The association between PM2.5 and frailty: Evidence from 122 cities in China and 7 countries in Europe

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PM 2.5 particulate matter exposure is a significant factor affecting human health and is crucial in the aging process. Methods : We utilized data from China Health and Retirement Longitudinal Study (CHARLS) and the Survey of Health, Aging, and Retirement in Europe (SHARE) to study the relationship between PM 2.5 exposure and the frailty index. Acquire PM 2.5 exposure data for China and Europe, match them according to geographic location within the database. Our study used frailty index to evaluate frailty, which comprises 29 items. We examined the association between PM 2.5 and frailty index using fixed-effects regression models and Mendelian randomization (MR) analysis. Results : We first examined the association between PM 2.5 and frailty index using fixed-effects regression models, revealing a notable positive link across populations in China (coefficient = 0.0003, P = 0.0380) and Europe (Coefficient = 0.0019, P < 0.0001). This suggests that PM 2.5 exposure is a significant risk factor for frailty, leading to accelerated frailty. Moreover, our MR analysis uncovered a possible causal association (OR = 1.2933，95%CI: 1.2045-1.3820, P < 0.0001) between PM 2.5 exposure and the frailty index. Conclusions : Our findings indicate that long-term exposure to PM 2.5 in the environment is a risk factor for physical frailty and may have a potential causal relationship. Given the rapid global aging trend, it is crucial to focus on how air pollution affects frailty and to combat its negative consequences. PM2.5 Frailty index Air pollution Aging Cohort study Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Aging is evolving into a worldwide trend, this is the development challenge faced by countries worldwide, evidenced by the rising count and percentage of individuals aged 60 and above in the population. As of 2019, the worldwide population aged 60 and older stood at 1 billion, with forecasts suggesting it will escalate to 1.4 billion by 2030 and 2.1 billion by 2050 (World Health Organization, 2022a ). The expansion is happening at a rate never seen before and is anticipated to accelerate in the upcoming years, especially in developing countries. As a result, the population of the elderly who are frail is expected to grow rapidly in the coming years, exerting considerable strain on worldwide healthcare infrastructures and placing a considerable strain on the aged (Dent et al., 2019). Frailty, characterized by a weakened ability to withstand health stress, often impacts the elderly and is linked to numerous negative health outcomes, including all-cause mortality (Fan et al., 2020), newly diagnosed chronic diseases (Jang et al., 2023), depressive symptoms (Zhu et al., 2023), and falls (de Vries et al., 2013 ). Additionally, research indicates that active improvement strategies can mitigate frailty (Clegg et al., 2013 a; Dent et al., 2016 ), suggesting that identifying and preventing frailty in older adults could significantly reduce the disease's effect on individuals and medical systems, thus improving the quality of life for the elderly. Air pollution, as the greatest environmental risks to health, may contribute to the aging process and impact successful aging, which is especially significant in understanding aging (Cohen and Gerber, 2017 ). In 2019, a staggering 99% of people worldwide resided in areas failing to comply with World Health Organization's (WHO) air quality criteria ( World Health Organization, 2022b ). Evidence from Global Burden of Disease study, ambient PM 2.5 pollution was the main contributor to the global burden of disease in 2021, leading to 6.7 million premature deaths and 8.0% of total Disability adjusted life years ( Global Burden of Disease Study, 2024). Air pollution's health impacts are diverse, encompassing respiratory diseases, allergic conditions (Sierra-Vargas and Teran, 2012 ), cardiovascular disorders (Lee et al., 2018 ), and brain health (Russ et al., 2019 ). Epidemiological evidence suggests a potential link between each of these illnesses and prolonged exposure to air pollution (Chen et al., 2008 ; Peters et al., 2015). And evidence suggests a connection between these illnesses and frailty (Vetrano et al., 2019; Pilotto et al., 2020). The frailty index (FI) is the proportion of accumulated deficits (signs, symptoms, functional impairments, etc.) used to reflect the relationship between individual status and disease severity and mortality risk (Mitnitski et al., 2001 ). This index is widely recognized as a standard measure of aging and can be used to assess the burden of age-related clinically evident health deficits (Jang et al., 2023). Unlike the need to construct a specific set of health defects, FI can utilize data extracted from large global healthcare databases and public datasets for building purposes. By utilizing these large datasets, researchers can conduct more efficient and comprehensive analyses. Studies have shown that the FI has excellent predictive power and can be used to predict adverse health outcomes, individual hospitalization rates, and disease mortality rates (Kaskirbayeva et al., 2023). Multiple recent studies have investigated the effects of air pollution on frailty. However, the direct link between PM 2.5 and frailty has not been thoroughly explored, particularly within longitudinal cohorts. Research examining the health consequences of extended exposure to ambient PM 2.5 in the elderly reported a 30% increase in the likelihood of frailty for every 10µg/m 3 rise in PM 2.5 levels in rural areas (Guo et al., 2022). A investigation of Chinese older adults indicated that indoor air pollution from traditional solid cooking fuels might be a contributing factor to frailty (Cao et al., 2022). Moreover, a population-based quasi-experimental study, utilizing propensity score matching and double difference analysis, showed a significant decrease in individuals' FI scores and the frailty status improved following the implementation of the Clean Air Action in China (Guo and Yang, 2024 ). Nevertheless, the current research predominantly leans towards cross-sectional studies with study populations typically confined to the same region. Therefore, there is a need for cross-regional studies to allow for more generalizable research outcomes. Additionally, there is a paucity of research concerning the causal association between air pollution and frailty. In our study, we conducted a detailed exploration of the relationship between PM 2.5 and the FI to enhance the understanding of air pollution's effects on debilitation comprehensively. To achieve this, we utilized a longitudinal cross-national study incorporating data from the China Health and Retirement Longitudinal Study (CHARLS), the Survey of Health, Ageing and Retirement in Europe (SHARE), and summary data from Genome-Wide Association Studies (GWAS). Employing fixed effect regression models and Mendelian randomization (MR) methods, we examined both the association and causal association between PM 2.5 and the FI. 2. Methods 2.1 Study Design The baseline survey of CHARLS, a nationally representative cohort study of Chinese residents aged 45 and above, began in 2011. It included 17,708 individuals and was followed up every two years (Zhao et al., 2014). The survey used multistage probability sampling to select respondents. In addition, data from SHARE, the largest longitudinal study in Europe, are available for people aged 50 years and older. We used data from three follow-up visits from 2011 to 2015. We obtained the following information from the two cohorts: gender, age, marital status, education, residence, smoking status, drinking status, retirement status, body mass index (BMI (kg/m 2 )), and a variety of chronic diseases. In addition, information was obtained on variables related to activities of daily living (ADL), instrumental activities of daily living (IADL), physical function limitations, cognitive abilities, and depression. Figure 2 shows the data screening flowchart. To ensure study consistency, we limited the age range of both cohorts to those over 50 years of age. During the research process, for CHARLS, we excluded participants who lacked information on key variables for constructing the FI, as well as participants data lacked geographical location information. Finally, we included 6,407 research subjects. The SHARE data is consistent with the CHARLS data above, and participants from European countries with land areas smaller than or comparable to the largest cities in China were retained (include Estonia, Belgium, Czech Republic, Austria, Switzerland, Denmark, Slovenia). Finally, 17,029 research subjects were included. Figure 3 shows the 122 cities in China and 7 countries in Europe included and the number of people surveyed. The darker the color, the more surveyed people are included. Furthermore, a MR analysis was performed using GWAS summary data to explore the causal association between FI and PM 2.5 . The summary data for PM 2.5 was sourced from the IEU Open GWAS project (ID: ukb-b-10817). For the FI, GWAS data were derived from a study that investigated British individuals of European descent (Pilling, 2019 ; Atkins et al., 2021). This study encompassed a GWAS meta-analysis of the FI conducted on Biobank participants and Swedish twin genome participants. Calculations for FI utilized self-reported health-related information from the UK Biobank (UKB) and Twin Genome, encompassing 49 to 44 elements related to symptoms, disability, and identified illnesses. 2.2 Air Pollution Data Ambient PM 2.5 data were obtained from previously established dataset through satellite-based remote sensing technology (Wei and Li, 2019 ; Hammer et al., 2020). The time span of air pollution data is consistent with that of the study population, which is 2011–2015. Specifically, air pollution data for China were sourced from the China High-Resolution, High-Quality Near Surface Air Pollutant dataset published by Wei et al (Wei and Li, 2019 ), which has a spatial resolution of 1 km × 1 km. Using the geographic coordinates provided by CHARLS, we matched and obtained the average PM 2.5 concentration levels from three follow-up visits between 2011 and 2015 across 122 cities in China. European PM 2.5 concentrations were obtained from the Atmospheric Composition Analysis Group Web site at Washington University, which is a 1km × 1km global model developed by HAMMER et al (Hammer et al., 2020). To facilitate the matching of PM 2.5 data for seven European countries, country-level geographic location information was obtained from SHARE. Figure 4 shows the average PM 2.5 concentrations from 2011 to 2015 across 122 cities in China and 7 European countries. The numbers represent the number of people surveyed included from each provincial-level unit in China or each European country. In addition to PM 2.5 concentration, NO 2 concentration and temperature levels are considered influential factors. Therefore, global NO 2 concentrations (with a spatial resolution of 1 km × 1 km) (Mohegh and Anenberg, 2020 ) and temperature data ( https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels ) were incorporated as control variables in the model. This comprehensive approach ensures a thorough examination of the impact of PM 2.5 concentration while accounting for potential confounding effects of NO 2 concentration and temperature levels. 