OBJECTIVE

We aim to compare the risk of nephrolithiasis among type 2 diabetes patients who initiated sodium–glucose cotransporter 2 inhibitors (SGLT2is) versus dipeptidyl peptidase 4 inhibitors (DPP4is), individually within stone never- and ever-formers.

RESEARCH DESIGN AND METHODS

Using the 2010–2021 Korea National Health Insurance Service database, we conducted a population-based cohort study, comparing initiators of SGLT2is versus DPP4is. The primary outcome was incident nephrolithiasis. Osteoarthritis encounters served as a negative control outcome. After 1:1 propensity score (PS) matching in stone never- and ever-formers, pooled and individual hazard ratios (HRs), incidence rate difference (IRD), and 95% CIs were reported. Subgroup analyses by sex, age, thiazide co-use, and baseline cardiovascular risk were done.

RESULTS

The 17,006 PS-matched pairs of SGLT2i and DPP4i initiators were pooled from stone never-formers (105,378 pairs) and ever-formers (11,628 pairs). Over a mean of 654 days, the risk of nephrolithiasis was lower in SGLT2i initiators than in DPP4i initiators: 0.65 vs. 1.12 events per 100 person-years, HR 0.54 (95% CI, 0.50–0.57), IRD −0.46 (95% CI, −0.21 to −0.52). Among never-formers, the HR was 0.43 (95% CI, 0.39–0.48) and IRD was −0.32 (95% CI, −0.27 to −0.36). Among ever-formers, the HR was 0.64 (95% CI, 0.59–0.69) and IRD was −2.26 (95% CI, −1.77 to −2.76). Near-null associations were found for osteoarthritis encounters. Results were consistent across subgroups.

CONCLUSIONS

We found a lower risk of nephrolithiasis associated with SGLT2is versus DPP4is in stone never- and ever-formers. Despite a greater relative risk reduction in the former, the absolute risk reduction was greater in the latter.

Nephrolithiasis is a global health issue with increasing prevalence and immense economic cost (1). According to the U.S. National Health and Nutrition Examination Survey, there was a threefold increase in the prevalence of kidney stones between 1976–1980 (3.2%) and 2007–2010 (8.8%), with a contemporary prevalence of 10.9% in 2017–2018 (2,3). Patients with type 2 diabetes are at 1.3–1.7 times the risk of nephrolithiasis compared with the general population (4). This association is highlighted for uric acid (UA) stones in that UA stones are prevalent as 35.7% of all stones in type 2 diabetes, whereas only 11.3% of those in nondiabetic patients (5). The link between UA stones and type 2 diabetes is thought to be driven by excess undissolved urinary UAs in relation to insulin resistance and unduly acidic urine (6). Insulin resistance is also associated with prolithogenic urinary profiles for calcium stones such as hypocitraturia, hyperoxaluria, and/or hypercalciuria (7–9). However, the role of insulin resistance in calcium stone formation is controversial, because calcium oxalate precipitation tends to be more heavily driven by factors other than insulin resistance (10,11).

Sodium–glucose cotransporter 2 inhibitors (SGLT2is) are a class of newer glucose-lowering drugs inhibiting proximal tubular reabsorption of glucose. Key randomized controlled trials have shown significant cardiorenal protection by SGLT2is beyond a glucose-lowering effect partly due to osmotic diuresis and reduced plasma volume (12). Increased urinary volume is a strong antilithogenic factor (13). On the basis of this background, dapagliflozin, an SGLT2i, was patented for the management of nephrolithiasis in 2009 (WO2009143021A1). In support of this, recent observational studies have shown a lowered risk of nephrolithiasis associated with SGLT2i initiators compared with initiators of glucagon-like peptide 1 receptor agonist (GLP1RA) or dipeptidyl peptidase 4 inhibitors (DPP4is) (14,15). However, these findings were limited to those free of a previous history of nephrolithiasis.

Considering that SGLT2is are becoming increasingly popular and DPP4is are currently one of the major classes of the second-line glucose-lowering therapy among type 2 diabetes patients (16), assessment of the comparative risk of nephrolithiasis between SGLT2is and DPP4is would provide useful information for the physicians’ decision-making on glucose-lowering drugs. Considering a high recurrence rate of urinary stones (17), antilithogenic properties of SGLT2i would be more relevant to stone ever-formers. In this context, we conducted a target trial emulation study of an active comparator new user cohort design to compare the risk of incident nephrolithiasis among type 2 diabetes patients treated with SGLT2is versus DPP4is, within stone never- and ever-formers, respectively.

Data Source

We used a nationally representative Korea National Health Insurance Service (KNHIS) database during 2010–2021 (18). KNHIS provides universal coverage of almost all Korean citizens, containing longitudinal patient data on demographics, International Classification of Diseases Tenth Revision (ICD10) diagnosis codes, procedures, pharmacy dispensing records, and type of medical utilization (outpatient, inpatient, or emergency department). This study complies with the Declaration of Helsinki. The Institutional Review Board of the Seoul National University Bundang Hospital exempted the study protocol (X-2311-867-905), waiving informed consent on the basis of the fully deidentified database.

Study Design and Population

We emulated a target trial for the outcomes of interest (see Supplementary Table 1 for the target trial emulation framework) using a propensity score (PS)-matched active comparator new user cohort study design (see Supplementary Fig. 1 for the detailed study design).

Eligible patients were those aged ≥40 and <70 years who had ICD10 diagnosis codes for type 2 diabetes and dispensed a study drug (SGLT2i or DPP4i) (see Supplementary Table 2 for ICD10 codes used to define study population). To avoid confounding by indication associated with varied type 2 diabetes severity (19), we only included new users of a study drug, free of dispensing any of the two study drugs during a 365-day period (baseline period) before the first dispensing date of the given study drug (index date). Stone never- and ever-formers were defined as those who had never and ever experienced any urolithiasis (ICD10 codes of N20–N23 and N13.2 at any position) before the index date, based on the look-back until 2010. We excluded those who had type 1 diabetes or end-stage renal disease at baseline and those who concomitantly dispensed thiazolidinedione or GLP1RA together with the study drug on the index date, considering that these drugs are known to improve insulin resistance, potentially affecting UA stone formation (20).

Outcomes

The primary outcome was incident nephrolithiasis from kidney and ureter, defined using ICD10 diagnosis codes (N20.xx) in the primary position (see Supplementary Table 3 for detailed ICD10 and procedural codes used to define outcomes): the positive predictive value has been reported to be >90% for active nephrolithiasis, using the diagnosis codes in the first three positions (21). The secondary outcome was a stone-removing intervention combined with the codes (N20.xx) in any position within 90 days apart from the intervention.

We used osteoarthritis (OA) encounters as a negative control outcome (22). OA and nephrolithiasis share common risk factors such as obesity, cardiovascular (CV) risk status, and type 2 diabetes severity (23–25). Since OA encounters derive from already symptomatic or advanced OA, they would be minimally affected by the study medications despite mild weight reduction effect by SGLT2is (12).

