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What is depression?

Everyone feels sad or low sometimes, but these feelings usually pass. Depression (also called major depression, major depressive disorder, or clinical depression) is different. It can cause severe symptoms that affect how a person feels, thinks, and handles daily activities, such as sleeping, eating, or working.

Depression can affect anyone regardless of age, gender, race or ethnicity, income, culture, or education. Research suggests that genetic, biological, environmental, and psychological factors play a role in the disorder.

Women are diagnosed with depression more often than men, but men can also be depressed. Because men may be less likely to recognize, talk about, and seek help for their negative feelings, they are at greater risk of their depression symptoms being undiagnosed and undertreated. Studies also show higher rates of depression and an increased risk for the disorder among members of the LGBTQI+ community.

In addition, depression can co-occur with other mental disorders or chronic illnesses, such as diabetes, cancer, heart disease, and chronic pain. Depression can make these conditions worse and vice versa. Sometimes, medications taken for an illness cause side effects that contribute to depression symptoms as well.

What are the different types of depression?

There are two common types of depression.

• Major depression includes symptoms of depressed mood or loss of interest, most of the time for at least 2 weeks, that interfere with daily activities.
• Persistent depressive disorder (also called dysthymia or dysthymic disorder) consists of less severe depression symptoms that last much longer, usually for at least 2 years.

Other types of depression include the following.

• Seasonal affective disorder comes and goes with the seasons, with symptoms typically starting in the late fall and early winter and going away during the spring and summer.
• Depression with symptoms of psychosis is a severe form of depression in which a person experiences psychosis symptoms, such as delusions or hallucinations.
• Bipolar disorder involves depressive episodes, as well as manic episodes (or less severe hypomanic episodes) with unusually elevated mood, greater irritability, or increased activity level.

Additional types of depression can occur at specific points in a woman’s life. Pregnancy, the postpartum period, the menstrual cycle, and menopause are associated with physical and hormonal changes that can bring on a depressive episode in some people.

• Premenstrual dysphoric disorder is a more severe form of premenstrual syndrome, or PMS, that occurs in the weeks before menstruation.
• Perinatal depression occurs during pregnancy or after childbirth. It is more than the “baby blues” many new moms experience after giving birth.
• Perimenopausal depression affects some women during the transition to menopause. Women may experience feelings of intense irritability, anxiety, sadness, or loss of enjoyment.

What are the signs and symptoms of depression?

Common signs and symptoms of depression include:

• Persistent sad, anxious, or “empty” mood
• Feelings of hopelessness or pessimism
• Feelings of irritability, frustration‚ or restlessness
• Feelings of guilt, worthlessness, or helplessness
• Loss of interest or pleasure in hobbies and activities
• Fatigue, lack of energy, or feeling slowed down
• Difficulty concentrating, remembering, or making decisions
• Difficulty sleeping, waking too early in the morning, or oversleeping
• Changes in appetite or unplanned weight changes
• Physical aches or pains, headaches, cramps, or digestive problems without a clear physical cause that do not go away with treatment
• Thoughts of death or suicide or suicide attempts

Depression can also involve other changes in mood or behavior that include:

• Increased anger or irritability
• Feeling restless or on edge
• Becoming withdrawn, negative, or detached
• Increased engagement in high-risk activities
• Greater impulsivity
• Increased use of alcohol or drugs
• Isolating from family and friends
• Inability to meet responsibilities or ignoring other important roles
• Problems with sexual desire and performance

Not everyone who is depressed shows all these symptoms. Some people experience only a few symptoms, while others experience many. Depression symptoms interfere with day-to-day functioning and cause significant distress for the person experiencing them.

If you show signs or symptoms of depression and they persist or do not go away, talk to a health care provider. If you see signs of depression in someone you know, encourage them to seek help from a mental health professional.

If you or someone you know is struggling or having thoughts of suicide, call or text the 988 Suicide and Crisis Lifeline   at 988 or chat at 988lifeline.org   . In life-threatening situations, call 911 .

How is depression diagnosed?

To be diagnosed with depression, a person must have symptoms most of the day, nearly every day, for at least 2 weeks. One of the symptoms must be a depressed mood or a loss of interest or pleasure in most activities. Children and adolescents may be irritable rather than sad.

Although several persistent symptoms, in addition to low mood, are required for a depression diagnosis, people with only a few symptoms may benefit from treatment. The severity and frequency of symptoms and how long they last vary depending on the person.

If you think you may have depression, talk to a health care provider, such as a primary care doctor, psychologist, or psychiatrist. During the visit, the provider may ask when your symptoms began, how long they have lasted, how often they occur, and if they keep you from going out or doing your usual activities. It may help to take some notes about your symptoms before the visit.

Certain medications and medical conditions, such as viruses or thyroid disorders, can cause the same symptoms as depression. A provider can rule out these possibilities by doing a physical exam, interview, and lab tests.

Does depression look the same in everyone?

Depression can affect people differently depending on their age.

• Children may be anxious or cranky, pretend to be sick, refuse to go to school, cling to a parent, or worry that a parent may die.
• Older children and teens may get into trouble at school, sulk, be easily frustrated‚ feel restless, or have low self-esteem. They may have other disorders, such as anxiety, an eating disorder, attention-deficit/hyperactivity disorder, or substance use disorder. Older children and teens are also more likely to experience excessive sleepiness (called hypersomnia) and increased appetite (called hyperphagia).
• Young adults are more likely to be irritable, complain of weight gain and hypersomnia, and have a negative view of life and the future. They often have other disorders, such as generalized anxiety disorder, social phobia, panic disorder, or substance use disorder.
• Middle-aged adults may have more depressive episodes, decreased libido, middle-of-the-night insomnia, or early morning waking. They often report stomach problems, such as diarrhea or constipation.
• Older adults often feel sadness, grief, or other less obvious symptoms. They may report a lack of emotions rather than a depressed mood. Older adults are also more likely to have other medical conditions or pain that can cause or contribute to depression. Memory and thinking problems (called pseudodementia) may be prominent in severe cases.

Depression can also look different in men versus women, such as the symptoms they show and the behaviors they use to cope with them. For instance, men (as well as women) may show symptoms other than sadness, instead seeming angry or irritable.

For some people, symptoms manifest as physical problems (for example, a racing heart, tightened chest, chronic headaches, or digestive issues). Many men are more likely to see a health care provider about these physical symptoms than their emotional ones. While increased use of alcohol or drugs can be a sign of depression in any person, men are also more likely to use these substances as a coping strategy.

How is depression treated?

Depression treatment typically involves psychotherapy (in person or virtual), medication, or both. If these treatments do not reduce symptoms sufficiently, brain stimulation therapy may be another option.

Choosing the right treatment plan is based on a person’s needs, preferences, and medical situation and in consultation with a mental health professional or a health care provider. Finding the best treatment may take trial and error.

For milder forms of depression, psychotherapy is often tried first, with medication added later if the therapy alone does not produce a good response. People with moderate or severe depression usually are prescribed medication as part of the initial treatment plan.

Psychotherapy

Psychotherapy (also called talk therapy or counseling) can help people with depression by teaching them new ways of thinking and behaving and helping them change habits that contribute to depression. Psychotherapy occurs under the care of a licensed, trained mental health professional in one-on-one sessions or with others in a group setting.

Psychotherapy can be effective when delivered in person or virtually via telehealth. A provider may support or supplement therapy using digital or mobile technology, like apps or other tools.

Evidence-based therapies to treat depression include cognitive behavioral therapy and interpersonal therapy. Using other forms of psychotherapy, such as psychodynamic therapy, for a limited time also may help some people with depression.

• Cognitive behavioral therapy (CBT) : With CBT, people learn to challenge and change unhelpful thoughts and behaviors to improve their depressive and anxious feelings. Recent advances in CBT include adding mindfulness principles and specializing the therapy to target specific symptoms like insomnia.
• Interpersonal therapy (IPT) : IPT focuses on interpersonal and life events that impact mood and vice versa. IPT aims to help people improve their communication skills within relationships, form social support networks, and develop realistic expectations to better deal with crises or other issues that may be contributing to or worsening their depression.

Learn more about psychotherapy .

Antidepressants are medications commonly used to treat depression. They work by changing how the brain produces or uses certain chemicals involved in mood or stress.

Antidepressants take time—usually 4−8 weeks—to work, and problems with sleep, appetite, and concentration often improve before mood lifts. Giving a medication a chance to work is important before deciding whether it is right for you.

Treatment-resistant depression occurs when a person doesn’t get better after trying at least two antidepressants. Esketamine is a medication approved by the U.S. Food and Drug Administration (FDA) for treatment-resistant depression. Delivered as a nasal spray in a doctor’s office, clinic, or hospital, the medication acts rapidly, typically within a couple of hours, to relieve depression symptoms. People will usually continue to take an antidepressant pill to maintain the improvement in their symptoms.

Another option for treatment-resistant depression is to combine an antidepressant with a different type of medication that may make it more effective, such as an antipsychotic or anticonvulsant medication.

All medications can have side effects. Talk to a health care provider before starting or stopping any medication. Learn more about antidepressants .

Note : In some cases, children, teenagers, and young adults under 25 years may experience an increase in suicidal thoughts or behavior when taking antidepressants, especially in the first few weeks after starting or when the dose is changed. The FDA advises that patients of all ages taking antidepressants be watched closely, especially during the first few weeks of treatment.

Information about medication changes frequently. Learn more about specific medications like esketamine, including the latest approvals, side effects, warnings, and patient information, on the FDA website  .

Brain stimulation therapy

Brain stimulation therapy is an option when other depression treatments have not worked. The therapy involves activating or inhibiting the brain with electricity or magnetic waves.

Although brain stimulation therapy is less frequently used than psychotherapy and medication, it can play an important role in treating depression in people who have not responded to other treatments. The therapy generally is used only after a person has tried psychotherapy and medication, and those treatments usually continue. Brain stimulation therapy is sometimes used as an earlier treatment option when severe depression has become life-threatening, such as when a person has stopped eating or drinking or is at a high risk of suicide.

The FDA has approved several types of brain stimulation therapy. The most used are electroconvulsive therapy (ECT) and repetitive transcranial magnetic stimulation (rTMS). Other brain stimulation therapies are newer and, in some cases, still considered experimental. Learn more about brain stimulation therapies .

Natural products

The FDA has not approved any natural products for treating depression. Although research is ongoing and findings are inconsistent, some people report that natural products, including vitamin D and the herbal dietary supplement St. John’s wort, helped their depression symptoms. However, these products can come with risks, including, in some cases, interactions with prescription medications.

Do not use vitamin D, St. John’s wort, or other dietary supplements or natural products without first talking to a health care provider. Rigorous studies must test whether these and other natural products are safe and effective.

How can I take care of myself?

Most people with depression benefit from mental health treatment. Once you begin treatment, you should gradually start to feel better. Go easy on yourself during this time. Try to do things you used to enjoy. Even if you don’t feel like doing them, they can improve your mood.

Other things that may help:

• Try to get physical activity. Just 30 minutes a day of walking can boost your mood.
• Try to maintain a regular bedtime and wake-up time.
• Eat regular, healthy meals.
• Do what you can as you can. Decide what must get done and what can wait.
• Connect with people. Talk to people you trust about how you are feeling.
• Delay making important life decisions until you feel better. Discuss decisions with people who know you well.
• Avoid using alcohol, nicotine, or drugs, including medications not prescribed for you.

How can I find help for depression?

You can learn about ways to get help and find tips for talking with a health care provider on the NIMH website.

The Substance Abuse and Mental Health Services Administration (SAMHSA) also has an online tool to help you find mental health services  in your area.

How can I help a loved one who is depressed?

If someone you know is depressed, help them see a health care provider or mental health professional. You also can:

• Offer support, understanding, patience, and encouragement.
• Invite them out for walks, outings, and other activities.
• Help them stick to their treatment plan, such as setting reminders to take prescribed medications.
• Make sure they have transportation or access to therapy appointments.
• Remind them that, with time and treatment, their depression can lift.

What are clinical trials and why are they important?

Clinical trials are research studies that look at ways to prevent, detect, or treat diseases and conditions. These studies help show whether a treatment is safe and effective in people. Some people join clinical trials to help doctors and researchers learn more about a disease and improve health care. Other people, such as those with health conditions, join to try treatments that aren’t widely available.

NIMH supports clinical trials across the United States. Talk to a health care provider about clinical trials and whether one is right for you. Learn more about  participating in clinical trials .

For more information

Learn more about mental health disorders and topics . For information about various health topics, visit the National Library of Medicine’s MedlinePlus   .

The information in this publication is in the public domain and may be reused or copied without permission. However, you may not reuse or copy images. Please cite the National Institute of Mental Health as the source. Read our copyright policy to learn more about our guidelines for reusing NIMH content.

U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES National Institutes of Health NIH Publication No. 24-MH-8079 Revised 2024

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Analysis: Depression is probably not caused by a chemical imbalance in the brain – new study

20 July 2022

Writing in The Conversation, Professor Joanna Moncrieff and Dr Mark Horowitz (both UCL Psychiatry) report on their new research showing no clear evidence that serotonin levels or serotonin activity are responsible for depression.

For three decades, people have been deluged with information suggesting that depression is caused by a “chemical imbalance” in the brain – namely an imbalance of a brain chemical called serotonin. However, our latest research review shows that the evidence does not support it.

Although first proposed in the 1960s, the serotonin theory of depression started to be widely promoted by the pharmaceutical industry in the 1990s in association with its efforts to market a new range of antidepressants, known as selective serotonin-reuptake inhibitors or SSRIs. The idea was also endorsed by official institutions such as the American Psychiatric Association, which still tells the public that “differences in certain chemicals in the brain may contribute to symptoms of depression”.

Countless doctors have repeated the message all over the world, in their private surgeries and in the media. People accepted what they were told. And many started taking antidepressants because they believed they had something wrong with their brain that required an antidepressant to put right. In the period of this marketing push, antidepressant use climbed dramatically, and they are now prescribed to one in six of the adult population in England, for example.

For a long time, certain academics, including some leading psychiatrists, have suggested that there is no satisfactory evidence to support the idea that depression is a result of abnormally low or inactive serotonin. Others continue to endorse the theory. Until now, however, there has been no comprehensive review of the research on serotonin and depression that could enable firm conclusions either way.

At first sight, the fact that SSRI-type antidepressants act on the serotonin system appears to support the serotonin theory of depression. SSRIs temporarily increase the availability of serotonin in the brain, but this does not necessarily imply that depression is caused by the opposite of this effect.

There are other explanations for antidepressants’ effects. In fact, drug trials show that antidepressants are barely distinguishable from a placebo (dummy pill) when it comes to treating depression. Also, antidepressants appear to have a generalised emotion-numbing effect which may influence people’s moods, although we do not know how this effect is produced or much about it.

There has been extensive research on the serotonin system since the 1990s, but it has not been collected systematically before. We conducted an “umbrella” review that involved systematically identifying and collating existing overviews of the evidence from each of the main areas of research into serotonin and depression. Although there have been systematic reviews of individual areas in the past, none have combined the evidence from all the different areas taking this approach.

One area of research we included was research comparing levels of serotonin and its breakdown products in the blood or brain fluid. Overall, this research did not show a difference between people with depression and those without depression.

Another area of research has focused on serotonin receptors, which are proteins on the ends of the nerves that serotonin links up with and which can transmit or inhibit serotonin’s effects. Research on the most commonly investigated serotonin receptor suggested either no difference between people with depression and people without depression, or that serotonin activity was actually increased in people with depression – the opposite of the serotonin theory’s prediction.

Research on the serotonin “transporter”, that is the protein which helps to terminate the effect of serotonin (this is the protein that SSRIs act on), also suggested that, if anything, there was increased serotonin activity in people with depression. However, these findings may be explained by the fact that many participants in these studies had used or were currently using antidepressants.

We also looked at research that explored whether depression can be induced in volunteers by artificially lowering levels of serotonin. Two systematic reviews from 2006 and 2007 and a sample of the ten most recent studies (at the time the current research was conducted) found that lowering serotonin did not produce depression in hundreds of healthy volunteers. One of the reviews showed very weak evidence of an effect in a small subgroup of people with a family history of depression, but this only involved 75 participants.

Very large studies involving tens of thousands of patients looked at gene variation, including the gene that has the instructions for making the serotonin transporter. They found no difference in the frequency of varieties of this gene between people with depression and healthy controls.

