Anxiety and Depression: Why They Are Common, Why They Are Hard to Heal, and How to Treat Them—A Mechanism-Informed, Evidence-Based Practice Essay

Anxiety and depression affect hundreds of millions of people, yet most never receive adequate care. This essay explains why these sibling disorders are so common, from evolved threat systems and early adversity to polygenic risk and modern stress, and why they resist healing, through circuit dysfunction, impaired neuroplasticity, inflammation, and self-reinforcing avoidance. It then lays out a stepped, evidence-based treatment framework: CBT and behavioral activation, SSRIs and SNRIs, combination care, and escalation options such as esketamine, rTMS, and ECT for treatment-resistant depression.

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Abstract

Background: Anxiety and depressive disorders are among the leading causes of disability worldwide, frequently co-occur, and share genetic, neurobiological, and psychological substrates. Despite effective treatments, fewer than half of affected people receive adequate care, and roughly one-third of patients with major depressive disorder (MDD) do not remit after two adequate antidepressant trials.

Objective: To integrate epidemiological, evolutionary, developmental, and neurobiological evidence to explain (a) why these disorders are so prevalent, (b) why they are difficult to heal, and (c) how mechanism-informed, stepped treatment can improve outcomes.

Methods: Narrative synthesis prioritizing Global Burden of Disease analyses, network meta-analyses, large randomized controlled trials, and mechanistic studies, including recent (2018–2026) literature.

Findings: Prevalence reflects an evolutionarily conserved threat-and-stress-response system, calibrated by early adversity, shaped by polygenic risk, and overloaded by contemporary stressors. Chronicity reflects distributed circuit dysfunction (amygdala–prefrontal–hippocampal networks), stress-axis and inflammatory dysregulation, impaired synaptic plasticity, and behavioral maintenance loops (avoidance, rumination, withdrawal, sleep disruption). Cognitive behavioral therapy (CBT) and behavioral activation are first-line psychotherapies with durable effects; SSRIs/SNRIs are first-line pharmacotherapy with modest average but clinically meaningful individual effects; combination treatment outperforms either alone in many patients. For treatment-resistant depression (TRD), augmentation, esketamine, repetitive transcranial magnetic stimulation (rTMS), and electroconvulsive therapy (ECT) provide escalation options. Measurement-based, collaborative, and lifestyle-integrated care improves outcomes across levels.

Conclusions: A stepped, measurement-guided, mechanism-informed approach—with early reassessment and timely escalation—offers the best route to remission and relapse prevention.

Keywords: anxiety disorders; major depressive disorder; cognitive behavioral therapy; SSRIs; treatment-resistant depression; HPA axis; neuroplasticity; stepped care; esketamine

1. Introduction

Depressive and anxiety disorders are the two most common mental disorders globally. In 2019, an estimated 970 million people lived with a mental disorder, with anxiety (301 million) and depression (280 million) dominating the total (GBD 2019 Mental Disorders Collaborators, 2022; World Health Organization [WHO], 2022). The COVID-19 pandemic added an estimated 76 million cases of anxiety and 53 million cases of major depression in 2020 alone (Santomauro et al., 2021), and the Global Burden of Disease 2021 analysis confirmed mental disorders among the leading contributors to years lived with disability (GBD 2021 Diseases and Injuries Collaborators, 2024). Projections suggest sustained burden through mid-century (Liu et al., 2024).

Anxiety and depression are "siblings" rather than strangers. Roughly half of people with lifetime MDD also meet criteria for an anxiety disorder (Kessler et al., 2005a; Lamers et al., 2011); they share a general internalizing genetic liability (Kendler et al., 2003; Hettema et al., 2001) and show overlapping neural circuit alterations (Etkin & Wager, 2007; Akiki et al., 2025). Anxiety typically precedes depression, and comorbid presentations are more severe, more chronic, and less treatment-responsive (Lamers et al., 2011).

Patients ask three practical questions: Why did this happen to me? Why is recovery so hard? What actually works? This essay answers each using mechanistic and clinical evidence, closing with a practical, staged treatment framework.

2. Why Are Anxiety and Depression So Common?

2.1 Epidemiology: A Lifetime Risk Measured in Tens of Percent

Lifetime prevalence estimates in the United States are approximately 29% for any anxiety disorder and 17% for major depression (Kessler et al., 2005a; Kessler et al., 2005b). Cross-national data show similar rank order across high-, middle-, and low-income countries (Kessler et al., 2009). Women have approximately twice the risk of men from adolescence onward (GBD 2019 Mental Disorders Collaborators, 2022), and onset is concentrated early—half of lifetime mental disorders begin by age 14 and three-quarters by age 24 (Kessler et al., 2005c; Solmi et al., 2022).

2.2 Evolutionary Mismatch and the "Smoke Detector" Principle

Anxiety and low mood are not defects in origin; they are evolved defenses. Because the cost of missing a genuine threat far exceeds the cost of a false alarm, natural selection favors defensive systems that fire too easily—the "smoke detector principle" (Nesse, 2005, 2019). Low mood may similarly function to disengage from unattainable goals or signal need for social support (Nesse, 2019). These systems were calibrated for acute, physical, and small-group threats. Modern environments impose chronic, abstract, and socially evaluative threats (financial insecurity, performance evaluation, constant social comparison), against which avoidance-based defense is maladaptive. Urban living, social fragmentation, sleep loss, and reduced physical activity further shift baseline risk (Peen et al., 2010; Baglioni et al., 2011).

