Long-term exposure to particulate matter and risk of dementia in Korea: A national population-based cohort study

doi:10.21203/rs.3.rs-2622781/v1

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Research Article

Long-term exposure to particulate matter and risk of dementia in Korea: A national population-based cohort study

https://doi.org/10.21203/rs.3.rs-2622781/v1

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published 14 Apr, 2023

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Background

The prevalence of age-related neurodegenerative diseases has risen in conjunction with an increase in life expectancy. Although there is emerging evidence that air pollution might accelerate or worsen dementia progression, studies on Asian regions remains limited. This study aimed to investigate the relationship between long-term exposure to PM₁₀ and the risk of developing Alzheimer's disease and vascular dementia in the elderly population in South Korea.

Methods

Insurance claims data from 2.4 million people aged 65 years and above who participated in at least one national health checkup program from 2008 to 2009 were obtained from the National Health Insurance Service. A nationwide retrospective cohort study was designed, and patients were followed from the date of cohort entry to the date of dementia occurrence, death, moving residence, or the end of the study period (December 31, 2019), whichever came first. Long-term average PM₁₀ exposure variable was constructed from national monitoring data considering time-dependent exposure. Extended Cox proportional hazard models with time-varying exposure were used to estimate hazard ratios (HR) for Alzheimer’s disease and vascular dementia.

Results

A total of 1,436,361 participants were selected, of whom 167,988 were newly diagnosed with dementia (134,811 with Alzheimer’s disease and 12,215 with vascular dementia). The results show that for every 10 µg/m³ increase in PM₁₀, the HR was 0.99 (95% CI 0.98-1.00) for Alzheimer’s disease and 1.05 (95% CI 1.02–1.08) for vascular dementia. Stratified analysis according to sex and age group showed that the risk of vascular dementia was higher in men and in those under 75 years of age.

Conclusion

The results found that long-term PM₁₀ exposure was significantly associated with the risk of developing vascular dementia but not with Alzheimer’s disease. These findings suggest that the mechanism behind the PM₁₀-dementia relationship could be linked to vascular damage.

Particulate matter (PM10)

vascular dementia

Alzheimer’s disease

dementia

cohort

An increase in life expectancy leads to an increase in the prevalence of age-related neurodegenerative diseases (Tipton and Graff-Radford 2018). Most of these diseases progress to dementia and are usually diagnosed when social and/or occupational functions cannot be performed because of acquired cognitive impairment (Hugo and Ganguli 2014). According to the American Psychiatric Association Diagnostic and Statistical Manual (DSM-5), dementia, a major neurocognitive disorder, results from severe dysfunction in one or more cognitive domains including memory, language, visuospatial ability, and social/behavioral function (Cunningham et al. 2015; Hugo and Ganguli 2014). Dementia is often divided into two broad categories: neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease, and non-neurodegenerative diseases such as vascular dementia (Gale et al. 2018). According to the pathophysiological process, the clinical classification of dementia is divided into Alzheimer's disease (50–70%), vascular dementia (20%), Lewy body dementia (5%), and frontotemporal dementia (5%) (Cunningham et al. 2015).

Currently, measures to prevent or delay dementia, particularly Alzheimer's disease are not available (Tipton and Graff-Radford 2018). In 2016, approximately 47 million people worldwide were reported to suffer from dementia; this number is expected to triple to approximately 115.4 million by 2050 (Prince et al. 2013; Tipton and Graff-Radford 2018). In particular, the prevalence of dementia in the population aged 65 years and older is estimated to double every five years (Jorm and Jolley 1998). The cost of health services, including caring for people with dementia, is increasing, and patients’ families are burdened by physical, emotional, and financial stress (Alzheimer's 2013; Cunningham et al. 2015; Hugo and Ganguli 2014).

Aging is a non-modifiable and strong risk factor for dementia; however, modifiable risk factors have the potential to delay or moderate it. Potential risk factors include apolipoprotein E (APOE) ↋4, hypertension, obesity, smoking, diabetes, depression, cardiovascular disease, head injury, and social isolation. Some protective factors include physical activity, healthy diet, and non-steroidal anti-inflammatory drugs (NSAIDs) (Cunningham et al. 2015; Ford et al. 2018; Hugo and Ganguli 2014; Livingston et al. 2017; Norton et al. 2014). Environmental exposure is another risk factor that can be modified, and recent epidemiological studies have suggested that air pollution may accelerate or worsen dementia (Bhatt et al. 2015; Shi et al. 2020). In a recent systematic review, long-term exposure to PM_2.5 was associated with the risk of dementia (Fu et al. 2019; Peters et al. 2019; Tsai et al. 2019) and cognitive decline (Peters et al. 2015; Thompson et al. 2023). Other findings provided inconsistent evidence on the effects of PM₁₀ and air pollution such as nitrogen oxides (NO_x), nitrogen dioxide (NO₂), or ozone (O₃) exposure on dementia development and cognitive function (Abolhasani et al. 2023; Weuve et al. 2021; Zhao et al. 2018).

