How does Clusters of Parental Characteristics Influences Offspring Adiposity: A Prospective Study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».