Long-Term Exposure to Transportation Noise and Risk for Type 2 Diabetes in a Nationwide Cohort Study from Denmark

Long-term exposure to transportation noise and diabetes mellitus mortality: a national cohort study and updated meta-analysis

BACKGROUND

Long-term exposure to transportation noise is related to cardio-metabolic diseases, with more recent evidence also showing associations with diabetes mellitus (DM) incidence. This study aimed to evaluate the association between transportation noise and DM mortality within the Swiss National Cohort.

METHODS

During 15 years of follow-up (2001-2015; 4.14 million adults), over 72,000 DM deaths were accrued. Source-specific noise was calculated at residential locations, considering moving history. Multi-exposure, time-varying Cox regression was used to derive hazard ratios (HR, and 95%-confidence intervals). Models included road traffic, railway and aircraft noise, air pollution, and individual and area-level covariates including socio-economic position. Analyses included exposure-response modelling, effect modification, and a subset analysis around airports. The main findings were integrated into meta-analyses with published studies on mortality and incidence (separately and combined).

RESULTS

HRs were 1.06 (1.05, 1.07), 1.02 (1.01, 1.03) and 1.01 (0.99, 1.02) per 10 dB day evening-night level (Lden) road traffic, railway and aircraft noise, respectively (adjusted model, including NO2). Splines suggested a threshold for road traffic noise (~ 46 dB Lden, well below the 53 dB Lden WHO guideline level), but not railway noise. Substituting for PM2.5, or including deaths with type 1 DM hardly changed the associations. HRs were higher for males compared to females, and in younger compared to older adults. Focusing only on type 1 DM showed an independent association with road traffic noise. Meta-analysis was only possible for road traffic noise in relation to mortality (1.08 [0.99, 1.18] per 10 dB, n = 4), with the point estimate broadly similar to that for incidence (1.07 [1.05, 1.09] per 10 dB, n = 10). Combining incidence and mortality studies indicated positive associations for each source, strongest for road traffic noise (1.07 [1.05, 1.08], 1.02 [1.01, 1.03], and 1.02 [1.00, 1.03] per 10 dB road traffic [n = 14], railway [n = 5] and aircraft noise [n = 5], respectively).

CONCLUSIONS

This study provides new evidence that transportation noise is associated with diabetes mortality. With the growing evidence and large disease burden, DM should be viewed as an important outcome in the noise and health discussion.

Association of transportation noise with cardiovascular diseases

This comprehensive article delves into the intricate and multifaceted issue of noise pollution, shedding light on its diverse sources, profound health implications, and the economic burden it imposes on societies. Noise pollution is an increasingly prevalent environmental challenge, impacting millions of people worldwide, often without their full awareness of its adverse effects. Drawing from a wealth of scientific research, the article underscores the well-established links between noise pollution and a spectrum of health issues, including cardiovascular diseases, sleep disturbances, and psychological stress. While exploring the sources and consequences of noise pollution, the article highlights the urgent need for a holistic and collaborative approach to mitigate its impact. This entails a combination of regulatory measures, technological innovations, urban planning strategies, and public education campaigns. It is increasingly evident that the detrimental effects of noise pollution extend beyond physical health, encompassing mental and social well-being. The article also addresses the synergistic relationship between noise pollution and other environmental stressors, emphasizing the importance of considering noise in conjunction with factors like air pollution and access to green spaces. It examines the potential of green spaces to mitigate the effects of noise pollution and enhance overall health.

The impact of co-exposure to air and noise pollution on the incidence of metabolic syndrome from a health checkup cohort

Previous studies have found associations between the incidence of metabolic syndrome (MetS) and exposure to air pollution or road traffic noise. However, investigations on environmental co-exposures are limited. This study aimed to investigate the association between co-exposure to air pollution and road traffic noise and MetS and its subcomponents. Participants living in Taipei City who underwent at least two health checkups between 2010 and 2016 were included in the study. Data were sourced from the MJ Health database, a longitudinal, large-scale cohort in Taiwan. The monthly traffic noise exposure (Lden and Lnight) was computed using a dynamic noise map. Monthly fine particulate data at one kilometer resolution were computed from satellite imagery data. Cox proportional hazards regression models with month as the underlying time scale were used to estimate hazard ratios (HRs) for the impact of PM2.5 and road traffic noise exposure on the risk of developing MetS or its subcomponents. Data from 10,773 participants were included. We found significant positive associations between incident MetS and PM2.5 (HR: 1.88; 95% CI 1.67, 2.12), Lden (HR: 1.10; 95% CI 1.06, 1.15), and Lnight (HR: 1.07; 95% CI 1.02, 1.13) in single exposure models. Results further showed significant associations with an elevated risk of incident MetS in co-exposure models, with HRs of 1.91 (95% CI 1.69, 2.16) and 1.11 (95% CI 1.06, 1.16) for co-exposure to PM2.5 and Lden, and 1.90 (95% CI 1.68, 2.14) and 1.08 (95% CI 1.02, 1.13) for co-exposure to PM2.5 and Lnight. The HRs for the co-exposure models were higher than those for models with only a single exposure. This study provides evidence that PM2.5 and noise exposure may elevate the risk of incident MetS and its components in both single and co-exposure models. Therefore, preventive approaches to mitigate the risk of MetS and its subcomponents should consider reducing exposure to PM2.5 and noise pollution.

Effects of long-term exposure to ambient fine particulate matter and its specific components on blood pressure and hypertension incidence

BACKGROUND

Epidemiological evidence on the association of PM2.5 (particulate matter with aerodynamic diameter ≤ 2.5 μm) and its specific components with hypertension and blood pressure is limited.

METHODS

We applied information of participants from the World Health Organization's (WHO) Study on Global Ageing and Adult Health (SAGE) to estimate the associations of long-term PM2.5 mass and its chemical components exposure with blood pressure (BP) and hypertension incidence in Chinese adults ≥ 50 years during 2007-2018. Generalized linear mixed model and Cox proportional hazard model were applied to investigate the effects of PM2.5 mass and its chemical components on the incidence of hypertension and BP, respectively.