Globally, many people with dementia place a significant burden on the healthcare system; however, studies on air pollution and dementia remain limited in Asian cities (Ran et al. 2021). In Korea, some studies have reported the effects of air pollution on cognitive function and Parkinson's disease (Lee et al. 2017; Park et al. 2022; Shin et al. 2019), and the Clinical Research Center for Dementia of South Korea (CREDOS) study cohort reported that PM_2.5 exposure was exacerbated neuropsychiatric symptoms in people with cognitive impairment (Lee et al. 2019a). There was no PM₁₀-dementia study in Korea, and this study is the first study to investigate the relationship between PM₁₀ and dementia. The prevalence of dementia in the elderly Korean population (≥ 65 years) was 9.2%. Comparing the prevalence of dementia among people aged 65 and over in other countries, it is about 7.1% in the UK (Cunningham et al. 2015), 6.3% in Japanese Americans, 12.9% in Caribbean Hispanic Americans, 7.2–20.9% in African Americans (Mehta et al. 2016), and 5.3% in China over 60 years of age (Jia et al. 2019). Korea is a serious aging society, and age-related neurodegenerative diseases will put an economic burden (Kim et al. 2014). This study aimed to determine the relationship between long-term exposure to PM₁₀ and the risk of developing Alzheimer’s disease and vascular dementia in an older population using the National Health Insurance Service (NHIS).

2.1. Data source

The National Health Insurance Service (NHIS) of Korea is a health insurance system that provides healthcare coverage for the entire population. The NHIS database contains demographic and medical claims data, including types of medical care facilities, dates of visits, diagnosis codes, medical costs, procedures, prescribed drug information, and examinations. In addition, this database is linked to the death records of National Statistics Korea. The diagnostic records were coded using the International Statistical Classification of Diseases (ICD-10). This study was approved by the Institutional Review Board of Korea University, which waived the need for informed consent because only de-identified data were used (IRB code KUIRB-2021-0003-01).

2.2. Study design and population

This was a nationwide, retrospective cohort study. A cohort entry date from January 1, 2008, was assumed. Claims data from 2.4 million people aged 65 years or older who participated in the national health checkup program at least once between 2008 and 2009 were obtained. Those who received ≥ 2 health records between January 1, 2008, and December 31, 2019, were selected. The following patients were excluded: (1) those who were diagnosed with dementia or had a history of dementia-related drugs between 2005 and 2007; (2) those who died before the cohort entry date; (3) those for whom there was no information on covariates; and (4) those for whom there was no air pollution information at baseline. Patients were followed from the date of cohort entry to the date of dementia occurrence, death, moving residence, or the end of the study period (December 31, 2019), whichever came first.

A total of 1,436,361 participants were followed after excluding 991,757 individuals by predefined exclusion criteria. A total of 167,988 participants were newly diagnosed with dementia during the study period, accounting for 11.7% of the study population. Among them, 134,811 and 12,215 individuals were identified as having Alzheimer’s disease and vascular dementia, respectively, accounting for 80.3% and 7.3% of the total number of dementia cases, respectively (Fig. 1).

2.3. Outcome definition

Referring to a previous study (Baek et al. 2020), we identified an incident case of dementia up to the fifth diagnosis code. A dementia event was defined as ≥ 1 inpatient or 2 outpatient records in the neurology or psychiatry department and prescription of dementia-related medications (donepezil, rivastigmine, galantamine hydrobromide, and memantine). Dementia type was classified as Alzheimer’s disease (ICD-10: F00, G30), vascular dementia (ICD-10: F01), and others (ICD-10: F02-F03, F05.1, G31.1, G31.0, G31.8). To detect incident cases of dementia, patients who developed dementia within one year of cohort entry were censored at the time of dementia onset in each subject. Moreover, several definitions were compared to validate the definition of outcome as follows: (1) restricted to primary and secondary diagnosis codes, presence of ≥ 1 inpatient or 2 outpatient diagnoses, and a prescription of dementia medication (strict definition); a case of dementia up to the fifth diagnosis code with (2) presence of ≥ 1 record of an inpatient or outpatient diagnosis, and a prescription of dementia medication; (3) presence of ≥ 1 inpatient or 2 outpatient diagnoses, or a prescription of dementia medication; and (4) presence of either a dementia diagnosis or a prescription of dementia medication.

2.4. Exposure assessment

Air Korea (www.airkorea.or.kr) was used to collect data from the region-specific sites. The data were sent to the National Ambient Air Monitoring Information System (NAMIS), and these were confirmed and finalized by the National Institute of Environmental Research. If more than 75% of the data were complete, they were considered valid.

