The association between greenness exposure and arthritis in middle-aged and older Chinese adults, mediated by climate factors

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While previous studies have explored various risk factors for arthritis, the relationship between green space exposure and arthritis risk remains underexplored. This study aims to investigate the correlation between green space exposure, as measured by the Normalized Difference Vegetation Index (NDVI), and arthritis risk among middle-aged and older adults in China using a cross-sectional approach. Methods Data for the present study were extracted from the 2015 wave of the China Health and Retirement Longitudinal Study (CHARLS), focusing specifically on middle-aged and older adults aged 45 years and above. Greenness exposure was quantified using the NDVI. Generalized linear models were used to assess the association between NDVI and arthritis. Climatic variables (relative humidity, precipitation) and metabolic equivalents were evaluated as correlates and potential mediators of this relationship. Results The study included a total of 7,985 participants, of whom 3,519 had arthritis and 4,466 did not. In the fully adjusted model, NDVI showed a positive correlation with arthritis. Specifically, the odds ratio (OR) of arthritis for each interquartile range (IQR) increase in NDVI was 1.14 (95% CI: 1.02–1.27). Additionally, annual precipitation, annual relative humidity, and metabolic equivalents all showed positive associations with arthritis incidence. Further mediation analysis indicated that annual precipitation significantly mediated the relationship between NDVI and arthritis, with a proportion mediated of 5.31%. Conclusion Increased NDVI is tied to a higher risk of arthritis, with climate factor (annual precipitation) partly mediating this relationship. Areas with higher levels of greenery should be considered for the prevention of joint diseases. Trial registration: The study was approved by the Institutional Review Board of Peking University (Code: IRB00001052-11015) and conducted in accordance with the Declaration of Helsinki, with written informed consent obtained from all participants. NDVI Arthritis CHARLS Mediation analysis Figures Figure 1 Figure 2 Figure 3 Key Messages Based on CHARLS 2015 data, per IQR increase in NDVI raises arthritis risk by 14%. Mediation analysis showed that annual precipitation partially explains the association between NDVI and arthritis incidence. Regions with higher levels of green space should consider implementing measures to prevent joint diseases. Background Arthritis is a general term encompassing a variety of arthritic conditions, characterized by inflammation of the joints that can manifest as either acute or chronic presentations. It is predominantly marked by joint pain and stiffness, which can substantially compromise patients' physical function and quality of life[ 1 , 2 ]. Meanwhile, the prevalence of arthritis has reached a higher level worldwide. The incidence of arthritis increases markedly with age, particularly after 45 years. Data from 2010 to 2012 revealed that 22.7% of the US population had doctor-diagnosed arthritis, with projections suggesting a 49% increase in prevalence by 2040[ 3 ]. Similarly, in China, the overall prevalence of arthritis among middle-aged and senior individuals grew from 31.4% to 44.7% between 2011 and 2018[ 4 ]. Furthermore, Research has shown that a substantial rise in medical expenditures has failed to enhance the health-related quality of life for individuals suffering from arthritis[ 5 ]. Considering the medical context, the importance of identifying relevant risk factors to prevent the onset of arthritis cannot be overstated. The etiology and pathogenesis of arthritis are highly complex and involve multiple factors. To date, genetics and environmental factors are among the key influences on the onset and progression of the condition [ 6 – 8 ]. In recent years, most relevant studies have concentrated on environmental pollution. Research has indicated that smoking and elevated levels of air pollution are significant risk factors for the development of arthritis[ 8 ]. Their primary pathogenic mechanisms may involve negatively altering the immune response. Furthermore, studies have shown that weather and seasonal changes can mitigate disease severity [ 9 ]. In clinical practice, many patients with rheumatoid arthritis (RA) report that their joint symptoms fluctuate with changes in climatic factors, such as temperature, humidity, and atmospheric pressure. However, strong scientific evidence to support this assumption is lacking[ 10 ]. As global urbanization continues to rise, and the adverse effects of long-term exposure to urban environmental risks have garnered significant attention, prompting extensive research into the potential health outcomes associated with exposure to green spaces. The most common marker for greenness exposure was NDVI[ 11 ]. Currently, numerous aspects of greenness are beneficial. For instance, exposure to greenness has beneficial effects in most studies regarding respiratory mortality, lung cancer incidence, respiratory hospitalisations and pulmonary function[ 12 ]. Similarly, greenness has been shown to improve air quality and enhance green spaces, thereby promoting visual health in aging populations[ 13 ]. While some aspects may be harmful, Studies have also emphasized greenness may have different health effects in different population subgroups[ 12 ]. Emerging evidence suggests a synergistic interaction between ambient greenness and climatic variables[ 14 ], coupled with consistent observations that greater residential greenness promotes higher levels of physical activity [ 15 ]. Concomitantly, both meteorological parameters and the intensity of physical activity have been causally implicated in the pathogenesis and progression of arthritis [ 16 , 17 ]. Synthesising these converging lines of evidence, this study conducted a cross-sectional analysis focusing on the relationship between green environment exposure and arthritis among middle-aged and elderly individuals in China. Furthermore, the study examined the mediating roles of climate factors (relative humidity, precipitation) and physical activity intensity (metabolic equivalents) in the association between green environment exposure and arthritis, thereby providing robust support for etiological research into the disease. Methods 2.1 Study population Data for this study were derived from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative survey of adults aged 45 and older. The study was conducted with the informed consent of all participants and received approval from the Institutional Review Board of Peking University (Code: IRB00001052-11015). The baseline survey utilized a multi-stage, stratified, probability-proportional-to-size sampling design, with further details on recruitment and sampling methods provided in accordance with the study protocol [ 18 ]. A total of 17,708 participants from 10,257 households across 28 provinces were recruited between 2011 and 2012, with subsequent follow-ups conducted in 2013, 2015, and 2018. For this study, we designed a cross-sectional analysis using CHARLS data captured in the 2015 survey, as this wave provided contemporaneous and complete biochemical data. The study aimed to explore the association between green space exposure and arthritis and to examine the mediating roles of climate factors (relative humidity, precipitation) and physical activity intensity. After excluding participants with missing data on arthritis, residential geolocation, key covariates, or those younger than 45 years, 7,985 individuals were included in the analysis (Fig. 1 ). 2.2 Greenness exposure To assess greenness exposure, we used NDVI, a satellite-derived measure of vegetation density. NDVI is calculated as (NIR − R)/(NIR + R), where NIR is near-infrared reflectance and R is red reflectance. NDVI values range from − 1 to 1, with higher values indicating denser green vegetation[ 19 ]. NDVI data were obtained from the MODIS Terra NDVI product ( https://modis.gsfc.nasa.gov/data/dataprod/mod13.php ). Annual mean NDVI for each province was calculated from 2015 data and assigned to participants based on their residential location. 2.3 Climate factors For this study, we assembled 2015 climate data encompassing relative humidity and precipitation. Relative humidity grids were obtained from the Geospatial Remote-sensing and Ecological Network ( https://www.gisrs.cn ), whereas precipitation indices were retrieved from the National Tibetan Plateau Scientific Data Center ( https://data.tpdc.ac.cn ). Consistent with our NDVI processing protocol, annual means were computed for each variable to attenuate seasonal fluctuations. 2.4 Physical activity assessment The CHARLS database records respondents' weekly physical activities, including intensity, duration, and frequency. Activities are categorized as light, moderate, and vigorous, with corresponding MET values assigned using the IPAQ scoring system[ 20 ]. MET minutes/week are calculated as follows: Light activity: 3.3 × (minutes × days) Moderate activity: 4.0 × (minutes × days) Vigorous activity: 8.0 × (minutes × days) 2.5 Arthritis assessment Participants were defined as having physician - diagnosed arthritis if they answered “yes” to the question “Have you ever been diagnosed with arthritis or rheumatism by a doctor?” [ 21 ]. The assessment did not differentiate between subtypes such as osteoarthritis or rheumatoid arthritis, but aimed to capture the overall burden of joint-related chronic conditions in the population. 