Bone health and mineral metabolism in 14- to 18-year-old adolescents with usual low intake of milk products: implications of micronutrient intakes and response to a motivational interviewing dietary intervention trial
Notice bibliographique
Résumé
Background: Maximizing peak bone mass during growth is an important strategy to prevent osteoporosis. To date, only 5 trials assessed bone health during adolescence; none were Canadian or included males. This dissertation aimed to: (1) assess the nutritional adequacy of bone-relevant nutrients in adolescents with low habitual intakes of MILK and to evaluate the relationship between intakes and bone density and serum biomarkers; (2) test the effect of increased MILK intake on changes in bone density in adolescents with usual MILK intake of < 2 servings per day; and (3) determine whether pentadecanoic acid (C15:0) and heptadecanoic acid (C17:0) can serve as biomarkers of compliance of milk intake. Design: Data are from the 1-year end-point from the Family Milk Product 2-Year dose-response study, a 2-year randomized controlled trial that took place in Montreal, QC. Eligibility included healthy adolescents 14-18.9 y who consumed < 2 servings of MILK/d, with a healthy body mass index (BMI), and no vitamin D deficiency or anemia. Adolescents (n=94) were randomized to 3 groups: control, Improved (IInt: consumed 3 MILK servings/d); or Recommended (RInt: consumed ≥ 4 servings/d). Visits occurred every 6 months (mo). Motivational interviewing technique was used to improve MILK intake and to optimize adherence. Bone outcomes were assessed by dual-energy X-ray absorptiometry and by peripheral quantitative computed tomography. Dietary intake was assessed by 24 h food recalls and a validated semi-quantitative, food-frequency questionnaire (FFQ). Physical activity (PA) was assessed using the Youth Physical Activity Questionnaire. C15:0 and C17:0 were measured in erythrocytes and plasma to assess compliance to the MILK intervention at 6 and 12 mo. Results: (Objective 1) Eighty-one adolescents (55 females and 26 males, 16.5 ± 1.6 y) were included. None of the participants met the Estimated Average Requirements (EAR) for potassium or vitamin D. In males, less than 30% met the EAR for calcium and 23% for magnesium. In females, 15% met the EAR for calcium and magnesium and 40% for phosphorus. FFQ data revealed significant positive associations between fluid milk and WB BMDZ; total MILK with trabecular density; fluid milk with tibia cortical thickness; and fluid milk and yogurt with bone turnover markers, after adjustment for covariates. (Objective 2) Ninety-four teens 14-18 y were recruited with a BMI z-score of 0.3 ± 0.9. At baseline, there were no differences in MILK intake among study groups, stratified by sex. At 12 mo, WB BMC and BMDZ and TH BMD and BMDZ significantly increased in the female RInt group (p<0.03) and greater percentage increases in WB BMC and WB and TH BMD were observed compared to control and IInt groups (p<0.05). Intervention effects on DXA outcomes were not significant in males. Furthermore, females in RInt and control groups increased radial trabecular area and cortical density at 66% radius and 38% tibia (p<0.02); males in the RInt group significantly increased tibial cortical density at 38% (p=0.02). However, intervention effects on pQCT outcomes, bone biomarkers and body composition were not significant in either sex. (Objective 3) At 12 mo, erythrocyte C15:0 increased in RInt group (+0.37 µg/ml, p=0.01). MILK intake improved significantly in both intervention groups from baseline to 12 mo (IInt: 1.65±1.53 vs 3.0±1.60 servings/d; RInt: 1.3±1.0 vs 3.7±1.0 servings/d, respectively). Further, MILK intake positively correlated with erythrocyte C15:0 and C17:0 at 12 mo. Conclusion: Increasing MILK intake may improve the adequacy of certain nutrients and thus bone health and metabolism in post-pubertal adolescents, specifically females with low habitual calcium intake. Further, erythrocyte C15:0 can detect short and longer-term MILK intakes during adolescence.
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 | 0,003 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,002 | 0,002 |
| 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 ».