The hourly concentrations of particles < 10 µm in diameter (PM₁₀) at each monitoring site were obtained from 2008 to 2019. The study area included 137 districts (study population per district:1,733 − 30,939) and 186 monitoring stations across South Korea (Supplementary Table 1), each of which had at least one monitoring site. If there were more than one monitoring station in a district, the average of the measurements was taken every hour within each district. We then calculated district-specific daily 24-hour mean concentrations. If more than 6 hours (25%) of the 24-hour measurements are missing, the concentration for that date was treated as missing. Next, we averaged the daily values over 12 months (all-season) for each district and calendar year. We also treated the values for a year as missing if more than 25% of the data were missing. To calculate the time-varying exposure, district-specific yearly mean concentrations were assigned to individuals based on their residence in each calendar year. The time-varying exposures were defined as the 1-year averages. When the study population moved away from their residence, the moving date was assumed to be July 1, the year in which they moved.

2.5. Covariates

Potential confounders were identified in the literature, including demographic variables (age, sex, body mass index (BMI)), socioeconomic factors (insurance premium), behavioral factors (smoking, drinking, physical activity), comorbidities (depression, traumatic brain injury, hypertension, diabetes mellitus, hyperlipidemia, coronary artery disease, cerebrovascular disease, atrial fibrillation, peripheral vascular disease, myocardial infarction, stroke, chronic obstructive pulmonary disease (COPD), chronic liver disease, chronic pulmonary disease, Charlson comorbidity index (CCI)), and ecological factors (Supplementary Table 2).

Ecological factors were collected from KOSIS (the Korean Statistical Information Service) at a province or district scale. The proportion of the elderly was calculated as the ratio of the population aged 65 years and older to the total population, and the proportion of basic livelihood security was the ratio of the number of recipients of basic livelihood security. The proportion of people without a high school diploma was defined as the population without a high school diploma, divided by those aged 25–64. Education data were available for 2005; therefore, the 2008–2009 study population’s education categories were based on the 2005 data. The ratio of the elderly and education was calculated as a single value for each district, and the proportion of basic livelihood security was calculated on the provincial scale.

2.6. Statistical analysis

Extended Cox proportional hazard models were used with time-varying exposure to estimate hazard ratios (HR) for the long-term effects of PM₁₀. As the exposure varied over the years, the dataset was prepared for analysis using the Andersen-Gill counting process. The counting process method had a yearly record for each participant, and records were generated until one of the following occurred first: dementia onset, death, moving, or ending of a cohort event. For example, one person with no events up to 2019 had 12 annual records. Furthermore, the association between the incidence of Alzheimer’s disease and vascular disease was estimated according to annual mean PM₁₀ exposure.

The final models were adjusted for potential confounders, including sex, age, BMI, smoking, physical activity, insurance premium, and comorbidities, including depression, traumatic brain injury, diabetes mellitus, stroke, CCI, proportion of basic livelihood security recipients, and proportion of people with no high school diploma. All covariates were treated as fixed variables except for age. Age was assigned to time-dependent covariates. The results are reported as estimated HRs with 95% CIs per 10 µg/m³ increase in PM₁₀. The effects of PM₁₀ on dementia were estimated as both continuous and categorical variables, and average exposure levels were categorized into quartiles (Q1, < 42.9 µg/m³; Q2, 42.9–47.6 µg/m³; Q3, 47.7–53.3 µg/m³; Q4, ≥ 53.4 µg/m³). To test the exposure-response relationship between PM₁₀ and dementia, we used a restricted natural cubic spline function with 4 knots.

Subgroup analyses were conducted to estimate the potential effect modifications according to sex (male and female), age (< 75 or ≥ 75 years), stroke (yes or no), depression (yes or no), brain injury (yes or no), and diabetes mellitus (yes or no). Sensitivity analyses were performed to explore the robustness of the results to the exposure time window, potential outcome misclassifications, and confounding factors. First, the annual average concentrations of PM₁₀ were computed for the previous three and five years (3-year, and 5-year moving). Second, alternative definitions of outcomes were applied. Third, participants who died during the study period were excluded. Fourth, regional indicators (16 province levels) as alternative ecological factor were applied. All analyses were performed using SAS Enterprise Guide 7.1 for Windows and R version 4.1.0. The statistical significance level was set at p = 0.05.

Of the 1,436,361 participants, 53.4% were female, 20.8% were aged 75 years or older, and 62.8% were overweight or obese. The majority of participants were non-smokers (75.5%), non-drinkers (78.0%), and exercised less (41.7%). Compared to all subjects, patients with Alzheimer’s disease and vascular dementia had a higher prevalence of comorbidities, particularly depression, hypertension, cerebrovascular disease, stroke, and CCI. The mean follow-up time was 8.6 years and the mean concentration of PM₁₀ was 48.4 µg/m³ (Table 1).

