on neurons compared to opioids that naturally occur in the brain (Stoeber et al., 2018)? They discovered that opioid medications used to treat pain bind to receptors  inside n erve cells, which is a quite different mechanism than the conventional wisdom that they behave the same way that naturally occurring (endogenous) opioids do—binding only on the surface  of nerve cells. Understanding this mechanism opens new options for developing pain relievers that are less- or non-addicting than current opioid medicines like morphine and oxycodone. Once these mechanisms of change are understood, interventions can be developed, then tested through intervention research approaches.

Intervention Research.  Interventions are designed around identified needs: epidemiology research helps to support intervention design by identify the needs. Epidemiology research also helps identify theories concerning the causes and factors affecting social work problems. Intervention development is further supported by later theory-testing and etiology research. However, developing an intervention is not sufficient: interventions need to be tested and evaluated to ensure that they are (1) safe, (2) effective, and (3) cost-efficient to deliver. This is where  intervention research  comes into play. Consider the example of Motivational Interviewing (MI) approaches to addressing client ambivalence about engaging in a behavior change effort. Early research concerning MI addressed questions about its effectiveness. For example, a meta-analytic review reported that “MI should be considered as a treatment for adolescent substance abuse” because the evidence demonstrated small, but significant effect sizes, and that the treatment gains were retained over time (Jensen et al., 2011). Subsequently, when its safety and effectiveness were consistently demonstrated through this kind of evidence, investigators assessed MI as cost-efficient or cost-effective. For example, MI combined with providing feedback was demonstrated to be cost-effective in reducing drinking among college students who engaged in heavy drinking behavior (Cowell et al., 2012).

Intervention research not only is concerned with the outcomes of delivering an intervention, but may also address the mechanisms of change  through which an intervention has its effects—not only what changes happen, but how  they happen. For example, investigators are exploring  how  psychotherapy works, moving beyond demonstrating that  it works (Ardito & Rabellino, 2011; Kazdin, 2007; Wampold, 2015). One mechanism that has garnered attention is the role of therapeutic alliance—the relationships, bonds, and interactions that occur in the context of treatment—on treatment outcomes.

Therapeutic alliance is one common factor identified across numerous types of effective psychotherapeutic approaches (Wampold, 2015). Authors summarizing a number of studies about therapeutic alliance and its positive relationship to treatment outcomes concluded that the quality of therapeutic alliance may be a more powerful predictor of positive outcome than is the nature or type of intervention delivered (Ardito & Rabellino, 2011). However, it is important to determine the extent to which (a) therapeutic alliance enhances clients’ symptom improvement, (b) gradual improvements in symptoms lead to enhanced therapeutic alliance, or (c) the relationship between therapeutic alliance and symptom improvement are iterative—they go back and forth, influencing each other over time (Kazdin, 2007).

Implementation Science . Social work and other disciplines have produced a great deal of evidence about “what works” for intervening around a great number of social work problems. Unfortunately, many best practices with this kind of evidence support are slow to become common practices.  Implementation science  is about understanding facilitators and barriers to these evidence-supported interventions becoming adopted into routine practice: characteristics of the interventions themselves, conditions and processes operating in the organizations where interventions are implemented, and factors external to these organizations all influence practitioners’ adoption of evidence supported interventions.

Even under optimal internal organizational conditions, implementation can be undermined by changes in organizations’ external environments, such as fluctuations in funding, adjustments in contracting practices, new technology, new legislation, changes in clinical practice guidelines and recommendations, or other environmental shifts” (Birken, et al, 2017).

Research for/about Research . In addition, social work investigators engage in research that is specifically about scientific methodology. This is where advances in measurement, participant recruitment and retention, and data analysis emerge. The results of these kinds of research studies are used to improve the research in basic, intervention, and implementation research. Later in the course you will see some of these products in action as we learn about best practices in research and evaluation methodology. Here are a few examples related to measurement methods:

• Concept mapping to assess community needs of sexual minority youth (Davis, Saltzburg, & Locke, 2010)
• Field methodologies for measuring college student drinking in natural environments (Clapp et al., 2007)
• Intergenerational contact measurement (Jarrott, Weaver, Bowen, & Wang, 2018)
• Perceived Social Competence Scale-II (Anderson-Butcher et al., 2016)
• Safe-At-Home Instrument to measure readiness to change intimate partner violence behavior (Begun et al., 2003; 2008; Sielski, Begun, & Hamel, 2015)
• Teamwork Scale for Youth (Lower, Newman, & Anderson-Butcher, 2016)

And, here are a few examples related to involving participants in research studies:

• Conducting safe research with at risk populations (Kyriakakis, Waller, Kagotho, & Edmond, 2015)
• Recruitment strategies for non-treatment samples in addiction studies (Subbaraman et al., 2015)
• Variations in recruitment results across Internet platforms (Shao et al., 2015)

Take a moment to complete the following activity.

Research Questions

In this section, we take a closer look at research questions and their relationship to the types of research conducted by investigators. It may be easier to understand research questions by first ruling out what are not research questions. In that spirit, let’s begin with examples of questions where applying research methods will not help to find answers:

• Trauma informed education. The first issue with this example is obvious: it is not worded as a question. The second is critically important: this is a general topic, it is not a research question. This topic is too vague and broad making it impossible to determine what answers would look like or how to approach finding answers.
• How is my client feeling about what just happened? This type of question about an individual is best answered by asking clinical questions of that individual, within the context of the therapeutic relationship, not by consulting research literature or conducting a systematic research study.
• Will my community come together in protest of a police-involved shooting incident? This type of question may best be answered by waiting to see what the future brings. Research might offer a guess based on data from how other communities behaved in the past but cannot predict how groups in individual situations will behave. A better research question might be: What factors predict community protest in response to police-involved shooting incidents?
• Should I order salad or soup to go with my sandwich? This type of question is not of general interest, making it a poor choice as a research question. The question might be reframed as a general interest question: Is it healthier to provide salad or soup along with a sandwich? The answer to that researchable question might inform a personal decision.
• Why divorce is bad for children. There are two problems with this example. First, it is a statement, not a question, despite starting with the word “why.” Second, this question starts out with a biased assumption—that divorce is bad for children. Research questions should support unbiased investigation, leading to evidence and answers representative of what exists rather than what someone sets out wanting to prove is the case. A better research question might be: How does divorce affect children?

Tuning back to our first example of what is not a research question, consider several possible school social work research questions related to that general topic:

• To what extent do elementary school personnel feel prepared to engage in trauma informed education with their students?
• What are the barriers and facilitators of integrating trauma informed education in middle school?
• Does integrating trauma informed education result in lower rates of suicidal ideation among high school students?

Is there a relationship between parent satisfaction and the implementation of trauma informed education in their children’s schools?

Does implementing trauma informed education in middle schools affect the rate of student discipline referrals?

What is the difference between these research questions and the earlier “not research” questions? First, research questions are specific. This is an important distinction between identifying a topic of interest (e.g., trauma informed education) and asking a researchable question. For example, the question “How does divorce affect children?” is not a good research question because it remains too broad. Instead, investigators might focus their research questions on one or two specific effects of interest, such as emotional or mental health, academic performance, sibling relationships, aggression, gender role, or dating relationship outcomes.

Related to a question being “researchable” is its feasibility for study. Being able to research a question requires that appropriate data can be collected with integrity. For example, it may not be feasible to study what would happen if every child was raised by two parents, because (a) it is impossible to study every child and (2) this reality cannot ethically be manipulated to systematically explore it. No one can ethically conduct a study whereby children are randomly assigned by study investigators to the compared conditions of being raised by two parents versus being raised by one or no parents. Instead, we settle for observing what has occurred naturally in different families.

Second, “good” research questions are relevant to knowledge building. For this reason, the question about what to eat was not a good research question—it is not relevant to others’ knowledge development. Relevance is in the “eye of the beholder,” however. A social work researcher may not see the relevance of using a 4-item stimulus array versus a 6-item stimulus array in testing children’s memory, but this may be an important research question for a cognitive psychology researcher. It may, eventually, have implications for assessment measures used in social work practice.

Third, is the issue of bias built into research questions. Remembering that investigators are a product of their own developmental and social contexts, what they choose to study and how they choose to study it are socially constructed. An important aspect at the heart of social work research relates to a question’s cultural appropriateness and acceptability. To demonstrate this point, consider an era (during the 1950s to early 1970s) when research questions were asked about the negative effects on child development of single-parent, black family households compared to two-parent, white family households in America. This “majority comparison” frame of reference is not culturally appropriate or culturally competent. Today, in social work, we adopt a strengths perspective, and avoid making comparisons of groups against a majority model. For example, we might ask questions like: What are the facilitators and barriers of children’s positive development as identified by single parents of diverse racial/ethnic backgrounds? What strengths do African American parents bring to the experience of single-parenting and how does it shape their children’s development? What are the similar and different experiences of single-parenting experienced by families of different racial/ethnic composition?

Research Questions versus Research Hypotheses . You have now seen examples of “good” research questions. Take, for example, the last one we listed about trauma informed education:

Based on a review of literature, practice experience, previous research efforts, and the school’s interests, an investigator may be prepared to be even more specific about the research question (see Figure 1-3). Assume that these sources led the investigator to believe that implementing the trauma informed education approach will have the effect of reducing the rate of disciplinary referrals. The investigator may then propose to test the following hypothesis:

Implementing trauma informed education in middle schools will result in a reduction in the number of student discipline referrals.

The research hypothesis  is a clear statement that can be tested with quantitative data and will either be rejected or not, depending on the evidence. Research hypotheses are predictions about study results—what the investigator expects the results will show. The prediction, or hypothesis, is based on theory and/or other evidence. A study hypothesis is, by definition, quantifiable—the answer lies in numerical data, which is why we do not generally see hypotheses in qualitative, descriptive research reports.

Hypotheses are also specific to one question at a time. Thus, an investigator would need to state and test a second hypothesis to answer the question:

The stated hypothesis might be:

Parent satisfaction is higher in middle schools where trauma informed education is implemented.

Figure 1-3. Increasing specificity from research topic to question to hypothesis

Social Work Questions

It is difficult to find a simple way to characterize social work research. The National Institutes of Health (NIH) described social work research in the following way:

Historically, social work research has focused on studies of the individual, family, group, community, policy and/or organizational level, focusing across the lifespan on prevention, intervention, treatment, aftercare and rehabilitation of acute and chronic conditions, including the effects of policy on social work practice (OBSSR, 2003, p. 5) .

For all the breadth expressed in this statement, it reflects only how social work research relates to the health arena—it does not indicate many other domains and service delivery systems of social work influence:

• physical, mental, and behavioral health
• substance misuse/addiction and other addictive behaviors
• income/poverty
• criminal justice
• child and family welfare
• housing and food security/insecurity
• environmental social work
• intimate partner, family, and community violence
• and others.

In addition to breadth of topic, social work research is characterized by its biopsychosocial nature. This means that social work researchers not only pursue questions relating to biological, psychological, and social context factors, but also questions relating to their intersections and interactions. Related to this observation is that social work not only addresses questions related to the multiple social system levels, social work also addresses the ways multiple levels intersect and interact (i.e., those levels represented in the NIH statement about individuals, families, groups, communities, organizations, and policy).

It is worth noting that research need not be conducted by social workers to be relevant to social work–many disciplines and professions contribute to the knowledge base which informs social work practice (medicine, nursing, education, occupational therapy, psychology, sociology, criminal justice, political science, economics, and more). Authors of one social work research textbook summarize the relevance issue in the following statement:

“To social workers, a relevant research question is one whose answers will have an impact on policies, theories, or practices related to the social work profession” (Grinnell & Unrau, 2014, p. 46).

Social Work Research Questions and Specific Aims

The kinds of questions that help inform social work practice and policy are relevant to understanding social work problems, diverse populations, social phenomena, or interventions. Most social work research questions can be divided into two general categories: background questions  and foreground questions . The major distinction between these two categories relates to the specific aims that emerge in relation to the research questions.

Background Questions.  This type of question is answerable with a fact or set of facts. Background questions are generally simple in structure, and they direct a straightforward search for evidence. This type of question can usually be formulated using the classic 5 question words: who, what, when, where, or why. Here are a few examples of social work background questions related to the topic of fetal alcohol exposure:

• Who is at greatest risk of fetal alcohol exposure?
• What are the developmental consequences of fetal alcohol exposure?
• When in gestation is the risk of fetal alcohol exposure greatest?
• Where do women get information about the hazards of drinking during pregnancy?
• Why is fetal alcohol exposure (FAE) presented as a spectrum disorder, different from fetal alcohol syndrome (FAS)?

These kinds of questions direct a social worker to review literature about human development, human behavior, the distribution of the problem across populations, and factors that determine the nature of a specific social work problem like fetal exposure to alcohol. Where the necessary knowledge is lacking, investigators aim to explore or describe the phenomenon of interest. Many background questions can be answered by epidemiology or etiology evidence.

Foreground Questions.  This type of question is more complex than the typical background question. Foreground questions typically are concerned with making specific choices by comparing or evaluating options. These types of questions required more specialized evidence and may lead to searching different types of resources than would be helpful for answering background questions. Foreground questions are dealt with in greater detail in our second course, SWK 3402 which is about understanding social work interventions. A quick foreground question example related to the fetal exposure to alcohol topic might be:

Which is the best tool for screening pregnant women for alcohol use with the aim of reducing fetal exposure, the T-ACE, TWEAK, or AUDIT?

This type of question leads the social worker to search for evidence that compares different approaches. These kinds of evidence are usually found in comparative reviews, or require the practitioner to conduct a review of literature, locating individual efficacy and effectiveness studies. Where knowledge is found to be lacking, investigators aim to experiment with different approaches or interventions.

Three Question Types and Their Associated Research Aims

Important distinctions exist related to different types of background questions. Consider three general categories of questions that social workers might ask about populations, problems, and social phenomena: exploratory, descriptive, and explanatory. The different types of questions matter because the nature of the research questions determines the specific aims and most appropriate research approaches investigators apply in answering them.

Exploratory Research Questions. Social workers may find themselves facing a new, emerging problem where there is little previously developed knowledge available—so little, in fact, that it is premature to begin asking any more complex questions about causes or developing testable theories. Exploratory research questions open the door to beginning understanding and are basic; answers would help build the foundation of knowledge for asking more complex descriptive and explanatory questions. For example, in the early days of recognition that HIV/AIDS was emerging as a significant public health problem, it was premature to jump to questions about how to treat or prevent the problem. Not enough was known about the nature and scope of the problem, for whom it was a problem, how the problem was transmitted, factors associated with risk for exposure, what factors influenced the transition from HIV exposure to AIDS as a disease state, and what issues or problems might co-occur along with either HIV exposure or AIDS. In terms of a knowledge evolution process, a certain degree of exploration had to occur before intervention strategies for prevention and treatment could be developed, tested, and implemented.

