Social and environmental stressors of cardiometabolic health.

Exposures to social and environmental stressors arise individual behavioural response and thus indirectly affect cardiometabolic health. The aim of this study was to investigate several social and environmental stressors and the paths of their influence on cardiometabolic health. The data of 2154 participants (aged 25-64 years) from the cross-sectional population-based study were analysed. The composite score of metabolic disorders (MS score) was calculated based on 5 biomarkers: waist circumference, blood pressure, fasting blood glucose, HDL-cholesterol, triglycerides. The effects of social stressors (education level, income), environmental stressors (NO2, noise) and behavioural factors (unhealthy diet, smoking, alcohol consumption, sedentary behaviours) on MS score were assessed using a structural model. We observed a direct effect of education on MS score, as well as an indirect effect mediated via an unhealthy diet, smoking, and sedentary behaviours. We also observed a significant indirect effect of income via sedentary behaviours. The only environmental stressor predicting MS was noise, which also mediated the effect of education. In summary, the effect of social stressors on the development of cardiometabolic risk had a higher magnitude than the effect of the assessed environmental factors. Social stressors lead to an individual's unhealthy behaviour and might predispose individuals to higher levels of environmental stressors exposures.

Living in poverty and accelerated biological aging: evidence from population-representative sample of U.S. adults.

BACKGROUND: Biological aging reflects a decline in the functions and integrity of the human body that is closely related to chronological aging. A variety of biomarkers have been found to predict biological age. Biological age higher than chronological age (biological age acceleration) indicates an accelerated state of biological aging and a higher risk of premature morbidity and mortality. This study investigated how socioeconomic disadvantages influence biological aging. METHODS: The data from the National Health and Nutrition Examination Survey (NHANES) IV, including 10 nationally representative cross-sectional surveys between 1999-2018, were utilized. The analytic sample consisted of N = 48,348 individuals (20-84 years). We used a total of 11 biomarkers for estimating the biological age. Our main outcome was biological age acceleration, indexed by PhenoAge acceleration (PAA) and Klemera-Doubal biological age acceleration (KDM-A). Poverty was measured as a ratio of family income to the poverty thresholds defined by the U.S. Census Bureau, adjusted annually for inflation and family size (5 categories). The PAA and KDM-A were regressed on poverty levels, age, their interaction, education, sex, race, and a data collection wave. Sample weights were used to make the estimates representative of the U.S. adult population. RESULTS: The results showed that higher poverty was associated with accelerated biological aging (PAA: unstandardized coefficient B = 1.38 p <.001, KDM: B = 0.96, p = .026 when comparing the highest and the lowest poverty level categories), above and beyond other covariates. The association between PAA and KDM-A and age was U-shaped. Importantly, there was an interaction between poverty levels and age (p <.001), as the effect of poverty was most pronounced in middle-aged categories while it was modest in younger and elderly groups. CONCLUSION: In a nationally representative US adult population, we found that higher poverty was positively associated with the acceleration of biological age, particularly among middle-aged persons.

The explanation of educational disparities in adiposity by lifestyle, socioeconomic and mental health mediators: a multiple mediation model.

BACKGROUND: The inverse association between education and obesity was previously found in numerous studies. This study aims to assess several possible mediators in the educational disparities in adiposity. We hypothesize the potential mediating role of lifestyle, socioeconomic, and mental health factors in the association between education and adiposity. METHODS: Cross-sectional population-based sample from Czechia included 2,154 25-64 years old subjects (54.6% women). Education was classified as high, middle, and low. Adiposity was assessed as a latent variable based on body fat percentage, BMI, waist circumference, and visceral fat. The mediation potential of unhealthy dietary behavior, alcohol intake, smoking, sedentary behaviors, income, stress, depression, and quality of life was assessed in age-adjusted sex-specific multiple mediation models. RESULTS: The negative direct effect of education on adiposity was statistically significant at 5% level of significance in both sexes. For men, the indirect effect was statistically significant via sedentary behavior (ß = 0.041; 95% CI [0.025-0.062]) with a mediation ratio of 23.7%. In women, the indirect effect was statistically significant via dietary risk (ß = -0.023, 95% CI [-0.037, -0.013]), alcohol intake (ß = -0.006; 95% CI [-0.014, -0.001]), sedentary behavior (ß = 0.012, 95% CI [0.004,0.023]), income (ß = -0.022; 95% CI [-0.041, -0.004]), and mental health (ß = -0.007; 95% CI [-0.019, -0.001]). The total mediation ratio in women was 30.5%. CONCLUSIONS: Sedentary behaviors had mediating role in the association between education and adiposity in both sexes, with more important role in men. In addition, unhealthy diet and lower income partially mediated the educational gradient in adiposity in women.

