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Characterizing visual field loss from past mercury exposure in an Indigenous riverine community (Grassy Narrows First Nation, Canada): a cluster-based approach
Environmental Health volume 23, Article number: 81 (2024)
Abstract
Background
Between 1962 and 1975, a chlor-alkali plant in Canada discharged approximately 9 metric tons of mercury (Hg) into the Wabigoon River. Over the following decades, biomarkers of Hg exposure of persons from Grassy Narrows First Nation (Asubpeeschoseewagong Anishinabek), located downriver from the discharge, reflected Hg concentrations in fish. Hg exposure is known to target the calcarine fissure, resulting in visual field (VF) loss. Most studies and clinical reports focus solely on peripheral VF loss; little is known about the impact of Hg on the central and paracentral portions. The present study sought to characterize the patterns of VF loss with respect to past and current Hg.
Methods
A 28-year hair-Hg (HHg) database, created from a 1970–97 government biomonitoring program, served to select study participants with ≥ 4 year-based HHg measurements (n = 81). Blood-Hg was assessed for current exposure. Light sensitivity thresholds across the VF were analyzed monocularly, using a Humphrey Field Analyzer (HFA). Following post-hoc exclusions, based on HFA interpretation indices, 65 participants were retained. Both eyes were combined for analyses (n = 130 eyes). Unsupervised hierarchical clustering of HFA plot data was used to identify patterns of VF loss. A series of mixed effects models (MEM) were performed to test the associations for current Hg exposure with respect to HFA interpretation indices and clusters, as well as for longitudinal past Hg exposure.
Results
The clustering approach decomposed the light sensitivity deficits into 5 concentric clusters, with greatest loss in the peripheral clusters. No relation was observed between any of the cluster scores and current blood-Hg. VF deficits increased with past Hg exposure. Longitudinal MEM showed that HHg was significantly (p < 0.05) associated with all peripheral, paracentral, and central cluster scores, as well as with HFA interpretation indices.
Conclusions
Past Hg exposure in Grassy Narrows First Nation was associated with present day VF loss. The cluster-based location-specific approach identified patterns of VF loss associated with long-term Hg exposure, in both the peripheral and the central areas. The functional implications of this type of visual loss should be investigated.
Background
Worldwide mercury (Hg) and production use peaked between 1960 and 1980, increasing the Hg load in the aquatic food chain [1]. In Canada, a 1969 report indicated that chlor-alkali plants accounted for more than 47% of the total Hg used and constituted the most important point source of Hg to the aquatic environment [2]. The First Nation community of Asubpeeschoseewagong Anishinabek (also known as Grassy Narrows First Nation) territorial waters are situated downstream of a former chlor-alkali plant, which, between 1962 and 1975, discharged approximately 9 metric tons of Hg into the watershed [3]. Very high concentrations of Hg were reported in fish from lakes directly on Grassy Narrows traditional territory. Fish Hg concentrations generally exceeded 2 µg/g and reached levels as high as 17 µg/g [3]. In the years following the control of the discharge, fish Hg declined; the trend transitioned in the mid 1980s and since the early nineties, concentrations have remained relatively stable [4, 5]. They are still among the highest in Canada [6]. For the Grassy Narrows community, fish was the dietary mainstay and biomonitoring programs, initiated in 1970, showed that biomarkers of Hg exposure (hair and blood) followed a similar pattern to the fish [7]. The programs ceased in 1997 when average biomarkers of Hg exposure were below Canadian guidelines.
Methylmercury (MeHg), a highly toxic substance [8,9,10], makes up most of the Hg content found in fish tissue [11,12,13]. In fish, sampled in the rivers downstream of the chlor-alkali plant, MeHg constituted 85–100% of total Hg [14]. MeHg in hair samples, from the government biomonitoring program in this region, constituted 88% of total Hg [15].
Peripheral visual field (VF) loss, resulting from damage to the primary visual cortex in the calcarine region, is a characteristic of MeHg poisoning [16,17,18,19,20,21,22,23]. Since 1975, there have been several reports of VF loss in the Grassy Narrows First Nation community. In the early studies, peripheral field constriction was reported for 10—15% of examinees, using the Foster Perimeter or confrontation test [24,25,26,27]. A more extensive examination of VF loss was recently performed as part of the Grassy Narrows’ Niibin study, using a Humphrey Field Analyzer (HFA); 36% of 70 adults (median age: 57 years) presented VF loss (Visual Field Index ≤ 81%) [28]. The authors indicated that the clinical picture presented differently from common eye and vision disorders [28].
The objectives of the present study were to conduct cluster analyses to identify patterns of light sensitivity threshold reduction across the entire VF and to examine the associations between HFA interpretation indices and VF clusters with respect to past long-term and current Hg exposure.
Methods
Study design
This research project is part of the Niibin study, developed in partnership with the Grassy Narrows First Nation (Northwestern Ontario) via their Mercury Justice Team, according to the OCAP® principles of ownership, control, access, and possession of information collected within First Nation communities [29]. OCAP® is a registered trademark of the First Nations Information Governance Centre (FNIGC) [29].
Two approaches were used: (i) longitudinal retrospective, based on historic biomarkers of Hg exposure, collected between 1970 and 1997, as part of government monitoring programs [30] and (ii) cross-sectional, based on current blood-Hg concentrations. Eye and vision examinations, carried out by three optometrists in summer 2021, were performed in a room set up in the Sakatcheway Anishinaabe School in Grassy Narrows [28].
Mercury exposure
Historic Hg biomarker data
In 1970, eight years after the beginning of the Hg discharge from the chlor-alkali plant upstream of Grassy Narrows, the Medical Service Branch of Health Canada and the Ontario Ministry of Health initiated Hg biomarker (blood and hair) testing programs, which continued until 1997 [30, 31]. Hair-Hg sample analyses were performed according to the methods published by Farant [15] and Giovanoli-Jakubczak [32]. Among the people of Grassy Narrows, hair-Hg was highly correlated to reported fish consumption [33, 34].