Table 1

Baseline characteristics of Alzheimer’s disease, vascular dementia, and non-dementia cohort for 2008–2019
		All		Alzheimer's disease		Vascular dementia
		N = 1,436,361		N = 134,811		N = 12,215
Sex
	Male	669,452	(46.6)	51,361	(38.1)	5,544	(45.4)
	Female	766,909	(53.4)	83,450	(61.9)	6,671	(54.6)
Age
	Mean ± SD	70.9	± 4.9	72.8	± 5.0	72.4	± 4.8
	64–69	660,135	(46.0)	38,348	(28.5)	3,812	(31.2)
	70–74	478,516	(33.3)	51,394	(38.1)	4,692	(38.4)
	75–79	209,352	(14.6)	31,689	(23.5)	2,697	(22.1)
	≥ 80	88,358	(6.2)	13,380	(9.9)	1,014	(8.3)
Insurance premium
	Q1	260,168	(18.1)	21,430	(15.9)	1,996	(16.3)
	Q2	204,095	(14.2)	18,959	(14.1)	1,698	(13.9)
	Q3	327,911	(22.8)	30,411	(22.6)	2,768	(22.7)
	Q4	644,187	(44.8)	64,011	(47.5)	5,753	(47.1)
Region^*
	7 Metropolitan	701,345	(48.8)	56,489	(41.9)	4,826	(39.5)
	9 Province	735,016	(51.2)	78,322	(58.1)	7,389	(60.5)
BMI
	< 23	534,276	(37.2)	53,849	(39.9)	4,682	(38.3)
	23-<25	377,439	(26.3)	34,251	(25.4)	3,123	(25.6)
	≥ 25	524,646	(36.5)	46,711	(34.7)	4,410	(36.1)
Smoking
	Non	1,084,788	(75.5)	108,045	(80.2)	9,270	(75.9)
	Past	175,085	(12.2)	13,069	(9.7)	1,397	(11.4)
	Present	176,488	(12.3)	13,697	(10.2)	1,548	(12.7)
Drinking
	< 1/week	1,120,964	(78.0)	111,217	(82.5)	9,722	(79.6)
	1–2/week	167,038	(11.6)	11,931	(8.9)	1,201	(9.8)
	≥ 3/week	148,359	(10.3)	11,663	(8.7)	1,292	(10.6)
Physical activity
	None	598,686	(41.7)	63,421	(47.0)	5,855	(47.9)
	1–2/week	191,296	(13.3)	16,578	(12.3)	1,529	(12.5)
	3–4/week	175,165	(12.2)	15,059	(11.2)	1,256	(10.3)
	≥ 5/week	471,214	(32.8)	39,753	(29.5)	3,575	(29.3)
Comorbidities
Depression		78,957	(5.5)	11,266	(8.4)	971	(8.0)
Traumatic brain injury		12,147	(0.8)	1,441	(1.1)	167	(1.4)
Hypertension		746,606	(52.0)	74,506	(55.3)	7,517	(61.5)
Diabetes mellitus		345,783	(24.1)	37,356	(27.7)	3,796	(31.1)
Hyperlipidemia		350,502	(24.4)	35,055	(26.0)	3,463	(28.4)
Coronary artery disease		178,569	(12.4)	18,684	(13.9)	1,779	(14.6)
Cerebrovascular disease		154,026	(10.7)	19,030	(14.1)	2,494	(20.4)
Atrial fibrillation		23,154	(1.6)	2,397	(1.8)	295	(2.4)
Peripheral vascular disease		123,727	(8.6)	14,042	(10.4)	1,288	(10.5)
Myocardial infarction		15,754	(1.1)	1,589	(1.2)	164	(1.3)
Stroke		110,242	(7.7)	13,836	(10.3)	1,984	(16.2)
COPD		186,585	(13.0)	19,378	(14.4)	1,758	(14.4)
Chronic liver disease		113,267	(7.9)	11,341	(8.4)	1,055	(8.6)
Chronic pulmonary disease		392,344	(27.3)	39,989	(29.7)	3,552	(29.1)
CCI	0	556,618	(38.8)	44,703	(33.2)	3,706	(30.3)
	1–2	575,048	(40.0)	55,114	(40.9)	4,883	(40.0)
	≥ 3	304,695	(21.2)	34,994	(26.0)	3,626	(29.7)
Proportion of elderly (mean ± SD)		9.8	± 3.2	10.2	± 3.5	9.9	± 3.5
Proportion of the basic livelihood security recipient (mean ± SD)		3.2	± 1.3	3.4	± 1.4	3.3	± 1.3
Proportion of people with no high school diploma (mean ± SD)		21.4	± 7.6	22.6	± 7.9	22.3	± 7.8
Follow-up (year) (mean ± SD)		8.6	± 4.1	7.4	± 3.0	6.6	± 2.8
PM₁₀(µg/m³) (mean ± SD)		48.4	± 7.7	46.0	± 7.0	47.2	± 7.1

BMI, body mass index; SD, standard deviation; COPD, chronic obstructive pulmonary disease; CCI, Charlson comorbidity index.

^*The region was categorized into seven metropolitans and nine rural areas. The metropolitan areas include Seoul, Busan, Incheon, Gwangju, Daegu, Daejeon, and Ulsan. The rural areas included Gyeonggi, Gangwon, Chungbuk, Chungnam, Jeonbuk, Jeonnam, Gyeongbuk, Gyeongnam, and Jeju.

