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Published in final edited form as: Environ Int. 2022 Sep 26;169:107528. doi: 10.1016/j.envint.2022.107528

Environmental exposure to metals and the development of tauopathies, synucleinopathies, and TDP-43 proteinopathies: A systematic evidence map protocol

Kirstin Hester a, Ellen Kirrane a, Timothy Anderson b, Nichole Kulikowski a, Jane Ellen Simmons a, David M Lehmann a,*
PMCID: PMC11694317  NIHMSID: NIHMS2030218  PMID: 36183491

Abstract

Background:

Neurodegenerative diseases such as Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis are incurable and expected to increase in prevalence in the upcoming decades. Environmental exposure to metals has been suggested as a contributing factor to the development of neurodegenerative disease. This systematic evidence map will identify and characterize the epidemiological and experimental data available on the intersection of eighteen metals of environmental concern (i.e., aluminum, antimony, arsenic, barium, beryllium, cadmium, chromium, cobalt, copper, lead, manganese, mercury, nickel, palladium, radium, silver, vanadium, and zinc) and three neurodegenerative disease clusters (i.e., tauopathies, synucleinopathies, and TDP-43 proteinopathies). We aim to describe the type and amount of evidence available (or lack thereof) for each metal and neurodegenerative disease combination and highlight important knowledge gaps and knowledge clusters for future research.

Methods:

We will conduct a thorough search using two databases (MEDLINE and Web of Science Core Collection) and grey literature resources. Pre-defined criteria have been developed to identify studies which evaluate at least one of the selected metals and neurodegenerative disease-relevant outcomes (e.g., neuropathology, cognitive function, motor function, disease mortality). At each phase of review, studies will be evaluated by two reviewers. Studies determined to be relevant will be extracted for population, exposure, and outcome information. We will conduct a narrative review of the included studies, and the extracted data will be available in a database hosted on Tableau Public.

Conclusion:

This protocol documents the decisions made a priori to data collection regarding these objectives.

Keywords: Systematic evidence map, Dementia, Motor neuron disease, Heavy metals, Metalloids

1. Introduction

1.1. Rationale

Worldwide, the number of people aged 65 and older is expected to reach 1.6 billion by 2050, representing an increase from 8.5 % (as of 2015) to 16.7 % of the total population (He et al., 2016). As the number of aging individuals increases, the prevalence of age-related neurodegenerative disorders is also likely to increase. Though these disorders are commonly associated with aging, substantial evidence suggests that other factors may play a significant role in their etiology (Brown et al., 2005). Neurodegenerative disease encompasses multiple conditions, all of which result in the progressive loss of vulnerable populations of neurons, followed by declines in cognitive function (e.g., memory, social skills, problem solving) and/or motor function significant enough to interfere with daily life. The two most common neurodegenerative diseases worldwide are Alzheimer’s disease (AD) and Parkinson’s disease (PD). In the United States, 6.2 million adults are living with AD, or approximately 11 % of all individuals over the age of 65 (Alzheimer’s Association, 2021). Recent estimates suggest that there are over 7 million cases of PD worldwide (Dorsey et al., 2018). The most common motor neuron disease, amyotrophic lateral sclerosis (ALS), impacts an additional 220,000 individuals worldwide (Arthur et al., 2016). Together, neurodegenerative disease in the U.S. has a projected economic burden of over 500 billion dollars yearly (Thorpe et al., 2021).

