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微量营养素和巨量营养素的营养摄入量和2型糖尿病:机器学习方案
Mohammad Rashidmayvan1, Amin Mansoori2, Elahe Derakhshan-Nezhad3,4
1Department of Nutrition, Food Sciences and Clinical Biochemistry, School of Medicine, Social Determinants of Health Research Center, Gonabad University of Medical Sciences, Gonabad, Iran.
Journal of health, population, and nutrition
|February 7, 2025
概括
具体的营养摄入量会影响2型糖尿病 (T2D) 的风险. 较低的某些矿物质和维生素摄入量与较低的T2D发病率相关,而较高的碳水化合物和蛋白质摄入量表明风险增加.
科学领域:
- 内分泌学 在内分泌学.
- 营养科学 营养科学
- 数据科学数据科学数据科学
背景情况:
- 糖尿病是一种广泛的内分泌疾病,在全球有众多患者.
- 关于营养与糖尿病相关性的现有研究往往缺乏大量人群和长期跟踪.
- 调查宏观/微量营养素摄入量和2型糖尿病 (T2D) 发病率至关重要.
研究的目的:
- 检查宏观/微量营养素摄入量与T2D发病率之间的关系.
- 使用机器学习算法来识别T2D风险因素.
- 分析来自大型队列的数据,随着十年的随访.
主要方法:
- 雇员监督机器学习模型,包括后勤回归 (LR) 和决策树 (DT).
- 利用马什哈德队列研究数据集,招收了9704名年龄在35-65岁的人.
- 监测参与者十年 (直到2020年) 来追踪T2D发病率.
主要成果:
- 二型糖尿病发病率与,,,,,,胡卜素,维生素D,三和维生素B12的摄入量之间的反向相关性 (p < 0.05).
- 观察到T2D风险与酸盐,铁和的摄入量之间存在积极的关系 (p < 0.05).
- 较高的碳水化合物和蛋白质摄入量与T2D组显著相关 (p <0.05).
结论:
- 机器学习模型有效地使用饮食和生物标志物数据识别T2D风险.
- 研究结果强调了特定营养与T2D发展的关联.
- 将这些模型整合到电子健康记录中的潜力,以积极主动地管理糖尿病.
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