Bertram L. Kasiske

Kidney Disease as a Risk Factor for Development of Cardiovascular Disease A Statement From the American Heart Association Councils on Kidney in Cardiovascular Disease, High Blood Pressure Research, Clinical Cardiology, and Epidemiology and Prevention

Chronic kidney disease1 (CKD) is a worldwide public health problem. In the United States, there is a rising incidence and prevalence of kidney failure, with poor outcomes and high cost. The number of individuals with kidney failure treated by dialysis and transplantation exceeded 320 000 in 1998 and is expected to surpass 650 000 by 2010.1,2 There is an even higher prevalence of earlier stages of CKD (Table 1).1,3 Kidney failure requiring treatment with dialysis or transplantation is the most visible outcome of CKD. However, cardiovascular disease (CVD) is also frequently associated with CKD, which is important because individuals with CKD are more likely to die of CVD than to develop kidney failure,4 CVD in CKD is treatable and potentially preventable, and CKD appears to be a risk factor for CVD. In 1998, the National Kidney Foundation (NKF) Task Force on Cardiovascular Disease in Chronic Renal Disease issued a report emphasizing the high risk of CVD in CKD.5 This report showed that there was a high prevalence of CVD in CKD and that mortality due to CVD was 10 to 30 times higher in dialysis patients than in the general population (Figure 1 and Table 2).6–18 The task force recommended that patients with CKD be considered in the “highest risk group” for subsequent CVD events and that treatment recommendations based on CVD risk stratification should take into account the highest-risk status of patients with CKD. View this table: TABLE 1. Stages of CKD Figure 1. Cardiovascular mortality defined by death due to arrhythmias, cardiomyopathy, cardiac arrest, myocardial infarction, atherosclerotic heart disease, and pulmonary edema in general population (GP; National Center for Health Statistics [NCHS] multiple cause of mortality data files International Classification of Diseases, 9th Revision [ICD 9] codes 402, 404, 410 to 414, and …

Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis.

BACKGROUND Substantial controversy surrounds the use of estimated glomerular filtration rate (eGFR) and albuminuria to define chronic kidney disease and assign its stages. We undertook a meta-analysis to assess the independent and combined associations of eGFR and albuminuria with mortality. METHODS In this collaborative meta-analysis of general population cohorts, we pooled standardised data for all-cause and cardiovascular mortality from studies containing at least 1000 participants and baseline information about eGFR and urine albumin concentrations. Cox proportional hazards models were used to estimate hazard ratios (HRs) for all-cause and cardiovascular mortality associated with eGFR and albuminuria, adjusted for potential confounders. FINDINGS The analysis included 105,872 participants (730,577 person-years) from 14 studies with urine albumin-to-creatinine ratio (ACR) measurements and 1,128,310 participants (4,732,110 person-years) from seven studies with urine protein dipstick measurements. In studies with ACR measurements, risk of mortality was unrelated to eGFR between 75 mL/min/1.73 m(2) and 105 mL/min/1.73 m(2) and increased at lower eGFRs. Compared with eGFR 95 mL/min/1.73 m(2), adjusted HRs for all-cause mortality were 1.18 (95% CI 1.05-1.32) for eGFR 60 mL/min/1.73 m(2), 1.57 (1.39-1.78) for 45 mL/min/1.73 m(2), and 3.14 (2.39-4.13) for 15 mL/min/1.73 m(2). ACR was associated with risk of mortality linearly on the log-log scale without threshold effects. Compared with ACR 0.6 mg/mmol, adjusted HRs for all-cause mortality were 1.20 (1.15-1.26) for ACR 1.1 mg/mmol, 1.63 (1.50-1.77) for 3.4 mg/mmol, and 2.22 (1.97-2.51) for 33.9 mg/mmol. eGFR and ACR were multiplicatively associated with risk of mortality without evidence of interaction. Similar findings were recorded for cardiovascular mortality and in studies with dipstick measurements. INTERPRETATION eGFR less than 60 mL/min/1.73 m(2) and ACR 1.1 mg/mmol (10 mg/g) or more are independent predictors of mortality risk in the general population. This study provides quantitative data for use of both kidney measures for risk assessment and definition and staging of chronic kidney disease. FUNDING Kidney Disease: Improving Global Outcomes (KDIGO), US National Kidney Foundation, and Dutch Kidney Foundation.Background A comprehensive evaluation of the independent and combined associations of estimated glomerular filtration rate (eGFR) and albuminuria with mortality is required for assessment of the impact of kidney function on risk in the general population, with implications for improving the definition and staging of chronic kidney disease (CKD).