RESULTS

Each interquartile range (IQR = 16.80 μg/m3) increase in the one-year average of PM2.5 mass concentration was associated with a 17 % increase in the risk of hypertension (HR = 1.17, 95 % CI: 1.10, 1.24), and the population attributable fraction (PAF) was 23.44 % (95 % CI: 14.69 %, 31.55 %). Each IQR μg/m3 increase in PM2.5 exposure was also related to increases of systolic blood pressure (SBP) by 2.54 mmHg (95 % CI:1.99, 3.10), and of diastolic blood pressure (DBP) by 1.36 mmHg (95 % CI: 1.04, 1.68). Additionally, the chemical components of SO42-, NO3-, NH4+, OM, and BC were also positively associated with an increased risk of hypertension incidence and elevated blood pressure.

CONCLUSIONS

These results indicate that long-term exposure to PM2.5 mass and its specific components may be major drivers of escalation in hypertension diseases.

Health position paper and redox perspectives - Disease burden by transportation noise

Transportation noise is a ubiquitous urban exposure. In 2018, the World Health Organization concluded that chronic exposure to road traffic noise is a risk factor for ischemic heart disease. In contrast, they concluded that the quality of evidence for a link to other diseases was very low to moderate. Since then, several studies on the impact of noise on various diseases have been published. Also, studies investigating the mechanistic pathways underlying noise-induced health effects are emerging. We review the current evidence regarding effects of noise on health and the related disease-mechanisms. Several high-quality cohort studies consistently found road traffic noise to be associated with a higher risk of ischemic heart disease, heart failure, diabetes, and all-cause mortality. Furthermore, recent studies have indicated that road traffic and railway noise may increase the risk of diseases not commonly investigated in an environmental noise context, including breast cancer, dementia, and tinnitus. The harmful effects of noise are related to activation of a physiological stress response and nighttime sleep disturbance. Oxidative stress and inflammation downstream of stress hormone signaling and dysregulated circadian rhythms are identified as major disease-relevant pathomechanistic drivers. We discuss the role of reactive oxygen species and present results from antioxidant interventions. Lastly, we provide an overview of oxidative stress markers and adverse redox processes reported for noise-exposed animals and humans. This position paper summarizes all available epidemiological, clinical, and preclinical evidence of transportation noise as an important environmental risk factor for public health and discusses its implications on the population level.

Aircraft and road traffic noise, insulin resistance, and diabetes: The role of neighborhood socioeconomic status in San Diego County

Evidence linking traffic noise to insulin resistance and diabetes is limited and unanswered questions remain regarding the potential effect modification by neighborhood socioeconomic status (nSES). We aimed to assess socioeconomic inequalities in noise exposure, whether road and aircraft noise exposures were associated with insulin resistance or diabetes, and whether nSES modified these relationships. Among the Community of Mine Study in San Diego County, road and aircraft noise exposure at enrollment was calculated based on the static (participant's administrative boundary, and circular buffer around participant homes), and dynamic (mobility data by global positioning system, GPS) spatio-temporal aggregation methods. Associations of noise with insulin resistance (HOMA-IR) or type 2 diabetes (T2DM) were quantified using generalized estimating equation models adjusted for sex, age, ethnicity, individual income, and air pollution (nitrogen dioxide) exposure. Additive interaction between noise and nSES was assessed. Among 573 participants (mean age 58.7 y), participants living in low nSES were exposed to higher levels of aircraft and road noise using noise level at the census tract, circular buffer, or Kernel Density Estimation (KDE) of GPS data. Participants exposed to road noise greater or equal to the median (53 dB(A)) at the census tract and living in low nSES had an increased level of insulin resistance (β = 0.15, 95%CI: -0.04, 0.34) and higher odds of T2DM (Odds Ratio = 2.34, 95%CI: 1.12, 4.90). A positive additive interaction was found as participants living in low nSES had higher odds of T2DM. The impact of noise exposure on insulin resistance and T2DM differs substantially by nSES. Public health benefits of reducing exposure to road or aircraft noise would be larger in individuals living in low nSES.

Too Loud to Handle? Transportation Noise and Cardiovascular Disease

The World Health Organization reported that more than 1.6 million healthy life-years are lost yearly from traffic-related noise in western Europe. In addition, the number of studies on health side effects in response to traffic noise is steadily growing, mainly cardiovascular disease, such as acute and chronic ischemic heart disease, heart failure, arrhythmia, and stroke. Pathophysiologically nighttime noise has been shown to cause sleep disturbances, including too short sleep periods and frequent interruption of sleep leading to an increase in the levels of circulating stress hormones and subsequently to a significant increase in the production of reactive oxygen species (oxidative stress) and inflammation in the vasculature and the brain. The consequence is arterial hypertension and vascular (endothelial) dysfunction, which might increase the risk of cardiovascular disease. With the present review, we give an overview of the "so-called" nonauditory cardiovascular health effects of noise, which have been proposed to be responsible for the future development of cardiovascular disease. We present epidemiological evidence but also evidence provided by translational human and experimental noise studies. Finally, we discuss manoeuvres to mitigate noise effectively.

Long-term exposure to ambient ozone and cardiovascular diseases: Evidence from two national cohort studies in China

INTRODUCTION

The health effects of ambient ozone have been investigated in many previous studies. However, the effects of long-term exposure to ambient ozone on the incidence of cardiovascular disease (CVD) remain inconclusive.

OBJECTIVES

To estimate the associations of long-term exposure to maximum daily 8-hours average ozone (MDA8 O3) with the incidence of total CVD, heart disease, hypertension, and stroke.

METHODS

This was a prospective cohort study, and the data was obtained from the China Health and Retirement Longitudinal Survey (CHARLS) implemented during 2011-2018 and the China Family Panel Studies (CFPS) implemented during 2010-2018. We applied a Cox proportional hazards regression model to evaluate the associations of MDA8 O3 with total CVD, heart disease, hypertension, and stroke risks, and the corresponding population-attributable fractions (PAF) attributable to MDA8 O3 were also calculated. All analyses were conducted by R software.

RESULTS

The mean MDA8 O3 concertation of all included participants in the CHARLS and CFPS were 51.03 part per billion (ppb) and 51.15 ppb, respectively. In the CHARLS including 18,177 participants, each 10 ppb increment in MDA8 O3 concentration was associated with a 31% increase [hazard ratio (HR) = 1.31, 95% confidence interval (CI): 1.22-1.42] in the risk of incident heart disease, and the corresponding population-attributable fractions (PAF) was 13.79% [10.12%-17.32%]. In the CFPS including 30,226 participants, each 10 ppb increment in MDA8 O3 concentration was associated with an increase in the risk of incident total CVD (1.07 [1.02-1.13]), and hypertension (1.10 [1.03-1.18]). The PAFs of total CVD, and hypertension attributable to MDA8 O3 were 3.53% [0.82%-6.16%], and 5.11% [1.73%-8.38%], respectively. Stratified analyses showed greater associations in males, urban areas, and Southern China.

CONCLUSIONS

Long-term exposure to MDA8 O3 may increase the incidence of CVD. Therefore, the policies that control O3 and related precursors are persistently needed.

Joint Effects of Long-Term Exposure to Ambient Fine Particulate Matter and Ozone on Asthmatic Symptoms: Prospective Cohort Study

BACKGROUND

The associations of long-term exposure to air pollutants in the presence of asthmatic symptoms remain inconclusive and the joint effects of air pollutants as a mixture are unclear.

OBJECTIVE

We aimed to investigate the individual and joint associations of long-term exposure to ambient fine particulate matter (PM2.5) and daily 8-hour maximum ozone concentrations (MDA8 O3) in the presence of asthmatic symptoms in Chinese adults.

METHODS

Data were derived from the World Health Organization Study on Global Ageing and Adult Health (WHO SAGE) cohort study among adults aged 50 years or older, which was implemented in 1 municipality and 7 provinces across China during 2007-2018. Annual average MDA8 O3 and PM2.5 at individual residential addresses were estimated by an iterative random forest model and a satellite-based spatiotemporal model, respectively. Participants who were diagnosed with asthma by a doctor or taking asthma-related therapies or experiencing related conditions within the past 12 months were recorded as having asthmatic symptoms. The individual associations of PM2.5 and MDA8 O3 with asthmatic symptoms were estimated by a Cox proportional hazards regression model, and the joint association was estimated by a quantile g-computation model. A series of subgroup analyses was applied to examine the potential modifications of some characteristics. We also calculated the population-attributable fraction (PAF) of asthmatic symptoms attributed to PM2.5 and MDA8 O3.

RESULTS

A total of 8490 adults older than 50 years were included, and the average follow-up duration was 6.9 years. During the follow-up periods, 586 (6.9%) participants reported asthmatic symptoms. Individual effect analyses showed that the risk of asthmatic symptoms was positively associated with MDA8 O3 (hazard ratio [HR] 1.12, 95% CI 1.01-1.24, for per quantile) and PM2.5 (HR 1.18, 95% CI 1.05-1.31, for per quantile). Joint effect analyses showed that per equal quantile increment of MDA8 O3 and PM2.5 was associated with an 18% (HR 1.18, 95% CI 1.05-1.33) increase in the risk of asthmatic symptoms, and PM2.5 contributed more (68%) in the joint effects. The individual PAFs of asthmatic symptoms attributable to PM2.5 and MDA8 O3 were 2.86% (95% CI 0.17%-5.50%) and 4.83% (95% CI 1.42%-7.25%), respectively, while the joint PAF of asthmatic symptoms attributable to exposure mixture was 4.32% (95% CI 1.10%-7.46%). The joint associations were greater in participants with obesity, in urban areas, with lower family income, and who used unclean household cooking fuel.

CONCLUSIONS

Long-term exposure to PM2.5 and MDA8 O3 may individually and jointly increase the risk of asthmatic symptoms, and the joint effects were smaller than the sum of individual effects. These findings informed the importance of joint associations of long-term exposure to air pollutants with asthma.

Spatial assessment of the attributable burden of disease due to transportation noise in England

BACKGROUND

Noise pollution from transportation is one of the leading contributors to the environmental disease burden in Europe. We provide a novel assessment of spatial variations of these health impacts within a country, using England as an example.

METHODS

We estimated the burden of annoyance (highly annoyed), sleep disturbance (highly sleep disturbed), ischemic heart disease (IHD), stroke, and diabetes attributable to long-term transportation noise exposures in England for the adult population in 2018 down to local authority level (average adult population: 136,000). To derive estimates, we combined literature-informed exposure-response relationships, with population data on noise exposures, disease, and mortalities. Long-term average noise exposures from road, rail and aircraft were sourced from strategic noise mapping, with a lower exposure threshold of 50 dB (decibels) Lden and Lnight.

RESULTS

40 %, 4.5 % and 4.8 % of adults in England were exposed to road, rail, and aircraft noise exceeding 50 dB Lden. We estimated close to a hundred thousand (∼97,000) disability adjusted life years (DALY) lost due to road-traffic, ∼13,000 from railway, and ∼ 17,000 from aircraft noise. This excludes some noise-outcome pairs as there were too few studies available to provide robust exposure-response estimates. Annoyance and sleep disturbance accounted for the majority of the DALYs, followed by strokes, IHD, and diabetes. London, the South East, and North West regions had the greatest number of road-traffic DALYs lost, while 63 % of all aircraft noise DALYs were found in London. The strategic noise mapping did not include all roads, which may still have significant traffic flows. In sensitivity analyses using modelled noise from all roads in London, the DALYs were 1.1x to 2.2x higher.

CONCLUSION

Transportation noise exposures contribute to a significant and unequal environmental disease burden in England. Omitting minor roads from the noise exposure modelling leads to underestimation of the disease burden.

Exposure to long-term source-specific transportation noise and incident breast cancer: A pooled study of eight Nordic cohorts

BACKGROUND

Environmental noise is an important environmental exposure that can affect health. An association between transportation noise and breast cancer incidence has been suggested, although current evidence is limited. We investigated the pooled association between long-term exposure to transportation noise and breast cancer incidence.