In 1981, medical providers, public health officials, and the Centers for Disease Control and Prevention (CDC) began to circulate and publish observations about a disproportionate, unexpectedly high incidence rate of an unusual pneumonia and Kaposi’s sarcoma appearing in New York City and San Francisco/California among homosexual men (Curran, & Jaffe, 2011). As a result, a task force was formed and charged with conducting an epidemiologic investigation of this outbreak; “Within 6 months, it was clear that a new, highly concentrated epidemic of life threatening illness was occurring in the United States” (Curran & Jaffe, 2011, p. 65). The newly recognized disease was named for its symptoms: acquired immune deficiency syndrome, or AIDS. Exploratory research into the social networks of 90 living patients in 10 different cities indicated that 40 had a sexual contact link with another member of the 90-patient group (Auerbach, Darrow, Jaffe, & Curran, 1984). Additionally, cases were identified among persons who had received blood products related to their having hemophilia, persons engaged in needle sharing during substance use, women who had sexual contact with a patient, and infants born to exposed women. Combined, these pieces of information led to an understanding that the causal infectious factor (eventually named the human immunodeficiency virus, HIV) was transmitted by sexual contact, blood, and placental connection. This, in turn, led to knowledge building activities to develop both preventive and treatment strategies which could be implemented and studied. Social justice concerns relate to the slow rate at which sufficient resources were committed for evolving to the point of effective solutions for saving lives among those at risk or already affected by a heavily stigmatized problem.

The exploratory research approaches utilized in the early HIV/AIDS studies were both qualitative and quantitative in nature. Qualitative studies included in-depth interviews with identified patients—anthropological and public health interviews about many aspects of their living, work, and recreational environments, as well as many types of behavior. Quantitative studies included comparisons between homosexually active men with and without the diseases of concern. In addition, social network study methods combined qualitative and quantitative approaches. These examples of early exploratory research supported next steps in knowledge building to get us to where we are today. “Today, someone diagnosed with HIV and treated before the disease is far advanced can live nearly as long as someone who does not have HIV” (hiv.gov). While HIV infection cannot (yet) be “cured,” it can be controlled and managed as a chronic condition.

Descriptive Research Questions.  Social workers often ask for descriptions about specific populations, problems, processes, or phenomena. Descriptive research questions  might be expressed in terms of searching to create a profile of a group or population, create categories or types (typology) to describe elements of a population, document facts that confirm or contradict existing beliefs about a topic or issue, describe a process, or identify steps/stages in a sequential process (Grinnell & Unrau, 2014). Investigators may elect to approach the descriptive question using qualitative methods that result in a rich, deep description of certain individuals’ experiences or perceptions (Yegidis, Weinbach, & Meyers, 2018). Or, the descriptive question might lead investigators to apply quantitative methods, assigning numeric values, measuring variables that describe a population, process, or situation of interest. In descriptive research, investigators do not manipulate or experiment with the variables; investigators seek to describe what naturally occurs (Yegidis, Weinbach, & Meyers, 2018). As a result of studies answering descriptive questions, tentative theories and hypotheses may be generated.

Here are several examples of descriptive questions.

• How do incarcerated women feel about the option of medication-assisted treatment for substance use disorders?
• What barriers to engaging in substance misuse treatment do previously incarcerated persons experience during community reentry?
• How often do emerging adults engage in binge drinking in different drinking contexts (e.g., bars, parties, sporting events, at home)?
• What percent of incarcerated adults experience a substance use disorder?
• What is the magnitude of racial/ethnic disparities in access to treatment for substance use disorders?
• Who provides supervision or coordination of services for aging adults with intellectual or other developmental disabilities?
• What is the nature of the debt load among students in doctoral social work programs?

An example of descriptive research, derived from a descriptive question, is represented in an article where investigators addressed the question: How is the topic of media violence and aggression reported in print media (Martins et al., 2013)? This question led the investigators to conduct a qualitative content analysis, resulting in a description showing a shift in tone where earlier articles (prior to 2000) emphasized the link as a point of concern and later articles (since 2000) assumed a more neutral stance.

Correlational Research Questions.  One important type of descriptive question asks about relationships that might exist between variables—looking to see if variable x  and variable y  are associated or correlated with each other. This is an example of a correlational research question; it does not indicate whether “x” causes “y” or “y” causes “x”, only whether these two are related. Consider again the topic of exposure to violence in the media and its relationship to aggression. A descriptive question asked about the existence of a relationship between exposure to media violence ( variable x ) and children’s expression of aggression ( variable y ). Investigators reported one study of school-aged children, examining the relationship between exposure to three types of media violence (television, video games, and movies/videos) and three types of aggression (verbal, relational, and physical; Gentile, Coyne, & Walsh, 2011). The study investigators reported that media violence exposure was, indeed, correlated with all three types of aggressive behavior (and less prosocial behavior, too).

For a positive correlation (the blue line), as the value of the “x” variable increases, so does the value of the “y” variable (see Figure 1-4 for a general demonstration). An example might be as age or grade in school increases (“x”), so does the number of preadolescent, adolescent, and emerging adults who have used alcohol (“y”). For a negative correlation (the orange line), as the value of the “x” variable increases, the value of the “y” variable decreases. An example might be as the number of weeks individuals are in treatment for depression symptoms (“x”), the reported depression symptoms decreases (“y”). The neutral of non-correlation line (grey) means that the two variables, “x” and “y” do not have an association with each other. For example, number of years of teachers’ education (“x”) might be unrelated to the number of students dropping out of high school (“y”).

Figure 1-4. Depicting positive, negative, and neutral correlation lines

Descriptive correlational studies are sometimes called comparison studies because the descriptive question is answered by comparing groups that differ on one of the variables (low versus high media violence exposure) to see how they might differ on the other variable (aggressive behavior).

Explanatory Research Questions. To inform the design of evidence-informed interventions, social workers need answers to questions about the nature of the relationships between potentially influential factors or variables. An explanatory research question  might be mapped as: Does variable x  cause, lead to or prevent changes in variable y  (Grinnell & Unrau, 2014)? These types of questions often test theory related to etiology.

Comparative research might provide information about a relationship between variables. For example, the difference in outcomes between persons experiencing a substance use disorder and have been incarcerated compared to others with the same problem but have not been incarcerated may be related to their employability and ability to generate a living-wage income for themselves and their families. However, to develop evidence-informed interventions, social workers need to know that variables are not only related, but that one variable actually plays a causal role in relation to the other. Imagine, for example, that evidence demonstrated a significant relationship between adolescent self-esteem and school performance. Social workers might spend a great deal of effort developing interventions to boost self-esteem in hopes of having a positive impact on school performance. However, what if self-esteem comes from strong school performance? The self-esteem intervention efforts will not likely have the desired effect on school performance. Just because research demonstrates a significant relationship between two variables does not mean that the research has demonstrated a  causal relationship between those variables. Investigators need to be cautious about the extent to which their study designs can support drawing conclusions about causality; anyone reviewing research reports also needs to be alert to where causal conclusions are properly and improperly drawn.

The questions that drive intervention and evaluation research studies are explanatory in nature: does the intervention ( x ) have a significant impact on outcomes of interest ( y )? Another type of explanatory question related to intervention research concerns the mechanisms of change. In other words, not only might social workers be interested to find out  what  outcomes or changes can be attributed to an intervention, they may also be interested to learn how  the intervention causes those changes or outcomes.

Chapter Summary

In this chapter, you learned about different aspects of the knowledge building process and where different types of research questions might fit into the big picture. No single research study covers the entire spectrum; each study contributes a piece of the puzzle as a whole. Research questions come in many different forms and several different types. What is important to recall as we move through the remainder of the course is that the decisions investigators make about research approaches, designs, and procedures all start with the nature of the question being asked. And, the questions being asked are influenced by multiple factors, including what is previously known and remains unknown, the culture and context of the questioners, and what theories they have about what is to be studied. That leads us to the next chapter.

Social Work 3401 Coursebook Copyright © by Dr. Audrey Begun is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License , except where otherwise noted.

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7.1.4 - developing and evaluating hypotheses, developing hypotheses section  .

After interviewing affected individuals, gathering data to characterize the outbreak by time, place, and person, and consulting with other health officials, a disease detective will have more focused hypotheses about the source of the disease, its mode of transmission, and the exposures which cause the disease. Hypotheses should be stated in a manner that can be tested.

Hypotheses are developed in a variety of ways. First, consider the known epidemiology for the disease: What is the agent's usual reservoir? How is it usually transmitted? What are the known risk factors? Consider all the 'usual suspects.'

Open-ended conversations with those who fell ill or even visiting homes to look for clues in refrigerators and shelves can be helpful. If the epidemic curve points to a short period of exposure, ask what events occurred around that time. If people living in a particular area have the highest attack rates, or if some groups with a particular age, sex, or other personal characteristics are at greatest risk, ask "why?". Such questions about the data should lead to hypotheses that can be tested.

Evaluating Hypotheses Section  

There are two approaches to evaluating hypotheses: comparison of the hypotheses with the established facts and analytic epidemiology , which allows testing hypotheses.

A comparison with established facts is useful when the evidence is so strong that the hypothesis does not need to be tested. A 1991 investigation of an outbreak of vitamin D intoxication in Massachusetts is a good example. All of the people affected drank milk delivered to their homes by a local dairy. Investigators hypothesized that the dairy was the source, and the milk was the vehicle of excess vitamin D. When they visited the dairy, they quickly recognized that far more than the recommended dose of vitamin D was inadvertently being added to the milk. No further analysis was necessary.

Analytic epidemiology is used when the cause is less clear. Hypotheses are tested, using a comparison group to quantify relationships between various exposures and disease. Case-control, occasionally cohort studies, are useful for this purpose.

Case-control studies Section  

As you recall from last week's lesson, in a case-control study case-patients and controls are asked about their exposures. An odds ratio is calculated to quantify the relationship between exposure and disease.

In general, the more case patients (and controls) you have, the easier it is to find an association. Often, however, an outbreak is small. For example, 4 or 5 cases may constitute an outbreak. An adequate number of potential controls is more easily located. In an outbreak of 50 or more cases, 1 control per case-patient will usually suffice. In smaller outbreaks, you might use 2, 3, or 4 controls per case-patient. More than 4 controls per case-patient are rarely worth the effort because the power of the study does not increase much when you have more than 4 controls per case-patient (we will talk more on power and sample size in epidemiologic studies later in this course!).

Testing statistical significance Section  

The final step in testing a hypothesis is to determine how likely it is that the study results could have occurred by chance alone. Is the exposure the study results suggest as the source of the outbreak related to the disease after all? The significance of the odds ratio can be assessed with a chi-square test. We will also discuss statistical tests that control for many possible factors later in the course.

Cohort studies Section  

If the outbreak occurs in a small, well-defined population a cohort study may be possible. For example, if an outbreak of gastroenteritis occurs among people who attended a particular social function, such as a banquet, and a complete list of guests is available, it is possible to ask each attendee the same set of questions about potential exposures and whether he or she had become ill with gastroenteritis.

After collecting this information from each guest, an attack rate can be calculated for people who ate a particular item (were exposed) and an attack rate for those who did not eat that item (were not exposed). For the exposed group, the attack rate is found by dividing the number of people who ate the item and became ill by the total number of people who ate that item. For those who were not exposed, the attack rate is found by dividing the number of people who did not eat the item but still became ill by the total number of people who did not eat that item.

To identify the source of the outbreak from this information, you would look for an item with:

• high attack rate among those exposed and
• a low attack rate among those not exposed (so the difference or ratio between attack rates for the two exposure groups is high); in addition
• most of the people who became ill should have consumed the item, so that the exposure could explain most, if not all, of the cases.

We will learn more about cohort studies in Week 9 of this course.

• Open access
• Published: 11 June 2019

Population health intervention research: the place of theories

• Graham Moore 1 ,
• Linda Cambon 2 ,
• Susan Michie 3 ,
• Pierre Arwidson 4 ,
• Grégory Ninot 5 ,
• Christine Ferron 6 ,
• Louise Potvin 7 ,
• Nadir Kellou 8 ,
• Julie Charlesworth 9 ,
• François Alla   ORCID: orcid.org/0000-0002-5793-7190 2 &

Discussion Panel

Trials volume  20 , Article number:  285 ( 2019 ) Cite this article

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A Commentary to this article was published on 30 May 2019

An international workshop on population health intervention research (PHIR) was organized to foster exchanges between experts from different disciplines and different fields. This paper aims to summarize the discussions around some of the issues addressed: (1) the place of theories in PHIR, (2) why theories can be useful, and (3) how to choose and use the most relevant of them in evaluating PHIR.

The workshop included formal presentations by participants and moderated discussions. An oral synthesis was produced by a rapporteur to validate, through an expert consensus, the key points of the discussion and the recommendations. All discussions were recorded and have been fully transcribed.

The following recommendations were generated through a consensus in the workshop discussions: (i) The evaluation of interventions, like their development, could be improved through better use of theory. (ii) The referenced theory and framework must be clarified. (iii) An intervention theory should be developed by a partnership of researchers and practitioners. (iv) More use of social theory is recommended. (v) Frameworks and a common language are helpful in selecting and communicating a theory. (vi) Better reporting of interventions and theories is needed.

Theory-driven interventions and evaluations are key in PHIR as they facilitate the understanding of mechanisms of change. There are many challenges in developing the most appropriate theories for interventions and evaluations. With the wealth of information now being generated, this subject is of increasing importance at many levels, including for public health policy. It is, therefore, timely to consider how to build on the experiences of many different disciplines to enable the development of better theories and facilitate evidence-based decisions.

Peer Review reports

Population health intervention research (PHIR) can be defined as the use of scientific research methods to produce knowledge on policy and intervention programs. Whether or not they are conducted in the context of the health system, these policies and programs have the potential to make an impact at the population level [ 1 ]. Population health interventions are generally, but not necessarily, considered as complex interventions, with complexity often seen as arising from their being “made up of various interconnecting parts” [ 2 ]. These interventions can also be considered as complex because of the influence of context on their implementation and outcomes [ 3 ]. The development of a complex intervention and in particular, the choice of levers targeted depends on explicit or implicit theories about their mechanism of action in their context, an understanding of which can then be enhanced through an evaluation.

The Medical Research Council (MRC) guidance [ 4 ] provides recommendations to guide researchers in designing, developing, and evaluating complex health interventions, and more specifically, in evaluating the process [ 5 ]. In the overarching MRC guidance, and to a greater extent in the guidance for evaluating the process, there is some emphasis on the use of theory to frame the evaluation: (1) to articulate the causal assumptions behind an intervention and then use this process evaluation frame to understand the implementation, (2) to test mechanisms (and contextual contingencies) and generate emerging insights into the mechanisms, (3) to guide the choice of mechanisms to test, and (4) to understand contextual interactions.

While this guidance represents a key milestone, methods and tools to conduct evaluations need to be refined and there are many outstanding challenges and questions. Notably, the overarching 2008 MRC framework is itself currently undergoing revision, since thinking has moved on significantly in the 11 years since it was published [ 6 , 7 ]. There is a need not only to develop methods, tools, and practical guidance for researchers, but also to clarify some underlying paradigms and to operationalize the overall research approach, from conceptualization to the dissemination of an intervention.

In France, where PHIR is well developed, the National Coordinated Action for Intervention Research [Action coordonnée pour la recherche interventionnelle en santé publique] (ACRISP) was created in 2015: (1) to support the development of research that is both scientifically sound and useful to practitioners and policy makers, (2) to promote the sharing of experiences between researchers, practitioners, and policy makers, (3) to encourage conceptual and methodological reflections, and (4) to make proposals in terms of organizing research, regulations, and funding.

