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Frontiers of Environmental Science & Engineering

ISSN 2095-2201

ISSN 2095-221X(Online)

CN 10-1013/X

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2018 Impact Factor: 3.883

Front. Environ. Sci. Eng.    2024, Vol. 18 Issue (9) : 109    https://doi.org/10.1007/s11783-024-1869-3
Cognitive impairment associated with individual and joint exposure to PM2.5 constituents in a Chinese national cohort
Boning Deng1, Yachen Li1, Lifeng Zhu1, Yuwei Zhou1, Aonan Sun1, Jingjing Zhang1, Yixiang Wang1, Yuxi Tan1, Jiajun Shen1, Yalin Zhang1, Zan Ding2(), Yunquan Zhang1()
1. Institute of Social Development and Health Management, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Public Health, Wuhan University of Science and Technology, Wuhan 430065, China
2. Baoan Central Hospital of Shenzhen, Shenzhen 518102, China
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Abstract

● A national cohort to assess nexus between cognition function and PM2.5 constituents.

● Cognitive impairment was related to individual and joint exposure to PM2.5 constituents.

● BC displayed the highest negative effect on PM2.5-related cognitive impairment.

● Female, younger, and well-educated individuals were more vulnerable.

Nationwide longitudinal evidence linking cognitive decline with exposure to fine particulate matter (PM2.5) constituents remains scarce in China. By constructing a dynamic cohort based on the China Health and Retirement Longitudinal Study, we aimed to assess individual and joint associations of PM2.5 constituents with cognitive function among middle-aged and older adults in China. Linear mixed-effects models incorporated with quantile-based g-computation were applied to investigate individual and joint associations of long-term exposures to PM2.5 constituents with cognitive function. Among 13,507 respondents, we evaluated 38,950 follow-up records of cognitive function tests. Declines in global cognitive score associated with an interquartile range (IQR) increase in exposure were −1.477 (95% CI: −1.722, −1.232) for nitrate, followed by −1.331 (−1.529, −1.133) for ammonium, −1.033 (−1.184, −0.883) for sulfate, −0.988 (−1.144, −0.832) for organic matter and −0.822 (−0.946, −0.699) for black carbon. An IQR-equivalent increase in joint exposure to these PM2.5 constituents was associated with a decline of −1.353 (−1.659, −1.048) in global cognitive score. Female, younger, and well-educated individuals were at greater vulnerability to cognitive impairment related to individual and joint exposure to PM2.5 constituents. This study suggested that later-life exposures to PM2.5 constituents were associated with cognitive decline in middle-aged and older adults in China.

