[Path analysis of lifestyle habits to the metabolic syndrome].
OBJECTIVE
To evaluate the relationship between lifestyle habits and the components of metabolic syndrome (MS).


METHODS
Based on the routine health check-up system in a certain Center for Health Management of Shandong Province, a longitudinal surveillance health check-up cohort from 2005 to 2010 was set up. There were 13 225 urban workers in Jinan included in the analysis. The content of the survey included demographic information, medical history, lifestyle habits, body mass index (BMI) and the level of blood pressure, fasting blood-glucose, and blood lipid, etc. The distribution of BMI, blood pressure, fasting blood-glucose, blood lipid and lifestyle habits between MS patients and non-MS population was compared, latent variables were extracted by exploratory factor analysis to determine the structure model, and then a partial least squares path model was constructed between lifestyle habits and the components of MS.


RESULTS
Participants'age was (46.62 ± 12.16) years old. The overall prevalence of the MS was 22.43% (2967/13 225), 26.49% (2535/9570) in males and 11.82% (432/3655) in females. The prevalence of the MS was statistically different between males and females (χ(2) = 327.08, P < 0.01). Between MS patients and non-MS population, the difference of dietary habits was statistically significant (χ(2) = 166.31, P < 0.01) in MS patients, the rate of vegetarian, mixed and animal food was 23.39% (694/2967), 42.50% (1261/2967) and 34.11% (1012/2967) respectively, while in non-MS population was 30.80% (3159/10 258), 46.37% (4757/10 258), 22.83% (2342/10 258) respectively. Their alcohol consumption has statistical difference (χ(2) = 374.22, P < 0.01) in MS patients, the rate of never or past, occasional and regular drinking was 27.37% (812/2967), 24.71% (733/2967), 47.93% (1422/2967) respectively, and in non-MS population was 39.60% (4062/10 258), 31.36% (3217/10 258), 29.04% (2979/10 258) respectively. The difference of their smoking status was statistically significant (χ(2) = 115.86, P < 0.01) in MS patients, the rate of never or past, occasional and regular smoking was 59.72% (1772/2967), 6.24% (185/2967), 34.04% (1010/2967) respectively, while in non-MS population was 70.03% (7184/10 258), 5.35% (549/10 258), 24.61% (2525/10 258) respectively. Both lifestyle habits and the components of MS were attributable to only one latent variable. After adjustment for age and gender, the path coefficient between the latent component of lifestyle habits and the latent component of MS was 0.22 with statistical significance (t = 6.46, P < 0.01) through bootstrap test. Reliability and validity of the model:the lifestyle latent variable: average variance extracted was 0.53, composite reliability was 0.77 and Cronbach's a was 0.57. The MS latent variable: average variance extracted was 0.45, composite reliability was 0.76 and Cronbach's a was 0.59.


CONCLUSIONS
Unhealthy lifestyle habits are closely related to MS. Meat diet, excessive drinking and smoking are risk factors for MS.
Institute of Epidemiology and Health Statistics, School of Public Health, Shandong University, Jinan 250012, China.
Zhen-xin
ZX
Zhu
Cheng-qi
CQ
Zhang
Fang
F
Tang
Xin-hong
XH
Song
Fu-zhong
FZ
Xue
D000328:Adult; D005260:Female; D006801:Humans; D008019:Life Style; D008137:Longitudinal Studies; D008297:Male; D024821:Metabolic Syndrome / Q000453:epidemiology*; D008875:Middle Aged; D015233:Models, Statistical; D015995:Prevalence; D012307:Risk Factors