Abstract
Aging is associated with many complex diseases. Reliable prediction of the aging process is important for assessing the risks of aging-associated diseases. However, despite intense research, so far there is no reliable aging marker. Here we addressed this problem by examining whether human 3D facial imaging features could be used as reliable aging markers. We collected > 300 3D human facial images and blood profiles well-distributed across ages of 17 to 77 years. By analyzing the morphological profiles, we generated the first comprehensive map of the aging human facial phenome. We identified quantitative facial features, such as eye slopes, highly associated with age. We constructed a robust age predictor and found that on average people of the same chronological age differ by ± 6 years in facial age, with the deviations increasing after age 40. Using this predictor, we identified slow and fast agers that are significantly supported by levels of health indicators. Despite a close relationship between facial morphological features and health indicators in the blood, facial features are more reliable aging biomarkers than blood profiles and can better reflect the general health status than chronological age.
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Acknowledgements
We are grateful to the individuals who volunteered for the study. We thank Chi Xu, Wenxuan Gong, Hao Cheng, Dan Wang, Jing Li, Bing Zhou, Wei Zhang, Na Sun, Veronica Chen, Yue Qu, Xia Tang, Jian Lu and Xiaoli Miao for the help in data collection. We thank Jing Guo and Hang Zhou for the help in the use of the 3dMDface system. This work was supported by the China Ministry of Science and Technology (2011CB504206 and 2015CB964803 to JDJH, and 2012AA020406 to GW), the National Natural Science Foundation of China (91019019, 31210103916 and 91329302 to JDJH, 31371188 to GW, and 31350110327 to CDG) and the Chinese Academy of Sciences (CAS; YZ201243 and XDA01010303 to JDJH). GW acknowledges supports from Shanghai Institutes for Biological Sciences (SIBS), CAS (2011KIP202) and the SA-SIBS Scholarship Program. CDG holds a CAS fellowship (2010Y2SB06), and Shanghai Institute for Biological Sciences Postdoctoral Fellowship (2013KIP315).
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( Supplementary information is linked to the online version of the paper on the Cell Research website.)
Supplementary information
Supplementary information, Figure S1
Correlation of age predicted by facial vertices-based PLSR predictors with the actual age of the subjects. (PDF 907 kb)
Supplementary information, Figure S2
Age predicted by 3D facial image vertices-based SVR predictors. (PDF 1322 kb)
Supplementary information, Figure S3
Differences between predicted and actual ages in each age group and the mean 3D facial images in well predicted, slow- and fast-ager classes of younger age groups. (PDF 190 kb)
Supplementary information, Figure S4
Levels of other age-correlated blood health indicators in predicted fast-agers, slow-agers and well predicted subjects when the classification is based on the age difference > 6 years between predicted age and chronological age. (PDF 373 kb)
Supplementary information, Figure S5
The distribution of RCCs between each Cluster 1/6 blood health indicator and the predicted slow- and fast-ager classifications or that between each health indicator and permuted classifications within each age group. (PDF 720 kb)
Supplementary information, Figure S6
Levels of the blood health indicators (showed in Fig. 3E) in predicted fast-agers, slow-agers and well-predicted subjects when the classification is based on the age difference > 7 years between predicted age and chronological age. (PDF 183 kb)
Supplementary information, Figure S7
Correlation between blood indicators and predicted/chronological age in slow- and fast-agers (|predicted age — chronological age| > 6). (PDF 184 kb)
Supplementary information, Figure S8
The loading values of PLS component 1 of the new PLS model adjusted for BMI (A) or for cholesterol, LDLC and albumin (B) in three dimensions. (PDF 161 kb)
Supplementary information, Figure S9
The heat map of 3D-effects showing loading values of PLS component 1 correlated with other indicators from 18 blood serum indicators and 24 blood-cell related indicators (except CHO, LDL-C, HDL-C and ALB in Fig. 4) on female and male faces. (PDF 1487 kb)
Supplementary information, Table S1
Age distribution of subjects. (PDF 15 kb)
Supplementary information, Table S2
Pearson Correlation Coefficients (PCCs) of each feature or component with chronological age. (PDF 21 kb)
Supplementary information, Table S3
The distribution of cumulative explained variance. (PDF 12 kb)
Supplementary information, Table S4
Correlations between ages predicted by the PLS model with or without adjusting for the age associated features, BMI, or LDLC, cholesterol and albumin. (PDF 10 kb)
Supplementary information, Table S5
Correlations between age predicted by the PLS model with or without removing image features/vertices that are highly correlated (PCC>=0.3) to the age associated features, BMI, or LDLC, cholesterol and albumin. (PDF 8 kb)
Supplementary information, Data S1
Supplementary Notes (PDF 289 kb)
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Chen, W., Qian, W., Wu, G. et al. Three-dimensional human facial morphologies as robust aging markers. Cell Res 25, 574–587 (2015). https://doi.org/10.1038/cr.2015.36
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DOI: https://doi.org/10.1038/cr.2015.36
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