Association of waist-to-hip ratio adjusted for body mass index with cognitive impairment in middle-aged and elderly patients with type 2 diabetes mellitus: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Numerous reports indicate that both obesity and type 2 diabetes mellitus (T2DM) are factors associated with cognitive impairment (CI). The objective was to assess the relationship between abdominal obesity as measured by waist-to-hip ratio adjusted for body mass index (WHRadjBMI) and CI in middle-aged and elderly patients with T2DM. METHODS: A cross-sectional study was conducted, in which a total of 1154 patients with T2DM aged ≥ 40 years were included. WHRadjBMI was calculated based on anthropometric measurements and CI was assessed utilizing the Montreal Cognitive Assessment (MoCA). Participants were divided into CI group (n = 509) and normal cognition group (n = 645). Correlation analysis and binary logistic regression were used to explore the relationship between obesity-related indicators including WHRadjBMI, BMI as well as waist circumference (WC) and CI. Meanwhile, the predictive power of these indicators for CI was estimated by receiver operating characteristic (ROC) curves. RESULTS: WHRadjBMI was positively correlated with MoCA scores, independent of sex. The Area Under the Curve (AUC) for WHRadjBMI, BMI and WC were 0.639, 0.521 and 0.533 respectively, and WHRadjBMI had the highest predictive power for CI. Whether or not covariates were adjusted, one-SD increase in WHRadjBMI was significantly related to an increased risk of CI with an adjusted OR of 1.451 (95% CI: 1.261-1.671). After multivariate adjustment, the risk of CI increased with rising WHRadjBMI quartiles (Q4 vs. Q1 OR: 2.980, 95%CI: 2.032-4.371, P for trend < 0.001). CONCLUSIONS: Our study illustrated that higher WHRadjBMI is likely to be associated with an increased risk of CI among patients with T2DM. These findings support the detrimental effects of excess visceral fat accumulation on cognitive function in middle-aged and elderly T2DM patients.
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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 | 0.001 |
| 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".