Objective Study on Tongue Diagnosis of Chronic Atrophic Gastritis
NCT Number: NCT06489132
Status: NOT_YET_RECRUITING
Study Type: OBSERVATIONAL
Conditions: Chronic Atrophic Gastritis
Sponsor: Peking University First Hospital
Sex: ALL
Age: ADULT, OLDER_ADULT
Enrollment: 498
Study Design: Observational Model: |Time Perspective: p
Start Date: 2024-07
Completion Date: 2026-03

Summary:
Chronic atrophic gastritis with intestinal metaplasia or dysplasia is called precancerous lesions of gastric cancer ( PLGC ). It is an important stage in the transformation of normal mucosa to gastric cancer and is significantly associated with the risk of gastric cancer. Early identification of PLGC high-risk groups is the focus of prevention and treatment of gastric cancer. Gastroscopy and pathological examination are the key means to diagnose PLGC. However, due to the invasiveness, high cost, strong professional operability and traumatic pathological sampling, the application of gastroscopy is greatly limited, and the predictive significance of serum markers for the occurrence and prognosis of PLGC is limited. The current PLGC monitoring methods are not targeted enough, and there is still a lack of effective risk prediction models for PLGC. New screening methods are needed to improve the early diagnosis rate of PLGC. Tongue diagnosis is simple, convenient, easy and inexpensive. It can dynamically monitor the risk of PLGC in patients with chronic atrophic gastritis in early real time, and timely formulate individualized intervention measures, which is of great significance for improving the prognosis of patients, controlling medical expenses and truncating disease progression. Based on the objectification of tongue diagnosis, it may become an important method for screening PLGC from the perspective of macroscopic tongue image characteristics and microscopic tongue coating flora.

Primary Outcome Measures: Correlation between tongue image characteristics and PLGC, Analysis of tongue color ( pale tongue, pale red tongue, crimson tongue, lavender tongue, ecchymosis tongue ) and tongue shape ( tooth-printed tongue, pricking tongue, ecchymosis tongue, fissured tongue, fat and thin tongue ) in PLGC patients and PLGC correlation ; 2. 2 Analysis of tongue coating characteristics : The correlation between tongue coating quality ( thick and thin coating, greasy coating, rotten coating, peeling coating ) and tongue coating color ( white and yellow ) and PLGC in PLGC patients was analyzed. Analysis of sublingual collaterals : According to the diagnostic criteria of sublingual collaterals grading, the sublingual collaterals were described according to the length, width ( thickness ), tortuosity and color of sublingual collaterals, and the correlation with the pathological changes of PLGC was analyzed. Analysis of tongue color and tongue coating color parameters : RGB, HIS and Lab color space types were used to analyze the RGB, Lab and HSV color space in, two years|Construction of PLGC risk prediction model based on tongue image features, Univariate conditional Logistic regression analysis was performed on the factors with statistical significance by t test and χ2 test. PLGC was used as the outcome variable to calculate the odds ratio ( OR ) and 95 % confidence interval of each factor. With αin = 0.05, αout = 0.1, the above variables were introduced into Logistic multivariate analysis to screen out the risk factors related to PLGC, and a risk prediction model was established. The receiver operating characteristic ( ROC ) curve was used to represent the ability of the prediction model to distinguish. The goodness of fit and prediction accuracy of the model were evaluated by Hosmer-Lemeshow goodness of fit, Pseudo R-square and area under the curve ( AUC )., two years
Secondary Outcome Measures: Correlation between tongue coating flora and PLGC, 1 α diversity analysis : The microbial community diversity among different groups was compared by α diversity analysis. 2 β diversity analysis : Through β analysis, the differences in community structure between different groups were explored, and the key flora was explored. 3 Species composition analysis : The differences in flora composition between groups were compared at the phylum level and genus level to find characteristic flora. Through species and functional contribution analysis, regression analysis and correspondence analysis, the function of microbial composition was predicted to find the function of characteristic flora., two years