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

Nature Metabolism volume 2, pages 946–957 (2020)Cite this article

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Abstract

Not all individuals age at the same rate. Methods such as the ‘methylation clock’ are invasive, rely on expensive assays of tissue samples and infer the ageing rate by training on chronological age, which is used as a reference for prediction errors. Here, we develop models based on convoluted neural networks through training on non-invasive three-dimensional (3D) facial images of approximately 5,000 Han Chinese individuals that achieve an average difference between chronological or perceived age and predicted age of ±2.8 and 2.9 yr, respectively. We further profile blood transcriptomes from 280 individuals and infer the molecular regulators mediating the impact of lifestyle on the facial-ageing rate through a causal-inference model. These relationships have been deposited and visualized in the Human Blood Gene Expression—3D Facial Image (HuB-Fi) database. Overall, we find that humans age at different rates both in the blood and in the face, but do so coherently and with heterogeneity peaking at middle age. Our study provides an example of how artificial intelligence can be leveraged to determine the perceived age of humans as a marker of biological age, while no longer relying on prediction errors of chronological age, and to estimate the heterogeneity of ageing rates within a population.

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Fig. 1: Accuracy of age predictors, and the health parameters and lifestyles associated with AgeDiffs.
Fig. 2: AgeDiff heterogeneity peaks at middle age.
Fig. 3: Inflammation is related to AgeDiff at the molecular and cellular level.
Fig. 4: Inferred molecular mediators of AgeDiffs.

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Data availability

The regulatory genes used in building the causal-inference network are from the KEGG pathway (https://www.genome.jp/kegg/pathway.html), Reactome (https://www.reactome.org/), Animal Transcription Factor Database (http://www.bioguo.org/AnimalTFDB/), human DEPhOsphorylation Database (http://www.depod.bioss.uni-freiburg.de/), IUPHAR/BPS database (http://www.guidetopharmacology.org/) and ImmPort42 (https://www.immport.org/shared/genelists). ImageNet (http://image-net.org/) was used for pretraining.

Results of facial-image, transcriptome and lifestyle associations are searchable at http://www.picb.ac.cn/hanlab/hub-fi/. Three-dimensional images and other metadata sensitive to personal identification cannot be publicized or shared according to our participant consent agreement. Individual sequencing raw data, as they contain genetic information, will be available on request under the condition of approval of the ethics committee of Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences abiding China Human Genetic Resource law. Mapped read counts and FPKM expression values of coding genes from the RNA-seq are deposited to the public repository NODE at https://www.biosino.org/node/project/detail/OEP001041.

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Acknowledgements

This work was supported by grants from the National Natural Science Foundation of China (91749205), China Ministry of Science and Technology (2016YFE0108700) and Shanghai Municipal Science and Technology Major Project (2017SHZDZX01) to J.-D.J.H.

Author information

Author notes

  1. These authors contributed equally: Xian Xia, Xingwei Chen.

Authors and Affiliations

  1. CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China

    Xian Xia, Xingwei Chen, Gang Wu, Fang Li, Yiyang Wang, Yang Chen, Mingxu Chen, Xinyu Wang, Weiyang Chen, Bo Xian, Weizhong Chen, Yaqiang Cao, Chi Xu, Wenxuan Gong, Guoyu Chen, Donghong Cai, Yizhen Yan & Jing-Dong J. Han

  2. Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, China

    Xian Xia, Xingwei Chen, Yiyang Wang, Yang Chen, Mingxu Chen, Xinyu Wang, Wenxuan Gong, Guoyu Chen, Donghong Cai, Yizhen Yan, Kangping Liu & Jing-Dong J. Han

  3. University of Chinese Academy of Sciences, Beijing, China

    Xian Xia, Xingwei Chen, Yiyang Wang, Yang Chen, Mingxu Chen, Wenxuan Gong, Guoyu Chen, Donghong Cai & Yizhen Yan

  4. School of Life Science and Technology, ShanghaiTech University, Shanghai, China

    Xinyu Wang

  5. Department of Hepatic Surgery, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China

    Wenxin Wei

  6. Accenture China Artificial Intelligence Lab, Shenzhen, China

    Nan Qiao, Xiaohui Zhao & Jin Jia

  7. School of Medical and Health Sciences, Edith Cowan University, Perth, Western Australia, Australia

    Wei Wang

  8. Departments of Biochemistry and Physiology, National University of Singapore, Singapore, Singapore

    Brian K. Kennedy

  9. Centre for Healthy Ageing, National University Health System, Singapore, Singapore

    Brian K. Kennedy

  10. Singapore Institute for Clinical Sciences, A*STAR, Singapore, Singapore

    Brian K. Kennedy

  11. Buck Institute for Research on Aging, Novato, CA, USA

    Brian K. Kennedy

  12. Faculty of Medicine, Macau University of Science and Technology, Macau, China

    Kang Zhang

  13. Biomedical Cybernetics Group, Biotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering (CMCB), Center for Systems Biology Dresden (CSBD), Cluster of Excellence Physics of Life (PoL), Department of Physics, Technische Universität Dresden, Dresden, Germany

    Carlo V. Cannistraci

  14. Center for Complex Network Intelligence (CCNI) at the Tsinghua Laboratory of Brain and Intelligence (THBI) and Department of Bioengineering, Tsinghua University, Beijing, China

    Carlo V. Cannistraci

  15. Clinical Research Institute, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Yong Zhou

Authors
  1. Xian Xia
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Contributions

