Identification for heavy metals exposure on osteoarthritis among aging people and Machine learning for prediction: A study based on NHANES 2011-2020

Association between sleep duration, depression and breast cancer in the United States: a national health and nutrition examination survey analysis 2009-2018

OBJECTIVE

Breast cancer is the most common cancer in women, threatening both physical and mental health. The epidemiological evidence for association between sleep duration, depression and breast cancer is inconsistent. The aim of this study was to determine the association between them and build machine-learning algorithms to predict breast cancer.

METHODS

A total of 1,789 participants from the National Health and Nutrition Examination Survey (NHANES) were included in the study, and 263 breast cancer patients were identified. Sleep duration was collected using a standardized questionnaire, and the Nine-item Patient Health Questionnaire (PHQ-9) was used to assess depression. Logistic regression yielded multivariable-adjusted breast cancer odds ratios (OR) and 95% confidence intervals (CI) for sleep duration and depression. Then, six machine learning algorithms, including AdaBoost, random forest, Boost tree, artificial neural network, limit gradient enhancement and support vector machine, were used to predict the development of breast cancer and find out the best algorithm.

RESULTS

Body mass index (BMI), race and smoking were statistically different between breast cancer and non-breast cancer groups. Participants with depression were associated with breast cancer (OR = 1.99, 95%CI: 1.55-3.51). Compared with 7-9h of sleep, the ORs for <7 and >9 h of sleep were 1.25 (95% CI: 0.85-1.37) and 1.05 (95% CI: 0.95-1.15), respectively. The AdaBoost model outperformed other machine learning algorithms and predicted well for breast cancer, with an area under curve (AUC) of 0.84 (95%CI: 0.81-0.87).

CONCLUSIONS

No significant association was observed between sleep duration and breast cancer, and participants with depression were associated with an increased risk for breast cancer. This finding provides new clues into the relationship between breast cancer and depression and sleep duration, and provides potential evidence for subsequent studies of pathological mechanisms.

Molecular mechanisms of environmental pollutant-induced cartilage damage: from developmental disorders to osteoarthritis

The objective of the present study was to review the molecular mechanisms of the adverse effects of environmental pollutants on chondrocytes and extracellular matrix (ECM). Existing data demonstrate that both heavy metals, including cadmium (Cd), lead (Pb), and arsenic (As), as well as organic pollutants, including polychlorinated dioxins and furans (PCDD/Fs) and polychlorinated biphenyls (PCB), bisphenol A, phthalates, polycyclic aromatic hydrocarbons (PAH), pesticides, and certain other organic pollutants that target cartilage ontogeny and functioning. Overall, environmental pollutants reduce chondrocyte viability through the induction apoptosis, senescence, and inflammatory response, resulting in cell death and impaired ECM production. The effects of organic pollutants on chondrocyte development and viability were shown to be mediated by binding to the aryl hydrocarbon receptor (AhR) signaling and modulation of non-coding RNA expression. Adverse effects of pollutant exposures were observed in articular and growth plate chondrocytes. These mechanisms also damage chondrocyte precursors and subsequently hinder cartilage development. In addition, pollutant exposure was shown to impair chondrogenesis by inhibiting the expression of Sox9 and other regulators. Along with altered Runx2 signaling, these effects also contribute to impaired chondrocyte hypertrophy and chondrocyte-to-osteoblast trans-differentiation, resulting in altered endochondral ossification. Several organic pollutants including PCDD/Fs, PCBs and PAHs, were shown to induce transgenerational adverse effects on cartilage development and the resulting skeletal deformities. Despite of epidemiological evidence linking human environmental pollutant exposure to osteoarthritis or other cartilage pathologies, the data on the molecular mechanisms of adverse effects of environmental pollutant exposure on cartilage tissue were obtained from studies in laboratory rodents, fish, or cell cultures and should be carefully extrapolated to humans, although they clearly demonstrate that cartilage should be considered a putative target for environmental pollutant toxicity.

Inflammatory burden index: associations between osteoarthritis and all-cause mortality among individuals with osteoarthritis

BACKGROUND

The newly described inflammatory burden index (IBI) reflects a patient's inflammatory burden. This study aimed to estimate the association between IBI, osteoarthritis (OA), and all-cause mortality in patients with OA.

METHODS

We extracted the data of adults from the National Health and Nutrition Examination Survey database between 1999 and 2018. After using appropriate survey weights to correct for sample bias, we conducted multivariate logistic regression analyses to explore the association between IBI and OA across three models: in the unadjusted model, partially adjusted model (adjusting age, sex, race, education level, marital status, PIR, BMI, smoking status, drinking status, stroke, CVD, DM, and hypertension) and fully adjusted model (which included additional variables: HBA1C, ALT, AST, BUN, TC, and HDL). And the odds ratios (OR) and 95% confidence intervals (CI) were calculated. Similarly, using comparable survey weights and covariates adjustments, we employed Cox proportional hazards regression analysis to investigate the association between IBI and all-cause mortality in the other 3 models. The Cox proportional hazards regression models were fitted to calculate the hazard ratios (HR) and 95% CI of the association between IBI and all-cause mortality. A restricted cubic spline (RCS) was used to explore the nonlinear relationships between association effects. Subgroup analysis was performed to validate the reliability of their effects.

RESULTS

In total, 22,343 eligible participants were included. Multiple logistic regression models revealed that participants with the highest IBI had 2.54 times (95%CI, 2.23, 2.90)) higher risk of OA than those with the lowest IBI in Model 1, whereas the OR was 1.21 (95%CI, 1.03, 1.42) in Model 2 and 1.23 (95%CI,1.05, 1.45) in Model 3. Multiple Cox regression models showed participants with the highest IBI had 186% (95%CI, 1.50, 2.31) times risk of developing all-cause death than those with the lowest IBI in Model 1. This trend remained stable in Models 2 (HR,1.54; 95%CI,1.22, 1.95) and 3 (HR, 1.41; 95%CI, 1.10, 1.80). The RCS revealed a significant positive association between IBI and OA risk. With respect to the association between IBI and all-cause mortality, a slight decrease in mortality was observed from the lowest quartile to the second quartile of IBI, and the mortality risk increased with increasing IBI. Subgroup analyses showed that age, cardiovascular disease, and hypertension were pivotal in the association of IBI with all-cause mortality, whereas the association of IBI with OA remained stable after stratification by other factors such as sex, race, education level, marital, smoking, and drinking status, hypertension, and most serological indices.

CONCLUSIONS

This study provides evidence of a positive association between IBI, OA, and all-cause mortality. IBI may be a promising signature for assessing the inflammatory burden in patients with OA, which, in turn, is conducive to precise references for high-risk population recognition, anti-inflammatory guidance, and reducing mortality intervention.

Assessment of EMR ML Mining Methods for Measuring Association between Metal Mixture and Mortality for Hypertension

INTRODUCTION

There are limited data available regarding the connection between heavy metal exposure and mortality among hypertension patients.

AIM

We intend to establish an interpretable machine learning (ML) model with high efficiency and robustness that monitors mortality based on heavy metal exposure among hypertension patients.