In the univariate model, the HR for Alzheimer’s disease with an increase in 10 µg/m³ of PM₁₀ was 0.94 (95% CI 0.94–0.95) and that for vascular dementia was 1.03 (95% CI 1.01–1.06). After adjustment for demographic variables, behavioral factors, socioeconomic factors, comorbidities and ecologic variables, the HR was 0.99 (95% CI 0.98–1.00) for Alzheimer’s disease and 1.05 (95% CI 1.02–1.08) for vascular dementia for every 10 µg/m³ increase in PM₁₀. In the case of vascular dementia, the risk of dementia increased in Q3 compared with the lowest quartile (Q1) of PM₁₀, but it was not statistically significant in Q4 (Table 2). The exposure-response curves between PM₁₀ and dementia showed a linear relationship up to a specific concentration but became constant at specific concentrations (Supplementary Fig. 1).

Table 2

Hazard ratio of Alzheimer’s disease and vascular dementia per 10 µg/m³ increase in PM₁₀
	Alzheimer's disease		Vascular dementia
	(No. of events = 134,811)		(No. of events 0= 12,215)
	HR^*	(95% CI)	HR^*	(95% CI)
Crude	0.94	(0.94, 0.95)	1.03	(1.01, 1.06)
Adjusted^*	0.99	(0.98, 1.00)	1.05	(1.02, 1.08)
Quintile
Q1(< 42.9)	1.00	(reference)	1.00	(reference)
Q2(42.9 ≤ 47.7)	1.00	(0.98, 1.01)	1.10	(1.05–1.16)
Q3(47.7 ≤ 53.4)	1.01	(1.00, 1.03)	1.11	(1.06, 1.17)
Q4(≥ 53.4)	0.99	(0.97, 1.00)	1.04	(0.98, 1.10)
^*Adjusted for sex, age as a time-varying covariate, BMI, smoking, physical activity, insurance premium, comorbidities including depression, traumatic brain injury, stroke, diabetes mellitus, CCI, proportion of basic livelihood security recipients, and proportion of people without a high school diploma
HR, hazard ratio; CI, confidence interval

In stratification analyses by sex and age, positive and statistically significant association between PM₁₀ and vascular dementia were observed in men and those younger than 75 years of age (HR [95% CI] per 10 µg/m³ for men:1.08 [1.03–1.12]], < 75 years:1.07 [1.03–1.11]). In the subgroup with or without stroke, the HRs of vascular dementia incidence associated with PM₁₀ exposure were 1.07 (95% CI 1.04–1.10) and 0.96 (95% CI 0.89–1.02), respectively (Table 3). In the sensitivity analyses, the robustness of the results was confirmed by exposure time windows, outcome definitions, exclusion of subjects who died, and adjustment for the indicator variable of region (Table 4).

Table 3

Hazard ratio of dementia per 10 µg/m³ increase in PM_10, according to sex, age, and comorbidities
	Alzheimer's disease		Vascular dementia
	HR^*	(95% CI)	HR^*	(95% CI)
Sex
Male	1.00	(0.98, 1.01)	1.08	(1.03, 1.12)
Female	0.98	(0.97, 1.00)	1.02	(0.99, 1.06)
Age group
< 75	1.01	(1.00, 1.02)	1.07	(1.03, 1.11)
≥ 75	0.96	(0.95, 0.98)	1.01	(0.96, 1.06)
Stroke
No	0.99	(0.98, 1.00)	1.07	(1.04, 1.10)
Yes	1.01	(0.98, 1.03)	0.96	(0.89, 1.02)
Depression
No	0.99	(0.98, 1.00)	1.05	(1.02, 1.08)
Yes	0.99	(0.96, 1.02)	1.03	(0.94, 1.13)
Brain injury
No	0.99	(0.98, 1.00)	1.05	(1.02, 1.08)
Yes	0.94	(0.86, 1.02)	1.06	(0.84, 1.35)
Diabetes mellitus
No	0.99	(0.98, 1.00)	1.05	(1.02, 1.09)
Yes	0.99	(0.95, 1.01)	1.04	(0.99, 1.09)

^*Adjusted for sex, age as a time-varying covariate, BMI, smoking, physical activity, insurance premium, comorbidities including depression, traumatic brain injury, stroke, diabetes mellitus, CCI, proportion of basic livelihood security recipients, and proportion of people without a high school diploma.

