Long-term exposure to ambient air pollution and cardiometabolic multimorbidity in Chinese adults over 45 years Article Swipe
Ruikang Liu
,
Guangyi Yang
,
Changqing Sun
,
Jun Li
,
Wenjie Zhang
,
Xuanchun Huang
,
Chao Meng
,
Yiying Liu
,
Lilan Su
,
Huanrong Jiang
,
Tiantian Xue
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-21165-5
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-21165-5
The rising prevalence of cardiometabolic multimorbidity (CMM), characterized by the coexistence of two or more cardiometabolic disorders, poses a significant public health challenge in aging populations. While ambient air pollution is a recognized environmental risk factor, its long-term impact on CMM remains underexplored, particularly in China. Utilizing data from the China Health and Retirement Longitudinal Study (CHARLS, 2015 wave), we analyzed 9,830 participants aged ≥ 45 years. CMM was defined as the concurrent presence of two or more conditions: diabetes/hyperglycemia, cardiovascular diseases (myocardial infarction, angina pectoris, coronary artery disease, or heart failure, or stroke. Annual pollutant exposures were estimated using a machine learning-based spatiotemporal model (space-time extremely randomized trees) based on residential addresses. Generalized linear models adjusted for sociodemographic, lifestyle, and meteorological covariates were employed to assess odds ratios (ORs) per interquartile range (IQR) increase in pollutants. Chronic exposure to particulate matter (PM) 10 demonstrated a consistent positive association with CMM prevalence across models. In fully adjusted analyses, each IQR increase in PM10 was associated with elevated CMM risk (OR = 1.01, 95% CI: 1.00-1.02, P = 0.039). Sensitivity analyses, including alternative exposure windows and adjustments for regional variations, reinforced PM10's robust association with CMM. Other pollutants (PM2.5, and sulfur dioxide) showed weaker or inconsistent associations. Long-term exposure to ambient particulate matter, particularly PM10, is significantly linked to increased CMM prevalence in China's aging population. The findings of this study provide epidemiological evidence, laying the foundation for future cohort studies and mechanistic investigations.
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- Language
- en
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- https://doi.org/10.1038/s41598-025-21165-5
- https://www.nature.com/articles/s41598-025-21165-5.pdf
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- 38
- OpenAlex ID
- https://openalex.org/W4415502526
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https://openalex.org/W4415502526Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1038/s41598-025-21165-5Digital Object Identifier
- Title
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Long-term exposure to ambient air pollution and cardiometabolic multimorbidity in Chinese adults over 45 yearsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
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2025Year of publication
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2025-10-24Full publication date if available
- Authors
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Ruikang Liu, Guangyi Yang, Changqing Sun, Jun Li, Wenjie Zhang, Xuanchun Huang, Chao Meng, Yiying Liu, Lilan Su, Huanrong Jiang, Tiantian XueList of authors in order
- Landing page
-
https://doi.org/10.1038/s41598-025-21165-5Publisher landing page
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https://www.nature.com/articles/s41598-025-21165-5.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.nature.com/articles/s41598-025-21165-5.pdfDirect OA link when available
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0Total citation count in OpenAlex
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38Number of works referenced by this work
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| abstract_inverted_index.failure, | 91 |
| abstract_inverted_index.findings | 226 |
| abstract_inverted_index.increase | 134, 160 |
| abstract_inverted_index.positive | 147 |
| abstract_inverted_index.presence | 73 |
| abstract_inverted_index.regional | 187 |
| abstract_inverted_index.Long-term | 206 |
| abstract_inverted_index.Utilizing | 46 |
| abstract_inverted_index.analyses, | 157, 179 |
| abstract_inverted_index.challenge | 22 |
| abstract_inverted_index.estimated | 98 |
| abstract_inverted_index.evidence, | 232 |
| abstract_inverted_index.exposures | 96 |
| abstract_inverted_index.extremely | 106 |
| abstract_inverted_index.including | 180 |
| abstract_inverted_index.increased | 218 |
| abstract_inverted_index.long-term | 37 |
| abstract_inverted_index.pectoris, | 85 |
| abstract_inverted_index.pollutant | 95 |
| abstract_inverted_index.pollution | 29 |
| abstract_inverted_index.1.00-1.02, | 174 |
| abstract_inverted_index.Retirement | 53 |
| abstract_inverted_index.addresses. | 112 |
| abstract_inverted_index.associated | 164 |
| abstract_inverted_index.concurrent | 72 |
| abstract_inverted_index.consistent | 146 |
| abstract_inverted_index.covariates | 122 |
| abstract_inverted_index.disorders, | 16 |
| abstract_inverted_index.foundation | 235 |
| abstract_inverted_index.lifestyle, | 119 |
| abstract_inverted_index.pollutants | 196 |
| abstract_inverted_index.prevalence | 2, 151, 220 |
| abstract_inverted_index.randomized | 107 |
| abstract_inverted_index.recognized | 32 |
| abstract_inverted_index.reinforced | 189 |
| abstract_inverted_index.(myocardial | 82 |
| abstract_inverted_index.(space-time | 105 |
| abstract_inverted_index.Generalized | 113 |
| abstract_inverted_index.Sensitivity | 178 |
| abstract_inverted_index.adjustments | 185 |
| abstract_inverted_index.alternative | 181 |
| abstract_inverted_index.association | 148, 192 |
| abstract_inverted_index.coexistence | 10 |
| abstract_inverted_index.conditions: | 78 |
| abstract_inverted_index.infarction, | 83 |
| abstract_inverted_index.mechanistic | 241 |
| abstract_inverted_index.particulate | 140, 210 |
| abstract_inverted_index.pollutants. | 136 |
| abstract_inverted_index.population. | 224 |
| abstract_inverted_index.residential | 111 |
| abstract_inverted_index.significant | 19 |
| abstract_inverted_index.variations, | 188 |
| abstract_inverted_index.Longitudinal | 54 |
| abstract_inverted_index.demonstrated | 144 |
| abstract_inverted_index.inconsistent | 204 |
| abstract_inverted_index.participants | 62 |
| abstract_inverted_index.particularly | 43, 212 |
| abstract_inverted_index.populations. | 25 |
| abstract_inverted_index.associations. | 205 |
| abstract_inverted_index.characterized | 7 |
| abstract_inverted_index.environmental | 33 |
| abstract_inverted_index.interquartile | 131 |
| abstract_inverted_index.significantly | 215 |
| abstract_inverted_index.cardiovascular | 80 |
| abstract_inverted_index.learning-based | 102 |
| abstract_inverted_index.meteorological | 121 |
| abstract_inverted_index.multimorbidity | 5 |
| abstract_inverted_index.spatiotemporal | 103 |
| abstract_inverted_index.underexplored, | 42 |
| abstract_inverted_index.cardiometabolic | 4, 15 |
| abstract_inverted_index.epidemiological | 231 |
| abstract_inverted_index.investigations. | 242 |
| abstract_inverted_index.sociodemographic, | 118 |
| abstract_inverted_index.diabetes/hyperglycemia, | 79 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 11 |
| citation_normalized_percentile.value | 0.63079519 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |