Association between type 2 diabetes mellitus and body composition based on MRI fat fraction mapping.

Purpose: To explore the association between type 2 diabetes mellitus (T2DM) and body composition based on magnetic resonance fat fraction (FF) mapping. Methods: A total of 341 subjects, who underwent abdominal MRI examination with FF mapping were enrolled in this study, including 68 T2DM patients and 273 non-T2DM patients. The FFs and areas of visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT) and abdominal muscle (AM) were measured at the level of the L1-L2 vertebral. The FF of bone marrow adipose tissue (BMAT) was determined by the averaged FF values measured at the level of T12 and L1 vertebral, respectively. The whole hepatic fat fraction (HFF) and pancreatic fat fraction (PFF) were measured based on 3D semi-automatic segmentation on the FF mapping. All data were analyzed by GraphPad Prism and MedCalc. Results: VAT area, VAT FF, HFF, PFF of T2DM group were higher than those of non-T2DM group after adjusting for age and sex (P < 0.05). However, there was no differences in SAT area, SAT FF, BMAT FF, AM area and AM FF between the two groups (P > 0.05). VAT area and PFF were independent risk factors of T2DM (all P < 0.05). The area under the curve (AUC) of the receiver operating characteristic (ROC) for VAT area and PFF in differentiating between T2DM and non-T2DM were 0.685 and 0.787, respectively, and the AUC of PFF was higher than VAT area (P < 0.05). Additionally, in seemingly healthy individuals, the SAT area, VAT area, and AM area were found to be significantly associated with being overweight and/or obese (BMI ≥ 25) (all P < 0.05). Conclusions: In this study, it was found that there were significant associations between T2DM and VAT area, VAT FF, HFF and PFF. In addition, VAT area and PFF were the independent risk factors of T2DM. Especially, PFF showed a high diagnostic performance in discrimination between T2DM and non-T2DM. These findings may highlight the crucial role of PFF in the pathophysiology of T2DM, and it might be served as a potential imaging biomarker of the prevention and treatment of T2DM. Additionally, in individuals without diabetes, focusing on SAT area, VAT area and AM area may help identify potential health risks and provide a basis for targeted weight management and prevention measures.

Quantification of the renal sinus fat and exploration of its relationship with ectopic fat deposition in normal subjects using MRI fat fraction mapping.

Purpose: To determine the renal sinus fat (RSF) volume and fat fraction (FF) in normal Chinese subjects using MRI fat fraction mapping and to explore their associations with age, gender, body mass index (BMI) and ectopic fat deposition. Methods: A total of 126 subjects were included in the analysis. RSF volume and FF, visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) area, and hepatic and pancreatic FFs were measured for each subject. The comparisons in gender were determined using two-tailed t-tests or the nonparametric Mann-Whitney U-test for normally or non-normally distributed data for continuous variables and the chi-square test for categorical variables. Comparisons of RFS volume and FF between right and left kidneys were determined using paired sample t-tests. Multivariable logistic models were performed to confirm whether RSF differences between men and women are independent of VAT or SAT area. When parameters were normally distributed, the Pearson correlation coefficient was used; otherwise, the Spearman correlation coefficient was applied. Results: The RSF volumes (cm3) of both kidneys in men (26.86 ± 8.81 for right and 31.62 ± 10.32 for left kidneys) were significantly bigger than those of women (21.47 ± 6.90 for right and 26.03 ± 8.55 for left kidneys) (P < 0.05). The RSF FFs (%) of both kidneys in men (28.33 ± 6.73 for right and 31.21 ± 6.29 for left kidneys) were significantly higher than those of the women (23.82 ± 7.74 for right and 27.92 ± 8.15 for left kidneys) (P < 0.05). The RSF differences between men and women are independent of SAT area and dependent of VAT area (except for right RSF volume). In addition, the RSF volumes and FFs in both kidneys in the overall subjects show significant correlations with age, BMI, VAT area, hepatic fat fraction and pancreatic fat fraction (P < 0.05). However, the patterns of these correlations varied by gender. The RSF volume and FF of left kidney were significantly larger than those of the right kidney (P < 0.05). Conclusion: The association between renal sinus fat and ectopic fat deposition explored in this study may help establish a consensus on the normal values of RSF volume and FF for the Chinese population. This will facilitate the identification of clinicopathological changes and aid in the investigation of whether RSF volume and FF can serve as early biomarkers for metabolic diseases and renal dysfunction in future studies.

Magnetic Resonance Imaging Assessment of Abdominal Ectopic Fat Deposition in Correlation With Cardiometabolic Risk Factors.

