A new finding for the obesity paradox? Evaluation of the relationship between muscle and adipose tissue in nuclear grade prediction in patients with clear cell renal cell carcinoma

Abstract

Background

Obesity is associated with an increased risk of developing clear cell renal cell carcinoma (ccRCC), but paradoxically there is a positive association between obesity and surveillance.

Purpose

To investigate the relationship between nucleus grade classification and body composition in patients with matched co-morbid conditions with non-metastatic ccRCC.

Materials and Methods

A total of 253 patients with non-metastatic ccRCC were included in the study. Body composition was assessed with abdominal computed tomography (CT) using an automated artificial intelligence software. Both adipose and muscle tissue parameters of the patients were calculated. In order to investigate the net effect of body composition, propensity score matching (PSM) procedure was applied over age, sex, and T stage parameters. In this way, selection bias and imbalance between groups were minimized. Univariate and multivariate logistic regression analyses were performed to identify the association between body composition and WHO/ISUP grade (I–IV).

Result

When the body composition of the patients was examined without matching the conditions, it was found that the subcutaneous adipose tissue (SAT) values were higher in patients with low grades (P = 0.001). Normal attenuation muscle area (NAMA) was higher in high-grade patients than low-grade patients (P < 0.05). In the post-matching evaluation, only SAT/NAMA was found to be associated with high-grade ccRCC (univariate analysis: odds ratio [OR]=0.899, 95% confidence interval [CI]=0.817−0.988, P = 0.028; multivariate analysis: OR=0.922, 95% CI=0.901−0.974, P = 0.042).

Conclusion

CT-based body composition parameters can be used as a prognostic marker in predicting nuclear grade when age, sex, and T stage match conditions. This finding offers a new perspective on the obesity paradox.

Keywords

Nucleus grade obesity paradox adipose muscle tomography

Introduction

Renal cell carcinoma (RCC) is the most common form of kidney cancer, accounting for 90% of all diagnosed types (1). RCC includes a heterogeneous group of malignant entities with different pathology, biology, and treatment aspects (2). The three most common subtypes are clear cell (about 75%), papillary (15%), and chromophobe (5%) (3). The World Health Organization / International Society of Urologic Pathologists (WHO/ISUP) grading system is widely used for grading RCC and has prognostic value regardless of tumor subtype. RCC is categorized as WHO/ISUP grade I–IV based on nuclear properties (4,5). In this classification, grades I–II are specified as low grade and III–IV as high grade. Recent studies have appeared where the WHO/ISUP classification is associated with the growth rate of tumors and the prognosis of patients (6). High-grade tumors are associated with higher invasive capacity, a higher likelihood of metastasis, and a poor prognosis (7).

Obesity is the real pandemic of today’s world (8). The relationship between obesity and RCC is quite complex. According to WHO, patients with a body mass index (BMI) above 30 kg/m² are considered obese. According to a meta-analysis, obesity increases the incidence of RCC (9). In contrast, there are studies in which obesity improves prognosis, even if it increases frequency (10,11). On the contrary, some studies show that it worsens prognosis and increases surgical complications (12,13). This exciting situation encountered in some malignant and non-malignant processes besides RCC is named the “obesity paradox” (14,15). The reverse causality of cachexia, which is more prominent in advanced metastatic disease, is added to this paradox (16). Although BMI is the most commonly used measure of body composition, it is an inconsistent prognostic marker. It can be said that the cause of this condition is because it reflects only weight and is not specific, as it cannot distinguish between skeletal muscle and adipose tissue (17,18). A recent study by Sanchez et al. (19) based on transcriptomic analysis has explored the biological basis of this paradoxical condition. Review studies were created with the new perspective formed after this study. It is necessary to examine whether adipose tissue or muscle tissue or endocrinological events that change with obesity influence the emergence of the obesity paradox. In most of these studies, it is said that patients should be evaluated with radiological measurements, although it is cumbersome so that the paradox can be understood more deeply (19). In RCC, parameters such as skeletal muscle area and distribution and amount of adipose tissue based on radiological measurements are associated with overall and cancer-specific survival, treatment-related toxicity, and survival after radical nephrectomy (20 –22).

