The predictive value of FAH model for the occurrence of colorectal cancer

Zhixuan Ma ¹ Qing Wu ¹ Qingming Wu ^2*

• ¹ 资阳市人民医院, Sichuan, China
• ² School of Public Health, Wuhan University of Science and Technology, Wuhan, Hebei Province, China

Background: Fatty liver is characterized by hepatic steatosis and is associated with dyslipidemia and insulin resistance. Carotid atherosclerosis, characterized by plaque formation, may be related to increased lipid deposition. High-density lipoprotein cholesterol (HDL-C) plays a role in reverse cholesterol transport. Colorectal cancer (CRC) is significantly associated with lipid metabolismrelated diseases. However, there is a paucity of research on the relationship between lipid metabolism disorders and CRC.Objective: To determine whether fatty liver (F), carotid atherosclerosis (A), and HDL-C (H) models (FAH) have predictive value for the occurrence of CRC and can be used for CRC screening.A case-control study was conducted on 166 patients with CRC and 448 patients who underwent physical examinations at Ziyang People's Hospital between September 2018 and August 2023. A 1:3 individual matching strategy was used to establish the independent risk factors for CRC using univariate and multivariate analyses. A model was constructed based on independent risk factors, and its accuracy and sensitivity were verified. The discriminative ability, calibration, and clinical utility of the predictive model were evaluated using the Receiver Operating Characteristic curve, bootstrap resampling method, the Hosmer-Lemeshow goodness-of-fit test, and Decision Curve Analysis (DCA).Results: Fatty liver (F), carotid atherosclerosis (A), HDL-C (H), and intestinal dysbiosis (D) were identified as independent risk factors for CRC. The odds ratios were 2. 885, 11.452, 24.659, and 22.445, respectively, P<0.001. Based on these results, an FAH prediction model was established. The Horser-Lemeshow test for the FAH prediction model yielded P=0.710. The cut-off value was 0.275, with the area under the curve of 0.902 (95% Confidence Interval: 0.875-0.929), P<0.001. The sensitivity was 86.7%, and the specificity was 78.1%. A nomogram was created, and the internal calibration chart showed that the calibration curve closely aligned with the standard curve, indicating good discrimination and predictive ability of the model. DCA demonstrated that the model had a favorable clinical net benefit.The FAH model has predictive value for CRC occurrence owing to its noninvasive nature and easy availability of data, making it worthy of further clinical research.