Purpose: Ectopic fat accumulation and abdominal fat distribution may have different cardiometabolic risk profiles. This study aimed to assess the associations between various magnetic resonance imaging (MRI)-acquired fat depots and cardiometabolic risk factors. Methods: A total of 320 subjects with median age of 59 years, 148 men and 172 women, were enrolled in the study. Visceral adipose tissue (VAT) area and fat fraction (FF), subcutaneous adipose tissue (SAT) area and FF at the L1-L2 levels, preperitoneal adipose tissue (pPAT) area and FF, hepatic FF, pancreatic FF, and intramuscular FF were assessed by MRI FF maps. The associations of various MRI-acquired fat depots with blood pressure, glucose, and lipid were examined using sex-stratified linear regression. Logistic regression stratified by sex was used to analyze the association of various MRI-acquired fat depots with the risk of hypertension, T2DM, and dyslipidemia. Results: The intraclass correlation coefficient (ICC) values were >0.9, which suggested good interobserver and intraobserver agreement. VAT area, V/S, hepatic fat, pancreatic fat, and pPAT rather than SAT area were significantly associated with multiple cardiometabolic risk factors (all p < 0.05). However, the patterns of these correlations varied by sex and specific risk factors. Also, VAT and SAT FF were only significantly associated with multiple cardiometabolic risk factors in women (all p < 0.05). Conclusions: VAT, hepatic fat, pancreatic fat, and pPAT were associated with cardiovascular metabolic risk factors independent of BMI. The patterns of these correlations were related to gender. These findings further the understanding of the association between ectopic fat deposition and cardiometabolic risk factors and help to better understand the obesity heterogeneity.

Hepatic fat quantification of magnetic resonance imaging whole-liver segmentation for assessing the severity of nonalcoholic fatty liver disease: comparison with a region of interest sampling method.

BACKGROUND: Accurate and early assessment of the hepatic fat content is crucial for patients with nonalcoholic fatty liver disease (NAFLD). For years, magnetic resonance imaging (MRI) has been considered the optimal noninvasive method for the assessment of fat accumulation. To avoid time-consuming manual placement of multiple regions of interest (ROI), the use of whole-liver segmentation has been proposed to measure liver fat, mainly for heterogeneous fat deposition. However, it remains uncertain whether the hepatic mean fat fraction (FF) obtained by whole-liver segmentation with the inclusion of intrahepatic vasculature is consistent with the traditional ROI sampling method. In this study, we assessed the accuracy of hepatic mean FF obtained by whole-liver segmentation in patients of NAFLD with different severities using the ROI sampling method as a reference standard. METHODS: Hepatic FFs were measured by whole-liver segmentation and the ROI sampling method (reference standard) using MRI scanning with the iterative decomposition of water and fat with echo an asymmetry at least-square estimation-iron quantification (IDEAL-IQ) sequence. SPSS version 25.0 software was used to analyze the correlation and consistency of data between the two methods. RESULTS: There was a strong correlation in hepatic FF between whole-liver segmentation and the ROI sampling method in healthy, mild, and moderate steatosis patients (r = 0.943, 0.990, and 0.961, respectively). Bland-Altman analysis showed a small bias of +0.50±0.27 and +0.05±0.30, which indicated a small overestimation when using whole-liver segmentation in healthy subjects and mild NAFLD patients. The 95% limits of agreement ranged from +1.02 to -0.03, and from +0.65 to -0.55, respectively. However, a small bias of -0.96±0.77 was also evident, which indicated a small underestimation when using whole-liver segmentation in moderate NAFLD patients. The 95% limits of agreement ranged from +0.56 to -2.48. CONCLUSIONS: Due to inclusion of the intrahepatic vasculature, whole-liver segmentation has some effects on hepatic FF assessment in patients with different NAFLD severities; yet, it does not significantly affect the assessment of whole-liver FF in MRI FF maps.

Preoperative vascular heterogeneity and aggressiveness assessment of pituitary macroadenoma based on dynamic contrast-enhanced MRI texture analysis.

