DXA-Measured Abdominal Adipose Depots and Structural Brain Integrity in Postmenopausal Women
Bibliographic record
Abstract
BACKGROUND: This study extends prior research from the MRI substudy of the Women's Health Initiative Memory Study (WHIMS-MRI) linking BMI to reduced brain atrophy and ischemic lesion load by examining DXA-based measurements of total body fat, total abdominal adipose tissue (TAT), abdominal visceral (VAT) and subcutaneous (SAT) adipose tissue, gynoid fat, and overall leg fat. METHODS: The analytic sample consisted of 61 postmenopausal women (baseline mean age 69.5 [3.6]) enrolled in WHIMS-MRI who had undergone DXA scans. DXA scans were completed at years 0, 3, and 6, and MRI scans were conducted ~8 years after baseline. Adjusted linear regression models were used to analyze the association between adiposity averaged across the 3-time points and volumes of brain regions previously linked to dementia. RESULTS: Higher levels of total body fat, TAT, VAT, SAT, gynoid, and overall leg fat were associated with larger hippocampal volume (β 0.02 [95% CI, 0.004-0.04]; 0.11 [0.02-0.21]; 0.26 [0.04-0.47]; 0.18 [0.03-0.33]; 0.18 [0.05-0.30]; 0.07 [0.009-0.12], respectively). No other significant associations were observed. CONCLUSION: Higher levels of adiposity were positively associated with hippocampal volume. Additional research with larger sample sizes is needed to ascertain the significance of this association.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".