Table_1_Machine learning for early prediction of sepsis-associated acute brain injury.DOCX

BackgroundSepsis-associated encephalopathy (SAE) is defined as diffuse brain dysfunction associated with sepsis and leads to a high mortality rate. We aimed to develop and validate an optimal machine-learning model based on clinical features for early predicting sepsis-associated acute brain injury.MethodsWe analyzed adult patients with sepsis from the Medical Information Mart for Intensive Care (MIMIC III) clinical database. Candidate models were trained using random forest, support vector machine (SVM), decision tree classifier, gradients boosting machine (GBM), multiple layer perception (MLP), extreme gradient boosting (XGBoost), light gradients boosting machine (LGBM) and a conventional logistic regression model. These methods were applied to develop and validate the optimal model based on its accuracy and area under curve (AUC).ResultsIn total, 12,460 patients with sepsis met inclusion criteria, and 6,284 (50.4%) patients suffered from sepsis-associated acute brain injury. Compared other models, the LGBM model achieved the best performance. The AUC for both train set and test set indicated excellent validity (Trainset AUC 0.91, Testset AUC 0.87). Feature importance analysis showed that glucose, age, mean arterial pressure, heart rate, hemoglobin, and length of ICU stay were the top 6 important clinical factors to predict occurrence of sepsis-associated acute brain injury.ConclusionAlmost half of patients admitted to ICU with sepsis had sepsis-associated acute brain injury. The LGBM model better identify patients with sepsis-associated acute brain injury than did other machine-learning models. Glucose, age, and mean arterial pressure were the three most important clinical factors to predict occurrence of sepsis-associated acute brain injury.</p

Image_3_Effect of basal metabolic rate on osteoporosis: A Mendelian randomization study.TIFF

PurposeBasal metabolic rate may play a key role in the pathogenesis and progression of osteoporosis. We performed Mendelian random analysis to evaluate the causal relationship between basal metabolic rate and osteoporosis.MethodsInstrumental variables for the basal metabolic rate were selected. We used the inverse variance weighting approach as the main Mendelian random analysis method to estimate causal effects based on the summary-level data for osteoporosis from genome-wide association studies.ResultsA potential causal association was observed between basal metabolic rate and risks of osteoporosis (odds ratio = 0.9923, 95% confidence interval: 0.9898–0.9949; P = 4.005e − 09). The secondary MR also revealed that BMR was causally associated with osteoporosis (odds ratio = 0.9939, 95% confidence interval: 0.9911–0.9966; P = 1.038e − 05). The accuracy and robustness of the findings were confirmed using sensitivity tests.ConclusionBasal metabolic rate may play a causal role in the development of osteoporosis, although the underlying mechanisms require further investigation.</p

Table_2_Effect of basal metabolic rate on osteoporosis: A Mendelian randomization study.XLSX

PurposeBasal metabolic rate may play a key role in the pathogenesis and progression of osteoporosis. We performed Mendelian random analysis to evaluate the causal relationship between basal metabolic rate and osteoporosis.MethodsInstrumental variables for the basal metabolic rate were selected. We used the inverse variance weighting approach as the main Mendelian random analysis method to estimate causal effects based on the summary-level data for osteoporosis from genome-wide association studies.ResultsA potential causal association was observed between basal metabolic rate and risks of osteoporosis (odds ratio = 0.9923, 95% confidence interval: 0.9898–0.9949; P = 4.005e − 09). The secondary MR also revealed that BMR was causally associated with osteoporosis (odds ratio = 0.9939, 95% confidence interval: 0.9911–0.9966; P = 1.038e − 05). The accuracy and robustness of the findings were confirmed using sensitivity tests.ConclusionBasal metabolic rate may play a causal role in the development of osteoporosis, although the underlying mechanisms require further investigation.</p

Image_2_Effect of basal metabolic rate on osteoporosis: A Mendelian randomization study.TIFF

PurposeBasal metabolic rate may play a key role in the pathogenesis and progression of osteoporosis. We performed Mendelian random analysis to evaluate the causal relationship between basal metabolic rate and osteoporosis.MethodsInstrumental variables for the basal metabolic rate were selected. We used the inverse variance weighting approach as the main Mendelian random analysis method to estimate causal effects based on the summary-level data for osteoporosis from genome-wide association studies.ResultsA potential causal association was observed between basal metabolic rate and risks of osteoporosis (odds ratio = 0.9923, 95% confidence interval: 0.9898–0.9949; P = 4.005e − 09). The secondary MR also revealed that BMR was causally associated with osteoporosis (odds ratio = 0.9939, 95% confidence interval: 0.9911–0.9966; P = 1.038e − 05). The accuracy and robustness of the findings were confirmed using sensitivity tests.ConclusionBasal metabolic rate may play a causal role in the development of osteoporosis, although the underlying mechanisms require further investigation.</p

