Geographical Effects In Familial Clustering Of Physical Activity, Adiposity And Metabolic Syndrome
Bibliographic record
Abstract
Metabolic syndrome (MetS) risk factors, adiposity and physical activity (PA) levels have a multifactorial aetiology, comprising genetic and non-genetic factors. Notwithstanding the consistent findings about their aetiology, biological and behavioural traits do not explain the total variation and the increase of metabolic disorders and physical inactivity over the past decade. Additionally, links may be identified between built and natural environments, namely PA environments, on adiposity, MetS and PA phenotypes, as well as how they may affect different behaviours, especially within families. PURPOSE: The purpose of this study was to estimate the magnitude of genetic and environmental factors on adiposity, MetS risk factors and PA levels, and to investigate the role of PA environments on these traits. METHODS: The sample comprised 259 nuclear families (781 individuals) from a rural city of Portugal. All PA facilities’ locations and families’ home addresses were geocoded and Euclidian distances were calculated. Percentage of total body fat was estimated by bioelectrical impedance. Systolic and diastolic blood pressure, waist circumference, fasting glucose, triglycerides and total cholesterol were measured. PA was estimated by the Baecke questionnaire. Quantitative genetic models were used and computations performed in SOLAR software. RESULTS: Genetic and shared environmental factors explained 22% and 38% of PA and body fat total variance, respectively. MetS risk factors were moderate-to-highly heritable, ranging from 26% to 73%. Spatially structured data of PA environments had significant effects on MetS risk factors, adiposity and PA phenotypes (p<0.05), except for waist circumference, contributing to low adiposity levels (p<0.05), increases in PA (p<0.05), and being protective against the development of MetS risk factors (p<0.05). CONCLUSIONS: Taken together, these results have important implications for the design of intervention programs, which need to consider the familial context and PA environments to promote physically active lifestyles and their positive effects on health.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".