Association of Low Protein-to-Carbohydrate Energy Ratio with Cognitive Impairment in Elderly Type 2 Diabetes Patients
Bibliographic record
Abstract
Background/Objectives: The relationship between macronutrient intake and cognitive decline in older adults with type 2 diabetes mellitus (T2DM) remains underexplored. Methods: This cross-sectional study aimed to evaluate the association between the protein-to-carbohydrate energy ratio (%E:P) and cognitive impairment among 192 elderly T2DM patients. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA) and the Self-Administered Gerocognitive Exam (SAGE), while dietary intake data, including (%E:P), was gathered using a validated semi-quantitative food frequency questionnaire. Results: Participants had a mean age of 71 ± 6 years, 46.4% were female, and the median BMI was 30 ± 4 kg/m2. After adjusting for confounding variables, patients in the highest (%E:P) tertile showed significantly higher MoCA and SAGE scores compared to those in the lowest tertile (p < 0.005). We identified an optimal (%E:P) threshold of 0.375 for predicting cognitive impairment, with a sensitivity of 53% and specificity of 64%. Conclusions: These findings suggest that a lower (%E:P) ratio may be a risk factor for cognitive impairment in elderly T2DM patients. Monitoring this ratio may serve as an early detection tool for cognitive deterioration. Moreover, current protein intake recommendations for older adults with T2DM may be insufficient to prevent cognitive impairment. Further research is needed to establish optimal dietary guidelines for this population.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".