2.3 Genetic Architecture

Heritability is approximately 30–40% for MDD (Sullivan et al., 2000) and 30–50% for anxiety disorders (Hettema et al., 2001). Genome-wide studies show that risk is highly polygenic: hundreds of loci, each of small effect, implicate synaptic, neuronal, and stress-related pathways (Wray et al., 2018; Howard et al., 2019). Early candidate-gene findings such as 5-HTTLPR × stress (Caspi et al., 2003) have not replicated robustly (Border et al., 2019), reinforcing that no single "depression gene" exists. Genetic risk is best understood as a diathesis that is expressed under environmental load.

2.4 Early-Life Adversity and Developmental Calibration

Childhood maltreatment approximately doubles the risk of later depression and anxiety and predicts worse course and poorer treatment response (Norman et al., 2012; Nanni et al., 2012). Early adversity sensitizes the HPA axis and amygdala reactivity, alters prefrontal and hippocampal development, and promotes epigenetic changes in stress-related genes—biologically adaptive calibration for a dangerous world that becomes maladaptive when conditions change (McEwen, 2007; Heim & Binder, 2012). Global burden analyses attribute a growing share of anxiety and depressive burden to childhood sexual abuse, bullying victimization, and intimate partner violence (Liu et al., 2024).

2.5 Contemporary Stressors and the Pandemic Natural Experiment

Population-level shocks shift prevalence rapidly. Santomauro et al. (2021) estimated a 27.6% increase in major depressive disorder and a 25.6% increase in anxiety disorders globally in 2020, with the largest increases among women and younger people, in locations with the greatest infection rates and mobility restrictions. Counterfactual modeling to 2021 corroborates this pattern (Chen et al., 2025). Chronic stressors—poverty, discrimination, job insecurity, loneliness—are reliably associated with incident disorder (WHO, 2022).

2.6 Sex and Age Differences

Sex differences emerge at puberty and likely reflect interactions among ovarian hormone sensitivity, greater exposure to interpersonal violence and caregiving strain, and ruminative coping styles (Nolen-Hoeksema, 2008; GBD 2019 Mental Disorders Collaborators, 2022). Late-life depression is often under-recognized and associated with vascular, inflammatory, and neurodegenerative contributors.

3. Why Are These Disorders So Difficult to Heal?

3.1 Distributed Circuit Dysfunction

Neither disorder reflects a single "chemical imbalance." Meta-analyses of neuroimaging show hyper-responsivity of the amygdala and insula to threat in anxiety disorders, with reduced regulatory control from the ventromedial and dorsolateral prefrontal cortex (Etkin & Wager, 2007; Shin & Liberzon, 2010). Contemporary circuit models extend this to negative valence, positive valence (reward), cognitive-control, and social-processing systems (Akiki et al., 2025), explaining why symptom-specific interventions often leave other domains untouched. In depression, altered subgenual cingulate, prefrontal, and default-mode network connectivity underlie rumination, anhedonia, and executive dysfunction (Drevets et al., 2008; Otte et al., 2016). Large ENIGMA analyses show smaller hippocampal volumes in MDD, most pronounced in recurrent and early-onset illness (Schmaal et al., 2016).

3.2 Stress-Axis Dysregulation and Loss of Plasticity

Chronic stress elevates glucocorticoids and impairs glucocorticoid-receptor feedback; sustained hypercortisolemia reduces dendritic complexity and neurogenesis in hippocampus and prefrontal cortex and suppresses brain-derived neurotrophic factor (BDNF) signaling (Pariante & Lightman, 2008; McEwen, 2007; Duman & Aghajanian, 2012). Depressed patients show reduced synaptic density and dendritic spine loss in prefrontal cortex, a "synaptic deficit" that undermines the very capacities—cognitive control, emotional regulation, motivation—required for recovery (Duman et al., 2016; He et al., 2026). This helps explain why healing is slow: repair requires new synapses and learned change, not merely a shifted neurotransmitter level.

3.3 Neuroimmune and Metabolic Contributions

A subgroup (perhaps one-quarter) of patients with depression shows elevated inflammatory markers (C-reactive protein, interleukin-6), which predict poorer response to conventional antidepressants (Raison et al., 2006; Miller & Raison, 2016). Cytokines alter glutamate metabolism, monoamine synthesis, and reward circuitry, contributing to fatigue, anhedonia, and psychomotor slowing (Dantzer et al., 2008; Miller & Raison, 2016). Sleep disturbance, obesity, and cardiometabolic disease bidirectionally reinforce mood symptoms (Baglioni et al., 2011).

3.4 Beyond the Monoamine Hypothesis

The claim that depression is caused by low serotonin lacks consistent support: an umbrella review found no convincing evidence of reduced serotonin activity or concentrations in depression (Moncrieff et al., 2023). Antidepressants nonetheless work for many patients, likely by gradually enhancing plasticity and modifying emotional processing rather than "correcting" a deficiency (Harmer et al., 2017; Duman et al., 2016). Glutamatergic, GABAergic, dopaminergic, and neuroimmune systems all contribute to prefrontal circuit function (He et al., 2026), and this pathophysiologic diversity is one reason single-mechanism drugs benefit only a proportion of patients.

3.5 Behavioral Maintenance Loops

Even where biology initiates the disorder, behavior perpetuates it:

  • Avoidance prevents extinction learning; the feared outcome is never disconfirmed, and the fear memory persists (Craske et al., 2014; Craske et al., 2017).

  • Rumination and worry amplify negative affect and impair problem solving (Nolen-Hoeksema et al., 2008).

  • Withdrawal and inactivity reduce reward exposure and reinforce anhedonia, the core target of behavioral activation (Dimidjian et al., 2006).

  • Insomnia both predicts incident depression (approximately doubling risk) and impairs emotional regulation (Baglioni et al., 2011).

