Association of triglyceride glucose-body mass index with Alzheimer’s disease pathology, cognition and brain structure in non-demented people
Bibliographic record
Abstract
Abstract The relationship between the triglyceride glucose-body mass index (TyG-BMI) index and Alzheimer’s disease (AD) pathology, cognition, and brain structure remains unclear. This study aimed to investigate these associations, focusing on cerebrospinal fluid (CSF) biomarkers, cognitive measures, and brain imaging data. Eight hundred and fifty-five non-demented participants were included. Linear regression was used to explore associations between the TyG-BMI index and AD pathology, cognition, and brain structure. The association between the TyG-BMI index and AD risk was assessed using Kaplan–Meier and Cox proportional hazards models. Longitudinal relationships were assessed using linear mixed-effects models. Mediation analyses were conducted to examine AD pathology’s potential mediating role between the TyG-BMI index and cognition as well as brain structure. In the linear regression analyses, higher TyG-BMI levels were associated with increased Aβ42 and decreased Tau, pTau, Tau/Aβ42, pTau/Aβ42, and pTau/Tau. Positive correlations were observed with mini-mental state examination (MMSE), memory (MEM), executive function (EF), and the volumes of the hippocampus, entorhinal cortex, and middle temporal regions, while negative correlations were found with Alzheimer’s Disease Assessment Scale (ADAS). Longitudinally, the TyG-BMI index was inversely associated with ADAS, and positively with MMSE, MEM, EF, hippocampus, entorhinal, and middle temporal. High TyG-BMI levels were correlated with lower AD risk (HR 0.996 [0.994, 0.999]). Mediation analyses revealed AD pathology mediated the association between TyG-BMI index and cognition as well as brain structure. Additionally, the TyG-BMI index could mediate cognitive changes by influencing brain structure. The TyG-BMI index is associated with AD pathology, cognition, and brain structure.
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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 | 0.001 |
| 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".