A subset of pregnant women treated with prophylactic low-dose aspirin (LDA) still develop preeclampsia (PE), a severe pregnancy-related complication. It is hypothesized that laboratory aspirin resistance (LAR), which may undermine the preventive efficacy of LDA, is associated with the onset of PE. However, this association remains unclear. This study aimed to investigate the relationship between LAR and the risk of developing PE despite LDA prophylaxis, a phenomenon we operationally define as clinical aspirin resistance (CAR), through a systematic review and meta-analysis. A comprehensive literature search was conducted in PubMed, Web of Science, Google Scholar, Scopus, Embase, and the Cochrane Library. Eligible studies that assessed the association between LAR and CAR were included. A random effect model was used to pool the data and calculate the combined odds ratio (OR) along with its corresponding 95

Importance Childhood hypertension is a growing health problem, yet its specific biomarkers are not yet fully elucidated. Objective The aim of this study was to investigate the functional metabolic alteration associated with hypertension in late adolescents. Design This study employed a cluster random sampling method based on the Health Promotion Program for Children and Adolescents. In the first stage in 2020-2021, three schools were selected for hypertension screening, followed by four schools in the second stage in 2022-2023. Hypertensive students in their late adolescent were matched 1:1 with normotensive controls for an untargeted metabolomics study to identify differential metabolites. In vitro cellular experiments were further performed to explore the functional roles of the interested metabolite. Setting Two separate case-control studies and cellular experiments. Participants In the first stage, a total of 51 late adolescents were identified with hypertension, and then were matched with 51 normotensive controls of similar sex and age from the same dormitory to collect their fasting urine samples. In the second stage, 91 hypertensive adolescents were identified and 91 matched normotensive adolescents were selected from the same dormitory to collect their fasting serum samples. Main outcomes and measures Hypertension was diagnosed by blood pressure measurements taken on three separate occasions. Results Detailed metabolomic evaluation revealed four distinct metabolites differentially expressed in hypertensive adolescents and control individuals in both urine and serum samples. As compared with the control group, higher levels of 2-hydroxycinnamic acid and xanthine, but lower levels of hypoxanthine and N-acetylornithine were observed in hypertensive adolescents. These metabolites also slightly enhance the discriminatory ability for hypertension based on body mass index Z score, as revealed by increased area under receiver operating characteristic curve. Notably, 2-hydroxycinnamic acid could inhibit cell proliferation, induce oxidative stress and inflammatory responses, and then disrupt the cellular function of human umbilical vein endothelial cells, inferring it as a potential detrimental metabolite for hypertension in adolescents. Conclusions and relevance Our result revealed distinct metabolic alterations in urine and serum samples of hypertensive adolescents. Combined with metabonomic and in vitro experiments, 2-hydroxycinnamic acid was identified as a potential detrimental metabolite for hypertension in adolescents.

Maternal preconception blood pressure (BP) is associated with various adverse pregnancy outcomes, but its relationship with spontaneous abortion (SA) remains controversial. We aimed to evaluate the relationship between preconception BP and SA. This population-based cohort study used data from participants in the National Free Preconception Examination Program between 2013 and 2019. Maternal preconception BP was categorized according to the 2017 ACC/AHA guidelines. The relationship between these BP categories and SA incidence was analyzed using multivariable logistic regression to generate adjusted odds ratios (aORs) with 95

ObjectiveBy reviewing existing literature and conducting a meta-analysis, this study aimed to investigate the critical window(s) of exposure to PM2.5 during pregnancy on offspring neurodevelopmental impairments and provide a reference basis for individual-level protection against PM2.5 exposure during pregnancy.MethodsAll literature on the associations between prenatal PM2.5 exposure and offspring neurodevelopmental impairments were searched from the three English databases including PubMed, Elsevier’s ScienceDirect, and Web of Science. Studies reporting odds ratio (OR) values for exposure during the first, second, and third trimesters, according to predetermined criteria, were selected for meta-analysis.ResultsA total of 14 studies were included in the final analysis. The meta-analysis of the extracted ORs revealed significant associations between PM2.5 exposure during all three trimesters and offspring neurodevelopmental impairments. The critical window showing the strongest association was observed during the second trimester (OR=1.11, 95%CI: 1.04‒1.18), followed by the first trimester (OR=1.10, 95%CI: 1.04‒1.15) and the third trimester (OR=1.09, 95%CI: 1.02‒1.16).ConclusionPM2.5 exposure during all gestational periods was positively correlated with an increased risk of neurodevelopmental impairments in offspring, with the strongest association observed during the second trimester. This period warrants targeted protective measurements for prenatal PM2.5 exposure.

