Age and metabolic syndrome
The weighted distribution of characteristics of selected population by age groups was shown in Table S2. Among three groups, the distribution of gender, education level, and dietary fiber intake was similar. Compared with the youngest age group (age ≥ 20 and < 40 years), participants in older age groups had higher proportions of 2015–2016 cycle, non-Hispanic White, non-single state, the ratio of family income to poverty equal to or greater than 3, being covered by health insurance, average alcoholic drinks less than 3, no current smoking, sedentary activity hours equal to or greater than 4, no work activity, no recreational activity, elevated waist circumference, elevated blood pressure, elevated triglyceride, and elevated fasting glucose. Besides, they had higher average BMI. On the contrary, they had less average energy, protein, carbohydrate, total sugars, and total fat intakes.
The correlation analysis between age and MS was presented in Table 1. Crude model is an unadjusted model. This model indicated that age was positively associated with MS (odds ratio (OR):1.04, 95%confidence interval (CI):1.03–1.04). In model 1, after adjusting for gender and race/ethnicity, the positive correlation between age and MS remained stable (OR:1.04, 95%CI:1.03–1.04). Similar results can be obtained in model 2 (OR:1.04, 95%CI:1.03–1.04) and model 3 (OR:1.05, 95%CI:1.04–1.05). We also grouped age by tertile and calculated P for trend. The results corroborated those of age as a continuous variable. All results were verified using multiply-imputed datasets (Table S3).
Table 1
Association between age and metabolic syndrome in different models
| Crude model (N = 4313) | Model 1 (N = 4313) | Model 2 (N = 4100) | Model 3 (N = 4091) |
OR (95%CI) | P value | OR (95%CI) | P value | OR (95%CI) | P value | OR (95%CI) | P value |
Age (year) | 1.04 (1.03–1.04) | < 0.01* | 1.04 (1.03–1.04) | < 0.01* | 1.04 (1.03–1.04) | < 0.01* | 1.05 (1.04–1.05) | < 0.01* |
Age groups (year) | | | | | | | | |
≥ 20,<40 | Reference | | Reference | | Reference | | Reference | |
≥ 40,<60 | 2.57 (2.18–3.04) | < 0.01* | 2.57 (2.18–3.04) | < 0.01* | 2.40 (2.01–2.88) | < 0.01* | 2.57 (2.09–3.16) | < 0.01* |
≥ 60 | 4.61 (3.90–5.45) | < 0.01* | 4.53 (3.83–5.35) | < 0.01* | 3.87 (3.17–4.71) | < 0.01* | 5.79 (4.59–7.29) | < 0.01* |
P for trend | | < 0.01* | | < 0.01* | | < 0.01* | | < 0.01* |
OR: odds ratio; CI: confidence interval. |
Crude model adjust for none. |
Model 1 adjust for gender and race/ethnicity. |
Model 2 adjust for all adjusted covariates in model 1 plus data release cycle, education level, marital status, the ratio of family income to poverty, energy intake, protein intake, carbohydrate intake, total sugars intake, dietary fiber intake, total fat intake, health insurance, average alcoholic drinks, current smoking, sedentary activity hours, total work activity minutes, and total recreational activity minutes. |
Model 3 adjust for all adjusted covariates in model 2 plus body mass index. |
* P value < 0.05. |
Gender and metabolic syndrome
The weighted distribution of characteristics of selected participants by gender was shown in Table S4. Between two groups, the distribution of percentages of data release cycle, race/ethnicity, sedentary activity hours, and elevated blood pressure was similar. Compared with their male counterparts, females had higher proportions of high school graduate above, the ratio of family income to poverty less than 1, being covered by health insurance, average alcoholic drinks less than 3, total work activity minutes equal to 0, total recreational activity minutes equal to or greater than 0 and less than 150, elevated waist circumference, and reduced HDL cholesterol. Besides, they had higher average age and BMI. In contrast, they had lower proportions of non-single state, no current smoking, elevated triglyceride, and elevated fasting glucose. They also had less average energy, protein, carbohydrate, total sugars, dietary fiber, and total fat intakes.