The effects of lowering LDL cholesterol with simvastatin plus ezetimibe in patients with chronic kidney disease (Study of Heart and Renal Protection) : a randomised placebo-controlled trial

Summary Background Lowering LDL cholesterol with statin regimens reduces the risk of myocardial infarction, ischaemic stroke, and the need for coronary revascularisation in people without kidney disease, but its effects in people with moderate-to-severe kidney disease are uncertain. The SHARP trial aimed to assess the efficacy and safety of the combination of simvastatin plus ezetimibe in such patients. Methods This randomised double-blind trial included 9270 patients with chronic kidney disease (3023 on dialysis and 6247 not) with no known history of myocardial infarction or coronary revascularisation. Patients were randomly assigned to simvastatin 20 mg plus ezetimibe 10 mg daily versus matching placebo. The key prespecified outcome was first major atherosclerotic event (non-fatal myocardial infarction or coronary death, non-haemorrhagic stroke, or any arterial revascularisation procedure). All analyses were by intention to treat. This trial is registered at ClinicalTrials.gov, NCT00125593, and ISRCTN54137607. Findings 4650 patients were assigned to receive simvastatin plus ezetimibe and 4620 to placebo. Allocation to simvastatin plus ezetimibe yielded an average LDL cholesterol difference of 0·85 mmol/L (SE 0·02; with about two-thirds compliance) during a median follow-up of 4·9 years and produced a 17% proportional reduction in major atherosclerotic events (526 [11·3%] simvastatin plus ezetimibe vs 619 [13·4%] placebo; rate ratio [RR] 0·83, 95% CI 0·74–0·94; log-rank p=0·0021). Non-significantly fewer patients allocated to simvastatin plus ezetimibe had a non-fatal myocardial infarction or died from coronary heart disease (213 [4·6%] vs 230 [5·0%]; RR 0·92, 95% CI 0·76–1·11; p=0·37) and there were significant reductions in non-haemorrhagic stroke (131 [2·8%] vs 174 [3·8%]; RR 0·75, 95% CI 0·60–0·94; p=0·01) and arterial revascularisation procedures (284 [6·1%] vs 352 [7·6%]; RR 0·79, 95% CI 0·68–0·93; p=0·0036). After weighting for subgroup-specific reductions in LDL cholesterol, there was no good evidence that the proportional effects on major atherosclerotic events differed from the summary rate ratio in any subgroup examined, and, in particular, they were similar in patients on dialysis and those who were not. The excess risk of myopathy was only two per 10 000 patients per year of treatment with this combination (9 [0·2%] vs 5 [0·1%]). There was no evidence of excess risks of hepatitis (21 [0·5%] vs 18 [0·4%]), gallstones (106 [2·3%] vs 106 [2·3%]), or cancer (438 [9·4%] vs 439 [9·5%], p=0·89) and there was no significant excess of death from any non-vascular cause (668 [14·4%] vs 612 [13·2%], p=0·13). Interpretation Reduction of LDL cholesterol with simvastatin 20 mg plus ezetimibe 10 mg daily safely reduced the incidence of major atherosclerotic events in a wide range of patients with advanced chronic kidney disease. Funding Merck/Schering-Plough Pharmaceuticals; Australian National Health and Medical Research Council; British Heart Foundation; UK Medical Research Council.

