Whole-exome sequencing study identifies four novel gene loci associated with diabetic kidney disease.

Diabetic kidney disease (DKD) is recognized as an important public health challenge. However, its genomic mechanisms are poorly understood. To identify rare variants for DKD, we conducted a whole-exome sequencing (WES) study leveraging large cohorts well-phenotyped for chronic kidney disease and diabetes. Our two-stage WES study included 4372 European and African ancestry participants from the Chronic Renal Insufficiency Cohort and Atherosclerosis Risk in Communities studies (stage 1) and 11 487 multi-ancestry Trans-Omics for Precision Medicine participants (stage 2). Generalized linear mixed models, which accounted for genetic relatedness and adjusted for age, sex and ancestry, were used to test associations between single variants and DKD. Gene-based aggregate rare variant analyses were conducted using an optimized sequence kernel association test implemented within our mixed model framework. We identified four novel exome-wide significant DKD-related loci through initiating diabetes. In single-variant analyses, participants carrying a rare, in-frame insertion in the DIS3L2 gene (rs141560952) exhibited a 193-fold increased odds [95% confidence interval (CI): 33.6, 1105] of DKD compared with noncarriers (P = 3.59 × 10-9). Likewise, each copy of a low-frequency KRT6B splice-site variant (rs425827) conferred a 5.31-fold higher odds (95% CI: 3.06, 9.21) of DKD (P = 2.72 × 10-9). Aggregate gene-based analyses further identified ERAP2 (P = 4.03 × 10-8) and NPEPPS (P = 1.51 × 10-7), which are both expressed in the kidney and implicated in renin-angiotensin-aldosterone system modulated immune response. In the largest WES study of DKD, we identified novel rare variant loci attaining exome-wide significance. These findings provide new insights into the molecular mechanisms underlying DKD.

Incident heart failure in chronic kidney disease: proteomics informs biology and risk stratification.

BACKGROUND AND AIMS: Incident heart failure (HF) among individuals with chronic kidney disease (CKD) incurs hospitalizations that burden patients and health care systems. There are few preventative therapies, and the Pooled Cohort equations to Prevent Heart Failure (PCP-HF) perform poorly in the setting of CKD. New drug targets and better risk stratification are urgently needed. METHODS: In this analysis of incident HF, SomaScan V4.0 (4638 proteins) was analysed in 2906 participants of the Chronic Renal Insufficiency Cohort (CRIC) with validation in the Atherosclerosis Risk in Communities (ARIC) study. The primary outcome was 14-year incident HF (390 events); secondary outcomes included 4-year HF (183 events), HF with reduced ejection fraction (137 events), and HF with preserved ejection fraction (165 events). Mendelian randomization and Gene Ontology were applied to examine causality and pathways. The performance of novel multi-protein risk models was compared to the PCP-HF risk score. RESULTS: Over 200 proteins were associated with incident HF after adjustment for estimated glomerular filtration rate at P < 1 × 10-5. After adjustment for covariates including N-terminal pro-B-type natriuretic peptide, 17 proteins remained associated at P < 1 × 10-5. Mendelian randomization associations were found for six proteins, of which four are druggable targets: FCG2B, IGFBP3, CAH6, and ASGR1. For the primary outcome, the C-statistic (95% confidence interval [CI]) for the 48-protein model in CRIC was 0.790 (0.735, 0.844) vs. 0.703 (0.644, 0.762) for the PCP-HF model (P = .001). C-statistic (95% CI) for the protein model in ARIC was 0.747 (0.707, 0.787). CONCLUSIONS: Large-scale proteomics reveal novel circulating protein biomarkers and potential mediators of HF in CKD. Proteomic risk models improve upon the PCP-HF risk score in this population.

Race, Genetic Ancestry, and Estimating Kidney Function in CKD.

BACKGROUND: The inclusion of race in equations to estimate the glomerular filtration rate (GFR) has become controversial. Alternative equations that can be used to achieve similar accuracy without the use of race are needed. METHODS: In a large national study involving adults with chronic kidney disease, we conducted cross-sectional analyses of baseline data from 1248 participants for whom data, including the following, had been collected: race as reported by the participant, genetic ancestry markers, and the serum creatinine, serum cystatin C, and 24-hour urinary creatinine levels. RESULTS: Using current formulations of GFR estimating equations, we found that in participants who identified as Black, a model that omitted race resulted in more underestimation of the GFR (median difference between measured and estimated GFR, 3.99 ml per minute per 1.73 m2 of body-surface area; 95% confidence interval [CI], 2.17 to 5.62) and lower accuracy (percent of estimated GFR within 10% of measured GFR [P10], 31%; 95% CI, 24 to 39) than models that included race (median difference, 1.11 ml per minute per 1.73 m2; 95% CI, -0.29 to 2.54; P10, 42%; 95% CI, 34 to 50). The incorporation of genetic ancestry data instead of race resulted in similar estimates of the GFR (median difference, 1.33 ml per minute per 1.73 m2; 95% CI, -0.12 to 2.33; P10, 42%; 95% CI, 34 to 50). The inclusion of non-GFR determinants of the serum creatinine level (e.g., body-composition metrics and urinary excretion of creatinine) that differed according to race reported by the participants and genetic ancestry did not eliminate the misclassification introduced by removing race (or ancestry) from serum creatinine-based GFR estimating equations. In contrast, the incorporation of race or ancestry was not necessary to achieve similarly statistically unbiased (median difference, 0.33 ml per minute per 1.73 m2; 95% CI, -1.43 to 1.92) and accurate (P10, 41%; 95% CI, 34 to 49) estimates in Black participants when GFR was estimated with the use of cystatin C. CONCLUSIONS: The use of the serum creatinine level to estimate the GFR without race (or genetic ancestry) introduced systematic misclassification that could not be eliminated even when numerous non-GFR determinants of the serum creatinine level were accounted for. The estimation of GFR with the use of cystatin C generated similar results while eliminating the negative consequences of the current race-based approaches. (Funded by the National Institute of Diabetes and Digestive and Kidney Diseases and others.).