METHODS

Pooled data from eight Nordic cohorts provided a study population of 111,492 women. Road, railway, and aircraft noise were modelled at residential addresses. Breast cancer incidence (all, estrogen receptor (ER) positive, and ER negative) was derived from cancer registries. Hazard ratios (HR) were estimated using Cox Proportional Hazards Models, adjusting main models for sociodemographic and lifestyle variables together with long-term exposure to air pollution.

RESULTS

A total of 93,859 women were included in the analyses, of whom 5,875 developed breast cancer. The median (5th-95th percentile) 5-year residential road traffic noise was 54.8 (40.0-67.8) dB Lden, and among those exposed, the median railway noise was 51.0 (41.2-65.8) dB Lden. We observed a pooled HR for breast cancer (95 % confidence interval (CI)) of 1.03 (0.99-1.06) per 10 dB increase in 5-year mean exposure to road traffic noise, and 1.03 (95 % CI: 0.96-1.11) for railway noise, after adjustment for lifestyle and sociodemographic covariates. HRs remained unchanged in analyses with further adjustment for PM2.5 and attenuated when adjusted for NO2 (HRs from 1.02 to 1.01), in analyses using the same sample. For aircraft noise, no association was observed. The associations did not vary by ER status for any noise source. In analyses using <60 dB as a cutoff, we found HRs of 1.08 (0.99-1.18) for road traffic and 1.19 (0.95-1.49) for railway noise.

CONCLUSIONS

We found weak associations between road and railway noise and breast cancer risk. More high-quality prospective studies are needed, particularly among those exposed to railway and aircraft noise before conclusions regarding noise as a risk factor for breast cancer can be made.

Association between occupational noise exposure and diabetes: A systematic review and meta-analysis

BACKGROUND

In addition to the well-known risk factors of diabetes, evidence is accumulating on the negative role of environmental and occupational factors such as noise exposure. We conducted a systematic review and meta-analysis on the association between long-term occupational noise exposure and diabetes.

METHODS

We systematically searched evidence in PubMed, Scopus, and Web of Science (until August 2022) according to the PRISMA protocol. Risk of bias was assessed using the Newcastle-Ottawa scale. Random-effects meta-analysis was applied separately for risk ratio (odds ratio, relative risk) and hazard ratio. We evaluated the heterogeneity and publication bias. We applied meta-regressions to identify sources of heterogeneity. The overall body of evidence was assessed using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) framework.

RESULTS

Of 533 retrieved articles, twelve studies (11 on non-gestational, and one on gestational diabetes) on total 106,045 population (23,996 diabetic cases) met our inclusion criteria; of which eight studies were cross-sectional, three were cohorts, and one was case-control. Only 40% of papers (five out of 12) had fair, good or very good quality, and most of the papers had poor or very poor quality in terms of risk of bias. We observed a non-significant increased risk of diabetes in association with occupational noise exposure (combined risk estimates: 1.16, 95% confidence interval [CI]: 0.97: 1.34; I² = 57.7%). Doing separate meta-analyses on cohort and rest of studies, we found similar findings (cohort studies (n = 3): combined risk estimate: 1.17; 95% CI: 0.84: 1.50; I² = 79%; cross-sectional studies (n = 8): combined risk estimate: 1.26; 95% CI: 0.93: 1.58; I² = 50.4%). We found no indication of publication bias.

CONCLUSIONS

The overall evidence on the association between occupational noise exposure and diabetes is heterogeneous, limited, and mostly with low quality. More robust studies in terms of population selection, exposure and outcome assessment, and adjustment for confounders are necessary.

Mortality burden based on the associations of ambient PM2.5 with cause-specific mortality in China: Evidence from a death-spectrum wide association study (DWAS)

Although studies have estimated the associations of PM_2.5 with total mortality or cardiopulmonary mortality, few have comprehensively examined cause-specific mortality risk and burden caused by ambient PM_2.5. Thus, this study investigated the association of short-term exposure to PM_2.5 with cause-specific mortality using a death-spectrum wide association study (DWAS). Individual information of 5,450,764 deaths during 2013-2018 were collected from six provinces in China. Daily PM_2.5 concentration in the case and control days were estimated by a random forest model. A time-stratified case-crossover study design was applied to estimate the associations (access risk, ER) of PM_2.5 with cause-specific mortality, which was then used to calculate the population-attributable fraction (PAF) of mortality and the corresponding mortality burden caused by PM_2.5. Each 10 μg/m³ increase in PM_2.5 concentration (lag03) was associated with a 0.80 % [95 % confidence interval (CI): 0.73 %, 0.86 %] rise in total mortality. We found greater mortality effect at PM_2.5 concentrations < 50 μg/m³. Stratified analyses showed greater ERs in females (1.01 %, 95 %CI: 0.91 %, 1.11 %), children ≤ 5 years (2.17 %, 95 %CI: 0.85 %, 3.51 %), and old people ≥ 70 years. We identified 33 specific causes (level 2) of death which had significant associations with PM_2.5, including 16 circulatory diseases, 9 respiratory diseases, and 8 other causes. The PAF estimated based on the overall association between PM_2.5 and total mortality was 3.16 % (95 %CI: 2.89 %, 3.40 %). However, the PAF was reduced to 2.88 % (95 %CI: 1.88 %, 3.81 %) using the associations of PM_2.5 with 33 level 2 causes of death, based on which 250.15 (95 %CI: 163.29, 330.93) thousand deaths were attributable to short-term PM_2.5 exposure across China in 2019. Overall, this study provided a comprehensive picture on the death-spectrum wide association between PM_2.5 and morality in China. We observed robust positive cause-specific associations of PM_2.5 with mortality risk, which may provide more precise basis in assessing the mortality burden of air pollution.

Mediation of metabolic syndrome in the association between long-term co-exposure to road traffic noise, air pollution and incident type 2 diabetes

OBJECTIVES

Recent studies have linked exposure to road traffic noise or air pollution with incident type 2 diabetes (T2D), but investigation on their co-exposure was limited and underlying mechanisms remain unclear. We hypothesized that long-term co-exposure to road traffic noise and air pollution increases the risk of incident T2D via the development of metabolic syndrome (MetS).

