| Article Access Statistics|
| Viewed||2816 |
| Printed||95 |
| Emailed||0 |
| PDF Downloaded||17 |
| Comments ||[Add] |
| Cited by others ||3 |
|Year : 2017
: 19 | Issue : 91 | Page
|Association between community noise and adiposity in patients with cardiovascular disease
Angel M Dzhambov1, Penka D Gatseva1, Mariya P Tokmakova2, Nikolai G Zdravkov2, Stefka V Vladeva3, Dolina G Gencheva2, Nevena G Ivanova4, Krasimir I Karastanev2, Emanuela V Vasileva5, Aleksandar T Donchev6
1 Department of Hygiene and Ecomedicine, Faculty of Public Health, Medical University of Plovdiv, Bulgaria
2 Section of Cardiology, First Department of Internal Diseases, Faculty of Medicine, Medical University of Plovdiv, Plovdiv; Clinic of Cardiology, UMHAT “Sv. Georgi” EAD Plovdiv, Bulgaria
3 Medical College, Medical University of Plovdiv, Plovdiv; Clinic of Endocrinology and Metabolic Disorders, “Kaspela” University Hospital, Plovdiv, Bulgaria
4 Department of Urology and General Medicine, Faculty of Medicine, Medical University of Plovdiv, Plovdiv; “St. Karidad” Hospital, Plovdiv, Bulgaria
5 Clinic of Endocrinology and Metabolic Disorders, “Kaspela” University Hospital, Plovdiv, Bulgaria
6 “St. Karidad” Hospital, Plovdiv, Bulgaria
Click here for correspondence address
|Date of Web Publication||29-Dec-2017|
Introduction: This study aimed to explore the effect of community noise on body mass index (BMI) and waist circumference (WC) in patients with cardiovascular disease (CVD). Materials and Methods: A representative sample of 132 patients from three tertiary hospitals in the city of Plovdiv, Bulgaria was collected. Anthropometric measurements were linked to global noise annoyance (GNA) based on different residential noise annoyances, day–evening–night (Lden), and nighttime (Lnight) road traffic noise exposure. Noise map Lden and Lnight were determined at the living room and bedroom façades, respectively, and further corrected to indoor exposure based on the window-opening frequency and soundproofing insulation. Results and Discussion: Results showed that BMI and WC increased (non-significantly) per 5 dB. The effect of indoor noise was stronger in comparison with that of outdoor noise. For indoor Lden, the effect was more pronounced in men, those with diabetes, family history of diabetes, high noise sensitivity, using solid fuel/gas for domestic heating/cooking, and living on the first floor. As regards indoor Lnight, its effect was more pronounced in those with low socioeconomic status, hearing loss, and using solid fuel/gas for domestic heating/cooking. GNA was associated with lower BMI and WC. Conclusion: Road traffic noise was associated with an increase in adiposity in some potentially vulnerable patients with CVD.
Keywords: Body mass index, noise annoyance, obesity, road traffic noise, waist circumference
|How to cite this article:|
Dzhambov AM, Gatseva PD, Tokmakova MP, Zdravkov NG, Vladeva SV, Gencheva DG, Ivanova NG, Karastanev KI, Vasileva EV, Donchev AT. Association between community noise and adiposity in patients with cardiovascular disease. Noise Health 2017;19:270-7
|How to cite this URL:|
Dzhambov AM, Gatseva PD, Tokmakova MP, Zdravkov NG, Vladeva SV, Gencheva DG, Ivanova NG, Karastanev KI, Vasileva EV, Donchev AT. Association between community noise and adiposity in patients with cardiovascular disease. Noise Health [serial online] 2017 [cited 2022 Jan 23];19:270-7. Available from: https://www.noiseandhealth.org/text.asp?2017/19/91/270/221985
| Introduction|| |
Recently, evidence of the metabolic effects of environmental noise has been emerging. Several studies explored the impact of road traffic noise on adiposity among adults,,,,,, youth, and children, but the associations were generally small and inconsistent. The underlying biomedical mechanism refers to noise acting as a stressor, involving the stimulation of the hypothalamic-pituitary-adrenal and simpatico-adrenal axes and resulting in increase in catecholamine, cortisol, and oxidative stress levels, which could engender, in the long run, insulin resistance and accumulation of adipose tissue., At the same time, nighttime noise causes sleep disturbances which are associated with adiposity through modulation of the cortical appetite control mechanisms (reduction of leptin and elevation of ghrelin levels).,, It was even suggested that maternal exposure during pregnancy might raise the risk of overweight of their offspring.
Another, behavioral pathway was proposed by Foraster et al. They observed lower level of physical activity with increasing noise annoyance. Roswall et al. recently extended these findings to modelled road traffic noise, which was also found to be negatively associated with leisure-time sports. This effect is possibly due to impaired sleep quality, leading to higher daytime sleepiness and decreased willingness to engage in physical activity. Another angle to this would be to view noise not merely as stressor, but also as a constraint on restorative qualities of the living environment, thereby leading to decreased residential satisfaction, which could entail less engagement in outdoor physical activity.,
No study has so far looked into the relationship noise − adiposity in vulnerable subgroups such as patients with pre-existing cardiovascular disease (CVD). This could be of relevance to cardiologists for two reasons. On one hand, adiposity is not only a risk factor for incident CVD, but for developing coronary artery disease among patients with pre-existing essential hypertension, and for mortality among patients with coronary artery disease. On the other, it is highly prevalent and inadequately managed in patients. EUROASPIRE IV, a cross-national European study, showed that 59.9% of the coronary artery disease patients in 2013 were physically inactive, 37.6% were obese, and 58.2% were centrally obese. In fact, these proportions had increased with some 7% since 1999. Clearly, targeting classic risk factors for adiposity like physical inactivity and poor dietary habits is not sufficient. Therefore this study aimed to explore the association of community noise exposure with body mass index (BMI) and waist circumference (WC) among Bulgarian patients with pre-existing CVD.
| Materials and Methods|| |
This was a cross-sectional study conducted in the city of Plovdiv, Bulgaria (March–May 2016). Detailed description of the design is reported elsewhere. Briefly, patients admitted in three tertiary hospitals were enrolled in the study. A cardiologist/internist took their medical history, reviewed their medical documentation and medication, and performed anthropometric measurements. Patients filled out a questionnaire asking about sociodemographic, lifestyle, socio-acoustic, housing, and medical factors. The questions were initially piloted in a sample of 20 patients. The study was reviewed and approved by the Ethics Review Committee at Medical University of Plovdiv. It adhered to the Declaration of Helsinki, participants signed informed consent declarations, and their participation was voluntary and anonymous. Participants received no incentives.
We included Bulgarian-speaking, capacitated, and literate patients, aged over 18 years, and resident in the Plovdiv Province during the preceding 12 months. Requirement was that participants were diagnosed with hypertension (ICD-10: I10–I13) and/or ischemic heart disease (ICD-10: I20–I25) at least 12 months prior to their current hospitalization. Exclusion criteria were as follows: secondary hypertension (ICD-10: I15), secondary diabetes (ICD-10: E08, E09, E13), cancer, and pregnancy.
BMI (weight in kg divided by height squared in cm) and WC were used as proxies for adiposity. Body weight was measured in light clothing on a calibrated digital scale. Height was measured standing without shoes. WC was measured at the midpoint between the lower margin of the last palpable rib and the top of the iliac crest, using a stretch-resistant tape. Measurements were performed twice at the end of a normal expiration.
We used the strategic noise map of Plovdiv (according to the Environmental Noise Directive 2002/49/EC) to assign modelled noise exposure to each participant living in the city (n = 132). It shows 5-dB noise contours for road traffic day–evening–night equivalent noise level (Lden) and night equivalent noise level (Lnight). Participants’ addresses were geocoded manually − they were inspected one-by-one using 3D Google Earth imagery, Google Street View, and in-person visits. The questionnaire asked about the orientation of rooms within the dwelling, which made it possible to assign Lden and Lnight to the living room and bedroom façades, respectively. This façade noise exposure was corrected to indoor exposure using the method proposed by Foraster et al. An insulation factor was subtracted from the façade noise level depending on the window-opening frequency (−15 dB for “always,” −20 dB for “sometimes,” and −30 dB for “never” keeping the living room windows open) and the presence of a soundproofing insulation (−40 dB for “never” and having an insulation). Lden and Lnight were treated as continuous (per 5 dB) variables.
