A Cross-Sectional Analysis of the Association between Physical Activity and Visceral Adipose Tissue Accumulation in a Multiethnic Cohort
Bibliographic record
Abstract
Higher levels of VAT at the same body size and lower levels of physical activity (PA) have been reported in persons of Chinese and South Asian origin compared to European origin. The purpose of this study was to test the hypothesis that higher levels of VAT in persons of Chinese and South Asian origin versus European origin are associated with lower levels of PA. Chinese, European, and South Asian participants were assessed for sociodemographics, obesity-related measures, anthropometrics, and PA. Bivariate correlations, analysis of covariance, and regression models were used to explore ethnic differences in PA and the role of PA in explaining obesity-related measures. We observed ethnic differences in both body fat distribution and PA. Chinese and South Asians had higher amounts of VAT at a given BMI but lower amounts of moderate PA, vigorous PA, and moderate-to-vigorous PA (MVPA). Furthermore, we found ethnic-specific differences in the associations between body fat distribution and PA with only Europeans showing a consistent negative relationship between body fat distribution and PA. When ethnic differences in PA were taken into account, there were no longer any differences in VAT between the Chinese and European groups, while VAT remained higher in South Asians than Europeans.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".