Sunshine and Synapses: Exploring the Vitamin D-Cognition Nexus among Young Tribal Adults of Meghalaya
Bibliographic record
Abstract
Abstract Introduction: The relationship between Vitamin D (VitD) levels and cognitive function in young adults remains unclear, with conflicting results in existing literature. This study aimed to assess the prevalence of VitD deficiency and mild cognitive impairment (MCI) and to explore the correlation between serum VitD levels and cognitive functions in young indigenous adults in Meghalaya. Methods: A hospital-based cross-sectional study was conducted in a tertiary care centre in Shillong, Meghalaya, involving 137 healthy young individuals associated with the centre. Serum VitD levels were measured and categorised according to the classifications of the Food and Nutrition Board, National Academies of Sciences, Engineering, and Medicine, and the Endocrine Society. Cognitive functions were assessed using the Montreal Cognitive Assessment scale. Results: Based on the Endocrine Society Classification, 85.4% (95% CI: 78.5%–90.3%) of participants were found to be VitD-deficient, and 51.1% (95% CI: 42.4%–59.7%) had MCI. A statistically significant positive correlation was observed between serum VitD levels and cognitive functions (r = 0.096; P = 0.025). Adequate sun exposure was independently associated with higher serum VitD levels, and females were found to have better cognition. Conclusion: The study revealed a high prevalence of VitD deficiency and MCI in the young indigenous population with an identified positive association between the two conditions. However, the findings highlight the need for large-scale, multi-centric, longitudinal studies with follow-up assessments to further understand these relationships.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".