Participants reported their annoyance by different noise sources: “To what extent, on a scale from 0 to 10, are you disturbed, annoyed or irritated by noise from different sources while you are at home (IN THE PAST 12 MONTHS)?” (“0, not at all” to “10, very much”).” Annoyances due to traffic, industrial, neighborhood (playgrounds, recreational facilities, etc.), building (e.g., plumbing, elevators, etc.), and dwelling/apartment noise (appliances, toddlers, etc.) were averaged on a global noise annoyance (GNA) scale (0–10).
The questionnaire asked about gender, age, ethnicity, highest educational attainment, marital status, self-rated socioeconomic status (SES), diet quality, alcohol drinking, cigarette smoking, physical activity, hearing loss, noise sensitivity, energy used for domestic heating/cooking, floor of the dwelling/apartment, duration of residence, and the average time/day spent at home. Participants’ diet quality was assessed with a food frequency questionnaire. Their family history of ischemic heart disease, stroke, and diabetes, and their co-morbidity with ischemic heart disease (ICD-10: I20–I25), stroke (ICD-10: I61, I63–I64), hypertension (ICD-10: I10–I13), and diabetes (ICD-10: E10–E12) were determined as well. Exposure to traffic fine particulate matter (PM2.5) was modeled. For more details on these covariates, refer Dzhambov et al.
Missing values were replaced using the expectation-maximization algorithm. The distribution of the dependent variables was explored with the D’Agostino–Pearson K2 test and histograms. Outliers were winsorized (recoded closer to the nearest non-outlier value).
The bivariate associations between road traffic noise exposure, GNA, and other variables were examined with Welch’s t-test/analysis of variance, Pearson’s chi-squared test/Fisher’s exact test/Fisher–Freeman–Halton test, and the Spearman correlation.
The multivariate association between road traffic noise and GNA and patients’ BMI and WC was explored using mixed linear models with a restricted maximum likelihood estimator and a random intercept to account for clustering on hospitals. Four adjusted models were fitted based on a priori selection of important covariates using DAGitty v. 2.3 (http://www.dagitty.net/).
Potential effect modification by participants’ characteristics was tested using the Wald test for interactions at the relaxed P < 0.2 level,, and the method of Altman and Bland. Other results were considered statistically significant at the P < 0.05 (two-tailed) level.
| Results|| |
Detailed description of the sample, associations between different variables in the dataset, and correlations between the exposure indicators are reported in Dzhambov et al. Briefly, mean age was 62.30 ± 14.61 years and 48.5% (n = 64) were men. Lden was significantly associated with male gender, sleep disturbance, floor of the dwelling/apartment, duration of residence, time spent at home/day, and traffic PM2.5. The mean BMI and WC in the sample were 29.55 ± 5.76 kg/m2 and 94.71 ± 19.96 cm, respectively. Higher BMI was significantly associated with lower SES (rs = −0.14, P = 0.035), quiet bedroom orientation (rs = −0.15, P = 0.027), solid fuel/gas used for domestic heating/cooking (rs = 0.20, P = 0.003), and lower floor of the dwelling/apartment (rs = −0.19, P = 0.006). Higher WC was associated with male gender (rs = −0.23, P = 0.001), solid fuel/gas used for domestic heating/cooking (rs = 0.15, P = 0.033), and lower floor of the dwelling/apartment (rs = −0.20, P = 0.003). In comparison with nationally representative data for CVD patients, most clinical variables in this study were representative.,
[Table 1] shows the adjusted linear models for road traffic noise. There was a non-significant increase in BMI and WC per 5-dB. The effect of indoor noise exposure was more pronounced. GNA was associated with a significantly lower BMI and WC [Table 2].
