Search

Abstract Background Extensive research has explored the association between heavy metal exposure and various health outcomes, including malignant neoplasms, hypertension, diabetes, and heart diseases. This study aimed to investigate the relationship between patterns of exposure to a mixture of seven heavy metals and these health outcomes. Methods Blood samples from 7,236 adults in the NHANES 2011–2016 studies were analyzed for levels of cadmium, manganese, lead, mercury, selenium, copper, and zinc. Cluster analysis and logistic regression identified three distinct patterns of mixed heavy metal exposure, and their associations with health outcomes were evaluated. Results Pattern 1 exhibited higher odds ratios (ORs) for malignancy during NHANES 2011–2012 (OR = 1.33) and 2015–2016 (OR = 1.29) compared to pattern 2. Pattern 3 showed a lower OR for malignancy during NHANES 2013–2014 (OR = 0.62). For hypertension, pattern 1 displayed higher ORs than pattern 2 for NHANES 2011–2012 (OR = 1.26), 2013–2014 (OR = 1.31), and 2015–2016 (OR = 1.41). Pattern 3 had lower ORs for hypertension during NHANES 2013–2014 (OR = 0.72) and 2015–2016 (OR = 0.67). In terms of heart diseases, pattern 1 exhibited higher ORs than pattern 2 for NHANES 2011–2012 (OR = 1.34), 2013–2014 (OR = 1.76), and 2015–2016 (OR = 1.68). Pattern 3 had lower ORs for heart diseases during NHANES 2013–2014 (OR = 0.59) and 2015–2016 (OR = 0.52). However, no significant trend was observed for diabetes. All three patterns showed the strongest association with hypertension among the health outcomes studied. Conclusions The identified patterns of seven-metal mixtures in NHANES 2011–2016 were robust. Pattern 1 exhibited higher correlations with hypertension, heart disease, and malignancy compared to pattern 2, suggesting an interaction between these metals. Particularly, the identified patterns could offer valuable insights into the management of hypertension in healthy populations.

Abstract Background Depression is a prevalent mental health disorder with a complex etiology and substantial public health implications. Early identification of individuals at risk for depression is crucial for effective intervention and prevention efforts. This study aimed to develop a predictive model for depression by integrating demographic factors (age, race, marital status, income), lifestyle factors (sleep duration, physical activity), and physiological measures (hypertension, blood lead levels). A key objective was to explore the role of physical activity and blood lead levels as predictors of current depression risk. Methods Data were extracted from the 2007–2014 National Health and Nutrition Examination Survey (NHANES). We applied a logistic regression analysis to these data to assess the predictive value of the above eight factors for depression to create the predictive model. Results The predictive model had bootstrap-corrected c-indexes of 0.68 (95% CI, 0.67–0.70) and 0.66 (95% CI, 0.64–0.68) for the training and validation cohorts, respectively, and well-calibrated curves. As the risk of depression increased, the proportion of participants with 1.76 ~ 68.90 µg/L blood lead gradually increased, and the proportion of participants with 0.05 ~ 0.66 µg/L blood lead gradually decreased. In addition, the proportion of sedentary participants increased as the risk of depression increased. Conclusions This study developed a depression risk assessment model that incorporates physical activity and blood lead factors. This model is a promising tool for screening, assessing, and treating depression in the general population. However, because the corrected c-indices of the predictive model have not yet reached an acceptable threshold of 0.70, caution should be exercised when drawing conclusions. Further research is required to improve the performance of this model.

Background: Liver cirrhosis (LC) is one of the most significant causes of morbidity and mortality in patients with chronic liver disease worldwide. Nutrition may be an important component of primary prevention of chronic liver disease. Diet–exercise patterns frame the eating behaviors and exercise habits of people through statistical methods related to nutritional epidemiology, which can explore the relationship between living habits and diseases among diverse populations. The purpose of this study was to explore the association between diet–exercise patterns and cirrhosis, and provide guidance on preventive diets for liver patients. Methods: This study identified diet–exercise patterns via clustering analysis of principal components and assessed their association with cirrhosis through the population samples of the National Health and Nutrition Examination Survey (NHANES) from 2017 to March 2020. Results: We identified two diet–exercise patterns that were named the “prudent pattern” (consumption of various staple foods, eggs, meat, fruits and vegetables; less sedentary) and the “dangerous pattern” (higher consumption of desserts, nuts, milk, meat, alcoholic beverages; recreational activities). The t-test demonstrated a significant relationship between patterns and multiple foods. The simple logistic regression test showed a lower risk of cirrhosis in those in the “prudent pattern” (OR = 0.73, 95%CI = 0.59–0.93). Conclusions: Two diet–exercise patterns associated with cirrhosis were identified: “prudent pattern” and “dangerous pattern”. The results of this study may be useful for suggesting preventive diets for people at risk of cirrhosis.

