六省老年人延缓神经退变膳食模式与认知功能的关系研究
Acta Nutrimenta Sinica（2023）
中国疾病预防控制中心
Abstract
目的 探索我国六省老年人延缓神经退变(Mediterranean-DASH diet intervention for neurodegenerative delay,MIND)膳食模式与认知功能的关系,为认知功能障碍及相关疾病的预防控制提供科学依据.方法 采用多阶段分层概率比例抽样方法(PPS)在我国 6 省 12 县抽取老年居民进行问卷调查、体格检查,收集调查对象的个人信息、行为生活方式及健康状况,通过食物频率表收集膳食摄入信息,通过简易精神状态评价量表(MMSE)评估认知功能.采用多重线性回归和 Logistic 回归分析 MIND 膳食模式与认知功能的相关性.结果 认知功能正常组 MIND 膳食得分显著高于认知功能受损组得分,分别为 6.56(±1.15)分和 6.33(±1.07)分.认知功能正常组MMSE总得分显著高于认知功能受损组得分,分别为 26.73(±2.74)分和 18.53(±5.86)分.MIND膳食得分与MMSE总得分呈正相关,与MIND膳食得分最低组相比,MIND膳食得分最高组老人MMSE总得分显著较高,β 值为 0.63(95%CI:0.20-1.07),其中在定向力、注意力和计算力、语言能力三个维度上的得分与 MIND 膳食得分均呈正相关,β 值分别为 0.20(95%CI:0.02-0.37)、0.17(95%CI:0.01-0.33)、0.29(95%CI:0.15-0.42).与MIND膳食得分最低组相比,最高组老人认知受损的风险较低,OR值为 0.71(95%CI:0.57-0.89).结论 MIND膳食模式与老年人较低的认知受损风险显著相关,坚持MIND膳食模式可能有助于保护老年人的认知功能.
More上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- 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
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined