What this is
- This nationwide cohort study evaluates the risk of new-onset atrial fibrillation (AF) in patients with type 2 diabetes using different glucose-lowering medications.
- Specifically, it compares sodium-glucose cotransporter-2 inhibitors (SGLT2i), glucagon-like peptide-1 receptor agonists (GLP-1RA), and dipeptidyl peptidase-4 inhibitors (DPP4i).
- The study analyzes data from over 344,000 patients, aiming to clarify conflicting results from previous studies regarding AF risk.
Essence
- SGLT2i treatment is associated with a lower risk of new-onset atrial fibrillation compared to DPP4i and GLP-1RA in type 2 diabetes patients. No significant difference in AF risk was found between GLP-1RA and DPP4i users.
Key takeaways
- SGLT2i users had a lower risk of new-onset AF compared to DPP4i users, with a hazard ratio (HR) of 0.90. This indicates a protective effect of SGLT2i against AF development.
- SGLT2i treatment also showed a lower risk of new-onset AF compared to GLP-1RA, with an HR of 0.74. This suggests SGLT2i may be a preferable option for diabetes management concerning AF risk.
- No difference in AF risk was observed between GLP-1RA and DPP4i users, with an HR of 1.01, indicating similar safety profiles for these medications regarding AF incidence.
Caveats
- This study is retrospective and observational, which may introduce biases and residual confounding despite propensity score matching.
- The Taiwan National Health Insurance Database does not include critical laboratory data, potentially affecting the analysis of AF risk factors.
- Findings are based on an Asian population, limiting the generalizability of results to other ethnic groups.
AI simplified
Background
Type 2 diabetes mellitus and atrial fibrillation (AF) represent common chronic clinical disease burdens and the ever-aging population globally [1]. Furthermore, diabetes is closely related to AF, and their coexistence is associated with substantial risks of adverse cardiovascular events, hospitalization for heart failure, morbidity, and mortality [1–4]. Diabetes itself also has a causal relationship with AF, being an independent risk factor for the development of incident AF [5, 6]. The underlying pathogenic mechanisms of the development of AF in diabetes include electrical-electromechanical, structural and autonomic remodeling, forming the substrate for AF development and maintenance [1]. Insulin resistance or obesity, even without overt diabetes (e.g., glycated hemoglobin levels < 6.5%), is associated with a proinflammatory state, atrial dilatation, pericardial fat accumulation, and autonomic dysfunction are all known to increase the risk of AF [1, 6, 7]. Furthermore, ischemic cerebrovascular and cardiovascular disease and heart failure (HF) can increase the risk for the development of AF in several ways [8, 9]. Therefore, glucose-lowering agents that lead to loss of body weight, improvement of insulin resistance, and cardiovascular outcomes may be associated with decreased risk of incident AF. Clinical studies have reported that both sodium-glucose cotransporter-2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1RA) reduce body weight and risks of major adverse cardiovascular events (MACE) and/or hospitalization for HF in patients with type 2 diabetes [10–18]. On the other hand, dipeptidyl peptidase-4 inhibitors (DPP4i) are generally safe and well-tolerated glucose-lowering agents that have been associated with a significant reduction in blood pressure and do not increase body weight [18–21]. Although several animal and pre-clinical studies demonstrated a cardioprotective effect of DPP4i [22, 23], these have not been clearly translated into clinically significant results in landmark cardiovascular outcome trials [19]. Also, whether these drugs are associated with a lower risk of AF remains unclear. Recent meta-analyses comparing the risk of incident atrial fibrillation (AF) between users of SGLT2i, GLP1-RA, and placebo (DPP4i and other anti-hypoglycemic agents) have shown conflicting results [24–29]. Some analyses have indicated that DPP4i treatment was associated with a higher risk for atrial arrhythmias [29, 30]. To date, a direct comparison between SGLT2i, GLP-1RA, and DPP4i regarding the risk of incident AF has not been conducted. We, therefore, aimed to investigate the risk of new-onset incident AF between DPP4i, GLP-1RA, and SGLT2i users using direct head-to-head comparisons, specifically focused on the Asian population with type 2 diabetes, in a large real-world setting.
Methods
Database
We performed a nationwide retrospective cohort study using the Taiwan National Health Insurance Research Database (NHIRD), which contains detailed health-care information for more than 23 million enrollees with a > 99% coverage rate of residents of Taiwan [31]. Informed consent was waived in the present study because the original identification number of each patient in the NHIRD had been encrypted and de-identified to protect their privacy. The study was approved by the Institutional Review Board of the Chang Gung Medical Foundation (201801427B0, and 201802075B0). The interpretation and conclusions contained herein do not represent the position of Chang Gung Memorial Hospital or the National Health Insurance Administration in Taiwan.
