Skip to main content
Download PDF
  • Systematic review update
  • Open access
  • Published:

Geospatial analysis of environmental atmospheric risk factors in neurodegenerative diseases: a systematic review update

Systematic Reviews volume 13, Article number: 267 (2024) Cite this article

Abstract

Following up the previously published systematic review on the same topic and realizing that new studies and evidence had emerged on the matter, we conducted an update on the same research terms. With the objective of updating the information relating environmental risk factors on neurodegenerative diseases and the geographic approaches used to address them, we searched PubMed, Web of Science and Scopus for all scientific studies considering the following three domains: neurodegenerative disease, environmental atmospheric factor and geographical analysis, using the same keywords as in the previously published systematic review. From February 2020 to February 2023, 35 papers were included versus 34 in the previous period, with dementia (including Alzheimer’s disease) being the most focused disease (60.0%) in this update, opposed to multiple sclerosis on the last review (55.9%). Also, environmental pollutants such as PM2.5 and NO2 have gained prominence, being represented in 65.7% and 42.9% of the new studies, opposed to 9.8% and 12.2% in the previous review, compared to environmental factors such as sun exposure (5.7% in the update vs 15.9% in the original). The mostly used geographic approach remained the patient’s residence (82.9% in the update vs 21.2% in the original and 62.3% in total), and remote sensing was used in 45.7% of the new studies versus 19.7% in the original review, with 42.0% of studies using it globally, being the second most common approach, usually to compute the environmental variable. This review has been registered in PROSPERO with the number CRD42020196188.

Peer Review reports

Introduction

The pathological changes suffered by the human brain during the ageing process lead to a range of neurodegenerative disorders [1], which are characterized by a progressive loss or damage of neuronal cell leading to compromised brain function. While the world’s population over the age of 60 is expected to reach 22% until 2050, the concern about the future of neurodegenerative diseases arises, and the development of better suitable health systems for elder population is urged forward. Alzheimer’s disease (AD) is the most prevalent of neurodegenerative pathologies worldwide and is responsible for an extensive cognitive damage which usually affects daily chores [2]. Parkinson’s disease (PD) closely follows AD in prevalence and leads to both motor and non-motor symptoms due to dopaminergic neuronal loss. Affecting around 2.5 million people globally, multiple sclerosis (MS) is the third most prevalent neurodegenerative disease and is usually an autoimmune response causing an inflammatory demyelination of the brain [3].

Other risk factors are known in the development of neurodegenerative disorders, including gender, aggravated clinical history — hypertension, diabetes, cranial injury and tumours — and smoking and drinking [4]. Nevertheless, the knowledge on the development of these pathologies remains incomplete, and it is thought environmental factors may have a contribution [4]. With 91% of the world’s population living under high pollution levels, with air quality levels above the established limits for health safety by the World Health Organization (WHO) [5], the urge for studying its implications on the population’s health is rising. Recent studies have revealed that atmospheric pollution can trigger mechanisms responsible for neurodegenerative diseases [6,7,8].

A useful tool to both measure and analyse environmental exposure to air pollution is remote sensing data/techniques, and it has been increasingly used in epidemiological studies with the number publications featuring remote sensing applied to health increasing from 5.6 to 13.3% between 2007 and 2016 [9]. Geospatial analysis allows to integrate information on health, environmental data and socio-economic information at a local and global focus. Future research may then maximize the data used and establish future policies, which may be critical in preventing and progressing neurodegenerative diseases.

Following our previous systematic review [10], this systematic review update aims to identify new studies concerning neurodegenerative diseases and their environmental atmospheric risk factors through a geographical approach.

Materials and methods

The methods for this review are similar to the ones previously used in the original review [10].

Information sources and search strategy

We searched PubMed, Web of Science and Scopus databases from the 1st February 2020 until the 31st January 2023, using the keywords on Table 1. This review has been previously registered in PROSPERO with the number CRD42020196188 and has currently been set as updated. Duplicates were removed prior to abstract screening.

Table 1 Keywords used to search databases by domain. Each domain’s keywords were concatenated by an AND condition
Full size table

Eligibility criteria

Records were included if they contained all three domains considered in this review, neurodegenerative disease, atmospheric pollutant or factor and geographical approach, and were excluded otherwise. Inclusion criteria were as follows: (1) Studying a neurodegenerative disease, (2) accounting for atmospheric environmental factors or pollutants, (3) using geospatial analysis or tools and (4) include all previous criteria in the same study. All languages were considered.

Exclusion criteria encompassed studying mechanisms and biospecimen behind a neurodegenerative disease, soil and water pollutants and simply stating a geographical area without further comparing nor analysing it.

Selection process

Abstracts were independently read by two authors to apply the inclusion and exclusion criteria explained above and select eligible papers for full-text screening. Rayyan was used to manage and perform all selection process in the abstracts phase. Abstracts were included if both reviewers agreed on the inclusion decision and excluded likewise. In abstracts with disagreement, reviewers discussed the individual cases until consensus was reached.

