Introduction
Large epidemiological studies of populations at increased risk of cardiovascular events (including those with diabetes, hypertension and a history of cardiovascular disease) show hyperuricaemia to powerfully predict cardiovascular adverse events and mortality
Elevated serum uric acid levels, obesity, insulin resistance and type 2 diabetes mellitus frequently coexist
Methods
SCOUT is an ongoing randomized, double-blind, placebo-controlled clinical study of cardiovascular outcome in overweight or obese patients at increased risk for cardiovascular adverse events. Patients aged 55 years or older, with a body mass index (BMI) ≥ 27 kg/m2 and ≤ 45 kg/m2, or a BMI ≥ 25 kg/m2 and <27 kg/m2 plus a waist circumference ≥ 102 cm if male and ≥ 88 cm if female, were included. A further requirement was either the presence of cardiovascular disease (CVD) (defined as a history of coronary artery disease, peripheral arterial occlusive disease or stroke), or diagnosed type 2 diabetes mellitus (DM) with at least one additional well defined risk factor (i.e. hypertension, dyslipidaemia, current smoker or diabetic nephropathy with evidence of microalbuminuria)
The present analyses deal with the first four weeks of the 6-week, single-blind, lead-in period of the SCOUT trial. The single-blind period preceded randomization into the main study, with all subjects receiving 10 mg sibutramine daily together with advice on diet and physical exercise. At the screening visit and every other week, a physical examination, including measurements of weight, height, blood pressure and pulse, was performed. Data on haematology and blood biochemistry were collected at the screening visit and after four weeks of treatment. All blood samples were taken fasting and analysed in a certified central laboratory. Some of the patients had a second haematology or blood biochemistry sample taken either in relation to the screening visit or in relation to the four week visit. For the present analysis, these secondary blood samples were used only if values from the first measurement were missing. Uric acid concentration had a normal reference defined as 150-450 μmol/L for women and 240-510 μmol/L for men. Creatinine clearance was calculated from measured creatinine concentrations, using the Modification of Diet in Renal Disease (MDRD) study equation
All patients provided urinary samples that were semi-quantitatively (i.e., by dip stick) analyzed for ketone bodies, blood, proteins and glucose. For glucose, the dip stick scale spanned from "negative", "trace", "1+", "2+" to "3+". A one unit change of urinary glucose was defined as a change of one step on the scale (e.g., from trace to 1+, or from trace to negative).
The population was classified according to the presence of diabetes mellitus, and/or cardiovascular disease, using the inclusion diagnoses. Thirteen of 10,742 patients (0.12%) could not be classified in any of these groups because of missing data, and were therefore not included in the present analysis. In analyses stratified by diabetes, the diabetes group included the patients originally grouped in DM only group or in CVD+DM group. In analyses stratified by weight change, patients who maintained or gained weight were defined as one group and the other groups were composed according to each percentage of weight loss (i.e. lost >0-1%; lost >1-2%; lost >2-3%; lost >3-4%; and lost > 4% of their initial weight, respectively; further information and distribution of patients in the different weight change groups are available elsewhere
Ethics
The study was performed in conformity with the Declaration of Helsinki and was approved by all relevant ethical committees. All patients gave their written, informed consent before participating. The trial is registered at ClinicalTrial.gov number: NCT00234832.
Statistics
Continuous variables were compared by t-test and analysis of variance (ANOVA). Discrete variables were compared with chi-square test. All tests were repeated with non-parametric methods (rank sum test for continuous variables and Mantel-Haenszel chi square for discrete variables). Unless specified, results from the parametric models have been reported. General linear models were used to examine the impact of different factors on the change in uric acid concentration. Irrespective of significance levels, all variables with a possible influence on uric acid levels were included in multivariable analysis, as were the medications previously reported to influence uric acid levels
Data available by September 2006 were used in all analyses. All calculations were made using SAS®, version 9.1 (SAS institute, Cary, North Carolina). The level of statistical significance was set at a p-value < 0.05.
Results
The study included 10729 patients, of whom 2584 (24%) were classified as having only DM, 1748 patients (16%) as having only CVD, and 6397 patients (60%) as having both DM and CVD. The main characteristics of the different groups are shown in Table
Screening characteristics
DM only
CVD only
CVD+DM
n = 2584
n = 1748
n = 6397
Age (years)
62 (± 6)
64 (± 6)
64 (± 6)
Male gender (%)
38%
66%
64%
BMI (kg/m2)
35.9 (± 4.8)
33.4 (± 4.1)
34.0 (± 4.4)
Weight (kg)
97.4 (± 16.1)
95.0 (± 14.8)
95.8 (± 15.4)
Waist circumference, men (cm)
117 (± 12)
112 (± 10)
114 (± 10)
Waist circumference, women (cm)
111 (± 12)
104 (± 11)
109 (± 11)
Systolic blood pressure (mmHg)
141 (± 12)
136 (± 13)
138 (± 13)
Diastolic blood pressure (mmHg)
78 (± 8)
78 (± 8)
77 (± 9)
Pulse (bpm)
75 (± 10)
68 (± 10)
71 (± 10)
Creatinine clearance (mL/min/1.73 m2)
76 [48;105]
72 [47;99]
71 [41;103]
Uric acid concentration (μmol/L)
342 (± 87)
369 (± 86)
374 (± 98)
Glucose concentration (mmol/L)
9.0 (± 3.1)
6.0 (± 1.0)
8.9 (± 3.2)
HDL cholesterol (mmol/L)
1.3 (± 0.3)
1.2 (± 0.3)
1.2 (± 0.3)
LDL cholesterol (mmol/L)
3.1 (± 0.9)
3.0 (± 0.9)
3.0 (± 1.0)
Triglycerides (mmol/L)
2.2 (± 1.5)
2.0 (± 1.1)
2.4 (± 1.4)
History of dyslipidaemia (%)
71%
76%
86%
History of stroke (%)
0%
9%
12%
Peripherial artery disease (%)
0%
8%
16%
Use of alcohol (%)
51%
63%
54%
Current smokers (%)
11%
10%
9%
Coronary artery disease (%)
4%
87%
83%
CHF (%)
2%
9%
11%
Use of diuretics (%)
45%
37%
51%
Use of Insulin (%)
26%
0.5%
32%
Use of statins (%)
42%
73%
73%
Use of ACE-inhibitors (%)
73%
64%
82%
Use of Ca2+-blockers (%)
32%
33%
40%
Use of fibrates (%)
10%
6%
10%
Negative (%)
81%
99%
79%
Trace (%)
6%
0.4%
6%
1+ (%)
4%
0.2%
5%
2+ (%)
3%
0.1%
3%
3+ (%)
6%
0.4%
6%
Continuous variables are presented as means (± standard deviation) and discrete variables as percentages (%). Creatinine clearance is presented as median [5th 95th percentiles] due to widely distributed values. Blood samples were obtained during screening visit, as were medical histories. Weight, BMI, waist circumference, pulse and blood pressure are values from the first day of sibutramine administration.
At screening, uric acid concentrations were significantly higher among patients with a history of CVD, compared to patients with only DM (CVD group, compared to DM group: p < 0.0001 and CVD+DM, compared to DM group: p < 0.0001). Patients using diuretics had significantly higher uric acid concentrations, compared to patients not using diuretics (mean ± standard deviation [SD]: 388 ± 101 μmol/L, and 345 ± 84 μmol/L, respectively, p < 0.0001). Analyses stratified by use of diuretics also found CVD patients had higher uric acid concentrations, compared to non-CVD patients irrespective of diuretic use (data not shown).
Over the first four weeks of sibutramine-based therapy, mean uric acid concentration decreased significantly in all three groups (Figure
Four week change in uric acid concentration, stratified for the presence of only diabetes mellitus (DM), only cardiovascular disease (CVD) or both (p-value for changes <0.0001 in respectively group)
Four week change in uric acid concentration, stratified for the presence of only diabetes mellitus (DM), only cardiovascular disease (CVD) or both (p-value for changes <0.0001 in respectively group). Error bars illustrate 95% confidence interval.
