What this is
- This study investigates the risk of new onset depression in patients with type 2 diabetes mellitus (T2DM) using sodium-glucose cotransporter 2 inhibitors (SGLT2I) compared to dipeptidyl peptidase-4 inhibitors (DPP4I).
- A population-based cohort of T2DM patients in Hong Kong from 2015 to 2019 was analyzed.
- The study utilized propensity score matching and Cox regression models to assess the relationship between medication use and depression risk.
Essence
- SGLT2I use is associated with a lower risk of new onset depression compared to DPP4I use in T2DM patients. The hazard ratio for depression risk with SGLT2I was 0.52, indicating a significant reduction.
Key takeaways
- SGLT2I users exhibited a 48% lower risk of new onset depression compared to DPP4I users. This was established through robust statistical analyses, including Cox regression and propensity score matching.
- The study included 18,309 SGLT2I users and 37,269 DPP4I users, providing a substantial dataset for analysis. The findings contribute to understanding the mental health implications of diabetes medications.
Caveats
- The observational nature of the study introduces potential biases, including under-coding and missing data. Compliance with medication was assessed indirectly, which may affect the results.
- Residual confounding may still exist despite matching, particularly due to unmeasured factors like socioeconomic status, which were not accounted for in the analyses.
AI simplified
Introduction
Type 2 diabetes mellitus (T2DM) has been described as an emerging pandemic, currently affecting over 462 million people worldwide [1]. Equally alarming is the recent increase in prevalence of depression especially during the COVID pandemic, a common mental health disorder which affects approximately 280 million people worldwide [2, 3]. A close link between the two conditions has long been recognised since the seventeenth century, with the famous British physician Thomas Phyllis describing diabetes as “a consequence of prolonged sorrow” [4]. This link has been confirmed by various studies demonstrating that there is an increased prevalence and diagnosis of depression in T2DM patients [5, 6] with one study reporting that T2DM doubles the risk of depression [5]. Conversely, it has also been shown that depression increases the risk of developing diabetes [7, 8] and diabetic complications [9, 10], indicating a bi-directional relationship between the two.
Given the close relationship between diabetes and depression, there has been growing interest to study the modulatory effects of anti-diabetic medications on depression, including novel agents such as dipeptidyl peptidase 4 inhibitor (DPP4I) and sodium-glucose co-transporter 2 inhibitor (SGLT2I). While early case reports suggested a potential association between incretin-based therapies and depression [11, 12], recent cohort studies have found that DPP4I use is generally associated with a reduced risk of depression [13–16]. This is confirmed by clinical findings that DPP4 enzymatic activity is increased in patients with depressive symptoms [17] as well as pre-clinical findings in rodent model that DPP4I use produces antidepressant effects [18, 19]. By contrast, there is limited data available to assess the anti-depressant effects of SGLT2I. A cohort study in 2019 found that both DPP4I and SGLT2I were associated with significantly lower risk of depression, but was only based on 1 SGLT2I user [13]. A case report in 2020 described a patient whose depressive symptoms and suicidal ideations resolved after 1 year of SGLT2I initiation [20]. While both showed promising results, it has not been possible to draw any definitive conclusions due to the small sample size of SGLT2I users in the respective studies.
To our knowledge, there has been no large-scale study so far exploring SGLT2I and its association with depression, either in isolation or in a head-to-head comparison with DPP4I. Hence, the aim of this study is to explore the largely unknown association with depression of SGLT2I use as compared against DPP4I, using a large database of Chinese T2DM patients in Hong Kong.
Methods
Ethics approval
This study was approved by the Joint Chinese University of Hong Kong–New Territories East Cluster Clinical Research Ethics Committee (Ethics Committee Approval Number NTEC-2018-0563).
Data sources and study population
The patients were identified from the Clinical Data Analysis and Reporting System (CDARS), a territory-wide database that centralizes patient information from individual local hospitals to establish a comprehensive set of medical data, including clinical characteristics, disease diagnosis, laboratory results, and drug treatment details. The system has previously been used by both our team and other teams in Hong Kong to conduct epidemiological studies [24–26].
As SGLT2I and DPP4I were only licensed for use in Hong Kong from 2015 onwards, the study is effectively a new user design with all users starting use of the medication during the study period. Patients were followed up from their first use of the medication either until the diagnosis of new-onset depression or until death. Certain patients were excluded from the study cohort, including patients with both DPP4I and SGLT2I use or discontinuation during the study period, without complete demographics data, without mortality data, with pregnancy or gestational diabetes and with prior diagnosis of psychiatric disease of antidepressant exposure. Users of both DPP4I and SGLT2I were excluded to ascertain the effects were due to one of the drugs, as it would be difficult to attribute whether the risk of new-onset depression was due to DPP4I use, SGLT2I use, or a combination of both with or without switching drugs. As drug compliance is not routinely collected within CDARs, users’ compliance to medication is only assessed indirectly through prescription refills.
Clinical and biochemical data were extracted for the present study. Patients' demographics included gender and age of initial drug use (baseline). Prior comorbidities before initial drug use were extracted based on standard International Classification of Diseases Ninth Edition (ICD-9) codes as shown in Supplementary Table 1 and the Charlson comorbidity index was also calculated. Baseline anti-5diabetic medication use, including metformin, sulphonylurea, insulin, acarbose, thiazolidinedione and glucagon-like peptide-1 agonist, was extracted. Baseline laboratory data were also extracted, including complete blood count, biochemical tests, glucose and lipid profiles.
Procedures of data processing for the study cohort. IR: Incidence rate; SGLT2I: Sodium-glucose cotransporter-2 inhibitors; DPP4I: Dipeptidyl peptidase-4 inhibitors
Statistical analysis
Descriptive statistics were used to summarize baseline characteristics of patients with SGLT2I and DPP4I use. For baseline clinical characteristics, the continuous variables were presented as mean (standard deviation [SD]) or median (95% confidence interval [CI]/ interquartile range [IQR]), and the categorical variables were presented as total number (percentage). Continuous variables were compared using the two-tailed Mann-Whitney U test, whilst the two-tailed Chi-square test with Yates’ correction was used to test 2 × 2 contingency data. 1:1 propensity score matching between SGLT2I and DPP4I users was performed based on demographics, prior comorbidities and non-SGLT2I/DPP4I medication using the nearest neighbour search strategy with calliper of 0.1. Propensity score matching results between treatment-group (SGLT2I) versus control-group (DPP4I) before and after matching are shown in Supplementary Fig. 1.
Univariate and multivariable Cox regression models were used to identify significant risk predictors for the study outcomes. Regression analysis with one-year lag time, competing risk analysis (cause-specific and sub-distribution models) and different propensity score approaches (propensity score stratification [27], propensity score matching with inverse probability weighting [28] and propensity score matching with stable inverse probability weighting [29]) were also considered. The hazard ratio (HR), 95% CI and P-value were reported. Statistical significance was defined as P-value < 0.05. All statistical analyses were performed with RStudio software (Version: 1.1.456), Python (Version: 3.6), and Stata (Version: SE 16.0).
Results
Baseline characteristics before and after propensity score matching
From the 76,147 patients identified on CDARS within the study period, we excluded 17,641 patients including patients with both DPP4I and SGLT2I use or discontinuation during the study period (N = 13,251), without complete demographics data (N = 17), without mortality data (N = 13), with pregnancy or gestational diabetes (N = 28), with prior diagnosis of psychiatric disease of antidepressant exposure (N = 4101) and with mortality within 30 days of initial drug exposure (N = 231).
