Decreased incidence of diabetes in patients with gout using benzbromarone

Decreased incidence of diabetes in patients with gout using benzbromarone Abstract Objective Insulin resistance is inversely correlated with the clearance rate of uric acid, which may indicate that improvement in the clearance rate of uric acid could reduce insulin resistance. Considering the increased prevalence of diabetes mellitus (DM) in the gout population, this study evaluated the effects of benzbromarone, a uricosuric agent, on the incidence of DM in the gout population. Methods We used data from the Taiwan National Health Insurance program. The benzbromarone user cohort included 8678 patients; each patient was age- and sex-matched with one benzbromarone non-user who was randomly selected from the gout population. The Cox proportional hazard regression analysis was conducted to estimate the effects of benzbromarone on the incidence of DM in the gout population. Results The incidence of DM was significantly lower in benzbromarone users than in benzbromarone non-users [adjusted hazard ratio (HR) = 0.86; 95% CI: 0.79, 0.94]. The HR for the incidence of DM was lower in male benzbromarone users (adjusted HR = 0.77; 95% CI: 0.69, 0.86) than in benzbromarone non-users. An analysis of three age groups (<40, 40–59 and ⩾60 years) indicated that the HRs of the age groups of 40–59 years (adjusted HR = 0.86; 95% CI: 0.76, 0.98) and ⩾60 years (adjusted HR = 0.82; 95% CI: 0.71, 0.94) were significantly lower among benzbromarone users than among benzbromarone non-users. Conclusion In the gout population, the incidence of DM was lower in benzbromarone users than in benzbromarone non-users. benzbromarone, diabetes mellitus, gout Rheumatology key messages There is a decreased incidence of diabetes in patients with gout with benzbromarone usage. There is a significantly trend of dose-dependent decrease of incidence of diabetes among benzbromarone users. Introduction The global prevalence (age standardized) of diabetes, which was estimated to be 422 million in 2014 compared with 108 million in 1980, has nearly doubled since 1980, increasing from 4.7 to 8.5% in the adult population [1]. Hyperuricaemia or gout is commonly observed in patients with metabolic syndrome (MetS) [2]. The increasing prevalence of gout is likely related to several factors including the increased incidence of obesity, hypertension, type 2 diabetes mellitus (DM), MetS, chronic kidney disease (CKD), lifestyle factors and the increased use of causative medications [3]. Thus, gout may be considered as one possible factor of MetS [4]. A study reported that in patients with gout, who had normal renal function were not receiving any medication but colchicine, insulin resistance (IR), measured as homeostasis model assessment, was inversely correlated with the clearance rate of uric acid (UA) [5]. This suggests that improvement of the clearance rate of UA can reduce IR. The clearance rate of UA appears to decrease in proportion to increases in IR and serum UA concentration. The modulation of the serum UA concentration by IR is performed at the kidney level [6]. Although increased UA absorption in the proximal tubule may be secondary to hyperinsulinaemia [7], increasing evidence has indicated that UA may predict the development of MetS, obesity and DM [8]. IR plays a crucial role in the causal relationship between MetS and hyperuricaemia [9]. Nakagawa et al. [10] reported that the treatment of rats with UA-lowering agents , such as benzbromarone, significantly alleviated the symptoms of MetS and reduced IR. Animal and epidemiological studies have established a strong relationship between hyperuricaemia and MetS [11]. UA may be a potential biomarker for the prevalence or severity of MetS [12]; however, a large-scale population study has not yet investigated the relationship between UA-lowering agents and MetS in humans. In the present study, we investigated the effect of benzbromarone on the incidence of DM. We investigated the incidence of new-onset DM in benzbromarone and comparison cohorts. Methods Data sources Taiwan launched a single-payer National Health Insurance (NHI) programme on 1 March 1995. The data used in this study were obtained from the Longitudinal Health Insurance Database 2000, which is a subset of the NHI database that contains all claims data (from 1996 to 2010) of 1 million beneficiaries. This sample was systematically and randomly selected in 2000. No significant differences in age, sex and healthcare costs were found between the sample group and all enrollees in the NHI programme. The Longitudinal Health Insurance Database 2000 provides encrypted patient identification numbers, sex information, date of birth, dates of admission and discharge, the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes for diagnoses and procedures, details of prescriptions, registry of the Catastrophic Illness Patient Database and costs covered and paid for by the NHI. The Institutional Review Board of Kaohsiung Medical University Hospital approved the protocol of this study [KMUHIRB-EXEMPT(II)-20170033]. Informed consent was not required because the datasets were devoid of identifiable personal information. Study sample A retrospective cohort study was conducted with two study groups during the recruitment period of 2000–05: a benzbromarone user group and a matched benzbromarone non-user control group (Fig. 1). Patients were defined as benzbromarone users if they had at least two outpatient service claims with the Anatomical Therapeutic Chemical (ATC) code of AB03 (ICD-9-CM code 274) at any hospital or a local medical clinic or any one single hospitalization with gout listed among the claims diagnosis codes with benzbromarone use. Patients who had received a diagnosis of DM before 2000, were <20-year-old and had incomplete demographic data were excluded. Because the outcome of interest was the new onset of DM, any patient who had received a diagnosis of DM before the index date (ICD-9-CM code 250) was excluded. Because patients with gout attacks may use steroids for symptom control and long-term steroid use may cause hyperglycaemia, we excluded those patients who used steroids for 1 month within 1 year before the index date. For each benzbromarone user, one benzbromarone non-user was randomly selected from the dataset as a control group match. Benzbromarone users were matched with members of the control group by age, hypertension, coronary heart disease, dyslipidaemia, the area of residence, monthly income and index date. The index date for benzbromarone users was the date of their first registration. The year of that index date was used to create an index date for each control patient. Fig. 1 View largeDownload slide Flow diagram of the study population DM: diabetes mellitus. Fig. 1 View largeDownload slide Flow diagram of the study population DM: diabetes mellitus. Demographic data such as sex, age, geographic area of Taiwan and monthly income (recorded in NT$) were collected. Baseline comorbidities of these patients, namely hypertension (ICD-9-CM codes 401–405), coronary heart disease (ICD-9-CM codes 410–414) and dyslipidaemia (ICD-9-CM code 272), were also recorded. These comorbidities were included because they are known to affect the risk of DM. The definition of dyslipidaemia includes low-density lipoprotein cholesterol (LDL-C) ⩾190 mg/dl without any risk factor; total cholesterol (TC) ⩾240 mg/dl or LDL-C ⩾160 mg/dl with one risk factor; TC ⩾200 mg/dl or LDL-C ⩾130 mg/dl with two risk factors; TC ⩾160 mg/dl or LDL-C ⩾100 mg/dl with DM or cardiovascular disease [including coronary artery disease (CAD) and cerebral vascular disease (CVD)]; triglyceride ⩾500 mg/dl; triglyceride ⩾200 mg/dl with TC/high-density lipoprotein cholesterol (HDL-C) >5 or HDL-C <40 mg/dl; and the risk factors include hypertension, male ⩾45 years old, female ⩾55 years old or menopause, family history of early cardiovascular disease (male ⩽55 year-old, female ⩽65 year-old), HDL-C <40 mg/dl; smoking [13]. We counted any of these comorbid conditions if the condition was diagnosed in an inpatient setting or in three or more ambulatory care claims coded 1 year before the index medical care date. The follow-up duration in person-years (PY) was calculated for each person until the diagnosis of DM, death or the end of 2011. Measurements of benzbromarone The commercially available benzbromarone (ATC code M04AB03) in Taiwan was analysed. According to the total supply in days and the quantity of benzbromarone, we calculated the cumulative defined daily dose (DDD) of benzbromarone for each benzbromarone user. For benzbromarone, the cumulative DDD was partitioned into three levels at the 33rd and 67th percentiles. The DDD is defined by the ATC/DDD system of the World Health Organization Collaborating Center for Drug Statistics and Methodology. Each product had to be referred to the appropriate ATC code and DDD. The unit of DDD is defined as the assumed average maintenance dose per day for a drug used for its major indication in adults. The DDD can provide a fixed unit of measurement independent of the price and dosage form. Trends in drug consumption can be assessed by comparisons between population groups and drug doses can be standardized and compared across multiple types of drugs. The number of DDDs is calculated as the total amount of drugs divided by the amount of drug in a DDD. The cumulative DDD, which expresses the dosage and exposure duration, could be used to estimate the sum of the dispensed DDD of benzbromarone. We correlated benzbromarone use and new-onset DM risk in patients with gout by using the cumulative DDD. Statistical analyses All statistical operations were performed using Statistical Package for Social Sciences (SPSS) software, version 