Evidence mapPaperPMID 41542680Full record

ArticlemedRxiv : the preprint server for health sciences2026

Heterogeneity of Treatment Effects Across Nine Glucose-Lowering Drug Classes in Type 2 Diabetes: Extension of the LEGEND-T2DM Network Study.

Hsin Yi Chen, Thomas Falconer, Anna Ostropolets, Tara V Anand, Xinzhuo Jiang, David Dávila-García, Linying Zhang, Ruochong Fan, Hannah Morgan-Cooper, Marc A Suchard and 1 more

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Hsin Yi ChenDepartment of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY.ORCID 0000-0002-7907-2335
Thomas FalconerDepartment of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY.
Anna OstropoletsDepartment of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY.ORCID 0000-0002-0847-6682
Tara V AnandDepartment of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY.
Xinzhuo JiangDepartment of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY.
David Dávila-GarcíaInstitute for Informatics, Data Science and Biostatistics, Washington University in St. Louis, St. Louis, MO.ORCID 0000-0002-9951-2270
Linying ZhangInstitute for Informatics, Data Science and Biostatistics, Washington University in St. Louis, St. Louis, MO.
Ruochong FanInstitute for Informatics, Data Science and Biostatistics, Washington University in St. Louis, St. Louis, MO.
Hannah Morgan-CooperStanford School of Medicine and Stanford Health Care, Palo Alto, CA.
Marc A SuchardDepartment of Biostatistics, Fielding School of Public Health, University of California, Los Angeles.
George HripcsakDepartment of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY.

Funding

Training in Biomedical Informatics at Columbia UniversityT15LM007079 · COLUMBIA UNIV NEW YORK MORNINGSIDE · 1992 to 2025
$8.3M
DISCOVERING AND APPLYING KNOWLEDGE IN CLINICAL DATABASESR01LM006910 · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2000 to 2005
$2.4M
Real-world Evidence to Inform Decisions for Hypertension Treatment EscalationR01HL169954 · YALE UNIVERSITY · 2025 to 2025
$644k
NHLBI NIH HHS R01 HL169954NLM NIH HHS R01 LM006910NLM NIH HHS T15 LM007079
6 · The paper itself

Abstract

Aims/Hypothesis: Understanding heterogeneous patient responses to various glucose-lowering therapies is crucial for advancing personalized treatment approaches and optimizing outcomes for type 2 diabetes mellitus. While average treatment effects are known for many drug classes, patient responses may differ by underlying clinical and demographic factors. We hypothesize that major glucose-lowering drug classes exhibit heterogeneous treatment effects (HTE) across patient subgroups defined by key clinical and demographic characteristics. Methods: This is a large-scale observational cohort study replicated in six data-sources across the Observational Health Data Sciences and Informatics network. New-user, active-comparator cohorts were constructed for patients with type 2 diabetes mellitus initiating one of the nine antihyperglycemic drug classes. Large-scale propensity score adjustment for measured confounding, empirical calibration using negative control outcomes, and random-effects meta-analysis were employed to estimate calibrated hazard ratios (HRs). HTE was assessed by comparing differences in log HRs across 10 demographic and clinical subgroups. Results: Evidence of HTE was observed across hyperlipidemia, hypertension, obesity, and sex subgroups. Biguanides (vs. DPP-4i) were protective against acute myocardial infarction in patients with hyperlipidemia, and against heart failure hospitalization in patients with obesity. SGLT-2 inhibitors (vs. GLP-1 receptor agonists) reduced stroke risk only in non-obese patients. Sex-specific patterns also emerged: women taking GLP-1 receptor agonists had a higher risk of diarrhea, and women taking SGLT-2 inhibitors had a lower risk of stroke compared with DPP-4 inhibitors; these associations were not seen for male patients. Conclusions: This hypothesis-generating study identified several potential signals (blood pressure status, lipid status, obesity status, and sex) where there exists treatment effect heterogeneity for several classes of type 2 diabetes mellitus drugs. These preliminary findings highlight the potential for personalized type 2 diabetes mellitus treatment recommendations based on patient characteristics.

Indexed as

Comparative effectivenessGlucose-lowering drugsHeterogeneity of treatment effectObservational studyPersonalized medicineReal-world evidence

Identifiers

PMID41542680
PMCPMC12803377

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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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 Socratic graph.