Grassy Narrows First Nation Chief and Council obtained the community’s archived Hg biomarker data from the First Nations and Inuit Health Branch of the Ministry of Indigenous Services Canada, and the Ontario Ministry of Health and Long-term Care. The data, which included monthly-based hair-Hg measurements and blood-Hg, were shared with the research team. Using these data, a retrospective longitudinal year-based database for the years 1970 – 1997 was created with the highest measurement of equivalent hair total-Hg (HHg) for each year [35]. The year-based database included 662 persons with 3621 data points: (3416 (94.3%)) hair-Hg and 205 (5.7%) blood-Hg measurements). The latter were converted into HHg, using the Canadian and JEFCA guideline for Hg hair/blood ratio of 250 [36, 37]. Since, Hg exposure varied throughout the year [30, 38], the corresponding month was noted and then merged into low and high peak seasons for fish-eating practices.
The government programs’ sampling schedules were not regular; persons sampled one year were not necessarily included in the following year(s). The highest number of persons sampled/year (> 250) were from 1975 to 1978. Of the 662 persons included in the 1970 – 1997 database, 296 (44.7%) have since died.
Current Hg exposure
Blood samples were collected by a single venipuncture using 21-gauge blood collection sets (BD Vacutainer™ Safety-Lok™ Blood Collection Sets, BD 367281) in BD Vacutainer™ tubes. For total-Hg analysis in whole blood, samples were collected in Sodium Heparin Vacutainer™ tubes, mixed thoroughly by gentle inversion, and kept refrigerated in upright position after mixing and during transportation. Samples were transported to LifeLabs’ office in Kenora the same day. Total-Hg in whole blood was analyzed at LifeLabs Medical Laboratory Services, Victoria Reference Laboratory (Victoria, BC, Canada) by inductively coupled plasma mass spectrometry (ICP-MS), with an Agilent 8800 Triple Quadrupole ICP-MS (Agilent Technologies). Three levels of whole blood Quality Control samples were run at the beginning of each session, after every 20 samples, and following the last sample. Results were released only for samples that met the internal acceptance criteria. Samples with Hg concentrations below the detection limit of 0.2 µg/L were assigned a concentration of 0.10 µg/L.
Eye and vision examination
Eye and vision examinations included visual acuity, autorefraction, slit lamp examination, color vision testing, contrast sensitivity testing, optical coherence tomography and automated VF testing. The full testing protocol is described elsewhere[28]. Here, the VF and visual acuity assessments are presented.
Visual acuity
Distance and near visual acuity (DVA and NVA) were performed. DVA was assessed monocularly using the distance Early Treatment Diabetic Retinopathy Study (ETDRS) computerized letter chart, with participants wearing habitual distance spectacle correction, if any. Pinhole acuity was determined when the monocular measure was poorer than 6/12 Snellen equivalent. NVA was measured with the near ETDRS logarithmic chart, with habitual near spectacle correction, when available. All acuity measurements were transformed to logarithm of the Minimum Angle of Resolution (LogMAR).
Visual Field (VF)
A comprehensive assessment of VF was conducted using the Humphrey Field Analyzer 3 Model 840 (HFA) (Carl Zeiss Meditec Inc, Dublin, CA, USA), with the 30–2 Swedish Interactive Threshold Algorithm Standard-Fast (SITA-Fast) algorithm. This test uses a size III white stimulus, across a 30 degrees field globe, testing 76 grid points. The participants were asked to fixate an illuminated cross in the center of the globe and indicate when they see sporadic lights at different intensities and locations. The test was performed monocularly and as needed, lenses were used to compensate for testing distance and autorefraction results. The HFA provides reliability measures for fixation loss (% time not fixating) and false negatives (% false responses). HFA interpretation indices include the following:
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Total Deviation Plot (TDP)—a numeric map of the difference between the measured VF sensitivity and the expected normal age-corrected sensitivity at each test point.
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Total Deviation Probability Plot (TDPP)—a numeric map of the probability that visual loss is outside the confidence interval of a normal age-corrected value (p < 5%, 2%, 1%, and 0.5%) [39]; for each test point; the probability level of < 0.5% is considered abnormal.
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Pattern Deviation Plot (PDP)—a numeric map of focal VF loss, obtained by adjustment to the height of the hill of vision.
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Pattern Deviation Probability Plot (PDPP)—a numeric map, representing the likelihood that each point is within the normal range (abnormal points P < 0.5%).
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Mean Deviation (MD)—a uniform loss index, derived from the weighted average of the TDP values, based on the differences between age-matched normative data and measured thresholds of retinal sensitivity.
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Pattern standard deviation (PSD) is a non-uniform sensitivity loss index, derived from the weighted standard deviation of the differences from the age-corrected normal threshold values, after adjustment for any overall elevation or depression of the field at each test point.
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Visual Field Index (VFI) is an index of the total amount of VF loss, expressed as a percentage of normal vision ranging from perimetrically blind (0%) to normal (100%).
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Glaucoma Hemifield Test Index (GHT) provides an indicator of asymmetry between superior and inferior hemifield, reflecting potential VF loss resulting from glaucoma. Its results are expressed as either “Within normal limits”, “Borderline”, “Outside normal limits”, “General Depression” or “Abnormally High Sensitivity”. These were grouped into Normal (“Within normal limits” and “Borderline”) and Abnormal (“Outside normal limits”, “General Depression” or “Abnormally High Sensitivity”).
Population
Participant selection and recruitment was based on the 1970 – 1997 Grassy Narrows historic equivalent year-based HHg biomarker database, which included 277 persons still living in 2021. Inclusion criteria were: (i) at least four year-based HHg biomarker measurements, and (ii) currently living in or nearby Grassy Narrows. A total of 131 community members (69 men and 62 women) were eligible and 81 (61.8%) persons (36 men and 45 women) underwent eye and vision examinations. There was no age difference between participants and non-participants (median: 57 years; interquartile range: 52 – 63; and median: 55 years; interquartile range: 51 – 60, respectively). All participants provided informed consent.
