Air Pollution, Built Environment, and Early Cardiovascular Disease

Abstract

As the world’s population becomes increasingly urbanized, there is growing concern about the impact of urban environments on cardiovascular health. Urban residents are exposed to a variety of adverse environmental exposures throughout their lives, including air pollution, built environment and lack of green space, which may contribute to the development of early cardiovascular disease (CVD) and related risk factors. While epidemiological studies have examined the role of a few environmental factors with early CVD, the relationship with the broader environment remains poorly defined. In this article, we provide a brief overview of studies that have examined the impact of the environment including the built physical environment, discuss current challenges in the field, and suggest potential directions for future research. Additionally, we highlight the clinical implications of these findings and propose multi-level interventions to promote cardiovascular health among children and young adults.

1. Introduction

There is overwhelming evidence that the environment in which humans live, has a profound effect on their overall health and life expectancy.^1-3 In the prior decades, the concept of the “exposome”, which encompasses the entire set of aspects people encounter and are exposed to throughout their life-course, has had growing scientific influence.⁴ For the majority of people living in the modern world, their exposome is shaped by urban environments. This is true not only for developed countries such as the United States (US) where ~82% of the population lives in cities,⁵ but also for lower- and middle-income nations, where the majority of the population will transition to urban areas by 2050.⁶ Therefore, it is critical to understand how the urban environment impacts public health and what aspects are most harmful and potentially remediable.

Here, we review the current state of knowledge regarding the impact of the modern envirome on cardiometabolic health. Key facets include multiple aspects of the built environment including greenspaces and pollution. While total pollution, including environmental noise and night-light pollution, accounts for over 9 million deaths per year worldwide, we only discuss air pollution given the scope of this review and because it is responsible for nearly three quarters of the mortality.² We also focus our review on early cardiovascular disease occurring across early stages of the life course and/or disease state to align with the principle emphasis of this compendium series.

2. Air Pollution

Air pollution is a complex mixture of particulate matter (PM) and gaseous pollutants (e.g., ozone, nitrogen and sulfur dioxide). Airborne PM itself is an amalgam of aerosols varying in size and composition (e.g., metals, elemental carbon, organic species and inorganic nitrates and sulfates). The major sources of anthropogenic air pollution include traffic (e.g., diesel and gasoline vehicles), industrial emissions, power generation (e.g., coal-fired plants), indoor and household pollution (e.g., cooking) as well as secondhand (SHS) smoke.^{7, 8} In light of climate change, wildfires are also an increasingly important source, especially in the Western United States (US). PM is generally categorized by aerodynamic diameter – PM₁₀ (<10 μm), PM_10-2.5 (2.5-10 μm), PM_2.5 (<2.5 μm), and ultrafine particles (<1 μm). Among size fractions, there is general scientific consensus that PM_2.5 is the most well-established to induce adverse health effects and accounts for the greatest global public health burden.^{9, 10} That said, a growing literature suggests that ultrafine PM (UFPM), typically fresh combustion particles (e.g., near-roadway diesel), can also elicit independent effects.^{8, 11} Indeed a significant portion of the PM2.5 effect may be driven by the ultrafine and nano-size subfractions, as there is strong evidence that ultrafine and nano PM fraction is correlated with CVD.¹² While clearly linked to pulmonary diseases, gaseous pollutants (especially ozone) may also promote adverse cardiovascular effects.¹³ However, from a real-world perspective, individuals are exposed not to a single pollutants, but rather to the entire air pollution mixture, which itself exists and is dependent upon local and regional sources, complex meteorology and other influences such as the built environments, tree cover, etc.

Due to the excess mortality following extreme air pollution episodes during the early 20^th century, the US Clean Air Act was passed in 1970 with the aim of regulating air pollutant emissions. The law requires that the Environmental Protection Agency (EPA) update their National Ambient Air Quality Standards (NAAQS) periodically based on the most recent evidence and to ensure that current standards protect public health within an adequate margin of safety. The latest revision of the US NAAQS in 2020 retained prior standards set in 2012. Contrarily, in 2021 the World Health Organization (WHO) promulgated markedly more stringent Air Quality Guidelines (AQG) (Table 1).¹⁴ This landmark update was based upon the fundamental observation of a monotonic exposure-risk relationship extending to very low levels, even below 5-10 μg/m³ for PM_2.5, such that no “safe” threshold exists at the population-level.¹⁵ Major improvements in overall air quality have occurred across the US over prior decades whereby current PM_2,5 levels average 7.7 μg/m³.¹⁶ Conversely, poor air quality persists across much of the globe with population-weighted PM_2.5 concentrations averaging above 40 μg/m³.¹⁶ In many developing countries (e.g., India, Nigeria, Pakistan, China) daily levels commonly exceed 100 μg/m³.¹⁶ Latest estimates show that roughly 99% of the world’s population remains exposed to PM_2.5 levels exceeding 2021 WHO AQG (i.e., 5 μg/m³).¹⁶

Table 1.

Current regulations and guidelines on EPA criteria pollutants

Pollutant	US EPA NAAQS (updated 2008)			WHO AQG (updated 2021)
	Primary/Secondary Regulation	Duration	Level	Duration	Level
PM2.5	Primary	Annual	12 μg/m3	Annual	5 μg/m3
	Primary/Secondary	24-hour	35 μg/m3	24-hour	15 μg/m3
PM10	Primary/Secondary	24-hour	150 μg/m3	Annual	15 μg/m3
				24-hour	45 μg/m3
Ozone	Primary/Secondary	8-hour	70 ppb	Peak-season	60 μg/m3
				8-hour	100 μg/m3
Nitrogen Oxide				Annual	10 μg/m3
				24-hour	25 μg/m3
Nitrogen Dioxide	Primary	1-hour	100 ppb
	Primary/Secondary	1-year	53 ppb
Sulfur Dioxide	Primary	1-hour	75 ppb	24-hour	40 μg/m3
	Secondary	3-hour	0.5 ppm
Carbon Monoxide	Primary	8-hour	9 ppm	24-hour	4 mg/m3
		1-hour	35 ppm
Lead	Primary/Secondary	3 months	0.15 μg/m3

Note: US EPA, United States Environmental Protection Agency; WHO AQG, World Health Organization Air Quality Guidelines.

