Moving Beyond BMI – Developments in Body Composition and Muscle Measurement in Renal Cell Carcinoma

Hannah Dzimitrowicz, MD,1 Jordan Infield, MD,1 Fides Regina Schwartz, MD,2 Rajan T. Gupta, MD,2,3 and Michael R. Harrison, MD,3,4

1. Department of Medicine, Duke University School of Medicine, Durham, NC, USA

2. Department of Radiology, Duke University, Durham, NC, USA

3. Duke Cancer Institute Center for Prostate and Urologic Cancers, Durham, NC, USA

4. Department of Medicine, Division of Medical Oncology, Duke University, Durham, NC, USA


Body composition, namely the distribution and quantification of muscle and adipose tissue, are of increasing interest as potential prognostic indicators in patients with renal cell carcinoma (RCC). Herein, we review the available literature examining body composition in relation to outcomes for patients with RCC, methodology used to quantify muscle and adipose tissue using cross-sectional imaging, and future directions for translation of these findings into clinical care.


The Obesity Paradox, BMI, and RCC

The Obesity Paradox, BMI, and RCC Excess body weight, primarily measured using body mass index (BMI), is associated with an increased risk of developing at least thirteen common cancers, including renal cell carcinoma (RCC), with the cumulative risk for RCC estimated to increase by 4% for each 1 kg/ m2 increment in BMI. 1-4 A variety of biologic mechanisms have been proposed as potential explanations for this relationship between adiposity and increased risk of cancers including RCC.5,6 These include: altered sex hormone metabolism, increased insulin and insulin-like growth factor (IGF) signaling, adipokine pathophysiology and its association with a state of chronic inflammation and microbiome effects.5,6 Importantly, no single mechanism is responsible for this relationship in RCC and it is likely a multifactorial and heterogenous process.

Paradoxically, patients who develop RCC and have an elevated BMI seem to have a more favorable prognosis and survival advantage compared to patients with normal BMI. This “obesity paradox” in RCC has been demonstrated in patients with locoregional disease undergoing nephrectomy7-9 as well as patients with metastatic RCC (mRCC) receiving systemic therapy. 10,11

The largest and most widely cited study demonstrating this protective role of elevated BMI in patients with mRCC included 1,975 patients from the International mRCC Database Consortium (IMDC) treated with anti-angiogenic targeted therapies. The authors demonstrated that high BMI (≥25 kg/m2) was associated with improved overall survival (OS) and progression free survival (PFS) even after adjustment for IMDC prognostic criteria and baseline characteristics.10 They then externally validated these findings in a pooled analysis of 4,657 patients with mRCC treated with tyrosine kinase inhibitors (TKI) in prospective clinical trials.10

After these initial studies that predominantly included patients on TKIs, in recent years, multiple groups have described outcomes by BMI in patients with mRCC receiving immune checkpoint inhibitor (ICI)-based regimens. Conflicting results were observed in smaller, earlier studies – some demonstrated a protective effect of elevated BMI and others found the opposite.12-16 Recently, however, a large study including 735 patients from the IMDC database receiving PD-1/PDL-1 inhibitors (alone or in combination) demonstrated that patients with BMI >/= 25 kg/ m2 had significantly improved OS (1 year OS rate of 79% vs 66%; P = 0.03) and had numerically higher response rates and time to treatment failure.11 This large study supports the conclusion that the obesity paradox, as measured by BMI, also extends beyond locoregional disease to patients with mRCC treated with modern ICI-based regimens.

Numerous hypotheses have attempted to explain this clinical observation, including potential confounding factors and biological differences between tumors in patients with elevated BMI versus normal BMI. Low fatty acid synthase gene expression, which is inversely correlated with BMI, was associated with longer OS in anti-angiogenic targeted therapy-treated patients and proposed as a potential reason for these differences in outcomes.10 Additionally, transcriptomic analysis suggests that patients with elevated BMI have tumors with upregulation of genes associated with angiogenesis and peritumoral adipose tissue with increased gene signatures of hypoxia, inflammation, and immune cell infiltration.17 In a recent issue of Kidney Cancer Journal, Dr. Ritesh Kotecha expertly reviewed recent mechanistic insights into the role of obesity in RCC biology and potential implications for future therapies.18 Contrastingly, an alternative explanation proposed by some groups for the commonly termed “obesity paradox” is that these obesity-survival analyses represent reverse causation with more aggressive cancers causing increased cachexia rather than increased adipose tissue impacting the growth and behavior of the cancer.19

