Literature DB >> 31960563

Liver-related long-term outcomes of thiazolidinedione use in persons with type 2 diabetes.

Fu-Shun Yen1, Yu-Cih Yang2,3, Chii-Min Hwu4,5, James C-C Wei6, Yi-Hsiang Huang7,8, Ming-Chih Hou4,7, Chih-Cheng Hsu9,10,11.   

Abstract

BACKGROUND & AIMS: Studies have described prominent histologic improvement in patients with nonalcoholic steatohepatitis (NASH) using thiazolidinedione (TZD); however, these were all short term with moderate sample size, no liver-related long-term outcomes could be noted.
METHODS: This retrospective cohort study enrolled patients with newly diagnosed type 2 diabetes mellitus (T2DM) from Taiwan's National Health Insurance Research Database between 1 January 2000 and 31 December 2013. We matched TZD users and nonusers at a 1:1 ratio through propensity score matching. This study included 5095 paired TZD users and nonusers. Cox proportional hazard models were used to compare the risks of cirrhosis, hepatic decompensation, hepatic failure and all-cause mortality between TZD users and nonusers. The Kaplan-Meier method was used to compare the cumulative incidence of these main outcomes.
RESULTS: The incidence rates of cirrhosis, hepatic decompensation, hepatic failure and all-cause mortality during follow-up were 0.77 vs 1.95, 1.43 vs 1.75, 0.36 vs 0.70, and 4.89 vs 3.78 per 1000 person-years between TZD users and nonusers. The adjusted hazard ratios of cirrhosis, hepatic decompensation, hepatic failure and all-cause mortality were 0.39 (95% confidence interval [CI]: 0.21-0.72), 0.86 (95% CI: 0.52-1.44), 0.46 (95% CI: 0.18-1.17) and 1.18 (95% CI: 0.87-1.61) respectively.
CONCLUSIONS: Our study demonstrated that TZD use could significantly lower the risk of cirrhosis. In clinical settings, TZD use might be able to improve liver-related long-term outcomes.
© 2020 The Authors. Liver International published by John Wiley & Sons Ltd.

Entities:  

Keywords:  all-cause mortality; cirrhosis; hepatic decompensation; hepatic failure; liver-related death; nonalcoholic fatty liver disease

Mesh:

Substances:

Year:  2020        PMID: 31960563      PMCID: PMC7317545          DOI: 10.1111/liv.14385

Source DB:  PubMed          Journal:  Liver Int        ISSN: 1478-3223            Impact factor:   5.828


Charlson comorbidity index confidence interval cardiovascular Diabetes Complications Severity Index diabetes mellitus hepatic B virus hepatocellular carcinoma hepatic C virus heart failure hazard ratios nonalcoholic fatty liver disease nonalcoholic steatohepatitis oral antidiabetic drugs peroxisome proliferator‐activated receptor gamma type 2 diabetes mellitus thiazolidinedione The prevalence of T2DM and NAFLD has dramatically increased worldwide. Our study disclosed that TZD use could significantly lower the risk of cirrhosis as compared with no use. TZD use in patients with T2DM might improve their liver‐related long‐term outcomes.

