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Kidney disease risk factors associate with urine biomarkers concentrations in HIV-positive persons; a cross-sectional study.

  • Author(s): Muiru, Anthony N
  • Shlipak, Michael G
  • Scherzer, Rebecca
  • Zhang, William R
  • Ascher, Simon B
  • Jotwani, Vasantha
  • Grunfeld, Carl
  • Parikh, Chirag R
  • Ng, Derek
  • Palella, Frank J
  • Ho, Ken
  • Kassaye, Seble
  • Sharma, Anjali
  • Cohen, Mardge
  • Wang, Ruibin
  • Qi, Qibin
  • Estrella, Michelle M
  • et al.
Abstract

Background

HIV-positive persons bear an excess burden of chronic kidney disease (CKD); however, conventional methods to assess kidney health are insensitive and non-specific for detecting early kidney injury. Urinary biomarkers can detect early kidney injury, and may help mitigate the risk of overt CKD.

Methods

Cross-sectional study of HIV-positive persons in the Multicenter AIDS Cohort Study and the Women's Interagency HIV Study. We measured levels of 14 biomarkers, capturing multiple dimensions of kidney injury. We then evaluated associations of known CKD risk factors with urine biomarkers using separate multivariable adjusted models for each biomarker.

Results

Of the 198 participants, one third were on HAART and virally suppressed. The vast majority (95%) had preserved kidney function as assessed by serum creatinine, with a median eGFR of 103 ml/min/1.73 m2 (interquartile range (IQR): 88, 116). In our multivariable analyses, the associations of each CKD risk factor with urinary biomarker levels varied in magnitude. For example, HIV viral load was predominantly associated with elevations in interleukin(IL)-18, and albuminuria, while higher CD4 levels were associated with lower monocyte chemoattractant protein-1 (MCP-1) and β2-microglobulin. In contrast, older age was significantly associated with elevations in α1-microglobulin, kidney injury marker-1, clusterin, MCP-1, and chitinase-3-like protein-1 levels, as well as lower epidermal growth factor, and uromodulin levels.

Conclusions

Among HIV-positive persons, CKD risk factors are associated with unique and heterogeneous patterns of changes in urine biomarkers levels. Additional work is needed to develop parsimonious algorithms that integrate multiple biomarkers and clinical data to discern the risk of overt CKD and its progression.

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