Fruit intake, genetic risk and type 2 diabetes: a population-based gene–diet interaction analysis

Abstract

Purpose Whether the association between fruit and type 2 diabetes (T2D) is modified by the genetic predisposition of T2D was yet elucidated. The current study is meant to examine the gene–dietary fruit intake interactions in the risk of T2D and related glycemic traits. Methods We performed a cross-sectional study in 11,657 participants aged ≥ 40 years from a community-based population in Shanghai, China. Fruit intake information was collected by a validated food frequency questionnaire by asking the frequency of consumption of typical food items over the previous 12 months. T2D-genetic risk score (GRS) was constructed by 34 well established T2D common variants in East Asians. The risk of T2D, fasting, 2 h-postprandial plasma glucose, and glycated hemoglobin A1c associated with T2D-GRS and each individual single nucleotide polymorphisms (SNPs) were tested. Results The risk of T2D associated with each 1-point of T2D-GRS was gradually decreased from the lower fruit intake level (< 1 times/week) [the odds ratio (OR) and 95% confidence interval (CI) was 1.10 (1.07–1.13)], to higher levels (1–3 and > 3 times/week) [the corresponding ORs and 95% CIs were 1.08 (1.05–1.10) and 1.07 (1.05–1.08); P for interaction = 0.04]. Analyses for associations with fasting, 2 h-postprandial plasma glucose and glycated hemoglobin A1c demonstrated consistent tendencies (all P for interaction ≤ 0.03). The inverse associations of fruit intake with risk of T2D and glucose traits were more prominent in the higher T2D-GRS tertile. Conclusions Fruit intakes interact with the genetic predisposition of T2D on the risk of diabetes and related glucose metabolic traits. Fruit intake alleviates the association between genetic predisposition of T2D and the risk of diabetes; the association of fruit intake with a lower risk of diabetes was more prominent in population with a stronger genetic predisposition of T2D.

The Association between Fasting Glucose and Sugar Sweetened Beverages Intake Is Greater in Latin Americans with a High Polygenic Risk Score for Type 2 Diabetes Mellitus

“…There is interest in identifying genetic loci implicated in fasting glucose and lifestyle-gene interactions that modify the incidence of T2D because they may suggest relevant biological pathways [ 26 , 27 ]. Interaction between fruit intake and polygenic genetic risk for type 2 diabetes have shown an effect on both diabetes risk and on fasting glucose [ 28 ]. Similar studies have explored interaction with SSB, without conclusive results [ 29 ].…”

Genome-Wide Interaction Study of Late-Onset Asthma With Seven Environmental Factors Using a Structured Linear Mixed Model in Europeans

“…Moreover, the association directions between asthma and six factors (BMI, insomnia, MET score, neuroticism score, time spent watching TV, and TDI) correspond to the findings of previous studies ( Hedlund et al, 2006 ; Eijkemans et al, 2012 ; Meltzer et al, 2014 ; Vancampfort et al, 2017 ; Kavanagh et al, 2018 ; Najjab et al, 2020 ), suggesting reasonably acceptable associations. Several studies have used disease outcomes from a cross-sectional dataset for interaction analysis and derived important results ( Eze et al, 2016 ; Jia et al, 2021 ). Next, some environmental data in the present study are from self-reported questionnaires (e.g., alcohol intake frequency, insomnia, MET score, neuroticism score, time spent watching TV, and TDI) that are prone to responder bias ( Rask-Andersen et al, 2017 ).…”

Associations of Urinary Phthalate Metabolites with Thyroid Function and the Mediated Role of Cytokines: A Panel Study of Healthy Children

“…A semistructured questionnaire collected information on demographic and socioeconomic characteristics, medication usage, activity, food frequency, and so on. Compared with the traditional dietary records, food frequency information was collected by asking the frequency of intake for particular food items (e.g., meat, vegetables, fruit, and so on) over the past year . Body mass index (BMI) was computed as body weight (kg)/height 2 (m 2 ).…”