METHODS

Our datasets were obtained from the US National Health and Nutrition Examination Survey (NHANES, 2013-2018). We developed 5 ML models for mortality prediction among hypertension patients by heavy metal exposure, and tested them by 10 discrimination characteristics. Further, we chose the optimally performing model after parameter adjustment by genetic algorithm (GA) for prediction. Finally, in order to visualize the model's ability to make decisions, we used SHapley Additive exPlanation (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) algorithm to illustrate the features. The study included 2347 participants in total.

RESULTS

A best-performing eXtreme Gradient Boosting (XGB) with GA for mortality prediction among hypertension patients by 13 heavy metals was selected (AUC 0.959; 95% CI 0.953-0.965; accuracy 96.8%). According to sum of SHAP values, cadmium (0.094), cobalt (2.048), lead (1.12), tungsten (0.129) in urine, and lead (2.026), mercury (1.703) in blood positively influenced the model, while barium (- 0.001), molybdenum (- 2.066), antimony (- 0.398), tin (- 0.498), thallium (- 2.297) in urine, and selenium (- 0.842), manganese (- 1.193) in blood negatively influenced the model.

CONCLUSIONS

Hypertension patients' mortality associated with heavy metal exposure was predicted by an efficient, robust, and interpretable GA-XGB model with SHAP and LIME. Cadmium, cobalt, lead, tungsten in urine, and mercury in blood are positively correlated with mortality, while barium, molybdenum, antimony, tin, thallium in urine, and lead, selenium, manganese in blood is negatively correlated with mortality.

Analyzing the impact of heavy metal exposure on osteoarthritis and rheumatoid arthritis: an approach based on interpretable machine learning

Introduction

This investigation leverages advanced machine learning (ML) techniques to dissect the complex relationship between heavy metal exposure and its impacts on osteoarthritis (OA) and rheumatoid arthritis (RA). Utilizing a comprehensive dataset from the National Health and Nutrition Examination Survey (NHANES) spanning from 2003 to 2020, this study aims to elucidate the roles specific heavy metals play in the incidence and differentiation of OA and RA.

Methods

Employing a phased ML strategy that encompasses a range of methodologies, including LASSO regression and SHapley Additive exPlanations (SHAP), our analytical framework integrates demographic, laboratory, and questionnaire data. Thirteen distinct ML models were applied across seven methodologies to enhance the predictability and interpretability of clinical outcomes. Each phase of model development was meticulously designed to progressively refine the algorithm's performance.

Results

The results reveal significant associations between certain heavy metals and an increased risk of arthritis. The phased ML approach enabled the precise identification of key predictors and their contributions to disease outcomes.

Discussion

These findings offer new insights into potential pathways for early detection, prevention, and management strategies for arthritis associated with environmental exposures. By improving the interpretability of ML models, this research provides a potent tool for clinicians and researchers, facilitating a deeper understanding of the environmental determinants of arthritis.

Spatial - temporal mapping of urine cadmium levels in China during 1980 - 2040: Dietary improvements lower exposure amid rising pollution

Persistent cadmium exposure poses significant health risks to the Chinese population, underscored by its prevalence as an environmental contaminant. This study leverages a machine-learning model, fed with a comprehensive dataset of environmental and socio-economic factors, to delineate trends in cadmium exposure from 1980 to 2040. We uncovered that urinary cadmium levels peaked at 1.09 μg/g Cr in the mid-2000 s. Encouragingly, a decline is projected to 0.92 μg/g Cr by 2025, tapering further to 0.87 μg/g Cr by 2040. Despite this trend, regions heavily influenced by industrialization, such as Hunan and Guizhou, as well as industrial counties in Jilin, report stubbornly high levels of exposure. Our demographic analysis reveals a higher vulnerability among adults & adolescents over 14, with males displaying elevated cadmium concentrations. Alarmingly, the projected data suggests that by 2040, an estimated 41% of the population will endure exposure beyond the safety threshold set by the European Food Safety Authority. Our research indicates disproportionate cadmium exposure impacts, necessitating targeted interventions and policy reforms to protect vulnerable groups and public health in China.

Exploring the relationship between heavy metals and diabetic retinopathy: a machine learning modeling approach

Diabetic retinopathy (DR) is one of the leading causes of adult blindness in the United States. Although studies applying traditional statistical methods have revealed that heavy metals may be essential environmental risk factors for diabetic retinopathy, there is a lack of analyses based on machine learning (ML) methods to adequately explain the complex relationship between heavy metals and DR and the interactions between variables. Based on characteristic variables of participants with and without DR and heavy metal exposure data obtained from the NHANES database (2003-2010), a ML model was developed for effective prediction of DR. The best predictive model for DR was selected from 11 models by receiver operating characteristic curve (ROC) analysis. Further permutation feature importance (PFI) analysis, partial dependence plots (PDP) analysis, and SHapley Additive exPlanations (SHAP) analysis were used to assess the model capability and key influencing factors. A total of 1042 eligible individuals were randomly assigned to two groups for training and testing set of the prediction model. ROC analysis showed that the k-nearest neighbour (KNN) model had the highest prediction performance, achieving close to 100% accuracy in the testing set. Urinary Sb level was identified as the critical heavy metal affecting the predicted risk of DR, with a contribution weight of 1.730632 ± 1.791722, which was much higher than that of other heavy metals and baseline variables. The results of the PDP analysis and the SHAP analysis also indicated that antimony (Sb) had a more significant effect on DR. The interaction between age and Sb was more significant compared to other variables and metal pairs. We found that Sb could serve as a potential predictor of DR and that Sb may influence the development of DR by mediating cellular and systemic senescence. The study revealed that monitoring urinary Sb levels can be useful for early non-invasive screening and intervention in DR development, and also highlighted the important role of constructed ML models in explaining the effects of heavy metal exposure on DR.

Possible non-linear relation between prostate specific antigen and vitamin D: a machine learning study based on cross-section data

Objective: Due to inconsistent results in earlier investigations regarding the relationship between vitamin D and prostate-specific antigen (PSA), this study was conducted to gain a deeper understanding of the association between vitamin D and PSA. Methods: A total of 7174 male samples with 25(OH)D, PSA, and other variables were obtained from the National Health and Nutrition Examination Survey (NHANES) database. Three models, created through stepwise logistic regression, were employed to examine the dose-response association between PSA and 25(OH)D. Subsequently, restricted cubic spline analysis (RCS) was used to explore the nonlinear association between 25(OH)D and PSA. The study also compared the performance of four machine learning models in predicting PSA levels. Results: The dose-response relationship indicated a negative impact of high 25(OH)D levels on PSA (p for trend 0.05). The odds ratio (OR) of Q4 (7.73 with 95% CI (0.26, 15.76)) was significantly higher than Q1 (6.23 with 95% CI (0.24, 12.57)). OR values in Q2 and Q3 were less than 1 (Q2= 0.57 with 95% CI (-6.37, 8.04) and Q3= 0.26 with 95% CI (-5.94, 6.86)), suggesting a potential protective effect of 25(OH)D on PSA. RCS analysis revealed a U-shaped relationship between blood 25(OH)D levels and PSA, with serum 25(OH)D in the range of 20-134 ng/ml showing a potential decrease in PSA levels. Above this range, an increase in 25(OH)D might elevate PSA levels. Age (2.67 with 95% CI 2.24 to 3.1) and BMI (17.52 with 95% CI 7.65 to 26.32), along with the OR of obesity (10.36 with 95% CI 0.68 to 20.18), were identified as potential PSA risk factors. Among the machine learning models, the random forest algorithm performed the best in predicting PSA levels. Conclusion: This study revealed a U-shaped relationship between 25(OH)D and PSA, with PSA potentially declining when 25(OH)D is between 20 and 134 ng/mL and possibly rising above this range. The random forest method proved effective in both predicting PSA levels and guiding vitamin D dosage.

