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|Year : 2022
: 24 | Issue : 115 | Page
|Impact of Noise Exposure on Risk of Developing Stress-Related Metabolic Effects: A Systematic Review and Meta-Analysis
Kapeena Sivakumaran1, Jennifer A Ritonja2, Haya Waseem1, Leena AlShenaiber1, Elissa Morgan1, Salman A Ahmadi3, Allison Denning4, David Michaud4, Rebecca L Morgan1
1 Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario; Evidence Foundation, Cleveland Heights, Ohio, USA, Canada
2 University of Montreal Hospital Research Centre (CRCHUM), Montreal, Quebec; Department of Social and Preventive Medicine, University of Montreal, Montreal, Quebec, Canada
3 Department of Public Health Sciences, Queen’s University, Kingston, Ontario, Canada
4 Health Canada, Environmental and Radiation Health Sciences Directorate, Consumer and Clinical Radiation Protection Bureau, Ottawa, Ontario, Canada
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|Date of Submission||19-Apr-2022|
|Date of Decision||09-Jun-2022|
|Date of Acceptance||18-Jun-2022|
|Date of Web Publication||19-Dec-2022|
Background: Exposure to noise can increase biological stress reactions, which may increase adverse health effects, including metabolic disorders; however, the certainty in the association between exposure to noise and metabolic outcomes has not been widely explored. The objective of this review is to evaluate the evidence between noise exposures and metabolic effects. Materials and Methods: A systematic review of English and comparative studies available in PubMed, Cochrane Central, EMBASE, and CINAHL databases between January 1, 1980 and December 29, 2021 was performed. Risk of Bias of Nonrandomized Studies of Exposures was used to assess risk of bias of individual studies and certainty of the body of evidence for each outcome was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. Results: Fifty-six primary studies reporting on cortisol, cholesterol levels, waist circumference, glucose levels, and adrenaline and/or noradrenaline were identified. Although meta-analyses suggested that there may be an increase in waist circumference and adrenaline with increased noise exposure, the certainty in the evidence is very low. Overall, the certainty in the evidence of an effect of increased noise on all the outcomes were low to very low due to concerns with risk of bias, inconsistency across exposure sources, populations, and studies, and imprecision in the estimates of effects. Conclusions: The certainty of the evidence of increased noise on metabolic effects was low to very low, which likely reflects the inability to compare across the totality of the evidence for each outcome. The findings from this review may be used to inform policies involving noise reduction and mitigation strategies, and to direct further research in areas that currently have limited evidence available.
Keywords: Environmental noise, GRADE, metabolic, sound, stress
|How to cite this article:|
Sivakumaran K, Ritonja JA, Waseem H, AlShenaiber L, Morgan E, Ahmadi SA, Denning A, Michaud D, Morgan RL. Impact of Noise Exposure on Risk of Developing Stress-Related Metabolic Effects: A Systematic Review and Meta-Analysis. Noise Health 2022;24:215-30
|How to cite this URL:|
Sivakumaran K, Ritonja JA, Waseem H, AlShenaiber L, Morgan E, Ahmadi SA, Denning A, Michaud D, Morgan RL. Impact of Noise Exposure on Risk of Developing Stress-Related Metabolic Effects: A Systematic Review and Meta-Analysis. Noise Health [serial online] 2022 [cited 2023 Mar 27];24:215-30. Available from: https://www.noiseandhealth.org/text.asp?2022/24/115/215/363982
| Introduction|| |
Exposure to noise has been linked to several ill-health effects, including disruptions in sleep, poorer mental health, and well-being, and cardiovascular outcomes such as hypertension, arteriosclerosis, ischemic heart disease, and stroke., It is increasingly recognized that exposure to noise, even acute exposures, can increase biological reactions associated with stress. It has been hypothesized that these biological reactions could increase the risk of stress-related health effects, such as metabolic disorders; however, it remains unclear how exposure to noise could impact particular stress-related metabolic end points such as changes in stress hormones and adiposity.