Although a famous early study found a relationship between the serotonin transporter gene and stressful life events, larger, more comprehensive studies suggest no such relationship exists. Stressful life events in themselves, however, exerted a strong effect on people’s subsequent risk of developing depression.

Some of the studies in our overview that included people who were taking or had previously taken antidepressants showed evidence that antidepressants may actually lower the concentration or activity of serotonin.

The serotonin theory of depression has been one of the most influential and extensively researched biological theories of the origins of depression. Our study shows that this view is not supported by scientific evidence. It also calls into question the basis for the use of antidepressants.

Most antidepressants now in use are presumed to act via their effects on serotonin. Some also affect the brain chemical noradrenaline. But experts agree that the evidence for the involvement of noradrenaline in depression is weaker than that for serotonin.

There is no other accepted pharmacological mechanism for how antidepressants might affect depression. If antidepressants exert their effects as placebos, or by numbing emotions, then it is not clear that they do more good than harm.

Although viewing depression as a biological disorder may seem like it would reduce stigma, in fact, research has shown the opposite, and also that people who believe their own depression is due to a chemical imbalance are more pessimistic about their chances of recovery.

It is important that people know that the idea that depression results from a “chemical imbalance” is hypothetical. And we do not understand what temporarily elevating serotonin or other biochemical changes produced by antidepressants do to the brain. We conclude that it is impossible to say that taking SSRI antidepressants is worthwhile, or even completely safe. People need all this information to make informed decisions about whether or not to take antidepressants.

This article originally appeared in  The Conversation on 20 July 2022.

• Article in  The Conversation
• Article in  The Conversation  (Spanish)
• Professor Joanna Moncrieff's academic profile
• Dr Mark Horowitz's academic profile
• UCL Division of Psychiatry
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New Research: Serotonin Does Have a Direct Role in Depression

Absence of evidence is not evidence of absence..

Posted November 5, 2022 | Reviewed by Vanessa Lancaster

• What Is Depression?
• Find a therapist to overcome depression
• There has been no direct evidence that serotonin plays a key role in depression until now.
• New research reported here uses a radioactive tracer and PET scans to show that serotonin activity is lower in people with clinical depression.
• While a small study, this work presents a new paradigm for directly assaying serotonin in psychiatry.
• Future research will seek to replicate or refute these findings and expand studies to larger and more diverse groups.

By Grant H. Brenner

Over the years, the serotonin hypothesis of depression , which suggests that low or impaired serotonin neurotransmission is related to the symptoms and possible causes of depression and related psychiatric disorders, has taken many hits.

Recently, a widely publicized review paper in Nature (Moncrieff et al., 2022) found no direct evidence that serotonin is involved in the pathophysiology of depression. This led to an explosion of reports and even attacks which reverberated across the internet about the serotonin hypothesis, the long-debunked “chemical imbalance” explanation, and, to an extent, psychiatry as a field.

The uproar is understandable–depression affects an estimated 20 million US citizens and nearly 300 million worldwide , and current treatments are only partially effective. There is a growing awareness that mental health problems are epidemic–a recent survey by the American Psychiatric Association found 79 percent of people see mental health as a public health emergency.

Moreover, rates of anxiety and depression are skyrocketing, especially among younger people , suicide has become a leading cause of death, and we are more aware every day of the hazardous effect of endemic stress and trauma on mental and physical health.

Despite various studies over the years and the recognition that antidepressant and psychedelic medications, 1 which improve depression, often increase serotonin levels and are associated with positive brain changes (such as increased neuronal complexity via "sprouting" and possible restoration of brain volume in areas like the hippocampus), there has been a startling lack of evidence showing a direct role for serotonin, perhaps because more sophisticated research methods have not been available.

Absence of Evidence Is Not Evidence of Absence

That is, until now. For the first time, study authors have found a clear association between altered serotonin activity and depression in a robust experimental design.

Researchers Erritzoe and colleagues (2022) report their findings in a recent paper in Biological Psychiatry entitled "Brain Serotonin Release Is Reduced in Patients With Depression." In this study, they compared two groups of people, 17 people with clinical depression (MDD group for major depressive disorder) rigorously diagnosed with depression and free from other conditions, not taking antidepressants at the time of the study (most never had, some had in the past), and 17 people without any mental illness (the HC or healthy control group).

The statistics analyzed clinical data and brain-imaging findings to determine if MDD and HC differed significantly in the serotonin neurotransmission, and if so, in what direction? If MDD showed lower serotonin, or 5HT, activity, this would directly support the serotonin hypothesis of depression.

Study participants were administered a mildly radioactive chemical developed to directly measure serotonin activity in the brain. Rather than the previous studies reviewed in the Nature article, which included indirect measures, using [11C]Cimbi-36 (a "radioligand") allows researchers to probe actual 5HT activity on PET (Positron Emission Tomography) and MRI (Magnetic Resonance Imaging) scan.

PET is an imaging approach more sophisticated than MRI. Unlike MRI, which produces a lower resolution scan indirectly estimating brain activity (often based on inferred blood flow), PET images positrons (a form of antimatter, antielectrons) to get a fine-grained, direct window into the brain's inner workings.

Why [11C]Cimbi-36? Prior PET studies have shown that when serotonin goes up, [11C]Cimbi-36 predictably and reliably goes down. It’s called a radioligand because the molecule contains a radioactive form of carbon that lights up on PET.

Study participants, in addition to careful clinical diagnosis, completed depression ratings and underwent PET-MRI imaging after being given a dose of d- amphetamine (a drug familiar for its use in treating ADHD and also for its abuse potential as a street drug and misused prescriptions).

Prior research has shown that if we give a dose of d-amphetamine to non-depressed individuals (HCs), [11C]Cimbi-36 levels drop significantly, reflecting an increase in 5HT activity. Is this response the same in people with depression? If not, it would lend new support to the serotonin hypothesis of depression.

The main study result was that in patients with depression, serotonin activity is indeed diminished compared with people without depression. Following d-amphetamine challenge, [11C]Cimbi-36 tracer binding across brain cortex (surface) areas was as expected for healthy individuals, consistent with prior experiments.

However, for depressed participants, a very different pattern emerged. When this group received d-amphetamine, [11C]Cimbi-36 binding was significantly lower than in the non-depressed group, reflecting underlying decreased serotonin activity in depressed patients compared with healthy controls. This activity was seen across many brain regions, reaching statistical significance in the temporal cortex (areas located on the sides of the brain, in rough proximity to the ears 2 ).

The effect was stronger in MDD participants who had never taken antidepressants (" medication naive") versus those who had but were not taking them during the study, but this finding is hard to interpret due to the small number of participants in the prior medication group.

Implications and Future Directions

Overall, this study found diminished serotonin activity in patients with diagnoses of clinical depression. This does not mean that depression is caused by serotonin deficiency or that medications that increase serotonin will treat depression. If only it were that easy.

The picture is much more complex than that, with an array of not only many different serotonin receptors and systems throughout the brain but also many other factors, including but not limited to an array of implicated non-serotonin neurotransmitters (norepinephrine, dopamine , glutamate, GABA, and others), the role of other biological factors and psychosocial factors, and the increasingly recognized role of supporting role of glial cells in brain function–cells which are not neurons but which support and modulate what neurons do. 3

This study is not the end of the discussion but rather a new beginning. It is the first study to show diminished serotonin activity in depression.

However, it is only one study with a relatively small number of participants, and as with any study requires replication to determine if it is really true and to build in more nuance to the study design to account for potential confounding factors (like time of day, clinical presentation in a more diverse population, the role of serotonin in disorders which overlap with depression, and so on).

In addition to the main finding that people with depression do have lower serotonin activity compared with those without depression, at least here, this work is important because it is proof of concept for a study approach to directly assess brain serotonin activity. This approach can and will be refined–for example, using challenges other than with d-amphetamine to perturb serotonin tracer activity.

Nevertheless, it is a powerful tool and one which, along with others under development such as computational tools to develop better ways of understanding psychiatric conditions (“ transdiagnostic approaches "), 4 will help to usher in a new era, with the expectation that growing understanding will enable more effective treatment and ultimate reduce the considerable suffering associated with mental illness.

Facebook image: Monkey Business Images/Shutterstock

1. Serotonin is also implicated in psychedelic medications, including psilocybin ("magic mushrooms") and LSD–however, the psychedelic effect may be different from the antidepressant effect (Kaplan et al., 2022), suggesting that aside from depression as a mental illness, culturally we may be looking for ways to break out of conventional, restrictive ways of thinking while folding this need into a medicalized view of human experience.

2. 3D Brain Online

3. A recent study found that astrocytes, a supporting cell in the brain, actually pull back part of their cell bodies ("leaflets") to expose the gap between neurons (the "synapse"), which in turn facilitates glutamate transmission involved in fear-based learning!

4. A recent paper discusses evolving diagnostic approaches in psychiatry: Identifying transdiagnostic mechanisms in mental health using computational factor modeling

Erritzoe D. CA, Searle G., Lewis Y., Passchier J., Azeem S., Beaver J., Nutt D., Knudsen G., Gunn R., Rabiner E. (2017): Serotonin release measured in the human brain: A PET study with [11C]Cimbi-36 and d-amphetamine challenge. BrainPET 2017. Berlin, Germany.

Erritzoe D., Godlewska B.R., Rizzo G., Searle G.E., Agnorelli C., Lewis Y., Ashok A.H., Colasanti A., Boura I., Farrell C., Parfit H., Howes O., Passchier J., Gunn R.N., Nutt D.J., Cowen P.J, Knudsen G. & Rabiner E.A., BRAIN SEROTONIN RELEASE IS REDUCED IN PATIENTS WITH DEPRESSION: A [11C]Cimbi-36 PET STUDY WITH A D-AMPHETAMINE CHALLENGE., Biological Psychiatry (2022), doi: https://doi.org/10.1016/j.biopsych.2022.10.012 .

Kaplan, A.L., Confair, D.N., Kim, K. et al. Bespoke library docking for 5-HT2A receptor agonists with antidepressant activity. Nature 610, 582–591 (2022). https://doi.org/10.1038/s41586-022-05258-z

Moncrieff, J., Cooper, R.E., Stockmann, T. et al. The serotonin theory of depression: a systematic umbrella review of the evidence. Mol Psychiatry (2022). https://doi.org/10.1038/s41380-022-01661-0

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Genetic determinants of depression: Recent findings and future directions

Erin c. dunn.

1 Center for Human Genetic Research, Massachusetts General Hospital

2 Department of Psychiatry, Harvard Medical School

3 Stanley Center for Psychiatric Research, The Broad Institute of Harvard and MIT

Ruth C. Brown

4 Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University

Jonathan Rosand

5 Department of Neurology, Massachusetts General Hospital

6 Program in Medical and Population Genetics, The Broad Institute of Harvard and MIT

Nicole R. Nugent

7 Department of Psychiatry and Human Behavior, Alpert Brown Medical School

Ananda B. Amstadter

Jordan w. smoller.

8 Center on the Developing Child, Harvard University

Depression is one of the most prevalent, disabling, and costly mental health conditions in the United States. One promising avenue for preventing depression and informing its clinical treatment lies in uncovering both the genetic and environmental determinants of the disorder as well as their interaction (i.e. gene-environment intervention; GxE). The overarching goal of this review paper is to translate recent findings from studies of genetic association and GxE related to depression, particularly for readers without in-depth knowledge of genetics or genetic methods. This review is organized into three major sections. In the first section, we summarize what is currently known about the genetic determinants of depression, focusing on findings from genome-wide association studies (GWAS). In the second section, we review findings from studies of GxE, which seek to simultaneously examine the role of genes and exposure to specific environments or experiences in the etiology of depression. In the third section, we describe the challenges to genetic discovery in depression and promising strategies for making progress.

Introduction

Depression is one of the most prevalent, disabling, and costly mental health conditions in the United States, with lifetime prevalence estimates of 11.7% among adolescents 1 and 16.6% among adults. 2 It is projected to be the leading cause of disease burden worldwide by 2030. 3 Although the impact of depression can be minimized or prevented through early detection, treatment, and ongoing care, numerous individual and structural barriers, including stigma, lack of health insurance, and other barriers to accessing mental health services, prevent many from seeking help. Indeed, only slightly more than half of all people who experience depression seek treatment and those who do tend to dropout prematurely or receive poor quality care. 4 , 5 Existing treatments for depression are also modestly effective; only about one-fifth of adults receiving cognitive behavioral therapy or psychodynamic therapy alone 6 and one-third of adults receiving antidepressant medication alone 7 , 8 will experience remission after an initial course of treatment. In children and adolescents, the efficacy of existing treatments is also limited. 9 - 11 Moreover, nearly three-quarters of people with depression will experience a relapse at some point in their life. 12 These findings underscore the urgent need to prioritize prevention, alongside treatment.

A deeper understanding of the etiology of depression, including both its genetic and environmental determinants, as well as their interplay (e.g., gene-environment interaction; GxE) will have implications for preventing depression and informing its clinical treatment. There are now numerous established environmental risk factors for depression, including poverty, 13 , 14 negative family relationships and parental divorce, 15 , 16 child maltreatment, 17 , 18 and other stressful life events more generally. 19 , 20 While the risk of depression is elevated in the immediate aftermath of experiencing these environmental adversities, the effects of adversity can persist over the lifecourse. 21 , 22

There is also now a robust literature implicating genetic factors in the etiology of depression and other psychiatric disorders. Depression is known to run in families; people with major depressive disorder are three times more likely than those without the disorder to have a first degree relative who also has depression. 23 Twin studies, which allow for simultaneous quantification of genetic and environmental influences, suggest that depression is moderately heritable. Specifically, twin studies have estimated that approximately 40% of the variation in the population risk of depression is attributable to genetic variation. 24

In recent years, the combination of advances in our understanding of human genomic variation (e.g., Human Genome Project; HapMap Project; 1,000 Genomes Project) and cost-effective genotyping techniques haveled to extraordinary growth in molecular genetic studies of depression and other “complex” psychiatric phenotypes. These studies typically examine whether specific alleles (e.g., alternative forms of DNA sequence at a specific locus) or genotypes (e.g., the combination of alleles at a given locus) are associated with the phenotype of interest. Until recently, genetic studies of depression focused largely on candidate genes, or genes hypothesized to be implicated in the neurobiology of depression. Some of the most commonly studied candidate genes have been those regulating serotonin (5-HT) and dopamine (DA) neurotransmission, given the suspected involvement of these neurotransmitters in the pathophysiology of depression and the fact that these are targets of antidepressant drugs. 25 - 27 Unfortunately, most candidate gene studies have been underpowered and replication of findings has been rare. More recently, the availability of DNA microarrays have enabled genomewide association studies (GWAS) that do not rely on prior hypotheses. The GWAS approach allows for the analysis of a million or more variants across the entire genome. The ultimate goal of these genetic association studies is to improve diagnosis, prevention, and treatment through a nuanced understanding of the genetic underpinnings of the disease.

In this paper, we review recent findings from studies of genetic association and GxE related to depression and outline areas for future research. Several excellent reviews of this literature aimed at the genetic research community have already been published (see for example 28 , 29 - 33 ). We aimed to provide a review for a broad audience of readers who may be unfamiliar with genetic concepts and methods. We organized this review into three major sections. In the first section, we describe recent findings based on GWAS of depression. We begin with GWAS, rather than older methods (i.e., linkage and candidate gene association studies), as the latter have already been extensively covered by prior reviews. We also do not review studies on genetic markers of antidepressant treatment response, or pharmacogenomics, 34 as our focus was on the genetic determinants of illness risk. In the second section, we review findings from GxE studies, which aim to simultaneously examine the role of genetic variants and environmental exposures in the etiology of depression. As described below, GxE studies have the potential to help identify genetic variants associated with risk or resilience against depression that are only revealed in specific subgroups of the population who have experienced a given environment. In the third section, we address the challenges that face genetic studies of depression and describe emerging strategies that may be useful for overcoming these challenges. We encourage readers who may be unfamiliar with basic genetic concepts to refer to the following resources 35 , 36 as well as the resources listed in Table 1 .