|Table 1: Associations between road traffic noise (per 5 dB) and adiposity (mixed linear models) (n = 132)|
Click here to view
|Table 2: Associations between global noise annoyance (per one interquartile range) and adiposity (mixed linear models) (n = 132)|
Click here to view
In sensitivity analysis, we checked for effect modification by participants’ characteristics. [Table 3] The effect of indoor Lden was more pronounced in men, those with diabetes, family history of diabetes, high noise sensitivity, using solid fuel/gas for domestic heating/cooking, and living on the first floor. A statistically significant effect was found in those over 63 years of age, highly sensitive to noise, with a living room facing a noisy street, and whose apartment was on the first floor. The effect of indoor Lnight was more pronounced in those with low SES, hearing loss, and using solid fuel/gas for domestic heating/cooking, and it was statistically significant in those over 63 years of age, reporting low SES, hearing loss, and living on the first floor.
|Table 3: Stratified analysis for identifying potential effect modification of the association between indoor road traffic noise (per 5 dB) and waist circumference (mixed linear models)|
Click here to view
| Discussion|| |
This study examined the association between road traffic noise and GNA and adiposity in patients with CVD. Road traffic noise was positively associated with BMI and WC in the total sample, but the estimates failed statistical significance. Indoor noise exposure was associated with a greater increase in BMI and WC.
Sensitivity analysis suggested that men, those with diabetes, with family history of diabetes, highly sensitive to noise, using solid fuel/gas for domestic heating/cooking, and living on the first floor were more vulnerable to indoor Lden. The effect of indoor Lnight was more pronounced in those with low SES, hearing loss, and using solid fuel/gas for domestic heating/cooking. GNA was associated with lower BMI and WC.
Although previous studies on traffic noise and obesity were population-based, our findings are in line with theirs. Pyko et al. reported 0.21 cm [95% confidence interval (CI): 0.01, 0.41] change in WC and −0.08 kg/m2 (95% CI: −0.16, 0.01) in BMI per 5 dB. Oftedal et al. found no effect on BMI or WC per 10 dB. In the study of Christensen et al., there was 0.25 cm (95% CI: 0.11, 0.39) increase in WC and 0.17 kg/m2 (95% CI: 0.12, 0.22) in BMI per 10 dB increase of 1-year-preceding noise levels. In a longitudinal study, Christensen et al. estimated a gain of 0.17 cm (95% CI: −0.036, 0.38) in WC per 10 dB for the 5 years preceding follow-up. As for air traffic noise, authors found 1.51 cm (95% CI: 1.13, 1.89) increase in WC and 0.04 kg/m2 (95% CI: −0.05, 0.13) in BMI for every 5 dB. This suggests that there may not be a significant difference in noise effects between people from the general population and patients with CVD. Our models for indoor noise exposure yield much larger effect size estimates, and this could not be compared to previous studies.
We found stronger association for indoor Lden in men, whereas most previous studies did not report significant gender differences.,, Still, Oftedal et al. reported greater odds of obesity in men albeit non-significant. In Eriksson et al., there was a significantly higher increase in WC per 5 dB in men (2.26 cm, 95% CI: 1.83, 2.69) versus women (1.58 cm, 95% CI: 1.13, 2.03).
With respect to age, only Pyko et al. evidenced a significant effect modification, with those under 60 years being at higher risk. In our study, the effect was stronger and statistically significant in people over 63 years although the interaction itself was not significant.
Herein, indoor Lden had a stronger effect in highly noise sensitive participants, as in Oftedal et al.’s work. Family history of diabetes was an effect modifier for indoor Lden in our study but not in previous studies.,
In contrast to Christensen et al., in our study there was a more pronounced association for indoor Lnight in people with low SES.
For people living on the first floor, the effect of indoor Lden was significantly stronger, possibly because the sound propagation model used for noise calculations assumed a receiver point at 4 m above the terrain level. Thus, for those living on higher floors the potential for exposure misclassification is expected to be greater. No other study has tested for effect modification by indoor air pollution or hearing impartment, both of which seemed to aggravate the effect of noise in ours.
Biomass fuel-combustion smoke deserves consideration as a co-exposure. Tentative evidence suggests a link between outdoor air pollution and obesity in children and adults. However, to our knowledge, no study has so far linked indoor air pollution to markers of adiposity or tested its interaction with road traffic noise. Given the potential of specific air contaminants to induce systemic inflammation in the body and modulate leptin levels and insulin resistance, such an association seems plausible and could explain the observed effect modification in our study.
