Relationships between the Planetary Health Diet Index, its food groups, and polygenic risk of obesity in the CARTaGENE cohort.

IF 3.9 2区 医学 Q2 NUTRITION & DIETETICS
Guiomar Masip, Daiva E Nielsen
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引用次数: 0

Abstract

Background: The Planetary Health Diet, proposed by the EAT-Lancet Commission, seeks to promote a sustainable and healthy diet for both humans and the environment. However, few studies have investigated relationships between the Planetary Health Diet and the genetic pathway of obesity. The aim of this study was to assess whether adherence to a Planetary Health Diet Index (PHDI) mediated or moderated the genetic susceptibility to obesity.

Methods: Participants were 7,037 adults (57% females, aged 55.6 ± 7.7) from the Quebec CARTaGENE Biobank. We constructed a primary polygenic risk score (PRS-Khera) for body mass index (BMI) comprised of ~ 2 million SNPs and utilized a secondary 97 SNPs polygenic risk score (PRS-Locke) for sensitivity analyses. The PHDI was based on 16 food groups. General linear models were conducted to assess main effect associations between the PRSs, the Planetary Health Diet Index (PHDI), and the individual food groups that comprise the PHDI on obesity outcomes. Causal mediation analyses (CMA) were used to evaluate mediation and interaction effects. All models were adjusted for age, sex, genetic ancestry, socio-demographic, and lifestyle variables, including those associated with dietary habits.

Results: The overall PHDI was inversely associated with BMI (β = - 0.11, 95% confidence interval (CI): - 0.13, - 0.09), waist circumference (WC) (β = - 0.12, 95% CI: - 0.14, - 0.10), and body fat % (β = - 0.10, 95% CI: - 0.12, - 0.08) for all participants, but did not mediate or moderate obesity polygenic risk. Associations between the PRS-Khera and obesity outcomes in all participants were partly mediated by the intake of red meat (mediation effect BMI: 1.72%, p = 0.01; WC: 2.22%, p = 0.01; body fat %: 2.14%, p = 0.01). Moreover, among males, whole grains intake partly mediated the association between the PRS-Khera and outcomes cross-sectionally (BMI: 1.28%, p = 0.03; WC: 1.71%, p = 0.02; body fat %: 2.19%, p = 0.02) and longitudinally (BMI: 3.80%, p = 0.02; WC: 7.38%, p = 0.04), but some observations were attenuated upon correction for multiple comparisons.

Conclusions: PHDI adherence was associated with a lower BMI, WC, and body fat % and genetic susceptibility to obesity was partly mediated by the intake of red meat and whole grains. Some components of a plant-based diet could be implicated in mechanisms underlying genetic susceptibility to obesity.

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来源期刊
Nutrition & Metabolism
Nutrition & Metabolism 医学-营养学
CiteScore
8.40
自引率
0.00%
发文量
78
审稿时长
4-8 weeks
期刊介绍: Nutrition & Metabolism publishes studies with a clear focus on nutrition and metabolism with applications ranging from nutrition needs, exercise physiology, clinical and population studies, as well as the underlying mechanisms in these aspects. The areas of interest for Nutrition & Metabolism encompass studies in molecular nutrition in the context of obesity, diabetes, lipedemias, metabolic syndrome and exercise physiology. Manuscripts related to molecular, cellular and human metabolism, nutrient sensing and nutrient–gene interactions are also in interest, as are submissions that have employed new and innovative strategies like metabolomics/lipidomics or other omic-based biomarkers to predict nutritional status and metabolic diseases. Key areas we wish to encourage submissions from include: -how diet and specific nutrients interact with genes, proteins or metabolites to influence metabolic phenotypes and disease outcomes; -the role of epigenetic factors and the microbiome in the pathogenesis of metabolic diseases and their influence on metabolic responses to diet and food components; -how diet and other environmental factors affect epigenetics and microbiota; the extent to which genetic and nongenetic factors modify personal metabolic responses to diet and food compositions and the mechanisms involved; -how specific biologic networks and nutrient sensing mechanisms attribute to metabolic variability.
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