Accurate early prognostic assessment in ischemic stroke—a major global health burden—remains challenging due to marked biological heterogeneity and the restricted predictive performance of conventional clinical factors. This study developed and validated an interpretable multimodal machine learning (ML) model for predicting adverse outcomes at 3 months following ischemic stroke. Herein, 3,381 patients with ischemic stroke were included. Multimodal predictors, including demographic and clinical characteristics, routine biochemical indices, and novel biomarkers, were selected using a double-layer feature reduction process. XGBoost-based feature selection was initially applied separately within each of the three data modalities to identify domain-specific variables. The retained variables were then pooled and further refined using backward stepwise regression to generate a parsimonious predictor set. Six ML algorithms (Random Forest, K-Nearest Neighbors, Logistic Regression, AdaBoost, Naive Bayes, and Multi-Layer Perceptron) were developed and evaluated using internal (n = 2,874) and external (n = 507) datasets. Model performance was assessed using accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), receiver operating characteristic area under the curve (AUC), precision-recall AUC (PR-AUC), and calibration metrics, and decision curve analysis. Model interpretability was evaluated using Shapley Additive Explanations (SHAP). Fourteen variables were retained in the analysis. In the internal development set, the simplified-variable Random Forest model demonstrated the best performance, achieving an accuracy of 0.88, sensitivity of 0.87, specificity of 0.89, PPV of 0.88, NPV of 0.87, AUC of 0.94, and PR-AUC of 0.94. The remaining algorithms yielded accuracy values of 0.57–0.86, sensitivity of 0.56–0.84, specificity of 0.58–0.86, PPV of 0.66–0.85, NPV of 0.57–0.83, AUC of 0.59–0.90, and PR-AUC of 0.56–0.90. Calibration and decision curve analyses supported the probabilistic reliability and potential clinical utility of the final model. In the external validation set, the simplified-variable Random Forest model remained the optimal classifier, with an accuracy of 0.76, AUC of 0.84, and PR-AUC of 0.83. SHAP-based interpretation identified baseline National Institutes of Health Stroke Scale score (48.2

Background Hypertensive disorders of pregnancy, including gestational hypertension (GH) and preeclampsia (PE), are associated with an elevated long-term risk of chronic hypertension (CH). However, evidence regarding the underlying mechanisms and effective risk prediction models remains limited. Methods This study utilized baseline metabolomics data from the UK Biobank, focusing on women with a history of GH or PE. Their CH status was assessed both at enrollment and during follow-up. In Phase I, a cross-sectional study employing logistic regression and Mendelian randomization analyses identified metabolites significantly associated with CH. The discriminatory performance of these metabolites for CH at enrollment was evaluated using the area under the receiver operating characteristic curve. In Phase II, a prospective study assessed the predictive performance of the identified metabolites for incident CH at three and five years. Results The study recruited 281 women with a history of GH or PE. Among them, 75 had prevalent CH at enrollment. Of the remaining 206 women without CH at baseline, 27 developed incident CH during follow-up. Nine metabolites were significantly associated with CH, including glycine and lipid components in lipoprotein subclasses. Notably, adding the metabolic profile to a conventional prediction model significantly improved the predictive performance for CH. Conclusion The findings suggest that metabolic dysregulation, particularly in lipid metabolism, is involved in the progression to CH following GH or PE. The identified metabolic profile may enhance CH risk prediction in this high-risk population.

There is a lack of data on the optimal gestational weight gain (GWG) in twin pregnancies in China. A multicenter retrospective study was conducted, containing 1247 twin pregnancies in both North and South China. Optimal GWG was defined as the interquartile range of GWG across pre-pregnancy body mass index stratum among low-risk women. The composite outcome was defined as any occurrence of preterm delivery, small for gestational age, large for gestational age, and hypertensive disorders during pregnancy. The study found that the optimal total gestational weight gain was 16–21.80 kg for underweight, 15.35–21.50 kg for normal weight, 12.10–20.25 kg for overweight, and 5.50–18.50 kg for obese subgroups. Corresponding gestational weight gain rates were 0.43–0.61 kg/week, 0.42–0.59 kg/week, 0.34–0.55 kg/week, and 0.15–0.51 kg/week. The proposed gestational weight gain ranges by our study were lower than the provisional Institute of Medicine (IOM) twin recommendation but higher than the Chinese Nutrition Society’s singleton recommendation. Additionally, 46.11

