Lagged Kernel Machine Regression For Identifying Time Windows Of Susceptibility To Exposures Of Complex Metal Mixtures

Background: In utero exposure to metals lead (Pb), cadmium (Cd), and mercury (Hg) may be associated with higher childhood systolic blood pressure (SBP), while trace elements manganese (Mn) and selenium (Se) may have protective, antioxidant effects that modify metal-SBP associations. No study has examined how in utero co-exposure to these metals affect offspring SBP. Objectives: To examine the individual and joint effects of in utero exposure to Cd, Pb, Hg, Mn, and Se on offspring SBP. Methods: We used data from the Boston Birth Cohort (enrolled 2002-2013). We measured metals in maternal red blood cells collected 24-72 hours after delivery. We calculated child age-, sex-, and height-specific SBP percentile per 2017 American Academy of Pediatrics guidelines. We used linear regression models to estimate associations of each metal, and Bayesian kernel machine regression (BKMR) to examine metal co-exposures, with child SBP between 3 to 15 years of age. Results: Our analytic sample comprised 1194 mother-child pairs (61% Black, 20% Hispanic). Hg and Pb were not associated with child SBP. Se and Mn were inversely associated with child SBP: each log2(Se) and log2(Mn) increment was associated with a 6.23 (95% CI: 0.96-11.51) and a 2.62 (95% CI: 0.04-5.20) percentile lower child SBP, respectively. BKMR models showed similar results ( Panel A ). While Cd was not overall associated with child SBP, there was an antagonistic interaction between Cd and Mn (P-interaction = 0.036): the association of Mn and lower child SBP was stronger with higher levels of Cd ( Panel B ). Consistent with this finding, in utero exposure to cigarette smoke (a major source of Cd) modified the association of Mn and child SBP: among children born mothers who smoked cigarette in pregnancy, each log2(Mn) increment was associated with a 10.09 (95% CI: 2.15-18.03) percentile lower SBP ( Panel C ). Conclusion: Optimizing in utero Se levels, as well as Mn levels in pregnant women who had high Cd or smoked during pregnancy, may protect offspring from developing high BP during childhood.

Download Full-text

Prenatal metal mixtures and child blood pressure in the Rhea mother-child cohort in Greece

Environmental Health ◽

10.1186/s12940-020-00685-9 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Caitlin G. Howe ◽

Katerina Margetaki ◽

Marina Vafeiadi ◽

Theano Roumeliotaki ◽

Marianna Karachaliou ◽

...

Keyword(s):

Blood Pressure ◽

Prenatal Exposure ◽

Metal Mixture ◽

Metal Mixtures ◽

Longitudinal Changes ◽

Kernel Machine ◽

Inflection Points ◽

Kernel Machine Regression ◽

Lead Magnesium ◽

Early Effects

Abstract Background Child blood pressure (BP) is predictive of future cardiovascular risk. Prenatal exposure to metals has been associated with higher BP in childhood, but most studies have evaluated elements individually and measured BP at a single time point. We investigated impacts of prenatal metal mixture exposures on longitudinal changes in BP during childhood and elevated BP at 11 years of age. Methods The current study included 176 mother-child pairs from the Rhea Study in Heraklion, Greece and focused on eight elements (antimony, arsenic, cadmium, cobalt, lead, magnesium, molybdenum, selenium) measured in maternal urine samples collected during pregnancy (median gestational age at collection: 12 weeks). BP was measured at approximately 4, 6, and 11 years of age. Covariate-adjusted Bayesian Varying Coefficient Kernel Machine Regression and Bayesian Kernel Machine Regression (BKMR) were used to evaluate metal mixture impacts on baseline and longitudinal changes in BP (from ages 4 to 11) and the development of elevated BP at age 11, respectively. BKMR results were compared using static versus percentile-based cutoffs to define elevated BP. Results Molybdenum and lead were the mixture components most consistently associated with BP. J-shaped relationships were observed between molybdenum and both systolic and diastolic BP at age 4. Similar associations were identified for both molybdenum and lead in relation to elevated BP at age 11. For molybdenum concentrations above the inflection points (~ 40–80 μg/L), positive associations with BP at age 4 were stronger at high levels of lead. Lead was positively associated with BP measures at age 4, but only at high levels of molybdenum. Potential interactions between molybdenum and lead were also identified for BP at age 11, but were sensitive to the cutoffs used to define elevated BP. Conclusions Prenatal exposure to high levels of molybdenum and lead, particularly in combination, may contribute to higher BP at age 4. These early effects appear to persist throughout childhood, contributing to elevated BP in adolescence. Future studies are needed to identify the major sources of molybdenum and lead in this population.

