Clustering of body composition, blood pressure and physical activity in Portuguese families
Bibliographic record
Abstract
AIM: The purposes of this study were: (i) to identify familial resemblances in body fat, blood pressure (BP) and total physical activity (TPA); (ii) to estimate the magnitude of their genetic and environmental influences; and (iii) to investigate shared familial aggregation among these phenotypes. SUBJECTS AND METHODS: The sample comprised 260 nuclear families from Portugal. Body fat was assessed by bioelectrical impedance. BP was measured by an oscillometric device. TPA was estimated by the Baecke questionnaire. Familial correlation analyses were performed using Generalized Estimating Equations. Quantitative genetic modelling was used to estimate maximal heritability, genetic and environmental correlations. RESULTS: Familial intra-trait correlations ranged from 0.15-0.38. Genetic and common environmental factors explained from 30%--44% of fat mass depots and BP and 24% of TPA. Genetic correlations were significant between BP and the fat mass traits (p < 0.05). Environmental correlations were statistically significant between diastolic BP and total body fat, trunk fat and arm fat (p < 0.05) and TPA and other phenotypes. CONCLUSIONS: The results suggest familial resemblance in the variation of body fat, BP and TPA, showing partial pleiotropic effects in the variation in body fat phenotypes and BP. TPA only shares common environmental influences with BP and body fat traits.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".