Abstract： Objective:To investigate the prevalence the influencing factors of metabolic syndrome among different age groups in Tianjin district.Methods:According to the ‘2015 Chinese Adult Chronic Disease and Nutrition Surveillance Program’, multi-stage cluster random sampling method was used to extract 42 communities from 7 districts in Tianjin. A total of 1 753 adult residents in the 42 communities were selected as the research subjects to analyze the relationship between the influencing factors and metabolic syndrome with descriptive epidemiological analysis. The χ2 test, non-conditional logistic regression and classification tree models were used to analyze the data. Results:The prevalence rate of metabolic syndrome in Tianjin was 30.6%, and the standardized rate was 24.5%. The prevalence rate of metabolic syndrome in Tianjin was increased with age, which was the highest in elderly people (38.9%, ≥60 years). According to the classification tree model, in youth group (18―44 years old), gender, smoking, subjective feeling of health, annual household income and sleep duration were important influencing factors of metabolic syndrome, And the standardized importance was 100%, 95.59%, 22.55%, 20.94% and 9.89%, respectively. In the middle-aged group (45―59 years old), secondhand smoke exposure, subjective feeling of health, sleep duration, the Chinese Food Pagoda (CHFP) score and living region were important influencing factors of metabolic syndrome, and the standardized importance was 100%, 98.08%, 91.04%, 45.74% and 20.15%, respectively. In the elderly group (≥60 years old), sleep duration, secondhand smoke exposure, gender, the CHFP score and living region were important influencing factors of metabolic syndrome, and the standardized importance was 100%, 46.75%, 41.87%, 41.82% and 7.60%, respectively.Conclusions:The prevalence rate of metabolic syndrome is quite high in Tianjin. Tobacco hazard (smoking and secondhand smoke exposure) and sleep duration are the common influencing factors of metabolic syndrome in all age groups. There are different emphases among different age groups in the distributions of those influencing factors, so pointed interventions should be adopted accordingly.