Association of diurnal temperature range and childhood asthma: a population-based cross-sectional study in a Tropical City, China

Abstract

Background

Climate change has significantly impacted the diurnal temperature range (DTR), particularly in tropical regions of China, where DTR fluctuations are more frequent. While previous studies have primarily focused on the link between short-term DTR exposure and childhood asthma, there is limited information on the long-term effects from large-scale studies.

Methods

In 2022, a cross-sectional survey involving 9,130 children aged 2–10 years was conducted using stratified cluster random sampling in tropical Sanya, Hainan Province, China. Data on demographics, and asthma symptoms were collected using the validated International Study of Asthma and Allergies in Childhood (ISAAC) questionnaire. Temperature, precipitation and Normalized Difference Vegetation Index (NDVI) were obtained from remote sensing satellite. A generalized linear model (GLM) was employed to analyze the association between DTR exposure and asthma, and stratified analyses were conducted based on environmental and lifestyle factors.

Results

The prevalence of childhood asthma was 7.57%, with the annual average DTR ranging from 5.15℃ to 7.26℃. After adjusting for potential confounders, each 1℃ increase in DTR was associated with a 65.9% higher risk of asthma (95% CI: 1.058, 2.602). Stratified analyses indicated that the impact of DTR on asthma risk was stronger among children living in areas with higher temperatures, higher precipitation, lower vegetation coverage (measured by NDVI), as well as those who were not breastfed, exposed to passive smoking, or whose mothers had pets during pregnancy.

Conclusions

In Sanya, increased annual DTR was significantly associated with a higher odds of childhood asthma, and this effect was influenced by environmental and lifestyle factors. Therefore, public health strategies could mitigate childhood asthma risk associated with DTR through urban greening, advocating for breastfeeding, reducing secondhand smoke, and avoiding pet ownership during pregnancy.

Introduction

Asthma is the second most common chronic respiratory disease in the world [1]. Globally, over 300 million people are affected, particularly children [2]. The International Study of Asthma and Allergies in Childhood (ISAAC) investigated the prevalence of symptoms and severity of allergic diseases, including asthma, across different populations worldwide. They reported on the global trends of asthma prevalence [3], and found that environmental factors maybe the most significant drivers of the persistent increase in childhood asthma prevalence [2, 4]. As global climate change intensifies, the impact of environmental factors on chronic diseases such as asthma becomes increasingly concerning.

Among various meteorological factors, the relationships between temperature, humidity, precipitation, and asthma are particularly close. In particular, the connection between asthma and diurnal temperature range (DTR) defined as the difference between the daily maximum and minimum temperatures, has garnered significant attention in recent years [5, 6]. As an important meteorological indicator reflecting daily temperature variations, DTR not only influences atmospheric conditions, but also is closely related to the pathogenesis of asthma. Zheng et al. found that the number of outpatient visits for asthma among children in Lanzhou was closely associated with various temperature variation indicators. These indicators included DTR, temperature changes between neighboring days (TCN), and temperature variability (TV0 − t) [7]. Yu et al.‘s study indicates that within a specific range of DTR, both high and low DTR significantly elevate the risk of childhood asthma hospitalizations [8]. Hu et al.‘s research indicates that DTR is widely recognized as a major trigger for asthma attacks in children [9]. Wu et al.‘s study found that short-term exposure to DTR increases the risk of hospitalization for asthma patients. Specifically, during an observation period spanning 0 to 7 days, a 1℃ increase in DTR was correlated with a 0.7% rise in hospitalization rates for children aged 0–4 years and a 1.1% increase for those aged 5–19 years [10].

Numerous studies through time-series analysis have provided increasing epidemiological evidence revealing a positive correlation between DTR exposure and disease mortality/morbidity, confirming the harmful short-term effects of DTR on health [11]. However, these studies cannot capture precise individual-level meteorological exposures or assess long-term DTR impacts. Moreover, large-scale cross-sectional surveys analyzing the association between DTR and childhood asthma in tropical cities are lacking. Unlike time-series studies, population-based cross-sectional studies can evaluate the long-term effects of environmental exposure, a capability that is crucial for chronic diseases like asthma [12]. The primary objective of this study is to explore the association between long-term exposure to DTR and childhood asthma, and to assess whether socio-demographic and environmental factors modify this relationship. This is aimed at further developing effective early prevention strategies related to DTR for asthma-prone populations, thereby playing a proactive role in addressing the health challenges posed by climate change.

Participants and methods

Participants and sampling

From October to December 2022, a cross-sectional survey was conducted in southernmost Sanya city, China, in a low-latitude tropical region. Sanya has a tropical marine monsoon climate and is composed of four districts, covering an area of 1,921.4 km² [13]. We randomly selected 20 kindergartens and 13 primary schools from the four districts. A total of 11,318 children participated in this study, of which 10,305 (response rate: 91.0%) completed questionnaires by their guardians. To ensure environmental exposure stability, inclusion criteria required children to be aged 2–10 years and have resided at their current address for ≥ 1 year. Exclusion criteria included incomplete questionnaires, missing home address data, and age entries outside the 2–10 year range or with errors. After applying these criteria, 9,130 children (80.7%) were retained for analysis, comprising 3,602 kindergarten children and 5,528 primary school children. Ethical approval for this study was obtained from the Ethics Committee of Sanya Women and Children’s Hospital (SYFYIRB20220063). All legal guardians provided informed consent prior to data collection.