In November 2016, ACRISP organized an international workshop, bringing together some of the world’s leading experts and researchers. Due to the complexity of the field, which requires an interdisciplinary approach, the objective was to promote exchanges between researchers from different disciplines. The workshop provided an opportunity to share experiences and learning between researchers from various fields, such as clinical research, health services research, and PHIR. The researchers invited were particularly interested in methodological research (most of them had published methodological papers).

Some of the key issues in PHIR were addressed. The presentations and discussions, in three successive sessions, covered various themes. One of them was the place of theory in PHIR. Indeed, according to the MRC guideline, a focal point is to explore the conditions under which an intervention is effective, that is why, for whom, and how does the intervention work? One way to answer these questions as well as possible is to integrate a theoretical reflection into the different steps of an evaluation [ 8 ] by: (i) defining the theoretical hypotheses about how the intervention works, (ii) choosing the data to be collected and how they are to be collected (especially for validating the hypotheses), and (iii) defining the transferability conditions, which are the key functions of an intervention, to guide the transfer or the scaling-up of the intervention. Theory could be used within these steps to inform the evaluation of the PHIR.

This article aims to share and synthesize the discussions, works, and recommendations put forward by experts on this subject during the workshop. Practically, the aim is to clarify why and how theories could be better used to improve PHIR, and how to choose and use the most relevant of them in evaluating PHIR. It is not intended to be a systematic synthesis of the science or to present new data, but to be a milestone for a common basis for discussion between researchers from different disciplines and fields.

The workshop was organized by GM and FA (who prepared the program and compiled a bibliographic file). It included formal presentations by participants and discussions moderated by GM. At the end of the workshop, an oral synthesis was produced by a rapporteur (PA) to validate, through an expert consensus, the key points of the discussion and the recommendations. All the discussions and the validated synthesis were recorded. The recordings have been fully transcribed. A first draft of the paper was prepared by JC and FA from this material, then corrected and validated by GM, then by all the coauthors (who all participated in the debates).

Why use theory in the development and evaluation of PHIR?

The potential focus of a process evaluation for PHIR can be considered as covering everything between the intervention as it is described in a manual and what happens in practice in terms of implementing it. This includes what mechanisms are used, how these processes are shaped, and how they interact with their contexts. Hence, it plays a particularly critical role in developing, testing, and refining theory as part of overarching evaluation studies. One of the objectives of a process evaluation is to understand the mechanisms by which an intervention has had its effect so that more effective interventions can be developed. For complex interventions, it is also necessary to understand which components within a complex intervention are linked with which mechanisms.

Various stages in the development and evaluation of an intervention, including a process evaluation, involve surfacing and interrogating the often latent (nonexplicit) theoretical assumptions regarding how a new set of actions will produce desired outcomes in a particular context, and using a theory of change to frame the questions that a process evaluation needs to pose and what methods will be used to address them. To measure the consistency of intervention delivery with underpinning theoretical principles, it is necessary to start from a point of clarity on what those assumptions are.

Hence, a theory, defined as “a set of analytical principles or statements designed to structure our observation, understanding and explanation of the world” [ 9 ], is a useful starting point for developing an intervention, while an evaluation can test and refine the theory. An evaluation to test and refine such theories can maximize their contribution to similar or different contexts and more generally. We agree with the position of various methodological works that a coherent theoretical basis for intervention development and use of evaluations to test key causal assumptions and build theory are crucial [ 10 ]. There is a need to be explicit regarding the causal assumptions driving an intervention, whether these are derived from formal social science theory, experience, common sense, or a combination of all of these various forms of “theory” [ 11 ].

Nevertheless, to a large extent, the group considered that sometimes people talk at cross-purposes in relation to the various kind of theory. As an illustration, theory has often been taken to mean formal academic theories, called classic theories by Nilsen [ 9 ], such as the theory of planned behavior or social cognitive theory. From a realist perspective, all interventions or programs can be viewed as theories [ 11 ], because they represent manifestations of assumptions about how an action produces change in a particular context.

So, causes and assumptions always drive interventions and they are all theories, whatever source they are derived from. In practice, interventions in this field draw on quite a wide range of types of theories based on academic research and experience (or common sense), and come from different groups such as researchers and practitioners/actors. Indeed, the outcome of an evaluation can be viewed as being the test of an underlying explicit program theory or the “reconstruction” a posteriori of an implicit theory. Finally, a third case could be the theory of change (ToC) [ 12 ] as “a theory of how and why an initiative works which can be empirically tested by measuring indicators for every expected step on the hypothesized causal pathway to impact.” In this case, the theory is different from classic sociological or psychological theories and the middle-range theories of Pawson and Tilley. It is a pragmatic framework used to design and evaluate development programs in many different contexts.

Moreover, using a theory-driven approach could contribute to improving theories [ 13 ]. The process of theorizing is always incomplete [ 14 ]. Hence, researchers should not treat existing theoretical knowledge as received wisdom and should make the effort to explain what the empirical findings mean for their theory(ies). We underline the necessity (i) to compare the empirical case under investigation and earlier studies that have contributed to the development of the theory(ies) used and (ii) to move beyond simply cataloguing different factors provided by theories and towards an exploration of how these factors work together. The aim of a theory-driven approach is not only to find similarities between the empirical case and extant theory(ies), but also to identify and explain the differences, thus moving the theory(ies) forward.

Hence, clarity about theory, particularly the causes and assumptions, is important for understanding outcome evaluations and implementation fidelity (consistency with function and underlying theory). Interventions delivered in complex systems often look very different, in terms of their precise forms, from one place to another. Therefore, in implementing a good understanding of functions, the causes and assumptions are important for transferability. Clarity about theory is also important for informing future interventions and finding the methods that are likely to be more transferable to other contexts where a problem is due to similar mechanisms, and for understanding which mechanisms work, for whom, and in what contexts. In keeping with this, one concern is to choose the most relevant type of theory to use in an evaluation. Guidance is currently in development for this aspect of uncertainty [ 15 ].

Which theory should be chosen?

The question of which theory to choose is often a dilemma and the subject of much debate. There is a wide range of theories based on academic research and experience (or common sense) and from different groups, such as researchers and practitioners/actors.

For classic theories, the dominant focus tends to be on individual psychology rather than more structural social theories applicable to the population level of the intervention. Indeed, the more individualized nature of intervention theory is commonly acknowledged [ 16 ], but there is also a wealth of alternative social science theory for intervention researchers to consider. A more pluralistic approach to the sources of theory could facilitate the development, evaluation, and implementation of interventions that are more effective in addressing PHIR problems [ 10 ]. For example, the socioecological approach is increasingly being used in intervention studies that aim to promote healthier behaviors. While socioecological frameworks are typically not explanatory, they provide a framework for drawing together theories from multiple disciplines at multiple system levels, by using social theories or frameworks, such as social determinant frameworks, the theory of diffusion, social networks theories, social capital theories, other professional theories, and organizational theories.

Socioecological theory-based interventions have multiple levels of influence. The individual, with their emotions, knowledge, beliefs, and norms, interacts with a social environment represented by family, friends, and co-workers, all within their living environment (natural or built, and organizational or public policy). So, population health researchers could usefully move towards considering the inclusion of forms of theory that address deeper influences on behavior rather than focusing only on a theory that addresses surface causes. Such changes in theoretical approach are challenging because more complex, system-level theories are not as readily accessible and easy to use as the more simplistic theoretical models. In addition, there is a tendency for recommendations for evaluations to be based on theories in a slightly simplistic way. There has, historically, been a tendency to pick a theory off the shelf (rather than using a bespoke theory of change) and to use it to drive the evaluation. The choice of theory based on conceptual simplicity and because others have used it can lead to the selection of weak theories. Many dominant theories have done little to make interventions more effective [ 10 ]. Moreover, evaluation theories are not necessarily organized to be user-friendly and are not necessarily adopted. When a theory is needed, even though there are a lot of intervention theories [ 17 ], they are often not used. To avoid this and to choose the best theory, the selection of theories to frame evaluations should be much more based on evidence.

Moreover, much theory is focused on how intervention actions impact health outcomes. However, triggering these mechanisms is contingent on introducing changes to complex systems. There is a need for further holistic approaches that would incorporate a focus on: which existing ways of working will be displaced and how; what new ways of working will be introduced; and how these changes would be expected to impact the target population. In this case, middle-range theories or ToC could accurately be used to hypothesize about and explore these elements. Indeed, these theories actually include elements from classic theories (e.g., motivation theories, Prochaska’s stages of change, etc.) and implementation theories [ 9 ] that describe how actions and levers trigger the mechanisms (e.g. self-efficacy, skills, emotional regulation, agentivity, etc.) involved in classic theories.

Therefore, careful consideration of the purpose of using theory is essential, because the purpose should guide the selection of the theory. To guide thinking, to help communicate across disciplines, and to structure research, theories that are less accurate as a representation of reality may be more helpful because they may be easier to work with. However, a beautifully operationalized plan may in some instances count for very little in reality. To really understand and to be able to work with reality and effect change, as well as understanding the complexity of it, we need to be able to drill down to find precision and then scale up to look at the interactions.

How should theory be used in a PHIR evaluation?

As outlined, evaluation has a role in theorizing and testing intervention mechanisms and also in how interventions interact with their context. There was a consensus among this group that intervention development and evaluation should be driven by theory. Indeed, a theory-driven evaluation [ 18 ] could be used to assess the efficacy and consequently the transferability [ 19 ] of an intervention.

It may be useful to consider the roles of theory in relation to the MRC guidance. An evaluation of an intervention includes assessing its effectiveness, understanding the change process, and assessing its cost-effectiveness, whilst development includes identifying the evidence base, identifying or developing a theory, and modelling processes and outcomes.

The move towards a greater emphasis on evaluation as a theory-building exercise has led to the notion that the evaluation and development phases can be integrated into the approach for developing and using theory. The development phase has a modelling process and evaluation seeks to understand the change process, which is at the heart of designing and evaluating interventions.

Beyond these frameworks, the group agreed that the key questions relating to theory are as follows:

Does the outcome fit the theory, i.e., what is the mechanism?

Can the theory be implemented?

Can the theory then be used in similar contexts?

Can the theory be used in other different contexts?

Moreover, we discussed the need in PHIR to take into account different classic theories, among other things, to balance the eco-sociological and behavioral approaches to interventions. We also talked about the particularities of middle-range theories and ToC, and gathered different types of theories that could best be adapted to fit the implementation context. The use of theories in complex interventions needs to take account of many different perspectives and it is important to understand different points of view. For example, investigating social networks may require a combination of epidemiological, psychological, sociological, and health promotion research perspectives, and an interdisciplinary approach may be required. More generally, a process evaluation could be helped by creating an empowering evaluation with public participation. It is very important to involve stakeholders in evaluating a theory because there may already be interventions that seem to be working well, and the aim may be to standardize them, to understand how they work, and to identify the different components needed to understand the intervention, and then to set up a trial or an evaluation of an intervention.

Therefore, the real contribution might be to develop systematic ways of thinking for all disciplines interested in PHIR evaluations, in terms of how to begin to think about the criteria to use and how to think about what theories could be helpful.

Position statements, further research directions, and recommendations

In these discussions, the experts of the international workshop organized by ACRISP defined six recommendations for using theory in PHIR.

Intervention evaluation, like intervention development, should be driven by theory

Exploring why, for whom, and how interventions work leads us to consider the necessity of integrating a theory-driven approach into the different steps of the evaluation: (1) the intervention mapping, which provides explicit hypothetical causal pathways to explore in the evaluation, (2) the choice of data and methods, (3) the definition of transferability, and (4) the scalability of the intervention.

Referenced theories and frameworks must be clarified

For classic theories, the use of a published and validated theory or framework should be systematically considered. However, we must distinguish frameworks used in a realistic evaluation or ToC framework where the theory is a combination of (i) academic and evidence-based classic theories, frameworks, and lessons drawn from other experiences and (ii) contextual parameters and stakeholders’ experiences and points of view. Moreover, by offering cases to test theories, the use of a theory-driven approach helps to build a cumulative understanding of the general processes and mechanisms of change, allowing these theories to be refined.

PHIR theory should be developed by researchers and practitioners

Theories can be academically driven or developed by the people who design the interventions. The evaluation has to explain the theory, or even reconstruct an implicit theory a posteriori. To do so, the involvement of actors and practitioners is important.

More use of social theory is recommended

Social theory should be considered in PHIR to address: (1) changes in social conditions, (2) context, (3) the ways that context shapes behavior, and (4) what happens when programs are unfolded in context. Involving people outside the usual scope needs to be considered, especially multidisciplinary teams in methodological work on how to develop interventions.

Frameworks and a common language are helpful in selecting and communicating a theory

Words can have subtle differences that may be really important. Key words need to be defined if they are to be translated to a different context. Using language that is understood by all is key, especially using the same term for the same component, where possible. The most important thing is that everyone knows what is meant. Moreover, transparency is important.

There is potential value in building ontologies with systematic methods for specifying concepts and the relationships between them using a controlled vocabulary framework and taxonomy. Ways of integrating and coordinating different definitions rather than trying to use the same terms and labels may also be useful.

Better reporting of interventions and theories is needed

Information on the theory and context is important when the results are as expected, as well as when they are unexpected. For a theory, it is important to know how it was designed in the intervention or the evaluation, or how it was selected, and also how it has been applied.

In developing a theory to guide a program, so that one can evaluate it in terms of the process and mechanisms of action, the important point is to indicate what the concepts are and how they were developed or sourced, and to be transparent about this. In this way, the theory can be evauated in its framework and everybody can learn from it.

Describing things transparently is essential. For example, for complex interventions, descriptions often lack many of the details required to facilitate their replication by others. Journal editors need to implement the guidelines for transparent reporting. Editors and authors should ensure that information on the theory is included and is understandable to the readers and sharable with others, while also taking into consideration both the context and mechanisms.

It may not be possible to describe absolutely everything involved in an intervention, especially a complex intervention, in a journal article, so such details could be provided elsewhere, e.g. protocols, forms, etc. or as appendices rather than summarized.

Theory has a key place in a process evaluation of PHIR. Theory highlights the role of mechanisms, an understanding of which is essential in the process evaluation.

PHIR should be driven by theory. There are many options for achieving this. The choice of theory and the many different approaches are often a subject of debate. The complexities in this field engender many challenges in developing the most appropriate theories for intervention development and evaluation in specific contexts and also those which can be transferred to similar contexts, or indeed more generally to different contexts.

Nonetheless, there is a wealth of information and experience across many different disciplines, and this subject is of increasing importance at many levels, including for public health policy. It is, therefore, timely to consider how to build on experience from many different disciplines to enable the development of better theories and facilitate evidence-based decisions.

The consensus reached by this group is that theory-driven intervention and theory-driven evaluation are key in PHIR. The group has provided some current thinking and suggestions to take this forward.

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Acknowledgements

Discussion panel: François Alla, Pierre Arwidson, Pierre Blaise, Christopher Bonell, Isabelle Boutron, Linda Cambon, Rona Campbell, Patrizia Carrieri, Franck Chauvin, François Dabis, Nancy Edwards, Christine Ferron, Marie-Renée Guevel, Nadir Kellou, Joëlle Kivits, Antony Lacouture, Thierry Lang, Susan Michie, Laëtitia Minary, Graham Moore, Grégory Ninot, Kareen Nour, Jeanine Pommier, Louise Potvin, Lehana Thabane.