Baseline Covariates

We assessed >70 variables at baseline for type 2 diabetes severity and risk factors of nephrolithiasis (see Supplementary Table 2 for ICD10 codes used to define comorbidities and Supplementary Table 4 for the full list of covariates): index year, demographics, diabetes complications (retinopathy, nephropathy, neuropathy, and diabetic foot), the number and classes of glucose-lowering drugs used, history of urolithiasis, CV diseases and cardiometabolic risk factors, chronic kidney disease, non-CV comorbidities, diuretics including thiazide, other medications for individual organ systems, and health care utilization patterns. Covariates for medications were binary, showing the percentage of patients who ever used the individual drugs listed in Supplementary Tables 4 for the baseline period, based on the pharmacy dispensing data. We calculated the Charlson comorbidity index to balance multimorbidities (26).

Statistical Analysis

In our target trial emulation study, we used PS matching to control baseline confounders. PS was estimated for each comparison, involving >70 baseline covariates listed in Supplementary Table 4. Nearest neighbor matching for SGLT2i versus DPP4i initiators was done among stone never- and ever-formers, respectively, with a 1:1 ratio and a caliper of 0.025 on the PS scale. Covariates after PS matching were considered balanced with absolute standardized differences of <0.1 between the two treatment groups (27). The PS-matched never- and ever-formers were pooled to generate the main PS-matched cohort.

For the primary as-treated analysis, follow-up started from the day after the index date and ended on the earliest censoring events among outcome occurrence, end of database, death, or treatment changes through discontinuation, switching, and adding. Study drug discontinuation occurred when no dispensing record was found within 90 days from the expected refill date, which was defined as the previous dispensing date plus number of days of supply. In case of study drug discontinuation, censoring occurred after a 30-day grace period from the last expected refill date. Switching between various SGLT2is or between various DPP4is was not a censoring event, but switching to or adding an glucose-lowering drug of a different class resulted in immediate censoring. We calculated drug adherence using a proportion of days covered, defined as the sum of days of supply divided by the total days of follow-up. The 1-year intention-to-treat (ITT) analysis was performed as a secondary analysis, where patients were followed up for either 365 days or until the earliest censoring event except for treatment change, whichever came first.

PS-matched incidence rates (IRs) per 100 person-years and 95% CIs were calculated for the outcomes. We used Cox proportional hazard models to estimate the hazard ratios (HRs) with 95% CIs. The proportional hazards assumption was tested by adding an interaction term between treatment and follow-up time in the model. With a significant interaction, we performed follow-up time-stratified analyses to look at the time-varying treatment effect. All analyses were completed using SAS 9.4 (SAS Institute) software.

Subgroup and Sensitivity Analysis

We separately examined stone never- and ever-formers. Subgroups stratified by sex, age (index age < and ≥60 years), co-use of thiazide, and baseline CV risk were also examined. We did not divide each subgroup into never- and ever-formers because of the limited sample size of the ever-formers. The high CV risk subgroup was defined as men aged ≥50 years and women aged ≥55 years, with at least one diagnosis of diabetes, angina, myocardial infarction, stroke, or peripheral vascular disease at baseline. We tested an interaction between treatment and individual stratifying factors by adding the interaction term in the Cox model.

To address the potential misclassification bias of ascertaining past/prevalent stones as incident ones among the stone ever-formers, we performed a sensitivity analysis by excluding those with stone-related claims during the 1-year baseline period from the ever-formers.

Data and Resource Availability

Patient-level data are not publicly allowed according to the data use agreement. Aggregate-level data can be requested from the corresponding author.

Baseline Characteristics of the PS-Matched Study Cohort and Drug Usage Pattern

Supplementary Figure 2 shows the study cohort selection process. We have identified 117,655 and 862,253 new users of SGLT2i and DPP4i, respectively, before PS matching (see Supplementary Table 4 for unmatched baseline characteristics). A total of 117,006 PS-matched pairs of SGLT2i and DPP4i initiators were included in the analysis (mean 62.0 years old, 54.9% males), pooled from 105,378 (90.1%) and 11,628 (9.9%) PS-matched pairs of stone never- and ever-formers, respectively (see Table 1 and Supplementary Table 5 for the select and full list of PS-matched covariates, respectively). The mean comorbidity score (SD) was higher in ever- than never-formers: 2.8 (2.0) vs. 2.4 (1.8). Most of the study participants had used one (76.4%) or two (23.4%) oral glucose-lowering drugs at baseline before they initiated the index study drug.

Table 1

Select baseline patient characteristics of PS-matched SGLT2i and DPP4i initiators

PooledNever-formersEver-formers
SGLT2i n = 117,006DPP4i n = 117,006s.d.aSGLT2i n = 105,378DPP4i n = 105,378s.d.aSGLT2i n = 11,628DPP4i n = 11,628s.d.a
Demographics          
 Age, years 62.0 ± 4.4 62.0 ± 4.5 <0.001 61.9 ± 4.4 61.9 ± 4.5 <0.001 62.3 ± 4.2 62.3 ± 4.3 <0.001 
 Male, % 55.0 54.8 0.004 54.4 54.2 0.004 60.1 59.9 0.004 
Income, %          
 Zero (medical aid) 4.4 4.5 <0.001 4.3 4.4 <0.001 4.9 5.0 0.027 
 Quartile 1 (lowest) 20.7 20.7   20.7 20.8   20.3 19.7   
 Quartile 2 20.1 20.1   20.3 20.2   19.0 18.9   
 Quartile 3 24.9 24.9   25.0 24.9   24.3 24.7   
 Quartile 4 (highest) 30.0 29.9   29.8 29.7   31.5 31.7   
Diabetes complications, %          
 Retinopathy 12.9 13.0 0.002 12.8 12.9 0.002 14.1 14.3 0.005 
 Nephropathy 9.9 10.0 <0.001 9.8 9.8 0.002 11.4 11.1 0.012 
 Neuropathy 16.2 16.3 0.001 16.1 16.1 <0.001 17.7 17.9 0.004 
 Diabetic foot 8.3 8.3 <0.001 8.2 8.2 <0.001 9.6 9.6 <0.001 
Glucose-lowering medications at baseline, %          
 Insulin 10.6 10.6 0.001 10.5 10.5 <0.001 11.5 11.7 0.006 
 Biguanide 52.2 52.2 0.001 52.0 51.9 0.002 54.2 54.4 0.004 
 Sulfonylurea 27.8 27.8 <0.001 27.9 27.9 <0.001 26.4 26.5 0.001 
 Meglitinide 0.6 0.6 0.001 0.6 0.6 <0.001 0.6 0.5 0.011 
 α-Glucosidase inhibitor 3.0 3.0 <0.001 2.9 2.9 <0.001 3.4 3.1 0.015 
 Thiazolidinedione 8.0 8.1 0.005 8.0 8.1 0.005 8.1 8.2 0.005 
 GLP1RA 0.5 0.4 0.014 0.5 0.4 0.016 0.4 0.5 0.005 
No. of oral glucose-lowering drugs at baseline          
 1 76.4 76.4 <0.001 76.4 76.3 <0.001 76.9 76.7 <0.001 
 2 23.4 23.4   23.5 23.5  23.0 23.1  
 ≥3 0.2 0.2   0.2 0.2  0.2 0.2  
CV comorbidities, %          
 Angina pectoris 16.9 17.2 0.007 16.4 16.7 0.007 21.5 22.0 0.011 
 Atrial fibrillation 3.0 3.1 0.004 3.0 3.0 0.004 3.6 3.8 0.011 
 Myocardial infarction 3.1 3.1 0.001 3.0 3.0 0.001 3.8 3.8 <0.001 
 Stroke 7.1 7.1 0.002 7.0 7.0 0.003 8.0 8.1 <0.001 
 Heart failure 7.9 8.0 0.004 7.7 7.8 0.005 10.2 10.1 0.003 
 Hypertension 67.0 66.8 0.004 66.5 66.3 0.004 71.3 71.4 0.004 
 Peripheral vascular disease. 19.8 19.9 0.003 19.5 19.6 0.003 21.7 22.2 0.011 
Other comorbidities, %          
 Chronic kidney disease 5.8 5.8 <0.001 5.6 5.6 <0.001 7.6 7.4 0.007 
 Malignancy 9.6 9.7 0.004 9.2 9.3 0.003 13.1 13.5 0.010 
 Hyperlipidemia 79.2 79.1 0.003 78.6 78.5 0.004 84.1 84.4 0.008 
 Liver disease 49.7 49.7 <0.001 49.1 49.0 0.003 55.2 56.0 0.015 
 COPD 20.6 20.8 0.005 20.3 20.5 0.006 23.2 23.1 0.003 
 Asthma 13.1 13.1 0.002 12.8 13.0 0.004 15.2 14.7 0.014 
 Alcoholism 4.6 4.6 <0.001 4.5 4.5 <0.001 5.4 5.5 0.005 
 Osteoporosis 11.8 11.8 0.001 11.7 11.7 0.001 13.0 13.1 <0.001 
 Gout 9.1 9.1 0.001 8.8 8.8 0.001 11.9 11.9 0.001 
 Urinary tract infection 13.2 13.2 <0.001 12.2 12.2 <0.001 21.8 21.9 0.002 
 Comorbidity score 2.4 ± 1.8 2.4 ± 1.9 0.006 2.4 ± 1.8 2.4 ± 1.8 0.005 2.8 ± 2.0 2.8 ± 2.0 0.009 
Gout-related medications, %          
 Any NSAIDs 50.0 50.1 0.003 49.1 49.2 0.002 57.9 58.5 0.013 
 Naproxen 2.0 2.0 0.003 1.9 2.0 0.003 2.6 2.6 0.000 
 Coxibs 10.6 10.6 <0.001 10.3 10.3 0.001 13.3 13.4 0.004 
 Opioids 10.3 10.5 0.006 10.1 10.3 0.006 12.2 12.4 0.009 
 Any steroid use 50.3 50.4 0.003 49.6 49.7 0.001 56.5 57.3 0.016 
 Xanthine oxidase inhibitor 2.4 2.4 0.002 2.3 2.3 0.001 3.8 3.9 0.002 
Other medications, %          
 ACEI or ARBs 54.8 54.7 0.002 54.4 54.3 0.002 58.1 58.3 0.005 
 β-blockers 22.1 22.2 0.003 21.7 21.8 0.003 25.3 25.7 0.009 
 Calcium channel blocker 41.2 41.1 0.004 41.0 40.8 0.003 43.9 43.5 0.008 
 Any diuretics 22.4 22.3 <0.001 22.1 22.0 0.002 24.4 25.0 0.013 
 Loop diuretics 5.6 5.6 0.003 5.3 5.4 0.003 7.7 7.8 0.003 
 Thiazide 16.0 15.9 0.003 16.0 15.8 0.004 16.2 16.6 0.011 
 Nitrate 8.7 8.8 0.005 8.4 8.6 0.005 11.1 11.2 0.005 
 Anticoagulants 5.2 5.3 0.004 5.1 5.2 0.004 6.7 6.8 0.002 
 Antiplatelets 27.7 27.6 0.002 27.5 27.4 0.003 29.9 29.8 0.002 
 Antiarrhythmic 8.4 8.6 0.005 8.2 8.3 0.003 10.5 10.9 0.013 
 Statins 61.5 61.5 <0.001 60.9 60.9 <0.001 67.2 67.4 0.005 
 Other lipid-lowering agents 18.2 18.1 0.002 17.9 17.8 0.003 21.0 21.2 0.006 
 Proton pump inhibitor 37.3 37.3 <0.001 36.4 36.4 <0.001 45.4 46.0 0.012 
 H2 blocker 43.3 43.5 0.003 42.6 42.8 0.003 49.7 50.1 0.008 
 Bisphosphonates 3.6 3.6 <0.001 3.5 3.5 <0.001 4.3 4.3 0.002 
 Calcium supplement 6.2 6.3 0.005 6.1 6.2 0.005 7.1 7.0 0.002 
Health care utilization pattern          
 Hospitalization, % 21.9 22.0 0.004 21.1 21.3 0.004 28.7 28.9 0.006 
 ER visits, % 11.1 11.1 0.002 10.4 10.4 0.002 17.2 17.5 0.007 
 ECG order, % 41.2 41.3 0.004 40.3 40.4 0.002 48.8 49.4 0.013 
 HbA1c order, % 34.4 34.5 0.002 35.0 35.0 0.001 29.6 29.8 0.004 
 Lipid/cholesterol test order, % 32.2 32.4 0.003 32.6 32.6 0.002 29.3 29.8 0.011 
 Serum creatinine test order, % 31.5 31.6 0.002 31.7 31.8 0.001 29.3 29.7 0.008 
 Uric acid test order, % 16.8 16.8 <0.001 16.7 16.7 <0.001 17.3 17.8 0.014 