INTRODUCTION

Owing to a sedentary lifestyle and westernized diet, the prevalence of type 2 diabetes mellitus (T2DM) and nonalcoholic fatty liver disease (NAFLD) has dramatically increased worldwide. According the IDF diabetes atlas, globally, diabetes mellitus (DM) cases increased from 151 million in 2000 to 425 million in 2017, representing an approximately 2.8‐fold increase in 17 years.1 In Taiwan, DM cases also increased from 707 000 in 20002 to 1 958 000 in 2017, representing an approximately 2.77‐fold increase in 17 years. NAFLD is a new epidemic and is the most common cause of chronic liver disease3; its estimated worldwide prevalence is approximately 15%‐30%.4 People with T2DM frequently have dyslipidaemia and NAFLD. Approximately 40%‐70% of patients with T2DM have NAFLD5, 6; in Taiwan, approximately 43.3% of T2DM have NAFLD.7 NAFLD can progress to nonalcoholic steatohepatitis (NASH), hepatic fibrosis, cirrhosis and even hepatocellular carcinoma (HCC)8; it can also aggravate cardiovascular events in patients with T2DM.9 Furthermore, in patients with NAFLD, diabetes can increase the risks of hepatic complications and death.10 Thiazolidinediones (TZDs) are one of the most promising medications for treating NAFLD; studies have revealed histological improvement in patients with NASH, and fibrosis was even attenuated in some patients.11, 12, 13, 14, 15 TZDs bind and activate the nuclear receptor of peroxisome proliferator‐activated receptor gamma (PPARγ) with strong insulin‐sensitizing activity. They ameliorate insulin resistance by acting on adipose tissue, muscle and liver to increase glucose utilization and decrease glucose production. They can also increase adiponectin levels, reduce free fatty acid influx, increase fatty acid oxidation, and then decrease liver fat and attenuate hepatic inflammation.16 But until now, most of the TZD studies in patients with NAFLD or NASH are short term with few number of participants, no long‐term liver outcomes were noted. Therefore, we performed this nationwide cohort study to evaluate the liver‐related outcomes of TZD use in persons with type 2 diabetes.

METHODS

Study design and patients

In Taiwan, the National Health Insurance (NHI) programme has been implemented since 1995. At least 99% of the 23.5 million population of Taiwan are registered in this insurance programme.17 The National Health Insurance Research Database (NHIRD) includes the healthcare data of the insurants of the NHI programme, including sex, date of birth, residency area, medical procedures, drug prescriptions and diagnosis according to the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD‐9‐CM). The Longitudinal Health Insurance Database 2000 (LHID2000) contains all the original claims data of 1 million beneficiaries randomly sampled from all insurants of the NHI programme in 2000. This cohort study was conducted using the LHID2000. All information that could be used to identify individuals or care providers was encrypted. This study was approved by the Research Ethics Committee of China Medical University and Hospital (CMUH104‐REC2‐115) and was granted a waiver of informed consent. We selected patients with type 2 diabetes (T2DM) diagnosis ascertained through the presence of the ICD‐9‐CM code 250.xx in at least two outpatient records over 1 year or in one inpatient record in the LHID2000 between 1 January 2000 and 31 December 2013. We excluded patients diagnosed with DM before 1 January 2000, to ensure that only incident T2DM cases were included. This study excluded individuals younger than 30 years or older than 80 years, having follow‐up less than 180 days and those diagnosed with type 1 diabetes (Table S1), hepatitis B virus (HBV) infection, hepatitis C virus (HCV) infection, alcoholism, dialysis and heart failure (HF). This study also excluded patients diagnosed with cirrhosis, oesophageal varices, hepatic ascites, hepatic encephalopathy, jaundice, hepatic failure and HCC before the index date or within 180 days after index date. The algorithm for the definitions of diabetes and cirrhosis based on ICD‐9 coding has been validated in previous studies.18, 19

Procedures

We defined the first date of TZD use by our patients as the index date. Those who had not used any TZD in the observation period were considered TZD nonusers. Each TZD nonuser was randomly assigned an index date according to the corresponding index date of a TZD user. The TZDs examined in this study included pioglitazone and rosiglitazone (Troglitazone and ciglitazone were not used in Taiwan). The covariates analysed in multivariable models included baseline demographics (we grouped the diagnoses of overweight, abnormal weight gain, and BMI 25‐29 as overweight; obesity, BMI 30‐39, obesity complicated pregnancy as obesity; severe obesity, BMI ≥ 40, and bariatric surgery status for obesity as severe obesity), comorbidities diagnosed 1 year before the index date, and medications including antidiabetic agents, antihypertensive drugs, statin and aspirin. We used the Charlson comorbidity index (CCI) to quantify patients’ comorbidity profiles20 and the Diabetes Complications Severity Index (DCSI) score21 to define the severity of diabetes. CCI and DCSI scores were calculated according to patients’ records in the NHIRD 1 year before the index date.