Data collection process

The review process was conducted independently by each of the two reviewers, who then convened to discuss their findings. Full texts were firstly screened for further selection. Rayyan kept being used in this phase to keep track of each reviewer’s decisions. Included full texts’ information was retrieved using semi-structured forms equal to the ones used in the original review. The forms included free writing inputs such as the title, year, country, authors, DOI, participants, aims and key findings. Additionally, multiple-choice inputs were available for study design, statistical methods, outcome measurements, study limitations, neurodegenerative disease, environmental risk factors, geographic approach and type of geographical approach. Some level of simplification was made on categorizing the study limitations, as to better fit most studies on the main biases and issues encountered. However, new methodologies from the papers made it necessary to add fields such as Bayesian methods in the statistical methods, as well as PM1 and black carbon (BC) as environmental factors. All respective options are listed in Table 2. Reviewers had the liberty to include supplementary free text comments when they felt it was necessary.

Table 2 Article forms’ options regarding multiple-choice domains
Full size table

Study risk-of-bias assessment

To evaluate the risk of bias in individual studies, the study’s limitations were categorized according to a set of predefined options. These options included the following: none given by the authors; conflict of interests (any conflict of interests stated by the authors); confounding factors (unassessed confounding factors); ecological bias (extrapolation of a conclusion from a population to a patient); exposure assessment (issues with assessing the patient’s exposure to the environmental factor); interpolation (issues with spatially interpolating the environmental factor); memory bias (data collection relying on patient’s memory); migration of patients (patients moving from one residence to another); referral bias (studies relying on the doctor’s referring similar cases); sampling issues (under sampling; over sampling or non-representative sampling); statistical issues (lacking of more relevant statistical methods); study design issues (studies acknowledging inappropriate study design); survival bias (relying on a patient being alive over a period of time); time-related issues (inability to assess the amount of time a patient was exposed to the environmental factor); and unassessed patients (patients outside the databases not being considered). The risk of bias was retrieved from the paper itself, as assessed by the respective authors, and was not further analysed by the reviewers.

Effect measurements

Outcomes from studies were extracted using the same forms, in which each reviewer could select which measurement was used (prevalence, correlation, relative risk, odds ratio, hazard ratio and regression coefficients), as well as the obtained values with simple plus signs (+) for statistically significative positive associations, minus signs (−) for statistically significative negative associations and question marks (?) for statistically non-significative associations. All studies’ measurements were considered, and only relative associations were noted to allow for more broad comparison. The strength of the association was not taken into account to simplify the relative comparison across different study types, population sizes and other variables that might influence the results.

Results

Identification, screening and assessment

Of the 3510 abstracts obtained, 2282 articles were initially screened after duplicates were removed, from which only 35 were included in the final study. The number of published papers on the scope of this review has clearly increased through the years, as visible in the graphic of Fig. 1. 2021 was the year with the most published articles in this scope, with 17 papers, while until 2015 only 15 had ever been published. The trend line presented in Fig. 1 is a fourth-degree polynomial line automatically obtained by fitting the number of studies over the years, and that shows the increasing trend in the amount of works published in the field. The selection process is summarized using PRISMA presented by a flow diagram (Fig. 2). The discrepancy in the total number of excluded studies and the sum of the exclusion reason categories are due to the overlapping of exclusion reasons. The full list with the studies results from the original review, and the present update is provided on Table S1 in the supplementary matterials.

Fig. 1
figure 1

Number of published articles on this review’s scope throughout the years

Full size image
Fig. 2
figure 2

Systematic review PRISMA flowchart, including original and update reviews and their junction. Neuro, neurodegenerative disease; env, environmental factor or pollutant; geo, geographic approach

Full size image

Overall, as in Table 3, most papers studied multiple sclerosis (43.5%) and dementia — including AD — (34.8%) as neurodegenerative diseases and compared them with either PM2.5, NOX and PM10 (44.9%, 36.2% and 23.2%, respectively). Using the patient’s residence as an estimator of exposure was used in 62.3% of the studies, and remote sensing was used in 42.0%. As categories from each domain are not mutually exclusive, the sum of all categories may be higher than the total number of articles. The graphics in Fig. 3 represent the number of articles in each domain with the corresponding categories.

Table 3 Number and percentage of papers included in the update for each domain categories
Full size table
Fig. 3
figure 3

Studies in each category by domain: neurodegenerative diseases, geographic approach and environmental factors and pollutants

Full size image

The main methodological characteristics of the included studies are summarized on Table 4. A more detailed table with each paper’s characteristics is included in supplementary materials in Table S1. The country of origin of the studies is represented in Fig. 4. The country with the most papers published was the USA, with 19 papers overall, 8 of which about dementia. It was also the country with the most variety of neurodegenerative diseases studied, which included dementia, MS, PD, amyotrophic lateral sclerosis (ALS) and paediatric multiple sclerosis (PMS). A list of all countries with their corresponding published papers is available on Table 5. As several times countries have joined their efforts to study a specific disease, the total number of studies included and the sum of the published papers by country do not match.

