Toenail metal concentrations and age at menopause

White, Alexandra J.; O’Brien, Katie M.; Niehoff, Nicole M.; Jackson, Brian P.; Weinberg, Clarice R.; Keil, Alexander P.

doi:10.1097/ee9.0000000000000104

Cited by 13 publications

(4 citation statements)

References 42 publications

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“…Prolonged exposure to this element may cause damage to the liver and kidneys, blood circulation and nervous system (Abdulrahman et al, 2012). In addition, according to the results obtained by White et al, (2020) in the Sister Study, the level of chromium in the nails is associated with the later age of menopause.…”

Section: Discussionmentioning

confidence: 99%

Chromium as a Risk Factor for Breast Cancer: A Meta-Analysis

Batyrova

Kononets

Amanzholkyzy

et al. 2022

Asian Pac J Cancer Prev

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Background: Chromium (Cr) is a transition metal, natural element. Chromium is the 21st most abundant element in Earth's crust. Cr is found in soil, rocks and living organisms. It may have various oxidation states, from -2 to +6, but most of these states are too unstable to exist in any significant quantities. The purpose of this review and meta-analysis is to critically assess the scientific evidence on the carcinogenic effects of chromium (Cr) and to determine whether there is currently sufficient evidence to suggest that that there is a link between chromium levels in hair and blood serum and breast cancer in women. Material and methods: Research on the relationship between heavy metal chromium and the risk of developing breast cancer has been searched in PubMed, EMBASE, Web of Science, Scopus among papers published between January 2000 and September 2020. The search used the following terms (MeSH): breast cancer, women, trace elements, metals, chromium, chemically-induced, hair, serum using additional terms. Results: In the second group of comparisons of women from "ecologically clean" districts of Aktobe Region, there were significantly lower indicators of the microelements in tumor tissue. The amount of Fe ranges from 38.46 to 65.39 ug/g (average 49.56±5.81 ug/g), Cu from 2.8 to 6.69 ug/g (average 5.06±1.01 ug/g), Zn from 1.89 to 5.38 ug/g (average 3.88±0.89 ug/g), Cr from zero to 6,1 ug/g (average 2.13±1.29 ug/g), Ni from 0.11 to 0.42 ug/g (average 0.28±0.067 ug/g) и Pb from zero to 0.19 ug/g (average 0.098±0.06 ug/g). Conclusion: The article established that women who live or work in ecologically polluted areas or have problems with micronutrient exchange need in-depth screening and more frequent screening for early detection of pre-and breast cancer.

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Section: Discussionmentioning

confidence: 99%

Chromium as a Risk Factor for Breast Cancer: A Meta-Analysis

Batyrova

Kononets

Amanzholkyzy

et al. 2022

Asian Pac J Cancer Prev

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“…An additional advancement was the implementation of quantile g-computation to better evaluate and quantify the joint impact of chemical groups. Understanding the joint impact is important because chemicals may have small effects that are missed when studied individually but may have a meaningful combined impact ( Niehoff et al, 2020 ; White et al, 2020 ). Methods to estimate the joint impact of exposure to multiple chemicals may help identify the effects of interventions that affect multiple chemicals.…”

Section: Discussionmentioning

confidence: 99%

Mixtures modeling identifies chemical inducers versus repressors of toxicity associated with wildfire smoke

Rager

Clark

Eaves

et al. 2021

Science of The Total Environment

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Exposure to wildfire smoke continues to be a growing threat to public health, yet the chemical components in wildfire smoke that primarily drive toxicity and associated disease are largely unknown. This study utilized a suite of computational approaches to identify groups of chemicals induced by variable biomass burn conditions that were associated with biological responses in the mouse lung, including pulmonary immune response and injury markers. Smoke condensate samples were collected and characterized, resulting in chemical distribution information for 86 constituents across ten different exposures. Mixtures-relevant statistical methods included (i) a chemical clustering and data-reduction method, weighted chemical co-expression network analysis (WCCNA), (ii) a quantile g-computation approach to address the joint effect of multiple chemicals in different groupings, and (iii) a correlation analysis to compare mixtures modeling results against individual chemical relationships. Seven chemical groups were identified using WCCNA based on co-occurrence showing both positive and negative relationships with biological responses. A group containing methoxyphenols (e.g., coniferyl aldehyde, eugenol, guaiacol, and vanillin) displayed highly significant, negative relationships with several biological esponses, including cytokines and lung injury markers. This group was further shown through quantile g-computation methods to associate with reduced biological responses. Specifically, mixtures modeling based on all chemicals excluding those in the methoxyphenol group demonstrated more significant, positive relationships with several biological responses; whereas mixtures modeling based on just those in the methoxyphenol group demonstrated significant negative relationships with several biological responses, suggesting potential protective effects. Mixtures-based analyses also identified other groups consisting of inorganic elements and ionic constituents showing positive relationships with several biological responses, including markers of inflammation. Many of the effects identified through mixtures modeling in this analysis were not captured through individual chemical analyses. Together, this study demonstrates the utility of mixtures-based approaches to identify potential drivers and inhibitors of toxicity relevant to wildfire exposures.

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“…Quantile gcomputation has recently been proposed to study the effects of complex exposure mixtures. This approach has been used to assess exposures such as metal (White et al, 2020;Xu et al, 2020;Niehoff et al, 2020) and chemicals (Lebeaux et al, 2020;Lin et al, 2021;Parada et al, 2021). To the best of our knowledge, only one study used g-computation to examine the effect of social/environmental exposure mixtures on fetal growth (Goin et al, 2020).…”

Section: Discussionmentioning

confidence: 99%