case study about major depressive disorder

Which of the following best describes risk factor(s) for the development of MDD?

• Adverse childhood experiences
• Family history of depression
• All of the above

There are several risk factors for depression to consider when gathering clinical evidence to support (or suggest against) a diagnosis of MDD (Table 2). 4 A family history of depression in first-degree family members is thought to increase risk of MDD by 2- to 4-fold 5 and should always be obtained during the initial assessment of a patient with concerns of depression.

Trauma and adverse childhood events are thought to be linked to increased incidence of many psychiatric illnesses, and they cause an earlier age of symptom onset, increased symptom severity, and worsened treatment responses in MDD. 6,7

What is another component of the patient’s history that should always be assessed during an initial clinical encounter for depression?

• Presence of pets in the home
• Current area of deprivation index (ADI) score
• History of mania / hypomania
• Family history of hypothyroidism
• Current dietary specifics and caloric intake

As always, diagnostic precision is a key to appropriate treatment and improved outcomes in depression care. Misdiagnosis of bipolar disorder I/II comes with considerable risk as rates of suicide with bipolar disorder are possibly 15 times greater than baseline, and suicide is one of the leading causes of death in this population. Clinical overlap between MDD and bipolar disorder is significant, as 90% of patients with a history of mania/bipolar I go on to have recurrent mood episodes. Approximately 60% of manic episodes occur before a major depressive episode, making questions about manic episodes a valuable tool for accurate diagnosis. In addition, the first-line treatments of bipolar depression are mood stabilizers and atypical antipsychotics rather than classic antidepressants. 4,8

A family history of bipolar disorder is thought to lead to an average 10-fold increased risk among adult relatives of individuals with bipolar disorders, making it one of the largest risk factors. Mania or hypomania that emerges during antidepressant treatment (eg, psychotropic medication, electroconvulsive therapy) should raise strong and immediate concern for a diagnosis of bipolar disorder rather than MDD. Traditionally, the introduction of antidepressants — first noticed with older tricyclic antidepressants and later with other classes such as selective serotonin reuptake inhibitors — in someone with bipolar disorder could precipitate mania. Although that remains an important treatment consideration, the incidence of this occurrence and the possible neurobiological causes is not clear.

Mania is a psychiatric emergency that requires immediate psychiatric consultation and/or psychiatric admission. Patients who are manic often do not perceive that they are ill and will sometimes engage in uncharacteristically dangerous behaviors that can lead to interactions with police or other problems with serious potential for harm (eg, gambling, serious financial difficulties, physical assaults, suicide). Prominent delusions or psychotic symptoms can also put these patients at risk of harming themselves or others.

The following define mania/hypomania:

An episode should not be attributable to the physiological effects of a substance (eg, drug of abuse, medication, other treatment) or another medical condition. By definition, during the period of mood disturbance and increased energy or activity, three (or more) of the following symptoms (four if the mood is only irritable) are present to a significant degree and represent a noticeable change from usual behavior:

For mania, the mood disturbance is sufficiently severe to cause marked impairment in social or occupational functioning, or to necessitate hospitalization to prevent harm to self or others, or if there are psychotic features. For hypomania, the episode is associated with an unequivocal change in functioning that is uncharacteristic of the individual when not symptomatic, but it does not cause marked impairment in functioning or lead to psychiatric admission. If the presence of psychotic features during hypomania emerges, then the diagnosis is then changed to mania (and subsequently bipolar I).

Which of the following conditions should be on the differential diagnosis in a patient presenting with concerns about depression?

• Substance/medication-induced depressive disorder
• Attention-deficit/hyperactivity disorder

Many conditions can resemble MDD, so one should be attentive to its broad differential when considering MDD as a diagnosis. 4,9,10 Alcohol and many illicit substances can cause substance induced mood disorders, so screening patients for substance use is an important part of the workup. Additionally, some medications such as barbiturates, vigabatrin, topiramate, flunarizine, corticosteroids, mefloquine, efavirenz, and interferon-alpha can cause depression.

Adjustment disorder with depressed mood is a condition that occurs in response to an identifiable stressor within 3 months of the stressor’s onset. Adjustment disorder is characterized by marked distress that is out of proportion to the severity or intensity of the stressor and causes significant impairment in social, occupational, or other important areas of functioning. Adjustment disorder does not persist for more than 6 months after a stressor or its consequences terminate. If a patient develops enough symptoms to meet the criteria for MDD, they cannot be diagnosed with an adjustment disorder because the MDD diagnosis takes precedence.

Attention-deficit/hyperactivity disorder (ADHD) causes problems with concentration that can be similar to those produced by depression. ADHD is frequently misdiagnosed in people who have MDD. If a patient mentions attention problems, it is important to screen for ADHD as well as depression. Finally, the experience of sadness by itself does not constitute a psychiatric condition. It is only in the context of other symptoms recognized as part of MDD, along with impairment in function, that sadness becomes part of a psychiatric condition that should be treated.

What is the first treatment step for mild MDD?

• Psychoeducation, self-management, and psychological treatments
• Selective serotonin reuptake inhibitors
• Tricyclic antidepressants
• Monoamine oxidase inhibitors
• Serotonin norepinephrine reuptake inhibitors

First-line treatments for individuals with mild depression include psychoeducation, self-management, and psychological treatments. Pharmacological treatments can be considered for mild depression in some situations, such as patient preference, previous improvement with antidepressants, or lack of response to other nonpharmacological interventions. 9,11

Assessment of severity and monitoring treatment response in depression care should incorporate the use of measurement-based outcomes (clinician-administered or patient-reported scales such as PHQ-9). The severity of MDD is determined by number of symptoms, symptom severity, depression scores on assessment tools, or by the practitioner’s clinical judgment of patient functioning.

Of the following, which is the most appropriate first-line pharmacological treatment for moderate to severe MDD?

• Psychoeducation
• Dialectical behavior therapy

Table 3 shows the commonly used medications for first- and second-line treatment of moderate to severe MDD. The selective serotonin reuptake inhibitors are generally well-tolerated, which makes them the preferred first-line treatment for moderate to severe depression. 9,11

It should be noted that most patients will require an increase in dose or a change in medication. This is best exemplified by the remission rates found in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial, which were only 36.8% for the first treatment. 12 Thus, it is important for the practitioner to bring the patient to an adequate treatment dose before giving up on a medication. Common pitfalls in initiating treatment for MDD are inadequate dose adjustments, inadequate duration of medication trial (prescribe for at least 3 months at therapeutic dose), and diagnostic errors.

There are a few additional considerations to make if there is suspicion for MDD with a specifier or other prominent clinical symptoms. These include the following:

Table 3: Commonly used first- and second-line pharmacological treatment of moderate and severe depression with treatment dose range.

NDRI = norepinephrine dopamine reuptake inhibitor; SNRIs = serotonin norepinephrine reuptake inhibitors; SSRIs = selective serotonin reuptake inhibitors

A patient with MDD presents with positive suicidal ideation, what is the next step in care?

• Increase antidepressant dose
• Suicide risk assessment
• Refer to psychological treatment
• Remove firearms from house
• Inform local police and transfer patient to psychiatric hospital

Patients with MDD are at increased risk of completing suicide or engaging in other suicidal behavior. Patients with depression, bipolar disorder, borderline personality disorder, and anorexia nervosa generally have the highest suicide risks. 13 There are many critical clinical steps to mitigate the risks for completing suicide and many standardized clinical assessment tools that can aid in determining risk and identifying modifiable risk factors. 14-17

While there is ongoing debate about how accurate these tools are in predicting suicide, clinically their importance remains paramount, and their use is recommended by the U.S. National Strategy for Suicide Prevention. 18 Using a standardized tool helps to identify important modifiable risk factors for suicide and to complete suicide risk documentation. Complete documentation of a risk assessment, as well as the steps that were taken to reduce risk, are key components of good clinical care and reducing liability risk. 19

Assessment of the nature of suicidal ideation — frequency, intensity, and characteristics of thoughts — as well as the presence of any planning or intent to suicide are the first steps. Suicide planning and intent generally require emergent psychiatric inpatient stabilization. Removing access to lethal means, treating underlying psychiatric disorders, and abstaining from alcohol and drug use are the three primary clinical targets in modifying suicide risk.

In monitoring treatment response in depression, what is the definition of remission?

• Complete resolution of depressive symptoms
• A period of 2 or more months with no symptoms or only one or two symptoms to no more than a mild degree
• Decrease in suicidal ideation
• Reduction in psychiatric admissions to 0-1 per year
• A period of at least 2 weeks with only one or two diagnostic symptoms

It is expected that symptom abatement should occur, generally, within the first 3 months of treatment with pharmacologic or psychological interventions. If the patient has an improvement in symptoms after 2 to 4 weeks, then treatment should continue for another 6 to 8 weeks. If the patient has remission of symptoms at that time, then the duration of antidepressant treatment is guided by the presence of risk factors for recurrence. If the patients has risk factors, then treatment should generally continue for 2 years or longer. Without risk factors, treatment should continue for approximately 6 to 9 months.

