The Effect of Mixture of Heavy Metals on Obesity in Individuals ≥50 Years of Age

Nguyen, Hai Duc; Oh, Hojin; Kim, Min-Sun

doi:10.1007/s12011-021-02972-z

Cited by 47 publications

(26 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…High-fat-diet has been shown to impact diabetes ( da Cruz et al, 2022 ), hypertension ( Logvinov et al, 2021 ), obesity ( Kang et al, 2022 ), and respiratory diseases ( Li et al, 2021a ). Organophosphate pesticides (and pesticides in general) have been shown to impact diabetes ( Miranda et al, 2022 ), hypertension ( Ledda et al, 2015 ), obesity ( Miranda et al, 2022 ), and respiratory diseases ( Bugay et al, 2022 ), and heavy metals have been shown to impact diabetes ( Wang et al, 2022 ), hypertension ( Tang et al, 2022a ), obesity ( Duc et al, 2022 ), and respiratory diseases ( Yao et al, 2021 ). More data are required to state with confidence that there are strong overlaps between the CFs to COVID-19 and its comorbidities.…”

Section: Resultsmentioning

confidence: 99%

Modifiable contributing factors to COVID-19: A comprehensive review

Kostoff

Briggs²,

Kanduc

et al. 2023

Food and Chemical Toxicology

View full text Add to dashboard Cite

Section: Resultsmentioning

confidence: 99%

Modifiable contributing factors to COVID-19: A comprehensive review

Kostoff

Briggs²,

Kanduc

et al. 2023

Food and Chemical Toxicology

View full text Add to dashboard Cite

“…In stage 3, we applied the Quantile G-Computation, a machine-learning method, to evaluate the importance of each food group and their joint effects on the risk of mortality [ 21 , 22 ]. The qgcomp.cox.noboot function in the R qgcomp package was used to estimate the exposure effects, which firstly categorizes all food groups into Q, then assigns each food group with a positive or negative weight on their relationship with the outcomes.…”

Section: Methodsmentioning

confidence: 99%

“…If the included food groups were associated with mortality in different directions, the positive or negative weights were interpreted as the percentage of exposure effects that had a positive or inverse association with mortality outcomes, with the positive and negative weights each adding up to one. In addition, a qgcomp index was computed based on the variable-specific coefficients for each included food group, and the association between the index and the risk of mortality was examined [ 21 , 22 ]. In other words, the qgcomp index summarized the joint effect of increasing one quartile of each food group with a negative (positive) weight with the cause-specific mortality risk, and the overall effect was presented as HR (95% CI).…”

Section: Methodsmentioning

confidence: 99%

Joint Associations of Food Groups with All-Cause and Cause-Specific Mortality in the Mr. OS and Ms. OS Study: A Prospective Cohort

Yang

Yeung

et al. 2022

Nutrients

View full text Add to dashboard Cite

Despite continuous growth in dietary pattern research, the relative importance of each dietary component in the overall pattern and their joint effects on mortality risk have not been examined adequately. We explored the individual and joint associations of multiple food groups with all-cause and cause-specific mortality (cardiovascular disease (CVD) or cancer), by analyzing data from a cohort of 3995 Hong Kong Chinese older adults in the Mr. Osteoporosis (OS) and Ms. OS Study. Cox proportional hazards models were used to examine the associations of food groups with mortality risk. The individual and joint contribution of food groups to mortality risk has been quantified by a machine learning approach, i.e., the Quantile G-Computation. When comparing the highest with the lowest quartile of intake, dark green and leafy vegetables (hazard ratio (HR) = 0.82, 95% confidence interval (CI) = 0.70 to 0.96, Ptrend = 0.049), fruit (HR = 0.79, 95% CI = 0.68 to 0.93, Ptrend = 0.006), legumes (HR = 0.75, 95% CI = 0.63 to 0.87, Ptrend = 0.052), mushroom and fungi (HR = 0.76, 95% CI = 0.65 to 0.88, Ptrend = 0.023), soy and soy products (HR = 0.77, 95% CI = 0.66 to 0.90, Ptrend = 0.143), and whole grains (HR = 0.76, 95% CI = 0.65 to 0.89, Ptrend = 0.008) were inversely associated with all-cause mortality. Legume intake was associated with a lower risk of CVD mortality, while fruit, nuts, soy and soy products were associated with a lower risk of cancer mortality. From the Quantile G-Computation, whole grains, legumes, fruits, mushroom and fungi, soy and soy products had a higher relative weighting on mortality risk, and the joint effect of food groups was inversely associated with the mortality risk due to all-causes (HR = 0.39, 95% CI = 0.27 to 0.55), CVD (HR = 0.78, 95% CI = 0.67 to 0.91), and cancer (HR = 0.31, 95% CI = 0.15 to 0.65). From a sex-stratified analysis, most associations between food groups (whole grains, legumes, fruits, mushroom and fungi, soy and soy products) and mortality risk remained significant among men. In conclusion, whole grains, legumes, fruits, mushroom and fungi, soy and soy products were the main contributors to a reduction in mortality risk, and their joint effects were stronger than individual food groups. Moreover, the sex-specific association of sweets and desserts with cancer mortality may be worth further investigation.

