Harold I. Feldman

Key Points: Using proteomics and Mendelian randomization, we identified causal proteins for the development of atrial fibrillation in patients with CKD. Immunologic function, cell growth, and neural development were common biologic pathways underlying atrial fibrillation and left atrial enlargement. Machine learning led to a nine-protein risk model that had a similar predictive performance for incident atrial fibrillation as clinical risk scores. Background: CKD is strongly associated with atrial fibrillation. Understanding the biologic pathways for this association and creating predictive models have been challenging. Left atrial enlargement is a substrate for atrial fibrillation, but any overlap between biomarkers of atrial fibrillation and left atrial enlargement in patients with CKD is unknown. Methods: We evaluated 4590 plasma proteins with SomaScan in two cohorts of adult patients with CKD: the Chronic Renal Insufficiency Cohort (CRIC; n =2654) and the Atherosclerosis Risk in Communities cohort (ARIC; n =1326). Using Mendelian randomization, we identified proteins along the causal pathway to atrial fibrillation. We also identified proteins and corresponding pathways associated with larger echocardiographic left atrial size, a recognized substrate for atrial fibrillation. Finally, we developed and validated a multiprotein risk score for incident atrial fibrillation in the CKD population. Results: Over 5 years, incident atrial fibrillation occurred among 150 patients in the CRIC and 140 in the ARIC cohort. We identified three proteins causally linked to incident atrial fibrillation: neural EGF like‑like protein 1, cartilage intermediate layer protein 2, and matrix metallopeptidase 12. Pathway analysis revealed an overlap in eight of the top ten canonical pathways for incident atrial fibrillation and left atrial enlargement. A risk model for incident atrial fibrillation composed of proteins had annualized areas under the receiver-operating characteristic curve over 5 years ranging from 0.65 to 0.76, a performance similar to the Cohorts for Heart and Aging Research in Genomic Epidemiology Atrial Fibrillation (CHARGE-AF) clinical risk score. Conclusions: This study identified causal proteins and biologic mechanisms underlying incident atrial fibrillation in CKD. A proteomic risk score for incident atrial fibrillation in CKD performed similarly to the CHARGE-AF clinical risk score.

Key Points: Family history of kidney disease was not associated with kidney disease progression in the context of established CKD. Family history of diabetes was a risk factor of CKD progression independently of diabetes status, polygenic risk, and traditional risk factors. Background: A family history of health conditions may reflect shared genetic and/or environmental risk. It is not well known to what extent family history affects outcomes among patients with CKD. In this study, we investigated the associations of family history of CKD, diabetes, and other conditions with common comorbidities and kidney disease progression among patients with CKD. Methods: We performed an observational study of two prospective CKD cohorts, 2573 adults and children from the Cure Glomerulopathy Network and 3939 Chronic Renal Insufficiency Cohort adult participants. Self-reported first-degree family history of CKD, diabetes, and other common diseases was tested for associations with the risk of comorbidities and CKD progression using multivariable models. Results: Family history of common comorbid conditions was associated with higher risk of these conditions in the context of CKD, including approximately by over three-fold for diabetes (adjusted odds ratio [OR], 3.37; 95% confidence interval [CI], 2.73 to 4.15), 48% for cancer (adjusted OR, 1.48; 95% CI, 1.05 to 2.09), and 69% for cardiovascular disease (adjusted OR, 1.69; 95% CI, 1.36 to 2.10 in combined cohorts). While polygenic risk score (PRS) for CKD was associated with kidney disease progression (adjusted hazards ratio, 1.11; 95% CI, 1.06 to 1.16 in combined cohorts), family history of kidney disease was not an independent risk factor of disease progression in the context of existing CKD. By contrast, family history of diabetes was significantly associated with a higher risk of CKD progression independently of diabetes occurrence or PRS for diabetes (adjusted hazards ratio, 1.19; 95% CI, 1.05 to 1.35 in combined cohorts). Conclusions: Broad collection of family history in the context of CKD improved clinical risk stratification. Family history of diabetes was consistently associated with a higher risk of CKD progression independently of diabetes status or PRS for diabetes in both cohorts.

Key Points: Machine learning and large-scale proteomics led to a 16-protein secondary cardiovascular risk model in patients with CKD. Hepatic fibrosis and liver X receptor activation represented biologic pathways that link kidney disease and risk of secondary cardiovascular events. An understanding of the circulating proteins associated with secondary cardiovascular events may help to identify novel therapeutic targets. Background: Cardiovascular risk models have been developed primarily for incident events. Well-performing models are lacking to predict secondary cardiovascular events among people with a history of coronary heart disease, stroke, or heart failure who also have CKD. We sought to develop a proteomic risk score for cardiovascular events in individuals with CKD and a history of cardiovascular disease. Methods: We measured 4638 plasma proteins among 1067 participants from the Chronic Renal Insufficiency Cohort (CRIC) and 536 individuals from the Atherosclerosis Risk in Communities (ARIC) Cohort. All had non–dialysis-dependent CKD and coronary heart disease, heart failure, or stroke at study baseline. A proteomic risk model for secondary cardiovascular events was derived by elastic net regression in CRIC, validated in ARIC, and compared with clinical models. Biologic mechanisms of secondary events were characterized through proteomic pathway analysis. Results: A 16-protein risk model was superior to the Framingham Risk Score for secondary events, including a modified score that included eGFR. In CRIC, the annualized area under the receiver operating characteristic curve (area under the curve) within 1–5 years ranged between 0.77 and 0.80 for the protein model and 0.57 and 0.72 for the clinical models. These findings were replicated in the ARIC validation cohort. Biologic pathway analysis identified pathways and proteins for cardiac remodeling and fibrosis, vascular disease, and thrombosis. Conclusions: The proteomic risk model for secondary cardiovascular events outperformed clinical models on the basis of traditional risk factors and eGFR.

Key Points: Support programs for self-management are underutilized among people with CKD. Implementing a smartphone support tool for self-monitoring physical activity and BP was feasible among people with CKD and hypertension. Despite low digital health literacy, Supporting Self-Management of Healthy Behaviors was observed to be readily usable because of high levels of adherence and usability scores. Background: Support programs for self-management are underutilized among people with CKD. We examined the feasibility of a smartphone-based intervention to support physical activity and BP monitoring, Supporting Self-Management of Healthy Behaviors (SMART-HABITS), for individuals with CKD and hypertension. Methods: SMART-HABITS was piloted in a 12-week randomized cross-over trial among people with CKD and hypertension. Participants were asked to monitor BP ≥3-times/wk and step counts ≥5-times/wk. Participants were randomized to BP communication approach–self-report through text message for 6 weeks versus automatic reporting with a smartphone application (app) paired to a Bluetooth enabled BP machine for the alternate 6 weeks. The approach to monitoring and reporting steps was the same during both phases. Primary outcomes were adoption (retention and use of SMART-HABITS dashboard), adherence (% of transmitted BP and step counts), and acceptability as assessed with surveys and interviews. Secondary outcomes were reach, maintenance, CKD knowledge, digital health literacy, self-management, self-efficacy, quality of life, step counts, and BP values. Interviews were conducted at study end. Results: Of the 47 randomized participants, 44 (94%) completed the text phase and 43 (92%) completed the app phase. The median age was 63 years, 49% were female, and 45% were Black. Retention was 91%. BP adherence was 87% in the text phase and 74% in the app phase, and step count adherence was 97%. Acceptability scores were high, and interviews largely conveyed acceptance. CKD knowledge increased but remaining survey scores did not change. Mean step counts increased from the prestudy period similarly in both phases. BP did not change over time. Conclusions: Implementing a smartphone support tool for self-management was feasible among people with CKD and hypertension. The approach can supplement clinic-based care and potentially lead to less cardiovascular disease and CKD progression. Clinical Trial registry name and registration number: NCT04858295. Podcast: This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2024_09_23_CJASNSeptember19992.mp3