The workshop was supported by the partners of ACRISP: Action coordonnée pour la recherche interventionnelle, AVIESAN: Alliance nationale pour els sciences de la vie et de la santé, INCa: Institut National du cancer, ANRS: France Recherche Nord&Sud, Sida-HIV Hépatite and IReSP: Institut de recherche en santé publique.

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Methods for testing theory and evaluating impact in randomized field trials: intent-to-treat analyses for integrating the perspectives of person, place, and time

Collaborators.

• Prevention Science and Methodology Group : Rick Price ,  George Howe ,  Irwin Sandler ,  Patrick Tolan ,  David Hawkins ,  José Szapocznik ,  Phil Leaf ,  Chen-Pin Wang ,  Pamela Moke ,  Terri Singer ,  Amelia Mackenzie

Affiliation

• 1 Department of Epidemiology and Biostatistics, College of Public Health, University of South Florida, 13201 Bruce B Downs Blvd., Tampa, FL 33612, United States. [email protected]
• PMID: 18215473
• PMCID: PMC2560173
• DOI: 10.1016/j.drugalcdep.2007.11.013

Randomized field trials provide unique opportunities to examine the effectiveness of an intervention in real world settings and to test and extend both theory of etiology and theory of intervention. These trials are designed not only to test for overall intervention impact but also to examine how impact varies as a function of individual level characteristics, context, and across time. Examination of such variation in impact requires analytical methods that take into account the trial's multiple nested structure and the evolving changes in outcomes over time. The models that we describe here merge multilevel modeling with growth modeling, allowing for variation in impact to be represented through discrete mixtures--growth mixture models--and nonparametric smooth functions--generalized additive mixed models. These methods are part of an emerging class of multilevel growth mixture models, and we illustrate these with models that examine overall impact and variation in impact. In this paper, we define intent-to-treat analyses in group-randomized multilevel field trials and discuss appropriate ways to identify, examine, and test for variation in impact without inflating the Type I error rate. We describe how to make causal inferences more robust to misspecification of covariates in such analyses and how to summarize and present these interactive intervention effects clearly. Practical strategies for reducing model complexity, checking model fit, and handling missing data are discussed using six randomized field trials to show how these methods may be used across trials randomized at different levels.

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13 4.2 Causality

Learning objectives.

• Define and provide an example of idiographic and nomothetic causal explanations
• Describe the role of causality in quantitative research as compared to qualitative research
• Identify, define, and describe each of the main criteria for nomothetic causal explanations
• Describe the difference between and provide examples of independent, dependent, and control variables
• Define hypothesis, be able to state a clear hypothesis, and discuss the respective roles of quantitative and qualitative research when it comes to hypotheses

Most social scientific studies attempt to provide some kind of causal explanation.  In other words, it is about cause and effect. A study on an intervention to prevent child abuse is trying to draw a connection between the intervention and changes in child abuse. Causality refers to the idea that one event, behavior, or belief will result in the occurrence of another, subsequent event, behavior, or belief.  It seems simple, but you may be surprised to learn there is more than one way to explain how one thing causes another. How can that be? How could there be many ways to understand causality?

Think back to our chapter on paradigms, which were analytic lenses comprised of assumptions about the world. You’ll remember the positivist paradigm as the one that believes in objectivity and social constructionist paradigm as the one that believes in subjectivity. Both paradigms are correct, though incomplete, viewpoints on the social world and social science.

A researcher operating in the social constructionist paradigm would view truth as subjective. In causality, that means that in order to try to understand what caused what, we would need to report what people tell us. Well, that seems pretty straightforward, right? Well, what if two different people saw the same event from the exact same viewpoint and came up with two totally different explanations about what caused what? A social constructionist might say that both people are correct. There is not one singular truth that is true for everyone, but many truths created and shared by people.

When social constructionists engage in science, they are trying to establish one type of causality—idiographic causality.  The word idiographic comes from the root word “idio” which means peculiar to one, personal, and distinct. An idiographic causal explanation means that you will attempt to explain or describe your phenomenon exhaustively, based on the subjective understandings of your participants. Idiographic causal explanations are intended to explain one particular context or phenomenon.  These explanations are bound with the narratives people create about their lives and experience, and are embedded in a cultural, historical, and environmental context. Idiographic causal explanations are so powerful because they convey a deep understanding of a phenomenon and its context. From a social constructionist perspective, the truth is messy. Idiographic research involves finding patterns and themes in the causal themes established by your research participants.

If that doesn’t sound like what you normally think of as “science,” you’re not alone. Although the ideas behind idiographic research are quite old in philosophy, they were only applied to the sciences at the start of the last century. If we think of famous Western scientists like Newton or Darwin, they never saw truth as subjective. They operated with the understanding there were objectively true laws of science that were applicable in all situations. In their time, another paradigm–the positivist paradigm–was dominant and continues its dominance today. When positivists try to establish causality, they are like Newton and Darwin, trying to come up with a broad, sweeping explanation that is universally true for all people. This is the hallmark of a nomothetic causal explanation .  The word nomothetic is derived from the root word “nomo” which means related to a law or legislative, and “thetic” which means something that establishes.  Put the root words together and it means something that is establishing a law, or in our case, a universal explanation.

Nomothetic causal explanations are incredibly powerful. They allow scientists to make predictions about what will happen in the future, with a certain margin of error. Moreover, they allow scientists to generalize —that is, make claims about a large population based on a smaller sample of people or items. Generalizing is important. We clearly do not have time to ask everyone their opinion on a topic, nor do we have the ability to look at every interaction in the social world. We need a type of causal explanation that helps us predict and estimate truth in all situations.

If these still seem like obscure philosophy terms, let’s consider an example. Imagine you are working for a community-based non-profit agency serving people with disabilities. You are putting together a report to help lobby the state government for additional funding for community support programs, and you need to support your argument for additional funding at your agency. If you looked at nomothetic research, you might learn how previous studies have shown that, in general, community-based programs like yours are linked with better health and employment outcomes for people with disabilities. Nomothetic research seeks to explain that community-based programs are better for everyone with disabilities. If you looked at idiographic research, you would get stories and experiences of people in community-based programs. These individual stories are full of detail about the lived experience of being in a community-based program. Using idiographic research, you can understand what it’s like to be a person with a disability and then communicate that to the state government. For example, a person might say “I feel at home when I’m at this agency because they treat me like a family member” or “this is the agency that helped me get my first paycheck.”

Neither kind of causal explanation is better than the other. A decision to conduct idiographic research means that you will attempt to explain or describe your phenomenon exhaustively, attending to cultural context and subjective interpretations. A decision to conduct nomothetic research, on the other hand, means that you will try to explain what is true for everyone and predict what will be true in the future. In short, idiographic explanations have greater depth, and nomothetic explanations have greater breadth. More importantly, social workers understand the value of both approaches to understanding the social world. A social worker helping a client with substance abuse issues seeks idiographic knowledge when they ask about that client’s life story, investigate their unique physical environment, or probe how they understand their addiction. At the same time, a social worker also uses nomothetic knowledge to guide their interventions. Nomothetic research may help guide them to minimize risk factors and maximize protective factors or use an evidence-based therapy, relying on knowledge about what in general helps people with substance abuse issues.

Nomothetic causal explanations

If you are trying to generalize about causality, or create a nomothetic causal explanation, then the rest of these statements are likely to be true: you will use quantitative methods, reason deductively, and engage in explanatory research. How can we make that prediction? Let’s take it part by part.

Because nomothetic causal explanations try to generalize, they must be able to reduce phenomena to a universal language, mathematics. Mathematics allows us to precisely measure, in universal terms, phenomena in the social world. Because explanatory researchers want a clean “x causes y” explanation, they need to use the universal language of mathematics to achieve their goal. That’s why nomothetic causal explanations use quantitative methods.  It’s helpful to note that not all quantitative studies are explanatory. For example, a descriptive study could reveal the number of people without homes in your county, though it won’t tell you why they are homeless. But nearly all explanatory studies are quantitative.

What we’ve been talking about here is an association between variables. When one variable precedes or predicts another, we have what researchers call independent and dependent variables. Two variables can be associated without having a causal relationship.  However, when certain conditions are met (which we describe later in this chapter), the independent variable is considered as a “ cause ” of the dependent variable.  For our example on spanking and aggressive behavior, spanking would be the independent variable and aggressive behavior addiction would be the dependent variable.  In causal explanations, the  independent variable is the cause, and the dependent variable is the effect.  Dependent variables depend on independent variables. If all of that gets confusing, just remember this graphical depiction:

The strength of the association between the independent variable and dependent variable is another important factor to take into consideration when attempting to make causal claims when your research approach is nomothetic.  In this context, strength refers to statistical significance . When the  association between two variables is shown to be statistically significant, we can have greater confidence that the data from our sample reflect a true association between those variables in the target population. Statistical significance is usually represented in statistics as the p- value .  Generally a p -value of .05 or less indicates the association between the two variables is statistically significant.

A hypothesis is a statement describing a researcher’s expectation regarding the research findings. Hypotheses in quantitative research are nomothetic causal explanations that the researcher expects to demonstrate. Hypotheses are written to describe the expected association between the independent and dependent variables. Your prediction should be taken from a theory or model of the social world. For example, you may hypothesize that treating clinical clients with warmth and positive regard is likely to help them achieve their therapeutic goals. That hypothesis would be using the humanistic theories of Carl Rogers. Using previous theories to generate hypotheses is an example of deductive research. If Rogers’ theory of unconditional positive regard is accurate, your hypothesis should be true.

Let’s consider a couple of examples. In research on sexual harassment (Uggen & Blackstone, 2004), one might hypothesize, based on feminist theories of sexual harassment, that more females than males will experience specific sexually harassing behaviors. What is the causal explanation being predicted here? Which is the independent and which is the dependent variable? In this case, we hypothesized that a person’s gender (independent variable) would predict their likelihood to experience sexual harassment (dependent variable).

Sometimes researchers will hypothesize that an association will take a specific direction. As a result, an increase or decrease in one area might be said to cause an increase or decrease in another. For example, you might choose to study the association between age and support for legalization of marijuana. Perhaps you’ve taken a sociology class and, based on the theories you’ve read, you hypothesize that age is negatively related to support for marijuana legalization. In fact, there are empirical data that support this hypothesis. Gallup has conducted research on this very question since the 1960s (Carroll, 2005). What have you just hypothesized? You have hypothesized that as people get older, the likelihood of their supporting marijuana legalization decreases. Thus, as age (your independent variable) moves in one direction (up), support for marijuana legalization (your dependent variable) moves in another direction (down). So, positive associations involve two variables going in the same direction and negative associations involve two variables going in opposite directions. If writing hypotheses feels tricky, it is sometimes helpful to draw them out and depict each of the two hypotheses we have just discussed.

It’s important to note that once a study starts, it is unethical to change your hypothesis to match the data that you found. For example, what happens if you conduct a study to test the hypothesis from Figure 4.3 on support for marijuana legalization, but you find no association between age and support for legalization? It means that your hypothesis was wrong, but that’s still valuable information. It would challenge what the existing literature says on your topic, demonstrating that more research needs to be done to figure out the factors that impact support for marijuana legalization. Don’t be embarrassed by negative results, and definitely don’t change your hypothesis to make it appear correct all along!

Establishing causality in nomothetic research

Let’s say you conduct your study and you find evidence that supports your hypothesis, as age increases, support for marijuana legalization decreases. Success! Causal explanation complete, right? Not quite. You’ve only established one of the criteria for causality. The main criteria for causality have to do with covariation, plausibility, temporality, and spuriousness. In our example from Figure 4.3, we have established only one criteria—covariation. When variables covary , they vary together. Both age and support for marijuana legalization vary in our study. Our sample contains people of varying ages and varying levels of support for marijuana legalization and they vary together in a patterned way–when age increases, support for legalization decreases.

Just because there might be some correlation between two variables does not mean that a causal explanation between the two is really plausible. Plausibility means that in order to make the claim that one event, behavior, or belief causes another, the claim has to make sense. It makes sense that people from previous generations would have different attitudes towards marijuana than younger generations. People who grew up in the time of Reefer Madness or the hippies may hold different views than those raised in an era of legalized medicinal and recreational use of marijuana.

Once we’ve established that there is a plausible association between the two variables, we also need to establish that the cause happened before the effect, the criterion of temporality . A person’s age is a quality that appears long before any opinions on drug policy, so temporally the cause comes before the effect. It wouldn’t make any sense to say that support for marijuana legalization makes a person’s age increase. Even if you could predict someone’s age based on their support for marijuana legalization, you couldn’t say someone’s age was caused by their support for legalization.

Finally, scientists must establish nonspuriousness. A spurious association is one in which an association between two variables appears to be causal but can in fact be explained by some third variable. For example, we could point to the fact that older cohorts are less likely to have used marijuana. Maybe it is actually use of marijuana that leads people to be more open to legalization, not their age. This is often referred to as the third variable problem, where a seemingly true causal explanation is actually caused by a third variable not in the hypothesis. In this example, the association between age and support for legalization could be more about having tried marijuana than the age of the person.

Quantitative researchers are sensitive to the effects of potentially spurious associations. They are an important form of critique of scientific work. As a result, they will often measure these third variables in their study, so they can control for their effects. These are called control variables , and they refer to variables whose effects are controlled for mathematically in the data analysis process. Control variables can be a bit confusing, but think about it as an argument between you, the researcher, and a critic.

Researcher: “The older a person is, the less likely they are to support marijuana legalization.” Critic: “Actually, it’s more about whether a person has used marijuana before. That is what truly determines whether someone supports marijuana legalization.” Researcher: “Well, I measured previous marijuana use in my study and mathematically controlled for its effects in my analysis. The association between age and support for marijuana legalization is still statistically significant and is the most important association here.”

Let’s consider a few additional, real-world examples of spuriousness. Did you know, for example, that high rates of ice cream sales have been shown to cause drowning? Of course, that’s not really true, but there is a positive association between the two. In this case, the third variable that causes both high ice cream sales and increased deaths by drowning is time of year, as the summer season sees increases in both (Babbie, 2010). Here’s another good one: it is true that as the salaries of Presbyterian ministers in Massachusetts rise, so too does the price of rum in Havana, Cuba. Well, duh, you might be saying to yourself. Everyone knows how much ministers in Massachusetts love their rum, right? Not so fast. Both salaries and rum prices have increased, true, but so has the price of just about everything else (Huff & Geis, 1993).

Finally, research shows that the more firefighters present at a fire, the more damage is done at the scene. What this statement leaves out, of course, is that as the size of a fire increases so too does the amount of damage caused as does the number of firefighters called on to help (Frankfort-Nachmias & Leon-Guerrero, 2011). In each of these examples, it is the presence of a third variable that explains the apparent association between the two original variables.

In sum, the following criteria must be met for a correlation to be considered causal:

• The two variables must vary together.
• The association must be plausible.
• The cause must precede the effect in time.
• The association must be nonspurious (not due to a third variable).

Once these criteria are met, there is a nomothetic causal explanation, one that is objectively true. However, this is difficult for researchers to achieve. You will almost never hear researchers say that they have proven their hypotheses. A statement that bold implies that a association has been shown to exist with absolute certainty and that there is no chance that there are conditions under which the hypothesis would not be true. Instead, researchers tend to say that their hypotheses have been supported (or not). This more cautious way of discussing findings allows for the possibility that new evidence or new ways of examining an association will be discovered. Researchers may also discuss a null hypothesis. The null hypothesis is one that predicts no association between the variables being studied. If a researcher fails to accept the null hypothesis, she is saying that the variables in question are likely to be related to one another.

