Associations Between Storm Exposure Patterns and Metabolic Syndrome Risk in Chinese Adults: A CHARLS-Based Prospective Cohort Study

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While acute impacts of extreme rainfall are documented, evidence on long-term associations with chronic metabolic conditions remains limited. Methods: This prospective cohort study analyzed 16,278 middle-aged and older adults from the China Health and Retirement Longitudinal Study (CHARLS, 2011–2020). Five rainstorm exposure indicators (frequency, duration, intensity, peak rainfall, total volume) were assessed. Cox regression models evaluated associations with MetS incidence, adjusted for sociodemographic, behavioral, and clinical confounders. Spatial analyses included:(1)Global/Local Moran’s I to detect spatial clustering of provincial MetS prevalence.(2)Geographically Weighted Regression (GWR) to quantify location-specific associations between rainstorm exposures and MetS. Results: (1)Spatial Clustering:Significant spatial autocorrelation in MetS prevalence was observed (Global Moran’s I= 0.288, z-score = 4.025, p< 0.001), identifying high-high clusters (hotspots)​in Northern China and low-low clusters (coldspots)in Southern China. (2)Rainstorm-MetS Associations:Rainstorm Frequency:Nationwide negative association with MetS risk (HR = 0.94, 95% CI: 0.93–0.95), strongest in coastal regions (GWR coefficients: −0.027 to −0.017).Rainstorm Duration:Positive association (HR = 1.03, 95% CI: 1.02–1.04), with pronounced effects in Central/Eastern provinces(e.g.,Henan,Shandong;GWR coefficients: up to+0.205).Peak Rainfall:Spatially heterogeneous—protective in the South (GWR: −0.175 to −0.195) but detrimental in the Northwest (GWR: +0.193 to +0.205).Dose-Response:Non-linear patterns (U/J-shaped) emerged, with extreme exposures attenuating protective effects. Conclusion: Rainstorm exposures exhibit dual protective-risk effects on MetS, moderated by spatial context. Frequency and moderate peak rainfall reduce risk, while prolonged duration and extreme intensity elevate it. Spatial analyses reveal distinct geographic vulnerability patterns, underscoring the need for region-specific public health interventions targeting climate-resilient metabolic health strategies. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Background Metabolic syndrome (MetS) is a constellation of interrelated metabolic abnormalities—including central obesity, hypertension, hyperglycemia, and dyslipidemia—that markedly elevate the risk of cardiovascular disease and type 2 diabetes [1]. In recent years, the prevalence of MetS has risen sharply in China, particularly among older adults, driven by lifestyle transitions, dietary changes, and environmental factors linked to rapid urbanization [2]. At the same time, extreme rainfall events have become increasingly frequent and intense in Chinese cities under climate change [3]. Beyond inflicting infrastructure damage and economic losses, urban rainstorms disrupt daily routines and healthcare services. Prolonged exposure to such conditions may restrict outdoor physical activity, heighten psychological stress, and complicate the management of chronic illnesses, thereby contributing to the onset and progression of MetS [4,5]. Although a growing body of research has examined the health consequences of climate variability, empirical evidence on the indirect, long-term effects of urban rainstorms on chronic metabolic conditions remains scarce. Most existing studies focus on acute outcomes such as infectious diseases or injuries, while the relationship between environmental hazards like urban flooding and non-communicable diseases is still underexplored, especially among aging populations. The China Health and Retirement Longitudinal Study (CHARLS), a nationally representative panel survey of middle-aged and older Chinese adults, offers a unique opportunity to address this gap. By integrating individual health data with geolocated meteorological information, this study investigates how urban rainstorm exposure relates to MetS risk, thereby advancing a more nuanced understanding of climate-related chronic disease burdens in rapidly urbanizing contexts. 2. Methods Study design and population This study drew on data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative cohort survey conducted between 2011 and 2020 to evaluate the social, economic, and health conditions of Chinese residents [6]. The baseline survey, carried out from June 2011 to March 2012, recruited 17,708 participants from 10,257 households [6], with follow-up waves conducted biennially through standardized face-to-face interviews. To ensure representativeness and minimize bias, CHARLS employed a multistage probability sampling design across four hierarchical levels: county (or district), village (or community), household, and individual. Stratified sampling and probability proportional to size (PPS) were used to randomly select 150 counties/districts and 450 villages/communities from 28 provinces, with PPS applied at both county/district and village/community stages. Rigorous quality control protocols were implemented throughout survey design and data collection. The Computer-Assisted Personal Interviewing (CAPI) system enabled real-time error detection and correction of interviewer misconduct. Non-responding households were revisited in subsequent waves to improve participation. These measures contributed to consistently high response rates across waves 1 to 5 (80.5%, 88.3%, 87.1%, 86.4%, and 86.8%). The CHARLS protocol was approved by the Institutional Review Board of Peking University, adhering to the principles of the 1964 Declaration of Helsinki. Written informed consent was obtained from all participants. The dataset and documentation are publicly available at the CHARLS project website (http://charls.pku.edu.cn/). For this study, participants were tracked across two survey waves using unique identifiers. After excluding individuals with missing information on metabolic syndrome, urban rainstorm exposure, or covariates, a final analytical sample of 16,278 participants was included (see Fig. 1). 2.2. Exposure: Storm Count,Storm Duration,Storm Level ,Storm Peak Rainfall,Storm Volume Storm count refers to the total number of rainstorm events within a given period, reflecting the frequency of extreme precipitation occurrences in a region [7]. Storm Duration represents the length of time that each rainstorm event persists, which can influence the extent of flooding and infrastructure impact [8]. Storm Level denotes the classification or intensity grade of rainstorm events, commonly based on standardized thresholds of rainfall amount [9]. Storm Peak Rainfall is the highest rainfall intensity observed during a single rainstorm event, serving as an indicator of short-term precipitation extremes [10]. Storm Volume indicates the total amount of precipitation produced during a rainstorm event, reflecting the potential for surface runoff and flood risk [11]. These six indicators are widely used in meteorological studies to evaluate the characteristics, intensity, and potential impacts of rainstorm events [12].In recent years, global climate change has led to frequent occurrences of extreme weather events. As one of these events, rainstorms have had a profound impact on both the ecological environment and public health in China. Nationwide rainstorm datasets provide an important foundation for studying the spatiotemporal distribution characteristics, intensity changes, and underlying mechanisms of rainstorms.All rainfall data were obtained from: Bo He, Ming Yisen, Liu Qihang, et al. China Rainstorm Dataset, 2001–2019 . https://cstr.cn/31253.41.sciencedb.j00001.00290.005E952E. In consideration of the potential lagged effects of environmental exposures, rainfall data from 2010 and 2014 were utilized in the present analysis. 2.3. Outcome: Metabolic syndrome Metabolic syndrome (MetS) is a cluster of interrelated metabolic abnormalities, including central obesity, insulin resistance, hypertension, hyperglycemia, and dyslipidemia. Individuals with metabolic syndrome are at a significantly increased risk for developing type 2 diabetes, cardiovascular diseases, and other related chronic conditions [13]. The diagnosis of MetS is typically based on the presence of at least three out of five criteria: increased waist circumference, elevated fasting glucose, elevated triglycerides, reduced high-density lipoprotein cholesterol, and elevated blood pressure [14]. In recent years, the prevalence of metabolic syndrome has risen globally, becoming a major public health concern due to shifts in lifestyle and dietary patterns [15]. The specific diagnostic criteria for metabolic syndrome are as follows: (1)Waist circumference: For Chinese individuals, ≥90 cm for men and ≥80 cm for women. (2)Hypertriglyceridemia: Triglycerides ≥1.7 mmol/L, or currently receiving treatment. (3)Reduced high-density lipoprotein cholesterol (HDL-C): <1.03 mmol/L for men, <1.29 mmol/L for women, or currently receiving treatment. (4)Elevated blood pressure: Systolic blood pressure ≥130 mmHg and/or diastolic blood pressure ≥85 mmHg, or currently receiving antihypertensive treatment. (5)Elevated fasting blood glucose: ≥5.6 mmol/L, or previously diagnosed with type 2 diabetes.Diagnosed as metabolic syndrome if any three of the above criteria are met [14]. 