Background: Manganese (Mn) plays a significant role in both human health and global industries. Epidemiological studies of exposed populations demonstrate a dose-dependent association between Mn and neuromotor effects ranging from subclinical effects to a clinically defined syndrome. However, little is known about the relationship between early life Mn biomarkers and adolescent postural balance.
Objectives: This study investigated the associations between childhood and adolescent Mn biomarkers and adolescent postural balance in partici-pants from the longitudinal Marietta Communities Actively Researching Exposures Study (CARES) cohort.
Methods: Participants were recruited into CARES when they were 7-9 y old, and reenrolled at 13-18 years of age. At both time points, participants provided samples of blood, hair, and toenails that were analyzed for blood Mn and lead (Pb), serum cotinine, hair Mn, and toenail Mn. In adoles-cence, participants completed a postural balance assessment. Greater sway indicates postural instability (harmful effect), whereas lesser sway indicates postural stability (beneficial effect). Multivariable linear regression models were conducted to investigate the associations between childhood and adolescent Mn biomarkers and adolescent postural balance adjusted for age, sex, height-weight ratio, parent/caregiver intelligence quotient, socioeco-nomic status, blood Pb, and serum cotinine.
Results: CARES participants who completed the adolescent postural balance assessment (n=123) were 98% White and 54% female and had a mean age of 16 y (range: 13-18 y). In both childhood and adolescence, higher Mn biomarker concentrations were significantly associated with greater adolescent sway measures. Supplemental analyses revealed sex-specific associations; higher childhood Mn biomarker concentrations were significantly associated with greater sway in females compared with males.
Discussion: This study found childhood and adolescent Mn biomarkers were associated with subclinical neuromotor effects in adolescence. This study demonstrates postural balance as a sensitive measure to assess the association between Mn biomarkers and neuromotor function. https://doi.org/ 10.1289/EHP13381
Introduction
Manganese (Mn) is an element that exists ubiquitously in the environment, often in combination with other elements.1 Mn plays a significant role in both human health and global indus-tries.1 Mn is an essential nutrient in trace quantities needed to sustain many health processes in the body, such as the formation of healthy cartilage and bone, as well as digestion, reproduction, and the antioxidant defense system, immune response, energy production, and neuronal regulation.2 The general population is exposed to sufficient quantities of Mn through diet given that Mn is present in a wide variety of foods, including shellfish, nuts, grains, legumes, dark chocolate, and pineapple, among others.3 Mn deficiency is rare and has been associated with skeletal abnormalities and an impaired oxidant defense system.4 Owing to its versatile physical and chemical properties, Mn is used in a number of applications that benefit our daily lives, including iron and steel manufacturing, aluminum alloys for beverage cans, welding, and dry cell and electric-vehicle batteries, as well as a component of some fungicides.1 Industrial processing of Mn can emit Mn compounds into the air, creating occupational exposure for workers and environmental exposure for residents living in the surrounding communities.5-8
The route of exposure and the amount (i.e., dose) are a key determinants in how Mn affects the body as either an essential nutrient or a neurotoxicant.9 Ingested Mn enters the body through the gastrointestinal tract and is subjected to a delicate homeostatic process of Mn transport across enterocytes of the intestinal wall and removal by the liver.10 Mn is absorbed into the body at rates of 1%-5% to sustain the essential levels needed to fulfill the nutri-tional requirements of the body.11 Conversely, inhaled airborne Mn particulate matter can enter the body at unregulated levels through the nose, where it is not subjected to the same homeo-static regulations as ingested Mn.12 From the nose, inhaled Mn can be directly transported into the brain and lung through two main pathways: a) via olfactory or trigeminal presynaptic nerve endings located in the nasal mucosa, traveling along the olfactory neuronal pathway, where Mn crosses the blood-brain barrier and accumulates in the brain12,13; and b) via transport across the re-spiratory epithelial lining of the lung and depositing into the lym-phatic system or blood to circulate throughout the body. When the intake of inhaled Mn exceeds elimination, Mn preferentially accumulates in the brain regions responsible for neuromotor function control, such as the basal ganglia, frontal cortex, and cerebellum.14-17 This accumulation can lead to neurotoxicity in a dose-dependent manner described as a "continuum of dysfunction," ranging from subclinical neuropsychological and neuromotor decrements to a clinically defined syndrome, manganism, which resembles the symptoms of Parkinson's disease (PD), although eti-ologies differ between diseases.18 Because cessation of exposure may not result in restored motor function, it is critical to identify preclinical signs of neurotoxic Mn accumulation.19,20
Mn-exposed workers in industries such as welding, mining, and refining were the first populations to show Mn poisoning resulting from high occupational exposures.21-23 In a summary of occupa-tional Mn exposure studies, the Agency for Toxic Substances and Disease Registry (ATSDR) reported that workers exposed to higher levels of occupational Mn, ranging from 2,000 to 22,000 ug=m3, demonstrated overt symptoms of manganism that included tremors, difficulty walking, and facial muscle spasms.6 Workers exposed to lower levels of occupational Mn exposure, ranging from 70 to 970 ug=m3, demonstrated subclinical neuromotor decrements related to eye-hand coordination, hand steadiness, reaction time, postural stability, motor efficiency and speed, and tremor.6,18,24 Environmental Mn exposures occur at concentrations two or three orders of magnitude lower than even the occupational levels. The US Environmental Protection Agency (EPA) reported ambient an-nual Mn concentrations in US EPA Region 5 from 26 monitoring sites: 5 sites in commercial areas, 11 sites in industrial areas, and 10 sites in residential areas. Industrial sites reported higher annual ambient Mn concentrations (10th percentile: 0:032 ug=m3, 90th percentile: 0:376 ug/m3) compared with residential sites (10th percentile: 0:009 ug/m3, 90th percentile: 0:085 ug=m3) and commercial sites (10th percentile: 0:048 ug=m3, 90th percentile: 0:192 ug=m3).25 The ATSDR has set the minimal risk level for chronic inhalation exposure to Mn at 0:30 Hg=m3.6
Given that the relationship between Mn exposure and neurotoxicity is dose dependent, investigating subclinical neuromotor effects of Mn in environmentally exposed adults and children is a relevant public health concern. It is important to study motor out-comes because it can inform motor limitations and potential for injury. In addition, motor function is an important neurological domain because it has a significant influence on psychological well-being.26 Related to exposure in adults, Ruiz-Azcona et al. recently published a systemic review and meta-analysis of pub-lished studies on environmentally exposed adults and cognitive and motor functions.27 Their pooled analysis included 11 studies with reported data susceptible for meta-analysis through pooled correlation or a standardized means difference (SMD) approach between exposed and nonexposed groups.18,28-37 The pooled correlation revealed statistically significant negative correlations between adult Mn levels and motor scores. The SMD approach revealed statistically significantly worse motor scores in the exposed adults compared with nonexposed adults.27
In children, the beneficial and neurotoxic effects of Mn exposure may be unique due to the developmental timing of exposure and neuromotor function assessment (i.e., prenatal, postnatal, early childhood, childhood, adolescence, adulthood).38,39 For instance, the prenatal developmental time point is considered a time point of increased demand for Mn as an essential nutrient to support healthy development of the central nervous system and fetus morphology, which is the initiation of development of neuromotor function.3,9 This role of Mn as essential and neurotoxic is supported by multiple studies that found prenatal Mn exposure demonstrated an inverse U-shaped association where both low and high Mn concentrations were associated with deficits in neonatal behavioral neurological assessments at 3 d of age40 and deficits in Bayley Scales of Infant Development (BSID)-11 measures at 6 months of age.41,42 Other prenatal Mn exposure studies have found inverse linear43-45 and null46 associations with motor function during infant years 0-2, a time point when postural reflexes and rudimentary movement skills are developed. During the early childhood years 3-5, general funda-mental skills such as locomotion, balance, and manipulation continue to develop. Starting in the childhood years 5-9, combinations of fundamental skills are developed into transitional skills (i.e., jumping rope, bicycling, kickball).26 By the late childhood years 10-12, specific skills of combinations of fundamental and transi-tional skills are developing (i.e., soccer kicking, gymnastics, hitting a ball), and by the adolescent years 13-18, specialized motor skills, such as achieving excellence in a sport, mature. Prospective studies found prenatal Mn exposure was not associated with motor out-comes from the McCarthy Scales of Children's Abilities at 4 or 5 years of age.47,48 Chiu et al. found higher prenatal Mn exposure was associated with better body stability in 11- to 14-y-old adolescent males compared with females.49 Similarly, Mora et al. reported higher postnatal Mn exposure was associated with better motor out-comesat7 and 10.5 years ofage for males compared with females.50 Cross-sectional studies conducted during childhood and adoles-cence have reported mixed findings. For example, some studies found Mn exposure results in inverse linear associations with motor function at 7-9,51 9-15,52 and 11-14 years of age,53 whereas other studies found no associations at 7-1254 and 12-16 years of age.55 Many questions remain related to the long-term impact of early childhood Mn exposure on neurodevelopment in general and on adolescent neuromotor function in particular.
The present study directly addresses this knowledge gap by leveraging our well-established longitudinal pediatric Marietta Communities Actively Researching Exposure Study (CARES) cohort, which was developed to address resident concern about Mn emissions from the longest-operating ferromanganese refin-ery in North America.56,57 In the present study, we report on investigating the associations between childhood and adolescent biomarkers of Mn exposure, indicated by blood, hair, and toenail Mn concentrations, and adolescent neuromotor function, as measured by postural balance. We hypothesized that both child-hood and adolescent Mn biomarkers will be significantly and positively associated with adolescent postural balance variables, indicating greater sway or postural instability.
Methods
Study Design and Population
Marietta CARES participants were invited to complete the adolescent postural balance sub-study through their enrollment in CARES. CARES eligibility included participants who were 13-17 y old, had resided in Marietta or Cambridge, Ohio, and their surrounding communities throughout their entire lives with no plans to move for at least 1 y and had completed a childhood clinic visit between 2008 and 2013 when they were 7-9 yold58 and were sched-uled for the adolescent clinic visit within 8 wk of the adolescent pos-tural balance assessment dates. CARES adolescent participants were recruited through mailed letters and phone calls. Adolescent balance sub-study exclusion criteria included any health condition that may impact the postural test result, such as uncorrected vision problems, vestibular or musculoskeletal disorders, or were >250 lb (113.4 kilograms) in weight. The postural balance assessment was conducted over eight weekends between May 2017 and September 2018. The University of Cincinnati Institutional Review Board approved this study. All participants and their parents or caregivers signed an informed consent, and the adolescents also signed an assent.
Internal Dose Markers ofExposure
Participants completed a childhood (7-9 years of age) and adolescent (13-17 years of age) clinic visit where they provided biospe-cimens of whole blood, hair, and toenails that were subsequently analyzed for Mn after each time point. Whole blood was also an-alyzed for blood lead (Pb) and serum cotinine, a marker of envi-ronmental tobacco smoke.59 The methods of collection and analysis were the same across both time points unless otherwise specified and are detailed below.
Whole blood. A nurse trained in pediatric phlebotomy collected venous whole-blood specimens from the antecubital vein. One 3-mL purple-top [dipotassium salt of ethylenediaminetetraacetic acid (K2EDTA)] tube of whole blood was collected for trace element analysis. A standard red-top tube (no preservative) was also collected, and serum was harvested off the clot for cotinine measure-ments. Whole-blood samples (for Mn and Pb analysis) were refri-gerated at 4°C and serum samples (for cotinine analysis) were frozen at - 20°C until shipped monthly to the New York State Department of Health's Wadsworth Center, Albany, New York. For samples collected in the childhood years 7-9 between 2008 and 2013, whole blood was analyzed for Mn by graphite furnace atomic absorption spectrometry (GFAAS)60 and for Pb by inductively coupled plasma-mass spectrometry (ICP-MS).61 The analytical methods used for analyzing childhood samples were optimized and validated for biomonitoring techniques, and the results are traceable to international standards.62,63 Blood samples collected in the adolescent years 13-18 between 2017 and 2018 were analyzed for Pb and Mn using an updated method optimized for a newer ICP-MS instrument (Tables S1 and S2). The new method was well validated for four toxic metals (Tables S3-S5), and a robust method compari-son was performed that showed good agreement with the prior ICP-MS method and with the GFAAS methods used for Mn in whole blood (Figure S1). Comparability and performance between the two ICP-MS instruments were monitored throughout the entire study pe-riod by analyzing the same internal quality control samples used previously (Table S4) and by participation in four international external quality assurance schemes for trace elements in whole blood (Table S5). Ongoing measurement accuracy was assured by routinely including blood-based National Institute of Standards and Technology Standard Reference Materials (NIST SRM 955c and SRM 1401) with certified values for several trace elements, including Pb and Mn (Table S3).60,64 The limit of detection (LOD) for blood Mn was <2:1 ug=L by GFAAS, whereas by ICP-MS it varied from <0:85 Hg=L to <1:4 ug=L. The LOD for blood Pb was <0:34 ug/dL on the older ICP-MS instrument compared with <0:04 ug/dL on the newer ICP-MS instrument.