Idiographic causal explanations

If you not trying to generalize, but instead are trying to establish an idiographic causal explanation, then you are likely going to use qualitative methods, reason inductively, and engage in exploratory or descriptive research. We can understand these assumptions by walking through them, one by one.

Researchers seeking idiographic causal explanation are not trying to generalize, so they have no need to reduce phenomena to mathematics. In fact, using the language of mathematics to reduce the social world down is a bad thing, as it robs the causality of its meaning and context. Idiographic causal explanations are bound within people’s stories and interpretations. Usually, these are expressed through words. Not all qualitative studies analyze words, as some can use interpretations of visual or performance art, but the vast majority of social science studies do.

But wait, we predicted that an idiographic causal explanation would use descriptive or exploratory research. How can we build causality if we are just describing or exploring a topic? Wouldn’t we need to do explanatory research to build any kind of causal explanation?  To clarify, explanatory research attempts to establish nomothetic causal explanations—an independent variable is demonstrated to cause changes a dependent variable. Exploratory and descriptive qualitative research are actually descriptions of the causal explanations established by the participants in your study. Instead of saying “x causes y,” your participants will describe their experiences with “x,” which they will tell you was caused by and influenced a variety of other factors, depending on time, environment, and subjective experience. As stated before, idiographic causal explanations are messy. The job of a social science researcher is to accurately identify patterns in what participants describe.

Let’s consider an example. What would you say if you were asked why you decided to become a social worker?  If we interviewed many social workers about their decisions to become social workers, we might begin to notice patterns. We might find out that many social workers begin their careers based on a variety of factors, such as: personal experience with a disability or social injustice, positive experiences with social workers, or a desire to help others. No one factor is the “most important factor,” like with nomothetic causal explanations. Instead, a complex web of factors, contingent on context, emerge in the dataset when you interpret what people have said.

Finding patterns in data, as you’ll remember from Chapter 2, is what inductive reasoning is all about. A qualitative researcher collects data, usually words, and notices patterns. Those patterns inform the theories we use in social work. In many ways, the idiographic causal explanations created in qualitative research are like the social theories we reviewed in Chapter 2  and other theories you use in your practice and theory courses. Theories are explanations about how different concepts are associated with each other how that network of associations works in the real world. While you can think of theories like Systems Theory as Theory (with a capital “T”), inductive causality is like theory with a small “t.” It may apply only to the participants, environment, and moment in time in which the data were gathered. Nevertheless, it contributes important information to the body of knowledge on the topic studied.

Unlike nomothetic causal explanations, there are no formal criteria (e.g., covariation) for establishing causality in idiographic causal explanations. In fact, some criteria like temporality and nonspuriousness may be violated. For example, if an adolescent client says, “It’s hard for me to tell whether my depression began before my drinking, but both got worse when I was expelled from my first high school,” they are recognizing that oftentimes it’s not so simple that one thing causes another. Sometimes, there is a reciprocal association where one variable (depression) impacts another (alcohol abuse), which then feeds back into the first variable (depression) and also into other variables (school). Other criteria, such as covariation and plausibility still make sense, as the associations you highlight as part of your idiographic causal explanation should still be plausibly true and it elements should vary together.

Similarly, idiographic causal explanations differ in terms of hypotheses. If you recall from the last section, hypotheses in nomothetic causal explanations are testable predictions based on previous theory. In idiographic research, instead of predicting that “x will decrease y,” researchers will use previous literature to figure out what concepts might be important to participants and how they believe participants might respond during the study. Based on an analysis of the literature a researcher may formulate a few tentative hypotheses about what they expect to find in their qualitative study. Unlike nomothetic hypotheses, these are likely to change during the research process. As the researcher learns more from their participants, they might introduce new concepts that participants talk about. Because the participants are the experts in idiographic causal explanation, a researcher should be open to emerging topics and shift their research questions and hypotheses accordingly.

Complementary approaches to causality

Over time, as more qualitative studies are done and patterns emerge across different studies and locations, more sophisticated theories emerge that explain phenomena across multiple contexts. In this way, qualitative researchers use idiographic causal explanations for theory building or the creation of new theories based on inductive reasoning. Quantitative researchers, on the other hand, use nomothetic causal explanations for theory testing , wherein a hypothesis is created from existing theory (big T or small t) and tested mathematically (i.e., deductive reasoning).  Once a theory is developed from qualitative data, a quantitative researcher can seek to test that theory. In this way, qualitatively-derived theory can inspire a hypothesis for a quantitative research project.

Two different baskets

Idiographic and nomothetic causal explanations form the “two baskets” of research design elements pictured in Figure 4.4 below. Later on, they will also determine the sampling approach, measures, and data analysis in your study.

In most cases, mixing components from one basket with the other would not make sense. If you are using quantitative methods with an idiographic question, you wouldn’t get the deep understanding you need to answer an idiographic question. Knowing, for example, that someone scores 20/35 on a numerical index of depression symptoms does not tell you what depression means to that person. Similarly, qualitative methods are not often used to deductive reasoning because qualitative methods usually seek to understand a participant’s perspective, rather than test what existing theory says about a concept.

However, these are not hard-and-fast rules. There are plenty of qualitative studies that attempt to test a theory. There are fewer social constructionist studies with quantitative methods, though studies will sometimes include quantitative information about participants. Researchers in the critical paradigm can fit into either bucket, depending on their research question, as they focus on the liberation of people from oppressive internal (subjective) or external (objective) forces.

We will explore later on in this chapter how researchers can use both buckets simultaneously in mixed methods research. For now, it’s important that you understand the logic that connects the ideas in each bucket. Not only is this fundamental to how knowledge is created and tested in social work, it speaks to the very assumptions and foundations upon which all theories of the social world are built!

Key Takeaways

• Idiographic research focuses on subjectivity, context, and meaning.
• Nomothetic research focuses on objectivity, prediction, and generalizing.
• In qualitative studies, the goal is generally to understand the multitude of causes that account for the specific instance the researcher is investigating.
• In quantitative studies, the goal may be to understand the more general causes of some phenomenon rather than the idiosyncrasies of one particular instance.
• For nomothetic causal explanations, an association must be plausible and nonspurious, and the cause must precede the effect in time.
• In a nomothetic causal explanations, the independent variable causes changes in a dependent variable.
• Hypotheses are statements, drawn from theory, which describe a researcher’s expectation about an association between two or more variables.
• Qualitative research may create theories that can be tested quantitatively.
• The choice of idiographic or nomothetic causal explanation requires a consideration of methods, paradigm, and reasoning.
• Depending on whether you seek a nomothetic or idiographic causal explanation, you are likely to employ specific research design components.
• Causality-the idea that one event, behavior, or belief will result in the occurrence of another, subsequent event, behavior, or belief
• Control variables- potential “third variables” effects are controlled for mathematically in the data analysis process to highlight the relationship between the independent and dependent variable
• Covariation- the degree to which two variables vary together
• Dependent variable- a variable that depends on changes in the independent variable
• Generalize- to make claims about a larger population based on an examination of a smaller sample
• Hypothesis- a statement describing a researcher’s expectation regarding what she anticipates finding
• Idiographic research- attempts to explain or describe your phenomenon exhaustively, based on the subjective understandings of your participants
• Independent variable- causes a change in the dependent variable
• Nomothetic research- provides a more general, sweeping explanation that is universally true for all people
• Plausibility- in order to make the claim that one event, behavior, or belief causes another, the claim has to make sense
• Spurious relationship- an association between two variables appears to be causal but can in fact be explained by some third variable
• Statistical significance- confidence researchers have in a mathematical relationship
• Temporality- whatever cause you identify must happen before the effect
• Theory building- the creation of new theories based on inductive reasoning
• Theory testing- when a hypothesis is created from existing theory and tested mathematically

Image attributions

Mikado by 3dman_eu CC-0

Weather TV Forecast by mohamed_hassan CC-0

Figures 4.2 and 4.3 were copied from Blackstone, A. (2012) Principles of sociological inquiry: Qualitative and quantitative methods. Saylor Foundation. Retrieved from: https://saylordotorg.github.io/text_principles-of-sociological-inquiry-qualitative-and-quantitative-methods/ Shared under CC-BY-NC-SA 3.0 License

Beatrice Birra Storytelling at African Art Museum by Anthony Cross public domain

Foundations of Social Work Research Copyright © 2020 by Rebecca L. Mauldin is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Schizophrenia: A Narrative Review of Etiopathogenetic, Diagnostic and Treatment Aspects

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Although schizophrenia is currently conceptualized as being characterized as a syndrome that includes a collection of signs and symptoms, there is strong evidence of heterogeneous and complex underpinned etiological, etiopathogenetic, and psychopathological mechanisms, which are still under investigation. Therefore, the present viewpoint review is aimed at providing some insights into the recently investigated schizophrenia research fields in order to discuss the potential future research directions in schizophrenia research. The traditional schizophrenia construct and diagnosis were progressively revised and revisited, based on the recently emerging neurobiological, genetic, and epidemiological research. Moreover, innovative diagnostic and therapeutic approaches are pointed to build a new construct, allowing the development of better clinical and treatment outcomes and characterization for schizophrenic individuals, considering a more patient-centered, personalized, and tailored-based dimensional approach. Further translational studies are needed in order to integrate neurobiological, genetic, and environmental studies into clinical practice and to help clinicians and researchers to understand how to redesign a new schizophrenia construct.

1. Introduction

Schizophrenia is a severe mental illness (SMI) affecting more than 21 million people worldwide that frequently leads to a persistent disability and impaired cognitive, social, and emotional functioning [ 1 ]. Schizophrenia is currently conceptualized as being characterized by at least positive symptoms (such as delusions and hallucinations), negative symptoms (including anhedonia, alogia, avolition, and social withdrawal), and cognitive symptoms (such as deficits in attention, processing speed, verbal learning, visuospatial learning, problem solving, working memory, and cognitive flexibility) [ 2 , 3 , 4 , 5 ]. Moreover, social cognition (including emotional intelligence, facial emotion recognition, emotion evaluation, and social inference) impairment may significantly impact the functional recovery in schizophrenia patients, due to the negative effects on interpersonal relationships, community adjustment, and vocational functioning [ 6 , 7 ]. Schizophrenia patients may also experience higher rates of co-occurring medical and/or mental illnesses, such as substance use disorders (mainly alcohol and cannabis), with prevalence rates up to 41% [ 8 ]. Due to a disordered lifestyle, an unhealthy diet, a lack of exercise, smoking, the adverse effects of antipsychotic treatment, a limited access to medical care, and the psychiatric illness itself [ 9 , 10 , 11 ], patients with schizophrenia are more likely to have a metabolic syndrome, a cardiovascular disease, diabetes, other endocrinopathies, an immune disease, and pulmonary illness, in particular, chronic obstructive pulmonary disease [ 10 , 11 , 12 , 13 , 14 ]. The concomitant comorbidity with other mental disorders determines the higher rates in symptomatology relapse, hospitalizations, suicidality, and family and social issues (such as higher rates of incarceration due to mental disorder relapse, treatment discontinuation, higher impulsivity and violent behaviors, and so forth), as well as a higher risk of negative outcomes in the short-term, including higher mortality rates [ 15 , 16 ]. A very recent meta-analysis showed that all causes of mortality were increased in people with schizophrenia, compared to the control group [ 17 ]. The specific causes of mortality included suicide, injury, poisoning, pulmonary diseases, endocrine diseases, respiratory diseases, urogenital diseases, diabetes, cancer, and cardio-cerebrovascular causes [ 17 ]. Moreover, it has also been found that treatment with an antipsychotic (AP) drug, in particular with second-generation long-acting injectable antipsychotics (SGA-LAIs), seems to be protective against all causes of mortality [ 17 ].

However, schizophrenia is a syndrome including a collection of signs and symptoms with heterogeneous etiology, etiopathogenesis, and psychopathological mechanisms that are potentially implicated, with many research directions and pathways currently under investigation [ 18 , 19 , 20 ]. Nowadays, there are several emerging neurobiological research directions that are suggested to be implicated in the pathogenesis of schizophrenia that could also be helpful in the clinical characterization of the disease, such as the following: (a) genetic factors (e.g., copy number variants [CNV], de novo nonsense genetic mutation, risk genes, polymorphisms in a gene, single nucleotide polymorphisms [SNPs], and so forth) that are implicated in the disrupted development at various stages of fetal life, which program the brain to manifest pre-psychotic features in the prepubertal or puberal age; (b) the neurodevelopmental model of schizophrenia, which considers several non-genetic factors, including perinatal complications, immigration status, and childhood maltreatment and neglect, which could mediate epigenetic changes, potentially determining structural and functional neurodevelopmental aberrations; (c) pathological alterations in multiple brain regions, including the frontal, temporal, parietal, cingulate, and glia components, as well as an excessive synaptic pruning and/or a disruption of neuroplasticity, and so forth; (d) the hypothesis of immune dysfunction and the neuroinflammatory model; (e) many others research pathways, including the emergence of the transdiagnostic model across multiple psychiatric disorders and the different abnormalities that are in the implicated neurotransmitters, such as the dopaminergic and glutammatergic pathways [ 21 , 22 , 23 ]. Indeed, there is an increased need for a better clinical characterization of individuals who are affected by schizophrenia, considering a more patient-centered, personalized, and tailored-based dimensional approach, which could consider all of the above-mentioned heterogeneous clinical manifestations and endophenotypes of the disease, including the investigation of all of the underpinned genetic and environmental factors [ 24 , 25 ]. Accordingly, the management of schizophrenic individuals should require better data integration towards the personalization of diagnosis and treatment [ 24 , 26 , 27 ]. Within this context, there have also been recently developed artificial intelligence (AI)- and machine learning (ML)-based approaches, which promise an interesting implementation of statistical tools to build more accurate and precise predictive models of schizophrenia onset, illness course, and potential therapeutic outcomes [ 28 ]. These can also identify candidate variables that are putative to be characteristics of schizophrenia spectrum disorders, by allowing a personalized diagnosis, such as a set of resting-state electroencephalographic (EEG) quantitative features, and magnetic resonance imaging of structural and functional anomalies, and so forth [ 29 , 30 , 31 ].

Therefore, due to the growing knowledge in schizophrenia research and the underpinned mechanisms, we aimed to provide some insights into and a viewpoint on the recently investigated schizophrenia research fields in order to discuss the potential future research directions in schizophrenia research, including the overview of recently developed new constructs and implemented classificatory systems.

2. Definitions and Concepts on Schizophrenia

While the cluster of symptoms that clinically define the schizophrenia concept has been noted historically before the 1990s, schizophrenia scientific research was mainly developed following the studies that were carried out by the German psychiatrist Emil Kraeplin (1856–1926) who identified a set of symptoms related to the schizophrenia disease in his Psychiatrie manual, which provided a descriptive classification of mental disorders that were based on his clinical observations and experience [ 32 ]. In his essay, he identified a set of mental disorders, which he named ‘ processes of psychic degeneration ’, that were characterized by a rapid development of a mental deterioration (later named ‘ dementia praecox ’) [ 33 ]. ‘Dementia praecox’ included catatonic syndrome (characterized by a tensive voluntary motor activity), the hebephrenic syndrome (characterized by a distinctive deteriorative course, based on the importance of silliness and minimal positive psychotic symptoms), and the paranoid dementia (characterized by the presence of hallucinations and delusions). Kraeplin [ 34 ] mainly focused on the illness course and the chronicity of the disease, rather than on a set of diagnostic criteria, in describing the concept of the ‘dementia praecox’. Kraeplin [ 34 ] defined those individuals as distinct from the insanity of tertiary syphilis or the cyclic, non-deteriorating psychosis of a manic-depressive illness. Accordingly, the dementia praecox diagnosis still contained the illness prognosis [ 33 ].

Indeed, Kraepelin’s system of mental diseases substantially contributed to the foundation of the modern psychiatric diagnosis in the Diagnostic and Statistical System of Mental Disorders (DSM) and the International Classification of Diseases (ICD). However, since the schizophrenia construct that was developed by Emil Kraeplin [ 33 ], several schizophrenia definitions and concepts have changed considerably over the past century, with an increasing disagreement about the core features of schizophrenia [ 35 , 36 ]. In fact, the originally developed Kraeplenian concept was subsequently revised by the Swiss psychiatrist Eugen Bleuler, who mainly focused, during his lecture at a meeting of the German Psychiatric Association in Berlin on 24 April 1908, on the dissociative symptomatology that is related to the illness [ 37 ]. At that meeting, Bleuler indeed argued that dementia praecox was associated with neither dementia nor precociousness and emphasized that the splitting of psychic functioning represented the essential schizophrenia feature [ 38 ]. Accordingly, Bleuler mainly described schizophrenia originally as a disorder in which “ emotionally charged ideas or drives attain a certain degree of autonomy so that the personality falls into pieces. These fragments can then exist side by side and alternately dominate the main part of the personality, the conscious part of the patient ” [ 37 ]. Accordingly, he coined the term “ schizophrenia ” (which was derived from the Greek verb ‘schizein’, indicating splitting, and ‘phren’ denoting the ‘soul, spirit, mind’). Bleuler also stated that schizophrenia was primarily represented by a thought and feeling disorder, comprising the ‘4 As’ (alogia, autistic isolation, ambivalence, and affect blunting) [ 37 , 38 ].

Indeed, the Bleulerian concept of schizophrenia, with the heterogeneity of prognosis and outcomes, indirectly paved the way for later subdivisions of the schizophrenia concept [ 39 ]. Consequently, the German psychiatrist Kurt Schneider (1887–1967) proposed a set of fundamental symptoms, named Schneider’s first-rank symptoms (FRS), of which the presence in the subject could be strongly suggestive of a schizophrenia diagnosis [ 40 ]. The FRS include the following: (a) auditory hallucinations; (b) thought withdrawal, insertion, and interruption; (c) thought broadcasting; (d) somatic hallucinations; (e) delusional perception; (f) feelings or actions that are made or are influenced by external agents [ 40 ]. The FRS are the so-called positive symptoms (i.e., the symptoms that are not usually experienced by people without schizophrenia), and they are usually given priority over other symptoms. In addition, second-rank symptoms include other perceptual disorders, delusional intuition, mood changes, affective flattening, perplexity, and other negative symptoms that represent the deficits of emotional responses and other thought processes [ 40 ]. The Schneiderian FRS, which were initially retained in the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM) [ 41 ] and were included in a special schizophrenia diagnostic status in the 10th edition of the International Classification of Diseases (ICD-10) [ 42 ], were later dropped in the DSM-5 [ 43 ], the DSM-5-TR [ 44 ], and in the ICD-11 [ 45 ].

The “Neo-Kraepelinian” movement of the 1960s and 1970s argued for the empirical psychometric validation of psychiatric syndromes and posed the basis for the proposal of schizophrenia diagnostic criteria, which was subsequently integrated into both the DSM and ICD versions. Within this context, John Feighner and his colleagues, Eli Robins, Samuel Guze, and George Winokur, at Washington University in St. Louis, Missouri, proposed the Feighner criteria, i.e., a set of influential psychiatric diagnostic criteria that was also developed for schizophrenia diagnosis [ 46 ]. In particular, Feighner et al. [ 46 ] required as essential criteria for a schizophrenia diagnosis the persistence of a limited set of symptoms (i.e., delusions, hallucinations, or thought disorders) for at least six months, without the return to the premorbid level of psychosocial adjustment. The Feighner criteria were later further expanded with the development of a set of specific diagnostic criteria (namely, research diagnostic criteria (RDC)) [ 46 ], which constituted the basis for the DSM-III, as developed by the American Psychiatric Association [ 47 ]. The RDC were, indeed, widely used in order to study a variety of schizophrenia-related research issues, particularly those that were related to genetics, psychobiology, and treatment outcomes [ 48 ].

Crow [ 49 ] simplified the schizophrenia description in terms of a positive form (type I schizophrenia syndrome) and a negative form (type II schizophrenia syndrome, occurring in the absence of positive symptoms), despite the fact that many patients with the type I syndrome can later acquire the features of the type II syndrome, and some patients can have both from an early stage. Type II syndrome is usually associated with the worst prognosis, corresponding more closely to the classical Kraepelinian schizophrenia diagnosis [ 49 ]. In addition, Carpenter et al. [ 50 ] distinguished between primary and secondary negative symptoms by reviving the long-standing question concerning the primary core deficits. More recently, Andreasen [ 51 ] more deeply investigated the negative symptoms that were originally described by Kraepelin [ 34 ] and Bleuler [ 37 ] as schizophrenic core symptoms. Both Andreasen [ 51 ] and Carpenter et al. [ 50 ] further investigated the originally developed Bleulerian concept of “thought disorder” as the primary defining feature of schizophrenia, rather than the presence of signs and symptoms such as delusions and hallucinations. Accordingly, Andreasen [ 51 ] proposed a neo-Bleulerian unitary model for schizophrenia, defining it as a neurodevelopmentally derived “misconnection syndrome” involving connections between the cortical regions and the cerebellum that are mediated through the thalamus (the cortico–cerebellar–thalamic–cortical circuit).

Meehl [ 52 ] proposed a model of the causes and the pathogenesis of schizophrenia and its related states, which emphasized on the presence of a genetically determined aberration in neural transmission that could be potentially responsible of the emergence of schizophrenia and non-psychotic schizotypal states within the diathesis-stressor framework [ 53 ]. Gottesman et al. [ 54 ] introduced the concept of the ‘epigenetic puzzle’ in schizophrenia, by proposing an explanatory model comprising the different causes of schizophrenia for etiological and phenomenological heterogeneity in schizophrenia [ 55 ]. Crow [ 56 ] proposed the viral hypothesis of schizophrenia, as derived by a mutagenesis that is caused by viral integration or transposition in human genomic DNA. Following studies that were carried out on subgroups of the non-psychotic relatives of patients who were affected with schizophrenia who displayed defects or abnormalities in clinical, cognitive, biological, social, and other dimensions of functioning that were similar to those shown in schizophrenic individuals [ 57 , 58 ], the hypothesis of schizophrenia liability syndrome [ 59 ] was proposed. In fact, based on Paul Meehl’s conceptualization of ‘schizotaxia’ [ 52 ], Stone et al. [ 59 ] reformulated the concept of liability syndrome based on observable, clinically meaningful symptoms involving the negative symptoms and neurocognitive deficits in non-psychotic relatives [ 60 ]. Furthermore, from a more phenomenological perspective, it has been hypothesized that, in schizophrenia spectrum disorders, a profound transformation of subjectivity antedating the onset of major symptoms is accompanied by micro-experiences of self-alienation (e.g., derealization, perplexity, depersonalization, reduced self-presence, and an alteration of the stream of thought) [ 61 ]. The self-experiences, indeed, represent fundamental and enduring (more a trait-like feature) distortions of subjectivity, which typically emerge in late childhood and early adolescence [ 61 ].

Finally, recent evidence supports the concept that schizophrenia represents a multifactorial disorder that results from a complex interplay between additive and interactive genetic and environmental determinants [ 62 ], displaying a highly variable and heterogeneous clinical presentation [ 63 ]. Therefore, due to the absence of clear boundaries and the multiplicity of implicated etiological factors, pathophysiological mechanisms, and hypotheses [ 64 , 65 , 66 ], the schizophrenia concept has been more recently broadened to a spectrum concept in the DSM-5 (and the recently released DSM-5-TR) [ 43 , 44 ] or as a primary psychosis in the ICD-11 [ 39 , 45 ].

3. The Heterogeneity and the New Nosological Schizophrenia Constructs

The heterogeneity of schizophrenia resides in the high variability of the phenotypic and clinical expression, with highly varying degrees of functionality, symptoms and personal recovery, and outcomes across individuals, together with a variable range of underlying neurobiological abnormalities, which are potentially implicated in its pathogenesis [ 67 , 68 ]. Indeed, the multifactorial nature of the etiological factors has worsened the difficulty in addressing the causal mechanisms in the disease pathophysiology of the illness [ 69 , 70 ]. However, Tandon et al. [ 36 ] exhorted that “ heterogeneity cannot just be an explanation for our failure, but is a problem to be explained ”. Indeed, Carpenter [ 71 ] first proposed that the schizophrenia construct should be reconstructed according to the following four major targets: (a) the identification of patient subgroups in order to enhance homogeneity; (b) deconstructing the traditional schizophrenia construct by identifying the specific core psychopathology domains; (c) deconstructing schizophrenia at the levels of neural circuits and behavioral constructs; (d) considering the different stages from the vulnerability of development to the illness onset and disease progression.

Indeed, the traditional schizophrenia construct has elicited a continual debate as the concept has fluctuated across the years, according to the different psychopathological perspectives and the emerging advances in multiple areas of schizophrenia research (e.g., genomics, neuroimaging, epidemiology, and cognitive science) [ 72 ]. One of the major obstacles of the traditional schizophrenia construct regards the fact that disorders continue to be defined almost exclusively by a set of symptoms and signs, despite the association between the specific diagnostic categories and biological or behavioral measures having been proven to be modest or inconsistent, therefore, not allowing a better understanding of schizophrenia or the development of more effective interventions for the illness [ 73 ]. In particular, the inconclusive findings coming from the neurobiological studies have demonstrated the inadequacy of the current schizophrenia diagnosis by underlining how the current nosological construct does not appear to be exhaustive in identifying all of the multiple and potentially different pathophysiological substrates that are implicated within schizophrenia spectrum disorder [ 74 ]. However, many experts in schizophrenia research have pointed to continuing to use the traditional schizophrenia construct because of its utility (at least clinically) and the absence of any current better alternative [ 20 , 36 , 39 , 66 , 74 , 75 ]. Carpenter [ 75 ] suggested replacing it with a broader construct of “primary psychosis”, while Gur [ 63 ] suggested replacing it with the “psychosis spectrum disorder” construct. On the other hand, Murray and Quattrone [ 76 ], Van Os and Goluksuz [ 77 ], and Zick et al. [ 68 ] proposed to completely eliminate it. The alternative proposed schizophrenia constructs include dimensional-based schizophrenia constructs [ 78 ], the hierarchical psychopathological model by Kotov et al. [ 79 ], and the biotype architecture [ 67 , 68 , 80 , 81 ], which is illustrated below. Therefore, in order to address these issues, an overview of the different diagnostic classificatory systems, from the traditional DSM/ICD to the recently developed alternative/integrative models, has been provided below.

4. The Systems of Diagnostic Classification

Overall, the diagnostic classifications have been ad hoc designed in order to address the following purposes: (a) facilitating research into the causes and the treatment of the illnesses; (b) guiding clinical decision making; (c) helping clinicians in more shared communication [ 82 ]. However, the extremely variable and discontinuing phenotypic presentation, diagnostic characteristics, illness trajectory, and treatment response in schizophrenic individuals, together with the highest rates of comorbid disorders, limit the feasibility and applicability of the current diagnostic systems and classifications in the clinical decision making practice in regard to schizophrenia [ 82 ]. Therefore, although the latest versions of the DSM-5-TR [ 44 ] and ICD-11 [ 45 ] might effectively represent some apparently useful approaches facilitating the information exchange among clinicians, they definitely fail to properly capture the biological and pathophysiological nature of schizophrenic individuals, as well as their phenotypical and clinical heterogeneity; indeed, not allowing for a personalized diagnosis or treatment [ 36 , 82 ]. For instance, neurocognitive deficits, which are commonly a core feature of schizophrenia, are not included in the criterion-based definition in the ICD-11 [ 45 ] nor in the DSM-5-TR [ 36 , 44 ]. Furthermore, the ICD-11 [ 45 ] also differs from the DSM-5 [ 43 ] (and the current DSM-5-TR) [ 44 ] according to the minimum duration of symptomatology. The ICD-11 [ 45 ] requires a minimum duration period of one month or more, whereas, the DSM-5 (and current DSM-5-TR) [ 36 , 44 ] requires the presence of continuous signs of the disturbances that should persist for at least six months beyond the required additional five months of symptoms, which could include prodromal or residual symptoms [ 83 ]. Obviously, the shorter duration requirement that is suggested in the ICD-11 was intended to encourage an earlier treatment in order to improve the patient’s outcome. Both the DSM-5-TR and the ICD-11 require at least two types of schizophrenia symptoms lasting at least one month, even though the ICD-11 also includes the presence of experiences of influence, passivity, or control as a separate core symptom in schizophrenia, which represent disturbances in the ‘ego-world boundary’, including passivity experiences, thought withdrawal, and thought broadcasting [ 83 ], which were previously included among Schneider’s FRS [ 40 ]. Finally, social processing dysfunction is represented as an integral part of the schizophrenia diagnostic criteria only in the DSM-5 [ 43 ] and the current DSM-5-TR [ 44 ], but not in the ICD-11 [ 24 , 45 ]. Indeed, although both the DSM-5-TR and ICD-11 incorporate, to a greater or lesser extent, the traditional clinical features that were investigated by Kraepelin [ 34 ], Bleuler [ 37 ] and Schneider [ 40 ], the latest iterations of the DSM and the ICD provide clinicians with dimensional assessments based on the key symptom domains covering the positive, negative, affective, and cognitive symptoms of the schizophrenia. However, as one of the most dominant etiological models for schizophrenia postulated that the illness can represent the final state following abnormal neurodevelopmental processes, which may have started years before the illness onset [ 84 ], and that it is possible to identify a schizophrenia spectrum disorder, rather than only the presence or absence of the illness, the current diagnostic systems have a series of limitations [ 85 ]. In fact, while the original aim of the current diagnostic systems was to allow clinicians to have a shared and homogeneous information exchange, as well as for research purposes, the traditional diagnostic systems, which are mainly based on a set of symptoms and signs, are not able to incorporate any etiology-based components, neurodevelopmental markers, the genetic liability, the subthreshold schizophrenia vulnerability status (i.e., schizotaxia), or many other currently investigated aspects of the disease [ 75 ].

