Differential effects of size-specific particulate matter on frailty transitions among middle-aged and older adults in China: findings from the China Health and Retirement Longitudinal Study (CHARLS), 2015–2018

Zhen Guo; Hui Xue; Lijun Fan; Di Wu; Yiming Wang; Younjin Chung; Yilan Liao; Zengliang Ruan; Wei Du

doi:10.1093/inthealth/ihad033

. 2023 May 10;16(2):182–193. doi: 10.1093/inthealth/ihad033

Differential effects of size-specific particulate matter on frailty transitions among middle-aged and older adults in China: findings from the China Health and Retirement Longitudinal Study (CHARLS), 2015–2018

Zhen Guo ¹, Hui Xue ², Lijun Fan ³, Di Wu ⁴, Yiming Wang ⁵, Younjin Chung ⁶, Yilan Liao ⁷, Zengliang Ruan ^8,^✉, Wei Du ^9,^✉

PMCID: PMC10939306 PMID: 37161970

Abstract

Background

This study aimed to assess the long-term effects of size-specific particulate matter (PM) on frailty transitions in middle-aged and older Chinese adults.

Methods

We included 13 910 participants ≥45 y of age from the China Health and Retirement Longitudinal Study (CHARLS) for 2015 and 2018 who were classified into three categories in 2015 according to their frailty states: robust, prefrail and frail. Air quality data were obtained from the National Urban Air Quality Real-time Publishing Platform. A two-level logistic regression model was used to examine the association between concentrations of PM and frailty transitions.

Results

At baseline, the total number of robust, prefrail and frail participants were 7516 (54.0%), 4324 (31.1%) and 2070 (14.9%), respectively. Significant associations were found between PM concentrations and frailty transitions. For each 10 μg/m³ increase in the 3-y averaged 2.5-μm PM (PM_2.5) concentrations, the risk of worsening in frailty increased in robust (odds ratio [OR] 1.06 [95% confidence interval {CI} 1.01 to 1.12]) and prefrail (OR 1.07 [95% CI 1.01 to 1.13]) participants, while the probability of improvement in frailty in prefrail (OR 0.91 [95% CI 0.84 to 0.98]) participants decreased. In addition, the associations of PM₁₀ and coarse fraction of PM with frailty transitions showed similar patterns.

Conclusions

Long-term exposure to PM was associated with higher risks of worsening and lower risks of improvement in frailty among middle-aged and older adults in China.

Keywords: air pollution, frailty, older adults, particulate matter

Introduction

Air pollution has become a serious threat to human health, especially particulate matter (PM) with aerodynamic diameters of ≤10 μm (PM₁₀), 2.5–10 μm (PM_c) and ≤2.5 μm (PM_2.5).¹ In 2019, >90% of the global population lived in areas where PM_2.5 concentrations exceed World Health Organization air quality guidelines and China was among those areas despite decreasing annual mean concentrations, from 67.4 μg/m³ in 2013 to 45.5 μg/m³ in 2017, due to a series of clean-air actions since 2013.^2,3 Previous studies have demonstrated the associations between PM exposure and numerous health outcomes, including cardiovascular and respiratory diseases, hand-grip strength, balance, gait speed, mental health and cancer.^4–9 According to the Global Burden of Disease Study 2019, ambient PM pollution was one of the top three risk factors that accounted for >1% of the world's total disability-adjusted life-years and ranked fifth in adults ages 50–74 y and sixth in adults ≥75 y of age.¹⁰ Around 6.45 million (95% confidence interval [CI] 5.72 to 7.24) total deaths in 2019 (11.4% [95% CI 10.3 to 12.6] of all deaths that year) were directly attributable to PM pollution, which ranked fifth in adults ages 50–69 y and fourth in adults ≥70 y of age.¹¹ According to a recent study with participants ≥45 y of age from the 45 and Up Study cohort in Australia, prolonged exposure to even low-level PM_2.5 (annual average 4.5 μg/m³) was associated with a higher risk of mortality.¹²

Frailty is generally defined as an age- and disease-related dynamic biologic syndrome that manifests as a decreased physiological reserve and leads to disproportionate vulnerability to adverse outcomes.^13–15 With the increasing aging population in China and other countries, the increasing burden of frailty requires continual monitoring. The prevalence of frailty greatly varies across studies and is expected to increase due to population aging.^16,17 One systematic review for participants from 62 countries reported that the prevalence of frailty was 12% using physical frailty measures and 24% using the frailty index.¹⁷ In China, one cohort study reported that the prevalence of frailty was 3.1% and increased with age, from 0.8% (people <50 y of age) to 3.5% (50–64 y) and 8.9% (≥65 y).¹⁸ The dynamic condition reveals the potential reversibility of frailty status, enabling intervention studies about risk factors on frailty transitions. Based on frailty status at baseline and follow-up, frailty transitions are associated with various risk factors and related research may contribute to predicting trends and identifying potential interventions for health policymaking.¹⁶