  • Safety behaviors and substance use provide short-term relief but sustain long-term symptoms.

3.6 Treatment Resistance as a Distinct Phenomenon

In the STAR*D trial, about 30% of patients remitted with first-line citalopram, and cumulative remission after up to four sequential steps was approximately 67% (Rush et al., 2006), although reanalyses applying the original protocol criteria estimated substantially lower cumulative remission (about 35%; Pigott et al., 2023). Treatment-resistant depression, conventionally defined as failure of at least two adequate antidepressant trials, affects roughly 15–30% of patients (Lucas et al., 2017; Rush et al., 2006). TRD is associated with greater illness severity, comorbid anxiety, early-life adversity, inflammation, and altered glutamatergic function (Miller & Raison, 2016; Duman et al., 2016). Notably, apparent "resistance" often reflects pseudo-resistance: inadequate dose or duration, poor adherence, misdiagnosis (e.g., bipolar depression), unrecognized medical or substance contributors, or unaddressed psychosocial drivers.

3.7 System-Level Barriers

Effective treatment is unreachable for many. Only about one in five people with MDD in high-income countries and fewer than one in ten in low- and middle-income countries receive minimally adequate treatment (Thornicroft et al., 2017; Kohn et al., 2004). Lack of trained therapists, cost, stigma, fragmented care, and infrequent symptom measurement contribute to persistent illness (WHO, 2022).

4. Evidence-Based Treatment: A Practical Framework

4.1 Principles

  1. Diagnose carefully: screen for bipolar disorder, substance use, thyroid and other medical contributors, trauma, and suicidality.

  2. Measure: use PHQ-9, GAD-7, or equivalents at baseline and every 2–4 weeks; measurement-based care improves remission rates (Guo et al., 2015).

  3. Stage treatment by severity and preference: mild–moderate → psychotherapy or medication; moderate–severe → combination or medication plus psychotherapy.

  4. Reassess early: less than 20% symptom improvement by weeks 2–4 predicts non-response and should prompt optimization (Szegedi et al., 2009).

  5. Treat to remission, not just response, and maintain treatment to prevent relapse.

4.2 Psychotherapy

CBT for anxiety. CBT is efficacious across anxiety disorders (Hofmann et al., 2012; Carpenter et al., 2018). In a network meta-analysis of 65 trials (N = 5,048), CBT reduced generalized anxiety symptoms compared with treatment as usual (SMD = −0.74; 95% CI −1.09 to −0.38), and was the only psychotherapy with a significant advantage at 3–12-month follow-up (SMD = −0.60; 95% CI −0.99 to −0.21) (Papola et al., 2024). Exposure-based methods that maximize inhibitory learning—rather than habituation alone—strengthen durability (Craske et al., 2014).

CBT and behavioral activation for depression. Network meta-analyses show that CBT, behavioral activation, interpersonal therapy, and problem-solving therapy all produce moderate-to-large effects versus control, with comparable efficacy among major approaches (Cuijpers et al., 2020). Behavioral activation delivered by non-specialist staff was non-inferior to CBT and cheaper in a large randomized trial (Richards et al., 2016), which is significant for scalability. Mindfulness-based cognitive therapy roughly halves relapse risk relative to usual care in patients with three or more prior episodes (Kuyken et al., 2016). Group and patient-centered group formats are effective and scalable (Yin et al., 2025).

Digital and low-intensity formats. Guided internet-delivered CBT achieves outcomes comparable to face-to-face therapy for depression (Karyotaki et al., 2021), making it a reasonable first step where access is limited.

Practical guidance: Standard courses run 12–20 weekly sessions. If there is less than meaningful improvement by sessions 6–8, reassess adherence, diagnosis, therapy fit, and consider adding medication.

4.3 Pharmacotherapy

Antidepressant efficacy. A network meta-analysis of 522 trials (116,477 participants) found all 21 studied antidepressants superior to placebo for acute MDD (odds ratios 1.37–2.13), with modest average effect sizes (SMD ≈ 0.30) and some agents (e.g., escitalopram, sertraline, mirtazapine, amitriptyline) offering better efficacy–acceptability balance (Cipriani et al., 2018). Average effects are modest partly because responders and non-responders are pooled; baseline severity, inflammation, and comorbidity moderate benefit (Fournier et al., 2010; Miller & Raison, 2016). Symptom improvement often begins within 2 weeks and continues over 6–8 weeks (Szegedi et al., 2009).

Anxiety disorders. SSRIs and SNRIs are first-line (Bandelow et al., 2015; Baldwin et al., 2014). For generalized anxiety disorder, network meta-analysis supports duloxetine, venlafaxine, and pregabalin, with several SSRIs also effective (Slee et al., 2019). Patients with anxiety are often sensitive to early somatic side effects; starting low and titrating slowly reduces dropout (Bandelow et al., 2015).

Benzodiazepines should be limited to short-term bridging because of tolerance, dependence, cognitive impairment, and falls (Baldwin et al., 2014).

Combination therapy. Adding psychotherapy to pharmacotherapy improves outcomes relative to medication alone in depression and anxiety disorders (Cuijpers et al., 2014), and is particularly appropriate for moderate–severe, chronic, or comorbid presentations.

Continuation and discontinuation. Relapse risk is substantially higher after stopping antidepressants (Lewis et al., 2021). Guidelines generally advise continuing for at least 6–12 months after remission, and longer after recurrent episodes. Discontinuation should be gradual, because withdrawal symptoms can be more frequent and prolonged than historically believed (Davies & Read, 2019).

Safety. Risk of suicidal thinking with antidepressants is small and concentrated in people under 25 (Stone et al., 2009). Close monitoring during the first weeks of treatment is prudent, while remembering that untreated depression is the far greater suicide risk.

