ArticleNutrition & diabetes2024
Relationships between minerals' intake and blood homocysteine levels based on three machine learning methods: a large cross-sectional study.
Article in Nutrition & diabetes, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Sex-specific association of plasma vitamin E with homocysteine concentrations in Chinese middle-aged and older men and women: evidence from pooled population-based study.European journal of nutrition · 2026Article
- Advanced Bayesian kernel machine regression for large-scale exposome studies: Making the impossible possible.Innovation (Cambridge (Mass.)) · 2026Article
- Association Between Multiple Plasma Toxic Metal and Metalloid Exposures and Hypertension in Elderly Chinese Adults.Biological trace element research · 2025Article
- Using Machine Learning to Detect Factors That Affect Homocysteine in Healthy Elderly Taiwanese Men.Biomedicines · 2025Article
- Machine learning-based exploration of the associations between multiple minerals' intake and thyroid dysfunction: data from the National Health and Nutrition Examination Survey.Frontiers in nutrition · 2025Article
- Cross-sectional study on the association between 24-hour urinary potassium excretion and the risk of H-type hypertension and non-H-type hypertension in Chinese adults.Frontiers in endocrinology · 2025Article
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Abstract
backgroundBlood 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.
methodsData 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.
resultsTraditional 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.
conclusionOverall, 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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