Co-prevalence relationship analysis on different metabolic syndrome scores and behavioral risk factors in adults from Urumqi based

Abstract

Objective: To investigate the influence of the prevalence and co-prevalence of risk factors for metabolic syndrome on the scores of different levels of metabolic syndrome in people receiving physical examination in Urumqi. Methods: Using the 2017 Xinjiang Health Examination Database, a total of 175 927 people from 7 districts and 1 county in Urumqi were selected as subjects. Face-to-face survey and body measurements were used to collect cardiovascular risk factors and metabolic syndrome scores. Metabolic syndrome scores were used. For the 0-5 points at 6 levels, χ(2), χ(2) trend test, correlation analysis of ordered variable Kendall's tau-b, and logistic regression analysis of ordered results were used to analyze the influence of prevalence and co-prevalence of behavioral risk factors on the MS scores. Results: The percentages of 6 metabolic syndrome scores in the sample population were 23.82%, 27.87%, 22.41%, 16.03%, 8.02%, and 1.85%, respectively. The scores of metabolic syndrome were different in different age groups, ethnic groups, groups with different drinking rates, and groups with different dietary types, with the differences all significant (P<0.05).The MS score in men increased with the increase of oil/salt rate and excessive drinking rate (P<0.01). The score in women increased with the increase of the current smoking rate, oil/salt rate, and increased with the decrease of physical activity (P≤0.01). There was no significant difference in the distribution of regular drinking rates between different score groups (P>0.05). The scores of metabolic syndrome increased with the increase of risk factors (P<0.05). Ordered results logistic analysis found that in the men with ≥3 risk factors and the metabolic syndrome score was 1.15 (1.06-1.26) times higher than that in the men without risk factor, as well as in women with 2 risk factors and≥3 risk factors. The metabolic syndrome scores were 1.38 (1.22-1.55), 2.02 (1.53-2.66) times higher than those in the women without risk factors. Conclusions: The physical examination group in Urumqi, the more the metabolic syndrome disease behavior risk factors clustered, the higher the metabolic syndrome score was. Therefore, comprehensive intervention measures should be taken to control the different forms of metabolic syndrome to prevent the occurrence and progress of the disease.