19 (IBM, Armonk, NY, USA). Pearson’s Chi-square test was used to compare differences in baseline characteristics, comorbidities and sociodemographic status between the study and control cohorts. The incidence rate was calculated as the number of newly diagnosed DM cases during the follow-up divided by the total PY for each group by age and duration. The risk of new-onset DM was compared between the study and control cohorts by estimating the incidence rate ratio using Poisson regression. Moreover, stratified Cox proportional hazard regression (stratified by the age groups of <50 and ⩾50 years) analysis was used to compute the adjusted hazard ratio (HR) for the new onset of DM between the study and control cohorts after adjustment for possible confounding factors (hypertension, coronary heart disease, dyslipidaemia, geographic area and monthly income). Kaplan–Meier analysis was also used to calculate the cumulative incidence rate of the new onset of DM in the two cohorts, and the log-rank test was used to analyse differences between the survival curves. A two-sided P < 0.05 was considered significant. Results Patients’ characteristics Among the gout patients without DM, we identified 8678 benzbromarone users and 8678 benzbromarone non-users (controls) between 2000 and 2005 (Fig. 1). The distribution of sex and age at entry was the same in both the cohorts (Table 1). The men accounted for 79.2% of the benzbromarone non-users and 78.2% of the benzbromarone users. The mean (s.d.) ages of the benzbromarone users and benzbromarone non-users were 52.28 (16.10) years and 52.43 (16.20) years, respectively. The prevalence of dyslipidaemia was higher in the benzbromarone users than in the benzbromarone non-users. Table 1 Demographic data of INN users and INN non-users in the gout population (N = 17 356) Variables INN non-users (N = 8678) INN users (N = 8678) n (%) n (%) P-value Age     <40 2037 (23.5) 2084 (24.0) 0.647     40–59 3634 (41.9) 3585 (41.3)     ≧60 3007 (34.7) 3009 (34.7)     Mean (s.d.) 52.43 (16.2) 52.28 (16.10) Gender     Female 1808 (20.8) 1896 (21.8) 0.103     Male 6870 (79.2) 6782 (78.2) Comorbidities     Chronic kidney disease 276 (3.2) 278 (3.2) 0.931     Hypertension 3033 (35.0) 3073 (35.4) 0.535     Dyslipidaemia 1531 (17.6) 1640 (18.9) 0.032     Cerebrovascular disease 277 (3.2) 252 (2.9) 0.270     Coronary artery disease 140 (1.6) 143 (1.6) 0.857     Depression 319 (3.7) 331 (3.8) 0.631     Chronic obstructive pulmonary disease 2008 (23.1) 2027 (23.4) 0.733     Congestive heart failure 100 (1.2) 106 (1.2) 0.674     Peripheral vascular disease 134 (1.5) 121 (1.4) 0.412 Variables INN non-users (N = 8678) INN users (N = 8678) n (%) n (%) P-value Age     <40 2037 (23.5) 2084 (24.0) 0.647     40–59 3634 (41.9) 3585 (41.3)     ≧60 3007 (34.7) 3009 (34.7)     Mean (s.d.) 52.43 (16.2) 52.28 (16.10) Gender     Female 1808 (20.8) 1896 (21.8) 0.103     Male 6870 (79.2) 6782 (78.2) Comorbidities     Chronic kidney disease 276 (3.2) 278 (3.2) 0.931     Hypertension 3033 (35.0) 3073 (35.4) 0.535     Dyslipidaemia 1531 (17.6) 1640 (18.9) 0.032     Cerebrovascular disease 277 (3.2) 252 (2.9) 0.270     Coronary artery disease 140 (1.6) 143 (1.6) 0.857     Depression 319 (3.7) 331 (3.8) 0.631     Chronic obstructive pulmonary disease 2008 (23.1) 2027 (23.4) 0.733     Congestive heart failure 100 (1.2) 106 (1.2) 0.674     Peripheral vascular disease 134 (1.5) 121 (1.4) 0.412 INN: benzbromarone. Table 1 Demographic data of INN users and INN non-users in the gout population (N = 17 356) Variables INN non-users (N = 8678) INN users (N = 8678) n (%) n (%) P-value Age     <40 2037 (23.5) 2084 (24.0) 0.647     40–59 3634 (41.9) 3585 (41.3)     ≧60 3007 (34.7) 3009 (34.7)     Mean (s.d.) 52.43 (16.2) 52.28 (16.10) Gender     Female 1808 (20.8) 1896 (21.8) 0.103     Male 6870 (79.2) 6782 (78.2) Comorbidities     Chronic kidney disease 276 (3.2) 278 (3.2) 0.931     Hypertension 3033 (35.0) 3073 (35.4) 0.535     Dyslipidaemia 1531 (17.6) 1640 (18.9) 0.032     Cerebrovascular disease 277 (3.2) 252 (2.9) 0.270     Coronary artery disease 140 (1.6) 143 (1.6) 0.857     Depression 319 (3.7) 331 (3.8) 0.631     Chronic obstructive pulmonary disease 2008 (23.1) 2027 (23.4) 0.733     Congestive heart failure 100 (1.2) 106 (1.2) 0.674     Peripheral vascular disease 134 (1.5) 121 (1.4) 0.412 Variables INN non-users (N = 8678) INN users (N = 8678) n (%) n (%) P-value Age     <40 2037 (23.5) 2084 (24.0) 0.647     40–59 3634 (41.9) 3585 (41.3)     ≧60 3007 (34.7) 3009 (34.7)     Mean (s.d.) 52.43 (16.2) 52.28 (16.10) Gender     Female 1808 (20.8) 1896 (21.8) 0.103     Male 6870 (79.2) 6782 (78.2) Comorbidities     Chronic kidney disease 276 (3.2) 278 (3.2) 0.931     Hypertension 3033 (35.0) 3073 (35.4) 0.535     Dyslipidaemia 1531 (17.6) 1640 (18.9) 0.032     Cerebrovascular disease 277 (3.2) 252 (2.9) 0.270     Coronary artery disease 140 (1.6) 143 (1.6) 0.857     Depression 319 (3.7) 331 (3.8) 0.631     Chronic obstructive pulmonary disease 2008 (23.1) 2027 (23.4) 0.733     Congestive heart failure 100 (1.2) 106 (1.2) 0.674     Peripheral vascular disease 134 (1.5) 121 (1.4) 0.412 INN: benzbromarone. The results of the log-rank test and the cumulative incidence curve of the new onset of DM events (Fig. 2) indicated that the incidence rate of the new onset of DM was significantly lower in the benzbromarone users than in the benzbromarone non-users (P < 0.001). During an average follow-up period of 7.31 years, 998 benzbromarone non-users and 932 benzbromarone users developed new-onset DM (Table 2). The average duration of the new onset of DM was significantly longer in the benzbromarone users [5.02 (2.21) years] than in the benzbromarone non-users [4.03 (2.53) years; P < 0.001; Table 3]. The incidence density rates of the new onset of DM in the benzbromarone non-users and benzbromarone users were 4.03 and 5.02 per 10 000 PY, respectively. After adjustment for sex, age, comorbidities and medication, the benzbromarone user group exhibited a 15% reduction (95% CI: 0.77, 0.93) in the risk of new-onset DM (Table 2). Table 2 Risk of new-onset DM between INN users and INN non-users (N = 17356) Group Case no. (%) cHR (95% CI) P-value aHR (95% CI) P-value aHR (95% CI) P-value Overall 1930 (11.1) INN non-user 998 (11.5) Ref. Ref. INN user 932 (10.7) 0.86 (0.79, 0.94) 0.001 0.85 (0.77, 0.93) <0.001 Cumulative DDDs <103 DDDs 344 (11.8) 1.01 (0.89, 1.14) 0.905 0.97 (0.86, 1.10) 0.648 Ref. 103–230 DDDs 315 (11.0) 0.89 (0.78, 1.00) 0.058 0.880 (0.78, 0.99) 0.049 0.91 (0.78, 1.06) 0.209 >230 DDDs 273 (9.4) 0.71 (0.62, 0.81) <0.001 0.70 (0.61, 0.80) <0.001 0.72 (0.61, 0.85) <0.001 P for trend <0.001 <0.001 Group Case no. (%) cHR (95% CI) P-value aHR (95% CI) P-value aHR (95% CI) P-value Overall 1930 (11.1) INN non-user 998 (11.5) Ref. Ref. INN user 932 (10.7) 0.86 (0.79, 0.94) 0.001 0.85 (0.77, 0.93) <0.001 Cumulative DDDs <103 DDDs 344 (11.8) 1.01 (0.89, 1.14) 0.905 0.97 (0.86, 1.10) 0.648 Ref. 103–230 DDDs 315 (11.0) 0.89 (0.78, 1.00) 0.058 0.880 (0.78, 0.99) 0.049 0.91 (0.78, 1.06) 0.209 >230 DDDs 273 (9.4) 0.71 (0.62, 0.81) <0.001 0.70 (0.61, 0.80) <0.001 0.72 (0.61, 0.85) <0.001 P for trend <0.001 <0.001 Adjusted for age, sex and comorbidities. DM: diabetes mellitus; INN: benzbromarone; DDD: defined daily dose; aHR: adjusted hazard ratio; cHR: crude hazard ratio. Table 2 Risk of new-onset DM between INN users and INN non-users (N = 17356) Group Case no. (%) cHR (95% CI) P-value aHR (95% CI) P-value aHR (95% CI) P-value Overall 1930 (11.1) INN non-user 998 (11.5) Ref. Ref. INN user 932 (10.7) 0.86 (0.79, 0.94) 0.001 0.85 (0.77, 0.93) <0.001 Cumulative DDDs <103 DDDs 344 (11.8) 1.01 (0.89, 1.14) 0.905 0.97 (0.86, 1.10) 0.648 Ref. 103–230 DDDs 315 (11.0) 0.89 (0.78, 1.00) 0.058 0.880 (0.78, 0.99) 0.049 0.91 (0.78, 1.06) 0.209 >230 DDDs 273 (9.4) 0.71 (0.62, 0.81) <0.001 0.70 (0.61, 0.80) <0.001 0.72 (0.61, 0.85) <0.001 P for trend <0.001 <0.001 Group Case no. (%) cHR (95% CI) P-value aHR (95% CI) P-value aHR (95% CI) P-value Overall 1930 (11.1) INN non-user 998 (11.5) Ref. Ref. INN user 932 (10.7) 0.86 (0.79, 0.94) 0.001 0.85 (0.77, 0.93) <0.001 Cumulative DDDs <103 DDDs 344 (11.8) 1.01 (0.89, 1.14) 0.905 0.97 (0.86, 1.10) 0.648 Ref. 103–230 DDDs 315 (11.0) 0.89 (0.78, 1.00) 0.058 0.880 (0.78, 0.99) 0.049 0.91 (0.78, 1.06) 0.209 >230 DDDs 273 (9.4) 0.71 (0.62, 0.81) <0.001 0.70 (0.61, 0.80) <0.001 0.72 (0.61, 0.85) <0.001 P for trend <0.001 <0.001 Adjusted for age, sex and comorbidities. DM: diabetes mellitus; INN: benzbromarone; DDD: defined daily dose; aHR: adjusted hazard ratio; cHR: crude hazard ratio. Table 3 Average follow-up duration and average duration of new-onset DM Average follow-up duration DM new onset average duration Group Mean P-value Mean(s.d.) P-value Overall 7.3 (12.41) 4.51(2.43) INN non-user 7.03 <0.001 4.03 (2.53) <0.001 INN user 7.59 (2.11) 5.02(2.21) Average follow-up duration DM new onset average duration Group Mean P-value Mean(s.d.) P-value Overall 7.3 (12.41) 4.51(2.43) INN non-user 7.03 <0.001 4.03 (2.53) <0.001 INN user 7.59 (2.11) 5.02(2.21) Adjusted for age, sex and comorbidities. DM: diabetes mellitus; INN: benzbromarone. Table 3 Average follow-up duration and average duration of new-onset DM Average follow-up duration DM new onset average duration Group Mean P-value Mean(s.d.) P-value Overall 7.3 (12.41) 4.51(2.43) INN non-user 7.03 <0.001 4.03 (2.53) <0.001 INN user 7.59 (2.11) 5.02(2.21) Average follow-up duration DM new onset average duration Group Mean P-value Mean(s.d.) P-value Overall 7.3 (12.41) 4.51(2.43) INN non-user 7.03 <0.001 4.03 (2.53) <0.001 INN user 7.59 (2.11) 5.02(2.21) Adjusted for age, sex and comorbidities. DM: diabetes mellitus; INN: benzbromarone. Fig. 2 View largeDownload slide Cumulative incidence of DM in benzbromarone