Global, regional, national trends of femur fracture and machine learning prediction: Comprehensive findings and questions from global burden of disease 1990-2019

Background

Femur fracture is a type of fracture with high disability and mortality. There is no comprehensive analysis and prediction of the global distribution of femur fractures, so we conducted this study.

Methods

Age-standardized incidence rate (ASIR), age-standardized prevalence rate (ASPR), and years living with disability (YLDs) of femur fractures (excluding femoral neck) were downloaded from the Global burden of disease database. Trend analysis was performed, and 6 time-series machine learning algorithms were applied to predict the global ASIR, ASPR, and YLDs.

Results

ASPR for femur fracture had been increasing in most countries worldwide from 1990 to 2019, with the highest in East Asia (AAPC = 1.25 95%Confidence Interval (1.2, 1.3)) and lowest in Central Latin America (AAPC = -0.74 95%CI (-0.81, -0.67)). However, ASIR showed a significant downward trend worldwide, with East Saharan Africa decreasing the most (AAPC = -4.04 95%CI (-5.56, -2.47)), and East Asia elevating the most (AAPC = 1.11 95%CI (0.87, 1.42)). YLDs were increasing over the world, with East Asia still elevating the most AAPC= (3.9 95%CI (3.85, 3.95)), with the only region of decrease being Eastern Europe (AAPC = -0.28 95%CI (-0.3, -0.26)). Both ASPR and ASIR were higher in women than in men in the >75 year group, whereas YLDs was lower in women than in men in the >60 year group. Globally, the ARIMA model was optimal in the prediction of ASPR, the PROPHET model effected in the prediction of ASIR, and the PROPHET WITH XGBOOST model was the best in the prediction of YLDs. The projections showed increase in both ASPR and YLDs, except for ASIR decreasing by 2030.

Conclusions

Our study found a rise in femur fracture ASPR and ASIR from 1990 to 2019 in war conflict areas and East Asia, meanwhile, the YLDs of femur fracture increased in populous countries. In both 1990 and 2019, both ASPR and ASIR were higher in women over 75 years than that in men, but YLDs was higher in men over 60 years than that in women. In 2020-2030, while global femur fracture ASIR might decline, both ASPR and YLDs might rise.

The Translational Potential of this article

Femur fracture is a high-energy injury due to direct violence, and in war, conflicting and underdeveloped regions such as East Asia. Accidental injuries may occur due to the rapid development of industry and the frequent traffic accidents. This study suggests that we should focus on elderly women (≥75 years) in the above regions in the future. For older men (>60 years old), more attention should be paid to post-fracture functional rehabilitation and early reintegration into society to reduce the disability rate and lower the socio-economic burden.

Circulating Copper Is Associated with Inflammatory Biomarkers in Greek Older Adults with Osteoarthritis

Osteoarthritis (OA) is the most common form of arthritis, that causes a significant decrease in the quality of life of the afflicted and constitutes a great burden for the socioeconomic system. Trace elements and heavy metals are implicated in the pathophysiology of OA, exacerbating inflammatory and oxidative stress responses. The aim of this cross-sectional study was to quantify metals in plasma samples of Greek OA patients and explore their link with disease related parameters, health status or quality of life, as well as epigenetic OA markers. This is the first study on plasma metal levels in Greek knee OA patients. To achieve precision in plasma metal and miRNA measurements, high-quality samples were selected from a subset of 34 participants (NCT04783792). Demographic, quality of life, clinical, biochemical, inflammation, oxidative stress, and anthropometric parameters, as well as microRNA levels were assessed. Significant correlations were found between circulating metals with OA related parameters or with measured microRNAs. Also, significant positive associations between plasma copper (Cu) levels and CRP (p = 0.033) or IL-6 (p = 0.001) occurred when adjusting for age, gender, BMI, physical activity level, smoking, disease severity, total arthroplasty, and dietary intake of the respective metal. Cu's role in OA is bidirectional, and this study confirms the findings that in OA, Cu is positively associated with inflammation. Such relationships between lifestyle, environment and OA enhance our understanding and encourage further study on metals related to OA inflammation.

Application of machine learning algorithms to identify people with low bone density

Background

Osteoporosis is becoming more common worldwide, imposing a substantial burden on individuals and society. The onset of osteoporosis is subtle, early detection is challenging, and population-wide screening is infeasible. Thus, there is a need to develop a method to identify those at high risk for osteoporosis.

Objective

This study aimed to develop a machine learning algorithm to effectively identify people with low bone density, using readily available demographic and blood biochemical data.

Methods

Using NHANES 2017-2020 data, participants over 50 years old with complete femoral neck BMD data were selected. This cohort was randomly divided into training (70%) and test (30%) sets. Lasso regression selected variables for inclusion in six machine learning models built on the training data: logistic regression (LR), support vector machine (SVM), gradient boosting machine (GBM), naive Bayes (NB), artificial neural network (ANN) and random forest (RF). NHANES data from the 2013-2014 cycle was used as an external validation set input into the models to verify their generalizability. Model discrimination was assessed via AUC, accuracy, sensitivity, specificity, precision and F1 score. Calibration curves evaluated goodness-of-fit. Decision curves determined clinical utility. The SHAP framework analyzed variable importance.

Results

A total of 3,545 participants were included in the internal validation set of this study, of whom 1870 had normal bone density and 1,675 had low bone density Lasso regression selected 19 variables. In the test set, AUC was 0.785 (LR), 0.780 (SVM), 0.775 (GBM), 0.729 (NB), 0.771 (ANN), and 0.768 (RF). The LR model has the best discrimination and a better calibration curve fit, the best clinical net benefit for the decision curve, and it also reflects good predictive power in the external validation dataset The top variables in the LR model were: age, BMI, gender, creatine phosphokinase, total cholesterol and alkaline phosphatase.

Conclusion

The machine learning model demonstrated effective classification of low BMD using blood biomarkers. This could aid clinical decision making for osteoporosis prevention and management.

Association of blood metals with anxiety among adults: A nationally representative cross-sectional study

BACKGROUND

Previous evidence demonstrated the inconsistent associations between metals and anxiety. The purpose of this study was to evaluate the individual and joint effects of blood lead (Pb), cadmium (Cd), mercury (Hg), selenium (Se) and manganese (Mn) on anxiety in the general population.

