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Major Depressive Disorder (MDD) is a common, frequently chronic condition characterized by substantial molecular alterations and pathway dysregulations. Single metabolite and targeted metabolomics platforms have revealed several metabolic alterations in depression, including energy metabolism, neurotransmission, and lipid metabolism. More comprehensive coverage of the metabolome is needed to further specify metabolic dysregulations in depression and reveal previously untargeted mechanisms. Here, we measured 820 metabolites using the metabolome-wide Metabolon platform in 2770 subjects from a large Dutch clinical cohort with extensive clinical phenotyping (1101 current MDD, 868 remitted MDD, 801 healthy controls) at baseline, which were repeated in 1805 subjects at 6-year follow up (327 current MDD, 1045 remitted MDD, 433 healthy controls). MDD diagnosis was based on DSM-IV psychiatric interviews. Depression severity was measured with the Inventory of Depressive Symptomatology Self-report. Associations between metabolites and MDD status and depression severity were assessed at baseline and at 6-year follow-up. At baseline, 139 and 126 metabolites were associated with current MDD status and depression severity, respectively, with 79 overlapping metabolites. Adding body mass index and lipid-lowering medication to the models changed results only marginally. Among the overlapping metabolites, 34 were confirmed in internal replication analyses using 6-year follow-up data. Downregulated metabolites were enriched with long-chain monounsaturated (P = 6.7e-07) and saturated (P = 3.2e-05) fatty acids; upregulated metabolites were enriched with lysophospholipids (P = 3.4e-4). Mendelian randomization analyses using genetic instruments for metabolites (N = 14,000) and MDD (N = 800,000) showed that genetically predicted higher levels of the lysophospholipid 1-linoleoyl-GPE (18:2) were associated with greater risk of depression. The identified metabolome-wide profile of depression indicated altered lipid metabolism with downregulation of long-chain fatty acids and upregulation of lysophospholipids, for which causal involvement was suggested using genetic tools. This metabolomics signature offers a window on depression pathophysiology and a potential access point for the development of novel therapeutic approaches.
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Pharmacotherapy is an effective treatment modality across psychiatric disorders. Nevertheless, many patients discontinue their medication at some point. Evidence-based guidance for patients, clinicians, and policymakers on rational discontinuation strategies is vital to enable the best, personalized treatment for any given patient. Nonetheless, there is a scarcity of guidelines on discontinuation strategies. In this perspective, we therefore summarize and critically appraise the evidence on discontinuation of six major psychotropic medication classes: antidepressants, antipsychotics, benzodiazepines, mood stabilizers, opioids, and stimulants. For each medication class, a wide range of topics pertaining to each of the following questions are discussed: (1) Who can discontinue (e.g., what are risk factors for relapse?); (2) When to discontinue (e.g., after 1 year or several years of antidepressant use?); and (3) How to discontinue (e.g., what's the efficacy of dose reduction compared to full cessation and interventions to mitigate relapse risk?). We thus highlight how comparing the evidence across medication classes can identify knowledge gaps, which may pave the way for more integrated research on discontinuation.
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Depression, anxiety and other psychosocial factors are hypothesized to be involved in cancer development. We examined whether psychosocial factors interact with or modify the effects of health behaviors, such as smoking and alcohol use, in relation to cancer incidence. Two-stage individual participant data meta-analyses were performed based on 22 cohorts of the PSYchosocial factors and CAncer (PSY-CA) study. We examined nine psychosocial factors (depression diagnosis, depression symptoms, anxiety diagnosis, anxiety symptoms, perceived social support, loss events, general distress, neuroticism, relationship status), seven health behaviors/behavior-related factors (smoking, alcohol use, physical activity, body mass index, sedentary behavior, sleep quality, sleep duration) and seven cancer outcomes (overall cancer, smoking-related, alcohol-related, breast, lung, prostate, colorectal). Effects of the psychosocial factor, health behavior and their product term on cancer incidence were estimated using Cox regression. We pooled cohort-specific estimates using multivariate random-effects meta-analyses. Additive and multiplicative interaction/effect modification was examined. This study involved 437,827 participants, 36,961 incident cancer diagnoses, and 4,749,481 person years of follow-up. Out of 744 combinations of psychosocial factors, health behaviors, and cancer outcomes, we found no evidence of interaction. Effect modification was found for some combinations, but there were no clear patterns for any particular factors or outcomes involved. In this first large study to systematically examine potential interaction and effect modification, we found no evidence for psychosocial factors to interact with or modify health behaviors in relation to cancer incidence. The behavioral risk profile for cancer incidence is similar in people with and without psychosocial stress.