Download Full-text

Environmental contaminant body burdens and the relationship with blood pressure measures among Indigenous adolescents using Bayesian Kernel Machine Regression: Results from the Nituuchischaayihtitaau Aschii: Multi-Community Environment-and-Health Study in Eeyou Istchee, Quebec, Canada, 2005-2009

Environmental Advances ◽

10.1016/j.envadv.2021.100048 ◽

2021 ◽

pp. 100048

Author(s):

Aleksandra M. Zuk ◽

Eric N. Liberda ◽

Leonard J.S. Tsuji

Keyword(s):

Blood Pressure ◽

Environmental Contaminant ◽

Health Study ◽

Community Environment ◽

Kernel Machine ◽

Environment And Health ◽

Kernel Machine Regression ◽

Indigenous Adolescents ◽

The Relationship

Download Full-text

Modeling the health effects of time-varying complex environmental mixtures: Mean field variational Bayes for lagged kernel machine regression

Environmetrics ◽

10.1002/env.2504 ◽

2018 ◽

Vol 29 (4) ◽

pp. e2504 ◽

Cited By ~ 6

Author(s):

Shelley H. Liu ◽

Jennifer F. Bobb ◽

Birgit Claus Henn ◽

Lourdes Schnaas ◽

Martha M. Tellez-Rojo ◽

...

Keyword(s):

Health Effects ◽

Mean Field ◽

Variational Bayes ◽

Time Varying ◽

Kernel Machine ◽

Kernel Machine Regression ◽

Environmental Mixtures

Download Full-text

Influence of combined exposure levels of total arsenic and inorganic arsenic on arsenic methylation capacity among university students: findings from Bayesian kernel machine regression analysis

Environmental Science and Pollution Research ◽

10.1007/s11356-021-17906-4 ◽

2022 ◽

Author(s):

Rui Jiang ◽

Qing Zhang ◽

Dongmei Ji ◽

Tingting Jiang ◽

Yuan Hu ◽

...

Keyword(s):

Regression Analysis ◽

University Students ◽

Inorganic Arsenic ◽

Total Arsenic ◽

Combined Exposure ◽

Kernel Machine ◽

Arsenic Methylation ◽

Methylation Capacity ◽

Kernel Machine Regression ◽

Exposure Levels

Download Full-text

MiRKAT: kernel machine regression-based global association tests for the microbiome

Bioinformatics ◽

10.1093/bioinformatics/btaa951 ◽

2020 ◽

Author(s):

Nehemiah Wilson ◽

Ni Zhao ◽

Xiang Zhan ◽

Hyunwook Koh ◽

Weijia Fu ◽

...

Keyword(s):

R Package ◽

Effect Sizes ◽

Supplementary Information ◽

Time To Event ◽

Kernel Machine ◽

Association Testing ◽

Higher Power ◽

Kernel Machine Regression ◽

Two Measures ◽

Rv Coefficient

Abstract Summary Distance-based tests of microbiome beta diversity are an integral part of many microbiome analyses. MiRKAT enables distance-based association testing with a wide variety of outcome types, including continuous, binary, censored time-to-event, multivariate, correlated and high-dimensional outcomes. Omnibus tests allow simultaneous consideration of multiple distance and dissimilarity measures, providing higher power across a range of simulation scenarios. Two measures of effect size, a modified R-squared coefficient and a kernel RV coefficient, are incorporated to allow comparison of effect sizes across multiple kernels. Availability and implementation MiRKAT is available on CRAN as an R package. Supplementary information Supplementary data are available at Bioinformatics online.

Download Full-text

Associating Multivariate Quantitative Phenotypes with Genetic Variants in Family Samples with a Novel Kernel Machine Regression Method

Genetics ◽

10.1534/genetics.115.178590 ◽

2015 ◽

Vol 201 (4) ◽

pp. 1329-1339 ◽

Cited By ~ 10

Author(s):

Qi Yan ◽

Daniel E. Weeks ◽

Juan C. Celedón ◽

Hemant K. Tiwari ◽

Bingshan Li ◽

...

Keyword(s):

Genetic Variants ◽

Regression Method ◽

Kernel Machine ◽

Kernel Machine Regression

Download Full-text

Model choice for estimating the association between exposure to chemical mixtures and health outcomes: A simulation study

PLoS ONE ◽

10.1371/journal.pone.0249236 ◽

2021 ◽

Vol 16 (3) ◽

pp. e0249236

Author(s):

Lauren Hoskovec ◽

Wande Benka-Coker ◽

Rachel Severson ◽

Sheryl Magzamen ◽

Ander Wilson

Keyword(s):

Health Outcomes ◽

Response Function ◽

Simulation Study ◽

Linear Models ◽

Model Choice ◽

Chemical Mixtures ◽

Kernel Machine ◽

Exposure Response ◽

Kernel Machine Regression ◽

Children With Asthma

Challenges arise in researching health effects associated with chemical mixtures. Several methods have recently been proposed for estimating the association between health outcomes and exposure to chemical mixtures, but a formal simulation study comparing broad-ranging methods is lacking. We select five recently developed methods and evaluate their performance in estimating the exposure-response function, identifying active mixture components, and identifying interactions in a simulation study. Bayesian kernel machine regression (BKMR) and nonparametric Bayes shrinkage (NPB) were top-performing methods in our simulation study. BKMR and NPB outperformed other contemporary methods and traditional linear models in estimating the exposure-response function and identifying active mixture components. BKMR and NPB produced similar results in a data analysis of the effects of multipollutant exposure on lung function in children with asthma.

Download Full-text