METHODS

Data of 4000 participants (aged≥20 years) in the study were retrieved from the National Health and Nutrition Examination Survey (NHANES) 2011-2012. Multiple logistic regression, restricted cubic splines (RCS) logistic analysis, and weighted quantile sum (WQS) regression were fitted to explore the possible effects of single and mixed metal exposures on anxiety. Moreover, this association was assessed by smoking group.

RESULTS

In the study, 24.60 % of participants were in an anxiety state. In logistic regression, blood Pb, Cd, Hg, Se and Mn were not significantly associated with anxiety in all participants. After stratified by smoking group, blood Cd was positively associated with anxiety in the current smoking group [P = 0.029, OR (95 %): 1.708(1.063, 3.040)], whereas not in other groups. In RCS regression, we observed a linear dose-response effect of blood Cd on anxiety stratified by smoking group. In WQS analysis, mixed metal exposures were positively associated with anxiety [P = 0.033, OR (95 %): 1.437(1.031, 2.003)], with Cd (33.69 %) contributing the largest weight to the index.

CONCLUSIONS

Our study showed that excessive exposure to Cd is a significant risk factor for anxiety, and the co-exposures to Pb, Cd, Hg, Se and Mn were positively related with the risk of anxiety in current smokers.

Association and mediation analyses among multiple metal exposure, mineralocorticoid levels, and serum ion balance in residents of northwest China

Toxic metals are vital risk factors affecting serum ion balance; however, the effect of their co-exposure on serum ions and the underlying mechanism remain unclear. We assessed the correlations of single metal and mixed metals with serum ion levels, and the mediating effects of mineralocorticoids by investigating toxic metal concentrations in the blood, as well as the levels of representative mineralocorticoids, such as deoxycorticosterone (DOC), and serum ions in 471 participants from the Dongdagou-Xinglong cohort. In the single-exposure model, sodium and chloride levels were positively correlated with arsenic, selenium, cadmium, and lead levels and negatively correlated with zinc levels, whereas potassium and iron levels and the anion gap were positively correlated with zinc levels and negatively correlated with selenium, cadmium and lead levels (all P < 0.05). Similar results were obtained in the mixed exposure models considering all metals, and the major contributions of cadmium, lead, arsenic, and selenium were highlighted. Significant dose-response relationships were detected between levels of serum DOC and toxic metals and serum ions. Mediation analysis showed that serum DOC partially mediated the relationship of metals (especially mixed metals) with serum iron and anion gap by 8.3% and 8.6%, respectively. These findings suggest that single and mixed metal exposure interferes with the homeostasis of serum mineralocorticoids, which is also related to altered serum ion levels. Furthermore, serum DOC may remarkably affect toxic metal-related serum ion disturbances, providing clues for further study of health risks associated with these toxic metals.

Effects of Various Heavy Metal Exposures on Insulin Resistance in Non-diabetic Populations: Interpretability Analysis from Machine Learning Modeling Perspective

Increasing and compelling evidence has been proved that heavy metal exposure is involved in the development of insulin resistance (IR). We trained an interpretable predictive machine learning (ML) model for IR in the non-diabetic populations based on levels of heavy metal exposure. A total of 4354 participants from the NHANES (2003-2020) with complete information were randomly divided into a training set and a test set. Twelve ML algorithms, including random forest (RF), XGBoost (XGB), logistic regression (LR), GaussianNB (GNB), ridge regression (RR), support vector machine (SVM), multilayer perceptron (MLP), decision tree (DT), AdaBoost (AB), Gradient Boosting Decision Tree (GBDT), Voting Classifier (VC), and K-Nearest Neighbour (KNN), were constructed for IR prediction using the training set. Among these models, the RF algorithm had the best predictive performance, showing an accuracy of 80.14%, an AUC of 0.856, and an F1 score of 0.74 in the test set. We embedded three interpretable methods, the permutation feature importance analysis, partial dependence plot (PDP), and Shapley additive explanations (SHAP) in RF model for model interpretation. Urinary Ba, urinary Mo, blood Pb, and blood Cd levels were identified as the main influencers of IR. Within a specific range, urinary Ba (0.56-3.56 µg/L) and urinary Mo (1.06-20.25 µg/L) levels exhibited the most pronounced upwards trend with the risk of IR, while blood Pb (0.05-2.81 µg/dL) and blood Cd (0.24-0.65 µg/L) levels showed a declining trend with IR. The findings on the synergistic effects demonstrated that controlling urinary Ba levels might be more crucial for the management of IR. The SHAP decision plot offered personalized care for IR based on heavy metal control. In conclusion, by utilizing interpretable ML approaches, we emphasize the predictive value of heavy metals for IR, especially Ba, Mo, Pb, and Cd.

The interpretable machine learning model associated with metal mixtures to identify hypertension via EMR mining method

There are limited data available regarding the connection between hypertension and heavy metal exposure. The authors intend to establish an interpretable machine learning (ML) model with high efficiency and robustness that identifies hypertension based on heavy metal exposure. Our datasets were obtained from the US National Health and Nutrition Examination Survey (NHANES, 2013-2020.3). The authors developed 5 ML models for hypertension identification by heavy metal exposure, and tested them by 10 discrimination characteristics. Further, the authors chose the optimally performing model after parameter adjustment by Genetic Algorithm (GA) for identification. Finally, in order to visualize the model's ability to make decisions, the authors used SHapley Additive exPlanation (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) algorithm to illustrate the features. The study included 19 368 participants in total. A best-performing eXtreme Gradient Boosting (XGB) with GA for hypertension identification by 16 heavy metals was selected (AUC: 0.774; 95% CI: 0.772-0.776; accuracy: 87.7%). According to SHAP values, Barium (0.02), Cadmium (0.017), Lead (0.017), Antimony (0.008), Tin (0.007), Manganese (0.006), Thallium (0.004), Tungsten (0.004) in urine, and Lead (0.048), Mercury (0.035), Selenium (0.05), Manganese (0.007) in blood positively influenced the model, while Cadmium (-0.001) in urine negatively influenced the model. Study participants' hypertension associated with heavy metal exposure was identified by an efficient, robust, and interpretable GA-XGB model with SHAP and LIME. Barium, Cadmium, Lead, Antimony, Tin, Manganese, Thallium, Tungsten in urine, and Lead, Mercury, Selenium, Manganese in blood are positively correlated with hypertension, while Cadmium in blood is negatively correlated with hypertension.

The Significant Role of Alcohol in the Relationship between C-Reactive Protein and Self-Reported Osteoarthritis

BACKGROUND

Despite the known inflammatory nature of osteoarthritis (OA) and the established role of C-reactive protein (CRP) as an inflammation marker, the influence of alcohol consumption on the CRP-OA relationship remains uncertain, with previous research providing conflicting results.

OBJECTIVES

This study aims to examine the potential moderating effect of alcohol on the association between CRP concentrations and self-reported OA.

