Integrated causal inference modeling uncovers novel causal factors and potential therapeutic targets of Qingjin Yiqi granules for chronic fatigue syndrome

Abstract

Objective: Chronic fatigue syndrome (CFS) is a prevalent symptom of post-coronavirus disease 2019 (COVID-19) and is associated with unclear disease mechanisms. The herbal medicine Qingjin Yiqi granules (QJYQ) constitute a clinically approved formula for treating post-COVID-19; however, its potential as a drug target for treating CFS remains largely unknown. This study aimed to identify novel causal factors for CFS and elucidate the potential targets and pharmacological mechanisms of action of QJYQ in treating CFS.
Methods: This prospective cohort analysis included 4,212 adults aged ≥65 years who were followed up for 7 years with 435 incident CFS cases. Causal modeling and multivariate logistic regression analysis were performed to identify the potential causal determinants of CFS. A proteome-wide, two-sample Mendelian randomization (MR) analysis was employed to explore the proteins associated with the identified causal factors of CFS, which may serve as potential drug targets. Furthermore, we performed a virtual screening analysis to assess the binding affinity between the bioactive compounds in QJYQ and CFS-associated proteins.
Results: Among 4,212 participants (47.5% men) with a median age of 69 years (interquartile range: 69-70 years) enrolled in 2004, 435 developed CFS by 2011. Causal graph analysis with multivariate logistic regression identified frequent cough (odds ratio: 1.74, 95% confidence interval [CI]: 1.15-2.63) and insomnia (odds ratio: 2.59, 95% CI: 1.77-3.79) as novel causal factors of CFS. Proteome-wide MR analysis revealed that the upregulation of endothelial cell-selective adhesion molecule (ESAM) was causally linked to both chronic cough (odds ratio: 1.019, 95% CI: 1.012-1.026, Pβ=β2.75 e^-05) and insomnia (odds ratio: 1.015, 95% CI: 1.008-1.022, Pβ=β4.40 e^-08) in CFS. The major bioactive compounds of QJYQ, ginsenoside Rb2 (docking score: -6.03) and RG4 (docking score: -6.15), bound to ESAM with high affinity based on virtual screening.
Conclusions: Our integrated analytical framework combining epidemiological, genetic, and in silico data provides a novel strategy for elucidating complex disease mechanisms, such as CFS, and informing models of action of traditional Chinese medicines, such as QJYQ. Further validation in animal models is warranted to confirm the potential pharmacological effects of QJYQ on ESAM and as a treatment for CFS.

Keywords

Causal factors / Causal graph analysis / Chronic fatigue syndrome / Drug targets / Mendelian randomization / Qingjin Yiqi

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