Table 4 Study methodological characteristics summary: limitations, study design, statistical methods and effect measures
Full size table
Fig. 4
figure 4

Country of origin of the studies and the diseases studied in each country. Different diseases are represented by different colours in the pie charts showing the diseases’ proportions, while the chart sizes represent the absolute number of studies issued in the respective country

Full size image
Table 5 Country of origin with the number of studies published (N) and the respectively studied diseases. Some studies have investigators or focus on data from more than one country
Full size table

Qualitative synthesis

The following analysis was performed taking into account all included articles from the original and the updated systematic review. Further, detailed information regarding the results presented in each study is presented on in the supplementary materials.

  • Amyotrophic lateral sclerosis: ALS was approached on four studies [11,12,13,14]. Of these, three studied its interaction with PM10 [12,13,14], and two analysed PM2.5 [12, 14]. Both pollutants generally showed a negative but not significant association. A study [11] identified a significant negative association between ALS and sun exposure and significant positive association with precipitation and humidity, while no significant associations were identified with both temperature and pressure. Another study [12] found a positive significant association with NOx, with an odds ratio (OR) between 1.872 and 2.703. This study found no more significant associations with the remaining pollutants studied: SO2, CO, O3 and Pb.

  • Dementia: Dementia, including AD, was studied 24 times [15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38], 18 of which focused on PM2.5, with 13 finding a significant positive association between dementia and PM2.5 [15, 19, 21, 22, 26,27,28,29,30,31,32, 36, 37], with hazard ratios ranging from 1.02 [36] to 1.67 [30] and the remaining not finding a significant association [23, 33, 34, 38]. No association was ever found between dementia and temperature [16, 20], humidity [20] nor black carbon [23, 26, 33]. The outcomes concerning NOx and NO2 were controversial, with six papers finding a positive association [15, 25, 35, 39,40,41], two finding a negative association [20] (in particular with Alzheimer [23]) and five finding no significant association [22, 23, 26, 33, 34]. Furthermore, a study [35] approached several air pollutants (PM10, NO2, SO2, CO and O3) and found a significant positive association with all of them.

  • Motor neuron disease: Only one study focused on motor neuron disease (MND) [42], and it positively related the disease to lead (Pb), with a correlation coefficient of 0.824.

  • Multiple sclerosis: MS was studied 30 times [23, 43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71], 13 of which in relation to sun exposure, where half the studies found a significant negative association [43,44,45, 47, 50, 51], half found no significant association [52, 55, 56, 58, 60, 70] and 1 found a positive association [62]. Other environmental factors such as temperature and precipitation brought up further conflicts: some studies found a negative relation to temperature [43, 44, 49, 66], opposing others [45, 47, 49], and precipitation was mostly inconclusive [43, 47]. Concerning the air pollutants, the results were more in line with each other, with most studies finding at least one significant positive association [23, 48, 53, 57, 59, 61, 67,68,69, 71].

  • Paediatric multiple sclerosis: Two studies focused on PMS [72, 73]: one positively relating an air quality index to the disease [72] and another one studying several pollutants, positively associating the disease with PM2.5, SO2, CO and Pb, and not finding significative association with PM10, NOX and O3 [73].

  • Parkinson’s disease: Finally, PD was studied 12 times [23, 29, 32, 74,75,76,77,78,79,80,81,82], mostly related to air pollutants, where significant positive relations were found between the disease and PM10 [81], PM2.5 [23, 32, 82], NOX/NO2 [23, 77, 81] and CO [77]. No associations were found with either black carbon [23, 82], copper [75], lead [75] and manganese [74]. Negative associations were also found in relation to sun exposure [78] and O3 [82].

Discussion

Overview

This systematic review has retrieved as many studies in this update, concerning 2020 to 2023 as in the original review, from inception to 2020, using the same methods, suggesting an increased concern in relating neurodegenerative diseases to, primarily, air pollution. Also, an increasing trend of studying dementia opposed to MS was clearly observed, which could indicate a new focus on less studied diseases. This updated version of the systematic review has hence brought new insights on the subject.

ALS studies are scarce, as well as MND and PMS ones, probably due to the low incidence of these diseases, and gaps addressing the related environmental risk factors have been found [83]. Considering the three most prevalent diseases, dementia (including AD) has nearly come level with MS in terms of the number of studies (24 versus 28), and PD, the second most prevalent neurodegenerative disease worldwide, has been studied not even half the times (10). Overall, all neurodegenerative diseases were positively related to air pollutants such as PM10, PM2.5, NO2, SO2 and CO [12, 15, 17, 19,20,21,22,23, 26,27,28,29,30,31,32, 35,36,37,38, 48, 53, 59, 61, 67,68,69, 71, 73, 75, 77, 81, 82].

In the studies reviewed, various environmental factors were analysed for their associations with neurological conditions, yielding mixed results across different diseases. For ALS, significant associations were noted with NOX, but not with PM10 or PM2.5. Dementia studies predominantly identified a positive link with PM2.5, while findings regarding NOX and NO2 were inconsistent. MND research indicated a positive correlation with lead. MS studies showed a divided stance on sun exposure, with air pollutants generally associated positively. Paediatric MS research also found positive associations with PM2.5, SO2, CO and lead. PD research demonstrated significant associations with PM10, PM2.5, NOX/NO2 and CO, but not with other pollutants like black carbon or heavy metals. These results underscore the varied impact of environmental factors on neurological conditions, with certain pollutants, as is the case of PM10 and PM2.5, consistently appearing as risk factors. The lack of studies in South America and Africa rises the concern of whether this group of diseases is being overlooked in these areas. Although Africa has the lowest rates of neurodegenerative diseases in the world, the same cannot be said about South America, and so further research on this region is advised.