Therefore, beyond these classical/traditional diagnostic systems, the National Institute of Mental Health (NIMH) Research Domain Criteria (RDoC) initiative was first developed in 2009 with the aim to build a new classification system for a better understanding of underlying dimensional processes and the development of psychopathology, by using a dimensional approach [ 63 , 82 ]. The RDoC may effectively provide a bridge between the basic behavioral neuroscience research and clinical research by using a dimensional approach in which each function is quantitatively mapped onto specific brain circuits [ 63 ]. The RDoC was conceived as an experimental framework in order to support translational research in psychopathology organized around basic functional domains (e.g., cognition, motivation, and motor activity) [ 72 , 86 ]. The focus of the RDoC program is on the fundamental operations of adaptive behavioral/cognitive and brain functioning (e.g., working memory, fear, and behavior) and psychopathology, according to a perspective in terms of the dysregulation of these systems rather than starting with clinical syndromes and then trying to determine their source/causes. The RDoC investigates the entire dimensions of functioning (i.e., negative valence, positive valence, cognition, social processes, arousal/regulatory systems, and sensorimotor systems) from the normal range to increasingly abnormal extents, and no specific cut points for each disorder are specified in order to facilitate studies on the transitions from normality to the different degrees of pathology [ 72 , 86 , 87 ].

In addition, other research directions have been proposed to reconceptualize schizophrenia psychopathology as consisting of continuous dimensions of maladaptive behaviors, emotions, and cognitions, with some hierarchical taxonomies of phenotypic psychopathological dimensions proposed [ 88 ]. Within this context, the Hierarchical Taxonomy of Psychopathology (HiTOP) consortium aimed to integrate the evidence from studies on the organization of psychopathology in order to overcome the arbitrary boundaries between psychopathology and normality, the diagnostic instability, the frequent co-occurring disorders, the heterogeneity within the same diagnosis, and the lack ability to identify the subthreshold clinical cases [ 79 , 89 , 90 , 91 ]. HiTOP was built in order to define psychopathology according to a dimensional approach, which also investigates those individuals with subthreshold symptoms or unusual symptom profiles, with the aim to reduce the heterogeneity within those constructs by grouping related symptoms together, independently, by an established diagnosis [ 68 , 79 ]. According to HiTOP, schizophrenia, schizophreniform disorder, schizoaffective disorder, and schizotypal and paranoid personality disorders reflect elevations on both thought disorders and “detachment” spectra dimensions [ 89 , 90 , 91 ]. In particular, the “detachment” dimension can be considered as a vulnerability trait for negative symptoms and schizophrenia, also among the relatives of people who are affected with schizophrenia, compared to the relatives of healthy probands or probands with mood disorders [ 92 ]. Furthermore, the biotype-based architecture model was investigated with the aim to incorporate the biomarkers for differentiating individual cases by subtype [ 93 ]. The Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP1) consortium sought to identify a broad range of biomarkers encompassing the neurocognitive and physiological correlations, with the aim to distinguish the three leading psychosis diagnoses (i.e., schizophrenia, schizoaffective disorder, and bipolar I disorder with psychosis) [ 67 ]. Identifying promising neurobiologically distinct subgroups of psychoses by using biomarkers could support genetic-based etiological investigations and may advance treatment developments [ 94 , 95 ]. Indeed, a biotype-based approach could significantly improve the development of new treatment targets, offering an opportunity to match interventions to pathophysiology and to implement more patient-centered, tailored, and personalized approaches to the disease towards a new precision, as well as personalized psychiatry in schizophrenia research and clinic.

5. Schizophrenia and Personalized Psychiatry

The concept of personalized medicine is based upon the hypothesis that each individual is unique, hence, diseases are heterogeneous regarding the specific contributing factors and also the specific treatment outcomes [ 96 ]. Personalized psychiatry aims to offer an individual and patient-centered approach, including an individualized clinical characterization (also using the tools of the precision psychiatry, including biomarkers, biotypes, endophenotypes, etc.), as well as tailored and personalized treatments for each individual real patient at the right time [ 97 ]. The topic of personalized psychiatry becomes more salient, particularly in the field of schizophrenia research, whereas a more concrete emphasis should be posed to the transdiagnostic conceptualization of psychopathology that is related to primary psychosis and schizophrenia, as already pointed out by Carpenter [ 98 ]. Furthermore, the investigation of specific biomarkers would be useful in early diagnosis, in clinical monitoring, and in treatment response [ 99 ].

According to the Biomarkers Definitions Working Group [ 100 ], a biomarker is “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacological responses to a therapeutic intervention”. Biomarkers can be molecular, histologic, derived from brain imaging, or physiologic in nature, being classified as diagnostic, prognostic, and theranostic [ 101 ]. Biomarkers are not represented by endophenotypes, which are heritable and are more specifically quantitative traits that are associated with disease liability, that instead explain the relationship between the genotype and the phenotype [ 102 ]. Indeed, biomarkers could be used as clinical predictors for schizophrenia, its illness course, and its phase, as well as for its treatment and intervention response [ 103 ]. A dimensional-based characterization along the RDoC domain [ 104 , 105 ] can help clinicians to identify the specific biomarkers for schizophrenia risk occurrence throughout the lifespan of the individual [ 63 ]. For example, dysregulated immunity and inflammatory processes were reported in schizophrenic individuals, despite the fact that the measurement and repeatability of these biomarkers display several challenges in terms of translating this approach in routine clinical practice [ 105 , 106 , 107 , 108 ]. Studies on those targeted on polygenic risk scores suggested better characterizing and identifying a specific subgroup of schizophrenic patients who had ongoing inflammation and immune dysfunction [ 107 , 108 ]. Therefore, blood-based biomarkers, including glucose and triglyceride levels, and pro-inflammatory markers (e.g., interleukin-6, tumor necrosis factor alpha, and so forth) have been investigated; however, their profile seems not to be specific to schizophrenia [ 79 ].

Further potential neurophysiological, immune, and endocrine biomarkers have been investigated through proteonomic gene expression (transcriptomic) and neuroimaging studies [ 63 , 99 , 109 , 110 , 111 ], even though these biomarkers have not yet been validated and, for this reason, they continue to be investigated in experimental settings rather than in clinical practice [ 100 , 112 ]. However, despite the fact that several biomarkers, including genetic biomarkers, have been identified or are currently under investigation, they have not yet been effectively implemented into routine clinical practice, mainly due to their inconclusive clinical reliability, with exception of a few pharmacogenetic-guided decision support tools [ 113 ]. In this regard, the pharmacogenetic research into antipsychotic drugs has examined a number of genetic variants and only a few polymorphisms have been found to be promising in explaining the therapeutic efficacy and side-effects of antipsychotic drugs in schizophrenia spectrum disorders, such as some polymorphisms in the brain-derived neurotrophic factor (BDNF), in some cytochrome CYP genes, and so forth [ 114 , 115 ]

6. Genomics

Genetic epidemiological studies have shown that schizophrenia is highly heritable, despite the fact that it has been better described as an underpinned multifactorial etiology with a complex polygenic genetic architecture [ 116 ]. Evidence has supported the role of both common and rare genetic variants that are implicated in the development of schizophrenia, as well as several environmental factors that may contribute to its etiology [ 63 ]. Indeed, few causal variants have been clearly identified and most of the genetic associations have not imparted any useful clinical implications [ 116 ]. For instance, many genome-wide associated variants (GWAV) have not been identified in genes, possibly indicating that they may have a regulatory role in modifying gene expression or they can represent the expression of quantitative trait loci [ 117 ]. Indeed, gene expression can be influenced by both genetic and environmental factors and the differences in gene expression can be a biomarker for the diagnosis, but also for a potential therapeutic target in schizophrenia [ 118 , 119 , 120 , 121 ]. However, although recent genome-wide association studies (GWAS) have identified more than 100 genetic risk loci in schizophrenia, they are overall responsible of a small effect on the schizophrenia risk [ 116 ]. The polygenic risk score (i.e., the measure of polygenic loading) [ 122 ] is able to address the polygenic architecture of schizophrenia and it can quantify the common risk allele burden that is carried by schizophrenia individuals [ 123 , 124 ]. Moreover, it has been suggested that the polygenic risk score may be useful in determining the association between schizophrenia and intermediate phenotypes, such as the brain structural alterations in schizophrenic individuals [ 125 ]. However, a recent systematic review by van der Merwe et al. [ 126 ] did not find any significant association between the polygenic risk score and the brain structural changes in schizophrenic individuals, suggesting the need for further research directions, particularly in the field of intermediate phenotypes other than altered brain structures. Furthermore, according to a recent systematic review, copy number variant (CNV)-based studies have identified five schizophrenia-associated CNV regions containing genes that were found to be differentially expressed in schizophrenia (i.e., PPP1R2 in 3q29, HSPB1 in 7q11.23, INO80E and YPEL3 in 16p11.2, DHRS11 in 17q12, and SEPT5, RTN4R, and SLC2A11 in 22q11.2) [ 121 ]. However, the CNVs in these regions are also associated with neurodevelopmental delays, intellectual disabilities [ 127 , 128 ], and other neuropsychiatric phenotypes, including anxiety (3q29, 7q11.23, and 17q12), autism spectrum disorder (ASD; 3q29, 7q11.23, 16p11.2, 17q12, and 22q), attention-deficit/hyperactivity disorder (ADHD; 7q11.23 and 22q), and bipolar disorder (3q29, 7q11.23, and 17q12), as well as in immune system dysfunction, cardiac pathologies, and many other medical issues [ 119 , 120 ]. The most well-investigated CNV that is associated with an increased risk of schizophrenia is the 22q11.2 deletion syndrome, with it being related to a 25-fold increase in schizophrenia risk [ 129 , 130 , 131 , 132 ]. Furthermore, several neurotransmitters (i.e., dopamine, serotonin, and glutamate), acting through metabotropic G protein-coupled receptors (GPCRs), which mediate the intracellular signal transduction and the induction of gene expression in order to exert antipsychotic activity, have been genetically investigated in schizophrenia [ 133 , 134 ]. The genetic studies have identified associations between the SNPs in genes that are related to GPCRs and schizophrenia [ 135 ]; in particular, some metabotropic glutamate receptors (mGlu), subtype 3 (mGlu 3 ), 5-hydroxytryptamine 2A receptor (5-HTA 2A ), and dopamine D 3 receptors (DRD 3 ). SNPs have been associated with schizophrenia, pathognomic measurable endophenotypes, and the treatment response to specific antipsychotics [ 136 , 137 , 138 , 139 , 140 ]. However, further studies are needed in order to investigate the role of GPCRs SNPs variants in schizophrenia and in the antipsychotic’s treatment response [ 133 , 141 ].

However, beyond the genetic susceptibility, epigenetics (including all postnatal modifications of gene expression that are not associated with changes in DNA sequences, such as DNA methylation, chemical modification of histone proteins, non-coding RNA, and other mechanisms that are involved in epigenetic regulation) have demonstrated that not only genetic factors are implicated in schizophrenia, but more specifically epigenetic factors [ 142 ]. Epigenetic factors are derived from the interplay between genetic factors and various environmental factors occurring from the fetal period to the developmental period that may potentially influence and modify the psychopathological trajectory of the illness, as well as other post-developmental factors influencing the onset of schizophrenia through an epigenetic mechanism [ 143 ].

7. Neuroimaging

A set of specific brain structural abnormalities have been widely reported in schizophrenia spectrum disorders, which are mainly considered to be a brain development disorder [ 144 , 145 , 146 ]. A large-scale metanalysis has reported a smaller hippocampus volume, together with smaller amygdala, thalamus, nucleus accumbens, and intracranial volumes in patients with schizophrenia compared to controls [ 144 ]. Moreover, it has been found that a larger palladium and lateral ventricle volume also occurs, compared to healthy controls [ 144 ]. Individuals with schizophrenia have also been reported to have widespread cortical thinning and smaller cortical surface [ 145 ]. Cortical thickness reductions are larger in individuals under antipsychotic treatment and are negatively correlated with medication dose, symptoms severity, and duration of illness [ 145 ]. Limitations in the imaging studies on schizophrenia are represented by the issue that most of them mainly recruited chronic patients and individuals taking antipsychotic treatment, therefore making it difficult to identify the time of the brain changes and the effect of the treatment exposure [ 146 , 147 ]. Functional neuroimaging studies have shown alterations in the brain metabolism and the blood flow in the frontal, cingulate, parietal, putamen, and sensorimotor regions [ 148 , 149 , 150 , 151 ]. Dopamine dysfunction has also been observed in schizophrenic patients. Indeed, dopamine D 2 receptor density and the occupancy of D 2 receptors by dopamine has been shown to be increased in schizophrenic patients, along with an increased dopamine transmission [ 152 , 153 ]. For other neurotransmitters, the findings coming from neuroimaging studies are still inconsistent; however, some studies have reported a reduced 5-HT 1 receptor concentration in the midbrain and pons, reduced 5HT 2 receptors in the neocortex, and a hypofunction of N-methyl-D-aspartate (NDMA) [ 154 , 155 ]. The data that are currently available on the glutamatergic system are still unclear [ 156 ].

Neuroimaging data have been more recently extensively investigated with the aim to identify individuals who are at risk of psychosis at an early stage or a prodromal phase [ 146 , 157 , 158 ]. High-risk individuals who will subsequently develop psychosis or a schizophrenia spectrum disorder showed several structural and functional brain abnormalities compared to the healthy controls, such as grey matter changes in the frontal, temporal, and cingulate cortices, a reduced integrity of striatal and temporal white matter, subcortical volumes of the thalamus, amygdala, striatum, and cerebellum, and changes in the functional connectivity and network organization [ 159 , 160 , 161 , 162 , 163 ]. Further studies have also investigated, through neuroimaging, whether it is possible to identify some predictors of the response to pharmacological medication [ 146 ] by demonstrating that a greater striatal dopamine synthesis, an enlarged gray matter volume, and normal gyrification, as well as an increased brain activity in the fronto-parietal regions may act as potential predictors of a positive response to antipsychotic treatments [ 164 , 165 , 166 , 167 , 168 ].

8. Environmental Factors

A set of environmental factors, such as childhood adversity, substance use and misuse, minority and ethnicity status, birth season, urbanity, and pregnancy and/or perinatal complications, have been associated with differential clinical manifestations of schizophrenia spectrum disorders [ 169 , 170 ]. A recent systematic review and meta-analysis assessed the evidence for a gene–environment correlation (genes influencing the likelihood of environmental exposure) between schizophrenia polygenic risk score and childhood adversities, observing only a small effect; however, there are still inconsistent findings that do not allow us to draw definitive conclusions [ 170 ]. Meta-analyses have also shown that substance use, particularly continued use, was significantly associated with higher rates of positive psychotic symptoms and a higher likelihood of a history of violence and aggressive behaviors [ 171 , 172 ]. In addition, cannabis use, especially with higher potency cannabis, is associated with an increased risk for schizophrenia [ 173 , 174 , 175 , 176 ]. In addition, ethnic minority status is correlated with more severe reality distortion, disorganization, and the onset of negative symptomatology [ 177 ].

Moreover, the paradigm of the exposome was only recently investigated in the field of schizophrenia [ 63 , 178 , 179 , 180 ]. The exposome represents the entirety of the environmental vulnerability underlying the pathoetiology of schizophrenia spectrum disorders, to which an individual is exposed to throughout their life [ 178 , 180 , 181 ]. According to the exposome model, environmental factors are bi-directionally interlinked, such that cannabis use is associated with childhood adversity, the effects of urbanicity variables (such as population density, deprivation, etc.) can be modified or influenced by individual level factors, such as cannabis use, exclusion, discrimination, and social adversity [ 178 , 180 ]. Moreover, there is evidence to suggest a dose–response relationship between environmental load scores and the severity of the mental health status, as well as the outcomes [ 179 , 180 , 182 , 183 ].

