The relationship between dietary protein intake distribution and lean mass loss in free‐living older adults: effect of sex and total protein intake.
Notice bibliographique
Résumé
Background Insufficient dietary protein is a plausible contributing factor to the age‐related loss of lean mass. In addition to quantity, an even protein intake distribution across meals has been shown to enhance 24h muscle protein synthesis in young adults. Whether these short‐term results translate into long‐term preservation of lean mass in older adults remains unknown. Objective To investigate the associations between the quantity and distribution of daily protein intake and lean mass (LM) and appendicular LM (aLM) at baseline (T1) and as a 2 y‐change (T3) in community‐dwelling older adults. Methods A secondary data analysis of the Quebec longitudinal study on nutrition as a determinant of successful aging (NuAge, n=1793, aged 67–84 y at T1) was performed among 351 men and 361 women with available body composition data measured by DXA at T1 and T3. Food intake was assessed from 3 non‐consecutive 24h food recalls at T1. Protein distribution across meals was calculated as the coefficient of variation (CV) of g protein ingested/meal, with lower values reflecting evenness of protein intake. Associations were examined using multivariate regression models adjusted for baseline age, energy intake, physical activity (PASE) questionnaire, smoking, fat mass, diabetes, and total protein intake (when studying protein distribution). Results Over 2 years, men lost 2.5±4.0% LM and 1.5±4.8% aLM, women lost 2.0±3.4% LM ( P <0.05 vs. men) and 1.2±5.3% aLM ( P =ns vs. men). Protein intake distribution at breakfast, lunch and dinner was 20/35/41% in men and 18/38/39% in women (P<0.05 vs. men). After adjustment for potential confounders, energy‐adjusted protein intake was associated with LM ( P <0.05), and aLM ( P <0.05) both at T1 and T3 only in men. However, the link between total protein intake and LM and aLM at T3 was abolished by further adjustment for baseline LM and aLM, indicating their predominant predictive values. The CV of protein intake distribution was negatively associated with T1 LM [β±SE: −3.27±1.31; P <0.05] and aLM [−1.43±0.67; P <0.05] in the fully adjusted model, again only in men; but not with T3 when adjusted for baseline values. Interestingly, in half of the cohort with protein intake below the median (<1.0 g/kg/d), protein distribution was associated with aLM in men and women at both time points, but not when controlling for T1 aLM. This association was not significant in participants with protein intake ≥1.0 g/kg/d. Consistently, 2‐y changes in LM or aLM were not related to the quantity‐ or distribution of protein intake in either sex. Conclusions Greater quantity and even distribution of daily protein intake were independently and cross‐sectionally associated with higher LM and aLM in men, and not related to changes over 2 years in either sex. Protein distribution may have more impact on lean mass when intakes are low, i.e. below 1.0 g/kg/d, in both men and women. These findings could have implications in establishing recommendations, upon confirmation with larger cohorts and longer‐term follow‐up. Support or Funding Information Funded by Dairy Farmers of Canada and Fonds de la recherche en sante‐Quebec.
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 | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| 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 ».