Environmental risk factors of type 2 diabetes-an exposome approach

Type 2 diabetes is one of the major chronic diseases accounting for a substantial proportion of disease burden in Western countries. The majority of the burden of type 2 diabetes is attributed to environmental risks and modifiable risk factors such as lifestyle. The environment we live in, and changes to it, can thus contribute substantially to the prevention of type 2 diabetes at a population level. The ‘exposome’ represents the (measurable) totality of environmental, i.e. nongenetic, drivers of health and disease. The external exposome comprises aspects of the built environment, the social environment, the physico-chemical environment and the lifestyle/food environment. The internal exposome comprises measurements at the epigenetic, transcript, proteome, microbiome or metabolome level to study either the exposures directly, the imprints these exposures leave in the biological system, the potential of the body to combat environmental insults and/or the biology itself. In this review, we describe the evidence for environmental risk factors of type 2 diabetes, focusing on both the general external exposome and imprints of this on the internal exposome. Studies provided established associations of air pollution, residential noise and area-level socioeconomic deprivation with an increased risk of type 2 diabetes, while neighbourhood walkability and green space are consistently associated with a reduced risk of type 2 diabetes. There is little or inconsistent evidence on the contribution of the food environment, other aspects of the social environment and outdoor temperature. These environmental factors are thought to affect type 2 diabetes risk mainly through mechanisms incorporating lifestyle factors such as physical activity or diet, the microbiome, inflammation or chronic stress. To further assess causality of these associations, future studies should focus on investigating the longitudinal effects of our environment (and changes to it) in relation to type 2 diabetes risk and whether these associations are explained by these proposed mechanisms. Graphical abstract: [Figure not available: see fulltext.

RENAL REPLACEMENT THERAPY IN A POLYTRAUMATIZED PATIENT WITH HEMOPHILIA

Zatajenje bubrežne funkcije je rijetka pojava u bolesnika s nasljednim koagulacijskim poremećajima. Međutim, kada nastupi veoma brzo napreduje do završnog stadija bubrežne bolesti i potrebe za nadomještanjem bubrežne funkcije. Javljaju se problemi vezani uz odabir metode dijalize, periproceduralne nadoknade nedostatnog faktora koagulacije te heparinizacije dijaliznog sustava. Kod hemoiličara uvijek treba biti oprezan tijekom samog postupka dijalize zbog mogućeg razvoja komplikacija koje ih mogu vitalno ugroziti. Ovo je prikaz slučaja teško politraumatiziranog bolesnika koji boluje od hemofilije A. Tijekom intenzivnog liječenja razvio je akutno bubrežno zatajenje te tešku sepsu. S obzirom na okolnosti najbolja metoda izbora za njega je bila kontinuirana veno-venska hemodijaliza. Unato uspješno provedenoj dijalizi bez komplikacija bolesnik umire od protrahirane sepseRenal failure is a rare complication of hereditary coagulopathies. However, when it occurs, it rapidly progresses to a stage that requires replacement of renal function. Major problems include the choice of dialysis method, prevention of complications and supplementation of deicient factor. In hemodialysis, it is challenging to prevent system clotting and avoid bleeding. We present a case of polytraumatized male patient with hemophilia A, who developed compartment syndrome with acute renal failure. Continuous venovenous hemodialysis (CVVHD) improved his condition and he recovered his kidney function. However, over the next few days he developed severe sepsis with deterioration of renal function. CVVHDF (hemodiailtration) was restarted. Several large hematomas were found in the abdominal cavity and in the inguinal region, one of them inducing compartment syndrome with leg necrosis. The patient died from cardiorespiratory arrest

Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression

Air pollution measurements collected through systematic mobile monitoring campaigns can provide outdoor concentration data at high spatial resolution. We explore approaches to minimize data requirements for mapping a city's air quality using mobile monitors with "data-only" versus predictive modeling approaches. We equipped two Google Street View cars with 1-Hz instruments to collect nitric oxide (NO) and black carbon (BC) measurements in Oakland, CA. We explore two strategies for efficiently mapping spatial air quality patterns through Monte Carlo analyses. First, we explore a "data-only" approach where we attempt to minimize the number of repeated visits needed to reliably estimate concentrations for all roads. Second, we combine our data with a land use regression-kriging (LUR-K) model to predict at unobserved locations; here, measurements from only a subset of roads or repeat visits are considered. Although LUR-K models did not capture the full variability of on-road concentrations, models trained with minimal data consistently captured important covariates and general spatial air pollution trends, with cross-validation R2 for log-transformed NO and BC of 0.65 and 0.43. Data-only mapping performed poorly with few (1-2) repeated drives but obtained better cross-validation R2 than the LUR-K approach within 4 to 8 repeated drive days per road segment

Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression

Air pollution measurements collected through systematic mobile monitoring campaigns can provide outdoor concentration data at high spatial resolution. We explore approaches to minimize data requirements for mapping a city's air quality using mobile monitors with "data-only" versus predictive modeling approaches. We equipped two Google Street View cars with 1-Hz instruments to collect nitric oxide (NO) and black carbon (BC) measurements in Oakland, CA. We explore two strategies for efficiently mapping spatial air quality patterns through Monte Carlo analyses. First, we explore a "data-only" approach where we attempt to minimize the number of repeated visits needed to reliably estimate concentrations for all roads. Second, we combine our data with a land use regression-kriging (LUR-K) model to predict at unobserved locations; here, measurements from only a subset of roads or repeat visits are considered. Although LUR-K models did not capture the full variability of on-road concentrations, models trained with minimal data consistently captured important covariates and general spatial air pollution trends, with cross-validation R2 for log-transformed NO and BC of 0.65 and 0.43. Data-only mapping performed poorly with few (1-2) repeated drives but obtained better cross-validation R2 than the LUR-K approach within 4 to 8 repeated drive days per road segment

Long-Term Exposure to Ultrafine Particles and Incidence of Cardiovascular and Cerebrovascular Disease in a Prospective Study of a Dutch Cohort

BACKGROUND: There is growing evidence that exposure to ultrafine particles (UFP; particles smaller than [Formula: see text]) may play an underexplored role in the etiology of several illnesses, including cardiovascular disease (CVD). OBJECTIVES: We aimed o investigate the relationship between long-term exposure to ambient UFP and incident cardiovascular and cerebrovascular disease (CVA). As a secondary objective, we sought to compare effect estimates for UFP with those derived for other air pollutants, including estimates from two-pollutant models. METHODS: Using a prospective cohort of 33,831 Dutch residents, we studied the association between long-term exposure to UFP (predicted via land use regression) and incident disease using Cox proportional hazard models. Hazard ratios (HR) for UFP were compared to HRs for more routinely monitored air pollutants, including particulate matter with aerodynamic diameter [Formula: see text] ([Formula: see text]), PM with aerodynamic diameter [Formula: see text] ([Formula: see text]), and [Formula: see text]. RESULTS: Long-term UFP exposure was associated with an increased risk for all incident CVD [[Formula: see text] per [Formula: see text]; 95% confidence interval (CI): 1.03, 1.34], myocardial infarction (MI) ([Formula: see text]; 95% CI: 1.00, 1.79), and heart failure ([Formula: see text]; 95% CI: 1.17, 2.66). Positive associations were also estimated for [Formula: see text] ([Formula: see text]; 95% CI: 1.01, 1.48 per [Formula: see text]) and coarse PM ([Formula: see text]; HR for all [Formula: see text]; 95% CI: 1.01, 1.45 per [Formula: see text]). CVD was not positively associated with [Formula: see text] (HR for all [Formula: see text]; 95% CI: 0.75, 1.28 per [Formula: see text]). HRs for UFP and CVAs were positive, but not significant. In two-pollutant models ([Formula: see text] and [Formula: see text]), positive associations tended to remain for UFP, while HRs for [Formula: see text] and [Formula: see text] generally attenuated towards the null. CONCLUSIONS: These findings strengthen the evidence that UFP exposure plays an important role in cardiovascular health and that risks of ambient air pollution may have been underestimated based on conventional air pollution metrics. https://doi.org/10.1289/EHP3047

Long-Term Exposure to Ultrafine Particles and Incidence of Cardiovascular and Cerebrovascular Disease in a Prospective Study of a Dutch Cohort.

