Antipsychotic-induced weight gain (AIWG) is of significant public health importance in mentally ill populations, potentially addressable with personalized, precision medicine. Antipsychotic medications increase body weight, thereby increasing cardiometabolic risk (CMR) conditions like type 2 diabetes and cardiovascular disease, conditions associated with accelerated cellular aging. This has contributed to a 10 to 15-year mortality gap between mentally ill individuals and the general population. Antipsychotic medications are commonly used at all ages, but are associated with differential patterns of fat gain, whereby children gain more and older adults gain less. Numerous genome-wide association studies (GWAS) have identified key genetic factors associated with AIWG, but are limited by the use of indirect measures of body fat, like weight or body mass index (BMI), that are less well correlated with metabolic disease risk. Additionally, existing research does not fully address age-related differences in AIWG. In response to NIH PA-17-088 ?Secondary Analyses of Existing Cohorts, Data Sets and Stored Biospecimens to Address Clinical Aging Research Questions,? we propose a novel approach applying population-based genetics, existing biospecimen with linked clinical data including precisely-measured adiposity and insulin sensitivity, and advanced molecular tools to identify and functionally validate key genetic determinants of AIWG and CMR across the age-span. This approach leverages 1) existing population-level data from large biobanking initiatives and epidemiological studies inclusive of approximately 15,000 individuals with genetic and relevant phenotypic data, 2) existing clinical and biospecimen data from NIH funded randomized clinical trials or RCTs characterizing the metabolic effects of antipsychotics in children, adults and older adults with direct and precise measures of body fat, together with data from approximately 600 individuals with genetic data and additional biomarkers of metabolic risk, and 3) CRISPR based in vitro drug exposure, followed by cellular functional assays to characterize molecular mechanisms impacted by antipsychotic. Additional sources of existing data will be available upon funding, including data on approximately 3000 individuals from large industry funded RCTs, data on up to 250,000 individuals from the Psychiatric Genetics Consortium (PGC, see letter of support), and data from more than 2,000 individuals from the Dutch Bipolar Cohort Study (see letter of support) will also be used for independent validation and replication. This study will combine unbiased genomic methods, including array-based genotyping, GWAS and GWAS meta-analysis, CRISPR-based gene inhibition/activation screens (CRISPRi/a), and functional molecular and cellular studies on prioritized variants of interest, combined with unique clinical data to identify genetic factors and generate predictive models of weight related physiological changes associated with accelerated aging. This combined set of molecular techniques will allow us to build on known genetic associations, while discovering new genes and genetic variants that are associated with the greatest risk for treatment-related fat gain in younger and older patients. This project will contribute to the development of a precision-based treatment algorithm that can accurately predict and prevent AIWG and cardiometabolic risk in youth, young, middle-aged, and older adults. The results from this study will also importantly contribute to publicly available datasets, and motivate future collection of similar data necessary for further validation of our results.
Antipsychotic treatment, which is known to cause differential age-related weight gain and related cardiometabolic risk (CMR) conditions in mentally ill populations, is an optimal example of how precision medicine approaches could rapidly impact treatment outcomes. This proposal leverages the existence of large population-based studies and hospital-based biobanks with randomized clinical trial data from studies funded by NIH, PCORI and pharma, and will apply genomics and novel molecular techniques, including array-based genotyping, CRISPR screening and cellular functional studies to understand the genetic risk for and mechanisms underlying antipsychotic-induced weight gain and associated CMR, from childhood to older adulthood. If funded, this project will substantially contribute to the understanding of mechanisms of accelerated aging associated with weight gain, and the development of an analytic algorithm that will eventually be able to predict and prevent antipsychotic-related weight gain.
