Anadromous fish populations in the Pacific Northwest face challenges along their migratory routes from declining habitat quality, harvest, and barriers to longitudinal connectivity. Changes in river temperature regimes are producing an additional challenge for upstream migrating adult salmon and steelhead, species that are sensitive to absolute and cumulative thermal exposure. Adult salmon populations have been shown to utilize cold water patches along migration routes when mainstem river temperatures exceed thermal optimums. We are employing an individual based model (IBM) to explore the advantages and disadvantages of spatially-distributed cold water refugia for adult migrating salmon and steelhead in the Columbia River. Our model, developed in the HexSim platform, is built around a mechanistic behavioral decision tree that drives individual interactions with their spatially explicit simulated environment. Population-scale responses to dynamic thermal regimes, coupled with other stressors such as disease and harvest, become emergent properties of the spatial IBM. Other model outputs include arrival times, species-specific survival rates, body energetic content, and reproductive fitness levels. Here, we discuss model development and the challenges associated with parameterizing an individual based model of salmon and steelhead in a section of the Columbia River.
1Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
2Centre for Occupational and Environmental Medicine, Stockholm County Council, Stockholm, Sweden
3Department of Cardiology and Internal Medicine, Belorussian State Medical University, Minsk, Belarus
4Department of Occupational and Environmental Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
5Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
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- Exposure to transportation noise is widespread and has been associated with obesity in some studies. However, the evidence from longitudinal studies is limited and little is known about effects of combined exposure to different noise sources.
- The aim of this longitudinal study was to estimate the association between exposure to noise from road traffic, railways, or aircraft and the development of obesity markers.
- We assessed individual long-term exposure to road traffic, railway, and aircraft noise based on residential histories in a cohort of 5,184 men and women from Stockholm County. Noise levels were estimated at the most exposed façade of each dwelling. Waist circumference, weight, and height were measured at recruitment and after an average of 8.9 y of follow-up. Extensive information on potential confounders was available from repeated questionnaires and registers.
- Waist circumference increased 0.04 cm/y (95% CI: 0.02, 0.06) and 0.16 cm/y (95% CI: 0.14, 0.17) per 10 dB Lden in relation to road traffic and aircraft noise, respectively. No corresponding association was seen for railway noise. Weight gain was only related to aircraft noise exposure. A similar pattern occurred for incidence rate ratios (IRRs) of central obesity and overweight. The IRR of central obesity increased from 1.22 (95% CI: 1.08, 1.39) in those exposed to only one source of transportation noise to 2.26 (95% CI: 1.55, 3.29) among those exposed to all three sources.
- Our results link transportation noise exposure to development of obesity and suggest that combined exposure from different sources may be particularly harmful. https://doi.org/10.1289/EHP1910
Received: 17 March 2017
Revised: 5 October 2017
Accepted: 9 October 2017
Published: 20 November 2017
Address correspondence to A. Pyko, Institute of Environmental Medicine, Karolinska Institutet, SE-171 77 Stockholm, Sweden. Telephone: 46(0) 852487561. Email: Andrei.email@example.com
Supplemental Material is available online (https://doi.org/10.1289/EHP1910).
The authors declare they have no actual or potential competing financial interests.
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Large parts of the population are exposed to elevated levels of noise, particularly in urban areas. Road traffic is a dominating source but both railway and aircraft noise contribute in certain areas. Exposure to transportation noise may result in annoyance and sleep disturbance (Basner et al. 2014; Miedema and Oudshoorn 2001; Miedema and Vos 2007) as well as in cardiovascular disease (Münzel et al. 2016). Recently, it has been suggested that markers of obesity, such as waist circumference and body mass index (BMI), may be associated with exposure to transportation noise in adults (Christensen et al. 2015a, 2015b; Eriksson et al. 2014; Oftedal et al. 2015; Pyko et al. 2015), but the evidence is not wholly consistent. Only one of the studies focusing on road traffic noise was longitudinal and used self-reported data on weight and waist circumference, which are prone to bias. In addition, there are studies on obesity in relation to residence near major roads (Li et al. 2016) and air pollution exposure (Jerrett et al. 2014), which indicates that it is important to assess both air pollution and noise exposure to elucidate causal associations when exposure from road traffic is focused.
It has been hypothesized that the relation between environmental noise and cardiovascular disease may involve sleep disturbances and psychological stress (Münzel et al. 2016) and the same mechanisms may also be relevant for metabolic diseases such as obesity and type 2 diabetes. Sleep deprivation may lead to dysregulation of appetite-regulating hormones, such as leptin and ghrelin, and contribute to overweight (Chaput et al. 2007; Van Cauter et al. 2008). Furthermore, noise may act as a stressor and lead to the elevation of cortisol levels, thereby promoting central fat deposition and impaired glucose regulation (Björntorp 1997; Rosmond 2003). For example, it has been shown that subjects living near airports have elevated saliva cortisol levels related to noise exposure (Selander et al. 2009a). Combined exposure to several stressors, such as different noise sources or work stress may be particularly harmful (Pyko et al. 2015; Selander et al. 2013; Tétreault et al. 2013). However, data on interactions between exposure to several stressors in relation to development of obesity are limited as well as on the combined effects of noise and air pollution exposure.
In a previous publication we reported results based on a cross-sectional analysis of transportation noise exposure and markers of obesity in a cohort from Stockholm County (Pyko et al. 2015). The present study is based on the same study population, but has a longitudinal design, and uses a newly developed methodology enabling more precise assessment of long-term exposure to transportation noise from different sources as well as objective outcome data. The aim was to estimate the association between exposure to transportation noise and development of obesity markers. As a secondary objective, we assessed the role of combined exposure to multiple sources of transportation noise, including road traffic, railways, and aircraft as well as interactions with air pollution exposure.
The present study was based on the Stockholm Diabetes Prevention Program cohort, which has been described in detail previously (Eriksson et al. 2008). Briefly, the program was conducted between 1992 and 2006 in Stockholm County with the primary aim to study risk factors for type 2 diabetes as well as to implement and evaluate methods for prevention. Participants were recruited between 1992 and 1998 from five municipalities in Stockholm County (Upplands Bro, Upplands Väsby, Sigtuna, Värmdö, and Tyresö). These municipalities mainly include suburban and semirural areas.
By an enrichment procedure in the original design of the study, approximately half of the participants had a family history of diabetes (52%), that is, at least one first-degree relative (mother, father, or sibling) or two second-degree relatives (grandparent, uncle, or aunt) with diabetes. Those with family history were matched on age and sex with individuals who did not have a family history of diabetes. A total of 3,162 (69.8%) men and 4,946 (70.3%) women accepted the invitation. After the baseline survey and medical examination 34 men and 125 women were excluded due to diabetes diagnosis or other medical reasons. Thus, 7,949 participants 35–56 y of age formed the diabetes-free baseline sample. Some members of the baseline sample died or moved out of Stockholm County during follow-up (n=838), and all remaining 7,111 participants were invited to a new survey 8 to 10 y after the baseline survey (see Figure S1). A total of 5,712 persons (corresponding to 80% of those invited) took part in the follow-up survey and medical examination.
At both baseline and follow-up investigations, participants filled out questionnaires and trained nurses measured weight, height, and waist circumference. The questionnaires covered health status as well as lifestyle habits such as smoking, alcohol intake, and physical activity during leisure time, dietary habits, psychological distress, shift work, insomnia, and job strain. Moreover, the follow-up questionnaire enquired about noise annoyance and noise sensitivity.
We obtained information on residential address history for the participants from the Swedish Population Register through the Swedish Taxation Authority. The residential address history included information on each address where the participants had lived starting from 1990, with precise times of address changes during follow-up.