users and benzbromarone non-users Dashed line: benzbromarone users; solid line: benzbromarone non-users. INN: benzbromarone; DM: diabetes mellitus. Fig. 2 View largeDownload slide Cumulative incidence of DM in benzbromarone users and benzbromarone non-users Dashed line: benzbromarone users; solid line: benzbromarone non-users. INN: benzbromarone; DM: diabetes mellitus. Dose–response relationship between benzbromarone use and the risk of new-onset DM Table 2 shows the dose–response relationship between individual benzbromarone use and the risk of new-onset DM in the patients with new-onset DM and controls (benzbromarone non-users). Among the benzbromarone users, the adjusted HR was 0.88 (95% CI: 0.78, 0.99) for those receiving a cumulative DDD of 103–230 and the adjusted HR was 0.70 (95% CI: 0.61, 0.80) for those receiving a cumulative DDD of >230. The higher the cumulative DDD of benzbromarone was, the more significant the risk reduction of new-onset DM was (P for trend <0.001). Multivariate analysis Sex stratification showed that male benzbromarone users had a significant risk reduction of new-onset DM relative to the benzbromarone non-users (adjusted HR = 0.77, 95% CI: 0.69, 0.86; Table 4). Age stratification showed a significant risk reduction of new-onset DM in both the 40- to 59-year-old and ⩾60-year-old subgroups of the benzbromarone users compared with the similar subgroups of the benzbromarone non-users (adjusted HR = 0.86 and 0.82 and 95% CI: 0.76, 0.98 and 0.71, 0.94, respectively). Comorbidity stratification with multivariate analysis (Table 4) showed that the benzbromarone users with hypertension and dyslipidaemia were associated with a significant risk reduction of new-onset DM compared with the benzbromarone non-users with these conditions (adjusted HR = 0.79 and 0.80 and 95% CI: 0.69, 0.89 and 0.67, 0.94, respectively); by contrast, the benzbromarone users without CKD, dyslipidaemia, ischaemic heart disease, CVD, depression, chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF) and peripheral vascular disease (PVD) were associated with a significant risk reduction of new-onset DM compared with the benzbromarone non-users without these conditions (adjusted HR = 0.84, 0.86, 0.84, 0.84, 0.84, 0.83, 0.84 and 0.83 and 95% CI: 0.77, 0.92, 0.78, 0.96, 0.77, 0.92, 0.77, 0.92, 0.77, 0.92, 0.75, 0.92, 0.77, 0.92 and 0.76, 0.91, respectively). After stratification for all factors, the risk reduction of new-onset DM was higher in the benzbromarone users than in the benzbromarone non-users. Table 4 The risk of new-onset DM stratified by sex, age, comorbidities and medication between INN non-users and INN users (N = 17 356) INN non-user INN user INN user vs non- INN user Variables No. cases (%) No. cases (%) aHR (95% CI) P-values P for interaction Gender     Female 289 (16.0) 335 (17.7) 1.01 (0.86, 1.19) 0.884 0.010     Male 709 (10.3) 597 (8.8) 0.77 (0.69, 0.86) <0.001 Age (years)     <40 126 (6.2) 123 (5.9) 0.87 (0.68, 1.12) 0.289 0.551     40–59 496 (13.6) 455 (12.7) 0.86 (0.76, 0.98) 0.021     ≥60 376 (12.5) 354 (11.8) 0.82 (0.71, 0.94) 0.006 Comorbidities     CKD         No 971 (11.6) 903 (10.8) 0.84 (0.77, 0.92) <0.001 0.708         Yes 27 (9.8) 29 (10.4) 0.89 (0.52, 1.53) 0.682     Hypertension         No 500 (8.9) 483 (8.6) 0.90 (0.79, 1.02) 0.099 0.090         Yes 498 (16.4) 449 (14.6) 0.79 (0.69, 0.89) <0.001  Dyslipidaemia         No 712 (10.0) 672 (9.5) 0.86 (0.78, 0.96) 0.005 0.380         Yes 286 (18.7) 260 (15.9) 0.80 (0.67, 0.94) 0.007  Ischaemic heart disease         No 983 (11.5) 913 (10.7) 0.84 (0.77, 0.92) <0.001 0.741         Yes 15 (10.7) 19 (13.3) 0.82 (0.41, 1.68) 0.594  Cerebral vascular disease         No 972 (11.6) 912 (10.8) 0.84 (0.77, 0.92) <0.001 0.727         Yes 26 (9.4) 20 (7.9) 0.72 (0.40, 1.30) 0.270     Depression         No 952 (11.4) 890 (10.7) 0.84 (0.77, 0.92) <0.001 0.972         Yes 46 (14.4) 42 (12.7) 0.84 (0.56, 1.29) 0.430  Chronic obstructive pulmonary disease         No 748 (11.2) 681 (10.2) 0.83 (0.75, 0.92) <0.001 0.505         Yes 250 (12.5) 251 (12.4) 0.89 (0.75, 1.06) 0.889  Congestive heart failure         No 987 (11.5) 919 (10.7) 0.84 (0.77, 0.92) <0.001 0.920         Yes 11 (11.0) 13 (12.3) 0.59 (0.25, 1.41) 0.236  Peripheral vascular disease         No 984 (11.5) 991 (10.6) 0.83 (0.76, 0.91) <0.001 0.173         Yes 14 (10.4) 221 (17.4) 1.17 (0.58, 2.37) 0.659 INN non-user INN user INN user vs non- INN user Variables No. cases (%) No. cases (%) aHR (95% CI) P-values P for interaction Gender     Female 289 (16.0) 335 (17.7) 1.01 (0.86, 1.19) 0.884 0.010     Male 709 (10.3) 597 (8.8) 0.77 (0.69, 0.86) <0.001 Age (years)     <40 126 (6.2) 123 (5.9) 0.87 (0.68, 1.12) 0.289 0.551     40–59 496 (13.6) 455 (12.7) 0.86 (0.76, 0.98) 0.021     ≥60 376 (12.5) 354 (11.8) 0.82 (0.71, 0.94) 0.006 Comorbidities     CKD         No 971 (11.6) 903 (10.8) 0.84 (0.77, 0.92) <0.001 0.708         Yes 27 (9.8) 29 (10.4) 0.89 (0.52, 1.53) 0.682     Hypertension         No 500 (8.9) 483 (8.6) 0.90 (0.79, 1.02) 0.099 0.090         Yes 498 (16.4) 449 (14.6) 0.79 (0.69, 0.89) <0.001  Dyslipidaemia         No 712 (10.0) 672 (9.5) 0.86 (0.78, 0.96) 0.005 0.380         Yes 286 (18.7) 260 (15.9) 0.80 (0.67, 0.94) 0.007  Ischaemic heart disease         No 983 (11.5) 913 (10.7) 0.84 (0.77, 0.92) <0.001 0.741         Yes 15 (10.7) 19 (13.3) 0.82 (0.41, 1.68) 0.594  Cerebral vascular disease         No 972 (11.6) 912 (10.8) 0.84 (0.77, 0.92) <0.001 0.727         Yes 26 (9.4) 20 (7.9) 0.72 (0.40, 1.30) 0.270     Depression         No 952 (11.4) 890 (10.7) 0.84 (0.77, 0.92) <0.001 0.972         Yes 46 (14.4) 42 (12.7) 0.84 (0.56, 1.29) 0.430  Chronic obstructive pulmonary disease         No 748 (11.2) 681 (10.2) 0.83 (0.75, 0.92) <0.001 0.505         Yes 250 (12.5) 251 (12.4) 0.89 (0.75, 1.06) 0.889  Congestive heart failure         No 987 (11.5) 919 (10.7) 0.84 (0.77, 0.92) <0.001 0.920         Yes 11 (11.0) 13 (12.3) 0.59 (0.25, 1.41) 0.236  Peripheral vascular disease         No 984 (11.5) 991 (10.6) 0.83 (0.76, 0.91) <0.001 0.173         Yes 14 (10.4) 221 (17.4) 1.17 (0.58, 2.37) 0.659 Adjusted for age, sex, comorbidities, history of medication and CCI score. CCI: charlson comorbidity index; DM: diabetes mellitus; INN: benzbromarone; aHR: adjusted hazard ratio. Table 4 The risk of new-onset DM stratified by sex, age, comorbidities and medication between INN non-users and INN users (N = 17 356) INN non-user INN user INN user vs non- INN user Variables No. cases (%) No. cases (%) aHR (95% CI) P-values P for interaction Gender     Female 289 (16.0) 335 (17.7) 1.01 (0.86, 1.19) 0.884 0.010     Male 709 (10.3) 597 (8.8) 0.77 (0.69, 0.86) <0.001 Age (years)     <40 126 (6.2) 123 (5.9) 0.87 (0.68, 1.12) 0.289 0.551     40–59 496 (13.6) 455 (12.7) 0.86 (0.76, 0.98) 0.021     ≥60 376 (12.5) 354 (11.8) 0.82 (0.71, 0.94) 0.006 Comorbidities     CKD         No 971 (11.6) 903 (10.8) 0.84 (0.77, 0.92) <0.001 0.708         Yes 27 (9.8) 29 (10.4) 0.89 (0.52, 1.53) 0.682     Hypertension         No 500 (8.9) 483 (8.6) 0.90 (0.79, 1.02) 0.099 0.090         Yes 498 (16.4) 449 (14.6) 0.79 (0.69, 0.89) <0.001  Dyslipidaemia         No 712 (10.0) 672 (9.5) 0.86 (0.78, 0.96) 0.005 0.380         Yes 286 (18.7) 260 (15.9) 0.80 (0.67, 0.94) 0.007  Ischaemic heart disease         No 983 (11.5) 913 (10.7) 0.84 (0.77, 0.92) <0.001 0.741         Yes 15 (10.7) 19 (13.3) 0.82 (0.41, 1.68) 0.594  Cerebral vascular disease         No 972 (11.6) 912 (10.8) 0.84 (0.77, 0.92) <0.001 0.727         Yes 26 (9.4) 20 (7.9) 0.72 (0.40, 1.30) 0.270     Depression         No 952 (11.4) 890 (10.7) 0.84 (0.77, 0.92) <0.001 0.972         Yes 46 (14.4) 42 (12.7) 0.84 (0.56, 1.29) 0.430  Chronic obstructive pulmonary disease         No 748 (11.2) 681 (10.2) 0.83 (0.75, 0.92) <0.001 0.505         Yes 250 (12.5) 251 (12.4) 0.89 (0.75, 1.06) 0.889  Congestive heart failure         No 987 (11.5) 919 (10.7) 0.84 (0.77, 0.92) <0.001 0.920         Yes 11 (11.0) 13 (12.3) 0.59 (0.25, 1.41) 0.236  Peripheral vascular disease         No 984 (11.5) 991 (10.6) 0.83 (0.76, 0.91) <0.001 0.173         Yes 14 (10.4) 221 (17.4) 1.17 (0.58, 2.37) 0.659 INN non-user INN user INN user vs non- INN user Variables No. cases (%) No. cases (%) aHR (95% CI) P-values P for interaction Gender     Female 289 (16.0) 335 (17.7) 1.01 (0.86, 1.19) 0.884 0.010     Male 709 (10.3) 597 (8.8) 0.77 (0.69, 0.86) <0.001 Age (years)     <40 126 (6.2) 123 (5.9) 0.87 (0.68, 1.12) 0.289 0.551     40–59 496 (13.6) 455 (12.7) 0.86 (0.76, 0.98) 0.021     ≥60 376 (12.5) 354 (11.8) 0.82 (0.71, 0.94) 0.006 Comorbidities     CKD         No 971 (11.6) 903 (10.8) 0.84 (0.77, 0.92) <0.001 0.708         Yes 27 (9.8) 29 (10.4) 0.89 (0.52, 1.53) 0.682     Hypertension         No 500 (8.9) 483 (8.6) 0.90 (0.79, 1.02) 0.099 0.090         Yes 498 (16.4) 449 (14.6) 0.79 (0.69, 0.89) <0.001  Dyslipidaemia         No 712 (10.0) 672 (9.5) 0.86 (0.78, 0.96) 0.005 0.380         Yes 286 (18.7) 260 (15.9) 0.80 (0.67, 0.94) 0.007  Ischaemic heart disease         No 983 (11.5) 913 (10.7) 0.84 (0.77, 0.92) <0.001 0.741         Yes 15 (10.7) 19 (13.3) 0.82 (0.41, 1.68) 0.594  Cerebral vascular disease         No 972 (11.6) 912 (10.8) 0.84 (0.77, 0.92) <0.001 0.727         Yes 26 (9.4) 20 (7.9) 0.72 (0.40, 1.30) 0.270     Depression         No 952 (11.4) 890 (10.7) 0.84 (0.77, 0.92) <0.001 0.972         Yes 46 (14.4) 42 (12.7) 0.84 (0.56, 1.29) 0.430  Chronic obstructive pulmonary disease         No 748 (11.2) 681 (10.2) 0.83 (0.75, 0.92) <0.001 0.505         Yes 250 (12.5) 251 (12.4) 0.89 (0.75, 1.06) 0.889  Congestive heart failure         No 987 (11.5) 919 (10.7) 0.84 (0.77, 0.92) <0.001 0.920         Yes 11 (11.0) 13 (12.3) 0.59 (0.25, 1.41) 0.236  Peripheral vascular disease         No 984 (11.5) 991 (10.6) 0.83 (0.76, 0.91) <0.001 