The effect of consuming bread contaminated with heavy metals on cardiovascular disease and calculating its risk assessment

Abstract

Heavy metals (HMs) may cause the generation of reactive oxygen species (ROS), which results in oxidative stress and eventually leads to an increase in cardiovascular diseases (CVD). The Hoveyzeh Cohort Study Center provided clinical data for cardiovascular cases. The collection of samples was done randomly. The association between CVD and HMs has been evaluated utilizing seven machine-learning techniques. The results showed that the effect coefficient (β) of bread consumption in the incidence of heart disease is 4.6908 × 10^-02. Consumption of bread contaminated with chromium (P value < 0.0217), cadmium (P value < 2.95 × 10^-6) and arsenic (P value < 1.15 × 10^-07) is significantly related to cardiovascular incidence. Each unit of bread consumption increases As intake by 0.494 (β = 4.940 × 10^-01) and CVD incidence by 11.9% (OR = 1.1190). Bread consumption increases Cd intake by 0.479 (β = 4.799 × 10^-1) and cardiovascular disease incidence by 11.97% (OR = 1.1197) per unit. The findings indicated that bread intake in the study region is not correlated with non-carcinogenic or carcinogenic risks, since the cancer risk and incremental lifetime cancer risk for both groups were below 1*10^^-6. In the present investigation, bread had HMs included As, Cd, Cr, and Pb higher than the limit declared by WHO. The results of the present study showed that bread is a mediating factor (between HMs and the incidence of CVD).

Keywords: Bread; Cohort study; Heart failure; Urinary.

Fig. 1

The ROC of the seven machine-learning models. Seven machine learning modes have been demonstrated in Fig. 1 to extract variables that have the highest effects on cardiovascular incidence. CVD is affected by characteristics like residence type, age, BMI, smoking, and wealth score. Besides these, four other HMs are affected by Al, Cr, Cd, and As. Residence type, age, BMI, smoking, and wealth score are important variables affecting CVD in the DTC, GNB, GB, KN, LDA, LR, and MLP models. Nevertheless, each of these factors was adjusted for in the evaluation. The research aimed to find out the effects of HMs on CVD. The findings of the measurements of HMs in consumption bread revealed that Al(0.65) was effective in models GB and GNB, while As, Cr, and Cd had the highest effects in DTC, GNB, GB, KN, LDA, LR, and MLP models. Finally, the elements with the greatest effects (as determined by various ML models) were As, Cr, and Cd, which have been added to the GAM statistical model.

Fig. 1

The ROC of the seven machine-learning models. Seven machine learning modes have been demonstrated in Fig. 1 to extract variables that have the highest effects on cardiovascular incidence. CVD is affected by characteristics like residence type, age, BMI, smoking, and wealth score. Besides these, four other HMs are affected by Al, Cr, Cd, and As. Residence type, age, BMI, smoking, and wealth score are important variables affecting CVD in the DTC, GNB, GB, KN, LDA, LR, and MLP models. Nevertheless, each of these factors was adjusted for in the evaluation. The research aimed to find out the effects of HMs on CVD. The findings of the measurements of HMs in consumption bread revealed that Al(0.65) was effective in models GB and GNB, while As, Cr, and Cd had the highest effects in DTC, GNB, GB, KN, LDA, LR, and MLP models. Finally, the elements with the greatest effects (as determined by various ML models) were As, Cr, and Cd, which have been added to the GAM statistical model.