***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: T HE characteristics of a disease as it appears in the general population are likely to be quite different from its characteristics in a hospital population , since only patients who are severely afflicted or in whom complications have developed are likely to be found in the hospital population . Analysis of a hospital population , therefore , will give an exaggerated idea of the usual severity of the disease and of the frequency with which complications occur . A similar bias may be introduced when patients coming to the hospital with other complaints are used as controls , and an even greater bias introduced when autopsy series are used . This paper presents some observations of a forward-going study of an adult population sample in respect to the characteristics of hyperuricemia and gouty arthritis . The characteristics investigated include ( 1 ) the prevalence of hyperuricemia and its relationship to age ; ( 2 ) the prevalence of secondary hyperuricemia ; ( 3 ) the prevalence of gouty arthritis ; ( 4 ) the incidence of gouty arthritis and its relationship to age ; ( 5 ) the relationship of gouty arthritis to hyperuricemia and the risk of developing gouty arthritis for each uric acid level ; ( 6 ) the clinical features of gouty arthritis in this population ; and ( 7 ) the prevalence of complications related to gouty arthritis and to hyperuricemia . In the Town of Framingham , Massachusetts , a population was selected by r and om sampling for the studp of coronaryartery disease and hJ , pertension . This population has been described in detail elsewhere [ /--31 . A total of 5,127 subjects ( 2,283 men and 2,844 women ) , age thirtythrough fifty-nine , free of evidence of coronary heart disease on initial examination , were included in the sample ( hereinafter referred to as the population ) . The mean age on admission was forty-four . Biennial examination included history , physical examination , electrocardiogram , roentgenograms and various laboratory tests . The results of these examinations , transcripts of admission to the community hospital , and information obtained from physicians and hospitals outside the community formed the record of each subject . At the time of this report the population had been examined an average of seven times for a follow-up period of twelve years . A few subjects have died , moved away or have missed one or two examinations for various reasons . Some have had eight examinations . The current health status of approxiluately 95 per cent of the original 5,127 subjects is known and only 0.7 per cent are considered lost to follow-up [ J ] Background Pegloticase is approved in the US for treatment of refractory chronic gout . Since chronic kidney disease ( CKD ) is common in these patients , we conducted a post-hoc analysis of 2 replicate phase 3 trials and the subsequent open-label extension study to determine the effects of pegloticase on renal function in patients with CKD stages 3 and 4 , as well as the effects of renal dysfunction on pegloticase efficacy and safety . Findings Patients with renal insufficiency were r and omized to pegloticase 8 mg every 2 weeks ( n = 42 ) , pegloticase 8 mg every 4 weeks ( n = 41 ) , or placebo ( n = 20 ) for 6 months as defined by the study protocol s. Renal function was assessed by estimated glomerular filtration rate ( eGFR ) . All patients completing the r and omized trials could participate in an open-label extension study for a further 2.5 years . Uric acid response , the primary end point in the trials , was plasma uric acid < 6.0 mg/dl for 80 % of months 3 and 6.Mean eGFR in both pegloticase dosing cohorts remained constant over the r and omized treatment phase and long-term open-label extension study . The number of patients achieving uric acid response was similar across CKD stages ( 32 % stage 1 , 23 % stage 2 , 35 % stage 3 , and 39 % stage 4 , respectively , P = 0.3 ) . There was no difference in the  pegloticase safety profile based on CKD stage . Conclusions Pegloticase treatment does not impact eGFR  in CKD patients and response to pegloticase is independent of CKD stage . Trial registration Clinical trial identifier : The glomerular filtration rate ( GFR ) is traditionally considered the best overall index of renal function in health and disease ( 1 ) . Because GFR is difficult to measure in clinical practice , most clinicians estimate the GFR from the serum creatinine concentration . However , the accuracy of this estimate is limited because the serum creatinine concentration is affected by factors other than creatinine filtration ( 2 , 3 ) . To circumvent these limitations , several formulas have been developed to estimate creatinine clearance from serum creatinine