Data collection

This study utilized the ISAAC questionnaire [14]. We used a history of asthma as the outcome of this study.

Definition of asthma:

History of asthma: has your child ever been diagnosed with asthma by a doctor?

Covariates were defined as follows:

Children’s age was calculated as the difference between the survey date and the birth date. The gender of the child was categorized as either boys or girls. Household income per month was measured in Chinese Yuan (CNY) and divided into the following brackets: less than 3000 CNY, 3000–5999 CNY, 6000–8999 CNY, 9000–11,999 CNY, and more than 12,000 CNY. A history of miscarriage was recorded as either yes or no. Exposure to antibiotics was classified as yes or no, based on whether they were used during pregnancy or within the first year of the child’s life. The gestational week was classified as less than 37 weeks, 37–42 weeks, or 42 weeks and above. Feeding patterns were categorized into exclusive breastfeeding, mixed feeding, and formula feeding. A family history of allergy was recorded as either yes or no. The presence of traffic within 50 m was categorized as much, not much, or less. Passive smoking was noted as either yes, if other household members smoked, or no. Pets were recorded if the household had a pet since conception. The presence of mouse, cockroaches, and other allergens was categorized as none, very few, less, or a lot. Insecticide use was recorded as either yes or no. The frequency of park visits was categorized as less than once per month, 2–3 times per month, 1–2 times per week, 3–5 times per week, or more than once per day.

Environmental exposure

Environmental data, including minimum temperature, maximum temperature, mean temperature, and precipitation, within a 1-kilometer buffer zone in Sanya for 2022, as well as 2022 normalized difference vegetation index (NDVI) data at a 250-meter resolution, were sourced from the National Tibetan Plateau / Third Pole Environment Data Center (http://data.tpdc.ac.cn). The DTR was calculated as the difference between the daily maximum and minimum temperatures. The datasets were rigorously validated by the data provider using ground-based meteorological stations and remote sensing measurements, ensuring high accuracy and reliability for environmental exposure assessment. For detailed validation methods, please refer to the data source documentation [15, 16].

Subsequently, we geocoded these environmental data with the current residential and school addresses of the participants to extract the corresponding environmental exposure values for each participant. Then, based on the time children aged 2–12 years spent at school and home in Sanya, where they generally spend 8 h at school, we calculated the comprehensive exposure values. The formula was: Comprehensive Exposure = School Exposure * 1/3 + Home Exposure * 2/3.

Statistical analysis

The analysis process was conducted in five stages. First, descriptive analysis was performed, where continuous variables (such as child age and environmental exposure) were described using means (standard deviation, SD) or medians (interquartile range, IQR), and categorical variables were represented by frequencies (percentages). Second, for observations with missing meteorological data, we applied multiple imputation using the Multivariate Imputation by Chained Equations (MICE) algorithm to generate five imputed datasets. Results were combined using Rubin’s rules to account for uncertainty [17,18,19]. Next, Spearman correlation was used to calculate the correlation coefficients (rs) between asthma, DTR, mean temperature, precipitation, and NDVI. In the fourth stage, based on the imputed dataset and given that the outcome variable (childhood asthma) is binary, we employed univariate and multivariate binomial generalized linear models (GLMs) with a logit link function to assess the association between DTR and childhood asthma, reporting odds ratios (OR) and 95% confidence intervals (CI). GLMs are a classical and well-established approach for such analyses, providing robust estimates of the association between exposure and outcome [20]. Finally, we selected the imputed dataset for stratified analysis based on akaike information criterion (AIC) and bayesian information criterion (BIC) criteria to explore the potential modifying effects of environmental and socio-demographic factors on the DTR-asthma association [21]. Additionally, we used z-test to analyze the differences in estimated values between subgroups [22].

We conducted a series of sensitivity analyses to comprehensively assess the robustness of the results. Specific measures included: (i) Using different multivariate models to compare with the final results to verify the consistency and reliability of the findings; (ii) Recalculating after removing all missing values from the original dataset to ensure that the results were not influenced by missing data. These steps aimed to ensure our results with high credibility and robustness.

Statistical analysis was performed with R software (version 4.3.2; R Core Team) where mainly using “tidyverse”, “tableone”, “mice”, “car”, “GGally”, “sf”, “grid”, “Hmisc”, and “forestploter” packages. Statistical significance level was set at p < 0.05 (two-sided).

