The Protective Role of Residential Greenness on Diabetes Risk and Insulin Sensitivity: Results from a Nationwide Cohort Studies

The Protective Role of Residential Greenness on Diabetes Risk and Insulin Sensitivity: Results from a Nationwide Cohort Studies | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Protective Role of Residential Greenness on Diabetes Risk and Insulin Sensitivity: Results from a Nationwide Cohort Studies Ye-xin Chen, Mao-xuan Lin, Bo Zhang, Han-zhang Hong, Yi-yu Dong, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7481579/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 20 You are reading this latest preprint version Abstract Objective: This study aimed to systematically evaluate the protective effects of residential greenness on diabetes risk and insulin sensitivity in a broad population. Methods: Normalized Difference Vegetation Index (NDVI) was used to assess the level of residential greenness, while estimated Glucose Disposal Rate (eGDR) was employed to assess insulin sensitivity. Based on the nationwide cohort data from the China Health and Retirement Longitudinal Study (CHARLS), multistage statistical analysis methods were applied. First, multivariable logistic regression and restricted cubic splines (RCS) were used to evaluate the association between NDVI and baseline diabetes risk. Generalized additive models (GAM) were utilized to reveal the non-linear relationship and threshold effects between NDVI and baseline eGDR in the general population. A linear mixed-effects model was implemented to analyze the dynamic impact of NDVI on longitudinal changes in eGDR. Results: The findings indicated a significant negative association between residential greenness and diabetes risk. For every 1-unit increase in NDVI, diabetes risk decreased by 64% (OR=0.36, 95% CI: 0.20-0.66, p<0.001). Non-linear analysis revealed a clear threshold for the protective effect. When NDVI = 0.318, diabetes risk significantly decreased. When NDVI = 0.348, eGDR levels showed a significant positive increase. Longitudinal tracking further confirmed that increases in NDVI significantly promoted the annual improvement rate of eGDR, with more pronounced improvements in highly greened areas. Conclusion: This study is the first to quantify the dual protective effects of residential greenness on glucose metabolism health in a national cohort, revealing non-linear threshold characteristics. These findings provide important scientific evidence for optimizing diabetes prevention and control strategies through urban greening. China Health and Retirement Longitudinal Study (CHARLS) estimated Glucose Disposal Rate (eGDR) Normalized Difference Vegetation Index (NDVI) Residential Greenness diabetes Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Diabetes, as a global chronic metabolic disease, poses a significant public health challenge due to its escalating burden. In 2021, the global prevalence of diabetes among individuals aged 20 to 79 reached 537 million, and it is projected to rise to 783 million by 2045, with direct medical costs amounting to $ 966 billion, resulting in substantial economic and social burdens [ 1 – 2 ]. Beyond conventional pharmacological interventions, growing evidence highlights the critical role of modifiable lifestyle and environmental factors in diabetes prevention and management. As a key component of the exposome, residential environments are increasingly recognized as complementary targets for mitigating metabolic disease progression. Residential greenness, as a key strategy to mitigate heat-related health risks, has shown increasing evidence of its protective effects [ 3 ]. Residential greenness can lower surface temperatures through transpiration, reduce the urban heat island effect, and absorb air pollutants. Previous studies have shown that increased greenness reduces the risks of various diseases, including depression, chronic obstructive pulmonary disease, and asthma [ 4 – 7 ]. Some studies also suggested that exposure to greenness might serve as a protective factor in the development of diabetes [ 8 – 10 ]. While existing studies in certain regions of China have partially revealed the association between residential greenness and diabetes risk [ 11 – 12 ], there is a lack of systematic nationwide research. More importantly, the potential protective effects of residential greenness on glucose metabolism abnormalities in a broader population remain undiscovered [ 13 – 14 ]. This study, based on the China Health and Retirement Longitudinal Study (CHARLS), aimed to investigate the protective effects of the Normalized Difference Vegetation Index (NDVI) on diabetes risk and its impact on insulin resistance indicators (estimated Glucose Disposal Rate, eGDR) in a wide population. Employing linear, nonlinear relationships and linear mixed-effects models, this study comprehensively examined the association between residential greenness exposure and glucose metabolism indicators. It also focused on the immediate effects and long-term trajectories of this association, providing a multi-dimensional evidence chain for the environmental exposure and metabolic regulation mechanisms. The findings not only offered scientific evidence for the development of integrated strategies for climate change adaptation and chronic disease prevention, but also provided important references for urban planning and public health interventions. 2 Method 2.1 Data source and study population The participants in the cross-sectional study were drawn from the CHARLS, a national cohort study covering approximately 150 county-level cities across 28 provinces in China. China is one of the countries with the highest diabetes burden globally. At the same time, the vast geographical area, diverse climate zones, and significant urban-rural differences in greenness levels make it a unique context. The CHARLS provides a reliable source of data to explore the impact of environmental heterogeneity on glucose metabolism health. In the CHARLS, all surveyors underwent systematic and professional training and conducted face-to-face interviews using standardized questionnaires. The CHARLS adheres to the principles of the Helsinki Declaration and was approved by the Institutional Review Board of Peking University (IRB00001052-11015). Written informed consent was obtained from all participants prior to their involvement [ 15 ]. In this study, an analytical group was constructed based on the CHARLS 2011 to 2015 data (initial sample size n = 11,847) by a phased exclusion approach. In the first phase, when exploring the association between NDVI and the diabetes prevalence in 2011, the following individuals were excluded: (1) those without diabetes diagnosis information in 2011; (2) those with missing residential greenness exposure data for 2011. Ultimately, n = 11,652 participants were included. In the second phase, when investigating the association between NDVI and eGDR in 2011, further exclusions were made from the first-phase cohort: (1) individuals on diabetes medication treatment; (2) those with missing parameters required for eGDR calculation. The final sample size for this analysis was n = 6,396; In the third phase, by tracking the 2015 data, the association between the level of residential greenness and the progression of eGDR from 2011 to 2015 was further analyzed. The following individuals were excluded: (1) individuals on diabetes medication treatment from 2011 to 2015; (2) those with missing parameters required for eGDR calculation and NDVI for 2011 and 2015. In this phase, n = 3988 participants were included. 2.2 Definition of variables In the CHARLS study, the NDVI was employed as the independent variable. NDVI is considered an effective measure of urban greenness and can accurately assess the level of vegetation in residential areas. The NDVI were sourced from the MOD13Q1 Version 5 dataset of the MODIS Land Product provided by NASA, which offers 16 day composite global vegetation index data with a spatial resolution of 250 meters by 250 meters. For each participant, the annual average NDVI value of their residing city for the corresponding year was employed for analysis. NDVI values range from − 1 to 1, with higher values indicating greater vegetation density, where negative values correspond to water bodies or snow/ice cover, and zero indicates bare ground with no vegetation. Diabetes and eGDR were respectively used as dependent variables in the study. The diagnosis of diabetes was based on a combination of a questionnaire and diagnostic criteria. Participants were diagnosed with diabetes if they met any of the following criteria: (1) fasting blood glucose ≥ 126 mg/dL; (2) glycated hemoglobin (HbA1c) ≥ 6.5%; (3) answering ‘Yes’ to the question ‘Take any meds for diabetes’ in the questionnaire; (4) answering ‘Yes’ to the question ‘Ever had diabetes’ in the questionnaire. The formula for calculating eGDR is as follows ( Eq. 1 ) [ 16 ]. Waist circumference was measured by asking participants to stand and using a tape measure at the level of the navel. Hypertension was defined as a self-reported physician diagnosis. HbA1c was measured using venous blood samples obtained after overnight fasting, analyzed by boronate affinity liquid chromatography. Eq1 eGDR = 21.158 − 0.09 × WC cm − 3.407 × hypertension (yes = 1/no = 0) − 0.551 × HbA1c % . The covariates include age and gender as demographic characteristicsand. And marital status (‘Single/divorced/widowed’ or ‘Married’), educational level (‘Primary school or below,’ ‘Middle school,’ or ‘High school or above’), residential area (‘Rural,’ ‘Urban’), regional GDP, and annual household expenditure were considered as socioeconomic characteristics. The GDP data for different cities were obtained from the website of the National Bureau of Statistics of China. There are natural environmental and economic development differences between northern/southern and eastern/western regions. Including the GDP as a variable minimized the potential confounding effect of socioeconomic development on the outcomes. Lifestyle factors recorded whether the participant currently consumes alcohol (‘Yes’ or ‘No’) and smoking status (‘Yes’ or ‘No’). Additionally, information on whether participants have hypertension or heart problem was included. Missing values for the independent and dependent variables were excluded, while missing data for covariates were imputed using multiple imputation (KNN, 10) to ensure completeness. 