Post hoc exclusion criteria were based on the visual examination: (i) persons that did not meet criteria for reliable test results on the HFA [40]: fixation loss for > 20% of stimuli points (excessive ocular movements during testing) and false positive responses exceeding 15% (participant responses in absence of stimuli) for one or the other eye; (ii) participants with cataracts with grade 3 or higher on the WHO cataract grading scale [41] upon slit lamp examination. Both eyes were eligible for a total of 65 persons.
A total of 554 retrospective equivalent hair-Hg samples were available for the 65 participants, of which 25 (4.5%) were derived from blood-Hg concentrations. Hg analyses for all current blood samples met the Quality Control criteria; two participants did not provide blood samples.
Statistical analyses
There are several approaches to examining data involving both eyes. Some authors present the left and right eyes separately, others use random selection of the right and left eye, and still others use both-eye data, accounting for the inter-eye correlation [42,43,44]. We opted for analyzing both eyes together since Hg is known to affect many components of the visual system, including the visual cortex [17, 18], optic nerve pathways [45, 46], and the retina [47,48,49,50]. Based on the VFI, the intra-class correlation between the two eyes was 0.73 [95% Confidence Interval (CI) 0.6 – 0.83]. Because many statistical methods assume independence of observations and two eyes are not independent since they belong to the same individual, participant ID code was included as a random factor in all mixed effects models to account for the intra-participant correlation between eyes [44, 51, 52]. The data points from the pattern deviation plot were combined by transposing the data points of the left eye to the right eye (mirror transposition).
Since diabetes can contribute to eye disease [53, 54], the possible contribution of diabetes to visual functions was examined. Binary and multivariate associations (with age and sex) were tested using data collected in other aspects of the Niibin study: physician-diagnosed diabetes, medication for diabetes and the current concentration of glycolate hemoglobin (HbA1c). No associations were observed (p > 0.20) and diabetes was not retained for the present analyses. Other potential covariates (smoking, drinking, blood pressure, socio-economic status (struggle to pay for food)) were likewise tested and did not reach significance threshold.
Clustering approach
Clustering can serve as a dimension-reduction tool to optimize our understanding of the distribution of sensitivity loss across VF by mapping. We used a mixed factorial approach (PCAMIX method in R software) on the 74 test points (two points corresponding to the blind spot) on PDP and then divided into clusters [55]. When there was no PDP due to exceeded deviation threshold, the deviation test points from the TDP were used.
The appropriate number of clusters was determined using a bootstrap approach for maximizing the homogeneity criterion within clusters, analysis of aggregation levels and stability of the partitions via bootstrapped mean-adjusted Rand Index and boxplots.
We validated the clustering results constructed in R software, using the VARCLUS function of the SAS computer application (JMP Professional 16.0 software), which uses a similar approach, and with Hopach package in R software, which is a hybrid approach to clustering.
For each cluster, a composite variable was calculated from the weighted linear combination of the visual test points. Along each composite variable, defect severity increased with higher deviation from age-adjusted standard values. The sum of the cluster scores (Cluster Sum) was tested with respect to the VFI. A series of heatmaps illustrated the distribution of light sensitivity thresholds across the VF.
Descriptive statistics
Simple and multiple comparisons were conducted with non-parametric Wilcoxon/Kruskal–Wallis Tests (Rank Sums). Matched-pairs analyses of light sensitivity loss between clustered compartments were conducted using participant ID code as a nested variable. The visual field concentric square model, proposed by Sayo et al. [56], was used to compare peripheral and central VF loss with respect to a year-based HHg maximum value at least once over the sampling period.
Mixed effects models
Mixed effects models (MEM) are powerful tools for analyzing complex datasets with nested and/or repeated observations. The possible contribution of current blood-Hg to vision outcomes was examined using MEM, with age and sex as fixed effects and participant ID code as random effect to account for the correlation between paired eyes [52]. Longitudinal MEM (LMEM) were performed using direct measurements of past longitudinal HHg with respect to vision outputs.
LMEM analyses were limited to persons with 10 or more HHg data to ensure measurement consistency over the years. These analyses included 56 eyes from 28 persons (10 men and 18 women), for a total of 352 HHg measurements, sampled over a period of 10 to 21 years (median: 11 years). To ensure that there were sufficient observations for the analyses, we estimated the minimal required sample size, using the G*power software [57]. Since one centimeter of a hair sample represents an accumulation of Hg during approximately one month, we used a correlation of repeated measures between yearly-based samples (rho of 0.2/0.5). Because the effect size was unknown, 0.25 was chosen [58]. Power analyses were set at 80%, with a two-tailed 5% hypothesis test, 10 time points, within and between factors were used to calculate sample size adequacy. The minimum number of participants required was between 23 and 30. LMEM were adjusted with age and sex as fixed effects, while age of sampling was nested in time of sampling; participant ID code was included as random effect.
The most appropriate model for MEM and LMEM was selected using the Wald test, the Akaike Information Criterion (AIC), the Bayesian Information Criteria (BIC) and the likelihood ratio (LR) test at p < = 0.05. In all models, we tested whether age and/or sex moderated the relation between past or current Hg exposure and vision outcomes. All model assumptions were verified by residual homogeneity.
To support the LMEM results, a series of sensitivity analyses were conducted with each eye separately and with all participants. The latter provides higher power, but a lower minimum number for repeated HHg measurements (at least 4 year-based HHg).
Threshold of significance in all statistical analyses was set at p ≤ 0.05.
Database management and descriptive statistical analyses were performed using JMP Professional 16.0 (Statistical Analysis Hardware, SAS Institute). All clustering analyses were computed using the following packages of R statistical software version 3.6.1. (R Core Team, 2016): PCAmixdata, cluster and ClustOfVar and HOPACH. Cronbach alpha data analyses were performed with SPSS software (IBM SPSS statistics version 28.0.1.0 (142)). MEM and LMEM were conducted with Stata 18 software. (Stata Statistical Software: Release 18.0 College Station, TX: Stata Corporation). The MEM and LMEM analyses, including the assumptions, conducted on Stata were verified using the lme4, lmerTest, robustlmm and ggplot2 R packages. Matched paired analyses on nested data used the lme4 R package. Heatmap representations were performed using the akima and ggplot2 R packages.
Results
Among the 65 participants with eligible VF test results for both eyes, there were 36 women and 29 men (mean age: 57 years (median: 56 years; IQR: 50.5 – 64.5 years)). Figure 1 shows their year-based HHg concentrations, between 1970 and 1997. Mean current blood-Hg was 6.19 µg/L (median 4.21 µg/L (IQR: 1.20 – 9.63 µg/L)).
Visual acuity and HFA interpretation indices for the combined eyes are presented in Table 1.
DVA decreased with age and was similar for men and women. The VFI for 20% of eyes was below 62%, the cut-off for abnormal VF score, representing 17 persons (26.1%) with at least one abnormal VFI. A total of 70.8% of eyes were classified as normal, with a VFI ≥ 81%. At least 43% of eyes had abnormal MD (< 0.5%) and more than 53% for PSD (< 0.5%). The GHT was outside of the normal range for more than two-thirds of participants. HFA interpretation indices are age-corrected, and indeed no relations were observed with age; no differences were observed between men and women.
The heatmap of mean sensitivity thresholds from the PDP matrices showed a series of concentric losses, increasing towards the periphery (Fig. 2).
The clustering approach revealed five clusters that were concentrically distributed, with the greatest loss in the periphery (Fig. 3).
Non-parametric matched-pair analyses of mean PDP in clusters showed that sensitivity loss in the peripheral superior and latero-peripheral clusters were similar, but significantly higher than the more central clusters (Wilcoxon Signed Rank one-way test; p ≤ 0.05) (Table 2).
Cluster scores were summed to provide a Cluster Sum (median: -7.14, IQR: -11.20 – 3.09), which was highly inversely correlated to the VFI (Spearman rank-order correlation: ρ: -0.70, p < 0.0001) (Fig. 4). The good correspondence between higher Cluster Sum scores and lower values of VFI, indicated that the clusters, taken together, reflected the VFI. Participants with abnormal GHT had significantly higher Cluster Sum scores compared to those with normal GHT scores (Wilcoxon / Kruskal–Wallis Tests (Rank Sums): Chi2 = 60.5; p < 0.0001).
No relation was observed between visual acuity and Cluster Sum score or any of the HFA interpretation indices.
Using PDP matrices, heatmaps were produced, using three categories of point HHg values over the sampling period (a) all HHg values < 3 µg/g (n = 24), (b) at least one HHg value ≥ 3 µg/g and < 10 µg/g (n = 50), and (c) at least one HHg ≥ 10 µg/g (n = 56) (Fig. 5).
To support the heatmap illustrations, the concentric VF diagram proposed by Sayo et al. in 2017 [56], was used and mean light sensitivity threshold loss for the 16 data points of the periphery and the 8 data points at the center were compared (Table 3). The light sensitivity loss threshold increased with increasing past Hg exposure.
Table 4 presents the results of the LMEM linking repeated past HHg concentrations and visual acuity and HFA interpretation indices for participants who had at least 10 year-based HHg measurements (704 hair measurements; n = 56 eyes). Past long-term Hg exposure over the biomonitoring period was significantly associated with lower VFI, abnormal GHT and lower MD.
Participants with higher longitudinal past HHg presented higher scores on all five clusters and Cluster Sum (Table 5). The position within the VF indicated in the Table, refers to the clusters displayed in Fig. 3. Similar results were found when using the average light sensitivity loss for each test point within each cluster rather than cluster scores.
Sensitivity analyses with each eye separately (≥ 10 HHg measurements) are presented in Supplementary Material for visual acuity and HFA interpretation indices (Supplementary Tables 1a and 1b), as well for clusters (Supplementary Tables 2a and 2b). Further sensitivity analyses with all participants are presented in Supplementary Tables 3 and 4. Results were similar, with lower coefficients and probability values.
No relations were observed between current blood-Hg and any of the vision parameters or clusters. The results are presented in Supplementary Tables 5 and 6.
Discussion
This study provides evidence of the magnitude of VF loss among persons from Grassy Narrows First Nation, with a history of Hg exposure through fish consumption between 1970 and 1997. While the commonly-used VF indices confirmed an association between global loss and long-term past Hg exposure, the clustering approach provided the means of identifying localized patterns. In this community, Hg-related VF loss was concentric, with the greatest reduction in the periphery. With increasing exposure, the central areas became more and more affected, as illustrated in the heatmap portraits of the entire study group and quantified in the longitudinal mixed model analyses.
Peripheral VF constriction was first noted among workers with MeHg poisoning [59]. It has since been identified as a common feature of Minamata Disease, resulting from the consumption of Hg-contaminated fish [16]. Several clinical reports and studies carried out in Grassy Narrows and in other First Nation communities in Canada likewise observed VF loss [26, 27, 38]. The present study demonstrates the severity of the VF loss among adults in Grassy Narrows First Nation and confirms the contribution of long-term Hg exposure. These findings are consistent with those from Minamata patients 40 years after their initial diagnosis [17, 60].
VF loss is not a common disorder, and is known to increase with aging [61,62,63,64]. In a two-eye populational study of older persons, the prevalence of VF loss among persons between 55 – 64 years of age was 3%, and it progressively increased to 17% for those 85 years and older [61]. In contrast, in the present study, 30.4% of participants in the 55 – 64 year-old age range (n = 46) presented at least one abnormal VFI, representing 28.3% of total eyes (n = 92).
It is noteworthy that although almost two-thirds of participants in the present study scored in the abnormal range of the GHT, and its association with past Hg exposure was significant with 10 repeated HHg measurements, no relation was observed for PSD, which is also used in the diagnosis of early glaucoma [65, 66]. Sensitivity analyses for these three indicators did not show consistent associations with long-term Hg exposure, as was the case for VFI, MD and the clusters. In situations where health care providers are unaware of past Hg exposure and the possible consequences of Hg poisoning, this might further complicate clinical diagnosis in cases of patients with suspected glaucoma, based on the presence of other risk factors[67]. Distance and near visual acuity were likewise not associated with long-term Hg exposure.
