Comparative Safety of Second-Line Antihyperglycemic Agents in Older Adults with Type 2 Diabetes: A Multinational Real-World Evidence From LEGEND-T2DM

Comparative Safety of Second-Line Antihyperglycemic Agents in Older Adults with Type 2 Diabetes: A Multinational Real-World Evidence From LEGEND-T2DM | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Comparative Safety of Second-Line Antihyperglycemic Agents in Older Adults with Type 2 Diabetes: A Multinational Real-World Evidence From LEGEND-T2DM View ORCID Profile Chungsoo Kim , Fan Bu , View ORCID Profile Clair Blacketer , Anna Ostropolets , View ORCID Profile Talita Duarte-Salles , Benjamin Viernes , Thomas Falconer , Andrea Pistillo , Jing Li , Can Yin , Mui Van Zandt , Paul Nagy , Akihiko Nishimura , Evan Minty , Seng Chan You , View ORCID Profile Mitsuaki Sawano , Shoko Sawano , Ja Young Jeon , View ORCID Profile Arya Aminorroaya , View ORCID Profile Lovedeep S. Dhingra , Aline F. Pedroso , Phyllis Thangraraj , View ORCID Profile David A. Dorr , Nicole Pratt , View ORCID Profile Kenneth K.C. Man , Wallis C.Y. Lau , Daniel R. Morales , View ORCID Profile Rohan Khera , Martijn Schuemie , Patrick B. Ryan , George Hripcsak , View ORCID Profile Harlan M. Krumholz , View ORCID Profile Marc A. Suchard , View ORCID Profile Yuan Lu doi: https://doi.org/10.1101/2025.05.08.25327248 Chungsoo Kim 1 Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine 2 Yale New Haven Hospital Center for Outcomes Research and Evaluation Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Chungsoo Kim Fan Bu 3 Department of Biostatistics, University of Michigan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Clair Blacketer 4 Johnson and Johnson Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Clair Blacketer Anna Ostropolets 4 Johnson and Johnson Find this author on Google Scholar Find this author on PubMed Search for this author on this site Talita Duarte-Salles 5 Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina 6 Department of Medical Informatics, Erasmus University Medical Center Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Talita Duarte-Salles Benjamin Viernes 7 VA Informatics and Computing Infrastructure, United States Department of Veterans Affairs Find this author on Google Scholar Find this author on PubMed Search for this author on this site Thomas Falconer 8 Department of Biomedical Informatics, Columbia University Find this author on Google Scholar Find this author on PubMed Search for this author on this site Andrea Pistillo 5 Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jing Li 9 Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Can Yin 9 Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mui Van Zandt 9 Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Paul Nagy 10 Department of Radiology, Johns Hopkins University School of Medicine Find this author on Google Scholar Find this author on PubMed Search for this author on this site Akihiko Nishimura 11 Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University Find this author on Google Scholar Find this author on PubMed Search for this author on this site Evan Minty 12 O’Brien Institute for Public Health, University of Calgary Find this author on Google Scholar Find this author on PubMed Search for this author on this site Seng Chan You 13 Department of Biomedicine Systems Informatics, Yonsei University College of Medicine Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mitsuaki Sawano 1 Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine 2 Yale New Haven Hospital Center for Outcomes Research and Evaluation Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Mitsuaki Sawano Shoko Sawano 2 Yale New Haven Hospital Center for Outcomes Research and Evaluation Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ja Young Jeon 14 Department of Endocrinology and Metabolism, Ajou University School of Medicine Find this author on Google Scholar Find this author on PubMed Search for this author on this site Arya Aminorroaya 1 Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Arya Aminorroaya Lovedeep S. Dhingra 1 Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lovedeep S. Dhingra Aline F. Pedroso 1 Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine Find this author on Google Scholar Find this author on PubMed Search for this author on this site Phyllis Thangraraj 1 Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine Find this author on Google Scholar Find this author on PubMed Search for this author on this site David A. Dorr 15 Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for David A. Dorr Nicole Pratt 16 Quality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kenneth K.C. Man 17 Research Department of Practice and Policy, UCL School of Pharmacy Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kenneth K.C. Man Wallis C.Y. Lau 17 Research Department of Practice and Policy, UCL School of Pharmacy Find this author on Google Scholar Find this author on PubMed Search for this author on this site Daniel R. Morales 18 Division of Population Health and Genomics, University of Dundee Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rohan Khera 1 Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine 2 Yale New Haven Hospital Center for Outcomes Research and Evaluation Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Rohan Khera Martijn Schuemie 4 Johnson and Johnson Find this author on Google Scholar Find this author on PubMed Search for this author on this site Patrick B. Ryan 4 Johnson and Johnson 8 Department of Biomedical Informatics, Columbia University Find this author on Google Scholar Find this author on PubMed Search for this author on this site George Hripcsak 8 Department of Biomedical Informatics, Columbia University Find this author on Google Scholar Find this author on PubMed Search for this author on this site Harlan M. Krumholz 1 Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine 2 Yale New Haven Hospital Center for Outcomes Research and Evaluation Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Harlan M. Krumholz Marc A. Suchard 7 VA Informatics and Computing Infrastructure, United States Department of Veterans Affairs 19 Department of Biostatistics, Fielding School of Public Health, University of California , Los Angeles Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Marc A. Suchard For correspondence: msuchard{at}ucla.edu y.lu{at}yale.edu Yuan Lu 1 Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine 2 Yale New Haven Hospital Center for Outcomes Research and Evaluation Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yuan Lu For correspondence: msuchard{at}ucla.edu y.lu{at}yale.edu Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF ABSTRACT Background As prescribing of newer antihyperglycemic agents expands, there remains limited comparative safety data for older adults—a population particularly vulnerable to adverse drug events and underrepresented in clinical trials. We aimed to evaluate the real-world safety of second-line antihyperglycemic agents among older adults with type 2 diabetes. Methods We conducted a multinational cohort study using nine harmonized electronic health record and claims databases from the U.S. and Europe, applying a consistent analytical framework based on the LEGEND-T2DM initiative. Among adults aged ≥65 years who initiated a second-line agent after metformin monotherapy, we compared safety outcomes across four drug classes: GLP-1 receptor agonists (GLP1RAs), SGLT2 inhibitors (SGLT2Is), DPP-4 inhibitors (DPP4Is), and sulfonylureas (SUs). We used propensity score adjustment, empirical calibration, and prespecified diagnostics to estimate hazard ratios (HRs) for 18 safety outcomes. Results In a cohort of 1.8 million older adults, both GLP1RAs and SGLT2Is were linked to significantly lower risks of hypoglycemia (HR 0.21 [95% CI, 0.16–0.27] for GLP1RA vs SU; HR 0.21 [0.13– 0.33] for SGLT2I vs SU) and hyperkalemia (HR 0.63 [0.50–0.81] for GLP1RA vs SU; HR 0.75 [0.63–0.90] for SGLT2I vs SU) and peripheral edema (HR 0.81 [0.71–0.92] for GLP1RAs vs. DPP4Is; HR 0.62 [0.46–0.84] for SGLT2Is vs. SU). However, SGLT2Is were associated with a higher risk of diabetic ketoacidosis compared to both GLP1RAs (HR 2.03 [1.38–2.99]) and SUs (HR 1.64 [1.27–2.11]). GLP1RAs had significantly higher risks of nausea (HR 0.63 [0.55–0.72]) and vomiting (HR 0.63 [0.57–0.69]) relative to SGLT2Is. Results were consistent across both on-treatment and intent-to-treat sensitivity analyses. Conclusion In older adults with type 2 diabetes, GLP1RAs and SGLT2Is demonstrated more favorable safety profiles than SUs and DPP4Is across multiple clinically relevant outcomes. These results support more informed, safety-conscious prescribing in a population underrepresented in clinical trials yet highly susceptible to adverse effects. INTRODUCTION As the global population ages, the burden of type 2 diabetes mellitus among older adults continues to rise. Second-line antihyperglycemic agents play a central role in managing hyperglycemia and preventing complications once metformin monotherapy is insufficient. In recent years, glucagon-like peptide-1 receptor agonists (GLP1RAs) and sodium-glucose cotransporter-2 inhibitors (SGLT2Is) have gained substantial attention for their cardiovascular and renal benefits, prompting guideline endorsements for broader use in high-risk patients. 1 , 2 However, despite increasing uptake, a critical knowledge gap persists: the comparative safety of these agents—particularly in older adults—remains insufficiently understood. Older adults are uniquely vulnerable to adverse drug events due to multimorbidity, frailty, and polypharmacy. 1 , 3 Yet, they remain underrepresented in randomized trials. The small sample sizes and selective populations of these trials limit their ability to detect rare, severe, or population-specific safety events—leaving clinicians with limited guidance on how to weigh safety considerations when initiating second-line therapy in real-world settings. Furthermore, much of the existing evidence is fragmented or focused on single outcomes, rather than offering a comprehensive assessment of safety across drug classes. 4 – 6 The need to address this gap is urgent. As prescribing patterns shift toward newer agents such as GLP1RA and the older adult population increases, 7 clinicians are making treatment decisions with incomplete safety data for a population at highest risk. Without robust, comparative evidence, efforts to personalize diabetes care and minimize avoidable harm fall short. To address this unmet need, we leveraged the LEGEND-T2DM initiative 8 – 10 —a multinational, large-scale, real-world study using harmonized data from nine electronic health record and claims databases—to evaluate the comparative safety of four major second-line drug classes: GLP1RAs, SGLT2Is, dipeptidyl peptidase-4 inhibitors (DPP4Is), and sulfonylureas (SUs). Using rigorous methods including large-scale propensity score adjustment, empirical calibration, and pre-specified diagnostics, we systematically assessed a broad range of clinically relevant safety outcomes. This study offers one of the most comprehensive safety evaluations to date of second-line antihyperglycemic agents in older adults. The findings aim to inform safer prescribing, reduce avoidable harms, and support evidence-based, patient-centered care in a population often left out of trials but central to clinical practice. METHODS Data source and study design This study is part of the LEGEND-T2DM initiative, led by the Observational Health Data Sciences and Informatics (OHDSI) research collaborative. We conducted a retrospective observational cohort study using international databases standardized to the Common Data Model (CDM). A total of nine databases from the United States, Germany, France, and Spain were included, all mapped to version 5.3.1 of the Observational Medical Outcomes Partnership (OMOP) CDM. 11 These comprised five U.S. insurance claims databases and several additional databases containing electronic health records (EHRs). All data were de-identified, and informed patient consent was waived. Ethical approvals for data use were obtained as required. Potential overlap of patients across some de-identified U.S. data sources was not explicitly addressed. Basic information, including data coverage periods and contributing entities for each source, is presented in Supplementary Table 1. Detailed definitions of the study population, exposures, outcomes, follow-up strategies, and statistical methods were prespecified, registered, and published prior to study execution. 12 , 13 The study utilized OHDSI’s Health Analytics Data-to-Evidence Suite, a suite of open-source tools for epidemiologic research. All study results were fully disclosed to support transparency and open science. The study followed the LEGEND principles and was reported in accordance with STROBE guidelines. 9 , 14 Study population and exposure The study population included older adults (aged ≥65 years) with a diagnosis of T2DM who initiated a second-line antihyperglycemic agent following first-line metformin monotherapy ( Figure 1 ). Second-line agents were defined as GLP1RAs, SGLT2Is, DPP4Is, and SUs. Download figure Open in new tab Figure 1. Study design scheme We employed a new-user, active-comparator design, with the index date defined as the date of the first prescription for a second-line agent. Patients were excluded if they met any of the following criteria: (1) less than one year of continuous observation prior to the index date; (2) a diagnosis of type 1 diabetes or secondary diabetes mellitus; (3) prior use of antihyperglycemic agents other than the specified second-line agents; or (4) fewer than 90 days of metformin monotherapy or evidence of long-term insulin use (defined as ≥30 days of continuous use). Outcome and follow-up All safety outcomes were selected based on the 2018 American Diabetes Association guidelines and prior randomized controlled trials. 15 We evaluated eighteen outcomes, grouped into three categories: (1) Metabolic and endocrine complications (abnormal weight gain, abnormal weight loss, diabetic ketoacidosis, hyperkalemia, hypoglycemia, and hypotension); (2) Organ system complications (acute pancreatitis, nausea, vomiting, diarrhea, bone fracture, joint pain, lower extremity amputation, peripheral edema, genitourinary infection, and photosensitivity); and (3) Systemic complications (all-cause mortality and venous thromboembolism). Outcome definitions were based on prior work and have been previously implemented and validated. 16 For each outcome, individuals with a prior history of the event were excluded. Follow-up was assessed using three approaches: (1) on-treatment regardless of treatment escalation (OT1), (2) on-treatment with censoring upon treatment escalation (OT2), and (3) intent-to-treat (ITT). In the OT1 approach, patients were followed from the day after the index date until the earliest occurrence of treatment discontinuation, outcome event, or end of observation. Treatment was considered continuous if a new prescription was recorded within 30 days of the previous one. Discontinuation was defined as the absence of a new prescription following the end of the prior prescription. The OT2 approach followed the same rules as OT1, but additionally censored follow-up upon initiation of any new antihyperglycemic agent. In contrast, the ITT approach followed patients from the index date until the end of observation or outcome occurrence, regardless of treatment discontinuation or escalation. Statistical analysis Large-scale propensity scores (PS) were estimated using L1-penalized logistic regression for each treatment comparison within each database, incorporating all available covariates across clinical data domains (including demographics, conditions, drugs, procedures, measurements, devices, and observations) over multiple time windows prior to treatment initiation (index date, 6 months prior, 1 year prior, and any time prior). 17 Confounding was addressed using PS stratification into 10 strata and variable-ratio matching for each pairwise comparison between second-line agents. To enhance generalizability, we prioritized reporting results based on PS stratification. Hazard ratios (HRs) for each outcome were estimated using Cox proportional hazards models within each database. Empirical calibration of HRs was conducted using negative control outcomes to correct for residual systematic error and to maintain nominal statistical properties despite the observational study design. 18 In total, we conducted 38,232 analyses, based on 9 databases × 6 comparison pairs × 118 outcomes (18 safety outcomes plus negative controls) × 2 PS methods × 3 follow-up approaches. Statistical significance was defined as a two-sided p-value <0.05. In addition, we applied Bonferroni correction for multiple testing in the meta-analytic results (Threshold with p < 0.00277778 from 18 outcomes hypotheses for each comparison), and both unadjusted and adjusted values are reported. Prior to evidence synthesis, we applied prespecified study diagnostics to ensure reliability. First, to confirm empirical equipoise, at least 25% of patients in each comparison within each database were required to have preference scores between 0.3 and 0.7. 19 Second, all covariates were required to have a maximum standardized mean difference (SMD) <0.15 following PS adjustment, indicating adequate balance. Third, we required a minimum detectable relative risk (MDRR) <4.0 and a minimum sample size of ≥1,000 patients per treatment group to ensure sufficient statistical power. 20 Only comparisons and databases that met all diagnostic criteria were included in the meta-analyses. Pooled HR estimates were obtained using a random-effects meta-analysis of the empirically calibrated database-specific HRs. All analyses were performed in a controlled computing environment using OHDSI and other R packages (version 4.2.3). 21 The full analytic code and results are publicly available at: https://github.com/ohdsi-studies/LegendT2dm , https://data.ohdsi.org/LegendT2dmClassEvidenceExplorer/ . RESULTS Characteristics A total of 1,808,003 adults aged 65 years or older met the eligibility criteria across the nine databases. There were more male patients at 984,590 (54.5%) than female patients, and the frequent age group was 65–74 years at 1,211,729 (67.0%). Among these, 73,603 (4.1%) patients initiated a GLP1RA, 173,465 (9.6%) initiated a SGLT2I, 485,016 (26.8%) initiated a DPP4I, and 1,075,919 (59.5%) received a SU as second-line therapy. The most commonly prescribed agents included dulaglutide (45.4%), semaglutide (36.1%), and exenatide (17.8%) among GLP1RAs; empagliflozin (58.6%), dapagliflozin (22.0%), and canagliflozin (18.9%) among SGLT2Is; sitagliptin (84.3%) among DPP4Is; and glipizide (53.9%) and glimepiride (35.3%) among SUs. In the on-treatment follow-up, the median days of follow-up periods ranged from 55 to 202 days for GLP1RAs and 59 to 246 days for SGLT2Is, with longer follow-up durations observed for DPP4Is (86–719 days) and SUs (84–741 days) ( Table 1 ). Table 2 presents baseline characteristics for the GLP1RA and SGLT2I groups in the US Open Claims database before and after propensity score stratification. Detailed baseline characteristics for all comparisons and databases, both before and after adjustment, are provided in Supplementary Tables 2–36. View this table: View inline View popup Download powerpoint Table 1. Population size and follow-up time for each drug class within each data source. We report the total patient counts and median and interquartile (IQR) times. When executing comparative studies, we included only within-database populations with > 1,000 new users per treatment-arm. View this table: View inline View popup Download powerpoint Table 2. Baseline patient characteristics for SGLT2I (T) and GLP1RA (C) new-users in the USOC data source. We report the proportion of initiators satisfying selected baseline characteristics and the standardized difference of population proportions (StdDiff) before and after propensity score adjustment. Less extreme StdDiffs through matching and stratification suggest improved balance between patient cohorts through adjustment. Study Diagnostics Following study diagnostics, the number of databases included for each drug comparison varied. For GLP1RA comparisons, a median of 2.0 (interquartile range [IQR] 1.0–2.0) databases were included for GLP1RA vs. DPP4I, and 1.0 (1.0–1.0) for GLP1RA vs. SU. For SGLT2I comparisons, a median of 4.0 (IQR 2.0–4.0) databases were included for SGLT2I vs. GLP1RA, 6.0 (5.0–7.0) for SGLT2I vs. DPP4I, and 3.0 (3.0–3.0) for SGLT2I vs. SU. Finally, for the DPP4I vs. SU comparison, a median of 6.5 (5.0–8.0) databases were included. Detailed diagnostic criteria—including thresholds for empirical equipoise, SMD, and MDRR—for each database-comparison pair are available in Supplementary Figure 36–53. Safety Outcomes Meta-analytic results from the primary analysis (on-treatment follow-up [OT1] with propensity score stratification) are presented in Figure 2 . Compared with SUs and DPP4Is, both GLP1RAs and SGLT2Is were associated with more favorable safety profiles across multiple metabolic and organ-related complications. Download figure Open in new tab Figure 2. Meta-analytic safety profiles comparing new users of SGLT2I to GLP1RA, DPP4I, and SU across 18 outcomes. Points and lines identify HR estimates with their 95% CIs, respectively. Outcomes in orange signify that the p<0.05 and outcomes in red mean statistically significant after Bonferroni correction (p<0.00277778) for the multiple testing. The result for all-cause mortality in the GLP1RA vs SU comparison was not presented because there was no valid result after the study diagnostic process. Download figure Open in new tab Figure 3. Safety profiles comparing new users of SGLT2I to GLP1RA, DPP4I, and SU across 18 outcomes from the intent-to-treat follow-up. Points and lines identify HR estimates with their 95% CIs, respectively. Outcomes in orange signify that the p<0.05 and outcomes in red mean statistically significant after Bonferroni correction (p<0.00277778) for the multiple testing. The result for all-cause mortality in the GLP1RA vs SU comparison was not presented because there was no valid result after the study diagnostic process. GLP1RA use was associated with a substantially lower risk of hypoglycemia compared to SU (HR 0.21 [95% CI, 0.16–0.28]), similar to SGLT2I (HR 0.20 [0.12–0.34]) and DPP4I (HR 0.26 [0.21–0.32]). Additionally, both GLP1RAs and SGLT2Is had significantly lower risks of hyperkalemia compared to SU (HR 0.63 [0.50–0.81] and 0.75 [0.63–0.90], respectively), with no meaningful difference observed between the two newer classes. SGLT2Is, in particular, demonstrated lower risk of abnormal weight gain (HR 0.57 [0.45–0.71]) and higher incidence of abnormal weight loss (HR 1.41 [1.18–1.67]) than SUs, reflecting their known weight-reducing properties. For peripheral edema, both GLP1RAs and SGLT2Is demonstrated lower risks than DPP4Is (HR 0.81 [0.72–0.90] and 0.69 [0.57–0.82], respectively), and were also favorable compared to SUs (HR 0.72 [0.58–0.90] and 0.62 [0.46–0.84], respectively). However, SGLT2Is showed a significantly increased risk compared to GLP1RAs (HR 2.03 [1.38–2.99]) and SUs (HR 1.64 [1.27–2.10]). GLP1RAs had shown high risks of nausea and vomiting (HR 1.58 [1.45–1.72] and 1.58 [1.44–1.74], respectively) compared to SGLT2Is. No significant differences across drug classes were observed for bone fracture, joint pain, lower extremity amputation, or venous thromboembolism. Importantly, SGLT2Is were associated with a significantly lower all-cause mortality risk compared to SUs (HR 0.49 [0.35– 0.68]), reinforcing the growing evidence supporting their long-term safety benefits in older adults. Sensitivity Analysis Findings from sensitivity analyses—using the OT2 follow-up design, which censors patients at treatment escalation—are presented in Supplementary Tables 216–323 and were largely consistent with the primary analysis. GLP1RAs continued to show a favorable safety profile, including a significantly lower risk of hyperkalemia compared to SUs (HR 0.62 [0.49–0.77]) and reduced risk of peripheral edema compared to SUs (HR 0.71 [0.58–0.86]). SGLT2Is also maintained a lower risk of peripheral edema compared to both DPP4Is and SUs (HR 0.70 [0.60– 0.83] and HR 0.65 [0.52–0.80], respectively). However, SGLT2Is remained associated with a significantly increased risk of diabetic ketoacidosis relative to both GLP1RAs (HR 1.99 [1.31–3.01]) and SUs (HR 1.70 [1.28–2.27]). In contrast, GLP1RAs continued to demonstrate inferior gastrointestinal tolerability, with significantly higher risks of nausea (HR 1.63 [1.50–1.77]) and vomiting (HR 1.62 [1.47–1.79]) compared to SGLT2Is. Across all drug classes, the risk of hypoglycemia remained significantly lower compared to SU, further reinforcing the advantages of GLP1RAs (HR 0.21 [0.16–0.27]), SGLT2Is (HR 0.17 [0.09–0.30]), and DPP4Is (HR 0.17 [0.13–0.21]). In the ITT analysis, which introduces greater bias toward the null due to persistent exposure misclassification, fewer relative risk estimates met the Bonferroni-corrected significance threshold. Nonetheless, key safety signals remained consistent. GLP1RAs continued to show higher risks of nausea (HR 1.24 [1.10–1.40]) and vomiting (HR 1.22 [1.10–1.34]) compared to SGLT2Is. Importantly, GLP1RAs and SGLT2Is showed a significantly lower risk of all-cause mortality compared to DPP4Is (HR 0.56 [0.39–0.79] and 0.79 [0.67–0.93], respectively), highlighting their potential long-term benefit in older adults. DISCUSSION This study provides new and comprehensive insight into the comparative safety of second-line antihyperglycemic agents in older adults—a population at high risk for drug-related complications yet historically underrepresented in clinical trials. By analyzing over 1.8 million patients across multiple international databases, we found that GLP1RAs and SGLT2Is generally offer more favorable safety profiles compared to SUs and DPP4Is. Importantly, we identified clinically relevant differences across a broad set of outcomes—not just the well-known risks like hypoglycemia or gastrointestinal side effects, 22 but also less frequently assessed events in clinical trials such as peripheral edema, diabetic ketoacidosis, and hyperkalemia. 23 – 27 These findings help move the field beyond efficacy comparisons to a more nuanced, safety-centered understanding of treatment choices in older adults with type 2 diabetes. The observed differences in safety profiles across drug classes likely reflect their distinct pharmacologic mechanisms and how those interact with age-related vulnerabilities. Sulfonylureas, which stimulate insulin secretion irrespective of glucose levels, predictably increase the risk of hypoglycemia, especially in older adults with fluctuating food intake or renal impairment. The weight gain and edema associated with sulfonylureas and DPP4Is may be tied to fluid retention and insulin-mediated anabolic effects. In contrast, SGLT2Is promote glucosuria and osmotic diuresis, which can reduce body weight and blood pressure but also predispose to volume depletion and ketoacidosis in frail or volume-sensitive patients. The lower incidence of peripheral edema observed with GLP1RAs and SGLT2Is may be due to their favorable renal and vascular effects. SGLT2I was associated with an elevated risk of diabetic ketoacidosis, which previous retrospective studies have similarly identified 28 – 32 but rarely detected in RCTs. 23 , 24 In a context where previous studies have limited evidence on hyperkalemia with SGLT2I and GLP1RA, 25 – 27 our study showed a significantly lower risk of hyperkalemia with SGLT2I and GLP1RA. However, potential differences between SGLT2I and GLP1RA suggested in previous literature were not observed in this study. 26 Bone fracture, which is specifically important to older adults and reported SGLT2Is can potentially cause in a previous trial, did not show significantly different risks in any comparison in line with other observational studies. 31 , 33 GLP1RAs, acting through central and gastrointestinal pathways, are known to cause nausea and vomiting, especially during initiation. Incretin-based glucose lowering agents have been reported to cause acute pancreatitis potentially, 34 – 36 however, no significant difference was found between SGLT2I and GLP1RA for acute pancreatitis. However, previous studies have shown that liraglutide strongly associated with pancreatitis among GLP1RAs, 37 – 40 further studies on individual ingredient are needed because we had very low rate of liraglutide in the population used GLP1RA. Finally, the signals for reduced all-cause mortality among GLP1RA and SGLT2I users align with emerging evidence of their cardioprotective and anti-inflammatory properties, which may translate into broader safety benefits in older populations. These results emphasize that prioritizing SGLT2Is and GLP1RAs as second-line treatment in older adults over the current pattern of prescribing DPP4s and SUs with T2DM may be potentially beneficial. 7 Our study extends the literature in three important ways. First, we focus explicitly on safety outcomes in older adults, a population frequently excluded or underrepresented in randomized controlled trials but disproportionately vulnerable to adverse drug events due to multimorbidity, frailty, and polypharmacy. Prior studies have either aggregated findings across age groups or relied on post hoc subgroup analyses, limiting the ability to draw robust conclusions specific to older adults. Second, we conduct a comprehensive, class-wide comparison of four major second-line antihyperglycemic drug classes—GLP1RA, SGLT2Is, DPP4Is, and SUs—rather than focusing on isolated pairwise comparisons or single adverse events. Previous real-world studies have typically concentrated on one or two outcomes (e.g., hypoglycemia or heart failure) or compared only a subset of these drug classes. Our study fills this gap by systematically examining 18 clinically relevant outcomes, allowing for a more balanced and practical evaluation of the relative safety of each class. Third, we applied a rigorous and transparent analytic framework, using harmonized multinational real-world data, large-scale propensity score adjustment, and empirical calibration. While prior observational studies often rely on a single database or apply variable methods across studies, our approach— anchored in the LEGEND and OHDSI frameworks—ensures consistency, reproducibility, and generalizability of the findings across diverse healthcare settings. This methodological strength not only confirms previously documented risks (such as hypoglycemia with sulfonylureas) but also uncovers less-recognized differences, such as the gastrointestinal tolerability advantages of SGLT2Is over GLP1RAs and the mortality benefits of SGLT2Is compared to sulfonylureas. These nuanced insights are particularly valuable for tailoring treatment decisions in older adults, where safety considerations often take precedence over modest differences in glycemic control. These findings have important clinical and policy implications. First, they reinforce current guideline recommendations—such as those from the American Diabetes Association and European Association for the Study of Diabetes—that prioritize GLP1RAs and SGLT2Is over SUs and DPP4Is, particularly in older adults. These preferences have largely been based on cardiometabolic benefits, but our results add further justification by demonstrating superior safety profiles across a wide range of adverse outcomes. In clinical practice, this means that clinicians should prioritize newer agents—especially in patients with multiple comorbidities, frailty, or a history of adverse drug reactions. Second, the findings highlight the need to reevaluate entrenched prescribing patterns that continue to favor SUs, which—despite their low cost—pose disproportionate risks of hypoglycemia, weight gain, and mortality in older adults. These patterns may reflect legacy habits, formulary limitations, or clinician concerns about the cost or side effects of newer agents. Addressing these barriers may require targeted educational efforts, updated formularies, or value-based pricing strategies to ensure that safety and effectiveness guide treatment choices. Third, these results can inform shared decision-making by providing patients and caregivers with more nuanced information about potential risks and tolerability profiles. For example, the lower rates of nausea and vomiting associated with SGLT2Is compared to GLP1RAs may be meaningful to patients who previously discontinued medications due to gastrointestinal side effects. Likewise, awareness of the increased risk of diabetic ketoacidosis with SGLT2Is may prompt closer monitoring or alternative choices in certain subgroups, such as those with low insulin reserve or volume depletion. At a systems level, this study underscores the feasibility and value of multinational, large-scale real-world safety surveillance. The use of harmonized data and transparent, reproducible methods provides a model for generating actionable evidence that is both rigorous and patient-centered. These approaches can be extended to other drug classes, populations, or outcomes—supporting a more responsive and evidence-driven healthcare system, especially for populations such as older adults who are often excluded from clinical trials. Several limitations should be considered when interpreting our findings. First, as with all observational studies, residual confounding cannot be fully excluded, despite our use of large-scale propensity score adjustment and empirical calibration. Second, we examined drug classes rather than individual agents, which may obscure important within-class heterogeneity— particularly relevant for GLP1RAs, where agents differ in gastrointestinal tolerability and cardiovascular effects. Third, medication exposure was based on prescription or dispensing records, which may not reflect actual patient adherence. Additionally, we did not incorporate laboratory data (e.g., glucose or electrolyte levels) or patient-reported outcomes, limiting the granularity of some safety assessments. Finally, certain outcomes of interest—such as cognitive decline or neuropsychiatric effects—were not included due to limitations in the underlying data or their absence at the time of protocol development. While these limitations may attenuate some findings or leave specific questions unanswered, they do not diminish the overall value of this large-scale, real-world evaluation of safety in a high-risk population. In conclusion, GLP1RAs and SGLT2Is are generally safer than SUs and DPP4Is in older adults with type 2 diabetes, with lower risks of hypoglycemia, hyperkalemia, edema, and mortality. These findings provide robust, real-world evidence to support safer prescribing choices in a high-risk population. Incorporating these safety considerations into clinical decision-making can improve outcomes and reduce avoidable harm in older adults. Data Availability All data produced are available online at https://data.ohdsi.org/LegendT2dmClassEvidenceExplorer/ https://ohdsi-studies.github.io/LegendT2dm/Protocol.html https://data.ohdsi.org/LegendT2dmClassEvidenceExplorer/ Funding This study was partially funded through National Institutes of Health grants R01 HL167858, K23 HL153775, R01 LM006910, R01 HG006139, and R01 HL169954 and the U.S. Department of Veterans Affairs under the research priority to “Put VA Data to Work for Veterans” (VA ORD 22-D4V). The funders had no role in the design and conduct of the protocol; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Disclosure Dr. Khera is an associate editor at JAMA; and has received support from the National Institutes of Health (awards R01 AG089981, R01 HL167858 and K23 HL153775) and the Doris Duke Charitable Foundation (award 2022060); has received research support, through Yale University, from Bristol Myers Squibb, Novo Nordisk, and BridgeBio; is a coinventor of U.S. provisional patent applications WO2023230345A1, US20220336048A1, 63/177,117, 63/346,610, 63/428,569, 63/484,426, 63/508,315, 63/580,137, 63/606,203, 63/619,241, and 63/562,335, unrelated to the present work; and is a cofounder of Evidence2Health and Ensight-AI, precision health platforms to improve evidence-based cardiovascular care and cardiovascular diagnostics. Dr. DuVall has received grants from Alnylam Pharmaceuticals, AstraZeneca Pharmaceuticals, Biodesix, Celgene, Cerner Enviza, GlaxoSmithKline, Janssen Pharmaceuticals, Novartis International, and Parexel International through the University of Utah or the Western Institute for Veteran Research (outside the submitted work). In the past 3 years, Dr. Krumholz has received expenses and/or personal fees from UnitedHealth, Element Science, Aetna, Reality Labs, Tesseract/4Catalyst, F-Prime, the Siegfried and Jensen Law Firm, the Arnold and Porter Law Firm, and the Martin/Baughman Law Firm; is a cofounder of Refactor Health, HugoHealth, and Ensight-AI; and is associated with contracts, through Yale New Haven Hospital, from the Centers for Medicare and Medicaid Services and, through Yale University, from Johnson & Johnson. Dr. Thangaraj is a coinventor on provisional patent 63/606,203, unrelated to the present work, and is funded by grant 5T32 HL155000-03. Ms. Blacketer, Dr. Ostropolets, and Dr. Ryan are employees of Johnson & Johnson. Dr. Schuemie is an employee and shareholder of Johnson & Johnson. Dr. Suchard receives additional contracts and grants from the US Food & Drug Administration, the US National Institutes of Health and Janssen Research & Development outside the scope of this work. Dr. Lau received research fundings from Diabetes UK outside the scope of this work. Dr. You reports being a chief executive officer of PHI Digital Healthcare; and grants from Daiichi Sankyo. He is a coinventor of granted Korea Patent DP-2023-1223 and DP-2023-0920, and pending Patent Applications DP-2024-0909, DP-2024-0908, DP-2022-1658, DP-2022-1478, and DP-2022-1365, unrelated to current work. Dr. Man has received support from the C.W. Maplethorpe Fellowship, the National Institute of Health Research, European Commission Framework Horizon 2020, the Hong Kong Research Grant Council, and the Innovation and Technology Commission of the Hong Kong Special Administration Region Government outside the submitted work). Dr. Morales is supported by a Wellcome Trust Clinical Research Fellowship (214588/Z/18/Z). Dr. Lu received support from the National Heart, Lung, and Blood Institute of the National Institutes of Health (under awards R01HL69954 and R01HL169171), the Patient-Centered Outcomes Research Institute (under award HM-2022C2-28354), Sentara Research Foundation, and Novartis through Yale University. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose. Footnotes ↵ * Drs. Suchard and Lu are co-senior authors for this paper. REFERENCES ↵ American Diabetes Association Professional Practice Committee; 9. 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