Risk factors for recurrence that prompt longer antidepressant treatment include the following:

Other factors that contribute to lower rates of remission or recovery include multiple depressive episodes, younger age, episode severity, co-occurring personality disorders, presence of psychotic features, and prominent anxiety. The longer someone remains in remission, the lower the chances of recurrence. 9,11  

Monitoring treatment response in depression should incorporate the use of clinician-administered or patient-reported scales. Assessment of symptom severity, severity scores, as well as functional improvement should be incorporated into routine clinical care. There are multiple well-validated scales to choose from and some examples are listed below. These scales provide quantification of symptoms and can be used to provide longitudinal comparison and evaluation of treatment. Many are quick to use in office and have become routine screening or severity tools in both clinical practice and research.

Well-validated scales for monitoring treatment response in patients with depression include the following:

Which of the following is/are first- or second-line psychological treatment(s) for MDD?

• Behavioral activation
• Mindfulness-based cognitive therapy
• Interpersonal therapy
• Cognitive behavioral therapy

All four of these psychological treatments are appropriate for managing a patient with MDD. 9,20 Table 4 shows their best use in clinical practice. 

Table 4. Common first and second line psychological treatments for MDD.

For a patient requesting the use of “alternative” or “complementary” treatment for their depression, which of the following treatments have sufficient evidence to be recommendation as first-line therapy for some forms of depression?

• Equine therapy
• Acupuncture
• St John’s Wort

There are several alternative and complementary treatments that have evidence for use as monotherapy or as adjunctive therapy for the treatment of MDD (Table 5).21 Although there is evidence that some of these can be used as monotherapy for mild depression, for moderate or severe depression these should generally only be used an adjunctive treatment to other well-established treatments.

Table 5. First-line and adjunctive alternative and complementary treatments for MDD.

It should be noted that unless there is a specific patient request for alternative or complementary treatment options, or other contraindications to well-established antidepressant treatment, these should not be used or offered as initial treatment for moderate to severe depression or where other clinically significant symptoms (eg, suicidal ideation, severe impairment) are present. Providers should be reminded that various forms of psychotherapy are available to those patients that may have contraindications to standard antidepressant therapy and that they should utilize consultation to psychiatry if treatment complications, nonresponse to medication trials, and other questions occur.

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Theses and Dissertations

Case study of a client diagnosed with major depressive disorder.

Barbara Ann Marie Allison , Rowan University

Date Approved

Embargo period, document type, degree name.

M.A. in Applied Psychology

College of Science & Mathematics

Cahill, Janet

Depressed persons--Case studies; Depression, Mental--Diagnosis

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The purpose of this study was to determine the best practice for a client diagnosed with major depressive disorder who was referred for treatment at a community mental health facility. The client was assessed, diagnosed, and a treatment plan was developed. Implemented treatment consisted of combined cognitive behavioral oriented psychotherapy and psychotropic medication. The Beck Depression Inventory (BDI-II) was used to assess changes in depressive symptoms. Results indicated a significant decline in depressive symptoms over the course of treatment. At the onset of treatment, the client's BDI scores were in the clinically depressed range, while at the conclusion of treatment they had decreased to the borderline range. The client self-reported an improvement in mood. A comparison between the client's current treatment and what might be considered "best" treatment is presented. Suggestions for treatment improvements are made, with a particular emphasis on concerns regarding a potential relapse.

Recommended Citation

Allison, Barbara Ann Marie, "Case study of a client diagnosed with major depressive disorder" (2005). Theses and Dissertations . 958. https://rdw.rowan.edu/etd/958

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Pathophysiology

Normal Physiology

Normal physiology of patient’s mood, perception, emotion and behavior focuses majorly on neurotransmitters in the brain. There are over 46 neurotransmitters in the brain and many have more than one function. Neurotransmitters are chemical messengers that are released and received by synapses of neurons to mediate intracellular communication in the nervous system. They use electrical signals to stimulate messages along the neurons where it affects ion channels and eventually performs a specific mechanism at a site of action (McCance & Huether, 2014).

Serotonin is involved with mood, happiness, anxiety, and sleep induction. Raphe-Serotonin System normally modulates homeostasis, emotionality, and tolerance to aversive experiences. Norepinephrine in the brain helps regulate alertness, mood, functions in dream sleep, and maintains arousal. It also can help in the response to stressful situations. The locus ceruleus has a group of norepinephrine containing cells implicated in global psychologic processes including attention, vigilance and orientation to stimuli. Dopamine in the brain regulates reward and motivation which could explain the loss of interest in patients with depression. Dopamine motivates people to take action toward goals, desires, and needs, and issues a surge of reinforcing pleasure once they’ve been accomplished (Garcia-Arocena). Sufficient levels are needed for the brain to function properly and decreased levels have been found in patients with depression (McCance & Huether, 2014).

Major Depressive Disorder

Major depression is classified as a unipolar mood disorder. Further, major depression can be classified as when an emotional state, such as sadness, becomes chronic and uncontrollable. It is the most common mood disorder (McCance & Huether, 2014). Mood disorders are still being studied due to the unclear nature of how they occur due to the difficult availability of human brain tissue for neurochemical measurement until patients are post-mortem. Each of the dysfunctions below focuses on what are thought to be the causes of major depressive disorder.

Pathophysiology: Genetic Predisposition and Environmental Influences

There is a genetic predisposition in major depression that runs in families. However, due to the large variance in symptoms, developmental and environmental factors also must be evaluated in the contributing factors to major depression. One view of mood disorders includes the connection between susceptible genes and environmental influence. The combination of life stressors and a potentially dysfunctional serotonin (5-HT) system. The serotonin transporter serves in the reuptake of serotonin at the synapse and may moderate the serotonergic response to stress. Individuals with 2 copies of the s allele were more likely to develop major depression and have suicidal thoughts in response to stressors than individuals homozygous for the l allele. As well as, individuals with 2 s alleles increased their risk for major depression episodes by twofold after experiencing 4 or more stressful events (McCance & Huether, 2014).

Pathophysiology: Neurochemical dysregulation

There are antidepressant drugs that can increase neurotransmitters in the body leading to another theory called the monoamine hypothesis of depression. In this hypothesis, there is a deficit in the concentration of the brain norepinephrine, dopamine, and/or serotonin resulting in depression. Antidepressant therapies focus on increasing the monoamine neurotransmitter levels within the synapses (McCance & Huether, 2014).

Pathophysiology: Neuroendocrine Dysregulation

There are 2 theories in the pathophysiology of depression that involve dysregulation of the neuroendocrine system. The first one focuses on stress and the hypothalamic-Pituitary-Adrenal system. The hypothalamic-pituitary-adrenal system (HPA) plays an essential role in an individual’s ability to cope with stress. Chronic activation of the HPA system and chronic glucocorticoid secretion are found in 30-70% of individuals with major depression suggesting the correlation between the dysfunctional system and depression. Chronic cortisol release in the body results in secretion of pro-inflammatory cytokines which causes immunosuppression and inflammation. Also, there is a Neurotrophic Hypothesis of depression. It is thought to focus on neuronal atrophy of the hippocampus resulting in no cell growth consequently causing in a reduction of the hippocampal brain derived neurotrophic factor (BDNF) and has been proposed as an extension of the monoamine hypothesis of depression.

The second neuroendocrine dysregulation is in the hypothalamic-pituitary-thyroid system. While this dysfunction is not completely understood, 20-30% cases of major depression have shown to have an altered hypothalamic-pituitary-thyroid (HPT) system. There is an increase in thyrotropin releasing hormone, blunted thyroid stimulating hormone in response to TRH challenge and decreased nocturnal rise in TSH level that normally occur. This all increases risk for relapse (McCance & Huether, 2014).

Pathophysiology: Neuroanatomic and Function Abnormalities

Depressed individuals post-mortem brains have shown widespread decrease in serotonin 5-HT1a receptor subtype binding in the frontal, temporal, and limbic cortex as well as serotonin transporter binding in the cerebral cortex and hippocampus, reflecting a dysfunction in the raphe-serotonin system. The activation of the locus ceruleus-norepinephrine system is capable of inhibiting the raphe-serotonin system. This suggests an indirect role in the modulation of serotonin function. Norepinephrine receptor alterations are found in the frontal cortex of some suicide victims with depression. Alterations in norepinephrine systems may be linked to attention or concentration difficulties as well as sleep and arousal disturbances in depression

Alterations in frontal and limbic regions (such as the amygdala) have shown a decreased number of glial cells in people with unipolar disorders. As well as, a decreased prefrontal cortex functioning and decreased frontal lobe volume.

