Zhou C, Cui GY, Tang YH, Zhang WY, Zou XL. Long-term and short-term exposure to outdoor air pollution and its association with glycolipid metabolic disorders. World J Diabetes 2025; 16(12): 110028 [DOI: 10.4239/wjd.v16.i12.110028]
Corresponding Author of This Article
Xue-Lun Zou, PhD, Department of Neurology, Xiangya Hospital, Central South University, No. 87 Xiangya Road, Changsha 410008, Hunan Province, China. 1609941099@qq.com
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Nutrition & Dietetics
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Prospective Study
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Dec 15, 2025 (publication date) through Dec 15, 2025
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World Journal of Diabetes
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Zhou C, Cui GY, Tang YH, Zhang WY, Zou XL. Long-term and short-term exposure to outdoor air pollution and its association with glycolipid metabolic disorders. World J Diabetes 2025; 16(12): 110028 [DOI: 10.4239/wjd.v16.i12.110028]
Author contributions: Zou XL and Zhou C designed the research and determined the structure of the manuscript. Zou XL, Tang YS, Cui GY, and Zhou C were involved in the implementation of this study. Zou XL, Cui GY, and Zhou C selected the references and contributed to the writing. Zou XL, Tang YS, and Zhou C contributed to the revision and finalization of the manuscript. The manuscript was polished by Zhang WY. All authors contributed to the article and approved the submitted version.
Supported by the Teaching Research and Reform Fund Project of Central South University, No. 2024jy178.
Institutional review board statement: There was no need to get informed consent or ethical approval for this study again because all of the data were taken from published sources, and the informed consent and approval were received.
Informed consent statement: There was no need to get informed consent or ethical approval for this study again because all of the data were taken from published sources, and the informed consent and approval were received.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
CONSORT 2010 statement: The authors have read the CONSORT 2010 Statement, and the manuscript was prepared and revised according to the CONSORT 2010 Statement.
Data sharing statement: The original contributions presented in the study are included in the manuscript. Further inquiries can be directed to the corresponding author.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Xue-Lun Zou, PhD, Department of Neurology, Xiangya Hospital, Central South University, No. 87 Xiangya Road, Changsha 410008, Hunan Province, China. 1609941099@qq.com
Received: May 28, 2025 Revised: July 25, 2025 Accepted: November 13, 2025 Published online: December 15, 2025 Processing time: 201 Days and 12.5 Hours
Abstract
BACKGROUND
The association between ambient air pollution and glycolipid metabolic disorders (GMDs, including diabetes mellitus and dyslipidemia) is still not well understood, especially when it comes to the different effects of long-term vs short-term exposure and the sources of pollutants (indoor or outdoor).
AIM
To look at how outdoor particulate matter (PM1, PM2.5, PM10) and ozone (O3), as well as indoor pollutants from solid fuels, are related to the risk of developing GMDs in a cohort that represents the national population.
METHODS
We used a longitudinal cohort design to look at how different time periods of air pollution exposure (long-term: 5-year averages; short-term: 1-year averages) affect the incidence of GMDs in middle-aged and elderly adults. Multivariable logistic regression models, which took into account key factors such as age, sex, and smoking status, were used to calculate odds ratios (ORs) and 95% confidence intervals (CIs).
RESULTS
Our study found that exposure to air pollution (1 μg/m3) has different effects on GMDs. Long-term exposure to outdoor pollutants like PM1, PM2.5, PM10, and O3 consistently increased the risk of diabetes (PM1: OR = 1.106, 95%CI: 1.018-1.205; PM2.5: OR = 1.038, 95%CI: 1.007-1.071; PM10: OR = 1.023, 95%CI: 1.004-1.043) and dyslipidemia (PM1: OR = 1.150, 95%CI: 1.064-1.249; PM2.5: OR = 1.053, 95%CI: 1.023-1.086; PM10: OR = 1.032, 95%CI: 1.014-1.052). Short-term exposure showed even stronger associations, particularly for PM1 with dyslipidemia (OR = 1.078, 95%CI: 1.044-1.114) and PM1 with diabetes (OR = 1.047, 95%CI: 1.007-1.089). Notably, certain components of PM2.5 - chloride (Cl-), ammonium (NH4+), sulfate (SO42-), and nitrate (NO3-) - showed a dose-dependent relationship with both conditions (for example, Cl-: Diabetes OR = 1.797 per 1 μg/m3, 95%CI: 1.086-2.991; dyslipidemia OR = 2.627, 95%CI: 1.728-4.012). However, neither long-term nor short-term exposure to indoor solid fuel pollutants was significantly associated with diabetes (long-term OR = 1.034, 95%CI: 0.801-1.333; short-term OR = 0.970, 95%CI: 0.774-1.209) or dyslipidemia (short-term OR = 1.159, 95%CI: 0.967-1.386).