4.4 Stepped Care for Treatment-Resistant Depression

Step 0—Verify adequacy. Confirm dose, duration (≥6 weeks at a therapeutic dose), adherence, diagnosis, and contributing factors (sleep apnea, alcohol, thyroid, pain).

Step 1—Optimize and switch or augment. Options include dose optimization, switching class, adding psychotherapy, or augmenting with lithium (Nelson et al., 2014), or second-generation antipsychotics such as aripiprazole or quetiapine (Berman et al., 2007).

Step 2—Esketamine and ketamine. Ketamine produces rapid antidepressant effects within hours (Berman et al., 2000; Zarate et al., 2006), mediated by NMDA-receptor blockade, glutamate surge, BDNF release, mTOR signaling, and rapid synaptogenesis (Li et al., 2010; Duman et al., 2016). Intranasal esketamine plus a newly initiated oral antidepressant improved depressive symptoms versus placebo plus antidepressant in TRD (Popova et al., 2019), and was superior to extended-release quetiapine augmentation for remission at 8 weeks (Reif et al., 2023). It is FDA-approved for TRD and for MDD with acute suicidal ideation, and must be administered under supervision because of dissociation, blood-pressure elevation, and abuse potential.

Step 3—Neuromodulation.

  • ECT remains the most effective acute treatment for severe or psychotic depression, with remission rates that exceed most pharmacological alternatives, but with cognitive side effects and high relapse risk without continuation therapy (UK ECT Review Group, 2003; Espinoza & Kellner, 2022).

  • rTMS is effective for TRD and non-invasive (O'Reardon et al., 2007); intermittent theta-burst stimulation is non-inferior to standard 10-Hz rTMS with much shorter sessions (Blumberger et al., 2018). Accelerated, image-guided protocols reported remission in a majority of participants in a sham-controlled trial (Cole et al., 2022).

Emerging options. Psilocybin-assisted therapy showed rapid, sustained benefit in early trials (Davis et al., 2021), and a randomized phase 2 trial in TRD showed dose-dependent effects (Goodwin et al., 2022). These remain investigational, and evidence is still limited by small samples, expectancy, and unblinding.

Practical note: If two adequate antidepressant trials have failed, discuss augmentation, esketamine, rTMS, and ECT with the treating clinician; ongoing medication changes without structured reassessment are unlikely to help.

4.5 Lifestyle and Health-System Interventions

  • Exercise: aerobic and resistance exercise have moderate-to-large antidepressant effects (Schuch et al., 2016; Noetel et al., 2024) and reduce anxiety symptoms; walking/jogging, yoga, and strength training performed well in a recent network meta-analysis (Noetel et al., 2024).

  • Sleep: CBT for insomnia improves sleep and reduces depressive symptoms; treating insomnia is a direct depression-risk intervention (Baglioni et al., 2011).

  • Diet: a modified Mediterranean diet improved depressive symptoms in a randomized trial (Jacka et al., 2017).

  • Collaborative care (care manager, primary care clinician, psychiatric consultant) improves depression outcomes compared with usual care (Archer et al., 2012).

  • Alcohol and substance reduction: these commonly maintain symptoms and lower treatment response.

5. Synthesis: Why Healing Is Hard—and How Treatment Maps onto Mechanism

Maintaining factor

Mechanism

Evidence-based response

Avoidance and safety behaviors

Blocked extinction learning

Exposure/inhibitory-learning CBT (Craske et al., 2014)

Inactivity and anhedonia

Loss of reward reinforcement

Behavioral activation (Dimidjian et al., 2006; Richards et al., 2016)

Rumination and negative schemas

Prefrontal control deficits

CBT, MBCT (Kuyken et al., 2016)

Plasticity deficit and HPA dysregulation

Reduced BDNF, synaptic loss

Antidepressants, exercise, ketamine/esketamine (Duman et al., 2016)

Inflammation

Altered glutamate/reward signaling

Treat comorbidity, lifestyle; investigational anti-inflammatory strategies (Miller & Raison, 2016)

Circuit-level hypoactivity

Dysregulated prefrontal networks

rTMS, ECT (Blumberger et al., 2018; Espinoza & Kellner, 2022)

Insomnia

Impaired emotion regulation

CBT-I (Baglioni et al., 2011)

Access and adherence barriers

System factors

Collaborative care, digital CBT, measurement-based care (Archer et al., 2012; Karyotaki et al., 2021; Guo et al., 2015)

6. Limitations and Future Directions

Average treatment effects mask substantial heterogeneity, and predicting who will respond to what remains an unsolved problem. Pharmacogenomic testing has shown modest benefit in some trials (Greden et al., 2019) but is not yet a substitute for clinical judgment. Biomarker-guided care (inflammation, imaging, EEG), rapid-acting glutamatergic and neurosteroid agents, and personalized neuromodulation are active areas. Trials in psychotherapy suffer from allegiance and blinding limitations, and many meta-analyses include studies at high risk of bias (Cuijpers et al., 2020). Equitable delivery—stepped, task-shared, and digital—may deliver larger population benefit than any single new therapy (WHO, 2022; Thornicroft et al., 2017).

7. Conclusion

Anxiety and depression are common because evolved threat and stress-response systems are sensitive by design, calibrated by early experience, shaped by polygenic risk, and taxed by modern environments. They are difficult to heal because they involve distributed circuit dysfunction, impaired plasticity, neuroimmune and sleep disruption, and self-reinforcing behavioral loops, all frequently compounded by inadequate access to care. Effective treatments exist: CBT and behavioral activation for durable psychotherapeutic benefit; SSRIs/SNRIs for pharmacological benefit; combination care for severe or chronic illness; and augmentation, esketamine, rTMS, and ECT for treatment resistance. Success depends on accurate diagnosis, routine measurement, early reassessment, timely escalation, and attention to lifestyle and access. These conditions are formidable, but they are treatable.

