Evaluating the UCP1 expression of brown adipose tissue by quantifying hepatic inflammation using synthetic magnetic...

The evaluation of uncoupling protein 1 (UCP1) expression in brown adipose tissue (BAT) is critical for assessing the efficacy and prognosis of BAT-targeted therapies in metabolic diseases. This study aimed to explore the association between BAT UCP1 expression and hepatic inflammation in metabolic dysfunction-associated steatotic liver disease (MASLD) mice, and to verify the feasibility of predicting UCP1 expression non-invasively by quantifying hepatic inflammation using synthetic magnetic resonance imaging (SyMRI).

In total, 80 SC57/BL6 and C57 db/db male mice with different diet modes were used for model construction. SyMRI was performed using a 3.0T magnetic resonance (MR) scanner. T1, T2, fat fraction (FF), and R2* values were obtained in the regions of interest (ROIs) delineated in the left and right liver lobes of each mouse. The liver T1 and T2 values were corrected by establishing a generalized linear model (GLM) to obtain fat- and iron-corrected T1 and T2 (cT1_A and cT2_A, respectively). The liver pathological scores were determined by two experienced pathologists using the clinical research network scoring standard for non-alcoholic steatosis hepatitis. BAT UCP1 expression was quantified as the percentage of the positively stained area in three representative regions. The association between the liver pathological score and BAT UCP1 expression was analyzed. The performance of the MRI parameters in evaluating liver inflammation was analyzed and compared. The efficacy of assessing BAT UCP1 expression using MRI parameters was also evaluated. Diagnostic thresholds were determined using Youden's J statistic. Pairwise comparisons of the area under the curve (AUC) values were performed using DeLong's test.

The mice models were divided into the normal control (NC; n=13) and MASLD (n=50) groups based on the liver pathological scores. There was a significant difference in BAT UCP1 expression between the NC and MASLD groups (P<0.001). UCP1 expression in the MASLD mice was positively correlated with liver inflammation activity (r=0.762, P<0.001). Among the MRI parameters, cT2_A was the best predictor of liver inflammation [AUC =0.717, 95% confidence interval (CI): 0.614-0.820]. K-means cluster analysis was used to divide the MASLD mice into high- and low-grade BAT UCP1 expression groups (F=370.404, P<0.001). The receiver operating characteristic (ROC) curve analysis showed that cT2_A demonstrated superior predictive value for UCP1 levels (AUC =0.741, 95% CI: 0.644-0.838).

SyMRI-derived cT2_A values were used in the quantitative assessment of hepatic inflammation and to predict BAT UCP1 expression levels in MASLD mice. The results suggest that cT2_A could serve as a non-invasive biomarker in metabolic disease monitoring.