Evidence map›Paper›PMID 26590418›Full record

Trial reportCell2015

Personalized Nutrition by Prediction of Glycemic Responses.

David Zeevi, Tal Korem, Niv Zmora, David Israeli, Daphna Rothschild, Adina Weinberger, Orly Ben-Yacov, Dar Lador, Tali Avnit-Sagi, Maya Lotan-Pompan and 13 more

25 registry-linked trialsAbstract readRandomized Controlled Trial
PubMed Publisher
In one paragraph

Trial report in Cell, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 25 registered trials, which are not on this map. Cited by 1,395 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1,395citing papers in PubMed, 4 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.

NCT04841668 completedstarted 2021, after this paper: background citation

Gut-Brain-axis: Targets for Improvement of Cognition in the Elderly

Ran2021Enrolled50Registered outcomes51Posted comparisons0ConditionsType 2 Diabetes MellitusArmsMetformin
PMID 8013758PMID 30397356PMID 16443885PMID 11118008PMID 18220635PMID 12086935PMID 28500216PMID 12930161PMID 16219009PMID 11602624PMID 16360788PMID 10718348other papers from this trial
Open the trial in the graph
NCT02945514 completednot on this mapstarted 2016, after this paper: background citation

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
NCT03009565 unknown statusnot on this mapstarted 2017, after this paper: background citation

Does the Human Gut Microbiome Serve as a Novel Personalized Therapeutic Target for Coronary Atherosclerosis?

TypeobservationalSponsorRabin Medical CenterRan2017 to 2019Enrolled800ConditionsCoronary (Artery) DiseaseArmscoronary artery disease
NCT03196895 nacompletednot on this mapstarted 2017, after this paper: background citation

Treating Type 2 Diabetes by Reducing Postprandial Glucose Elevations: A Paradigm Shift in Lifestyle Modification

TypeinterventionalSponsorUniversity of VirginiaRan2017 to 2020Enrolled192ConditionsDiabetes Mellitus, Type 2ArmsWeight reduction training, PPG training, discrete BG feedback, continuous BG feedback
NCT03207893 nacompletednot on this mapstarted 2018, after this paper: background citation

Benefits of Adding Continuous Glucose Monitoring to Glycemic Load, Exercise, and Monitoring of Blood Glucose (GEM) for Adults With Type 2 Diabetes - Phase 2

TypeinterventionalSponsorUniversity of VirginiaRan2018 to 2020Enrolled24ConditionsDiabetes Mellitus, Type 2ArmsGEM lifestyle modification & continuous glucose monitoring, Routine Care
NCT03543644 nacompletednot on this mapstarted 2018, after this paper: background citation

A Randomized Controlled Trial of the Effect of Replacing Sugar-sweetened Beverages With Non-nutritive Sweetened Beverages or Water on Gut Microbiome and Metabolic Outcomes: STOP Sugars NOW Trial

TypeinterventionalSponsorUniversity of TorontoRan2018 to 2020Enrolled81ConditionsHealthy, Overweight and Obesity, Metabolic Syndrome, PreDiabetesArmsSugar-sweetened beverage (SSB), Non-nutritive sweetened beverages (NSB), Water
NCT03662217 naunknown statusnot on this mapstarted 2018, after this paper: background citation

Personalized Nutrition for Diabetes Type 2

TypeinterventionalSponsorDayTwoRan2018 to 2020Enrolled200ConditionsDiabetes Type 2ArmsAlgorithm-based diet, ADA- based diet
NCT03889132 completednot on this mapstarted 2019, after this paper: background citation

Integrated Analysis of the Interactions Between Glycemia and Microbiota Composition, and Their Impact on Brain Iron Deposition and Cognition in Subjects With Obesity

TypeobservationalSponsorInstitut d'Investigació Biomèdica de Girona Dr. Josep TruetaRan2019 to 2025Enrolled128ConditionsObesityArmsBariatric Surgery
NCT04079270 naunknown statusnot on this mapstarted 2019, after this paper: background citation

A Phase 2 Single-blinded Randomized Study of Algorithm-based Personalized Nutrition Intervention Compared to Standard Diet Intervention in Patients Treated With Endocrine Therapy for Early Stage, Hormone Receptor Positive Breast Cancer

TypeinterventionalSponsorSheba Medical CenterRan2019 to 2025Enrolled200ConditionsBreast CancerArmsNutrition intervention
NCT04369833 completednot on this mapstarted 2020, after this paper: background citation

