Impact of Multiple Air Pollutants on Probable Sarcopenia in the UK Population: The Mediating Role of Physical Activity and Biological Aging

Impact of Multiple Air Pollutants on Probable Sarcopenia in the UK Population: The Mediating Role of Physical Activity and Biological Aging | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Impact of Multiple Air Pollutants on Probable Sarcopenia in the UK Population: The Mediating Role of Physical Activity and Biological Aging Jiaxiang Gao, Tong Li, Yufei Gu, Ran Ding, Yue Peng, Cheng Huang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7721102/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Mar, 2026 Read the published version in BMC Public Health → Version 1 posted 11 You are reading this latest preprint version Abstract Background Emerging evidence links air pollution to probable sarcopenia, yet combined effects of multiple pollutants and their mediating pathways remain unclear. We aimed to evaluate both individual and combined associations of air pollutants with probable sarcopenia, and to explore mediating roles of physical activity and biological aging. Methods Data from 211,808 UK Biobank participants were analyzed. Probable sarcopenia was defined by the EWGSOP criteria. Concentrations of NO x , NO 2 , PM 2.5 , PM 10 , and PM 2.5−10 were estimated using land use regression models. Multivariate logistic regression, weighted quantile sum (WQS) analysis, and Cox regression were employed to investigate cross-sectional, combined and longitudinal associations, respectively. Parallel mediation analyses quantified contributions of physical inactivity and accelerated biological aging. Results In cross-sectional analyses, each 10 µg/m³ increase in NO 10 (OR = 1.11, 95% CI:1.08–1.15), NO 2 (1.03, 1.02–1.04), PM 2.5 (1.41, 1.15–1.72), and PM 10 (1.34, 1.21–1.49) was associated with elevated probable sarcopenia risk. The combined effects, indicated by WQS index, yielded an OR of 1.14 (95% CI: 1.11–1.17), primarily driven by NO x and PM 10 . Physical inactivity mediated 7.0–10.2% of total effects, while accelerated biological aging mediated 9.7% to 30.7%. Longitudinal analyses over 2.9 years further confirmed an increased incident risk. Conclusion Individual and combined exposures to air pollutants elevate probable sarcopenia risk, partially mediated by physical inactivity and accelerated biological aging, underscoring the need for improved air quality, active lifestyles and healthy aging to mitigate sarcopenia burden. Air Pollution Sarcopenia Physical Activity Biological Aging UK Biobank Figures Figure 1 Figure 2 Figure 3 Introduction Sarcopenia is an age-related degenerative musculoskeletal disorder characterized by loss of muscle mass, strength, and/or physical performance [ 1 ], and has been estimated to affect between 5.5% and 25.7% of populations worldwide [ 2 ]. The condition imposes a substantial public-health burden because it is robustly associated with falls and fractures [ 3 ], cardiopulmonary diseases [ 4 , 5 ], loss of functional independence [ 6 ], and increased mortality [ 7 ]. In recognition of the prognostic importance of muscle strength, the European Working Group on Sarcopenia in Older People (EWGSOP) in 2019 emphasized low muscle strength as the primary case-finding criterion and introduced the concept of “probable sarcopenia” to promote early detection and intervention in clinical practice [ 1 ]. A growing body of evidence suggests that environmental exposures may contribute to the pathogenesis of sarcopenia. Ambient air pollution induces oxidative stress and systemic inflammation, mechanisms that are known to impair muscle structure and function [ 8 ] [ 9 ]. Large-scale analyses from the UK Biobank have reported positive associations between long-term pollutant exposure and the prevalence of probable sarcopenia [ 10 , 11 ]. Most prior studies, however, have examined single pollutants in isolation and therefore may not capture the health consequences of real-world concurrent exposures to pollutant mixtures. Neglecting such mixture effects could underestimate or mischaracterize true environmental risk. Moreover, inconsistencies across cohorts exist; for example, a community-based study in South Korea did not observe an association between medium- to long-term particulate matter exposure and sarcopenia [ 12 ], which suggests that findings may depend on exposure assessment, population characteristics, or residual confounding. Reductions in physical activity are a well-established determinant of sarcopenia: sedentary behavior or disease-related activity limitation accelerates declines in muscle strength and functional capacity [ 13 ]. Ambient air pollution may decrease physical activity by provoking respiratory or cardiopulmonary symptoms and discouraging outdoor exercise; such reductions could plausibly contribute to muscle deconditioning. Moreover, accumulating evidence indicates that exposures to air pollutants accelerate biological aging via oxidative DNA damage and epigenetic dysregulation [ 14 , 15 ], processes that are mechanistically linked to loss of muscle mass and strength. Given that sarcopenia is fundamentally age-related, we hypothesize that long-term air pollution exposure increases the risk of probable sarcopenia through two complementary pathways: reduced physical activity and accelerated biological aging. Hence, the aim of present study was to assess the individual and combined effects of various air pollutants on probable sarcopenia and the other sarcopenia-related measures, using nationally representative survey data from the UK biobank. We further sought to explore the potential mechanistic pathways linking air pollution to probable sarcopenia by assessing physical inactivity and biological aging as mediators of this relationship. Materials and methods Study population Data were retrieved from the UK Biobank, a large-scale national cohort encompassing over 500,000 participants aged from 37 to 73 years. The North West Multi-Center Research Ethics Committee has approved the UK Biobank study (application number 95082), with additional details online ( http://www.ukbiobank.ac.uk/ and https://biobank.ndph.ox.ac.uk/ukb/ ). As illustrated in Fig. 1 , participants with complete baseline information on air pollution exposures and sarcopenia-related measures were included in the cross-sectional analysis (n = 211,808). For the longitudinal analysis, individuals with probable sarcopenia at baseline or lacking follow-up data were excluded, yielding a final sample of 200903 participants, who were followed for an average of 2.9 years. Definition of sarcopenia components and diagnosis of probable sarcopenia According to the EWGSOP in 2019, three components of sarcopenia were assessed containing hand grip strength, appendicular lean mass (ALM) index, and gait speed [ 1 ]. Low muscle strength was identified as hand grip strength < 27 kg for males and < 16 kg for females. ALM index was defined as dividing ALM by height 2 and low muscle mass was defined as ALM index < 7.0 kg/m 2 for males and < 5.5 kg/m 2 for females. Slow walking pace of < 3 miles/h was used to estimate physical performance, instead of low gait speed of < 0.8m/s in EWGSOP criteria. Participants were categorized into probable sarcopenia group, defined by low grip strength, and non-sarcopenia group, characterized by normal grip strength. Assessment of air pollution exposure Land use regression (LUR) models were employed to assess the annual concentrations of NO X , NO 2 , PM 2.5 , PM 10 , and PM 2.5−10 , created in the European Study of Cohorts for Air Pollution Effects (ESCAPE). The LUR models could explain 88%, 87%, 77%, 88%, and 57% of the variance (cross-validation R 2 ) for NO X , NO 2 , PM 2.5 , PM 10 , and PM 2.5−10 , respectively, demonstrating moderate to good performance. Covariates Information on sociodemographic status include age, sex, ethnicity, educational level and the Townsend deprivation index (TDI). Health-related factors contained smoking and drinking status, body mass index (BMI) and comorbidities relevant to either air pollution or sarcopenia. In terms of potential mediators, physical activity was quantified by metabolic equivalent task (MET) hours per week for different types of activities and physical inactivity was identified using the short form international physical activity questionnaire (IPAQ). Biological aging was accessed through Klemera-Doubal method biological age (KDM-BA) and the phenotypic age (PhenoAge), which reflects the chronological age with approximately normal physiological function [ 16 ] and normal mortality risk within a reference population [ 17 ], respectively. The calculation process was performed by R package BioAge [ 18 ]. In this study, age accelerations, calculated as the residuals of these biomarkers adjusted for chronological age, was used to indicate the rate of biological aging. Statistical analysis Continuous variables are presented as mean ± standard deviation and categorical variables as n (%). Baseline characteristics were compared by standardized mean differences (SMD). Air pollutant concentrations were modelled both continuously and by quartiles (Q1–Q4). Records with missing key categorical or demographic identifiers were excluded. Continuous covariates with incomplete values (physical activity, BMI, ALM) were multiply imputed by chained equations (MICE). Biological aging indicators (KDM-BA and PhenoAge) were not imputed to avoid structural distortion and additional uncertainty. To address the study objectives — estimating individual and joint (mixture) effects of ambient pollutants and examining potential mediating pathways — analyses proceeded as follows. Cross-sectional analysis of individual effects. Associations between each pollutant and probable sarcopenia at baseline were estimated using univariate and multivariable logistic regression. Odds ratios (ORs) and 95% confidence intervals (CIs) are reported. Four hierarchical covariate models were fitted: Model 1 adjusted for sociodemographic variables; Model 2 additionally adjusted for health-related factors; Model 3 further adjusted for physical activity; and a DAG-based model adjusted for confounders selected using a directed acyclic graph (Figure S1 ). Combined effects. To characterize real-world joint exposures, weighted quantile sum (WQS) regression was applied to derive a composite mixture index and pollutant contributions (weights). Restricted cubic splines (RCS) were used to examine concentration–response shapes for individual pollutants and the WQS index. Bayesian kernel machine regression (BKMR) was implemented as a complementary approach to accommodate potential non-linear and non-additive interactions within the pollutant mixture. Sensitivity analyses. We performed multiple sensitivity checks including: (1) BKMR as described above; (2) stratified analyses to detect vulnerable subgroups defined by age, sex, ethnicity, deprivation, smoking, BMI and IPAQ status; (3) analyses of other sarcopenia components (low ALM index and slow walking pace) as secondary outcomes; and (4) additional adjustment for participants’ occupation and osteoarthritis status. Results from multiply imputed datasets were compared with complete-case analyses to assess robustness. Mediation analyses. Parallel mediation models were employed. The direct effect indicated the effect of air pollution on probable sarcopenia without mediation. The indirect effect denoted the effect of air pollution on probable sarcopenia via mediators. The proportion of mediation was calculated by dividing the indirect effect by the total effect. Longitudinal analyses. Incident probable sarcopenia was modelled using Cox proportional hazards regression with DAG-based covariate adjustment; individuals with probable sarcopenia at baseline were excluded. Follow-up time was defined from baseline assessment to incident event, death, loss to follow-up, or last available follow-up date. Proportional hazards assumptions were assessed using Schoenfeld residuals. Results are reported as hazard ratios (HRs) with 95% CIs. All analyses were conducted using R version 4.3.2. Statistical significance was established at a two-sided P value of less than 0.05. Results Characteristics of participants and distribution of air pollutants In the cross-sectional analysis, 211,808 participants were finally included, of whom 10,905 were diagnosed with probable sarcopenia. Table 1 represents the demographic details. Participants with probable sarcopenia were generally older, predominantly female, non-white, and exhibited higher TDI levels. They were also more likely to be non-drinkers, combined with hypertension and diabetes, along with lower physical activity levels (SMD > 0.1). Table 1 Demographic characteristics of participants stratified by probable sarcopenia status (n = 211908). Characteristics Total Non-sarcopenia Probable sarcopenia SMD (n = 200903) (n = 10905) Age, mean (SD), years 52.05 (7.02) 51.92 (7.00) 54.49 (7.03) 0.366 Sex, n (%) 0.171 Female 