Previous analyses have suggested a possible link between PM and frailty.^19–21 For example, one cross-sectional study reported higher PM_2.5 exposure in prefrail and frail adults ≥65 y of age than in healthy participants in Taiwan.²² In addition, exposure to PM_2.5 and PM₁₀ was generally associated with prefrail and frail among 2912 Korean community-dwelling individuals ≥70 y of age.²⁰ Also, Eckel et al.²³ examined the modification by frailty history of the associations of PM₁₀ with lung function utilizing cohort data of adults ≥65 y of age from four American counties. However, researchers did not specify the association between PM and frailty transitions, despite the dynamic frailty status evolving over time. In fact, some participants improved and most remained stable during follow-up.^24,25 Investigating the associations between the various transitions is critically important in preventing and reversing the health consequences of frailty. Moreover, frailty has been studied in adults ages 45–59 y, and early-onset frailty in participants may lead to worse health status in later years.^26–28 After analysing data from the UK Biobank, Hanlon et al.²⁷ concluded that individuals 45–59 y of age with early-onset frailty had a higher risk of premature death. Similarly, another cohort study, also based on UK Biobank data, revealed that higher frailty index values were most strongly linked to mortality in younger age groups (45–59 y of age).²⁶ Identification of frailty status earlier in the life course is needed to promote healthy aging.²⁹ Overall, the impact of PM on frailty transitions in Chinese middle-aged and older adults remain unclear.

Using data from the China Health and Retirement Longitudinal Study (CHARLS) and the National Urban Air Quality Real-time Publishing Platform for 2015–2018, this study aimed to explore whether exposure to PM was associated with frailty transitions among Chinese adults ≥45 y of age.

Methods

Study participants

The CHARLS is designed to assess the social, economic and health status of middle-aged and older adults living in China. Details of the study design and methodology were published previously.³⁰ The representative sample of Chinese residents ≥45 y of age was selected from 125 cities in 2011 (wave 1) and then followed in 2013 (wave 2), 2015 (wave 3) and 2018 (wave 4) through face-to-face conversations. Our study only included data from waves 3 and 4 because the monitoring data for major air pollutants in China were available after the year 2014 and the wave 3 investigation was considered the baseline survey.³¹ Participants ≥45 y of age with fixed home addresses throughout waves 3 and 4 were included, and those who had missing data on the key variables, including the frailty index and concentrations of air pollutants, were excluded (Figure 1). The CHARLS was approved by the Ethics Review Committee of Peking University (IRB00001052-11015).

Figure 1. — Flowchart of the study subjects from the CHARLS.

Measurement of frailty

In the present study, frailty status was measured using the frailty index (FI, also called deficit accumulation index), a widely used indicator to evaluate frailty through cumulating age-related deficits such as comorbidities, functional impairments and psychosocial issues.^14,32,33 Defined as the proportion of accumulated health deficits, the FI is suitable for participants >45 y of age.^18,27,34 The FI checklist has 39 deficit items scoring from 0 to 1, which represent eight dimensions including current comorbidities, disability, self-rated health, ability in activities of daily living (ADL), instrumental activities of daily living (IADL), functional activities, cognition and depression (Table S1). The FI was calculated as the ratio of the number of deficits in a person to the total number of deficits considered, ranging from 0 to 1. The states of frailty were measured and divided into three levels (robust: FI ≤0.10, prefrail: FI >0.10–≤0.21 and frail: FI >0.21) in both the baseline and follow-up investigations.^35–37 Then the frailty transitions during the study period were measured as the changes in frailty status from 2015 to 2018, which were classified as stability, improvement or worsening.³⁸ Depending upon the previously defined frailty states and transitions, the study population was divided into seven groups: robust worsening (robust in 2015, prefrail or frail in 2018), prefrail improvement (prefrail in 2015, robust in 2018), prefrail worsening (prefrail in 2015, frail in 2018), frail improvement (frail in 2015, prefrail or robust in 2018), robust stability (robust in 2015 and 2018), prefrail stability (prefrail in 2015 and 2018) and frail stability (frail in 2015 and 2018). Given the potential modification of baseline frailty states, we further categorized the participants into three groups by frailty states measured in 2015.³⁹

Air quality data

Air quality data were obtained from the National Urban Air Quality Real-time Publishing Platform, which includes air pollution data (hourly concentrations of PM_2.5, PM₁₀, carbon monoxide [CO], ozone [O₃], sulphur dioxide [SO₂[ and nitrogen dioxide [NO₂]) in all monitoring sites during the study period, and the concentration of PM_c was calculated by subtracting PM_2.5 from PM₁₀.³¹ We averaged the hourly data from all monitoring sites of each city to construct the city-level hourly mean concentrations of the air pollutants. We defined 1- and 3-y air pollution exposure as long-term exposure. In addition, 1- and 3-y mean concentrations of air pollutants prior to the wave 4 investigation were calculated based on the hourly data.

Covariates

We considered a set of covariates that may be related to the association between PM and frailty, including sex, age, educational attainment, single status, type of residence and smoking status.^19–22 Educational attainment was divided into five categories: illiterate, literate but did not finish primary school, primary school, middle school and high school and above. Single status included two categories: no (married and living with or without the spouse) and yes (including separated, divorced, widowed and never married). The type of residence was classified as urban or rural according to the urban–rural division in CHARLS. Smoking status included current smoker, former smoker and never smoked. Moreover, the natural logarithm of population density in 2015 was also included as an area-level confounder in our study.

Statistical analysis

The distributions of the FI and 3-y mean concentrations of the major air pollutants during the study period were presented as the 25th, 50th, and 75th percentiles as well as mean, minimum and maximum values. Spearman's correlation was performed to quantify the correlation between 3-y mean concentrations of the major air pollutants. The characteristics of both air pollutants and covariates between different frailty transitions were compared by χ² test for categorical variables and analysis of variance for continuous variables.

