Aircraft noise exposure linked to higher obesity risk in women

In a recent study published in Environment International, researchers investigated the associations between aircraft noise and obesity among female nurses living near 90 United States (US) airports.

Study: Aircraft noise exposure and body mass index among female participants in two Nurses’ Health Study prospective cohorts living around 90 airports in the United States. Image Credit: Steve Mann/Shutterstock.comStudy: Aircraft noise exposure and body mass index among female participants in two Nurses’ Health Study prospective cohorts living around 90 airports in the United States. Image Credit: Steve Mann/Shutterstock.com

Background

Aircraft noise exposure is associated with various health problems, including poor sleep, hypertension, stroke, psychological health, cancer, coronary heart disease, cardiovascular disease, and death.

Obesity is an understudied mechanism associated with stress responses that influence physiological, metabolic, and immunological function. Sustained stress reactions raise the risk of obesity.

Aircraft noise is associated with higher salivary cortisol levels in women and lower sleep quality. Chronic psychological stress may also cause increased stress reactions to perceived stimuli.

Studies associate environmental noise with general and central obesity, with a significant correlation between central obesity and diabetes. However, subsequent systematic evaluations found no evidence of a link between noise and obesity markers.

About the study

In the present study, researchers investigated whether exposure to aircraft noise would be associated with a higher body mass index (BMI) among US nurses.

The study included Nurses’ Health Study (NHS) and NHS-II participants with at least one successfully geocoded residential address at baseline linkable to environmental metrics (e.g., aircraft noise estimates) who lived within 22 mi or 36 km of the study airports.

They excluded individuals with incident cancer or diabetes diagnoses during the study and those who were pregnant, those with missing data, or those who died during the study.

The researchers determined airplane day-night average sound levels (DNLs) at the residential locations of the participants using modeled 1.0-decibel noise contours exceeding 44 decibels for US airports across five-year intervals from 1995 to 2010.

From 1994 to 2017, biennial surveys collected data on BMI (categorical, dichotomized) values and other variables.

The researchers computed BMI and BMI from 18 years (BMI18) throughout each two-year survey cycle (NHS: even years 1994–2016; NHSII: odd years 1995–2017). They also calculated BMI18 tertiles differences.

The researchers classified airplane noise exposures as dichotomized (45 dB, 55 dB), continuous (≥45 dB), or categorized (below 45 dB, 45 to 54 dB, or ≥55 dB). The day-night average sound level (DNL) measures cumulative exposure to aircraft noises over 24 hours. Every five years from 1995 to 2010, the team used the US Federal Aviation Administration (FAA) Aviation Environmental Design Tool to generate noise contours for airports in 1 dB increments above 44 dB using the Official Aviation Guide (OAG) for 1995 and the Enhanced Traffic Management Systems for 2000, 2005, and 2010.

Every two years, they matched addresses to recent airplane noise contours published at five-year intervals (1995, 2000, 2005, 2010), where the survey cycle did not overlap with the year with aircraft noise contours.

The researchers used multivariate multinomial logistic regressions with generalized estimating equations (GEEs) for analysis. They evaluated effect modification based on the US Census area, climatic border, hearing loss, airline hub types, and smoking habits.

Study covariates included age, race, individual socioeconomic status (SES) metrics of living alone, spouse education, parity, postmenopausal status, hormone treatment, smoking status, alcohol intake, food quality, and physical activity.

Potential environmental confounders were air pollution quintiles, population density, greenness, neighborhood SES (nSES), and environmental noises. The researchers represented continuous BMI as a linear outcome in the sensitivity analysis.

Results

Among 74,848 participants, 43% lived in the Northeast US, 95% were white, 10% lived alone, and 20% had a spouse with a high school education or less.

The mean participant age was 50 years, with 83%, 15%, and 2.20% exposed to aircraft noise levels of less than 45 dB, 45 to 54 dB, and ≥55.0 dB, respectively.

Exposure to 55 dB and above was related to 11% higher chances of BMI values ≥30 and a 15% higher probability of participation in the uppermost BMI18 tertile (ΔBMI between 6.70% and 71.60%). Attenuated associations were detected for the second BMI18 tertile (ΔBMI between 2.90 and 6.60), BMI 25 to 29.9, and exposures ≥45 vs. <45 decibels. The team observed DNL-BMI patterns.

The study found robust exposure-response relationships, with higher probabilities of being in the BMI 25–29.9 or ≥30.0 group compared to the BMI 18.5-24.9 category for increased exposure to aircraft noise of 45–54 and ≥55 dB against <45 dB.

Participants residing in Western regions, in dry climatic zones, and who had previously smoked showed stronger connections. Population density, greenness, neighborhood socioeconomic level, and environmental noise influenced study associations.

The study showed that residential exposure to aircraft noise above 45 decibels (DNL) was related to higher self-reported BMI and changes in BMI since age 18, regardless of individual or community characteristics. The relationship was stronger among Westerners, those living in dry climates, and smokers.

Exposure to DNL ≥45 dB was related to increased BMI since the age of 18 years. However, variables such as area greenness, environmental noise, population density, and nSES impacted the association.

Journal reference:
Pooja Toshniwal Paharia

Written by

Pooja Toshniwal Paharia

Pooja Toshniwal Paharia is an oral and maxillofacial physician and radiologist based in Pune, India. Her academic background is in Oral Medicine and Radiology. She has extensive experience in research and evidence-based clinical-radiological diagnosis and management of oral lesions and conditions and associated maxillofacial disorders.

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