The Association between Zinc and Copper Circulating Levels and Cardiometabolic Risk Factors in Adults: A Study of Qatar Biobank Data

Kerkadi, Abdelhamid; Alkudsi, Dana Samir; Hamad, Sara; Alkeldi, Hanan Mohamed; Salih, Reem Omar Mohamed; Agouni, Abdelali

doi:10.3390/nu13082729

Cited by 13 publications

(8 citation statements)

References 112 publications

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“…found a negative association between plasma Zn levels and obesity (54) . Several other studies similarly found a negative association between plasma Zn levels and waist circumference and/or BMI (55–57) , which is consistent with our findings that plasma Zn was negatively associated with general obesity. In our study, the median dietary Zn intake of the total population was 6·74 mg/d, which was lower than the study by Ju et al .…”

Section: Discussionsupporting

confidence: 93%

Association between plasma and dietary trace elements and obesity in a rural Chinese population

Liu

Huang

et al. 2023

Br J Nutr

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Trace elements may play an important role in obesity. This study aimed to assess the plasma and dietary intake levels of four trace elements, manganese, copper, zinc and selenium in a rural Chinese population and analyse the relationship between trace elements and obesity. A cross-sectional study involving 2587 participants was conducted. Logistic regression models were used to analyse the association between trace elements and obesity; Restricted cubic spline(RCS) models were used to assess the dose-response relationship between trace elements and obesity; The Weighted Quantile Sum(WQS) model was used to examine the potential interaction of four plasma trace elements on obesity. Logistic regression analysis showed that plasma selenium concentrations in the fourth quartile(Q4) exhibited a lower risk of developing obesity than the first quartile(Q1)(central obesity: OR=0.634, P=0.002; general obesity: OR=0.525, P=0.005). Plasma zinc concentration in the third quartile(Q3) showed a lower risk of developing obesity in general obesity compared to the first quartile(Q1)(OR=0.625, P=0.036). In general obesity, the risk of morbidity was 1.727 and 1.923 times higher for the second and third(Q2, Q3) quartiles of dietary manganese intake than for Q1, respectively. RCS indicated an inverse U-shaped correlation between plasma selenium and obesity. WQS revealed the combined effects of four trace elements were negatively associated with central obesity. Plasma zinc and selenium were negatively associated with obesity and dietary manganese was positively associated with obesity. The combined action of the four plasma trace elements had a negative effect on obesity.

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Section: Discussionsupporting

confidence: 93%

Association between plasma and dietary trace elements and obesity in a rural Chinese population

Liu

Huang

et al. 2023

Br J Nutr

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“…However, in this Mediterranean population, no significant associations were found between plasma Zn levels and these parameters. Other authors found no association between diabetes and the levels of Zn or other trace elements [ 148 , 151 ], even an inverse association with glycated hemoglobin has been reported [ 155 ]. Interestingly, despite not detecting statistically significant associations between plasma Zn concentrations and fasting glucose or diabetes risk, we identified a highly significant positive association between urine Zn concentrations and these parameters in our population.…”

Section: Discussionmentioning

confidence: 99%

“…Other authors inversely related low circulating levels of Zn with systemic inflammatory activity [ 80 ], and with dyslipidemia in the presence of hypertension and hypercholesterolemia [ 149 ]. High Cu levels have been linked with hypertriglyceridemia in newborns [ 150 ], cardiovascular risk [ 77 ], and a positive correlation between Cu levels and total cholesterol and HDL-cholesterol has been described [ 151 ]. However, other authors found no correlation between plasma Cu and Zn and lipid parameters [ 125 , 152 ].…”

Section: Discussionmentioning

confidence: 99%

Single and Combined Associations of Plasma and Urine Essential Trace Elements (Zn, Cu, Se, and Mn) with Cardiovascular Risk Factors in a Mediterranean Population