2.3 Statistical analyses The baseline analysis of CHARLS included the mean (standard deviation) for continuous variables and the frequencies and percentages for categorical variables. Group comparisons for continuous variables were performed using Welch’s t-test or ANOVA. For group comparisons of categorical data, the Fisher exact test was used when expected frequencies were < 5; otherwise, the Chi-squared test was applied. A multi-stage statistical analysis method was implemented to systematically explore the complex associations between NDVI and the risk of diabetes, as well as the insulin resistance surrogate index (eGDR). A multivariable logistic regression model was first employed to analyze the relationship between NDVI and the risk of diabetes. Three progressively adjusted models were constructed: Model 1 did not adjust for any covariates; Model 2 adjusted for demographic and socioeconomic factors (age, gender, education level, marital status, residential area, regional GDP, annual household expenditure); Model 3 further included health behaviors and medical history variables (smoking, drinking, ever had heart disease and hypertension). To uncover potential nonlinear relationships, a restricted cubic spline (RCS) model was further applied. Specifically, we utilized the functions of the rms package to test between 3 and 7 knots, and the model with the lowest Akaike Information Criterion (AIC) value was selected for the RCS analysis. The shape characteristics of the dose-response curve were assessed through global tests (P-overall) and nonlinear tests (P-nonlinear). Potential breakpoints were identified using a piecewise Logistic regression model, where the relationship between NDVI and diabetes risks may change at certain threshold values. The segmented model was applied using the R segmented package, which estimates breakpoints and fits a separate linear model for each segment defined by these breakpoints. The value of breakpoints was determined based on model fit and statistical tests. Furthermore, a combination of the generalized additive model (GAM) and piecewise linear regression methods was employed to explore the nonlinear association between participants’ eGDR and the NDVI of the cities where the participants resided. GAM is a semi-parametric flexible form of GLM that can handle both linear and nonlinear relationships between the dependent and independent variables. In GAM, nonlinear relationships are addressed using smoothing functions. The “mgcv” package was used to fit the GAM model, and the curve shape was estimated using the smoothing function. The “segmented” package was used for breakpoint search, and the log-likelihood ratio test (LRT) was applied to compare the goodness of fit between the piecewise model and the linear model. A linear mixed-effects model was used to assess the longitudinal association between eGDR progression from 2011 to 2015 and NDVI. The model was constructed using the lmer function from the R’s lme4 package, with eGDR as the continuous outcome variable and the continuous NDVI index as the core independent variable. The model included the follow-up time variable and its interaction term with NDVI to capture dynamic trends. The fixed effects component covered baseline NDVI levels, follow-up time span, and the interaction effect between NDVI and time, while controlling for the same covariates as in previous models. The random effect incorporated an individual random intercept structure to account for heterogeneity between individuals, and model fitting was based on the restricted maximum likelihood algorithm. Parameter estimation was performed with the lmerTest package, using Satterthwaite degrees of freedom correction. The interaction term coefficient was used to quantify the moderating effect of each unit change in NDVI on the annual rate of change in eGDR. Since the eGDR involved hypertension as a component of the index itself, the history of hypertension variable was excluded from Model 3 in eGDR-related analysis. The strength and significance of these associations were quantified through regression coefficients (Beta), 95% CI, and corresponding p-values. At the same time, sensitivity analysis was also conducted by reconstructing the NDVI index and adding climate covariates (annual average temperature, PM2.5, and annual average dryness) to verify the robustness of the results. The temperature and dryness data used in the sensitivity analysis were sourced from the Global Surface Summary of the Day (GSOD) dataset, provided by the National Oceanic and Atmospheric Administration (NOAA). This dataset is publicly available through the official FTP server of NOAA’s National Centers for Environmental Information (NCEI) ( https://www.ncei.noaa.gov/data/global-summary-of-the-day/archive/ ). The PM2.5 data was obtained from China High Air Pollutants (CHAP) [ 17 ]. In our study, all statistical analyses were performed using the R software (version 4.2.2). A P-value of less than 0.05 was considered statistically significant. 3 Result 3.1 Baseline characteristics of the cohort study population The baseline characteristics of the study population (N=11,652), stratified by diabetes status, were presented in Table 1 . The average age of the included population was 59±10 years and diabetic participants were significantly older than non-diabetic participants (p<0.001). In the entire study population, a higher proportion of participants were female, had an education level of primary school or below, and were married. However, no significant differences were observed between diabetic and non-diabetic groups in terms of gender, education level, marital status, or annual household expenditure. Additionally, diabetic participants were less likely to reside in rural areas (75.0% vs. 82.0%, p<0.001), and had a higher prevalence of heart disease (18.3% vs. 11.3%, p<0.001) and hypertension (41.9% vs. 24.3%, p<0.001). In terms of lifestyle habits, the group of non-diabetic participants exhibited a higher proportion of individuals with a history of smoking and drinking, although no significant differences were observed between the two groups. 3.2 The association between NDVI and baseline diabetes risk A multivariable logistic regression model was used to explore the association between NDVI and the risk of diabetes. (Table 2) Three progressively adjusted regression models were constructed, and the results revealed the following: In Model 1, which was unadjusted for any covariates, each unit increase in NDVI was associated with a 65% reduction in diabetes risk (OR=0.35, 95% CI: 0.20-0.61, p<0.001). After adjusting for demographic characteristics and socioeconomic factors, the effect size remained significant in Model 2 (OR=0.41, 95% CI: 0.23 to 0.74, p=0.003). Further inclusion of health behaviors and medical history variables in Model 3 showed that NDVI remained independently negatively associated with diabetes risk (OR=0.36, 95% CI: 0.20-0.66, p<0.001). When NDVI was grouped into quartiles, the highest NDVI group (Q4) showed a 27%, 25%, and 26% reduction in risk compared to the reference group (Q1) in the three models (p<0.05), with significant dose-response trends observed in Models 1 to 3. The Q2 group also showed a significant negative correlation when compared to the Q1 group. The results indicated a consistent negative association between residential greenness and diabetes risk. 3.3 Subgroup analysis of the association between NDVI and baseline diabetes risk Subgroup analysis further revealed that the protective effect was consistent across different populations. (Figure 2) In the age stratification, the elderly group aged 60 to 75 years showed a more significant benefit (OR=0.32, 95% CI 0.13-0.79, p=0.013). The male group demonstrated a stronger negative association compared to females, with an OR as low as 0.25 (95% CI 0.11-0.56, p=0.001). Groups with an education level of primary school or below, married individuals, and rural residents all showed significant trends of reduced risk, with OR values of 0.30 (95% CI 0.16-0.58, p < 0.001), 0.31 (95% CI 0.17-0.56, p < 0.001), and 0.32 (95% CI 0.17-0.61, p < 0.001), respectively. Although there were differences in the effect size across subgroups, all interaction p-values were greater than 0.05, indicating that the protective effect of NDVI remains stable across different age, gender, education level, marital status, residence, smoking and drinking habits, and underlying disease populations. This highlighted the broad applicability of residence greenness as a diabetes prevention and control strategy. 