HR, hazard ratio; CI, confidence interval

Table 4

Sensitivity analysis for hazard ratio of dementia per 10 µg/m³ increase in PM₁₀
	Alzheimer's disease			Vascular dementia
	No. of event	HR^*	(95% CI)	No. of event	HR^*	(95% CI)
Main analysis (1-year moving average)	134,811	0.99	(0.98, 1.00)	12,215	1.05	(1.02, 1.08)
Exposure time window
3-year moving average	134,811	0.98	(0.97, 0.99)	12,215	1.04	(1.01, 1.07)
5-year moving average	134,811	0.97	(0.96, 0.99)	12,215	1.02	(0.99, 1.05)
Outcome definition
Restricted to secondary diagnosis and treatment for dementia	117,014	0.99	(0.98, 1.00)	9,128	1.06	(1.03, 1.10)
≥ 1 inpatient or outpatient diagnosis and treatment for dementia	149,579	1.00	(0.99, 1.00)	13,090	1.05	(1.03, 1.08)
≥ 1 inpatient or 2 outpatient diagnoses or treatment for dementia	126,323	0.98	(0.97, 0.99)	16,363	1.06	(1.03, 1.08)
Dementia diagnosis or treatment for dementia	137,080	0.99	(0.98, 1.00)	18,790	1.06	(1.03, 1.08)
Excluded participants with death	134,732	0.99	(0.98, 1.00)	12,205	1.05	(1.02, 1.08)
Region variable
Region indicator (16 province)^**	134,811	0.96	(0.95, 0.97)	12,215	1.08	(1.05, 1.12)
EDU + RECIP + Region indicator^†	134,811	0.96	(0.95, 0.97)	12,215	1.08	(1.05, 1.12)

^**Adjusted for sex, age as a time-varying covariate, BMI, smoking, physical activity, insurance premium, comorbidities including depression, traumatic brain injury, stroke, diabetes mellitus, CCI, region

^**Region indicator was classified into 16 provinces including Seoul, Busan, Daegu, Incheon, Gwangju, Ulsan, Daejeon, Gyeonggi, Gangwon, Chungbuk, Chungnam, Jeonbuk, Jeonnam, Gyeongbuk, Gyeongnam, and Jeju.

^†Adjusted for sex, age as a time-varying covariate, BMI, smoking, physical activity, insurance premium, comorbidities including depression, traumatic brain injury, stroke, diabetes mellitus, CCI, proportion of basic livelihood security recipients, proportion of people with no high school diploma, and region.

EDU, proportion of people with no high school diploma; RECIP, proportion of basic livelihood security recipients.

This study investigated the association between long-term exposure to PM₁₀ and risk of dementia in Korea from 2008 to 2019. The results revealed that long-term exposure to PM₁₀ increased the risk of developing vascular dementia but was not significantly associated with Alzheimer’s disease. Stratified analysis according to sex and age group showed that the risk of vascular dementia was higher in men and in those under 75 years of age. The associations were robust to various sensitivity analyses, including exposure time window and outcome definition. These findings provide evidence of a potentially significant implication of exposure to PM₁₀ on vascular dementia.

Previous studies in Western countries have suggested that long-term exposure to PM_2.5 significantly increases the risk of dementia (Chen et al. 2017a; Kioumourtzoglou et al. 2016; Shi et al. 2020; Smargiassi et al. 2020). A meta-analysis showed a strong association between PM_2.5 exposure and dementia (OR = 1.16, 95% CI 1.07–1.24) and Alzheimer’s disease (OR = 3.26, 95% CI 0.84–12.74) (Fu et al. 2019). An open cohort study in the southeastern United States found that the HR of hospitalization for Alzheimer’s disease was 1.060 (per 1 µg/m³ increase in annual PM_2.5; 95% CI 1.057–1.062) and the HR for vascular dementia was 1.086 (95% CI 1.081–1.090) (Lee et al. 2019b). In a cohort study of London, exposure to PM_2.5 was observed to increase the risk of dementia; the HRs for Alzheimer’s disease and vascular dementia were 1.10 (per IQR change; 95% CI 1.02–1.18) and 1.06 (95% CI 0.97–1.23), respectively (Carey et al. 2018).

In Asia, a Hong Kong study suggested that the estimated HRs for Alzheimer’s disease and vascular dementia were 1.03 (95% CI 0.94–1.12) and 1.09 (95% CI 0.98–1.22), respectively, for every 3.8 µg/m³ increase in PM_2.5 exposure (Ran et al. 2021). A nested case-control study in Taiwan found that higher NO₂ exposure was associated with an increased risk of vascular dementia (Q4 vs. Q1: OR = 2.22, 95% CI 1.35–3.65), whereas PM₁₀ exposure was not significantly associated with vascular dementia risk (per 10 µg/m³: OR = 0.99, 95% CI 0.98–1.01) (Li et al. 2019a).

Similar to this study, in the Rome Longitudinal study, exposure to PM_2.5 and PM₁₀ was positively associated with vascular dementia (PM_2.5 per 5 µg/m³: HR = 1.07, 95% CI 1.01–1.12; PM₁₀ per 10 µg/m³: HR = 1.06, 95% CI 1.02–1.10) and negatively associated with Alzheimer’s disease (PM_2.5: HR = 0.91, 95% CI 0.85–0.97; PM₁₀: HR = 0.95, 95% CI 0.91–0.99) (Cerza et al. 2019). Although previous studies had limitations in the effect estimates, the evidence between long-term exposure to PM_2.5 and cognitive decline supported an adverse association, whereas the relationship between particulate matter (PM) and NO₂ was not conclusively established (Weuve et al. 2021). The results of this study showed a positive association between vascular dementia and PM₁₀ compared to previous studies but was not consistent with Alzheimer's dementia. In addition, previous studies suggested that the effect estimate of PM_2.5 tended to be large for PM₁₀, and the PM₁₀ estimate per 10 µg/m³ in the previous two studies (Cerza et al. 2019; Li et al. 2019b) was comparable to our results.