CONCLUSION
This national cohort study shows that outdoor air pollution - particularly PM1, PM2.5, and their chemical components - is an important environmental factor contributing to GMDs, with long-term exposure showing greater metabolic toxicity than short-term exposure. The lack of association between indoor solid fuel pollutants and GMDs underscores the urgent need for targeted interventions to improve outdoor air quality and reduce metabolic risks at the population level.
Core Tip: This national cohort study shows that outdoor air pollution - particularly PM1, PM2.5, and their chemical components - is an important environmental factor contributing to glycolipid metabolic disorders, with long-term exposure showing greater metabolic toxicity than short-term exposure. The lack of association between indoor solid fuel pollutants and glycolipid metabolic disorders underscores the urgent need for targeted interventions to improve outdoor air quality and reduce metabolic risks at the population level.
Citation: Zhou C, Cui GY, Tang YH, Zhang WY, Zou XL. Long-term and short-term exposure to outdoor air pollution and its association with glycolipid metabolic disorders. World J Diabetes 2025; 16(12): 110028
Glycolipid metabolic disorders (GMDs), including diabetes mellitus and dyslipidemia, pose severe global health threats. Diabetes is one of the top causes of death and disability globally, hitting middle-aged and elderly people especially hard[1-3]. Due to their part in hastening cardiovascular complications and metabolic dysfunction, these disorders place a huge burden on healthcare systems[1,4]. In China, GMDs are strikingly prevalent-12.4% of adults have diabetes, and over 40% are affected by dyslipidemia. This causes a sharp rise in years of life lost and healthcare costs[5,6]. The worsening GMDs situation makes it urgent to find modifiable environmental risk factors for targeted public health steps.
Air pollution, a major public health issue in China, is viewed as a likely environmental factor causing metabolic problems[7,8]. Particulate pollutants [outdoor Particulate matter (PM)1, PM2.5, PM10, and ozone (O3)] and indoor pollutants from solid fuel burning may mess with glucose homeostasis and lipid metabolism via systemic inflammation and oxidative stress[9,10]. PM2.5 stands out as a key metabolic disruptor. Longitudinal studies in Chinese groups show that every 10 µg/m3 rise in PM2.5 exposure is linked to a 26% higher type 2 diabetes risk [Hazard ratio = 1.26, 95% confidence intervals (95%CI): 1.22-1.31], with population-attributable fractions over 13%[11]. Also, PM1 and PM2.5 parts like black carbon are connected to dyslipidemia progression, showing dose-dependent links with higher LDL cholesterol and triglyceride levels[12,13]. Mechanistic studies indicate PM2.5 components (such as sulfate SO42- and nitrate NO3-) impair insulin signaling and boost hepatic lipid buildup[14,15].
However, despite more awareness of air pollution’s metabolic impacts, big knowledge gaps still exist. First, most current studies focus on single-pollutant or single-disease models, ignoring the synergistic effects of combined outdoor and indoor pollutant exposure on GMD progression. Second, the time-related aspects of exposure, especially how acute and chronic pollution exposure differently affect glycemic control and lipid homeostasis, are not well understood. It’s worth noting that while long-term PM2.5 increases (each 1 µg/m3) are tied to worse dyslipidemia [odds ratios (ORs) = 1.14, 95%CI: 1.10-1.18], the short-term metabolic effects of sustained PM1 exposure need more study[16,17].
To tackle these gaps, we used the China Health and Retirement Longitudinal Study (CHARLS) data to check how household solid fuel use and its interaction with outdoor air pollutants affect GMDs. By looking at both short-term (1-year) and long-term (5-year) exposure, this study uniquely reveals the time-related air pollution impacts on metabolic problems and considers the combined indoor-outdoor pollutant risks. We have three goals: (1) To measure the separate and combined effects of outdoor PM1, PM2.5, PM10, O3, and indoor solid-fuel pollutants on GMDs incidence; (2) To spot key PM2.5 chemical parts (like sulfate, nitrate) that disrupt glucose and lipid metabolism; and (3) To offer evidence-based policy advice on energy transition (e.g., clean household fuel use), air quality standards, and metabolic health steps. By linking environmental exposure mixtures to population-level metabolic risks, this work gives practical ideas to boost China’s public health and environmental governance, helping ease the growing GMDs burden through joined-up pollution control and healthcare prioritization.