Results

Basic characters

Among the 9,130 children, including 5,014 boys (54.92%) and 4,116 girls (45.08%), 691 of 9,130 (7.57%) had a history of asthma. The average age was 6.75 (2.83) years. Table 1 presents the characteristics of sociodemographic factors and environmental exposures. Overall, the median levels (IQR) for minimum temperature, maximum temperature, mean temperature, DTR, precipitation, and NDVI were 23.23 (0.31)℃, 29.30 (0.13)℃, 26.28 (0.18)℃, 6.09 (0.27)℃, 146.54 (4.25) mm, and 0.48 (0.14), respectively. Children with asthma exhibited statistically significant differences (p < 0.05) from those without asthma in terms of gender, maternal history of miscarriage, exposure to antibiotics during pregnancy and the first year of life, maternal gestational week, feeding patterns, family history of allergies, traffic within 50 m, passive smoking, having a pet since conception, allergens exposure, insecticide exposure, park visits, DTR, and precipitation.

[IMAGE OMITTED: SEE PDF]

Figure 1 illustrates the geographic distribution of participants’ residential locations and the corresponding DTR at a 1 km spatial resolution. The map in Fig. 1A represents the spatial distribution of 500 randomly selected children with asthma, while Fig. 1B displays the distribution of 500 matched controls without asthma. Both groups show similar spatial distribution patterns across the study area, indicating comparable geographic representation between asthmatic and non-asthmatic children. Fig. S1 presents the geographic distribution of the current home addresses of participants without missing meteorological data and the DTR at a 1 km resolution. The detailed distributions of minimum temperature, maximum temperature, DTR, mean temperature, precipitation, and NDVI for the entire year of 2022 are presented in Table S1.

[IMAGE OMITTED: SEE PDF]

The Spearman correlation between asthma and environmental factors

Table 2 presents the Spearman correlations between asthma and environmental exposure in 2022. Asthma was positively correlated with DTR (r = 0.035, P < 0.01) and negatively correlated with precipitation (r = -0.035, P < 0.01). DTR was negatively correlated with mean temperature (r = -0.664, P < 0.01) and precipitation (r = -0.375, P < 0.01), and positively correlated with NDVI (r = 0.114, P < 0.01). Mean temperature was positively correlated with precipitation (r = 0.408, P < 0.01) and negatively correlated with NDVI (r = -0.304, P < 0.01). Precipitation was positively correlated with NDVI (r = 0.087, P < 0.01). These findings reveal the complex relationships between asthma and environmental factors. After excluding missing data, similar results were found among environmental exposures in 2022 (Table S2).

[IMAGE OMITTED: SEE PDF]

Associations of annual DTR with childhood asthma

Figure 2 illustrates the association between DTR and childhood asthma. In the univariate model (Model 1), each 1℃ increasing in DTR was associated with a 68.0% increasing in the OR of asthma (95% CI: 1.205, 2.343). In Model 2, after adjusting child age, gestational week, child gender, feeding patterns (< 4 months), and antibiotic use within one year, each 1℃ increasing in DTR was still positively associated with asthma (OR = 1.732, 95% CI: 1.236, 2.427). In Model 3, history of miscarriage, antibiotic use during pregnancy, family history of allergies, and pet ownership since conception were further adjusted, each 1℃ increasing in DTR remained positively associated with childhood asthma (OR = 1.850, 95% CI: 1.318, 2.595). In Model 4, further controlling traffic within 50 m, passive smoking, allergen exposure, insecticide exposure, and frequency of park visits, each 1℃ increasing in DTR was positively associated with childhood asthma (OR = 1.448, 95% CI: 1.018, 2.059). Finally, in Model 5, further adjustment for environmental factors including mean temperature, precipitation, NDVI, and socioeconomic level, each 1℃ increasing in DTR remained positively associated with childhood asthma (OR = 1.659, 95% CI: 1.058, 2.602).

Stratified analyses

[IMAGE OMITTED: SEE PDF]

Figure 3 illustrates the associations between DTR and childhood asthma, which was stratified by factors such as mean temperature, precipitation, NDVI, feeding patterns, passive smoking, and pet ownership since conception. Table S3 presents the effect modification of these covariates. At a 1 km resolution, for each 1℃ increase in DTR, the OR for asthma in the high temperature group (> median) was 3.278(95% CI: 1.184, 9.024), and in the high precipitation group (> median) was 2.999(95% CI: 1.270, 7.085). In the low NDVI group (< median), an increase of 1℃ in DTR was associated with an increased OR for asthma (OR = 2.228, 95% CI: 1.164, 4.290). In the subgroups of feeding patterns, children fed with formula showed an increased OR for asthma associated with a 1℃ increase in DTR at a 1 km resolution (OR = 3.720, 95% CI: 1.210, 11.439). In the subgroups exposed to passive smoking (OR = 1.845, 95% CI: 1.122, 3.027) and pet ownership since conception (OR = 5.069, 95% CI: 1.508, 16.884), each 1℃ increase in DTR was significantly associated with an increased OR for childhood asthma.

[IMAGE OMITTED: SEE PDF]

In the sensitivity analysis, we used the dataset excluding all missing data to recalculate the association between long-term DTR exposure and childhood asthma in 2022, obtaining similar results (Fig. S2, and S3).