Clinically, neurodegenerative diseases are primarily differentiated by their symptomology which may be supported by diagnostic imaging. The primary features of AD are cognitive dysfunction and gross brain atrophy beginning in the temporal lobe and progressing to the parietal and frontal lobe (Scahill et al., 2002). In PD, loss of dopaminergic neurons in the substantia nigra manifest as resting tremor, bradykinesia, and rigidity (Obeso et al, 2017). ALS impacts the upper and lower motor neurons and symptoms generally begin with peripheral muscle weakness progressing to total paralysis and respiratory system failure (Masrori and Van Damme, 2020). While these broad disease classifications are useful, there is increasing recognition that substantial heterogeneity exists within the pathological and clinical manifestations of each of these neurodegenerative diseases, and many patients present with mixed clinical features (Das et al., 2020). Several less common diseases (e.g., frontotemporal dementia, dementia with Lewy bodies, and corticobasal degeneration) fall into this spectrum (Bang et al., 2015; Mahapatra et al., 2004; Walker et al., 2015).

Due to the complex clinical features, the pathological hallmarks underlying these diseases are often a useful way to conceptualize these diseases. The prion hypothesis of neurodegenerative disease proposes that certain misfolded proteins have ‘prion-like’ properties that cause them to progressively aggregate and spread throughout the nervous system, eventually leading to impaired synaptic function and cell death (Jucker and Walker, 2013; Walker and Jucker, 2015). Based on this hypothesis, three diseases clusters can be defined: tauopathies (related to tau), synucleinopathies (related to α-synuclein), and TDP-43 proteinopathies (related to TAR DNA-binding protein 43). AD is considered a tauopathy, though both tau and β-amyloid contribute to the formation of the disease’s hallmark senile plaques and neurofibrillary tangles (LaFerla and Oddo, 2005). Synucleinopathies include PD and Lewy body dementia, and ALS and frontotemporal dementia are in the TDP-43 proteinopathy cluster (Geser et al., 2009; Goedert et al., 2017). Although it is not clear if these misfolded proteins initiate the disease, they result in the characteristic pathologies which are observed (Hardy and Selkoe, 2002; Tompkins and Hill, 1997).

The etiology of these neurodegenerative diseases remains unclear, but several risk factors have been identified, including (1) age (2) genetic background and (3) environmental exposures. The prevalence of dementia is highest in older age groups and increases exponentially after age 65 (Hou et al., 2019). Specific monogenic mutations have been identified as causative agents for the familial forms of AD, PD, and ALS; however, these familial disease forms are rare (Pihlstrom et al., 2017). Additional genetic factors, such as polymorphisms, have been identified as contributing to sporadic cases of the disease and likely represent gene-environment interactions (Angelopoulou et al., 2021; Gao and Hong, 2011). Indeed, several environmental agents/chemicals, including metals, have been suggested to confer increased risk of neurodegenerative diseases (Chin-Chan et al., 2015).

Toxic metals/metalloids (for simplicity referred to as metals, here-after) are present in the environment as both a constituent of air pollution and in other environmental media (Ahmad et al., 2021; Hanfi et al., 2019; Sanderson et al. 2014). Eighteen metals of environmental concern are included on the 2019 U.S. Agency for Toxic Substances and Disease Registry (ATSDR) Substance Priority List (SPL), which ranks chemicals based on their toxicity, prevalence at Superfund sites (i.e., federally recognized hazardous waste sites), and potential for human exposure (ATSDR, 2019). Epidemiologic and experimental studies have identified prospective associations between neurodegenerative disease and increased exposure to certain metals (e.g., lead, mercury, aluminum, cadmium, manganese) (Azar et al., 2021; Bakulski et al., 2020; Farace et al., 2020; Raj et al., 2021; Tesauro et al., 2021; Weiss, 2011). The environmental contribution to neurodegenerative disease is particularly challenging to investigate in humans as it is hypothesized that the etiologically relevant exposure period occurs in early life long before disease onset (Barlow et al., 2007; Basha et al., 2005; Lahiri et al, 2009; Wu et al., 2008), and the easily available biological matrices (e.g., blood) are often indicative of recent exposure rather than lifetime exposure. Experimental animal studies address some of these challenges owing to their shorter lifespans and general ease in evaluating pathological endpoints, making experimental studies particularly informative for this research area.

