Regarding noise pollution with MetS, the findings show that the concentration of noise pollutants in Yazd is higher than those mentioned in the WHO guidelines (55dB [A]). The chance of developing MetS increases with age. The mean exposure to noise pollution in patients with and without MetS was estimated to be close to each other. In the present study, the relationship between noise and air pollution and MetS was investigated. At the level of exposure to noise pollution, there was no significant relationship with the prevalence of MetS. According to our findings, this is the first study to evaluate the relationship between noise pollution and MetS in Yazd. The results of some studies support our findings, in a cross-sectional study of 424 industrial workers with an average noise exposure of 79.9dB (A), exposure to workplace noise, has sharply associated with increased triglyceride levels, cholesterol ratio to HDL, and lower HDL cholesterol levels. After modifying the effect of potential factors, there was no correlation between occupational noise exposure and serum fat levels (Arlien-Søborg et al., 2016); similarly, in a cohort study, there was a positive correlation between components of MetS (Hypertriglyceridemia) with noise exposure whitest this association was not seen with abdominal obesity (Yu et al., 2020). In another study, noise exposure was associated with significant increase in the risks of hypertriglyceridemia, abdominal obesity, and hyperglycemia while the risk ratio for noise on hypertension and low HDL cholesterol was lower and not statistically significant. Correlation was found to be periodically stronger among people exposed to moderate or high noise than in people exposed to low noise (Huang et al., 2020), in another cohort study, no increase was seen in the risk of exposure to the air traffic noise and the incidence of diabetes (Eriksson et al., 2014); similarly, in another cohort study associated with enrollment, no risk of hypertension was seen with exposure to noise in the lower half of range of 80–90 dB (A) (Stokholm et al., 2013), in several other studies no increased risk of hypertension related to exposure to traffic noise was found (Sørensen et al., 2011, Sørensen et al., 2012, Eriksson et al., 2010), also in a cross-sectional study of road traffic noise and fat markers in a Danish nurse group, no positive association was found between exposure to road traffic noise and BMI (Cramer et al., 2019).
On the other hand, there are a number of studies indicating opposite results showing a significant association between environmental noise exposure and lipid markers (Pyko et al., 2017, Oftedal et al., 2015, Eriksson et al., 2014, Pyko et al., 2015), and a number of studies have shown a significant association between noise exposure and hyperglycemia (Dendup et al., 2018, Wang et al., 2020, Dzhambov, 2015, Sørensen et al., 2013), also several other studies have found a positive association between exposure to noise and high blood pressure (Van Poll et al., 2014, Oftedal et al., 2015, Chang et al., 2014, Dratva et al., 2012, Liu et al., 2014); as well as, several cross-sectional studies have shown significant positive relationship between road traffic noise, waist circumference, and BMI. Road traffic noise was also associated with a higher prevalence of overweight and obesity (Christensen et al., 2016, Christensen et al., 2015, Pyko et al., 2015, Oftedal et al., 2015, Foraster et al., 2018, Pyko et al., 2017, Eriksson et al., 2014).
In our study, for every 5 dB increase in exposure to noise pollution, the chance of developing MetS was assessed and there was no significant change and no significant correlation was seen; similarly, no correlation was observed between road traffic noise in the fully adjusted model (in every 10 dB increase in a 1-year average of Lden) and BMI (Cramer et al., 2019). In another study, a positive correlation was found between rail noise exposure > 60 dB (vs. 0–20 dB) and BMI, waist circumference (Christensen et al., 2016). In another study, exposure to higher levels of noise per day was positively associated with higher triglycerides (Cai et al., 2017), also a cohort study showed that by increasing noise to 11.6 dB, the level of High-Density Lipoprotein Cholesterol (HDL-C) decreases (HR = 1.17, 95% CI: 1.01–1.35) and the risk of MetS increases by 17% (Yu et al., 2020); moreover, a cohort study showed that a 10-dB higher level of average road traffic noise at diagnosis and during the 5 years preceding diagnosis was associated with an increased risk of incident diabetes and also this association was slightly stronger with long-term exposure (5 years) compared to short-term exposure (1 year) (Sørensen et al., 2013). Similarly, in several other studies, exposure to higher levels of noise was associated with a higher risk of hyperglycemia (Eze et al., 2017, Ohlwein et al., 2019, Chang et al., 2020, Clark et al., 2017, Sakhvidi et al., 2018, Van Poll et al., 2014). In another study, the strongest correlation between the effect of noise and hypertension at 4 kHz was observed. An increase in 20-dB exposure to noise at 4 kHz was associated with a 34% risk of hypertension (Liu et al., 2016). Similarly, in several other studies, the increased risk of hypertension increased with increasing exposure to noise pollution (Van Kempen and Babisch, 2012, Fuks et al., 2017, Lin et al., 2020).
