Abstract Background The associations between serum uric acid and osteoporosis or osteopenia remain controversial, and few studies have explored whether BMI acts as a mediators in the association between the SUA and OP/ osteopenia. Objective To explore the relationship between serum uric acid and osteoporosis or osteopenia among US adults. Methods A cross-sectional study was conducted to examine the association between serum uric acid and osteoporosis or osteopenia from four cycles of NHANES. Binary logistic regression models and restricted cubic spline models were used to evaluate the association between serum uric acid and osteoporosis or osteopenia, and interaction analysis was used to test the differences between subgroups. Mediation analysis was utilized to investigate whether BMI acts as a mediator in the association between SUA and OP/ osteopenia. Results 12581 participants aged ≥ 18 years were included. A U-shape nonlinear relationship between SUA and osteoporosis or osteopenia in all people was found (P < 0.0001, P for nonlinear = 0.0287). There were significant interactions in age subgroups (P for interaction = 0.044), sex subgroups (P for interaction = 0.005), and BMI subgroups (P for interaction = 0.017). We further assessed the subgroups and found the optimal range of serum uric acid levels with a lower risk of osteoporosis or osteopenia was 357–535 µmol/L in males, 327–417 µmol/L in people aged ≥ 50 years, above 309 µmol/L in people aged < 50 years, 344–445 µmol/L in people with BMI ≥ 30, and above 308 µmol/L in people with BMI < 30. BMI fully mediated the association of SUA and OP/osteopenia, with a value of -0.0024(-0.0026–-0.0021). These results were robust in sensitivity analyses. Conclusions A complicated relationship between SUA and bone health in different populations was observed. Maintaining SUA within a specific range may be beneficial to bone health. In addition, BMI may play an important role in the association between SUA and bone health, but considering the limitations of this study, further prospective research is required.

Objective:To establish a decision tree model based on the synthetic minority over-sampling technique (SMOTE) algorithm for the prediction of post-operative 30-day death in patients undergoing surgery with cardiovascular events.Methods:A total of 3 086 Chinese patients undergoing surgery with cardiovascular events (the history of ischemic heart disease and/or congestive heart failure) admitted to the Singapore General Hospital for operation from 2012 to 2016 were enrolled, and their clinical information, history of diseases and surgical scores were extracted. The original data was reconstructed by the SMOTE algorithm, and predictors were selected by best subset regression. Data was divided into a training group and a validation group by the ratio of 7 ∶ 3, of which the training group was used to establish the decision tree model and the validation group was used for internal verification.Results:The mortality rate was 3.0% (93/3 086) at 30 days after surgery and 4.5% (140/3 086) of patients were admitted to ICU at 24 h after surgery. The best subset regression analysis showed age > 75 years [odds ratio (OR) = 1.033, 95% confidence interval (CI) (1.024, 1.042), P < 0.001], anemia severity [OR = 1.368, 95%CI (1.211, 1.546), P < 0.001], chronic kidney disease stage > 2 [OR = 1.381, 95%CI (1.277, 1.494), P < 0.001], preoperative blood transfusion [OR = 4.496, 95%CI (3.268, 6.185), P < 0.001], surgical types [OR = 3.344, 95%CI (2.752, 4.064), P < 0.001], red blood cell distribution width > 15.7% [OR = 2.097, 95%CI (1.658, 2.652), P < 0.001] and American Society of Anesthesiologists classification > 2 [OR = 3.362, 95%CI (2.734, 4.135), P < 0.001] were risk factors for 30-day death after surgery in patients with cardiovascular events. The above seven predictors were selected to build a decision tree model. The results showed that the area under the receiver operating characteristic curve of the decision tree model was 0.853 [95%CI (0.837, 0.868), P < 0.001], and the sensitivity and specificity were 0.765 and 0.756 respectively in the training group; the area under the receiver operating characteristic curve of the decision tree model was 0.858 [95%CI (0.834, 0.882), P < 0.001], and the sensitivity and specificity were 0.938 and 0.612 respectively in the validation group, with good overall discrimination.Conclusions:The risk of post-operative 30-day death in patients with cardiovascular events is an issue of unbalanced data classification, with minor cases for outcomes. In this study, the SMOTE algorithm was adopted avoiding the poor clinical applicability caused by the overfitting in conventional modeling. At the same time, the decision tree model presents visual, convenient and personalized characteristics, which is a useful clinical prediction tool for physicians.

Objective Sarcopenia has been recognized as a third category of complications in people with diabetes. However, few studies focus on the reduction of skeletal muscle mass in young people with diabetes. The aim of this study was to investigate risk factors of pre-sarcopenia in young patients with diabetes and establish a practical tool to diagnose pre-sarcopenia in those people. Methods Patients ( n = 1246) enrolled from the National Health and Nutrition Examination Survey (NHANES) cycle year of 2011 to 2018 were randomly divided into the training set and validation set. The all-subsets regression analysis was used to select the risk factors of pre-sarcopenia. A nomogram model for the prediction of pre-sarcopenia in the diabetic population was established based on the risk factors. The model was evaluated by the area under the receiver operating characteristic curve for discrimination, calibration curves for calibration, and decision curve analysis curves for clinical utility. Results In this study, gender, height, and waist circumference were elected as predictive factors for pre-sarcopenia. The nomogram model presented excellent discrimination in training and validation sets with areas under the curve of 0.907 and 0.912, respectively. The calibration curve illustrated excellent calibration, and the decision curve analysis showed a wide range of good clinical utility. Conclusions This study develops a novel nomogram that integrates gender, height, and waist circumference and can be used to easily predict pre-sarcopenia in diabetics. The novel screen tool is accurate, specific, and low-cost, highlighting its potential value in clinical application.