109555 (51.7) 103041 (51.3) 6514 (59.7) Male 102253 (48.3) 97862 (48.7) 4391 (40.3) Ethnicity, n (%) 0.254 White 199527 (94.2) 189853 (94.5) 9674 (88.7) Asian 4818 (2.3) 4069 (2.0) 749 (6.9) Black 4009 (1.9) 3813 (1.9) 196 (1.8) Mixed 1551 (0.7) 1464 (0.7) 87 (0.8) Others 1903 (0.9) 1704 (0.8) 199 (1.8) Education level, n (%) 0.031 Under high school 66710 (31.5) 63124 (31.4) 3586 (32.9) High school or equivalent 29002 (13.7) 27536 (13.7) 1466 (13.4) Above high school 116096 (54.8) 110243 (54.9) 5853 (53.7) Townsend deprivation index, mean (SD) -1.46 (2.92) -1.49 (2.91) -0.94 (3.08) 0.184 Smoking status, n (%) 0.049 Non-smoker 123641 (58.4) 117026 (58.3) 6615 (60.7) Smoker 88167 (41.6) 83877 (41.7) 4290 (39.3) Drinking status, n (%) Non-drinker 6365 (3.0) 5699 (2.8) 666 (6.1) 0.159 Drinker 205443 (97.0) 195204 (97.2) 10239 (93.9) BMI, mean (SD) 27.18 (4.69) 27.16 (4.66) 27.52 (5.17) 0.074 Comorbidity Hypertension, n (%) 0.130 No 169138 (79.9) 160990 (80.1) 8148 (74.7) Yes 42670 (20.1) 39913 (19.9) 2757 (25.3) Cancer, n (%) 0.072 No 200003 (94.4) 189887 (94.5) 10116 (92.8) Yes 11805 (5.6) 11016 (5.5) 789 (7.2) Diabetes, n (%) 0.159 No 204621 (96.6) 194440 (96.8) 10181 (93.4) Yes 7187 (3.4) 6463 (3.2) 724 (6.6) Cardiovascular diseases, n (%) 0.087 No 206296 (97.4) 195835 (97.5) 10461 (95.9) Yes 5512 (2.6) 5068 (2.5) 444 (4.1) Respiratory diseases, n (%) 0.029 No 142210 (67.1) 135029 (67.2) 7181 (65.9) Yes 69598 (32.9) 65874 (32.8) 3724 (34.1) Physical activity 0.086 MET hours/week, mean (SD) 41.82 (43.49) 42.02 (43.56) 38.32 (42.00) IPAQ activity group, n (%) 0.125 Normal 168467 (79.5) 160157 (79.7) 8310 (76.2) Physical inactivity 43341 (20.5) 40746 (20.3) 8310 (23.8) ALM index, mean (SD) 8.35 (1.38) 8.36 (1.38) 8.16 (1.37) 0.144 Grip strength, mean (SD), kg 32.71 (10.98) 33.58 (10.52) 16.71 (5.76) 1.989 Walking pace, n (%) 0.279 Normal 204041 (96.3) 194254 (96.7) 9787 (79.7) Slow 7767 (3.7) 6649 (3.3) 1118 (10.3) Air pollution, mean (SD), ug/m 3 NO x 26.73 (7.73) 26.66 (7.73) 27.99 (7.56) 0.174 NO 2 44.10 (15.70) 43.99 (15.70) 46.05 (15.67) 0.131 PM 2.5 9.99 (1.06) 9.99 (1.06) 10.11 (1.04) 0.114 PM 10 16.23 (1.90) 16.22 (1.90) 16.43 (1.84) 0.109 PM 2.5−10 6.43 (0.90) 6.42 (0.90) 6.46 (0.88) 0.044 Continuous variables were presented as mean ± SD. Categorical variables were presented as n (%). SMD, standard mean difference (SMD ≥ 0.1 indicates imbalanced characteristics between groups); BMI, body mass index; MET, metabolic equivalent task; IPAQ, international physical activity questionnaire. Estimated exposures to NO X , NO 2 , PM 2.5 , PM 10 , and PM 2.5−10 in the probable sarcopenia group were 27.99 ± 7.56, 46.05 ± 15.67, 10.11 ± 1.04, 16.43 ± 1.84, and 6.46 ± 0.88 ug/m 3 , respectively, compared to 26.66 ± 7.73, 43.99 ± 15.70, 9.99 ± 1.06, 16.22 ± 1.90, and 6.42 ± 0.90 ug/m 3 in the non-sarcopenia group. Moderate to strong correlations (r > 0.3) were observed among most air pollutants (Fig. 2 a). Individual exposure effects of air pollutants on probable sarcopenia Table 2 illustrates significant associations between each air pollutant and probable sarcopenia in unadjusted models, regardless of whether air pollutants were analyzed as continuous or categorical variables. Following the adjustment of various factors (models 1, 2, and 3), associations remained consistent for NO X , NO 2 , PM 2.5 , and PM 10 . In terms of PM 2.5−10 , no association was found with probable sarcopenia when treated as a continuous variable, but a significant relationship emerged when categorized into quartiles. The highest exposure quartile increased the risk by 11% compared to quartile 1, suggesting a potential non-linear concentration-response relationship with probable sarcopenia. Similar results were confirmed in the DAG-based model. For each 10 µg/m³ increase in NO X , NO 2 , PM 2.5 , and PM 10 , the ORs (95% CIs) for probable sarcopenia were 1.11 (1.08, 1.15), 1.03 (1.02, 1.04), 1.41 (1.15, 1.72), and 1.34 (1.21, 1.49), respectively (all P < 0.05). Table 2 Association between individual air pollutant and probable sarcopenia. Air pollutants Category Unadjusted Model 1 Model 2 Model 3 DAG-based model NO x Continuous (10ug/m 3 ) 1.23 (1.20, 1.26) 1.11 (1.08, 1.14) 1.12 (1.09, 1.15) 1.11 (1.08, 1.15) 1.11 (1.08, 1.14) Q1 ref ref ref ref ref Q2 1.14 (1.08, 1.21) 1.12 (1.06, 1.19) 1.11 (1.05, 1.18) 1.11 (1.05, 1.18) 1.12 (1.06, 1.19) Q3 1.41 (1.34, 1.50) 1.31 (1.24, 1.39) 1.31 (1.23, 1.39) 1.31 (1.23, 1.39) 1.32 (1.24, 1.40) Q4 1.63 (1.54, 1.72) 1.35 (1.27, 1.43) 1.36 (1.23, 1.39) 1.35 (1.27, 1.44) 1.35 (1.27, 1.44) NO 2 Continuous (10ug/m 3 ) 1.08 (1.06, 1.09) 1.03 (1.02, 1.04) 1.03 (1.02, 1.04) 1.03 (1.02, 1.04) 1.03 (1.02, 1.04) Q1 ref ref ref ref ref Q2 1.16 (1.09, 1.23) 1.12 (1.06, 1.19) 1.11 (1.05, 1.18) 1.11 (1.05, 1.18) 1.12 (1.06, 1.19) Q3 1.42 (1.35, 1.51) 1.31 (1.23, 1.39) 1.30 (1.23, 1.38) 1.30 (1.23, 1.38) 1.31 (1.24, 1.39) Q4 1.48 (1.40, 1.57) 1.23 (1.15, 1.31) 1.23 (1.16, 1.31) 1.23 (1.16, 1.31) 1.23 (1.16, 1.31) PM 2.5 Continuous (10ug/m 3 ) 2.77 (2.33, 3.30) 1.39 (1.14, 1.70) 1.42 (1.16, 1.73) 1.41 (1.15, 1.72) 1.41 (1.16, 1.72) Q1 ref ref ref ref ref Q2 1.17 (1.10, 1.38) 1.12 (1.06, 1.19) 1.11 (1.05, 1.18) 1.11 (1.05, 1.18) 1.12 (1.06, 1.19) Q3 1.30 (1.23, 1.38) 1.20 (1.13, 1.27) 1.19 (1.12, 1.26) 1.19 (1.12, 1.26) 1.20 (1.13, 1.27) Q4 1.36 (1.29, 1.44) 1.13 (1.06, 1.20) 1.14 (1.07, 1.21) 1.14 (1.07, 1.21) 1.14 (1.07, 1.21) PM 10 Continuous (10ug/m 3 ) 1.73 (1.57, 1.92) 1.34 (1.21, 1.49) 1.34 (1.21, 1.49) 1.34 (1.21, 1.49) 1.35 (1.22, 1.50) Q1 ref ref ref ref ref Q2 1.15 (1.09, 1.22) 1.11 (1.05, 1.18) 1.11 (1.05, 1.18) 1.11 (1.05, 1.18) 1.12 (1.05, 1.18) Q3 1.33 (1.26, 1.41) 1.20 (1.14, 1.28) 1.21 (1.14, 1.28) 1.21 (1.14, 1.28) 1.21 (1.14, 1.28) Q4 1.39 (1.32, 1.47) 1.19 (1.13, 1.27) 1.20 (1.13, 1.27) 1.20 (1.13,1.27) 1.20 (1.13, 1.27) PM 2.5−10 Continuous (10ug/m 3 ) 1.60 (1.30, 1.96) 1.15 (0.92, 1.42) 1.14 (0.92, 1.42) 1.14 (0.92, 1.42) 1.15 (0.92, 1.43) Q1 ref ref ref ref ref Q2 1.12 (1.06, 1.19) 1.08 (1.02, 1.14) 1.08 (1.02, 1.14) 1.08 (1.02, 1.14) 1.08 (1.02, 1.14) Q3 1.19 (1.13, 1.26) 1.08 (1.02, 1.15) 1.08 (1.02, 1.15) 1.08 (1.02, 1.15) 1.08 (1.02, 1.15) Q4 1.28 (1.21, 1.35) 1.11 (1.05, 1.18) 1.11 (1.05, 1.18) 1.11 (1.06, 1.18) 1.12 (1.05, 1.18) Bold represents P value < 0.05 Model 1: adjusted for the sociodemographic status (age, sex, ethnicity, education level, TDI). Model 2: further adjusted for health-related factors (smoking, drinking, BMI and comorbidity). Model 3: further adjusted for physical activity (MET hours/week). DAG-based model: adjusted for confounders selected from directed acyclic graph (age, sex, ethnicity, education level, TDI and smoking status). Combined exposure effects of mixed air pollution on probable sarcopenia According to WQS regression models, NO x (79.1%) and PM 10 (12.6%) contributed the most significant weights to the combined exposure effects of mixed air pollution on probable sarcopenia (Fig. 2 b). The WQS index for mixed air pollution demonstrated a positive correlation with probable sarcopenia (OR: 1.14, 95% CI: 1.11 to 1.17). When categorized into quartiles, the highest quantile of combined exposure elevated the probability of probable sarcopenia by 36% relative to the lowest quantile (OR: 1.36, 95% CI: 1.28 to 1.45, Fig. 2 c). As displayed in Fig. 2 d, the concentration-effect curve demonstrated a positive non-linear relationship between mixed air pollution and probable sarcopenia, with significant effects observed at higher exposure levels. Sensitivity analysis First, the BKMR analysis revealed a significant, positive correlation between mixed air pollution and probable sarcopenia. Figure S2a depicts the exposure-response relationship for each air pollutant, with other pollutants held at the median. The overall effect of mixed air pollution on probable sarcopenia was significantly positive when all air pollutants reached or exceeded the 55th percentile of their concentrations, compared to their medians (Figure S2b). Furthermore, when the concentrations of other air pollutants were held at the 25th, 50th, and 75th percentiles, PM 10 and NO X exhibited a notably positive correlation with the estimated risk of probable sarcopenia (Figure S2c). Second, the outcomes of stratified analyses (Figure S3) were mainly consistent with those of the primary analyses. Males, the White, individuals with lower TDI, and drinkers were more susceptible to both individual and combined air pollution exposure. Additionally, non-smokers demonstrated increased sensitivity specifically to PM 10 and PM 2.5−10 . Third, DAG-based logistic regression analyses demonstrated that significant, adverse effects of mixed air pollution on both muscle mass and physical performance. The concentration-effect curves of mixed air pollution on low ALM index and slow walking pace were shown in Figure S4. Fourth, the results remained largely unchanged following additional adjustments for the participants' occupations and osteoarthritis status. (Table S1 and Table S2). Mediation analyses Prior to mediation testing, we examined exposure-mediator relationships and found that both individual and combined air pollutants were inversely associated with physical activity (except PM 2.5−10 ) and positively associated with biological aging (Table S3). For mediator-outcome associations (Table S4), higher physical activity was protective against probable sarcopenia (OR 0.989, 95% CI 0.988–0.989, P < 0.001), whereas biological aging increased risk (KDM-BA: OR 1.026, 95% CI 1.024–1.028; PhenoAge: OR 1.063, 95% CI 1.059–1.066; both P < 0.001). Parallel mediation analyses indicated that both physical activity and biological aging acted as intermediary pathways linking air pollution to probable sarcopenia (Table S5). Physical activity explained a modest proportion of the associations (7.0–10.2%), with no significant mediation observed for PM 2.5–10 . In contrast, biological aging contributed more substantially, with KDM-BA mediating 9.7–23.9% and PhenoAge 14.5–30.7% of the associations, although some direct effects (particularly for PM 2.5–10 ) were not statistically significant. Notably, for mixed air pollution, physical activity, KDM-BA, and PhenoAge mediated 9.6%, 10.2%, and 15.0% of the association with probable sarcopenia risk, respectively (all P < 0.001; Fig. 3 ). Longitudinal impact of air pollution on the onset risk of probable sarcopenia During an average follow-up duration of 2.9 years, 4242 subjects (1.3%) developed probable sarcopenia. Individual exposure to NO x , NO 2 , PM 2.5 , PM 10 , and combined exposure to mixed air pollution at baseline were all correlated with the incidence risk of probable sarcopenia (Table 3 ). Table 3 Impact of multiple air pollutants on the incident risk of probable sarcopenia Air pollution Hazard Ratio Lower 95% CI Upper 95% CI P for trend NO x Continuous 1.06 1.02 1.11 0.004* Q1 ref Q2 1.20 1.11 1.29 < 0.001** Q3 1.21 1.12 1.31 < 0.001** Q4 1.15 1.05 1.26 0.002* NO 2 Continuous 1.03 1.01 1.05 0.003* Q1 ref Q2 1.14 1.06 1.24 0.001* Q3 1.19 1.09 1.29 < 0.001** Q4 1.18 1.08 1.28 < 0.001** PM 2.5 Continuous 1.73 1.30 2.31 < 0.001** Q1 ref Q2 1.08 0.99 1.17 0.065 Q3 1.09 1.01 1.19 0.031* Q4 1.18 1.09 1.29 < 0.001** PM 10 Continuous 0.89 0.76 1.04 0.136 Q1 ref Q2 1.04 0.96 1.12 0.312 Q3 1.02 0.94 1.10 0.678 Q4 0.89 0.82 0.97 0.009* PM 2.5−10 Continuous 0.64 0.50 0.90 0.01* Q1 ref Q2 1.11 1.03 1.20 0.005* Q3 1.06 0.98 1.15 0.142 Q4 0.92 0.84 0.99 0.004* Mixed air pollution Continuous 1.07 1.02 1.12 0.005* Q1 ref Q2 1.17 1.08 1.26 < 0.001** Q3 1.19 1.10 1.29 < 0.001** Q4 1.13 1.04 1.24 0.007* Models were adjusted for confounders selected from directed acyclic graph (age, sex, ethnicity, education level, TDI and smoking status) Discussion This study presents two novel findings derived from the UK Biobank dataset. First, exposure to ambient air pollutants, whether in isolation or combination, were linked to an increased risk of probable sarcopenia, as well as low muscle mass and slow walking pace. Notably, the combined effects were predominantly driven by NO x and PM 10 . The longitudinal analysis of the incidence of probable sarcopenia further substantiated these associations. Second, physical activity and biological aging were recognized as potential mediators in the relationship between air pollution and probable sarcopenia. Existing literature, though limited, has shown a positive association between individual air pollution exposure and probable sarcopenia. A nationwide cross-sectional study conducted by Lai et al. among UK adults found that each interquartile range increase in air pollutants (PM 2.5 , PM 10 , PM coarse , NO 2 and NO x ) was substantially linked to a higher risk of probable sarcopenia [ 11 ]. Longitudinal analysis also confirmed similar results concerning the incidence of probable sarcopenia. Cai et al. reported that for each 10 µg/m³ increase in concentrations of PM 2.5 , NO 2 , and NO x , the relative incidence risk of probable sarcopenia increased by 23.2%, 5.5%, and 1.6%, respectively[ 10 ]. Our