Real-world evidence for comparative safety of second-line antihyperglycemic agents in older adults with type 2 diabetes

Domain: neuroscience
Published: 2026-04-04
Editorial outcome: minor_revision

Abstract
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 conduct a multinational cohort study using nine databases
from the U.S. and Europe. Among adults aged ≥65, we compare eighteen safety outcomes across
four classes of second-line antihyperglycemic agents. We use propensity score adjustment,
empirical calibration, and prespecified diagnostics and estimated hazard ratios. In a cohort of 1.8
million, both GLP-1 receptor agonists and SGLT2 inhibitors are linked to significantly lower
risks of hypoglycemia and hyperkalemia than sulfonylureas. GLP-1 receptor agonists show
lower risks of peripheral edema than DPP-4 inhibitors. However, SGLT2 inhibitors are
associated with a higher risk of diabetic ketoacidosis compared to GLP-1 receptor agonists and
sulfonylureas. These results support informed, safety-conscious prescribing in the older adults
underrepresented in clinical trials yet highly susceptible to adverse events.

Introduction
As the global population ages, the burden of type 2 diabetes mellitus among older adults
continues to rise. According to global estimates, approximately 19.3% of adults aged over 65
years, representing 135.6 million individuals, were living with diabetes in 2019, and this number
is projected to increase to 276.2 million by 20451. However, treating this population is
challenging, as older adults are uniquely vulnerable to adverse drug events due to
multimorbidity, frailty, and polypharmacy.2,3 Yet, they remain underrepresented in randomized
trials. The small sample sizes and selective populations in randomized 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.4,5,6

Clinical guidelines have traditionally recommended a stepwise treatment approach that
begins with lifestyle modification and metformin therapy.7 However, antihyperglycemic agents,
including glucagon-like peptide 1 receptor agonists (GLP1RAs), sodium-glucose cotransporter-2
inhibitors (SGLT2Is), dipeptidyl peptidase-4 inhibitors (DPP4Is), and sulfonylureas (SUs), play a
more pivotal role in achieving glycemic control than before. These therapies are commonly
employed as second-line options for patients who do not reach target glucose levels with first-line
treatment, and they are also considered as first-line agents in individuals with comorbidities or
clinical indications.7 In particular, GLP1RAs and SGLT2Is have gained substantial attention for
their cardiovascular and renal benefits, prompting guideline endorsements for broader use in high-
risk patients.3,8 However, despite increasing uptake, a critical knowledge gap persists: the
comparative safety of these agents, particularly in older adults, remains insufficiently understood.

The need to address this gap is urgent. As prescribing patterns increasingly shift toward
newer agents such as GLP1RA9 and the older adult population continues to grow, clinicians are
often required to make treatment decisions with incomplete safety data for those at highest risk.
Without robust comparative evidence, efforts to personalize diabetes care and minimize
preventable harm remain limited. Comprehensive, up-to-date evaluations of the safety of second-
line antihyperglycemic agents in older adults are therefore essential to inform safer prescribing,
reduce avoidable harms, and support evidence-based, patient-centered care in a population
frequently underrepresented in clinical trials yet central to everyday clinical practice.

Here, to address this unmet need, we leverage the LEGEND-T2DM initiative10,11,12, a
multinational, large-scale, real-world analytics using a harmonized data model, to evaluate the
comparative safety of four major second-line drug classes: GLP1RAs, SGLT2Is, DPP4Is, and SUs.
Using rigorous methods including large-scale propensity score adjustment, empirical calibration,
and pre-specified diagnostics, we systematically assess a broad range of clinically relevant
metabolic, organ system-specific, and systemic safety outcomes.

Results
Characteristics
Nine international databases comprised five insurance claims databases from the US and four
electronic health records (EHRs) from the US, Germany, and Spain13,14,15,16 were included in this
study (Supplementary Table 1). Following pre-specified eligibility criteria (Figure 1), a total of
1,808,003 adults aged 65 years or older with type 2 diabetes who were prescribed a secondary
antihyperglycemic agent met the eligibility criteria. There were more male patients with 984,590
(54.5%) than female patients, and 1,211,729 (67.0%) were aged 65-74, and 596,274 (32.9%) were
aged 75 or older. 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 Ope n Claims database before and after propensity
score stratification. Detailed baseline characteristics and standardized mean differences (SMDs)
for all comparisons across nine databases before and after propensity score adjustment are
provided in Supplementary Tables 2–36.

Study Diagnostics
We applied prespecified study diagnostics to ensure reliability, including requirements for
empirical equipoise, appropriate covariate balance after propensity score adjustment, and
sufficient statistical power based on a minimum number of patients (at least a thousand patients in
each group), and a minimum detection relative risk (MDRR). Only treatment comparisons in each
database meeting all diagnostic criteria were included in the meta-analyses. Figure 2 shows
whether the results from each database passed the diagnostic criteria for the synthesis of
hyperkalemia meta-analysis results. For example, for the hyperkalemia outcome, results
comparing GLP1RAs and SGLT2Is were generated from four databases, and these four met all
diagnostic criteria and were ultimately included in the meta-analysis. Similarly, in the GLP1RA
vs. DPP4I comparison, the OptumDOD and MDCR databases did not meet the covariate balance
criteria and were therefore excluded from the final meta-analysis.

Following study diagnostics, the number of databases included for each drug comparison
varied across study outcomes. 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
For eighteen safety outcomes, meta-analytic results from the primary analysis (on-treatment
follow-up with propensity score stratification) are presented in Figure 3. Overall, both GLP1RAs
and SGLT2Is were associated with more favorable safety profiles across multiple outcomes
compared with SUs and DPP4Is (Supplementary Tables 108–215).

GLP1RA use was associated with a substantially lower risk of hypoglycemia compared to
SU, similar to SGLT2I and DPP4I (Hazard ratio [HR] 0.21 [95% CI, 0.16–0.28], HR 0.20 [0.12–
0.34], and HR 0.26 [0.21–0.32], respectively). 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 [1.27–2.11]) for diabetic ketoacidosis. GLP1RAs had shown high
risks of nausea (HR 1.58 [1.45–1.72]) and vomiting (HR 1.58 [1.44–1.74]) 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 an on-treatment follow-up design with additionally
censored 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 also remained associated with a significantly increased risk of
diabetic ketoacidosis relative to both GLP1RAs and SUs (HR 1.99 [1.31–3.01] and HR 1.70
[1.28–2.27], respectively). 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 intention-to-treat 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 (Figure 4 and
Supplementary Tables 108–215). 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.

Results of the meta-analyses, pooled separately by database type (Claims vs. EHR) and
region (US vs. non-US), are presented in Supplemental Figure. 58–59. Although the meta-
analysis results from Claims and EHR databases showed a high degree of agreement in their
significance, the results between US and non-US databases were generally consistent only for the
DPP4I vs. SU comparison due to the limited numbers of GLP1RA and SGLT2I cohorts in the
non-US database. In a leave-one-database-out meta-analysis that exclude one database for each
outcome conducted to determine the influence of individual databases, most comparisons
showed consistent significances (Supplemental Figure. 60–95).

Methods
Ethical considerations
All informed patient consent was waived or deemed not applicable due to the retrospective, de-
identified nature of the data. Ethical approvals and exemptions were obtained as required for each
data source. For CCAE, MDCR, MDCD, OptumDOD, and OptumEHR databases, the New
England Independent Review Board determined the study to be exempt from ongoing IRB
approval. Because the data are fully de-identified, the project does not constitute human subject
research, which inherently waives the need for informed consent. For US Open Claims and IQVIA
Disease Analyzer, Germany (DAG) databases, no ethics committee oversight is applicable. These
are commercially available, de-identified data assets; their usage is governed by compliance with
Data Use Agreements rather than institutional ethics review.

For the VA-OMOP database, the study was overseen by the VA Salk Lake City Health
Care System Research and Development Committee. The University of Utah IRB (IRB #00192565)
determined the study met the criteria for exemption from full ethics review under Exemption
Category 4 (45 CRF 46.104(d)) and officially approved the Waiver of HIPAA Authorization (45
CRF 164.512(i)), which serves as the waiver for informed consent.

Lastly, for the SIDIAP database, the study was approved by the Clinical Research Ethics
Committee (CElm) of the IDIAP Jordi Gol (Project code: 22/020-P). The committee certified
compliance with the Declaration of Helsinki and applicable data protection regulations, thereby
waiving the requirement for individual informed consent for this retrospective anonymized study.

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.48,49 We conducted a retrospective observational
cohort study using international databases standardized to the Common Data Model (CDM).13 All
databases mapped to version 5.3.1 of the Observational Medical Outcomes Partnership (OMOP)
CDM.50 Basic information, including data coverage periods and contributing entities for each
source, is presented in Supplementary Table 1. Potential overlap of patients across de-identified
U.S. data sources could not be evaluated or removed due to the fully de-identified nature of the
data.

Detailed definitions of the study population, exposures, outcomes, follow-up strategies,
and statistical methods were prespecified, registered, and published prior to study execution.48,49
The study utilized OHDSI’s Health Analytics Data-to-Evidence Suite, a suite of open-source tools
for epidemiologic research51. 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 guidelines11,52.

Study population and exposure
The study population included older adults (aged ≥65 years) with a diagnosis of type 2 diabetes
who initiated a second-line antihyperglycemic agent following first-line metformin monotherapy
(Figure 4). Second-line agents were defined as GLP1RAs, SGLT2Is, DPP4Is, and SUs.

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).

Study Outcome
All safety outcomes were selected based on the 2018 American Diabetes Association guidelines
and prior randomized controlled trials53. 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 have been previously implemented and validated54. Detailed definitions of study
outcome were presented in the study protocol48. For each outcome, individuals with a prior history
of the event were excluded.

Follow-up
Follow-up was assessed using three approaches: (1) on-treatment regardless of treatment
escalation, (2) on-treatment with additional censoring upon treatment escalation, and (3) intention-
to-treat. In the on-treatment 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 on-treatment with additional censoring upon treatment escalation
approach followed the same rules as on-treatment, but additionally censored follow-up upon
initiation of any new antihyperglycemic agent. In contrast, the intention-to-treat 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 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)55. Sex was based on coded information from individual data
sources. The measurements were included as only the measurement status as a covariate, not the
value. Values that did not exist in the database were treated as unobserved, and no separate method
(e.g., imputation) was used to supplement missing data.

Confounding was addressed using propensity score 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 propensity score stratification. HR 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 design56.

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 0.757. Second, all covariates
were required to have a maximum SMD <0.15 following PS adjustment, indicating adequate
balance. Third, we required a MDRR <4.0 and a minimum sample size of ≥1,000 patients per
treatment group to ensure sufficient statistical power58. 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)51. The full analytic
code and results are publicly available at: https://github.com/ohdsi-studies/LegendT2dm,
https://data.ohdsi.org/LegendT2dmClassEvidenceExplorer/.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Reviewer’s comments for the paper:\nComparative Safety of Second-Line Antihyperglycemic Agents in Older Adults with Type 2 diabetes: A Multinational Real-\nWorld Evidence From LEGEND-T2DM\nThis is a well-written paper from a respected group of researchers. This paper is an excellent example of using real-world\ndata to provide the evidence for medication safety and utilisation not found in randomised trials. It was a pleasure to read,\nhowever there are some suggestions that the authors may consider before publishing.\nTitle: The subtitle does not seem to read correctly, it almost seems that something is missing. Can the authors just confirm\nthe subtitle is grammatically correct? “A multinational real world evidence [study?] from LEGEND-T2DM.”\nAbstract: Can the authors review the results in the abstract and compare these with the results in the paper and ensure that\nthey are the same numbers. Some results are a few numbers out and others are different.\nMethods: Line 118; the authors should consider putting a reference in after (CDM).\nLine 122-126; the authors might consider putting this information into a reference. This journal is not only read by American\nresearchers that would be interested in the US database content, but world-wide, so information on the other country\ndatabases should also be presented or left to the reader to find in detailed reference.\nResults: Line 240-242; the sentence gives the HRs of a comparison of two drugs and shows an increased risk. But\nincreased risk of what?\nReferences: reference 11 seems not to have a title, is this correct?\nReference 20 is formatted differently to the other references.\nTable 1 title is very long, not sure if the journal will want a title this long. Table 2 also has a very long title.\nNot sure if the meta-analytic figure (Figure 3) is required. The important information from the table is described well in the\ntext of the paper.", "reviewer_2": "This is a well-written manuscript evaluating the real-world safety of second-line antihyperglycemic agents among older\nadults with type 2 diabetes.\nAbstract\nThe conclusion at the end of the manuscript is more balanced/specified than the conclusion in the abstract. Please balance\nthe conclusion in the abstract regarding the sentence: “In older adults with type 2 diabetes, GLP1RAs and SGLT2Is\ndemonstrated more favorable safety profiles than SUs and DPP4Is across multiple clinically relevant outcomes”. Please\nspecify by using the categories or by outcomes as in the conclusion at the end of the manuscript (lower risks of\nhypoglycemia, hyperkalemia, edema, and mortality).\nIntroduction\n1. Please insert a reference after the first sentence.\n2. As this is not a diabetes specific journal, please insert a few sentences on type 2 diabetes in the beginning. There is a\nsomewhat abrupt transition between sentence 1 and sentence 2.\n3. A slightly unusual ending to the introduction, where the authors state what they have done and touch upon implications. I\nwould recommend explicit state the aim at the end of the introduction.\n4. Minor comment: recommend revising the use of long hyphens throughout the manuscript. In several places, a shorter\nhyphen (–) or, where appropriate, a comma would improve readability and consistency\nMethods\n1. I would suggest a reference after the following sentence on line number 116-117: “This study is part of the LEGEND-\nT2DM initiative, led by the Observational Health Data Sciences and Informatics (OHDSI) research collaborative.”\n2. The manuscript currently provides only a single sentence on ethical approvals (Line 123: “Ethical approvals for data use\nwere obtained as required”). Please expand this section with more details. Was the study reviewed and approved by an\nethics committee? If so, include the committee’s name and approval/reference number. For improved clarity, I also\nrecommend presenting all ethical considerations under a dedicated heading to ensure a coherent overview.\n3. On line number 123 you write the following “Potential overlap of patients\nacross some de-identified U.S. data sources was not explicitly addressed. ” This should be further explained. Does the data\nhave duplicates?\n4. Line 154: For each outcome, individuals with a prior history of the event were excluded. Please add numbers/percentages\nand reason for excluding. This included as well hypoglycemia, hypotension. Is this a bias? This information is not in the text\nor in the overview/flowchart.\n5. Line number 155: “Follow-up was assessed using three approaches….”. Have this under a separate headline for better\nclarity? New paragraph?\n6. In the introduction it is stated: “Older adults are uniquely vulnerable to adverse drug events due to multimorbidity, frailty,\nand polypharmacy. Yet, they remain underrepresented in randomized trials.”. Please elaborate more on older adults vs\nelderly. Most vulnerable are those over 74. Focus in the manuscript is on those between 65-74, it is a lost opportunity not to\nanalyze/report on those 75 years and older.\n7. None of the analyses are gender or age-stratified.\nResults\n1. Please check language in line 202. Should it be “most” frequent?\n2. Gender and age stratified analyses are missing. Prior studies have reported that women are more likely to report GLP-1–\nrelated side effects. This issue is particularly relevant in older populations, and it would be valuable to differentiate between\nparticipants aged 65–74 and those ≥75 years (20% of the study sample is ≥75). Without such analyses, there is a risk that\nimportant differences may be overlooked, potentially leading to overmedication in women and increased risk of adverse\neffects such as nausea. Addressing this gap would strengthen the study, especially as sex and gender considerations are\nincreasingly emphasized in clinical research. Stratified analyses could therefore add significant value to the current work.\n3. The sentence on line 240 appears to be incomplete.\n4. First paragraph, it would be helpful to include information on the frequency of the other/older age group as well. This\nparagraph does not reference any table. Please, add as well information for those above 75 years.\nDiscussion\nThe discussion is well-written.\n- The first paragraph clearly inform about the study findings without drawing to strong conclusions.\n- I appreciate the clear documentation on how the study contributes to the existing literature (line number 310).\n- There are several instances in the manuscript where references are lacking. I recommend a thorough review of the entire\nmanuscript to ensure appropriate citation throughout. Here are some examples from the discussion:\no Line number 284- 290\no Line number 299\no Line number 304-307\no Line number 332- 335\n- Line number 296: “Bone fracture, which is specifically important to older adults and reported SGLT2Is can potentially cause\nin a previous trial, did not show significantly different risks in any comparison in line with other observational studies”.\nSuggest rewriting this sentence as it is difficult to understand.\n- Please add a paragraph with discussion on gender and age.\n- In the limitations section, it should be clarified whether excluding participants with a prior history of the event may have\nintroduced bias.\nConclusion\nThe conclusion is appropriately aligned with the study’s acknowledged limitations.\nReferences:\nReference 16 seems inaccurate; please verify and correct if necessary.\nTables and figures\nTable 1:\n- I suggest avoiding explanations in the title. Move the two last lines to foot notes?\nTable 2:\n- Same comment for the title as for Table 1.\n- I recommend moving “gender” to the top, placing it above the “age group” section for improved logical flow.\n- In the “Age group” column, the word “year” is missing for the age group ranges.\nFigure 1:\n- The title does not fully explain the context of the figure. Consider revising.\n- Be consisted with space between symbol and number, before/after comma etc.\n- The light scatter blue color at the top of the figure makes the text difficult to read. I suggest changes here.\n- Abbreviations should be explained. This concerns all figures.\nFigure 2 and 3\n- Thank you for the clear presentation of the results in the figures.\n- Please place explanations for each figure below the respective figure (not in the title).", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "This study applies a large-scale, federated, observational analysis using OHDSI tools to compare safety outcomes of\nantihyperglycemic agents. The scope, standardized analytics, and empirical calibration are strengths. However, several\naspects of the design and reporting could be clarified or strengthened. In particular, more transparency around missing data\nhandling, potential biases introduced by trimming/selection, heterogeneity across data sources, and sensitivity analyses\nwould improve interpretability and robustness.\nMajor Comments\n1. Missing Data\nThe manuscript does not provide details on how missing data were addressed. In LEGEND/OHDSI pipelines, covariates are\ntypically coded as “0 = not observed,” but this implicitly treats missingness as absence rather than truly unmeasured. This\nmay be problematic for systematically missing variables (e.g., BMI, labs, smoking). I recommend:\n* Explicitly stating the missing data handling strategy.\n* Considering sensitivity analyses comparing complete-case analyses, missing-indicator approaches, and, where feasible,\nmultiple imputation.\n* Discussing the potential for bias if missingness differs systematically between treatment arms or databases.\n2. Potential Bias from Trimming and Selection of Analyses\nThe study applies PS trimming, exclusion of analyses failing balance diagnostics, and MDRR thresholds. While these steps\nimprove internal validity, they introduce potential biases:\n* Loss of generalizability: trimming removes patients at the extremes of PS, you’re discarding those who are almost always\nor almost never treated. These patients may represent an important clinical subgroup (e.g., very sick or very healthy).\n* Selection bias: excluding “unbalanced” or underpowered comparisons means the evidence base is skewed toward\nsettings with inherently better overlap and less confounding.\n* Interpretational Bias: Policymakers or clinicians may mistakenly interpret the findings as generalizable to all patients, when\nin fact the filters mean the results are for a subset (patients with overlap, balance, and sufficient data). I suggest explicitly\ndiscussing these trade-offs and clarifying that estimates apply primarily to patients in regions of overlap, not to the broader\npopulation.\n3. Heterogeneity Across Data Sources (Table 1)\nTable 1 reveals substantial heterogeneity in treatment persistence and follow-up across databases (e.g., very long on-\ntreatment durations in SIDIAP vs much shorter in OEHR and CCAE). This raises concerns about exposure construction and\nharmonization. Additional checks may be warranted:\n* Varying grace period and stockpiling assumptions (15/30/60/90 days; stockpiling on/off).\n* Assessing whether heterogeneity reflects true practice differences versus database artifacts.\n* Meta-regression or subgroup analyses by data source type (claims vs EHR; US vs EU).\n4. Transparency in Figure 2\nThe forest plot summarizing outcomes does not show sample sizes or event counts for each comparison. Without these\ndenominators, it is difficult to assess evidentiary weight and precision. I recommend adding sample sizes (and ideally event\ncounts) for each treatment arm within each comparison, although I am not sure what is the best way to add these information\nwithout making the plot too busy.\n5. Additional Sensitivity Analyses\nThe ITT and OT2 sensitivity analyses are helpful, but further robustness checks would strengthen conclusions:\n* Exposure definitions: Vary grace periods, stockpiling rules, induction lags, and carry-over windows after discontinuation.\n* Database influence: Perform leave-one-database-out meta-analysis.\n* Competing risks: Apply competing-risk models for outcomes with high mortality."}, "author_response": "We are grateful to the editor and reviewers for the thoughtful comments. We have responded to\nall comments raised by the editor and reviewers in a point-by-point manner and revised the\nmanuscript accordingly.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nReviewer’s comments for the paper:\nThis is a well-written paper from a respected group of researchers. This paper is an excellent\nexample of using real-world data to provide the evidence for medication safety and utilisation\nnot found in randomised trials. It was a pleasure to read, however there are some suggestions that\nthe authors may consider before publishing.\nResponse:\nWe appreciate your thoughtful and encouraging comments. We sincerely appreciate your positive\nassessment of our work and are grateful that you found the manuscript clear and enjoyable to\nread. We also appreciate your recognition of the value of real-world evidence in addressing gaps\nleft by randomized trials. We carefully considered all of your suggestions and incorporated the\nrecommended revisions. Your feedback has been helpful, and we are grateful for the time and\nexpertise you devoted to reviewing our study.\n1. Title: The subtitle does not seem to read correctly, it almost seems that something is missing.\nCan the authors just confirm the subtitle is grammatically correct? “A multinational real world\nevidence [study?] from LEGEND-T2DM.”\nResponse:\nWe have changed the title as follows in accordance with the journal's editorial policy.\n“Comparative Safety of Second-Line Antihyperglycemic Agents in Older Adults with Type\n2. Abstract: Can the authors review the results in the abstract and compare these with the results\nin the paper and ensure that they are the same numbers. Some results are a few numbers out and\nothers are different.\nResponse:\nThank you for your comment. We have revised the abstract to a concise form (less than 150\nwords) in accordance with the journal's policy.\n3. Methods: Line 118; the authors should consider putting a reference in after (CDM).\nLine 122-126; the authors might consider putting this information into a reference. This journal\nis not only read by American researchers that would be interested in the US database content, but\nworld-wide, so information on the other country databases should also be presented or left to the\nreader to find in detailed reference.\nResponse:\nThank you for your valuable feedback. We have cited prior research papers and other resources\nthat provide information on each database, as you suggested.\nIn Results section (Line 189),\nNine international databases comprised five insurance claims databases from the US and\nfour electronic health records (EHRs) from the US, Germany, and Spain13,14,15,16 were\nincluded in this study (Supplementary Table 1).\nIn References (Line 868),\n13.Voss EA, et al. Feasibility and utility of applications of the common data model to\nmultiple, disparate observational health databases. Journal of the American Medical\nInformatics Association 22, 553-564 (2015).\n14.Blacketer C. Janssen CDM Documentation. https://ohdsi.github.io/ETL-\nLambdaBuilder/ (2025).\n15. Rathmann W, Bongaerts B, Carius HJ, Kruppert S, Kostev K. Basic characteristics\nand representativeness of the German Disease Analyzer database . Int J Clin Pharmacol\nTher 56, 459-466 (2018).\n16. Recalde M, et al. Data Resource Profile: The Information System for Research in\nPrimary Care (SIDIAP). International Journal of Epidemiology 51, e324-e336 (2022).\n4. Results: Line 240-242; the sentence gives the HRs of a comparison of two drugs and shows\nan increased risk. But increased risk of what?\nResponse:\nThank you for pointing this out. We have confirmed that this was accidentally removed from the\nediting process. The revised sentence is below.\nIn Results section (Line 357)\nHowever, SGLT2Is showed a significantly increased risk compared to GLP1RAs (HR 2.03\n[1.38–2.99]) and SUs (HR 1.64 [1.27–2.11]) for diabetic ketoacidosis.\n5. References: reference 11 seems not to have a title, is this correct? Reference 20 is formatted\ndifferently to the other references.\nResponse:\nThe references the reviewer mentioned have been modified (and rearranged) to conform to the\nNature Communications format.\nIn References (Line 1002)\n49. Hripcsak G, et al. Observational Health Data Sciences and Informatics (OHDSI):\nopportunities for observational researchers. In: MEDINFO 2015: eHealth-enabled\nHealth). IOS Press (2015).\n57. Armstrong B. A Simple Estimator of Minimum Detectable Relative Risk, Sample Size,\nor Power in Cohort Studies. American Journal of Epidemiology 126, 356-358 (1987).\n6. Table 1 title is very long, not sure if the journal will want a title this long. Table 2 also has a\nvery long title.\nResponse:\nWe apologize for the confusion. This was because the table titles are accidentally connected to\nthe legends for self-explanation. We separated the titles and legends for Table 1 and 2.\nIn Table (Line 1095, 1109)\nTable 1. Population size and follow-up time for each drug class within each data source.\nTable 2. Baseline patient characteristics for SGLT2I (T) and GLP1RA (C) new-users in\nthe USOC data source.\n7. Figure: Not sure if the meta-analytic figure (Figure 3) is required. The important information\nfrom the table is described well in the text of the paper.\nResponse:\nWe appreciate your feedback. However, we also considered the sensitivity analysis, which\nanalyzed the results based on different follow-up periods, to be important. Other reviewers also\nhave expressed support for the clear presentation of the results in Figures 2 and 3, therefore, we\nintend to maintain this. However, to present the results more richly and effectively, we have\nnewly presented Figure 2, which represents the study diagnostics.\nFigure 2. Pre-specified diagnostics results by target-comparator pairs and database\nused for hyperkalemia outcome.\nThis plot shows whether cohort diagnostic criteria are met before and after propensity\nscore adjustment for each database. Only exposure-comparator pairs with more than\n1,000 individuals in each group within the database are displayed. The Equipoise\nrepresents the percentage of the preference score in the corresponding interval (0.3–0.7)\nfor each database. A database passes diagnostics if it exceeds 25%. The Max SMD\nrepresents the maximum SMD value after propensity score adjustment, and a database\npasses diagnostics if it is less than 0.15. An MDRR of 4 or less passes diagnostics.\nGLP1RA: glucagon-like peptide-1 receptor agonist; SGLT2I: sodium-glucose\ntransporter-2 inhibitor; DPP4I: dipeptidyl peptidase 4 inhibitor; SU: sulfonylurea;\nCCAE: IBM MarketScan Commercial Claims and Encounter (CCAE); MDCR: IBM\nMarketScan Medicare Supplemental Database; MDCD: IBM MarketScan Multi-state\nMedicaid Database; USOC: IQVIA Open Claims; ODOD: Optum Clinformatics Data\nMart; DAG: IQVIA Disease Analyzer Germany; OEHR: Optum Electronic Health\nRecords; SIDIAP: Information System for Research in Primary Care; VA: US\nDepartment of Venterans Affairs; SMD: standardized mean difference.\nReviewer #2 (Remarks to the Author):\nThis is a well-written manuscript evaluating the real-world safety of second-line\nantihyperglycemic agents among older adults with type 2 diabetes.\nResponse:\nWe appreciate your positive and encouraging feedback. Your supportive comments are greatly\nappreciated and motivate us to further refine and strengthen the manuscript.\n1. Abstract\nThe conclusion at the end of the manuscript is more balanced/specified than the conclusion in the\nabstract. Please balance the conclusion in the abstract regarding the sentence: “In older adults\nwith type 2 diabetes, GLP1RAs and SGLT2Is demonstrated more favorable safety profiles than\nSUs and DPP4Is across multiple clinically relevant outcomes”. Please specify by using the\ncategories or by outcomes as in the conclusion at the end of the manuscript (lower risks of\nhypoglycemia, hyperkalemia, edema, and mortality).\nResponse:\nThank you for your comment. We have revised the abstract to a concise form (less than 150\nwords) in accordance with the journal's policy. At the same time, we have modified the abstract\nto be more explicit in your comment.\nIn Abstract (Line 62)\nIn a cohort of 1.8 million, both GLP-1 receptor agonists and SGLT2 inhibitors were\nlinked to significantly lower risks of hypoglycemia and hyperkalemia than sulfonylureas.\nGLP-1 receptor agonists showed lower risks of peripheral edema than DPP-4 inhibitors.\nHowever, SGLT2 inhibitors were associated with a higher risk of diabetic ketoacidosis\ncompared to GLP-1 receptor agonists and sulfonylureas.\nIntroduction\n2. Please insert a reference after the first sentence.\nResponse:\nWe added the following references:\nIn Reference (Line 821)\n1. Sinclair A, Saeedi P, Kaundal A, Karuranga S, Malanda B, Williams R. Diabetes and\nglobal ageing among 65–99-year-old adults: Findings from the International Diabetes\nFederation Diabetes Atlas, 9th edition. Diabetes Research and Clinical Practice 162,\n108078 (2020).\n3. As this is not a diabetes specific journal, please insert a few sentences on type 2 diabetes in the\nbeginning. There is a somewhat abrupt transition between sentence 1 and sentence 2.\nResponse:\nThank you for your suggestion. We agree with your comments and have improved the overall\nstructure and flow of the Introduction. In the first paragraph, we now describe the growing\nburden of diabetes in older adults and explain why managing diabetes in the older adult\npopulation is particularly challenging. In the second paragraph, we outlined the fundamental\npharmacologic treatment strategies for diabetes. We hope that these revisions will enhance clarity\nand improve comprehension for general readers.\nIn Introduction (Line 139)\nAs the global population ages, the burden of type 2 diabetes mellitus among older adults\ncontinues to rise. According to global estimates, approximately 19.3% of adults aged\nover 65 years, representing 135.6 million individuals, were living with diabetes in 2019,\nand this number is projected to increase to 276.2 million by 20451.\n4. A slightly unusual ending to the introduction, where the authors state what they have done and\ntouch upon implications. I would recommend explicit state the aim at the end of the introduction.\nResponse:\nThank you for your suggestion. We have divided and rearranged the existing final paragraph into\ntwo, and the new final paragraph more explicitly states the purpose of the study, as follows:\nIn Introduction (Line 186)\nTo address this unmet need, we leveraged the LEGEND-T2DM initiative10, 11, 12, a\nmultinational, large-scale, real-world analytics using harmonized data model, to evaluate\nthe comparative safety of four major second-line drug classes: GLP1RAs, SGLT2Is,\nDPP4Is, and SUs. Using rigorous methods including large-scale propensity score\nadjustment, empirical calibration, and pre-specified diagnostics, we systematically\nassessed a broad range of clinically relevant metabolic, organ system-specific, and\nsystemic safety outcomes.\n5. Minor comment: recommend revising the use of long hyphens throughout the manuscript. In\nseveral places, a shorter hyphen (–) or, where appropriate, a comma would improve readability\nand consistency\nResponse:\nWe have revised the manuscript to ensure accurate use of hyphens throughout.\nMethods\n6. I would suggest a reference after the following sentence on line number 116-117: “This study\nis part of the LEGEND-T2DM initiative, led by the Observational Health Data Sciences and\nInformatics (OHDSI) research collaborative.”\nResponse:\nWe have added the following two references:\nIn Reference (Line 1008, 1012)\n47. Hripcsak G, et al. RESEARCH PROTOCOL Large-scale evidence generation and\nevaluation across a network of databases for type 2 diabetes mellitus. https://ohdsi-\nstudies.github.io/LegendT2dm/Protocol.html (2021).\n48. Khera R, et al. Large-scale evidence generation and evaluation across a network of\ndatabases for type 2 diabetes mellitus (LEGEND-T2DM): a protocol for a series of\nmultinational, real-world comparative cardiovascular effectiveness and safety studies.\nBMJ Open 12, e057977 (2022).\n7. The manuscript currently provides only a single sentence on ethical approvals (Line 123:\n“Ethical approvals for data use were obtained as required”). Please expand this section with more\ndetails. Was the study reviewed and approved by an ethics committee? If so, include the\ncommittee’s name and approval/reference number. For improved clarity, I also recommend\npresenting all ethical considerations under a dedicated heading to ensure a coherent overview.\nResponse:\nTo address your comment, we created a separate the Ethical Considerations section in the\nmanuscript and wrote it as follows:\nIn Ethical consideration (Line 685)\nAll informed patient consent was waived. Ethical approvals for data use were obtained as\nrequired. For CCAE, MDCR, MDCD, OptumDOD, and OptumEHR databases, New\nEngland IRB and was determined to be exempt from broad IRB approval, as this research\nproject using de-identified data did not involve human subject research. For US Open\nClaims and IQVIA Disease Analyzer, Germany (DAG) databases, approval is provided\nfor OHDSI network studies, though this was a retrospective database study on de-\nidentified data and was deemed not human subject research. For the VA database, use of\nVA-OMOP was reviewed by the VA Salt Lake City Health Care System Research and\nDevelopment Committee and was determined to meet the criteria for exemption under\nExemption Category 4 (3) and approved the request for Waiver of HIPAA Authorization\n(IRBnet #1875081-3). For SIDIAP database, use of the SIDIAP data source was\napproved by the Clinical Research Ethics Committee of IDIAPJGol (Project code:\n20/070-PCV).\n8. On line number 123 you write the following “Potential overlap of patients\nacross some de-identified U.S. data sources was not explicitly addressed. ” This should be\nfurther explained. Does the data have duplicates?\nResponse:\nThank you for this important comment. Several U.S. databases used in our study are\nindependently curated and fully de-identified, so any potential patient overlap across data\nsources cannot be directly assessed or removed. While some duplication is theoretically possible,\nsuch overlap is expected to be minimal. Please see description on data source in our previous\nprotocol publication (Khera, et al., doi:10.1136/bmjopen-2021-057977, quoted the relevant\nparagraph below).\n“Across Commercial Claims and Encounters (CCAE; commercially insured), MCDR\n(Medicare) and MDCD (Medicaid), we expect little overlap in terms of the same\nobservations recorded at the same time for a patient; patients can flow between sources\n(eg, a CCAE patient who retires can opt-in to become an MDCR patient), but the\nenrolment time periods stand distinct. On the other hand, Optum, OpenClaims, CUIMC\nmay overlap in time with the other US data sources. While it remains against licensing\nagreements to attempt to link patients between most data sources, Optum reports <20%\noverlap between their claims and electronic health record (EHR) data sources that is\nreassuringly small.”\nMoreover, because all analyses were conducted separately within each database and then\ncombined through meta-analysis, any minor overlap would not materially affect the overall\nresults. We have clarified this point in the revised manuscript. Additionally, the negative control\ncalibration introduced in this study should correct for the misestimation of the effect size\nvariance due to overlap. That is, if dependencies cause the variance to be underestimated, the\ncalibration on negative controls should correct for that.\nIn Methods (Line 564)\nPotential overlap of patients across de-identified U.S. data sources could not be\nevaluated or removed due to the fully de-identified nature of the data.\n9. Line 154: For each outcome, individuals with a prior history of the event were excluded.\nPlease add numbers/percentages and reason for excluding. This included as well hypoglycemia,\nhypotension. Is this a bias? This information is not in the text or in the overview/flowchart.\nResponse:\nThank you for your insightful comment. We agree that events such as hypoglycemia and\nhypotension may be transient event in nature; however, we excluded individuals with a prior\nhistory of each outcome to ensure the validity of the comparative analysis across treatment and\ncomparator groups. Including prevalent cases could introduce several methodological biases.\nFirst, individual who already experienced the outcome before cohort entry may have different\nunderlying risk profiles or clinical vulnerabilities, which could confound the association between\ntreatment and subsequent events. Second, prevalent cases may obscure the temporal relationship\nbetween exposure and outcome, making it difficult to determine whether the treatment preceded\nthe event.\nRestricting the cohort to incident cases allows all individuals to start follow-up free the event,\nthereby ensuring a uniform baseline risk and clearer temporal ordering. This approach minimizes\nsurvivor bias, reduces unmeasured confounding, and improve causal interpretability. We also\nacknowledge that the optimal approach may vary depending on the research question; in some\ncontext, including prevalent events may be appropriate if recurrence is of interest. However,\ngiven that our study focuses on the comparative risk of new-onset outcomes associated with\ntreatment, we believe that incident-only design is most suitable.\nIn response to your comment, we have added the detailed tables show the proportion of patients\nexcluded due to previous outcome occurrence within each comparison unit (Table R1–R6).\nTable R1. Proportion of patients excluded due to the occurrence of prior outcomes per\ndatabase in GLP1RA vs. SGLT2I comparisons.\nMDCR ODOD OEHR USOC\nPrevious outcome GLP1RA SGLT2I GLP1RA SGLT2I GLP1RA SGLT2I GLP1RA SGLT2I\n(%) (%) (%) (%) (%) (%) (%) (%)\nAbnormal weight gain 4.4 1.9 6.1 3.0 6.4 3.4 7.2 3.8\nAbnormal weight loss 2.3 3.4 4.0 4.5 3.1 3.8 4.1 4.5\nAcute pancreatitis 0.4 0.9 0.7 0.9 0.5 0.3 1.2 1.5\nAll-cause mortality 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\nBone fracture 13.3 10.5 11.9 10.3 9.9 8.3 19.1 15.6\nDiabetic ketoacidosis 0.1 0.1 0.1 0.1 0.0 0.1 0.1 0.1\nDiarrhea 14.2 13.6 16.6 15.0 12.4 11.7 17.1 14.5\nEdema 27.3 24.6 30.4 26.9 24.4 19.9 32.8 28.2\nGenitourinary infection 25.1 17.5 27.4 22.4 19.1 16.1 30.9 25.9\nHyperkalemia 2.4 2.9 5.4 5.9 2.9 2.9 3.3 3.5\nHypoglycemia 1.3 1.0 1.8 1.5 1.4 1.0 2.1 1.6\nHypotension 5.1 4.8 6.5 6.8 3.7 4.0 5.6 5.8\nJoint pain 6.9 4.0 8.7 6.2 7.3 5.4 9.1 6.9\nLower extremity\n0.1 0.3 0.3 0.3 0.1 0.1 0.2 0.2\namputation\nNausea 10.8 11.1 16.8 14.2 11.0 9.4 17.0 14.3\nPhotosensitivity 2.1 1.3 0.8 0.9 1.1 0.9 1.7 1.6\nVenous thromboembolism 5.0 4.1 5.1 3.8 4.1 3.7 5.6 4.6\nVomiting 7.2 6.8 10.7 9.8 6.7 5.5 12.0 10.5\nTable R2. Proportion of patients excluded due to the occurrence of prior outcomes per\ndatabase in GLP1RA vs. DPP4I comparisons.\nMDCR ODOD OEHR USOC\nPrevious outcome GLP1RA DPP4I GLP1RA DPP4I GLP1RA DPP4I GLP1RA DPP4I\n(%) (%) (%) (%) (%) (%) (%) (%)\nAbnormal weight gain 3.9 1.3 6.1 2.7 6.2 2.3 7.2 2.8\nAbnormal weight loss 2.0 4.5 3.9 6.5 3.1 4.1 4.1 5.4\nAcute pancreatitis 0.4 0.8 0.6 0.9 0.5 0.4 1.2 1.2\nAll-cause mortality 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\nBone fracture 12.5 12.6 11.8 11.7 9.6 8.2 19.1 15.4\nDiabetic ketoacidosis 0.1 0.2 0.1 0.2 0.0 0.0 0.1 0.1\nDiarrhea 12.9 14.1 16.4 17.2 12.2 12.5 17.1 15.0\nEdema 26.2 25.9 30.3 28.8 23.8 19.0 32.8 27.4\nGenitourinary infection 24.0 23.5 27.2 29.7 18.8 19.0 30.9 28.3\nHyperkalemia 2.1 3.0 5.3 7.0 2.9 3.6 3.3 3.8\nHypoglycemia 1.3 1.1 1.8 1.7 1.4 0.9 2.1 1.7\nHypotension 4.6 5.8 6.3 7.5 3.6 3.7 5.6 5.6\nJoint pain 6.2 4.2 8.5 6.3 7.2 5.1 9.1 6.3\nLower extremity\n0.1 0.3 0.2 0.3 0.1 0.1 0.2 0.2\namputation\nNausea 9.7 11.1 16.6 15.2 10.7 9.1 17.0 13.7\nPhotosensitivity 2.1 2.0 0.9 1.4 1.1 1.0 1.7 1.5\nVenous thromboembolism 4.5 4.3 5.0 4.2 4.0 3.5 5.6 4.5\nVomiting 6.6 8.6 10.6 11.1 6.6 5.6 12.0 10.6\nTable R3. Proportion of patients excluded due to the occurrence of prior outcomes per\ndatabase in GLP1RA vs. SU comparisons.\nMDCR ODOD OEHR USOC\nPrevious outcome GLP1RA SU GLP1RA SU GLP1RA SU GLP1RA SU\n(%) (%) (%) (%) (%) (%) (%) (%)\nAbnormal weight gain 3.7 0.9 6.1 2.3 6.2 1.9 7.2 2.5\nAbnormal weight loss 1.9 4.5 3.9 6.1 3.1 4.1 4.1 4.9\nAcute pancreatitis 0.4 1.2 0.6 1.2 0.5 0.6 1.2 1.5\nAll-cause mortality 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\nBone fracture 12.1 12.3 11.8 10.4 9.6 8.2 19.1 14.5\nDiabetic ketoacidosis 0.0 0.1 0.1 0.2 0.0 0.1 0.1 0.2\nDiarrhea 12.7 13.0 16.4 16.0 12.2 11.9 17.1 13.8\nEdema 25.7 24.4 30.3 27.1 23.8 18.8 32.8 25.6\nGenitourinary infection 23.5 22.0 27.2 27.0 18.8 17.4 30.9 25.3\nHyperkalemia 2.0 3.1 5.3 6.8 2.9 3.5 3.3 3.6\nHypoglycemia 1.3 0.9 1.8 1.4 1.4 0.9 2.1 1.6\nHypotension 4.4 5.5 6.3 7.2 3.6 3.8 5.6 5.2\nJoint pain 6.0 3.6 8.5 5.5 7.2 4.4 9.1 5.5\nLower extremity\n0.1 0.3 0.2 0.2 0.1 0.1 0.2 0.2\namputation\nNausea 9.5 10.9 16.6 14.7 10.7 8.8 17.0 13.1\nPhotosensitivity 2.0 1.7 0.9 1.2 1.1 1.1 1.7 1.4\nVenous thromboembolism 4.3 4.3 5.0 4.2 4.0 3.5 5.6 4.4\nVomiting 6.3 8.5 10.6 11.0 6.6 5.7 12.0 10.1\nTable R4. Proportion of patients excluded due to the occurrence of prior outcomes per database in SGLT2I vs. DPP4I\ncomparisons.\nMDCR ODOD OEHR USOC DAG SIDIAP VA\nPrevious outcome SGLT2I DPP4I SGLT2I DPP4I SGLT2I DPP4I SGLT2I DPP4I SGLT2I DPP4I SGLT2I DPP4I SGLT2I DPP4I\n(%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%)\nAbnormal weight gain 1.9 1.6 3.0 2.7 3.4 2.4 3.8 2.8 0.9 0.7 1.0 1.2 2.4 1.9\nAbnormal weight loss 3.4 5.1 4.5 6.9 3.8 4.2 4.5 5.4 2.9 3.2 1.9 1.9 4.6 4.7\nAcute pancreatitis 0.9 0.7 0.9 1.0 0.3 0.4 1.5 1.2 0.0 0.0 0.3 0.2 1.2 0.9\nAll-cause mortality 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\nBone fracture 10.5 13.1 10.3 12.1 8.3 8.7 15.6 15.4 13.1 15.1 12.8 14.6 11.0 9.9\nDiabetic ketoacidosis 0.1 0.2 0.1 0.2 0.1 0.0 0.1 0.1 0.0 0.0 0.0 0.0 0.1 0.0\nDiarrhea 13.6 15.8 15.0 18.0 11.7 12.9 14.5 15.0 1.5 1.2 5.2 6.5 14.8 13.3\nEdema 24.6 28.8 26.9 30.1 19.9 19.7 28.2 27.4 18.8 18.9 14.8 12.5 28.6 24.6\nGenitourinary\n17.5 24.6 22.4 30.7 16.1 19.7 25.9 28.3 23.3 25.3 24.9 26.4 12.2 13.6\ninfection\nHyperkalemia 2.9 3.8 5.9 7.8 2.9 3.8 3.5 3.8 8.5 9.0 6.3 8.4 10.7 7.7\nHypoglycemia 1.0 1.3 1.5 1.7 1.0 0.9 1.6 1.7 0.5 0.7 0.2 0.2 1.4 0.9\nHypotension 4.8 6.5 6.8 8.0 4.0 3.9 5.8 5.6 3.5 3.0 1.1 1.1 11.1 7.8\nJoint pain 4.0 4.7 6.2 6.6 5.4 5.4 6.9 6.3 15.4 14.7 24.0 21.8 12.1 11.5\nLower extremity\n0.3 0.3 0.3 0.3 0.1 0.1 0.2 0.2 0.0 0.0 0.0 0.1 0.2 0.2\namputation\nNausea 11.1 12.5 14.2 16.2 9.4 9.7 14.3 13.7 6.4 7.3 4.2 4.3 7.5 6.2\nPhotosensitivity 1.3 2.1 0.9 1.2 0.9 1.0 1.6 1.5 0.2 0.4 0.2 0.2 1.9 1.9\nVenous\n4.1 4.9 3.8 4.6 3.7 3.6 4.6 4.5 7.6 8.6 4.0 4.0 5.0 4.0\nthromboembolism\nVomiting 6.8 9.4 9.8 11.8 5.5 5.9 10.5 10.6 6.4 7.4 4.4 4.5 5.6 4.4\nTable R5. Proportion of patients excluded due to the occurrence of prior outcomes per database in SGLT2I vs. SU\ncomparisons.\nMDCR ODOD OEHR USOC DAG SIDIAP VA\nPrevious outcome SGLT2I SU SGLT2I SU SGLT2I SU SGLT2I SU SGLT2I SU SGLT2I SU SGLT2I SU\n(%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%)\nAbnormal weight gain 1.3 1.9 2.4 3.0 2.1 3.4 2.5 3.8 0.6 0.9 1.1 1.0 1.7 2.4\nAbnormal weight loss 5.3 3.4 6.3 4.5 4.4 3.8 4.9 4.5 3.0 2.9 1.1 1.9 4.7 4.6\nAcute pancreatitis 1.4 0.9 1.3 0.9 0.6 0.3 1.5 1.5 0.0 0.0 0.4 0.3 1.2 1.2\nAll-cause mortality 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\nBone fracture 13.0 10.5 10.8 10.3 8.9 8.3 14.5 15.6 13.4 13.1 12.3 12.8 10.2 11.0\nDiabetic ketoacidosis 0.1 0.1 0.2 0.1 0.1 0.1 0.2 0.1 0.0 0.0 0.0 0.0 0.1 0.1\nDiarrhea 16.3 13.6 16.8 15.0 12.9 11.7 13.8 14.5 1.4 1.5 6.5 5.2 13.1 14.8\nEdema 28.7 24.6 28.6 26.9 20.1 19.9 25.7 28.2 17.1 18.8 10.3 14.8 24.5 28.6\nGenitourinary\n23.5 17.5 27.5 22.4 18.3 16.1 25.4 25.9 24.2 23.3 21.4 24.9 14.1 12.2\ninfection\nHyperkalemia 4.2 2.9 7.4 5.9 3.8 2.9 3.6 3.5 5.7 8.5 5.3 6.3 8.4 10.7\nHypoglycemia 1.1 1.0 1.6 1.5 1.0 1.0 1.6 1.6 0.6 0.5 0.1 0.2 1.0 1.4\nHypotension 6.7 4.8 7.6 6.8 4.1 4.0 5.2 5.8 2.8 3.5 0.6 1.1 8.0 11.1\nJoint pain 4.7 4.0 6.0 6.2 4.8 5.4 5.5 6.9 12.5 15.3 18.6 24.0 11.9 12.1\nLower extremity\n0.2 0.3 0.2 0.3 0.1 0.1 0.2 0.2 0.0 0.0 0.1 0.0 0.3 0.2\namputation\nNausea 13.8 11.1 15.5 14.2 9.7 9.4 13.1 14.3 7.7 6.4 3.8 4.2 6.4 7.5\nPhotosensitivity 1.9 1.3 1.2 0.9 1.2 0.9 1.4 1.6 0.2 0.2 0.2 0.2 1.9 1.9\nVenous\n5.2 4.1 4.5 3.8 3.8 3.7 4.4 4.6 7.8 7.6 4.0 4.0 4.1 5.0\nthromboembolism\nVomiting 10.3 6.8 11.4 9.8 6.3 5.5 10.2 10.5 7.7 6.4 4.3 4.4 4.7 5.6\nTable R6. Proportion of patients excluded due to the occurrence of prior outcomes per database in DPP4I vs. SU comparisons.\nCCAE MDCD MDCR ODOD OEHR USOC DAG SIDIAP VA\nPrevious outcome DPP4I SU DPP4I SU DPP4I SU DPP4I SU DPP4I SU DPP4I SU DPP4I SU DPP4I SU DPP4I SU\n(%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%)\nAbnormal weight gain 3.2 2.8 3.5 2.6 1.3 1.0 2.7 2.3 2.3 1.9 2.8 2.5 0.7 0.6 1.0 0.5 1.9 1.5\nAbnormal weight loss 2.8 2.9 7.9 7.4 4.5 4.6 6.5 6.1 4.1 4.1 5.4 4.9 3.0 3.0 1.6 0.4 4.7 4.2\nAcute pancreatitis 0.7 0.8 1.7 2.3 0.8 1.2 0.9 1.2 0.4 0.6 1.2 1.5 0.0 0.0 0.2 0.2 0.9 1.0\nAll-cause mortality 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\nBone fracture 10.1 8.5 18.3 13.9 12.6 12.4 11.7 10.4 8.2 8.2 15.4 14.5 14.3 12.1 12.8 7.8 9.7 8.2\nDiabetic ketoacidosis 0.0 0.1 0.2 0.3 0.2 0.1 0.2 0.2 0.0 0.1 0.1 0.2 0.0 0.0 0.0 0.0 0.0 0.0\nDiarrhea 12.8 11.4 19.3 17.5 14.1 13.5 17.2 16.0 12.5 11.9 15.0 13.8 1.3 1.5 5.4 2.7 13.0 11.0\nEdema 21.2 19.3 40.7 33.0 25.9 25.1 28.8 27.1 19.0 18.8 27.4 25.6 18.2 15.8 10.4 5.1 24.4 21.3\nGenitourinary infection 22.8 20.3 34.0 29.8 23.5 22.1 29.7 27.0 19.0 17.4 28.3 25.3 25.0 22.8 22.4 11.5 13.4 12.6\nHyperkalemia 2.6 2.5 8.1 7.1 3.0 3.2 7.0 6.8 3.6 3.5 3.8 3.6 8.6 5.2 7.6 4.4 7.7 7.8\nHypoglycemia 1.8 1.3 3.0 3.1 1.1 0.9 1.7 1.4 0.9 0.9 1.7 1.6 0.7 0.7 0.2 0.1 0.9 0.8\nHypotension 3.3 2.7 11.2 10.9 5.8 5.7 7.5 7.2 3.7 3.8 5.6 5.2 3.0 2.7 0.9 0.3 7.8 7.0\nJoint pain 6.8 4.3 8.5 6.7 4.2 3.8 6.3 5.5 5.1 4.4 6.3 5.5 14.0 10.5 18.5 9.5 11.4 10.4\nLower extremity amputation 0.2 0.2 0.4 0.3 0.3 0.3 0.3 0.2 0.1 0.1 0.2 0.2 0.0 0.0 0.1 0.2 0.2 0.2\nNausea 11.3 9.9 24.7 21.2 11.1 11.3 15.2 14.7 9.1 8.8 13.7 13.1 7.2 6.7 3.5 1.7 6.1 5.3\nPhotosensitivity 1.5 1.6 0.4 0.4 2.0 1.8 1.4 1.2 1.0 1.1 1.5 1.4 0.4 0.2 0.2 0.1 1.9 1.5\nVenous thromboembolism 3.5 3.0 5.8 4.7 4.3 4.5 4.2 4.2 3.5 3.5 4.5 4.4 8.4 7.2 3.4 1.9 4.0 3.2\nVomiting 8.4 7.1 20.2 18.4 8.6 8.8 11.1 11.0 5.6 5.7 10.6 10.1 7.2 6.7 3.7 1.9 4.3 3.9\n10. Line number 155: “Follow-up was assessed using three approaches….”. Have this under a\nseparate headline for better clarity? New paragraph?\nResponse:\nWe appreciated your suggestion. We have separated the paragraphs as you suggested.\n11. In the introduction it is stated: “Older adults are uniquely vulnerable to adverse drug events\ndue to multimorbidity, frailty, and polypharmacy. Yet, they remain underrepresented in\nrandomized trials.”. Please elaborate more on older adults vs elderly. Most vulnerable are those\nover 74. Focus in the manuscript is on those between 65-74, it is a lost opportunity not to\nanalyze/report on those 75 years and older.\n12. None of the analyses are gender or age-stratified.\n13. Gender and age stratified analyses are missing. Prior studies have reported that women are\nmore likely to report GLP-1–related side effects. This issue is particularly relevant in older\npopulations, and it would be valuable to differentiate between participants aged 65–74 and those\n≥75 years (20% of the study sample is ≥75). Without such analyses, there is a risk that important\ndifferences may be overlooked, potentially leading to overmedication in women and increased\nrisk of adverse effects such as nausea. Addressing this gap would strengthen the study, especially\nas sex and gender considerations are increasingly emphasized in clinical research. Stratified\nanalyses could therefore add significant value to the current work.\nResponse:\nWe thank the reviewer for the thoughtful comment regarding the ≥75-year subgroup. We fully\nagree that individuals in this age range often exhibit higher vulnerability and deserve careful\nconsideration. However, there are several methodological reasons we did not perform a separate\nanalysis specifically for participants aged ≥75 years.\nFirst, all analyses in this study were conducted strictly in accordance with a pre-specified\nprotocol established prior to data collection. Introducing a new age-based subgroup post hoc\nwould deviate from this protocol and increase the risk of overinterpretation or false-positive\nfindings, which we sought to avoid in order to maintain analytic rigor.\nSecond, although the ≥75-year group is clinically meaningful, the available sample becomes\nconsiderably limited when subdivided across treatment arms and outcome categories. Such\nfragmentation would yield unstable estimates with wide uncertainty, ultimately reducing the\ninterpretability and reliability of the results.\nWith regard to sex differences specifically, we would like to clarify that a separate, dedicated\nresearch project using the same overall cohort is currently being conducted to examine sex-\nspecific patterns and adverse effects in detail (Khera et al., BMJ Open; 2022. doi:10.1136/\nbmjopen-2021-057977). Those analyses will be reported comprehensively in the corresponding\nmanuscript. Given this parallel project, we intentionally did not duplicate or partially reproduce\nsex-stratified analyses in the present study.\nWe have added the following to the Discussion section:\nIn Discussion (Line 576),\nFourth, we were unable to address the emerging questions after the protocol was\ndeveloped. Certain outcomes, such as cognitive decline, neuropsychiatric outcomes, and\nophthalmological complications, were not included. Furthermore, the anticipated small\nsample size precluded conducting interesting subgroup analyses (e.g., age ≥75 years or\nby sex) in the older adult population.\n14. Please check language in line 202. Should it be “most” frequent?\nResponse:\nThank you for pointing out the typo. We've corrected the sentence as follows.\nIn Results (Line 199)\nThere were more male patients with 984,590 (54.5%) than female patients, and 1,211,729\n(67.0%) were aged 65-74, and 596,274 (32.9%) were aged 75 or older.\n15. The sentence on line 240 appears to be incomplete.\nResponse:\nWe apologize for the confusion. The sentence was revised accordingly.\nIn Results, (Line 372)\nHowever, SGLT2Is showed a significantly increased risk compared to GLP1RAs (HR 2.03\n[1.38–2.99]) and SUs (HR 1.64 [1.27–2.11]) for diabetic ketoacidosis.\n16. First paragraph, it would be helpful to include information on the frequency of the\nother/older age group as well. This paragraph does not reference any table. Please, add as well\ninformation for those above 75 years.\nResponse:\nWe have revised the sentence as follow:\nIn Results (Line 199)\nThere were more male patients with 984,590 (54.5%) than female patients, and 1,211,729\n(67.0%) were aged 65-74, and 596,274 (32.9%) were aged 75 or older.\nDiscussion:\nThe discussion is well-written.\n17. The first paragraph clearly inform about the study findings without drawing to strong\nconclusions.\n18. I appreciate the clear documentation on how the study contributes to the existing literature\n(line number 310).\nResponse:\nWe thank the reviewer for the positive feedback and appreciate your recognition of the clarity\nand acknowledgment of how the study’s contribution to the existing literature was presented.\n19. There are several instances in the manuscript where references are lacking. I recommend a\nthorough review of the entire manuscript to ensure appropriate citation throughout. Here are\nsome examples from the discussion:\no Line number 284- 290\no Line number 299\no Line number 304-307\no Line number 332- 335\nResponse:\nThank you for your suggestion. We have now cited more references to strengthen our argument\nfurther.\nIn Discussion\n(Line 433)\nSulfonylureas, which stimulate insulin secretion irrespective of glucose levels, predictably\nincrease the risk of hypoglycemia, especially in older adults with fluctuating food intake\nor renal impairment23. The weight gain and edema associated with sulfonylureas may be\ntied to fluid retention and insulin-mediated anabolic effects24,25. In contrast, SGLT2Is\npromote glucosuria and osmotic diuresis, which can reduce body weight and blood\npressure but also predispose to volume depletion in frail or volume-sensitive patients.26\nThe lower incidence of peripheral edema observed with GLP1RAs and SGLT2Is may be\ndue to their favorable renal and vascular effects27,28.\n(Line 455)\nGLP1RAs, acting through central and gastrointestinal pathways, are known to cause\nnausea and vomiting, especially during initiation35.\n(Line 462)\nFinally, the signals for reduced all-cause mortality among GLP1RA and SGLT2I users\nalign with emerging evidence of their cardioprotective and anti-inflammatory properties,\nwhich may translate into broader safety benefits in older populations43,44,45.\n(Line 513)\nFirst, they reinforce current guideline recommendations—such as those from the\nAmerican Diabetes Association and European Association for the Study of Diabetes—\nthat prioritize GLP1RAs and SGLT2Is over SUs and DPP4Is, particularly in older\nadults46,47.\n20. Line number 296: “Bone fracture, which is specifically important to older adults and reported\nSGLT2Is can potentially cause in a previous trial, did not show significantly different risks in any\ncomparison in line with other observational studies”. Suggest rewriting this sentence as it is\ndifficult to understand.\nResponse:\nWe apologize for the confusion. We have tried to improve comprehension by rephrasing the\nsentence.\nIn Discussion (Line 443)\nBone fractures are an outcome of particular relevance in older adults, and a previous\ntrial suggested that SGLT2I may increase this risk. In contrast, we did not observe any\nsignificant differences across comparison pairs, which is consistent with findings from\nprior observation studies32,34\n21. Please add a paragraph with discussion on gender and age.\nResponse:\nPlease see our responses on Questions 11–13. We have added the following to the Discussion\nsection:\nIn Discussion (Line 576),\nFourth, we were unable to address the emerging questions after the protocol was\ndeveloped. Certain outcomes, such as cognitive decline, neuropsychiatric outcomes, and\nophthalmological complications, were not included. Furthermore, the anticipated small\nsample size precluded conducting interesting subgroup analyses (e.g., age ≥75 years or\nby sex) in the older adult population.\n22. In the limitations section, it should be clarified whether excluding participants with a prior\nhistory of the event may have introduced bias.\nResponse:\nWe added the following limitations on generalizability to the discussion section:\nIn Discussion (Line 561)\nSecond, although this study applied rigorous study diagnostics to ensure the internal\nvalidity and credible estimation, these procedures may introduce limitations in\ngeneralizability. For example, only U.S. databases met all diagnostics for the GLP1RA\ncomparisons; the corresponding results may not extend to older adults in Europe and\nAsia. In addition, focusing exclusively on incident outcomes, individuals with a prior\nhistory of the outcome were excluded; therefore, our estimate should not be interpreted as\nreflecting risk of recurrence among patients with pre-existing outcome.\n23. Conclusion: The conclusion is appropriately aligned with the study’s acknowledged\nlimitations.\nResponse:\nWe appreciate the reviewer’s supportive comment\n24. References: Reference 16 seems inaccurate; please verify and correct if necessary.\nResponse:\nWe have confirmed that this was an accidental edit. We appreciate the reviewer’s attention\nIn Reference (Line 1049)\n54. Rao G. PhenotypeLibrary: The OHDSI Phenotype Library.\nhttps://ohdsi.github.io/PhenotypeLibrary/ (2024).\nTables and figures\n25. Table 1: I suggest avoiding explanations in the title. Move the two last lines to foot notes?\n26. Table 2: Same comment for the title as for Table 1. I recommend moving “gender” to the top,\nplacing it above the “age group” section for improved logical flow. In the “Age group” column,\nthe word “year” is missing for the age group ranges.\nResponse:\nThank you for your nice suggestion. We moved the explanation that was included in the table\ntitle down for both Tables. For Table 2, we have moved the “Gender” section above the “Age\ngroup” section to improve the logical flow, and we have added the word “years” to “Age group”.\nWe have updated Table 2 accordingly.\nIn Table (Line 1095, 1109)\nTable 1. Population size and follow-up time for each drug class within each data source.\nTable 2. Baseline patient characteristics for SGLT2I (T) and GLP1RA (C) new-users in\nthe USOC data source.\nTable 2. Baseline patient characteristics for SGLT2I (T) and GLP1RA (C) new-users in the\nUSOC data source.\nBefore adjustment After matching After stratification\nCharacteristics\nT (%) C(%) SMD T (%) C(%) SMD T (%) C(%) SMD\nGender: Female 44.7 57.3 0.25 55.3 54.7 -0.01 48.8 48.3 -0.01\nAge group, years\n65-69 43.1 49.1 0.12 44.9 44.9 0.00 44.9 44.9 0.00\n70-74 29.2 29.8 0.01 30.1 29.9 0.00 29.5 29.4 0.00\n75-79 17.5 14.2 -0.09 14.7 14.8 0.00 16.4 16.6 0.00\n80-84 9.5 6.4 -0.12 6.7 6.8 0.00 8.5 8.6 0.00\n85-89 0.8 0.5 -0.03 0.5 0.5 0.00 0.7 0.6 -0.01\nMedical history: General\nChronic liver disease 0.9 0.9 -0.01 0.9 0.9 0.00 0.9 0.9 0.00\nChronic obstructive lung disease 7.2 7.1 0.00 7.1 6.9 -0.01 7.3 7.1 0.00\nDementia 1.0 0.8 -0.02 0.9 0.9 0.00 1.0 1.0 0.00\nDepressive disorder 6.0 8.8 0.10 8.0 7.7 -0.01 6.9 7.0 0.00\nHyperlipidemia 48.5 46.1 -0.05 45.8 45.6 0.00 47.8 47.8 0.00\nHypertensive disorder 54.8 53.5 -0.03 53.1 52.7 -0.01 54.5 54.6 0.00\nObesity 9.6 16.2 0.20 13.8 13.4 -0.01 11.6 11.9 0.01\nOsteoarthritis 18.0 22.1 0.10 20.9 20.5 -0.01 19.3 19.4 0.00\nRenal impairment 10.4 9.4 -0.03 9.7 9.2 -0.02 10.2 10.0 -0.01\nRheumatoid arthritis 1.2 1.5 0.03 1.4 1.3 -0.01 1.3 1.3 0.00\nMedical history: Cardiovascular disease\nCerebrovascular disease 4.7 3.9 -0.04 4.0 3.9 0.00 4.5 4.5 0.00\nCoronary arteriosclerosis 16.4 11.4 -0.14 12.0 11.7 -0.01 15.0 14.7 -0.01\nHeart failure 8.6 5.0 -0.14 5.2 5.0 -0.01 7.6 6.8 -0.03\nIschemic heart disease 7.2 4.6 -0.11 4.8 4.7 -0.01 6.4 6.1 -0.01\nPeripheral vascular disease 7.5 6.7 -0.03 6.8 6.6 -0.01 7.3 7.3 0.00\nMedical history: Neoplasms\nMalignant neoplastic disease 9.1 8.7 -0.02 8.8 8.5 -0.01 9.0 8.8 -0.01\nMalignant tumor of breast 1.5 1.8 0.02 1.8 1.7 0.00 1.6 1.5 -0.01\nMalignant tumor of colon 0.4 0.3 -0.01 0.4 0.3 0.00 0.4 0.3 0.00\nMalignant tumor of lung 0.3 0.3 -0.01 0.3 0.3 0.00 0.3 0.3 0.00\nMalignant tumor of urinary bladder 0.4 0.4 0.00 0.4 0.4 0.00 0.4 0.5 0.01\nPrimary malignant neoplasm of prostate 2.3 1.8 -0.03 1.9 1.9 0.00 2.2 2.2 0.00\nMedication use\nAgents acting on the renin-angiotensin\n74.4 70.2 -0.10 71.0 70.9 0.00 73.2 73.0 0.00\nsystem\nAntibacterials for systemic use 55.4 59.1 0.07 58.5 57.8 -0.01 56.7 56.3 -0.01\nAntiinflammatory and antirheumatic\n33.6 36.0 0.05 35.5 34.9 -0.01 34.5 34.0 -0.01\nproducts\nAntithrombotic agents 28.7 22.2 -0.15 22.6 22.7 0.00 26.6 26.4 0.00\nBeta blocking agents 47.5 41.1 -0.13 41.8 41.7 0.00 45.5 45.4 0.00\nCalcium channel blockers 33.2 31.1 -0.04 31.5 31.5 0.00 32.5 32.8 0.01\nDiuretics 47.9 49.4 0.03 49.3 48.5 -0.02 48.6 48.1 -0.01\nDrugs for obstructive airway disease 39.5 44.3 0.10 43.4 42.7 -0.01 41.2 41.0 0.00\nLipid modifying agents 81.1 77.0 -0.10 77.7 77.8 0.00 79.8 79.8 0.00\nOpioids 22.8 26.2 0.08 25.4 24.8 -0.01 24.0 23.8 -0.01\nThis table reports the proportion of initiators satisfying selected baseline characteristics and the standardized difference of population\nproportions before and after propensity score adjustment. Less extreme SMD (<0.1) through matching and stratification suggest improved\nbalance between patient cohorts through adjustment. Abbreviations: SGLT2I: sodium-glucose transporter-2 inhibitor; GLP1RA: glucagon-like\npeptide-1 receptor agonist; SMD: standardized mean difference.\n27. Figure 1\n- The title does not fully explain the context of the figure. Consider revising.\n- Be consisted with space between symbol and number, before/after comma etc.\n- The light scatter blue color at the top of the figure makes the text difficult to read. I suggest\nchanges here.\n- Abbreviations should be explained. This concerns all figures.\nResponse:\nAs you suggested, we've changed the title of Figure 1 to a more descriptive one. We've also\nremoved the spacing between the symbols and numbers to maintain consistency. The light scatter\ncolor has been removed. Abbreviations have been added to the figure legend.\nFigure 1. Study design that depicts target-comparator pairs, eligibility criteria, and\nfollow-up strategies.\nT2DM: type 2 diabetes mellitus; Rx: prescription.\n28. Figure 2 and 3\n- Thank you for the clear presentation of the results in the figures.\n- Please place explanations for each figure below the respective figure (not in the title).\nResponse:\nThank you for your helpful comments. We have revised the manuscript so that explanatory text\nfor each Figure is now place below the respective Figure rather than in the title. Again, we\nappreciate your positive feedback on the clarity of our manuscript.\nReviewer #3 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is\npart of the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts.\nResponse:\nWe appreciate the involvement of Early-Career Researchers.\nReviewer #4 (Remarks to the Author):\nThis study applies a large-scale, federated, observational analysis using OHDSI tools to compare\nsafety outcomes of antihyperglycemic agents. The scope, standardized analytics, and empirical\ncalibration are strengths. However, several aspects of the design and reporting could be clarified\nor strengthened. In particular, more transparency around missing data handling, potential biases\nintroduced by trimming/selection, heterogeneity across data sources, and sensitivity analyses\nwould improve interpretability and robustness.\nMajor Comments\n1. Missing Data\nThe manuscript does not provide details on how missing data were addressed. In\nLEGEND/OHDSI pipelines, covariates are typically coded as “0 = not observed,” but this\nimplicitly treats missingness as absence rather than truly unmeasured. This may be problematic\nfor systematically missing variables (e.g., BMI, labs, smoking). I recommend:\n* Explicitly stating the missing data handling strategy.\n* Considering sensitivity analyses comparing complete-case analyses, missing-indicator\napproaches, and, where feasible, multiple imputation.\n* Discussing the potential for bias if missingness differs systematically between treatment arms\nor databases.\nResponse:\nThank you for your valuable question. As you understand, we considered unobserved data in the\ndatabase as non-existent. Furthermore, to clarify, while \"measurement\" was used in the\npropensity score model, this refers to whether a measurement was performed, not the numerical\nresult itself. To clarify this, the text has been revised as follows:\nIn Methods (Line 671)\nThe measurements were included as only the measurement status as a covariate, not the\nvalue. Values that did not exist in the database were treated as unobserved, and no\nseparate method (e.g., imputation) was used to supplement missing data.\n2. Potential Bias from Trimming and Selection of Analyses\nThe study applies PS trimming, exclusion of analyses failing balance diagnostics, and MDRR\nthresholds. While these steps improve internal validity, they introduce potential biases:\n* Loss of generalizability: trimming removes patients at the extremes of PS, you’re discarding\nthose who are almost always or almost never treated. These patients may represent an important\nclinical subgroup (e.g., very sick or very healthy).\n* Selection bias: excluding “unbalanced” or underpowered comparisons means the evidence base\nis skewed toward settings with inherently better overlap and less confounding.\n* Interpretational Bias: Policymakers or clinicians may mistakenly interpret the findings as\ngeneralizable to all patients, when in fact the filters mean the results are for a subset (patients\nwith overlap, balance, and sufficient data). I suggest explicitly discussing these trade-offs and\nclarifying that estimates apply primarily to patients in regions of overlap, not to the broader\npopulation.\nResponse:\nWe appreciate the reviewer’s thoughtful comment. We would like to clarify that we did not apply\npropensity score trimming in this study. Instead, we assessed covariate balance and overlap\nacross comparisons and excluded only those with poor overlap or extreme imbalance to ensure\nvalid estimation. Therefore, no patients were removed based on their position in the propensity\nscore distribution.\nFor the selection bias, we acknowledge that excluding unbalanced or underpowered comparisons\ncould introduce selection bias toward more well-behaved datasets. However, this decision was\nmade to ensure the validity of causal inference, as highly unbalanced comparisons are inherently\nunreliable. We clarified this limitation and emphasized that our findings apply primarily to data\nsources and comparisons with adequate covariate overlap.\nFor the interpretational bias, we appreciate the reviewer’s thoughtful comment. We agree that\nrestricting analyses to comparisons with adequate covariate overlap and balance improves\ninternal validity but limits external generalizability. Accordingly, we have clarified in the\nDiscussion that our estimates primarily apply to patients within the region of common support—\nthose for whom treatment A and B were both plausible options—and should not be interpreted as\nrepresentative of all patients or settings. This trade-off between validity and generalizability has\nbeen explicitly discussed as a limitation.\nIn Discussion (Line 559)\nSecond, although this study applied rigorous study diagnostics to ensure the internal\nvalidity and credible estimation, these procedures may introduce limitations in\ngeneralizability. For example, only U.S. databases met all diagnostics for the GLP1RA\ncomparisons; the corresponding results may not extend to older adults in Europe and\nAsia. In addition, focusing exclusively on incident outcomes, individuals with a prior\nhistory of the outcome were excluded; therefore, our estimate should not be interpreted as\nreflecting risk of recurrence among patients with pre-existing outcome.\n3. Heterogeneity Across Data Sources (Table 1)\nTable 1 reveals substantial heterogeneity in treatment persistence and follow-up across databases\n(e.g., very long on-treatment durations in SIDIAP vs much shorter in OEHR and CCAE). This\nraises concerns about exposure construction and harmonization. Additional checks may be\nwarranted:\n* Varying grace period and stockpiling assumptions (15/30/60/90 days; stockpiling on/off).\n* Assessing whether heterogeneity reflects true practice differences versus database artifacts.\n* Meta-regression or subgroup analyses by data source type (claims vs EHR; US vs EU).\nResponse:\nThank you for your suggestion. We believe this question is very important, and we have\nproduced meta-analysis results according to the database subgroups (Claims vs. EHR; US vs.\nnon-US) you suggested. The table and graph below show the results of separate meta-analyses by\ndatabase subgroup. Through this analysis, we were able to confirm that Claims and EHR showed\nhigh overall significance. However, due to the limited number of GLP1RA and SGLT2I patients\nin the non-US database, only the comparison of DPP4I vs. SU showed overall agreement\nbetween the US and non-US databases and other comparisons could not be determined.\nTherefore, we added this result to the main text results section as shown below, and also added\nan explanation of the limited generalizability in the limitation section of the discussion section.\nIn Results, (Line 426)\nResults of the meta-analyses, pooled separately by database type (Claims vs. EHR) and\nregion (US vs. non-US), are presented in Supplemental Fig. 58–59. Although the meta-\nanalysis results from Claims and EHR databases showed a high degree of agreement in\ntheir significance, the results between US and non-US databases were generally\nconsistent only for the DPP4I vs. SU comparison due to the limited numbers of GLP1RA\nand SGLT2I cohorts in the non-US database.\nIn Discussion (Line 559)\nSecond, although this study applied rigorous study diagnostics to ensure the internal\nvalidity and credible estimation, these procedures may introduce limitations in\ngeneralizability. For example, only U.S. databases met all diagnostics for the GLP1RA\ncomparisons; the corresponding results may not extend to older adults in Europe and\nAsia. In addition, focusing exclusively on incident outcomes, individuals with a prior\nhistory of the outcome were excluded; therefore, our estimate should not be interpreted as\nreflecting risk of recurrence among patients with pre-existing outcome.\nTable R7. Meta-analytic results of key study outcomes by data source subgroups (Claims\nvs. EHR)\n*Red color means statistically significant and blue shadow means agreement between subgroups\nGLP1RA vs. GLP1RA vs. GLP1RA vs. SGLT2I vs. SGLT2I vs. SU DPP4I vs. SU\nOutcome SGLT2I DPP4I SU DPP4I\nClaims EHR Claims EHR Claims EHR Claims EHR Claims EHR Claims EHR\nDiabetic 0.49 NA 0.80 NA 0.96 NA 1.57 14.7 1.62 2.73 1.04 1.30\nKetoacidosis [0.33– [0.51– [0.62– [1.21– [1.10– [1.27– [0.85– [0.89– [0.68–\n0.73] 1.26] 1.48] 2.05] 196.8] 2.06] 8.73] 1.21] 2.48]\nHyperkalemia 0.93 0.65 0.64 0.32 0.63 NA 0.73 0.76 0.75 0.83 1.04 1.15\n[0.81– [0.30– [0.52– [0.16– [0.50– [0.60– [0.60– [0.63– [0.56– [0.96– [0.93–\n1.06] 1.39] 0.80] 0.65] 0.81] 0.89] 0.97] 0.88] 1.24] 1.12] 1.43]\nHypoglycemia 0.95 NA 0.81 0.21 0.21 NA 0.81 0.77 0.23 0.09 0.26 0.28\n[0.64– [0.62– [0.04– [0.16– [0.59– [0.25– [0.15– [0.03– [0.23– [0.15–\n1.42] 1.07] 0.96] 0.28] 1.11] 2.32] 0.37] 0.25] 0.28] 0.53]\nNausea 1.56 1.79 1.40 1.62 1.34 NA 0.89 0.80 0.84 1.13 0.97 1.00\n[1.32– [1.21– [1.16– [1.10– [1.07– [0.77– [0.54– [0.72– [0.84– [0.90– [0.86–\n1.84] 2.65] 1.68] 2.43] 1.66] 1.03] 1.18] 0.98] 1.50] 1.04] 1.18]\nPeripheral 1.03 1.44 0.79 0.72 0.72 NA 0.72 0.60 0.68 0.43 0.95 0.87\nedema [0.89– [0.87– [0.65– [0.46– [0.58– [0.62– [0.50– [0.58– [0.32– [0.89– [0.76–\n1.19] 2.37] 0.94] 1.12] 0.90] 0.84] 0.71] 0.79] 0.59] 1.03] 1.00]\nVomiting 1.59 1.82 1.38 1.58 1.30 NA 0.87 0.79 0.83 1.06 0.97 1.05\n[1.44– [1.11– [1.14– [0.98– [1.04– [0.75– [0.56– [0.71– [0.74– [0.88– [0.89–\n1.75] 2.98] 1.66] 2.53] 1.63] 1.01] 1.12] 0.96] 1.50] 1.06] 1.24]\nTable R8. Meta-analytic results of key study outcomes by data source subgroups (US vs.\nnon-US)\n*Red color means statistically significant and blue shadow means agreement between subgroups\nGLP1RA vs. GLP1RA vs. GLP1RA vs. SGLT2I vs. SGLT2I vs. SU DPP4I vs. SU\nSGLT2I DPP4I SU DPP4I\nOutcome\nUS Non- US Non- US Non- US Non- US Non- US Non-\nUS US US US US US\nDiabetic 0.49 NA 0.82 NA 0.96 NA 1.69 NA 1.64 NA 1.05 NA\nKetoacidosis [0.33– [0.53– [0.62– [1.07– [1.27– [0.90–\n0.73] 1.26] 1.48] 2.68] 2.11] 1.22]\nHyperkalemia 0.92 NA 0.59 NA 0.63 NA 0.74 0.80 0.74 NA 1.06 1.06\n[0.80– [0.40– [0.50– [0.60– [0.58– [0.62– [0.95– [0.88–\n1.05] 0.67] 0.81] 0.91] 1.10] 0.89] 1.18] 1.27]\nHypoglycemia 0.95 NA 0.62 NA 0.21 NA 0.78 1.10 0.21 NA 0.28 0.23\n[0.63– [0.23– [0.16– [0.52– [0.30– [0.13– [0.23– [0.11–\n1.42] 1.63] 0.28] 1.16] 4.03] 0.33] 0.33] 0.46]\nNausea 1.59 NA 1.69 NA 1.34 NA 0.89 0.62 0.88 NA 0.97 1.09\n[1.39– [1.13– [1.07– [0.76– [0.41– [0.71– [0.90– [0.87–\n1.81] 2.53] 1.66] 1.04] 0.95] 1.09] 1.05] 1.37]\nPeripheral 1.05 NA 0.81 NA 0.72 NA 0.72 0.55 0.64 NA 0.93 0.89\nedema [0.91– [0.71– [0.58– [0.61– [0.43– [0.51– [0.86– [0.74–\n1.23] 0.92] 0.90] 0.83] 0.70] 0.80] 1.01] 1.07]\nVomiting 1.59 NA 1.62 NA 1.30 NA 0.87 0.64 0.83 NA 0.98 1.10\n[1.45– [1.10– [1.04– [0.74– [0.44– [0.69– [0.90– [0.88–\n1.75] 2.39] 1.63] 1.02] 0.92] 1.01] 1.06] 1.39]\nSupplementary Figure 58. Meta-analytic effects comparing SGLT2I, GLP1RA, DPP4I and\nSU new-users from Claims (Dark brown) and EHR (Dark blue) databases. Points indicate\nHR estimates and lines show their 95% CIs.\nSupplementary Figure 59: Meta-analytic effects comparing SGLT2I, GLP1RA, DPP4I and\nSU new-users from US (Dark brown) and Non-US (Dark blue) databases. Points indicate\nHR estimates and lines show their 95% CIs\n4. Transparency in Figure 2\nThe forest plot summarizing outcomes does not show sample sizes or event counts for each\ncomparison. Without these denominators, it is difficult to assess evidentiary weight and\nprecision. I recommend adding sample sizes (and ideally event counts) for each treatment arm\nwithin each comparison, although I am not sure what is the best way to add this information\nwithout making the plot too busy.\nResponse:\nWe appreciate your suggestion. However, rather than adding more complexity by including\npatient numbers in the figure, we preferred to provide our analysis results in supplementary\ntables, making them transparent to researchers. We added instructions for the supplementary\ntable to the results, as shown below.\nIn Results (Line 353)\nFor eighteen safety outcomes, meta-analytic results from the primary analysis (on-\ntreatment follow-up with propensity score stratification) are presented in Figure 3.\nOverall, both GLP1RAs and SGLT2Is were associated with more favorable safety profiles\nacross multiple outcomes compared with SUs and DPP4Is (Supplementary Tables 108–\n215).\n5. Additional Sensitivity Analyses\nThe ITT and OT2 sensitivity analyses are helpful, but further robustness checks would\nstrengthen conclusions:\n* Exposure definitions: Vary grace periods, stockpiling rules, induction lags, and carry-over\nwindows after discontinuation.\n* Database influence: Perform leave-one-database-out meta-analysis.\n* Competing risks: Apply competing-risk models for outcomes with high mortality.\nResponse:\nThank you for your excellent suggestion. We performed a leave-one-database-out meta-analysis\nusing the method you suggested and included it in the Supplementary Fig. 60–95. Figures for\nsome key outcomes (hypoglycemia, hyperkalemia, peripheral edema, diabetic ketoacidosis,\nnausea, and vomiting) are included in this response letter too. As you can see, most of the results\nremain consistent even when one database is excluded.\nIn Results (Line 431),\nIn a leave-one-database-out meta-analysis that exclude one database for each outcome\nconducted to determine the influence of individual databases, most comparisons showed\nconsistent significances (Supplemental Fig. 60–95).\nReviewer #4 (Remarks on code availability):\nI reviewed the provided codebase. Overall, the authors have made the code available in a usable\nform, with functions that can be run successfully. I was able to install the package and execute\nsome of the core functions without major issues, which supports the reproducibility of the\nreported analyses.\nThat said, the repository is quite large, with many files and scripts. While this reflects the\ncomplexity of the analyses, it makes it difficult to comprehensively review every component in\nthe scope of peer review.\nResponse:\nThank you for your review of the code repository. Our code is maintained within a repository\nthat supports the entire LEGEND T2DM project. Because this repository contains a large\nnumber of analyses, it may appear complex. However, the analysis package is based on the\nOHDSI analytic framework and coding structure, which are well documented and can be readily\nunderstood through previously published materials, e.g., Book of OHDSI\n(https://ohdsi.github.io/TheBookOfOhdsi/).\nRESPONSE LETTER (Manuscript Number: NCOMMS-25-50794)\nWe are grateful to the editor and reviewers for the encouraging comments.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nThis paper has gone through considerable revision and reconstruction to present the study in a\nclear and concise manner. In the new form, I found it hard to fault. There are minor issues that\nshould be considered in the editing such as the use of Fig. and Figure is inconsistent, indents at\nparagraphs, to name a few.\nResponse: We thank the reviewer for the positive feedback. We have carefully proofread the\nmanuscript to ensure consistent use of \"Figure\" and corrected all paragraph indentation and\nformatting issues.\nReviewer #2 (Remarks to the Author):\nThank you for the opportunity to review the revised version of the manuscript. I am pleased to\nsee that the previous comments from both myself and the early-career researcher have been\nthoroughly addressed and appropriately incorporated into the revised manuscript.\nI have no further comments at this stage. In my view, the manuscript now represents an\nimportant and valuable scientific contribution to the field, and I support its publication.\nResponse: We sincerely appreciate the reviewer’s recognition of our work as a valuable\nscientific contribution and for supporting its publication.\nReviewer #3 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is\npart of the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts.\nResponse: We thank Reviewer 3 for their time and contribution to the peer-review process and\nthe co-review initiative.\nReviewer #4 (Remarks to the Author):\nThe authors have addressed all of my concerns, and I have no additional comments.\nReviewer #4 (Remarks on code availability):\nNo additional comments\nResponse: We thank Reviewer 4 for confirming that all concerns have been fully addressed."}, {"round": 2, "reviewer_comments": {"reviewer_1": "This paper has gone through considerable revision and reconstruction to present the study in a clear and concise manner. In the new form, I found it hard to fault. There are minor issues that should be considered in the editing such as the use of Fig. and Figure is inconsistent, indents at paragraphs, to name a few.", "reviewer_2": "Thank you for the opportunity to review the revised version of the manuscript. I am pleased to see that the previous comments from both myself and the early-career researcher have been thoroughly addressed and appropriately incorporated into the revised manuscript.\nI have no further comments at this stage. In my view, the manuscript now represents an important and valuable scientific contribution to the field, and I support its publication.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "The authors have addressed all of my concerns, and I have no additional comments."}, "author_response": "RESPONSE LETTER (Manuscript Number: NCOMMS-25-50794)\nWe are grateful to the editor and reviewers for the encouraging comments.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nThis paper has gone through considerable revision and reconstruction to present the study in a\nclear and concise manner. In the new form, I found it hard to fault. There are minor issues that\nshould be considered in the editing such as the use of Fig. and Figure is inconsistent, indents at\nparagraphs, to name a few.\nResponse: We thank the reviewer for the positive feedback. We have carefully proofread the\nmanuscript to ensure consistent use of \"Figure\" and corrected all paragraph indentation and\nformatting issues.\nReviewer #2 (Remarks to the Author):\nThank you for the opportunity to review the revised version of the manuscript. I am pleased to\nsee that the previous comments from both myself and the early-career researcher have been\nthoroughly addressed and appropriately incorporated into the revised manuscript.\nI have no further comments at this stage. In my view, the manuscript now represents an\nimportant and valuable scientific contribution to the field, and I support its publication.\nResponse: We sincerely appreciate the reviewer’s recognition of our work as a valuable\nscientific contribution and for supporting its publication.\nReviewer #3 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is\npart of the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts.\nResponse: We thank Reviewer 3 for their time and contribution to the peer-review process and\nthe co-review initiative.\nReviewer #4 (Remarks to the Author):\nThe authors have addressed all of my concerns, and I have no additional comments.\nReviewer #4 (Remarks on code availability):\nNo additional comments\nResponse: We thank Reviewer 4 for confirming that all concerns have been fully addressed."}]

Decision letter