0.173         Yes 14 (10.4) 221 (17.4) 1.17 (0.58, 2.37) 0.659 Adjusted for age, sex, comorbidities, history of medication and CCI score. CCI: charlson comorbidity index; DM: diabetes mellitus; INN: benzbromarone; aHR: adjusted hazard ratio. Discussion Our study results demonstrated that the incidence rate of new-onset DM was lower in the patients with gout using benzbromarone than in those not using benzbromarone. The higher the cumulative dose of benzbromarone, the more significantly the risk of new-onset of DM was reduced. A significantly longer duration of new-onset DM was observed among benzbromarone users than among benzbromarone non-users. Hyperuricaemia may play a role in the development and pathogenesis of many metabolic, haemodynamic and systemic pathological diseases, including MetS, hypertension, stroke and atherosclerosis [14]. At the same time, IR may potentially play a key role in the causal relationships between hyperuricaemia, MetS and type 2 DM [11]. A study by Kuwabara et al. [15] observed that hyperuricaemia was a significant risk factor for incident hypertension and DM compared with normouricaemia without MetS. One study reported that insulin may enhance renal urate reabsorption through the stimulation of the urate-anion exchanger Urate Transporter 1 (URAT1) in the brush border membranes of the renal proximal tubule [16]. By contrast, the proposed mechanisms related to these findings indicate the effect of UA on endothelial dysfunction and the overproduction of reactive oxygen species. UA inactivates the production of nitrogen oxide (NO) in animals [17]; thus, hyperuricemia stimulates intracellular reactive oxygen species production and reduces the effects of NO on adipose cells. Such oxidative stress in adipocytes is considered to play a key role in the induction of IR increase and MetS [18]. Benzbromarone inhibits URAT1, thereby reducing the serum urate level [19]. URAT1 (SLC22A12) mediates the entry (uptake) of urate into cells. A decreased renal UA excretion indicates increased URAT1 activity, which can be correlated with higher intracellular UA levels [20]. URAT1 is located not only on the renal proximal tubular cells, but also on adipocytes [21]. URAT1 transporter gene polymorphisms in Caucasians with hypertension were associated with BMI, waist circumference, HDL cholesterol and MetS [20]. A clinical trial of lowering urate levels in patients with CHF by using benzbromarone indicated that lowering urate levels improved IR. However, it cannot be ruled out that this may have been a secondary pharmacological effect of benzbromarone related to its peroxisome proliferator-activated receptor agonist activity [22]. The aforementioned findings indicate that benzbromarone may improve IR, DM and even MetS. A previous study revealed that UA-lowering agents reduced IR [23]; however, no incidence reduction effect of DM has been investigated. In our study, a decreasing trend of new-onset DM after benzbromarone use and a dose-dependent risk reduction were observed. Several theories may account for this finding. A close relationship between serum UA levels and the presence of MetS was demonstrated in a study conducted in the USA [24]. An Indian study reported that UA contributed to 66.84% of the variance in MetS in multivariate regression analysis, indicating that UA can be considered as a marker and potential modifier of MetS [25]. In a study from the USA, the prevalence (95% CI) of MetS according to the revised National cholesterol education program/ adult treatment panel III (NCEP/ATP III) criteria was 62.8% (51.9, 73.6) in patients with gout and 25.4% (23.5, 27.3) in individuals without gout [26]. The renal handling of urate in gout patients with MetS indicates that the severity of MetS increases with an increase in the serum urate level, whereas the renal excretion of UA is inversely correlated. This suggests that the disturbance is related to the severity of MetS [27]. In Korea, the prevalence of MetS in patients with gout was 50.8%, which is higher than the prevalence in the general Korean population [28]. In a study from Iran, hyperuricaemia increased the risk of MetS more than 2-fold [29]. Gout contributes to a high risk of MetS [30]. The prevalence of MetS increases significantly with the serum UA level [31]. A meta-analysis of 11 studies revealed that every milligram per decilitre increase in the UA level is associated with a 17% increased risk of DM [32]. However, whether gout therapy improves DM or MetS in humans remains unclear. In our study, the risk of new-onset DM could be reduced by 14% with the usage of benzbromarone. Comorbidity stratification by using multivariate analysis (Table 4) indicated that the benzbromarone users with CKD, CAD, CVD, depression, COPD, CHF and PVD exhibited a non-significant risk reduction for new-onset DM compared with the benzbromarone non-users. The negative results might be due to small sample sizes. Additional large-sample studies evaluating these subgroups are required. The present study also revealed risk reduction of new-onset of DM in male benzbromarone users, but females did not demonstrate any effect. It demonstrated that serum UA level was significantly associated with pre-hypertension [33], carotid-to-femoral pulse wave velocity [34] and cognitive impairment [35] only in men, and not in women. Female hormones are associated with lower levels of serum UA via renal clearance [36]. Several types of oral hypoglycaemic agents are available. Metformin improves IR and targets both the liver and skeletal muscle [37]. Thiazolidinediones, by activating peroxisome proliferator-activated receptors, improve IR, reduce hepatic glucose production and improve glucose disposal indirectly by altering lipid metabolism in adipose tissues [38]. Sulfonylureas and meglitinides (glinides) both target pancreatic β cells and increase insulin secretion. Glucagon-like peptide-1 agonists reduce excess glucagon secretion by pancreatic α-cells and slow down gastric emptying [39]. Dipeptidyl peptidase-4 inhibitors reduce the breakdown and increase the levels of the endogenous incretin hormones glucagon-like peptide-1 and glucose-dependent insulin-releasing polypeptide [40]. α-Glucosidase inhibitors slow down the rate of carbohydrate digestion by the small intestine [41]. In recent years, sodium glucose cotransporter 2 (SGLT-2) inhibitors, a new type of oral hypoglycemic agents (OHA), have been developed; they inhibit SGLT2 in the proximal nephron of the kidney and block glucose reabsorption [42]. SGLT-2 inhibitors have three characteristics. First, these are independent of insulin action; thus, there is a low risk of hypoglycemia [43]. Second, similar to benzbromarone (URAT1 inhibitor), SGLT-2 inhibitor is the first type of OHA targeting the kidneys and proximal tubules. Third, SGLT-2 inhibitor can lower not only baseline haemoglobin A1c but also UA [44]. In our study, the incidence of DM in the gout population was 111 per 1000 PY (Table 2). Furthermore, a study conducted in Taiwan reported that the incidence of DM in patients aged 20–79 years was 11.60/1000 PY in the general population in 2008 [45]; thus, the incidence of DM in the gout population was ∼9.57 times that in the general population. Therefore, improving the incidence of DM in the gout population may be more important than improving that in the general population. In our study, benzbromarone users in the gout population exhibited a 14% risk reduction (95% CI: 0.79, 0.94) for new-onset DM, and the higher the cumulative DDD of benzbromarone use was, the more significant was the risk reduction of new-onset DM. In the gout population, the benzbromarone users with hypertension and dyslipidaemia were associated with a significant risk reduction of new-onset DM compared with the benzbromarone non-users with these conditions; by contrast, those without CKD, hypertension, dyslipidaemia, CAD, CVD, depression, COPD, CHF and PVD were associated with a significant risk reduction of new-onset DM compared with the benzbromarone non-users without these conditions. Limitations The present study has some limitations. First, this is not a study of prospective nature and dedicated intervention. Second, this study only demonstrated the preventive effects of benzbromarone for DM in a gout population. In general, benzbromarone is not used in the non-gout population. Existing literature provides the aspect that UA levels might be linked with the incidence of IR and diabetes, but does not look into the potential effect of the therapeutic intervention. Third, the NHI database does not include laboratory data, such as sugar, glycated haemoglobin and UA levels; therefore, our case definition was based on physician-recorded diagnoses instead of the ACR criteria [46] or urate crystal identification. In our study, gout and DM were both accurately diagnosed and coded (ICD-9-CM codes) by specialists according to the standard diagnostic criteria, including typical symptoms and signs, laboratory data and imaging findings. Lastly, real targets of UA are unknown in the different DDD groups. Conclusions In this cohort study, the incidence of DM was lower in the benzbromarone users than in the benzbromarone non-users in the gout population. 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Diabetes Obes Metabol 2017 . 45 Jiang YD , Chang CH , Tai TY , Chen JF , Chuang LM. Incidence and prevalence rates of diabetes mellitus in Taiwan: analysis of the 2000-2009 Nationwide Health Insurance database . J Formos Med Assoc 2012 ; 111 : 599 – 604 . Google Scholar CrossRef Search ADS PubMed 46 Wallace SL , Robinson H , Masi AT et al. Preliminary criteria for the classification of the acute arthritis of primary gout . Arthritis Rheum 1977 ; 20 : 895 – 900 . Google Scholar CrossRef Search ADS PubMed © The Author(s) 2018. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Rheumatology Oxford University Press