Serum cotinine was analyzed by liquid chromatography/ tandem mass spectrometry (LC-MS/MS) using a modification of the techniques used by the Centers for Disease Control and Prevention (CDC) for the National Health and Nutrition Examination Survey (NHANES)65 and the New York State Wadsworth Laboratories for the New York City Health and Nutrition Examination Survey studies.66 Each serum specimen underwent the following process: It was equilibrated with a trideuterated cotinine internal standard solution, extracted using a 96-well Bond Elut Plexa Solid Phase Extraction plate (Varian). The acetonitrile sample extract was evaporated to dry-ness and then reconstituted in a solution containing 96% acetonitrile and 4% water. Subsequently, the reconstituted sample was analyzed using LC-MS/MS with electrospray ionization. To ensure quality control, three different pools were employed, each having varying target cotinine concentrations (0.173, 1.61, and 15:7 ug=L) at low, medium, and high levels. The final results were blank corrected using the mean batch blank value. The same method and LC-MS/ MS instrument were used for serum cotinine throughout childhood and adolescence. The LOD for serum cotinine was 0:05 lg=L.58 For values below the LOD, machine readings provided by the labo-ratory were used in statistical analysis,67 and for values <0, concen-trations imputed with the LOD divided by the square root of 2 were used in statistical analysis68,69 Hair. Participants were asked in advance for hair to be free of all gels, oils, and hair creams or sprays prior to sample collection. Hair samples were not collected if the participant had chemically treated hair. Collection was completed by a research team mem-ber trained in proper collection procedures with a clean environment. Approximately 20 strands of hair from the occipital region were cut with ceramic scissors as close to the scalp as possible. If the hair was long, it was trimmed to 6 cm. The hair sample was taped toward the nonscalp-side of the hair shaft onto an index card with an arrow pointing in the direction of the scalp end, placed into a prelabeled envelope, and stored at room temperature until shipped for analysis. Childhood hair samples were shipped to Channing Trace Metals Laboratory at the Brigham and Women's Hospital, Harvard School of Public Health for analysis, and adolescent hair samples were sent to the lab relocation to the Molecular Environmental Health Laboratory at the Mount Sinai Hospital. Hair samples were first washed in a 1% (vol/vol) Triton X-100 solution, digested using concentrated nitric acid and then acid digestates were analyzed for Mn by ICP-MS.51 The LOD for Mn in hair was <2 ng=g.58
Toenails. Parents/caregivers were asked to bring collected clip-pings of their child toenails inside a labeled envelope to the child-hood and adolescent clinic visits. The toenails were to be clipped after growing forat least2 wk, removing all nail polish, and washing feet with soap and water. Toenail samples were stored at room tem-perature until shipped to the Microbiology and Environmental Toxicology Department at the University of California Santa Cruz for analysis. Toenails were analyzed for Mn by magnetic sector ICP-MS, and the LOD forMn intoenails was <0:03 lg=g.
Parent/Caregiver Intellectual Function and Socioeconomic Status
Full-scale intelligence quotient (IQ) of the parent/caregiver as meas-ured by the Wechsler Abbreviated Scale of Intelligence (WASI) at the childhood clinic visit was used in this study.58,70 Adult IQ is con-sidered relatively stable over time; therefore, we did not reassess this caregiver measure at the adolescent visit.71 Socioeconomic status (SES) of the parent/caregiver was measured at the adolescent clinic visit using the Barratt Simplified Measure of Social Status (BSMSS), which was completed by the parent/caregiver.72 Although the care-giver had previously completed this measure of social status at the childhood clinic visit, the BSMSS was readministered at the adolescent clinic visit because it is based on factors that may change with time. For analysis, we used the parent/caregiver responses at the adolescent clinic visit. The BSMSS includes educational attainment scores and occupational prestige scores of the parent/caregiver and the parent/caregiver's mother, father, and spouse/partner. These ques-tions yielded a calculated ordinal score that ranged from 8 to 66. A higher number reflects more advanced SES.
Postural Balance Assessment
Daily setup for balance testing involved a standardized protocol to maintain identical test conditions. The experimental setup required the connection of a Hall-effect force plate, Accusway-O (model Accusway-O, Advanced Mechanical Technology Inc.) and laptop installed with Balance Clinic software (Advanced
Mechanical Technology Inc.) and KineLysis software (University of Cincinnati). Before testing and halfway through testing, a standard 18-kg weight was analyzed on the force plate at five specific locations to confirm calibration to <3% error for the field studies.73 This experimental setup quantifies the movement pattern of the body's center of pressure associated with body sway captured at a frequency of 50 Hz. The software programs use the raw data col-lected from the force plate during testing to generate a "fingerprint" of body sway called a stabilogram.74 For each stabilogram, the fol-lowing four postural balance/sway (the terms balance and sway are interchangeable) measures were quantified: sway area, sway length, medio-lateral excursion, and anterior-posterior excursion.75 Sway area (in centimeters squared) is defined as the area encompassed by the x-y plot of the excursion of the center of pressure. Sway length (in centimeters squared) is defined as the total distance traveled by the center of pressure. Medio-lateral (in centimeters squared) and anterior-posterior (in centimeters squared) excursion are the net deviations of the center of pressure in the medio-lateral and anterior-posterior directions, respectively. Figure S2 represents the experimental setup, including the reference coordinate system for the force platform used to quantify a participant's postural sway and the connected laptop used to generate the stabilogram.
The maintenance of upright postural balance requires the inte-gration of three sensory afferents: visual (eyes), vestibular (inner ear organs that act as an accelerometer and gyroscope),76 and proprio-ceptive (conscious and unconscious awareness of joint position)77 systems, which are weighted differently according to specific tasks.78-81 Our research team administered a postural balance test protocol of six test conditions designed to challenge the visual, vestibular, and proprioceptive afferent systems required for the maintenance of postural balance, as detailed in Figure 1, which was adapted from Bhattacharya et al.82 Each test lasted 30 s. The pro-gression of the six-test protocol was designed for each subsequent condition tobe more difficult, as reflected byan expected increase in sway area: a) stand with eyes open on the force plate; b) stand with eyes closed on the force plate; c) stand with eyes open on 0.10 meter thick foam pad placed on the force plate; d) stand with eyes closed on 0.10 meter thick foam pad placedon the force plate; e) stand with eyes open for 12 s, then on verbal command, bend torso at the waist and stay in that position for 5 s and return to the upright position and stand for the remainder of the 30-s test; f ) same as e but with eyes closed. Repeat trials were conducted for the static tests with eyes open and eyes closed to establish a representative baseline before adding the foam and bending tests. The average of each condition was used for statistical analyses.
Other Study Covariates
Participant height was measured with a stadiometer at the adolescent clinic visit, and participant weight was measured with a scale at the adolescent postural balance assessment. Height and weight were combined into a height-weight ratio to be used for all statis-tical analysis. The height-weight ratio was used as a direct corre-lation with the physical proportions of the participant and has been used as a covariate in our previous postural balance analy-ses.37,51 Birthday and biological sex at birth were established at the childhood clinic visit. Age at the balance assessment was cal-culated using birthday and date of postural balance assessment to be used for all statistical analysis.
Statistical Analyses
All statistical analyses were conducted using SAS statistical software (version 9.4 for Windows; SAS Institute Inc.) unless other-wise specified. All biomarkers and postural balance outcomes were natural log-transformed (ln) to approximate normal distri-butions for statistical analyses. Descriptive statistics were calcu-lated to describe the demographic and biomarker variables of the study participants. Demographic variables are presented as the mean ± standard deviation (SD), and biomarker variables are pre-sented as the geometric meanðGMÞ ± geometric standard deviation (GSD). Pearson's bivariate correlations were examined between the biomarkers of child and adolescent environmental exposures. Pearson's correlation was defined as low if the correlation coeffi-cient was <0:29, medium if the correlation coefficient was between 0.30 and 0.49, and high if the correlation coefficient was >0:50. Multivariable linear regression models were conducted to investigate the associations between childhood and adolescent Mn biomarkers and adolescent postural balance while adjusting for covariates. Independent variables included childhood blood Mn, childhood hair Mn, childhood toenail Mn, adolescent blood Mn, adolescent hair Mn, and adolescent toenail Mn levels. Dependent variables included sway area, sway length, medio-lateral sway, and anterior-posterior sway for each of the six postural balance test conditions: eyes open, eyes closed, foam open, foam closed, bending open, and bending closed. Covariates were the same across all models and included age, sex, height-weight ratio, parent/care-giver IQ, parent/caregiver SES, ln blood Pb, and ln serum cotinine. These covariates were chosen a priori based on our previous studies and factors knowntoimpact postural balance.37,51 Individual factors of age, sex, and height-weight ratio83,84 and environmental expo-sures of blood Pb85 and environmental tobacco smoke, measured by serum cotinine,86 have been associated with postural balance. Both parent/caregiver IQ and parent/caregiver SES were included given the unique roles they demonstrate with neuromotor develop-ment.51,87 Regression coefficients (bs), 95% confidence intervals (CIs), and p-values were reported from multivariable regression models for the Mn biomarker coefficient (i.e., blood, hair, or toe-nail). To interpret the b coefficient, a positive value reflects greater sway or postural instability (i.e., harmful effect), and a negative value reflects less sway or postural stability (i.e., beneficial effect). For all regression models, any participant with a missing value for anyofthe variables was excluded from the analytical sample.
Supplemental analyses were conducted to investigate different associations between Mn biomarkers and motor outcomes by sex by including a sex × Mn (blood, hair, toenail) interaction term in all models. Regression coefficients (b) and 95% CIs are reported separately for males and females, and p-values are reported from multivariable regression models for Mn × sex interaction coeffi-cient. Statistical significance was set at p < 0:05. To adjust for multiple comparisons, we applied the Benjamini-Hochberg pro-cedure88 to the p-values from all of the regression models using R (version 4.1.3; R Developmental Core Team).
Additional supplemental analyses were conducted to further investigate our findings from the adjusted multivariable linear regression models between childhood and adolescent Mn bio-markers and adolescent postural balance.To explore potential different findings due in part to different sample sizes for each biomarker, a supplemental analysis was conducted using a restricted sample comprising participants with complete dataonall biomarkers at both time points (childhood blood Mn and Pb, childhood serum cotinine, childhood hair Mn, childhood toenail Mn, adolescent blood Mn and Pb, adolescent serum cotinine, adolescent hair Mn, and adolescent toenail Mn levels) on the adjusted multivariable linear regression models. In another supplemental analysis aimed to understand whether there was confounding by exposure at the other time point (childhood or adolescence), multiple informant models using gener-alized estimating equations (MIM GEEs) were employed to test for differences in associations across Mn exposure time point (child-hood, adolescence).89-91
Results
Participant Characteristics
CARES participants who completed the adolescent postural balance assessment (n= 123) were 98% White and 53% female, with a mean age of 16.1 y (range: 13-18.1 y) (Table 1). All participants con-sented and enrolled before they were 18 y, but due to scheduling of balance, one participant turned 18 within the 8 week time frame between consenting and balance assessment. Their childhood and adolescent biomarker concentrations of blood Mn, blood Pb, serum cotinine, hair Mn, and toenail Mn are reported as GM±GSD and ranges inTable 2. Missing childhood blood Mn and blood Pb values (n =17) were due to participant refusing blood draw (n = 7) and research nurse unable to collect sample (n =10). Missing childhood serum cotinine values (n =15) were due to participant refusing blood draw (n = 7), research nurse unable to collect sample (n = 7), and lab unable to analyze sample (n = 1). Missing childhood hair Mn values (n = 4) were due to lab unable to analyze sample (n =4). Missing childhood toenail Mn values (n= 12) were due to parent/ caregiver not providing sample (n= 10) and lab unable to analyze sample (n =2). Missing adolescent blood Mn and Pb values (n =14) were due to participant not completing adolescent clinic visit (n = 2), participant refusing blood draw (n = 7), participant refusing blood draw of one tube (n =1), and research nurse unable to collect sample (n = 4). Missing adolescent serum cotinine values (n =13) were due to participant not completing adolescent clinic visit (n= 2), participant refusing blood draw (n= 7), and research nurse unable to collect sample (n=4). Missing adolescent hair Mn values (n =4) were due to participant not completing adolescent clinic visit (n=2) and research nurse unable to collect sample (n =2). Missing adolescent values (n=13) were due to participant not completing adolescent clinic visit (n =2) and parent/caregiver not providing sample (n = 11).