The biological mechanisms through which PM exposure induces brain-related diseases are not completely understood. The most common mechanisms are believed to be inflammation and oxidative stress, and PM can affect the central nervous system (CNS) through direct and indirect pathways (Block and Calderon-Garciduenas 2009; Mills et al. 2009). Fine particles less than 2.5 µm (PM_2.5), and ultrafine particles (UFPM) less than 0.1 µm can enter directly the nasal olfactory pathway, where the inhaled particles can enter systemic circulation or penetrate through cellular membranes to reach the brain (Block and Calderon-Garciduenas 2009). Another pathway involves the peripheral immune system, where PM₁₀ and PM_2.5 induce pro-inflammatory signals in the immune system, triggering a cytokine response that transmits inflammation to the brain (Block and Calderon-Garciduenas 2009; Ran et al. 2021). PM-induced systemic inflammation, especially interleukin-1 alpha (IL-1α) and tumor necrosis factor-alpha (TNF-α), is likely to cause stroke (McColl et al. 2009), lung disease (Tamagawa and van Eeden 2006), cardiovascular disease (Ruckerl et al. 2007) and neurodegenerative diseases (Clifford et al. 2016; Cunningham et al. 2009; Perry et al. 2007). In an in vivo study, long-term exposure to PM₁₀ was associated with an increased risk of amyloid-ß (Aβ) positivity with regard to CNS pathologies (Lee et al. 2020). Another study reported that exposure to PM₁₀ and PM_2.5 was associated with higher concentrations of cerebrospinal fluid, a biomarker of Alzheimer’s disease (Alemany et al. 2021).

Vascular dementia causes damage to brain function owing to vascular lesions. From the perspective of vascular pathology, large and small vascular atherosclerosis can lead to cortical and subcortical infarctions, sub-infarct ischemic lesions, and large and small cerebral hemorrhages, which can occur throughout the brain and lead to vascular dementia (O'Brien and Thomas 2015). Additionally, the effects of air pollution on cardiovascular and cerebrovascular diseases can have a negative effect on the brain (Clifford et al. 2016). Cardiovascular disease (CVD) associated with chronic PM exposure leads to cerebrovascular dysregulation through vasoconstriction (Li et al. 2019a). Air pollution does not reach the brain but adversely affects cognitive function owing to the negative effects of cardiovascular disease (Grande et al. 2020; Qiu and Fratiglioni 2015). In animals, exposure to nanoparticles aggravates white matter hypoperfusion and worsens blood-brain barrier (BBB) permeability, thereby inducing cerebrovascular pathologies associated with dementia (Huuskonen et al. 2021). Vascular dementia and other types of dementia such as Alzheimer’s disease have different etiological features. Considering the research on the effects of air pollution on cardiovascular disease, it is not surprising that air pollution affects the risk of vascular dementia (Cerza et al. 2019). The harmful effects of cardiovascular and cerebrovascular diseases on the brain have also been observed in vascular dementia and Alzheimer's disease. This indicates that the onset of dementia could be the result of all lesion types in the brain, including vascular, neurodegenerative, brain trauma, and inflammatory lesions (Thal et al. 2012). Therefore, the association between PM and vascular dementia, and the impact of Alzheimer's disease remains a challenge.

Stratified analysis according to sex and age group showed that the risk of vascular dementia was higher in men and in those under 75 years of age. However, the differences between the two sexes have not yet been fully characterized. In some studies, sex-specific effects of air pollution have been observed on inflammatory markers and atherosclerosis (Kunzli et al. 2005; Steinvil et al. 2008). Hoffmann et al. (2009) reported that various exposure patterns contribute to heterogeneity and suggested the possibility of modifying endogenous estrogen and postmenopausal hormone replacement therapy (Hoffmann et al. 2009; Stork et al. 2008; Wong et al. 2008). Another study reported differences in cognitive decline between men and women in the progression of Alzheimer's disease (Sohn et al. 2018). Although it is generally believed that the association between air pollution and cognition does not depend on age or sex, we assumed that there may be age-related cognitive decline in people aged 75 years or older (Gatto et al. 2014).

Stratified analysis was performed according to comorbidities such as stroke, depression, brain injury, and diabetes mellitus. Previous studies have used effect modifiers and mediators via CVD to establish an association between air pollution and dementia (Grande et al. 2020; Ilango et al. 2020). In this study, the association of exposure to PM₁₀ with vascular dementia in the stroke group was slightly lower, but the difference was not statistically significant. Therefore, it was assumed that there would be no indirect effect of CVD; however, further research is required.