Decreased incidence of diabetes in patients with gout using benzbromarone

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© The Author(s) 2018. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For permissions, please email: journals.permissions@oup.com
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10.1093/rheumatology/key138
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Abstract

Abstract Objective Insulin resistance is inversely correlated with the clearance rate of uric acid, which may indicate that improvement in the clearance rate of uric acid could reduce insulin resistance. Considering the increased prevalence of diabetes mellitus (DM) in the gout population, this study evaluated the effects of benzbromarone, a uricosuric agent, on the incidence of DM in the gout population. Methods We used data from the Taiwan National Health Insurance program. The benzbromarone user cohort included 8678 patients; each patient was age- and sex-matched with one benzbromarone non-user who was randomly selected from the gout population. The Cox proportional hazard regression analysis was conducted to estimate the effects of benzbromarone on the incidence of DM in the gout population. Results The incidence of DM was significantly lower in benzbromarone users than in benzbromarone non-users [adjusted hazard ratio (HR) = 0.86; 95% CI: 0.79, 0.94]. The HR for the incidence of DM was lower in male benzbromarone users (adjusted HR = 0.77; 95% CI: 0.69, 0.86) than in benzbromarone non-users. An analysis of three age groups (<40, 40–59 and ⩾60 years) indicated that the HRs of the age groups of 40–59 years (adjusted HR = 0.86; 95% CI: 0.76, 0.98) and ⩾60 years (adjusted HR = 0.82; 95% CI: 0.71, 0.94) were significantly lower among benzbromarone users than among benzbromarone non-users. Conclusion In the gout population, the incidence of DM was lower in benzbromarone users than in benzbromarone non-users. benzbromarone, diabetes mellitus, gout Rheumatology key messages There is a decreased incidence of diabetes in patients with gout with benzbromarone usage. There is a significantly trend of dose-dependent decrease of incidence of diabetes among benzbromarone users. Introduction The global prevalence (age standardized) of diabetes, which was estimated to be 422 million in 2014 compared with 108 million in 1980, has nearly doubled since 1980, increasing from 4.7 to 8.5% in the adult population [1]. Hyperuricaemia or gout is commonly observed in patients with metabolic syndrome (MetS) [2]. The increasing prevalence of gout is likely related to several factors including the increased incidence of obesity, hypertension, type 2 diabetes mellitus (DM), MetS, chronic kidney disease (CKD), lifestyle factors and the increased use of causative medications [3]. Thus, gout may be considered as one possible factor of MetS [4]. A study reported that in patients with gout, who had normal renal function were not receiving any medication but colchicine, insulin resistance (IR), measured as homeostasis model assessment, was inversely correlated with the clearance rate of uric acid (UA) [5]. This suggests that improvement of the clearance rate of UA can reduce IR. The clearance rate of UA appears to decrease in proportion to increases in IR and serum UA concentration. The modulation of the serum UA concentration by IR is performed at the kidney level [6]. Although increased UA absorption in the proximal tubule may be secondary to hyperinsulinaemia [7], increasing evidence has indicated that UA may predict the development of MetS, obesity and DM [8]. IR plays a crucial role in the causal relationship between MetS and hyperuricaemia [9]. Nakagawa et al. [10] reported that the treatment of rats with UA-lowering agents , such as benzbromarone, significantly alleviated the symptoms of MetS and reduced IR. Animal and epidemiological studies have established a strong relationship between hyperuricaemia and MetS [11]. UA may be a potential biomarker for the prevalence or severity of MetS [12]; however, a large-scale population study has not yet investigated the relationship between UA-lowering agents and MetS in humans. In the present study, we investigated the effect of benzbromarone on the incidence of DM. We investigated the incidence of new-onset DM in benzbromarone and comparison cohorts. Methods Data sources Taiwan launched a single-payer National Health Insurance (NHI) programme on 1 March 1995. The data used in this study were obtained from the Longitudinal Health Insurance Database 2000, which is a subset of the NHI database that contains all claims data (from 1996 to 2010) of 1 million beneficiaries. This sample was systematically and randomly selected in 2000. No significant differences in age, sex and healthcare costs were found between the sample group and all enrollees in the NHI programme. The Longitudinal Health Insurance Database 2000 provides encrypted patient identification numbers, sex information, date of birth, dates of admission and discharge, the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes for diagnoses and procedures, details of prescriptions, registry of the Catastrophic Illness Patient Database and costs covered and paid for by the NHI. The Institutional Review Board of Kaohsiung Medical University Hospital approved the protocol of this study [KMUHIRB-EXEMPT(II)-20170033]. Informed consent was not required because the datasets were devoid of identifiable personal information. Study sample A retrospective cohort study was conducted with two study groups during the recruitment period of 2000–05: a benzbromarone user group and a matched benzbromarone non-user control group (Fig. 1). Patients were defined as benzbromarone users if they had at least two outpatient service claims with the Anatomical Therapeutic Chemical (ATC) code of AB03 (ICD-9-CM code 274) at any hospital or a local medical clinic or any one single hospitalization with gout listed among the claims diagnosis codes with benzbromarone use. Patients who had received a diagnosis of DM before 2000, were <20-year-old and had incomplete demographic data were excluded. Because the outcome of interest was the new onset of DM, any patient who had received a diagnosis of DM before the index date (ICD-9-CM code 250) was excluded. Because patients with gout attacks may use steroids for symptom control and long-term steroid use may cause hyperglycaemia, we excluded those patients who used steroids for 1 month within 1 year before the index date. For each benzbromarone user, one benzbromarone non-user was randomly selected from the dataset as a control group match. Benzbromarone users were matched with members of the control group by age, hypertension, coronary heart disease, dyslipidaemia, the area of residence, monthly income and index date. The index date for benzbromarone users was the date of their first registration. The year of that index date was used to create an index date for each control patient. Fig. 1 View largeDownload slide Flow diagram of the study population DM: diabetes mellitus. Fig. 1 View largeDownload slide Flow diagram of the study population DM: diabetes mellitus. Demographic data such as sex, age, geographic area of Taiwan and monthly income (recorded in NT$) were collected. Baseline comorbidities of these patients, namely hypertension (ICD-9-CM codes 401–405), coronary heart disease (ICD-9-CM codes 410–414) and dyslipidaemia (ICD-9-CM code 272), were also recorded. These comorbidities were included because they are known to affect the risk of DM. The definition of dyslipidaemia includes low-density lipoprotein cholesterol (LDL-C) ⩾190 mg/dl without any risk factor; total cholesterol (TC) ⩾240 mg/dl or LDL-C ⩾160 mg/dl with one risk factor; TC ⩾200 mg/dl or LDL-C ⩾130 mg/dl with two risk factors; TC ⩾160 mg/dl or LDL-C ⩾100 mg/dl with DM or cardiovascular disease [including coronary artery disease (CAD) and cerebral vascular disease (CVD)]; triglyceride ⩾500 mg/dl; triglyceride ⩾200 mg/dl with TC/high-density lipoprotein cholesterol (HDL-C) >5 or HDL-C <40 mg/dl; and the risk factors include hypertension, male ⩾45 years old, female ⩾55 years old or menopause, family history of early cardiovascular disease (male ⩽55 year-old, female ⩽65 year-old), HDL-C <40 mg/dl; smoking [13]. We counted any of these comorbid conditions if the condition was diagnosed in an inpatient setting or in three or more ambulatory care claims coded 1 year before the index medical care date. The follow-up duration in person-years (PY) was calculated for each person until the diagnosis of DM, death or the end of 2011. Measurements of benzbromarone The commercially available benzbromarone (ATC code M04AB03) in Taiwan was analysed. According to the total supply in days and the quantity of benzbromarone, we calculated the cumulative defined daily dose (DDD) of benzbromarone for each benzbromarone user. For benzbromarone, the cumulative DDD was partitioned into three levels at the 33rd and 67th percentiles. The DDD is defined by the ATC/DDD system of the World Health Organization Collaborating Center for Drug Statistics and Methodology. Each product had to be referred to the appropriate ATC code and DDD. The unit of DDD is defined as the assumed average maintenance dose per day for a drug used for its major indication in adults. The DDD can provide a fixed unit of measurement independent of the price and dosage form. Trends in drug consumption can be assessed by comparisons between population groups and drug doses can be standardized and compared across multiple types of drugs. The number of DDDs is calculated as the total amount of drugs divided by the amount of drug in