Study limitations

Our study has some limitations. For instance, despite this being an update, rarer diseases such as Creutzfeldt-Jakob and Huntington’s are still unstudied in the domains of this review. It is thus identified as a potential future study case to focus on the diseases not yet found. Also, despite not having excluded any study based on its language, and having scanned three different relevant databases in the scientific field, no grey literature was analysed, and it could potentially provide further studies of interest. Also, no particular tool was used to assess the risk of bias from the included studies. It is relevant to refer that the studies included in this review focus on association and not causality; thus, no causal inference can be taken from the results collected.

Study implications

Future works on the field of neurodegenerative diseases and their relation to environmental factors might refer to this review as a starting point to identify studies in the area, along with which diseases have been studied and those which still lack analysis, and which environmental factors have they been related to, hypothesizing environmental factors relatable to unstudied diseases or related environmental factors yet to analyse. The geographic approaches also present diverse methods that can be used to add the geographical dimension to the studies, as well as exposures assessment.

Conclusions

The most studied neurodegenerative diseases were in line with the most prevalent ones worldwide. It is mostly unanimous that environmental pollutants significantly influence the incidence of these pathologies, increasing their rates. Particulate matter and nitric oxides were the most studied pollutants and have mostly contributed positively to the rates of neurodegenerative diseases. The present systematic review update provides an insight of the evidence being made regarding the association between environmental factors and neurodegenerative diseases using geospatial analysis. As with the original systematic review, the ever-increasing amount of data support the development of further research on this topic. Less prevalent diseases such as ALS, MND and PMS have been targeted as less studied, as well as the regions of South America and Africa, and are an interesting starting point for future works.

Data availability

All data generated or analysed during this study are included in this published article and its supplementary information files.

References

  1. Spencer P, Palmer V, Kisby G. Seeking environmental causes of neurodegenerative disease and envisioning primary prevention. Neurotoxicology. 2016;56. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.neuro.2016.03.017.

  2. Pang SY, et al. The interplay of aging, genetics and environmental factors in the pathogenesis of Parkinson’s disease. Transl Neurodegener. 2019;8:23. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s40035-019-0165-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. WHO, W.H.O. Neurological disorders: public health challenges. 2006.

    Google Scholar 

  4. Brown RC, Lockwood AH, Sonawane BR. Neurodegenerative Diseases: An Overview of Environmental Risk Factors. Environ Health Perspect. 2005;113(9):1250–6. https://doiorg.publicaciones.saludcastillayleon.es/10.1289/ehp.7567.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Organization, W.H. Air pollution. 2020. Available from: https://www.who.int/health-topics/air-pollution#tab=tab_2.  Cited 2023 June.

  6. Fu P, et al. The association between PM(2.5) exposure and neurological disorders: a systematic review and meta-analysis. Sci Total Environ. 2019;655:1240–8. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.scitotenv.2018.11.218.

    Article  CAS  PubMed  Google Scholar 

  7. Moulton PV, Yang W. Air pollution, oxidative stress, and Alzheimer’s disease. J Environ Public Health. 2012;2012: 472751. https://doiorg.publicaciones.saludcastillayleon.es/10.1155/2012/472751.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Chin-Chan M, Navarro-Yepes J, Quintanilla-Vega B. Environmental pollutants as risk factors for neurodegenerative disorders: Alzheimer and Parkinson diseases. Front Cell Neurosci. 2015;9:124. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fncel.2015.00124.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Viana J, et al. Remote sensing in human health: a 10-year bibliometric analysis. Remote Sensing. 2017;9(12):1225. https://doiorg.publicaciones.saludcastillayleon.es/10.3390/rs9121225.

    Article  Google Scholar 

  10. Oliveira M, et al. Geospatial analysis of environmental atmospheric risk factors in neurodegenerative diseases: a systematic review. Int J Environ Res Public Health. 2020;17(22):8414.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Tsai CP, Tzu-Chi Lee C. Climatic factors associated with amyotrophic lateral sclerosis: a spatial analysis from Taiwan. Geospat Health. 2013;8(1):45–52. https://doiorg.publicaciones.saludcastillayleon.es/10.4081/gh.2013.53.

    Article  PubMed  Google Scholar 

  12. Povedano M, et al. Spatial assessment of the association between long-term exposure to environmental factors and the occurrence of amyotrophic lateral sclerosis in Catalonia, Spain: a population-based nested case-control study. Neuroepidemiology. 2018;51(1):33–49. https://doiorg.publicaciones.saludcastillayleon.es/10.1159/000489664.

    Article  PubMed  Google Scholar 

  13. Filippini T, et al. Risk of amyotrophic lateral sclerosis and exposure to particulate matter from vehicular traffic: a case-control study. Int J Environ Res Public Health. 2021;18(3):1–14. https://doiorg.publicaciones.saludcastillayleon.es/10.3390/ijerph18030973.

    Article  CAS  Google Scholar 

  14. Malek AM, et al. Long-term air pollution and risk of amyotrophic lateral sclerosis mortality in the Women’s Health Initiative cohort. Environ Res. 2023;216. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.envres.2022.114510.

  15. Chen H, et al. Exposure to ambient air pollution and the incidence of dementia: a population-based cohort study. Environ Int. 2017;108:271–7. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.envint.2017.08.020.

    Article  CAS  PubMed  Google Scholar 

  16. Wei Y, et al. Associations between seasonal temperature and dementia-associated hospitalizations in New England. Environ Int. 2019;126:228–33. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.envint.2018.12.054.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Li CY, et al. Association between air pollution and risk of vascular dementia: a multipollutant analysis in Taiwan. Environ Int. 2019;133: 105233. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.envint.2019.105233.

    Article  CAS  PubMed  Google Scholar 

  18. Cerin E, et al. International Mind, Activities and Urban Places (iMAP) study: methods of a cohort study on environmental and lifestyle influences on brain and cognitive health. BMJ Open. 2020;10(3). https://doiorg.publicaciones.saludcastillayleon.es/10.1136/bmjopen-2019-036607.

  19. Crous-Bou M, et al. Impact of urban environmental exposures on cognitive performance and brain structure of healthy individuals at risk for Alzheimer’s dementia. Environ Int. 2020;138. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.envint.2020.105546.

  20. Ho HC, et al. The associations between social, built and geophysical environment and age-specific dementia mortality among older adults in a high-density Asian city. Int J Health Geogr. 2020;19(1):53. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12942-020-00252-y.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Jung CR, Lin YT, Hwang BF. Ozone, particulate matter, and newly diagnosed Alzheimer’s disease: a population-based cohort study in Taiwan, in Advances in Alzheimer’s Disease. 2020. p. 3–14. https://doiorg.publicaciones.saludcastillayleon.es/10.3233/AIAD210002.

  22. Smargiassi A, et al. Exposure to ambient air pollutants and the onset of dementia in Québec, Canada. Environ Res. 2020;190. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.envres.2020.109870.

  23. Yuchi W, et al. Road proximity, air pollution, noise, green space and neurologic disease incidence: a population-based cohort study. Environ Health Global Access Sci Source. 2020;19(1). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12940-020-0565-4.

  24. Cleary EG, et al. Association of low-level ozone with cognitive decline in older adults. J Alzheimers Dis. 2018;61(1):67–78. https://doiorg.publicaciones.saludcastillayleon.es/10.3233/jad-170658.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Chen GB, et al. Long-term exposures to ambient PM1 and NO2 pollution in relation to mild cognitive impairment of male veterans in China. Environ Res Letters. 2021;16(2). https://doiorg.publicaciones.saludcastillayleon.es/10.1088/1748-9326/abde5c.

  26. Mortamais M, et al. Long-term exposure to ambient air pollution and risk of dementia: results of the prospective Three-City Study. Environ Int. 2021;148. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.envint.2020.106376.

  27. Ran J, et al. Long-term exposure to fine particulate matter and dementia incidence: A cohort study in Hong Kong. Environ Pollut. 2021;271: 116303. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.envpol.2020.116303.

    Article  CAS  PubMed  Google Scholar 

  28. Ran J, et al. The joint association of physical activity and fine particulate matter exposure with incident dementia in elderly Hong Kong residents. Environ Int. 2021;156: 106645. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.envint.2021.106645.

    Article  CAS  PubMed  Google Scholar 

  29. Rhew SH, Kravchenko J, Lyerly HK. Exposure to low-dose ambient fine particulate matter PM2.5 and Alzheimer’s disease, non-Alzheimer’s dementia, and Parkinson’s disease in North Carolina. Plos One. 2021;16(7). https://doiorg.publicaciones.saludcastillayleon.es/10.1371/journal.pone.0253253.

  30. Sullivan KJ, et al. Ambient fine particulate matter exposure and incident mild cognitive impairment and dementia. J Am Geriatr Soc. 2021;69(8):2185–94. https://doiorg.publicaciones.saludcastillayleon.es/10.1111/jgs.17188.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Tan J, et al. Associations of particulate matter with dementia and mild cognitive impairment in China: a multicenter cross-sectional study. Innovation. 2021;2(3). https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.xinn.2021.100147.

  32. Yitshak-Sade M, et al. PM2.5 and hospital admissions among Medicare enrollees with chronic debilitating brain disorders. Sci Total Environ. 2021;755. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.scitotenv.2020.142524.

  33. Andersen ZJ, et al. Long-term exposure to air pollution and mortality from dementia, psychiatric disorders, and suicide in a large pooled European cohort: ELAPSE study. Environ Int. 2022;170. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.envint.2022.107581.

  34. Wang X, et al. Association of improved air quality with lower dementia risk in older women. Proc Natl Acad Sci USA. 2022;119(2). https://doiorg.publicaciones.saludcastillayleon.es/10.1073/pnas.2107833119.

  35. Xie J, Lu C. Is there a casual relation between air pollution and dementia? Environ Sci Pollut Res. 2022. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s11356-022-23226-y.

