Depressive Symptoms
Introduction
Section titled “Introduction”Depressive symptoms represent a spectrum of emotional, cognitive, and physical changes that are a significant public health concern worldwide. These symptoms can range from subthreshold or minor depression to severe major depressive disorder (MDD).[1]Accurate assessment of depressive symptoms is fundamental for both clinical practice and research endeavors. Various standardized self-report scales are employed globally to quantify the severity and prevalence of these symptoms. Widely used instruments include the Beck Depression Inventory (BDI).[2] its revised version BDI-II.[3] the Center for Epidemiologic Studies Depression (CES-D) scale.[4] and the K6 scale, particularly in populations like the Japanese.[5] These tools provide consistent metrics essential for understanding, diagnosing, and managing depression.
Biological Basis
Section titled “Biological Basis”Research, particularly through Genome-Wide Association Studies (GWAS), has advanced the understanding of the biological underpinnings of depressive symptoms. Studies indicate a significant genetic component, with heritability estimates for depressive symptoms ranging between 23% and 29%.[1]Specific genetic loci and genes have been implicated in the development and severity of depressive symptoms. For instance, a GWAS in the Korean population identified a role forTBXAS1in the pathogenesis of depressive symptoms.[2] Other investigations have associated variants in genes such as ZNF354C with depression, particularly in the context of interferon-based therapies.[3] Furthermore, broad depression phenotypes and psychological distress have been linked to multiple genetic loci, underscoring the polygenic nature of these conditions.[1] The complex interplay between genetic predispositions and environmental factors, such as psychosocial stress, is also being explored through gene-environment interaction studies.[6]
Clinical Relevance
Section titled “Clinical Relevance”The precise assessment of depressive symptoms is paramount in clinical settings for several reasons. It enables healthcare professionals to accurately diagnose depressive disorders, tailor treatment plans, and monitor patient responses to interventions.[7] Standardized scales offer objective measures that help differentiate between varying degrees of depression severity and track changes over time, guiding decisions regarding pharmacological, psychological, or other therapeutic approaches. Moreover, these measurements are crucial for stratifying participants in clinical trials, ensuring that research findings are applicable to specific patient populations and contributing to the development of more effective treatments.
Social Importance
Section titled “Social Importance”Depressive symptoms carry a substantial social burden, impacting individuals, families, and communities. They are associated with reduced quality of life, significant functional impairment.[8]and considerable economic costs due to lost productivity and increased healthcare utilization. By understanding the genetic and environmental factors that contribute to depressive symptoms through precise , public health initiatives can be improved. This includes developing targeted prevention strategies, facilitating early intervention programs, and implementing personalized treatment approaches. Population-specific studies, such as those conducted in Korean or Japanese populations.[2]are vital for addressing health disparities and ensuring culturally sensitive care. Ultimately, research into the biological basis of depressive symptoms helps to destigmatize mental health conditions and advocates for evidence-based care.