show abstract

“…qgcomp used a parametric GLM-based application of g-calculation to evaluate the effect of increasing all metals and metalloids in the mixture by a quarter simultaneously ( 31 ). The advantage of qgcomp is that metals and metalloids can interact with outcomes in any direction ( 21 ). This method is applied in following steps.…”

Section: Methodsmentioning

confidence: 99%

“…It is difficult to evaluate the real exposure of human body through a single or several external exposure media (i.e., air or water). Previous studies have reported that metals can enter the body through oral, inhalational, or transdermal routes, they circulate in the blood and are excreted in the kidney as urine ( 19 – 21 ). Urinary metal concentrations were mostly regarded as a reliable indicator of exposure as they can integrate multiple exposure sources ( 22 , 23 ).…”

Section: Introductionmentioning

confidence: 99%

Association of Metals and Metalloids With Urinary Albumin/Creatinine Ratio: Evidence From a Cross-Sectional Study Among Elderly in Beijing

Zhao

Liu

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

BackgroundEnvironmental exposure to toxic elements contributes to the pathogenesis of chronic kidney disease (CKD). Few studies focus on the association of urinary metals and metalloids concentrations with the urinary albumin/creatinine ratio (UACR) among elderly, especially in areas and seasons with severe air pollution.ObjectiveWe aimed to evaluate the associations of urinary metals and metalloids concentration with UACR, which is an early and sensitive indicator of CKD.MethodWe conducted a cross-sectional study among 275 elderly people in Beijing from November to December 2016, which has experienced the most severe air pollution in China. We measured 15 urinary metals and metalloids concentration and estimated their association with UACR using a generalized linear model (GLM). Bayesian kernel machine regression (BKMR) and quantile g-computation (qgcomp) models were also conducted to evaluate the combined effect of metal and metalloid mixtures concentration.ResultsOf the 275 elderly people included in the analysis, we found that higher urinary Cu concentration was positively associated with UACR using GLM (β = 0.36, 95% CI: 0.25, 0.46). Using the BKMR model, we found that the change in UACR was positively associated with a change in urinary Cu concentration from its 25th to 75th percentile value with all other metals and metalloids concentration fixed at their 25th, 50th, or 75th percentile levels. Urinary Cu concentration had the most significant positive contribution (59.15%) in the qgcomp model. Our finding was largely robust in three mixture modeling approaches: GLM, qgcomp, and BKMR.ConclusionThis finding suggests that urinary Cu concentration was strongly positively associated with UACR. Further analyses in cohort studies are required to corroborate this finding.

show abstract

The Effect of Mixture of Heavy Metals on Obesity in Individuals ≥50 Years of Age

Cited by 47 publications

References 67 publications

Modifiable contributing factors to COVID-19: A comprehensive review

Modifiable contributing factors to COVID-19: A comprehensive review

Joint Associations of Food Groups with All-Cause and Cause-Specific Mortality in the Mr. OS and Ms. OS Study: A Prospective Cohort

Association of Metals and Metalloids With Urinary Albumin/Creatinine Ratio: Evidence From a Cross-Sectional Study Among Elderly in Beijing

Contact Info

Product

Resources

About