a DDD. The cumulative DDD, which expresses the dosage and exposure duration, could be used to estimate the sum of the dispensed DDD of benzbromarone. We correlated benzbromarone use and new-onset DM risk in patients with gout by using the cumulative DDD. Statistical analyses All statistical operations were performed using Statistical Package for Social Sciences (SPSS) software, version 19 (IBM, Armonk, NY, USA). Pearson’s Chi-square test was used to compare differences in baseline characteristics, comorbidities and sociodemographic status between the study and control cohorts. The incidence rate was calculated as the number of newly diagnosed DM cases during the follow-up divided by the total PY for each group by age and duration. The risk of new-onset DM was compared between the study and control cohorts by estimating the incidence rate ratio using Poisson regression. Moreover, stratified Cox proportional hazard regression (stratified by the age groups of <50 and ⩾50 years) analysis was used to compute the adjusted hazard ratio (HR) for the new onset of DM between the study and control cohorts after adjustment for possible confounding factors (hypertension, coronary heart disease, dyslipidaemia, geographic area and monthly income). Kaplan–Meier analysis was also used to calculate the cumulative incidence rate of the new onset of DM in the two cohorts, and the log-rank test was used to analyse differences between the survival curves. A two-sided P < 0.05 was considered significant. Results Patients’ characteristics Among the gout patients without DM, we identified 8678 benzbromarone users and 8678 benzbromarone non-users (controls) between 2000 and 2005 (Fig. 1). The distribution of sex and age at entry was the same in both the cohorts (Table 1). The men accounted for 79.2% of the benzbromarone non-users and 78.2% of the benzbromarone users. The mean (s.d.) ages of the benzbromarone users and benzbromarone non-users were 52.28 (16.10) years and 52.43 (16.20) years, respectively. The prevalence of dyslipidaemia was higher in the benzbromarone users than in the benzbromarone non-users. Table 1 Demographic data of INN users and INN non-users in the gout population (N = 17 356) Variables INN non-users (N = 8678) INN users (N = 8678) n (%) n (%) P-value Age     <40 2037 (23.5) 2084 (24.0) 0.647     40–59 3634 (41.9) 3585 (41.3)     ≧60 3007 (34.7) 3009 (34.7)     Mean (s.d.) 52.43 (16.2) 52.28 (16.10) Gender     Female 1808 (20.8) 1896 (21.8) 0.103     Male 6870 (79.2) 6782 (78.2) Comorbidities     Chronic kidney disease 276 (3.2) 278 (3.2) 0.931     Hypertension 3033 (35.0) 3073 (35.4) 0.535     Dyslipidaemia 1531 (17.6) 1640 (18.9) 0.032     Cerebrovascular disease 277 (3.2) 252 (2.9) 0.270     Coronary artery disease 140 (1.6) 143 (1.6) 0.857     Depression 319 (3.7) 331 (3.8) 0.631     Chronic obstructive pulmonary disease 2008 (23.1) 2027 (23.4) 0.733     Congestive heart failure 100 (1.2) 106 (1.2) 0.674     Peripheral vascular disease 134 (1.5) 121 (1.4) 0.412 Variables INN non-users (N = 8678) INN users (N = 8678) n (%) n (%) P-value Age     <40 2037 (23.5) 2084 (24.0) 0.647     40–59 3634 (41.9) 3585 (41.3)     ≧60 3007 (34.7) 3009 (34.7)     Mean (s.d.) 52.43 (16.2) 52.28 (16.10) Gender     Female 1808 (20.8) 1896 (21.8) 0.103     Male 6870 (79.2) 6782 (78.2) Comorbidities     Chronic kidney disease 276 (3.2) 278 (3.2) 0.931     Hypertension 3033 (35.0) 3073 (35.4) 0.535     Dyslipidaemia 1531 (17.6) 1640 (18.9) 0.032     Cerebrovascular disease 277 (3.2) 252 (2.9) 0.270     Coronary artery disease 140 (1.6) 143 (1.6) 0.857     Depression 319 (3.7) 331 (3.8) 0.631     Chronic obstructive pulmonary disease 2008 (23.1) 2027 (23.4) 0.733     Congestive heart failure 100 (1.2) 106 (1.2) 0.674     Peripheral vascular disease 134 (1.5) 121 (1.4) 0.412 INN: benzbromarone. Table 1 Demographic data of INN users and INN non-users in the gout population (N = 17 356) Variables INN non-users (N = 8678) INN users (N = 8678) n (%) n (%) P-value Age     <40 2037 (23.5) 2084 (24.0) 0.647     40–59 3634 (41.9) 3585 (41.3)     ≧60 3007 (34.7) 3009 (34.7)     Mean (s.d.) 52.43 (16.2) 52.28 (16.10) Gender     Female 1808 (20.8) 1896 (21.8) 0.103     Male 6870 (79.2) 6782 (78.2) Comorbidities     Chronic kidney disease 276 (3.2) 278 (3.2) 0.931     Hypertension 3033 (35.0) 3073 (35.4) 0.535     Dyslipidaemia 1531 (17.6) 1640 (18.9) 0.032     Cerebrovascular disease 277 (3.2) 252 (2.9) 0.270     Coronary artery disease 140 (1.6) 143 (1.6) 0.857     Depression 319 (3.7) 331 (3.8) 0.631     Chronic obstructive pulmonary disease 2008 (23.1) 2027 (23.4) 0.733     Congestive heart failure 100 (1.2) 106 (1.2) 0.674     Peripheral vascular disease 134 (1.5) 121 (1.4) 0.412 Variables INN non-users (N = 8678) INN users (N = 8678) n (%) n (%) P-value Age     <40 2037 (23.5) 2084 (24.0) 0.647     40–59 3634 (41.9) 3585 (41.3)     ≧60 3007 (34.7) 3009 (34.7)     Mean (s.d.) 52.43 (16.2) 52.28 (16.10) Gender     Female 1808 (20.8) 1896 (21.8) 0.103     Male 6870 (79.2) 6782 (78.2) Comorbidities     Chronic kidney disease 276 (3.2) 278 (3.2) 0.931     Hypertension 3033 (35.0) 3073 (35.4) 0.535     Dyslipidaemia 1531 (17.6) 1640 (18.9) 0.032     Cerebrovascular disease 277 (3.2) 252 (2.9) 0.270     Coronary artery disease 140 (1.6) 143 (1.6) 0.857     Depression 319 (3.7) 331 (3.8) 0.631     Chronic obstructive pulmonary disease 2008 (23.1) 2027 (23.4) 0.733     Congestive heart failure 100 (1.2) 106 (1.2) 0.674     Peripheral vascular disease 134 (1.5) 121 (1.4) 0.412 INN: benzbromarone. The results of the log-rank test and the cumulative incidence curve of the new onset of DM events (Fig. 2) indicated that the incidence rate of the new onset of DM was significantly lower in the benzbromarone users than in the benzbromarone non-users (P < 0.001). During an average follow-up period of 7.31 years, 998 benzbromarone non-users and 932 benzbromarone users developed new-onset DM (Table 2). The average duration of the new onset of DM was significantly longer in the benzbromarone users [5.02 (2.21) years] than in the benzbromarone non-users [4.03 (2.53) years; P < 0.001; Table 3]. The incidence density rates of the new onset of DM in the benzbromarone non-users and benzbromarone users were 4.03 and 5.02 per 10 000 PY, respectively. After adjustment for sex, age, comorbidities and medication, the benzbromarone user group exhibited a 15% reduction (95% CI: 0.77, 0.93) in the risk of new-onset DM (Table 2). Table 2 Risk of new-onset DM between INN users and INN non-users (N = 17356) Group Case no. (%) cHR (95% CI) P-value aHR (95% CI) P-value aHR (95% CI) P-value Overall 1930 (11.1) INN non-user 998 (11.5) Ref. Ref. INN user 932 (10.7) 0.86 (0.79, 0.94) 0.001 0.85 (0.77, 0.93) <0.001 Cumulative DDDs <103 DDDs 344 (11.8) 1.01 (0.89, 1.14) 0.905 0.97 (0.86, 1.10) 0.648 Ref. 103–230 DDDs 315 (11.0) 0.89 (0.78, 1.00) 0.058 0.880 (0.78, 0.99) 0.049 0.91 (0.78, 1.06) 0.209 >230 DDDs 273 (9.4) 0.71 (0.62, 0.81) <0.001 0.70 (0.61, 0.80) <0.001 0.72 (0.61, 0.85) <0.001 P for trend <0.001 <0.001 Group Case no. (%) cHR (95% CI) P-value aHR (95% CI) P-value aHR (95% CI) P-value Overall 1930 (11.1) INN non-user 998 (11.5) Ref. Ref. INN user 932 (10.7) 0.86 (0.79, 0.94) 0.001 0.85 (0.77, 0.93) <0.001 Cumulative DDDs <103 DDDs 344 (11.8) 1.01 (0.89, 1.14) 0.905 0.97 (0.86, 1.10) 0.648 Ref. 103–230 DDDs 315 (11.0) 0.89 (0.78, 1.00) 0.058 0.880 (0.78, 0.99) 0.049 0.91 (0.78, 1.06) 0.209 >230 DDDs 273 (9.4) 0.71 (0.62, 0.81) <0.001 0.70 (0.61, 0.80) <0.001 0.72 (0.61, 0.85) <0.001 P for trend <0.001 <0.001 Adjusted for age, sex and comorbidities. DM: diabetes mellitus; INN: benzbromarone; DDD: defined daily dose; aHR: adjusted hazard ratio; cHR: crude hazard ratio. Table 2 Risk of new-onset DM between INN users and INN non-users (N = 17356) Group Case no. (%) cHR (95% CI) P-value aHR (95% CI) P-value aHR (95% CI) P-value Overall 1930 (11.1) INN non-user 998 (11.5) Ref. Ref. INN user 932 (10.7) 0.86 (0.79, 0.94) 0.001 0.85 (0.77, 0.93) <0.001 Cumulative DDDs <103 DDDs 344 (11.8) 1.01 (0.89, 1.14) 0.905 0.97 (0.86, 1.10) 0.648 Ref. 103–230 DDDs 315 (11.0) 0.89 (0.78, 1.00) 0.058 0.880 (0.78, 0.99) 0.049 0.91 (0.78, 1.06) 0.209 >230 DDDs 273 (9.4) 0.71 (0.62, 0.81) <0.001 0.70 (0.61, 0.80) <0.001 0.72 (0.61, 0.85) <0.001 P for trend <0.001 <0.001 Group Case no. (%) cHR (95% CI) P-value aHR (95% CI) P-value aHR (95% CI) P-value Overall 1930 (11.1) INN non-user 998 (11.5) Ref. Ref. INN user 932 (10.7) 0.86 (0.79, 0.94) 0.001 0.85 (0.77, 0.93) <0.001 Cumulative DDDs <103 DDDs 344 (11.8) 1.01 (0.89, 1.14) 0.905 0.97 (0.86, 1.10) 0.648 Ref. 103–230 DDDs 315 (11.0) 0.89 (0.78, 1.00) 0.058 0.880 (0.78, 0.99) 0.049 0.91 (0.78, 1.06) 0.209 >230 DDDs 273 (9.4) 0.71 (0.62, 0.81) <0.001 0.70 (0.61, 0.80) <0.001 0.72 (0.61, 0.85) <0.001 P for trend <0.001 <0.001 Adjusted for age, sex and comorbidities. DM: diabetes mellitus; INN: benzbromarone; DDD: defined daily dose; aHR: adjusted hazard ratio; cHR: crude hazard ratio. Table 3 Average follow-up duration and average duration of new-onset DM Average follow-up duration DM new onset average duration Group Mean P-value Mean(s.d.) P-value Overall 7.3 (12.41) 4.51(2.43) INN non-user 7.03 <0.001 4.03 (2.53) <0.001 INN user 7.59 (2.11) 5.02(2.21) Average follow-up duration DM new onset average duration Group Mean P-value Mean(s.d.) P-value Overall 7.3 (12.41) 4.51(2.43) INN non-user 7.03 <0.001 4.03 (2.53) <0.001 INN user 7.59 (2.11) 5.02(2.21) Adjusted for age, sex and comorbidities. DM: diabetes mellitus; INN: benzbromarone. Table 3 Average follow-up duration and average duration of new-onset DM Average follow-up duration DM new onset average duration Group Mean P-value Mean(s.d.) P-value Overall 7.3 (12.41) 4.51(2.43) INN non-user 7.03 <0.001 4.03 (2.53) <0.001 INN user 