9. Schizophrenia Treatment and Interventions

Despite several evidence- and consensus-based schizophrenia guidelines that have been generated over the last decades [ 184 , 185 , 186 , 187 , 188 ], the treatment interventions in schizophrenia research are far from being effective and many factors are involved in treatment response based on theoretical groundings, with some innovative fields of research yet to be implemented [ 21 , 185 , 189 ]. The current approach to schizophrenia in routine clinical practice worldwide is often stereotyped, being mostly prescribed a second-generation antipsychotic drug [ 190 ]. Indeed, antipsychotic treatments for schizophrenic individuals have been demonstrated to be effective in managing the core symptoms of schizophrenia, but also they has been reported to be associated with a decreased risk of all-cause, cardiovascular, and suicide mortality, also, in terms of cumulative antipsychotic exposure, particularly in those patients under clozapine treatment [ 191 , 192 , 193 ].

Furthermore, from a pharmacological perspective, despite the fact that the dopaminergic system has been hugely investigated in the pathophysiology of schizophrenia and has been guided in initially targeting antipsychotic treatments, there is clinical evidence that dopamine blockade is not effective in managing the negative and cognitive symptoms and, in some schizophrenic patients, it does not improve the positive symptoms either [ 194 , 195 , 196 ]. Therefore, researchers have recently directed their research interest towards new neurochemical targets, such as the glutamatergic system [ 194 , 197 , 198 ]. While, on the other hand, from a non-pharmacological perspective, despite the demonstrated evidence-based efficacy of cognitive–behavioral approach [ 199 , 200 , 201 , 202 ], its use is still poor in routine clinical practice for schizophrenic individuals [ 24 , 203 ]. In patients with treatment-resistant schizophrenia (TRS), researchers have explored the utility of brain stimulation procedures [ 204 ], such as electroconvulsive therapy (ECT), repetitive transcranial magnetic stimulation (rTMS), and deep brain stimulation (DBS). Despite the promising preliminary results [ 205 , 206 ], further studies are needed in order to better understand the potential role of these neuromodulatory techniques in the treatment of TRS patients [ 204 ]. Finally, psychosocial interventions and recovery-oriented rehabilitative interventions (e.g., cognitive remediation and metacognitive reflection and insight therapy (MERIT) etc.) have been rapidly developed in order to target cognitive and/or metacognitive deficits that can hamper the functional recovery of schizophrenic patients and in subjects at ultra-high risk of psychosis [ 203 , 207 , 208 , 209 , 210 ], even though they do not seem to be adequately integrated in the mental health services [ 24 , 203 ]. Similarly, family-based interventions and supported employment programs are seldom implemented in routine clinical practice [ 24 , 203 , 211 ]. In particular, there are also initiatives that are aimed at implementing and favoring social integration, regular employment, and reducing the social exclusion of all individuals who are affected by severe mental illnesses, including schizophrenia, such as the Individual Placement and Support (IPS) initiative [ 212 , 213 ]. The IPS became the standard of supported employment and the only evidence-based employment model for people with schizophrenia, indicating a moderate-to-large effect size [ 214 , 215 ], which has also been confirmed in long-term studies [ 216 ]. However, despite the recovery-oriented approach that is needed for the management of schizophrenic patients, a resilience-promoting environment (i.e., an environment that integrates interventions in order to increase a positive outcome, despite adversities, in order to implement wellbeing [ 217 ]) is often missing in many mental health services [ 24 ].

10. Discussion

Overall, schizophrenia could better represent an encompassing term referring to a group of related disorders, which have distinct etiologies and that require different treatment strategies [ 55 ]. Schizophrenia, indeed, describes a clinical syndrome, not a disease entity. A syndrome consists of co-emerging specific symptoms of unknown etiology and has no clear boundaries with other entities. Symptom dimensions explain more clinical characteristics than diagnostic categories and they are specifically associated with genetic and environmental risk factors that may operate across diagnostic categories [ 76 , 116 , 117 , 118 , 119 , 121 , 169 , 170 , 178 , 179 ]. However, the current conceptualization of schizophrenia only appears to be useful to establish evidence-based guidelines for diagnosis and treatment, and to provide valuable information on psychosocial outcomes [ 39 ]. In fact, if schizophrenia continues to be defined almost exclusively by a set of symptoms and signs, despite the modest and/or inconsistent association between the diagnostic categories and the biological and/or behavioral measures, the traditional construct of schizophrenia will not be able to clearly reach a comprehensive understanding of the disorder, its heterogeneous clinical presentation and treatment outcomes, or the development of more effective treatments [ 72 ]. In fact, it has been well documented that the real-life functioning of schizophrenic patients does not exclusively depend on their symptoms and/or signs, but is more strictly related to context-related factors rather than illness-related ones [ 218 , 219 ], by demonstrating an ability to be stable in their relationships after a four-year follow-up, as reported in the multicenter study that was carried out by the Italian Network for Research on Psychoses [ 220 ].

Therefore, the current concept and traditional constructs of schizophrenia appear to be not exhaustive enough in explaining the heterogeneity and the complexity, as well as the complex interplaying roles of additive factors (both genetic and environmental determinants) in the pathogenesis of schizophrenia spectrum disorders [ 67 , 68 , 72 , 81 ]. Moreover, the current and traditional schizophrenia constructs are not able to adequately provide a clinical characterization, nor a dimensional and personalized approach to the understanding of each individual who is affected by schizophrenia [ 24 , 25 , 63 ]. However, there is still no relatively easily applicable and precise biologically-based diagnostic technique for schizophrenia that has enough specificity and sensitivity to replace the traditional schizophrenia constructs [ 20 , 36 ]. The highest clinical utility for the diagnosis of severe brain diseases, such as schizophrenia, is still provided by another brain, the long-term trained brain of a psychiatrist [ 221 ]. However, it has been also proposed that a precise psychiatry-based approach could better clinicians to move from a categorical (i.e., ICD and DSM-based criteria) to a dimensional approach in order to better identify people who are at risk for schizophrenia onset, and better clinically and psychopathologically characterize individuals who are affected with schizophrenia spectrum disorder [ 63 , 72 , 86 , 87 , 98 , 99 ]. However, there is still an intense debate in the scientific community and, despite overcoming the categorical approach that could apparently represent the best way to implement knowledge about schizophrenia, the boundaries of the currently termed schizophrenia could be limited to a neurodevelopmental syndrome that is characterized by disorganization, negative and cognitive symptoms, with a significant presence of anomalous self-experiences that may be distinguishable from other forms of psychosis [ 222 , 223 ].

Our current knowledge and understanding of schizophrenia have been influenced by its multi-level and multi-causal etiology, and the advances in deepening our understanding of its underpinned neurobiology and genetics [ 63 , 94 , 95 , 99 , 109 , 110 , 111 , 224 ]. Therefore, transdiagnostic psychosis spectrum and multi-dimensional frameworks, or multiple functional domains whose combinations comprise significant biotypes that are associated with schizophrenia, have been proposed as replacements for the schizophrenia construct due to the many shared characteristics and the blurring of boundaries between schizophrenia and related entities [ 36 , 67 , 68 , 78 , 79 , 80 , 94 ].

Furthermore, advances in multiple areas of neurosciences, including genomics, neuroimaging, cognitive science, and epidemiology, have facilitated the emergence of new conceptions and constructs of schizophrenia, and have allowed us to bridge animal and human research in order to probe the underlying mechanisms of typical and abnormal behaviors in schizophrenia [ 63 ]. The genomic data provide increasing support for the concept of systematic transdiagnostic components of neurodevelopmental spectra in schizophrenia [ 130 ], although the high heritability has not been translated into satisfying evidence for genetic lesions. In fact, both GWAS- and CNV-based studies that were looking for common genetic variants that are associated with schizophrenia were disappointing, either because the early findings failed to replicate or the large-scale studies failed to detect genome-wide significance [ 68 , 94 ].

Finally, considering that schizophrenia is a severe mental illness that is most strongly associated with stereotyping, prejudice, and a stigmatizing attitude [ 63 ], recently several researchers have proposed renaming the word ‘schizophrenia’ (etymologically meaning ‘ split mind ’) [ 63 , 68 , 225 , 226 ]. A recent systematic review has demonstrated that renaming schizophrenia could be associated with improvements in attitudes towards patients who are affected with the illness and may increase early diagnosis, mental health access, and reduce stigmatizing behaviors towards the disease and the patients who are affected [ 227 ]. Moreover, two recent large surveys of stakeholders demonstrated that approximately 75% of participants agreed to change the name, with the hope of reducing the stigma and the discrimination [ 228 ]. Accordingly, some authors have proposed to substitute it with the expression ‘ psychosis spectrum syndrome ’ or ‘ psychosis spectrum illness (PSI )’, which would be further characterized by key temporal features, such as the age of onset (i.e., childhood, adolescent, or adult), the symptom onset (i.e., acute/insidious), the illness course (i.e., single episode, intermittent, remitting/relapsing, or persistent), and the phase of the illness (i.e., clinical high risk, first episode, recent-onset/early phase, ongoing, or recovered), and so forth [ 68 ]. Furthermore, different names have been proposed to refer to schizophrenia in other countries [ 39 ]. For instance, the Taiwanese Society of Psychiatry introduced a new name for schizophrenia that means “disorder with dysfunction in thought and perception” in 2012 [ 229 ]. In 2022, the Japanese Society of Psychiatry and Neurology renamed the Japanese translation of schizophrenia from “seishin-bunretsu-byo” (meaning mind-split disease) to “togo-shitcho-sho” (meaning integration disorder) [ 230 ]. In addition, the Korean Neuropsychiatric Association changed the original Korean name for schizophrenia “jeongshin-bunyeol-byung” (meaning mind-split disorder) to “johyun-byung” (meaning attunement disorder) [ 231 ]. Several studies have demonstrated that renaming has significantly modified the attitude toward schizophrenia in health professionals and in the general population [ 217 , 231 , 232 ]. However, despite these pro-renaming movements, other authors have still declared themselves to be against changing the name for schizophrenia by supporting the idea that changing the name of the condition (or even abolishing the concept) will not affect the root cause of the stigma and will not provide clinicians with a more complete understanding of the causes and the pathophysiological mechanisms underlying schizophrenia [ 233 , 234 , 235 ].

Therefore, current emerging research supports the need to revise the schizophrenia concept, to implement and readapt the traditional and original schizophrenia constructs by developing new integrative, personalized approaches, to consider the unicity of each individual, the need to clinically characterize the illness onset, the clinical course, the clinical manifestation, the phenotypes, and to personalize the treatment interventions towards a better personalized and dimensional psychiatry. Furthermore, there is also the need to think about renaming, not only the schizophrenia concept, from a neurobiological perspective, but also renaming the term, in order to facilitate a changing mind of health professionals and of the general population.

Funding Statement

This research received no external funding.

Author Contributions

All authors equally contributed to the data collection and worked on the manuscript draft. All authors have read and agreed to the published version of the manuscript.

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Informed consent statement, data availability statement, conflicts of interest.

The authors declare no conflict of interest.

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4 minute read

The change in theory

The study and investigation into the root causes of a psychological disorder so that it might be resolved.

Psychological etiology refers to the scientific investigation into the origins of a disorder that cannot be explained biologically. Etiology is complicated by the fact that most disorders have more than one cause. Early etiological theories were the Freudian and post-Freudian psychoanalytic beliefs. Sigmund Freud attributed mental or neurotic disorders to deep-seated or hidden psychic motivations. The unconscious played the primary role in Freud's approach. According to Freud, the person in conflict was unaware of the cause because it was too deeply embedded in an inaccessible part of the mind. Freud postulated that the occurrence of previous traumas, unacceptable feelings, or wanton drives enacted a defense mechanism that enabled this burial into the unconscious. As a means of survival, a person might push such unsavory thoughts and memories as far from the conscious mind as possible.

Childhood , according to Freud, was the time when many repressed motivations and defense mechanisms began to thrive. Without control over their own lives, children have no way to resolve such emotions that include frustration, insecurity, or guilt . These emotions essentially build up while the child's personality is developing into adulthood. Every psychological disorder from sexual dysfunction to anxiety might be explained after talking about the repressed feelings a person has harbored since childhood.

A new trend in determining the causes of psychological disorders began to thrive after World War II. Some of the psychologists who proposed this new etiology had studied under Freud but ultimately looked further to explain the nature and causes of psychological disorders. Carl Jung , for example, believed that a person's need for spirituality lead to dissatisfaction if it were not met. The inability to thus define oneself spiritually contributed to the rise of psychological or neurotic disorders. For Alfred Adler , feelings of inferiority captured the focus of conflict. Others, including Harry Stack Sullivan, Karen Horney , and Erick Fromm, used Freud's theory as a basis for their thought but emphasized instead the importance of social, cultural, and environmental factors in uncovering the causes for psychological problems.

Another type of etiology that emerged after Freud is called behavioral etiology. This focuses on learned behaviors as causes of mental disorders. Ivan Pavlov and B. F. Skinner are two famous behavioral psychologists. Behaviorists argue that the mind can be "trained" to respond to stimuli in various ways. A conditioned response is one which is learned when a stimulus produces a response, and that response is somehow reinforced. A young girl, for example, is told she is cute for screeching at the sight of a spider. She learns that this screech produces a favorable response from onlookers. Over time, this learned behavior may develop into a truly paralyzing fear of spiders. Behaviorists believe that just as a person can be conditioned to respond to a stimulus in a particular way, that same person can be conditioned to respond differently. In other words, more appropriate behavior can be learned, which is the basis for behavioral therapy.

With modern approaches to therapy, including the existential and cognitive approach, psychology has moved away from depicting mental illnesses as emerging from one root cause. For example, clinical psychologists generally search for a complexity of issues that stem from emotional, psychosexual, social, cultural, or existential causes. The cognitive approach, such as that developed by Aaron Beck , attempts to readapt behavioral responses through a rational process that demands an honesty and discipline to undo fears and anxieties. Cognitive therapies might even have a positive role in treating schizophrenia without medication.

While psychologists have focused on the mind itself as the location where psychological impairment might begin, medical doctors and researchers have continued to understand the biology that might influence mental disorders. Many of these studies have resulted in the refinement of prescription medications that alter a person's biochemistry to prevent or control various illnesses such as depression or schizophrenia. Neuropathology, or damage to brain tissue, can also serve as a biological cause of psychological disorders. Genetic research has been conducted to determine causes of certain disorders at the level of DNA. Researchers have been working for decades to isolate a gene that contains the "program" for schizophrenia. In fact, reports of fully recovered schizophrenics treated without medication continued to rise by 2000. Psychological intervention seems to be just as effective as medical treatment for schizophrenic episodes.

Crucial to the treatment of any disorder is an understanding of its possible causes. Psychologists need to determine the etiology of a disorder before they can modify behavior.

See also Behaviorism ; Cognitive therapy

Further Reading

Benner, David G., and Hill, Peter C., ed. Baker Encyclopedia of Psychology & Counseling. Grand Rapids, MI: Baker Books, 1999.

Corsini, Ray, ed. Encyclopedia of Psychology, Second Edition. New York: John Wiley & Sons 1994.

McGuire, Patrick A. New Hope for People with Schizophrenia. APA Monitor on Psychology, February 2, 2000.

Further Information

American Psychological Association. 750 First Street, N.E., Washington, D.C., USA. 20002-4242, 202-336-5500, 800-374-2721.

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• Existential Psychology - History of the movement
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