Phenotypic Heterogeneity and Challenges
Section titled “Phenotypic Heterogeneity and Challenges”Studies often combine data from heterogeneous phenotype measures of depressive symptoms to increase statistical power, a strategy that, while effective for signal detection, complicates the interpretation of discovered genetic associations.[9]This aggregation of diverse measures, ranging from continuous self-report scales like the Center for Epidemiological Studies Depression (CES-D) or specific UK Biobank questions to ascertained case-control data for major depressive disorder, introduces variability in the underlying phenotype being studied.[2], [4], [9], [10]Such phenotypic heterogeneity can also lead to and scoring differences across study centers and may reduce the estimated heritability of depressive symptoms, hindering the identification of genetic variants.[9], [11]Furthermore, the presentation of depressive symptoms can differ significantly by age, and the exclusion of individuals on antidepressant medication in some analyses can introduce further biases, limiting the general applicability of findings to the broader population.[11]These challenges underscore the critical need for research employing more fine-grained and standardized measures to better characterize the various facets of depressive symptoms.[9]
Statistical Power, Replication, and Generalizability
Section titled “Statistical Power, Replication, and Generalizability”Despite assembling large sample sizes by combining data, many studies of depressive symptoms remain underpowered to detect all genetic variants with small effect sizes, suggesting that numerous loci comparable to identified SNPs likely evade detection.[1], [9]For instance, the statistical power to detect even lead SNPs can be as low as ~13%, indicating that significantly larger samples are required to fully uncover the polygenic architecture of depressive symptoms.[9] While some studies report inflation of median test statistics, methods like LD Score regression often indicate that this is largely due to polygenicity rather than population stratification bias, though careful assessment of such inflation is always necessary.[9], [12] Replication of findings is also a persistent challenge, with observed gaps possibly stemming from differences in how stressful life events are captured across studies or variations in instruments, necessitating further validation in independent cohorts with equivalent tools.[6] Moreover, many large-scale genetic analyses primarily focus on populations of European ancestry, with separate analyses for East Asian or Korean populations, highlighting a significant limitation in the generalizability of findings across diverse global ancestries.[2], [9], [13], [14] This ancestral bias limits the direct applicability of identified variants and polygenic risk scores to non-European populations.
Complex Genetic Architecture and Environmental Influences
Section titled “Complex Genetic Architecture and Environmental Influences”A significant limitation in understanding the genetics of depressive symptoms is the phenomenon of missing heritability, where SNP-based heritability estimates are substantially lower (e.g., 1-13%) than those derived from twin studies (30-40%).[11], [15] This discrepancy suggests that the variance of genetic factors may be underestimated in current models, potentially due to the exclusion of the X chromosome or the predominant reliance on simple additive genetic models that do not fully capture the trait’s complex architecture.[11] Many identified significant SNPs are also located in non-coding regions, implying complex regulatory roles rather than direct protein-coding effects.[16]Furthermore, existing research often neglects the intricate interplay between genetic determinants and environmental factors, such as stressful life events, traumas, or lifestyle choices like smoking, which are known to influence depression risk.[11], [15]Cross-sectional study designs are limited in their ability to elucidate how specific environmental factors affect gene expression in the pathogenesis of depressive symptoms, calling for more controlled experimental studies or longitudinal designs to fully unravel gene-environment interactions.[15] Modelling these gene-environment effects could significantly improve predictive genetic models and explain deviations from purely additive genetic influences.[6]
Variants
Section titled “Variants”Genetic variations play a crucial role in understanding individual differences in susceptibility to and experience of depressive symptoms, often by influencing fundamental biological pathways related to metabolism, neurodevelopment, and cellular function. Many of these variants are located within or near genes that have broad effects on physiological systems, and their impact on depressive symptom is an active area of research.