We also examined the potential effects of some potential confounders, with no significant effect on correction for age, sex, socioeconomic status, marital and employment status, education, insurance and housing status, blood pressure, physical activity, BMI, waist circumference, smoking, family history of diabetes, and stress levels; similarly, in another study, there was no correlation between road traffic noise and BMI in correcting the effect of age (Christensen et al., 2016). In one study, the effects of road traffic noise on type 2 diabetes in the age group over 60 years or older showed more severe effects whereas no association was found between road traffic noise and diabetes and education (Sørensen et al., 2013). In some age-modifying studies, the association between noise exposure and hypertension is significant and is more likely in the age group under (Pyko et al., 2015). A Norwegian cross-sectional study showed no significant association between road traffic noise and obesity markers in the general population; however, there is a positive association between noise-sensitive women (Oftedal et al., 2015). Also, in another study, the association of road noise with abdominal obesity was seen only among women (Pyko et al., 2015). In another study, the association between road traffic noise and diabetes was stronger among women than men (Sørensen et al., 2013); whilst in another study, there was association between noise and increased triglyceride levels, cholesterol to HDL ratio, decrease in HDL cholesterol with male gender, smoking, low level of education, low personal income, waist circumference, as well as BMI (Arlien-Søborg et al., 2016); several studies have also shown that exposure to road traffic noise may be significantly associated with BMI or waist circumference of individuals with specific allergies, such as those working under stress or living in urban areas (Cramer et al., 2019, Eriksson et al., 2014, Selander et al., 2013).
Regarding the relationship between air pollution and MetS, the findings showed that the concentration of air pollutants (PM2.5 index) in Yazd is higher than those mentioned in the WHO guidelines (10 µg/m3). The median exposure to air pollution in individuals with or without MetS was estimated to be close to each other. In the present study, no positive and significant relationship was observed between exposure to air pollution (PM2.5 index) and the possibility of MetS. According to our findings, this is the first study to evaluate the relationship between air pollution and MetS in Yazd. Our study provides evidence that PM2.5 is not associated with MetS or components of the MetS. Similarly the results of a cross-sectional study showed no significant relationship between air pollution (PM2.5 and black carbon) with blood lipids or waist circumference (Rajkumar et al., 2019). One study in Taiwan reported no association between air pollution and triglycerides or HDL, while a positive association was found between total cholesterol and PM2.5 (Chuang et al., 2011). Several other studies found no association between exposure to air pollution and incidence of diabetes (Coogan et al., 2012, Puett et al., 2011).
Whereas the results of several epidemiological studies have shown that prolonged exposure to ambient air pollutants increased the risk of MetS (Hou et al., 2020, Matthiessen et al., 2018, Wallwork et al., 2017, Yang et al., 2018a). Some studies have presented results related to MetS. In a study of life closer to a main road and exposure to the 1-year average level of particulate matter (PM2.5) in the air was associated with an increased likelihood of overall obesity and abdominal obesity (increased BMI) (Li et al., 2016). Some experimental studies also provided evidence of a possible association of exposure to higher PM2.5 which leads to the induction of lipid metabolic disorders and an increased risk of obesity and MetS (Wei et al., 2016, Wu et al., 2019, Mendez et al., 2013, Sun et al., 2009). A Danish cross-sectional study showed with an increase in IQR in PM2.5 environment, increased 0.78 (0.22, 1.34) mg/dL in total cholesterol (Sørensen et al., 2015). A retrospective cohort indicated that higher LDL and triglycerides as well as lower HDL associated with higher PM2.5 concentrations (Yitshak Sade et al., 2016). Another study showed that short-term exposure to PM2.5 accelerated the progression of systemic insulin resistance and affected fat metabolism (Haberzettl et al., 2016). In some studies, short-term or long-term exposure to air pollution was associated with an increased risk of hypertension (Giorgini et al., 2016, Wu et al., 2013, Dong et al., 2013, Chen et al., 2014). Another study indicated exposure to air pollutants, especially traffic-related pollutants, may increase the risk of type 2 diabetes and hypertension (Coogan et al., 2012, Brook and Kousha, 2015, Brook et al., 2016, Eze et al., 2015, Liu et al., 2014, Ying et al., 2014, Fuks et al., 2017, Weinmayr et al., 2015, Thiering et al., 2013). In another study among non-diabetics, short- and medium-term exposure to PM2.5 was associated with increased blood sugar (Peng et al., 2016).
In our study, for an increase in 10 µg/m3 exposure to air pollution, the chance of developing MetS was assessed and no significant change and correlation was observed. Similarly, in another study after adjusting potential disruptors, an increase in 1-year mean of air pollutants (PM2.5) led to an increase in systolic blood pressure, diastolic blood pressure, total cholesterol, triglyceride, HDL-C, fasting glucose, hemoglobin A1c (Chuang et al., 2011). Another study showed that increasing 10 µg/m3 of PM2.5, significantly associated with the prevalence of MetS, hyperglycemia and hypertension (Shamy et al., 2018). An increase of 1 µg/m3 at an average annual concentration of PM2.5 was associated with a 27% increase in the risk of MetS in 587 elderly men (Wallwork et al., 2017). A cross-sectional study also showed that an increase of 10 µg/m3 of PM2.5, was associated with a 9% increased risk for MetS in Chinese adults (Yang et al., 2018a). In a study with an increase of 10 µg/m3 of PM2.5, the risk ratio of diabetes increased (Chen et al., 2013). In another study with an increase of 10 µg/m3 of PM2.5, the risk ratio of diabetes and blood pressure increased(Coogan et al., 2012). ). Similarly, in another study with an increase of 5 µg/m3 of PM2.5, a 22% increased risk of self-report hypertension was observed (Fuks et al., 2017).
We also examined the potential effects of some potential confounders, with no significant effect on correction for age, sex, socioeconomic status, marital and employment status, education, insurance and housing status, blood pressure, physical activity, BMI, waist circumference, smoking, family history of diabetes, or stress levels. Similarly, in another study, long-term exposure to air pollution and diabetes was not significantly associated with correction of gender and education (Eze et al., 2014), whilst in another study the association between air pollution and diabetes was stronger among women than men (Chen et al., 2013), in another study men residents of areas with PM2.5 or temperatures higher than the average level associated with an increased risk of metabolic disorders (Wallwork et al., 2017). In another study, there was evidence of a stronger effect in women ≥ 40 years old than women < 40 years old for association of pollution of PM2.5 with MetS (Rajkumar et al., 2019), while the findings of a study showed that in men, the elderly, and those who had an unhealthy lifestyle (smoking, drinking, not exercising regularly, as well as consuming more sweet soft drinks, high-calorie, and high-fat foods), and low-income people, the relation between ambient air pollution and MetS was very high and they may become more vulnerable (Yang et al., 2018a). Another study showed that physical activity reduced the effects of ambient air pollutants in increasing the risk of MetS (Hou et al., 2020); similarly, Kubesch et al. indicated that physical activity has potentially a preventive effect against the negative impact of air pollution on systolic blood pressure (Kubesch et al., 2015).
Limitations
In this study, only one serum sample from each participant was collected, and serum lipids as well as blood sugar levels may vary between days (Arlien-Søborg et al., 2016). Also, measurements were taken over a relatively short period of time, and various measurement factors included and may cause defects in the assessment.