PURPOSE: To assess the vascular heterogeneity and aggressiveness of pituitary macroadenomas (PM) using texture analysis based on Dynamic Contrast-Enhanced MRI (DCE-MRI). METHOD: Fifty patients with pathologically confirmed PM, including 32 patients with aggressive PM (aggressive group) and 18 patients with non-aggressive PM (non-aggressive group), were included in this study. The preoperative DCE-MRI and clinical data were collected from all patients. The features based on Ktrans, Ve, and Kep were generated using Omni-Kinetics software. Independent-samples t-test and Mann-Whitney U test were used for comparison between two groups. Logistic regression analysis was used to determine the optimal model for distinguishing aggressive and non-aggressive PM. RESULTS: Six features related to tumor morphology, 24 features in Ktrans, 20 features in Ve, and 3 features in Kep were significantly different between the aggressive and non-aggressive groups. Volume count, gray-level non-uniformity in Ktrans, voxel value sum in Ve and run-length non-uniformity in Kep (AUC = 0.816, 0.903, 0.785, 0.813) were considered the best feature for tumor diagnosis. After modeling, the diagnosis efficiency of mean model and total model was desirable (AUC = 0.859 and 0.957), and the diagnostic efficiency of morphological, Ktrans, Ve and Kep features model was improved (AUC = 0.845, 0.951, 0.847, 0.804). CONCLUSIONS: Texture analysis based on DCE-MRI elucidates the vascular heterogeneity and aggressiveness of pituitary adenoma. The total model could be used as a new noninvasive method for predicting the aggressiveness of pituitary macroadenoma.

DTI Study of Cerebral Normal-Appearing White Matter in Hereditary Neuropathy With Liability to Pressure Palsies (HNPP).

The majority of previous studies on hereditary neuropathy with liability to pressure palsies (HNPP) were focused on peripheral nerves, whereas cerebral alterations in HNPP have been less attended to. In this work, Diffusion tensor imaging (DTI) was used to detect the changes in WM, especially in the normal-appearing white matter (NAWM) in HNPP patients for its sensitivity in probing the microstructure of WM, the sensitive metric was searched for probing cerebral alterations and the regional distribution of cerebral abnormalities was identified. Twelve HNPP patients and 12 age- and gender-matched healthy controls underwent the conventional MRI, DTI scan, and electrophysiological examination. The conventional MRI images were first analyzed to identify abnormal intense regions and the NAWM regions. NAWM refers to the white matter regions that do not include the lesions on conventional MRI. The apparent diffusion coefficient and fractional anisotropy (FA) values of the NAWM were then measured and compared between patient and control groups. The sensitivity and specificity of 3 methods and the cerebral regional distribution of MR signal abnormalities were further analyzed. Hyperintense foci were observed on T2 weighted image and fluid attenuated inversion recovery images in 6 patients. Compared to the controls, FA values of the patients were significantly lower in bilateral frontal, orbitofrontal, and temporal NAWMs; whereas the electrophysiological examination results of patients and controls exhibited no statistically significant difference. The sensitivity of FA value was higher than that of electrophysiological examination and conventional MRI. The majority of abnormal signals on conventional MRI images and abnormal FA values were located in the frontal and temporal lobes. The results of our study show cerebral WM changes in HNPP patients. FA value in DTI has been shown to be sensitive to the cerebral microstructural changes in HNPP. The frontal lobe is the predilection site that is most involved in HNPP.

Use of enhanced T2 star-weighted angiography (ESWAN) and R2* values to distinguish ovarian cysts due to endometriosis from other causes.

PURPOSE: To evaluate the feasibility of enhanced T2 star-weighted angiography (ESWAN) in differentiating endometrial from non-endometrial cysts. METHODS: Forty-nine patients with 60 histopathologically proven ovarian cystic lesions underwent pelvic MRI including T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), liver acquisition with volume acceleration, and ESWAN. Ovarian cystic lesions were divided into endometrial cysts (group 1; n = 28), pyosalpinx and hydrosalpinx (group 2; n = 13), and ovarian cystic and cystic-solid tumors (group 3; n = 19). R2* (effective transverse relaxation rate) values were measured and pairwise comparison of the R2* values among the three groups was made using Kruskal-Wallis test. Receiver operating characteristic curves were used to calculate cutoff values and performance of R2* values for distinguishing among groups. T1WI signal intensity and R2* value were also compared using area under curve values. RESULTS: R2* values for group 1 were statistically higher than groups 2 and 3 (15.37, 1.40, and 1.79 Hz, respectively; P < 0.001). The cutoff value for R2* was 7.43 Hz with a sensitivity, specificity, PPV, NPV, and accuracy of 96.43, 87.50, 87.10, 96.55, and 91.67%, respectively. There was no significant difference between the R2* value and T1WI in diagnosing endometrial cysts. CONCLUSIONS: The R2* value provides an effective way to discriminate endometrial cysts from other ovarian cystic lesions.