Platelet-Derived Biomaterials Exert Chondroprotective and Chondroregenerative Effects on Diabetes Mellitus-Induced Intervertebral Disc Degeneration.

Complications of diabetes mellitus (DM) range from acute to chronic conditions, leading to multiorgan disorders such as nephropathy, retinopathy, and neuropathy. However, little is known about the influence of DM on intervertebral disc degeneration (IVDD). Moreover, traditional surgical outcomes in DM patients have been found poor, and to date, no definitive alternative treatment exists for DM-induced IVDD. Recently, among various novel approaches in regenerative medicine, the concentrated platelet-derived biomaterials (PDB), which is comprised of transforming growth factor-ß1 (TGF-ß1), platelet-derived growth factor (PDGF), etc., have been reported as safe, biocompatible, and efficacious alternatives for various disorders. Therefore, we initially investigated the correlations between DM and IVDD, through establishing in vitro and in vivo DM models, and further evaluated the therapeutic effects of PDB in this comorbid pathology. In vitro model was established by culturing immortalized human nucleus pulposus cells (ihNPs) in high-glucose medium, whereas in vivo DM model was developed by administering streptozotocin, nicotinamide and high-fat diet to the mice. Our results revealed that DM deteriorates both ihNPs and IVD tissues, by elevating reactive oxygen species (ROS)-induced oxidative stress, inhibiting chondrogenic markers and disc height. Contrarily, PDB ameliorated IVDD by restoring cellular growth, chondrogenic markers and disc height, possibly through suppressing ROS levels. These data imply that PDB may serve as a potential chondroprotective and chondroregenerative candidate for DM-induced IVDD.

Identify the Characteristics of Metabolic Syndrome and Non-obese Phenotype: Data Visualization and a Machine Learning Approach.

Introduction: A third of the world's population is classified as having Metabolic Syndrome (MetS). Traditional diagnostic criteria for MetS are based on three or more of five components. However, the outcomes of patients with different combinations of specific metabolic components are undefined. It is challenging to be discovered and introduce treatment in advance for intervention, since the related research is still insufficient. Methods: This retrospective cohort study attempted to establish a method of visualizing metabolic components by using unsupervised machine learning and treemap technology to discover the relations between predicting factors and different metabolic components. Several supervised machine-learning models were used to explore significant predictors of MetS and to construct a powerful prediction model for preventive medicine. Results: The random forest had the best performance with accuracy and c-statistic of 0.947 and 0.921, respectively, and found that body mass index, glycated hemoglobin, and controlled attenuation parameter (CAP) score were the optimal primary predictors of MetS. In treemap, high triglyceride level plus high fasting blood glucose or large waist circumference group had higher CAP scores (>260) than other groups. Moreover, 32.2% of patients with high CAP scores during 3 years of follow-up had metabolic diseases are observed. This reveals that the CAP score may be used for detecting MetS, especially for the non-obese MetS phenotype. Conclusions: Machine learning and data visualization can illustrate the complicated relationships between metabolic components and potential risk factors for MetS.

Exploring and predicting mortality among patients with end-stage liver disease without cancer: a machine learning approach.

OBJECTIVE: End-stage liver disease is a global public health problem with a high mortality rate. Early identification of people at risk of poor prognosis is fundamental for decision-making in clinical settings. This study created a machine learning prediction system that provides several related models with visualized graphs, including decision trees, ensemble learning and clustering, to predict mortality in patients with end-stage liver disease. METHODS: A retrospective cohort study was conducted: the training data were from patients enrolled from January 2009 to December 2010 and followed up to December 2014; validation data were from patients enrolled from January 2015 to December 2016 and followed up to January 2019. Hospitalized patients with noncancer-related chronic liver disease were identified from the hospital's electrical medical records. RESULTS: In traditional multivariable logistic regression and Cox proportional hazard model, prothrombin time of international normalized ratio, which was significant with P value = 0.002, odds ratio = 2.790 and hazard ratio 1.363. Besides, blood urea nitrogen and C-reactive protein were also significant, with P value <0.001 and 0.026. The area under the curve was 0.771 in the receiver operating characteristic curve. In machine learning, blood urea nitrogen and age were regarded as the primary factors for predicting mortality. Creatinine, prothrombin time of international normalized ratio and bilirubin were also significant mortality predictors. The area under the curve of the random forest and AdaBoost was 0.838 and 0.792. CONCLUSION: The machine learning techniques provided a comprehensive assessment of patient conditions; it could help physicians make an accurate diagnosis of chronic liver disease and improve healthcare management.

Effect of Probiotic Supplementation on Newborn Birth Weight for Mother with Gestational Diabetes Mellitus or Overweight/Obesity: A Systematic Review and Meta-Analysis.

High birth weight indicates the future risk of obesity and increased fat mass in childhood. Maternal gestational diabetes mellitus (GDM) or overweight are powerful predictors of high birth weight. Studies on probiotic supplementation during pregnancy have reported its benefits in modulating gut microbiota composition and improving glucose and lipid metabolism in pregnant women. Therefore, probiotic intervention during pregnancy was proposed to interrupt the transmission of obesity from mothers to newborns. Thus, we performed a meta-analysis to investigate the effect of probiotic intervention in pregnant women with GDM or overweight on newborn birth weight. We searched PubMed, EMBASE, Cochrane Library, and Web of Science databases up to 18 December 2019. Randomized controlled trials (RCTs) comparing pregnant women with GDM or overweight who received probiotic intervention during pregnancy with those receiving placebo were eligible for the analysis. Newborn birth weights were pooled to calculate the mean difference with a 95% confidence interval (CI). Two reviewers assessed the trial quality and extracted data independently. Seven RCTs involving 1093 participants were included in the analysis. Compared with the placebo, probiotics had little effect on newborn birth weight of pregnant women with GDM or overweight (mean difference = -10.27, 95% CI = -90.17 to 69.63, p = 0.801). The subgroup analysis revealed that probiotic intake by women with GDM decreased newborn birth weight, whereas probiotic intake by obese pregnant women increased newborn birth weight. Thus, no evidence indicates that probiotic intake by pregnant women with GDM or overweight can control newborn birth weight.

Correction: Development of an Online Health Care Assessment for Preventive Medicine: A Machine Learning Approach.

[This corrects the article DOI: 10.2196/18585.].

Development of an Online Health Care Assessment for Preventive Medicine: A Machine Learning Approach.

BACKGROUND: In the era of information explosion, the use of the internet to assist with clinical practice and diagnosis has become a cutting-edge area of research. The application of medical informatics allows patients to be aware of their clinical conditions, which may contribute toward the prevention of several chronic diseases and disorders. OBJECTIVE: In this study, we applied machine learning techniques to construct a medical database system from electronic medical records (EMRs) of subjects who have undergone health examination. This system aims to provide online self-health evaluation to clinicians and patients worldwide, enabling personalized health and preventive health. METHODS: We built a medical database system based on the literature, and data preprocessing and cleaning were performed for the database. We utilized both supervised and unsupervised machine learning technology to analyze the EMR data to establish prediction models. The models with EMR databases were then applied to the internet platform. RESULTS: The validation data were used to validate the online diagnosis prediction system. The accuracy of the prediction model for metabolic syndrome reached 91%, and the area under the receiver operating characteristic (ROC) curve was 0.904 in this system. For chronic kidney disease, the prediction accuracy of the model reached 94.7%, and the area under the ROC curve (AUC) was 0.982. In addition, the system also provided disease diagnosis visualization via clustering, allowing users to check their outcome compared with those in the medical database, enabling increased awareness for a healthier lifestyle. CONCLUSIONS: Our web-based health care machine learning system allowed users to access online diagnosis predictions and provided a health examination report. Users could understand and review their health status accordingly. In the future, we aim to connect hospitals worldwide with our platform, so that health care practitioners can make diagnoses or provide patient education to remote patients. This platform can increase the value of preventive medicine and telemedicine.

Predicting Metabolic Syndrome With Machine Learning Models Using a Decision Tree Algorithm: Retrospective Cohort Study.

BACKGROUND: Metabolic syndrome is a cluster of disorders that significantly influence the development and deterioration of numerous diseases. FibroScan is an ultrasound device that was recently shown to predict metabolic syndrome with moderate accuracy. However, previous research regarding prediction of metabolic syndrome in subjects examined with FibroScan has been mainly based on conventional statistical models. Alternatively, machine learning, whereby a computer algorithm learns from prior experience, has better predictive performance over conventional statistical modeling. OBJECTIVE: We aimed to evaluate the accuracy of different decision tree machine learning algorithms to predict the state of metabolic syndrome in self-paid health examination subjects who were examined with FibroScan. METHODS: Multivariate logistic regression was conducted for every known risk factor of metabolic syndrome. Principal components analysis was used to visualize the distribution of metabolic syndrome patients. We further applied various statistical machine learning techniques to visualize and investigate the pattern and relationship between metabolic syndrome and several risk variables. RESULTS: Obesity, serum glutamic-oxalocetic transaminase, serum glutamic pyruvic transaminase, controlled attenuation parameter score, and glycated hemoglobin emerged as significant risk factors in multivariate logistic regression. The area under the receiver operating characteristic curve values for classification and regression trees and for the random forest were 0.831 and 0.904, respectively. CONCLUSIONS: Machine learning technology facilitates the identification of metabolic syndrome in self-paid health examination subjects with high accuracy.

Clustering Heatmap for Visualizing and Exploring Complex and High-dimensional Data Related to Chronic Kidney Disease.

