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Assessing muscle quality as a key predictor to differentiate fallers from non-fallers in older adults.
Michel, Emeline; Zory, Raphael; Guerin, Olivier; Prate, Frederic; Sacco, Guillaume; Chorin, Fréderic.
Affiliation
  • Michel E; Department of Geriatric Medicine, Université Côte d'Azur, Centre Hospitalier Universitaire de Nice, Clinique Gériatrique de Soins Ambulatoires, 06003, Nice, France. michel.e2@chu-nice.fr.
  • Zory R; Université Côte d'Azur, LAMHESS, Nice, France. michel.e2@chu-nice.fr.
  • Guerin O; Université Côte d'Azur, LAMHESS, Nice, France.
  • Prate F; Institut Universitaire de France (IUF), Paris, France.
  • Sacco G; Department of Geriatric Medicine, Université Côte d'Azur, Centre Hospitalier Universitaire de Nice, Clinique Gériatrique de Soins Ambulatoires, 06003, Nice, France.
  • Chorin F; Université Côte d'Azur, INSERM, CNRS, Nice, France.
Eur Geriatr Med ; 2024 Aug 03.
Article in En | MEDLINE | ID: mdl-39096327
ABSTRACT

BACKGROUND:

Falling is an important public health issue because of its prevalence and severe consequences. Evaluating muscle performance is important when assessing fall risk. The study aimed to identify factors [namely muscle capacity (strength, quality, and power) and spatio-temporal gait attributes] that best discriminate between fallers and non-fallers in older adults. The hypothesis is that muscle quality, defined as the ratio of muscle strength to muscle mass, is the best predictor of fall risk.

METHODS:

184 patients were included, 81% (n = 150) were women and the mean age was 73.6 ± 6.83 years. We compared body composition, mean grip strength, spatio-temporal parameters, and muscle capacity of fallers and non-fallers. Muscle quality was calculated as the ratio of maximum strength to fat-free mass. Mean handgrip and power were also controlled by fat-free mass. We performed univariate analysis, logistic regression, and ROC curves.

RESULTS:

The falling patients had lower muscle quality, muscle mass-controlled power, and mean weighted handgrip than the non-faller. Results showing that lower muscle quality increases fall risk (effect size = 0.891). Logistic regression confirmed muscle quality as a significant predictor (p < .001, OR = 0.82, CI [0.74; 0.89]). ROC curves demonstrated muscle quality as the most predictive factor of falling (AUC = 0.794).

CONCLUSION:

This retrospective study showed that muscle quality is the best predictor of fall risk, above spatial and temporal gait parameters. Our results underscore muscle quality as a clinically meaningful assessment and may be a useful complement to other assessments for fall prevention in the aging population.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Eur Geriatr Med Year: 2024 Document type: Article Affiliation country: France Country of publication: Switzerland

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Eur Geriatr Med Year: 2024 Document type: Article Affiliation country: France Country of publication: Switzerland