Depressed individuals have also been found to have abnormalities in Cerebral blood flow and glucose metabolism. Dorsolateral prefrontal abnormalities in depression may be responsible for the retardation in cognitive processing and speech deficits similar to those found in schizophrenia. Dorsomedial frontal dysfunction may be associated with mnemonic and attentional impairments that accompany mood disorders. The frontal brain has increased blood flow and metabolism. It is positively related to negative affect in depressed individuals (McCance & Huether, 2014).

Clinical Manifestations/Diagnostic Criteria

To diagnosis depression, symptoms must be present for at least two weeks. There are unremitting feelings of sadness and despair. Depressive episodes may occur or recur suddenly, gradually or continue from a few weeks to months. Twenty percent of all people with depression exhibit chronic forms of depression. Symptoms vary widely depending on the individual. The timing and length of the depression also varies.

To be diagnosed with Major Depressive Disorder, patients have to have several, usually five or more, symptoms including low mood that is present for at least two weeks (Depression, 2018). Other symptoms of major depressive disorder include:

• Depressed or irritable mood
• Loss of interests and pleasures – this includes interpersonal relationships
• Significant weight gain or loss (5%) in a month
• Sleep Disturbances: Insomnia/Hypersomnia
• Psychomotor agitation or retardation: Restlessness or agitation can occur
• Fatigue or loss of energy
• Feelings of worthlessness or excessive guilt: Pessimistic/Negative outcomes are perceived
• Poor concentration or indecisiveness
• Recent thoughts of suicide/death: Suicidal risk increases with depression

Garcia-Arocena, D. (n.d.). Happy or SAD: The chemistry behind depression. Retrieved October 29, 2018, from https://www.jax.org/news-and-insights/jax-blog/2015/december/happy-or-sad-the-chemistry-behind-depression

LA NEUROSCIENZA DEL CERVELLO CON ADHD. (2018, September 17). Retrieved from https://mondoadhd.blog/2018/09/17/la-neuroscienza-del-cervello-adhd/

(n.d.). Retrieved October 29, 2018, from https://www.nature.com/articles/nrdp201665/figures/3

What are neurotransmitters? (2017, November 09). Retrieved from https://qbi.uq.edu.au/brain/brain-physiology/what-are-neurotransmitters

Major Depressive Disorder Case Queries: Where Do Rapid-Acting Therapies Fit Into Care?

How soon is now? Identifying which patients with major depressive disorder (MDD) are candidates for treatment with a rapid-acting antidepressant .

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This education is intended for psychiatrists, primary care physicians (PCPs), obstetricians and gynecologists (Ob/Gyns), nurse practitioners (NPs), nurses, and other clinicians who manage patients with MDD, as well as people living with depression and their care partners.

The goal of this activity is for learners to be better able to navigate existing clinical evidence for novel, rapid-onset antidepressant therapy and incorporate novel agents into the treatment paradigm.

Upon completion of this activity, participants will:

• Identifying patients with MDD who may benefit from a novel, rapid-acting medication as part of their treatment plan
• Recognize patient-specific factors that allow clinicians to tailor antidepressant medication selection

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John F. Greden Professor of Depression and Clinical Neuroscience  Professor of Psychiatry Michigan Medicine University of Michigan, Ann Arbor Ann Arbor, Michigan

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Abbreviations.

• Hashimoto K. Molecular mechanisms of the rapid-acting and long-lasting antidepressant actions of (R)-ketamine. Biochem Pharmacol. 2020;177:113935.
• Chen T, et al. Molecular mechanisms of rapid-acting antidepressants: new perspectives for developing antidepressants. Pharmacol Res. 2023;194:106837.
• Duman RS, et al. Synaptic plasticity and depression: new insights from stress and rapid-acting antidepressants. Nat Med. 2016;22:238-249.
• Kennedy S. Full remission: a return to normal functioning. J Psychiatry Neurosci. 2002;27:233-234.
• Phillips ML. Multimodal predictors of treatment response in major depressive disorder: advancing personalized medicine in psychiatry. Am J Psychiatry. 2024;181:180-182.
• Kennedy SH. Beyond response: aiming for quality remission in depression. Adv Ther. 2022;39:20-28.
• Lam RW, et al. Canadian Network for Mood and Anxiety Treatments (CANMAT) 2023 update on clinical guidelines for management of major depressive disorder in adults. Cam J Psychiatry. 2024. doi: 10.1177/07067437241245384 [Epub ahead of print]
• Scala M, et al. Clinical specificity profile for novel rapid acting antidepressant drugs. Int Clin Psychopharmacol. 2023;38:297-328.
• Cornett EM, et al. Brexanolone to treat postpartum depression in adult women. Psychopharmacol Bull. 2021;51:115-130.
• Meltzer-Brody S, et al. Brexanolone injection in post-partum depression: two multicentre, double-blind, randomised, placebo-controlled, phase 3 trials. Lancet. 2018;392:1058-1070.
• Ionescu DF, et al. Esketamine nasal spray for rapid reduction of depressive symptoms in patients with major depressive disorder who have active suicide ideation with intent: results of a phase 3, double-blind, randomized study (ASPIRE II). Int J Neuropsychopharmacol. 2021;24:22-31.
• Loo C, et al. Efficacy and safety of a 4-week course of repeated subcutaneous ketamine injections for treatment-resistant depression (KADS study): randomised double-blind active-controlled trial. Br J Psychiatry. 2023;223:533-541.
• Krystal JH, et al. Ketamine and rapid antidepressant action: new treatments and novel synaptic signaling mechanisms. Neuropsychopharmacology. 2024;49:41-50.
• Fu DJ, et al. Esketamine nasal spray for rapid reduction of major depressive disorder symptoms in patients who have active suicidal ideation with intent: double-blind, randomized study (ASPIRE I). J Clin Psychiatry. 2020;81:19m13191.
• Deligiannidis KM, et al. Effect of zuranolone vs placebo in postpartum depression: a randomized clinical trial. JAMA Psychiatry. 2021;78:951-959.
• Deligiannidis KM, et al. Zuranolone for the treatment of postpartum depression. Am J Psychiatry. 2023;180:668-675.
• Iosifescu DV, et al. Efficacy and safety of AXS-05 (dextromethorphan-bupropion) in patients with major depressive disorder: a phase 3 randomized clinical trial (GEMINI). J Clin Psychiatry. 2022;83:21m14345.
• Tabuteau H, et al. Effect of AXS-05 (dextromethorphan-bupropion) in major depressive disorder: a randomized double-blind controlled trial. Am J Psychiatry. 2022;179:490-499.
• Bahji A, et al. Efficacy and safety of racemic ketamine and esketamine for depression: a systematic review and meta-analysis. Expert Opin Drug Saf. 2022;21:853-866.
• Recourt K, et al. The selective orexin-2 antagonist seltorexant (JNJ-42847922/MIN-202) shows antidepressant and sleep-promoting effects in patients with major depressive disorder. Transl Psychiatry. 2019;9:216.
• Savitz A, et al. Efficacy and safety of seltorexant as adjunctive therapy in major depressive disorder: a phase 2b, randomized, placebo-controlled, adaptive dose-finding study. Int J Neuropsychopharmacol. 2021;24:965-976.
• Carhart-Harris RL, et al. Psilocybin with psychological support for treatment-resistant depression: an open-label feasibility study. Lancet Psychiatry. 2016;3:619-627.
• Carhart-Harris RL, et al. Trial of psilocybin versus escitalopram for depression. N Engl J Med. 2021;384:1402-1411. 

The educational activity presented above may involve simulated, case-based scenarios. The patients depicted in these scenarios are fictitious and no association with any actual patient, whether living or deceased, is intended or should be inferred.

The material presented here does not necessarily reflect the views of Medscape, LLC, or any individuals or commercial entities that support companies that support educational programming on medscape.org. These materials may include discussion of therapeutic products that have not been approved by the US Food and Drug Administration, off-label uses of approved products, or data that were presented in abstract form. These data should be considered preliminary until published in a peer-reviewed journal. Readers should verify all information and data before treating patients or employing any therapies described in this or any educational activity. A qualified healthcare professional should be consulted before using any therapeutic product discussed herein.

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Six distinct types of depression identified in Stanford Medicine-led study

Brain imaging, known as functional MRI, combined with machine learning can predict a treatment response based on one’s depression “biotype.”

June 17, 2024 - By Rachel Tompa

Researchers have identified six subtypes of depression, paving the way toward personalized treatment. Damerfie -   stock.adobe.com

In the not-too-distant future, a screening assessment for depression could include a quick brain scan to identify the best treatment.

Brain imaging combined with machine learning can reveal subtypes of depression and anxiety, according to a new study led by researchers at Stanford Medicine. The study , published June 17 in the journal Nature Medicine , sorts depression into six biological subtypes, or “biotypes,” and identifies treatments that are more likely or less likely to work for three of these subtypes.