| Characteristics | Before matching | Standardised mean difference (SMD)# | After matching | Standardised mean difference (SMD)# | ||||
|---|---|---|---|---|---|---|---|---|
| All (N = 58,506) Mean(SD);N or Count(%) | SGLT2I users (N = 19,381) Mean(SD);N or Count(%) | DPP4I users (N = 39,125) Mean(SD);N or Count(%) | All (N = 38,762) Mean(SD);N or Count(%) | SGLT2I users (N = 19,381) Mean(SD);N or Count(%) | DPP4I users (N = 19,381) Mean(SD);N or Count(%) | |||
| Demographics | ||||||||
| Male gender | 32,825(56.10%) | 12,007(61.95%) | 20,818(53.20%) | 0.18 | 24,102(62.17%) | 12,007(61.95%) | 12,095(62.40%) | 0.01 |
| Female gender | 25,681(43.89%) | 7374(38.04%) | 18,307(46.79%) | 0.18 | 14,660(37.82%) | 7374(38.04%) | 7286(37.59%) | 0.01 |
| Baseline age, years | 63.4(12.9);n = 58,506 | 57.7(11.2);n = 19,381 | 66.2(12.7);n = 39,125 | 0.71* | 58.1(11.2);n = 38,762 | 57.7(11.2);n = 19,381 | 58.5(11.2);n = 19,381 | 0.07 |
| Diabetes duration, days | 500.7(1314.4);n = 58,506 | 499.6(1160.4);n = 19,381 | 501.2(1384.3);n = 39,125 | < 0.01 | 459.8(1188.6);n = 38,762 | 499.6(1160.4);n = 19,381 | 420.0(1214.8);n = 19,381 | 0.07 |
| Past comorbidities | ||||||||
| Charlson comorbidity index | 2.1(1.5);n = 58,506 | 1.6(1.2);n = 19,381 | 2.4(1.6);n = 39,125 | 0.61* | 1.6(1.2);n = 38,762 | 1.55(1.24);n = 19,381 | 1.56(1.24);n = 19,381 | 0.01 |
| Heart failure | 1939(3.31%) | 490(2.52%) | 1449(3.70%) | 0.07 | 968(2.49%) | 490(2.52%) | 478(2.46%) | < 0.01 |
| Hypertension | 13,924(23.79%) | 4519(23.31%) | 9405(24.03%) | 0.02 | 8865(22.87%) | 4519(23.31%) | 4346(22.42%) | 0.02 |
| Hypoglycaemia | 476(0.81%) | 45(0.23%) | 431(1.10%) | 0.11 | 90(0.23%) | 45(0.23%) | 45(0.23%) | < 0.01 |
| Hyperlipidaemia | 1559(2.66%) | 682(3.51%) | 877(2.24%) | 0.08 | 1344(3.46%) | 682(3.51%) | 662(3.41%) | 0.01 |
| Ischemic heart disease | 5823(9.95%) | 2502(12.90%) | 3321(8.48%) | 0.14 | 4610(11.89%) | 2502(12.90%) | 2108(10.87%) | 0.06 |
| Liver diseases | 2210(3.77%) | 904(4.66%) | 1306(3.33%) | 0.07 | 1746(4.50%) | 904(4.66%) | 842(4.34%) | 0.02 |
| Autoimmune disease tissue | 591(1.01%) | 189(0.97%) | 402(1.02%) | 0.01 | 377(0.97%) | 189(0.97%) | 188(0.97%) | < 0.01 |
| Gastrointestinal disease | 1378(2.35%) | 347(1.79%) | 1031(2.63%) | 0.06 | 688(1.77%) | 347(1.79%) | 341(1.75%) | < 0.01 |
| Acute myocardial infarction | 1598(2.73%) | 660(3.40%) | 938(2.39%) | 0.06 | 1311(3.38%) | 660(3.40%) | 651(3.35%) | < 0.01 |
| Peripheral vascular disease | 455(0.77%) | 101(0.52%) | 354(0.90%) | 0.05 | 202(0.52%) | 101(0.52%) | 101(0.52%) | < 0.01 |
| Chronic obstructive pulmonary disease | 684(1.16%) | 139(0.71%) | 545(1.39%) | 0.07 | 278(0.71%) | 139(0.71%) | 139(0.71%) | < 0.01 |
| Renal diseases | 1152(1.96%) | 102(0.52%) | 1050(2.68%) | 0.17 | 204(0.52%) | 102(0.52%) | 102(0.52%) | < 0.01 |
| Sleep disorders | 1749(2.98%) | 1005(5.18%) | 744(1.90%) | 0.18 | 1908(4.92%) | 1005(5.18%) | 903(4.65%) | 0.02 |