Considering the nested data structure and insufficient number of cities included per province, we applied a two-level logistic regression model to examine the associations between the 3-y averaged concentrations of PM and frailty transitions, with the individuals specified as level 1 and cities as level 2. The dependent variable here was frailty transition from 2015 to 2018, taking the corresponding stable group as the reference group. The results were expressed as the odds ratio (OR) and its 95% CI for each 10 μg/m³ increase in PM concentration. Confounders of the regression model included sex, age, education, residence, single status, smoking status and regional population density. Furthermore, we also explored the effects of demographic characteristics and regional factors on the association in the stratified analyses divided by sex, age and residence type. We checked the interaction effects by adding a multiplicative term in the model and significant difference across all three strata by examining CIs as Inline graphic .^40,41 In each stratum, and were the coefficient estimates and and were the standard error. The concentration–response associations of the PM with frailty transitions were analysed using the natural spline smoothing function.

For sensitivity analyses, two-pollutant models were used to examine the associations between PM and frailty after adjusting for other gaseous pollutants. Additionally, the associations between 1- and 3-y average PM concentrations prior to the wave 4 investigation and frailty status in 2018 were also analysed. Finally, we compared characteristics of included and excluded individuals to evaluate potential selection bias.

The test level for this study was α=0.05 (two-sided). Statistical analyses were performed in R version 4.2.0 (R Foundation for Statistical Computing, Vienna, Austria) and Stata 16 (StataCorp, College Station, TX, USA).

Results

Descriptive summary

This study involved a total of 13 910 participants from 114 cities in 26 provincial-level administrative regions of China (Figure 1). The summary statistics of FI and air pollutants are presented in Table 1. The mean FI was 0.12 in 2015 and 0.15 in 2018 and showed an increasing trend in most of the respondents (68.1%). The 3-y average concentrations of PM_2.5, PM_c, PM₁₀, O₃, NO₂, SO₂ and CO before the CHARLS wave 4 investigation were 49.90 μg/m³, 36.91 μg/m³, 87.06 μg/m³, 104.70 μg/m³, 33.25 μg/m³, 20.73 μg/m³ and 1.03 mg/m³, respectively.

Table 1.

Summary of the FI of the participants and 3-y averaged concentrations of air pollutants during the study period

				Percentiles
Pollutants	Mean	Minimum	Maximum	25th	50th	75th
FI in 2015	0.12	0.00	0.72	0.05	0.09	0.16
FI in 2018	0.15	0.00	0.83	0.07	0.12	0.21
Change in FI^a	0.04	−0.47	0.71	−0.01	0.02	0.07
PM_2.5 (μg/m³)	49.90	15.46	83.15	37.05	48.33	61.71
PM_c (μg/m³)	36.91	14.22	71.45	25.35	33.07	47.71
PM₁₀ (μg/m³)	87.06	29.64	148.10	64.16	81.39	108.20
O₃ (μg/m³)	104.70	70.92	126.80	96.68	105.90	114.00
NO₂ (μg/m³)	33.25	13.38	55.64	25.20	33.32	39.45
SO₂ (μg/m³)	20.73	5.64	52.23	13.29	18.75	26.30
CO (mg/m³)	1.03	0.47	2.07	0.83	0.96	1.18

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^aChange in frailty was calculated as the difference in FI from wave 3 to wave 4.

The demographic characteristics across different states of frailty during the study period are presented in Table 2. In 2015, 7516 (54.0%) of the participants’ states of frailty were identified as robust, among which 5094 (67.8%) remained robust while the other 2422 (32.2%) participants worsened in 2018. Moreover, of the 4324 (31.1%) participants who were prefrail in 2015, 573 (13.3%) improved, 2371 (54.8%) stayed prefrail and 1380 (31.9%) worsened in 2018. We also found that 1745 (84.3%) of the 2070 (14.9%) frail participants in 2015 remained stable in 2018. In the prefrail baseline groups, the average age (mean±standard deviation [SD]) demonstrated older in the worsening group (64.79±9.75) and younger in the improvement group (60.27±8.64) compared with the stable individuals (61.79±9.11). Compared with those of the stably robust group, the major characteristics of worsened robust respondents were female, lower education level, single, never or former smoker and less regional population density. The mean PM_c concentrations of three transitions of the prefrail baseline group are, in descending order, worsening (37.55±13.86), stability (36.85±13.84) and improvement (35.47±13.80).

Table 2.