    Article  Google Scholar 

  36. Yang L, et al. Associations between PM2.5 exposure and Alzheimer’s disease prevalence among elderly in eastern China. Environ Health Global Access Sci Source. 2022;21(1). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12940-022-00937-w.

  37. Younan D, et al. Racial/ethnic disparities in Alzheimer’s disease risk: role of exposure to ambient fine particles. Journals of Gerontology - Series A Biological Sciences and Medical Sciences. 2022;77(5):977–85. https://doiorg.publicaciones.saludcastillayleon.es/10.1093/gerona/glab231.

    Article  PubMed  Google Scholar 

  38. Zhang H, et al. Short-term associations between ambient air pollution and emergency department visits for Alzheimer’s disease and related dementias. Environ Epidemiol. 2023;7(1). https://doiorg.publicaciones.saludcastillayleon.es/10.1097/EE9.0000000000000237.

  39. Li CY, et al. Association between air pollution and risk of vascular dementia: a multipollutant analysis in Taiwan. Environ Int. 2019;133(Pt B): 105233. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.envint.2019.105233.

    Article  CAS  PubMed  Google Scholar 

  40. Crous-Bou M, et al. Impact of urban environmental exposures on cognitive performance and brain structure of healthy individuals at risk for Alzheimer’s dementia. Environ Int. 2020;138: 105546. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.envint.2020.105546.

    Article  CAS  PubMed  Google Scholar 

  41. Zhang H, et al. Short-term associations between ambient air pollution and emergency department visits for Alzheimer’s disease and related dementias. Environ Epidemiol. 2023;7(1): e237. https://doiorg.publicaciones.saludcastillayleon.es/10.1097/ee9.0000000000000237.

    Article  PubMed  Google Scholar 

  42. Santurtún A, et al. Trends in motor neuron disease: association with latitude and air lead levels in Spain. Neurol Sci. 2016;37(8):1271–5. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s10072-016-2581-2.

    Article  PubMed  Google Scholar 

  43. Leibowitz U. Multiple sclerosis: progress in epidemiologic and experimental research. A review. J Neurol Sci. 1971;12(3):307–18. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/0022-510X(71)90065-7.

    Article  CAS  PubMed  Google Scholar 

  44. Norman JE Jr, Kurtzke JF, Beebe GW. Epidemiology of multiple sclerosis in U.S. veterans: 2. Latitude, climate and the risk of multiple sclerosis. J Chronic Dis. 1983;36(8):551–9. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/0021-9681(83)90142-X.

    Article  PubMed  Google Scholar 

  45. Kalafatova O. Geographic and climatic factors and multiple sclerosis in some districts of Bulgaria. Neuroepidemiology. 1987;6(3):116–9. https://doiorg.publicaciones.saludcastillayleon.es/10.1159/000110106.

    Article  CAS  PubMed  Google Scholar 

  46. Bølviken B, Nilsen R, Ukkelberg Å. A new method for spatially moving correlation analysis in geomedicine. Environ Geochem Health. 1997;19(4):143–53. https://doiorg.publicaciones.saludcastillayleon.es/10.1023/A:1018410807648.

    Article  Google Scholar 

  47. Van Der Mei IAF, et al. Regional variation in multiple sclerosis prevalence in Australia and its association with ambient ultraviolet radiation. Neuroepidemiology. 2001;20(3):168–74. https://doiorg.publicaciones.saludcastillayleon.es/10.1159/000054783.

    Article  PubMed  Google Scholar 

  48. Gregory AC 2nd, et al. Multiple sclerosis disease distribution and potential impact of environmental air pollutants in Georgia. Sci Total Environ. 2008;396(1):42–51. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.scitotenv.2008.01.065.

    Article  CAS  PubMed  Google Scholar 

  49. Risberg G, et al. Prevalence and incidence of multiple sclerosis in Oppland county - a cross-sectional population-based study in a landlocked county of Eastern Norway. Acta Neurol Scand. 2010;124(4):250–7. https://doiorg.publicaciones.saludcastillayleon.es/10.1111/j.1600-0404.2010.01465.x.

    Article  PubMed  Google Scholar 

  50. Sloka S, et al. A quantitative analysis of suspected environmental causes of MS. Can J Neurol Sci. 2011;38(1):98–105. https://doiorg.publicaciones.saludcastillayleon.es/10.1017/s0317167100011124.

    Article  PubMed  Google Scholar 

  51. Ramagopalan SV, et al. Relationship of UV exposure to prevalence of multiple sclerosis in England. Neurology. 2011;76(16):1410–4. https://doiorg.publicaciones.saludcastillayleon.es/10.1212/WNL.0b013e318216715e.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Schuurman N, et al. A proposed methodology to estimate the cumulative life-time UVB exposure using geographic information systems: an application to multiple sclerosis. Mult Scler Relat Disord. 2012;2(1):29–35. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.msard.2012.07.003.

    Article  PubMed  Google Scholar 

  53. Heydarpour P, et al. Potential impact of air pollution on multiple sclerosis in Tehran. Iran Neuroepidemiology. 2014;43(3):233–8. https://doiorg.publicaciones.saludcastillayleon.es/10.1159/000368553.