MATERIALS AND METHODS
Research population
This study utilized data from the CHARLS, a comprehensive national survey in China that combines face-to-face interviews with a web-based system[18]. The survey collects detailed household and individual information, including basic demographics, health status, and health-related behaviors. Launched in 2011, it covers 17708 participants across 150 counties in 28 provinces, with follow-ups every two years. The study was approved by the Biomedical Ethics Committee of Peking University (approval number No. IRB00001052-11015), and all participants provided informed consent. For this research, we focused on survey data from 2013 to 2018. Since GMDs primarily affect middle-aged and elderly individuals, our study included participants aged 45 and older. Inclusion criteria for this study: (1) Age ≥ 45 years; (2) Medical records indicate no history of diabetes or dyslipidemia prior to 2013; (3) New-onset diabetes or dyslipidemia was first recorded in the medical system during follow-up in 2013 or 2018, and the patient’s self-reported information aligns with the recorded data; (4) Complete key laboratory data (Fasting plasma glucose, glycated hemoglobin A1c, total cholesterol, triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol); and (5) Complete household fuel use information. Exclusion criteria: (1) Age < 45 years; (2) Missing key laboratory indicators; (3) Missing household fuel use information; (4) Air pollutant (PM2.5, O3, etc.) exposure data unable to be accurately spatially linked to the residential address; (5) Heart problems, chronic kidney disease, chronic liver disease, chronic gastrointestinal disease, rheumatoid arthritis, hyperthyroidism/hypothyroidis-m, etc., present prior to 2013; and (6) Missing rate of other covariates > 20%.
Sources of air pollutants
The outdoor air pollutants examined in this study were PM1, PM2.5, PM10, and O3. Annual average data for these pollutants were sourced from the China High Air Pollutants dataset (Available from: https://weijing-rs.github.io/product.html). This dataset is renowned for its high-quality air pollutant data, derived from ground-based measurements, satellite observations, atmospheric reanalysis, and artificial intelligence-enhanced model simulations. The exposure data for these pollutants have a spatial resolution of one kilometer. Using Geographic Information Systems, we determined the annual average concentrations of these air pollutants for each city. These data were then merged with the municipal geographic locations in the CHARLS database to derive the annual average exposures for each participant’s city of residence. Indoor air pollutants were primarily associated with solid fuels used for cooking, assessed through a structured questionnaire. The questionnaire asked, “What is the main source of fuel used for cooking in your home?” at both baseline and follow-up surveys. Solid fuels included coal, briquettes, straw, firewood, and similar materials, while clean fuels primarily included biogas, liquefied petroleum gas, electricity, piped natural gas, or gas.
To assess the health impacts of air pollution, we defined short-term and long-term exposure based on prior studies that have shown significant health effects from both short-term and long-term exposure to air pollutants. Specifically, we defined short-term exposure as the average exposure over one year and long-term exposure as the average exposure over five years. This approach allows us to capture both acute and chronic health impacts of air pollution. The one-year average for short-term exposure and the five-year average for long-term exposure were calculated using individual-level data from our cohort. For each participant, we computed the mean exposure over a consecutive 12-month period for short-term exposure and over a consecutive 60-month period for long-term exposure. This ensured that each participant’s exposure was accurately represented over the study period. The long-term averages for O3, PM1, PM2.5, and PM10 are 84.743 ± 4.9 µg/m3, 31.375 ± 9.5 µg/m3, 98.960 ± 30.6 µg/m3, 57.005 ± 17.5 µg/m3, as shown in Table 1. The short-term averages for O3, PM1, PM2.5, and PM10 are 97.432 ± 9.143 µg/m3, 20.164 ± 5.936 µg/m3, 68.759 ± 22.849 µg/m3, 36.152 ± 10.231 µg/m3, as shown in Supplementary Table 1.
Table 1 Long-term exposure baseline data overview, n (%).
GMDs investigated in this study included dyslipidemia and diabetes. The diagnosis of these conditions was confirmed through a structured questionnaire at both baseline and follow-up visits, which asked participants, “Has a doctor ever told you that you have dyslipidemia or diabetes?” This was verified in subsequent follow-ups with the question, “The record from your last visit indicates that you have/do not have dyslipidemia or diabetes. Is this record correct?” To ensure the accuracy of the GMDs records, pre- and post-checks were employed. Only respondents who affirmed with certainty and could confirm a doctor’s diagnosis for conditions such as “dyslipidemia (high or low blood lipids)”, “diabetes mellitus or elevated blood glucose (including abnormal glucose tolerance and elevated fasting blood glucose)”, were considered as having GMDs. This rigorous data collection and verification approach ensures the reliability of the study’s findings on the prevalence and impact of GMDs.
Covariates
Based on existing literature[11-13,16-17] and variables in our questionnaire, we selected factors that could potentially influence the relationship between air pollutants and GMDs. These included age, sex (male/female), education level (primary school or below, secondary school or above), marital status (currently married and living with spouse, unmarried but living with partner, single, divorced, or widowed), smoking status (smokers and non-smokers), drinking status (drinkers and non-drinkers), place of residence (urban/rural), and geographical region (east, midland, and west). In this study, former smokers who have quit smoking for more than one year are defined as non-smokers, while those who have quit smoking for less than one year are defined as smokers. We also considered provincial variations, categorizing data by specific provinces and regions across China, covering all provinces and autonomous regions.
Statistical analyses
The statistical analyses were designed to account for variations in outdoor air pollutants, indoor household fuel types, and GMDs classifications. Baseline continuous variables were described as mean (standard deviation), and categorical variables as counts (percentage). To investigate the relationship between long-term and short-term exposure to outdoor air pollutants, solid fuel use, and GMDs+ prevalence, we used multivariate logistic regression models and generalized linear mixed-effects models. We included air pollutants (PM1, PM2.5, PM10, and O3) and solid fuel use as fixed factors, and the province as a random-effects term in the mixed-effects models. We developed four models to ensure robust findings: Crude (unadjusted), model 1 (adjusted for age, sex, residence, education, and marital status), model 2 (further adjusted for smoking and drinking status), and model 3 (incorporating all factors plus provincial distribution). OR and 95%CI quantified the associations, indicating the risk of GMDs per 1 µg/m3 increase in air pollutant levels.
Subgroup analyses explored the relationship between air pollutants and GMDs across demographic subsets (age, sex, education, marital status, residence, smoking, alcohol use, etc.), assessing whether air pollutant impacts on GMDs varied by these groups. Sensitivity analyses also excluded individuals with other chronic conditions (e.g., liver and lung diseases) and those with memory-related disorders (e.g., dementia, Alzheimer’s disease) to minimize bias. Records with incomplete key covariate information were omitted in preliminary analyses. All analyses were conducted using R (version 4.4.1), with P-values < 0.05 considered statistically significant for two-tailed tests. This rigorous approach ensures reliable findings to guide future research and policy decisions.
RESULTS
Demographic characteristics of participants at baseline
Long-term exposure analysis: As delineated in Table 1 and Supplementary Figure 1, our longitudinal assessment encompassed 18605 participants. Compared to individuals without GMDs, those diagnosed with chronic conditions exhibited distinct demographic characteristics: Participants with GMDs were significantly older than those without (mean difference: 6.3 years), more likely to reside in rural areas (62.4% vs 41.7%), and had a higher prevalence of solid fuel usage (coal/biomass: 58.9% vs 32.1%). Baseline ambient air pollution levels revealed substantial exposure burdens, with PM1, PM2.5, PM10, and O3 concentrations reaching 84.743 ± 4.852 µg/m3, 31.375 ± 9.532 µg/m3, 98.960 ± 30.634 µg/m3, and 57.005 ± 17.543 µg/m3, respectively. Notably, temporal trends from 2013 to 2018 demonstrated divergent patterns: While O3 levels increased to 88.878 ± 6.986 µg/m3, particulate matter exhibited significant reductions (PM1: 25.536 ± 7.573 µg/m3; PM2.5: 46.006 ± 13.574 µg/m3; PM10: 81.508 ± 25.312 µg/m3).
Short-term exposure analysis: In contrast, the cross-sectional evaluation included 19816 subjects, with 5539 GMDs cases identified (Supplementary Table 1). This cohort displayed pronounced socioeconomic gradients: 73.2% resided in rural regions, 68.4% attained only primary education, and 82.6% reported cohabitation with spouses. A striking transition emerged in energy consumption patterns - clean fuel adoption rates surged from 28.1% (2013) to 64.7% (2018), paralleling improvements in air quality metrics. By 2018, pollutant concentrations reached 97.432 ± 9.143 µg/m3 for O3, 20.164 ± 5.936 µg/m3 for PM1, 68.759 ± 22.849 µg/m3 for PM2.5, and 36.152 ± 10.231 µg/m3 for PM10, underscoring persistent challenges in particulate matter mitigation (Supplementary Table 1).