The study was conducted in accordance with the Helsinki Declaration and approved by the Regional Ethics Review Board at Karolinska Institutet. All participants gave informed consent to the original study which also applied to the present analyses.
Noise Exposure Assessment
To assess long-term individual road transportation noise exposure, a noise database was constructed for Stockholm County to represent the period 1990–2010. The database contains information from several national, regional, and local authorities and includes 3-dimensional terrain data as well as information on ground surface, road net, daily traffic flows, speed limits, and percentage of heavy vehicles. Data were available on the road traffic situation every fifth year (1990, 1995, 2000, 2005, and 2010). To calculate noise levels, we developed and used a modification of the Nordic prediction method for road traffic (Bendtsen 1999; Nielsen 1997). The Nordic method uses traffic flow, distribution of heavy and light vehicles, speed, and pavement type to calculate the noise emission from each road link. The noise level at a receiving point is then calculated by summing the contribution of every road link using a propagation correction based on distance between road and receiver, presence of screening objects such as noise barriers or buildings, terrain shape, and whether the ground is acoustically soft or hard. Meteorological effects are included to some extent, but vegetation is not considered other than as soft ground. We modified the Nordic method for dense urban areas where possible reflection and shielding are taken into account by a ground space index based on building density (Salomons and Pont 2012). Thus, instead of the detailed information on buildings a typical reflection and shielding scenario based on building density was applied. The higher building density and the longer the distance from a source to a receiver, the higher the probability of both reflections and screening by the buildings. This modification led to decreased computational time as well as to avoiding geometrical errors such as the receiver positions being placed inside buildings instead of being exactly on the façade. Our method was validated against the full Nordic prediction method modeled with SoundPLAN (version 6.3; SoundPLAN GmbH) and showed coherent estimates. A more detailed description of the simplified noise modeling methodology instead of using detailed information on buildings is provided elsewhere (Ögren and Barregard 2016). Information on road traffic noise barriers was not included because of insufficient data on year of construction.
Using the residential address history, we estimated noise levels due to road traffic at the most exposed façade for all relevant addresses. The equivalent continuous A-weighted sound pressure level (LAeq) was calculated, and assuming a 24-h distribution of road traffic as 75%/20%/5% for day, evening, and night, respectively, we expressed noise levels as Lden, which corresponds to the equivalent level with a penalty of 3.4 dB considering noise during evening (+5 dB) and night (+10 dB) (Murphy and King 2010). The modeled noise levels were interpolated between the years with road traffic noise estimates. For each participant, we calculated the time-weighted average noise exposure during follow-up taking into account all addresses in Stockholm County where the subject had lived and considering the duration of residence at each address.
For railway noise, we used parts of the same database (3-dimensional terrain data, ground surface, residential history) as for road traffic noise supplemented with relevant information on the railway net, such as speed limits, train counts, and train types, as well as the exact 24-h distribution for different parts of the railway net for the years 1990–2012. Information on railway noise barriers was not taken into account because we generally lacked data on year of construction. As for road traffic, we applied the typical reflection and shielding scenario based on building density instead of detailed information on buildings. Railway noise levels were expressed as Lden with time-weighted exposure during the follow-up computed in a similar way as for road traffic noise.
With assistance from Swedavia, which operates the two main airports (Arlanda and Bromma) in Stockholm County, we obtained noise contour data of annual levels around the airports for the years 1995, 2000, 2005, and 2010. We assumed the same noise level in 1990 as in 1995 because there were no major structural changes in the airports between these years. The noise contour data ranged from 45 dB Lden near Arlanda and from 40 dB Lden near Bromma and were combined with the residential address history data. The annual noise levels at each address point were interpolated between the years with data during the follow-up period. Time-weighted exposure to aircraft noise was computed in relation to residential time at each address.
All measurements of weight and height as well as of waist circumference were performed by trained nurses according to a standard protocol during the baseline and follow-up investigations. Height and weight were measured with the participant standing without shoes and were rounded to the nearest 0.5 cm or 100 g, respectively. Waist circumference was measured with the participant in lying face up, midway between the lower costal margin and iliac crest. Anthropometric markers of obesity were defined according to the WHO criteria for the European population. BMI was calculated as the weight divided by the squared height (kilograms per meter squared) with a cutoff at ≥25 to define overweight. Sex-specific cutoff values for central obesity were applied for waist circumference: ≥88 cm for women and ≥102 cm for men (WHO 2008). We used weight gain in the analyses based on continuous outcomes because it is more easily interpretable than changes in BMI as well as for comparability with previous evidence.
Men and women were investigated during partly different time periods, leading to differences in follow-up time with means of 10.2 and 8.0 y, respectively. Moreover, the follow-up time varied from 6.1 to 11.0 y between individual participants. Therefore, the change in weight and waist circumference was calculated by dividing weight gain and waist circumference increase from baseline to follow-up with the individual follow-up time in years (kilograms per year and centimeters per year, respectively).
The covariates evaluated as confounders were identified based on a literature search and by development of a directed acyclic graph (DAG) with DAGitty.net software (see Figure S2) (Textor et al. 2011). We used the DAG to select a set of confounders for assessment of the direct effect of transportation noise on the development of obesity. Information on age, sex, physical activity, dietary habits, psychological distress, family history of diabetes, occupational status, shift work, educational level, marital status, alcohol consumption, smoking status, sleep disturbance, dietary habits, psychological distress, and job strain was obtained from the baseline questionnaire. Data on noise sensitivity and annoyance by transportation noise were obtained from the follow-up questionnaire. Furthermore, information on household mean income in small geographical areas with an average population of 1,000–2,000 subjects was obtained from registers held by Statistics Sweden to adjust for contextual confounding.
Dietary habits were assessed using “recommended” and “non-recommended” food scores (Pyko et al. 2015). In recommended foods we included consumption of low-fat dairy products, whole-meal or hard bread, fruits, vegetables (score +1 if consumed at least two to three times per week), and porridge (+1 if consumed at least one to three times per month). Among the non-recommended foods we included consumption of high-fat dairy products, white bread (score +1 if consumed at least two to three times per week), fast foods (+1 if consumed at least one to three times per month), cakes and sweets (+1 if consumed at least once a week). Summarized, the two scores for recommended and non-recommended foods ranged from 0 to 15 and 0 to 16, respectively. We considered more than 8 of 15 in recommended food score as a healthy diet indicator and more than 8 of 16 in non-recommended food score as an unhealthy diet indicator.
We assessed job strain based on the Swedish version of the Karasek and Theorell demand–decision latitude questionnaire (Agardh et al. 2003). Baseline indices for work-related demands and decision latitude were created and categorized in tertiles, and the highest tertile of demand together with the lowest tertile of decision latitude defined job strain. An index was also created for psychological distress that was assessed from baseline questions on anxiety, apathy, depression, fatigue, and insomnia (Eriksson et al. 2008).
Based on the individual residential history, we calculated time-weighted exposure to nitrogen oxides (NOx) from road traffic for each participant during follow-up using a dispersion modeling methodology developed to assess long-term source-specific exposure in Stockholm County (Bellander et al. 2001; Gruzieva et al. 2012).
Further covariates used in the adjusted models included physical activity during leisure time (sedentary: exercise less than 2 h per week/moderate: exercise at least 2 h per week/regular: exercise at least 30 min one to two times per week/frequent regular: exercise at least 30 min three times or more per week), alcohol consumption (daily/weekly/seldom/never), education (primary school/upper secondary school/university), smoking status (never/former/current), employment status (gainfully employed/unemployed/retired), psychological distress (yes/no), job strain (yes/no), and shift work (yes/no).
