Interleukin 6 Is a Stronger Predictor of Clinical Events Than High-Sensitivity C-Reactive Protein or D-Dimer During HIV Infection

Abstract

Background: Interleukin 6 (IL-6), high-sensitivity C-reactive protein (hsCRP), and D-dimer levels are linked to adverse outcomes in human immunodeficiency virus (HIV) infection, but the strength of their associations with different clinical end points warrants investigation.

Methods: Participants receiving standard of care in 2 HIV trials with measured biomarker levels were followed to ascertain all-cause death, non-AIDS-related death, AIDS, cardiovascular disease (CVD), and non-AIDS-defining malignancies. Hazard ratios (HRs) and 95% confidence intervals (CIs) of each end point for quartiles and log2-transformed IL-6, hsCRP, and D-dimer levels were calculated using Cox models. Marginal models modelling multiple events tested for equal effects of biomarker levels on different end points.

Results: Among 4304 participants, there were 157 all-cause deaths, 117 non-AIDS-related deaths, 101 AIDS cases, 121 CVD cases, and 99 non-AIDS-defining malignancies. IL-6 was more strongly associated with most end points, compared with hsCRP. IL-6 appeared to be a stronger predictor than D-dimer for CVD and non-AIDS-defining malignancies, but 95% CIs overlapped. Independent associations of IL-6 were stronger for non-AIDS-related death (HR, 1.71; 95% CI, 1.43-2.04) and all-cause death (HR, 1.56; 95% CI, 1.33-1.84) and similar for CVD (HR, 1.35; 95% CI, 1.12-1.62) and non-AIDS-defining malignancies (HR, 1.30; 95% CI, 1.06-1.61). There was heterogeneity of IL-6 (P < .001) but not hsCRP (P = .15) or D-dimer (P = .20) as a predictor for different end points.

Conclusions: IL-6 is a stronger predictor of fatal events than of CVD and non-AIDS-defining malignancies. Adjuvant antiinflammatory and antithrombotic therapies should be tested in HIV-infected individuals.

Keywords: D-dimer; HIV; IL-6; cancer; cardiovascular disease; hsCRP; inflammation.

Figure 1.

Crude incidence rates of clinical end points across biomarker quartiles. Quartile cut points were defined differently for each study, as described in Supplementary Table 2. Abbreviations: CI, confidence interval; hsCRP, high-sensitivity C-reactive protein; IL-6, interleukin 6; PYFU, person-years of follow-up.

Figure 2.

Hazard ratios (HRs) for clinical end points associated with baseline levels of biomarkers. Model 1: unadjusted associations, stratified by study. Model 2: adjusted for demographic characteristics (age, sex, race, and body mass index [calculated as the weight in kilograms divided by the height in meters squared]), antiretroviral therapy use, nadir and baseline CD4⁺ T-cell counts, baseline human immunodeficiency virus RNA level, prior AIDS and cardiovascular disease, diabetes mellitus, and hepatitis B virus and hepatitis C virus coinfection, stratified by study. Model 3: HRs for interleukin 6 (IL-6) and D-dimer levels, using model 2 and including both biomarkers simultaneously, and HRs for high-sensitivity C-reactive protein (hsCRP) level, using model 2 and also adjusted for D-dimer level, stratified by study. Abbreviation: CI, confidence interval.

Figure 3.

Adjusted hazard ratios (HRs) for clinical end points associated with biomarker quartiles. Quartile cut points were defined differently for each study, as described in Supplementary Table 2. HRs were adjusted for factors included in model 2, namely demographic characteristics (age, sex, race, and body mass index [calculated as the weight in kilograms divided by the height in meters squared]), antiretroviral therapy use, nadir and baseline CD4⁺ T-cell counts, baseline human immunodeficiency virus RNA level, prior AIDS and cardiovascular disease, diabetes mellitus, and hepatitis B virus and hepatitis C virus coinfection. HRs calculated as in models 1 and 3 showed consistent results (data not shown). Abbreviations: CI, confidence interval; hsCRP, high-sensitivity C-reactive protein; IL-6, interleukin 6.