Limb Muscular Strength and Bone Mineral Density in Elderly Subjects with Low Skeletal Muscle Mass Index

Saddik, Hayman; Pinti, Antonio; Antoun, Amal; Rassy, Nathalie Al; Hage, Zaher El; Berro, Abdel-Jalil; Lespessailles, Éric; Toumi, Hechmi; Hage, Rawad El

doi:10.1016/j.jocd.2021.03.011

Cited by 9 publications

(1 citation statement)

References 29 publications

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“…LMI as one of the evaluation parameters of SOP diagnosis, our research showed that the higher the LMI, the lower the prevalence of SOP ( OR = 0.66, 95% CI : 0.52–0.85, P < 0.001). Previous studies have shown [ 24 ]that the correlation of skeletal muscle is also reflected in the change of one of the components will also cause the change of other components. Patients with sarcopenia had lower bone mineral density, and the occurrence of sarcopenia increased the risk of OP ( OR = 7.3, P < 0.001) [ 25 ].…”

Section: Discussionmentioning

confidence: 99%

Analysis of related factors for sarco-osteoporosis in middle-aged and elderly inpatients and development and validation of a nomogram

Juan Peng,

Gao,

Lou

et al. 2024

BMC Musculoskelet Disord

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Background Sarco-osteoporosis is a skeletal muscle disease associated with aging and complex pathological factors. At present, there are few studies on the analysis of its related factors, and a nomogram to estimate the risk of sarco-osteoporosis in middle-aged and elderly patients is not available. Methods A total of 386 patients admitted to our hospital from October 2021 to October 2022 were collected, and the general demographic data and clinical data of the patients were collected.386 subjects were enrolled in the study and randomly divided into training set and validation set at a ratio of 7:3. In the training set, the Least absolute shrinkage and selection operator(LASSO)regression technique was used to select the optimal predictive features, and multivariate logistic regression was used to screen the factors associated with sarco-osteoporosis, and a nomogram was constructed using meaningful variables from multivariate analysis. The performance of the nomograms was assessed and validated by Area Under Curve (AUC) and calibration curves. Results There were no significant differences in baseline characteristic of individuals in training set and validation set, six variables with non-zero coefficients were screened based on LASSO regression in the training set. Multivariate logistic regression analysis showed that the related factors for sarco-osteoporosis in middle-aged and elderly inpatients included age (OR = 1.08, 95%CI 1.03 ∼ 1.14), regular exercise (OR = 0.29, 95%CI 0.15 ∼ 0.56), albumin (OR = 0.9, 95%CI 0.82 ∼ 0.98), height (OR = 0.93, 95%CI 0.88 ∼ 0.99) and lean mass index (OR = 0.66, 95%CI 0.52 ∼ 0.85), and a nomogram was constructed based on the above factors. AUC of nomogram were 0.868(95%CI 0.825 ∼ 0.912) in the training set and 0.737(95%CI 0.646 ∼ 0.828) in the validation set. Calibration curve analysis showed that the predicted probability of sarco-osteoporosis had high consistency with the actual probability, and the absolute error of the training set and verification set was 0.018 and 0.03, respectively. Conclusions Our research showed that the occurrence of sarco-osteoporosis was associated with age, regular exercise, albumin, height and lean mass index, and we have developed a nomogram that can be effectively used in the preliminary and in-depth risk prediction of sarco-osteoporosis in middle-aged and elderly hospitalized patients.

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Section: Discussionmentioning

confidence: 99%