Comparison^a of the general characteristics between frailty transitions

	Frailty transitions during 3 y
	Robust (n=7516 [54.0%])			Prefrail (n=4324 [31.1%])				Frail (n=2070 [14.9%])
Characteristics	Stability	Worsening	p-Value^a	Improvement	Stability	Worsening	p-Value^a	Improvement	Stability	p-Value^a
Participants, n (%)	5094 (67.8)	2422 (32.2)		573 (13.3)	2371 (54.8)	1380 (31.9)		325 (15.7)	1745 (84.3)
Age at interview (years), mean±SD	56.07±8.31	59.34±9.47	<0.001	60.27±8.64	61.79±9.11	64.79±9.75	<0.001	64.60±9.36	66.81±9.49	<0.001
Age group (years), n (%)			<0.001				<0.001			<0.001
45–50	1326 (26.0)	411 (17.0)		49 (8.6)	206 (8.7)	63 (4.6)		11 (3.4)	58 (3.3)
50–60	2068 (40.6)	846 (34.9)		234 (40.8)	782 (33.0)	354 (25.7)		99 (30.5)	321 (18.4)
60–70	1337 (26.3)	803 (33.2)		206 (36.0)	910 (38.4)	537 (38.9)		122 (37.5)	705 (40.4)
>70	363 (7.1)	362 (15.0)		84 (14.7)	473 (20.0)	426 (30.9)		93 (28.6)	661 (37.9)
Sex, n (%)			<0.001				<0.01			0.28
Female	2184 (42.9)	1233 (50.9)		327 (57.1)	1301 (54.9)	840 (60.9)		218 (67.1)	1116 (64.0)
Male	2910 (57.1)	1189 (49.1)		246 (42.9)	1070 (45.1)	540 (39.1)		107 (32.9)	629 (36.0)
Education level, n (%)			<0.001				<0.001			0.34
Illiterate	800 (15.7)	555 (22.9)		175 (30.5)	623 (26.3)	494 (35.8)		150 (46.2)	701 (40.2)
Literate	757 (14.9)	430 (17.8)		99 (17.3)	478 (20.2)	285 (20.7)		62 (19.1)	382 (21.9)
Primary school	1191 (23.4)	546 (22.5)		124 (21.6)	590 (24.9)	304 (22.0)		62 (19.1)	350 (20.1)
Middle school	1463 (28.7)	580 (24.0)		114 (19.9)	427 (18.0)	209 (15.1)		33 (10.2)	214 (12.3)
Higher and further education	883 (17.3)	311 (12.8)		61 (10.7)	253 (10.7)	88 (6.4)		18 (5.5)	98 (5.6)
Residence, n (%)			0.09				0.25			0.85
Rural	2867 (56.3)	1414 (58.4)		371 (64.7)	1488 (62.8)	902 (65.4)		228 (70.2)	1215 (69.6)
Urban	2227 (43.7)	1008 (41.6)		202 (35.3)	883 (37.2)	478 (34.6)		97 (29.8)	530 (30.4)
Single status, n (%)			<0.001				<0.001			0.07
Yes	341 (6.7)	237 (9.8)		74 (12.9)	289 (12.2)	263 (19.1)		58 (17.8)	390 (22.3)
No	4753 (93.3)	2185 (90.2)		499 (87.1)	2082 (87.8)	1117 (80.9)		267 (82.2)	1355 (77.7)
Smoking status, n (%)			<0.001				<0.05			0.20
Never	2754 (54.1)	1421 (58.7)		364 (63.5)	1443 (60.9)	859 (62.3)		224 (68.9)	1130 (64.8)
Former	565 (11.1)	306 (12.6)		58 (10.1)	313 (13.2)	203 (14.7)		39 (12.0)	274 (15.7)
Current	1775 (34.8)	695 (28.7)		151 (26.4)	615 (25.9)	318 (23.0)		62 (19.1)	341 (19.5)
Regional population density, mean±SD	6.00±0.82	5.95±0.85	<0.05	5.96±0.83	5.92±0.90	5.81±1.01	<0.001	5.80±0.89	5.74±1.05	0.38
PM_2.5 concentration (μg/m³), mean±SD	49.72±15.53	50.07±15.51	0.36	48.99±14.78	50.15±15.38	50.03±15.98	0.27	49.04±15.22	50.25±15.63	0.20
PM₁₀ concentration (μg/m³), mean±SD	86.91±28.62	87.17±27.71	0.71	84.67±27.08	87.24±27.58	87.86±28.04	0.06	84.56±27.13	87.77±27.16	0.051
PM_c concentration (μg/m³), mean±SD	36.95±14.47	36.85±13.78	0.78	35.47±13.80	36.85±13.84	37.55±13.86	<0.05	35.30±13.70	37.25±13.58	<0.05

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^aχ² test for categorical variables and analysis of variance for continuous variables.

Figure S1 shows Spearman's correlations between the 3-y averaged concentrations of major air pollutants. PM_2.5 was strongly positively correlated with PM_c (r=0.73) and with PM₁₀ (r=0.93), as was PM_c and PM₁₀ (r=0.92). Moderate correlations were observed between PM and gaseous pollutants; e.g. the r coefficients were 0.63, 0.69, 0.58 and 0.65 for the correlations between PM_2.5 and SO₂, NO₂, O₃ and CO, respectively. Gaseous pollutants were lowly or moderately correlated with gaseous pollutants other than themselves (r≥0.31–≤0.65).

Associations between PM concentrations and frailty transitions

The associations between long-term exposure to PM and the frailty transitions of the participants after adjusting for potential confounders are presented in Table 3. For robust participants, each 10 μg/m³ increase in 3-y mean ambient PM_2.5 or PM₁₀ concentration was associated with a higher risk of worsening state (PM_2.5: OR 1.06 [95% CI 1.01 to 1.12], PM₁₀: OR 1.02 [95% CI 1.00 to 1.05]). For prefrail participants, each 10 μg/m³ increase in ambient PM₁₀ concentration was associated with an increased risk of worsening state (OR 1.04 [95% CI 1.01 to 1.07]) and a decreased risk of improved state (OR 0.95 [95% CI 0.91 to 0.99]). Such results remained consistent among prefrail participants when assessing the influence of each 10 μg/m³ increase in ambient PM_2.5 or PM_c concentration. For frail participants, each 10 μg/m³ increase in ambient PM_c or PM₁₀ concentration was associated with a decrease of improvement in frailty (PM_c: OR 0.87 [95% CI 0.77 to 0.98], PM₁₀: OR 0.93 [95% CI 0.87 to 0.99]). However, stratified analyses did not exhibit significant associations of PM exposure with frailty transition risks. Also, the interaction terms added no significance to the explanation of frailty transitions in the same baseline groups, providing no evidence that long-term PM exposure has a differential impact on frailty at different sex/age/residence levels. Similarly, the difference across the sex/age/residence strata was not statistically significant among stratified analyses performed separately for the four transition groups. In addition, Figure 2 illustrated the exposure–response curves for the associations between PM concentrations and frailty transitions, which showed linear shapes in most groups. However, non-linear curves with a threshold at high concentrations of PM_c and PM₁₀ were also observed in the robust worsening group.

