Genomic and transcriptomic insights into antipsychotic-induced changes in total cholesterol and body mass index in a multi-ancestry cohort of the US veterans
Year: 2026
Type: preprint
Study design: Observational Study
Abstract: Objective Antipsychotic medications are a cornerstone in the treatment of many psychiatric disorders, but they are associated with adverse metabolic effects including weight gain and hypercholesterolemia. To investigate the underlying genetic architecture of these effects, we conducted the largest-by-far and most ethnically diverse genome-wide association study (GWAS) of longitudinal changes in total cholesterol (ΔTC) and body mass index (ΔBMI) using an antipsychotic-treated cohort from the Million Veteran Program (MVP). Methods The study included 59,372 participants for ΔTC and 39,112 for ΔBMI across European (EUR), African American (AFR), and Hispanic (HIS) ancestries. GWAS, trans-ancestry meta-analysis, functional annotation, and summary-data-based Mendelian randomization (SMR) analyses were performed to identify associated loci, enriched biological pathways, and gene expression signals. Results We identified genome-wide significant and suggestive loci for both traits, with stronger associations for ΔTC and clinically significant weight gain (ΔBMI > 1.5 kg/m²) than for overall BMI change. Significant loci included genes involved in cholesterol metabolism and lipid homeostasis for ΔTC and genes previously implicated in BMI-related traits for ΔBMI > 1.5 kg/m². Trans-ancestry meta-analysis highlighted suggestive loci shared across ancestries, and functional annotation demonstrated significant enrichment of protein homeostasis and synaptic function gene sets for ΔBMI > 1.5 kg/m². SMR analyses further identified suggestive gene expression associations in whole blood and liver. Conclusions These findings implicate lipid metabolism, body weight regulation, and central nervous system mechanisms in antipsychotic-associated metabolic changes. This work advances understanding of genetic susceptibility to metabolic adverse effects of antipsychotic treatment and may inform future precision medicine approaches to risk prediction and treatment selection.
Source: medRxiv
Authors: Hamed Kazemi, John E. Drake, Silviu-Alin Bacanu, Benjamin H. Mcmahon, Khushbu Agarwal, Alejandro M. Zuniga, Sutanay Choudhury, Hamed Abbaszadegan, Sayera Dhaubhadel, Kory A. Johnson, Julie A. Kreyenbuhl, Stephen R. Marder, Philip D. Harvey, Vladimir I. Vladimirov, Million Veteran Program, MVP Program Office, MVP Co-Principal Investigators, MVP Core Operations, Ayman H. Fanous
Institutions: University of Arizona, University of Arizona College of Medicine- Phoenix, Virginia Commonwealth University, Los Alamos National Laboratory, Pacific Northwest National Laboratory, Carl T. Hayden Veterans Affairs Medical Center, University of Maryland, Baltimore, University of California, Los Angeles, VA Desert Pacific Healthcare Network, University of Miami
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7170049662