Sphingomyelin is involved in multisite musculoskeletal pain: evidence from metabolomic analysis in 2 independent cohorts
Bibliographic record
Abstract
ABSTRACT: Metabolic dysfunction has been suggested to be involved in musculoskeletal pain; however, few studies have identified metabolic markers associated with multisite musculoskeletal pain (MSMP). This study sought to identify metabolic marker(s) for MSMP by metabolomic analysis. The Tasmanian Older Adult Cohort Study (TASOAC) provided the discovery cohort with the Newfoundland Osteoarthritis Study (NFOAS) providing the replication cohort. Multisite musculoskeletal pain was assessed by a self-reported pain questionnaire and defined as painful sites ≥4 in both the TASOAC and the NFOAS. Furthermore, MSMP was also defined as painful sites ≥7, whereas non-MSMP was defined as either painful sites <7 or ≤1 in the NFOAS. Serum samples of the TASOAC received metabolic profiling using The Metabolomics Innovation Centre Prime Metabolomics Profiling Assay. The data on the identified metabolites were retrieved from NFOAS metabolomic database for the purpose of replication. A total of 409 participants were included in the TASOAC, 38% of them had MSMP. Among the 143 metabolites assessed, 129 passed quality control and were included in the analysis. Sphingomyelin (SM) C18:1 was significantly associated with MSMP (odds ratio [OR] per log µM increase = 3.96, 95% confidence interval, 1.95-8.22; P = 0.0002). The significance remained in multivariable analysis (OR per log µM increase = 2.70, 95% confidence interval, 1.25-5.95). A total of 610 participants were included in the NFOAS, and the association with SM C18:1 was successfully replicated with 3 MSMP definitions (OR ranging from 1.89 to 2.82; all P < 0.03). Our findings suggest that sphingomyelin metabolism is involved in the pathogenesis of MSMP, and the circulating level of SM C18:1 could serve as a potential marker in the management of MSMP.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".