Variables with a significant effect on the four week change in uric acid. Results from multivariable regression analysis
p-value:
p-value:
p-value:
Change in glucose concentration (for 1 mmol/L decrease) *
1.6 (± 0.3)
<0.0001
1.7 (± 0.3)
<0.0001
-3.8 (± 1.5)
0.01
Weight loss, for 1% decrease *
2.5 (± 0.3)
<0.0001
2.9 (± 0.4)
<0.0001
0.6 (± 0.7)
0.4
LDL change (for 1 mmol/L decrease) †
-7.4 (± 0.9)
<0.0001
-6.4 (± 1.1)
<0.0001
-11.7 (± 2.0)
<0.0001
Triglyceride change (for 1 mmol/L decrease)
-6.3 (± 1.1)
<0.0001
-6.0 (± 1.2)
<0.0001
-7.4 (± 2.6)
0.004
Change in systolic blood pressure (for 1 mmHg decrease)
0.3 (± 0.07)
<0.0001
0.3 (± 0.08)
0.0002
0.1 (± 0.1)
0.5
Use of fibrates
-14.7 (± 2.1)
<0.0001
-15.3 (± 2.2)
<0.0001
-8.9 (± 5.7)
0.1
Triglyceride concentration at baseline (for 1 mmol/L increment)
5.5 (± 0.9)
<0.0001
5.8 (± 1.0)
<0.0001
3.6 (± 2.2)
0.1
Use of diuretics
11.9 (± 1.2)
<0.0001
12.1 (± 1.4)
<0.0001
10.0 (± 2.9)
0.0007
Uric acid concentration at baseline (for 1 μmol/L increment)
-0.2 (± 0.01)
<0.0001
-0.3 (± 0.007)
<0.0001
-0.2 (± 0.02)
<0.0001
Creatinine clearance at baseline (for 1 mL/min/1.73 m2 increment)
-0.08 (± 0.02)
<0.0001
-0.07 (± 0.02)
<0.0001
-0.2 (± 0.06)
<0.0001
Change in creatinine clearance (for 1 mL/min/1.73 m2 increase)
0.1 (± 0.01)
<0.0001
0.1 (± 0.02)
<0.0001
0.2 (± 0.05)
<0.0001
Male gender
10.2 (± 1.4)
<0.0001
10.2 (± 1.6)
<0.0001
11.1 (± 3.5)
0.001
Waist circumference at baseline (for 1 cm increment)
0.2 (± 0.07)
0.001
0.2 (± 0.08)
0.007
0.2 (± 0.1)
0.1
BMI at screening (for 1 kg/m2 increment)
0.5 (± 0.2)
0.01
0.5 (± 0.2)
0.02
0.5 (± 0.4)
0.3
HDL change (for 1 mmol/L decrease) *
5.7 (± 4.0)
0.2
10.0 (± 4.5)
0.03
-10.8 (± 8.3)
0.2
LDL at screening (for 1 mmol/L decrement)
0.3 (± 0.7)
0.6
0.8 (± 0.8)
0.3
-3.6 (± 1.6)
0.02
Type 2 diabetes mellitus
5.9 (± 1.7)
0.0004
-
-
-
The stratum "diabetes" includes the patients in the DM only group and the CVD+DM group. "No diabetes" includes the patients in CVD only group. Only variables at a significance level of ≤ 0.05 in overall group or in any of the two groups are presented in table. Analysis was also adjusted for screening features (age, smoking status, a diagnosis of heart failure, systolic and diastolic blood pressure, pulse, glucose concentration at baseline, and HDL cholesterol at baseline), medication use (insulin, aspirin, ACE-inhibitors, calcium channel blockers, statins) and dynamic variables (changes in diastolic blood pressure and pulse). SE = standard error. (*) and (†) indicate a p-value ≤ 0.01 and p-value ≤ 0.05 respectively for differences in the influence on uric acid between patients with and without diabetes.
