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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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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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.
Authors declare no competing interests.
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.
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).
The p value is derived from ANOVA (n=341). All results with p < 0.05 are shown here. Data are presented as mean +/- SD.
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.
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).
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.
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).
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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