Long-term exposure to particulate matter was associated with increased dementia risk using both traditional approaches and novel machine learning…

WHO releases country estimates on air pollution exposure and health impact, <https://www.who.int/news/item/27-09-2016-who-releases-country-estimates-on-air-pollution-exposure-and-health-impact> (2016).

Faridi, S. et al. Long-term trends and health impact of PM2.5 and O3 in Tehran, Iran, 20062015. Environ. Int. 114, 3749. https://doi.org/10.1016/j.envint.2018.02.026 (2018).

CAS PubMed Google Scholar

Sun, G. et al. Association between air pollution and the development of rheumatic disease: A systematic review. Int. J. Rheumatol. 2016, 111 (2016).

Google Scholar

Zhang, H. et al. Ambient air pollution exposure and gestational diabetes mellitus in Guangzhou, China: A prospective cohort study. Sci. Total Environ. 699, 134390. https://doi.org/10.1016/j.scitotenv.2019.134390 (2020).

ADS CAS PubMed Google Scholar

Rovira, J., Domingo, J. L. & Schuhmacher, M. Air quality, health impacts and burden of disease due to air pollution (PM10, PM2.5, NO2 and O3): Application of AirQ+ model to the Camp de Tarragona County Catalonia. Spain. Sci. Total Environ. 703, 135538. https://doi.org/10.1016/j.scitotenv.2019.135538 (2020).

ADS CAS PubMed Google Scholar

Mullen, C., Grineski, S. E., Collins, T. W. & Mendoza, D. L. Effects of PM2.5 on third grade students proficiency in math and english language arts. Int. J. Environ. Res. Public Health. 17, 6931. https://doi.org/10.3390/ijerph17186931 (2020).

PubMed Central Google Scholar

Delgado-Saborit, J. M. et al. A critical review of the epidemiological evidence of effects of air pollution on dementia, cognitive function and cognitive decline in adult population. Sci. Total Environ. 757, 143734 (2021).

ADS CAS PubMed Google Scholar

Peters, R. et al. Air pollution and dementia: A systematic review. J. Alzheimers Dis. 70, S145S163 (2019).

CAS PubMed PubMed Central Google Scholar

Shi, L. et al. A national cohort study (20002018) of long-term air pollution exposure and incident dementia in older adults in the United States. Nat. Commun. 12, 19 (2021).

ADS Google Scholar

Weuve, J. et al. Exposure to air pollution in relation to risk of dementia and related outcomes: An updated systematic review of the epidemiological literature. Environ. Health Perspect. 129, 096001 (2021).

CAS PubMed Central Google Scholar

Chen, J.-H. et al. Long-term exposure to air pollutants and cognitive function in taiwanese community-dwelling older adults: A four-year cohort study. J. Alzheimers Dis. 8, 115 (2020).

Google Scholar

Gao, Q. et al. Long-term ozone exposure and cognitive impairment among Chinese older adults: A cohort study. Environ. Int. 160, 107072 (2022).

CAS PubMed Google Scholar

He, F. et al. Impact of air pollution exposure on the risk of Alzheimers disease in China: A community-based cohort study. Environ. Res. 205, 112318 (2022).

CAS PubMed Google Scholar

Ran, J. et al. Long-term exposure to fine particulate matter and dementia incidence: A cohort study in Hong Kong. Environ. Pollut. 271, 116303 (2021).

CAS PubMed Google Scholar

Garcia, C. A., Yap, P.-S., Park, H.-Y. & Weller, B. L. Association of long-term PM2.5 exposure with mortality using different air pollution exposure models: Impacts in rural and urban California. Int J. Environ. Health Res. 26, 145157. https://doi.org/10.1080/09603123.2015.1061113 (2016).

CAS PubMed Google Scholar

Wang, B. et al. The impact of long-term PM2. 5 exposure on specific causes of death: exposure-response curves and effect modification among 53 million US Medicare beneficiaries. Environ. Health 19, 112 (2020).

CAS PubMed PubMed Central Google Scholar

Yu, W., Guo, Y., Shi, L. & Li, S. The association between long-term exposure to low-level PM2.5 and mortality in the state of Queensland, Australia: A modelling study with the difference-in-differences approach. PLOS Med. 17, e1003141. https://doi.org/10.1371/journal.pmed.1003141 (2020).

CAS PubMed PubMed Central Google Scholar

Bellinger, C., Jabbar, M. S. M., Zaane, O. & Osornio-Vargas, A. A systematic review of data mining and machine learning for air pollution epidemiology. BMC Public Health 17, 119 (2017).