Adipose tissue has some functional functions. Adipose tissue is the primary place in our body where excess energy in the form of triacylglycerol is stored. In addition to normal biological activities, some biomolecules such as adipokines, proinflammatory cytokines, hormones, and fatty acid metabolism regulators have been suggested to promote the risk and progression of obesity-induced cancer by altering related pathways (23,24). Leptin and adiponectin are the main cytokines released from adipocytes whose balance has proven to be critical in carcinogenesis (25). Serum leptin levels in obese people increase 5–10 times in size compared to people of normal weight (26). The rise in leptin levels has been linked to a rising risk of many metabolic diseases, including cancer. Leptin can stimulate the proliferation of cancer cells and activate pathways in oncogenic signaling, angiogenesis, and immunomodulation, promoting the survival of cancer cells. On the contrary, a considerable decrease in adiponectin levels is noticed in obese individuals (27,28). Adiponectin, an antagonist to leptin activity, can exert antitumor effects by inhibiting angiogenesis and reduce macrophage infiltration by suppressing mTOR and Stat3 pathways, stimulating AMPK and caspase activity (29). In an experiment on mice, lowering adiponectin levels were associated with increased tumor formation in the liver (30). Therefore, reduced levels of adiponectin in obese individuals make them susceptible to the development of cancer. On the one hand, it is a fact that obesity increases the risk of cancer from the above pathways; on the other hand, it creates a contrast that obese patients have better survival than normal-weight patients with regard to RCC.

We think it is helpful to understand the complex effects of obesity and body composition on RCC through the tumor cell's nucleus. Studies show the relation of Fuhrman nucleus classification with visceral adipose tissue in RCC (31). However, there is no study in which the WHO/ISUP classification was evaluated together with muscle mass and adipose tissue components (visceral and subcutaneous adipose tissue). In order to evaluate this relationship more clearly, we think that patients with similar conditions, such as age, sex, tumor stage, should be evaluated. The aim of the present study was to investigate the relationship between the WHO/ISUP nucleus classification and body composition in patients with non-metastatic RCC with matched co-morbid conditions.

Material and Methods

Patient selection

A total of 210 patients with RCC patients from the 2019 Kidney and Kidney Tumor Segmentation Challenge (C4KC-KiTS) dataset (32,33) and 236 patients with RCC from the Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) dataset (34) were retrieved from the Cancer Imaging Archive (TCIA) (35). Patients’ characteristics were obtained from TCIA, including age, sex, pathologic grade, and T stage. T stage was divided into low-T (T 1–2) and high-T (T 3–4). Pathologic grade was divided into low-grade (grades 1–2) and high-grade (grades 3–4). Informed consent was not required since TCIA data contained no personal identifying information.

Inclusion criteria were as follows: (i) preoperative abdominal CT examination; and (ii) adequate images and clinical information. The exclusion criteria were as follows: (i) metastatic patients and uncertain metastasis evaluation; (ii) patients receiving preoperative chemotherapy or radiotherapy treatment; and (iii) patients with lumbar surgical material. As a result of the criteria, 60 patients with non-metastatic ccRCC from the C4KC-KiTS dataset and 153 from the TCGA-KIRC dataset (total of 253 patients) were included in the study (Fig. 1).

Following the 2012 ISUP Vancouver conference results, a new WHO/ISUP rating system was introduced and recommended for WHO (36). The system is also a four-grade system with nucleolar salience evaluated to determine grades 1–3 and the presence of highly atypical “pleomorphic” cells and/or sarcomatoid or rhabdoid morphology defining grade 4. It is assigned based on the highest-grade cells available rather than the most dominant. In practice, the new WHO/ISUP rating, similar to the Fuhrman system, is easier to implement and should be more reproducible and clinically relevant.

According to the simplified grading system, the four-tier ISUP grading system was dichotomized as low-grade (grades 1 and 2) and high-grade (grades 3 and 4) (37). T stage system was dichotomized as low T stage (T1 and T2) and high T stage (T3 and T4).

Assessment of body composition

Body composition was assessed with abdominal CT using an automated artificial intelligence software (AID-U™; iAID Inc., Seoul, Republic of Korea), which was developed using a fully convolutional network (FCN) segmentation technique (38). A specialized abdominal radiologist, blinded to the clinical information, selected the axial CT slice at the L3 vertebral inferior endplate level in a semi-automatic manner with the aid of sagittal reconstructed images. Then, the chosen images were automatically segmented to generate the boundary of total abdominal muscles and subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT). For the evaluation of muscle quality, the cross-sectional area of selected axial muscle images (i.e. the psoas, paraspinal, transversus abdominis, rectus abdominis, quadratus lumborum, and internal and external obliques) were further segmented by predetermined Hounsfield units (HU) thresholds as follows: (i) normal attenuation muscle area (NAMA; +30 to +150 HU), reflecting healthy muscle with little intramuscular fat; (ii) low attenuation muscle area (LAMA; −29 to +29 HU), reflecting unhealthy muscle with intramuscular lipid pool; and (iii) intramuscular adipose tissue (IMAT; −190 to −30 HU), reflecting the apparent fat tissue between muscle groups and muscle fibers (39,40). Total abdominal muscle area (TAMA; −190 to +150 HU) was defined as a whole area including all skeletal muscles and fat tissues (TAMA = NAMA + LAMA + IMAT). Conventional parameters such as patient height and BMI are not common in the whole dataset, so it is from the patient's height. In order to make an independent assessment, SAT/TAMA, VAT/TAMA, SAT/NAMA, VAT/NAMA, NAMA/ TAMA, LAMA/TAMA, VAT (VAT/VAT +SAT), and rSAT (SAT/VAT +SAT) values were calculated. An example of the interface of the tool can be seen in Fig. 2.