Study cohort
Enrollment of patients with type 2 diabetes (T2D) treated with sodium-glucose cotransporter 2 inhibitors (SGLT2i), glucagon-like peptide-1 receptor agonist (GLP-1RA), and dipeptidyl peptidase-4 inhibitors (DPP4i). A total of 344,893, 44,370, and 393,100 patients with T2D without pre-existing atrial fibrillation (AF) treated with SGLT2i, GLP-1RA, and DPP4i from May 1, 2016 to December 31, 2019, respectively, were enrolled in the present study. There were 245,442, 43,682, and 39,190 paired cohorts of SGLT2i versus DPP4i, SGLT2i versus GLP-1RA, and GLP-1RA versus DPP4i, respectively, after propensity score matching (PSM)
Covariates
Baseline covariates were obtained from all claim records with diagnoses, procedures, or medication codes before the index date. A history of all prescription medications was confined to medications used at least once within 3 months before the index date. The ICD-10-CM codes used to identify baseline covariates are summarized in Additional file: Tables S1. 1
Outcomes
The main study outcome was new-onset AF (ICD-10-CM code I48) diagnosed in accordance with one inpatient or at least two outpatient diagnostic codes assigned after the drug index date. Patients were followed from the drug-index date until the development of incident AF, discontinuation of index drug, mortality, or the end of the study period (December 31, 2020), whichever occurred first.
Statistical analysis
| Baseline characteristics | Before PSM | After PSM | After PSM | After PSM | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SGLT2i (n = 344,893) | GLP-1RA (n = 44,370) | DPP4i (n = 393,100) | Max ASMD | SGLT2i (n = 245,442) | DPP4i (n = 245,442) | ASMD | SGLT2i (n = 43,682) | GLP-1RA (n = 43,682) | ASMD | GLP-1RA (n = 39,190) | DPP4i (n = 39,190) | ASMD | |
| Age (mean ± SD) | 58.6 ± 12.1 | 55.3 ± 13.2 | 63.0 ± 13.2 | 59.0 (12.8) | 60.3 (11.7) | 56.8 (11.9) | 55.2 (13.2) | 55.4 (13.5) | 58.6 (11.3) | ||||
| < 65 y/o | 233,520 (67.71) | 32,834 (74.00) | 214,564 (54.58) | 0.447 | 157,801 (64.29) | 159,193 (64.86) | 0.035 | 33,160 (75.91) | 32,434 (74.25) | 0.088 | 28,492 (72.70) | 29,092 (74.23) | 0.094 |
| 65–74 y/o | 79,935 (23.18) | 8175 (18.42) | 99,054 (25.20) | 59,845 (24.38) | 59,794 (24.36) | 7626 (17.46) | 7984 (18.28) | 7425 (18.95) | 7197 (18.36) | ||||
| 75–84 y/o | 27,000 (7.83) | 2901 (6.54) | 58,700 (14.93) | 23,522 (9.58) | 22,579 (9.20) | 2507 (5.74) | 2817 (6.45) | 2814 (7.18) | 2509 (6.40) | ||||
| ≥ 85 y/o | 4438 (1.29) | 460 (1.04) | 20,782 (5.29) | 4274 (1.74) | 3876 (1.58) | 389 (0.89) | 447 (1.02) | 459 (1.17) | 392 (1.00) | ||||
| Male | 201,889 (58.54) | 22,834 (51.46) | 214,352 (54.53) | 0.143 | 138,611 (56.47) | 138,350 (56.37) | 0.002 | 22,333 (51.13) | 22,517 (51.55) | 0.008 | 20,440 (52.16) | 20,355 (51.94) | 0.004 |
| Chronic lung disease | 8215 (2.38) | 1462 (3.30) | 14,065 (3.58) | 0.07 | 5749 (2.34) | 5604 (2.28) | 0.004 | 1243 (2.85) | 1417 (3.24) | 0.023 | 1256 (3.20) | 1178 (3.01) | 0.012 |
| Chronic liver disease | 61,198 (17.74) | 8322 (18.76) | 54,461 (13.85) | 0.133 | 38,880 (15.84) | 39,177 (15.96) | 0.003 | 8179 (18.72) | 8241 (18.87) | 0.004 | 7041 (17.97) | 6943 (17.72) | 0.007 |