2.1. Air pollution as a cardiovascular risk factor

The landmark Global Burden of Disease study has repetitively shown that cardiovascular diseases account for the majority (~60%) of the excess in morbidity and mortality due to PM_2.5.⁹ A host of large epidemiologic studies conducted across the world have established a consistent association between air pollution exposure (especially PM_2.5) and increased cardiovascular risk factors (e.g., hypertension, diabetes), surrogates markers of disease (e.g., coronary artery calcium) and cardiovascular morbidity and mortality (e.g., ischemic heart disease, stroke, and heart failure). Most of these studies have been conducted in adults as summarized extensively in previous meta-analyses, reviews and consensus documents.^17-20 Although air pollution poses smaller relative risks than traditional risk factors (e.g., tobacco, hyperlipidemia), it presents an enormous public health threat due to its ubiquitous nature, involuntarily impacting billions of people on a daily basis. Hence, the absolute global burden of disease is enormous and explains why PM_2.5 ranks as the fourth leading risk factor for morbidity and mortality.¹⁰ Low- and middle-income countries once again suffer by far the greatest consequence.²¹ Health organizations across the world, including the American Heart Association (AHA) and European Society of Cardiology (ESC), have therefore deemed PM_2.5 as an important causal risk factor for cardiovascular morbidity and mortality.^22-24

Of note and specific relevance for this review is the consistent finding across epidemiological studies of the key health importance of exposure duration. Both short (hours-to-days) and long-term (months-to-years) exposures increase cardiovascular risk. However, the former does so by only 1% whereas the latter does so by roughly 10% (per 10 μg/m³ of PM_2.5). The health risks posed by chronic exposures, are thus an order of magnitude greater than those induced by acute episodes.²⁵ This core concept has pivotal ramifications for public health, especially for children and young adults potentially facing a lifetime of higher exposures.^{7, 26}

2.2. Cardiovascular risk from air pollution during gestation, childhood and young adulthood

There is growing consensus that the adverse cardiovascular heath impacts attributed to air pollution are cumulative over time.^{7, 27-29} One plausible explanation is that long-term or repetitive exposures elicit underlying cardiovascular damage (e.g., atherosclerosis, endothelial dysfunction, left ventricular alterations, kidney disease) and promote the worsening of chronic cardiometabolic conditions (e.g., hypertension, diabetes). Over years, these pathobiological changes synergistically enhance an individual’s prevailing risk for cardiovascular events, while also increasing their susceptibility to future acute pollution exposures. Long-term exposures can thus worsen the life-course trajectory of cardiovascular health, as outlined in Figure 1. This finding has increased the focus on the imperative to intervene earlier to minimize the life-long risk of excess cardiovascular morbidity and mortality.³⁰

Figure 1.

A life-course perspective on exposure to air pollution and adverse built environment and cardiovascular disease (CVD). Note: Built environment and air pollution make up a key element of one’s social determinants of health. Early exposure to pollution and adverse environment beginning in-utero and in childhood can have a profound effect on cardiovascular health due to the cumulative nature of harm on cardiometabolic risk factors and disease. Early intervention is critical in reducing the lifetime burden of CVD.

The ubiquitous and omnipresent nature of air pollution exposure, places nearly 2 billion children at serious lifetime risk of cardiovascular diseases.^{14, 31} This is even more important, due to several factors that make children potentially more susceptible. Children have higher baseline ventilation rates, are more physically active than adults and spend more time outdoors, exposing their lungs to greater air pollution. The higher ventilation rates and mouth-breathing may also drive pollutants deeper into the lungs making clearance of particulates more difficult. Furthermore, their lungs as well as the immune system are not fully developed which may have lasting impact from air pollution induced injury. These vulnerabilities are compounded for children that are in low socioeconomic status (SES) households, with little control over their home and social environment. Maternal exposure to air pollution during pregnancy has also been found to worsen multiple cardiovascular risk factors in the progeny. While there have been many excellent reviews that have examined the impact of environmental pollution on risk factors and CVD in the adult, there have been comparatively fewer studies at early life stages. Given the critical importance of aggregate exposures in the development of CVD , we review the literature that has studied the cardiovascular impact of air pollution at early life stages (Table 2).

Table 2.

Key studies of how air pollution exposure before adulthood (fetus to adolescents) affect cardiovascular risk factors