Findings from Genome-Wide Association Studies (GWAS)

GWAS have been one of the most widely used methods for identifying risk loci in the past decade. 37 - 40 In a typical GWAS, one million or more common variants known as single nucleotide polymorphisms (SNPs) are examined for their association to disease. “Common variants” are generally defined as those alleles that are carried by at least 5% of the population. GWAS are typically conducted using a case-control design in which allele frequencies are compared between cases with a disease to controls without the disease. Compared to candidate gene studies, GWAS provide a hypothesis free or “unbiased” approach to detecting susceptibility loci. However, to account for the large number of tests conducted, the threshold for declaring genome-wide significance in a GWAS is a p-value of less than 5×10 -8 , equivalent to a p-value of 0.05 corrected for a million independent tests (p<0.00000005). 41 Because common variant effects are typically modest, large samples (on the order of 10,000 or more cases and controls) are usually needed to have sufficient power to detect such effects at this statistical threshold.

According to the National Human Genome Research Institute (NHGRI) GWAS catalog, more than 2,000 GWAS have been published to date. 42 A total of 14 GWAS have been conducted for either major depressive disorder (MDD) or depressive symptoms. In addition, one GWAS focusing on age at onset of major depressive disorder was conducted. These 15 studies were identified by conducting a systematic search of PubMed for papers published before October 2013. We searched the PubMed database using the following MESH terms: (“Depression”[Mesh] OR “Depressive Disorder”[Mesh] OR “Depressive Disorder, Major”[Mesh] OR “Depressive Disorder, Treatment-Resistant”[Mesh]) AND “Genome-Wide Association Study”[Mesh]. We also searched for articles by examining the references pages of review articles, meta-analyses, and other empirical articles published since 2005. As shown in Table 2 , all of these studies were based on samples of European ancestry and represent a combination of population-and clinic-based samples.

BDI = Beck Depression Inventory; CESD = Center for Epidemiological Studies of Depression Scale; CIDI = Composite International Diagnostic Interview; DIGS = Diagnostic Interview for Genetic Studies; fMRI = functional magnetic resonance imaging; GAIN = Genetic Association Information Network; GCTA = genome-wide complex trait analysis; GenRED = Genetics of Recurrent Early-Onset Depression; GWS = genome wide significant; HADS = Hospital Anxiety and Depression Scale; HAM-D = Hamilton Depression Rating Scale; LD = linkage disequilibrium; POMS = Profile of Mood States; SCAN = Schedules for Clinical Assessment in Neuropsychiatry; SCI = Structured Clinical Interview; SNPs = single nucleotide polymorphism; STAR*D = Sequenced Treatments to Relieve Depression

The first GWAS of depression was published in 2009 and included 1,738 cases and 1,802 controls. Although no SNPs reached genome-wide significance, 11 of the top 200 SNPs were found in a 167 kilobase (kb) region overlapping the gene PCLO (piccolo presynaptic cytomatrix protein), which is involved in establishing active synaptic zones and synaptic vesicle tracking. 43 In several subsequent studies, 44 , 49 , 58 investigators found mixed evidence regarding the association of PCLO SNPs and MDD. In the first study to report a genome-wide significant association for depression, Kohli and colleagues 50 found support for a recessive effect of a SNP (rs1545843) in the gene SLC6A15 (solute carrier family 6, neutral amino acid transporter, member 15) that is involved in transporting neutral amino acids. They provided additional evidence in support of this association by demonstrating that risk alleles were correlated with reduced SLC6A15 expression in hippocampal tissue (taken from individuals undergoing surgery for epilepsy) and reduced hippocampal volume and neuronal integrity using neuroimaging. Mice susceptible to chronic stress were also found to have reduced hippocampal SLC6A15 expression. Of note, however, this locus has not emerged as a prominent finding in subsequent depression GWAS (described below).

One of the major lessons from these early GWAS of depression, as with other complex traits, 59 , 60 was that the effect of most SNPs was small in magnitude (allelic odd ratios of around 1.3 or less) and therefore considerably larger samples would be needed to identify genetic loci associated with depression. To enhance the power of psychiatric GWAS studies, the Psychiatric Genomics Consortium (PGC) was established in 2007 as an international collaborative effort to define the spectrum of risk variants across psychiatric disorders ( http://www.med.unc.edu/pgc ). One of the major goals of the PGC was to conduct mega-analyses for MDD in addition to schizophrenia, bipolar disorder, autism, and attention deficit hyperactivity disorder. 61 - 63 A mega-analysis pools individual-level phenotype and genotype data from across many studies; this approach differs from a meta-analysis, where the summary statistics produced by each study are analyzed. In 2012, the PGC published the results of a GWAS mega-analysis of MDD comprising 9,240 cases and 9,519 controls across 9 primary samples, all of European ancestry. 64 Although this was the largest sample to date, no SNP reached genome-wide significance. The most significant SNPs in the discovery sample were rs11579964 (p=1.0 × 10 -7 ), a variant closest to several genes ( CNIH4 , NVL , WDR26 ) and rs7647854 (p=6.5 × 10 -7 ) a variant closest to C3orf70 and EHHADH . However, these findings were not supported in a large independent replication sample.

GWAS of depressive symptoms have also been largely unrevealing. The first GWAS of depressive symptoms did not find any SNPs reaching genome-wide significance. 55 One modestly associated (p=1.59×10 -6 ) SNP (rs7582472) did show evidence of replication in two independent cohorts. However, this SNP was more than 300kb away from two genes and neither gene showed significant association to depression in a gene-based analysis. A second study of depressed mood also did not find any genome-wide significant SNP, but did find an intronic SNP (rs12912233) in RORA (retinoid related orphan receptor alpha gene) was modestly associated in the meta-analysis (p-6.3×10 -7 ). While interesting, because another RORA SNP has been linked through GWAS to post-traumatic stress disorder, 65 this result awaits replication. In the largest study, which was a meta-analysis comprising 17 population-based studies (n=34,549 individuals) as the discovery sample, no SNP reached genome-wide significance. 56 The strongest association was for rs8020095 (p=1.05 × 10 -7 ), located in the gene GPHN . When the discovery and replication samples were combined into one meta-analysis of 22 studies with 51,258 respondents, one region (indexed by the SNP rs40465) was associated with depressive symptoms at genome-wide levels of significance. 56 This variant is in a “gene desert,” an area of the genome where there are long regions without protein-coding sequences and unknown biological function.

Another major lesson from depression GWAS has been that popular candidate genes have generally not shown evidence of association. Prior to the GWAS era, meta-analyses of candidate gene studies concluded there was nominally significant evidence (at p<0.05) for six candidate genes in depression: APOE , DRD4 , GNB3 , MTHFR , SLC6A3 , and SLC6A4 . 66 , 67 However, none of these genes nor any of the more than 100 frequently examined candidate genes have shown evidence of significant association in the published GWAS of depression to date. Replication of candidate genes in GWAS is challenging, however, as several widely studied candidate gene markers, including the serotonin transporter 5-HTTLPR variable tandem number repeat (VNTR), are not directly captured by typical GWAS platform. Some groups have developed techniques to impute or derive best-guess estimates of these genetic markers using available SNP data, 68 , 69 though these efforts have not yet been widely adopted. However, the overwhelming evidence for many candidate genes has not been compelling.

Another interesting observation from GWAS has been the lack of consideration of the role of environment. As we describe below, we think GWAS may be limited by not examining how genetic influences on depression may vary among individuals with certain environmental exposures. One exception is a study by Powers and colleagues, 57 who used propensity score matching to conduct a GWAS among case-control pairs matched on exposure to recent stressful life events. Use of propensity score matching enabled them to reduce sample heterogeneity and compare cases to controls with a similar level of exposure, though they did not formally test for GxE. In their analysis, no SNPs however, were genome-wide significant or even suggestive (p<5×10 -6 ), though this was likely due to the very small sample size (n=805).

Findings from Gene-Environnent Interaction (GxE) Studies

The longstanding recognition that both genes (“nature”) and environments (“nurture”) contribute to the etiology of depression has motivated a great deal of interest in studying GxE. GxE studies examine the degree to which genetic variants modify the association between environmental factors and depression (or similarly, the extent to which environmental factors modify the association between genes and depression). 70 - 72 Typically, GxE studies have assumed a “diathesis-stress” model, where a genetic liability, also referred to as a diathesis, interacts with a stressful life event to give rise to depression. In this model, genes either exacerbate or buffer the effects of stress. 73 More recently, however, the concept of GxE has been expanded to incorporate more positive aspects of the environment, such as social support, psychosocial interventions, and other protective factors that reduce risk for disease. 74 , 75 Here, emerging work has focused on differential susceptibility to the environment, 76 , 77 or the extent to which genetic variation makes individuals more likely to respond adversely to negative environments, but more positively to salutary environments.

Research on GxE in depression was essentially launched with a publication in Science in 2003. In this study, Caspi and colleagues 78 used data from a 26-year longitudinal study in New Zealand to test whether a functional length polymorphism in the promoter region (5-HTTLPR) of the serotonin transporter gene ( SLC6A4 ) interacted with stressful life events to increase risk for depression. Results of the Caspi study suggested that individuals with at least one short (s) allele (i.e. who had the “s/s” or “s/l” genotype of the biallelic coded version) had more depression whether measured in terms of level of depressive symptoms, a depression diagnosis, or incident depression, as well as suicidality, in response to the number of stressful life events when compared to subjects who were not s allele carriers. They also found that s allele carriers had a greater probability of experiencing depression relative to those without an s allele as a result of exposure to probable or severe childhood maltreatment. The Caspi paper has become one of the most influential studies in the field, having been cited more than 5,000 times.

Since the publication of Caspi et al.'s seminal research, numerous replication attempts have been made. Most of these have also focused on 5-HTTLPR, though other genetic variants have also been studied, including variants in BDNF (brain derived neurotropic factor), MAOA (monoamine oxidase A), FKBP5 (FK506 binding protein 51), CRHR1 (corticotropin releasing hormone receptor 1), COMT (catechol- O -methyltransferase), and CREB1 (also known as CAMP or responsive element binding protein 1). Many replication attempts have focused on recent or childhood stressful life events, as well as child maltreatment, namely physical abuse, sexual abuse, or neglect. All of these are appropriate “candidate” environments to study in GxE research. Child maltreatment, for example, is one of the most potent environmental stressors in the etiology and course of depression and other types of psychopathology. Extant studies suggest that childhood maltreatment at least doubles the risk for internalizing problems, including depression. 18 , 20 , 21 , 79 , 80

The large number of empirical studies trying to replicate Caspi's GxE findings for depression have been summarized in several reviews focusing on GxE with 5-HTTLPR (see for example 72 , 81 , 82 - 88 ). These reviews ultimately fueled a heated debate regarding the plausibility of the Caspi findings. Including somewhat identical individual studies, review papers have drawn opposing conclusions about the support for GxE effects, with some studies finding consistent GxE effects and others failing to detect them. 84 , 85 Meta-analyses have provided a quantitative summary of these studies, but have also reached opposing conclusions. Specifically, the results of two meta-analyses, 82 , 86 which found evidence against a consistent GxE effect, differed from a third meta-analysis, 83 which concluded there was strong evidence to support the 5-HTTLPR GxE. These conflicting results may be explained by differences in the selection of studies for inclusion in the meta-analyses. 89 , 90 For example, the meta-analyses that used the most stringent inclusion criteria 82 , 86 failed to support the GxE association. 91 Some have also noted there is an inverse relationship between the power of the replication studies and support for the 5-HTTLPR association, precisely the opposite of what one would expect if the association is valid. 91 Moreover, the most direct replication attempt of the Caspi findings, which was not included in any prior meta-analysis, found no evidence in support of the GxE effect on depression. This was a longitudinal birth cohort study following a similar population (New Zealand residents), for a similar length of time (30 years), and using comparable phenotypic measures. 92 The authors observed no interaction between stressful life events and 5HTTLPR genotype, even after conducting 104 different regression models. 92

On the other hand, some have argued that support for the 5HTTLPR GxE has been more consistent when childhood maltreatment is the exposure variable 83 - 85 or when direct-interview assessments (as opposed to self-report questionnaires) have been used. 84 , 85 This finding is important, as there has been substantial variability in the characteristics of study populations, measurements of depression and environmental exposures, and analytic methods used across empirical studies to test for GxE in depression. 72 Some have also tried to place these individual GxE studies in the context of the broader literature examining genetic variability and stress sensitivity on depression. Here, some have appealed to the more consistent findings from animal studies showing that loss of function mutations in the serotonin gene have been associated with depressive-like behavior in rodents and that genetic variation in the serotonin transporter gene has been linked to depression among non-human primates. 93 Proponents have also noted that the results are more convincing when considered alongside experimental imaging studies showing 5-HTTLPR variation in amygdala activity, and treatment response studies showing 5-HTTLPR variation in antidepressant treatment response. 93 , 94 Overall, the validity of the influential 5-HTTLPR GxE finding remains unclear.

GxE studies focusing on other candidate genes, however, have found more consistent results. For example, studies examining FKBP5 and CHRH1 have shown that variants in these genes moderate the effect of exposure to child maltreatment, childhood adversities, or negative life events on adult depression. 95 - 98 These genes are interesting candidates because they regulate the stress response via the hypothalamic-pituitary-adrenal (HPA) axis. 99 Additional replications of these candidates would be helpful to further evaluate their role in shaping risk for depression. Evidence for other candidates, such as BDNF , has been more mixed. For instance, a recent review found stronger evidence to support interactions with the BDNF Val66Met polymorphism and stressful life events compared to childhood adversity. 100 As we later discuss, genome-wide approaches to GxE remain an important, but relatively unexplored area.

Current and Future Directions for Research

The limited success of GWAS for depression is in contrast with other psychiatric disorders, where established risk variants are accumulating through GWAS. For example, at the time of this writing, there are now more than 100 loci that have been associated with schizophrenia and bipolar disorder at stringent levels of statistical significance. 101 - 106 Despite the fact that individual risk loci have not been identified for depression, we know that such variants will be found given adequate sample sizes. For example, it is now possible to use genome-wide complex trait analysis (GCTA) to estimate the common variant contribution to depression using genome-wide SNP data (these estimates are sometimes referred to as SNP-heritability). 107 Through these methods, estimates of the common variant contribution to depression have ranged from a high of 32% 108 to a low of 21%. 109 It should be noted that these are lower bound estimates because SNP-chip heritability only reflects the effect of common variation that is captured on genotyping arrays.

Thus, the field faces two major questions: what explains the lack success of GWAS and GxE studies for depression and how can we best move forward? As described below (and summarized in Table 3 ), there are several likely explanations for the limited progress to date and several strategies that may help overcome these challenges.

Genome-Wide Association Studies = GWAS; Genome-Environment Wide Interaction Studies = GEWIS; National Institute of Mental Health Research Domain Criteria Initiative (RDoC)

Genetic Architecture and the Need for Larger Studies

The genetic architecture of depression is likely to be highly complex. Genetic architecture refers to the number of genetic loci associated with a phenotype, the effect size of each locus, and the manner in which these loci behave (e.g., whether they have additive or multiplicative effects). While all psychiatric disorders are thought to be polygenic, or influenced by multiple genes, the genetic basis of depression may reflect an even larger number of loci of individually small effect. Results from studies that have calculated polygenic risk scores (capturing aggregate effects of loci across the genome) support such a hypothesis. 64 , 110 Therefore, it is likely that much larger samples than those examined to date will be needed to detect these individually small effects. Simulations suggest that, to have comparable power to GWAS of schizophrenia or bipolar disorder, studies of depression will need to have sample sizes as much as five times larger. 52 Experience with GWAS for other disorders has established that, once a critical sample size threshold is crossed, larger and larger sample size yields more and more loci.

If depression is driven by many thousands of loci of weak effect, another strategy may be to combine genetic signals across many SNPs into functionally-defined gene sets or pathways. Pathway approaches can be considerably more powerful than single variant analyses, as the aggregation of weak signals from multiple causal variants may yield statistically significant evidence in support of a given gene or pathway. 111 , 112 Thus far, investigators have primarily examined pathways related to specific biological functions (e.g., axon guidance, cell functioning) as defined by human-curated bioinformatics resources, such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) 113 or Gene Ontology. 114 Recent studies of candidate gene pathways have found evidence that genes involved in glutamatergic synaptic neurotransmission, 115 among others, 116 were significantly associated with depression. Evidence in support of gene sets or pathways also comes from several GWAS described previously and shown in Table 2 , which found significant support for some pathways. 46 , 56 One of the major drawbacks of gene-set analyses is that they require predefined sets of genes. Gene sets defined by current annotation databases, such as KEGG or GO, vary in their completeness; some pathways are more complete than others. Moreover, databases also vary in how they define gene sets. Thus, a given gene may belong to one pathway in one database and a second pathway in another. Although these challenges are not unsubstantial, we think greater use of pathway-type analyses is needed.