In childhood years 7-9, the GM ± GSD concentrations for blood Mn (n= 106) was 9:7 ± 1:3 lg=L (range: 5.30-18.8), blood Pb (n= 106) was 0:79 ±1:5 lg=dL (range: 0.36-2.7), serum cotinine (n= 108) was 0:044 ±6:6 lg=L (range: 0.00060-6.1), hair Mn (n= 119) was 407± 2:51 ng=g (range: 63.19-7,379), and toenail Mn (n = 111) was 0:65± 2:7 lg=g (range: 0.060- 9.7). For childhood serum cotinine, 71 (66%) values were below the LOD. In adolescent years 13-18, the GM ±GSD concentra-tions for blood Mn (n =109) was 10± 1:3 lg=L (range: 5.0-30), blood Pb (n = 109) was 0:42 ± 1:7 lg=dL (range: 0.18-6.8), serum cotinine (n = 110) was 0:10 ± 9:8 lg=L (range: 0.000700- 180), hair Mn (n =119) was 189 ± 2:69 ng=g (range: 29.90- 3,330), and toenail Mn (n= 110) was 0:35 ± 2:5 lg=g (range: 0.040-2.4). For adolescent serum cotinine, 57 (52%) of values were below the LOD, and for these values, machine readings provided by the laboratory were used. The GM ±GSD and ranges of childhood and adolescent biomarker concentrations by sex are reported in Table 3.
Pearson's correlations among the biomarkers of environmen-tal exposure at both time points are reported in Table 4. A high degree of correlation was demonstrated between childhood ln blood Mn and adolescent ln blood Mn (Pearson's r = 0:75, p < 0:001) and also between childhood ln serum cotinine and adolescent ln serum cotinine (Pearson's r = 0:59, p < 0:001).
Stabilograms from two participants are presented in Figure 2 to illustrate the differences in center of pressure movement sway patterns associated with blood Mn. The participant with lower blood Mn has smaller postural sway measures than the participant with higher blood Mn, demonstrating the effects of blood Mn under the static test condition, eyes open.
Associations of Mn Biomarkers with Postural Balance
Adjusted linear regression models were conducted between child-hood and adolescent biomarkers of Mn (blood, hair, toenail) and postural balance outcomes measured during adolescence. The results are summarized in Tables 5 and 6.
Higher childhood hair Mn concentrations were consistently associated with greater adolescent sway measures under the test condition of eyes open standing on foam above the force plate. Under foam open, for each nanogram-per-gram increase childhood ln hair Mn, adolescent ln sway area increased 0:14 cm2 [(95% CI: 0.039, 0.24), p = 0:0073]; ln sway length increased 0:050 cm [(95% CI: 0.0054, 0.095), p = 0:029]; and ln anterior-posterior sway increased 0:078 cm [(95% CI: 0.019, 0.14), p= 0:010]. Higher childhood blood Mn and childhood toenail Mn concentra-tions also demonstrated significant associations with adolescent sway. Under foam open, for each microgram-per-gram increase childhood ln toenail Mn, adolescent ln sway area increased 0:099 cm2 [(95% CI: 0.0065, 0.19), p = 0:036]. Under bending open, for each microgram-per-gram increase childhood ln toenail Mn, adolescent ln anterior-posterior sway increased 0:076 cm [(95% CI: 0.02, 0.13), p = 0:0038]. Under foam closed, for each microgram-per-liter increase childhood ln blood Mn, adolescent ln anterior-posterior sway increased 0:32 cm [(95% CI: 0.05, 0.60), p = 0:023] (Table 5).
Higher adolescent blood Mn concentrations were consis-tently associated with greater sway measures under the static test conditions eyes open and eyes closed on the force plate. Under eyes open, for each microgram-per-liter increase adolescent ln blood Mn, adolescent ln sway area increased 0:45cm2 [(95% CI: 0.063, 0.83), p = 0:022]; ln medio-lateral excursion increased 0:27 cm [(95% CI: 0.018, 0.52), p = 0:036]; and ln anterior-posterior excursion increased 0:24 cm [(95% CI: 0.026, 0.46), p = 0:029]. Under eyes closed, for each microgram-per-liter increase adolescent ln blood Mn, adolescent ln sway area increased 0:44 cm2 [(95% CI: 0.039, 0.85), p = 0:032] and ln anterior-posterior excursion increased 0:37 cm [(95% CI: 0.15, 0.59), p = 0:001]. Higher blood Mn concentrations were also significantly associated with increased sway under the semi-dynamic test conditions. Under both bending eyes open and bending eyes closed on the force plate, for each microgram-per-liter increase adolescent ln blood Mn, adolescent medio-lateral sway increased 0:21 cm [(95% CI: 0.0064, 0.41), p = 0:043] and 0:23 cm [(95% CI: 0.020, 0.43), p = 0:032], respectively. No significant associations were found between adolescent hair or toenail Mn and concurrent postural balance measures (Table 6). Supplemental analyses revealed sex-specific associations between childhood biomarkers of Mn exposure and adolescent postural balance (Table 7). Generally, males with higher childhood Mn biomarker concentrations had significantly smaller adolescent sway measures (i.e., postural stability), whereas females with higher childhood Mn concentrations had significantly greater adolescent sway measures (i.e., postural instability). For males in childhood, under the test condition eyes open standing on foam above the force plate, for each microgram-per-liter increase ln blood Mn, adolescent ln sway area and anterior-posterior sway decreased -0:77 cm2 [(95% CI: -1:36, -0:18), p= 0:011] and -0:61 cm [(95% CI: -0:95, -0:27), p = 0:0006], respectively. In contrast, for females, ln sway area and anterior-posterior sway increased 0:49 cm2 [(95% CI: 0.011, 0.97), p = 0:045] and 0:29 cm [(95% CI: 0.018, <0:0001), p = 0:037], respectively. The p-values for interaction for these models were 0.0013 and <0:0001, respectively. This same pattern between males and females was identified under the test condition bending open with all child-hood Mn biomarker (blood, hair, toenail) models. There were no significant sex-specific associations between adolescent bio-markers of Mn exposure and postural balance (Table 8).
After application of the Benjamini-Hochberg procedure88 to control for multiple comparisons, adjusted p-values are reported in Tables S6and S7. Of allof the multivariable linear regression models between childhood and adolescent Mn biomarkers and adolescent postural balance, only the association between adolescent blood Mn and adolescent anterior-posterior sway under eyes closed remained significant. From the supplemental analyses of the multivariable linear regression models between childhood and ad-olescentMn biomarkers and adolescent postural balance, including a Mn × sex interaction term, the sex interaction term remained significant for the associations between childhood blood Mn and adolescent sway area and anterior-posterior sway under foam open.
There were 76 participants with complete data on all bio-markers at both time points. The descriptive characteristics and biomarker concentrations of this restricted sample are described in Tables S8 and S9. The results from the supplemental analysis of the adjusted linear regression models between childhood and adolescent Mn biomarkers (blood, hair, toenail) and adolescent postural balance outcomes using the restricted sample comprising of the 76 participants with complete biomarker data are summar-ized in Tables S10 and S11. When the sample was restricted to participants with data available on all biomarkers at both time points, additional significant associations were discovered, but none of the significant associations initially identified in Tables 5 and 6 were lost. This may be attributed to differences in variabili-ty in the environmental exposures or postural balance outcomes between the analytical sample (n = 123) and the restricted sample (n =76). One exception that lost significance in the restricted analyses was the association between adolescent blood Mn expo-sure and adolescent medio-lateral sway under bending open.
For the supplemental analysis using MIM GEEs, results are reported for the p-valueofthe Mn(blood, hair, toenail) × time point (childhood, adolescence) interaction term in Table S12. There were two significant interactions, which represent associations where the regression coefficient changes direction between childhood and adolescence: for blood Mn, under bending open, for medio-lateral sway, the regression coefficient in childhood was -0:08 and in ado-lescence was 0.21 (p = 0:003). For hair Mn, under foam open, for anterior-posterior sway, the regression coefficient in childhood was 0.078, and inadolescence,it was -0:015 (p = 0:038).
Discussion
The goal of this CARES substudy was to investigate the associa-tions between childhood and adolescent Mn biomarkers, measured in blood, hair, and toenails, and adolescent postural balance. This study found that Mn biomarkers measured at both time points, childhood and adolescence, were significantly associated with adolescent postural instability. This study also found sex-specific asso-ciations between childhood Mn biomarkers and adolescent postural balance, with females demonstrating greater sway than males.
Within our cohort, participants with higher childhood blood Mn concentrations had significantly greater adolescent postural sway measures under the foam open test condition, and partici-pants with higher childhood hair and toenail Mn concentrations had significantly greater postural sway measures under the foam closed test conditions. These results are consistent with our previ-ous findings in a subset of 55 CARES children 7-9 years of age located in the Marietta community. Rugless et al. reported that participants with higher childhood hair Mn concentrations had significantly greater childhood postural sway measures under the foam open and foam closed test conditions.51 The present study found a significant association between childhood hair and toenail Mn and foam open in the adolescent postural balance assessment. Foam open involves standing on foam to challenge the propriocep-tive receptors on the feet, forcing dependence on the visual and vestibular sensory afferents. Increased sway under the foam open tests implies that the participants with higher childhood hair Mn and toenail Mn concentrations are increasing muscular activity and body velocity to compensate when forced to depend on the visual and vestibular sensory afferents. Foam closed further removes visual cues and forces reliance on the vestibular sensory afferent. Thus, increased sway under the foam closed balance tests implies that the participants with higher childhood blood Mn are increasing muscular activity and body velocity to compensate when forced to depend on the vestibular sensory afferent.
These results may be indicative of the pediatric developmental trajectories of postural control. During childhood, the neuromuscu-lar mechanisms involving sensory and motor processes are imma-ture. Researchers such as Hirabayashi and Iwasaki,92 Cumberworth et al.,93 Steindl et al.,94 and Ferber-Viart et al.95 have found that the visual sensory afferent develops much earlier than the vestibular system in children. Given that the vestibular sensory afferent is not fully developed like adulthood until 14-16 years of age,95 our find-ings support that childhoodMnbiomarkers may impact the develop-ing vestibular system. Animal and epidemiological studies provide some support for the neurotoxic impact of Mn on the vestibular sys-tem.37,96 For example, Ding et al. reported in vivo studies of manga-nese chloride (MnCl2)-exposed cochlear organotype cultures on postnatal day-3 rats caused damage to the sensory hair cells, pe-ripheral auditory nerve fibers, and spiral ganglionic neurons in cochlear implants from the vestibular system. In the 1980s, Khalkova and Kostadinova studied Bulgarian coke chemical production and ferroalloy production workers. They found pro-nounced changes in the hearing and vestibular indices in workers with the lowest degree of exposure. The authors proposed analy-sis of the vestibular system as criteria for early diagnosis of chronic Mn intoxiciation.97 Since then, no studies have fully elu-cidated Mn's neurotoxic effect on the vestibular system related to neuromotor function.
Within our study's cohort, participants with higher adolescent blood Mn concentrations had significantly greater adolescent sway measures under the eyes open and eyes closed postural bal-ance test conditions. This association was also demonstrated in our previous study of adults living in Marietta, Ohio. Standridge et al. found that higher hair Mn concentrations in 22 nonoccupa-tionally exposed 19- to 68-old Marietta adults were significantly associated with greater sway measures under the eyes open and eyes closed test conditions.37 Eyes open and eyes closed are the least difficult postural balance test conditions given the availabil-ity of more sensory afferents than the subsequent test conditions. Thus, increased sway under the eyes open and eyes closed bal-ance tests implies that, despite availability of all or most sensory afferents, participants with higher adolescent blood Mn must increase muscular activity and body velocity to maintain upright postural balance. During adolescence, the pediatric development of the visual, vestibular, and proprioceptive systems has matured to healthy adult status, whereas multisensory integration strat-egies may not mature until young adulthood.93,98 Our results sug-gest adolescent blood Mn may impact mechanisms beyond the three sensory afferents that control postural balance, which is indic-ative of the development of pediatric postural control. Relevant mechanisms during adolescence may include the rapid maturation of the frontal brain system,99 dopaminergic pathways,100-102 and complex interacting musculoskeletal processes.103 Mn is postulated to selectively exert toxicity on dopaminergic neurons,104 resulting in motor changes.105 Despite maturity of postural control, brain de-velopment studies indicate adolescence is a significant period of plasticity and neurodevelopment related to patterns of gray matter thinning, white matter pathway integrity, and synapse prolifera-tion.106 The ongoing maturation of the adolescent brain provides support for adolescence as a unique neurodevelopmental time point.106 Given the uniqueness of the adolescent brain develop-ment, the subclinical neuromotor effects of adolescent blood Mn on adolescent postural balance reported in this study may reflect mechanisms of neurotoxicity that persist into adulthood, as demon-strated by Standridge et al.37
A growing body of epidemiological and animal studies sug-gest Mn may impact males and females differently.42,50,107-112
This study found significant sex differences in the associations between childhood Mn biomarkers and adolescent balance; how-ever, this study did not find any sex differences in the associa-tions between concurrent adolescent Mn biomarkers and balance. Specifically, this study found that childhood blood and hair Mn was associated with decreased adolescent sway in males (i.e., postural stability) and increased adolescent sway in females (i.e., postural instability) under various postural balance outcomes. These results are in opposition to several studies on sex differences in postural stability that reported male children exhibit greater sway compared with female children of similar ages.94,113-119 Potential explanations for this may be attributed to sway parameters,117 physi-cal growth, and the neuromuscular system120 developing earlier in female children compared with males. Despite support for female children to have better neuromotor function than male counterparts, other pediatric Mn exposure studies that examined sex effects have also found similar Mn effects impacting females worse than males. These same sex effects were found previously by Chiu et al., who reported higher prenatal dentin Mn was associated with decreased adolescent sway in males and increased adolescent sway in females in 11- to 14-y-old participants from the Italian Public Health Impact of Manganese Exposure cohort living near historical ferroalloy industries.49 Similar findings were reported in another pediatric Mn cohort, the California Center for the Health Assessment of Mother and Children of Salinas (CHAMACOS) cohort study. Mora et al. found that higher prenatal and postnatal dentin Mn levels were associated with better childhood motor outcomes at 7, 9, and 10.5 years of age in males but not in females.50 Also in the CHAMACOS cohort, Gunnier et al. reported an effect modifica-tion by sex for the association between postnatal dentin Mn lev-els and motor function at 6 months of age, measured by the Psychomotor Development Index of the Bayley Scales of Infant Development, where a significant adverse linear relationship was found in females only.42
In contrast, Takser et al. conducted a study of mother-newborn couples from a French maternity hospital and found higher umbili-cal blood Mn concentration at birth was negatively associated with hand skill at 3 years of age in males only.121 The majority of the studies suggest early life Mn exposure impacts development of motor function differently in males and females. However, because of the lack of childhood and adolescent Mn biomarker and motor studies investigating sex effects, there is not sufficient information to conclude why we might be finding significant associations related to adolescent motor function in the childhood Mn expo-sure time point only and not also in adolescence. A potential explanations for the sex effects we did see between childhood Mn biomarkers and adolescent balance may be attributed to biologi-cal differences between males and females in response to Mn exposure.122 There is also evidence from animal studies in rats that Mn accumulation differs between males and females in stria-tal morphology across body tissues.108,109
Currently, there is no ideal biomarker of Mn exposure in envi-ronmental studies. This study was strengthened by our use of multiple Mn biomarkers (blood, hair, and toenails) at two time points of pediatric neurodevelopment (childhood and adoles-cence). Whole blood is a commonly collected biomarker of Mn exposure. Because Mn has a relatively short half-life of hours in blood, it is often used to reflect recent exposure rather than body burden.123 However, interpreting blood Mn as a biomarker of recent exposure should be done cautiously when considering the discrepancy between the half-life of Mn in blood vs. tissue/body. In monkeys exposed to 1:5 mg Mn=m3 for 13 wk, the half-life of elimination in soft tissues of kidney and heart was 18.3 and 27.3 d, respectively. Halftime of elimination of Mn from the monkey brain varied by region, ranging 4.9 d from the olfactory bulb; 15.7-16.7 d
from the globus pallidus, putamen, and caudate; 19.4 d from the olfactory cortex; 23.6 d from the pituitary; and 32.3 d from the cerebellum.124 Based on animal studies conducted in rats, the half-life of Mn in hard tissue of human bone is expected to be 8-9 y.125 Chronic exposure to Mn can lead to accumulated stores in the body, which may release Mn into the blood circula-tion, opening up the possibility that blood Mn may also act as a measure of more long-term exposure.126
It is interesting that we found adolescent blood Mn was signifi-cantly associated with greater adolescent sway when childhood blood Mn was not associated with adolescent sway, given the high correlation between blood Mn at the two time points (Pearson's r = 0:75, p< 0:0001). In addition, as participants aged over the decade from childhood into adolescence, the participants were exposed to increasingly less ambient Mn emitted from the local fer-romanganese refinery. Over the past decade, total Mn emissions (sum of fugitive or non-point air emissions and stack or point air emissions) reported by Eramet Marietta Inc. have varied widely but, in general, decreased from 108,380 kg Mn compounds in 2008, this study's first year of child recruitment, to 14,576 kg Mn compounds in 2017, the first year of recruitment for the adolescent postural balance sub-study.127 Despite the lower Mn emissions during the adolescent study visit years, adolescent blood Mn was uniquely associated with subclinical adolescent neuro-motor effects. To interpret this adolescent cross-sectional find-ing, further discussion of blood as a biomarker of Mn exposure and the adolescent time point is needed. A literature-based anal-ysis conducted by Baker et al.128 hypothesized that blood Mn acts as an exposure biomarker for inhaled Mn at concentrations >10 ng=m3. The adolescents in the present study were exposed to environmental Mn well below this threshold. The pharmaco-kinetics of inhaled Mn at lower environmental concentrations have yet to be elucidated; however, the research that underlies the pharmacokinetic models for Mn suggests blood Mn levels serve as a viable biological indicator for inhalation exposures in controlled experimental conditions.129 In the present study, it was beyond the scope of our analysis to interpret whether adolescent blood Mn reflects recent or chronic exposure. However, the lack of associations between adolescent hair or adolescent toe-nail Mn with adolescent postural balance lends support to adolescent blood Mn as a reflection of recent exposure. This is plausible given the rapid neurodevelopment occurring during the adolescent time point, despite the lower levels of environmental Mn during adolescence compared with childhood.
Hair and toenails are other commonly collected tissues of Mn exposure used to reflect chronic exposure. Many metals deposit in keratin, a protein found in hair and nails. Because the growth rate of hair and toenails are ~ 1:27 cm=month130 and 0:35 cm=month,131 respectively, mean hair and toenail levels are used to represent cu-mulative exposure over 2-4 months132 and 7-12 months131,133 before sampling, respectively. Hair and toenails are easy to collect because they require noninvasive collection methods and also are easy to store.134,135 Although the mechanisms of the ambient air Mn to hair Mn pathway need to be further elucidated, several studies found significant associations between Mn levels in hair and dis-tance to industrial point source of Mn.136-138 Although hair Mn has been regarded by Coetzee et al. as the "most consistent and valid biomarker in pediatric epidemiolog this statement is problematic because it is based on the greater number of pediatric studies that identify significant associations between pediatric hair Mn and neurodevelopmental outcomes compared with other Mn bio-markers. Few studies include measures of both internal dose Mn and environmental Mn, and those that do yield diverse findings regarding the relationship between Mn concentrations in hair or toe-nails and environmental mediums. For example, in Italian children living in a community of historical ferroalloy emissions, children's hair Mn concentrations exhibited a significantly low correlation with Mn in household dust and airborne particles.140 In Bangladeshi children exposed to elevated levels of Mn in drinking water, Skröder et al. reported no correlation between hair Mn and water Mn.141 On the contrary, Bouchard et al. and Oulhote et al. reported that Canadian children living in homes with elevated levelsof Mn in water had higher hair Mn concentrations compared with children living in homes with lower levels of Mn in water.20,142 Regarding toenails, in Brazilian children living near a ferromanganese alloy plant, toenail Mn was significantly correlated with Mn in exterior dust and interior environment.143 The utility of hair as a Mn bio-marker is limited by the potential for external contamination and lack of standardized cleaning methods to remove exogenous Mn from hair before analysis that has led to varying hair Mn ranges across environmentally exposed children.53,144-148 Similar limita-tions exist for the utility of toenails as a Mn biomarker. Generally, toenails are considered less susceptible to external contamination than hair because, for a given sample weight, toenails have a smaller surface-to-volume ratio.149,150 However, there is also a lack of standardized cleaning methods for toenails.151
There was no correlation between the childhood and adolescent measures of hair Mn and toenail Mn. Compared with their childhood measurements, participants had lower hair Mn and toe-nail Mn concentrations during the adolescent time point. Given the decrease in emissions from the ferromanganese refinery, this difference was anticipated. We observed that participants with greater childhood hair Mn and toenail Mn concentrations had sig-nificantly greater adolescent sway measures under the foam open test condition. This finding suggests that early life Mn biomarkers may impact adolescent balance.
This study has public health, as well as occupational health and safety, relevance for Mn-exposed children and adolescents because greater sway is associated with increased risk of prospec-tive fall.152 Adolescents are identified as one of the most high-risk group for falls by the World Health Organization.153 In addi-tion, adolescence is a time when the majority of youth enter the labor force before they finish high school.154 The CDC identifies adolescents as a high-risk group for work-related injuries owing to their unique physical and psychosocial characteristics.154 Given the well-established relationship between greater sway and increased risk of falls,152 adolescents in this study with higher in-ternal Mn dose may be at increased risk of injuries, including at work, compared with adolescents with lower levels. A study con-ducted in Pb-exposed children provides support for potential injury trends.155 Pb and Mn are both metals with evidence of low exposure associated with neurotoxicity in pediatric populations. Pb-exposed children who demonstrated postural instability were followed up in adolescent years. Study results indicated that falls among these subjects were the most common event leading to injury and were associated with increased blood Pb concentra-tions.155 Injuries are a largely preventable public health problem; thus, balance training as a public health intervention may be use-ful for pediatric populations living near Mn emissions.156
This study had several strengths that enabled it to address gaps in understanding regarding the associations between Mn ex-posure at childhood and adolescent time points and adolescent neuromotor function. One strength lies in the long-standing bidir-ectional academic-community partnership between our research team and the Marietta community.57 This relationship enabled the high participation rate and sample size, providing new information on the impact of Mn biomarkers during childhood and adolescence on adolescent balance. Adolescent participants in our study had a mean age of 16 y, which is older than other adolescent studies on Mn that have an average or median age of ?12 y.52,55,157 This study was also strengthened by our choice of neuromotor assessment. Postural balance testing is a validated bio-marker of neuromotor function and long-established method used in Mn exposure studies dating back to the early 1990s.158,159(p199) Neuromotor function is a primary outcome of interest in occupa-tional studies owing to its role in occupational safety and prevention of injury. Increasingly, environmental exposure studies of Mn-exposed adults and children living near industrial emissions are investigating neuromotor outcomes. Across pediatric Mn expo-sure studies, a myriad of tests has been employed in attempts to quantify neuromotor function, contributing to the heterogeneity of study findings. Popular tests include fingertapping,42,86,142 visual
motor,50,52,160,161 pursuit aiming,49 pegboard,54,86,161 and postural balance tests.51,53 Our postural balance testing methods exceed the criteria outlined by Zoni et al. for a standardized neuromotor test to emerge that is validated, reproducible, sensitive to early neuro-toxic alternations affected by Mn, and can be administered in the field under standard conditions.162,163 The experimental setup for balance testing is portable, sensitive enough to detect <3% error in field studies, and our methods are validated37,51,164 and sensitive enough to effectively distinguish differences between patients exhibiting manganism and PD, something which is challenging to do clinically.165 Our findings support postural balance as the standard test of neuromotor function to aid early detection of subclinical Mn neurotoxicity.
This article is the first step in investigating the associations between childhood and adolescent Mn biomarkers and adolescent postural balance. Our results add valuable information to the limited body of knowledge surrounding pediatric Mn biomarkers and neuromotor function. The supplemental analyses strengthen the interpretation of our findings. When restricting the analytical sample to participants with data on all biomarkers at both time points (n = 76), the significant associations initially found in the adjusted multivariable linear regression models between child-hood Mn and adolescent Mn and adolescent balance (n= 123) remained, with the exception of one. Additional associations were found. This may be attributed to differences in variability in the environmental exposures or postural balance outcomes between the analytical sample (n= 123) and the restricted sample (n= 76). In addition, the supplemental analyses using MIM GEEs identified the exposure time points that have the greatest impact on postural balance outcomes. Under foam open, for anterior-posterior sway, childhood hair Mn was associated with increased adolescent sway (i.e., postural instability), whereas adolescent hair Mn was associated with decreased adolescent sway (i.e., postural stability). Under bending open, for medio- lateral sway, childhood blood Mn was associated with increased adolescent sway (i.e., postural instability), whereas adolescent blood Mn was associated with decreased adolescent sway (i.e., postural stability).
This study was limited by the subset of Marietta CARES ado-lescents who completed the adolescent neuromotor study visit, who were predominantly White. Although this is generally repre-sentative of the Appalachian Ohio region, it did not reflect the diversity of the study catchment area, Washington County's 2.1% Black, 1.9% two or more races, 1.3% American Indian and Alaska Native, and 1.2% Hispanic population.166 In conclusion, this study adds new knowledge about the effects of childhood and adolescent Mn biomarkers on adolescent neuromotor out-comes. Postural balance is a sensitive measure to assess the impact of neurotoxic exposure on neuromotor function. Given the association between Mn biomarkers and postural instability, future research should investigate injury trends in Mn-exposed adolescents. It is important for future research to include ambient Mn concentrations to better approximate ambient exposures with health outcomes, such as balance. This will contribute signifi-cantly to the translational efforts of our findings to public health impact for protection of adolescent workers and aid regulatory standards for ambient Mn concentrations.
Acknowledgments
We express gratitude to the children and families from the Marietta Communities Actively Researching Exposures Study (CARES) cohort who kindly participated in the study, as well as for the research support provided by Jody Alden, Emily Embresica, Brooke Stanley, Ethan Bunnell, Dylan Vlaovich, Cassandra Bezi, Ashley Turner, Nick Ferrara, and Nell Wickstrom. The University of Kentucky College of Public Health's Office of Scientific Writing assisted in the preparation of this manuscript. The authors also extend gratitude for the thoughtful and constructive feedback from reviewers.Ithas enhanced the quality ofthe manuscript.