2.4. Confounding variables Cofounders were chosen based on previous evidence [2,14,16–18] and included: (1) demographic variables: age group, sex, residence, marital status. (2) health-related behaviors: smoking status, chronic conditions , alcohol consumption, BMI,and physical activity; (3) socioeconomic status (SES): medical insurance,educational level, and employment status. 2.5 Spatial autocorrelation analysis Spatial autocorrelation analysis was performed to examine whether the prevalence of metabolic syndrome (MetS) exhibits spatial clustering across provincial units in China. Global Moran’s I was calculated to assess the overall spatial dependence, where positive values indicate clustering of similar values and negative values suggest spatial dispersion . In addition, the Local Indicators of Spatial Association (LISA) were employed to identify specific spatial clusters and outliers, distinguishing statistically significant hot spots (high–high clusters) and cold spots (low–low clusters) at the local level . These analyses provide critical insights into the spatial heterogeneity of MetS distribution, offering a foundation for subsequent geographically weighted regression (GWR) modeling.[19] 2.6 Geographically weighted regression (GWR) To further explore the spatial heterogeneity in the relationship between heavy rainfall exposure and the prevalence of metabolic syndrome (MetS), a geographically weighted regression (GWR) model was applied. Unlike traditional ordinary least squares (OLS) regression, which assumes spatial stationarity of relationships, GWR allows the regression coefficients to vary across geographic space, thereby capturing local variations in the strength and direction of associations . By calibrating a separate regression equation for each spatial unit, GWR provides location-specific parameter estimates and local goodness-of-fit measures, offering deeper insights into spatially varying processes that cannot be revealed by global models. This approach is particularly suitable for public health research in the context of environmental exposures, where the effects may differ significantly across regions.[20] 2.7. Statistical analysis Baseline characteristics of participants were summarized by metabolic syndrome status. Categorical variables were reported as counts and percentages, while continuous variables were first assessed for normality using the Kolmogorov-Smirnov test. Normally distributed variables were expressed as mean ± standard deviation, and skewed variables as median with interquartile range. To examine the association between urban heavy rainfall and the risk of metabolic syndrome, Cox proportional hazards models were employed, using survey waves as the timescale. Time-varying Cox regression models were applied to account for updated exposures and covariates [21,22]. Three models were specified: Model I (unadjusted), Model II (adjusted for age, sex, education, residence, and marital status), and Model III (further adjusted for medical insurance, employment, physical activity, BMI, and chronic disease status). Hazard ratios (HRs) with 95% confidence intervals (CIs) were estimated. The proportional hazards assumption was tested using Schoenfeld residuals, and no violations were detected (all p > 0.05). Effect modification was evaluated by interaction analysis, and significant modifiers were further explored through stratified regression models. To assess potential nonlinear relationships, rainfall exposures were modeled using natural cubic splines (df = 3), with likelihood ratio tests applied to evaluate nonlinearity. Multi-pollutant models incorporating two or three rainfall metrics were also constructed to control for confounding. Sensitivity analyses were conducted to ensure robustness. Additional adjustments included social activity participation (yes/no), physical exercise (insufficiently vs. sufficiently active), and sleep duration (9 hours) [23]. BMI was further categorized into low, moderate, and high groups, while physical activity intensity was divided into low, moderate, and high based on the International Physical Activity Questionnaire (IPAQ) [24,25]. In addition to individual-level analyses, geographic analyses were performed by linking provincial-level incidence of metabolic syndrome with urban heavy rainfall exposure. Spatial autocorrelation analysis was used to detect clustering patterns, and geographically weighted regression (GWR) was applied to evaluate spatial heterogeneity in the association between rainfall and metabolic syndrome across provinces. All statistical analyses and visualizations were performed in R version 4.3.2, with statistical significance set at p < 0.05. Spatial autocorrelation and geographically weighted regression analyses were conducted in ArcMap 10.7. 3. Results After excluding samples with missing data on exposure, outcomes, and confounding factors, a total of 16278 participants were finally included in this study . Compared with participants without missing data, those with missing data on any relevant variables were more likely to be older; illiterate or had received technical school education or above; lived in urban areas; were unemployed; and were physically active. Table 1 presents the baseline characteristics of the study participants by group. Among the 16278 eligible participants included in this study, 4235 (26.01%) were diagnosed with metabolic syndrome.(see Table 1) In a cohort of 16,278 participants, with 4,235 incident cases of metabolic syndrome, all heavy rainfall exposure indicators were significantly associated with a reduced risk of metabolic syndrome (HR < 1, all p < 0.001). These associations remained consistent across univariable, baseline-adjusted, and fully adjusted Cox regression models. Among the five exposure metrics, peak rainfall intensity showed the strongest protective effect , with each unit increase associated with a 43% lower risk of metabolic syndrome (HR = 0.57, 95% CI: 0.53–0.62). In contrast, rainfall duration demonstrated the weakest effect , corresponding to only a 3% risk reduction (HR = 0.97, 95% CI: 0.97–0.98). Both rainfall grade and total rainfall amount exhibited moderate protective effects (HR ≈ 0.79, risk reduction ≈ 21%), while frequency of rainfall events reduced risk by 6% (HR = 0.94, 95% CI: 0.93–0.95). Further analysis revealed dose–response relationships for peak rainfall intensity and rainfall frequency, with the highest exposure group (Q4) deriving the greatest benefit (risk reduction 42–43%). In contrast, rainfall duration exhibited an inverted U-shaped relationship , with the third quartile (Q3) showing the most favorable effect, suggesting that excessively prolonged rainfall exposure may attenuate the protective benefit. Collectively, these findings demonstrate that heavy rainfall exposure confers statistically significant and directionally consistent protective effects against metabolic syndrome, while the magnitude and shape of associations vary across different exposure indicators. (see Fig. 2 and Table2) Across all heavy rainfall exposure indicators, a consistent protective window was observed, with hazard ratios (HR) falling below 1 at moderate exposure levels. Dose–response analyses revealed predominantly non-linear associations , including U-shaped, J-shaped, and wave-like curves, highlighting the complexity of exposure–outcome relationships. At the extremes of exposure, particularly in the highest categories, wider confidence intervals indicated increased uncertainty in risk estimates.Regarding protective effects, the strongest associations were observed for total rainfall amount (HR as low as 0.0005), followed by rainfall frequency (HR = 0.20) and peak rainfall intensity (HR = 0.20) . In contrast, risk elevations were most pronounced for prolonged rainfall duration (HR = 1.50) and, to a lesser extent, for high-frequency exposure (HR = 0.80) . Collectively, these findings demonstrate that heavy rainfall exposure exerts a dual “protective–risk” effect on metabolic syndrome. While rainfall duration emerges as the principal risk indicator , rainfall frequency and total rainfall amount function as key protective factors .(see Fig. 3) Effect modification analyses indicated that BMI significantly altered the association between storm intensity and the primary endpoint (P<0.01 for interaction). The protective effect of storm intensity was strongest among participants with normal BMI (30; RR=0.76, 95% CI: 0.57–0.99; P>0.05). Urban residents showed significantly stronger protective effects of storm intensity, peak rainfall, and total precipitation than rural residents (all P interaction<0.05), with relative risks ranging from 0.43–0.54 in urban settings versus 0.60–0.78 in rural settings. No significant urban–rural differences were observed for storm duration or storm frequency (P interaction>0.10). Sleep duration predominantly modified the effect of storm duration (P interaction<0.05). The protective effect of longer storm duration was evident among participants with normal sleep (7–9 h; RR=0.99, 95% CI: 0.98–1.00), but was attenuated in short sleepers (9 h; RR=0.98, 95% CI: 0.94–1.01; P>0.05). No significant modifying effects of sleep duration were found for storm frequency, intensity, peak rainfall, or total precipitation.