Diabetes Mellitus After Kidney Transplantation in the United States

New onset diabetes is a major complication after kidney transplantation. However, the incidence, risk factors and clinical relevance of post‐transplant diabetes mellitus (PTDM) vary among reports from single‐center observational studies and clinical trials. Using data from the United Renal Data System we identified 11 659 Medicare beneficiaries who received their first kidney transplant in 1996–2000. The cumulative incidence of PTDM was 9.1% (95% confidence interval = 8.6–9.7%), 16.0% (15.3–16.7%), and 24.0% (23.1–24.9%) at 3, 12, and 36 months post‐transplant, respectively. Using Coxs proportional hazards analysis, risk factors for PTDM included age, African American race (relative risk = 1.68, range: 1.52–1.85, p < 0.0001), Hispanic ethnicity (1.35, range: 1.19–1.54, p < 0.0001), male donor (1.12, range: 1.03–1.21, p = 0.0090), increasing HLA mismatches, hepatitis C infection (1.33, range: 1.15–1.55, p < 0.0001), body mass index ≥30 kg/m2 (1.73, range: 1.57–1.90, p < 0.0001), and the use of tacrolimus as the initial maintenance immunosuppressive medication (1.53, range: 1.29–1.81, p < 0.0001). Factors that reduced the risk for PTDM included the use of mycophenolate mofetil, azathioprine, younger recipient age, glomerulonephritis as a cause of kidney failure, and a college education. As a time‐dependent covariate in Cox analyses that also included multiple other risk factors, PTDM was associated with increased graft failure (1.63, 1.46–1.84, p < 0.0001), death‐censored graft failure (1.46, 1.25–1.70, p < 0.0001), and mortality (1.87, 1.60–2.18, p < 0.0001). We conclude that high incidences of PTDM are associated with the type of initial maintenance immunosuppression, race, ethnicity, obesity and hepatitis C infection. It is a strong, independent predictor of graft failure and mortality. Efforts should be made to minimize the risk of this important complication.

KDIGO clinical practice guideline for the care of kidney transplant recipients: a summary

The 2009 Kidney Disease: Improving Global Outcomes (KDIGO) clinical practice guideline on the monitoring, management, and treatment of kidney transplant recipients is intended to assist the practitioner caring for adults and children after kidney transplantation. The guideline development process followed an evidence-based approach, and management recommendations are based on systematic reviews of relevant treatment trials. Critical appraisal of the quality of the evidence and the strength of recommendations followed the Grades of Recommendation Assessment, Development, and Evaluation (GRADE) approach. The guideline makes recommendations for immunosuppression and graft monitoring, as well as prevention and treatment of infection, cardiovascular disease, malignancy, and other complications that are common in kidney transplant recipients, including hematological and bone disorders. Limitations of the evidence, especially the lack of definitive clinical outcome trials, are discussed and suggestions are provided for future research. This summary includes a brief description of methodology and the complete guideline recommendations but does not include the rationale and references for each recommendation, which are published elsewhere.

Cancer after Kidney Transplantation in the United States

Previous reports of cancer after kidney transplantation have been limited by small numbers of patients in single‐center studies and incomplete ascertainment of cases in large registries.