The correlation analysis between gender and MS was presented in Table 2. In all four models, compared with the male group, there was no significant correlation between female and MS. However, we found that the effect size of OR in the female group changed from 1.13 in model 2 to 0.88 in model 3 after further adjusting for BMI. To investigate whether there is any interaction between gender and BMI for metabolic syndrome, an interaction test was performed. We found an interaction between gender and BMI for MS(Table S5). After that, a subgroup analysis by BMI was undertaken. The results demonstrated that, compared with the male group, female was positively associated with MS in participants with BMI less than 25 kg/m2 (OR:1.84, 95%CI:1.07–3.18) whereas it was negatively associated with MS in those with BMI equal to or greater than 30 kg/m2 (i.e., obesity) (OR:0.62, 95%CI:0.48–0.81) (Table 3). The results of correlation analysis were confirmed by those from multiply-imputed datasets (Tables S6). The subgroup analysis by BMI using multiply-imputed datasets also showed similar results(Table S7).
Table 2
Association between gender and metabolic syndrome in different models
| Crude model (N = 4313) | Model 1 (N = 4313) | Model 2 (N = 4100) | Model 3 (N = 4091) |
OR (95%CI) | P value | OR (95%CI) | P value | OR (95%CI) | P value | OR (95%CI) | P value |
Male | Reference | | Reference | | Reference | | Reference | |
Female | 1.09 (0.96–1.23) | 0.17 | 1.09 (0.96–1.24) | 0.19 | 1.13 (0.97–1.31) | 0.12 | 0.88 (0.75–1.05) | 0.16 |
OR: odds ratio; CI: confidence interval. |
Crude model adjust for none. |
Model 1 adjust for age and race/ethnicity. |
Model 2 adjust for all adjusted covariates in model 1 plus data release cycle, education level, marital status, the ratio of family income to poverty, energy intake, protein intake, carbohydrate intake, total sugars intake, dietary fiber intake, total fat intake, health insurance, average alcoholic drinks, current smoking, sedentary activity hours, total work activity minutes, and total recreational activity minutes. |
Model 3 adjust for all adjusted covariates in model 2 plus body mass index. |
Table 3
Association between gender and metabolic syndrome in BMI groups
| BMI < 25 kg/m2 (N = 1156) | BMI ≥ 25,<30 kg/m2 (N = 1329) | BMI ≥ 30 kg/m2 (N = 1606) |
OR (95%CI) | P value | OR (95%CI) | P value | OR (95%CI) | P value |
Male | Reference | | Reference | | Reference | |
Female | 1.84 (1.07–3.18) | 0.03* | 1.28 (0.96–1.71) | 0.10 | 0.62 (0.48–0.81) | < 0.01* |
BMI:body mass index; OR: odds ratio; CI: confidence interval. |
Adjusted for age, race/ethnicity, data release cycle, education level, marital status, the ratio of family income to poverty, energy intake, protein intake, carbohydrate intake, total sugars intake, dietary fiber intake, total fat intake, health insurance, average alcoholic drinks, current smoking, sedentary activity hours, total work activity minutes, total recreational activity minutes, and body mass index. |
* P value < 0.05. |
Race/ethnicity and metabolic syndrome
The weighted distribution of characteristics of selected population by race/ethnicity was shown in Table S8. Among five groups, the distribution of percentages of data release cycle and gender was similar. Compared with other groups, Mexican American had the highest proportions of high school graduate/GED or equivalent or below, the ratio of family income to poverty less than 1, average alcoholic drinks equal to or greater than 3, sedentary activity hours less than 4, total recreational activity minutes equal to 0, elevated waist circumference, elevated triglyceride, reduced HDL cholesterol, and elevated fasting glucose. Non-Hispanic White had the highest proportion of no current smoking. Besides, they had the highest average energy, protein, carbohydrate, total sugars, dietary fiber, and total fat intakes. In contrast, they had the lowest proportion of being covered by health insurance and average age. Non-Hispanic Black had the highest proportion of elevated blood pressure and average BMI. Non-Hispanic Asian had the highest proportions of non-single state and total work activity minutes equal to 0.