METHODS

This prospective study included 390,834 participants in UK Biobank. Cumulative risk index (CRI), the health-based weighted levels of multiple exposures, was applied to characterize the co-exposure to 24-hour road traffic noise (L_den), particulate matter with aerodynamic diameter ≤ 2.5 µm (PM_2.5), and nitrogen dioxide (NO₂). L_den was modeled by the Common Noise Assessment Methods in Europe and air pollutant levels were measured by the Land Use Regression model at participants' residential addresses. Incident T2D was ascertained through linkages to inpatient hospital records. MetS was defined by five (central obesity, triglycerides, HDL cholesterol, glucose, and blood pressure) or six factors (C-reactive protein additionally). Cox proportional hazard models were used to assess the association between environmental exposures and incident T2D, and mediation analyses were applied to investigate the role of MetS.

RESULTS

After a median of 10.9 years of follow-up, 13,214 (3.4%) incident T2D cases were ascertained. The exposure to L_den, PM_2.5, and NO₂, as well as their co-exposure, were significantly associated with an elevated risk of incident T2D, with HRs of 1.03 (95%CI: 1.00, 1.05) per 3.5 dB(A) increase in L_den, 1.05 (95%CI: 1.01, 1.10) per 1.3 μg/m³ increase in PM_2.5, 1.07 (95%CI: 1.02, 1.11) per 9.8 μg/m³ increase in NO₂, and 1.06 (95%CI: 1.02, 1.09) per interquartile range increase in CRI. MetS significantly mediated 43.5%- 54.7% of the CRI-T2D relationship.

CONCLUSIONS

Long-term co-exposure to road traffic noise and air pollution is associated with an elevated risk of incident T2D, which may partly be mediated by MetS.

The association between road traffic noise and type 2 diabetes: a systematic review and meta-analysis of cohort studies

The association between road traffic noise and type 2 diabetes (T2DM) was inconsistent. To address this, we have synthesized available cohort studies about their association by meta-analysis. PubMed, Web of Science, EBSCO, Cochrane Library, EMBASE, and Scopus databases were searched up to July 2022. The Quality-effect model (QE) was used to incorporate the results of included studies. The possibility of publication bias was assessed by the Doi plots and Luis Furuya-Kanamori index. Sensitivity analyses included leave-one-out meta-analysis, subgroup meta-analysis, and meta-regressions. The Recommendations for Assessment, Development, and Evaluation (GRADE) guidelines were conducted to evaluate the overall quality of evidence. Eight cohort studies with 4,989,846 participants and 416,799 diabetes cases were included. Based on the fully adjusted models from 8 cohort studies (10 estimates; Lden range ≈ 15-98.5 dB(A)), we found "high" evidence of RR per 10 dB(A) = 1.07 (1.05, 1.10), high heterogeneity (I2 = 0.91%, p < 0.001), and high publication bias (LKF index = 4.55). Sensitivity analyses showed stable model results, and the GRADE assessment suggested the current overall quality of evidence is high. Comprehensive evidence from cohort studies supports that increasing exposure to road traffic noise may be associated with higher risk of T2DM.

Transportation Noise and Risk of Tinnitus: A Nationwide Cohort Study from Denmark

BACKGROUND

There is a growing body of evidence linking residential exposure to transportation noise with several nonauditory health outcomes. However, auditory outcomes, such as tinnitus, are virtually unexplored.

OBJECTIVES

We aimed to investigate the association between residential transportation noise and risk of incident tinnitus.

METHODS

We conducted a nationwide cohort study including all residents in Denmark age ≥30y, of whom 40,692 were diagnosed with tinnitus. We modeled road traffic and railway noise at the most (Ldenmax) and least (Ldenmin) exposed façades of all Danish addresses from 1990 until 2017. For all participants, we calculated 1-, 5-, and 10-y time-weighted mean noise exposure and retrieved detailed information on individual- and area-level socioeconomic covariates. We conducted analyses using Cox proportional hazards models.

RESULTS

We found positive associations between exposure to road traffic noise and risk of tinnitus, with hazard ratios of 1.06 [95% confidence interval (CI): 1.04, 1.08] and 1.02 (95% CI: 1.01, 1.03) per 10-dB increase in 10-y Ldenmin and Ldenmax, respectively. Highest risk estimates were found for women, people without a hearing loss, people with high education and income, and people who had never been in a blue-collar job. The association with road Ldenmin followed a positive, monotonic exposure-response relationship. We found no association between railway noise and tinnitus.

DISCUSSION

To our knowledge, this is the first study to show that residential exposure to road traffic noise may increase risk of tinnitus, suggesting noise may negatively affect the auditory system. If confirmed, this finding adds to the growing evidence of road traffic noise as a harmful pollutant with a substantial health burden. https://doi.org/10.1289/EHP11248.

Effects of Sociodemographic Characteristics, Comorbidity, and Coexposures on the Association between Air Pollution and Type 2 Diabetes: A Nationwide Cohort Study

BACKGROUND

Exposure to air pollution has been associated with a higher risk of type 2 diabetes (T2D), but studies investigating whether deprived groups are more susceptible to the harmful effects of air pollution are inconsistent.

OBJECTIVES

We aimed to investigate whether the association between air pollution and T2D differed according to sociodemographic characteristics, comorbidity, and coexposures.

METHODS

We estimated residential exposure to PM2.5, ultrafine particles (UFP), elemental carbon, and NO2 for all persons living in Denmark in the period 2005-2017. In total, 1.8 million persons 50-80 y of age were included for main analyses of whom 113,985 developed T2D during follow-up. We conducted additional analyses on 1.3 million persons age 35-50 y. Using Cox proportional hazards model (relative risk) and Aalens additive hazard model (absolute risk), we calculated associations between 5-y time-weighted running means of air pollution and T2D in strata of sociodemographic variables, comorbidity, population density, road traffic noise, and green space proximity.