In the present study, past Hg exposure, but not current blood-Hg concentration, was associated with VF loss, suggesting that the process leading to Hg-related VF constriction occurs over time or is a delayed reaction to long-term exposure. Delayed Hg visuo-toxicity has been put forward by several authors [22, 68]. In a study of 6 macaques with low MeHg exposure, Merigan and co-authors noted reversible early VF loss, especially in the inferior-nasal field; more severe poisoning resulted in persistent VF constriction [69]. A case study of children, who had eaten MeHg-contaminated pork for a period of 3 months, reported neurologic signs and symptoms, and constricted VF, 22 years following the poisoning [70]. Although in the present study, we did not have VF measurements prior to exposure, the absence of a relation with current Hg exposure suggests that the effect may be cumulative over time, or that this is a manifestation of delayed neurotoxicity. Permanent adverse effects on spatial vision in adult monkeys (n = 21) were observed in relation to in utero MeHg exposure [71]. Weiss and co-authors [68] have proposed mechanisms for delayed Hg neurotoxicity.
The calcarine fissure (also known as striate or visual cortex) of the occipital lobe is a major target for MeHg toxicity, resulting in VF loss [48, 72, 73]. Korogi and co-authors [17] reported on magnetic resonance imaging (MRI) of 8 patients with MeHg poisoning (Minamata Disease) with moderate to severe concentric VF loss. The MRI showed significant dilation of the ventral portion of the calcarine fissure and T2-weighted images showed hyperintense lesions sparing the most posterior portion of the calcarine cortex [17]. There was a logarithmic correlation between VF loss and the extent of dilation of the calcarine fissure [17]. On autopsy, microscopic examination of Minamata patients have revealed neuropathological lesions including disintegration and loss of neurons in the calcarine cortex [17, 18].
The central portion of the VF (macula) has a considerably higher representation in the calcarine fissure compared to the periphery [74]. The anterior portion of the calcarine fissure receives input from the peripheral field, while the posterior portion is linked to the central VF [48]. In the present study, there appears to be progressive involvement not only of the peripheral portion of the VF, but also of the paracentral and central portions, with increasing Hg exposure. Autopsy data from 21 persons from Grassy Narrows, who died between 1976 and 1986, show a significant correlation between Hg content of the calcarine cortex and hair Hg, but no difference in Hg concentration between the anterior and posterior calcarine cortex (submitted manuscript).
This study has several strengths. The unique 28-year HHg database provided the means of examining longitudinal effects of Hg exposure on today’s VF loss. The mean values of HFA plots were used to derive heatmaps which illustrated increasing severity of concentric VF constriction. Clustering HFA outputs provided a segmental map of regions of sensitivity loss in the VF that could be individually related to long-term Hg exposure. The strong correlation between the Cluster Sum and VFI provided credibility to the segmentation of deficits into clusters and the underlying methodology.
Since VF loss can be monocular [61, 62], most analyses were performed with the two eyes. Data collected from both eyes from each individual cannot be jointly analyzed without taking into account their intra-correlation. Such a procedure, however, is likely to underestimate standard errors, result in lower probability values, and the calculation of the confidence intervals may be imprecise [51]. These problems become more profound as the degree of correlation between eyes increases [51]. Among the statistical procedures that are available for two-eye analyses, incorporating eyes as a ‘within subjects’ factor in paired analyses and in longitudinal mixed effect model analyses constituted the best alternative [52].
Although the HHg database, used in the present study, is unique in providing a portrait of long-term exposure, the information was derived from government monitoring programs that did not rely on rigorous sampling strategies. Although HHg measurements constituted the large majority of points in the database, approximately 4.8% of equivalent HHg values were derived from blood-Hg, using the conversion ratio of 250:1, as recommended in the Canadian guidelines [36, 37]. This ratio has been questioned by several studies that suggest that it is underestimated and highly variable [75,76,77]. Removal of the derived data points did not change the outcome of the analyses.
For this community, as illustrated in Fig. 1, Hg exposure decreased over the sampling period [30, 31, 38]. No information was available regarding previous VF status. It would have been useful to have longitudinal VF measures to better understand the progression of the disorder. Further studies should follow-up VF constriction in this population.
The findings of this study demonstrate the importance of long-term follow-up, even after biomarkers of Hg exposure are below official guidelines. Eye care professionals such as ophthalmologists and optometrists, especially in the context of suspected glaucoma, would benefit from knowledge of participants’ past Hg exposure. Coastal and riparian Indigenous communities, where fish consumption is historically much higher than in non-Indigenous communities [78], may be at increased risk for VF loss.
Conclusion
This study of adults from Grassy Narrows First Nation demonstrates the relationship between long-term Hg exposure and VF constriction, involving not only the periphery, but also the more central areas. To date, there are no programs available for visual rehabilitation in this community. The people of Grassy Narrows have fought for the past 50 years for recognition of the impact of Hg poisoning on their health and their lives. The Canadian government has recently committed to their long-stated demand for a Mercury Care Home and Wellness Centre, which should include eye and vision examinations and a visual rehabilitation program.
Availability of data and materials
The datasets generated and analysed in the present study are the property of Grassy Narrows First Nation. Permission for use of the data lies with Grassy Narrows Chief and Council.
References
Swain EB, Jakus PM, Rice G, Lupi F, Maxson PA, Pacyna JM, Penn A, Spiegel SJ, Veiga MM. Socioeconomic consequences of mercury use and pollution. Ambio. 2007;36:45–61.
Buffa L. Mercury losses from chlor alkali plants: the Canadian experience: environmental protection service, department of environment. 1973.
Bishop J, Neary B. Mercury Levels in Fish from Northwestern Ontario 1970 - 1975. Edited by Environment Mot. Ontario, Canada: Queen's Printer for Ontario; 1976.