results are predominantly aligned with these findings, albeit with marginally higher odds/hazard ratios. The effect of air pollution on muscle mass and walking pace has also been observed. Published findings indicated that each interquartile range rise in NO 2 exposure correlated with reduced gait speed and chair-stand test [ 19 ]. Studies on the relationship between air pollution and muscle mass were relatively scarce. Hu et al. identified an inverse relationship between indoor air pollution—indirectly measured by solid fuel usage for cooking and heating—and muscle mass, which also corresponded with an elevated risk of low muscle mass [ 20 ]. However, regarding outdoor ambient pollutants, Dong et al. reported that particulate matter, NO 2 , and O 3 did not elevate the risk of low muscle mass [ 21 ]. Notably, these studies were conducted on Chinese population, with low muscle mass defined by the AWGS 2019 criteria [ 2 ]. Our findings, which demonstrated a significant concentration-response relationship between ambient air pollution and low muscle mass, contributed to the existing European population data to some extent. It is widely recognized that individuals are routinely exposed to a combination of air pollutants in everyday life. Thus, the effects of air pollutants may depend on their cooperation and interaction. To our knowledge, this study is the first large-scale, European population-based study demonstrating notable combined effects of mixed air pollution on probable sarcopenia, which corroborated earlier findings in Chinese population [ 21 ]. Dong et al. also demonstrated substantial PM 10 -induced combined interactions among multiple air pollutants, which contributed to an elevated risk of sarcopenia. As for our study, however, utilizing WQS and BKMR models, NO x emerged as the dominant contributor, while PM 10 still played an important role. This disparity may reflect regional variations in major pollutants and could possibly stem from our focus on probable sarcopenia as the target condition. Subgroup analysis revealed that males are more susceptible to both individual and combined effects of air pollution on probable sarcopenia, contrasting with findings from previous studies in Chinese population [ 21 , 22 ]. Despite the relatively lower prevalence of sarcopenia in European males compared to females [ 23 ], this discrepancy underscores the necessity of prioritizing male individuals as a high-risk group. Interestingly, most air pollutants exhibited a beneficial role in non-white participants, in accordance with Cai et al. [ 10 ]. This paradox may stem from biases due to the limited proportion of non-white participants, genetic variations, or distinct lifestyle factors influencing sarcopenia risk [ 24 ]. Our study also identified individuals with lower deprivation levels (TDI ≤ 0) as a susceptible population, potentially due to healthier habits rendering them more vulnerable to challenges posed by air pollutant [ 10 ]. Additionally, experimental studies have suggested that alcohol consumption increases vulnerability to adverse effects of air pollutants, likely due to alcohol-induced reductions in lean body mass and protein synthesis, a finding corroborated by our results [ 21 , 22 , 25 ]. Our mediation analysis indicated that both physical activity and biological aging serve as partial mediators in the relationship between air pollution and probable sarcopenia. Engaging in physical activity has been recognized as a protective factor against sarcopenia, aiding in the maintenance of muscle mass and strength [ 2 ]. Elevated air pollution levels correlated with decreased total weekly MET hours, possibly undermining the protective benefits of physical exercise. Aerobic training may mitigate oxidative damage in rat skeletal muscle exposed to air pollution from a molecular standpoint [ 8 ]. Interestingly, Liu et al. reported that vigorous exercise alongside ozone exposure might trigger apoptosis in quadriceps femoris muscle cells of rats through the mitochondria-mediated pathway [ 26 ]. Consequently, further research is warranted to ascertain whether different types of physical exercise have varying benefits in alleviating the impact of air pollution on (probable) sarcopenia. Consequently, further research is warranted to determine whether different types of physical activity confer varying benefits in mitigating the effects of air pollution on (probable) sarcopenia. Regarding biological aging, numerous studies have suggested that air pollution contributes to DNA mutations, epigenetic changes, and alterations in epitranscriptomics [ 27 ], which subsequently accelerated DNA methylation aging [ 28 ] and clinical biomarker-based biological aging [ 29 ]. Aging is a significant risk factor for various chronic diseases, including sarcopenia, which is associated with type II myofiber atrophy [ 30 ], fat infiltration [ 31 ], and changes in muscle metabolism [ 32 ]..These factors negatively impact muscle strength and contribute to the onset of probable sarcopenia. Notably, the indirect mediation effects of physical activity and biological aging ranged from 7.0% to 30.7%, suggesting that a considerable portion of the association between air pollution and probable sarcopenia is attributable to direct effects. First, air pollutants such as NO 2 and PM 2.5 have been reported to trigger oxidative stress and inflammatory responses [ 33 – 35 ], resulting in the loss of muscle mass and strength through oxidative damage to membrane lipids and proteins [ 36 ]. Second, emerging evidence suggests that airborne nanoparticles interfere with mitochondrial function by altering intracellular Ca 2+ homeostasis, hindering mitochondrial dynamics, and damaging mtDNA [ 37 ], which leads to a decrease in muscle fiber cross-sectional area [ 11 , 38 ]. Third, insulin resistance and gut microbiota dysbiosis partially mediate the association between air pollution exposure and sarcopenia onset [ 21 , 39 , 40 ], underscoring the role of dysregulated nutrition sensing [ 41 ]. Based on these findings, we hypothesize that exposure to ambient air pollution elevates the risk of probable sarcopenia by inducing oxidative stress, chronic inflammation, mitochondrial dysfunction, dysregulated nutrient sensing, and epigenetic alterations, which adversely affects muscle function and mass. Physical activity and biological aging indirectly mediate effects by altering certain pathways. Several limitations must be acknowledged. First, air pollution exposure was estimated via LUR models based on participants' residential addresses, omitting non-residential sources and potentially resulting in exposure misclassification. Second, participants of UK Biobank generally reside in areas with lower socioeconomic deprivation, and only five air pollutants were accessible, which may introduce selection biases. Third, the absence of time-varying air pollution exposure was attributable to the characteristics of the UK Biobank [ 10 ]. Thus, the baseline concentrations may not fully represent long-term exposure. Fourth, walking pace was self-reported in the UK Biobank, which differs from the EWGSOP criteria; however, this is likely to have limited impact as the key outcome in our study was probable sarcopenia. Lastly, despite adjusting for various covariates, some possible confounders may not have been considered, potentially resulting in residual confounding. Conclusions In conclusion, we underscored that both individual and combined exposures to air pollutants correlates with an elevated risk of probable sarcopenia, as well as low muscle mass and slow walking pace, which is partially mediated by physical inactivity and accelerated biological aging. These findings highlight the need for public health initiatives aimed at improving air quality, promoting active lifestyles, and fostering healthy aging to mitigate the growing burden of sarcopenia. Abbreviations EWGSOP, European Working Group on Sarcopenia in Older People; ALM, appendicular lean mass; LUR, land use regression; ESCAPE, European Study of Cohorts for Air Pollution Effects; TDI, Townsend deprivation index; BMI, body mass index; MET, metabolic equivalent task; IPAQ, international physical activity questionnaire; KDM-BA, Klemera-Doubal method biological age; PhenoAge, phenotypic age; SMD, standardized mean differences; MICE, multiply imputed by chained equations; OR, odds ratio; CI, confidence intervals; WQS, weighted quantile sum; RCS, restricted cubic splines; BKMR, Bayesian kernel machine regression; HR, hazard ratios. Declarations Clinical trial number: Not applicable Ethics approval and consent to participate: UK Biobank study is an open dataset. The North West Multi-Center Research Ethics Committee has approved the UK Biobank study (application number 95082) and all participants signed an informed consent. Consent for publication: Not applicable. Availability of data and material: Details of how to access the UK biobank data and details of the data release schedule are available from http://www.ukbiobank.ac.uk/ and https://biobank.ndph.ox.ac.uk/ukb/ Competing interests: The authors declare that they have no conflict of interests. Funding: 1. National High Level Hospital Clinical Research Funding; 2. Elite Medical Professionals Project of China-Japan Friendship Hospital (NO. ZRJY2023-QM04) Author’s contributions: Jiaxiang Gao: Investigation, Methodology, Software, Data curation, Writing – original draft, Funding acquisition; Tong Li: Investigation, Methodology, Data curation; Yufei Gu: Investigation, Formal analysis, Data curation; Ran Ding: Software, Formal analysis; Yue Peng: Methodology, Visualization; Cheng Huang: Validation, Visualization, Resources, Project administration Weiguo Wang: Writing – review and editing, Project administration, Resources, Supervision; Jun Lin: Writing – review and editing, Data curation, Investigation, Project administration, Resources, Supervision. Acknowledgements: We would like to acknowledge all the investigators and participants involved in the UK Biobank for providing high quality, nationally representative data, which make it possible for our study. References Cruz-Jentoft AJ, Bahat G, Bauer J, et al. Sarcopenia: revised European consensus on definition and diagnosis. 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Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials2025930.docx Table S1. OR (95% CI) for probable sarcopenia in relation to individual and mixed air pollution, with additional adjustment for occupation. Table S2. OR (95% CI) for probable sarcopenia in relation to individual and mixed air pollution, with additional adjustment for osteoarthritis. Table S3. Association of multiple air pollutants with physical activity (n=211808) and biological aging (n=172627). Table S4. Association of physical activity (n=211808) and biological aging (n=172627) with probable sarcopenia. Table S5. Estimated proportion of the relationships between multiple air pollutants and probable sarcopenia mediated by physical activity and biological aging. Figure S1. Directed acyclic graph for the association between air pollution and probable sarcopenia. Figure S2. Association between mixed air pollution and probable sarcopenia based on BKMR model. Figure S3. Individual and synergistic exposure effects of air pollutants on probable sarcopenia stratified by potential modifiers. Figure S4.Concentration-response relationship between mixed air pollution and (A) low ALM index and (B) slow walking pace. GA.png Graphic abstract Cite Share Download PDF Status: Published Journal Publication published 14 Mar, 2026 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 20 Jan, 2026 Reviews received at journal 20 Jan, 2026 Reviewers agreed at journal 29 Dec, 2025 Reviews received at journal 02 Nov, 2025 Reviewers agreed at journal 15 Oct, 2025 Reviewers agreed at journal 14 Oct, 2025 Reviewers invited by journal 09 Oct, 2025 Editor invited by journal 01 Oct, 2025 Editor assigned by journal 01 Oct, 2025 Submission checks completed at journal 01 Oct, 2025 First submitted to journal 26 Sep, 2025 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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16:07:11","extension":"html","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":181356,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7721102/v1/1a9b4f3d8b12072b53bdf70e.html"},{"id":94213539,"identity":"8481d838-60d2-4412-896d-71efe61a7c47","added_by":"auto","created_at":"2025-10-23 16:07:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":143134,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the participants selection.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7721102/v1/d93754c3d2bde64495780eb7.png"},{"id":94214568,"identity":"23df7a8d-b633-4326-adb9-3bf047de4d73","added_by":"auto","created_at":"2025-10-23 16:15:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":94247,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between mixed air pollution and probable sarcopenia based on WQS model. (a) Pearson’s correlation matrix among multiple air pollutants; (b) Weights of multiple air pollutants; (d) Combined exposure effects of mixed air pollution on probable sarcopenia; (d) Concentration-response relationship between mixed air pollution and probable sarcopenia.