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Journal of Peking University (Health Sciences) ›› 2024, Vol. 56 ›› Issue (3): 424-430. doi: 10.19723/j.issn.1671-167X.2024.03.008

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Cardiovascular safety of sitagliptin added to metformin in real world patients with type 2 diabetes

Zuoxiang LIU1,2,Xiaowei CHEN1,2,Houyu ZHAO1,2,Siyan ZHAN1,2,3,*(),Feng SUN1,2,4,*()   

  1. 1. Department of Epidemiology and Biostatistics, Peking University School of Public Health, Beijing 100191, China
    2. Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
    3. Clinical Epidemiology Research Center, Peking University Third Hospital, Beijing 100191, China
    4. Hainan Institute of Real World Data, Qionghai 571437, Hainan, China
  • Received:2024-02-15 Online:2024-06-18 Published:2024-06-12
  • Contact: Siyan ZHAN,Feng SUN E-mail:siyan-zhan@bjmu.edu.cn;sunfeng@bjmu.edu.cn
  • Supported by:
    the National Natural Science Foundation of China(72074011);The Second Batch of Key Projects of China Drug Regulatory Scientific Action Plan([2021]37-10);Real World Research Project of Boao Lecheng International Medical Tourism Pilot Zone Administration of Hainan Province(HNLC2022RWS012)

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Abstract:

Objective: To assess the safety of sitagliptin added to metformin on cardiovascular adverse events in real world patients with type 2 diabetes mellitus (T2DM). Methods: Real world data from Yinzhou Regional Health Care Database were used to select T2DM patients with diagnosis and treatment records in the platform from January 1, 2017 to December 31, 2022. According to drug prescription records, the patients were divided into metformin plus sitagliptin group (combination group) and metformin monotherapy group(monotherapy group). A series of retrospective cohorts were constructed according to the index date.Finally, full retrospective cohorts were constructed according to propensity score model, including baseline covariates that might be related to outcomes, to match the subjects in the combination group and monotherapy group for the purpose of increasing the comparability of baseline characteristics. The participants were followed up from the index date until the first occurrence of the following events: Diagnosis of outcomes, death, or the end of the study period (December 31, 2022). Cox proportional risk model was used to estimate the hazard ratio(HR)and 95% confidence interval (CI) of sitagliptin added to metformin on 3-point major adverse cardiovascular events (3P-MACE) combination outcome and secondary cardiovascular outcomes. Results: Before propensity score matching, the proportion of the patients in combination group using insulin, α glucosidase inhibitors, sodium-glucose transporter 2 inhibitors (SGLT-2I) and glienides at baseline was higher than that in monotherapy group, and the baseline fasting blood glucose (FBG) and hemoglobin A1c (HbA1c) levels in combination group were higher than those in monotherapy group. After propensity score matching, 5 416 subjects were included in the combination group and the monotherapy group, and baseline characteristics were effectively balanced between the groups. The incidence densities of 3P-MACE were 6.41/100 person years and 6.35/100 person years, respectively. Sitagliptin added to metformin did not increase or decrease the risk of 3P-MACE compared with the metformin monotherapy (HR=1.00, 95% CI: 0.91-1.10). In secondary outcomes analysis, the incidence of cardiovascular death was lower in the combination group than in the monotherapy group (HR=0.59, 95% CI: 0.41-0.85), and no association was found between sitagliptin and the risk of myocardial infarction and stroke (HR=1.12, 95% CI: 0.89-1.41; HR=0.99, 95% CI: 0.91-1.12). Conclusion: In T2DM patients in Yinzhou district of Ningbo, compared with metformin alone, sitagliptin added to metformin may reduce the risk of cardiovascular death, and do not increase the incidence of overall cardiovascular events. The results of this study can provide real-world evidence for post-marketing cardiovascular safety evaluation of sitagliptin.

Key words: Real world study, Cardiovascular safety, Cox proportional risk model, Sitagliptin

CLC Number: 

  • R195.4

Figure 1

Flow chart of participants in the study cohort T2DM, type 2 diabetes mellitus; DPP-4I, dipeptidyl peptidase-4 inhibitor; PS, propensity score."

Table 1

Baseline characteristics of monotherapy group and combination group"

Characteristics Pre-PS matching Post-PS matching
Monotherapy group (n=118 854) Combination group (n=5 416) SMD Monotherapy group (n=5 416) Combination group (n=5 416) SMD
Male 57 587 (48.5) 2 834 (52.3) 0.083 2 818 (52.0) 2 834 (52.3) 0.006
Age/years 67.18±10.65 66.19±10.20 0.095 65.91±10.88 66.19±10.20 0.026
Smoke 18 120 (15.2) 971 (17.9) 0.071 964 (17.8) 971 (17.9) 0.003
Alcohol 20 632 (17.4) 1 046 (19.3) 0.057 1 072 (19.8) 1 046 (19.3) 0.012
T2DM duration/years 5.86±3.72 5.37±3.90 0.129 5.22±3.77 5.37±3.90 0.039
Insulin 14 556 (12.2) 1 112 (20.5) 0.231 1 104 (20.4) 1 112 (20.5) 0.004
Sulfonylurea 58 358 (49.1) 2 676 (49.4) 0.006 2 655 (49.0) 2 676 (49.4) 0.008
α-glucosidase inhibitor 30 697 (25.8) 1 943 (35.9) 0.205 1 979 (36.5) 1 943 (35.9) 0.014
Thiazolidinedione 10 259 (8.6) 525 (9.7) 0.039 525 (9.7) 525 (9.7) < 0.001
SGLT-2I 4 763 (4.0) 715 (13.2) 0.197 699 (12.9) 715 (13.2) 0.008
Glienide 9 526 (8.0) 611 (11.3) 0.107 597 (11.0) 611 (11.3) 0.008
NSAID 45 966 (38.7) 2 310 (42.7) 0.086 2 307 (42.6) 2 310 (42.7) 0.001
Lipid-lowering drug 50 435 (42.4) 2 870 (53.0) 0.223 2 934 (54.2) 2 870 (53.0) 0.024
ACEI 45 523 (38.3) 2 085 (38.5) 0.004 2 060 (38.0) 2 085 (38.5) 0.009
ARB 6 402 (5.4) 275 (5.1) 0.010 302 (5.6) 275 (5.1) 0.022
CCB 59 597 (50.1) 2 923 (54.0) 0.082 2 927 (54.0) 2 923 (54.0) 0.001
β-blocker 26 848 (22.6) 1 481 (27.3) 0.097 1 536 (28.4) 1 481 (27.3) 0.023
Diuretic 26 238 (22.1) 1 507 (27.8) 0.102 1 466 (27.1) 1 507 (27.8) 0.017
PPI 21 000 (17.7) 1 286 (23.7) 0.104 1 316 (24.3) 1 286 (23.7) 0.013
Antipsychotic drug 3 919 (3.3) 229 (4.2) 0.050 230 (4.2) 229 (4.2) 0.001
Sedative-hypnotic drug 22 853 (19.2) 1 104 (20.4) 0.031 1 118 (20.6) 1 104 (20.4) 0.006
Antidepressant 1 178 (1.0) 85 (1.6) 0.048 93 (1.7) 85 (1.6) 0.012
Antitumor and immune agent 1 045 (0.9) 54 (1.0) 0.012 56 (1.0) 54 (1.0) 0.004
BMI/(kg/m2) 24.86±3.50 24.83±3.48 0.009 24.79±3.45 24.83±3.48 0.011
FBG/(mmol/L) 6.82±1.64 7.59±2.50 0.364 7.44±2.54 7.59±2.50 0.058
HbA1c/% 7.27±1.69 7.99±1.98 0.390 7.99±2.15 7.99±1.98 0.003
SBP/mmHg 132.36±12.84 132.16±13.31 0.016 132.12±12.66 132.16±13.31 0.002
DBP/mmHg 77.33±6.80 77.08±6.83 0.038 77.15±6.70 77.08±6.83 0.011
CCI score 0.050 0.022
   < 5 71 770 (60.4) 3 141 (58.0) 3 089 (57.0) 3 141 (58.0)
  5-10 45 584 (38.4) 2 194 (40.5) 2 251 (41.6) 2 194 (40.5)
  >10 1 500 (1.3) 81 (1.5) 76 (1.4) 81 (1.5)

Table 2

Cardiovascular outcomes between combination group and monotherapy group"

Outcomes Combination group Monotherapy group HR(95% CI) P value
Follow-up/person years Cases/(incidence density/100 person years) Follow-up/person years Cases/(incidence density/100 person years)
3P-MACE 12 562 805 (6.41) 13 061 830 (6.35) 1.00 (0.91, 1.10) 0.98
Cardiovascular death 14 008 46 (0.33) 14 469 80 (0.55) 0.59 (0.41, 0.85) < 0.01
MI 13 724 156 (1.14) 14 232 140 (0.98) 1.12 (0.89, 1.41) 0.34
Stroke 12 794 668 (5.22) 13 260 680 (5.13) 0.99 (0.91，1.12) 0.86

Figure 2

Kaplan-Meier curve of 3P-MACE outcome in combination group and monotherapy group 3P-MACE, 3-point major cardiovascular events outcome."