Spectrum of Cancer Risk Among US Solid Organ Transplant Recipients

CONTEXT Solid organ transplant recipients have elevated cancer risk due to immunosuppression and oncogenic viral infections. Because most prior research has concerned kidney recipients, large studies that include recipients of differing organs can inform cancer etiology. OBJECTIVE To describe the overall pattern of cancer following solid organ transplantation. DESIGN, SETTING, AND PARTICIPANTS Cohort study using linked data on solid organ transplant recipients from the US Scientific Registry of Transplant Recipients (1987-2008) and 13 state and regional cancer registries. MAIN OUTCOME MEASURES Standardized incidence ratios (SIRs) and excess absolute risks (EARs) assessing relative and absolute cancer risk in transplant recipients compared with the general population. RESULTS The registry linkages yielded data on 175,732 solid organ transplants (58.4% for kidney, 21.6% for liver, 10.0% for heart, and 4.0% for lung). The overall cancer risk was elevated with 10,656 cases and an incidence of 1375 per 100,000 person-years (SIR, 2.10 [95% CI, 2.06-2.14]; EAR, 719.3 [95% CI, 693.3-745.6] per 100,000 person-years). Risk was increased for 32 different malignancies, some related to known infections (eg, anal cancer, Kaposi sarcoma) and others unrelated (eg, melanoma, thyroid and lip cancers). The most common malignancies with elevated risk were non-Hodgkin lymphoma (n = 1504; incidence: 194.0 per 100,000 person-years; SIR, 7.54 [95% CI, 7.17-7.93]; EAR, 168.3 [95% CI, 158.6-178.4] per 100,000 person-years) and cancers of the lung (n = 1344; incidence: 173.4 per 100,000 person-years; SIR, 1.97 [95% CI, 1.86-2.08]; EAR, 85.3 [95% CI, 76.2-94.8] per 100,000 person-years), liver (n = 930; incidence: 120.0 per 100,000 person-years; SIR, 11.56 [95% CI, 10.83-12.33]; EAR, 109.6 [95% CI, 102.0-117.6] per 100,000 person-years), and kidney (n = 752; incidence: 97.0 per 100,000 person-years; SIR, 4.65 [95% CI, 4.32-4.99]; EAR, 76.1 [95% CI, 69.3-83.3] per 100,000 person-years). Lung cancer risk was most elevated in lung recipients (SIR, 6.13 [95% CI, 5.18-7.21]) but also increased among other recipients (kidney: SIR, 1.46 [95% CI, 1.34-1.59]; liver: SIR, 1.95 [95% CI, 1.74-2.19]; and heart: SIR, 2.67 [95% CI, 2.40-2.95]). Liver cancer risk was elevated only among liver recipients (SIR, 43.83 [95% CI, 40.90-46.91]), who manifested exceptional risk in the first 6 months (SIR, 508.97 [95% CI, 474.16-545.66]) and a 2-fold excess risk for 10 to 15 years thereafter (SIR, 2.22 [95% CI, 1.57-3.04]). Among kidney recipients, kidney cancer risk was elevated (SIR, 6.66 [95% CI, 6.12-7.23]) and bimodal in onset time. Kidney cancer risk also was increased in liver recipients (SIR, 1.80 [95% CI, 1.40-2.29]) and heart recipients (SIR, 2.90 [95% CI, 2.32-3.59]). CONCLUSION Compared with the general population, recipients of a kidney, liver, heart, or lung transplant have an increased risk for diverse infection-related and unrelated cancers.

Effect of antihypertensive therapy on the kidney in patients with diabetes : a meta-regression analysis