[15] Variants in genes associated with lipid metabolism and cellular processes are frequently investigated for their potential links to mood disorders. The APOEgene, for example, encodes apolipoprotein E, a key protein in lipid transport and neuronal repair, with variants likers7412 and rs429358 influencing lipid levels and brain health. Similarly, LPL(Lipoprotein Lipase), represented by variants such asrs12679834 , rs3208305 , and rs117199990 , is essential for breaking down triglycerides, and variations can affect the availability of fatty acids vital for brain function. The APOB gene, with variants like rs668948 and rs563290 , encodes a primary structural component of low-density lipoproteins, influencing cholesterol transport. Variants in the HERPUD1-CETP region, including rs247617 , rs247616 , and rs12446515 , impact cholesteryl ester transfer protein activity, thereby affecting HDL cholesterol levels. Furthermore,GCKR(Glucokinase Regulatory Protein), with variants likers1260326 , rs780094 , and rs780093 , regulates glucose metabolism, andCELSR2 (Cadherin EGF LAG Seven-Pass G-Type Receptor 2), with rs7528419 and rs12740374 , is associated with lipid traits, suggesting a role in metabolic health that can indirectly affect brain function and depressive symptoms.[11] Other variants influence chromatin remodeling, cell proliferation, and key signaling pathways. The SMARCA4 gene, with variants such as rs114846969 , rs138294113 , rs138175288 , and rs56315738 , is part of a complex that regulates gene expression by altering chromatin structure, a process critical for neuronal development and plasticity. Alterations in chromatin remodeling are increasingly recognized as contributors to mood disorders. The ZPR1 gene, with variant rs964184 , encodes a zinc finger protein involved in cell proliferation and survival, impacting fundamental cellular processes that, if disrupted, could contribute to the cellular mechanisms underlying depressive symptoms. Additionally,ALDH1A2 (Aldehyde Dehydrogenase 1 Family Member A2), featuring variants like rs261291 and rs1532085 , is involved in the synthesis of retinoic acid, a vital signaling molecule for brain development and neuroplasticity, whose dysregulation may affect neuronal health and synaptic function relevant to depression.[2]These genetic influences underscore the complex interplay between systemic biological processes and mental well-being, highlighting how diverse genetic backgrounds can modulate an individual’s predisposition to or experience of depressive symptoms.[15]
Key Variants
Section titled “Key Variants”Conceptualizing Depressive Symptoms and Their Classification
Section titled “Conceptualizing Depressive Symptoms and Their Classification”Depressive symptoms are a constellation of affective, cognitive, and somatic complaints that can manifest with varying patterns and severity.[17] The diagnosis of depressive disorders is primarily based on these symptom presentations, as a validated biomarker for depression does not currently exist.[11]Conceptually, depression can be understood along a continuum of severity, ranging from subthreshold or minor depression to Major Depressive Disorder (MDD) of mild, moderate, or severe intensity.[1] This dimensional approach suggests that depressive experiences exist on a spectrum rather than as strictly categorical distinctions, a perspective supported by longitudinal studies showing an increased risk of MDD in individuals with minor or subthreshold depression.[1] The classification of depressive disorders traditionally relies on nosological systems such as the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV).[15]These systems provide specific diagnostic criteria that define what constitutes a depressive disorder, guiding both clinical diagnosis and research.[15] While categorical distinctions are useful for clinical decision-making, the debate between categorical and dimensional assessment of mental illness acknowledges the continuous nature of symptoms and the potential for a continuum approach to enhance statistical power in research by including a broader range of individuals who might fall into a “grey area” of symptom expression.[1] The recognition of phenotypic heterogeneity, where depression presents with diverse symptom patterns, suggests that analyzing more narrowly defined symptom clusters could help reduce genetic heterogeneity and identify distinct genetic pathways.[11]
Operational Definitions and Tools
Section titled “Operational Definitions and Tools”The operational definition of depressive symptoms involves their assessment through standardized approaches, primarily self-report questionnaires, which capture the presence and severity of various symptoms.[2] These instruments translate the complex experience of depression into quantifiable scores, often reflecting how participants have been feeling over a specified period, such as the last two weeks or 30 days.[2] Widely used self-scored measures include the Patient Health Questionnaire (PHQ-9), the Beck Depression Inventory (BDI) and its revised version (BDI-II), the Center for Epidemiologic Studies-Depression Scale (CES-D), and the K6 scale.[15] For instance, the PHQ-9, a 9-item scale, is based on DSM-IV diagnostic criteria, while the BDI-II is a 21-item self-report questionnaire also revised to correspond to DSM-IV criteria, both designed to measure depression severity.[15] The CES-D scale is another common self-report measure for research in the general population.[4] and the K6 scale is often employed for large-scale screening, asking six questions about the frequency of depressive and anxious symptoms over the past month.[5] These scales typically use ordinal rating systems, where a higher total score signifies a higher level of depression-related feelings.[2] While some studies use these scores as continuous variables to capture the full range of symptom expression.[15] they can also be used to define binary phenotypes, such as classifying individuals as “cases” of depression based on self-report or exceeding specific score thresholds.[15] The careful selection of multiple measures can increase research potential, as differences in results between various self-reported measures may provide complementary information.[16]