Increasing evidence revealed a significant relationship between the fine particulate matter (PM2.5) exposure and the incidence of liver diseases. The contributions of ferroptosis and pyroptosis were recently reported in the progression of hepatic diseases. This study aimed to investigate the possible role of ferroptosis and pyroptosis in PM2.5-induced hepatocyte toxicity in LO2 cells (25, 50, or 100 μg/mL, 24 h) and liver injury in mice (10 mg/kg, intranasal administration, twice a week for 15 times). Our results revealed that PM2.5 induced a dose-related cytotoxic effect on LO2 cells and mouse liver injuries as well as increased activities of aspartate aminotransferase and alanine aminotransferase. These findings were accompanied by Fe2+ accumulation, resulting from iron transport system disruption and ferritinophagy activation. The excess free Fe2+ induced reactive oxygen species (ROS) overproduction, glutathione (GSH) depletion, glutathione peroxidase (GPX) activity loss, differential regulation of Nrf2 pathway elements, decreasing expression of the GPX4 protein, increasing expression of PTGS2 and ACSL4 genes and lipid peroxides, and finally initiation of ferroptosis. Along with tissue inflammation, overexpression of HMGB1 and proinflammatory cytokines, hepatocyte pyroptosis as indicated by cellular pyroptotic characteristics, NLRP3 inflammasome activation, and Gasdermin D cleavage were also induced by PM2.5. The mitigating effects of deferoxamine on the above effects suggested ferroptosis and pyroptosis and the subsequent inflammation responses as potential mechanisms underlying PM2.5-induced liver toxicity. These results demonstrated that PM2.5 induced inflammation-associated hepatocyte toxicity and liver injury via the activation of ferroptosis and pyroptosis caused by iron accumulation-triggered oxidative stress. Our study revealed the potential role of different programmed cell death patterns in PM2.5-induced hepatic toxicity.

Construction a troublemaking risk assessment tool to predict the risk of troublemaking for patients with severe mental disorders in the community of China. 28,000 cases registered in the Jiangsu Provincial Severe Mental Disorder Management System from January 2017 to December 2019 were collected. The risk factors of troublemaking among patients with severe mental disorders in the community were analyzed through Logistic regression analysis, then the troublemaking risk assessment tool was established and verified. The incidence of troublemaking among patients with severe mental disorders in the community was 7.15%. The results of multivariate logistic regression analysis showed that males, ≤ 44 years old, duration of disease ≤ 14 years, high school education and below, unemployed, subsistence allowances, schizophrenia, major symptoms > 1, psychiatric visits ≥ 1 time per year, unwilling to participate in community management and community rehabilitation activities, and delayed diagnosis < 2 months were risk factors for troublemaking. The above factors were incorporated into the nomogram model, and the area under the ROC curve of the nomogram model was 0.688 (95%CI: 0.563–0.726). The calibration curve proved that the probability predicted by the model was in good agreement with the actual probability. The established troublemaking risk assessment tool for patients with severe mental disorders in the community based on Logistic regression analysis had good predictive performance, which could be applied to assess the probability of troublemaking among patients with severe mental disorders in the community.

Objective:Lyme disease, caused by Borrelia burgdorferi and transmitted by blacklegged ticks (Ixodes species), is the most common vector-borne disease in the United States. Its spatiotemporal dynamics are influenced by environmental and socioeconomic factors, yet the impacts of the COVID-19 pandemic on Lyme disease remain unclear. Methods:We analyzed county-level Lyme disease surveillance data (2001-2022) alongside environmental, socioeconomic, and tick vector data. Using machine learning models (Random Forest, Boosted Regression Trees, and XGBoost) and Shapley Additive Explanations (SHAP), we evaluated the influence of key predictors on Lyme disease risk. Predicted cases for 2020-2022 were compared with actual reports to assess the pandemic's effects. Results:Lyme disease cases rose from 16,862 in 2001 to 61,802 in 2022, with geographic expansion into southeastern regions. Population density, ecological niche of I. scapularis, and maximum temperature were presented as the key predictors of disease risk. The COVID-19 pandemic severely disrupted reporting dynamics, with 2020 and 2021 cases falling 43.9 % (95 % CI: 41.2-46.7 %) and 22.0 % (95 % CI: 19.5-24.5 %) below predictions, respectively-a decline most pronounced in the Northeast and linked to reduced healthcare access and outdoor activity during lockdowns. Conclusion:Our findings highlight the complex interactions of environmental, socioeconomic, and behavioral factors in Lyme disease dynamics, including the significant impact of the COVID-19 pandemic on disease reporting. These insights underscore the need for integrated, data-driven public health strategies to mitigate Lyme disease risk in the United States.

