首页|基于机器学习的饮食质量指数及其对血压水平及高血压的预测研究

基于机器学习的饮食质量指数及其对血压水平及高血压的预测研究

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目的 基于机器学习弹性网变量选择方法提出一种数据自适应的饮食质量指数,并探讨其与血压及高血压患病风险的关联.方法 数据来源于美国营养调查协会(NHANES)2011~2018年4个周期的1 143个亚裔样本数据,其中男性占59.06%,平均年龄为(43.12±15.10)岁;其它变量包括人口统计学变量、29种食物摄入成分以及舒张压和收缩压测量值.在控制相关混杂因素的前提下,利用弹性网变量选择方法筛选出重要食物摄入成分,并以食物成分对应的回归系数为权重计算加权平均值作为数据自适应饮食质量指数(ddDQS).进一步分析新指数与高血压风险的关联,并与健康饮食指数(HEI-2015)、替代健康饮食指数(AHEI)、得舒饮食指数(DASH)以及地中海饮食指数(MED)4种常用的饮食指数进行比较.结果 高血压患病率为26.15%,患高血压个体的平均年龄[(52.94±14.77)岁]显著高于没有患高血压的个体[(39.64±13.62)岁].ddDQS包含5个重要的膳食成分(精制谷物、油、酒精、添加糖以及马铃薯),与收缩压(SBP)(β=-2.08,95%CI=3.24 0.92,P<0.001)及高血压(HTN)(β=-0.482,95%CI=-0.72~-0.25,OR=0.62,P<0.001)均相关.摄入马铃薯会增加ddDQS指数,而摄入精制谷物、油、酒精、添加糖则会降低ddDQS指数.亚组分析结果表明40~60岁以及男性人群对于ddDQS更加敏感,这些亚群将更多地从健康膳食模式中获益.HEI-2015与DBP、SBP以及HTN存在一定关联,但P值均大于ddDQS对应P值.本研究未发现DASH、AHEI和MED与DBP、SBP以及HTN的关联.结论 与常用饮食指数相比,ddDQS对高血压有更好的预测.ddDQS越高越有益于血压及高血压风险的降低.针对亚裔人群的饮食指数的构建可指导未来研究进行高血压预防与控制.
Construction of machine learning-based diet quality index and its association with blood pressure levels and hypertension risk
Objective This paper aimed to propose a data-adaptive diet quality index based on the elastic net variable selection method in machine learning.Additionally,it explored the association of this index with blood pressure levels and the risk of hypertension.Methods The data source was from 1 143 Asian samples in the National Health and Nutrition Examination Survey(NHANES)across four cycles from 2011 to 2018,with 59.06%being male and an average age of(43.12±15.10)years.Other variables included demographic variables,intake levels of 29 food components and diastolic and systolic blood pressure measurements.Control-ling for related confounding factors,the elastic net variable selection method was used to screen important food components.The weighted average of food components was defined as the data-driven diet quality score(ddDQS),using their corresponding regression coefficients as weights.Furthermore,the association between the new index and the risk of hypertension was investigated and compared with four commonly used diet quality indices:the Healthy Eating Index 2015(HEI-2015),the Alternative Healthy Eating Index(AHEI),the Dieta-ry Approaches to Stop Hypertension(DASH)index,and the Mediterranean Dietary Index(MED).Results The prevalence of hypertension was 26.15%,and the average age of individuals with hypertension(52.94±14.77)years was significantly higher than those without hypertension(39.64±13.62)years.The ddDQS in-cluded five important dietary components:refined grains,oils,alcohol,added sugars and starchy potatoes.It was significantly associated with systolic blood pressure(SBP)(β=-2.08,95%CI=-3.24~-0.92,P<0.001)and hypertension(HTN)(β=-0.482,95%CI=-0.72~-0.25,OR=0.62,P<0.001).Intake of starchy potatoes increased the ddDQS,while intake of refined grains,oils,alcohol,and added sugars de-creased the ddDQS.Subgroup analysis results showed that individuals aged 40~60 years and males were more sensitive to the ddDQS,suggesting that these subgroups would benefit more from adhering to healthy dietary patterns.The HEI-2015 was associated with diastolic blood pressure(DBP),SBP,and HTN to some extent,but the P-values were greater than those corresponding to the ddDQS.This study did not find associations be-tween the DASH,AHEI,and MED indices and DBP,SBP,or HTN.Conclusion Compared to commonly used diet quality indices,the ddDQS constructed using machine learning methods demonstrates better predic-tive ability for hypertension.A higher ddDQS is beneficial for reducing blood pressure levels and the risk of hy-pertension.The development of a diet quality index tailored to the Asian population can guide future research on hypertension prevention and control.

hypertensiondiet quality indexelastic net variable selectionmachine learning

刘炎、张玲、李琦、杨灿、杜亚楠、袁敏

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安徽医科大学卫生管理学院卫生健康数据科学系,安徽 合肥 230032

高血压 饮食质量指数 弹性网变量选择 机器学习

国家自然科学基金项目安徽省自然科学面上项目人口健康与优生安徽省重点实验室课题

820735782008085MA09JKYS20233

2024

右江民族医学院学报
右江民族医学院

右江民族医学院学报

影响因子:0.708
ISSN:1001-5817
年,卷(期):2024.46(3)