Discussion

Main findings

In this city-wide cross-sectional study, we found a significant association between higher exposure to annual DTR and an increased risk of childhood asthma. Unlike previous studies that focused on short-term exposure, our research highlights the long-term impact of DTR on asthma risk. The association was more significant in regions with high temperatures (> 26.28℃), high precipitation (> 146.54 mm), and low NDVI (< 0.48). Additionally, our study revealed several potential risk factors for childhood asthma, including formula feeding, exposure to passive smoking, and pet ownership since conception.

Comparison with other studies

Some studies have reported that exposure to DTR poses a potential risk for childhood asthma. Seven studies from China [23], the United States [24], Australia [25, 26], Korea [27], Japan [28], and Trinidad [29] found that a larger short-term DTR was associated with an increased incidence of childhood asthma. A study conducted in 21 cities across China also found that high DTR was associated with an increased risk of chronic respiratory diseases, including asthma [30]. Our study further reinforces this association, providing additional evidence of the potential adverse effects of long-term DTR on childhood asthma. In tropical and subtropical regions, where temperature variability is pronounced, DTR significantly impacts respiratory health. A Saudi study identified temperature fluctuations as a trigger for respiratory allergic diseases [31], while a Brazilian study linked a 1℃ increase in temperature variability over 0–7 days to a 1.0% rise in asthma hospitalizations [10]. In Taiwan, DTR was associated with adverse cardiopulmonary outcomes [32], and a Global Burden of Disease (GBD) study estimated that a 1℃ increase in maximum temperature variability raises asthma risk by 5.0% [33]. These findings underscore the widespread impact of DTR across diverse climates. A series of studies have shown that high temperature, high precipitation, and low NDVI may exacerbate the adverse effects of DTR on childhood asthma [34,35,36]. However, our study found some inconsistencies with other reports. Yu et al. found that low DTR increased the risk of asthma hospitalizations, suggesting that under low DTR conditions, where temperature fluctuations are minimal, the respiratory system may gradually lose its adaptability to these changes, leading to reduced respiratory stability [8].

Potential mechanisms

DTR may influence childhood asthma through several pathways. Firstly, elevated DTR can lead to a decrease in peak expiratory flow, thereby exacerbating respiratory symptoms and inducing asthma [1, 34]. Secondly, the airways may be damaged when coping with drastic DTR, leading to the opening of intercellular junctional complexes and further increasing the risk of asthma [37]. Thirdly, DTR may affect the regulation of the immune system, leading to an imbalance in Th1/Th2 cell-mediated immunity and subsequently triggering inflammatory responses [37,38,39]. Specifically, temperature fluctuations may induce pyroptosis, a form of programmed cell death, resulting in chronic inflammation and altered Th1/Th2 balance, which exacerbates airway inflammation [40,41,42]. Additionally, DTR may directly disrupt Th1/Th2 balance, leading to the activation of transient receptor potential ankyrin 1 (TRPA1) channels and subsequent airway inflammation [43,44,45].

Moreover, temperature variability may increase the expression of heat shock proteins (HSPs), particularly HSP70, which has been linked to asthma pathogenesis [46,47,48,49]. DTR may also upregulate the expression of transient receptor potential vanilloid 1 (TRPV1) and the production of substance P (SP), exacerbating neurogenic inflammation and pulmonary inflammation [43, 50,51,52]. Finally, the activation of TRPV1 and TRPA1 by temperature changes may stimulate thymic stromal lymphopoietin (TSLP) production through the Ca2+/NFAT pathway, upregulating IL-25 and IL-33 gene expression and increasing IL-33 protein levels, ultimately contributing to airway inflammation [44, 53].

In high-temperature environment, children’s respiratory tracts may be more sensitive to temperature changes, and even a relatively small DTR could cause significant airway constriction [54]. Simultaneously, high-temperature environment may lead to an imbalance in immune regulation, making the body more sensitive to allergens [55]. In high-precipitation environment, high DTR and severe thunderstorms with heavy precipitation may cause pollen to rupture, increasing the release of allergenic bioaerosols and elevating the total concentration of environmental allergens. This could disrupt the Th1/Th2 balance, further increasing the risk of childhood asthma [56,57,58]. In low NDVI environment, reduced green space exposure may lead to an enhanced perception of heat stress in the body, thereby intensifying the impact of DTR on childhood asthma [59, 60]. Exposure to passive smoking and pet ownership starting during pregnancy may also exacerbate asthma risk. Specifically, benzo[a]pyrene (BaP), a pollutant in secondhand smoke, induces oxidative stress by generating reactive oxygen species (ROS), disrupting antioxidant enzyme activity, and reducing non-enzymatic antioxidant levels, thereby amplifying the adverse effects of DTR on asthma [61, 62]. Additionally, BaP exposure may trigger macrophage pyroptosis, promote pro-inflammatory macrophage polarization, and increase the secretion of pro-inflammatory cytokines, further contributing to asthma pathogenesis [63]. Furthermore, prenatal exposure to high concentrations of pet al.lergens (e.g., dust mites, cat and dog dander) may affect fetal immune system development through the placenta, increasing susceptibility to asthma later in life [64, 65]. On the other hand, breastfeeding can reduce infant infections, lower the incidence of atopic predisposition, and promote the overall development of the child’s immune system, thereby reducing the risk of childhood asthma [66,67,68].