Backgrounds: Research indicates that metal exposure and gut microbiota may influence weight gain in children. This study aimed to examine the relationship between metal mixture exposure, gut microbiota composition, and weight gain trajectories among children admitted to the neonatal intensive care unit. Methods: A total of 207 children were recruited from the Children's Hospital of Hunan Province, Central China. Blood metal concentrations were measured using ICP-MS, and gut microbiota composition was determined through 16S rRNA gene sequencing. Group-based trajectory modeling, logistic regression and Bayesian Kernel Machine Regression (BKMR) were used to assess the effects of metal mixtures and gut microbiota on children's weight gain patterns. Results: Four distinct weight gain trajectories were identified over the five-year follow-up: growth retardation group, catch-up growth group, high-end weight group, and normal growth group. Among 26 metals, Cs (OR = 3.208, 95 %CI: 1.107, 9.963), Cu (OR = 3.270, 95 %CI: 1.132, 10.148), W (OR = 3.393, 95 %CI: 1.208, 10.101), As (OR = 0.272, 95 %CI: 0.069, 0.872) and Mn (OR = 0.201, 95 %CI: 0.047, 0.674) were significantly correlated with the growth retardation group. RB41(OR = 9.630, p = 0.016) and Bacteroides (OR=0.272, p = 0.011) in gut microbiota were correlated with the growth retardation group. Conclusion: Mn, As, Cs, Cu, W, Zn and Sb in 26 metals, RB41 and Bacteroides in gut microbiota were associated with the trajectory patterns of children. Maintaining optimal blood metal levels and regulating gut microbiota composition during early infancy may have important implications for promoting healthy weight gain trajectories in early childhood.

Purpose:This study aimed to investigate the predictive effects of atherogenic indices and remnant cholesterol on the risk of GDM. Patients and Methods:This observational study was conducted based on the Hospital's clinical information system. A total of 6619 participants including 2054 GDM patients and 4565 controls were obtained. Serum lipid data, including triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) levels were collected. Atherogenic indices including TG/HDL-C, TC/HDL-C, and LDL-C/HDL-C, and remnant cholesterol were regarded as independent variables, and GDM was regarded as the dependent variable. Logistic regression analyses, nomogram analysis, decision curve analysis (DCA), and restricted cubic spline (RCS) analysis were applied to explore the impact of atherogenic indices and remnant cholesterol on GDM. Results:The prevalence of GDM was 31.03% in this study. In comparison to the 1st quartile, the 4th quartile levels of atherogenic indices and remnant cholesterols were significantly associated with increased risks of gestational diabetes mellitus (GDM). The odds ratios (OR) for these associations were as follows: OR = 1.66 (95% CI: 1.41, 1.96) for TG/HDL-C; OR = 1.47 (95% CI: 1.24, 1.73) for TC/HDL-C; OR = 1.47 (95% CI: 1.24, 1.73) for LDL-C/HDL-C; and OR = 1.39 (95% CI: 1.18, 1.64) for remnant cholesterol. The DCA results confirmed the reliable clinical utility of GDM prediction by atherogenic indices and remnant cholesterol. The RCS regression analysis revealed the nonlinear relationships between the atherogenic indices, remnant cholesterol and GDM. Conclusion:This study revealed the potential predictive effects of atherogenic indices and remnant cholesterol on GDM. These findings underscore the potential of routine lipid testing as a cost-effective strategy for the early identification and management of GDM in clinical settings.