METHODS

We conducted a cross-sectional study involving 50,259 participants, all data collected from NHANES between 2005-2010 and 2015-2018. A multivariable logistic regression model was used to analyze the relationship between CRP and OA.

RESULTS

We found a nonsignificant positive association between CRP concentration and prevalence of self-reported OA after adjusting for covariates in the raw dataset or 5 multiple imputed datasets. In the stratified analysis by alcohol drinking, for every 10 mg/L higher in CRP concentration, the prevalence of self-reported OA was higher by 13 % in nondrinkers (P = 0.007, adjusted for covariates). Conversely, for every 10 mg/L higher in CRP concentration, the prevalence of self-reported OA was lower by 59 % in drinkers (P = 0.005, adjusted for covariates). Furthermore, we discovered that the directions of the association between CRP concentrations (10 mg/L) and prevalence of self-reported OA [odds ratio (OR) < 1 in the drinking subgroup and OR > 1 in the no-drinking subgroup] were stable in both the main and sensitivity analyses. The significant interaction between CRP concentration and alcohol drinking on the prevalence of self-reported OA was shown in most of our analyses (P-interaction < 0.05).

CONCLUSION

Alcohol consumption may be an interaction factor between CRP and self-reported OA. To our knowledge, our findings are the first to highlight the importance of incorporating analysis of alcohol consumption differences into future studies of CRP and self-reported OA.

Machine learning algorithms predicting bladder cancer associated with diabetes and hypertension: NHANES 2009 to 2018

Bladder cancer is 1 of the 10 most common cancers in the world. However, the relationship between diabetes, hypertension and bladder cancer are still controversial, limited study used machine learning models to predict the development of bladder cancer. This study aimed to explore the association between diabetes, hypertension and bladder cancer, and build predictive models of bladder cancer. A total of 1789 patients from the National Health and Nutrition Examination Survey were enrolled in this study. We examined the association between diabetes, hypertension and bladder cancer using multivariate logistic regression model, after adjusting for confounding factors. Four machine learning models, including extreme gradient boosting (XGBoost), Artificial Neural Networks, Random Forest and Support Vector Machine were compared to predict for bladder cancer. Model performance was assessed by examining the area under the subject operating characteristic curve, accuracy, recall, specificity, precision, and F1 score. The mean age of bladder cancer group was older than that of the non-bladder cancer (74.4 years vs 65.6 years, P < .001), and men were more likely to have bladder cancer. Diabetes was associated with increased risk of bladder cancer (odds ratio = 1.24, 95%confidence interval [95%CI]: 1.17-3.02). The XGBoost model was the best algorithm for predicting bladder cancer; an accuracy and kappa value was 0.978 with 95%CI:0.976 to 0.986 and 0.01 with 95%CI:0.01 to 0.52, respectively. The sensitivity was 0.90 (95%CI:0.74-0.97) and the area under the curve was 0.78. These results suggested that diabetes is associated with risk of bladder cancer, and XGBoost model was the best algorithm to predict bladder cancer.

Association of vitamins with bone mineral density and osteoporosis measured by dual-energy x-ray absorptiometry: a cross-sectional study

BACKGROUND

We aimed to assess the associations of vitamins intake with osteoporosis based on a national sample from US adults.

METHODS

A total of 1536 participants were included in this cross-sectional study to investigate the relationship between vitamins intake and osteoporosis from National Health and Nutrition Examination Survey, including vitamin A, C, D. Logistic regression models were used to assess the associations between dietary vitamin intake and osteoporosis.

RESULTS

We found that vitamins intake were negatively associated with osteoporosis. For vitamin A, compared with the first tertile, the odds ratios (ORs) and 95% confidential intervals (CIs) were 0.93 (0.81-1.04) for the second tertile and 0.85 (0.78-0.96) for the third tertile (P < 0.01). For vitamin C, compared with the first tertile, the ORs and 95% CIs were 0.89 (0.78-1.05) for the second tertile and 0.79 (0.67-0.93) for the third tertile (P < 0.01). For vitamin D, compared with the first tertile, the odds ratios (ORs) and 95% confidential intervals (CIs) were 0.94 (0.82-1.07) for the second tertile and 0.88 (0.75-0.98) for the third tertile (P < 0.01). And the negative association between vitamins intake and osteoporosis were more evident for female, aged ≥ 60, and BMI > 30, including vitamin A, C and D.

CONCLUSIONS

Our findings provide evidence that vitamins intake is linked with decreased prevalence of osteoporosis, including vitamin A, C, D. Further large-scale prospective cohort studies are needed to verify our findings.

Global, regional, and national mortality of tuberculosis attributable to alcohol and tobacco from 1990 to 2019: A modelling study based on the Global Burden of Disease study 2019

Background

Tuberculosis (TB) is expected to become the second leading single cause of death with several risk factors, but the related disease burden is currently unknown. We aimed to analyse the pre-coronavirus disease 2019 (COVID-19) changes in mortality of TB attributable to alcohol and tobacco worldwide from 1990 to 2019.

Methods

We obtained data of TB deaths and age-standardised death rates attributed to alcohol and cigarette in 204 countries and territories from the Global Burden of Disease 2019 public database. We performed a spatial-temporal analysis of age-standardised death rate and the average annual per cent change (AAPC), after which we analysed the effects of gender and socio-demographic index on age-standardised death rate using an age-period-cohort model. Finally, we built machine learning models to predict the TB age-standardised death rate in 2035.

Results

We found that the global age-standardised death rate of TB attributable to alcohol consumption declined from 5.35 (95% uncertainty interval (UI) = 3.51, 7.00) in 1990 to 2.54 (95% UI = 1.65, 3.33) in 2019, with significant declines in Andean Latin America (AAPC = -7.59; 95% confidence interval (CI) = -8, -7.16, P < 0.05), East Asia (AAPC = -7.32; 95% CI = -8.00, -6.62, P < 0.05), and Central Latin America (AAPC = -7.31; 95% CI = -7.63, -6.99, P < 0.05). However, there was an increase in a few regions, especially in parts of Central Asia. The age-standardised death rate for TB attributable to cigarette smoking declined more rapidly than that for TB attributable to alcohol, from 7.45 (95% UI = 6.17, 8.72) to 2.21 (95% UI = 1.78, 2.64), especially in East Asia (AAPC = -6.64; 95% CI = -7.07, -6.2, P < 0.05), North Africa and Middle East (AAPC = -6.47; 95% CI = -6.67, -6.28, P < 0.05), and Andean Latin America (AAPC = -6.31; 95% CI = -6.87, -5.75, P < 0.05). However, TB attributable to cigarette smoking increased in parts of Eastern Europe. In both TB attributable to alcohol consumption and to cigarette smoking, the age-standardised death rate was much higher in men than in women. The age-period-cohort model results showed that TB attributable to alcohol consumption was the highest in older adults aged 60-80 years, while TB attributable to cigarette smoking was the highest in adults aged 40-60 years. Machine learning models projected that by 2035, the age-standardised death rate for TB attributable to alcohol consumption would be 1.29 (per 100 000 population), while the age-standardised death rate for TB attributable to cigarette consumption would be 0.37 (per 100 000 population), which might not achieve the 2035 global target for eliminating TB.