Statistical analysis

Continuous variables were reported as mean ± standard deviation, and categorical variables as number (ratio). Kolmogorov–Smirnov and Shapiro–Wilk tests were performed for continuous variables. Comparisons between groups were made using the following statistical tests: chi-square test for categorical variables; Student's t test for normally distributed continuous variables; and Mann–Whitney U test for non-normally distributed continuous variables. Univariable associations with the occurrence of any ISUP high-grade were calculated for each of the predictor variables using binary logistic regression. Subsequently, a multivariable least absolute shrinkage and selection operator (LASSO) penalized logistic regression model was employed to select the variables most strongly associated with the high-grade tumor. Finally, the variables as identified in the penalized regression were entered into a non-penalized multivariable logistic regression model. The odds ratios (ORs) of this reduced model are reported with 95% confidence intervals (CI), alongside the ORs of the penalized regression model as well as the ORs of the full multivariable logistic model.

We also employed propensity score matching (PSM) with a 1:1 ratio to minimize selection bias and adjust the imbalance between groups. SPSS R plug-in (SPSS R Essentials) was applied for matching (41). We used the SPSS “PS Matching” feature to perform propensity score-matched analysis. Matching factors included age, sex, and T stage. Low-grade and high-grade patients were matched 1:1 in a multivariable logistic analysis using stepwise regression based on a greedy matching algorithm with a caliper of 0.05 times the logit standard deviation. After applying 1:1 propensity score matching, 53 eligible patients were matched to each group.

Results

In our study, 11 patients were evaluated as Grade I, 125 patients as Grade II, 95 patients as Grade III, and 22 patients as Grade IV. In the evaluation made without matching all the patients’ conditions, high-grade tumors in the dataset are more common in the male sex (P < 0.05). The high-grade tumor was detected in patients with high T stage (P <0.05). Detailed evaluations are shown in Table 1.

Table 1.

The relationship between ISUP grade classification according to sex and T stage.

		Low grade (n = 136)	High grade (n = 117)	P value
Sex	Male	77 (56.6)	87 (74.4)	0.003
Sex	Female	59 (53.4)	30 (25.6)	0.003
T stage	High T stage	17 (12.5)	48 (41)	<0.001
T stage	Low T stage	119 (87.5)	69 (59)	<0.001

Values are given as n (%).

ISUP, International Society of Urologic Pathologists.

When the body composition of the patients was examined without matching the conditions, it was found that the SAT and TAT values were higher in patients with low grades (P = 0.001, P = 0.013, respectively). NAMA was higher in high-grade patients than low-grade patients (P < 0.05). Again, the TAT/TAMA, SAT/TAMA, TAT/NAMA, and VAT/NAMA ratios were higher in low-grade tumors. The evaluation made by matching the conditions, SAT/NAMA, myosteatosis, and LAMA/NAMA ratios were higher in patients with low-grade tumors. Detailed data are shown in Table 2.

Table 2.

Evaluation of age and body composition parameters before and after propensity score matching in patients with high- and low-grade tumors.