RESULTS

Air pollution was associated with T2D, especially among people age 50-80 y, with hazard ratios of 1.17 [95% confidence interval (CI): 1.13, 1.21] per 5 μg/m3 PM2.5 and 1.16 (95% CI: 1.13, 1.19) per 10,000  UFP/cm3. In the age 50-80 y population, we found higher associations between air pollution and T2D among men in comparison with women, people with lower education vs. individuals with high education, people with medium income vs. those with low or high income, people cohabiting vs. those living alone, and people with comorbidities vs. those without comorbidities. We observed no marked changes according to occupation, population density, road noise, or surrounding greenness. In the age 35-50 y population, similar tendencies were observed, except in relation to sex and occupation, where we observed associations with air pollution only among women and blue-collar workers.

DISCUSSION

We found stronger associations between air pollution and T2D among people with existing comorbidities and weaker associations among people with high socioeconomic status in comparison with those with lower socioeconomic status. https://doi.org/10.1289/EHP11347.

Dose-response association between transportation noise exposure and type 2 diabetes: A systematic review and meta-analysis of prospective cohort studies

AIMS

To examine the longitudinal association between transportation noise exposure (road traffic, aircraft, and railway noise) and T2D in a meta-analysis.

MATERIALS AND METHODS

We systematically searched PubMed, Embase, Scopus, Cochrane, and Web of Science published up to February 2022. The GRADE approach was used to evaluate the study quality, and the pooled effect estimate was calculated by the fixed-effects model or the random-effects model.

RESULTS

We included 10 prospective studies with a total of 4,994,171 participants and 417,332 T2D cases in the meta-analysis. According to the Navigation guide, 8 studies out of 10 were rated as having a probably high or high risk of bias. For road noise, the pooled relative risk (RR) per 10 dB higher L_den for developing T2D was 1.06 (95% CI:1.03, 1.09) with high heterogeneity (I²  = 90.1%, p < 0.001). Similar associations were also observed in aircraft and railway noise: the pooled RR were separately were: 1.01 (1.00, 1.01) and 1.02 (1.01, 1.03) separately. A 'dose-response' analysis found a similar linear association between road noise exposure and the risk of T2D.

CONCLUSIONS

An overall 6% increase in the risk of T2D per 10 dB increase in road exposure was observed. Further studies are needed to confirm our findings, especially for aircraft and railway noise, and to identify the mechanisms involved.

Long-term exposure to transportation noise and risk of type 2 diabetes: A cohort study

BACKGROUND

Some studies have found transportation noise to be associated with higher diabetes risk. This includes studies based on millions of participants, relying entirely on register-based confounder adjustment, which raises concern about residual lifestyle confounding. We aimed to investigate associations between noise and type 2 diabetes (T2D), including investigation of effects of increasing confounder adjustment for register-data and lifestyle.

METHODS

In a cohort of 286,151 participants randomly selected across Denmark in 2010-2013 and followed up until 2017, we identified 7574 incident T2D cases. Based on residential address-history for all participants linked with exposure assessment of high spatial resolution, we calculated 10-year time-weighted mean road and railway noise at the most (LdenMax) and least (LdenMin) exposed façades and air pollution (PM_2.5). We used Cox models to calculate hazard ratios (HR) with increasing adjustment for individual- and area-level register-based sociodemographic covariates, self-reported lifestyle and air pollution.

RESULTS

We found that a 10 dB increase in 10-year mean road LdenMin was associated with HRs (95% CI) of 1.06 (1.02-1.10) after adjustment for age, sex and year, 1.08 (1.04-1.13) after further adjustment for register-based sociodemographic covariates, 1.07 (1.03-1.12) after further lifestyle adjustment (e.g. smoking, diet and alcohol) and 1.06 (1.02-1.11) after further PM_2.5 adjustment. For road LdenMax, the corresponding HRs were 1.07 (1.04-1.10), 1.05 (1.02-1.08), 1.04 (1.01-1.07) and 1.03 (1.00-1.06). Railway noise was associated with HRs of 1.04 (0.98-1.11) for LdenMax and 1.02 (0.92-1.12) for LdenMin after adjustment for sociodemographic and lifestyle covariates and PM_2.5.

CONCLUSIONS

Long-term exposure to road traffic noise was associated with T2D, which together with previous literature indicates that T2D should be considered when calculating health impacts of noise. After sociodemographic adjustment, further lifestyle adjustment only changed HRs slightly, suggesting that large register-based studies with adjustment for key sociodemographic covariates can produce reliable results.

Effect of long-term exposure to PM2.5 on the risk of type 2 diabetes and arthritis in type 2 diabetes patients: Evidence from a national cohort in China

BACKGROUND

It remains unclear whether type 2 diabetes and the complication of arthritis are causally related to the PM_2.5 pollutant. Therefore, we aimed to investigate the associations of long-term PM_2.5 exposure with type 2 diabetes and with arthritis in type 2 diabetes patients.

MATERIALS AND METHODS

This study used data from the China Health and Retirement Longitudinal Survey (CHARLS) implemented during 2011-2018. The associations were analyzed by Cox proportional hazards regression models, and the population-attributable fraction (PAF) was calculated to assess the burden of type 2 diabetes and arthritis-attributable to PM_2.5.

RESULTS

A total of 21,075 participants were finally included, with 19,121 analyzed for PM_2.5 and type 2 diabetes risk and 12,427 analyzed for PM_2.5 and arthritis risk, of which 1,382 with newly-diagnosed type 2 diabetes and 1,328 with arthritis during the follow-up. Overall, each 10 μg/m³ increment in PM_2.5 concentration was significantly associated with an increase in the risk of type 2 diabetes (HR = 1.26, 95 %CI1.22 to 1.31), and the PAF of type 2 diabetes attributable to PM_2.5 was 13.54 %. In type 2 diabetes patients, each 10 μg/m³ increment in PM_2.5 exposure was associated with an increase in arthritis (HR = 1.42, 95 %CI: 1.28 to 1.57), and the association was significantly greater than that (H = 1.23, 95 %CI: 1.19 to 1.28) in adults without type 2 diabetes. The PAFs of arthritis-attributable to PM_2.5 in participants with and without type 2 diabetes were 18.54 % and 10.69 %, respectively.

CONCLUSION

Long-term exposure to PM_2.5 may increase the risk of type 2 diabetes and make type 2 diabetes patients susceptible to arthritis.