| Chronic kidney disease | 55,458 (16.08) | 11,254 (25.36) | 63,786 (16.23) | 0.231 | 36,689 (14.95) | 36,301 (14.79) | 0.004 | 10,153 (23.24) | 10,576 (24.21) | 0.023 | 9157 (23.37) | 8768 (22.37) | 0.024 |
| Congestive heart failure | 3378 (0.98) | 756 (1.70) | 3439 (0.87) | 0.074 | 1629 (0.66) | 1666 (0.68) | 0.002 | 575 (1.32) | 657 (1.50) | 0.016 | 568 (1.45) | 541 (1.38) | 0.006 |
| Hypertension | 240,837 (69.83) | 32,220 (72.62) | 245,978 (62.57) | 0.216 | 163,972 (66.81) | 163,490 (66.61) | 0.004 | 31,190 (71.40) | 31,549 (72.22) | 0.018 | 27,854 (71.07) | 27,634 (70.51) | 0.012 |
| Dyslipidemia | 290,201 (84.14) | 38,868 (87.60) | 275,391 (70.06) | 0.44 | 197,217 (80.35) | 197,488 (80.46) | 0.003 | 38,283 (87.64) | 38,219 (87.49) | 0.004 | 33,741 (86.10) | 34,001 (86.76) | 0.019 |
| Diabetic ulcer | 2258 (0.65) | 614 (1.38) | 2806 (0.71) | 0.073 | 1469 (0.60) | 1436 (0.59) | 0.002 | 501 (1.15) | 547 (1.25) | 0.01 | 467 (1.19) | 435 (1.11) | 0.008 |
| Stroke | 12,758 (3.70) | 1721 (3.88) | 21,150 (5.38) | 0.081 | 9637 (3.93) | 9431 (3.84) | 0.004 | 1531 (3.50) | 1676 (3.84) | 0.018 | 1577 (4.02) | 1495 (3.81) | 0.011 |
| Ischemic heart disease | 23,570 (6.83) | 2936 (6.62) | 13,116 (3.34) | 0.16 | 9808 (4.00) | 10,232 (4.17) | 0.009 | 2557 (5.85) | 2809 (6.43) | 0.024 | 2226 (5.68) | 2119 (5.41) | 0.012 |
| Chronic gout | 52,487 (15.22) | 7242 (16.32) | 59,616 (15.17) | 0.032 | 37,704 (15.36) | 37,865 (15.43) | 0.002 | 6748 (15.45) | 7002 (16.03) | 0.016 | 6430 (16.41) | 6270 (16.00) | 0.011 |
| Peripheral artery disease | 704 (0.20) | 209 (0.47) | 863 (0.22) | 0.046 | 422 (0.17) | 411 (0.17) | 0.001 | 155 (0.35) | 186 (0.43) | 0.011 | 169 (0.43) | 163 (0.42) | 0.002 |
| Malignancy | 19,538 (5.66) | 2430 (5.48) | 32,100 (8.17) | 0.107 | 15,226 (6.20) | 14,840 (6.05) | 0.007 | 2219 (5.08) | 2390 (5.47) | 0.018 | 2234 (5.70) | 2127 (5.43) | 0.012 |
| History of bleeding | 1264 (0.37) | 230 (0.52) | 3784 (0.96) | 0.073 | 1079 (0.44) | 987 (0.40) | 0.006 | 191 (0.44) | 219 (0.50) | 0.009 | 218 (0.56) | 214 (0.55) | 0.001 |
| History of PCI | 23,625 (6.85) | 2895 (6.52) | 12,230 (3.11) | 0.173 | 9151 (3.73) | 9612 (3.92) | 0.01 | 2461 (5.63) | 2759 (6.32) | 0.029 | 2180 (5.56) | 2084 (5.32) | 0.011 |
| History of CABG | 1984 (0.58) | 337 (0.76) | 1238 (0.31) | 0.061 | 840 (0.34) | 866 (0.35) | 0.002 | 283 (0.65) | 315 (0.72) | 0.009 | 249 (0.64) | 243 (0.62) | 0.002 |
| APT | 96,424 (27.96) | 12,021 (27.09) | 92,230 (23.46) | 0.103 | 58,905 (24.00) | 59,184 (24.11) | 0.003 | 11,063 (25.33) | 11,696 (26.78) | 0.033 | 10,334 (26.37) | 9927 (25.33) | 0.024 |
| ACEI/ARB | 198,252 (57.48) | 25,822 (58.20) | 193,019 (49.10) | 0.183 | 132,347 (53.92) | 132,130 (53.83) | 0.002 | 25,031 (57.30) | 25,348 (58.03) | 0.015 | 22,289 (56.87) | 22,120 (56.44) | 0.009 |
| ARNI | 1590 (0.46) | 185 (0.42) | 465 (0.12) | 0.064 | 367 (0.15) | 410 (0.17) | 0.004 | 150 (0.34) | 179 (0.41) | 0.011 | 123 (0.31) | 122 (0.31) | 0.001 |
| Beta blocker | 103,432 (29.99) | 13,027 (29.36) | 101,697 (25.87) | 0.092 | 66,552 (27.12) | 66,650 (27.16) | 0.001 | 12,040 (27.56) | 12,639 (28.93) | 0.031 | 11,203 (28.59) | 10,849 (27.68) | 0.02 |
| Loop diuretics | 17,484 (5.07) | 3472 (7.83) | 30,088 (7.65) | 0.112 | 12,256 (4.99) | 11,938 (4.86) | 0.006 | 2817 (6.45) | 3164 (7.24) | 0.032 | 3095 (7.90) | 2874 (7.33) | 0.021 |
| MRA | 11,098 (3.22) | 1525 (3.44) | 13,938 (3.55) | 0.018 | 6608 (2.69) | 6491 (2.64) | 0.003 | 1292 (2.96) | 1480 (3.39) | 0.025 | 1337 (3.41) | 1243 (3.17) | 0.013 |
| Nitrate | 16,002 (4.64) | 2180 (4.91) | 17,124 (4.36) | 0.027 | 9568 (3.90) | 9635 (3.93) | 0.001 | 1852 (4.24) | 2060 (4.72) | 0.023 | 1872 (4.78) | 1789 (4.56) | 0.01 |
| Non-DHP CCB | 10,068 (2.92) | 1245 (2.81) | 12,216 (3.11) | 0.018 | 6462 (2.63) | 6412 (2.61) | 0.001 | 1096 (2.51) | 1219 (2.79) | 0.018 | 1113 (2.84) | 1017 (2.60) | 0.015 |
| Digoxin | 1986 (0.58) | 205 (0.46) | 2604 (0.66) | 0.027 | 1239 (0.50) | 1197 (0.49) | 0.002 | 174 (0.40) | 203 (0.46) | 0.01 | 182 (0.46) | 172 (0.44) | 0.004 |
| DHP CCB | 145,011 (42.05) | 18,226 (41.08) | 169,847 (43.21) | 0.043 | 100,712 (41.03) | 107,373 (43.75) | 0.055 | 18,132 (41.51) | 17,796 (40.74) | 0.016 | 15,802 (40.32) | 17,991 (45.91) | 0.113 |
| Statin | 235,174 (68.19) | 32,023 (72.17) | 207,551 (52.80) | 0.408 | 152,641 (62.19) | 152,713 (62.22) | 0.001 | 31,443 (71.98) | 31,469 (72.04) | 0.001 | 27,162 (69.31) | 27,087 (69.12) | 0.004 |
| NSAIDs | 79,453 (23.04) | 10,719 (24.16) | 100,346 (25.53) | 0.058 | 59,192 (24.12) | 59,463 (24.23) | 0.003 | 10,650 (24.38) | 10,572 (24.20) | 0.004 | 9775 (24.94) | 9768 (24.92) | 0 |
| PPI | 19,067 (5.53) | 2669 (6.02) | 31,754 (8.08) | 0.101 | 14,558 (5.93) | 14,277 (5.82) | 0.005 | 2336 (5.35) | 2603 (5.96) | 0.027 | 2553 (6.51) | 2313 (5.90) | 0.025 |
| H2 blocker | 63,552 (18.43) | 8123 (18.31) | 87,960 (22.38) | 0.101 | 46,910 (19.11) | 46,947 (19.13) | < .001 | 7539 (17.26) | 7931 (18.16) | 0.024 | 7494 (19.12) | 7299 (18.62) | 0.013 |
| Anti-diabetic agent | |||||||||||||
| Metformin | 198,656 (57.60) | 25,551 (57.59) | 245,340 (62.41) | 0.099 | 157,015 (63.97) | 158,769 (64.69) | 0.015 | 25,842 (59.16) | 25,513 (58.41) | 0.015 | 23,305 (59.47) | 23,736 (60.57) | 0.023 |
| Sulfonylurea | 203,927 (59.13) | 26,406 (59.51) | 170,955 (43.49) | 0.325 | 131,131 (53.43) | 130,340 (53.10) | 0.007 | 26,423 (60.49) | 26,227 (60.04) | 0.009 | 23,314 (59.49) | 23,593 (60.20) | 0.015 |
| Glinide | 15,568 (4.51) | 4300 (9.69) | 24,902 (6.33) | 0.203 | 11,696 (4.77) | 11,478 (4.68) | 0.004 | 3648 (8.35) | 3806 (8.71) | 0.013 | 3634 (9.27) | 3623 (9.24) | 0.001 |
| Acarbose | 41,010 (11.89) | 7037 (15.86) | 28,343 (7.21) | 0.273 | 21,909 (8.93) | 22,173 (9.03) | 0.004 | 6788 (15.54) | 6888 (15.77) | 0.006 | 5879 (15.00) | 5959 (15.21) | 0.006 |
| Glitazone | 63,680 (18.46) | 10,281 (23.17) | 30,652 (7.80) | 0.435 | 27,968 (11.39) | 28,016 (11.41) | 0.001 | 10,092 (23.10) | 10,067 (23.05) | 0.001 | 8178 (20.87) | 8336 (21.27) | 0.01 |
| Insulin | 58,063 (16.84) | 23,232 (52.36) | 70,838 (18.02) | 0.805 | 35,077 (14.29) | 34,991 (14.26) | 0.001 | 22,653 (51.86) | 22,553 (51.63) | 0.005 | 18,147 (46.31) | 18,048 (46.05) | 0.005 |
