Literature DB >> 31112536

Risk of cardiovascular events associated with dipeptidyl peptidase-4 inhibitors in patients with diabetes with and without chronic kidney disease: A nationwide cohort study.

Tzu-Lan Huang1,2, Fei-Yuan Hsiao1,2,3, Chih-Kang Chiang4,5, Li-Jiuan Shen1,2,3, Chih-Fen Huang2,3.   

Abstract

BACKGROUND: Cardiovascular events associated with oral hypoglycemic agents (OHAs) have raised significant safety concerns. This study assessed the association between dipeptidyl peptidase-4 inhibitors (DPP-4i) and the risk of cardiovascular events in patients with type 2 diabetes mellitus with or without chronic kidney disease (CKD). STUDY
DESIGN: A retrospective cohort study using Taiwan's National Health Insurance Research Database. SETTINGS AND PARTICIPANTS: Our study included patients with type 2 diabetes who received OHAs between March 1, 2009, and December 31, 2012. All eligible subjects were classified into CKD and non-CKD cohorts and further categorized as the DPP-4i and non-DPP-4i users in each cohort.
METHODS: The DPP-4i and non-DPP-4i groups were matched 1:1 by propensity score to attenuate potential selection bias. Propensity score was estimated by logistic regression, using demographics, co-medications, comorbidities. and adapted diabetic complication severity index at baseline. OUTCOMES: Outcomes of interest included a composite endpoint of ischemic stroke, myocardial infarction, cardiovascular death (major adverse cardiac events [MACE]), and hospitalization for heart failure (hHF). COX proportional hazard models were applied to examine the association between DPP-4i and outcomes of interest.
RESULTS: We identified 37,641 and 87,604 patients with type 2 diabetes with and without CKD, respectively. After propensity score matching, 8,213 pairs of CKD patients and 12,313 pairs of non-CKD patients were included for analysis. In the CKD cohort, DPP-4i were associated with a 25% increased risk of hHF (DPP-4i vs. non-DPP-4i incidence/1,000 person-years: 15.0 vs. 9.9, HR = 1.25; 95% CI 1.01-1.54, p = 0.037) but not with the risk of MACE (HR = 0.89, p = 0.144). In the non-CKD cohort, DPP-4i were associated with a lower risk of MACE (DPP-4i vs. non-DPP-4i incidence/1,000 person-years: 9.8 vs. 12.6 HR = 0.73; 95% CI 0.61-0.87, p = 0.0007), but not the risk of hHF (HR = 1.09, p = 0.631).
CONCLUSIONS: DPP-4i were found to be associated with decreased risk of MACE in the non-CKD cohort in our study. However, DPP-4i were associated with increased risk of hHF in the CKD cohort. DPP-4i in the CKD cohort should be used cautiously.

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Year:  2019        PMID: 31112536      PMCID: PMC6528980          DOI: 10.1371/journal.pone.0215248

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


Introduction

Oral antidiabetic-agents-associated cardiovascular events have raised serious concerns since the debates about such risks among thiazolidinedione users that have arisen in the past decades[1]. As a result, the U.S. Food and Drug Administration /European Medical Association have requested cardiovascular safety trials with emerging antidiabetic agents, including Dipeptidyl peptidase-4 inhibitors (DPP-4i). However, three major trials of DPP-4i have reported conflicting results. The SAVOR-TIMI-53, the first large randomized controlled safety trial aimed to assess the risk between DPP-4i and cardiovascular events, unexpectedly found a 27% increase in hospitalization for heart failure (hHF) in patients receiving saxagliptin compared to placebo[2]. However, results from the TECOS trial (sitagliptin) and EXAMINE trial (alogliptin) remained neutral regarding the risk of hHF in patients receiving DPP-4i [3, 4]. In addition, observational studies tried to answer this question and reported inconsistent findings[5-11]. Furthermore, despite the fact that numerous studies have explored the relationship between DPP-4i and cardiovascular events, studies that examined such risk among patients with diabetes and chronic kidney disease (CKD) remain scarce. Existing studies were often conducted in general diabetes populations, which include only a few CKD patients, if any[4-14]. The CKD subgroup had significant clinical relevance as the risk of cardiovascular events have been reported to be greater when patients develop both diabetes and CKD [15]. Other studies also suggested that diabetes and end-stage renal disease could synergistically increase risks of cardiovascular events—up to 5 times greater risk in some cases[16]. With limited available data, this study aims to assess the risk between DPP-4i and cardiovascular events in patients with type 2 diabetes with or without CKD. Particularly, we tested the hypothesis as to whether dialysis status or different DPP-4i contribute differently to our results.