7.59 (2.11) 5.02(2.21) Average follow-up duration DM new onset average duration Group Mean P-value Mean(s.d.) P-value Overall 7.3 (12.41) 4.51(2.43) INN non-user 7.03 <0.001 4.03 (2.53) <0.001 INN user 7.59 (2.11) 5.02(2.21) Adjusted for age, sex and comorbidities. DM: diabetes mellitus; INN: benzbromarone. Fig. 2 View largeDownload slide Cumulative incidence of DM in benzbromarone users and benzbromarone non-users Dashed line: benzbromarone users; solid line: benzbromarone non-users. INN: benzbromarone; DM: diabetes mellitus. Fig. 2 View largeDownload slide Cumulative incidence of DM in benzbromarone users and benzbromarone non-users Dashed line: benzbromarone users; solid line: benzbromarone non-users. INN: benzbromarone; DM: diabetes mellitus. Dose–response relationship between benzbromarone use and the risk of new-onset DM Table 2 shows the dose–response relationship between individual benzbromarone use and the risk of new-onset DM in the patients with new-onset DM and controls (benzbromarone non-users). Among the benzbromarone users, the adjusted HR was 0.88 (95% CI: 0.78, 0.99) for those receiving a cumulative DDD of 103–230 and the adjusted HR was 0.70 (95% CI: 0.61, 0.80) for those receiving a cumulative DDD of >230. The higher the cumulative DDD of benzbromarone was, the more significant the risk reduction of new-onset DM was (P for trend <0.001). Multivariate analysis Sex stratification showed that male benzbromarone users had a significant risk reduction of new-onset DM relative to the benzbromarone non-users (adjusted HR = 0.77, 95% CI: 0.69, 0.86; Table 4). Age stratification showed a significant risk reduction of new-onset DM in both the 40- to 59-year-old and ⩾60-year-old subgroups of the benzbromarone users compared with the similar subgroups of the benzbromarone non-users (adjusted HR = 0.86 and 0.82 and 95% CI: 0.76, 0.98 and 0.71, 0.94, respectively). Comorbidity stratification with multivariate analysis (Table 4) showed that the benzbromarone users with hypertension and dyslipidaemia were associated with a significant risk reduction of new-onset DM compared with the benzbromarone non-users with these conditions (adjusted HR = 0.79 and 0.80 and 95% CI: 0.69, 0.89 and 0.67, 0.94, respectively); by contrast, the benzbromarone users without CKD, dyslipidaemia, ischaemic heart disease, CVD, depression, chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF) and peripheral vascular disease (PVD) were associated with a significant risk reduction of new-onset DM compared with the benzbromarone non-users without these conditions (adjusted HR = 0.84, 0.86, 0.84, 0.84, 0.84, 0.83, 0.84 and 0.83 and 95% CI: 0.77, 0.92, 0.78, 0.96, 0.77, 0.92, 0.77, 0.92, 0.77, 0.92, 0.75, 0.92, 0.77, 0.92 and 0.76, 0.91, respectively). After stratification for all factors, the risk reduction of new-onset DM was higher in the benzbromarone users than in the benzbromarone non-users. Table 4 The risk of new-onset DM stratified by sex, age, comorbidities and medication between INN non-users and INN users (N = 17 356) INN non-user INN user INN user vs non- INN user Variables No. cases (%) No. cases (%) aHR (95% CI) P-values P for interaction Gender     Female 289 (16.0) 335 (17.7) 1.01 (0.86, 1.19) 0.884 0.010     Male 709 (10.3) 597 (8.8) 0.77 (0.69, 0.86) <0.001 Age (years)     <40 126 (6.2) 123 (5.9) 0.87 (0.68, 1.12) 0.289 0.551     40–59 496 (13.6) 455 (12.7) 0.86 (0.76, 0.98) 0.021     ≥60 376 (12.5) 354 (11.8) 0.82 (0.71, 0.94) 0.006 Comorbidities     CKD         No 971 (11.6) 903 (10.8) 0.84 (0.77, 0.92) <0.001 0.708         Yes 27 (9.8) 29 (10.4) 0.89 (0.52, 1.53) 0.682     Hypertension         No 500 (8.9) 483 (8.6) 0.90 (0.79, 1.02) 0.099 0.090         Yes 498 (16.4) 449 (14.6) 0.79 (0.69, 0.89) <0.001  Dyslipidaemia         No 712 (10.0) 672 (9.5) 0.86 (0.78, 0.96) 0.005 0.380         Yes 286 (18.7) 260 (15.9) 0.80 (0.67, 0.94) 0.007  Ischaemic heart disease         No 983 (11.5) 913 (10.7) 0.84 (0.77, 0.92) <0.001 0.741         Yes 15 (10.7) 19 (13.3) 0.82 (0.41, 1.68) 0.594  Cerebral vascular disease         No 972 (11.6) 912 (10.8) 0.84 (0.77, 0.92) <0.001 0.727         Yes 26 (9.4) 20 (7.9) 0.72 (0.40, 1.30) 0.270     Depression         No 952 (11.4) 890 (10.7) 0.84 (0.77, 0.92) <0.001 0.972         Yes 46 (14.4) 42 (12.7) 0.84 (0.56, 1.29) 0.430  Chronic obstructive pulmonary disease         No 748 (11.2) 681 (10.2) 0.83 (0.75, 0.92) <0.001 0.505         Yes 250 (12.5) 251 (12.4) 0.89 (0.75, 1.06) 0.889  Congestive heart failure         No 987 (11.5) 919 (10.7) 0.84 (0.77, 0.92) <0.001 0.920         Yes 11 (11.0) 13 (12.3) 0.59 (0.25, 1.41) 0.236  Peripheral vascular disease         No 984 (11.5) 991 (10.6) 0.83 (0.76, 0.91) <0.001 0.173         Yes 14 (10.4) 221 (17.4) 1.17 (0.58, 2.37) 0.659 INN non-user INN user INN user vs non- INN user Variables No. cases (%) No. cases (%) aHR (95% CI) P-values P for interaction Gender     Female 289 (16.0) 335 (17.7) 1.01 (0.86, 1.19) 0.884 0.010     Male 709 (10.3) 597 (8.8) 0.77 (0.69, 0.86) <0.001 Age (years)     <40 126 (6.2) 123 (5.9) 0.87 (0.68, 1.12) 0.289 0.551     40–59 496 (13.6) 455 (12.7) 0.86 (0.76, 0.98) 0.021     ≥60 376 (12.5) 354 (11.8) 0.82 (0.71, 0.94) 0.006 Comorbidities     CKD         No 971 (11.6) 903 (10.8) 0.84 (0.77, 0.92) <0.001 0.708         Yes 27 (9.8) 29 (10.4) 0.89 (0.52, 1.53) 0.682     Hypertension         No 500 (8.9) 483 (8.6) 0.90 (0.79, 1.02) 0.099 0.090         Yes 498 (16.4) 449 (14.6) 0.79 (0.69, 0.89) <0.001  Dyslipidaemia         No 712 (10.0) 672 (9.5) 0.86 (0.78, 0.96) 0.005 0.380         Yes 286 (18.7) 260 (15.9) 0.80 (0.67, 0.94) 0.007  Ischaemic heart disease         No 983 (11.5) 913 (10.7) 0.84 (0.77, 0.92) <0.001 0.741         Yes 15 (10.7) 19 (13.3) 0.82 (0.41, 1.68) 0.594  Cerebral vascular disease         No 972 (11.6) 912 (10.8) 0.84 (0.77, 0.92) <0.001 0.727         Yes 26 (9.4) 20 (7.9) 0.72 (0.40, 1.30) 0.270     Depression         No 952 (11.4) 890 (10.7) 0.84 (0.77, 0.92) <0.001 0.972         Yes 46 (14.4) 42 (12.7) 0.84 (0.56, 1.29) 0.430  Chronic obstructive pulmonary disease         No 748 (11.2) 681 (10.2) 0.83 (0.75, 0.92) <0.001 0.505         Yes 250 (12.5) 251 (12.4) 0.89 (0.75, 1.06) 0.889  Congestive heart failure         No 987 (11.5) 919 (10.7) 0.84 (0.77, 0.92) <0.001 0.920         Yes 11 (11.0) 13 (12.3) 0.59 (0.25, 1.41) 0.236  Peripheral vascular disease         No 984 (11.5) 991 (10.6) 0.83 (0.76, 0.91) <0.001 0.173         Yes 14 (10.4) 221 (17.4) 1.17 (0.58, 2.37) 0.659 Adjusted for age, sex, comorbidities, history of medication and CCI score. CCI: charlson comorbidity index; DM: diabetes mellitus; INN: benzbromarone; aHR: adjusted hazard ratio. Table 4 The risk of new-onset DM stratified by sex, age, comorbidities and medication between INN non-users and INN users (N = 17 356) INN non-user INN user INN user vs non- INN user Variables No. cases (%) No. cases (%) aHR (95% CI) P-values P for interaction Gender     Female 289 (16.0) 335 (17.7) 1.01 (0.86, 1.19) 0.884 0.010     Male 709 (10.3) 597 (8.8) 0.77 (0.69, 0.86) <0.001 Age (years)     <40 126 (6.2) 123 (5.9) 0.87 (0.68, 1.12) 0.289 0.551     40–59 496 (13.6) 455 (12.7) 0.86 (0.76, 0.98) 0.021     ≥60 376 (12.5) 354 (11.8) 0.82 (0.71, 0.94) 0.006 Comorbidities     CKD         No 971 (11.6) 903 (10.8) 0.84 (0.77, 0.92) <0.001 0.708         Yes 27 (9.8) 29 (10.4) 0.89 (0.52, 1.53) 0.682     Hypertension         No 500 (8.9) 483 (8.6) 0.90 (0.79, 1.02) 0.099 0.090         Yes 498 (16.4) 449 (14.6) 0.79 (0.69, 0.89) <0.001  Dyslipidaemia         No 712 (10.0) 672 (9.5) 0.86 (0.78, 0.96) 0.005 0.380         Yes 286 (18.7) 260 (15.9) 0.80 (0.67, 0.94) 0.007  Ischaemic heart disease         No 983 (11.5) 913 (10.7) 0.84 (0.77, 0.92) <0.001 0.741         Yes 15 (10.7) 19 (13.3) 0.82 (0.41, 1.68) 0.594  Cerebral vascular disease         No 972 (11.6) 912 (10.8) 0.84 (0.77, 0.92) <0.001 0.727         Yes 26 (9.4) 20 (7.9) 0.72 (0.40, 1.30) 0.270     Depression         No 952 (11.4) 890 (10.7) 0.84 (0.77, 0.92) <0.001 0.972         Yes 46 (14.4) 42 (12.7) 0.84 (0.56, 1.29) 0.430  Chronic obstructive pulmonary disease         No 748 (11.2) 681 (10.2) 0.83 (0.75, 0.92) <0.001 0.505         Yes 250 (12.5) 251 (12.4) 0.89 (0.75, 1.06) 0.889  Congestive heart failure         No 987 (11.5) 919 (10.7) 0.84 (0.77, 0.92) <0.001 0.920         Yes 11 (11.0) 13 (12.3) 0.59 (0.25, 1.41) 0.236  Peripheral vascular disease         No 984 (11.5) 991 (10.6) 0.83 (0.76, 0.91) <0.001 0.173         Yes 14 (10.4) 221 (17.4) 1.17 (0.58, 2.37) 0.659 INN non-user INN user INN user vs non- INN user Variables No. cases (%) No. cases (%) aHR (95% CI) P-values P for interaction Gender     Female 289 (16.0) 335 (17.7) 1.01 (0.86, 1.19) 0.884 0.010     Male 709 (10.3) 597 (8.8) 0.77 (0.69, 0.86) <0.001 Age (years)     <40 126 (6.2) 123 (5.9) 0.87 (0.68, 1.12) 0.289 0.551     40–59 496 (13.6) 455 (12.7) 0.86 (0.76, 0.98) 0.021     ≥60 376 (12.5) 354 (11.8) 0.82 (0.71, 0.94) 0.006 Comorbidities     CKD         No 971 (11.6) 903 (10.8) 0.84 (0.77, 0.92) <0.001 0.708         Yes 27 (9.8) 29 (10.4) 0.89 (0.52, 1.53) 0.682     Hypertension         No 500 (8.9) 483 (8.6) 0.90 (0.79, 1.02) 0.099 0.090         Yes 498 (16.4) 449 (14.6) 0.79 (0.69, 0.89) <0.001  Dyslipidaemia         No 712 (10.0) 672 (9.5) 0.86 (0.78, 0.96) 0.005 0.380         Yes 286 (18.7) 260 (15.9) 0.80 (0.67, 0.94) 0.007  Ischaemic heart disease         No 983 (11.5) 913 (10.7) 0.84 (0.77, 0.92) <0.001 0.741         Yes 15 (10.7) 19 (13.3) 0.82 (0.41, 1.68) 0.594  Cerebral vascular disease         No 972 (11.6) 912 (10.8) 0.84 (0.77, 0.92) <0.001 0.727         Yes 26 (9.4) 20 (7.9) 0.72 (0.40, 1.30) 0.270     Depression         No 952 (11.4) 890 (10.7) 0.84 (0.77, 0.92) <0.001 0.972         Yes 46 (14.4) 42 (12.7) 0.84 (0.56, 1.29) 0.430  Chronic