3.4 RCS analysis of the association between NDVI and baseline diabetes risk Nonlinear analysis by RCS model was employed to further investigate the nonlinear relationship between NDVI and diabetes risk. (Figure 3) From Model 1 to Model 2, the nonlinear effect of NDVI remained significant (Model 1: P-overall <0.001, P-nonlinear = 0.020; Model 2: P-overall = 0.001, P-nonlinear = 0.016). After adjusting for covariates in Model 3, the p-value no longer maintained statistical significance (Model 3: P-overall = 0.002, P-nonlinear = 0.092). In these models, areas with low NDVI (0.3). All three models demonstrated a threshold effect in the dose-response relationship between NDVI and diabetes risk, suggesting that environments with higher greenness coverage may offer a protective effect. Further exploration using piecewise logistic regression analysis identified a threshold effect at NDVI = 0.318. No significant association was observed below this threshold. In contrast, above this threshold (NDVI ≥ 0.318), a strong protective effect was observed (OR = 0.03, 95% CI: 0.00-0.21, P < 0.001). The likelihood ratio test confirmed that the piecewise model provided a significantly better fit than the standard logistic regression model (P = 0.037). (Table 3) 3.5 GAM analysis of the association between NDVI and baseline eGDR GAM analysis was used to explore the nonlinear association between NDVI and baseline eGDR in a broad population. (Figure 4) A significant nonlinear relationship was found between the two variables. From Model 1 to Model 3, the curve showed an initial increase followed by a decrease when NDVI 0.3. Moreover, piecewise regression identified a significant threshold effect at NDVI = 0.348 (P for log likelihood ratio <0.001). Below this threshold, NDVI showed a positive correlation followed by a negative correlation with eGDR. (the first threshold was found in NDVI = 0.194). Conversely, above the threshold of NDVI = 0.346, NDVI demonstrated a strong positive association with eGDR (β= 7.24, 95% CI: 3.46 to 11.02, P < 0.001). The piecewise model provided significantly better fit than the linear model (P < 0.001). (Table 4) 3.6 Linear mixed-effects model analysis of the association between NDVI and eGDR progression The linear mixed-effects model ( Table 5 ) revealed that increases in NDVI at the longitudinal association level significantly promoted the enhancement of eGDR levels. In the base model, the interaction between continuous NDVI and time demonstrated a consistent positive association with eGDR. In Model 1, the coefficient for the interaction term was 0.23 (95% CI: 0.04–0.41, p = 0.015). In Model 2, the effect size remained at 0.19 (95% CI: 0.01–0.38, p = 0.041). Further adjustment for covariates in Model 3 indicated that for each unit increase in NDVI, the annual improvement rate of eGDR increased by 19% (95% CI: 0.01–0.38, p = 0.041). Quartile group analysis showed that the eGDR improvement in the Q2 group (Model 3: Beta = 0.07, 95% CI: 0.02–0.12, p = 0.003) and the Q4 group (Model 3: Beta = 0.09, 95% CI: 0.04–0.13, p < 0.001) was significantly higher than in the reference group, while no significant association was observed in the Q3 group. This suggested a non-linear threshold effect of residential greenness on insulin sensitivity modulation. These findings highlighted the continuous benefits of residential greenness in improving glucose disposal capacity, contributing to sustained metabolic health improvements. 3.7 Sensitivity analysis This study verified the robustness of the results through sensitivity analysis. Sensitivity analysis incorporated the average annual temperature, PM2.5, and annual average dryness of participants’ residential locations to more accurately assess the independent protective effect of NDVI. The results of the sensitivity analyses remained consistent with the main analysis. (Online Supplementary Material) 4 Discussion This study, based on the nationwide CHARLS cohort data, systematically revealed the dual protective effects of residential greenness level (NDVI) on diabetes risk and insulin sensitivity. Through multidimensional analytical models, it was confirmed that for each 1-unit increase in residential area NDVI, the risk of diabetes was significantly reduced by 64%. When NDVI exceeded the threshold of 0.318, its protective effect was notably enhanced. A threshold effect was also observed for NDVI’s protection on eGDR, with a significant increase in insulin sensitivity protection when NDVI exceeded 0.346. Longitudinal studies showed that an increase in NDVI significantly promoted a 19% improvement rate in eGDR progression. High-greenery areas showed particularly remarkable improvements. These findings not only validated, from an epidemiological perspective, the preventive value of green exposure for diabetes but also provided a quantifiable reference for urban planning through nonlinear threshold effects, offering critical scientific evidence for integrating environmental interventions with metabolic disease prevention and control strategies. Residential greenness has been shown to alleviate heat stress and reduce the heat-related disease burden across various populations [ 18 ]. The transpiration of vegetation significantly lowers surface temperatures and mitigates the urban heat island effect to reduce the intensity and duration of extreme heat exposure [ 19 ]. A study in China demonstrated that for every 1.4% increase in fraction vegetation coverage, summer temperatures decreased by an average of 0.11°C [ 20 ]. Additionally, residential greenness reduces oxidative stress on pancreatic and endothelial cells by adsorbing air pollutants such as PM2.5 and ozone[ 21 ]. Residential greenness also encourages healthier lifestyles, which is crucial for the prevention and management of metabolic diseases [ 22 – 23 ]. Research has shown that exposure to green spaces enhances immune regulation and reduces inflammation [ 24 ]. Our study further revealed a nonlinear relationship and threshold effect between NDVI and diabetes risk. High greening environments (NDVI > 0.3) provide significant protective effects through combined cooling, pollution reduction, and promotion of healthy behaviors. Similar studies have confirmed that the association between NDVI and mental health outcomes is more pronounced in high-greening percentiles (the top 20%) [ 25 ]. These intriguing threshold effects of NDVI offer new insights for urban planning and disease prevention. The findings of this study also provide insights for optimizing public health intervention strategies. For example, subgroup analyses showed that residential greenness has a particularly significant protective effect for individuals aged 60 plus. This suggests that prioritizing the enhancement of greenness in communities with higher levels of aging may be an effective approach to alleviating the burden of metabolic diseases. Dose-response analysis further revealed that when the NDVI exceeds the threshold of 0.3, the risk of diabetes decreases, providing a quantifiable scientific basis for urban planning. Governments could draw on this NDVI threshold as the reference standard for community green space development. Employing vertical greening technologies to enhance vegetation cooling effects in densely built areas is also a measure that can be adopted. It is noteworthy that while environmental interventions have population-wide protective value, individual health management remains the core of disease prevention and control. International practical experience indicates that systematically increasing residential greenness coverage, combined with proactive monitoring measures, can significantly reduce the diabetes disease burden in certain regions. Taking Singapore as an example, it implemented systematic green urban planning, such as the construction of higher density and diversity of green facades [ 26 ]. It helped cool the environment and reduce pollution, lowering the metabolic risks associated with extreme heat. Additionally, Singapore has launched a nationwide health promotion program, incorporating community fitness facilities and subsidies for healthy eating, which effectively control obesity and hypertension prevalence [ 27 ]. Moreover, Singapore integrated its heat warning system with a tiered healthcare network, providing free cooling centers and real-time blood glucose monitoring services for diabetic patients [ 28 – 29 ]. These findings highlight that integrating environmental optimization, climate adaptation, and precision health management strategies may offer a systematic solution to address the chronic disease burden effectively. This study has certain limitations. First, using the NDVI of the participants’ city as a variable makes it difficult to accurately quantify the green exposure within an individual’s daily activity range, which may weaken the strength of the actual association. Secondly, the CHARLS cohort only covers the Chinese population, and although its sample is diverse in terms of climate and demographics, caution is needed when extrapolating the conclusions to regions with different cultural, geographical, and socio-economic backgrounds. Thirdly, the interaction between residential greenness and various climate changes, such as extreme temperatures and air pollution, on metabolic health requires further exploration. Future studies should integrate higher-resolution environmental data, multinational prospective cohorts, and multi-omics techniques to explore deeper associations and mechanisms in heat-related metabolic disorders. 