According to the general noise stress model, the human body perceives chronic exposure to noise as a stressor that results in activation of neurohormonal systems, such as the activation of sympathetic responses., Such stress responses, in turn, are hypothesized to cause changes in a number of physiological functions, including changes to blood pressure, cardiac output, and metabolic markers such as blood glucose and lipids (e.g., cholesterol)., Some experimental and epidemiologic studies have investigated the association between environmental noise exposure to cardiovascular and metabolic diseases, such as hypertension, ischemic heart disease, and stroke.,,, In a prospective cohort study investigating road traffic noise and stroke, Sørensen et al. reported that exposure to residential road traffic noise was associated with a higher risk of stroke for individuals >64.5 years old; however, this study noted uncertainty in noise exposure assessment and residual confounding as limitations. Additionally, a study investigating the association between traffic noise and cardiovascular diseases concluded that traffic noise was associated with a higher prevalence of hypertension; however, temporality could not be assessed as it was a cross-sectional study. Further, there is a need to examine the strength of the association between noise and stress-related metabolic effects and the certainty of evidence (CoE) from available studies.
Past reviews examining noise exposure and long-term metabolic outcomes such as diabetes and obesity suggest a positive association.,, However, few reviews have been conducted to comprehensively assess how noise may affect metabolic-related biomarkers of stress, including hormones. In addition, few reviews have considered the CoE, using updated methodology. A recent systematic review conducted by van Kempen et al. used a modified Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach to examine the relationship between environmental noise and long-term cardiometabolic outcomes, including waist circumference, as an obesity marker, and diabetes. Their review found that noise from road traffic may increase the incidence of diabetes, and found insignificant associations for the other traffic noises, including air traffic and rail; however, the authors rated the quality of the evidence supporting an association between traffic noise and diabetes to be low. Similarly, the authors rated the quality of the evidence supporting an association between traffic noise and obesity markers to be low, even though all the studies showed that an increase in traffic noise was associated with an increase in obesity markers.
The objective of this systematic review was to examine the association between noise exposure and the risk of stress-related metabolic effects. This included examining the strength of the association using meta-analytic methods and CoE, as well as updating the available evidence. This is one in a series of three systematic reviews reporting on the associations between noise levels and cardiovascular, metabolic, and obstetric health outcomes.,
| Methods|| |
Details of the methods have been published previously. In brief, a systematic review and meta-analysis of exposure to incremental increases in noise on biological markers of stress was performed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist for the development of this review [Table S1]. The protocol has been registered in PROSPERO (CRD42020209353). Supplemental material includes PRISMA checklist, literature search strategies, data extraction form, Risk of Bias analyses and study characteristics for each evaluated outcome.
Searches for peer-reviewed primary studies and systematic reviews were conducted in PubMed, EMBASE, Cochrane CENTRAL, and CINAHL by an informational specialist between January 1, 1980 and December 29, 2021 [Table S2]. Additional references were also searched using the reference lists of included systematic reviews and studies. Results of the Literature search and screening are summarized in a PRISMA flow diagram.
Study screening and eligibility
Studies were eligible if they met the following criteria: 1) available in English, 2) conducted in humans, 3) measured noise exposure using A-weighted noise metrics: dB(A), Lden, Lnight, LAeq16, LAeq8h, Ldn, Lmax, sound exposure level, and, 4) provided at least one comparison of noise levels in relation to cortisol, cholesterol, waist circumference, glucose, and adrenaline/noradrenaline[Table 1].
As previously reported, to determine study relevance and eligibility, two study authors independently reviewed titles and abstracts, as well as full texts, for all potential articles that were identified as a result of the searches. Studies failing to meet the inclusion criteria were excluded. Study screening was conducted using the screening software program Covidence (Covidence systematic review software, Veritas Health Innovation, Melbourne, Australia. Available at: www.covidence.org). Disagreements that arose during study selection were resolved either by discussion or by consulting a third reviewer.