Table 3.

Adjusted OR^a (95% CI) of frailty transitions^b per 10 increments in 3-y averaged concentrations of PM

Groups	Robust worsening^c	Prefrail improvement^c	Prefrail worsening^c	Frail improvement^c
PM_2.5
All	1.06 (1.01 to 1.12)	0.91 (0.84 to 0.98)	1.07 (1.01 to 1.13)	0.89 (0.79 to 1.01)
Sex
Female	1.05 (0.98 to 1.11)	0.88 (0.79 to 0.97)	1.09 (1.02 to 1.18)	0.90 (0.78 to 1.05)
Male	1.08 (1.02 to 1.15)	0.94 (0.84 to 1.04)	1.04 (0.96 to 1.13)	0.83 (0.68 to 1.01)
p-Value for interaction	0.78	0.61	0.38	0.17
Age group (years)
45–50	1.08 (0.99 to 1.19)	0.72 (0.54 to 0.95)	1.15 (0.86 to, 1.55)	—^d
50–60	1.08 (0.9998 to 1.16)	0.93 (0.84 to 1.04)	1.06 (0.96 to 1.17)	0.77 (0.63 to 0.93)
60–70	1.06 (0.99 to 1.14)	0.94 (0.83 to 1.06)	1.05 (0.96 to 1.15)	0.97 (0.81 to 1.17)
>70	1.03 (0.92 to 1.17)	0.87 (0.69 to 1.08)	1.09 (0.98 to 1.21)	0.91 (0.74 to 1.11)
p-Value for interaction	0.63	0.91	0.58	0.61
Residence
Rural	1.04 (0.97 to 1.11)	0.93 (0.86 to 1.01)	1.05 (0.99 to 1.13)	0.87 (0.75 to 1.02)
Urban	1.10 (1.02 to 1.18)	0.86 (0.75 to 0.99)	1.10 (0.99 to 1.22)	0.91 (0.75 to 1.10)
p-Value for interaction	0.11	0.09	0.64	0.48
PM_c
All	1.03 (0.97 to 1.08)	0.90 (0.84 to 0.98)	1.09 (1.03 to 1.15)	0.87 (0.77 to 0.98)
Sex
Female	1.02 (0.96 to 1.08)	0.87 (0.79 to 0.97)	1.12 (1.04 to 1.20)	0.86 (0.75 to 0.99)
Male	1.04 (0.97 to 1.10)	0.94 (0.84 to 1.05)	1.04 (0.96 to 1.13)	0.88 (0.72 to 1.08)
p-Value for interaction	0.90	0.43	0.18	0.93
Age group (years)
45–50	1.04 (0.95 to 1.14)	0.82 (0.62 to 1.09)	1.04 (0.79 to 1.35)	—^d
50–60	1.02 (0.94 to 1.10)	0.91 (0.81 to 1.02)	1.15 (1.04 to 1.27)	0.81 (0.66 to 0.99)
60–70	1.02 (0.95 to 1.10)	0.94 (0.83 to 1.06)	1.03 (0.94 to 1.12)	0.92 (0.78 to 1.09)
>70	1.07 (0.94 to 1.20)	0.89 (0.71 to 1.11)	1.12 (1.01 to 1.24)	0.87 (0.71 to 1.06)
p-Value for interaction	0.49	0.69	0.79	0.98
Residence
Rural	1.02 (0.96 to 1.09)	0.93 (0.86 to 1.01)	1.06 (0.99 to 1.13)	0.85 (0.73 to 0.98)
Urban	1.03 (0.96 to 1.12)	0.88 (0.76 to 1.01)	1.14 (1.03 to 1.26)	0.95 (0.79 to 1.14)
p-Value for interaction	0.47	0.25	0.24	0.24
PM₁₀
All	1.02 (1.00 to 1.05)	0.95 (0.91 to 0.99)	1.04 (1.01 to 1.07)	0.93 (0.87 to 0.99)
Sex
Female	1.02 (0.99 to 1.05)	0.93 (0.88 to 0.98)	1.06 (1.02 to 1.10)	0.93 (0.86 to 1.00)
Male	1.03 (0.999 to 1.07)	0.96 (0.91 to 1.02)	1.02 (0.98 to 1.07)	0.91 (0.82 to 1.02)
p-Value for interaction	0.82	0.51	0.24	0.40
Age group (years)
45–50	1.03 (0.98 to 1.09)	0.86 (0.74 to 1.00)	1.05 (0.90 to 1.22)	—^d
50–60	1.03 (0.99 to 1.07)	0.96 (0.90 to 1.01)	1.06 (1.01 to 1.11)	0.87 (0.78 to 0.97)
60–70	1.02 (0.98 to 1.06)	0.97 (0.90 to 1.03)	1.02 (0.97 to 1.07)	0.97 (0.88 to 1.06)
>70	1.03 (0.96 to 1.10)	0.93 (0.83 to 1.04)	1.06 (1.00 to 1.12)	0.93 (0.84 to 1.04)
p-Value for interaction	0.54	0.78	0.65	0.77
Residence
Rural	1.02 (0.98 to 1.05)	0.96 (0.92 to 1.00)	1.03 (0.997 to 1.07)	0.92 (0.85 to 0.99)
Urban	1.04 (0.995 to 1.08)	0.92 (0.85 to 0.99)	1.07 (1.01 to 1.13)	0.96 (0.86 to 1.06)
p-Value for interaction	0.21	0.12	0.38	0.32

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^aAdjusted for sex, age, single status, residence, education level, smoking status and ln(population density).