Objective: Insulin -like growth factor binding protein 7 (IGFBP7) has a strong affinity to insulin. This study aimed to evaluate the relationship between IGFBP7 and complications among type 2 diabetes mellitus (T2DM) patients. Design: A total of 1449 T2DM patients were selected from a cross-sectional study for disease management registered in the National Basic Public Health Service in Changshu, China, and further tested for their plasma IGFBP7 levels. Logistic regressions and Spearman 's rank correlation analyses were used to explore the associations of IGFBP7 with diabetic complications and clinical characteristics, respectively. Results: Among the 1449 included T2DM patients, 403 (27.81%) had complications. In patients with shorter duration (less than five years), the base 10 logarithms of IGFBP7 concentration were associated with T2DM complications, with an adjusted odds ratio (OR) of 2.41 [95% confidence interval (95%CI) = 1.06 -5.48]; while in patients with longer duration (more than five years), plasma IGFBP7 levels were not associated with T2DM complications. Furthermore, in T2DM patients with shorter duration, those with two or more types of complications were more likely to have higher levels of IGFBP7. Conclusion: IGFBP7 is positively associated with the risk of complication in T2DM patients with shorter duration.

Background:Growth differentiation factor-15 (GDF-15) is a stress response protein and is related to cardiovascular diseases (CVD). This study aimed to investigate the association between GDF-15 and pre-eclampsia (PE). Method:The study involved 299 pregnant women, out of which 236 had normal pregnancies, while 63 participants had PE. Maternal serum levels of GDF-15 were measured by using enzyme-linked immunosorbent assay kits and then translated into multiple of median (MOM) to avoid the influence of gestational week at blood sampling. Logistic models were performed to estimate the association between GDF-15 MOM and PE, presenting as odd ratios (ORs) and 95% confidence intervals (CIs). Results:MOM of GDF-15 in PE participants was higher compared with controls (1.588 vs. 1.000, p < 0.001). In the logistic model, pregnant women with higher MOM of GDF-15 (>1) had a 4.74-fold (95% CI = 2.23-10.08, p < 0.001) increased risk of PE, adjusted by age, preconceptional body mass index, gravidity, and parity. Conclusions:These results demonstrated that higher levels of serum GDF-15 were associated with PE. GDF-15 may serve as a biomarker for diagnosing PE.

Importance:Many studies have reported that the interpregnancy interval (IPI) is a potential modifiable risk factor for adverse perinatal outcomes. However, the association between IPI after live birth and subsequent spontaneous abortion (SA) is unclear. Objective:To investigate the association of IPI after a healthy live birth and subsequent SA. Design, Setting, and Participants:This prospective cohort study used data from 180 921 women aged 20 to 49 years who had a single healthy live birth and planned for another pregnancy and who participated in the Chinese National Free Prepregnancy Checkups Project from January 1, 2010, to December 31, 2020. Statistical analysis was conducted from June 20 to October 5, 2023. Exposure:Interpregnancy interval, defined as the interval between the delivery date and conception of the subsequent pregnancy, was categorized as follows: less than 18 months, 18 to 23 months, 24 to 35 months, 36 to 59 months, and 60 months or longer. Main Outcomes and Measures:The main outcome was SA. Multivariable-adjusted odds ratios (ORs) were calculated by logistic regression models to examine the association between IPI and the risk of SA. Dose-response associations were evaluated by restricted cubic splines. Results:The analyses included 180 921 multiparous women (mean [SD] age at current pregnancy, 26.3 [2.8] years); 4380 SA events (2.4% of all participants) were recorded. A J-shaped association between IPI levels and SA was identified. In the fully adjusted model, compared with IPIs of 18 to 23 months, both short (<18 months) and long (≥36 months) IPIs showed an increased risk of SA (IPIs of <18 months: OR, 1.15 [95% CI, 1.04-1.27]; IPIs of 36-59 months: OR, 1.28 [95% CI, 1.15-1.43]; IPIs of ≥60 months: OR, 2.13 [95% CI, 1.78-2.56]). Results of the subgroup analysis by mode of previous delivery were consistent with the main analysis. Conclusions and Relevance:This cohort study of multiparous women suggests that an IPI of shorter than 18 months or an IPI of 36 months or longer after a healthy live birth was associated with an increased risk of subsequent SA. The findings are valuable to make a rational prepregnancy plan and may facilitate the prevention of SA and improvement in neonatal outcomes.