Notably, these studies characterizing the role of obesity in relation to RCC utilize BMI, usually from a single baseline measurement, as a surrogate marker of adiposity. BMI, simply a person’s weight in kilograms divided by the square of height in meters is easily captured and has widely accepted classifications of underweight (BMI <18.5 kg/m2), normal weight (BMI 18.5 to 24.9 kg/m2), overweight (BMI 25.0 to 29.9 kg/m2), and obese (BMI ≥30.0 kg/m2). BMI, however, treats all mass as equal, not distinguishing the proportion of adipose or muscle mass or distribution of these tissues, and allowing for great heterogeneity in body composition for patients at the same BMI. Thus, more precise measures of body composition, including adipose and muscle mass and their distribution (visceral versus subcutaneous adipose tissue, for example) are of interest to better understand their impact on outcomes in patients with RCC and other cancers.

Getting more granular on BMI: Radiologic measurement of body composition The use of computed tomography (CT) imaging allows detailed assessment and measurement of different body tissues, including adipose tissue and skeletal muscle, providing more accurate measurement of body composition than BMI; however, due to associated costs and radiation exposure, the value of CT would be limited if its role were solely for assessment of body composition. Uniquely, though, patients with cancer are routinely assessed with serial high-resolution diagnostic CT imaging to monitor tumor growth and response to therapy and thus these images could be used for opportunistic body composition analysis.

The cross-sectional area of tissues in single images from the region of the third lumbar vertebrae (L3) appears to correlate strongly with whole body adipose tissue, including visceral and subcutaneous adipose tissue, and skeletal muscle, while not including most visceral organs; thus, images taken from L3 are widely used to quantify these tissues. 20-22 Specific tissues are identified based on anatomic features and demarcated based on well characterized Hounsfield unit (HU) reference ranges using commercially available software for analysis.22 This methodology allows for the quantification of multiple tissue types, including skeletal muscle, subcutaneous adipose tissue, visceral adipose tissue, and intermuscular adipose tissue. Commonly, these values are then converted to indices (skeletal muscle index, subcutaneous fat index, visceral fat index, and intermuscular fat index, for example) by dividing by height (m2) to allow cross-patient comparison. Commercially available software makes this analysis more accessible to researchers, however, it does still require segmentation of different tissue areas by a trained clinician or researcher with anatomical and imaging knowledge (Slice-O-matic; Tomovision, Montreal, Canada).22 Automated and semi-automated software are being developed to make this methodology more accessible. For example, ABACS (Automatic Body composition Analyzer using Computed tomography image Segmentation) is a commercially available add-on software to Slice-O-Matic that has the ability to automatically segment skeletal muscle and adipose tissue at L3 to estimate tissue areas and their mean radiodensities and has been externally validated with similar measurements to manual segmentation analysis.23

The importance of adipose tissue assessment by radiologic measurements There can be substantial variation of body composition within each BMI group; that is, patients may have drastically different adipose components to their body composition at the same BMI (Figure 1). With CT scans readily available for patients undergoing oncologic treatment and using the methodology described above, researchers have aimed to better understand the role that adipose tissue plays along with BMI in RCC outcomes, with varying results.

Primarily, researchers have aimed to quantify different adipose tissue components, subcutaneous fat area (SFA) and visceral fat area (VFA), at a baseline timepoint and evaluate whether these values have prognostic value in patients with RCC (Table 1). In addition to obvious differences in anatomic location, visceral and subcutaneous adipose tissue are physiologically and structurally different. Visceral adipose tissue is more cellular, vascular, innervated, and generally contains more inflammatory cells, immune cells, and glucocorticoid and androgen receptors than subcutaneous adipose tissue, among other differences.24

In patients with localized RCC who underwent nephrectomy, adipose tissue measurements did not account for the positive impact of elevated BMI on outcome.25 Baseline SFA was highly correlated with BMI (r = 0.804), however, after adjustment for sex, neither SFA nor VFA was significantly associated with tumor grade, stage, or overall survival, despite BMI being associated with improved OS.25

In contrast, in the metastatic setting, primarily studied in patients receiving anti-angiogenic targeted therapies, adipose tissue measurement appears to have a stronger association with outcomes based on available studies. In a study of 114 patients with mRCC receiving systemic therapy, baseline elevated visceral fat accumulation (defined as ≥100 cm2) correlated with improved PFS (P = 0.0070) and OS (P = 0.0001) and its addition to the Memorial Sloan Kettering Cancer Center (MSKCC) classification improved the model’s prognostic value in this patient cohort.26 Increased adipose tissue may more accurately reflect the role of body composition in predicting outcomes of patients with mRCC treated with anti-angiogenic therapies. Another study measuring baseline BMI, BSA, VFA, and SFA in patients with mRCC treated with a TKI (sunitinib, sorafenib, or axitinib) or bevacizumab found that higher than average VFA and SFA were significant predictors of longer PFS and OS with BMI and BSA only demonstrating a trend towards association.27 Contrastingly, in a population of patients with mRCC who received anti-angiogenic targeted therapies (bevacizumab, sunitinib, or sorafenib) (n = 64) or cytokines (n = 49) as first line treatment evaluating VFA and SFA, after multivariate analysis, high VFA was associated with shorter TTP and OS in the antiangiogenic agenttreated patients with no association seen in the patients treated with cytokine therapy.28 Of note, none of these studies included patients receiving immune checkpoint inhibitors, so any prognostic role adipose tissue composition may play in this setting is unknown.

Skeletal muscle measurement, sarcopenia, and sarcopenic obesity

While we commonly associate BMI with adiposity, body mass also includes muscle mass and thus there has been interest in the role that muscle mass and muscle loss may play in the obesity paradox in RCC (Table 1). Sarcopenia, a decline in muscle mass, strength, and conditioning, is known to be common in patients with advanced cancer and associated with worse prognosis and has thus been of interest in assessing outcomes of patients with RCC.29

Similar to the m easurement of adipose tissue, cross sectional imaging can be used to calculate skeletal muscle area and radiodensity, which can serve as surrogates for body muscle mass and classification of sarcopenia. Using optimal stratification and OS as the outcome, sex-specific skeletal muscle index thresholds have been developed to define sarcopenia as measured on CT images.29,30 Initially characterized by Prado et al. in a population of obese Canadians with GI and lung malignancies and later extended to include nonobese patients by Martin et al., SMI thresholds for sarcopenia are widely accepted according to sex and BMI.

The prevalence of sarcopenia in patients with RCC, measured by SMI, has ranged across studies, including rates of 47% in localized RCC, 29% in patients with mRCC undergoing cytoreductive nephrectomy, and 36 – 68% in patients with mRCC receiving systemic therapy.31-35 In patients with localized RCC undergoing nephrectomy, sarcopenia was independently associated with cancer-specific and all-cause mortality after radical nephrectomy.31 In patients with mRCC undergoing cytoreductive nephrectomy, sarcopenia was an independent predictor of OS with sarcopenia associated with worse OS.32 Notably, these studies measured SMI at baseline as a measure of sarcopenia rather than measuring changes in muscle mass or development of sarcopenia over time.

In patients with mRCC receiving primarily anti-angiogenic targeted therapies, studies have evaluated the impacts of baseline sarcopenia and changes in skeletal muscle over time on survival and therapyrelated toxicities. In a study of 92 patients with mRCC receiving systemic therapy (33% targeted agents, 47% cytokine therapy), baseline sarcopenia was associated with worse OS and integration of sarcopenia into the MSKCC risk model improved the c-index, suggesting that baseline sarcopenia was an important prognosticator in addition to previously identified clinical factors.33 Patients treated with sorafenib in a phase III clinical trial lost skeletal muscle mass progressively over the course of therapy compared to patients receiving placebo based on analysis of baseline, 6-month, and 12-month CT images.34