Neff MR, Bhavsar SP, Arhonditsis GB, Fletcher R, Jackson DA. Long-term changes in fish mercury levels in the historically impacted English-Wabigoon River system (Canada). J Environ Monit. 2012;14(9):2327–37.
Kinghorn A, Solomon P, Chan HM. Temporal and spatial trends of mercury in fish collected in the English-Wabigoon river system in Ontario, Canada. Sci Total Environ. 2007;372(2–3):615–23.
Rudd JWM, Kelly CA, Sellers P, Flett RJ, Townsend BE. Why the English-Wabigoon river system is still polluted by mercury 57 years after its contamination. FACETS. 2021;6:2002–27.
Wheatley B, Paradis S. Northern exposure: further analysis of the results of the Canadian aboriginal methylmercury program. Int J Circumpolar Health. 1998;57(Suppl 1):586–90.
Wu Y-S, Osman AI, Hosny M, Elgarahy AM, Eltaweil AS, Rooney DW, Chen Z, Rahim NS, Sekar M, Gopinath SC. The toxicity of mercury and its chemical compounds: molecular mechanisms and environmental and human health implications: a comprehensive review. ACS Omega. 2024;9(5):5100–26.
National Research Council (US) Committee on the Toxicological Effects of Methylmercury. Toxicological Effects of Methylmercury. Washington (DC): National Academies Press (US); 2000.
Poulin J, Gibb H, Prüss-Üstün A, Organization WH. Mercury: assessing the environmental burden of disease at national and local levels. 2008.
U.S. Environmental Protection Agency (USEPA) Guidance for Assessing Chemical Contaminant Data for Use in Fish Advisories: Vol. 1, Fish Sampling And Analysis (Third edition), Office of Water, Office of Science and Technology. Washington, DC: 2000;EPA–823–B–00–007.
Scheuhammer AM, Basu N, Evers DC, Heinz G, Sandheinrich MB, Bank MS. Ecotoxicology of mercury in fish and wildlife: recent advances. 2012.
Bradley MA, Barst BD, Basu N. A review of mercury bioavailability in humans and fish. Int J Environ Res Public Health. 2017;14(2):169.
Fimreite N, Reynolds LM. Mercury contamination of fish in northwestern Ontario. J Wildlife Manage. 1973;37:62–68.
Farant JP, Brissette D, Moncion L, Bigras L, Chartrand A. Improved cold-vapor atomic absorption technique for the microdetermination of total and inorganic mercury in biological samples. J Anal Toxicol. 1981;5(1):47–51.
Harada M. Minamata disease: methylmercury poisoning in Japan caused by environmental pollution. Crit Rev Toxicol. 1995;25(1):1–24.
Korogi Y, Takahashi M, Hirai T, Ikushima I, Kitajima M, Sugahara T, Shigematsu Y, Okajima T, Mukuno K. Representation of the visual field in the striate cortex: comparison of MR findings with visual field deficits in organic mercury poisoning (Minamata disease). Am J Neuroradiol. 1997;18(6):1127–30.
Korogi Y, Takahashi M, Okajima T, Eto K. MR findings of Minamata disease–organic mercury poisoning. J Magn Reson Imaging. 1998;8(2):308–16.
Ekino S, Susa M, Ninomiya T, Imamura K, Kitamura T. Minamata disease revisited: an update on the acute and chronic manifestations of methyl mercury poisoning. J Neurol Sci. 2007;262(1–2):131–44.
Lacerda EMdCB, Souza GdS, Cortes MIT, Rodrigues AR, Pinheiro MCN, Silveira LCdL, Ventura DF. Comparison of visual functions of two Amazonian populations: possible consequences of different mercury exposure. Front Neurosci. 2020;13:1428.
Merigan WH. Visual fields and flicker thresholds in methylmercury-poisoned monkeys. In: Merigan WH, Weiss B, editors. Neurotoxicity of the visual system. New York: Raven Press; 1980. p. 149–63.
Rice DC, Gilbert SG. Effects of developmental exposure to methyl mercury on spatial and temporal visual function in monkeys. Toxicol Appl Pharmacol. 1990;102(1):151–63.
Merigan WH, Maurissen J, Weiss B, Eskin T, Lapham L. Neurotoxic actions of methylmercury on the primate visual system. Neurobehav Toxicol Teratol. 1983;5(6):649–58.
Harada M, Fujino T, Akagi, T, Nishigaki S. Epidemiological and clinical study and historical background of mercury pollution on Indian reservations in northwestern Ontario. Canada: Bull Inst Const Med. 1976;26:169–84.
Harada M, Fujino T, Oorui T, Nakachi S, Nou T, Kizaki T, Hitomi Y, Nakano N, Ohno H. Followup study of mercury pollution in indigenous tribe reservations in the Province of Ontario, Canada, 1975–2002. Bull Environ Contam Toxicol. 2005;74(4):689–97.
Harada M, Hanada M, Tajiri M, Inoue Y, Hotta N, Takehiko F, Takaoka S, Ueda K. Mercury poisoning in first nations groups in Ontario, Canada 35 years of Minamata disease in Canada. J Minamata Stud. 2011;3:3–30.
Takaoka S, Fujino T, Hotta N, Ueda K, Hanada M, Tajiri M, Inoue Y. Signs and symptoms of methylmercury contamination in a first Nations community in Northwestern Ontario, Canada. Sci Total Environ. 2014;468–469:950–7.
Tousignant B, Chatillon A, Philibert A, Da Silva J, Fillion M, Mergler D. Visual characteristics of adults with long-standing history of dietary exposure to mercury in Grassy Narrows First Nation, Canada. Int J Environ Res Public Health. 2023;20(6):4827.
The First Nations Principles of OCAP® [https://fnigc.ca/ocap-training/].
Wheatley B, Paradis S. Exposure of Canadian aboriginal peoples to methylmercury. Water Air Soil Pollut. 1995;80:3–11.
Wheatley B, Paradis S, Lassonde M, Giguere MF, Tanguay S. Exposure patterns and long term sequelae on adults and children in two Canadian indigenous communities exposed to Methylmercury. Water Air Soil Pollut. 1997;97(1–2):63–73.
