Relationship between protein intake and bone architecture or bone mineral density among dynapenic-obese older adults
Abstract
OBJECTIVE: The current study aimed to assess the relationship between protein intake and bone parameters among dynapenic-obese older adults.
DESIGN: The current study is a secondary analysis with an a posteriori and exploratory design.
SETTING: Subjects were recruited from the community via social communication (flyers and meetings in community centres) in the Great Montreal area.
PARTICIPANTS: Twenty-six subjects were divided a posteriori into two groups according to their usual protein intake: PROT-: 1·2 g/kg per d (n 13; women: 61·5 %; 67·2 (sd 2·7) years).
RESULTS: Both groups were comparable for age (PROT-: 66·5 (sd 3·3) v. PROT+: 67·2 (sd 2·7) years, P = 0·61) and gender (women: PROT-: n 7; 53·8 % v. PROT+: n 8; 61·5 %, P = 0·69). The PROT- group had a higher marrow area (P = 0·049), a greater bone compressive strength (P = 0·048) and a larger total bone area (P = 0·045) than the PROT+ group. However, no significant difference between the two groups was observed regarding body composition (fat and lean masses) or muscle composition.
CONCLUSIONS: A lower protein intake seems to be associated with bone sizes, which influence bone strength, but do not influence bone density among dynapenic-obese older people.
Read the paper
Related studies
- Development of a crushable foam model for human trabecular bone
- Data-driven motion compensation in HR-pQCT.
- Modeling of structure, quality, and function in the orthodontic patient
- Exploring the mechanism of Heidihuang Pill in the treatment of osteoporosis based on network pharmacology, molecular docking, and experimental validation
- Effect of fabric on the accuracy of computed tomography-based finite element analyses of the vertebra
- Use of interventions involving virtual reality tasks during the climacteric: a systematic review
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.