Advanced Lung Cancer Inflammation Index as a new predictor for colon cancer in elderly patients: An NHANES-Based Study

Hangyan Zhong¹Yisheng Chen²Weigen Wu³Suhua Liu⁴Youlong Fan⁵Haiqin Liu⁶Rongqi Lin⁷Junjie Wan^8*Meifang He^9*

• ¹Department of Proctology, Shanghang Hospital of Traditional Chinese Medicine, Longyan, Fujian Province, 364200, China, longyan, China
• ²Department of Neurology, Shanghang County Hospital, Longyan, Fujian Provl, longyan, China
• ³Laboratory of General Surgery, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510080, China, Guangzhou, China
• ⁴Department of Neurology, Shanghang County Hospital, Longyan, Fujian Province, 364200, China, longyan, China
• ⁵Department of Internal Medicine, Shanghang Hospital of Traditional Chinese Medicine, Longyan, Fujian Province, 364200, China, longyan, China
• ⁶Department of Internal Medicine, Shanghai Seventh People's Hospital, Shanghai, 200137, China, shanghai, China
• ⁷Department of Pharmacy, Shanghang County Hospital, Longyan, Fujian Province, 364200, China, longyan, China
• ⁸National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116, China, shenzhen, China
• ⁹Laboratory of General Surgery, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510080, China, GuangZhou, China

Background: Previous research suggests that anti-inflammatory diets may reduce the risk of colon cancer. However, the combined impact of diet and inflammation on colon cancer prevalence in elderly patients remain unclear.Methods: We used the Advanced Lung Cancer Inflammation Index (ALI) as a composite marker reflecting both inflammation status and nutritional health. To evaluate the association between ALI and colon cancer prevalence, we conducted multivariate logistic regression, applied an Extreme Gradient Boosting (XGBoost) machine learning model, and performed subgroup analyses. Additionally, a smoothed two-piece logistic regression model was used to identify the ALI threshold predictive of colon cancer.The study included 10,137, with a colon cancer prevalence of 2.45%. The ALI was significantly lower with colon cancer group compared to those without (P < 0.001). Multivariable logistic regression revealed a significant inverse association between ALI and colon cancer (P < 0.05), with individuals in the highest ALI tertile experiencing a 67% lower risk compared to those in the lowest tertile (P for trend = 0.008). Generalized additive models demonstrated a linear relationship, identifying an inflection point at 4.73 and a predictive threshold of 113.3. Sensitivity analyses confirmed the robustness of these findings, particularly among individuals aged over 70 years,females, unmarried individuals, alcohol consumers, and those with a BMI below 30. In the XGBoost model, ALI emerged as the strongest predictor of colon cancer (AUC = 0.717). Furthermore, ALI showed a positive association with dietary health status (P < 0.05) but was not significantly related to bowel habits.This study highlights a significant inverse association between ALI and colon cancer risk. ALI may serve as a promising, non-invasive biomarker for early risk assessment, especially among high-risk groups such as elderly unmarried females, alcohol consumers, and those with lower BMI. The strong predictive capacity of ALI, validated through machine learning, underscores its potential in guiding personalized prevention strategies. Further research is needed to investigate the roles of dietary patterns and gut microbiota in the observed association.