Diagnostic and Criteria
Section titled “Diagnostic and Criteria”Diagnostic and criteria for depressive symptoms encompass clinical thresholds, research cut-off values, and the consideration of symptom clusters. Clinical criteria are derived from diagnostic manuals like the DSM-IV, which outline the necessary symptoms and duration for a formal diagnosis of depressive disorders.[15] For research purposes, continuous scores from self-report questionnaires are frequently employed, with specific cut-off values established to differentiate between different levels of symptom severity or to identify probable cases.[5]For example, a cut-off of ≥9 on the K6 scale has been suggested for identifying subjects at high risk of depressive symptoms in the Japanese population, demonstrating specific sensitivity and specificity.[5] Similarly, researchers may calculate cut-off values to maximize differences between groups or to define symptom clusters for more nuanced analyses.[3] Severity gradations, such as mild, moderate, and severe depression, are often determined by the total score on these standardized scales, reflecting the intensity and impact of the symptoms.[1] The absence of a biological biomarker means that these symptom-based assessments and their derived cut-offs are critical for both clinical decision-making and for defining phenotypes in genetic studies.[11] Analyzing specific symptom clusters, such as somatic, positive, and negative domains, can help reduce phenotypic and genetic heterogeneity, potentially increasing the power of association studies to identify genetic factors.[11] The criteria for handling missing data in these assessments, such as excluding participants with more than half of the items missing, also represent an important aspect of criteria in research.[2]
Diagnostic and Prognostic Utility
Section titled “Diagnostic and Prognostic Utility”Measuring depressive symptoms is crucial for diagnostic utility, helping clinicians identify and characterize mental health conditions. Standardized self-report scales like the Center for Epidemiologic Studies Depression Scale (CES-D), the Beck Depression Inventory-II (BDI-II), and the Patient Health Questionnaire-9 (PHQ-9) are widely used to assess the presence and severity of symptoms.[4] The PHQ-9, for instance, is based on the diagnostic criteria for depressive disorders outlined in the DSM-IV, providing a brief yet valid measure of depression severity.[18] These tools allow for systematic evaluation, moving beyond subjective impressions and supporting a more consistent diagnostic process across various clinical settings.[10]Beyond diagnosis, symptom assessment holds significant prognostic value, aiding in the prediction of disease progression and long-term outcomes. Longitudinal studies have demonstrated that individuals with minor or subthreshold depressive symptoms face an increased risk of developing major depressive disorder (MDD).[1]Furthermore, the severity of depressive symptoms is associated with differential mortality rates, highlighting the importance of early identification and intervention.[19] Assessing these symptoms also helps predict functional impairment, particularly in primary care settings, allowing clinicians to anticipate and address potential challenges in patients’ daily lives.[8] Conceptualizing depression along a continuum of severity, rather than purely categorical diagnoses, can enhance statistical power in research and provide a more nuanced understanding of patient trajectories, including those in ‘grey areas’ of severity.[1]
Guiding Treatment and Monitoring
Section titled “Guiding Treatment and Monitoring”Systematic of depressive symptoms is fundamental for guiding treatment selection and monitoring patient progress effectively. Clinicians utilize symptom scores to inform decisions regarding pharmacotherapy, such as the use of specific antidepressants or augmentation strategies for treatment-resistant depression.[20] For instance, understanding a patient’s symptom profile can help in considering treatments like PPAR-γ agonists for major depression or melatonin receptor agonists such as agomelatine.[21] Regular symptom assessment also enables -based care, where treatment adjustments are made based on objective changes in symptom severity, optimizing therapeutic outcomes.[22]The precise of depressive symptoms allows for the evaluation of treatment response, distinguishing between symptom improvement and full remission, which is considered a new gold standard in antidepressant care.[23]Researchers define antidepressant response through metrics like remission (achieving a prespecified symptom threshold) or percentage improvement, critical for clinical trials and individual patient management.[14] Early prediction of response to specific treatments, such as escitalopram monotherapy or adjunctive aripiprazole, can be facilitated by careful symptom monitoring, leading to more personalized and effective care pathways.[24] Furthermore, incorporating genetic insights, such as those from genome-wide by environment interaction studies (GWEIS) and polygenic risk scores, holds promise for improving predictive genetic models and tailoring prevention and treatment strategies to individuals at higher risk.[6]