If you or someone you know is experiencing thoughts of suicide, contact a local emergency number or, in the U.S., call or text 988 (Suicide & Crisis Lifeline).

References

Akiki, T. J., Abdallah, C. G., et al. (2025). Neural circuit basis of pathological anxiety. Nature Reviews Neuroscience, 26(1). https://doi.org/10.1038/s41583-024-00880-4

Archer, J., Bower, P., Gilbody, S., Lovell, K., Richards, D., Gask, L., Dickens, C., & Coventry, P. (2012). Collaborative care for depression and anxiety problems. Cochrane Database of Systematic Reviews, 10, CD006525.

Baglioni, C., Battagliese, G., Feige, B., Spiegelhalder, K., Nissen, C., Voderholzer, U., Lombardo, C., & Riemann, D. (2011). Insomnia as a predictor of depression: A meta-analytic evaluation of longitudinal epidemiological studies. Journal of Affective Disorders, 135(1–3), 10–19.

Baldwin, D. S., Anderson, I. M., Nutt, D. J., Allgulander, C., Bandelow, B., den Boer, J. A., et al. (2014). Evidence-based pharmacological treatment of anxiety disorders, post-traumatic stress disorder and obsessive-compulsive disorder: A revision of the 2005 guidelines from the British Association for Psychopharmacology. Journal of Psychopharmacology, 28(5), 403–439.

Bandelow, B., Michaelis, S., & Wedekind, D. (2015). Treatment of anxiety disorders. Dialogues in Clinical Neuroscience, 19(2), 93–107.

Berman, R. M., Cappiello, A., Anand, A., Oren, D. A., Heninger, G. R., Charney, D. S., & Krystal, J. H. (2000). Antidepressant effects of ketamine in depressed patients. Biological Psychiatry, 47(4), 351–354.

Berman, R. M., Marcus, R. N., Swanink, R., McQuade, R. D., Carson, W. H., Corey-Lisle, P. K., & Khan, A. (2007). The efficacy and safety of aripiprazole as adjunctive therapy in major depressive disorder: A multicenter, randomized, double-blind, placebo-controlled study. Journal of Clinical Psychiatry, 68(6), 843–853.

Blumberger, D. M., Vila-Rodriguez, F., Thorpe, K. E., Feffer, K., Noda, Y., Giacobbe, P., et al. (2018). Effectiveness of theta burst versus high-frequency repetitive transcranial magnetic stimulation in patients with depression (THREE-D): A randomised non-inferiority trial. The Lancet, 391(10131), 1683–1692.

Border, R., Johnson, E. C., Evans, L. M., Smolen, A., Berley, N., Sullivan, P. F., & Keller, M. C. (2019). No support for historical candidate gene or candidate gene-by-interaction hypotheses for major depression across multiple large samples. American Journal of Psychiatry, 176(5), 376–387.

Carpenter, J. K., Andrews, L. A., Witcraft, S. M., Powers, M. B., Smits, J. A. J., & Hofmann, S. G. (2018). Cognitive behavioral therapy for anxiety and related disorders: A meta-analysis of randomized placebo-controlled trials. Depression and Anxiety, 35(6), 502–514.

Caspi, A., Sugden, K., Moffitt, T. E., Taylor, A., Craig, I. W., Harrington, H., et al. (2003). Influence of life stress on depression: Moderation by a polymorphism in the 5-HTT gene. Science, 301(5631), 386–389.

Chen, M., et al. (2025). The impact of the COVID-19 pandemic on the global burden of mental disorders: A counterfactual modeling study from 1990 to 2021. Translational Psychiatry, 15.

Cipriani, A., Furukawa, T. A., Salanti, G., Chaimani, A., Atkinson, L. Z., Ogawa, Y., et al. (2018). Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder: A systematic review and network meta-analysis. The Lancet, 391(10128), 1357–1366.

Cole, E. J., Phillips, A. L., Bentzley, B. S., Stimpson, K. H., Nejad, R., Barmak, F., et al. (2022). Stanford Neuromodulation Therapy (SNT): A double-blind randomized controlled trial. American Journal of Psychiatry, 179(2), 132–141.

Craske, M. G., Treanor, M., Conway, C. C., Zbozinek, T., & Vervliet, B. (2014). Maximizing exposure therapy: An inhibitory learning approach. Behaviour Research and Therapy, 58, 10–23.

Craske, M. G., Stein, M. B., Eley, T. C., Milad, M. R., Holmes, A., Rapee, R. M., & Wittchen, H.-U. (2017). Anxiety disorders. Nature Reviews Disease Primers, 3, 17024.

Cuijpers, P., Sijbrandij, M., Koole, S. L., Andersson, G., Beekman, A. T., & Reynolds, C. F. (2014). Adding psychotherapy to antidepressant medication in depression and anxiety disorders: A meta-analysis. World Psychiatry, 13(1), 56–67.

Cuijpers, P., Karyotaki, E., Reijnders, M., & Ebert, D. D. (2020). Psychotherapies for depression: A network meta-analysis covering efficacy, acceptability and long-term outcomes of all main treatment types. World Psychiatry, 19(1), 92–107.

Dantzer, R., O'Connor, J. C., Freund, G. G., Johnson, R. W., & Kelley, K. W. (2008). From inflammation to sickness and depression: When the immune system subjugates the brain. Nature Reviews Neuroscience, 9(1), 46–57.

