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.

并非所有人的年龄增长速度都相同。诸如“甲基化时钟”之类的方法是侵入性的，依赖于昂贵的组织样品测定，并通过按时间顺序进行训练来推断衰老率，这可作为预测误差的参考。在这里，我们通过对约5,000名汉族人的非侵入性三维（3D）面部图像进行训练，开发了基于卷积神经网络的模型，这些人在年龄或预期年龄与预测年龄之间的平均差异为±2.8和2.9岁，分别。我们进一步分析了280个个体的血液转录组，并通过因果推断模型推断了介导生活方式对面部老龄化率影响的分子调节剂。这些关系已在“人类血液基因表达-3D面部图像（HuB-Fi）”数据库中存储和可视化。总的来说，我们发现人类在血液和面部中的衰老速率不同，但是一致，并且异质性在中年达到高峰。我们的研究提供了一个示例，说明了如何利用人工智能来确定人类的感知年龄作为生物年龄的标志，而不再依赖于时间年龄的预测误差，并估计人口中老龄化率的异质性。