Pre-eclampsia is a complex multi-system pregnancy disorder with limited treatment options. Therefore, we aimed to screen for metabolites that have causal associations with preeclampsia and to predict target-mediated side effects based on Mendelian randomization (MR) analysis. A two-sample MR analysis was firstly conducted to systematically assess causal associations of blood metabolites with pre-eclampsia, by using metabolites related large-scale genome-wide association studies (GWASs) involving 147,827 European participants, as well as GWASs summary data about pre-eclampsia from the FinnGen consortium R8 release data that included 182,035 Finnish adult female subjects (5922 cases and 176,113 controls). Subsequently, a phenome-wide MR (Phe-MR) analysis was applied to assess the potential on-target side effects associated with hypothetical interventions that reduced the burden of pre-eclampsia by targeting identified metabolites. Four metabolites were identified as potential causal mediators for pre-eclampsia by using the inverse-variance weighted method, including cholesterol in large HDL (L-HDL-C) [odds ratio (OR): 0.88; 95% confidence interval (95% CI): 0.83–0.93; P = 2.14 × 10−5), cholesteryl esters in large HDL (L-HDL-CE) (OR: 0.88; 95% CI: 0.83–0.94; P = 5.93 × 10−5), free cholesterol in very large HDL (XL-HDL-FC) (OR: 0.88; 95% CI: 0.82–0.94; P = 1.10 × 10−4) and free cholesterol in large HDL (L-HDL-FC) (OR: 0.89; 95% CI: 0.84–0.95; P = 1.45 × 10−4). Phe-MR analysis showed that targeting L-HDL-CE had beneficial effects on the risk of 24 diseases from seven disease chapters. Based on this systematic MR analysis, L-HDL-C, L-HDL-CE, XL-HDL-FC, and L-HDL-FC were inversely associated with the risk of pre-eclampsia. Interestingly, L-HDL-CE may be a promising drug target for preventing pre-eclampsia with no predicted detrimental side effects. The study consists of a two-stage design that conducts MR at both stages. First, we assessed the causality for the associations between 194 blood metabolites and the risk of pre-eclampsia. Second, we investigated a broad spectrum of side effects associated with the targeting identified metabolites in 693 non-preeclampsia diseases. Our results suggested that Cholesteryl esters in large HDL may serve as a promising drug target for the prevention or treatment of pre-eclampsia with no predicted detrimental side effects.

Background Poststroke cognitive impairment is a severe and common clinical complication that constitutes a substantial global health burden. We aimed to evaluate the association of 3 cardiac biomarkers in combination with poststroke cognitive impairment and their prognostic significance. Methods and Results This prospective study included 566 patients with ischemic stroke. Cardiac biomarkers, including sST2 (soluble suppression of tumorigenicity‐2 receptor), GDF‐15 (growth differentiation factor‐15), and NT‐proBNP (N‐terminal pro‐B‐type natriuretic peptide), were measured. Cognitive impairment was defined as a Mini‐Mental State Examination score of <27 or a Montreal Cognitive Assessment score of <25 at 3 months after ischemic stroke. Odds of cognitive impairment 3 months after ischemic stroke increased with the number of elevated cardiac biomarkers (sST2, GDF‐15, and NT‐proBNP; Ptrend<0.001). The multivariable adjusted odds ratios (95% CIs) of cognitive impairment defined by the Mini‐Mental State Examination and Montreal Cognitive Assessment were 2.45 (1.48–4.07) and 1.86 (1.10–3.14) for the participants with ≥2 elevated cardiac biomarkers, respectively, compared with those without any elevated cardiac biomarker. Additionally, higher cardiac biomarker scores were associated with an increased risk of cognitive impairment (Ptrend<0.05). Simultaneously adding all 3 cardiac biomarkers to the basic model with traditional risk factors significantly improved the risk prediction of Mini‐Mental State Examination‐defined cognitive impairment (net reclassification improvement=34.99%, P<0.001; integrated discrimination index=2.67%, P<0.001). Similar findings were observed using the Montreal Cognitive Assessment scores. Conclusions An increased number of elevated novel cardiac biomarkers were associated with an increased odds of poststroke cognitive impairment, suggesting that a combination of these cardiac biomarkers may improve the risk prediction of cognitive impairment. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT01840072.