Metabolites Associated With Uremic Symptoms in Patients With CKD: Findings From the Chronic Renal Insufficiency Cohort (CRIC) Study.

RATIONALE & OBJECTIVE: The toxins that contribute to uremic symptoms in patients with chronic kidney disease (CKD) are unknown. We sought to apply complementary statistical modeling approaches to data from untargeted plasma metabolomic profiling to identify solutes associated with uremic symptoms in patients with CKD. STUDY DESIGN: Cross-sectional. SETTING & PARTICIPANTS: 1,761 Chronic Renal Insufficiency Cohort (CRIC) participants with CKD not treated with dialysis. PREDICTORS: Measurement of 448 known plasma metabolites. OUTCOMES: The uremic symptoms of fatigue, anorexia, pruritus, nausea, paresthesia, and pain were assessed by single items on the Kidney Disease Quality of Life-36 instrument. ANALYTICAL APPROACH: Multivariable adjusted linear regression, least absolute shrinkage and selection operator linear regression, and random forest models were used to identify metabolites associated with symptom severity. After adjustment for multiple comparisons, metabolites selected in at least 2 of the 3 modeling approaches were deemed "overall significant." RESULTS: Participant mean estimated glomerular filtration rate was 43mL/min/1.73m2, with 44% self-identifying as female and 41% as non-Hispanic Black. The prevalence of uremic symptoms ranged from 22% to 55%. We identified 17 metabolites for which a higher level was associated with greater severity of at least one uremic symptom and 9 metabolites inversely associated with uremic symptom severity. Many of these metabolites exhibited at least a moderate correlation with estimated glomerular filtration rate (Pearson's r≥0.5), and some were also associated with the risk of developing kidney failure or death in multivariable adjusted Cox regression models. LIMITATIONS: Lack of a second independent cohort for external validation of our findings. CONCLUSIONS: Metabolomic profiling was used to identify multiple solutes associated with uremic symptoms in adults with CKD, but future validation and mechanistic studies are needed. PLAIN-LANGUAGE SUMMARY: Individuals living with chronic kidney disease (CKD) often experience symptoms related to CKD, traditionally called uremic symptoms. It is likely that CKD results in alterations in the levels of numerous circulating substances that, in turn, cause uremic symptoms; however, the identity of these solutes is not known. In this study, we used metabolomic profiling in patients with CKD to gain insights into the pathophysiology of uremic symptoms. We identified 26 metabolites whose levels were significantly associated with at least one of the symptoms of fatigue, anorexia, itchiness, nausea, paresthesia, and pain. The results of this study lay the groundwork for future research into the biological causes of symptoms in patients with CKD.

Testican-2 Is Associated with Reduced Risk of Incident ESKD.

BACKGROUND: Testican-2 was recently identified as a podocyte-derived protein that is released into circulation by the kidneys and is positively correlated with eGFR and eGFR slope. However, whether higher testican-2 levels are associated with lower risk of ESKD is unknown. METHODS: Aptamer-based proteomics assessed blood testican-2 levels among participants in the African American Study of Kidney Disease and Hypertension (AASK, n =703), the Chronic Renal Insufficiency Cohort (CRIC) study ( n =3196), and the Atherosclerosis Risk in Communities (ARIC) study ( n =4378). We compared baseline characteristics by testican-2 tertile and used Cox proportional hazards models to study the association of testican-2 with incident ESKD. RESULTS: Higher testican-2 levels were associated with higher measured GFR (mGFR) in AASK, higher eGFR in the CRIC and ARIC studies, and lower albuminuria in all cohorts. Baseline testican-2 levels were significantly associated with incident ESKD in Cox proportional hazards models adjusted for age, sex, and race (model 1) and model 1+mGFR or eGFR+comorbidities (model 2). In model 3 (model 2+proteinuria), the associations between testican-2 (per SD increase) and incident ESKD were AASK (hazard ratio [HR]=0.84 [0.72 to 0.98], P =0.023), CRIC (HR=0.95 [0.89 to 1.02], P =0.14), ARIC (HR=0.54 [0.36 to 0.83], P =0.0044), and meta-analysis (HR=0.92 [0.86 to 0.98], P =0.0073). CONCLUSIONS: Across three cohorts spanning >8000 individuals, testican-2 is associated with kidney health and prognosis, with higher levels associated with reduced risk of ESKD.