Image_1_Effect of basal metabolic rate on osteoporosis: A Mendelian randomization study.TIFF

PurposeBasal metabolic rate may play a key role in the pathogenesis and progression of osteoporosis. We performed Mendelian random analysis to evaluate the causal relationship between basal metabolic rate and osteoporosis.MethodsInstrumental variables for the basal metabolic rate were selected. We used the inverse variance weighting approach as the main Mendelian random analysis method to estimate causal effects based on the summary-level data for osteoporosis from genome-wide association studies.ResultsA potential causal association was observed between basal metabolic rate and risks of osteoporosis (odds ratio = 0.9923, 95% confidence interval: 0.9898–0.9949; P = 4.005e − 09). The secondary MR also revealed that BMR was causally associated with osteoporosis (odds ratio = 0.9939, 95% confidence interval: 0.9911–0.9966; P = 1.038e − 05). The accuracy and robustness of the findings were confirmed using sensitivity tests.ConclusionBasal metabolic rate may play a causal role in the development of osteoporosis, although the underlying mechanisms require further investigation.</p

Table_1_Effect of basal metabolic rate on osteoporosis: A Mendelian randomization study.XLSX

PurposeBasal metabolic rate may play a key role in the pathogenesis and progression of osteoporosis. We performed Mendelian random analysis to evaluate the causal relationship between basal metabolic rate and osteoporosis.MethodsInstrumental variables for the basal metabolic rate were selected. We used the inverse variance weighting approach as the main Mendelian random analysis method to estimate causal effects based on the summary-level data for osteoporosis from genome-wide association studies.ResultsA potential causal association was observed between basal metabolic rate and risks of osteoporosis (odds ratio = 0.9923, 95% confidence interval: 0.9898–0.9949; P = 4.005e − 09). The secondary MR also revealed that BMR was causally associated with osteoporosis (odds ratio = 0.9939, 95% confidence interval: 0.9911–0.9966; P = 1.038e − 05). The accuracy and robustness of the findings were confirmed using sensitivity tests.ConclusionBasal metabolic rate may play a causal role in the development of osteoporosis, although the underlying mechanisms require further investigation.</p

Image_4_Effect of basal metabolic rate on osteoporosis: A Mendelian randomization study.TIFF

PurposeBasal metabolic rate may play a key role in the pathogenesis and progression of osteoporosis. We performed Mendelian random analysis to evaluate the causal relationship between basal metabolic rate and osteoporosis.MethodsInstrumental variables for the basal metabolic rate were selected. We used the inverse variance weighting approach as the main Mendelian random analysis method to estimate causal effects based on the summary-level data for osteoporosis from genome-wide association studies.ResultsA potential causal association was observed between basal metabolic rate and risks of osteoporosis (odds ratio = 0.9923, 95% confidence interval: 0.9898–0.9949; P = 4.005e − 09). The secondary MR also revealed that BMR was causally associated with osteoporosis (odds ratio = 0.9939, 95% confidence interval: 0.9911–0.9966; P = 1.038e − 05). The accuracy and robustness of the findings were confirmed using sensitivity tests.ConclusionBasal metabolic rate may play a causal role in the development of osteoporosis, although the underlying mechanisms require further investigation.</p

Table_3_Effect of basal metabolic rate on osteoporosis: A Mendelian randomization study.XLSX

PurposeBasal metabolic rate may play a key role in the pathogenesis and progression of osteoporosis. We performed Mendelian random analysis to evaluate the causal relationship between basal metabolic rate and osteoporosis.MethodsInstrumental variables for the basal metabolic rate were selected. We used the inverse variance weighting approach as the main Mendelian random analysis method to estimate causal effects based on the summary-level data for osteoporosis from genome-wide association studies.ResultsA potential causal association was observed between basal metabolic rate and risks of osteoporosis (odds ratio = 0.9923, 95% confidence interval: 0.9898–0.9949; P = 4.005e − 09). The secondary MR also revealed that BMR was causally associated with osteoporosis (odds ratio = 0.9939, 95% confidence interval: 0.9911–0.9966; P = 1.038e − 05). The accuracy and robustness of the findings were confirmed using sensitivity tests.ConclusionBasal metabolic rate may play a causal role in the development of osteoporosis, although the underlying mechanisms require further investigation.</p