Cardiovascular Risk Factor	Age at exposure and number of participants	Air pollutants and location	Study Design and Major Finding	Reference
Hypertension	5-17 (n=9,354)	PM1, PM2.5 China	Cross-sectional; Increased systolic blood pressure and greater odds for hypertension	Wu, QZ (2020)
	6-12 (n=7,225)	PM2.5, SO2, O3 China	Cross-sectional; Positive association with hypertension	Zhang, J (2020)
	6-12 (n=130)	Ultrafine particles (<100 nm)	Cross-sectional; Increased systolic blood pressure	Pieters, N (2015)
	0-12 (n=1,432)	Long-term exposure to NO2 and PM2.5 The Netherlands	Prospective birth cohort; Increased diastolic blood pressure at age 12	Bilenko, N (2015)
	Mean age 7.9 (n=4,303)	Long-term exposure to PM2.5 China	Prospective longitudinal cohort; Increased blood pressure	Liang, X (2022)
	7-18 (n=43,745)	PM2.5, PM10 China	Cross-sectional; Increased association with systolic blood pressure	Zhang, Z (2019)
	Mean age 16, less than 18 (n=4,272)	PM2.5 Taiwan, Hong Kong	Prospective longitudinal cohort; Increased risk of hypertension during adulthood	Guo, C (2022)
	Gestational (n=1,293 mothers and their children)	PM2.5 China	Prospective longitudinal birth cohort; Increased blood pressure at age 3-9	Zhang, M (2018)
	Gestational (n=1,131 mother-infant pairs)	PM2.5 MA, USA	Prospective longitudinal birth cohort; Increased systolic blood pressure in newborns	Van Rossem, L (2015)
Obesity	Childhood (15 studies, n=683,081)	PM1, PM2.5, PM10, NO2 Location: Multiple	Systemic review/meta-analysis; Increased BMI and obesity	Huang, C (2022)
	6-8 (n=4,284)	PM2.5 China	Prospective cohort; Increased risk of central obesity at year 4-5	Tong, J (2022)
	7-18 (n=44,718)	PM1, PM2.5, PM10 and NO2 China	Cross-sectional; Increased BMI Z-score, waist circumference and waist-to-hip ratio, and higher prevalence of both general and central obesity	Zhang, Z (2021)
	5-6, followed for 4 years (n=4,550)	Traffic-related air pollution CA, USA	Prospective cohort; A 13.6% increase in the rate of average annual BMI growth between the children exposed to the lowest to the highest tenth percentile of air pollution	Jerrett, M (2014)
	9-10, followed till 18 (n=3,318)	Traffic-related pollution CA, USA	Prospective cohort; Increased BMI, mostly in females at age 18 years	Jerrett, M (2010)
	Prenatal (n=136 mother-infant pairs)	Traffic-related air pollution CA, USA	Prospective birth cohort; Higher cord blood levels of leptin and high molecular weight adiponectin, adipokines associated with increased infant weight change in female infants.	Alderete, TL (2018)
	Prenatal (n=535)	Polycyclic aromatic hydrocarbons and fine PM NY, USA	Prospective birth cohort; The prevalence of obesity was 20.6% at age 5 and increased across follow-ups until age 11 when it was 33.0%	Rundle, A (2019)
Insulin Resistance, Diabetes	6-17 (n=16,489)	PM2.5 China	Cross-sectional; Finding: Increased fasting plasma glucose	Wang, M (2020)
	6-13 (n=4,234)	PM2.5 and PM10 China	Cross-sectional; Higher fasting blood glucose levels	Cai, L (2019)
	14-18 (n=482)	PM2.5 Indonesia	Cross-sectional; Increased odds of fasting blood glucose	Yu, W (2020)
	8-18 (n=429)	Ambient and Traffic Related Ambient Pollution CA, USA	Cross-sectional; Higher insulin resistance and secretion, which was observed in conjunction with higher glycaemia	Toledo-Corral, C (2018)
	10 (n=397)	Traffic-related air pollution Germany	Prospective birth cohort; Insulin resistance increased by 17% for every 2 standard deviations of increase in ambient PM and NO2	Thiering, E (2013)
	6-8 (n=299)	Traffic-related air pollution CA, USA	Cross-sectional; Increased HbA1c percentage	Mann, JK (2021)
	<15 (n=33,000 children with DM)	Total air pollutant Russian Federation	Cross-sectional; Increased type I and type II diabetes	Choi, HS (2021)
Dyslipidemia	Children and adults (n=7,578)	PM10 Taiwan	Cross-sectional; PM10 associated with elevated triglycerides, apolipoprotein B, and reduced High density lipoprotein level	Chuang, K (2010)
	7-18 (n=12,814)	PM1, PM2.5, NO2 China	Cross-sectional; Increased risk of hypercholesterolemia	Gui, ZH (2020)
	Gestational (n=465 mother-child pairs)	PM2.5 Mexico City, Mexico	Prospective birth cohort; Exposure during third trimester associated with increase in childhood total cholesterol and LDL-C.	McGuinn, LA (2020)
Atherosclerosis	12-30 (n=789)	Long-term PM2.5, NOx Taipei, Taiwan	Prospective cohort; Increased carotid intimal media thickness	Chen, SY (2022)
	12-30 (n=755)	PM2.5, heavy metals Taipei, Taiwan	Cross-sectional; Increased carotid intimal media thickness	Chen, SY (2022)
	16 (n=363)	PM2.5 The Netherlands	Prospective cohort; Increased carotid intimal medial thickness	Peralta, AA (2022)
	Age 10, follow up at 21-22 (n=70)	Total NOx from TRAP CA, USA	Prospective longitudinal cohort; Greater yearly rate of change in carotid intimal medial thickness	Farzan, SF (2021)
	7-12 (n=287)	Distance from heavy traffic road Ecuador	Prospective longitudinal cohort; Greater cIMT in children living <100m compared to >200m	Armijos, RX (2015)
Cardiac Arrhythmias	Young adults (mean age 28, n=10,427,948)	PM2.5 China	Cross-sectional; Increase in resting heart rate	Xie, X (2018)
	Fetus, childhood (n=4,976)	Second-hand smoke CA, USA; internet-based	Prospective longitudinal cohort; Exposure during gestational development and during childhood was associated with having Atrial fibrillation later in life	Dixit, S (2016)
	6-12 (n=322)	PM2.5 PA, USA	Prospective longitudinal cohort; Increased burden of premature ventricular contractions	He, F (2022)
	Young adults (mean age 23, n=73)	Ultrafine particles (5-560 nm), black carbon, NO2 and CO, SO2, and O3	Prospective longitudinal cohort; Significant increases in QTc, indicating cardiac repolarization abnormalities	Xu, H (2019)

2.3. Impact on cardiovascular risk factors

Atherosclerotic vascular changes begin early in life as suggested by human autopsy studies of coronary arteries in children and teenagers, and are exacerbated by the presence of traditional risk factors at a young age.^32-37 For example, in the Young Finns Study, childhood LDL cholesterol, blood pressure, obesity, and smoking status were associated with increased carotid intimal medial thickness (cIMT) in adulthood.³⁸ In a recent landmark study of 38,589 individuals, both childhood risk factors and their change into adulthood were shown to be independent predictors of mid-life cardiovascular risk.³⁹ Hence, the early development of cardiovascular risk factors during childhood is critically important for lifetime risk as they carry on into adulthood and can predispose to early onset of myocardial infarction and stroke.

In children and adolescents, there is clear evidence that air pollution not only has a direct impact on the cardiovascular system, but can also adversely influence traditional risk factors.²² PM exposures can worsen risk factors (e.g., raise blood pressure and glucose) and even promote the earlier onset of chronic cardiometabolic conditions (e.g., hypertension and diabetes) in previously healthy individuals, thus further contributing to the lifetime burden of disease.²⁷ In the following section, we review the evidence linking air pollution to major risk factors during early life-stages (Table 2).