Davies, J., & Read, J. (2019). A systematic review into the incidence, severity and duration of antidepressant withdrawal effects. Addictive Behaviors, 97, 111–121.

Davis, A. K., Barrett, F. S., May, D. G., Cosimano, M. P., Sepeda, N. D., Johnson, M. W., et al. (2021). Effects of psilocybin-assisted therapy on major depressive disorder: A randomized clinical trial. JAMA Psychiatry, 78(5), 481–489.

Dimidjian, S., Hollon, S. D., Dobson, K. S., Schmaling, K. B., Kohlenberg, R. J., Addis, M. E., et al. (2006). Randomized trial of behavioral activation, cognitive therapy, and antidepressant medication in the acute treatment of adults with major depression. Journal of Consulting and Clinical Psychology, 74(4), 658–670.

Drevets, W. C., Price, J. L., & Furey, M. L. (2008). Brain structural and functional abnormalities in mood disorders: Implications for neurocircuitry models of depression. Brain Structure and Function, 213(1–2), 93–118.

Duman, R. S., & Aghajanian, G. K. (2012). Synaptic dysfunction in depression: Potential therapeutic targets. Science, 338(6103), 68–72.

Duman, R. S., Aghajanian, G. K., Sanacora, G., & Krystal, J. H. (2016). Synaptic plasticity and depression: New insights from stress and rapid-acting antidepressants. Nature Medicine, 22(3), 238–249.

Espinoza, R. T., & Kellner, C. H. (2022). Electroconvulsive therapy. New England Journal of Medicine, 386(7), 667–672.

Etkin, A., & Wager, T. D. (2007). Functional neuroimaging of anxiety: A meta-analysis of emotional processing in PTSD, social anxiety disorder, and specific phobia. American Journal of Psychiatry, 164(10), 1476–1488.

Fournier, J. C., DeRubeis, R. J., Hollon, S. D., Dimidjian, S., Amsterdam, J. D., Shelton, R. C., & Fawcett, J. (2010). Antidepressant drug effects and depression severity: A patient-level meta-analysis. JAMA, 303(1), 47–53.

GBD 2019 Mental Disorders Collaborators. (2022). Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. The Lancet Psychiatry, 9(2), 137–150.

GBD 2021 Diseases and Injuries Collaborators. (2024). Global incidence, prevalence, years lived with disability, disability-adjusted life-years, and healthy life expectancy for 371 diseases and injuries in 204 countries and territories, 1990–2021. The Lancet, 403(10440), 2133–2161.

Goodwin, G. M., Aaronson, S. T., Alvarez, O., Arden, P. C., Baker, A., Bennett, J. C., et al. (2022). Single-dose psilocybin for a treatment-resistant episode of major depression. New England Journal of Medicine, 387(18), 1637–1648.

Greden, J. F., Parikh, S. V., Rothschild, A. J., Thase, M. E., Dunlop, B. W., DeBattista, C., et al. (2019). Impact of pharmacogenomics on clinical outcomes in major depressive disorder in the GUIDED trial. Journal of Psychiatric Research, 111, 59–67.

Guo, T., Xiang, Y. T., Xiao, L., Hu, C. Q., Chiu, H. F., Ungvari, G. S., et al. (2015). Measurement-based care versus standard care for major depression: A randomized controlled trial with blind raters. American Journal of Psychiatry, 172(10), 1004–1013.

Harmer, C. J., Duman, R. S., & Cowen, P. J. (2017). How do antidepressants work? New perspectives for refining future treatment approaches. The Lancet Psychiatry, 4(5), 409–418.

He, M., Shi, Z., Zhan, R., et al. (2026). Network dysregulation in depression: A synthesis of HPA axis, BDNF signaling, and neurotransmitter interactions across multidimensional systems. Neuroscience & Biobehavioral Reviews.

Heim, C., & Binder, E. B. (2012). Current research trends in early life stress and depression: Review of human studies on sensitive periods, gene–environment interactions, and epigenetics. Experimental Neurology, 233(1), 102–111.

Hettema, J. M., Neale, M. C., & Kendler, K. S. (2001). A review and meta-analysis of the genetic epidemiology of anxiety disorders. American Journal of Psychiatry, 158(10), 1568–1578.

Hofmann, S. G., Asnaani, A., Vonk, I. J. J., Sawyer, A. T., & Fang, A. (2012). The efficacy of cognitive behavioral therapy: A review of meta-analyses. Cognitive Therapy and Research, 36(5), 427–440.

Howard, D. M., Adams, M. J., Clarke, T.-K., Hafferty, J. D., Gibson, J., Shirali, M., et al. (2019). Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nature Neuroscience, 22(3), 343–352.

Jacka, F. N., O'Neil, A., Opie, R., Itsiopoulos, C., Cotton, S., Mohebbi, M., et al. (2017). A randomised controlled trial of dietary improvement for adults with major depression (the 'SMILES' trial). BMC Medicine, 15, 23.

Karyotaki, E., Efthimiou, O., Miguel, C., Bermpohl, F. M. G., Furukawa, T. A., Cuijpers, P., et al. (2021). Internet-based cognitive behavioral therapy for depression: A systematic review and individual patient data network meta-analysis. JAMA Psychiatry, 78(4), 361–371.

Kendler, K. S., Prescott, C. A., Myers, J., & Neale, M. C. (2003). The structure of genetic and environmental risk factors for common psychiatric and substance use disorders in men and women. Archives of General Psychiatry, 60(9), 929–937.

Kessler, R. C., Berglund, P., Demler, O., Jin, R., Merikangas, K. R., & Walters, E. E. (2005a). Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Archives of General Psychiatry, 62(6), 593–602.