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Neoplasias , Masculino , Humanos , Neoplasias/psicologia , Ansiedade/etiologia , Fumar , Consumo de Bebidas Alcoólicas , Comportamentos Relacionados com a SaúdeRESUMO
BACKGROUND: Profiling patients on a proposed 'immunometabolic depression' (IMD) dimension, described as a cluster of atypical depressive symptoms related to energy regulation and immunometabolic dysregulations, may optimise personalised treatment. AIMS: To test the hypothesis that baseline IMD features predict poorer treatment outcomes with antidepressants. METHOD: Data on 2551 individuals with depression across the iSPOT-D (n = 967), CO-MED (n = 665), GENDEP (n = 773) and EMBARC (n = 146) clinical trials were used. Predictors included baseline severity of atypical energy-related symptoms (AES), body mass index (BMI) and C-reactive protein levels (CRP, three trials only) separately and aggregated into an IMD index. Mixed models on the primary outcome (change in depressive symptom severity) and logistic regressions on secondary outcomes (response and remission) were conducted for the individual trial data-sets and pooled using random-effects meta-analyses. RESULTS: Although AES severity and BMI did not predict changes in depressive symptom severity, higher baseline CRP predicted smaller reductions in depressive symptoms (n = 376, ßpooled = 0.06, P = 0.049, 95% CI 0.0001-0.12, I2 = 3.61%); this was also found for an IMD index combining these features (n = 372, ßpooled = 0.12, s.e. = 0.12, P = 0.031, 95% CI 0.01-0.22, I2= 23.91%), with a higher - but still small - effect size compared with CRP. Confining analyses to selective serotonin reuptake inhibitor users indicated larger effects of CRP (ßpooled = 0.16) and the IMD index (ßpooled = 0.20). Baseline IMD features, both separately and combined, did not predict response or remission. CONCLUSIONS: Depressive symptoms of people with more IMD features improved less when treated with antidepressants. However, clinical relevance is limited owing to small effect sizes in inconsistent associations. Whether these patients would benefit more from treatments targeting immunometabolic pathways remains to be investigated.
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Antidepressivos , Depressão , Humanos , Depressão/tratamento farmacológico , Antidepressivos/uso terapêutico , Inibidores Seletivos de Recaptação de Serotonina/farmacologia , Inibidores Seletivos de Recaptação de Serotonina/uso terapêutico , Resultado do TratamentoRESUMO
BACKGROUND: Although behavioral mechanisms in the association among depression, anxiety, and cancer are plausible, few studies have empirically studied mediation by health behaviors. We aimed to examine the mediating role of several health behaviors in the associations among depression, anxiety, and the incidence of various cancer types (overall, breast, prostate, lung, colorectal, smoking-related, and alcohol-related cancers). METHODS: Two-stage individual participant data meta-analyses were performed based on 18 cohorts within the Psychosocial Factors and Cancer Incidence consortium that had a measure of depression or anxiety (N = 319 613, cancer incidence = 25 803). Health behaviors included smoking, physical inactivity, alcohol use, body mass index (BMI), sedentary behavior, and sleep duration and quality. In stage one, path-specific regression estimates were obtained in each cohort. In stage two, cohort-specific estimates were pooled using random-effects multivariate meta-analysis, and natural indirect effects (i.e. mediating effects) were calculated as hazard ratios (HRs). RESULTS: Smoking (HRs range 1.04-1.10) and physical inactivity (HRs range 1.01-1.02) significantly mediated the associations among depression, anxiety, and lung cancer. Smoking was also a mediator for smoking-related cancers (HRs range 1.03-1.06). There was mediation by health behaviors, especially smoking, physical inactivity, alcohol use, and a higher BMI, in the associations among depression, anxiety, and overall cancer or other types of cancer, but effects were small (HRs generally below 1.01). CONCLUSIONS: Smoking constitutes a mediating pathway linking depression and anxiety to lung cancer and smoking-related cancers. Our findings underline the importance of smoking cessation interventions for persons with depression or anxiety.
