BACKGROUND: Colorectal polyps are the most important precursor lesions of colorectal cancer. Their epidemiological characteristics and risk factors exhibit substantial regional and population-based variations. Qinghai Province, located on the Qinghai–Tibet Plateau in China, is characterized by a unique hypoxic environment and a multi-ethnic population. However, large-scale epidemiological data on colorectal polyps in this region is limited. OBJECTIVE: To characterize the detection rate, temporal trends, demographic distribution, and risk factors for colorectal polyps in Qinghai Province. DESIGN: Retrospective study SETTING: A single tertiary medical center in Qinghai Province, China. PATIENTS AND METHODS: We enrolled 33059 patients who underwent colonoscopy at Qinghai University Affiliated Hospital (2021–2025). Data on demographics and endoscopic findings were collected. Temporal trends were analyzed using the linear-by-linear association LLA test, and independent risk factors were identified by binary logistic regression. MAIN OUTCOME MEASURES: Colorectal polyp detection rate and risk factors. SAMPLE SIZE: 33059 patients RESULTS: The overall colorectal polyp detection rate was 31.82% (10519/33059), with a slight upward trend over the study period. Rates were significantly higher in males (39.01%) than females (24.04%) and increased sharply with age. Multivariable analysis identified male sex (OR=2.493, 95 %CI:2.106–2.950, P <.001), age 41–64 years (OR=2.535, 95% CI:2.056–3.125, P <.001), and age ≥65 years (OR=4.379, 95% CI:3.328–5.761, P <.001) as independent risk factors. Tibetan and Hui ethnicities were associated with lower risk compared with Han (OR=0.611, 95% CI:0.448–0.833, P =.002). CONCLUSIONS: Colorectal polyp detection is high and rising in Qinghai. Male sex and advanced age are major risk factors. Notably, Tibetan or Hui ethnicity appear as potential protective factors, offering new insights into genetic interactions that may inform region-specific screening strategies. LIMITATIONS: Single-center retrospective design, potential selection bias, lack of lifestyle and metabolic data.
Tao et al. (Fri,) studied this question.