Funding for this work was provided by the National Institutes of Health/National Institute of Environmental Health Sciences [NIEHS; R01 ES026446 and R01 ES016531 (both to Erin N. Haynes) and P30 ES006096], NIEHS Molecular Epidemiology in Children's Environmental Health T-32 training program [T32 ES010957 (to Danielle E. McBride)], and the National Institute for Occupational Safety and Health through the Pilot Research Project Training Program of the University of Cincinnati Education and Research Center Grant [T42 OH008432 (to Danielle E. McBride)].
References
1. Clarke C, Upson S. 2017. A global portrait of the manganese industry-a socioeconomic perspective. Neurotoxicology 58:173-179, PMID: 27182045, https://doi.org/10.1016/j.neuro.2016.03.013.
2. Chen P, Bornhorst J, Aschner M. 2018. Manganese metabolism in humans. Front Biosci (Landmark Ed) 23:1655-1679, PMID: 29293455, https://doi.org/10. 2741/4665.
3. Horning KJ, Caito SW, Tipps KG, Bowman AB, Aschner M. 2015. Manganese is essential for neuronal health. Annu Rev Nutr 35:71-108, PMID: 25974698, https://doi.org/10.1146/annurev-nutr-071714-034419.
4. Keen CL, Ensunsa JL, Watson MH, Baly DL, Donovan SM, Monaco MH, et al. 1999. Nutritional aspects of manganese from experimental studies. Neurotoxicology 20(2-3):213-223, PMID: 10385885.
5. Pellizzari ED, Clayton CA, Rodes CE, Mason RE, Piper LL, Fort B, et al. 2001. Particulate matter and manganese exposures in Indianapolis, Indiana. J Expo Anal Environ Epidemiol 11(6):423-440, PMID: 11791160, https://doi.org/10.1038/ sj.jea.7500186.
6. ATSDR (Agency for Toxic Substances and Disease Registry). 2012. Toxicological Profile for Manganese. https://www.atsdr.cdc.gov/ToxProfiles/ tp151.pdf [accessed 7 October 2023].
7. Lucchini RG, Dorman DC, Elder A, Veronesi B. 2012. Neurological impacts from inhalation of pollutants and the nose-brain connection. Neurotoxicology 33(4):838-841, PMID: 22178536, https://doi.org/10.1016/j.neuro.2011.12.001.
8. Shaffer RM, Wright JM, Cote I, Bateson TF. 2023. Comparative susceptibility of children and adults to neurological effects of inhaled manganese: a review of the published literature. Environ Res 221:115319, PMID: 36669586, https://doi.org/10.1016/j.envres.2023.115319.
9. Balachandran RC, Mukhopadhyay S, McBride D, Veevers J, Harrison FE, Aschner M, et al. 2020. Brain manganese and the balance between essential roles and neurotoxicity. J Biol Chem 295(19):6312-6329, PMID: 32188696, https://doi.org/10.1074/jbc.REV119.009453.
10. Papavasiliou PS, Miller ST, Cotzias GC. 1966. Role of liver in regulating distri-bution and excretion of manganese. Am J Physiol 211(1):211-216, PMID: 5911040, https://doi.org/10.1152/ajplegacy.1966.211.1.211.
11. Roth JA. 2006. Homeostatic and toxic mechanisms regulating manganese uptake, retention, and elimination. Biol Res 39(1):45-57, PMID: 16629164, https://doi.org/10.4067/s0716-97602006000100006.
12. Dorman DC, Struve MF, Wong BA. 2002. Brain manganese concentrations in rats following manganese tetroxide inhalation are unaffected by dietary man-ganese intake. Neurotoxicology 23(2):185-195, PMID: 12224760, https://doi.org/ 10.1016/s0161-813x(01)00075-4.
13. Elder A, Gelein R, Silva V, Feikert T, Opanashuk L, Carter J, et al. 2006. Translocation of inhaled ultrafine manganese oxide particles to the central nervous system. Environ Health Perspect 114(8):1172-1178, PMID: 16882521, https://doi.org/10.1289/ehp.9030.
14. Pajarillo E, Johnson J Jr, Rizor A, Nyarko-Danquah I, Adinew G, Bornhorst J, et al. 2020. Astrocyte-specific deletion of the transcription factor Yin Yang 1 in murine substantia nigra mitigates manganese-induced dopaminergic neuro-toxicity. J Biol Chem 295(46):15662-15676, PMID: 32893191, https://doi.org/10. 1074/jbc.RA120.015552.
15. Nelson K, Golnick J, Korn T, Angle C. 1993. Manganese encephalopathy: utility of early magnetic resonance imaging. Br J Ind Med 50(6):510-513, PMID: 8329316, https://doi.org/10.1136/oem.50.6.510.
16. Dorman DC, Struve MF, Wong BA, Dye JA, Robertson ID. 2006. Correlation of brain magnetic resonance imaging changes with pallidal manganese concentrations in rhesus monkeys following subchronic manganese inha-lation. Toxicol Sci 92(1):219-227, PMID: 16638924, https://doi.org/10.1093/ toxsci/kfj209.
17. Yamada M, Ohno S, Okayasu I, Okeda R, Hatakeyama S, Watanabe H, et al. 1986. Chronic manganese poisoning: a neuropathological study with determi-nation of manganese distribution in the brain. Acta Neuropathol 70(3-4):273- 278, PMID: 3766127, https://doi.org/10.1007/BF00686083.
18. Mergler D, Baldwin M, Bélanger S, Larribe F, Beuter A, Bowler R, et al. 1999. Manganese neurotoxicity, a continuum of dysfunction: results from a commu-nity based study. Neurotoxicology 20(2-3):327-342, PMID: 10385894.
19. Bowler RM, Gocheva V, Harris M, Ngo L, Abdelouahab N, Wilkinson J, et al. 2011. Prospective study on neurotoxic effects in manganese-exposed bridge construction welders. Neurotoxicology 32(5):596-605, PMID: 21762725, https://doi.org/10. 1016/j.neuro.2011.06.004.
20. Bouchard M, Mergler D, Baldwin M, Panisset M, Bowler R, Roels HA. 2007. Neurobehavioral functioning after cessation of manganese exposure: a follow-up after 14 years. Am J Ind Med 50(11):831-840, PMID: 17096374, https://doi.org/10.1002/ajim.20407.
21. Aschner M, Erikson KM, Herrero Hernández E, Tjalkens R. 2009. Manganese and its role in Parkinson's disease: from transport to neuropathology. Neuromolecular Med 11(4):252-266, PMID: 19657747, https://doi.org/10.1007/ s12017-009-8083-0.
22. Olanow CW. 2004. Manganese-induced parkinsonism and Parkinson's dis-ease. Ann NY Acad Sci 1012(1):209-223, PMID: 15105268, https://doi.org/10. 1196/annals.1306.018.
23. Perl DP, Olanow CW. 2007. The neuropathology of manganese-induced parkin-sonism. J Neuropathol Exp Neurol 66(8):675-682, PMID: 17882011, https://doi.org/ 10.1097/nen.0b013e31812503cf.
24. Meyer-Baron M, Schäper M, Knapp G, Lucchini R, Zoni S, Bast-Pettersen R, et al. 2013. The neurobehavioral impact of manganese: results and challenges obtained by a meta-analysis of individual participant data. Neurotoxicology 36:1-9, PMID: 23419685, https://doi.org/10.1016/j.neuro.2013.02.003.
25. US EPA (US Environmental Protection Agency). 2015. Ambient Concentrations of Manganese Compounds in EPA Region 5. https://cfpub.epa.gov/roe/indicator. cfm?i=6 [accessed 2 February 2023].
26. Zaichkowsky LD, Larson GA. 1995. Physical, motor, and fitness development in children and adolescents. J Educ (Boston) 177(2):55-79, https://doi.org/10. 1177/002205749517700205.
27. Ruiz-Azcona L, Fernández-Olmo I, Expósito A, Markiv B, Paz-Zulueta M, Parás-Bravo P, et al. 2021. Impact of environmental airborne manganese ex-posure on cognitive and motor functions in adults: a systematic review and meta-analysis. Int J Environ Res Public Health 18(8):4075, PMID: 33924318, https://doi.org/10.3390/ijerph18084075.
28. Bowler RM, Harris M, Gocheva V, Wilson K, Kim Y, Davis SI, et al. 2012. Anxiety affecting parkinsonian outcome and motor efficiency in adults of an Ohio community with environmental airborne manganese exposure. Int J Hyg Environ Health 215(3):393-405, PMID: 22112744, https://doi.org/10. 1016/j.ijheh.2011.10.005.
29. Bowler RM, Kornblith ES, Gocheva VV, Colledge MA, Bollweg G, Kim Y, et al. 2015. Environmental exposure to manganese in air: associations with cogni-tive functions. Neurotoxicology 49:139-148, PMID: 26096496, https://doi.org/10. 1016/j.neuro.2015.06.004.
30. Bowler RM, Beseler CL, Gocheva VV, Colledge M, Kornblith ES, Julian JR, et al. 2016. Environmental exposure to manganese in air: associations with tremor and motor function. Sci Total Environ 541:646-654, PMID: 26437342, https://doi.org/10.1016/j.scitotenv.2015.09.084.
31. Ghazali AR, Kamarulzaman F, Normah CD, Ahmad M, Ghazali SE, Ibrahim N, et al. 2013. Levels of metallic elements and their potential relationships to cognitive function among elderly from Federal Land Development Authority (FELDA) settlement in Selangor Malaysia. Biol Trace Elem Res 153(1-3):16-21, PMID: 23475372, https://doi.org/10.1007/s12011-013-9642-7.
32. Iqbal G, Zada W, Mannan A, Ahmed T. 2018. Elevated heavy metals levels in cognitively impaired patients from Pakistan. Environ Toxicol Pharmacol 60:100-109, PMID: 29684799, https://doi.org/10.1016/j.etap.2018.04.011.
33. KimY, Bowler RM, AbdelouahabN, HarrisM, Gocheva V, Roels HA. 2011. Motor function in adults of an Ohio community with environmental manganese exposure. Neurotoxicology 32(5):606-614, PMID: 21840336, https://doi.org/10. 1016/j.neuro.2011.07.011.
34. Lucchini RG, Guazzetti S, Zoni S, Benedetti C, Fedrighi C, Peli M, et al. 2014. Neurofunctional dopaminergic impairment in elderly after lifetime exposure to manganese. Neurotoxicology 45:309-317, PMID: 24881811, https://doi.org/10. 1016/j.neuro.2014.05.006.
35. Guarneros M, Ortiz-Romo N, Alcaraz-Zubeldia M, Drucker-Colín R, Hudson R. 2013. Nonoccupational environmental exposure to manganese is linked to deficits in peripheral and central olfactory function. Chem Senses 38(9):783- 791, PMID: 24097266, https://doi.org/10.1093/chemse/bjt045.
36. de Sousa Viana GF, de Carvalho CF, Nunes LS, Rodrigues JLG, Ribeiro NS, de Almeida DA, et al. 2014. Noninvasive biomarkers of manganese exposure and neuropsychological effects in environmentally exposed adults in Brazil. Toxicol Lett 231(2):169-178, PMID: 24992226, https://doi.org/10.1016/j.toxlet. 2014.06.018.
37. Standridge JS, Bhattacharya A, Succop P, Cox C, Haynes E. 2008. Effect of chronic low level manganese exposure on postural balance: a pilot study of residents in southern Ohio. J Occup Environ Med 50(12):1421-1429, PMID: 19092498, https://doi.org/10.1097/JOM.0b013e3181896936.
38. Bauer JA, White RF, Coull BA, Austin C, Oppini M, Zoni S, et al. 2021. Critical windows of susceptibility in the association between manganese and neuro-cognition in Italian adolescents living near ferro-manganese industry. Neurotoxicology 87:51-61, PMID: 34478771, https://doi.org/10.1016/j.neuro. 2021.08.014.
39. Rechtman E, Navarro E, de Water E, Tang CY, Curtin P, Papazaharias DM, et al. 2022. Early-life critical windows of susceptibility to manganese exposure and sex-specific changes in brain connectivity in late adolescence. Biol Psychiatry Glob Open Sci 3(3):460-469, PMID: 37519473, https://doi.org/10. 1016/j.bpsgos.2022.03.016.
40. Yu XD, Zhang J, Yan CH, Shen XM. 2014. Prenatal exposure to manganese at environment relevant level and neonatal neurobehavioral development. Environ Res 133:232-238, PMID: 24971720, https://doi.org/10.1016/j.envres. 2014.04.012.
41. Chung SE, Cheong HK, Ha EH, Kim BN, Ha M, Kim Y, et al. 2015. Maternal blood manganese and early neurodevelopment: the Mothers and Children's Environmental Health (MOCEH) study. Environ Health Perspect 123(7):717-722, PMID: 25734517, https://doi.org/10.1289/ehp.1307865.