PooledNever-formersEver-formers
SGLT2i n = 117,006DPP4i n = 117,006s.d.aSGLT2i n = 105,378DPP4i n = 105,378s.d.aSGLT2i n = 11,628DPP4i n = 11,628s.d.a
Demographics          
 Age, years 62.0 ± 4.4 62.0 ± 4.5 <0.001 61.9 ± 4.4 61.9 ± 4.5 <0.001 62.3 ± 4.2 62.3 ± 4.3 <0.001 
 Male, % 55.0 54.8 0.004 54.4 54.2 0.004 60.1 59.9 0.004 
Income, %          
 Zero (medical aid) 4.4 4.5 <0.001 4.3 4.4 <0.001 4.9 5.0 0.027 
 Quartile 1 (lowest) 20.7 20.7   20.7 20.8   20.3 19.7   
 Quartile 2 20.1 20.1   20.3 20.2   19.0 18.9   
 Quartile 3 24.9 24.9   25.0 24.9   24.3 24.7   
 Quartile 4 (highest) 30.0 29.9   29.8 29.7   31.5 31.7   
Diabetes complications, %          
 Retinopathy 12.9 13.0 0.002 12.8 12.9 0.002 14.1 14.3 0.005 
 Nephropathy 9.9 10.0 <0.001 9.8 9.8 0.002 11.4 11.1 0.012 
 Neuropathy 16.2 16.3 0.001 16.1 16.1 <0.001 17.7 17.9 0.004 
 Diabetic foot 8.3 8.3 <0.001 8.2 8.2 <0.001 9.6 9.6 <0.001 
Glucose-lowering medications at baseline, %          
 Insulin 10.6 10.6 0.001 10.5 10.5 <0.001 11.5 11.7 0.006 
 Biguanide 52.2 52.2 0.001 52.0 51.9 0.002 54.2 54.4 0.004 
 Sulfonylurea 27.8 27.8 <0.001 27.9 27.9 <0.001 26.4 26.5 0.001 
 Meglitinide 0.6 0.6 0.001 0.6 0.6 <0.001 0.6 0.5 0.011 
 α-Glucosidase inhibitor 3.0 3.0 <0.001 2.9 2.9 <0.001 3.4 3.1 0.015 
 Thiazolidinedione 8.0 8.1 0.005 8.0 8.1 0.005 8.1 8.2 0.005 
 GLP1RA 0.5 0.4 0.014 0.5 0.4 0.016 0.4 0.5 0.005 
No. of oral glucose-lowering drugs at baseline          
 1 76.4 76.4 <0.001 76.4 76.3 <0.001 76.9 76.7 <0.001 
 2 23.4 23.4   23.5 23.5  23.0 23.1  
 ≥3 0.2 0.2   0.2 0.2  0.2 0.2  
CV comorbidities, %          
 Angina pectoris 16.9 17.2 0.007 16.4 16.7 0.007 21.5 22.0 0.011 
 Atrial fibrillation 3.0 3.1 0.004 3.0 3.0 0.004 3.6 3.8 0.011 
 Myocardial infarction 3.1 3.1 0.001 3.0 3.0 0.001 3.8 3.8 <0.001 
 Stroke 7.1 7.1 0.002 7.0 7.0 0.003 8.0 8.1 <0.001 
 Heart failure 7.9 8.0 0.004 7.7 7.8 0.005 10.2 10.1 0.003 
 Hypertension 67.0 66.8 0.004 66.5 66.3 0.004 71.3 71.4 0.004 
 Peripheral vascular disease. 19.8 19.9 0.003 19.5 19.6 0.003 21.7 22.2 0.011 
Other comorbidities, %          
 Chronic kidney disease 5.8 5.8 <0.001 5.6 5.6 <0.001 7.6 7.4 0.007 
 Malignancy 9.6 9.7 0.004 9.2 9.3 0.003 13.1 13.5 0.010 
 Hyperlipidemia 79.2 79.1 0.003 78.6 78.5 0.004 84.1 84.4 0.008 
 Liver disease 49.7 49.7 <0.001 49.1 49.0 0.003 55.2 56.0 0.015 
 COPD 20.6 20.8 0.005 20.3 20.5 0.006 23.2 23.1 0.003 
 Asthma 13.1 13.1 0.002 12.8 13.0 0.004 15.2 14.7 0.014 
 Alcoholism 4.6 4.6 <0.001 4.5 4.5 <0.001 5.4 5.5 0.005 
 Osteoporosis 11.8 11.8 0.001 11.7 11.7 0.001 13.0 13.1 <0.001 
 Gout 9.1 9.1 0.001 8.8 8.8 0.001 11.9 11.9 0.001 
 Urinary tract infection 13.2 13.2 <0.001 12.2 12.2 <0.001 21.8 21.9 0.002 
 Comorbidity score 2.4 ± 1.8 2.4 ± 1.9 0.006 2.4 ± 1.8 2.4 ± 1.8 0.005 2.8 ± 2.0 2.8 ± 2.0 0.009 
Gout-related medications, %          
 Any NSAIDs 50.0 50.1 0.003 49.1 49.2 0.002 57.9 58.5 0.013 
 Naproxen 2.0 2.0 0.003 1.9 2.0 0.003 2.6 2.6 0.000 
 Coxibs 10.6 10.6 <0.001 10.3 10.3 0.001 13.3 13.4 0.004 
 Opioids 10.3 10.5 0.006 10.1 10.3 0.006 12.2 12.4 0.009 
 Any steroid use 50.3 50.4 0.003 49.6 49.7 0.001 56.5 57.3 0.016 
 Xanthine oxidase inhibitor 2.4 2.4 0.002 2.3 2.3 0.001 3.8 3.9 0.002 
Other medications, %          
 ACEI or ARBs 54.8 54.7 0.002 54.4 54.3 0.002 58.1 58.3 0.005 
 β-blockers 22.1 22.2 0.003 21.7 21.8 0.003 25.3 25.7 0.009 
 Calcium channel blocker 41.2 41.1 0.004 41.0 40.8 0.003 43.9 43.5 0.008 
 Any diuretics 22.4 22.3 <0.001 22.1 22.0 0.002 24.4 25.0 0.013 
 Loop diuretics 5.6 5.6 0.003 5.3 5.4 0.003 7.7 7.8 0.003 
 Thiazide 16.0 15.9 0.003 16.0 15.8 0.004 16.2 16.6 0.011 
 Nitrate 8.7 8.8 0.005 8.4 8.6 0.005 11.1 11.2 0.005 
 Anticoagulants 5.2 5.3 0.004 5.1 5.2 0.004 6.7 6.8 0.002 
 Antiplatelets 27.7 27.6 0.002 27.5 27.4 0.003 29.9 29.8 0.002 
 Antiarrhythmic 8.4 8.6 0.005 8.2 8.3 0.003 10.5 10.9 0.013 
 Statins 61.5 61.5 <0.001 60.9 60.9 <0.001 67.2 67.4 0.005 
 Other lipid-lowering agents 18.2 18.1 0.002 17.9 17.8 0.003 21.0 21.2 0.006 
 Proton pump inhibitor 37.3 37.3 <0.001 36.4 36.4 <0.001 45.4 46.0 0.012 
 H2 blocker 43.3 43.5 0.003 42.6 42.8 0.003 49.7 50.1 0.008 
 Bisphosphonates 3.6 3.6 <0.001 3.5 3.5 <0.001 4.3 4.3 0.002 
 Calcium supplement 6.2 6.3 0.005 6.1 6.2 0.005 7.1 7.0 0.002 
Health care utilization pattern          
 Hospitalization, % 21.9 22.0 0.004 21.1 21.3 0.004 28.7 28.9 0.006 
 ER visits, % 11.1 11.1 0.002 10.4 10.4 0.002 17.2 17.5 0.007 
 ECG order, % 41.2 41.3 0.004 40.3 40.4 0.002 48.8 49.4 0.013 
 HbA1c order, % 34.4 34.5 0.002 35.0 35.0 0.001 29.6 29.8 0.004 
 Lipid/cholesterol test order, % 32.2 32.4 0.003 32.6 32.6 0.002 29.3 29.8 0.011 
 Serum creatinine test order, % 31.5 31.6 0.002 31.7 31.8 0.001 29.3 29.7 0.008 
 Uric acid test order, % 16.8 16.8 <0.001 16.7 16.7 <0.001 17.3 17.8 0.014 