Liver‐related long‐term outcomes

Using ICD‐9‐CM codes in medical records, we assessed the incidence rates of cirrhosis, hepatic decompensation (the composite of oesophageal varices, ascites, hepatic encephalopathy and jaundice),22 oesophageal varices, abdominal ascites, hepatic encephalopathy, jaundice, hepatic failure and HCC to determine liver‐related long‐term outcomes. We did a sensitivity analysis by excluding patients diagnosed with cirrhosis, oesophageal varices, hepatic ascites, hepatic encephalopathy, jaundice, hepatic failure, HCC or death within 365 days after index date.

Statistical analyses

Propensity score matching was used to optimize comparability between TZD users and nonusers.23 The propensity score was estimated for every patient using a nonparsimonious multivariable logistic regression, with TZD use as the dependent variable. In all, 26 clinically relevant covariates were used as independent variables (Table 1) in the regression. The nearest‐neighbour algorithm was applied to construct matched pairs, assuming that a proportion of 0.995‐1.0 was perfect.
Table 1

Baseline characteristics of study population

VariableOriginal populationStandardized differencea PS‐matching populationStandardized differencea
Type II DM with TZDs (n = 6420)Type II DM without TZDs (n = 66 766)Type II DM with TZDs (n = 5095)Type II DM without TZDs (n = 5095)
N%N%N%N%
Gender
Female313348.836 61354.80.121243647.8239346.90.017
Male328751.230 15345.20.121265952.2270253.10.017
Age at baseline, year
Mean (SD)59.7 (10.7)55.8 (12.3)0.33859.0 (10.9)59.0 (11.1)0.001
Comorbidity
Overweight290.453760.560.016220.43190.370.009
Obesity1902.9616322.440.0321543.021533.000.001
Severe obesity250.391430.210.032200.39130.260.024
CCI score
0476274.256 49384.60.26389576.5393077.10.016
177812.156678.490.1259711.757611.30.013
≥288013.746066.900.22560311.858911.60.009
DCSI score
0516280.460 64490.80.3413581.2413481.10.001
13896.0619822.970.1493146.163406.670.021
≥286913.541406.200.24864612.762112.20.015
Medication
Oral antidiabetic drugs
0‐12794.3548 21572.21.952795.482895.670.009
2112017.411 43217.10.009111821.9124024.30.057
≥3502178.2711910.61.853369872.6356669.90.057
Metformin618296.323 55635.31.679485795.3487595.70.017
Sulfonylurea601293.619 93429.81.74468791.9466991.60.013
DPP‐4 inhibitors360756.242666.391.273241747.4226244.40.061
AGIs339552.847797.161.151229445.0216042.40.053
Meglitinides216833.731234.680.794141527.8133126.10.037
Insulin347154.1825512.30.987242547.6230745.30.046
Antihypertensive drugs
0‐1130320.324 72337.00.377112322.0111221.80.005
286113.410 31915.50.05868913.571814.10.016
≥3425666.331 72447.50.386328364.5326564.10.007
Statin281143.812 84819.20.548205440.3203339.90.008
Aspirin298246.518 77628.10.386221343.4218542.90.011
Follow‐up time, y
Mean (SD)3.53 (2.58)4.91 (3.90)0.4163.82 (2.71)3.90 (3.01)0.027

Abbreviations: AGI, Alpha‐glucosidase inhibitors; CCI, Charlson comorbidity index; DCSI, Diabetes complications severity index; DPP‐4 inhibitors, dipeptidyl peptidase‐4 inhibitors; TZDs, Thiazolidinediones.

A standardized mean difference of ≤0.10 indicates a negligible difference between the two cohorts.