Methods

Data source

We obtained healthcare data on patients with diabetes from the National Health Insurance Database (NHIRD). The National Health Insurance was a single-payer health insurance program initiated in 1995 and covered over 99% of total population in Taiwan. The NHIRD provides records on diagnoses, procedures, and drug prescriptions from the outpatient, inpatient, and emergency departments. For the current study, we used a subset of the NHIRD, the Longitudinal Health Insurance Database, which contains all the original claim data of 1 million beneficiaries randomly sampled from the NHIRD[17]. The study protocol was approved by the Research Ethics Committee of National Taiwan University Hospital (NTUH-REC-201406124W).

Study cohort

All eligible patients had to have at least one diagnosis of diabetes mellitus (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] code: 250) and at least one prescription of oral hypoglycemic agents (OHAs) between March 1, 2009 and December 31, 2012. The cohort identification period was defined as such because the first DPP-i was reimbursed in Taiwan since March 2009. Patients were excluded if they had type 1 diabetes (ICD-9-CM: 250.x1) and were under 20 years old upon cohort entry. We then divided the patients into two cohorts, the CKD group and non-CKD group, according to their underlying renal diseases (S2 Table) and dialysis status.

Exposure assessment

Within the study cohort, patients with at least one prescription of DPP-4i during the cohort identification period were classified as the DPP-4i users, while all other were classified as the non-DPP-4i users. DPP-4i included in our study were sitagliptin, saxagliptin, vildagliptin, and linagliptin. To attenuate potential selection bias, we matched the non-DPP-4i users to the DPP-4i users using propensity score. We estimated the propensity score by logistic regression, using covariates, including age, gender, dialysis status, adapted diabetic complication severity index (aDCSI)[18-20], history of antidiabetic agent use, comorbidities, and selected co-medication use, and the greedy 5 to 1 technique was adopted for matching[21].

Outcome measurement

The main outcomes of interest were hHF (ICD-9-CM: 428) and the composite endpoint of major adverse cardiovascular events (MACE), including myocardial infarction (ICD-9-CM: 410), ischemic stroke (ICD-9-CM: 433–435), and cardiovascular death. The definition of cardiovascular death meets the criteria of Standardized Definitions for End Point Events in Cardiovascular Trials draft by the U.S. Food and Drug Administration[22].

Covariates

Baseline demographics included age, gender, dialysis status, aDCSI[18-20], history of antidiabetic agent use, comorbidities, and selected co-medication use. Baseline comorbidities were defined by diagnostic codes, and dialysis status was confirmed using data in the Registry for Catastrophic illness.

Statistical analysis

Comparisons of demographics, comorbidities, and co-medications between the two user groups were conducted by paired statistical methods, paired t-tests for numeric variables, and McNemar’s test or Cochran’s Q test for categorical variables. All participants were followed until any events, death, end-of-follow-up, or if the patients dropped out of their original group (i.e., if they stop taking study medications, which mimics the as-treated analysis approach in clinical trials). Reentry into cohort was not allowed. Cox proportional hazard models were used to estimate hazard ratio (HR) with 95% confidence intervals (CIs).

Results

Between March 1, 2009, and December 31, 2012, we identified 8,213 pairs of CKD patients and 12,313 pairs of non-CKD patients after propensity-score matching. Baseline demographics were similar between the two user groups in both cohorts after matching (). Median follow-up for HF was 567 days (Q1-Q3: 176–1190) and 611 (Q1-Q3: 202–1298) days in the CKD and the non-CKD cohort, respectively. For MACE, the median follow-up was 553 days (Q1-Q3: 170–1161) and 600 (Q1-Q3: 196–1273) days in the CKD and the non-CKD cohort, respectively. * Abbreviations: AMI, acute myocardial infarction; CHF, congestive heart failure; VHD, valvular heart disease; Afib, atrial fibrillation; PAD, peripheral artery disease; PE, pulmonary embolism; TZD, thiazolidinedione; CCB, calcium channel blocker; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin-2 receptor blocker; NSAID, nonsteroidal anti-inflammatory drugs; Anticoagulant., anticoagulation agents; EPO, erythropoietin; aDCSI, adjusted Diabetes Complications Severity Index; OHA, oral hyperglycemic agent