Data are presented as percent for binary variables and mean ± SD for continuous variables. ARB, angiotensin receptor blocker; COPD, chronic obstructive pulmonary disease; COXIBS, cyclo-oxygenase-2 inhibitors; ECG, electrocardiogram; ER, emergency room; HbA1c, hemoglobin A1c; NSAID, nonsteroidal anti-inflammatory drug.

as.d., standardized differenced.

The most common PS-matched index drugs were dapagliflozin (58.6%) and empagliflozin (35.5%) for SGLT2is, and linagliptin (22.6%), gemigliptin (22.5%), and sitagliptin (20.3%) for DPP4is (Supplementary Table 6). The mean proportion of days covered (SD) was 0.97 (0.08) and 0.96 (0.08) for SGLT2i and DPP4i initiators, respectively.

Risk of Nephrolithiasis Associated With SGLT2i Versus DPP4i

During a mean (SD) follow-up of 654 (642) days (median 423, interquartile range 123–1,014), a total of 3,783 nephrolithiasis events were identified. The IR (95% CI) per 100 person-years was 0.65 (0.62–0.69) among SGLT2i initiators and 1.12 (1.07–1.16) among DPP4i initiators, with an HR (95% CI) of 0.54 (0.50–0.57) and an IR difference (IRD) (95% CI) of −0.46 (−0.41 to −0.52) (Table 2). The HR (95% CI) for the stone-removing interventions was 0.41 (0.37–0.45), and the IRD (95% CI) was −0.30 (−0.26 to −0.34). We found a near-null association between treatment and a negative control outcome (HR 0.98, 95% CI 0.97–1.00). The 1-year ITT analyses showed similar results.

Table 2

Risk of nephrolithiasis associated with PS-matched SGLT2i versus DPP4i initiators

SGLT2iDPP4i (ref)PS-matched
HR (95% CI)
IRD
(95% CI)
EventsIR* (95% CI)EventsIR* (95% CI)
Pooled analysis (n = 117,006 PS-matched pairs)    
 AT Urinary stones 1,254 0.65 (0.62–0.69) 2,529 1.12 (1.07–1.16) 0.54 (0.50–0.57) −0.46 (−0.41 to −0.52) 
 Interventions 462 0.24 (0.22–0.26) 1,238 0.54 (0.51–0.57) 0.41 (0.37–0.45) −0.30 (−0.26 to −0.34) 
 OA encounters 26,556 17.8 (17.6–18.0) 29,979 17.5 (17.3–17.7) 0.98 (0.97–1.00)  
 1-year ITT Urinary stones 903 0.97 (0.91–1.04) 1,482 1.49 (1.41–1.56) 0.64 (0.60–0.68) −0.51 (−0.41 to −0.61) 
 Interventions 316 0.34 (0.30–0.38) 666 0.67 (0.61–0.72) 0.49 (0.44–0.54) −0.33 (−0.26 to −0.39) 
 OA encounters 20,216 24.8 (24.4–25.1) 21,767 24.9 (24.6–25.2) 0.98 (0.97–1.00)  
Never-formers (105,378 PS-matched pairs) 
 AT Urinary stones 554 0.31 (0.29–0.34) 1,315 0.63 (0.60–0.67) 0.43 (0.39–0.48) −0.32 (−0.27 to −0.36) 
 Interventions 206 0.12 (0.10–0.13) 649 0.31 (0.29–0.33) 0.33 (0.28–0.39) −0.19 (−0.16 to −0.22) 
 OA encounters 23,724 17.4 (17.2–17.6) 26,869 17.2 (17.0–17.4) 0.98 (0.96–0.99)  
 1-year ITT Urinary stones 334 0.40 (0.36–0.44) 642 0.71 (0.65–0.76) 0.55 (0.50–0.62) −0.31 (−0.24 to −0.38) 
 Interventions 123 0.15 (0.12–0.17) 315 0.35 (0.31–0.39) 0.41 (0.35–0.49) −0.20 (−0.15 to −0.25) 
 OA encounters 17,940 24.3 (23.9–24.6) 19,374 24.5 (24.1–24.8) 0.98 (0.96–0.99)  
Ever-formers (11,628 PS-matched pairs) 
 AT Urinary stones 700 4.28 (3.96–4.60) 1,214 6.54 (6.18–6.91) 0.64 (0.59–0.69) −2.26 (−1.77 to −2.76) 
 Interventions 256 1.50 (1.32–1.68) 589 2.98 (2.74–3.22) 0.49 (0.43–0.57) −1.48 (−1.17 to −1.79) 
 OA encounters 2,832 21.5 (20.7–22.3) 3,110 20.3 (19.6–21.0) 1.02 (0.97–1.07)  
 1-year ITT Urinary stones 569 6.54 (6.00–7.08) 840 9.15 (8.53–9.77) 0.70 (0.65–0.76) −2.61 (−1.79 to −3.43) 
 Interventions 193 2.17 (1.86–2.47) 351 3.70 (3.32–4.09) 0.56 (0.49–0.64) −1.54 (−1.04 to −2.04) 
 OA encounters 2,276 29.3 (28.1–30.5) 2,393 28.9 (27.8–30.1) 1.02 (0.97–1.06)  
SGLT2iDPP4i (ref)PS-matched
HR (95% CI)
IRD
(95% CI)
EventsIR* (95% CI)EventsIR* (95% CI)
Pooled analysis (n = 117,006 PS-matched pairs)    
 AT Urinary stones 1,254 0.65 (0.62–0.69) 2,529 1.12 (1.07–1.16) 0.54 (0.50–0.57) −0.46 (−0.41 to −0.52) 
 Interventions 462 0.24 (0.22–0.26) 1,238 0.54 (0.51–0.57) 0.41 (0.37–0.45) −0.30 (−0.26 to −0.34) 
 OA encounters 26,556 17.8 (17.6–18.0) 29,979 17.5 (17.3–17.7) 0.98 (0.97–1.00)  
 1-year ITT Urinary stones 903 0.97 (0.91–1.04) 1,482 1.49 (1.41–1.56) 0.64 (0.60–0.68) −0.51 (−0.41 to −0.61) 
 Interventions 316 0.34 (0.30–0.38) 666 0.67 (0.61–0.72) 0.49 (0.44–0.54) −0.33 (−0.26 to −0.39) 
 OA encounters 20,216 24.8 (24.4–25.1) 21,767 24.9 (24.6–25.2) 0.98 (0.97–1.00)  
Never-formers (105,378 PS-matched pairs) 
 AT Urinary stones 554 0.31 (0.29–0.34) 1,315 0.63 (0.60–0.67) 0.43 (0.39–0.48) −0.32 (−0.27 to −0.36) 
 Interventions 206 0.12 (0.10–0.13) 649 0.31 (0.29–0.33) 0.33 (0.28–0.39) −0.19 (−0.16 to −0.22) 
 OA encounters 23,724 17.4 (17.2–17.6) 26,869 17.2 (17.0–17.4) 0.98 (0.96–0.99)  
 1-year ITT Urinary stones 334 0.40 (0.36–0.44) 642 0.71 (0.65–0.76) 0.55 (0.50–0.62) −0.31 (−0.24 to −0.38) 
 Interventions 123 0.15 (0.12–0.17) 315 0.35 (0.31–0.39) 0.41 (0.35–0.49) −0.20 (−0.15 to −0.25) 
 OA encounters 17,940 24.3 (23.9–24.6) 19,374 24.5 (24.1–24.8) 0.98 (0.96–0.99)  
Ever-formers (11,628 PS-matched pairs) 
 AT Urinary stones 700 4.28 (3.96–4.60) 1,214 6.54 (6.18–6.91) 0.64 (0.59–0.69) −2.26 (−1.77 to −2.76) 
 Interventions 256 1.50 (1.32–1.68) 589 2.98 (2.74–3.22) 0.49 (0.43–0.57) −1.48 (−1.17 to −1.79) 
 OA encounters 2,832 21.5 (20.7–22.3) 3,110 20.3 (19.6–21.0) 1.02 (0.97–1.07)  
 1-year ITT Urinary stones 569 6.54 (6.00–7.08) 840 9.15 (8.53–9.77) 0.70 (0.65–0.76) −2.61 (−1.79 to −3.43) 
 Interventions 193 2.17 (1.86–2.47) 351 3.70 (3.32–4.09) 0.56 (0.49–0.64) −1.54 (−1.04 to −2.04) 
 OA encounters 2,276 29.3 (28.1–30.5) 2,393 28.9 (27.8–30.1) 1.02 (0.97–1.06)  

