Biomarkers for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS): a systematic review
Notice bibliographique
Résumé
Abstract Background Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a multifaceted condition that affects most body systems. There is currently no known diagnostic biomarker; instead, diagnosis is dependent on application of symptom-based case criteria following exclusion of any other potential medical conditions. While there are some studies that report potential biomarkers for ME/CFS, their efficacy has not been validated. The aim of this systematic review is to collate and appraise literature pertaining to a potential biomarker(s) which may effectively differentiate ME/CFS patients from healthy controls. Methods This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and Cochrane review guidelines. PubMed, Embase and Scopus were systematically searched for articles containing “biomarker” and “ME/CFS” keywords in the abstract or title and if they included the following criteria: (1) were observational studies published between December 1994 and April 2022; (2) involved adult human participants; (3) full text is available in English (4) original research; (5) diagnosis of ME/CFS patients made according to the Fukuda criteria (1994), Canadian Consensus Criteria (2003), International Consensus Criteria (2011) or Institute of Medicine Criteria (2015); (6) study investigated potential biomarkers of ME/CFS compared to healthy controls. Quality and Bias were assessed using the Joanna Briggs Institute Critical Appraisal Checklist for Case Control Studies. Results A total of 101 publications were included in this systematic review. Potential biomarkers ranged from genetic/epigenetic (19.8%), immunological (29.7%), metabolomics/mitochondrial/microbiome (14.85%), endovascular/circulatory (17.82%), neurological (7.92%), ion channel (8.91%) and physical dysfunction biomarkers (8.91%). Most of the potential biomarkers reported were blood-based (79.2%). Use of lymphocytes as a model to investigate ME/CFS pathology was prominent among immune-based biomarkers. Most biomarkers had secondary (43.56%) or tertiary (54.47%) selectivity, which is the ability for the biomarker to identify a disease-causing agent, and a moderate (59.40%) to complex (39.60%) ease-of-detection, including the requirement of specialised equipment. Conclusions All potential ME/CFS biomarkers differed in efficiency, quality, and translatability as a diagnostic marker. Reproducibility of findings between the included publications were limited, however, several studies validated the involvement of immune dysfunction in the pathology of ME/CFS and the use of lymphocytes as a model to investigate the pathomechanism of illness. The heterogeneity shown across many of the included studies highlights the need for multidisciplinary research and uniform protocols in ME/CFS biomarker research.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,012 | 0,050 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,011 | 0,011 |
| Bibliométrie | 0,010 | 0,010 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».