Keywords Air pollution      PM2.5 constituents      Cognitive function      Joint exposure      Middle-aged and older adults     
Corresponding Author(s): Zan Ding,Yunquan Zhang   
Issue Date: 27 June 2024
 Cite this article:   
Boning Deng,Yachen Li,Lifeng Zhu, et al. Cognitive impairment associated with individual and joint exposure to PM2.5 constituents in a Chinese national cohort[J]. Front. Environ. Sci. Eng., 2024, 18(9): 109.
 URL:  
https://academic.hep.com.cn/fese/EN/10.1007/s11783-024-1869-3
https://academic.hep.com.cn/fese/EN/Y2024/V18/I9/109
Characteristics Statistics
Total No. of participants 13,507
Cognitive score, mean ± SD
 Global cognition 16.0 ± 5.1
 Episodic memory 7.7 ± 3.6
 Mental status 8.3 ± 2.9
Demographic characteristics, n (%)
Age, mean ± SD, years 56.6 ± 8.7
Male sex 7462 (55.2)
Low educational attainment 7106 (52.6)
Married 11,761 (87.1)
Employment 9817 (72.7)
Rural residence 7340 (54.3)
Behavioral factors, n (%)
Cigarette smoking 5861 (43.4)
Alcohol consumption 4952 (36.7)
Social activity participation 5604 (41.5)
Intensity of physical activity
 High 4258 (33.5)
 Medium 4295 (31.8)
 Low 3605 (26.7)
 Never 1079 (8.0)
Night sleep duration, hour
 ≤6 3572 (26.4)
 (6, 8] 6082 (45.0)
 >8 3853 (28.5)
Household characteristics, n (%)
Annual income, RMB
 ≤6997 2797 (20.7)
 (6997, 25,100] 3445 (25.5)
 (25100, 52710] 3494 (25.9)
 >57210 3771 (27.9)
Clean cooking fuel 7612 (56.4)
Health conditions, n (%)
Prevalence of chronic diseases 8796 (65.1)
Self-rated health status
 Poor 1625 (12.0)
 Fair 4703 (34.8)
 Good 7179 (53.2)
Depression (CES-D-10 ≥10) 3895 (28.8)
Tab.1  Baseline characteristics of study participants
Fig.1  Estimated changes in cognitive scores associated with an IQR increase in exposure to PM2.5 and individual constituents or an IQR-equivalent increase in joint exposure. Abbreviations: IQR, interquartile range; CI, confidence interval; PM2.5, particulate matter with an aerodynamic diameter 2.5 μm; BC, black carbon; OM, organic matter; NO3, nitrate; SO42–, sulfate; NH4+, ammonium.
Fig.2  The probability distributions of changes in global cognitive score associated with PM2.5 constituents estimated from 1000 Monte Carlo simulations. Abbreviations: PM2.5, particulate matter with an aerodynamic diameter ≤2.5 μm; BC, black carbon; OM, organic matter; NO3, nitrate; SO42–, sulfate; NH4+, ammonium.
Fig.3  Concentration-response associations of exposure to PM2.5 and its constituents with changes in global cognitive score. Abbreviations: PM2.5, particulate matter with an aerodynamic diameter ≤2.5 μm; BC, black carbon; OM, organic matter; NO3, nitrate; SO42–, sulfate; NH4+, ammonium.
Fig.4  Quasi-concentration-response associations (A) and estimated weights (B) in joint-exposure analyses using qg-computation. Abbreviations: PM2.5, particulate matter with an aerodynamic diameter ≤2.5 μm; BC, black carbon; OM, organic matter; NO3, nitrate; SO42–, sulfate; NH4+, ammonium.
Fig.5  Estimated changes in domain-specific cognitive scores associated with an IQR-equivalent increase in joint exposure, stratified by participant characteristics. Abbreviations: CI, confidence interval; IQR, interquartile range.
1 S Ayton, NG Faux, AI Bush, Disease Neuroimaging Initiative Alzheimer’s. (2015). Ferritin levels in the cerebrospinal fluid predict Alzheimer’s disease outcomes and are regulated by APOE. Nature Communications, 6(1): 6760
https://doi.org/10.1038/ncomms7760