Baseline characteristics of study population Abbreviations: AGI, Alpha‐glucosidase inhibitors; CCI, Charlson comorbidity index; DCSI, Diabetes complications severity index; DPP‐4 inhibitors, dipeptidyl peptidase‐4 inhibitors; TZDs, Thiazolidinediones. A standardized mean difference of ≤0.10 indicates a negligible difference between the two cohorts. Cox proportional hazard models were used to compare the outcomes between TZD users and nonusers. All analyses were conducted using an intention‐to‐treat approach in accordance with the initial TZD assignment, irrespective of subsequent changes to other antidiabetic medications. The results are expressed as hazard ratios (HRs) with 95% confidence intervals (CIs). To calculate the risk of mortality, we censored patients at the time of death or the end of study, whichever occurred first. To calculate the risks of other investigated outcomes, we censored patients on the respective events or at the end of follow‐up on 31 December 2013, whichever occurred first. Using the Kaplan‐Meier method, we compared the cumulative incidence of cirrhosis over time between TZD users and nonusers. We performed subgroup analysis according to pre‐specified strata of clinical interest to assess effect modification. The subgroup strata included overall rosiglitazone and pioglitazone use; sex; CCI score; oral antidiabetic drugs; insulin; antihypertensive drugs and statin. We calculate the P for interaction to see the different effects of variables in the same subgroup. A two‐tailed P value less than .05 was considered significant. SAS version 9.2 was used for analyses.

RESULTS

Patients

We identified 22 856 patients newly diagnosed type 2 diabetes who used TZD and 74 126 patients newly diagnosed T2DM who had never used TZD between 1 January 2000 and 31 December 2013. The flowchart for patient selection for this study is depicted in Figure 1. After propensity score matching, 5095 pairs of patients were selected. The matched pairs were similar in terms of all covariates. The mean age of the cohort was 59.0 years, 52.7% of patients were men. The mean follow‐up time (mean [standard deviations, SD]) of the TZD users and non‐TZD users were 3.84 (2.71) and 3.90 (3.01) years respectively (Table 1).
Figure 1

Flow chart of patient selection for this cohort study

Flow chart of patient selection for this cohort study

Main outcomes

In this study, 96 (1.88%) TZD users and 76 (1.49%) nonusers died during the follow‐up period (incidence rate: 4.89 vs 3.78 per 1000 person‐years). Multivariable models showed that TZD users had no significant difference of mortality (aHR: 1.18, P = .27; Table 2). For liver‐related outcomes, TZD users appeared to have lower risks of cirrhosis (incidence rate: 0.77 vs 1.95 per 1000 person‐years; aHR: 0.39, P = .002; Table 2); TZD users appeared to have no significant difference in the risks of hepatic decompensation (aHR: 0.86, P = .58), hepatic failure (aHR: 0.46, P = .10) and HCC (aHR:1.22, P = .35) during the follow‐up period. Figure 2 delineates the cumulative incidence rates of cirrhosis between TZD users and nonusers, which were determined using the Kaplan‐Meier method.
Table 2

TZD users vs. nonusers in patients with type 2 diabetes after propensity matching

OutcomeTZDs userTZDs nonuserCrudeMultivariable adjusted
EventPYIREventPYIRHR (95% CI) P valueHR (95% CI) P value
All‐cause mortality9619 6214.897620 1243.781.30 (0.96‐1.76).081.18 (0.87‐1.61).27
Cirrhosis1519 5650.773920 0231.950.41 (0.22‐0.75).0040.39 (0.21‐0.72).002
Hepatic decompensation2819 5331.433520 0371.750.89 (0.53‐1.47).650.86 (0.52‐1.44).58
Oesophageal varices319 6030.15320 1170.150.98 (0.19‐4.88).981.11 (0.22‐5.62).89
Hepatic ascites919 6130.461320 1100.650.74 (0.31‐1.77).500.73 (0.30‐1.75).48
Hepatic encephalopathy1196 1610.01020 1210
Jaundice1019 5840.511320 0930.650.77 (0.34‐1.77).550.74 (0.32‐1.70).48
Hepatic failure719 6070.361420 0560.700.51 (0.20‐1.27).150.46 (0.18‐1.17).10
Hepatic carcinoma2119 5821.072320 0851.151.08 (0.97‐3.49).091.22 (0.95‐2.49).35

Decompensated cirrhosis contains oesophageal varices, hepatic ascites, hepatic encephalopathy, hepatic Jaundice.

HR adjusted for gender, age, comorbidities, CCI score, DCSI score and medications use.

— Unable to calculate because there are few or no events in with and without TZD cohorts.

Abbreviations: CI, confidence interval; HR, hazard ratio; IR, incidence rate, per 1000 person‐years; PY, person‐years; TZDs, Thiazolidinediones.