For data extraction, two study authors independently extracted study characteristics into a standardized and pilot-tested data extraction form [Table S3]. The following information was collected for each included study: publication information, study design, study population, source and ascertainment of exposure, ascertainment of outcome, statistical analysis, study results for relevant outcomes, and funding information. Discrepancies were resolved by consensus or by discussion with a third reviewer.
Data were pooled in a random-effects meta-analysis, where appropriate. Separate random-effects meta-analyses were conducted for each individual outcome and presented as a mean difference (MD) or beta [exp(β)]. When possible, studies were grouped based on the continuous measures of noise exposure reported (per 10 dBA or Lden increase in noise). If continuous measures were not available, studies were grouped by similar noise exposure categories reported in studies (e.g., <80 versus ≥80 dBA) and analyzed in a separate meta-analysis. When study results were too heterogeneous to be pooled, findings were described narratively. Meta-analyses were performed using the metafor package in R (version 4.0.3).,
Given the expected heterogeneity between studies reporting exposure to noise from different sources (e.g., rail versus road traffic) and different study designs (e.g., cross-sectional versus trials), studies were pooled separately by study design and noise source. Between-study heterogeneity was assessed in several ways, including visual inspection of forest plots, chi-square tests (using P < 0.1 as a threshold for clinical significance), and I2 statistics. As fewer than 10 studies were identified for any prespecified outcome, funnel plots could not be used to assess publication bias.
Risk of bias
For risk of bias, two reviewers independently assessed each study using the Cochrane Risk of Bias tool for randomized controlled trials (RCTs) or a preliminary version of the Risk of Bias Instrument for Non-randomized Studies of Exposures (ROBINS-E) for nonrandomized (i.e., observational) studies [Table S4]., Any discrepancies that occurred were resolved either through consultation with a third reviewer or by consensus.
The following confounders were identified as critical for adjustment a priori, for the risk of bias assessment using the ROBINS-E: age, sex, and smoking status. All publications or records for a single primary study were considered when making risk of bias judgments.
GRADE evidence assessment
For each outcome, the overall certainty of the evidence was assessed by noise-exposure source. Study authors assessed the CoE using the GRADE approach by considering the five domains for rating down (risk of bias, inconsistency, indirectness, imprecision, publication bias) and three domains for rating up (large or very large magnitude of effect, dose-response gradient, opposing residual confounding). Within the GRADE approach, for RCTs the body of evidence was started at high CoE, while nonrandomized studies started at low CoE.
| Results|| |
The search identified 11,482 records, of which 54 primary studies reporting on metabolic outcomes were included [Figure 1]. The effects of noise exposure on the following outcomes are presented: cortisol, cholesterol, waist circumference, glucose, and adrenaline/noradrenaline [Table 2]. Characteristics of eligible studies, risk of bias assessments, and estimates extracted from the studies (with transformations) are presented in Tables S5–S22.
|Figure 1 PRISMA flow diagram. PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses.|
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A total of 25 studies were identified that reported on the impact of noise exposure on cortisol levels [Table S5],,,,,,,,,,,,,,,,,,,,,,,,; however the majority of studies (n = 21) could not be pooled into a meta-analysis due to differences in populations, measurement of exposures, measurement of outcomes, and reporting of outcomes. Therefore, the results are presented narratively in the summary of findings table.,,,,,,,,,, Concerns with risk of bias due to confounding, exposure assessment, missing data, and measurement of outcomes were identified [Tables S6 and S7]. As shown in [Table 3], there was very low CoE for the effects of increased noise exposure on cortisol levels.
Four studies examined the relationship between road traffic noise and cortisol.,,, Among cohort studies that assessed road traffic noise and cortisol, it was reported that an increase in road traffic noise may have little to no effect on cortisol levels. Among cross-sectional studies exploring road traffic noise, one study reported that increased road traffic noise may increase cortisol levels (MD: 1.70; 95% confidence interval, CI: 1.20–2.20); however, the certainty in the evidence was very low [Table 3].