Novel Genetic Variants Associated with Chronic Kidney Disease Progression.

SIGNIFICANCE STATEMENT: eGFR slope has been used as a surrogate outcome for progression of CKD. However, genetic markers associated with eGFR slope among patients with CKD were unknown. We aimed to identify genetic susceptibility loci associated with eGFR slope. A two-phase genome-wide association study identified single nucleotide polymorphisms (SNPs) in TPPP and FAT1-LINC02374 , and 22 of them were used to derive polygenic risk scores that mark the decline of eGFR by disrupting binding of nearby transcription factors. This work is the first to identify the impact of TPPP and FAT1-LINC02374 on CKD progression, providing predictive markers for the decline of eGFR in patients with CKD. BACKGROUND: The incidence of CKD is associated with genetic factors. However, genetic markers associated with the progression of CKD have not been fully elucidated. METHODS: We conducted a genome-wide association study among 1738 patients with CKD, mainly from the KoreaN cohort study for Outcomes in patients With CKD. The outcome was eGFR slope. We performed a replication study for discovered single nucleotide polymorphisms (SNPs) with P <10 -6 in 2498 patients with CKD from the Chronic Renal Insufficiency Cohort study. Several expression quantitative trait loci (eQTL) studies, pathway enrichment analyses, exploration of epigenetic architecture, and predicting disruption of transcription factor (TF) binding sites explored potential biological implications of the loci. We developed and evaluated the effect of polygenic risk scores (PRS) on incident CKD outcomes. RESULTS: SNPs in two novel loci, TPPP and FAT1-LINC02374 , were replicated (rs59402340 in TPPP , Pdiscovery =7.11×10 -7 , PCRIC =8.13×10 -4 , Pmeta =7.23×10 -8 ; rs28629773 in FAT1-LINC02374 , Pdiscovery =6.08×10 -7 , PCRIC =4.33×10 -2 , Pmeta =1.87×10 -7 ). The eQTL studies revealed that the replicated SNPs regulated the expression level of nearby genes associated with kidney function. Furthermore, these SNPs were near gene enhancer regions and predicted to disrupt the binding of TFs. PRS based on the independently significant top 22 SNPs were significantly associated with CKD outcomes. CONCLUSIONS: This study demonstrates that SNP markers in the TPPP and FAT1-LINC02374 loci could be predictive markers for the decline of eGFR in patients with CKD.

Genomic Disorders in CKD across the Lifespan.

SIGNIFICANCE STATEMENT: Pathogenic structural genetic variants, also known as genomic disorders, have been associated with pediatric CKD. This study extends those results across the lifespan, with genomic disorders enriched in both pediatric and adult patients compared with controls. In the Chronic Renal Insufficiency Cohort study, genomic disorders were also associated with lower serum Mg, lower educational performance, and a higher risk of death. A phenome-wide association study confirmed the link between kidney disease and genomic disorders in an unbiased way. Systematic detection of genomic disorders can provide a molecular diagnosis and refine prediction of risk and prognosis. BACKGROUND: Genomic disorders (GDs) are associated with many comorbid outcomes, including CKD. Identification of GDs has diagnostic utility. METHODS: We examined the prevalence of GDs among participants in the Chronic Kidney Disease in Children (CKiD) cohort II ( n =248), Chronic Renal Insufficiency Cohort (CRIC) study ( n =3375), Columbia University CKD Biobank (CU-CKD; n =1986), and the Family Investigation of Nephropathy and Diabetes (FIND; n =1318) compared with 30,746 controls. We also performed a phenome-wide association analysis (PheWAS) of GDs in the electronic MEdical Records and GEnomics (eMERGE; n =11,146) cohort. RESULTS: We found nine out of 248 (3.6%) CKiD II participants carried a GD, replicating prior findings in pediatric CKD. We also identified GDs in 72 out of 6679 (1.1%) adult patients with CKD in the CRIC, CU-CKD, and FIND cohorts, compared with 199 out of 30,746 (0.65%) GDs in controls (OR, 1.7; 95% CI, 1.3 to 2.2). Among adults with CKD, we found recurrent GDs at the 1q21.1, 16p11.2, 17q12, and 22q11.2 loci. The 17q12 GD (diagnostic of renal cyst and diabetes syndrome) was most frequent, present in 1:252 patients with CKD and diabetes. In the PheWAS, dialysis and neuropsychiatric phenotypes were the top associations with GDs. In CRIC participants, GDs were associated with lower serum magnesium, lower educational achievement, and higher mortality risk. CONCLUSION: Undiagnosed GDs are detected both in children and adults with CKD. Identification of GDs in these patients can enable a precise genetic diagnosis, inform prognosis, and help stratify risk in clinical studies. GDs could also provide a molecular explanation for nephropathy and comorbidities, such as poorer neurocognition for a subset of patients.

Proteomic cardiovascular risk assessment in chronic kidney disease.