Estimating traffic noise over a large urban area: An evaluation of methods

Unlike air pollution, traffic-related noise remains unregulated and has been under-studied despite evidence of its deleterious health impacts. To characterize population exposure to traffic noise, both acoustic-based numerical models and data-driven statistical approaches can generate estimates over large urban areas. The aim of this work is to formally compare the performances of the most common traffic noise models by evaluating their estimates for different categories of roads and validating them against a unique dataset of measured noise in Long Beach, California. Specifically, a statistical land use regression model, an extreme gradient boosting machine learning model (XGB), and three numerical/acoustic traffic noise models: the US Noise Model (FHWA-TNM2.5), a commercial noise model (CadnaA), and an open-source European model (Harmonoise) were optimized and compared. The results demonstrate that XGB and CadnaA were the most effective models for estimating traffic noise, and they are particularly adept at differentiating noise levels on different categories of road.

Air pollution, road traffic noise and lack of greenness and risk of type 2 diabetes: A multi-exposure prospective study covering Denmark

OBJECTIVE

Air pollution, road traffic noise and lack of greenness coexist in urban environments and have all been associated with type 2 diabetes. We aimed to investigate how these co-exposures were associated with type 2 diabetes in a multi-exposure perspective.

METHODS

We estimated 5-year residential mean exposure to fine particles (PM_2.5), ultrafine particles (UFP), elemental carbon (EC), nitrogen dioxide (NO₂) and road traffic noise at the most (LdenMax) and least (LdenMin) exposed facade for all persons aged > 50 years living in Denmark in 2005 to 2017. For each air pollutant, we estimated total concentrations and traffic contributions. Based on land use maps, we estimated proportion of green and non-green space within 150 and 1000 m of all residences. In total, 1.9 million persons were included and 128,358 developed type 2 diabetes during follow-up. We performed analyses using Cox proportional hazards models, with adjustment for individual and neighborhood-level sociodemographic co-variates.

RESULTS

In single-pollutant models, all air pollutants, noise and lack of green space were associated with higher risk of diabetes. In two-, three- and four-pollutant analyses of the air pollutants, only UFP and NO₂ remained associated with higher diabetes risk in all models. LdenMax, LdenMin and the two proxies of green space remained associated with diabetes in two-pollutant models of, respectively, noise and green space. In a multi-pollutant analysis, we found hazard ratios (95 % confidence intervals) per interquartile range of 1.021 (1.005; 1.038) for UFP, 1.012 (0.996; 1.028) for NO₂, 1.022 (1.012; 1.033) for LdenMin, 1.013 (1.004; 1.022) for LdenMax, and 1.038 (1.031; 1.044) and 1.018 (1.010; 1.025) for lack of green space within, respectively, 150 m and 1000 m, and a cumulative risk index of 1.131 (1.113; 1.149).

CONCLUSIONS

Air pollution, road traffic noise and lack of green space were independently associated with higher risk of type 2 diabetes.

Ambient air pollution associated with incidence and dynamic progression of type 2 diabetes: a trajectory analysis of a population-based cohort

BACKGROUND

Though the association between air pollution and incident type 2 diabetes (T2D) has been well documented, evidence on the association with development of subsequent diabetes complications and post-diabetes mortality is scarce. We investigate whether air pollution is associated with different progressions and outcomes of T2D.

METHODS

Based on the UK Biobank, 398,993 participants free of diabetes and diabetes-related events at recruitment were included in this analysis. Exposures to particulate matter with a diameter ≤ 10 μm (PM₁₀), PM_2.5, nitrogen oxides (NO_x), and NO₂ for each transition stage were estimated at each participant's residential addresses using data from the UK's Department for Environment, Food and Rural Affairs. The outcomes were incident T2D, diabetes complications (diabetic kidney disease, diabetic eye disease, diabetic neuropathy disease, peripheral vascular disease, cardiovascular events, and metabolic events), all-cause mortality, and cause-specific mortality. Multi-state model was used to analyze the impact of air pollution on different progressions of T2D. Cumulative transition probabilities of different stages of T2D under different air pollution levels were estimated.

RESULTS

During the 12-year follow-up, 13,393 incident T2D patients were identified, of whom, 3791 developed diabetes complications and 1335 died. We observed that air pollution was associated with different progression stages of T2D with different magnitudes. In a multivariate model, the hazard ratios [95% confidence interval (CI)] per interquartile range elevation in PM_2.5 were 1.63 (1.59, 1.67) and 1.08 (1.03, 1.13) for transitions from healthy to T2D and from T2D to complications, and 1.50 (1.47, 1.53), 1.49 (1.36, 1.64), and 1.54 (1.35, 1.76) for mortality risk from baseline, T2D, and diabetes complications, respectively. Generally, we observed stronger estimates of four air pollutants on transition from baseline to incident T2D than those on other transitions. Moreover, we found significant associations between four air pollutants and mortality risk due to cancer and cardiovascular diseases from T2D or diabetes complications. The cumulative transition probability was generally higher among those with higher levels of air pollution exposure.

CONCLUSIONS

This study indicates that ambient air pollution exposure may contribute to increased risk of incidence and progressions of T2D, but to diverse extents for different progressions.

Road Traffic Noise, Obesity, and the Risk of Incident Type 2 Diabetes: A Cohort Study in UK Biobank

Objectives: To assess the association of road traffic noise exposure with Type 2 Diabetes (T2D) risk, and to explore the potential moderation effect of obesity. Methods: A total of 305,969 participants from the UK Biobank Cohort - an open access cohort of 500,000 participants recruited in the United Kingdom (UK) between 2006 and 2010 - were included in the study. A Cox proportional hazard model was fitted to assess the association between road traffic noise exposure and T2D. Results: A total of 19,303 participants were diagnosed with T2D during the 11.9-year median follow-up period. For every 10 dB increase in road traffic noise, there was a 4% increase in T2D risk (HR = 1.04, 95%CI: 1.01, 1.07). Besides, a significant positive interaction was observed between obesity and road traffic noise (P interaction <0.001) for the risk of T2D. The association of road traffic noise with T2D was stronger in overweight and obese participants (HR = 1.04, 95% CI: 1.01-1.08), but not significant among lean ones (HR = 0.96, 95% CI: 0.86-1.07). Conclusion: Our study observed a longitudinal association of road traffic noise exposure with T2D risk, which was stronger among overweight and obese individuals than the lean ones.

