Sex-different changes of body composition in aging: a systemic review

Kim, Sun Young; Won, Chang Won

doi:10.1016/j.archger.2022.104711

Cited by 36 publications

(13 citation statements)

References 69 publications

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“…This result confirms what is previously demonstrated, since ageing leads to loss in muscle mass and increase in muscle fatty infiltration. 24,25 The agerelated reduction in skeletal muscle mass does not occur at the same time in all anatomic regions but is greater in lower limbs compared to the upper limbs, particularly at the anterior tight muscles. 11 Moreover, the functional correlates of quantitative muscle ultrasonographic parameters demonstrated that variations of muscle mass are good predictors of changes in muscle function (26).…”

Section: Discussionmentioning

confidence: 99%

Validity of ultrasound rectus femoris quantitative assessment: A comparative study between linear and curved array transducers

Tognolo

Coraci

Farì³

et al. 2022

Eur J Transl Myol

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Appendicular skeletal mass is commonly used to assess the loss in muscle mass and US represents a valid, and reliable method. However, the procedural protocols are still heterogeneous. The aim of this study was to compare the intertransducers validity of thickness, width, and CSA measurements of RF muscle. The AP, LL and CSA of RF muscle were evaluated with both linear and curve probes in ten healthy subjects and six sarcopenic patients. In the healthy group the mean AP diameters measured with the linear array were significantly higher than those measured with the curved array. AP and CSA were higher in the healthy group compared with the sarcopenic group with both transducers. There was a positive correlation between weight and LL diameter, and a negative correlation between age and muscle AP, measured with the linear probe. Both linear and curved probes represent valid methods in US evaluation of the CSA of the RF muscle. However, in the healthy subjects, the thickness and width of the of the same muscle, are affected by the type of probe.

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Section: Discussionmentioning

confidence: 99%

Validity of ultrasound rectus femoris quantitative assessment: A comparative study between linear and curved array transducers

Tognolo

Coraci

Farì³

et al. 2022

Eur J Transl Myol

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“…In addition to aging mechanisms that may lead to changes in body composition, numerous studies have also drawn connections between body features and poor age‐related outcomes such as disability, age‐related diseases, and mortality (Kim & Won, 2022 ; Santanasto et al, 2016 ; Schorr et al, 2018 ). Evidence suggests that the bidirectional relationship between aging and body composition can eventually turn into a vicious cycle that results in an aggravation of age acceleration (Salvestrini et al, 2019 ) and that this phenomenon is heavily influenced by sex dimorphisms (Goossens et al, 2021 ; Heid et al, 2010 ; Pomatto et al, 2018 ; Sampathkumar et al, 2020 ; Wells, 2007 ).…”

Section: Discussionmentioning

confidence: 99%

AnthropoAge, a novel approach to integrate body composition into the estimation of biological age

et al. 2022

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Aging is believed to occur across multiple domains, one of which is body composition; however, attempts to integrate it into biological age (BA) have been limited. Here, we consider the sex‐dependent role of anthropometry for the prediction of 10‐year all‐cause mortality using data from 18,794 NHANES participants to generate and validate a new BA metric. Our data‐driven approach pointed to sex‐specific contributors for BA estimation: WHtR, arm and thigh circumferences for men; weight, WHtR, thigh circumference, subscapular and triceps skinfolds for women. We used these measurements to generate AnthropoAge, which predicted all‐cause mortality (AUROC 0.876, 95%CI 0.864–0.887) and cause‐specific mortality independently of ethnicity, sex, and comorbidities; AnthropoAge was a better predictor than PhenoAge for cerebrovascular, Alzheimer, and COPD mortality. A metric of age acceleration was also derived and used to assess sexual dimorphisms linked to accelerated aging, where women had an increase in overall body mass plus an important subcutaneous to visceral fat redistribution, and men displayed a marked decrease in fat and muscle mass. Finally, we showed that consideration of multiple BA metrics may identify unique aging trajectories with increased mortality (HR for multidomain acceleration 2.43, 95%CI 2.25–2.62) and comorbidity profiles. A simplified version of AnthropoAge (S‐AnthropoAge) was generated using only BMI and WHtR, all results were preserved using this metric. In conclusion, AnthropoAge is a useful proxy of BA that captures cause‐specific mortality and sex dimorphisms in body composition, and it could be used for future multidomain assessments of aging to better characterize the heterogeneity of this phenomenon.

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“…In addition to aging mechanisms that may lead to changes in body composition, numerous studies have also drawn connections between body features and poor age-related outcomes such as disability, age-related diseases, and mortality (Kim & Won, 2022; Santanasto et al, 2016; Schorr et al, 2018). Evidence suggests that the bidirectional relationship between aging and body composition can eventually turn into a vicious cycle that results in an aggravation of age acceleration (Salvestrini et al, 2019), and that this phenomenon is heavily influenced by sex dimorphisms (Goossens et al, 2021; MAGIC et al, 2010; Pomatto et al, 2018; Sampathkumar et al, 2020; Wells, 2007).…”

Section: Discussionmentioning

confidence: 99%

AnthropoAge, a novel approach to integrate body composition into the estimation of biological age

Márquez‐Salinas¹,

Guerra²,

Zavala-Romero³

et al. 2021

Preprint

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Methods to estimate biological age (BA) capture different aspects of aging. Here, we consider the role of changes in body composition related to aging as a starting point to incorporate anthropometry into the estimation of BA. To that end, we developed AnthropoAge, a metric to estimate 10-year mortality risk as a proxy of BA using anthropometric and linked mortality data from NHANES-III (n=11,865) and validated it in NHANES-IV (n=7,065). We identified that thigh circumference, arm circumference, body-mass index (BMI), waist-to-height ratio (WHtR) and arm length were useful to predict BA in men, whilst weight, thigh circumference, subscapular and tricipital skinfolds and WHtR in women. We also developed a simplified version of AnthropoAge (S-AnthropoAge) which used only BMI and WHtR, with strong concordance with the complete metric. Both AnthropoAge and S-AnthropoAge were useful to predict 10-year mortality independent of ethnicity, sex, and comorbidities. In comparison to PhenoAge, AnthropoAge/S-AnthropoAge were superior for prediction of cardiovascular, cerebrovascular, cancer-related and nephritis/nephrosis related mortality risk in contrast with other causes. Accelerated aging metrics AnthropoAgeAccel/S-AnthropoAgeAccel identified males with phenotypes of decreased lean and fat mass and females with phenotypes of increased fat mass and increased abdominal adiposity, which likely reflected sexual dimorphisms related to accelerated body composition aging. When jointly assessing PhenoAge and AnthropoAge/S-AnthropoAge, we identified unique aging trajectories with differential mortality risk and comorbidity clustering. AnthropoAge is a useful proxy of BA, which captures cause-specific mortality risk; assessing aging using different BA measures may be useful to better characterize the heterogeneity of the aging process.

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Sex-different changes of body composition in aging: a systemic review

Cited by 36 publications

References 69 publications

Validity of ultrasound rectus femoris quantitative assessment: A comparative study between linear and curved array transducers

Validity of ultrasound rectus femoris quantitative assessment: A comparative study between linear and curved array transducers

AnthropoAge, a novel approach to integrate body composition into the estimation of biological age

AnthropoAge, a novel approach to integrate body composition into the estimation of biological age

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