Long-term exposure to outdoor air pollutants elevates GMDs risk, whereas indoor solid fuel combustion shows no association
As outlined in Figure 1A and B, chronic exposure to outdoor air pollutant PM1 is associated with an increased risk of dyslipidaemia (OR = 1.150, 95%CI: 1.064-1.249, P = 0.00062) and diabetes (OR = 1.106, 95%CI: 1.018-1.205, P = 0.0189). This indicates that a 1 μg/m3 increase in PM1 concentration is associated with a 15% increase in the risk of new-onset dyslipidemia and a 10.6% increase in the risk of new-onset diabetes. Similarly, long-term exposure to PM2.5 influences the risk of dyslipidaemia (OR = 1.053, 95%CI: 1.023-1.086, P = 0.00062) and diabetes (OR = 1.038, 95%CI: 1.007-1.071, P = 0.0189). A 1 μg/m3 increase in PM2.5 concentration is associated with a 5.3% increase in the incidence of new cases of dyslipidemia and a 3.8% increase in the incidence of new cases of diabetes. Long-term exposure to PM10 also impacts the risk of dyslipidaemia (OR = 1.032, 95%CI: 1.014-1.052, P = 0.00062) and diabetes (OR = 1.023, 95%CI: 1.004-1.043, P = 0.0189). The impact of a 1 μg/m3 change in PM10 on GMDs is relatively lower compared to that of PM2.5 and PM1. However, there is no evidence to suggest a link between long-term exposure to solid fuels and the risk of GMDs.
Figure 1 Associations of long-term air pollutant exposure with the risk of diabetes and dyslipidaemia, and of specific PM2.5 components with the risk of diabetes and dyslipidaemia.
A: Associations of long-term air pollutant exposure with the risk of diabetes; B: Associations of long-term air pollutant exposure with the risk of dyslipidaemia; C: Specific PM2.5 components with the risk of diabetes; D: Specific PM2.5 components with the risk of dyslipidaemia. Results are presented across sequentially adjusted models: Crude model (unadjusted); model 1 (adjusted for age, gender, residence, education, and marital status); model 2 (further adjusted for smoking and drinking status); model 3 (additionally adjusted for provincial distribution). Arrow indicates direction of effect. PM1: Particulate matter 1; PM2.5: Particulate matter 2.5; PM10: Particulate matter 10; O3: Ozone. SO42-: Sulfate ion; NO3-: Nitrate ion; Cl-: Chloride ion; NH4+: Ammonium ion.
Further analysis of PM2.5 components - Cl-, NH4+, SO42-, and NO3- - reveals distinct effects on dyslipidaemia, diabetes (Figure 1C and D). Cl- emerges as a significant risk factor for all three health issues: Dyslipidaemia (OR = 5.862, 95%CI: 2.184-16.705, P = 0.00062), diabetes (OR = 3.556, 95%CI: 1.258-10.598, P = 0.019). A one-unit increase in the Cl- component of PM2.5 significantly elevates the risk of dyslipidemia and diabetes. SO42- primarily contributes to the risks of dyslipidaemia (OR = 1.432, 95%CI: 1.172-1.772, P = 0.00062) and diabetes (OR = 1.294, 95%CI: 1.048-1.615, P = 0.019). This indicates that for each unit increase in SO₄2-, the risk of new-onset dyslipidemia and diabetes rises by 43.2% and 29.4%, respectively. Notably, NO3-, a key component of PM2.5, is found to elevate the risks of dyslipidaemia (OR = 1.209, 95%CI: 1.087-1.352, P = 0.00062), diabetes (OR = 1.146, 95%CI: 1.025-1.288, P = 0.019), and NH4+ is associated with an increased risk of dyslipidaemia (OR = 1.352, 95%CI: 1.081-1.711, P = 0.00967).
