Chen Weiyang, Qian Wei, Wu Gang, Chen Weizhong, Xian Bo, Chen Xingwei, Cao Yaqiang, Green Christopher D, Zhao Fanghong, Tang Kun, Han Jing-Dong J
1] Chinese Academy of Sciences Key Laboratory of Computational Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China [2] University of Chinese Academy of Sciences, Beijing 100049, China.
Chinese Academy of Sciences Key Laboratory of Computational Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China.
Cell Res. 2015 May;25(5):574-87. doi: 10.1038/cr.2015.36. Epub 2015 Mar 31.
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.
衰老与许多复杂疾病相关。可靠地预测衰老过程对于评估衰老相关疾病的风险很重要。然而，尽管进行了深入研究，但迄今为止尚无可靠的衰老标志物。在此，我们通过研究人类三维面部成像特征是否可作为可靠的衰老标志物来解决这一问题。我们收集了300多张17至77岁各年龄段分布均匀的人类三维面部图像和血液样本。通过分析形态学特征，我们生成了首张全面的衰老人类面部表型图谱。我们确定了与年龄高度相关的定量面部特征，如眼斜率。我们构建了一个强大的年龄预测模型，发现相同实际年龄的人面部年龄平均相差±6岁，40岁以后偏差增大。使用这个预测模型，我们识别出了衰老速度慢和快的人，这在健康指标水平上得到了显著支持。尽管面部形态特征与血液中的健康指标密切相关，但面部特征作为衰老生物标志物比血液样本更可靠，并且比实际年龄能更好地反映总体健康状况。