| Stroke/transient ischemic attack | 1854(3.16%) | 489(2.52%) | 1365(3.48%) | 0.06 | 970(2.50%) | 489(2.52%) | 481(2.48%) | < 0.01 |
| Atrial fibrillation | 1523(2.60%) | 426(2.19%) | 1097(2.80%) | 0.04 | 845(2.17%) | 426(2.19%) | 419(2.16%) | < 0.01 |
| Anaemia | 2462(4.20%) | 423(2.18%) | 2039(5.21%) | 0.16 | 847(2.18%) | 423(2.18%) | 424(2.18%) | < 0.01 |
| Cancer | 1629(2.78%) | 391(2.01%) | 1238(3.16%) | 0.07 | 780(2.01%) | 391(2.01%) | 389(2.00%) | < 0.01 |
| Medications | ||||||||
| Metformin | 51,824(88.57%) | 18,016(92.95%) | 33,808(86.41%) | 0.22* | 36,057(93.02%) | 18,016(92.95%) | 18,041(93.08%) | 0.01 |
| Sulphonylurea | 44,983(76.88%) | 13,618(70.26%) | 31,365(80.16%) | 0.23* | 27,844(71.83%) | 13,618(70.26%) | 14,226(73.40%) | 0.07 |
| Insulin | 29,437(50.31%) | 9829(50.71%) | 19,608(50.11%) | 0.01 | 20,695(53.38%) | 9829(50.71%) | 10,866(56.06%) | 0.11 |
| Acarbose | 1470(2.51%) | 778(4.01%) | 692(1.76%) | 0.13 | 1455(3.75%) | 778(4.01%) | 677(3.49%) | 0.03 |
| Thiazolidinedione | 10,758(18.38%) | 5330(27.50%) | 5428(13.87%) | 0.34* | 9524(24.57%) | 5330(27.50%) | 4194(21.63%) | 0.14 |
| Glucagon-like peptide-1 receptor agonists | 1572(2.68%) | 1407(7.25%) | 165(0.42%) | 0.36* | 2432(6.27%) | 1407(7.25%) | 1025(5.28%) | 0.08 |
| Statins and fibrates | 15,292(26.13%) | 2344(12.09%) | 12,948(33.09%) | 0.52* | 4517(11.65%) | 2344(12.09%) | 2173(11.21%) | 0.03 |
| ACEI/ARB | 12,420(21.22%) | 8089(41.73%) | 4331(11.06%) | 0.74* | 15,588(40.21%) | 8089(41.73%) | 7499(38.69%) | 0.06 |
| Antihypertensive drugs | 1018(1.73%) | 943(4.86%) | 75(0.19%) | 0.30* | 1227(3.16%) | 943(4.86%) | 284(1.46%) | 0.2 |
| Anticoagulants | 17,378(29.70%) | 11,514(59.40%) | 5864(14.98%) | 1.03* | 23,078(59.53%) | 11,514(59.40%) | 11,564(59.66%) | 0.01 |
| Antiplatelets | 9154(15.64%) | 6272(32.36%) | 2882(7.36%) | 0.66* | 11,252(29.02%) | 6272(32.36%) | 4980(25.69%) | 0.15 |
| Lipid-lowering drugs | 10,999(18.79%) | 6624(34.17%) | 4375(11.18%) | 0.57* | 13,875(35.79%) | 6624(34.17%) | 7251(37.41%) | 0.07 |
| Nitrates | 4239(7.24%) | 2861(14.76%) | 1378(3.52%) | 0.40* | 5212(13.44%) | 2861(14.76%) | 2351(12.13%) | 0.08 |
| Non-steroidal anti-inflammatory drugs | 8796(15.03%) | 6020(31.06%) | 2776(7.09%) | 0.64* | 10,950(28.24%) | 6020(31.06%) | 4930(25.43%) | 0.13 |
| Diuretics | 9467(16.18%) | 6014(31.03%) | 3453(8.82%) | 0.58* | 10,857(28.00%) | 6014(31.03%) | 4843(24.98%) | 0.13 |
| Beta-blockers | 7293(12.46%) | 5016(25.88%) | 2277(5.81%) | 0.57* | 9044(23.33%) | 5016(25.88%) | 4028(20.78%) | 0.12 |
| Calcium channel blockers | 12,606(21.54%) | 8458(43.64%) | 4148(10.60%) | 0.80* | 15,173(39.14%) | 8458(43.64%) | 6715(34.64%) | 0.19 |
| Complete blood counts | ||||||||
| Mean corpuscular volume, fL | 87.2(7.6);n = 29,704 | 86.7(7.2);n = 10,965 | 87.6(7.8);n = 18,739 | 0.12 | 86.7(7.4);n = 21,160 | 86.7(7.2);n = 10,965 | 86.8(7.7);n = 10,195 | 0.01 |
| Eosinophil, × 10^9/L | 0.2(0.3);n = 23,916 | 0.22(0.2);n = 8569 | 0.22(0.28);n = 15,347 | < 0.01 | 0.2(0.2);n = 17,345 | 0.22(0.2);n = 8569 | 0.23(0.28);n = 8776 | 0.05 |