Short-term exposure to air pollutants increases the risk of GMDs
As detailed in Figure 2A and B, short-term exposure to indoor and outdoor air pollution is linked to GMDs risk variations. Notably, O3 (OR = 1.048, 95%CI: 1.027-1.069, P = 0.00000707), PM1 (OR = 1.078, 95%CI: 1.044-1.114, P = 0.00000707), PM2.5 (OR = 1.035, 95%CI: 1.020-1.051, P = 0.00000707), and PM10 (OR = 1.021, 95%CI: 1.012-1.031, P = 0.00000707) are key determinants of dyslipidaemia risk. A 1 μg/m3 increase in short-term exposure to outdoor air pollutants PM1, PM2.5, PM10, and O3 is associated with a 4.8%, 7.8%, 3.5%, and 2.1% increase in the risk of dyslipidemia, respectively. These outdoor pollutants similarly influence diabetes risk. Figure 2C and D highlight four PM2.5 components - Cl-, NH4+, SO42-, and NO3- - that contribute to diabetes and dyslipidaemia occurrence. For diabetes risk, these pollutants are also significantly associated across various subgroups (Supplementary Tables 2 and 3), except for specific age and marital status categories. Across nearly all subgroups, PM1, PM2.5, PM10, and O3 show significant associations with dyslipidaemia (Supplementary Tables 4 and 5), with non-significant results only in a few age and education subgroups. In contrast, solid fuel use correlates with diabetes risk mainly among those with an elementary school education or less. Sensitivity analyses consistently align with the primary findings, underscoring the robustness of the conclusions.
Figure 2 Associations between short-term air pollutant exposure and the risk of diabetes and dyslipidaemia, and between specific PM2.5 components and the risk of diabetes and dyslipidaemia.
A: Associations of short-term air pollutant exposure with the risk of diabetes; B: Associations of short-term air pollutant exposure with the risk of dyslipidaemia; C: Specific PM2.5 components with the risk of diabetes; D: Specific PM2.5 components with the risk of dyslipidaemia. Results are shown across sequentially adjusted models: The crude model (unadjusted); model 1 (adjusted for age, gender, residence, education, and marital status); model 2 (additionally adjusted for smoking and drinking status); and model 3 (further adjusted for provincial distribution). PM1: Particulate matter 1; PM2.5: Particulate matter 2.5; PM10: Particulate matter 10; O3: Ozone. SO42-: Sulfate ion; NO3-: Nitrate ion; Cl-: Chloride ion; NH4+: Ammonium ion.
DISCUSSION
The environmental epidemiological study explored links between long-term and short-term exposure to indoor air pollutants (from solid fuel use) and outdoor air pollutants (PM1, PM2.5, PM10, and O3) and the risk of GMDs, including diabetes mellitus and dyslipidemia. Analysis indicated a significant association between long-term exposure to outdoor pollutants and higher prevalence rates of dyslipidemia and diabetes mellitus. However, no such correlation was observed for indoor solid fuel use alone. Notably, certain long-term exposures to outdoor pollutants demonstrated notable metabolic toxicity, particularly for PM1 and PM2.5, and their chemical constituents (e.g., sulfate, nitrate), which were significantly linked to dyslipidemia and diabetes. The study also highlighted the complex interplay of combined exposures, as synergistic effects between indoor solid fuels and outdoor pollutants were found to further increase the risk of dyslipidemia.
The temporal variations in pollutant effects align with distinct biological mechanisms of metabolic disruption. Short-term peaks in PM1/PM2.5 exposure can trigger acute oxidative stress and systemic inflammation[19-22], which may disrupt insulin signaling[23] and impair hepatic lipid metabolism[23-26]. In contrast, chronic exposure to these pollutants may induce epigenetic changes in adipose tissue[27,28], such as continuous activation of the NF-κB pathway[29,30] and mitochondrial dysfunction[31], contributing to persistent metabolic dysfunction. Notably, specific components of PM2.5, like SO42-, and NO3-, can directly interfere with glucose homeostasis by inhibiting insulin receptor substrate-1 phosphorylation in adipocytes[32-34]. Clinical evidence also indicates that ammonia exposure can enhance basophil and mast cell activation, amplifying systemic inflammatory responses[35]. Moreover, elevated exposure to NO3- and SO42 correlates with increased levels of high-sensitivity C-reactive protein and oxidized DNA markers such as 8-OHdG[35], potentially worsening glucolipid metabolism impairments. Experimental research further demonstrates that PM2.5 can induce endoplasmic reticulum stress in pancreatic β-cells[36,37] and modulate proliferator-activated receptor gamma activity in hepatocytes[38], collectively elucidating the mechanisms through which PM1 and PM2.5 impact glucose-lipid metabolism disorders.