Risks of hHF and MACE

In the CKD cohort, exposure to DPP-4i were associated with a 25% increase risk of hHF (DPP-4i vs. non-DPP-4i incidence/1,000 person-years: 15.0 vs. 9.9, HR = 1.25; 95% CI 1.01–1.54, p = 0.037) but not with the risk of MACE (HR = 0.89, p = 0.144) (). When we further stratified the CKD cohort according to dialysis status (S2B Table), only those not undergoing dialysis showed an increased risk of hHF associated with DPP-4i use. However, the number of patients undergoing dialysis in this cohort (n = 1,687) was relatively small, which may lead to inadequate statistic power with only numeric increased risk of hHF (HR = 1.18, p = 0.48). Cardiovascular death showed a decreased risk toward DPP-4i, but the case number was very small (1 vs. 11) and therefore could not be further analyzed. aIncidence = number of events/ 1000 person-year bAbbreviations: DPP-4i, DPP-4 inhibitors; hHF, hospitalization for heart failure; MACE, major adverse cardiovascular disease; MI, myocardial infarction; CV death, cardiovascular death On the contrary, exposure to DPP-4i was associated with a lower risk of MACE (DPP-4i vs. non-DPP-4i incidence/1,000 person-years: 9.8 vs. 12.6, HR = 0.73; 95% CI 0.61–0.87, p = 0.0007), but not the risk of hHF (HR = 1.09, p = 0.631) in the non-CKD cohort (). The decreased risk of MACE in the non-CKD population is mainly contributed to the decrease risk of ischemic stroke (DPP-4i vs. non-DPP-4i incidence/1,000 person-years: 7.4 vs. 10.0, HR = 0.68; 95% CI 0.55–0.84, p = 0.0003) related to exposure of DPP-4i. No association between DPP-4i and decreased risk of the other two components of MACE were seen. Overall, results were consistent in subgroup analysis stratified by age, gender, aDCSI, and other covariates (S3–S7 Tables).

Subgroup analysis stratified by DPP-4i

When stratified by different DPP-4i use, the CKD group included 14,138, 1,146, and 1,066 participants in the sitagliptin, saxagliptin, and vildagliptin group, respectively. Similar distribution of patients was seen in the non-CKD group. Only sitagliptin showed consistent results with the main analysis (). Small case numbers of saxagliptin and vildagliptin led to wide CIs and made the results difficult to interpret. *Abbreviations: DPP-4i, DPP-4 inhibitors; hHF, hospitalization for heart failure; MACE, major adverse cardiovascular disease; CKD, chronic kidney disease ** Shown as DPP-4/ Non-DPP-4 group, incidence rates are expressed in events/ 1000-person-year