Understudied Components of the Genetic Architecture of Depression

A related consideration is that GWAS are designed to capture common but not rare genetic variation. Rare variants can include genetic single nucleotide variations (“SNVs” present in <1% of the population) and rare copy number variations (“CNVs,” that is, structural variations in DNA sequence that involve the duplication or deletion of thousands or more than a million base pairs). Such variants have now been shown to play a role in autism 117 , 118 and schizophrenia 119 , 120 and bipolar disorder, 121 but to date these components of the genetic architecture of depression have been largely unexplored.

Fortunately, advances in sequencing technology now provide an opportunity to address the role of rare SNVs. In recent years, the cost of direct DNA sequencing has dropped dramatically and technologic advances have facilitated the development of “high-throughput” sequencing. 122 , 123 To date, these “next generation sequencing technologies” have been largely applied to study variants in exons, which are the protein-coding regions of the genome collectively known as the “exome.” Exons comprise about 30 megabases of DNA or 1% of the total genome. Although no exome-sequencing studies of depression have been reported at the time of this writing, such studies are underway. Next generation sequencing technologies can also be applied to the entire genome (“whole genome sequencing”), enabling researchers to explore a full range of genetic variants in both coding as well as non-coding regions of the genome.

The major strength of sequencing is that it captures variants that have been previously uncharacterized by candidate gene and GWAS methods and thus may provide new insights into the genetic underpinnings of depression. Like all techniques, however, sequencing approaches face a number of challenges. For example, despite enormous reductions in the cost of sequencing, well-powered studies are still very expensive. Whole genome sequencing costs at least $1,000 US per genome, whereas exome sequencing costs several hundreds of dollars. Exome sequencing also assesses polymorphisms that by definition are rare and thus occur with much less frequently than common variants. To have sufficient statistical power to identify an association between these rare variants and depression, very large sample sizes, on the order of 10,000 or more cases, are needed. In addition, rare variant association methods are still largely under development.

Structural variation, including CNVs, are also a potential source of depression risk loci. CNVs can be inherited or spontaneous, also referred to as de novo . De novo CNVs—those that are present in offspring but not in either parent, have been shown to be important risk factors for several neuropsychiatric disorders, namely autism, 117 , 118 schizophrenia 119 , 120 and bipolar disorder. 121 After conducting a systematic literature search of PubMed for papers published by December 2013 using the MESH terms for depression described previously and the phrase “Copy Number Var*,” we identified four studies, which provide preliminary evidence implicating CNVs in depression. 124 - 127 In the largest of these studies, Glessner and colleagues found 12 CNV regions that were exclusive to cases with MDD. The region with the highest frequency in cases was a locus on chromosome 5 (5q35.1) that overlapped the genes SLIT3 , CCDC99 , and DOCK2 . The finding of a CNV overlapping the gene SLIT3 is interesting, as SLIT3 is known to play a role in axon development and neurodevelopmental disorders.

One of the major strengths of studying CNVs is that the methods for association testing are similar, by and large, to examining common variants. Simultaneous examination of SNPs and CNVs in large samples may identify whether CNVs play a significant role in depression and what their importance is relative to common variants. One of the major drawbacks of association testing with CNVs is that catalogs of these variants do not exist with the same level of number or specificity as they do for SNPs. For example, the location, size, and boundary of CNVs in these publicly available resources have been relatively imprecise. As a result, opportunities for misclassification of variants is much higher for CNVs than for SNPs. 128 Efforts are now underway to provide a more comprehensive catalog of CNVs (see for example: http://www.sanger.ac.uk/research/areas/humangenetics/cnv/ ). Moreover, until recently there has also not been a commercially-available genotyping array that could detect both SNPs and CNVs. With the advent of the “PsychChip,” a customized genotyping chip for psychiatric phenotypes, investigators will soon be able to simultaneously examine multiple genetic variants, including SNPs, CNVs, and rare variants. The importance of rare variants to depression risk remains to be seen, but large-scale studies will be needed to clarify their contribution.

Accounting for the Role of Gene-Environment Interaction

As noted previously, existing studies have not systematically addressed the possibility that a substantial proportion of the risk of depression is attributable to non-additive effects, including GxE. Moreover, GxE studies to date have focused on a limited set of candidate genes and have typically been underpowered, creating a risk of both false positive and false negative results. It is well established that environmental factors, including exposure to stressful life events and child maltreatment, are important risk factors for depression, but we still know little about whether these environmental effects are moderated by genetic variation and, if so, which genetic variants are relevant.

One approach to filling this gap may come from genome-environment wide interaction studies (GEWIS), pronounced “G-Whiz.” 129 , 130 In a GEWIS, investigators test for statistical interaction or GxE, with the “G” defined as the genetic loci (e.g., SNPs) included in a GWAS and the “E” defined as a known environmental exposure. Unlike candidate gene GxE, GEWIS offers the opportunity to conduct a genetically unbiased search—that is, one in which prior genetic or biologic hypotheses are not required. In one type of GEWIS, investigators could focus on loci for which a main effect of a genetic variant has been established by GWAS. In this scenario, loci identified by GWAS become candidates for GxE analysis, but with the advantage over traditional candidate gene studies that the locus is already known to influence the phenotype of interest.

To our knowledge, no GEWIS of depression has been published to date. Though research on GEWIS of depression and other psychiatric phenotypes is lacking, a small but emerging body of research on other complex phenotypes suggests GEWIS can yield important new gains. For example, studies have identified significant genome-wide GxE interactions in cancer, 131 , 132 diabetes 133 and insulin resistance, 134 Parkinson's disease 135 , pulmonary function 136 , and nonsyndromic cleft palate. 137 While interest in GEWIS is growing, there are several challenges to conducting this type of study. 129 The first is identifying the best methods to test for genome-wide GxE. Several methodological approaches have been developed (see for example reviews by 138 , 139 ); however there is no consensus on what methods are most ideal. Selection of a specific analytic method depends largely on whether the goal is to leverage GxE to discover novel loci or characterize the joint effect of genetic variants and environmental factors. 140

Second, the “environment” is to some extent unbounded in a way the genome is not. Both children and adults are exposed to a range of experiences across the multiple social and physical contexts in which they are embedded (e.g., families, school, neighborhoods, workplaces); all of these experiences and exposures can contribute to health. 141 Focusing on well-defined measures of environment where there has been robust and consistent evidence to support a relationship between the exposure and depression is one way to start. Such a list of measures could include in utero exposures (e.g., viruses, toxins, alcohol and drugs), social deprivation (e.g., poverty, child maltreatment), and enrichment (e.g., psychosocial interventions and treatments). However, even if we select the same environment, such as child maltreatment, there are still multiple different types of maltreatment, multiple ages to consider when the maltreatment occurred, and multiple ways to measure maltreatment (e.g., self report, administrative records, clinical interview).

Finally, and perhaps the biggest challenge, is the need to balance the trade-off between the need for large samples and identifying precise measures of environmental exposure. Large samples are needed to detect GxE (larger even than those needed in standard GWAS). However, large samples often lack the depth and breadth necessary to capture data on environmental or phenotype measures. Although smaller samples frequently have rich and repeated measures, they are underpowered to establish robust associations. Smaller samples can be combined to increase statistical power. However, challenges will arise in trying to harmonize measures of environment across these datasets. In other words, efforts to ensure adequate sample size for each unique combination of risk factors and GxE strata can lead to a “watered-down” environmental measure that lacks any meaningful variability; a classic example would be an instance where respondents are simply classified as “exposed” or “non-exposed.” Longitudinal birth cohort studies, which can include prospective measures of environmental exposures along with detailed phenotype data and genome-wide data, may be one promising avenue for conducting GEWIS in the future. Moreover, the growing interest in the concept of the “exposome,” environment-wide association studies (EWAS) and ways to systematically identify relevant environmental factors (see for example 142 , 143 ) could yield new insights to guide GEWIS in the future.

The Phenotypic Complexity of Depression

Another obstacle to progress in identifying susceptibility loci is the fact that depression is a heterogeneous phenotype. Indeed, it is possible to meet DSM-IV or DSM-5 diagnostic criteria for a major depressive episode through at least 227 different symptom combinations. 144 As currently described by DSM-5, MDD can manifest with or without: (1) anxious distress; (2) mixed features; (3) melancholic features; (4) atypical features; (5) mood-congruent psychotic features; (6) mood-incongruent psychotic features; (7) catatonia; (8) peripartum onset; and (9) a seasonal pattern. 145 These subtypes of major depressive disorder could reflect different genetic contributions. Consistent with such a hypothesis, studies suggest that depression with a history of child maltreatment has a different onset, course, and response to treatment when compared a depression that arises among individuals without a history of abuse. 146 , 147 Recent twin studies have also suggested that genetic liability to MDD reflects not one , but three distinct symptom dimensions (psychomotor/cognitive, mood, and neurovegetative symptoms). 148 Thus, GWAS that simply examine “depressed” cases versus controls may decrease the ratio of “signal to noise” by combining multiple disorder subtypes that vary in their genetic etiology. In light of evidence suggesting that there is no truly categorical threshold for depression caseness, 149 and that different lifetime prevalence estimates of depression are found when comparing cross-sectional retrospective reports to cumulative evaluations based on multiple interviews, 150 it is reasonable to posit that misclassification of individuals as cases or controls may be undermining the power of typical case-control GWAS.

We think there are several strategies to reduce the heterogeneity in depression. First, examination of the full range of variation in depression (e.g., depressive symptoms), rather than dichotomizing the phenotype (cases and controls), could be a statistically more powerful approach to identify variants associated with depression. 151 This would be consistent with evidence that the diagnostic threshold for MDD has been artificially imposed on a continuity of depression risk. 149 Second, more data-driven approaches to examine shared features or subtypes of depression through use of latent class analysis 152 may also prove helpful. Prior studies applying such methods in both adolescents and adults have found distinct subtypes that differ based on severity, symptoms, and episode length. 153 , 154 Examination of these subtypes in a genetic association study may help to identify variants that are common across or unique to specific subtypes. Third, another strategy would be to continue efforts to examine phenotypes thought to be more proximal to a genetic substrate than are clinically-defined categories. 155 Putative “intermediate” or “endophenotypes” related to depression include emotion-based attention biases, 156 , 157 impaired reward function, 158 and deficits in domains of executive functioning, such as learning and memory. 159 Investigation of endophenotypes is consistent with the National Institute on Mental Health (NIMH) Research Domain Criteria Initiative (RDoC;), 160 - 163 which aims to provide a bottom-up characterization of psychopathology incorporating genetics, neural circuitry, and behavioral phenotypes. Endophenotypes have not yet been the subject of large-scale studies that might fully evaluate their power relative. One exception is the ENIGMA consortium, through which GWAS meta-analyses of structural MRI phenotypes yielded a genome-wide significant association with hippocampal volume, 164 one of the best-established biomarkers of depression risk. However, this result still required sample sizes in the thousands, challenging the view that endophenotype-based studies will be more powerful than studies of major depressive disorder itself.

Conclusions

Research on the genetic underpinnings of depression is at an exciting, yet challenging crossroad. On the one hand, genotyping technologies have allowed for the characterization of individual and population-based genetic variation and have provided analytic tools to examine the individual and joint effects of genetic and environmental determinants. On the other hand, GWAS of depression have yet to see the same success achieved with other psychiatric or medical disorders. Moreover, studies of GxE have thus far not led to great clarity but have fueled plenty of debate. Some argue that positive findings reflect chance results among small, underpowered studies, 86 while others see consistencies when focusing on studies that are methodologically comparable. 83 - 85

We have also reviewed some of the potential explanations for the lack of success to date for GWAS and GxE studies of depression. Given the established heritability of depression, there is every reason to expect that increasingly well-powered studies will indeed identify risk loci. However, the genetic and phenotypic complexity of depression may mean that such successes will require samples on the order of tens of thousands of participants. Efforts to parse the heterogeneity of depression and validate phenotypic subtypes may also be essential to facilitate gene identification. Further, as we have noted, potentially important areas of the genetic basis of depression--specifically, rare variation and GxE--remain relatively unexplored on a large-scale. It remains to be seen how much of the “missing heritability” of depression will be revealed thorough studies of these components.

Although the path forward to detect genetic risk loci for depression remains challenging, what is certain is that a deeper understanding of the etiology of depression is needed. Existing treatments for depression are based on decades-old biology and genetic discoveries have already begun to identify promising targets for novel therapies in other disorders. Given the enormous burden of depression, identifying its genetic underpinnings may be essential to preventing the onset of this disorder and improving the lives of those who already suffer.

Acknowledgments

The authors thank Caitlin Clements, Patience Gallagher, Stephanie Kravitz, and Preetha Palasuberniam for their assistance in conducting the literature review for this paper. Dr. Dunn was supported in part by funding from the Center on the Developing Child at Harvard University. Dr. Smoller was funded in part by NIMH grant K24MH094614. Dr. Nugent was funded in part by NIMH grant K01MH087240. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Mental Health or the National Institutes of Health.

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Study says depression not caused by chemical imbalance, raising questions about antidepressants

University college london research says depression is not a serotonin imbalance and drugs that target it may not be the answer.

By Lois M. Collins

Millions of Americans take antidepressants, but a new study suggests the theory underpinning their use may be entirely wrong. Research from the University College London raises doubt that chemical imbalance in the brain is responsible for depression .

A major review of previous studies on serotonin’s role in depression, just published in the journal Molecular Psychiatry, concluded that serotonin level — the target of antidepressants — is not responsible for depression.

The researchers found “no support for the hypothesis that depression is caused by lowered serotonin activity or concentrations.”

They found stronger evidence that stressful life events can lead to depression.

The question is, do antidepressants help, and if so, how? If not, could they be doing harm?

Experts are divided and the study has drawn some pushback.

“Some of the studies in our overview that included people who were taking or had previously taken antidepressants showed evidence that antidepressants may actually lower the concentration or activity of serotonin,” according to an article in The Conversation by the study’s authors, Joanna Moncrieff, professor of psychiatry, and Mark Horowitz, clinical research fellow in psychiatry, both of University College London.

“Most antidepressants are selective serotonin reuptake inhibitors, which were originally said to work by correcting abnormally low serotonin levels. There is no other accepted pharmacological mechanism by which antidepressants affect the symptoms of depression,” the researchers said in a news release . 

The research suggests depression is not biochemical and questions how, given that, a biochemical solution would work. Horowitz and Moncrieff also question whether that kind of treatment, which acts on brain chemistry, does more harm than good.

“Our view is that patients should not be told that depression is caused by low serotonin or by a chemical imbalance, and they should not be led to believe that antidepressants work by targeting these unproven abnormalities. We do not understand what antidepressants are doing to the brain exactly, and giving people this sort of misinformation prevents them from making an informed decision about whether to take antidepressants or not,” Moncrieff said.

Targeting serotonin

The “chemical imbalance” theory has dominated the thinking about depression for several decades, according to the researchers.

“It is always difficult to prove a negative, but I think we can safely say that after a vast amount of research conducted over several decades, there is no convincing evidence that depression is caused by serotonin abnormalities, particularly by lower levels or reduced activity of serotonin,” Moncrieff said.

In the United States between 2015 and 2018, 13.9% of adults took antidepressants for depression, according to the National Center for Health Statistics in the Centers for Disease Control and Prevention.

As many as 1 in 6 adults in England are now prescribed antidepressants every year, according to the study.

“I had been taught that depression was caused by low serotonin in my psychiatry training and had even taught this to students in my own lectures. Being involved in this research was eye-opening and feels like everything I thought I knew has been flipped upside down,” said Horowitz in background material.

As Mike McRae wrote for ScienceAlert , “This doesn’t necessarily mean serotonin-based treatments aren’t working on some other mechanism we don’t yet understand. And  no one should consider ditching their meds  without consulting their doctors. But given so many people are relying on these drugs, it is important to figure out what’s really going on.”

