Metabolic dysfunctions in multiple sclerosis: implications as to causation, early detection, and treatment, a case control study
Bibliographic record
Abstract
BACKGROUND: Biochemical changes associated with multiple sclerosis (MS), and its various clinical forms have not been characterized well. Therefore, we investigated the biochemistry of MS in relation to its natural history using targeted lipidomics platforms. METHODS: Cross-sectional serum samples from 24 secondary progressive (SPMS), 100 relapsing remitting (RRMS), 19 primary progressive MS (PPMS), and 55 age-matched control subjects were analyzed by flow injection tandem mass spectrometry for very long chain fatty acid (VLCFA) containing phosphatidyl ethanolamines (PtdEtn), plasmalogen ethanolamines (PlsEtn) and for novel anti-inflammatory gastrointestinal tract acids (GTAs). Changes in analyte levels relative to healthy controls were correlated with the disease stage and disease duration. RESULTS: RRMS subjects having <13 years disease duration had elevated levels (p < 0.05) of anti-inflammatory metabolites (GTAs) and normal levels (p > 0.05) of mitochondrial stress biomarkers (VLCFA-PtdEtn), compared to controls. SPMS subjects had statistically similar levels of anti-inflammatory metabolites (GTAs), elevated mitochondrial stress metabolites (VLCFA-PtdEtn) and elevated peroxisomal metabolites (PlsEtn) compared to controls (p < 0.05). RRMS subjects with > = 13 years disease duration exhibited metabolic profiles intermediate between short-duration RRMS and SPMS, based on statistical significance. Therefore, RRMS cohort appear to comprise of two metabolically distinct subpopulations. The key clinical discriminator of these two groups was disease duration. PPMS patients exhibited metabolic profiles distinct from RRMS and SPMS. CONCLUSIONS: These data indicate that inflammation and mitochondrial stress are intricately involved in the etiology of MS and that progression in MS can potentially be monitored using serum metabolic biomarkers.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".