Aims/Introduction: To investigate the impact of visceral adipose tissue (VAT) on atherosclerosis in type 2 diabetes patients with normal bodyweight (OB[−]) in the Chinese population, and to further assess the sex–age differences between them.Materials and Methods: A total of 8,839 type 2 diabetes patients from two of the National Metabolic Management Centers in China were included in this study. Participants were classified into four groups by visceral fat area (VFA; cm²) and body mass index (BMI; kg/m²): VFA < 100 and BMI < 23.9 (VA[−]OB[−]), VFA < 100 and BMI ≥ 23.9 (VA[−]OB[+]), VFA ≥ 100 and BMI < 23.9 (VA[+]OB[−]), VFA ≥ 100 and BMI ≥ 23.9 (VA[+]OB[+]). Atherosclerosis was defined by brachial‐ankle pulse wave velocity (baPWV; cm/s), and we analyzed the association between VFA, BMI and the tertiles of baPWV values.Results: The VA(+)OB(−) prevalence was 3.7% among these participants. Patients with VA(+)OB(−) had the highest baPWV value (P < 0.001) and the highest proportion of the tertile 3 of baPWV (P < 0.001) among four groups, and were significantly associated with baPWV (standardized β = 0.026, P = 0.008). VFA was significantly related to tertile 2 to tertile 3 of baPWV in (OB[−]) type 2 diabetes patients, when compared with tertile 1 of baPWV, respectively. In sex–age stratified analysis, the association of VFA and the tertiles of baPWV showed sex differences. For the 55 years age stratification analysis, there was no age difference in the relationship between VFA and baPWV in (OB[−]) patients.Conclusion: Increased VAT was an independent risk factor for atherosclerosis in female type 2 diabetes patients with normal weight.

Background: Coronary computed tomography-derived fractional flow reserve (CT-FFR) and intravascular ultrasound-derived fractional flow reserve (IVUS-FFR) are two functional assessment methods for coronary stenoses. However, the calculation algorithms for these methods differ significantly. This study aimed to compare the diagnostic performance of CT-FFR and IVUS-FFR using invasive fractional flow reserve (FFR) as the reference standard. Methods: Six hundred and seventy patients (698 lesions) with known or suspected coronary artery disease were screened for this retrospective analysis between January 2020 and July 2021. A total of 40 patients (41 lesions) underwent intravascular ultrasound (IVUS) and FFR evaluations within six months after completing coronary CT angiography were included. Two novel CFD-based models (AccuFFRct and AccuFFRivus) were used to compute the CT-FFR and IVUS-FFR values, respectively. The invasive FFR ≤ 0.80 was used as the reference standard for evaluating the diagnostic performance of CT-FFR and IVUS-FFR. Results: Both AccuFFRivus and AccuFFRct demonstrated a strong correlation with invasive FFR (R = 0.7913, P < 0.0001; and R = 0.6296, P < 0.0001), and both methods showed good agreement with FFR. The area under the receiver operating characteristic curve was 0.960 (P < 0.001) for AccuFFRivus and 0.897 (P < 0.001) for AccuFFRct in predicting FFR ≤ 0.80. FFR ≤ 0.80 were predicted with high sensitivity (96.6%), specificity (85.7%), and the Youden index (0.823) using the same cutoff value of 0.80 for AccuFFRivus. A good diagnostic performance (sensitivity 89.7%, specificity 85.7%, and Youden index 0.754) was also demonstrated by AccuFFRct. Conclusions: AccuFFRivus, computed from IVUS images, exhibited a high diagnostic performance for detecting myocardial ischemia. It demonstrated better diagnostic power than AccuFFRct, and could serve as an accurate computational tool for ischemia diagnosis and assist in clinical decision-making.

The present study aimed to further investigate the effects of high glucose on the function of circulating fibrocytes and its underlying mechanisms. The total peripheral blood mononuclear cells were obtained from normal glucose tolerance patients and type 2 diabetic mellitus patients. Circulating fibrocytes were stimulated with different glucose concentrations for different time periods (24, 48 and 72 h). Cell proliferation was determined by Cell Counting Kit‑8 assay. The expression of connective tissue growth factor (CTGF) was detected by western blotting. The expression of COL‑I was detected by flow cytometry. The apoptotic bodies of cells were detected by fluorescence microscopy after Hoechst33258 staining. The invasive and migration abilities of fibrocytes were detected by Transwell chamber assay. Secretion of stromal cell‑derived factor 1 (SDF‑1) was measured by ELISA. The circulating fibrocytes showed a typical spindle‑shape and were double‑positive for cluster of differentiation 45 (green) and COL‑I (red). Compared with the 5.5 mmol/l glucose group, a high glucose concentration significantly promoted the proliferation of circulating fibrocytes and showed the most significant effects at 30 mmol/l after treatment for 48 h. AMD3100 showed no effects on the proliferation of circulating fibrocytes. Flow cytometry revealed that 30 mmol/l glucose significantly promoted the expression of COL‑I vs. 5.5 mmol/l glucose group (P<0.01), while AMD3100 reversed this (P0.05). Western blotting revealed that the expression of CTGF was decreased significantly by AMD3100 pretreatment (P<0.01). Transwell chamber assay showed that 30 mmol/l glucose significantly promoted the invasive and transfer abilities (P<0.01) of fibrocytes when compared with the 5.5 mmol/l glucose group. While AMD3100 reversed the cell migratory effects induced by high glucose (P0.05). High glucose stimulated the expressions of CTGF and COL‑I, and promoted migration of circulating fibrocytes via the CXC chemokine receptor 4/SDF‑1 axis.

