Topological network properties of resting-state functional connectivity patterns are associated with metal mixture exposure in adolescents

Abstract

IntroductionAdolescent exposure to neurotoxic metals adversely impacts cognitive, motor, and behavioral development. Few studies have addressed the underlying brain mechanisms of these metal–associated developmental outcomes. Furthermore, metal exposure occurs as a mixture, yet previous studies most often consider impacts of each metal individually. In this cross–sectional study, we investigated the relationship between exposure to neurotoxic metals and topological brain metrics in adolescents.MethodsIn 193 participants (53% females, ages: 15–25 years) enrolled in the Public Health Impact of Metals Exposure (PHIME) study, we measured concentrations of four metals (manganese, lead, copper, and chromium) in multiple biological media (blood, urine, hair, and saliva) and acquired resting–state functional magnetic resonance imaging scans. Using graph theory metrics, we computed global and local efficiency (global:GE; local:LE) in 111 brain areas (Harvard Oxford Atlas). We used weighted quantile sum (WQS) regression models to examine association between metal mixtures and each graph metric (GE or LE), adjusted for sex and age.ResultsWe observed significant negative associations between the metal mixture and GE and LE [βGE = −0.076, 95% CI (−0.122, −0.031); βLE= −0.051, 95% CI (−0.095, −0.006)]. Lead and chromium measured in blood contributed most to this association for GE, while chromium measured in hair contributed the most for LE.DiscussionOur results suggest that exposure to this metal mixture during adolescence reduces the efficiency of integrating information in brain networks at both local and global levels, informing potential neural mechanisms underlying the developmental toxicity of metals. Results further suggest these associations are due to combined joint effects to different metals, rather than to a single metal.

Neuro-environmental interactions: a time sensitive matter

“…For example, the distribution of SHAP scores for urinary Pb (UPb), the top contributor to rs dynamics prediction, relative to the observed values of UPb indicated a non-linear association, with high UPb values contributing disproportionally to model efficacy. Globally, UPb values were negatively associated with global efficiency, consistent with previous studies showing high lead levels disrupt neuronal activity ( Invernizzi et al, 2023 ) and are associated with altered structural connectivity and functional activation patterns in children and adults ( Bressler and Goldstein, 1991 ). On the contrary, the SHAP scores for blood Zinc (BZn) revealed a positive linear association in predicting rs dynamics.…”

“…Metals within our mixture have been shown to produce such synergistic neurotoxic effects ( Tao et al, 1999 ; Chen et al, 2016 ; Lu et al, 2018 ), and epidemiological studies suggest that co-exposure to multiple metals, compared to individuals, increases disruption to human neurodevelopment ( Kim et al, 2009 ; Claus Henn et al, 2012 ; Claus et al, 2014 ; Levin-Schwartz et al, 2021 ). Few neuroimaging studies account for this synergistic action of multiple metals mixture on neurophysiological dynamics ( Invernizzi et al, 2023 ) and rather focus on single-metal assessments ( de Water et al, 2018 , 2019 ). Here, we applied a multi-modal exposure assessment (i.e., multiple metals in several media) combined with ML-based modeling to investigate the impact of mixed metal exposures on brain dynamics while retaining the discrete information regarding each individual metal.…”

“…To accommodate this reality, recent studies leverage mixtures-based methods to combine information from multiple exposure matrices ( Bobb et al, 2015 ; Levin-Schwartz et al, 2020 ; Bauer et al, 2021 ). These metals mixture-based models demonstrated a more-negative impact on neurodevelopment than single metal model ( Claus Henn et al, 2012 ; Claus et al, 2014 ; Freire et al, 2018 ; Horton et al, 2018 ; Invernizzi et al, 2023 ). A previous study in our group used an integrated measure of metal mixtures across multiple media, called multi-media biomarkes (MMB) ( Levin-Schwartz et al, 2020 ), to analyze the impact on the brain of different neurotoxic metals across multiple media ( Invernizzi et al, 2023 ).…”

“…These metals mixture-based models demonstrated a more-negative impact on neurodevelopment than single metal model ( Claus Henn et al, 2012 ; Claus et al, 2014 ; Freire et al, 2018 ; Horton et al, 2018 ; Invernizzi et al, 2023 ). A previous study in our group used an integrated measure of metal mixtures across multiple media, called multi-media biomarkes (MMB) ( Levin-Schwartz et al, 2020 ), to analyze the impact on the brain of different neurotoxic metals across multiple media ( Invernizzi et al, 2023 ). Robust associations were found between metals and graph metrics (GE and LE), however the small sample size of the used dataset resulted in relatively small effect sizes and assumption of linear associations between outcomes were made.…”