Childhood maltreatment stands out as a pivotal risk factor for depression, with gene-by-environment interaction serving as a crucial mechanism. Here we perform genome-wide interaction analyzes of childhood maltreatment in the UK Biobank, integrating methylation evidence through colocalization analysis and identifying associated brain structure abnormalities from childhood to adulthood. A genome-wide significant genomic region interacting with childhood maltreatment is identified at 8p11.21 (IDO2 rs7846217, P = 2.02e–08), implicating the tryptophan-kynurenine pathway. Colocalization analysis reveals that IDO2 rs11777027, rs2340953 and rs28631334 are associated with depression in individuals exposed to childhood maltreatment and colocalize with methylation signals in both blood and brain for IDO2. These interactions affect cortical thickness of the left supramarginal gyrus in children (P = 9.72e–04) and adults (P = 1.34e–04), as well as cortical volume in the right angular gyrus in children (P = 1.02e–04). Furthermore, the interactions significantly predict new-onset depression at a 2-year follow-up in children. Stunted increase in cortical thickness of the left middle-anterior cingulate gyrus and sulcus significantly mediates the interaction between childhood maltreatment and IDO2 on childhood depression. These interactions also moderate antidepressant treatment efficacy at 4–6 weeks. Childhood maltreatment interacts with genes to increase depression risk. Here, Sun et al. use a genome-wide approach to identify variants of IDO2 associated with depression in individuals who have undergone child maltreatment.
Antipsychotic-induced hyperprolactinemia (AP-induced HPRL) occurs overall in up to 70% of patients with schizophrenia, which is associated with hypogonadism and sexual dysfunction. We summarized the latest evidence for the benefits of prolactin-lowering drugs. We performed network meta-analyses to summarize the evidence and applied Grading of Recommendations Assessment, Development, and Evaluation frameworks (GRADE) to rate the certainty of evidence, categorize interventions, and present the findings. The search identified 3,022 citations, 31 studies of which with 1999 participants were included in network meta-analysis. All options were not significantly better than placebo among patients with prolactin (PRL) less than 50 ng/ml. However, adjunctive aripiprazole (ARI) (5 mg: MD = -64.26, 95% CI = -87.00 to -41.37; 10 mg: MD = -59.81, 95% CI = -90.10 to -29.76; more than 10 mg: MD = -68.01, 95% CI = -97.12 to -39.72), switching to ARI in titration (MD = -74.80, 95% CI = -134.22 to -15.99) and adjunctive vitamin B6 (MD = -91.84, 95% CI = -165.31 to -17.74) were associated with significant decrease in AP-induced PRL among patients with PRL more than 50 ng/ml with moderated (adjunctive vitamin B6) to high (adjunctive ARI) certainty of evidence. Pharmacological treatment strategies for AP-induced HPRL depends on initial PRL level. No effective strategy was found for patients with AP-induced HPRL less than 50 ng/ml, while adjunctive ARI, switching to ARI in titration and adjunctive high-dose vitamin B6 showed better PRL decrease effect on AP-induced HPRL more than 50 ng/ml.
