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Background: CKD is a heterogeneous condition with multiple underlying causes, risk factors, and outcomes. Subtyping CKD with multidimensional patient data holds the key to precision medicine. Consensus clustering may reveal CKD subgroups with different risk profiles of adverse outcomes., Methods: We used unsupervised consensus clustering on 72 baseline characteristics among 2696 participants in the prospective Chronic Renal Insufficiency Cohort (CRIC) study to identify novel CKD subgroups that best represent the data pattern. Calculation of the standardized difference of each parameter used the cutoff of ±0.3 to show subgroup features. CKD subgroup associations were examined with the clinical end points of kidney failure, the composite outcome of cardiovascular diseases, and death., Results: The algorithm revealed three unique CKD subgroups that best represented patients' baseline characteristics. Patients with relatively favorable levels of bone density and cardiac and kidney function markers, with lower prevalence of diabetes and obesity, and who used fewer medications formed cluster 1 ( n =1203). Patients with higher prevalence of diabetes and obesity and who used more medications formed cluster 2 ( n =1098). Patients with less favorable levels of bone mineral density, poor cardiac and kidney function markers, and inflammation delineated cluster 3 ( n =395). These three subgroups, when linked with future clinical end points, were associated with different risks of CKD progression, cardiovascular disease, and death. Furthermore, patient heterogeneity among predefined subgroups with similar baseline kidney function emerged., Conclusions: Consensus clustering synthesized the patterns of baseline clinical and laboratory measures and revealed distinct CKD subgroups, which were associated with markedly different risks of important clinical outcomes. Further examination of patient subgroups and associated biomarkers may provide next steps toward precision medicine., (Copyright © 2021 by the American Society of Nephrology.)

Background: Insulin resistance contributes to the metabolic syndrome, which is associated with the development of kidney disease. However, it is unclear if insulin resistance independently contributes to an increased risk of chronic kidney disease (CKD) progression or CKD complications. Additionally, predisposing factors responsible for insulin resistance in the absence of diabetes in CKD are not well described. This study aimed to describe factors associated with insulin resistance and characterize the relationship of insulin resistance to CKD progression, cardiovascular events and death among a cohort of non-diabetics with CKD., Methods: Data was utilized from Chronic Renal Insufficiency Cohort Study participants without diabetes (N = 1883). Linear regression was used to assess associations with insulin resistance, defined using the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR). The relationship of HOMA-IR, fasting glucose, hemoglobin A1c (HbA1c), and C-peptide with CKD progression, cardiovascular events, and all-cause mortality was examined with Cox proportional hazards models., Results: Novel positive associations with HOMA-IR included serum albumin, uric acid, and hemoglobin A1c. After adjustment, HOMA-IR was not associated with CKD progression, cardiovascular events, or all-cause mortality. There was a notable positive association of one standard deviation increase in HbA1c with the cardiovascular endpoint (HR 1.16, 95% CI: 1.00-1.34)., Conclusion: We describe potential determinants of HOMA-IR among a cohort of non-diabetics with mild-moderate CKD. HOMA-IR was not associated with renal or cardiovascular events, or all-cause mortality, which adds to the growing literature describing an inconsistent relationship of insulin resistance with CKD-related outcomes.

Cardiovascular events, such as hospitalizations because of congestive heart failure, often occur repeatedly in patients with CKD. Many studies focus on analyses of the first occurrence of these events, and discard subsequent information. In this article, we review a number of statistical methods for analyzing ordered recurrent events of the same type, including Poisson regression and three commonly used survival models that are extensions of Cox proportional hazards regression. We illustrate the models by analyzing data from the Chronic Renal Insufficiency Cohort Study to identify risk factors for congestive heart failure hospitalizations in patients with CKD. We show that recurrent event analyses provide additional insights about the data compared with a standard survival analysis of time to the first event., (Copyright © 2017 by the American Society of Nephrology.)

