Visceral Obesity in Non-Small Cell Lung Cancer

Body composition alterations in patients with lung cancer

Most patients with lung cancer experience cancer cachexia (CC), a syndrome of skeletal muscle and adipose tissue wasting. Knowledge of body composition changes in patients is limited, however, because most studies have been cross-sectional, comparing patients with noncancer controls or patients with and without CC. Few studies, in contrast, have evaluated body composition in patients with lung cancer over time. This review examines our current understanding of longitudinal body composition changes in patients with lung cancer and identifies modifying factors contributing to variation in muscle and adipose tissue wasting, focusing on biological sex. We identified 32 studies conducting longitudinal measurements of body composition by computed tomography, bioelectrical impedance, dual X-ray absorptiometry, or total body nitrogen, with a total of n = 3,951 patients (35% female). All studies evaluated changes following diagnosis while patients were receiving treatment. Most studies reporting muscle-specific outcomes show decreased skeletal muscle mass, with more pronounced muscle wasting in males and male-enriched populations. In a small number of studies reporting muscle density, the majority show increased myosteatosis. Adiposity changes are less frequently reported, although wasting appears more prevalent in late-stage disease. Further studies are needed to define adipose changes along the lung cancer continuum. Our review emphasizes the need for balanced recruitment based on biological sex and sex-based analyses. In addition, consensus reporting of relevant patient data and outcomes in future studies will allow for meta-analysis and assist in the development of effective treatments for lung CC.

Non-cancerous CT findings as predictors of survival outcome in advanced non-small cell lung cancer patients treated with first-generation EGFR-TKIs

PURPOSE

To identify non-cancerous factors from baseline CT chest affecting survival in advanced non-small cell lung cancer (NSCLC) treated with first-generation Epidermal Growth Factor Receptor-Tyrosine Kinase Inhibitors (EGFR-TKIs).

METHODS

Retrospective study of 172 advanced NSCLC patients treated with first-generation EGFR-TKIs as a first-line systemic treatment (January 2012 to September 2022). Baseline CT chest assessed visceral/subcutaneous fat (L1 level), sarcopenia, and myosteatosis (multiple levels), main pulmonary artery (MPA) size, MPA to aorta ratio, emphysema, and bone mineral density. Cox regression analyzed prognostic factors at 18-month outcome.

RESULTS

Median overall survival was 17.57 months (14.87-20.10) with 76 (44.19%) patients died at 18 months. Deceased had lower baseline BMI (21.10 ± 3.44) vs. survived (23.25 ± 4.45) (p < 0.001). Univariable analysis showed 5 significant prognostic factors: low total adiposity with/without cutoff [HR 2.65 (1.68-4.18), p < 0.001; 1.00 (0.99-1.00), p = 0.006;], low subcutaneous adipose tissue (SAT) with/without cutoff [HR 1.95 (1.23-3.11), p = 0.005; 0.99 (0.98-0.99), p = 0.005], low SAT index (SATI) with/without cutoff [1.74 (1.10-2.78), p = 0.019; 0.98 (0.97-0.99), p = 0.003], high VSR [1.67 (1.06-2.62), p = 0.026], and high MPA size with/without cutoff [2.23 (1.23-4.04), p = 0.005; 1.09 (1.04-1.16), p = 0.001]. MPA size, MPA size > 29 mm, and total adiposity ≤85 cm2 remained significant in multivariable analysis, adjusted by BMI [HR 1.14 (1.07-1.21), p < 0.001; 3.10 (1.81-5.28), p < 0.001; 3.91 (1.63-9.40), p = 0.002]. There was no significant difference of sarcopenic and myosteatotic parameters between the two groups.

CONCLUSION

In advanced EGFR-mutated NSCLC patients, assessing pre-treatment prognosis is warranted to predict the survival outcome and guide decision regarding EGFR-TKI therapy. Enlarged MPA size, low total adiposity, and low subcutaneous fat (lower SAT, lower SATI, and higher VSR) are indicators of poor survival. Large MPA size (>29 mm) or low total adiposity (≤85 cm2) alone predict 18-month death.