Understanding Comorbidity and Risk Stratification
Section titled “Understanding Comorbidity and Risk Stratification”Depressive symptom is vital for understanding and managing the complex interplay of depression with various comorbidities and overlapping phenotypes. Depressive symptoms frequently co-occur with anxiety, and tools like the PHQ-9 and Generalized Anxiety Disorder Scale (GAD-7) are often used together to assess these interconnected mental health phenotypes.[25]Beyond psychiatric conditions, depressive symptoms are associated with systemic health issues, including inflammatory markers linked to cardiovascular disease.[26]Furthermore, specific contexts like interferon-based therapy for chronic hepatitis C can induce depressive symptoms, underscoring the need for careful monitoring in patients with chronic medical conditions.[3]Precise symptom assessment contributes significantly to risk stratification, enabling the identification of high-risk individuals and the development of targeted prevention strategies. Genetic studies, including genome-wide association studies (GWAS), have identified specific loci associated with depressive symptoms, such as theTBXAS1 gene in the Korean population and ZNF354C variants in interferon-induced depression, pointing towards biological underpinnings for risk.[2] Environmental and psychosocial factors also play a critical role; for example, socioeconomic deprivation, low social support, and personal debt are strongly associated with psychiatric disorders and suicidal ideation, indicating areas for public health intervention.[25] By integrating genetic predispositions with environmental interactions, as explored in genome-wide by environment interaction studies, clinicians can move towards personalized prevention approaches that consider both biological vulnerability and psychosocial stressors.[15]
Population Studies in Depressive Symptom Assessment
Section titled “Population Studies in Depressive Symptom Assessment”Understanding depressive symptoms at a population level involves diverse study designs, large cohorts, and cross-cultural comparisons to identify prevalence, risk factors, and genetic underpinnings. These studies utilize various methodologies to capture the complex nature of depression across different demographic and socioeconomic contexts.
Population Cohorts and Longitudinal Research
Section titled “Population Cohorts and Longitudinal Research”Large-scale cohort studies are crucial for elucidating the genetic and environmental factors contributing to depressive symptoms over time. In the Korean population, the Health and Prevention Enhancement (H-PEACE) and Gene-Environment of Interaction and Phenotype (GENIE) cohorts, comprising participants from health check-ups between 2003 and 2017, have been instrumental. These cohorts involved genotyping thousands of individuals on Korean-specific chips and measuring depressive symptoms using the Beck Depression Inventory (BDI), allowing for the identification of genetic loci likeTBXAS1associated with depressive symptom pathogenesis , the collection and storage of such sensitive information raise concerns about who has access to this data and how it might be used. Individuals undergoing genetic screening for depressive symptom must provide truly informed consent, understanding the complex implications of having their genetic predisposition known, including the potential for future genetic discrimination in areas like employment or insurance, even if not explicitly for a clinical diagnosis. Furthermore, the ability to predict or infer genetic predispositions for depressive symptom could influence reproductive choices, introducing complex moral and societal debates about selective breeding or genetic interventions.
The aggregation of genetic data into large databases, such as those used in meta-analyses.[1] or platforms like LD Hub.[27] necessitates robust data protection policies to prevent misuse or breaches. While these datasets are invaluable for identifying polygenicity and genetic correlations.[12]the de-identification of genetic information is challenging and imperfect, creating persistent privacy risks. Researchers must adhere to stringent ethical guidelines, ensuring that participants’ data is handled with the utmost care and that the potential benefits of research are carefully weighed against the risks to individual autonomy and privacy. The evolving landscape of genetic understanding demands continuous re-evaluation of ethical frameworks to protect individuals from unintended negative consequences of genetic discoveries related to depressive symptom.
