Higher buccal mtDNA content is associated with residential surrounding green in a panel study of primary school children

Background Mitochondria are known to respond to environmental stressors but whether green space is associated with mitochondrial abundance is unexplored. Furthermore, as exposures may affect health from early life onwards, we here evaluate if residential green space is associated with mitochondria DNA content (mtDNAc) in&nbsp;children. Methods In primary schoolchildren (COGNAC study), between 2012 and 2014, buccal mtDNAc was repeatedly (three times) assessed using qPCR. Surrounding low (&lt;3m), high (≥3m) and total (sum of low and high) green space within different radii (100m–1000m) from the residence and distance to the nearest large green space (&gt;0.5ha) were estimated using a&nbsp;remote sensing&nbsp;derived map. Given the repeated measures design, we applied a mixed-effects model with school and subject as random effect while adjusting for&nbsp;a priori&nbsp;chosen fixed&nbsp;covariates. Results: mtDNAc was assessed in 246 children with a total of 436 measurements (mean age 10.3 years). Within a 1000m radius around the residential address, an IQR increment in low (11.0%), high (9.5%), and total (13.9%) green space was associated with a respectively 15.2% (95% CI: 7.2%–23.7%), 10.8% (95% CI: 4.5%–17.5%), and 13.4% (95% CI: 7.4%–19.7%) higher mtDNAc. Conversely, an IQR increment (11.6%) in agricultural area in the same radius was associated with a −3.4% (95% CI: 6.7% to −0.1%) lower mtDNAc. Finally, a doubling in distance to large green space was associated with a −5.2% (95% CI: 7.9 to −2.4%) lower&nbsp;mtDNAc. Conclusion To our knowledge, this is the first study evaluating associations between residential surrounding green space and mtDNAc in children. Our results showed that green space was associated with a higher mtDNAc in children, which indicates the importance of the early life environment. To what extent these findings contribute to later life health effects should be further&nbsp;examined.</p

A healthy lifestyle is positively associated with mental health and well-being and core markers in ageing

BACKGROUND: Studies often evaluate mental health and well-being in association with individual health behaviours although evaluating multiple health behaviours that co-occur in real life may reveal important insights into the overall association. Also, the underlying pathways of how lifestyle might affect our health are still under debate. Here, we studied the mediation of different health behaviours or lifestyle factors on mental health and its effect on core markers of ageing: telomere length (TL) and mitochondrial DNA content&nbsp;(mtDNAc). METHODS: In this study, 6054 adults from the 2018 Belgian Health Interview Survey (BHIS) were included. Mental health and well-being outcomes included psychological and severe psychological distress, vitality, life satisfaction, self-perceived health, depressive and generalised anxiety disorder and suicidal ideation. A lifestyle score integrating diet, physical activity, smoking status, alcohol consumption and BMI was created and validated. On a subset of 739 participants, leucocyte TL and mtDNAc were assessed using qPCR. Generalised linear mixed models were used while adjusting for a priori chosen&nbsp;covariates. RESULTS: The average age (SD) of the study population was 49.9 (17.5) years, and 48.8% were men. A one-point increment in the lifestyle score was associated with lower odds (ranging from 0.56 to 0.74) for all studied mental health outcomes and with a 1.74% (95% CI: 0.11, 3.40%) longer TL and 4.07% (95% CI: 2.01, 6.17%) higher mtDNAc. Psychological distress and suicidal ideation were associated with a lower mtDNAc of - 4.62% (95% CI: - 8.85, - 0.20%) and - 7.83% (95% CI: - 14.77, - 0.34%), respectively. No associations were found between mental health and TL. CONCLUSIONS: In this large-scale study, we showed the positive association between a healthy lifestyle and both biological ageing and different dimensions of mental health and well-being. We also indicated that living a healthy lifestyle contributes to more favourable biological&nbsp;ageing.</p

Environmental exposures and health behavior in association with mental health: a study design.

Background: Air pollution, green space and smoking are known to affect human health. However, less is known about their underlying biological mechanisms. One of these mechanisms could be biological aging. In this study, we explore the mediation of biomarkers of exposure and biological aging to explain the associations between environmental exposures, health behavior and mental&nbsp;health. Methods: The study population of this cross-sectional study (&nbsp;= 1168) is a subsample of the Belgian 2018 Health Interview Survey (BHIS). Mental health indicators including psychological and severe psychological distress, life satisfaction, vitality, eating disorders, suicidal ideation, subjective health and depressive and anxiety disorders, demographics and health behavior such as smoking are derived from the BHIS. Urine and blood samples are collected to measure respectively the biomarkers of exposure (urinary black carbon (BC) and (hydroxy)cotinine) and the biomarkers of biological aging (mitochondrial DNA content (mtDNAc) and telomere length (TL)). Recent and chronic exposure (μg/m) to nitrogen dioxide (NO), particulate matter ≤2.5 μm (PM) and ≤ 10 μm (PM) and BC at the participants’ residence are modelled using a high resolution spatial temporal interpolation model. Residential green space is defined in buffers of different size (50 m&#8239;—&thinsp;5000 m) using land cover data in ArcGIS 10 software. For the statistical analysis multivariate linear and logistic regressions as well as mediation analyses are used taking into account a priori selected covariates and&nbsp;confounders. Results: As this study combined data of BHIS and laboratory analyses, not all data is available for all participants. Therefore, data analyses will be conducted on different subsets. Data on air pollution and green space exposure is available for all BHIS participants. Questions on smoking and mental health were answered by respectively 7829 and 7213 BHIS participants. For biomarker assessment, (hydroxy) cotinine, urinary BC and the biomarkers of biological aging are measured for respectively 1130, 1120 and 985&nbsp;participants. Conclusion: By use of personal markers of air pollution and smoking, as well as biological aging, we will gain knowledge about the association between environmental exposures, health behavior, and the mental health status. The results of the study can provide insights on the health of the Belgian population, making it a nationwide interesting&nbsp;study.</p