obstructive pulmonary disease         No 748 (11.2) 681 (10.2) 0.83 (0.75, 0.92) <0.001 0.505         Yes 250 (12.5) 251 (12.4) 0.89 (0.75, 1.06) 0.889  Congestive heart failure         No 987 (11.5) 919 (10.7) 0.84 (0.77, 0.92) <0.001 0.920         Yes 11 (11.0) 13 (12.3) 0.59 (0.25, 1.41) 0.236  Peripheral vascular disease         No 984 (11.5) 991 (10.6) 0.83 (0.76, 0.91) <0.001 0.173         Yes 14 (10.4) 221 (17.4) 1.17 (0.58, 2.37) 0.659 Adjusted for age, sex, comorbidities, history of medication and CCI score. CCI: charlson comorbidity index; DM: diabetes mellitus; INN: benzbromarone; aHR: adjusted hazard ratio. Discussion Our study results demonstrated that the incidence rate of new-onset DM was lower in the patients with gout using benzbromarone than in those not using benzbromarone. The higher the cumulative dose of benzbromarone, the more significantly the risk of new-onset of DM was reduced. A significantly longer duration of new-onset DM was observed among benzbromarone users than among benzbromarone non-users. Hyperuricaemia may play a role in the development and pathogenesis of many metabolic, haemodynamic and systemic pathological diseases, including MetS, hypertension, stroke and atherosclerosis [14]. At the same time, IR may potentially play a key role in the causal relationships between hyperuricaemia, MetS and type 2 DM [11]. A study by Kuwabara et al. [15] observed that hyperuricaemia was a significant risk factor for incident hypertension and DM compared with normouricaemia without MetS. One study reported that insulin may enhance renal urate reabsorption through the stimulation of the urate-anion exchanger Urate Transporter 1 (URAT1) in the brush border membranes of the renal proximal tubule [16]. By contrast, the proposed mechanisms related to these findings indicate the effect of UA on endothelial dysfunction and the overproduction of reactive oxygen species. UA inactivates the production of nitrogen oxide (NO) in animals [17]; thus, hyperuricemia stimulates intracellular reactive oxygen species production and reduces the effects of NO on adipose cells. Such oxidative stress in adipocytes is considered to play a key role in the induction of IR increase and MetS [18]. Benzbromarone inhibits URAT1, thereby reducing the serum urate level [19]. URAT1 (SLC22A12) mediates the entry (uptake) of urate into cells. A decreased renal UA excretion indicates increased URAT1 activity, which can be correlated with higher intracellular UA levels [20]. URAT1 is located not only on the renal proximal tubular cells, but also on adipocytes [21]. URAT1 transporter gene polymorphisms in Caucasians with hypertension were associated with BMI, waist circumference, HDL cholesterol and MetS [20]. A clinical trial of lowering urate levels in patients with CHF by using benzbromarone indicated that lowering urate levels improved IR. However, it cannot be ruled out that this may have been a secondary pharmacological effect of benzbromarone related to its peroxisome proliferator-activated receptor agonist activity [22]. The aforementioned findings indicate that benzbromarone may improve IR, DM and even MetS. A previous study revealed that UA-lowering agents reduced IR [23]; however, no incidence reduction effect of DM has been investigated. In our study, a decreasing trend of new-onset DM after benzbromarone use and a dose-dependent risk reduction were observed. Several theories may account for this finding. A close relationship between serum UA levels and the presence of MetS was demonstrated in a study conducted in the USA [24]. An Indian study reported that UA contributed to 66.84% of the variance in MetS in multivariate regression analysis, indicating that UA can be considered as a marker and potential modifier of MetS [25]. In a study from the USA, the prevalence (95% CI) of MetS according to the revised National cholesterol education program/ adult treatment panel III (NCEP/ATP III) criteria was 62.8% (51.9, 73.6) in patients with gout and 25.4% (23.5, 27.3) in individuals without gout [26]. The renal handling of urate in gout patients with MetS indicates that the severity of MetS increases with an increase in the serum urate level, whereas the renal excretion of UA is inversely correlated. This suggests that the disturbance is related to the severity of MetS [27]. In Korea, the prevalence of MetS in patients with gout was 50.8%, which is higher than the prevalence in the general Korean population [28]. In a study from Iran, hyperuricaemia increased the risk of MetS more than 2-fold [29]. Gout contributes to a high risk of MetS [30]. The prevalence of MetS increases significantly with the serum UA level [31]. A meta-analysis of 11 studies revealed that every milligram per decilitre increase in the UA level is associated with a 17% increased risk of DM [32]. However, whether gout therapy improves DM or MetS in humans remains unclear. In our study, the risk of new-onset DM could be reduced by 14% with the usage of benzbromarone. Comorbidity stratification by using multivariate analysis (Table 4) indicated that the benzbromarone users with CKD, CAD, CVD, depression, COPD, CHF and PVD exhibited a non-significant risk reduction for new-onset DM compared with the benzbromarone non-users. The negative results might be due to small sample sizes. Additional large-sample studies evaluating these subgroups are required. The present study also revealed risk reduction of new-onset of DM in male benzbromarone users, but females did not demonstrate any effect. It demonstrated that serum UA level was significantly associated with pre-hypertension [33], carotid-to-femoral pulse wave velocity [34] and cognitive impairment [35] only in men, and not in women. Female hormones are associated with lower levels of serum UA via renal clearance [36]. Several types of oral hypoglycaemic agents are available. Metformin improves IR and targets both the liver and skeletal muscle [37]. Thiazolidinediones, by activating peroxisome proliferator-activated receptors, improve IR, reduce hepatic glucose production and improve glucose disposal indirectly by altering lipid metabolism in adipose tissues [38]. Sulfonylureas and meglitinides (glinides) both target pancreatic β cells and increase insulin secretion. Glucagon-like peptide-1 agonists reduce excess glucagon secretion by pancreatic α-cells and slow down gastric emptying [39]. Dipeptidyl peptidase-4 inhibitors reduce the breakdown and increase the levels of the endogenous incretin hormones glucagon-like peptide-1 and glucose-dependent insulin-releasing polypeptide [40]. α-Glucosidase inhibitors slow down the rate of carbohydrate digestion by the small intestine [41]. In recent years, sodium glucose cotransporter 2 (SGLT-2) inhibitors, a new type of oral hypoglycemic agents (OHA), have been developed; they inhibit SGLT2 in the proximal nephron of the kidney and block glucose reabsorption [42]. SGLT-2 inhibitors have three characteristics. First, these are independent of insulin action; thus, there is a low risk of hypoglycemia [43]. Second, similar to benzbromarone (URAT1 inhibitor), SGLT-2 inhibitor is the first type of OHA targeting the kidneys and proximal tubules. Third, SGLT-2 inhibitor can lower not only baseline haemoglobin A1c but also UA [44]. In our study, the incidence of DM in the gout population was 111 per 1000 PY (Table 2). Furthermore, a study conducted in Taiwan reported that the incidence of DM in patients aged 20–79 years was 11.60/1000 PY in the general population in 2008 [45]; thus, the incidence of DM in the gout population was ∼9.57 times that in the general population. Therefore, improving the incidence of DM in the gout population may be more important than improving that in the general population. In our study, benzbromarone users in the gout population exhibited a 14% risk reduction (95% CI: 0.79, 0.94) for new-onset DM, and the higher the cumulative DDD of benzbromarone use was, the more significant was the risk reduction of new-onset DM. In the gout population, the benzbromarone users with hypertension and dyslipidaemia were associated with a significant risk reduction of new-onset DM compared with the benzbromarone non-users with these conditions; by contrast, those without CKD, hypertension, dyslipidaemia, CAD, CVD, depression, COPD, CHF and PVD were associated with a significant risk reduction of new-onset DM compared with the benzbromarone non-users without these conditions. Limitations The present study has some limitations. First, this is not a study of prospective nature and dedicated intervention. Second, this study only demonstrated the preventive effects of benzbromarone for DM in a gout population. In general, benzbromarone is not used in the non-gout population. Existing literature provides the aspect that UA levels might be linked with the incidence of IR and diabetes, but does not look into the potential effect of the therapeutic intervention. Third, the NHI database does not include laboratory data, such as sugar, glycated haemoglobin and UA levels; therefore, our case definition was based on physician-recorded diagnoses instead of the ACR criteria [46] or urate crystal identification. In our study, gout and DM were both accurately diagnosed and coded (ICD-9-CM codes) by specialists according to the standard diagnostic criteria, including typical symptoms and signs, laboratory data and imaging findings. Lastly, real targets of UA are unknown in the different DDD groups. Conclusions In this cohort study, the incidence of DM was lower in the benzbromarone users than in the benzbromarone non-users in the gout population. 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RheumatologyOxford University Press

Published: May 22, 2018

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