Obesity and renal cell carcinoma: Biological mechanisms and perspectives

Abstract

Obesity, defined by body mass index (BMI), is an established risk factor for specific renal cell carcinoma (RCC) subtypes such as clear cell RCC, the most common RCC histology. Many studies have identified an association between obesity and improved survival after diagnosis of RCC, a potential "obesity paradox." Clinically, there is uncertainty whether improved outcomes observed after diagnosis are driven by stage, type of treatment received, or artifacts of longitudinal changes in weight and body composition. The biological mechanisms underlying obesity's influence on RCC are not fully established, but multiomic and mechanistic studies suggest an impact on tumor metabolism, particularly fatty acid metabolism, angiogenesis, and peritumoral inflammation, which are known to be key biological hallmarks of clear cell RCC. Conversely, high-intensity exercise associated with increased muscle mass may be a risk factor for renal medullary carcinoma, a rare RCC subtype that predominantly occurs in individuals with sickle hemoglobinopathies. Herein, we highlight methodologic challenges associated with studying the influence of obesity on RCC and review the clinical evidence and potential underlying mechanisms associating RCC with BMI and body composition.

Keywords: Angiogenesis; Body composition; Fatty acid; Metabolism; Obesity; Renal cell carcinoma.

Figure 1:

Deregulation in RCC subytpes of metabolic pathways related to fatty acid biosynthesis and energy metabolism. Obesity is associated with hyperlipidemia, with the excess free (non-esterified) fatty acids being transported into the mitochondria and catabolized via beta-oxidation which results in depletion of NAD+ and FAD+ and increased levels of acetyl-CoA. NAD+ depletion is an intracellular signal of hypoxia that induces FASN upregulation. When NAD+ is depleted, glucose is metabolized through the pentose-phosphate pathway (not shown), leading to higher levels of NADPH. FASN relies on increased NADPH levels for the biosynthesis of lipids such as phospholipids, sphingolipids, and cholesterol esters, which serve as a primary building blocks for cancer cell membranes, energy storage, and generation of oncogenic signaling molecules. The loss of VHL gene in clear cell RCC upregulates the HIF pathway resulting in a pseudohypoxic state where lipid biosynthesis is favored via FASN upregulation. Fumarate hydratase and succinate dehydrogenase deficiency result in deregulation of the tricarboxylic acid cycle (Krebs cycle) which induces a pseudohypoxic state. However, the subsequent disruption of acetyl-CoA entry in the TCA cycle reduces free fatty acid metabolism via the buildup of NADH and decrease in NAD+ which slows down beta oxidation. Compensatory metabolic shifts following the disruption of the TCA cycle in FH or SDH deficiency may include the upregulation of glycolysis or aminoacid catabolism to produce energy, which may further reduce the cell’s reliance on beta oxidation for energy production. Created with BioRender.com

Figure 2:

Causal relationships represented by DAGs. (A) X is an exposure that causes an outcome Y. No other variables other than X and Y need to be adjusted for to estimate the causal effect of X on Y. (B) Z is a confounder that induces a false causal relationship between X and Y. Z must be adjusted for to properly estimate the causal effect of X on Y. (C) M is a mediator that transfers the indirect effect of X on Y. Adjusting for M in a statistical model will block the indirect effect of X on Y. (D) W is a collider and therefore adjusting for W will bias the estimation of the causal effect of X on Y by opening a false path between X and Y. (E) More complex causal relationship whereby W is both a mediator of the indirect effect of X on Y and also a collider of X and the confounder Z. Adjusting for W without adjusting for Z will open a false path from X to Z resulting in a biased estimate of the effect on X on Y. Adjusting for both Z and W will prevent this collider bias. (F) The obesity paradox can occur when increased BMI due to obesity has a direct detrimental effect on survival but also an indirect effect on survival by increasing RCC risk. Studies that adjust for RCC by selecting only for patients with RCC are prone to collider stratification bias, a type of selection bias, which can make a harmful effect of BMI appear protective unless the statistical analysis also adjusts for confounders such as smoking.

Figure 3:

DAG for a study investigating the effect of BMI on the risk of developing RCC. Because height can influence both BMI and RCC risk, it is a confounder that needs to be adjusted for.

Figure 4:

DAG for a study investigating the effect of different variables with the risk of RMC compared with other genitourinary (GU) malignancies. High-intensity exercise can increase red blood cell sickling, thus aggravating hypoxia and promoting SMARCB1 loss in renal medulla cells, increasing the risk for RMC. The highly aggressive nature of RMC distinctly affects cachexia and adiposity compared with other malignancies. Other variables such as race, age, and exercise intensity also influence adiposity. Adiposity is a collider that multiple arrowheads converge toward. Thus, adjusting for adiposity can produce collider biases.

Figure 5:

DAG depicting the relationship between RCC aggressiveness, adiposity as measured by BMI, and survival outcomes. Aggressive RCC biology often results in cachexia and subsequent weight loss due to loss of appetite and increased metabolic demands. Thus, lower BMI measured at the time of diagnosis may be associated with worse outcomes, such as increased mortality risk, which may also be directly influenced by RCC aggressiveness.

Figure 6:

Increased oncogenic signaling at the cellular level driven by proteins enriched in the obese state. Obesity is generally associated with insulin resistance and a chronic inflammatory status at the systemic level. Obese patients tend to have higher levels of circulating pro-inflammatory molecules such as Insulin Like Growth Factor 1 (IGF1), Interleukin 6 (IL-6) and tumor necrosis factor alpha (TNF-α). Additionally, there is increased production of leptin by white adipocytes, which reduces the levels of circulating adiponectin. Cancer cell growth, proliferation, survival, motility, and migration are promoted by IGF1, IL-6, and leptin. IGF1 signaling is mediated by receptor tyrosine kinases (RTKs), which activate the RAS and PI3K signaling pathways. The latter ultimately activates mTOR. The lack of adiponectin prevents mTOR inhibition trough AMPK (adiponectin signaling is mediated by a G protein-coupled receptor [GPCR]). Leptin and IL-6 signaling is mediated by the JAK/STAT pathway. TNF-α through TNF-receptor 2 promotes inflammation and cell survival by promoting the activation of NF-kB and its translocation to the nucleus. Created with BioRender.com

Figure 7:

The complex interplay among host factors and tumor cells in obese patients. In the case of obesity (left), there are (1) high levels of circulating fatty acids; (2) a pro-inflammatory state induced by obesity, and (3) potential for lymphocyte dysfunction (mid). In obese patients with clear cell RCC (right), there is downregulation of the fatty acid synthase (FASN) gene and increased angiogenesis relative to tumors originating in normal-weight individuals, and the peritumoral fat serves as a reservoir of dysfunctional T cells. Created with BioRender.com