(see Fig. 4) Table 3 presents the results of the multi-rainstorm indicator models. Overall, heavy rainfall exposure was associated with a reduced risk of metabolic syndrome (HR = 0.950–0.971), but the strength of protection varied substantially across different indicator combinations. When rainfall duration was excluded, the combination of rainfall frequency, grade, peak intensity, and total amount showed the strongest protective effect (HR = 0.950). Peak rainfall intensity emerged as the core protective factor, whereas prolonged duration may attenuate the overall protective effect by inducing secondary risks. Fig.5 shows the results of the global spatial autocorrelation analysis (Global Moran’s I) indicated a significant clustering pattern in the spatial distribution of metabolic syndrome prevalence across Chinese provincial units (Moran’s I = 0.288, z-score = 4.025, p < 0.001). Furthermore, the local spatial autocorrelation analysis (LISA) revealed specific clusters, identifying statistically significant high–high clusters (hot spots) and low–low clusters (cold spots). The health effects of heavy rainfall exposure are not unidimensional but rather multifaceted, complex, and strongly dependent on geographical context. At the national level, rainfall frequency exhibited a consistently negative association with the prevalence of metabolic syndrome (MetS) (all coefficients negative). The magnitude of this effect (−0.027 to −0.017) suggests a relatively substantial impact, indicating that provinces experiencing more frequent heavy rainfall events tended to have lower MetS prevalence. In contrast, rainfall duration demonstrated a consistently positive correlation across all provinces (all coefficients positive), with particularly pronounced effects observed in the eastern and central regions (e.g., Henan, Shandong, Hubei), where longer rainfall duration was more strongly associated with higher MetS prevalence. Similarly, rainfall intensity (grade) showed a uniformly positive association (all coefficients positive), implying that stronger rainfall events were linked to higher prevalence. This effect was most evident in northern provinces such as Heilongjiang, Jilin, and Inner Mongolia.(Fig.6) Rainfall peak volume exhibited spatial heterogeneity (coefficients both positive and negative), representing the most complex finding. In much of the south, negative coefficients suggested that higher peak volumes were associated with lower prevalence, mirroring the pattern observed for rainfall frequency. In contrast, parts of the northwest displayed positive coefficients, indicating that larger peak volumes were linked to higher prevalence. Finally, total rainfall amount demonstrated a uniformly positive correlation nationwide (all coefficients positive), with the strongest effects concentrated in the southwest, where higher cumulative rainfall was most strongly associated with increased prevalence. Taken together, these findings highlight the necessity of employing multidimensional exposure indicators and geographically weighted approaches when assessing the health risks of climate change. Such methodological considerations are essential to generate more precise and scientifically robust conclusions, thereby informing the design of targeted and evidence-based public health interventions. 4. Discussion In this large population-based cohort, we found that exposure to multiple heavy rainfall indicators was consistently associated with a lower risk of metabolic syndrome, with peak rainfall intensity emerging as the strongest protective factor. These findings highlight the potential for short-term intense precipitation events to confer physiological or environmental benefits, possibly through mechanisms such as improved air quality, reduced ambient temperature, or altered physical activity patterns [22,26]. In contrast, prolonged rainfall duration was associated with diminished or even adverse effects, suggesting that excessive exposure may negate protective benefits, potentially due to disruptions in mobility, psychosocial stress, or infrastructure-related hazards [18]. The dose–response analyses further revealed non-linear associations, including U-shaped and J-shaped relationships, emphasizing the complexity of rainfall–health interactions. At moderate exposure levels, protective effects were most pronounced, whereas extreme exposure was accompanied by wider confidence intervals and greater uncertainty. This pattern is consistent with prior evidence that extreme climatic exposures may act as double-edged swords, conferring both protective and harmful influences depending on intensity, frequency, and duration [27,28]. Effect modification analyses revealed important heterogeneity across population subgroups. BMI significantly modified the relationship between storm intensity and metabolic syndrome, with normal-weight individuals benefiting the most, whereas the protective effect was attenuated among overweight and obese participants [28]. Similarly, urban residents derived greater protective effects compared with rural residents, which may reflect differences in infrastructure, housing quality, or healthcare accessibility [28]. Sleep duration emerged as an additional modifier, with normal sleepers (7–9h) maintaining moderate protection, whereas both short and long sleepers showed diminished effects, particularly for rainfall duration. These findings underscore the role of individual- and context-specific vulnerability factors in shaping climate–health relationships [28]. Our results align with previous studies reporting that meteorological and extreme weather events often show non-linear associations with cardiometabolic outcomes. Prior research has shown that moderate levels of precipitation and humidity may improve air pollutant dispersion and reduce cardiovascular risk [22], while excessive or prolonged rainfall can increase risks through flooding, infectious disease transmission, and stress-related pathways [29]. Similarly, non-linear U- or J-shaped associations have been reported between temperature and cardiometabolic outcomes [30], suggesting that both insufficient and excessive climatic exposures can undermine protective effects. The stronger protective associations observed in urban compared to rural areas are also consistent with earlier findings that urban infrastructure may buffer against certain climate-related risks, though potentially at the cost of greater vulnerability to extreme events [5]. A substantial body of research has demonstrated that air pollution, particularly fine particulate matter (PM₂.₅) and traffic-related pollutants, exerts adverse effects on metabolic health. Epidemiological studies consistently show that long-term exposure to ambient air pollution is associated with increased prevalence of obesity, insulin resistance, and metabolic syndrome (MetS)[31]. Mechanistically, inflammation has been identified as a key mediator of these associations. Inhaled pollutants induce oxidative stress in the lungs, activating inflammatory pathways and leading to the systemic release of cytokines such as interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α) [32]. These circulating inflammatory mediators contribute to endothelial dysfunction, impaired glucose uptake, and altered lipid metabolism, all of which are central components of MetS. Experimental studies provide further biological plausibility. Rodent models exposed to PM₂.₅ show increased macrophage infiltration in adipose tissue, mitochondrial dysfunction, and impaired insulin signaling [33]. Similarly, human cohort studies have linked chronic air pollution exposure to elevated C-reactive protein and other inflammatory biomarkers, reinforcing the hypothesis that low-grade systemic inflammation serves as a biological bridge between environmental exposure and metabolic dysfunction [34]. Beyond direct metabolic effects, air pollution has also been shown to exacerbate visceral adiposity, a critical driver of cardiometabolic risk, through inflammation-induced adipose tissue remodeling [35]. More recent reviews emphasize the heterogeneity of effects across populations. Urban residents, individuals with obesity, and those with pre-existing cardiometabolic risk appear more susceptible to the pro-inflammatory and metabolic consequences of pollution exposure [36]. Moreover, interactions between air pollution and other lifestyle factors such as diet and physical activity may further amplify inflammatory responses, suggesting that the health burden of air pollution is not uniform but context-dependent. Together, these findings support a multifactorial model in which air pollution contributes to metabolic syndrome via inflammatory and oxidative stress pathways, moderated by individual and environmental vulnerabilities. Through spatial analysis, it was found that the north–south differences in the association between storm exposure and metabolic syndrome are the result of multiple interacting factors, including climate environment, socioeconomic status, and individual lifestyle:Climate conditions: Frequent and moderate rainfall in the south brings protective effects, while prolonged, intense, and irregular rainfall in the north increases risks.Diet and obesity: High-salt, high-fat diets and higher BMI levels in the north amplify the risk of metabolic syndrome.Health behaviors: The south has advantages in sleep, physical activity, and access to medical resources.Adaptive differences: Southern residents and infrastructure are better adapted to heavy rainfall, whereas the north is more vulnerable to extreme precipitation events. 