Three-dimensional human facial morphologies as robust aging markers

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.

[1]  Y. Marcel,et al.  Serum albumin is a significant intermediate in cholesterol transfer between cells and lipoproteins. , 1996, Biochemistry.

[2]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[3]  M. Zimbler,et al.  Anatomy and pathophysiology of facial aging. , 2001, Facial plastic surgery clinics of North America.

[4]  Peter Hammond,et al.  Estimating average growth trajectories in shape-space using kernel smoothing , 2003, IEEE Transactions on Medical Imaging.

[5]  Rajiv Grover,et al.  The anatomy of the aging face: volume loss and changes in 3-dimensional topography. , 2006, Aesthetic surgery journal.

[6]  P. Hammond,et al.  Three-dimensional face shape in Fabry disease , 2007, European Journal of Human Genetics.

[7]  Ron Wehrens,et al.  The pls Package: Principal Component and Partial Least Squares Regression in R , 2007 .

[8]  Hans-Peter Seidel,et al.  Prediction of Individual Non‐Linear Aging Trajectories of Faces , 2007, Comput. Graph. Forum.

[9]  Liu Xianming,et al.  A Time Petri Net Extended with Price Information , 2007 .

[10]  Zhi-Hua Zhou,et al.  Automatic Age Estimation Based on Facial Aging Patterns , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Yun Fu,et al.  Image-Based Human Age Estimation by Manifold Learning and Locally Adjusted Robust Regression , 2008, IEEE Transactions on Image Processing.

[12]  A. M. Burton,et al.  100% Accuracy in Automatic Face Recognition , 2008, Science.

[13]  L. Xi,et al.  The interaction between cholesterol and human serum albumin. , 2008, Protein and peptide letters.

[14]  Robert I. Damper,et al.  Customizing Kernel Functions for SVM-Based Hyperspectral Image Classification , 2008, IEEE Transactions on Image Processing.

[15]  G. Hillebrand,et al.  Lifestyle Factors and Visible Skin Aging in a Population of Japanese Elders , 2009, Journal of epidemiology.

[16]  Rama Chellappa,et al.  The role of geometry in age estimation , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[17]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[18]  Brent E Larson,et al.  Accuracy and precision of a 3D anthropometric facial analysis with and without landmark labeling before image acquisition. , 2011, The Angle orthodontist.

[19]  Robert B Shaw,et al.  Aging of the Facial Skeleton: Aesthetic Implications and Rejuvenation Strategies , 2011, Plastic and reconstructive surgery.

[20]  R. Hennekam,et al.  Uncovering Genomic Causes of Co-Morbidity in Epilepsy: Gene-Driven Phenotypic Characterization of Rare Microdeletions , 2011, PloS one.

[21]  J. Han.  An aging program at the systems level? , 2012, Birth defects research. Part C, Embryo today : reviews.

[22]  P. Hammond,et al.  Large‐scale objective phenotyping of 3D facial morphology , 2012, Human mutation.

[23]  Howard Y. Chang,et al.  Aging, Rejuvenation, and Epigenetic Reprogramming: Resetting the Aging Clock , 2012, Cell.

[24]  Guodong Guo,et al.  Joint estimation of age, gender and ethnicity: CCA vs. PLS , 2013, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).

[25]  Kun Tang,et al.  Automatic landmark annotation and dense correspondence registration for 3D human facial images , 2012, BMC Bioinformatics.

[26]  Raimund Erbel,et al.  Aging of blood can be tracked by DNA methylation changes at just three CpG sites , 2014, Genome Biology.

[27]  M. Phillips,et al.  Serum albumin acts as a shuttle to enhance cholesterol efflux from cells[S] , 2013, Journal of Lipid Research.

[28]  Dan Wang,et al.  Integrating genomic, epigenomic, and transcriptomic features reveals modular signatures underlying poor prognosis in ovarian cancer. , 2013, Cell reports.

[29]  Manuel Serrano,et al.  The Hallmarks of Aging , 2013, Cell.

[30]  C. H. Kau,et al.  Comparison of facial morphologies between adult Chinese and Houstonian Caucasian populations using three-dimensional imaging. , 2013, International journal of oral and maxillofacial surgery.

[31]  Hang Zhou,et al.  Detecting Genetic Association of Common Human Facial Morphological Variation Using High Density 3D Image Registration , 2013, PLoS Comput. Biol..

[32]  Hervé Abdi,et al.  Partial least squares methods: partial least squares correlation and partial least square regression. , 2013, Methods in molecular biology.

[33]  Yuling Li,et al.  Variation of head and facial morphological characteristics with increased age of Han in Southern China , 2013 .

[34]  M. Padgett,et al.  3D Computational Imaging with Single-Pixel Detectors , 2013, Science.

[35]  H. Wildiers,et al.  Evaluating the physiological reserves of older patients with cancer: the value of potential biomarkers of aging? , 2014, Journal of geriatric oncology.