Background and objectives: Both fractional flow reserve (FFR) and instantaneous wave-free ratio (iFR) are widely used to evaluate ischemia-causing coronary lesions. A new method of CT-iFR, namely AccuiFRct, for calculating iFR based on deep learning and computational fluid dynamics (CFD) using coronary computed tomography angiography (CCTA) has been proposed. In this study, the diagnostic performance of AccuiFRct was thoroughly assessed using iFR as the reference standard. Methods: Data of a total of 36 consecutive patients with 36 vessels from a single-center who underwent CCTA, invasive FFR, and iFR were retrospectively analyzed. The CT-derived iFR values were computed using a novel deep learning and CFD-based model. Results: Mean values of FFR and iFR were 0.80 ± 0.10 and 0.91 ± 0.06, respectively. AccuiFRct was well correlated with FFR and iFR (correlation coefficients, 0.67 and 0.68, respectively). The diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of AccuiFRct ≤ 0.89 for predicting FFR ≤ 0.80 were 78%, 73%, 81%, 73%, and 81%, respectively. Those of AccuiFRct ≤ 0.89 for predicting iFR ≤ 0.89 were 81%, 73%, 86%, 79%, and 82%, respectively. AccuiFRct showed a similar discriminant function when FFR or iFR were used as reference standards. Conclusion: AccuiFRct could be a promising noninvasive tool for detection of ischemia-causing coronary stenosis, as well as facilitating in making reliable clinical decisions.

Background: Accurate segmentation of the left ventricle (LV) is an important step in assessing cardiac function. Cardiac CT angiography (CCTA) has become an important means of clinical diagnosis of cardiovascular diseases (CVDs) because of its advantages of non-invasive, short examination time and low cost. In order to obtain the segmentation of LV in CCTA scans, we propose a deep learning method based on 8-layer residual U-Net with deep supervision. In this study we compared the left ventricular ejection fraction (LVEF) calculated by deep learning (DL) method (AccuLV) from CCTA to LVEF by conventional two-dimensional echocardiography (EC). Methods: This was a retrospective cross-sectional study, and consecutive patients who had undergone CCTA and EC in our hospital from February 2021 to May 2021 were recruited. The current study included 180 patients who had undergone CCTA and EC. To obtain LVEF, we used an 8-layer residual U-Net with deep supervision to segment LV contours from CCTA images and compute LVEF (DL-LVEF). The EC and DL-LVEF measurements were compared. A 50% EC-LVEF cut-off value was used as a reference standard to assess the diagnostic performance of AccuLV in assessing LV function. Results: The overall mean EC-LVEF and DL-LVEF values were 64.0% (52.3%, 69.0%) and 73.0% (52.3%, 77.0%), respectively. Three patient groups were studied: (I) hypertensive patients, (II) postmenopausal women, and (III) diabetes. EC-LVEF and DL-LVEF were found to be positively correlated for all of the included patients (r=0.82, P<0.001), with the detailed results for the three groups as follows: hypertensive patients (r=0.77, P<0.001), postmenopausal women (r=0.92, P<0.001) and diabetes (r=0.88, P<0.001). The diagnostic accuracy, sensitivity, and specificity of the DL method in predicting EC-LVEF <50% for all patients were 93.9%, 92.3%, and 94.3%, and for hypertensive patients were 95.4%, 93.8%, and 95.8%, for postmenopausal women were 87.0%, 100%, and 84.2%, for diabetes were 97.4%, 100%, and 96.6%. Conclusions: In comparison to echocardiography, which is commonly used in clinical setting, AccuLV may be a promising, fully automated tool for rapid and accurate quantification of LV function and thus for making reliable clinical decisions.

Background: We aim to compare with the diagnostic performance of target-position quantitative flow ratio derived from Murray Law (target-μFR) and vessel quantitative flow ratio derived from Murray Law (vessel-μFR) using the fractional flow reserve (FFR) as reference standard. This study may provide more evidence for the novel clinical usage of target-μFR in the diagnosis of coronary artery disease. Methods: Six hundreds and fifty-six patients (685 lesions) with known or suspected coronary artery disease were screened for this retrospective analysis between January 2021 to March 2023. A total of 161 patients (190 lesions) underwent quantitative coronary angiography and FFR evaluations. In the final analysis, 137 patients (146 lesions) were included in this study. Both of target-μFR and vessel-μFR were compared the diagnostic performance using the FFR ≤ 0.80 as the reference standard. Results: Both target-μFR (R = 0.84) and vessel-μFR (R = 0.83) demonstrated a strong correlation with FFR, and both methods showed great agreement with FFR. The area under the receiver operating characteristic curve was 0.937 for target-μFR and 0.936 for vessel-μFR in predicting FFR ≤ 0.80. FFR ≤ 0.80 were predicted with high sensitivity (86.44%) and specificity (88.51%) using the pre-defined cutt-off of 0.80 for target-μFR. A good diagnostic performance (sensitivity 92.98% and specificity 91.01%) was also demonstrated by vessel-μFR which the pre-defined cutt-off was 0.80. Conclusion: The target-μFR has the similar diagnostic performance with vessel-μFR. The accuracy of μFR does not seem to be affected by the selection of the measurement point. Both of the virtual models have been validated as computational tools for diagnosing ischemia and are instrumental in aiding clinical decision-making.