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. 2015 Feb;1(1):e1400121.
doi: 10.1126/sciadv.1400121.

Distinct Plasma Immune Signatures in ME/CFS Are Present Early in the Course of Illness

Free PMC article

Distinct Plasma Immune Signatures in ME/CFS Are Present Early in the Course of Illness

Mady Hornig et al. Sci Adv. .
Free PMC article


Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is an unexplained incapacitating illness that may affect up to 4 million people in the United States alone. There are no validated laboratory tests for diagnosis or management despite global efforts to find biomarkers of disease. We considered the possibility that inability to identify such biomarkers reflected variations in diagnostic criteria and laboratory methods as well as the timing of sample collection during the course of the illness. Accordingly, we leveraged two large, multicenter cohort studies of ME/CFS to assess the relationship of immune signatures with diagnosis, illness duration, and other clinical variables. Controls were frequency-matched on key variables known to affect immune status, including season of sampling and geographic site, in addition to age and sex. We report here distinct alterations in plasma immune signatures early in the course of ME/CFS (n = 52) relative to healthy controls (n = 348) that are not present in subjects with longer duration of illness (n = 246). Analyses based on disease duration revealed that early ME/CFS cases had a prominent activation of both pro- and anti-inflammatory cytokines as well as dissociation of intercytokine regulatory networks. We found a stronger correlation of cytokine alterations with illness duration than with measures of illness severity, suggesting that the immunopathology of ME/CFS is not static. These findings have critical implications for discovery of interventional strategies and early diagnosis of ME/CFS.


Fig. 1
Fig. 1. Comparison of plasma cytokine levels in short-duration ME/CFS, long-duration ME/CFS, and control subjects.
(A) Proinflammatory cytokines. (B) Anti-inflammatory cytokines. The means ± SEM for each cytokine are shown. Only cytokines meeting significance criteria (P < 0.05) in either the one-way or the two-way GLM are represented. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001 by two-sample t-test comparisons.
Fig. 2
Fig. 2. Network cytokine-cytokine associations differ for short-duration versus long-duration ME/CFS versus control subjects.
(A to C) Network diagrams for short-duration ME/CFS subjects (A, n = 52), long-duration ME/CFS subjects (B, n = 246), and healthy controls (C, n = 348). Network diagrams of the 51 measured cytokines were created in NodeXL ( using a 0.01 family-wise false discovery rate (FDR) to adjust for multiple comparisons (A, short-duration group, P = 0.0065; B, long-duration group, P = 0.0081; C, control group, P = 0.0075). Red lines (edges) indicate negative correlations, and gray lines indicate positive cytokine-cytokine correlations with associated P values that fall below the corrected P value criterion for each group. Note that whereas CD40L drives most of the inverse relationships with other immune molecules in both the long-duration ME/CFS and the control groups, CD40L is only related to five other cytokines in the short-duration ME/CFS group, and only one of these associations is negative (inverse relationship with IL-12p40). Similarly, PDGFBB is a negative driver of many other cytokines in both long-duration ME/CFS and control subjects, but shows no negative correlations with other cytokines in the short-duration subset.
Fig. 3
Fig. 3. CART analysis of cytokine and clinical predictors in subjects with short- and long-duration ME/CFS.
The CART decision tree machine learning method was applied to plasma cytokine and clinical covariate data to derive predictors associated with ME/CFS of short (≤3 years, n = 52) versus long (>3 years, n = 246) duration. Predictor variables and cutoffs at each of the nodes in the decision tree are those with the maximum capacity to differentiate between the different levels of the dependent variable (here, short versus long duration of illness). Resulting cytokine classifiers are highly dependent on subject age within both the short-duration and long-duration ME/CFS subgroups, but predictor patterns are shown to vary differently with age across different cytokines. These data provide evidence that cytokine differences are not solely due to the older mean age of the long-duration ME/CFS subgroup.

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