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Investigating gene-diet interactions impacting the association between macronutrient intake and glycemic traits

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posted on 2023-02-15, 21:48 authored by Kenneth E. Westerman, Maura E. Walker, Sheila M. Gaynor, Jennifer Wessel, Daniel DiCorpo, Jiantao Ma, Alvaro Alonso, Stella Aslibekyan, Abigail S. Baldridge, Alain G. Bertoni, Mary L. Biggs, Jennifer A. Brody, Yii-Der Ida Chen, Josee Dupuis, Mark O. Goodarzi, Xiuqing Guo, Natalie R. Hasbani, Adam Heath, Bertha Hidalgo, Marguerite R. Irvin, W. Craig Johnson, Rita R. Kalyani, Leslie Lange, Rozenn N. Lemaitre, Ching-Ti Liu, Simin Liu, Jee-Young Moon, Rami Nassir, James S. Pankow, Mary Pettinger, Laura Raffield, Laura J. Rasmussen-Torvik, Elizabeth Selvin, Mackenzie K. Senn, Aladdin H. Shadyab, Albert V. Smith, Nicholas L. Smith, Lyn Steffen, Sameera Talegakwar, Kent D. Taylor, Paul S. de Vries, James G. Wilson, Alexis C. Wood, Lisa R. Yanek, Jie Yao, Yinan Zheng, Eric Boerwinkle, Alanna C. Morrison, Miriam Fornage, Tracy P. Russell, Bruce M. Psaty, Daniel Levy, Nancy L. Heard-Costa, Vasan S. Ramachandran, Rasika A. Mathias, Donna K. Arnett, Robert Kaplan, Kari E. North, Adolfo Correa, April Carson, Jerome I. Rotter, Stephen S. Rich, JoAnn E. Manson, Alexander P. Reiner, Charles Kooperberg, Jose C. Florez, James B. Meigs, Jordi Merino, Deirdre K. Tobias, Han Chen, Alisa K. Manning

Few studies have demonstrated reproducible gene-diet interactions (GDIs) impacting metabolic disease risk factors, likely due in part to measurement error in dietary intake estimation and insufficient capture of rare genetic variation. We aimed to identify GDIs across the genetic frequency spectrum impacting the macronutrient-glycemia relationship in genetically and culturally diverse cohorts. We analyzed N=33,187 diabetes-free participants from 10 NHLBI Trans-Omics for Precision Medicine (TOPMed) program cohorts with whole-genome sequencing, self-reported diet, and glycemic trait data. We fit cohort-specific, multivariable-adjusted linear mixed models for the effect of diet, modeled as an isocaloric substitution of carbohydrate for fat, and its interactions with common and rare variants genome-wide. In main effect meta-analyses, participants consuming more carbohydrate had modestly lower glycemic trait values (e.g. for hemoglobin A1c [HbA1c], -0.013 %HbA1c per 250 kcal substitution). In GDI meta-analyses, a common African ancestry-enriched variant (rs79762542) reached study-wide significance and replicated in the UK Biobank cohort, indicating a negative carbohydrate-HbA1c association among major allele homozygotes only. Simulations revealed that over 150,000 samples may be necessary to identify similar macronutrient GDIs under realistic assumptions about effect size and measurement error. These results generate hypotheses for further exploration of modifiable metabolic disease risk in additional cohorts with African ancestry.

Funding

KEW, HC, and AKM were supported by NIH R01 HL145025. LMR was supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant KL2TR002490 (LMR). VSR is supported in part by the Evans Medical Foundation and the Jay and Louis Coffman Endowment from the Department of Medicine, Boston University School of Medicine. MEW is supported in part by the American Heart Association (20CDA35310237, the Doris Duke Charitable Foundation (2021261), and the National Center for Advancing Translational Sciences, National Institutes of Health, through BU-CTSI (1UL1TR001430). The contents of this work are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.

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