*Per 100 person-years. AT, as-treated.

The Kaplan-Meier curves diverged early and separated more in the later follow-up (Fig. 1), indicating greater effects with longer treatment. Consistent with this, there was a significant interaction between treatment and follow-up time (P < 0.05 for both nephrolithiasis and intervention). The follow-up time-stratified analyses (50,722 and 16,690 PS-matched pairs treated for ≤2 years and >2 years, respectively) (see Supplementary Table 7 for their baseline covariate distribution) showed a greater magnitude of association for >2 years vs. ≤2 years of treatment: HR (95% CI) of 0.37 (0.30–0.46) vs. 0.50 (0.45–0.55) for the nephrolithiasis; 0.32 (0.23–0.44) vs. 0.35 (0.30–0.42) for the interventions (Supplementary Table 8).

Figure 1

Kaplan-Meier curves for stone-free survival comparing PS-matched SGLT2i and DPP4i initiators.

Figure 1

Kaplan-Meier curves for stone-free survival comparing PS-matched SGLT2i and DPP4i initiators.

Close modal

Risk of Nephrolithiasis Associated With SGLT2i Versus DPP4i in Stone Never- and Ever-Formers

Among the stone never-formers, 105,378 PS-matched pairs were followed up for a mean (SD) of 666 (649) days (median 435, interquartile range 126–1,033), generating 1,869 new cases of nephrolithiasis. The IR (95% CI) per 100 person-years was 0.31 (0.29–0.34) in SGLT2i initiators and 0.63 (0.60–0.67) in DPP4i initiators, with an HR (95% CI) of 0.43 (0.39–0.48). The HR (95% CI) for the intervention was 0.33 (0.28–0.39). Among the stone ever-formers, 11,628 PS-matched pairs were followed up for a mean (SD) of 548 (569) days (median 333, interquartile range 103–834), generating 1,914 cases of nephrolithiasis. The IR (95% CI) was 4.28 (3.96–4.59) in SGLT2i and 6.54 (6.18–6.91) in DPP4i initiators, indicating a high recurrence rate. The HR (95% CI) was 0.64 (0.59–0.69) for nephrolithiasis and 0.49 (0.43–0.57) for the intervention (Fig. 1and Table 2).

A significant interaction (P < 0.05) between previous stone history and treatment indicated a greater HR reduction among never-formers. However, the IRD of nephrolithiasis was greater among the ever-formers (−2.26, 95% CI −1.77 to −2.76) than never-formers (−0.32, 95% CI −0.27 to −0.36) because of the high IR of stones in the former (Table 2). The negative control outcome showed near-null associations for both stone never- and ever-formers.

Subgroup and Sensitivity Analyses

The results were consistent across subgroups by sex, age, thiazide use, and CV risk (Fig. 2and Supplementary Tables 9 and 10). There was no significant interaction between treatment and these stratifying factors. In our sensitivity analysis on the stone ever-formers free of stone-related claims for the 1-year baseline period (see Supplementary Table 11 for the PS-matched baseline covariate distribution), the IR (95% CI) of nephrolithiasis was 2.74 (2.41–3.07) in SGLT2i initiators and 4.63 (4.23–5.04) in DPP4i initiators, with an HR (95% CI) of 0.58 (0.50–0.67) and IRD (95% CI) of −1.89 (−1.37 to −2.42) (Supplementary Table 12). The HR (95% CI) for stone-removing interventions was 0.49 (0.40–0.61), with an IRD of −1.29 (−0.93 to −1.66).

Figure 2

Risk of nephrolithiasis between initiators of SGLT2i and DPP4i in PS-matched subgroups. A: Nephrolithiasis. B: Stone-removing interventions.

Figure 2

Risk of nephrolithiasis between initiators of SGLT2i and DPP4i in PS-matched subgroups. A: Nephrolithiasis. B: Stone-removing interventions.

Close modal

In this large population-based cohort study on type 2 diabetes patients, we found a 46% lower risk of nephrolithiasis associated with SGLT2i initiation versus DPP4i: 57% and 36% risk reduction among the stone never- and ever-formers, respectively. Despite a greater relative risk reduction among the former group, absolute IR decrease was greater in the latter. Our study findings were consistent with the secondary outcome and across various subgroups, and robust enough to persist with ITT approaches. Near-null associations from a negative control outcome also add internal validity of the study findings. Our follow-up time-stratified analysis suggested a trend of greater benefits with longer treatment.

Our results are in line with a recent meta-analysis pooled from 27 randomized controlled trials on SGLT2is that showed a 36% reduced IR of nephrolithiasis compared with placebo (28), and also with a recent U.S. study that showed a 26% risk reduction compared with DPP4i (15). The IR of nephrolithiasis in the U.S. study was approximately two times that of our study. This could be because of a generally higher stone risk among the U.S. than Asian population (29), or because of the diagnoses codes in any position used in the U.S study compared with the primary position in our study.

We observed a far higher IR of nephrolithiasis in ever- compared with never-formers, due to a high recurrence rate among ever-formers (17). For our primary outcome (incident nephrolithiasis) defined by the diagnosis codes, past/prevalent stones may have been misclassified as incident stones among the ever-formers, resulting in overestimated IR. To address this, we performed a sensitivity analysis regarding the ever-formers free of stone-related claims for at least a 1-year baseline period. For these patients, we still observed a considerably higher IR of nephrolithiasis and a greater IRD than for never-formers. We also observed a far higher IR and IRD of the secondary outcome (stone-removing interventions) in ever- than never-formers; this outcome is expected to have a high specificity.

The mechanisms underlying stone prevention by SGLT2is have been considered to encompass increased urinary flow, higher citrate excretion, and reduced inflammation (28,30,31). SGLT2is cause proximal natriuresis, particularly at the initiation of treatment, which is considered transient following distal tubular and/or neurohormonal counterregulation (32,33). However, glycosuria and thereby osmotic diuresis by SGLT2i persists even with blood glucose concentrations in the normal range (34,35). Also, enhanced renal citrate excretion and decreased urine pH (but no change in oxalate excretion) has been found in a post hoc analysis on type 2 diabetes patients treated with empagliflozin versus placebo, together with an observation of reduced supersaturation ratios of calcium phosphate minerals (30). In an ethylene glycol–treated rate model, SGLT2i treatment ameliorated calcium oxalate stone formation by reducing inflammation (31).

