Higher Framingham Risk Scores are associated with greater loss of brain volume over time in multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Few studies have evaluated the association between comorbidities associated with increased vascular risk and brain volume changes in multiple sclerosis (MS). To date, findings have not been consistent with respect to which comorbidities are associated with lower brain volumes or whether comorbidities associated with increased vascular risk are associated with greater brain volume loss over time. OBJECTIVES: We aimed to evaluate the association between the Framingham Risk Score (FRS) which evaluates vascular risk and normalized whole brain volume in MS. METHODS: We included 98 participants with MS who underwent two brain MRIs two years apart, from which whole brain volumes were calculated. Each participant reported their comorbidities and medications taken. Blood pressure, height and weight were recorded and we calculated the FRS. We tested the association between the FRS at baseline and brain volume at the second time point using quantile regression adjusting for baseline normalized brain volume, age, gender and use of disease-modifying therapy. RESULTS: As the FRS increased, brain volume was lower, both at enrollment (β= -0.24; 95%CI: -0.42, -0.04) and at follow-up (-0.27; 95%CI: -0.45, -0.08). After further adjustment for age, gender, and use of disease modifying therapy, higher FRS remained associated with lower brain volume at follow-up at the 90th percentile of brain volume (β= -2.22; 95%CI: -3.40, -1.04) but not at the 10th or 50th percentiles. CONCLUSION: Higher FRS were associated with lower brain volumes in persons with MS at baseline, and with brain volume loss over time. This effect was most pronounced for persons with higher brain volumes at baseline, which suggests that prevention, detection and effective management of comorbidities associated with vascular risk in people with MS is particularly important early in the disease course.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".