BACKGROUND: Preventive medicine and primary health care are essential for patients with chronic kidney disease (CKD) because the symptoms of CKD may not appear until the renal function is severely compromised. Early identification of the risk factors of CKD is critical for preventing kidney damage and adverse outcomes. Early recognition of rapid progression to advanced CKD in certain high-risk populations is vital. METHODS: This is a retrospective cohort study, the population screened and the site where the study has been performed. Multivariate statistical analysis was used to assess the prediction of CKD as many potential risk factors are involved. The clustering heatmap and random forest provides an interactive visualization for the classification of patients with different CKD stages. RESULTS: uric acid, blood urea nitrogen, waist circumference, serum glutamic oxaloacetic transaminase, and hemoglobin A1c (HbA1c) were significantly associated with CKD. CKD was highly associated with obesity, hyperglycemia, and liver function. Hypertension and HbA1c were in the same cluster with a similar pattern, whereas high-density lipoprotein cholesterol had an opposite pattern, which was also verified using heatmap. Early staged CKD patients who are grouped into the same cluster as advanced staged CKD patients could be at high risk for rapid decline of kidney function and should be closely monitored. CONCLUSIONS: The clustering heatmap provided a new predictive model of health care management for patients at high risk of rapid CKD progression. This model could help physicians make an accurate diagnosis of this progressive and complex disease.

Evaluation of urate-lowering therapy in hyperuricemia patients: a systematic review and Bayesian network meta-analysis of randomized controlled trials.

OBJECTIVE: Hyperuricemia is a strong precursor of gout, which deteriorates patients' health and quality of life. Sustained adherence to urate-lowering therapies (ULTs) is crucial for efficacy and therapeutic cost-effectiveness. Recently, several new ULTs have been proposed. We performed a systematic review and meta-analysis of randomized controlled trials (RCTs) to reassess the efficacy and safety of the current ULTs, focusing on adherence attrition-related adverse event reporting. METHOD: The Bayesian network meta-analysis was applied to compare ULTs. Drug efficacy and safety were measured by whether the target level of serum urate acid was achieved and whether any adverse events occurred. The results were summarized using the pooled estimates of effect sizes (odds ratios), their precisions (95% credible interval), and the ranking probabilities. RESULTS AND CONCLUSIONS: Thirty-nine RCTs were identified, accumulating 19,401 patients. Consistent with previous studies, febuxostat (≥ 40 mg/day) was superior to other monoagent ULTs. The new findings were as follows: (i) dual-agent ULTs were superior to febuxostat alone, and further surveillance on the adverse effects when lesinurad is uptitrated is needed, and (ii) terminalia bellerica 500 mg/day, a novel xanthine oxidase inhibitor (XOI) made of natural fruit extracts, and topiroxostat ≥ 80 mg/day, an XOI used mostly in Japan, could be new effective options for lowering the occurrence of adherence attrition events. Evidence from RCTs regarding second-line agents, such as probenecid and pegloticase, remains insufficient for clinical decision-making.Key Points• Dual-agent ULTs were superior to febuxostat alone, and further surveillance on the adverse-effects when lesinurad is uptitrated is needed.• Terminalia bellerica 500 mg/day, a novel xanthine oxidase inhibitor (XOI) made of natural fruit extracts, and topiroxostat 80 mg/day, an XOI used mostly in Japan, could be new effective options for lowering the occurrence of adherence attrition events.

Noninvasive and Convenient Screening of Metabolic Syndrome Using the Controlled Attenuation Parameter Technology: An Evaluation Based on Self-Paid Health Examination Participants.

BACKGROUND: There is a medical need for an easy, fast, and non-invasive method for metabolic syndrome (MetS) screening. This study aimed to assess the ability of FibroScan to detect MetS, in participants who underwent a self-paid health examination. METHODS: A retrospective cohort study was conducted on all adults who underwent a self-paid health examination comprising of an abdominal transient elastography inspection using FibroScan 502 Touch from March 2015 to February 2019. FibroScan can assess the level of liver fibrosis by using a liver stiffness score, and the level of liver steatosis by using the controlled attenuation parameter (CAP) score. The logistic regression analysis and receiver operating characteristic curve were applied to select significant predictors and assess their predictability. A final model that included all significant predictors that are found by univariate analysis, and a convenient model that excluded all invasive parameters were created. RESULTS: Of 1983 participants, 13.6% had a physical status that fulfilled MetS criteria. The results showed that the CAP score solely could achieve an area under the curve (AUC) of 0.79 (0.76-0.82) in predicting MetS, and the AUC can be improved to 0.88 (0.85-0.90) in the final model. An AUC of 0.85 (0.83-0.88) in predicting MetS was obtained in the convenient model, which includes only 4 parameters (CAP score, gender, age, and BMI). A panel of predictability indices (the ranges of sensitivity, specificity, positive and negative likelihood ratio: 0.78-0.89, 0.66-0.82, 2.64-4.47, and 0.17-0.26) concerning gender- and BMI-specific CAP cut-off values (range: 191.65-564.95) were presented for practical reference. CONCLUSIONS: Two prediction systems were proposed for identifying individuals with a physical status that fulfilled the MetS criteria, and a panel of predictability indices was presented for practical reference. Both systems had moderate predictive performance. The findings suggested that FibroScan evaluation is appropriate as a first-line MetS screening; however, the variation in prediction performance of such systems among groups with varying metabolic derangements warrants further studies in the future.