Uric acid is not associated with diabetic nephropathy and other complications in type 1 diabetes

Uric acid is not associated with diabetic nephropathy and other complications in type 1 diabetes Abstract Background To examine the association between plasma uric acid (UA) and the presence of diabetic complications including diabetic nephropathy and cardiovascular risk factors in patients with type 1 diabetes. Methods This study, which is cross-sectional in design, included 676 Caucasian type 1 diabetes patients from the Steno Diabetes Center Copenhagen. Participants with UA within the three lowest sex-specific quartiles were compared with participants with levels in the highest quartile. Unadjusted and adjusted linear regression analyses were applied. Adjustment included sex, age, diabetes duration, body mass index, high-density lipoprotein cholesterol, smoking, haemoglobin A1c, 24-h pulse pressure, urinary albumin excretion rate (UAER), estimated glomerular filtration rate (eGFR) and treatment with renin–angiotensin–aldosterone system blockers. Results Of the 676 patients, 372 (55%) were male, mean ± SD age was 55 ± 13 years and eGFR was 82 ± 26 mL/min/1.73 m2. The median UA was 0.30 (interquartile range 0.23–0.37) mmol/L. UA in the upper sex-specific quartile was associated with lower eGFR, higher UAER and carotid–femoral pulse wave velocity and lower 24 h and daytime diastolic blood pressure (BP) in unadjusted analyses (P < 0.001). Moreover, UA in the upper sex-specific quartile was associated with higher nighttime systolic BP and the presence of cardiovascular disease in unadjusted analyses (P ≤ 0.01), but significance was lost after adjustment (P ≥ 0.17). UA was higher across the retinopathy groups [nil (n = 142), simplex (n = 277), proliferative (n = 229) and blind (n = 19)] in unadjusted analyses (P < 0.0001), but not after adjustment (P = 0.12). Patients with an accelerated decline in eGFR (≥3 mL/min/year) had significantly higher UA at baseline (P = 0.006) compared with slow decliners (<3 mL/min/year), but significance was lost after adjustment (P = 0.10). Conclusions In type 1 diabetes patients, higher UA was associated with lower kidney function and other diabetic complications. The association between higher UA and lower eGFR and lower diastolic BP was independent of traditional risk factors. coronary artery, diabetes mellitus, diabetic kidney disease, disease uric acid, GFR INTRODUCTION The complex pathogenesis of diabetic complications is not fully understood [1, 2] and so far many risk factors have been proposed. One of these is uric acid (UA). Elevated levels of UA have been proposed as a risk factor for chronic kidney disease (CKD), hypertension and cardiovascular disease in people with as well as without diabetes [3–7]. In the clinical setting, the majority of observational studies have shown that higher UA is associated with the incidence and development of CKD in patients with type 1 diabetes independent of other risk factors [5, 6, 8–10]. Prospective data from the Second Joslin Kidney Study showed that higher UA is one of the strongest risk factors for early loss of renal function (measured by cystatin C) among patients with type 1 diabetes characterized by microalbuminuria and normal renal function at baseline [9]. The relationship between UA and other diabetic complications, such as retinopathy and neuropathy, has only sparsely been investigated in patients with type 1 diabetes. Moreover, only one study have investigated the association between UA and arterial stiffness in patients with type 1 diabetes and the study included adolescents ages 11–16 years [11]. Also, the association between UA and 24-h blood pressure has never been investigated in patients with type 1 diabetes. At Steno Diabetes Center Copenhagen, we performed a cross-sectional study of patients with type 1 diabetes characterized by different degrees of diabetic nephropathy [12]. The objective of this analysis is to examine the association between plasma UA and the presence of diabetic nephropathy as well as other diabetic complications and cardiovascular risk factors in patients with type 1 diabetes. We hypothesized that higher levels of UA are associated with (i) the presence of and increased progression of renal impairment, (ii) the presence of diabetic complications and (iii) higher 24-h blood pressure and arterial stiffness. MATERIALS AND METHODS Participants Between 2009 and 2011, Caucasian patients with type 1 diabetes were recruited to enter a cross-sectional study at Steno Diabetes Center Copenhagen. Inclusion criteria were type 1 diabetes [World Health Organization (WHO) criteria], age > 18 years and patient at the tertiary outpatient clinic at Steno Diabetes Center Copenhagen. The study was designed to investigate the associations between blood pressure, arterial stiffness and diabetic complications [12, 13]. The cohort was stratified by levels of albuminuria (normo-, micro- and macroalbuminuria). Patients with end-stage renal disease, defined as receiving dialysis or renal transplantation or glomerular filtration rate (GFR) or estimated glomerular filtration rate (eGFR) <15 mL/min/1.73 m2 were not included. All participants gave written informed consent and the study was approved by the regional ethics committee. Procedures UA was measured in plasma by the Vitros 5600 MicroSlide system (Ortho Clinical Diagnostics, Raritan, NJ, USA). The plasma samples were stored immediately after collection in freezers at −80°C and were stored for up to 8 years prior to analysis. The eGFR was calculated by the Chronic Kidney Disease Epidemiology Collaboration equation [14]. Sufficient follow-up information to calculate the annual decline in eGFR (≥2 measures and a mean of 5.7 measures) was available in 476 patients through the medical records. The rate of decline in kidney function was analysed with regression lines for eGFR over the follow-up period (mean 4.1 years) using all measurements during the study period. Three 24-h urine collections were obtained to measure the urinary albumin excretion rate (UAER) using an enzyme immunoassay (Vitros). Participants were categorized as normoalbuminuric if UAER was <30 mg/day, microalbuminuric if UAER was or previously had been recorded between 30 and 299 mg/day and macroalbuminuric if UAER was or previously had been recorded at >300 mg/day in two of three consecutive measurements. All patients classified as normoalbuminuric did not have any history of micro- or macroalbuminuria prior to enrolment in the study. Of the patients with macroalbuminuria, 183 (95.8%) had retinopathy. Haemoglobin A1c (HbA1c) was measured by high-performance liquid chromatography (Bio-Rad Laboratories, Munich, Germany) and serum creatinine concentration by an enzyme method (Hitachi 912; Rocked Diagnostics, Mannheim, Germany). The 24-h ambulatory blood pressure was recorded with a validated tonometric watch-like device (BPro; HeathStats, Singapore) that captures radial pulse wave reflection and calculates blood pressure [12, 13]. The device was programmed to measure every 15 min during the entire 24-h period. The 24-h ambulatory blood pressure was considered adequate if  ≥14 and  ≥7 recordings were obtained during the day and night, respectively. Mean arterial pressure was calculated as diastolic blood pressure plus one-third of pulse pressure. Following 15 min of supine rest, the carotid–femoral pulse wave velocity was obtained with the SphygmoCor device (Actor Medical, Sydney, NSW, Australia) by trained laboratory technicians. Three measurements were performed and averaged. A history of cardiovascular disease included myocardial infarction, stroke or peripheral arterial disease based on standardized WHO questionnaires and patient records. Participants were classified as current smokers if using one or more cigarettes, cigars or pipes per day and all others as non-smokers. Information on antihypertensive treatment was recorded from patient files and cross-checked by interview with the patient. Antihypertensive treatment was classified into drug classes. Analyses of heart rate variability were applied to access cardiac autonomic dysfunction (as a measure of neuropathy) and were recorded during paced deep breathing [12, 13]. A heart rate variability ≥15 beats per min was classified as normal, between 11 and 14 beats per min as borderline and <11 beats per min as abnormal. Retinopathy status was obtained from medical records based on standardized analysis of retinal photos through dilated pupils by certified eye nurses and classified into four groups (nil, simplex, proliferative or blind). The assignment was based on the severity of the most severely affected eye. Statistical analysis The distribution of UA and UAER was skewed and therefore these variables were log2 transformed in all the linear analyses and given as medians with interquartile range (IQR). All other continuous variables are given as means ± SD and the categorical variables are given as percentages. When analysing differences between participants based on the four UA sex-specific quartiles, we used the analysis of covariance for continuous and the χ2 test for categorical variables. In regression analyses we compared participants with a level of UA within the three lowest sex-specific quartiles with participants with a level in the highest quartile [1]. We applied unadjusted and adjusted linear regression analyses. We used unadjusted models to determine if any association existed between the level of UA and covariates/predefined groups. The subsequent multivariate adjustment included traditional risk factors based on prior evidence, namely, age, sex, diabetes duration, high-density lipoprotein (HDL) cholesterol, body mass index, smoking, HbA1c, 24-h pulse pressure, eGFR, UAER and treatment with renin–angiotensin–aldosterone system (RAAS) blockers, as appropriate. The analyses of carotid–femoral pulse wave velocity also included office mean arterial pressure to account for the steady component of the blood pressure at the time of measurement. The proportion of the variability in the dependent variable explained by the model is presented as R2 and the F-test was applied to determine whether this relationship was statistically significant. A two-tailed P-value <0.05 was considered statistical significant. Statistical analyses were performed using SAS software (version 9.3; SAS Institute, Cary, NC, USA). RESULTS Clinical characteristics Of the original cohort (n = 676), the plasma UA level was available in 670 (99.1%) patients. The median UA was 0.30 (IQR 0.23–0.37) mmol/L, equivalent to 5.04 (IQR 3.87–6.22) mg/dL. Figure 1 illustrates median values of UA according to the albuminuria group. UA was significantly higher with increasing levels of albuminuria (P < 0.001). FIGURE 1: View largeDownload slide Plasma UA in groups of albuminuria. Data represent median (IQR). Participants were categorized as normoalbuminuric (UAER <30 mg/day), microalbuminuric (UAER 30–299 mg/day) and macroalbuminuric (UAER >300 mg/day). P < 0.001 after adjustment for sex, age, diabetes duration, body mass index, HDL cholesterol, smoking, HbA1c, 24-h pulse pressure, eGFR and treatment with RAAS blockers. FIGURE 1: View largeDownload slide Plasma UA in groups of albuminuria. Data represent median (IQR). Participants were categorized as normoalbuminuric (UAER <30 mg/day), microalbuminuric (UAER 30–299 mg/day) and macroalbuminuric (UAER >300 mg/day). P < 0.001 after adjustment for sex, age, diabetes duration, body mass index, HDL cholesterol, smoking, HbA1c, 24-h pulse pressure, eGFR and treatment with RAAS blockers. The clinical characteristics in the 670 participants divided according to sex-specific quartiles of UA are shown in Table 1. Participants with higher UA were older; had longer diabetes duration; had higher albuminuria level, body mass index, carotid–femoral pulse wave velocity and triglycerides; were prescribed more antihypertensive medication, including diuretics, RAAS blockers, beta-blockers and calcium channel blockers; and had lower eGFR compared with those with lower UA (P < 0.001 for all). However, there was no significant difference across sex-specific quartiles of UA and low-density lipoprotein cholesterol or HbA1c. A total of 454 (67.8%) were treated with RAAS blockers. Table 1. Clinical characteristics divided according to sex-specific quartiles of plasma UA Characteristics  Sex-specific quartiles of plasma UA   Plasma UA (mmol/L)  Women: <0.21 Men: <0.27  Women: ≥0.21–<0.25 Men: ≥0.27–<0.32  Women: ≥0.25–≤0.33 Men: ≥0.32–≤0.37  Women: >0.33 Men: >0.37  P-value  Number of participants  162  147  190  175    Female, %  42  45  52  40  0.11  Age (years)  50 ± 14  52 ± 12  57 ± 11  58 ± 11  <0.001  Diabetes duration (years)  26 ± 16  30 ± 16  35 ± 15  39 ± 13  <0.001  eGFR (mL/min/1.73 m2)  99 ± 16  95 ± 14  81 ± 20  55 ± 23  <0.001  UAER (mg/24 h), median (IQR)  12 (7–26)  11 (6–32)  15 (7–64)  50 (16–234)  <0.001  HbA1c (mmol/mol)  64 ± 10  64 ± 10  64 ± 5  64 ± 10  0.92  HbA1c (%)  8 ± 2  8 ± 2  8 ± 1  8 ± 2  0.92  HDL cholesterol (mmol/L)  1.7 ± 0.5  1.7 ± 0.5  1.8 ± 0.6  1.6 ± 0.5  0.002  LDL cholesterol (mmol/L)  2.5 ± 0.7  2.4 ± 0.7  2.5 ± 0.8  2.4 ± 0.8  0.63  Triglycerides (mmol/L)  1.03 ± 0.5  1.09 ± 0.7  1.10 ± 0.6  1.31 ± 0.9  <0.001  Body mass index (kg/m2)  24.2 ± 3.8  24.6 ± 3.5  25.1 ± 4.0  27.2 ± 9.2  <0.001  Treatment with, (%)   Antihypertensive drugs  50  58  78  97  <0.001   Diuretics  27  32  50  87  <0.001   RAAS blockers  45  53  74  92  <0.001   Beta-blockers  11  6  11  21  <0.001   Calcium channel blockers  22  17  32  48  <0.001  Smokers, %  18  23  25  18  0.25  24-h systolic blood pressure (mmHg)  127 ± 16  130 ± 12  128 ± 87  129 ± 15  0.44  24-h diastolic blood pressure (mmHg)  76 ± 9  78 ± 9  74 ± 11  74 ± 11  0.012  Carotid–femoral pulse wave velocity (m/s)  9.2 ± 3.1  9.6 ± 3.1  10.7 ± 3.1  11.9 ± 3.5  <0.001  Characteristics  Sex-specific quartiles of plasma UA   Plasma UA (mmol/L)  Women: <0.21 Men: <0.27  Women: ≥0.21–<0.25 Men: ≥0.27–<0.32  Women: ≥0.25–≤0.33 Men: ≥0.32–≤0.37  Women: >0.33 Men: >0.37  P-value  Number of participants  162  147  190  175    Female, %  42  45  52  40  0.11  Age (years)  50 ± 14  52 ± 12  57 ± 11  58 ± 11  <0.001  Diabetes duration (years)  26 ± 16  30 ± 16  35 ± 15  39 ± 13  <0.001  eGFR (mL/min/1.73 m2)  99 ± 16  95 ± 14  81 ± 20  55 ± 23  <0.001  UAER (mg/24 h), median (IQR)  12 (7–26)  11 (6–32)  15 (7–64)  50 (16–234)  <0.001  HbA1c (mmol/mol)  64 ± 10  64 ± 10  64 ± 5  64 ± 10  0.92  HbA1c (%)  8 ± 2  8 ± 2  8 ± 1  8 ± 2  0.92  HDL cholesterol (mmol/L)  1.7 ± 0.5  1.7 ± 0.5  1.8 ± 0.6  1.6 ± 0.5  0.002  LDL cholesterol (mmol/L)  2.5 ± 0.7  2.4 ± 0.7  2.5 ± 0.8  2.4 ± 0.8  0.63  Triglycerides (mmol/L)  1.03 ± 0.5  1.09 ± 0.7  1.10 ± 0.6  1.31 ± 0.9  <0.001  Body mass index (kg/m2)  24.2 ± 3.8  24.6 ± 3.5  25.1 ± 4.0  27.2 ± 9.2  <0.001  Treatment with, (%)   Antihypertensive drugs  50  58  78  97  <0.001   Diuretics  27  32  50  87  <0.001   RAAS blockers  45  53  74  92  <0.001   Beta-blockers  11  6  11  21  <0.001   Calcium channel blockers  22  17  32  48  <0.001  Smokers, %  18  23  25  18  0.25  24-h systolic blood pressure (mmHg)  127 ± 16  130 ± 12  128 ± 87  129 ± 15  0.44  24-h diastolic blood pressure (mmHg)  76 ± 9  78 ± 9  74 ± 11  74 ± 11  0.012  Carotid–femoral pulse wave velocity (m/s)  9.2 ± 3.1  9.6 ± 3.1  10.7 ± 3.1  11.9 ± 3.5  <0.001  Data presented as mean ± SD unless stated otherwise. P-values are for trend across quartiles. LDL, low-density lipoprotein. Table 1. Clinical characteristics divided according to sex-specific quartiles of plasma UA Characteristics  Sex-specific quartiles of plasma UA   Plasma UA (mmol/L)  Women: <0.21 Men: <0.27  Women: ≥0.21–<0.25 Men: ≥0.27–<0.32  Women: ≥0.25–≤0.33 Men: ≥0.32–≤0.37  Women: >0.33 Men: >0.37  P-value  Number of participants  162  147  190  175    Female, %  42  45  52  40  0.11  Age (years)  50 ± 14  52 ± 12  57 ± 11  58 ± 11  <0.001  Diabetes duration (years)  26 ± 16  30 ± 16  35 ± 15  39 ± 13  <0.001  eGFR (mL/min/1.73 m2)  99 ± 16  95 ± 14  81 ± 20  55 ± 23  <0.001  UAER (mg/24 h), median (IQR)  12 (7–26)  11 (6–32)  15 (7–64)  50 (16–234)  <0.001  HbA1c (mmol/mol)  64 ± 10  64 ± 10  64 ± 5  64 ± 10  0.92  HbA1c (%)  8 ± 2  8 ± 2  8 ± 1  8 ± 2  0.92  HDL cholesterol (mmol/L)  1.7 ± 0.5  1.7 ± 0.5  1.8 ± 0.6  1.6 ± 0.5  0.002  LDL cholesterol (mmol/L)  2.5 ± 0.7  2.4 ± 0.7  2.5 ± 0.8  2.4 ± 0.8  0.63  Triglycerides (mmol/L)  1.03 ± 0.5  1.09 ± 0.7  1.10 ± 0.6  1.31 ± 0.9  <0.001  Body mass index (kg/m2)  24.2 ± 3.8  24.6 ± 3.5  25.1 ± 4.0  27.2 ± 9.2  <0.001  Treatment with, (%)   Antihypertensive drugs  50  58  78  97  <0.001   Diuretics  27  32  50  87  <0.001   RAAS blockers  45  53  74  92  <0.001   Beta-blockers  11  6  11  21  <0.001   Calcium channel blockers  22  17  32  48  <0.001  Smokers, %  18  23  25  18  0.25  24-h systolic blood pressure (mmHg)  127 ± 16  130 ± 12  128 ± 87  129 ± 15  0.44  24-h diastolic blood pressure (mmHg)  76 ± 9  78 ± 9  74 ± 11  74 ± 11  0.012  Carotid–femoral pulse wave velocity (m/s)  9.2 ± 3.1  9.6 ± 3.1  10.7 ± 3.1  11.9 ± 3.5  <0.001  Characteristics  Sex-specific quartiles of plasma UA   Plasma UA (mmol/L)  Women: <0.21 Men: <0.27  Women: ≥0.21–<0.25 Men: ≥0.27–<0.32  Women: ≥0.25–≤0.33 Men: ≥0.32–≤0.37  Women: >0.33 Men: >0.37  P-value  Number of participants  162  147  190  175    Female, %  42  45  52  40  0.11  Age (years)  50 ± 14  52 ± 12  57 ± 11  58 ± 11  <0.001  Diabetes duration (years)  26 ± 16  30 ± 16  35 ± 15  39 ± 13  <0.001  eGFR (mL/min/1.73 m2)  99 ± 16  95 ± 14  81 ± 20  55 ± 23  <0.001  UAER (mg/24 h), median (IQR)  12 (7–26)  11 (6–32)  15 (7–64)  50 (16–234)  <0.001  HbA1c (mmol/mol)  64 ± 10  64 ± 10  64 ± 5  64 ± 10  0.92  HbA1c (%)  8 ± 2  8 ± 2  8 ± 1  8 ± 2  0.92  HDL cholesterol (mmol/L)  1.7 ± 0.5  1.7 ± 0.5  1.8 ± 0.6  1.6 ± 0.5  0.002  LDL cholesterol (mmol/L)  2.5 ± 0.7  2.4 ± 0.7  2.5 ± 0.8  2.4 ± 0.8  0.63  Triglycerides (mmol/L)  1.03 ± 0.5  1.09 ± 0.7  1.10 ± 0.6  1.31 ± 0.9  <0.001  Body mass index (kg/m2)  24.2 ± 3.8  24.6 ± 3.5  25.1 ± 4.0  27.2 ± 9.2  <0.001  Treatment with, (%)   Antihypertensive drugs  50  58  78  97  <0.001   Diuretics  27  32  50  87  <0.001   RAAS blockers  45  53  74  92  <0.001   Beta-blockers  11  6  11  21  <0.001   Calcium channel blockers  22  17  32  48  <0.001  Smokers, %  18  23  25  18  0.25  24-h systolic blood pressure (mmHg)  127 ± 16  130 ± 12  128 ± 87  129 ± 15  0.44  24-h diastolic blood pressure (mmHg)  76 ± 9  78 ± 9  74 ± 11  74 ± 11  0.012  Carotid–femoral pulse wave velocity (m/s)  9.2 ± 3.1  9.6 ± 3.1  10.7 ± 3.1  11.9 ± 3.5  <0.001  Data presented as mean ± SD unless stated otherwise. P-values are for trend across quartiles. LDL, low-density lipoprotein. UA and eGFR Higher UA was significantly associated with lower eGFR in unadjusted (Figure 2A;R2 = 0.37; F-test P < 0.0001) and adjusted analyses (P < 0.001 for all). The level of UA in the upper sex-specific quartile was significantly associated with lower eGFR in unadjusted and adjusted analyses compared with the level in the three lowest quartiles, both in the total cohort and in analyses stratified by albuminuria group (unadjusted and adjusted P < 0.001 for all; Table 2). Table 2. Associations between plasma UA and eGFR in sex-specific quartile analyses Model  Quartile 4 versus Quartiles 1–3  P-value  All (n = 670)       Unadjusted  −35.4 ± 1.8  <0.001   Adjusted  −27.4 ± 1.7  <0.001  Normoalbuminuria (n = 312)       Unadjusted  −29.0 ± 3.4  <0.001   Adjusted  −23.0 ± 2.7  <0.001  Microalbuminuria (n = 168)       Unadjusted  −22.0 ± 3.5  <0.001   Adjusted  −18.6 ± 3.3  <0.001  Macroalbuminuria (n = 190)       Unadjusted  −33.6 ± 3.3  <0.001   Adjusted  −31.4 ± 3.3  <0.001  Model  Quartile 4 versus Quartiles 1–3  P-value  All (n = 670)       Unadjusted  −35.4 ± 1.8  <0.001   Adjusted  −27.4 ± 1.7  <0.001  Normoalbuminuria (n = 312)       Unadjusted  −29.0 ± 3.4  <0.001   Adjusted  −23.0 ± 2.7  <0.001  Microalbuminuria (n = 168)       Unadjusted  −22.0 ± 3.5  <0.001   Adjusted  −18.6 ± 3.3  <0.001  Macroalbuminuria (n = 190)       Unadjusted  −33.6 ± 3.3  <0.001   Adjusted  −31.4 ± 3.3  <0.001  Adjustment included sex, age, diabetes duration, body mass index, HDL cholesterol, smoking, HbA1c, 24-h pulse pressure, UAER and treatment with RAAS blockers. Table 2. Associations between plasma UA and eGFR in sex-specific quartile analyses Model  Quartile 4 versus Quartiles 1–3  P-value  All (n = 670)       Unadjusted  −35.4 ± 1.8  <0.001   Adjusted  −27.4 ± 1.7  <0.001  Normoalbuminuria (n = 312)       Unadjusted  −29.0 ± 3.4  <0.001   Adjusted  −23.0 ± 2.7  <0.001  Microalbuminuria (n = 168)       Unadjusted  −22.0 ± 3.5  <0.001   Adjusted  −18.6 ± 3.3  <0.001  Macroalbuminuria (n = 190)       Unadjusted  −33.6 ± 3.3  <0.001   Adjusted  −31.4 ± 3.3  <0.001  Model  Quartile 4 versus Quartiles 1–3  P-value  All (n = 670)       Unadjusted  −35.4 ± 1.8  <0.001   Adjusted  −27.4 ± 1.7  <0.001  Normoalbuminuria (n = 312)       Unadjusted  −29.0 ± 3.4  <0.001   Adjusted  −23.0 ± 2.7  <0.001  Microalbuminuria (n = 168)       Unadjusted  −22.0 ± 3.5  <0.001   Adjusted  −18.6 ± 3.3  <0.001  Macroalbuminuria (n = 190)       Unadjusted  −33.6 ± 3.3  <0.001   Adjusted  −31.4 ± 3.3  <0.001  Adjustment included sex, age, diabetes duration, body mass index, HDL cholesterol, smoking, HbA1c, 24-h pulse pressure, UAER and treatment with RAAS blockers. FIGURE 2: View largeDownload slide Correlations with plasma UA and (A) eGFR and (B) carotid–femoral pulse wave velocity. (A and B) The proportion of the variability in the dependent variable explained by the model is presented as the R2. FIGURE 2: View largeDownload slide Correlations with plasma UA and (A) eGFR and (B) carotid–femoral pulse wave velocity. (A and B) The proportion of the variability in the dependent variable explained by the model is presented as the R2. The baseline level of UA in the upper sex-specific quartile was not associated with the annual decline in eGFR (mean follow-up 4.1 years) neither in the total cohort (P = 0.33; n = 475) nor in the patients with normoalbuminuria (P = 0.84; n = 259), microalbuminuria (P = 0.99; n = 126) or macroalbuminuria (P = 0.70; n = 90) as compared with the patients in the three lowest quartiles. Participants with an accelerated decline in eGFR (≥3 mL/min/year; n = 56) had significantly higher UA at baseline (P = 0.006) compared with slow decliners (<3 mL/min/year; n = 420), but after adjustment this difference attenuated (P = 0.10; Table 3). Table 3. Comparison of plasma UA between groups Groups  P-UA (mmol/L),  P-value   median (IQR)  Unadjusted  Adjusted  Slow (<−3.0) versus fast (≥−3.0) annual eGFR decline (n = 420 versus 56)  0.26 (0.22–0.32) versus  0.006  0.10  0.31 (0.25–0.36)  Normal versus reduced nocturnal blood pressure decrease (≥10 versus <10%) (n = 343 versus 313)  0.29 (0.23–0.35) versus  0.018  0.20  0.30 (0.24–0.39)  Presence of cardiovascular disease (no versus yes) (n = 527 versus 143)  0.28 (0.23–0.35) versus  <0.001  0.17  0.36 (0.27–0.44)  Heart rate variability (normal, borderline or abnormal)a (n = 178, 77, 371)  0.25 (0.21–0.31) versus  <0.001  0.75  0.28 (0.23–0.33) versus  0.32 (0.25–0.40)  Retinopathy (nil, simplex, proliferative or blind) (n = 142, 277, 229, 19)  0.25 (0.20–0.31) versus  <0.001  0.12  0.28 (0.23–0.34) versus  0.33 (0.27–0.45) versus  0.34 (0.31–0.51)  Groups  P-UA (mmol/L),  P-value   median (IQR)  Unadjusted  Adjusted  Slow (<−3.0) versus fast (≥−3.0) annual eGFR decline (n = 420 versus 56)  0.26 (0.22–0.32) versus  0.006  0.10  0.31 (0.25–0.36)  Normal versus reduced nocturnal blood pressure decrease (≥10 versus <10%) (n = 343 versus 313)  0.29 (0.23–0.35) versus  0.018  0.20  0.30 (0.24–0.39)  Presence of cardiovascular disease (no versus yes) (n = 527 versus 143)  0.28 (0.23–0.35) versus  <0.001  0.17  0.36 (0.27–0.44)  Heart rate variability (normal, borderline or abnormal)a (n = 178, 77, 371)  0.25 (0.21–0.31) versus  <0.001  0.75  0.28 (0.23–0.33) versus  0.32 (0.25–0.40)  Retinopathy (nil, simplex, proliferative or blind) (n = 142, 277, 229, 19)  0.25 (0.20–0.31) versus  <0.001  0.12  0.28 (0.23–0.34) versus  0.33 (0.27–0.45) versus  0.34 (0.31–0.51)  Adjustment included sex, age, diabetes duration, body mass index, HDL cholesterol, smoking, HbA1c, 24-h pulse pressure, UAER, eGFR and treatment with RAAS blockers. a Heart rate variability defined as normal ≥15, borderline 11–14 and abnormal <11 beats per min. Table 3. Comparison of plasma UA between groups Groups  P-UA (mmol/L),  P-value   median (IQR)  Unadjusted  Adjusted  Slow (<−3.0) versus fast (≥−3.0) annual eGFR decline (n = 420 versus 56)  0.26 (0.22–0.32) versus  0.006  0.10  0.31 (0.25–0.36)  Normal versus reduced nocturnal blood pressure decrease (≥10 versus <10%) (n = 343 versus 313)  0.29 (0.23–0.35) versus  0.018  0.20  0.30 (0.24–0.39)  Presence of cardiovascular disease (no versus yes) (n = 527 versus 143)  0.28 (0.23–0.35) versus  <0.001  0.17  0.36 (0.27–0.44)  Heart rate variability (normal, borderline or abnormal)a (n = 178, 77, 371)  0.25 (0.21–0.31) versus  <0.001  0.75  0.28 (0.23–0.33) versus  0.32 (0.25–0.40)  Retinopathy (nil, simplex, proliferative or blind) (n = 142, 277, 229, 19)  0.25 (0.20–0.31) versus  <0.001  0.12  0.28 (0.23–0.34) versus  0.33 (0.27–0.45) versus  0.34 (0.31–0.51)  Groups  P-UA (mmol/L),  P-value   median (IQR)  Unadjusted  Adjusted  Slow (<−3.0) versus fast (≥−3.0) annual eGFR decline (n = 420 versus 56)  0.26 (0.22–0.32) versus  0.006  0.10  0.31 (0.25–0.36)  Normal versus reduced nocturnal blood pressure decrease (≥10 versus <10%) (n = 343 versus 313)  0.29 (0.23–0.35) versus  0.018  0.20  0.30 (0.24–0.39)  Presence of cardiovascular disease (no versus yes) (n = 527 versus 143)  0.28 (0.23–0.35) versus  <0.001  0.17  0.36 (0.27–0.44)  Heart rate variability (normal, borderline or abnormal)a (n = 178, 77, 371)  0.25 (0.21–0.31) versus  <0.001  0.75  0.28 (0.23–0.33) versus  0.32 (0.25–0.40)  Retinopathy (nil, simplex, proliferative or blind) (n = 142, 277, 229, 19)  0.25 (0.20–0.31) versus  <0.001  0.12  0.28 (0.23–0.34) versus  0.33 (0.27–0.45) versus  0.34 (0.31–0.51)  Adjustment included sex, age, diabetes duration, body mass index, HDL cholesterol, smoking, HbA1c, 24-h pulse pressure, UAER, eGFR and treatment with RAAS blockers. a Heart rate variability defined as normal ≥15, borderline 11–14 and abnormal <11 beats per min. UA and albuminuria In the total cohort, UA in the upper sex-specific quartile was associated with higher UAER in unadjusted analyses (P < 0.001; Table 1 and Figure 3), but significance was lost after adjustment (P = 0.20). In analyses stratified by albuminuria group, UA in the upper sex-specific quartile was not associated with UAER in any of the three groups in the unadjusted (P ≥ 0.76) or adjusted (P ≥ 0.44) model. FIGURE 3: View largeDownload slide UAER in sex-specific quartiles of UA. Data represent the median (IQR). P = 0.20 after adjustment for sex, age, diabetes duration, body mass index, HDL cholesterol, smoking, HbA1c, 24-h pulse pressure, UAER, eGFR and treatment with RAAS blockers. FIGURE 3: View largeDownload slide UAER in sex-specific quartiles of UA. Data represent the median (IQR). P = 0.20 after adjustment for sex, age, diabetes duration, body mass index, HDL cholesterol, smoking, HbA1c, 24-h pulse pressure, UAER, eGFR and treatment with RAAS blockers. UA and cardiovascular status UA in the upper sex-specific quartile was associated with lower 24-h and daytime diastolic blood pressure both in unadjusted and adjusted analyses (P ≤ 0.01), but was not associated with nighttime diastolic blood pressure (P ≥ 0.11). The association between higher UA and lower diastolic blood pressure was not owing to the effect of antihypertensive treatment or specific to any of the different antihypertensive drug classes, as the association remained significant if the different drug classes were entered individually in the adjusted model (P ≤ 0.035). UA in the upper sex-specific quartile was associated with higher nighttime systolic blood pressure in unadjusted analyses (P = 0.02), but the association lost significance after adjustment (P = 0.11). UA in the upper sex-specific quartile was not associated with 24-h systolic blood pressure (P = 0.44) or daytime systolic blood pressure (P = 0.91). The median UA was significantly higher (P = 0.018; Table 3) in patients with reduced nocturnal systolic blood pressure decrease (<10%; n = 313) compared with patients with normal nocturnal systolic blood pressure decrease (≥10%; n = 343) in unadjusted analyses (P = 0.018). However, significance was lost after adjustment (P = 0.20). UA in the upper sex-specific quartile was associated with higher carotid–femoral pulse wave velocity in unadjusted analyses (P < 0.0001; Figure 2B;R2 = 0.09; F-test P < 0.0001), but significance was lost after adjustment (P = 0.07). Higher UA was associated with the presence of known cardiovascular disease (n = 143) in the unadjusted analysis (P < 0.001; Table 3), but the association lost significance in the adjusted model (P = 0.17; Table 3). UA and other diabetic complications We used heart rate variability as a measure of neuropathy. When comparing the median level of UA within the heart rate variability groups [normal (n = 178), borderline (n = 77) and abnormal (n = 371)], there was a trend (P < 0.0001; Table 3) of increasing UA level across the groups, with the highest level in patients with abnormal heart rate variability. However, significance was lost after adjustment (P = 0.75). There was an increased level of UA across the retinopathy groups [nil (n = 142), simplex (n = 277), proliferative (n = 229) and blind (n = 19); P < 0.0001; Table 3 and Figure 4]. However, significance was lost after adjustment (P = 0.12; Table 3). Since age and duration of diabetes are associated, we repeated the analyses excluding diabetes duration from the adjustment and the association was then significant (P = 0.037). FIGURE 4: View largeDownload slide Plasma UA in groups of retinopathy. Data represent the median (IQR). Participants were categorized based on their level of retinopathy: nil, simplex, proliferative or blind (based on worst eye). P = 0.12 after adjustment for sex, age, diabetes duration, body mass index, HDL cholesterol, smoking, HbA1c, 24-h pulse pressure, UAER, eGFR and treatment with RAAS blockers. FIGURE 4: View largeDownload slide Plasma UA in groups of retinopathy. Data represent the median (IQR). Participants were categorized based on their level of retinopathy: nil, simplex, proliferative or blind (based on worst eye). P = 0.12 after adjustment for sex, age, diabetes duration, body mass index, HDL cholesterol, smoking, HbA1c, 24-h pulse pressure, UAER, eGFR and treatment with RAAS blockers. Sensitivity analysis The results were overall similar when treatment with diuretics was included in the adjusted analyses instead of treatment with RAAS blockers. DISCUSSIONS We investigated the associations between UA and renal function, cardiovascular risk factors (blood pressure and pulse wave velocity) and diabetic complications in 670 type 1 diabetic patients. UA in the upper sex-specific quartile was independently associated with lower eGFR and lower diastolic blood pressure. Moreover, higher UA was associated with more severe retinopathy independent of risk factors; however, after further adjustment for diabetes duration the significance was lost. We also revealed a relationship between UA in the upper sex-specific quartile and accelerated decline in eGFR and higher nighttime systolic blood pressure, UAER and carotid–femoral pulse wave velocity and between higher UA and reduced nocturnal blood pressure decrease, previous cardiovascular disease and the presence of neuropathy. However, these relationships attenuated after adjustment. We hypothesized that higher levels of UA were associated with the presence of renal impairment, but we did not find an association between UA levels and progression of kidney disease or other complications in type 1 diabetes after adjustments for traditional risk factors. We have previously reported an association between higher UA and the development of persistent macroalbuminuria. A level of UA in the upper quartile, measured shortly after the onset of type 1 diabetes, was a significant and independent predictor of later development of macroalbuminuria during 18 years of follow-up compared with the three lower quartiles [1]. Jalal et al. [8] showed in a prospective study that for every 1 mg/dL (0.06 mmol/L) higher UA at baseline, patients with type 1 diabetes had an 80% increased risk of developing micro- or macroalbuminuria after adjustment for known risk factors. We divided the participants into sex-specific quartiles of UA, as previously [1], as data suggests that the relationship between levels of UA and albuminuria might not be linear. Our results in relation to eGFR are in accordance with findings by Rosolowsky et al. [15], who found that renal function [GFR measured by cystatin C (cGFR)] decreased progressively with higher UA and higher UAER, even within values in the normal range. UA and UAER were found to be independent of each other [15]. Moreover, prospective data from the Second Joslin Kidney Study have suggested that elevated UA is one of the strongest risk factor for early loss of renal function (cGFR) among type 1 diabetic patients with microalbuminuria and normal renal function at baseline [9]. Interestingly, the concentrations of UA in our cohort were largely within the normal range (0.23–0.48 mmol/L in men and 0.15–0.35 mmol/L in women), suggesting that UA also has adverse effects at levels considered to be normal. In our study, UA did not predict eGFR decline in a period of 5 years. UA in the upper sex-specific quartile was associated with an accelerated decline (>3 mL/min/year) in eGFR in the unadjusted model but lost significance when we adjusted for known risk factors. Ficociello et al. [9] found that patients with type 1 diabetes and with a rapid decline in measured GFR (>3.3% per year) had significantly higher UA at baseline than patients with stable renal function (<3.3% per year). They found a clear dose–response relationship between increasing UA and the occurrence of early GFR loss, that is, for each 1 mg/dL (0.06 mmol/L) increase in UA, there was a 40% increase in the odds of developing early GFR loss (>3.3% per year), even after adjustment for baseline cGFR, albumin:creatinine ratio, sex and HbA1c [9]. This relation was linear across the entire range of UA levels [9]. Kuwabara et al. [4] found both baseline UA and the increase in UA over time were independent risk factors for accelerated eGFR decline over a period of 5 years. This study was, however, a retrospective cohort study and the results should be interpreted with caution. We have unpublished data demonstrating a weak association between UA and a very rapid decline (>5 mL/min/1.73 m2) in measured GFR in type 1 diabetic patients with overt nephropathy [16]. The lack of a significant association between high UA and an accelerated decline in eGFR in our cohort may be due to a power problem, as sufficient follow-up information on creatinine to calculate the annual decline in eGFR was only available in 476 (71%) individuals. In our study, UA in the upper sex-specific quartile was associated with lower 24-h and daytime diastolic blood pressure after adjustment for known risk factors. Higher nighttime systolic blood pressure and reduced nocturnal blood pressure decrease were also associated with higher UA, but the significance was lost after adjustment for other risk factors. Experimental and clinical evidence suggest that an elevated UA level increases the relative risk for development of essential hypertension within 5 years, independent of other risk factors [17]. We found that UA in the upper sex-specific quartile was significantly associated with higher carotid–femoral pulse wave velocity, although not after adjustment. Only one previous study has investigated the relation between UA and carotid–femoral pulse wave velocity in patients with type 1 diabetes [11]. The study included adolescents ages 11–16 years and could not demonstrate any association between UA and carotid–femoral pulse wave velocity. This might be explained by the young age of the study population [11]. In our cohort, we showed an association between higher UA and the presence of known cardiovascular disease, but the association was not independent of traditional risk factors. Previously only one study has examined the association between UA and coronary heart disease in patients with type 1 diabetes. The study found that hyperuricaemia was correlated with the presence of coronary heart disease in women but not in men [18]. The Coronary Artery Calcification in Type 1 Diabetes (CACTI) Study found that the risk of progression in coronary artery calcification (during 6 years of follow-up) increased by 30–50% per 0.011 mmol/L higher UA at baseline. Notably, this finding was independent of traditional risk factors [19, 20]. We evaluated heart rate variability as a measure of cardiac autonomic neuropathy. Patients with borderline or abnormal heart rate variability had higher UA, but we could not establish an association independent of other risk factors. Huang et al. [21] investigated cardiac autonomic neuropathy by beat-to-beat test and heart rate response to deep breathing and the Valsalva manoeuvre test in type 2 diabetic patients from Taiwan. They found that UA had positive correlations with adrenergic subscores. A systemic review and meta-analysis including cross-sectional and case–control studies reported that patients with type 2 diabetes and peripheral neuropathy had higher levels of UA compared with patients without in unadjusted analysis [22]. The meta-analysis also demonstrated that higher UA increased the risk of developing peripheral neuropathy independent of other risk factors, although adjustment was not consistent across studies [22]. We revealed an association between higher levels of UA and more advanced diabetic retinopathy and this relation remained after adjustment for risk factors but not after further adjustment for diabetes duration. Only a handful of studies have investigated this relation before. Bjornstad et al. [19] found that the risk of developing diabetic retinopathy increased with higher levels of UA in type 1 diabetic patients, independent of known risk factors. However, the diagnosis of retinopathy was self-reported. Lee et al. [23] showed that higher levels of UA in addition to higher HbA1c and longer diabetes duration were independent risk factors for worsening in severity of diabetic retinopathy over a period of 3 years in patients with type 2 diabetes in Taiwan. These observations suggest that higher UA may play a role in the pathogenesis of diabetic retinopathy. The many findings of a relationship between higher UA and the presence and development of diabetic complications highlights that UA may be a target for pharmaceutical intervention to reduce the burden of complications. Indeed, several clinical trials, albeit of varying quality, have investigated UA as a target for pharmaceutical intervention [5, 24–31]. The results are conflicting, as some have shown promising effects of lowering UA by allopurinol treatment [5, 24–27, 29, 31] on reduction of albuminuria, improvement of cGFR and hypertension and a reduction of cardiovascular risk factors. Others have shown little or no effect [28, 32, 33]. An observational study using data from the UK Clinical Practice Research Database showed that treatment with high-dose allopurinol (≥300 mg/day) was associated with lower rates of stroke (−50%) and cardiac events (−39%) in older adults with hypertension [34]. Additional randomized controlled trials are warranted to further clarify the effect of UA-lowering treatment. As part of a multicentre trial, we are currently investigating the effect of UA lowering by allopurinol in patients with type 1 diabetes, using a randomized, placebo-controlled study design in Preventing Early Renal Loss in diabetes (PERL) study [35]. Strengths and limitations Strengths of the study include that the cohort represents 20% of the type 1 diabetic patients followed in our outpatient clinic at Steno Diabetes Center Copenhagen, thus representing a broad segment of the population of adult type 1 diabetic patients in the region. Moreover, the cohort is large and well described and all stages of albuminuria are represented. Limitations included the following. First, the study included a homogeneous population at a single centre, which doesn’t allow for generalization of the results to other, diverse populations. Second, the cross-sectional design of many of the analyses is a limitation, preventing us from making a causal association. Third, UA concentrations were measured in stored plasma samples, but all samples were handled similarly regardless of renal status. There is no consensus in the literature as to whether UA is stable when stored at −80°C [36, 37]. Fourth, information on prescription of UA-lowering therapy, including specific information on treatment with losartan, was not available. Moreover, information on diagnosis of gout was lacking. Fifth, the definition of kidney function was based on eGFR rather than using a more precise measure of kidney function and sufficient follow-up information on creatinine to calculate the annual decline in eGFR was only available in 476 (71%) individuals, limiting the power of these analyses. CONCLUSION In type 1 diabetic patients, higher levels of UA were associated with the presence of impaired kidney function and other diabetic complications. The association between UA in the upper sex-specific quartile and lower eGFR and lower diastolic blood pressure was independent of traditional risk factors. ACKNOWLEDGEMENTS We thank all participants and acknowledge the work of study nurse Lone Jelstrup and lab technicians Anne G. Lundgaard, Berit R. Jensen, Tina R. Juhl and Jessie A. Hermann, employees at Steno Diabetes Center Copenhagen (SDCC), Gentofte, Denmark, including the eye clinic at SDCC headed by Prof Henrik Lund-Andersen and eye nurse Marianne Valerius. FUNDING The study was supported with a grant from the Poul and Erna Sehested Foundation and from the Danish Diabetes Academy supported by the Novo Nordisk Foundation. AUTHORS’ CONTRIBUTIONS S.P.-L. analysed and interpreted the data and wrote the manuscript. T.W.H. helped in analysing the data and reviewed/edited the manuscript. S.T. performed the original study, including data collection, and reviewed/edited the manuscript. T.S.A. calculated the follow-up data on eGFR decline and reviewed/edited the manuscript. F.P., P.R. and J.F. reviewed/edited the manuscript. CONFLICT OF INTEREST STATEMENT None declared. REFERENCES 1 Hovind P, Rossing P, Tarnow L et al.   Serum uric acid as a predictor for development of diabetic nephropathy in type 1 diabetes: an inception cohort study. 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Curr Diab Rep  2013; 13: 550– 559 Google Scholar CrossRef Search ADS PubMed  36 Remer T, Montenegro-Bethancourt G, Shi L. Long-term urine biobanking: storage stability of clinical chemical parameters under moderate freezing conditions without use of preservatives. Clin Biochem  2014; 47: 307– 311 Google Scholar CrossRef Search ADS PubMed  37 Jansen EH, Beekhof PK, Cremers JW et al.   Long-term stability of parameters of antioxidant status in human serum. Free Radic Res  2013; 47: 535– 540 Google Scholar CrossRef Search ADS PubMed  © The Author(s) 2018. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved. 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 Nephrology Dialysis Transplantation Oxford University Press

Uric acid is not associated with diabetic nephropathy and other complications in type 1 diabetes

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Oxford University Press
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© The Author(s) 2018. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.