There is growing evidence that exposure to ultrafine particles (UFP; particles smaller than [Formula: see text]) may play an underexplored role in the etiology of several illnesses, including cardiovascular disease (CVD). We aimed o investigate the relationship between long-term exposure to ambient UFP and incident cardiovascular and cerebrovascular disease (CVA). As a secondary objective, we sought to compare effect estimates for UFP with those derived for other air pollutants, including estimates from two-pollutant models. Using a prospective cohort of 33,831 Dutch residents, we studied the association between long-term exposure to UFP (predicted via land use regression) and incident disease using Cox proportional hazard models. Hazard ratios (HR) for UFP were compared to HRs for more routinely monitored air pollutants, including particulate matter with aerodynamic diameter [Formula: see text] ([Formula: see text]), PM with aerodynamic diameter [Formula: see text] ([Formula: see text]), and [Formula: see text]. Long-term UFP exposure was associated with an increased risk for all incident CVD [[Formula: see text] per [Formula: see text]; 95% confidence interval (CI): 1.03, 1.34], myocardial infarction (MI) ([Formula: see text]; 95% CI: 1.00, 1.79), and heart failure ([Formula: see text]; 95% CI: 1.17, 2.66). Positive associations were also estimated for [Formula: see text] ([Formula: see text]; 95% CI: 1.01, 1.48 per [Formula: see text]) and coarse PM ([Formula: see text]; HR for all [Formula: see text]; 95% CI: 1.01, 1.45 per [Formula: see text]). CVD was not positively associated with [Formula: see text] (HR for all [Formula: see text]; 95% CI: 0.75, 1.28 per [Formula: see text]). HRs for UFP and CVAs were positive, but not significant. In two-pollutant models ([Formula: see text] and [Formula: see text]), positive associations tended to remain for UFP, while HRs for [Formula: see text] and [Formula: see text] generally attenuated towards the null

Environmental risk factors of type 2 diabetes-an exposome approach

Type 2 diabetes is one of the major chronic diseases accounting for a substantial proportion of disease burden in Western countries. The majority of the burden of type 2 diabetes is attributed to environmental risks and modifiable risk factors such as lifestyle. The environment we live in, and changes to it, can thus contribute substantially to the prevention of type 2 diabetes at a population level. The ‘exposome’ represents the (measurable) totality of environmental, i.e. nongenetic, drivers of health and disease. The external exposome comprises aspects of the built environment, the social environment, the physico-chemical environment and the lifestyle/food environment. The internal exposome comprises measurements at the epigenetic, transcript, proteome, microbiome or metabolome level to study either the exposures directly, the imprints these exposures leave in the biological system, the potential of the body to combat environmental insults and/or the biology itself. In this review, we describe the evidence for environmental risk factors of type 2 diabetes, focusing on both the general external exposome and imprints of this on the internal exposome. Studies provided established associations of air pollution, residential noise and area-level socioeconomic deprivation with an increased risk of type 2 diabetes, while neighbourhood walkability and green space are consistently associated with a reduced risk of type 2 diabetes. There is little or inconsistent evidence on the contribution of the food environment, other aspects of the social environment and outdoor temperature. These environmental factors are thought to affect type 2 diabetes risk mainly through mechanisms incorporating lifestyle factors such as physical activity or diet, the microbiome, inflammation or chronic stress. To further assess causality of these associations, future studies should focus on investigating the longitudinal effects of our environment (and changes to it) in relation to type 2 diabetes risk and whether these associations are explained by these proposed mechanisms

LongITools:dynamic longitudinal exposome trajectories in cardiovascular and metabolic noncommunicable diseases

Abstract The current epidemics of cardiovascular and metabolic noncommunicable diseases have emerged alongside dramatic modifications in lifestyle and living environments. These correspond to changes in our ”modern” postwar societies globally characterized by rural-to-urban migration, modernization of agricultural practices, and transportation, climate change, and aging. Evidence suggests that these changes are related to each other, although the social and biological mechanisms as well as their interactions have yet to be uncovered. LongITools, as one of the 9 projects included in the European Human Exposome Network, will tackle this environmental health equation linking multidimensional environmental exposures to the occurrence of cardiovascular and metabolic noncommunicable diseases