Wittmelvin3618

In recent years, many studies have revealed the importance of heart failure (HF) development in type 2 diabetes (T2D), which increases the morbidity and mortality during the course of diabetes. In this context, it became important to emphasize the role of both cardiologists and diabetologists in the early diagnosis and further adequate treatment of HF in T2D. While HF appears in two major forms, with reduced or preserved ejection fraction (EF), namely HFrEF and HFpEF, it became important to define the optimal approach to the diagnostics. Regarding HFrEF, the role of cardiological methods remained dominant, while the complexity of early diagnosis requires nowadays more active participation of diabetologists. The absence of abundant symptoms and echocardiographic findings imposed the need for the use of risk markers based on metabolic variables and low-grade inflammation parameters. Following that unmet need, numerous studies have defined the possible relationship between metabolic variables in diabetes and the risk for HF. Moreover, attempts have been made to integrate biochemical and clinical parameters into risk score engines and some of them gave promising results. However, the follow-up studies in T2D subjects are needed to determine the clinical relevance of these new approaches.Personal care products (PCPs) are important and modifiable sources of exposure to endocrine disrupting chemicals (EDCs). Research is limited on how EDC-associated PCP use differs by race/ethnicity and socioeconomic status (SES), particularly during the sensitive period of pregnancy. We investigated differences in PCP use by race/ethnicity and SES among 497 participants in the LIFECODES pregnancy cohort (Boston, Massachusetts). Participants self-reported race/ethnicity, SES indicators (maternal education; insurance status), and recent PCP use via questionnaire at ≤4 prenatal visits. We evaluated trimester-specific differences in use of individual PCP categories by race/ethnicity and SES indicators. We used Poisson regression to estimate trimester-specific mean total product categories used by race/ethnicity and SES indicators. In the first trimester, compared to non-Hispanic White women, Hispanic women reported higher use of hair gel (45% vs. 28%), perfume (75% vs. 39%), and "other" hair products (37% vs. 19%). Compared to women with a college degree, women without a college degree reported higher use of perfume (79% vs. 41%) and bar soap (74% vs. 56%); patterns were similar for insurance status. find more The estimated mean total product categories used was significantly lower in Asian compared to non-Hispanic White women in all trimesters (e.g., Trimester 1 4.8 vs. 6.7 categories; p less then 0.001). Patterns of PCP use differed by race/ethnicity and SES, with implications for potentially modifiable differential EDC exposure and associated pregnancy outcomes.Amid the COVID-19 pandemic, a nationwide lockdown was imposed in the United Kingdom (UK) on March 23, 2020. These sudden control measures led to radical changes in human activities in the Greater London Area (GLA). During this lockdown, transportation use was significantly reduced and non-key workers were required to work from home. This study aims to understand how population exposure to PM2.5 and NO2 changed spatially and temporally across London, in different microenvironments, following the lockdown period relative to the previous three-year average in the same calendar period. Our research shows that population exposure to NO2 declined significantly (52.3% ± 6.1%), while population exposure to PM2.5 showed a smaller relative reduction (15.7% ± 4.1%). Changes in population activity had the strongest relative influence on exposure levels during morning rush hours, when prior to the lockdown a large percentage of people would normally commute or be at the workplace. In particular, a very high exposure decrease was observed for both pollutants (approximately 66% for NO2 and 19% for PM2.5) at 0800am, consistent with the radical changes in population commuting. The infiltration of outdoor air pollution into housing modifies the degree of exposure change both temporally and spatially. Moreover, this study shows that the impacts on air pollution exposure vary across groups with different socioeconomic status (SES), with a disproportionate positive effect on the areas of the city home to more economically deprived communities.

Asthma affects millions of people worldwide. Lima, Peru is one of the most polluted cities in the Americas but has insufficient ground PM

(particulate matter that are 2.5 μm or less in diameter) measurements to conduct epidemiologic studies regarding air pollution. PM

estimates from a satellite-driven model have recently been made, enabling a study between asthma and PM

.

We conducted a daily time-series analysis to determine the association between asthma emergency department (ED) visits and estimated ambient PM

levels in Lima, Peru from 2010 to 2016.

We used Poisson generalized linear models to regress aggregated counts of asthma on district-level population weighted PM

. Indicator variables for hospitals, districts, and day of week were included to account for spatial and temporal autocorrelation while assessing same day, previous day, day before previous and average across all 3-day exposures. We also included temperature and humidity to account for meteorology and used dichotomous percent p PM

and asthma in a low- and middle-income (LMIC) country.

Results from this study provide additional literature on use of satellite-driven exposure estimates in time-series analyses and evidence for the association between PM2.5 and asthma in a low- and middle-income (LMIC) country.The frequency and intensity of compound hot extremes will be likely to increase in the context of global warming. Epidemiological studies have demonstrated the adverse effect of simple hot extreme events on mortality, but little is known about the effects of compound hot extremes on mortality. Daily meteorological, demographic, and mortality data during 2011-2017 were collected from 160 streets in Guangzhou City, China. We used distributed lag non-linear model (DLNM) to analyze the associations of different hot extremes with mortality risk in each street. Street-specific associations were then combined using a meta-analysis approach. To assess the spatial distribution of vulnerability to compound hot extremes, vulnerable characteristics at street level were selected using random forest model, and then we calculated and mapped spatial vulnerability index (SVI) at each street in Guangzhou. At street level, compared with normal day, compound hot extreme significantly increased mortality risk (relative risk(RR)=1.