Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.
Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.
Gut. 2023 Aug;72(8):1486-1496. doi: 10.1136/gutjnl-2022-329201. Epub 2023 May 3.
To explore the interplay between dietary modifications, microbiome composition and host metabolic responses in a dietary intervention setting of a personalised postprandial-targeting (PPT) diet versus a Mediterranean (MED) diet in pre-diabetes.
In a 6-month dietary intervention, adults with pre-diabetes were randomly assigned to follow an MED or PPT diet (based on a machine-learning algorithm for predicting postprandial glucose responses). Data collected at baseline and 6 months from 200 participants who completed the intervention included: dietary data from self-recorded logging using a smartphone application, gut microbiome data from shotgun metagenomics sequencing of faecal samples, and clinical data from continuous glucose monitoring, blood biomarkers and anthropometrics.
PPT diet induced more prominent changes to the gut microbiome composition, compared with MED diet, consistent with overall greater dietary modifications observed. Particularly, microbiome alpha-diversity increased significantly in PPT (p=0.007) but not in MED arm (p=0.18). Post hoc analysis of changes in multiple dietary features, including food-categories, nutrients and PPT-adherence score across the cohort, demonstrated significant associations between specific dietary changes and species-level changes in microbiome composition. Furthermore, using causal mediation analysis we detect nine microbial species that partially mediate the association between specific dietary changes and clinical outcomes, including three species (from , , orders) that mediate the association between PPT-adherence score and clinical outcomes of hemoglobin A1c (HbA1c), high-density lipoprotein cholesterol (HDL-C) and triglycerides. Finally, using machine-learning models trained on dietary changes and baseline clinical data, we predict personalised metabolic responses to dietary modifications and assess features importance for clinical improvement in cardiometabolic markers of blood lipids, glycaemic control and body weight.
Our findings support the role of gut microbiome in modulating the effects of dietary modifications on cardiometabolic outcomes, and advance the concept of precision nutrition strategies for reducing comorbidities in pre-diabetes.
NCT03222791.
在个性化餐后靶向(PPT)饮食与地中海(MED)饮食的饮食干预研究中,探索饮食改变、微生物组组成和宿主代谢反应之间的相互作用,以治疗糖尿病前期患者。
在为期 6 个月的饮食干预中,将糖尿病前期成年人随机分配到 MED 或 PPT 饮食组(基于预测餐后血糖反应的机器学习算法)。从完成干预的 200 名参与者中收集了基线和 6 个月的数据,包括:使用智能手机应用程序进行自我记录的饮食数据、粪便样本的 shotgun 宏基因组测序的肠道微生物组数据以及连续血糖监测、血液生物标志物和人体测量数据的临床数据。
与 MED 饮食相比,PPT 饮食引起了更显著的肠道微生物组组成变化,这与观察到的总体更大的饮食改变一致。特别是,PPT 组的微生物组 alpha 多样性显著增加(p=0.007),而 MED 组则没有增加(p=0.18)。对整个队列中多种饮食特征的变化进行了事后分析,包括食物类别、营养素和 PPT 依从性评分,发现特定饮食变化与微生物组组成的物种水平变化之间存在显著关联。此外,使用因果中介分析,我们检测到九个微生物物种,它们部分介导了特定饮食变化与临床结局之间的关联,包括三个物种(来自,, 目),它们介导了 PPT 依从性评分与血红蛋白 A1c(HbA1c)、高密度脂蛋白胆固醇(HDL-C)和甘油三酯等临床结局之间的关联。最后,使用基于饮食变化和基线临床数据的机器学习模型,我们预测了个性化的代谢对饮食改变的反应,并评估了对改善血脂、血糖控制和体重等血液生化标志物的临床改善的特征重要性。
我们的研究结果支持肠道微生物组在调节饮食改变对心血管代谢结局的影响方面的作用,并推进了个性化营养策略的概念,以减少糖尿病前期的合并症。
NCT03222791。