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. 2016 Jun:8:265-276.
doi: 10.1016/j.ebiom.2016.05.008. Epub 2016 May 10.

One of the immune activation profiles observed in HIV-1-infected adults with suppressed viremia is linked to metabolic syndrome: The ACTIVIH study

Christina Psomas  1 , Mehwish Younas  2 , Christelle Reynes  3 , Renaud Cezar  4 , Pierre Portalès  5 , Edouard Tuaillon  6 , Adeline Guigues  2 , Corinne Merle  7 , Nadine Atoui  7 , Céline Fernandez  7 , Vincent Le Moing  8 , Claudine Barbuat  9 , Grégory Marin  10 , Nicolas Nagot  10 , Albert Sotto  11 , Jean-François Eliaou  12 , Robert Sabatier  3 , Jacques Reynes  8 , Pierre Corbeau  13
Affiliations

One of the immune activation profiles observed in HIV-1-infected adults with suppressed viremia is linked to metabolic syndrome: The ACTIVIH study

Christina Psomas et al. EBioMedicine. 2016 Jun.

Erratum in

Abstract

Immune activation in HIV-1-infected individuals is reduced under antiretroviral therapies, but persists, resulting in various morbidities. To better characterize this phenomenon, using a panel of 68 soluble and cell surface markers, we measured the level of activation in circulating CD4+ and CD8+ T cells, B cells, monocytes, NK cells, polynuclear and endothelial cells as well as of inflammation and fibrinolysis in 120 virologic responders over 45years of age. As compared with age- and sex-matched uninfected individuals, we observed a persistence of activation in all the cell subpopulations analyzed, together with marks of inflammation and fibrinolysis. Two independent hierarchical clustering analyses allowed us to identify five clusters of markers that varied concurrently, and five patient groups, each with the same activation profile. The five groups of patients could be characterized by a marker of CD4+ T cell, CD8+ T cell, NK cell, monocyte activation or of inflammation, respectively. One of these profiles was strongly associated with marks of metabolic syndrome, particularly with hyperinsulinemia (OR 12.17 [95% CI 1.79-82.86], p=0.011). In conclusion, our study unveils biomarkers linked to metabolic syndrome that could be tested as predictive markers, and opens the way to new therapeutic approaches tailored to each patient group.

Keywords: Cell activation; Coagulation; Endothelium; Hyperinsulinemia; Inflammation.

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Figures

Fig. 1
Fig. 1
Immune activation in virologic responders. Percentages of various cell populations and plasma levels of soluble markers in healthy donors (HIV-), treated (HIV+ ART+), and untreated (HIV+ ART–) HIV patients. Data are presented as mean values and 95% confidence intervals; p-values are shown.
Fig. 2
Fig. 2
Variables factor map resulting from Principal Component Analysis. The variables are represented by arrows. The elbow test was carried out in order to select the number of components to consider. This was done by plotting the components' eigenvalues according to their size and analyzing the point in the graph where the slope goes from “steep” to “flat” in order to keep only the components that are placed before the elbow, which were 2 in our case. The length of each of these arrows depends on the correlation of the variable with the component. Highly positively correlated variables are represented by arrows close to each other. Strongly negatively correlated variables are represented by arrows diametrically opposed.
Fig. 3
Fig. 3
Virologic responders present with different immune activation profiles. Heatmap showing the hierarchical clustering of the activation markers (vertical) as well as of the virologic responders according to their profile of activation (horizontal). Each group number is indicated.
Fig. 4
Fig. 4
Characterization of the five different immune activation profiles. Differences in the levels of key activation markers between each group of patients and the other groups are represented. Data are presented as mean values and 95% confidence intervals; p-values are shown.
Fig. 5
Fig. 5
Link between immune activation profile 2 and marks of metabolic syndrome. Odd ratios relating each profile of immune activation to risk of hyperinsulinemia (a), hypertriglyceridemia (b), and lipodystrophy (c). Data are presented as OR and 95% confidence intervals.
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Comment in

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