Peripheral blood age-sensitive immune markers in multiple sclerosis: relation to sex, cytomegalovirus status, and treatment
Bibliographic record
Abstract
BACKGROUND: Immunosenescence is accelerated by chronic infectious and autoimmune diseases and could contribute to the pathobiology of multiple sclerosis (MS). How MS and disease-modifying therapies (DMTs) impact age-sensitive immune biomarkers is only partially understood. METHODS: We analyzed 771 serum samples from 147 healthy controls and 289 people with MS (PwMS) by multiplex immunoassays. We determined cytomegalovirus (CMV) serostatus and collected retrospective clinical information. We performed unsupervised and multivariable analyses. FINDINGS: Unsupervised analyses revealed that MS immune profile was characterized by low relative levels of anti-inflammatory/neuroprotective factors IL-4, IL-10, TNF, and β-NGF but high levels of growth factors EGF and bFGF. Serum levels of IL-4, β-NGF, IL-27, BDNF, and leptin were significantly influenced by sex and/or CMV status. IL-4 and β-NGF levels were lower in untreated PwMS compared to controls, while EGF and bFGF levels were influenced by age and markedly elevated in PwMS in multivariable analysis. Samples from treated PwMS, but not untreated PwMS, showed lower levels of BDNF and TNF than controls. Initiation of high efficacy DMTs, but not low efficacy DMTs, was associated with reduced levels of bFGF and EGF. Samples associated with distinct DMTs exhibited specific profiles for age-sensitive immune markers. Finally, lower levels of IL-6, TNF, IL-10, and β-NGF were observed at baseline in PwMS who subsequently experienced clinical failure after DMTs initiation. INTERPRETATION: Age, sex, CMV status, and specific DMTs significantly influence levels of age-sensitive immune biomarkers associated with MS and must be considered when investigating inflammation-related biomarkers. FUNDING: This work was supported by a Grant for Multiple Sclerosis Innovation by Merck KGaA (ID: 10.12039/100009945).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".