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

1. Peters, M. J. et al. The transcriptional landscape of age in human peripheral blood.

  • 1.

    Peters, M. J. et al. The transcriptional landscape of age in human peripheral blood. Nat. Commun. 6, 8570 (2015).

    CAS 
    Article 

    Google Scholar
     

  • 2.

    Lehallier, B. et al. Undulating changes in human plasma proteome profiles across the lifespan. Nat. Med. 25, 1843–1850 (2019).

    CAS 
    Article 

    Google Scholar
     

  • 3.

    Hannum, G. et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol. Cell 49, 359–367 (2013).

    CAS 
    Article 

    Google Scholar
     

  • 4.

    Horvath, S. DNA methylation age of human tissues and cell types. Genome Biol. 14, 3156 (2013).

    Article 

    Google Scholar
     

  • 5.

    Unschuld, P. U. & Tessenow, H. Huang Di Nei Jing Su Wen (University of California Press, 2011).

  • 6.

    Chen, W., Xia, X., Huang, Y., Chen, X. & Han, J.-D. J. Bioimaging for quantitative phenotype analysis. Methods 102, 20–25 (2016).

    CAS 
    Article 

    Google Scholar
     

  • 7.

    Chen, W. et al. Three-dimensional human facial morphologies as robust aging markers. Cell Res. 25, 574–587 (2015).

    Article 

    Google Scholar
     

  • 8.

    López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M. & Kroemer, G. The Hallmarks of Aging. Cell 153, 1194–1217 (2013).

    Article 

    Google Scholar
     

  • 9.

    Gao, X. W., Hui, R. & Tian, Z. Classification of CT brain images based on deep learning networks. Computer Methods Programs Biomed. 138, 49–56 (2017).

    Article 

    Google Scholar
     

  • 10.

    Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017).

    CAS 
    Article 

    Google Scholar
     

  • 11.

    Millstein, J., Zhang, B., Zhu, J. & Schadt, E. E. Disentangling molecular relationships with a causal inference test. BMC Genet. 10, 23 (2009).

    Article 

    Google Scholar
     

  • 12.

    Debrabant, B. et al. DNA methylation age and perceived age in elderly Danish twins. Mechanisms Ageing Dev. 169, 40–44 (2018).

    CAS 
    Article 

    Google Scholar
     

  • 13.

    Lu, T. et al. Gene regulation and DNA damage in the ageing human brain. Nature 429, 883–891 (2004).

    CAS 
    Article 

    Google Scholar
     

  • 14.

    Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).

    CAS 
    Article 

    Google Scholar
     

  • 15.

    Jefferson, A. L. et al. Inflammatory biomarkers are associated with total brain volume. Neurology 68, 1032–1038 (2007).

    CAS 
    Article 

    Google Scholar
     

  • 16.

    Frąckiewicz, J. et al. Hematological parameters and all-cause mortality: a prospective study of older people. Aging Clin. Exp. Res. 30, 517–526 (2018).

    Article 

    Google Scholar
     

  • 17.

    Chatthanawaree, W. Biomarkers of cobalamin (vitamin B12) deficiency and its application. J. Nutr. Health Aging 15, 227–231 (2011).

    CAS 
    Article 

    Google Scholar
     

  • 18.

    Conigrave, K. M., Davies, P., Haber, P. & Whitfield, J. B. Traditional markers of excessive alcohol use. Addiction 98, 31–43 (2003).

    Article 

    Google Scholar
     

  • 19.

    Liu, Y. et al. Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis. Nat. Biotechnol. 31, 142–147 (2013).

    CAS 
    Article 

    Google Scholar
     

  • 20.

    Ahmed, Z. et al. Accelerated lipofuscinosis and ubiquitination in granulin knockout mice suggest a role for progranulin in successful aging. Am. J. Pathol. 177, 311–324 (2010).

    CAS 
    Article 

    Google Scholar
     

  • 21.

    He, Z., Ong, C. H. P., Halper, J. & Bateman, A. Progranulin is a mediator of the wound response. Nat. Med. 9, 225–229 (2003).

    CAS 
    Article 

    Google Scholar
     

  • 22.

    Elkabets, M. et al. Human tumors instigate granulin-expressing hematopoietic cells that promote malignancy by activating stromal fibroblasts in mice. J. Clin. Invest 121, 784–799 (2011).