Better methods for matching patients with treatments are desperately needed, said the study’s senior author,  Leanne Williams , PhD, the Vincent V.C. Woo Professor, a professor of psychiatry and behavioral sciences, and the director of Stanford Medicine’s Center for Precision Mental Health and Wellness . Williams, who lost her partner to depression in 2015, has focused her work on pioneering the field of precision psychiatry .

Around 30% of people with depression have what’s known as treatment-resistant depression , meaning multiple kinds of medication or therapy have failed to improve their symptoms. And for up to two-thirds of people with depression, treatment fails to fully reverse their symptoms to healthy levels.  

That’s in part because there’s no good way to know which antidepressant or type of therapy could help a given patient. Medications are prescribed through a trial-and-error method, so it can take months or years to land on a drug that works — if it ever happens. And spending so long trying treatment after treatment, only to experience no relief, can worsen depression symptoms.

“The goal of our work is figuring out how we can get it right the first time,” Williams said. “It’s very frustrating to be in the field of depression and not have a better alternative to this one-size-fits-all approach.”

Biotypes predict treatment response

To better understand the biology underlying depression and anxiety, Williams and her colleagues assessed 801 study participants who were previously diagnosed with depression or anxiety using the imaging technology known as functional MRI, or fMRI, to measure brain activity. They scanned the volunteers’ brains at rest and when they were engaged in different tasks designed to test their cognitive and emotional functioning. The scientists narrowed in on regions of the brain, and the connections between them, that were already known to play a role in depression.

Using a machine learning approach known as cluster analysis to group the patients’ brain images, they identified six distinct patterns of activity in the brain regions they studied.

Leanne Williams

The scientists also randomly assigned 250 of the study participants to receive one of three commonly used antidepressants or behavioral talk therapy. Patients with one subtype, which is characterized by overactivity in cognitive regions of the brain, experienced the best response to the antidepressant venlafaxine (commonly known as Effexor) compared with those who have other biotypes. Those with another subtype, whose brains at rest had higher levels of activity among three regions associated with depression and problem-solving, had better alleviation of symptoms with behavioral talk therapy. And those with a third subtype, who had lower levels of activity at rest in the brain circuit that controls attention, were less likely to see improvement of their symptoms with talk therapy than those with other biotypes.

The biotypes and their response to behavioral therapy make sense based on what they know about these regions of the brain, said Jun Ma, MD, PhD, the Beth and George Vitoux Professor of Medicine at the University of Illinois Chicago and one of the authors of the study. The type of therapy used in their trial teaches patients skills to better address daily problems, so the high levels of activity in these brain regions may allow patients with that biotype to more readily adopt new skills. As for those with lower activity in the region associated with attention and engagement, Ma said it’s possible that pharmaceutical treatment to first address that lower activity could help those patients gain more from talk therapy.

“To our knowledge, this is the first time we’ve been able to demonstrate that depression can be explained by different disruptions to the functioning of the brain,” Williams said. “In essence, it’s a demonstration of a personalized medicine approach for mental health based on objective measures of brain function.”

In another recently published study , Williams and her team showed that using fMRI brain imaging improves their ability to identify individuals likely to respond to antidepressant treatment. In that study, the scientists focused on a subtype they call the cognitive biotype of depression, which affects more than a quarter of those with depression and is less likely to respond to standard antidepressants. By identifying those with the cognitive biotype using fMRI, the researchers accurately predicted the likelihood of remission in 63% of patients, compared with 36% accuracy without using brain imaging. That improved accuracy means that providers may be more likely to get the treatment right the first time. The scientists are now studying novel treatments for this biotype with the hope of finding more options for those who don’t respond to standard antidepressants.

Further explorations of depression

The different biotypes also correlate with differences in symptoms and task performance among the trial participants. Those with overactive cognitive regions of the brain, for example, had higher levels of anhedonia (inability to feel pleasure) than those with other biotypes; they also performed worse on executive function tasks. Those with the subtype that responded best to talk therapy also made errors on executive function tasks but performed well on cognitive tasks.

One of the six biotypes uncovered in the study showed no noticeable brain activity differences in the imaged regions from the activity of people without depression. Williams believes they likely haven’t explored the full range of brain biology underlying this disorder — their study focused on regions known to be involved in depression and anxiety, but there could be other types of dysfunction in this biotype that their imaging didn’t capture.

Williams and her team are expanding the imaging study to include more participants. She also wants to test more kinds of treatments in all six biotypes, including medicines that haven’t traditionally been used for depression.

Her colleague  Laura Hack , MD, PhD, an assistant professor of psychiatry and behavioral sciences, has begun using the imaging technique in her clinical practice at Stanford Medicine through an experimental protocol . The team also wants to establish easy-to-follow standards for the method so that other practicing psychiatrists can begin implementing it.

“To really move the field toward precision psychiatry, we need to identify treatments most likely to be effective for patients and get them on that treatment as soon as possible,” Ma said. “Having information on their brain function, in particular the validated signatures we evaluated in this study, would help inform more precise treatment and prescriptions for individuals.”

Researchers from Columbia University; Yale University School of Medicine; the University of California, Los Angeles; UC San Francisco; the University of Sydney; the University of Texas MD Anderson; and the University of Illinois Chicago also contributed to the study.

Datasets in the study were funded by the National Institutes of Health (grant numbers R01MH101496, UH2HL132368, U01MH109985 and U01MH136062) and by Brain Resource Ltd.

• Rachel Tompa Rachel Tompa is a freelance science writer.

About Stanford Medicine

Stanford Medicine is an integrated academic health system comprising the Stanford School of Medicine and adult and pediatric health care delivery systems. Together, they harness the full potential of biomedicine through collaborative research, education and clinical care for patients. For more information, please visit med.stanford.edu .

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Understanding Sleep, Activity, and Circadian Rhythms in Major Depressive Disorder

A new study in JAMA Psychiatry aimed to understand how sleep, physical activity, and circadian rhythms relate to major depressive disorder (MDD). The study included adults with both current MDD and those with a history of MDD but who were symptom-free at the time of the study (remitted MDD). MDD is a serious mental health disorder with symptoms that include a lack of desire to enjoy life and difficulty doing simple things like eating and being socially engaged. 

The researchers found that the amount and quality of physical activity, and timing of both sleep and physical activity combined with the stability of their 24-hour circadian rhythm, was a better indicator of MDD than any of these factors individually. With this knowledge, doctors can provide potentially more efficient MDD interventions, jointly targeting these aspects of 24-hour behaviors.

In a Q&A, lead author Vadim Zipunnikov, PhD, MS, MA , associate professor in the Department of Biostatistics, answers questions about the study, which shows the importance of looking at a person’s 24-hour behaviors comprehensively.

Your study used a technology called accelerometry to measure sleep and activity. What exactly is it, and how did you use it? 

An accelerometer is a sensor that measures movement. It can be used to assess intensity and duration of physical activity, the amount of movement during sleep and for other things. Built-in accelerometers can be found in devices like Fitbits and smartphones. In the study, we used a research-grade device called GeneActiv to estimate both physical activity during the day and quality of sleep during the night.

How are the research-grade devices you used similar to and different from common trackers such as Apple watches or Fitbits?

Fitbit and similar devices have photoplethysmography (PPG) sensors that estimate heart rate and heart rate variability. Having a person’s heart rate, along with accelerometry information, makes the algorithms for estimating physical activity and sleep more accurate; this is a big advantage over research-grade devices that typically only record accelerometry data. A major advantage of research-grade accelerometer devices is that researchers have a lot more control over the algorithm processing pipeline.

You found two key contributors that are both strongly associated with MDD: physical activity and the timing of sleep and physical activity. What does this mean in terms of the prevention and treatment of major depression?

We found that the amount and quality of physical activity, and the timing of sleep and physical activity, were the two most important components of 24-hour behaviors, both for people with MDD and without it. People with MDD engaged in less physical activity and it was of poorer quality (more fragmented). They also had later timing of both sleep and physical activity. Thus, late timing of sleep and physical activity, or delayed circadian rhythm, is related to a higher chance of having current or remitted depression.

We also found that people with current MDD tend to go to bed and be physically active even later than those with remitted MDD. Of course, later timing is relative to a person’s natural rhythm; for some people going to sleep at 11 p.m. naturally aligns with their internal clock, while for others it doesn’t. 

Our study adds to the existing literature that shows that not staying up too late and going to sleep at a regular time, based on your body’s natural clock, may be a healthy sleep routine. And the same may be equally applied to other daily routines including physical activity. 

How might these findings help clinicians treat people with MDD?

Clinically, it means that intervention approaches jointly targeting these aspects of the 24-hour behaviors may potentially be more efficient in prevention/treatment of depression and beyond. It is important to understand what healthier behaviors are within the context of depression and how we could switch the person back if or when they fall out of them. 

What big questions was this study trying to answer? 