42. Gunier RB, Arora M, Jerrett M, Bradman A, Harley KG, Mora AM, et al. 2015. Manganese in teeth and neurodevelopment in young Mexican-American chil-dren. Environ Res 142:688-695, PMID: 26381693, https://doi.org/10.1016/j. envres.2015.09.003.
43. Yu X, Chen L, Wang C, Yang X, Gao Y, Tian Y. 2016. The role of cord blood BDNF in infant cognitive impairment induced by low-level prenatal manga-nese exposure: LW birth cohort, China. Chemosphere 163:446-451, PMID: 27565312, https://doi.org/10.1016/j.chemosphere.2016.07.095.
44. Muñoz-Rocha TV, Tamayo y Ortiz M, Romero M, Pantic I, Schnaas L, Bellinger D, et al. 2018. Prenatal co-exposure to manganese and depres-sion and 24-months neurodevelopment. Neurotoxicology 64:134-141, PMID: 28728787, https://doi.org/10.1016/j.neuro.2017.07.007.
45. Claus Henn B, Bellinger DC, Hopkins MR, Coull BA, Ettinger AS, Jim R, et al. 2017. Maternal and cord blood manganese concentrations and early child-hood neurodevelopment among residents near a mining-impacted Superfund Site. Environ Health Perspect 125(6):067020, PMID: 28665786, https://doi.org/ 10.1289/EHP925.
46. Mora AM, Córdoba L, Cano JC, Hernandez-Bonilla D, Pardo L, Schnaas L, et al. 2018. Prenatal mancozeb exposure, excess manganese, and neurodevel-opment at 1 year of age in the Infants' Environmental Health (ISA) Study. Environ Health Perspect 126(5):057007, PMID: 29847083, https://doi.org/10. 1289/EHP1955.
47. Andiarena A, Irizar A, Molinuevo A, Urbieta N, Babarro I, Subiza-Pérez M, et al. 2020. Prenatal manganese exposure and long-term neuropsychological de-velopment at 4 years of age in a population-based birth cohort. Int J Environ Res Public Health 17(5):1665, PMID: 32143391, https://doi.org/10.3390/ ijerph17051665.
48. Freire C, Amaya E, Gil F, Fernández MF, Murcia M, Llop S, et al. 2018. Prenatal co-exposure to neurotoxic metals and neurodevelopment in preschool chil-dren: the Environment and Childhood (INMA) Project. Sci Total Environ 621:340-351, PMID: 29190557, https://doi.org/10.1016/j.scitotenv.2017.11.273.
49. Chiu YHM, Claus Henn B, Hsu HHL, Pendo MP, Coull BA, Austin C, et al. 2017. Sex differences in sensitivity to prenatal and early childhood manganese ex-posure on neuromotor function in adolescents. Environ Res 159:458-465, PMID: 28858760, https://doi.org/10.1016/j.envres.2017.08.035.
50. Mora AM, Arora M, Harley KG, Kogut K, Parra K, Hernández-Bonilla D, et al. 2015. Prenatal and postnatal manganese teeth levels and neurodevelopment at 7, 9, and 10.5 years in the CHAMACOS cohort. Environ Int 84:39-54, PMID: 26209874, https://doi.org/10.1016/j.envint.2015.07.009.
51. Rugless F, Bhattacharya A, Succop P, Dietrich KN, Cox C, Alden J, et al. 2014. Childhood exposure to manganese and postural instability in children living near a ferromanganese refinery in southeastern Ohio. Neurotoxicol Teratol 41:71-79, PMID: 24370548, https://doi.org/10.1016/j.ntt.2013.12.005.
52. Dion LA, Bouchard MF, Sauvé S, Barbeau B, Tucholka A, Major P, et al. 2016. MRI pallidal signal in children exposed to manganese in drinking water. Neurotoxicology 53:124-131, PMID: 26801245, https://doi.org/10.1016/j.neuro. 2016.01.004.
53. Lucchini RG, Guazzetti S, Zoni S, Donna F, Peter S, Zacco A, et al. 2012. Tremor, olfactory and motor changes in Italian adolescents exposed to histori-cal ferro-manganese emission. Neurotoxicology 33(4):687-696, PMID: 22322213, https://doi.org/10.1016/j.neuro.2012.01.005.
54. Carvalho CFD, Oulhote Y, Martorelli M, Carvalho COD, Menezes-Filho JA, Argollo N, et al. 2018. Environmental manganese exposure and associations with memory, executive functions, and hyperactivity in Brazilian children. Neurotoxicology 69:253-259, PMID: 29432852, https://doi.org/10.1016/j.neuro. 2018.02.002.
55. Vibol S, Hashim JH, Sarmani S. 2015. Neurobehavioral effects of arsenic ex-posure among secondary school children in the Kandal Province, Cambodia. Environ Res 137:329-337, PMID: 25601736, https://doi.org/10.1016/j.envres. 2014.12.001.
56. Eramet Marietta. 2022. About Eramet Marietta. https://marietta.eramet.com/ eramet/about-us/ [accessed 20 June 2022].
57. Haynes EN, Beidler C, Wittberg R, Meloncon L, Parin M, Kopras EJ, et al. 2011. Developing a bidirectional academic-community partnership with an Appalachian-American community for environmental health research and risk communication. Environ Health Perspect 119(10):1364-1372, PMID: 21680278, https://doi.org/10.1289/ehp.1003164.
58. Haynes EN, Sucharew H, Kuhnell P, Alden J, Barnas M, Wright RO, et al. 2015. Manganese exposure and neurocognitive outcomes in rural school-age children: The communities actively researching exposure study (Ohio, USA). Environ Health Perspect 123(10):1066-1071, PMID: 25902278, https://doi.org/10. 1289/ehp.1408993.
59. Seccareccia F, Zuccaro P, Pacifici R, Meli P, Pannozzo F, Freeman KM, et al. 2003. Serum cotinine as a marker of environmental tobacco smoke exposure in epidemiological studies: the experience of the MATISS project. Eur J Epidemiol 18(6):487-492, PMID: 12908713, https://doi.org/10.1023/a:1024672522802.
60. Praamsma ML, Arnason JG, Parsons PJ. 2011. Monitoring Mn in whole blood and urine: a comparison between electrothermal atomic absorption and inor-ganic mass spectrometry. J Anal At Spectrom 26(6):1224-1232, https://doi.org/ 10.1039/c1ja10039d.
61. Palmer CD, Lewis ME Jr, Geraghty CM, Barbosa F Jr, Parsons PJ. 2006. Determination of lead, cadmium and mercury in blood for assessment of envi-ronmental exposure: a comparison between inductively coupled plasma- mass spectrometry and atomic absorption spectrometry. Spectrochim Acta Part B At Spectrosc 61(8):980-990, https://doi.org/10.1016/j.sab.2006.09.001.
62. Birdsall RE, Kiley MP, Segu ZM, Palmer CD, Madera M, Gump BB, et al. 2010. Effects of lead and mercury on the blood proteome of children. J Proteome Res 9(9):4443-4453, PMID: 20681587, https://doi.org/10.1021/pr100204g.
63. McKelvey W, Gwynn RC, Jeffery N, Kass D, Thorpe LE, Garg RK, et al. 2007. A biomonitoring study of lead, cadmium, and mercury in the blood of New York City adults. Environ Health Perspect 115(10):1435-1441, PMID: 17938732, https://doi.org/10.1289/ehp.10056.
64. Yu LL, Murphy KE, Bryan Sallee CE, Vetter TW, Hagwood RC, Caceres GC, et al. 2020. Certification of Standard Reference Material(r) 955d, Toxic Elements and Metabolites in Frozen Human Blood. Special Publication 260-206. Washington DC: US Department of Commerce. https://doi.org/10.6028/ NIST.SP.260-206.
65. Bernert JT Jr, Turner WE, Pirkle JL, Sosnoff CS, Akins JR, Waldrep MK, et al. 1997. Development and validation of sensitive method for determination of serum cotinine in smokers and nonsmokers by liquid chromatography/ atmospheric pressure ionization tandem mass spectrometry. Clin Chem 43(12):2281-2291, PMID: 9439445, https://doi.org/10.1093/clinchem/43.12. 2281.
66. Ellis JA, Gwynn C, Garg RK, Philburn R, Aldous KM, Perl SB, et al. 2009. Secondhand smoke exposure among nonsmokers nationally and in New York City. Nicotine Tob Res 11(4):362-370, PMID: 19351780, https://doi.org/10.1093/ ntr/ntp021.
67. Succop PA, Clark S, Chen M, Galke W. 2004. Imputation of data values that are less than a detection limit. J Occup Environ Hyg 1(7):436-441, PMID: 15238313, https://doi.org/10.1080/15459620490462797.
68. Croghan C, Egeghy PP. 2003. Methods of Dealing with Values Below the Limit of Detection Using SAS. https://cfpub.epa.gov/si/si_public_record_report. cfm?Lab=NERL&dirEntryId=64046 [accessed 14 February 2024].
69. Lubin JH, Colt JS, Camann D, Davis S, Cerhan JR, Severson RK, et al. 2004. Epidemiologic evaluation of measurement data in the presence of detection limits. Environ Health Perspect 112(17):1691 1696, PMID: 15579415, https://doi.org/ 10.1289/eh p.7199.
70. Wechsler D. 1999. Wechsler Abbreviated Scale of Intelligence. San Antonio, TX: Psychological Corporation.
71. Jäncke L, Sele S, Liem F, Oschwald J, Merillat S. 2020. Brain aging and psychometric intelligence: a longitudinal study. Brain Struct Funct 225(2):519-536, PMID: 31863184, https://doi.org/10.1007/s00429-019-02005-5.
72. Barratt W. 2006. The Barratt Simplified Measure of Social Status (BSMSS): measuring SES. [Unpublished manuscript] Terre Haute, IN: Indiana State University. http://socialclassoncampus.blogspot.com/2012/06/barratt-simplified-measure-of-social.html [accessed 20 June 2022].
73. Bhattacharya A, Morgan R, Shukla R, Ramakrishanan HK, Wang L 1987. Non-invasive estimation of afferent inputs for postural stability under low levels of alcohol. Ann Biomed Eng 15(6):533-550, PMID: 3688583, https://doi.org/10. 1007/BF02364247.
74. Bhattacharya A. 1999. Quantitative posturagraphy as an alternative non-invasive tool for alcohol/drug/chemical testing-preliminary thoughts. Drug Chem Toxicol 22(1 ):201 212, PMID: 10189579, https://doi.org/10. 3109/01480549909029732.
75. Bhattacharya A, Watts NB, Dwivedi A, Shukla R, Mani A, Diab D. 2016. Combined measures of dynamic bone quality and postural balance-a frac-ture riskassessment approach in osteoporosis. J Clin Densitom 19(2):154-164, PMID: 25936482, https://doi.org/10.1016/j.jocd.2015.03.005.
76. Purves D, Augustine GJ, Fitzpatrick D, Katz LC, LaMantia A-S, McNamara JO, et al. 2001. The vestibular system. In: Neuroscience. 2nd ed. Sunderland, MA: Sinauer Associates. https://www.ncbi.nlm.nih.gov/books/NBK10819/ [accessed 18 October 2023].
77. Han J, Waddington G, Adams R, Anson J, Liu Y. 2016. Assessing propriocep-tion: a critical review of methods. J Sport Health Sci 5(1):80-90, PMID: 30356896, https://doi.org/10.1016/j.jshs.2014.10.004.
78. Mergner T, Rosemeier T. 1998. Interaction of vestibular, somatosensory and visual signals for postural control and motion perception under terres-trial and microgravity conditions-a conceptual model. Brain Res Brain Res Rev 28(1 2):118-135, PMID: 9795180, https://doi.org/10.1016/s0165-0173 (98)00032-0.
79. Peterka RJ, Benolken MS. 1995. Role of somatosensory and vestibular cues in attenuating visually induced human postural sway. Exp Brain Res 105(1):101 110, PMID: 7589307, https://doi.org/10.1007/BF00242186.
80. Simoneau GG, Ulbrecht JS, Derr JA, Cavanagh PR. 1995. Role of somatosensory input in the control of human posture. Gait Posture 3(3):115-122, https://doi.org/10.1016/0966-6362(95)99061-O.
81. Diener HC, Dichgans J. 1988. On the role of vestibular, visual and somatosensory information for dynamic postural control in humans. Prog Brain Res 76:253-262, PMID: 3064150, https://doi.org/10.1016/s0079-6123(08)64512-4.
82. Bhattacharya A, Shukla R, Dietrich K, Bornschein R, Berger O. 1995. Effect of early lead exposure on children's postural balance. Dev Med Child Neurol 37(10):861-878, PMID: 7493720, https://doi.org/10.1111/j.1469-8749. 1995.tb 11939.x.
83. Orendorz-Fraczkowska K, Kubacka M. 2019. The development of postural control in 6-17 old years healthy children. Part I postural control evaluation in modified Clinical TestforThe Sensory Interaction on Balance in 6-17 old year children (mCTSIB). Otolaryngol Pol 74(1):1 7, PMID: 32020900, https://doi.org/ 10.5604/01.3001.0013.2965.
