Diabetes and anxiety adversely affect cognition in multiple sclerosis
Bibliographic record
Abstract
OBJECTIVE: To determine whether comorbid diabetes and hypertension are associated with cognition in multiple sclerosis (MS) after accounting for psychiatric comorbidities. METHODS: Participants completed a structured psychiatric interview, the Hospital Anxiety and Depression Scale (HADS), a comorbidity questionnaire, and cognitive testing including the Symbol Digit Modalities Test (SDMT), California Verbal Learning Test (CVLT-II), Brief Visuospatial Memory Test-Revised (BVMT-R), and verbal fluency. Test scores were converted to age-, sex- and education-adjusted z-scores. We evaluated associations between diabetes and hypertension and the four cognitive z-scores using a multivariate linear model, adjusting for comorbid depression and anxiety disorders, psychotropic medications, disease-modifying therapies, smoking status and body mass index. RESULTS: Of 111 participants, most were women (82.9%) with relapsing remitting MS (83.5%), of mean (SD) age 49.6 (12.7) years. Comorbidity was common; 22.7% participants had hypertension, 10.8% had diabetes, 9.9% had current major depression, and 9.9% had current anxiety disorders. Mean (SD) z-scores were: SDMT -0.66 (1.15), CVLT-II -0.43 (1.32), BVMT-R -0.49 (1.07) and fluency -0.59 (0.86). Diabetes (p = 0.02) and anxiety disorder (p = 0.02) were associated with cognitive function overall. Diabetes was associated with lower BVMT-R (β = -1.18, p = 0.0015) and fluency (β = -0.63, p = 0.037) z-scores. Anxiety was associated with lower SDMT (β = -1.07, p = 0.0074) z-scores. Elevated anxiety symptoms (HADS-A ≥ 11) were associated with lower z-scores on the SDMT and CVLT-II. CONCLUSION: Comorbidities, including diabetes and anxiety, are associated with cognitive dysfunction in MS. Their presence may contribute to the heterogeneous pattern of impairments seen across individuals and they may represent targets for improved management of cognitive symptoms.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".