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BACKGROUND: One of the most robust risk factors for developing a mood disorder is having a parent with a mood disorder. Unfortunately, mechanisms explaining the transmission of mood disorders from one generation to the next remain largely elusive. Since timely intervention is associated with a better outcome and prognosis, early detection of intergenerational transmission of mood disorders is of paramount importance. Here, we describe the design of the Mood and Resilience in Offspring (MARIO) cohort study in which we investigate: 1. differences in clinical, biological and environmental (e.g., psychosocial factors, substance use or stressful life events) risk and resilience factors in children of parents with and without mood disorders, and 2. mechanisms of intergenerational transmission of mood disorders via clinical, biological and environmental risk and resilience factors. METHODS: MARIO is an observational, longitudinal cohort study that aims to include 450 offspring of parents with a mood disorder (uni- or bipolar mood disorders) and 100-150 offspring of parents without a mood disorder aged 10-25 years. Power analyses indicate that this sample size is sufficient to detect small to medium sized effects. Offspring are recruited via existing Dutch studies involving patients with a mood disorder and healthy controls, for which detailed clinical, environmental and biological data of the index-parent (i.e., the initially identified parent with or without a mood disorder) is available. Over a period of three years, four assessments will take place, in which extensive clinical, biological and environmental data and data on risk and resilience are collected through e.g., blood sampling, face-to-face interviews, online questionnaires, actigraphy and Experience Sampling Method assessment. For co-parents, information on demographics, mental disorder status and a DNA-sample are collected. DISCUSSION: The MARIO cohort study is a large longitudinal cohort study among offspring of parents with and without mood disorders. A unique aspect is the collection of granular data on clinical, biological and environmental risk and resilience factors in offspring, in addition to available parental data on many similar factors. We aim to investigate the mechanisms underlying intergenerational transmission of mood disorders, which will ultimately lead to better outcomes for offspring at high familial risk.
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Filho de Pais com Deficiência , Resiliência Psicológica , Criança , Humanos , Filho de Pais com Deficiência/psicologia , Estudos de Coortes , Estudos Longitudinais , Transtornos do Humor/psicologia , Pais/psicologiaRESUMO
BACKGROUND: Tailoring antidepressant drugs (AD) to patients' genetic drug-metabolism profile is promising. However, literature regarding associations of ADs' treatment effect and/or side effects with drug metabolizing genes CYP2D6 and CYP2C19 has yielded inconsistent results. Therefore, our aim was to longitudinally investigate associations between CYP2D6 (poor, intermediate, and normal) and CYP2C19 (poor, intermediate, normal, and ultrarapid) metabolizer-status, and switching/discontinuing of ADs. Next, we investigated whether the number of perceived side effects differed between metabolizer statuses. METHODS: Data came from the multi-site naturalistic longitudinal cohort Netherlands Study of Depression and Anxiety (NESDA). We selected depression- and/or anxiety patients, who used AD at some point in the course of the 9 years follow-up period (n = 928). Medication use was followed to assess patterns of AD switching/discontinuation over time. CYP2D6 and CYP2C19 alleles were derived using genome-wide data of the NESDA samples and haplotype data from the PharmGKB database. Logistic regression analyses were conducted to investigate the association of metabolizer status with switching/discontinuing ADs. Mann-Whitney U-tests were conducted to compare the number of patient-perceived side effects between metabolizer statuses. RESULTS: No significant associations were observed of CYP metabolizer status with switching/discontinuing ADs, nor with the number of perceived side effects. CONCLUSIONS: We found no evidence for associations between CYP metabolizer statuses and switching/discontinuing AD, nor with side effects of ADs, suggesting that metabolizer status only plays a limited role in switching/discontinuing ADs. Additional studies with larger numbers of PM and UM patients are needed to further determine the potential added value of pharmacogenetics to guide pharmacotherapy.
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Antidepressivos , Citocromo P-450 CYP2C19 , Citocromo P-450 CYP2D6 , Humanos , Citocromo P-450 CYP2D6/genética , Citocromo P-450 CYP2C19/genética , Masculino , Antidepressivos/uso terapêutico , Feminino , Pessoa de Meia-Idade , Adulto , Estudos Longitudinais , Países Baixos , Transtornos de Ansiedade/genética , Transtornos de Ansiedade/tratamento farmacológico , Transtorno Depressivo/tratamento farmacológico , Transtorno Depressivo/genéticaRESUMO
In this cohort profile article we describe the lifetime major depressive disorder (MDD) database that has been established as part of the BIObanks Netherlands Internet Collaboration (BIONIC). Across the Netherlands we collected data on Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) lifetime MDD diagnosis in 132,850 Dutch individuals. Currently, N = 66,684 of these also have genomewide single nucleotide polymorphism (SNP) data. We initiated this project because the complex genetic basis of MDD requires large population-wide studies with uniform in-depth phenotyping. For standardized phenotyping we developed the LIDAS (LIfetime Depression Assessment Survey), which then was used to measure MDD in 11 Dutch cohorts. Data from these cohorts were combined with diagnostic interview depression data from 5 clinical cohorts to create a dataset of N = 29,650 lifetime MDD cases (22%) meeting DSM-5 criteria and 94,300 screened controls. In addition, genomewide genotype data from the cohorts were assembled into a genomewide association study (GWAS) dataset of N = 66,684 Dutch individuals (25.3% cases). Phenotype data include DSM-5-based MDD diagnoses, sociodemographic variables, information on lifestyle and BMI, characteristics of depressive symptoms and episodes, and psychiatric diagnosis and treatment history. We describe the establishment and harmonization of the BIONIC phenotype and GWAS datasets and provide an overview of the available information and sample characteristics. Our next step is the GWAS of lifetime MDD in the Netherlands, with future plans including fine-grained genetic analyses of depression characteristics, international collaborations and multi-omics studies.
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Bancos de Espécimes Biológicos , Transtorno Depressivo Maior , Estudo de Associação Genômica Ampla , Humanos , Países Baixos/epidemiologia , Feminino , Masculino , Transtorno Depressivo Maior/genética , Transtorno Depressivo Maior/epidemiologia , Pessoa de Meia-Idade , Adulto , Internet , Genômica , Polimorfismo de Nucleotídeo Único , Estudos de Coortes , Fenótipo , IdosoRESUMO
OBJECTIVES: Despite expanding knowledge about the internal and external resources that contribute to resilience among individuals who have experienced depression, the long-term accessibility and protectiveness of these resources across different stressors is unknown. We investigated whether and how the resilience resources of individuals who previously recovered from late-life depression remained protective during the COVID-19 pandemic. METHODS: We used a sequential explanatory mixed methods design. Quantitative data were derived from two psychiatric case-control cohorts and included twelve repeated measures during the COVID-19 pandemic (n = 465, aged ≥ 60). Qualitative data included two sequential interviews held in 2020 (n = 25) and 2021 (n = 19). We used thematic analysis to determine the protective resources after depression and during the COVID-19 pandemic and linear mixed models to examine the effect of these resources on change in depressive symptoms during the COVID-19 pandemic. RESULTS: While resources of 'Taking agency', 'Need for rest', 'Managing thought processes' and 'Learning from depression' remained accessible and protective during the pandemic, 'Social support' and 'Engaging in activities' did not. 'Negotiating with lockdown measures', 'changing social contact' and 'changing activities' were compensating strategies. Quantitative data confirmed the protectiveness of social contact, social cohesion, sense of mastery, physical activity, staying active and entertained and not following the media. CONCLUSION: Many of the resources that previously helped to recover from depression also helped to maintain good mental health during the COVID-19 pandemic. Where accessibility and protectiveness declined, compensatory strategies or new resources were used. Hence, the sustainability of resilience is enabled through adaptation and compensation processes.
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COVID-19 , Depressão , Resiliência Psicológica , Humanos , COVID-19/psicologia , COVID-19/epidemiologia , Idoso , Feminino , Masculino , Depressão/psicologia , Pessoa de Meia-Idade , Apoio Social , SARS-CoV-2 , Estudos de Casos e Controles , Pesquisa Qualitativa , Idoso de 80 Anos ou maisAssuntos
Restrição Calórica , Depressão , Obesidade , Sobrepeso , Humanos , Obesidade/psicologia , Sobrepeso/psicologia , Restrição Calórica/métodosRESUMO
BACKGROUND: Childhood trauma, including emotional neglect, emotional abuse, physical abuse, and sexual abuse, may contribute to borderline personality features like affective instability, identity problems, negative relationships, and self-harm. This study aims to explore how different types of childhood trauma affect these features in bipolar versus unipolar depressive disorders. METHODS: We included 839 participants of the Netherlands Study of Depression and Anxiety (NESDA) with a lifetime diagnosis of major depressive disorder single episode (MDDS; N = 443), recurrent major depressive disorder (MDD-R; N = 331), or bipolar disorder (BD; N = 65). Multivariate regression was used to analyze data from the Childhood Trauma Interview and borderline features (from the self-report Personality Assessment Inventory). RESULTS: On average, participants were 48.6 years old (SD: 12.6), with 69.2 % being women, and 50.3 % of participants assessed positive for childhood trauma. Adjusted analyses revealed that participants diagnosed with BD, followed by MDD-R, exhibited the highest number of borderline personality features. Additionally, within the entire group, a strong association was found between childhood trauma, especially emotional neglect, and the presence of borderline personality features. CONCLUSION: Given the high prevalence of childhood trauma and borderline personality features, screening for these factors in individuals with mood disorders is crucial. Identifying these elements can inform and enhance the management of the often fluctuating and complex nature of these comorbid conditions, leading to more effective and tailored treatment strategies.
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Transtorno Bipolar , Transtorno da Personalidade Borderline , Transtorno Depressivo Maior , Humanos , Feminino , Masculino , Transtorno da Personalidade Borderline/epidemiologia , Transtorno da Personalidade Borderline/psicologia , Transtorno Bipolar/psicologia , Transtorno Bipolar/epidemiologia , Pessoa de Meia-Idade , Transtorno Depressivo Maior/psicologia , Transtorno Depressivo Maior/epidemiologia , Adulto , Países Baixos/epidemiologia , Experiências Adversas da Infância/estatística & dados numéricos , Experiências Adversas da Infância/psicologia , Sobreviventes Adultos de Maus-Tratos Infantis/psicologia , Sobreviventes Adultos de Maus-Tratos Infantis/estatística & dados numéricos , Maus-Tratos Infantis/psicologia , Maus-Tratos Infantis/estatística & dados numéricosRESUMO
OBJECTIVE: Immuno-metabolic depression (IMD) is proposed to be a form of depression encompassing atypical, energy-related symptoms (AES), low-grade inflammation and metabolic dysregulations. Light therapy may alleviate AES by modulating inflammatory and metabolic pathways. We investigated whether light therapy improves clinical and biological IMD features and whether effects of light therapy on AES or depressive symptom severity are moderated by baseline IMD features. Associations between changes in symptoms and biomarkers were explored. METHODS: In secondary analyses, clinical trial data was used from 77 individuals with depression and type 2 diabetes mellitus (T2DM) randomized to four weeks of light therapy or placebo. AES severity and depressive symptom severity were based on the Inventory of Depressive Symptomatology. Biomarkers included 73 metabolites (Nightingale) summarized in three principal components and CRP, IL-6, TNF-α, INF-γ. Linear regression analyses were performed. RESULTS: Light therapy had no effect on AES severity, inflammatory markers and metabolite principle components versus placebo. None of these baseline features moderated the effects of light therapy on AES severity. Only a principle component reflecting metabolites implicated in glucose homeostasis moderated the effects of light therapy on depressive symptom severity (ßinteraction = 0.65, P = 0.001, FDR = 0.003). Changes in AES were not associated with changes in biomarkers. CONCLUSION: Findings do not support the efficacy of light therapy in reducing IMD features in patients with depression and T2DM. We find limited evidence that light therapy is a more beneficial depression treatment among those with more IMD features. Changes in clinical and biological IMD features did not align over four-weeks' time. TRIAL REGISTRATION: The Netherlands Trial Register (NTR) NTR4942.
