Our study shows no correlation between blood Mn and psychomotor skills in school-age children from the general population, contrary to our hypothesis. This finding aligns with previous reports by Lucchini (28) and Wasserman (18). In addition, we observe no association with the diagnosis or suspicion of ADHD, as well as non-significant associations found in the attention scores from TEA-Ch. We also found no association between high blood Mn and the risk of DCD, confirmed by our results from the motor scores in NEPSY II.
One potential explanation for these non-significant findings are the low absolute levels of Mn in the blood that may not reach a toxic level in our population, thus not affecting children psychomotor skills. Also, at 6–7 years of age the physiological regulation of Mn in the body -reduction of absorption in the intestines and increase excretion by the liver- may be sufficient to reduce blood Mn levels and its potential toxic effect (30). Another potential explanation is that our population is not exposed to any Mn-releasing industry as seen in other studies (31, 32). The only Mn-releasing industry in the Eastern Townships is a small paper mill plant, that was closed approximately 2 years before our study. Thus, we may speculate that for the majority of children in our study group the major source of Mn would be water and/or food. However, we do not have any individual measurement of Mn concentrations in drinking water or air to confirm this hypothesis. Blood Mn levels in our study group are similar to those reported in U.S. samples (33). Moreover, our findings regarding blood Mn concentration (mean : 9.9 µg/L, range : 4.67–21.4) are similar to those for children aged 6 to 14 published by Lucchini et al (mean: 11.24 µg/L, range: 4.25–24.10) (34), Bhang et al (mean: 14.10 µg/L, range: 4.00–24.10) (35) and Haynes et al (36) (Geometric mean: 9.67 µg/L, range: 6.1–18.8) in Italy, Korea and Bangladesh, respectively. A similar range of studies highlight the effects of blood Mn on psychomotor scores (see Table 1).
A recent publication discusses the presence and the role of Type III error in environmental health science. Type III error is defined as correctly rejecting the null hypothesis (in our case Mn in drinking water is not toxic for school age children) for the wrong reasons (37, 38). This error occurs most often when a causal factor, such as socio-economic status or other environmental factors, etc., is homogeneously distributed in the population. Type III error mostly comes from a dichotomic reasoning. We may inconsistently conclude that only one factor is the cause rather than the interaction between factors (e.g. genetic and environment, socio-economic status and environment, etc.) (38).
It is possible that some studies did not consider the interactions that may lead to a toxic effect of Mn. Studies based on socio-economically disadvantaged populations often show a toxic effect of Mn. These are predominantly people living in poor rural areas in Mexico, Brazil (31, 32), and Bangladesh (39). Low socio-economic status can lead to an increasing of malnutrition in the population. The exposure to Mn is higher among malnourished children, they absorb Mn more efficiently and eliminate it less efficiently (40, 41). Socio-economically disadvantaged communities also face higher concentrations of pollutants in their environment (42), thus increasing the risk of toxic interaction with Mn.
Studies focused mainly on communities exposed to Mn through food and water remain inconsistent. Bouchard et al (2011) (19) and Oulhote et al (2014) (43), found a negative association between water and hair Mn and IQ. In contrast, Wasserman et al (2006) (18), Khan et al (2011) (39) and Bouchard et al (2018) (7) found no association between Mn levels in blood, hair, or water and decreased psychomotor performances.
In adults, the route of exposure plays a crucial role in total intake and Mn accumulation. When exposed to Mn through air, it will bypass physiological control and leads to a rate of absorption of almost 70%. This inhaled Mn will be directly transported to the brain through the olfactive nerve (14). The ingestion of significant quantities of Mn is tightly controlled by the liver, which allows only about 2% of ingested Mn to reach the general circulation, the rest being eliminated through bile into the GI tract (14). Elimination of Mn by hepatobiliary mechanisms is decreased in children (44), however only evidence from neonates are cited to support this claim. There is no evidence that the hepatic elimination of Mn in school aged children might be different compared to adults. The route of exposure may also have an important role in Mn toxicity. This was proposed by Winder et al. (2010) (45), when they estimated that at the same exposure level a child’s Mn inhalation is at a greater proportion of the maximum recommended levels compared to adults. Inhalation provides more rapid uptake of Mn into the blood from lungs, as shown in welders and mice (45). In a review of the inhalation dosimetry methods applied to children’s risk assessment, the authors (46) recommended to use a higher uncertainty factor due to increased toxicity of most inhaled chemicals in children.