We tested differences in background characteristics related to road traffic noise exposure with Pearson chi-squared tests for categorical variables and one-way ANOVA for continuous variables. Pearson correlations were used to describe relationships between different transportation noise sources and road traffic–related NOx.
Linear regression models were used to analyze associations between transportation noise exposure and weight and waist circumference changes with the estimation of regression coefficients and 95% confidence intervals (CIs). Homoscedastic variance was checked by residual plots, and normality was assessed by normal probability plots of the residuals. The analyses were performed for continuous transportation noise exposures, and associations are presented for an increment of 10 dB Lden. To examine associations between transportation noise exposure and incidence of central obesity as well as overweight, we used Poisson regression models estimating incidence rate ratios (IRRs) and 95% CIs. In each analysis, those with the outcome at baseline were excluded. We approximated person-time with the length of follow-up and this was specified as an offset parameter in the model.
Additionally, we tested the assumption of linearity between transportation noise and weight as well as waist circumference changes. First, we performed analyses with a categorical exposure variable (<45, 45–49, 50–54, and ≥55 dB Lden) by inserting it in the linear model. Second, we performed restricted cubic splines analyses with 3 knots determined by Harell’s method (knots placed at the 10th, 50, and 90 percentiles). Also, we assessed the effect of combined exposure to multiple noise sources by creating dummy variables, indicating subjects exposed to none, one, two, or three transportation noise sources ≥45 dB Lden. We performed Cuzick nonparametric trend tests for the ranks across exposure groups to estimate p-values for trend.
Results are mainly presented based on two adjustment models. First, a crude model is used with adjustment for only sex and age (35/40/45/50/55 y of age). Second, a fully adjusted model is presented with additional adjustment for dietary habits, physical activity during leisure time, alcohol consumption, education level, physical activity, smoking status, psychological distress, job strain, and shift work.
Effect modification of the association between road traffic noise and development of obesity measures by characteristics from baseline (sex, education, BMI, and waist circumference vs. railway noise and diabetes heredity) as well as follow-up (age, noise annoyance, noise sensitivity, and air pollution) were evaluated by introducing interaction terms into the models and by using F-test statistics.
We performed sensitivity analyses to investigate how the association between road traffic noise exposure and the IRR for central obesity was affected by restriction of the population to those who did not change their residential address, those were exposed to only road traffic noise, or those without diabetes heredity. Moreover, results of additional adjustment for other transportation noise sources, baseline waist circumference, municipality, and contextual confounding (area based mean income) are also presented. In particular, the effects were evaluated of additional adjustments for air pollution from local traffic using NOx as the indicator.
Hypothesis testing for all analyses was based on two-tailed rejection regions and p-values less than 5% were considered statistically significant, except for the interaction terms, where 10% was used as significance level. Statistical analyses were performed using Stata/SE (version 13.1; StataCorp, College Station, TX); and spatial manipulation of data and exposure assessment were performed in QGIS (version 2.10.1; QGIS Development Team).
Of the 5,712 persons participating in the follow-up survey 82 (1.4%) were excluded because of missing exposure data; 94 (1.6%) because of missing data on anthropometric variables at baseline or follow-up; and 352 (6.1%) because of missing data on the covariates included in the main model. Following these exclusions, a study population of 5,184 (91%) individuals remained with complete information and a mean follow-up time of 8.9 y.
Of those included in the study 2,739 (53%) were not exposed to a noise level of ≥45 dB Lden from any transportation noise source; 1,901 (37%) participants were exposed to one of three transportation noise sources at this level or higher; 487 (9%) to two sources of transportation noise, and 57 (1%) to all three sources ≥45 dB Lden (see Figure S3).
Women reported road traffic noise exposure ≥45 dB Lden more often than men because some of them were recruited from one municipality (Upplands-Väsby) that was not included in the recruitment of men (Table 1). Furthermore, participants with higher noise exposure had lower education and socioeconomic status, were less physically active during leisure time, had more job strain and psychological distress, and reported that they were less noise sensitive. Furthermore, those exposed were more annoyed by road traffic noise and more often exposed to railway and aircraft noise.
|Individual characteristicsa||Time-weighted average road traffic exposure|
|<45 dB Lden(n=3,457)||≥45 dB Lden(n=1,727)||p-Value|
|Women||1,914 (55)||1,069 (62)||0.007|
|35–39||333 (10)||141 (8)|
|40–44||659 (19)||316 (18)|
|45–49||1,196 (35)||573 (33)|
|50–55||1,269 (37)||697 (40)|
|Low||889 (26)||483 (28)|
|Medium||704 (20)||390 (23)|
|High||1,609 (47)||746 (43)|
|Other||166 (5)||67 (4)|
|Gainfully employed||3,162 (91)||1,573 (91)|
|Unemployed||217 (6)||101 (6)|
|Retired||78 (2)||53 (3)|
|Shift work||329 (10)||181 (10)||0.272|
|Current||812 (23)||439 (25)|
|Former||1,274 (37)||631 (37)|
|Never||1,371 (40)||657 (38)|
|Physical activity during leisure timeb||0.012|
|Sedentary||329 (10)||206 (12)|
|Moderate||1,853 (54)||909 (53)|
|Regular||986 (29)||497 (29)|
|Frequent regular||289 (8)||115 (7)|
|Daily||153 (4)||73 (4)|
|Weekly||2,241 (65)||1,131 (65)|
|Seldom||949 (27)||451 (26)|
|Never||114 (3)||72 (4)|
|Primary school||1,017 (29)||565 (33)|
|Secondary school||1,340 (39)||666 (39)|
|University degree or higher||1,100 (32)||496 (29)|
|Job strainc||381 (11)||221 (13)||0.060|
|Psychological distressd||696 (20)||410 (24)||0.003|
|Less sensitive than others||637 (18)||357 (21)|
|Same sensitivity as others||2,441 (71)||1,210 (70)|
|More sensitive that others||378 (11)||158 (9)|
|Noise annoyance from road traffice||<0.001|
|Seldom/never||3,191 (92)||1,263 (73)|
|Few times per month||134 (4)||187 (11)|
|Few times per week||79 (2)||149 (9)|
|Each day||46 (1)||123 (7)|
|Healthy dietf||318 (9)||175 (10)||0.280|
|Unhealthy dietf||2,167 (63)||1,030 (60)||0.034|
|Diabetes heredityg||1,776 (51)||924 (54)||0.148|
|Railway noise over 45 dB Ldenh||213 (6)||183 (11)||<0.001|
|Aircraft noise over 45 dB Lden h||578 (17)||345 (20)||0.004|
aCharacteristics are from the baseline investigation unless stated otherwise. Number of participants in each group, percentages in parenthesis and p-values are reported.
bPhysical activity during leisure time is defined as sedentary (regular exercise less than 2 h per week), moderate (regular exercise at least 2 h per week), regular (regular exercise at least 30 min one to two times per week), frequent regular (at least 30 min three times or more per week).
cJob strain is defined as a combination of the highest tertile of demand together with the lowest tertile of decision latitude at work.
dPsychological distress is assessed as the highest quartile of a summed index based on questions on anxiety, apathy, depression, fatigue, and insomnia.
eFrom follow-up investigation.
fMore than 8 of 15 in recommended food score and 8 of 16 in non-recommended food score (see “Methods” section).
gFamily history of diabetes defined if participants had at least one first-degree relative (parent or sibling) with diabetes or at least two second-degree relatives (grandparents, aunts, uncles) with diabetes.
hTime-weighted average during follow-up period.