Social Impact: Stigma, Disparities, and Cultural Context
Section titled “Social Impact: Stigma, Disparities, and Cultural Context”The of depressive symptom carries profound social implications, particularly regarding stigma, health disparities, and the critical role of cultural context. Identifying genetic predispositions for depression could exacerbate existing societal stigma surrounding mental illness, potentially leading to increased labeling or discrimination against individuals deemed “at risk.” This is especially concerning given that depression is often conceptualized along a continuum of severity.[1] making clear diagnostic boundaries elusive and potentially broadening the scope of stigmatization. Moreover, socioeconomic factors, such as adverse childhood experiences, personal debt, social exclusion, and neighborhood stressors, are strongly associated with depression.[15] highlighting how social determinants interact with individual vulnerabilities.
Health disparities are a significant concern, as access to genetic testing, counseling, and subsequent mental healthcare may not be equitably distributed, further widening the gap between privileged and vulnerable populations. Cultural considerations are paramount in both the assessment and interpretation of depressive symptom, as symptom presentation and understanding of mental distress vary significantly across different populations.[28] Self-report scales like the CES-D, BDI, PHQ-9, and K6, while widely used, require careful validation and contextualization within specific cultural groups to ensure accurate and culturally sensitive.[4] Ignoring these cultural nuances can lead to misdiagnosis, inappropriate interventions, and further marginalization of specific communities.
Governance, Equity, and Resource Allocation
Section titled “Governance, Equity, and Resource Allocation”Effective policy and regulation are crucial for navigating the ethical complexities of depressive symptom , especially in the realm of genetic data, while simultaneously promoting health equity and justice. Robust genetic testing regulations and data protection laws are essential to govern the collection, analysis, and sharing of genetic information, ensuring that research ethics are upheld and individual rights are protected. Clinical guidelines need to be developed and regularly updated to integrate genetic insights responsibly into mental health practice, advising on appropriate use of genetic information without over-medicalizing or over-simplifying the multifaceted nature of depression.
From an equity and justice perspective, global health initiatives must address the equitable allocation of resources for both research and clinical application of advanced depressive symptom techniques. Studies increasingly involve multi-ancestry or population-specific analyses.[25] underscoring the need to ensure that findings benefit all populations, not just those in well-resourced regions. Vulnerable populations, who often bear a disproportionate burden of mental health challenges and face barriers to care.[29] must be prioritized in both the development and implementation of new diagnostic and treatment strategies, ensuring that genetic information serves to reduce, rather than exacerbate, existing health inequalities.
Frequently Asked Questions About Depressive Symptom
Section titled “Frequently Asked Questions About Depressive Symptom”These questions address the most important and specific aspects of depressive symptom based on current genetic research.
1. Do my family’s struggles mean I’ll get depression too?
Section titled “1. Do my family’s struggles mean I’ll get depression too?”There’s a genetic component to depressive symptoms, with heritability estimates ranging from 23% to 29%. This means you might have a predisposition, but it’s not a guarantee. Environmental factors and life experiences also play a significant role.
2. Can stress actually worsen my depression if it runs in my family?
Section titled “2. Can stress actually worsen my depression if it runs in my family?”Yes, absolutely. Your genetic predispositions can interact with environmental factors like psychosocial stress, potentially making you more vulnerable to depressive symptoms. This complex gene-environment interplay is an active area of research.
3. Does my ethnic background change my risk for depression?
Section titled “3. Does my ethnic background change my risk for depression?”Yes, population-specific studies have identified different genetic links. For example, research in Korean or Japanese populations has found unique genetic variants, such as those in the TBXAS1gene, associated with depressive symptoms. This highlights the importance of culturally sensitive care.
4. Why do I feel so down when my friends seem fine?
Section titled “4. Why do I feel so down when my friends seem fine?”Depressive symptoms are influenced by a complex interplay of many genetic loci, not just one. Your unique genetic makeup, combined with your life experiences, can lead to different emotional responses and symptom severity compared to others.