Hypertension

Higher blood pressure levels during childhood are recognized as a risk factor for developing hypertension and metabolic syndrome later in life.⁴⁰ There is strong evidence that air pollution exposures are associated with increased blood pressure in children and adolescents-. Wu et al found that each 10 μg/m³ greater exposure to PM_2.5 was significantly associated with 2.4 mm Hg higher systolic BP levels and 55% greater odds for developing hypertension.⁴¹ Another study found that long-term exposure to PM_2.5, SO₂, and O₃ were associated with increased BP in ~7000 elementary school children in China.⁴² Exposure to a combination of SO₂, O₃, NO₂, and PM_2.5 were also found to be associated with elevated systolic and diastolic blood pressures.^43-47 Several studies have also reported significant associations between PM_2.5 exposure during childhood and higher blood pressures, or hypertension in adulthood during extended follow up.^{45, 48} A recent meta-analysis of 14 studies, confirmed that both short- and long-term exposures to PM_2.5 are associated with increased blood pressure in children and adolescents.⁴⁷

Gestational exposure to air pollutants can lead to increased blood pressure in the newborn and later childhood. In a prospective birth cohort of 1200 mothers, PM_2.5 exposure during pregnancy was subsequently associated with elevated blood pressure measured between ages 3 to 9 of the child.⁴⁹ Exposure to PM_2.5 during late pregnancy has been associated with systolic hypertension in newborns, a similar association found in neonates of mothers who are active smokers.^{50, 51}

Obesity

Obesity and in particular visceral fat, plays a central role in promoting atherosclerosis and cardiovascular diseases. The rise in obesity in children has increased the prevalence of insulin resistance and type 2 diabetes.⁵² In a meta-analysis of 15 studies of air pollution exposure in children, a significant association was found between PM₁, PM_2.5, PM₁₀, with a 28% increase in risk of obesity per 10 μg/m³ increase in PM_2.5.⁵³ Tong et al found that every 5 μg/m³ increase in PM_2.5 levels in children 6 to 8 years of age followed for 4 years led to a 26% increase in relative risk of central obesity.⁵⁴ In a large cohort of 44,718 children and adolescents aged 7 to 18 years in China, PM₁, PM_2.5, PM₁₀, and NO₂ were associated with increased body mass index (BMI) Z-score, waist circumference and Waist-to-Height ratio, and higher prevalence of general and central obesity.⁵⁵ In another series of studies examining traffic pollution around family homes in the US, higher levels of vehicular traffic and pollution were associated with higher attained BMI in children aged 5 to 7 years as well as 10 to18 years.^{56, 57}

Pregnant women exposed to higher level of traffic-related air pollution (TRAP) had increase in leptin and adiponectin in the cord blood, and were more likely to have an infant with increased weight gain ⁵⁸. Similarly, in a study by Rundle et al. exposure to air pollution during gestational period was associated with increased prevalence of obesity and diabetes during adolescence.⁵⁹ In fact, a study of tobacco exposure in 568 pairs of mother-child found that the exposure period of late gestation and early infancy to tobacco may have the greatest impact on childhood adiposity.⁶⁰

Interestingly, interactions between exposure to air pollution and different risk factors have also been observed. For example, Dong et al. found that obesity amplified the association of long-term pollution exposure with blood pressure and hypertension in ~10,000 children aged 5-17 years in China.⁶¹ A similar interaction was found by Wu et al, where stronger associations of pollution exposure with blood pressure was seen among younger (aged ≤11 years) and overweight/obese children.⁴¹

Insulin resistance and diabetes

Childhood onset diabetes is a risk factor for early cardiovascular disease. In studies of adolescents with diabetes, longer duration of diabetes and higher HgbA1c have been associated with increase in cIMT and arterial stiffness.^62-64 A number of studies including a large cross-sectional analysis have found associations between long-term PM_2.5 exposure and impaired fasting glucose in children and adolescents.^65-67 In a cohort of 429 overweight minority children in Los Angeles, prior-year exposure to PM_2.5 was associated with 25% increase in fasting insulin, 9% decrease in insulin sensitivity, and increase in fasting glucose.⁶⁸ In another study, insulin resistance and type-2 diabetes were greater in 10-year-old children exposed to high levels of traffic-related air pollution compared with those children exposed to lower levels.⁶⁹ Also in children living in Fresno, longer-term increases in TRAP was associated with increased HgbA1c percentage.⁷⁰ Furthermore, type-1 diabetes has been associated with air pollution, and suggested that exposure to fine particulates may be a specific contributing factor for type-1 diabetes in children under 5 years of age.^{71, 72}

Hyperlipidemia

Hyperlipidemia is a well-established risk factor with ample evidence supporting a life-time burden of disease correlating with increased risk for cardiovascular disease. Childhood hyperlipidemia clearly increases risk for early development of cardiovascular disease as evidenced by the literature on familial hypercholesterolemia. A cohort study of Taiwanese children and adults found that long-term exposure to PM₁₀ associated with increased apolipoprotein B, triglycerides and reduced HDL.⁷³ In a more recent study of 12,814 children aged 7-18 years in China, each 10 ug/m3 increase in PM₁, PM_2.5 and NO₂ was associated with increased risk for hypercholesterolemia (OR 2.15 (95%CI: 1.27, 3.65), OR 1.70 (95%CI 1.12, 2.60), and OR 1.43 (95%CI 1.05,1.93), respectively).⁷⁴ Furthermore, in a longitudinal birth cohort of 465 mother-child pairs in Mexico, PM2.5 exposure during the third trimester associated with increased total cholesterol, LDL cholesterol and non-HDL cholesterol.⁷⁵

The total body of evidence for hyperlipidemia in children is relatively limited compared to other risk factors such as hypertension and impaired glucose metabolism, but along with several studies in adults showing significant correlations of PM_2.5 exposure and hyperlipidemia and dyslipidemia, the trend appears to be significant.^76-78

Atherosclerotic vascular changes

Several large longitudinal cohort studies have demonstrated a consistent link between cumulative burden of exposure on the prevalence and progression of coronary and carotid atherosclerotic disease in adults.^79-82 These studies utilized coronary artery or aortic calcium scores, carotid ultrasounds for plaque or cIMT, and coronary angiograms to find correlations between the degree of exposure and atherosclerotic disease burden.^{17, 83} In children and adolescents, the study of vascular remodeling and progression of disease is difficult due to the slowly progressive nature and the typical timeline of disease pathogenesis. However, measures of subclinical atherosclerosis, such as cIMT, which predict cardiovascular events later in life, have been found to correlate with tobacco exposure and ambient air pollution.^84-86

