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While noise pollution from transportation has become an important public health problem, the relationships between different sources of traffic noise and cardiovascular diseases (CVDs) remain inconclusive. A comprehensive meta-analysis was therefore conducted to quantitatively assess the effects of long-term exposure to road traffic, railway, and aircraft noise on CVDs and relevant subtypes. We systematically retrieved PubMed, Embase, and Web of Science for articles published before April 4, 2022. Summary relative risks (RRs) and 95% confidence intervals (CIs) were estimated by the fixed- or random-effects models. In total, 23 articles were included in our meta-analysis. The risk of CVDs increased by 2% (RR 1.020, 95% CI 1.006–1.035) and 1.6% (RR 1.016, 95% CI 1.000–1.032) for every 10 dB increment of road traffic and aircraft noise. For CVD subtypes, the risk increased by 3.4% (1.034, 1.026–1.043) for stroke and 5% (1.050, 1.006–1.096) for heart failure with each 10 dB increment of road traffic noise; the risk of atrial fibrillation increased by 1.1% (1.011, 1.002–1.021) with each 10 dB increment of railway noise; and the risk increased by 1% (1.010, 1.003–1.017) for myocardial infarction, 2.7% (1.027, 1.004–1.050) for atrial fibrillation, and 2.3% (1.023, 1.016–1.030) for heart failure with each 10 dB increment in aircraft noise. Further, effects from road traffic, railway, and aircraft noise all followed positive linear trends with CVDs. Long-term exposure to traffic noise is positively related to the incidence risk of cardiovascular events, especially road traffic noise which significantly increases the risk of CVDs, stroke, and heart failure. [ABSTRACT FROM AUTHOR]

Background: Accumulating studies indicate that maternal obesity is associated with the risk of cerebral palsy (CP); however, their conclusions have been inconsistent. Objectives: To quantitatively estimate the association between maternal body mass index (BMI) and CP in offspring. Data Sources: PubMed, Embase and Web of Science. Study Selection and Data Extraction: Articles published up to 18 September 2022 were searched that reported the correlation between maternal BMI and CP in children. Two reviewers independently extracted data and critically assessed articles. Synthesis: Pooled relative risks (RR) and 95% confidence intervals (CI) were estimated by the random‐effects model. Subgroup analysis and meta‐regression were performed to explore sources of heterogeneity. Results: In total, 11 articles (8,407,668 participants) were identified for inclusion in our meta‐analysis. For maternal underweight, no significant association was found with CP risk (RR 1.11, 95% CI 0.90, 1.38). The risk of CP was increased by 25% (RR 1.25, 95% CI 1.06, 1.47), 38% (RR 1.38, 95% CI 1.18, 1.61) and 127% (RR 2.27, 95% CI 1.82, 2.83) for maternal overweight, obesity and obesity grade 3, respectively. In addition, we observed a positive linear dose‐response relationship, with the pooled risk of cerebral palsy in offspring increasing by 3% with each unit increase in maternal BMI. Conclusion: This meta‐analysis indicates that the risk of CP in offspring grew with maternal overweight or obesity grades increasing, and was positively correlated with maternal BMI. [ABSTRACT FROM AUTHOR]

The relationship between pregnancy loss and the risk of cardiovascular diseases (CVDs) remains a matter of debate. Our intention in conducting this meta -analysis was to analyze the relationship between miscarriage and stillbirth and risk of CVDs. PubMed, Embase, and Web of Science were systematically searched up to May 30, 2023 for all relevant studies. The random-effects model was applied to estimate the pooled relative risks (RRs) and 95% confidence intervals (95% CIs). We evaluated RR estimates for the risk of CVDs with each additional miscarriage and stillbirth through generalized least squares regression. Twenty-three articles were incorporated into the meta -analysis. For women with a history of miscarriage, the pooled RRs for the risk of total CVDs, coronary heart disease (CHD), stroke, and total CVD deaths were 1.16 (95 % CI 1.10–1.22), 1.26 (1.12–1.41), 1.13 (1.03–1.24), and 1.20 (1.01–1.42), respectively. For women with a history of stillbirth, the pooled RRs for the risk of total CVDs, CHD, stroke, and total CVD deaths were 1.60 (1.34–1.89), 1.30 (1.12–1.50), 1.37 (1.06–1.78), and 1.95 (1.05–3.63), respectively. With each additional miscarriage, the risk increased for total CVDs (1.08, 1.04–1.13), CHD (1.08, 1.04–1.13), and stroke (1.05, 1.00–1.10). With each additional stillbirth, the risk increased for total CVDs (1.11, 1.03–1.21) and CHD (1.13, 1.07–1.19). This meta -analysis indicates that both miscarriages and stillbirths are related to a higher risk of total CVDs, CHD, stroke, and total CVD deaths. The risk of total CVDs and CHD increased with the number of miscarriages or stillbirths. [ABSTRACT FROM AUTHOR]

