Machine learning model for depression based on heavy metals among aging people: A study with National Health and Nutrition Examination Survey 2017–2018

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.

Association Between Vitamin Intake and Colorectal Cancer: Evidence from NHANES Data

PURPOSE

This study aims to investigate the associations between vitamins and colorectal cancer (CRC) based on a national sample of US adults.

METHODS

A total of 6200 samples were collected from the National Health and Nutrition Examination Survey to explore the relationship between vitamins (specifically, A, C, and D) and CRC. Logistic regression models were employed to assess the associations between dietary vitamin intake and CRC.

RESULTS

Our findings indicate a negative association between vitamin C intake and CRC. However, the associations of vitamin A and vitamin D with CRC were not statistically significant. For vitamin C, compared to the first tertile, the odds ratios (ORs) and 95% confidence intervals (CIs) were 0.91 (0.76-0.97) for the second tertile and 0.81 (0.64-0.95) for the third tertile (P < 0.01). Conversely, for vitamin A, compared to the first tertile, the odds ratios (ORs) and 95% confidence intervals (CIs) were 1.02 (0.82-1.22) for the second tertile and 1.04 (0.75-1.25) for the third tertile (P < 0.01). For vitamin D, compared to the first tertile, the odds ratios (ORs) and 95% confidence intervals (CIs) were 0.96 (0.84-1.06) for the second tertile and 1.01 (0.83-1.15) for the third tertile (P < 0.01). Additionally, the negative association between vitamin C and CRC was more pronounced among females (0.76, 0.67-0.92), individuals aged 60 and above (0.75, 0.69-0.95), and those with a BMI > 30 (0.78, 0.67-0.93).

CONCLUSION

Our findings suggest that higher vitamin C intake is associated with a reduced prevalence of CRC. However, further large-scale prospective cohort studies are warranted to validate our results.

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.

Association between high or low-quality carbohydrate with depressive symptoms and socioeconomic-dietary factors model based on XGboost algorithm: From NHANES 2007-2018

BACKGROUND

Depressive symptoms are a serious public mental health problem, and dietary intake is often considered to be associated with depressive symptoms. However, the relationship between the quality of dietary carbohydrates and depressive symptoms remains unclear. Therefore, this study aimed to investigate the relationship between high and low-quality carbohydrates and depressive symptoms and to attempt to construct an integrated model using machine learning to predict depressive symptoms.

METHODS

A total of 4982 samples from the National Health and Nutrition Examination Survey (NHANES) were included in this study. Carbohydrate intake was assessed by a 24-h dietary review, and depressive symptoms were assessed using the Patient Health Questionnaire-9 (PHQ9). Variance inflation factor (VIF) and Relief-F algorithms were used for variable feature selection.

RESULTS

The results of multivariate linear regression showed a negative association between high-quality carbohydrates and depressive symptoms (β: -0.147, 95 % CI: -0.239, -0.056, p = 0.002) and a positive association between low-quality carbohydrates and depressive symptoms (β: 0.018, 95 % CI: 0.007, 0.280, p = 0.001). Subsequently, we used the XGboost model to produce a comprehensive depressive symptom evaluation model and developed a corresponding online tool (http://8.130.128.194:5000/) to evaluate depressive symptoms clinically.

LIMITATIONS

The cross-sectional study could not yield any conclusions regarding causality, and the model has not been validated with external data.

CONCLUSIONS

Carbohydrate quality is associated with depressive symptoms, and machine learning models that combine diet with socioeconomic factors can be a tool for predicting depression severity.

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.

Association between oxidative balance score and prostate specific antigen among older US adults

Objective

Oxidative Balance Score (OBS) is an index affecting the oxidative stress of dietary and lifestyle factors. We aimed to explore the association of OBS with prostate specific antigen (PSA) among older males.

Methods

A total of 5,136 samples were collected in this study to investigate the relationship between OBS and PSA from the National Health and Nutrition Examination Survey. Logistic regression models and restricted cubic spline were used to assess the associations between OBS and PSA.

Results

Compared with the Q1 group, the odds ratios for the association between OBS and PSA were 1.005 (1.003, 1.009), 1.003 (1.001, 1.006), and 1.001 (0.978, 1.022) for Q2, Q3, and Q4, respectively. In the age-specific analyses, the association was significant among individuals aged 65 years old and over: the odds ratios for the association between OBS and PSA were 1.019 (1.005, 1.028), 1.028 (1.018, 1.039), and 1.038 (1.022, 1.049) for Q2, Q3, and Q4, respectively. But it was not significant among individuals aged less than 65 years old: the odds ratios for the association between OBS and PSA were 1.016 (0.995, 1.026), 1.015 (0.985, 1.022), and 0.988 (0.978, 1.016) for Q2, Q3, and Q4, respectively. The restricted cubic splines also indicated a nonlinear relationship between OBS and PSA among individuals aged 65 years old and over (Poverall = 0.006, Pnonlinear = 0.021).