Figure 2

Cumulative incidence of cirrhosis between Thiazolidinediones users and nonusers in T2DM through Kaplan‐Meier

TZD users vs. nonusers in patients with type 2 diabetes after propensity matching Decompensated cirrhosis contains oesophageal varices, hepatic ascites, hepatic encephalopathy, hepatic Jaundice. HR adjusted for gender, age, comorbidities, CCI score, DCSI score and medications use. — Unable to calculate because there are few or no events in with and without TZD cohorts. Abbreviations: CI, confidence interval; HR, hazard ratio; IR, incidence rate, per 1000 person‐years; PY, person‐years; TZDs, Thiazolidinediones. Cumulative incidence of cirrhosis between Thiazolidinediones users and nonusers in T2DM through Kaplan‐Meier

Subgroup analysis

Table 3 presents the results of subgroup analysis of cirrhosis between TZD users and nonusers. Compared with TZD nonusers, rosiglitazone and pioglitazone users, men, patients undergoing insulin treatment, using ≧3 oral antidiabetic drugs, and statin nonusers had a significantly lower risk of cirrhosis. We used the p for interaction to compare rosiglitazone and pioglitazone on the effect of cirrhosis, which show no significant difference (P = .9209).
Table 3

Incidence and Cox proportional hazard regression with hazard ratios and 95% confidence intervals of cirrhosis associated with and without TZD by gender, age group and comorbidities

VariableTZDAdjusted HR (95% CI) P for interaction
NoYes
EventPerson‐yearIREventPerson‐yearIR
Overall3920 0231.951519 5650.770.39 (0.21‐0.72)**  
Rosiglitazone3920 0231.95689340.670.43 (0.20‐0.90)* .9209
Pioglitazone3920 0231.95910 6310.840.35 (0.15‐0.85)*  
Gender
Female1596041.56695600.630.72 (0.25‐2.00).1648
Male2410 4192.3910 0050.90.31 (0.10‐0.92)*
CCI index
03015 8821.891315 0270.870.46 (0.20‐1.08).7319
1621372.81123180.430.22 (0.02‐1.97)
≥2320041.5122200.450.13 (0.007‐2.65)
OAD
0‐101006019751.03.3839
21041222.43441190.970.31 (0.06‐1.51)
≥32914 8951.951014 4710.690.40 (0.16‐0.98)*
Insulin
No1110 0471.09393050.320.52 (0.14‐1.90).0017
Yes2899762.811210 2601.170.39 (0.16‐0.97)*
Antihypertensive drugs
0‐1238110.52139500.25.0588
2826093.07125170.40.24 (0.02‐1.97)
≥32913 6032.131313 0980.990.51 (0.23‐1.14)
Statin
No2711 4102.37811 2550.710.41 (0.16‐0.99)* .2356
Yes1286131.39783100.840.45 (0.11‐1.71)

HR adjusted for gender, age, comorbidities, CCI score, DCSI score, and medications use.

— Unable to calculate because there are few or no events in with and without TZD cohorts.

Abbreviations: CCI, Charlson comorbidity index; CI, confidence interval; HR, hazard ratio; IR, incidence rate, per 1000 person‐years; OAD, oral antidiabetic drugs; PY, person‐years; TZDs, Thiazolidinediones.

P < .05.

P < .01.

Incidence and Cox proportional hazard regression with hazard ratios and 95% confidence intervals of cirrhosis associated with and without TZD by gender, age group and comorbidities HR adjusted for gender, age, comorbidities, CCI score, DCSI score, and medications use. — Unable to calculate because there are few or no events in with and without TZD cohorts. Abbreviations: CCI, Charlson comorbidity index; CI, confidence interval; HR, hazard ratio; IR, incidence rate, per 1000 person‐years; OAD, oral antidiabetic drugs; PY, person‐years; TZDs, Thiazolidinediones. P < .05. P < .01.

Sensitivity test

After excluding patients with liver‐related events or death within 365 days after index date, the adjusted HR of cirrhosis in TZD users was 0.37 (95% CI: 0.20‐0.65, P = .0006) compared with nonusers. TZD users had a significantly lower risk of cirrhosis (Tables S2 and S3).