	Before matching (n = 253)			After matching (n = 106)
	High grade (n = 117)	Low grade (n = 136)	P	High grade (n = 53)	Low grade (n = 53)	P
Age (years)	60.6 ± 12.9	58.8 ± 12.4	0.26	57.08 ± 11.4	57.43 ± 11.6	0.874
SAT (cm²)	207.6 ± 138.2	261.3 ± 112.1	0.001	209.1 ± 125.8	247.2 ± 120.7	0.115
VAT (cm²)	225.3 ± 128.4	236.2 ± 112.1	0.495	247.19 ± 120.7	236.3 ± 116.2	0.654
TAT (cm²)	432.9 ± 199	497.5 ± 210.5	0.013	483.5 ± 198.3	435.2 ± 197.7	0.212
IMAT (cm²)	27.6 ± 14.6	30.7 ± 15.4	0.105	24.7 ± 13.7	29.8 ± 17.3	0.093
LAMA (cm²)	55.6 ± 24.7	59.5 ± 22.5	0.189	62.8 ± 25.4	54.2 ± 26.2	0.091
NAMA (cm²)	99.4 ± 36.4	89.1 ± 39.3	0.047	106.3 ± 36.9	94.8 ± 40.7	0.129
TAMA	182.7 ± 44.3	180.4 ± 40.9	0.669	187.5 ± 38.7	185.3 ± 42.2	0.786
rSAT %	0.45 ± 0.15	0.53 ± 0.15	0.056	0.48 ± 0.16	0.52 ± 0.13	0.199
rVAT %	0.5 ± 0.15	0.47 ± 0.15	0.056	0.51 ± 0.17	0.49 ± 0.13	0.199
TAT/TAMA	2.34 ± 0.82	2.77 ± 1.09	0.001	2.32 ± 0.77	2.62 ± 1.08	0.103
SAT/TAMA	1.16 ± 0.53	1.49 ± 0.82	<0.001	1.14 ± 0.57	1.36 ± 0.7	0.084
VAT/TAMA	1.19 ± 0.56	1.19 ± 0.56	0.219	1.18 ± 0.5	1.26 ± 0.61	0.445
TAT/NAMA	5.15 ± 3.5	7.34 ± 6.41	0.001	4.88 ± 0.82	7.8 ± 7.95	0.081
SAT/NAMA	2.55 ± 1.96	3.95 ± 3.87	<0.001	2.38 ± 1.99	4 ± 4.31	0.037
VAT/NAMA	2.6 ± 2.06	3.39 ± 3.18	0.022	2.5 ± 2.43	3.81 ± 3.97	0.234
Myosteatosis	0.46 ± 0.16	0.51 ± 0.17	0.014	0.42 ± 0.16	0.5 ± 0.19	0.045
LAMA/NAMA	0.69 ± 0.55	0.9 ± 0.81	0.021	0.63 ± 0.53	1.03 ± 1.12	0.046

Values are given as mean ± SD.

IMAT, intramuscular adipose tissue; LAMA, low attenuation muscle area; NAMA, normal attenuation muscle area; SAT, subcutaneous adipose tissue; SD, standard deviation; TAMA, total abdominal muscle area; TAT, total adipose tissue; VAT, visceral adipose tissue.

Logistic regression was performed on each variable for the univariate model, WHO/ISUP ccRCC grade, and to explore relationships with body composition, age, sex, and T stage (Table 3). In the univariate model, T stage (OR = 2.824, 95% CI = 1.624–4.909; P < 0.001), SAT (OR = 0.999, 95% CI = 0.998–1.000; P = 0.012), TAT (OR = 0.999, 95% CI = 0.999–1.000; P = 0.040), TAT/TAMA (OR = 0.898, 95% CI = 0.820−0.983; P = 0.020), SAT/TAMA (OR = 0.788, 95% CI = 0.665–0.933; P = 0.006), TAT/NAMA (OR = 0.952, 95% CI = 0.921–0.984; P = 0.004), SAT/NAMA (OR = 0.904, 95% CI = 0.849–0.963; P = 0.002), VAT/NAMA (OR = 0.924, 95% CI = 0.865–0.987, P = 0.018), myosteatosis (OR = 0.831, 95% CI = 0.710–0.972; P = 0.020), LAMA/NAMA (OR = 0.740, 95% CI = 0.577–0.950; P = 0.018) were found to be associated with high-grade ccRCC. LASSO regression was then performed for the multivariate model (Table 3). In the multivariate model, T stage (OR = 5.132, 95% CI = 3.415−10.634; P < 0.001), being male (OR = 2.724, 95% CI = 1.322−5.285; P < 0.014), NAMA (OR = 0.960, 95% CI = 0.938−0.979; P <0.018), and SAT/NAMA (OR = 0.822, 95% CI = 0.745−0.893; P < 0.001) remained predictors.

Table 3.

Univariate and multivariate models for predicting high-grade ccRCC.