Recently, Chen et al showed correlations of ambient air pollution and PM_2.5 levels with subclinical atherosclerosis in 789 Taiwanese adolescents and young adults (age range 12-30) as measured by cIMT.^{87, 88} In a Dutch birth cohort of 363 adolescents, Peralta et al. also found that early exposure to ambient pollution correlated with cIMT measurement at age 16 using a quantile regression modeling.⁸⁹ Furthermore, in a prospective study of 70 participants, Farzan et al. measured the cIMT at average age of 10, then repeated cIMT measurement at age 21-22 where they found increased change in cIMT with greater exposure to traffic density and NOx.⁹⁰ Children residing less than 100 meters from heavy traffic were also found to have cIMT greater than those of children living >200m away.⁹¹

Arrhythmias

Changes in resting heart rate and heart rate variability have been reported with acute and chronic exposure to air pollution in young adults.^{92, 93} There is limited literature linking air pollution exposure to incidence of arrythmia presentation.^94-96 Data on the long-term effect of air pollution on specific atrial and ventricular arrythmias is relatively sparse, compared to short-term exposure.^{97 98} Atrial fibrillation has been reported in adults who have had greater long-term exposure to second-hand smoke pollution during their childhood, including gestation.⁹⁹ Furthermore, changes in corrected QT interval (QTc) on electrocardiogram, and repolarization abnormalities and premature ventricular contractions have been associated with air pollution exposure.^{100, 101}

2.4. Paradigm of magnified risks posed by a high life-long burden of risk factors

Studies of families with familial hypercholesterolemia (FH) have demonstrated that accelerated atherosclerotic cardiovascular disease may develop, as a consequence of high levels of exposures to atherogenic lipoproteins even over short durations . Exposure to high levels of LDL-C, can rapidly progress the disease even during childhood and adolescence, manifesting as early myocardial infarctions and stroke. Fortunately, institution of available therapy against hyperlipidemia can drastically reduce the rate of adverse cardiac outcomes. These findings are further supported by Mendelian randomization studies using genetic variants associated with traditional risk factors such as lipids, hypertension, and obesity as instrumental variables, which have not only established the causal nature of many risk factors, but show the cumulative lifetime burden of exposure to such risk factors.^102-105 Previous reviews have summarized the totality of evidence with regards to the dose, timing and mechanisms of chronic air pollution mediated potentiation of atherosclerosis.^{17, 83} Overall, the lessons learned suggest that much like conventional risk factors, a high lifelong burden of exposure to air pollution, will likely pose cumulative adverse health effects, that translate into a synergistically heightened risk for cardiovascular diseases over the life-course (Figure 1). ^{25, 27}

2.5. Mechanisms of air pollution induced cardiovascular disease

Potential biological mechanisms have been reviewed in detail previously.^{7, 27, 28, 106} (Figure 2) PM, including PM2.5, UFPM, and NP, individual pollutant constituents (e.g., metals, organic chemicals), or secondarily-generated compounds (e.g., oxidization by-products) coming into contact with pulmonary and immune cells (e.g., resident macrophages, alveolar cells), neuronal receptors, and/or entering into the systemic circulation can lead to a cascade of events responsible for the acute and chronic effects of pollution on cardiovascular diseases. Briefly, several alterations including systemic inflammation, endothelial dysfunction, neurohormonal activation, and cardiac infiltration of NP have been described in children as potential mechanisms conferring long-term risk.^{107, 108} Several “mediating (transmission) pathways” whereby the inhalation of PM elicits a harmful signal in remote cardiovascular organs have been implicated in human and animal experiments.⁷ Controlled human exposure studies have been valuable in establishing the immediate physiologic effects of PM exposure on biomarkers and pathological processes linked to cardiovascular risk (e.g., endothelial dysfunction, thrombogenesis).^{109, 110} A general schema illustrating the pathways and end organ responses to air pollution exposure is provided in Figure 2. While most human studies have been performed in adults, many of the findings and results from animal experiments may be relevant in children.

Figure 2.

A general schema of the pathways and end organ responses to air pollution exposure. Note: PM_2.5 exposure from multiple sources lead to cardiovascular disease by several potential mechanisms, including lung inflammation and oxidative stress, PM spillover into circulation, oral intake leading to change in gut microbiome, and neurohormonal activation resulting in adrenal and autonomic imbalance.^{7, 22, 27, 28}

3. Built Environment

Built environment refers to the physical environment constructed and shaped by humans as opposed to the natural environment. It comprises the transportation system, land use patterns, and design features that could either promote or undermine population health.¹¹¹ The transportation system includes street design and connectivity, pedestrian and bicycle infrastructure, and public transit; and components of land use and design include mixed land use, residential density, destination proximity, parks, and recreational facility access. Green space refers to places with natural vegetation, such as grass, plants or trees, including those in the natural (e.g., wetlands), social (e.g., parks), and personal environment (e.g., yards).¹¹²

3.1. Relevance of built environment for cardiovascular health

There is abundant evidence showing an association between the built environment, green space, and CVD and related morbidity and mortality, which has been well documented by several recent reviews.^{112, 113} For instance, CHD risk is found to be positively associated with traffic density and proximity to major roads, and negatively associated with access to parks and recreational facilities.

There are many ways that built environment and green space may affect individual CVD risks (Figure 3).¹¹⁴ First, it has a direct effect by exposing individuals to environmental factors, including air pollution, noise and high/low temperature, that are found to be closely associated with the development and incidence of CVD. Second, they may influence CVD risks indirectly by changing individuals’ behaviors and lifestyles. For instance, people living in neighborhoods with little green space may find it difficult to maintain regular physical activities that are beneficial to cardiovascular health. These influences, especially the ones associated with environmental exposure, are likely to start even during pregnancy.

Figure 3.

Conceptual framework between built environment, green space and cardiovascular disease

3.2. Built environment and CVD risk factors

Research has found that built environment and green space are closely related to common CVD risk factors among adults, including high blood pressure, obesity or overweight, diabetes mellitus, metabolic syndrome, and abnormal levels of blood lipids.¹¹⁵ For instance, othese risks factors are likely to be affected by neighborhood walkability as indicated by street connectivity, public transit, population density, land-use mix, and green and open spaces. Many studies have also examined these relationships among children, while research focusing on young adults is relatively rare (Table 3).