Kessler, R. C., Chiu, W. T., Demler, O., Merikangas, K. R., & Walters, E. E. (2005b). Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication. Archives of General Psychiatry, 62(6), 617–627.

Kessler, R. C., Berglund, P., Borges, G., Nock, M., & Wang, P. S. (2005c). Trends in suicide ideation, plans, gestures, and attempts in the United States, 1990–1992 to 2001–2003. JAMA, 293(20), 2487–2495.

Kessler, R. C., Aguilar-Gaxiola, S., Alonso, J., Chatterji, S., Lee, S., Ormel, J., Üstün, T. B., & Wang, P. S. (2009). The global burden of mental disorders: An update from the WHO World Mental Health (WMH) Surveys. Epidemiologia e Psichiatria Sociale, 18(1), 23–33.

Kohn, R., Saxena, S., Levav, I., & Saraceno, B. (2004). The treatment gap in mental health care. Bulletin of the World Health Organization, 82(11), 858–866.

Kuyken, W., Warren, F. C., Taylor, R. S., Whalley, B., Crane, C., Bondolfi, G., et al. (2016). Efficacy of mindfulness-based cognitive therapy in prevention of depressive relapse: An individual patient data meta-analysis from randomized trials. JAMA Psychiatry, 73(6), 565–574.

Lamers, F., van Oppen, P., Comijs, H. C., Smit, J. H., Spinhoven, P., van Balkom, A. J. L. M., et al. (2011). Comorbidity patterns of anxiety and depressive disorders in a large cohort study: The Netherlands Study of Depression and Anxiety (NESDA). Journal of Clinical Psychiatry, 72(3), 341–348.

Lewis, G., Marston, L., Duffy, L., Freemantle, N., Gilbody, S., Hunter, R., et al. (2021). Maintenance or discontinuation of antidepressants in primary care. New England Journal of Medicine, 385(14), 1257–1267.

Li, N., Lee, B., Liu, R.-J., Banasr, M., Dwyer, J. M., Iwata, M., et al. (2010). mTOR-dependent synapse formation underlies the rapid antidepressant effects of NMDA antagonists. Science, 329(5994), 959–964.

Liu, J., et al. (2024). Estimation of the global disease burden of depression and anxiety between 1990 and 2044: An analysis of the Global Burden of Disease Study 2019. Healthcare, 12(17), 1721. https://doi.org/10.3390/healthcare12171721

Lucas, N., et al. (2017). Treatment resistant depression: Actuality and perspectives in 2017. Revue Médicale de Bruxelles, 38(3).

McEwen, B. S. (2007). Physiology and neurobiology of stress and adaptation: Central role of the brain. Physiological Reviews, 87(3), 873–904.

Miller, A. H., & Raison, C. L. (2016). The role of inflammation in depression: From evolutionary imperative to modern treatment target. Nature Reviews Immunology, 16(1), 22–34.

Moncrieff, J., Cooper, R. E., Stockmann, T., Amendola, S., Hengartner, M. P., & Horowitz, M. A. (2023). The serotonin theory of depression: A systematic umbrella review of the evidence. Molecular Psychiatry, 28(8), 3243–3256.

Nanni, V., Uher, R., & Danese, A. (2012). Childhood maltreatment predicts unfavorable course of illness and treatment outcome in depression: A meta-analysis. American Journal of Psychiatry, 169(2), 141–151.

Nelson, J. C., Baumann, P., Delucchi, K., Joffe, R., & Katona, C. (2014). A systematic review and meta-analysis of lithium augmentation of tricyclic and second generation antidepressants in major depression. Journal of Affective Disorders, 168, 269–275.

Nesse, R. M. (2005). Natural selection and the regulation of defenses: A signal detection analysis of the smoke detector principle. Evolution and Human Behavior, 26(1), 88–105.

Nesse, R. M. (2019). Good reasons for bad feelings: Insights from the frontier of evolutionary psychiatry. Dutton.

Noetel, M., Sanders, T., Gallardo-Gómez, D., Taylor, P., del Pozo Cruz, B., van den Hoek, D., et al. (2024). Effect of exercise for depression: Systematic review and network meta-analysis of randomised controlled trials. BMJ, 384, e075847.

Nolen-Hoeksema, S., Wisco, B. E., & Lyubomirsky, S. (2008). Rethinking rumination. Perspectives on Psychological Science, 3(5), 400–424.

Norman, R. E., Byambaa, M., De, R., Butchart, A., Scott, J., & Vos, T. (2012). The long-term health consequences of child physical abuse, emotional abuse, and neglect: A systematic review and meta-analysis. PLoS Medicine, 9(11), e1001349.

O'Reardon, J. P., Solvason, H. B., Janicak, P. G., Sampson, S., Isenberg, K. E., Nahas, Z., et al. (2007). Efficacy and safety of transcranial magnetic stimulation in the acute treatment of major depression: A multisite randomized controlled trial. Biological Psychiatry, 62(11), 1208–1216.

Otte, C., Gold, S. M., Penninx, B. W., Pariante, C. M., Etkin, A., Fava, M., Mohr, D. C., & Schatzberg, A. F. (2016). Major depressive disorder. Nature Reviews Disease Primers, 2, 16065.

Papola, D., Miguel, C., Mazzaglia, M., et al. (2024). Psychotherapies for generalized anxiety disorder in adults: A systematic review and network meta-analysis of randomized clinical trials. JAMA Psychiatry, 81(3), 250–259. https://doi.org/10.1001/jamapsychiatry.2023.3971

Pariante, C. M., & Lightman, S. L. (2008). The HPA axis in major depression: Classical theories and new developments. Trends in Neurosciences, 31(9), 464–468.

