Evidence map›Paper›PMID 42565199›Full record

ArticlePain reports2026

Longitudinal stability of data-driven fibromyalgia phenotypes across repeated clinical assessments.

Kevin V Hackshaw, Michelle M Osuna-Diaz, Katherine R Sebastian, Lianbo Yu, Zhanna Mikulik, M Monica Giusti, Luis Rodriguez-Saona

Abstract read
In one paragraph

Article in Pain reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Kevin V HackshawDivision of Rheumatology, Department of Internal Medicine, Dell Medical School, The University of Texas at Austin, Austin, TX, USA.ORCID https://orcid.org/0000-0002-9780-7171
Michelle M Osuna-DiazDepartment of Internal Medicine, Dell Medical School, The University of Texas at Austin, Austin, TX, USA.
Katherine R SebastianDepartment of Internal Medicine, Dell Medical School, The University of Texas at Austin, Austin, TX, USA.
Lianbo YuCenter of Biostatistics and Bioinformatics, The Ohio State University, Columbus, OH, USA.
Zhanna MikulikDivision of Immunology and Rheumatology, Department of Internal Medicine, The Ohio State University, Columbus, OH, USA.
M Monica GiustiDepartment of Food Science and Technology, The Ohio State University, Columbus, OH, USA.
Luis Rodriguez-SaonaDepartment of Food Science and Technology, The Ohio State University, Columbus, OH, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Fibromyalgia is a heterogeneous chronic condition characterized by variability in pain, psychological distress, central sensitization, and functional impairment. Cross-sectional studies have identified reproducible symptom phenotypes; however, their longitudinal stability remains incompletely understood. Methods: We analyzed longitudinal data from 821 adults with fibromyalgia recruited from 2 academic medical centers. Standardized assessments included the Revised Fibromyalgia Impact Questionnaire, Beck Depression Inventory, Central Sensitization Inventory, McGill Pain Questionnaire, and selected SF-36 functional domains. Unsupervised clustering was performed at baseline to derive symptom phenotypes, which were ordered by overall severity. Using baseline-derived model parameters, phenotype membership was assigned at subsequent visits. Phenotype transitions were examined between visit 1 and visit 2 (n = 191) and across visits 1 to 3 (n = 72) using transition matrices and alluvial visualizations. Results: Two clinically interpretable fibromyalgia phenotypes were identified at baseline, representing lower-severity/preserved function and higher-severity/global impairment profiles. Across 6-month follow-up intervals, most participants remained within their baseline phenotype (61.9% from visit 1→2 and 64.8% from visit 2→3). Transitions occurred in a minority of individuals and were primarily between adjacent severity phenotypes. Among participants with 3 or more visits, phenotype membership remained stable across repeated assessments. Conclusion: Data-driven fibromyalgia phenotypes demonstrate substantial longitudinal stability, supporting their validity as enduring clinical constructs rather than transient symptom states. These findings support phenotype-based stratification for longitudinal research and precision treatment approaches in fibromyalgia. Longitudinal analysis shows that fibromyalgia symptom phenotypes are largely stable over time, with infrequent transitions occurring mainly between adjacent severity groups.

Indexed as

Central sensitizationChronic painFibromyalgiaFunctional impairmentLongitudinal stabilitySymptom phenotyping

Identifiers

PMID42565199
PMCPMC13446975

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.