The incidence and prevalence of heart failure (HF) and chronic kidney disease (CKD) are increasing, and as such a better understanding of the interface between both conditions is imperative for developing optimal strategies for their detection, prevention, diagnosis, and management. To this end, Kidney Disease: Improving Global Outcomes (KDIGO) convened an international, multidisciplinary Controversies Conference titled Heart Failure in CKD. Breakout group discussions included (i) HF with preserved ejection fraction (HFpEF) and nondialysis CKD, (ii) HF with reduced ejection fraction (HFrEF) and nondialysis CKD, (iii) HFpEF and dialysis-dependent CKD, (iv) HFrEF and dialysis-dependent CKD, and (v) HF in kidney transplant patients. The questions that formed the basis of discussions are available on the KDIGO website http:// kdigo.org/conferences/heart-failure-in-ckd/, and the deliberations from the conference are summarized here.

Aims Chronic kidney disease (CKD) is associated with worse outcomes in heart failure with preserved ejection fraction (HFpEF). Whether this association is due the effect of CKD on intrinsic abnormalities in cardiac function is unknown. We hypothesized that CKD is independently associated with worse cardiac mechanics in HFpEF. Methods and Results We prospectively studied 299 patients enrolled in the Northwestern University HFpEF Program. Using the creatinine-based CKD-Epi equation to calculate estimated glomerular filtration rate (eGFR), study participants were analyzed by CKD status (using eGFR <60 ml/min/1.73 m2 to denote CKD). Indices of cardiac mechanics (longitudinal strain parameters) were measured using speckle-tracking echocardiography. Using multivariable-adjusted linear and Cox regression analyses, we determined the association between CKD and echocardiographic parameters and clinical outcomes (cardiovascular hospitalization or death). Of 299 study participants, 48% had CKD. CKD (dichotomous variable) and reduced eGFR (continuous variable) were both associated with worse cardiac mechanics indices including LA reservoir strain, left ventricular longitudinal strain, and right ventricular free wall strain, even after adjusting for potential confounders, including comorbidities, EF and volume status. For example, for each 1-SD decrease in eGFR, LA reservoir strain was 3.52%-units lower (P<0.0001) after multivariable adjustment. Reduced eGFR was also associated with worse outcomes (adjusted hazard ratio [HR] 1.28 [95% CI 1.01–1.61] per 1-SD decrease in eGFR; P=0.039). The association was attenuated after adjustment for indices of cardiac mechanics (P=0.064). Conclusion In HFpEF, CKD is independently associated with worse cardiac mechanics, which may explain why HFpEF patients with CKD have worse outcomes.

BackgroundCardiac troponin T is independently associated with cardiovascular events and mortality in patients with chronic kidney disease (CKD). Serum levels of high sensitivity cardiac troponin T (hs-TnT) reflect subclinical myocardial injury in ambulatory patients. We sought to determine the distribution and predictors of hs-TnT in CKD patients without overt cardiovascular disease (CVD).MethodsWe studied 2464 participants within the multi-ethnic Chronic Renal Insufficiency Cohort (CRIC) who did not have self-reported CVD. We considered renal and non-renal factors as potential determinants of hs-TnT, including demographics, comorbidities, left ventricular (LV) mass, serologic factors, estimated glomerular filtration rate (eGFR) and albumin to creatinine ratio.ResultsHs-TnT was detectable in 81% of subjects, and the median (IQR) hs-TnT was 9.4 pg/ml (4.3-18.3). Analysis was performed using Tobit regression, adjusting for renal and non-renal factors. After adjustment, lower eGFR was associated with higher expected hs-TnT; participants with eGFR < 30 ml/min/1.73 m2 had 3-fold higher expected hs-TnT compared to subjects with eGFR > 60. Older age, male gender, black race, LV mass, diabetes and higher blood pressure all had strong, independent associations with higher expected hs-TnT.ConclusionsKnowledge of the determinants of hs-TnT in this cohort may guide further research on the pathology of heart disease in patients with CKD and help to stratify sub-groups of CKD patients at higher cardiovascular risk.

In this review of the application of proteomics and metabolomics to kidney disease research, we review key concepts, highlight illustrative examples, and outline future directions. The proteome and metabolome reflect the influence of environmental exposures in addition to genetic coding. Circulating levels of proteins and metabolites are dynamic and modifiable, and thus amenable to therapeutic targeting. Design and analytic considerations in proteomics and metabolomics studies should be tailored to the investigator's goals. For the identification of clinical biomarkers, adjustment for all potential confounding variables, particularly GFR, and strict significance thresholds are warranted. However, this approach has the potential to obscure biologic signals and can be overly conservative given the high degree of intercorrelation within the proteome and metabolome. Mass spectrometry, often coupled to up-front chromatographic separation techniques, is a major workhorse in both proteomics and metabolomics. High-throughput antibody-and aptamer-based proteomic platforms have emerged as additional, powerful approaches to assay the proteome. As the breadth of coverage for these methodologies continues to expand, machine learning tools and pathway analyses can help select the molecules of greatest interest and categorize them in distinct biologic themes. Studies to date have already made a substantial effect, for example elucidating target antigens in membranous nephropathy, identifying a signature of urinary peptides that adds prognostic information to urinary albumin in CKD, implicating circulating inflammatory proteins as potential mediators of diabetic nephropathy, demonstrating the key role of the microbiome in the uremic milieu, and highlighting kidney bioenergetics as a modifiable factor in AKI. Additional studies are required to replicate and expand on these findings in independent cohorts. Further, more work is needed to understand the longitudinal trajectory of select protein and metabolite markers, perform transomics analyses within merged datasets, and incorporate more kidney tissue-based investigation.