{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T13:05:43Z","timestamp":1768050343001,"version":"3.49.0"},"posted":{"date-parts":[[2026,1,8]]},"group-title":"Endocrinology (including Diabetes Mellitus and Metabolic Disease)","reference-count":69,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T00:00:00Z","timestamp":1767830400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,1,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                <jats:sec>\n                  <jats:title>Aims\/Hypothesis<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n                <\/jats:sec>","DOI":"10.64898\/2026.01.06.26343548","type":"posted-content","created":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T20:40:14Z","timestamp":1767904814000},"source":"Crossref","is-referenced-by-count":0,"title":["Heterogeneity of Treatment Effects Across Nine Glucose-Lowering Drug Classes in Type 2 Diabetes: Extension of the LEGEND-T2DM Network Study"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7907-2335","authenticated-orcid":false,"given":"Hsin Yi","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Falconer","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0847-6682","authenticated-orcid":false,"given":"Anna","family":"Ostropolets","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY"},{"name":"Johnson C Johnson, Titusville, NJ"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tara V.","family":"Anand","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinzhuo","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9951-2270","authenticated-orcid":false,"given":"David","family":"D\u00e1vila-Garc\u00eda","sequence":"additional","affiliation":[{"name":"Institute for Informatics, Data Science and Biostatistics, Washington University in St. Louis, St. Louis, MO"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linying","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute for Informatics, Data Science and Biostatistics, Washington University in St. Louis, St. Louis, MO"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruochong","family":"Fan","sequence":"additional","affiliation":[{"name":"Institute for Informatics, Data Science and Biostatistics, Washington University in St. Louis, St. Louis, MO"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hannah","family":"Morgan-Cooper","sequence":"additional","affiliation":[{"name":"Stanford School of Medicine and Stanford Health Care, Palo Alto, CA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marc A.","family":"Suchard","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles"},{"name":"VA Informatics and Computing Infrastructure, United States Department of Veterans Affairs"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"George","family":"Hripcsak","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2026010920511607000_2026.01.06.26343548v1.1","unstructured":"2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association. https:\/\/www.ahajournals.org\/doi\/epub\/10.1161\/CIR.0000000000001209. 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