The mediating role of body surface area-adjusted basal metabolic rate: effects of low muscle mass and central obesity on cognitive impairment in Chinese patients with type 2 diabetes mellitus
Bibliographic record
Abstract
Background This study investigates the relationship between basal metabolic rate (BMR), body composition, obesity indices, and cognitive impairment (CI) in middle-aged and older type 2 diabetes mellitus (T2DM) patients, assessing their potential role in CI screening. Methods A cross-sectional study included 1243 T2DM patients over 45 years old. CI was assessed using the Montreal Cognitive Assessment. BMR and body composition indices were measured through bioelectrical impedance analysis. The associations and predictions related to CI were explored using multivariable-adjusted logistic regression, restricted cubic spline (RCS) models, and receiver operating characteristic (ROC) curve analyses. Mediation analysis explored the role of BMR adjusted by body surface area (BMR/BSA) in CI risk. Results Patients with CI showed significantly lower BMR, BMR adjusted for height squared (BMR/Height²), BMR/BSA, appendicular skeletal muscle mass (ASM), and fat-free mass (FFM), alongside higher waist circumference (WC) and percentage of body fat. Logistic regression showed that participants in the fourth quartile of BMR, BMR/Height2, and BMR/BSA had approximately a 54% reduced risk of CI (odds ratio range 0.457 to 0.463). RCS analysis indicated a linear decrease in CI risk with increasing BMR metrics. ROC analysis indicated high predictive efficacy for CI with combined indicators, particularly BMR and FFM (area under the curve 0.645). Mediation analysis suggested that BMR/BSA played a significant mediating role in WC, ASM and FFM on CI risk, with a mediation proportion ranging from 45.73% to 50.87%. Conclusion Low energy expenditure assessed by BMR/BSA is an independent risk factor for increased CI risk in middle-aged and elderly T2DM patients. Central obesity, low muscle mass, and low energy expenditure significantly elevate CI risk in 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 | 0.001 |
| 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 | 0.000 |
| 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".