Diabetes status and the influence of weight loss and changes in fasting glucose on uric acid concentration
Figure
Mean change in uric acid concentration, according to four week weight change (left) and four week mean change in fasting serum glucose (FSG, right) in patients with diabetes
Mean change in uric acid concentration, according to four week weight change (left) and four week mean change in fasting serum glucose (FSG, right) in patients with diabetes. "Numbers" refers to the numbers of patients with available values on the uric acid change in the respective group. The mean uric acid concentration change was found to differ in both the groups of weight change and the groups of FSG change (p < 0.0001 in both analyses). Error bars illustrate 95% confidence interval.
Mean change in uric acid concentration, according to four week weight change (left) and four week mean change in fasting serum glucose (FSG, right) in patients without diabetes
Mean change in uric acid concentration, according to four week weight change (left) and four week mean change in fasting serum glucose (FSG, right) in patients without diabetes. "Numbers" refers to the numbers of patients with available values on the uric acid change in the respective group. No difference was found in mean uric acid concentration change between the weight change groups (p = 0.3). The mean uric acid concentration change was found to differ over the groups of FSG change (p = 0.0004). Error bars illustrate the 95% confidence interval.
In patients with diabetes, increasing weight loss with sibutramine and lifestyle changes was associated with smaller reductions in uric acid. Adjusted for multiple variables, each 1% reduction in weight was associated with a 2.9 (± standard error [SE] 0.4, p < 0.0001) μmol/L smaller fall in uric acid concentration. In patients without diabetes, weight loss had no detectable impact on the uric acid levels: a 1% reduction in weight was associated with a 0.6 (± 0.7, p = 0.4) μmol/L increase in uric acid concentration. The difference in impact of weight loss between patients with and without diabetes was statistically significant (p for difference in multivariable analysis = 0.0003).
Decreasing levels of fasting serum glucose were associated with different changes in the uric acid levels in patients with and without diabetes (p for interaction in multivariable analysis <0.0001). Adjusted for multiple variables, each 1 mmol/L decrease in fasting serum glucose was associated with a 1.7 (± 0.3, p < 0.0001) μmol/L smaller fall in uric acid concentration in patients with diabetes. Conversely, each 1 mmol/L decrease in fasting serum glucose was associated with a 3.7 (± 1.5, p = 0.01) μmol/L larger fall in uric acid concentration in patients without diabetes.
Urinary glucose and its influence on serum uric acid concentration
At the screening visit almost all patients without diabetes (99%) and approximately 80% of the patients with diabetes had a negative dip stick for urinary glucose. However, those patients showing a change in urinary glucose, measured by dip stick, were found to significantly alter their uric acid levels, as illustrated for all patients with diabetes in Figure
Mean change in uric acid concentration for patients with diabetes, according to four week change in urinary glucose, estimated by dip stick
Mean change in uric acid concentration for patients with diabetes, according to four week change in urinary glucose, estimated by dip stick. The dip stick scale ranged between "negative", "trace", "1+", "2+" and "3+" for glucose content. FSG = fasting serum glucose. Error bars illustrate 95% confidence interval. Variables in table are presented as means (± standard deviation). Analysis for patients without diabetes was not performed, since 99% of the patients had a negative dip stick at screening, and 99% of the patients were found to have no change in glucose dip stick.
Discussion
The once-daily treatment with sibutramine plus diet and exercise for four weeks induced significant mean reductions in uric acid concentrations overall, and patients without diabetes had greater reductions than those patients with diabetes. However, the significant fall in uric acid in the subgroup of patients gaining/maintaining their initial weight suggests a specific uric acid lowering effect of sibutramine and the results from some previous placebo controlled studies of sibutramine agree with this hypothesis with greater falls in uric acid for the same weight loss in sibutramine compared with placebo treated patients
The present study found the response of weight loss and falling serum glucose levels on uric acid levels to be strongly modified by diabetes. In patients without diabetes weight loss had no detectable influence on uric acid concentrations and decreasing serum glucose concentrations were associated with decreasing uric acid levels. In patients with diabetes weight loss were found to have a paradoxical effect on the fall in uric acid concentrations - those with no weight loss and limited changes in blood glucose had the greatest uric acid reductions whereas those with maximum weight loss had negligible falls.