Conclusion

Our findings provide evidence that OBS is positively associated with higher levels of PSA among older adults. Further large-scale prospective cohort studies are needed to verify our findings.

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.

A machine learning model to predict the risk of depression in US adults with obstructive sleep apnea hypopnea syndrome: a cross-sectional study

Objective

Depression is very common and harmful in patients with obstructive sleep apnea hypopnea syndrome (OSAHS). It is necessary to screen OSAHS patients for depression early. However, there are no validated tools to assess the likelihood of depression in patients with OSAHS. This study used data from the National Health and Nutrition Examination Survey (NHANES) database and machine learning (ML) methods to construct a risk prediction model for depression, aiming to predict the probability of depression in the OSAHS population. Relevant features were analyzed and a nomogram was drawn to visually predict and easily estimate the risk of depression according to the best performing model.

Study design

This is a cross-sectional study.

Methods

Data from three cycles (2005-2006, 2007-2008, and 2015-2016) were selected from the NHANES database, and 16 influencing factors were screened and included. Three prediction models were established by the logistic regression algorithm, least absolute shrinkage and selection operator (LASSO) algorithm, and random forest algorithm, respectively. The receiver operating characteristic (ROC) area under the curve (AUC), specificity, sensitivity, and decision curve analysis (DCA) were used to assess evaluate and compare the different ML models.

Results

The logistic regression model had lower sensitivity than the lasso model, while the specificity and AUC area were higher than the random forest and lasso models. Moreover, when the threshold probability range was 0.19-0.25 and 0.45-0.82, the net benefit of the logistic regression model was the largest. The logistic regression model clarified the factors contributing to depression, including gender, general health condition, body mass index (BMI), smoking, OSAHS severity, age, education level, ratio of family income to poverty (PIR), and asthma.

Conclusion

This study developed three machine learning (ML) models (logistic regression model, lasso model, and random forest model) using the NHANES database to predict depression and identify influencing factors among OSAHS patients. Among them, the logistic regression model was superior to the lasso and random forest models in overall prediction performance. By drawing the nomogram and applying it to the sleep testing center or sleep clinic, sleep technicians and medical staff can quickly and easily identify whether OSAHS patients have depression to carry out the necessary referral and psychological treatment.

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.

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 of anxiety, depression symptoms and sleep quality with chronic kidney disease among older Chinese

This study aimed to explore the association of anxiety, depression symptoms and sleep quality with chronic kidney disease (CKD) among older Chinese adults. A total of 1025 participants from the Chinese Longitudinal Healthy Longevity Survey (CLHLS, 2011-2012) were included in our study. The Generalized Anxiety Disorder scale was used to assess anxiety and the Center for Epidemiologic Studies Depression Scale was used to evaluate depressive symptoms. Logistic regression models were conducted to explore the odds ratios (ORs) and 95% confidential intervals (CIs). We found that anxiety, depression symptoms and poor sleep quality were positively associated with albuminuria, impaired estimated glomerular filtration (eGFR) and CKD, after adjusting for other covariates. For anxiety symptom, the ORs and 95% CIs were 1.20 (1.15-1.38) for albuminuria, 1.16 (1.12-1.35) for impaired eGFR and 1.18 (1.12-1.36) for CKD, respectively. For depression symptom, the ORs and 95% CIs were 1.15 (1.05-1.23) for albuminuria, 1.14 (1.05-1.20) for impaired eGFR and 1.14 (1.05-1.22) for CKD, respectively. Compared with good sleep quality, the OR and 95% CI of poor sleep quality were 1.12 (1.04-1.35) for albuminuria, 1.10 (1.02-1.30) for impaired eGFR and 1.11 (1.03-1.32) for CKD, respectively. And the positive association was more evident among females, body mass index ≥ 28, smoking and drinking adults. Anxiety, depression symptoms and poor sleep quality are positively associated with CKD. Future cohort studies are needed to confirm the results.

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.