Giovanoli-Jakubczak T, Greenwood MR, Smith JC, Clarkson T. Determination of total and inorganic mercury in hair by flameless atomic absorption, and of methylmercury by gas chromatography. Clin Chem. 1974;20(2):222–9.
Chan L, Solomon P, Kinghorn A, Mandamin B, Fobister Jr, S, Fobister B. “Our Waters, Our Fish, Our People” Mercury Contamination in Fish Resources of Two Treaty #3 Communities. Final Report submitted to Grassy Narrows and Wabaseemoong First Nations. 2005. p. 120.
Philibert A, Fillion M, Da Silva J, Lena TS, Mergler D. Past mercury exposure and current symptoms of nervous system dysfunction in adults of a First Nation community (Canada). Environ Health. 2022;21(1):34.
Philibert A, Fillion M, Mergler D. Mercury exposure and premature mortality in the Grassy Narrows First Nation community: a retrospective longitudinal study. Lancet Planet Health. 2020;4(4):e141–8.
Legrand M, Feeley M, Tikhonov C, Schoen D, Li-Muller A. Methylmercury blood guidance values for Canada. Can J Public Health. 2010;101(1):28–31.
World Health Organization Technical Report. Evaluation of certain food additives and contaminants: sixty-first report of the joint FAO/WHO Expert committee on food additives, vol. 61. Geneva: World Health Organization; 2004.
Wheatley B, Barbeau A, Clarkson TW, Lapham LW. Methylmercury Poisoning in Canadian Indians — The Elusive Diagnosis. Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques. 1979;6(4):417–22.
Yaqub M. Visual fields interpretation in glaucoma: a focus on static automated perimetry. Community eye health. 2012;25(79–80):1.
Heijl A, Patella VM, Bengtsson B. The field analyzer primer: effective perimetry. 4th ed. Dublin: Carl Zeiss Meditec Incorporated; 2012.
Thylefors B, Chylack L Jr, Konyama K, Sasaki K, Sperduto R, Taylor H, West4 S. A simplified cataract grading system The WHO Cataract Grading Group. Ophthalmic Epidemiol. 2002;9(2):83–95.
Ying GS, Maguire MG, Glynn R, Rosner B. Tutorial on biostatistics: statistical analysis for correlated binary eye data. Ophthalmic Epidemiol. 2018;25(1):1–12.
Ying GS, Maguire MG, Glynn RJ, Rosner B. Tutorial on biostatistics: longitudinal analysis of correlated continuous eye data. Ophthalmic Epidemiol. 2021;28(1):3–20.
Murdoch I. People and eyes: statistics in ophthalmology. Community Eye Health. 1998;11(27):43.
Pamphlett R, Kum Jew S, Cherepanoff S. Mercury in the retina and optic nerve following prenatal exposure to mercury vapor. PLoS ONE. 2019;14(8):e0220859.
Pamphlett R, Cherepanoff S, Too LK, Kum Jew S, Doble PA, Bishop DP. The distribution of toxic metals in the human retina and optic nerve head: implications for age-related macular degeneration. PLoS ONE. 2020;15(10):e0241054.
Fox DA, Boyes WK. In: Toxic responses of the ocular and visual system. Casarett and Doull’s Toxicology: the science of poisons (Klaassen CD, ed). 7th ed. New York: McGraw-Hill; 2008. p. 665–97.
Fox DA. Retinal and visual system: occupational and environmental toxicology. Handb Clin Neurol. 2015;131:325–40.
Mela M, Grötzner SR, Legeay A, Mesmer-Dudons N, Massabuau J-C, Ventura DF, de Oliveira Ribeiro CA. Morphological evidence of neurotoxicity in retina after methylmercury exposure. Neurotoxicology. 2012;33(3):407–15.
de Los SC, Pastor JC, Calonge M. Mercury intoxication and ophthalmic involvement: an update review. Frontiers in Toxicology. 2023;5:1148357.
Armstrong RA. Statistical guidelines for the analysis of data obtained from one or both eyes. Ophthalmic Physiol Opt. 2013;33(1):7–14.
Mollan SP, Homer V, Gates S, Brock K, Sinclair AJ. One Eye or Two: Statistical Considerations in Ophthalmology With a Focus on Interventional Clinical Trials. J Neuroophthalmol. 2021;41(4):421–3.
Klein R, Klein BE. Diabetic eye disease. Lancet. 1997;350(9072):197–204.
Welp A, Woodbury RB, McCoy MA, Teutsch SM, National Academies of Sciences E, and Medicine: Understanding the epidemiology of vision loss and impairment in the United States. In: Making eye health a population health imperative: Vision for tomorrow. Washington, DC: National Academies Press (US); 2016.
Chavent M, Genuer R, Kuentz-Simonet V, Liquet B, Saracco J. ClustOfVar : an R package for dimension reduction via clustering of variables. Application in supervised classification and variable selection in gene expressions data. Netherlands: Statistical Methods for (post)-Genomics Data (SMPGD 2013); 2013.
Sayo A, Ueno S, Kominami T, Nishida K, Inooka D, Nakanishi A, Yasuda S, Okado S, Takahashi K, Matsui S. Longitudinal study of visual field changes determined by Humphrey Field Analyzer 10–2 in patients with retinitis pigmentosa. Sci Rep. 2017;7(1):1–8.
Faul F, Erdfelder E, Buchner A, Lang A. G* Power Version 3.1. 7 [computer software]. Universität Kiel by Heinrich Heine Universität, Dusseldorf, Germany. https://www.psychologie.hhu.de/arbeitsgruppen/allgemeine-psychologie-und-arbeitspsychologie/gpower.
Kang H. Sample size determination and power analysis using the G* Power software. J Educ Eval Health Prof. 2021;18:17.
Hunter D, Russell DS. Focal cerebral and cerebellar atrophy in a human subject due to organic mercury compounds. J Neurol Neurosurg Psychiatry. 1954;17(4):235.
