Chronic Pain Fits Three Types | 医療法人社団EPSDC

1. Introduction: Why Is “Your Pain” So Hard to Treat?

Chronic pain is now a serious public health challenge, accounting for 71% of global disability. Yet what is the reality in clinical practice? Over 90% of low back pain is still diagnosed as “nonspecific,” and many patients spend significant time and money repeating trial-and-error before finding a treatment that fits them.

As thousands of randomized controlled trials have shown, the one-size-fits-all approach has reached its limit. To break this stalemate, the latest research (Hodges et al., 2025) used machine learning to pinpoint the biological mechanisms of pain and chart a course toward individualized treatments.

2. Finding 1: Pain Converges into “Three Clusters”—Machine Learning Confirms the Validity of This Classification

The International Association for the Study of Pain (IASP) defines pain mechanisms in three categories: nociceptive pain, neuropathic pain, and nociplastic pain.

Hodges and colleagues analyzed data from 350 patients with chronic musculoskeletal pain using unsupervised machine learning. Crucially, the choice of algorithm mattered. Hard clustering (K-means), which strictly partitions data, matched expert clinical judgment only 59% of the time. In contrast, a soft-clustering approach—Gaussian Mixture Models (GMM), which accept overlap and ambiguity—achieved 82% agreement with experienced clinicians.

“Results confirmed that data could be best explained by 3 clusters… agreed with the designation of the experienced clinician with 82% accuracy.”

This suggests that probabilistic models that account for overlapping distributions are better suited to capturing complex human biological responses than black-and-white methods.

3. Finding 2: Common Comorbidities Lack Discriminative Validity

In clinics, sleep disturbance, fatigue, and cognitive issues (e.g., poor concentration) are often referenced as markers of pain severity. However, supervised machine learning in this study revealed a surprising truth:

These comorbidities show little discriminative validity for identifying pain mechanisms. Sleep problems and fatigue are common features across pain types and are not decisive signs for mechanistic classification.

Furthermore, tools often used to flag neuropathic pain—such as the Neuropathic Pain Questionnaire (NPQ)—contributed little to discrimination in the present analysis. This likely reflects the stricter definition of neuropathic pain (from “dysfunction” to “lesion or disease” of the nervous system) and the overlap of features like sensory hypersensitivity with the newly defined nociplastic pain.

4. Finding 3: The “Obligatory” Criterion for Nociplastic Pain

Nociplastic pain, which occurs in the absence of clear tissue or nerve damage, has been the hardest to identify. The Discussion and data (Table 2) highlight key elements that help define this category:

– Regional distribution of pain: a non-dermatomal, widespread pattern—this is an obligatory criterion.

– Objective hypersensitivity on physical testing: heightened responses to mechanical or thermal stimuli.

– Aftersensations: unpleasant sensations persisting after the stimulus ends.

A crucial insight is that objective responses measured by physical testing carry greater value for mechanism identification than subjective reports of being “sensitive.”

5. Finding 4: The Real-World Perspective of “Mixed Types”

Probabilistic analysis showed that pain mechanisms are not always singular. The fuzzy partition coefficient (FPC) did not indicate 100% certainty, with notable overlap—especially for neuropathic pain. This likely reflects a process in which pain may begin nociceptively, then evolve or coexist with central plastic changes characteristic of nociplastic pain.

For clinicians, the key is not fixating on a single diagnostic label but determining which mechanism is currently dominant and tailoring strategy to that proportional mix.

6. Conclusion: Toward Individualized Pain Care

This study provides strong evidence for a shift from “symptom suppression” to “mechanism-based matching” for each patient. The 82% agreement achieved by GMM demonstrates that the true nature of pain behind complex clinical presentations can be scientifically classified. By reading pain not merely as an unpleasant symptom but as a sign of underlying mechanisms, we can put an end to unproductive trial-and-error.

Scientific progress is steadily unlocking the black box of pain.

What message is your pain conveying about your body right now?

Reference
Hodges, P. W., Sanchez, R., Pritchard, S., Turnbull, A., Hahne, A., & Ford, J. (2025). Toward validation of clinical measures to discriminate between nociceptive, neuropathic, and nociplastic pain: cluster analysis of a cohort with chronic musculoskeletal pain. The Clinical Journal of Pain, 41(5), e1281.

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