84. Greve J, Alonso A, Bordini ACPG, Camanho GL 2007. Correlation between body mass index and postural balance. Clinics (Sao Paulo) 62(6):717-720, PMID: 18209913, https://doi.org/10.1590/s1807-59322007000600010.
85. Bhattacharya A, Shukla R, Dietrich KN, Bornschein RL. 2006. Effect of early lead exposure on the maturation of children's postural balance: a longitudinal study. Neurotoxicol Teratol 28(3):376-385, PMID: 16624520, https://doi.org/10. 1016/j.ntt.2006.02.003.
86. Yeramaneni S, Dietrich KN, Yolton K, Parsons PJ, Aldous KM, Haynes EN. 2015. Secondhand tobacco smoke exposure and neuromotor function in rural children. J Pediatr 167(2):253-259.e1, PMID: 25882879, https://doi.org/10.1016/j. jpeds.2015.03.014.
87. Ye A, Yan S, Huang K, Mao L, Ge X, Weng T, et al. 2019. Maternal intelligence quotient and motor development in early childhood: the mediating role of mother's education. J Paediatr Child Health 55(1):87-94, PMID: 30051946, https://doi.org/10.1111/jpc.14123.
88. Benjamini Y, Hochberg Y. 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol 57(1):289-300, https://doi.org/10.1111/j.2517-6161.1995.tb02031.x.
89. Horton NJ, Laird NM, Zahner GEP. 1999. Use of multiple informant data as a predictor in psychiatric epidemiology. Int J Methods Psych Res 8(1):6 18, https://doi.org/10.1002/mpr.52.
90. Pepe MS, Whitaker RC, Seidel K. 1999. Estimating and comparing univariate associations with application to the prediction of adult obesity. Stat Med 18(2):163-173, PMID: 10028137, https://doi.org/10.1002/(SICI)1097-0258(19990130) 18:2<163:AID-SIM11>3.0.CO;2-F.
91. Bather JR, Horton NJ, Coull BA, Williams PL. 2023. The impact of correlated exposures and missing data on multiple informant models used to identify critical exposure windows. Stat Med 42(8):1171-1187, PMID: 36647625, https://doi.org/10.1002/sim.9664.
92. Hirabayashi S, Iwasaki Y. 1995. Developmental perspective of sensory organiza-tion on postural control. Brain Dev 17(2):111-113, PMID: 7542846, https://doi.org/ 10.1016/0387-7604(95)00009-z.
93. Cumberworth VL, Patel NN, Rogers W, Kenyon GS. 2007. The maturation of balance in children. J Laryngol Otol 121(5):449-454, PMID: 17105679, https://doi.org/10.1017/S0022215106004051.
94. Steindl R, Kunz K, Schrott-Fischer A, Scholtz AW. 2006. Effect of age and sex on maturation of sensory systems and balance control. Dev Med Child Neurol 48(6):477-482, PMID: 16700940, https://doi.org/10.1111/j.1469-8749. 2006.tb01299.x.
95. Ferber-Viart C, Ionescu E, Morlet T, Froehlich P, Dubreuil C. 2007. Balance in healthy individuals assessed with Equitest: maturation and normative data for children and young adults. Int J Pediatr Otorhinolaryngol 71(7):1041-1046, PMID: 17467063, https://doi.org/10.1016/j.ijporl.2007.03.012.
96. Ding D, Roth J, Salvi R. 2011. Manganese is toxic to spiral ganglion neurons and hair cells in vitro. Neurotoxicology 32(2):233-241, PMID: 21182863, https://doi.org/10.1016/j.neuro.2010.12.003.
97. Khalkova Z, Kostadinova G. 1986. Auditory-vestibular changes in workers in ferrous metallurgy manufacture [in Bulgarian]. Probl Khig 11:134-138, PMID: 3493484.
98. Sinno S, Dumas G, Mallinson A, Najem F, Abouchacra KS, Nashner L, et al. 2021. Changes in the sensory weighting strategies in balance control through-out maturation in children. J Am Acad Audiol 32(2):122-136, PMID: 33296934, https://doi.org/10.1055/s-0040-1718706.
99. Konrad K, Firk C, Uhlhaas PJ. 2013. Brain development during adolescence: neuroscientific insights into this developmental period. Dtsch Arztebl Int 110(25):425-431, PMID: 23840287, https://doi.org/10.3238/arztebl.2013.0425.
100. Guilarte TR. 2010. Manganese and Parkinson's disease: a critical review and new findings. Environ Health Perspect 118(8):1071-1080, PMID: 20403794, https://doi.org/10.1289/ehp.0901748.
101. Ward RJ, Zucca FA, Duyn JH, Crichton RR, Zecca L. 2014. The role of iron in brain ageing and neurodegenerative disorders. Lancet Neurol 13(10):1045- 1060, PMID: 25231526, https://doi.org/10.1016/S1474-4422(14)70117-6.
102. Zucca FA, Segura-Aguilar J, Ferrari E, Muñoz P, Paris I, Sulzer D, et al. 2017. Interactions of iron, dopamine and neuromelanin pathways in brain aging and Parkinson's disease. Prog Neurobiol 155:96-119, PMID: 26455458, https://doi.org/ 10.1016/j.pneurobio.2015.09.012.
103. Hayes AM, Herning MM, Gonzalez-Snyder C. 2016. Chapter 8 - Musculoskeletal system. In: Occupational Therapy with Aging Adults. Barney KF, Emerita C, Perkinson MA, eds. Maryland Heights, MO: Mosby, 97-124.
104. Nyarko-Danquah I, Pajarillo E, Digman A, Soliman KFA, Aschner M, Lee E. 2020. Manganese accumulation in the brain via various transporters and its neurotox-icity mechanisms. Molecules 25(24):5880, PMID: 33322668, https://doi.org/10. 3390/molecules25245880.
105. Zoni S, Bonetti G, Lucchini R. 2012. Olfactory functions at the intersection between environmental exposure to manganese and parkinsonism. J Trace Elem Med Biol 26(2-3):179-182, PMID: 22664337, https://doi.org/10.1016/j. jtemb.2012.04.023.
106. Larsen B, Luna B. 2018. Adolescence as a neurobiological critical period for the development of higher-order cognition. Neurosci Biobehav Rev 94:179- 195, PMID: 30201220, https://doi.org/10.1016/j.neubiorev.2018.09.005.
107. Richter Schmitz CR, Eichwald T, Branco Flores MV, Varela KG, Mantovani A, Steffani JA, et al. 2019. Sex differences in subacute manganese intoxica-tion: oxidative parameters and metal deposition in peripheral organs of adult Wistar rats. Regul Toxicol Pharmacol 104:98-107, PMID: 30878574, https://doi.org/10.1016/j.yrtph.2019.03.005.
108. Dorman DC, McManus BE, Marshall MW, James RA, Struve MF. 2004. Old age and gender influence the pharmacokinetics of inhaled manganese sulfate and manganese phosphate in rats. Toxicol Appl Pharmacol 197(2):113-124, PMID: 15163547, https://doi.org/10.1016/j.taap.2004.02.010.
109. Madison JL, Wegrzynowicz M, Aschner M, Bowman AB. 2011. Gender and manganese exposure interactions on mouse striatal neuron morphology. Neurotoxicology 32(6):896-906, PMID: 21641932, https://doi.org/10.1016/j. neuro.2011.05.007.
110. Roels HA, Bowler RM, Kim Y, Claus Henn B, Mergler D, Hoet P, et al. 2012. Manganese exposure and cognitive deficits: a growing concern for manganese neurotoxicity. Neurotoxicology 33(4):872-880, PMID: 22498092, https://doi.org/10. 1016/j.neuro.2012.03.009.
111. Torres-Agustín R, Rodríguez-Agudelo Y, Schilmann A, Solís-Vivanco R, Montes S, Riojas-Rodríguez H, et al. 2013. Effect of environmental manganese exposure on verbal learning and memory in Mexican children. Environ Res 121:39-44, PMID: 23141434, https://doi.org/10.1016/j.envres.2012.10.007.
112. Menezes-Filho JA, de Carvalho-Vivas CF, Viana GFS, Ferreira JRD, Nunes LS, Mergler D, et al. 2014. Elevated manganese exposure and school-aged child-ren's behavior: a gender-stratified analysis. Neurotoxicology 45:293-300, PMID: 24121006, https://doi.org/10.1016/j.neuro.2013.09.006.
113. Demura S, Kitabayashi T, Uchiyama M. 2006. Body sway characteristics during static upright posture in young children. Sport Sci Health 1(4):158-161, https://doi.org/10.1007/s11332-006-0028-5.
114. Geldhof E, Cardon G, De Bourdeaudhuij I, Danneels L, Coorevits P, Vanderstraeten G, et al. 2006. Static and dynamic standing balance: test-retest reliability and reference values in 9 to 10 year old children. Eur J Pediatr 165(11):779-786, PMID: 16738867, https://doi.org/10.1007/s00431-006-0173-5.
115. Lee AJY, Lin WH. 2007. The influence of gender and somatotype on single-leg upright standing postural stability in children. J Appl Biomech 23(3):173-179, PMID: 18089914, https://doi.org/10.1123/jab.23.3.173.
116. Nolan L, Grigorenko A, Thorstensson A. 2007. Balance control: sex and age differences in 9- to 16-year-olds. Dev Med Child Neurol 47(7):449-454, https://doi.org/10.1111/j.1469-8749.2005.tb01170.x.
117. Odenrick P, Sandstedt P. 1984. Development of postural sway in the normal child. Hum Neurobiol 3(4):241-244, PMID: 6526710.
118. Peterson ML, Christou E, Rosengren KS. 2006. Children achieve adult-like sen-sory integration during stance at 12-years-old. Gait Posture 23(4):455-463, PMID: 16002294, https://doi.org/10.1016/j.gaitpost.2005.05.003.
119. Varsha S, Anbalagan D, Adalarasu K, Jagannath M, Celestin Jerald A. 2022. Gender differences in postural stability in a cohort of adolescent age. J Phys: Conf Ser 2318(1):012002, https://doi.org/10.1088/1742-6596/2318/1/012002.
120. Lebiedowska MK, Syczewska M. 2000. Invariant sway properties in children. Gait Posture 12(3):200-204, PMID: 11154930, https://doi.org/10.1016/s0966-6362 (00)00080-1.
121. Takser L, Mergler D, Hellier G, Sahuquillo J, Huel G. 2003. Manganese, monoamine metabolite levels at birth, and child psychomotor develop-ment. Neurotoxicology 24(4-5):667-674, PMID: 12900080, https://doi.org/10. 1016/S0161-813X(03)00058-5.
122. Oulhote Y, Mergler D, Bouchard MF. 2014. Sex- and age-differences in blood manganese levels in the U.S. general population: National Health and Nutrition Examination Survey 2011-2012. Environ Health 13:87, PMID: 25342305, https://doi.org/10.1186/1476-069X-13-87.
123. Baker MG, Simpson CD, Sheppard L, Stover B, Morton J, Cocker J, et al. 2015. Variance components of short-term biomarkers of manganese exposure in an inception cohort of welding trainees. J Trace Elem Med Biol 29:123-129, PMID: 24916793, https://doi.org/10.1016/j.jtemb.2014.05.004.
124. Dorman DC, Struve MF, Marshall MW, Parkinson CU, James RA, Wong BA. 2006. Tissue manganese concentrations in young male rhesus monkeys fol-lowing subchronic manganese sulfate inhalation. Toxicol Sci 92(1):201-210, PMID: 16624849, https://doi.org/10.1093/toxsci/kfj206.
125. O'Neal SL, Hong L, Fu S, Jiang W, Jones A, Nie LH, et al. 2014. Manganese accumulation in bone following chronic exposure in rats: steady-state con-centration and half-life in bone. Toxicol Lett 229(1):93-100, PMID: 24930841, https://doi.org/10.1016/j.toxlet.2014.06.019.
126. Costa LG, Aschner M. 2015. Manganese in Health and Disease. London, UK: Royal Society of Chemistry.
127. US EPA. n.d. TRI Form R Search. https://enviro.epa.gov/facts/tri/form_r_search. html [accessed 10 November 2023].
128. Baker MG, Simpson CD, Stover B, Sheppard L, Checkoway H, Racette BA, et al. 2014. Blood manganese as an exposure biomarker: state of the evidence. J Occup Environ Hyg 11(4):210-217, PMID: 24579750, https://doi.org/10.1080/ 15459624.2013.852280.
129. Ramoju SP, Mattison DR, Milton B, McGough D, Shilnikova N, Clewell HJ, et al. 2017. The application of PBPK models in estimating human brain tissue manganese concentrations. Neurotoxicology 58:226-237, PMID: 27989617, https://doi.org/10.1016/j.neuro.2016.12.001.
130. Murphrey MB, Agarwal S, Zito PM. 2023. Anatomy, hair. In: StatPearls. Treasure Island, FL: StatPearls Publishing. http://www.ncbi.nlm.nih.gov/ books/NBK513312/ [accessed 25 October 2023].
