Background/Purpose: The diagnosis of fibromyalgia (FM) is challenging due to its reliance on patient-reported symptoms, the absence of definitive biomarkers, and numerous overlapping comorbidities. Discrepancies between diagnostic criteria and clinical practice imply the possibility of diagnostic biases, complicating timely and accurate identification. This study aimed to identify factors associated with receiving a FM diagnosis, among individuals who met 2016 criteria, and explore potential barriers to diagnosis.

Methods: Using data from UK Biobank, a total of 27 potential factors across four biopsychosocial domains were analysed for differences between diagnosed and undiagnosed individuals. Multivariate log-binomial regression was used within and across domains to evaluate prevalence ratios (PR) regarding the independent association of factors with receiving a FM diagnosis. To avoid potential selection bias, we conducted a sensitivity analysis including all UK Biobank participants who completed the Experience of Pain question, regardless of whether they met the 2016 FM criteria.

Results: Among the 4,197 individuals meeting FM criteria, 72.6% reported not having received a diagnosis. We identified eight factors significantly associated with not being diagnosed: being male (fully adjusted PR 0.47, 95% confidence interval 0.41-0.56), older age (0.85, 0.75-0.96), self-reported concomitant diagnosis of neuropathy (0.91, 0.83-0.99), shorter pain duration (0.56, 0.41-0.76), lower polysymptomatic distress scores (0.56, 0.51-0.62), and less frequent fatigue (0.85, 0.76-0.96). In contrast diagnosis was more likely amongst people who also reported a diagnosis of chronic fatigue syndromes (1.65, 1.50-1.82) and complex regional pain syndrome (1.13, 1.03-1.25). The sensitivity analysis conducted on the whole population sample demonstrated results generally consistent with the main analysis.

Conclusion: Our research has identified significant disparities in the diagnosis of FM at the population-level in the UK, particularly among patients without stereotypical characteristics, with less severe symptoms, or the existence of concomitant chronic pain conditions. Understanding these diagnostic patterns can help improve alignment between criteria for identifying FM and real-world practice.

Figure 1. Diagnosis status among people meeting the 2016 fibromyalgia criteria

Figure 2. Factors associated with fibromyalgia diagnosis in the fully adjusted model 3

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ACR Meeting Abstracts - https://acrabstracts.org/abstract/factors-associated-with-fibromyalgia-diagnosis-amongst-people-meeting-criteria-results-from-uk-biobank/