Discussion

To the best of our knowledge, our study is the first to examine the association between DPP-4i and cardiovascular events in patients with type 2 diabetes with or without CKD. Compared to the non-DPP-4i users, DPP-4 significantly increased 25% risk of hHF in CKD patients. This is consistent with the SAVOR-TIMI-53 trial, which shows a 27% increased risk of hHF associated with saxagliptin use, regardless of kidney disease status[2]. Udell et al. stratified patients according to kidney function in their post hoc analysis and found that increased risk of hHF is seen only in patients with moderately impaired renal function (30 ml/min/1.73 m2 < estimated glomerular filtration rate [eGFR] <50 ml/min/1.73 m2), but not the normal-to-mildly impaired and the severely impaired renal function group (eGFR <30 ml/min/1.73 m2) [23]. Their results are similar to those of our study, since the increased risk is seen only in the CKD cohort but not the non-CKD cohort or dialysis subgroup. However, we have to bear in mind that the number of patients in the most severely impaired renal function group is small in both our study (i.e., dialysis subgroup) and the post hoc analysis. Another difference between our study and the post hoc analysis is that our patients were predominately sitagliptin users. In another observational study done in Taiwan, Ou et al. found no association between DPP-4i and hHF, whether or not the patients had CKD[6]. However, Ou et al.’s comparator groups are different from ours, and they analyzed the CKD group by using subgroup analysis instead of matched cohort, which we emphasized to counter the action of selection bias. Our work was also consistent with another study, which specifically focused on the dialysis patients and found no increased risk between DPP-4i and hHF (HR = 1.14, 95% CI 0.85–1.54)[24]. DPP-4i are a class of drug with pleiotropic effects, and the mechanism between DPP-4i and heart failure is unclear. In an animal model, selectively using DPP-4i in old, diabetic mice results in modest cardiac hypertrophy, impairment of cardiac function, and dysregulated expression of genes and proteins controlling inflammation and cardiac fibrosis[25]. All these findings were not seen in the young DPP4 knockout mice. Further mechanistic experimental studies were warranted to explore the myth of DPP4i in the risk of hHF in different patient characteristics. Although there was no association between DPP-4i and MACE in all three clinical trials, our study shows that DPP-4i were associated with decreased risk of MACE in the non-CKD cohort. When broken down to specific components, DPP-4i use is associated with a 32% decrease in risk of ischemic stroke. These results are similar to those of most of the observational studies done in Taiwan[5, 6, 24]. However, in Ou et al.’s study, DPP-4i decreased risk of ischemic stroke regardless of kidney disease status[6]. More and more studies have been showing that DPP-4 inhibition exhibit neuroprotective effect, either through glucagon-like peptide-mediated or other pathways[26, 27]. Our studies have the following strengths in answering a significant clinical question. First, using the Longitudinal Health Insurance Database, we have a very large sample size (N = 42,864) of unselected patient coverage, which would fit to the general practices of care of patients with type 2 diabetes. Unlike many others, our study population is not restricted to patients with newly diagnosed disease or patients using certain combination of OHA only, which more closely resembles real-world practice. Second, although we are unable to differentiate the effects of saxagliptin, sitagliptin, vildagliptin, and linagliptin on cardiovascular events, we include all four DPP-4i available. Thirdly, our study’s follow-up period is relatively long, with medians of 1.3 and 1.6 years in different models. Finally, we demonstrated the differences between CKD and non-CKD patients, stratified patients into two matched groups, and analyzed them in similar fashion. However, our study has several limitations, which are similar to those of other observational studies based on claims databases. Firstly, due to the lack of laboratory data and other information in NHIRD, we cannot adjust for confounders such as creatinine clearance, HbA1c, or information such as smoking status, body mass index, and other socioeconomic factors, which may lead to unbalanced characteristics between groups and generate bias. However, we applied aDCSI, history of antidiabetic agent use as diabetes severity markers, erythropoietin (EPO) use, and anemia percentage as CKD severity markers. We found only minor differences in CKD and diabetes duration between the treatment groups. Secondly, although the diabetes definition[28-30], CKD definition,[31-33] and outcome codes we used in the study have been validated in NHIRD or large databases, misclassification of events or drug exposure may still exist when conducting database research. As-treated analysis may be able to minimize exposure misclassifications. Thirdly, drug exposure definition relied solely on prescriptions; therefore, patient adherence is unknown and uncontrolled. Another limitation in the study is the possibility of CKD case misclassification; since our screening period of CKD followed those with diabetes, some CKD group patients may not have developed CKD upon cohort entry. However, after excluding those patients and their matched participants, the results remain consistent with those of the main analysis (hHF, HR = 1.54, p = 0.001; MACE, HR = 0.93, p = 0.49). Fourthly, amongst the various DPP-4is, linagliptin is currently being used quite frequently in CKD patients; however, linagliptin was not reimbursed by Taiwan’s National Health Insurance program until June, 2012. As our study cohort was identified as those with diagnosis of diabetes mellitus and received prescription of OHAs between March 1, 2009 and December 31, 2012, less than 20 linagliptin users were included in our study. The low number of participants make it implausible to analyze linaglipin solely. Further studies evaluating the CV profiles of linagliptin are thus warranted.

Conclusion

For patients with type 2 diabetes without CKD, we found that DPP-4i exposure is associated with a lower risk of MACE and ischemic stroke. However, DPP-4i are associated with a higher risk of hHF in patients with type 2 diabetes with CKD. DPP-4i in the CKD cohort should be used cautiously. Future studies with more comprehensive hospital-based cohorts with more detailed patient data are warranted.

OHA drug lists and ATC codes.

(DOCX) Click here for additional data file.

CKD definition: Diagnosis codes and dialysis codes.

(a) CKD-related diagnosis codes (b) Dialysis codes. (DOCX) Click here for additional data file.