5 Conclusion This study provided nationwide cohort evidence that an increase in residential greenness levels can significantly reduce the risk of diabetes and improve insulin sensitivity, offering important epidemiological support for the metabolic protective effects of greenness. The dose-response relationship revealed through multi-model analysis offers valuable insights for the quantification of greenness standards in urban planning. These findings support the inclusion of environmental interventions in the diabetes prevention and control system, and by integrating strategies such as green space optimization, community health promotion, and precise monitoring, provide a sustainable solution to the metabolic disease burden brought by climate change. Declarations Consent to Publish declaration: not applicable. Data Availability: The data supporting the findings of this study are available from the corresponding author upon reasonable request. Competing interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Ethics and Consent to Participate declarations: not applicable. Clinical trial number: not applicable. Funding Declaration: Beijing Natural Science Foundation-Youth Science Fund Project (No.7244487) Contribution: Ye-xin Chen and Mao-xuan Lin contributed to the data analysis and interpretation, providing key insights into the results. Bo Zhang, Han-zhang Hong worked on the design and execution, ensuring the reliability of the study conducted. Run-ze Wang focused on the development of the methodology used in the study, contributing to the technical aspects of the research. Yi-yu Dong played a pivotal role in the writing and review of the manuscript, refining the final report and ensuring clarity in the presentation of the findings. References Magliano DJ, Boyko EJ. & IDF Diabetes Atlas 10th edition scientific committee, 2021. IDF DIABETES ATLAS, tenth ed., International Diabetes Federation, Brussels. Bommer C, Heesemann E, Sagalova V, Manne-Goehler J, Atun R, Bärnighausen T, Vollmer S. 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Healthy living. https://www.moh.gov.sg/staying-healthy/healthy-living , 2025(Accessed 19 April 2025). Health Promotion Board (HPB). Let’s BEAT Diabetes. https://www.healthhub.sg/programmes/diabetes-mellitus#beaware , 2025(Accessed 19 April 2025). Heat R. & Performance Centre, National heatwave response plan a signal that Singapore should start taking heat seriously. https://medicine.nus.edu.sg/hrpc/national-heatwave-response-plan-a-signal-that-singapore-should-start-taking-heat-seriously/ , 2025(Accessed 19 April 2025). Tables Tables 1 to 5 are available in the Supplementary Files section. Additional Declarations No competing interests reported. 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rate.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7481579/v1/e45cc7dca003b1afcae48565.jpg"},{"id":92897236,"identity":"9eb79024-bb0b-4539-a7bf-d9d93444b047","added_by":"auto","created_at":"2025-10-06 19:56:38","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":826326,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis forest plot of the association between NDVI and baseline diabetes risk.\u003c/p\u003e\n\u003cp\u003eNDVI, Normalized Difference Vegetation Index.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7481579/v1/d6e6793cc05af2f4fd8b9fc2.jpg"},{"id":92897918,"identity":"ec6298f0-8012-4603-baa4-f165c547e727","added_by":"auto","created_at":"2025-10-06 20:12:38","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":77985,"visible":true,"origin":"","legend":"\u003cp\u003eRCS of association between NDVI and baseline diabetes risk.\u003c/p\u003e\n\u003cp\u003eRCS, Restricted Cubic Spline; NDVI, Normalized Difference Vegetation Index.\u003c/p\u003e\n\u003cp\u003eModel 1 : no covariates were adjusted \u003cbr\u003e\nModel 2 : adjusted for Age, Gender, Education, Marital status, Residence, GDP, and Annual household expenditure \u003cbr\u003e\nModel 3 : adjusted for Age, Gender, Education, Marital status, Residence, GDP, Annual household expenditure, Smoking status, Drinking status, Ever had heart problem, and Ever had high blood pressure.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7481579/v1/6449bb42e6cca577ea7a52d0.jpg"},{"id":92897378,"identity":"ac028e06-b1bb-4c2a-8e83-a778aadf21c9","added_by":"auto","created_at":"2025-10-06 20:04:38","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":69353,"visible":true,"origin":"","legend":"\u003cp\u003eModel 1 : no covariates were adjusted \u003cbr\u003e\nModel 2 : adjusted for Age, Gender, Education, Marital status, Residence, GDP, and Annual household expenditure \u003cbr\u003e\nModel 3 : adjusted for Age, Gender, Education, Marital status, Residence, GDP, Annual household expenditure, Smoking status, Drinking status, Ever had heart problem.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig 4:\u003c/strong\u003e GAM of association between NDVI and baseline eGDR.\u003c/p\u003e\n\u003cp\u003eGAM, Generalized Additive Models; NDVI, Normalized Difference Vegetation Index; eGDR, the estimated glucose disposal rate; GDP, Gross Domestic Product.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7481579/v1/d5e4c0af6de9bfb32b0023a5.jpg"},{"id":92945244,"identity":"0bcf5635-4da8-4475-9546-7584207b41af","added_by":"auto","created_at":"2025-10-07 12:31:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1759099,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7481579/v1/6acb21b6-8a86-4630-8855-ead4aac850b8.pdf"},{"id":92897238,"identity":"96220aac-0b87-4d26-9541-2741265f996b","added_by":"auto","created_at":"2025-10-06 19:56:38","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":237725,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineSupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7481579/v1/561589b1dab21eaa1e49a3b9.docx"},{"id":92898194,"identity":"72c41e9d-7d87-48b6-b112-5be44ede03ee","added_by":"auto","created_at":"2025-10-06 20:20:38","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":29566,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7481579/v1/ef70957f55ee5d5e2773fb83.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Protective Role of Residential Greenness on Diabetes Risk and Insulin Sensitivity: Results from a Nationwide Cohort Studies","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eDiabetes, as a global chronic metabolic disease, poses a significant public health challenge due to its escalating burden. In 2021, the global prevalence of diabetes among individuals aged 20 to 79 reached 537\u0026nbsp;million, and it is projected to rise to 783\u0026nbsp;million by 2045, with direct medical costs amounting to \u003cspan\u003e$\u003c/span\u003e966\u0026nbsp;billion, resulting in substantial economic and social burdens [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBeyond conventional pharmacological interventions, growing evidence highlights the critical role of modifiable lifestyle and environmental factors in diabetes prevention and management. As a key component of the exposome, residential environments are increasingly recognized as complementary targets for mitigating metabolic disease progression. Residential greenness, as a key strategy to mitigate heat-related health risks, has shown increasing evidence of its protective effects [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Residential greenness can lower surface temperatures through transpiration, reduce the urban heat island effect, and absorb air pollutants. Previous studies have shown that increased greenness reduces the risks of various diseases, including depression, chronic obstructive pulmonary disease, and asthma [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Some studies also suggested that exposure to greenness might serve as a protective factor in the development of diabetes [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhile existing studies in certain regions of China have partially revealed the association between residential greenness and diabetes risk [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], there is a lack of systematic nationwide research. More importantly, the potential protective effects of residential greenness on glucose metabolism abnormalities in a broader population remain undiscovered [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This study, based on the China Health and Retirement Longitudinal Study (CHARLS), aimed to investigate the protective effects of the Normalized Difference Vegetation Index (NDVI) on diabetes risk and its impact on insulin resistance indicators (estimated Glucose Disposal Rate, eGDR) in a wide population. Employing linear, nonlinear relationships and linear mixed-effects models, this study comprehensively examined the association between residential greenness exposure and glucose metabolism indicators. It also focused on the immediate effects and long-term trajectories of this association, providing a multi-dimensional evidence chain for the environmental exposure and metabolic regulation mechanisms. The findings not only offered scientific evidence for the development of integrated strategies for climate change adaptation and chronic disease prevention, but also provided important references for urban planning and public health interventions.