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0931-0509
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1460-2385
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10.1093/ndt/gfy076
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Abstract

Abstract Background To examine the association between plasma uric acid (UA) and the presence of diabetic complications including diabetic nephropathy and cardiovascular risk factors in patients with type 1 diabetes. Methods This study, which is cross-sectional in design, included 676 Caucasian type 1 diabetes patients from the Steno Diabetes Center Copenhagen. Participants with UA within the three lowest sex-specific quartiles were compared with participants with levels in the highest quartile. Unadjusted and adjusted linear regression analyses were applied. Adjustment included sex, age, diabetes duration, body mass index, high-density lipoprotein cholesterol, smoking, haemoglobin A1c, 24-h pulse pressure, urinary albumin excretion rate (UAER), estimated glomerular filtration rate (eGFR) and treatment with renin–angiotensin–aldosterone system blockers. Results Of the 676 patients, 372 (55%) were male, mean ± SD age was 55 ± 13 years and eGFR was 82 ± 26 mL/min/1.73 m2. The median UA was 0.30 (interquartile range 0.23–0.37) mmol/L. UA in the upper sex-specific quartile was associated with lower eGFR, higher UAER and carotid–femoral pulse wave velocity and lower 24 h and daytime diastolic blood pressure (BP) in unadjusted analyses (P < 0.001). Moreover, UA in the upper sex-specific quartile was associated with higher nighttime systolic BP and the presence of cardiovascular disease in unadjusted analyses (P ≤ 0.01), but significance was lost after adjustment (P ≥ 0.17). UA was higher across the retinopathy groups [nil (n = 142), simplex (n = 277), proliferative (n = 229) and blind (n = 19)] in unadjusted analyses (P < 0.0001), but not after adjustment (P = 0.12). Patients with an accelerated decline in eGFR (≥3 mL/min/year) had significantly higher UA at baseline (P = 0.006) compared with slow decliners (<3 mL/min/year), but significance was lost after adjustment (P = 0.10). Conclusions In type 1 diabetes patients, higher UA was associated with lower kidney function and other diabetic complications. The association between higher UA and lower eGFR and lower diastolic BP was independent of traditional risk factors. coronary artery, diabetes mellitus, diabetic kidney disease, disease uric acid, GFR INTRODUCTION The complex pathogenesis of diabetic complications is not fully understood [1, 2] and so far many risk factors have been proposed. One of these is uric acid (UA). Elevated levels of UA have been proposed as a risk factor for chronic kidney disease (CKD), hypertension and cardiovascular disease in people with as well as without diabetes [3–7]. In the clinical setting, the majority of observational studies have shown that higher UA is associated with the incidence and development of CKD in patients with type 1 diabetes independent of other risk factors [5, 6, 8–10]. Prospective data from the Second Joslin Kidney Study showed that higher UA is one of the strongest risk factors for early loss of renal function (measured by cystatin C) among patients with type 1 diabetes characterized by microalbuminuria and normal renal function at baseline [9]. The relationship between UA and other diabetic complications, such as retinopathy and neuropathy, has only sparsely been investigated in patients with type 1 diabetes. Moreover, only one study have investigated the association between UA and arterial stiffness in patients with type 1 diabetes and the study included adolescents ages 11–16 years [11]. Also, the association between UA and 24-h blood pressure has never been investigated in patients with type 1 diabetes. At Steno Diabetes Center Copenhagen, we performed a cross-sectional study of patients with type 1 diabetes characterized by different degrees of diabetic nephropathy [12]. The objective of this analysis is to examine the association between plasma UA and the presence of diabetic nephropathy as well as other diabetic complications and cardiovascular risk factors in patients with type 1 diabetes. We hypothesized that higher levels of UA are associated with (i) the presence of and increased progression of renal impairment, (ii) the presence of diabetic complications and (iii) higher 24-h blood pressure and arterial stiffness. MATERIALS AND METHODS Participants Between 2009 and 2011, Caucasian patients with type 1 diabetes were recruited to enter a cross-sectional study at Steno Diabetes Center Copenhagen. Inclusion criteria were type 1 diabetes [World Health Organization (WHO) criteria], age > 18 years and patient at the tertiary outpatient clinic at Steno Diabetes Center Copenhagen. The study was designed to investigate the associations between blood pressure, arterial stiffness and diabetic complications [12, 13]. The cohort was stratified by levels of albuminuria (normo-, micro- and macroalbuminuria). Patients with end-stage renal disease, defined as receiving dialysis or renal transplantation or glomerular filtration rate (GFR) or estimated glomerular filtration rate (eGFR) <15 mL/min/1.73 m2 were not included. All participants gave written informed consent and the study was approved by the regional ethics committee. Procedures UA was measured in plasma by the Vitros 5600 MicroSlide system (Ortho Clinical Diagnostics, Raritan, NJ, USA). The plasma samples were stored immediately after collection in freezers at −80°C and were stored for up to 8 years prior to analysis. The eGFR was calculated by the Chronic Kidney Disease Epidemiology Collaboration equation [14]. Sufficient follow-up information to calculate the annual decline in eGFR (≥2 measures and a mean of 5.7 measures) was available in 476 patients through the medical records. The rate of decline in kidney function was analysed with regression lines for eGFR over the follow-up period (mean 4.1 years) using all measurements during the study period. Three 24-h urine collections were obtained to measure the urinary albumin excretion rate (UAER) using an enzyme immunoassay (Vitros). Participants were categorized as normoalbuminuric if UAER was <30 mg/day, microalbuminuric if UAER was or previously had been recorded between 30 and 299 mg/day and macroalbuminuric if UAER was or previously had been recorded at >300 mg/day in two of three consecutive measurements. All patients classified as normoalbuminuric did not have any history of micro- or macroalbuminuria prior to enrolment in the study. Of the patients with macroalbuminuria, 183 (95.8%) had retinopathy. Haemoglobin A1c (HbA1c) was measured by high-performance liquid chromatography (Bio-Rad Laboratories, Munich, Germany) and serum creatinine concentration by an enzyme method (Hitachi 912; Rocked Diagnostics, Mannheim, Germany). The 24-h ambulatory blood pressure was recorded with a validated tonometric watch-like device (BPro; HeathStats, Singapore) that captures radial pulse wave reflection and calculates blood pressure [12, 13]. The device was programmed to measure every 15 min during the entire 24-h period. The 24-h ambulatory blood pressure was considered adequate if  ≥14 and  ≥7 recordings were obtained during the day and night, respectively. Mean arterial pressure was calculated as diastolic blood pressure plus one-third of pulse pressure. Following 15 min of supine rest, the carotid–femoral pulse wave velocity was obtained with the SphygmoCor device (Actor Medical, Sydney, NSW, Australia) by trained laboratory technicians. Three measurements were performed and averaged. A history of cardiovascular disease included myocardial infarction, stroke or peripheral arterial disease based on standardized WHO questionnaires and patient records. Participants were classified as current smokers if using one or more cigarettes, cigars or pipes per day and all others as non-smokers. Information on antihypertensive treatment was recorded from patient files and cross-checked by interview with the patient. Antihypertensive treatment was classified into drug classes. Analyses of heart rate variability were applied to access cardiac autonomic dysfunction (as a measure of neuropathy) and were recorded during paced deep breathing [12, 13]. A heart rate variability ≥15 beats per min was classified as normal, between 11 and 14 beats per min as borderline and <11 beats per min as abnormal. Retinopathy status was obtained from medical records based on standardized analysis of retinal photos through dilated pupils by certified eye nurses and classified into four groups (nil, simplex, proliferative or blind). The assignment was based on the severity of the most severely affected eye. Statistical analysis The distribution of UA and UAER was skewed and therefore these variables were log2 transformed in all the linear analyses and given as medians with interquartile range (IQR). All other continuous variables are given as means ± SD and the categorical variables are given as percentages. When analysing differences between participants based on the four UA sex-specific quartiles, we used the analysis of covariance for continuous and the χ2 test for categorical variables. In regression analyses we compared participants with a level of UA within the three lowest sex-specific quartiles with participants with a level in the highest quartile [1]. We applied unadjusted and adjusted linear regression analyses. We used unadjusted models to determine if any association existed between the level of UA and covariates/predefined groups. The subsequent multivariate adjustment included traditional risk factors based on prior evidence, namely, age, sex, diabetes duration, high-density lipoprotein (HDL) cholesterol, body mass index, smoking, HbA1c, 24-h pulse pressure, eGFR, UAER and treatment with renin–angiotensin–aldosterone system (RAAS) blockers, as appropriate. The analyses of carotid–femoral pulse wave velocity also included office mean arterial pressure to account for the steady component of the blood pressure at the time of measurement. The proportion of the variability in the dependent variable explained by the model is presented as R2 and the F-test was applied to determine whether this relationship was statistically significant. A two-tailed P-value <0.05 was considered statistical significant. Statistical analyses were performed using SAS software (version 9.3; SAS Institute, Cary, NC, USA). RESULTS Clinical characteristics Of the original cohort (n = 676), the plasma UA level was available in 670 (99.1%) patients. The median UA was 0.30 (IQR 0.23–0.37) mmol/L, equivalent to 5.04 (IQR 3.87–6.22) mg/dL. Figure 1 illustrates median values of UA according to the albuminuria group. UA was significantly higher with increasing levels