Sensitivity analysis
We performed several sensitivity analyses to examine the robustness of the present results. First, some severe diabetic patients die before AF can occur. Therefore, the HR for risk of new-onset AF between the two paired study groups was analyzed after PSM and using death as a competing risk factor. Second, we repeated the analyses using new-onset AF with a hospital discharge diagnosis. Third, we repeated the analyses using the restricted AF outcome defined as the specified AF outcome with the consequent use of oral anticoagulant (OAC) and rhythm control management either using anti-arrhythmic drug, cardioversion, or catheter ablation after the AF outcome was established. Fourth, we considered the study groups with no previous SGLT2i, DPP4i, or GLP1-RA exposure based on a 1-year washout period. Fifth, SGLT2i use is contraindicated among individuals with end-stage kidney disease (ESKD). In sensitivity analysis, we excluded patients with ESKD (confirmed by both relevant ICD-9-CM or ICD-10-CM codes and enrollment in the Registry of Catastrophic Illness Patient Database, a subpart of the NHI database) to avoid confounding since ESKD is associated with an increased risk of AF. Indeed, insulin use may also be associated with an increased risk of AF [3, 37, 38]. Therefore, we excluded the study patients with concomitant use of insulin.
Results
A total of 344,893, 44,370, and 393,100 patients treated with SGLT2i, GLP-1RA, and DPP4i were eligible for the present study. Among the SGLT2i users, 144,058 (41.8%), 179,004 (51.9%), 21,762 (6.3%), and 69 (0.0%) were treated with empagliflozin, dapagliflozin, canagliflozin, ertugliflozin, respectively. Among the GLP-1RA users, 20,677 (46.6%), 19,895 (44.9%), 3,432 (7.7%), 91 (0.2%), 275 (0.6%) were treated with liraglutide, dulaglutide, lixisenatide, exenatide, and semaglutide, respectively. Most of the DPP4i users were prescribed with sitagliptin (n = 125,767, 32.0%), followed by linagliptin (n = 119,537, 30.4%), vildagliptin (n = 114,713, 29.2%), saxagliptin (n = 28,625, 7.3%) and alogliptin (n = 4,458, 1.1%).
Table 1 summarizes the baseline demographic characteristics of the three groups before and after paired PSM. Before PSM, there were many differences in the baseline characteristics across the three study groups (most ASMD > 0.10). After PSM, there were 245,442, 43,682, and 39,190 paired cohorts of SGLT2i versus DPP4i, SGLT2i versus GLP-1RA, and GLP-1RA versus DPP4i, respectively: the three paired cohorts were well-balanced in most characteristics (ASMD < 0.10).
AF incidence rates among the three study groups
Before PSM, there were 1964 (0.29 per 100 patient-years), 314 (0.32 per 100 patient-years), 3883 (0.46 per 100 patient-years) new-onset AF for the SGLT2i, GLP-1RA, DPP4i groups, respectively. DPP4i treatment were associated with a higher risk of new-onset AF in participants with type 2 diabetes compared with SGLT2i [HR 1.59; 95% confidential interval (CI) 1.51–1.68; P < 0.0001] and GLP-1RA [HR 1.44; 95% CI 1.28–1.61; P < 0.0001] before PSM, respectively.