\u003c/p\u003e"},{"header":"2 Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data source and study population\u003c/h2\u003e\u003cp\u003eThe participants in the cross-sectional study were drawn from the CHARLS, a national cohort study covering approximately 150 county-level cities across 28 provinces in China. China is one of the countries with the highest diabetes burden globally. At the same time, the vast geographical area, diverse climate zones, and significant urban-rural differences in greenness levels make it a unique context. The CHARLS provides a reliable source of data to explore the impact of environmental heterogeneity on glucose metabolism health. In the CHARLS, all surveyors underwent systematic and professional training and conducted face-to-face interviews using standardized questionnaires. The CHARLS adheres to the principles of the Helsinki Declaration and was approved by the Institutional Review Board of Peking University (IRB00001052-11015). Written informed consent was obtained from all participants prior to their involvement [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn this study, an analytical group was constructed based on the CHARLS 2011 to 2015 data (initial sample size n\u0026thinsp;=\u0026thinsp;11,847) by a phased exclusion approach. In the first phase, when exploring the association between NDVI and the diabetes prevalence in 2011, the following individuals were excluded: (1) those without diabetes diagnosis information in 2011; (2) those with missing residential greenness exposure data for 2011. Ultimately, n\u0026thinsp;=\u0026thinsp;11,652 participants were included. In the second phase, when investigating the association between NDVI and eGDR in 2011, further exclusions were made from the first-phase cohort: (1) individuals on diabetes medication treatment; (2) those with missing parameters required for eGDR calculation. The final sample size for this analysis was n\u0026thinsp;=\u0026thinsp;6,396; In the third phase, by tracking the 2015 data, the association between the level of residential greenness and the progression of eGDR from 2011 to 2015 was further analyzed. The following individuals were excluded: (1) individuals on diabetes medication treatment from 2011 to 2015; (2) those with missing parameters required for eGDR calculation and NDVI for 2011 and 2015. In this phase, n\u0026thinsp;=\u0026thinsp;3988 participants were included.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Definition of variables\u003c/h2\u003e\u003cp\u003eIn the CHARLS study, the NDVI was employed as the independent variable. NDVI is considered an effective measure of urban greenness and can accurately assess the level of vegetation in residential areas. The NDVI were sourced from the MOD13Q1 Version 5 dataset of the MODIS Land Product provided by NASA, which offers 16 day composite global vegetation index data with a spatial resolution of 250 meters by 250 meters. For each participant, the annual average NDVI value of their residing city for the corresponding year was employed for analysis. NDVI values range from \u0026minus;\u0026thinsp;1 to 1, with higher values indicating greater vegetation density, where negative values correspond to water bodies or snow/ice cover, and zero indicates bare ground with no vegetation.\u003c/p\u003e\u003cp\u003eDiabetes and eGDR were respectively used as dependent variables in the study. The diagnosis of diabetes was based on a combination of a questionnaire and diagnostic criteria. Participants were diagnosed with diabetes if they met any of the following criteria: (1) fasting blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;126 mg/dL; (2) glycated hemoglobin (HbA1c)\u0026thinsp;\u0026ge;\u0026thinsp;6.5%; (3) answering \u0026lsquo;Yes\u0026rsquo; to the question \u0026lsquo;Take any meds for diabetes\u0026rsquo; in the questionnaire; (4) answering \u0026lsquo;Yes\u0026rsquo; to the question \u0026lsquo;Ever had diabetes\u0026rsquo; in the questionnaire. The formula for calculating eGDR is as follows (\u003cb\u003eEq.\u0026nbsp;1\u003c/b\u003e) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Waist circumference was measured by asking participants to stand and using a tape measure at the level of the navel. Hypertension was defined as a self-reported physician diagnosis. HbA1c was measured using venous blood samples obtained after overnight fasting, analyzed by boronate affinity liquid chromatography.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEq1\u003c/strong\u003e\u003cp\u003e\u003cem\u003eeGDR\u0026thinsp;=\u0026thinsp;21.158\u0026thinsp;\u0026minus;\u0026thinsp;0.09 \u0026times; WC\u003c/em\u003e\u003csub\u003e\u003cem\u003ecm\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e\u0026minus; 3.407 \u0026times; hypertension\u003c/em\u003e\u003csub\u003e\u003cem\u003e(yes = 1/no = 0)\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e\u0026minus;\u0026thinsp;0.551 \u0026times; HbA1c\u003c/em\u003e\u003csub\u003e\u003cem\u003e%\u003c/em\u003e\u003c/sub\u003e.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eThe covariates include age and gender as demographic characteristicsand. And marital status (\u0026lsquo;Single/divorced/widowed\u0026rsquo; or \u0026lsquo;Married\u0026rsquo;), educational level (\u0026lsquo;Primary school or below,\u0026rsquo; \u0026lsquo;Middle school,\u0026rsquo; or \u0026lsquo;High school or above\u0026rsquo;), residential area (\u0026lsquo;Rural,\u0026rsquo; \u0026lsquo;Urban\u0026rsquo;), regional GDP, and annual household expenditure were considered as socioeconomic characteristics. The GDP data for different cities were obtained from the website of the National Bureau of Statistics of China. There are natural environmental and economic development differences between northern/southern and eastern/western regions. Including the GDP as a variable minimized the potential confounding effect of socioeconomic development on the outcomes. Lifestyle factors recorded whether the participant currently consumes alcohol (\u0026lsquo;Yes\u0026rsquo; or \u0026lsquo;No\u0026rsquo;) and smoking status (\u0026lsquo;Yes\u0026rsquo; or \u0026lsquo;No\u0026rsquo;). Additionally, information on whether participants have hypertension or heart problem was included.\u003c/p\u003e\u003cp\u003eMissing values for the independent and dependent variables were excluded, while missing data for covariates were imputed using multiple imputation (KNN, 10) to ensure completeness.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Statistical analyses\u003c/h2\u003e\u003cp\u003eThe baseline analysis of CHARLS included the mean (standard deviation) for continuous variables and the frequencies and percentages for categorical variables. Group comparisons for continuous variables were performed using Welch\u0026rsquo;s t-test or ANOVA. For group comparisons of categorical data, the Fisher exact test was used when expected frequencies were \u0026lt;\u0026thinsp;5; otherwise, the Chi-squared test was applied.\u003c/p\u003e\u003cp\u003eA multi-stage statistical analysis method was implemented to systematically explore the complex associations between NDVI and the risk of diabetes, as well as the insulin resistance surrogate index (eGDR). A multivariable logistic regression model was first employed to analyze the relationship between NDVI and the risk of diabetes. Three progressively adjusted models were constructed: Model 1 did not adjust for any covariates; Model 2 adjusted for demographic and socioeconomic factors (age, gender, education level, marital status, residential area, regional GDP, annual household expenditure); Model 3 further included health behaviors and medical history variables (smoking, drinking, ever had heart disease and hypertension). To uncover potential nonlinear relationships, a restricted cubic spline (RCS) model was further applied. Specifically, we utilized the functions of the rms package to test between 3 and 7 knots, and the model with the lowest Akaike Information Criterion (AIC) value was selected for the RCS analysis. The shape characteristics of the dose-response curve were assessed through global tests (P-overall) and nonlinear tests (P-nonlinear). Potential breakpoints were identified using a piecewise Logistic regression model, where the relationship between NDVI and diabetes risks may change at certain threshold values. The segmented model was applied using the R segmented package, which estimates breakpoints and fits a separate linear model for each segment defined by these breakpoints. The value of breakpoints was determined based on model fit and statistical tests.