2.6 Covariates Baseline data on covariates were collected by trained interviewers using a structured questionnaire. Variable distributions are detailed in Supplementary Table S1 . Covariates included: (1) Sociodemographic characteristics : Gender, age, residence, marital status, and education. (2) Lifestyle factors : Smoking, alcohol use, cooking fuel, and household expenditure. (3) Anthropometric measurements : Recorded measurements included body mass index (BMI), waist circumference, and systolic and diastolic blood pressure (SBP and DBP). (4) Laboratory data : Triglycerides, high-density lipoprotein (HDL), fasting glucose, and C-reactive protein (CRP). 2.7 Statistical analysis We used descriptive statistics to summarize baseline characteristics (mean ± SD or median with IQR for continuous variables, counts with proportions for categorical variables) and assessed group differences with t-tests or chi-square/Fisher's exact tests. Generalized Linear Models explored the NDVI–arthritis link: Model 1 was unadjusted, Model 2 included socioeconomic and lifestyle factors, and Model 3 added anthropometric and biological indicators. Results are shown as odds ratios (OR) with confidence intervals (CI). Mediation analysis using the R package “mediation” evaluated climate factors and physical activity as potential mediators, with bootstrapping (1,000 replications) to estimate mediation effects. Stratified analyses examined NDVI effects across subgroups defined by gender, residence, marital status, education, smoking, drinking, and cooking fuel use, using fully adjusted models to account for confounders. All statistical analyses were conducted using R version 4.4.3, which is available at https://www.r-project.org/ , Two-sided p-values less than 0.05 were considered statistically significant. Results 3.1. The baseline characteristics of study participants Table 1 presents the summary statistics of the study participants. Continuous variables are shown as mean with standard deviation (SD) or median with IQR, and categorical variables are presented as counts with percentages. Our final analysis included 7,985 participants, with a mean age of 61.34 ± 9.75 years. Among these, 4117(51.56% )were female, 6453(80.88%) were currently married or living with a partner, and 4964 (62.17%) resided in rural areas. Approximately two - thirds (67.25%) of the participants had an educational level of elementary school or below. Anthropometric measurements and laboratory test results for all participants are summarized in Supplementary Table S2. Table 1 Baseline Characteristics of the Study Participant. Characteristics Total (n = 7985) Non-Arthritis (n = 4466) Arthritis (n = 3519) p Age ,mean (SD), years 61.34 (9.75) 60.55 (9.83) 62.33 (9.54) < 0.001 BMI, mean (SD), kg/m 2 24.83 (33.14) 24.39 (15.17) 25.37 (46.54) 0.24 gender < 0.001 Female 4117 (51.56) 2104 (47.11) 2013 (57.20) Male 3868 (48.44) 2362 (52.89) 1506 (42.80) Residence < 0.001 Rural 4964 (62.17) 2618 (58.62) 2346 (66.67) Urban 3021 (37.83) 1848 (41.38) 1173 (33.33) Marital_status < 0.001 Married and living with a spouse 6453 (80.88) 3703 (82.95) 2750 (78.26) Married but living without a spouse 433 ( 5.43) 237 ( 5.31) 196 ( 5.58) Single, divorced, and windowed 1092 (13.69) 524 (11.74) 568 (16.16) Education_Status < 0.001 Elementary school or below 5370 (67.25) 2743 (61.42) 2627 (74.65) Middle school or above 2615 (32.75) 1723 (38.58) 892 (25.35) NDVI, mean (IQR) 0.00 (0.76) -0.04 (0.77) 0.05 (0.75) < 0.001 Annual RH, mean mean (IQR) -0.04 (0.58) -0.08 (0.58) 0.00 (0.59) < 0.001 Annual_Precip, mean (IQR), mm 0.10 (0.57) 0.08 (0.58) 0.12 (0.54) 0.009 metabolic_equivalent, mean (IQR) 0.19 (0.70) 0.16 (0.68) 0.24 (0.73) < 0.001 SD, standard deviation; BMI, body mass index. The total percentage may not equal to 100 due to rounding. 3.2. Associations between NDVI and arthritis in generalized linear models Generalized linear models demonstrated a positive association between NDVI and arthritis. Per IQR increment in NDVI corresponded to an OR of 1.16 (95%CI: 1.10–1.23) unadjusted, 1.15 (1.05–1.26) after adjustment for sociodemographic and lifestyle factors, and 1.14 (1.02–1.27) with further control for anthropometric and biological variables. We also calculated the associations between climate factors (relative humidity, precipitation), metabolic equivalents and arthritis under three models. The results showed that these factors all had significant impacts on arthritis (Fig. 2 ). After adjusting for all covariates, per IQR increase in annual relative humidity, precipitation, and metabolic equivalents was associated with higher arthritis odds (OR = 1.45, 95%CI 1.25–1.68; OR = 1.25, 95%CI 1.08–1.45 and OR = 1.33, 95%CI:1.17–1.50; all P < 0.01). Full results of the generalized linear models are provided in Supplementary Table S3. 3.3 Mediation effects As shown in Supplementary Table S4, after adjusting for covariates, annual precipitation was confirmed as a significant mediator, accounting for 5.31% of the association between NDVI and arthritis [Average Causal Mediation Effect (ACME) = 0.002, 95% CI: 0.000–0.010, P < 0.01]. In contrast, annual relative humidity and metabolic equivalents did not exhibit statistically significant mediation effects. The mediation pathways are illustrated in Fig. 3 . 3.4 Subgroup analysis results Subgroup analyses (Supplementary Table S5) indicated that, across most strata, NDVI, relative humidity, precipitation, and metabolic equivalents were positively associated with arthritis risk. Notably, the association between vegetation increase and arthritis risk was stronger among individuals residing in rural areas, those with single, divorced, or widowed marital status, those with lower education levels, those living in the Eastern region, and those using solid fuel for cooking. Interestingly, the risk was also relatively higher among non-smokers and non-drinkers. Discussion This study is a large-scale, nationally representative cohort involving over 7,000 middle-aged and older adults in China, focusing on the relationship between green space exposure and arthritis. Through this extensive epidemiological survey, we revealed a positive correlation between NDVI (a proxy for green space exposure) and the incidence of arthritis. Fully adjusted models also showed that annual relative humidity, precipitation and physical activity intensity independently predicted arthritis; notably, precipitation emerged as significant mediators of the NDVI–arthritis association. In recent years, the impact of greenness exposure on human health has gradually attracted public attention, and its beneficial effects have been confirmed in multiple studies. However, our study found a positive correlation between NDVI and arthritis, indicating that higher levels of green space exposure are associated with an increased incidence of arthritis. Currently, there are few studies on the correlation between NDVI and arthritis. One epidemiological study[ 22 ], which included 30,684 participants from the Ningbo Yinzhou cohort in China and used a Cox proportional hazards model to assess the relationship between PM2.5, green space, and RA, found that individuals living in areas with more green space had a lower risk of RA. These differences can be explained by variations in arthritis classification, study populations, and research regions. The dependent variable of arthritis in our study was derived from the CHARLS database, which did not involve specific classification, whereas the aforementioned study only included RA. Additionally, our study focused on middle-aged and older adults aged 45 years and above, while the other study included adults aged 18 years and older. More importantly, our study covered a large part of China, whereas the prior study was limited to the Ningbo Yinzhou cohort. Another study applied a generalized linear mixed-effects model to estimate the impact of PM2.5 components, NDVI, and their interactions on arthritis and rheumatoid arthritis[ 23 ]. The results indicated that the odds ratio of arthritis for per 0.1-unit decrease in NDVI was 1.091 (95%CI:1.033–1.151). This discrepancy can be traced to design differences: this study pooled four CHARLS waves (2011–2018) and modeled NDVI in quartiles, revealing OR > 1 across all strata but declining with higher greenness—a potential threshold effect. While that work emphasized PM₂.₅ × NDVI interactions, we examined mediating pathways (relative humidity, precipitation, physical activity), underscoring the need for refined, non-linear NDVI–arthritis analyses. The overall body of evidence supports the notion that exposure to green spaces can exert beneficial effects on health, such as reducing the risk of cardiovascular diseases, mental health disorders, adverse birth outcomes, and mortality [ 24 , 25 ]. However, given that the characteristics of green spaces (such as types, structure, and tree species) vary between countries and regions, their health effects on populations may also differ. Fan et al. [ 26 ] conducted a cross-sectional study across multiple regions in China, involving 66,752 middle-aged and older adults. They found that, after adjusting for various covariates, an increase of one interquartile range in NDVI at 100 m was associated with an approximately 8% increased risk of chronic obstructive pulmonary disease (COPD). A few studies have also assessed the relationship between green space exposure and infectious diseases. Chen et al.