SGLT2i versus DPP4i after PSM
Cumulative risk of incident AF for the paired study cohorts treated with SGTL2i versus DPP4i (, SGLT2i versus GLP-1RA (), and GLP-1RA versus DPP4i () after PSM. There was a clear separation of event curves for new-onset AF between the paired SGLT2i and DPP4i group and the paired SGLT2i and GLP-1RA group after PSM adjustment. SGLT2i treatment was associated with a lower risk of new-onset AF in participants with type 2 diabetes compared with DPP4i or GLP-1RA treatment after PSM. Conversely, there was no difference in the risk of incident AF between the GLP-1RA and DPP4i treatment A) B C
SGLT2i versus GLP-1RA after PSM
For the 43,682 paired cohort of SGLT2i and GLP-1RA, the mean follow-up periods for the SGLT2i and GLP-1RA groups were 2.24 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pm$$\end{document}± 1.32 and 2.24 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pm$$\end{document}± 1.34 years, respectively. There were 228 (0.23 per 100 person-year) and 305 (0.31 per 100 person-year) new-onset AF events in the SGLT2i and GLP-1RA groups, respectively. After PSM, SGLT2i treatment was associated with lower risk of new-onset AF in participants with type 2 diabetes compared with GLP-1RA [HR 0.74; 95% CI 0.63–0.88; P = 0.0007] (Fig. 2B).
GLP-1RA versus DPP4i after PSM
For the 39,190 paired cohort of GLP-1RA and DPP4i, the mean following-up periods for the GLP-1RA and DPP4i groups were 2.30 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pm$$\end{document}± 1.34 and 2.26 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pm$$\end{document}± 1.33 years, respectively. There were 289 (0.32 per 100 person-year) and 283 (0.32 per 100 person-year) new-onset AF events in the SGLT2i and GLP-1RA groups, respectively. There was no difference in the risk of incident AF between the GLP-1RA and DPP4i users [HR 1.01; 95% CI 0.86–1.19; P = 0.8980] (Fig. 2C).
Sensitivity analyses
Sensitivity analyses of the hazard ratio for incident AF for the paired study cohorts treated with SGTL2i versus DPP4i, SGLT2i versus GLP-1RA, and GLP-1RA versus DPP4i after PSM. The sensitivity analyses showed the results were robust, and consistent with the main analysis. The use of SGLT2i was still associated with a lower risk of new-onset AF compared with either DPP4i or GLP-1RA after PSM, using death as a competing risk factor consistent with the main analysis. Specifically, we identified incident AF using either hospitalized diagnosed AF; AF with treatment using anti-arrhythmic drugs, cardioversion, or catheter ablation; or only patients without previous drug exposure, underlying ESKD, or concomitant use of insulin, the results remained consistent with the main analyses. There were no differences in the risk of incident AF with consequent use of oral anticoagulant treatment for three paired study groups
Subgroup analysis
Subgroup analysis revealed that SGLT2i was associated with a lower risk of new-onset AF than DPP4i or GLP-1RA across most subgroups (Additional file 2: Figure S1, Additional file 3: Figure S2). Dapagliflozin was specifically associated with a lower risk of new-onset AF than DPP4i (P interaction = 0.02) (Additional file 2: Figure S1). In addition, the use of SGLT2i was associated with greater reductions in new-onset AF events in subgroups, including those without concomitant use of sulfonylurea, when compared with GLP-1RA (P interaction < 0.01) (Additional file 3: Figure S2). There were no differences in the risk of incident AF between the GLP-1RA and DPP4i in all subgroups (P interaction all > 0.05) (Additional file 4: Figure S3).
Discussion
To the best of our knowledge, this is the largest observational study to specifically evaluate the risk of new-onset AF focused on an Asian population with type 2 diabetes treated with SGLT2i, GLP-1RA, and DPP4i. The main finding of this study was that SGLT2i treatment was associated with a lower risk of new-onset AF in participants with type 2 diabetes compared with either GLP-1RA or DPP4i treatment. Conversely, there was no difference in the risk of incident AF between the DPP4i and GLP-1RA. Furthermore, the above findings persisted among several important subgroups and sensitivity analyses.
Diabetes mellitus is an independent risk factor of stroke development in AF patients and an independent prognostic predictor for the development of AF [3, 4]. Nonetheless, there is conflicting literature concerning the association between AF and anti-diabetic medications. Several medications, such as insulin and sulfonylureas, have been associated with an increased risk of AF, possibly through hypoglycemia and glycemic fluctuation, and stimulation of the sympathetic nervous system is associated with an increased incidence of AF [3, 37, 38]. By contrast, other medications such as metformin, pioglitazone, and DPP4i may reduce the degree of atrial remodeling as an upstream therapy to prevent the development of AF [39–42]. In contrast to the treatment benefit of AF reduction for pioglitazone and DPP4i observed in retrospective observational studies, no significant differences in the risk of new-onset AF with the use of pioglitazone were reported in the PROactive, RECORD, and BARI 2D trials [43–45]. Furthermore, the meta-analysis of landmark cardiovascular outcome trials (CVOTs) showed that DPP4i treatment did not significantly affect the risk for AF, while they were associated with a significant increase in the risk for atrial flutter (AFL) [30].