5. Conclusion Based on a large population-based cohort study integrating meteorological and health survey data in China, this research provides robust evidence on the spatially heterogeneous associations between multiple dimensions of heavy rainfall exposure and the risk of metabolic syndrome (MetS). The findings demonstrate that rainfall characteristics exhibit both protective and adverse effects on MetS prevalence, influenced by geographic context and individual vulnerabilities. Key results from spatial analyses revealed significant global spatial autocorrelation in MetS prevalence (Global Moran’s I = 0.288, p < 0.001), identifying high-high clusters (hotspots) in Northern China and low-low clusters (coldspots) in Southern China. Geographically Weighted Regression (GWR) further illustrated the spatial non-stationarity of associations: rainfall frequency showed a consistent negative correlation with MetS risk, suggesting protective effects, particularly in coastal regions. In contrast, rainfall duration and intensity exhibited positive associations, with prolonged duration and higher intensity linked to increased MetS prevalence, especially in Central and Northern provinces. Peak rainfall revealed marked spatial heterogeneity—protective in the South (negative correlation) but detrimental in the Northwest (positive correlation). These results underscore the complexity of climate-health interactions and highlight the necessity of employing multidimensional exposure metrics and spatially explicit approaches to inform targeted public health interventions and climate-resilient health policies across diverse regions. Declarations Authors’ contributions ZiJie Cai: Writing – original draft, Supervision, Project administration, Funding acquisition. ChunZhi Tang: Writing – review,Supervision, Project administration. LiXiang Gan: Writing – review & editing, Supervision, Project administration. GuangPeng Zhang: Writing – review & editing, Writing – Software, Methodology, Investigation, Formal anal ysis, Data curation. YaLong Qiu:Writing – review & editing, Methodology, Investigation. HongYing Tian: Writing – review & editing, Visualization, Validation, Software, Resources, Formal analysis. Funding Not applicable. Data Availability Not applicable. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests Not applicable. References Alberti KGMM, Zimmet P, Shaw J. Metabolic syndrome--a new world-wide definition. A Consensus Statement from the International Diabetes Federation. Diabet Med England; 2006 May;23(5):469–480. PMID:16681555 Pan Y, Wu X, Liu Y, Li Z, Yang Y, Luo Y. Urbanization and Cognitive Function Among Middle-Aged and Old Adults in China. J Gerontol B Psychol Sci Soc Sci United States; 2022 Dec 29;77(12):2338–2347. PMID:35908238 Deshpande A, Chang HH, Levy K. 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PMID:16805397 Sun Q, Yue P, Deiuliis JA, Lumeng CN, Kampfrath T, Mikolaj MB, Cai Y, Ostrowski MC, Lu B, Parthasarathy S, Brook RD, Moffatt-Bruce SD, Chen LC, Rajagopalan S. Ambient air pollution exaggerates adipose inflammation and insulin resistance in a mouse model of diet-induced obesity. Circulation United States; 2009 Feb 3;119(4):538–546. PMID:19153269 Eze IC, Schaffner E, Fischer E, Schikowski T, Adam M, Imboden M, Tsai M, Carballo D, von Eckardstein A, Künzli N, Schindler C, Probst-Hensch N. Long-term air pollution exposure and diabetes in a population-based Swiss cohort. Environ Int Netherlands; 2014 Sep;70:95–105. PMID:24912113 Tables Tables 1 to 3 are available in the Supplementary Files section Additional Declarations No competing interests reported. Supplementary Files sampleInfocharlshuimian2011.xlsx sampleInfocharlshuimian2015.xlsx merge.filtercharlsshuimian2011.xlsx merge.filtercharlsshuimian2015.xlsx SupplementaryMaterial.tif Tables.docx Cite Share Download PDF Status: Published Journal Publication published 15 Feb, 2026 Read the published version in International Journal of Health Geographics → Version 1 posted Editorial decision: Revision requested 23 Oct, 2025 Reviews received at journal 21 Oct, 2025 Reviews received at journal 18 Oct, 2025 Reviews received at journal 25 Sep, 2025 Reviewers agreed at journal 24 Sep, 2025 Reviewers agreed at journal 21 Sep, 2025 Reviewers agreed at journal 19 Sep, 2025 Reviewers invited by journal 19 Sep, 2025 Submission checks completed at journal 10 Sep, 2025 First submitted to journal 08 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. 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2","display":"","copyAsset":false,"role":"figure","size":445865,"visible":true,"origin":"","legend":"\u003cp\u003eUnivariable regression analysis\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7504116/v1/694cdf26b1a952ef3133734f.png"},{"id":92611209,"identity":"4b7b031d-1faf-4f9d-a15a-5831eb3ab34c","added_by":"auto","created_at":"2025-10-01 16:24:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":967888,"visible":true,"origin":"","legend":"\u003cp\u003eNatural cubic splines of urban rainstorm and the risk of metabolic syndrome. (a)Storm duration, (b) Storm count, (c) Storm level, (d) Storm peak rainfall, (e)Storm volume. The solid dark blue line indicates the estimated effect, and the light blue shaded area represents the 95% confidence interval (CI). The analysis results are adjusted for age group, sex, educational level, residence (urban or rural), marital status, medical insurance, employment status, smoking status, sleep duration, physical activity, and alcohol consumption. This figure shows that as urban rainstorm changes, its relationship with the risk of metabolic syndrome also varies. When both the estimated effect and the 95% confidence interval are greater than 1 (reference line), it indicates a risk factor; when both are less than 1, it indicates a protective factor; if the 95% confidence interval includes the reference line, it suggests that the association is not statistically significant.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7504116/v1/e52c21f712596c93e8817128.png"},{"id":92611210,"identity":"90a02d40-b8d6-4b4b-98a1-f31cffd9d7b4","added_by":"auto","created_at":"2025-10-01 16:24:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1503059,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis of the relationship between exposure to ,Storm duration, Storm count, Storm level, Storm peak rainfall, Storm volume. and the risk of metabolic syndrome stratified by sleep duration and BMI among 3,608 participants: results from time-varying Cox regression analysis.\u003c/p\u003e\n\u003cp\u003eEach rainstorm indicator was analyzed separately, and each model was adjusted for age group, sex, educational level, residence (urban or rural), marital status, medical insurance, employment status, smoking status, alcohol consumption, sleep duration, and physical activity.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7504116/v1/4878356441c209bfa455b596.png"},{"id":92610482,"identity":"8a3cdd7a-769c-429f-8b56-3815685990c1","added_by":"auto","created_at":"2025-10-01 16:16:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":694926,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial autocorrelation analysis\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7504116/v1/fe7efa2bbd8fb77c27d41a35.png"},{"id":92611397,"identity":"ddd8d71a-4ec3-47de-942c-079cb0b313f7","added_by":"auto","created_at":"2025-10-01 16:32:35","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":777721,"visible":true,"origin":"","legend":"\u003cp\u003eGeographically weighted regression\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7504116/v1/41e69ba4aa96b43e2270ecce.png"},{"id":102785273,"identity":"051fb62d-b29e-4300-a99e-65581b3ecf58","added_by":"auto","created_at":"2026-02-16 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Background","content":"\u003cp\u003eMetabolic syndrome (MetS) is a constellation of interrelated metabolic abnormalities—including central obesity, hypertension, hyperglycemia, and dyslipidemia—that markedly elevate the risk of cardiovascular disease and type 2 diabetes [1]. In recent years, the prevalence of MetS has risen sharply in China, particularly among older adults, driven by lifestyle transitions, dietary changes, and environmental factors linked to rapid urbanization [2].\u003c/p\u003e\n\u003cp\u003eAt the same time, extreme rainfall events have become increasingly frequent and intense in Chinese cities under climate change [3]. Beyond inflicting infrastructure damage and economic losses, urban rainstorms disrupt daily routines and healthcare services. Prolonged exposure to such conditions may restrict outdoor physical activity, heighten psychological stress, and complicate the management of chronic illnesses, thereby contributing to the onset and progression of MetS [4,5].\u003c/p\u003e\n\u003cp\u003eAlthough a growing body of research has examined the health consequences of climate variability, empirical evidence on the indirect, long-term effects of urban rainstorms on chronic metabolic conditions remains scarce. Most existing studies focus on acute outcomes such as infectious diseases or injuries, while the relationship between environmental hazards like urban flooding and non-communicable diseases is still underexplored, especially among aging populations.