Aims: To identify the trajectories of body mass index (BMI) and waist circumference (WC), and assess the associations of BMI trajectory, WC trajectory, or the two combined, with type 2 diabetes mellitus (T2DM) risk in Chinese adults. Materials and Methods: This study was based on a prospective project—the Prediction for Atherosclerotic Cardiovascular Disease Risk in China (China‐PAR). A total of 54 434 participants (39.21% men) who were measured on at least two occasions were included. Three slowly increasing trajectory patterns were identified for BMI, and four for WC, by latent mixed modelling. A nine‐category variable was derived by combining the WC trajectory (low, moderate, moderate‐high/high) and the BMI trajectory (low, moderate, high). Logistic regression models were applied to estimate the odds ratios (ORs) and 95% confidence intervals (CIs). Results: The risk of developing T2DM increased with elevated BMI or WC trajectory levels (all ptrend <0.001). The risks were 2.85 (2.59–3.14) for high BMI trajectory and 4.34 (3.78–4.99) for high WC trajectory versus low trajectory groups, respectively. The association was more pronounced among younger individuals (pinteraction <0.001). In the joint analysis, compared to participants with low WC and BMI trajectory, those with moderate‐high/high WC combined with high BMI trajectory had the highest risk of T2DM (OR 3.96, 95% CI 3.48–4.50); even those who maintained moderate‐high/high WC but low BMI trajectory showed a higher T2DM risk (OR 3.00, 95% CI 2.31–3.91). Conclusions: This study suggests that simultaneous dynamic and continuous monitoring of BMI and WC may contribute more than single measurements to predicting T2DM risk and determining preventive strategies. [ABSTRACT FROM AUTHOR]

Background: The triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio and triglyceride-glucose (TyG) index are novel indexes for insulin resistance (IR). We aimed to evaluate associations of TG/HDL-C and TyG with arterial stiffness risk. Methods: We enrolled 1979 participants from the Rural Chinese Cohort Study, examining arterial stiffness by brachial-ankle pulse wave velocity (baPWV). Logistic and linear regression models were employed to calculate effect estimates. For meta-analysis, we searched relevant articles from PubMed, Embase and Web of Science up to August 26, 2023. The fixed-effects or random-effects models were used to calculate the pooled estimates. We evaluated dose-response associations using restricted cubic splines. Results: For cross-sectional studies, the adjusted ORs (95%CIs) for arterial stiffness were 1.12 (1.01–1.23) and 1.78 (1.38–2.30) for per 1 unit increment in TG/HDL-C and TyG. In the meta-analysis, the pooled ORs (95% CIs) were 1.26 (1.14–1.39) and 1.57 (1.36–1.82) for per 1 unit increment of TG/HDL-C and TyG. Additionally, both TG/HDL-C and TyG were positively related to PWV, with β of 0.09 (95% CI 0.04–0.14) and 0.57 (95% CI 0.35–0.78) m/s. We also found linear associations of TG/HDL-C and TyG with arterial stiffness risk. Conclusions: High TG/HDL-C and TyG were related to increased arterial stiffness risk, indicating TG/HDL-C and TyG may be convincing predictors of arterial stiffness. [ABSTRACT FROM AUTHOR]