We assumed a linear relationship between PM₁₀ and dementia and assessed the shape of the exposure-response curve by using a restricted natural cubic spline function with 4 knots. The curve of PM₁₀ and Alzheimer’s disease was constant up to approximately 60 µg/m³ of PM₁₀ concentration, and then decreased at higher concentrations. In the case of vascular dementia, the risk of dementia increased for the low-moderate exposure but became constant at higher concentrations. The shape of the exposure-response could play an important role in determining disease burden by suggesting whether a safe threshold exists (Roberts and Martin 2006). Some studies have suggested a violation of the log-linearity assumption for particulate matter effects on population mortality and morbidity (Forastiere et al. 2020; Li et al. 2019b; Liu et al. 2019). In our vascular dementia model, the risk of dementia tended to increase when PM₁₀ concentration increased by 53.4 µg/m³ (Q3) and remained constant thereafter. Our results are meaningful because the national regulation for the PM₁₀ annual concentration was below 50 µg/m³. However, it is possible that our linear effect models underestimated the burden of Alzheimer’s disease and vascular dementia due to PM₁₀ exposure.

This study has some limitations. First, there is a possibility of misclassification the onset of dementia. Owing to the nature of the administrative dataset, some dementia patients might not had been diagnosed. Also, the dataset included both incident and progressive cases among people diagnosed with dementia according to ICD-10 codes. To reduce outcome misclassification, predefined criteria were defined, and the robustness of the results was tested. The dementia subtypes in this study were Alzheimer’s disease (80.3%) and vascular dementia (7.3%), which showed a distribution similar to a previous study conducted in Korea (Alzheimer’s disease:86.1% in 2016; vascular dementia:10.6% in 2016) (Choi et al. 2021).

Second, our exposure assessment was based on district-level address information at the baseline, which did not completely reflect personal exposure. Although assigning an average exposure to each individual at a fixed monitoring site would introduce a Berkson error, the error is not expected to significantly affect the measurements and estimates (Heid et al. 2004). In addition, there was no information regarding the distance from the road. In a cohort study, living close to a major road was associated with an increased risk of dementia, particularly Alzheimer’s disease (Chen et al. 2017b).

Previous studies have mostly reported the effect of PM_2.5 exposure on dementia, and PM_2.5 may be a more appropriate indicator in terms of biological mechanism. Although PM_2.5 exposure estimates were not available because the data were established in 2015, the correlation between PM_2.5 and PM₁₀ concentrations was high (r > 0.73) within our data (2015–2019). When other air pollutants, including SO₂, NO₂, O₃, and CO, were added to the model, PM₁₀ estimates were robust (Supplementary Table 3).

One of the strengths of this study is the first study to examine the long-term effect of PM₁₀ on dementia, in a representative, a large population-based cohort using data from a nationwide database collected over 15 years. This provided significant statistical power to detect the association between PM₁₀ and dementia. Second, we were able to determine the residential mobility of the study population, which could have reduced the exposure measurement error. Third, we included various covariates in the model to minimize the residual confounding. Using medical records, pre-existing diseases were identified at baseline and behavioral variables were obtained in connection with health examination data. In addition, the models were adjusted for ecological variables such as the proportion of basic livelihood security recipients and people with no high school diploma.

In this large population-based cohort, long-term exposure to PM₁₀ was associated with a higher incidence of vascular dementia but not Alzheimer’s disease. Moreover, we assessed potential effect modification by sex, age, and cardiovascular disease, and these findings could contribute to our understanding of the relationship between air pollution and dementia by providing information on susceptible population. This study may implicate the evidence that exposure to air pollution may be more associated with dementia, especially in terms of vascular damage.

BMI, body mass index; CCI, Charlson comorbidity index; CI, confidence interval; COPD, chronic obstructive pulmonary disease; CVD, Cardiovascular disease; EDU, proportion of people with no high school diploma; HR, hazard ratio; ICD-10, International Statistical Classification of Diseases; NHIS, National Health Insurance Service; SD, standard deviation; PM, particulate matter; RECIP, proportion of basic livelihood security recipients.

Acknowledgments

We would like to thank the National Health Insurance Service for offering raw data (NHIS-2021-1-323). The paper’s contents are solely the responsibility of the author and do not necessarily represent the official views of the National Health Insurance Service.

Author’s contributions

Jung-Im Shim: Conceptualization, Methodology, Investigation, Writing—original draft. Garam Byun: Methodology, Writing—review.

Jong-Tae Lee: Supervision, Methodology, Writing—review and editing.

All authors have read and agreed to the published version of the manuscript.

Funding sources:

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2022R1A2C1006661).

Availability of data and materials

The data that support the findings of this study was used under license for the current study, and hence not publicly available. Data codebooks and syntaxes used for the statistical analyses are however available from the authors upon request.

Ethics approval and consent to participate

Ethical review and approval of Korea university were waived for this study due to the use of only de-identified data (IRB code KUIRB-2021-0003-01).

Consent for publication

Not applicable.

Competing interests

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.

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Long-term exposure to particulate matter and risk of dementia in Korea: A national population-based cohort study

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Abstract

Background

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1. Introduction

2. Methods

2.1. Data source

2.2. Study design and population

2.3. Outcome definition

2.4. Exposure assessment

2.5. Covariates

2.6. Statistical analysis

3. Results

4. Discussion

5. Conclusions

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