DISCUSSION

Our study demonstrated that TZD use in T2DM could significantly decrease the risk of cirrhosis. Subgroup analysis revealed that both rosiglitazone and pioglitazone could lower the risks of cirrhosis with no significantly different effects between these two drugs. Belfort et al randomly compared a hypocaloric diet plus pioglitazone with the diet plus placebo in 55 patients with impaired glucose tolerance or T2DM. The pioglitazone group showed reduced liver function and hepatic fat content, increased insulin sensitivity, and improved histologic steatosis, ballooning necrosis and inflammation but no significant difference in improvement of fibrosis compared with the placebo group.11 Cusi et al conducted a similar RCT to compare 101 pre‐diabetes or T2DM patients, consuming a hypocaloric diet plus pioglitazone or the diet plus placebo. The pioglitazone group showed reduced liver triglyceride content and improved histological scores of steatosis, inflammation, ballooning and fibrosis.14 Aithal et al compared the standard diet and exercise with pioglitazone or with placebo in 74 nondiabetic patients with NASH.12 The pioglitazone group showed reduced alanine aminotransferase levels and improved histologic features of hepatic injury, Mallory bodies and fibrosis compared with the placebo group. Sanyal et al conducted a RCT to compare vitamin E and pioglitazone with placebo in nondiabetic patients with NASH.13 Compared with placebo, both vitamin E and pioglitazone could reduce aminotransferase levels, decrease hepatic steatosis and lobular inflammation, but they could not improve fibrosis scores. Ratziu et al randomly assigned 63 patients with NASH to receive rosiglitazone or placebo treatment. The rosiglitazone group had improved 21% of steatosis and normalized 21% of transaminase levels. No improvement in ballooning, inflammation and fibrosis was noted.15 The systemic review and meta‐analysis of TZD use in patents with NASH has revealed that TZD could reduce liver fat, normalize aminotransferase levels and improve histological steatosis, ballooning and inflammation.24 These researches indicated that TZD use in patients with NASH could attenuate hepatic injury, inflammation and even fibrosis. However, these studies were all short‐term clinical trials with a moderate sample size; no large series study of long‐term liver‐related outcomes has been conducted. To the best of our knowledge, our study is the first large series cohort study investigating liver‐related long‐term outcomes of TZD use in patients with T2DM. Though our patients were not image or histology confirmed NAFLD or NASH cases; however, based on epidemiological studies, at least 50% of patients with T2DM exhibit NAFLD.5 In addition, we excluded patients with previous viral hepatitis and alcoholism to make our population more similar to patients with NAFLD. This study indicated that TZD use in patients with T2DM might be able to prevent the development of cirrhosis; T2DM patients with the risk of hepatic injury could use TZD to prevent bad liver‐related long‐term outcomes. Through the activation of PPARγ, TZD can reduce insulin resistance, sequester fatty acid in adipose tissue, and alleviate fat storage, steatosis and ballooning in the liver. TZDs also can activate AMP‐activated protein kinase (AMPK) and reduce hepatic fat content.25 TZD use can increase adiponectin and reduce high‐sensitivity C‐reactive protein (hs‐CRP), TNFα, IL‐1β and IL‐6 levels, which can reduce hepatic inflammation.26, 27 In preclinical studies, TZDs bind to PPARγ and thus inhibit the activation of hepatic stellate cells, reduce extracellular matrix production, decrease transforming growth factor β1 expression, attenuate matrix remodelling, and protect for tissue repair, fibrosis and even cirrhosis.28 TZD can induce cell cycle arrest and apoptosis and inhibit cancer cell proliferation and invasion through the activation of PPARγ.29 Some studies have revealed that TZD use may decrease the risk of HCC in T2DM.30, 31 However, another study provided contrasting results.32 A meta‐analysis disclosed that TZD does not decrease the risk of HCC,33 which is consistent with our result. Our study has several strengths. We recruited patients from the NHIRD, which covers approximately 99% of the population of Taiwan. This might be able to decrease the risk of selection bias in the study. We used medical records instead of self‐reports, which might decrease recall bias and more correctly censor the incident rates of the main outcomes. The events noted within 6 months after the index date were excluded to decrease the possibility of latent morbidity and mortality. We did a sensitivity test to exclude patients with liver‐related events or death within 365 days after the index date, which also revealed that TZD could significantly lower the risk of cirrhosis. Our study has some limitations. First, the NHIRD does not contain detailed information on patients’ lifestyle, height and body weight, and family history; all of which might influence the measured outcomes. Although we took many codings to include overweight, obesity and severe obesity as covariates in analysis, many patientsobesity might not be recorded in the database, which could lead to the underestimation of the prevalence of obesity in our study. Second, the use of ICD‐9 codings for the censoring of cases in administrative databases has been criticized about its accuracy. In this study, the algorithm of using ICD‐9 to define type 2 diabetes and cirrhosis has been validated in the Taiwan National Health Insurance Research Database18 and in the administrative database from Parkland health and Hospital System,19 with acceptable accuracy. Using the NHI claim databases, we may avoid the measurement errors introduced by poor patient recall. Third, we lacked the results of biochemical tests and image examinations; therefore, we could not confirm the diagnosis of NAFLD in this dataset. This made us be difficult to observe the effects of TZD on NAFLD progression, which may be considered as intermediate processes in hepatic pathological changes. However, instead of the proxy indictor, we used several hard outcomes, including cirrhosis, hepatic failure, hepatic decompensation and mortality, to elucidate liver outcomes resulting from the TZD use. We believe important clinical implications can be derived from our study. Finally, our study was a cohort study with inevitable biases certainly existed. A larger randomized control study should be conducted to observe the liver‐specific endpoints after TZD use in patients with T2DM.