[ 27 ] evaluated the association between over 70 environmental and socioeconomic factors and the incidence of dengue fever in Guangzhou and Foshan, finding that forest cover in Foshan significantly increased the risk of dengue fever. Additionally, Hundessa et al. [ 28 ]identified green spaces as an important determinant of the environmental suitability for Plasmodium ovale, a parasite causing malaria. Given that the transmission of infectious diseases is significantly influenced by external climatic factors (such as temperature, humidity, and air quality), and that green space exposure is associated with several climatic factors related to infectious diseases (such as lowering temperatures, increasing humidity, and providing habitats for vectors), this may facilitate the transmission of certain infectious diseases. Notably, environmental factors are closely linked to the onset and progression of arthritis. It is widely reported that climate and environment are factors associated with the etiology of rheumatoid arthritis (RA) [ 10 ]. Cold and damp conditions are climatic and environmental factors associated with increased risk [ 29 , 30 ] Low temperature, high atmospheric pressure, and high humidity are significantly correlated with pain in patients with RA [ 31 , 32 ]. A retrospective longitudinal study [ 33 ] utilized multivariate linear regression to explore the links between weather factors and rheumatoid arthritis (RA) symptoms. The findings showed that winter humidity and summer rainfall significantly correlated with the number of tender joints in RA patients. In related research, humidity is a key climatic factor. We examined annual humidity and precipitation as potential mediators in the NDVI–arthritis link. Initially, both showed significant mediation effects. However, after adjusting for covariates, only precipitation remained significant. Slight fluctuations in atmospheric pressure (AP) related to weather changes can profoundly impact human health, The influence can be attributed to both direct mechanical effects and the modulation of oxygen partial pressure.[ 34 ]. These changes may directly influence the onset and progression of arthritis, as demonstrated by studies showing that variations in barometric pressure are independently associated with the severity of knee pain in osteoarthritis patients [ 35 ]. Additionally, residing in areas with higher vegetation cover (i.e., higher NDVI values) is associated with increased levels of physical activity[ 36 – 38 ], which may pose a risk for arthritis. In this study, we used MET to represent physical activity intensity Similarly, in the unadjusted model, the mediating effect of physical activity intensity was significant; however, after adjusting for covariates, this mediating effect became statistically insignificant. There is a scarcity of both basic and clinical research on the relationship between greenness exposure and arthritis, and the underlying mechanisms for these associations remain largely unclear. Overall, the pathogenesis of arthritis is highly complex, involving the interplay of multiple factors. Recent studies have investigated the relationships between residential greenness and human microbial characteristics, including alpha-diversity, composition, and genus abundance, using data from 34 countries. The findings revealed that higher levels of residential greenness are significantly associated with increased richness in both palm and gut microbiota, while this greenness also corresponds to decreased evenness in the gut microbiota[ 39 ]. The fecal microbiome influences both innate and adaptive immunity, and its imbalance can trigger inflammatory responses and increase the risk of autoimmune diseases, leading to joint damage [ 40 , 41 ]. Moreover, a recent small-scale population study highlighted the potential impact of climatic conditions (such as air temperature and pressure) on the proportions of T-cell and B-cell subsets, which may trigger autoimmunity in rheumatoid arthritis (RA) [ 9 ]. Collectively, these studies suggest that the interplay between the gut, environment, and immune system may underlie the pathogenesis of arthritis. Our study has several strengths. Initially, we leveraged a sizable, nationally representative cohort to amass a broad spectrum of variables, encompassing demographic traits, lifestyle behaviors, health metrics, and both physiological and biochemical markers. The comprehensive nature of this data collection enhances the reliability and precision of our findings, thereby bolstering the validity of our study's conclusions. Secondly, our study represents the pioneering effort to establish a positive correlation between NDVI and the prevalence of arthritis, while also providing insights into the intermediary roles played by climatic factors, including humidity and precipitation, as well as metabolic equivalents. This investigation contributes novel perspectives to the understanding of how environmental determinants may influence the development of musculoskeletal conditions. Lastly, subgroup analyses were performed to further validate the robustness of our findings. This approach allowed us to explore potential heterogeneity across different demographic and clinical subgroups, thus strengthening the generalizability of our results. However, our study also has limitations. Firstly, the cross-sectional design of the study precludes the establishment of causality between greenness exposure and arthritis. Secondly, the use of NDVI as a proxy for greenness exposure may not fully capture the complexity of green space characteristics and human interactions with these spaces. Thirdly, the study relies on self-reported data for arthritis diagnosis, which may introduce recall bias. Finally, the study population is limited to middle-aged and older adults in China, which may limit the generalizability of the findings to other age groups or populations. Conclusions In summary, our study establishes a positive association between NDVI and arthritis incidence in a large cohort of middle-aged and older adults. Notably, the climate factor of annual precipitation partially mediates this relationship. This discovery not only enriches our comprehension of the health impacts linked to greenness exposure and climate factors, but also acts as a crucial link in pinpointing risk factors for arthritis. Abbreviations CHARLS China Health and Retirement Longitudinal Study IRB Institutional Review Board NDVI Normalized Difference Vegetation Index MODIS Moderate Resolution Imaging Spectroradiometer MET Metabolic Equivalents IPAQ International Physical Activity Questionnaire BMI Body Mass Index SBP Systolic Blood Pressure DBP Diastolic Blood Pressure HDL High-Density Lipoprotein CRP C-Reactive Protein RA Rheumatoid Arthritis COPD Chronic Obstructive Pulmonary Disease AP Atmospheric Pressure OR Odds Ratio CI Confidence Interval ACME Average Causal Mediation Effect Declarations Ethics approval and consent to participate: The data for this study were derived from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative survey of adults aged 45 and older. The study was conducted with the informed consent of all participants, who were provided with detailed information about the study objectives, procedures, potential risks and benefits, and their rights to withdraw from the study at any time without penalty. Written informed consent was obtained from each participant before the start of the study. The study received approval from the Institutional Review Board of Peking University (Code: IRB00001052-11015) and was conducted in accordance with the principles of the Declaration of Helsinki. Consent for publication: All participants provided written informed consent for the publication of anonymized data from the study. The consent form explicitly stated that the data collected would be used for research purposes and that any published results would not include any identifying information that could link the data to individual participants. The study adhered to strict confidentiality protocols to ensure the privacy and anonymity of all participants. The Institutional Review Board of Peking University (Code: IRB00001052-11015) reviewed and approved the consent form and the procedures for data publication. Competing interests: The authors declare that they have no competing interests. Funding: This work was supported by grants from the Liaoning Provincial Natural Science Fund for Distinguished Young Scholars, Science and Technology Program of Liaoning Province (Project No.: 2024-MSLH-159). The study sponsor has no role in study design, data analysis and interpretation of data, the writing of manuscript, or the decision to submit the paper for publication. Availability of data and materials: The datasets generated and analyzed during the current study are available in the China Health and Retirement Longitudinal Study (CHARLS) repository [insert specific repository link or reference here]. 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McAlindon T, Formica M, Schmid CH, Fletcher J. Changes in barometric pressure and ambient temperature influence osteoarthritis pain. Am J Med. 2007;120(5):429–34. Xia QF, Qin GY, Liu Q, Hu YZ. Green space exposure and Chinese residents' physical activity participation: empirical evidence from a health geography perspective. Front public health. 