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7721102/v1/22a37e6dbdf26851a2d2b5be.png"},{"id":94213541,"identity":"0ca73a4f-b079-453d-a5d6-39cf63dd52c9","added_by":"auto","created_at":"2025-10-23 16:07:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":93249,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated proportion of the association between mixed air pollution and probable sarcopenia mediated by physical activity (a), KDM-BA (b), and PhenoAge (c).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7721102/v1/f1c367459e93e8fa51cc9c93.png"},{"id":104739538,"identity":"bc144414-6f5e-434a-99a9-f8da0f009edb","added_by":"auto","created_at":"2026-03-16 16:08:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2203703,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7721102/v1/fe4b461f-e903-40ff-bf3f-49e27f7802f0.pdf"},{"id":94214570,"identity":"ef06af82-4910-4e2f-8ba9-18fcd38957b4","added_by":"auto","created_at":"2025-10-23 16:15:11","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4862736,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S1. \u003c/strong\u003eOR (95% CI) for probable sarcopenia in relation to individual and mixed air pollution, with additional adjustment for occupation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S2. \u003c/strong\u003eOR (95% CI) for probable sarcopenia in relation to individual and mixed air pollution, with additional adjustment for osteoarthritis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S3.\u003c/strong\u003e Association of multiple air pollutants with physical activity (n=211808) and biological aging (n=172627).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S4.\u003c/strong\u003e Association of physical activity (n=211808) and biological aging (n=172627) with probable sarcopenia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S5.\u003c/strong\u003e Estimated proportion of the relationships between multiple air pollutants and probable sarcopenia mediated by physical activity and biological aging.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S1. \u003c/strong\u003eDirected acyclic graph for the association between air pollution and probable sarcopenia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S2. \u003c/strong\u003eAssociation between mixed air pollution and probable sarcopenia based on BKMR model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S3. \u003c/strong\u003eIndividual and synergistic exposure effects of air pollutants on probable sarcopenia stratified by potential modifiers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S4.\u003c/strong\u003eConcentration-response relationship between mixed air pollution and (A) low ALM index and (B) slow walking pace.\u003c/p\u003e","description":"","filename":"SupplementaryMaterials2025930.docx","url":"https://assets-eu.researchsquare.com/files/rs-7721102/v1/c1308107275aba0c71945015.docx"},{"id":94213543,"identity":"8bd37ee5-b31b-4432-afc8-b26fea6a5916","added_by":"auto","created_at":"2025-10-23 16:07:11","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":168935,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphic abstract\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"GA.png","url":"https://assets-eu.researchsquare.com/files/rs-7721102/v1/516a2cca2deb5a6ab7fcf5bb.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of Multiple Air Pollutants on Probable Sarcopenia in the UK Population: The Mediating Role of Physical Activity and Biological Aging","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSarcopenia is an age-related degenerative musculoskeletal disorder characterized by loss of muscle mass, strength, and/or physical performance [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], and has been estimated to affect between 5.5% and 25.7% of populations worldwide [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The condition imposes a substantial public-health burden because it is robustly associated with falls and fractures [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], cardiopulmonary diseases [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], loss of functional independence [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], and increased mortality [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In recognition of the prognostic importance of muscle strength, the European Working Group on Sarcopenia in Older People (EWGSOP) in 2019 emphasized low muscle strength as the primary case-finding criterion and introduced the concept of \u0026ldquo;probable sarcopenia\u0026rdquo; to promote early detection and intervention in clinical practice [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA growing body of evidence suggests that environmental exposures may contribute to the pathogenesis of sarcopenia. Ambient air pollution induces oxidative stress and systemic inflammation, mechanisms that are known to impair muscle structure and function [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Large-scale analyses from the UK Biobank have reported positive associations between long-term pollutant exposure and the prevalence of probable sarcopenia [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Most prior studies, however, have examined single pollutants in isolation and therefore may not capture the health consequences of real-world concurrent exposures to pollutant mixtures. Neglecting such mixture effects could underestimate or mischaracterize true environmental risk. Moreover, inconsistencies across cohorts exist; for example, a community-based study in South Korea did not observe an association between medium- to long-term particulate matter exposure and sarcopenia [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], which suggests that findings may depend on exposure assessment, population characteristics, or residual confounding.\u003c/p\u003e\u003cp\u003eReductions in physical activity are a well-established determinant of sarcopenia: sedentary behavior or disease-related activity limitation accelerates declines in muscle strength and functional capacity [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Ambient air pollution may decrease physical activity by provoking respiratory or cardiopulmonary symptoms and discouraging outdoor exercise; such reductions could plausibly contribute to muscle deconditioning. Moreover, accumulating evidence indicates that exposures to air pollutants accelerate biological aging via oxidative DNA damage and epigenetic dysregulation [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], processes that are mechanistically linked to loss of muscle mass and strength. Given that sarcopenia is fundamentally age-related, we hypothesize that long-term air pollution exposure increases the risk of probable sarcopenia through two complementary pathways: reduced physical activity and accelerated biological aging.\u003c/p\u003e\u003cp\u003eHence, the aim of present study was to assess the individual and combined effects of various air pollutants on probable sarcopenia and the other sarcopenia-related measures, using nationally representative survey data from the UK biobank. We further sought to explore the potential mechanistic pathways linking air pollution to probable sarcopenia by assessing physical inactivity and biological aging as mediators of this relationship.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy population\u003c/h2\u003e\u003cp\u003eData were retrieved from the UK Biobank, a large-scale national cohort encompassing over 500,000 participants aged from 37 to 73 years. The North West Multi-Center Research Ethics Committee has approved the UK Biobank study (application number 95082), with additional details online (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ukbiobank.ac.uk/\u003c/span\u003e\u003cspan address=\"http://www.ukbiobank.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://biobank.ndph.ox.ac.uk/ukb/\u003c/span\u003e\u003cspan address=\"https://biobank.ndph.ox.ac.uk/ukb/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, participants with complete baseline information on air pollution exposures and sarcopenia-related measures were included in the cross-sectional analysis (n\u0026thinsp;=\u0026thinsp;211,808). For the longitudinal analysis, individuals with probable sarcopenia at baseline or lacking follow-up data were excluded, yielding a final sample of 200903 participants, who were followed for an average of 2.9 years.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDefinition of sarcopenia components and diagnosis of probable sarcopenia\u003c/h3\u003e\n\u003cp\u003eAccording to the EWGSOP in 2019, three components of sarcopenia were assessed containing hand grip strength, appendicular lean mass (ALM) index, and gait speed [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Low muscle strength was identified as hand grip strength\u0026thinsp;\u0026lt;\u0026thinsp;27 kg for males and \u0026lt;\u0026thinsp;16 kg for females. ALM index was defined as dividing ALM by height\u003csup\u003e2\u003c/sup\u003e and low muscle mass was defined as ALM index\u0026thinsp;\u0026lt;\u0026thinsp;7.0 kg/m\u003csup\u003e2\u003c/sup\u003e for males and \u0026lt;\u0026thinsp;5.5 kg/m\u003csup\u003e2\u003c/sup\u003e for females. Slow walking pace of \u0026lt;\u0026thinsp;3 miles/h was used to estimate physical performance, instead of low gait speed of \u0026lt;\u0026thinsp;0.8m/s in EWGSOP criteria.\u003c/p\u003e\u003cp\u003eParticipants were categorized into probable sarcopenia group, defined by low grip strength, and non-sarcopenia group, characterized by normal grip strength.\u003c/p\u003e\n\u003ch3\u003eAssessment of air pollution exposure\u003c/h3\u003e\n\u003cp\u003eLand use regression (LUR) models were employed to assess the annual concentrations of NO\u003csub\u003eX\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, and PM\u003csub\u003e2.5\u0026minus;10\u003c/sub\u003e, created in the European Study of Cohorts for Air Pollution Effects (ESCAPE). The LUR models could explain 88%, 87%, 77%, 88%, and 57% of the variance (cross-validation R\u003csup\u003e2\u003c/sup\u003e) for NO\u003csub\u003eX\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, and PM\u003csub\u003e2.5\u0026minus;10\u003c/sub\u003e, respectively, demonstrating moderate to good performance.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eInformation on sociodemographic status include age, sex, ethnicity, educational level and the Townsend deprivation index (TDI). Health-related factors contained smoking and drinking status, body mass index (BMI) and comorbidities relevant to either air pollution or sarcopenia. In terms of potential mediators, physical activity was quantified by metabolic equivalent task (MET) hours per week for different types of activities and physical inactivity was identified using the short form international physical activity questionnaire (IPAQ). Biological aging was accessed through Klemera-Doubal method biological age (KDM-BA) and the phenotypic age (PhenoAge), which reflects the chronological age with approximately normal physiological function [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and normal mortality risk within a reference population [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], respectively. The calculation process was performed by R package \u003cem\u003eBioAge\u003c/em\u003e [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In this study, age accelerations, calculated as the residuals of these biomarkers adjusted for chronological age, was used to indicate the rate of biological aging.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eContinuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and categorical variables as n (%). Baseline characteristics were compared by standardized mean differences (SMD). Air pollutant concentrations were modelled both continuously and by quartiles (Q1\u0026ndash;Q4). Records with missing key categorical or demographic identifiers were excluded. Continuous covariates with incomplete values (physical activity, BMI, ALM) were multiply imputed by chained equations (MICE). Biological aging indicators (KDM-BA and PhenoAge) were not imputed to avoid structural distortion and additional uncertainty. To address the study objectives \u0026mdash; estimating individual and joint (mixture) effects of ambient pollutants and examining potential mediating pathways \u0026mdash; analyses proceeded as follows.