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Background: Kidney disease disproportionately affects minority populations, including African Americans and Hispanics; therefore, understanding the relationship of kidney function to cardiovascular (CV) outcomes within different racial/ethnic groups is of considerable interest. We investigated the relationship between kidney function and CV events and assessed effect modification by race/ethnicity in the Women's Health Initiative., Study Design: Prospective cohort study., Setting & Participants: Baseline serum creatinine concentrations (assay traceable to isotope-dilution mass spectrometry standard) of 19,411 postmenopausal women aged 50 to 79 years who self-identified as either non-Hispanic white (n=8,921), African American (n=7,436), or Hispanic (n=3,054) were used to calculate estimated glomerular filtration rates (eGFRs)., Predictors: Categories of eGFR (exposure); race/ethnicity (effect modifier)., Outcomes: The primary outcome was the composite of 3 physician-adjudicated CV events: myocardial infarction, stroke, or CV-related death., Measurements: We evaluated the multivariable-adjusted associations between categories of eGFR and CV events using proportional hazards regression and formally tested for effect modification by race/ethnicity., Results: During a mean follow-up of 7.6 years, 1,424 CV events (653 myocardial infarctions, 627 strokes, and 297 CV-related deaths) were observed. The association between eGFR and CV events was curvilinear; however, the association of eGFR with CV outcomes differed by race (P=0.006). In stratified analyses, we observed that the U-shaped association was present in non-Hispanic whites, whereas African American participants had a rather curvilinear relationship, with lower eGFR being associated with higher CV risk, and higher eGFR, with reduced CV risk. Analyses among Hispanic women were inconclusive owing to few Hispanic women having very low or high eGFRs and very few events occurring in these categories., Limitations: Lack of urinary albumin measurements; residual confounding by unmeasured or imprecisely measured characteristics., Conclusions: In postmenopausal women, the patterns of association between eGFR and CV risk differed between non-Hispanic whites and African American women., (Copyright © 2016 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.)

Improved accuracy in predicting coronary heart disease (CHD) risk in patients with diabetes and kidney disease is needed. The addition of albuminuria to established methods of CHD risk calculation was reported in the Strong Heart Study (SHS) cohort. In this study, the addition of estimated glomerular filtration rate (eGFR) was evaluated using data from 4,549 American Indian SHS participants aged 45 to 74 years. After adjustment for Framingham CHD risk factors, hazard ratios for eGFR as a predictor of CHD were 1.69 (95% confidence interval 1.34 to 2.13) in women and 1.41 (95% confidence interval 0.94 to 2.13) in men. Models including albuminuria, eGFR, or both scored higher in discriminatory power than models using conventional risk factors alone in women; in men, the improvement was seen only for albuminuria and the combination of albuminuria and eGFR. Hosmer-Lemeshow assessments showed good calibration for the models using eGFR alone in both genders, followed by models including albuminuria alone in both genders. Adding eGFR improved the net reclassification improvement (NRI) in women (0.085, p = 0.0004) but not in men (0.010, p = 0.1967). NRI and integrated discrimination improvement (IDI) were improved in both genders using albuminuria and eGFR (NRI 0.135, p <0.0001, and IDI 0.027, p <0.0001 in women; NRI 0.035, p <0.0196, and IDI 0.008, p <0.0156 in men). Therefore, a risk calculator including albuminuria enhances CHD prediction compared to a calculator using only standard risk factors in men and women. Including eGFR alone improves risk prediction in women, but for men, it is preferable to include eGFR and albuminuria. In conclusion, this enhanced calculator should be useful in estimating CHD risk in populations with high prevalence of diabetes and renal disease., (Copyright © 2011 Elsevier Inc. All rights reserved.)

American Indians have a disproportionately high rate of kidney disease likely due to a combination of environmental and genetic factors. We performed a genome wide scan of estimated glomerular filtration rate in 3665 participants of the Strong Heart Family Study to localize genes influencing kidney disease risk factors. The participants were men and women from 13 American Indian tribes recruited from 3 centers located in Arizona, the Dakotas and Oklahoma. Multipoint variance component linkage analysis was performed for each center and on the entire cohort after controlling for center effects. Modeling strategies that incorporated age, gender and interaction terms (model 1) and another that also controlled for diabetes mellitus, systolic and diastolic blood pressure, body mass index, low density and high density lipoproteins, triglycerides and smoking status (model 2) were used. Significant evidence for linkage in the Arizona group was found on chromosome 12p12.2 at 39cM (nearest marker D12S310) using model 1. Additional loci with very suggestive evidence for linkage were detected at 1p36.31 for all groups using both models and at 2q33.3 and 9q34.2 for the Dakotas group each using model 1. No significant evidence for additive interaction with diabetes, hypertension or obesity was noted. This evidence for linkage of a quantitative trait locus influencing estimated glomerular filtration rate to a region of chromosome 12p in a large cohort of American Indians will be worth studying in more detail in the future.

Kidney and cardiovascular disease are widespread among populations with high prevalence of diabetes, such as American Indians participating in the Strong Heart Study (SHS). Studying these conditions simultaneously in longitudinal studies is challenging, because the morbidity and mortality associated with these diseases result in missing data, and these data are likely not missing at random. When such data are merely excluded, study findings may be compromised. In this article, a subset of 2264 participants with complete renal function data from Strong Heart Exams 1 (1989–1991), 2 (1993–1995), and 3 (1998–1999) was used to examine the performance of five methods used to impute missing data: listwise deletion, mean of serial measures, adjacent value, multiple imputation, and pattern-mixture. Three missing at random models and one non-missing at random model were used to compare the performance of the imputation techniques on randomly and non-randomly missing data. The pattern-mixture method was found to perform best for imputing renal function data that were not missing at random. Determining whether data are missing at random or not can help in choosing the imputation method that will provide the most accurate results. [ABSTRACT FROM AUTHOR]

Type 2 diabetes is highly prevalent and is the major cause of progressive chronic kidney disease in American Indians. Genome-wide association studies identified several loci associated with diabetes but their impact on susceptibility to diabetic complications is unknown. We studied the association of 18 type 2 diabetes genome-wide association single-nucleotide polymorphisms (SNPs) with estimated glomerular filtration rate (eGFR; MDRD equation) and urine albumin-to-creatinine ratio in 6958 Strong Heart Study family and cohort participants. Center-specific residuals of eGFR and log urine albumin-to-creatinine ratio, obtained from linear regression models adjusted for age, sex, and body mass index, were regressed onto SNP dosage using variance component models in family data and linear regression in unrelated individuals. Estimates were then combined across centers. Four diabetic loci were associated with eGFR and one locus with urine albumin-to-creatinine ratio. A SNP in the WFS1 gene (rs10010131) was associated with higher eGFR in younger individuals and with increased albuminuria. SNPs in the FTO, KCNJ11, and TCF7L2 genes were associated with lower eGFR, but not albuminuria, and were not significant in prospective analyses. Our findings suggest a shared genetic risk for type 2 diabetes and its kidney complications, and a potential role for WFS1 in early-onset diabetic nephropathy in American Indian populations. [ABSTRACT FROM AUTHOR]