Korogi Y, Takahashi M, Shinzato J, Okajima T. MR findings in seven patients with organic mercury poisoning (Minamata disease). Am J Neuroradiol. 1994;15(8):1575–8.
Ramrattan RS, Wolfs RC, Panda-Jonas S, Jonas JB, Bakker D, Pols HA, Hofman A, de Jong PT. Prevalence and causes of visual field loss in the elderly and associations with impairment in daily functioning: the Rotterdam Study. Arch Ophthalmol. 2001;119(12):1788–94.
Wang YX, Xu L, Sun XY, Zou Y, Zhang HT, Jonas JB. Five year incidence of visual field loss in adult Chinese. The Beijing eye study. PLoS One. 2012;7(5):e37232.
Taylor HR, Livingston PM, Stanislavsky YL, McCarty CA. Visual impairment in Australia: distance visual acuity, near vision, and visual field findings of the Melbourne Visual Impairment Project. Am J Ophthalmol. 1997;123(3):328–37.
Wang Y, Xu L, Jonas JB. Prevalence and causes of visual field loss as determined by frequency doubling perimetry in urban and rural adult Chinese. Am J Ophthalmol. 2006;141(6):1078–1086. e1071.
Heo DW, Kim KN, Lee MW, Lee SB, Kim C-s. Properties of pattern standard deviation in open-angle glaucoma patients with hemi-optic neuropathy and bi-optic neuropathy. PLoS ONE. 2017;12(3):e0171960.
Shin HJ, Oh SE, Park CK, Park H-YL. Importance of pattern standard deviation of Humphrey 10–2 visual field to evaluate central visual function in patients with early-stage Glaucoma. J Clin Med. 2023;12(15):5091.
Gedde SJ, Lind JT, Wright MM, Chen PP, Muir KW, Vinod K, Li T, Mansberger SL. Primary open-angle glaucoma suspect preferred practice pattern®. Ophthalmology. 2021;128(1):P151–92.
Weiss B, Clarkson TW, Simon W. Silent latency periods in methylmercury poisoning and in neurodegenerative disease. Environ Health Perspect. 2002;110 Suppl 5(Suppl 5):851–4.
Merigan WH, Maurissen JP, Weiss B, Eskin T, Lapham LW. Neurotoxic actions of methylmercury on the primate visual system. Neurobehav Toxicol Teratol. 1983;5:649–58.
Davis LE, Kornfeld M, Mooney HS, Fiedler KJ, Haaland KY, Orrison WW, Cernichiari E, Clarkson TW. Methylmercury poisoning: long-term clinical, radiological, toxicological, and pathological studies of an affected family. Ann Neurol. 1994;35(6):680–8.
Burbacher TM, Grant KS, Mayfield DB, Gilbert SG, Rice DC. Prenatal methylmercury exposure affects spatial vision in adult monkeys. Toxicol Appl Pharmacol. 2005;208(1):21–8.
Jackson AC. Chronic neurological disease due to Methylmercury poisoning. Can J Neurol Sci. 2018;45(6):620–3.
Eto K, Yasutake A, Kuwana T, Korogi Y, Akima M, Shimozeki T, Tokunaga H, Kaneko Y. Methylmercury poisoning in common marmosets–a study of selective vulnerability within the cerebral cortex. Toxicol Pathol. 2001;29(5):565–73.
Horton JC, Hoyt WF. The representation of the visual field in human striate cortex: a revision of the classic Holmes map. Arch Ophthalmol. 1991;109(6):816–24.
Zareba G, Cernichiari E, Goldsmith LA, Clarkson TW. Validity of methyl mercury hair analysis: mercury monitoring in human scalp/nude mouse model. J Appl Toxicol: An International Journal. 2008;28(4):535–42.
Packull-McCormick S, Ratelle M, Lam C, Napenas J, Bouchard M, Swanson H, Laird BD. Hair to blood mercury concentration ratios and a retrospective hair segmental mercury analysis in the Northwest Territories, Canada. Environ Res. 2022;203:111800.
Liberda EN, Tsuji LJ, Martin ID, Ayotte P, Dewailly E, Nieboer E. The complexity of hair/blood mercury concentration ratios and its implications. Environ Res. 2014;134:286–94.
Cisneros-Montemayor AM, Pauly D, Weatherdon LV, Ota Y. A global estimate of seafood consumption by coastal indigenous peoples. PLoS ONE. 2016;11(12):e0166681.
Acknowledgements
The study was carried out in partnership with the Mercury Justice Team of Grassy Narrows. We thank the people of Grassy Narrows who contributed their time as participants and as discussants to the understanding and interpretation of the findings. Without the hard work and support of the many people from Grassy Narrows First Nation for recruitment, community organization and data collection, this study would not have been possible. We thank Drs. Annie Chatillon and Mathieu Khoury, optometrists, who performed the eye and visual examinations. We greatly appreciate the work of Florence Fiola-Racine and Rosa Nalini, who entered the HFA pattern deviation data by hand. We are particularly grateful to Vanessa Tremblay-Carter who provided the backbone administrative support throughout the project.
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A.P. first author, D.M. principal investigator and M.F. and J.D., co-principal investigators, ensured the community collaboration, grant proposal and data collection. B.T. designed the eye and vision examination protocol and supervised the optometrists who performed the examinations. A.P. conceived the innovative statistical approach and carried out the analyses. A.P., D.M and B.T. wrote the main manuscript. A.P. prepared all of the figures. All authors reviewed the manuscript.
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Ethics approval for the Niibin Study was obtained from the Université du Québec à Montréal (UQAM) Research Ethics Board (certificate #3763_e_2020) and Manitoulin Anishinaabek Research Review Committee (certificate #2022-06). This manuscript has been reviewed and approved for publication by Grassy Narrows Chief and Council.
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The authors declare no competing interests.
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Philibert, A., Tousignant, B., Fillion, M. et al. Characterizing visual field loss from past mercury exposure in an Indigenous riverine community (Grassy Narrows First Nation, Canada): a cluster-based approach. Environ Health 23, 81 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12940-024-01119-6
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DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12940-024-01119-6