Overall, given the emerging evidence on toxic metals and neurodegeneration, a systematic examination of the literature is warranted to further explore this complex association. Systematic evidence maps (SEMs) have recently emerged as a tool to create a descriptive catalogue of all evidence relating to a broad topic area. In contrast to systematic reviews, SEMs do not aim to draw conclusions. Instead, they organize research (often in the form of an interactive database) to support identification of both knowledge gaps and knowledge clusters which can be used in future primary and secondary research (including systematic reviews) (Wolffe et al., 2019). In essence, they are a prioritization tool and have been increasingly used in many contexts, including human health assessments (Thayer et al, 2022).

Herein, we will generate a SEM examining both epidemiologic and experimental animal data on the intersection of eighteen environmental metals and tauopathies, synucleinopathies, and TDP-43 proteinopathies. This SEM will serve as a useful database for the design of future experimental research and future systematic reviews and/or meta-analyses of specific metals and specific neurodegenerative outcomes.

1.2. Objectives

The objectives of this SEM are:

  1. To identify and organize the available epidemiological and experimental animal evidence relating to metal pollutants and neurodegenerative diseases, as defined in the respective Population, Exposure, Comparator and Outcomes (PECO) statements (Table 1).

  2. To provide the complete dataset to the public in a user-friendly, searchable database.

  3. To generate a narrative description of the type and amount of evidence available (or lack thereof) for each metal and neurodegenerative disease combination to highlight knowledge clusters, data gaps, and research needs.

Table 1.

Human and Animal PECO Statements.

Human Epidemiological Studies
Population Studies of any adult with a diagnosed tauopathy, synucleinopathy, or TDP-43 proteinopathy, as defined in the MeSH index (e.g., AD, PD, ALS, Lewy body dementia, frontotemporal dementia); or studies of adults where the mean population age is 55 years old and above.
Exposure Exposure to at least one of the selected metals/metalloids (Ag, Al, As, Ba, Be, Cd, Co, Cr, Cu, Hg, Mn, Ni, Pb, Pd, Ra, Sb, V, or Zn) at any life stage through any route of exposure assessed using in vivo biomarkers of exposure (e.g., blood, bone, urine, toenail clippings) or estimated based on historical geographical data.
Comparator A comparison or reference population exposed to lower levels (or no exposure/exposure below detection limits) of comparable metal or exposed for shorter periods of time.
Outcomes Incidence of clinical diagnoses, behavioral testing or assessments (e. g., MMSE, MDS-UPDRS, ALSFRS-R), in vivo biomarkers of disease (e. g., CSF measurements, PET imaging), post-mortem neuropathology.
Experimental Animal Studies
Population Any laboratory non-human mammalian species (e.g., rodents, primates, dogs, cats, rabbits) at either any life stage (for nonbehavioral outcomes) or middle-age (for behavioral outcomes)a.
Exposure Exposure to metals/metalloids (Ag, Al, As, Ba, Be, Cd, Co, Cr, Cu, Hg, Mn, Ni, Pb, Pd, Ra, Sb, V, or Zn) at any life stage (including prenatal, post-natal, adolescence, adulthood) through any route of exposure (e. g., oral, inhalation, dermal, intravenous, intraperitoneal).
Comparator A concurrent control group exposed to vehicle-only treatment or untreated control.
Outcomes Relevant behavioral assessments (e.g., cognitive and motor function), neuropathological or biochemical endpoints (e.g., tau, amyloid plaques, α-synuclein, TDP-43, dopaminergic or motor neuron loss), electrophysiology.
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a

Behavioral tests in relevant transgenic models will be considered at any life stage.

This protocol documents the decisions made a priori to data collection regarding these objectives.

2. Methods

This protocol was registered with Zenodo on May 13th, 2022 (https://doi.org/10.5281/zenodo.6543952). Our methods are in accordance with Environment International’s PRISMA-SM-P guidelines and incorporates recommendations from James et al. (2016) and Wolffe et al. (2020). Any modifications to the protocol will be added as amendments in Zenodo and addressed in the final publication.

