BackgroundAir pollution is hypothesized to be a risk factor for diabetes. Epidemiological evidence is inconsistent and has not been systematically evaluated.ObjectivesWe systematically reviewed epidemiological evidence on the association between air pollution and diabetes, and synthesized results of studies on type 2 diabetes mellitus (T2DM).MethodsWe systematically searched electronic literature databases (last search, 29 April 2014) for studies reporting the association between air pollution (particle concentration or traffic exposure) and diabetes (type 1, type 2, or gestational). We systematically evaluated risk of bias and role of potential confounders in all studies. We synthesized reported associations with T2DM in meta-analyses using random-effects models and conducted various sensitivity analyses.ResultsWe included 13 studies (8 on T2DM, 2 on type 1, 3 on gestational diabetes), all conducted in Europe or North America. Five studies were longitudinal, 5 cross-sectional, 2 case–control, and 1 ecolo...

The chronic impact of ambient air pollutants on lung function in adults is not fully understood. The objective of this study was to investigate the association of long-term exposure to ambient air pollution with lung function in adult participants from five cohorts in the European Study of Cohorts for Air Pollution Effects (ESCAPE).Residential exposure to nitrogen oxides (NO₂, NO_x) and particulate matter (PM) was modelled and traffic indicators were assessed in a standardised manner. The spirometric parameters forced expiratory volume in 1 s (FEV₁) and forced vital capacity (FVC) from 7613 subjects were considered as outcomes. Cohort-specific results were combined using meta-analysis.We did not observe an association of air pollution with longitudinal change in lung function, but we observed that a 10 μg·m⁻³increase in NO₂exposure was associated with lower levels of FEV₁(−14.0 mL, 95% CI −25.8 to −2.1) and FVC (−14.9 mL, 95% CI −28.7 to −1.1). An increase of 10 μg·m⁻³in PM₁₀, but not other PM metrics (PM_2.5, coarse fraction of PM, PM absorbance), was associated with a lower level of FEV₁(−44.6 mL, 95% CI −85.4 to −3.8) and FVC (−59.0 mL, 95% CI −112.3 to −5.6). The associations were particularly strong in obese persons.This study adds to the evidence for an adverse association of ambient air pollution with lung function in adults at very low levels in Europe.

Air pollution is an important risk factor for global burden of disease. There has been recent interest in its possible role in the etiology of diabetes mellitus. Experimental evidence is suggestive, but epidemiological evidence is limited and mixed. We therefore explored the association between air pollution and prevalent diabetes, in a population-based Swiss cohort. We did cross-sectional analyses of 6392 participants of the Swiss Cohort Study on Air Pollution and Lung and Heart Diseases in Adults [SAPALDIA], aged between 29 and 73 years. We used estimates of average individual home outdoor PM10 [particulate matter <10μm in diameter] and NO2 [nitrogen dioxide] exposure over the 10 years preceding the survey. Their association with diabetes was modeled using mixed logistic regression models, including participants' study area as random effect, with incremental adjustment for confounders. There were 315 cases of diabetes (prevalence: 5.5% [95% confidence interval (CI): 2.8, 7.2%]). Both PM10 and NO2 were associated with prevalent diabetes with respective odds ratios of 1.40 [95% CI: 1.17, 1.67] and 1.19 [95% CI: 1.03, 1.38] per 10μg/m(3) increase in the average home outdoor level. Associations with PM10 were generally stronger than with NO2, even in the two-pollutant model. There was some indication that beta blockers mitigated the effect of PM10. The associations remained stable across different sensitivity analyses. Our study adds to the evidence that long term air pollution exposure is associated with diabetes mellitus. PM10 appears to be a useful marker of aspects of air pollution relevant for diabetes. This association can be observed at concentrations below air quality guidelines.