AIMS: Chronic kidney disease (CKD) is widely prevalent and independently increases cardiovascular risk. Cardiovascular risk prediction tools derived in the general population perform poorly in CKD. Through large-scale proteomics discovery, this study aimed to create more accurate cardiovascular risk models. METHODS AND RESULTS: Elastic net regression was used to derive a proteomic risk model for incident cardiovascular risk in 2182 participants from the Chronic Renal Insufficiency Cohort. The model was then validated in 485 participants from the Atherosclerosis Risk in Communities cohort. All participants had CKD and no history of cardiovascular disease at study baseline when ∼5000 proteins were measured. The proteomic risk model, which consisted of 32 proteins, was superior to both the 2013 ACC/AHA Pooled Cohort Equation and a modified Pooled Cohort Equation that included estimated glomerular filtrate rate. The Chronic Renal Insufficiency Cohort internal validation set demonstrated annualized receiver operating characteristic area under the curve values from 1 to 10 years ranging between 0.84 and 0.89 for the protein and 0.70 and 0.73 for the clinical models. Similar findings were observed in the Atherosclerosis Risk in Communities validation cohort. For nearly half of the individual proteins independently associated with cardiovascular risk, Mendelian randomization suggested a causal link to cardiovascular events or risk factors. Pathway analyses revealed enrichment of proteins involved in immunologic function, vascular and neuronal development, and hepatic fibrosis. CONCLUSION: In two sizeable populations with CKD, a proteomic risk model for incident cardiovascular disease surpassed clinical risk models recommended in clinical practice, even after including estimated glomerular filtration rate. New biological insights may prioritize the development of therapeutic strategies for cardiovascular risk reduction in the CKD population.

Low concentrations of medium-sized HDL particles predict incident CVD in chronic kidney disease patients.

Patients with chronic kidney disease (CKD) are at high risk for CVD. However, traditional CVD risk factors cannot completely explain the increased risk. Altered HDL proteome is linked with incident CVD in CKD patients, but it is unclear whether other HDL metrics are associated with incident CVD in this population. In the current study, we analyzed samples from two independent prospective case-control cohorts of CKD patients, the Clinical Phenotyping and Resource Biobank Core (CPROBE) and the Chronic Renal Insufficiency Cohort (CRIC). We measured HDL particle sizes and concentrations (HDL-P) by calibrated ion mobility analysis and HDL cholesterol efflux capacity (CEC) by cAMP-stimulated J774 macrophages in 92 subjects from the CPROBE cohort (46 CVD and 46 controls) and in 91 subjects from the CRIC cohort (34 CVD and 57 controls). We tested associations of HDL metrics with incident CVD using logistic regression analysis. No significant associations were found for HDL-C or HDL-CEC in either cohort. Total HDL-P was only negatively associated with incident CVD in the CRIC cohort in unadjusted analysis. Among the six sized HDL subspecies, only medium-sized HDL-P was significantly and negatively associated with incident CVD in both cohorts after adjusting for clinical confounders and lipid risk factors with odds ratios (per 1-SD) of 0.45 (0.22-0.93, P = 0.032) and 0.42 (0.20-0.87, P = 0.019) for CPROBE and CRIC cohorts, respectively. Our observations indicate that medium-sized HDL-P-but not other-sized HDL-P or total HDL-P, HDL-C, or HDL-CEC-may be a prognostic cardiovascular risk marker in CKD.

Excess risk of cardiovascular events in patients in the United States vs. Japan with chronic kidney disease is mediated mainly by left ventricular structure and function.

While patients receiving dialysis therapy in the United States are more likely to develop cardiovascular disease (CVD) than those in Japan, direct comparisons of patients with predialysis chronic kidney disease (CKD) are rare. To study this, we compared various outcomes in patients with predialysis CKD using data from the Chronic Renal Insufficiency Cohort (CRIC) and CKD Japan Cohort (CKD-JAC) studies and determined mediators of any differences. Candidate mediators included left ventricular (LV) indices assessed by echocardiography. Among 3125 CRIC and 1097 CKD-JAC participants, the mean LV mass index (LVMI) and ejection fraction (EF) were 55.7 and 46.6 g/m2 and 54% and 65%, respectively (both significant). The difference in body mass index (32 and 24 kg/m2, respectively) largely accounted for the differences in LVMI and C-reactive protein levels across cohorts. Low EF and high LVMI were significantly associated with subsequent CVD in both cohorts. During a median follow-up of five years, CRIC participants were at higher risk for CVD (adjusted hazard ratio [95% confidence interval]: 3.66 [2.74-4.89]) and death (4.69 [3.05-7.19]). A three-fold higher C-reactive protein concentration and higher phosphate levels in the United States cohort were moderately strong mediators of the differences in CVD. However, echocardiographic parameters were stronger mediators than these laboratory measures. LVMI, EF and their combination mediated the observed difference in CVD (27%, 50%, and 57%, respectively) and congestive heart failure (33%, 62%, and 70%, respectively). Thus, higher LV mass and lower EF, even in the normal range, were found to be predictive of CVD in CKD.

Association of urine and plasma ADMA with atherosclerotic risk in DKD cardiovascular disease risk in diabetic kidney disease: findings from the Chronic Renal Insufficiency Cohort (CRIC) study.

