Lidbury, Brett A, Kita, Badia, Richardson, Alice M et al. · Diagnostics (Basel, Switzerland) · 2019 · DOI
This study looked for blood test markers that could help diagnose ME/CFS and measure how severe someone's illness is. Researchers tested a protein called activin B along with routine blood work from pathology labs in people with ME/CFS and healthy controls. They used a computer learning method to see which combinations of blood markers worked best for identifying ME/CFS and predicting symptom severity.
ME/CFS currently lacks objective diagnostic criteria, forcing reliance on symptom-based diagnosis. Identifying reliable blood biomarkers like activin B could enable faster, objective diagnosis and provide clinicians with tools to objectively measure disease severity—potentially improving patient outcomes and enabling better monitoring of disease progression.
This study does not prove that activin B causes ME/CFS or explain the biological mechanism behind elevation. The cross-sectional design cannot establish causality or temporal relationships. Results require validation in independent prospective cohorts before clinical implementation; machine learning models trained on one dataset may not generalize reliably to different populations.
About the PEM badge: “PEM required” means post-exertional malaise was an explicit required diagnostic criterion for participant inclusion in this study — not that PEM was studied, observed, or discussed. Studies using criteria that do not require PEM (e.g. Fukuda, Oxford) are tagged “PEM not required”. How the atlas works →
The first block is for the primary paper and is the citation you should use in research work. The atlas-snapshot line only applies if you are specifically referring to this atlas’s reading of the paper on the date shown.
Primary citation
Lidbury, Brett A, Kita, Badia, Richardson, Alice M, Lewis, Donald P, Privitera, Edwina, Hayward, Susan, et al. (2019). Rethinking ME/CFS Diagnostic Reference Intervals via Machine Learning, and the Utility of Activin B for Defining Symptom Severity.. Diagnostics (Basel, Switzerland). https://doi.org/10.3390/diagnostics9030079
BibTeX
@article{mecfsatlas-lidbury-2019-rethinking-cfs,
author = {Lidbury, Brett A and Kita, Badia and Richardson, Alice M and Lewis, Donald P and Privitera, Edwina and Hayward, Susan and de Kretser, David and Hedger, Mark},
title = {Rethinking ME/CFS Diagnostic Reference Intervals via Machine Learning, and the Utility of Activin B for Defining Symptom Severity.},
journal = {Diagnostics (Basel, Switzerland)},
year = {2019},
doi = {10.3390/diagnostics9030079},
note = {PubMed: 31331036},
url = {https://www.mecfsatlas.com/evidence/lidbury-2019-rethinking-cfs},
}Atlas snapshot reference
ME/CFS Atlas. Generator v1 / Scanner v1.4 / policy v0.1. Accessed 2026-05-26. https://www.mecfsatlas.com/evidence/lidbury-2019-rethinking-cfs
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