Key Points: We provide an atlas of cross-sectional and longitudinal serum and urine metabolite associations with eGFR and urine albumin-creatinine ratio in an older community-based cohort. Metabolic profiling in serum and urine provides distinct and complementary insights into disease. Background: Metabolites represent a read-out of cellular processes underlying states of health and disease. Methods: We evaluated cross-sectional and longitudinal associations between 1255 serum and 1398 urine known and unknown (denoted with “X” in name) metabolites (Metabolon HD4, 721 detected in both biofluids) and kidney function in 1612 participants of the Atherosclerosis Risk in Communities study. All analyses were adjusted for clinical and demographic covariates, including for baseline eGFR and urine albumin-creatinine ratio (UACR) in longitudinal analyses. Results: At visit 5 of the Atherosclerosis Risk in Communities study, the mean age of participants was 76 years (SD 6); 56% were women, mean eGFR was 62 ml/min per 1.73 m² (SD 20), and median UACR level was 13 mg/g (interquartile range, 25). In cross-sectional analysis, 675 serum and 542 urine metabolites were associated with eGFR (Bonferroni-corrected P < 4.0E-5 for serum analyses and P < 3.6E-5 for urine analyses), including 248 metabolites shared across biofluids. Fewer metabolites (75 serum and 91 urine metabolites, including seven metabolites shared across biofluids) were cross-sectionally associated with albuminuria. Guanidinosuccinate; N2,N2-dimethylguanosine; hydroxy-N6,N6,N6-trimethyllysine; X-13844; and X-25422 were significantly associated with both eGFR and albuminuria. Over a mean follow-up of 6.6 years, serum mannose (hazard ratio [HR], 2.3 [1.6–3.2], P = 2.7E-5) and urine X-12117 (HR, 1.7 [1.3–2.2], P = 1.9E-5) were risk factors of UACR doubling, whereas urine sebacate (HR, 0.86 [0.80–0.92], P = 1.9E-5) was inversely associated. Compared with clinical characteristics alone, including the top five endogenous metabolites in serum and urine associated with longitudinal outcomes improved the outcome prediction (area under the receiver operating characteristic curves for eGFR decline: clinical model=0.79, clinical+metabolites model=0.87, P = 8.1E-6; for UACR doubling: clinical model=0.66, clinical+metabolites model=0.73, P = 2.9E-5). Conclusions: Metabolomic profiling in different biofluids provided distinct and potentially complementary insights into the biology and prognosis of kidney diseases.

Key Points: Integrated analysis of proteome and metabolome identifies modules associated with CKD progression and kidney failure. Ephrin transmembrane proteins and podocyte-expressed CRIM1 and NPNT emerged as central components and warrant experimental and clinical investigation. Background: Proteins and metabolites play crucial roles in various biological functions and are frequently interconnected through enzymatic or transport processes. Methods: We present an integrated analysis of 4091 proteins and 630 metabolites in the Chronic Renal Insufficiency Cohort study ( N =1708; average follow-up for kidney failure, 9.5 years, with 537 events). Proteins and metabolites were integrated using an unsupervised clustering method, and we assessed associations between clusters and CKD progression and kidney failure using Cox proportional hazards models. Analyses were adjusted for demographics and risk factors, including the eGFR and urine protein–creatinine ratio. Associations were identified in a discovery sample (random two thirds, n =1139) and then evaluated in a replication sample (one third, n =569). Results: We identified 139 modules of correlated proteins and metabolites, which were represented by their principal components. Modules and principal component loadings were projected onto the replication sample, which demonstrated a consistent network structure. Two modules, representing a total of 236 proteins and 82 metabolites, were robustly associated with both CKD progression and kidney failure in both discovery and validation samples. Using gene set enrichment, several transmembrane-related terms were identified as overrepresented in these modules. Transmembrane–ephrin receptor activity displayed the largest odds (odds ratio=13.2, P value = 5.5×10 ⁻⁵ ). A module containing CRIM1 and NPNT expressed in podocytes demonstrated particularly strong associations with kidney failure ( P value = 2.6×10 ⁻⁵ ). Conclusions: This study demonstrates that integration of the proteome and metabolome can identify functions of pathophysiologic importance in kidney disease.