Subgroup analysis-hHF in CKD population.

(DOCX) Click here for additional data file.

Subgroup analysis-hHF in non-CKD population.

(DOCX) Click here for additional data file.

Subgroup analysis-MACE in CKD population.

(DOCX) Click here for additional data file.

Subgroup analysis-MACE in non-CKD population.

(DOCX) Click here for additional data file.

Subgroup analysis-ischemic stroke in non-CKD population.

(DOCX) Click here for additional data file.
Table 1

Baseline characteristics of study cohort after matching.

Cohort/GroupCharacteristicsCKD populationNon-CKD population
DPP-4iNon DPP-4iPDPP-4iNon DPP-4iP
N8,2138,213-12,31312,313-
Sex, male (%)52.351.80.518450.250.40.8086
Age, year (mean)65.765.70.823460.860.90.4163
Age, year (std)12.611.912.711.9
aDSCI(median, %)2211
    018.016.50.117648.848.70.6965
    120.820.624.724.5
    222.021.716.116.5
    314.815.56.26.0
    411.812.53.12.9
    5+12.713.21.31.4
Dialysis status9.910.60.1611---
Comorbidity, prior 24 months (%)
    Lipid disorder57.658.00.656955.355.00.7052
    Obesity1.31.30.94421.71.80.7302
    Hypertension78.178.00.892864.163.60.4002
    Transplant0.60.70.55070.10.10.8601
    AMI*3.43.70.37912.12.00.7849
    CHF*11.211.60.50234.34.40.7520
    CHD28.728.90.76802019.70.5513
    VHD*3.23.21.00002.82.90.7308
    AFib*3.03.20.49721.61.70.6888
    PAD*12.612.80.63996.36.31.0000
    Ischemicstroke11.211.10.86147.37.60.4140
    Anemia0.60.70.76880.010.011.0000
    PE*0.10.20.83180.090.110.8388
    Chronic lungdisease18.117.90.806312.913.30.3944
    Cancer8.38.40.90976.66.50.9177
    Hypoglycemia8.69.10.22323.13.00.5287
    Liver cirrhosis2.52.70.29611.81.90.5088
    Thyroid disorder4.44.60.47334.84.90.6772
    Autoimmunedisease3.33.31.00002.52.50.9671
History of Antidiabetics Agent Use, Prior 24 months (%)
    α-glucosidaseinhibitor28.229.00.225420.619.70.0713
    Biguanide76.078.10.001182.781.5<0.001
    Meglitinide19.219.10.902910.09.70.4769
    TZD*30.830.40.543925.124.40.1435
    Sulfonylurea76.778.70.001471.170.00.0348
    Insulin25.727.60.003611.2411.10.7563
OHA* Usage, At Index Date (%)
Median No.2222
    Monotherapy27.426.8< .000121.4422.10.0064
    Dual therapy43.145.444.543.3
    Triple therapy26.925.932.132.2
    >3 therapy2.71.92.12.4
Co-medications (%)
    CCB*49.249.10.912637.637.30.6751
    ACEI*17.918.40.444812.712.90.6696
    ARB*49.450.00.474536.737.00.6561
    β-blocker35.535.50.934128.427.70.2047
    α-blocker7.48.00.20633.53.51.0000
    Diuretics65.565.40.826548.849.20.5860
    Nitrate15.416.20.16489.28.80.2564
    Aspirin34.635.10.463327.827.60.7162
    Antiplatelet other than aspirin20.820.60.797610.610.60.9832
    Anticoagulant*3.53.70.61311.61.70.8805
    Statins39.639.40.754136.235.70.4046
    Fibrates11.811.90.845210.010.00.6973
    NSAIDs*54.054.20.789453.453.30.8184
    COX-2 inhibitors6.66.70.77554.94.91.0000
    EPO*5.05.40.2237---
    Hyperkalemia/ hyperphosphatemia agents2.62.90.36070.20.21.0000

* Abbreviations: AMI, acute myocardial infarction; CHF, congestive heart failure; VHD, valvular heart disease; Afib, atrial fibrillation; PAD, peripheral artery disease; PE, pulmonary embolism; TZD, thiazolidinedione; CCB, calcium channel blocker; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin-2 receptor blocker; NSAID, nonsteroidal anti-inflammatory drugs; Anticoagulant., anticoagulation agents; EPO, erythropoietin; aDCSI, adjusted Diabetes Complications Severity Index; OHA, oral hyperglycemic agent

Table 2

Risks of hHF and MACE.

