Investigating Gene–Diet Interactions Impacting the Association Between Macronutrient Intake and Glycemic Traits

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, Joseé Dupuis, Mark O. Goodarzi, Xiuqing Guo, Natalie R. Hasbani, Adam Heath, Bertha Hidalgo, Marguerite R. IrvinW. 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 M. 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*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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 33,187 participants free of diabetes from 10 National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine 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 glycated hemoglobin [HbA1c], 20.013% HbA1c/250 kcal substitution). In GDI meta-analyses, a common African ancestry–enriched variant (rs79762542) reached studywide significance and replicated in the UK Biobank cohort, indicating a negative carbohydrate–HbA1c association among major allele homozygotes only. Simulations revealed that >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.

Original languageEnglish (US)
Pages (from-to)653-665
Number of pages13
JournalDiabetes
Volume72
Issue number5
DOIs
StatePublished - May 2023

ASJC Scopus subject areas

  • Internal Medicine
  • Endocrinology, Diabetes and Metabolism

Fingerprint

Dive into the research topics of 'Investigating Gene–Diet Interactions Impacting the Association Between Macronutrient Intake and Glycemic Traits'. Together they form a unique fingerprint.

Cite this