	Univariate analysis			Multivariate analysis
	OR	95% CI	P	OR	95% CI	P
Age	0.998	0.994 − 1.002	0.350
T stage	2.824	1.624 − 4.909	<0.001	5.132	3.415 − 10.634	<0.001
Sex (M/F)	1.130	0.831 − 1.535	0.435	2.724	1.322 − 5.285	0.014
SAT (cm²)	0.999	0.998 − 1.000	0.010
VAT (cm²)	0.999	0.998 − 1.000	0.172
IMAT (cm²)	0.993	0.986 − 1.000	0.073
LAMA (cm²)	0.997	0.993 − 1.001	0.110
NAMA (cm²)	1.000	0.997 − 1.002	0.700	0.960	0.938 − 0.979	0.018
TAMA (cm²)	0.999	0.998–1.001	0.288
TAT (cm²)	0.999	0.999 − 1.000	.040
rSAT %	0.873	0.424 − 1.797	0.712
rVAT %	1.152	0.551 − 2.409	0.706
TAT/TAMA	0.898	0.820 − 0.983	0.020
SAT/TAMA	0.788	0.665 − 0.933	0.006
VAT/TAMA	0.862	0.718 − 1.035	0.111
TAT/NAMA	0.952	0.921 − 0.984	0.004
SAT/NAMA	0.904	0.849 − 0.963	0.002	0.822	0.745 − 0.893	<0.001
VAT/NAMA	0.924	0.865 − 0.987	0.018
Myosteatosis	0.831	0.710 − 0.972	0.020
LAMA/NAMA	0.740	0.577 − 0.950	0.018

ccRCC, clear cell renal cell carcinoma; IMAT, intramuscular adipose tissue; LAMA, low attenuation muscle area; NAMA, normal attenuation muscle area; SAT, subcutaneous adipose tissue; SD, standard deviation; TAMA, total abdominal muscle area; TAT, total adipose tissue; VAT, visceral adipose tissue.

Logistic regression was performed on each variable for the univariate model to evaluate the relationship between body composition and WHO/ISUP ccRCC grade after matching body composition's age, sex, and T stage status. Then, logistic regression was performed for the multivariate model (Table 4). In the post-matching evaluation, only SAT/NAMA (OR = 0.899, 95% CI = 0.817−0.988; P = 0.028) was found to be associated with high-grade ccRCC in univariate evaluation. In multivariate analysis after matching, the SAT/NAMA ratio (OR = 0.922, 95% CI = 0.901−0.974; P = 0.042) was associated with high-grade ccRCC.

Table 4.

Univariate and multivariate models to predict high grade ccRCC after propensity score matching.

	Univariate analysis			Multivariate analysis
	OR	95% CI	P	OR	95% CI	P
SAT (cm²)	0.999	0.998 − 1.001	0.453
VAT (cm²)	1.000	0.998 − 1.001	0.839
IMAT (cm²)	0.995	0.983 − 1.007	0.401
LAMA (cm²)	0.998	0.992 − 1.004	0.492
NAMA (cm²)	1.001	0.997 − 1.005	0.582
TAMA (cm²)	1.000	0.998 − 1.002	0.954
TAT (cm²)	1.000	0.999 − 1.001	0.620
SAT/VAT	0.958	0.747 − 1.228	0.734
rSAT %	0.873	0.424 − 1.797	0.712
rVAT %	1.152	0.551 − 2.409	0.706
TAT/TAMA	0.958	0.829 − 1.107	0.560
SAT/TAMA	0.896	0.682 − 1.177	0.429
VAT/TAMA	0.955	0.718 − 1.269	0.749
TAT/NAMA	0.923	0.843–1.011	0.085
SAT/NAMA	0.899	0.817 − 0.988	0.028	0.908	0.897 − 0.962	0.031
VAT/NAMA	0.937	0.857 − 1.025	0.153
Myosteatosis	0.824	0.654 − 1.038	0.101
LAMA/NAMA	0.751	0.529 − 1.065	0.108

Discussion

In our study, in accordance with the literature, we found that the patient group with high-grade tumors also had a high T stage. Furthermore, we also found that the male sex was predisposed to higher-grade tumors (42). Therefore, we preferred the PSM procedure to evaluate the relationship between WHO/ISUP grade and body composition, which is the primary purpose of our study. In evaluating adipose tissue components before PSM, we found that SAT and TAT values were higher in patients with low-grade RCC (P < 0.05). There was no statistically significant difference after PSM. We think that this difference is affected by age, sex, and T stage. Another essential body tissue composition we evaluated in our study is muscle tissue. Our study showed that none of the areas except NAMA (LAMA, IMAT, and TAT) in muscle tissue alone contributed to grade prediction. However, it was a pretty surprising result that NAMA was found to be high in high-grade patients. Normal muscle area can be highly variable between individuals (depending on sex, race, age, physical fitness, and structural properties).

