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, 2 (2), e188102

Assessment of a Personalized Approach to Predicting Postprandial Glycemic Responses to Food Among Individuals Without Diabetes

Assessment of a Personalized Approach to Predicting Postprandial Glycemic Responses to Food Among Individuals Without Diabetes

Helena Mendes-Soares et al. JAMA Netw Open.

Abstract

Importance: Emerging evidence suggests that postprandial glycemic responses (PPGRs) to food may be influenced by and predicted according to characteristics unique to each individual, including anthropometric and microbiome variables. Interindividual diversity in PPGRs to food requires a personalized approach for the maintenance of healthy glycemic levels.

Objectives: To describe and predict the glycemic responses of individuals to a diverse array of foods using a model that considers the physiology and microbiome of the individual in addition to the characteristics of the foods consumed.

Design, setting, and participants: This cohort study using a personalized predictive model enrolled 327 individuals without diabetes from October 11, 2016, to December 13, 2017, in Minnesota and Florida to be part of a study lasting 6 days. The study measured anthropometric variables, described the gut microbial composition, and assessed blood glucose levels every 5 minutes using a continuous glucose monitor. Participants logged their food and activity information for the duration of the study. A predictive model of individualized PPGRs to a diverse array of foods was trained and applied.

Main outcomes and measures: Glycemic responses to food consumed over 6 days for each participant. The predictive model of personalized PPGRs considered individual features, including the microbiome, in addition to the features of the foods consumed.

Results: Postprandial response to the same foods varied across 327 individuals (mean [SD] age, 45 [12] years; 78.0% female). A model predicting each individual's responses to food that considers several individual factors in addition to food features had better overall performance (R = 0.62) than current standard-of-care approaches using nutritional content alone (R = 0.34 for calories and R = 0.40 for carbohydrates) to control postprandial glycemic levels.

Conclusions and relevance: Across the cohort of adults without diabetes who were examined, a personalized predictive model that considers unique features of the individual, such as clinical characteristics, physiological variables, and the microbiome, in addition to nutrient content was more predictive than current dietary approaches that focus only on the calorie or carbohydrate content of foods. Providing individuals with tools to manage their glycemic responses to food based on personalized predictions of their PPGRs may allow them to maintain their blood glucose levels within limits associated with good health.

Conflict of interest statement

Conflict of Interest Disclosures: Dr Mendes-Soares reported that Mayo Clinic received support for this study from DayTwo, reported that Mayo Clinic has a financial interest in DayTwo, and reported receiving grants and nonfinancial support from DayTwo. Dr Raveh-Sadka reported being an employee of DayTwo. Mr Azulay reported being an employee of DayTwo. Ms Edens reported receiving grants from the Center for Individualized Medicine at Mayo Clinic, reported that Mayo Clinic received support for this study from DayTwo, and reported that Mayo Clinic has a financial interest in DayTwo. Mr Ben-Shlomo reported being an employee of DayTwo. Mr Cohen reported being an employee of DayTwo. Dr Ofek reported being an employee of DayTwo. Mr Bachrach reported being an employee of DayTwo. Mr Stevens reported being an employee of DayTwo. Dr Colibaseanu reported that Mayo Clinic received support for this study from DayTwo and reported that Mayo Clinic has a financial interest in DayTwo. Ms Segal reported being cofounder and CEO of DayTwo; reported receiving support from Angles Hi-tech Investments, Mayo Foundation for Medical Education and Research (Mayo Clinic), Johnson & Johnson Innovation–JJDC, Inc, Health For Life (HFL) SCA, HFL ALPHA, and I.B.I. Trust Management Ltd; and reported having a patent to US 2016/0232311 A1 pending and a patent to WO 2015/166489 A2 pending. Dr Kashyap reported that Mayo Clinic received support for this study from DayTwo, reported that Mayo Clinic has a financial interest in DayTwo, and reported receiving support from Mayo Clinic (Rochester, Minnesota) and from Salix Pharmaceuticals. Dr Nelson reported that Mayo Clinic received support for this study from DayTwo, reported that Mayo Clinic has a financial interest in Day Two, and reported receiving support from Mayo Clinic and from DayTwo.

Figures

Figure 1.
Figure 1.. Flow Diagram of Study Participants
CGM indicates continuous glucose monitor. aOne participant experienced a microbiome sequencing run less than 5 million and an invalid CGM data set.
Figure 2.
Figure 2.. Variability in Glycemic Responses to Food Among the Individuals in the Cohort
A, Shown is an example of intraindividual consistency and interindividual variability in response to the bagel and cream cheese meal. The key shows the participant number and the computed postprandial glycemic response (PPGR) for meals eaten by the same participant on different days. Postprandial glycemic response was computed as the incremental area under the curve of blood glucose levels in the 2 hours after a meal (to convert glucose to mmol/L, multiply by 0.0555). B, Carbohydrate sensitivity is measured as the correlation between carbohydrates (in grams) in the meal consumed and the PPGR.
Figure 3.
Figure 3.. Distribution of the Glycemic Responses to the Different Proportions and Ratios of Nutrients
Postprandial glycemic response (PPGR) is defined as the incremental area under the curve of blood glucose levels in the 2 hours after a meal. Boxes show the 25th percentile (bottom of box), median (box midline), and 75th percentile (top of box). The error bars show the distribution, excluding outliers (Q1 − 1.5*IQR and Q3 + 1.5*IQR, where IQR = Q3 − Q1, Q3 is the 75th percentile, and Q1 is the 25th percentile), and the yellow line shows how the median changes across different ranges of nutrients. IQR indicates interquartile range; Q, quantile.
Figure 4.
Figure 4.. Performance of the Model Developed in This Study, as Well as Calorie-Only or Carbohydrate-Only Models, in Predicting the Postprandial Glycemic Response (PPGR) to Food
A-C, Correlation between measured and various PPGR predictors. For definition of the measured PPGRs, see the Outcomes subsection in the Methods section. C, Note that rare cases in which meals were reported to have more than 40 g of carbohydrates showed a remarkably low glycemic response (<5 mg/dL*h), and were excluded to reduce potential influences of meal misreporting. D, Receiver operating characteristic curve analysis for comparison of the model presented herein, as well as models based on the calorie or carbohydrate content alone for classifying high PPGR, where high PPGR was defined as the 75th percentile of all measured PPGRs in the US cohort. Additional receiver operating characteristic curve analyses at other PPGR quantiles are available in the eFigure in the Supplement.

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