Air traffic noise may have little to no effect on cortisol levels based on findings from two studies [Table 3]., However, one study demonstrated a trend toward decreased cortisol levels per 10 dBA increase in noise exposure among females across LAeq 16 hour [exp(β): 0.92; 95% CI: 0.86–0.98], LAeq 24 hour [exp(β): 0.92; 95% CI: 0.85–0.98], Lden [exp(β): 0.90; 95% CI: 0.84–0.97], and Lnight[exp(β): 0.89; 95% CI: 0.83–0.96]. This study did not report a similar trend for males.
Among five studies exploring occupational noise and cortisol, it was found that exposure to higher levels of occupational noise may have little to no effect on cortisol, but the evidence was very uncertain due to concerns with risk of bias and imprecision.,, Evidence from a clinical trial on occupational noise and cortisol levels suggested that exposure to high noise (8-hour time-weighted average of personal noise exposure was 76.8 dBA among participants) may cause an increase in the percent change of cortisol levels (2.02% per 1 dBA increase in noise; 95% CI: 1.39–2.66); with an increase in cortisol serum levels when exposed to higher noise days compared to lower noise days. However, the certainty in the evidence was very low and this effect was not reported among subjects exposed to low noise levels [Table 3].
One study explored railway noise and cortisol levels and reported that exposure to higher levels of railway noise may have little to no effect on cortisol; however, there was very low certainty in the evidence due to concerns with risk of bias and imprecision [Table 3]. Similarly, exposure to higher levels of mixed noise may have little to no effect on cortisol, as informed by one study, but the evidence was very uncertain due to concerns with risk of bias and imprecision [Table 3]. Further, five clinical trials assessing laboratory-simulated noise could not detect a difference in cortisol levels across measures of Noise 60, Noise 30, (60 and 30 events, mean SPL of approximately 46 and 43 dBA, respectively), nor when comparing 90 dBA to control levels [Figure 2].,,,,
|Figure 2 Forest plot of lab-simulated noise and mean difference in cortisol (ug/L) for experimental studies.|
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Twenty studies were identified that reported on the impact of noise exposure on cholesterol levels [Table S9],,,,,,,,,,,,,,,,,,,; however, many of them (n = 13) are reported narratively in the summary of findings table based on differences in populations, measurement of exposures, measurement of outcome, and reporting of outcomes.,, Concerns with risk of bias due to confounding, exposure assessment, missing data, and measurement of outcomes were identified [Table S10]. As shown in [Table 4], there was very low CoE for the effects of increased noise exposure on cholesterol levels.
Five studies examined the relationship between road traffic noise and cholesterol.,,,, Among these studies, it was reported that exposure to higher levels of road traffic noise may have little to no effect on cholesterol, but the evidence is very uncertain due to concerns with risk of bias and imprecision [Table 4].
One cohort study explored the relationship between railway noise and cholesterol and reported that exposure to higher levels of railway noise may have little to no effect on cholesterol, but the evidence is very uncertain due to concerns with risk of bias [Table 4].
Eight studies examined the relationship between occupational noise and cholesterol.,,,,,,, Among cohort studies, it was found that occupational noise over 75 dBA (versus ≤75 dBA) may have little to no effect on cholesterol (MD: 2.43; 95% CI: −7.02–11.89); however, the evidence was very uncertain due to concerns with risk of bias and imprecision [Table 4]. Similarly, among the cross-sectional studies, it was found that occupational noise over ≥80 dBA (versus <80 dBA) may have little to no effect on cholesterol (MD: 4.54; 95% CI: −5.03–14.12) [Figure 3]; however, the certainty in the evidence was very low due to concerns with risk of bias, imprecision, and inconsistency [Table 4]. Evidence from a case–control study reporting on occupational noise and cholesterol suggested that exposure to higher levels of occupational noise may cause a decrease in cholesterol; however, the certainty in the evidence was very low due to concerns with risk of bias and imprecision [Table 4].
