Machine learning identifies fatigue as a key symptom of [FM] reflected in tyrosine, purine, pyrimidine, and glutaminergic metabolism 2024 Zetterman+

Abstract

Fibromyalgia patients vary in clinical phenotype and treatment can be challenging. The pathophysiology of fibromyalgia is incompletely understood but appears to involve metabolic changes at rest or in response to stress. We enrolled 54 fibromyalgia patients and 31 healthy controls to this prospective study. Symptoms were assessed using the Fibromyalgia Impact Questionnaire (FIQ) and blood samples were collected for metabolomics analysis at baseline and after an oral glucose tolerance test and a cardiopulmonary exercise test.

We identified key symptoms of fibromyalgia and related them to changes in metabolic pathways with supervised and unsupervised machine learning methods. Algorithms trained with the FIQ information assigned the fibromyalgia diagnosis in new data with balanced accuracy of 88% while fatigue alone already provided the diagnosis with 86% accuracy. Supervised analyses reduced the metabolomic information from 77 to 13 key markers. With these metabolites, fibromyalgia could be identified in new cases with 79% accuracy. In addition, 5-hydroxyindole-3-acetic acid and glutamine levels correlated with the severity of fatigue. Patients differed from controls at baseline in tyrosine and purine pathways, and in the pyrimidine pathway after the stress challenges. Several key markers are involved in glutaminergic neurotransmission. This data-driven analysis highlights fatigue as a key symptom of fibromyalgia. Fibromyalgia is associated with metabolic changes which also reflect the degree of fatigue. Responses to metabolic and physical stresses result in a metabolic pattern that allows discrimination of fibromyalgia patients from controls and narrows the focus on key pathophysiological processes in fibromyalgia as treatment targets.


Study Highlights

• WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC?

Among fibromyalgia patients, there is a lot of variation in symptom profiles. There are also many hypothesized pathophysiological mechanisms for fibromyalgia, including changes in metabolism. However, it remains unclear what are the contributions of individual symptoms to fibromyalgia symptom burden, what metabolic changes are relevant for fibromyalgia, and whether different metabolic anomalies could be linked to individual symptoms of fibromyalgia.

• WHAT QUESTION DID THIS STUDY ADDRESS?

Through data-driven analysis we assessed (1) the impact of individual symptoms in fibromyalgia; (2) metabolic differences between fibromyalgia patients and healthy controls at baseline and in stress responses; and (3) the association of the most significant symptom and metabolism.

• WHAT DOES THIS STUDY ADD TO OUR KNOWLEDGE?

Our results highlight the impact of fatigue on fibromyalgia symptom burden. We also indicate tyrosine, purine, and pyrimidine pathways as well as changes in metabolites involved in glutaminergic neurotransmission as possible pathophysiological mechanisms of fibromyalgia, and 5-hydroxyindole-3-acetic acid and glutamine as associating with fatigue in fibromyalgia. Importantly, most of the metabolic findings only became evident in stress responses.

• HOW MIGHT THIS CHANGE CLINICAL PHARMACOLOGY OR TRANSLATIONAL SCIENCE?

Metabolomic research could benefit from more emphasis on dynamic assessment of metabolism under diverse metabolic conditions, such as the stress challenges employed in our study.

Open access, https://ascpt.onlinelibrary.wiley.com/doi/10.1111/cts.13740

 

Or it could mean that if you're fatigued, your body can't process chemicals as effectively. They might not be tired because of less serotonin production or catabolism, but production is down because of being tired.

A reliable marker would be useful, and understanding what causes the abnormal levels of metabolites could lead to treatments, but it seems too early in the research to claim what is cause and what is effect. I suppose suggesting that it's a cause increases the chance of more funding.