ObjectiveHeavy metals are present in many environmental pollutants, and have cumulative effects on the human body through water or food, which can lead to several diseases, including osteoarthritis (OA). In this research, we aimed to explore the association between heavy metals and OA.MethodsWe extracted 18 variables including age, gender, race, education level, marital status, smoking status, body mass index (BMI), physical activity, diabetes mellitus, hypertension, poverty level index (PLI), Lead (Pb), cadmium (Cd), mercury (Hg), selenium (Se), manganese (Mn), and OA status from National Health and Nutrition Examination Survey (NHANES) 2011-2020 datasets.ResultsIn the baseline data, the t test and Chi-square test were conducted. For heavy metals, quartile description and limit of detection (LOD) were adopted. To analyze the association between heavy metals and OA among elderly subjects, multivariable logistic regression was conducted and subgroup logistic by gender was also carried out. Furthermore, to make predictions based on heavy metals for OA, we compared eight machine learning algorithms, and XGBoost (AUC of 0.8, accuracy value of 0.773, and kappa value of 0.358) was the best machine learning model for prediction. For interactive use, a shiny application was made (https://alanwu.shinyapps.io/NHANES-OA/).ConclusionThe overall and gender subgroup logistic regressions all showed that Pb and Cd promoted the prevalence of OA while Mn could be a protective factor of OA prevalence among the elderly population of the United States. Furthermore, XGBoost model was trained for OA prediction.

ObjectiveTo explore the association between depression and blood metal elements, we conducted this machine learning model fitting research.MethodsDatasets from the National Health and Nutrition Examination Survey (NHANES) in 2017–2018 were downloaded (https://www.cdc.gov/nchs/nhanes). After screening, 3,247 aging samples with 10 different metals [lead (Pb), mercury (Hg), cadmium (Cd), manganese (Mn), selenium (Se), chromium (Cr), cobalt (Co), inorganic mercury (InHg), methylmercury (MeHg) and ethyl mercury (EtHg)] were included. Eight machine learning algorithms were compared for analyzing metal and depression. After comparison, XGBoost showed optimal effects. Poisson regression and XGBoost model (a kind of decision tree algorithm) were conducted to find the risk factors and prediction for depression.ResultsA total of 344 individuals out of 3247 participants were diagnosed with depression. In the Poisson model, we found Cd (β = 0.22, P = 0.00000941), EtHg (β = 3.43, P = 0.003216), and Hg (β=-0.15, P = 0.001524) were related with depression. XGBoost model was the suitable algorithm for the evaluation of depression, the accuracy was 0.89 with 95%CI (0.87, 0.92) and Kappa value was 0.006. Area under the curve (AUC) was 0.88. After that, an online XGBoost application for depression prediction was developed.ConclusionBlood heavy metals, especially Cd, EtHg, and Hg were significantly associated with depression and the prediction of depression was imperative.

Background According to previous reports, obesity especially visceral fat has become an important public health problem, causing an estimation of 20.5 disability-adjusted life years per 1000 inhabitants. Those who exercised for 1 or 2 days per week and reached the recommended 150 minutes of moderate physical activity (PA) per week have been defined as “weekend warriors” (WWs). Although the benefits of PA in suppressing obesity have been widely studied, the association of WWs with the Visceral Adiposity Index (VAI) and the difference between WW activity and regular PA are yet to be explored. Objective This study aims to explore the association between WW activity and other PA patterns with VAI in US adults. Methods The National Health and Nutrition Examination Survey 2007-2016 data set was used, and the analytic sample was limited to adults 20 years and older who had complete information about VAI, PA patterns, and other covariates, including demographic characteristics, behavioral factors, and disease conditions. Participants’ characteristics in different PA pattern groups were tested using the Rao and Scott adjusted χ2 test and ANOVA. Univariate and multivariate stepped linear regression models were then used to explore the association between the PA pattern and VAI. Finally, stratified analyses and interaction effects were conducted to investigate whether the association was stable among subgroups. Results The final sample included 9642 adults 20 years or older, which is representative of 158.1 million noninstitutionalized US adults, with 52.15% (n=5169) being male and 70.8% (n=4443) being non-Hispanic White. Gender, age group, race, education level, income level, marital status, smoking status, alcoholism, VAI, cardiovascular disease, and diabetes were all correlated with the PA pattern, but no relationship between hypertension and PA pattern was observed. After adjusting for demographic covariates, smoking status, alcoholism, cardiovascular disease, diabetes, and hypertension, WW and regularly active adults had a β of .307 (95% CI –0.611 to –0.003) and .354 (95% CI –0.467 to –0.241), respectively, for reduced VAI when compared with inactive adults, but no significant effect of lowering VAI (β=–.132, 95% CI –0.282 to 0.018) was observed in insufficiently active adults when compared with inactive adults. Besides, no significant difference was exhibited between WW adults and regularly active adults (β=.047, 95% CI –0.258 to 0.352), suggesting WW adults had the same benefit of decreasing VAI as regularly active adults. Stratified analyses results exhibited that WW activity was related to reduced VAI in female adults aged 20-44 years who were non-Hispanic Black, other, or multiracial; high school or General Educational Development education; and never married, and the association between PA pattern and VAI remained stable in all demographic subgroups. Conclusions Compared with inactive adults, WWs could reduce VAI, and there was no significant difference between WWs and regular active adults in decreasing VAI. Our study provides compelling evidence of the beneficial effect of WW activity on visceral obesity.

ObjectiveLeukocytes telomere length (LTL) was reported to be associated with cellular aging and aging related disease. Urine metal also might accelerate the development of aging related disease. We aimed to analyze the association between LTL and urinary metals.MethodsIn this research, we screened all cycles of National Health and Nutrition Examination Survey (NHANES) dataset, and download the eligible dataset in NHANES 1999–2002 containing demographic, disease history, eight urine metal, and LTL. The analysis in this research had three steps including baseline difference comparison, multiple linear regression (MLR) for hazardous urine metals, and artificial neural network (ANN, based on Tensorflow framework) to make LTL prediction.ResultsThe MLR results showed that urinary cadmium (Cd) was negatively correlated with LTL in the USA population [third quantile: −9.36, 95% confidential interval (CI) = (−19.7, −2.32)], and in the elderly urinary molybdenum (Mo) was positively associated with LTL [third quantile: 24.37, 95%CI = (5.42, 63.55)]. An ANN model was constructed, which had 24 neurons, 0.375 exit rate in the first layer, 15 neurons with 0.53 exit rate in the second layer, and 7 neurons with 0.86 exit rate in the third layer. The squared error loss (LOSS) and mean absolute error (MAE) in the ANN model were 0.054 and 0.181, respectively, which showed a low error rate.ConclusionIn conclusion, in adults especially the elderly, the relationships between urinary Cd and Mo might be worthy of further research. An accurate prediction model based on ANN could be further analyzed.