Previous studies have not investigated the association between noise annoyance and adiposity. We speculate that the lack of an effect in this study is due to several reasons. First, GNA may not be a good exposure indicator because people may not be able to accurately average their annoyance over a long period of time, needed to observe an effect on adiposity. Also, GNA refers to noise during the waking hours, whereas both Lden and Lnight take into account nighttime exposure during sleep. An inverse association was previously reported between annoyance and blood pressure, with significantly higher systolic blood pressure and higher prevalence of hypertension in less annoyed participants; this counterintuitive finding was explained “with the much higher adaptive efforts that higher annoyed subjects invested to reduce noise exposure compared with less annoyed subjects.” Moreover, in the study of Oftedal et al., there was some increase in the effect on WC after it was adjusted for noise annoyance, also suggesting an inverse relationship.
Strengths and limitations
This was the first study to examine the relationship between community noise and adiposity in CVD patients who are typically either excluded from the analyses or simply analyzed as a subgroup. Thus, out findings suggest that noise may be important not only to Bulgarian public health officers, but to clinicians and general practitioners as well, because 43% of the patients with ischemic heart disease in Bulgaria are obese, 57% have central obesity, and 74% are physically inactive. Another strength of the study is the fact that we had detailed exposure data, including the location of rooms and indoor noise exposure, which showed good validity with respect to hypertension in the studies of Babisch et al. and Foraster et al., respectively. Our results also indicated stronger effect of indoor noise (in comparison with façade-level noise) which might explain the inconclusive results of previous studies in the field. Finally, we gathered data on multiple confounders and effect modifiers.
There are several important limitations. First, the study was cross-sectional precluding causal inference. This was addressed by running a time window analysis. Oftedal et al. found stronger (but not significantly so) effect among long-term residents. Eriksson et al. reported significantly stronger effect on WC among non-movers during the study period.
The sample size was smallish although justified by an a priori simulation. This fact may account for some non-significant effects. We had to relax the P-value for interaction terms to have more power. Moreover, we lacked power to dichotomize BMI and WC, which could be important if noise affects the right side of the distribution of the measures of adiposity more than its mean.
The limitations of the noise maps and air pollution models for Plovdiv are discussed elsewhere. We refined the noise assessment method but the END noise maps have well-known issues − for example, they often use traffic count data only for major roads, free sound propagation, and do not consider the shielding of buildings, which leads to exposure misclassification.
We did not test whether sleep disturbance and physical activity mediated the effect of road traffic noise on adiposity. However, this is an issue that may be addressed using structural equation modeling in future studies in order to disentangle the mechanisms underlying the observed associations.
Some studies show a correlation between the distribution of urban traffic noise and social inequalities., Therefore, critics may argue that the effect on adiposity observed in our study is due to the fact that patients living in more disadvantaged areas were also exposed to higher noise levels and that, in fact, social deprivation was the leading adverse factor. Still, the picture is not so clear, and this spatial correlation appears to be region-specific. For instance, one study indicated higher noise exposure in advantaged neighbourhoods and another one, a non-linear relationship between noise and SES. Another related limitation is that we lacked aggregated spatial data to construct a neighborhood-level SES index. There are no Bulgarian studies on this subject, but reassuringly, there was no correlation between individual-level SES and road traffic noise in our study. Moreover, in Bulgaria, the scatter of people of diverse SES in the same residential area or even at the same street is common due to both cultural and social factors. It is more than likely that neighborhood selection among elderly CVD patients, who face many daily challenges due to the high poverty rates and inadequate access to healthcare in Bulgaria, is driven by other factors in contrast to more affluent countries.
Initially, when we piloted the preliminary, lengthier version of the questionnaire, we observed lower compliance and quality of patients’ responses (e.g., omitted or implausible responses). This prompted us to reduce the total number of questions and to use fairly simplistic measures for some covariates, for instance, noise sensitivity. This could have biased the results since single-item ratings of noise sensitivity have notable shortcomings compared to multi-item questionnaires, such as crude precision, lower retest reliability, and undesired correlations with noise exposure levels. In our sample, noise sensitivity was related to Lnight (rs = 0.26–0.28).