The mean follow-up time was 8.0 y in women and 10.2 y in men and mean weight gain was 0.28 kg/y (SD 0.72) among women and 0.32 kg/y (SD 0.56) among men (see Table S1). Mean waist circumference increase differed among women and men and was 0.64 cm/y (SD 0.80) and 0.33 cm/y (SD 0.64), respectively. Excluding those with overweight at baseline, 2,560 (49%), the cumulative incidence of overweight in the remaining 2,624 participants during follow-up was 25% and 36% for women and men, respectively. In contrast, the cumulative incidence of central obesity in those 4,386 without central obesity at baseline was 23% and 16% in women and men, respectively.
Table 2 presents associations between transportation noise exposures from different sources and continuous outcomes. In the fully adjusted model, we observed an association between road traffic noise and waist circumference increase of 0.04 cm/y (95% CI: 0.02, 0.06) per 10 dB Lden. For aircraft noise, the waist circumference increase was 0.16 cm/y (95% CI: 0.14, 0.17) per 10 dB Lden. No clear association was observed between railway noise and waist circumference increase. Weight gain was associated with aircraft noise, and changed 0.03 kg/y (95% CI: 0.01, 0.04) per 10 dB Lden, but not with road or railway traffic noise exposure.
|Exposurea||No. total||Waist circumference increase (cm/y)||Weight gain (kg/y)|
|Model 1b β (95% CI)||Model 2c β (95% CI)||Model 1b β (95% CI)||Model 2c β (95% CI)|
|Road traffic noise|
|Continuous per 10 dB Lden||0.04 (0.02, 0.07)||0.04 (0.02, 0.06)||0.01 (−0.006, 0.03)||0.01 (−0.009, 0.03)|
|Categorical, dB Lden|
|<45||3,457||0 Referent||0 Referent||0 Referent||0 Referent|
|45–49||958||−0.04 (−0.09, 0.01)||−0.05 (−0.10, 0.003)||−0.02 (−0.07, 0.03)||−0.02 (−0.07, 0.02)|
|50–54||565||0.13 (0.06, 0.19)||0.12 (0.06, 0.18)||0.04 (−0.02, 0.10)||0.03 (−0.03, 0.09)|
|≥55||204||0.15 (0.04, 0.25)||0.14 (0.04, 0.25)||0.03 (−0.06, 0.12)||0.03 (−0.07, 0.12)|
|Railway noise exposure|
|Continuous per 10 dB Lden||0.02 (−0.008, 0.04)||0.01 (−0.01, 0.03)||0.02 (−0.003, 0.04)||0.01 (−0.005, 0.04)|
|Categorical, dB Lden|
|<45||4,788||0 Referent||0 Referent||0 Referent||0 Referent|
|45–49||161||0.02 (−0.09, 0.14)||0.01 (−0.10, 0.13)||0.01 (−0.11, 0.10)||−0.01 (−0.11, 0.09)|
|50–54||125||0.07 (−0.06, 0.20)||0.07 (−0.06, 0.20)||0.10 (−0.02, 0.21)||0.09 (−0.03, 0.20)|
|≥55||110||−0.04 (−0.18, 0.10)||−0.05 (−0.19, 0.09)||−0.01 (−0.13, 0.12)||−0.02 (−0.14, 0.11)|
|Continuous per 10 dB Lden||0.16 (0.14, 0.17)||0.16 (0.14, 0.17)||0.03 (0.01, 0.04)||0.03 (0.01, 0.04)|
|Categorical, dB Lden|
|<45||4,261||0 Referent||0 Referent||0 Referent||0 Referent|
|45–49||126||0.32 (0.19, 0.44)||0.31 (0.18, 0.44)||0.12 (0.00, 0.23)||0.11 (−0.008, 0.22)|
|50–54||590||0.45 (0.39, 0.51)||0.44 (0.38, 0.50)||0.06 (0.008, 0.12)||0.06 (0.006, 0.12)|
|≥55||207||0.49 (0.39, 0.59)||0.48 (0.39, 0.58)||0.09 (−0.003, 0.18)||0.09 (−0.005, 0.18)|
aTime-weighted noise exposure expressed as Lden taking into account all addresses where the subject had lived during the follow-up period.
bResults of linear regression model adjusted only for sex and age.
cResults of linear regression model adjusted for sex, age, dietary habits, alcohol consumption, education level, physical activity, smoking status, psychological distress, job strain, and shift work.
Both categorical and restricted cubic splines analyses suggested nonlinearity in the association between road traffic noise exposure and waist circumference increase (Table 2, Figure 1). It is suggested that there might be a threshold in the exposure–response relation at around 45–50 dB Lden. However, departure from linearity was not statistically significant (p-value of departure = 0.099). No corresponding threshold in the exposure–response curve was suggested for aircraft noise.
There were positive trends in incidence of central obesity in relation to aircraft and road traffic noise exposure, with IRRs of 1.19 (95% CI: 1.14, 1.24) and 1.07 (95% CI: 1.00, 1.14) per 10 dB Lden, respectively (Table 3). Aircraft noise was also associated with an increased risk of overweight, showing an IRR of 1.06 (95% CI: 1.01, 1.12) per 10 dB Lden. In sex-specific analyses, clear associations between road traffic or railway noise exposure and central obesity were only seen in women, whereas trends were apparent in both sexes for aircraft noise (see Table S2). For overweight, a statistically significant trend was only seen in women in relation to aircraft noise exposure. Just as for waist circumference, there seemed to be an increased IRR for central obesity primarily above 45–50 dB Lden in relation to road traffic noise exposure, whereas for aircraft noise the trend appears to extend to even lower levels (Table 3, Figure 2).
|No. of subjects/cases||Model 1cIRR (95% CI)||Model 2dIRR (95% CI)||No. of subjects/cases||Model 1cIRR (95% CI)||Model 2dIRR (95% CI)|
|Road traffic noisee|
|Continuous per 10 dB Lden||4,386/872||1.09 (1.02, 1.16)||1.07 (1.00, 1.14)||2,624/760||1.00 (0.94, 1.07)||0.99 (0.92, 1.05)|
|Categorical, dB Lden|
|<45||2,932/548||1.00 Referent||1.00 Referent||1,784/522||1.00 Referent||1.00 Referent|
|45–49||796/154||1.03 (0.88, 1.21)||1.00 (0.85, 1.17)||461/130||0.97 (0.83, 1.14)||0.96 (0.82, 1.13)|
|50–54||479/124||1.35 (1.14, 1.60)||1.33 (1.12, 1.58)||276/77||1.02 (0.83, 1.24)||0.99 (0.81, 1.21)|
|≥55||179/46||1.33 (1.02, 1.72)||1.26 (0.96, 1.64)||103/31||1.06 (0.78, 1.43)||1.04 (0.77, 1.39)|
|Continuous per 10 dB Lden||4,386/872||1.07 (1.00, 1.13)||1.05 (0.99, 1.12)||2,624/760||1.04 (0.97, 1.11)||1.03 (0.96, 1.09)|
|Categorical, dB Lden|
|<45||4,057/791||1.00 Referent||1.00 Referent||2,423/702||1.00 Referent||1.00 Referent|
|45–49||128/25||0.98 (0.69, 1.40)||0.97 (0.68, 1.38)||77/22||1.08 (0.75, 1.54)||1.04 (0.73, 1.49)|
|50–54||110/33||1.48 (1.10, 1.99)||1.43 (1.07, 1.92)||65/17||0.95 (0.63, 1.44)||0.91 (0.61, 1.36)|
|≥55||91/23||1.31 (0.92, 1.87)||1.27 (0.88, 1.81)||59/19||1.09 (0.74, 1.59)||1.04 (0.71, 1.52)|
|Aircraft traffic noisee|
|Continuous per 10 dB Lden||4,386/872||1.20 (1.15, 1.26)||1.19 (1.14, 1.24)||2,624/760||1.06 (1.01, 1.11)||1.06 (1.01, 1.12)|
|Categorical, dB Lden|
|<45||3,590/647||1.00 Referent||1.00 Referent||2,200/620||1.00 Referent||1.00 Referent|
|45–49||103/22||1.31 (0.90, 1.92)||1.27 (0.87, 1.85)||55/19||1.17 (0.82, 1.67)||1.10 (0.76, 1.58)|
|50–54||508/145||1.69 (1.45, 1.97)||1.62 (1.39, 1.89)||277/90||1.18 (0.99, 1.41)||1.20 (1.00, 1.44)|
|≥55||185/58||2.04 (1.64, 2.56)||1.99 (1.58, 2.50)||92/31||1.16 (0.86, 1.56)||1.15 (0.85, 1.55)|
aGender-specific cutoff values for central obesity were applied for waist circumference: ≥88 cm for women and ≥102 cm for men. Subjects with central obesity at baseline were excluded from analysis.