Although the net effect of SGLT2is decreased the risk of nephrolithiasis compared with DPP4is, the antilithogenic effect of SGLT2is can be differential, depending on the stone composition. For example, increased UA excretion and lowered urine pH among those treated with SGLT2is might encourage UA stone formation, while renal citrate excretion and lowered urine pH have been suggested to lower calcium phosphate stone formation (30). Moreover, increased urinary flow rate will be preventive against both UA and calcium stones.

There was a greater absolute IR decrease among the stone ever- than never-formers, implying a greater public health impact among the ever-formers. Symptomatic recurrence rate in the first nephrolithiasis formers was reported as high as 20% at 5 years (17). Considering an increased risk of nephrolithiasis in patients with type 2 diabetes and the heavy economic burden associated with it (1,4,5), preventive efforts are particularly required for the stone ever-formers. In this context, our study provides clinically relevant information on the comparative risk of nephrolithiasis between different glucose-lowering drugs.

This study has the following strengths. First, we used rigorous pharmacoepidemiologic methods to control confounding. The active comparator new user design is a powerful approach that effectively copes with both measured and unmeasured confounding (19). Also, we adjusted for >70 potential baseline confounders by extensive PS matching, and obtained near-null associations for the negative control outcome. Second, we separately examined stone never- and ever-formers. Third, we used a nationally representative database, providing high generalizability. Lastly, we examined various subgroups stratified by relevant clinical features, and observed consistent results.

There are also limitations. As an observational study, our study is inevitably susceptible to residual or unmeasured confounding. Although we achieved good balance for many proxies for type 2 diabetes severity and other comorbidities, we could not directly measure renal function or blood glucose levels, leaving concerns for residual confounding. Second, despite consistent results across different outcome definitions and subgroups, there could have been outcome misclassification bias, particularly among the stone ever-formers. Third, we did not perform dose-respond relationship. However, significant interaction between treatment and follow-up time together with the results from the follow-up time stratified analyses suggest cumulative drug effect. Lastly, this study does not provide the mechanism of the association, thus, there is uncertainty regarding whether the benefits are similar between calcium and UA stones. Also, we did not include a nonuser comparator group analogous to the placebo group. However, it has been noted that 12-week DPP4 inhibition did not affect glomerular filtration rate, urinary flow rate, or tubular functions including urine pH, which makes it plausible that the stone IR among the DPP4i initiators would be similar to the placebo group (36,37).

In conclusion, this large population-based cohort study found that SGLT2i initiation compared with DPP4i was associated with reduced risk of nephrolithiasis and related interventions in both stone never- and ever-formers with type 2 diabetes. Despite a greater relative risk reduction among the former, absolute IR reduction was greater in the latter. The lowered risk of stone development was consistent across subgroups stratified by sex, age, concurrent use of thiazide, and baseline CV risk status.

This article contains supplementary material online at https://doi.org/10.2337/figshare.27890151.

This article is featured in a podcast available at diabetesjournals.org/journals/pages/diabetes-core-update-podcasts.

Funding. This study was supported by a grant from the Korea Health Industry Development Institute-AZ Diabetes Research Program (#08-2022-0261) and by a research grant from Seoul National University Bundang Hospital (#21-2024-0006).

The study was designed and drafted independently of the funding source, and the authors retained the right for the final wording.

Duality of Interest. J.-Y.S. has received grants from the Ministry of Food and Drug Safety, the National Research Foundation of Korea, and pharmaceutical companies, including Pfizer, Celltrion, and SK Bioscience for unrelated studies. E.H.K. has received grants from Celltrion to Seoul National University Bundang Hospital for unrelated studies. No other potential conflicts of interest relevant to this article were reported.

Author Contributions. E.H.K. was involved in the study concept and design and writing a first draft of the manuscript. J.-Y.S. reviewed and edited the study protocol. A.S. conducted the study. All authors were involved in the analysis and interpretation of the results, reviewed and edited the manuscript, and approved the final version of the manuscript. E.H.K. and A.S. are guarantors of this work and, as such, had full access to all the data in the study and take the responsibility for the integrity of the data and the accuracy of the data analysis.

Handling Editors. The journal editors responsible for overseeing the review of the manuscript were Cheryl A.M. Anderson and Csaba P. Kovesdy.