2024;12:1430706. Bancroft C, Joshi S, Rundle A, Hutson M, Chong C, Weiss CC et al. Association of proximity and density of parks and objectively measured physical activity in the United States: A systematic review. Social science & medicine (1982). 2015;138:22–30. James P, Hart JE, Banay RF, Laden F. Exposure to Greenness and Mortality in a Nationwide Prospective Cohort Study of Women. Environ Health Perspect. 2016;124(9):1344–52. Zhang YD, Fan SJ, Zhang Z, Li JX, Liu XX, Hu LX, et al. Association between Residential Greenness and Human Microbiota: Evidence from Multiple Countries. Environ Health Perspect. 2023;131(8):87010. Rodrigues GSP, Cayres LCF, Gonçalves FP, Takaoka NNC, Lengert AH, Tansini A et al. Detection of Increased Relative Expression Units of Bacteroides and Prevotella, and Decreased Clostridium leptum in Stool Samples from Brazilian Rheumatoid Arthritis Patients: A Pilot Study. Microorganisms. 2019;7(10). Opazo MC, Ortega-Rocha EM, Coronado-Arrázola I, Bonifaz LC, Boudin H, Neunlist M, et al. Intestinal Microbiota Influences Non-intestinal Related Autoimmune Diseases. Front Microbiol. 2018;9:432. Additional Declarations No competing interests reported. Supplementary Files supplementaryinformation.xlsx Cite Share Download PDF Status: Published Journal Publication published 06 Mar, 2026 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 27 Jan, 2026 Reviews received at journal 05 Jan, 2026 Reviewers agreed at journal 29 Dec, 2025 Reviews received at journal 20 Oct, 2025 Reviewers agreed at journal 06 Oct, 2025 Reviewers invited by journal 15 Sep, 2025 Editor assigned by journal 12 Sep, 2025 Submission checks completed at journal 11 Sep, 2025 First submitted to journal 11 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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13:35:35","extension":"html","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":115819,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7483957/v1/33252d819eab1f42a72637c8.html"},{"id":92180466,"identity":"9943be46-d845-4fd7-9f1d-c633478dbbd2","added_by":"auto","created_at":"2025-09-25 13:35:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":41408,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of participants selection.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7483957/v1/bebb419b44b6f33845012ece.png"},{"id":92180467,"identity":"cb1bff0b-3204-4f7f-a290-088bf7a050ed","added_by":"auto","created_at":"2025-09-25 13:35:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":57938,"visible":true,"origin":"","legend":"\u003cp\u003eOR (with 95% CIs) for arthritis associated with per IQR increase in exposure to annual relative humidity, Annual Precipitation and Metabolic equivalent. Abbreviations: OR, Odds ratios; CI, confidence interval; IQR, interquartile range; Model 1: unadjusted for any covariates. Model 2: adjusted for gender, age, BMI,residence, marital status, education level, smoking status, and drinking status, cooking fuel use. Model 3: adjusted for gender, age, BMI, residence, marital status, education level, smoking status, drinking status, cooking fuel use, waist circumference, and systolic and diastolic blood pressure, triglycerides, high-density lipoprotein (HDL), fasting blood glucose, and C-reactive protein (CRP).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7483957/v1/2fdb4460a3cbb5f4f844345c.png"},{"id":92181859,"identity":"c8e27567-c79d-4137-aaff-06ed3e33d091","added_by":"auto","created_at":"2025-09-25 13:43:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":51075,"visible":true,"origin":"","legend":"\u003cp\u003ePath diagram of mediation effects between NDVI and arthritis, Annual relative humidity, annual precipitation and metabolic equivalent as mediators. * \u003cem\u003eP-\u003c/em\u003e value \u0026lt; 0.05, **\u003cem\u003e P-\u003c/em\u003e value \u0026lt; 0.01, ***\u003cem\u003e P-\u003c/em\u003evalue \u0026lt; 0.001, 95% CI in the parentheses are shown. Model control for gender, age, BMI,residence, marital status, education level, smoking status, and drinking status, cooking fuel use.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7483957/v1/6081e51b43142c9888c8b580.png"},{"id":104250789,"identity":"af389f86-8e8e-490f-90a8-a699d44dece9","added_by":"auto","created_at":"2026-03-09 16:08:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":888892,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7483957/v1/fdc3405c-5a7a-4ff1-83ec-f8efca9d8da3.pdf"},{"id":92180468,"identity":"afaedb50-37f6-4617-90d3-51dc5228d7ec","added_by":"auto","created_at":"2025-09-25 13:35:35","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":45865,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryinformation.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7483957/v1/3f467b8d77ce167271ede105.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The association between greenness exposure and arthritis in middle-aged and older Chinese adults, mediated by climate factors","fulltext":[{"header":"Key Messages","content":"\u003cul\u003e\n \u003cli\u003eBased on CHARLS 2015 data, per IQR increase in NDVI raises arthritis risk by 14%.\u003c/li\u003e\n \u003cli\u003eMediation analysis showed that annual precipitation partially explains the association between NDVI and arthritis incidence.\u003c/li\u003e\n \u003cli\u003eRegions with higher levels of green space should consider implementing measures to prevent joint diseases.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Background","content":"\u003cp\u003eArthritis is a general term encompassing a variety of arthritic conditions, characterized by inflammation of the joints that can manifest as either acute or chronic presentations. It is predominantly marked by joint pain and stiffness, which can substantially compromise patients' physical function and quality of life[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Meanwhile, the prevalence of arthritis has reached a higher level worldwide. The incidence of arthritis increases markedly with age, particularly after 45 years. Data from 2010 to 2012 revealed that 22.7% of the US population had doctor-diagnosed arthritis, with projections suggesting a 49% increase in prevalence by 2040[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Similarly, in China, the overall prevalence of arthritis among middle-aged and senior individuals grew from 31.4% to 44.7% between 2011 and 2018[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Furthermore, Research has shown that a substantial rise in medical expenditures has failed to enhance the health-related quality of life for individuals suffering from arthritis[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Considering the medical context, the importance of identifying relevant risk factors to prevent the onset of arthritis cannot be overstated.\u003c/p\u003e\u003cp\u003eThe etiology and pathogenesis of arthritis are highly complex and involve multiple factors. To date, genetics and environmental factors are among the key influences on the onset and progression of the condition [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In recent years, most relevant studies have concentrated on environmental pollution. Research has indicated that smoking and elevated levels of air pollution are significant risk factors for the development of arthritis[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Their primary pathogenic mechanisms may involve negatively altering the immune response. Furthermore, studies have shown that weather and seasonal changes can mitigate disease severity [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In clinical practice, many patients with rheumatoid arthritis (RA) report that their joint symptoms fluctuate with changes in climatic factors, such as temperature, humidity, and atmospheric pressure. However, strong scientific evidence to support this assumption is lacking[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAs global urbanization continues to rise, and the adverse effects of long-term exposure to urban environmental risks have garnered significant attention, prompting extensive research into the potential health outcomes associated with exposure to green spaces. The most common marker for greenness exposure was NDVI[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Currently, numerous aspects of greenness are beneficial. For instance, exposure to greenness has beneficial effects in most studies regarding respiratory mortality, lung cancer incidence, respiratory hospitalisations and pulmonary function[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Similarly, greenness has been shown to improve air quality and enhance green spaces, thereby promoting visual health in aging populations[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. While some aspects may be harmful, Studies have also emphasized greenness may have different health effects in different population subgroups[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Emerging evidence suggests a synergistic interaction between ambient greenness and climatic variables[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], coupled with consistent observations that greater residential greenness promotes higher levels of physical activity [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Concomitantly, both meteorological parameters and the intensity of physical activity have been causally implicated in the pathogenesis and progression of arthritis [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Synthesising these converging lines of evidence, this study conducted a cross-sectional analysis focusing on the relationship between green environment exposure and arthritis among middle-aged and elderly individuals in China. Furthermore, the study examined the mediating roles of climate factors (relative humidity, precipitation) and physical activity intensity (metabolic equivalents) in the association between green environment exposure and arthritis, thereby providing robust support for etiological research into the disease.