Google Scholar

Belotti, J. T. et al. Air pollution epidemiology: A simplified Generalized Linear Model approach optimized by bio-inspired metaheuristics. Environ. Res. 191, 110106. https://doi.org/10.1016/j.envres.2020.110106 (2020).

CAS PubMed Google Scholar

Stingone, J. A., Pandey, O. P., Claudio, L. & Pandey, G. Using machine learning to identify air pollution exposure profiles associated with early cognitive skills among U.S. children. Environ. Pollut. 230, 730740. https://doi.org/10.1016/j.envpol.2017.07.023 (2017).

CAS PubMed PubMed Central Google Scholar

Chang, F.-J., Chang, L.-C., Kang, C.-C., Wang, Y.-S. & Huang, A. Explore spatio-temporal PM2.5 features in northern Taiwan using machine learning techniques. Sci. Total Environ. 736, 139656. https://doi.org/10.1016/j.scitotenv.2020.139656 (2020).

ADS CAS PubMed Google Scholar

Silibello, C. et al. Spatial-temporal prediction of ambient nitrogen dioxide and ozone levels over Italy using a random forest model for population exposure assessment. Air Qual. Atmos. Health 14, 817829. https://doi.org/10.1007/s11869-021-00981-4 (2021).

CAS Google Scholar

Fecho, K. et al. A novel approach for exposing and sharing clinical data: The translator integrated clinical and environmental exposures service. J. Am. Med. Inform. Assoc. 26, 10641073. https://doi.org/10.1093/jamia/ocz042 (2019).

PubMed PubMed Central Google Scholar

Chang, V., Ni, P. & Li, Y. K-clustering methods for investigating social-environmental and natural-environmental features based on air quality index. IT Prof. 22, 2834. https://doi.org/10.1109/MITP.2020.2993851 (2020).

Google Scholar

Wu, X., Cheng, C., Zurita-Milla, R. & Song, C. An overview of clustering methods for geo-referenced time series: From one-way clustering to co- and tri-clustering. Int. J. Geogr. Inf. Sci. 34, 18221848. https://doi.org/10.1080/13658816.2020.1726922 (2020).

Google Scholar

Karri, R., Chen, Y.-P.P. & Drummond, K. J. Using machine learning to predict health-related quality of life outcomes in patients with low grade glioma, meningioma, and acoustic neuroma. PLoS ONE 17, e0267931. https://doi.org/10.1371/journal.pone.0267931 (2022).

CAS PubMed PubMed Central Google Scholar

Hautamki, M. et al. The association between charlson comorbidity index and mortality in acute coronary syndromethe MADDEC study. Scand. Cardiovasc. J. 54, 146152. https://doi.org/10.1080/14017431.2019.1693615 (2020).

PubMed Google Scholar

Kantidakis, G. et al. Survival prediction models since liver transplantationcomparisons between Cox models and machine learning techniques. BMC Med. Res. Methodol. 20, 277. https://doi.org/10.1186/s12874-020-01153-1 (2020).

PubMed PubMed Central Google Scholar

Blom, M. C. et al. Training machine learning models to predict 30-day mortality in patients discharged from the emergency department: A retrospective, population-based registry study. BMJ Open 9, e028015. https://doi.org/10.1136/bmjopen-2018-028015 (2019).

PubMed PubMed Central Google Scholar

Weng, S. F., Reps, J., Kai, J., Garibaldi, J. M. & Qureshi, N. Can machine-learning improve cardiovascular risk prediction using routine clinical data?. PLoS ONE 12, e0174944. https://doi.org/10.1371/journal.pone.0174944 (2017).

CAS PubMed PubMed Central Google Scholar

Weng, S. F., Vaz, L., Qureshi, N. & Kai, J. Prediction of premature all-cause mortality: A prospective general population cohort study comparing machine-learning and standard epidemiological approaches. PLoS ONE 14, e0214365. https://doi.org/10.1371/journal.pone.0214365 (2019).

CAS PubMed PubMed Central Google Scholar

Chun, M. et al. Stroke risk prediction using machine learning: a prospective cohort study of 0.5 million Chinese adults. J. Am. Med. Inf. Assoc. 28, 17191727. https://doi.org/10.1093/jamia/ocab068 (2021).

Google Scholar

Moncada-Torres, A., van Maaren, M. C., Hendriks, M. P., Siesling, S. & Geleijnse, G. Explainable machine learning can outperform Cox regression predictions and provide insights in breast cancer survival. Sci. Rep. 11, 6968. https://doi.org/10.1038/s41598-021-86327-7 (2021).

