Oh GxE! The Complexity of Body Mass Index and Colon Cancer Risk

Investigation of the Trajectory of Muscle and Body Mass as a Prognostic Factor in Patients With Colorectal Cancer: Longitudinal Cohort Study

BACKGROUND

Skeletal muscle and BMI are essential prognostic factors for survival in colorectal cancer (CRC). However, there is a lack of understanding due to scarce studies on the continuous aspects of these variables.

OBJECTIVE

This study aimed to evaluate the prognostic impact of the initial status and trajectories of muscle and BMI on overall survival (OS) and assess whether these 4 profiles within 1 year can represent the profiles 6 years later.

METHODS

We analyzed 4056 newly diagnosed patients with CRC between 2010 to 2020. The volume of the muscle with 5-mm thickness at the third lumbar spine level was measured using a pretrained deep learning algorithm. The skeletal muscle volume index (SMVI) was defined as the muscle volume divided by the square of the height. The correlation between BMI status at the first, third, and sixth years of diagnosis was analyzed and assessed similarly for muscle profiles. Prognostic significances of baseline BMI and SMVI and their 1-year trajectories for OS were evaluated by restricted cubic spline analysis and survival analysis. Patients were categorized based on these 4 dimensions, and prognostic risks were predicted and demonstrated using heat maps.

RESULTS

Trajectories of SMVI were categorized as decreased (812/4056, 20%), steady (2014/4056, 49.7%), or increased (1230/4056, 30.3%). Similarly, BMI trajectories were categorized as decreased (792/4056, 19.5%), steady (2253/4056, 55.5%), or increased (1011/4056, 24.9%). BMI and SMVI values in the first year after diagnosis showed a statistically significant correlation with those in the third and sixth years (P<.001). Restricted cubic spline analysis showed a nonlinear relationship between baseline BMI and SMVI change ratio and OS; BMI, in particular, showed a U-shaped correlation. According to survival analysis, increased BMI (hazard ratio [HR] 0.83; P=.02), high baseline SMVI (HR 0.82; P=.04), and obesity stage 1 (HR 0.80; P=.02) showed a favorable impact, whereas decreased SMVI trajectory (HR 1.31; P=.001), decreased BMI (HR 1.23; P=.02), and initial underweight (HR 1.38; P=.02) or obesity stages 2-3 (HR 1.79; P=.01) were negative prognostic factors for OS. Considered simultaneously, BMI >30 kg/m² with a low SMVI at the time of diagnosis resulted in the highest mortality risk. We observed improved survival in patients with increased muscle mass without BMI loss compared to those with steady muscle mass and BMI.

CONCLUSIONS

Profiles within 1 year of both BMI and muscle were surrogate indicators for predicting the later profiles. Continuous trajectories of body and muscle mass are independent prognostic factors of patients with CRC. An automatic algorithm provides a unique opportunity to conduct longitudinal evaluations of body compositions. Further studies to understand the complicated natural courses of muscularity and adiposity are necessary for clinical application.

PTEN hamartoma tumour syndrome: what happens when there is no PTEN germline mutation?

Hereditary cancer syndromes represent ~10% of all incident cancers. It is important to identify individuals having these disorders because, unlike patients with sporadic cancer, these patients require specialised life-long care, with implications for their families. Importantly, the identification of alterations in cancer-predisposing genes facilitates gene-informed molecular diagnosis, cancer risk assessment and gene-specific clinical management. Moreover, knowledge about gene function in the inherited cancers offers insights towards biological processes pertinent to the more common sporadic cancers. Conversely, without a known gene, clinical management is less precise, and it is impossible to offer predictive testing of family members. PTEN hamartoma tumour syndrome (PHTS) is an umbrella term encompassing four overgrowth and cancer predisposition disorders associated with germline PTEN mutations. With time, it became evident that only a finite subset of individuals with PHTS-associated phenotypes harbour germline PTEN mutations. Therefore, non-PTEN aetiologies exist in PTEN wildtype patients. Indeed, gene discovery efforts over the last decade elucidated multiple candidate cancer predisposition genes. While a subset of genes (e.g. AKT1, PIK3CA) are biologically plausible as being key effectors within the PTEN signalling cascade, other genes required meticulous functional interrogation to explain their contribution to PHTS-related phenotypes. Collectively, the extensive phenotypic heterogeneity of the clinical syndromes typically united by PTEN is reflected by the genetic heterogeneity revealed through gene discovery. Validating these gene discoveries is critical because, while PTEN wildtype patients can be diagnosed clinically, they do not have the benefit of specific gene-informed risk assessment and subsequent management.