2.3 Assessment of the Frailty Index In our study, we used the FI to evaluate frailty, which is characterized by the accumulation of various age-related health problems. During the follow-up period between 2011 and 2015, three repeated measurements of FI were taken for each individual. The FI was developed according to established procedures and principles outlined by Searle SD (Searle et al., 2008; Searle and Rockwood, 2018 ),and informed by relevant previous research (He et al., 2024; Fan et al., 2020). Based on data from CHARLS and SHARE, a total of 29 items were selected for the construction of the FI. These items included illness, disability in ADL and disability in IADL, physical functioning, depression, and cognition. Each item was scored as 1 (deficit present) or 0 (no deficit), respectively. The scores for items 1–29 were summed to obtain the FI score, which ranged from 0 to 29. Items 28 and 29 representing cognitive and depression scores, are continuous variables, their values spanning from 0 to 1. Currently, research focuses on categorizing FI. However, existing studies have revealed discrepancies in the classification of FI and in identifying older adults as "frail", thereby limiting our understanding of frailty. Maintaining FI as a continuous variable may be beneficial until further research establishes the optimal FI category for this population (Fletcher et al., 2023). Therefore, the FI for each participant is calculated by dividing the total current health deficits by 29. As a continuous variable, FI ranges from 0 to 1, with higher values indicating greater frailty. The variables for constructing the FI are shown in the Supplementary table (Supplementary Table 1). 2.4 Statistical Analysis After obtaining air pollution exposure data and FI outcome data, we matched them based on the geographic location information provided by the CHARLS and SHARE databases. The data for each individual included three PM 2.5 concentration data and FI measurements, as well as a series of covariate data. First, to reduce the impact of missing values on the analysis, we excluded individuals with missing information on key variables in the construction of the FI. For the remaining individuals, we performed multiple imputation of covariates to obtain complete data for analysis (Van Buuren, 2018 ; Huque et al., 2018). The proportion of missing variables is shown in the supplementary table (Supplementary Table 2). Fixed effects regression was used to evaluate the longitudinal data. Fixed effects regression can account for both observed and unobserved time-invariant confounding variables (Isong et al., 2016). Consequently, fixed effect regression is deemed more robust than conventional regression models in investigating the correlation between predictor alterations and outcome variations. European SHARE data was employed to explore the impact of regional variances on the link between PM 2.5 and FI. The following covariates were controlled for in the analysis: age, marital status, education, smoking status, drinking status, retirement status, BMI, mean annual NO 2 concentration and temperature. Furthermore, to explore the causal association between the two variables, we performed MR analysis using GWAS summary data. In this analysis, we initially filtered out outlier single nucleotide polymorphisms (SNPs), retaining only those SNPs deemed reliable for further investigation. The MR analysis was primarily conducted employing the inverse variance weighted with modified weights (MW-IVW) method (Bowden et al., 2019) as the main analytical approach. Additionally, sensitivity analyses were performed using the Inverse Variance Weighted (IVW) (Burgess et al., 2013 ) and weighted median (WM) methods (Bowden et al., 2016) to assess the robustness of the findings. To test for pleiotropy, the MR-Egger method was employed, and the F statistic was applied to gauge the strength of instrumental variables in the study (Pierce et al., 2011 ; Bowden et al., 2015 ). The statistical evaluations were performed utilizing R (version 4.2.3), ArcGIS (version 10.8), Stata 16, and SPSS 25 software, considering a P -value less than 0.05 as statistically significant. 3. Results 3.1. Basic Characteristics of Study Participants The baseline basic characteristics of CHARLS and SHARE participants are presented in Table 1 . The mean age (SD) of 6,407 participants in CHARLS was 60.67 years (7.11 years), with males comprising 49.9%. And the proportion of elderly people aged 65 and above is 25.63%. The mean annual exposure concentration of PM 2.5 in 2011 at baseline was 58.1757µg/m 3 , the average FI in 2011 was 0.1268. The mean age (SD) of all 17,029 participants in SHARE was 65.06 years (9.13 years), with males comprising 41.2% and over 65 years old 46.63%. The mean annual exposure concentration of 14.8600µg/m 3 of PM 2.5 in 2011 at baseline, the average FI in 2011 was 0.1037. Table 1 Fundamental traits of the population studied Characteristic CHARLS (N (%)) SHARE (N (%)) Total sample (N) 6,407 17,029 Males 3,194 (49.9%) 6,992 (41.1%) ≥ 65 years old 1706 (26.63%) 7940 (46.63%) Age (Mean ± SD, years) 60.67 ± 7.11 65.06 ± 9.13 BMI (Mean ± SD, years) 23.42 ± 3.84 27.11 ± 4.80 Marital status (married) 5,964 (88.9%) 11,062 (65.0%) Educational level Less than lower secondary 5,740 (89.9%) 5,003 (29.4%) Upper secondary & vocational training 594 (9.3%) 7,785 (45.7%) Tertiary 72 (1.1%) 4,341 (24.9%) Residence (Rural) 4,178 (65.2%) 5,993 (35.2%) Smoking status (Smoker) 2,009 (32.8%) 3,348 (19.7%) Drinking status (Drinker) 2,191 (34.2%) 8,081 (47.1%) Frailty Index in 2011 (Mean ± SD) 0.1268 ± 0.1078 0.1037 ± 0.1031 PM 2.5 in 2011 (Mean ± SD, µg/m 3 ) 58.1757 ± 17.8567 14.8600 ± 3.6226 NO 2 in 2011 (Mean ± SD, µg/m 3 ) 7.1756 ± 4.5943 5.3475 ± 2.3394 Temperature in 2011 (Mean ± SD, ℃) 13.9071 ± 5.0140 8.2235 ± 1.6146 3.2. Association between PM and Frailty Index in cohort study In Table 2 , the association between PM 2.5 and the FI is displayed. Initially, a significant positive correlation was identified without controlling for potential confounding variables: The FI increases by 0.0030 for every 10µg/m 3 rise in PM 2.5 concentration, P = 0.0290. Upon adjusting for these confounding factors, a positive relationship between PM 2.5 and the FI persisted (coefficient = 0.0003, P = 0.0380). This suggests that with each 10 µg/m 3 rise in PM 2.5 concentration in the atmosphere, the FI also increases by 0.0030. In the SHARE data, we observed similar results. Prior to adjustment: coefficient = 0.0022, P < 0.0001, after adjustment: coefficient = 0.0019, P < 0.0001. This indicates that the FI was found to increase by 0.0190 for every 10 µg/m 3 increase in PM 2.5 concentration. Limiting the study to individuals aged 65 years and older revealed that PM 2.5 remained a risk factor for frailty in the European population. While a positive association was observed in the Chinese population, it was not statistically significant ( P = 0.3140). Additionally, to further explore the urban-rural associations. CHARLS data revealed a notable positive relationship between PM 2.5 and the FI in rural areas : coefficient = 0.0004, P = 0.0250. However, no such association was found in urban areas: coefficient = 0.0001, P = 0.6220. In the SHARE, the impact of PM 2.5 on the FI remains consistent, indicating that it serves as a risk factor for frailty. 3.2. Casual association between PM and Frailty Index in MR The F-statistic (MeanF = 23.9521) and MR-Egger results ( P = 0.5290) demonstrated that the analysis was not influenced by weak instrumental variables and pleiotropy. The MR results indicated that MWIVW: Odds ratio (OR) = 1.2933, 95% CI: 1.2045–1.3820, P < 0.0001; the results indicated that PM 2.5 was a significant risk for the FI. Sensitivity analyses showed similar results: IVW: OR = 1.2820, 95% CI: 1.1874–1.3767, P < 0.0001; WM: OR = 1.2630, 95% CI: 1.1271–1.3989, P = 0.0008. Our findings demonstrate that higher PM 2.5 levels expedite the frailty process., with each standard deviation increase in PM 2.5 associated with a 29.33% rise in frailty risk. Table 2 Main results Method CHARLS SHARE Coefficient P Coefficient P Fixed effects regression (Model 1) 0.0003 0.0290 0.0022 < 0.0001 Fixed effects regression (Model 2) 0.0003 0.0380 0.0019 < 0.0001 Fixed effects regression (Rural) 0.0004 0.0250 0.0031 < 0.0001 Fixed effects regression (Urban) 0.0001 0.6220 0.0016 0.0005 Fixed effects regression (≥ 65 years old) 0.0003 0.3140 0.0025 < 0.0001 MR \(\:\text{O}\text{R}\:\) = 1.2933, 95%CI: 1.2045–1.3820, P < 0.0001 Note: Model 1 is not adjusted. Model 2 adjusted for age, marital status, education, smoking status, drinking status, retirement status, BMI, mean annual NO 2 concentration and temperature. OR: Odds ratio; CHARLS: the China Health and Retirement Longitudinal Study; SHARE: the Survey of Health, Aging and Retirement in Europe; MR: Mendelian randomization. 4. Discussion The longitudinal study demonstrated a significant positive association between PM 2.5 exposure and FI, and MR results indicated a causal association. Our study investigated the association between PM 2.5 and FI through a follow-up design. It is noteworthy that a generally accepted instrument was used to measure frailty, and the FI was measured using a combination of 29 items. Through cross-ethnic studies, we obtained similar results in European populations, indicating that exposure to PM 2.5 can accelerate frailty in people of different ethnic groups. Our study revealed that exposure to PM 2.5 may speed up the aging process, similar findings were observed across different regions, and suggesting a possible causal relationship. Numerous research efforts have focused on exploring the link between air pollution and frailty among older adults. Research involving prospective cohorts revealed a correlation between air pollutants, like PM 2.5 , and a heightened likelihood of frailty (Guo et al., 2024). Consistent with the finding, a population-based study involving 220,079 UKB participants revealed that higher exposure to PM 2.5 was associated with an elevated risk of frailty (Veronese et al., 2023). Likewise, studies derived from the Chinese Longitudinal Healthy Longevity Survey revealed an increased occurrence of frailty linked to heightened exposure to air pollution in the year preceding the interview (Hu et al., 2020 ). Notably, frailty scores were significantly higher in older adults residing in areas with severe air pollution. This implies that air contamination could be a major factor in shaping the progression of healthy aging (Hu et al., 2020 ). Furthermore, after China implemented the air pollution control policy, namely the Clean Air Action Plan, the FI scores of healthy individuals were significantly reduced by 0.0205, while the FI scores of pre-frail individuals were significantly reduced by 0.0114 (Guo and Yang, 2024 ). Our research corroborates the negative impact of environmental air pollution on frailty. Exposure to PM 2.5 is positively correlated with the FI, thereby accelerating the aging process. Interestingly, when we restricted our study subjects to individuals aged 65 and above, we observed a positive correlation between PM 2.5 exposure and FI. However, in the sample of the elderly population in China, this association did not reach statistical significance. This phenomenon may be related to significant lifestyle differences between elderly populations in China and Europe, which could influence the level of PM 2.5 exposure among those aged 65 and above and differences in genetic backgrounds and physiological characteristics might also contribute to this phenomenon (Kodavanti, 2019 ; Jiang et al., 2023; Eurostat, 2017 ). In addition, our investigation brought to light disparities in results between China rural and urban settings. One potential explanation for this variation is that rural regions heavily rely on traditional energy sources like biomass burning, leading to higher levels of both outdoor and indoor air pollution (Guo et al., 2022). Moreover, compared to urban residents, rural areas lack proper housing and transportation planning, which may exacerbate environmental exposures for rural residents and result in their limited understanding of the significance of air pollution prevention and control (Zhao et al., 2021; Mueller et al., 2017). Exposure to air pollution is widely recognized for causing a range of detrimental health impacts, including inflammatory reactions, oxidative stress, metabolic disorders, and epigenetic modifications. For instance, by upsetting mitochondria, air pollution can cause pro-inflammatory reactions in different immune cells, and since inflammation is thought to be a possible source of weakness, thus collectively leading to the onset of weakness (Glencross et al., 2020; Zhang et al., 2023 ). Moreover, air pollutants may disrupt the body's balance and reduce its ability to handle stress, hastening the decline in functional abilities and capacities associated with aging levels of cells, organs, and the entire system, ultimately resulting in frailty (Clegg et al., 2013 b). Clearly, air pollution plays a role in frailty to a certain extent, making the reduction of air pollution crucial for diminishing frailty in the elderly. As populations age, the prevalence of frailty is on the rise globally, leading to adverse disease outcomes and increasing healthcare expenditures (Hoogendijk et al., 2019). At the same time, the disease burden caused by chronic diseases, which are a component of frailty, continues to increase, bringing tremendous pressure to public health prevention work (Shilian et al., 2020). Therefore, a series of research and measures are needed to reduce the concentration of PM 2.5 , slow down the process of aging, and alleviate the heavy burden brought by aging. The present study has several significant strengths. Firstly, a cohort study design was employed to investigate the longitudinal association between PM 2.5 and FI in depth. During the research process, the effects of factors such as temperature, NO 2 and important covariates were carefully controlled to ensure the reliability of the results. Secondly, we constructed a comprehensive FI, taking into account multiple factors, including disease, physical functional limitations, disability in ADL, disability in IADL, physical function, depression, and cognition, to comprehensively assess the FI. Finally, we utilized data from CHARLS and SHARE. Moreover, we ensured consistency in variables used to construct the FI between SHARE and CHARLS, with a consistent data timeframe from 2011 to 2015. Through cross-regional observations, we obtained consistent results. Additionally, we further established causal relationship through MR analysis. Therefore, our study findings are generalizable and demonstrate the impact of PM 2.5 on frailty, providing robust support for the credibility of our research. Although our study yielded some important findings, its limitations must also be acknowledged. First, the pollutant data used in the study are based on city-level data, which may not fully capture small changes within cities. Since we did not account for intra-city variability, this may have introduced bias into the results. Second, we used a validated tool to detect FI, but we adapted it based on information available in the research database used. The use of existing data may introduce bias from the original version. Third, to ensure the maximum inclusion of the sample size, we performed multiple imputation of cognitive variables required to construct FI in the SHARE database (such as: Orient variable: missing proportion: wave 4: 28.32%, wave 5: 99.62%, wave 6: 0.23%), which may have some impact on the results. Therefore, it is important to be cautious in interpreting the findings and to address these limitations in future studies. 5. Conclusion The results indicate that long-term exposure to PM 2.5 serves as a risk factor for frailty. In the context of aging, it is crucial to consider the accelerated effect of environmental issues on the aging process. Therefore, implementing public health measures aimed at decreasing the PM 2.5 concentration in the environment is necessary to mitigate the aging acceleration and alleviate the associated pension and fiscal burdens. Declarations Data availability The China Health and Retirement Longitudinal Study (CHARLS): https://charls.charlsdata.com/pages/data/111/en.html. The Survey of Health, Ageing and Retirement in Europe (SHARE): http://www.share-project.org/data-access.html. Harmonized data for CHARLS and SHARE can be accessed via: https://g2aging.org/hrd/get-data. GWAS summary data for PM 2.5 : https://gwas.mrcieu.ac.uk/datasets/ukb-b-10817/. GWAS summary data for frailty index: https://figshare.com/articles/dataset/Genome-Wide_Association_Study_of_the_Frailty_Index_-_Atkins_et_al_2019/9204998. Ethics approval CHARLS : CHARLS received ethical approval from the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015) and all participants provided informed written consent. SHARE: SHARE received ethical approval from the Ethics Council of the Max Planck Society and all participants provided informed written consent. Acknowledgments The authors sincerely thank the CHARLS and SHARE data management teams for data collection and management, as well as GWAS and related consortia for the collection and management of large-scale data resources. Thanks to the financial support provided by the National Natural Science Foundation of China. Funding This study was supported by the National Natural Science Foundation of China (Grant numbers: No.82073674& No.82373692). Consent for publication All authors approved the final manuscript and the submission to this journal. Conflicts of Interest The authors declare no competing interests. CRediT Authorship Contribution Statement Yanchao Wen: Data Curation, Writing-Original Draft, Writing-Review & Editing, Visualization. Guiming Zhu: Data Curation, Writing-Review & Editing, Visualization. Kexin Cao: Data Curation, Writing-Review & Editing. Jie Liang: Writing-Review & Editing. Xiangfeng Lu: Writing-Review & Editing. Tong Wang: Writing-Review & Editing, Supervision, Funding acquisition. Supplementary Material See supplementary table. References Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet 403: 2162-2203.https://10.1016/s0140-6736(24)00933-4. 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Fan J, Yu C, Guo Y, et al. 2020 Frailty index and all-cause and cause-specific mortality in Chinese adults: a prospective cohort study. Lancet Public Health 5: e650-e660.https://10.1016/s2468-2667(20)30113-4. Fletcher JA, Logan B, Reid N, et al. 2023 How frail is frail in oncology studies? A scoping review. BMC Cancer 23: 498.https://10.1186/s12885-023-10933-z. Glencross DA, Ho TR, Camiña N, et al. 2020 Air pollution and its effects on the immune system. Free Radic Biol Med 151: 56-68.https://10.1016/j.freeradbiomed.2020.01.179. Guo X, Su W, Wang X, et al. 2024 Assessing the effects of air pollution and residential greenness on frailty in older adults: a prospective cohort study from China. Environ Sci Pollut Res Int 31: 9091-9105.https://10.1007/s11356-023-31741-9. Guo Y and Yang F. 2024 Effects of China's Clean Air Act on Frailty Levels Among Middle-Aged and Older Adults: A Population-Based Quasi-Experimental Study. J Gerontol A Biol Sci Med Sci 79.https://10.1093/gerona/glae040. Guo YF, Ng N, Kowal P, et al. 2022 Frailty Risk in Older Adults Associated With Long-Term Exposure to Ambient PM2.5 in 6 Middle-Income Countries. J Gerontol A Biol Sci Med Sci 77: 970-976.https://10.1093/gerona/glac022. Hammer MS, van Donkelaar A, Li C, et al. 2020 Global Estimates and Long-Term Trends of Fine Particulate Matter Concentrations (1998–2018). Environmental Science & Technology 54: 7879-7890.https://10.1021/acs.est.0c01764. He D, Wang Z, Li J, et al. 2024 Changes in frailty and incident cardiovascular disease in three prospective cohorts. Eur Heart J 45: 1058-1068.https://10.1093/eurheartj/ehad885. Hoogendijk EO, Afilalo J, Ensrud KE, et al. 2019 Frailty: implications for clinical practice and public health. Lancet 394: 1365-1375.https://10.1016/s0140-6736(19)31786-6. Hu K, Keenan K, Hale JM, et al. 2020 The association between city-level air pollution and frailty among the elderly population in China. Health Place 64: 102362.https://10.1016/j.healthplace.2020.102362. Huque MH, Carlin JB, Simpson JA, et al. 2018 A comparison of multiple imputation methods for missing data in longitudinal studies. BMC Med Res Methodol 