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Current Research Provides Insight into the Biological Basis and Diagnostic Potential for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS)
Open AccessArticle

Rethinking ME/CFS Diagnostic Reference Intervals via Machine Learning, and the Utility of Activin B for Defining Symptom Severity

1
National Centre for Epidemiology and Population Health, RSPH, College of Health and Medicine, The Australian National University, Canberra, ACT 2601, Australia
2
Paranta Biosciences Limited, Suite 549, 1 Queens Rd, Melbourne, VIC 3004, Australia
3
CFS Discovery, Donvale Specialist Medical Centre, Donvale, VIC 3111, Australia
4
Centre for Reproductive Health, Hudson Institute of Medical Research, Clayton, VIC 3168, Australia
5
Department of Anatomy and Developmental Biology, School of Biomedical Sciences, Monash University, Clayton, VIC 3800, Australia
*
Author to whom correspondence should be addressed.
Diagnostics 2019, 9(3), 79; https://doi.org/10.3390/diagnostics9030079
Received: 28 May 2019 / Revised: 9 July 2019 / Accepted: 15 July 2019 / Published: 19 July 2019
(This article belongs to the Special Issue Biomedical Insights that Inform the Diagnosis of ME/CFS)
Biomarker discovery applied to myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), a disabling disease of inconclusive aetiology, has identified several cytokines to potentially fulfil a role as a quantitative blood/serum marker for laboratory diagnosis, with activin B a recent addition. We explored further the potential of serum activin B as a ME/CFS biomarker, alone and in combination with a range of routine test results obtained from pathology laboratories. Previous pilot study results showed that activin B was significantly elevated for the ME/CFS participants compared to healthy (control) participants. All the participants were recruited via CFS Discovery and assessed via the Canadian/International Consensus Criteria. A significant difference for serum activin B was also detected for ME/CFS and control cohorts recruited for this study, but median levels were significantly lower for the ME/CFS cohort. Random Forest (RF) modelling identified five routine pathology blood test markers that collectively predicted ME/CFS at ≥62% when compared via weighted standing time (WST) severity classes. A closer analysis revealed that the inclusion of activin B to the panel of pathology markers improved the prediction of mild to moderate ME/CFS cases. Applying correct WST class prediction from RFA modelling, new reference intervals were calculated for activin B and associated pathology markers, where 24-h urinary creatinine clearance, serum urea and serum activin B showed the best potential as diagnostic markers. While the serum activin B results remained statistically significant for the new participant cohorts, activin B was found to also have utility in enhancing the prediction of symptom severity, as represented by WST class. View Full-Text
Keywords: myalgic encephalomyelitis; chronic fatigue syndrome; activin; pathology; biomarker; cytokine; machine learning; reference intervals myalgic encephalomyelitis; chronic fatigue syndrome; activin; pathology; biomarker; cytokine; machine learning; reference intervals
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Lidbury, B.A.; Kita, B.; Richardson, A.M.; Lewis, D.P.; Privitera, E.; Hayward, S.; de Kretser, D.; Hedger, M. Rethinking ME/CFS Diagnostic Reference Intervals via Machine Learning, and the Utility of Activin B for Defining Symptom Severity. Diagnostics 2019, 9, 79.

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