What is important to understand is that people working in the field of sleep are typically focused on sleep period, and physical activity experts primarily focus on daytime physical activity. In our work, we try to better understand the interplay between sleep and physical activity, looking at them as integral parts of the 24-hour daily cycle. 

In this study we focused on understanding the interdependencies between sleep, physical activity and circadian rhythms and seeing how these three domains related to current or remitted major depression. Interestingly, we learned that sleep timing, not duration, was related to MDD status, which indicates that circadian misalignment plays an important role in MDD.

Were you surprised by any of the findings of the study?

Some of the findings reconfirmed results that were previously published. That being said, our work used a novel methodological approach, which allowed us to jointly study the domains of sleep, physical activity, and circadian rhythmicity. Our approach allowed us to take a step forward, seeing which measures are closely related to each other and essentially allowing us to group these variables.

What can the general public learn from this study?

Understand your sleep, not just from a durational point of view but in terms of timing and how your sleep timing is related to the timing of other activities you do, such as physical activity. And there is no one right answer. Some people naturally stay up later or rise earlier. There is nothing wrong with either. But look at your history: Did you shift to this later timing? Additionally, any amount of physical activity takes you in the right direction, especially for those who are highly sedentary.

How does this relate to other work you’ve done using accelerometry?

Along with my colleagues at Johns Hopkins University and Motor Activity Research Consortium for Health (mMarch) at the National Institute of Mental Health (NIMH) I have been doing a lot of work to understand the role of sleep and physical activity across multiple diseases and disorders. Converging sleep and physical activity, and thinking about 24-hour behaviors jointly, is a very natural step both for me and these two fields. I think we will see more and more research approaching 24-hour behaviors in this comprehensive way of thinking.

What’s next?  

This paper is based on our study in Switzerland with middle-aged to older people (the mean age was 61.79 years). Since then we have applied this approach across three continents (North America, Europe and Australia) and across the entire lifespan (from 5 to 85), and expanded to include people who experience ADHD, anxiety, and other mental health disorders. Our findings are very consistent across all three studies. Though these studies have not yet been published, we are in the process of doing so now and this will be our next step. We were really surprised in some sense to see such a consistency across continents and across lifespans.

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• Published: 21 June 2024

Gamma oscillations of visual cortex underlying emotion and cognition deficits associated with suicide attempt in major depressive disorder

• Zhongpeng Dai   ORCID: orcid.org/0000-0001-9645-5824 1 , 2 ,
• Wei Zhang 1 ,
• Hongliang Zhou 3 ,
• Siqi Zhang 4 ,
• Zhilu Chen 3 ,
• Zhijian Yao   ORCID: orcid.org/0000-0002-4573-1697 1 , 3 , 5 &
• Qing Lu   ORCID: orcid.org/0000-0001-7717-391X 1  

Nature Mental Health ( 2024 ) Cite this article

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• Predictive markers

Altered neural oscillations in response to negative or positive emotional stimuli may be related to severe clinical symptoms in patients with major depressive disorder, particularly high suicidality. However, the underlying neurobiological mechanisms of this aberrant oscillatory activity and its potential emotional and cognitive functions remain unclear. Here we conducted a cross-sectional study of 107 participants, including 40 healthy controls and 67 patients with major depressive disorder (33 with suicide attempts and 34 without). All participants underwent an emotional expression recognition task during the magnetoencephalography scanning and completed neurocognitive assessments. Time–frequency characteristics and phase connections were analysed and compared between groups in sensor and source space using cluster-based permutation tests. The association between abnormal oscillatory features and neurocognitive performance was also evaluated. We found that increased gamma oscillations (50–70 Hz) of the visual cortices were considerably associated with suicide attempts in depression. Moreover, gamma-band source power in happy or sad conditions could predict individualized suicide risk. Gamma-band phase connections under the happy or sad condition were related to deficits in large-scale cognitive functions. Overall, gamma oscillations of the visual areas induced by the emotional stimuli were reliable biomarkers for identifying suicide attempts in depressive patients. Abnormal gamma-band connection involving visual cortex under both happy and sad expressions were significantly correlated with broad cognitive deficits.

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Data availability

The data that support the key findings are available from the Open Science Framework ( https://osf.io/q5zyg/ ). For any inquiries regarding the data, requests can be made to the corresponding authors.

Code availability

MEG data were preprocessed and analysed with the MATLAB r2016b based Fieldtrip (v.2017) toolbox ( https://github.com/fieldtrip/fieldtrip ) 55 . The sample size was estimated with G*Power_3.1.9.7 software ( https://www.psychologie.hhu.de/arbeitsgruppen/allgemeine-psychologie-und-arbeitspsychologie/gpower ) 47 . The statistical comparisons involving demographic variables, behavioral data were conducted with SPSS software (v.22) 59 . The data distribution plots were conducted with GraphPad Prism software (8.0.2) 60 . The brain maps were plotted with the BrainNet Viewer (v.2017) toolbox ( https://github.com/mingruixia/BrainNet-Viewer ) 32 . The custom codes are available from the Open Science Framework ( https://osf.io/q5zyg/ ).

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Acknowledgements

This research received support from the National Natural Science Foundation of China (grants 81871066, 81571639 and 81701784) awarded to Q.L.; the Fundamental Research Funds for the Central Universities (2242021k30014, 2242021k30059) awarded to Q.L.; the Jiangsu Provincial Medical Innovation Team of the Project of Invigorating Health Care through Science, Technology, and Education (grant CXTDC2016004) awarded to Z.Y.; and the Jiangsu Provincial key research and development program (grant BE2018609) awarded to Z.Y.; Z.D. was supported by the Chinese Scholarship Council. We are grateful to O. Jensen from the University of Birmingham and H. Jiang from Zhejiang University for their valuable comments on this work. We would also like to express our appreciation to Y. Pan and T. Ghafari from the University of Birmingham for their helpful suggestions. Additionally, we acknowledge the editing and proofreading services provided by American Journal Experts, which substantially contributed to improving the clarity and language of this paper.

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Zhongpeng Dai, Wei Zhang, Zhijian Yao & Qing Lu

Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK

Zhongpeng Dai

Department of Psychiatry, Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China

Hongliang Zhou, Zhilu Chen & Zhijian Yao

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Q.L. and Z.Y. were responsible for conceptualization. Z.D., W.Z. and S.Z. were responsible for methodology. Z.D., H.Z. and W.Z. were responsible for formal analysis. Z.D., H.Z., Z.C. and W.Z. were responsible for investigation. Z.D. wrote the orignal draft; Z.D., Q.L., H.Z. and S.Z. reviewed and edited the paper. Z.D. was responsible for visualization. Q.L. and Z.Y. supervised the project and acquired funding.

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Dai, Z., Zhang, W., Zhou, H. et al. Gamma oscillations of visual cortex underlying emotion and cognition deficits associated with suicide attempt in major depressive disorder. Nat. Mental Health (2024). https://doi.org/10.1038/s44220-024-00269-3

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Contributing studies for clinically elevated depression symptoms are presented in order of largest to smallest prevalence rate. Square data markers represent prevalence rates, with lines around the marker indicating 95% CIs. The diamond data marker represents the overall effect size based on included studies.

Contributing studies for clinically elevated anxiety symptoms are presented in order of largest to smallest prevalence rate. Square data markers represent prevalence rates, with lines around the marker indicating 95% CIs. The diamond data marker represents the overall effect size based on included studies.

eTable 1. Example Search Strategy from Medline

eTable 2. Study Quality Evaluation Criteria

eTable 3. Quality Assessment of Studies Included

eTable 4. Sensitivity analysis excluding low quality studies (score=2) for moderators of the prevalence of clinically elevated depressive symptoms in children and adolescence during COVID-19

eTable 5. Sensitivity analysis excluding low quality studies (score=2) for moderators of the prevalence of clinically elevated anxiety symptoms in children and adolescence during COVID-19

eFigure 1. PRISMA diagram of review search strategy

eFigure 2. Funnel plot for studies included in the clinically elevated depressive symptoms

eFigure 3. Funnel plot for studies included in the clinically elevated anxiety symptoms

• Pediatric Depression and Anxiety Doubled During the Pandemic JAMA News From the JAMA Network October 5, 2021 Anita Slomski
• Guidelines Synopsis: Screening for Anxiety in Adolescent and Adult Women JAMA JAMA Clinical Guidelines Synopsis March 8, 2022 This JAMA Clinical Guidelines Synopsis summarizes the 2020 Women’s Preventive Services Initiative recommendation on screening for anxiety in adolescent and adult women. Tiffany I. Leung, MD, MPH; Adam S. Cifu, MD; Wei Wei Lee, MD, MPH
• Addressing the Global Crisis of Child and Adolescent Mental Health JAMA Pediatrics Editorial November 1, 2021 Tami D. Benton, MD; Rhonda C. Boyd, PhD; Wanjikũ F.M. Njoroge, MD
• Effect of the COVID-19 pandemic on Adolescents With Eating Disorders JAMA Pediatrics Comment & Response February 1, 2022 Thonmoy Dey, BSc; Zachariah John Mansell, BSc; Jasmin Ranu, BSc