We cannot rule out the possibility that using solid fuel/gas for domestic heating/cooking instead of electricity, which is more expensive, acted as a proxy for low SES and household deprivation. This is also implied by the correlation between this variable and SES (rs = −0.24). We attempted to address this issue by adjusting for SES, education, and ethnicity.
Finally, other noise sources, including occupational noise, were not considered, since very few people were living close to a railway or reported exposure to noise at work to allow for meaningful analysis.
| Conclusion|| |
This study showed evidence that road traffic noise was associated with an increase in adiposity in some potentially vulnerable patients with CVD. Indoor noise exposure was a better predictor of BMI and WC. Men, diabetics, those with family history of diabetes, high noise sensitivity, using solid fuel/gas for domestic heating/cooking, and living on the first floor were more vulnerable to indoor Lden. The association for indoor Lnight was stronger for those with low SES, hearing loss, and using solid fuel/gas for domestic heating/cooking. GNA was associated with lower adiposity.
The authors would like to thank all patients who participated in the study, the administrations of the involved hospitals, and the staff members who facilitated data collection.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Oftedal B, Krog NH, Pyko A, Eriksson C, Graff-Iversen S, Haugen M et al.
Road traffic noise and markers of obesity − A population-based study. Environ Res 2015;138:144-53.
Christensen JS, Raaschou-Nielsen O, Tjønneland A, Nordsborg RB, Jensen SS, Sørensen TI et al.
Long-term exposure to residential traffic noise and changes in body weight and waist circumference: A cohort study. Environ Res 2015;143:154-61.
Christensen JS, Raaschou-Nielsen O, Tjønneland A, Overvad K, Nordsborg RB, Ketzel M et al.
Road traffic and railway noise exposures and adiposity in adults: A cross-sectional analysis of the Danish diet, cancer, and health cohort. Environ Health Perspect 2016;124:329-35.
Pyko A, Eriksson C, Oftedal B, Hilding A, Östenson CG, Krog NH et al.
Exposure to traffic noise and markers of obesity. Occup Environ Med 2015;72:594-601.
Eriksson C, Hilding A, Pyko A, Bluhm G, Pershagen G, Östenson CG. Long-term aircraft noise exposure and body mass index, waist circumference, and type 2 diabetes: A prospective study. Environ Health Perspect 2014;122:687-94.
Dzhambov AM, Dimitrova DD. Road traffic noise exposure association with self-reported body mass index. Noise Control Eng J 2015;63:572-81.
Argalášová Ľ, Ševčíková Ľ, Štefániková Z, Babjaková J, Vondrová D, Filová A et al.
Is transportation noise associated with obesity? Proc Eng 2017;190:597-602.
Christensen JS, Hjortebjerg D, Raaschou-Nielsen O, Ketzel M, Sørensen TI, Sørensen M. Pregnancy and childhood exposure to residential traffic noise and overweight at 7 years of age. Environ Int 2016;94:170-6.
Recio A, Linares C, Banegas JR, Díaz J. Road traffic noise effects on cardiovascular, respiratory, and metabolic health: An integrative model of biological mechanisms. Environ Res 2016;146:359-70.
Björntorp P, Rosmond R. Obesity and cortisol. Nutrition 2000;16:924-36.
Chaput JP, Despres JP, Bouchard C, Tremblay A. Short sleep duration is associated with reduced leptin levels and increased adiposity: Results from the Quebec family study. Obesity (Silver Spring) 2007;15:253-61.
Spiegel K, Leproult R, L’hermite-Balériaux M, Copinschi G, Penev PD, Van Cauter E. Leptin levels are dependent on sleep duration: Relationships with sympathovagal balance, carbohydrate regulation, cortisol, and thyrotropin. J Clin Endocrinol Metab 2004;89:5762-71.
Taheri S, Lin L, Austin D, Young T, Mignot E. Short sleep duration is associated with reduced leptin, elevated ghrelin, and increased body mass index. PLoS Med 2004;1:e62. doi: 10.1371/journal.pmed.0010062
Foraster M, Eze IC, Vienneau D, Brink M, Cajochen C, Caviezel S et al.
Long-term transportation noise annoyance is associated with subsequent lower levels of physical activity. Environ Int 2016;91:341-9.