2.1. Eligibility criteria

References will be evaluated for inclusion in the SEM based on the epidemiological and the experimental animal PECO statements (Table 1). Eligible studies must contain primary research investigating the intersection of exposure to the PECO-relevant metals (i.e., aluminum, antimony, arsenic, barium, beryllium, cadmium, chromium, cobalt, copper, lead, manganese, mercury, nickel, palladium, radium, silver, vanadium, and zinc) and a relevant neurodegenerative disease endpoint. To qualify for inclusion, epidemiologic studies must include individuals diagnosed with a neurodegenerative disease categorized as a tauopathy, synucleinopathy, or TDP-43 proteinopathy by MEDLINE’s MeSH indexing terms or have a mean participant age of 55 years old at the time of outcome assessment. Experimental studies which use non-human mammalian species will also be considered for the SEM. For animal studies, there are no restrictions on the age of exposure or outcome assessment, unless the study exclusively focuses on behavioral endpoints. Studies that perform only behavioral tests (e.g., cognitive or motor function tests) without any supporting biochemical data will only be included if the animals are classified as middle-age or older (≥10 months old in rodents) at the time of outcome assessment or in cases where the study population is a relevant transgenic model.

Studies that do not meet all the inclusion criteria (i.e., PECO-relevance), but contain information on the relevant metals and neurodegenerative endpoints will be classified as supplemental material during screening. Supplemental material may be useful for answering further research efforts or for the users of the developed database. Examples of supplemental information include review articles, meta-analyses, book chapters, case studies, conference proceedings, in vitro data, non-mammalian in vivo data, in silico analyses, and foreign language studies. Theses and dissertations may be included in the primary evidence stream when the original research they contain is not published elsewhere, otherwise they will be considered supplemental. Supplementary information will be evaluated separately from the primary evidence streams and will undergo an abridged data extraction process.

2.2. Search strategy and sources

Peer-reviewed literature will be identified using the following databases: MEDLINE (via PubMed) and Web of Science (WoS) Core Collection. When available, database searches will utilize both keyword and MeSH (or equivalent indexing term) searching. In collaboration with research librarians, we developed the search strategy for MEDLINE (Supplementary Data 1 Table 1). Additional search strings will be developed with further consultation from librarians and will have a similar construction as the MEDLINE search described herein. To be retrieved, records must contain at least one matching search term from both the neurodegenerative keywords and exposure keywords. Generally, each metal will be searched for using the full name (e.g., cadmium) and relevant MeSH terms; however, due to the high number of irrelevant search results, the keyword “lead” will not be used. Instead, targeted keywords (e.g., “Pb”, “lead poisoning”, “exposure to lead”) will be used to focus the results on relevant literature (in addition to relevant MeSH terms). To provide additional coverage, “metal” will also be used as a generic metals search term. Initial searches will not include restrictions on publication date, article type or any other available filters. If necessary, an updated search will be run prior to manuscript submission with dates restricted to only after the primary search.

We will also conduct a search of grey literature, using the Bielefeld Academic Search Engine (BASE) and ‘opendissertations.org’. Grey literature may include but is not limited to theses/dissertations, conference proceedings, government reports and white papers. Studies may also be identified outside of database searches (e.g., hand-searching reference lists from existing reviews).

2.3. Data management

The results from the database searches will be exported and combined into a single library in EndNote X9.2 (Clarivate; Philadelphia, PA). Any additional records identified through hand-searching will be added into this library, and the number of identified records will be documented. A second EndNote library will be created to contain all records identified though grey literature searches, as they will be processed independently from the traditional literature (i.e., peer-reviewed journal publications). Both libraries will be uploaded into DistillerSR (Evidence Partners; Ottawa, ON), and duplicates will be quarantined (i.e., removed from the screening pool) using the program’s duplicate detection function. All screening and data extraction steps will be performed using DistillerSR, so each record will have a complete audit-trail documenting its progression throughout the review process.

