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Lee, Jeong, Park, Hwang, and Park: Association between Mothers’ Working Hours and Metabolic Syndrome in Children and Adolescents: Data from the Korea National Health and Nutrition Examination Survey, 2016–2020

Abstract

Background

Prevention and management of metabolic syndrome (MetS) during childhood are crucial. Recently, obesity among children and adolescents has increased with an increase in mothers’ working hours. The present study was conducted to determine the relationship between mothers’ working hours and MetS in their children.

Methods

Data from the 2016–2020 National Health and Nutrition Examination Survey were used, and 2,598 children and adolescents aged 10–18 years were included. Logistic regression analysis was conducted to confirm the association between MetS and mothers’ working hours for each risk factor. Linear regression analysis was conducted to confirm the association between mothers’ working hours and the number of risk factors for MetS.

Results

Abdominal obesity in children was higher when the mothers’ working hours were 53 hours or more (odds ratio [OR], 2.267; 95% confidence interval [CI], 1.21–4.25). In the trend analysis, the OR of children’s abdominal obesity increased significantly as mothers’ working hours increased (P-value <0.05). Additionally, sex-stratified analysis revealed a significant trend between maternal work hours and the presence of MetS in female children (P=0.016). The adjusted OR of the presence of MetS in female children with mothers working 53 hours or more weekly was 6.065 (95% CI, 1.954–18.822).

Conclusion

Mothers’ working hours were highly correlated with the risk of abdominal obesity in their children. The OR of the presence of MetS significantly increased in female children with mothers having longer working hours compared with those with stay-at-home mothers.

INTRODUCTION

Metabolic syndrome (MetS) is not a single disease but is characterized by a combination of risk factors, such as abdominal obesity, hyperglycemia, hypertension (HTN), and dyslipidemia. Diabetes mellitus (DM), HTN, and obesity directly affect blood vessels and synergistically increase the risk of coronary artery disease, stroke, and other vascular disorders [1,2].
The global prevalence of obesity in children and adolescents is rapidly increasing [3]. Childhood obesity increases the risk of chronic metabolic disorders such as HTN and DM, and those affected are more likely to develop MetS in adulthood [4]. Therefore, promptly detecting and managing obesity and MetS in the young is imperative.
However, research in this area is limited by ambiguous diagnostic criteria for MetS in children and adolescents. Many studies have adopted modified adult MetS diagnostic criteria, which differ and impede consistent analysis [5,6].
In 2007, the International Diabetes Federation (IDF) became the first major organization to introduce diagnostic criteria for MetS in the youth [7]. The IDF diagnostic criteria mandated the presence of abdominal obesity and the fulfillment of two out of four other criteria. A study conducted by Cho et al. [8] in 2009 based on these criteria revealed a MetS prevalence of 2.0%±0.5% among Korean youth in 2015.
Double-income households surged as more women entered the workforce. Mothers’ lifestyles significantly influence their children and adolescents. Working mothers may face challenges in precisely planning their children’s nutrition and activities. Brown et al. [9] in 2010 found a correlation between maternal work hours and difficulties in managing the diet and exercise of children, potentially contributing to increased obesity rates.
Prior research has established a link between obesity in the youth and maternal work hours. Kim et al. [10] in 2018 demonstrated that maternal work hours is correlated with the body mass index (BMI) and waist circumference (WC) of the child. Similarly, Lee and Kim [11] in 2013 found that girls aged 13–18 years had higher odds ratio (OR) of being overweight or obese when their mothers worked more than 60 hours weekly.
Although associations between childhood obesity and maternal working hours have been reported in previous studies, the relationship between MetS in youth and maternal working hours remains unexplored. Therefore, in this study, we aimed to investigate the relationship between maternal work hours and children’s susceptibility to MetS.

METHODS

1. Study Population

We used data from the 2016 to 2020 Korea National Health and Nutrition Examination Survey (KNHANES), incorporating population-based weighting. This study focused on individuals aged 10–18 years, excluding those with incomplete or missing data for study variables. Of the 39,738 participants, 3,554 were in the 10–18-year age group. After excluding those without data on maternal work hours and MetS-related variables, 2,611 remained. Subsequently, individuals with incomplete data for other study variables were excluded, leaving 2,598 participants for analysis. After adjusting for the Korean population, the weighted count was 3,540,581. The KNHANES was reviewed and approved by the Ethics Committee of the Korea Centers for Disease Control and Prevention. Informed consent was obtained from every participant at the time when the surveys were conducted. This study was approved by the Institutional Review Board (IRB) of Hanyang University Hospital (IRB approval no., 2022-06-024).

