预测结肠癌的最佳人体测量指标。

The best anthropometric indices to predict colon cancer.

作者信息

Wang Kaihao, Wang Anlei, Wang Shaozu, Luo Zhijiang, Gong Yazhao, Hao Xiaoliang, Yang Guanglei, Xu Shuqing

机构信息

Department of General Surgery, Xingtai People's Hospital, No.16 Hongxing Street, Xiangdu District, Xingtai, 054000, Hebei, China.

出版信息

BMC Gastroenterol. 2025 Apr 10;25(1):242. doi: 10.1186/s12876-025-03832-z.

DOI:10.1186/s12876-025-03832-z

PMID:40211175

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11987457/

Abstract

BACKGROUND

Colon cancer is one of the most prevalent malignancies globally. Anthropometric data are widely accessible. However, numerous anthropometric indicators exist, and no study has identified the most reliable predictor for colon cancer. This study aims to identify the most effective anthropometric indicators for predicting colon cancer.

RESULTS

Significant differences were observed in age, race, smoking, diabetes, hypertension, waist circumference, a body shape index (ABSI), Body Roundness Index (BRI), Conicity Index (ConI), Waist-to-Height Ratio(WHtR), and Weight-adjusted Waist Index(WWI) between colon cancer patients and controls (P < 0.05). ABSI (AUC: 0.68, 95% CI: 0.64-0.71), ConI (AUC: 0.68, 95% CI: 0.64-0.71), and WWI (AUC: 0.67, 95% CI: 0.63-0.70) were the most accurate anthropometric indices for predicting colon cancer. ABSI, ConI, and WWI demonstrated a strong correlation.

CONCLUSION

Analysis of NHANES data (2005-2018) identified ABSI, ConI, and WWI (AUC:0.67-0.68) as optimal anthropometric predictors of colon cancer, emphasizing abdominal obesity's clinical relevance. These cost-effective indices could enhance early screening in resource-limited settings.

摘要

背景

结肠癌是全球最常见的恶性肿瘤之一。人体测量数据易于获取。然而，存在众多人体测量指标，且尚无研究确定结肠癌最可靠的预测指标。本研究旨在确定预测结肠癌最有效的人体测量指标。

结果

结肠癌患者与对照组在年龄、种族、吸烟、糖尿病、高血压、腰围、体型指数（ABSI）、体圆度指数（BRI）、锥度指数（ConI）、腰高比（WHtR）和体重调整腰围指数（WWI）方面存在显著差异（P < 0.05）。ABSI（AUC：0.68，95%CI：0.64 - 0.71）、ConI（AUC：0.68，95%CI：0.64 - 0.71）和WWI（AUC：0.67，95%CI：0.63 - 0.70）是预测结肠癌最准确的人体测量指标。ABSI、ConI和WWI显示出强相关性。