Abstract Background The high incidence of catastrophic health expenditure (ICHE) among middle-aged and elderly population is a major deterrent for reducing the financial risk of disease. Current research is predominantly based on the assumption of spatial homogeneity of nationwide population characteristics, ignoring the differences in regional characteristics. Thus, our study aimed to explore the impact of various influencing factors on the ICHE from a spatiotemporal perspective. Methods We used data from the China Health and Retirement Longitudinal Study (waves 1 to 4), to conduct a retrospective cohort study across 28 provinces, from 2011 to 2018. We measured regional incidences of catastrophic health expenditure using methods recommended by the World Health Organization. Ordinary least squares (OLS) and geographical and temporal weighted regression (GTWR) were used as the global and local estimation models, respectively. The Fortheringham method was used to test the spatiotemporal non-stationarity. Results National ICHE showed a gradual increase from 2011 to 2015, but suddenly decreased from 2015 to 2018, also showing the spatial heterogeneity. And the southwest area and Hebei showed persistently high ICHE (Qinghai even reached the highest value of 27.5% in 2015). Out-of-pocket payment, gross domestic product, PM2.5, ageing, incidence of non-communicable diseases and disabilities, number of nurses, and health insurance coverage in the global estimation passed the significance test, and the GTWR model showed a better model fit (0.769) than the OLS model (0.388). Furthermore, except for health insurance coverage, all seven variables had spatiotemporal non-stationarity among their impacts on ICHE. Conclusion In this longitudinal study, we found spatiotemporal non-stationarity among the variable relationships, supporting regional governments’ adoption of regional-target policies. First, after the completion of universal health insurance coverage, the spatiotemporal non-stationarity of the prevalence of non-communicable diseases and disability and ageing should be the focus of the next phase of health insurance design, where improvements to compensation coverage and benefit packages are possible policy instruments. Second, the governance and causes of catastrophic health expenditure need to be laid out from a macro perspective rather than only from the individual/household perspective, especially for the potential impact of economic development, air pollution and nursing resources.

Abstract Background Depression has received a lot of attention as a common and serious illness. However, people are rarely aware of their current depression risk probabilities. We aimed to develop and validate a predictive model applicable to the risk of depression in US adults. Methods This study was conducted using the database of the National Health and Nutrition Examination Survey (NHANES, 2017–2012). In particular, NHANES (2007–2010) was used as the training cohort (n = 6015) for prediction model construction and NHANES (2011–2012) was used as the validation cohort (n = 2812) to test the model. Depression was assessed (defined as a binary variable) by the Patient Health Questionnaire (PHQ-9). Socio-demographic characteristics, sleep time, illicit drug use and anxious days were assessed using a self-report questionnaire. Logistic regression analysis was used to evaluate independent risk factors for depression. The nomogram has the advantage of being able to visualize complex statistical prediction models as risk estimates of individualized disease probabilities. Then, we developed two depression risk nomograms based on the results of logistic regression. Finally, several validation methods were used to evaluate the prediction performance of nomograms. Results The predictors of model 1 included gender, age, income, education, marital status, sleep time and illicit drug use, and model 2, furthermore, included anxious days. Both model 1 and model 2 showed good discrimination ability, with a bootstrap-corrected C index of 0.71 (95% CI, 0.69–0.73) and 0.85 (95% CI, 0.83–0.86), and an externally validated C index of 0.71 (95% CI, 0.68–0.74) and 0.83 (95% CI, 0.81–0.86), respectively, and had well-fitted calibration curves. The area under the receiver operating characteristic curve (AUC) values of the models with 1000 different weighted random sampling and depression scores of 10–17 threshold range were higher than 0.7 and 0.8, respectively. Calculated net reclassification improvement (NRI) and integrated discrimination improvement (IDI) showed the discrimination or accuracy of the prediction models. Decision curve analysis (DCA) demonstrated that the depression models were practically useful. The network calculators work for participants to make personalized predictions. Conclusions This study presents two prediction models of depression, which can effectively and accurately predict the probability of depression as well as helping the U.S. civilian non-institutionalized population to make optimal treatment decisions.