    CAS 
    Article 

    Google Scholar
     

  • 23.

    Chitramuthu, B. P., Bennett, H. P. J. & Bateman, A. Progranulin: a new avenue towards the understanding and treatment of neurodegenerative disease. Brain 140, 3081–3104 (2017).

    Article 

    Google Scholar
     

  • 24.

    Knupp, D. & Miura, P. CircRNA accumulation: a new hallmark of aging? Mechanisms Ageing Dev. 173, 71–79 (2018).

    CAS 
    Article 

    Google Scholar
     

  • 25.

    Ruiz, R. et al. Sterol regulatory element-binding protein-1 (SREBP-1) is required to regulate glycogen synthesis and gluconeogenic gene expression in mouse liver. J. Biol. Chem. 289, 5510–5517 (2014).

    CAS 
    Article 

    Google Scholar
     

  • 26.

    Oishi, Y. et al. SREBP1 contributes to resolution of pro-inflammatory TLR4 signaling by reprogramming fatty acid metabolism. Cell Metab. 25, 412–427 (2017).

    CAS 
    Article 

    Google Scholar
     

  • 27.

    Li, S. et al. Metabolic phenotypes of response to vaccination in humans. Cell 169, 862–877.e817 (2017).

    CAS 
    Article 

    Google Scholar
     

  • 28.

    Schoenborn, N. L. et al. Preferred clinician communication about stopping cancer screening among older US adults: results from a national survey. JAMA Oncol. 4, 1126–1128 (2018).

    Article 

    Google Scholar
     

  • 29.

    World Medical Association Inc Declaration of Helsinki. Ethical principles for medical research involving human subjects. J. Indian Med. Assoc. 107, 403–405 (2009).


    Google Scholar
     

  • 30.

    Guo, J., Mei, X. & Tang, K. Automatic landmark annotation and dense correspondence registration for 3D human facial images. BMC Bioinformatics 14, 232 (2013).

    Article 

    Google Scholar
     

  • 31.

    King, D. E. Dlib-ml: a Machine Learning Toolkit J. Mach. Learn. Res. 10, 1755–1758 (2009).

  • 32.

    Szegedy, C. et al. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 1–9 (2015).

  • 33.

    Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. Preprint at https://https://arxiv.org/abs/1409.1556 (2014).

  • 34.

    He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. in IEEE Conference on Computer Vision and Pattern Recognition 770–778 (2016).

  • 35.

    Kingma, D. & Ba, J. Adam: A method for stochastic optimization. Preprint at https://arxiv.org/abs/1412.6980 (2014).

  • 36.

    Harrow, J. et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 22, 1760–1774 (2012).

    CAS 
    Article 

    Google Scholar
     

  • 37.

    Trapnell, C. et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 7, 562–578 (2012).

    CAS 
    Article 

    Google Scholar
     

  • 38.

    Zhang, X. O. et al. Diverse alternative back-splicing and alternative splicing landscape of circular RNAs. Genome Res. 26, 1277–1287 (2016).

    CAS 
    Article 

    Google Scholar
     

  • 39.

    Wold, S., Sjöström, M. & Eriksson, L. PLS-regression: a basic tool of chemometrics. Chemometrics Intell. Lab. Syst. 58, 109–130 (2001).

    CAS 
    Article 

    Google Scholar
     

  • 40.

    Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    CAS 
    Article 

    Google Scholar
     

  • 41.

    Dennis, G. et al. DAVID: database for annotation, visualization, and integrated discovery. Genome Biol. 4, P3 (2003).

    Article 

    Google Scholar
     

  • 42.

    Bhattacharya, S. et al. ImmPort, toward repurposing of open access immunological assay data for translational and clinical research. Sci. Data 5, 180015 (2018).

    CAS 
    Article 

    Google Scholar
     

  • 43.

    Coppé, J.-P., Desprez, P.-Y., Krtolica, A. & Campisi, J. The senescence-associated secretory phenotype: the dark side of tumor suppression. Annu Rev. Pathol. 5, 99–118 (2010).

    Article 

    Google Scholar
     

  • 44.

    Millstein, J., Chen, G. K. & Breton, C. V. cit: hypothesis testing software for mediation analysis in genomic applications. Bioinformatics 32, 2364–2365 (2016).

    CAS 
    Article 

    Google Scholar
     

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