Data created in DistillerSR will be exported in .csv format for further processing. Extracted study information from included records will be exported into three files, one for each evidence stream (human and animal) and one for supplemental records. These .csv files will be used to develop visualizations in Tableau (Seattle, WA) and RStudio version 1.3 (Boston, MA). A Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) flow diagram will be created using information recorded in DistillerSR to indicate the number of records identified from each data source, the number of references evaluated in each screening step, and reasons for record exclusion, using the formatting shown in Fig. 1.

Fig. 1.

Fig. 1.

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PRISMA flow diagram template. This template will be used to show the flow of records through the identification and screening process to make up the final number of included studies. Ti/Ab = Title and Abstract, WoS = Web of Science

2.4. Study selection workflow

Two reference datasets will be created in DistillerSR (i.e., traditional database search records and grey literature records) and will be screened separately. The traditional database search results will be screened in two phases – a title and abstract (Ti/Ab) level followed by full-text level. Grey literature records will be screened in a single phase. Prior to each screening step, custom forms will be developed and piloted to maximize consistency between reviewers.

Database records will first be screened by reviewing only the Ti/Ab. Records that appear to meet the inclusion criteria, contain supplemental information, or are unclear will be included during this phase and moved forward to the full-text screening level. Due to the large number of expected results, human screeners will be aided by DistillerSR’s Artificial Intelligence (AI) reviewer. A diagram showing the flow of each record through Ti/Ab screening is shown in Fig. 2. Each record will first be evaluated by a human screener, and if the record is recommended for inclusion, it will move forward to the full-text screening phase without further Ti/Ab screening. If the human screener recommends that the record be excluded, this decision will be reviewed by the AI reviewer. The record will be excluded if the AI reviewer also recommends the record for exclusion. If the AI reviewer disagrees with the first human screener, the record will be passed to a different human screener to make the final decision to include or exclude the record. This method is designed to allow clearly relevant or clearly irrelevant papers to be sorted with only one human reviewer, while still utilizing dual human screening for records where the relevance is less clear. In instances where the AI reviewer is unavailable (e.g., during the algorithm training period), dual human screening will be used as a substitute.

Fig. 2.

Fig. 2.

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Decision Tree for Title & Abstract Screening. While the Artificial Intellegence (AI) Review is training, records will be screened as shown in Panel A. If the first reviewer includes a record it will be moved directly to the next level of review (i.e., full-text screening); however, if the first reviewer excludes a record it will be evaluated by a second reviewer. The record will only be excluded if both reviewers agree. Panel B shows the workflow which will be used after DistillerSR’s AI Review is deployed. The AI Review will be used as an intermediate screener, allowing for some records to be excluded at this step. Levels of screening are displayed as rectangles, and outcomes are displayed as ovals.

During the Ti/Ab phase, we will also implement a stop screening approach using DistillerSR’s continuous reprioritization model. This model uses machine learning algorithms to continuously reorder records during screening based on an estimated likelihood of relevance (Hamel et al., 2020). Each include/exclude decision during screening will continuously improve performance of the model, allowing the most relevant papers to be screened first. To jumpstart the model performance, we will upload 20 “seed” references (i.e., examples of relevant literature) into DistillerSR prior to screening initiation. Seed references will be identified by hand searching currently available reviews. Reviewers will screen references until 95 % of the predicted relevant references are found, and the remaining unscreened papers will be excluded. This threshold is commonly utilized as it maximizes the reduction in screening workload while maintaining a high level of accuracy (i.e., approximately the human error rate) (Hamel et al., 2020; Wang et al., 2020). Tests of DistillerSR’s algorithm performed by Hamel et al. (2020) found that within their example datasets, no records which merited inclusion in the final review were missed by using this stop screening rule.