^bFrailty transition groups compared with the corresponding stable group (with the same baseline state).

^cRobust worsening: robust in 2015, prefrail or frail in 2018; prefrail improvement: prefrail in 2015, robust in 2018; prefrail worsening: prefrail in 2015, frail in 2018; frail improvement: frail in 2015, prefrail or robust in 2018.

^dThe regression model was not converged.

Correlations of statistical significance are in bold.

Figure 2. — The smoothing curve of the exposure–response relationship between 3-y averaged concentrations of PM and frailty transitions. ^aAdjusted for sex, age group, single status, residence, education level, smoking status and ln(population density).

Sensitivity analyses

Our sensitivity analyses by two-pollutant models suggested the robustness of our main findings (Table 4). For example, adjusting for O₃, SO₂, NO₂ and CO per 10 μg/m³ increase in the 3-y mean PM₁₀ was associated with increased risks of worsening status in prefrail participants, with ORs of 1.07 (95% CI 1.03 to 1.11), 1.04 (95% CI 1.00 to 1.08), 1.08 (95% CI 1.04 to 1.13) and 1.07 (95% CI 1.03 to 1.11), respectively. Similarly, we observed a decreased probability of improvement transitions in frail participants, with ORs of 0.91 (95% CI 0.84 to 0.98), 0.91 (95% CI 0.84 to 0.99), 0.91 (95% CI 0.83 to 0.99), 0.86 (95% CI 0.79 to 0.93) for per 10 μg/m³ increase in the 3-y mean PM₁₀ when adjusting for O₃, SO₂, NO₂ and CO, respectively. In addition, when we used the annual mean PM concentrations as the exposure and frailty status in 2018 as the outcome, the associations displayed patterns similar to our main findings (Table S2). Of note, included and excluded individuals differed significantly (p<0.05) in baseline characteristics, except for FI scores and regional population density (Table S3). For example, compared with included participants, those excluded were more likely to be young (58.07±11.96 vs 60.20±9.78), female (52.97% vs 51.90%), single (15.25% vs 11.88%) and living in urban areas (46.96% vs 39.00%).

Table 4.

Adjusted OR^a (95% CI) of frailty transitions^b per 10 increments in 3-y averaged concentrations of PM in two-pollutant models

Pollutants	Models	Robust worsening^c	Prefrail improvement^c	Prefrail worsening^c	Frail improvement^c
PM_2.5
	Single-pollutant model	1.06 (1.01 to 1.12)	0.91 (0.84 to 0.98)	1.07 (1.01 to 1.13)	0.89 (0.79 to 1.01)
	Two-pollutant models
	Control for O₃	1.10 (1.04 to 1.16)	0.89 (0.82 to 0.97)	1.09 (1.03 to 1.17)	0.88 (0.76 to 1.00)
	Control for SO₂	1.09 (1.02 to 1.17)	0.92 (0.84 to 1.02)	1.06 (0.99 to 1.14)	0.86 (0.74 to 1.01)
	Control for NO₂	1.09 (1.02 to 1.17)	0.89 (0.81 to 0.98)	1.12 (1.04 to 1.21)	0.87 (0.74 to 1.01)
	Control for CO	1.04 (0.98 to 1.10)	0.94 (0.85 to 1.04)	1.10 (1.02 to 1.19)	0.78 (0.67 to 0.91)
PM_c
	Singl-pollutant model	1.03 (0.97 to 1.08)	0.90 (0.84 to 0.98)	1.09 (1.03 to 1.15)	0.87 (0.77 to 0.98)
	Two-pollutant models
	Control for O₃	1.07 (1.00 to 1.14)	0.87 (0.80 to 0.96)	1.14 (1.06 to 1.22)	0.84 (0.72 to 0.97)
	Control for SO₂	1.02 (0.96 to 1.09)	0.92 (0.84 to 1.01)	1.08 (1.01 to 1.15)	0.86 (0.75 to 0.99)
	Control for NO₂	1.03 (0.96 to 1.10)	0.89 (0.81 to 0.98)	1.14 (1.06 to 1.22)	0.85 (0.73 to 0.99)
	Control for CO	1.01 (0.94 to 1.08)	0.93 (0.85 to 1.03)	1.12 (1.04 to 1.20)	0.78 (0.68 to 0.90)
PM₁₀
	Singl-pollutant model	1.02 (1.00 to 1.05)	0.95 (0.91 to 0.99)	1.04 (1.01 to 1.07)	0.93 (0.87 to 0.99)
	Two-pollutant models
	Control for O₃	1.05 (1.01 to 1.08)	0.93 (0.89 to 0.98)	1.07 (1.03 to 1.11)	0.91 (0.84 to 0.98)
	Control for SO₂	1.03 (0.99 to 1.07)	0.95 (0.90 to 1.00)	1.04 (1.00 to 1.08)	0.91 (0.84 to 0.99)
	Control for NO₂	1.04 (0.999 to 1.08)	0.93 (0.88 to 0.98)	1.08 (1.04 to 1.13)	0.91 (0.83 to 0.99)
	Control for CO	1.02 (0.98 to 1.06)	0.96 (0.91 to 1.01)	1.07 (1.03 to 1.11)	0.86 (0.79 to 0.93)

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^aAdjusted for sex, age, single status, residence, education level, smoking status and ln(population density).