concentration , age , sex , and body size ( 4 - 12 ) . Despite more recent studies that have related serum creatinine concentration to GFR ( 13 - 24 ) , no formula is more widely used to predict creatinine clearance than that proposed by Cockcroft and Gault ( 4 ) . This formula is used to detect the onset of renal insufficiency , to adjust the dose of drugs excreted by the kidney , and to evaluate the effectiveness of therapy for progressive renal disease . More recently , it has been used to document eligibility for reimbursement from the Medicare End Stage Renal Disease Program ( 25 ) and for accrual of points for patients on the waiting list for cadaveric renal transplantation ( 26 ) . Major clinical decisions in general medicine , geriatrics , and oncology ( as well as nephrology ) are made by using the Cockcroft-Gault formula and other formulas to predict the level of renal function . Therefore , these formulas must predict GFR as accurately as possible . The Modification of Diet in Renal Disease ( MDRD ) Study , a multicenter , controlled trial , evaluated the effect of dietary protein restriction and strict blood pressure control on the progression of renal disease ( 27 - 30 ) . During the baseline period , GFR , serum creatinine , and several variables that affect the relation between them were measured in patients with chronic renal disease . The purpose of our study was to develop an equation from MDRD Study data that could improve the prediction of GFR from serum creatinine concentration . Methods Baseline Cohort and Measurement Methods in the Modification of Diet in Renal Disease Study The overall study design and methods of recruitment for the MDRD Study have been described elsewhere ( 31 , 32 ) . A total of 1785 patients entered the baseline period . Of these patients , 1628 ( 91 % ) also underwent measurement of GFR  and the other variables described below ; these patients constitute the study group for these analyses . Glomerular filtration rate was measured as the renal clearance of 125I-iothalamate ( 33 , 34 ) . Creatinine clearance was computed from creatinine excretion in a 24-hour urine collection and a single measurement of serum creatinine . Serum and urine creatinine were measured by using a kinetic alkaline picrate assay with a normal range in serum of 62 to 124 mol/L ( 0.7 to 1.4 mg/dL ) ( 35 ) . Glomerular filtration rate and creatinine clearance were expressed per 1.73 m2 of body surface area by multiplying measured values by 1.73/body surface area ( 36 ) . The serum and urine specimens were also used for other measurements , including serum albumin ( bromcresol green method [ 35 ] ) , serum urea nitrogen ( urease method [ 35 ] ) , and urine urea nitrogen ( urease method [ 35 ] ) . Protein intake ( g/d ) was estimated as 6.25 [ UUN ( g/d ) + 0.031 ( g/kg per day ) SBW ( kg ) ] , where UUN is urine urea nitrogen , SBW is st and ard body weight , and 0.031 g/kg per day is a constant reflecting the rate of excretion of nitrogen in compounds other than urine urea ( 37 , 38 ) . The diagnosis of diabetes and the cause of renal disease were assigned on the basis of chart review at the clinical center ( 39 ) . Statistical Analysis Descriptive Statistics The relation of renal function measurements to other baseline characteristics was assessed by using contingency tables , t-tests , analysis of variance , and linear regression , as appropriate . Nonparametric tests ( Wilcoxon rank-sum tests and Kruskal-Wallis tests ) gave consistent results . A P value less than 0.01 was considered statistically significant . Multivariable Analysis of Glomerular Filtration Rate We used stepwise multiple regression to determine a set of variables that jointly predicted GFR . The stepwise regression models were developed by using a training sample consisting of a r and om sample of 1070 of the 1628 patients . We found that the variability of the difference between the observed and predicted GFR values was greater for higher GFR values . This increase was eliminated by performing multiple regressions on log-transformed data . To facilitate clinical interpretation , the results were re-expressed in terms of the original units . Consequently , the prediction equation is a multiplicative model ; regression coefficients refer to the change in geometric mean GFR associated with unit changes in the independent variable . Predicted GFR is expressed in mL/min per 1.73 m2 . The following variables were considered for possible inclusion in the regression model : weight , height , sex , ethnicity , age , diagnosis of diabetes , serum creatinine concentration , serum urea nitrogen level , serum albumin level , serum phosphorus level , serum calcium level , mean arterial pressure , urine creatinine level , urine urea nitrogen level , urine protein level , and urine phosphorus level . The cause of renal disease was not included because in clinical practice , the cause may be unknown or clinicians may not use the same classification method as the investigators in the MDRD Study . A P value less than 0.001 was used as the criterion for entry of a variable into the model . Because of the difficulty in collecting complete 24-hour urine sample s in clinical practice , an additional stepwise regression was performed to develop a prediction model that did not include urine biochemistry variables . Finally , because of the interest in developing a prediction equation to assess eligibility for Medicare reimbursement and listing for cadaveric renal transplantation , we repeated the analysis restricting the population to the subgroup of patients with higher serum creatinine concentrations ( > 221 mol/L [ 2.5 mg/dL ] ; n=509 in the training sample ) . Methods for Comparing Equations To Predict Glomerular Filtration Rate We first developed coefficients for each prediction equation ( including the selection of the predictor variables for the stepwise regressions ) using the data from the training sample to predict log GFR . Each prediction equation also included a multiplicative constant to account for any consistent bias in the application of that equation in the MDRD Study Group . This was particularly important for equations that are intended to estimate creatinine clearance , which is known to be higher than GFR . The regression coefficients determined in the training sample were then applied to obtain predicted GFRs in a separate validation sample consisting of the remaining 558 patients ( 172 patients with serum creatinine concentration>221 mol/L [ 2.5 mg/dL ] ) . These predicted GFR values were compared with the actual GFRs in the validation sample to evaluate the performance of each prediction equation . In this way , separate data sets were used to construct the equations and assess their accuracy after removal of systematic bias . For each equation , we computed overall R 2 ( percentage of variability in log GFR explained by the regression model ) and the 50th , 75th , and 90th percentiles of the distribution of the percentage absolute difference between measured and predicted GFRs in the validation sample . The 50th percentiles indicate the typical size of the errors in prediction of GFR , and the 75th and 90th percentiles assess the sizes of the larger errors that occurred for each model . Development of Final Prediction Equations To improve the accuracy of the final MDRD Study prediction equations , the regression coefficients derived from the training sample were up date d on the basis of data from all 1628 patients . As a result , the st and ard errors of the regression coefficients in the final MDRD Study prediction equations are slightly smaller than those derived from the training sample ; thus , the accuracy of the final prediction equations may be slightly better ( by about 0.1 % to 0.2 % ) than their accuracy as assessed in the validation sample . Results Demographic and Clinical Characteristics The mean age ( SD ) of the cohort was 50.6 12.7 years . Sixty percent of patients were male , 88 % were white , and 6 % were diabetic . Causes of renal disease were glomerular disease ( 32 % ) , polycystic kidney disease ( 22 % ) , tubulointerstitial disease ( 7 % ) , and other or unknown renal diseases ( 40 % ) . Mean protein intake was 0.99 0.24 g/kg of body weight per day and mean arterial pressure was 99.4 12.2 mm Hg . Mean weight was 79.6 16.8 kg , body surface area was 1.91 0.23 m2 , serum urea nitrogen concentration was 11.4 5.7 mmol/L [ 32 16 mg/dL ] , and serum albumin concentration was 40.0 4.0 g/L [ 4.0 0.4 g/dL ] , respectively . Glomerular Filtration Rate , Creatinine Clearance , and Serum Creatinine Concentration Renal function measurements for the study group and for various subgroups are shown in Table 1 . Mean GFR for the population was 0.38 mL s 2 m 2 ( 39.8 mL/min per 1.73 m2 ) , with lower values in patients with lower protein intake , white patients compared with black patients , and older patients ( 55 years ) compared with younger patients ( P<0 
    Output:
    
Uric acid lowering therapy may make little of no difference ( low certainty evidence ) in the incidence of ESKD at one or two years .
Uric acid lowering therapy reduced uric acid levels at all time points ( 3 , 4 , 6 , 12 and 24 months ) ( high certainty evidence ) .There is insufficient evidence to support an effect on blood pressure , proteinuria or other cardiovascular markers by uric acid lowering therapy .
There is limited data which suggests uric acid lowering therapy may prevent progression of chronic kidney disease but the conclusion is very uncertain .