Fried food consumption, genetic risk, and body mass index: gene-diet interaction analysis in three US cohort studies

Abstract

Objective: To examine the interactions between genetic predisposition and consumption of fried food in relation to body mass index (BMI) and obesity.

Design: Prospective cohort study.

Setting: Health professionals in the United States.

Participants: 9623 women from the Nurses' Health Study, 6379 men from the Health Professionals Follow-up Study, and a replication cohort of 21,421 women from the Women's Genome Health Study.

Main outcome measure: Repeated measurement of BMI over follow-up.

Results: There was an interaction between fried food consumption and a genetic risk score based on 32 BMI-associated variants on BMI in both the Nurses' Health Study and Health Professionals Follow-up Study (P ≤ 0.001 for interaction). Among participants in the highest third of the genetic risk score, the differences in BMI between individuals who consumed fried foods four or more times a week and those who consumed fried foods less than once a week amounted to 1.0 (SE 0.2) in women and 0.7 (SE 0.2) in men, whereas the corresponding differences were 0.5 (SE 0.2) and 0.4 (SE 0.2) in the lowest third of the genetic risk score. The gene-diet interaction was replicated in the Women's Genome Health Study (P<0.001 for interaction). Viewed differently, the genetic association with adiposity was strengthened with higher consumption of fried foods. In the combined three cohorts, the differences in BMI per 10 risk alleles were 1.1 (SE 0.2), 1.6 (SE 0.3), and 2.2 (SE 0.6) for fried food consumption less than once, one to three times, and four or more times a week (P<0.001 for interaction); and the odds ratios (95% confidence intervals) for obesity per 10 risk alleles were 1.61 (1.40 to 1.87), 2.12 (1.73 to 2.59), and 2.72 (2.12 to 3.48) across the three categories of consumption (P=0.002 for interaction). In addition, the variants in or near genes highly expressed or known to act in the central nervous system showed significant interactions with fried food consumption, with the FTO (fat mass and obesity associated) variant showing the strongest result (P<0.001 for interaction).

Conclusion: Our findings suggest that consumption of fried food could interact with genetic background in relation to obesity, highlighting the particular importance of reducing fried food consumption in individuals genetically predisposed to obesity.

Fig 1 BMI according to frequency of fried food consumption and thirds of genetic risk score in pooled data of three cohorts. Data adjusted for age, source of genotyping data, physical activity, television watching, smoking, alcohol intake, intake of sugar sweetened beverages, alternative healthy eating index, and total energy intake in Nurses’ Health Study (NHS) and Health Professionals Follow-Up Study (HPFS); and age, physical activity, smoking, alcohol intake, intake of sugar sweetened beverages, alternative healthy eating index, and total energy intake In the Women’s Genome Health Study (WGHS). Data from three cohorts were pooled by means of fixed effects meta-analyses (if P≥0.05 for heterogeneity between studies) or random effects meta-analyses (if P<0.05 for heterogeneity between studies)

Fig 2 Genetic associations with BMI according to frequency of fried food consumption in three cohorts. Data are differences (SE) in BMI per 10 risk alleles of genetic risk score and differences (SE) in BMI per risk allele (A-allele) of the FTO (fat mass and obesity associated) variant rs1558902. In Nurses’ Health Study (NHS) and Health Professionals Follow-Up Study (HPFS), data were adjusted for age, source of genotyping data, physical activity, television watching, smoking, alcohol intake, intake of sugar sweetened beverages, alternative healthy eating index, and total energy intake. In Women’s Genome Health Study (WGHS), data were adjusted for age, physical activity, smoking, alcohol intake, intake of sugar sweetened beverages, alternative healthy eating index, and total energy intake. Data from three cohorts pooled by means of fixed effects meta-analyses (if P≥0.05 for heterogeneity between studies) or random effects meta-analyses (if P<0.05 for heterogeneity between studies)