|Figure 3 Forest plot of occupational noise and mean difference in cholesterol (mg/dL).|
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Twelve studies were identified that reported on the impact of noise exposure on waist circumference [Table S12],,,,,,,,,,,; however, some (n = 7) are reported narratively in the summary of findings table based on differences in populations, measurement of exposures, measurement of outcome, and reporting of outcomes.,,,,,, Concerns with risk of bias due to confounding, exposure assessment, missing data, and measurement of outcomes were identified [Table S13]. As shown in [Table 5], there was very low CoE for the effects of increased noise exposure on waist circumference.
Seven studies examined the relationship between road traffic noise and waist circumference.,,,,,, Among cohort studies assessing road traffic noise and waist circumference, one study suggested an inverse relationship between road traffic noise and waist circumference (beta per 10 dBA: 0.04; 95% CI: 0.02–0.06); however, the certainty in the evidence was very low due to concerns with risk of bias and imprecision [Table 5]. Among cross-sectional studies, it was found that an increase in road traffic noise may have little to no effect on waist circumference (MD: 0.13; 95% CI: −0.31–0.57) [Figure 4]; however, the certainty in the evidence was very low due to concerns with risk of bias and imprecision [Table 5].
|Figure 4 Forest plot of road/railway traffic noise and mean difference in waist circumference (cm for cross-sectional studies, and cm per year for cohort studies).|
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Five studies explored the relationship between railway noise and waist circumference.,,,, Among cohort studies, it was found that an increase in railway noise may have little to no effect on waist circumference (MD: 0.00; 95% CI: −0.02–0.02) [Figure 4]; however, the evidence is very uncertain [Table 5]. Similarly, the cross-sectional study assessing railway noise and waist circumference suggested there may be an increase in waist circumference when there is an increase in railway noise, but the evidence was very uncertain due to risk of bias [Table 5].
Among three cohort and cross-sectional studies examining the relationship between air traffic noise and waist circumference, it was found that an increase in air traffic noise may be associated with an increase in waist circumference, but the CoE was very low due to concerns with risk of bias and imprecision [Table 5].,,
One cross-sectional study explored the relationship between occupational noise and waist circumference. This study found that exposure to increased occupational noise levels may decrease waist circumference when looking at production line workers exposed to <85, 95 to <100, and ≥100 dBA compared to office workers; however, this was not observed for production-line workers exposed to 90 to <95 dBA. The certainty in this evidence was very low due to concerns with risk of bias and imprecision [Table 5].
Eleven studies were identified that reported on the impact of noise exposure on glucose levels [Table S15],,,,,,; however, they could not be pooled into a meta-analysis due to differences in populations, measurement of exposures, measurement of outcome, and reporting of outcomes. Concerns with risk of bias due to confounding, exposure assessment, missing data, and measurement of outcomes were identified [Table S16]. As shown in [Table 6], there was very low CoE for the effects of increased noise exposure on glucose.
Exposure to higher levels of road traffic, railway (including laboratory simulated), or occupational noise may have little to no effect on glucose, but the evidence is very uncertain [Table 6].