\u003c/p\u003e\u003cp\u003eCross-sectional analysis of individual effects. Associations between each pollutant and probable sarcopenia at baseline were estimated using univariate and multivariable logistic regression. Odds ratios (ORs) and 95% confidence intervals (CIs) are reported. Four hierarchical covariate models were fitted: Model 1 adjusted for sociodemographic variables; Model 2 additionally adjusted for health-related factors; Model 3 further adjusted for physical activity; and a DAG-based model adjusted for confounders selected using a directed acyclic graph (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eCombined effects. To characterize real-world joint exposures, weighted quantile sum (WQS) regression was applied to derive a composite mixture index and pollutant contributions (weights). Restricted cubic splines (RCS) were used to examine concentration\u0026ndash;response shapes for individual pollutants and the WQS index. Bayesian kernel machine regression (BKMR) was implemented as a complementary approach to accommodate potential non-linear and non-additive interactions within the pollutant mixture.\u003c/p\u003e\u003cp\u003eSensitivity analyses. We performed multiple sensitivity checks including: (1) BKMR as described above; (2) stratified analyses to detect vulnerable subgroups defined by age, sex, ethnicity, deprivation, smoking, BMI and IPAQ status; (3) analyses of other sarcopenia components (low ALM index and slow walking pace) as secondary outcomes; and (4) additional adjustment for participants\u0026rsquo; occupation and osteoarthritis status. Results from multiply imputed datasets were compared with complete-case analyses to assess robustness.\u003c/p\u003e\u003cp\u003eMediation analyses. Parallel mediation models were employed. The direct effect indicated the effect of air pollution on probable sarcopenia without mediation. The indirect effect denoted the effect of air pollution on probable sarcopenia via mediators. The proportion of mediation was calculated by dividing the indirect effect by the total effect.\u003c/p\u003e\u003cp\u003eLongitudinal analyses. Incident probable sarcopenia was modelled using Cox proportional hazards regression with DAG-based covariate adjustment; individuals with probable sarcopenia at baseline were excluded. Follow-up time was defined from baseline assessment to incident event, death, loss to follow-up, or last available follow-up date. Proportional hazards assumptions were assessed using Schoenfeld residuals. Results are reported as hazard ratios (HRs) with 95% CIs.\u003c/p\u003e\u003cp\u003eAll analyses were conducted using R version 4.3.2. Statistical significance was established at a two-sided P value of less than 0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eCharacteristics of participants and distribution of air pollutants\u003c/h2\u003e\u003cp\u003eIn the cross-sectional analysis, 211,808 participants were finally included, of whom 10,905 were diagnosed with probable sarcopenia. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e represents the demographic details. Participants with probable sarcopenia were generally older, predominantly female, non-white, and exhibited higher TDI levels. They were also more likely to be non-drinkers, combined with hypertension and diabetes, along with lower physical activity levels (SMD\u0026thinsp;\u0026gt;\u0026thinsp;0.1).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\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\u003eDemographic characteristics of participants stratified by probable sarcopenia status (n\u0026thinsp;=\u0026thinsp;211908).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-sarcopenia\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eProbable sarcopenia\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSMD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;200903)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;10905)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge, mean (SD), years\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e52.05 (7.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51.92 (7.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e54.49 (7.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.366\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex, n (%)\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.171\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e109555 (51.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e103041 (51.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6514 (59.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e102253 (48.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e97862 (48.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4391 (40.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEthnicity, n (%)\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.254\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e199527 (94.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e189853 (94.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9674 (88.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4818 (2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4069 (2.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e749 (6.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlack\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4009 (1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3813 (1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e196 (1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMixed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1551 (0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1464 (0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e87 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1903 (0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1704 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e199 (1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation level, n (%)\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnder high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e66710 (31.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63124 (31.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3586 (32.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school or equivalent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29002 (13.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27536 (13.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1466 (13.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbove high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e116096 (54.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e110243 (54.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5853 (53.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTownsend deprivation index, mean (SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.46 (2.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.49 (2.91)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.94 (3.08)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.184\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoking status, n (%)\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e123641 (58.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e117026 (58.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6615 (60.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e88167 (41.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e83877 (41.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4290 (39.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDrinking status, n (%)\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-drinker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6365 (3.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5699 (2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e666 (6.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.159\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrinker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e205443 (97.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e195204 (97.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10239 (93.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI, mean (SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27.18 (4.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27.16 (4.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27.52 (5.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComorbidity\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension, n (%)\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.130\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e169138 (79.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e160990 (80.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8148 (74.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e42670 (20.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39913 (19.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2757 (25.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCancer, n (%)\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e200003 (94.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e189887 (94.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10116 (92.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11805 (5.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11016 (5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e789 (7.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes, n (%)\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.159\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e204621 (96.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e194440 (96.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10181 (93.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7187 (3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6463 (3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e724 (6.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardiovascular diseases, n (%)\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.087\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e206296 (97.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e195835 (97.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10461 (95.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5512 (2.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5068 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e444 (4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespiratory diseases, n (%)\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e142210 (67.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e135029 (67.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7181 (65.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e69598 (32.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e65874 (32.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3724 (34.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePhysical activity\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.086\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMET hours/week, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e41.82 (43.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e42.02 (43.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38.32 (42.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIPAQ activity group, n (%)\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.125\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e168467 (79.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e160157 (79.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8310 (76.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical inactivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e43341 (20.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40746 (20.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8310 (23.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eALM index, mean (SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.35 (1.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.36 (1.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.16 (1.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.144\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGrip strength, mean (SD), kg\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32.71 (10.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33.58 (10.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.71 (5.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.989\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWalking pace, n (%)\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.279\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e204041 (96.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e194254 (96.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9787 (79.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSlow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7767 (3.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6649 (3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1118 (10.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAir pollution, mean (SD), ug/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sup\u003e\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003csub\u003ex\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26.73 (7.