Study nuts and bolts

In all, studies in the review included tens of thousands of participants. Among the serotonin mechanisms studied and the findings:

• No difference was seen between people with depression and healthy control subjects in levels of serotonin and breakdown products in the blood or brain fluids.
• In studies of serotonin receptors and the serotonin transporter protein most antidepressants target, they found “weak and inconsistent evidence” suggesting higher levels of serotonin activity in those who are depressed. They believe that was caused by use of antidepressants.
• Studies that lowered serotonin levels in hundreds of healthy volunteers did not produce depression. The researchers saw “very weak evidence” in a small 75-person subgroup of people with a family history of depression. A study after that was inconclusive.
• No evidence of variation in the serotonin transporter gene was found between those with depression and healthy control subjects. 

On the other hand, stressful life events had a “strong effect” on the risk of becoming depressed. And the more one experienced stress or trauma, the greater the likelihood of depression. 

“A famous early study found a relationship between stressful events, the type of serotonin transporter gene a person had and the chance of depression. But larger, more comprehensive studies suggest this was a false finding,” the release said.

In the piece from The Conversation , Moncrieff and Horowitz wrote, “It is important that people know that the idea that depression results from a ‘chemical imbalance’ is hypothetical. And we do not understand what temporarily elevating serotonin or other biochemical changes produced by antidepressants do to the brain. We conclude that it is impossible to say that taking SSRI antidepressants is worthwhile, or even completely safe.”

Public perception

Surveys suggest as many as 90% of people believe depression is caused by low serotonin or chemical imbalance. There’s evidence believing that creates a “pessimistic outlook on the likelihood of recovery” and the hope of managing depression without medical help, the study said.

Doubts about brain chemistry’s role in depression have been around a while.

“If you’re among those who are hearing all of this for the first time, the hypothesis has been on shaky ground practically since it took off in the 1990s, with study after study failing to support the idea,” wrote ScienceAlert’s McRae . He noted the Moncrieff and Horowitz limited their research to high-quality, peer-evaluated studies.

“Just 17 studies made the cut, which included a genetic association study, another umbrella review, and a dozen systematic reviews and meta-analyses,” he wrote.

The impact is huge, given most people will have diagnosable levels of anxiety or depression at some point, the researchers said.

The researchers also said one large meta-analysis found people using antidepressants had less serotonin in their blood, which could mean that antidepressants designed to raise levels of serotonin may do the opposite over time.

The researchers note they didn’t look at the efficacy of antidepressants. Their hope, they said, is that more research and treatment will focus on helping people manage stressful or traumatic events, “such as with psychotherapy, alongside other practices such as exercise or mindfulness, or addressing underlying contributors such as poverty, stress and loneliness.”

Some experts disagree

The research has attracted some pushback.

The Guardian quoted Dr. Michael Bloomfield, a consultant psychiatrist and principal clinical research fellow at University College London, who was not involved in the study: “Many of us know that taking paracetamol can be helpful for headaches, and I don’t think anyone believes that headaches are caused by not enough paracetamol in the brain. The same logic applies to depression and medicines used to treat depression.”

He added, “There is consistent evidence that antidepressant medicines can be helpful in the treatment of depression and can be life-saving.”

Johan Lundberg  at the Karolinska Institute in Sweden told New Scientist that one limitation of the study is failure to distinguish between those with long-term depression and those having episodes of depression, because their state during the study could be different in terms of serotonin. “It is key to separately analyze data from studies that examine the same patients when ill and when in remission, to have optimal conditions to examine the hypothesis,” he said.

The same article quoted a spokesperson for the Royal College of Psychiatrists who was talking about treatment guidelines from public health officials in England, who said antidepressants are an effective treatment for depression and some other physical and mental health conditions.

The spokesperson noted that “antidepressants will vary in effectiveness for different people, and the reasons for this are complex. We would not recommend for anyone to stop taking their antidepressants based on this review, and encourage anyone with concerns about their medication to contact their (family doctor).”

Psychological Theories of Depression

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Depression is a mood disorder that prevents individuals from leading a normal life at work, socially, or within their family. Seligman (1973) referred to depression as the ‘common cold’ of psychiatry because of its frequency of diagnosis.

Depending on how data are gathered and how diagnoses are made, as many as 27% of some population groups may be suffering from depression at any one time (NIMH, 2001; data for older adults).

Behaviorist Theory

Behaviorism emphasizes the importance of the environment in shaping behavior. The focus is on observable behavior and the conditions through which individuals” learn behavior, namely classical conditioning, operant conditioning, and social learning theory.

Therefore, depression is the result of a person’s interaction with their environment.

For example, classical conditioning proposes depression is learned through associating certain stimuli with negative emotional states. Social learning theory states behavior is learned through observation, imitation, and reinforcement.

Operant Conditioning

Operant conditioning states that depression is caused by the removal of positive reinforcement from the environment (Lewinsohn, 1974). Certain events, such as losing your job, induce depression because they reduce positive reinforcement from others (e.g., being around people who like you).

Depressed people usually become much less socially active. In addition, depression can also be caused by inadvertent reinforcement of depressed behavior by others.

For example, when a loved one is lost, an important source of positive reinforcement has lost as well. This leads to inactivity. The main source of reinforcement is now the sympathy and attention of friends and relatives.

However, this tends to reinforce maladaptive behavior, i.e., weeping, complaining, and talking of suicide. This eventually alienates even close friends leading to even less reinforcement and increasing social isolation and unhappiness. In other words, depression is a vicious cycle in which the person is driven further and further down.

Also, if the person lacks social skills or has a very rigid personality structure, they may find it difficult to make the adjustments needed to look for new and alternative sources of reinforcement (Lewinsohn, 1974). So they get locked into a negative downward spiral.

Critical Evaluation

Behavioral/learning theories make sense in terms of reactive depression, where there is a clearly identifiable cause of depression. However, one of the biggest problems for the theory is that of endogenous depression. This is depression that has no apparent cause (i.e., nothing bad has happened to the person).

An additional problem of the behaviorist approach is that it fails to consider cognitions (thoughts) influence on mood.

Psychodynamic Theory

During the 1960s, psychodynamic theories dominated psychology and psychiatry. Depression was understood in terms of the following:

• inwardly directed anger (Freud, 1917),
• introjection of love object loss,
• severe super-ego demands (Freud, 1917),
• excessive narcissistic , oral, and/or anal personality needs (Chodoff, 1972),
• loss of self-esteem (Bibring, 1953; Fenichel, 1968), and
• deprivation in the mother-child relationship during the first year (Kleine, 1934).

Freud’s psychoanalytic theory is an example of the psychodynamic approach . Freud (1917) proposed that many cases of depression were due to biological factors.

However, Freud also argued that some cases of depression could be linked to loss or rejection by a parent. Depression is like grief in that it often occurs as a reaction to the loss of an important relationship.

However, there is an important difference because depressed people regard themselves as worthless. What happens is that the individual identifies with the lost person so that repressed anger towards the lost person is directed inwards towards the self. The inner-directed anger reduces the individual’s self-esteem  and makes him/her vulnerable to experiencing depression in the future.

Freud distinguished between actual losses (e.g., the death of a loved one) and symbolic losses (e.g., the loss of a job). Both kinds of losses can produce depression by causing the individual to re-experience childhood episodes when they experience loss of affection from some significant person (e.g., a parent).

Later, Freud modified his theory stating that the tendency to internalize lost objects is normal and that depression is simply due to an excessively severe super-ego. Thus, the depressive phase occurs when the individual’s super-ego or conscience is dominant. In contrast, the manic phase occurs when the individual’s ego or rational mind asserts itself, and s/he feels control.

In order to avoid loss turning into depression, the individual needs to engage in a period of mourning work, during which s/he recalls memories of the lost one.

This allows the individual to separate himself/herself from the lost person and reduce inner-directed anger. However, individuals very dependent on others for their sense of self-esteem may be unable to do this and so remain extremely depressed.

Psychoanalytic theories of depression have had a profound impact on contemporary theories of depression.

For example, Beck’s (1983) model of depression was influenced by psychoanalytic ideas such as the loss of self-esteem (re: Beck’s negative view of self), object loss (re: the importance of loss events), external narcissistic deprivation (re: hypersensitivity to loss of social resources) and oral personality (re: sociotropic personality).

However, although highly influential, psychoanalytic theories are difficult to test scientifically. For example, its central features cannot be operationally defined with sufficient precision to allow empirical investigation. Mendelson (1990) concluded his review of psychoanalytic theories of depression by stating:

“A striking feature of the impressionistic pictures of depression painted by many writers is that they have the flavor of art rather than of science and may well represent profound personal intuitions as much as they depict they raw clinical data” (p. 31).

Another criticism concerns the psychanalytic emphasis on the unconscious, intrapsychic processes, and early childhood experience as being limiting in that they cause clinicians to overlook additional aspects of depression. For example, conscious negative self-verbalization (Beck, 1967) or ongoing distressing life events (Brown & Harris, 1978).

Cognitive Explanation of Depression

This approach focuses on people’s beliefs rather than their behavior. Depression results from systematic negative bias in thinking processes.

Emotional, behavioral (and possibly physical) symptoms result from cognitive abnormality. This means that depressed patients think differently from clinically normal people. The cognitive approach also assumes changes in thinking precede (i.e., come before) the onset of a depressed mood.

Beck’s (1967) Theory

One major cognitive theorist is Aaron Beck. He studied people suffering from depression and found that they appraised events in a negative way.

Beck (1967) identified three mechanisms that he thought were responsible for depression:

The cognitive triad (of negative automatic thinking) Negative self schemas Errors in Logic (i.e. faulty information processing)

The cognitive triad is three forms of negative (i.e., helpless and critical) thinking that are typical of individuals with depression: namely, negative thoughts about the self, the world, and the future. These thoughts tended to be automatic in depressed people as they occurred spontaneously.

For example, depressed individuals tend to view themselves as helpless, worthless, and inadequate. They interpret events in the world in an unrealistically negative and defeatist way, and they see the world as posing obstacles that can’t be handled.

Finally, they see the future as totally hopeless because their worthlessness will prevent their situation from improving.

As these three components interact, they interfere with normal cognitive processing, leading to impairments in perception, memory, and problem-solving, with the person becoming obsessed with negative thoughts.

Beck believed that depression-prone individuals develop a negative self-schema . They possess a set of beliefs and expectations about themselves that are essentially negative and pessimistic. Beck claimed that negative schemas might be acquired in childhood as a result of a traumatic event. Experiences that might contribute to negative schemas include:

• Death of a parent or sibling.
• Parental rejection, criticism, overprotection, neglect, or abuse.
• Bullying at school or exclusion from a peer group.

However, a negative self-schema predisposes the individual to depression, and therefore someone who has acquired a cognitive triad will not necessarily develop depression.

Some kind of stressful life event is required to activate this negative schema later in life. Once the negative schema is activated, a number of illogical thoughts or cognitive biases seem to dominate thinking .

People with negative self-schemas become prone to making logical errors in their thinking, and they tend to focus selectively on certain aspects of a situation while ignoring equally relevant information.

Beck (1967) identified a number of systematic negative biases in information processing known as logical errors or faulty thinking. These illogical thought patterns are self-defeating and can cause great anxiety or depression for the individual. For example:

• Arbitrary Inference:  Drawing a negative conclusion in the absence of supporting data.
• Selective Abstraction:  Focusing on the worst aspects of any situation.
• Magnification and Minimisation: If they have a problem, they make it appear bigger than it is. If they have a solution they make it smaller.
• Personalization:  Negative events are interpreted as their fault.
• Dichotomous Thinking:  Everything is seen as black and white. There is no in between.

Such thoughts exacerbate and are exacerbated by the cognitive triad. Beck believed these thoughts or this way of thinking become automatic.

When a person’s stream of automatic thoughts is very negative, you would expect a person to become depressed. Quite often these negative thoughts will persist even in the face of contrary evidence.

Alloy et al. (1999) followed the thinking styles of young Americans in their early 20s for six years. Their thinking style was tested, and they were placed in either the ‘positive thinking group’ or ‘negative thinking group’.

After six years, the researchers found that only 1% of the positive group developed depression compared to 17% of the ‘negative’ group. These results indicate there may be a link between cognitive style and the development of depression.

However, such a study may suffer from demand characteristics. The results are also correlational. It is important to remember that the precise role of cognitive processes is yet to be determined. The maladaptive cognitions seen in depressed people may be a consequence rather than a cause of depression.

Learned Helplessness

Martin Seligman (1974) proposed a cognitive explanation of depression called learned helplessness .

According to Seligman’s learned helplessness theory, depression occurs when a person learns that their attempts to escape negative situations make no difference.

Consequently, they become passive and will endure aversive stimuli or environments even when escape is possible.

Seligman based his theory on research using dogs.

A dog put into a partitioned cage learns to escape when the floor is electrified. If the dog is restrained whilst being shocked, it eventually stops trying to escape.

Dogs subjected to inescapable electric shocks later failed to escape from shocks even when it was possible to do so. Moreover, they exhibited some of the symptoms of depression found in humans (lethargy, sluggishness, passive in the face of stress, and appetite loss).

This led Seligman (1974) to explain depression in humans in terms of learned helplessness , whereby the individual gives up trying to influence their environment because they have learned that they are helpless as a consequence of having no control over what happens to them.

Although Seligman’s account may explain depression to a certain extent, it fails to take into account cognitions (thoughts). Abramson, Seligman, and Teasdale (1978) consequently introduced a cognitive version of the theory by reformulating learned helplessness in terms of attributional processes (i.e., how people explain the cause of an event).

The depression attributional style is based on three dimensions, namely locus (whether the cause is internal – to do with a person themselves, or external – to do with some aspect of the situation), stability (whether the cause is stable and permanent or unstable and transient) and global or specific (whether the cause relates to the “whole” person or just some particular feature characteristic).

In this new version of the theory, the mere presence of a negative event was not considered sufficient to produce a helpless or depressive state. Instead, Abramson et al. argued that people who attribute failure to internal, stable, and global causes are more likely to become depressed than those who attribute failure to external, unstable, and specific causes.

This is because the former attributional style leads people to the conclusion that they are unable to change things for the better.

Gotlib and Colby (1987) found that people who were formerly depressed are actually no different from people who have never been depressed in terms of their tendencies to view negative events with an attitude of helpless resignation.

This suggests that helplessness could be a symptom rather than a cause of depression. Moreover, it may be that negative thinking generally is also an effect rather than a cause of depression.

Humanist Approach

Humanists believe that there are needs that are unique to the human species. According to Maslow (1962), the most important of these is the need for self-actualization (achieving our potential). The self-actualizing human being has a meaningful life. Anything that blocks our striving to fulfill this need can be a cause of depression. What could cause this?

• Parents impose conditions of worth on their children. I.e., rather than accepting the child for who s/he is and giving unconditional love , parents make love conditional on good behavior. E.g., a child may be blamed for not doing well at school, develop a negative self-image and feel depressed because of a failure to live up to parentally imposed standards.
• Some children may seek to avoid this by denying their true selves and projecting an image of the kind of person they want to be. This façade or false self is an effort to please others. However, the splitting off of the real self from the person you are pretending to cause hatred of the self. The person then comes to despise themselves for living a lie.
• As adults, self-actualization can be undermined by unhappy relationships and unfulfilling jobs. An empty shell marriage means the person is unable to give and receive love from their partner. An alienating job means the person is denied the opportunity to be creative at work.

Abramson, L. Y., Seligman, M. E., & Teasdale, J. D. (1978). Learned helplessness in humans: critique and reformulation . Journal of abnormal psychology, 87(1) , 49.

Alloy, L. B., Abramson, L. Y., Whitehouse, W. G., Hogan, M. E., Tashman, N. A., Steinberg, D. L., … & Donovan, P. (1999). Depressogenic cognitive styles : Predictive validity, information processing and personality characteristics, and developmental origins. behavior research and therapy, 37(6) , 503-531.

Beck, A. T. (1967). Depression: Causes and treatment . Philadelphia: University of Pennsylvania Press.

Beck, A. T., Epstein, N., & Harrison, R. (1983). Cognitions, attitudes and personality dimensions in depression. British Journal of Cognitive Psychotherapy .

Bibring, E. (1953). The mechanism of depression .

Brown, G. W., & Harris, T. (1978). Social origins of depression: a reply. Psychological Medicine, 8(04) , 577-588.

Chodoff, P. (1972). The depressive personality: A critical review. Archives of General Psychiatry, 27(5) , 666-673.

Fenichel, O. (1968). Depression and mania. The Meaning of Despair . New York: Science House.

Freud, S. (1917). Mourning and melancholia. Standard edition, 14(19) , 17.