2 Azur MJ, Stuart EA, Frangakis C, Leaf PJ (2011). Multiple imputation by chained equations: What is it and how does it work? International Journal of Methods in Psychiatric Research, 20(1): 40–49
3 PC Bello-Medina, E Rodriguez-Martinez, RA Prado-Alcala, S Rivas-Arancibia. (2022). Ozone pollution, oxidative stress, synaptic plasticity, and neurodegeneration. Neurología, 37(4): 277–286
https://doi.org/10.1016/j.nrleng.2018.10.025
4 X DaoS Di X ZhangP GaoL WangL YanG Tang L HeT Krafft F (2022) Zhang. Composition and sources of particulate matter in the Beijing-Tianjin-Hebei region and its surrounding areas during the heating season. Chemosphere, 291(Pt 1): 132779
5 SG Gaidin, VP Zinchenko, AM Kosenkov. (2020). Mechanisms of ammonium-induced neurotoxicity. Neuroprotective effect of alpha-2 adrenergic agonists. Archives of Biochemistry and Biophysics, 693: 108593
https://doi.org/10.1016/j.abb.2020.108593
6 J Gong, G Wang, Y Wang, X Chen, Y Chen, Q Meng, P Yang, Y Yao, Y Zhao. (2022). Nowcasting and forecasting the care needs of the older population in China: analysis of data from the China Health and Retirement Longitudinal Study (CHARLS). Lancet Public Health, 7(12): e1005–e1013
https://doi.org/10.1016/S2468-2667(22)00203-1
7 MS Goyal, AG Vlassenko, TM Blazey, Y Su, LE Couture, TJ Durbin, RJ Bateman, TL Benzinger, JC Morris, ME Raichle. (2017). Loss of brain aerobic glycolysis in normal human aging. Cell Metabolism, 26(2): 353–360.e3
https://doi.org/10.1016/j.cmet.2017.07.010
8 Harrell J F E (2001). Regression Modeling Strategies: with Applications to Linear Models, Logistic Regression, and Survival Analysis. New York: Springer
9 P Hautekiet, ND Saenen, S Demarest, H Keune, I Pelgrims, J Van Der Heyden, EM De Clercq, TS Nawrot. (2022). Air pollution in association with mental and self-rated health and the mediating effect of physical activity. Environmental Health, 21(1): 29
https://doi.org/10.1186/s12940-022-00839-x
10 M Helbich, Y Yao, Y Liu, J Zhang, P Liu, R Wang. (2019). Using deep learning to examine street view green and blue spaces and their associations with geriatric depression in Beijing, China. Environment International, 126: 107–117
https://doi.org/10.1016/j.envint.2019.02.013
11 Y Hu, W Peng, R Ren, Y Wang, G Wang. (2022). Sarcopenia and mild cognitive impairment among elderly adults: the first longitudinal evidence from CHARLS. Journal of Cachexia, Sarcopenia and Muscle, 13(6): 2944–2952
https://doi.org/10.1002/jcsm.13081
12 S HuangQ SongW HuB YuanJ Liu B JiangW LiC WuF JiangW Chen, et al.. (2022). Chemical composition and sources of amines in PM2.5 in an urban site of PRD, China. Environmental Research, 212(Pt B): 113261
13 W Huang, Y Zhou, X Chen, X Zeng, L D Knibbs, Y Zhang, B Jalaludin, SC Dharmage, L Morawska, Y Guo. et al.. (2023). Individual and joint associations of long-term exposure to air pollutants and cardiopulmonary mortality: a 22-year cohort study in Northern China. Lancet Regional Health. Western Pacific, 36: 100776
https://doi.org/10.1016/j.lanwpc.2023.100776
14 L Jia, Y Du, L Chu, Z Zhang, F Li, D Lyu, Y Li, Y Li, M Zhu, H Jiao. et al.. (2020). Prevalence, risk factors, and management of dementia and mild cognitive impairment in adults aged 60 years or older in China: a cross-sectional study. Lancet Public Health, 5(12): e661–e671
https://doi.org/10.1016/S2468-2667(20)30185-7
15 S Kaumbekova, MA Torkmahalleh, D Shah. (2021). Impact of ultrafine particles and secondary inorganic ions on early onset and progression of amyloid aggregation: insights from molecular simulations. Environmental Pollution, 284: 117147