Table 3.

Key studies examining the association between built environment, green space and early CVD

Cardiovascular Risk Factor	Age at exposure and number of participants	Built Environment and Green Space measures and location	Study Design and Major Finding	Reference
Hypertension	12 (n=1,505) and 16 (n=797)	GS The Netherlands	Cross-sectional; No effect on BP	Bloemsma, LD (2019)
	10-16 (n=188)	Density of public transit St. Paul-Minneapolis MA, USA	Cross-sectional; Negative association with systolic BP	Dengel, DR (2009)
	4-5 (n=4,279)	Traffic and GS France, Greece, Spain, and UK	Cross-sectional; Higher diastolic BP associated with high traffic and low GS	Warembourg, C (2021)
	6-18 (n=61,229)	Greenness China	Cross-sectional; Negatively associated with hypertension, and systolic and diastolic BP	Luo, YN (2022)
	8-12 (n=1,251)	GS Austria and Italy	Cross-sectional; BP negatively associated with GS and presence of a home garden	Dzhambov, AM (2022)
	10 (n=2,078)	GS (NDVI) Germany	Cross-sectional; Negatively associated with systolic and diastolic BP	Markevych, I (2014)
	7-18 (n=588,004)	Greenness around schools (NDVI) Beijing and Zhongshan city, China	Negative association with high BP	Chen, L (2022)
	Mean age 10.9 (n=9,354)	Greenness around schools (NDVI; SAVI) Shenyang, Dalian, Anshan, Fushun, Benxi, Liaoyang, and Dandong, China	Cross-sectional; Negative association with systolic BP and hypertension	Xiao, X (2020)
	18-25, twins (n=278)	Greenness East-Flanders, Belgium	Cross-sectional; Early-life greenness negatively associated with night-time systolic BP	Bijnens, EM (2017)
	20-45, women (n=3,416)	GS Kaunas, Lithuania	Cross-sectional; Distance to GS positively associated with BP	Grazuleviciene, R (2014)
Obesity	8-12 (n=1,251)	GS Austria and Italy	Cross-sectional; BMI negatively associated with GS and presence of a home garden	Dzhambov, AM (2022)
	25-37 at baseline (n=4,143)	Road connectivity, parks and PA facilities USA	Longitudinal; Inconsistently associated with BMI	Meyer, KA (2015)
	18-34 (n=280)	Urban sprawl USA	Cross-sectional; Positive association with BMI	Arcaya, M (2014)
	Mean age 36.9 (n= 2,848)	Recreational facility Jamaica	Cross-sectional; Negative association with BMI among men but not women	Cunningham-Myrie, CA (2015)
	12-17, followed till 18-23 (n=8,984)	County sprawl USA	Cross-sectional and longitudinal; Positive association with BMI in cross-sectional analysis, no association in longitudinal analysis	Ewing, R (2006)
	14-21 (n=6,111)	Urban sprawl USA	Longitudinal; No association with BMI	Eid, J (2008)
	35-43 (n=3,867)	Urban sprawl USA	Cross-sectional; Positive association with BMI	Plantinga, AJ (2007)
Insulin Resistance, Diabetes	7-18 (n=3,844)	GS Iran	Cross-sectional; FBG negatively associated with time spent in GS, and IFG negatively associated with time spent in natural GS	Dadvand, P (2018)
	8-18 (n=453, Hispanic)	Park space	Cross-sectional; Negatively associated with IR	Hsieh, S (2014)
	25-37 at baseline (n=4,143)	Road connectivity, parks and PA facilities USA	Longitudinal; Inconsistently associated with IR	Meyer, KA (2015)
Dyslipidemia	10-15 (n=1,552)	Greenness Munich and Wesel area, Germany	Cross-sectional; No association with BL levels	Markevych, I (2016)
Atherosclerosis	45-74 (n=4,301)	Traffic Essen, Bochum, and Mulheim, Germany	Cross-sectional; Distance to major roads negatively associated with CAC, and participants with low SES and simultaneous exposure to high traffic had highest levels of CAC	Dragano, N; Hoffmann, B; Moebus, S (2009)
	45-74 (n=4,301)	Traffic Essen, Bochum, and Mulheim, Germany	Cross-sectional; Distance to major roads negatively associated with CAC	Hoffmann, B (2007)
	45-74 (n=4,301)	Traffic Essen, Bochum, and Mulheim, Germany	Cross-sectional; Distance to major roads negatively associated with CAC, and significant results for men with a distance to major roads <=50m, but none for women	Dragano, N; Hoffmann, B; Stang, A (2009)
	45-84 at baseline (n=5,950)	Walking environment Baltimore, MD; Chicago, IL; Forsyth County, NC; LA, CA; New York, NY; and St. Paul, MN, USA	Longitudinal; No association with coronary artery calcium	Wing, JJ (2016)
	Adults (n=2,030)	Greenness (NDVI)	Cross-sectional; Negative association with coronary artery stenosis and myocardial injury biomarkers	Wu, J (2023)
	Mean age 55.5 (SD: 12.7) (n=4,800)	Proximity to major roads USA	Longitudinal; Living less than 150 m (versus more than 300 m) from major roadways negatively associated with CIMT, but not associated with peripheral artery disease (PAD), CAC, and abdominal aortic calcification (AAC)	Wang, Y (2016)
	7-12 (n=287)	Traffic Ecuador	Cross-sectional; Residential proximity and distance-weighted traffic density (DWTD) negatively associated with CIMT, but not associated with systemic inflammation indicators	Armijos, RX (2015)

Hypertension

With a few exceptions,¹¹⁶ many studies have found a link between built environment, green space and hypertension among children and adolescents. More green space in neighborhoods or around schools where children and adolescents live or study is often associated with lower blood pressure.^117-122 For example, a study of German children found that for children living at residences with low and moderate greenness, systolic BP was 0.90 ± 0.50 mmHg and 1.23 ± 0.50 mmHg higher, respectively, than that of their counterparts living in areas of high greenness; similar disparities were also found for their diastolic BP.¹²⁰ Moreover, some of these studies found important differences among subgroups. In the case of green space, its positive effects on the youth’s BP are stronger in more urbanized settings,^{118, 120} and the benefits of greenness exposure are more pronounced among older respondents and boys than among their counterparts.¹¹⁸