The most important result of our study, considering the post-matching data, is that both adipose tissue and muscle tissue areas alone cannot be used in WHO/ISUP grade prediction in patients with non-metastatic RCC. Previous studies aimed to create standardized indexes by using the square of the height of the individuals (43). However, we think that evaluating the condition of adipose and muscle tissue relative to each other to evaluate body composition will be more successful than an index for height. For this purpose, we proportioned the components of fat and muscle tissues (Table 2). We developed logistic regression models to evaluate further the relationship of body composition parameters to high-grade tumors (Tables 3 and 4).

Although no fundamental theory can explain the relationship between fat and muscle tissue with the nuclear grade, we think two conditions can explain this situation. One of them is the obesity paradox, which we consider the body's self-protection mechanism, and the other is the effect of tumor tissue on the body. Although we have two different theories, these two cases are related to each other. Recent advances in the assessment of body composition reveal the complexity of obesity in carcinogenesis. According to the results of a comprehensive meta-analysis for obesity, the relative risk increased 1.57 times in men and 1.72 times in women with high BMI compared to individuals with normal BMI (9). However, more prolonged survival was found in people with paradoxically high BMI (44,45). This condition is called the obesity paradox and, similar to this situation, is seen in malignant and non-malignant processes (14,15). Beyond these, the reverse causality caused by cachexia, more common in advanced metastatic oncological processes, feeds this paradox. In the present study, we tried to overcome the problem of reverse causation by excluding metastatic patients.

In the literature, studies conducted to predict Furhman nuclear grade in patients with non-metastatic RCC have shown a positive relationship, especially with high VAT (24). However, in our study, according to the WHO/ISUP grade system, we found that VAT was not related to the grade, but it was slightly higher in patients with high-grade tumors, although it was not statistically significant. One of the critical results we obtained in our study is that SAT is higher in low-grade patients. In many studies of kidney tumors and various types of cancer, different results, especially with VAT, are reported in studies on survival with body adipose tissue components. While high VAT is a good prognostic indicator in some studies, it appears as a poor prognostic factor in some studies (46,47). However, it has been stated that only the increase in SAT can be used as a favorable prognostic marker in survival (48).

In recent years, studies have aimed to reveal the genetic/molecular mechanisms underlying this obesity paradox. A recent study by Sanchez et al. (19) based on transcriptomic analysis has explored the biological basis of this paradoxical condition. These analyses showed that compared with tumors of patients with normal BMI, those from obese patients have a significant upregulation in genes associated with hypoxia, angiogenesis, transforming growth factor, and epithelial-mesenchymal transition (19). With this information, the researchers claim that visceral adiposity creates zones of hypoxia that support changes in the tumor microenvironment that regulate angiogenesis and RCC growth. Hence, they hypothesize that upregulation of obesity-related angiogenesis in RCC may explain the rising vulnerability of these tumors to tyrosine kinase inhibitors. Growing tumor vascularization may raise delivery to tumors and raise local drug concentrations. In addition, the investigators propose that peritumoral adipose tissue may act as a reservoir of immune cells that enhances the antitumor immune response in the asset of immune checkpoint inhibitors.

We think that adipose and muscle tissue should be evaluated together in predicting nuclear grade in patients with non-metastatic ccRCC. Although adipose and muscle tissue have different tissues, we think that the primary condition affecting both is the obesity paradox. The obesity paradox does not only affect adipose tissue. The difference between myosteatosis and LAMA/NAMA values between grades before and after PSM can be evaluated for its effect on adipose tissue reserve in muscle tissue. It would not be wrong to say that this result is a new finding added to the obesity paradox. New studies are needed to explain this paradox: automatic segmentation will make things easier from a radiological point of view. Especially with these systems, subcutaneous, visceral, intra- and intermuscular adipose tissue areas with adiposity measurements will be quickly evaluated.

The present study has some limitations. The most important of these is that the study was retrospective, and we did not have detailed information about the patients’ height and weight and their lymph node stages. We planned our study with completely public data to minimize selection bias, and as a result, we could not calculate the index criteria related to height. However, we think that evaluating parameters with each other in order to evaluate body composition may be more accurate than evaluating according to height.

