Background: Metabolomic studies of depression have yielded heterogeneous findings, potentially because metabolic correlates differ across symptoms and metabolic states. We examined symptom-specific metabolomic associations and whether body mass index (BMI) modifies these relationships. Methods: We analyzed 83,717 Estonian Biobank participants (70.6% female) with 249 Nightingale metabolite measures and 14 lifetime depressive symptoms. Logistic regression models progressively adjusted for sociodemographic, lifestyle, medication, and BMI factors. BMI-related attenuation and metabolite x BMI interactions were evaluated, followed by self-organizing map analyses of broader metabolic context. Results: Before BMI adjustment, 660 metabolite-symptom associations were Bonferroni-significant; 136 were significant after BMI adjustment, including 105 retained associations. Weight-related associations showed the strongest BMI dependence: none of 199 weight-gain associations and 2 of 115 weight-loss associations were retained. Among 691 preselected metabolite-symptom pairs, 211 (30.5%) showed significant metabolite x BMI interactions after false discovery rate correction. Six systemic metabolic profiles were identified, but only 3 of 211 BMI-sensitive pairs showed additional profile-dependent heterogeneity. Conclusions: Circulating metabolic correlates of depressive symptoms are heterogeneous and strongly dependent on symptom phenotype and BMI-related metabolic context. These findings suggest that metabolic biomarkers in depression should be interpreted in relation to both symptom presentation and metabolic state rather than as uniform correlates of the disorder.
S. Kurvits, N. Taba, Estonian biobank research team et al.· medRxiv· 0 citations
Objective: The objective of this study was to detect genetic factors associated with dermatochalasis using a genome-wide association study (GWAS) across three large cohorts. Design: GWAS meta-analysis Participants: A total of 13,200 dermatochalasis cases and 962,513 controls were included. Methods: A GWAS meta-analysis of dermatochalasis combining data from the FinnGen, the Estonian Biobank and the UK Biobank was conducted. We also performed colocalization analyses, a phenome-wide association study and age-at-onset analysis, and assessed genetic correlations with various diseases and traits. Main outcome measures: Identification of genetic variants associated with dermatochalasis. Results: We identified 18 loci associated with dermatochalasis at genome-wide significance, 16 of which were novel. Most of these loci had genes involved in skin biology and cutaneous diseases, such as the genes encoding elastin (ELN) and Latent TGF-{beta} binding protein 1 (LTBP1). Phenome-wide association study revealed previous associations with morphology-related traits, while genetic correlation analysis highlighted multiple genetic correlations, especially with smoking and pain. Conclusions: We detected 18 genetic loci associated with dermatochalasis, characterized these loci in detail and demonstrated their relevance in skin biology and related processes. These findings give novel information on the genetic background of dermatochalasis and provide a solid basis for further research.
K. Rajueni, F. Koskimaki, V. Salo et al.· medRxiv· 0 citations
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