of albuminuria (P < 0.001). FIGURE 1: View largeDownload slide Plasma UA in groups of albuminuria. Data represent median (IQR). Participants were categorized as normoalbuminuric (UAER <30 mg/day), microalbuminuric (UAER 30–299 mg/day) and macroalbuminuric (UAER >300 mg/day). P < 0.001 after adjustment for sex, age, diabetes duration, body mass index, HDL cholesterol, smoking, HbA1c, 24-h pulse pressure, eGFR and treatment with RAAS blockers. FIGURE 1: View largeDownload slide Plasma UA in groups of albuminuria. Data represent median (IQR). Participants were categorized as normoalbuminuric (UAER <30 mg/day), microalbuminuric (UAER 30–299 mg/day) and macroalbuminuric (UAER >300 mg/day). P < 0.001 after adjustment for sex, age, diabetes duration, body mass index, HDL cholesterol, smoking, HbA1c, 24-h pulse pressure, eGFR and treatment with RAAS blockers. The clinical characteristics in the 670 participants divided according to sex-specific quartiles of UA are shown in Table 1. Participants with higher UA were older; had longer diabetes duration; had higher albuminuria level, body mass index, carotid–femoral pulse wave velocity and triglycerides; were prescribed more antihypertensive medication, including diuretics, RAAS blockers, beta-blockers and calcium channel blockers; and had lower eGFR compared with those with lower UA (P < 0.001 for all). However, there was no significant difference across sex-specific quartiles of UA and low-density lipoprotein cholesterol or HbA1c. A total of 454 (67.8%) were treated with RAAS blockers. Table 1. Clinical characteristics divided according to sex-specific quartiles of plasma UA Characteristics  Sex-specific quartiles of plasma UA   Plasma UA (mmol/L)  Women: <0.21 Men: <0.27  Women: ≥0.21–<0.25 Men: ≥0.27–<0.32  Women: ≥0.25–≤0.33 Men: ≥0.32–≤0.37  Women: >0.33 Men: >0.37  P-value  Number of participants  162  147  190  175    Female, %  42  45  52  40  0.11  Age (years)  50 ± 14  52 ± 12  57 ± 11  58 ± 11  <0.001  Diabetes duration (years)  26 ± 16  30 ± 16  35 ± 15  39 ± 13  <0.001  eGFR (mL/min/1.73 m2)  99 ± 16  95 ± 14  81 ± 20  55 ± 23  <0.001  UAER (mg/24 h), median (IQR)  12 (7–26)  11 (6–32)  15 (7–64)  50 (16–234)  <0.001  HbA1c (mmol/mol)  64 ± 10  64 ± 10  64 ± 5  64 ± 10  0.92  HbA1c (%)  8 ± 2  8 ± 2  8 ± 1  8 ± 2  0.92  HDL cholesterol (mmol/L)  1.7 ± 0.5  1.7 ± 0.5  1.8 ± 0.6  1.6 ± 0.5  0.002  LDL cholesterol (mmol/L)  2.5 ± 0.7  2.4 ± 0.7  2.5 ± 0.8  2.4 ± 0.8  0.63  Triglycerides (mmol/L)  1.03 ± 0.5  1.09 ± 0.7  1.10 ± 0.6  1.31 ± 0.9  <0.001  Body mass index (kg/m2)  24.2 ± 3.8  24.6 ± 3.5  25.1 ± 4.0  27.2 ± 9.2  <0.001  Treatment with, (%)   Antihypertensive drugs  50  58  78  97  <0.001   Diuretics  27  32  50  87  <0.001   RAAS blockers  45  53  74  92  <0.001   Beta-blockers  11  6  11  21  <0.001   Calcium channel blockers  22  17  32  48  <0.001  Smokers, %  18  23  25  18  0.25  24-h systolic blood pressure (mmHg)  127 ± 16  130 ± 12  128 ± 87  129 ± 15  0.44  24-h diastolic blood pressure (mmHg)  76 ± 9  78 ± 9  74 ± 11  74 ± 11  0.012  Carotid–femoral pulse wave velocity (m/s)  9.2 ± 3.1  9.6 ± 3.1  10.7 ± 3.1  11.9 ± 3.5  <0.001  Characteristics  Sex-specific quartiles of plasma UA   Plasma UA (mmol/L)  Women: <0.21 Men: <0.27  Women: ≥0.21–<0.25 Men: ≥0.27–<0.32  Women: ≥0.25–≤0.33 Men: ≥0.32–≤0.37  Women: >0.33 Men: >0.37  P-value  Number of participants  162  147  190  175    Female, %  42  45  52  40  0.11  Age (years)  50 ± 14  52 ± 12  57 ± 11  58 ± 11  <0.001  Diabetes duration (years)  26 ± 16  30 ± 16  35 ± 15  39 ± 13  <0.001  eGFR (mL/min/1.73 m2)  99 ± 16  95 ± 14  81 ± 20  55 ± 23  <0.001  UAER (mg/24 h), median (IQR)  12 (7–26)  11 (6–32)  15 (7–64)  50 (16–234)  <0.001  HbA1c (mmol/mol)  64 ± 10  64 ± 10  64 ± 5  64 ± 10  0.92  HbA1c (%)  8 ± 2  8 ± 2  8 ± 1  8 ± 2  0.92  HDL cholesterol (mmol/L)  1.7 ± 0.5  1.7 ± 0.5  1.8 ± 0.6  1.6 ± 0.5  0.002  LDL cholesterol (mmol/L)  2.5 ± 0.7  2.4 ± 0.7  2.5 ± 0.8  2.4 ± 0.8  0.63  Triglycerides (mmol/L)  1.03 ± 0.5  1.09 ± 0.7  1.10 ± 0.6  1.31 ± 0.9  <0.001  Body mass index (kg/m2)  24.2 ± 3.8  24.6 ± 3.5  25.1 ± 4.0  27.2 ± 9.2  <0.001  Treatment with, (%)   Antihypertensive drugs  50  58  78  97  <0.001   Diuretics  27  32  50  87  <0.001   RAAS blockers  45  53  74  92  <0.001   Beta-blockers  11  6  11  21  <0.001   Calcium channel blockers  22  17  32  48  <0.001  Smokers, %  18  23  25  18  0.25  24-h systolic blood pressure (mmHg)  127 ± 16  130 ± 12  128 ± 87  129 ± 15  0.44  24-h diastolic blood pressure (mmHg)  76 ± 9  78 ± 9  74 ± 11  74 ± 11  0.012  Carotid–femoral pulse wave velocity (m/s)  9.2 ± 3.1  9.6 ± 3.1  10.7 ± 3.1  11.9 ± 3.5  <0.001  Data presented as mean ± SD unless stated otherwise. P-values are for trend across quartiles. LDL, low-density lipoprotein. Table 1. Clinical characteristics divided according to sex-specific quartiles of plasma UA Characteristics  Sex-specific quartiles of plasma UA   Plasma UA (mmol/L)  Women: <0.21 Men: <0.27  Women: ≥0.21–<0.25 Men: ≥0.27–<0.32  Women: ≥0.25–≤0.33 Men: ≥0.32–≤0.37  Women: >0.33 Men: >0.37  P-value  Number of participants  162  147  190  175    Female, %  42  45  52  40  0.11  Age (years)  50 ± 14  52 ± 12  57 ± 11  58 ± 11  <0.001  Diabetes duration (years)  26 ± 16  30 ± 16  35 ± 15  39 ± 13  <0.001  eGFR (mL/min/1.73 m2)  99 ± 16  95 ± 14  81 ± 20  55 ± 23  <0.001  UAER (mg/24 h), median (IQR)  12 (7–26)  11 (6–32)  15 (7–64)  50 (16–234)  <0.001  HbA1c (mmol/mol)  64 ± 10  64 ± 10  64 ± 5  64 ± 10  0.92  HbA1c (%)  8 ± 2  8 ± 2  8 ± 1  8 ± 2  0.92  HDL cholesterol (mmol/L)  1.7 ± 0.5  1.7 ± 0.5  1.8 ± 0.6  1.6 ± 0.5  0.002  LDL cholesterol (mmol/L)  2.5 ± 0.7  2.4 ± 0.7  2.5 ± 0.8  2.4 ± 0.8  0.63  Triglycerides (mmol/L)  1.03 ± 0.5  1.09 ± 0.7  1.10 ± 0.6  1.31 ± 0.9  <0.001  Body mass index (kg/m2)  24.2 ± 3.8  24.6 ± 3.5  25.1 ± 4.0  27.2 ± 9.2  <0.001  Treatment with, (%)   Antihypertensive drugs  50  58  78  97  <0.001   Diuretics  27  32  50  87  <0.001   RAAS blockers  45  53  74  92  <0.001   Beta-blockers  11  6  11  21  <0.001   Calcium channel blockers  22  17  32  48  <0.001  Smokers, %  18  23  25  18  0.25  24-h systolic blood pressure (mmHg)  127 ± 16  130 ± 12  128 ± 87  129 ± 15  0.44  24-h diastolic blood pressure (mmHg)  76 ± 9  78 ± 9  74 ± 11  74 ± 11  0.012  Carotid–femoral pulse wave velocity (m/s)  9.2 ± 3.1  9.6 ± 3.1  10.7 ± 3.1  11.9 ± 3.5  <0.001  Characteristics  Sex-specific quartiles of plasma UA   Plasma UA (mmol/L)  Women: <0.21 Men: <0.27  Women: ≥0.21–<0.25 Men: ≥0.27–<0.32  Women: ≥0.25–≤0.33 Men: ≥0.32–≤0.37  Women: >0.33 Men: >0.37  P-value  Number of participants  162  147  190  175    Female, %  42  45  52  40  0.11  Age (years)  50 ± 14  52 ± 12  57 ± 11  58 ± 11  <0.001  Diabetes duration (years)  26 ± 16  30 ± 16  35 ± 15  39 ± 13  <0.001  eGFR (mL/min/1.73 m2)  99 ± 16  95 ± 14  81 ± 20  55 ± 23  <0.001  UAER (mg/24 h), median (IQR)  12 (7–26)  11 (6–32)  15 (7–64)  50 (16–234)  <0.001  HbA1c (mmol/mol)  64 ± 10  64 ± 10  64 ± 5  64 ± 10  0.92  HbA1c (%)  8 ± 2  8 ± 2  8 ± 1  8 ± 2  0.92  HDL cholesterol (mmol/L)  1.7 ± 0.5  1.7 ± 0.5  1.8 ± 0.6  1.6 ± 0.5  0.002  LDL cholesterol (mmol/L)  2.5 ± 0.7  2.4 ± 0.7  2.5 ± 0.8  2.4 ± 0.8  0.63  Triglycerides (mmol/L)  1.03 ± 0.5  1.09 ± 0.7  1.10 ± 0.6  1.31 ± 0.9  <0.001  Body mass index (kg/m2)  24.2 ± 3.8  24.6 ± 3.5  25.1 ± 4.0  27.2 ± 9.2  <0.001  Treatment with, (%)   Antihypertensive drugs  50  58  78  97  <0.001   Diuretics  27  32  50  87  <0.001   RAAS blockers  45  53  74  92  <0.001   Beta-blockers  11  6  11  21  <0.001   Calcium channel blockers  22  17  32  48  <0.001  Smokers, %  18  23  25  18  0.25  24-h systolic blood pressure (mmHg)  127 ± 16  130 ± 12  128 ± 87  129 ± 15  0.44  24-h diastolic blood pressure (mmHg)  76 ± 9  78 ± 9  74 ± 11  74 ± 11  0.012  Carotid–femoral pulse wave velocity (m/s)  9.2 ± 3.1  9.6 ± 3.1  10.7 ± 3.1  11.9 ± 3.5  <0.001  Data presented as mean ± SD unless stated otherwise. P-values are for trend across quartiles. LDL, low-density lipoprotein. UA and eGFR Higher UA was significantly associated with lower eGFR in unadjusted (Figure 2A;R2 = 0.37; F-test P < 0.0001) and adjusted analyses (P < 0.001 for all). The level of UA in the upper sex-specific quartile was significantly associated with lower eGFR in unadjusted and adjusted analyses compared with the level in the three lowest quartiles, both in the total cohort and in analyses stratified by albuminuria group (unadjusted and adjusted P < 0.001 for all; Table 2). Table 2. Associations between plasma UA and eGFR in sex-specific quartile analyses Model  Quartile 4 versus Quartiles 1–3  P-value  All (n = 670)       Unadjusted  −35.4 ± 1.8  <0.001   Adjusted  −27.4 ± 1.7  <0.001  Normoalbuminuria (n = 312)       Unadjusted  −29.0 ± 3.4  <0.001   Adjusted  −23.0 ± 2.7  <0.001  Microalbuminuria (n = 168)       Unadjusted  −22.0 ± 3.5  <0.001   Adjusted  −18.6 ± 3.3  <0.001  Macroalbuminuria (n = 190)       Unadjusted  −33.6 ± 3.3  <0.001   Adjusted  −31.4 ± 3.3  <0.001  Model  Quartile 4 versus Quartiles 1–3  P-value  All (n = 670)       Unadjusted  −35.4 ± 1.8  <0.001   Adjusted  −27.4 ± 1.7  <0.001  Normoalbuminuria (n = 312)       Unadjusted  −29.0 ± 3.4  <0.001   Adjusted  −23.0 ± 2.7  <0.001  Microalbuminuria (n = 168)       Unadjusted  −22.0 ± 3.5  <0.001   Adjusted  −18.6 ± 3.3  <0.001  Macroalbuminuria (n = 190)       Unadjusted  −33.6 ± 3.3  <0.001   Adjusted  −31.4 ± 3.3  <0.001  Adjustment included sex, age, diabetes duration, body mass index, HDL cholesterol, smoking, HbA1c, 24-h pulse pressure, UAER and treatment with RAAS blockers. Table 2. Associations between plasma UA and eGFR in sex-specific quartile analyses Model  Quartile 4 versus Quartiles 1–3  P-value  All (n = 670)       Unadjusted  −35.4 ± 1.8  <0.001   Adjusted  −27.4 ± 1.7  <0.001  Normoalbuminuria (n = 312)       Unadjusted  −29.0 ± 3.4  <0.001   Adjusted  −23.0 ± 2.7  <0.001  Microalbuminuria (n = 168)       Unadjusted  −22.0 ± 3.5  <0.001   Adjusted  −18.6 ± 3.3  <0.001  Macroalbuminuria (n = 190)       Unadjusted  −33.6 ± 3.3  <0.001   Adjusted  −31.4 ± 3.3  <0.001  Model  Quartile 4 versus Quartiles 1–3  P-value  All (n = 670)       Unadjusted  −35.4 ± 1.8  <0.001   Adjusted  −27.4 ± 1.7  <0.001  Normoalbuminuria (n = 312)       Unadjusted  −29.0 ± 3.4  <0.001   Adjusted  −23.0 ± 2.7  <0.001  Microalbuminuria (n = 168)       Unadjusted  −22.0 ± 3.5  <0.001   Adjusted  −18.6 ± 3.3  <0.001  Macroalbuminuria (n = 190)       Unadjusted  −33.6 ± 3.3  <0.001   Adjusted  −31.4 ± 3.3  <0.001  Adjustment included sex, age, diabetes duration, body mass index, HDL cholesterol, smoking, HbA1c, 24-h pulse pressure, UAER and treatment with RAAS blockers. FIGURE 2: View largeDownload slide Correlations with plasma UA and (A) eGFR and (B) carotid–femoral pulse wave velocity. (A and B) The proportion of the variability