Joint effects of prenatal exposure to air pollution and pregnancy-related anxiety on birth weight: A prospective birth cohort study in Ma'anshan, China.

BACKGROUND: A growing number of studies have shown that prenatal exposure to chemical and non-chemical stressors has effects on fetal growth. The co-exposure of both better reflects real-life exposure patterns. However, no studies have included air pollutants and pregnancy-related anxiety (PrA) as mixtures in the analysis. METHOD: Using the birth cohort study method, 576 mother-child pairs were included in the Ma'anshan Maternal and Child Health Hospital. Evaluate the exposure levels of six air pollutants during pregnancy using inverse distance weighting (IDW) based on the pregnant woman's residential address and air pollution data from monitoring stations. Prenatal anxiety levels were assessed using the PrA Questionnaire. Generalized linear regression (GLR), quantile g-computation (QgC) and bayesian kernel machine regression (BKMR) were used to assess the independent or combined effects of air pollutants and PrA on birth weight for gestational age z-score (BWz). RESULT: The results of GLR indicate that the correlation between the six air pollutants and PrA with BWz varies depending on the different stages of pregnancy and pollutants. The QgC shows that during trimester 1, when air pollutants and PrA are considered as a whole exposure, an increase of one quartile is significantly negatively correlated with BWz. The BKMR similarly indicates that during trimester 1, the combined exposure of air pollutants and PrA is moderately correlated with a decrease in BWz. CONCLUSION: Using the method of analyzing mixed exposures, we found that during pregnancy, the combined exposure of air pollutants and PrA, particularly during trimester 1, is associated with BWz decrease. This supports the view that prenatal exposure to chemical and non-chemical stressors has an impact on fetal growth.

Association of urinary heavy metal combined exposure with periodontitis among US adults from NHANES 2011-2014.

Some heavy metals are associated with periodontitis; whereas most of these associations have focused on individual metal, there are no specific studies on the effects of combined heavy metal exposure on periodontitis. We conducted an analysis on the association between urinary heavy metal exposure and periodontitis in participants aged 30 years and older using multiple logistic regression and Bayesian kernel machine regression (BKMR). This analysis was performed on data from the National Health and Nutrition Examination Survey from 2011 to 2014. The study found that using logistic regression, the 4th quartile of urinary lead and molybdenum and the 3rd quartile of urinary strontium were positively associated with periodontitis compared to the reference quartile after adjusting for covariates. Odds ratio (OR) with 95% confidence interval (CI) was 1.738 (1.069-2.826), 1.515 (1.025-2.239), and 1.498 (1.010-2.222), respectively. The 3rd and 4th quartiles of urinary cobalt were negatively associated with periodontitis, and their ORs and 95% CIs were 0.639 (0.438-0.934) and 0.571 (0.377-0.964), respectively. The BKMR model showed that urinary barium, lead, and molybdenum were positively associated with periodontitis in a range of concentrations and urinary cobalt, manganese, tin, and strontium were negatively correlated with periodontitis. Furthermore, the overall association between urinary heavy metals and periodontitis was positive. Our study provides evidence for an association between exposure to multiple urinary heavy metals and periodontitis. However, further longitudinal studies are needed to explore the specific mechanisms involved.

The modification of meteorological factors on the relationship between air pollution and periodontal diseases: an exploration based on different interaction strategies.

We aimed to characterize the association between air pollutants exposure and periodontal diseases outpatient visits and to explore the interactions between ambient air pollutants and meteorological factors. The outpatient visits data of several large stomatological and general hospitals in Hefei during 2015-2020 were collected to explore the relationship between daily air pollutants exposure and periodontal diseases by combining Poisson's generalized linear model (GLMs) and distributed lag nonlinear model (DLNMs). Subgroup analysis was performed to identify the vulnerability of different populations to air pollutants exposure. The interaction between air pollutants and meteorological factors was verified in both multiplicative and additive interaction models. An interquartile range (IQR) increased in nitrogen dioxide (NO2) concentration was associated with the greatest lag-specific relative risk (RR) of gingivitis at lag 3 days (RR = 1.087, 95% CI 1.008-1.173). Fine particulate matter (PM2.5) exposure also increased the risk of periodontitis at the day of exposure (RR = 1.049, 95% CI 1.004-1.096). Elderly patients with gingivitis and periodontitis were both vulnerable to PM2.5 exposure. The interaction analyses showed that exposure to high levels of NO2 at low temperatures was related to an increased risk of gingivitis, while exposure to high levels of NO2 and PM2.5 may also increase the risk of gingivitis and periodontitis in the high-humidity environment, respectively. This study supported that NO2 and PM2.5 exposure increased the risk of gingivitis and periodontitis outpatient visits, respectively. Besides, the adverse effects of air pollutants exposure on periodontal diseases may vary depending on ambient temperature and humidity.

Cardiorenal protective effects of sodium-glucose cotransporter 2 inhibition in combination with angiotensin II type 1 receptor blockade in salt-sensitive Dahl rats.

