Correlation between the Cognitive Status (SIRT1) and the Metabolic Function in Geriatric Patients Using the Indonesian Version of the Montreal Cognitive Assessment (MoCA-INA)
Bibliographic record
Abstract
A growing life expectancy may result in a chronic medical condition and multimorbidity because the aging process leads to a decrease in cognitive and physiological function. These risks may affect the quality of life of geriatrics. The present study aims to determine the correlation between cognitive status (in terms of SIRT1, a nicotinamide adenine dinucleotide (NAD+)-dependent class III deacetylase) and metabolic function (in terms of the lipid profile, kidney function, and blood glucose) in geriatric patients. The differences in the parameters of metabolic function in the participants’ cognitive status were determined by using the Indonesian version of the Montreal Cognitive Assessments (MoCA-Ina). The elderly participants (n = 120) were recruited at three sites in Indonesia from March to October 2022. Our study demonstrated a negative correlation between the cognitive status of geriatric patients and their metabolic function, represented by the MoCA-Ina score with a linear regression equation of y = 0.27 − 2.4 ×10−3x. Higher levels of LDL-C, cystatin C, and HbA1c were found in the Severe-Moderate Cognitive Impairment group. Determining the SIRT1 levels may be beneficial in predicting both the cognitive and metabolic status of geriatrics because this protein is among numerous metabolic sensors in the hypothalamus.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".