A Parsimonious Host Inflammatory Biomarker Signature Predicts Incident Tuberculosis and Mortality in Advanced Human Immunodeficiency Virus

Abstract

Background: People with advanced human immunodeficiency virus (HIV) (CD4 < 50) remain at high risk of tuberculosis (TB) or death despite the initiation of antiretroviral therapy (ART). We aimed to identify immunological profiles that were most predictive of incident TB disease and death.

Methods: The REMEMBER randomized clinical trial enrolled 850 participants with HIV (CD4 < 50 cells/µL) at ART initiation to receive either empiric TB treatment or isoniazid preventive therapy (IPT). A case-cohort study (n = 257) stratified by country and treatment arm was performed. Cases were defined as incident TB or all-cause death within 48 weeks after ART initiation. Using multiplexed immunoassay panels and ELISA, 26 biomarkers were assessed in plasma.

Results: In total, 52 (6.1%) of 850 participants developed TB; 47 (5.5%) died (13 of whom had antecedent TB). Biomarkers associated with incident TB overlapped with those associated with death (interleukin [IL]-1β, IL-6). Biomarker levels declined over time in individuals with incident TB while remaining persistently elevated in those who died. Dividing the cohort into development and validation sets, the final model of 6 biomarkers (CXCL10, IL-1β, IL-10, sCD14, tumor necrosis factor [TNF]-α, and TNF-β) achieved a sensitivity of 0.90 (95% confidence interval [CI]: .87-.94) and a specificity of 0.71(95% CI: .68-.75) with an area under the curve (AUC) of 0.81 (95% CI: .78-.83) for incident TB.

Conclusion: Among people with advanced HIV, a parsimonious inflammatory biomarker signature predicted those at highest risk for developing TB despite initiation of ART and TB preventive therapies. The signature may be a promising stratification tool to select patients who may benefit from increased monitoring and novel interventions.

Clinical trials registration: NCT01380080.

Keywords: antiretroviral therapy; biomarker; early mortality; tuberculosis.

© The Author(s) 2019. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.

Figures

Figure 1.

Cox regression model for biomarkers. Association with (A) incident TB, adjusted model includes age, sex, BMI; (B) death, adjusted model includes age, sex, and BMI presented as a Forest plot. Panels on the right display the c-statistics values. Abbreviations: BMI, body mass index; CI, confidence interval; HR, hazard ratio; MLR, monocyte to lymphocyte ratio; TB, tuberculosis.

Figure 2.

Canonical discriminant analysis of baseline biomarkers most influential for incident TB and death. A, ROC curve analysis of plasma levels of all biomarkers measured combined at study baseline (week 0) to distinguish TB vs. no TB or patients that died from those who survived. B, Canonical discriminant analyses of the biomarkers were performed independently for TB and death. Those above 0.2 and below −0.2 were considered most influential in the ROC curve analyses and are shown in the grey boxes. Abbreviations: AUC, area under the curve; min, minimum; max, maximum; ROC, receiver operator characteristics; sens, sensitivity; spec, specificity; TB, tuberculosis.

Figure 3.

Plasma biomarker levels measured serially in patients stratified by clinical endpoints. A–B, Mean values of log10-transformed concentration of each plasma marker per time point were calculated for the entire population and also per clinical outcomes. Biomarker values were z-score normalized and illustrated in a heatmap in which biomarkers were grouped using hierarchical clustering (Ward’s method with 100× bootstrap). Dendrograms represent Euclidean distance. Abbreviation: SD, standard deviation; TB, tuberculosis.

Figure 4.

A. Associations between baseline plasma inflammatory biomarkers and time to TB diagnosis. Left: Data were log-transformed and ranked and colored in a heatmap from minimum to maximum values detected for each marker. Patients were ordered based on time to TB diagnosis (in weeks) and plasma biomarkers were clustered (Ward’s method with 100× bootstrap) according to the distribution profile in the study population. Dendrograms represent Euclidean distance. Right: Spearman correlations for each marker and time to TB diagnosis. Blue bars indicate statistically significant correlations after corrections for multiple measurements (Holm-Bonferroni’s method). Abbreviation: TB, tuberculosis. B. Associations between baseline plasma inflammatory biomarkers and time to death. Left: Data were log-transformed and ranked and colored in a heatmap from minimum to maximum values detected for each marker. Patients were ordered based on time to death (in weeks) and plasma biomarkers were clustered (Ward’s method with 100× bootstrap) according to the distribution profile in the study population. Dendrograms represent Euclidean distance. Right: Spearman correlations or each marker and time to death. Only soluble IL1-R1 remained statistically significant after corrections for multiple measurements (Holm-Bonferroni’s method). Only participants with valid values for all markers were included (n = 46). Abbreviation: IL1-R1, interleukin 1 receptor, type 1 min, minimum; max, maximum.