\u003c/p\u003e\u003cp\u003eFurthermore, a combination of the generalized additive model (GAM) and piecewise linear regression methods was employed to explore the nonlinear association between participants\u0026rsquo; eGDR and the NDVI of the cities where the participants resided. GAM is a semi-parametric flexible form of GLM that can handle both linear and nonlinear relationships between the dependent and independent variables. In GAM, nonlinear relationships are addressed using smoothing functions. The \u0026ldquo;mgcv\u0026rdquo; package was used to fit the GAM model, and the curve shape was estimated using the smoothing function. The \u0026ldquo;segmented\u0026rdquo; package was used for breakpoint search, and the log-likelihood ratio test (LRT) was applied to compare the goodness of fit between the piecewise model and the linear model.\u003c/p\u003e\u003cp\u003eA linear mixed-effects model was used to assess the longitudinal association between eGDR progression from 2011 to 2015 and NDVI. The model was constructed using the lmer function from the R\u0026rsquo;s lme4 package, with eGDR as the continuous outcome variable and the continuous NDVI index as the core independent variable. The model included the follow-up time variable and its interaction term with NDVI to capture dynamic trends. The fixed effects component covered baseline NDVI levels, follow-up time span, and the interaction effect between NDVI and time, while controlling for the same covariates as in previous models. The random effect incorporated an individual random intercept structure to account for heterogeneity between individuals, and model fitting was based on the restricted maximum likelihood algorithm. Parameter estimation was performed with the lmerTest package, using Satterthwaite degrees of freedom correction. The interaction term coefficient was used to quantify the moderating effect of each unit change in NDVI on the annual rate of change in eGDR.\u003c/p\u003e\u003cp\u003eSince the eGDR involved hypertension as a component of the index itself, the history of hypertension variable was excluded from Model 3 in eGDR-related analysis. The strength and significance of these associations were quantified through regression coefficients (Beta), 95% CI, and corresponding p-values.\u003c/p\u003e\u003cp\u003eAt the same time, sensitivity analysis was also conducted by reconstructing the NDVI index and adding climate covariates (annual average temperature, PM2.5, and annual average dryness) to verify the robustness of the results. The temperature and dryness data used in the sensitivity analysis were sourced from the Global Surface Summary of the Day (GSOD) dataset, provided by the National Oceanic and Atmospheric Administration (NOAA). This dataset is publicly available through the official FTP server of NOAA\u0026rsquo;s National Centers for Environmental Information (NCEI) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncei.noaa.gov/data/global-summary-of-the-day/archive/\u003c/span\u003e\u003cspan address=\"https://www.ncei.noaa.gov/data/global-summary-of-the-day/archive/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003e).\u003c/em\u003e The PM2.5 data was obtained from China High Air Pollutants (CHAP) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn our study, all statistical analyses were performed using the R software (version 4.2.2). A P-value of less than 0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Result","content":"\u003cp\u003e\u003cstrong\u003e3.1 Baseline characteristics of the cohort study population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe baseline characteristics of the study population (N=11,652), stratified by diabetes status, were presented in\u0026nbsp;\u003cstrong\u003eTable 1\u003c/strong\u003e. The average age of the included population was 59±10 years and diabetic participants were significantly older than non-diabetic participants (p\u0026lt;0.001). In the entire study population, a higher proportion of participants were female, had an education level of primary school or below, and were married. However, no significant differences were observed between diabetic and non-diabetic groups in terms of gender, education level, marital status, or annual household expenditure. Additionally, diabetic participants were less likely to reside in rural areas (75.0% vs. 82.0%, p\u0026lt;0.001), and had a higher prevalence of heart disease (18.3% vs. 11.3%, p\u0026lt;0.001) and hypertension (41.9% vs. 24.3%, p\u0026lt;0.001). In terms of lifestyle habits, the group of \u0026nbsp; non-diabetic participants exhibited a higher proportion of individuals with a history of smoking and drinking, although no significant differences were observed between the two groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 The association between NDVI and baseline diabetes risk\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA multivariable logistic regression model was used to explore the association between NDVI and the risk of diabetes.\u0026nbsp;\u003cstrong\u003e(Table 2)\u003c/strong\u003e Three progressively adjusted regression models were constructed, and the results revealed the following: In Model 1, which was unadjusted for any covariates, each unit increase in NDVI was associated with a 65% reduction in diabetes risk (OR=0.35, 95% CI: 0.20-0.61, p\u0026lt;0.001). After adjusting for demographic characteristics and socioeconomic factors, the effect size remained significant in Model 2 (OR=0.41, 95% CI: 0.23 to 0.74, p=0.003). Further inclusion of health behaviors and medical history variables in Model 3 showed that NDVI remained independently negatively associated with diabetes risk (OR=0.36, 95% CI: 0.20-0.66, p\u0026lt;0.001). When NDVI was grouped into quartiles, the highest NDVI group (Q4) showed a 27%, 25%, and 26% reduction in risk compared to the reference group (Q1) in the three models (p\u0026lt;0.05), with significant dose-response trends observed in Models 1 to 3. The Q2 group also showed a significant negative correlation when compared to the Q1 group. The results indicated a consistent negative association between residential greenness and diabetes risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Subgroup analysis of the association between NDVI and baseline diabetes risk\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSubgroup analysis further revealed that the protective effect was consistent across different populations.\u0026nbsp;\u003cstrong\u003e(Figure 2)\u003c/strong\u003e In the age stratification, the elderly group aged 60 to 75 years showed a more significant benefit (OR=0.32, 95% CI 0.13-0.79, p=0.013). The male group demonstrated a stronger negative association compared to females, with an OR as low as 0.25 (95% CI 0.11-0.56, p=0.001). Groups with an education level of primary school or below, married individuals, and rural residents all showed significant trends of reduced risk, with OR values of 0.30 (95% CI 0.16-0.58, p \u0026lt; 0.001), 0.31 (95% CI 0.17-0.56, p \u0026lt; 0.001), and 0.32 (95% CI 0.17-0.61, p \u0026lt; 0.001), respectively. Although there were differences in the effect size across subgroups, all interaction p-values were greater than 0.05, indicating that the protective effect of NDVI remains stable across different age, gender, education level, marital status, residence, smoking and drinking habits, and underlying disease populations. This highlighted the broad applicability of residence greenness as a diabetes prevention and control strategy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 RCS analysis of the association between NDVI and baseline diabetes risk\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNonlinear analysis by RCS model was employed to further investigate the nonlinear relationship between NDVI and diabetes risk.\u0026nbsp;\u003cstrong\u003e(Figure 3)\u0026nbsp;\u003c/strong\u003eFrom Model 1 to Model 2, the nonlinear effect of NDVI remained significant (Model 1: P-overall \u0026lt;0.001, P-nonlinear = 0.020; Model 2: P-overall = 0.001, P-nonlinear = 0.016). After adjusting for covariates in Model 3, the p-value no longer maintained statistical significance (Model 3: P-overall = 0.002, P-nonlinear = 0.092). In these models, areas with low NDVI (\u0026lt;0.3) showed an increasing trend in risk, which significantly decreased beyond the threshold (NDVI\u0026gt;0.3). All three models demonstrated a threshold effect in the dose-response relationship between NDVI and diabetes risk, suggesting that environments with higher greenness coverage may offer a protective effect. Further exploration using piecewise logistic regression analysis identified a threshold effect at NDVI = 0.318. No significant association was observed below this threshold. In contrast, above this threshold (NDVI\u0026nbsp;≥\u0026nbsp;0.318), a strong protective effect was observed (OR = 0.03, 95% CI: 0.00-0.21, P \u0026lt; 0.001). The likelihood ratio test confirmed that the piecewise model provided a significantly better fit than the standard logistic regression model (P = 0.037).