The correlation analysis between race/ethnicity and MS was presented in Table 4. In the crude model, compared with Mexican American, non-Hispanic Asian had a negative correlation with MS (OR:0.40, 95%CI:0.31–0.51). Similar results can be seen in model 1 and 2. Besides, in model 1 and 2, non-Hispanic White had a negative correlation with MS (OR: 0.78, 95%CI: 0.64–0.94 and OR: 0.80, 95%CI: 0.64–0.99, respectively). Moreover, in model 1 and 3, non-Hispanic Black had a negative correlation with MS (OR: 0.77, 95%CI: 0.62–0.96 and OR: 0.72, 95%CI: 0.55–0.95, respectively). Of note, the effect size of OR in non-Hispanic Asian changed from 0.44 in model 2 to 1.15 in model 3 after further adjusting for BMI. To investigate whether there is any interaction between race/ethnicity and BMI for MS, an interaction test was carried out. We found an interaction between race/ethnicity and BMI for MS (Table S9). Then, a subgroup analysis by BMI was performed. The results indicated that, compared with Mexican American, non-Hispanic Asian and other race/ethnicity were positively associated with MS in participants with BMI less than 25 kg/m2 (OR: 10.09, 95%CI: 1.97–51.59 and OR: 6.79, 95%CI: 1.33–34.68, respectively) whereas non-Hispanic Black was negatively associated with MS in those with BMI equal to or greater than 25 kg/m2 and less than 30 kg/m2 (i.e.,overweight) (OR: 0.56, 95%CI: 0.35–0.89) (Table 5). However, the results of correlation analysis using multiply-imputed datasets demonstrated that, in model 2, there was no significant correlation between non-Hispanic White and MS when compared with Mexican American(Table S10). The results of subgroup analysis by BMI were generally supported by those from multiply-imputed datasets (Tables S11).
Table 4
Association between race/ethnicity and metabolic syndrome in different models
| Crude model (N = 4313) | Model 1 (N = 4313) | Model 2 (N = 4100) | Model 3 (N = 4091) |
OR (95%CI) | P value | OR (95%CI) | P value | OR (95%CI) | P value | OR (95%CI) | P value |
Mexican American | Reference | | Reference | | Reference | | Reference | |
Non-Hispanic White | 0.94 (0.79–1.13) | 0.53 | 0.78 (0.64–0.94) | < 0.01* | 0.80 (0.64–0.99) | 0.04* | 0.94 (0.73–1.20) | 0.62 |
Non-Hispanic Black | 0.83 (0.68–1.03) | 0.09 | 0.77 (0.62–0.96) | 0.02* | 0.81 (0.64–1.03) | 0.09 | 0.72 (0.55–0.95) | 0.02* |
Non-Hispanic Asian | 0.40 (0.31–0.51) | < 0.01* | 0.40 (0.31–0.52) | < 0.01* | 0.44 (0.32–0.60) | < 0.01* | 1.15 (0.82–1.63) | 0.42 |
Other race/ethnicity | 0.87 (0.70–1.09) | 0.22 | 0.82 (0.65–1.03) | 0.09 | 0.81 (0.63–1.03) | 0.09 | 0.96 (0.73–1.27) | 0.78 |
OR: odds ratio; CI: confidence interval. |
Crude model adjust for none. |
Model 1 adjust for age and gender. |
Model 2 adjust for all adjusted covariates in model 1 plus data release cycle, education level, marital status, the ratio of family income to poverty, energy intake, protein intake, carbohydrate intake, total sugars intake, dietary fiber intake, total fat intake, health insurance, average alcoholic drinks, current smoking, sedentary activity hours, total work activity minutes, and total recreational activity minutes. |
Model 3 adjust for all adjusted covariates in model 2 plus body mass index. |
* P value < 0.05. |
Table 5
Association between race/ethnicity and metabolic syndrome in BMI groups
| BMI < 25 kg/m2 (N = 1156) | BMI ≥ 25,<30 kg/m2 (N = 1329) | BMI ≥ 30 kg/m2 (N = 1606) |
OR (95%CI) | P value | OR (95%CI) | P value | OR (95%CI) | P value |
Mexican American | Reference | | Reference | | Reference | |
Non-Hispanic White | 4.34 (0.89–21.19) | 0.07 | 0.84 (0.57–1.26) | 0.41 | 1.05 (0.74–1.50) | 0.78 |
Non-Hispanic Black | 2.45 (0.46–13.10) | 0.30 | 0.56 (0.35–0.89) | 0.02* | 0.87 (0.60–1.26) | 0.45 |
Non-Hispanic Asian | 10.09 (1.97–51.59) | < 0.01* | 1.63 (0.96–2.79) | 0.07 | 0.86 (0.41–1.77) | 0.68 |
Other race/ethnicity | 6.79 (1.33–34.68) | 0.02* | 0.80 (0.51–1.25) | 0.32 | 0.97 (0.65–1.44) | 0.86 |
