Computed tomography-measured adipose tissue attenuation and area both predict adipocyte size and cardiometabolic risk in women
Bibliographic record
Abstract
OBJECTIVE: To assess the ability of CT-derived measurements including adipose tissue attenuation and area to predict fat cell hypertrophy and related cardiometabolic risk. METHODS: Abdominal adipose tissue areas and radiologic attenuation were assessed using 4 CT images in 241 women (age: 47 years, BMI: 26.5 kg/m(2)). Fat cell weight was measured in paired VAT and SAT samples. Fasting plasma lipids, glucose and insulin levels were measured. RESULTS: Adipose tissue attenuation was negatively correlated with SAT (r=-0.46) and VAT (r=-0.67) fat cell weight in the corresponding depot (p<0.0001 for both). Women with visceral adipocyte hypertrophy had higher total-, VLDL-, LDL- and HDL-triglyceride and apoB levels as well as a higher cholesterol/HDL-cholesterol ratio, fasting glucose and insulin levels compared to women with smaller visceral adipocytes. Adjustment for VAT area minimized these differences while subsequent adjustment for attenuation eliminated all differences, with the exception of fasting glycaemia. In SAT, adjustment for VAT area and attenuation eliminated all adipocyte hypertrophy-related alterations except for fasting hyperglycaemia. CONCLUSION: CT-derived adipose tissue attenuation and area both contribute to explain variation in the cardiometabolic risk profile associated with the same biological parameter: visceral fat cell hypertrophy.
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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 | 0.003 |
| 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".