\u0026nbsp;\u003cstrong\u003e(Table 3)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 GAM analysis of the association between NDVI and baseline eGDR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGAM analysis was used to explore the nonlinear association between NDVI and baseline eGDR in a broad population.\u0026nbsp;\u003cstrong\u003e(Figure 4)\u0026nbsp;\u003c/strong\u003eA significant nonlinear relationship was found between the two variables. From Model 1 to Model 3, the curve showed an initial increase followed by a decrease when NDVI \u0026lt; 0.3, and an upward trend when NDVI \u0026gt; 0.3. Moreover, piecewise regression identified a significant threshold effect at NDVI = 0.348 (P for log likelihood ratio \u0026lt;0.001). Below this threshold, NDVI showed a positive correlation followed by a negative correlation with eGDR. (the first threshold was found in NDVI = 0.194). Conversely, above the threshold of NDVI = 0.346, NDVI demonstrated a strong positive association with eGDR (β= 7.24, 95% CI: 3.46 to 11.02, P \u0026lt; 0.001). The piecewise model provided significantly better fit than the linear model (P \u0026lt; 0.001).\u0026nbsp;\u003cstrong\u003e(Table 4)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Linear mixed-effects model analysis of the association between NDVI and eGDR progression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe linear mixed-effects model (\u003cstrong\u003eTable 5\u003c/strong\u003e) revealed that increases in NDVI at the longitudinal association level significantly promoted the enhancement of eGDR levels. In the base model, the interaction between continuous NDVI and time demonstrated a consistent positive association with eGDR. In Model 1, the coefficient for the interaction term was 0.23 (95% CI: 0.04–0.41, p = 0.015). In Model 2, the effect size remained at 0.19 (95% CI: 0.01–0.38, p = 0.041). Further adjustment for covariates in Model 3 indicated that for each unit increase in NDVI, the annual improvement rate of eGDR increased by 19% (95% CI: 0.01–0.38, p = 0.041). Quartile group analysis showed that the eGDR improvement in the Q2 group (Model 3: Beta = 0.07, 95% CI: 0.02–0.12, p = 0.003) and the Q4 group (Model 3: Beta = 0.09, 95% CI: 0.04–0.13, p \u0026lt; 0.001) was significantly higher than in the reference group, while no significant association was observed in the Q3 group. This suggested a non-linear threshold effect of residential greenness on insulin sensitivity modulation. These findings highlighted the continuous benefits of residential greenness in improving glucose disposal capacity, contributing to sustained metabolic health improvements.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.7 Sensitivity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study verified the robustness of the results through sensitivity analysis. Sensitivity analysis incorporated the average annual temperature, PM2.5, and annual average dryness of participants’ residential locations to more accurately assess the independent protective effect of NDVI. The results of the sensitivity analyses remained consistent with the main analysis.\u0026nbsp;\u003cstrong\u003e(Online Supplementary Material)\u003c/strong\u003e\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study, based on the nationwide CHARLS cohort data, systematically revealed the dual protective effects of residential greenness level (NDVI) on diabetes risk and insulin sensitivity. Through multidimensional analytical models, it was confirmed that for each 1-unit increase in residential area NDVI, the risk of diabetes was significantly reduced by 64%. When NDVI exceeded the threshold of 0.318, its protective effect was notably enhanced. A threshold effect was also observed for NDVI\u0026rsquo;s protection on eGDR, with a significant increase in insulin sensitivity protection when NDVI exceeded 0.346. Longitudinal studies showed that an increase in NDVI significantly promoted a 19% improvement rate in eGDR progression. High-greenery areas showed particularly remarkable improvements. These findings not only validated, from an epidemiological perspective, the preventive value of green exposure for diabetes but also provided a quantifiable reference for urban planning through nonlinear threshold effects, offering critical scientific evidence for integrating environmental interventions with metabolic disease prevention and control strategies.\u003c/p\u003e\u003cp\u003eResidential greenness has been shown to alleviate heat stress and reduce the heat-related disease burden across various populations [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The transpiration of vegetation significantly lowers surface temperatures and mitigates the urban heat island effect to reduce the intensity and duration of extreme heat exposure [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. A study in China demonstrated that for every 1.4% increase in fraction vegetation coverage, summer temperatures decreased by an average of 0.11\u0026deg;C [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Additionally, residential greenness reduces oxidative stress on pancreatic and endothelial cells by adsorbing air pollutants such as PM2.5 and ozone[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Residential greenness also encourages healthier lifestyles, which is crucial for the prevention and management of metabolic diseases [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Research has shown that exposure to green spaces enhances immune regulation and reduces inflammation [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Our study further revealed a nonlinear relationship and threshold effect between NDVI and diabetes risk. High greening environments (NDVI\u0026thinsp;\u0026gt;\u0026thinsp;0.3) provide significant protective effects through combined cooling, pollution reduction, and promotion of healthy behaviors. Similar studies have confirmed that the association between NDVI and mental health outcomes is more pronounced in high-greening percentiles (the top 20%) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. These intriguing threshold effects of NDVI offer new insights for urban planning and disease prevention.\u003c/p\u003e\u003cp\u003eThe findings of this study also provide insights for optimizing public health intervention strategies. For example, subgroup analyses showed that residential greenness has a particularly significant protective effect for individuals aged 60 plus. This suggests that prioritizing the enhancement of greenness in communities with higher levels of aging may be an effective approach to alleviating the burden of metabolic diseases. Dose-response analysis further revealed that when the NDVI exceeds the threshold of 0.3, the risk of diabetes decreases, providing a quantifiable scientific basis for urban planning. Governments could draw on this NDVI threshold as the reference standard for community green space development. Employing vertical greening technologies to enhance vegetation cooling effects in densely built areas is also a measure that can be adopted. It is noteworthy that while environmental interventions have population-wide protective value, individual health management remains the core of disease prevention and control. International practical experience indicates that systematically increasing residential greenness coverage, combined with proactive monitoring measures, can significantly reduce the diabetes disease burden in certain regions. Taking Singapore as an example, it implemented systematic green urban planning, such as the construction of higher density and diversity of green facades [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. It helped cool the environment and reduce pollution, lowering the metabolic risks associated with extreme heat. Additionally, Singapore has launched a nationwide health promotion program, incorporating community fitness facilities and subsidies for healthy eating, which effectively control obesity and hypertension prevalence [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Moreover, Singapore integrated its heat warning system with a tiered healthcare network, providing free cooling centers and real-time blood glucose monitoring services for diabetic patients [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. These findings highlight that integrating environmental optimization, climate adaptation, and precision health management strategies may offer a systematic solution to address the chronic disease burden effectively.\u003c/p\u003e\u003cp\u003eThis study has certain limitations. First, using the NDVI of the participants\u0026rsquo; city as a variable makes it difficult to accurately quantify the green exposure within an individual\u0026rsquo;s daily activity range, which may weaken the strength of the actual association. Secondly, the CHARLS cohort only covers the Chinese population, and although its sample is diverse in terms of climate and demographics, caution is needed when extrapolating the conclusions to regions with different cultural, geographical, and socio-economic backgrounds. Thirdly, the interaction between residential greenness and various climate changes, such as extreme temperatures and air pollution, on metabolic health requires further exploration. Future studies should integrate higher-resolution environmental data, multinational prospective cohorts, and multi-omics techniques to explore deeper associations and mechanisms in heat-related metabolic disorders.