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Effects of personalized diets by prediction of glycemic responses on glycemic control and metabolic health in newly diagnosed T2DM: a randomized dietary intervention pilot trial.
Rein, M, Ben-Yacov, O, Godneva, A, Shilo, S, Zmora, N, Kolobkov, D, Cohen-Dolev, N, Wolf, BC, Kosower, N, Lotan-Pompan, M, et al
BMC medicine. 2022;20(1):56
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Type 2 diabetes mellitus (T2DM) affects around 10% of the global population. The primary goal in its management is to improve glycemic control. Modifying the diet can help, but many patients fail to achieve improvements with diet alone. The aim of the randomized dietary intervention pilot trial is to evaluate the effects of a personalized postprandial-targeting (PPT) diet on glycemic control and metabolic health in 23 adults with newly diagnosed T2DM, as compared to the commonly recommended Mediterranean-style (MED) diet. The PPT diet led to significant lower levels of continuous-glucose-monitoring (CGM)-based measures as compared to the MED diet. In the additional 6-months intervention, metabolic parameters were further improved and 61% of the participants exhibited diabetes remission. Improvements in clinical outcomes were also accompanied by changes in the gut microbiome. These findings may be useful for the design of larger studies in the future that may have implications for dietary advice in clinical practice.
Abstract
BACKGROUND Dietary modifications are crucial for managing newly diagnosed type 2 diabetes mellitus (T2DM) and preventing its health complications, but many patients fail to achieve clinical goals with diet alone. We sought to evaluate the clinical effects of a personalized postprandial-targeting (PPT) diet on glycemic control and metabolic health in individuals with newly diagnosed T2DM as compared to the commonly recommended Mediterranean-style (MED) diet. METHODS We enrolled 23 adults with newly diagnosed T2DM (aged 53.5 ± 8.9 years, 48% males) for a randomized crossover trial of two 2-week-long dietary interventions. Participants were blinded to their assignment to one of the two sequence groups: either PPT-MED or MED-PPT diets. The PPT diet relies on a machine learning algorithm that integrates clinical and microbiome features to predict personal postprandial glucose responses (PPGR). We further evaluated the long-term effects of PPT diet on glycemic control and metabolic health by an additional 6-month PPT intervention (n = 16). Participants were connected to continuous glucose monitoring (CGM) throughout the study and self-recorded dietary intake using a smartphone application. RESULTS In the crossover intervention, the PPT diet lead to significant lower levels of CGM-based measures as compared to the MED diet, including average PPGR (mean difference between diets, - 19.8 ± 16.3 mg/dl × h, p < 0.001), mean glucose (mean difference between diets, - 7.8 ± 5.5 mg/dl, p < 0.001), and daily time of glucose levels > 140 mg/dl (mean difference between diets, - 2.42 ± 1.7 h/day, p < 0.001). Blood fructosamine also decreased significantly more during PPT compared to MED intervention (mean change difference between diets, - 16.4 ± 37 μmol/dl, p < 0.0001). At the end of 6 months, the PPT intervention leads to significant improvements in multiple metabolic health parameters, among them HbA1c (mean ± SD, - 0.39 ± 0.48%, p < 0.001), fasting glucose (- 16.4 ± 24.2 mg/dl, p = 0.02) and triglycerides (- 49 ± 46 mg/dl, p < 0.001). Importantly, 61% of the participants exhibited diabetes remission, as measured by HbA1c < 6.5%. Finally, some clinical improvements were significantly associated with gut microbiome changes per person. CONCLUSION In this crossover trial in subjects with newly diagnosed T2DM, a PPT diet improved CGM-based glycemic measures significantly more than a Mediterranean-style MED diet. Additional 6-month PPT intervention further improved glycemic control and metabolic health parameters, supporting the clinical efficacy of this approach. TRIAL REGISTRATION ClinicalTrials.gov number, NCT01892956.
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Personalized Postprandial Glucose Response-Targeting Diet Versus Mediterranean Diet for Glycemic Control in Prediabetes.
Ben-Yacov, O, Godneva, A, Rein, M, Shilo, S, Kolobkov, D, Koren, N, Cohen Dolev, N, Travinsky Shmul, T, Wolf, BC, Kosower, N, et al
Diabetes care. 2021;(9):1980-1991
Abstract
OBJECTIVE To compare the clinical effects of a personalized postprandial-targeting (PPT) diet versus a Mediterranean (MED) diet on glycemic control and metabolic health in prediabetes. RESEARCH DESIGN AND METHODS We randomly assigned adults with prediabetes (n = 225) to follow a MED diet or a PPT diet for a 6-month dietary intervention and additional 6-month follow-up. The PPT diet relies on a machine learning algorithm that integrates clinical and microbiome features to predict personal postprandial glucose responses. During the intervention, all participants were connected to continuous glucose monitoring (CGM) and self-reported dietary intake using a smartphone application. RESULTS Among 225 participants randomized (58.7% women, mean ± SD age 50 ± 7 years, BMI 31.3 ± 5.8 kg/m2, HbA1c, 5.9 ± 0.2% [41 ± 2.4 mmol/mol], fasting plasma glucose 114 ± 12 mg/dL [6.33 ± 0.67 mmol/L]), 200 (89%) completed the 6-month intervention. A total of 177 participants also contributed 12-month follow-up data. Both interventions reduced the daily time with glucose levels >140 mg/dL (7.8 mmol/L) and HbA1c levels, but reductions were significantly greater in PPT compared with MED. The mean 6-month change in "time above 140" was -0.3 ± 0.8 h/day and -1.3 ± 1.5 h/day for MED and PPT, respectively (95% CI between-group difference -1.29 to -0.66, P < 0.001). The mean 6-month change in HbA1c was -0.08 ± 0.19% (-0.9 ± 2.1 mmol/mol) and -0.16 ± 0.24% (-1.7 ± 2.6 mmol/mol) for MED and PPT, respectively (95% CI between-group difference -0.14 to -0.02, P = 0.007). The significant between-group differences were maintained at 12-month follow-up. No significant differences were noted between the groups in a CGM-measured oral glucose tolerance test. CONCLUSIONS In this clinical trial in prediabetes, a PPT diet improved glycemic control significantly more than a MED diet as measured by daily time of glucose levels >140 mg/dL (7.8 mmol/L) and HbA1c. These findings may have implications for dietary advice in clinical practice.
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A reference map of potential determinants for the human serum metabolome.
Bar, N, Korem, T, Weissbrod, O, Zeevi, D, Rothschild, D, Leviatan, S, Kosower, N, Lotan-Pompan, M, Weinberger, A, Le Roy, CI, et al
Nature. 2020;(7836):135-140
Abstract
The serum metabolome contains a plethora of biomarkers and causative agents of various diseases, some of which are endogenously produced and some that have been taken up from the environment1. The origins of specific compounds are known, including metabolites that are highly heritable2,3, or those that are influenced by the gut microbiome4, by lifestyle choices such as smoking5, or by diet6. However, the key determinants of most metabolites are still poorly understood. Here we measured the levels of 1,251 metabolites in serum samples from a unique and deeply phenotyped healthy human cohort of 491 individuals. We applied machine-learning algorithms to predict metabolite levels in held-out individuals on the basis of host genetics, gut microbiome, clinical parameters, diet, lifestyle and anthropometric measurements, and obtained statistically significant predictions for more than 76% of the profiled metabolites. Diet and microbiome had the strongest predictive power, and each explained hundreds of metabolites-in some cases, explaining more than 50% of the observed variance. We further validated microbiome-related predictions by showing a high replication rate in two geographically independent cohorts7,8 that were not available to us when we trained the algorithms. We used feature attribution analysis9 to reveal specific dietary and bacterial interactions. We further demonstrate that some of these interactions might be causal, as some metabolites that we predicted to be positively associated with bread were found to increase after a randomized clinical trial of bread intervention. Overall, our results reveal potential determinants of more than 800 metabolites, paving the way towards a mechanistic understanding of alterations in metabolites under different conditions and to designing interventions for manipulating the levels of circulating metabolites.
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Bread Affects Clinical Parameters and Induces Gut Microbiome-Associated Personal Glycemic Responses.
Korem, T, Zeevi, D, Zmora, N, Weissbrod, O, Bar, N, Lotan-Pompan, M, Avnit-Sagi, T, Kosower, N, Malka, G, Rein, M, et al
Cell metabolism. 2017;25(6):1243-1253.e5
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Bread is a key ingredient of the human diet. Wheat is the most commonly used cereal for baking bread. The aim of this study was to compare the effects of traditionally milled and prepared whole-grain sourdough bread and industrial white bread made from refined wheat on multiple clinical and disease markers and on the composition and function of the gut microbiome. The study is a randomized crossover trial with 20 healthy subjects. Participants received either industrial white bread made from mostly refined wheat flour or a sourdough-leavened bread made from whole-grain wheat flour. Results indicate that there were no significant differences on a broad array of clinical parameters between the two 1-week-long dietary interventions. Additionally, gut microbiome analysis showed that the microbiota composition remained generally stable and person specific throughout the trial. Authors conclude that their study underlines the importance of personalisation in dietary recommendations as the interpersonal variation in the effect of bread would allow the personalisation of bread-related nutritional recommendations and optimisation of food choices worldwide.
Abstract
Bread is consumed daily by billions of people, yet evidence regarding its clinical effects is contradicting. Here, we performed a randomized crossover trial of two 1-week-long dietary interventions comprising consumption of either traditionally made sourdough-leavened whole-grain bread or industrially made white bread. We found no significant differential effects of bread type on multiple clinical parameters. The gut microbiota composition remained person specific throughout this trial and was generally resilient to the intervention. We demonstrate statistically significant interpersonal variability in the glycemic response to different bread types, suggesting that the lack of phenotypic difference between the bread types stems from a person-specific effect. We further show that the type of bread that induces the lower glycemic response in each person can be predicted based solely on microbiome data prior to the intervention. Together, we present marked personalization in both bread metabolism and the gut microbiome, suggesting that understanding dietary effects requires integration of person-specific factors.
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Personalized Nutrition by Prediction of Glycemic Responses.
Zeevi, D, Korem, T, Zmora, N, Israeli, D, Rothschild, D, Weinberger, A, Ben-Yacov, O, Lador, D, Avnit-Sagi, T, Lotan-Pompan, M, et al
Cell. 2015;(5):1079-1094
Abstract
Elevated postprandial blood glucose levels constitute a global epidemic and a major risk factor for prediabetes and type II diabetes, but existing dietary methods for controlling them have limited efficacy. Here, we continuously monitored week-long glucose levels in an 800-person cohort, measured responses to 46,898 meals, and found high variability in the response to identical meals, suggesting that universal dietary recommendations may have limited utility. We devised a machine-learning algorithm that integrates blood parameters, dietary habits, anthropometrics, physical activity, and gut microbiota measured in this cohort and showed that it accurately predicts personalized postprandial glycemic response to real-life meals. We validated these predictions in an independent 100-person cohort. Finally, a blinded randomized controlled dietary intervention based on this algorithm resulted in significantly lower postprandial responses and consistent alterations to gut microbiota configuration. Together, our results suggest that personalized diets may successfully modify elevated postprandial blood glucose and its metabolic consequences. VIDEO ABSTRACT.