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Machine Learning to Understand the Immune-Inflammatory Pathways in Fibromyalgia

Group of Psychological Research in Fibromyalgia & Chronic Pain (AGORA), Institut de Recerca Sant Joan de Déu, 08950 Esplugues de Llobregat, Spain
Teaching, Research & Innovation Unit, Parc Sanitari Sant Joan de Déu, 08830 St. Boi de Llobregat, Spain
Primary Care Prevention and Health Promotion Research Network, RedIAPP, 28013 Madrid, Spain
Faculty of Psychology, Universitat Autònoma de Barcelona, 08193 Bellaterra (Cerdanyola del Vallès Barcelona, Spain
Department of Rheumatology, Parc Sanitari Sant Joan de Déu, 08830 St. Boi de Llobregat, Spain
Parc Sanitari Sant Joan de Déu, CIBERSAM, 08950 Sant Boi de llobregat, Spain
Department of Psychiatry, Miguel Servet Hospital, Aragon Institute of Health Sciences (I+CS), 50009 Zaragoza, Spain
Department of Psychiatry, Chulalongkorn University, Bangkok 10330, Thailand
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Int. J. Mol. Sci. 2019, 20(17), 4231;
Received: 8 August 2019 / Revised: 24 August 2019 / Accepted: 26 August 2019 / Published: 29 August 2019
Fibromyalgia (FM) is a chronic syndrome characterized by widespread musculoskeletal pain, and physical and emotional symptoms. Although its pathophysiology is largely unknown, immune-inflammatory pathways may be involved. We examined serum interleukin (IL)-6, high sensitivity C-reactive protein (hs-CRP), CXCL-8, and IL-10 in 67 female FM patients and 35 healthy women while adjusting for age, body mass index (BMI), and comorbid disorders. We scored the Fibromyalgia Severity Score, Widespread Pain Index (WPI), Symptom Severity Scale (SSS), Hospital Anxiety (HADS-A), and Depression Scale and the Perceived Stress Scale (PSS-10). Clinical rating scales were significantly higher in FM patients than in controls. After adjusting for covariates, IL-6, IL-10, and CXCL-8 were lower in FM than in HC, whereas hs-CRP did not show any difference. Binary regression analyses showed that the diagnosis FM was associated with lowered IL-10, quality of sleep, aerobic activities, and increased HADS-A and comorbidities. Neural networks showed that WPI was best predicted by quality of sleep, PSS-10, HADS-A, and the cytokines, while SSS was best predicted by PSS-10, HADS-A, and IL-10. Lowered levels of cytokines are associated with FM independently from confounders. Lowered IL-6 and IL-10 signaling may play a role in the pathophysiology of FM. View Full-Text
Keywords: fibromyalgia; widespread pain; cytokines; inflammation; neuro-immune fibromyalgia; widespread pain; cytokines; inflammation; neuro-immune
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MDPI and ACS Style

Andrés-Rodríguez, L.; Borràs, X.; Feliu-Soler, A.; Pérez-Aranda, A.; Rozadilla-Sacanell, A.; Arranz, B.; Montero-Marin, J.; García-Campayo, J.; Angarita-Osorio, N.; Maes, M.; Luciano, J.V. Machine Learning to Understand the Immune-Inflammatory Pathways in Fibromyalgia. Int. J. Mol. Sci. 2019, 20, 4231.

AMA Style

Andrés-Rodríguez L, Borràs X, Feliu-Soler A, Pérez-Aranda A, Rozadilla-Sacanell A, Arranz B, Montero-Marin J, García-Campayo J, Angarita-Osorio N, Maes M, Luciano JV. Machine Learning to Understand the Immune-Inflammatory Pathways in Fibromyalgia. International Journal of Molecular Sciences. 2019; 20(17):4231.

Chicago/Turabian Style

Andrés-Rodríguez, Laura; Borràs, Xavier; Feliu-Soler, Albert; Pérez-Aranda, Adrián; Rozadilla-Sacanell, Antoni; Arranz, Belén; Montero-Marin, Jesús; García-Campayo, Javier; Angarita-Osorio, Natalia; Maes, Michael; Luciano, Juan V. 2019. "Machine Learning to Understand the Immune-Inflammatory Pathways in Fibromyalgia" Int. J. Mol. Sci. 20, no. 17: 4231.

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