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Depressão , Diabetes Mellitus Tipo 2 , Fototerapia , Humanos , Diabetes Mellitus Tipo 2/terapia , Masculino , Feminino , Pessoa de Meia-Idade , Fototerapia/métodos , Depressão/terapia , Depressão/metabolismo , Biomarcadores/sangue , Idoso , Adulto , Inflamação , Resultado do Tratamento , Índice de Gravidade de DoençaRESUMO
Background: Acylcarnitines (ACs) are involved in bioenergetics processes that may play a role in the pathophysiology of depression. Studies linking AC levels to depression are few and provide mixed findings. We examined the association of circulating ACs levels with Major Depressive Disorder (MDD) diagnosis, overall depression severity and specific symptom profiles. Methods: The sample from the Netherlands Study of Depression and Anxiety included participants with current (n=1035) or remitted (n=739) MDD and healthy controls (n=800). Plasma levels of four ACs (short-chain: acetylcarnitine C2 and propionylcarnitine C3; medium-chain: octanoylcarnitine C8 and decanoylcarnitine C10) were measured. Overall depression severity as well as atypical/energy-related (AES), anhedonic and melancholic symptom profiles were derived from the Inventory of Depressive Symptomatology. Results: As compared to healthy controls, subjects with current or remitted MDD presented similarly lower mean C2 levels (Cohen's d=0.2, p≤1e-4). Higher overall depression severity was significantly associated with higher C3 levels (ß=0.06, SE=0.02, p=1.21e-3). No associations were found for C8 and C10. Focusing on symptom profiles, only higher AES scores were linked to lower C2 (ß=-0.05, SE=0.02, p=1.85e-2) and higher C3 (ß=0.08, SE=0.02, p=3.41e-5) levels. Results were confirmed in analyses pooling data with an additional internal replication sample from the same subjects measured at 6-year follow-up (totaling 4195 observations). Conclusions: Small alterations in levels of short-chain acylcarnitine levels were related to the presence and severity of depression, especially for symptoms reflecting altered energy homeostasis. Cellular metabolic dysfunctions may represent a key pathway in depression pathophysiology potentially accessible through AC metabolism.
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BACKGROUND: Self-esteem is an important psychological concept that can be measured explicitly (reflective processing) and implicitly (associative processing). The current study examined 1) the association between childhood trauma (CT) and both explicit and implicit self-esteem, and 2) whether self-esteem mediated the association between CT and depression/anxiety. METHODS: In 1479 adult participants of the Netherlands Study of Depression and Anxiety, CT was assessed with a semi-structured interview, depression/anxiety symptoms with self-report questionnaires and explicit and implicit self-esteem with the Rosenberg Self-Esteem Scale and Implicit Association Test, respectively. ANOVAs and regression analyses determined the association between CT (no/mild/severe CT), its subtypes (abuse/neglect) and self-esteem. Finally, we examined whether self-esteem mediated the relationship between CT and depression/anxiety. RESULTS: Participants with CT reported lower explicit (but not lower implicit) self-esteem compared to those without CT (p < .001, partial η2 = 0.06). All CT types were associated with lower explicit self-esteem (p = .05 for sexual abuse, p < .001 for other CT types), while only emotional neglect significantly associated with lower implicit self-esteem after adjusting for sociodemographic characteristics (p = .03). Explicit self-esteem mediated the relationship between CT and depression/anxiety symptoms (proportion mediated = 48-77 %). LIMITATIONS: The cross-sectional design precludes from drawing firm conclusions about the direction of the proposed relationships. CONCLUSIONS: Our results suggested that the relationship between CT and depression/anxiety symptoms can at least partly be explained by explicit self-esteem. This is of clinical relevance as it points to explicit self-esteem as a potential relevant treatment target for people with CT.
