The gut microbiota has a specific signature for fibromyalgia

Fibromyalgia (FM) is one of the most common forms of chronic generalized pain, with an estimated prevalence of 2–4% in the adult population. FM is characterized by pain, tiredness, sleeping difficulties and cognitive symptoms. Since there is no objective diagnostic criterion other than these symptoms, FM causes a significant deterioration in people’s quality of life. The growing understanding of the interactions between the gut microbiota and the central nervous system, also known as the “gut–brain axis”, suggests that it may also affect pain processing and perception. To better understand the pathophysiology of FM, the gut microbiota of 77 Canadian women suffering from FM (on average, aged 46 years and diagnosed 12 years earlier) and of 79 control subjects (11 first-degree relatives, 20 members of patients’ households and 48 unrelated controls) were compared based on the analysis of 16S rRNA and whole genomes

Taxa associated with the severity of FM

No difference was observed between FM patients and healthy subjects in terms of diversity and overall structure of the microbial population. However, closer examination showed an association between the abundance of several taxa and the severity of symptoms linked to FM, including pain intensity, pain localization, tiredness, sleep disorders and cognitive symptoms.

A specific signature

The comparison of patients and control subjects also revealed a specific signature in the fecal microbiota of FM patients: 19 species were identified–some relatively less abundant in FM patients, while others were more abundant. Several are involved in the metabolism of butyrate and propionate, two short-chain fatty acids. The serum concentrations of these fatty acids are altered in FM patients, who have higher levels of butyrate and lower levels of propionate. These fatty acids could be involved in the mechanisms linking the dysbiosis and the symptoms observed. Finally, certain taxa in FM patients are also observed in other dysfunctional syndromes–irritable bowel syndrome, chronic fatigue syndrome, interstitial cystitis–while others seem to be specific to FM.

An identification algorithm?

Beyond a better understanding of FM, or even potential therapeutic applications, the researchers also open the path to a possible diagnostic test for FM patients. A machine learning (sidenote: Machine Learning an artificial intelligence technology that allows computers to learn solely on the basis of a very large collection of data )  algorithm based only on the composition of the microbiota seems able to distinguish FM patients from control subjects with a prediction accuracy of 87.8%.