5. Could a DNA test help my doctor personalize my depression treatment?
Section titled “5. Could a DNA test help my doctor personalize my depression treatment?”While not yet standard for routine clinical care, understanding genetic factors is crucial for research. It helps scientists develop more targeted and personalized treatment approaches in the future, moving beyond a one-size-fits-all model.
6. Is my depression “all in my head” or is it a real health issue?
Section titled “6. Is my depression “all in my head” or is it a real health issue?”Depression is a real health issue with a significant biological basis. Research, including Genome-Wide Association Studies, has identified specific genetic components and pathways involved in the development and severity of depressive symptoms.
7. Why do my depressive symptoms feel different from my sibling’s?
Section titled “7. Why do my depressive symptoms feel different from my sibling’s?”Depressive symptoms can manifest differently due to the complex interplay of various genetic predispositions and unique life experiences. Even within families, these factors can lead to diverse presentations of the condition, affecting how it’s experienced.
8. How do doctors really know how bad my depression is?
Section titled “8. How do doctors really know how bad my depression is?”Healthcare professionals use standardized self-report scales like the Beck Depression Inventory (BDI) or the Center for Epidemiologic Studies Depression (CES-D) scale to quantify your symptoms. These tools provide objective measures to assess severity, track changes, and guide treatment decisions.
9. Can my age change how my depression shows up?
Section titled “9. Can my age change how my depression shows up?”Yes, the presentation of depressive symptoms can differ significantly by age. This variability is an important consideration for healthcare professionals when they are assessing and diagnosing your condition, as symptoms might be expressed differently at various life stages.
10. Is my depression affecting more than just my mood?
Section titled “10. Is my depression affecting more than just my mood?”Yes, depressive symptoms carry a substantial burden, impacting your quality of life, functional abilities, and even contributing to broader social and economic costs. Understanding its biological basis helps in developing comprehensive support and destigmatizing mental health conditions.
This FAQ was automatically generated based on current genetic research and may be updated as new information becomes available.
Disclaimer: This information is for educational purposes only and should not be used as a substitute for professional medical advice. Always consult with a healthcare provider for personalized medical guidance.
References
Section titled “References”[1] Direk N et al. “An Analysis of Two Genome-wide Association Meta-analyses Identifies a New Locus for Broad Depression Phenotype.” Biol Psychiatry, vol. 81, no. 3, 2017, pp. 265-71.
[2] Park K et al. “Genome-wide association study implicates the role of TBXAS1 in the pathogenesis of depressive symptoms among the Korean population.”Transl Psychiatry, vol. 14, no. 1, 2024, p. 81.
[3] Matsunami, K., et al. “Genome-Wide Association Study Identifies ZNF354CVariants Associated with Depression from Interferon-Based Therapy for Chronic Hepatitis C.”PLoS One, vol. 11, no. 10, 2016, e0164222.
[4] Radloff, L. S. “The CES-D Scale: A Self-Report Depression Scale for Research in the General Population.” _Applied Psychological _, vol. 1, 1977, p. 385.
[5] Otowa, T., et al. “The First Pilot Genome-Wide Gene-Environment Study of Depression in the Japanese Population.” PLoS One, 2016.
[6] Arnau-Soler A et al. “Genome-wide by environment interaction studies of depressive symptoms and psychosocial stress in UK Biobank and Generation Scotland.”Transl Psychiatry, vol. 9, no. 1, 2019, p. 55.
[7] Rakofsky, J. J., et al. “The prevalence and severity of depressive symptoms along the spectrum of unipolar depressive disorders: a post hoc analysis.”The Journal of Clinical Psychiatry, vol. 74, no. 11, 2013, pp. 1084-1091.
[8] Backenstrass, M., et al. “A comparative study of nonspecific depressive symptoms and minor depression regarding functional impairment and associated characteristics in primary care.”Comprehensive Psychiatry, vol. 47, no. 1, 2006, pp. 35-41.
[9] Okbay A et al. “Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses.”Nat Genet, vol. 48, no. 6, 2016, pp. 624-33.
[10] Weissman MM et al. “Assessing depressive symptoms in five psychiatric populations: a validation study.”Am J Epidemiol, vol. 106, no. 3, 1977, pp. 203-14.
