Evidence mapPaperPMID 40497454Full record

Trial reportThe Journal of clinical endocrinology and metabolism2025

Prediabetes Subgroups, Type 2 Diabetes Risk, and Differential Effects of Preventive Interventions.

Jeanette M Stafford, Ramon Casanova, Byron C Jaeger, Yitbarek Demesie, Brian J Wells, Michael P Bancks

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in The Journal of clinical endocrinology and metabolism, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Prediabetes Subgroups, Type 2 Diabetes Risk, and Differential Effects of Preventive Interventions.The Journal of clinical endocrinology and metabolism · 2025 · on this map
    Trial
  2. Subphenotyping Obesity in Pursuit of Personalized Medicine: The Devil is in the Details.Diabetes, metabolic syndrome and obesity : targets and therapy · 2025
    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

6 authors.

Jeanette M StaffordWake Forest University School of Medicine, Winston-Salem, NC 27157, USA.
Ramon CasanovaWake Forest University School of Medicine, Winston-Salem, NC 27157, USA.
Byron C JaegerWake Forest University School of Medicine, Winston-Salem, NC 27157, USA.
Yitbarek DemesieWake Forest University School of Medicine, Winston-Salem, NC 27157, USA.
Brian J WellsWake Forest University School of Medicine, Winston-Salem, NC 27157, USA.
Michael P BancksWake Forest University School of Medicine, Winston-Salem, NC 27157, USA.ORCID 0000-0003-3694-6060

Funding

American Diabetes Association 11-22-ICTSPM-18Bristol-Myers Squibb and Parke-DavisCDC HHSDiabetes Prevention ProgramGeneral Clinical Research Center ProgramNational Institute of Child Health and Human DevelopmentNIA NIH HHSNIDDK NIH HHSOffice of Research on Minority HealthOffice of Research on Women's Health
6 · The paper itself

Abstract

objectivePrior studies have subclassified type 2 diabetes using statistical clustering approaches with clinical data, but few have subclassified prediabetes and assessed the effects of preventive interventions. Our objective was to derive prediabetes subgroups based on clinical biomarkers and assess risk for incident diabetes and differential preventive intervention effects within the derived subgroups, with comparison to more simple modeling approaches.

methodsBaseline data for 3145 participants in the Diabetes Prevention Program trial were used to derive prediabetes subgroups using K-means clustering with data for 22 clinical biomarkers (sex-standardized). Cox proportional hazards regression was used to estimate hazard ratios (HRs) for diabetes and differential intervention effects (intensive lifestyle, metformin, or placebo) by prediabetes subgroups and to compare the clustering strategy to a model with clinical variables.

resultsWe identified 2 prediabetes subgroups characterized by severe insulin resistance with severe obesity (subgroup 1, 31% of sample) and moderate insulin resistance with overweight or obesity (subgroup 2, 69%). Subgroup 1 had a 58% higher risk for diabetes (HR: 1.58, 95% confidence interval: 1.31, 1.91) compared to subgroup 2. Randomization to lifestyle (compared to placebo) halved diabetes risk for both subgroups, while metformin provided greater benefit to subgroup 1 vs subgroup 2 (P for interaction <.05). A clinical variable model discriminated diabetes risk better than the clustering strategy.

conclusionPathophysiologically distinct prediabetes subgroups differ in risk for diabetes and preventive benefit from metformin. These results support distinct mechanisms of diabetes susceptibility; however, the use of clinical prediction models to guide treatment decisions may provide adequate risk profiling.

Indexed as

Diabetes Mellitus, Type 2Prediabetic StateAdultAgedBiomarkersFemaleHumansHypoglycemic AgentsInsulin ResistanceLife StyleMaleMetforminMiddle AgedObesityRisk FactorsBiomarkersHypoglycemic AgentsMetforminintensive lifestyle interventionmetformin therapyprediabetes subgroupstype 2 diabetes prevention

Identifiers

PMID40497454
PMCPMC12712972

What Socratic holds

Texttitle and abstract
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.