Several insulin-independent mechanisms include reducing blood pressure, body weight, uric acid, epicardial adipose tissue, interstitial volume, atrial dilatation, increasing serum magnesium, and promoting mitochondrial biogenesis, hinting that SGLT2i might reduce the incidence of AF development [46]. SGLT2i inhibits sodium–hydrogen exchange in cardiac myocytes, which has been linked to ameliorating myocardial hypertrophy, fibrosis, remodeling, and HF [47]. Preclinical data showed that treatment of SGLT2i inhibits sympathetic overdrive, which also plays an important role in the development and maintenance of AF [48, 49]. Post hoc analysis of DECLARE–TIMI 58 showed that dapagliflozin was significantly associated with a relative risk reduction of AF/AFL by 19% compared with placebo among participants with type 2 diabetes, which was consistent regardless of the established atherosclerotic cardiovascular disease, HF, or preexisting AF at baseline [50]. However, because atrial tachyarrhythmia was not a predefined and monitored outcome, the risk reduction in AF/AFL associated with dapagliflozin versus placebo has not been reported consistently for other SGLT2is, such as the case of empagliflozin and canagliflozin. Furthermore, the DAPA-HF trial indicated that dapagliflozin did not significantly reduce the risk of new-onset AF compared to patients with HF and reduced ejection fraction without AF at baseline [51]. Recently, a pooled meta-analysis of 32 trials reported that SGLT2i treatment was associated with a significant reduction in the risk of incident AF/AFL by 19% compared with the control (OR 0.81; 95% CI 0.69–0.95; p = 0.008) [27]. The average cumulative incidence of AF/AFL was around 3.6 per 1,000 patient-years. Further subgroup analysis showed that only dapagliflozin (10 trials) was associated with a significantly lower risk of AF/AFL (OR 0.74; 95% CI 0.60–0.91; p = 0.005). Conversely, empagliflozin (9 trials) was associated with no significant difference in risk (OR 1.17; 95% CI 0.75–1.82; p = 0.49), and canagliflozin (8 trials) was associated with a numerically but non-significant lower risk of AF/AFL (OR 0.81; 95% CI 0.60–1.08; p = 0.15) when compared with placebo [27]. The above findings are of particular interest as they are more in line with our present study showing that dapagliflozin was specifically associated with a lower risk of incident AF when compared with either DPP4i or GLP-1RA (Additional file 3: Figure S2, Additional file 4: Figure S3).
Several CVOTs have shown that human-based GLP-1RAs reduce MACE and mortality risks in patients with type 2 diabetes[13–17]. However, other cardiovascular outcomes such as the incidence of incident AF have not been thoroughly investigated in those CVOTs associated with GLP-1RAs. Currently, there is conflicting literature regarding the risk of AF associated with treating GLP-1RA. Potential underlying mechanisms of treatment benefit GLP1-RA include several nonglycemic effects, such as reductions in body weight and blood pressure; anti-fibrotic effects; and improvement of microcirculation, endothelial function, and conduction properties, all of which may reduce the development of AF [52]. Conversely, GLP-1RAs were associated with an elevated heart rate by around 2–8 beats per minute higher than that of the control group, which may be related to GLP-1RA leading to systemic vasodilation with subsequent reflex tachycardia or a direct effect of the GLP-1RA on the autonomic nervous system and/or sinus node [52–54]. Due to the important role in the initiation and maintenance of AF medicated by sympathetic overflow [55], this effect may raise the possibility of an increased risk of AF. Indeed, recent observational studies have shown that the use of GLP-1RA was independently associated with a higher risk of incident AF (HR 2.27; 95% CI 1.49–3.47) among participants with diabetes [29]. The post hoc analysis of the HARMONY trial also showed that albiglutide was associated with a greater risk of AF and tachyarrhythmia [56]. However, these results were not consistent across all the CVOTs regarding GLP-1RAs, and the pooled meta-analysis of CVOTs (the LEADER, SUSTAIN‐6, REWIND, HARMONY, ELIXA, and PIONEER trials) showed no significant differences in the risk of incident AF between GLP1‐RA and placebo among participants with type 2 diabetes (OR 0.93; 95% CI 0.70–1.23; I2 = 58%) [28]. Conversely, some meta-analyses indicated that GLP-1RA was associated with a lower risk of AF/AFL compared to other glucose-lowering agents in patients with type 2 diabetes [24, 26]. These above trials are limited by AF not being a predefined and monitored endpoint. Further prospective trials are warranted to confirm the potential arrhythmogenic or antiarrhythmic effect of GLP-1RAs and investigate further whether the effect is a drug-specific or class effect.