[11] Demirkan, A., et al. “Somatic, positive and negative domains of the Center for Epidemiological Studies Depression (CES-D) scale: a meta-analysis of genome-wide association studies.” Psychological Medicine, 2016.
[12] Bulik-Sullivan BK et al. “LD Score regression distinguishes confounding from polygenicity in genome-wide association studies.” Nat Genet, vol. 47, no. 3, 2015, pp. 291-95.
[13] Koshimizu H et al. “Genome-wide association study identifies a novel locus associated with psychological distress in the Japanese population.” Transl Psychiatry, vol. 9, no. 1, 2019, p. 30.
[14] Pain O et al. “Identifying the Common Genetic Basis of Antidepressant Response.”Biol Psychiatry Glob Open Sci, vol. 2, no. 3, 2022, pp. 263-72.
[15] Pan C et al. “A multidimensional social risk atlas of depression and anxiety: An observational and genome-wide environmental interaction study.”J Glob Health, vol. 13, 2023, p. 03038.
[16] Ye J et al. “Association between herpes simplex virus 1 exposure and the risk of depression in UK Biobank.” Clin Transl Med, vol. 10, no. 3, 2020, p. e112.
[17] American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. 4th ed. American Psychiatric Association, 1994.
[18] Kroenke, K., et al. “The PHQ-9: validity of a brief depression severity measure.” Journal of General Internal Medicine, vol. 16, no. 9, 2001, pp. 606-13.
[19] Cuijpers, P., et al. “Differential mortality rates in major and subthreshold depression: meta-analysis of studies that measured both.”The British Journal of Psychiatry : The Journal of Mental Science, vol. 202, 2013, pp. 22-27.
[20] Lenze, E. J., et al. “Efficacy, safety, and tolerability of augmentation pharmacotherapy with aripiprazole for treatment-resistant depression in late life: a randomised, double-blind, placebo-controlled trial.” Lancet, vol. 386, no. 10004, 2015, pp. 2404-12.
[21] Bourin, M., and C. Prica. “Melatonin receptor agonist agomelatine: a new drug for treating unipolar depression.” Current Pharmaceutical Design, vol. 15, no. 15, 2009, pp. 1675-1682.
[22] Trivedi, M. H., et al. “Evaluation of outcomes with citalopram for depression using -based care in STAR*D: implications for clinical practice.” *American Journal of Psychiatry*, vol. 163, no. 1, 2006, pp. 28-40.
[23] Keller, M. B. “Remission versus response: the new gold standard of anti-depressant care.”The Journal of Clinical Psychiatry, vol. 65, Suppl 4, 2004, pp. 53-59.
[24] Kennedy, S. H., et al. “Symptomatic and functional outcomes and early prediction of response to escitalopram monotherapy and sequential adjunctive aripiprazole therapy in patients with major depressive disorder: a CAN-BIND-1.”Translational Psychiatry, vol. 6, no. 11, 2016, p. e950.
[25] Bentley, A. R., et al. “Multi-ancestry genome-wide association analyses incorporating SNP-by-psychosocial interactions identify novel loci for serum lipids.” Translational Psychiatry, 2024.
[26] Cesari, M., et al. “Inflammatory markers and cardiovascular disease (The Health, Aging and Body Composition [Health ABC] Study).”American Journal of Cardiology, vol. 92, no. 5, 2003, pp. 522-528.
[27] Zheng, J., et al. “LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis.” Bioinformatics, vol. 33, 2017, pp. 272–279.
[28] Bernert, S., et al. “Is it always the same? Variability of depressive symptoms across six European countries.”Psychiatry Research, vol. 168, 2009, pp. 137–144.
[29] Kawakami, N., et al. “Twelve-month prevalence, severity, and treatment of common mental disorders in communities in Japan: preliminary finding from the World Mental Health Japan Survey 2002–2003.” Psychiatry and Clinical Neurosciences, vol. 59, no. 4, 2005, pp. 441–52.