Gastric cancer (GC) causes a heavy burden in China. Predicting individual GC risk can help to identify high-risk groups early, and then take targeted interventions to avoid or delay GC progression.

Establish and validate a nomogram model for predicting individual GC risk.

From January 2020 to July 2021, GC patients ≥40 years were diagnosed from the cancer registry system in 14 counties (districts) of Anhui province, Henan province, Shandong province and Jiangsu province were selected as the case group (684 cases). Match the general population with a frequency of 1∶2 based on gender, age, place of residence and health status matching people as a control group (1 368 cases). All subjects were randomly divided into training set (1 641 cases) and validation set (411 cases) according to a ratio of 8∶2. Multivariate Logistic regression analysis was used to screen variables and establish nomogram prediction model. The receiver operating characteristic (ROC) curve of the model predicting the risk of GC was drawn, the discrimination and calibration of the model were evaluated via the area under the ROC curve (AUC) and the Hosmer-Lemeshow test. The model was verified by Bootstrap method and the decision curve analysis (DCA) was used to evaluate the clinical practicability of the model.

Multivariate Logistic regression analysis showed that salty tastes (OR=1.690, 95%CI=1.333-2.142), dry and hard diet (OR=1.596, 95%CI=1.145-2.225), spicy food tastes (OR=1.387, 95%CI=1.093-1.760), exposure to secondhand smoking (OR=1.880, 95%CI=1.473-2.399), frequent tantrums (OR=3.283, 95%CI=2.236-4.819), history of stomach disease (OR=4.008, 95%CI=3.046-5.273), the family history of cancer (OR=1.549, 95%CI=1.170-2.051), Helicobacter pylori (Hp) infection (OR=1.298, 95%CI=1.028-1.693), high-salt diet (OR=1.338, 95%CI=1.033-1.734) were independent risk factors for GC (P<0.05). Junior high school education (OR=0.616, 95%CI=0.468-0.811), high school education or above (OR=0.491, 95%CI=0.342-0.703), regular diet (OR=0.542, 95%CI=0.405-0.726), the garlic consumption (OR=0.501, 95%CI=0.394-0.636) were protective factors for GC (P<0.05). The AUC for predicting GC risk in the training and validation sets was 0.768 (95%CI=0.744-0.792) and 0.776 (95%CI=0.728-0.823), respectively. The verification results of Bootstrap method showed that the calibration curve was in good agreement with the actual curve (Brier score of training set=0.177; Brier score of verification set=0.176) ; Hosmer-Lemeshow results showed that the model had a good fit (training set: χ²=4.408, P=0.819; verification set: χ²=4.650, P=0.794). The DCA curve showed that when the threshold is between 0.05 and 0.79, patients can benefit clinically using the nomogram model to predict the risk of GC occurrence.

The nomogram model constructed in this study could predict individual GC risk, early identify high-risk groups and help to formulate targeted and individualized interventions.