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26.66 (7.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27.99 (7.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.174\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44.10 (15.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e43.99 (15.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46.05 (15.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.131\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\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.99 (1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.99 (1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.11 (1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.114\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16.23 (1.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16.22 (1.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.43 (1.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.109\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePM\u003csub\u003e2.5\u0026minus;10\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.43 (0.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.42 (0.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.46 (0.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eContinuous variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Categorical variables were presented as n (%). SMD, standard mean difference (SMD\u0026thinsp;\u0026ge;\u0026thinsp;0.1 indicates imbalanced characteristics between groups); BMI, body mass index; MET, metabolic equivalent task; IPAQ, international physical activity questionnaire.\u003c/p\u003e\u003cp\u003eEstimated exposures to NO\u003csub\u003eX\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, and PM\u003csub\u003e2.5\u0026minus;10\u003c/sub\u003e in the probable sarcopenia group were 27.99\u0026thinsp;\u0026plusmn;\u0026thinsp;7.56, 46.05\u0026thinsp;\u0026plusmn;\u0026thinsp;15.67, 10.11\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04, 16.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.84, and 6.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88 ug/m\u003csup\u003e3\u003c/sup\u003e, respectively, compared to 26.66\u0026thinsp;\u0026plusmn;\u0026thinsp;7.73, 43.99\u0026thinsp;\u0026plusmn;\u0026thinsp;15.70, 9.99\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06, 16.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.90, and 6.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90 ug/m\u003csup\u003e3\u003c/sup\u003e in the non-sarcopenia group. Moderate to strong correlations (r\u0026thinsp;\u0026gt;\u0026thinsp;0.3) were observed among most air pollutants (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ea).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eIndividual exposure effects of air pollutants on probable sarcopenia\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates significant associations between each air pollutant and probable sarcopenia in unadjusted models, regardless of whether air pollutants were analyzed as continuous or categorical variables. Following the adjustment of various factors (models 1, 2, and 3), associations remained consistent for NO\u003csub\u003eX\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e, and PM\u003csub\u003e10\u003c/sub\u003e. In terms of PM\u003csub\u003e2.5\u0026minus;10\u003c/sub\u003e, no association was found with probable sarcopenia when treated as a continuous variable, but a significant relationship emerged when categorized into quartiles. The highest exposure quartile increased the risk by 11% compared to quartile 1, suggesting a potential non-linear concentration-response relationship with probable sarcopenia. Similar results were confirmed in the DAG-based model. For each 10 \u0026micro;g/m\u0026sup3; increase in NO\u003csub\u003eX\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e, and PM\u003csub\u003e10\u003c/sub\u003e, the ORs (95% CIs) for probable sarcopenia were 1.11 (1.08, 1.15), 1.03 (1.02, 1.04), 1.41 (1.15, 1.72), and 1.34 (1.21, 1.49), respectively (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\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\u003eAssociation between individual air pollutant and probable sarcopenia.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAir pollutants\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUnadjusted\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDAG-based model\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNO\u003c/b\u003e\u003csub\u003e\u003cb\u003ex\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eContinuous (10ug/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.23 (1.20, 1.26)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.11 (1.08, 1.14)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.12 (1.09, 1.15)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.11 (1.08, 1.15)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1.11 (1.08, 1.14)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.14 (1.08, 1.21)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.12 (1.06, 1.19)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.11 (1.05, 1.18)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.11 (1.05, 1.18)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1.12 (1.06, 1.19)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.41 (1.34, 1.50)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.31 (1.24, 1.39)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.31 (1.23, 1.39)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.31 (1.23, 1.39)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1.32 (1.24, 1.40)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.63 (1.54, 1.72)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.35 (1.27, 1.43)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.36 (1.23, 1.39)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.35 (1.27, 1.44)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1.35 (1.27, 1.44)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eContinuous (10ug/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.08 (1.06, 1.09)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.03 (1.02, 1.04)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.03 (1.02, 1.04)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.03 (1.02, 1.04)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1.03 (1.02, 1.04)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.16 (1.09, 1.23)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.12 (1.06, 1.19)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.11 (1.05, 1.18)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.11 (1.05, 1.18)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1.12 (1.06, 1.19)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.42 (1.35, 1.51)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.31 (1.23, 1.39)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.30 (1.23, 1.38)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.30 (1.23, 1.38)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1.31 (1.24, 1.39)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.48 (1.40, 1.57)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.23 (1.15, 1.31)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.23 (1.16, 1.31)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.23 (1.16, 1.31)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1.23 (1.16, 1.31)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePM\u003c/b\u003e\u003csub\u003e\u003cb\u003e2.5\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eContinuous (10ug/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e2.77 (2.33, 3.30)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.39 (1.14, 1.70)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.42 (1.16, 1.73)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.41 (1.15, 1.72)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1.41 (1.16, 1.72)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.17 (1.10, 1.38)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.12 (1.06, 1.19)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.11 (1.05, 1.18)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.11 (1.05, 1.18)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1.12 (1.06, 1.19)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.30 (1.23, 1.38)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.20 (1.13, 1.27)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.19 (1.12, 1.26)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.19 (1.12, 1.26)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1.20 (1.13, 1.27)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.36 (1.29, 1.44)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.13 (1.06, 1.20)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.14 (1.07, 1.21)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.14 (1.07, 1.21)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1.14 (1.07, 1.21)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePM\u003c/b\u003e\u003csub\u003e\u003cb\u003e10\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eContinuous (10ug/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.73 (1.57, 1.92)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.34 (1.21, 1.49)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.34 (1.21, 1.49)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.34 (1.21, 1.49)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1.35 (1.22, 1.50)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.15 (1.09, 1.22)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.11 (1.05, 1.18)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.11 (1.05, 1.18)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.11 (1.05, 1.18)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1.12 (1.05, 1.18)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.33 (1.26, 1.41)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.20 (1.14, 1.28)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.21 (1.14, 1.28)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.21 (1.14, 1.28)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1.21 (1.14, 1.28)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.39 (1.32, 1.47)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.19 (1.13, 1.27)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.20 (1.13, 1.27)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.20 (1.13,1.27)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1.20 (1.13, 1.27)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePM\u003c/b\u003e\u003csub\u003e\u003cb\u003e2.5\u0026minus;10\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eContinuous (10ug/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.60 (1.30, 1.96)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.15 (0.92, 1.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.14 (0.92, 1.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.14 (0.92, 1.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.15 (0.92, 1.43)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.12 (1.06, 1.19)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.08 (1.02, 1.14)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.08 (1.02, 1.14)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.08 (1.02, 1.14)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1.08 (1.02, 1.14)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.19 (1.13, 1.26)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.08 (1.02, 1.15)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.08 (1.02, 1.15)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.08 (1.02, 1.15)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1.08 (1.02, 1.15)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.28 (1.21, 1.35)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.11 (1.05, 1.18)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.11 (1.05, 1.18)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.11 (1.06, 1.18)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1.12 (1.05, 1.18)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eBold represents P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eModel 1: adjusted for the sociodemographic status (age, sex, ethnicity, education level, TDI).\u003c/p\u003e\u003cp\u003eModel 2: further adjusted for health-related factors (smoking, drinking, BMI and comorbidity).\u003c/p\u003e\u003cp\u003eModel 3: further adjusted for physical activity (MET hours/week).\u003c/p\u003e\u003cp\u003eDAG-based model: adjusted for confounders selected from directed acyclic graph (age, sex, ethnicity, education level, TDI and smoking status).