Adrenaline and noradrenaline
A total of nine studies were identified that reported on the effect of noise exposure on adrenaline (i.e., epinephrine) and/or noradrenaline (i.e., norepinephrine) levels [Table S18],,,,,,,; however, five of them could not be pooled into a meta-analysis due to differences in populations, measurement of exposures, measurement of outcome, and reporting of outcomes.,,,, Concerns with risk of bias due to confounding, exposure assessment, missing data, and measurement of outcomes were identified [Tables S19 and S20]. As shown in [Table 7], there was very low CoE for the effects of increased noise exposure on adrenaline and noradrenaline.
|Table 7 Summary of findings for noise exposure and adrenaline/noradrenaline|
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Exposure to high (>63 dBA) versus low (<57 dBA) levels of air traffic noise may have little to no effect on adrenaline and noradrenaline levels, but the evidence is very uncertain [Table 7]. Exposure to occupational noise >90 dBA (versus <78 dBA) may increase adrenaline levels when compared to lower levels of occupational noise during midshift; however, this may have little to no effect on noradrenaline levels, but the evidence is very uncertain [Table 7]. Exposure to higher levels of noise in a laboratory-simulation may have little to no effect on adrenaline levels (MD: 1.57; 95% CI: −5.74–8.87; MD: 2.53; 95% CI: −0.57–5.63; Noise 30 and Noise 60, respectively) [Figure 5], and little to no effect on noradrenaline levels; however, the certainty in the evidence is very low [Table 7].
|Figure 5 Forest plot of laboratory-simulated noise and mean difference in adrenaline (ng/L) in experimental studies.|
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| Discussion|| |
Statement of the principal findings
This review identified the evidence on metabolic biomarkers that are relevant in the context of noise exposure. Overall, as indicated in some studies and pooled meta-analytic estimates, we found that higher levels of noise may have impacts on the metabolic outcomes of adrenaline, noradrenaline, and cortisol. However, this was based on low or very low CoE across different exposure sources and noise levels, which makes it difficult to draw conclusions about the effect of higher levels of noise on metabolic outcomes.
This review demonstrates the need to further discussions on the strength of evidence available for an association between exposure to noise and the development of adverse metabolic health effects that are caused or exacerbated by chronic stressor exposure. The certainty in the evidence of an effect of increased noise on these outcomes was low or very low due to concerns with risk of bias, inconsistency across exposure sources, differences across populations and studies, and imprecision.
This systematic review is one in a series reporting on the associations between the exposure to noise and cardiovascular, metabolic, and obstetric health outcomes., For cardiovascular outcomes, it was seen that there may be signals of increased response to higher noise exposure for cardiac output, vascular resistance, hypertension, blood pressure, and heart rate; however, the CoE was very low across the exposure sources and noise levels, making it difficult to draw definite conclusions. Similarly, for obstetric outcomes, it was seen that increased noise exposure may be associated with increases in the risks of preeclampsia and gestational hypertension but the certainty in the evidence was very low.
Strengths and limitations of the study
This review has a number of strengths, including the use of rigorous and transparent systematic review methods, as well as a comprehensive literature search. Individual studies were assessed using a risk of bias tool developed for the assessment of exposures outside the context of a clinical trial, and the certainty of the body of evidence for each outcome by noise source was assessing using the GRADE approach. However, there are limitations. Due to heterogeneity across studies (i.e., populations, noise exposure and sources, outcome measurement), we were unable to pool most studies using meta-analytic techniques. Most of the eligible study results instead were presented narratively within the evidence profiles. This also limited the ability to conduct sensitivity analyses to explore the role of risk of bias across different outcomes. Similarly, as stated a priori in the protocol, subgroup analyses were not performed as there were not sufficient studies. The inability to conduct a publication bias assessment, due to insufficient studies, is also recognized as a limitation of this review.
Relation to other studies
Similar to the review by van Kempen et al., this review also identified low to very low CoE for the outcomes of interest. However, the review by van Kempen et al. only explored long-term metabolic effects, including diabetes and obesity, while the current review also assessed short-term biomarkers such as glucose levels and waist circumference, which would serve as risk factors to developing long-term metabolic outcomes. The review by van Kempen et al. reported that road traffic may increase the incidence of diabetes and that an increase in traffic noise may be associated with an increase in obesity markers, including waist circumference; however, the quality of evidence was low. van Kempen et al. also recognized that based on the quality of evidence, it would be too early to draw definite conclusions about the impact of noise on the metabolic system. Both reviews identified low and very low CoE for the relevant outcomes. This review had very low certainty in the body of evidence due to concerns with risk of bias, which included bias due to confounding, exposure assessment, missing data, and in measurement of outcomes. The body of evidence could also not be rated up, as there were no signals of large or very large magnitudes of effect, no dose-response gradients or opposing residual confounding.