Conclusions

Globally, the age-standardised death rate for TB attributable to alcohol consumption declined slower than that attributable to cigarette smoking. Controlling these two factors would help achieve the global goal of TB elimination, especially for the elderly who are at high risk.

Association between combined exposure to dioxins and arthritis among US adults: a cross-sectional study

Dioxins and dioxin-like compounds (DLCs) are common pollutants hazardous to human health. We applied 12 dioxins and DLCs data of 1851 participants (including 484 arthritis patients) from National Health Examination Survey (NHANES) 2001-2004 and quadrupled them into rank variables. Multivariate logistic regression, weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR) models were used to explore the relationship between individual or mixed exposure to the pollutants and arthritis after adjusting for multiple covariates. In multivariable logistic regression with an individual dioxin or DLC, almost every chemical was significantly positively associated with arthritis, except PCB66 (polychlorinated biphenyl 66) and 1,2,3,4,6,7,8-heptachlorodibenzofuran (hpcdf). The WQS model indicated that the combined exposure to the 12 dioxins and DLCs was positively linked to arthritis (OR: 1.884, 95% CI: 1.514-2.346), with PCB156 (weighted 0.281) making the greatest contribution. A positive trend between combined exposure and arthritis was observed in the BKMR model, with a posterior inclusion probability (PIP) of 0.987 for PCB156, which was also higher than the other contaminants.

The association of blood metals with latent tuberculosis infection among adults and adolescents

Objective

We aimed to investigate the relationship of metal exposure and latent tuberculosis infection (LTBI) among US adults and adolescents.

Methods

Participants from the National Health and Nutrition Examination Surveys (NHANES 2011 ~ 2012) were included. Multiple logistic regression models were used to explore the associations between metal exposure and LTBI. A total of 5,248 adults and 1,860 adolescents were included in the present analysis.

Results

For adults, we only found a positive association between total mercury and LTBI (OR: 1.411; 95% CI: 1.164 ~ 1.710) when used as a continuous variable. Compared with Q1, Q4 increased the prevalence of LTBI (2.303; 1.455 ~ 3.644) when used as a quartile. The OR of total mercury and LTBI was higher among females (1.517; 1.009 ~ 2.279), individuals aged 45 ~ 64 (1.457; 1.060 ~ 2.002), and non-Hispanic White individuals (1.773; 1.316 ~ 2.388). A relationship was observed among only participants with obesity (1.553; 1.040 ~ 2.319) or underweight (1.380; 1.076 ~ 1.771), with college or above (1.645; 1.184 ~ 2.286), with PIR > 3.0 (1.701; 1.217 ~ 2.376), reported smoking (1.535; 1.235 ~ 1.907) and drinking (1.464; 1.232 ~ 1.739). For adolescents, blood manganese was positively associated with LTBI. The OR and 95% CIs for each one-unit increase in the log-transformed level of blood manganese with LTBI were 9.954 (1.389 ~ 71.344).

Conclusion

Significant associations were observed in girls, aged ≥12 years and in the non-Hispanic white population. In conclusion, total mercury is associated with an increased prevalence of LTBI among adults and positive association between blood manganese and LTBI was observed among adolescents. Further studies should be conducted to verify the results and explore potential biological mechanisms.

Association between systemic immune-inflammation index and diabetes: a population-based study from the NHANES

Background

Systemic Immune-Inflammation Index (SII) has been reported to be associated with diabetes. We aimed to assess possible links between SII and diabetes.

Methods

Data were obtained from the 2017-2020 National Health and Nutrition Examination Survey (NHANES) database. After removing missing data for SII and diabetes, we examined patients older than 20 years. Simultaneously, the relationship between SII and diabetes was examined using weighted multivariate regression analysis, subgroup analysis, and smooth curve fitting.

Results

There were 7877 subjects in this study, the average SII was 524.91 ± 358.90, and the prevalence of diabetes was 16.07%. Weighted multivariate regression analysis found that SII was positively associated with diabetes, and in model 3, this positive association remained stable (OR = 1.04; 95% CI: 1.02-1.06; p = 0.0006), indicating that each additional unit of SII, the possibility of having diabetes increased by 4%. Gender, age, BMI, regular exercise, high blood pressure, and smoking did not significantly affect this positive link, according to the interaction test (p for trend>0.05).

Discussion

Additional prospective studies are required to examine the precise connection between higher SII levels and diabetes, which may be associated with higher SII levels.

Association of Serum Vitamin D With Periodontal Disease

OBJECTIVE

There are conflicting reports on the relationship between vitamin D and periodontal disease. Our research is intended to further analyse the association between serum 25(OH)D3, a vitamin D precursor and periodontal disease based on a large national survey sample in Japan.

METHODS

We downloaded the 2009-2018 National Health and Nutrition Examination Survey (NHANES) cycle, which included a total of 23,324 samples. Logistic regression of factors influencing perioral disease including periodntal disease, and subgroup logistic regression were performed to analyse the relationship between serum vitamin D and perioral disease, using WTMEC2YR as weights for regression analysis. Then machine learning model-based prediction of perioral disease onset was performed, and the machine learning algorithms used included boost tree, artificial neural network, AdaBoost, and random forest.

RESULTS

We evaluated the vitamin D, age, sex, race, education, marriage, body mass index, ratio of family income to poverty (PIR), smoking, alcohol consumption, diabetes, and hypertension as variables in the included samples. Vitamin D was negatively associated with perioral disease; compared with Q1, the odds ratios and 95% CI were 0.8 (0.67-0.96) for Q2, 0.84 (0.71-1.00) for Q3, and 0.74 (0.6-0.92) for Q4 (P for trend <.05), respectively. The results of the subgroup analysis showed that the effect of 25(OH)D3 on periodontal disease was more pronounced in women younger than 60 years. Based on the accuracy and receiver operating characteristic curve, we concluded that a boost tree was a relatively good model to predict periodontal disease.

CONCLUSIONS

Vitamin D might be a protective factor for periodontal disease, and boost tree analysis we emplyed was a relatively good model to predict perioral disease.

Primary antibiotic resistance in Helicobacter pylori in China: a systematic review and meta-analysis

OBJECTIVES

The incidence of Helicobacter pylori (HP) is 25-50% in developed countries and 80% in developing countries, including 56.2% in China. However, antibiotic resistance of HP is a threat to HP control. The purpose of this study was to comprehensively evaluate primary drug resistance of HP in China.

METHODS

The full text of reports of the primary antibiotic resistance prevalence of HP was obtained from multiple databases (PubMed, Web of Science, Evimed, Cochrane Library, and China National Knowledge Internet). Review Manager 5.2 was adopted for meta-analysis, sensitivity analysis, and bias analysis. The Newcastle-Ottawa Scale was used to assess the article quality.