\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eCombined exposure effects of mixed air pollution on probable sarcopenia\u003c/h2\u003e\u003cp\u003eAccording to WQS regression models, NO\u003csub\u003ex\u003c/sub\u003e (79.1%) and PM\u003csub\u003e10\u003c/sub\u003e (12.6%) contributed the most significant weights to the combined exposure effects of mixed air pollution on probable sarcopenia (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The WQS index for mixed air pollution demonstrated a positive correlation with probable sarcopenia (OR: 1.14, 95% CI: 1.11 to 1.17). When categorized into quartiles, the highest quantile of combined exposure elevated the probability of probable sarcopenia by 36% relative to the lowest quantile (OR: 1.36, 95% CI: 1.28 to 1.45, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). As displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ed, the concentration-effect curve demonstrated a positive non-linear relationship between mixed air pollution and probable sarcopenia, with significant effects observed at higher exposure levels.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eSensitivity analysis\u003c/h2\u003e\u003cp\u003eFirst, the BKMR analysis revealed a significant, positive correlation between mixed air pollution and probable sarcopenia. Figure S2a depicts the exposure-response relationship for each air pollutant, with other pollutants held at the median. The overall effect of mixed air pollution on probable sarcopenia was significantly positive when all air pollutants reached or exceeded the 55th percentile of their concentrations, compared to their medians (Figure S2b). Furthermore, when the concentrations of other air pollutants were held at the 25th, 50th, and 75th percentiles, PM\u003csub\u003e10\u003c/sub\u003e and NO\u003csub\u003eX\u003c/sub\u003e exhibited a notably positive correlation with the estimated risk of probable sarcopenia (Figure S2c).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSecond, the outcomes of stratified analyses (Figure S3) were mainly consistent with those of the primary analyses. Males, the White, individuals with lower TDI, and drinkers were more susceptible to both individual and combined air pollution exposure. Additionally, non-smokers demonstrated increased sensitivity specifically to PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u0026minus;10\u003c/sub\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThird, DAG-based logistic regression analyses demonstrated that significant, adverse effects of mixed air pollution on both muscle mass and physical performance. The concentration-effect curves of mixed air pollution on low ALM index and slow walking pace were shown in Figure S4.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFourth, the results remained largely unchanged following additional adjustments for the participants' occupations and osteoarthritis status. (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Table S2).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eMediation analyses\u003c/h2\u003e\u003cp\u003ePrior to mediation testing, we examined exposure-mediator relationships and found that both individual and combined air pollutants were inversely associated with physical activity (except PM\u003csub\u003e2.5\u0026minus;10\u003c/sub\u003e) and positively associated with biological aging (Table S3). For mediator-outcome associations (Table S4), higher physical activity was protective against probable sarcopenia (OR 0.989, 95% CI 0.988\u0026ndash;0.989, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas biological aging increased risk (KDM-BA: OR 1.026, 95% CI 1.024\u0026ndash;1.028; PhenoAge: OR 1.063, 95% CI 1.059\u0026ndash;1.066; both P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eParallel mediation analyses indicated that both physical activity and biological aging acted as intermediary pathways linking air pollution to probable sarcopenia (Table S5). Physical activity explained a modest proportion of the associations (7.0\u0026ndash;10.2%), with no significant mediation observed for PM\u003csub\u003e2.5\u0026ndash;10\u003c/sub\u003e. In contrast, biological aging contributed more substantially, with KDM-BA mediating 9.7\u0026ndash;23.9% and PhenoAge 14.5\u0026ndash;30.7% of the associations, although some direct effects (particularly for PM\u003csub\u003e2.5\u0026ndash;10\u003c/sub\u003e) were not statistically significant. Notably, for mixed air pollution, physical activity, KDM-BA, and PhenoAge mediated 9.6%, 10.2%, and 15.0% of the association with probable sarcopenia risk, respectively (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eLongitudinal impact of air pollution on the onset risk of probable sarcopenia\u003c/h2\u003e\u003cp\u003eDuring an average follow-up duration of 2.9 years, 4242 subjects (1.3%) developed probable sarcopenia. Individual exposure to NO\u003csub\u003ex\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, and combined exposure to mixed air pollution at baseline were all correlated with the incidence risk of probable sarcopenia (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eImpact of multiple air pollutants on the incident risk of probable sarcopenia\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAir pollution\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHazard Ratio\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLower\u003c/p\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUpper\u003c/p\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eNO\u003c/b\u003e\u003csub\u003e\u003cb\u003ex\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eContinuous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.004*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.002*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eNO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eContinuous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.003*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003ePM\u003c/b\u003e\u003csub\u003e\u003cb\u003e2.5\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eContinuous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.031*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003ePM\u003c/b\u003e\u003csub\u003e\u003cb\u003e10\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eContinuous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.136\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.312\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.678\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.009*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003ePM\u003c/b\u003e\u003csub\u003e\u003cb\u003e2.5\u0026minus;10\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eContinuous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.01*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.005*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.142\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.004*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eMixed air pollution\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eContinuous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.005*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.007*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eModels were adjusted for confounders selected from directed acyclic graph (age, sex, ethnicity, education level, TDI and smoking status)\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study presents two novel findings derived from the UK Biobank dataset. First, exposure to ambient air pollutants, whether in isolation or combination, were linked to an increased risk of probable sarcopenia, as well as low muscle mass and slow walking pace. Notably, the combined effects were predominantly driven by NO\u003csub\u003ex\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e. The longitudinal analysis of the incidence of probable sarcopenia further substantiated these associations. Second, physical activity and biological aging were recognized as potential mediators in the relationship between air pollution and probable sarcopenia.\u003c/p\u003e\u003cp\u003eExisting literature, though limited, has shown a positive association between individual air pollution exposure and probable sarcopenia. A nationwide cross-sectional study conducted by Lai et al. among UK adults found that each interquartile range increase in air pollutants (PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, PM\u003csub\u003ecoarse\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e and NO\u003csub\u003e\u003cem\u003ex\u003c/em\u003e\u003c/sub\u003e) was substantially linked to a higher risk of probable sarcopenia [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Longitudinal analysis also confirmed similar results concerning the incidence of probable sarcopenia. Cai et al. reported that for each 10 \u0026micro;g/m\u0026sup3; increase in concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and NO\u003csub\u003ex\u003c/sub\u003e, the relative incidence risk of probable sarcopenia increased by 23.2%, 5.5%, and 1.6%, respectively[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Our results are predominantly aligned with these findings, albeit with marginally higher odds/hazard ratios.\u003c/p\u003e\u003cp\u003eThe effect of air pollution on muscle mass and walking pace has also been observed. Published findings indicated that each interquartile range rise in NO\u003csub\u003e2\u003c/sub\u003e exposure correlated with reduced gait speed and chair-stand test [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Studies on the relationship between air pollution and muscle mass were relatively scarce. Hu et al. identified an inverse relationship between indoor air pollution\u0026mdash;indirectly measured by solid fuel usage for cooking and heating\u0026mdash;and muscle mass, which also corresponded with an elevated risk of low muscle mass [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, regarding outdoor ambient pollutants, Dong et al. reported that particulate matter, NO\u003csub\u003e2\u003c/sub\u003e, and O\u003csub\u003e3\u003c/sub\u003e did not elevate the risk of low muscle mass [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Notably, these studies were conducted on Chinese population, with low muscle mass defined by the AWGS 2019 criteria [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Our findings, which demonstrated a significant concentration-response relationship between ambient air pollution and low muscle mass, contributed to the existing European population data to some extent.\u003c/p\u003e\u003cp\u003eIt is widely recognized that individuals are routinely exposed to a combination of air pollutants in everyday life. Thus, the effects of air pollutants may depend on their cooperation and interaction. To our knowledge, this study is the first large-scale, European population-based study demonstrating notable combined effects of mixed air pollution on probable sarcopenia, which corroborated earlier findings in Chinese population [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Dong et al. also demonstrated substantial PM\u003csub\u003e10\u003c/sub\u003e-induced combined interactions among multiple air pollutants, which contributed to an elevated risk of sarcopenia. As for our study, however, utilizing WQS and BKMR models, NO\u003csub\u003ex\u003c/sub\u003e emerged as the dominant contributor, while PM\u003csub\u003e10\u003c/sub\u003e still played an important role. This disparity may reflect regional variations in major pollutants and could possibly stem from our focus on probable sarcopenia as the target condition.