Country of residence is associated with distinct inflammatory biomarker signatures in HIV-infected patients

Abstract

Background: Inflammation and coagulation biomarkers are independent predictors of morbidity and mortality in HIV-infected patients. The impact of country of residence on these biomarkers is unknown and was investigated in persons at similar stages of HIV infection.

Methods: Cryopreserved plasma specimens were analysed from 267 ART-naive patients with CD4 cell counts <100 cells/μl from Mexico (n=124) and South Africa (n=143). Biomarkers were compared and dimension reduction analyses were performed to highlight biosignatures according to nationality, gender and tuberculosis co-infection.

Results: Mexican patients were significantly different from South Africans with regard to age, gender, CD4 cell count, haemoglobin, presence of AIDS-defining illness and prevalence of active tuberculosis. After adjusting for baseline characteristics, patients from Mexico had higher levels of IFN-γ, IL-8, and CXCL-10 whereas patients from South Africa had higher levels of fibrinogen, LTB4, P-selectin, protein S, and sCD40 ligand. The effect of country on the profile of biomarker expression was stronger than gender differences and tuberculosis co-infection.

Conclusion: Inflammation and coagulation biomarkers vary significantly by country. Further studies are needed to evaluate how these differences may contribute to HIV pathogenesis and prognosis in diverse populations and how they can be accounted for in studies using biomarkers as surrogate end points.

Keywords: HIV; biomarkers; inflammation; nationality.

Figure 1.

Differences in expression profile of plasma biomarkers between Mexico and South Africa. (A, B) Plasma concentrations of several biomarkers were compared between patients from Mexico (n=124) or South Africa (n=143) with univariate analysis using a generalised linear model. (C) Principal component analysis was employed using data from the biomarkers displayed in (A) and (B). Normal ellipsoids represent area estimated to contain 50% of the population of a group. (D) Spearman correlations between HIV viremia and several biomarkers are shown for patients from Mexico or South Africa. P-values of significant correlations are shown

Figure 2.

Discrimination of country of origin using combination of plasma biomarkers. In an exploratory approach, a sparse canonical correlation analysis (sCCA) was employed to test whether patients from Mexico or South Africa could be distinguished. The sCCA performs dimensionality reduction for two co-dependent data sets (biomarker profile and baseline characteristics profile) so that the trends of correlations between parameters from both countries represent a combination of variables that are maximally correlated. In the biomarker profile dataset, values of variables that were exhibited significant differences (P<0.05) in the multivariate analysis (CXCL10, fibrinogen, IFN-γ, IL-8, LTB4, P-selectin, protein S, vitamin D and sCD40L; see Appendix 1 for details). The overall performance of the model in discriminating participants from Mexico versus South Africa is shown

Figure 3.

Gender differences in expression of plasma biomarkers. (A) Network analysis shows significant correlations (P<0.001, after bootstrap of 100X) between several biomarkers assessed by Spearman correlation matrices in male (n=175) and female (n=92) patients. Network densities were compared between the groups using the permutation test. (B) Plasma concentrations of selected biomarkers were compared between male and female patients with univariate analysis using a generalised linear model. (C) A hierarchical clustering analysis (Ward's method) of the plasma expression profile of several biomarkers was employed to test whether male and female patients from either Mexico or South Africa could be clustered separately. The colours represent the level of expression relative to the median

Figure 4.

Differential expression of plasma biomarkers in tuberculosis. (A) Network analysis shows significant correlations (P<0.001, after bootstrap of 100X) between several biomarkers assessed by Spearman correlation matrices in patients with no TB (n=206) or with a TB diagnosis (n=61). Network densities were compared between the groups using the permutation test. (B) Plasma concentrations of selected biomarkers were compared between patients with (green) or without TB (pink) with univariate analysis using a generalised linear model. (C) A hierarchical clustering analysis (Ward's method) of the plasma expression profile of several biomarkers was employed to test whether patients with or without TB and from either Mexico or South Africa could be clustered separately. The colours represent the level of expression relative to the median