DXA‐derived Abdominal Fat Mass, Waist Circumference, and Blood Lipids in Postmenopausal Women
Bibliographic record
Abstract
The purpose of this study was to determine the utility of dual-energy X-ray absorptiometry (DXA)-derived fat mass indices for predicting blood lipid profile in postmenopausal women. A secondary purpose was to determine whether waist circumference is comparable with DXA-derived measurements in predicting blood lipid profile. Subjects were 423 postmenopausal women (age 58.1 +/- 6.3 years). Fat mass was assessed at abdomen, trunk, and total body using DXA. Anthropometric measurements included BMI and waist circumference. Blood samples were analyzed for total cholesterol (TC), triglyceride (TAG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), and cholesterol/HDL ratio. Of the DXA-derived measures, abdominal-fat mass was the best predictor of blood lipid profiles. DXA-derived abdominal fat mass and waist girth explained 20 and 16.5% of variation in TC/HDL ratio, respectively, in univariate analysis, with no difference between the slopes of the regression coefficients. Eighty-four percent of subjects were common to the top quartiles of waist circumference and abdominal fat mass, and blood lipid profiles generally worsened across increasing quartiles. DXA-derived abdominal fat mass and waist circumference are of equivalent utility for predicting alterations in blood lipids. Waist circumference is, therefore, ideal as an inexpensive means in primary health-care services for predicting risk of cardiovascular diseases in postmenopausal women.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".