The incidence and prevalence of renal failure from diabetes have grown alarmingly during the past decade [1]. The incidence of hypertension associated with nephropathy is high in patients with type I and type II diabetes [2, 3], and hypertension can contribute to a progressive deterioration in renal function in patients with diabetic nephropathy [4, 5]. Indeed, antihypertensive treatment has been shown to retard the rate of declining renal function in patients with diabetic nephropathy [6-8]. Moreover, experiments in animal models of diabetes have shown that antihypertensive agents may vary in their ability to prevent renal injury [9-20]. The effect of different antihypertensive agents on glomerular filtration rate and proteinuria in patients with diabetes has been examined in many clinical trials, with conflicting results. Several possible reasons for the differing results can be advanced. First, different agents were studied, including various angiotensin-converting enzyme (ACE) inhibitors, calcium antagonists, -blockers, and other agents alone or in combination. Second, study samples differed with regard to the presence or absence of hypertension, the type of diabetes, and the stage of nephropathy. Third, although some investigations were well controlled, most were not, and the duration of treatment varied from days to years. To weigh and interpret the different results of these studies, we codified the data from each study and carried out a meta-regression analysis [21]. We used multiple linear regression to determine the extent to which differences in agents, patient characteristics, study design features, and the duration of treatment might have affected the renal response to antihypertensive therapy in patients with diabetes. Methods Studies Using MEDLINE and bibliographies in recent publications to identify clinical trials that examined the effects of antihypertensive agents on blood pressure, glomerular filtration rate, renal blood flow, urine protein excretion, and urine albumin excretion in patients with diabetes, we found 101 studies with extractable data [4,6-8,22-118]. For nine studies, data on blood pressure were estimated from figures. In one instance, data on urine albumin excretion were extracted from a figure. We excluded one study, much larger than the others, that included only blood pressure response and did not indicate whether the standard deviation or standard error was reported [90]. Of the 100 studies analyzed, 87 have been reported since 1985. Fifty-two studies had only one experimental group, 35 had two groups, 9 had three groups, 3 had four groups, and 1 had seven groups. The analysis included 168 experimental groups, totalling 2494 patients. Study End Points The end points we examined included mean arterial pressure (calculated as one third of the pulse pressure plus the diastolic pressure [mm Hg]); glomerular filtration rate (mL/min); renal blood flow (mL/min); filtration fraction (glomerular filtration rate divided by the renal blood flow); urine protein excretion defined as either albumin or, in its absence, total protein excretion (mg/mL); and urine albumin excretion alone (mg/24 h). Each end point, except for urine protein and albumin excretion, was defined by the change from baseline, that is, the value after treatment or placebo minus the value at baseline within each experimental group. Because the frequency distributions of the changes in urine protein and albumin excretion were not normal, these end points were transformed using the natural logarithm (ln). For both urine protein excretion and albuminuria, the end point analyzed was the natural logarithm of the value after treatment or placebo minus the natural logarithm of the baseline value (ln [treatment] ln [baseline]). We estimated the variances for each end point using the standard deviations of the values before and after treatment for each experimental group [119]. Thus, Var(X Y) = Var(X) + Var(Y) 2 xy x radical(Var[X]) x radical (Var[Y]), where X and Y were the means of the treatment and baseline measurements, respectively, and XY was the correlation coefficient between X and Y estimated from the experimental group means across all studies. Complete data for calculating the end point and its variance were available as follows: mean arterial pressure, 147 experimental groups; glomerular filtration rate, 74 groups; renal blood flow, 35 groups; filtration fraction, 34 groups; urine protein excretion, 78 groups; and urine albumin excretion alone, 55 groups. Independent Explanatory Variables To examine the extent to which differences in study end points could be explained by differences in the agents used, characteristics of the patients studied, or study design features, several independent explanatory variables were used to describe each experimental group. The specific agent (for example, enalapril, captopril, nifedipine), the class of agent (for example, ACE inhibitor, calcium antagonist, -blocker), and the duration of treatment were used to characterize each group. The patient characteristics examined were the type of diabetes (type I or type II), the presence or absence of diagnosed hypertension, the World Health Organization (WHO) stage of diabetic nephropathy, the duration of diabetes, age, and gender. Study design features that indicated how well each experimental group had been controlled were also examined. These design features included whether the study allocated patients randomly, used a single or double-blinded design, included the use of a placebo, incorporated a wash-in phase, used an untreated control group (placebo or not), or used stratification for hypertension, diabetes stage, or other patient characteristics. In addition, publication of the study in abstract form only was also included as an independent variable to explain differences among the results of the studies. In general, data for most of the independent explanatory variables were complete for most of the experimental groups. Exceptions included variables describing the type, stage, and duration of diabetes, and the proportion of male patients in each group: Data on type, stage, and duration of diabetes were available for 153 (91%), 102 (61%), and 81 (48%) experimental groups, respectively; data on age and gender were available for 112 (67%) and 122 (73%) experimental groups, respectively. Univariate Analysis We examined the mean effect of each antihypertensive agent or each combination of agents (included in two or more experimental groups) on mean arterial pressure, glomerular filtration rate, renal blood flow, urine protein excretion, and urine albumin excretion by pooling all experimental groups using the same agent or combination. We also examined the effect of each class of agent for each of the study end points. The mean effect of each different agent or class of agent was compared with the mean effects of the other agents or classes as well as with those of no antihypertensive drug treatment. The differences between these agent-specific groups were tested using analysis of variance. Multiple Linear Regression Analysis We used multiple linear regression analysis to determine the relative magnitude and independent effects of different agents, treatment duration, patient characteristics, and study features on each of the study end points. We analyzed the change in mean arterial pressure, glomerular filtration rate, urine protein excretion, and urine albumin excretion separately. For each of these dependent variables, we tested several independent explanatory variables. The change in blood pressure was also included as a possible independent explanatory variable to determine the extent to which changes in glomerular filtration rate, urine protein excretion, and urine albumin excretion could be explained by changes in systemic blood pressure. Regression models were weighted by the inverse of the variance of the change in measured end point. For the regression analysis of each end point, we included all experimental groups that had data on the effect of treatment (treatment or placebo value minus the baseline value) and data that allowed an estimate of the variance of the change in the end point. In the case of independent variables for which data were missing, models were tested using only cases with complete data for all variables, and separately using mean substitution of missing data. When it was found that a variable with missing data did not enter into either of these models, the variable was dropped from subsequent models to permit the inclusion of all experimental groups. Thus, for each end point that was analyzed, the final regression model included virtually all experimental groups that measured that end point. Meta-Analysis of Randomized, Controlled Trials Only 12 randomized, controlled trials could be subjected to a meta-analysis of treatment effects calculated using a parallel control group. Because all but 1 of the 12 trials examined the effects of an ACE inhibitor [101], only treatment effects of ACE inhibitors could be analyzed [22, 30-33, 35, 44, 49-51, 57]. The treatment effect of each study was defined as = T C, where T and C were the changes in end points for the treatment and control groups, respectively. For example, in the case of mean arterial pressure, = (mean arterial pressure after treatment mean arterial pressure before treatment) (mean arterial pressure after placebo mean arterial pressure before placebo). In the case of urine albumin excretion, values were transformed using the natural logarithm before calculating the treatment effect. As in the regression analysis, treatment effects were weighted by the inverse variance. The pooled treatment effect and pooled estimates of 95% CIs were calculated as described by Cappuccio and coworkers [120]. The results were considered significant at = 0.05 when the 95% CI did not include 0. Statistical Conventions Values in the text and tables are expressed as the mean