et al. 2022

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Trace elements are micronutrients that are required in very small quantities through diet but are crucial for the prevention of acute and chronic diseases. Despite the fact that initial studies demonstrated inverse associations between some of the most important essential trace elements (Zn, Cu, Se, and Mn) and cardiovascular disease, several recent studies have reported a direct association with cardiovascular risk factors due to the fact that these elements can act as both antioxidants and pro-oxidants, depending on several factors. This study aims to investigate the association between plasma and urine concentrations of trace elements and cardiovascular risk factors in a general population from the Mediterranean region, including 484 men and women aged 18–80 years and considering trace elements individually and as joint exposure. Zn, Cu, Se, and Mn were determined in plasma and urine using an inductively coupled plasma mass spectrometer (ICP-MS). Single and combined analysis of trace elements with plasma lipid, blood pressure, diabetes, and anthropometric variables was undertaken. Principal component analysis, quantile-based g-computation, and calculation of trace element risk scores (TERS) were used for the combined analyses. Models were adjusted for covariates. In single trace element models, we found statistically significant associations between plasma Se and increased total cholesterol and systolic blood pressure; plasma Cu and increased triglycerides and body mass index; and urine Zn and increased glucose. Moreover, in the joint exposure analysis using quantile g-computation and TERS, the combined plasma levels of Zn, Cu, Se (directly), and Mn (inversely) were strongly associated with hypercholesterolemia (OR: 2.03; 95%CI: 1.37–2.99; p < 0.001 per quartile increase in the g-computation approach). The analysis of urine mixtures revealed a significant relationship with both fasting glucose and diabetes (OR: 1.91; 95%CI: 1.01–3.04; p = 0.046). In conclusion, in this Mediterranean population, the combined effect of higher plasma trace element levels (primarily Se, Cu, and Zn) was directly associated with elevated plasma lipids, whereas the mixture effect in urine was primarily associated with plasma glucose. Both parameters are relevant cardiovascular risk factors, and increased trace element exposures should be considered with caution.

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“…5 Metals can get into people's bodies in a variety of ways, such as air inhalation, skin contact, and digestion. 6 Essential elements could assume an imperative part in human physiological activities, including immunity, metabolism, and development. 7 However, insufficient, or excessive essential elements might apply antagonistic impacts on people health.…”

Section: Introductionmentioning

confidence: 99%

“…Genetics, diet, and lifestyle are currently well‐established risk factors for hypertension, 4 while previous researches have suggested that metal exposure may also be the cause of hypertension's etiology 5 . Metals can get into people's bodies in a variety of ways, such as air inhalation, skin contact, and digestion 6 . Essential elements could assume an imperative part in human physiological activities, including immunity, metabolism, and development 7 .…”

Section: Introductionmentioning

confidence: 99%

The interpretable machine learning model associated with metal mixtures to identify hypertension via EMR mining method

Xu,

Sun

2024

J of Clinical Hypertension

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There are limited data available regarding the connection between hypertension and heavy metal exposure. The authors intend to establish an interpretable machine learning (ML) model with high efficiency and robustness that identifies hypertension based on heavy metal exposure. Our datasets were obtained from the US National Health and Nutrition Examination Survey (NHANES, 2013–2020.3). The authors developed 5 ML models for hypertension identification by heavy metal exposure, and tested them by 10 discrimination characteristics. Further, the authors chose the optimally performing model after parameter adjustment by Genetic Algorithm (GA) for identification. Finally, in order to visualize the model's ability to make decisions, the authors used SHapley Additive exPlanation (SHAP) and Local Interpretable Model‐Agnostic Explanations (LIME) algorithm to illustrate the features. The study included 19 368 participants in total. A best‐performing eXtreme Gradient Boosting (XGB) with GA for hypertension identification by 16 heavy metals was selected (AUC: 0.774; 95% CI: 0.772–0.776; accuracy: 87.7%). According to SHAP values, Barium (0.02), Cadmium (0.017), Lead (0.017), Antimony (0.008), Tin (0.007), Manganese (0.006), Thallium (0.004), Tungsten (0.004) in urine, and Lead (0.048), Mercury (0.035), Selenium (0.05), Manganese (0.007) in blood positively influenced the model, while Cadmium (−0.001) in urine negatively influenced the model. Study participants' hypertension associated with heavy metal exposure was identified by an efficient, robust, and interpretable GA‐XGB model with SHAP and LIME. Barium, Cadmium, Lead, Antimony, Tin, Manganese, Thallium, Tungsten in urine, and Lead, Mercury, Selenium, Manganese in blood are positively correlated with hypertension, while Cadmium in blood is negatively correlated with hypertension.

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The Association between Zinc and Copper Circulating Levels and Cardiometabolic Risk Factors in Adults: A Study of Qatar Biobank Data

Cited by 13 publications

References 112 publications

Association between plasma and dietary trace elements and obesity in a rural Chinese population

Association between plasma and dietary trace elements and obesity in a rural Chinese population

Single and Combined Associations of Plasma and Urine Essential Trace Elements (Zn, Cu, Se, and Mn) with Cardiovascular Risk Factors in a Mediterranean Population

The interpretable machine learning model associated with metal mixtures to identify hypertension via EMR mining method

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