scholarly journals Integrative Analysis of Metabolome and Microbiome in Patients with Progressive Alcohol-Associated Liver Disease

Metabolites ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 766
Author(s):  
Bei Gao ◽  
Yixin Zhu ◽  
Nan Gao ◽  
Weishou Shen ◽  
Peter Stärkel ◽  
...  
Keyword(s):  
Liver Disease ◽  
Predictive Power ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Disease Status ◽  
Integrated Analysis ◽  
Data Types ◽  
Serum Metabolites ◽  
Microbial Species ◽  
Progressive Liver Disease

Alcohol-associated liver disease is one of the most prevalent diseases around the world, with 10–20% of patients developing progressive liver disease. To identify the complex and correlated nature of metabolic and microbial data types in progressive liver disease, we performed an integrated analysis of the fecal and serum metabolomes with the gut microbiome in a cohort of 38 subjects, including 15 patients with progressive liver disease, 16 patients with non-progressive liver disease, and 7 control subjects. We found that although patients were generally clustered in three groups according to disease status, metabolites showed better separation than microbial species. Furthermore, eight serum metabolites were correlated with two microbial species, among which seven metabolites were decreased in patients with progressive liver disease. Five fecal metabolites were correlated with three microbial species, among which four metabolites were decreased in patients with progressive liver disease. When predicting progressive liver disease from non-progressive liver disease using correlated metabolic and microbial signatures with the random forest model, correlated serum metabolites and microbial species showed great predictive power, with the area under the receiver operating characteristic curve achieving 0.91. The multi-omics signatures identified in this study are helpful for the early identification of patients with progressive alcohol-associated liver disease, which is a key step for therapeutic intervention.

scholarly journals Shoulder Muscle Imbalance as a Risk for Shoulder Injury in Elite Adolescent Swimmers: A Prospective Study

2020 ◽  
Vol 75 (1) ◽  
pp. 103-113
Author(s):  
Joffrey Drigny ◽  
Antoine Gauthier ◽  
Emmanuel Reboursière ◽  
Henri Guermont ◽  
Vincent Gremeaux ◽  
...  
Keyword(s):  
Predictive Power ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Curve Analysis ◽  
Shoulder Injuries ◽  
Shoulder Injury ◽  
Shoulder Muscle ◽  
Increased Risk ◽  
A Prospective Study ◽  
External Rotator

Abstract Muscle strength imbalances between the internal and external rotators of the shoulder are frequent in swimmers, but their role in shoulder injury remains unknown. We aimed to evaluate the association of shoulder rotator strength and injury in elite adolescent swimmers. Eighteen adolescent swimmers performed preseason isokinetic tests of the internal and external rotator muscles in concentric (con) and eccentric (ecc) modes. Conventional (conER:conIR and eccER:eccIR) and functional ratios (eccER:conIR and eccIR:conER) were calculated. Thirteen swimmers completed a weekly questionnaire about swimming habits and shoulder injuries throughout the season. Preseason testing showed a significant negative association between the functional eccER:conIR ratio and years of practice (p < 0.05). Over the season, 46% of athletes experienced at least one shoulder injury. At the end of the season, peak torques increased for both internal and external rotator muscles strength, but only concentrically, resulting in a decrease in the eccER:conIR functional ratio (p < 0.05). The receiver operating characteristic curve analysis highlighted good predictive power for the preseason functional eccER:conIR ratio, as values below 0.68 were associated with a 4.5-fold (95% CI 1.33-15.28, p < 0.05) increased risk of shoulder injuries during the season.

Clinical value and potential mechanisms of LINC00221 in hepatocellular carcinoma based on integrated analysis

Epigenomics ◽  
2021 ◽  
Author(s):  
Yanlin Feng ◽  
Souraka Tapara Dramani Maman ◽  
Xinyu Zhu ◽  
Xuefang Liu ◽  
Christian Cedric Bongolo ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Operating Characteristic ◽  
Bioinformatics Analysis ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Integrated Analysis ◽  
Clinical Value ◽  
Serum Samples ◽  
Functional Roles

This study aimed to unveil the functional roles of LINC00221 in hepatocellular carcinoma (HCC). A discovery cohort and a validation cohort were respectively used to identify and verify the clinical value of LINC00221 in HCC. Bioinformatics analysis was performed to explore its potential mechanisms. LINC00221 was upregulated in HCC tissues and serum samples. Survival analysis and receiver operating characteristic curve further revealed its prognostic and diagnostic roles. Exploration of the mechanism showed that LINC00221 might exert a pro-cancer role via the lncRNA–miRNA–mRNA network. Our study reveals that upregulated LINC00221 can serve as a potential diagnostic and prognostic biomarker and provides novel clues as to the role of LINC00221 in HCC.

scholarly journals On the Binormal Predictive Receiver Operating Characteristic Curve for the Joint Assessment of Positive and Negative Predictive Values

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 593 ◽  
Author(s):  
Gareth Hughes
Keyword(s):  
Receiver Operating Characteristic ◽  
Roc Curve ◽  
Operating Characteristic ◽  
Statistical Evaluation ◽  
Characteristic Curve ◽  
Disease Status ◽  
Curve Analysis ◽  
Roc Curve Analysis ◽  
Predictive Values ◽  
Receiver Operating

The predictive receiver operating characteristic (PROC) curve is a diagrammatic format with application in the statistical evaluation of probabilistic disease forecasts. The PROC curve differs from the more well-known receiver operating characteristic (ROC) curve in that it provides a basis for evaluation using metrics defined conditionally on the outcome of the forecast rather than metrics defined conditionally on the actual disease status. Starting from the binormal ROC curve formulation, an overview of some previously published binormal PROC curves is presented in order to place the PROC curve in the context of other methods used in statistical evaluation of probabilistic disease forecasts based on the analysis of predictive values; in particular, the index of separation (PSEP) and the leaf plot. An information theoretic perspective on evaluation is also outlined. Five straightforward recommendations are made with a view to aiding understanding and interpretation of the sometimes-complex patterns generated by PROC curve analysis. The PROC curve and related analyses augment the perspective provided by traditional ROC curve analysis. Here, the binormal ROC model provides the exemplar for investigation of the PROC curve, but potential application extends to analysis based on other distributional models as well as to empirical analysis.

Comparison of CHA2DS2-VASc and C2HEST scores for predicting the incidence of atrial fibrillation among patients with end-stage renal disease

Perfusion ◽  
2020 ◽  
Vol 35 (8) ◽  
pp. 842-846
Author(s):  
Wei-Syun Hu ◽  
Cheng-Li Lin
Keyword(s):  
Atrial Fibrillation ◽  
Renal Disease ◽  
End Stage Renal Disease ◽  
Predictive Power ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Predictive Capability ◽  
Stage Renal Disease ◽  
End Stage ◽  
Insurance Database

Purpose: The authors have studied assessment of CHA2DS2-VASc score versus C2HEST score in atrial fibrillation risk prediction in end-stage renal disease patients. Methods: The authors conducted this study by Longitudinal Health Insurance Database 2000. The authors totally enrolled 4,601 end-stage renal disease patients. The predictive capability of atrial fibrillation by using CHA2DS2-VASc and C2HEST score was estimated by area under the receiver operating characteristic curve (AUROC). Results: The AUROC for CHA2DS2-VASc score in predicting atrial fibrillation events was 0.5786, and AUROC for C2HEST score for atrial fibrillation prediction was 0.5983. Conclusion: Both scores yield almost identical AUROC values implying no difference in predictive power. Further work is warranted to verify the prognostic value of the current scores.

scholarly journals A Comparison of KL-6 and Clara Cell Protein as Markers for Predicting Bronchopulmonary Dysplasia in Preterm Infants

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Keyi Wang ◽  
Xianmei Huang ◽  
Hui Lu ◽  
Zhiqun Zhang
Keyword(s):  
Preterm Infants ◽  
Bronchopulmonary Dysplasia ◽  
Predictive Power ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Clara Cell ◽  
Cell Protein ◽  
Preterm Neonates ◽  
Operating Characteristic Curve ◽  
Time Point

Objectives. To evaluate the predictive characteristics of KL-6 and CC16 for bronchopulmonary dysplasia (BPD) in preterm infants, either independently or in combination.Study Design. This prospective cohort study was performed from 2011 to 2013 with preterm neonates of gestational age ≤32 weeks and birth weight ≤1500 g. Serum KL-6 and CC16 levels were determined 7 and 14 days after birth.Results. Seventy-three preterm infants were studied. BPD was identified in 24 of these infants. After adjusting for potential confounders, serum KL-6 concentrations were found to be elevated in BPD infants at both time points relative to non-BPD infants, while serum CC16 concentrations were lower at 14 days. At both 7 d and 14 d of life the predictive power of KL-6 levels exceeded that of CC16 (area under receiver operating characteristic curve: at 7 d, 0.91 cf. 0.73,P=0.02; at 14 d, 0.95 cf. 0.85,P=0.05). The combination of these markers enhanced the sensitivity further.Conclusions. Serum KL-6 levels higher than 79.26 ng/mL at 14 days postpartum in preterm infants predict the occurrence of BPD. CC16 was less predictive than KL-6 at this time point, but KL-6 and CC16 together enhanced the prediction.