The full text of each article included during Ti/Ab screening will be sought for retrieval to be used during the second level of screening. At this level, every paper will be reviewed independently by two members of the study team (i.e., no AI or continuous reprioritization will be implemented). Records that are confirmed to meet the inclusion criteria will be sorted to the human and/or animal evidence stream. Records that do not meet the inclusion criteria will be excluded and the specific reason for exclusion indicated (e.g., population not relevant, exposure not relevant, etc.). Records containing supplemental information will also be identified and categorized during the full text screening, with additional tags to indicate the nature of the record (e.g., in vitro study, review article). When reviewer conflicts occur during full-text screening, the two reviewers will attempt to resolve it. If the original reviewers cannot reach a consensus, a separate third reviewer will be consulted to resolve discrepancies.

Due to the expected heterogeneity of the material, grey literature will be screened in a single phase without the use of automated tools. Two individuals will review each record against the inclusion criteria, and sort studies similarly to the full-text screening phase described above. If a record can be excluded or tagged as supplemental based on the record’s title, abstract, or summary, no further evaluation will occur. Full text will be retrieved as necessary to determine inclusion/exclusion decisions. Reviewer conflicts will be resolved according to the process described above.

2.5. Data extraction

Evidence inventories will be generated for all PECO-relevant studies and supplemental records using custom extraction forms created in DistillerSR to maintain consistent language and formatting. Three separate extraction forms have been developed and piloted for the separate evidence steams (i.e., human, animal, or supplemental). References will be imported into the appropriate category based on tags in the previous screening steps.

Tables S2, S3, and S4 (in Supplementary Data 1) detail the information which will be extracted for each evidence stream. Bibliographic information will be extracted from every study, including title, author (s), journal, publication year, and DOI. For supplemental data, general information about the data type (e.g., in vitro, review article, conference proceeding) and the research question (i.e., metal(s) and neurodegenerative cluster studied) will be extracted. In the case of human and animal evidence, detailed information about the study population, exposure, and outcome will be collected using a mixture of pick lists and text entry. Each relevant endpoint within a study will be extracted using the EPA’s Environmental Health Vocabulary (EHV), which implements a hierarchal labeling scheme to characterize each endpoint at increasing levels of specificity (i.e., System, Organ, Effect, Sub-Effect, Endpoint) and increases interoperability of data (Whaley et al., 2020). New endpoints identified during this study will be submitted for incorporation into EHV. Generally, the information extracted from each study will be displayed in a single entry within the final database; however, if a study evaluates two (or more) independent populations, each population within a study will be displayed as separate entries. Examples of the flat file database are provided in Supplementary Data 2.

Each reference will be extracted independently by two separate reviewers to ensure accuracy of results. When extraction is complete, conflicts will be reviewed by one of the original reviewers. Minor mistakes (e.g., spelling or formatting) will be immediately resolved, and any major discrepancies between the forms (e.g., different animal strains, age groupings, outcomes, etc. indicated) will be discussed between the two reviewers before correction. If a study is missing information relevant to extraction endpoints, we will contact the corresponding author via email twice with a two-week follow-up interval. If no response is received within one month of the second contact (i.e., 6 weeks after initial email), the author query attempts will be documented, and the missing information may be coded as not available. Prior to publication, a high-level quality check will be performed for each database file to ensure consistency across reviewers and corrections will be discussed with the study team.

2.6. Study quality evaluation

This SEM will not perform formal study evaluations for risk of bias or sensitivity. All studies determined to be PECO-relevant at the full-text screening level will undergo data extraction and be included in the SEM. However, information relating to study characteristics (e.g., study design) may be included in the narrative synthesis if it helps to explain important differences between studies.

