Volumetric evaluation of renal sinus adipose tissue on computed tomography images in bilateral nephrolithiasis patients

Does the Location of Fat Accumulation Affect the Degree of Aortic and Renal Arterial Calcification?

The vascular risk associated with obesity is particularly associated with visceral adiposity, but recent studies suggest that ectopic fat might contribute to the increased risk of atherosclerotic cardiovascular disease. Our study aimed to explore the connection between arterial calcification of the aorta and renal arteries with visceral and ectopic fat deposits, including liver, pancreatic, and renal sinus fat. Retrospective analysis of thoracoabdominal multi-slice computed tomography (MSCT) scans of 302 patients included measurements of calcification volumes of thoracic and abdominal aorta, and of both renal arteries. On the same scans, the visceral fat volume, liver-to-spleen ratio, pancreatic-to-spleen ratio, and both renal sinus fat areas were retrieved. Logistic regression showed the left kidney sinus fat area to be the most strongly associated with calcifications in the aorta and both renal arteries (coef. from 0.578 to 0.913, p < 0.05). The visceral fat positively predicted aortic calcification (coef. = 0.462, p = 0.008), and on the contrary, the pancreatic fat accumulation even showed protective effects on thoracic and abdominal aorta calcification (coef. = -0.611 and -0.761, p < 0.001, respectively). The results suggest that ectopic fat locations differently impact the calcification of arteries, which should be further explored.

Renal sinus fat is associated with intrarenal hemodynamic abnormalities independent of visceral fat in patients with chronic kidney disease

OBJECTIVE

Obesity is a risk factor of chronic kidney disease (CKD), contributing to the rising incidence of cardiometabolic diseases. Renal sinus fat (RSF) is an ectopic fat depot located at the renal cavity that could impair renal function and hemodynamic through compression of renal structures. The major purpose of this study was to explore the relationship between RSF accumulation and renal dysfunction in CKD patients.

METHODS

We evaluated the associations between computed tomography measured RSF volume and key clinical and histologic parameters involved in renal function and hemodynamics in 132 well-characterized CKD patients who underwent renal biopsy (median age: 62 years; 63.6% men).

RESULTS

RSF volume normalized by renal volume (RSF%) positively correlated with obesity-related traits such body mass index and visceral fat volume (VFV) (all P < 0.001) whereas it negatively correlated with estimated glomerular filtration rate (eGFR) (ρ = -0.42, P < 0.001) and 24-h urinary creatinine clearance (CCr) (ρ = -0.34, P < 0.001). Notably, we found robust positive correlations between RSF% and renal resistive index (RRI) measured by the Doppler ultrasound (ρ = 0.40, P < 0.001), and the histological severity of global glomerular sclerosis (ρ = 0.48, P < 0.001) and interstitial fibrosis and tubular atrophy (IFTA) (ρ = 0.35, P < 0.001). In the multivariate linear regression models, after accounting for potential confounders including VFV, RSF% remained significantly associated with CCr (β = -0.26, P < 0.001), RRI (β = 0.17, P = 0.022), global glomerular sclerosis (β = 0.21, P = 0.002), and IFTA (β = 0.17, P = 0.012).

CONCLUSION

RSF accumulation is associated with renal dysfunction and hemodynamic abnormalities independent of visceral adiposity. Our results suggest that RSF may have a potential unique role in the pathogenesis of CKD.

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.

Basic principles and new advances in kidney imaging

Over the past few years, clinical renal imaging has seen great advances, allowing assessments of kidney structure and morphology, perfusion, function and metabolism, and oxygenation, as well as microstructure and the interstitium. Medical imaging is becoming increasingly important in the evaluation of kidney physiology and pathophysiology, showing promise in management of patients with renal disease, in particular with regard to diagnosis, classification, and prediction of disease development and progression, monitoring response to therapy, detection of drug toxicity, and patient selection for clinical trials. A variety of imaging modalities, ranging from routine to advanced tools, are currently available to probe the kidney both spatially and temporally, particularly ultrasonography, computed tomography, positron emission tomography, renal scintigraphy, and multiparametric magnetic resonance imaging. Given that the range is broad and varied, kidney imaging techniques should be chosen based on the clinical question and the specific underlying pathologic mechanism, taking into account contraindications and possible adverse effects. Integration of various modalities providing complementary information will likely provide the greatest insight into renal pathophysiology. This review aims to highlight major recent advances in key tools that are currently available or potentially relevant for clinical kidney imaging, with a focus on non-oncological applications. The review also outlines the context of use, limitations, and advantages of various techniques, and highlights gaps to be filled with future development and clinical adoption.

Machine Learning in Body Composition Analysis

Body composition analysis (BCA) generates objective anthropometric data that can inform prognostication and treatment decisions across a wide variety of urologic conditions. A patient's body composition, specifically muscle and adipose tissue mass, may be characterized via segmentation of cross-sectional images (computed tomography, magnetic resonance imaging) obtained as part of routine clinical care. Unfortunately, conventional semi-automated segmentation techniques are time- and resource-intensive, precluding translation into clinical practice. Machine learning (ML) offers the potential to automate and scale rapid and accurate BCA. To date, ML for BCA has relied on algorithms called convolutional neural networks designed to detect and analyze images in ways similar to human neuronal connections. This mini review provides a clinically oriented overview of ML and its use in BCA. We address current limitations and future directions for translating ML and BCA into clinical practice. PATIENT SUMMARY: Body composition analysis is the measurement of muscle and fat in your body based on analysis of computed tomography or magnetic resonance imaging scans. We discuss the use of machine learning to automate body composition analysis. The information provided can be used to guide shared decision-making and to help in identifying the best therapy option.