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study provided nationwide cohort evidence that an increase in residential greenness levels can significantly reduce the risk of diabetes and improve insulin sensitivity, offering important epidemiological support for the metabolic protective effects of greenness. The dose-response relationship revealed through multi-model analysis offers valuable insights for the quantification of greenness standards in urban planning. These findings support the inclusion of environmental interventions in the diabetes prevention and control system, and by integrating strategies such as green space optimization, community health promotion, and precise monitoring, provide a sustainable solution to the metabolic disease burden brought by climate change.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConsent to Publish declaration:\u0026nbsp;\u003c/strong\u003enot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u0026nbsp;\u003c/strong\u003eThe data supporting the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics and Consent to Participate declarations:\u003c/strong\u003e not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u0026nbsp;\u003c/strong\u003enot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration:\u003c/strong\u003e Beijing Natural Science Foundation-Youth Science Fund Project (No.7244487)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContribution:\u0026nbsp;\u003c/strong\u003eYe-xin Chen and Mao-xuan Lin contributed to the data analysis and interpretation, providing key insights into the results. Bo Zhang, Han-zhang Hong worked on the design and execution, ensuring the reliability of the study conducted. Run-ze Wang focused on the development of the methodology used in the study, contributing to the technical aspects of the research. Yi-yu Dong played a pivotal role in the writing and review of the manuscript, refining the final report and ensuring clarity in the presentation of the findings.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMagliano DJ, Boyko EJ. \u0026amp; IDF Diabetes Atlas 10th edition scientific committee, 2021. IDF DIABETES ATLAS, tenth ed., International Diabetes Federation, Brussels.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBommer C, Heesemann E, Sagalova V, Manne-Goehler J, Atun R, B\u0026auml;rnighausen T, Vollmer S. 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Let\u0026rsquo;s BEAT Diabetes. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.healthhub.sg/programmes/diabetes-mellitus#beaware\u003c/span\u003e\u003cspan address=\"https://www.healthhub.sg/programmes/diabetes-mellitus#beaware\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, 2025(Accessed 19 April 2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHeat R. \u0026amp; Performance Centre, National heatwave response plan a signal that Singapore should start taking heat seriously. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://medicine.nus.edu.sg/hrpc/national-heatwave-response-plan-a-signal-that-singapore-should-start-taking-heat-seriously/\u003c/span\u003e\u003cspan address=\"https://medicine.nus.edu.sg/hrpc/national-heatwave-response-plan-a-signal-that-singapore-should-start-taking-heat-seriously/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, 2025(Accessed 19 April 2025).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 5 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"diabetology-and-metabolic-syndrome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dims","sideBox":"Learn more about [Diabetology \u0026 Metabolic Syndrome](http://dmsjournal.biomedcentral.com/)","snPcode":"13098","submissionUrl":"https://submission.nature.com/new-submission/13098/3","title":"Diabetology \u0026 Metabolic Syndrome","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"China Health and Retirement Longitudinal Study (CHARLS), estimated Glucose Disposal Rate (eGDR), Normalized Difference Vegetation Index (NDVI), Residential Greenness, diabetes","lastPublishedDoi":"10.21203/rs.3.rs-7481579/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7481579/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eThis study aimed to systematically evaluate the protective effects of residential greenness on diabetes risk and insulin sensitivity in a broad population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eNormalized Difference Vegetation Index (NDVI) was used to assess the level of residential greenness, while estimated Glucose Disposal Rate (eGDR) was employed to assess insulin sensitivity. Based on the nationwide cohort data from the China Health and Retirement Longitudinal Study (CHARLS), multistage statistical analysis methods were applied. First, multivariable logistic regression and restricted cubic splines (RCS) were used to evaluate the association between NDVI and baseline diabetes risk. Generalized additive models (GAM) were utilized to reveal the non-linear relationship and threshold effects between NDVI and baseline eGDR in the general population. A linear mixed-effects model was implemented to analyze the dynamic impact of NDVI on longitudinal changes in eGDR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe findings indicated a significant negative association between residential greenness and diabetes risk. For every 1-unit increase in NDVI, diabetes risk decreased by 64% (OR=0.36, 95% CI: 0.20-0.66, p\u0026lt;0.001). Non-linear analysis revealed a clear threshold for the protective effect. When NDVI = 0.318, diabetes risk significantly decreased. When NDVI = 0.348, eGDR levels showed a significant positive increase. Longitudinal tracking further confirmed that increases in NDVI significantly promoted the annual improvement rate of eGDR, with more pronounced improvements in highly greened areas.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThis study is the first to quantify the dual protective effects of residential greenness on glucose metabolism health in a national cohort, revealing non-linear threshold characteristics. These findings provide important scientific evidence for optimizing diabetes prevention and control strategies through urban greening.\u003c/p\u003e","manuscriptTitle":"The Protective Role of Residential Greenness on Diabetes Risk and Insulin Sensitivity: Results from a Nationwide Cohort Studies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-06 19:48:33","doi":"10.21203/rs.3.rs-7481579/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-15T22:01:39+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-26T17:59:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-20T11:30:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-14T19:30:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"93034784389273739743511184161541291104","date":"2026-02-07T20:46:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"217004056907656282587841332028259240008","date":"2026-02-07T16:08:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"317950605123563033396058953639377044795","date":"2026-02-05T18:46:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-05T17:08:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"103286564890191956118291145054669224400","date":"2026-02-05T16:54:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-28T13:57:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"272432990145030323620832154291533216999","date":"2026-01-26T18:46:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"32953318334923365093671927648991268440","date":"2026-01-23T09:24:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"209011996842054441252177342023546319382","date":"2026-01-21T18:46:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-08T23:42:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"186986135422894576791305897987481450446","date":"2025-10-01T15:36:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"65847331695070093435705479272515656525","date":"2025-09-28T21:50:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-23T09:02:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-08T05:32:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-08T05:31:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"Diabetology \u0026 Metabolic Syndrome","date":"2025-08-28T15:02:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"diabetology-and-metabolic-syndrome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dims","sideBox":"Learn more about [Diabetology \u0026 Metabolic Syndrome](http://dmsjournal.biomedcentral.com/)","snPcode":"13098","submissionUrl":"https://submission.nature.com/new-submission/13098/3","title":"Diabetology \u0026 Metabolic Syndrome","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"24169340-a491-42eb-bcaa-aa97f3678e77","owner":[],"postedDate":"October 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-03-15T22:08:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-06 19:48:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7481579","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7481579","identity":"rs-7481579","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}