B. Penninx

Longitudinal associations between inflammatory biomarkers, polygenic risk scores and cardiometabolic outcomes in major depressive and anxiety disorders: Findings from the Netherlands Study of Depression and Anxiety (NESDA).

Cardiometabolic dysfunctions are prevalent in individuals with major depressive (MDD) and/or anxiety disorders, yet the longitudinal relationships between inflammation, polygenic susceptibility, and cardiometabolic outcomes remain incompletely understood. We analyzed longitudinal data from 2,716 participants retrieved from the Netherlands Study of Depression and Anxiety (NESDA) across three measurements over six years. Longitudinal associations between inflammatory biomarkers (CRP, IL-6, and TNF-α), polygenic risk scores (PRS), and ten cardiometabolic outcomes were assessed using linear mixed-effects models. Between-group differences were tested via current diagnostic subgroup interaction terms while adjusting for common covariates. Population-level associations were evaluated in the full sample, and PRS-related variance explained was quantified using changes in marginal R2. No interactions between inflammatory biomarkers or PRSes and current MDD and/or anxiety diagnosis survived FDR correction, although several nominally significant interactions were observed. In the full sample, higher CRP, IL-6, and TNF-α levels were consistently associated with higher BMI, waist circumference, and triglyceride levels, and lower HDL cholesterol, while CRP and IL-6 were additionally associated with blood pressure and glucose-related outcomes. PRSCRP was associated with multiple cardiometabolic outcomes, explaining an additional 0.16 % of the variance in systolic blood pressure to 0.72 % in BMI. All eight cardiometabolic PRSes showed positive associations with their corresponding outcomes and explained up to 12.88 % of the variance in LDL cholesterol. Overall, inflammatory biomarkers and polygenic susceptibility to inflammatory and cardiometabolic traits are associated with cardiometabolic outcomes. These findings may help inform future mechanistic and causal studies aimed at improving cardiometabolic risk assessment in people with mental health disorders.

Multilingual lexical feature analysis of spoken language for predicting major depression symptom severity.

BACKGROUND Remotely captured spoken language could provide objective, regular indicators of depression symptom severity. However, research to date has largely used non-clinical, cross-sectional written language and complex machine learning (ML) approaches with limited interpretability. METHODS We used linear mixed-effect models to identify interpretable lexical features associated with symptom severity in data from the RADAR-MDD study that comprised 5846 smartphone recordings and Patient Health Questionnaire (PHQ-8) scores from 467 participants in the UK, Netherlands and Spain. We then developed ML models and systematically assessed via nested cross-validation whether interpretable lexical features or high-dimensional vector embeddings improved the accuracy of PHQ-8 prediction over sociodemographic and confounding features. RESULTS Depression symptom severity was associated with five lexical features, including reductions in word count measures, use of first-person plural pronouns and positive word frequency. Associations were stable across countries, except for positive word frequency. Lexical features and vector embeddings did improve prediction accuracy beyond baseline models. LIMITATIONS Our cohort was skewed in age (median = 53, IQR 35 to 62) and majority female (n = 357), potentially affecting the generalizability of our results. A lack of natural language processing tools for non-English languages restricted our feature choices. CONCLUSION Further research is required to realise the value of spoken lexical markers in clinical research and practice including larger and more diverse samples, elicitation protocol development and analytical methods that account for within- and between-individual variations.

Determinants of the plasma metabolome: cross-sectional and longitudinal associations over six years in the NESDA cohort

Summary Background The plasma metabolome represents a valuable molecular readout of a person’s physiological state, yet its relation to health, stress and lifestyle remains underexplored collectively. Methods Here, we conducted an untargeted metabolomics analysis using 3804 paired samples from 1902 participants of the observational Netherlands Study of Depression and Anxiety at baseline and six-year follow-up, quantifying 680 plasma metabolites. We characterised five metabolome principal components, three with distinct biochemical enrichments related to transmembrane transport, sphingolipid, and amino acid metabolism. Findings Metabolite levels showed moderate intrapersonal correlation between baseline and six-year follow-up (ICCmedian = 0.482), and 22% of metabolites showed standardised mean differences >0.2. Multivariate linear modelling on 18 baseline determinants across demographics, psychosocial environment, lifestyle, somatic and mental health explained a maximum of 35% of baseline metabolome PC variance and 12% of six-year change ΔPC variance. Demographic (e.g., sex, age), somatic health (e.g., BMI, medication) and lifestyle factors (e.g., smoking, alcohol intake) demonstrated strong associations both cross-sectionally and longitudinally, while psychosocial factors and mental health contributed minor explained variance in comparison. Interpretation Altogether, our study provides hierarchical insights into the cross-sectional and longitudinal implications of health, stress, and lifestyle exposures for the plasma metabolome. Funding Geestkracht program of the Netherlands Organisation for Health Research and Development, the Dutch Research Council and the Dutch Ministry of Education, Culture and Science, Stress in Action, Amsterdam Neuroscience, ImmunoMIND, the National Institute of Mental Health, National Institute on Ageing and the Foundation for the National Institutes of Health.

Distinguishing different psychiatric disorders using DDx-PRS.

Childhood trauma and suicide ideation and attempts: Examining the mediating role of psychosocial, personality, lifestyle and biological factors.

BACKGROUND Childhood trauma (CT) is robustly associated with suicide ideation (SI) and attempts (SA) in individuals with depressive and/or anxiety disorders. However, pathways underlying these associations and whether they differ between suicide ideation only (SI + SA-) and suicide ideation with attempt (SI + SA+) remain unclear. Variables that may statistically account for CT-SI + SA- and CT-SI + SA+ associations were examined. METHODS Data from 1572 respondents with a 12-month depressive and/or anxiety disorder from the Netherlands Study of Depression and Anxiety were used. The NEMESIS childhood trauma interview (0-8 score) measured CT severity. Single and multiple-mediator models via generalized structural equation modeling (GSEM) were used to investigate whether psychosocial, personality, lifestyle and biological variables showed indirect associations consistent with mediation of the association between CT and suicide outcomes. RESULTS Higher CT scores were associated with SI + SA- (OR = 1.51, 95% CI: 1.32-1.73) and SI + SA+ (OR = 2.68, 95% CI: 2.21-3.27). In single mediator models, personality traits, insomnia, low social support and loneliness statistically accounted for a part of the association between CT and both outcomes. Interleukin-6 statistically accounted for a small portion of the CT-SI + SA+ link. In multiple-mediator models, direct effects reduced by 65.6% (SI + SA-) and 39.1% (SI + SA+) and aggression, insomnia, social support (both outcomes), neuroticism, introversion, hopelessness (SI + SA-) and locus of control (SI + SA+) remained significant. LIMITATIONS The cross-sectional design limits causal inferences. CONCLUSION Multiple personality traits, insomnia and lower social support statistically accounted for a portion of the association between CT and suicide outcomes. Especially aggression, insomnia and social support may represent actionable suicide prevention targets, pending longitudinal validation.

Atypical energy-related symptoms define biologically distinct subtypes of major depressive disorder

Findings show that the directionality of neurovegetative symptoms indexes genetic heterogeneity within MDD, with metabolic biology as a central axis of differentiation.