Steroid dynamics in myalgic encephalomyelitis / chronic fatigue syndrome: a case-control study using ultra performance supercritical fluid chromatography tandem mass spectrometry

Abstract

Background: Myalgic encephalomyelitis / chronic fatigue syndrome (ME/CFS) is a multisystem disorder characterised by unrelenting fatigue, post-exertional malaise, and dysfunction across immune, nervous, metabolism, and endocrine systems. Given the broad role of steroid hormones in regulating these systems, this study investigated differences in the steroid metabolome and network dynamics between ME/CFS patients and matched controls.

Methods: Blood plasma steroid levels were quantified using Ultra-Performance Supercritical Fluid Chromatography- Tandem Mass Spectrometry (UPSFC-MS/MS) in ME/CFS patients (n = 24) and age and gender matched controls (n = 24). Group comparisons of absolute steroid concentrations were performed using Mann-Whitney U tests. Partial Spearman correlation networks were evaluated to examine direct associations between steroids within each group, and centrality metrics were used to evaluate structural differences. Steroid-steroid ratios were analysed to reflect biochemical relationships. Multivariate analysis with Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) was also conducted.

Results: No significant group differences in absolute steroid concentrations were observed following FDR correction. However, network analysis revealed a marked reduction in direct steroid-steroid relationships in ME/CFS, with controls exhibiting 52 significant partial correlations, while the ME/CFS group retained only one (cortisol - corticosterone). Centrality analysis further revealed a shift in network structure, with cortisone emerging as highly central in ME/CFS (degree = 7, betweenness = 16.7), despite being peripheral in controls, and progesterone showing reduced integration in ME/CFS (degree = 3 vs. 12, eigenvector = 0.40 vs. 0.93). Steroid-steroid ratio analysis revealed a higher cortisol-to-pregnanolone ratio and a lower pregnanolone-to-progesterone ratio in ME/CFS, although these findings did not remain significant after FDR correction. OPLS-DA indicated a modest relationship between steroid levels and group classification (R²Y = 22.8%), but negative Q² values suggested poor predictive power.

Conclusions: Despite no significant differences in absolute steroid levels, network analysis revealed profound disruptions in steroid-steroid relationships in ME/CFS compared to controls, suggesting disrupted steroid homeostasis. Collectively the results suggest dysregulation of HPA axis function and progestogen pathways, as demonstrated by altered partial correlations, centrality profiles, and steroid ratios. These findings illustrate the importance of hormone network dynamics in ME/CFS pathophysiology and underscores the need for more research into steroid metabolism.

Keywords: Chronic illness; Homeostasis; Hormone metabolism; ME/CFS; Network analysis; Neuroendocrine dysfunction; Partial correlations; Steroidomics; Ultra-performance supercritical fluid chromatography tandem mass spectrometry (UPSFC-MS/MS).

Fig. 1

Steroid level comparisons in myalgic encephalomyelitis / chronic fatigue syndrome and control groups. (A) Absolute (untransformed) steroid levels in ME/CFS vs. control cohorts cortisol, cortisone, DHEA, estrone, and estradiol are plotted on a 0-150 ng/mol y-axis (SEM), while all other steroids use a 0–10 ng/mol scale (SEM) (B) and (C) Heatmaps displaying normalized (z-score) steroid levels in the ME/CFS and control group, respectively. Rows represent individual samples clustered using complete linkage, and columns represent specific steroids. Blue indicates lower-than-average levels, white represents the mean, and red indicates higher-than-average levels

Fig. 2

Absolute steroid level comparison in ME/CFS vs. control cohorts by gender. (A) Females and (B) Males. Steroid levels are reported in ng/mol, except for estradiol and estrone, which are shown in pmol/mol. Cortisol, cortisone, DHEA, estrone, and estradiol are plotted on a 0 -150 ng/mol y-axis, while all other steroids are displayed on a 0–10 ng/mol scale

Fig. 3

Steroid concentration network analysis based on spearman partial correlations. (A) (B) Network analysis for the control and ME/CFS groups, respectively, where nodes represent individual steroids and edges represent partial correlations with r > 0.3. (C) (D) Network analysis for the control and ME/CFS groups, respectively, where edges represent only significant correlations (r > 0.3, q < 0.3). These figures highlight the higher density and number of correlations in the control group compared to ME/CFS (A vs. B). Additionally, more significant correlations are observed in the control group (C vs. D), whereas the ME/CFS group only shows correlations between cortisol and corticosterone. Abbreviations: ME/CFS; myalgic encephalomyelitis/ chronic fatigue syndrome. Aldo; aldosterone, ADT; androsterone, AED; androstenedione, F; cortisol, Cot; cortisone, B; corticosterone, DOC; 1-deoxycorticosterone, 11DOC; 11-deoxycortisol, DHEA; dehydroepiandrosterone, Etn; etiocholanolone, 17OHP; 17α-hydroxyprogesterone, PNL; pregnanolone, P₅; pregnenolone, P₄; progesterone, T; testosterone, E1; estrone, E2; estradiol

Fig. 4

(A) Scores plot from the orthogonal partial least squares discriminant analysis (OPLS-DA) showing group separation between ME/CFS and control. (B) VIP scores plot from OPLS-DA identifying the most important variables driving group separation. Variables with VIP > 1.2 are considered significant contributors to the model

Fig. 5

OPLS-DA Scores and VIP plots for ME/CFS vs. control groups by gender. (A) Scores plot (Females) from orthogonal partial least squares discriminant analysis (OPLS-DA) showing group separation between ME/CFS and Control. (B) VIP Scores plot (Females) from OPLS-DA highlighting the most important variables contributing to group separation. (C) Scores plot (Males) from OPLS-DA showing group separation between ME/CFS and Control. (D) VIP scores plot (Males) from OPLS-DA highlighting the most important variables contributing to group separation