bCutoff of BMI at ≥25 kg/m2 to define overweight. Subjects with overweight at baseline were excluded from analysis.
cResults of Poisson regression model adjusted only for sex and age.
dResults of Poisson regression model adjusted for sex, age, dietary habits, alcohol consumption, education level, physical activity, smoking status, psychological distress, job strain, and shift work.
eTime-weighted noise exposures expressed as Lden taking into account all addresses where the subject had lived during follow-up period.
We observed an exposure–response relation between the number of transportation noise sources and risk of central obesity (Figure 3). The IRR increased from 1.22 (95% CI: 1.08, 1.39) among those exposed to only one source to 2.26 (95% CI: 1.55, 3.29) among those exposed to all three transportation noise sources (p-value for trend <0.001). No corresponding trend was seen for overweight. For waist circumference a positive trend was observed with a change of 0.14 cm/y (95% CI: 0.09, 0.18) among those exposed to only one source and of 0.48 cm/y (95% CI: 0.29, 0.67) among those exposed to all three transportation noise sources (p-value for trend <0.001) (see Figure S4). On the other hand, no trend was seen in relation to weight gain.
The association between exposure to road traffic noise and waist circumference increase was modified by age, with a higher waist circumference increase among those younger than 60 y at follow-up (see Table S3). No other characteristics showed an interaction with road traffic noise exposure. In particular, there was no apparent effect modification by sex as was seen for central obesity (see Table S2). The association between road traffic noise and weight gain also appeared stronger in the younger age group. The effect modification for aircraft noise exposure generally showed the same pattern as for road traffic noise (data not shown).
In sensitivity analyses, we first explored how the results for central obesity related to exposure to road traffic noise were affected by different restrictions and adjustments (see Figure S5). For exposures of ≥45 dB Lden there was an adjusted IRR per 10 dB Lden of 1.22 (95% CI: 1.06, 1.42). The IRR appeared to be little affected by additional adjustment for railway noise, aircraft noise, baseline waist circumference, or contextual confounding (using household mean income in small areas) or following exclusion of those with exposure to railway or aircraft noise ≥45 dB Lden, address change during follow-up or diabetes heredity. However, a lower IRR was suggested after adjustment for municipality. Second, we explored how results were affected by additional adjustment for local air pollution from road traffic using NOx as indicator. No marked influence by adjustment for NOx was seen on the results for different transportation noise sources using waist circumference and weight as continuous outcomes (see Table S4). However, the results on IRR for central obesity related to road traffic noise were affected by adjustment for NOx (see Table S5). On the other hand, results for overweight or for aircraft or railway noise were not influenced. NOx was moderately related to road traffic noise (r=0.56) but not to aircraft or railway noise (r=−0.02 and 0.14, respectively), which contributes to explaining the consequences of NOx adjustment on the associations with obesity markers for different transportation noise sources.
This cohort study showed an association between exposure to road traffic noise as well as aircraft noise and waist circumference increase. No corresponding association was observed for railway noise. There were positive trends in risk for central obesity related to each of the sources of transportation noise with a particularly high risk in those exposed to all three sources. The excess risk for central obesity primarily occurred at road traffic noise levels of around 50 dB Lden and higher, whereas the excess risk related to aircraft noise exposure seemed to occur even at lower levels.
There is growing evidence that transportation noise affects adiposity markers. The first study to investigate this relationship focused on aircraft noise and showed an association particularly for waist circumference (Eriksson et al. 2014). A Norwegian cross-sectional study found a positive association between road traffic noise and BMI only among some subgroups (Oftedal et al. 2015). However, based on a longitudinal study a Danish group reported associations between residential exposure to road traffic or railway noise and waist circumference as well as weight changes in adults (Christensen et al. 2015a), confirming earlier cross-sectional evidence in the same cohort (Christensen et al. 2015b). Our observed waist circumference increase related to road traffic (0.02–0.06 cm/y per 10 dB Lden) corresponds well to the findings in the Danish cohort (0.02–0.12 cm/y per 10 dB Lden) (Christensen et al. 2015a). A limitation with the Danish study is that the anthropometric data at follow-up were based on self-reports, which are prone to bias. We already published results on transportation noise exposure and obesity markers based on cross-sectional analyses of the same cohort as in present study (Pyko et al. 2015). This new longitudinal analysis used a much more detailed methodology for assessment of noise exposure and showed associations primarily for waist circumference increase in relation to road traffic or aircraft noise exposure as well as for weight gain in relation to aircraft noise exposure. Overall, the evidence is not fully consistent regarding a role for transportation noise in the development of central obesity (increased waist circumference or waist–hip ratio) or general adiposity (weight gain or BMI). Stress mechanisms as mediated by cortisol excretion would be expected to primarily result in central obesity, although noise-induced sleep disturbances and behavioral changes might also mediate effects on general adiposity (Zaharna and Guilleminault 2010). Elucidation of noise effects on specific adiposity markers may provide evidence on causal mechanisms.
In our study, the waist circumference increase per unit of noise exposure was highest for aircraft noise. The effect appeared lower for road traffic noise, and there was no clear association for railway noise. This pattern is well in line with the effect of noise exposure on annoyance and sleep disturbances (Miedema and Oudshoorn 2001; Miedema and Vos 2007) where aircraft noise causes more pronounced effects than road traffic noise at the same noise levels, and railway noise is less harmful than road traffic noise (WHO 2009). Furthermore, our data suggest a threshold in the effects by road traffic noise on waist circumference and central obesity at around 45–50 dB Lden, which was not apparent for aircraft noise. Aircraft and road traffic noise are qualitatively different, for example, aircraft noise is transient, more intense in a short period, and usually causes a higher arousal level for areas that are directly under the flight paths, which may contribute to the stronger effects. Furthermore, the exposure assessment approaches to both sources of noise were different in our study. There is a great need for further longitudinal evidence on the association between transportation noise from different sources and obesity.
The risk of central obesity related to road traffic noise exposure ≥45 dB Lden was mostly unaffected by different adjustments and restrictions. However, the excess risk was no longer statistically significant following adjustment for air pollution from road traffic, using NOx as marker. On the other hand, no major effect was seen following adjustment for NOx in analyses of road traffic noise and waist circumference increase. We observed a correlation of 0.56 between exposure to noise and air pollution from road traffic in individuals, which is similar to the correlation in another epidemiological study from Stockholm County (Selander et al. 2009b). Other studies on road traffic noise and obesity have generally not found major changes in associations following adjustment for air pollution (Christensen et al. 2015a, 2015b, 2016; Oftedal et al. 2015). It cannot be excluded that air pollution exposure also contributed to the development of obesity in our study population, although we did not find evidence of any interaction between the two exposures. Furthermore, the association between road traffic noise exposure and central obesity appeared somewhat weaker following adjustment for municipality. However, we did not see any marked effect by adjustment for a large number of individual characteristics or contextual confounding. To generally adjust for municipality is not meaningful in our study because aircraft noise primarily affected only two municipalities and due to risks of overadjustment for road traffic noise, it would not have made optimal use of the full range of exposure in our study area given the quite low exposures overall. However, we cannot exclude that residual confounding may have affected some of our results.
We saw clear exposure–response associations related to number of noise sources for both risk of central obesity risks and waist circumference increase. This goes in line with previous studies of sleep effects and annoyance in relation to combined exposure to different noise sources (Griefahn et al. 2006; Miedema 2004). Our findings speak in favor of the multiple environmental stressors theory, where several stressors may enhance the effect of each other (Stansfeld and Matheson 2003). Moreover, interactions have been observed between traffic and occupational noise as well as job strain in relation to the risk of myocardial infarction (Selander et al. 2013). Unfortunately, we did not have any data on stress markers for our study subjects, such as saliva cortisol levels. It is important to further investigate interactions between different environmental stressors including noise for both cardiovascular and metabolic outcomes.
We did not observe significant interactions between exposure to road traffic noise and other risk factors in relation to waist circumference increase, except for age. Our results showed that the association between waist circumference increase and road traffic noise were mainly driven by the age group below 60 y. This is congruent with some noise studies on hypertension (Bodin et al. 2009; De Kluizenaar et al. 2007) but opposite to studies focused on stroke and type 2 diabetes, which indicated stronger effects in those over 60 and 64 y of age, respectively (Sørensen et al. 2011, 2013). Moreover, no age interactions were apparent in other noise studies of obesity (Christensen et al. 2015a, 2015b; Oftedal et al. 2015). We did not see an interaction with BMI or waist circumference at baseline. In contrast, Danish longitudinal results showed stronger effects of noise on adiposity development in those obese or with central obesity at baseline (Christensen et al. 2015a). Furthermore, there was no clear effect modification by noise annoyance or sensitivity, which is opposite to Norwegian results with the strongest effect in noise-sensitive women (Oftedal et al. 2015). Although the literature is limited regarding the influence by these factors on the association between noise exposure and obesity, there is evidence of noise annoyance and sensitivity acting as effect modifiers of the relationship between the noise exposure and cardiovascular outcomes (Babisch et al. 2013; Eriksson et al. 2010). All in all, it is not clear if age or other risk factors modify the association between noise and adiposity.
A limitation of our study is the relatively low road traffic noise levels and the small number of highly exposed participants. Furthermore, in certain aspects, the data on noise exposures are imprecise. For example, we lack information on noise exposure other than from the three transportation sources, such as occupational exposure. Additionally, we do not have information on noise modifiers, such as façade and window insulation as well as bedroom location, open/closed windows, use of earplugs, and so forth. Moreover, by design, the study population was enriched with persons with a family history of diabetes and the results may not be generalizable to the population as a whole. However, the associations were confirmed when we restricted the analysis on road traffic and waist circumference to those without a family history of diabetes.
The strengths of the present study include the prospective design and anthropometric data measured by trained nurses at recruitment as well as at follow-up. Additionally, detailed information was available regarding potential individual confounders (socioeconomic position, diet, alcohol consumption, smoking, physical activity, and so forth) as well as area–base confounders. Nevertheless, residual confounding cannot be excluded. Furthermore, we had a detailed residential history for all participants, allowing exposure assessment for the whole follow-up period. A particular feature of our study is that a sizable proportion of the study participants was exposed to several noise sources, allowing evaluation of health effect following exposure to multiple noise sources.
In conclusion, our study showed associations between exposure to noise from road traffic or aircraft and development of central obesity. The risk appeared particularly high for aircraft noise and in those with concomitant exposure to different sources of transportation noise. These findings support the evidence linking noise to development of obesity, which is an outcome of great public health significance.
The authors wish to thank the staff and participants of the Stockholm Diabetes Prevention Program. We thank B. Julin for assistance with the assessment of dietary habits and A. Hilding for valuable comments regarding the manuscript.
The study was supported by grants from the Swedish Research Council for Health, Working Life and Welfare, the Swedish Heart and Lung Foundation, the Stockholm County Council, the Swedish Research Council, the Diabetes Fund of the Swedish Diabetes Association, Novo Nordisk Scandinavia, and GlaxoSmithKline. Both Novo Nordisk Scandinavia and GlaxoSmithKline, Sweden supported the SDPP (Stockholm Diabetes Prevention Program) by unconditional grants, which were used for data base handling and epidemiologic studies. However, none of these grants was used for salary of any author. The authors’freedom to design, conduct, interpret, and publish research was not compromised by any sponsor or funding agency.
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Agencies announce threshold for smaller loan exemption from appraisal requirements for higher-priced mortgage loans
Janet L. Yellen will step down as a Member of the Board of Governors of the Federal Reserve System, effective upon the swearing in of her successor as Chair
none, this is a slide presenation
This collection of 3 AOPs describe varying outcomes of adversity dependent upon species in response to inhibition of thyroperoxidase (TPO) during development. Chemical inhibition of TPO, the molecular-initiating event (MIE), results in decreased thyroid hormone (TH) synthesis, and subsequent reduction in circulating concentrations of THs. THs are essential for normal human brain development, metamorphorfic change from tadpole to frog in amphibians, and deficits in inflation of the anterior swim bladder in young fishes. Chemicals that interfere with TH synthesis have the potential to cause TH insufficiency that may result in adverse neurodevelopmental effects in offspring. The biochemistry of TPO and its essentiality for TH synthesis is well known across species. Although quantitative information at all levels of KERs is limited a number of applications of this AOP have been identified.
Adequate levels of thyroid hormones (TH) are needed for proper brain development, deficiencies may lead to adverse neurological outcomes in humans and animal models. Environmental chemicals have been linked to TH disruption, yet the relationship between developmental exposures and decline in serum TH resulting in neurodevelopmental impairment is poorly understood. The present study developed a quantitative adverse outcome pathway (qAOP) where serum thyroxin (T4) reduction following inhibition of thyroperoxidase in the thyroid gland are described and related to deficits in fetal brain TH and the development of a brain malformation, subcortical band heterotopia. Pregnant dams were exposed to 6-propylthiouracil (PTU 0, 0.1, 0.5, 1, 2, or 3 ppm) from gestational day 6-20, increasing PTU concentrations in maternal thyroid gland and serum as well as in fetal serum. Dams exposed to 0.5 ppm PTU and higher exhibited dose-dependent decreases in thyroidal T4. Serum T4 levels in the dam were significantly decreased with exposure to 2 and 3 ppm PTU. In the fetus, T4 decrements were first observed at a lower dose of 0.5 ppm PTU. Based on these data, fetal brain T4 levels were estimated from published literature sources, and quantitatively linked to increases in the size of the heterotopia present in the brains of offspring. These data show the potential of in vivo assessments and computational descriptions of biological responses to predict the development of this structural brain malformation and use of qAOP approach to evaluate brain deficits that may result from exposure to other TH disruptors.