^bFrailty transition groups compared with the corresponding stable group (with the same baseline state).

Correlations of statistical significance are in bold.

Discussion

In this community-based cohort study comprising 13 910 middle-aged and older individuals, our findings suggested that long-term PM exposure was associated with frailty transitions (including stability, worsening and improvement). To our knowledge, our study is the first attempt to determine whether PM concentrations contribute to frailty transitions, which reveals more about the dynamic process of frailty than previous studies. We noted a consistent association among main and sensitive analyses as well as nearly linear exposure–response smoothing curves in most groups. Meanwhile, with an unanticipated lack of statistical significance when testing the interaction effects and the differences across the strata, limited pronounced effects were observed in the stratified analysis. These observations provide evidence that PM exposure plays a pervasive role in frailty transitions and targeted interventions may have important implications for delaying or even reversing the process of frailty and promoting healthy aging for both middle-aged and older adults.

Given previous studies have found the differing prevalence of frailty or prefrailty, our results at baseline made sense for applications. For example, one systematic review of the prevalence of frailty and prefrailty using population-level data showed 23% (range 9–41%) and 45% (range 30–61%) in the USA, 16% (range 13–19%) and 43% (range 39–47%) in the Republic of Korea and 20% (range 16–25%) and 37% (range 35–40%) in the UK for the FI, respectively.¹⁷ In China, Wu et al.⁴² reported a much lower prevalence of 7% being frail and 51.2% being prefrail in 2011 for the frailty phenotype. We observed an overall prevalence incongruent with previous research, which may be due to diversity in diagnostic criteria, country income level, periods, populations etc.⁴³ Meanwhile, the frailty transitions exhibited a consistent pattern in that most respondents stay stable, a few worsened and even fewer improved. Many longitudinal epidemiologic experiments have explored the patterns above, which were commensurate with our findings.^24,44 Also, the moderate or high positive correlations for the seven air pollutants were mostly in line with those reported by the US Environmental Protection Agency in 2019 and a pooled analysis of six European cohorts,^45,46 as well as correlations reported in China.^47,48

An increasing number of studies have reported associations between long-term exposure to ambient PM and the risk of non-communicable diseases or mental disorders, including cardiovascular disease, chronic obstructive pulmonary disease, depression and cognitive functions, all of which were considered when assessing frailty.^4,5,7,49,50 However, the direct linkage between PM and frailty has not been well investigated, especially in longitudinal cohort studies. Previous cohort studies have highlighted the association between air quality index (involving the six major air pollutants, including PM_2.5, PM₁₀, O₃, NO₂ and SO₂) and the FI in an adult population ≥65 y of age.²¹ Based on the cohort design, only rare studies have explored the effects of PM on frailty transitions in populations ≥45 y of age. Therefore, the significant associations between PM and frailty in the middle-aged group of our study suggest we should identify frailty earlier in the lifespan and intervene to reduce the burden of frailty.⁵¹ Moreover, there is ample evidence supporting inflammation and oxidative stress as key mechanisms linking PM exposure to adverse health effects.^52,53 With respect to frailty, numerous studies have demonstrated the involvement of inflammatory and oxidative stress mechanisms in the progress of frailty.^54,55 Therefore, it is reasonable to assume that frailty progression may be exacerbated by PM-induced inflammation and oxidative stress. Our research may offer epidemiological evidence to support the hypothesis proposed from a pathological mechanism perspective.

The exposure–response relationships were generally linear without thresholds, displaying increased risks in the worsening groups and decreased risks in the improvement groups. Although there are few published curves for PM exposure with frailty, frail-related diseases and symptoms displayed a monotonic increase in the risk of PM concentrations, among which considerable studies have observed near-linear or linear curves. For example, previous research in China observed almost linear curves for PM_2.5 associated with lung cancer and cardiovascular disease.^56–58 Regarding the apparent plateauing trend at high levels of PM_c and PM₁₀ in the robust worsening group, it might be due to our study population, which excluded those susceptible people who had died because of poor health status or sensitivity to PM exposure.

Previous cross-sectional studies have reflected inconclusive susceptibility with sex, age or residence, reporting evident associations mostly in male, older and rural populations, whereas differences were not always significant.^19,20,22 Similarly, we did not find significant heterogeneity between these subpopulations, with no significant differences. Moreover, the sensitivity analyses demonstrated the robustness of our findings. First, the associations between PM and frailty after further adjustment for gaseous pollutants generally remained positive and significant, despite the fact that two-pollutant models generally present more unstable parameter estimates compared with single-pollutant models.⁵⁹ Also, the magnitude of difference between single- and two-pollutant models was limited, which was convergent with previous studies.⁶⁰ Moreover, the associations between 1- and 3-y average PM concentrations and frailty status in 2018 demonstrated the contribution of PM exposure to the prevalence of frailty, just as has been published in cross-sectional studies.^20,22,61

This study provides empirical data to support evaluation of the frailty trajectory among middle-aged and older Chinese individuals in the context of decreasing concentrations of air pollution, indicating significant clinical and public health implications.^2,3 While China has made great progress since the implementation of clean air policies in 2013, exposure to PM still accelerates frailty development in both middle-aged and older populations, and there were no significant differences in such associations across subgroups. These results highlight the urgent need for policies and interventions aimed at reducing ambient air pollution levels to protect public health⁶² and the importance of mitigating the economic and societal burden of frailty in aging populations, regardless of gender, age and residence differences. Healthcare professionals and policymakers should continue to focus on aging-related risks in both older people and the previously underrepresented middle-aged population, as well as to expand frailty-related prevention and intervention measures to enhance life course care. Our study reinforces the existing knowledge that air quality is a critical determinant of public health and should be a priority for public health research, practice and policy.