Roswall N, Ammitzbøll G, Christensen JS, Raaschou-Nielsen O, Jensen SS, Tjønneland A et al.
Residential exposure to traffic noise and leisure-time sports − A population-based study. Int J Hyg Environ Health 2017;220:1006-13.
von Lindern E, Hartig T, Lercher P. Traffic-related exposures, constrained restoration, and health in the residential context. Health Place 2016;39:92-100.
Barton J, Bragg R, Wood C, Pretty J. Green Exercise: Linking Nature, Health and Well-Being. New York: Routledge; 2016.
Hartig T. Green space, psychological restoration, and health inequality. Lancet 2008;372:1614-5.
de Koning L, Merchant AT, Pogue J, Anand SS. Waist circumference and waist-to-hip ratio as predictors of cardiovascular events: Meta-regression analysis of prospective studies. Eur Heart J 2007;28:850-6.
Dimitriadis K, Tsioufis C, Mazaraki A, Liatakis I, Koutra E, Kordalis A et al.
Waist circumference compared with other obesity parameters as determinants of coronary artery disease in essential hypertension: A 6-year follow-up study. Hypertens Res 2016;39:475-9.
Sobiczewski W, Wirtwein M, Jarosz D, Gruchala M. Superiority of waist circumference and body mass index in cardiovascular risk assessment in hypertensive patients with coronary heart disease. Blood Press 2015;24:90-5.
Kotseva K, Wood D, De Bacquer D, De Backer G, Rydén L, Jennings C et al.
EUROASPIRE IV: A European Society of Cardiology survey on the lifestyle, risk factor and therapeutic management of coronary patients from 24 European countries. Eur J Prev Cardiol 2016;23:636-48.
Kotseva K, De Bacquer D, Jennings C, Gyberg V, De Backer G, Rydén L et al.
Time trends in lifestyle, risk factor control, and use of evidence-based medications in patients with coronary heart disease in Europe: Results from 3 EUROASPIRE surveys, 1999–2013. Glob Heart 2016. pii: S2211-8160(15)00295-1. doi: 10.1016/j.gheart.2015.11.003.
Dzhambov AM, Tokmakova MP, Gatseva PD, Vladeva SV, Zdravkov NG, Vasileva EV et al.
Is community noise associated with metabolic control in patients with cardiovascular disease? Acoust Aust 2017;45:61-75.
WHO. Waist Circumference and Waist-Hip Ratio: Report of a WHO Expert Consultation. Geneva: World Health Organization; 2008.
SPECTRI. Acoustic Monitoring and Specific Traffic Counting at Locations Used for the Development of “Strategic Noise Map of Plovdiv Agglomeration”. Sofia: SPECTRI; 2015. [in Bulgarian].
Foraster M, Künzli N, Aguilera I, Rivera M, Agis D, Vila J et al.
High blood pressure and long-term exposure to indoor noise and air pollution from road traffic. Environ Health Perspect 2014;122:1193-200.
Greenland S, Rothman KJ. Chapter 18: Concepts of interaction. In: Rothman KJ, Greenland S, editors. Modern Epidemiology. 2nd ed. New York: Lippincott-Raven; 1998. p. 329-42.
Marshall SW. Power for tests of interaction: Effect of raising the Type I error rate. Epidemiol Perspect Innov 2007;4:4.
Altman DG, Bland JM. Interaction revisited: The difference between two estimates. BMJ 2003;326:219.
Kotseva K, Wood D, De Backer G, De Bacquer D, Pyörälä K, Keil U et al.
EUROASPIRE III: A survey on the lifestyle, risk factors and use of cardioprotective drug therapies in coronary patients from 22 European countries. Eur J Cardiovasc Prev Rehabil 2009;16:121-37.
Gotcheva NN, Raev DH, Trendafilova EG, Postadzhiyan AS, Runev NM, Vandenhoven G et al.
Assessment of lipid-lowering treatment in Bulgaria − The CEPHEUS study. Cent Eur J Med 2013;8:627-37.
Jerrett M, McConnell R, Wolch J, Chang R, Lam C, Dunton G et al.