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Racine N , McArthur BA , Cooke JE , Eirich R , Zhu J , Madigan S. Global Prevalence of Depressive and Anxiety Symptoms in Children and Adolescents During COVID-19 : A Meta-analysis . JAMA Pediatr. 2021;175(11):1142–1150. doi:10.1001/jamapediatrics.2021.2482

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Global Prevalence of Depressive and Anxiety Symptoms in Children and Adolescents During COVID-19 : A Meta-analysis

• 1 Department of Psychology, University of Calgary, Calgary, Alberta, Canada
• 2 Alberta Children’s Hospital Research Institute, Calgary, Alberta, Canada
• Editorial Addressing the Global Crisis of Child and Adolescent Mental Health Tami D. Benton, MD; Rhonda C. Boyd, PhD; Wanjikũ F.M. Njoroge, MD JAMA Pediatrics
• News From the JAMA Network Pediatric Depression and Anxiety Doubled During the Pandemic Anita Slomski JAMA
• JAMA Clinical Guidelines Synopsis Guidelines Synopsis: Screening for Anxiety in Adolescent and Adult Women Tiffany I. Leung, MD, MPH; Adam S. Cifu, MD; Wei Wei Lee, MD, MPH JAMA
• Comment & Response Effect of the COVID-19 pandemic on Adolescents With Eating Disorders Thonmoy Dey, BSc; Zachariah John Mansell, BSc; Jasmin Ranu, BSc JAMA Pediatrics

Question   What is the global prevalence of clinically elevated child and adolescent anxiety and depression symptoms during COVID-19?

Findings   In this meta-analysis of 29 studies including 80 879 youth globally, the pooled prevalence estimates of clinically elevated child and adolescent depression and anxiety were 25.2% and 20.5%, respectively. The prevalence of depression and anxiety symptoms during COVID-19 have doubled, compared with prepandemic estimates, and moderator analyses revealed that prevalence rates were higher when collected later in the pandemic, in older adolescents, and in girls.

Meaning   The global estimates of child and adolescent mental illness observed in the first year of the COVID-19 pandemic in this study indicate that the prevalence has significantly increased, remains high, and therefore warrants attention for mental health recovery planning.

Importance   Emerging research suggests that the global prevalence of child and adolescent mental illness has increased considerably during COVID-19. However, substantial variability in prevalence rates have been reported across the literature.

Objective   To ascertain more precise estimates of the global prevalence of child and adolescent clinically elevated depression and anxiety symptoms during COVID-19; to compare these rates with prepandemic estimates; and to examine whether demographic (eg, age, sex), geographical (ie, global region), or methodological (eg, pandemic data collection time point, informant of mental illness, study quality) factors explained variation in prevalence rates across studies.

Data Sources   Four databases were searched (PsycInfo, Embase, MEDLINE, and Cochrane Central Register of Controlled Trials) from January 1, 2020, to February 16, 2021, and unpublished studies were searched in PsycArXiv on March 8, 2021, for studies reporting on child/adolescent depression and anxiety symptoms. The search strategy combined search terms from 3 themes: (1) mental illness (including depression and anxiety), (2) COVID-19, and (3) children and adolescents (age ≤18 years). For PsycArXiv , the key terms COVID-19 , mental health , and child/adolescent were used.

Study Selection   Studies were included if they were published in English, had quantitative data, and reported prevalence of clinically elevated depression or anxiety in youth (age ≤18 years).

Data Extraction and Synthesis   A total of 3094 nonduplicate titles/abstracts were retrieved, and 136 full-text articles were reviewed. Data were analyzed from March 8 to 22, 2021.

Main Outcomes and Measures   Prevalence rates of clinically elevated depression and anxiety symptoms in youth.

Results   Random-effect meta-analyses were conducted. Twenty-nine studies including 80 879 participants met full inclusion criteria. Pooled prevalence estimates of clinically elevated depression and anxiety symptoms were 25.2% (95% CI, 21.2%-29.7%) and 20.5% (95% CI, 17.2%-24.4%), respectively. Moderator analyses revealed that the prevalence of clinically elevated depression and anxiety symptoms were higher in studies collected later in the pandemic and in girls. Depression symptoms were higher in older children.

Conclusions and Relevance   Pooled estimates obtained in the first year of the COVID-19 pandemic suggest that 1 in 4 youth globally are experiencing clinically elevated depression symptoms, while 1 in 5 youth are experiencing clinically elevated anxiety symptoms. These pooled estimates, which increased over time, are double of prepandemic estimates. An influx of mental health care utilization is expected, and allocation of resources to address child and adolescent mental health concerns are essential.

Prior to the COVID-19 pandemic, rates of clinically significant generalized anxiety and depressive symptoms in large youth cohorts were approximately 11.6% 1 and 12.9%, 2 respectively. Since COVID-19 was declared an international public health emergency, youth around the world have experienced dramatic disruptions to their everyday lives. 3 Youth are enduring pervasive social isolation and missed milestones, along with school closures, quarantine orders, increased family stress, and decreased peer interactions, all potential precipitants of psychological distress and mental health difficulties in youth. 4 - 7 Indeed, in both cross-sectional 8 , 9 and longitudinal studies 10 , 11 amassed to date, the prevalence of youth mental illness appears to have increased during the COVID-19 pandemic. 3 However, data collected vary considerably. Specifically, ranges from 2.2% 12 to 63.8% 13 and 1.8% 12 to 49.5% 13 for clinically elevated depression and anxiety symptoms, respectively. As governments and policy makers deploy and implement recovery plans, ascertaining precise estimates of the burden of mental illness for youth are urgently needed to inform service deployment and resource allocation.

Depression and generalized anxiety are 2 of the most common mental health concerns in youth. 14 Depressive symptoms, which include feelings of sadness, loss of interest and pleasure in activities, as well as disruption to regulatory functions such as sleep and appetite, 15 could be elevated during the pandemic as a result of social isolation due to school closures and physical distancing requirements. 6 Generalized anxiety symptoms in youth manifest as uncontrollable worry, fear, and hyperarousal. 15 Uncertainty, disruptions in daily routines, and concerns for the health and well-being of family and loved ones during the COVID-19 pandemic are likely associated with increases in generalized anxiety in youth. 16

When heterogeneity is observed across studies, as is the case with youth mental illness during COVID-19, it often points to the need to examine demographic, geographical, and methodological moderators. Moderator analyses can determine for whom and under what circumstances prevalence is higher vs lower. With regard to demographic factors, prevalence rates of mental illness both prior to and during the COVID-19 pandemic are differentially reported across child age and sex, with girls 17 , 18 and older children 17 , 19 being at greater risk for internalizing disorders. Studies have also shown that youth living in regions that experienced greater disease burden 2 and urban areas 20 had greater mental illness severity. Methodological characteristics of studies also have the potential to influence the estimated prevalence rates. For example, studies of poorer methodological quality may be more likely to overestimate prevalence rates. 21 The symptom reporter (ie, child vs parent) may also contribute to variability in the prevalence of mental illness across studies. Indeed, previous research prior to the pandemic has demonstrated that child and parent reports of internalizing symptoms vary, 22 with children/adolescents reporting more internalizing symptoms than parents. 23 Lastly, it is important to consider the role of data collection timing on potential prevalence rates. While feelings of stress and overwhelm may have been greater in the early months of the pandemic compared with later, 24 extended social isolation and school closures may have exerted mental health concerns.

Although a narrative systematic review of 6 studies early in the pandemic was conducted, 8 to our knowledge, no meta-analysis of prevalence rates of child and adolescent mental illness during the pandemic has been undertaken. In the current study, we conducted a meta-analysis of the global prevalence of clinically elevated symptoms of depression and anxiety (ie, exceeding a clinical cutoff score on a validated measure or falling in the moderate to severe symptom range of anxiety and depression) in youth during the first year of the COVID-19 pandemic. While research has documented a worsening of symptoms for children and youth with a wide range of anxiety disorders, 25 including social anxiety, 26 clinically elevated symptoms of generalized anxiety are the focus of the current meta-analysis. In addition to deriving pooled prevalence estimates, we examined demographic, geographical, and methodological factors that may explain between-study differences. Given that there have been several precipitants of psychological distress for youth during COVID-19, we hypothesized that pooled prevalence rates would be higher compared with prepandemic estimates. We also hypothesized that child mental illness would be higher among studies with older children, a higher percentage of female individuals, studies conducted later in the pandemic, and that higher-quality studies would have lower prevalence rates.

