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Objective: Non-alcoholic fatty liver disease (NAFLD) is one of the leading causes of chronic liver disease. The controlled attenuation parameter (CAP) obtained by FibroScan reflects the level of liver steatosis in patients with obesity. Our study aimed to construct a simple equation to predict the CAP, to facilitate the screening and monitoring of patients at high risk for NAFLD., Methods: A total of 272 subjects were randomly divided into derivation and validation cohorts at a ratio of 1:2. The derivation set was used for constructing a multiple linear regression model; the validation set was used to verify the validity of the model., Results: Several variables strongly correlated with the CAP were used to construct the following equation for predicting CAP values:CAP1 = 2.4 × BMI + 10.5 × TG+ 3.6 × NC + 10.3 × CP +31.0, where BMI is body mass index, TG is triglyceride, NC is neck circumference and CP is C-peptide. The CAP1 model had an R 2 of 0.764 and adjusted R 2 of 0.753. It was then simplified to derive CAP2 included only simple anthropometric parameters: CAP2 = 3.5 × BMI + 4.2 × NC + 20.3 ( R 2 = 0.696, adjusted R 2 = 0.689). The data were well fitted by both models. In the verification group, the predicted (CAP1 and CAP2) values were compared to the actual CAP values. For the CAP1 equation, R 2 = 0.653, adjusted R 2 = 0.651. For the CAP2 equation, R 2 = 0.625, adjusted R 2 = 0.623. The intra-class correlation coefficient (ICC) values were 0.781 for CAP1 and 0.716 for CAP2 ( p < 0.001). The actual CAP and the predicted CAP also showed good agreement in Bland-Altman plot., Conclusion: The equations for predicting the CAP value comprise easily accessible variables, and showed good stability and predictive power. Thus, they can be used as simple surrogate tools for early screening and follow-up of NAFLD in the Chinese population., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Liu, Li, Han, Zhang, Gu, Sun, Han, Tu, Bao, Bai and Yu.)

Background: Type A aortic dissection (TAAD) has a rapid onset and high mortality. Currently, aortic diameter is the major criterion for evaluating the risk of TAAD. We attempted to find other aortic morphological indicators to further analyze their relationships with the risk of type A dissection., Methods: We included the imaging and clinical data of 112 patients. The patients were divided into three groups, of which Group 1 had 49 patients with normal aortic diameter, Group 2 had 22 patients with ascending aortic aneurysm, and Group 3 had 41 patients with TAAD. We used AW Server software, version 3.2, to measure aorta-related morphological indicators., Results: First, in Group 1, the univariate analysis results showed that ascending aortic diameter was correlated with patient age ( r 2 = 0.35) and ascending aortic length (AAL) ( r 2 = 0.43). AAL was correlated with age ( r 2 = 0.12) and height ( r 2 = 0.11). Further analysis of the aortic morphological indicators among the three groups found that the median aortic diameter was 36.20 mm in Group 1 (Q1-Q3: 33.40-37.70 mm), 42.5 mm in Group 2 (Q1-Q3: 41.52-44.17 mm) and 48.6 mm in Group 3 (Q1-Q3: 42.4-55.3 mm). There was no significant difference between Groups 2 and 3 ( P > 0.05). Group 3 had the longest AAL (median: 109.4 mm, Q1-Q3: 118.3-105.3 mm), followed by Group 2 (median: 91.0 mm, Q1-Q3: 95.97-84.12 mm) and Group 1 (81.20 mm, Q1-Q3: 76.90-86.20 mm), and there were statistically significant differences among the three groups ( P < 0.05). The Aortic Bending Index (ABI) was 14.95 mm/cm in Group 3 (Q1-Q3: 14.42-15.78 mm/cm), 13.80 mm/cm in Group 2 (Q1-Q3: 13.42-14.42 mm/cm), and 13.29 mm/cm in Group 1 (Q1-Q3: 12.71-13.78 mm/cm), and the difference was statistically significant in comparisons between any two groups ( P < 0.05). Regression analysis showed that aortic diameter + AAL + ABI differentiated Group 2 and Group 3 with statistical significance (area under the curve (AUC) = 0.834), which was better than aortic diameter alone (AUC = 0.657; P < 0.05)., Conclusions: We introduced the new concept of ABI, which has certain clinical significance in distinguishing patients with aortic dissection and aneurysm. Perhaps the ascending aortic diameter combined with AAL and ABI could be helpful in predicting the occurrence of TAAD., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Sun, Li, Wang, Sun, Zhang, Chi, Cao, Ma, Yan and Liu.)

Objective: Vascular endothelial dysfunction is considered an early predictor of endothelial injury and the initiating factor of atherosclerosis (AS). Brachial artery flow-mediated dilation (FMD) can detect endothelial injury early and provide important prognostic information beyond traditional cardiovascular (CV) risk factors. This study aimed to find the influencing factors of FMD and develop a simple prediction model in populations with different body mass indices (BMIs)., Methods: In total, 420 volunteers with different BMIs were recruited in our study. Subjects were randomly assigned to the derivation and validation cohorts (the ratio of the two was 1:2) with simple random sampling. The former was used for influencing factors searching and model construction of FMD and the latter was used for verification and performance evaluation., Results: The population was divided into two groups, i.e., 140 people in the derivation group and 280 people in the verification group. Analyzing in the training data, we found that females had higher FMD than males ( p < 0.05), and FMD decreased with age ( p < 0.05). In people with diabetes, hypertension or obesity, FMD was lower than that in normal individuals ( p < 0.05). Through correlation analysis and linear regression, we found the main influencing factors of FMD: BMI, age, waist-to-hip radio (WHR), aspartate aminotransferase (AST) and low-density lipoprotein (LDL). And we developed a simple FMD prediction model: FMD = -0.096BMI-0.069age-4.551WHR-0.015AST-0.242LDL+17.938, where R 2 = 0.599, and adjusted R 2 = 0.583. There was no statistically significant difference between the actual FMD and the predicted FMD in the verification group ( p > 0.05). The intra-class correlation coefficient (ICC) was 0.77. In a Bland-Altman plot, the actual FMD and the predicted FMD also showed good agreement. This prediction model had good hints in CV risk stratification (area under curve [AUC]: 0.780, 95 % confidence intervals [95% CI]: 0.708-0.852, p < 0.001), with a sensitivity and specificity of 73.8 and 72.1%, respectively., Conclusions: Males, older, obesity, hypertension, diabetes, smoking, etc. were risk factors for FMD, which was closely related to CV disease (CVD). We developed a simple equation to predict FMD, which showed good agreement between the training and validation groups. And it would greatly simplify clinical work and may help physicians follow up the condition and monitor therapeutic effect. But further validation and modification bears great significance., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflictof interest., (Copyright © 2022 Li, Liu, Zhang, Gu, Sun, Yu and Bai.)