CONCLUSIONS

Our nationwide cohort study revealed that compared with TZD nonuse, TZD use in type 2 diabetes could significantly lower the risk of cirrhosis. In clinical settings, TZD use in T2DM patients might be able to improve their liver‐related long‐term outcomes.

CONFLICT OF INTEREST

The authors do not have any disclosures to report.

AUTHORS' CONTRIBUTIONS

Specific author contributions: Dr Ming‐Chih Hou and Chih‐Cheng Hsu had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of data analysis. Concept and design: Fu‐Shun Yen, James C.‐C. Wei and Yi‐Hsiang Huang; acquisition, analysis and interpretation of data: Fu‐Shun Yen and Chii‐Min Hwu; drafting of the manuscript: Fu‐Shun Yen, Ming‐Chih Hou and Chih‐Cheng Hsu; critical revision of the manuscript for important intellectual content: All authors; statistical analysis: Fu‐Shun Yen, Yu‐Cih Yang, Chii‐Min Hwu and Chih‐Cheng Hsu; obtained funding: Yu‐Cih Yang; administrative, technical and material support: Yu‐Cih Yang and James C.‐C. Wei; supervision: Ming‐Chih Hou and Chih‐Cheng Hsu. Click here for additional data file.
  32 in total

Review 1.  Thiazolidinediones.

Authors:  Hannele Yki-Järvinen
Journal:  N Engl J Med       Date:  2004-09-09       Impact factor: 91.245

Review 2.  Natural history and prognostic indicators of survival in cirrhosis: a systematic review of 118 studies.

Authors:  Gennaro D'Amico; Guadalupe Garcia-Tsao; Luigi Pagliaro
Journal:  J Hepatol       Date:  2005-11-09       Impact factor: 25.083

3.  Epidemiology and natural history of non-alcoholic steatohepatitis.

Authors:  Curtis K Argo; Stephen H Caldwell
Journal:  Clin Liver Dis       Date:  2009-11       Impact factor: 6.126

Review 4.  Systematic review: the epidemiology and natural history of non-alcoholic fatty liver disease and non-alcoholic steatohepatitis in adults.

Authors:  G Vernon; A Baranova; Z M Younossi
Journal:  Aliment Pharmacol Ther       Date:  2011-05-30       Impact factor: 8.171

5.  Changes in the prevalence of the most common causes of chronic liver diseases in the United States from 1988 to 2008.