\u003c/p\u003e\n\u003cp\u003eThe China Health and Retirement Longitudinal Study (CHARLS), a nationally representative panel survey of middle-aged and older Chinese adults, offers a unique opportunity to address this gap. By integrating individual health data with geolocated meteorological information, this study investigates how urban rainstorm exposure relates to MetS risk, thereby advancing a more nuanced understanding of climate-related chronic disease burdens in rapidly urbanizing contexts.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003ch3\u003eStudy design and population\u003c/h3\u003e\n\u003cp\u003eThis study drew on data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative cohort survey conducted between 2011 and 2020 to evaluate the social, economic, and health conditions of Chinese residents [6]. The baseline survey, carried out from June 2011 to March 2012, recruited 17,708 participants from 10,257 households [6], with follow-up waves conducted biennially through standardized face-to-face interviews.\u003c/p\u003e\n\u003cp\u003eTo ensure representativeness and minimize bias, CHARLS employed a multistage probability sampling design across four hierarchical levels: county (or district), village (or community), household, and individual. Stratified sampling and probability proportional to size (PPS) were used to randomly select 150 counties/districts and 450 villages/communities from 28 provinces, with PPS applied at both county/district and village/community stages.\u003c/p\u003e\n\u003cp\u003eRigorous quality control protocols were implemented throughout survey design and data collection. The Computer-Assisted Personal Interviewing (CAPI) system enabled real-time error detection and correction of interviewer misconduct. Non-responding households were revisited in subsequent waves to improve participation. These measures contributed to consistently high response rates across waves 1 to 5 (80.5%, 88.3%, 87.1%, 86.4%, and 86.8%).\u003c/p\u003e\n\u003cp\u003eThe CHARLS protocol was approved by the Institutional Review Board of Peking University, adhering to the principles of the 1964 Declaration of Helsinki. Written informed consent was obtained from all participants. The dataset and documentation are publicly available at the CHARLS project website (http://charls.pku.edu.cn/).\u003c/p\u003e\n\u003cp\u003eFor this study, participants were tracked across two survey waves using unique identifiers. After excluding individuals with missing information on metabolic syndrome, urban rainstorm exposure, or covariates, a final analytical sample of 16,278 participants was included (see Fig. 1).\u003c/p\u003e\n\u003ch3\u003e2.2.\u0026nbsp;Exposure: Storm Count,Storm Duration,Storm Level ,Storm Peak Rainfall,Storm Volume\u003c/h3\u003e\n\u003cp\u003eStorm\u0026nbsp;count\u0026nbsp;refers to the total number of rainstorm events within a given period, reflecting the frequency of extreme precipitation occurrences in a region\u0026nbsp;[7]. Storm Duration represents the length of time that each rainstorm event persists, which can influence the extent of flooding and infrastructure impact\u0026nbsp;[8]. Storm Level denotes the classification or intensity grade of rainstorm events, commonly based on standardized thresholds of rainfall amount\u0026nbsp;[9]. Storm Peak Rainfall is the highest rainfall intensity observed during a single rainstorm event, serving as an indicator of short-term precipitation extremes\u0026nbsp;[10]. Storm Volume indicates the total amount of precipitation produced during a rainstorm event, reflecting the potential for surface runoff and flood risk\u0026nbsp;[11]. These six indicators are widely used in meteorological studies to evaluate the characteristics, intensity, and potential impacts of rainstorm events\u0026nbsp;[12].In recent years, global climate change has led to frequent occurrences of extreme weather events. As one of these events, rainstorms have had a profound impact on both the ecological environment and public health in China. Nationwide rainstorm datasets provide an important foundation for studying the spatiotemporal distribution characteristics, intensity changes, and underlying mechanisms of rainstorms.All rainfall data were obtained from: Bo He, Ming Yisen, Liu Qihang, et al. \u003cem\u003eChina Rainstorm Dataset, 2001\u0026ndash;2019\u003c/em\u003e. https://cstr.cn/31253.41.sciencedb.j00001.00290.005E952E. In consideration of the potential lagged effects of environmental exposures, rainfall data from 2010 and 2014 were utilized in the present analysis.\u003c/p\u003e\n\u003ch3\u003e2.3. Outcome:\u0026nbsp;Metabolic syndrome\u003c/h3\u003e\n\u003cp\u003eMetabolic syndrome (MetS) is a cluster of interrelated metabolic abnormalities, including central obesity, insulin resistance, hypertension, hyperglycemia, and dyslipidemia. Individuals with metabolic syndrome are at a significantly increased risk for developing type 2 diabetes, cardiovascular diseases, and other related chronic conditions\u0026nbsp;[13]. The diagnosis of MetS is typically based on the presence of at least three out of five criteria: increased waist circumference, elevated fasting glucose, elevated triglycerides, reduced high-density lipoprotein cholesterol, and elevated blood pressure\u0026nbsp;[14]. In recent years, the prevalence of metabolic syndrome has risen globally, becoming a major public health concern due to shifts in lifestyle and dietary patterns\u0026nbsp;[15].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe specific diagnostic criteria for metabolic syndrome are as follows:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(1)Waist circumference:\u003c/strong\u003e For Chinese individuals, \u0026ge;90 cm for men and \u0026ge;80 cm for women.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(2)Hypertriglyceridemia:\u003c/strong\u003e Triglycerides \u0026ge;1.7 mmol/L, or currently receiving treatment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(3)Reduced high-density lipoprotein cholesterol (HDL-C):\u003c/strong\u003e \u0026lt;1.03 mmol/L for men, \u0026lt;1.29 mmol/L for women, or currently receiving treatment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(4)Elevated blood pressure:\u003c/strong\u003e Systolic blood pressure \u0026ge;130 mmHg and/or diastolic blood pressure \u0026ge;85 mmHg, or currently receiving antihypertensive treatment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(5)Elevated fasting blood glucose:\u003c/strong\u003e \u0026ge;5.6 mmol/L, or previously diagnosed with type 2 diabetes.Diagnosed as metabolic syndrome if any three of the above criteria are met\u0026nbsp;[14].\u003c/p\u003e\n\u003ch3\u003e2.4.\u0026nbsp;Confounding variables\u003c/h3\u003e\n\u003cp\u003e\u003cbr\u003e\u0026nbsp;Cofounders were chosen based on previous evidence [2,14,16\u0026ndash;18] and included: (1) demographic variables: age group, sex, residence, marital status. (2) health-related behaviors: smoking status, \u003cstrong\u003echronic conditions\u003c/strong\u003e, alcohol consumption, BMI,and\u0026nbsp;physical activity; (3) socioeconomic status (SES):\u0026nbsp;medical insurance,educational level, and\u0026nbsp;employment status.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Spatial autocorrelation analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpatial autocorrelation analysis was performed to examine whether the prevalence of metabolic syndrome (MetS) exhibits spatial clustering across provincial units in China. Global Moran\u0026rsquo;s I was calculated to assess the overall spatial dependence, where positive values indicate clustering of similar values and negative values suggest spatial dispersion . In addition, the Local Indicators of Spatial Association (LISA) were employed to identify specific spatial clusters and outliers, distinguishing statistically significant hot spots (high\u0026ndash;high clusters) and cold spots (low\u0026ndash;low clusters) at the local level . These analyses provide critical insights into the spatial heterogeneity of MetS distribution, offering a foundation for subsequent geographically weighted regression (GWR) modeling.[19]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Geographically weighted regression (GWR)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further explore the spatial heterogeneity in the relationship between heavy rainfall exposure and the prevalence of metabolic syndrome (MetS), a geographically weighted regression (GWR) model was applied. Unlike traditional ordinary least squares (OLS) regression, which assumes spatial stationarity of relationships, GWR allows the regression coefficients to vary across geographic space, thereby capturing local variations in the strength and direction of associations . By calibrating a separate regression equation for each spatial unit, GWR provides location-specific parameter estimates and local goodness-of-fit measures, offering deeper insights into spatially varying processes that cannot be revealed by global models. This approach is particularly suitable for public health research in the context of environmental exposures, where the effects may differ significantly across regions.[20]\u003c/p\u003e\n\u003ch3\u003e2.7.\u0026nbsp;Statistical analysis\u003c/h3\u003e\n\u003cp\u003eBaseline characteristics of participants were summarized by metabolic syndrome status. Categorical variables were reported as counts and percentages, while continuous variables were first assessed for normality using the Kolmogorov-Smirnov test. Normally distributed variables were expressed as mean \u0026plusmn; standard deviation, and skewed variables as median with interquartile range.