Epithelial-mesenchymal interactions drive embryonic fusion events during development and upon perturbation can result in birth defects. Cleft palate and neural tube defects can result from genetic defects or environmental exposures during development, yet very little is known about the effect of chemical exposures on fusion defects in humans because of the lack of relevant and robust human in vitro assays of developmental fusion behavior. Given the etiology and prevalence of cleft palate and the relatively simple architecture and composition of the embryonic palate, we sought to develop a three-dimensional culture system that could be used to study fusion behavior in vitro using human cells. We engineered human Wharton’s Jelly stromal cell (HWJSC) spheroids of defined size and established that 7 days of culture in osteogenesis differentiation medium was sufficient to promote an osteogenic phenotype consistent with embryonic palatal mesenchyme. HWJSC spheroids supported the attachment of human epidermal keratinocyte progenitor cells on the outer spheroid surface likely through deposition of collagens I and IV, fibronectin, and laminin, and co-cultured spheroids exhibited fusion behavior that was dependent on epidermal growth factor signaling and fibroblast growth factor signaling in agreement with palate fusion literature. The method described here may broadly apply to the generation of three-dimensional epithelial-mesenchymal co-cultures to study developmental fusion events in a format that is amenable to predictive toxicology applications.
cological Thresholds for Toxicologic Concern (eco-TTC) employs an assessment of distributions of Predicted No-Effect Concentrations (PNECs) for compounds following chemical grouping. Grouping can be by mode of action, structural fragments, or by chemical functional use. Thus, eco-TTCs summarize the wealth of ecotoxicological information as probability distributions of PNECs and the 5th percentile lower value is chosen to represent the eco-TTC per se. Ecotoxicological hazards for untested chemicals, grouped using the same attributes, could be conservatively estimated. PNEC determinations vary fundamentally by regulatory jurisdiction. Application factors assigned to different levels of ecotoxicological data (species breadth, acute or chronic toxicity) result in different extrapolations for a potential “safe concentration” of a chemical. We compared PNECs derived by US and European environmental regulatory PNEC approaches in detail for ~5000 compounds and Japan and Canadian environmental PNEC approaches for a subset of these. Algorithms were written in R for US and Europe PNEC processes, then implemented into an eco-TTC web application as envisioned in Belanger et al. (2015). Cumulative PNEC probability distributions for European, Canadian, and Japan approaches are somewhat more conservative than the US approach driven principally by smaller assessment factors applied to data sets at earlier stages of hazard assessment. For example, the AF for the US when a full toxicity data set is available for all 3 trophic levels and a chronic test is available on the most sensitive acute species is 10; however, in other jurisdictions this may be as high as 100. On average, European PNECs were 11 times more conservative than US PNECs. PNEC distributions across geographies are driven by the large number of compounds that lack full chronic toxicity data. All assessments derive similar PNECs when full chronic toxicity data sets are available but this is only ~5% of cases encountered. The PNEC derivation logic, embedded in the eco-TTC web application, will be a useful tool to allow assessors to quickly and consistently compare hazard extrapolations across geographies minimizing animal testing requirements and maximizing use of existing information.
The Threshold for Toxicological Concern (TTC) is well-established for assessing human safety of indirect food-contact substances and has been applied to a variety of endpoints. Recently, we have proposed an extension to the human safety TTC concept for environmental applications, termed the ecological TTC (eco-TTC). The strengths and limitations of an eco-TTC approach are still being investigated. Algal tests are an important component of chemical environmental risk assessments and are the most sensitive taxon approximately 50% of the time. The complete eco-TTC database contains approximately 120,000 toxicological records (tests) employing some 2500 different species. Further, the eco-TTC database contains over 14,000 curated records from 300 unique algal species dominated by standard test species such as the green algal genera Pseudokirchneriella, Scenedesmus, and Desmodesmus, the marine Skeletonema and Phaeodactylum and the blue-green Microcystis and Anabaena. Here, we explore how hazard values for final PNEC derivation may change with the inclusion of standard and non-standard algal test species supported by analyses of eco-TTC and USEPA Web-ICE (Web-based Inter-species Correlation Estimation) applications. An eco-TTC derived hazard value will also be compared against an algal SSD for data rich chemicals such as cadmium chloride, triclosan, and a cationic surfactant. This work was performed with input from the HESI Animal Alternatives in ERA Technical Committee. * Disclaimer: The views, conclusions and recommendations expressed in this article are those of the author and do not necessarily represent views or policies of the US Environmental Protection Agency
The present study investigated whether inhibition of deiodinase, the enzyme which converts thyroxine (T4) to the more biologically-active form, 3,5,3′-triiodothyronine (T3), would impact inflation of the posterior and/or anterior chamber of the swim bladder, processes previously demonstrated to be thyroid-hormone regulated. Two experiments were conducted using a model deiodinase inhibitor, iopanoic acid (IOP). In the first study, fathead minnow (Pimephales promelas) embryos were exposed to 0.6, 1.9, or 6.0 mg IOP/L or control water in a flow-through system until reaching 6 days post-fertilization (dpf) at which time posterior swim bladder inflation was assessed. To examine effects on anterior swim bladder inflation, a second study was conducted with 6 dpf larvae exposed to the same IOP concentrations until reaching 21 dpf. Fish from both studies were sampled for T4/T3 measurements, gene transcription analyses, and thyroid histopathology. In the embryo study, incidence and length of inflated posterior swim bladders were significantly reduced in the 6.0 mg/L treatment at 6 dpf. Incidence of inflation and length of anterior swim bladder in larval fish were significantly reduced in all IOP treatments at 14 dpf, but inflation recovered by 18 dpf. Throughout the larval study, whole body T4 concentrations were significantly increased and T3 concentrations were significantly decreased in all IOP treatments. Consistent with hypothesized compensatory responses, significant up-regulation of deiodinase-2 mRNA was observed in the larval study, and down-regulation of thyroperoxidase mRNA was observed in all IOP treatments in both studies. Taken together, these results support the hypothesized adverse outcome pathways linking inhibition of deiodinase activity to impaired swim bladder inflation.
NASA astronaut Scott Tingle will be available at 6 a.m. EST Friday, Dec. 1 for live satellite interviews one last time prior to his upcoming launch to the International Space Station Dec. 17, on what will be his first mission in space.
In vitro high-throughput screening (HTS) and in silico technologies have emerged as 21st century tools for chemical hazard identification. In 2007 the U.S. Environmental Protection Agency (EPA) launched the ToxCast Program, which has screened thousands of chemicals in hundreds of (primarily) mammalian-based HTS assays for biological activity suggestive of potential toxic effects. Data generated through this effort are being used to prioritize toxicity testing on chemicals most likely to lead to adverse health effects. To realize the full potential of the ToxCast data for predicting adverse effects to both humans and wildlife, it is necessary to understand how broadly these data may plausibly be extrapolated across species. Therefore, the U.S. EPA Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool was used to assess conservation of the 460 protein targets represented in the ToxCast assay suite. The SeqAPASS query sequence was selected based on the model organism used in the ToxCast assay (e.g., human, cattle, chimpanzee, guinea pig, rabbit, rat, mouse, pig, or sheep). Similarity of primary amino acid sequences and sequences from appropriate functional domains were compared across species to understand conservation of each assay target across taxa and probe questions of species extrapolation. To demonstrate the applicability of the SeqAPASS data for extrapolation of ToxCast targets, we developed case studies focused on the extrapolation of targets being evaluated as a component of the Endocrine Disruptor Screening Program, including the androgen receptor, enzymes involved in steroidogenesis, and proteins in thyroid axis function. These case studies demonstrate the utility of SeqAPASS for informing the extrapolation of HTS data and identification of model organisms likely to be suitable for follow-up or complementary in vivo toxicity tests.