Limitations
To date, direct comparisons of SGLT2i, GLP-1RA, and DPP4i regarding the risk of AF among patients with type 2 diabetes are scarce. Although a few network meta-analyses were aimed to compare the risk of incident AF between SGLT2i, GLP-1RA, and other anti-hypoglycemic agents, the above studies show conflicting results [24–29]. Also, the data from randomized and placebo-controlled studies may be incomplete because they were derived from the documentation of adverse effects and were not predefined for systematically identifying AF. Nevertheless, there are several limitations to our present study. First, this was a retrospective and observational study. The clinical characteristics of the patients were different across three study groups. Although we have adjusted for several important parameters relevant to clinical outcomes using PSM models, residual unmeasured confounders are probably present. Nevertheless, we suggest that future prospective randomized studies are needed to determine whether our findings apply to patients with type 2 diabetes treated with these drugs. Second, the NHIRD does not contain several important laboratory data such as body weight, glycohemoglobin (HbA1c), and serum creatinine, all of which may be associated with the risk of incident AF among participants with type 2 diabetes [57]. In addition, even with adjustment for CKD, the diagnosis of CKD by coding could not reflect the severity of renal disease, which may interfere with DPP4i, GLP-1RA, and SGLT2i selection for each patient. Third, the Taiwan National Health Insurance (NHI) Program only covers prescription of only one of DPP4i, GLP-1RA, or SGLT2i at the same time due to financial considerations (DPP4i, SGLT2i, and GLP1-RA are the relatively new and expensive anti-diabetic medications). If physicians prescribe two or three of these drugs simultaneously (or add-on) for patients, only one drug can be covered by the NHI program (and the drug will be therefore captured in the claims database), whereas the other one or two drugs should be paid by patients themselves (which will not be captured in the claims database). As obtaining the medical information of each participant outside the Taiwan NHIRD scheme is difficult, there would be a small number of patients in the specific group (e.g. SGLT2i, which was captured in the NHIRD) receiving other combination (or add-on) therapies outside the NHIRD (e.g., GLP1-RA and/or DPP4i, which was not captured in the NHIRD). Fourth, AF does not necessarily lead to hospitalization, nor is it always associated with extensive symptoms. AF can remain entirely asymptomatic. Therefore, detection bias due to common contacts with the healthcare system in one of the exposure groups could bias the results. Also, AF/AFL was not a predefined outcome lacking rigorous prospective collection and monitoring observed in the pivotal CVOT trials regarding the SGLT2i, GLP-1RA, or DPP4i. We call for further study regarding the clinical importance and reliability of this finding. Fifth, the present study was conducted based on an on-treatment design. It did not consider the changes in medical status or activity (e.g., new diagnosis of comorbidities or discontinuation/add-on of co-medication) during their follow-up period. Given that the mean follow-up was more than 2 years, time-dependent confounding could become critical. Finally, we only investigated Asian patients, and whether our result can be extrapolated to other ethnicities is unclear.
Conclusions
SGLT2i treatment was associated with a lower risk of incident AF than DPP4i or GLP-1RA treatment among patients with type 2 diabetes, irrespective of underlying comorbidities in a large real-world setting. There was no difference in the risk of incident AF between the DPP4i and GLP-1RA treatment.
Supplementary Information
Additional file 1: Table S1 International Classification of Diseases (10th edition) Clinical Modification (ICD 10-CM) codes used to define comorbidities and clinical outcomes in this study.Additional file 2: Figure S1. Subgroup analysis of forest plot of hazard ratio (HR) for sodium-glucose cotransporter 2 inhibitors (SGLT2i) versus dipeptidyl peptidase-4 inhibitors (DPP4i) among patients with type 2 diabetes (T2D) after propensity score matching (PSM). Subgroup analysis revealed that use of SGLT2i was associated with a lower risk of new-onset AF compared with use of DPP4i across most subgroups. It is noted that dapagliflozin was specifically associated with a lower risk of new-onset AF compared with DPP4i (P interaction = 0.02).Additional file 3: Figure S2. Subgroup analysis of forest plot of HR for SGLT2i versus glucagon-like peptide-1 receptor agonist (GLP-1RA) among patients with T2D after PSM. Subgroup analysis revealed that use of SGLT2i was associated with a lower risk of new-onset AF compared with use of DPP4i across most subgroups. Use of SGLT2i was associated with greater reductions in new-onset AF events in subgroup including those without concomitant use of sulfonylurea when compared with GLP-1RA (P interaction < 0.01).Additional file 4: Figure S3. Subgroup analysis of forest plot of HR for GLP-1RA versus DPP4i among patients with T2D after PSM. There was no difference of the risk of incident AF between the GLP-1RA and DPP4i across all subgroups (P interaction > 0.05).