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Experiências Adversas da Infância , Depressão , Adulto , Humanos , Depressão/psicologia , Estudos Transversais , Transtornos de Ansiedade , Ansiedade , AutoimagemRESUMO
Recently the 1/f signal of human electroencephalography has attracted attention, as it could potentially reveal a quantitative measure of neural excitation and inhibition in the brain, that may be relevant in a clinical setting. The purpose of this short article is to show that the 1/f signal depends on the vigilance state of the brain in both humans and mice. Therefore, proper labelling of the EEG signal is important as improper labelling may obscure disease-related changes in the 1/f signal. We demonstrate this by comparing EEG results from a longitudinal study in a genetic mouse model for synaptic dysfunction in schizophrenia and autism spectrum disorders to results from a large European cohort study with schizophrenia and mild Alzheimer's disease patients. The comparison shows when the 1/f is corrected for vigilance state there is a difference between groups, and this effect disappears when vigilance state is not corrected for. In conclusion, more attention should be paid to the vigilance state during analysis of EEG signals regardless of the species.
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Encéfalo , Eletroencefalografia , Animais , Camundongos , Humanos , Masculino , Encéfalo/fisiopatologia , Esquizofrenia/fisiopatologia , Feminino , Doença de Alzheimer/fisiopatologia , Idoso , Nível de Alerta/fisiologia , Pessoa de Meia-Idade , Transtorno do Espectro Autista/fisiopatologia , Estudos LongitudinaisRESUMO
Despite great progress on methods for case-control polygenic prediction (e.g. schizophrenia vs. control), there remains an unmet need for a method that genetically distinguishes clinically related disorders (e.g. schizophrenia (SCZ) vs. bipolar disorder (BIP) vs. depression (MDD) vs. control); such a method could have important clinical value, especially at disorder onset when differential diagnosis can be challenging. Here, we introduce a method, Differential Diagnosis-Polygenic Risk Score (DDx-PRS), that jointly estimates posterior probabilities of each possible diagnostic category (e.g. SCZ=50%, BIP=25%, MDD=15%, control=10%) by modeling variance/covariance structure across disorders, leveraging case-control polygenic risk scores (PRS) for each disorder (computed using existing methods) and prior clinical probabilities for each diagnostic category. DDx-PRS uses only summary-level training data and does not use tuning data, facilitating implementation in clinical settings. In simulations, DDx-PRS was well-calibrated (whereas a simpler approach that analyzes each disorder marginally was poorly calibrated), and effective in distinguishing each diagnostic category vs. the rest. We then applied DDx-PRS to Psychiatric Genomics Consortium SCZ/BIP/MDD/control data, including summary-level training data from 3 case-control GWAS ( N =41,917-173,140 cases; total N =1,048,683) and held-out test data from different cohorts with equal numbers of each diagnostic category (total N =11,460). DDx-PRS was well-calibrated and well-powered relative to these training sample sizes, attaining AUCs of 0.66 for SCZ vs. rest, 0.64 for BIP vs. rest, 0.59 for MDD vs. rest, and 0.68 for control vs. rest. DDx-PRS produced comparable results to methods that leverage tuning data, confirming that DDx-PRS is an effective method. True diagnosis probabilities in top deciles of predicted diagnosis probabilities were considerably larger than prior baseline probabilities, particularly in projections to larger training sample sizes, implying considerable potential for clinical utility under certain circumstances. In conclusion, DDx-PRS is an effective method for distinguishing clinically related disorders.