Repeated measures of various biomarkers provide opportunities for us to enhance understanding of many important clinical aspects of CKD, including patterns of disease progression, rates of kidney function decline under different risk factors, and the degree of heterogeneity in disease manifestations across patients. However, because of unique features, such as correlations across visits and time dependency, these data must be appropriately handled using longitudinal data analysis methods. We provide a general overview of the characteristics of data collected in cohort studies and compare appropriate statistical methods for the analysis of longitudinal exposures and outcomes. We use examples from the Chronic Renal Insufficiency Cohort Study to illustrate these methods. More specifically, we model longitudinal kidney outcomes over annual clinical visits and assess the association with both baseline and longitudinal risk factors., (Copyright © 2017 by the American Society of Nephrology.)

Survival analysis is commonly used to evaluate factors associated with time to an event of interest ( e.g., ESRD, cardiovascular disease, and mortality) among CKD populations. Time to the event of interest is typically observed only for some participants. Other participants have their event time censored because of the end of the study, death, withdrawal from the study, or some other competing event. Classic survival analysis methods, such as Cox proportional hazards regression, rely on the assumption that any censoring is independent of the event of interest. However, in most clinical settings, such as in CKD populations, this assumption is unlikely to be true. For example, participants whose follow-up time is censored because of health-related death likely would have had a shorter time to ESRD, had they not died. These types of competing events that cause dependent censoring are referred to as competing risks. Here, we first describe common circumstances in clinical renal research where competing risks operate and then review statistical approaches for dealing with competing risks. We compare two of the most popular analytical methods used in settings of competing risks: cause-specific hazards models and the Fine and Gray approach (subdistribution hazards models). We also discuss practical recommendations for analysis and interpretation of survival data that incorporate competing risks. To demonstrate each of the analytical tools, we use a study of fibroblast growth factor 23 and risks of mortality and ESRD in participants with CKD from the Chronic Renal Insufficiency Cohort Study., (Copyright © 2017 by the American Society of Nephrology.)

Prediction models are often developed in and applied to CKD populations. These models can be used to inform patients and clinicians about the potential risks of disease development or progression. With increasing availability of large datasets from CKD cohorts, there is opportunity to develop better prediction models that will lead to more informed treatment decisions. It is important that prediction modeling be done using appropriate statistical methods to achieve the highest accuracy, while avoiding overfitting and poor calibration. In this paper, we review prediction modeling methods in general from model building to assessing model performance as well as the application to new patient populations. Throughout, the methods are illustrated using data from the Chronic Renal Insufficiency Cohort Study., (Copyright © 2017 by the American Society of Nephrology.)

Background: Glomerular filtration rate (GFR) is considered the best measure of kidney function, but repeated assessment is not feasible in most research studies., Study Design: Cross-sectional study of 1,433 participants in the Chronic Renal Insufficiency Cohort (CRIC) Study (ie, the GFR subcohort) to derive an internal GFR estimating equation using a split-sample approach., Setting & Participants: Adults from 7 US metropolitan areas with mild to moderate chronic kidney disease; 48% had diabetes and 37% were black., Index Test: CRIC GFR estimating equation., Reference Test or Outcome: Urinary (125)I-iothalamate clearance testing (measured GFR [mGFR])., Other Measurements: Laboratory measures, including serum creatinine and cystatin C, and anthropometrics., Results: In the validation data set, the model that included serum creatinine level, serum cystatin C level, age, sex, and race was the most parsimonious and similarly predictive of mGFR compared with a model additionally including bioelectrical impedance analysis phase angle, CRIC clinical center, and 24-hour urinary creatinine excretion. Specifically, root mean square errors for the separate models were 0.207 versus 0.202, respectively. Performance of the CRIC GFR estimating equation was most accurate for the subgroups of younger participants, men, nonblacks, non-Hispanics, those without diabetes, those with body mass index <30 kg/m(2), those with higher 24-hour urine creatinine excretion, those with lower high-sensitivity C-reactive protein levels, and those with higher mGFRs., Limitations: Urinary clearance of (125)I-iothalamate is an imperfect measure of true GFR; cystatin C level is not standardized to certified reference material; lack of external validation; small sample sizes limit analyses of subgroup-specific predictors., Conclusions: The CRIC GFR estimating equation predicts mGFR accurately in the CRIC cohort using serum creatinine and cystatin C levels, age, sex, and race. Its performance was best in younger and healthier participants., (Copyright © 2012 National Kidney Foundation, Inc. All rights reserved.)

