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

Background: Blood homocysteine (Hcy) is a sensitive predictor of cardiovascular disease. Prior studies indicate links between individual mineral intakes and Hcy, but the impact of combined (mixed) mineral intake is unknown. Methods: Baseline data from the Shanghai Suburban Adult Cohort and Biobank (SSACB) 2016 were analyzed, including 38,273 adults aged 20–74 years. A food frequency questionnaire (FFQ) estimated intakes of 10 minerals (calcium, potassium, magnesium, sodium, iron, zinc, selenium, phosphorus, copper, manganese). Morning fasting blood Hcy was measured. Traditional regression evaluated single-mineral associations with Hcy. Three mixture-focused machine learning approaches—Weighted Quantile Sum (WQS), Quantile g-computation (Qg-comp), and Bayesian Kernel Machine Regression (BKMR)—assessed the joint effect of the mineral mixture and individual mineral weights in the mixture. Results: In traditional models, higher intakes of calcium, phosphorus, potassium, magnesium, iron, zinc, copper, and manganese were associated with lower Hcy levels. Both Qg-comp and BKMR indicated that greater mixed mineral intake was associated with lower Hcy. In WQS, calcium had the highest weight in the joint effect. In Qg-comp, iron showed the highest positive weight, and manganese the highest negative weight. BKMR (10% stratified subsample; 10,000 iterations) showed that, except sodium, all nine other minerals had high weights in the joint effect on Hcy. Conclusion: Higher mixed mineral intake was associated with lower Hcy levels, with minerals contributing differently to the joint effect. Further work should explore underlying mechanisms and potential effects of mixed mineral intake on other health indicators.