2. Definition of Metabolic Syndrome

In this study, MetS was defined in alignment with the 2007 IDF diagnostic criteria for children and adolescents [7]. Per the IDF diagnostic criteria for MetS in children and adolescents, abdominal obesity must be present. Furthermore, the diagnostic criteria vary depending on the age of the child or adolescent. For youth aged 10–15 years, MetS was diagnosed based on the presence of abdominal obesity and two of the remaining four components. Abdominal obesity for ages 10–15 years is ≥90th percentile of sex- and age-specific WC or the adult cutoff if lower. The four remaining MetS components are: (1) triglyceride (TG) ≥150 mg/dL, (2) HDL-C <40 mg/dL, (3) systolic blood pressure (SBP) ≥130 mm Hg or diastolic blood pressure (DBP) ≥85 mm Hg, and (4) fasting blood glucose (FBG) ≥100 mg/dL or diagnosis of type 2 DM (T2DM). In this study, sex- and age-specific WC percentiles for ages 10–15 years were obtained from the 2007 Korean Standard Growth Chart [12]. For ages ≥16 years, adult IDF MetS criteria apply, with abdominal obesity and two of four components defining MetS. For abdominal obesity diagnosis, ≥90 cm for males and ≥85 cm for females, as presented by the Korean Society for the Study of Obesity, were used [13]. The components for ages ≥16 years were: (1) TG ≥150 mg/dL or therapeutic agent use, (2) HDL-C <40 mg/dL (boys), HDL-C <50 mg/dL (girls) or therapeutic agent use, (3) SBP ≥130 mm Hg or DBP ≥85 mm Hg or antihypertensive agent use, and (4) FBG≥100 mg/dL or diagnosis of T2DM.

3. Maternal Work Hours

Maternal working hours were sourced from self-reported KNHANES data. Working hours were divided into five categories: 0, 1–19, 20–39, 40–52, and ≥53 hours. These hours were obtained based on legal work hours (40 h/wk) and maximum overtime (52 h/wk) as per the Labor Standards Act.

4. Other Factors

BMI was calculated by dividing the weight (kg) by height squared (m2). Obesity in children and adolescents was defined according to the 2007 Korean Standard Growth Chart for Children and Adolescents [12]. We defined participants below the 5th percentile for sex- and age-specific BMI as underweight, those between 85th–94.99th percentiles as overweight, and those below the 95th percentile as obese.
Family household income was categorized as low income, mediumlow, medium-high, or high income. Maternal educational level was divided into elementary school or lower, middle school, high school, and college or higher. In the KNHANES, maternal marital status was classified as either “married,” “separated,” “widowed,” or “divorced.” However, for the purpose of this study, only the categories of “married” and “unmarried” were used. Within the “unmarried” category, responses indicating “separated,” “widowed,” or “divorced” were included. Maternal smoking status was divided into “smoker” and “nonsmoker” based on reported current smoking status.

5. Statistical Analysis

Statistical analyses were performed using the IBM SPSS ver. 26.0 software (IBM Corp., Armonk, NY, USA), using techniques for complex samples. First, to describe participant characteristics, categorical variables were presented as frequencies and weighted percentages and analyzed using the chi-square test with Rao-Scott adjustment. Continuous variables were expressed as mean and standard error (SE) and analyzed using Student t-test. Next, the associations among maternal work hours, MetS, and individual MetS components were analyzed using logistic regression and presented as OR and 95% confidence intervals (CI). An initial unadjusted analysis was conducted by adjusting the analyses to incorporate participant characteristics. Furthermore, logistic regression analysis was used to explore the association between sex, age, household income, and maternal BMI, in addition to maternal work hours, and the presence of MetS. Stratified analysis of the association between maternal working hours and the presence of MetS according to sex, age, maternal BMI, and household income was performed using logistic regression analysis to calculate the OR and 95% CI.