1.
Thongprayoon
C
,
Krambeck
AE
,
Rule
AD
.
Determining the true burden of kidney stone disease
.
Nat Rev Nephrol
2020
;
16
:
736
746
2.
Abufaraj
M
,
Xu
T
,
Cao
C
, et al
.
Prevalence and trends in kidney stone among adults in the USA: analyses of National Health and Nutrition Examination Survey 2007-2018 data
.
Eur Urol Focus
2021
;
7
:
1468
1475
3.
Antonelli
JA
,
Maalouf
NM
,
Pearle
MS
,
Lotan
Y
.
Use of the National Health and Nutrition Examination Survey to calculate the impact of obesity and diabetes on cost and prevalence of urolithiasis in 2030
.
Eur Urol
2014
;
66
:
724
729
4.
Taylor
EN
,
Stampfer
MJ
,
Curhan
GC
.
Diabetes mellitus and the risk of nephrolithiasis
.
Kidney Int
2005
;
68
:
1230
1235
5.
Daudon
M
,
Traxer
O
,
Conort
P
,
Lacour
B
,
Jungers
P
.
Type 2 diabetes increases the risk for uric acid stones
.
J Am Soc Nephrol
2006
;
17
:
2026
2033
6.
Sakhaee
K
,
Adams-Huet
B
,
Moe
OW
,
Pak
CYC
.
Pathophysiologic basis for normouricosuric uric acid nephrolithiasis
.
Kidney Int
2002
;
62
:
971
979
7.
Kohjimoto
Y
,
Sasaki
Y
,
Iguchi
M
,
Matsumura
N
,
Inagaki
T
,
Hara
I
.
Association of metabolic syndrome traits and severity of kidney stones: results from a nationwide survey on urolithiasis in Japan
.
Am J Kidney Dis
2013
;
61
:
923
929
8.
Taylor
EN
,
Curhan
GC
.
Determinants of 24-hour urinary oxalate excretion
.
Clin J Am Soc Nephrol
2008
;
3
:
1453
1460
9.
Tran
TY
,
Flynn
M
,
O’Bell
J
,
Pareek
G
.
Calculated insulin resistance correlates with stone-forming urinary metabolic changes and greater stone burden in high-risk stone patients
.
Clin Nephrol
2016
;
85
:
316
320
10.
Sakhaee
K
,
Capolongo
G
,
Maalouf
NM
, et al
.
Metabolic syndrome and the risk of calcium stones
.
Nephrol Dial Transplant
2012
;
27
:
3201
3209
11.
Shavit
L
,
Ferraro
PM
,
Johri
N
, et al
.
Effect of being overweight on urinary metabolic risk factors for kidney stone formation
.
Nephrol Dial Transplant
2015
;
30
:
607
613
12.
Brown
E
,
Heerspink
HJL
,
Cuthbertson
DJ
,
Wilding
JPH
.
SGLT2 inhibitors and GLP-1 receptor agonists: established and emerging indications
.
Lancet
2021
;
398
:
262
276
13.
Borghi
L
,
Meschi
T
,
Amato
F
,
Briganti
A
,
Novarini
A
,
Giannini
A
.
Urinary volume, water and recurrences in idiopathic calcium nephrolithiasis: a 5-year randomized prospective study
.
J Urol
1996
;
155
:
839
843
14.
Kristensen
KB
,
Henriksen
DP
,
Hallas
J
,
Pottegård
A
,
Lund
LC
.
Sodium-glucose cotransporter 2 inhibitors and risk of nephrolithiasis
.
Diabetologia
2021
;
64
:
1563
1571
15.
Paik
JM
,
Tesfaye
H
,
Curhan
GC
,
Zakoul
H
,
Wexler
DJ
,
Patorno
E
.
Sodium-glucose cotransporter 2 inhibitors and nephrolithiasis risk in patients with type 2 diabetes
.
JAMA Intern Med
2024
;
184
:
265
274
16.
Abrahami
D
,
D’Andrea
E
,
Yin
H
, et al
.
Contemporary trends in the utilization of second-line pharmacological therapies for type 2 diabetes in the United States and the United Kingdom
.
Diabetes Obes Metab
2023
;
25
:
2980
2988
17.
Rule
AD
,
Lieske
JC
,
Li
X
,
Melton
LJ
,
Krambeck
AE
,
Bergstralh
EJ
.
The ROKS nomogram for predicting a second symptomatic stone episode
.
J Am Soc Nephrol
2014
;
25
:
2878
2886
18.
Cheol Seong
S
,
Kim
Y-Y
,
Khang
Y-H
, et al
.
Data resource profile: the National Health Information Database of the National Health Insurance Service in South Korea
.
Int J Epidemiol
2017
;
46
:
799
800
19.
Yoshida
K
,
Solomon
DH
,
Kim
SC
.
Active-comparator design and new-user design in observational studies
.
Nat Rev Rheumatol
2015
;
11
:
437
441
20.
Abdul-Ghani
M
,
Maffei
P
,
DeFronzo
RA
.
Managing insulin resistance: the forgotten pathophysiological component of type 2 diabetes
.
Lancet Diabetes Endocrinol
2024
;
12
:
674
680
21.
Semins
MJ
,
Trock
BJ
,
Matlaga
BR
.
Validity of administrative coding in identifying patients with upper urinary tract calculi
.
J Urol
2010
;
184
:
190
192
22.
Lipsitch
M
,
Tchetgen Tchetgen
E
,
Cohen
T
.
Negative controls: a tool for detecting confounding and bias in observational studies
.
Epidemiology
2010
;
21
:
383
388
23.
Rahman
MM
,
Kopec
JA
,
Anis
AH
,
Cibere
J
,
Goldsmith
CH
.
Risk of cardiovascular disease in patients with osteoarthritis: a prospective longitudinal study
.
Arthritis Care Res (Hoboken)
2013
;
65
:
1951
1958
24.
Muschialli
L
,
Mannath
A
,
Moochhala
SH
,
Shroff
R
,
Ferraro
PM
.
Epidemiological and biological associations between cardiovascular disease and kidney stone formation: a systematic review and meta-analysis
.
Nutr Metab Cardiovasc Dis
2024
;
34
:
559
568
25.
Schett
G
,
Kleyer
A
,
Perricone
C
, et al
.
Diabetes is an independent predictor for severe osteoarthritis: results from a longitudinal cohort study
.
Diabetes Care
2013
;
36
:
403
409
26.
Sundararajan
V
,
Henderson
T
,
Perry
C
,
Muggivan
A
,
Quan
H
,
Ghali
WA
.
New ICD-10 version of the Charlson comorbidity index predicted in-hospital mortality
.
J Clin Epidemiol
2004
;
57
:
1288
1294
27.
Austin
PC
.
Using the standardized difference to compare the prevalence of a binary variable between two groups in observational research
.
Commun Stat-Simul C
2009
;
38
:
1228
1234
28.
Balasubramanian
P
,
Wanner
C
,
Ferreira
JP
, et al
.
Empagliflozin and decreased risk of nephrolithiasis: a potential new role for SGLT2 inhibition?
J Clin Endocrinol Metab
2022
;
107
:
e3003
e3007
29.
Sorokin
I
,
Mamoulakis
C
,
Miyazawa
K
,
Rodgers
A
,
Talati
J
,
Lotan
Y
.
Epidemiology of stone disease across the world
.
World J Urol
2017
;
35
:
1301
1320
30.
Harmacek
D
,
Pruijm
M
,
Burnier
M
, et al
.
Empagliflozin changes urine supersaturation by decreasing pH and increasing citrate
.
J Am Soc Nephrol
2022
;
33
:
1073
1075
31.
Anan
G
,
Hirose
T
,
Kikuchi
D
, et al
.
Inhibition of sodium-glucose cotransporter 2 suppresses renal stone formation
.
Pharmacol Res
2022
;
186
:
106524
32.
Rao
VS
,
Ivey-Miranda
JB
,
Cox
ZL
, et al
.
Empagliflozin in heart failure: regional nephron sodium handling effects
.
J Am Soc Nephrol
2024
;
35
:
189
201
33.
Zanchi
A
,
Burnier
M
,
Muller
ME
, et al
.
Acute and chronic effects of SGLT2 inhibitor empagliflozin on renal oxygenation and blood pressure control in nondiabetic normotensive subjects: a randomized, placebo-controlled trial
.
J Am Heart Assoc
2020
;
9
:
e016173
34.
Komoroski
B
,
Vachharajani
N
,
Boulton
D
, et al
.
Dapagliflozin, a novel SGLT2 inhibitor, induces dose-dependent glucosuria in healthy subjects
.
Clin Pharmacol Ther
2009
;
85
:
520
526
35.
Cianciolo
G
,
De Pascalis
A
,
Capelli
I
, et al
.
Mineral and electrolyte disorders with SGLT2i therapy
.
JBMR Plus
2019
;
3
:
e10242
36.
Tonneijck
L
,
Smits
MM
,
Muskiet
MHA
, et al
.
Erratum. Renal effects of DPP-4 inhibitor sitagliptin or GLP-1 receptor agonist liraglutide in overweight patients with type 2 diabetes: a 12-week, randomized, double-blind, placebo-controlled trial [Diabetes Care 2016;39:2042–2050]
.
Diabetes Care
2019
;
42
:
494
450
37.
Muskiet
MHA
,
Tonneijck
L
,
Smits
MM
, et al
.
Effects of DPP-4 inhibitor linagliptin versus sulfonylurea glimepiride as add-on to metformin on renal physiology in overweight patients with type 2 diabetes (RENALIS): a randomized, double-blind trial
.
Diabetes Care
2020
;
43
:
2889
2893
Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at https://www.diabetesjournals.org/journals/pages/license.