This systematic review was registered as a protocol with PROSPERO (CRD42020184903) and the Preferred Reporting Items for Systematic Reviews and Meta-analyses ( PRISMA ) reporting guideline was followed. 27 Ethics review was not required for the study. Electronic searches were conducted in collaboration with a health sciences librarian in PsycInfo, Cochrane Central Register of Controlled Trials (CENTRAL), Embase, and MEDLINE from inception to February 16, 2021. The search strategy (eTable 1 in the Supplement ) combined search terms from 3 themes: (1) mental illness (including depression and anxiety), (2) COVID-19, and (3) children and adolescents (age ≤18 years). Both database and subject headings were used to search keywords. As a result of the rapidly evolving nature of research during the COVID-19 pandemic, we also searched a repository of unpublished preprints, PsycArXiv . The key terms COVID-19 , mental health , and child/adolescent were used on March 8, 2021, and yielded 38 studies of which 1 met inclusion criteria.

The following inclusion criteria were applied: (1) sample was drawn from a general population; (2) proportion of individuals meeting clinical cutoff scores or falling in the moderate to severe symptom range of anxiety or depression as predetermined by validated self-report measures were provided; (3) data were collected during COVID-19; (4) participants were 18 years or younger; (5) study was empirical; and (6) studies were written in English. Samples of participants who may be affected differently from a mental health perspective during COVID-19 were excluded (eg, children with preexisting psychiatric diagnoses, children with chronic illnesses, children diagnosed or suspected of having COVID-19). We also excluded case studies and qualitative analyses.

Five (N.R., B.A.M., J.E.C., R.E. and J.Z.) authors used Covidence software (Covidence Inc) to review all abstracts and to determine if the study met criteria for inclusion. Twenty percent of abstracts reviewed for inclusion were double-coded, and the mean random agreement probability was 0.89; disagreements were resolved via consensus with the first author (N.R.). Two authors (N.R. and B.A.M.) reviewed full-text articles to determine if they met all inclusion criteria and the percent agreement was 0.80; discrepancies were resolved via consensus.

When studies met inclusion criteria, prevalence rates for anxiety and depression were extracted, as well as potential moderators. When more than 1 wave of data was provided, the wave with the largest sample size was selected. For 1 study in which both parent and youth reports were provided, 26 the youth report was selected, given research that they are the reliable informants of their own behavior. 28 The following moderators were extracted: (1) study quality (see the next subsection); (2) participant age (continuously as a mean); (3) sex (% female in a sample); (4) geographical region (eg, East Asia, Europe, North America), (5) informant (child, parent), (6) month in 2020 when data were collected (range, 1-12). Data from all studies were extracted by 1 coder and the first author (N.R.). Discrepancies were resolved via consensus.

Adapted from the National Institute of Health Quality Assessment Tool for Observation Cohort and Cross-Sectional Studies, a short 5-item questionnaire was used (eTable 2 in the Supplement ). 29 Studies were given a score of 0 (no) or 1 (yes) for each of the 5 criteria (validated measure; peer-reviewed, response rate ≥50%, objective assessment, sufficient exposure time) and summed to give a total score of 5. When information was unclear or not provided by the study authors, it was marked as 0 (no).

All included studies are from independent samples. Comprehensive Meta-Analysis version 3.0 (Biostat) software was used for data analysis. Pooled prevalence estimates with associated 95% confidence intervals around the estimate were computed. We weighted pooled prevalence estimates by the weight of the inverse of their variance, which gives greater weight to large sample sizes.

We used random-effects models to reflect the variations observed across studies and assessed between-study heterogeneity using the Q and I 2 statistics. Pooled prevalence is reported as an event rate (ie, 0.30) but interpreted as prevalence (ie, 30.0%). Significant Q statistics and I 2 values more than 75% suggest moderator analyses should be explored. 30 As recommended by Bornstein et al, 30 we examined categorical moderators when k of 10 or higher and a minimum cell size of k more than 3 were available. A P value of .05 was considered statistically significant. For continuous moderators, random-effect meta-regression analyses were conducted. Publication bias was examined using the Egger test 31 and by inspecting funnel plots for symmetry.

Our electronic search yielded 3094 nonduplicate records (eFigure 1 in the Supplement ). Based on the abstract review, a total of 136 full-text articles were retrieved to examine against inclusion criteria, and 29 nonoverlapping studies 10 , 12 , 13 , 17 , 19 , 20 , 26 , 32 - 53 met full inclusion criteria.

A total of 29 studies were included in the meta-analyses, of which 26 had youth symptom reports and 3 studies 39 , 42 , 48 had parent reports of child symptoms. As outlined in Table 1 , across all 29 studies, 80 879 participants were included, of which the mean (SD) perecentage of female individuals was 52.7% (12.3%), and the mean age was 13.0 years (range, 4.1-17.6 years). All studies provided binary reports of sex or gender. Sixteen studies (55.2%) were from East Asia, 4 were from Europe (13.8%), 6 were from North America (20.7%), 2 were from Central America and South America (6.9%), and 1 study was from the Middle East (3.4%). Eight studies (27.6%) reported having racial or ethnic minority participants with the mean across studies being 36.9%. Examining study quality, the mean score was 3.10 (range, 2-4; eTable 3 in the Supplement ).

The pooled prevalence from a random-effects meta-analysis of 26 studies revealed a pooled prevalence rate of 0.25 (95% CI, 0.21-0.30; Figure 1 ) or 25.2%. The funnel plot was symmetrical (eFigure 2 in the Supplement ); however, the Egger test was statistically significant (intercept, −9.5; 95% CI, −18.4 to −0.48; P  = .02). The between-study heterogeneity statistic was significant ( Q  = 4675.91; P  < .001; I 2  = 99.47). Significant moderators are reported below, and all moderator analyses are presented in Table 2 .

As the number of months in the year increased, so too did the prevalence of depressive symptoms ( b  = 0.26; 95% CI, 0.06-0.46). Prevalence rates were higher as child age increased ( b  = 0.08; 95% CI, 0.01-0.15), and as the percentage of female individuals ( b  = 0.03; 95% CI, 0.01-0.05) in samples increased. Sensitivity analyses removing low-quality studies were conducted (ie, scores of 2) 32 , 43 (eTable 4 in the Supplement ). Moderators remained significant, except for age, which became nonsignificant ( b  = 0.06; 95% CI, −0.02 to 0.13; P  = .14).

The overall pooled prevalence rate across 25 studies for elevated anxiety was 0.21 (95% CI, 0.17-0.24; Figure 2 ) or 20.5%. The funnel plot was symmetrical (eFigure 3 in the Supplement ) and the Egger test was nonsignificant (intercept, −6.24; 95% CI, −14.10 to 1.62; P  = .06). The heterogeneity statistic was significant ( Q  = 3300.17; P  < .001; I 2  = 99.27). Significant moderators are reported below, and all moderator analyses are presented in Table 3 .

As the number of months in the year increased, so too did the prevalence of anxiety symptoms ( b  = 0.27; 95% CI, 0.10-0.44). Prevalence rates of clinically elevated anxiety was higher as the percentage of female individuals in the sample increased ( b  = 0.04; 95% CI, 0.01-0.07) and also higher in European countries ( k  = 4; rate = 0.34; 95% CI, 0.23-0.46; P  = .01) compared with East Asian countries ( k  = 14; rate = 0.17; 95% CI, 0.13-0.21; P  < .001). Lastly, the prevalence of clinically elevated anxiety was higher in studies deemed to have poorer quality ( k  = 21; rate = 0.22; 95% CI, 0.18-0.27; P  < .001) compared with studies with better study quality scores ( k  = 4; rate = 0.12; 95% CI, 0.07-0.20; P  < .001). Sensitivity analyses removing low quality studies (ie, scores of 2) 32 , 43 yielded the same pattern of results (eTable 5 in the Supplement ).

The current meta-analysis provides a timely estimate of clinically elevated depression and generalized anxiety symptoms globally among youth during the COVID-19 pandemic. Across 29 samples and 80 879 youth, the pooled prevalence of clinically elevated depression and anxiety symptoms was 25.2% and 20.5%, respectively. Thus, 1 in 4 youth globally are experiencing clinically elevated depression symptoms, while 1 in 5 youth are experiencing clinically elevated anxiety symptoms. A comparison of these findings to prepandemic estimates (12.9% for depression 2 and 11.6% for anxiety 1 ) suggests that youth mental health difficulties during the COVID-19 pandemic has likely doubled.

The COVID-19 pandemic, and its associated restrictions and consequences, appear to have taken a considerable toll on youth and their psychological well-being. Loss of peer interactions, social isolation, and reduced contact with buffering supports (eg, teachers, coaches) may have precipitated these increases. 3 In addition, schools are often a primary location for receiving psychological services, with 80% of children relying on school-based services to address their mental health needs. 54 For many children, these services were rendered unavailable owing to school closures.