The effects of O3 on GMDs follow a dual-phase pattern. In the short term, O3 exposure can trigger oxidative stress, which may cause β-cell dysfunction and hinder glucose uptake in skeletal muscle[39-41]. Long-term exposure, on the other hand, activates NLRP3 inflammasomes in adipose tissue[42-44], worsening lipid peroxidation and insulin resistance[45-47]. This nonlinear dose-response relationship reflects how O3 disrupts nuclear factor E2-related factor antioxidant pathways in a threshold-dependent manner[48], which are key to maintaining redox balance in metabolic tissues[49]. The impact of O3 on blood lipids is closely linked to its role in systemic inflammation and oxidative stress, factors that can impair lipid metabolism and oxidation[50]. Short-term O3 exposure may activate neurohormone-mediated stress pathways, increasing stress hormone levels and altering peripheral lipid metabolism in humans[51]. Furthermore, animal studies show that O3 can induce glucose intolerance and systemic metabolic responses, with oxidative stress and inflammation potentially increasing insulin resistance and metabolic damage[50,52-54], providing a possible mechanism for its impact on diabetes.
Indoor air pollution from solid fuels substantially impacts global disease burden, assessed via disability-adjusted life years, with significant effects in Southeast Asia and beyond[55]. In China, solid fuels are still widely used, especially in rural areas, where they’re a main source of indoor air pollution. Burning these fuels produces numerous pollutants, including fine particulate matter like PM2.5[56]. Although solid fuel use isn’t independently tied to GMDs, it worsens outdoor pollutant effects through inflammatory synergy[57]. PM2.5 from indoor and outdoor combustion sources can overwhelm antioxidant defenses[58,59]. This occurs via redox cycling of transition metals (e.g., iron in coal ash)[60-63] and persistent organic pollutants[63,64]. Rural cohort studies also show that solid fuel use worsens PM2.5-related dyslipidemia by chronically increasing interleukin-6 and tumour necrosis factor alpha[65,66]. These cytokines reduce hepatic LDL receptor expression and encourage lipid accumulation[67,68].
Based on our research findings, we can now translate the exposure–response relationship into specific health benefits. First, if the annual average concentrations of PM1, PM2.5, and PM10 in the study area were reduced by 1 μg/m3, the attributable risk of diabetes would decrease by 1.06%, 0.38%, and 0.23%, respectively. This is equivalent to preventing approximately 1060, 380, and 230 new cases of diabetes per 100000 adults; the attributable risk of dyslipidemia would decrease by 1.15%, 0.53%, and 0.32%, respectively, equivalent to preventing 1150, 530, and 320 new cases of dyslipidemia among 100000 adults. Therefore, based on the long-term and short-term effects of air pollutants on GMDs, targeted reductions in exposure concentrations of PM1, PM2.5, PM10, and O3 nationwide would effectively prevent tens of thousands of new GMD cases. Secondly, if we can implement targeted control measures for specific components of PM2.5, such as SO₄2- and Cl-, for example, by retrofitting coal-fired power plants, this will significantly reduce the risk of GMDs. Additionally, to address the impacts of air pollution, we should promote clean industries nationwide, expand green spaces, and increase the coverage of street trees and shrubs, which can also help reduce the threat of air pollutants to GMDs. Finally, during daily pollution peak periods for PM1, PM2.5, PM10, and O3, high-exposure populations wearing N95 masks for two hours can significantly reduce the intake of outdoor air pollutants. In summary, these evidence-based interventions - from national air quality standards to local green infrastructure and individual behavior - provide a clear roadmap for translating our epidemiological research findings into substantial reductions in blood glucose and metabolic disorders.
This study achieves several groundbreaking methodological and public health advancements in environmental epidemiology. First, it is the first large-scale, nationally representative longitudinal study in China to systematically evaluate the combined effects of indoor (solid fuel-derived) and outdoor (PM1, PM2.5, PM10, and O3) air pollution on GMDs. Using a prospective cohort design with 5-year and 1-year exposure windows, it uniquely reveals the temporal dynamics of pollution impacts, showing how acute and chronic exposures differently contribute to diabetes and dyslipidemia. Importantly, the multi-pollutant interaction analysis pioneers the quantification of synergistic risks from combined indoor-outdoor exposures, which is crucial given their coexistence in real-world settings. By bridging gaps between environmental exposure science and population-level metabolic health outcomes, this work offers an evidence-based roadmap for targeted air quality interventions, directly informing China’s national strategies on energy transition, industrial emission controls, and metabolic disease prevention.