Psychiatric disorders present a substantial global public health burden with limited drug options. The gut-brain axis connects inflammatory bowel diseases and psychiatric disorders, which often have comorbidities. While some evidence hints at anti-inflammatory drugs aiding in treating psychiatric conditions, the specific effects of intestinal anti-inflammatory drugs remain unclear. This study investigates the causal effect of intestinal anti-inflammatory drug targets on psychiatric disorders. We hypothesize that these drug targets may offer new insights into the treatment and prevention of such disorders. Additionally, we explore gut microbiota's mediating role between drug target genes and psychiatric disorders. We performed two-sample Mendelian randomization (MR) using summary data from existing expression quantitative trait loci (eQTL) and protein QTL in the brain, along with public genome-wide association studies of disease. We also explored gut microbiota's mediating effect. The statistics encompassed six psychiatric disorders involving 9,725–500,199 individuals. Colocalization analysis enhanced the MR evidence. We uncovered a causal link between TPMT (a target of olsalazine) expression in the amygdala and bipolar disorder (BD) risk (odds ratio [OR] = 1.08; P = 4.29 × 10−4). This association was observed even when the sigmoid colon and whole blood eQTL were considered as exposures. Colocalization analysis revealed a shared genetic variant (rs11751561) between TPMT expression and BD, with a posterior probability of 61.6 %. Interestingly, this causal effect was influenced by a decrease in the gut microbiota abundance of the genus Roseburia (effect proportion = 10.05 %). Moreover, elevated ACAT1 expression was associated with higher obsessive–compulsive disorder risk (OR = 1.62; P = 3.64 × 10−4; posterior probability = 3.1 %). These findings provide novel targets for the treatment of psychiatric disorders, underscore the potential of repurposing olsalazine, and emphasize the importance of TPMT and ACAT1 in future drug development.
This study identified discrepant therapeutic outcomes of antipsychotics.A total of 5191 patients with schizophrenia were enrolled, 3030 as discovery cohort, 1395 as validation cohort, and 766 as multi-ancestry validation cohort. Therapeutic Outcomes Wide Association Scan was conducted. Types of antipsychotics (one antipsychotic vs other antipsychotics) were dependent variables, therapeutic outcomes including efficacy and safety were independent variables.In discovery cohort, olanzapine related to higher risk of weight gain (AIWG, OR: 2.21-2.86), liver dysfunction (OR: 1.75-2.33), sedation (OR: 1.76-2.86), increased lipid level (OR: 2.04-2.12), and lower risk of extrapyramidal syndrome (EPS, OR: 0.14-0.46); risperidone related to higher risk of hyperprolactinemia (OR: 12.45-20.53); quetiapine related to higher risk of sedation (OR = 1.73), palpitation (OR = 2.87), increased lipid level (OR = 1.69), lower risk of hyperprolactinemia (OR: 0.09-0.11), and EPS (OR: 0.15-0.44); aripiprazole related to lower risk of hyperprolactinemia (OR: 0.09-0.14), AIWG (OR = 0.44), sedation (OR: 0.33-0.47), and QTc prolongation (β = -2.17); ziprasidone related to higher risk of increased QT interval (β range: 3.11-3.22), nausea (OR: 3.22-3.91), lower risk of AIWG (OR: 0.27-0.46), liver dysfunction (OR: 0.41-0.38), and increased lipid level (OR: 0.41-0.55); haloperidol related to higher risk of EPS (OR: 2.64-6.29), hyperprolactinemia (OR: 5.45-9.44), and increased salivation (OR: 3.50-3.68). Perphenazine related to higher risk of EPS (OR: 1.89-2.54). Higher risk of liver dysfunction in olanzapine and lower risk of hyperprolactinemia in aripiprazole were confirmed in validation cohort, and higher risk of AIWG in olanzapine and hyperprolactinemia in risperidone were confirmed in multi-ancestry validation cohort.Future precision medicine should focus on personalized side-effects.
A growing body of research suggests that social or physical activity can affect the risk of Major depressive disorder (MDD). However, the bidirectional relationship between them remains to be clarified further, especially between inactivity and MDD. Here, we performed a two-sample Mendelian Randomization analysis using genetic variants associated with social/physical activities and MDD, and assessed the mediating effect of obesity-related measures and brain imaging phenotypes. The dataset on MDD, social activities, and physical activities included 500,199; 461,369; 460,376 individuals, respectively. Information regarding body mass index (BMI), body fat percentage (BFP), IDPs for 454,633; 461,460; 8,428 participants, respectively. We identified bidirectional causal relationships between sport clubs or gyms, strenuous sports, heavy do-it-youself, other exercises and MDD. We also observed that leisure/social inactivity (odds ratio [OR] = 1.64; P = 5.14 × 10
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)