| Lymphocyte, × 10^9/L | 2.0(0.9);n = 23,941 | 2.2(0.9);n = 8574 | 1.9(0.9);n = 15,367 | 0.32* | 2.1(0.9);n = 17,353 | 2.2(0.9);n = 8574 | 2.0(0.9);n = 8779 | 0.17 |
| Neutrophil, × 10^9/L | 5.4(2.8);n = 23,941 | 5.1(2.4);n = 8574 | 5.5(3.1);n = 15,367 | 0.14 | 5.3(2.8);n = 17,353 | 5.1(2.4);n = 8574 | 5.5(3.2);n = 8779 | 0.12 |
| White cell count, × 10^9/L | 8.0(3.0);n = 29,713 | 7.97(2.58);n = 10,971 | 8.04(3.23);n = 18,742 | 0.02 | 8.1(3.1);n = 21,177 | 8.0(2.6);n = 10,971 | 8.1(3.5);n = 10,206 | 0.06 |
| Mean cell haemoglobin, pg | 29.4(3.0);n = 29,704 | 29.2(2.9);n = 10,965 | 29.6(3.1);n = 18,739 | 0.15 | 29.2(3.0);n = 21,160 | 29.2(2.9);n = 10,965 | 29.3(3.2);n = 10,195 | 0.06 |
| Platelet, × 10^9/L | 239.3(72.4);n = 29,711 | 244.1(66.9);n = 10,969 | 236.4(75.3);n = 18,742 | 0.11 | 245.6(72.6);n = 21,175 | 244.1(66.9);n = 10,969 | 247.1(78.2);n = 10,206 | 0.04 |
| Red cell count, × 10^12/L | 4.5(0.7);n = 29,704 | 4.8(0.6);n = 10,965 | 4.4(0.7);n = 18,739 | 0.61* | 4.7(0.7);n = 21,160 | 4.8(0.6);n = 10,965 | 4.6(0.7);n = 10,195 | 0.28* |
| Liver and renal functions | ||||||||
| Potassium, mmol/L | 4.4(0.5);n = 48,394 | 4.3(0.4);n = 16,344 | 4.4(0.5);n = 32,050 | 0.14 | 4.3(0.5);n = 31,796 | 4.31(0.43);n = 16,344 | 4.31(0.49);n = 15,452 | 0.01 |
| Albumin, g/L | 41.5(4.0);n = 37,036 | 42.5(3.3);n = 13,865 | 40.9(4.3);n = 23,171 | 0.41* | 42.1(3.8);n = 26,483 | 42.5(3.3);n = 13,865 | 41.6(4.3);n = 12,618 | 0.24* |
| Sodium, mmol/L | 139.3(3.0);n = 48,419 | 139.2(2.7);n = 16,346 | 139.3(3.1);n = 32,073 | 0.04 | 139.3(2.9);n = 31,824 | 139.2(2.7);n = 16,346 | 139.4(3.0);n = 15,478 | 0.06 |
| Urea, mmol/L | 6.7(3.7);n = 48,403 | 5.7(2.0);n = 16,340 | 7.2(4.2);n = 32,063 | 0.45* | 6.2(2.9);n = 31,829 | 5.7(2.0);n = 16,340 | 6.7(3.5);n = 15,489 | 0.33* |
| Protein, g/L | 73.8(5.6);n = 34,805 | 74.4(4.9);n = 13,072 | 73.4(5.9);n = 21,733 | 0.18 | 74.3(5.4);n = 25,214 | 74.4(4.9);n = 13,072 | 74.2(6.0);n = 12,142 | 0.03 |
| Creatinine, umol/L | 97.2(80.2);n = 48,545 | 79.1(28.0);n = 16,375 | 106.4(95.1);n = 32,170 | 0.39* | 87.8(53.9);n = 31,883 | 79.1(28.0);n = 16,375 | 97.1(70.5);n = 15,508 | 0.34* |
| Alkaline phosphatase, U/L | 77.2(32.9);n = 37,156 | 73.5(26.2);n = 13,869 | 79.5(36.2);n = 23,287 | 0.19 | 76.6(30.6);n = 26,541 | 73.5(26.2);n = 13,869 | 80.1(34.4);n = 12,672 | 0.21* |
| Aspartate transaminase, U/L | 28.2(54.7);n = 14,759 | 28.4(29.0);n = 5574 | 28.1(65.5);n = 9185 | 0.01 | 29.4(33.4);n = 11,266 | 28.4(29.0);n = 5574 | 30.4(37.3);n = 5692 | 0.06 |
| Alanine transaminase, U/L | 28.7(34.3);n = 31,584 | 32.3(28.3);n = 11,792 | 26.6(37.3);n = 19,792 | 0.17 | 31.5(28.3);n = 22,033 | 32.3(28.3);n = 11,792 | 30.5(28.3);n = 10,241 | 0.06 |
| Bilirubin, umol/L | 11.2(6.9);n = 36,964 | 11.5(5.6);n = 13,837 | 11.1(7.6);n = 23,127 | 0.06 | 11.2(6.0);n = 26,437 | 11.5(5.6);n = 13,837 | 10.9(6.4);n = 12,600 | 0.09 |
| Lipid and glucose profiles | ||||||||