in the dependent variable explained by the model is presented as the R2. FIGURE 2: View largeDownload slide Correlations with plasma UA and (A) eGFR and (B) carotid–femoral pulse wave velocity. (A and B) The proportion of the variability in the dependent variable explained by the model is presented as the R2. The baseline level of UA in the upper sex-specific quartile was not associated with the annual decline in eGFR (mean follow-up 4.1 years) neither in the total cohort (P = 0.33; n = 475) nor in the patients with normoalbuminuria (P = 0.84; n = 259), microalbuminuria (P = 0.99; n = 126) or macroalbuminuria (P = 0.70; n = 90) as compared with the patients in the three lowest quartiles. Participants with an accelerated decline in eGFR (≥3 mL/min/year; n = 56) had significantly higher UA at baseline (P = 0.006) compared with slow decliners (<3 mL/min/year; n = 420), but after adjustment this difference attenuated (P = 0.10; Table 3). Table 3. Comparison of plasma UA between groups Groups  P-UA (mmol/L),  P-value   median (IQR)  Unadjusted  Adjusted  Slow (<−3.0) versus fast (≥−3.0) annual eGFR decline (n = 420 versus 56)  0.26 (0.22–0.32) versus  0.006  0.10  0.31 (0.25–0.36)  Normal versus reduced nocturnal blood pressure decrease (≥10 versus <10%) (n = 343 versus 313)  0.29 (0.23–0.35) versus  0.018  0.20  0.30 (0.24–0.39)  Presence of cardiovascular disease (no versus yes) (n = 527 versus 143)  0.28 (0.23–0.35) versus  <0.001  0.17  0.36 (0.27–0.44)  Heart rate variability (normal, borderline or abnormal)a (n = 178, 77, 371)  0.25 (0.21–0.31) versus  <0.001  0.75  0.28 (0.23–0.33) versus  0.32 (0.25–0.40)  Retinopathy (nil, simplex, proliferative or blind) (n = 142, 277, 229, 19)  0.25 (0.20–0.31) versus  <0.001  0.12  0.28 (0.23–0.34) versus  0.33 (0.27–0.45) versus  0.34 (0.31–0.51)  Groups  P-UA (mmol/L),  P-value   median (IQR)  Unadjusted  Adjusted  Slow (<−3.0) versus fast (≥−3.0) annual eGFR decline (n = 420 versus 56)  0.26 (0.22–0.32) versus  0.006  0.10  0.31 (0.25–0.36)  Normal versus reduced nocturnal blood pressure decrease (≥10 versus <10%) (n = 343 versus 313)  0.29 (0.23–0.35) versus  0.018  0.20  0.30 (0.24–0.39)  Presence of cardiovascular disease (no versus yes) (n = 527 versus 143)  0.28 (0.23–0.35) versus  <0.001  0.17  0.36 (0.27–0.44)  Heart rate variability (normal, borderline or abnormal)a (n = 178, 77, 371)  0.25 (0.21–0.31) versus  <0.001  0.75  0.28 (0.23–0.33) versus  0.32 (0.25–0.40)  Retinopathy (nil, simplex, proliferative or blind) (n = 142, 277, 229, 19)  0.25 (0.20–0.31) versus  <0.001  0.12  0.28 (0.23–0.34) versus  0.33 (0.27–0.45) versus  0.34 (0.31–0.51)  Adjustment included sex, age, diabetes duration, body mass index, HDL cholesterol, smoking, HbA1c, 24-h pulse pressure, UAER, eGFR and treatment with RAAS blockers. a Heart rate variability defined as normal ≥15, borderline 11–14 and abnormal <11 beats per min. Table 3. Comparison of plasma UA between groups Groups  P-UA (mmol/L),  P-value   median (IQR)  Unadjusted  Adjusted  Slow (<−3.0) versus fast (≥−3.0) annual eGFR decline (n = 420 versus 56)  0.26 (0.22–0.32) versus  0.006  0.10  0.31 (0.25–0.36)  Normal versus reduced nocturnal blood pressure decrease (≥10 versus <10%) (n = 343 versus 313)  0.29 (0.23–0.35) versus  0.018  0.20  0.30 (0.24–0.39)  Presence of cardiovascular disease (no versus yes) (n = 527 versus 143)  0.28 (0.23–0.35) versus  <0.001  0.17  0.36 (0.27–0.44)  Heart rate variability (normal, borderline or abnormal)a (n = 178, 77, 371)  0.25 (0.21–0.31) versus  <0.001  0.75  0.28 (0.23–0.33) versus  0.32 (0.25–0.40)  Retinopathy (nil, simplex, proliferative or blind) (n = 142, 277, 229, 19)  0.25 (0.20–0.31) versus  <0.001  0.12  0.28 (0.23–0.34) versus  0.33 (0.27–0.45) versus  0.34 (0.31–0.51)  Groups  P-UA (mmol/L),  P-value   median (IQR)  Unadjusted  Adjusted  Slow (<−3.0) versus fast (≥−3.0) annual eGFR decline (n = 420 versus 56)  0.26 (0.22–0.32) versus  0.006  0.10  0.31 (0.25–0.36)  Normal versus reduced nocturnal blood pressure decrease (≥10 versus <10%) (n = 343 versus 313)  0.29 (0.23–0.35) versus  0.018  0.20  0.30 (0.24–0.39)  Presence of cardiovascular disease (no versus yes) (n = 527 versus 143)  0.28 (0.23–0.35) versus  <0.001  0.17  0.36 (0.27–0.44)  Heart rate variability (normal, borderline or abnormal)a (n = 178, 77, 371)  0.25 (0.21–0.31) versus  <0.001  0.75  0.28 (0.23–0.33) versus  0.32 (0.25–0.40)  Retinopathy (nil, simplex, proliferative or blind) (n = 142, 277, 229, 19)  0.25 (0.20–0.31) versus  <0.001  0.12  0.28 (0.23–0.34) versus  0.33 (0.27–0.45) versus  0.34 (0.31–0.51)  Adjustment included sex, age, diabetes duration, body mass index, HDL cholesterol, smoking, HbA1c, 24-h pulse pressure, UAER, eGFR and treatment with RAAS blockers. a Heart rate variability defined as normal ≥15, borderline 11–14 and abnormal <11 beats per min. UA and albuminuria In the total cohort, UA in the upper sex-specific quartile was associated with higher UAER in unadjusted analyses (P < 0.001; Table 1 and Figure 3), but significance was lost after adjustment (P = 0.20). In analyses stratified by albuminuria group, UA in the upper sex-specific quartile was not associated with UAER in any of the three groups in the unadjusted (P ≥ 0.76) or adjusted (P ≥ 0.44) model. FIGURE 3: View largeDownload slide UAER in sex-specific quartiles of UA. Data represent the median (IQR). P = 0.20 after adjustment for sex, age, diabetes duration, body mass index, HDL cholesterol, smoking, HbA1c, 24-h pulse pressure, UAER, eGFR and treatment with RAAS blockers. FIGURE 3: View largeDownload slide UAER in sex-specific quartiles of UA. Data represent the median (IQR). P = 0.20 after adjustment for sex, age, diabetes duration, body mass index, HDL cholesterol, smoking, HbA1c, 24-h pulse pressure, UAER, eGFR and treatment with RAAS blockers. UA and cardiovascular status UA in the upper sex-specific quartile was associated with lower 24-h and daytime diastolic blood pressure both in unadjusted and adjusted analyses (P ≤ 0.01), but was not associated with nighttime diastolic blood pressure (P ≥ 0.11). The association between higher UA and lower diastolic blood pressure was not owing to the effect of antihypertensive treatment or specific to any of the different antihypertensive drug classes, as the association remained significant if the different drug classes were entered individually in the adjusted model (P ≤ 0.035). UA in the upper sex-specific quartile was associated with higher nighttime systolic blood pressure in unadjusted analyses (P = 0.02), but the association lost significance after adjustment (P = 0.11). UA in the upper sex-specific quartile was not associated with 24-h systolic blood pressure (P = 0.44) or daytime systolic blood pressure (P = 0.91). The median UA was significantly higher (P = 0.018; Table 3) in patients with reduced nocturnal systolic blood pressure decrease (<10%; n = 313) compared with patients with normal nocturnal systolic blood pressure decrease (≥10%; n = 343) in unadjusted analyses (P = 0.018). However, significance was lost after adjustment (P = 0.20). UA in the upper sex-specific quartile was associated with higher carotid–femoral pulse wave velocity in unadjusted analyses (P < 0.0001; Figure 2B;R2 = 0.09; F-test P < 0.0001), but significance was lost after adjustment (P = 0.07). Higher UA was associated with the presence of known cardiovascular disease (n = 143) in the unadjusted analysis (P < 0.001; Table 3), but the association lost significance in the adjusted model (P = 0.17; Table 3). UA and other diabetic complications We used heart rate variability as a measure of neuropathy. When comparing the median level of UA within the heart rate variability groups [normal (n = 178), borderline (n = 77) and abnormal (n = 371)], there was a trend (P < 0.0001; Table 3) of increasing UA level across the groups, with the highest level in patients with abnormal heart rate variability. However, significance was lost after adjustment (P = 0.75). There was an increased level of UA across the retinopathy groups [nil (n = 142), simplex (n = 277), proliferative (n = 229) and blind (n = 19); P < 0.0001; Table 3 and Figure 4]. However, significance was lost after adjustment (P = 0.12; Table 3). Since age and duration of diabetes are associated, we repeated the analyses excluding diabetes duration from the adjustment and the association was then significant (P = 0.037). FIGURE 4: View largeDownload slide Plasma UA in groups of retinopathy. Data represent the median (IQR). Participants were categorized based on their level of retinopathy: nil, simplex, proliferative or blind (based on worst eye). P = 0.12 after adjustment for sex, age, diabetes duration, body mass index, HDL cholesterol, smoking, HbA1c, 24-h pulse pressure, UAER, eGFR and treatment with RAAS blockers. FIGURE 4: View largeDownload slide Plasma UA in groups of retinopathy. Data represent the median (IQR). Participants were categorized based on their level of retinopathy: nil, simplex, proliferative or blind (based on worst eye). P = 0.12 after adjustment for sex, age, diabetes duration, body mass index, HDL cholesterol, smoking, HbA1c, 24-h pulse pressure, UAER, eGFR and treatment with RAAS blockers. Sensitivity analysis The results were overall similar when treatment with diuretics was included in the adjusted analyses instead of treatment with RAAS blockers. DISCUSSIONS We investigated the associations between UA and renal function, cardiovascular risk factors (blood pressure and pulse wave velocity) and diabetic complications in 670 type 1 diabetic patients. UA in the upper sex-specific quartile was independently associated with lower eGFR and lower diastolic blood pressure. Moreover, higher UA was associated with more severe retinopathy independent of risk factors; however, after further adjustment for diabetes duration the significance was lost. We also revealed a relationship between UA in the upper sex-specific quartile and accelerated decline in eGFR and higher nighttime systolic blood pressure, UAER and carotid–femoral pulse wave velocity and between higher UA and reduced nocturnal blood pressure decrease, previous cardiovascular disease and the presence of neuropathy. However, these relationships attenuated after adjustment. We hypothesized that higher levels of UA were associated with the presence of renal impairment, but we did not find an association between UA levels and progression of kidney disease or other complications in type 1 diabetes after adjustments for traditional risk factors. We have previously reported an association between higher UA and the development of persistent macroalbuminuria. A level of UA in the upper quartile, measured shortly after the onset of type 1 diabetes, was a significant and independent predictor of later development of macroalbuminuria