https://doi.org/10.1016/j.envpol.2021.117147
16 AP Keil, JP Buckley, KM O’Brien, KK Ferguson, S Zhao, AJ White. (2020). A quantile-based g-computation approach to addressing the effects of exposure mixtures. Environmental Health Perspectives, 128(4): 047004
https://doi.org/10.1289/EHP5838
17 ER Kulick, GA Wellenius, AK Boehme, NR Joyce, N Schupf, JD Kaufman, R Mayeux, RL Sacco, JJ Manly, MSV Elkind. (2020). Long-term exposure to air pollution and trajectories of cognitive decline among older adults. Neurology, 94(17): e1782–e1792
https://doi.org/10.1212/WNL.0000000000009314
18 J Liu, R Liu, Y Zhang, X Lao, KL Mandeville, X Ma, Q Di. (2023). Leisure-time physical activity mitigated the cognitive effect of PM2.5 and PM2.5 components exposure: evidence from a nationwide longitudinal study. Environment International, 179: 108143
https://doi.org/10.1016/j.envint.2023.108143
19 S Liu, G Geng, Q Xiao, Y Zheng, X Liu, J Cheng, Q Zhang. (2022). Tracking daily concentrations of PM2.5 chemical composition in China since 2000. Environmental Science & Technology, 56(22): 16517–16527
https://doi.org/10.1021/acs.est.2c06510
20 G Livingston, J Huntley, A Sommerlad, D Ames, C Ballard, S Banerjee, C Brayne, A Burns, J Cohen-Mansfield, C Cooper. et al.. (2020). Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet, 396(10248): 413–446
https://doi.org/10.1016/S0140-6736(20)30367-6
21 YC Lo, YC Lu, YH Chang, S Kao, HB Huang. (2019). Air Pollution exposure and cognitive function in Taiwan older adults: a repeated measurement study. International Journal of Environmental Research and Public Health, 16(16): 2976
https://doi.org/10.3390/ijerph16162976
22 L Luo, X Bai, S Liu, B Wu, W Liu, Y Lv, Z Guo, S Lin, S Zhao, Y Hao. et al.. (2022). Fine particulate matter (PM2.5/PM1.0) in Beijing, China: variations and chemical compositions as well as sources. Journal of Environmental Sciences, 121: 187–198
https://doi.org/10.1016/j.jes.2021.12.014
23 S Mo, Y Wang, M Peng, Q Wang, H Zheng, Y Zhan, Z Ma, Z Yang, L Liu, K Hu. et al.. (2023). Sex disparity in cognitive aging related to later-life exposure to ambient air pollution. Science of the Total Environment, 886: 163980
https://doi.org/10.1016/j.scitotenv.2023.163980
24 2019 Dementia Forecasting Collaborators GBD. (2022). Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019. Lancet. Public Health, 7(2): e105–e125
https://doi.org/10.1016/S2468-2667(21)00249-8
25 R Niranjan, A K Thakur. (2017). The toxicological mechanisms of environmental soot (black carbon) and carbon black: focus on oxidative stress and inflammatory pathways. Frontiers in Immunology, 8: 763
https://doi.org/10.3389/fimmu.2017.00763
26 R Pan, Y Zhang, Z Xu, W Yi, F Zhao, J Song, Q Sun, P Du, J Fang, J Cheng. et al.. (2022). Exposure to fine particulate matter constituents and cognitive function performance, potential mediation by sleep quality: a multicenter study among Chinese adults aged 40–89 years. Environment International, 170: 107566
https://doi.org/10.1016/j.envint.2022.107566
27 LL Popescu, RS Popescu, T Catalina. (2022). Indoor particle’s pollution in bucharest, Romania. Toxics, 10(12): 757
https://doi.org/10.3390/toxics10120757
28 J Qi, N Zhao, M Liu, Y Guo, J Fu, Y Zhang, W Wang, Z Su, Y Zeng, Y Yao. et al.. (2024). Long-term exposure to fine particulate matter constituents and cognitive impairment among older adults: an 18-year Chinese nationwide cohort study. Journal of Hazardous Materials, 468: 133785