OBJECTIVE:Radon ( 222 Rn) is a naturally occurring radioactive gas that has been closely linked with the development of lung cancer. In this study, we investigated the radon-induced DNA strand breaks, a critical event in lung carcinogenesis, and the corresponding DNA damage response (DDR) in mice and human bronchial epithelial (BEAS-2B) cells. METHODS:Biomarkers of DNA double-strand breaks (DSBs), DNA repair response to DSBs, ataxia-telangiectasia mutated (ATM) kinase, autophagy, and a cell apoptosis signaling pathway as well as cell-cycle arrest and the rate of apoptosis were determined in mouse lung and BEAS-2B cells after radon exposure. RESULTS:Repeated radon exposure induced DSBs indicated by the increasing expressions of γ-Histone 2AX (H2AX) protein and H2AX gene in a time and dose-dependent manner. Additionally, a panel of ATM-dependent repair cascades [i.e. non-homologous DNA end joining (NHEJ), cell-cycle arrest and the p38 mitogen activated protein kinase (p38MAPK)/Bax apoptosis signaling pathway] as well as the autophagy process were activated. Inhibition of autophagy by 3-methyladenine pre-treatment partially reversed the expression of NHEJ-related genes induced by radon exposure in BEAS-2B cells. CONCLUSIONS:The findings demonstrated that long-term exposure to radon gas induced DNA lesions in the form of DSBs and a series of ATM-dependent DDR pathways. Activation of the ATM-mediated autophagy may provide a protective and pro-survival effect on radon-induced DSBs.

BackgroundEarly intervention and diagnosis of Metabolic Syndrome (MetS) are crucial for preventing adult cardiovascular disease. However, the optimal indicator for identifying MetS in adolescent remains controversial.MethodsIn total,1408 Chinese adolescents and 3550 American adolescents aged 12-17 years were included. MetS was defined according to the modified version for adolescents based on Adult Treatment Panel III (NCEP-ATP III) criteria. Areas under the curve (AUC) and corresponding 95% confidence interval (95% CI) of 8 anthropometric/metabolic indexes, such as waist circumference (WC), body mass index (BMI), a body shape index (ABSI), waist triglyceride index (WTI), were calculated to illustrate their ability to differentiate MetS. Sensitivity analysis using the other MetS criteria was performed.ResultsUnder the modified NCEP-ATP III criteria, WTI had the best discriminating ability in overall adolescents, with AUC of 0.922 (95% CI: 0.900-0.945) in Chinese and 0.959 (95% CI: 0.949-0.969) in American. In contrast, ABSI had the lowest AUCs. Results of sensitivity analysis were generally consistent for the whole Chinese and American population, with the AUC for WC being the highest under some criteria, but it was not statistically different from that of WTI.ConclusionsWTI had relatively high discriminatory power for MetS detection in Chinese and American adolescents, but the performance of ABSI was poor.ImpactWhile many studies have compared the discriminatory power of some anthropometric indicators for MetS, there are few focused on pediatrics.The current study is the first to compare the discriminating ability of anthropometric/metabolic indicators (WC, BMI, TMI, ABSI, WHtR, VAI, WTI, and TyG) for MetS in adolescents.WTI remains the optimal indicator in screening for MetS in adolescents.WC was also a simple and reliable indicator when screening for MetS in adolescents, but the performance of ABSI was poor.This study provides a theoretical basis for the early identification of MetS in adolescents by adopting effective indicators.

BackgroundThe associations of gut microbial metabolites, such as trimethylamine N-oxide (TMAO), its precursors, and phenylacetylglutamine (PAGln), with the risk of gestational diabetes mellitus (GDM) remain unclear.MethodsSerum samples of 201 women with GDM and 201 matched controls were collected and then targeted metabolomics was performed to examine the metabolites of interest. Multivariable conditional logistic regression was applied to investigate the relationship between metabolites and GDM. Meta-analysis was performed to combine our results and four similar articles searched from online databases, and Mendelian randomization (MR) analysis was eventually conducted to explore the causalities.ResultsIn the case-control study, after dichotomization and comparing the higher versus the lower group, the adjusted odds ratio and 95% confidence interval of choline and L-carnitine with GDM were 2.124 (1.186-3.803) and 0.293 (0.134-0.638), respectively; but neutral relationships between TMAO, betaine, and PAGln with GDM were observed. The following meta-analysis consistently revealed that L-carnitine was negatively associated with GDM. However, MR analyses showed no evidence of causalities.ConclusionsMaternal levels of L-carnitine were related to the risk of GDM in both the original case-control study and meta-analysis. However, we did not observe any genetic evidence to establish a causal relationship between this metabolite and GDM.