Agencies amend CRA regulations to conform to HMDA regulation changes and remove references to the Neighborhood Stabilization Program
Original release date: November 20, 2017
The CERT Coordination Center (CERT/CC) has released information on a vulnerability in Windows Address Space Layout Randomization (ASLR) that affects Windows 8, Windows 8.1, and Windows 10. A remote attacker could exploit this vulnerability to take control of an affected system.
US-CERT encourages users and administrators to review CERT/CC VU #817544 and apply the necessary workaround until a patch is released.
Conservation of a molecular target across species can be used as a line-of-evidence to predict the likelihood of chemical susceptibility. The web-based Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS; https://seqapass.epa.gov/seqapass/) application was developed to simplify, streamline, and quantitatively assess protein sequence/structural similarity across taxonomic groups as a means to predict relative intrinsic susceptibility. The intent of the tool is to allow for evaluation of any potential protein target while remaining amenable to variable degrees of protein characterization, in the context of available information about the chemical/protein interaction and the molecular target itself. To accommodate this flexibility in the analysis, three levels of evaluation were developed. The first level of the SeqAPASS analysis compares primary amino acid sequences to a query sequence, calculating a metric for sequence similarity (including detection of orthologs); the second level evaluates sequence similarity within selected functional domains (e.g., ligand-binding domain); and the third level of analysis compares individual amino acid residue positions of importance for protein conformation and/or interaction with the chemical upon binding. Each level of the SeqAPASS analysis provides additional evidence to apply toward rapid, screening-level assessments of probable cross species susceptibility. Such analysis can provide valuable insights as to the potential chemical susceptibility of such species lacking empirical toxicity test data such as the case with threatened and endangered species. To better understand the potential for chemicals to act on threatened and endangered species, a case study was developed demonstrating how SeqAPASS can be used to evaluate the potential susceptibility of endangered butterfly to molt-accelerating compounds.
Exposure to manufactured chemicals is a fact of contemporary life for both humans and wildlife. In many cases, these exposures occur at safe environmental concentrations. However, spectacular exceptions have occurred (e.g., DDT and eggshell thinning, monocrotophos and Swainson’s Hawk mortality). USEPA is responsible for ensuring that regulated chemicals used in accordance with their labeling will not pose risks to humans or wildlife. This is done through a process called Ecological Risk Assessment. I will describe how USEPA conducts Ecological Risk Assessment for birds potentially exposed to pesticides, focusing on spatio-temporal considerations in the Risk Assessment process. I will also describe recent research on new ecological models for population-level risk assessment at USEPA.
Product Description: To understand how some chemicals affect the endocrine system, controlled lab experiments often monitor how chemicals impact natural steroid hormones in fish. Current methods can target only one or two hormones in a single sample, limiting the information that can be obtained. A new method was developed to identify fourteen steroid hormones in a single sample. Using this method in toxicity testing will aid our understanding of how chemicals induce toxic effects along the endocrine axis of vertebrates.
Abstract: Exposure to endocrine active chemicals can lead to perturbations of the hypothalamic-pituitary-gonadal (HPG) axis, ultimately leading to adverse reproductive or developmental effects. To evaluate potential effects, studies with possible HPG-active chemicals often rely on radioimmunoassay (RIA) for determination of biologically-active steroids and their precursors in biological matrices. While RIAs provide high sensitivity, there is potential for cross reactivity of antibodies, and assays are limited to a single steroid. To address these limitations, in the present study analytical methods were developed for the simultaneous analysis of fourteen steroid hormones including androgens, estrogens, progestogens, and glucocorticoids by liquid chromatography tandem mass spectrometry (LC-MS/MS). Atmospheric pressure photoionization (APPI) with a toluene dopant was utilized to allow ionization of all compounds in positive ionization mode without the need for derivatization. This method was applied to the analysis of fathead minnow (Pimephales promelas) plasma and exposure tank water from zebrafish (Danio rerio) experiments. Application to tank water analysis during flow-through chemical exposures provides a possible non-invasive endpoint for time-course experiments. The method demonstrated high sensitivity in both matrices with detection limits of most steroids at low µg/L in plasma and sub ng/L in water. Application of this method to aquatic vertebrate toxicity testing will lead to better understanding of specific mechanisms of HPG axis disruption and inform development of adverse outcome pathways (AOPs). The contents of this presentation neither constitute nor necessarily reflect US EPA policy.
Many, if not most, ecological models developed for chemical risk assessment have not been (and will never be?) adopted for routine use by public agencies. There may be many valid reasons for this: 1) models are often published as “case studies” giving little insight into how they could be routinely applied under varying conditions, 2) model complexity is not commensurate with either the objectives of a specific regulatory risk assessment or available data, 3) lack of guidance about uncertainties associated with decisions made using the proposed model, 4) model output is not provided in a format that is useful for risk assessment, 5) time and expertise required for adoption and use of the model by a regulatory agency is greater than the perceived value added through model use. Nevertheless, calls for the adoption and use of population models in ecological risk assessment have been ongoing for many decades. We will explore some of the reasons why population models have been rarely used in ecological risk assessment at USEPA. Using the MCnest model as an example, we will also explore the factors that led to its formal adoption as a tool for avian population-level ecological risk assessment by the Office of Pesticide Programs at USEPA, a process that took at least 12 years from inception to adoption. Our goal is to initiate an ongoing discussion about the types of collaboration that lead to successful adoption of models as tools for regulatory risk assessment.
The ability of an organism to metabolize a pollutant is critical to understanding the risk the chemical poses to the organism. In the environment, fish are uniquely exposed to pollutants found in agricultural runoff and discharges from industry and wastewater treatment plants. Most research on chemical toxicity in fish rely on a few model fish species, like the zebrafish. For other fish, scientists make predictions of pollutant effects based on the model species. However, fish are extremely diverse. There is already good evidence that fish possess very different metabolizing enzymes which act on specific types of pollutants, making predictions of chemical toxicity challenging. This study examines the variability in metabolizing enzymes found among diverse fish species, including: model species, endangered species (e.g., American eel), species which have changed very little over time (e.g., sturgeon), and the most highly-evolved species (e.g., pufferfish). There is increasing evidence that diverse xenobiotic metabolizing enzymes exist among fishes, potentially resulting in different chemical sensitivities and accumulation, but this has never been systematically evaluated. One concern is that model test species such as rainbow trout, zebrafish and fathead minnows may not adequately represent the xenobiotic metabolizing capacity of other fish species. Our current study mined available fish liver transcriptome data and performed full-transcript, isoform sequencing on liver samples from two dozen phylogenetically diverse fish species. This novel RNAseq approach eliminated the need for transcriptome reconstruction resulting in reference genomes of the highest precision, allowing for detection of enzyme isoform orthologs among the species, as well as the nuclear receptors that control expression of the enzymes. Species were selected for broad phylogenetic coverage, as well as economic, research, and conservation importance, and included: sea lamprey (Petromyzon marinus), lake sturgeon (Acipenser fluvenscens), American eel (Anguilla rostrate), alligator gar (Atractosteus spatula), paddlefish (Polyodon spathula), rainbow trout (Oncorhynchus mykiss), rainbow smelt (Osmerus mordax), fathead minnow (Pimephales promelas), Antarctic icefish (Trematomus loennbergii), common carp (Cyprinus carpio), and channel catfish (Ictalurus punctatus). In addition to comparing information across fish species, the resolved isoforms were compared to human xenobiotic metabolizing enzymes. This comparison aids in evaluating the utility of human-based biotransformation tools such as ToxCast chemical screening assays or metabolism prediction software for potential relevance in fish. The content of this presentation neither constitute nor necessarily reflect US EPA policy..