Lung cancer and obesity: A contentious relationship (Review)

The global obesity epidemic, attributed to sedentary lifestyles, unhealthy diets, genetics and environmental factors, has led to over 1.9 billion adults being classified as overweight and 650 million living with obesity. Despite advancements in early detection and treatment, lung cancer prognosis remains poor due to late diagnoses and limited therapies. The obesity paradox challenges conventional thinking by suggesting that individuals with obesity and certain diseases, including cancer, may have an improved prognosis compared with their counterparts of a normal weight. This observation has prompted investigations to understand protective mechanisms, including potentially favorable adipokine secretion and metabolic reserves that contribute to tolerating cancer treatments. However, understanding the association between obesity and lung cancer is complex. While smoking is the primary risk factor of lung cancer, obesity may independently impact lung cancer risk, particularly in non‑smokers. Adipose tissue dysfunction, including low‑grade chronic inflammation, and hormonal changes contribute to lung cancer development and progression. Obesity‑related factors may also influence treatment responses and survival outcomes in patients with lung cancer. The impact of obesity on treatment modalities such as chemotherapy, radiotherapy and surgery is still under investigation. Challenges in managing patients with obesity and cancer include increased surgical complexity, higher rates of postoperative complications and limited treatment options due to comorbidities. Targeted interventions aimed at reducing obesity prevalence and promoting healthy lifestyles are crucial for lung cancer prevention. The impact of obesity on lung cancer is multifaceted and requires further research to elucidate the underlying mechanisms and develop personalized interventions for prevention and treatment.

Predicting the risk of lung cancer using machine learning: A large study based on UK Biobank

In response to the high incidence and poor prognosis of lung cancer, this study tends to develop a generalizable lung-cancer prediction model by using machine learning to define high-risk groups and realize the early identification and prevention of lung cancer. We included 467,888 participants from UK Biobank, using lung cancer incidence as an outcome variable, including 49 previously known high-risk factors and less studied or unstudied predictors. We developed multivariate prediction models using multiple machine learning models, namely logistic regression, naïve Bayes, random forest, and extreme gradient boosting models. The performance of the models was evaluated by calculating the areas under their receiver operating characteristic curves, Brier loss, log loss, precision, recall, and F1 scores. The Shapley additive explanations interpreter was used to visualize the models. Three were ultimately 4299 cases of lung cancer that were diagnosed in our sample. The model containing all the predictors had good predictive power, and the extreme gradient boosting model had the best performance with an area under curve of 0.998. New important predictive factors for lung cancer were also identified, namely hip circumference, waist circumference, number of cigarettes previously smoked daily, neuroticism score, age, and forced expiratory volume in 1 second. The predictive model established by incorporating novel predictive factors can be of value in the early identification of lung cancer. It may be helpful in stratifying individuals and selecting those at higher risk for inclusion in screening programs.

Influence of abdominal fat distribution and inflammatory status on post-operative prognosis in non-small cell lung cancer patients: a retrospective cohort study

PURPOSE

To evaluate the influence of visceral fat area (VFA), subcutaneous fat area (SFA), the systemic immune-inflammation index (SII) and total inflammation-based systemic index (AISI) on the postoperative prognosis of non-small cell lung cancers (NSCLC) patients.

METHODS

266 NSCLC patients received surgery from two academic medical centers were included. To assess the effect of abdominal fat measured by computed tomography (CT) imaging and inflammatory indicators on patients' overall survival (OS) and progression-free survival (PFS), Kaplan-Meier survival analysis and Cox proportional hazards models were used.

RESULTS

Kaplan-Meier analysis showed the OS and PFS of patients in high-VFA group was better than low-VFA group (p < 0.05). AISI and SII were shown to be risk factors for OS and PFS (p < 0.05) after additional adjustment for BMI (Cox regression model II). After further adjustment for VFA (Cox regression model III), low-SFA group had longer OS (p < 0.05). Among the four subgroups based on VFA (high/low) and SFA (high/low) (p < 0.05), the high-VFA & low-SFA group had the longest median OS (108 months; 95% CI 74-117 months) and PFS (85 months; 95% CI 65-117 months), as well as the lowest SII and AISI (p < 0.05). Low-SFA was a protective factor for OS with different VFA stratification (p < 0.05).

CONCLUSION

VFA, SFA, SII and AISI may be employed as significant prognostic markers of postoperative survival in NSCLC patients. Moreover, excessive SFA levels may encourage systemic inflammation decreasing the protective impact of VFA, which may help to provide targeted nutritional support and interventions for postoperative NSCLC patients with poor prognosis.