Background: The ratio of 24,25‐dihydroxyvitamin D ₃ /25‐hydroxyvitamin D ₃ (vitamin D metabolite ratio [VDMR]) may reflect functional vitamin D activity. We examined associations of the VDMR, 25‐hydroxyvitamin D (25[OH]D), and 1,25‐dihydroxyvitamin D (1,25[OH] ₂ D) with cardiovascular disease (CVD) in patients with chronic kidney disease. Methods and Results: This study included longitudinal and cross‐sectional analyses of 1786 participants from the CRIC (Chronic Renal Insufficiency Cohort) Study. Serum 24,25‐dihydroxyvitamin D ₃ , 25(OH)D, and 1,25(OH) ₂ D were measured by liquid chromatography–tandem mass spectrometry 1 year after enrollment. The primary outcome was composite CVD (heart failure, myocardial infarction, stroke, and peripheral arterial disease). We used Cox regression with regression‐calibrated weights to test associations of the VDMR, 25(OH)D, and 1,25(OH) ₂ D with incident CVD. We examined cross‐sectional associations of these metabolites with left ventricular mass index using linear regression. Analytic models adjusted for demographics, comorbidity, medications, estimated glomerular filtration rate, and proteinuria. The cohort was 42% non‐Hispanic White race and ethnicity, 42% non‐Hispanic Black race and ethnicity, and 12% Hispanic ethnicity. Mean age was 59 years, and 43% were women. Among 1066 participants without prevalent CVD, there were 298 composite first CVD events over a mean follow‐up of 8.6 years. Lower VDMR and 1,25(OH) ₂ D were associated with incident CVD before, but not after, adjustment for estimated glomerular filtration rate and proteinuria (hazard ratio, 1.11 per 1 SD lower VDMR [95% CI, 0.95–1.31]). Only 25(OH)D was associated with left ventricular mass index after full covariate adjustment (0.6 g/m ^2.7 per 10 ng/mL lower [95% CI, 0.0–1.3]). Conclusions: Despite modest associations of 25(OH)D with left ventricular mass index, 25(OH)D, the VDMR, and 1,25(OH) ₂ D were not associated with incident CVD in chronic kidney disease.

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. Podcast: This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/JASN/2023_04_03_JASN2022060725.mp3

BACKGROUND: Metabolomic profiling in individuals with chronic kidney disease (CKD) has the potential to identify novel biomarkers and provide insight into disease pathogenesis. METHODS: We examined the association between blood metabolites and CKD progression, defined as the subsequent development of end-stage renal disease (ESRD) or estimated glomerular filtrate rate (eGFR) halving, in 1,773 participants of the Chronic Renal Insufficiency Cohort (CRIC) study, 962 participants of the African-American Study of Kidney Disease and Hypertension (AASK), and 5,305 participants of the Atherosclerosis Risk in Communities (ARIC) study. RESULTS: In CRIC, more than half of the measured metabolites were associated with CKD progression in minimally adjusted Cox proportional hazards models, but the number and strength of associations were markedly attenuated by serial adjustment for covariates, particularly eGFR. Ten metabolites were significantly associated with CKD progression in fully adjusted models in CRIC; 3 of these metabolites were also significant in fully adjusted models in AASK and ARIC, highlighting potential markers of glomerular filtration (pseudouridine), histamine metabolism (methylimidazoleacetate), and azotemia (homocitrulline). Our findings also highlight N-acetylserine as a potential marker of kidney tubular function, with significant associations with CKD progression observed in CRIC and ARIC. CONCLUSION: Our findings demonstrate the application of metabolomics to identify potential biomarkers and causal pathways in CKD progression. FUNDING: This study was supported by the NIH (U01 DK106981, U01 DK106982, U01 DK085689, R01 DK108803, and R01 DK124399).