CKD populationNon-CKD population
DPP-4ibNon-DPP-4iDPP-4iNon-DPP-4i
N (%)aN (%)aHR (95% CI)p-valueN (%)aN (%)aHR (95% CI)p-value
hHFb166 (2.0)15.01207 (2.5)9.851.25(1.01, 1.54)0.037350 (0.4)2.8179 (0.6)2.371.09(0.76,1.59)0.6314
MACEb230 (2.8)20.95447 (5.4)21.700.89(0.75, 1.04)0.1443174 (1.4)9.84412 (3.3)12.580.73(0.61,0.87)0.0007
    MIb72 (0.9)6.48129 (1.6)6.110.98(0.73, 1.32)0.898244 (0.4)2.4785 (0.7)2.551.00(0.65, 1.40)0.8105
    Ischemic stroke160 (1.9)14.50319 (3.9)15.390.87(0.72, 1.06)0.1595131 (1.1)7.40327 (2.7)9.960.68(0.55,0.84)0.0003
    CV deathb1 (<0.1)0.0911 (0.1)0.520.11(0.01, 0.82)0.03182 (<0.01)0.115 (<0.1)0.150.42(0.08, 2.17)0.3017

aIncidence = number of events/ 1000 person-year

bAbbreviations: DPP-4i, DPP-4 inhibitors; hHF, hospitalization for heart failure; MACE, major adverse cardiovascular disease; MI, myocardial infarction; CV death, cardiovascular death

Table 3

DPP-4 inhibitors subgroup analysis.

(a) hHF subgroup analysis
hHFDPP-4i vs. Non-DPP-4i
AllSitagliptinSaxagliptinVildagliptin
CKD population
N16,42814,1381,1461,066
Events**166/ 207137/ 17110/ 234/ 12
Person-year**10004/ 209429087/ 17996499/ 1534418/ 1411
Incidence**15.01/ 9.8515.08/ 9.5020.02/ 14.999.57/ 8.50
HR (95% CI)1.25(1.01, 1.54)1.26(1.00, 1.58)1.08(0.48, 2.42)0.79(0.24, 2.63)
P-value0.03730.04800.85750.6979
Non-CKD population
N26,43620,9641,4922,016
Events**50/ 7945/ 702/ 13/ 8
Person-year**17353/ 3305915706 / 28414690/ 1999956/ 2645
Incidence**2.81/ 2.372.87/ 2.462.89/ 0.503.14/ 3.02
HR (95% CI)1.09(0.76, 1.58)1.09(0.73, 1.61)4.31(0.32, 57.72)0.78(0.18, 3.29)
P-value0.63140.67060.26980.7314
(b) MACE subgroup analysis
MACEDPP-4i vs. Non-DPP-4i
AllSitagliptinSaxagliptinVildagliptin
CKD population
Events**230/447200/3997/212/22
Person-year**9932/ 205239014/ 17599498/1528419/ 1395
Incidence**20.95/ 21.7022.19/ 22.6714.03/13.744.77/ 15.76
HR (95% CI)0.89(0.75, 1.04)0.89(0.74, 1.05)0.92(0.36, 2.35)0.40(0.09, 1.82)
P-value0.14430.170.860.23
Non-CKD population
Events**174/ 412156/ 34811/ 2113/ 41
Person-year**17213/ 3255415574/ 27999685/ 1960953/ 2595
Incidence**9.84/ 12.5810.02/ 12.4316.04/ 10.7113.63/ 15.80
HR (95% CI)0.73(0.61, 0.87)0.75(0.62, 0.91)1.12(0.51, 2.46)0.89(0.44, 1.80)
P-value0.00070.0040.790.74

*Abbreviations: DPP-4i, DPP-4 inhibitors; hHF, hospitalization for heart failure; MACE, major adverse cardiovascular disease; CKD, chronic kidney disease

** Shown as DPP-4/ Non-DPP-4 group, incidence rates are expressed in events/ 1000-person-year

  30 in total

Review 1.  Validity of administrative database coding for kidney disease: a systematic review.

