Abstract：
Dietary restriction (DR), including caloric restriction (CR), intermittent fasting (IF), and restriction of specific food compositions, can delay aging, and the main mechanisms include regulation of nutrient-sensing pathways and gut microbiota. However, the effects of DR regimens on longevity remain controversial, as some studies have demonstrated that IF, rather than CR or diet composition, influences longevity, while other studies have shown that the restricted-carbohydrate or -protein diets, rather than CR, determine health and longevity. Many factors, including DR-related factors (carbohydrate or protein composition, degree and duration of DR), and individual differences (health status, sex, genotype, and age of starting DR), would be used to explain the controversial anti-aging effects of DR, thus highlighting the necessity of precise DR intervention for anti-aging. Personalized DR intervention in humans is challenging because of the lack of accurate aging molecular biomarkers and vast individual variability. Using machine learning to build a predictive model based on the data set of clinical features, gut microbiome and metabolome, may be a good method to achieve precise DR intervention. Therefore, this review analyzed the anti-aging effects of various DR regimens, summarized their mechanisms and influencing factors, and proposed a future research direction for achieving personalized DR regimens for slowing aging.
摘要：
饮食限制（DR），包括热量限制（CR），间歇性禁食（IF），和特定食物成分的限制，可以延缓衰老，主要机制包括营养传感途径和肠道菌群的调节。然而,DR方案对寿命的影响仍存在争议，正如一些研究表明，如果，而不是CR或饮食组成，影响寿命，而其他研究表明，限制碳水化合物或蛋白质饮食，而不是CR，决定健康和长寿。许多因素,包括DR相关因素(碳水化合物或蛋白质组成，DR的程度和持续时间)，和个体差异(健康状况，性别,基因型，和开始DR的年龄)，将被用来解释DR的有争议的抗衰老作用，从而凸显了精准DR干预抗衰老的必要性。由于缺乏准确的衰老分子生物标志物和巨大的个体差异，人类的个性化DR干预具有挑战性。利用机器学习建立基于临床特征数据集的预测模型,肠道微生物组和代谢组，可能是实现精确DR干预的好方法。因此,这篇综述分析了各种DR方案的抗衰老作用，总结了其作用机理和影响因素,并提出了实现个性化DR方案延缓衰老的未来研究方向。
