Biomarkers and personalized therapy in chronic kidney diseases

Current State and Future Trends to Optimize the Care of African Americans with End-Stage Renal Disease

BACKGROUND

Chronic kidney disease is a progressive disease, which terminates in end-stage renal diseases (ESRD) that requires either dialysis or kidney transplantation for the patient to survive. There is an alarming trend in the disparities of ESRD in African Americans (AAs). Currently, AAs represent more than 30% of incident ESRD cases, yet they constitute 15% of the overall US population. Despite the reductions in mortality, increases in access to patient-centered home dialysis and preemptive kidney transplantation for the overall US ESRD population over the last decade, disparities in the care of AAs with ESRD remain largely unaffected.

SUMMARY

This review discusses patient-, community-, and practitioner-related factors that contribute to disparities in ESRD care for AAs. In particular, the review addresses issues related to end-of-life support, the importance of Apolipoprotein-1 gene variants, and the advent of pharmacogenomics toward achieving precision care. The need for accessible clinical intelligence for the ESRD population is discussed. Several interventions and a call to action to address the disparities are presented. Key Messages: Significant disparities in ESRD care exist for AAs. Strategies to enhance patient engagement, education, accountable partnerships, and clinical intelligence may reduce these disparities.

Metabolomics for clinical use and research in chronic kidney disease

Chronic kidney disease (CKD) has a high prevalence in the general population and is associated with high mortality; a need therefore exists for better biomarkers for diagnosis, monitoring of disease progression and therapy stratification. Moreover, very sensitive biomarkers are needed in drug development and clinical research to increase understanding of the efficacy and safety of potential and existing therapies. Metabolomics analyses can identify and quantify all metabolites present in a given sample, covering hundreds to thousands of metabolites. Sample preparation for metabolomics requires a very fast arrest of biochemical processes. Present key technologies for metabolomics are mass spectrometry and proton nuclear magnetic resonance spectroscopy, which require sophisticated biostatistic and bioinformatic data analyses. The use of metabolomics has been instrumental in identifying new biomarkers of CKD such as acylcarnitines, glycerolipids, dimethylarginines and metabolites of tryptophan, the citric acid cycle and the urea cycle. Biomarkers such as c-mannosyl tryptophan and pseudouridine have better performance in CKD stratification than does creatinine. Future challenges in metabolomics analyses are prospective studies and deconvolution of CKD biomarkers from those of other diseases such as metabolic syndrome, diabetes mellitus, inflammatory conditions, stress and cancer.

The renal protective effect of angiotensin receptor blockers depends on intra-individual response variation in multiple risk markers

AIMS

Angiotensin receptor blockers (ARBs) are renoprotective and targeted to blood pressure. However, ARBs have multiple other (off-target) effects which may affect renal outcome. It is unknown whether on-target and off-target effects are congruent within individuals. If not, this variation in short term effects may have important implications for the prediction of individual long term renal outcomes. Our aim was to assess intra-individual variability in multiple parameters in response to ARBs in type 2 diabetes.

METHODS

Changes in systolic blood pressure (SBP), albuminuria, potassium, haemoglobin, cholesterol and uric acid after 6 months of losartan treatment were assessed in the RENAAL database. Improvement in predictive performance of renal outcomes (ESRD or doubling serum creatinine) for each individual using ARB-induced changes in all risk markers was assessed by the relative integrative discrimination index (RIDI).

RESULTS

SBP response showed high variability (mean -5.7 mmHg, 5(th) to 95(th) percentile -36.5 to +24.0 mmHg) between individuals. Changes in off-target parameters also showed high variability between individuals. No congruency was observed between responses to losartan in multiple parameters within individuals. Using individual responses in all risk markers significantly improved renal risk prediction (RIDI 30.4%, P < 0.01) compared with using only SBP changes. Results were successfully replicated in two independent trials with irbesartan, IDNT and IRMA-2.

CONCLUSIONS

In this post hoc analysis we showed that ARBs have multiple off-target effects which vary between and within individuals. Combining all ARB-induced responses beyond SBP provides a more accurate prediction of who will benefit from ARB therapy. Prospective trials are required to validate these findings.