By comparison, the evidence for young adults is far less. In a study focusing on young adults aged 18-25, researchers found that among both movers and non-movers, greater residential greenness is associated with lower night-time systolic BP, although the effect becomes insignificant for the movers once their early-life greenness exposure was considered.¹²³ Another study on a group of 20- to 45-year-old women found that the probability of high-normal BP increased by 14% for each 300-meter increase in the distance from their home to urban green space.¹²⁴

Obesity

The clearest influences of built environment and green space on children, adolescents and young adults are observed with obesity.¹²⁵ However, relevant evidence is less conclusive. For example, a study on adolescents aged 12 and 16 did not find an association between waist circumference and the exposure to green space.¹¹⁶ Moreover, the evidence is inconsistent for the association between urban sprawl (often characterized by low-density residential housing, single-use zoning, etc.) and childhood obesity. A positive association was often documented by cross-sectional analyses, although research using longitudinal data often failed to establish a causal relationship.^{126, 127}

For young adults, some studies reported a positive association between urban sprawl with their BMI.^{128, 129} And there is evidence that men are more likely than women to be affected by environmental attributes related to physical activities (PA), such as commercial PA facilities.¹³⁰

Insulin resistance and diabetes

There is also evidence for the link between built environment, green space, and the risk of diabetes mellitus among children. A study investigating children and adolescents aged 7-18 years found that increases in the total time spent in green space are associated with decreased levels of fasting blood glucose (FBG) and reduced risk of impaired fasting glucose (IFG).¹³¹ In another study,¹³² researchers found that even controlling for obesity measures, higher insulin resistance is still significantly associated with less park space in the neighborhood for a group of Hispanic youth aged 8-18 years. In a study on Jamaican adults with an average age of 36.9, researchers failed to find an association between diabetes and neighborhood availability of recreational facilities.¹³⁰

Hyperlipidemia

Only a few studies examined the relationship between green space and blood lipids among children, and most did not find any significant association.^{116, 133-135} For instance, using a set of different measures of green space exposure, such as total/urban/rural/natural green space within 300-meter distance of respondents’ home, Bloemsma and colleagues examined total cholesterol levels, the total/HDL cholesterol ratio and HbA1c, but did not find any significant associations in adolescents aged 12 and 16 years.¹¹⁶

Atherosclerotic vascular changes

Among adults, research has also begun to link built environment attributes and green space to common early CVD conditions, such as coronary atherosclerosis. Exposure to high traffic, for example, shows a clear link to greater levels of CAC, and men and people of low SES are particularly vulnerable,^136-138 although one study showed an insignificant association.¹³⁹ There have been limited studies linking active mobility, walkable streets and public transporation with atherosclerosis surrogates. However, greater active mobility is expected to lower the risks of early CVD conditions, this benefit might be undermined by high levels of exposures to ambient air pollution in heavily polluted environments.¹⁴⁰

Proximity to major roadways (living less than 150 meters versus more than 300 meters) was associated with 6.67% increase in carotid intima-media thickness.¹³⁹ A similar association was also found among healthy children.⁹¹ Moreover, researchers also documented an association between greenness in decreasing the odds of severe coronary stenosis among adults.¹⁴¹

3.3. Potential biological mechanisms

As documented by epidemiological studies focusing on the effects of multiple environmental exposures across the life course on CVD, there are biological pathways through which built environment and green space could influence the onset and progression of CVD, and research has identified several biomarkers being involved in these processes, such as DNA methylation and gut microbiota changes.^{142, 143}

DNA methylation modifications induced by environmental factors can persist over time¹⁴⁴ There is evidence that the health benefits of green space may be related to its role in certain DNA methylation changes.^{145, 146} In Jeong and colleagues’ study,¹⁴⁶ they found that residential greennesswas associated with DNA methylation enriched in the allostatic load that is linked to metabolic syndrome, blood lipids, impaired fasting glucose, insulin, obesity, blood pressure, and cardiac autonomic nervous system.

Changes in gut microbiota might also be relevant due to the close links with food and physical activities.¹⁴³ While much research has tried to clarify the underlying biological mechanisms, direct evidence on the other end is still very rare. Several intervention studies on children did find that exposure to nature or vegetation is likely to change their gut microbiota, fecal serotonin, and/or blood immune markers that regulate the human’s immune system.^{147, 148}

3.4. Modification effects of social determinants

Increasing evidence also suggests that common social determinants of health (SDH) often interact with air pollution, built environment attributes and green space in affecting CVD outcomes.¹⁴⁹ Social deprivation and PM_2.5 exposures were independently associated with county level age-adjusted CV mortality.^150-152 The associations between PM2.5 and CV mortality were stronger in counties with high vs low social deprivation. In a prior study examining the association with CAC,¹³⁶ the highest levels of CAC were observed among participants with low SES and simultaneous exposure to high traffic. And Ward-Caviness et al.’s study on DNA methylation also documented a significant interaction between neighborhood green space (presence of large mature trees) and neighborhood disadvantages, as indicated by the presence of abandoned cars, poor streets, and non-art graffiti.¹⁴⁵

4. Challenges and Future Directions

4.1. Study design and causal inference

Unlike air pollution, built environment and green space have not been established as risk factors for early CVD and relevant findings are mostly associations, highlighting a critical need to conduct robust studies (e.g., longitudinal cohort or natural experiment or intervention studies) and perform novel statistical methods to infer causality. Firstly, natural experiments or intervention studies (e.g., randomized community or school trials) should be considered in future studies. For example, with a prospective cohort study design, the Green Heart Louisville explicitly selected part of a region in Louisville, KY for a mature vegetation intervention and provided reliable data and methodological foundations to assess the causality between urban green space and cardiovascular health.¹⁵³ Secondly, because human body responds dynamically to exposures to built environment/green space that also change over time and vary spatially, a study on a longitudinally followed population with repeated exposure profiles from a young age to later in life with detailed subclinical and clinical CVD phenotype information will provide biological insights into the initiation and progression of CVD associated with long-term exposure to built environment and green space. Lastly, emerging causal inference methods such as integrating genetic data within the exposome context¹⁵⁴ should be employed to strengthen the causality.

