Genetic and Metabolite Variability in One-Carbon Metabolism Applied to an Insulin Resistance Model in Patients With Schizophrenia Receiving Atypical Antipsychotics

Frontiers in Psychiatry

Andrew Jaeger¹  Kristen M. Ward¹  Kyle Burghardt³  Vicki L. Ellingrod⁴  Kathleen A. Stringer⁵  Alla Karnovsky⁶  Cora McHugh⁷  Larisa Yeomans⁷  A. Zarina Kraal⁸ 

[1] Department of Clinical Pharmacy, College of Pharmacy, University of Michigan, Ann Arbor, MI, United States;Department of Computational Medicine and Bioinformatics, School of Medicine, University of Michigan, Ann Arbor, MI, United States;Department of Pharmacy Practice, Eugene Applebaum College of Pharmacy and Health Sciences, Wayne State University, Detroit, MI, United States;Department of Psychiatry, School of Medicine, University of Michigan, Ann Arbor, MI, United States;Division of Pulmonary and Critical Care Medicine, School of Medicine, University of Michigan, Ann Arbor, MI, United States;Michigan Regional Comprehensive Metabolomics Resource Core, University of Michigan, Ann Arbor, MI, United States;Nuclear Magnetic Resonance (NMR) Metabolomics Laboratory, College of Pharmacy, University of Michigan, Ann Arbor, MI, United States;Psychology Department, College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI, United States;

关键词: pharmacogenomics;    metabolomics;    folate;    one-carbon metabolism;    cardiovascular disease;    antipsychotics;   

DOI  :  10.3389/fpsyt.2021.623143

来源: DOAJ

【 摘 要 】

Background: Patients with schizophrenia are at high risk of pre-mature mortality due to cardiovascular disease (CVD). Our group has completed studies in pharmacogenomics and metabolomics that have independently identified perturbations in one-carbon metabolism as associated with risk factors for CVD in this patient population. Therefore, this study aimed to use genetic and metabolomic data to determine the relationship between folate pharmacogenomics, one-carbon metabolites, and insulin resistance as measured using the homeostatic model assessment for insulin resistance (HOMA-IR) as a marker of CVD.Methods: Participants in this pilot analysis were on a stable atypical antipsychotic regimen for at least 6 months, with no diabetes diagnosis or use of antidiabetic medications. Participant samples were genotyped for MTHFR variants rs1801131 (MTHFR A1298C) and rs1801133 (MTHFR C677T). Serum metabolite concentrations were obtained with NMR. A least squares regression model was used to predict log(HOMA-IR) values based on the following independent variables: serum glutamate, glycine, betaine, serine, and threonine concentrations, and carrier status of the variant alleles for the selected genotypes.Results: A total of 67 participants were included, with a median age of 47 years old (IQR 42–52), 39% were female, and the median BMI was 30.3 (IQR 26.3–37.1). Overall, the model demonstrated an ability to predict log(HOMA-IR) values with an adjusted R2 of 0.44 and a p-value of < 0.001. Glutamate, threonine, and carrier status of the MTHFR 1298 C or MTHFR 677 T allele were positively correlated with log(HOMA-IR), whereas glycine, serine, and betaine concentrations trended inversely with log(HOMA-IR). All factors included in this final model were considered as having a possible effect on predicting log(HOMA-IR) as measured with a p-value < 0.1.Conclusions: Presence of pharmacogenomic variants that decrease the functional capacity of the MTHFR enzyme are associated with increased risk for cardiovascular disease, as measured in this instance by log(HOMA-IR). Furthermore, serine, glycine, and betaine concentrations trended inversely with HOMA-IR, suggesting that increased presence of methyl-donating groups is associated with lower measures of insulin resistance. Ultimately, these results will need to be replicated in a significantly larger population.

【 授权许可】

Unknown