Figure 3

Kaplan-Meier curve of cardiocvascular death outcome in combination group and monotherapy group"

Figure 4

Kaplan-Meier curve of MI outcome in combination group and monotherapy group MI, myocardial infarction."

Figure 5

Kaplan-Meier curve of stroke outcome in combination group and monotherapy group"

1 中华医学会糖尿病学分会. 中国2型糖尿病防治指南(2020年版)[J]. 国际内分泌代谢杂志, 2021, 41 (5): 482- 548.
doi: 10.3760/cma.j.cn121383-20210825-08063
2 Ji L , Hu D , Pan C , et al. Primacy of the 3B approach to control risk factors for cardiovascular disease in type 2 diabetes patients[J]. Am J Med, 2013, 126 (10): 925.e11- 925.e 22.
doi: 10.1016/j.amjmed.2013.02.035
3 Schmidt AM . Diabetes mellitus and cardiovascular disease[J]. Arterioscler Thromb Vasc Biol, 2019, 39 (4): 558- 568.
doi: 10.1161/ATVBAHA.119.310961
4 Goldfine AB . Assessing the cardiovascular safety of diabetes therapies[J]. N Engl J Med, 2008, 359 (11): 1092- 1095.
doi: 10.1056/NEJMp0805758
5 Griffin SJ , Leaver JK , Irving GJ . Impact of metformin on cardiovascular disease: A meta-analysis of randomised trials among people with type 2 diabetes[J]. Diabetologia, 2017, 60 (9): 1620- 1629.
doi: 10.1007/s00125-017-4337-9
6 Garber AJ , Abrahamson MJ , Barzilay JI , et al. Consensus statement by the American Association of Clinical Endocrinologists and American College of Endocrinology on the comprehensive Type 2 diabetes management algorithm: 2020 executive summary[J]. Endocr Pract, 2020, 26 (1): 107- 139.
doi: 10.4158/CS-2019-0472
7 余学锋, 王爱红. 基层医疗机构二肽基肽酶4抑制剂临床应用常见问题专家指导建议[J]. 中国糖尿病杂志, 2022, 30 (2): 81- 85.
doi: 10.3969/j.issn.1006-6187.2022.02.001
8 Fei Y , Tsoi MF , Cheung BMY . Cardiovascular outcomes in trials of new antidiabetic drug classes: A network meta-analysis[J]. Cardiovasc Diabetol, 2019, 18 (1): 112.
doi: 10.1186/s12933-019-0916-z
9 Green JB , Bethel MA , Armstrong PW , et al. Effect of sitagliptin on cardiovascular outcomes in type 2 diabetes[J]. N Engl J Med, 2015, 373 (3): 232- 242.
doi: 10.1056/NEJMoa1501352
10 El Sanadi CE , Ji X , Kattan MW . 3-point major cardiovascular event outcome for patients with T2D treated with dipeptidyl peptidase-4 inhibitor or glucagon-like peptide-1 receptor agonist in addition to metformin monotherapy[J]. Ann Transl Med, 2020, 8 (21): 1345.
doi: 10.21037/atm-20-4063
11 Zhao H , Chen X , Sun Y , et al. Associations between thiazolidi-nediones use and incidence of rheumatoid arthritis: A retrospective population-based cohort study[J]. Arthritis Care Res (Hoboken), 2024, 76 (4): 486- 496.
doi: 10.1002/acr.25277
12 Lin H , Tang X , Shen P , et al. Using big data to improve cardiovascular care and outcomes in China: A protocol for the Chinese electronic health records research in Yinzhou (CHERRY) study[J]. BMJ Open, 2018, 8 (2): e019698.
doi: 10.1136/bmjopen-2017-019698
13 Robins JM , Hernán MA , Rotnitzky A . Effect modification by time-varying covariates[J]. Am J Epidemiol, 2007, 166 (9): 994- 1002.
doi: 10.1093/aje/kwm231
14 Borah BJ , Moriarty JP , Crown WH , et al. Applications of propensity score methods in observational comparative effectiveness and safety research: Where have we come and where should we go?[J]. J Comp Eff Res, 2014, 3 (1): 63- 78.