\u003c/p\u003e\n\u003cp\u003eTo examine the association between urban heavy rainfall and the risk of metabolic syndrome, Cox proportional hazards models were employed, using survey waves as the timescale. Time-varying Cox regression models were applied to account for updated exposures and covariates [21,22]. Three models were specified: Model I (unadjusted), Model II (adjusted for age, sex, education, residence, and marital status), and Model III (further adjusted for medical insurance, employment, physical activity, BMI, and chronic disease status). Hazard ratios (HRs) with 95% confidence intervals (CIs) were estimated. The proportional hazards assumption was tested using Schoenfeld residuals, and no violations were detected (all p \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003eEffect modification was evaluated by interaction analysis, and significant modifiers were further explored through stratified regression models. To assess potential nonlinear relationships, rainfall exposures were modeled using natural cubic splines (df = 3), with likelihood ratio tests applied to evaluate nonlinearity. Multi-pollutant models incorporating two or three rainfall metrics were also constructed to control for confounding.\u003c/p\u003e\n\u003cp\u003eSensitivity analyses were conducted to ensure robustness. Additional adjustments included social activity participation (yes/no), physical exercise (insufficiently vs. sufficiently active), and sleep duration (\u0026lt;7, 7\u0026ndash;9, \u0026gt;9 hours) [23]. BMI was further categorized into low, moderate, and high groups, while physical activity intensity was divided into low, moderate, and high based on the International Physical Activity Questionnaire (IPAQ) [24,25].\u003c/p\u003e\n\u003cp\u003eIn addition to individual-level analyses, geographic analyses were performed by linking provincial-level incidence of metabolic syndrome with urban heavy rainfall exposure. Spatial autocorrelation analysis was used to detect clustering patterns, and geographically weighted regression (GWR) was applied to evaluate spatial heterogeneity in the association between rainfall and metabolic syndrome across provinces.\u003c/p\u003e\n\u003cp\u003eAll statistical analyses and visualizations were performed in R version 4.3.2, with statistical significance set at p \u0026lt; 0.05. Spatial autocorrelation and geographically weighted regression analyses were conducted in ArcMap 10.7.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eAfter excluding samples with missing data on exposure, outcomes, and confounding factors, a total of 16278 participants were finally included in this study . Compared with participants without missing data, those with missing data on any relevant variables were more likely to be older; illiterate or had received technical school education or above; lived in urban areas; were unemployed; and were physically active.\u003c/p\u003e\n\u003cp\u003eTable 1 presents the baseline characteristics of the study participants by group. Among the 16278 eligible participants included in this study, 4235 (26.01%) were diagnosed with metabolic syndrome.(see Table 1)\u003c/p\u003e\n\u003cp\u003eIn a cohort of 16,278 participants, with 4,235 incident cases of metabolic syndrome, all heavy rainfall exposure indicators were significantly associated with a reduced risk of metabolic syndrome (HR \u0026lt; 1, all p \u0026lt; 0.001). These associations remained consistent across univariable, baseline-adjusted, and fully adjusted Cox regression models.\u003c/p\u003e\n\u003cp\u003eAmong the five exposure metrics, \u003cstrong\u003epeak rainfall intensity showed the strongest protective effect\u003c/strong\u003e, with each unit increase associated with a 43% lower risk of metabolic syndrome (HR = 0.57, 95% CI: 0.53\u0026ndash;0.62). In contrast, \u003cstrong\u003erainfall duration demonstrated the weakest effect\u003c/strong\u003e, corresponding to only a 3% risk reduction (HR = 0.97, 95% CI: 0.97\u0026ndash;0.98). Both \u003cstrong\u003erainfall grade and total rainfall amount exhibited moderate protective effects\u003c/strong\u003e (HR \u0026asymp; 0.79, risk reduction \u0026asymp; 21%), while \u003cstrong\u003efrequency of rainfall events reduced risk by 6%\u003c/strong\u003e (HR = 0.94, 95% CI: 0.93\u0026ndash;0.95).\u003c/p\u003e\n\u003cp\u003eFurther analysis revealed \u003cstrong\u003edose\u0026ndash;response relationships\u003c/strong\u003e for peak rainfall intensity and rainfall frequency, with the \u003cstrong\u003ehighest exposure group (Q4) deriving the greatest benefit\u003c/strong\u003e (risk reduction 42\u0026ndash;43%). In contrast, \u003cstrong\u003erainfall duration exhibited an inverted U-shaped relationship\u003c/strong\u003e, with the third quartile (Q3) showing the most favorable effect, suggesting that excessively prolonged rainfall exposure may attenuate the protective benefit.\u003c/p\u003e\n\u003cp\u003eCollectively, these findings demonstrate that heavy rainfall exposure confers statistically significant and directionally consistent protective effects against metabolic syndrome, while the magnitude and shape of associations vary across different exposure indicators.\u003c/p\u003e\n\u003cp\u003e(see Fig. 2 and Table2)\u003c/p\u003e\n\u003cp\u003eAcross all heavy rainfall exposure indicators, a consistent \u003cstrong\u003eprotective window\u003c/strong\u003e was observed, with hazard ratios (HR) falling below 1 at moderate exposure levels. Dose\u0026ndash;response analyses revealed predominantly \u003cstrong\u003enon-linear associations\u003c/strong\u003e, including U-shaped, J-shaped, and wave-like curves, highlighting the complexity of exposure\u0026ndash;outcome relationships. At the extremes of exposure, particularly in the highest categories, wider confidence intervals indicated increased uncertainty in risk estimates.Regarding protective effects, the \u003cstrong\u003estrongest associations were observed for total rainfall amount\u003c/strong\u003e (HR as low as 0.0005), followed by \u003cstrong\u003erainfall frequency (HR = 0.20)\u003c/strong\u003e and \u003cstrong\u003epeak rainfall intensity (HR = 0.20)\u003c/strong\u003e. In contrast, risk elevations were most pronounced for \u003cstrong\u003eprolonged rainfall duration (HR = 1.50)\u003c/strong\u003e and, to a lesser extent, for \u003cstrong\u003ehigh-frequency exposure (HR = 0.80)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eCollectively, these findings demonstrate that heavy rainfall exposure exerts a \u003cstrong\u003edual \u0026ldquo;protective\u0026ndash;risk\u0026rdquo; effect\u003c/strong\u003e on metabolic syndrome. While \u003cstrong\u003erainfall duration emerges as the principal risk indicator\u003c/strong\u003e, \u003cstrong\u003erainfall frequency and total rainfall amount function as key protective factors\u003c/strong\u003e.(see Fig. 3)\u003c/p\u003e\n\u003cp\u003eEffect modification analyses indicated that BMI significantly altered the association between storm intensity and the primary endpoint (P\u0026lt;0.01 for interaction). The protective effect of storm intensity was strongest among participants with normal BMI (\u0026lt;25), with a relative risk (RR) of 0.79 (95% CI: 0.72\u0026ndash;0.87), attenuated among those overweight (BMI 25\u0026ndash;30; RR=0.7, 95% CI: 0.61\u0026ndash;0.79), and no longer evident in the obese group (BMI \u0026gt;30; RR=0.76, 95% CI: 0.57\u0026ndash;0.99; P\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003eUrban residents showed significantly stronger protective effects of storm intensity, peak rainfall, and total precipitation than rural residents (all P interaction\u0026lt;0.05), with relative risks ranging from 0.43\u0026ndash;0.54 in urban settings versus 0.60\u0026ndash;0.78 in rural settings. No significant urban\u0026ndash;rural differences were observed for storm duration or storm frequency (P interaction\u0026gt;0.10).\u003c/p\u003e\n\u003cp\u003eSleep duration predominantly modified the effect of storm duration (P interaction\u0026lt;0.05). The protective effect of longer storm duration was evident among participants with normal sleep (7\u0026ndash;9 h; RR=0.99, 95% CI: 0.98\u0026ndash;1.00), but was attenuated in short sleepers (\u0026lt;7 h; RR=0.97, 95% CI: 0.96\u0026ndash;0.98) and disappeared among long sleepers (\u0026gt;9 h; RR=0.98, 95% CI: 0.94\u0026ndash;1.01; P\u0026gt;0.05). No significant modifying effects of sleep duration were found for storm frequency, intensity, peak rainfall, or total precipitation.(see Fig. 4)\u003c/p\u003e\n\u003cp\u003eTable 3 presents the results of the multi-rainstorm indicator models. Overall, heavy rainfall exposure was associated with a reduced risk of metabolic syndrome (HR = 0.950\u0026ndash;0.971), but the strength of protection varied substantially across different indicator combinations. When rainfall duration was excluded, the combination of rainfall frequency, grade, peak intensity, and total amount showed the strongest protective effect (HR = 0.950). Peak rainfall intensity emerged as the core protective factor, whereas prolonged duration may attenuate the overall protective effect by inducing secondary risks.