A Comparative Study of the Diagnostic Potential of Plasma and Erythrocytic α-Synuclein in Parkinson’s Disease

2019 ◽  
Vol 19 (5-6) ◽  
pp. 204-210
Author(s):  
Luxuan Wang ◽  
Guowei Wang ◽  
Yangyang Duan ◽  
Feng Wang ◽  
Shaoqing Lin ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Predictive Power ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Expression Level ◽  
Enzyme Linked Immunosorbent Assay ◽  
Control Subjects ◽  
Diagnostic Potential

Background: Parkinson’s disease (PD) is a neurodegenerative disease characterized by intracellular α-synuclein (α-Syn) deposition. Alternation of the α-Syn expression level in plasma or erythrocytes may be used as a potential PD biomarker. However, no studies have compared their prognostic value directly with the same cohort. Methods: The levels of α-Syn in plasma and erythrocytes, obtained from 45 PD patients and 45 control subjects, were measured with enzyme-linked immunosorbent assay. Then, correlation and receiver operating characteristic curve (ROC) analysis were performed to characterize the predictive power of erythrocytic and plasma α-Syn. Results: Our results showed that α-Syn expression levels in both plasma and erythrocytes were significantly higher in PD patients than in control subjects (823.14 ± 257.79 vs. 297.10 ± 192.82 pg/mL, p < 0.0001 in plasma; 3,104.14 ± 143.03 vs. 2,944.82 ± 200.41 pg/mL, p < 0.001 in erythrocytes, respectively). The results of the ROC analysis suggested that plasma α-Syn exhibited better predictive power than erythrocytic α-Syn with a sensitivity of 80.0%, specificity of 97.7%, and a positive predictive value of 77.8%. The expression level of plasma α-Syn correlated well with the age of patients, H-Y stage, MoCA scale, and UPDRS motor scale. On the contrary, there was no correlation between erythrocytic α-Syn level and clinical parameters in this study. Conclusion: Our results suggest that plasma α-Syn could be a specific and sensitive potential diagnostic biomarker for PD.

scholarly journals The value of the atherogenic index of plasma in non-obese people with non-alcoholic fatty liver disease: a secondary analysis based on a cross-sectional study

2020 ◽  
Author(s):  
Buyuan Dong ◽  
Yuqing Mao ◽  
Zhengyang Li ◽  
Fujun Yu
Keyword(s):  
Liver Disease ◽  
Fatty Liver ◽  
Fatty Liver Disease ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Atherogenic Index ◽  
Obese People ◽  
Alcoholic Fatty Liver ◽  
Atherogenic Index Of Plasma ◽  
Alcoholic Fatty Liver Disease

Abstract Background and objectives: The atherogenic index of plasma (AIP) is elevated in fatty liver disease, but its value in non-obese people with non-alcoholic fatty liver disease (NAFLD) is unclear. This study aimed to investigate the relationship between AIP and NAFLD as well as to determine whether AIP might be used as an indicator of NAFLD in non-obese individuals. Methods : The present study involved non-obese Chinese and Japanese participants. Univariate and multivariate logistic regression models were used to determine risk factors. The performance of risk factors was compared according to the area under the receiver operating characteristic curve. Results : In the unadjusted model, the odds ratio (OR) for every 1 standard deviation (SD) increase in AIP was 52.30. In adjusted models I and II, the OR for every 1 SD increase in AIP was 36.57 and 50.84, respectively. The area under the receiver operating characteristic curve for AIP was 0.803 and 0.802 in the development and validation groups, respectively. The best cut-off value of AIP for discrimination between NAFLD and non-NAFLD was 0.005 in the Chinese group and -0.220 in the Japanese group. Conclusions : AIP and NAFLD are positively correlated in Chinese and Japanese populations. Therefore, AIP can be used as a new screening indicator for non-obese people with NAFLD in different nations.

