Dataset Information

Proteinuria, but Not eGFR, Predicts Stroke Risk in Chronic Kidney Disease: Chronic Renal Insufficiency Cohort Study.

ABSTRACT: Chronic kidney disease is associated with an increased risk of cardiovascular events. However, the impact of chronic kidney disease on cerebrovascular disease is less well understood. We hypothesized that renal function severity would be predictive of stroke risk, independent of other vascular risk factors.The study population included 3939 subjects enrolled in the Chronic Renal Insufficiency Cohort (CRIC) study, a prospective observational cohort. Stroke events were reported by participants and adjudicated by 2 vascular neurologists. Cox proportional hazard models were used to compare measures of baseline renal function with stroke events. Multivariable analysis was performed to adjust for key covariates.In 3939 subjects, 143 new stroke events (0.62 events per 100 person-years) occurred over a mean follow-up of 6.4 years. Stroke risk was increased in subjects who had worse baseline measurements of renal function (estimated glomerular filtration rate and total proteinuria or albuminuria). When adjusted for variables known to influence stroke risk, total proteinuria or albuminuria, but not estimated glomerular filtration rate, were associated with an increased risk of stroke. Treatment with blockers of the renin-angiotensin system did not decrease stroke risk in individuals with albuminuria.Proteinuria and albuminuria are better predictors of stroke risk in patients with chronic kidney disease than estimated glomerular filtration rate. The impact of therapies targeting proteinuria/albuminuria in individuals with chronic kidney disease on stroke prevention warrants further investigation.

SUBMITTER: Sandsmark DK

PROVIDER: S-EPMC4519405 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

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Proteinuria, but Not eGFR, Predicts Stroke Risk in Chronic Kidney Disease: Chronic Renal Insufficiency Cohort Study.

Sandsmark Danielle K DK Messé Steven R SR Zhang Xiaoming X Roy Jason J Nessel Lisa L Lee Hamm Lotuce L He Jiang J Horwitz Edward J EJ Jaar Bernard G BG Kallem Radhakrishna R RR Kusek John W JW Mohler Emile R ER Porter Anna A Seliger Stephen L SL Sozio Stephen M SM Townsend Raymond R RR Feldman Harold I HI Kasner Scott E SE

Stroke 20150630 8

<h4>Background and purpose</h4>Chronic kidney disease is associated with an increased risk of cardiovascular events. However, the impact of chronic kidney disease on cerebrovascular disease is less well understood. We hypothesized that renal function severity would be predictive of stroke risk, independent of other vascular risk factors.<h4>Methods</h4>The study population included 3939 subjects enrolled in the Chronic Renal Insufficiency Cohort (CRIC) study, a prospective observational cohort. ...[more]

PMID: 26130097

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Project description:Rationale & objectivePatients with a high-risk Apolipoprotein L1 (APOL1) genotype are more likely to develop chronic kidney disease and kidney failure. It is unclear whether this increased risk is entirely mediated by the development of proteinuria.Study designRetrospective observational study of the African American Study of Kidney Disease and Hypertension cohort and Chronic Renal Insufficiency Cohort.Exposures & predictorsSelf-identified race (Black/non-Black) and presence of high-risk APOL1 genotype. The primary model was adjusted for age, sex, diabetes, estimated glomerular filtration rate, and urinary protein-creatinine ratio.OutcomesTime to kidney failure defined as time to dialysis or transplantation.Analytical approachWe used Cox proportional hazard models to study how proteinuria mediates the association between APOL1 and kidney failure. We modeled proteinuria at baseline and as a time-varying covariate.ResultsA high-risk APOL1 genotype was associated with a significantly higher risk of kidney failure, even for patients with minimal proteinuria (HR, 1.87; 95% CI, 1.23-2.84). The association was not significant among patients with high proteinuria (HR, 1.22; 95% CI, 0.93-1.61). When modeling proteinuria as a time-varying covariate, a high-risk APOL1 genotype was associated with higher kidney failure risk even among patients who never developed proteinuria (HR, 2.04; 95% CI, 1.10-3.77). Compared to non-Black patients, Black patients without the high-risk genotype did not have higher risk of kidney failure (HR, 0.96; 95% CI, 0.85-1.10).LimitationsTwo datasets were combined to increase statistical power. Limited generalizability beyond the study cohorts. Residual confounding common to observational studies.ConclusionsA high-risk APOL1 genotype is significantly associated with increased kidney failure risk, especially among patients without baseline proteinuria. Although our results suggest that the risk is partially mediated through proteinuria, higher kidney failure risk was present even among patients who never developed proteinuria. Providers should consider screening for the high-risk APOL1 genotype, especially among Black patients without proteinuria in populations with chronic kidney disease.

Project description:Background and objectivesAtrial fibrillation (AF) is common in CKD and associated with poor kidney and cardiovascular outcomes. Prediction models developed using novel methods may be useful to identify patients with CKD at highest risk of incident AF. We compared a previously published prediction model with models developed using machine learning methods in a CKD population.Design, setting, participants, & measurementsWe studied 2766 participants in the Chronic Renal Insufficiency Cohort study without prior AF with complete cardiac biomarker (N-terminal pro-B-type natriuretic peptide and high-sensitivity troponin T) and clinical data. We evaluated the utility of machine learning methods as well as a previously validated clinical prediction model (Cohorts for Heart and Aging Research in Genomic Epidemiology [CHARGE]-AF, which included 11 predictors, using original and re-estimated coefficients) to predict incident AF. Discriminatory ability of each model was assessed using the ten-fold cross-validated C-index; calibration was evaluated graphically and with the Grønnesby and Borgan test.ResultsMean (SD) age of participants was 57 (11) years, 55% were men, 38% were Black, and mean (SD) eGFR was 45 (15) ml/min per 1.73 m2; 259 incident AF events occurred during a median of 8 years of follow-up. The CHARGE-AF prediction equation using original and re-estimated coefficients had C-indices of 0.67 (95% confidence interval, 0.64 to 0.71) and 0.67 (95% confidence interval, 0.64 to 0.70), respectively. A likelihood-based boosting model using clinical variables only had a C-index of 0.67 (95% confidence interval, 0.64 to 0.70); adding N-terminal pro-B-type natriuretic peptide, high-sensitivity troponin T, or both biomarkers improved the C-index by 0.04, 0.01, and 0.04, respectively. In addition to N-terminal pro-B-type natriuretic peptide and high-sensitivity troponin T, the final model included age, non-Hispanic Black race/ethnicity, Hispanic race/ethnicity, cardiovascular disease, chronic obstructive pulmonary disease, myocardial infarction, peripheral vascular disease, use of angiotensin-converting enzyme inhibitor/angiotensin receptor blockers, calcium channel blockers, diuretics, height, and weight.ConclusionsUsing machine learning algorithms, a model that included 12 standard clinical variables and cardiac-specific biomarkers N-terminal pro-B-type natriuretic peptide and high-sensitivity troponin T had moderate discrimination for incident AF in a CKD population.

Dataset Information

Proteinuria, but Not eGFR, Predicts Stroke Risk in Chronic Kidney Disease: Chronic Renal Insufficiency Cohort Study.

Publications

Proteinuria, but Not eGFR, Predicts Stroke Risk in Chronic Kidney Disease: Chronic Renal Insufficiency Cohort Study.

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