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O21.2 One profile or many? plasma biomarkers cxcl10, scd163 and scd14 reveal distinct associations with hiv treatment response, choice of treatment regimen, and cardiovascular risk factors
  1. A Castley1,2,
  2. I James3,
  3. L Williams1,
  4. C Berry2,
  5. D Nolan1,3
  1. 1Department of Clinical Immunology, 2nd Floor, North Block, Royal Perth Hospital, Wellington Street, Perth, Western Australia 6000
  2. 2School of Veterinary and Life Sciences, Murdoch University, Perth, Western Australia 6150
  3. 3Centre for Clinical Immunology and Biomedical Statistics (CCIBS), Murdoch University, Perth, Western Australia 6150

Abstract

 Introduction Persistent systemic immune activation despite effective HIV treatment may be revealed by measuring plasma ‘biomarker’ levels. Here we investigate three established biomarkers within a well-characterised HIV cohort.

Methods Plasma sCD14, sCD163 and CXCL10 levels were measured by ELISA methods in 475 consecutive patients with documented CVD risk (age, ethnicity, gender, smoking, blood pressure, BMI, fasting metabolic profile), as well as HIV treatment history and immunological/virological outcomes, and analysed using multiple regression analysis.

Results All biomarkers were reduced with higher CD4 counts (p < 0.05), but showed distinct associations with virological response: CXCL10 strongly correlated with viral load (p < 0.001), sCD163 was significantly reduced among ‘aviremic’ patients only (p = 0.02), while sCD14 was unaffected by virological status under 10000 cpms (p > 0.2) however sCD14 was increased if HIV RNA viral load was >10000 cpm (p = 0.003). The choice of HIV treatment did not affect CXCL10, however, higher sCD163 was associated with PI’s (p = 0.05) and lower sCD14 was associated with integrase inhibitors (p = 0.02). Several CVD risk factors were associated with sCD163 (age, ethnicity, HDL, BMI), with a favourable influence of Framingham score <10% (p = 0.04). Soluble CD14 levels were higher among smokers (p = 0.002), with no effect of other CVD risk factors, except age (p = 0.045), or overall Framingham score.

Conclusion These biomarkers reveal remarkably distinct associations, with levels of CXCL10 most readily explained by routinely monitored variables (viral load, CD4 counts), while sCD163 levels appear to reflect a deeper level of virological suppression as well as the influence of CVD risk factors. Levels of sCD14, which have been linked to overall mortality risk, are least associated with routinely monitored variables, with evidence of specific effects of smoking and integrase inhibitor therapy that warrant further investigation.

Disclosure of interest statement None to disclose.

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