Evidence map›Paper›PMID 39715896›Full record

ArticleNature biomedical engineering2025

Prediction of metabolic subphenotypes of type 2 diabetes via continuous glucose monitoring and machine learning.

Ahmed A Metwally, Dalia Perelman, Heyjun Park, Yue Wu, Alokkumar Jha, Seth Sharp, Alessandra Celli, Ekrem Ayhan, Fahim Abbasi, Anna L Gloyn and 2 more

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature biomedical engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03919877 (Precision Diets for Diabetes Prevention), which is not on this map. Cited by 34 papers.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed
–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.

NCT03919877 nacompletednot on this map

Precision Diets for Diabetes Prevention

TypeinterventionalSponsorStanford UniversityRan2018 to 2023Enrolled115ConditionsPre Diabetes, Insulin Resistance, Diabetes Mellitus, Type 2ArmsDietary, Oral Food Challege
3 · Its place in the literature

Who cites it

34 citing papers in PubMed.

  1. Review
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  4. Article
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  8. Review
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  11. Article
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  15. Materials and System Design for Self-Decision Bioelectronic Systems.Advanced materials (Deerfield Beach, Fla.) · 2026
    Review
  16. Observational
  17. Article
  18. Review
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Ahmed A MetwallyDepartment of Genetics, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-0155-7412
Dalia PerelmanDepartment of Genetics, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0003-3335-1950
Heyjun ParkDepartment of Genetics, Stanford University, Stanford, CA, USA.
Yue WuDepartment of Genetics, Stanford University, Stanford, CA, USA.
Alokkumar JhaDepartment of Pediatrics, Stanford University, Stanford, CA, USA.
Seth SharpDepartment of Pediatrics, Stanford University, Stanford, CA, USA.
Alessandra CelliDepartment of Genetics, Stanford University, Stanford, CA, USA.
Ekrem AyhanDepartment of Medicine, Stanford University, Stanford, CA, USA.
Fahim AbbasiDepartment of Medicine, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-3932-8375
Anna L GloynDepartment of Pediatrics, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0003-1205-1844
Tracey McLaughlin *Department of Medicine, Stanford University, Stanford, CA, USA. tmclaugh@stanford.edu.
Michael P Snyder *Department of Genetics, Stanford University, Stanford, CA, USA. mpsnyder@stanford.edu.ORCID http://orcid.org/0000-0003-0784-7987

Funding

Stanford Islet Research CoreP30DK116074 · STANFORD UNIVERSITY · 2025 to 2025
$2.0M
NIDDK NIH HHS P30 DK116074NIDDK NIH HHS R01 DK110186NIDDK NIH HHS U01 DK085545NIDDK NIH HHS U01 DK105535NIDDK NIH HHS UM1 DK126185Wellcome Trust
6 · The paper itself

Abstract

The classification of type 2 diabetes and prediabetes does not consider heterogeneity in the pathophysiology of glucose dysregulation. Here we show that prediabetes is characterized by metabolic heterogeneity, and that metabolic subphenotypes can be predicted by the shape of the glucose curve measured via a continuous glucose monitor (CGM) during standardized oral glucose-tolerance tests (OGTTs) performed in at-home settings. Gold-standard metabolic tests in 32 individuals with early glucose dysregulation revealed dominant or co-dominant subphenotypes (muscle or hepatic insulin-resistance phenotypes in 34% of the individuals, and β-cell-dysfunction or impaired-incretin-action phenotypes in 40% of them). Machine-learning models trained with glucose time series from OGTTs from the 32 individuals predicted the subphenotypes with areas under the curve (AUCs) of 95% for muscle insulin resistance, 89% for β-cell deficiency and 88% for impaired incretin action. With CGM-generated glucose curves obtained during at-home OGTTs, the models predicted the muscle-insulin-resistance and β-cell-deficiency subphenotypes of 29 individuals with AUCs of 88% and 84%, respectively. At-home identification of metabolic subphenotypes via a CGM may aid the risk stratification of individuals with early glucose dysregulation.

Indexed as

Blood GlucoseDiabetes Mellitus, Type 2Machine LearningAdultAgedBlood Glucose Self-MonitoringContinuous Glucose MonitoringFemaleGlucose Tolerance TestHumansInsulin ResistanceInsulin-Secreting CellsMaleMiddle AgedPhenotypePrediabetic StateBlood Glucose

Identifiers

PMID39715896
PMCPMC12183321

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.