Air pollution in association with mental and self-rated health and the mediating effect of physical activity

Background Recent studies showed that air pollution might play a role in the etiology of mental disorders. In this study we evaluated the association between air pollution and mental and self-rated health and the possible mediating effect of physical activity in this&nbsp;association. Methods In 2008, 2013 and 2018 the Belgian Health Interview Survey (BHIS) enrolled 16,455 participants who completed following mental health dimensions: psychological distress, suboptimal vitality, suicidal ideation, and depressive and generalized anxiety disorder and self-rated health. Annual exposure to nitrogen dioxide (NO2), particulate matter ≤ 2.5&nbsp;µm (PM2.5) and black carbon (BC) were estimated at the participants’ residence by a high resolution spatiotemporal model. Multivariate logistic regressions were carried out taking into account a priori selected&nbsp;covariates. Results Long-term exposure to PM2.5, BC and NO2&nbsp;averaged 14.5, 1.4, and 21.8&nbsp;µg/m3, respectively. An interquartile range (IQR) increment in PM2.5&nbsp;exposure was associated with higher odds of suboptimal vitality (OR = 1.27; 95% CI: 1.13, 1.42), poor self-rated health (OR = 1.20; 95% CI: 1.09, 1.32) and depressive disorder (OR = 1.19; 95% CI: 1.00, 1.41). Secondly, an association was found between BC exposure and higher odds of poor self-rated health and depressive and generalized anxiety disorder and between NO2&nbsp;exposure and higher odds of psychological distress, suboptimal vitality and poor self-rated health. No association was found between long-term ambient air pollution and suicidal ideation or severe psychological distress. The mediation analysis suggested that between 15.2% (PM2.5-generalized anxiety disorder) and 40.1% (NO2-poor self-rated health) of the association may be mediated by a difference in physical&nbsp;activity. Conclusions Long-term exposure to PM2.5, BC or NO2&nbsp;was adversely associated with multiple mental health dimensions and self-rated health and part of the association was mediated by physical activity. Our results suggest that policies aiming to reduce air pollution levels could also reduce the burden of mental health disorders in&nbsp;Belgium.</p

Validity of self-reported air pollution annoyance to assess long-term exposure to air pollutants in Belgium

In epidemiological studies, assessment of long term exposure to air pollution is often estimated using air pollution measurements at fixed monitoring stations, and interpolated to the residence of survey participants through Geographical Information Systems (GIS). However, obtaining georeferenced address data from national registries requires a long and cumbersome administrative procedure, since this kind of personal data is protected by privacy regulations. This paper aims to assess whether information collected in health interview surveys, including air pollution annoyance, could be used to build prediction models for assessing individual long term exposure to air pollution, removing the need for data on personal residence&nbsp;address. Analyses were carried out based on data from the Belgian Health Interview Survey (BHIS) 2013 linked to GIS-modelled air pollution exposure at the residence place of participants older than 15 years (n&nbsp;=&nbsp;9347). First, univariate linear regressions were performed to assess the relationship between air pollution annoyance and modelled exposure to each air pollutant. Secondly, a multivariable linear regression was performed for each air pollutant based on a set of variables selected with elastic net cross-validation, including variables related to environmental annoyance, socio-economic and health status of participants. Finally, the performance of the models to classify individuals in three levels of exposure was assessed by means of a confusion&nbsp;matrix. Our results suggest a limited validity of self-reported air pollution annoyance as a direct proxy for air pollution exposure and a weak contribution of environmental annoyance variables in prediction models. Models using variables related to the socio-economic status, region, urban level and environmental annoyance allow to predict individual air pollution exposure with a percentage of error ranging from 8% to 18%. Although these models do not provide very accurate predictions in terms of absolute exposure to air pollution, they do allow to classify individuals in groups of relative exposure levels, ranking participants from low over medium to high air pollution exposure. This model represents a rapid assessment tool to identify groups within the BHIS participants undergoing the highest levels of environmental&nbsp;stress.</p