Cardiovascular disease in chronic kidney disease. A clinical update from Kidney Disease: Improving Global Outcomes (KDIGO)

Cardiovascular morbidity and mortality in patients with chronic kidney disease (CKD) is high, and the presence of CKD worsens outcomes of cardiovascular disease (CVD). CKD is associated with specific risk factors. Emerging evidence indicates that the pathology and manifestation of CVD differ in the presence of CKD. During a clinical update conference convened by the Kidney Disease: Improving Global Outcomes (KDIGO), an international group of experts defined the current state of knowledge and the implications for patient care in important topic areas, including coronary artery disease and myocardial infarction, congestive heart failure, cerebrovascular disease, atrial fibrillation, peripheral arterial disease, and sudden cardiac death. Although optimal strategies for prevention, diagnosis, and management of these complications likely should be modified in the presence of CKD, the evidence base for decision making is limited. Trials targeting CVD in patients with CKD have a large potential to improve outcomes.

Lower estimated glomerular filtration rate and higher albuminuria are associated with mortality and end-stage renal disease. A collaborative meta-analysis of kidney disease population cohorts

We studied here the independent associations of estimated glomerular filtration rate (eGFR) and albuminuria with mortality and end-stage renal disease (ESRD) in individuals with chronic kidney disease (CKD). We performed a collaborative meta-analysis of 13 studies totaling 21,688 patients selected for CKD of diverse etiology. After adjustment for potential confounders and albuminuria, we found that a 15 ml/min per 1.73 m² lower eGFR below a threshold of 45 ml/min per 1.73 m² was significantly associated with mortality and ESRD (pooled hazard ratios (HRs) of 1.47 and 6.24, respectively). There was significant heterogeneity between studies for both HR estimates. After adjustment for risk factors and eGFR, an eightfold higher albumin- or protein-to-creatinine ratio was significantly associated with mortality (pooled HR 1.40) without evidence of significant heterogeneity and with ESRD (pooled HR 3.04), with significant heterogeneity between HR estimates. Lower eGFR and more severe albuminuria independently predict mortality and ESRD among individuals selected for CKD, with the associations stronger for ESRD than for mortality. Thus, these relationships are consistent with CKD stage classifications based on eGFR and suggest that albuminuria provides additional prognostic information among individuals with CKD.