Gotlib, I. H., & Colby, C. A. (1987). Treatment of depression: An interpersonal systems approach. Pergamon Press.

Klein, M. (1934). Psychogenesis of manic-depressive states: contributions to psychoanalysis . London: Hogarth.

Lewinsohn, P. M. (1974). A behavioral approach to depression .

Maslow, A. H. (1962). Towards a psychology of being . Princeton: D. Van Nostrand Company.

National Institute of Mental Health. (2001). Depression research at the National Institute of Mental Health http://www.nimh.nih.gov/health/publications/depression/complete-index.shtml.

Seligman, M. E. (1973). Fall into helplessness. Psychology today, 7(1) , 43-48.

Seligman, M. E. (1974). Depression and learned helplessness . John Wiley & Sons.

Further Information

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Touch can reduce pain, depression and anxiety, say researchers

More consensual touch helps ease or buffer against mental and physical complaints, meta-analysis shows

Whether it is a hug from a friend or the caress of a weighted blanket, the sensation of touch appears to bring benefits for the body and mind, researchers say.

The sense of touch is the first to develop in babies and is crucial in allowing us to experience the environment around us as well as communicate. Indeed, the loss of touch from others during the Covid pandemic hit many hard.

However, while myriad studies have suggested touch is beneficial for our health, few have attempted to draw the vast field of research together.

Now experts have done just that, revealing a simple message: touch helps.

Dr Helena Hartmann, a co-author of the research from University Hospital Essen , said: “More consensual touch events throughout our day can help alleviate or potentially buffer against mental and physical complaints.”

Published in the journal Nature Human Behaviour , the research encompassed 212 previously published studies and included a statistical analysis of 85 studies involving adults and 52 involving newborns.

Among the results, the team found touch was just as beneficial for mental health as physical health – a finding that held for adults and newborns – although touch had a bigger impact on some areas than others.

“Our work illustrates that touch interventions are best suited for reducing pain, depression and anxiety in adults and children as well as for increasing weight gain in newborns,” the researchers write.

The analysis revealed humans gained similar benefits in terms of their physical health when touched by other humans as by objects – such as social robots or weighted blankets.

Hartmann said that was a surprise. “This means we need to undertake more research on the potential of weighted blankets or social robots to improve people’s wellbeing, especially during contact-limiting situations like the recent Covid-19 pandemic,” she said.

The positive impact on mental health was larger for human touch than touch from objects – possibly, the team said, because it involved skin-to-skin contact.

Among other results, the team found touch was beneficial for both healthy and unwell people, although the impact was larger among the latter for mental health benefits.

The type of touch and its duration was not important, although greater frequency was associated with greater benefits in adults.

Further, touching the head was associated with greater health benefits than touching other parts of the body.

The team cautioned that some of the findings could be false positives, while it was not clear if they would hold across different cultures.

Dr Mariana von Mohr, from Royal Holloway, University of London, who was not involved in the work, said if future robots could more accurately replicate the texture and warmth of human skin, they may be able to provide comparable mental health benefits to human touch.

“[These properties are] important because our skin contains specialised sensors, known as C-tactile afferents, which are particularly receptive to gentle, caressing touch and temperature similar to that of human skin, factors that are also thought to facilitate emotional regulation,” she said.

Prof Katerina Fotopoulou, at University College London, said the research gave a bird’s-eye view of the benefits of touch interventions on health.

She cautioned that the work could not offer more specific conclusions, such as the particular types of touch that may be associated with specific health benefits.

Dr Susannah Walker, at Liverpool John Moores University, agreed, noting that many of the studies considered were small and included varied types of touch and different measures of their outcomes. “This means it is hard to draw firm conclusions about why they work,” she said.

Fotopoulou added that the research could fuel new work in the field, including how touch could be used alongside other treatments.

“It is a historical misfortune that we have prioritised talking over touch or other somatic therapies in the past couple of centuries. This review gives us the necessary emphasis and confidence to redress this balance with further, careful study on touch interventions,” she said.

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• Published: 13 July 2021

Systematic review and meta-analysis of depression, anxiety, and suicidal ideation among Ph.D. students

• Emily N. Satinsky 1 ,
• Tomoki Kimura 2 ,
• Mathew V. Kiang 3 , 4 ,
• Rediet Abebe 5 , 6 ,
• Scott Cunningham 7 ,
• Hedwig Lee 8 ,
• Xiaofei Lin 9 ,
• Cindy H. Liu 10 , 11 ,
• Igor Rudan 12 ,
• Srijan Sen 13 ,
• Mark Tomlinson 14 , 15 ,
• Miranda Yaver 16 &
• Alexander C. Tsai 1 , 11 , 17  

Scientific Reports volume  11 , Article number:  14370 ( 2021 ) Cite this article

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• Epidemiology
• Health policy
• Quality of life

University administrators and mental health clinicians have raised concerns about depression and anxiety among Ph.D. students, yet no study has systematically synthesized the available evidence in this area. After searching the literature for studies reporting on depression, anxiety, and/or suicidal ideation among Ph.D. students, we included 32 articles. Among 16 studies reporting the prevalence of clinically significant symptoms of depression across 23,469 Ph.D. students, the pooled estimate of the proportion of students with depression was 0.24 (95% confidence interval [CI], 0.18–0.31; I 2  = 98.75%). In a meta-analysis of the nine studies reporting the prevalence of clinically significant symptoms of anxiety across 15,626 students, the estimated proportion of students with anxiety was 0.17 (95% CI, 0.12–0.23; I 2  = 98.05%). We conclude that depression and anxiety are highly prevalent among Ph.D. students. Data limitations precluded our ability to obtain a pooled estimate of suicidal ideation prevalence. Programs that systematically monitor and promote the mental health of Ph.D. students are urgently needed.

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Introduction

Mental health problems among graduate students in doctoral degree programs have received increasing attention 1 , 2 , 3 , 4 . Ph.D. students (and students completing equivalent degrees, such as the Sc.D.) face training periods of unpredictable duration, financial insecurity and food insecurity, competitive markets for tenure-track positions, and unsparing publishing and funding models 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 —all of which may have greater adverse impacts on students from marginalized and underrepresented populations 13 , 14 , 15 . Ph.D. students’ mental health problems may negatively affect their physical health 16 , interpersonal relationships 17 , academic output, and work performance 18 , 19 , and may also contribute to program attrition 20 , 21 , 22 . As many as 30 to 50% of Ph.D. students drop out of their programs, depending on the country and discipline 23 , 24 , 25 , 26 , 27 . Further, while mental health problems among Ph.D. students raise concerns for the wellbeing of the individuals themselves and their personal networks, they also have broader repercussions for their institutions and academia as a whole 22 .

Despite the potential public health significance of this problem, most evidence syntheses on student mental health have focused on undergraduate students 28 , 29 or graduate students in professional degree programs (e.g., medical students) 30 . In non-systematic summaries, estimates of the prevalence of clinically significant depressive symptoms among Ph.D. students vary considerably 31 , 32 , 33 . Reliable estimates of depression and other mental health problems among Ph.D. students are needed to inform preventive, screening, or treatment efforts. To address this gap in the literature, we conducted a systematic review and meta-analysis to explore patterns of depression, anxiety, and suicidal ideation among Ph.D. students.

Flowchart of included articles.

The evidence search yielded 886 articles, of which 286 were excluded as duplicates (Fig.  1 ). An additional nine articles were identified through reference lists or grey literature reports published on university websites. Following a title/abstract review and subsequent full-text review, 520 additional articles were excluded.

Of the 89 remaining articles, 74 were unclear about their definition of graduate students or grouped Ph.D. and non-Ph.D. students without disaggregating the estimates by degree level. We obtained contact information for the authors of most of these articles (69 [93%]), requesting additional data. Three authors clarified that their study samples only included Ph.D. students 34 , 35 , 36 . Fourteen authors confirmed that their study samples included both Ph.D. and non-Ph.D. students but provided us with data on the subsample of Ph.D. students 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 . Where authors clarified that the sample was limited to graduate students in non-doctoral degree programs, did not provide additional data on the subsample of Ph.D. students, or did not reply to our information requests, we excluded the studies due to insufficient information (Supplementary Table S1 ).

Ultimately, 32 articles describing the findings of 29 unique studies were identified and included in the review 16 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 (Table 1 ). Overall, 26 studies measured depression, 19 studies measured anxiety, and six studies measured suicidal ideation. Three pairs of articles reported data on the same sample of Ph.D. students 33 , 38 , 45 , 51 , 53 , 56 and were therefore grouped in Table 1 and reported as three studies. Publication dates ranged from 1979 to 2019, but most articles (22/32 [69%]) were published after 2015. Most studies were conducted in the United States (20/29 [69%]), with additional studies conducted in Australia, Belgium, China, Iran, Mexico, and South Korea. Two studies were conducted in cross-national settings representing 48 additional countries. None were conducted in sub-Saharan Africa or South America. Most studies included students completing their degrees in a mix of disciplines (17/29 [59%]), while 12 studies were limited to students in a specific field (e.g., biomedicine, education). The median sample size was 172 students (interquartile range [IQR], 68–654; range, 6–6405). Seven studies focused on mental health outcomes in demographic subgroups, including ethnic or racialized minority students 37 , 41 , 43 , international students 47 , 50 , and sexual and gender minority students 42 , 54 .

In all, 16 studies reported the prevalence of depression among a total of 23,469 Ph.D. students (Fig.  2 ; range, 10–47%). Of these, the most widely used depression scales were the PHQ-9 (9 studies) and variants of the Center for Epidemiologic Studies-Depression scale (CES-D, 4 studies) 63 , and all studies assessed clinically significant symptoms of depression over the past one to two weeks. Three of these studies reported findings based on data from different survey years of the same parent study (the Healthy Minds Study) 40 , 42 , 43 , but due to overlap in the survey years reported across articles, these data were pooled. Most of these studies were based on data collected through online surveys (13/16 [81%]). Ten studies (63%) used random or systematic sampling, four studies (25%) used convenience sampling, and two studies (13%) used multiple sampling techniques.

Pooled estimate of the proportion of Ph.D. students with clinically significant symptoms of depression.

The estimated proportion of Ph.D. students assessed as having clinically significant symptoms of depression was 0.24 (95% confidence interval [CI], 0.18–0.31; 95% predictive interval [PI], 0.04–0.54), with significant evidence of between-study heterogeneity (I 2  = 98.75%). A subgroup analysis restricted to the twelve studies conducted in the United States yielded similar findings (pooled estimate [ES] = 0.23; 95% CI, 0.15–0.32; 95% PI, 0.01–0.60), with no appreciable difference in heterogeneity (I 2  = 98.91%). A subgroup analysis restricted to the studies that used the PHQ-9 to assess depression yielded a slightly lower prevalence estimate and a slight reduction in heterogeneity (ES = 0.18; 95% CI, 0.14–0.22; 95% PI, 0.07–0.34; I 2  = 90.59%).

Nine studies reported the prevalence of clinically significant symptoms of anxiety among a total of 15,626 Ph.D. students (Fig.  3 ; range 4–49%). Of these, the most widely used anxiety scale was the 7-item Generalized Anxiety Disorder scale (GAD-7, 5 studies) 64 . Data from three of the Healthy Minds Study articles were pooled into two estimates, because the scale used to measure anxiety changed midway through the parent study (i.e., the Patient Health Questionnaire-Generalized Anxiety Disorder [PHQ-GAD] scale was used from 2007 to 2012 and then switched to the GAD-7 in 2013 40 ). Most studies (8/9 [89%]) assessed clinically significant symptoms of anxiety over the past two to four weeks, with the one remaining study measuring anxiety over the past year. Again, most of these studies were based on data collected through online surveys (7/9 [78%]). Five studies (56%) used random or systematic sampling, two studies (22%) used convenience sampling, and two studies (22%) used multiple sampling techniques.

Pooled estimate of the proportion of Ph.D. students with clinically significant symptoms of anxiety.

The estimated proportion of Ph.D. students assessed as having anxiety was 0.17 (95% CI, 0.12–0.23; 95% PI, 0.02–0.41), with significant evidence of between-study heterogeneity (I 2  = 98.05%). The subgroup analysis restricted to the five studies conducted in the United States yielded a slightly lower proportion of students assessed as having anxiety (ES = 0.14; 95% CI, 0.08–0.20; 95% PI, 0.00–0.43), with no appreciable difference in heterogeneity (I 2  = 98.54%).

Six studies reported the prevalence of suicidal ideation (range, 2–12%), but the recall windows varied greatly (e.g., ideation within the past 2 weeks vs. past year), precluding pooled estimation.

Additional stratified pooled estimates could not be obtained. One study of Ph.D. students across 54 countries found that phase of study was a significant moderator of mental health, with students in the comprehensive examination and dissertation phases more likely to experience distress compared with students primarily engaged in coursework 59 . Other studies identified a higher prevalence of mental ill-health among women 54 ; lesbian, gay, bisexual, transgender, and queer (LGBTQ) students 42 , 54 , 60 ; and students with multiple intersecting identities 54 .

Several studies identified correlates of mental health problems including: project- and supervisor-related issues, stress about productivity, and self-doubt 53 , 62 ; uncertain career prospects, poor living conditions, financial stressors, lack of sleep, feeling devalued, social isolation, and advisor relationships 61 ; financial challenges 38 ; difficulties with work-life balance 58 ; and feelings of isolation and loneliness 52 . Despite these challenges, help-seeking appeared to be limited, with only about one-quarter of Ph.D. students reporting mental health problems also reporting that they were receiving treatment 40 , 52 .

Risk of bias

Twenty-one of 32 articles were assessed as having low risk of bias (Supplementary Table S2 ). Five articles received one point for all five categories on the risk of bias assessment (lowest risk of bias), and one article received no points (highest risk). The mean risk of bias score was 3.22 (standard deviation, 1.34; median, 4; IQR, 2–4). Restricting the estimation sample to 12 studies assessed as having low risk of bias, the estimated proportion of Ph.D. students with depression was 0.25 (95% CI, 0.18–0.33; 95% PI, 0.04–0.57; I 2  = 99.11%), nearly identical to the primary estimate, with no reduction in heterogeneity. The estimated proportion of Ph.D. students with anxiety, among the 7 studies assessed as having low risk of bias, was 0.12 (95% CI, 0.07–0.17; 95% PI, 0.01–0.34; I 2  = 98.17%), again with no appreciable reduction in heterogeneity.

In our meta-analysis of 16 studies representing 23,469 Ph.D. students, we estimated that the pooled prevalence of clinically significant symptoms of depression was 24%. This estimate is consistent with estimated prevalence rates in other high-stress biomedical trainee populations, including medical students (27%) 30 , resident physicians (29%) 65 , and postdoctoral research fellows (29%) 66 . In the sample of nine studies representing 15,626 Ph.D. students, we estimated that the pooled prevalence of clinically significant symptoms of anxiety was 17%. While validated screening instruments tend to over-identify cases of depression (relative to structured clinical interviews) by approximately a factor of two 67 , 68 , our findings nonetheless point to a major public health problem among Ph.D. students. Available data suggest that the prevalence of depressive and anxiety disorders in the general population ranges from 5 to 7% worldwide 69 , 70 . In contrast, prevalence estimates of major depressive disorder among young adults have ranged from 13% (for young adults between the ages of 18 and 29 years in the 2012–2013 National Epidemiologic Survey on Alcohol and Related Conditions III 71 ) to 15% (for young adults between the ages of 18 and 25 in the 2019 U.S. National Survey on Drug Use and Health 72 ). Likewise, the prevalence of generalized anxiety disorder was estimated at 4% among young adults between the ages of 18 and 29 in the 2001–03 U.S. National Comorbidity Survey Replication 73 . Thus, even accounting for potential upward bias inherent in these studies’ use of screening instruments, our estimates suggest that the rates of recent clinically significant symptoms of depression and anxiety are greater among Ph.D. students compared with young adults in the general population.

