Shi Ni, Nepal Sushma, Hoobler Rachel, Menni Cristina, Playdon Mary C, Spakowicz Daniel, Wells Philippa M, Steves Claire J, Clinton Steven K, Tabung Fred K
Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA.
Interdisciplinary Ph.D. Program in Nutrition, The Ohio State University, Columbus, OH, USA.
Gut Microbiome (Camb). 2024 Dec 5;5:e12. doi: 10.1017/gmb.2024.14. eCollection 2024.
Metabolic dietary patterns, including the Empirical Dietary Index for Hyperinsulinaemia (EDIH) and Empirical Dietary Inflammatory Pattern (EDIP), are known to impact multiple chronic diseases, but the role of the colonic microbiome in mediating such relationships is poorly understood. Among 1,610 adults with faecal 16S rRNA data in the TwinsUK cohort, we identified the microbiome profiles for EDIH and EDIP (from food frequency questionnaires) cross-sectionally using elastic net regression. We assessed the association of the dietary pattern-related microbiome profile scores with circulating biomarkers in multivariable-adjusted linear regression. In addition, we used PICRUSt2 to predict biological pathways associated with the enriched microbiome profiles, and further screened pathways for associations with the dietary scores in linear regression analyses. Microbiome profile scores developed with 32 (EDIH) and 15 (EDIP) genera were associated with higher insulin and homeostatic model assessment of insulin resistance. Six genera were associated with both dietary scores: , inversely and , positively. Further, pathways in fatty acid biosynthesis, sugar acid degradation, and mevalonate metabolism were associated with insulinaemic and inflammatory diets. Dietary patterns that exert metabolic effects on insulin and inflammation may influence chronic disease risk by modulating gut microbial composition and function.
代谢性饮食模式,包括高胰岛素血症经验性饮食指数(EDIH)和经验性饮食炎症模式(EDIP),已知会影响多种慢性疾病,但结肠微生物群在介导这种关系中的作用却知之甚少。在英国双胞胎队列中1610名有粪便16S rRNA数据的成年人中,我们使用弹性网络回归横断面确定了EDIH和EDIP(来自食物频率问卷)的微生物群特征。我们在多变量调整线性回归中评估了饮食模式相关微生物群特征评分与循环生物标志物之间的关联。此外,我们使用PICRUSt2预测与丰富的微生物群特征相关的生物途径,并在线性回归分析中进一步筛选途径与饮食评分之间的关联。由32个属(EDIH)和15个属(EDIP)构建的微生物群特征评分与较高的胰岛素水平和胰岛素抵抗的稳态模型评估相关。有6个属与两种饮食评分均相关:与EDIH呈负相关,与EDIP呈正相关。此外,脂肪酸生物合成、糖酸降解和甲羟戊酸代谢途径与胰岛素血症和炎症性饮食相关。对胰岛素和炎症产生代谢影响的饮食模式可能通过调节肠道微生物组成和功能来影响慢性病风险。