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BACKGROUND: Prior research has associated spoken language use with depression, yet studies often involve small or non-clinical samples and face challenges in the manual transcription of speech. This paper aimed to automatically identify depression-related topics in speech recordings collected from clinical samples. METHODS: The data included 3919 English free-response speech recordings collected via smartphones from 265 participants with a depression history. We transcribed speech recordings via automatic speech recognition (Whisper tool, OpenAI) and identified principal topics from transcriptions using a deep learning topic model (BERTopic). To identify depression risk topics and understand the context, we compared participants' depression severity and behavioral (extracted from wearable devices) and linguistic (extracted from transcribed texts) characteristics across identified topics. RESULTS: From the 29 topics identified, we identified 6 risk topics for depression: 'No Expectations', 'Sleep', 'Mental Therapy', 'Haircut', 'Studying', and 'Coursework'. Participants mentioning depression risk topics exhibited higher sleep variability, later sleep onset, and fewer daily steps and used fewer words, more negative language, and fewer leisure-related words in their speech recordings. LIMITATIONS: Our findings were derived from a depressed cohort with a specific speech task, potentially limiting the generalizability to non-clinical populations or other speech tasks. Additionally, some topics had small sample sizes, necessitating further validation in larger datasets. CONCLUSION: This study demonstrates that specific speech topics can indicate depression severity. The employed data-driven workflow provides a practical approach for analyzing large-scale speech data collected from real-world settings.
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Aprendizado Profundo , Fala , Humanos , Smartphone , Depressão/diagnóstico , Interface para o Reconhecimento da FalaRESUMO
BACKGROUND: Overweight and obesity impose a considerable individual and social burden, and the urban environments might encompass factors that contribute to obesity. Nevertheless, there is a scarcity of research that takes into account the simultaneous interaction of multiple environmental factors. OBJECTIVES: Our objective was to perform an exposome-wide association study of body mass index (BMI) in a multicohort setting of 15 studies. METHODS: Studies were affiliated with the Dutch Geoscience and Health Cohort Consortium (GECCO), had different population sizes (688-141,825), and covered the entire Netherlands. Ten studies contained general population samples, others focused on specific populations including people with diabetes or impaired hearing. BMI was calculated from self-reported or measured height and weight. Associations with 69 residential neighborhood environmental factors (air pollution, noise, temperature, neighborhood socioeconomic and demographic factors, food environment, drivability, and walkability) were explored. Random forest (RF) regression addressed potential nonlinear and nonadditive associations. In the absence of formal methods for multimodel inference for RF, a rank aggregation-based meta-analytic strategy was used to summarize the results across the studies. RESULTS: Six exposures were associated with BMI: five indicating neighborhood economic or social environments (average home values, percentage of high-income residents, average income, livability score, share of single residents) and one indicating the physical activity environment (walkability in 5-km buffer area). Living in high-income neighborhoods and neighborhoods with higher livability scores was associated with lower BMI. Nonlinear associations were observed with neighborhood home values in all studies. Lower neighborhood home values were associated with higher BMI scores but only for values up to 300,000. The directions of associations were less consistent for walkability and share of single residents. DISCUSSION: Rank aggregation made it possible to flexibly combine the results from various studies, although between-study heterogeneity could not be estimated quantitatively based on RF models. Neighborhood social, economic, and physical environments had the strongest associations with BMI. https://doi.org/10.1289/EHP13393.
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Índice de Massa Corporal , Exposição Ambiental , Expossoma , Humanos , Países Baixos , Exposição Ambiental/estatística & dados numéricos , Características de Residência/estatística & dados numéricos , Masculino , Feminino , Obesidade/epidemiologia , Estudos de Coortes , Algoritmo Florestas AleatóriasRESUMO
Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects.