The MHS database includes around 180,000 patients with type 2 diabetes among its over 2 million registrees. We included adults with type 2 diabetes who initiated a GLP-1 RA (exenatide [introduced in Israel in 12.2007], liraglutide [2.2010], lixisenatide [1.2015], exenatide extended-release [11.2015], dulaglutide [4.2016], and semaglutide [8.2019]) or basal insulin between February 2010 to December 2019. The day of drug initiation was defined as the index date, and the year preceding the index date was defined as the baseline period. We selected basal insulin as a comparator to ensure comparability between the groups, because during these years in Israel both drugs were mainly used as injectable drugs for glycemic control in advanced stages of diabetes. Accordingly, we included patients treated with at least two other glucose-lowering agents (GLAs) at the baseline period, reflecting the common use at Israel at the time. Only those with at least one eGFR measurement at the baseline period were included. We excluded patients with type 1 diabetes, eGFR < 15 mL/min/1.73 m2, an indication of kidney transplantation or dialysis treatment, or those treated with the comparator drug within the year prior index date. Patients with an indication of pregnancy within 9 months before the index date were also excluded. To reduce bias associated with physicians' preference to treat severely ill patients with familiar and less costly drugs, we excluded patients with a diagnosis of dementia; history of organ transplantation; in MHS’ cancer (within the past 5 years) or heart failure registers; or those hospitalized for  ≥ 5 consecutive days within the past 180 days (Additional file 1: Figure S1).

In the protocol, we defined two follow-up periods. In the intention to treat (ITT) analysis, follow-up continued until the end of data availability, death, or October 2021. In the as-treated (AT) analysis, follow-up was censored also at exposure discontinuation (added by 180 days of grace period) or the initiation of the comparator. In addition, we performed a sensitivity analysis censoring the ITT follow-up for all patients after 4 years of follow-up (ITT-48mo). The rationale behind this analysis was to terminate follow-up when a large portion of the participants was still exposed to the study drugs. Four years cut-off was also selected to emulate the follow-up duration of the LEADER trial [19].

The study was approved by the institutional review board (IRB) at MHS. Due to the de-identified nature of the data, the IRB did not require obtaining informed consent from the participants.

Validated MHS registries were used to identify patients with type 2 diabetes, cardiovascular disease (including heart failure), hypertension, or cancer [20,21,22]. The relevant International Classification of Diseases-9 diagnosis codes, Anatomical Therapeutic Chemical medications codes, and MHS registries are presented in Additional file 1: Table S1. Blood and urine samples included in this study were collected in community settings and were measured in the MHS-certified central laboratory. eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation [23]. Residential socioeconomic status (SES) was ranked on a 1 (lowest) to 10 (highest) scale. This score was derived by Points Location Intelligence Ltd, combining geographic and socioeconomic information for each neighborhood (e.g., expenditures related to retail chains, credit cards, and housing). This score is highly correlated with the SES measured by the Israeli Central Bureau of Statistics. This parameter was categorized into 4 groups (low [1–3], low-medium [4, 5], medium [6, 7] and high [8–10]) [24].

The main kidney outcome was a composite of confirmed  ≥ 40% eGFR reduction from baseline or new ESKD. Additional outcomes were confirmed or single-measurement eGFR reductions of  ≥ 30, ≥ 40, ≥ 50, or  ≥ 57% (corresponding to a doubling of serum creatinine) or new ESKD alone. We also assessed albuminuria outcomes: (1) a categorical increase of urine albumin-to-creatinine ratio (UACR) for the following categories:  < 30, 30- < 300 or  ≥ 300 mg/g; (2) or new-onset macroalbuminuria (UACR ≥ 300) among those with UACR ≤ 230 mg/g at baseline (resembling a ≥ 30% increase in UACR). The albuminuria-based outcomes were assessed either as a single- or as confirmed -measurement. In addition, we compared the eGFR slopes between the groups.

In addition to the entire study population, analyses were performed in subgroups defined by sex, age (< 60 or ≥ 60 years), presence of CVD, years in diabetes registry (≤ 10 or > 10 years), body mass index (< 30 or ≥ 30 kg/m2), HbA1c (< 8, or ≥ 8%), UACR (< 30, 30- < 300, or ≥ 300 mg/g), treatment with ACEi/ARBs, and treatment with SGLT2i. Patients were also divided into subgroups by their baseline eGFR (≥ 90 and < 90 mL/min/1.73 m2); this threshold was selected owing to the relatively preserved kidney function of the study population. As a sensitivity analysis, we also divided patients into three eGFR subgroups (≥ 90, 60- < 90, or < 60 mL/min/1.73 m2).

Participants were propensity-score (PS) matched in 1:1 ratio using greedy matching, as previously described [25]. The model included 88 baseline parameters, including demographic variables (including SES), medical history, concomitant medications, and laboratory values (see the complete list in the Additional file 1). Continuous variables were categorized, and missing values were defined as a distinct ‘missing’ category to allow all patients to be matched. The PS matching was carried out by layers of baseline eGFR (> 90, 60–90, 45–60, 30–45, and 15–30 mL/min/1.73 m2).

Baseline values were described using mean and standard deviation (continuous variables with approximately normal distribution), median and IQR (continuous variables with skewed distribution), or proportions (categorical variables). Standardized difference (STD) was used to assess differences between the GLP-1 RAs and basal insulin group, with values of  < 10% considered negligible.

Cumulative incidence functions were used to describe the incidence of the outcomes in each group. Cox proportional hazard regression models were applied to estimate hazard ratios, confidence intervals, and p-value. The models were adjusted for the competing risk of mortality using cause-specific hazard models for the cumulative incidence functions and by using sub-distribution hazard functions for the Cox model [26].

The eGFR change from baseline at different time points was estimated using mixed models for repeated measures. We defined time windows of 6 months during the first 3 years and each year thereafter. At each time window, we included for each patient the eGFR measurement closest to the end of the period. Differences between groups at different time points were estimated using mixed-effect models with repeated measures. In randomized controlled trials (RCTs), eGFR slopes are also estimated using mixed-effect models with repeated measures; however, this approach may not fit the irregular sampling in real-world settings, where the times of measurements vary between patients. Therefore, to assess eGFR slopes, we fitted a linear regression model per patient (with time from index date as the independent variable and eGFR values as the dependent variable), enabling us to use all available eGFR measurements. The between-group difference in the linear slope estimates were compared using a t-test. We included in this analysis only patients with  ≥ 2 eGFR measurements with  ≥ 180 days between the first and last evaluations.

This study did not include formal hypothesis testing, and the p-values are presented for descriptive purposes only. No correction for multiple testing was performed. We consider a p < 0.05 as statistically significant. Analyses were performed using SAS version 9.4.

The study was funded by Novo Nordisk. The funder was involved in the study design, data analysis, data interpretation, writing of the report, and the decision to submit the paper for publication. This report was written according to a predefined protocol, including main and additional outcomes.