Purpose: This meta‐analysis evaluated the relationship between overweight/obesity and depressive disorders in children and adolescents. Methods: We examined the databases of PubMed, Embase and Web of Science for pertinent observational studies released up until 20 February 2022. The pooled relative risks (RRs) and 95% confidence intervals (CIs) of obesity and overweight with depressive disorder were calculated by means of random‐effects models. The Newcastle‐Ottawa Quality Assessment Scale and Agency for Healthcare Research and Quality scale were adopted to evaluate the study quality. Results: Finally, for this meta‐analysis, we evaluated 22 observational publications covering 175 135 participants (5 cohort study articles, 1 case–control study article and 16 cross‐sectional study articles). A significant positive association was found between obesity and the risk of depression (RR 1.32, 95% CI 1.09–1.60, I2 = 79.90%, Pheterogeneity < 0.001) and in the association between obesity and depressive symptoms (RR 1.16, 95% CI: 1.00–1.35, I2 = 25.0%, Pheterogeneity = 0.247). On sensitivity analysis, the pooled RRs remained robust. Subgroup analysis indicated that obese children and teenagers in western countries were more prone to depression. Conclusion: Evidence from this meta‐analysis, based on observational studies, supported the idea that obese children and adolescents are more likely to experience depression and depressive symptoms. [ABSTRACT FROM AUTHOR]

Aims: To investigate the association of methylation risk score (MRS) and its interactions with environmental factors with type 2 diabetes mellitus (T2DM) risk. Methods: We conducted a nested case–control study with 241 onset cases and 241 matched controls. Conditional logistic regression models were employed to identify risk CpG sites. Simple and weighted MRSs were constructed based on the methylation levels of ATP‐binding cassette G1 gene, fat mass and obesity associated gene, potassium voltage‐gated channel member 1 gene, and thioredoxin‐interacting protein gene previously associated with T2DM to estimate the association of MRS with T2DM risk. Stratified analyses were used to investigate interactions between MRS and environmental factors. Results: A total of 10 CpG loci were identified from the aforementioned genes to calculate MRS. After controlling for potential confounding factors, taking tertile 1 as reference, the odds ratios (ORs) and 95% confidence intervals (CIs) for T2DM of tertile 3 was 2.39 (1.36–4.20) for simple MRS and 2.59 (1.45–4.63) for weighted MRS. With per SD score increment in MRS, the OR (95% CI) was 1.66 (1.29–2.14) and 1.60 (1.24–2.08) for simple and weighted MRSs, respectively. J‐curved associations were observed between both simple and weighted MRSs and T2DM risks. Additionally, multiplication interactions for smoking and hypertension with simple MRS on the risk of T2DM were found, similarly for smoking and obesity with weighted MRS on the risk of T2DM (all Pinteraction <.05). Conclusion: Elevated simple and weighted MRSs were associated with increased risk of T2DM. Environmental risk factors may influence the association between MRS and T2DM. [ABSTRACT FROM AUTHOR]

Limited information is available on the links between heavy metals' exposure and coronary heart disease (CHD). We aim to establish an efficient and explainable machine learning (ML) model that associates heavy metals' exposure with CHD identification. Our datasets for investigating the associations between heavy metals and CHD were sourced from the US National Health and Nutrition Examination Survey (US NHANES, 2003–2018). Five ML models were established to identify CHD by heavy metals' exposure. Further, 11 discrimination characteristics were used to test the strength of the models. The optimally performing model was selected for identification. Finally, the SHapley Additive exPlanations (SHAP) tool was used for interpreting the features to visualize the selected model's decision-making capacity. In total, 12,554 participants were eligible for this study. The best performing random forest classifier (RF) based on 13 heavy metals to identify CHD was chosen (AUC: 0.827; 95%CI: 0.777–0.877; accuracy: 95.9%). SHAP values indicated that cesium (1.62), thallium (1.17), antimony (1.63), dimethylarsonic acid (0.91), barium (0.76), arsenous acid (0.79), total arsenic (0.01) in urine, and lead (3.58) and cadmium (4.66) in blood positively contributed to the model, while cobalt (−0.15), cadmium (−2.93), and uranium (−0.13) in urine negatively contributed to the model. The RF model was efficient, accurate, and robust in identifying an association between heavy metals' exposure and CHD among US NHANES 2003–2018 participants. Cesium, thallium, antimony, dimethylarsonic acid, barium, arsenous acid, and total arsenic in urine, and lead and cadmium in blood show positive relationships with CHD, while cobalt, cadmium, and uranium in urine show negative relationships with CHD. [Display omitted] • An explainable ML model for CHD identification was built by using multi-source data over 16 years. • RF model associated with heavy metal for identifying CHD was efficient, accurate, and robust. • Shap explained positive and negative relationships between heavy metals with CHD. [ABSTRACT FROM AUTHOR]