Strengths and limitations

Our study has several strengths. Firstly, it is a population-based study that focuses on the long-term impact of DTR on childhood asthma. Secondly, the sample size is relatively large, with a high response rate (91.0%), providing high statistical power for assessing the DTR-asthma relationship. Thirdly, we used DTR satellite data for both current residential and school addresses to accurately match participants and calculate comprehensive exposure values, ensuring the most accurate individual DTR exposure. Finally, we conducted numerous sensitivity analyses to demonstrate the robustness of our results.

Several limitations should also be acknowledged. Firstly, due to the cross-sectional design of this study, we cannot establish a causal relationship between DTR and childhood asthma. However, we used the average DTR exposure for the entire year prior to the study to explore its association with childhood asthma, which provides a robust temporal perspective. Secondly, the data on childhood asthma were collected through questionnaires completed by the children’s guardians, which may introduce recall bias. Nevertheless, the use of the validated ISAAC questionnaire helps mitigate this limitation. Thirdly, while the comprehensive exposure assessment is based on the current residential and school addresses using high-resolution satellite data, it may not fully capture indoor environmental conditions, such as ventilation, humidity, and indoor air quality. These factors could potentially influence asthma prevalence and may lead to an overestimation of the effects of DTR. Future studies should incorporate indoor environmental variables to provide a more comprehensive analysis. Finally, our study did not include air pollution data in the analysis, although air pollutants are known to significantly impact asthma occurrence. This omission may lead to an overestimation of the association between DTR and childhood asthma. However, it is important to note that Sanya City maintains excellent air quality year-round, with air pollutant concentrations significantly lower than the national average. While this reduces the potential confounding effect of air pollution, future research should still consider including air quality metrics to further refine the analysis.

Application and future directions

The present preliminary study has shown that children who are not breastfed, exposed to passive smoking, and whose mothers started keeping pets during pregnancy are a vulnerable group for DTR-induced asthma. Based on this finding, we recommend breastfeeding for susceptible children and taking measures to avoid their exposure to passive smoking and household pets during pregnancy.

For future research, prospective cohort studies are needed to establish causal relationships between long-term DTR and childhood asthma. Integrating machine learning methodologies could help build spatiotemporal prediction models to identify high-risk areas and periods for asthma, enabling targeted public health interventions. Additionally, population-based studies using personal monitoring devices should be conducted to evaluate individual-level DTR exposure and its effects on asthma symptoms, providing personalized insights into exposure thresholds and mitigation strategies.

Conclusions

Our study indicates that an increase in annual DTR is associated with an increased odds of childhood asthma, and that this association is influenced by factors such as high temperature, high precipitation, low NDVI, formula feeding, passive smoking, and pet ownership since conception. These findings provide new insights into the role of environmental and lifestyle factors in childhood asthma and highlight the need for targeted public health interventions to mitigate these risks. Specifically, we recommend the development of DTR warning systems to alert vulnerable populations, the implementation of urban greening initiatives to mitigate temperature variability, and behavioral interventions such as promoting breastfeeding, enforcing indoor smoking bans, and advising against pet ownership during pregnancy. Future research should focus on refining these associations, exploring additional risk factors, and evaluating the effectiveness of these interventions, ultimately contributing to more effective public health policies.

Data availability

Environmental data are publicly available from the National Tibetan Plateau/Third Pole Environment Data Center (http://data.tpdc.ac.cn). The database of participants used and analyzed during the current study is available from the corresponding author ([email protected]) on reasonable request.

Abbreviations

ISAAC:

International study of asthma and allergies in childhood

DTR:

Diurnal temperature range

TCN:

Temperature changes between neighboring days

TV0-t:

Temperature variability

CNY:

Chinese Yuan

NDVI:

Normalized difference vegetation index

SD:

Standard deviation

IQR:

Interquartile range

MICE:

Multivariate imputation by chained equations

r:

Correlation coefficients

GLMs:

Generalized linear models

OR:

Odds ratios

CI:

Confidence intervals

AIC:

Akaike information criterion

BIC:

Bayesian information criterion

GBD:

Global burden of disease

TRPA1:

Transient receptor potential ankyrin 1

HSPS:

Heat shock proteins

TRPV1:

Transient receptor potential vanilloid 1

SP:

Substance P

TSLP:

Thymic stromal lymphopoietin

BaP:

Benzo[a]pyrene

ROS:

Reactive oxygen species

References

Zhu Y, Yang T, Huang S, Li H, Lei J, Xue X, et al. Cold temperature and sudden temperature drop as novel risk factors of asthma exacerbation: a longitudinal study in 18 Chinese cities. Sci Total Environ. 2022;814:151959.

