Metabolic phenotyping with computed tomography deep learning for metabolic syndrome, osteoporosis and sarcopenia predicts mortality in adults.

BACKGROUND: Computed tomography (CT) body compositions reflect age-related metabolic derangements. We aimed to develop a multi-outcome deep learning model using CT multi-level body composition parameters to detect metabolic syndrome (MS), osteoporosis and sarcopenia by identifying metabolic clusters simultaneously. We also investigated the prognostic value of metabolic phenotyping by CT model for long-term mortality. METHODS: The derivation set (n = 516; 75% train set, 25% internal test set) was constructed using age- and sex-stratified random sampling from two community-based cohorts. Data from participants in the individual health assessment programme (n = 380) were used as the external test set 1. Semi-automatic quantification of body compositions at multiple levels of abdominal CT scans was performed to train a multi-layer perceptron (MLP)-based multi-label classification model. External test set 2 to test the prognostic value of the model output for mortality was built using data from individuals who underwent abdominal CT in a tertiary-level institution (n = 10 141). RESULTS: The mean ages of the derivation and external sets were 62.8 and 59.7 years, respectively, without difference in sex distribution (women 50%) or body mass index (BMI; 23.9 kg/m2). Skeletal muscle density (SMD) and bone density (BD) showed a more linear decrement across age than skeletal muscle area. Alternatively, an increase in visceral fat area (VFA) was observed in both men and women. Hierarchical clustering based on multi-level CT body composition parameters revealed three distinctive phenotype clusters: normal, MS and osteosarcopenia clusters. The L3 CT-parameter-based model, with or without clinical variables (age, sex and BMI), outperformed clinical model predictions of all outcomes (area under the receiver operating characteristic curve: MS, 0.76 vs. 0.55; osteoporosis, 0.90 vs. 0.79; sarcopenia, 0.85 vs. 0.81 in external test set 1; P < 0.05 for all). VFA contributed the most to the MS predictions, whereas SMD, BD and subcutaneous fat area were features of high importance for detecting osteoporosis and sarcopenia. In external test set 2 (mean age 63.5 years, women 79%; median follow-up 4.9 years), a total of 907 individuals (8.9%) died during follow-up. Among model-predicted metabolic phenotypes, sarcopenia alone (adjusted hazard ratio [aHR] 1.55), MS + sarcopenia (aHR 1.65), osteoporosis + sarcopenia (aHR 1.83) and all three combined (aHR 1.87) remained robust predictors of mortality after adjustment for age, sex and comorbidities. CONCLUSIONS: A CT body composition-based MLP model detected MS, osteoporosis and sarcopenia simultaneously in community-dwelling and hospitalized adults. Metabolic phenotypes predicted by the CT MLP model were associated with long-term mortality, independent of covariates.

Preclinical assessment of rodent jumping power with a novel electrical stimulation-assisted device.

Sarcopenia is a progressive loss of muscle mass and strength that is associated with increasing the risk of falls, musculoskeletal diseases, and chronic metabolic diseases. However, the animal models adopted to study sarcopenia face limitations since the functional tests conducted on human cannot be directly adapted to animals because the animals do not follow instructions. Moreover, current preclinical research tools for muscle function assessment, such as the rotarod, grip strength, and treadmill, have limitations, including low-intensity simple movements, evaluator subjectivity, and limited power indicators. Hence, in this study, we present a new jumping-power assessment tool in a preclinical rodent model to demonstrate muscle functions. To overcome the light weight and command issues in the rodent model, we developed an electrical stimulation-assisted jump power assessment device. Precisely, the device utilizes a load cell with a 0.1 g resolution and a 50 points/s data acquisition rate to capture the short period of the mouse jump. Additionally, interdigitated electrodes are used to electrically stimulate the mice and make them jump. While our primary focus in this article is the validation of the newly developed jump power assessment device, it is worth noting that this tool has several potential utilities. These include the phenotypic comparison of sarcopenia models, the exploration of muscle function reduction mechanisms, muscle function-related blood biomarkers, and the evaluation of drug intervention effects.

Deep-Learning-Based Detection of Vertebral Fracture and Osteoporosis Using Lateral Spine X-Ray Radiography.

Osteoporosis and vertebral fractures (VFs) remain underdiagnosed. The addition of deep learning methods to lateral spine radiography (a simple, widely available, low-cost test) can potentially solve this problem. In this study, we develop deep learning scores to detect osteoporosis and VF based on lateral spine radiography and investigate whether their use can improve referral of high-risk individuals to bone-density testing. The derivation cohort consisted of patients aged 50 years or older who underwent lateral spine radiography in Severance Hospital, Korea, from January 2007 to December 2018, providing a total of 26,299 lateral spine plain X-rays for 9276 patients (VF prevalence, 18.6%; osteoporosis prevalence, 40.3%). Two individual deep convolutional neural network scores to detect prevalent VF (VERTE-X pVF score) and osteoporosis (VERTE-X osteo score) were tested on an internal test set (20% hold-out set) and external test set (another hospital cohort [Yongin], 395 patients). VERTE-X pVF, osteo scores, and clinical models to detect prevalent VF or osteoporosis were compared in terms of the areas under the receiver-operating-characteristics curves (AUROCs). Net reclassification improvement (NRI) was calculated when using deep-learning scores to supplement clinical indications for classification of high-risk individuals to dual-energy X-ray absorptiometry (DXA) testing. VERTE-X pVF and osteo scores outperformed clinical models in both the internal (AUROC: VF, 0.93 versus 0.78; osteoporosis, 0.85 versus 0.79) and external (VF, 0.92 versus 0.79; osteoporosis, 0.83 versus 0.65; p < 0.01 for all) test sets. VERTE-X pVF and osteo scores improved the reclassification of individuals with osteoporosis to the DXA testing group when applied together with the clinical indications for DXA testing in both the internal (NRI 0.10) and external (NRI 0.14, p < 0.001 for all) test sets. The proposed method could detect prevalent VFs and osteoporosis, and it improved referral of individuals at high risk of fracture to DXA testing more than clinical indications alone. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Evidence-based nursing outputs and hot spot analysis of the last 5 years in mainland China: Results of a bibliometric analysis.

BACKGROUND: Evidence-based nursing has been highlighted and highly developed in recent decades in mainland China. Nevertheless, little is known about its overall development. AIMS: To gain insights on the overall development of evidence-based nursing in the most recent 5 years and to inform future evidence-based nursing research in mainland China. METHOD: Four Chinese and four English databases were searched with the search terms "evidence-based practice," "nurse or nursing," and "China or Chinese" from 2012 to 2016. Bibliometric and co-word cluster analysis were conducted with the final included publications. RESULTS: A total of 9036 papers published by 13 808 authors in 606 journals were included. Publication numbers were increasing. None of the top ten journals publishing evidence-based nursing papers were core nursing journals. The research hot spots on evidence-based nursing in the recent five years were cardiovascular disease, mental health, and complication prevention. However, little attention has been paid to education for evidence-based nursing. CONCLUSION: Evidence-based nursing has penetrated into various nursing branches in mainland China and become a well-recognized and relatively mature research domain. More importance should be attached to the study design, methodological, and reporting quality of evidence-based nursing projects.