Mn is suspected to have gender-dependent effect (6). In our study, only one attentional score is positively affected in boys, and tends to be negatively affected in girls. To date, mechanisms underlying this relationship remain unclear and there is no published data on the gender-dependent toxic effect. As only one score was affected, we presume that this result may be due to chance.
Our study has several strengths. Children included in the analyses participated in a prospective population-based birth cohort decreasing the risk of selection bias. We also adjusted for lead levels and most of confounders and effect modifiers we had access to. Our statistical analyses included non-linear modelling to account for a potential U shape association. Our study population is relatively homogenous socially, economically and racially, given that our root population is stable, composed of historical descendants from French and Irish families. This homogeneity is advantageous when we are looking for small effects of environmental pollutants because it reduces a background noise from potential confounders (i.e. “quasi-experimental design”). Blood Mn, our biomarker of exposure, has been shown to better approximate pallidal index, an indicator of Mn accumulation in the brain (relevant in the context of studying its neurodevelopmental effects), than other potential markers including hair or water Mn.
This study has some limitations. The homogeneity of our population can decrease the external validity of our results, which should be carefully extrapolated to a community with different genetic or socioeconomic characteristics. In the same way, we noticed that more educated families tend to stay within the follow up (data not shown), as it was recently observed for other prospective cohorts (47).
Another potential limitation is that we used blood Mn as the only biomarker of exposure, and blood Mn is weakly corelated with ingested Mn (18, 48). However, blood Mn has been reported to have longitudinal stability (33). Furthermore, data on iron deficiency in the cohort was not collected, but there is low prevalence – 3.5% of the children population - of iron deficiency in Canada (49). Children with iron deficiency tend to have high blood Mn (50). Yet increased blood Mn in patients with iron deficiency do not typically increase Mn accumulation in the brain (51, 52). However, even if iron deficiency increases blood Mn, we observed no effect of blood Mn on psychomotor scores thus we expect the direction of results to be the same. In addition, we have no estimation of Mn intake. Most of the Mn in our region comes from water (19) and there is no association between dietary Mn intake and cognitive scores in children (19). Moreover it has been reported that blood Mn is poorly correlated with intake in children and is not affected by subtle intake variation (18). Thus, the lack of estimation of Mn intake should not alter our results.
For this study we used five subscales of the WISC-IV - which did not allow us to estimate the children’s full IQ -, two of the NEPSY II and three of the TEACh. These subscales were chosen in order to cover a variety of non-verbal skills within a limit of about 1.5 hour of evaluation, which is feasible in children aged 6–7. ADHD group selection was based on whether the child took prescription ADHD medication and if the caregiver reported a diagnosis. We may have misclassified ADHD cases as controls if their caregiver did not report physician’s diagnosis. Children with ADHD were not asked to stop taking their medicines during the visit, meaning they may appear more neurotypical on continuous assessments of their behaviors, which could weaken any true association. TEA-Ch is a test commonly used to diagnose ADHD and is known to differentiate children medicated for ADHD and those non-medicated (24). However, because children diagnosed with ADHD who were medicated had similar scores on the psychomotor evaluations compared to those who were unmedicated (for more details see additional file 2), any effect of medication is likely minimal. Although we could not confirm DCD diagnosis in medical records, our finding of no association between Mn and DCD as assessed by the parentally-administered DCDQ is supported by our null findings with the NEPSY-II, which also measures dexterity but is more objective because it is administered by study staff.
In conclusion, our study in school-aged children from the general population we did not show evidence of Mn toxicity related to psychomotor and attention skills. Our study does not rule out prenatal Mn toxicity given that fetuses/neonates have immature hepatobiliary mechanisms of Mn elimination (44). Pre and postnatal exposure to Mn has toxic effects in toddlers (48). Those effects may be persistent during the childhood and alter the developmental trajectories of the child (53). There is a need for further comprehensive studies using different matrices and MRI to confirm these results. New biomarker that can better reflect ingested Mn (like meconium or stools) should also be considered in consort with other established biomarkers. Additionally, MRIs can be used to confirm Mn accumulation in children’s brains and provide more information on the related anatomical or functional modifications.