How does Clusters of Parental Characteristics Influences Offspring Adiposity: A Prospective Study
Bibliographic record
Abstract
Introduction: Childhood obesity rates have increased exponentially in the past three decades. Parental characteristics, such as weight status, physical activity (PA), education and smoking habits have been identified individually as being potential determinants of offspring obesity. However, no prospective studies have examined the joint impact of parental lifestyle habits on their offspring's adiposity. We identified clusters of parental characteristics, and estimated their influence on offspring adiposity in late adolescence. \n \n Methods: Data stem from the QUALITY Cohort, a longitudinal study of children with at least one obese parent. Children were evaluated at 8-10y (n=630), 10-12y (n=564), and 15-17y (n=377). Parental smoking habits, PA and education were self-reported. Weight and height were obtained and body mass index (BMI) was calculated. Cluster analysis was performed on 209 families with complete data across all 3 evaluation cycles. We performed cluster analysis on mothers and fathers separately using partitioning around medoids (PAM) to identify parental phenotype clusters based on 4 parental characteristics (BMI, PA, education and smoking habits). Linear regressions, adjusted for child age, sex and Tanner stage, were used to assess associations between clusters (mothers and fathers) and measures of childhood adiposity (BMI z-score) at 15-17y. \n \n Results: Three clusters were identified among mothers and four clusters among fathers. Mothers in cluster 1 (n=18) were obese, less educated, smoked, and tended to be more active; cluster 2 (n=109) were overweight, educated and non-smokers; cluster 3 (n=82) were overweight, less educated, non-smokers and tended to be less active. Fathers in cluster 1 (n=109) were less educated and non-smokers, cluster 2 (n=68) were educated and non-smokers, cluster 3 (n=23) were less educated and smokers and cluster 4 (n=9) were older, educated and smokers. \n \n Children of obese, less educated and smoking mothers(cluster 1) had higher adiposity measurements compared with children of overweight, educated, non-smokingmothers (cluster 2), with an increase in BMI z-score of +0.94 (95% CI: 0.35-1.53); P=0.002. Child adiposity measurements were comparable across father phenotype clusters. \n \n Conclusions: Targeting obese and less educated mothers who smoke to promote the adoption of healthier lifestyle habits may be effective at preventing later adiposity in their offspring.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".