Evidence map›Paper›PMID 31095300›Full record

ArticleThe American journal of clinical nutrition2019

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

Helena Mendes-Soares, Tali Raveh-Sadka, Shahar Azulay, Yatir Ben-Shlomo, Yossi Cohen, Tal Ofek, Josh Stevens, Davidi Bachrach, Purna Kashyap, Lihi Segal and 1 more

Erratum issued 3 registry-linked trialsAbstract read
In one paragraph

Article in The American journal of clinical nutrition, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. It is linked to 3 registered trials, which are not on this map. Cited by 48 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
48citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT02945514 completednot on this map

Validation of the Measurement and Prediction of the Postprandial Glycemic Response to Food and Providing Personally Tailored Dietary Recommendations

TypeobservationalSponsorMayo ClinicRan2016 to 2018Enrolled327ConditionsHealthy VolunteersArmsDayTwo machine learning algorithm
NCT05230342 naactive not recruitingnot on this mapstarted 2022, after this paper: background citation

Personalized Nutrition Based on the Glycemic Response: Effect of Diet and Intestinal

TypeinterventionalSponsorInstituto Nacional de Ciencias Medicas y Nutricion Salvador ZubiranRan2022 to 2025Enrolled100ConditionsObesity, Pre DiabetesArmsA package containing a mix of functional foods, Placebo ingredient group
NCT05723913 narecruitingnot on this mapstarted 2023, after this paper: background citation

Postprandial Glucose Levels, Gut Microbiota and Supplementation With Functional Foods in Adults

TypeinterventionalSponsorInstituto Nacional de Ciencias Medicas y Nutricion Salvador ZubiranRan2023 to 2026Enrolled200ConditionsHealthy, Nutritional and Metabolic DiseasesArmsDietary Supplement: A package containing a mix of functional foods
3 · Its place in the literature

Who cites it

48 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  15. Obesity Adversely Affects Dietary Metabolism: Time to Inform Guidelines?Journal of the American Heart Association · 2025
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Helena Mendes-SoaresCenter for Individualized Medicine, Mayo Clinic, Rochester, MN.
Tali Raveh-SadkaDayTwo, Adanim, IL.
Shahar AzulayDayTwo, Adanim, IL.
Yatir Ben-ShlomoDayTwo, Adanim, IL.
Yossi CohenDayTwo, Adanim, IL.
Tal OfekDayTwo, Adanim, IL.
Josh StevensDayTwo, Adanim, IL.
Davidi BachrachDayTwo, Adanim, IL.
Purna KashyapCenter for Individualized Medicine, Mayo Clinic, Rochester, MN.
Lihi SegalDayTwo, Adanim, IL.
Heidi NelsonCenter for Individualized Medicine, Mayo Clinic, Rochester, MN.

Funding

Mechanisms of alteration of GI physiology by gut microbesR01DK114007 · NIDDK · MAYO CLINIC ROCHESTER · PI Purna C Kashyap · 2017 to 2026
$4.7M
NIDDK NIH HHS R01 DK114007
6 · The paper itself

Abstract

backgroundControlled 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.

objectiveWe 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.

designWe 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.

resultsWhen 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.

conclusionsWe 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.

Indexed as

FoodMealsPostprandial PeriodBlood GlucoseCohort StudiesDietDietary CarbohydratesEnergy IntakeGlycemic IndexHumansIsraelMicrobiotaModels, BiologicalPrecision MedicineUnited StatesBlood GlucoseDietary Carbohydratescarbohydrate contentcontinuous glucose monitorsdiabetesglycemic responsemicrobiomepersonalized nutrition

Identifiers

PMID31095300
PMCPMC6599737

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.