Diet-related inflammation increases the odds of multiple sclerosis: Results from a large population-based prevalent case-control study in Jordan
Bibliographic record
Abstract
Background Multiple sclerosis, a chronic inflammatory disease in young and middle-aged adults, is one of the leading causes of non-traumatic disability in adults. Diet is known to have an important role in the modulating inflammatory processes and influencing molecular pathways. Purpose This study aims to examine the association of the inflammatory capacity of diet measured by DII with MS in Jordan. Methods This prevalent case-control study included participants of both sexes, aged between 20 and 60 years. The cases (n = 541) had a confirmed diagnosis of prevalent Multiple Sclerosis (MS) in the previous 3 years, and controls (n = 607) were apparently healthy individuals matched on sex and age (42 ± 4 years). A validated Arabic food frequency questionnaire (FFQ) was utilized to obtain estimated dietary intake. Dietary data from the FFQ were analyzed using ESHA’s Food Processor® nutrition analysis software, and the results were used to calculate the DII scores. Logistic regression analyses, controlling for covariates such as age, sex, body mass index, and smoking status, were used to measure the association between DII score and MS outcomes. Results Cases represent a mixed sample of MS phenotypes and controls were comparable on age and sex. However, controls tended to be taller, lighter, had a lower BMI, and had a lower smoking rate. After controlling for age, BMI, sex, and smoking status, there was a consistent increase in MS risk according to DII score, with a 10-fold increase in odds in quartile 4 vs. quartile 1 [ORquartile 4vs1 = 10.17 (95% CI: 6.88; 15.04)]. For each point increase in DII score, there was nearly a doubling of odds [OR1 = 1.75 (95% CI: 1.59; 1.92)]. Individual nutrients and food values aligned according to their contribution to the DII score calculations. Conclusion The findings of this study, obtained in MS patients with varied illness duration over the previous 3 years, are consistent with an association between the overall inflammatory potential of diet and MS odds. Our findings among MS participants showed a significantly more pro-inflammatory DII scores than age- and sex-matched controls. Our results also suggest that MS group had a diet rich in pro-inflammatory foods and nutrients.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".