Enhancer RNAs (eRNAs) are present specifically in tumors, where they affect the expression of eRNA-regulated genes (ERGs). Owing to this characteristic, ERGs were hypothesized to improve prognosis of overall survival in heterogeneous low-grade and intermediate-grade gliomas. This study aimed to construct and validate an ERG prognostic tool to facilitate clinical management, and offer more effective diagnostic and therapeutic biomarkers for glioma. Survival-related eRNAs were identified, and their ERGs were selected based on eRNA and target gene information. The ERG prognostic model was constructed and validated using internal and external validation cohorts. Finally, biological differences related to the ERG signature were analysed to explore the potential mechanisms influencing survival outcomes. Thirteen ERGs were identified and used to build an ERG risk signature, which included five super-enhancer RNA (seRNA)-regulated genes and five LGG-specific eRNA-regulated genes. The prognostic nomogram established based on combining the ERG score, age, and sex was evaluated by calibration curves, clinical utility, Harrell’s concordance index (0.86; 95% CI: 0.83-0.90), and time-dependent receiver operator characteristic curves. We also explored potential immune-related mechanisms that might cause variation in survival. The established prognostic model displayed high validity and robustness. Several immune-related genes regulated by seRNAs or specific eRNAs were identified, indicating that these transcripts or their genes were potential targets for improving immunotherapeutic/therapeutic outcomes. The functions of an important specific eRNA-regulated gene (USP28) were validated in robust vitro experiments. In addition, the ERG risk signature was significantly associated with the immune microenvironment and other immune-related features.

In order to master the vortex-induced vibration laws of marine riser system, the generalized α method was developed to solve the cylinder vibration equation, and the stability of the solution method was enhanced. After the flow field equation was solved by the improved Characteristic Based Split (CBS), a fluid-solid coupling finite element method was developed to calculate the problem of vortex-induced vibration. In addition, the accuracy and stability of the solution method were verified using a benchmark example. Finally, this solution method was applied to numerically simulate the vortex-induced vibration process of two staggered circular cylinders under various working conditions at low Reynolds number. The results show that when the attack angle is small, the vortex shedding from the upstream cylinder interacts directly with the downstream cylinder, which increases the pressure difference between the upper and lower surfaces of the downstream cylinder, resulting in large-scale vortex-induced vibration. And at this moment, the vibrating frequency of the downstream cylinder is dominated by the shedding process of the vortex from the upstream cylinder. When the attack angle is large, the downstream cylinder will be separated from the wake region of the upstream cylinder, and its flow field, force and vibrating frequency are close to the case of single cylinder. The research results can provide some reference for the layout of marine riser system in engineering practice.

Background: Gliomas are the most malignant tumors of the nervous system. Even though their survival outcome is closely affected by immune-related genes (IRGs) in the tumor microenvironment (TME), the corresponding regulatory mechanism remains poorly characterized.Methods: Specific enhancer RNAs (eRNAs) can be found in tumors, where they control downstream genes. The present study aimed to identify eRNA-regulated IRGs, evaluate their influence on the TME, and use them to construct a novel prognostic model for gliomas.Results: Thirteen target genes (ADCYAP1R1, BMP2, BMPR1A, CD4, DDX17, ELN, FGF13, MAPT, PDIA2, PSMB8, PTPN6, SEMA6C, and SSTR5) were identified and integrated into a comprehensive risk signature, which distinguished two risk subclasses. Discrepancies between these subclasses were compared to explore potential mechanisms attributed to eRNA-regulated genes, including immune cell infiltration, clinicopathological features, survival outcomes, and chemotherapeutic drug sensitivity. Furthermore, the risk signature was used to construct a prognostic tool that was evaluated by calibration curve, clinical utility, Harrell’s concordance index (0.87; 95% CI: 0.84–0.90), and time-dependent receiver operator characteristic curves (AUCs: 0.93 and 0.89 at 3 and 5 years, respectively). The strong reliability and robustness of the established prognostic tool were validated in another independent cohort. Finally, potential subtypes were explored in patients with grade III tumors.Conclusion: Overall, eRNAs were associated with immune-related dysfunctions in the TME. Targeting of IRGs regulated by eRNAs could improve immunotherapeutic/therapeutic outcomes.