131. Yaemsiri S, Hou N, Slining MM, He K. 2010. Growth rate of human finger-nails and toenails in healthy American young adults. J Eur Acad Dermatol Venereol 24(4):420-423, PMID: 19744178, https://doi.org/10.1111/j.1468-3083. 2009.03426.x.
132. Robbins CR. 2002. Chemical and Physical Behavior of Human Hair. New York, NY: Springer.
133. Laohaudomchok W, Lin X, Herrick RF, Fang SC, Cavallari JM, Christiani DC, et al. 2011. Toenail, blood, and urine as biomarkers of manganese exposure. J Occup Environ Med 53(5):506-510, PMID: 21494156, https://doi.org/10.1097/ JOM.0b013e31821854da.
134. Zheng W, Fu SX, Dydak U, Cowan DM. 2011. Biomarkers of manganese intoxi-cation. Neurotoxicology 32(1):1-8, PMID: 20946915, https://doi.org/10.1016/j. neuro.2010.10.002.
135. He K. 2011. Trace elements in nails as biomarkers in clinical research. Eur J Clin Invest 41(1):98-102, PMID: 20813017, https://doi.org/10.1111/j.1365-2362.2010.02373.x.
136. Menezes-Filho JA, Bouchard M, Sarcinelli PdeN, Moreira JC. 2009. Manganese exposure and the neuropsychological effect on children and adolescents: a review. Rev Panam Salud Publica 26(6):541-548, PMID: 20107709, https://doi.org/10. 1590/s1020-49892009001200010.
137. Haynes EN, Heckel P, Ryan P, Roda S, Leung YK, Sebastian K, et al. 2010. Environmental manganese exposure in residents living near a ferromanga-nese refinery in Southeast Ohio: a pilot study. Neurotoxicology 31(5):468-474, PMID: 19879291, https://doi.org/10.1016/j.neuro.2009.10.011.
138. Riojas-Rodríguez H, Solís-Vivanco R, Schilmann A, Montes S, Rodríguez S, Ríos C, et al. 2010. Intellectual function in Mexican children living in a mining area and environmentally exposed to manganese. Environ Health Perspect 118(10):1465-1470, PMID: 20936744, https://doi.org/10.1289/ehp.0901229.
139. Coetzee DJ, McGovern PM, Rao R, Harnack LJ, Georgieff MK, Stepanov I. 2016. Measuring the impact of manganese exposure on children's neurodevel-opment: advances and research gaps in biomarker-based approaches. Environ Health 15(1):91, PMID: 27576472, https://doi.org/10.1186/s12940-016-0174-4.
140. Lucas EL, Bertrand P, Guazzetti S, Donna F, Peli M, Jursa TP, et al. 2015. Impact of ferromanganese alloy plants on household dust manganese levels: implications for childhood exposure. Environ Res 138:279-290, PMID: 25747819, https://doi.org/10.1016/j.envres.2015.01.019.
141. Skröder H, Kippler M, Nermell B, Tofail F, Levi M, Rahman SM, et al. 2017. Major limitations in using element concentrations in hair as biomarkers of ex-posure to toxic and essential trace elements in children. Environ Health Perspect 125(6):067021, PMID: 28669939, https://doi.org/10.1289/EHP1239.
142. Oulhote Y, Mergler D, Barbeau B, Bellinger DC, Bouffard T, Brodeur MÈ, et al. 2014. Neurobehavioral function in school-age children exposed to manganese in drinking water. Environ Health Perspect 122(12):1343-1350, PMID: 25260096, https://doi.org/10.1289/ehp.1307918.
143. Rodrigues JLG, Bandeira MJ, Araújo CFS, Dos Santos NR, Anjos ALS, Koin NL, et al. 2018. Manganese and lead levels in settled dust in elementary schools are correlated with biomarkers of exposure in school-aged children. Environ Pollut 236:1004-1013, PMID: 29287923, https://doi.org/10.1016/j.envpol.2017.10.132.
144. Bouchard MF, Sauvé S, Barbeau B, Legrand M, Brodeur MÈ, Bouffard T, et al. 2011. Intellectual impairment in school-age children exposed to manga-nese from drinking water. Environ Health Perspect 119(1):138-143, PMID: 20855239, https://doi.org/10.1289/ehp.1002321.
145. Wright RO, Amarasiriwardena C, Woolf AD, Jim R, Bellinger DC. 2006. Neuropsychological correlates of hair arsenic, manganese, and cadmium levels in school-age children residing near a hazardous waste site. Neurotoxicology 27(2):210-216, PMID: 16310252, https://doi.org/10.1016/j.neuro.2005.10.001.
146. Bouchard M, Laforest F, Vandelac L, Bellinger D, Mergler D. 2007. Hair man-ganese and hyperactive behaviors: pilot study of school-age children exposed through tap water. Environ Health Perspect 115(1):122-127, PMID: 17366831, https://doi.org/10.1289/ehp.9504.
147. Menezes-Filho JA, Novaes CdeO, Moreira JC, Sarcinelli PN, Mergler D. 2011. Elevated manganese and cognitive performance in school-aged children and their mothers. Environ Res 111(1):156-163, PMID: 20943219, https://doi.org/10. 1016/j.envres.2010.09.006.
148. Sakai T, Wariishi M, Nishiyama K. 2000. Changes in trace element concentra-tions in hair of growing children. Biol Trace Elem Res 77(1):43-51, PMID: 11097470, https://doi.org/10.1385/BTER:77:1:43.
149. Shilnikova N, Karyakina N, Farhat N, Ramoju S, Cline B, Momoli F, et al. 2022. Biomarkers of environmental manganese exposure. Crit Rev Toxicol 52(4):325- 343, PMID: 35894753, https://doi.org/10.1080/10408444.2022.2095979.
150. Adair BM, Hudgens EE, Schmitt MT, Calderon RL, Thomas DJ. 2006. Total ar-senic concentrations in toenails quantified by two techniques provide a useful biomarker of chronic arsenic exposure in drinking water. Environ Res 101(2):213-220, PMID: 16188251, https://doi.org/10.1016/j.envres.2005.08.004.
151. Salcedo-Bellido I, Gutiérrez-González E, García-Esquinas E, Fernández de Larrea-Baz N, Navas-Acien A, Téllez-Plaza M, et al. 2021. Toxic metals in toe-nails as biomarkers of exposure: a review. Environ Res 197:111028, PMID: 33753073, https://doi.org/10.1016/j.envres.2021.111028.
152. Johansson J, Nordström A, Gustafson Y, Westling G, Nordström P. 2017. Increased postural sway during quiet stance as a risk factor for prospective falls in community-dwelling elderly individuals. Age Ageing 46(6):964-970, PMID: 28531243, https://doi.org/10.1093/ageing/afx083.
153. WHO (World Health Organization). 2021. Step Safely: Strategies for Preventing and Managing Falls across the Life-Course. https://iris.who.int/ bitstream/handle/10665/340962/9789240021914-eng.pdf?sequence=1 [accessed 23 March 2023].
154. Perritt KR, Hendricks KJ, Goldcamp EM. 2017. Young Worker Injury Deaths: A Historical Summary of Surveillance and Investigative Findings. https://www. cdc.gov/niosh/docs/2017-168/pdfs/2017-168.pdf [accessed 20 June 2022].
155. Kincl LD, Dietrich KN, Bhattacharya A. 2006. Injury trends for adolescents with early childhood lead exposure. J Adolesc Health 39(4):604-606, PMID: 16982401, https://doi.org/10.1016/j.jadohealth.2006.02.008.
156. Montero-Odasso M, van der Velde N, Martin FC, Petrovic M, Tan MP, Ryg J, et al. 2022. World guidelines for falls prevention and management for older adults: a global initiative. Age Ageing 51(9):afac205, PMID: 36178003, https://doi.org/10.1093/ageing/afac205.
157. Rentschler G, Covolo L, Ahmadi Haddad A, Lucchini RG, Zoni S, Broberg K. 2012. ATP13A2 (PARK9) polymorphisms influence the neurotoxic effects of manganese. Neurotoxicology 33(4):697-702, PMID: 22285144, https://doi.org/ 10.1016/j.neuro.2012.01.007.
158. Chia SE, Goh J, Lee G, Foo SC, Gan SL, Bose K, et al. 1993. Use of a computer-ized postural sway measurement system for assessing workers exposed to manganese. Clin Exp Pharmacol Physiol 20(9):549-553, PMID: 8222334, https://doi.org/10.1111/j.1440-1681.1993.tb01740.x.
159. Chia SE, Gan SL, Chua LH, Foo SC, Jeyaratnam J. 1995. Postural stability among manganese exposed workers. Neurotoxicology 16(3):519-526, PMID: 8584283.
160. Claus Henn B, Austin C, Coull BA, Schnaas L, Gennings C, Horton MK, et al. 2018. Uncovering neurodevelopmental windows of susceptibility to manga-nese exposure using dentine microspatial analyses. Environ Res 161:588-598, PMID: 29247915, https://doi.org/10.1016/j.envres.2017.12.003.
161. Sears L, Myers JV, Sears CG, Brock GN, Zhang C, Zierold KM. 2021. Manganese body burden in children is associated with reduced visual motor and attention skills. Neurotoxicol Teratol 88:107021, PMID: 34428495, https://doi.org/10.1016/j.ntt.2021.107021.
162. Zoni S, Albini E, Lucchini R. 2007. Neuropsychological testing for the assess-ment of manganese neurotoxicity: a review and a proposal. Am J Ind Med 50(11):812-830, PMID: 17918193, https://doi.org/10.1002/ajim.20518.
163. Zoni S, Lucchini RG. 2013. Manganese exposure: cognitive, motor and be-havioral effects on children: a review of recent findings. Curr Opin Pediatr 25(2):255-260, PMID: 23486422, https://doi.org/10.1097/MOP.0b0 13e32835e906b.
164. Bhattacharya A, Shukla R, Auyang ED, Dietrich KN, Bornschein R. 2007. Effect of succimer chelation therapy on postural balance and gait outcomes in chil-dren with early exposure to environmental lead. Neurotoxicology 28(3):686- 695, PMID: 17499360, https://doi.org/10.1016/j.neuro.2007.03.007.
165. Ellingsen DG, Shvartsman G, Bast-Pettersen R, Chashchin M, Thomassen Y, Chashchin V. 2019. Neurobehavioral performance of patients diagnosed with manganism and idiopathic Parkinson disease. Int Arch Occup Environ Health 92(3):383-394, PMID: 30790043, https://doi.org/10.1007/s00420-019-01415-6.
166. US Census Bureau. n.d. QuickFacts: Washington County, Ohio. https://www. census.gov/quickfacts/fact/table/washingtoncountyohio/PST040222#PST040222 [accessed 21 March 2023].
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Abstract
Background: Manganese (Mn) plays a significant role in both human health and global industries. Epidemiological studies of exposed populations demonstrate a dose-dependent association between Mn and neuromotor effects ranging from subclinical effects to a clinically defined syndrome. However, little is known about the relationship between early life Mn biomarkers and adolescent postural balance. Objectives: This study investigated the associations between childhood and adolescent Mn biomarkers and adolescent postural balance in partici-pants from the longitudinal Marietta Communities Actively Researching Exposures Study (CARES) cohort. Methods: Participants were recruited into CARES when they were 7-9 y old, and reenrolled at 13-18 years of age. At both time points, participants provided samples of blood, hair, and toenails that were analyzed for blood Mn and lead (Pb), serum cotinine, hair Mn, and toenail Mn. In adoles-cence, participants completed a postural balance assessment. Greater sway indicates postural instability (harmful effect), whereas lesser sway indicates postural stability (beneficial effect). Multivariable linear regression models were conducted to investigate the associations between childhood and adolescent Mn biomarkers and adolescent postural balance adjusted for age, sex, height-weight ratio, parent/caregiver intelligence quotient, socioeco-nomic status, blood Pb, and serum cotinine. Results: CARES participants who completed the adolescent postural balance assessment (n=123) were 98% White and 54% female and had a mean age of 16 y (range: 13-18 y). In both childhood and adolescence, higher Mn biomarker concentrations were significantly associated with greater adolescent sway measures. Supplemental analyses revealed sex-specific associations; higher childhood Mn biomarker concentrations were significantly associated with greater sway in females compared with males. Discussion: This study found childhood and adolescent Mn biomarkers were associated with subclinical neuromotor effects in adolescence. This study demonstrates postural balance as a sensitive measure to assess the association between Mn biomarkers and neuromotor function.
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Details
1 Department of Epidemiology and Environmental Health, College of Public Health, University of Kentucky, Lexington, Kentucky, USA
2 Department of Environmental and Public Health Sciences, College of Medicine, University of Cincinnati, Cincinnati, Ohio, USA
3 Department of Emergency Medicine, College of Medicine, University of Cincinnati, Cincinnati, Ohio, USA
4 Department of Psychology, Marietta College, Marietta, Ohio, USA