BACKGROUND: Chronic kidney disease (CKD) is associated with atherosclerotic cardiovascular disease (ASCVD) risk, especially among those with diabetes. Altered metabolism of solutes that accumulate in CKD [asymmetric dimethylarginine (ADMA), symmetric dimethylarginine (SDMA) and trimethylamine N-oxide (TMAO)] may reflect pathways linking CKD with ASCVD. METHODS: This case-cohort study included Chronic Renal Insufficiency Cohort participants with baseline diabetes, estimated glomerular filtration rate <60 mL/min/1.73 m2, and without prior history for each outcome. The primary outcome was incident ASCVD (time to first myocardial infarction, stroke or peripheral artery disease event) and secondary outcome was incident heart failure. The subcohort comprised randomly selected participants meeting entry criteria. Plasma and urine ADMA, SDMA and TMAO concentrations were determined by liquid chromatography-tandem mass spectrometry. Associations of uremic solute plasma concentrations and urinary fractional excretions with outcomes were evaluated by weighted multivariable Cox regression models, adjusted for confounding covariables. RESULTS: Higher plasma ADMA concentrations (per standard deviation) were associated with ASCVD risk [hazard ratio (HR) 1.30, 95% confidence interval (CI) 1.01-1.68]. Lower fractional excretion of ADMA (per standard deviation) was associated with ASCVD risk (HR 1.42, 95% CI 1.07-1.89). The lowest quartile of ADMA fractional excretion was associated with greater ASCVD risk (HR 2.25, 95% CI 1.08-4.69) compared with the highest quartile. Plasma SDMA and TMAO concentration and fractional excretion were not associated with ASCVD. Neither plasma nor fractional excretion of ADMA, SDMA and TMAO were associated with incident heart failure. CONCLUSION: These data suggest that decreased kidney excretion of ADMA leads to increased plasma concentrations and ASCVD risk.

Systematic integrated analysis of genetic and epigenetic variation in diabetic kidney disease.

Poor metabolic control and host genetic predisposition are critical for diabetic kidney disease (DKD) development. The epigenome integrates information from sequence variations and metabolic alterations. Here, we performed a genome-wide methylome association analysis in 500 subjects with DKD from the Chronic Renal Insufficiency Cohort for DKD phenotypes, including glycemic control, albuminuria, kidney function, and kidney function decline. We show distinct methylation patterns associated with each phenotype. We define methylation variations that are associated with underlying nucleotide variations (methylation quantitative trait loci) and show that underlying genetic variations are important drivers of methylation changes. We implemented Bayesian multitrait colocalization analysis (moloc) and summary data-based Mendelian randomization to systematically annotate genomic regions that show association with kidney function, methylation, and gene expression. We prioritized 40 loci, where methylation and gene-expression changes likely mediate the genotype effect on kidney disease development. Functional annotation suggested the role of inflammation, specifically, apoptotic cell clearance and complement activation in kidney disease development. Our study defines methylation changes associated with DKD phenotypes, the key role of underlying genetic variations driving methylation variations, and prioritizes methylome and gene-expression changes that likely mediate the genotype effect on kidney disease pathogenesis.

Socioeconomic characteristics of those with peripheral artery disease in the chronic renal insufficiency cohort.

BACKGROUND: The association between socioeconomic factors and peripheral arterial disease (PAD) has not been as well characterized as other cardiovascular conditions. We sought to define how annual income and education level are associated with PAD in a well-characterized diverse set of adults with chronic kidney disease (CKD). METHODS: The Chronic Renal Insufficiency Cohort Study (CRIC) is a multi-center, prospective cohort study designed to examine risk factors for progression of CKD and cardiovascular disease. Demographic, income, and education-level data, as well as clinical data including ankle-brachial index (ABI) were collected at baseline. Annual income was categorized as < $25,000, $25,000-50,000, $50,000-100,000, or above $100,000; educational level was categorized as some high school, high school graduate, some college, or college graduate. Participants with missing income data or incompressible ABI (>1.4) were excluded from initial analysis. Logistic regression was used to estimate the association of income and/or education level with PAD, defined as an enrollment ABI of <0.90, history of PAD, or history of PAD intervention. RESULTS: A total of 4122 were included, mean age of participants was 59.5 years, 56% were male, and 44% were Black. There were 763 CRIC participants with PAD at study enrollment (18.5%). In the final multivariable logistic regression model, Black race (OR = 1.3, 95% CI 1.1-1.6, p = 0.004) and level of annual household income remained independently associated with PAD at the time of enrollment (income <$25,000 OR = 1.9, 95% CI 1.3-2.8, p < 0.001; income $25,000-50,000 OR = 1.6, 95% CI 1.1-2.3, p = 0.011; income $50,000-100,000 OR = 1.2, 95% CI 0.9-1.8, p = 0.246), relative to a baseline annual income of >$100,000 (overall p-value <0.001). Decreasing level of educational attainment was not independently associated with increased PAD at enrollment, but lower level of educational attainment was associated with increased PAD when income data was not adjusted for (p = 0.001). Interestingly, Black race (OR = 0.7, 95% CI 0.6-0.8, p < 0.001), female gender (OR = 0.8, 95% CI 0.7-0.9, p = 0.007), and income <$25,000 (OR = 0.7, 95% CI 0.5-0.9, p = 0.008) were significantly associated with decreased statin use even after controlling for cardiovascular conditions. CONCLUSIONS: In this prospectively followed CKD cohort, lower annual household income and Black race were significantly associated with increased PAD at study enrollment. In contrast, educational level was not associated with PAD when adjusted for patient income data. Black race, female gender, and low income were independently associated with decreased statin use, populations which could be targets of future public health programs.