Further underscoring the importance of this public health issue, Ph.D. students face unique stressors and uncertainties that may put them at increased risk for mental health and substance use problems. Students grapple with competing responsibilities, including coursework, teaching, and research, while also managing interpersonal relationships, social isolation, caregiving, and financial insecurity 3 , 10 . Increasing enrollment in doctoral degree programs has not been matched with a commensurate increase in tenure-track academic job opportunities, intensifying competition and pressure to find employment post-graduation 5 . Advisor-student power relations rarely offer options for recourse if and when such relationships become strained, particularly in the setting of sexual harassment, unwanted sexual attention, sexual coercion, and rape 74 , 75 , 76 , 77 , 78 . All of these stressors may be magnified—and compounded by stressors unrelated to graduate school—for subgroups of students who are underrepresented in doctoral degree programs and among whom mental health problems are either more prevalent and/or undertreated compared with the general population, including Black, indigenous, and other people of color 13 , 79 , 80 ; women 81 , 82 ; first-generation students 14 , 15 ; people who identify as LGBTQ 83 , 84 , 85 ; people with disabilities; and people with multiple intersecting identities.

Structural- and individual-level interventions will be needed to reduce the burden of mental ill-health among Ph.D. students worldwide 31 , 86 . Despite the high prevalence of mental health and substance use problems 87 , Ph.D. students demonstrate low rates of help-seeking 40 , 52 , 88 . Common barriers to help-seeking include fears of harming one’s academic career, financial insecurity, lack of time, and lack of awareness 89 , 90 , 91 , as well as health care systems-related barriers, including insufficient numbers of culturally competent counseling staff, limited access to psychological services beyond time-limited psychotherapies, and lack of programs that address the specific needs either of Ph.D. students in general 92 or of Ph.D. students belonging to marginalized groups 93 , 94 . Structural interventions focused solely on enhancing student resilience might include programs aimed at reducing stigma, fostering social cohesion, and reducing social isolation, while changing norms around help-seeking behavior 95 , 96 . However, structural interventions focused on changing stressogenic aspects of the graduate student environment itself are also needed 97 , beyond any enhancements to Ph.D. student resilience, including: undercutting power differentials between graduate students and individual faculty advisors, e.g., by diffusing power among multiple faculty advisors; eliminating racist, sexist, and other discriminatory behaviors by faculty advisors 74 , 75 , 98 ; valuing mentorship and other aspects of “invisible work” that are often disproportionately borne by women faculty and faculty of color 99 , 100 ; and training faculty members to emphasize the dignity of, and adequately prepare Ph.D. students for, non-academic careers 101 , 102 .

Our findings should be interpreted with several limitations in mind. First, the pooled estimates are characterized by a high degree of heterogeneity, similar to meta-analyses of depression prevalence in other populations 30 , 65 , 103 , 104 , 105 . Second, we were only able to aggregate depression prevalence across 16 studies and anxiety prevalence across nine studies (the majority of which were conducted in the U.S.) – far fewer than the 183 studies included in a meta-analysis of depression prevalence among medical students 30 and the 54 studies included in a meta-analysis of resident physicians 65 . These differences underscore the need for more rigorous study in this critical area. Many articles were either excluded from the review or from the meta-analyses for not meeting inclusion criteria or not reporting relevant statistics. Future research in this area should ensure the systematic collection of high-quality, clinically relevant data from a comprehensive set of institutions, across disciplines and countries, and disaggregated by graduate student type. As part of conducting research and addressing student mental health and wellbeing, university deans, provosts, and chancellors should partner with national survey and program institutions (e.g., Graduate Student Experience in the Research University [gradSERU] 106 , the American College Health Association National College Health Assessment [ACHA-NCHA], and HealthyMinds). Furthermore, federal agencies that oversee health and higher education should provide resources for these efforts, and accreditation agencies should require monitoring of mental health and programmatic responses to stressors among Ph.D. students.

Third, heterogeneity in reporting precluded a meta-analysis of the suicidality outcomes among the few studies that reported such data. While reducing the burden of mental health problems among graduate students is an important public health aim in itself, more research into understanding non-suicidal self-injurious behavior, suicide attempts, and completed suicide among Ph.D. students is warranted. Fourth, it is possible that the grey literature reports included in our meta-analysis are more likely to be undertaken at research-intensive institutions 52 , 60 , 61 . However, the direction of bias is unpredictable: mental health problems among Ph.D. students in research-intensive environments may be more prevalent due to detection bias, but such institutions may also have more resources devoted to preventive, screening, or treatment efforts 92 . Fifth, inclusion in this meta-analysis and systematic review was limited to those based on community samples. Inclusion of clinic-based samples, or of studies conducted before or after specific milestones (e.g., the qualifying examination or dissertation prospectus defense), likely would have yielded even higher pooled prevalence estimates of mental health problems. And finally, few studies provided disaggregated data according to sociodemographic factors, stage of training (e.g., first year, pre-prospectus defense, all-but-dissertation), or discipline of study. These factors might be investigated further for differences in mental health outcomes.

Clinically significant symptoms of depression and anxiety are pervasive among graduate students in doctoral degree programs, but these are understudied relative to other trainee populations. Structural and clinical interventions to systematically monitor and promote the mental health and wellbeing of Ph.D. students are urgently needed.

This systematic review and meta-analysis follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach (Supplementary Table S3 ) 107 . This study was based on data collected from publicly available bibliometric databases and did not require ethical approval from our institutional review boards.

Eligibility criteria

Studies were included if they provided data on either: (a) the number or proportion of Ph.D. students with clinically significant symptoms of depression or anxiety, ascertained using a validated scale; or (b) the mean depression or anxiety symptom severity score and its standard deviation among Ph.D. students. Suicidal ideation was examined as a secondary outcome.

We excluded studies that focused on graduate students in non-doctoral degree programs (e.g., Master of Public Health) or professional degree programs (e.g., Doctor of Medicine, Juris Doctor) because more is known about mental health problems in these populations 30 , 108 , 109 , 110 and because Ph.D. students face unique uncertainties. To minimize the potential for upward bias in our pooled prevalence estimates, we excluded studies that recruited students from campus counseling centers or other clinic-based settings. Studies that measured affective states, or state anxiety, before or after specific events (e.g., terrorist attacks, qualifying examinations) were also excluded.

If articles described the study sample in general terms (i.e., without clarifying the degree level of the participants), we contacted the authors by email for clarification. Similarly, if articles pooled results across graduate students in doctoral and non-doctoral degree programs (e.g., reporting a single estimate for a mixed sample of graduate students), we contacted the authors by email to request disaggregated data on the subsample of Ph.D. students. If authors did not reply after two contact attempts spaced over 2 months, or were unable to provide these data, we excluded these studies from further consideration.

Search strategy and data extraction

PubMed, Embase, PsycINFO, ERIC, and Business Source Complete were searched from inception of each database to November 5, 2019. The search strategy included terms related to mental health symptoms (e.g., depression, anxiety, suicide), the study population (e.g., graduate, doctoral), and measurement category (e.g., depression, Columbia-Suicide Severity Rating Scale) (Supplementary Table S4 ). In addition, we searched the reference lists and the grey literature.

After duplicates were removed, we screened the remaining titles and abstracts, followed by a full-text review. We excluded articles following the eligibility criteria listed above (i.e., those that were not focused on Ph.D. students; those that did not assess depression and/or anxiety using a validated screening tool; those that did not report relevant statistics of depression and/or anxiety; and those that recruited students from clinic-based settings). Reasons for exclusion were tracked at each stage. Following selection of included articles, two members of the research team extracted data and conducted risk of bias assessments. Discrepancies were discussed with a third member of the research team. Key extraction variables included: study design, geographic region, sample size, response rate, demographic characteristics of the sample, screening instrument(s) used for assessment, mean depression or anxiety symptom severity score (and its standard deviation), and the number (or proportion) of students experiencing clinically significant symptoms of depression or anxiety.

Risk of bias assessment

Following prior work 30 , 65 , the Newcastle–Ottawa Scale 111 was adapted and used to assess risk of bias in the included studies. Each study was assessed across 5 categories: sample representativeness, sample size, non-respondents, ascertainment of outcomes, and quality of descriptive statistics reporting (Supplementary Information S5 ). Studies were judged as having either low risk of bias (≥ 3 points) or high risk of bias (< 3 points).

Analysis and synthesis

Before pooling the estimated prevalence rates across studies, we first transformed the proportions using a variance-stabilizing double arcsine transformation 112 . We then computed pooled estimates of prevalence using a random effects model 113 . Study specific confidence intervals were estimated using the score method 114 , 115 . We estimated between-study heterogeneity using the I 2 statistic 116 . In an attempt to reduce the extent of heterogeneity, we re-estimated pooled prevalence restricting the analysis to studies conducted in the United States and to studies in which depression assessment was based on the 9-item Patient Health Questionnaire (PHQ-9) 117 . All analyses were conducted using Stata (version 16; StataCorp LP, College Station, Tex.). Where heterogeneity limited our ability to summarize the findings using meta-analysis, we synthesized the data using narrative review.

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Acknowledgements

We thank the following investigators for generously sharing their time and/or data: Gordon J. G. Asmundson, Ph.D., Amy J. L. Baker, Ph.D., Hillel W. Cohen, Dr.P.H., Alcir L. Dafre, Ph.D., Deborah Danoff, M.D., Daniel Eisenberg, Ph.D., Lou Farrer, Ph.D., Christy B. Fraenza, Ph.D., Patricia A. Frazier, Ph.D., Nadia Corral-Frías, Ph.D., Hanga Galfalvy, Ph.D., Edward E. Goldenberg, Ph.D., Robert K. Hindman, Ph.D., Jürgen Hoyer, Ph.D., Ayako Isato, Ph.D., Azharul Islam, Ph.D., Shanna E. Smith Jaggars, Ph.D., Bumseok Jeong, M.D., Ph.D., Ju R. Joeng, Nadine J. Kaslow, Ph.D., Rukhsana Kausar, Ph.D., Flavius R. W. Lilly, Ph.D., Sarah K. Lipson, Ph.D., Frances Meeten, D.Phil., D.Clin.Psy., Dhara T. Meghani, Ph.D., Sterett H. Mercer, Ph.D., Masaki Mori, Ph.D., Arif Musa, M.D., Shizar Nahidi, M.D., Ph.D., Arthur M. Nezu, Ph.D., D.H.L., Angelo Picardi, M.D., Nicole E. Rossi, Ph.D., Denise M. Saint Arnault, Ph.D., Sagar Sharma, Ph.D., Bryony Sheaves, D.Clin.Psy., Kennon M. Sheldon, Ph.D., Daniel Shepherd, Ph.D., Keisuke Takano, Ph.D., Sara Tement, Ph.D., Sherri Turner, Ph.D., Shawn O. Utsey, Ph.D., Ron Valle, Ph.D., Caleb Wang, B.S., Pengju Wang, Katsuyuki Yamasaki, Ph.D.

A.C.T. acknowledges funding from the Sullivan Family Foundation. This paper does not reflect an official statement or opinion from the County of San Mateo.  

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A.C.T. conceptualized the study and provided supervision. T.K. conducted the search. E.N.S. contacted authors for additional information not reported in published articles. E.N.S. and T.K. extracted data and performed the quality assessment appraisal. E.N.S. and A.C.T. conducted the statistical analysis and drafted the manuscript. T.K., M.V.K., R.A., S.C., H.L., X.L., C.H.L., I.R., S.S., M.T. and M.Y. contributed to the interpretation of the results. All authors provided critical feedback on drafts and approved the final manuscript.

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Satinsky, E.N., Kimura, T., Kiang, M.V. et al. Systematic review and meta-analysis of depression, anxiety, and suicidal ideation among Ph.D. students. Sci Rep 11 , 14370 (2021). https://doi.org/10.1038/s41598-021-93687-7

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4 Scents That Can Reduce Depression, New Research Shows

Posted: March 15, 2024 | Last updated: March 15, 2024

For centuries, people have used aromatherapy for its therapeutic benefits, including emotional regulation. Now, research is exploring how it could be used to combat specific mental health problems, such as depression and anxiety.

For instance, a 2022 study published in the medical journal Frontiers in Neuroscience looked at the relationship between one's sense of smell and their likelihood of developing depressive traits. They note that people who lost their senses of smell were more likely than those who did not to develop clinical depression within five to 10 years, and that the severity of one's sensory loss could actually predict the severity of their eventual depression.

Based on these findings, the team proposed that enhancing your sense of smell may help improve your symptoms of depression. Olfactory enrichment in the form of aromatherapy "modifies brain structures and improves cognitive and emotional status," the researchers wrote.

If you're looking to improve your own mental wellbeing with aromatherapy, these are the four specific scents that researchers recommend for reducing symptoms of depression.

RELATED:  8 Houseplants That Improve Your Mental Health, Science Says .

New research published last month in JAMA Open Network suggests that familiar scents can improve symptoms in people with depressive disorders. That's because individuals who suffer from depression are known to have more difficulty recalling specific autobiographical memories. However, smelling familiar scents helped the study subjects recall more memories. Kymberly Young , an associate professor of psychiatry at the University of Pittsburgh and a co-author on the study, suggested while speaking to NBC News that with a little training, those with depression might be able to lessen their symptoms by using scents to conjure positive memories.

And, it seems any scent you have a personal memory of will do. The scents used in the study included orange, vanilla extract, cumin, whiskey, red wine, cough syrup, disinfectant, shoe polish, and more.

Many of us associate lavender with calm, and in keeping with the findings of the last study, that association likely enhances its ability to improve depression and anxiety. However, studies show that there also appear to be other mechanisms behind lavender's antidepressant and anxiolytic (anti-anxiety) effects.

RELATED: Taking a "Smell Walk" Slashes Stress and Boosts Your Mood—Here's How to Do It .

Bergamot orange is a citrus fruit that's too bitter to eat on its own. However, Bergamot oil is commonly used in perfumes and aromatherapy—and touted for its mood-boosting benefits.

"Fifteen minutes of Bergamot essential oil exposure improved participants' positive feelings compared with the control group (17 percent higher)," says a 2017 study published in the journal Phytotherapy Research . The study authors note that Bergamot is characterized by a high content of limonene, linalool, and linalyl acetate—three compounds associated with antidepressant and anxiolytic benefits.

Ultimately, they concluded, "that bergamot essential oil aromatherapy can be an effective adjunct treatment to improve individuals' mental health and well‐being."

A 2021 study published in the International Journal of Molecular Sciences found that chamomile was yet another scent that could improve mental well-being when used as aromatherapy. The study noted that inhaling chamomile oil helped to decrease levels of depression, anxiety, and stress in older adults.

"It was suggested that the anxiolytic and antidepressant effects could be associated with the suppression of the activity of the sympathetic nervous system," the researchers note.

RELATED:For more up-to-date information, sign up for our daily newsletter.

Read the original article on Best Life .

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Pregnancy may speed up 'biological aging,' study suggests

A study conducted in the Philippines links pregnancy to changes in the chemical tags that sit atop DNA, hinting that pregnancy speeds biological aging.

Women in their early 20s who have been pregnant are "biologically older" than those who have never been pregnant, and by some measures, this age gap seems to widen in people who have had multiple pregnancies, a new study suggests.

The research, conducted in the Philippines, used various tools to look at people's epigenetics, meaning the chemical tags attached to their DNA . These tags don't change the DNA's underlying code but rather help control which genes are activated and to what degree. The new study specifically looked at methyl groups, a type of molecule long linked to different aspects of the aging process .

By studying patterns of methylation seen throughout the human life span, scientists have created a number of "epigenetic clocks" that can be used to assess a person's biological age. While chronological age simply reflects how long someone's been alive, biological age reflects their physiological state and chances of age-related diseases and death.

"What epigenetic clocks are doing is they're serving a predictive function rather than a sort of causal explanation," said first study author Calen Ryan , an associate research scientist in the Columbia Aging Center . "They're trained to predict things that we think of as representing aspects of aging." So one clock may be designed to predict a person's chronological age, while others predict a person's likelihood of death and still others estimate the length of their telomeres , the protective caps at the end of DNA that keep it from fraying.

Related: 'Biological aging' speeds up in times of great stress, but it can be reversed during recovery

The research, published Monday (April 8) in the journal PNAS , used six different epigenetic clocks to make predictions about 1,735 young women and men in the Philippines. The full group had blood samples taken in 2005, between the ages of 20 and 22. A subset of the women — around 330 — who became pregnant in the years following their first blood sample also had a second sample taken about four to nine years afterward.

Across all of the clocks used, women who'd had at least one pregnancy showed accelerated aging compared with women with no pregnancy history, the analysis revealed; the pregnancies included those that resulted in miscarriages, stillbirths and live births. The pattern still showed up when the scientists controlled for other factors that also affect a person's rate of biological aging, such as socioeconomic status, smoking history and some genetic risk factors.