Hu Y-B, Chen Y-T, Liu S-J, Jiang F, Wu M-Q, Yan C-H, et al. Increasing prevalence and influencing factors of childhood asthma: a cross-sectional study in Shanghai, China. World J Pediatr. 2021;17:419–28.

Asher MI, Montefort S, Björkstén B, Lai CKW, Strachan DP, Weiland SK, et al. Worldwide time trends in the prevalence of symptoms of asthma, allergic rhinoconjunctivitis, and eczema in childhood: ISAAC phases one and three repeat multicountry cross-sectional surveys. Lancet. 2006;368:733–43.

The International Study of Asthma and Allergies in Childhood (ISAAC). Steering committee. Worldwide variation in prevalence of symptoms of asthma, allergic rhinoconjunctivitis, and atopic eczema: ISAAC. Lancet. 1998;351:1225–32.

Braganza K, Karoly DJ, Arblaster JM. Diurnal temperature range as an index of global climate change during the twentieth century. Geophys Res Lett. 2004;31.

Wei Q, Zhong L, Gao J, Yi W, Pan R, Gao J, et al. Diurnal temperature range and childhood asthma in Hefei, China: does temperature modify the association? Sci Total Environ. 2020;724:138206.

Zheng J, Yue L, Wang B, Li Y, Zhang L, Xue B, et al. Seasonal characteristics of ambient temperature variation (DTR, TCN, and TV0-t) and air pollutants on childhood asthma attack in a dry and cold City in China. Environ Res. 2023;217:114872.

Yu J, Zhu A, Liu M, Dong J, Tian T, Liu T, et al. The correlation between daily temperature, diurnal temperature range, and asthma hospital admissions in Lanzhou City, 2013–2020. BMC Public Health. 2024;24:2454.

Hu Y, Cheng J, Yin Y, Liu S, Tan J, Li S, et al. Association of childhood asthma with intra-day and inter-day temperature variability in Shanghai, China. Environ Res. 2022;204:112350.

Wu Y, Xu R, Wen B, Saldiva PH, Li S, Guo Y. Temperature variability and asthma hospitalisation in Brazil, 2000–2015: a nationwide case-crossover study. Thorax. 2021;76:962–9.

Zhang Y, Peng M, Wang L, Yu C. Association of diurnal temperature range with daily mortality in England and Wales: A nationwide time-series study. Sci Total Environ. 2018;619–620:291–300.

Tang H, Wang X, Kang Y, Zheng C, Cao X, Tian Y, et al. Long-Term impacts of diurnal temperature range on mortality and cardiovascular disease: a nationwide prospective cohort study. Metabolites. 2022;12:1287.

Hu Y, Yang G, Wang D, Gu W, Xie D, Huang T, et al. Associations of insecticide exposure with childhood asthma and wheezing: a population-based cross-sectional study in Sanya, China. Toxics. 2024;12:392.

Asher M, Keil U, Anderson H, Beasley R, Crane J, Martinez F, et al. International study of asthma and allergies in childhood (ISAAC): rationale and methods. Eur Respir J. 1995;8:483–91.

Gao J, Shi Y, Zhang H, Chen X, Zhang W, Shen W, et al. China regional 250m normalized difference vegetation index data set (2000–2023). National Tibetan Plateau Data Center; 2024.

Peng S. High-spatial-resolution monthly temperatures dataset over China during 1901–2017. Earth Syst Sci Data. 2019;11:1931–46.

Gong R, Pu X, Cheng Z, Ding J, Chen Z, Wang Y. The association between serum cadmium and diabetes in the general population: A cross-sectional study from NHANES (1999–2020). Front Nutr. 2022;9:966500.

Sterne JAC, White IR, Carlin JB, Spratt M, Royston P, Kenward MG, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ. 2009;338:b2393.

Lee KJ, Simpson JA. Introduction to multiple imputation for dealing with missing data. Respirology. 2014;19:162–7.

Austin PC, Van Buuren S. The effect of high prevalence of missing data on Estimation of the coefficients of a logistic regression model when using multiple imputation. BMC Med Res Methodol. 2022;22:196.

Noghrehchi F, Stoklosa J, Penev S, Warton DI. Selecting the model for multiple imputation of missing data: just use an IC! Stat Med. 2021;40:2467–97.

Altman DG, Bland JM. Interaction revisited: the difference between two estimates. BMJ. 2003;326:219.

Qiu H, Yu IT, Tse LA, Chan EYY, Wong TW, Tian L. Greater temperature variation within a day associated with increased emergency hospital admissions for asthma. Sci Total Environ. 2015;505:508–13.

Mireku N, Wang Y, Ager J, Reddy RC, Baptist AP. Changes in weather and the effects on pediatric asthma exacerbations. Ann Allergy Asthma Immunol. 2009;103:220–4.

Xu Z, Huang C, Hu W, Turner LR, Su H, Tong S. Extreme temperatures and emergency department admissions for childhood asthma in Brisbane, Australia. Occup Environ Med. 2013;70:730–5.