18: 168.https://10.1186/s12874-018-0615-6. Isong IA, Richmond T, Kawachi I, et al. 2016 Childcare Attendance and Obesity Risk. Pediatrics 138.https://10.1542/peds.2016-1539. Jang J, Jung H, Shin J, et al. 2023 Assessment of Frailty Index at 66 Years of Age and Association With Age-Related Diseases, Disability, and Death Over 10 Years in Korea. JAMA Netw Open 6: e2248995.https://10.1001/jamanetworkopen.2022.48995. Jiang L, Chen X, Liang W, et al. 2023 Alike but also different: a spatiotemporal analysis of the older populations in Zhejiang and Jilin provinces, China. BMC Public Health 23: 1529.https://10.1186/s12889-023-16433-w. Kaskirbayeva D, West R, Jaafari H, et al. 2023 Progression of frailty as measured by a cumulative deficit index: A systematic review. Ageing Research Reviews 84: 101789.https://https://doi.org/10.1016/j.arr.2022.101789. Kodavanti UP. 2019 Susceptibility Variations in Air Pollution Health Effects: Incorporating Neuroendocrine Activation. Toxicol Pathol 47: 962-975.https://10.1177/0192623319878402. Lee KK, Miller MR and Shah ASV. 2018 Air Pollution and Stroke. J Stroke 20: 2-11.https://10.5853/jos.2017.02894. Mitnitski AB, Mogilner AJ and Rockwood K. 2001 Accumulation of deficits as a proxy measure of aging. ScientificWorldJournal 1: 323-336.https://10.1100/tsw.2001.58. Mohegh A and Anenberg S. (2020) Global surface NO2 concentrations 1990-2020; figshare. Dataset. Mueller N, Rojas-Rueda D, Basagaña X, et al. 2017 Urban and Transport Planning Related Exposures and Mortality: A Health Impact Assessment for Cities. Environ Health Perspect 125: 89-96.https://10.1289/ehp220. Organization WH. (2022a) https://www.who.int/news-room/fact-sheets/detail/ageing-and-health , 2022. Aging and Health. Organization WH. (2022b) https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health , Ambient (outdoor) air pollution . Peters R, Peters J, Booth A, et al. 2015 Is air pollution associated with increased risk of cognitive decline? A systematic review. Age Ageing 44: 755-760.https://10.1093/ageing/afv087. Pierce BL, Ahsan H and Vanderweele TJ. 2011 Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants. Int J Epidemiol 40: 740-752.https://10.1093/ije/dyq151. Pilling L. 2019 Genome-Wide Association Study of the Frailty Index - Atkins et al. 2021. figshare. Dataset. .https://https://doi.org/10.6084/m9.figshare.9204998.v4 Pilotto A, Custodero C, Maggi S, et al. 2020 A multidimensional approach to frailty in older people. Ageing Res Rev 60: 101047.https://10.1016/j.arr.2020.101047. Russ TC, Reis S and van Tongeren M. 2019 Air pollution and brain health: defining the research agenda. Curr Opin Psychiatry 32: 97-104.https://10.1097/yco.0000000000000480. Searle SD, Mitnitski A, Gahbauer EA, et al. 2008 A standard procedure for creating a frailty index. BMC Geriatr 8: 24.https://10.1186/1471-2318-8-24. Searle SD and Rockwood K. 2018 What proportion of older adults in hospital are frail? Lancet 391: 1751-1752.https://10.1016/S0140-6736(18)30907-3. Shilian H, Jing W, Cui C, et al. 2020 Analysis of epidemiological trends in chronic diseases of Chinese residents. Aging Med (Milton) 3: 226-233.https://10.1002/agm2.12134. Sierra-Vargas MP and Teran LM. 2012 Air pollution: impact and prevention. Respirology 17: 1031-1038.https://10.1111/j.1440-1843.2012.02213.x. Van Buuren S. (2018) Flexible Imputation of Missing Data : Chapman & Hall/CRC. Boca Raton, FL. Veronese N, Maniscalco L, Matranga D, et al. 2023 Association Between Pollution and Frailty in Older People: A Cross-Sectional Analysis of the UK Biobank. J Am Med Dir Assoc 24: 475-481.e473.https://10.1016/j.jamda.2022.12.027. Vetrano DL, Palmer K, Marengoni A, et al. 2019 Frailty and Multimorbidity: A Systematic Review and Meta-analysis. J Gerontol A Biol Sci Med Sci 74: 659-666.https://10.1093/gerona/gly110. Wei J and Li Z. (2019) ChinaHighPM2.5: Big Data Seamless 1 km Ground-level PM2.5 Dataset for China. In: Center NTPTPED (ed). Zhang L, Zeng X, He F, et al. 2023 Inflammatory biomarkers of frailty: A review. Exp Gerontol 179: 112253.https://10.1016/j.exger.2023.112253. Zhao S, Liu S, Hou X, et al. 2021 Air pollution and cause-specific mortality: A comparative study of urban and rural areas in China. Chemosphere 262: 127884.https://10.1016/j.chemosphere.2020.127884. Zhao Y, Hu Y, Smith JP, et al. 2014 Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS). Int J Epidemiol 43: 61-68.https://10.1093/ije/dys203. Zhu J, Zhou D, Nie Y, et al. 2023 Assessment of the bidirectional causal association between frailty and depression: A Mendelian randomization study. J Cachexia Sarcopenia Muscle 14: 2327-2334.https://10.1002/jcsm.13319. Additional Declarations No competing interests reported. Supplementary Files Supplementarytable.docx Cite Share Download PDF Status: Published Journal Publication published 30 Dec, 2024 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 04 Dec, 2024 Reviews received at journal 04 Dec, 2024 Reviews received at journal 26 Nov, 2024 Reviewers agreed at journal 12 Nov, 2024 Reviewers agreed at journal 12 Nov, 2024 Reviewers agreed at journal 13 Oct, 2024 Reviewers invited by journal 10 Oct, 2024 Editor invited by journal 30 Sep, 2024 Editor assigned by journal 30 Sep, 2024 Submission checks completed at journal 30 Sep, 2024 First submitted to journal 28 Sep, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5172427","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":386462866,"identity":"0f25c490-e662-4107-ba65-42351ad317de","order_by":0,"name":"Yanchao Wen","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yanchao","middleName":"","lastName":"Wen","suffix":""},{"id":386462867,"identity":"385538ce-412f-47f2-96c2-428c43875d8f","order_by":1,"name":"Guiming Zhu","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Guiming","middleName":"","lastName":"Zhu","suffix":""},{"id":386462868,"identity":"e8a99400-b235-4c91-9de1-ebd5da3dd911","order_by":2,"name":"Kexin Cao","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kexin","middleName":"","lastName":"Cao","suffix":""},{"id":386462869,"identity":"f6c7f5e5-7666-4901-b2ce-a035041936b5","order_by":3,"name":"Jie Liang","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Liang","suffix":""},{"id":386462870,"identity":"571d7a8f-35d3-42b1-b370-d4778320f17d","order_by":4,"name":"Xiangfeng Lu","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences and Peking Union Medical College, National Center for Cardiovascular Diseases","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiangfeng","middleName":"","lastName":"Lu","suffix":""},{"id":386462871,"identity":"63ca1e5e-7d46-49d7-ba39-18b2cbdcaa88","order_by":5,"name":"Tong Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYDADfijN2EBIJQ+MIdlAshaDA8RqsWfvPfyap+aO3ebzZ0w38zDYyG44wPzsAV5beM6lWfMce5a87UZa2m0ehjTjDQfYzA3wapHIMTPOYTucbHaD+RhQy+HEDQd42CQIa/l3ONm4/2AbUMt/orQYP85tO2xnwJAMsuUAEVrOnDFj/tt3OEEC6JebcwySjWceZjPDq4W9vcf444xvh+35+8+Y3XhTYSfbd7z5GV4tQAB2RmIDmA0KKmYC6kFKPgAJe8LqRsEoGAWjYMQCAIMxSgoou4eRAAAAAElFTkSuQmCC","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Tong","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-09-29 02:08:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5172427/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5172427/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-024-21121-4","type":"published","date":"2024-12-30T15:57:30+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":71347895,"identity":"c1b68ea3-05e4-45b4-88f8-c7f6638d75fa","added_by":"auto","created_at":"2024-12-13 14:11:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":669205,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical abstract. Note：CHARLS: the China Health and Retirement Longitudinal Study; SHARE: the Survey of Health, Aging and Retirement in Europe; GWAS: Genome-Wide Association Studies; ADL: Activities of Daily Living; IADL: Instrumental Activities of Daily Living; CESD: Center for Epidemiological Studies Depression Scale\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5172427/v1/e21c7eb3d9c9e195512a4fbb.png"},{"id":71347896,"identity":"feb276da-6656-4300-af8f-ae6f05f6da03","added_by":"auto","created_at":"2024-12-13 14:11:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":574292,"visible":true,"origin":"","legend":"\u003cp\u003eData Screening Flowchart\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5172427/v1/b8a5d73d9826ba7aa6b4aa5b.png"},{"id":71348122,"identity":"85a70f12-5c56-4a06-a5e8-6b344e8f6832","added_by":"auto","created_at":"2024-12-13 14:19:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":808110,"visible":true,"origin":"","legend":"\u003cp\u003e122 Chinese cities and 7 European countries included and the number of respondents. The darker the color, the more respondents were included. A: China; B: Europe.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5172427/v1/7e7e1b8c70b58ddfb07df6b9.png"},{"id":71347897,"identity":"96030e4f-c5f0-46fe-a8cf-4adee4c93d49","added_by":"auto","created_at":"2024-12-13 14:11:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":936074,"visible":true,"origin":"","legend":"\u003cp\u003eThe average PM\u003csub\u003e2.5\u003c/sub\u003e concentrations from 2011 to 2015 across 122 cities in China and 7 European countries. The numbers represent the number of people surveyed included from each provincial-level unit in China or each European country. A: China; B: Europe.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5172427/v1/321eed9af8a9cf28aceefe1e.png"},{"id":73093326,"identity":"b41677d5-ba68-41ee-992f-5d9d7972682b","added_by":"auto","created_at":"2025-01-06 16:13:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4192791,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5172427/v1/8a313a2e-726e-48a9-adba-9856bb4667e9.pdf"},{"id":71347894,"identity":"cb4e1f02-55b3-489d-960b-f5aa9f2aca5c","added_by":"auto","created_at":"2024-12-13 14:11:13","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":20925,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable.docx","url":"https://assets-eu.researchsquare.com/files/rs-5172427/v1/24f5d1a3dd96372e3c51d3c1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The association between PM2.5 and frailty: Evidence from 122 cities in China and 7 countries in Europe","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAging is evolving into a worldwide trend, this is the development challenge faced by countries worldwide, evidenced by the rising count and percentage of individuals aged 60 and above in the population. As of 2019, the worldwide population aged 60 and older stood at 1\u0026nbsp;billion, with forecasts suggesting it will escalate to 1.4\u0026nbsp;billion by 2030 and 2.1\u0026nbsp;billion by 2050 (World Health Organization, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e). The expansion is happening at a rate never seen before and is anticipated to accelerate in the upcoming years, especially in developing countries. As a result, the population of the elderly who are frail is expected to grow rapidly in the coming years, exerting considerable strain on worldwide healthcare infrastructures and placing a considerable strain on the aged (Dent et al., 2019). Frailty, characterized by a weakened ability to withstand health stress, often impacts the elderly and is linked to numerous negative health outcomes, including all-cause mortality (Fan et al., 2020), newly diagnosed chronic diseases (Jang et al., 2023), depressive symptoms (Zhu et al., 2023), and falls (de Vries et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Additionally, research indicates that active improvement strategies can mitigate frailty (Clegg et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003ea; Dent et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), suggesting that identifying and preventing frailty in older adults could significantly reduce the disease's effect on individuals and medical systems, thus improving the quality of life for the elderly.