Three-dimensional facial-image analysis to predict heterogeneity of the human ageing rate and the impact of lifestyle

Xia, Xian; Chen, Xingwei; Wu, Gang; Fang, Li; Wang, Yiyang; Yang, Chen; Chen, Mingxu; Wang, Xinyu; Chen, Weiyang; Xian, Bo; Chen, Weizhong; Yun, Cao; Xu, Chi; Gong, W. X.; Chen, Guoyu; Cai, Donghong; Wei, Wenxin; Yan, Yizhen; Liu, Kangping; Qiao, Nan; Zhao, Xiaohui; Jia, Jin; Wang, Wei; Kennedy, Brian K.; Zhang, Kang; Cannistraci, Carlo Vittorio; Zhou, Yingling; Han, Jing‐Dong J.

doi:10.1038/s42255-020-00270-x

Cited by 65 publications

(57 citation statements)

References 39 publications

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“…A reduced model could achieve an MAE of 5.37 with 200 genes (FigS3-C). This model outperformed the previous blood transcriptome-based aging clock constructed in ribo-minus PBMC 24 (MAE=5.68) and multiple cohort model constructed in whole-blood gene expression array data 6 (MAE=7.8), as well as other transcriptome-based aging clocks constructed in muscle gene expression 7 (MAE=6.24).…”

Section: Resultsmentioning

confidence: 81%

Scale Bar of Aging Trajectories for Screening Personal Rejuvenation Treatments

Shen

Jiang

et al. 2022

Preprint

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Although aging is an increasingly severe healthy, economic, and social global problem, it is far from well-modeling aging due to the aging process's complexity. To promote the aging modeling, here we did the quantitative measurement based on aging blood transcriptome. Specifically, the aging blood transcriptome landscape was constructed through ensemble modeling in a cohort of 505 people, and 1138 age-related genes were identified. To assess the aging rate in the linear dimension of aging, we constructed a simplified linear aging clock, which distinguished fast-aging and slow-aging populations and showed the differences in the composition of immune cells. Meanwhile, the non-linear dimension of aging revealed the transcriptome fluctuations with a crest around the age of 40 and showed that this crest came earlier and was more vigorous in the fast-aging population. Moreover, the aging clock was applied to evaluate the rejuvenation effect of molecules in vitro, such as Nicotinamide Mononucleotide (NMN) and Metformin. In sum, this study developed a de novo aging clock to evaluate age-dependent precise medicine by revealing its fluctuation nature based on comprehensively mining the aging blood transcriptome, promoting the development of personal aging monitoring and anti-aging therapies.

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Section: Resultsmentioning

confidence: 81%

Scale Bar of Aging Trajectories for Screening Personal Rejuvenation Treatments

Shen

Jiang

et al. 2022

Preprint

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“…Previous studies have demonstrated MAEs of 3·3-5·2 years for DNA methylation clock, 18,19 5·5-5·9 years MAEs for blood profiles, 20,21 and 6·2-7·8 years MAEs for the transcriptome ageing clock. 22,23 Neuroimaging and 3D facial imaging have achieved accurate performances in age prediction with MAEs between 4·3 and 7·3, 7,24 and 2·8 and 6·4 years, 6,25 respectively. Despite these reasonable accuracies, the invasiveness of cellular and molecular ageing biomarkers, high cost and time-consuming nature of neuroimaging and 3D facial ages, and ethical and privacy concerns of facial imaging, have limited their utilities.…”

Section: Discussionmentioning

confidence: 98%

Retinal Age as a Predictive Biomarker for Mortality Risk

Zhu

Shi

Peng

et al. 2020

Preprint

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SummaryBackgroundAgeing varies substantially, thus an accurate quantification of ageing is important. We developed a deep learning (DL) model that predicted age from fundus images (retinal age). We investigated the association between retinal age gap (retinal age-chronological age) and mortality risk in a population-based sample of middle-aged and elderly adults.MethodsThe DL model was trained, validated and tested on 46,834, 15,612 and 8,212 fundus images respectively from participants of the UK Biobank study alive on 28th February 2018. Retinal age gap was calculated for participants in the test (n=8,212) and death (n=1,117) datasets. Cox regression models were used to assess association between retinal age gap and mortality risk. A restricted cubic spline analyses was conducted to investigate possible non-linear association between retinal age gap and mortality risk.FindingsThe DL model achieved a strong correlation of 0·83 (P<0·001) between retinal age and chronological age, and an overall mean absolute error of 3·50 years. Cox regression models showed that each one-year increase in the retinal age gap was associated with a 2% increase in mortality risk (hazard ratio=1·02, 95% confidence interval:1·00-1·04, P=0·021). Restricted cubic spline analyses showed a non-linear relationship between retinal age gap and mortality (Pnon-linear=0·001). Higher retinal age gaps were associated with substantially increased risks of mortality, but only if the gap exceeded 3·71 years.InterpretationOur findings indicate that retinal age gap is a robust biomarker of ageing that is closely related to risk of mortality.FundingNational Health and Medical Research Council Investigator Grant, Science and Technology Program of Guangzhou.Research in contextEvidence before this studyAgeing at an individual level is heterogeneous. An accurate quantification of the biological ageing process is significant for risk stratification and delivery of tailored interventions. To date, cell-, molecular-, and imaging-based biomarkers have been developed, such as epigenetic clock, brain age and facial age. While the invasiveness of cellular and molecular ageing biomarkers, high cost and time-consuming nature of neuroimaging and facial ages, as well as ethical and privacy concerns of facial imaging, have limited their utilities. The retina is considered a window to the whole body, implying that the retina could provide clues for ageing.Added value of this studyWe developed a deep learning (DL) model that can detect footprints of aging in fundus images and predict age with high accuracy for the UK population between 40 and 69 years old. Further, we have been the first to demonstrate that each one-year increase in retinal age gap (retinal age-chronological age) was significantly associated with a 2% increase in mortality risk. Evidence of a non-linear association between retinal age gap and mortality risk was observed. Higher retinal age gaps were associated with substantially increased risks of mortality, but only if the retinal age gap exceeded 3·71 years.Implications of all the available evidenceThis is the first study to link the retinal age gap and mortality risk, implying that retinal age is a clinically significant biomarker of ageing. Our findings show the potential of retinal images as a screening tool for risk stratification and delivery of tailored interventions. Further, the capability to use fundus imaging in predicting ageing may improve the potential health benefits of eye disease screening, beyond the detection of sight-threatening eye diseases.

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“…In contrast to these previous studies, AnthropoAge and S-AnthropoAge attempt to capture the contribution of anthropometric measurements to 10-year all-cause mortality risk independently of CA, which may clarify the relationship between body composition and aging beyond BMI, given that this latter metric may not completely capture the complexity of this phenomenon 22,23 . Recent analyses have also shown that a richer diversity of biomarkers may lead to more precise assessments of BA and that aging phenotypes may have influence on body composition and even facial expressions [24][25][26] .…”

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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Three-dimensional facial-image analysis to predict heterogeneity of the human ageing rate and the impact of lifestyle

Cited by 65 publications

References 39 publications

Scale Bar of Aging Trajectories for Screening Personal Rejuvenation Treatments

Scale Bar of Aging Trajectories for Screening Personal Rejuvenation Treatments

Retinal Age as a Predictive Biomarker for Mortality Risk

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

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