Association between triglyceride glucose index and suicide attempts in patients with first-episode drug-naïve major depressive disorder

Objective

Triglyceride glucose (TyG) index has been suggested as an alternative indicator of insulin resistance (IR); however, the association between TyG index and suicide attempts (SA) in major depressive disorder (MDD) is unclear. The aim of this study was to investigate the relationship between TyG index and SA in Chinese patients with first-episode drug-naïve (FEDN) MDD.

Methods

This cross-sectional study enrolled 1,718 patients with FEDN MDD aged 34.9 ± 12.4 years from the First Hospital of Shanxi Medical University (Taiyuan, Shanxi Province, China) from September 2016 to December 2018. Multivariable binary logistic regression analysis was used to estimate the association between TyG index and the risk of SA. A two-piecewise linear regression model was used to investigate the threshold effects if non-linearity associations existed. Interaction and stratified analyses were performed based on sex, education, marital status, comorbid anxiety, and psychotic symptoms.

Results

Multivariable logistic regression analysis revealed that TyG index was positively associated with the risk of SA after adjusting for confounders (OR = 1.35, 95% CI: 1.04-1.75, p = 0.03). Smoothing plots also showed a nonlinear relationship between TyG index and SA, with the inflection point of TyG index being 9.29. On the right of the inflection point, a positive association between TyG index and SA was detected (OR = 3.47, 95% CI: 1.81 to 6.66, p < 0.001), while no significant association was observed on the left side of the inflection point (OR = 1.14, 95% CI: 0.79 to 1.66, p = 0.476).

Conclusion

The relationship between TyG index and SA risk was non-linear and exhibited a threshold effect in Chinese patients with FEDN MDD. When TyG index was greater than 9.29, they showed a significant positive correlation.

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.

Risk assessment of combined exposure to lead, cadmium, and total mercury among the elderly in Shanghai, China

Lead (Pb), cadmium (Cd) and total mercury (THg) are toxic heavy metals (THMs) that are widely present in the environment and can cause substantial health problems. However, previous risk assessment studies have rarely focused on the elderly population and have usually targeted a single heavy metal, which might underestimate the long-term accumulative and synergistic effects of THMs in humans. Based on the food frequency questionnaire and inductively coupled plasma mass spectrometry, this study assessed external and internal exposures to Pb, Cd and THg in 1747 elderly people in Shanghai. Probabilistic risk assessment with the relative potential factor (RPF) model was used to assess the neurotoxicity and nephrotoxicity risks of combined THMs exposures. The mean external exposures of Pb, Cd and THg in Shanghai elderly were 46.8, 27.2 and 4.9 μg/day, respectively. Plant-based foods are the main source of Pb and THg exposure, while Cd is mainly from animal-based foods. The mean concentrations of Pb, Cd and THg were 23.3, 1.1 and 2.3 μg/L in the whole blood, and 6.2, 1.0 and 2.0 μg/L in the morning urine, respectively. Combined exposure to THMs leading to 10.0 % and 7.1 % of Shanghai elderly at risk of neurotoxicity and nephrotoxicity. The results of this study have important implications for understanding the profiles of Pb, Cd and THg exposure in the elderly living in Shanghai and provide data support for risk assessment and control of nephrotoxicity and neurotoxicity from combined THMs exposure in the elderly.

Association between incubator standards and newborn nosocomial infection with machine-learning prediction

Background

Newborns have a high incidence of nosocomial infection (NI). We conducted a logistic regression to analyze different incubator standards and other risk factors for newborn NI, which could better help clinical choice of incubator standard.

Methods

Newborns with complete necessary clinical data were included. We collected the demographic and incubator data of 76 patients (40 uninfected and 36 infected) at the Heping Hospital Affiliated to Changzhi Medical College. An analysis of variance, Pearson correlation matrix analysis, and logistic regression analysis were conducted to explore the different incubator standards and other risk factors for neonatal hospital infections. In addition, 4 machine-learning algorithms were used to predict neonatal hospital infections.

Results

We found differences in the gestational age, incubator type, paternal age, and maternal age between the 2 groups. The correlation analysis only revealed a correlation between paternal age and maternal age. The logistic regression showed that gestational age [odds ratio (OR)= 0.77574, 95% confidence interval (CI): 0.583513-0.996354] and the new standard incubator (OR =0.011639, 95% CI: 0.000958-0.067897) may be protective factors for infant infection during hospitalization. Among the extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and decision tree (DT) algorithms, XGBoost had the best performance in terms of accuracy, sensitivity, specificity, and precision.

Conclusions

We found early gestational age and incubator standards may be risk factors for the NIs of newborns, which might help clinicians to improve the health and safety standards for incubators. XGBoost can be used to predict newborn NIs.

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.

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.