Traffic-related air pollution and obesity formation in children: A longitudinal, multilevel analysis. Environ Health 2014;13:49.
Ponticiello BG, Capozzella A, Di Giorgio V, Casale T, Giubilati R, Tomei G et al.
Overweight and urban pollution: Preliminary results. Sci Total Environ 2015;518-519:61-4.
Wang Y, Eliot MN, Kuchel GA, Schwartz J, Coull BA, Mittleman MA et al.
Long-term exposure to ambient air pollution and serum leptin in older adults: Results from the MOBILIZE Boston study. J Occup Environ Med 2014;56:e73-7.
Wolf K, Popp A, Schneider A, Breitner S, Hampel R, Rathmann W et al.
Association between long-term exposure to air pollution and biomarkers related to insulin resistance, subclinical inflammation, and adipokines. Diabetes 2016;65:3314-26.
Lercher P, Botteldooren D, Widmann U, Uhrner U, Kammeringer E. Cardiovascular effects of environmental noise: Research in Austria. Noise Health 2011;13:234-50.
] [Full text]
Babisch W, Wölke G, Heinrich J, Straff W. Road traffic noise and hypertension − Accounting for the location of rooms. Environ Res 2014;133:380-7.
Dzhambov AM, Dimitrova DD. Exposures to road traffic, noise, and air pollution as risk factors for type 2 diabetes: A feasibility study in Bulgaria. Noise Health 2016;18:133-42.
] [Full text]
Morley DW, Gulliver J. Methods to improve traffic flow and noise exposure estimation on minor roads. Environ Pollut 2016;216:746-54.
Lam KC, Chan PK. Socio-economic status and inequalities in exposure to transportation noise in Hong Kong. Transportation 2006;2:107-13.
Nega TH, Chihara L, Smith K, Jayaraman M. Traffic noise and inequality in the twin cities, Minnesota. Hum Ecol Risk Assess Int J 2013;19:601-19.
Havard S, Reich BJ, Bean K, Chaix B. Social inequalities in residential exposure to road traffic noise: An environmental justice analysis based on the RECORD Cohort Study. Occup Environ Med 2011;68:366-74.
Bocquier A, Cortaredona S, Boutin C, David A, Bigot A, Chaix B et al.
Small-area analysis of social inequalities in residential exposure to road traffic noise in Marseilles, France. Eur J Public Health 2013;23:540-6.
Zimmer K, Ellermeier W. Psychometric properties of four measures of noise sensitivity: A comparison. J Environ Psychol 1999;19:295-302.
Angel M Dzhambov
Department of Hygiene and Ecomedicine, Faculty of Public Health, Medical University of Plovdiv, No. 15-A Vassil Aprilov Boulevard, 4002 Plovdiv
Source of Support: None, Conflict of Interest: None
[Table 1], [Table 2], [Table 3]
|This article has been cited by|
||Associations between ambient fine particulate (PM2.5) exposure and cardiovascular disease: findings from the China Health and Retirement Longitudinal Study (CHARLS)
| ||Xiaowei Mai, Houfeng Zhou, Yangyang Li, Xin Huang, Tao Yang |
| ||Environmental Science and Pollution Research. 2021; |
|[Pubmed] | [DOI]|
||The independent association of source-specific transportation noise exposure, noise annoyance and noise sensitivity with health-related quality of life
| ||Paco Cerletti, Ikenna C. Eze, Emmanuel Schaffner, Maria Foraster, Danielle Viennau, Christian Cajochen, Jean-Marc Wunderli, Martin Röösli, Daiana Stolz, Marco Pons, Medea Imboden, Nicole Probst-Hensch |
| ||Environment International. 2020; 143: 105960 |
|[Pubmed] | [DOI]|
||Impact of road traffic noise on obesity measures: Observational study of three European cohorts
| ||Yutong Cai, Wilma L. Zijlema, Elin Pettersen Sørgjerd, Dany Doiron, Kees de Hoogh, Susan Hodgson, Bruce Wolffenbuttel, John Gulliver, Anna L. Hansell, Mark Nieuwenhuijsen, Kazem Rahimi, Kirsti Kvaløy |
| ||Environmental Research. 2020; 191: 110013 |
|[Pubmed] | [DOI]|