RESULTS

In total, 38804 HP samples from 22 trials were extracted. The results suggested that the overall prevalence of amoxicillin, clarithromycin, metronidazole, and levofloxacin resistance among HP in adults was as follows: mean difference (MD) = 1.35%, 95% confidence interval (CI) [1.03%, 1.68%]; MD = 23.76%, 95% CI [20.23%, 27.3%]; MD = 69.32%, 95% CI [64.85%, 73.8%]; and MD = 29.45%, 95% CI [4.90, 176.96], respectively. From the results of sensitivity and publication bias, we find that these results are robust and had little publication bias.

CONCLUSION

Our research showed that in China, the prevalence of HP resistance to primary antibiotics warrants attention, especially with regard to metronidazole, levofloxacin, and clarithromycin.

Analysis of misdiagnosed or delayed-diagnosed Leprosy bacillus infection from 1990 to 2020 with a prophet time series prediction in Hubei Province, China

To analyze the misdiagnosis or delayed diagnosis of leprosy in Hubei Province, China during the past 30 years, which can provide a scientific basis for improving the prevention and treatment of leprosy by proposing targeted intervention measures. A retrospective study was conducted to compile 161 cases of misdiagnosed or delayed diagnosis of leprosy in Hubei Province during 1990 to 2020 from the National Leprosy Prevention and Control Management Information System and the background information of regional leprosy control centers in Hubei Province. Among 161 study subjects, the shortest delay period was 25.30 months for cases aged 15 to 20 years, the longest delay period was 67.09 months for cases aged 51 to 60 years, the shortest delay period was 35.33 months for type TN cases, and the longest delay period was 75.17 months for type I cases. There were 71 cases (44.10%) misdiagnosed, and the top 5 misdiagnosed disease names were rash 23 cases (32.39%). Top 5 misdiagnosed cases were rash 23 (32.39%), rheumatism 10 (14.08%), skin ulceration 9 (12.68%), dermatitis 9 (12.68%), neuritis 9 (12.68%). In the prophet prediction, the overall trend of leprosy misdiagnosis was increasing and within 1 year the number is fluctuant. The training of medical personnel at all levels on leprosy prevention and treatment should be strengthened, and the public awareness of leprosy prevention and treatment should be enhanced.

Effects of heavy metal exposure on hypertension: A machine learning modeling approach

Heavy metal exposure is a common risk factor for hypertension. To develop an interpretable predictive machine learning (ML) model for hypertension based on levels of heavy metal exposure, data from the NHANES (2003-2016) were employed. Random forest (RF), support vector machine (SVM), decision tree (DT), multilayer perceptron (MLP), ridge regression (RR), AdaBoost (AB), gradient boosting decision tree (GBDT), voting classifier (VC), and K-nearest neighbour (KNN) algorithms were utilized to generate an optimal predictive model for hypertension. Three interpretable methods, the permutation feature importance analysis, partial dependence plot (PDP), and Shapley additive explanations (SHAP) methods, were integrated into a pipeline and embedded in ML for model interpretation. A total of 9005 eligible individuals were randomly allocated into two distinct sets for predictive model training and validation. The results showed that among the predictive models, the RF model demonstrated the highest performance, achieving an accuracy rate of 77.40% in the validation set. The AUC and F1 score for the model were 0.84 and 0.76, respectively. Blood Pb, urinary Cd, urinary Tl, and urinary Co levels were identified as the main influencers of hypertension, and their contribution weights were 0.0504 ± 0.0482, 0.0389 ± 0.0256, 0.0307 ± 0.0179, and 0.0296 ± 0.0162, respectively. Blood Pb (0.55-2.93 μg/dL) and urinary Cd (0.06-0.15 μg/L) levels exhibited the most pronounced upwards trend with the risk of hypertension within a specific value range, while urinary Tl (0.06-0.26 μg/L) and urinary Co (0.02-0.32 μg/L) levels demonstrated a declining trend with hypertension. The findings on the synergistic effects indicated that Pb and Cd were the primary determinants of hypertension. Our findings underscore the predictive value of heavy metals for hypertension. By utilizing interpretable methods, we discerned that Pb, Cd, Tl, and Co emerged as noteworthy contributors within the predictive model.

Meta analysis of clinical prognosis of radiofrequency ablation versus partial nephrectomy in the treatment of early renal cell carcinoma

Objective

To systematically review the differences between radiofrequency ablation and partial nephrectomy in patients with early-stage renal cell carcinoma, and to provide evidence-based medical evidence for the choice of surgery for patients with early-stage renal cell carcinoma.

Methods

According to the search strategy recommended by the Cochrane Collaboration, Chinese databases such as CNKI, VIP Chinese Science and Technology Periodicals Database (VIP), and Wanfang Full-text Database were searched with Chinese search terms. And PubMed and MEDLINE as databases for English literature retrieval. Retrieve the relevant literature on renal cell carcinoma surgical methods published before May 2022, and further screen radiofrequency ablation and partial nephrectomy in patients with renal cell carcinoma The relevant literature on the application is analyzed. RevMan5.3 software was used for heterogeneity test and combined statistical analysis, sensitivity analysis, and subgroup analysis. Analysis, and draw forest plot, using Stata software Begger quantitative assessment of publication bias.

Results

A total of 11 articles were involved, including 2958 patients. According to the Jadad scale, 2 articles were of low quality, and the remaining 9 articles were of high quality. Results of this study demonstrates the advantages of radiofrequency ablation in early-stage renal cell carcinoma. The results of this meta-analysis showed that compared with partial nephrectomy, there was significant difference in the 5-year overall survival rate between radiofrequency ablation and partial nephrectomy and there was a statistically significant difference between the two surgical methods in the 5-year relapse free survival rate of early renal cell carcinoma.

Conclusion

1. Compared with partial nephrectomy, the 5-year relapse-free survival rate, the 5-year cancer specific survival rate and the overall 5-year survival rate were higher in the radiofrequency ablation group. 2. Compared with partial nephrectomy, there was no significant difference in the postoperative local tumor recurrence rate of radiofrequency ablation. 3. Compared with partial resection, radiofrequency ablation is more beneficial to patients with renal cell carcinoma.

Relationship of blood heavy metals and osteoporosis among the middle-aged and elderly adults: A secondary analysis from NHANES 2013 to 2014 and 2017 to 2018

Objective

This study aimed to assess the relationship between blood heavy metals and a higher prevalence of osteoporosis in middle-aged and elderly US adults using the National Health and Nutritional Examination Surveys (NHANES).

Methods

The secondary data analysis was performed using the data of NHANES 2013–2014 and 2017–2018. We used the information, including physical examination, laboratory tests, questionnaires, and interviews, provided by participants in NHANES. Logistic regression and weighted quantile sum (WQS) regression models were used to explore the relationships between levels of blood heavy metals and a higher prevalence of osteoporosis.

Results

A total of 1,777 middle-aged and elderly participants were analyzed in this study, comprising 115 participants with osteoporosis and 1,662 without osteoporosis. Adjusted model 1 showed a significant positive relationship between cadmium (Cd) levels and a higher prevalence of osteoporosis (quartile 2, OR = 7.62; 95% CI, 2.01–29.03; p = 0.003; quartile 3, OR = 12.38; 95% CI, 3.88–39.60; p &lt; 0.001; and quartile 4, OR = 15.64; 95% CI, 3.22–76.08; p = 0.001). The fourth quartile of selenium (Se) level (OR = 0.34; 95% CI, 0.14–0.39; p &lt; 0.001) led to a lower prevalence of osteoporosis and exerted a protective effect on model 1. Other models produced similar results to those of model 1. A subgroup analysis showed that Cd levels were positively related to a higher prevalence of osteoporosis in all three models in women, while this relationship was not found in men. The fourth quartile of the Se level was related to a lower prevalence of osteoporosis in both male and female analyses. A significant positive relationship was found between the blood Cd level and a higher prevalence of osteoporosis in the non-smoking subgroup. Blood Se level showed a protective effect on the fourth quartile in both the smoking and non-smoking subgroups.

Conclusion

Blood Cd level aggravated the prevalence of osteoporosis, while blood Se level could be a protective factor in osteoporosis among the US middle-aged and older populations.

Association between skin disease and anxiety: a logistic analysis and prediction

Background

Dermatosis has symptoms of flushing, itching, pain, and burning, which causes psychological distress in patients.

Methods

We collected the data of 214 patients from the Wuhan Institute of Dermatology and Venereology from January 2020 to January 2022, including age, gender, diagnosis, Hamilton Anxiety Rating Scale (HAMA), and anxiety. First, descriptive analysis and difference analysis of the included data were carried out. Second, a correlation matrix was used to exclude the confounding factors with strong collinearity. Finally, logistic regression was used to analyze and predict the risk factors of anxiety.

Results

In the anxiety group and nonanxiety group, eczema and generalized eczema accounted for the largest proportion, and there was no difference in the composition of the diagnosis between the 2 groups. Several related analyses proved the accuracy of the logistic model. The results showed that age had a protective effect on the anxiety of patients with skin diseases [odds ratio (OR) =0.8606 with 95% confidence interval (CI): 0.7812, 0.8987]. Neurodermatitis (OR =1.0853 with 95% CI: 1.0115, 1.2512), eczema (OR =1.1358 with 95% CI: 1.0215, 1.2129), generalized eczema (OR =1.3346 with 95% CI: 1.1212, 1.5521), and psoriasis (OR =1.3685 with 95% CI: 1.1728, 1.6215) were associated with the anxiety of patients. Prediction analysis showed that with increase of patients' age, the likelihood of anxiety decreased.

Conclusions

This study demonstrated a strong correlation between skin diseases and anxiety and that the likelihood of anxiety decreases as age increases. Therefore, psychological intervention for patients with skin diseases, especially young patients, is essential.

Identification of key genes, biological functions, and pathways of empagliflozin by network pharmacology and its significance in the treatment of type 2 diabetes mellitus

Background

To explore the key genes, biological functions, and pathways of empagliflozin in the treatment of type 2 diabetes mellitus (T2DM) through network pharmacology.

Methods

The TCMSP (a traditional Chinese medicine system pharmacology database and analysis platform) was used to screen empagliflozin's active components and targets. The target genes of T2DM were screened according to the GeneCards and OMIM databases, and a Venn diagram was constructed to obtain the target for T2DM treatment. Cytoscape 3.7.2 software was adopted to construct the drug-component-target-disease network. Functional annotation of Gene Ontology (GO) and enrichment analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were performed using R software.

Results

Target genes with a probability >0 were selected, among which Compound 012, Compound 060, Compound 093, Compound 111, and Compound 119 Swiss Target Prediction suggested that no similar active substances or predictable target genes were found. A "compound-target gene-disease" network was constructed, in which SLC5A2, SLC5A1, SLC5A4, SLC5A11, ADK, and ADORA2A were the core genes of T2DM. The key factors of the GO summary map included chemical reaction, membrane organelle, protein binding, and so on. The KEGG pathway summary map included the AMPK pathway, insulin resistance, the MAPK pathway, longevity-related pathway regulation, and so on. The top 10 pathways were endocrine resistance, the NF-κB signaling pathway, the HIF-1 signaling pathway, apoptosis, cell senescence, the Ras signaling pathway, the MAPK signaling pathway, the FoxO signaling pathway, the P13K-Akt signaling pathway, and the p53 signaling pathway. The binding of active compounds to key proteins was verified based on the Swiss Dock database, and the molecular docking of 193 bioactive compounds was finally verified. Among them, SLC5A2, SLC5A1, LDHA, KLK1, KLF5, and GSTP1 had better binding to the protein molecules.

Conclusions

Empagliflozin may regulate the targets of SLC5A2, SLC5A1, LDHA, KLK1, KLF5, and GSTP1. There are numerous ways of treating T2DM with empagliflozin, including by regulating apoptosis, cell aging, as well as the NF-κB, HIF-1HIF-1, Ras, MAPK, FoxO, P13K-Akt, and p53 pathways.

A study on the correlation between patellofemoral joint morphology and early patella malacia in young adults: quantitative analysis based on magnetic resonance

Background

The aim of this study was to quantitatively measure the morphology of the patellofemoral joint by magnetic resonance imaging (MRI) in young adults, to investigate the relationship between the morphology of the patellofemoral joint and chondromalacia patellae (CP), and to provide an imaging reference for early clinical diagnosis of CP.

Methods

A total of 57 cases of CP and 138 cases of normal knees were retrospectively collected and assigned to the CP group and control group, respectively. The morphological parameters of the patellofemoral joint were measured systematically. The statistical software SPSS 22.0 was used in correlation analysis. Logistic regression was used for univariate analysis. A P value <0.05 was defined as statistically significant.

Results

Wiberg index, patellar depth, and the lateral angle of the patella in the CP group were significantly lower than those in the control group (P=0.043, 0.001, 0.040, respectively). The width of the medial side of the trochlea, the ratio of the internal and external articular surfaces of the trochlea, the depth of the trochlea, and the inclination angle of the lateral trochlea in the CP group were significantly lower than those in the control group (P=0.003, 0.020, 0.000, 0.040, respectively), whereas the angle of the trochlea in the CP group was significantly higher than that in the control group (P=0.010). The lateral patellofemoral angle (LPA) and lateral patellofemoral contact width in the CP group were significantly lower than those in the control group (P=0.010, 0.010, respectively). In the CP group, with gender as a covariate, there were significant statistical differences between male and female in patellar depth, lateral patellar inclination angle, lateral trochlear inclination (LTI) angle, and lateral patellar contact surface width (P<0.05), whereas in the control group, there were significant differences in Wiberg index, patellar depth, medial trochlear width, and lateral patellofemoral contact surface width between male and female gender (P<0.05).

Conclusions

The prevalence of CP in women was significantly higher than that in men and the reduction of trochlea width and the patella depth is an important factor for the incidence of female CP. Morphological characteristics of the patellofemoral joint are significantly correlated with the occurrence of CP in young adults.