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A hint for the obesity paradox and the link between obesity, perirenal adipose tissue and Renal Cell Carcinoma progression.

ABSTRACT: Increasing evidence supports a role for local fat depots in cancer outcomes. Despite the robust positive association of obesity with renal cell carcinoma (RCCa) diagnosis, increased adiposity is inversely related to RCCa oncological outcomes. Here, we sought to ascertain whether imagiologically assessed local fat depots associate with RCCa progression and survival and account for this apparent paradox. A retrospective cohort of renal carcinoma patients elective for nephrectomy (n = 137) were included. Beyond baseline clinicopathological characteristics, computed tomography (CT)-scans at the level of renal hilum evaluated areas and densities of different adipose tissue depots (perirenal, subcutaneous, visceral) and skeletal muscle (erector spinae, psoas and quadratus lumborum muscles) were analyzed. Univariate and multivariable Cox proportional hazards models were estimated following empirical analysis using stepwise Cox regression. Age, visceral adipose tissue (VAT) area and body mass index (BMI) predicted tumour-sided perirenal fat area (R² = 0.584), which presented upregulated UCP1 expression by 27-fold (P = 0.026) and smaller adipocyte areas, compared with subcutaneous depot. Multivariate analyses revealed that increased area of perirenal adipose tissue (PRAT) on the contralateral and tumour side associate with improved progression-free survival (HR = 0.3, 95CI = 0.1-0.8, P = 0.019) and overall survival (HR = 0.3, 95CI = 0.1-0.7, P = 0.009). PRAT measurements using CT, might become a possible tool, well correlated with other measures of obesity such as VAT and BMI, that will improve determination of obesity and contribute to assess the risk for disease progression and mortality in renal cancer patients. Present data supports the obesity paradox in RCCa, assumed that larger PRAT areas seem to protect from disease progression and death.

SUBMITTER: Preza-Fernandes J

PROVIDER: S-EPMC9675816 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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A hint for the obesity paradox and the link between obesity, perirenal adipose tissue and Renal Cell Carcinoma progression.

Preza-Fernandes José J Passos Pedro P Mendes-Ferreira Miguel M Rodrigues Adriana R AR Gouveia Alexandra A Fraga Avelino A Medeiros Rui R Ribeiro Ricardo R

Scientific reports 20221119 1

Increasing evidence supports a role for local fat depots in cancer outcomes. Despite the robust positive association of obesity with renal cell carcinoma (RCCa) diagnosis, increased adiposity is inversely related to RCCa oncological outcomes. Here, we sought to ascertain whether imagiologically assessed local fat depots associate with RCCa progression and survival and account for this apparent paradox. A retrospective cohort of renal carcinoma patients elective for nephrectomy (n = 137) were inc ...[more]

PMID: 36402906

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Project description:ObjectivesAccurate preoperative prediction of the pathological grade of clear cell renal cell carcinoma (ccRCC) is crucial for optimal treatment planning and patient outcomes. This study aims to develop and validate a deep-learning (DL) algorithm to automatically segment renal tumours, kidneys, and perirenal adipose tissue (PRAT) from computed tomography (CT) images and extract radiomics features to predict the pathological grade of ccRCC.MethodsIn this cross-ethnic retrospective study, a total of 614 patients were divided into a training set (383 patients from the local hospital), an internal validation set (88 patients from the local hospital), and an external validation set (143 patients from the public dataset). A two-dimensional TransUNet-based DL model combined with the train-while-annotation method was trained for automatic volumetric segmentation of renal tumours, kidneys, and visceral adipose tissue (VAT) on images from two groups of datasets. PRAT was extracted using a dilation algorithm by calculating voxels of VAT surrounding the kidneys. Radiomics features were subsequently extracted from three regions of interest of CT images, adopting multiple filtering strategies. The least absolute shrinkage and selection operator (LASSO) regression was used for feature selection, and the support vector machine (SVM) for developing the pathological grading model. Ensemble learning was used for imbalanced data classification. Performance evaluation included the Dice coefficient for segmentation and metrics such as accuracy and area under curve (AUC) for classification. The WHO/International Society of Urological Pathology (ISUP) grading models were finally interpreted and visualized using the SHapley Additive exPlanations (SHAP) method.ResultsFor automatic segmentation, the mean Dice coefficient achieved 0.836 for renal tumours and 0.967 for VAT on the internal validation dataset. For WHO/ISUP grading, a model built with features of PRAT achieved a moderate AUC of 0.711 (95% CI, 0.604-0.802) in the internal validation set, coupled with a sensitivity of 0.400 and a specificity of 0.781. While model built with combination features of the renal tumour, kidney, and PRAT showed an AUC of 0.814 (95% CI, 0.717-0.889) in the internal validation set, with a sensitivity of 0.800 and a specificity of 0.753, significantly higher than the model built with features solely from tumour lesion (0.760; 95% CI, 0.657-0.845), with a sensitivity of 0.533 and a specificity of 0.767.ConclusionAutomated segmentation of kidneys and visceral adipose tissue (VAT) through TransUNet combined with a conventional image morphology processing algorithm offers a standardized approach to extract PRAT with high reproducibility. The radiomics features of PRAT and tumour lesions, along with machine learning, accurately predict the pathological grade of ccRCC and reveal the incremental significance of PRAT in this prediction.

Project description:De novo lipogenesis and hypercaloric diets are thought to contribute to increased fat mass, particularly in abdominal fat depots. CB1 is highly expressed in adipose tissue, and CB1-mediated signalling is associated with stimulation of lipogenesis and diet-induced obesity, though its contribution to increasing fat deposition in adipose tissue is controversial. Lipogenesis is regulated by transcription factors such as liver X receptor (LXR), sterol-response element binding protein (SREBP) and carbohydrate-responsive-element-binding protein (ChREBP). We evaluated the role of CB1 in the gene expression of these factors and their target genes in relation to lipogenesis in the perirenal adipose tissue (PrAT) of rats fed a high-carbohydrate diet (HCHD) or a high-fat diet (HFD). Both obesity models showed an up-regulated gene expression of CB1 and Lxrα in this adipose pad. The Srebf-1 and ChREBP gene expressions were down-regulated in HFD but not in HCHD. The expression of their target genes encoding for lipogenic enzymes showed a decrease in diet-induced obesity and was particularly dramatic in HFD. In HCHD, CB1 blockade by AM251 reduced the Srebf-1 and ChREBP expression and totally abrogated the remnant gene expression of their target lipogenic enzymes. The phosphorylated form of the extracellular signal-regulated kinase (ERK-p), which participates in the CB1-mediated signalling pathway, was markedly present in the PrAT of obese rats. ERK-p was drastically repressed by AM251 indicating that CB1 is actually functional in PrAT of obese animals, though its activation loses the ability to stimulate lipogenesis in PrAT of obese rats. Even so, the remnant expression levels of lipogenic transcription factors found in HCHD-fed rats are still dependent on CB1 activity. Hence, in HCHD-induced obesity, CB1 blockade may help to further potentiate the reduction of lipogenesis in PrAT by means of inducing down-regulation of the ChREBP and Srebf-1 gene expression, and consequently in the expression of lipogenic enzymes.

Dataset Information

A hint for the obesity paradox and the link between obesity, perirenal adipose tissue and Renal Cell Carcinoma progression.

Publications

A hint for the obesity paradox and the link between obesity, perirenal adipose tissue and Renal Cell Carcinoma progression.

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