Comparative Effectiveness of Second-line Antihyperglycemic Agents for Cardiovascular Outcomes: A Large-scale, Multinational, Federated Analysis of the LEGEND-T2DM Study.
| العنوان: | Comparative Effectiveness of Second-line Antihyperglycemic Agents for Cardiovascular Outcomes: A Large-scale, Multinational, Federated Analysis of the LEGEND-T2DM Study. |
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| المؤلفون: | Khera R; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA.; Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, 06510, USA.; Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT, 06520, USA., Aminorroaya A; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA., Dhingra LS; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA., Thangaraj PM; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA., Camargos AP; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA., Bu F; Department of Biostatistics, University of Michigan - Ann Arbor, Ann Arbor, MI, 48105, USA., Ding X; Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21205, USA., Nishimura A; Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21205, USA., Anand TV; Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10027, USA., Arshad F; Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA., Blacketer C; Observational Health Data Analytics, Janssen Research and Development, LLC, Titusville, NJ, 8560, USA., Chai Y; Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong., Chattopadhyay S; Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA., Cook M; Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21205, USA., Dorr DA; Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA., Duarte-Salles T; Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, 8007, Spain.; Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands., DuVall SL; Veterans Affairs Informatics and Computing Infrastructure, United States Department of Veterans Affairs, Salt Lake City, UT, USA.; Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA., Falconer T; Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10027, USA., French TE; Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA.; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA., Hanchrow EE; Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA.; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA., Kaur G; Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, DD1 9SY, United Kingdom., Lau WC; Research Department of Practice and Policy, School of Pharmacy, University College London, London, WC1H 9JP, United Kingdom.; Centre for Medicines Optimisation Research and Education, University College London Hospitals NHS Foundation Trust, London, United Kingdom.; Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong.; Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong, Hong Kong., Li J; Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA, Durham, NC, USA., Li K; Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA., Liu Y; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA.; Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, 06510, USA., Lu Y; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA., Man KK; Research Department of Practice and Policy, School of Pharmacy, University College London, London, WC1H 9JP, United Kingdom.; Centre for Medicines Optimisation Research and Education, University College London Hospitals NHS Foundation Trust, London, United Kingdom.; Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong.; Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong, Hong Kong., Matheny ME; Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA.; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA., Mathioudakis N; Division of Endocrinology, Diabetes, and Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD, USA., McLeggon JA; Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10027, USA., McLemore MF; Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA.; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA., Minty E; Faculty of Medicine, O'Brien Institute for Public Health, University of Calgary, Calgary, AB, T2N4N1, Canada., Morales DR; Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, DD1 9SY, United Kingdom., Nagy P; Division of Endocrinology, Diabetes, and Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD, USA., Ostropolets A; Observational Health Data Analytics, Janssen Research and Development, LLC, Titusville, NJ, 8560, USA., Pistillo A; Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, 8007, Spain., Phan TP; School of Pharmacy, Taipei Medical University., Pratt N; Quality Use of Medicines and Pharmacy Research Centre, UniSA Clinical and Health Sciences, University of South Australia, Adelaide, Australia., Reyes C; Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, 8007, Spain., Richter L; Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10027, USA., Ross J; Section of General Medicine and National Clinician Scholars Program, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.; Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, 06510, USA., Ruan E; Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10027, USA., Seager SL; Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA, London, UK., Simon KR; Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA.; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA., Viernes B; Veterans Affairs Informatics and Computing Infrastructure, United States Department of Veterans Affairs, Salt Lake City, UT, USA.; Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA., Yang J; Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA., Yin C; Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA, Shanghai, China., You SC; Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, South Korea.; Institute for Innovation in Digital Healthcare, Yonsei University College of Medicine, Seoul, South Korea., Zhou JJ; Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA.; Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90024, USA., Ryan PB; Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10027, USA., Schuemie MJ; Epidemiology, Office of the Chief Medical Officer, Johnson & Johnson, Titusville, NJ, 8560, USA., Krumholz HM; Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, 06510, USA.; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA.; Section of Cardiovascular Medicine, Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, 06510, USA., Hripcsak G; Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10027, USA., Suchard MA; Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA.; Department of Biomathematics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.; Veterans Affairs Informatics and Computing Infrastructure, United States Department of Veterans Affairs, Salt Lake City, UT, USA. |
| المصدر: | MedRxiv : the preprint server for health sciences [medRxiv] 2024 Feb 08. Date of Electronic Publication: 2024 Feb 08. |
| نوع المنشور: | Preprint |
| اللغة: | English |
| بيانات الدورية: | Country of Publication: United States NLM ID: 101767986 Publication Model: Electronic Cited Medium: Internet NLM ISO Abbreviation: medRxiv Subsets: PubMed not MEDLINE |
| مستخلص: | Background: SGLT2 inhibitors (SGLT2is) and GLP-1 receptor agonists (GLP1-RAs) reduce major adverse cardiovascular events (MACE) in patients with type 2 diabetes mellitus (T2DM). However, their effectiveness relative to each other and other second-line antihyperglycemic agents is unknown, without any major ongoing head-to-head trials. Methods: Across the LEGEND-T2DM network, we included ten federated international data sources, spanning 1992-2021. We identified 1,492,855 patients with T2DM and established cardiovascular disease (CVD) on metformin monotherapy who initiated one of four second-line agents (SGLT2is, GLP1-RAs, dipeptidyl peptidase 4 inhibitor [DPP4is], sulfonylureas [SUs]). We used large-scale propensity score models to conduct an active comparator, target trial emulation for pairwise comparisons. After evaluating empirical equipoise and population generalizability, we fit on-treatment Cox proportional hazard models for 3-point MACE (myocardial infarction, stroke, death) and 4-point MACE (3-point MACE + heart failure hospitalization) risk, and combined hazard ratio (HR) estimates in a random-effects meta-analysis. Findings: Across cohorts, 16·4%, 8·3%, 27·7%, and 47·6% of individuals with T2DM initiated SGLT2is, GLP1-RAs, DPP4is, and SUs, respectively. Over 5·2 million patient-years of follow-up and 489 million patient-days of time at-risk, there were 25,982 3-point MACE and 41,447 4-point MACE events. SGLT2is and GLP1-RAs were associated with a lower risk for 3-point MACE compared with DPP4is (HR 0·89 [95% CI, 0·79-1·00] and 0·83 [0·70-0·98]), and SUs (HR 0·76 [0·65-0·89] and 0·71 [0·59-0·86]). DPP4is were associated with a lower 3-point MACE risk versus SUs (HR 0·87 [0·79-0·95]). The pattern was consistent for 4-point MACE for the comparisons above. There were no significant differences between SGLT2is and GLP1-RAs for 3-point or 4-point MACE (HR 1·06 [0·96-1·17] and 1·05 [0·97-1·13]). Interpretation: In patients with T2DM and established CVD, we found comparable cardiovascular risk reduction with SGLT2is and GLP1-RAs, with both agents more effective than DPP4is, which in turn were more effective than SUs. These findings suggest that the use of GLP1-RAs and SGLT2is should be prioritized as second-line agents in those with established CVD. Funding: National Institutes of Health, United States Department of Veterans Affairs. |
| معلومات مُعتمدة: | T15 LM007079 United States LM NLM NIH HHS; R01 HL144644 United States HL NHLBI NIH HHS; R01 LM006910 United States LM NLM NIH HHS; R01 HG006139 United States HG NHGRI NIH HHS; R01 HS022882 United States HS AHRQ HHS; R01 HL169954 United States HL NHLBI NIH HHS; U01 FD005938 United States FD FDA HHS; T32 HL155000 United States HL NHLBI NIH HHS; R01 HS025164 United States HS AHRQ HHS; United Kingdom WT_ Wellcome Trust; K23 HL153775 United States HL NHLBI NIH HHS |
| فهرسة مساهمة: | Keywords: Cardiovascular Diseases; Comparative Effectiveness Research; Diabetes Mellitus; Glucagon-Like Peptide-1 Receptor Agonists; Hypoglycemic Agents; Sodium-Glucose Transporter 2 Inhibitors; Type 2 |
| تواريخ الأحداث: | Date Created: 20240219 Latest Revision: 20240409 |
| رمز التحديث: | 20240409 |
| مُعرف محوري في PubMed: | PMC10871374 |
| DOI: | 10.1101/2024.02.05.24302354 |
| PMID: | 38370787 |
| قاعدة البيانات: | MEDLINE |
| DOI: | 10.1101/2024.02.05.24302354 |
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