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The researchers also found that women who'd had more pregnancies showed faster aging than those with fewer pregnancies "for all six of the clocks," Ryan told Live Science. "We do not find that relationship among the men we looked at cross-sectionally." In other words, the number of pregnancies a man fathered didn't seem to affect the speed at which his epigenetic clock ticked.

(Notably, the men looked biologically older than the women overall, regardless of pregnancy status; it's just that impregnanting people didn't increase the men's biological ages even higher. This pattern of biological aging in men is consistently seen across epigenetic-clock studies and may be connected to men generally dying at younger ages than women , Ryan said.)

The team then looked at the 330 women they followed over time, to see if there were differences between the women's first and second blood samples. In that analysis, experiencing more pregnancies also was associated with faster aging compared with fewer pregnancies. However, this pattern showed up for only two of the six clocks — specifically the two designed to predict chronological age.

Based on all of these data, the team estimates that each pregnancy was tied to about 4 to 4.5 months of biological aging among the women in the study.

Related: Epigenetics linked to the maximum life spans of mammals

The study's findings may have been affected by where it was conducted. For instance, people's access to adequate nutrition, health care and social support during pregnancy vary throughout the Philippines, and these factors may influence the extent to which pregnancy influences aging. It's also relevant that most epigenetic clocks have been confirmed to work well at tracking aging in white people in developed countries, but many clocks still need to be fully validated in people of other demographics elsewhere in the world, Ryan noted.

"They're still basically our best measures yet," but they could likely be improved for different populations, he said. More work is also needed to tease out the effects of parenting on aging from those tied to being pregnant and giving birth, the authors noted in their report.

In addition, "these women are quite young at the time of the sample," Ryan said of the study participants. So it's not clear if women who are older at the time of their first pregnancy would show the same patterns. That said, it was helpful for the team to study young women because the researchers were trying to see if biological aging tied to pregnancy could be seen early, before the health outcomes of accelerated age show up.

If you can catch this accelerated aging early, that could theoretically inform future treatments to help prevent or reverse the process, Ryan said — although at this early stage of research, it's unclear what such treatments would entail.

Similar upticks in biological aging have been seen in some other contexts, but not all. For example, they've been observed among Filipino women in the U.S. but not in women in Finland . A recent Yale study also found that epigenetic clocks accelerate during pregnancy but that much of that effect disappears after the child's birth, especially in people who breastfeed.

So "we do have decent evidence of biological aging being sped up from pregnancy, but maybe not in all contexts," Ryan said.

— Sped-up 'biological aging' linked to worse memory

— Could a drug cocktail reverse biological aging?

— Cells age prematurely in those with depression, study suggests

For now, this new study is helping scientists start to unpack the impact of pregnancy on the aging process. Someday, though, it could pave the way for medical interventions.

"My hope is that we can start to maybe use tools like this [epigenetic clocks] to identify at-risk individuals," meaning people who may age more with each pregnancy, Ryan said. If they can identify factors that help buffer against biological aging, scientists could potentially design interventions that mimic those factors in people more susceptible to it.

Ever wonder why some people build muscle more easily than others or why freckles come out in the sun ? Send us your questions about how the human body works to [email protected] with the subject line "Health Desk Q," and you may see your question answered on the website!

Nicoletta Lanese is the health channel editor at Live Science and was previously a news editor and staff writer at the site. She holds a graduate certificate in science communication from UC Santa Cruz and degrees in neuroscience and dance from the University of Florida. Her work has appeared in The Scientist, Science News, the Mercury News, Mongabay and Stanford Medicine Magazine, among other outlets. Based in NYC, she also remains heavily involved in dance and performs in local choreographers' work.

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Healthy Living with Diabetes

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How can I plan what to eat or drink when I have diabetes?

How can physical activity help manage my diabetes, what can i do to reach or maintain a healthy weight, should i quit smoking, how can i take care of my mental health, clinical trials for healthy living with diabetes.

Healthy living is a way to manage diabetes . To have a healthy lifestyle, take steps now to plan healthy meals and snacks, do physical activities, get enough sleep, and quit smoking or using tobacco products.

Healthy living may help keep your body’s blood pressure , cholesterol , and blood glucose level, also called blood sugar level, in the range your primary health care professional recommends. Your primary health care professional may be a doctor, a physician assistant, or a nurse practitioner. Healthy living may also help prevent or delay health problems  from diabetes that can affect your heart, kidneys, eyes, brain, and other parts of your body.

Making lifestyle changes can be hard, but starting with small changes and building from there may benefit your health. You may want to get help from family, loved ones, friends, and other trusted people in your community. You can also get information from your health care professionals.

What you choose to eat, how much you eat, and when you eat are parts of a meal plan. Having healthy foods and drinks can help keep your blood glucose, blood pressure, and cholesterol levels in the ranges your health care professional recommends. If you have overweight or obesity, a healthy meal plan—along with regular physical activity, getting enough sleep, and other healthy behaviors—may help you reach and maintain a healthy weight. In some cases, health care professionals may also recommend diabetes medicines that may help you lose weight, or weight-loss surgery, also called metabolic and bariatric surgery.

Choose healthy foods and drinks

There is no right or wrong way to choose healthy foods and drinks that may help manage your diabetes. Healthy meal plans for people who have diabetes may include

• dairy or plant-based dairy products
• nonstarchy vegetables
• protein foods
• whole grains

Try to choose foods that include nutrients such as vitamins, calcium , fiber , and healthy fats . Also try to choose drinks with little or no added sugar , such as tap or bottled water, low-fat or non-fat milk, and unsweetened tea, coffee, or sparkling water.

Try to plan meals and snacks that have fewer

• foods high in saturated fat
• foods high in sodium, a mineral found in salt
• sugary foods , such as cookies and cakes, and sweet drinks, such as soda, juice, flavored coffee, and sports drinks

Your body turns carbohydrates , or carbs, from food into glucose, which can raise your blood glucose level. Some fruits, beans, and starchy vegetables—such as potatoes and corn—have more carbs than other foods. Keep carbs in mind when planning your meals.

You should also limit how much alcohol you drink. If you take insulin  or certain diabetes medicines , drinking alcohol can make your blood glucose level drop too low, which is called hypoglycemia . If you do drink alcohol, be sure to eat food when you drink and remember to check your blood glucose level after drinking. Talk with your health care team about your alcohol-drinking habits.

Find the best times to eat or drink

Talk with your health care professional or health care team about when you should eat or drink. The best time to have meals and snacks may depend on

• what medicines you take for diabetes
• what your level of physical activity or your work schedule is
• whether you have other health conditions or diseases

Ask your health care team if you should eat before, during, or after physical activity. Some diabetes medicines, such as sulfonylureas  or insulin, may make your blood glucose level drop too low during exercise or if you skip or delay a meal.

Plan how much to eat or drink

You may worry that having diabetes means giving up foods and drinks you enjoy. The good news is you can still have your favorite foods and drinks, but you might need to have them in smaller portions  or enjoy them less often.

For people who have diabetes, carb counting and the plate method are two common ways to plan how much to eat or drink. Talk with your health care professional or health care team to find a method that works for you.

Carb counting

Carbohydrate counting , or carb counting, means planning and keeping track of the amount of carbs you eat and drink in each meal or snack. Not all people with diabetes need to count carbs. However, if you take insulin, counting carbs can help you know how much insulin to take.

Plate method

The plate method helps you control portion sizes  without counting and measuring. This method divides a 9-inch plate into the following three sections to help you choose the types and amounts of foods to eat for each meal.

• Nonstarchy vegetables—such as leafy greens, peppers, carrots, or green beans—should make up half of your plate.
• Carb foods that are high in fiber—such as brown rice, whole grains, beans, or fruits—should make up one-quarter of your plate.
• Protein foods—such as lean meats, fish, dairy, or tofu or other soy products—should make up one quarter of your plate.

If you are not taking insulin, you may not need to count carbs when using the plate method.

Work with your health care team to create a meal plan that works for you. You may want to have a diabetes educator  or a registered dietitian  on your team. A registered dietitian can provide medical nutrition therapy , which includes counseling to help you create and follow a meal plan. Your health care team may be able to recommend other resources, such as a healthy lifestyle coach, to help you with making changes. Ask your health care team or your insurance company if your benefits include medical nutrition therapy or other diabetes care resources.

Talk with your health care professional before taking dietary supplements

There is no clear proof that specific foods, herbs, spices, or dietary supplements —such as vitamins or minerals—can help manage diabetes. Your health care professional may ask you to take vitamins or minerals if you can’t get enough from foods. Talk with your health care professional before you take any supplements, because some may cause side effects or affect how well your diabetes medicines work.

Research shows that regular physical activity helps people manage their diabetes and stay healthy. Benefits of physical activity may include

• lower blood glucose, blood pressure, and cholesterol levels
• better heart health
• healthier weight
• better mood and sleep
• better balance and memory

Talk with your health care professional before starting a new physical activity or changing how much physical activity you do. They may suggest types of activities based on your ability, schedule, meal plan, interests, and diabetes medicines. Your health care professional may also tell you the best times of day to be active or what to do if your blood glucose level goes out of the range recommended for you.

Do different types of physical activity

People with diabetes can be active, even if they take insulin or use technology such as insulin pumps .

Try to do different kinds of activities . While being more active may have more health benefits, any physical activity is better than none. Start slowly with activities you enjoy. You may be able to change your level of effort and try other activities over time. Having a friend or family member join you may help you stick to your routine.

The physical activities you do may need to be different if you are age 65 or older , are pregnant , or have a disability or health condition . Physical activities may also need to be different for children and teens . Ask your health care professional or health care team about activities that are safe for you.

Aerobic activities

Aerobic activities make you breathe harder and make your heart beat faster. You can try walking, dancing, wheelchair rolling, or swimming. Most adults should try to get at least 150 minutes of moderate-intensity physical activity each week. Aim to do 30 minutes a day on most days of the week. You don’t have to do all 30 minutes at one time. You can break up physical activity into small amounts during your day and still get the benefit. 1

Strength training or resistance training

Strength training or resistance training may make your muscles and bones stronger. You can try lifting weights or doing other exercises such as wall pushups or arm raises. Try to do this kind of training two times a week. 1

Balance and stretching activities

Balance and stretching activities may help you move better and have stronger muscles and bones. You may want to try standing on one leg or stretching your legs when sitting on the floor. Try to do these kinds of activities two or three times a week. 1

Some activities that need balance may be unsafe for people with nerve damage or vision problems caused by diabetes. Ask your health care professional or health care team about activities that are safe for you.

Stay safe during physical activity

Staying safe during physical activity is important. Here are some tips to keep in mind.

Drink liquids

Drinking liquids helps prevent dehydration , or the loss of too much water in your body. Drinking water is a way to stay hydrated. Sports drinks often have a lot of sugar and calories , and you don’t need them for most moderate physical activities.

Avoid low blood glucose

Check your blood glucose level before, during, and right after physical activity. Physical activity often lowers the level of glucose in your blood. Low blood glucose levels may last for hours or days after physical activity. You are most likely to have low blood glucose if you take insulin or some other diabetes medicines, such as sulfonylureas.

Ask your health care professional if you should take less insulin or eat carbs before, during, or after physical activity. Low blood glucose can be a serious medical emergency that must be treated right away. Take steps to protect yourself. You can learn how to treat low blood glucose , let other people know what to do if you need help, and use a medical alert bracelet.

Avoid high blood glucose and ketoacidosis

Taking less insulin before physical activity may help prevent low blood glucose, but it may also make you more likely to have high blood glucose. If your body does not have enough insulin, it can’t use glucose as a source of energy and will use fat instead. When your body uses fat for energy, your body makes chemicals called ketones .

High levels of ketones in your blood can lead to a condition called diabetic ketoacidosis (DKA) . DKA is a medical emergency that should be treated right away. DKA is most common in people with type 1 diabetes . Occasionally, DKA may affect people with type 2 diabetes  who have lost their ability to produce insulin. Ask your health care professional how much insulin you should take before physical activity, whether you need to test your urine for ketones, and what level of ketones is dangerous for you.

Take care of your feet

People with diabetes may have problems with their feet because high blood glucose levels can damage blood vessels and nerves. To help prevent foot problems, wear comfortable and supportive shoes and take care of your feet  before, during, and after physical activity.

If you have diabetes, managing your weight  may bring you several health benefits. Ask your health care professional or health care team if you are at a healthy weight  or if you should try to lose weight.

If you are an adult with overweight or obesity, work with your health care team to create a weight-loss plan. Losing 5% to 7% of your current weight may help you prevent or improve some health problems  and manage your blood glucose, cholesterol, and blood pressure levels. 2 If you are worried about your child’s weight  and they have diabetes, talk with their health care professional before your child starts a new weight-loss plan.

You may be able to reach and maintain a healthy weight by

• following a healthy meal plan
• consuming fewer calories
• being physically active
• getting 7 to 8 hours of sleep each night 3

If you have type 2 diabetes, your health care professional may recommend diabetes medicines that may help you lose weight.

Online tools such as the Body Weight Planner  may help you create eating and physical activity plans. You may want to talk with your health care professional about other options for managing your weight, including joining a weight-loss program  that can provide helpful information, support, and behavioral or lifestyle counseling. These options may have a cost, so make sure to check the details of the programs.

Your health care professional may recommend weight-loss surgery  if you aren’t able to reach a healthy weight with meal planning, physical activity, and taking diabetes medicines that help with weight loss.

If you are pregnant , trying to lose weight may not be healthy. However, you should ask your health care professional whether it makes sense to monitor or limit your weight gain during pregnancy.

Both diabetes and smoking —including using tobacco products and e-cigarettes—cause your blood vessels to narrow. Both diabetes and smoking increase your risk of having a heart attack or stroke , nerve damage , kidney disease , eye disease , or amputation . Secondhand smoke can also affect the health of your family or others who live with you.

If you smoke or use other tobacco products, stop. Ask for help . You don’t have to do it alone.

Feeling stressed, sad, or angry can be common for people with diabetes. Managing diabetes or learning to cope with new information about your health can be hard. People with chronic illnesses such as diabetes may develop anxiety or other mental health conditions .

Learn healthy ways to lower your stress , and ask for help from your health care team or a mental health professional. While it may be uncomfortable to talk about your feelings, finding a health care professional whom you trust and want to talk with may help you

• lower your feelings of stress, depression, or anxiety
• manage problems sleeping or remembering things
• see how diabetes affects your family, school, work, or financial situation

Ask your health care team for mental health resources for people with diabetes.

Sleeping too much or too little may raise your blood glucose levels. Your sleep habits may also affect your mental health and vice versa. People with diabetes and overweight or obesity can also have other health conditions that affect sleep, such as sleep apnea , which can raise your blood pressure and risk of heart disease.

NIDDK conducts and supports clinical trials in many diseases and conditions, including diabetes. The trials look to find new ways to prevent, detect, or treat disease and improve quality of life.

What are clinical trials for healthy living with diabetes?

Clinical trials—and other types of clinical studies —are part of medical research and involve people like you. When you volunteer to take part in a clinical study, you help health care professionals and researchers learn more about disease and improve health care for people in the future.

Researchers are studying many aspects of healthy living for people with diabetes, such as

• how changing when you eat may affect body weight and metabolism
• how less access to healthy foods may affect diabetes management, other health problems, and risk of dying
• whether low-carbohydrate meal plans can help lower blood glucose levels
• which diabetes medicines are more likely to help people lose weight

Find out if clinical trials are right for you .

Watch a video of NIDDK Director Dr. Griffin P. Rodgers explaining the importance of participating in clinical trials.

What clinical trials for healthy living with diabetes are looking for participants?

You can view a filtered list of clinical studies on healthy living with diabetes that are federally funded, open, and recruiting at www.ClinicalTrials.gov . You can expand or narrow the list to include clinical studies from industry, universities, and individuals; however, the National Institutes of Health does not review these studies and cannot ensure they are safe for you. Always talk with your primary health care professional before you participate in a clinical study.

This content is provided as a service of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), part of the National Institutes of Health. NIDDK translates and disseminates research findings to increase knowledge and understanding about health and disease among patients, health professionals, and the public. Content produced by NIDDK is carefully reviewed by NIDDK scientists and other experts.

NIDDK would like to thank: Elizabeth M. Venditti, Ph.D., University of Pittsburgh School of Medicine.