Authors:  Meghan E O Vlasschaert; Shayna A D Bejaimal; Daniel G Hackam; Robert Quinn; Meaghan S Cuerden; Matthew J Oliver; Arthur Iansavichus; Nabil Sultan; Alison Mills; Amit X Garg
Journal:  Am J Kidney Dis       Date:  2011-01       Impact factor: 8.860

2.  Accuracy of diabetes diagnosis in health insurance claims data in Taiwan.

Authors:  Cheng-Ching Lin; Mei-Shu Lai; Ci-Yong Syu; Shuan-Chuan Chang; Fen-Yu Tseng
Journal:  J Formos Med Assoc       Date:  2005-03       Impact factor: 3.282

3.  Patients with diagnosed diabetes mellitus can be accurately identified in an Indian Health Service patient registration database.

Authors:  C Wilson; L Susan; A Lynch; R Saria; D Peterson
Journal:  Public Health Rep       Date:  2001 Jan-Feb       Impact factor: 2.792

4.  Saxagliptin and cardiovascular outcomes in patients with type 2 diabetes and moderate or severe renal impairment: observations from the SAVOR-TIMI 53 Trial.

Authors:  Jacob A Udell; Deepak L Bhatt; Eugene Braunwald; Matthew A Cavender; Ofri Mosenzon; Ph Gabriel Steg; Jaime A Davidson; Jose C Nicolau; Ramon Corbalan; Boaz Hirshberg; Robert Frederich; KyungAh Im; Amarachi A Umez-Eronini; Ping He; Darren K McGuire; Lawrence A Leiter; Itamar Raz; Benjamin M Scirica
Journal:  Diabetes Care       Date:  2014-12-31       Impact factor: 19.112

5.  Failure of ICD-9-CM codes to identify patients with comorbid chronic kidney disease in diabetes.

Authors:  Elizabeth F O Kern; Miriam Maney; Donald R Miller; Chin-Lin Tseng; Anjali Tiwari; Mangala Rajan; David Aron; Leonard Pogach
Journal:  Health Serv Res       Date:  2006-04       Impact factor: 3.402

6.  Diabetes complications severity index and risk of mortality, hospitalization, and healthcare utilization.

Authors:  Bessie Ann Young; Elizabeth Lin; Michael Von Korff; Greg Simon; Paul Ciechanowski; Evette J Ludman; Siobhan Everson-Stewart; Leslie Kinder; Malia Oliver; Edward J Boyko; Wayne J Katon
Journal:  Am J Manag Care       Date:  2008-01       Impact factor: 2.229

7.  Sitagliptin After Ischemic Stroke in Type 2 Diabetic Patients: A Nationwide Cohort Study.

Authors:  Dong-Yi Chen; Szu-Heng Wang; Chun-Tai Mao; Ming-Lung Tsai; Yu-Sheng Lin; Feng-Chieh Su; Chung-Chuan Chou; Ming-Shien Wen; Chun-Chieh Wang; I-Chang Hsieh; Kuo-Chun Hung; Wen-Jin Cherng; Tien-Hsing Chen
Journal:  Medicine (Baltimore)       Date:  2015-07       Impact factor: 1.889

8.  Validating the adapted Diabetes Complications Severity Index in claims data.

Authors:  Hsien-Yen Chang; Jonathan P Weiner; Thomas M Richards; Sara N Bleich; Jodi B Segal
Journal:  Am J Manag Care       Date:  2012-11       Impact factor: 2.229

9.  DPP-4 inhibition and neuroprotection: do mechanisms matter?

Authors:  Richard P Shannon
Journal:  Diabetes       Date:  2013-04       Impact factor: 9.461

10.  The DPP-4 inhibitor linagliptin counteracts stroke in the normal and diabetic mouse brain: a comparison with glimepiride.

Authors:  Vladimer Darsalia; Henrik Ortsäter; Anna Olverling; Emilia Darlöf; Petra Wolbert; Thomas Nyström; Thomas Klein; Åke Sjöholm; Cesare Patrone
Journal:  Diabetes       Date:  2012-12-03       Impact factor: 9.461

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1.  Dipeptidyl peptidase-4 inhibitors and cardiovascular events in patients with type 2 diabetes, without cardiovascular or renal disease.

Authors:  Sheriza N Baksh; Jodi B Segal; Mara McAdams-DeMarco; Rita R Kalyani; G Caleb Alexander; Stephan Ehrhardt
Journal:  PLoS One       Date:  2020-10-15       Impact factor: 3.240

  1 in total

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