    Article  PubMed  Google Scholar 

  54. Groves-Kirkby CJ, et al. Is environmental radon gas associated with the incidence of neurodegenerative conditions? A retrospective study of multiple sclerosis in radon affected areas in England and Wales. J Environ Radioact. 2016;154:1–14. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.jenvrad.2015.12.003.

    Article  CAS  PubMed  Google Scholar 

  55. Monti MC, et al. Is geo-environmental exposure a risk factor for multiple sclerosis? A population-based cross-sectional study in South-Western Sardinia. PLoS ONE. 2016;11(9): e0163313. https://doiorg.publicaciones.saludcastillayleon.es/10.1371/journal.pone.0163313.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Sun H. Temperature dependence of multiple sclerosis mortality rates in the United States. Mult Scler. 2017;23(14):1839–46. https://doiorg.publicaciones.saludcastillayleon.es/10.1177/1352458516688954.

    Article  PubMed  Google Scholar 

  57. Ashtari F, et al. An 8-year study of people with multiple sclerosis in Isfahan, Iran: association between environmental air pollutants and severity of disease. J Neuroimmunol. 2018;319:106–11. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.jneuroim.2018.02.019.

    Article  CAS  PubMed  Google Scholar 

  58. Gallagher LG, et al. Lifetime exposure to ultraviolet radiation and the risk of multiple sclerosis in the US radiologic technologists cohort study. Mult Scler. 2018;25(8):1162–9. https://doiorg.publicaciones.saludcastillayleon.es/10.1177/1352458418783343.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Tateo F, et al. PM2.5 levels strongly associate with multiple sclerosis prevalence in the province of Padua, Veneto region. North-East Italy Mult Scler. 2018;25(13):1719–27. https://doiorg.publicaciones.saludcastillayleon.es/10.1177/1352458518803273.

    Article  PubMed  Google Scholar 

  60. Amram O, et al. The use of satellite data to measure ultraviolet-B penetrance and its potential association with age of multiple sclerosis onset. Mult Scler Relat Disord. 2018;21:30–4. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.msard.2018.02.005.

    Article  PubMed  Google Scholar 

  61. Iuliano G. Geography based hypotheses about multiple sclerosis. Rivista Italiana di Neurobiologia. 2005;2:213–20.

    Google Scholar 

  62. Adamczyk-Sowa M, Gębka-Kępińska B, Kępiński M. Multiple sclerosis - risk factors. Wiad Lek. 2020;73(12 cz 1):2677–82.

    Article  PubMed  Google Scholar 

  63. Bergamaschi R, et al. PM2.5 exposure as a risk factor for multiple sclerosis. An ecological study with a Bayesian mapping approach. Environ Sci Pollut Res. 2021;28(3):2804–9. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s11356-020-10595-5.

    Article  Google Scholar 

  64. Chacko G, et al. Heat exposure and multiple sclerosis-a regional and temporal analysis. Int J Environ Res Public Health. 2021;18(11). https://doiorg.publicaciones.saludcastillayleon.es/10.3390/ijerph18115962.

  65. Elgabsi M, et al. An impact of air pollution on moderate to severe relapses among multiple sclerosis patients. Multiple Sclerosis Relat Disord. 2021;53. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.msard.2021.103043.

  66. Elser H, et al. Anomalously warm weather and acute care visits in patients with multiple sclerosis: a retrospective study of privately insured individuals in the US. Plos Medicine. 2021;18(4). https://doiorg.publicaciones.saludcastillayleon.es/10.1371/journal.pmed.1003580.

  67. Januel E, et al. Fine particulate matter related to multiple sclerosis relapse in young patients. Front Neurol. 2021;12: 651084. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fneur.2021.651084.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Kazemi Moghadam V, et al. Association of the global distribution of multiple sclerosis with ultraviolet radiation and air pollution: an ecological study based on GBD data. Environ Sci Pollut Res. 2021;28(14):17802–11. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s11356-020-11761-5.

    Article  CAS  Google Scholar 

  69. Scartezzini A, et al. Association of multiple sclerosis with PM 2.5 levels. Further evidence from the highly polluted area of Padua province, Italy. Multiple Sclerosis and Related Disord. 2021;48. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.msard.2020.102677.

  70. Vitkova M, et al. Association of latitude and exposure to ultraviolet B radiation with severity of multiple sclerosis an international registry study. Neurology. 2022;98(24):E2401–12. https://doiorg.publicaciones.saludcastillayleon.es/10.1212/wnl.0000000000200545.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Hedström AK, et al. Association between exposure to combustion-related air pollution and multiple sclerosis risk. Int J Epidemiol. 2023. https://doiorg.publicaciones.saludcastillayleon.es/10.1093/ije/dyac234.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Lavery AM, et al. Examining the contributions of environmental quality to pediatric multiple sclerosis. Mult Scler Relat Disord. 2017;18:164–9. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.msard.2017.09.004.

    Article  PubMed  PubMed Central  Google Scholar 

  73. Lavery AM, et al. Urban air quality and associations with pediatric multiple sclerosis. Annals of Clinical and Translational Neurology. 2018;5(10):1146–53. https://doiorg.publicaciones.saludcastillayleon.es/10.1002/acn3.616.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Finkelstein MM, Jerrett M. A study of the relationships between Parkinson’s disease and markers of traffic-derived and environmental manganese air pollution in two Canadian cities. Environ Res. 2007;104(3):420–32. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.envres.2007.03.002.

