Understanding the role of physical activity, physical performance and dietary protein intake on muscle mass and insulin resistance in seniors
Notice bibliographique
Résumé
The decrease in physical performance (PP) with aging is in part mediated through body composition changes. With aging there is a gain and central redistribution of fat depots and a loss of lean tissue, mainly skeletal muscle. The loss of muscle mass has been implicated in the risk of developing insulin resistance. Physical activity could act as a predictor and an outcome of PP and is a modulator of insulin resistance. Age, sex, energy intake and chronic diseases can also affect PP and insulin resistance. Dietary protein intake is an easy and inexpensive modality to combat loss of muscle mass. Contrary to plant source of protein intake, animal source of dietary protein may confer an increased risk of insulin resistance and diabetes. Determining insulin resistant subjects in epidemiological studies is challenging because of the lack of cut-off for scores used to assess insulin sensitivity. Analyses of effects of interrelationships are complex and leads to challenging interpretations. Our first objective was to explore the complex interrelationships of body composition, physical performance, physical activity, protein intakes and insulin resistance. Our second objective was to determine subjects who were insulin resistant subjects over a 3-year period and compare them to insulin sensitive subjects in regard to body composition measures and other baseline characteristics. A sample of elderly men and women, non-diabetic, community-dwellers participants of the Quebec Longitudinal Study on Nutrition and Successful Aging (NuAge Study) were analysed. Tests employed to assess PP were analyzed by principal component analysis and generated two indices, one related to strength and the other to mobility. Muscle mass index (MMI; kg/height in m2) and % body fat were derived from dual X-ray absorptiometry and bioimpedance analysis. Physical activity was assessed by the Physical Activity Scale for the Elderly, energy intakes and protein intakes were calculated from three non-consecutive 24h-food recalls. Insulin resistance was estimated based on the Homeostasis Model Assessment score. Proposed models associating these variables were tested for validity with the NuAge data using path analysis and employed trajectory analyses to established incidence of insulin resistance. MMI and % body fat were both negatively associated with mobility score, however, muscle mass was positively associated with strength independently of other variables. Direct positive associations were observed for HOMA-IR score with MMI and % body fat. There was a significant, direct negative association for plant protein intake with MMI, whereas there was no association with HOMA-IR. There were significant, positive indirect associations between animal protein intake and HOMA-IR score and significant negative indirect associations between plant protein intake and HOMA-IR mediated through MMI and % body fat. In the longitudinal analyses, 7 group-based trajectories were identified with good posterior probabilities. An inspection of the curves allowed for determination of insulin sensitive subjects and classification of insulin resistance subjects. The logistic regression with the most parsimonious model provided only 3 significant predictors of insulin resistance: higher MMI, higher body fat% and male sex. Muscle mass was associated with strength but positively associated with HOMA score. This relationship is counterintuitive since it suggests muscle mass with aging is positively associated with insulin resistance. There were significant, positive indirect associations between animal protein intake and HOMA-IR score and significant negative indirect associations between plant protein intake and HOMA-IR. These indirect associations were mediated through MMI and % body fat. Our longitudinal analyses showed that a higher muscle mass, % body fat and male sex contribute to a higher odd of insulin resistance with aging.
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 | 0,001 |
| 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 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| 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 ».