https://doi.org/10.1016/j.jhazmat.2024.133785
29 R Ren, J Qi, S Lin, X Liu, P Yin, Z Wang, R Tang, J Wang, Q Huang, J Li. et al.. (2022). The China alzheimer report 2022. General Psychiatry, 35(1): e100751
https://doi.org/10.1136/gpsych-2022-100751
30 LS Richmond-Rakerd, S D’Souza, B J Milne, A Caspi, T E Moffitt. (2022). Longitudinal associations of mental disorders with dementia: 30-year analysis of 1.7 million New Zealand citizens. JAMA Psychiatry, 79(4): 333–340
https://doi.org/10.1001/jamapsychiatry.2021.4377
31 L S Rotenstein, M A Ramos, M Torre, J B Segal, M J Peluso, C Guille, S Sen, D A Mata. (2016). Prevalence of depression, depressive symptoms, and suicidal ideation among medical students: a systematic review and meta-analysis. Journal of the American Medical Association, 316(21): 2214–2236
https://doi.org/10.1001/jama.2016.17324
32 A Salinas-Rodríguez, J A Fernandez-Nino, B Manrique-Espinoza, G L Moreno-Banda, A L Sosa-Ortiz, Z M Qian, H Lin. (2018). Exposure to ambient PM2.5 concentrations and cognitive function among older Mexican adults. Environment International, 117: 1–9
https://doi.org/10.1016/j.envint.2018.04.033
33 L Shi, Q Zhu, Y Wang, H Hao, H Zhang, J Schwartz, H Amini, Donkelaar A Van, RV Martin, K Steenland. et al.. (2023). Incident dementia and long-term exposure to constituents of fine particle air pollution: a national cohort study in the United States. Proceedings of the National Academy of Sciences of the United States of America, 120(1): e2211282119
https://doi.org/10.1073/pnas.2211282119
34 N Sumien, JT Cunningham, DL Davis, R Engelland, O Fadeyibi, G E Farmer, S Mabry, P Mensah-Kane, O T P Trinh, P H Vann. et al.. (2021). Neurodegenerative disease: roles for sex, hormones, and oxidative stress. Endocrinology, 162(11): bqab185
https://doi.org/10.1210/endocr/bqab185
35 L Tan, E Nakanishi, M Lee. (2022). Association between exposure to air pollution and late-life neurodegenerative disorders: an umbrella review. Environment International, 158: 106956
https://doi.org/10.1016/j.envint.2021.106956
36 Y Tian, Z Shi. (2022). Effects of physical activity on daily physical function in Chinese middle-aged and older adults: a longitudinal study from CHARLS. Journal of Clinical Medicine, 11(21): 6514
https://doi.org/10.3390/jcm11216514
37 J Wang, J Wang, W Nie, X Chi, D Ge, C Zhu, L Wang, Y Li, X Huang, X Qi. et al.. (2023). Response of organic aerosol characteristics to emission reduction in Yangtze River Delta region. Frontiers of Environmental Science & Engineering, 17(9): 114
https://doi.org/10.1007/s11783-023-1714-0
38 Q Wang, D Fan, L Zhao, W Wu. (2019). A Study on the design method of indoor fine particulate matter (PM2.5) pollution control in China. International Journal of Environmental Research and Public Health, 16(23): 4588
https://doi.org/10.3390/ijerph16234588
39 X Q Wang, P J Chen. (2014). Population ageing challenges health care in China. Lancet, 383(9920): 870
https://doi.org/10.1016/S0140-6736(14)60443-8
40 B Winblad, P Amouyel, S Andrieu, C Ballard, C Brayne, H Brodaty, A Cedazo-Minguez, B Dubois, D Edvardsson, H Feldman. et al.. (2016). Defeating Alzheimer’s disease and other dementias: a priority for European science and society. Lancet Neurology, 15(5): 455–532
https://doi.org/10.1016/S1474-4422(16)00062-4
41 S WuB Wang D YangH WeiH LiL PanJ Huang X WangY QinC Zheng, et al.. (2016). Ambient particulate air pollution and circulating antioxidant enzymes: a repeated-measure study in healthy adults in Beijing, China. Environmental Pollution, 208(Pt A): 16-24
