Childhood exposure to air pollution linked to higher metabolic syndrome risk
Home and school exposure to PM2.5 and NO2 is associated with increased metabolic syndrome risk
Barcelona Institute for Global Health (ISGlobal)
Although the effects of air pollution on respiratory diseases are well established, its effects on cardiometabolic health are often overlooked. This population-based European cohort study led by the Barcelona Institute for Global Health (ISGlobal), a centre supported by the ”la Caixa” Foundation, suggests that higher exposure to air pollution during childhood at home and at school may increase the risk of metabolic syndrome. The study was published in Environmental Research.
Air pollution is a major environmental risk factor for health and has been linked to a wide range of adverse health outcomes in adults, including cardiovascular disease and metabolic syndrome. Children are particularly vulnerable because their organs and immune systems are still developing, they breathe more air relative to their body weight, and they tend to spend more time outdoors. Metabolic syndrome refers to a cluster of risk factors, including abdominal obesity, high blood pressure, abnormal blood sugar and dyslipidaemia, that can increase the risk of type 2 diabetes and heart disease later in life.
To investigate this, researchers analysed data from 1,147 children aged 6 to 11 years from France, Greece, Lithuania, Norway, Spain, and the United Kingdom who were followed up with between 2013 and 2016 within the HELIX project. “We estimated exposure to PM2.5 and NO2 at home, school and during commuting, and examined its relationship with a metabolic syndrome risk score,” says Anne van Rooijen Korving, researcher at ISGlobal and first author of the study.
Using validated air pollution models based on residential, school and commuting environments, the team estimated annual exposure to both pollutants during the year prior to clinical assessment. Associations with metabolic syndrome risk were examined using multivariable regression models, and metabolic syndrome risk was calculated using a previously validated score based on waist circumference, blood pressure, blood lipids and insulin levels, following International Diabetes Federation criteria.
Stronger associations at school than at home
Air pollution was more strongly associated with metabolic syndrome risk at school than at home, suggesting that children may be particularly vulnerable in the school environment. Although pollution levels at home and at school were highly correlated (with annual PM2.5 and NO2 concentrations consistently exceeding World Health Organization guidelines), susceptibility appears to be greater at school. Previous research indicates that higher levels of physical activity at school increase breathing rates, which could amplify the adverse effects of pollution exposure. Conversely, no associations were found between exposure on commuting routes and metabolic syndrome risk. Similarly, no associations were observed for short-term exposure in the days or week before clinical assessment, supporting the idea that metabolic changes develop gradually over time rather than in response to acute exposure.
Potential biological mechanisms and public health implications
Several biological mechanisms may explain these findings. Air pollution exposure has been linked to systemic inflammation, oxidative stress, and disruptions in lipid metabolism, glucose regulation and blood pressure control — all processes involved in the development of metabolic syndrome.
“Given the widespread exposure to air pollution and the long-term health consequences of metabolic syndrome, these findings have important public health implications,” says Martine Vrijheid, Director of ISGlobal's Environment and Health over the Lifecourse programme. “Our results reinforce the need to continue reducing air pollution levels to protect children’s cardiometabolic health.”
Reference
Anne van Rooijen Korving, Parisa Montazeri, Léa Maitre, Sandra Marquez, Jose Urquiza, Nuria Güil-Oumrait, Antònia Valentín, Regina Grazuleviciene, Kristine Bjerve Gützkow, Johanna Lepeule, Rosemary R.C. McEachan, Bente Oftedal, Euripides G. Stephanou, John Wright, Wen Lun Yuan, Martine Vrijheid, Childhood outdoor air pollution exposure across multiple microenvironments and metabolic syndrome: A HELIX cohort analysis, Environmental Research, Volume 305, Part 1, 2026, 124892,ISSN 0013-9351, https://doi.org/10.1016/j.envres.2026.124892
Journal
Environmental Research
Method of Research
Observational study
Subject of Research
People
COI Statement
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Air pollution mixtures may pose hidden risks during pregnancy
Using machine learning, University of Utah researchers found that repeated exposure to combinations of common air pollutants in early pregnancy was associated with nearly three-times greater odds of early preterm birth
University of Utah
image:
Typical cluster characters, determined by comparing the average of each cluster’s pollutant concentrations to the entire distribution (very low, low, moderate, high, very high). Cluster 10 had the strongest relationship to preterm birth.
view moreCredit: Kelly et. al., J Expo Sci Environ Epidemiol (2026)
Pregnancy puts higher oxygen demands on the body, so expectant mothers breathe significantly more air over the course of a day. When air quality is bad, they’re also breathing in more harmful pollutants. Many studies have linked individual air pollutants, such as fine particulate matter (PM2.5) or ozone, to early preterm birth—delivery before 34 weeks’ gestation, when infants face higher risks of health complications. But those studies don’t reflect the reality that people are often exposed to mixtures of pollutants simultaneously.