Physiological mechanisms involved in uric acid metabolism
Renal elimination is the single most important factor regulating blood uric acid levels
SLC2A9, uric acid and glucose
The SLC2A9 transporter seems to be comprised of two splice variants: the Glut9 transporter, which is situated in the basolateral membrane, and the Glut9ΔN, which is situated in the apical membrane
Differential effects of decreasing serum glucose levels on uric acid concentrations in diabetes
The different serum uric acid responses to decreasing levels of fasting glucose in patients with and without diabetes have been previously described with Chinese adults without diabetes also showing higher uric acid levels with higher fasting blood glucose whereas the opposite effect was seen in those with newly diagnosed diabetes
The present study demonstrated that a decrease in serum glucose concentration was associated with a decrease in uric acid levels in patients without diabetes (Figure
The paradoxical relationship found between weight loss and uric acid levels in patients with diabetes is assumed to be secondary to the previously discussed differences in response of uric acid to falling serum glucose levels. However, it cannot be excluded that some of the inverse relationship between uric acid decrease and weight loss also potentially could be explained by factors like increased cell catabolism, as uric acid has previously been shown to increase during fasting
Other factors influencing uric acid concentrations
The relationship of uric acid levels to a change in HDL and LDL cholesterol seems of minor significance but the use of fibrates did seem to amplify the falls in serum uric acid. Multivariable analysis suggested that fibrates reduce by about a further 10 μmol/L the fall in uric acid observed over the four week period. Previous studies have shown a uric acid lowering effect of fenofibrate, with suggested mechanisms linked to increased urinary uric acid excretion
Limitations of the study
The present study was based on data obtained during four weeks of treatment with sibutramine, diet and exercise. Because fat masses and body fluids may have changed dynamically during this period, any observed relationship between uric acid, sibutramine, serum/renal glucose, and diabetes should be regarded as hypothesis-generating only. The lead-in period was non-randomized, single-blind and had no control group; therefore a direct uric acid lowering effect of sibutramine could not be isolated in this study, but has been demonstrated in previous placebo-controlled trials
There is also a possibility that the results were affected by regression towards the mean or other factors. In particular, the higher screening values of uric acid among patients with CVD compared to that in patients without CVD could potentially account for some of the difference found in uric acid decrease between patients with and without diabetes. However, the fact that the group with CVD only and the group with DM+CVD had comparable screening values of uric acid but differences in decrement over the study period suggests that other factors were of importance. In addition, baseline values of uric acid were included in multivariable regression analysis.
Furthermore, measurements of insulin concentrations were lacking and their measurement would have helped in the interpretation of the data. On the other hand, serum insulin levels in patients with diabetes can be variable depending on the length of time with the disease, the type of therapy and the presence of differing degrees of insulin resistance. Also glycated haemoglobin could have been helpful in the interpretation of data; however, due to the design of the study and run-in period, there was not data available to permit meaningful analysis.
Anti-gout medication was not recorded in the study, but since nearly all uric acid values were within the normal range, the influence of such medication can be assumed to be minor. In addition genetic variation of the renal SCL2A9 transporter for uric acid transporters is now recognised to account for some of the variation in uric acid concentrations
Conclusion
A four week daily intake of sibutramine and life style changes was associated with a significant reduction in mean uric acid levels but the renal glucose load in patients with diabetes tended to counteract any selective uricosuric effect of sibutramine. The uric acid reduction during this short period of weight management was more pronounced in patients without diabetes.
Competing interests
The SCOUT Executive Steering Committee comprises WPTJ (Chair), IDC, WC, LVG, NF, AM, CTP and AMS. RAR is employed by Abbott Laboratories.
Authors' contributions
CA, PW, ELF, BB, LK and CTP analyzed the data for the present paper. CA wrote the initial draft of the manuscript. All authors interpreted the data and revised the manuscript.