42 R Wurth, MA Kioumourtzoglou, KL Tucker, J Griffith, J Manjourides, H Suh. (2018). Fine particle sources and cognitive function in an older Puerto Rican cohort in Greater Boston. Environmental Epidemiology, 2(3): e022
https://doi.org/10.1097/EE9.0000000000000022
43 Q Xiao, G Geng, T Xue, S Liu, C Cai, K He, Q Zhang. (2022). Tracking PM2.5 and O3 pollution and the related health burden in China 2013–2020. Environmental Science & Technology, 56(11): 6922–6932
https://doi.org/10.1021/acs.est.1c04548
44 Y Xie, D Wu, S Zhu. (2021). Can new energy vehicles subsidy curb the urban air pollution? Empirical evidence from pilot cities in China. Science of the Total Environment, 754: 142232
https://doi.org/10.1016/j.scitotenv.2020.142232
45 J Xu, J Wang, A Wimo, L Fratiglioni, C Qiu. (2017). The economic burden of dementia in China, 1990–2030: implications for health policy. Bulletin of the World Health Organization, 95(1): 18–26
https://doi.org/10.2471/BLT.15.167726
46 J Xue, J Li, J Liang, S Chen. (2018). The prevalence of mild cognitive impairment in China: a systematic review. Aging and Disease, 9(4): 706–715
https://doi.org/10.14336/AD.2017.0928
47 T Xue, Y Han, Y Fan, Y Zheng, G Geng, Q Zhang, T Zhu. (2021). Association between a rapid reduction in air particle pollution and improved lung function in adults. Annals of the American Thoracic Society, 18(2): 247–256
https://doi.org/10.1513/AnnalsATS.202003-246OC
48 H Yang, X Huang, J Hu, J R Thompson, R J Flower. (2022). Achievements, challenges and global implications of China’s carbon neutral pledge. Frontiers of Environmental Science & Engineering, 16(8): 111
https://doi.org/10.1007/s11783-022-1532-9
49 Y Yang, Z Ruan, X Wang, Y Yang, T G Mason, H Lin, L Tian. (2019). Short-term and long-term exposures to fine particulate matter constituents and health: a systematic review and meta-analysis. Environmental Pollution, 247: 874–882
https://doi.org/10.1016/j.envpol.2018.12.060
50 Y Yao, K Wang, H Xiang. (2022). Association between cognitive function and ambient particulate matters in middle-aged and elderly Chinese adults: evidence from the China Health and Retirement Longitudinal Study (CHARLS). Science of the Total Environment, 828: 154297
https://doi.org/10.1016/j.scitotenv.2022.154297
51 D Younan, X Wang, R Casanova, R Barnard, S A Gaussoin, S Saldana, A J Petkus, D P Beavers, S M Resnick, J E Manson. et al.. (2020). PM2.5 associated with gray matter atrophy reflecting increased Alzheimers risk in older women. Neurology, 96(8): e1190–e1201
https://doi.org/10.1212/WNL.0000000000011149
52 Y Yuan, K Wang, H Z Sun, Y Zhan, Z Yang, K Hu, Y Zhang. (2023). Excess mortality associated with high ozone exposure: a national cohort study in China. Environmental Science and Ecotechnology, 15: 100241
https://doi.org/10.1016/j.ese.2023.100241
53 Y Zeng, Q Feng, T Hesketh, K Christensen, J W Vaupel. (2017). Survival, disabilities in activities of daily living, and physical and cognitive functioning among the oldest-old in China: a cohort study. Lancet, 389(10079): 1619–1629
https://doi.org/10.1016/S0140-6736(17)30548-2
54 N Zhao, Z Al-Aly, B Zheng, A Van Donkelaar, RV Martin, CA Pineau, S Bernatsky. (2022). Fine particulate matter components and interstitial lung disease in rheumatoid arthritis. European Respiratory Journal, 60(1): 2102149
https://doi.org/10.1183/13993003.02149-2021
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