In a new University of Utah study of 44,874 first-time mothers in Utah, researchers found that exposure to even moderate levels of multiple air pollutants may increase the risk of preterm birth when experienced together—especially during critical stages of pregnancy.
The findings raise questions about whether current air quality index (AQI) ratings—typically based on the single pollutant posing the greatest harm—may be missing serious health risks to the public.
“People are exposed to multiple things at once, over multiple times,” said Brenna Kelly, lead author on the paper. Kelly is a recent graduate of the University of Utah’s Population Health Sciences PhD program and an incoming Responsible AI Postdoctoral Fellow. “It may matter when pollution is slightly elevated for multiple chemicals.”
The study published on May 27, 2026, in the Journal of Exposure Science & Environmental Epidemiology, a Nature journal.
Using AI to untangle complex exposures
Growing evidence suggests that environmental mixtures may have a greater impact on health than individual pollutants alone. But identifying which combinations to study remains a major challenge.
“Finding these patterns is like searching for a needle in a haystack—without the machine learning component, testing all combinations of mixtures would have been intractable,” Kelly said.
This study focused on air pollution and preterm births in Utah, a state that intermittently has some of the worst air quality in the world. The researchers developed an epidemiologic machine learning framework using a self-organizing map, a type of neural network that finds patterns in data. Similar models have assessed environmental mixtures, but this is the first to link them to health outcomes over time.
"Brenna’s work helps demonstrate the potential of machine learning and artificial intelligence in tackling complex environmental problems and in the assessment of their impacts,” said Simon Brewer, professor in the U’s School of Environment, Society & Sustainability and coauthor of the study. “U has been key in supporting this work, both through the DELPHI initiative, and more broadly across campus as thematic areas within the U’s Responsible AI Initiative.”
The scientists provided the model with high-resolution air pollution data from Utah between 2013 and 2016, focusing on temperature and three common pollutants: nitrogen dioxide (NO2), ozone (O3) and fine particulate matter PM2.5. The algorithm identified 12 distinct mixtures, and the researchers then modeled the effect of these exposures on early preterm birth during each week of pregnancy.
Early pregnancy: A critical window
Exposure to a mixture of O3 and PM2.5 in the late first trimester had the strongest relationship to early preterm birth. Because the AQI does not take multiple exposures into account, the EPA would have considered these levels “safe” air quality, although it may still pose risks to health. Women exposed to this mixture in week 11 of pregnancy had 53% greater odds of a preterm birth later in pregnancy. Additionally, those exposed repeatedly over weeks 9-14 had almost three-times greater odds of preterm birth.
“Many different pathways could lead to early preterm birth, including inflammation, infection or problems with placental development,” said Michelle Debbink, associate professor of obstetrics and gynecology at the U and coauthor of the study. “Early pregnancy is a critical period because the placenta and arteries that supply blood and oxygen to the fetus are still developing. Exposure to pollutants at this juncture could impair this process, increasing the risk of complications such as preeclampsia, which can require preterm delivery. Repeated exposures could also cause inflammation and damage that accumulates over time, further increasing the risk of preterm birth.”
Rethinking air quality standards
The authors hope that scientists will use this framework to better understand how other types of environmental hazards may impact human health.
“Public health policy definitely oversimplifies real-world exposures, but research into the health effects of complex mixtures is the first step in improving these policies,” Kelly said.
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Other authors include Robert Silver of the U’s Department of Obstetrics and Gynecology; Heather Holmes of the U’s Department of Chemical Engineering; Heidi Hanson of both the U’s Department of Population Health Sciences (DPH) and Oak Ridge National Laboratory; and Jennifer Doherty of the U’s DPH and Huntsman Cancer Institute; and Joel Schwartz of the Harvard T.H. Chan School of Public Health.
The University of Utah’s Center for High Performance Computing Center hosted the study data and provided the computational resources for model training.
The research was supported by a University of Utah DELPHI Data Science Initiative Seed Grant.
The study, “Linking mixtures of air pollution exposures and preterm birth with a self-organizing map,” published in the Journal of Exposure Science & Environmental Epidemiology on May 27, 2026.
A time series showing the frequency of air pollution clusters during the study period.
Credit
Kelly et. al., J Expo Sci Environ Epidemiol (2026)
Journal
Journal of Exposure Science & Environmental Epidemiology
Article Title
Linking mixtures of air pollution exposures and preterm birth with a self-organizing map
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