To characterize m uscle loss over time and the potential impact on outcomes, skeletal muscle was measured on CT scans at baseline and at 3-4 months into treatment for 101 patients with mRCC receiving an anti-angiogenic TKI (sunitinib, sorafenib, or axitinib) or mTOR inhibitor everolimus on clinical trial.35 Muscle loss ≥ 5% was a significant prognostic factor for PFS (hazard ratio [HR]: 1.744, 95% confidence interval [CI]: 1.077–2.826, P = 0.024) and overall survival (HR: 2.367, 95%CI: 1.253–4.469, P = 0.008), and the addition of muscle loss to the Heng model significantly improved its discriminative ability. Additionally, patients with early skeletal muscle loss experienced more doselimiting toxicities. Notably, baseline sarcopenia was not associated with patient survival, suggesting that decline in muscle mass has prognostic value rather than baseline decreased muscle mass in this setting.35 Contrastingly, low baseline SMI was an independent prognostic factor compared to high SMI for patients with mRCC receiving everolimus in another study, and there was no difference in toxicity attributed to everolimus based on SMI.36 Based on available data, decreased skeletal muscle, both at baseline and its progressive loss, appears to be associated with worse outcomes in patients with mRCC, but findings across studies and treatment regimens are somewhat heterogenous, suggesting additional contributing factors and lack of generalizability. Assessment of muscle mass and sarcopenia in patients with mRCC on ICI-based regimens is limited, similar to the dearth of knowledge regarding studies of adipose tissue in patients on ICIs. Limited data available across cancer types suggests that sarcopenia may impact outcomes in patients on ICIs. In a study of 100 patients across cancer types (15 patients had RCC) treated with PD-1/ PD-L1 inhibitors, patients with low SMI had significantly shorter OS, however, there was no significant association with clinical response, suggesting in this small population, that sarcopenia may be prognostic but not predictive of response to immunotherapy.37

Complicating clinical assessment of muscle mass over time, obesity often masks the loss of skeletal muscle, and skeletal muscle can be lost concurrently with an increase in adipose tissue, a condition termed sarcopenic obesity (Figure 2).29 It is estimated that approximately 1 in 10 patients with advanced cancer meets criteria for sarcopenic obesity and 1 in 4 obese patients is sarcopenic.38 Across cancer types and treatments, sarcopenic obesity is independently associated with higher mortality and higher complication rates. Thus, the identification of sarcopenic obesity is of interest and has not been well characterized in patients with mRCC.30 In patients with mRCC enrolled in a clinical trial receiving sorafenib or placebo, 34% of patients with a BMI >25 kg/m2 were sarcopenic, suggesting that a significant number of patients with mRCC have sarcopenic obesity which may mask muscle loss if only BMI is measured.34 In this study, the median time from diagnosis of RCC to randomization was 38 +/- 4.4 months, so it is unclear if these rates of sarcopenic obesity are applicable to patients at the time of diagnosis or earlier in treatment. There is not data describing the impact of sarcopenic obesity on patients with RCC receiving ICIs, however, in a single center study of 68 patients with melanoma receiving anti-PD1 therapy, sarcopenic overweight (BMI ≥ 25 kg/m2) was associated with increased early acute limiting toxicities.39

Clinical application and future directions

BMI, while acknowledged as a limited marker of body composition, is easily captured and therefore, widely utilized. Measurement of body composition using radiologic images, while more detailed and of interest as described, is challenging to translate into routine clinical practice.