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study population\u003c/h2\u003e\u003cp\u003eData for this study were derived from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative survey of adults aged 45 and older. The study was conducted with the informed consent of all participants and received approval from the Institutional Review Board of Peking University (Code: IRB00001052-11015). The baseline survey utilized a multi-stage, stratified, probability-proportional-to-size sampling design, with further details on recruitment and sampling methods provided in accordance with the study protocol [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. A total of 17,708 participants from 10,257 households across 28 provinces were recruited between 2011 and 2012, with subsequent follow-ups conducted in 2013, 2015, and 2018.\u003c/p\u003e\u003cp\u003eFor this study, we designed a cross-sectional analysis using CHARLS data captured in the 2015 survey, as this wave provided contemporaneous and complete biochemical data. The study aimed to explore the association between green space exposure and arthritis and to examine the mediating roles of climate factors (relative humidity, precipitation) and physical activity intensity. After excluding participants with missing data on arthritis, residential geolocation, key covariates, or those younger than 45 years, 7,985 individuals were included in the analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Greenness exposure\u003c/h2\u003e\u003cp\u003eTo assess greenness exposure, we used NDVI, a satellite-derived measure of vegetation density. NDVI is calculated as (NIR\u0026thinsp;\u0026minus;\u0026thinsp;R)/(NIR\u0026thinsp;+\u0026thinsp;R), where NIR is near-infrared reflectance and R is red reflectance. NDVI values range from \u0026minus;\u0026thinsp;1 to 1, with higher values indicating denser green vegetation[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. NDVI data were obtained from the MODIS Terra NDVI product (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://modis.gsfc.nasa.gov/data/dataprod/mod13.php\u003c/span\u003e\u003cspan address=\"https://modis.gsfc.nasa.gov/data/dataprod/mod13.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Annual mean NDVI for each province was calculated from 2015 data and assigned to participants based on their residential location.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Climate factors\u003c/h2\u003e\u003cp\u003e For this study, we assembled 2015 climate data encompassing relative humidity and precipitation. Relative humidity grids were obtained from the Geospatial Remote-sensing and Ecological Network (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gisrs.cn\u003c/span\u003e\u003cspan address=\"https://www.gisrs.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), whereas precipitation indices were retrieved from the National Tibetan Plateau Scientific Data Center (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.tpdc.ac.cn\u003c/span\u003e\u003cspan address=\"https://data.tpdc.ac.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Consistent with our NDVI processing protocol, annual means were computed for each variable to attenuate seasonal fluctuations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Physical activity assessment\u003c/h2\u003e\u003cp\u003eThe CHARLS database records respondents' weekly physical activities, including intensity, duration, and frequency. Activities are categorized as light, moderate, and vigorous, with corresponding MET values assigned using the IPAQ scoring system[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. MET minutes/week are calculated as follows:\u003c/p\u003e\u003cp\u003eLight activity: 3.3 \u0026times; (minutes \u0026times; days)\u003c/p\u003e\u003cp\u003eModerate activity: 4.0 \u0026times; (minutes \u0026times; days)\u003c/p\u003e\u003cp\u003eVigorous activity: 8.0 \u0026times; (minutes \u0026times; days)\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Arthritis assessment\u003c/h2\u003e\u003cp\u003eParticipants were defined as having physician - diagnosed arthritis if they answered \u0026ldquo;yes\u0026rdquo; to the question \u0026ldquo;Have you ever been diagnosed with arthritis or rheumatism by a doctor?\u0026rdquo; [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The assessment did not differentiate between subtypes such as osteoarthritis or rheumatoid arthritis, but aimed to capture the overall burden of joint-related chronic conditions in the population.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Covariates\u003c/h2\u003e\u003cp\u003eBaseline data on covariates were collected by trained interviewers using a structured questionnaire. Variable distributions are detailed in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Covariates included:\u003c/p\u003e\u003cp\u003e(1) \u003cb\u003eSociodemographic characteristics\u003c/b\u003e: Gender, age, residence, marital status, and education.\u003c/p\u003e\u003cp\u003e(2) \u003cb\u003eLifestyle factors\u003c/b\u003e: Smoking, alcohol use, cooking fuel, and household expenditure.\u003c/p\u003e\u003cp\u003e(3) \u003cb\u003eAnthropometric measurements\u003c/b\u003e: Recorded measurements included body mass index (BMI), waist circumference, and systolic and diastolic blood pressure (SBP and DBP).\u003c/p\u003e\u003cp\u003e(4) \u003cb\u003eLaboratory data\u003c/b\u003e: Triglycerides, high-density lipoprotein (HDL), fasting glucose, and C-reactive protein (CRP).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Statistical analysis\u003c/h2\u003e\u003cp\u003eWe used descriptive statistics to summarize baseline characteristics (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median with IQR for continuous variables, counts with proportions for categorical variables) and assessed group differences with t-tests or chi-square/Fisher's exact tests. Generalized Linear Models explored the NDVI\u0026ndash;arthritis link: Model 1 was unadjusted, Model 2 included socioeconomic and lifestyle factors, and Model 3 added anthropometric and biological indicators. Results are shown as odds ratios (OR) with confidence intervals (CI). Mediation analysis using the R package \u0026ldquo;mediation\u0026rdquo; evaluated climate factors and physical activity as potential mediators, with bootstrapping (1,000 replications) to estimate mediation effects. Stratified analyses examined NDVI effects across subgroups defined by gender, residence, marital status, education, smoking, drinking, and cooking fuel use, using fully adjusted models to account for confounders.\u003c/p\u003e\u003cp\u003eAll statistical analyses were conducted using R version 4.4.3, which is available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, Two-sided p-values less than 0.05 were considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.1. The baseline characteristics of study participants\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the summary statistics of the study participants. Continuous variables are shown as mean with standard deviation (SD) or median with IQR, and categorical variables are presented as counts with percentages. Our final analysis included 7,985 participants, with a mean age of 61.34\u0026thinsp;\u0026plusmn;\u0026thinsp;9.75 years. Among these, 4117(51.56% )were female, 6453(80.88%) were currently married or living with a partner, and 4964 (62.17%) resided in rural areas. Approximately two - thirds (67.25%) of the participants had an educational level of elementary school or below. Anthropometric measurements and laboratory test results for all participants are summarized in Supplementary Table S2.\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\u003eBaseline Characteristics of the Study Participant.\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\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;7985)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-Arthritis\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;4466)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eArthritis\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3519)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge ,mean (SD), years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e61.34 (9.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60.55 (9.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e62.33 (9.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI, mean (SD), kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24.83 (33.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24.39 (15.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25.37 (46.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003egender\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\u003e\u0026lt;\u0026thinsp;0.001\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\u003e4117 (51.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2104 (47.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2013 (57.20)\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\u003e3868 (48.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2362 (52.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1506 (42.80)\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\u003eResidence\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\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4964 (62.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2618 (58.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2346 (66.67)\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\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3021 (37.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1848 (41.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1173 (33.33)\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\u003eMarital_status\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\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried and living with a spouse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6453 (80.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3703 (82.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2750 (78.26)\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\u003eMarried but living without a spouse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e433 ( 5.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e237 ( 5.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e196 ( 5.58)\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\u003eSingle, divorced, and windowed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1092 (13.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e524 (11.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e568 (16.16)\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\u003eEducation_Status\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\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElementary school or below\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5370 (67.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2743 (61.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2627 (74.65)\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\u003eMiddle school or above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2615 (32.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1723 (38.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e892 (25.35)\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\u003eNDVI, mean (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.00 (0.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.04 (0.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.05 (0.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnnual RH, mean mean (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.04 (0.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.08 (0.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00 (0.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnnual_Precip, mean (IQR), mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.10 (0.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.08 (0.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.12 (0.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emetabolic_equivalent, mean (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.19 (0.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.16 (0.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.24 (0.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eSD, standard deviation; BMI, body mass index. The total percentage may not equal to 100 due to rounding.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Associations between NDVI and arthritis in generalized linear models\u003c/h2\u003e\u003cp\u003eGeneralized linear models demonstrated a positive association between NDVI and arthritis. Per IQR increment in NDVI corresponded to an OR of 1.16 (95%CI: 1.10\u0026ndash;1.23) unadjusted, 1.15 (1.05\u0026ndash;1.26) after adjustment for sociodemographic and lifestyle factors, and 1.14 (1.02\u0026ndash;1.27) with further control for anthropometric and biological variables.\u003c/p\u003e\u003cp\u003eWe also calculated the associations between climate factors (relative humidity, precipitation), metabolic equivalents and arthritis under three models. The results showed that these factors all had significant impacts on arthritis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). After adjusting for all covariates, per IQR increase in annual relative humidity, precipitation, and metabolic equivalents was associated with higher arthritis odds (OR\u0026thinsp;=\u0026thinsp;1.45, 95%CI 1.25\u0026ndash;1.68; OR\u0026thinsp;=\u0026thinsp;1.25, 95%CI 1.08\u0026ndash;1.45 and OR\u0026thinsp;=\u0026thinsp;1.33, 95%CI:1.17\u0026ndash;1.50; all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Full results of the generalized linear models are provided in Supplementary Table S3.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Mediation effects\u003c/h2\u003e\u003cp\u003eAs shown in Supplementary Table S4, after adjusting for covariates, annual precipitation was confirmed as a significant mediator, accounting for 5.31% of the association between NDVI and arthritis [Average Causal Mediation Effect (ACME)\u0026thinsp;=\u0026thinsp;0.002, 95% CI: 0.000\u0026ndash;0.010, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01]. In contrast, annual relative humidity and metabolic equivalents did not exhibit statistically significant mediation effects. The mediation pathways are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Subgroup analysis results\u003c/h2\u003e\u003cp\u003eSubgroup analyses (Supplementary Table S5) indicated that, across most strata, NDVI, relative humidity, precipitation, and metabolic equivalents were positively associated with arthritis risk. Notably, the association between vegetation increase and arthritis risk was stronger among individuals residing in rural areas, those with single, divorced, or widowed marital status, those with lower education levels, those living in the Eastern region, and those using solid fuel for cooking. Interestingly, the risk was also relatively higher among non-smokers and non-drinkers.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study is a large-scale, nationally representative cohort involving over 7,000 middle-aged and older adults in China, focusing on the relationship between green space exposure and arthritis. Through this extensive epidemiological survey, we revealed a positive correlation between NDVI (a proxy for green space exposure) and the incidence of arthritis. Fully adjusted models also showed that annual relative humidity, precipitation and physical activity intensity independently predicted arthritis; notably, precipitation emerged as significant mediators of the NDVI\u0026ndash;arthritis association.\u003c/p\u003e\u003cp\u003eIn recent years, the impact of greenness exposure on human health has gradually attracted public attention, and its beneficial effects have been confirmed in multiple studies. However, our study found a positive correlation between NDVI and arthritis, indicating that higher levels of green space exposure are associated with an increased incidence of arthritis. Currently, there are few studies on the correlation between NDVI and arthritis. One epidemiological study[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], which included 30,684 participants from the Ningbo Yinzhou cohort in China and used a Cox proportional hazards model to assess the relationship between PM2.5, green space, and RA, found that individuals living in areas with more green space had a lower risk of RA. These differences can be explained by variations in arthritis classification, study populations, and research regions. The dependent variable of arthritis in our study was derived from the CHARLS database, which did not involve specific classification, whereas the aforementioned study only included RA. Additionally, our study focused on middle-aged and older adults aged 45 years and above, while the other study included adults aged 18 years and older. More importantly, our study covered a large part of China, whereas the prior study was limited to the Ningbo Yinzhou cohort.\u003c/p\u003e\u003cp\u003eAnother study applied a generalized linear mixed-effects model to estimate the impact of PM2.5 components, NDVI, and their interactions on arthritis and rheumatoid arthritis[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The results indicated that the odds ratio of arthritis for per 0.1-unit decrease in NDVI was 1.091 (95%CI:1.033\u0026ndash;1.151). This discrepancy can be traced to design differences: this study pooled four CHARLS waves (2011\u0026ndash;2018) and modeled NDVI in quartiles, revealing OR\u0026thinsp;\u0026gt;\u0026thinsp;1 across all strata but declining with higher greenness\u0026mdash;a potential threshold effect. While that work emphasized PM₂.₅ \u0026times; NDVI interactions, we examined mediating pathways (relative humidity, precipitation, physical activity), underscoring the need for refined, non-linear NDVI\u0026ndash;arthritis analyses.