Firefighters and the liver: Exposure to PFAS and PAHs in relation to liver function and serum lipids (CELSPAC-FIREexpo study).

INTRODUCTION: Firefighting is one of the most hazardous occupations due to exposure to per- and polyfluoroalkyl substances (PFAS) and polycyclic aromatic hydrocarbons (PAHs). Such exposure is suspected to affect the cardiometabolic profile, e.g., liver function and serum lipids. However, only a few studies have investigated the impact of this specific exposure among firefighters. METHODS: Men included in the CELSPAC-FIREexpo study were professional firefighters (n = 52), newly recruited firefighters in training (n = 58), and controls (n = 54). They completed exposure questionnaires and provided 1-3 samples of urine and blood during the 11-week study period to allow assessment of their exposure to PFAS (6 compounds) and PAHs (6 compounds), and to determine biomarkers of liver function (alanine aminotransferase (ALT), gamma-glutamyl transferase (GGT), aspartate aminotransferase (AST), alkaline phosphatase (ALP) and total bilirubin (BIL)) and levels of serum lipids (total cholesterol (CHOL), low-density lipoprotein cholesterol (LDL) and triglycerides (TG)). The associations between biomarkers were investigated both cross-sectionally using multiple linear regression (MLR) and Bayesian weighted quantile sum (BWQS) regression and prospectively using MLR. The models were adjusted for potential confounders and false discovery rate correction was applied to account for multiplicity. RESULTS: A positive association between exposure to PFAS and PAH mixture and BIL (ß = 28.6%, 95% CrI = 14.6-45.7%) was observed by the BWQS model. When the study population was stratified, in professional firefighters and controls the mixture showed a positive association with CHOL (ß = 29.5%, CrI = 10.3-53.6%) and LDL (ß = 26.7%, CrI = 8.3-48.5%). No statistically significant associations with individual compounds were detected using MLR. CONCLUSIONS: This study investigated the associations between exposure to PFAS and PAHs and biomarkers of cardiometabolic health in the Czech men, including firefighters. The results suggest that higher exposure to a mixture of these compounds is associated with an increase in BIL and the alteration of serum lipids, which can result in an unfavourable cardiometabolic profile.

Comparison of social gradient in cardiometabolic health in Czechia and Venezuela: a cross-sectional study.

OBJECTIVES: This study compared the relationships of social determinants with cardiometabolic risk in different socioeconomic contexts: sociopolitically unstable Venezuela (VE) and stable Czechia (CZ). DESIGN: cross-sectional analysis involving two population-based studies. SETTING: Brno, Czechia and 23 cities of Venezuela. PARTICIPANTS: 25-64 years old subjects from CZ (2013-2014, n=1579, 56% females) and VE (2014-2017, n=1652, 70% females). MAIN OUTCOME MEASURES: The composite cardiometabolic risk score (CMRS) (scaled 0-8) was calculated using eight biomarkers (body mass index, waist circumference, blood glucose, systolic and diastolic blood pressure, total and high-density lipoprotein-cholesterol, triglycerides). Social characteristics included education in both countries, income in CZ and a composite measure of social position (SP) in VE. Sex stratified ordinal regression examined the social gradient in having less favourable CMRS. RESULTS: In CZ, men and women with low education and women with low income had higher odds of higher CMRS compared with those with high education and income with OR 1.45 (95% CI 1.01 to 2.21), 2.29 (95% CI 1.62 to 3.24) and 1.69 (95% CI 1.23 to 2.35). In VE, women with low education and low SP had higher odds to have higher CMRS OR 1.47 (95% CI 1.09 to 1.97) and 1.51 (95% CI 1.16 to 1.97), while men with low education and low SP had lower odds to have higher CMRS OR 0.64 (95% CI 0.41 to 1.00) and 0.61 (95% CI 0.40 to 0.97), compared with those with high education and high SP. Independently of age, sex and socioeconomic characteristics, Venezuelans had higher odds to have higher CMRS than Czechs (OR 2.70; 95% CI 2.37 to 3.08). CONCLUSIONS: The results suggest that the associations of socioeconomic status indices and cardiometabolic risk differed between CZ and VE, likely reflecting differences in the social environment among countries. Further research is needed to confirm and quantify these differences.