4.2. Life-course exposure to built environment and SDH and their interaction

Exposures to environmental and social stressors at various life stages shape the development of CVD differently.²⁷ Therefore, it is crucial to adopt a life-course approach to identify sensitive periods or windows of susceptibility. Robust evidence has demonstrated that individual- and neighborhood-level SDH contribute to cardiovascular risk and the development of CVD.¹⁵⁵ However, interactions between built and social environments (e.g., neighborhood crime and social connections) in relation to CVD risk factors and outcomes have rarely been studied, particularly considering a life-course approach. A better understanding of such interactions is essential to design multi-level interventions to reduce environmental and social exposure at the same time that likely optimize their effectiveness.

4.3. Incorporating mobility into exposure assessment

Apart from integrating quality assessment into existing exposure measures, emerging big mobility data collected through smart phones, sensors, and smart technologies also provide unprecedented opportunities to better characterize dynamic exposures to air pollution, built environment and green space. The majority of prior studies relied on residential addresses to calculate short- and long-term exposures to outdoor environmental factors while ignoring human activity patterns, which is a significant omission as shown in the literature.¹⁵⁶ Newly available mobility data on peoples’ location, movement and/or activity collected through various enabled smartphone applications or sensors hold great promise to fill this gap for future research. For instance, in a study examining park use in three U.S. cities, researchers used smartphone data with weekly foot traffic information and found that a large share of park visits actually occurred outside individuals’ residential neighborhood.¹⁵⁷

4.4. Exposure inequity and health disparity

More work is required to elucidate how exposure inequality to environmental factors may contribute to racial/ethnic disparity in early CVD. This will provide critical information for interventions and regulations to reduce exposure inequality considering environmental exposures are modifiable risk factors. Racial/ethnic disparities in common CVD outcomes have been well documented in the literature,¹⁴² which is also the case for early CVD. And there is abundant evidence of racial/ethnic inequalities in built environmental exposure and green space access.¹⁵⁸ However, very little research has explicitly explored how inequalities in environmental exposures might contribute to disparities in early CVD among racial/ethnic groups. As the plateau in improvements in CVD rates among U.S. young adults in recent decades is partly driven by increasing racial/ethnic disparities,¹⁵⁹ more advances can thus be expected if such disparities can be properly reduced.

4.5. Biological mechanisms

Efforts to clarify the underlying biological mechanisms are essential to deepen our understanding of the role of air pollution, built environment and green space in early CVD and to help identify the most effective interventions. Although there has been abundant evidence on air pollution,¹⁶⁰ this area of research on exposure to built environment and green space is still in its infancy to elucidate the biological pathways. Thriving omics technologies (e.g., epigenetics, microbiome, and metabolomics) in recent years hold great promise to identify biomarkers in the pathways between exposures and early CVD, and these biomarkers provide an important opportunity to understand the early-stage changes underlying diseases of the cardiovascular system (and potentially others).

4.6. Clinical implications

Altogether, these findings support the importance of urgent actions to reduce the burden of lifelong exposures to air pollution in children and young adults. We are unaware of any trials that show cardiovascular benefits of preventing exposures in children. However, a number of studies in adults, including younger college-age individuals, have shown that personal-level interventions, most notably using portable air cleaners, can improve biomarkers of cardiovascular health including inflammation, neurohormonal stress and blood pressure.^{161, 162} Lifestyle actions such as following Air Quality Index guidelines on activity changes, increasing exercise, a healthy diet, and certain oral agents (e.g., fish oil, antioxidants) have preliminary evidence of potential benefits.¹⁶³ Without a doubt, air quality regulations that lower ambient PM_2.5 at a regional or country-level reduce morbidity and mortality.¹⁶¹ We believe clinicians and medical societies should be aware of the risk posed by air pollution to young individuals and support stronger US EPA NAAQS that accord with the 2021 WHO AQG.

Although green space-related activities are supported by growing evidence of epidemiological and intervention studies, they have rarely been incorporated into healthcare practice, particularly for children and young adults. This is mainly due to the lack of: 1) standard measures for green space while traditional medicines have a specific direction for dose and treatment duration; 2) clinical guidelines; 3) tools to monitor the effectiveness of green space activities; and 4) understanding of local contextual factors (e.g., neighborhood safety).¹⁶⁴ Currently, green space-related activities are promising as preventive measures or assisted therapeutic interventions to increase physical activities and reduce stress. Considering children and young adults spend much time at school or workplace, specific changes to these places can be integrated to promote physical activities, such as adding green schoolyards or building exercise facilities at worksites.

4.7. Multi-level intervention

Although recent practices of CVD risk reduction have been shifting to multi-level interventions that target individuals, social and physical environments, and policies,¹⁶⁵ the evidence for mitigating multiple environmental exposures and/or social environments in relation to CVD is very limited but potentially promising. For example, re-imaging cities with walkable neighborhoods, recreational spaces that reduce stress and a sense of community and areas of green space, together with safe and ready access to locally sourced and procured nutritious food may redefine a new approach to cardiovascular disease prevention. Redesigning communities by careful consideration to improves access and utilization among the socioeconomically disadvantaged and racial/ethnic minorities is vitally important.¹⁶⁶ However, the benefits are likely to be undermined if necessary conditions (e.g., neighborhood safety) are not present. Smart cities that reduce exposures to environmental pollutants, provide access to clean food and water devoid of chemicals and simultaneously encouraging physical activity may allow primordial risk mitigation. Urban policy integrating health and importantly cardiovascular and metabolic health in all decisions is going to be critical.^{167, 168}

Non-standard Abbreviations and Acronyms

CHD

coronary heart disease

CVD

cardiovascular disease

BP

blood pressure

DM

diabetes mellitus

IR

insulin resistance

BL

blood lipids

FBG

fasting blood glucose

IFG

impaired fasting glucose

OB

obesity

BMI

body mass index

CA

coronary atherosclerosis

CAC

coronary artery calcification

CIMT

carotid intima-media thickness

GS

green space

NDVI

normalized difference vegetation index

SAVI

soil-adjusted vegetation index

PA

physical activity

SDH

social determinants of health