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      Model of personalized postprandial glycemic response to food developed for an Israeli cohort predicts responses in Midwestern American individuals

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          ABSTRACT

          Background

          Controlled glycemic concentrations are associated with a lower risk of conditions such as cardiovascular disease and diabetes. Models commonly used to guide interventions to control the glycemic response to food have low efficacy, with recent clinical guidelines arguing for the use of personalized approaches.

          Objective

          We tested the efficacy of a predictive model of personalized postprandial glycemic response to foods that was developed with an Israeli cohort and that takes into consideration food components and specific features, including the microbiome, when applied to individuals from the Midwestern US.

          Design

          We recruited 327 individuals for this study. Participants provided information regarding lifestyle, dietary habits, and health, as well as a stool sample for characterization of their gut microbiome. Participants were connected to continuous glucose monitors for 6 d, and the glycemic response to meals logged during this time was computed. The ability of a model trained using meals logged by the Israeli cohort to correctly predict glycemic responses in the Midwestern cohort was assessed and compared with that of a model trained using meals logged by both cohorts.

          Results

          When trained on the Israeli cohort meals only, model performance for predicting responses of individuals in the Midwestern cohort was better (R = 0.596) than that observed for models taking into consideration the carbohydrate (R = 0.395) or calorie content of the meals alone (R = 0.336). Performance increased (R = 0.618) when the model was trained on meals from both cohorts, likely because of the observed differences in age distribution, diet, and microbiome.

          Conclusions

          We show that the modeling framework described in Zeevi et al. for an Israeli cohort is applicable to a Midwestern population, and outperforms commonly used approaches for the control of blood glucose responses. The adaptation of the model to the Midwestern cohort further enhances performance and is a promising means for designing effective nutritional interventions to control glycemic responses to foods. This trial was registered at clinicaltrials.gov as NCT02945514.

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          Most cited references19

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          Dietary Fiber-Induced Improvement in Glucose Metabolism Is Associated with Increased Abundance of Prevotella.

          The gut microbiota plays an important role in human health by interacting with host diet, but there is substantial inter-individual variation in the response to diet. Here we compared the gut microbiota composition of healthy subjects who exhibited improved glucose metabolism following 3-day consumption of barley kernel-based bread (BKB) with those who responded least to this dietary intervention. The Prevotella/Bacteroides ratio was higher in responders than non-responders after BKB. Metagenomic analysis showed that the gut microbiota of responders was enriched in Prevotella copri and had increased potential to ferment complex polysaccharides after BKB. Finally, germ-free mice transplanted with microbiota from responder human donors exhibited improved glucose metabolism and increased abundance of Prevotella and liver glycogen content compared with germ-free mice that received non-responder microbiota. Our findings indicate that Prevotella plays a role in the BKB-induced improvement in glucose metabolism observed in certain individuals, potentially by promoting increased glycogen storage.
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            Nutrition recommendations and interventions for diabetes: a position statement of the American Diabetes Association.

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              Adiposity, gut microbiota and faecal short chain fatty acids are linked in adult humans

              Background/Objectives: High dietary fibre intakes may protect against obesity by influencing colonic fermentation and the colonic microbiota. Though, recent studies suggest that increased colonic fermentation contributes to adiposity. Diet influences the composition of the gut microbiota. Previous research has not evaluated dietary intakes, body mass index (BMI), faecal microbiota and short chain fatty acid (SCFA) in the same cohort. Our objectives were to compare dietary intakes, faecal SCFA concentrations and gut microbial profiles in healthy lean (LN, BMI⩽25) and overweight or obese (OWOB, BMI>25) participants. Design: We collected demographic information, 3-day diet records, physical activity questionnaires and breath and faecal samples from 94 participants of whom 52 were LN and 42 OWOB. Results: Dietary intakes and physical activity levels did not differ significantly between groups. OWOB participants had higher faecal acetate (P=0.05), propionate (P=0.03), butyrate (P=0.05), valerate (P=0.03) and total short chain fatty acid (SCFA; P=0.02) concentrations than LN. No significant differences in Firmicutes to Bacteroides/Prevotella (F:B) ratio was observed between groups. However, in the entire cohort, Bacteroides/Prevotella counts were negatively correlated with faecal total SCFA (r=−0.32, P=0.002) and F:B ratio was positively correlated with faecal total SCFA (r=0.42, P<0.0001). Principal component analysis identified distinct gut microbiota and SCFA–F:B ratio components, which together accounted for 59% of the variation. F:B ratio loaded with the SCFA and not with the microbiota suggesting that SCFA and F:B ratio vary together and may be interrelated. Conclusions: The results support the hypothesis that colonic fermentation patterns may be altered, leading to different faecal SCFA concentrations in OWOB compared with LN humans. More in-depth studies looking at the metabolic fate of SCFA produced in LN and OWOB participants are needed in order to determine the role of SCFA in obesity.
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                Author and article information

                Journal
                Am J Clin Nutr
                Am. J. Clin. Nutr
                ajcn
                The American Journal of Clinical Nutrition
                Oxford University Press
                0002-9165
                1938-3207
                July 2019
                16 May 2019
                16 May 2019
                : 110
                : 1
                : 63-75
                Affiliations
                [1 ]Center for Individualized Medicine, Mayo Clinic, Rochester, MN
                [2 ]Department of Surgery, Mayo Clinic, Rochester, MN
                [3 ]DayTwo, Adanim, IL
                [4 ]Department of Gastroenterology, Mayo Clinic, Rochester, MN
                Author notes
                Address correspondence to TR-S (e-mail: Tali.Raveh@ 123456daytwo.com ).
                Author information
                http://orcid.org/0000-0002-6727-4327
                http://orcid.org/0000-0002-8670-8450
                http://orcid.org/0000-0002-0583-2442
                Article
                nqz028
                10.1093/ajcn/nqz028
                6599737
                31095300
                52f46301-01ec-4085-951f-fb8a45fa3867
                Copyright © American Society for Nutrition 2019.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence ( http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com

                History
                : 25 September 2018
                : 04 February 2019
                Page count
                Pages: 13
                Funding
                Funded by: Mayo Foundation for Medical Education and Research 10.13039/100007048
                Categories
                Original Research Communications
                Carbohydrate Metabolism and Diabetes

                Nutrition & Dietetics
                glycemic response,personalized nutrition,diabetes,continuous glucose monitors,carbohydrate content,microbiome

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