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      A mixed meal tolerance test predicts onset of type 2 diabetes in Southwestern Indigenous adults

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          Abstract

          Background/Objective

          To identify predictors of incident type 2 diabetes using a mixed meal tolerance test (MMTT).

          Methods

          Adult Indigenous Americans without diabetes ( n = 501) from a longitudinal cohort underwent at baseline a 4-h MMTT, measures of body composition, an oral glucose tolerance test, an intravenous glucose tolerance test for acute insulin response (AIR), and a hyperinsulinemic-euglycemic clamp for insulin action (M). Plasma glucose responses from the MMTT were quantified by the total and incremental area under the curve (AUC/iAUC).

          Results

          At follow-up (median time 9.6 [inter-quartile range: 5.6–13.5] years), 169 participants were diagnosed with diabetes. Unadjusted Cox proportional hazards models, glucose AUC 180-min (HR: 1.98, 95% CI: 1.67, 2.34, p < 0.0001), AUC 240-min (HR: 1.93, 95% CI: 1.62, 2.31, p < 0.0001), and iAUC 180-min (HR: 1.43, 95% CI: 1.20, 1.71, p < 0.0001) were associated with an increased risk of diabetes. After adjustment for covariates (age, sex, body fat percentage, M, AIR, Indigenous American heritage) in three subsequent models, AUC 180-min (HR: 1.44, 95% CI: 1.10, 1.88, p = 0.007) and AUC 240-min (HR: 1.41, 95% CI: 1.09, 1.84, p < 0.01) remained associated with increased risk of diabetes.

          Conclusions

          Glucose responses to a mixed meal predicted the development of type 2 diabetes. This indicates that a mixed nutritional challenge provides important information on disease risk.

          Clinical Trial Registry

          ClinicalTrials.gov identifier : NCT00340132, NCT00339482

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          Most cited references27

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          Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach

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            Personalized Nutrition by Prediction of Glycemic Responses.

            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.
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              Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation.

              The assessment of the discrimination ability of a survival analysis model is a problem of considerable theoretical interest and important practical applications. This issue is, however, more complex than evaluating the performance of a linear or logistic regression. Several different measures have been proposed in the biostatistical literature. In this paper we investigate the properties of the overall C index introduced by Harrell as a natural extension of the ROC curve area to survival analysis. We develop the overall C index as a parameter describing the performance of a given model applied to the population under consideration and discuss the statistic used as its sample estimate. We discover a relationship between the overall C and the modified Kendall's tau and construct a confidence interval for our measure based on the asymptotic normality of its estimate. Then we investigate via simulations the length and coverage probability of this interval. Finally, we present a real life example evaluating the performance of a Framingham Heart Study model. Copyright 2004 John Wiley & Sons, Ltd.
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                Author and article information

                Contributors
                cassie-mitchell@ouhsc.edu
                Journal
                Nutr Diabetes
                Nutr Diabetes
                Nutrition & Diabetes
                Nature Publishing Group UK (London )
                2044-4052
                10 July 2024
                10 July 2024
                2024
                : 14
                : 50
                Affiliations
                [1 ]GRID grid.94365.3d, ISNI 0000 0001 2297 5165, Obesity and Diabetes Clinical Research Section, Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, , National Institutes of Health, ; Phoenix, AZ USA
                [2 ]Harold Hamm Diabetes Center, University of Oklahoma Health Sciences Center, ( https://ror.org/0457zbj98) Oklahoma City, USA
                Author information
                http://orcid.org/0000-0002-6988-3900
                http://orcid.org/0000-0003-4875-7048
                http://orcid.org/0000-0001-6074-5600
                Article
                269
                10.1038/s41387-024-00269-3
                11237083
                38987291
                02ced3b9-9c75-40ba-9a25-ba739b814b87
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 5 May 2023
                : 5 January 2024
                : 23 February 2024
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                © Springer Nature Limited 2024

                Endocrinology & Diabetes
                physiology,risk factors
                Endocrinology & Diabetes
                physiology, risk factors

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