Lifestyle habits and their relation to insulin sensitivity and insulin secretion in youth
Notice bibliographique
Résumé
Background: Decreased insulin sensitivity and impaired pancreatic B-cell function have been identified as key components in the pathogenesis of type 2 diabetes mellitus. Understanding how to best measure insulin dynamics in epidemiologic studies in youth, and determining how lifestyle habits influence these measures are essential to the development of preventive strategies for at risk youth. Objectives: 1) To identify the best measures of insulin sensitivity and insulin secretion that can be used in large epidemiologic studies in children 2) To determine how physical activity, fitness, sedentary behavior, and macronutrient intake are associated with these measures of insulin sensitivity and insulin secretion in children 3) To determine if lifestyle habits predict insulin sensitivity over a 2 year period in childrenMethods: For the first objective, 20 healthy children with normal glucose metabolism (9 boys and 11 girls, mean (SD) age: 9(2) years) were studied. Each child underwent a 3-hour hyperinsulinemic-euglycemic clamp study (gold standard for measuring insulin sensitivity), an insulin modified minimal model FSIVGTT, and a 3-hour oral glucose tolerance test (OGTT). Various measures of both insulin sensitivity and insulin secretion were calculated, and correlations against the reference method were established using Spearman's rank correlations. For objectives 2 and 3, data were drawn from the baseline and first follow-up assessments of the QUALITY cohort, which includes 630 Caucasian youth (aged 8-10 years at recruitment) with at least one obese biological parent. Measures of insulin sensitivity and secretion were derived from fasting data and OGTT data. Fitness was measured by VO2 peak; percent fat mass (PFM) was measured by DXA; 7-day moderate-to-vigorous physical activity was measured using accelerometry. Screen time was determined by the average daily hours of self-reported television, video game or computer use. Multivariable linear regression models were adjusted for age, sex, season and puberty. Non-parametric smoothing splines were used to model non-linear associations between lifestyle habits and measures of insulin sensitivity or secretion. Results: Several measures of insulin sensitivity and insulin secretion derived from the OGTT, as well as from fasting-based data, performed well against the reference methods. Physical activity and screen time were associated with insulin sensitivity both cross-sectionally and longitudinally. This association was largely mediated by adiposity. Fitness was independently associated with insulin sensitivity. Finally, dietary composition was not associated with insulin sensitivity or secretion in children.Conclusions:The OGTT allows the estimation of insulin sensitivity and insulin secretion in youth and is the method that most closely mimics normal physiology. Lifestyle habits play an important role in insulin dynamics in youth, with adiposity however showing the greatest influence.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».