Childhood type 1 diabetes: an environment-wide association study across England

Identifying modifiable factors and their joint effect on dementia risk in the UK Biobank

Previous hypothesis-driven research has identified many risk factors linked to dementia. However, the multiplicity and co-occurrence of risk factors have been underestimated. Here we analysed data of 344,324 participants from the UK Biobank with 15 yr of follow-up data for 210 modifiable risk factors. We first conducted an exposure-wide association study and then combined factors associated with dementia to generate composite scores for different domains. We then evaluated their joint associations with dementia in a multivariate Cox model. We estimated the potential impact of eliminating the unfavourable profiles of risk domains on dementia using population attributable fraction. The associations varied by domain, with lifestyle (16.6%), medical history (14.0%) and socioeconomic status (13.5%) contributing to the majority of dementia cases. Overall, we estimated that up to 47.0%-72.6% of dementia cases could be prevented.

Critical Overview on Endocrine Disruptors in Diabetes Mellitus

Diabetes mellitus is a major public health problem in all countries due to its high human and economic burden. Major metabolic alterations are associated with the chronic hyperglycemia that characterizes diabetes and causes devastating complications, including retinopathy, kidney failure, coronary disease and increased cardiovascular mortality. The most common form is type 2 diabetes (T2D) accounting for 90 to 95% of the cases. These chronic metabolic disorders are heterogeneous to which genetic factors contribute, but so do prenatal and postnatal life environmental factors including a sedentary lifestyle, overweight, and obesity. However, these classical risk factors alone cannot explain the rapid evolution of the prevalence of T2D and the high prevalence of type 1 diabetes in particular areas. Among environmental factors, we are in fact exposed to a growing amount of chemical molecules produced by our industries or by our way of life. In this narrative review, we aim to give a critical overview of the role of these pollutants that can interfere with our endocrine system, the so-called endocrine-disrupting chemicals (EDCs), in the pathophysiology of diabetes and metabolic disorders.

The Association between Exposure to Air Pollution and Type 1 Diabetes Mellitus: A Systematic Review and Meta-Analysis

Background

This systematic review and meta-analysis aimed to overview the observational studies on the association of exposure to air pollution and type 1 diabetes mellitus (T1DM).

Materials and Methods

Based on PRISMA guidelines, we systematically reviewed the databases of PubMed, Scopus, Embase, and Web of Science databases to determine the association of air pollution exposure and T1DM. Quality assessment of the papers was evaluated using the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist for observational studies. The odds ratios (OR) and their 95% confidence intervals (CI) were calculated to assess the strength of the associations between air pollutants (gases and particulate matter air pollutants including PM10, PM2.5, NO2, volatile organic compound, SO4, SO2, O3) and T1DM.

Results

Out of 385 initially identified papers, 6 studies were used for this meta-analysis. Fixed effects meta-analysis showed a significant association between per 10 μg/m³ increase in O3 and PM2.5 exposures with the increased risk of T1DM (3 studies, OR = 1.51, 95% CI: 1.26, 1.80, I ² = 83.5% for O3 and two studies, OR = 1.03, 95% CI: 1.01, 1.05, I ² = 76.3% for PM2.5). There was no evidence of association between increased risk of T1DM and exposure to PM10 (OR = 1.02, 95% CI: 0.99-1.06, I ² = 59.4%), SO4 (OR = 1.16, 95% CI: 0.91-1.49, I ² = 93.8%), SO2 (OR = 0.94, 95% CI: 0.83-1.06, I ² = 85.0%), and NO2 (OR = 0.995,95% CI: 1.05-1.04, I ² = 24.7%).

Conclusion

Recent publications indicated that exposure to ozone and PM2.5 may be a risk factor for T1DM. However, due to limited available studies, more prospective cohort studies are needed to clarify the role of air pollutants in T1DM occurrence.

Air Pollution, Oxidative Stress, and the Risk of Development of Type 1 Diabetes

Despite multiple studies focusing on environmental factors conducive to the development of type 1 diabetes mellitus (T1DM), knowledge about the involvement of long-term exposure to air pollution seems insufficient. The main focus of epidemiological studies is placed on the relationship between exposure to various concentrations of particulate matter (PM): PM1, PM2.5, PM10, and sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (O3), versus the risk of T1DM development. Although the specific molecular mechanism(s) behind the link between increased air pollution exposure and a higher risk of diabetes and metabolic dysfunction is yet unknown, available data indicate air pollution-induced inflammation and oxidative stress as a significant pathway. The purpose of this paper is to assess recent research examining the association between inhalation exposure to PM and associated metals and the increasing rates of T1DM worldwide. The development of modern and more adequate methods for air quality monitoring is also introduced. A particular emphasis on microsensors, mobile and autonomous measuring platforms, satellites, and innovative approaches of IoT, 5G connections, and Block chain technologies are also presented. Reputable databases, including PubMed, Scopus, and Web of Science, were used to search for relevant literature. Eligibility criteria involved recent publication years, particularly publications within the last five years (except for papers presenting a certain novelty or mechanism for the first time). Population, toxicological and epidemiological studies that focused particularly on fine and ultra-fine PM and associated ambient metals, were preferred, as well as full-text publications.

Adult-onset autoimmune diabetes

Adult-onset autoimmune (AOA) diabetes pathophysiology starts with immune changes, followed by dysglycaemia and overt disease. AOA diabetes can occur as classic type 1 diabetes when associated with severe loss of insulin secretion. More frequently, it is diagnosed as latent autoimmune diabetes in adults, a slowly progressing form with late onset, a long period not requiring insulin, and it is often misdiagnosed as type 2 diabetes. As its clinical presentation varies remarkably and immune markers often lack specificity, it is challenging to classify each case ad hoc, especially when insulin treatment is not required at diagnosis. Proper care of AOA diabetes aims to prevent complications and to improve quality of life and life expectancy. To achieve these goals, attention should be paid to lifestyle factors, with the aid of pharmacological therapies properly tailored to each individual clinical setting. Given the heterogeneity of the disease, choosing the right therapy for AOA diabetes is challenging. Most of the trials testing disease-modifying therapies for autoimmune diabetes are conducted in people with childhood onset, whereas non-insulin diabetes therapies have mostly been studied in the larger population with type 2 diabetes. More randomized controlled trials of therapeutic agents in AOA diabetes are needed.

Investigating the association of environmental exposures and all-cause mortality in the UK Biobank using sparse principal component analysis

Multicollinearity refers to the presence of collinearity between multiple variables and renders the results of statistical inference erroneous (Type II error). This is particularly important in environmental health research where multicollinearity can hinder inference. To address this, correlated variables are often excluded from the analysis, limiting the discovery of new associations. An alternative approach to address this problem is the use of principal component analysis. This method, combines and projects a group of correlated variables onto a new orthogonal space. While this resolves the multicollinearity problem, it poses another challenge in relation to interpretability of results. Standard hypothesis testing methods can be used to evaluate the association of projected predictors, called principal components, with the outcomes of interest, however, there is no established way to trace the significance of principal components back to individual variables. To address this problem, we investigated the use of sparse principal component analysis which enforces a parsimonious projection. We hypothesise that this parsimony could facilitate the interpretability of findings. To this end, we investigated the association of 20 environmental predictors with all-cause mortality adjusting for demographic, socioeconomic, physiological, and behavioural factors. The study was conducted in a cohort of 379,690 individuals in the UK. During an average follow-up of 8.05 years (3,055,166 total person-years), 14,996 deaths were observed. We used Cox regression models to estimate the hazard ratio (HR) and 95% confidence intervals (CI). The Cox models were fitted to the standardised environmental predictors (a) without any transformation (b) transformed with PCA, and (c) transformed with SPCA. The comparison of findings underlined the potential of SPCA for conducting inference in scenarios where multicollinearity can increase the risk of Type II error. Our analysis unravelled a significant association between average noise pollution and increased risk of all-cause mortality. Specifically, those in the upper deciles of noise exposure have between 5 and 10% increased risk of all-cause mortality compared to the lowest decile.

Is Population Density Associated with Non-Communicable Disease in Western Developed Countries? A Systematic Review

Over the last three decades, researchers have investigated population density and health outcomes at differing scale. There has not been a systematic review conducted in order to synthesise this evidence. Following the Preferred Reporting Items for Systematic Reviews (PRISMA) guidelines, we systematically reviewed quantitative evidence published since 1990 on population density and non-communicable disease (NCD) within Westernised countries. Fifty-four studies met the inclusion criteria and were evaluated utilising a quality assessment tool for ecological studies. High population density appears to be associated with higher mortality rates of a range of cancers, cardiovascular disease and COPD, and a higher incidence of a range of cancers, asthma and club foot. In contrast, diabetes incidence was found to be associated with low population density. High and low population density are therefore risk markers for a range of NCDs, indicating that there are unidentified factors and mechanisms underlying aetiology. On closer examination, our synthesis revealed important and complex relationships between population density, the built environment, the nature of greenspace and man-made exposures. In light of increasing rates of morbidity and mortality, future research is required to investigate these associations in order to establish causative agents for each NCD.

Relationships between emissions of toxic airborne molecules and type 1 diabetes incidence in children: An ecologic study

BACKGROUND

Type 1 diabetes originates from gene-environment interactions, with increasing incidence over time.

AIM

To identify correlates of childhood type 1 diabetes in European countries using an ecological approach. Several environmental variables potentially influencing the onset of type 1 diabetes have been previously evaluated. However, the relationships between epidemiologic data and exposure to toxic airborne molecules are scarcely studied.

METHODS

We employed an ecological model to explore, in a wide time period (1990-2018), associations between type 1 diabetes incidence in 19 European countries (systematic literature review) and the nationwide production of five widely diffused air pollutants: particulate matter < 10 μm (PM10), nitrogen oxides (NO), non-methane volatile organic compounds (VOCs), sulphur oxide (SO₂), and ammonia.

RESULTS

Data confirm a raising incidence of type 1 diabetes in 18 out of 19 explored countries. The average difference (last vs first report, all countries) was +6.9 × 100000/year, with values ranging from -1.4 (Germany) to +16.6 (Sweden) per 100000/year. Although the overall production of pollutants decreased progressively from 1990 to 2018, type 1 diabetes incidence was positively associated with the nationwide emissions of PM10, VOCs, and NO but not with those of SO2 and ammonia. Type 1 diabetes incidence was significantly higher in countries with high emissions than in those with low emissions of PM10 (27.5 ± 2.4 vs 14.6 ± 2.4 × 100000 residents, respectively), VOCs (24.5 ± 4.4 vs 13.2 ± 1.7 × 100000 residents, respectively), and NO (26.6 ± 3 vs 13.4 ± 2.4 × 100000 residents, respectively), but not of SO₂ or ammonia.

CONCLUSION

Evidence justify further studies to explore better links between long-term air quality and type 1 diabetes onset at the individual level, which should include exposures during pregnancy. In this respect, type 1 diabetes could be, at least in part, a preventable condition. Thus, primary prevention policies acting through a marked abatement of pollutant emissions might attenuate future type 1 diabetes incidence throughout Europe.

Why is the Incidence of Type 1 Diabetes Increasing?

Type 1 diabetes is a condition that can lead to serious long-term complications and can have significant psychological and quality of life implications. Its incidence is increasing in all parts of the world, but the reasons for this are incompletely understood. Genetic factors alone cannot explain such a rapid increase in incidence; therefore, environmental factors must be implicated. Lifestyle factors have been classically associated with type 2 diabetes. However, there are data implicating obesity and insulin resistance to type 1 diabetes as well (accelerator hypothesis). Cholesterol has also been shown to be correlated with the incidence of type 1 diabetes; this may be mediated by immunomodulatory effects of cholesterol. There is considerable interest in early life factors, including maternal diet, mode of delivery, infant feeding, childhood diet, microbial exposure (hygiene hypothesis), and use of anti-microbials in early childhood. Distance from the sea has recently been shown to be negatively correlated with the incidence of type 1 diabetes. This may contribute to the increasing incidence of type 1 diabetes since people are increasingly living closer to the sea. Postulated mediating mechanisms include hours of sunshine (and possibly vitamin D levels), mean temperature, dietary habits, and pollution. Ozone, polychlorinated biphenyls, phthalates, trichloroethylene, dioxin, heavy metals, bisphenol, nitrates/nitrites, and mercury are amongst the chemicals which may increase the risk of type 1 diabetes. Another area of research concerns the role of the skin and gut microbiome. The microbiome is affected by many of the factors mentioned above, including the mode of delivery, infant feeding, exposure to microbes, antibiotic use, and dietary habits. Research on the reasons why the incidence of type 1 diabetes is increasing not only sheds light on its pathogenesis but also offers insights into ways we can prevent type 1 diabetes.