# Moving Toward Objective Diagnosis in Fibromyalgia: Emerging Biomarkers and Digital Phenotyping Tools

## TL;DR

This paper explores new biomarkers and digital tools that could help diagnose fibromyalgia more objectively, improving diagnosis and treatment.

## Contribution

The paper highlights novel biomarkers and digital phenotyping approaches for objective fibromyalgia diagnosis.

## Key findings

- Neuroimaging and inflammatory markers show promise as objective fibromyalgia indicators.
- Digital tools like wearables and machine learning can capture real-world symptom patterns.
- Combining biological and digital data may lead to more accurate fibromyalgia classification.

## Abstract

Fibromyalgia is a complex chronic pain condition characterized by pervasive pain, persistent fatigue, and cognitive disturbances. Despite advances in understanding its neurobiological mechanisms, diagnosis largely relies on subjective symptom assessment and exclusion criteria, contributing to underdiagnosis and treatment delays. Recent research has increasingly focused on identifying objective biomarkers and leveraging digital phenotyping to improve diagnostic precision. Promising biomarkers include neuroimaging indicators of altered pain processing, neuroinflammatory signatures in cerebrospinal fluid and blood, and dysregulated neuroendocrine and autonomic patterns. In addition, metabolomics and transcriptomics have revealed molecular profiles associated with fibromyalgia pathophysiology. Concurrently, digital health tools (e.g., wearable sensors, ecological momentary assessment, and machine learning-based symptom clustering) offer opportunities for continuous, real-world data collection and individualized disease characterization. This body of work suggests that integrating biological and digital metrics could enable a transition from subjective to objective data-driven fibromyalgia classification, facilitating earlier diagnosis and improved therapeutic outcomes.