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
BACKGROUND AND AIMS
Up to half of patients switch or discontinue antihypertensive medications within the first year, but underlying mechanisms remain elusive. This study aimed to identify genetic predictors of antihypertensive medication use trajectories within the first year.
METHODS
Using longitudinal medication data from >400 000 genotyped antihypertensive medication users across three cohorts (FinnGen, the UK Biobank, and the Estonian Biobank), short-term antihypertensive medication use trajectories were classified as Continue, Switch, or Discontinue. Genome-wide association studies were performed across five medication classes.
RESULTS
In total, 14 genome-wide significant loci were identified for switching from angiotensin-converting enzyme inhibitors (ACEI) and dihydropyridine calcium channel blockers (dCCB) to other antihypertensive medications. For ACEI switching, evidence converged on the neurotensin-NTSR1 pathway, including a 320-fold Finnish-enriched protective missense variant in the neurotensin receptor gene NTSR1 (rs148569146 [G301R], odds ratio [OR] 0.49, P = 3.3 × 10-43) and a variant near RASSF9 (rs181941187, OR = 0.74, P = 1.2 × 10-49) tagging the neurotensin gene NTS. In drug-gene interaction analyses, NTSR1 G301R was associated with reduced ACEI-induced cough risk (OR 0.39, P = 8.1 × 10-4). The dCCB switching locus at CYP3A43 was in near-complete linkage (r2 = 0.99) with the functional CYP3A4*22 allele (rs35599367, OR 1.23, P = 6.1 × 10-10). A polygenic risk score (PRS) for ACEI switching predicted two-fold ACEI cough risk in the top 10% PRS compared with the middle 20% in an independent sample of the Estonian Biobank.
CONCLUSIONS
These findings extend the bradykinin hypothesis of ACEI-induced cough by implicating neurotensin-NTSR1 signalling, pinpoint CYP3A4*22 as a novel functional predictor of dCCB switching with potential for genotype-guided prescribing, and validate medication use trajectories as a framework for pharmacogenetic discovery.
F. Vaura, Kristi Krebs, T. Kiiskinen et al.· European Heart Journal· 0 citations
This work developed a neural network model, nnCNV, to predict deletions in the CYP2C19 pharmacogene region from array intensity signals and demonstrated that long-range information, which cannot be utilized by hidden Markov models, can improve CNV calling.
Burak Yelmen, R. Hofmeister, Viido Kaur Lutsar et al.· bioRxiv· 0 citations
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