\u003c/p\u003e\n\u003cp\u003eFig.5 shows the results of the global spatial autocorrelation analysis (Global Moran\u0026rsquo;s I) indicated a significant clustering pattern in the spatial distribution of metabolic syndrome prevalence across Chinese provincial units (Moran\u0026rsquo;s I = 0.288, z-score = 4.025, p \u0026lt; 0.001). Furthermore, the local spatial autocorrelation analysis (LISA) revealed specific clusters, identifying statistically significant high\u0026ndash;high clusters (hot spots) and low\u0026ndash;low clusters (cold spots).\u003c/p\u003e\n\u003cp\u003eThe health effects of heavy rainfall exposure are not unidimensional but rather multifaceted, complex, and strongly dependent on geographical context. At the national level, rainfall frequency exhibited a consistently negative association with the prevalence of metabolic syndrome (MetS) (all coefficients negative). The magnitude of this effect (\u0026minus;0.027 to\u0026nbsp;\u0026minus;0.017) suggests a relatively substantial impact, indicating that provinces experiencing more frequent heavy rainfall events tended to have lower MetS prevalence. In contrast, rainfall duration demonstrated a consistently positive correlation across all provinces (all coefficients positive), with particularly pronounced effects observed in the eastern and central regions (e.g., Henan, Shandong, Hubei), where longer rainfall duration was more strongly associated with higher MetS prevalence. Similarly, rainfall intensity (grade) showed a uniformly positive association (all coefficients positive), implying that stronger rainfall events were linked to higher prevalence. This effect was most evident in northern provinces such as Heilongjiang, Jilin, and Inner Mongolia.(Fig.6)\u003c/p\u003e\n\u003cp\u003eRainfall peak volume exhibited spatial heterogeneity (coefficients both positive and negative), representing the most complex finding. In much of the south, negative coefficients suggested that higher peak volumes were associated with lower prevalence, mirroring the pattern observed for rainfall frequency. In contrast, parts of the northwest displayed positive coefficients, indicating that larger peak volumes were linked to higher prevalence. Finally, total rainfall amount demonstrated a uniformly positive correlation nationwide (all coefficients positive), with the strongest effects concentrated in the southwest, where higher cumulative rainfall was most strongly associated with increased prevalence.\u003c/p\u003e\n\u003cp\u003eTaken together, these findings highlight the necessity of employing multidimensional exposure indicators and geographically weighted approaches when assessing the health risks of climate change. Such methodological considerations are essential to generate more precise and scientifically robust conclusions, thereby informing the design of targeted and evidence-based public health interventions.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this large population-based cohort, we found that exposure to multiple heavy rainfall indicators was consistently associated with a lower risk of metabolic syndrome, with peak rainfall intensity emerging as the strongest protective factor. These findings highlight the potential for short-term intense precipitation events to confer physiological or environmental benefits, possibly through mechanisms such as improved air quality, reduced ambient temperature, or altered physical activity patterns\u0026nbsp;[22,26]. In contrast, prolonged rainfall duration was associated with diminished or even adverse effects, suggesting that excessive exposure may negate protective benefits, potentially due to disruptions in mobility, psychosocial stress, or infrastructure-related hazards\u0026nbsp;[18].\u003c/p\u003e\n\u003cp\u003eThe dose\u0026ndash;response analyses further revealed non-linear associations, including U-shaped and J-shaped relationships, emphasizing the complexity of rainfall\u0026ndash;health interactions. At moderate exposure levels, protective effects were most pronounced, whereas extreme exposure was accompanied by wider confidence intervals and greater uncertainty. This pattern is consistent with prior evidence that extreme climatic exposures may act as double-edged swords, conferring both protective and harmful influences depending on intensity, frequency, and duration\u0026nbsp;[27,28].\u003c/p\u003e\n\u003cp\u003eEffect modification analyses revealed important heterogeneity across population subgroups. BMI significantly modified the relationship between storm intensity and metabolic syndrome, with normal-weight individuals benefiting the most, whereas the protective effect was attenuated among overweight and obese participants\u0026nbsp;[28]. Similarly, urban residents derived greater protective effects compared with rural residents, which may reflect differences in infrastructure, housing quality, or healthcare accessibility\u0026nbsp;[28]. Sleep duration emerged as an additional modifier, with normal sleepers (7\u0026ndash;9h) maintaining moderate protection, whereas both short and long sleepers showed diminished effects, particularly for rainfall duration. These findings underscore the role of individual- and context-specific vulnerability factors in shaping climate\u0026ndash;health relationships\u0026nbsp;[28].\u003c/p\u003e\n\u003cp\u003eOur results align with previous studies reporting that meteorological and extreme weather events often show non-linear associations with cardiometabolic outcomes. Prior research has shown that moderate levels of precipitation and humidity may improve air pollutant dispersion and reduce cardiovascular risk\u0026nbsp;[22], while excessive or prolonged rainfall can increase risks through flooding, infectious disease transmission, and stress-related pathways\u0026nbsp;[29]. Similarly, non-linear U- or J-shaped associations have been reported between temperature and cardiometabolic outcomes\u0026nbsp;[30], suggesting that both insufficient and excessive climatic exposures can undermine protective effects. The stronger protective associations observed in urban compared to rural areas are also consistent with earlier findings that urban infrastructure may buffer against certain climate-related risks, though potentially at the cost of greater vulnerability to extreme events\u0026nbsp;[5].\u003c/p\u003e\n\u003cp\u003eA substantial body of research has demonstrated that air pollution, particularly fine particulate matter (PM₂.₅) and traffic-related pollutants, exerts adverse effects on metabolic health. Epidemiological studies consistently show that long-term exposure to ambient air pollution is associated with increased prevalence of obesity, insulin resistance, and metabolic syndrome (MetS)[31]. Mechanistically, inflammation has been identified as a key mediator of these associations. Inhaled pollutants induce oxidative stress in the lungs, activating inflammatory pathways and leading to the systemic release of cytokines such as interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-\u0026alpha;)\u0026nbsp;[32]. These circulating inflammatory mediators contribute to endothelial dysfunction, impaired glucose uptake, and altered lipid metabolism, all of which are central components of MetS.\u003c/p\u003e\n\u003cp\u003eExperimental studies provide further biological plausibility. Rodent models exposed to PM₂.₅ show increased macrophage infiltration in adipose tissue, mitochondrial dysfunction, and impaired insulin signaling\u0026nbsp;[33]. Similarly, human cohort studies have linked chronic air pollution exposure to elevated C-reactive protein and other inflammatory biomarkers, reinforcing the hypothesis that low-grade systemic inflammation serves as a biological bridge between environmental exposure and metabolic dysfunction\u0026nbsp;[34]. Beyond direct metabolic effects, air pollution has also been shown to exacerbate visceral adiposity, a critical driver of cardiometabolic risk, through inflammation-induced adipose tissue remodeling\u0026nbsp;[35].\u003c/p\u003e\n\u003cp\u003eMore recent reviews emphasize the heterogeneity of effects across populations. Urban residents, individuals with obesity, and those with pre-existing cardiometabolic risk appear more susceptible to the pro-inflammatory and metabolic consequences of pollution exposure\u0026nbsp;[36]. Moreover, interactions between air pollution and other lifestyle factors such as diet and physical activity may further amplify inflammatory responses, suggesting that the health burden of air pollution is not uniform but context-dependent. Together, these findings support a multifactorial model in which air pollution contributes to metabolic syndrome via inflammatory and oxidative stress pathways, moderated by individual and environmental vulnerabilities.\u003c/p\u003e\n\u003cp\u003eThrough spatial analysis, it was found that the north\u0026ndash;south differences in the association between storm exposure and metabolic syndrome are the result of multiple interacting factors, including climate environment, socioeconomic status, and individual lifestyle:Climate conditions: Frequent and moderate rainfall in the south brings protective effects, while prolonged, intense, and irregular rainfall in the north increases risks.Diet and obesity: High-salt, high-fat diets and higher BMI levels in the north amplify the risk of metabolic syndrome.Health behaviors: The south has advantages in sleep, physical activity, and access to medical resources.Adaptive differences: Southern residents and infrastructure are better adapted to heavy rainfall, whereas the north is more vulnerable to extreme precipitation events.