Increased glutamate and deep brain atrophy can predict the severity of multiple sclerosis
Bibliographic record
Abstract
OBJECTIVE: In multiple sclerosis (MS), deep grey matter (DGM) atrophy has been recognised as a crucial component of the disease that presents early and it has been associated with disability. Although the precise mechanism underlying grey matter atrophy is unknown, several hypotheses have been postulated. Our previous research pointed to correlations of hypothalamic metabolic alterations with clinical outcomes of MS, therefore we decided to further test the relationship of these alterations with DGM atrophy. METHODS: H-MRS) of the hypothalamus to test its metabolites in 26 patients with RRMS and 22 healthy age-matched controls. DGM atrophy was evaluated by simple planimetry of third ventricular width on the hypothalamic level (3VW) in T1 weighted MRI pictures. Metabolite ratios of N-acetyl aspartate (NAA), choline (Cho), glutamate and glutamine (Glx), myo-inositol (mIns) and creatine (Cr) were correlated with Multiple Sclerosis Severity Scale (MSSS) and 3VW. RESULTS: Metabolite concentrations were compared between patients and controls using multiple regression models allowing for age, 3VW and metabolites. It revealed that the only relevant predictors of MSSS were 3VW and Glx/NAA. At a significance level of P<0.05, a unit increase of 3VW was associated with a 0.35 increase of MSSS, for a typical value of Glx/NAA; P value 0.0039. A unit increase of Glx/NAA was associated with a 0.93 increase of MSSS, for a typical value of atrophy; P value 0.090. There were significant linear correlations between Glx/Cr and MSSS, Glx/NAA and MSSS, and between mIns/NAA and 3VW. CONCLUSIONS: H-MRS parameters seem to be superior to other metabolites in determining disease burden, independently of otherwise powerful 3VW planimetry. Significantly increased mIns/NAA in MS patients compared to controls point to gliosis, which parallels the atrophy of hypothalamic DGM.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".