In conclusion, body composition analysis has a prognostic significance in predicting WHO/ISUP nuclear grade in non-metastatic RCCs. When conditions such as age, sex, and T stage are matched, the SAT/NAMA ratio can be used as a prognostic marker for predicting nuclear grade. The findings suggest that the definition of paradoxical obesity should be considered holistic, including adipose tissue and muscle tissue.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Emin Demirel

Okan Dilek

References

Capitanio

Montorsi

. Renal cancer. Lancet 2016;387:894–906.

Moch

Cubilla

Humphrey

, et al. The 2016 WHO classification of tumours of the urinary system and male genital organs-part A: renal, penile, and testicular tumours. Eur Urol 2016;70:93–105.

Hsieh

Purdue

Signoretti

, et al. Renal cell carcinoma. Nat Rev Dis Primers 2017;3:17009.

Moch

. The WHO/ISUP grading system for renal carcinoma. Pathologe 2016;37:355–360.

Delahunt

Cheville

Martignoni

, et al. The International Society of Urological Pathology (ISUP) grading system for renal cell carcinoma and other prognostic parameters. Am J Surg Pathol 2013;37:1490–1504.

Collins

Epstein

. Prognostic significance of extensive necrosis in renal cell carcinoma. Hum Path 2017;66:108–114.

Donat

Diaz

Bishoff

, et al. Follow-up for clinically localized renal neoplasms: AUA guideline. J Urol 2013;190:407–416

Meldrum

Morris

Gambone

. Obesity pandemic: causes, consequences, and solutions-but do we have the will? Fertil Steri 2017;107: 833–839.

Dobbins M, Decorby K, Choi BCK. The association between obesity and cancer risk: a meta-analysis of observational studies from 1985 to 2011. ISRN Prev Med 2013;2013:16.

10.

Park

Jeong

Seo

, et al. Influence of body mass index, smoking, and blood pressure on survival of patients with surgically-treated, low stage renal cell carcinoma: a 14-year retrospective cohort study. J Korean Med Sci 2013;28:227–236.

11.

Ohno

Nakashima

Nakagami

, et al. Sex and the clinical value of body mass index in patients with clear cell renal cell carcinoma. Br J Cancer 2013;109:1899–1903.

12.

Reeves

Pirie

Beral

, et al. Cancer incidence and mortality in relation to body mass index in the Million Women Study: cohort study. BMJ 2007;335:1134.

13.

Rogde

Gudbrandsdottir

Hjelle

, et al. Obesity is associated with an improved cancer-specific survival, but an increased rate of postoperative complications after surgery for renal cell carcinoma. Scand J Urol Nephrol 2012;46:348–357.

14.

Feliciano

EMC

Kroenke

Caan

. The obesity paradox in cancer: how important is muscle? Annu Rev Nutr 2018;38:357–379.

15.

Clark

Fonarow

Horwich

. Obesity and the obesity paradox in heart failure. Prog Cardiovasc Dis 2014;56:409–414.

16.

Pinto

Carnier

Oyama

, et al. Cancer as a proinflammatory environment: metastasis and cachexia. Mediators Inflamm 2015;2015:791060.

17.

Albiges

Hakimi

Xie

, et al. Body mass index and metastatic renal cell carcinoma: clinical and biological correlations. J Clin Oncol 2016; 34:3655.

18.

Psutka

Boorjian

Moynagh

, et al. Decreased skeletal muscle mass is associated with an increased risk of mortality after radical nephrectomy for localized renal cell cancer. J Urol 2016;195:270–276.

19.

. Biological support to obesity paradox in renal cell carcinoma: a review. Urol Int 2020;104:837–848.

20.

Fukushima

Nakanishi

Kataoka

, et al. Prognostic significance of sarcopenia in patients with metastatic renal cell carcinoma. J Urol 2016;195:26–32.

21.

Cushen

Power

Teo

, et al. Body composition by computed tomography as a predictor of toxicity in patients with renal cell carcinoma treated with sunitinib. Am J Clin Oncol 2017;40:47–52.

22.

Martini

Kline

Liu

, et al. Adiposity may predict survival in patients with advanced stage cancer treated with immunotherapy in phase 1 clinical trials. Cancer 2020;126:575–582.

23.

Aurilio

Piva

Santoni

, et al. The role of obesity in renal cell carcinoma patients: clinical-pathological implications. Int J Mol Sci 2019;20:5683.

24.

Quail

Dannenberg

. The obese adipose tissue microenvironment in cancer development and progression. Nat Rev Endocrinol 2019;15:139–154.

25.

Grossmann

Cleary

. The balance between leptin and adiponectin in the control of carcinogenesis–focus on mammary tumorigenesis. Biochimie 2012;94:2164–2171.

