Clustering of lifestyle behaviours and analysis of their associations with MAFLD: a cross-sectional study of 196,515 individuals in China | BMC Public Health

Clustering of lifestyle behaviours and analysis of their associations with MAFLD: a cross-sectional study of 196,515 individuals in China | BMC Public Health

The ultimate assessment involved 196,515 subjects. The sociodemographic and life style traits of the male and feminine samples are offered individually in Desk 1. Males accounted for 54.92% of the sample, while females comprised 45.08%. The proportion of middle-aged and elderly persons aged ≥ 45 years is 45.55%, and the proportion of younger folks aged < 45 years old is 54.45%. A total of 77,172 cases of MAFLD (39.27%) were identified between 2016 and 2020, and men had a prevalence of 54.37%, which was higher than that of women (20.88%, p < 0.001).

Table 1 Descriptive statistics on the sociodemographic and lifestyle characteristics of participants separately by sex

The defined lifestyle scoring system, including optimal cut points for the options of the lifestyle items, is presented in Appendix Table S1 in the Supplementary materials. The factors extracted by the PCA method corresponded to dietary factors and mental models. The factor loadings for each dietary factor are shown in Appendix Table S2 in the Supplementary materials.

Based on the results of the silhouette coefficient and elbow method, we identified nine clusters for males and four clusters for females. A relatively high agreement was found between the cluster solution derived from the full sample and the random subsample (males: Cohen’s kappa = 0.55, p < 0.01 females: Cohen’s kappa = 0.89, p < 0.01). The specific characteristics of each cluster are presented in Fig. 3 and Table 2.

Fig. 3
figure 3

Lifestyle clusters of males and females (Z scores). Note: YPA: Years of consistent exercise, SS: Smoking status, WPI: Physical intensity at work, NCS: Number of cigarettes smoked per day, NWD: Number of working days per week, YCS: Number of years of continuous smoking, DWH: Daily working hours, YQS: Number of years to quit smoking, SD: Sedentary duration, DAS: Drinking alcohol status, SQ: Sleep quality, FAC: Frequency of alcohol consumption per week, ST: Sleep time, AAC: Amount of alcohol consumption per drink, MM: Mental model, YCD: Number of years of continuous drinking, DF1: Dietary Factor 1, YAA: Number of years of alcohol abstinence, DF2: Dietary Factor 2, PAS: Physical activity status, DF3: Dietary Factor 3, FPA: Frequency of exercise per week, DF4: Dietary Factor 4, DPA: Duration of each exercise, DF5: Dietary Factor 5

Table 2 Sociodemographic characteristics of lifestyle clusters

For males, the largest cluster (C1, n = 21,494, 19.91%) was referred to as a “healthy” lifestyle pattern. It excluded all risky health behaviours and was characterized by being physically active, not smoking, not drinking, having low levels of sedentariness, having moderate physical activity at work, sleeping well, and having a healthy mind and diet. Cluster 2 (C2, n = 10,790, 10.00%) was characterized by physical inactivity, an unhealthy diet, not smoking, not drinking, being sedentary, and having poor mental status. Cluster 3 (C3, n = 10,092, 9.35%) was characterized by “heavy smoking” but maintaining other healthy lifestyle behaviours. Cluster 4 (C4, n = 16,160, 14.97%) was significantly characterized by heavily consuming alcohol, smoking in small quantities, being physically active, and having unhealthy dietary behaviours. Cluster 5 (C5, n = 3917, 3.63%) was described as having the “most physically intense work,” being physically active, having a healthy mentality, and having a high-fat and high-cholesterol diet. Cluster 6 (C6, n = 7656, 7.09%) was almost the exact opposite of Cluster 4 and was characterized by “heavy smoking,” being physically inactive, having “poor sleep quality,” and having an unhealthy diet but no alcohol consumption. Cluster 7 (C7, n = 17,723,16.42%) was the opposite of Cluster 2 and was characterized by “heavy smoking” and “heavy drinking” while also being physically active. Cluster 8 (C8, n = 1865,1.73%), similar to Cluster 5, was characterized by performing the “most physically intense work,” “heavy drinking,” “heavy smoking,” being physically inactive, being highly sedentary, and having an “unhealthy mentality and diet.” Last, Cluster 9 (C9, n = 18,234,16.89%), known as the “unhealthiest” lifestyle pattern, was characterized by heavy drinking and smoking, being physically inactive, being sedentary, having poor sleep quality, and having an unhealthy mental status and diet.

The largest cluster of females (CF1, n = 80,551, 90.93%), known as the “relatively healthy” lifestyle pattern, differed from the largest cluster of males in that they lacked physical activity. Cluster 2 (CF2, n = 1040, 1.17%) was characterized by heavy smoking, being highly sedentary, having poor mental status, and having an unhealthy diet. Cluster 3 (CF3, n = 718,0.81%), known as the “least healthy” lifestyle pattern, had similar characteristics to Cluster 2 except for heavy drinking. Cluster 4 (CF4, n = 6275, 7.08%) was characterized by heavy alcohol consumption but was the most active in exercise and had relatively healthy dietary behaviours.

In men, C4, C7, and C9 had the highest prevalence of MAFLD (58.36%, 58.63%, and 58.06%, respectively), whereas the lowest prevalence was observed for C1 (48.07%). The prevalence of MAFLD was lower in women than in men (20.88% vs. 54.37%, p < 0.001). In women, a very similar prevalence of MAFLD was found in CF1, CF3, and CF4, whereas CF2 had the highest prevalence of MAFLD (26.92%).

The results of the binary logistic regression models are presented in Table 3, stratified by sex. C1 and CF1 were chosen as the reference clusters for males and females because they had the largest populations and showed a lower prevalence of MAFLD compared with the other clusters. After adjusting for confounding variables, C2, C6, C8, and C9 among males were significantly associated with a higher risk of MAFLD. C3, C4, and C7 had significantly lower odds of belonging to the MAFLD group. In the female clusters, the odds of CF2 belonging to MAFLD were higher (AOR = 1.370, 95% CI = 1.168–1.607).

Table 3 Associations between MAFLD and lifestyle clusters