during 18 years of follow-up compared with the three lower quartiles [1]. Jalal et al. [8] showed in a prospective study that for every 1 mg/dL (0.06 mmol/L) higher UA at baseline, patients with type 1 diabetes had an 80% increased risk of developing micro- or macroalbuminuria after adjustment for known risk factors. We divided the participants into sex-specific quartiles of UA, as previously [1], as data suggests that the relationship between levels of UA and albuminuria might not be linear. Our results in relation to eGFR are in accordance with findings by Rosolowsky et al. [15], who found that renal function [GFR measured by cystatin C (cGFR)] decreased progressively with higher UA and higher UAER, even within values in the normal range. UA and UAER were found to be independent of each other [15]. Moreover, prospective data from the Second Joslin Kidney Study have suggested that elevated UA is one of the strongest risk factor for early loss of renal function (cGFR) among type 1 diabetic patients with microalbuminuria and normal renal function at baseline [9]. Interestingly, the concentrations of UA in our cohort were largely within the normal range (0.23–0.48 mmol/L in men and 0.15–0.35 mmol/L in women), suggesting that UA also has adverse effects at levels considered to be normal. In our study, UA did not predict eGFR decline in a period of 5 years. UA in the upper sex-specific quartile was associated with an accelerated decline (>3 mL/min/year) in eGFR in the unadjusted model but lost significance when we adjusted for known risk factors. Ficociello et al. [9] found that patients with type 1 diabetes and with a rapid decline in measured GFR (>3.3% per year) had significantly higher UA at baseline than patients with stable renal function (<3.3% per year). They found a clear dose–response relationship between increasing UA and the occurrence of early GFR loss, that is, for each 1 mg/dL (0.06 mmol/L) increase in UA, there was a 40% increase in the odds of developing early GFR loss (>3.3% per year), even after adjustment for baseline cGFR, albumin:creatinine ratio, sex and HbA1c [9]. This relation was linear across the entire range of UA levels [9]. Kuwabara et al. [4] found both baseline UA and the increase in UA over time were independent risk factors for accelerated eGFR decline over a period of 5 years. This study was, however, a retrospective cohort study and the results should be interpreted with caution. We have unpublished data demonstrating a weak association between UA and a very rapid decline (>5 mL/min/1.73 m2) in measured GFR in type 1 diabetic patients with overt nephropathy [16]. The lack of a significant association between high UA and an accelerated decline in eGFR in our cohort may be due to a power problem, as sufficient follow-up information on creatinine to calculate the annual decline in eGFR was only available in 476 (71%) individuals. In our study, UA in the upper sex-specific quartile was associated with lower 24-h and daytime diastolic blood pressure after adjustment for known risk factors. Higher nighttime systolic blood pressure and reduced nocturnal blood pressure decrease were also associated with higher UA, but the significance was lost after adjustment for other risk factors. Experimental and clinical evidence suggest that an elevated UA level increases the relative risk for development of essential hypertension within 5 years, independent of other risk factors [17]. We found that UA in the upper sex-specific quartile was significantly associated with higher carotid–femoral pulse wave velocity, although not after adjustment. Only one previous study has investigated the relation between UA and carotid–femoral pulse wave velocity in patients with type 1 diabetes [11]. The study included adolescents ages 11–16 years and could not demonstrate any association between UA and carotid–femoral pulse wave velocity. This might be explained by the young age of the study population [11]. In our cohort, we showed an association between higher UA and the presence of known cardiovascular disease, but the association was not independent of traditional risk factors. Previously only one study has examined the association between UA and coronary heart disease in patients with type 1 diabetes. The study found that hyperuricaemia was correlated with the presence of coronary heart disease in women but not in men [18]. The Coronary Artery Calcification in Type 1 Diabetes (CACTI) Study found that the risk of progression in coronary artery calcification (during 6 years of follow-up) increased by 30–50% per 0.011 mmol/L higher UA at baseline. Notably, this finding was independent of traditional risk factors [19, 20]. We evaluated heart rate variability as a measure of cardiac autonomic neuropathy. Patients with borderline or abnormal heart rate variability had higher UA, but we could not establish an association independent of other risk factors. Huang et al. [21] investigated cardiac autonomic neuropathy by beat-to-beat test and heart rate response to deep breathing and the Valsalva manoeuvre test in type 2 diabetic patients from Taiwan. They found that UA had positive correlations with adrenergic subscores. A systemic review and meta-analysis including cross-sectional and case–control studies reported that patients with type 2 diabetes and peripheral neuropathy had higher levels of UA compared with patients without in unadjusted analysis [22]. The meta-analysis also demonstrated that higher UA increased the risk of developing peripheral neuropathy independent of other risk factors, although adjustment was not consistent across studies [22]. We revealed an association between higher levels of UA and more advanced diabetic retinopathy and this relation remained after adjustment for risk factors but not after further adjustment for diabetes duration. Only a handful of studies have investigated this relation before. Bjornstad et al. [19] found that the risk of developing diabetic retinopathy increased with higher levels of UA in type 1 diabetic patients, independent of known risk factors. However, the diagnosis of retinopathy was self-reported. Lee et al. [23] showed that higher levels of UA in addition to higher HbA1c and longer diabetes duration were independent risk factors for worsening in severity of diabetic retinopathy over a period of 3 years in patients with type 2 diabetes in Taiwan. These observations suggest that higher UA may play a role in the pathogenesis of diabetic retinopathy. The many findings of a relationship between higher UA and the presence and development of diabetic complications highlights that UA may be a target for pharmaceutical intervention to reduce the burden of complications. Indeed, several clinical trials, albeit of varying quality, have investigated UA as a target for pharmaceutical intervention [5, 24–31]. The results are conflicting, as some have shown promising effects of lowering UA by allopurinol treatment [5, 24–27, 29, 31] on reduction of albuminuria, improvement of cGFR and hypertension and a reduction of cardiovascular risk factors. Others have shown little or no effect [28, 32, 33]. An observational study using data from the UK Clinical Practice Research Database showed that treatment with high-dose allopurinol (≥300 mg/day) was associated with lower rates of stroke (−50%) and cardiac events (−39%) in older adults with hypertension [34]. Additional randomized controlled trials are warranted to further clarify the effect of UA-lowering treatment. As part of a multicentre trial, we are currently investigating the effect of UA lowering by allopurinol in patients with type 1 diabetes, using a randomized, placebo-controlled study design in Preventing Early Renal Loss in diabetes (PERL) study [35]. Strengths and limitations Strengths of the study include that the cohort represents 20% of the type 1 diabetic patients followed in our outpatient clinic at Steno Diabetes Center Copenhagen, thus representing a broad segment of the population of adult type 1 diabetic patients in the region. Moreover, the cohort is large and well described and all stages of albuminuria are represented. Limitations included the following. First, the study included a homogeneous population at a single centre, which doesn’t allow for generalization of the results to other, diverse populations. Second, the cross-sectional design of many of the analyses is a limitation, preventing us from making a causal association. Third, UA concentrations were measured in stored plasma samples, but all samples were handled similarly regardless of renal status. There is no consensus in the literature as to whether UA is stable when stored at −80°C [36, 37]. Fourth, information on prescription of UA-lowering therapy, including specific information on treatment with losartan, was not available. Moreover, information on diagnosis of gout was lacking. Fifth, the definition of kidney function was based on eGFR rather than using a more precise measure of kidney function and sufficient follow-up information on creatinine to calculate the annual decline in eGFR was only available in 476 (71%) individuals, limiting the power of these analyses. CONCLUSION In type 1 diabetic patients, higher levels of UA were associated with the presence of impaired kidney function and other diabetic complications. The association between UA in the upper sex-specific quartile and lower eGFR and lower diastolic blood pressure was independent of traditional risk factors. ACKNOWLEDGEMENTS We thank all participants and acknowledge the work of study nurse Lone Jelstrup and lab technicians Anne G. Lundgaard, Berit R. Jensen, Tina R. Juhl and Jessie A. Hermann, employees at Steno Diabetes Center Copenhagen (SDCC), Gentofte, Denmark, including the eye clinic at SDCC headed by Prof Henrik Lund-Andersen and eye nurse Marianne Valerius. FUNDING The study was supported with a grant from the Poul and Erna Sehested Foundation and from the Danish Diabetes Academy supported by the Novo Nordisk Foundation. AUTHORS’ CONTRIBUTIONS S.P.-L. analysed and interpreted the data and wrote the manuscript. T.W.H. helped in analysing the data and reviewed/edited the manuscript. S.T. performed the original study, including data collection, and reviewed/edited the manuscript. T.S.A. calculated the follow-up data on eGFR decline and reviewed/edited the manuscript. F.P., P.R. and J.F. reviewed/edited the manuscript. CONFLICT OF INTEREST STATEMENT None declared. REFERENCES 1 Hovind P, Rossing P, Tarnow L et al.   Serum uric acid as a predictor for development of diabetic nephropathy in type 1 diabetes: an inception cohort study. 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Nephrology Dialysis TransplantationOxford University Press

Published: Apr 11, 2018

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