| Triglyceride, mmol/L | 1.7(1.5);n = 45,525 | 1.8(1.7);n = 15,707 | 1.6(1.3);n = 29,818 | 0.09 | 1.8(1.6);n = 30,166 | 1.79(1.74);n = 15,707 | 1.81(1.53);n = 14,459 | 0.01 |
| Low-density lipoprotein, mmol/L | 2.4(0.8);n = 44,805 | 2.37(0.8);n = 15,458 | 2.38(0.8);n = 29,347 | < 0.01 | 2.4(0.8);n = 29,638 | 2.37(0.8);n = 15,458 | 2.42(0.85);n = 14,180 | 0.06 |
| High-density lipoprotein, mmol/L | 1.2(0.3);n = 45,465 | 1.16(0.31);n = 15,684 | 1.22(0.34);n = 29,781 | 0.16 | 1.2(0.3);n = 30,116 | 1.16(0.31);n = 15,684 | 1.19(0.36);n = 14,432 | 0.1 |
| Total cholesterol, mmol/L | 4.3(1.0);n = 45,570 | 4.31(1.0);n = 15,727 | 4.32(0.98);n = 29,843 | 0.01 | 4.4(1.0);n = 30,192 | 4.3(1.0);n = 15,727 | 4.4(1.0);n = 14,465 | 0.09 |
| Haemoglobin A1C, % | 8.0(1.5);n = 47,584 | 8.3(1.6);n = 16,132 | 7.9(1.5);n = 31,452 | 0.24* | 8.2(1.6);n = 31,318 | 8.3(1.6);n = 16,132 | 8.1(1.7);n = 15,186 | 0.1 |
| Fasting glucose, mmol/L.1 | 8.9(3.9);n = 43,006 | 9.2(3.6);n = 14,806 | 8.7(4.0);n = 28,200 | 0.11 | 9.2(4.4);n = 28,132 | 9.15(3.59);n = 14,806 | 9.19(5.12);n = 13,326 | 0.01 |
Univariate and multivariable cox regression analyses
Cumulative incidence curves for new onset depression by SGLT2I vs DPP4I use before and after propensity score matching (1:1)
| Characteristics | N or count (%) | Model 1 | Model 2 | Model 3 |
|---|---|---|---|---|
| DepressionHR [95% CI];P value | DepressionHR [95% CI];P value | DepressionHR [95% CI];P value | ||
| SGLT2I.v.s. DPP4I | 19,381(50.00%) | 0.35[0.30–0.41]; < 0.0001*** | 0.35[0.29–0.41]; < 0.0001*** | 0.33[0.27–0.77]; < 0.0001*** |
| Dapagliflozin v.s. DPP4I | 11,169(28.81%) | 0.47[0.39–0.58]; < 0.0001*** | 0.44[0.32–0.67]; < 0.0001*** | 0.44[0.33–0.86]; < 0.0001*** |
| Empagliflozin v.s. DPP4I | 4286(11.05%) | 0.44[0.32–0.61]; < 0.0001*** | 0.51[0.26–0.81]; < 0.0001*** | 0.45[0.30–0.90]; < 0.0001*** |
| Canagliflozin v.s. DPP4I | 4667(12.04%) | 0.34[0.24–0.49]; < 0.0001*** | 0.36[0.21–0.59]; < 0.0001*** | 0.39[0.20–0.83]; < 0.0001*** |
| Ertugliflozin v.s. DPP4I | 2367(6.10%) | 0.34[0.21–0.57]; < 0.0001*** | 0.39[0.31–0.77]; < 0.0001*** | 0.36[0.19–0.82]; < 0.0001*** |
Sensitivity analysis
| Model | New onset depression |
|---|---|
| Regression analysis with one-year lag time | 0.34[0.21–0.65];0.0004*** |
| Cause-specific hazard model | 0.43[0.32–0.85]; < 0.0001*** |
| Sub-distribution hazard model | 0.62[0.33–0.77]; < 0.0001*** |
| PS stratification | 0.38[0.24–0.73]; < 0.0001*** |
| PS with IPTW | 0.49[0.34–0.88];0.0015** |
| PS with SIPTW | 0.58[0.44–0.91];0.0033** |
Discussion
This key finding of the present study is that SGLT2I users are associated with a lower risk of depression compared to DPP4I users after 1:1 propensity score matching for demographics, prior comorbidities, non-SGLT2I/DPP4I medication use, glycaemic indices and duration of diabetes. This was demonstrated by Cox regression models and further confirmed by competing risk analysis and different propensity score approaches.