\u003c/p\u003e\u003cp\u003eThe overall body of evidence supports the notion that exposure to green spaces can exert beneficial effects on health, such as reducing the risk of cardiovascular diseases, mental health disorders, adverse birth outcomes, and mortality [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, given that the characteristics of green spaces (such as types, structure, and tree species) vary between countries and regions, their health effects on populations may also differ. Fan et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] conducted a cross-sectional study across multiple regions in China, involving 66,752 middle-aged and older adults. They found that, after adjusting for various covariates, an increase of one interquartile range in NDVI at 100 m was associated with an approximately 8% increased risk of chronic obstructive pulmonary disease (COPD). A few studies have also assessed the relationship between green space exposure and infectious diseases. Chen et al.[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] evaluated the association between over 70 environmental and socioeconomic factors and the incidence of dengue fever in Guangzhou and Foshan, finding that forest cover in Foshan significantly increased the risk of dengue fever. Additionally, Hundessa et al. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]identified green spaces as an important determinant of the environmental suitability for Plasmodium ovale, a parasite causing malaria. Given that the transmission of infectious diseases is significantly influenced by external climatic factors (such as temperature, humidity, and air quality), and that green space exposure is associated with several climatic factors related to infectious diseases (such as lowering temperatures, increasing humidity, and providing habitats for vectors), this may facilitate the transmission of certain infectious diseases.\u003c/p\u003e\u003cp\u003eNotably, environmental factors are closely linked to the onset and progression of arthritis. It is widely reported that climate and environment are factors associated with the etiology of rheumatoid arthritis (RA) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Cold and damp conditions are climatic and environmental factors associated with increased risk [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] Low temperature, high atmospheric pressure, and high humidity are significantly correlated with pain in patients with RA [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. A retrospective longitudinal study [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] utilized multivariate linear regression to explore the links between weather factors and rheumatoid arthritis (RA) symptoms. The findings showed that winter humidity and summer rainfall significantly correlated with the number of tender joints in RA patients. In related research, humidity is a key climatic factor. We examined annual humidity and precipitation as potential mediators in the NDVI\u0026ndash;arthritis link. Initially, both showed significant mediation effects. However, after adjusting for covariates, only precipitation remained significant. Slight fluctuations in atmospheric pressure (AP) related to weather changes can profoundly impact human health, The influence can be attributed to both direct mechanical effects and the modulation of oxygen partial pressure.[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. These changes may directly influence the onset and progression of arthritis, as demonstrated by studies showing that variations in barometric pressure are independently associated with the severity of knee pain in osteoarthritis patients [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAdditionally, residing in areas with higher vegetation cover (i.e., higher NDVI values) is associated with increased levels of physical activity[\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], which may pose a risk for arthritis. In this study, we used MET to represent physical activity intensity Similarly, in the unadjusted model, the mediating effect of physical activity intensity was significant; however, after adjusting for covariates, this mediating effect became statistically insignificant.\u003c/p\u003e\u003cp\u003eThere is a scarcity of both basic and clinical research on the relationship between greenness exposure and arthritis, and the underlying mechanisms for these associations remain largely unclear. Overall, the pathogenesis of arthritis is highly complex, involving the interplay of multiple factors. Recent studies have investigated the relationships between residential greenness and human microbial characteristics, including alpha-diversity, composition, and genus abundance, using data from 34 countries. The findings revealed that higher levels of residential greenness are significantly associated with increased richness in both palm and gut microbiota, while this greenness also corresponds to decreased evenness in the gut microbiota[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The fecal microbiome influences both innate and adaptive immunity, and its imbalance can trigger inflammatory responses and increase the risk of autoimmune diseases, leading to joint damage [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Moreover, a recent small-scale population study highlighted the potential impact of climatic conditions (such as air temperature and pressure) on the proportions of T-cell and B-cell subsets, which may trigger autoimmunity in rheumatoid arthritis (RA) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Collectively, these studies suggest that the interplay between the gut, environment, and immune system may underlie the pathogenesis of arthritis.\u003c/p\u003e\u003cp\u003eOur study has several strengths. Initially, we leveraged a sizable, nationally representative cohort to amass a broad spectrum of variables, encompassing demographic traits, lifestyle behaviors, health metrics, and both physiological and biochemical markers. The comprehensive nature of this data collection enhances the reliability and precision of our findings, thereby bolstering the validity of our study's conclusions. Secondly, our study represents the pioneering effort to establish a positive correlation between NDVI and the prevalence of arthritis, while also providing insights into the intermediary roles played by climatic factors, including humidity and precipitation, as well as metabolic equivalents. This investigation contributes novel perspectives to the understanding of how environmental determinants may influence the development of musculoskeletal conditions. Lastly, subgroup analyses were performed to further validate the robustness of our findings. This approach allowed us to explore potential heterogeneity across different demographic and clinical subgroups, thus strengthening the generalizability of our results.\u003c/p\u003e\u003cp\u003eHowever, our study also has limitations. Firstly, the cross-sectional design of the study precludes the establishment of causality between greenness exposure and arthritis. Secondly, the use of NDVI as a proxy for greenness exposure may not fully capture the complexity of green space characteristics and human interactions with these spaces. Thirdly, the study relies on self-reported data for arthritis diagnosis, which may introduce recall bias. Finally, the study population is limited to middle-aged and older adults in China, which may limit the generalizability of the findings to other age groups or populations.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, our study establishes a positive association between NDVI and arthritis incidence in a large cohort of middle-aged and older adults. Notably, the climate factor of annual precipitation partially mediates this relationship. This discovery not only enriches our comprehension of the health impacts linked to greenness exposure and climate factors, but also acts as a crucial link in pinpointing risk factors for arthritis.