基于三种机器学习方法的矿物质摄入量与血液中同型半胱氨酸水平之间的关系:一项大型横断面研究。

IF 4.6 2区 医学 Q1 ENDOCRINOLOGY & METABOLISM
Jing Fan, Shaojie Liu, Lanxin Wei, Qi Zhao, Genming Zhao, Ruihua Dong, Bo Chen
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引用次数: 0

摘要

背景:血液中的同型半胱氨酸(Hcy)水平已成为预测心血管疾病发生的一个敏感指标。研究表明,单个矿物质摄入量与血液中 Hcy 水平之间存在关联。混合矿物质摄入量对血液中 Hcy 水平的影响尚不清楚:数据来自 2016 年上海郊区成人队列与生物库(SSACB)的基线调查数据。共有38273名年龄在20-74岁之间的参与者符合我们的纳入和排除标准。食物频率问卷(FFQ)用于计算10种矿物质(钙、钾、镁、钠、铁、锌、硒、磷、铜和锰)的摄入量。测量早晨空腹血样中 Hcy 的浓度。采用传统的回归模型来评估各种矿物质的摄入量与血液中 Hcy 水平之间的关系。使用三种机器学习模型(WQS、Qg-comp 和 BKMR)来评估混合矿物质摄入量与血液中 Hcy 水平之间的关系,区分每种矿物质的单独效应,并确定它们在联合效应中各自的权重:传统回归模型显示,钙、磷、钾、镁、铁、锌、铜和锰的摄入量越高,血 Hcy 水平越低。Qg-comp 和 BKMR 的结果一致表明,混合矿物质摄入量越高,血 Hcy 水平越低。在 WQS 模型中,钙在联合效应中的权重最高。在 Qg-comp 中,铁的正效应权重最高,而锰的负效应权重最高。子样本经过 10,000 次迭代后的 BKMR 结果表明,除钠外,其他九种矿物质对血液 Hcy 水平影响的联合效应权重都很高:总体而言,较高的混合矿物质摄入量与较低的血液 Hcy 水平相关,而每种矿物质对联合效应的贡献各不相同。未来的研究将进一步探讨这种关联的内在机制,并进一步研究混合矿物质的摄入对其他健康指标的潜在影响。这些努力将有助于提供更多的见解,加深我们对混合矿物质及其在维护健康方面的潜在作用的了解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Relationships between minerals' intake and blood homocysteine levels based on three machine learning methods: a large cross-sectional study.

Relationships between minerals' intake and blood homocysteine levels based on three machine learning methods: a large cross-sectional study.

Background: Blood homocysteine (Hcy) level has become a sensitive indicator in predicting the development of cardiovascular disease. Studies have shown an association between individual mineral intake and blood Hcy levels. The effect of mixed minerals' intake on blood Hcy levels is unknown.

Methods: Data were obtained from the baseline survey data of the Shanghai Suburban Adult Cohort and Biobank(SSACB) in 2016. A total of 38273 participants aged 20-74 years met our inclusion and exclusion criteria. Food frequency questionnaire (FFQ) was used to calculate the intake of 10 minerals (calcium, potassium, magnesium, sodium, iron, zinc, selenium, phosphorus, copper and manganese). Measuring the concentration of Hcy in the morning fasting blood sample. Traditional regression models were used to assess the relationship between individual minerals' intake and blood Hcy levels. Three machine learning models (WQS, Qg-comp, and BKMR) were used to the relationship between mixed minerals' intake and blood Hcy levels, distinguishing the individual effects of each mineral and determining their respective weights in the joint effect.

Results: Traditional regression model showed that higher intake of calcium, phosphorus, potassium, magnesium, iron, zinc, copper, and manganese was associated with lower blood Hcy levels. Both Qg-comp and BKMR results consistently indicate that higher intake of mixed minerals is associated with lower blood Hcy levels. Calcium exhibits the highest weight in the joint effect in the WQS model. In Qg-comp, iron has the highest positive weight, while manganese has the highest negative weight. The BKMR results of the subsample after 10,000 iterations showed that except for sodium, all nine minerals had the high weights in the joint effect on the effect of blood Hcy levels.

Conclusion: Overall, higher mixed mineral's intake was associated with lower blood Hcy levels, and each mineral contributed differently to the joint effect. Future studies are available to further explore the mechanisms underlying this association, and the potential impact of mixed minerals' intake on other health indicators needs to be further investigated. These efforts will help provide additional insights to deepen our understanding of mixed minerals and their potential role in health maintenance.

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来源期刊
Nutrition & Diabetes
Nutrition & Diabetes ENDOCRINOLOGY & METABOLISM-NUTRITION & DIETETICS
CiteScore
9.20
自引率
0.00%
发文量
50
审稿时长
>12 weeks
期刊介绍: Nutrition & Diabetes is a peer-reviewed, online, open access journal bringing to the fore outstanding research in the areas of nutrition and chronic disease, including diabetes, from the molecular to the population level.
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