\u003c/p\u003e \u003cp\u003eAir pollution, as the greatest environmental risks to health, may contribute to the aging process and impact successful aging, which is especially significant in understanding aging (Cohen and Gerber, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In 2019, a staggering 99% of people worldwide resided in areas failing to comply with World Health Organization's (WHO) air quality criteria ( World Health Organization, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e). Evidence from Global Burden of Disease study, ambient PM\u003csub\u003e2.5\u003c/sub\u003e pollution was the main contributor to the global burden of disease in 2021, leading to 6.7\u0026nbsp;million premature deaths and 8.0% of total Disability adjusted life years ( Global Burden of Disease Study, 2024). Air pollution's health impacts are diverse, encompassing respiratory diseases, allergic conditions (Sierra-Vargas and Teran, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), cardiovascular disorders (Lee et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and brain health (Russ et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Epidemiological evidence suggests a potential link between each of these illnesses and prolonged exposure to air pollution (Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Peters et al., 2015). And evidence suggests a connection between these illnesses and frailty (Vetrano et al., 2019; Pilotto et al., 2020).\u003c/p\u003e \u003cp\u003eThe frailty index (FI) is the proportion of accumulated deficits (signs, symptoms, functional impairments, etc.) used to reflect the relationship between individual status and disease severity and mortality risk (Mitnitski et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). This index is widely recognized as a standard measure of aging and can be used to assess the burden of age-related clinically evident health deficits (Jang et al., 2023). Unlike the need to construct a specific set of health defects, FI can utilize data extracted from large global healthcare databases and public datasets for building purposes. By utilizing these large datasets, researchers can conduct more efficient and comprehensive analyses. Studies have shown that the FI has excellent predictive power and can be used to predict adverse health outcomes, individual hospitalization rates, and disease mortality rates (Kaskirbayeva et al., 2023).\u003c/p\u003e \u003cp\u003eMultiple recent studies have investigated the effects of air pollution on frailty. However, the direct link between PM\u003csub\u003e2.5\u003c/sub\u003e and frailty has not been thoroughly explored, particularly within longitudinal cohorts. Research examining the health consequences of extended exposure to ambient PM\u003csub\u003e2.5\u003c/sub\u003e in the elderly reported a 30% increase in the likelihood of frailty for every 10\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e rise in PM\u003csub\u003e2.5\u003c/sub\u003e levels in rural areas (Guo et al., 2022). A investigation of Chinese older adults indicated that indoor air pollution from traditional solid cooking fuels might be a contributing factor to frailty (Cao et al., 2022). Moreover, a population-based quasi-experimental study, utilizing propensity score matching and double difference analysis, showed a significant decrease in individuals' FI scores and the frailty status improved following the implementation of the Clean Air Action in China (Guo and Yang, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Nevertheless, the current research predominantly leans towards cross-sectional studies with study populations typically confined to the same region. Therefore, there is a need for cross-regional studies to allow for more generalizable research outcomes. Additionally, there is a paucity of research concerning the causal association between air pollution and frailty.\u003c/p\u003e \u003cp\u003eIn our study, we conducted a detailed exploration of the relationship between PM\u003csub\u003e2.5\u003c/sub\u003e and the FI to enhance the understanding of air pollution's effects on debilitation comprehensively. To achieve this, we utilized a longitudinal cross-national study incorporating data from the China Health and Retirement Longitudinal Study (CHARLS), the Survey of Health, Ageing and Retirement in Europe (SHARE), and summary data from Genome-Wide Association Studies (GWAS). Employing fixed effect regression models and Mendelian randomization (MR) methods, we examined both the association and causal association between PM\u003csub\u003e2.5\u003c/sub\u003e and the FI.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design\u003c/h2\u003e \u003cp\u003eThe baseline survey of CHARLS, a nationally representative cohort study of Chinese residents aged 45 and above, began in 2011. It included 17,708 individuals and was followed up every two years (Zhao et al., 2014). The survey used multistage probability sampling to select respondents. In addition, data from SHARE, the largest longitudinal study in Europe, are available for people aged 50 years and older. We used data from three follow-up visits from 2011 to 2015. We obtained the following information from the two cohorts: gender, age, marital status, education, residence, smoking status, drinking status, retirement status, body mass index (BMI (kg/m\u003csup\u003e2\u003c/sup\u003e)), and a variety of chronic diseases. In addition, information was obtained on variables related to activities of daily living (ADL), instrumental activities of daily living (IADL), physical function limitations, cognitive abilities, and depression. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the data screening flowchart.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo ensure study consistency, we limited the age range of both cohorts to those over 50 years of age. During the research process, for CHARLS, we excluded participants who lacked information on key variables for constructing the FI, as well as participants data lacked geographical location information. Finally, we included 6,407 research subjects. The SHARE data is consistent with the CHARLS data above, and participants from European countries with land areas smaller than or comparable to the largest cities in China were retained (include Estonia, Belgium, Czech Republic, Austria, Switzerland, Denmark, Slovenia). Finally, 17,029 research subjects were included. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the 122 cities in China and 7 countries in Europe included and the number of people surveyed. The darker the color, the more surveyed people are included.\u003c/p\u003e \u003cp\u003eFurthermore, a MR analysis was performed using GWAS summary data to explore the causal association between FI and PM\u003csub\u003e2.5\u003c/sub\u003e. The summary data for PM\u003csub\u003e2.5\u003c/sub\u003e was sourced from the IEU Open GWAS project (ID: ukb-b-10817). For the FI, GWAS data were derived from a study that investigated British individuals of European descent (Pilling, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Atkins et al., 2021). This study encompassed a GWAS meta-analysis of the FI conducted on Biobank participants and Swedish twin genome participants. Calculations for FI utilized self-reported health-related information from the UK Biobank (UKB) and Twin Genome, encompassing 49 to 44 elements related to symptoms, disability, and identified illnesses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2.2 Air Pollution Data\u003c/h3\u003e\n\u003cp\u003eAmbient PM\u003csub\u003e2.5\u003c/sub\u003e data were obtained from previously established dataset through satellite-based remote sensing technology (Wei and Li, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hammer et al., 2020). The time span of air pollution data is consistent with that of the study population, which is 2011\u0026ndash;2015. Specifically, air pollution data for China were sourced from the China High-Resolution, High-Quality Near Surface Air Pollutant dataset published by Wei et al (Wei and Li, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which has a spatial resolution of 1 km \u0026times; 1 km. Using the geographic coordinates provided by CHARLS, we matched and obtained the average PM\u003csub\u003e2.5\u003c/sub\u003e concentration levels from three follow-up visits between 2011 and 2015 across 122 cities in China. European PM\u003csub\u003e2.5\u003c/sub\u003e concentrations were obtained from the Atmospheric Composition Analysis Group Web site at Washington University, which is a 1km \u0026times; 1km global model developed by HAMMER et al (Hammer et al., 2020). To facilitate the matching of PM\u003csub\u003e2.5\u003c/sub\u003e data for seven European countries, country-level geographic location information was obtained from SHARE. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the average PM\u003csub\u003e2.5\u003c/sub\u003e concentrations from 2011 to 2015 across 122 cities in China and 7 European countries. The numbers represent the number of people surveyed included from each provincial-level unit in China or each European country.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition to PM\u003csub\u003e2.5\u003c/sub\u003e concentration, NO\u003csub\u003e2\u003c/sub\u003e concentration and temperature levels are considered influential factors. Therefore, global NO\u003csub\u003e2\u003c/sub\u003e concentrations (with a spatial resolution of 1 km \u0026times; 1 km) (Mohegh and Anenberg, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and temperature data (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels\u003c/span\u003e\u003cspan address=\"https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e were incorporated as control variables in the model. This comprehensive approach ensures a thorough examination of the impact of PM\u003csub\u003e2.5\u003c/sub\u003e concentration while accounting for potential confounding effects of NO\u003csub\u003e2\u003c/sub\u003e concentration and temperature levels.\u003c/p\u003e\n\u003ch3\u003e2.3 Assessment of the Frailty Index\u003c/h3\u003e\n\u003cp\u003eIn our study, we used the FI to evaluate frailty, which is characterized by the accumulation of various age-related health problems. During the follow-up period between 2011 and 2015, three repeated measurements of FI were taken for each individual. The FI was developed according to established procedures and principles outlined by Searle SD (Searle et al., 2008; Searle and Rockwood, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e),and informed by relevant previous research (He et al., 2024; Fan et al., 2020). Based on data from CHARLS and SHARE, a total of 29 items were selected for the construction of the FI. These items included illness, disability in ADL and disability in IADL, physical functioning, depression, and cognition. Each item was scored as 1 (deficit present) or 0 (no deficit), respectively. The scores for items 1\u0026ndash;29 were summed to obtain the FI score, which ranged from 0 to 29. Items 28 and 29 representing cognitive and depression scores, are continuous variables, their values spanning from 0 to 1. Currently, research focuses on categorizing FI. However, existing studies have revealed discrepancies in the classification of FI and in identifying older adults as \"frail\", thereby limiting our understanding of frailty. Maintaining FI as a continuous variable may be beneficial until further research establishes the optimal FI category for this population (Fletcher et al., 2023). Therefore, the FI for each participant is calculated by dividing the total current health deficits by 29. As a continuous variable, FI ranges from 0 to 1, with higher values indicating greater frailty. The variables for constructing the FI are shown in the Supplementary table (Supplementary Table\u0026nbsp;1).\u003c/p\u003e\n\u003ch3\u003e2.4 Statistical Analysis\u003c/h3\u003e\n\u003cp\u003eAfter obtaining air pollution exposure data and FI outcome data, we matched them based on the geographic location information provided by the CHARLS and SHARE databases. The data for each individual included three PM\u003csub\u003e2.5\u003c/sub\u003e concentration data and FI measurements, as well as a series of covariate data. First, to reduce the impact of missing values on the analysis, we excluded individuals with missing information on key variables in the construction of the FI. For the remaining individuals, we performed multiple imputation of covariates to obtain complete data for analysis (Van Buuren, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Huque et al., 2018). The proportion of missing variables is shown in the supplementary table (Supplementary Table\u0026nbsp;2). Fixed effects regression was used to evaluate the longitudinal data. Fixed effects regression can account for both observed and unobserved time-invariant confounding variables (Isong et al., 2016). Consequently, fixed effect regression is deemed more robust than conventional regression models in investigating the correlation between predictor alterations and outcome variations. European SHARE data was employed to explore the impact of regional variances on the link between PM\u003csub\u003e2.5\u003c/sub\u003e and FI. The following covariates were controlled for in the analysis: age, marital status, education, smoking status, drinking status, retirement status, BMI, mean annual NO\u003csub\u003e2\u003c/sub\u003e concentration and temperature.\u003c/p\u003e \u003cp\u003eFurthermore, to explore the causal association between the two variables, we performed MR analysis using GWAS summary data. In this analysis, we initially filtered out outlier single nucleotide polymorphisms (SNPs), retaining only those SNPs deemed reliable for further investigation. The MR analysis was primarily conducted employing the inverse variance weighted with modified weights (MW-IVW) method (Bowden et al., 2019) as the main analytical approach. Additionally, sensitivity analyses were performed using the Inverse Variance Weighted (IVW) (Burgess et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and weighted median (WM) methods (Bowden et al., 2016) to assess the robustness of the findings. To test for pleiotropy, the MR-Egger method was employed, and the F statistic was applied to gauge the strength of instrumental variables in the study (Pierce et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Bowden et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe statistical evaluations were performed utilizing R (version 4.2.3), ArcGIS (version 10.8), Stata 16, and SPSS 25 software, considering a \u003cem\u003eP\u003c/em\u003e-value less than 0.05 as statistically significant.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Basic Characteristics of Study Participants\u003c/h2\u003e \u003cp\u003eThe baseline basic characteristics of CHARLS and SHARE participants are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The mean age (SD) of 6,407 participants in CHARLS was 60.67 years (7.11 years), with males comprising 49.9%. And the proportion of elderly people aged 65 and above is 25.63%. The mean annual exposure concentration of PM\u003csub\u003e2.5\u003c/sub\u003e in 2011 at baseline was 58.1757µg/m\u003csup\u003e3\u003c/sup\u003e, the average FI in 2011 was 0.1268. The mean age (SD) of all 17,029 participants in SHARE was 65.06 years (9.13 years), with males comprising 41.2% and over 65 years old 46.63%. The mean annual exposure concentration of 14.8600µg/m\u003csup\u003e3\u003c/sup\u003e of PM\u003csub\u003e2.5\u003c/sub\u003e in 2011 at baseline, the average FI in 2011 was 0.1037.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFundamental traits of the population studied\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHARLS (N (%))\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSHARE (N (%))\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal sample (N)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,407\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17,029\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMales\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,194 (49.9%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,992 (41.1%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e≥ 65 years old\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1706 (26.63%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7940 (46.63%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (Mean ± SD, years)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.67 ± 7.11\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.06 ± 9.13\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (Mean ± SD, years)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.42 ± 3.84\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.11 ± 4.80\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status (married)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,964 (88.9%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11,062 (65.0%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational level\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than lower secondary\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,740 (89.9%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,003 (29.4%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper secondary \u0026amp; vocational training\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e594 (9.3%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,785 (45.7%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 (1.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,341 (24.9%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence (Rural)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,178 (65.2%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,993 (35.2%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status (Smoker)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,009 (32.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,348 (19.7%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking status (Drinker)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,191 (34.2%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,081 (47.1%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrailty Index in 2011 (Mean ± SD)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1268 ± 0.1078\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1037 ± 0.1031\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e in 2011 (Mean ± SD, µg/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.1757 ± 17.8567\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.8600 ± 3.6226\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e in 2011 (Mean ± SD, µg/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.1756 ± 4.5943\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.3475 ± 2.3394\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature in 2011 (Mean ± SD, ℃)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.9071 ± 5.0140\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.2235 ± 1.6146\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e3.2. Association between PM and Frailty Index in cohort study\u003c/h3\u003e\n\u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the association between PM\u003csub\u003e2.5\u003c/sub\u003e and the FI is displayed. Initially, a significant positive correlation was identified without controlling for potential confounding variables: The FI increases by 0.0030 for every 10µg/m\u003csup\u003e3\u003c/sup\u003e rise in PM\u003csub\u003e2.5\u003c/sub\u003e concentration, \u003cem\u003eP\u003c/em\u003e = 0.0290. Upon adjusting for these confounding factors, a positive relationship between PM\u003csub\u003e2.5\u003c/sub\u003e and the FI persisted (coefficient = 0.0003, \u003cem\u003eP\u003c/em\u003e = 0.0380). This suggests that with each 10 µg/m\u003csup\u003e3\u003c/sup\u003e rise in PM\u003csub\u003e2.5\u003c/sub\u003e concentration in the atmosphere, the FI also increases by 0.0030. In the SHARE data, we observed similar results. Prior to adjustment: coefficient = 0.0022, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001, after adjustment: coefficient = 0.0019, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001. This indicates that the FI was found to increase by 0.0190 for every 10 µg/m\u003csup\u003e3\u003c/sup\u003e increase in PM\u003csub\u003e2.5\u003c/sub\u003e concentration. Limiting the study to individuals aged 65 years and older revealed that PM\u003csub\u003e2.5\u003c/sub\u003e remained a risk factor for frailty in the European population. While a positive association was observed in the Chinese population, it was not statistically significant (\u003cem\u003eP\u003c/em\u003e = 0.3140). Additionally, to further explore the urban-rural associations. CHARLS data revealed a notable positive relationship between PM\u003csub\u003e2.5\u003c/sub\u003e and the FI in rural areas : coefficient = 0.0004, \u003cem\u003eP\u003c/em\u003e = 0.0250. However, no such association was found in urban areas: coefficient = 0.0001, \u003cem\u003eP\u003c/em\u003e = 0.6220. In the SHARE, the impact of PM\u003csub\u003e2.5\u003c/sub\u003e on the FI remains consistent, indicating that it serves as a risk factor for frailty.\u003c/p\u003e\n\u003ch3\u003e3.2. Casual association between PM and Frailty Index in MR\u003c/h3\u003e\n\u003cp\u003eThe F-statistic (MeanF = 23.9521) and MR-Egger results (\u003cem\u003eP\u003c/em\u003e = 0.5290) demonstrated that the analysis was not influenced by weak instrumental variables and pleiotropy. The MR results indicated that MWIVW: Odds ratio (OR) = 1.2933, 95% CI: 1.2045–1.3820, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001; the results indicated that PM\u003csub\u003e2.5\u003c/sub\u003e was a significant risk for the FI. Sensitivity analyses showed similar results: IVW: OR = 1.2820, 95% CI: 1.1874–1.3767, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001; WM: OR = 1.2630, 95% CI: 1.1271–1.3989, \u003cem\u003eP\u003c/em\u003e = 0.0008. Our findings demonstrate that higher PM\u003csub\u003e2.5\u003c/sub\u003e levels expedite the frailty process., with each standard deviation increase in PM\u003csub\u003e2.5\u003c/sub\u003e associated with a 29.33% rise in frailty risk.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMain results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCHARLS\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eSHARE\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed effects regression (Model 1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0290\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0022\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed effects regression (Model 2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0380\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0019\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed effects regression (Rural)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0250\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0031\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed effects regression (Urban)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6220\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0016\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed effects regression (≥ 65 years old)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3140\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0025\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{O}\\text{R}\\:\\)\u003c/span\u003e\u003c/span\u003e= 1.2933, 95%CI: 1.2045–1.3820, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Model 1 is not adjusted.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eModel 2 adjusted for age, marital status, education, smoking status, drinking status, retirement status, BMI, mean annual NO\u003csub\u003e2\u003c/sub\u003e concentration and temperature. OR: Odds ratio; CHARLS: the China Health and Retirement Longitudinal Study; SHARE: the Survey of Health, Aging and Retirement in Europe; MR: Mendelian randomization.\u003c/p\u003e "},{"header":"4. Discussion","content":"\u003cp\u003eThe longitudinal study demonstrated a significant positive association between PM\u003csub\u003e2.5\u003c/sub\u003e exposure and FI, and MR results indicated a causal association. Our study investigated the association between PM\u003csub\u003e2.5\u003c/sub\u003e and FI through a follow-up design. It is noteworthy that a generally accepted instrument was used to measure frailty, and the FI was measured using a combination of 29 items. Through cross-ethnic studies, we obtained similar results in European populations, indicating that exposure to PM\u003csub\u003e2.5\u003c/sub\u003e can accelerate frailty in people of different ethnic groups. Our study revealed that exposure to PM\u003csub\u003e2.5\u003c/sub\u003e may speed up the aging process, similar findings were observed across different regions, and suggesting a possible causal relationship.\u003c/p\u003e\u003cp\u003eNumerous research efforts have focused on exploring the link between air pollution and frailty among older adults. Research involving prospective cohorts revealed a correlation between air pollutants, like PM\u003csub\u003e2.5\u003c/sub\u003e, and a heightened likelihood of frailty (Guo et al., 2024). Consistent with the finding, a population-based study involving 220,079 UKB participants revealed that higher exposure to PM\u003csub\u003e2.5\u003c/sub\u003e was associated with an elevated risk of frailty (Veronese et al., 2023). Likewise, studies derived from the Chinese Longitudinal Healthy Longevity Survey revealed an increased occurrence of frailty linked to heightened exposure to air pollution in the year preceding the interview (Hu et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Notably, frailty scores were significantly higher in older adults residing in areas with severe air pollution. This implies that air contamination could be a major factor in shaping the progression of healthy aging (Hu et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, after China implemented the air pollution control policy, namely the Clean Air Action Plan, the FI scores of healthy individuals were significantly reduced by 0.0205, while the FI scores of pre-frail individuals were significantly reduced by 0.0114 (Guo and Yang, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Our research corroborates the negative impact of environmental air pollution on frailty. Exposure to PM\u003csub\u003e2.5\u003c/sub\u003e is positively correlated with the FI, thereby accelerating the aging process. Interestingly, when we restricted our study subjects to individuals aged 65 and above, we observed a positive correlation between PM\u003csub\u003e2.5\u003c/sub\u003e exposure and FI. However, in the sample of the elderly population in China, this association did not reach statistical significance. This phenomenon may be related to significant lifestyle differences between elderly populations in China and Europe, which could influence the level of PM\u003csub\u003e2.5\u003c/sub\u003e exposure among those aged 65 and above and differences in genetic backgrounds and physiological characteristics might also contribute to this phenomenon (Kodavanti, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jiang et al., 2023; Eurostat, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In addition, our investigation brought to light disparities in results between China rural and urban settings. One potential explanation for this variation is that rural regions heavily rely on traditional energy sources like biomass burning, leading to higher levels of both outdoor and indoor air pollution (Guo et al., 2022). Moreover, compared to urban residents, rural areas lack proper housing and transportation planning, which may exacerbate environmental exposures for rural residents and result in their limited understanding of the significance of air pollution prevention and control (Zhao et al., 2021; Mueller et al., 2017).\u003c/p\u003e\u003cp\u003eExposure to air pollution is widely recognized for causing a range of detrimental health impacts, including inflammatory reactions, oxidative stress, metabolic disorders, and epigenetic modifications. For instance, by upsetting mitochondria, air pollution can cause pro-inflammatory reactions in different immune cells, and since inflammation is thought to be a possible source of weakness, thus collectively leading to the onset of weakness (Glencross et al., 2020; Zhang et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Moreover, air pollutants may disrupt the body's balance and reduce its ability to handle stress, hastening the decline in functional abilities and capacities associated with aging levels of cells, organs, and the entire system, ultimately resulting in frailty (Clegg et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003eb). Clearly, air pollution plays a role in frailty to a certain extent, making the reduction of air pollution crucial for diminishing frailty in the elderly. As populations age, the prevalence of frailty is on the rise globally, leading to adverse disease outcomes and increasing healthcare expenditures (Hoogendijk et al., 2019). At the same time, the disease burden caused by chronic diseases, which are a component of frailty, continues to increase, bringing tremendous pressure to public health prevention work (Shilian et al., 2020). Therefore, a series of research and measures are needed to reduce the concentration of PM\u003csub\u003e2.5\u003c/sub\u003e, slow down the process of aging, and alleviate the heavy burden brought by aging.\u003c/p\u003e\u003cp\u003eThe present study has several significant strengths. Firstly, a cohort study design was employed to investigate the longitudinal association between PM\u003csub\u003e2.5\u003c/sub\u003e and FI in depth. During the research process, the effects of factors such as temperature, NO\u003csub\u003e2\u003c/sub\u003e and important covariates were carefully controlled to ensure the reliability of the results. Secondly, we constructed a comprehensive FI, taking into account multiple factors, including disease, physical functional limitations, disability in ADL, disability in IADL, physical function, depression, and cognition, to comprehensively assess the FI. Finally, we utilized data from CHARLS and SHARE. Moreover, we ensured consistency in variables used to construct the FI between SHARE and CHARLS, with a consistent data timeframe from 2011 to 2015. Through cross-regional observations, we obtained consistent results. Additionally, we further established causal relationship through MR analysis. Therefore, our study findings are generalizable and demonstrate the impact of PM\u003csub\u003e2.5\u003c/sub\u003e on frailty, providing robust support for the credibility of our research.\u003c/p\u003e\u003cp\u003eAlthough our study yielded some important findings, its limitations must also be acknowledged. First, the pollutant data used in the study are based on city-level data, which may not fully capture small changes within cities. Since we did not account for intra-city variability, this may have introduced bias into the results. Second, we used a validated tool to detect FI, but we adapted it based on information available in the research database used. The use of existing data may introduce bias from the original version. Third, to ensure the maximum inclusion of the sample size, we performed multiple imputation of cognitive variables required to construct FI in the SHARE database (such as: Orient variable: missing proportion: wave 4: 28.32%, wave 5: 99.62%, wave 6: 0.23%), which may have some impact on the results. Therefore, it is important to be cautious in interpreting the findings and to address these limitations in future studies.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe results indicate that long-term exposure to PM\u003csub\u003e2.5\u003c/sub\u003e serves as a risk factor for frailty. In the context of aging, it is crucial to consider the accelerated effect of environmental issues on the aging process. Therefore, implementing public health measures aimed at decreasing the PM\u003csub\u003e2.5\u003c/sub\u003e concentration in the environment is necessary to mitigate the aging acceleration and alleviate the associated pension and fiscal burdens.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe China Health and Retirement Longitudinal Study (CHARLS): https://charls.charlsdata.com/pages/data/111/en.html. The Survey of Health, Ageing and Retirement in Europe (SHARE): http://www.share-project.org/data-access.html.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHarmonized data for CHARLS and SHARE can be accessed via: https://g2aging.org/hrd/get-data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGWAS summary data for PM\u003csub\u003e2.5\u003c/sub\u003e: https://gwas.mrcieu.ac.uk/datasets/ukb-b-10817/. GWAS summary data for frailty index: https://figshare.com/articles/dataset/Genome-Wide_Association_Study_of_the_Frailty_Index_-_Atkins_et_al_2019/9204998.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCHARLS\u003c/strong\u003e: CHARLS received ethical approval from the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015) and all participants provided informed written consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSHARE:\u0026nbsp;\u003c/strong\u003eSHARE received ethical approval from the Ethics Council of the Max Planck Society and all participants provided informed written consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors sincerely thank the CHARLS and SHARE data management teams for data collection and management, as well as GWAS and related consortia for the collection and management of large-scale data resources. Thanks to the financial support provided by the National Natural Science Foundation of China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (Grant numbers: No.82073674\u0026amp; No.82373692).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors approved the final manuscript and the submission to this journal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT Authorship Contribution Statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYanchao Wen: Data Curation, Writing-Original Draft, Writing-Review \u0026amp; Editing, Visualization.\u003c/p\u003e\n\u003cp\u003eGuiming Zhu: Data Curation, Writing-Review \u0026amp; Editing, Visualization.\u003c/p\u003e\n\u003cp\u003eKexin Cao: Data Curation, Writing-Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003eJie Liang: Writing-Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003eXiangfeng Lu: Writing-Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003eTong Wang: Writing-Review \u0026amp; Editing, Supervision, Funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSee supplementary table.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGlobal burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. \u003cem\u003eLancet\u003c/em\u003e 403: 2162-2203.https://10.1016/s0140-6736(24)00933-4.\u003c/li\u003e\n\u003cli\u003eAtkins JL, Jylh\u0026auml;v\u0026auml; J, Pedersen NL, et al. 2021 A genome-wide association study of the frailty index highlights brain pathways in ageing. \u003cem\u003eAging Cell\u003c/em\u003e 20: e13459.https://10.1111/acel.13459.\u003c/li\u003e\n\u003cli\u003eBowden J, Davey Smith G and Burgess S. 2015 Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. \u003cem\u003eInt J Epidemiol\u003c/em\u003e 44: 512-525.https://10.1093/ije/dyv080.\u003c/li\u003e\n\u003cli\u003eBowden J, Davey Smith G, Haycock PC, et al. 2016 Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. \u003cem\u003eGenet Epidemiol\u003c/em\u003e 40: 304-314.https://10.1002/gepi.21965.\u003c/li\u003e\n\u003cli\u003eBowden J, Del Greco MF, Minelli C, et al. 2019 Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption. \u003cem\u003eInt J Epidemiol\u003c/em\u003e 48: 728-742.https://10.1093/ije/dyy258.\u003c/li\u003e\n\u003cli\u003eBurgess S, Butterworth A and Thompson SG. 2013 Mendelian randomization analysis with multiple genetic variants using summarized data. \u003cem\u003eGenet Epidemiol\u003c/em\u003e 37: 658-665.https://10.1002/gepi.21758.\u003c/li\u003e\n\u003cli\u003eCao L, Zhai D, Kuang M, et al. 2022 Indoor air pollution and frailty: A cross-sectional and follow-up study among older Chinese adults. \u003cem\u003eEnvironmental Research\u003c/em\u003e 204: 112006.https://https://doi.org/10.1016/j.envres.2021.112006.\u003c/li\u003e\n\u003cli\u003eChen H, Goldberg MS and Villeneuve PJ. 2008 A systematic review of the relation between long-term exposure to ambient air pollution and chronic diseases. \u003cem\u003eRev Environ Health\u003c/em\u003e 23: 243-297.https://10.1515/reveh.2008.23.4.243.\u003c/li\u003e\n\u003cli\u003eClegg A, Young J, Iliffe S, et al. 2013a Frailty in elderly people. \u003cem\u003eLancet\u003c/em\u003e 381: 752-762.https://10.1016/s0140-6736(12)62167-9.\u003c/li\u003e\n\u003cli\u003eClegg A, Young J, Iliffe S, et al. 2013b Frailty in elderly people. \u003cem\u003eThe Lancet\u003c/em\u003e 381: 752-762.https://10.1016/s0140-6736(12)62167-9.\u003c/li\u003e\n\u003cli\u003eCohen G and Gerber Y. 2017 Air Pollution and Successful Aging: Recent Evidence and New Perspectives. \u003cem\u003eCurr Environ Health Rep\u003c/em\u003e 4: 1-11.https://10.1007/s40572-017-0127-2.\u003c/li\u003e\n\u003cli\u003ede Vries OJ, Peeters GM, Lips P, et al. 2013 Does frailty predict increased risk of falls and fractures? 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PM\u003csub\u003e2.5\u003c/sub\u003e particulate matter exposure is a significant factor affecting human health and is crucial in the aging process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: We utilized data from China Health and Retirement Longitudinal Study (CHARLS) and the Survey of Health, Aging, and Retirement in Europe (SHARE) to study the relationship between PM\u003csub\u003e2.5\u003c/sub\u003e exposure and the frailty index. Acquire PM\u003csub\u003e2.5\u003c/sub\u003e exposure data for China and Europe, match them according to geographic location within the database. Our study used frailty index to evaluate frailty, which comprises 29 items. We examined the association between PM\u003csub\u003e2.5\u003c/sub\u003e and frailty index using fixed-effects regression models and Mendelian randomization (MR) analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: We first examined the association between PM\u003csub\u003e2.5\u003c/sub\u003e and frailty index using fixed-effects regression models, revealing a notable positive link across populations in China (coefficient = 0.0003, \u003cem\u003eP\u003c/em\u003e = 0.0380) and Europe (Coefficient = 0.0019, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001). This suggests that PM\u003csub\u003e2.5\u003c/sub\u003e exposure is a significant risk factor for frailty, leading to accelerated frailty. Moreover, our MR analysis uncovered a possible causal association (OR = 1.2933，95%CI: 1.2045-1.3820, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001) between PM\u003csub\u003e2.5\u003c/sub\u003e exposure and the frailty index.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: Our findings indicate that long-term exposure to PM\u003csub\u003e2.5\u003c/sub\u003e in the environment is a risk factor for physical frailty and may have a potential causal relationship. Given the rapid global aging trend, it is crucial to focus on how air pollution affects frailty and to combat its negative consequences.\u003c/p\u003e","manuscriptTitle":"The association between PM2.5 and frailty: Evidence from 122 cities in China and 7 countries in Europe","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-13 14:11:09","doi":"10.21203/rs.3.rs-5172427/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-05T04:24:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-04T12:40:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-26T12:45:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5178904190889381170438404895233384363","date":"2024-11-13T03:55:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"186295032167597956291011813929635658250","date":"2024-11-13T03:31:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"155444745263057169144600383425172612918","date":"2024-10-13T16:19:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-10T17:46:07+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-09-30T12:34:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-30T10:02:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-30T10:01:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-09-29T02:01:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b510ddc6-40b5-4b48-8fd6-3381466bd499","owner":[],"postedDate":"December 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-06T16:01:38+00:00","versionOfRecord":{"articleIdentity":"rs-5172427","link":"https://doi.org/10.1186/s12889-024-21121-4","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2024-12-30 15:57:30","publishedOnDateReadable":"December 30th, 2024"},"versionCreatedAt":"2024-12-13 14:11:09","video":"","vorDoi":"10.1186/s12889-024-21121-4","vorDoiUrl":"https://doi.org/10.1186/s12889-024-21121-4","workflowStages":[]},"version":"v1","identity":"rs-5172427","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5172427","identity":"rs-5172427","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}