Our study has several advantages over previous studies. We considered the middle-aged population and explored the corresponding frailty transitions that might exist. Additionally, this study utilized population-based longitudinal data to explore the association of PM exposure with frailty transitions.¹⁵ Moreover, we considered the effect of city-level population migration for controlling confounding factors, as well as adjusted models with and without four gaseous pollutants to provide evidence of the independent health effects of PM.

Inevitably, this study had some limitations. First, we assessed the PM concentrations of participants through city-level average exposure, thus misclassification may have occurred due to not reflecting actual individual exposure. However, considering the number of participants and multistage stratified probability proportionate to size sampling in CHARLS, using city-level exposure should be less likely to result in exposure misclassifications. Second, the characteristics of the participants we excluded due to missing information were found to be significantly different from those who were included. Hence selection bias may prevent us from applying our findings to the whole population. Also, considering the large amount of time spent indoors by middle-aged and elderly people, excluding indoor air pollution may lead to an underestimation of the association, which is predominantly attributed to outdoor air pollution.^63,64 In addition, CHARLS has collected most of the essential data of respondents, but biological data such as weight, lung capacity and walking speed were not tested in 2018. Weight and lung capacity are considered frequently in frailty research and walking speed is a component of the Fried Frailty Phenotype.^18,65 Moreover, long-term follow-up data could not be considered due to the lack of air quality monitoring data in China before 2013, which lowered the capacity of causal inference. Furthermore, not all cities participating in CHARLS were included in our study, due to missing cities in the China Urban Statistical Yearbook and the National Urban Air Quality Real-time Publishing Platform.

Conclusions

In conclusion, we found that long-term exposure to PM was positively associated with worsened states and negatively associated with improved states when considering frailty transitions. Also, irrespective of sex, age and residence, the risks remained similar. Our finding extends knowledge on the association between the two risk factors with a high burden of disease—PM pollution and frailty status—in a community-based cohort design and emphasizes the effects in middle-aged individuals. Policymakers should recognize the urgency of sustainable reductions in air pollution levels to reverse the frailty status, as well as early identification and management of frailty in middle-aged and older adults.

Supplementary Material

ihad033_Supplemental_File

ihad033_supplemental_file.docx^{(27.7KB, docx)}

Contributor Information

Zhen Guo, Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing 210009, China.

Hui Xue, Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing 210009, China.

Lijun Fan, Department of Medical Insurance, School of Public Health, Southeast University, Nanjing 210009, China.

Di Wu, Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing 210009, China.

Yiming Wang, Department of Medical Insurance, School of Public Health, Southeast University, Nanjing 210009, China.

Younjin Chung, National Centre for Epidemiology and Population Health, College of Health and Medicine, Australian National University, Canberra, ACT, Australia.

Yilan Liao, State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.

Zengliang Ruan, Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing 210009, China.

Wei Du, Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing 210009, China.

Authors’ contributions

ZG, ZLR and WD conceived the study. ZLR, LJF and WD designed the study protocol. ZG and HX carried out the analysis and interpretation of data. ZG, DW and YMW drafted the manuscript. LJF, YJC, YLL and ZLR critically revised the manuscript for intellectual content. All authors read and approved the final manuscript. ZLR is the guarantor of the article.

Funding

This work was supported by the National Natural Science Foundation of China (71704192), Department of Education (1125000172), Fundamental Research Funds for the Central Universities (3225002002A1) and Guangdong Basic and Applied Basic Research Foundation (2019A1515012038).

Competing interests

None declared.

Ethical approval

All the procedures and contents in the CHARLS were approved by the Ethics Review Committee of Peking University (IRB00001052-11015). There were no human or animal experiments in this study. Data were analysed at the aggregate level and no participants were contacted.

Data availability

The data underlying this article were accessed from CHARLS (http://opendata.pku.edu.cn/) and the National Urban Air Quality Real-time Publishing Platform (https://air.cnemc.cn:18007/). The derived data generated in this research will be shared upon reasonable request to the corresponding author.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

ihad033_Supplemental_File

ihad033_supplemental_file.docx^{(27.7KB, docx)}

Data Availability Statement

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PERMALINK

Differential effects of size-specific particulate matter on frailty transitions among middle-aged and older adults in China: findings from the China Health and Retirement Longitudinal Study (CHARLS), 2015–2018

Zhen Guo

Hui Xue

Lijun Fan

Di Wu

Yiming Wang

Younjin Chung

Yilan Liao

Zengliang Ruan

Wei Du

Abstract

Background

Methods

Results

Conclusions

Introduction

Methods

Study participants

Figure 1.

Measurement of frailty

Air quality data

Covariates

Statistical analysis

Results

Descriptive summary

Table 1.

Table 2.

Associations between PM concentrations and frailty transitions

Table 3.

Figure 2.

Sensitivity analyses

Table 4.

Discussion

Conclusions

Supplementary Material

Contributor Information

Authors’ contributions

Funding

Competing interests

Ethical approval

Data availability

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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