Visceral Obesity in Non-Small Cell Lung Cancer

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.