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCHARLS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eChina Health and Retirement Longitudinal Study\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eIRB\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInstitutional Review Board\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eNDVI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNormalized Difference Vegetation Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eMODIS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eModerate Resolution Imaging Spectroradiometer\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eMET\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMetabolic Equivalents\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eIPAQ\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInternational Physical Activity Questionnaire\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBody Mass Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eSBP\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSystolic Blood Pressure\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eDBP\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDiastolic Blood Pressure\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eHDL\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHigh-Density Lipoprotein\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCRP\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eC-Reactive Protein\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eRA\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRheumatoid Arthritis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCOPD\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eChronic Obstructive Pulmonary Disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eAP\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAtmospheric Pressure\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOdds Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eConfidence Interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eACME\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAverage Causal Mediation Effect\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate:\u0026nbsp;The data for this study were derived from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative survey of adults aged 45 and older. The study was conducted with the informed consent of all participants, who were provided with detailed information about the study objectives, procedures, potential risks and benefits, and their rights to withdraw from the study at any time without penalty. Written informed consent was obtained from each participant before the start of the study. The study received approval from the Institutional Review Board of Peking University (Code: IRB00001052-11015) and was conducted in accordance with the principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003eConsent for publication:\u0026nbsp;All participants provided written informed consent for the publication of anonymized data from the study. The consent form explicitly stated that the data collected would be used for research purposes and that any published results would not include any identifying information that could link the data to individual participants. The study adhered to strict confidentiality protocols to ensure the privacy and anonymity of all participants. The Institutional Review Board of Peking University (Code: IRB00001052-11015) reviewed and approved the consent form and the procedures for data publication.\u003c/p\u003e\n\u003cp\u003eCompeting interests:\u0026nbsp;The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding:\u0026nbsp;This work was supported by grants from the Liaoning Provincial Natural Science Fund for Distinguished Young Scholars, Science and Technology Program of Liaoning Province (Project No.: 2024-MSLH-159). The study sponsor has no role in study design, data analysis and interpretation of data, the writing of manuscript, or the decision to submit the paper for publication.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials: The datasets generated and analyzed during the current study are available in the China Health and Retirement Longitudinal Study (CHARLS) repository [insert specific repository link or reference here]. Access to the data can be requested through the official CHARLS website following their data access policy. The authors declare that all data supporting the findings of this study are available upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSafiri S, Kolahi AA, Cross M, Hill C, Smith E, Carson-Chahhoud K et al. Prevalence, Deaths, and Disability-Adjusted Life Years Due to Musculoskeletal Disorders for 195 Countries and Territories 1990\u0026ndash;2017. Arthritis \u0026amp; rheumatology (Hoboken, NJ). 2021;73(4):702\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYan H, Guo J, Zhou W, Dong C, Liu J. Health-related quality of life in osteoarthritis patients: a systematic review and meta-analysis. Psychol health Med. 2022;27(8):1859\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHootman JM, Helmick CG, Barbour KE, Theis KA, Boring MA. Updated Projected Prevalence of Self-Reported Doctor-Diagnosed Arthritis and Arthritis-Attributable Activity Limitation Among US Adults, 2015\u0026ndash;2040. Arthritis \u0026amp; rheumatology (Hoboken, NJ). 2016;68(7):1582\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHe D, Fan Y, Qiao Y, Liu S, Zheng X, Zhu J. Depressive symptom trajectories and new-onset arthritis in a middle-aged and elderly Chinese population. J Psychosom Res. 2023;172:111422.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJin X, Liang W, Zhang L, Cao S, Yang L, Xie F. Economic and Humanistic Burden of Osteoarthritis: An Updated Systematic Review of Large Sample Studies. PharmacoEconomics. 2023;41(11):1453\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePetrovsk\u0026aacute; N, Prajzlerov\u0026aacute; K, Vencovsk\u0026yacute; J, Šenolt L, Filkov\u0026aacute; M. The pre-clinical phase of rheumatoid arthritis: From risk factors to prevention of arthritis. Autoimmun rev. 2021;20(5):102797.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDeane KD, Demoruelle MK, Kelmenson LB, Kuhn KA, Norris JM, Holers VM. Genetic and environmental risk factors for rheumatoid arthritis. Best Pract Res Clin Rheumatol. 2017;31(1):3\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRuzzon F, Adami G. Environment and arthritis. 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Front Microbiol. 2018;9:432.\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":"NDVI, Arthritis, CHARLS, Mediation analysis","lastPublishedDoi":"10.21203/rs.3.rs-7483957/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7483957/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eArthritis is a common health issue among middle-aged and older adults, significantly impacting their quality of life. While previous studies have explored various risk factors for arthritis, the relationship between green space exposure and arthritis risk remains underexplored. This study aims to investigate the correlation between green space exposure, as measured by the Normalized Difference Vegetation Index (NDVI), and arthritis risk among middle-aged and older adults in China using a cross-sectional approach.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eData for the present study were extracted from the 2015 wave of the China Health and Retirement Longitudinal Study (CHARLS), focusing specifically on middle-aged and older adults aged 45 years and above. Greenness exposure was quantified using the NDVI. Generalized linear models were used to assess the association between NDVI and arthritis. Climatic variables (relative humidity, precipitation) and metabolic equivalents were evaluated as correlates and potential mediators of this relationship.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe study included a total of 7,985 participants, of whom 3,519 had arthritis and 4,466 did not. In the fully adjusted model, NDVI showed a positive correlation with arthritis. Specifically, the odds ratio (OR) of arthritis for each interquartile range (IQR) increase in NDVI was 1.14 (95% CI: 1.02\u0026ndash;1.27). Additionally, annual precipitation, annual relative humidity, and metabolic equivalents all showed positive associations with arthritis incidence. Further mediation analysis indicated that annual precipitation significantly mediated the relationship between NDVI and arthritis, with a proportion mediated of 5.31%.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eIncreased NDVI is tied to a higher risk of arthritis, with climate factor (annual precipitation) partly mediating this relationship. Areas with higher levels of greenery should be considered for the prevention of joint diseases.\u003c/p\u003e\u003ch2\u003eTrial registration:\u003c/h2\u003e\u003cp\u003e The study was approved by the Institutional Review Board of Peking University (Code: IRB00001052-11015) and conducted in accordance with the Declaration of Helsinki, with written informed consent obtained from all participants.\u003c/p\u003e","manuscriptTitle":"The association between greenness exposure and arthritis in middle-aged and older Chinese adults, mediated by climate factors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-25 13:35:31","doi":"10.21203/rs.3.rs-7483957/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-27T07:33:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-05T10:47:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"88470825483991666758519706273665145763","date":"2025-12-29T17:47:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-20T17:04:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"149559790155004198435763374631998852397","date":"2025-10-06T17:17:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-15T14:54:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-12T06:21:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-12T02:25:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-09-12T02:22:25+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":"590024b0-3402-4ba4-8859-26bdab9cd0bb","owner":[],"postedDate":"September 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-09T16:04:37+00:00","versionOfRecord":{"articleIdentity":"rs-7483957","link":"https://doi.org/10.1186/s12889-026-26813-7","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2026-03-06 15:58:21","publishedOnDateReadable":"March 6th, 2026"},"versionCreatedAt":"2025-09-25 13:35:31","video":"","vorDoi":"10.1186/s12889-026-26813-7","vorDoiUrl":"https://doi.org/10.1186/s12889-026-26813-7","workflowStages":[]},"version":"v1","identity":"rs-7483957","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7483957","identity":"rs-7483957","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}