2.7. Data mapping and visualization

The extracted study information will be used to create two interactive data maps (one for each evidence stream), published on Tableau Public. These data maps will be accessible via the internet with a unique and stable URL, and the .csv files used to create the Tableau visualization will be provided as a supplemental document to the published SEM. Data will be organized in a tabular format according to metal and the type of endpoint studied, creating a heatmap of the identified studies. Users will be able to easily filter the data based on selected characteristics. For human studies, study type, country of origin, and exposure assessment method will be used as filters. Species, exposure, and exposure/outcome lifestages will be filters available for animal data. A list of the studies included in each category will be visible in Tableau and selecting a study will reveal full bibliographic details and extracted information. Studies may appear in more than one list when merited (e.g., multiple metals are examined). Additional, non-interactive, visualizations and tables will be created using RStudio.

2.8. Synthesis of results

Data collected for this SEM will be summarized narratively with supporting tables and visualizations. The narrative description will examine trends in the data, including publications by year, publication types (e.g., epidemiological vs experimental), types and combinations of metal(s) and type of endpoint studied. Specific to epidemiologic studies, we will describe studies according to study design types, population characteristics, geographic locations, and exposure metrics. As some evidence suggests that the pathogenesis of neurodegenerative disease begins much earlier than cognitive decline begins, we are interested in categorizing studies which look at long-term measures of exposure as compared to studies which capture current exposure. We will describe the animal evidence similarly, in addition to reporting frequency of species/strain studied (including transgenic models), exposure timing, route of administration, and types/timing of the outcomes collected. Within the supplemental data stream, we intend to analyze the topic areas covered by any previous systematic reviews. These data will allow for further discussion on knowledge gaps to help inform and prioritize areas of future primary research and specific questions which could be addressed in future systematic reviews.

Supplementary Material

SI2
SI1

Acknowledgements

We thank Drs. Rachel Schaffer, Chelsea Weitekamp, Krista Christensen, and Christelene Horton for their thoughtful and constructive comments. Thank you to Taylor Johnson, Corinne Foster, and Sally Smith for their assistance in the development of the literature search strategy. We are also grateful for guidance and technical assistance provided by the team at DistillerSR, particularly Chia Lian and Derek Lord.

Abbreviations:

AD

Alzheimer’s Disease

AI

Artificial Intelligence

ALS

Amyotrophic Lateral Sclerosis

ALSFRS-R

Revised Amyotrophic Lateral Sclerosis Functional Rating Scale

ATSDR

Agency for Toxic Substances and Disease Registry

BASE

Bielefeld Academic Search Engine

CSF

Cerebrospinal Fluid

EHV

Environmental Health Vocabulary

MDS-UPDRS

Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale

MeSH

Medical Subject Headings

MMSE

Mini-Mental State Examination

PD

Parkinson’s Disease

PECO

Population, Exposure, Comparator, and Outcomes statement

PET

Positron Emission Tomography

PRISMA

Preferred Reporting Items for Systematic Reviews and Meta-Analysis

SEM

Systematic Evidence Map

SPL

Substance Priority List

Ti/Ab

Title and Abstract

Footnotes

CRediT authorship contribution statement

Kirstin Hester: Conceptualization, Writing – original draft, Methodology, Visualization. Ellen Kirrane: Methodology, Writing – review & editing. Timothy Anderson: Methodology, Writing – review & editing. Nichole Kulikowski: Methodology, Writing – review & editing. Jane Ellen Simmons: Writing – review & editing, Methodology, Supervision, Project administration. David M. Lehmann: Conceptualization, Methodology, Writing – review & editing, Supervision, Project administration.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A. Supplementary material

Supplementary data to this article can be found online at https://doi.org/10.1016/j.envint.2022.107528.

Data availability

No data was used for the research described in the article.

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Associated Data

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Supplementary Materials

SI2
NIHMS2030218-supplement-SI2.xlsx (51.6KB, xlsx)
SI1
NIHMS2030218-supplement-SI1.docx (42.1KB, docx)

Data Availability Statement

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