Express Estimation of the Biological Age by the Parameters of Body Composition in Men and Women over 50 Years

Negasheva, Marina A.; Зимина, С.Н.; Lapshina, Natalia; Sineva, Irina M.

doi:10.1007/s10517-017-3814-y

Cited by 4 publications

(5 citation statements)

References 12 publications

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“…Furthermore, these measures could be combined with other known and emerging biological correlates of aging, including molecular markers (obtained from the peripheral blood, for example 52,53 ), gene expression 8 , body composition 54 , microbiome 55 , and other imaging modalities such as x-rays 56 , CT 57 or MRI 58 . We have already demonstrated that FaceAge is correlated with genes implicated in cellular senescence, as well as with skeletal muscle index, a body composition measure already known to be prognostic of survival in cancer patients 44,45 .…”

Section: Discussionmentioning

confidence: 99%

Decoding biological age from face photographs using deep learning

Zalay,

Bontempi,

Bitterman

et al. 2023

Preprint

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Because humans age at different rates, a person’s physical appearance may yield insights into their biological age and physiological health more reliably than their chronological age. In medicine, however, appearance is incorporated into medical judgments in a subjective and non-standardized fashion. In this study, we developed and validated FaceAge, a deep learning system to estimate biological age from easily obtainable and low-cost face photographs. FaceAge was trained on data from 58,851 healthy individuals, and clinical utility was evaluated on data from 6,196 patients with cancer diagnoses from two institutions in the United States and The Netherlands. To assess the prognostic relevance of FaceAge estimation, we performed Kaplan Meier survival analysis. To test a relevant clinical application of FaceAge, we assessed the performance of FaceAge in end-of-life patients with metastatic cancer who received palliative treatment by incorporating FaceAge into clinical prediction models. We found that, on average, cancer patients look older than their chronological age, and looking older is correlated with worse overall survival. FaceAge demonstrated significant independent prognostic performance in a range of cancer types and stages. We found that FaceAge can improve physicians’ survival predictions in incurable patients receiving palliative treatments, highlighting the clinical utility of the algorithm to support end-of-life decision-making. FaceAge was also significantly associated with molecular mechanisms of senescence through gene analysis, while age was not. These findings may extend to diseases beyond cancer, motivating using deep learning algorithms to translate a patient’s visual appearance into objective, quantitative, and clinically useful measures.

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Section: Discussionmentioning

confidence: 99%

Decoding biological age from face photographs using deep learning

Zalay,

Bontempi,

Bitterman

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…Body composition has been previously explored for the evaluation of BA, identifying sex‐based differences in anthropometric parameters that are linearly associated with CA (Bae et al, 2012 ; Negasheva et al, 2017 ). In contrast to these previous studies, AnthropoAge uses a nonlinear approach to link body composition patterns to mortality risk, which has been speculated to be a better surrogate of BA; this in turn may clarify the relationship of body composition with aging and age‐related outcomes beyond individual metrics such as BMI, given that these alone do not completely capture the complexity of this phenomenon (Donini et al, 2020 ; Lavie et al, 2009 ).…”

Section: Discussionmentioning

confidence: 99%

“…With all this, it is unsurprising that body composition is often recognized as one of the main domains in which aging takes place (Kuo et al, 2020 ). However, despite the overwhelming evidence supporting the contribution of body composition and anthropometry to predict age and age‐related outcomes, their proper implementation in the development of BA estimations has been limited (Bae et al, 2012 ; Negasheva et al, 2017 ). Here, we hypothesize that the integration of sex‐based differences in multiple anthropometric measurements would result in the development of a reliable and easily implementable BA estimation that captures the body composition domain of aging.…”

Section: Introductionmentioning

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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“…Body composition has been previously explored for the evaluation of BA, identifying sex-based differences of anthropometric parameters that are linearly associated with CA (Kang et al, 2012; Negasheva et al, 2017). In contrast to these previous studies, AnthropoAge uses a non-linear approach to link body composition patterns to mortality risk, which has been speculated to be a better surrogate of BA; this in turn may clarify the relationship of body composition with aging and age-related outcomes beyond individual metrics such as BMI, given that these alone do not completely capture the complexity of this phenomenon (Donini et al, 2020; Lavie et al, 2009).…”

Section: Discussionmentioning

confidence: 99%

“…With all this, it is unsurprising that body composition is often recognized as one of the main domains in which aging takes place (P. -L. Kuo et al, 2020). However, despite the overwhelming evidence supporting the contribution of body composition and anthropometry to predict age and age-related outcomes, their proper implementation in the development of BA estimations has been limited (Kang et al, 2012; Negasheva et al, 2017). Here, we hypothesize that the integration of sex-based differences in multiple anthropometric measurements would result in the development of a reliable and easily implementable BA estimation that captures the body composition domain of aging.…”

Section: Introductionmentioning

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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Express Estimation of the Biological Age by the Parameters of Body Composition in Men and Women over 50 Years

Cited by 4 publications

References 12 publications

Decoding biological age from face photographs using deep learning

Decoding biological age from face photographs using deep learning

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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