Railway noise and diabetes among residents living close to the railways in Västra Götaland, Sweden: Cross-sectional mediation analysis on obesity indicators

Railway noise is expected to increase in Europe and Sweden as well, following recommendations for a more sustainable transportation mode. This forecasted increase might lead to higher level of noise exposure, higher population exposure and potentially increased night-time exposure. Evidence supports an effect of transportation noise on several health outcomes, including metabolic conditions such as diabetes. However, few studies were directed to railway noise; present studies on railway noise and diabetes so far show ambiguous results while some studies report an association between railway noise and obesity. The aim of this study is to investigate the relationship between railway noise and diabetes prevalence and to assess whether obesity might be a mediator in this association. The study population (N = 5381) was randomly selected from residents living within 1 km of a trafficked railway in Västra Götaland, Sweden. Survey data was combined with modelled exposures and health register data (ICD10 codes for diabetes). The study uses a cross sectional design, logistic regression analysis and a counterfactual mediation analysis. We found an increase in the prevalence of diabetes associated with the exposure to railway noise: OR = 1.33 per 10 dB increase L_den (95% CI 1.09-1.63) accounting for sociodemographic and lifestyle factors. BMI and waist circumference partially mediate the association between railway noise and diabetes. This is the first study to report an association between railway noise and diabetes in Sweden. BMI and waist circumference seem to be a potential mediators in this association. Still, future studies should further explore the mechanisms from noise to diabetes, considering different pathways in relation to obesity but also exploring other potential mediators.

Associations of road traffic noise with cardiovascular diseases and mortality: Longitudinal results from UK Biobank and meta-analysis

BACKGROUND

Epidemiological evidence suggests potential associations of road traffic noise exposure with cardiovascular disease (CVD) and mortality, but uncertainty remains.

OBJECTIVES

We examined the association of road traffic noise with the risk of CVD and mortality in a large longitudinal cohort study and meta-analysis.

METHODS

We analyzed 342, 566 participants from the UK Biobank who were free of CVD at baseline and had complete covariate data. We also performed a meta-analysis of road traffic noise effects on CVD and mortality by including qualified cohort studies published before April 2021.

RESULTS

After adjustment for potential confounders, the odds for the risk of stroke, CVD, and all-cause mortality increased by 1.07 (95%CI: 1.01-1.13, P = 0.019), 1.13 (95%CI: 1.04-1.22, P = 0.003) and 1.08 (95%CI: 1.04-1.12, P < 0.001) times per 10 dB increases in road traffic noise, respectively. Among men, high road traffic noise exposure was significantly associated with an increased risk in stroke (HR = 1.08 per 10 dB increase, 95%CI: 1.00-1.16, P = 0.043), CVD (HR = 1.12 per 10 dB increase, 95%CI: 1.02-1.23, P = 0.020) and all-cause mortality (HR = 1.12 per 10 dB increase, 95%CI: 1.07-1.17 P < 0.001), whereas we did not find a significant association in women. The meta-analysis showed that road traffic noise exposure was significantly associated with a high risk of stroke (risk ratio [RR]: 1.06, 95% CI: 1.02-1.11), CVD mortality (RR: 1.03, 95% CI: 1.00-1.05), all-cause mortality (RR: 1.05, 95% CI: 1.02-1.07).

CONCLUSIONS

This study provides more evidence of increased risk of stroke, CVD, and all-cause mortality in association with exposure to road traffic noise pollution, especially in men.

Impact of road traffic noise on annoyance and preventable mortality in European cities: A health impact assessment

BACKGROUND

Road traffic is the main source of environmental noise in European cities and one of the main environmental risks to health and wellbeing. In this study we aimed to provide an in-depth assessment of available road traffic noise data and to estimate population exposure and health impacts for cities in Europe.

METHODS

We conducted the analysis for 724 cities and 25 greater cities in 25 European countries. We retrieved road traffic strategic noise maps delivered under the Environmental Noise Directive (END) or available from local sources. We assessed noise exposure using the 24 h day-evening-night noise level indicator (L_den) starting at exposure levels of 55 dB L_den - based on data availability - for the adult population aged 20 and over (n = 123,966,346). For the adults exposed to noise levels above 55 dB L_den we estimated the health impacts of compliance with the World Health Organization (WHO) recommendation of 53 dB L_den. Two primary health outcomes were assessed: high noise annoyance and Ischemic Heart Disease (IHD), using mortality from IHD causes as indicator. Exposure Response Functions (ERFs) relating road traffic noise exposure to annoyance and IHD mortality were retrieved from the literature. Uncertainties in input parameters were propagated using Monte Carlo simulations to obtain point estimates and empirical 95% Confidence Intervals (CIs). Lastly, the noise maps were categorized as high, moderate and low quality following a qualitative approach.

RESULTS

Strategic noise map data was delivered in three distinct formats (i.e. raster, polygon or polyline) and had distinct noise ranges and levels of categorization. The majority of noise maps (i.e. 83.2%) were considered of moderate or low quality. Based on the data provided, almost 60 million adults were exposed to road traffic noise levels above 55 dB L_den, equating to a median of 42% (Interquartile Range (IQR): 31.8-64.8) of the adult population across the analysed cities. We estimated that approximately 11 million adults were highly annoyed by road traffic noise and that 3608 deaths from IHD (95% CI: 843-6266) could be prevented annually with compliance of the WHO recommendation. The proportion of highly annoyed adults by city had a median value of 7.6% (IQR: 5.6-11.8) across the analysed cities, while the number preventable deaths had a median of 2.2 deaths per 100,000 population (IQR: 1.4-3.1).

CONCLUSIONS

Based on the provided strategic noise maps a considerable number of adults in European cities are exposed to road traffic noise levels harmful for health. Efforts to standardize the strategic noise maps and to increase noise and disease data availability at the city level are needed. These would allow for a more accurate and comprehensive assessment of the health impacts and further help local governments to address the adverse health effects of road traffic noise.