J.-D.J.H. conceived and designed the study and analyses. Y.Z. set up the cohort and designed and collected baseline data. C.V.C., together with J.D.J.H., conceived the paradigm-shift idea to train the CNN on perceived age instead of chronological age for an AI-based perceived-age predictor. X.X. and X.C. analysed the data, with help from Y.W. and C.X. Y. Cao, W. Wei, G.C. Y.Y., X.X., K.L. and D.C. helped in collecting and preprocessing data. N.Q., X.Z. and J.J. helped to set up GPU, AI systems and training. G.W. and F.L. performed the PBMC-isolation and RNA-extraction experiments. Weiyang Chen provided FacePlsAge. B.X., Weizhong Chen, Y. Cao, C.X. and W.G. helped to recruit the Beijing cohorts. G.C. quantified the wrinkle and symmetry on faces. Y. Chen analysed circRNA. X.W., M.C. and D.C. helped with data analysis. X.X., X.C., J.D.J.H, C.V.C., K.Z., B.K.K. and W. Wang wrote the manuscript. All authors contributed to preparation of the manuscript. G.W., F.L. and Y.W. contributed equally.

Corresponding authors

Correspondence to Yong Zhou or Jing-Dong J. Han.

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Authors declare no competing interests.

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Peer review information Primary Handling Editor: Pooja Jha.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Age and perceived age prediction using CNN.

a, Deep learning performance in training, validation and testing datasets for age prediction. Average loss (mean average difference (MAD), upper panel) and accuracy (Pearson Correlation Coefficient (PCC), lower panel) were plotted over training epochs. One epoch indicates the network weights updates over the whole training dataset for one time. b, Overlap between FaceCnnAge, FaceCnnPerceivedAge, FacePerceivedAge, and FacePlsAge in younger (left), normal (middle), and older (right) samples. The lower table shows the one-tailed Fisher’s exact test p-values with light red indicate p < 0.05. c, d, Independent validation of FaceCnnAge (c) and FaceCnnPerceivedAge (d) on 332 facial images collected in Beijing, 2012 (left) and 358 facial images collected in Beijing, 2015 (right).

Extended Data Fig. 2 AgeDiff-associated lifestyles in Beijing (2012) cohort by ANOVA test.

The p value is derived from ANOVA (n=341). All results with p < 0.05 are shown here. Data are presented as mean +/- SD.

Extended Data Fig. 3 Heterogeneity of aging rate at different ages in Jidong and Beijing cohort.

a, The standard deviation of randomly guess a number between 20–85 (n=4719, the boxes show 25%, 50% and 75% quantile and whiskers show maximum and minimum value). b, Relationship between age and the standard deviation of four AgeDiffs with bin size as 20 in Jidong cohort. Data are presented as mean +/- SD. c, Heatmap of aging-related facial features, health parameters and RNAs in Beijing (2012) cohort sorted by increasing chronological age (PCC with age, FDR < 0.05). Features were ranked by PCC from low to high (ALB: albumin, A/G: albumin/ globulin, TP: total protein, GGT: glutamyl transpeptidase, ALP: alkline phosphatase, CREA: creatinine, CHO: total cholesterol). d, e, Relationship between age and the standard deviation of four AgeDiffs with bin size as 20 (d) and 10 (e) in Beijing (2012) cohort. Data are presented as mean +/- SD.

Extended Data Fig. 4 Broken stick regression of SD of AgeDiffs against age.

a, b, Broken stick regression in Jidong cohort with bin size 100 (a) and 20 (b). c, d, Broken stick regression in Beijing (2012) cohort with bin size 20 (c) and 10 (d).

Extended Data Fig. 5 Age prediction using transcriptome.

a–c, Mean absolute difference (MAD) (top panel), Pearson correlation coefficient (PCC) (bottom panel) saturation analysis and correlation against chronological age of transcriptomes PLS age prediction for all the samples (a), female (b) and male (c), respectively. d, Enriched GO biological processes terms of PLS top 10% (upper) or 20% (lower) loading genes. P values are derived from hypergeometric test (Methods). e, Overlap between FacePlsAge, FaceCnnAge, RnaPlsAge, and FaceCnnPerceivedAge in younger (left), normal (middle), and older (right) samples. The lower table shows the one-tailed Fisher’s exact test p-values with light red highlight indicate p < 0.05.

Extended Data Fig. 6 Associations between cell types and AgeDiffs.

a, Cytokines (left panels) and antigen processing and presentation (right panels) enrichment scores as a function of four AgeDiffs. P values are derived from permutation test (Methods). b, Association of RNA-seq deconvoluted cell type fractions and AgeDiffs (* p<0.1, ** p<0.05, *** p<0.01 derived from two-sided t test, and * Benjamini-Hochberg correction derived FDR < 0.1).

Extended Data Fig. 7 Expression profile of expressed non-coding RNAs against chronological age.

a, The heatmap of expressed lncRNAs (FPKM > 2) significantly related to chronological age (FDR < 0.1). The samples (columns) were sorted by age and lncRNAs were sorted by PCC of expression to age from high to low. b, The heatmap of expressed circRNAs (TPM > 2) significantly related to chronological age (FDR < 0.1). The samples (columns) were sorted by age and circRNAs were sorted by PCC of expression to age from high to low. c, Top three enriched terms for parent genes of age-up (top) and age-down circRNAs. P values are derived from hypergeometric test (Methods). d, The correlation between age and total expression level of circRNAs. P value is derived from two-sided t test.

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Xia, X., Chen, X., Wu, G. et al. Three-dimensional facial-image analysis to predict heterogeneity of the human ageing rate and the impact of lifestyle. Nat Metab 2, 946–957 (2020). https://doi.org/10.1038/s42255-020-00270-x

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