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Biomarkers and Health-Related Quality of Life in End-Stage Renal Disease: A Systematic Review

Brennan M.R. Spiegel^*,,, Gil Melmed^,, Sean Robbins^||, and Eric Esrailian^,

^* Department of Medicine, VA Greater Los Angeles Healthcare System, Department of Medicine, David Geffen School of Medicine at University of California, Los Angeles, Department of Medicine, University of California, Cedars-Sinai Medical Center, UCLA/VA Center for Outcomes Research and Education, and ^|| Global Health Economics, Amgen, Inc., Los Angeles, California

Correspondence: Dr. Brennan M.R. Spiegel, Department of Medicine, VA Greater Los Angeles Healthcare System, David Geffen School of Medicine at UCLA, UCLA/VA Center for Outcomes Research and Education (CORE), 11301 Wilshire Boulevard, Building 115 Room 215, Los Angeles, CA 90073. Phone: 310-268-3256; Fax: 310-268-4510; E-mail: bspiegel{at}mednet.ucla.edu

Background and objectives: Health-related quality of life (HRQOL) predicts mortality in ESRD, yet adoption of HRQOL monitoring is not widespread, and regulatory authorities remain predominantly concerned with monitoring traditional biologic parameters. To assist with future efforts to adopt HRQOL monitoring while acknowledging the importance of biomarkers, this study sought to establish which domains of HRQOL are most affected by ESRD and to measure the strength of evidence linking common biomarkers to HRQOL in ESRD.

Design, setting, participants, & measurements: A systematic review was performed to identify studies that measured HRQOL in ESRD. Data were abstracted according to a conceptual model regarding the measurement of HRQOL differences, and HRQOL data were converted to weighted mean effect sizes and correlation coefficients.

Results: The impact of ESRD was largest in the Short Form 36 domains of physical functioning (e.g., role-physical, vitality) and smallest in mental functioning (e.g., mental health, role-emotional). Dialysis adequacy, as measured by Kt/V, was a poor correlate for Short Form 36 scores. Similarly, mineral metabolism (e.g., calcium x phosphorous, parathyroid hormone) and inflammatory (e.g., C-reactive protein, TNF) biomarkers had small effect sizes and correlations with HRQOL. In contrast, hematocrit demonstrated small to moderate relationships with mental and physical HRQOL, and nutritional biomarkers (e.g., albumin, creatinine, body mass index) demonstrated moderate to large relationships.

Conclusions: HRQOL in ESRD is most affected in the physical domains, and nutritional biomarkers are most closely associated with these domains. In contrast, Kt/V, mineral metabolism indices, and inflammatory markers are poor HRQOL correlates.