This study, while offering valuable insights into air pollution’s metabolic impacts, has several methodological limitations to consider. First, our municipal-level pollution data, though spatially comprehensive, lacks individual residence-specific exposure parameters. This coarse spatial resolution may cause exposure misclassification, especially for highly mobile populations or those near localized pollution sources like industrial zones, potentially weakening true effect estimates. Second, the lack of detailed data on microenvironmental exposures (e.g., home ventilation efficiency, cooking duration) and lifestyle factors (e.g., occupational pollutant contact, commuting routes) limits our ability to fully account for exposure heterogeneity, possibly leaving residual confounding in risk estimates. Third, as the CHARLS cohort focuses on adults aged ≥ 45 years, our findings might not fully capture pollution sensitivity in younger populations who are undergoing critical metabolic development phases, so caution is needed when extrapolating results to adolescents and young adults. Furthermore, although we excluded participants with physician-diagnosed diabetes or dyslipidaemia at baseline - verified through electronic medical records - undiagnosed or subclinical chronic conditions (e.g., hypertension, prior cardiovascular events) may still have been present. Such unmeasured factors could bias the observed associations by influencing both the incidence and severity of GMD. Because we did not perform universal screening for all potential comorbidities (e.g., mild cognitive impairment or inflammatory disorders), residual confounding remains possible and may modestly attenuate the internal validity of our findings. Future multicentre studies with comprehensive clinical phenotyping and standardized examinations are needed to confirm these results. Besides, Individual height, weight, and body mass index were not available, precluding direct adjustment for obesity - a well-established risk factor for GMDs. This may introduce residual confounding, leading to estimates of the effects of air pollution on GMDs that deviate from the true values. Consequently, residual confounding may bias the observed associations between air pollution and GMDs. We attempted to mitigate this by adjusting for detailed area-level socioeconomic status and other obesity-related covariates, but unmeasured confounding cannot be ruled out. Future studies that collect individual body mass index data or leverage longitudinal cohorts with electronic health records will be better positioned to isolate the true effect of air pollution on GMDs. Additionally, since the units of change for air pollutants in our calculations are 1 μg/m3, the observed effects on disease risk are relatively weaker due to the small unit size. While some analyses showed significant differences in OR values after adjustment, we did not use these as primary conclusions in our study. Further research is needed to confirm the effects of these environmentally contested pollutants on GMDs. In this study, using cooking fuel use as an important assessment factor may introduce measurement bias. Firstly, we primarily relied on self-reported data to assess cooking fuel use. This method is susceptible to recall bias, meaning participants may overestimate clean fuel use or underestimate traditional fuel use due to social expectations. Secondly, when households use multiple fuel types or cooking stoves simultaneously, this may lead to inaccurate exposure assessments. This issue is particularly pronounced in certain regions where households may concurrently use both traditional and clean fuels. Such practices may dilute the true impact of clean fuel use on health outcomes, as the primary fuel type may not fully represent actual pollutant exposure levels. Thirdly, this study relies on self-reported data and does not utilize objective measurement tools such as air quality monitors or biomarkers. This limitation may result in exposure classification errors and weaken the strength of the true association between cooking fuel use and health outcomes. Future studies should consider incorporating objective exposure measurement methods to enhance the accuracy of exposure assessment. Finally, while we identified GMDs (diabetes/dyslipidemia) as primary outcomes, the database’s reliance on diagnostic codes rather than biomarker-stratified subtypes (e.g., insulin-resistant vs. deficient diabetes) prevents deeper mechanistic insights into pollution’s pathophysiological pathways.
CONCLUSION
Exposure to outdoor air pollutants, especially PM1 and PM2.5 and their chemical components, is a key environmental factor contributing to GMDs nationwide, with long-term exposure posing greater risks. Indoor solid fuel use, however, has not been shown to increase GMDs risk. Thus, enforcing air pollution controls and advancing energy transition to improve air quality are vital for preventing GMDs like diabetes and dyslipidemia, protecting public health, and promoting a healthier, more sustainable environment.
ACKNOWLEDGEMENTS
We thank all workers who contributed to the CHARLS cohort.
Footnotes
Provenance and peer review: Unsolicited article; Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Endocrinology and metabolism
Country of origin: China
Peer-review report’s classification
Scientific Quality: Grade B, Grade C, Grade C
Novelty: Grade B, Grade B, Grade B
Creativity or Innovation: Grade B, Grade B, Grade C
Scientific Significance: Grade B, Grade B, Grade B
P-Reviewer: Ling YW, PhD, United States; Xin YJ, PhD, Assistant Professor, China S-Editor: Bai SR L-Editor: A P-Editor: Yu HG
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