Development of Continuous Glucose Monitoring System Cohort for Personalized Diabetes Prevention and Management Platform Based on Artificial Intelligence Model Using Mathematical Analysis

Typeobservational_patient_registrySponsorPusan National University HospitalRan2020 to 2020Enrolled130ConditionsPreDiabetes, Glucose Metabolism Disorders, Diabetes MellitusArmscontinuous glucose monitoring system
NCT04757233 nacompletednot on this mapstarted 2018, after this paper: background citation

Dynamically Tailoring Interventions for Problem-Solving in Diabetes Self-Management Using Self-Monitoring Data

TypeinterventionalSponsorColumbia UniversityRan2018 to 2018Enrolled20ConditionsType 2 Diabetes MellitusArmsGlucoType
NCT04920058 nacompletednot on this mapstarted 2021, after this paper: background citation

Improving Cognitive-Behavioral and Cardio-Metabolic Health Through Continuous Glucose Monitoring (CGM)

TypeinterventionalSponsorUniversity of South FloridaRan2021 to 2022Enrolled66ConditionsMetabolic SyndromeArmsContinuous Glucose Monitor, <Active Comparator?>
NCT05121844 naactive not recruitingnot on this mapstarted 2021, after this paper: background citation

Use of Continuous Glucose Monitoring in Non-Diabetic Population to Compliment Signos Mobile Health Platform: Comprehensive Weight Optimization Program and Customized Lifestyle Modifications

TypeinterventionalSponsorSignos IncRan2021 to 2027Enrolled31,187ConditionsWeight Loss, Metabolic Syndrome, Pre-diabetes, Central ObesityArmsContinuous Glucose Monitor Device
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
NCT05373381 nawithdrawnnot on this mapstarted 2022, after this paper: background citation

A Modified Ketogenic, Anti-Inflammatory Diet for Patients With High-Grade Gliomas

TypeinterventionalSponsorTufts Medical CenterRan2022 to 2022Enrolled0ConditionsGliomaArmsSupplemental High Fat Low Carbohydrate (sHFLC) + KetoPhyt
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
NCT05874635 naactive not recruitingnot on this mapstarted 2023, after this paper: background citation

Use of Signos mHealth Platform in Participants With Non-Insulin Dependent Type 2 Diabetes: Weight and Blood Glucose

TypeinterventionalSponsorSignos IncRan2023 to 2029Enrolled20,000ConditionsWeight Loss, Metabolic Syndrome, Diabetes Mellitus, Type 2, Central ObesityArmsContinuous Glucose Monitor Device
NCT06057246 naunknown statusnot on this mapstarted 2023, after this paper: background citation

Exploring Individual Determinants of postpranDIAl Glucose Response in Type 2 diaBEteS to opTimize Therapeutic Strategies With a Personalized Approach (DIABEST)

TypeinterventionalSponsorFederico II UniversityRan2023 to 2025Enrolled144ConditionsType 2 DiabetesArmsTest meal with postprandial samples
NCT06163794 narecruitingnot on this mapstarted 2024, after this paper: background citation

New Participatory Research Strategy and Multidimensional Phenotyping Towards Personalized Preventive Nutritional Support for Elderly People

TypeinterventionalSponsorInstitut National de Recherche pour l'Agriculture, l'Alimentation et l'EnvironnementRan2024 to 2027Enrolled250ConditionsNon Dependant 60 to 75 Year-old Men and WomenArmsNutritional postprandial test
NCT06214221 naactive not recruitingnot on this mapstarted 2024, after this paper: background citation

A Randomized Controlled Trial Evaluating the Safety and Effectiveness of the Intelligently-Driven Signos System for Personalized Weight Management in Overweight and Obese Adults

TypeinterventionalSponsorSignos IncRan2024 to 2026Enrolled380ConditionsOverweight and Obesity, Metabolic Syndrome, Weight LossArmsSignos System, Standard Lifestyle Education
NCT06416254 nacompletednot on this mapstarted 2024, after this paper: background citation

The Effects of a Diet Enriched With Dietary Fibre on Glycaemic Response, Gastrointestinal Tolerability, Satiety, and Microbiome Functionality (The Fibre Full Study).

