Association between the Geriatric Nutritional Risk Index and mild cognitive impairment in elderly patients with type 2 diabetes mellitus
Bibliographic record
Abstract
Background Diabetes increases the risk of mild cognitive impairment (MCI). The Geriatric Nutritional Risk Index (GNRI) is an objective indicator for assessing malnutrition risk, and aging and malnutrition are closely associated with MCI. However, the relationship between GNRI and MCI in older type 2 diabetes mellitus (T2DM) remains unclear. Objective To investigate the correlation between GNRI and MCI in elderly patients with T2DM. Methods In this cross-sectional study, 366 T2DM patients aged ≥ 60 years were divided into MCI and normal cognitive function (NCF) group according to the Montreal Cognitive Assessment (MoCA). Nutritional status was evaluated by calculating GNRI levels. GNRI levels and MoCA scores were compared between two groups, and correlation and regression analysis were performed to explore the association between GNRI and MCI. Results The GNRI level in the MCI group was significantly lower than that in the NCF group ( p < 0.05) and positively correlated with MoCA scores ( r = 0.783, p < 0.001). MCI prevalence increased progressively with decreasing GNRI levels. After adjusting for confounders, the odds of MCI were significantly higher in the 92 ≤ GNRI ≤ 98 and GNR < 92 groups compared to GNRI < 98 ( p < 0.05). Binary logistic regression identified that after adjusting for age, education level, body mass index, HbA1c, HOMA-IR, 25(OH)D and DR, GNRI as an independent protective factor for T2DM with MCI (OR = 0.783, 95%CI 0.648–0.874, p < 0.001). Conclusions Lower GNRI levels are associated with increased risk of MCI in elderly T2DM patients; GNRI is a potential predictor of MCI. Assessing nutritional status of elderly with T2DM facilitates the early clinical recognition of MCI.
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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 | 0.002 |
| 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 | 0.001 |
| 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".