Objectives: Estimating equations using serum creatinine (SCr) are often used to assess glomerular filtration rate (GFR). Such creatinine (Cr)-based formulae may produce biased estimates of GFR when using Cr measurements that have not been calibrated to reference laboratories. In this paper, we sought to examine the degree of this variation in Cr assays in several laboratories associated with academic medical centers affiliated with the Chronic Renal Insufficiency Cohort (CRIC) Study; to consider how best to correct for this variation, and to quantify the impact of such corrections on eligibility for participation in CRIC. Variability of Cr is of particular concern in the conduct of CRIC, a large multicenter study of subjects with chronic renal disease, because eligibility for the study depends on Cr-based assessment of GFR., Methods: A library of 5 large volume plasma specimens from apheresis patients was assembled, representing levels of plasma Cr from 0.8 to 2.4 mg/dl. Samples from this library were used for measurement of Cr at each of the 14 CRIC laboratories repetitively over time. We used graphical displays and linear regression methods to examine the variability in Cr, and used linear regression to develop calibration equations. We also examined the impact of the various calibration equations on the proportion of subjects screened as potential participants who were actually eligible for the study., Results: There was substantial variability in Cr assays across laboratories and over time. We developed calibration equations for each laboratory; these equations varied substantially among laboratories and somewhat over time in some laboratories. The laboratory site contributed the most to variability (51% of the variance unexplained by the specimen) and variation with time accounted for another 15%. In some laboratories, calibration equations resulted in differences in eligibility for CRIC of as much as 20%., Conclusions: The substantial variability in SCr assays across laboratories necessitates calibration of SCr measures to a common standard. Failing to do so may substantially affect study eligibility and clinical interpretations when they are determined by Cr-based estimates of GFR., (2010 S. Karger AG, Basel.)

Background and Objectives: The Chronic Renal Insufficiency Cohort (CRIC) Study was established to examine risk factors for the progression of chronic kidney disease (CKD) and cardiovascular disease (CVD) in patients with CKD. We examined baseline demographic and clinical characteristics., Design, Setting, Participants, & Measurements: Seven clinical centers recruited adults who were aged 21 to 74 yr and had CKD using age-based estimated GFR (eGFR) inclusion criteria. At baseline, blood and urine specimens were collected and information regarding health behaviors, diet, quality of life, and functional status was obtained. GFR was measured using radiolabeled iothalamate in one third of participants., Results: A total of 3612 participants were enrolled with mean age +/- SD of 58.2 +/- 11.0 yr; 46% were women, and 47% had diabetes. Overall, 45% were non-Hispanic white, 46% were non-Hispanic black, and 5% were Hispanic. Eighty-six percent reported hypertension, 22% coronary disease, and 10% heart failure. Mean body mass index was 32.1 +/- 7.9 kg/m(2), and 47% had a BP >130/80 mmHg. Mean eGFR was 43.4 +/- 13.5 ml/min per 1.73 m(2), and median (interquartile range) protein excretion was 0.17 g/24 h (0.07 to 0.81 g/24 h). Lower eGFR was associated with older age, lower socioeconomic and educational level, cigarette smoking, self-reported CVD, peripheral arterial disease, and elevated BP., Conclusions: Lower level of eGFR was associated with a greater burden of CVD as well as lower socioeconomic and educational status. Long-term follow-up of participants will provide critical insights into the epidemiology of CKD and its relationship to adverse outcomes.