As the month of data collection increased, rates of depression and anxiety increased correspondingly. One possibility is that ongoing social isolation, 6 family financial difficulties, 55 missed milestones, and school disruptions 3 are compounding over time for youth and having a cumulative association. However, longitudinal research supporting this possibility is currently scarce and urgently needed. A second possibility is that studies conducted in the earlier months of the pandemic (February to March 2020) 12 , 51 were more likely to be conducted in East Asia where self-reported prevalence of mental health symptoms tends to be lower. 56 Longitudinal trajectory research on youth well-being as the pandemic progresses and in pandemic recovery phases will be needed to confirm the long-term mental health implications of the COVID-19 pandemic on youth mental illness.

Prevalence rates for anxiety varied according to study quality, with lower-quality studies yielding higher prevalence rates. It is important to note that in sensitivity analyses removing lower-quality studies, other significant moderators (ie, child sex and data collection time point) remained significant. There has been a rapid proliferation of youth mental health research during the COVID-19 pandemic; however, the rapid execution of these studies has been criticized owing to the potential for some studies to sacrifice methodological quality for methodological rigor. 21 , 57 Additionally, several studies estimating prevalence rates of mental illness during the pandemic have used nonprobability or convenience samples, which increases the likelihood of bias in reporting. 21 Studies with representative samples and/or longitudinal follow-up studies that have the potential to demonstrate changes in mental health symptoms from before to after the pandemic should be prioritized in future research.

In line with previous research on mental illness in childhood and adolescence, 58 female sex was associated with both increased depressive and anxiety symptoms. Biological susceptibility, lower baseline self-esteem, a higher likelihood of having experienced interpersonal violence, and exposure to stress associated with gender inequity may all be contributing factors. 59 Higher rates of depression in older children were observed and may be due to puberty and hormonal changes 60 in addition to the added effects of social isolation and physical distancing on older children who particularly rely on socialization with peers. 6 , 61 However, age was not a significant moderator for prevalence rates of anxiety. Although older children may be more acutely aware of the stress of their parents and the implications of the current global pandemic, younger children may be able to recognize changes to their routine, both of which may contribute to similar rates of anxiety with different underlying mechanisms.

In terms of practice implications, a routine touch point for many youth is the family physician or pediatrician’s office. Within this context, it is critical to inquire about or screen for youth mental health difficulties. Emerging research 42 suggests that in families using more routines during COVID-19, lower child depression and conduct problems are observed. Thus, a tangible solution to help mitigate the adverse effects of COVID-19 on youth is working with children and families to implement consistent and predictable routines around schoolwork, sleep, screen use, and physical activity. Additional resources should be made available, and clinical referrals should be placed when children experience clinically elevated mental distress. At a policy level, research suggests that social isolation may contribute to and confer risk for mental health concerns. 4 , 5 As such, the closure of schools and recreational activities should be considered a last resort. 62 In addition, methods of delivering mental health resources widely to youth, such as group and individual telemental health services, need to be adapted to increase scalability, while also prioritizing equitable access across diverse populations. 63

There are some limitations to the current study. First, although the current meta-analysis includes global estimates of child and adolescent mental illness, it will be important to reexamine cross-regional differences once additional data from underrepresented countries are available. Second, most study designs were cross-sectional in nature, which precluded an examination of the long-term association of COVID-19 with child mental health over time. To determine whether clinically elevated symptoms are sustained, exacerbated, or mitigated, longitudinal studies with baseline estimates of anxiety and depression are needed. Third, few studies included racial or ethnic minority participants (27.6%), and no studies included gender-minority youth. Given that racial and ethnic minority 64 and gender-diverse youth 65 , 66 may be at increased risk for mental health difficulties during the pandemic, future work should include and focus on these groups. Finally, all studies used self- or parent-reported questionnaires to examine the prevalence of clinically elevated (ie, moderate to high) symptoms. Thus, studies using criterion standard assessments of child depression and anxiety disorders via diagnostic interviews or multimethod approaches may supplement current findings and provide further details on changes beyond generalized anxiety symptoms, such symptoms of social anxiety, separation anxiety, and panic.

Overall, this meta-analysis shows increased rates of clinically elevated anxiety and depression symptoms for youth during the COVID-19 pandemic. While this meta-analysis supports an urgent need for intervention and recovery efforts aimed at improving child and adolescent well-being, it also highlights that individual differences need to be considered when determining targets for intervention (eg, age, sex, exposure to COVID-19 stressors). Research on the long-term effect of the COVID-19 pandemic on mental health, including studies with pre– to post–COVID-19 measurement, is needed to augment understanding of the implications of this crisis on the mental health trajectories of today’s children and youth.

Corresponding Author: Sheri Madigan, PhD, RPsych, Department of Psychology University of Calgary, Calgary, AB T2N 1N4, Canada ( [email protected] ).

Accepted for Publication: May 19, 2021.

Published Online: August 9, 2021. doi:10.1001/jamapediatrics.2021.2482

Author Contributions: Drs Racine and Madigan had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Racine, Madigan.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Racine, McArthur, Eirich, Zhu, Madigan.

Critical revision of the manuscript for important intellectual content: Racine, Cooke, Eirich, Madigan.

Statistical analysis: Racine, McArthur.

Administrative, technical, or material support: Madigan.

Supervision: Racine, Madigan.

Conflict of Interest Disclosures: Dr Racine reported fellowship support from Alberta Innovates. Dr McArthur reported a postdoctoral fellowship award from the Alberta Children’s Hospital Research Institute. Ms Cooke reported graduate scholarship support from Vanier Canada and Alberta Innovates Health Solutions outside the submitted work. Ms Eirich reported graduate scholarship support from the Social Science and Humanities Research Council. No other disclosures were reported.

Additional Contributions: We acknowledge Nicole Dunnewold, MLIS (Research and Learning Librarian, Health Sciences Library, University of Calgary), for her assistance with the search strategy, for which they were not compensated outside of their salary. We also acknowledge the contribution of members of the Determinants of Child Development Laboratory at the University of Calgary, in particular, Julianna Watt, BA, and Katarina Padilla, BSc, for their contribution to data extraction, for which they were paid as research assistants.

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Evaluation of serum MIP-1β and MCP-2 levels in major depressive disorder: A case-control study

Affiliations.

• 1 Department of Pharmacy, University of Asia Pacific, Farmgate, Dhaka, Bangladesh.
• 2 Department of Psychiatry, Bangabandhu Sheikh Mujib Medical University, Shahabagh, Dhaka, Bangladesh.
• 3 School of Pharmacy, BRAC University, Merul Badda, Dhaka, Bangladesh.
• PMID: 38889138
• PMCID: PMC11185482
• DOI: 10.1371/journal.pone.0305734

Background: Major depressive disorder (MDD) is a common and debilitating mental illness characterized by persistent feelings of sadness, hopelessness, and a lack of interest in daily activities. The objective of this study was to investigate whether levels of macrophage inflammatory protein-1β (MIP-1β) and macrophage chemoattractant protein-2 (MCP-2) in the blood were associated with the pathophysiology and development of MDD compared to healthy controls (HCs).

Methods: This case-control study was conducted involving 50 MDD patients and 38 HCs. We performed a comprehensive assessment to match age, sex, BMI, and socio-demographic profile between the groups. The study excluded participants with chronic infection, inflammatory diseases, coexisting psychiatric disorder, history of liver and kidney diseases, and individuals who are under antipsychotic medications. A professional psychiatrist diagnosed MDD patients and evaluated HCs based on the Diagnostic and Statistical Manual of Mental Disorders-5 (DSM-5) criteria. The severity of depression was assessed using the Hamilton Depression (Ham-D) rating scale. Commercially available enzyme-linked immunosorbent assay (ELISA) kits were used to quantify the serum MIP-1β and MCP-2 levels.

Results: The results indicated elevated serum MIP-1β levels (207.73±24.24 pg/ml) in MDD patients compared to HCs (58.77±9.14 pg/ml). This difference in concentration is positively correlated with severity of disease symptoms (r = 0.451; p<0.001). Similarly, the levels of MCP-2 were found to be elevated in patients compared to controls (143.61±19.92 vs. 56.84±4.02 pg/ml; p = 0.003), with a positive correlation with the Ham-D scores (r = 0.373; p = 0.004).

Conclusion: According to this study, elevated levels of MIP-1β and MCP-2 may be associated with the pathophysiology and development of MDD. These increased serum MIP-1β and MCP-2 levels could be used as risk assessment tools for MDD. The present findings urge further research and the development of therapeutic and diagnostic approaches for depression.

Copyright: © 2024 Akter et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Fig 1. Comparison of serum MIP-1β and…

Fig 1. Comparison of serum MIP-1β and MCP-2 levels between MDD patients and healthy controls.

Receiver operating characteristic (ROC) curves…

Receiver operating characteristic (ROC) curves of serum MIP-1β levels (a) and serum MCP-2…

• Depressive disorder (depression). World Health Organization. March 31, 2023. Accessed February 22, 2024. https://www.who.int/news-room/fact-sheets/detail/depression .
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