Meaning of the study: possible explanations and implications for stakeholders
Findings from this review may be used to investigate the effects of noise exposure on metabolic outcomes, as well as to direct further research in the areas with limited evidence for the effect of noise exposures on metabolic outcomes (e.g., railway). Findings from this review may also be used to inform decision makers tasked with establishing guidelines and/or making policy decisions, as this review focuses on the totality of the evidence, as opposed to the disaggregated evidence streams. However, when making policy decisions, the evidence and its certainty, as well as benefits and harms, resource use, equity, acceptability, and feasibility need to be considered, in conjunction with the GRADE Evidence to Decision (EtD) framework.
Unanswered questions and future research
Given the high heterogeneity evident across study designs, particularly in terms of exposure/outcome measurement, additional research is needed to understand the effect of noise for multiple exposure types on metabolic outcomes. In general, few studies were available that examined various types of noise exposures (e.g., occupational, road, air) and even fewer that examined various aspects of noise (e.g., in terms of duration, intensity, timing of exposure). Studies that were available had limitations with lack of adjustments for critical confounders, as well as differences in the measurement of the noise exposure and outcome. Also, while none of the included studies reported on participants with measures of impaired hearing, this could be an important avenue to further investigate. Additionally, future studies exploring the impact of noise exposure on adverse metabolic outcomes that appropriately adjust for critical confounders, reduce the risk of exposure misclassification, and explore the need for standardized techniques to measure exposures and outcomes are needed. Similar reviews exploring the effects on noise on other endpoints (e.g., oxidative stress parameters) are warranted as they may inform a more comprehensive evidence base of the effects of noise on cardiovascular events. Further, future studies are needed that consider long-term exposure to noise from various sources of exposure and at multiple time points.
It should be acknowledged that several of the biological markers evaluated in this review are notoriously challenging to evaluate insofar as they are susceptible to circadian rhythms, which may require repeated sampling and a level of invasiveness that can affect participation and subject attrition. Although salivary sampling is relatively noninvasive, adrenaline and noradrenaline are often measured in blood/urine and this introduced added complexities into the study design. Moreover, it is not always possible to identify and adequately control the many non-noise sources of influence on the evaluated outcomes, but these needs to be considered in sampling strategies. One potential area of investigation could be to assess the stress biomarker, cortisol, in scalp hair. Hair cortisol has been proposed as an alternative means of assessing chronic stress as cortisol may integrate into hair over time during stress, overcoming several of the shortcomings associated with salivary, plasma, and urine sampling.,,, To our knowledge, the only study to date to evaluate hair cortisol concentrations in relation to environmental noise was by Michaud et al. Their study considered the association between wind turbine noise and multiple measures of self-reported and measured stress reactions including an assessment of hair cortisol. While there was no association between any measure of stress and wind turbine noise, there was a significant correlation between hair cortisol and self-reported stress over the previous 30-days. Some clarity may emerge on the putative association between environmental noise and stress as more studies include hair cortisol in their design. The assessment of hair cortisol may prove to be a valuable methodological tool in this regard for future studies.
Authors thank Skye Bickett for constructing the search strategies.
Financial support and sponsorship
This review was funded by Health Canada under contract no. 4500414567 with Rebecca Morgan.Conflicts of interest
There are no conflicts of interest.
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PhD David Michaud
Health Canada, Environmental and Radiation Health Sciences Directorate, Research Scientist, 775 Brookfield Road, Ottawa, K1A1C1, Ontario
Source of Support: None, Conflict of Interest: None
[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7]