Xu Z, Crooks JL, Davies JM, Khan AF, Hu W, Tong S. The association between ambient temperature and childhood asthma: a systematic review. Int J Biometeorol. 2018;62:471–81.

Lim Y-H, Hong Y-C, Kim H. Effects of diurnal temperature range on cardiovascular and respiratory hospital admissions in Korea. Sci Total Environ. 2012;417–418:55–60.

Ueda K, Nitta H, Odajima H. The effects of weather, air pollutants, and Asian dust on hospitalization for asthma in Fukuoka. Environ Health Prev Med. 2010;15:350–7.

Ivey MA, Simeon DT, Monteil MA. Climatic variables are associated with seasonal acute asthma admissions to accident and emergency room facilities in Trinidad, West Indies. Clin Exp Allergy. 2003;33:1526–30.

Wang Z, Zhou Y, Luo M, Yang H, Xiao S, Huang X, et al. Association of diurnal temperature range with daily hospitalization for exacerbation of chronic respiratory diseases in 21 cities, China. Respir Res. 2020;21:251.

Aburiziza A, Almatrafi MA, Alonazi AS, Zatari MH, Alqouzi SA, Mandili RA, et al. The prevalence, clinical picture, and triggers of allergic rhinitis in Saudi population: a systematic review and meta-analysis. J Asthma Allergy. 2022;15:1831–49.

Lung S-CC, Yeh J-CJ, Hwang J-S, Chen L-S. Diurnal temperature range and cardiopulmonary health in Taiwan: evaluating impacts, thresholds, and vulnerable groups. Environ Res. 2024;263:120083.

Xu Q, Zhou Q, Chen J, Li T, Ma J, Du R, et al. The incidence of asthma attributable to temperature variability: an ecological study based on 1990–2019 GBD data. Sci Total Environ. 2023;904:166726.

Li S, Baker PJ, Jalaludin BB, Guo Y, Marks GB, Denison LS, et al. Are children׳s asthmatic symptoms related to ambient temperature? A panel study in Australia. Environ Res. 2014;133:239–45.

Soneja S, Jiang C, Fisher J, Upperman CR, Mitchell C, Sapkota A. Exposure to extreme heat and precipitation events associated with increased risk of hospitalization for asthma in Maryland, U.S.A. Environ Health. 2016;15:57.

Hu Y, Chen Y, Liu S, Tan J, Yu G, Yan C, et al. Residential greenspace and childhood asthma: an intra-city study. Sci Total Environ. 2023;857:159792.

Komlósi ZI, Van De Veen W, Kovács N, Szűcs G, Sokolowska M, O’Mahony L, et al. Cellular and molecular mechanisms of allergic asthma. Mol Aspects Med. 2022;85:100995.

Han A, Deng S, Yu J, Zhang Y, Jalaludin B, Huang C. Asthma triggered by extreme temperatures: from epidemiological evidence to biological plausibility. Environ Res. 2023;216:114489.

Wang H, Ülgen M, Trajkovski M. Importance of temperature on immuno-metabolic regulation and cancer progression. FEBS J. 2024;291:832–45.

Qiao Y-L, Zhu M-W, Xu S, Jiao W-E, Ni H-F, Tao Z-Z, et al. Allergen-induced CD11c + dendritic cell pyroptosis aggravates allergic rhinitis. Cell Commun Signal. 2023;21:281.

Berkel C. Potential impact of climate change-induced alterations on pyroptotic cell death in animal cells: a review. Mol Biotechnol. 2024. https://doi.org/10.1007/s12033-024-01182-x.

Li M, Wang C, Xu W-T, Zhong X. Sodium houttuyfonate plays a protective role in the asthmatic airway by alleviating the NLRP3-related pyroptosis and Th1/Th2 immune imbalance. Mol Immunol. 2023;160:103–11.

Wang Y, Shi Y, Zuo M, Yu Y, Huang X. Advances in the study for modulators of transient receptor potential vanilloid (TRPV) channel family. Curr Top Med Chem. 2025. https://doi.org/10.2174/0115680266294569241115053420.

Du C, Kang J, Yu W, Chen M, Li B, Liu H, et al. Repeated exposure to temperature variation exacerbates airway inflammation through TRPA1 in a mouse model of asthma. Respirology. 2019;24:238–45.

Luostarinen S, Hämäläinen M, Pemmari A, Moilanen E. The regulation of TRPA1 expression and function by Th1 and Th2-type inflammation in human A549 lung epithelial cells. Inflamm Res. 2023;72:1327–39.

Sugiura S, Yoshida H, Sugiura H, Uehara M, Sugiura Y, Maruo Y, et al. Increased intracellular stress responses and decreased KLF2 in adult patients with atopic dermatitis. Cell Stress Chaperones. 2025;30:84–99.

Zarei S, Ghafoori H, Vahdatiraad L, Sohrabi T, Heidari B. Effects of HSP inducers on the gene expression of heat shock proteins (HSPs) in cells extracted from Sterlet sturgeon under temperature stress with antioxidant and immunity responses. Fish Physiol Biochem. 2024;50:1409–28.

