Sanjiv M Baxi1, Rebecca Scherzer, Vasantha Jotwani, Michelle M Estrella, Alison G Abraham, Chirag R Parikh, Michael R Bennett, Mardge H Cohen, Marek J Nowicki, Deborah R Gustafson, Anjali Sharma, Mary A Young, Michael G Shlipak. 1. *Department of Medicine, University of California San Francisco (UCSF), San Francisco, CA; †Division is Epidemiology, School of Public Health, University of California, Berkeley, Berkeley, CA; ‡Department of Medicine, Kidney Health Research Collaborative, San Francisco Veterans Affairs Hospital, San Francisco, CA; §Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD; ‖Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; ¶Section of Nephrology, Department of Medicine, Program of Applied Translational Research, Yale University, New Haven, CT; #Division of Nephrology and Hypertension, Cincinnati Children's Hospital Medical Center, Cincinnati, OH; **Department of Medicine, John H. Stroger Jr. Hospital of Cook County, Chicago, IL; ††Department of Medicine, University of Southern California, Los Angeles, CA; ‡‡Department of Neurology, State University of New York, Downstate Medical Center, Brooklyn, NY; §§Department of Medicine, Albert Einstein College of Medicine, Bronx, NY; and ‖‖Department of Medicine, Division of Infectious Diseases and Travel Medicine, Georgetown University Medical Center, Washington, DC.
Abstract
BACKGROUND: Urine biomarkers have helped identify persons at risk for progressing to kidney disease in the setting of HIV infection. We explored factors associated with changes in 3 urine biomarkers over 10 years among women living with HIV. METHODS: Prospective cohort of 294 HIV-infected women from the multicenter Women's Interagency HIV Study. Predictors included HIV viral and immunological parameters, comorbid conditions, and health-related behaviors. Outcomes were patterns of changes of urine interleukin-18 (IL-18), albumin-to-creatinine ratio (ACR), and alpha-1-microglobulin (α1m) over 10 years. We used quantile regression to examine patterns of change in each urine biomarker during follow-up and multivariable analysis of variance regression to identify predictors of biomarker changes. RESULTS: Over 10 years, the median concentrations of IL-18 declined from 120 to 64 pg/mL, α1m rose from 0.7 to 1.5 ng/mL, and ACR remained stable (9-8 mg/g). In multivariate analyses, the strongest predictors of increases in IL-18 were higher baseline body mass index, increase in waist circumference, higher follow-up HIV viral load, lower follow-up CD4 cell count, hepatitis C virus (HCV) coinfection, and higher follow-up high density lipoprotein cholesterol. Predictors of increasing concentration of α1m were lower CD4 cell counts, higher diastolic blood pressure, HCV coinfection, and smoking. Finally, determinants of ACR increases during follow-up were higher follow-up diastolic blood pressure, HCV coinfection, higher follow-up HIV viral load, and triglyceride concentration. CONCLUSIONS: Over 10 years, HIV disease status had different associations with each urine biomarker under study. Overall, the associations with changes in each biomarker support research into their use for longitudinal monitoring of kidney health.
BACKGROUND: Urine biomarkers have helped identify persons at risk for progressing to kidney disease in the setting of HIV infection. We explored factors associated with changes in 3 urine biomarkers over 10 years among women living with HIV. METHODS: Prospective cohort of 294 HIV-infected women from the multicenter Women's Interagency HIV Study. Predictors included HIV viral and immunological parameters, comorbid conditions, and health-related behaviors. Outcomes were patterns of changes of urine interleukin-18 (IL-18), albumin-to-creatinine ratio (ACR), and alpha-1-microglobulin (α1m) over 10 years. We used quantile regression to examine patterns of change in each urine biomarker during follow-up and multivariable analysis of variance regression to identify predictors of biomarker changes. RESULTS: Over 10 years, the median concentrations of IL-18 declined from 120 to 64 pg/mL, α1m rose from 0.7 to 1.5 ng/mL, and ACR remained stable (9-8 mg/g). In multivariate analyses, the strongest predictors of increases in IL-18 were higher baseline body mass index, increase in waist circumference, higher follow-up HIV viral load, lower follow-up CD4 cell count, hepatitis C virus (HCV) coinfection, and higher follow-up high density lipoprotein cholesterol. Predictors of increasing concentration of α1m were lower CD4 cell counts, higher diastolic blood pressure, HCV coinfection, and smoking. Finally, determinants of ACR increases during follow-up were higher follow-up diastolic blood pressure, HCV coinfection, higher follow-up HIV viral load, and triglyceride concentration. CONCLUSIONS: Over 10 years, HIV disease status had different associations with each urine biomarker under study. Overall, the associations with changes in each biomarker support research into their use for longitudinal monitoring of kidney health.
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