TypeinterventionalSponsorUniversity College CorkRan2024 to 2024Enrolled24ConditionsOverweight and ObesityArmsFibre-enriched diet, Control diet
NCT06657001 enrolling by invitationnot on this mapstarted 2025, after this paper: background citation

Enabling Microbiomics- Driven Personalized Nutrition (EMPOWER)

Typeobservational_patient_registrySponsorMayo ClinicRan2025 to 2028Enrolled4,001ConditionsInflammatory Bowel Disease (IBD), Irritable Bowel Syndrome (IBS), Celiac Disease, Diarrhea
NCT06878781 narecruitingnot on this mapstarted 2024, after this paper: background citation

Standardized Meals With Different Macronutrient Ratios on the Postprandial Metabolome and Metabolic Flexibility

TypeinterventionalSponsorInstituto Nacional de Medicina GenomicaRan2024 to 2026Enrolled300ConditionsFasting, Postprandial Hyperglycemia, Lipid Metabolism Disorders, Glucose Metabolism DisordersArmsHigh carbohydrate challenge, High lipid challenge, High proteinchallenge
NCT07278063 nanot yet recruitingnot on this mapstarted 2026, after this paper: background citation

Effect of Gender and Meal Composition on the Postprandial Glycemic Response in People With Type 1 Diabetes: Impact of Fat Content and Menstrual Cycle Phases

TypeinterventionalSponsorInstituto de Investigacion Sanitaria La FeRan2026 to 2028Enrolled50ConditionsType 1 Diabetes Mellitus, Glycemic Variability, Sex Characteristics, Menstrual CycleArmsLow Fat Mixed Meal, High Fat Mixed Meal
NCT07626658 not yet recruitingnot on this mapstarted 2026, after this paper: background citation

Food_i Sense Analytics: Integrando la Inteligencia Artificial Con la monitorización Continua de la Glucosa Para la nutrición de precisión

TypeobservationalSponsorIMDEA FoodRan2026 to 2028Enrolled471ConditionsPrediabetes (Insulin Resistance, Impaired Glucose Tolerance), Artificial Intelligence Mobile ApplicationArmsContinuous glucose monitoring using a wearable sensor (flash interstitial glucose monitor)
3 · Its place in the literature

Who cites it

1,395 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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  5. Effect of pasteurizedGut microbes · 2026
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1,335 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

23 authors.

David ZeeviDepartment of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel.
Tal KoremDepartment of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel.
Niv ZmoraImmunology Department, Weizmann Institute of Science, Rehovot 7610001, Israel; Internal Medicine Department, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel; Research Center for Digestive Tract and Liver Diseases, Tel Aviv Sourasky Medical Center, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6423906, Israel.
David IsraeliDay Care Unit and the Laboratory of Imaging and Brain Stimulation, Kfar Shaul Hospital, Jerusalem Center for Mental Health, Jerusalem 9106000, Israel.
Daphna RothschildDepartment of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel.
Adina WeinbergerDepartment of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel.
Orly Ben-YacovDepartment of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel.
Dar LadorDepartment of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel.
Tali Avnit-SagiDepartment of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel.
Maya Lotan-PompanDepartment of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel.
Jotham SuezImmunology Department, Weizmann Institute of Science, Rehovot 7610001, Israel.
Jemal Ali MahdiImmunology Department, Weizmann Institute of Science, Rehovot 7610001, Israel.
Elad MatotDepartment of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel.
Gal MalkaDepartment of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel.
Noa KosowerDepartment of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel.
Michal ReinDepartment of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel.
Gili Zilberman-SchapiraImmunology Department, Weizmann Institute of Science, Rehovot 7610001, Israel.
Lenka DohnalováImmunology Department, Weizmann Institute of Science, Rehovot 7610001, Israel.
Meirav Pevsner-FischerImmunology Department, Weizmann Institute of Science, Rehovot 7610001, Israel.
Rony BikovskyDepartment of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel.
Zamir HalpernResearch Center for Digestive Tract and Liver Diseases, Tel Aviv Sourasky Medical Center, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6423906, Israel; Digestive Center, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel.
Eran ElinavImmunology Department, Weizmann Institute of Science, Rehovot 7610001, Israel. Electronic address: eran.elinav@weizmann.ac.il.
Eran SegalDepartment of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel. Electronic address: eran.segal@weizmann.ac.il.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AlgorithmsPostprandial PeriodBlood GlucoseDiabetes Mellitus, Type 2Diet, DiabeticGastrointestinal MicrobiomeHumansSmartphoneBlood Glucose

Identifiers

PMID26590418

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

and 19 more above

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.