Assessment of left atrioventricular coupling and left atrial function impairment in diabetes with and without hypertension using CMR feature tracking.

PURPOSE: The study was designed to assess the effect of co-occurrence of diabetes mellitus (DM) and hypertension on the deterioration of left atrioventricular coupling index (LACI) and left atrial (LA) function in comparison to individuals suffering from DM only. METHODS: From December 2015 to June 2022, we consecutively recruited patients with clinically diagnosed DM who underwent cardiac magnetic resonance (CMR) at our hospital. The study comprised a total of 176 patients with DM, who were divided into two groups based on their blood pressure status: 103 with hypertension (DM + HP) and 73 without hypertension (DM-HP). LA reservoir function (reservoir strain (εs), total LA ejection fraction (LAEF)), conduit function (conduit strain (εe), passive LAEF), booster-pump function (booster strain (εa) and active LAEF), LA volume index (LAVI), LV global longitudinal strain (LVGLS), and LACI were evaluated and compared between the two groups. RESULTS: After adjusting for age, sex, body surface area (BSA), and history of current smoking, total LAEF (61.16 ± 14.04 vs. 56.05 ± 12.72, p = 0.013) and active LAEF (43.98 ± 14.33 vs. 38.72 ± 13.51, p = 0.017) were lower, while passive LAEF (33.22 ± 14.11 vs. 31.28 ± 15.01, p = 0.807) remained unchanged in the DM + HP group compared to the DM-HP group. The DM + HP group had decreased εs (41.27 ± 18.89 vs. 33.41 ± 13.94, p = 0.006), εe (23.69 ± 12.96 vs. 18.90 ± 9.90, p = 0.037), εa (17.83 ± 8.09 vs. 14.93 ± 6.63, p = 0.019), and increased LACI (17.40±10.28 vs. 22.72±15.01, p = 0.049) when compared to the DM-HP group. In patients with DM, multivariate analysis revealed significant independent associations between LV GLS and εs (ß=-1.286, p < 0.001), εe (ß=-0.919, p < 0.001), and εa (ß=-0.324, p = 0.036). However, there was no significant association observed between LV GLS and LACI (ß=-0.003, p = 0.075). Additionally, hypertension was found to independently contribute to decreased εa (ß=-2.508, p = 0.027) and increased LACI in individuals with DM (ß = 0.05, p = 0.011). CONCLUSIONS: In DM patients, LV GLS showed a significant association with LA phasic strain. Hypertension was found to exacerbate the decline in LA booster strain and increase LACI in DM patients, indicating potential atrioventricular coupling index alterations.

The right ventricular dysfunction and ventricular interdependence in patients with DM: assessment using cardiac MR feature tracking.

BACKGROUND: To investigate the difference of right ventricular (RV) structural and functional alteration in patients with diabetes mellitus (DM) with preserved left ventricular ejection fraction (LVEF), and the ventricular interdependence in these patients, using cardiac MR (CMR) feature tracking. METHODS: From December 2016 to February 2022, 148 clinically diagnosed patients with DM who underwent cardiac MR (CMR) in our hospital were consecutively recruited. Fifty-four healthy individuals were included as normal controls. Biventricular strains, including left/right ventricular global longitudinal strain (LV-/RVGLS), left/right ventricular global circumferential strain (LV-/RVGCS), left/right ventricular global radial strain (LV-/RVGRS) were evaluated, and compared between patients with DM and healthy controls. Multiple linear regression and mediation analyses were used to evaluate DM's direct and indirect effects on RV strains. RESULTS: No differences were found in age (56.98 ± 10.98 vs. 57.37 ± 8.41, p = 0.985), sex (53.4% vs. 48.1%, p = 0.715), and body surface area (BSA) (1.70 ± 0.21 vs. 1.69 ± 0.17, p = 0.472) between DM and normal controls. Patients with DM had decreased RVGLS (- 21.86 ± 4.14 vs. - 24.49 ± 4.47, p = 0.001), RVGCS (- 13.16 ± 3.86 vs. - 14.92 ± 3.08, p = 0.011), and no decrease was found in RVGRS (22.62 ± 8.11 vs. 23.15 ± 9.05, p = 0.743) in patients with DM compared with normal controls. The difference in RVGLS between normal controls and patients with DM was totally mediated by LVGLS (indirect effecting: 0.655, bootstrapped 95%CI 0.138-0.265). The difference in RVGCS between normal controls and DM was partly mediated by the LVGLS (indirect effecting: 0.336, bootstrapped 95%CI 0.002-0.820) and LVGCS (indirect effecting: 0.368, bootstrapped 95%CI 0.028-0.855). CONCLUSIONS: In the patients with DM and preserved LVEF, the difference in RVGLS between DM and normal controls was totally mediated by LVGLS. Although there were partly mediating effects of LVGLS and LVGCS, the decrease in RVGCS might be directly affected by the DM.

Radiomic assessment as a method for predicting tumor mutation burden (TMB) of bladder cancer patients: a feasibility study.

BACKGROUND: Tumor mutation burden (TMB) is an emerging prognostic biomarker of immunotherapy for bladder cancer (BLCA). We aim at investigating radiomic features' value in predicting the TMB status of BLCA patients. METHODS: Totally, 75 patients with BLCA were enrolled. Radiomic features extracted from the volume of interest of preoperative pelvic contrast-enhanced computed tomography (CECT) were obtained for each case. Unsupervised hierarchical clustering analysis was performed based on radiomic features. Sequential univariate Logistic regression, the least absolute shrinkage and selection operator (LASSO) regression and the backward stepwise regression were used to develop a TMB-predicting model using radiomic features. RESULTS: The unsupervised clustering analysis divided the total cohort into two groups, i.e., group A (32.0%) and B (68.0%). Patients in group A had a significantly larger proportion of having high TMB against those in group B (66.7% vs. 41.2%, p = 0.039), indicating the intrinsic ability of radiomic features in TMB-predicting. In univariate analysis, 27 radiomic features could predict TMB. Based on six radiomic features selected by logistic and LASSO regression, a TMB-predicting model was built and visualized by nomogram. The area under the ROC curve of the model reached 0.853. Besides, the calibration curve and the decision curve also revealed the good performance of the model. CONCLUSIONS: Our work firstly proved the feasibility of using radiomics to predict TMB for patients with BLCA. The predictive model based on radiomic features from pelvic CECT has a promising ability to predict TMB. Future study with a larger cohort is needed to verify our findings.