Peen, J., Schoevers, R. A., Beekman, A. T., & Dekker, J. (2010). The current status of urban–rural differences in psychiatric disorders. Acta Psychiatrica Scandinavica, 121(2), 84–93.

Pigott, H. E., Kim, T., Xu, C., Kirsch, I., & Amsterdam, J. (2023). What are the treatment remission, response and extent of improvement rates after up to four trials of antidepressant therapies in real-world depressed patients? A reanalysis of the STAR*D study's patient-level data files. BMJ Open, 13(7), e063095.

Popova, V., Daly, E. J., Trivedi, M., Cooper, K., Lane, R., Lim, P., et al. (2019). Efficacy and safety of flexibly dosed esketamine nasal spray combined with a newly initiated oral antidepressant in treatment-resistant depression: A randomized double-blind active-controlled study. American Journal of Psychiatry, 176(6), 428–438.

Raison, C. L., Capuron, L., & Miller, A. H. (2006). Cytokines sing the blues: Inflammation and the pathogenesis of depression. Trends in Immunology, 27(1), 24–31.

Reif, A., Bitter, I., Buyze, J., Cebulla, K., Frey, R., Fu, D.-J., et al. (2023). Esketamine nasal spray versus quetiapine for treatment-resistant depression. New England Journal of Medicine, 389(14), 1298–1309.

Richards, D. A., Ekers, D., McMillan, D., Taylor, R. S., Byford, S., Warren, F. C., et al. (2016). Cost and outcome of behavioural activation versus cognitive behavioural therapy for depression (COBRA): A randomised, controlled, non-inferiority trial. The Lancet, 388(10047), 871–880.

Rush, A. J., Trivedi, M. H., Wisniewski, S. R., Nierenberg, A. A., Stewart, J. W., Warden, D., et al. (2006). Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: A STAR*D report. American Journal of Psychiatry, 163(11), 1905–1917.

Santomauro, D. F., Mantilla Herrera, A. M., Shadid, J., Zheng, P., Ashbaugh, C., Pigott, D. M., et al. (2021). Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. The Lancet, 398(10312), 1700–1712.

Schmaal, L., Veltman, D. J., van Erp, T. G. M., Sämann, P. G., Frodl, T., Jahanshad, N., et al. (2016). Subcortical brain alterations in major depressive disorder: Findings from the ENIGMA Major Depressive Disorder working group. Molecular Psychiatry, 21(6), 806–812.

Schuch, F. B., Vancampfort, D., Richards, J., Rosenbaum, S., Ward, P. B., & Stubbs, B. (2016). Exercise as a treatment for depression: A meta-analysis adjusting for publication bias. Journal of Psychiatric Research, 77, 42–51.

Shin, L. M., & Liberzon, I. (2010). The neurocircuitry of fear, stress, and anxiety disorders. Neuropsychopharmacology, 35(1), 169–191.

Slee, A., Nazareth, I., Bondaronek, P., Liu, Y., Cheng, Z., & Freemantle, N. (2019). Pharmacological treatments for generalised anxiety disorder: A systematic review and network meta-analysis. The Lancet, 393(10173), 768–777.

Solmi, M., Radua, J., Olivola, M., Croce, E., Soardo, L., Salazar de Pablo, G., et al. (2022). Age at onset of mental disorders worldwide: Large-scale meta-analysis of 192 epidemiological studies. Molecular Psychiatry, 27(1), 281–295.

Stone, M., Laughren, T., Jones, M. L., Levenson, M., Holland, P. C., Hughes, A., et al. (2009). Risk of suicidality in clinical trials of antidepressants in adults: Analysis of proprietary data submitted to US Food and Drug Administration. BMJ, 339, b2880.

Sullivan, P. F., Neale, M. C., & Kendler, K. S. (2000). Genetic epidemiology of major depression: Review and meta-analysis. American Journal of Psychiatry, 157(10), 1552–1562.

Szegedi, A., Jansen, W. T., van Willigenburg, A. P., van der Meulen, E., Stassen, H. H., & Thase, M. E. (2009). Early improvement in the first 2 weeks as a predictor of treatment outcome in patients with major depressive disorder: A meta-analysis including 6562 patients. Journal of Clinical Psychiatry, 70(3), 344–353.

Thornicroft, G., Chatterji, S., Evans-Lacko, S., Gruber, M., Sampson, N., Aguilar-Gaxiola, S., et al. (2017). Undertreatment of people with major depressive disorder in 21 countries. British Journal of Psychiatry, 210(2), 119–124.

UK ECT Review Group. (2003). Efficacy and safety of electroconvulsive therapy in depressive disorders: A systematic review and meta-analysis. The Lancet, 361(9360), 799–808.

World Health Organization. (2022). World mental health report: Transforming mental health for all. WHO.

Wray, N. R., Ripke, S., Mattheisen, M., Trzaskowski, M., Byrne, E. M., Abdellaoui, A., et al. (2018). Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nature Genetics, 50(5), 668–681.

Yin, Y.-Y., et al. (2025). Patient-centered group psychotherapy for depression and negative emotions: A systematic review and meta-analysis. Frontiers in Psychiatry, 16, 1530615.

Zarate, C. A., Jr., Singh, J. B., Carlson, P. J., Brutsche, N. E., Ameli, R., Luckenbaugh, D. A., Charney, D. S., & Manji, H. K. (2006). A randomized trial of an N-methyl-D-aspartate antagonist in treatment-resistant major depression. Archives of General Psychiatry, 63(8), 856–864.

#anxiety disorders; major depressive disorder; cognitive behavioral therapy; SSRIs; treatment-resistant depression; HPA axis; neuroplasticity; stepped care; esketamine

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