Several studies have previously shown that the risk of depression is significantly lowered by DPP4I use in T2DM patients. A prospective study in 2016 of 1735 T2DM patients found that one year of incretin-based therapy use, defined as glucagon-like peptide-1 receptor agonist (GLP1-RA) or DPP4I, was correlated with significant improvement in depressive symptoms as measured by the Patient Health Questionnaire-9 [15]. A UK cohort study in 2018 found that DPP4I use is associated with a lower risk of new-onset depression and self-harm compared to sulphonylurea (HR: 0.80, 95% CI: [0.57, 1.13]) but did not reach statistical threshold [14]. A Japanese cohort study in 2019 of 40,214 patients investigated all classes of anti-diabetic medications and found that only DPP4I use was associated with significantly lower risk for development of depression (HR: 0.31, 95% CI: [0.24, 0.42], P < 0.0001) [13]. This has also been confirmed in animal models, such as a study in 2016 demonstrating that sitagliptin has anti-nociceptive and antidepressant effects using a rodent model of depression [18]. Compared to DPP4I, research on the association between SGLT2I and depression has been very limited. The aforementioned 2019 Japanese study is the only study to-date to investigate the association between SGLT2I use and depression [13]. The study suggested that SGLT2I use significantly reduces the risk of depression (HR: 0.09, 95% CI: [0.01–0.63], P = 0.0153), but was only based on 1 SGLT2I patient and therefore inconclusive.
Multiple studies have demonstrated the neuroprotective effects of SGLT2I, highlighting their potential to improve brain mitochondrial function, hippocampal synaptic plasticity and inhibit acetylcholinesterase [30–33]. It is therefore very possible that SGLT2I exerts its anti-depressant effects via direct effects on the brain. One such mechanism was suggested in a recent study by Muhammad et al. using a rodent model of depression [34]. The neuroimmune hypothesis of depression suggests that mood disorders are mediated by a state of systemic inflammation, defined by activated inflammatory pathways and elevated cytokine levels [35–37]. One such pathway is the nod-like receptor pyrin containing 3 (NLRP3) inflammasome which, when activated in chronic stress, leads to release of pro-inflammatory cytokines such as IL-1β and IL-18 [38]. Muhammad et al. demonstrated that dapagliflozin suppresses NLRP3 inflammasome activation and downstream inflammatory mediators, thus inhibiting neuro-inflammation and blood-brain barrier disturbances. The study also demonstrated that the mechanism of action and efficacy shown by dapagliflozin was analogous, and sometimes superior, to the commonly prescribed anti-depressant Escitalopram [34]. While further studies are required to confirm whether such effects are observed in humans, it gives credence to the exciting anti-depressant potentials of SGLT2I in addition to its main anti-diabetic effects among T2DM patients.
Limitations
Several limitations should be noted for the present study. First, given its observational nature, there is inherent information bias due to under-coding, coding errors and missing data. Secondly, as drug compliance is not routinely collected within CDARS, patient compliance to SGLT2I and DPP4I was only assessed indirectly through prescription refills and was not accounted for in Cox regression analyses. Thirdly, residual and unmeasured confounding may be present despite robust propensity-matching, particularly with the unavailability of information such as patient-level socioeconomic status. Patients’ drug exposure duration has not been controlled, which may affect their risk against the study outcomes.
Conclusion
SGLT2I use is associated with significantly lower risk of depression compared to DPP4 use in patients with type-2 diabetes mellitus using propensity score matching and Cox regression analyses.
Supplementary Information
Below is the link to the electronic supplementary material. Supplementary file1 (DOCX 93 KB)