ADS CAS PubMed PubMed Central Google Scholar

Du, M., Haag, D. G., Lynch, J. W. & Mittinty, M. N. Comparison of the tree-based machine learning algorithms to cox regression in predicting the survival of oral and pharyngeal cancers: Analyses based on SEER database. Cancers 12, 2802. https://doi.org/10.3390/cancers12102802 (2020).

CAS PubMed Central Google Scholar

Kim, H., Park, T., Jang, J. & Lee, S. Comparison of survival prediction models for pancreatic cancer: Cox model versus machine learning models. Genomics Inform. 20, e23. https://doi.org/10.5808/gi.22036 (2022).

PubMed PubMed Central Google Scholar

Kattan Michael, W. Comparison of Cox Regression with other methods for determining prediction models and nomograms. J. Urol. 170, S6S10. https://doi.org/10.1097/01.ju.0000094764.56269.2d (2003).

CAS PubMed Google Scholar

Lin, J., Li, K. & Luo, S. Functional survival forests for multivariate longitudinal outcomes: Dynamic prediction of Alzheimers disease progression. Stat. Methods Med. Res. 30, 99111 (2021).

MathSciNet PubMed Google Scholar

Facal, D. et al. Machine learning approaches to studying the role of cognitive reserve in conversion from mild cognitive impairment to dementia. Int. J. Geriatr. Psychiatry 34, 941949 (2019).

PubMed Google Scholar

Spooner, A. et al. A comparison of machine learning methods for survival analysis of high-dimensional clinical data for dementia prediction. Sci. Rep. 10, 110 (2020).

MathSciNet Google Scholar

Wang, J. et al. Random forest model in the diagnosis of dementia patients with normal mini-mental state examination scores. J. Personal. Med. 12, 37. https://doi.org/10.3390/jpm12010037 (2022).

Google Scholar

Pinheiro, L. I. C. C. et al. Application of data mining algorithms for dementia in people with HIV/AIDS. Comput. Math. Methods Med. 2021, 4602465. https://doi.org/10.1155/2021/4602465 (2021).

PubMed PubMed Central Google Scholar

Brickell, E., Whitford, A., Boettcher, A., Pereira, C. & Sawyer, R. J. A-1 the influence of base rate and sample size on performance of a random forest classifier for dementia prediction: Implications for recruitment. Arch. Clin. Neuropsychol. 36, 10401040. https://doi.org/10.1093/arclin/acab062.19 (2021).

Google Scholar

Dauwan, M. et al. Random forest to differentiate dementia with Lewy bodies from Alzheimers disease. Alzheimers Dement. Diagn. Assess. Dis. Monit. 4, 99106. https://doi.org/10.1016/j.dadm.2016.07.003 (2016).

Google Scholar

Mar, J. et al. Validation of random forest machine learning models to predict dementia-related neuropsychiatric symptoms in real-world data. J. Alzheimers Dis. 77, 855864. https://doi.org/10.3233/JAD-200345 (2020).

PubMed PubMed Central Google Scholar

World Medical Association. World medical association declaration of Helsinki: Ethical principles for medical research involving human subjects. JAMA 310, 21912194. https://doi.org/10.1001/jama.2013.281053 (2013).

CAS Google Scholar

Taiwan Environmental Protection Administration (EPA) website, <https://airtw.epa.gov.tw/CHT/Query/His_Data.aspx>

Yu, H.-L. et al. Interactive spatiotemporal modelling of health systems: The SEKSGUI framework. Stoch. Env. Res. Risk Assess. 21, 555572. https://doi.org/10.1007/s00477-007-0135-0 (2007).

MathSciNet Google Scholar

Charlson, M. E., Pompei, P., Ales, K. L. & MacKenzie, C. R. A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. J. Chronic Dis. 40, 373383. https://doi.org/10.1016/0021-9681(87)90171-8 (1987).

CAS PubMed Google Scholar

Hude, Q. et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med. Care 43, 11301139 (2005).

Google Scholar

Heagerty, P. J. & Saha, P. SurvivalROC: Time-dependent ROC curve estimation from censored survival data. Biometrics 56, 337344 (2000).

CAS PubMed Google Scholar

Harrell Jr, F. E., Harrell Jr, M. F. E. & Hmisc, D. Package rms. Vanderbilt University, 229 (2017).

Harrell, F. E. Jr., Califf, R. M., Pryor, D. B., Lee, K. L. & Rosati, R. A. Evaluating the yield of medical tests. JAMA 247, 25432546. https://doi.org/10.1001/jama.1982.03320430047030 (1982).

Read this article:
Long-term exposure to particulate matter was associated with increased dementia risk using both traditional approaches and novel machine learning...