One argument against existing methods to assess body composition using CT images is that available methodology is time consuming, expensive, and requires specialized training; easily implementable measurement tools would improve this. Several studies have explored the use of the digital ruler available in most radiologic software as a way of measuring skeletal muscle area at L3, assessing height and width of the psoas and paraspinal muscles to compute their combined “linear area”.40 This linear area was highly correlated with total cross-sectional area assessed using standard methods, and low linear area was associated with increased risk of death in 807 patients with non-metastatic colon cancer (HR 1.66; 95% CI: 1.22, 2.25).40 Additionally, increasingly available automated and semi-automated software to segment skeletal muscle and adipose tissue at L3 makes these measurements implementable in research settings and potentially in future clinical practice.23

As highlighted in the American Society of Clinical Oncology guideline on the management of cancer cachexia, improvements in methodology that ease clinical implementation of radiologic imaging of muscle mass are needed as is the development of novel biomarkers to easily measure and follow skeletal muscle mass in clinical settings.41 One such potential biomarker is the D3-creatine dilution method (D3Cr), which provides a direct, non-invasive, and accurate measure of muscle mass. The D3Cr dilution method has been previously described as a method to measure muscle mass.42 Briefly, total body creatinine pool size, and subsequently total body muscle mass, are assessed using a single oral dose of deuterated creatine (D3-creatine) which is absorbed and diluted by entry into the endogenous pool of creatine in skeletal muscle. Labelled creatinine and unlabeled creatinine are then measured in a urine sample 3-6 days later and included in an algorithm to determine total body creatine pool size and thus skeletal muscle mass.42 Preclinical and clinical studies demonstrate that D3Cr dilution is a promising method for the assessment of skeletal muscle mass. A clinical validation study was performed in both young and older men and women in which D3Cr muscle mass was strongly associated with whole-body magnetic resonance imaging (MRI) of muscle mass (r = 0.868, P < 0.0001), with less bias compared with lean body mass assessment by dual-energy x-ray absorptiometry (DXA), which overestimated muscle mass compared with MRI.43 In the Health, Aging and Body Composition (Health ABC) study (2,292 participants aged 70-79), strength (grip or quadriceps) but not lean mass (assessed by CT cross-sectional area or DXA) was associated with mortality, although muscle mass (only lean mass and CSA) was not measured.44 In the Osteoporotic Fractures in Men (MrOS) study in more than 1,300 older men (>80 years), men in the lowest quartile of D3Cr muscle mass/body mass had increased mobility limitation and injurious falls, worse physical performance, and lower strength compared with higher muscle mass, while these associations were not seen with DXA lean mass.45 Although additional data is needed to demonstrate its applicability in oncology, the D3- creatine dilution method may present an easily implementable measure of muscle mass over time and an alternative or companion to radiologic measurement.

With increasing data supporting the prognostic and predictive roles of body composition measures in patients with mRCC, efforts at incorporating these findings into existing and new models is of interest. In the first of these studies, a single institution retrospective study of 79 patients with mRCC treated with ICI-based regimens, investigators analyzed baseline CT images to investigate the association between body composition and clinical outcomes.46 They created a body composition risk score in which patients were classified as poor (0- 1), intermediate (2), or favorable risk (3-4) based on measures of skeletal muscle and adipose tissue, demonstrating that the poor-risk patients had significantly shorter OS (HR: 6.37, p<0.001), PFS (HR: 4.19, p<0.001), and lower chance of clinical benefit (OR: 0.23, p=0.044) compared to favorable risk patients in multivariable analysis. Patients with low total fat index (TFI) had significantly shorter OS (HR: 2.72, p=0.002), PFS (HR: 1.91, p=0.025), and lower chance of clinical benefit (OR: 0.25, p=0.008) compared to high TFI patients in multivariable analysis. C-statistics were higher for body composition risk groups and TFI compared to IMDC and BMI.46 While this study was limited by its small size and heterogenous patient population, it suggests the potentially valuable prognostic and predictive roles for radiologic body composition measures in patients with mRCC treated with modern ICI-based regimens.


While available studies suggest potentially important roles for body composition measures, both muscle and adipose tissue, in the outcomes of patients with mRCC, many questions remain unanswered regarding their clinical applicability and relevance to modern ICI-based treatment regimens. As radiologic measurement of body composition and laboratory-based muscle mass measurements become clinically accessible, we increasingly will be equipped to explore heterogeneity and changes in body mass over time and their potential role in understanding and ultimately influencing the outcomes of patients with renal cell carcinoma.


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Correspondence to: Hannah Dzimitrowicz, MD Department of Medicine, Duke University School of Medicine, Durham, NC, USA