Publications & Reports

Immunometabolic and lipidomic markers associated with the frailtyindex and quality of life in aging HIV+ men on antiretroviral therapy.

Yeoh HL, Cheng AC, Cherry CL, Weir JM, Meikle PJ, Hoy JF, Crowe SM, Palmer CS


Chronic immune activation persists despite antiretroviral therapy (ART) in HIV+ individuals and underpins an increased risk of age-related co-morbidities. We assessed the Frailty Index in older HIV+ Australian men on ART. Immunometabolic markers on monocytes and T cells were analyzed using flow cytometry, plasma innate immune activation markers by ELISA, and lipidomic profiling by mass spectrometry. The study population consisted of 80 HIV+ men with a median age of 59 (IQR, 56-65), and most had an undetectable viral load (92%). 24% were frail, and 76% were non-frail. Frailty was associated with elevated Glucose transporter-1 (Glut1) expression on the total monocytes (p=0.04), increased plasma levels of innate immune activation marker sCD163 (OR, 4.8; CI 1.4-15.9, p=0.01), phosphatidylethanolamine PE(36:3) (OR, 5.1; CI 1.7-15.5, p=0.004) and triacylglycerol TG(16:1_18:1_18:1) (OR, 3.4; CI 1.3-9.2, p=0.02), but decreased expression of GM3 ganglioside, GM3(d18:1/18:0) (OR, 0.1; CI 0.0-0.6, p=0.01) and monohexosylceramide HexCerd(d18:1/22:0) (OR, 0.1; CI 0.0-0.5, p=0.004). There is a strong inverse correlation between quality of life and the concentration of PE(36:3) (ρ=-0.33, p=0.004) and PE(36:4) (ρ=-0.37, p=0.001). These data suggest that frailty is associated with increased innate immune activation and abnormal lipidomic profile. These markers should be investigated in larger, longitudinal studies to determine their potential as biomarkers for frailty.

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The authors gratefully acknowledge the patients and clinicians in the Department of Infectious Diseases, Alfred Health for support in recruiting participants for this study, and Kerrie Watson for support utilizing information from the HIV database. We gratefully appreciate statistical advice from consultant Baki Billah, School of Public Health and Preventive Medicine, Monash University. We thank Robinder Gauba for assisting with the hierarchical clustering and correspondence factor map analysis and James Marijanovic for assisting with the storage of samples and extraction of lipids. The authors gratefully acknowledge the contribution to this work of the Victorian Operational Infrastructure Support Program received by the Burnet Institute. We acknowledge the assistance of Geza Paukovic and Eva Orlowski-Oliver from the AMREP Flow Cytometry Core Facility for flow cytometry training and technical advice.


  • Journal: EBioMedicine
  • Published: 01/08/2017
  • Volume: 22
  • Pagination: 112-21