Air pollutants (AP) play a role in subclinical inflammation, and are associated with cardiovascular morbidity and mortality. Metabolic syndrome (MetS) is inflammatory and precedes cardiovascular morbidity and type 2 diabetes. Thus, a positive association between AP and MetS may be hypothesized. We explored this association, (taking into account, pathway-specific MetS definitions), and its potential modifiers in Swiss adults. We studied 3769 participants of the Swiss Cohort Study on Air Pollution and Lung and Heart Diseases in Adults, reporting at least four-hour fasting time before venepuncture. AP exposures were 10-year mean residential PM₁₀ (particulate matter <10μm in diameter) and NO₂ (nitrogen dioxide). Outcomes included MetS defined by World Health Organization (MetS-W), International Diabetes Federation (MetS-I) and Adult Treatment Panel-III (MetS-A) using four- and eight-hour fasting time limits. We also explored associations with individual components of MetS. We applied mixed logistic regression models to explore these associations. The prevalence of MetS-W, MetS-I and MetS-A were 10%, 22% and 18% respectively. Odds of MetS-W, MetS-I and MetS-A increased by 72% (51-102%), 31% (11-54%) and 18% (4-34%) per 10μg/m³ increase in 10-year mean PM₁₀. We observed weaker associations with NO₂. Associations were stronger among physically-active, ever-smokers and non-diabetic participants especially with PM₁₀ (p<0.05). Associations remained robust across various sensitivity analyses including ten imputations of missing observations and exclusion of diabetes cases. The observed associations between AP exposure and MetS were sensitive to MetS definitions. Regarding the MetS components, we observed strongest associations with impaired fasting glycemia, and positive but weaker associations with hypertension and waist-circumference-based obesity. Cardio-metabolic effects of AP may be majorly driven by impairment of glucose homeostasis, and to a less-strong extent, visceral adiposity. Well-designed prospective studies are needed to confirm these findings.

Background: Comprehensive breast cancer risk prediction models enable identifying and targeting women at high-risk, while reducing interventions in those at low-risk. Breast cancer risk prediction models used in clinical practice have low discriminatory accuracy (0.53–0.64). Machine learning (ML) offers an alternative approach to standard prediction modeling that may address current limitations and improve accuracy of those tools. The purpose of this study was to compare the discriminatory accuracy of ML-based estimates against a pair of established methods—the Breast Cancer Risk Assessment Tool (BCRAT) and Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) models. Methods: We quantified and compared the performance of eight different ML methods to the performance of BCRAT and BOADICEA using eight simulated datasets and two retrospective samples: a random population-based sample of U.S. breast cancer patients and their cancer-free female relatives (N = 1143), and a clinical sample of Swiss breast cancer patients and cancer-free women seeking genetic evaluation and/or testing (N = 2481). Results: Predictive accuracy (AU-ROC curve) reached 88.28% using ML-Adaptive Boosting and 88.89% using ML-random forest versus 62.40% with BCRAT for the U.S. population-based sample. Predictive accuracy reached 90.17% using ML-adaptive boosting and 89.32% using ML-Markov chain Monte Carlo generalized linear mixed model versus 59.31% with BOADICEA for the Swiss clinic-based sample. Conclusions: There was a striking improvement in the accuracy of classification of women with and without breast cancer achieved with ML algorithms compared to the state-of-the-art model-based approaches. High-accuracy prediction techniques are important in personalized medicine because they facilitate stratification of prevention strategies and individualized clinical management.

The contribution of different transportation noise sources to metabolic disorders such as obesity remains understudied. We evaluated the associations of long-term exposure to road, railway and aircraft noise with measures of obesity and its subphenotypes using cross-sectional and longitudinal designs. We assessed 3796 participants from the population-based Swiss Cohort Study on Air Pollution and Lung and Heart Diseases (SAPALDIA), who attended the visits in 2001 (SAP2) and 2010/2011 (SAP3) and who were aged 29-72 at SAP2. At SAP2 we measured body mass index (BMI, kg/m2). At SAP3 we measured BMI, waist circumference (centimetres) and Kyle body Fat Index (%) and derived overweight, central and general obesity. Longitudinally for BMI, we derived change in BMI, incidence of overweight and obesity and a 3-category outcome combining the latter two. We assigned source-specific 5-year mean noise levels before visits and during follow-up at the most exposed dwelling façade (Lden, dB), using Swiss noise models for 2001 and 2011 and participants' residential history. Models were adjusted for relevant confounders, including traffic-related air pollution. Exposure to road traffic noise was significantly associated with all adiposity subphenotypes, cross-sectionally (at SAP3) [e.g. beta (95% CI) per 10 dB, BMI: 0.39 (0.18; 0.59); waist circumference: 0.93 (0.37; 1.50)], and with increased risk of obesity, longitudinally (e.g. RR = 1.25, 95% CI: 1.04; 1.51, per 10 dB in 5-year mean). Railway noise was significantly related to increased risk of overweight. In cross-sectional analyses, we further identified a stronger association between road traffic noise and BMI among participants with cardiovascular disease and an association between railway noise and BMI among participants reporting bad sleep. Associations were independent of the other noise sources, air pollution and robust to all adjustment sets. No associations were observed for aircraft noise. Long-term exposure to transportation noise, particularly road traffic noise, may increase the risk of obesity and could constitute a pathway towards cardiometabolic and other diseases.