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eBased on a large population-based cohort study integrating meteorological and health survey data in China, this research provides robust evidence on the spatially heterogeneous associations between multiple dimensions of heavy rainfall exposure and the risk of metabolic syndrome (MetS). The findings demonstrate that rainfall characteristics exhibit both protective and adverse effects on MetS prevalence, influenced by geographic context and individual vulnerabilities. Key results from spatial analyses revealed significant global spatial autocorrelation in MetS prevalence (Global Moran’s I = 0.288, p \u0026lt; 0.001), identifying high-high clusters (hotspots) in Northern China and low-low clusters (coldspots) in Southern China. Geographically Weighted Regression (GWR) further illustrated the spatial non-stationarity of associations: rainfall frequency showed a consistent negative correlation with MetS risk, suggesting protective effects, particularly in coastal regions. In contrast, rainfall duration and intensity exhibited positive associations, with prolonged duration and higher intensity linked to increased MetS prevalence, especially in Central and Northern provinces. Peak rainfall revealed marked spatial heterogeneity—protective in the South (negative correlation) but detrimental in the Northwest (positive correlation). These results underscore the complexity of climate-health interactions and highlight the necessity of employing multidimensional exposure metrics and spatially explicit approaches to inform targeted public health interventions and climate-resilient health policies across diverse regions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAuthors\u0026rsquo; contributions\u003c/p\u003e\n\u003cp\u003eZiJie Cai: Writing \u0026ndash; original draft, Supervision, Project administration, Funding acquisition.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eChunZhi Tang: Writing \u0026ndash; review,Supervision, Project administration.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLiXiang Gan: Writing \u0026ndash; review \u0026amp; editing, Supervision, Project administration.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGuangPeng Zhang: Writing \u0026ndash; review \u0026amp; editing, Writing \u0026ndash; Software, Methodology, Investigation, Formal anal\u003c/p\u003e\n\u003cp\u003eysis, Data curation.\u003c/p\u003e\n\u003cp\u003eYaLong Qiu:Writing \u0026ndash; review \u0026amp; editing, Methodology, Investigation.\u003c/p\u003e\n\u003cp\u003eHongYing Tian: Writing \u0026ndash; review \u0026amp; editing, Visualization, Validation, Software, Resources, Formal analysis.\u003c/p\u003e\n\u003cp\u003eFunding\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData Availability\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent for publication\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAlberti KGMM, Zimmet P, Shaw J. 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PLoS One United States; 2012;7(10):e47791. PMID:23082217\u003c/li\u003e\n \u003cli\u003eYang W, Guo S, Wang H, Li Y, Zhang X, Hu Y, Guo H, Wang K, Yan Y, Zhang J, Ma J, Mao L, Mu L, Liu J, Song Y, Li C, Ma Z, Ma R, He J. The Association of Metabolic Syndrome with the development of cardiovascular disease among Kazakhs in remote rural areas of Xinjiang, China: a cohort study. BMC PUBLIC HEALTH 2021 Jan 26;21(1). doi: 10.1186/s12889-021-10241-w\u003c/li\u003e\n \u003cli\u003eChen Y. Spatial autocorrelation equation based on Moran\u0026rsquo;s index. Sci Rep England; 2023 Nov 7;13(1):19296. PMID:37935705\u003c/li\u003e\n \u003cli\u003eShen Y, de Hoogh K, Schmitz O, Clinton N, Tuxen-Bettman K, Brandt J, Christensen JH, Frohn LM, Geels C, Karssenberg D, Vermeulen R, Hoek G. Europe-wide air pollution modeling from 2000 to 2019 using geographically weighted regression. Environ Int Netherlands; 2022 Oct;168:107485. 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PMID:24912113\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 3 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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-health-geographics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijhg","sideBox":"Learn more about [International Journal of Health Geographics](http://ij-healthgeographics.biomedcentral.com/)","snPcode":"12942","submissionUrl":"https://submission.nature.com/new-submission/12942/3","title":"International Journal of Health Geographics","twitterHandle":"@IJHGeo","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7504116/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7504116/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Rising metabolic syndrome (MetS) prevalence in China coincides with increased frequency and intensity of urban rainstorms under climate change. While acute impacts of extreme rainfall are documented, evidence on long-term associations with chronic metabolic conditions remains limited.\u003c/p\u003e\n\u003cp\u003eMethods: This prospective cohort study analyzed 16,278 middle-aged and older adults from the China Health and Retirement Longitudinal Study (CHARLS, 2011–2020). Five rainstorm exposure indicators (frequency, duration, intensity, peak rainfall, total volume) were assessed. Cox regression models evaluated associations with MetS incidence, adjusted for sociodemographic, behavioral, and clinical confounders. Spatial analyses included:(1)Global/Local Moran’s I to detect spatial clustering of provincial MetS prevalence.(2)Geographically Weighted Regression (GWR) to quantify location-specific associations between rainstorm exposures and MetS.\u003c/p\u003e\n\u003cp\u003eResults: (1)Spatial Clustering:Significant spatial autocorrelation in MetS prevalence was observed (Global Moran’s I= 0.288, z-score = 4.025, p\u0026lt; 0.001), identifying high-high clusters (hotspots)​in Northern China and low-low clusters (coldspots)in Southern China.\u003c/p\u003e\n\u003cp\u003e(2)Rainstorm-MetS Associations:Rainstorm Frequency:Nationwide negative association with MetS risk (HR = 0.94, 95% CI: 0.93–0.95), strongest in coastal regions (GWR coefficients: −0.027 to −0.017).Rainstorm Duration:Positive association (HR = 1.03, 95% CI: 1.02–1.04), with pronounced effects in Central/Eastern provinces(e.g.,Henan,Shandong;GWR coefficients: up to+0.205).Peak Rainfall:Spatially heterogeneous—protective in the South (GWR: −0.175 to −0.195) but detrimental in the Northwest (GWR: +0.193 to +0.205).Dose-Response:Non-linear patterns (U/J-shaped) emerged, with extreme exposures attenuating protective effects.\u003c/p\u003e\n\u003cp\u003eConclusion: Rainstorm exposures exhibit dual protective-risk effects on MetS, moderated by spatial context. Frequency and moderate peak rainfall reduce risk, while prolonged duration and extreme intensity elevate it. Spatial analyses reveal distinct geographic vulnerability patterns, underscoring the need for region-specific public health interventions targeting climate-resilient metabolic health strategies.\u003c/p\u003e","manuscriptTitle":"Associations Between Storm Exposure Patterns and Metabolic Syndrome Risk in Chinese Adults: A CHARLS-Based Prospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-01 16:16:30","doi":"10.21203/rs.3.rs-7504116/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-23T20:30:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-22T02:53:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-18T08:47:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-25T15:17:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"270067093029405631678976279702877008844","date":"2025-09-24T15:54:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"160473994294137273391277438238033195819","date":"2025-09-21T13:47:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6453871907220726252106103017256428179","date":"2025-09-19T15:37:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-19T15:33:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-10T23:27:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Health Geographics","date":"2025-09-09T03:29:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-health-geographics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijhg","sideBox":"Learn more about [International Journal of Health Geographics](http://ij-healthgeographics.biomedcentral.com/)","snPcode":"12942","submissionUrl":"https://submission.nature.com/new-submission/12942/3","title":"International Journal of Health Geographics","twitterHandle":"@IJHGeo","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9ca59647-5736-4e84-a36d-051f7ba9ea07","owner":[],"postedDate":"October 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-16T16:01:19+00:00","versionOfRecord":{"articleIdentity":"rs-7504116","link":"https://doi.org/10.1186/s12942-026-00457-7","journal":{"identity":"international-journal-of-health-geographics","isVorOnly":false,"title":"International Journal of Health Geographics"},"publishedOn":"2026-02-15 15:58:27","publishedOnDateReadable":"February 15th, 2026"},"versionCreatedAt":"2025-10-01 16:16:30","video":"","vorDoi":"10.1186/s12942-026-00457-7","vorDoiUrl":"https://doi.org/10.1186/s12942-026-00457-7","workflowStages":[]},"version":"v1","identity":"rs-7504116","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7504116","identity":"rs-7504116","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}