BMI:body mass index; OR: odds ratio; CI: confidence interval. |
Adjusted for age, gender, data release cycle, education level, marital status, the ratio of family income to poverty, energy intake, protein intake, carbohydrate intake, total sugars intake, dietary fiber intake, total fat intake, health insurance, average alcoholic drinks, current smoking, sedentary activity hours, total work activity minutes, total recreational activity minutes, and body mass index. |
* P value < 0.05. |
The ratio of family income to poverty and metabolic syndrome
The weighted distribution of characteristics of selected participants by the ratio of family income to poverty was shown in Table S12. Among three groups, the distribution of percentages of elevated waist circumference, blood pressure, triglyceride, and fasting glucose along with energy intake was similar. Compared with the lowest ratio of family income to poverty group (the ratio of family income to poverty < 1), participants with higher ratio of family income to poverty had higher proportions of 2015–2016 cycle, males, non-Hispanic White, college graduate or above, non-single state, being covered by health insurance, average alcoholic drinks less than 3, no current smoking, sedentary activity hours equal to or greater than 6, total work activity minutes greater than 0 and equal to or less than 300, total recreational activity minutes greater than 0. Besides, they had higher average age, protein intake, dietary fiber intake. In contrast, they had a lower proportion of reduced HDL cholesterol as well as less average carbohydrate intake, total sugars intake, total fat intake, and BMI.
The correlation analysis between the ratio of family income to poverty and MS was presented in Table 6. The unadjusted crude model demonstrated that, compared with the group of ratio of family income to poverty less than 1, the group of ratio of family income to poverty equal to or greater than 3 was inversely associated with MS (OR:0.78, 95%CI:0.66–0.93). In model 1, after adjusting for demographic variables, this inverse correlation between the ratio of family income to poverty and MS remained stable (OR:0.71, 95%CI:0.59–0.86). However, in model 2 and 3, no significant inverse correlation between them was detected. The results in crude model, model 1 and 3 were ascertained by those from multiply-imputed datasets (Table S13). Nonetheless, the results in model 2 using multiply-imputed datasets indicated that there was an inverse correlation between the ratio of family income to poverty and MS (OR:0.78, 95%CI:0.63–0.98) (Tables S13).
Table 6
Association between the ratio of family income to poverty and metabolic syndrome in different models
| Crude model (N = 3972) | Model 1 (N = 3972) | Model 2 (N = 3793) | Model 3 (N = 3785) |
OR (95%CI) | P value | OR (95%CI) | P value | OR (95%CI) | P value | OR (95%CI) | P value |
The ratio of family income to poverty < 1 | Reference | | Reference | | Reference | | Reference | |
The ratio of family income to poverty ≥ 1,<3 | 1.02 (0.86–1.20) | 0.82 | 0.91 (0.76–1.09) | 0.30 | 0.93 (0.77–1.13) | 0.47 | 0.96 (0.78–1.20) | 0.74 |
The ratio of family income to poverty ≥ 3 | 0.78 (0.66–0.93) | < 0.01* | 0.71 (0.59–0.86) | < 0.01* | 0.82 (0.65–1.02) | 0.08 | 0.93 (0.72–1.19) | 0.56 |
P for trend | | < 0.01* | | < 0.01* | | 0.07 | | 0.56 |
OR: odds ratio; CI: confidence interval. |
Crude model adjust for none. |
Model 1 adjust for age, gender, and race/ethnicity. |
Model 2 adjust for all adjusted covariates in model 1 plus data release cycle, education level, marital status, energy intake, protein intake, carbohydrate intake, total sugars intake, dietary fiber intake, total fat intake, health insurance, average alcoholic drinks, current smoking, sedentary activity hours, total work activity minutes, and total recreational activity minutes. |
Model 3 adjust for all adjusted covariates in model 2 plus body mass index. |
* P value < 0.05. |