Genetic variation of habitual coffee consumption and glycemic changes in response to weight-loss diet intervention: the Preventing Overweight Using Novel Dietary Strategies (POUNDS LOST) trial

Abstract

Background: Coffee consumption has been associated with glucose metabolism and risk of type 2 diabetes.Objective: We examined whether the genetic variation determining habitual coffee consumption affected glycemic changes in response to weight-loss dietary intervention.Design: A genetic risk score (GRS) was calculated based on 8 habitual coffee consumption-associated single nucleotide polymorphisms. We used general linear models to test changes in glycemic traits in groups randomly assigned to high- and low-fat diets according to tertiles of the GRS.Results: We observed significant interactions between the GRS and low compared with high dietary fat intake on 6-mo changes in fasting insulin and homeostasis model assessment of insulin resistance (HOMA-IR) (P-interaction = 0.023 and 0.022, respectively), adjusting for age, sex, race, physical activity, smoking, alcohol, seasonal variation, and baseline values of the respective outcomes. Participants with a higher GRS of habitual coffee consumption showed a greater reduction in fasting insulin and a marginally greater decrease in HOMA-IR in the low-fat diet intervention group.Conclusions: Our data suggest that participants with genetically determined high coffee consumption may benefit more by eating a low-fat diet in improving fasting insulin and HOMA-IR in a short term. This trial was registered at clinicaltrials.gov as NCT00072995 and NCT03258203.

Keywords: gene-diet interaction; genetic risk score; glycemic traits; habitual coffee consumption; weight-loss diets.

© 2017 American Society for Nutrition.

Figures

FIGURE 1

The effects of high- and low-fat diets (x-axis) on changes in insulin (A) and HOMA-IR (B) (y-axis) over 6 mo were based on the genetic risk score. T1 = lowest; T3 = highest. Data are expressed as means ± SDs. General linear models were used to calculate P values with adjustment for age, sex, race, physical activity, smoking, alcohol, seasonal variation, and baseline values of the respective outcomes. Values are means ± SEs. T, tertile.