Risk Prediction Models for Atherosclerotic Cardiovascular Disease in Patients with Chronic Kidney Disease: The CRIC Study.

BACKGROUND: Individuals with CKD may be at high risk for atherosclerotic cardiovascular disease (ASCVD). However, there are no ASCVD risk prediction models developed in CKD populations to inform clinical care and prevention. METHODS: We developed and validated 10-year ASCVD risk prediction models in patients with CKD that included participants without self-reported cardiovascular disease from the Chronic Renal Insufficiency Cohort (CRIC) study. ASCVD was defined as the first occurrence of adjudicated fatal and nonfatal stroke or myocardial infarction. Our models used clinically available variables and novel biomarkers. Model performance was evaluated based on discrimination, calibration, and net reclassification improvement. RESULTS: Of 2604 participants (mean age 55.8 years; 52.0% male) included in the analyses, 252 had incident ASCVD within 10 years of baseline. Compared with the American College of Cardiology/American Heart Association pooled cohort equations (area under the receiver operating characteristic curve [AUC]=0.730), a model with coefficients estimated within the CRIC sample had higher discrimination (P=0.03), achieving an AUC of 0.736 (95% confidence interval [CI], 0.649 to 0.826). The CRIC model developed using clinically available variables had an AUC of 0.760 (95% CI, 0.678 to 0.851). The CRIC biomarker-enriched model had an AUC of 0.771 (95% CI, 0.674 to 0.853), which was significantly higher than the clinical model (P=0.001). Both the clinical and biomarker-enriched models were well-calibrated and improved reclassification of nonevents compared with the pooled cohort equations (6.6%; 95% CI, 3.7% to 9.6% and 10.0%; 95% CI, 6.8% to 13.3%, respectively). CONCLUSIONS: The 10-year ASCVD risk prediction models developed in patients with CKD, including novel kidney and cardiac biomarkers, performed better than equations developed for the general population using only traditional risk factors.

Using Machine Learning to Identify Metabolomic Signatures of Pediatric Chronic Kidney Disease Etiology.

BACKGROUND: Untargeted plasma metabolomic profiling combined with machine learning (ML) may lead to discovery of metabolic profiles that inform our understanding of pediatric CKD causes. We sought to identify metabolomic signatures in pediatric CKD based on diagnosis: FSGS, obstructive uropathy (OU), aplasia/dysplasia/hypoplasia (A/D/H), and reflux nephropathy (RN). METHODS: Untargeted metabolomic quantification (GC-MS/LC-MS, Metabolon) was performed on plasma from 702 Chronic Kidney Disease in Children study participants (n: FSGS=63, OU=122, A/D/H=109, and RN=86). Lasso regression was used for feature selection, adjusting for clinical covariates. Four methods were then applied to stratify significance: logistic regression, support vector machine, random forest, and extreme gradient boosting. ML training was performed on 80% total cohort subsets and validated on 20% holdout subsets. Important features were selected based on being significant in at least two of the four modeling approaches. We additionally performed pathway enrichment analysis to identify metabolic subpathways associated with CKD cause. RESULTS: ML models were evaluated on holdout subsets with receiver-operator and precision-recall area-under-the-curve, F1 score, and Matthews correlation coefficient. ML models outperformed no-skill prediction. Metabolomic profiles were identified based on cause. FSGS was associated with the sphingomyelin-ceramide axis. FSGS was also associated with individual plasmalogen metabolites and the subpathway. OU was associated with gut microbiome-derived histidine metabolites. CONCLUSION: ML models identified metabolomic signatures based on CKD cause. Using ML techniques in conjunction with traditional biostatistics, we demonstrated that sphingomyelin-ceramide and plasmalogen dysmetabolism are associated with FSGS and that gut microbiome-derived histidine metabolites are associated with OU.

Trans-ethnic genome-wide association study of blood metabolites in the Chronic Renal Insufficiency Cohort (CRIC) study.