doi: 10.2217/cer.13.89
15 中国医师协会内分泌代谢科医师分会. DPP-4抑制剂临床应用专家共识[J]. 中华内分泌代谢杂志, 2018, 34 (11): 899- 903.
doi: 10.3760/cma.j.issn.1000-6699.2018.11.001
16 Ussher JR , Drucker DJ . Cardiovascular biology of the incretin system[J]. Endocr Rev, 2012, 33 (2): 187- 215.
doi: 10.1210/er.2011-1052
17 Mccormick LM , Kydd AC , Read PA , et al. Chronic dipeptidyl peptidase-4 inhibition with sitagliptin is associated with sustained protection against ischemic left ventricular dysfunction in a pilot study of patients with type 2 diabetes mellitus and coronary artery disease[J]. Circ Cardiovasc Imaging, 2014, 7 (2): 274- 281.
doi: 10.1161/CIRCIMAGING.113.000785
18 Anderluh M , Kocic G , Tomovic K , et al. Cross-talk between the dipeptidyl peptidase-4 and stromal cell-derived factor-1 in stem cell homing and myocardial repair: Potential impact of dipeptidyl peptidase-4 inhibitors[J]. Pharmacol Ther, 2016, 167, 100- 107.
doi: 10.1016/j.pharmthera.2016.07.009
19 Ussher JR , Drucker DJ . Cardiovascular actions of incretin-based therapies[J]. Circ Res, 2014, 114 (11): 1788- 1803.
doi: 10.1161/CIRCRESAHA.114.301958
20 Scirica BM , Bhatt DL , Braunwald E , et al. Saxagliptin and cardiovascular outcomes in patients with type 2 diabetes mellitus[J]. N Engl J Med, 2013, 369 (14): 1317- 1326.
doi: 10.1056/NEJMoa1307684
21 Zheng SL , Roddick AJ , Aghar-Jaffar R , et al. Association between use of sodium-glucose cotransporter 2 inhibitors, glucagon-like peptide 1 agonists, and dipeptidyl peptidase 4 inhibitors with all-cause mortality in patients with type 2 diabetes: A systematic review and meta-analysis[J]. Jama, 2018, 319 (15): 1580- 1591.
doi: 10.1001/jama.2018.3024
22 Kaneko M , Narukawa M . Meta-analysis of dipeptidyl peptidase-4 inhibitors use and cardiovascular risk in patients with type 2 diabetes mellitus[J]. Diabetes Res Clin Pract, 2016, 116, 171- 182.
doi: 10.1016/j.diabres.2016.04.012
23 Giugliano D , Longo M , Signoriello S , et al. The effect of DPP-4 inhibitors, GLP-1 receptor agonists and SGLT-2 inhibitors on cardiorenal outcomes: A network meta-analysis of 23 CVOTs[J]. Cardiovasc Diabetol, 2022, 21 (1): 42.
doi: 10.1186/s12933-022-01474-z
24 Ou SM , Shih CJ , Chao PW , et al. Effects on clinical outcomes of adding dipeptidyl peptidase-4 inhibitors versus sulfonylureas to metformin therapy in patients with type 2 diabetes mellitus[J]. Ann Intern Med, 2015, 163 (9): 663- 672.
doi: 10.7326/M15-0308
25 Baksh S , Wen J , Mansour O , et al. Dipeptidyl peptidase-4 inhi-bitor cardiovascular safety in patients with type 2 diabetes, with cardiovascular and renal disease: A retrospective cohort study[J]. Sci Rep, 2021, 11 (1): 16637.
doi: 10.1038/s41598-021-95687-z
26 Noguchi Y , Yoshizawa S , Tachi T , et al. Effect of dipeptidyl peptidase-4 inhibitors vs. metformin on major cardiovascular events using spontaneous reporting system and real-world database study[J]. J Clin Med, 2022, 11 (17): 4988.
doi: 10.3390/jcm11174988
27 Ray WA . Evaluating medication effects outside of clinical trials: New-user designs[J]. Am J Epidemiol, 2003, 158 (9): 915- 920.
doi: 10.1093/aje/kwg231
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