OBJECTIVE: The kidney plays a central role in regulating the salt sensitivity of blood pressure (BP) by governing sodium excretion and reabsorption via renal sodium transporters. We hypothesized that sodium-glucose cotransporter 2 (SGLT2) inhibition and angiotensin II type 1 receptor (AT1R) blockade can synergistically reduce renal sodium reabsorption by beneficially effects on these transporters, leading to lower BP and ameliorating renal and cardiac damage. METHODS AND RESULTS: Dahl salt-sensitive rats were treated orally for 8weeks with a normal salt diet (0.3% NaCl), a high-salt diet (8% NaCl), high-salt diet with ipragliflozin (0.04%), high-salt diet with losartan (0.05%) or high-salt diet with a combination of ipragliflozin and losartan. The combination treatment significantly reduced BP and increased daily urine sodium excretion compared with losartan or ipragliflozin monotherapy, leading to greater improvement in BP salt sensitivity than ipragliflozin monotherapy. The combination treatment significantly ameliorated glomerulosclerosis and reduced cardiomyocyte hypertrophy compared with losartan or ipragliflozin monotherapy. The protein expression levels of Na+/H+ exchanger isoform 3 (NHE3) and Na+-K+-CI- cotransporter 2 (NKCC2) in the kidney were significantly decreased with losartan monotherapy and combination treatment, but not with ipragliflozin monotherapy. CONCLUSION: Inhibition of SGLT2 in combination with an angiotensin II receptor blocker effectively improved BP salt sensitivity by reducing renal expression levels of sodium transporters including NHE3 and NKCC2, which eventually led to improvement of BP salt sensitivity and cardiorenal protection.

Serum-Induced Expression of Brain Natriuretic Peptide Contributes to Its Increase in Patients with HFpEF.

Brain natriuretic peptide (BNP) levels are increased in both patients with heart failure with preserved (HFpEF) and reduced ejection fraction (HFrEF), but the reasons for this remain unclear. Our purpose was to examine whether serum-induced BNP (iBNP) expression partly contributes to increased BNP in patients with HFpEF. BNP reporter cardiomyocytes from pBNP-luc-KI mice were stimulated with serum from patients with HFpEF or HFrEF (n = 114 and n = 82, respectively). Luciferase activity was examined as iBNP and the iBNP-to-BNP ratio was evaluated. Patient characteristics and clinical parameters were compared, and multivariate regression analysis was performed to determine independent predictors of the iBNP-to-BNP ratio. Female sex and frequencies of atrial fibrillation, hypertension and the use of a calcium channel blocker (CCB) were higher in HFpEF. The iBNP-to-BNP ratio was significantly higher in HFpEF (26.9) than in HFrEF (16.1, p < 0.001). Multivariate regression analysis identified the existence of HFpEF as an independent predictor of the iBNP-to-BNP ratio after adjusting for all other measurements (ß = 0.154, p = 0.032). Age, hemoglobin, CCB usage and deceleration time were also independent predictors (ß = 0.167, p = 0.025; ß = 0.203, p = 0.006; ß = 0.138, p = 0.049; and ß = 0.143, p = 0.049, respectively). These results indicate that the elevated BNP in patients with HFpEF is partly due to iBNP from the heart.

Regulation of myosin light-chain phosphorylation and its roles in cardiovascular physiology and pathophysiology.

The regulation of muscle contraction is a critical function in the cardiovascular system, and abnormalities may be life-threatening or cause illness. The common basic mechanism in muscle contraction is the interaction between the protein filaments myosin and actin. Although this interaction is primarily regulated by intracellular Ca2+, the primary targets and intracellular signaling pathways differ in vascular smooth muscle and cardiac muscle. Phosphorylation of the myosin regulatory light chain (RLC) is a primary molecular switch for smooth muscle contraction. The equilibrium between phosphorylated and unphosphorylated RLC is dynamically achieved through two enzymes, myosin light chain kinase, a Ca2+-dependent enzyme, and myosin phosphatase, which modifies the Ca2+ sensitivity of contractions. In cardiac muscle, the primary target protein for Ca2+ is troponin C on thin filaments; however, RLC phosphorylation also plays a modulatory role in contraction. This review summarizes recent advances in our understanding of the regulation, physiological function, and pathophysiological involvement of RLC phosphorylation in smooth and cardiac muscles.

Research progress in machine learning methods for gene-gene interaction detection.

Complex diseases are results of gene-gene and gene-environment interactions. However, the detection of high-dimensional gene-gene interactions is computationally challenging. In the last two decades, machine-learning approaches have been developed to detect gene-gene interactions with some successes. In this review, we summarize the progress in research on machine learning methods, as applied to gene-gene interaction detection. It systematically examines the principles and limitations of the current machine learning methods used in genome wide association studies (GWAS) to detect gene-gene interactions, such as neural networks (NN), random forest (RF), support vector machines (SVM) and multifactor dimensionality reduction (MDR), and provides some insights on the future research directions in the field.