669 Mortality Risk Prediction for Emergency Laparotomy: Are We Utilising the Best Tool?

2021 ◽  
Vol 108 (Supplement_6) ◽  
Author(s):  
M Barghash ◽  
A Iskandar ◽  
S Fawzy ◽  
T Elghobashy ◽  
F Salimi ◽  
...  
Keyword(s):  
Predictive Power ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Scoring Systems ◽  
Emergency Laparotomy ◽  
Discriminative Power ◽  
Entire Cohort ◽  
Possum Score ◽  
The Common ◽  
The Uk

Abstract Aim Emergency laparotomy is one of the common operations performed in the UK. To aid in more objective decision making, several scoring systems have been formulated. In this project, we aimed to explore the predictive power of both NELA and P-Possum mortality scores against 30 days and 90 days observed mortality for emergency laparotomy patients. Method Patient details from two large district general hospitals were extracted from the NELA database over a period of three years. Pre-operative NELA, post-operative NELA, and P-POSSUM predicted mortality were calculated and compared with the observed 30 days and 90 days mortality for the entire cohort. Model discrimination (statistical accuracy) was tested by calculating the area under the receiver operating characteristic curve (AUC), which was used to assess how accurately the model could discriminate. Results There were 378 patients eligible for inclusion with a median age of 64. 39 patients (10.3%) died within 30 days and 52 patients (13.8%) died within 90 days. P-POSSUM score, pre-operative NELA, and post-operative NELA scores predicted the 30 days mortality as (2.7%, 3.7%, and 2.4%) and 90 days mortality as (2.9%, 4.8%, and 4%) respectively. The discriminative power for 30 days and 90 days mortality was highest for the pre-operative NELA score (AUC 0.870, CI: 0.824 – 0.916), (AUC 0.826, CI: 0.769 – 0.884) respectively. Conclusions Both NELA and P-Possum scores underpredicted actual 30 days and 90 days mortality. It was however noted that the pre-operative NELA mortality score showed more accurate mortality discriminative power than the other 2 tested tools.

scholarly journals Composite BMI and Waist-to-Height Ratio Index for Risk Assessment of Non-alcoholic Fatty Liver Disease in Adult Populations

2021 ◽  
Vol In Press (In Press) ◽  
Author(s):  
Na Li ◽  
Ying Liu ◽  
Shujun Yu ◽  
Bin Hu ◽  
Hui Zhao
Keyword(s):  
Risk Factors ◽  
Liver Disease ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Height Ratio ◽  
Alcoholic Fatty Liver ◽  
High Risk Factors ◽  
Waist To Height Ratio ◽  
Operating Characteristic Curve ◽  
Alcoholic Fatty Liver Disease

Background: As obesity becomes more prevalent, non-alcoholic fatty liver disease (NAFLD) is also becoming a major worldwide health problem and the most common cause of chronic liver disease. A new obesity classification method based on a composite index which includes both the body mass index (BMI) and the waist-to-height ratio (WHtR) was recently proposed. However, the usefulness of this approach to assess the risk of NAFLD is unclear. Methods: This is a cross-sectional study of 1,276 adult individuals in Dalian, China. The Mann Whitney U test, χ2 test and t-test were used to compare differences between groups. Binary logistic regression analysis was used to identify independent risk factors. Based on BMI and WHtR tertiles, individuals were divided into five new groups. Spearman correlation and receiver operating characteristic curve (ROC) analyses were performed to compare the NAFLD risk factors among groups based on BMI alone, WHtR alone, or the combination of both indexes. Results: BMI, waistline circumference (WC), WHtR, alanine aminotransferase (ALT), weight, triglycerides (TG), γ-glutamyl transpeptidase (GGT), serum uric acid (SUA), red blood cell (RBC) counts, hemoglobin levels (HGB), fasting blood glucose (FBG) and aspartate aminotransferase (AST) levels were identified as high risk factors for NAFLD (all AUC > 0.7). Logistic regression analysis suggested that BMI and WHtR were independent predictors of the appearance of NAFLD (the ORs for BMI and WHtR were 1.595 and 4.060E-11, respectively; all P < 0.001). The combination of BMI and WHtR tertiles significantly improved the correlation coefficient and Area under the receiver operating characteristic curve (AUC) for NAFLD risk factors in subjects classified as overweight or obese when compared with either BMI or WHtR alone. Conclusions: BMI, WC, WHtR, ALT, weight, TG, GGT, SUA, RBC, HGB, FBG, AST were high risk factors for NAFLD. The composite BMI and WHtR index improved body fat classification and the ability to detect individuals with NAFLD risk, offering a more precise method for the early identification of high- and low-risk NAFLD patients.

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