Major depressive disorder (MDD) and insulin resistance-related conditions are major contributors to global disability. Their co-occurrence complicates clinical outcomes, increasing mortality and symptom severity.

In this study, we investigated the association of insulin resistance-related conditions and related polygenic scores (PGSs) with MDD clinical profile and treatment outcomes, using primary care records from UK Biobank.

We identified MDD cases and insulin resistance-related conditions, as well as measures of depression treatment outcomes (e.g. resistance) from the records. Clinical-demographic variables were derived from self-reports, and insulin resistance-related PGSs were calculated using PRS-CS. Univariable analyses were conducted to compare sociodemographic and clinical variables of MDD cases with (IR+) and without (IR−) lifetime insulin resistance-related conditions. Multiple regressions were performed to identify factors, including insulin resistance-related PGSs, potentially associated with treatment outcomes, adjusting for confounders.

Among 30 919 MDD cases, 51.95% were IR+. These had more antidepressant prescriptions and classes utilisation and longer treatment duration than patients without insulin resistance-related conditions (P < 0.001). IR+ participants showed distinctive depressive profiles, characterised by concentration issues, loneliness and inadequacy feelings, which varied according to the timing of MDD diagnosis relative to insulin resistance-related conditions. After adjusting for confounders, insulin resistance-related conditions (i.e. cardiovascular diseases, hypertension, non-alcoholic fatty liver disease, obesity/overweight, prediabetes and type 2 diabetes mellitus) were associated with antidepressant non-response/resistance and longer treatment duration, particularly when MDD preceded insulin resistance-related conditions. No significant PGS associations were found with antidepressant treatment outcomes.

Our findings support an integrated treatment approach, prioritising both psychiatric and metabolic health, and public health strategies aimed at early intervention and prevention of insulin resistance in MDD.

The brain’s default mode network (DMN) plays a role in social cognition, with altered DMN function being associated with social impairments across various neuropsychiatric disorders. However, the genetic basis linking sociability with DMN function remains underexplored. This study aimed to elucidate the shared genetics and causal relationship between sociability and DMN-related resting-state functional MRI (rs-fMRI) traits.

We conducted a comprehensive genomic analysis using large-scale genome-wide association study (GWAS) summary statistics for sociability and 31 activity and 64 connectivity DMN-related rs-fMRI traits (N = 34,691–342,461). We performed global and local genetic correlations analyses and bi-directional Mendelian randomization (MR) to assess shared and causal effects. We prioritized genes influencing both sociability and rs-fMRI traits by combining expression quantitative trait loci MR analyses, the CELLECT framework – integrating single-nucleus RNA sequencing (snRNA-seq) data with GWAS – and network propagation within a protein–protein interaction network.

Significant local genetic correlations were identified between sociability and two rs-fMRI traits, one representing spontaneous activity within the temporal cortex, the other representing connectivity between the cingulate and angular/temporal cortices. MR analyses suggested potential causal effects of sociability on 12 rs-fMRI traits. Seventeen genes were highly prioritized, with LINGO1, ELAVL2, and CTNND1 emerging as top candidates. Among these, DRD2 was also identified, serving as a robust internal validation of our approach.

By combining genomic and transcriptomic data, our gene prioritization strategy may serve as a blueprint for future studies. Our findings can guide further research into the biological mechanisms underlying sociability and its role in the development, prognosis, and treatment of neuropsychiatric disorders.

Psychiatric disorders and type 2 diabetes mellitus (T2DM) are heritable, polygenic, and often comorbid conditions, yet knowledge about their potential shared familial risk is lacking. We used family designs and T2DM polygenic risk score (T2DM-PRS) to investigate the genetic associations between psychiatric disorders and T2DM.

We linked 659 906 individuals born in Denmark 1990–2000 to their parents, grandparents, and aunts/uncles using population-based registers. We compared rates of T2DM in relatives of children with and without a diagnosis of any or one of 11 specific psychiatric disorders, including neuropsychiatric and neurodevelopmental disorders, using Cox regression. In a genotyped sample (iPSYCH2015) of individuals born 1981–2008 (n = 134 403), we used logistic regression to estimate associations between a T2DM-PRS and these psychiatric disorders.

Among 5 235 300 relative pairs, relatives of individuals with a psychiatric disorder had an increased risk for T2DM with stronger associations for closer relatives (parents:hazard ratio = 1.38, 95% confidence interval 1.35–1.42; grandparents: 1.14, 1.13–1.15; and aunts/uncles: 1.19, 1.16–1.22). In the genetic sample, one standard deviation increase in T2DM-PRS was associated with an increased risk for any psychiatric disorder (odds ratio = 1.11, 1.08–1.14). Both familial T2DM and T2DM-PRS were significantly associated with seven of 11 psychiatric disorders, most strongly with attention-deficit/hyperactivity disorder and conduct disorder, and inversely with anorexia nervosa.

Our findings of familial co-aggregation and higher T2DM polygenic liability associated with psychiatric disorders point toward shared familial risk. This suggests that part of the comorbidity is explained by shared familial risks. The underlying mechanisms still remain largely unknown and the contributions of genetics and environment need further investigation.