BackgroundSunburn is a common problem for outdoor workers and casual outdoor walkers. Carotenoids are important elements in normal function of skin tissue and skin metabolism and are critical in the development of some cancers. However, the possible relationships between sunburn sensitivity, carotenoids and the risk of cancers remain unknown.ObjectivesTo explore the associations of serum carotenoids with sunburn severity and the risk of cancers.MethodsA cross-sectional study from the National Health and Nutrition Examination Survey from 1999 to 2018 were conducted. The relationship between sunburn and serum carotenoids, cancers were investigated by unconditional or ordinal logistic regression. Mediation analysis was used to explore the effect of carotenoids on the relationship between sunburn and cancers.ResultsA total of 25,440 US adults from 1999 to 2018 were enrolled in this study. There were significant differences in sex, race and natural hair color between the sunburn and non-sunburn people. The severity of sunburn was significantly associated with serum trans-β-carotene, cis-β-carotene, combined lutein, and vitamin A. The odds ratios of severe reactions were 5.065 (95% CI: 2.266–11.318) in melanoma patients, 5.776 (95% CI: 3.362–9.922) in non-melanoma patients, and 1.880 (95% CI: 1.484–2.380) in non-skin cancers patients. Additionally, serum carotenoids were partially attributable to the effect of sunburn on skin and non-skin cancers.ConclusionSunburn severity was associated with cancers, and severer sunburn was related with higher risk of cancers. Serum carotenoids were also associated with sunburn severity. Moreover, the relationship between sunburn and cancers was mediated by some serum carotenoids.

Xie, Tian1; Wang, Shikai1; Zhang, Xiangda2; Ou, Sihua3; Mo, Xiaoqiao4 Author Information

Background Accurate evaluation of mortality risk in polytrauma patients is crucial for guiding the precision treatment strategy. There are few scales designed to provide an early assessment of mortality risk. However, the complexity of available scoring systems limits their application in pre-hospital care. Here, we established a GAS-TRS system to estimate the risk of early death for individual polytrauma patients and assess the early mortality risk in the individual patient. Methods We performed a secondary analysis from public Database. RCS and Multivariate Logistic regression analyses were used to screen potential prognostic factors for nomogram model. The VIF method examined multicollinearity, and VIF ≥ 5 suggested multicollinearity in this model. CMA was used to characterize the causality relationship in nomogram model. A four-layer back-propagation artificial neural network (BP-ANN) model was built by neuralnet package on R software. AUC of ROC analysis or F1 score were used to analyze the quality of predictive performance of GAS-TRS system. DCA and precision-recall curves were used to make up for the limitations of ROC curves. Results A total of 2406 patients were included in this analysis. Logistic regression analysis predicted four independent risk factors for nomogram model, including age (OR=1.03, 95%CI:1.02~1.03), GCS (OR=0.83, 95%CI:0.79~0.86), BE (OR=0.95, 95%CI:0.91~0.99) and serum lactic acid (OR=1.30, 95%CI:1.20~1.41) with an AUC of 0.88. Causal mediation analysis performed the mediation effect that lactate, age and BE accounted for 1.7%,0.7% and 3.0% indirect effect.The calibration curve showed model has good highly consistent with actual condition after bootstrapping. DCA showed the net benefit probability was between 2% and 85% and could bring more benefits for predicting early mortality.Then the input neurons were selected step by step in BP-ANN model. An optimal BP-ANN with an AUC of 0.91and AUPRC of 0.79 was established. Conclusion We established a GAS-TRS predictive system which includes a quick prognostic nomogram model and a precise BP-ANN model to evaluate early mortality within 72 hours for polytrauma patients. This scoring system might be practical and more efficient in identifying high-risk polytrauma patients. Moreover, this system may also guide triaging and precise initial individual treatment strategy for pre-hospital medical personnel.

BACKGROUND:To assess the efficacy and safety of corticosteroids in COVID-19 patients compared with standard care or placebo. METHODS:Electronic databases were searched to identify relevant studies. The mortality, adverse events, and other data from studies were pooled for statistical analysis. RESULTS:Ten randomized clinical trials were eligible for inclusion. Corticosteroid treatment in COVID-19 patients did not significantly reduce the risk of death (RR: 0.93; CI: 0.82, 1.05) and the need for mechanical ventilation (RR: 0.82; CI: 0.62, 1.08). No mortality reduction was also observed in the subgroup of patients requiring mechanical ventilation (RR: 0.90; CI: 0.79-1.03). The use of corticosteroids increased mortality in the subgroup of patients not requiring oxygen support (RR: 1.24; CI: 1.00-1.55). The survival benefit was observed in a low dosage of corticosteroids (RR: 0.90; CI: 0.84-0.97) and dexamethasone (RR: 0.90; 95% CI: 0.79-1.04). There was no difference in the rates of adverse events (RR: 1.13; CI: 0.58, 2.20) and secondary infections (RR: 0.87; CI: 0.66, 1.15). CONCLUSION:Corticosteroid treatment did not convincingly improve survival in severe COVID-19 patients. Low-dose dexamethasone could be considered as a drug for the treatment of COVID-19 patients. More high-quality trials are needed to further verify this conclusion.Expert Opinion: The effect of corticosteroids on patient survival highly depended on the selection of the right dosage and type and in a specific subgroup of patients. This meta-analysis, which included more RCTs, evaluated the safety and efficacy in severe COVID-19 patients and analyzed the effects of different types of corticosteroid treatments. Corticosteroid treatment did not convincingly improve survival in severe COVID-19 patients. But the low dose dexamethasone appear to have a role in the management of severe COVID-19 patients.