\u003c/p\u003e\u003cp\u003eSubgroup analysis revealed that males are more susceptible to both individual and combined effects of air pollution on probable sarcopenia, contrasting with findings from previous studies in Chinese population [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Despite the relatively lower prevalence of sarcopenia in European males compared to females [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], this discrepancy underscores the necessity of prioritizing male individuals as a high-risk group. Interestingly, most air pollutants exhibited a beneficial role in non-white participants, in accordance with Cai et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This paradox may stem from biases due to the limited proportion of non-white participants, genetic variations, or distinct lifestyle factors influencing sarcopenia risk [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Our study also identified individuals with lower deprivation levels (TDI\u0026thinsp;\u0026le;\u0026thinsp;0) as a susceptible population, potentially due to healthier habits rendering them more vulnerable to challenges posed by air pollutant [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Additionally, experimental studies have suggested that alcohol consumption increases vulnerability to adverse effects of air pollutants, likely due to alcohol-induced reductions in lean body mass and protein synthesis, a finding corroborated by our results [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur mediation analysis indicated that both physical activity and biological aging serve as partial mediators in the relationship between air pollution and probable sarcopenia. Engaging in physical activity has been recognized as a protective factor against sarcopenia, aiding in the maintenance of muscle mass and strength [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Elevated air pollution levels correlated with decreased total weekly MET hours, possibly undermining the protective benefits of physical exercise. Aerobic training may mitigate oxidative damage in rat skeletal muscle exposed to air pollution from a molecular standpoint [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Interestingly, Liu et al. reported that vigorous exercise alongside ozone exposure might trigger apoptosis in quadriceps femoris muscle cells of rats through the mitochondria-mediated pathway [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Consequently, further research is warranted to ascertain whether different types of physical exercise have varying benefits in alleviating the impact of air pollution on (probable) sarcopenia. Consequently, further research is warranted to determine whether different types of physical activity confer varying benefits in mitigating the effects of air pollution on (probable) sarcopenia. Regarding biological aging, numerous studies have suggested that air pollution contributes to DNA mutations, epigenetic changes, and alterations in epitranscriptomics [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], which subsequently accelerated DNA methylation aging [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] and clinical biomarker-based biological aging [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Aging is a significant risk factor for various chronic diseases, including sarcopenia, which is associated with type II myofiber atrophy [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], fat infiltration [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and changes in muscle metabolism [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]..These factors negatively impact muscle strength and contribute to the onset of probable sarcopenia.\u003c/p\u003e\u003cp\u003eNotably, the indirect mediation effects of physical activity and biological aging ranged from 7.0% to 30.7%, suggesting that a considerable portion of the association between air pollution and probable sarcopenia is attributable to direct effects. First, air pollutants such as NO\u003csub\u003e2\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e have been reported to trigger oxidative stress and inflammatory responses [\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], resulting in the loss of muscle mass and strength through oxidative damage to membrane lipids and proteins [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Second, emerging evidence suggests that airborne nanoparticles interfere with mitochondrial function by altering intracellular Ca\u003csup\u003e2+\u003c/sup\u003e homeostasis, hindering mitochondrial dynamics, and damaging mtDNA [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], which leads to a decrease in muscle fiber cross-sectional area [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Third, insulin resistance and gut microbiota dysbiosis partially mediate the association between air pollution exposure and sarcopenia onset [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], underscoring the role of dysregulated nutrition sensing [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Based on these findings, we hypothesize that exposure to ambient air pollution elevates the risk of probable sarcopenia by inducing oxidative stress, chronic inflammation, mitochondrial dysfunction, dysregulated nutrient sensing, and epigenetic alterations, which adversely affects muscle function and mass. Physical activity and biological aging indirectly mediate effects by altering certain pathways.\u003c/p\u003e\u003cp\u003eSeveral limitations must be acknowledged. First, air pollution exposure was estimated via LUR models based on participants' residential addresses, omitting non-residential sources and potentially resulting in exposure misclassification. Second, participants of UK Biobank generally reside in areas with lower socioeconomic deprivation, and only five air pollutants were accessible, which may introduce selection biases. Third, the absence of time-varying air pollution exposure was attributable to the characteristics of the UK Biobank [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Thus, the baseline concentrations may not fully represent long-term exposure. Fourth, walking pace was self-reported in the UK Biobank, which differs from the EWGSOP criteria; however, this is likely to have limited impact as the key outcome in our study was probable sarcopenia. Lastly, despite adjusting for various covariates, some possible confounders may not have been considered, potentially resulting in residual confounding.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, we underscored that both individual and combined exposures to air pollutants correlates with an elevated risk of probable sarcopenia, as well as low muscle mass and slow walking pace, which is partially mediated by physical inactivity and accelerated biological aging. These findings highlight the need for public health initiatives aimed at improving air quality, promoting active lifestyles, and fostering healthy aging to mitigate the growing burden of sarcopenia.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eEWGSOP, European Working Group on Sarcopenia in Older People; ALM, appendicular lean mass; LUR, land use regression; ESCAPE, European Study of Cohorts for Air Pollution Effects; TDI, Townsend deprivation index; BMI, body mass index; MET, metabolic equivalent task; IPAQ, international physical activity questionnaire; KDM-BA, Klemera-Doubal method biological age; PhenoAge, phenotypic age; SMD, standardized mean differences; MICE, multiply imputed by chained equations; OR, odds ratio; CI, confidence intervals; WQS, weighted quantile sum; RCS, restricted cubic splines; BKMR, Bayesian kernel machine regression; HR, hazard ratios.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eClinical trial number:\u003c/em\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthics approval and consent to participate:\u003c/em\u003e UK Biobank study is an open dataset. The North West Multi-Center Research Ethics Committee has approved the UK Biobank study (application number 95082) and all participants signed an informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication:\u003c/em\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and material:\u003c/em\u003e Details of how to access the UK biobank data and details of the data release schedule are available from http://www.ukbiobank.ac.uk/ and https://biobank.ndph.ox.ac.uk/ukb/\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests:\u0026nbsp;\u003c/em\u003eThe authors declare that they have no conflict of interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding:\u003c/em\u003e 1. National High Level Hospital Clinical Research Funding; 2. Elite Medical Professionals Project of China-Japan Friendship Hospital (NO. ZRJY2023-QM04)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthor\u0026rsquo;s contributions:\u0026nbsp;\u003c/em\u003eJiaxiang Gao:\u003cem\u003e\u0026nbsp;\u003c/em\u003eInvestigation, Methodology, Software, Data curation, Writing \u0026ndash; original draft, Funding acquisition; Tong Li:\u003cem\u003e\u0026nbsp;\u003c/em\u003eInvestigation, Methodology, Data curation; \u0026nbsp;Yufei Gu: Investigation, Formal analysis, Data curation; Ran Ding:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eSoftware, Formal analysis; Yue Peng:\u003cem\u003e\u0026nbsp;\u003c/em\u003eMethodology, Visualization; Cheng Huang:\u003cem\u003e\u0026nbsp;\u003c/em\u003eValidation, Visualization, Resources, Project administration Weiguo Wang: Writing \u0026ndash; review and editing, Project administration, Resources, Supervision; Jun Lin:\u003cem\u003e\u0026nbsp;\u003c/em\u003eWriting \u0026ndash; review and editing, Data curation, Investigation, Project administration, Resources, Supervision.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements:\u0026nbsp;\u003c/em\u003eWe would like to acknowledge all the investigators and participants involved in the UK Biobank for providing high quality, nationally representative data, which make it possible for our study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCruz-Jentoft AJ, Bahat G, Bauer J, et al. 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Ageing Res Rev. 2025;103:102587. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://10.1016/j.arr.2024.102587\u003c/span\u003e\u003cspan address=\"https://10.1016/j.arr.2024.102587\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"Air Pollution, Sarcopenia, Physical Activity, Biological Aging, UK Biobank","lastPublishedDoi":"10.21203/rs.3.rs-7721102/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7721102/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEmerging evidence links air pollution to probable sarcopenia, yet combined effects of multiple pollutants and their mediating pathways remain unclear. We aimed to evaluate both individual and combined associations of air pollutants with probable sarcopenia, and to explore mediating roles of physical activity and biological aging.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData from 211,808 UK Biobank participants were analyzed. Probable sarcopenia was defined by the EWGSOP criteria. Concentrations of NO\u003csub\u003ex\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, and PM\u003csub\u003e2.5−10\u003c/sub\u003e were estimated using land use regression models. Multivariate logistic regression, weighted quantile sum (WQS) analysis, and Cox regression were employed to investigate cross-sectional, combined and longitudinal associations, respectively. Parallel mediation analyses quantified contributions of physical inactivity and accelerated biological aging.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn cross-sectional analyses, each 10 µg/m³ increase in NO\u003csub\u003e10\u003c/sub\u003e (OR = 1.11, 95% CI:1.08–1.15), NO\u003csub\u003e2\u003c/sub\u003e (1.03, 1.02–1.04), PM\u003csub\u003e2.5\u003c/sub\u003e (1.41, 1.15–1.72), and PM\u003csub\u003e10\u003c/sub\u003e (1.34, 1.21–1.49) was associated with elevated probable sarcopenia risk. The combined effects, indicated by WQS index, yielded an OR of 1.14 (95% CI: 1.11–1.17), primarily driven by NO\u003csub\u003ex\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e. Physical inactivity mediated 7.0–10.2% of total effects, while accelerated biological aging mediated 9.7% to 30.7%. Longitudinal analyses over 2.9 years further confirmed an increased incident risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIndividual and combined exposures to air pollutants elevate probable sarcopenia risk, partially mediated by physical inactivity and accelerated biological aging, underscoring the need for improved air quality, active lifestyles and healthy aging to mitigate sarcopenia burden.\u003c/p\u003e","manuscriptTitle":"Impact of Multiple Air Pollutants on Probable Sarcopenia in the UK Population: The Mediating Role of Physical Activity and Biological Aging","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-23 16:07:06","doi":"10.21203/rs.3.rs-7721102/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-21T04:15:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-20T06:44:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"307499675517390090444792964275443473106","date":"2025-12-29T08:16:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-02T21:26:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"272532696551287820562925758234145981325","date":"2025-10-15T17:55:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"111454774319517711631672760903307326545","date":"2025-10-14T08:40:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-09T18:45:18+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-01T11:20:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-01T07:37:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-01T07:37:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-09-26T10:54:59+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":"3c22b6b0-1a52-4a21-be4d-d910a8fc4e07","owner":[],"postedDate":"October 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-16T16:04:50+00:00","versionOfRecord":{"articleIdentity":"rs-7721102","link":"https://doi.org/10.1186/s12889-026-26962-9","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2026-03-14 15:59:17","publishedOnDateReadable":"March 14th, 2026"},"versionCreatedAt":"2025-10-23 16:07:06","video":"","vorDoi":"10.1186/s12889-026-26962-9","vorDoiUrl":"https://doi.org/10.1186/s12889-026-26962-9","workflowStages":[]},"version":"v1","identity":"rs-7721102","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7721102","identity":"rs-7721102","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}