Wu D, Liu L, Zhu J, Liu C, Long C, Liu C, et al. CTNNAL1 enhances glucocorticoid sensitivity in HDM-induced asthma mouse model through deactivating hsp90 signaling pathway. Life Sci. 2023;313:121304.

Chai T, Liu Y, Zeng Y, Kang S-Y, Li J. CLCA1 and BPIFB1 are potential novel biomarkers for asthma: an iTRAQ analysis. J Thorac Dis. 2024;16:6955–68.

Deng L, Chen X, Ma P, Wu Y, Okoye CO, Du D, et al. The combined effect of oxidative stress and TRPV1 on temperature-induced asthma: evidence in a mouse model. Environ Pollut. 2024;344:123313.

Wang N, Wang J, Zhang Y, Hu S, Zhang T, Wu Y, et al. Substance P-induced lung inflammation in mice is mast cell dependent. Clin Exp Allergy. 2022;52:46–58.

Lian Z, Qi H, Liu X, Zhang Y, Xu R, Yang X, et al. Ambient Ozone, and urban PM2.5 co-exposure, aggravate allergic asthma via transient receptor potential vanilloid 1-mediated neurogenic inflammation. Ecotoxicol Environ Saf. 2022;243:114000.

Li J, Wang F, Meng C, Zhu D. Role of TRPV1 and TRPA1 in TSLP production in nasal epithelial cells. Int Immunopharmacol. 2024;131:111916.

Cantet JM, Yu Z, Ríus AG. Heat Stress-Mediated activation of Immune-Inflammatory pathways. Antibiot (Basel). 2021;10:1285.

Song J, Pan R, Yi W, Wei Q, Qin W, Song S, et al. Ambient high temperature exposure and global disease burden during 1990–2019: an analysis of the global burden of disease study 2019. Sci Total Environ. 2021;787:147540.

Park J-H, Lee E, Fechter-Leggett ED, Williams E, Yadav S, Bakshi A, et al. Associations of emergency department visits for asthma with precipitation and temperature on thunderstorm days: a time-series analysis of data from Louisiana, USA, 2010–2012. Environ Health Perspect. 2022;130:087003.

Jiang F, Yan A. Correlation of pollen concentration and meteorological factors with medical condition of allergic rhinitis in Shenyang area. Comput Math Methods Med. 2022;2022:4619693.

Koskela HO, Kaulamo JT, Lätti AM. The associations of long-term temperature and precipitation with chronic respiratory symptoms: projections for the changing climate. Lung. 2024;203:7.

Beele E, Aerts R, Reyniers M, Somers B. Urban green space, human heat perception and sleep quality: a repeated cross-sectional study. Environ Res. 2024;:120129.

Keith RJ, Hart JL, Bhatnagar A. Greenspaces and cardiovascular health. Circ Res. 2024;134:1179–96.

Bukowska B, Duchnowicz P. Molecular mechanisms of action of selected substances involved in the reduction of benzo[a]pyrene-Induced oxidative stress. Molecules. 2022;27:1379.

Deng J, Wei L, Chen Y, Li X, Zhang H, Wei X, et al. Identification of benzo(a)pyrene-related toxicological targets and their role in chronic obstructive pulmonary disease pathogenesis: a comprehensive bioinformatics and machine learning approach. BMC Pharmacol Toxicol. 2025;26:33.

You M, Song Y, Chen J, Liu Y, Chen W, Cen Y, et al. Combined exposure to benzo(a)pyrene and dibutyl phthalate aggravates pro-inflammatory macrophage polarization in spleen via pyroptosis involving cathepsin B. Sci Total Environ. 2023;881:163460.

De Pinot A, Pearce N, Pedersen M, Nybo Andersen A-M. The influence of early-life animal exposure on the risk of childhood atopic dermatitis, asthma and allergic rhinoconjunctivitis: findings from the Danish National birth cohort. Int J Epidemiol. 2023;52:1231–42.

Biagioni B, Cecchi L, D’Amato G, Annesi-Maesano I. Environmental influences on childhood asthma: climate change. Pediatr Allergy Immunol. 2023;34:e13961.

Hu Y, Chen Y, Liu S, Jiang F, Wu M, Yan C, et al. Breastfeeding duration modified the effects of neonatal and Familial risk factors on childhood asthma and allergy: a population-based study. Respir Res. 2021;22:41.

Papandreou D, Pavlidou E, Tyrovolas S, Mantzorou M, Andreou E, Psara E, et al. Relation of maternal pre-pregnancy factors and childhood asthma: a cross-sectional survey in pre-school children aged 2–5 years old. Med (Kaunas). 2023;59:179.

Esmeraldino L, Traebert E, Nunes RD, Traebert J. Association between prenatal and neonatal factors and occurrence of asthma symptoms in six-year-old children. Ciênc Saúde Coletiva. 2022;27:545–54.