    Article  CAS  PubMed  Google Scholar 

  75. Willis AW, et al. Metal emissions and urban incident Parkinson disease: a community health study of Medicare beneficiaries by using geographic information systems. Am J Epidemiol. 2010;172(12):1357–63. https://doiorg.publicaciones.saludcastillayleon.es/10.1093/aje/kwq303.

    Article  PubMed  PubMed Central  Google Scholar 

  76. Liu R, et al. Ambient air pollution exposures and risk of Parkinson disease. Environ Health Perspect. 2016;124(11):1759–65. https://doiorg.publicaciones.saludcastillayleon.es/10.1289/EHP135.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Lee PC, et al. Gene-environment interactions linking air pollution and inflammation in Parkinson’s disease. Environ Res. 2016;151:713–20. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.envres.2016.09.006.

    Article  CAS  PubMed  Google Scholar 

  78. Kravietz A, et al. Association of UV radiation with Parkinson disease incidence: a nationwide French ecologic study. Environ Res. 2017;154:50–6. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.envres.2016.12.008.

    Article  CAS  PubMed  Google Scholar 

  79. Salimi F, et al. Associations between long-term exposure to ambient air pollution and Parkinson’s disease prevalence: a cross-sectional study. Neurochem Int. 2020;133: 104615. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.neuint.2019.104615.

    Article  CAS  PubMed  Google Scholar 

  80. Santurtún A, et al. Geographical distribution of mortality by Parkinson’s disease and its association with air lead levels in Spain. Med Clin (Barc). 2016;147(11):481–7. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.medcli.2016.07.022.

    Article  PubMed  Google Scholar 

  81. Fleury V, et al. Geospatial analysis of individual-based Parkinson’s disease data supports a link with air pollution: a case-control study. Parkinsonism Relat Disord. 2021;83:41–8. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.parkreldis.2020.12.013.

    Article  PubMed  Google Scholar 

  82. Cole-Hunter T, et al. Long-term air pollution exposure and Parkinson’s disease mortality in a large pooled European cohort: an ELAPSE study. Environ Int. 2023;171: 107667. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.envint.2022.107667.

    Article  CAS  PubMed  Google Scholar 

  83. Caller TA, et al. Spatial clustering of amyotrophic lateral sclerosis and the potential role of BMAA. Amyotroph Lateral Scler. 2012;13(1):25–32. https://doiorg.publicaciones.saludcastillayleon.es/10.3109/17482968.2011.621436.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors would like to kindly thank the authors who made their studies available for full paper screening when these were not available online.

Funding

This research was funded by FCT — Fundação para a Ciência e Tecnologia, grant number SFRH/BD/147324/2019, and national MCTES funds and supported also by National Funds through FCT — Fundação para a Ciência e a Tecnologia, I.P., within CINTESIS, R&D Unit (reference UIDB/4255/2020).

Author information

Authors and Affiliations

  1. Center for Health Technology and Services Research (CINTESIS@RISE), Faculty of Medicine, University of Porto, Rua Doutor Plácido da Costa, s/n, Porto, 4200-450, Portugal

    Mariana Oliveira, Alberto Freitas & Hernâni Gonçalves

  2. Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Rua Doutor Plácido da Costa s/n, 4200-450, Porto, Portugal

    Mariana Oliveira, Alberto Freitas & Hernâni Gonçalves

  3. Floradata — Biodiversidade, Ambiente e Recursos Naturais Lda, Campo 24 de Agosto, 129 — Escritório 704, 4300-504, Porto, Portugal

    André Padrão

  4. Department of Geosciences, Environment and Land Planning, Faculty of Sciences, University of Porto, Rua Do Campo Alegre 687, 4169-007, Porto, Portugal

    Ana Cláudia Teodoro

  5. Earth Sciences Institute (ICT), Pole of the FCUP, University of Porto, 4169-007, Porto, Portugal

    Ana Cláudia Teodoro

Authors
  1. Mariana Oliveira
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. André Padrão
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Ana Cláudia Teodoro
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Alberto Freitas
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Hernâni Gonçalves
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Methodology, MO; first reviewers, MO and AP; writing—original draft preparation, MO; writing—review and editing, AF, ACT and HG; and supervision, AF, ACT and HG. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Mariana Oliveira.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

13643_2024_2637_MOESM1_ESM.docx

Supplementary Material 1. Supplementary tables: Table S1. Detailed characteristics of each included study. ALS: amyotrophic lateral sclerosis; GIS: geographic information system; MS: multiple sclerosis. Table S2. Study results summary. + : positive statistically significative association, -: negative statistically significative association; ?: no statistically significative association found.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Oliveira, M., Padrão, A., Teodoro, A.C. et al. Geospatial analysis of environmental atmospheric risk factors in neurodegenerative diseases: a systematic review update. Syst Rev 13, 267 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13643-024-02637-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13643-024-02637-7

Keywords

Systematic Reviews

ISSN: 2046-4053