Evidence map›Paper›PMID 41407881›Full record

Articlenpj metabolic health and disease2025

Risk stratification at prediabetes onset and association with diabetes outcomes using EHR data.

Junjie Luo, Di Hu, Rui Han, Diyang Lyu, Ritu Agarwal, Nestoras Mathioudakis, Jehan El-Bayoumi, Gordon Gao, Nawar Shara

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in npj metabolic health and disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

9 authors.

Junjie LuoSchool of Medicine, Johns Hopkins University, Baltimore, MD, USA.
Di HuDonald Bren School of Information and Computer Sciences, University of California Irvine, Irvine, CA, USA.
Rui HanCenter for Digital Health and AI, Johns Hopkins University, Baltimore, MD, USA.
Diyang LyuMedStar Health, Columbia, MD, USA.
Ritu AgarwalCenter for Digital Health and AI, Johns Hopkins University, Baltimore, MD, USA.
Nestoras MathioudakisSchool of Medicine, Johns Hopkins University, Baltimore, MD, USA.
Jehan El-BayoumiGeorgetown University and MedStar, Washington, DC, USA.
Gordon GaoCenter for Digital Health and AI, Johns Hopkins University, Baltimore, MD, USA. gordon.gao@jhu.edu.ORCID http://orcid.org/0000-0002-2336-9682
Nawar SharaMedStar Health, Columbia, MD, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Prediabetes can progress to type 2 diabetes (T2D), but individual risk varies widely. Few studies have rigorously characterized subgroups at the point of prediabetes (PD) onset. Using electronic health records (EHRs), we developed a machine learning approach to stratify PD and analyze T2D progression risk. We defined PD onset based on strict HbA1c criteria and excluded patients with missing follow-ups or atypical clinical events, yielding a high-fidelity cohort of 14,436 patients from an initial pool of 74,054 (2017-2023, MedStar Health). An XGBoost model using routine features, including HbA1c, BMI, blood pressure, lipids, ALT, medication history, and lifestyle factors, was trained on 2018-2020 data and tested on 2021-2022 patients, achieving an AUC of 81.6%. Risk scores enabled subtyping into high-, medium-, and low-risk groups with distinct progression trajectories. Stratification patterns remained consistent in future cohorts. This approach supports earlier, personalized intervention and diabetes risk prediction using real-world EHR data.

Identifiers

PMID41407881
PMCPMC12711876

What OpenQuestion holds

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Registered trials

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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.