Fibromyalgia diagnosis from a multi-omics approach: a gut feeling
Bibliographic record
Abstract
Background: Fibromyalgia is a complex disorder whose main symptoms are chronic widespread pain and fatigue and affects between 0.2 and 6.6% of the world population. Nowadays, there are no molecular biomarkers that could facilitate diagnosis. The latest efforts by researchers have focused on studying problems at the level of central nervous system sensitivity, inflammation, and oxidative disorders. Methods: A total of 892 women were initially enrolled in the study. For individuals who met the inclusion criteria, a plasma proteome analysis was conducted using blood samples. Briefly, blood was collected, centrifuged, and analyzed by liquid nano-chromatography coupled to tandem mass spectrometry. After the raw data analysis, proteins with statistically significant differential abundance and a fold change over 1.2 (20% increase in fibromyalgia compared with control samples) or under 0.8 (20% decrease in fibromyalgia compared with control samples) in fibromyalgia were selected. For fecal metagenome analysis, fecal samples were collected and processed for DNA extraction. Amplicon sequencing of V3-V4 regions from the 16S ribosomal RNA gene was performed using the Illumina MiSeq platform. The statistical analysis was conducted using R v4.3.2 base packages. Results: After applying exclusion criteria, 242 women (199 patients and 43 age- and environmentally paired controls) provided plasma and feces samples, as well as properly filled health questionnaires. A total of 30 proteins and 19 taxa were differentially expressed in fibromyalgia patients, and their integration into an algorithm allows for discrimination between cases and controls. The multi-omic approach for biomarker discovery in this study proposes a multifactorial connection between gut microbiota and mitochondria-derived oxidative stress and inflammation. Conclusions: as leading actors. Trial registration: NCT05921409.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".