Evidence map›Paper›PMID 30040822›Full record

ArticlePLoS biology2018

Glucotypes reveal new patterns of glucose dysregulation.

Heather Hall, Dalia Perelman, Alessandra Breschi, Patricia Limcaoco, Ryan Kellogg, Tracey McLaughlin, Michael Snyder

8 registry-linked trialsAbstract read
In one paragraph

Article in PLoS biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 8 registered trials, which are not on this map. Cited by 162 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
162citing papers in PubMed, 3 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.

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
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
NCT06070025 completednot on this mapstarted 2023, after this paper: background citation

Impact of Pre-Military Life Experiences/Exposures on Active-Duty Service Members' Psychological Health and Adjustment Over a Marine Battalion Deployment Cycle

TypeobservationalSponsorUniversity of Southern CaliforniaRan2023 to 2025Enrolled208ConditionsTraumatic Brain Injury
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
NCT07064499 active not recruitingnot on this mapstarted 2023, after this paper: background citation

CGM Boosted by Artificial Intelligence for Better Glycaemic Control in T2 Diabetes: a Real-life Multicentre Observational Study in Belgium.

TypeobservationalSponsorErasme University HospitalRan2023 to 2027Enrolled440ConditionsType 2 Diabetes
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

162 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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  20. CGM glycemic persistence reflects OGTT dysglycemia.medRxiv : the preprint server for health sciences · 2026
    Article

102 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Heather HallStanford University, Stem Cell Biology and Regenerative Medicine, Stanford, California, United States of America.
Dalia PerelmanStanford University, Department of Genetics, Stanford, California, United States of America.
Alessandra BreschiStanford University, Department of Genetics, Stanford, California, United States of America.ORCID 0000-0001-6464-6693
Patricia LimcaocoStanford University, Department of Genetics, Stanford, California, United States of America.
Ryan KelloggStanford University, Department of Genetics, Stanford, California, United States of America.
Tracey McLaughlinStanford University, Department of Medicine, Division of Endocrinology, Stanford, California, United States of America.
Michael SnyderStanford University, Department of Genetics, Stanford, California, United States of America.

Funding

Stanford Islet Research CoreP30DK116074 · NIDDK · STANFORD UNIVERSITY · PI Seung K Kim · 2017 to 2026
$19.5M
Longitudinal Multi-Omic Profiles to Reveal Mechanisms of Obesity-Mediated Insulin ResistanceR01DK110186 · NIDDK · STANFORD UNIVERSITY · PI MCLAUGHLIN, TRACEY, SNYDER, MICHAEL P. · 2017 to 2021
$3.2M
NIDDK NIH HHS P30 DK116074NIDDK NIH HHS R01 DK110186
6 · The paper itself

Abstract

Diabetes is an increasing problem worldwide; almost 30 million people, nearly 10% of the population, in the United States are diagnosed with diabetes. Another 84 million are prediabetic, and without intervention, up to 70% of these individuals may progress to type 2 diabetes. Current methods for quantifying blood glucose dysregulation in diabetes and prediabetes are limited by reliance on single-time-point measurements or on average measures of overall glycemia and neglect glucose dynamics. We have used continuous glucose monitoring (CGM) to evaluate the frequency with which individuals demonstrate elevations in postprandial glucose, the types of patterns, and how patterns vary between individuals given an identical nutrient challenge. Measurement of insulin resistance and secretion highlights the fact that the physiology underlying dysglycemia is highly variable between individuals. We developed an analytical framework that can group individuals according to specific patterns of glycemic responses called "glucotypes" that reveal heterogeneity, or subphenotypes, within traditional diagnostic categories of glucose regulation. Importantly, we found that even individuals considered normoglycemic by standard measures exhibit high glucose variability using CGM, with glucose levels reaching prediabetic and diabetic ranges 15% and 2% of the time, respectively. We thus show that glucose dysregulation, as characterized by CGM, is more prevalent and heterogeneous than previously thought and can affect individuals considered normoglycemic by standard measures, and specific patterns of glycemic responses reflect variable underlying physiology. The interindividual variability in glycemic responses to standardized meals also highlights the personal nature of glucose regulation. Through extensive phenotyping, we developed a model for identifying potential mechanisms of personal glucose dysregulation and built a webtool for visualizing a user-uploaded CGM profile and classifying individualized glucose patterns into glucotypes.

Indexed as

Blood Glucose Self-MonitoringAdultAgedBlood GlucoseCarbohydrate MetabolismCohort StudiesDiabetes Mellitus, Type 2FemaleHomeostasisHumansHyperglycemiaInsulinInternetMaleMealsMiddle AgedBlood GlucoseInsulin

Identifiers

PMID30040822
PMCPMC6057684

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

and 2 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 Socratic graph.