中老年人饮食类型与结构抗衰老因素分析和模式探索

LIU Xing,JING Zhengwei,TU Jiaxin, WU Lei,HUANG Helang,WANG Zhifeng

Chongqing Medicine（2022）

南昌大学第一附属医院

Cited 2|Views18

Abstract

目的通过社区中老年人群衰老度测量,探索抗/缓衰老饮食类型、结构等因素和模式.方法以江西省南昌市部分社区为研究现场,以中老年健康人群为研究对象,采用分层随机抽样方法选取其中年龄≥40岁的8个年龄组人群共1167人.采用生理-心理-社会三维人体衰老度量表(PPSHAS)开展衰老度测量和饮食因素的现场调查,分析比较功能年龄明显轻和明显老两类人群的饮食类型与结构,筛选相关因素,在此基础上采用Clementine决策树C5.0模型探索性构建抗/缓衰老方案.结果共发放问卷1167份,回收有效问卷1130份,有效回收率为96.83％.衰老度测量结果显示,功能年龄明显轻者占22.48％,明显老者占27.79％,与实际年龄相符者占49.73％.对明显轻者(254人)和明显老者(314人)两类人群的饮食类型与结构的单因素分析结果显示,功能年龄与主食、叶菜类、根茎类、坚果类摄入及饮用水量等12个因素相关.决策树模型拟合显示,饮水量、主食、坚果类、奶类、叶菜类、鱼虾类、茄果豇豆类7个因素入选,提供了6套抗/缓衰老方案,其中第6套最为理想,抗/缓衰老贡献率为83.33％.结论科学、合理的饮食类型与结构有助于延缓衰老,预防慢性病.

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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