Abstract

Diagnosing complex illnesses like Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is complicated due to the diverse symptomology and presence of comorbid conditions. ME/CFS patients often present with multiple health issues, therefore, incorporating comorbidities into research can provide a more accurate understanding of the condition's symptomatology and severity, to better reflect real-life patient experiences. We performed association studies and machine learning on 1194 ME/CFS individuals with blood plasma nuclear magnetic resonance (NMR) metabolomics profiles, and seven exclusive comorbid cohorts: hypertension (n = 13,559), depression (n = 2522), asthma (n = 6406), irritable bowel syndrome (n = 859), hay fever (n = 3025), hypothyroidism (n = 1226), migraine (n = 1551) and a non-diseased control group (n = 53,009). We present a lipoprotein perspective on ME/CFS pathophysiology, highlighting gender-specific differences and identifying overlapping associations with comorbid conditions, specifically surface lipids, and ketone bodies from 168 significant individual biomarker associations. Additionally, we searched for, trained, and optimised a machine learning algorithm, resulting in a predictive model using 19 baseline characteristics and nine NMR biomarkers which could identify ME/CFS with an AUC of 0.83 and recall of 0.70. A multi-variable score was subsequently derived from the same 28 features, which exhibited ~2.5 times greater association than the top individual biomarker. This study provides an end-to-end analytical workflow that explores the potential clinical utility that association scores may have for ME/CFS and other difficult to diagnose conditions. Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is an illness with severe fatigue without a known cause. Further symptoms of ME/CFS often overlap with other medical problems making diagnosis difficult. We wanted to find a way to easily identify people with this condition, so we used data from the UK Biobank to compare people with and without ME/CFS who had other medical problems. We developed a mathematical calculation, using 19 basic health factors and nine blood markers, which could classify ME/CFS and non-ME/CFS individuals correctly 83% of the time, and recognise this condition in individuals 70% of the time. This research could lead to a better way to diagnose ME/CFS and serve as an example for diseases lacking definite laboratory testing. Huang et al. train and optimize a machine learning model using patient characteristics and NMR biomarkers to predict Myalgic Encephalomyelitis/Chronic Fatigue Syndrome cases in the UK Biobank. This works explores the heterogenous symptoms and comorbidities of this condition.