Metabolomics genome wide association study (GWAS) help outline the genetic contribution to human metabolism. However, studies to date have focused on relatively healthy, population-based samples of White individuals. Here, we conducted a GWAS of 537 blood metabolites measured in the Chronic Renal Insufficiency Cohort (CRIC) Study, with separate analyses in 822 White and 687 Black study participants. Trans-ethnic meta-analysis was then applied to improve fine-mapping of potential causal variants. Mean estimated glomerular filtration rate was 44.4 and 41.5 mL/min/1.73m2 in the White and Black participants, respectively. There were 45 significant metabolite associations at 19 loci, including novel associations at PYROXD2, PHYHD1, FADS1-3, ACOT2, MYRF, FAAH, and LIPC. The strength of associations was unchanged in models additionally adjusted for estimated glomerular filtration rate and proteinuria, consistent with a direct biochemical effect of gene products on associated metabolites. At several loci, trans-ethnic meta-analysis, which leverages differences in linkage disequilibrium across populations, reduced the number and/or genomic interval spanned by potentially causal single nucleotide polymorphisms compared to fine-mapping in the White participant cohort alone. Across all validated associations, we found strong concordance in effect sizes of the potentially causal single nucleotide polymorphisms between White and Black study participants. Thus, our study identifies novel genetic determinants of blood metabolites in chronic kidney disease, demonstrates the value of diverse cohorts to improve causal inference in metabolomics GWAS, and underscores the shared genetic basis of metabolism across race.

Heart failure-type symptom scores in chronic kidney disease: The importance of body mass index.

OBJECTIVES: This analysis sought to determine factors (including adiposity-related factors) most associated with HF-type symptoms (fatigue, shortness of breath, and edema) in adults with chronic kidney disease (CKD). BACKGROUND: Symptom burden impairs quality of life in CKD, especially symptoms that overlap with HF. These symptoms are common regardless of clinical HF diagnosis, and may be affected by subtle cardiac dysfunction, kidney dysfunction, and other factors. We used machine learning to investigate cross-sectional relationships of clinical variables with symptom scores in a CKD cohort. METHODS: Participants in the Chronic Renal Insufficiency Cohort (CRIC) with a baseline modified Kansas City Cardiomyopathy Questionnaire (KCCQ) score were included, regardless of prior HF diagnosis. The primary outcome was Overall Summary Score as a continuous measure. Predictors were 99 clinical variables representing demographic, cardiac, kidney and other health dimensions. A correlation filter was applied. Random forest regression models were fitted. Variable importance scores and adjusted predicted outcomes are presented. RESULTS: The cohort included 3426 individuals, 10.3% with prior HF diagnosis. BMI was the most important factor, with BMI 24.3 kg/m2 associated with the least symptoms. Symptoms worsened with higher or lower BMIs, with a potentially clinically relevant 5 point score decline at 35.7 kg/m2 and a 1-point decline at the threshold for low BMI, 18.5 kg/m2. The most important cardiac and kidney factors were heart rate and eGFR, the 4th and 5th most important variables, respectively. Results were similar for secondary analyses. CONCLUSIONS: In a CKD cohort, BMI was the most important feature for explaining HF-type symptoms regardless of clinical HF diagnosis, identifying an important focus for symptom directed investigations.

Association of Uremic Solutes With Cardiovascular Death in Diabetic Kidney Disease.

RATIONALE & OBJECTIVE: Cardiovascular disease (CVD) is a major cause of mortality among people with diabetic kidney disease (DKD). The pathophysiology is inadequately explained by traditional CVD risk factors. The uremic solutes trimethylamine-N-oxide (TMAO) and asymmetric and symmetric dimethylarginine (ADMA, SDMA) have been linked to CVD in kidney failure with replacement therapy (KFRT), but data are limited in populations with diabetes and less severe kidney disease. STUDY DESIGN: Observational cohort. SETTINGS & PARTICIPANTS: Random subcohort of 555 REGARDS (Reasons for Geographic and Racial Differences in Stroke) study participants with diabetes and estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2 at study entry. EXPOSURE: ADMA, SDMA, and TMAO assayed by liquid chromatography-mass spectrometry in plasma and urine. OUTCOME: Cardiovascular mortality (primary outcome); all-cause mortality and incident KFRT (secondary outcomes). ANALYTICAL APPROACH: Plasma concentrations and ratios of urine to plasma concentrations of ADMA, SDMA, and TMAO were tested for association with outcomes. Adjusted Cox regression models were fitted and hazard ratios of outcomes calculated per standard deviation and per doubling, and as interquartile comparisons. RESULTS: The mean baseline eGFR was 44 mL/min/1.73 m2. Cardiovascular death, overall mortality, and KFRT occurred in 120, 285, and 89 participants, respectively, during a mean 6.2 years of follow-up. Higher plasma ADMA and SDMA (HRs of 1.20 and 1.28 per 1-SD greater concentration), and lower ratios of urine to plasma concentrations of ADMA, SDMA, and TMAO (HRs per halving of 1.53, 1.69, and 1.38) were associated with cardiovascular mortality. Higher plasma concentrations of ADMA, SDMA, and TMAO (HRs of 1.31, 1.42, and 1.13 per 1-SD greater concentration) and lower urine to plasma ratios of ADMA, SDMA, and TMAO (HRs per halving of 1.34, 1.37, and 1.26) were associated with all-cause mortality. Higher plasma ADMA and SDMA were associated with incident KFRT by categorical comparisons (HRs of 2.75 and 2.96, comparing quartile 4 to quartile 1), but not in continuous analyses. LIMITATIONS: Single cohort, restricted to patients with diabetes and eGFR < 60 mL/min/1.73 m2, potential residual confounding by GFR, no dietary information. CONCLUSIONS: Higher plasma concentrations and lower ratios of urine to plasma concentrations of uremic solutes were independently associated with cardiovascular and all-cause mortality in DKD. Associations of ratios of urine to plasma concentrations with mortality suggest a connection between renal uremic solute clearance and CVD pathogenesis.

