Homocysteine and Nutritional Biomarkers In Cognitive Impairment
Bibliographic record
Abstract
Abstract Background: Cognitive impairment is a progressive disorder that affects the aging population. With the increase in the mean age of our population, it is becoming a public health problem. Homocysteinemia has been implicated in cognitive impairment. While it is modulated by vitamins B12 and folate, it acts through MMPs 2 and 9.Objectives: To assesses the relationship of cognitive impairment with homocysteine, B12, folate, and MMPs 2 and 9, with the intention of detecting cases of mild cognitive impairment which are potentially reversible.Materials and Methods: Blood samples were drawn from 73 enrolled subjects, with and without cognitive impairment on basis of Montreal cognitive assessment (MoCA) score <25 or >25, respectively. Homocysteine, B12, folate, and MMPs2 and 9 were estimated. Correlation between MoCA score and these parameters was elucidated.Results: After adjusting for age and sex, homocysteine was the only significant independent predictor of MoCA score. Cut off of homocysteine for prediction of MoCA <25 was derived at 13.5 µmol/L (PPV=59.6%; NPV=79.2%; Sensitivity=84.8%; Specificity=50%). The equation derived for calculation of MoCA score from homocysteine is: MoCA score = 32.893 + [(-0.223)(homocysteine in mmol/L)] Conclusions: Homocysteine >13.5mmol/L predicts low MoCA (<25) with 84.8% sensitivity and 50% specificity. So patients with a Hcy >13.5mmol/L should be carefully evaluated for the presence/progression of dementia and administered vitamins of the B group as a measure towards amelioration of the modifiable risk factor of cognitive decline, i.e. homocysteinemia.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".