Authors:  Zobair M Younossi; Maria Stepanova; Mariam Afendy; Yun Fang; Youssef Younossi; Hesham Mir; Manirath Srishord
Journal:  Clin Gastroenterol Hepatol       Date:  2011-03-25       Impact factor: 11.382

6.  Functional role of peroxisome-proliferator-activated receptor γ in hepatocellular carcinoma.

Authors:  Chung-Wah Wu; Geoffery C Farrell; Jun Yu
Journal:  J Gastroenterol Hepatol       Date:  2012-11       Impact factor: 4.029

7.  The natural history of nonalcoholic fatty liver disease: a population-based cohort study.

Authors:  Leon A Adams; James F Lymp; Jenny St Sauver; Schuyler O Sanderson; Keith D Lindor; Ariel Feldstein; Paul Angulo
Journal:  Gastroenterology       Date:  2005-07       Impact factor: 22.682

8.  A placebo-controlled trial of pioglitazone in subjects with nonalcoholic steatohepatitis.

Authors:  Renata Belfort; Stephen A Harrison; Kenneth Brown; Celia Darland; Joan Finch; Jean Hardies; Bogdan Balas; Amalia Gastaldelli; Fermin Tio; Joseph Pulcini; Rachele Berria; Jennie Z Ma; Sunil Dwivedi; Russell Havranek; Chris Fincke; Ralph DeFronzo; George A Bannayan; Steven Schenker; Kenneth Cusi
Journal:  N Engl J Med       Date:  2006-11-30       Impact factor: 91.245

9.  Incidence and prevalence rates of diabetes mellitus in Taiwan: analysis of the 2000-2009 Nationwide Health Insurance database.

Authors:  Yi-Der Jiang; Chia-Hsuin Chang; Tong-Yuan Tai; Jung-Fu Chen; Lee-Ming Chuang
Journal:  J Formos Med Assoc       Date:  2012-10-23       Impact factor: 3.282

10.  Liver-related long-term outcomes of thiazolidinedione use in persons with type 2 diabetes.

Authors:  Fu-Shun Yen; Yu-Cih Yang; Chii-Min Hwu; James C-C Wei; Yi-Hsiang Huang; Ming-Chih Hou; Chih-Cheng Hsu
Journal:  Liver Int       Date:  2020-02-10       Impact factor: 5.828

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1.  Epidemiology and disease burden of non-alcoholic steatohepatitis in greater China: a systematic review.

Authors:  Huimin Zou; Ying Ge; Qing Lei; Carolina Oi Lam Ung; Zhen Ruan; Yunfeng Lai; Dongning Yao; Hao Hu
Journal:  Hepatol Int       Date:  2022-01-31       Impact factor: 6.047

Review 2.  Post-transplant diabetes mellitus and preexisting liver disease - a bidirectional relationship affecting treatment and management.

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Journal:  World J Gastroenterol       Date:  2020-06-07       Impact factor: 5.742

Review 3.  Selection and Warning of Evidence-Based Antidiabetic Medications for Patients With Chronic Liver Disease.

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4.  Non-alcoholic fatty liver disease in adults 2021: A clinical practice guideline of the Italian Association for the Study of the Liver (AISF), the Italian Society of Diabetology (SID) and the Italian Society of Obesity (SIO).

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Journal:  Eat Weight Disord       Date:  2021-12-16       Impact factor: 3.008

5.  Liver-related long-term outcomes of thiazolidinedione use in persons with type 2 diabetes.

Authors:  Fu-Shun Yen; Yu-Cih Yang; Chii-Min Hwu; James C-C Wei; Yi-Hsiang Huang; Ming-Chih Hou; Chih-Cheng Hsu
Journal:  Liver Int       Date:  2020-02-10       Impact factor: 5.828

6.  Thiazolidinediones and Glucagon-Like Peptide-1 Receptor Agonists and the Risk of Nonalcoholic Fatty Liver Disease: A Cohort Study.

Authors:  Judith van Dalem; Johanna H M Driessen; Andrea M Burden; Coen D A Stehouwer; Olaf H Klungel; Frank de Vries; Martijn C G J Brouwers
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