Black and White Adults With CKD Hospitalized With Acute Kidney Injury: Findings From the Chronic Renal Insufficiency Cohort (CRIC) Study.

RATIONALE & OBJECTIVE: Few studies have investigated racial disparities in acute kidney injury (AKI), in contrast to the extensive literature on racial differences in the risk of kidney failure. We sought to study potential differences in risk in the setting of chronic kidney disease (CKD). STUDY DESIGN: Prospective cohort study. SETTING & PARTICIPANTS: We studied 2,720 self-identified Black or White participants with CKD enrolled in the Chronic Renal Insufficiency Cohort (CRIC) Study from July 1, 2013, to December 31, 2017. EXPOSURE: Self-reported race (Black vs White). OUTCOME: Hospitalized AKI (≥50% increase from nadir to peak serum creatinine). ANALYTICAL APPROACH: Cox regression models adjusting for demographics (age and sex), prehospitalization clinical risk factors (diabetes, blood pressure, cardiovascular disease, estimated glomerular filtration rate, proteinuria, receipt of angiotensin-converting enzyme inhibitors or angiotensin-receptor blockers), and socioeconomic status (insurance status and education level). In a subset of participants with genotype data, we adjusted for apolipoprotein L1 gene (APOL1) high-risk status and sickle cell trait. RESULTS: Black participants (n = 1,266) were younger but had a higher burden of prehospitalization clinical risk factors. The incidence rate of first AKI hospitalization among Black participants was 6.3 (95% CI, 5.5-7.2) per 100 person-years versus 5.3 (95% CI, 4.6-6.1) per 100 person-years among White participants. In an unadjusted Cox regression model, Black participants were at a modestly increased risk of incident AKI (HR, 1.22 [95% CI, 1.01-1.48]) compared with White participants. However, this risk was attenuated and no longer significant after adjusting for prehospitalization clinical risk factors (adjusted HR, 1.02 [95% CI, 0.83-1.25]). There were only 11 AKI hospitalizations among individuals with high-risk APOL1 risk status and 14 AKI hospitalizations among individuals with sickle cell trait. LIMITATIONS: Participants were limited to research volunteers and potentially not fully representative of all CKD patients. CONCLUSIONS: In this multicenter prospective cohort of CKD patients, racial disparities in AKI incidence were modest and were explained by differences in prehospitalization clinical risk factors.

Time-Updated Changes in Estimated GFR and Proteinuria and Major Adverse Cardiac Events: Findings from the Chronic Renal Insufficiency Cohort (CRIC) Study.

RATIONALE & OBJECTIVE: Evaluating repeated measures of estimated glomerular filtration rate (eGFR) and urinary protein-creatinine ratio (UPCR) over time may enhance our ability to understand the association between changes in kidney parameters and cardiovascular disease risk. STUDY DESIGN: Prospective cohort study. SETTING & PARTICIPANTS: Annual visit data from 2,438 participants in the Chronic Renal Insufficiency Cohort (CRIC). EXPOSURES: Average and slope of eGFR and UPCR in time-updated, 1-year exposure windows. OUTCOMES: Incident heart failure, atherosclerotic cardiovascular disease events, death, and a composite of incident heart failure, atherosclerotic cardiovascular disease events, and death. ANALYTICAL APPROACH: A landmark analysis, a dynamic approach to survival modeling that leverages longitudinal, iterative profiles of laboratory and clinical information to assess the time-updated 3-year risk of adverse cardiovascular outcomes. RESULTS: Adjusting for baseline and time-updated covariates, every standard deviation lower mean eGFR (19mL/min/1.73m2) and declining slope of eGFR (8mL/min/1.73m2 per year) were independently associated with higher risks of heart failure (hazard ratios [HRs] of 1.82 [95% CI, 1.39-2.44] and 1.28 [95% CI, 1.12-1.45], respectively) and the composite outcome (HRs of 1.32 [95% CI, 1.11-1.54] and 1.11 [95% CI, 1.03-1.20], respectively). Every standard deviation higher mean UPCR (136mg/g) and increasing UPCR (240mg/g per year) were also independently associated with higher risks of heart failure (HRs of 1.58 [95% CI, 1.28-1.97] and 1.20 [95% CI, 1.10-1.29], respectively) and the composite outcome (HRs of 1.33 [95% CI, 1.17-1.50] and 1.12 [95% CI, 1.06-1.18], respectively). LIMITATIONS: Limited generalizability of annual eGFR and UPCR assessments; several biomarkers for cardiovascular disease risk were not available annually. CONCLUSIONS: Using the landmark approach to account for time-updated patterns of kidney function, average and slope of eGFR and proteinuria were independently associated with 3-year cardiovascular risk. Short-term changes in kidney function provide information about cardiovascular risk incremental to level of kidney function, representing possible opportunities for more effective management of patients with chronic kidney disease.