26.

Considine

Sinha

Heiman

, et al. Serum immunoreactive-leptin concentrations in normal-weight and obese humans. N Engl J Med 1996;334:292–295.

27.

Arita

Kihara

Ouchi

, et al. Paradoxical decrease of an adipose-specific protein, adiponectin, in obesity. Biochem Biophys Res Commun 1999;257: 79−83.

28.

Horiguchi

Ito

Sumitomo

, et al. Decreased serum adiponectin levels in patients with metastatic renal cell carcinoma. Jpn J Clin Oncol 2008;38:106–111.

29.

Park

Morley

Kim

, et al. Obesity and cancer—mechanisms underlying tumour progression and recurrence. Nat Rev Endocrinol 2014;10:455.

30.

Kamada

Matsumoto

Tamura

, et al. Hypoadiponectinemia accelerates hepatic tumor formation in a nonalcoholic steatohepatitis mouse model. J Hepatol 2007;47:556–564.

31.

Lai

, et al. Clear cell renal cell carcinoma: the value of sex-specific abdominal visceral fat measured on CT for prediction of Fuhrman nuclear grade. Eur Radiol 2020;30:3977–3986.

32.

Heller

Isensee

Maier-Hein

, et al. The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: results of the KiTS19 challenge. Med Image Anal 2021;67:101821.

33.

Heller N, Sathianathen N, Kalapara A, et al. The kits19 challenge data: 300 kidney tumor cases with clinical context, CT semantic segmentations, and surgical outcomes. arXiv:1904.00445, 2019. https://arxiv.org/abs/1904.00445

34.

Akin O, Elnajjar P, Heller M, et al. Radiology data from The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma [TCGA-KIRC] collection. The Cancer Imaging Archive website. wiki.cancerimagingarchive.net/display/Public/TCGAKIRC#329b9f4f31cf4586831934c19c4f10f4 (2016, accessed 4 May 2018).

35.

Clark

Vendt

Smith

, et al. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 2013;26:1045–1057.

36.

Moch

Cubilla

Humphrey

, et al. The 2016 WHO classification of tumours of the urinary system and male genital organs—part A: renal, penile, and testicular tumours. Eur Urol 2016;70:93–105.

37.

Becker

Hickmann

Hansen

, et al. Critical analysis of a simplified Fuhrman grading scheme for prediction of cancer specific mortality in patients with clear cell renal cell carcinoma--impact on prognosis. Eur J Surg Oncol 2016;42:419–425.

38.

Park

Shin

Park

, et al. Development and validation of a deep learning system for segmentation of abdominal muscle and fat on computed tomography. Korean J Radiol 2020;21:88.

39.

Lee

Shin

Huh

, et al. Recent issues on body composition imaging for sarcopenia evaluation. Korean J Radiol 2019;20:205–217.

40.

Aubrey

Esfandiari

Baracos

, et al. Measurement of skeletal muscle radiation attenuation and basis of its biological variation. Acta Physiol 2014;210:489–497.

41.

Thoemmes

. Propensity score matching in SPSS, 2012. http://arxiv.org/abs/1201.6385 (accessed 18 September 2012).

42.

Aron

Nguyen

Stein

, et al. Impact of gender in renal cell carcinoma: an analysis of the SEER database. Eur Uro 2008;54:133–142.

43.

Derstine

Holcombe

Ross

, et al. Skeletal muscle cutoff values for sarcopenia diagnosis using T10 to L5 measurements in a healthy US population. Sci Rep 2018;8:11369–11369.

44.

Liu

Wang

Chen

, et al. Obesity: an independent protective factor for localized renal cell carcinoma in a systemic inflammation state. Int Braz J Urol 2020;46:585–598.

45.

Choi

Park

Jeong

, et al. Body mass index and survival in patients with renal cell carcinoma: a clinical-based cohort and meta-analysis. Int J Cancer 2013;132:625–634.

46.

Antoun

Bayar

Ileana

, et al. High subcutaneous adipose tissue predicts the prognosis in metastatic castration-resistant prostate cancer patients in post chemotherapy setting. Eur J Cancer 2015;51:2570–2577.

47.

Fujiwara

Nakagawa

Kudo

, et al. Sarcopenia, intramuscular fat deposition, and visceral adiposity independently predict the outcomes of hepatocellular carcinoma. J Hepatol 2015;63:131–140.

48.

Ebadi

Martin

Ghosh

, et al. Subcutaneous adiposity is an independent predictor of mortality in cancer patients. Br J Cancer 2017;117:148–155.