Background: Low muscle mass is associated with sarcopenia and increased mortality. Muscle mass, especially that of the limbs, is commonly estimated by dual-energy X-ray absorptiometry (DXA) or bioimpedance analysis (BIA). However, BIA-based predictive equations for estimating lean appendicular soft tissue mass (ALST) do not take into account body fat distribution, an important factor influencing DXA and BIA measurements.Objectives: To develop and cross-validate a BIA-based equation for estimating ALST with DXA as criterion, and to compare our new formula to three previously published models.Methods: One-hundred eighty-four older adults (140 women and 44 men) (age 71.5 +/- 7.3 years, body mass index 27.9 +/- 5.3 kg/m2) were recruited. Participants were randomly split into validation (n = 118) and crossvalidation groups (n = 66). Bioelectrical resistance was obtained with a phase-sensitive 50 kHz BIA device. Results: A BIA-based model was developed for appendicular lean soft tissue mass [ALST (kg) = 5.982 + (0.188 x S2 / resistance) + (0.014 x waist circumference) + (0.046 x Wt) + (3.881 x sex) - (0.053 x age), where sex is 0 if female or 1 if male, Wt is weight (kg), and S is stature (cm) (R2 = 0.86, SEE = 1.35 kg)]. Cross validation revealed r2 of 0.91 and no mean bias. Two of three previously published models showed a trend to significantly overestimate ALST in our sample (p < 0.01).Conclusions: The new equation can be considered valid, with no observed bias and trend, thus affording practical means to quantify ALST mass in older adults.

Predictive equation for assessing appendicular lean soft tissue mass using bioelectric impedance analysis in older adults: Effect of body fat distribution

Campa, F
;
2021

Abstract

Background: Low muscle mass is associated with sarcopenia and increased mortality. Muscle mass, especially that of the limbs, is commonly estimated by dual-energy X-ray absorptiometry (DXA) or bioimpedance analysis (BIA). However, BIA-based predictive equations for estimating lean appendicular soft tissue mass (ALST) do not take into account body fat distribution, an important factor influencing DXA and BIA measurements.Objectives: To develop and cross-validate a BIA-based equation for estimating ALST with DXA as criterion, and to compare our new formula to three previously published models.Methods: One-hundred eighty-four older adults (140 women and 44 men) (age 71.5 +/- 7.3 years, body mass index 27.9 +/- 5.3 kg/m2) were recruited. Participants were randomly split into validation (n = 118) and crossvalidation groups (n = 66). Bioelectrical resistance was obtained with a phase-sensitive 50 kHz BIA device. Results: A BIA-based model was developed for appendicular lean soft tissue mass [ALST (kg) = 5.982 + (0.188 x S2 / resistance) + (0.014 x waist circumference) + (0.046 x Wt) + (3.881 x sex) - (0.053 x age), where sex is 0 if female or 1 if male, Wt is weight (kg), and S is stature (cm) (R2 = 0.86, SEE = 1.35 kg)]. Cross validation revealed r2 of 0.91 and no mean bias. Two of three previously published models showed a trend to significantly overestimate ALST in our sample (p < 0.01).Conclusions: The new equation can be considered valid, with no observed bias and trend, thus affording practical means to quantify ALST mass in older adults.
2021
File in questo prodotto:
File Dimensione Formato  
ALST older adults.pdf

Accesso riservato

Tipologia: Published (Publisher's Version of Record)
Licenza: Accesso privato - non pubblico
Dimensione 810.5 kB
Formato Adobe PDF
810.5 kB Adobe PDF Visualizza/Apri   Richiedi una copia
h_11585_843271_aam.pdf

accesso aperto

Tipologia: Preprint (AM - Author's Manuscript - submitted)
Licenza: Creative commons
Dimensione 1.12 MB
Formato Adobe PDF
1.12 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3464128
Citazioni
  • ???jsp.display-item.citation.pmc??? 6
  • Scopus 11
  • ???jsp.display-item.citation.isi??? 11
  • OpenAlex 12
social impact