Gialluisi Alessandro, Di Castelnuovo Augusto, Donati Maria Benedetta, de Gaetano Giovanni, Iacoviello Licia
Department of Epidemiology and Prevention, IRCCS NEUROMED, Pozzilli, Italy.
Mediterranea Cardiocentro, Naples, Italy.
Front Med (Lausanne). 2019 Jul 3;6:146. doi: 10.3389/fmed.2019.00146. eCollection 2019.
In recent years, different machine learning algorithms have been developed for the estimation of Biological Age (BA), defined as the hypothetical underlying age of an organism. BA can be computed based on different circulating and non-circulating biomarkers. In this perspective, identifying biomarkers with a prominent influence on BA and developing reliable models for its estimation is of fundamental importance for monitoring healthy aging, and could provide new tools to screen health status and the risk of clinical events in the general population. Here, we briefly review the different machine learning (ML) approaches used for BA estimation, focusing on those methods with potential application to the Moli-sani study, a prospective population-based cohort study of 24,325 subjects (35-99 years). In particular, we discuss the potential of BA estimation based on blood biomarkers, which likely represents the easiest and most immediate way to compute organismal BA. Similarly, we describe ML methods for the estimation of brain age based on structural neuroimaging features. For each method, we discuss the relation with epidemiological variables (e.g., mortality), genetic and environmental factors, and common age-related diseases (e.g., Alzheimer disease), to examine the potential as aging biomarker in the general population. Finally, we hypothesize new approaches for BA estimation, both at the single organ and at the whole organism level. Overall, here we trace the road ahead in the Big Data era for our and other prospective general population cohorts, presenting ways to exploit the notable amount of data available nowadays.
近年来，已经开发出了不同的机器学习算法来估计生物学年龄（BA），生物学年龄被定义为生物体假设的潜在年龄。BA可以基于不同的循环和非循环生物标志物来计算。从这个角度来看，识别对BA有显著影响的生物标志物并开发可靠的估计模型对于监测健康衰老至关重要，并且可以提供新的工具来筛查普通人群的健康状况和临床事件风险。在这里，我们简要回顾用于BA估计的不同机器学习（ML）方法，重点关注那些有可能应用于莫利萨尼研究的方法，该研究是一项基于人群的前瞻性队列研究，涉及24325名受试者（35 - 99岁）。特别是，我们讨论基于血液生物标志物估计BA的潜力，这可能是计算生物体BA最简单、最直接的方法。同样，我们描述基于结构神经影像学特征估计脑年龄的ML方法。对于每种方法，我们讨论其与流行病学变量（如死亡率）、遗传和环境因素以及常见的年龄相关疾病（如阿尔茨海默病）的关系，以检验其作为普通人群衰老生物标志物的潜力。最后，我们设想了在单器官和全生物体水平上估计BA的新方法。总体而言，我们在这里描绘了大数据时代我们以及其他前瞻性普通人群队列的前进道路，展示了利用当今可用大量数据的方法。