RESULTS

Table 1 presents the characteristics of the participants. The prevalence of MetS was 3.1% (78 out of 2,598). Age (years) was lower in the normal group (mean±SE, 14.33±0.06) than in the MetS group (mean±SE, 14.41±0.31); however, this difference was not significant. Maternal age (years) was higher in the control group (mean±SE, 44.34±0.12) than in the MetS group (mean±SE, 43.40±0.56), but the difference was not significant. Maternal BMI (kg/m2) was significantly lower in the normal group (mean±SE, 23.25±0.10) than in the MetS group (mean±SE, 25.45±0.61) (P<0.001). There was no significant difference in sex between the two groups; the MetS group comprised 38 males (45.9%) and 40 females (54.1%), whereas the normal group comprised 1,341 males (52.5%) and 1,179 females (47.5%). There was a significant difference in BMI percentile (P<0.001); the MetS group comprised 0 underweight (<5th percentile; 0%), 0 normal (5th–84.99th percentile; 0%), 10 overweight (85th–94.99th percentile; 13.5%), and 68 obese (≥95th percentile; 86.5%) individuals. In contrast, the normal group comprised 201 underweight (<5th percentile; 8.1%), 1,834 normal (5th–84.99th percentile; 73.1%), 242 overweight (85th–94.99th percentile; 9.0%), and 243 obese (≥95th percentile; 9.8%) participants. Household income, maternal educational level, maternal marital status, and maternal smoking status did not differ significantly between the two groups. Maternal working hours were lower in the normal group (mean±SE, 26.33±0.61) than in the MetS group (mean±SE, 29.50±2.84); however, this difference was not significant. Maternal working hours were not significantly different between the groups (P=0.087). In the MetS group, work hours were none (n=20, 26.7%), 1–19 hours (n=3, 3.1%), 20–39 hours (n=20, 26.6%), 40–52 hours (n=26, 30.0%), and ≥53 hours (n=9, 13.7%). In the normal group, work hours were none (n=690, 26.0%), 1–19 hours (n=271, 11.6%), 20–39 hours (n=601, 24.6%), 40–52 hours (n=799, 31.2%), and ≥53 hours (n=159, 6.6%).
Table 2 presents the results of the logistic regression analysis of the association between maternal work hours, MetS, and its components. In the adjusted model, the OR for high WC was 2.267 (95% CI, 1.210–4.246) among those whose mothers worked ≥53 hours than for those whose mothers did not work at all (P<0.05). In the adjusted model, the OR for MetS was 0.291 (0.086–0.988) in the group with 1–19 working hours compared with none (P<0.05). The OR for MetS with ≥53 work hours was 1.93 (95% CI, 0.68–5.49) and was not significant. Notably, trend analysis indicated a significant increase in the OR for high WC with higher maternal work hours in both the unadjusted and adjusted models (P<0.05).
Table 3 shows the associations between sex, age, household income, maternal BMI, maternal working hours, and the presence of MetS. When the maternal BMI was 25 kg/m2 or higher, the adjusted OR of MetS in children was high at 2.739 (95% CI, 1.557–4.817). The probability of MetS was lower in male than in female; however, the difference was not statistically significant.
Table 4 shows the stratified analysis of the association between maternal working hours and the presence of MetS according to sex, age, maternal BMI, and household income. In female children, the OR of presence of MetS was high at 6.065 (95% CI, 1.954–18.822) when the maternal work hours were ≥53 hours with significant trend (P for trend=0.016).

DISCUSSION

In this study, we investigated the association between maternal work hours and MetS in children using the KNHANES data from 2016 to 2020. Our investigation revealed a 3.1% prevalence of MetS among the adolescents. Although the unadjusted model indicated a higher risk of MetS development in children whose mothers worked ≥53 hours weekly compared with stay-at-home mothers, this association vanished after accounting for multiple variables. Conversely, children whose mothers worked 1–19 hours a week exhibited reduced MetS risk after adjustment. Regarding specific MetS risk factors, the waist circumference of a child increased proportionally with maternal work hours compared with stay-at-home mothers. In female children, the OR for the presence of MetS tended to increase as maternal work hours increased, reaching 6.065 in children whose mothers worked ≥53 hours.
Using data from 2007 to 2018, Chae et al. [14] in 2021 reported that the prevalence of MetS increased from 1.7% to 2.2% among Korean children and adolescents. In our study, which incorporated 2016–2020 data, the prevalence reached 3.1%, likely owing to the inclusion of more recent data. Although the annual prevalence was not analyzed, the significance of our study lies in employing the IDF criteria on the latest data showing MetS growth in Korean youth, paralleling prior findings.
Mothers notably influenced their children’s behaviors, including diet, screen time, and exercise. Working mothers with less child-focused time may struggle to foster healthy habits, potentially leading to obesity and MetS. As a result, extended maternal working hours could potentially negatively affect the health of children in relation to lifestyle variables such as obesity and MetS [15,16].
Our results suggest that maternal working hours are associated with abdominal obesity in children. In line with this, North American and European research has revealed that increasing maternal work hours correspond to a child’s BMI elevation [17-19]. Gwozdz et al. [20] in 2013 investigated the association between maternal work hours and childhood obesity by measuring BMI, WC, and body fat mass and found a partial association in the European population. Cutting et al. [21] in 1999 and Heude et al. [22] in 2005 reported a stronger connection between maternal work hours and the average BMI and WC of a child in cases where mothers were overweight or obese.
For instance, Kim et al. [10] in 2018 demonstrated an association between maternal work hours and the average child BMI and WC. This association was particularly robust when mothers worked longer weekly hours, the children were overweight or obese, and the children frequently consumed EDNP (energy-dense, nutrient-poor) foods [10].
Working mothers spend less time with their children and have difficulty in overseeing their children’s daily activities [19,23]. Additionally, families tend to dine out or eat late at night when a mother works long work hours or works shifts [24]. A United Kingdom found that children of mothers who worked a regular, permanent job tended to watch TV more frequently [25]. Longer screen time (TV and computers) has been linked to a higher risk of obesity among children and adolescents [26]. Therefore, extended maternal working hours elevate the risk of obesity in children, consequently increasing the risk of MetS.
In the present study, female children with mothers working ≥53 hours weekly had a higher OR than those with stay-at-home mothers. Among all participants, children with mothers working 1–19 hours per week demonstrated a lower risk of MetS. This suggest the possible J-shape association between the maternal work hours and the presence of MetS in their children, but our sample size was limited owing to the fact that we enrolled children and adolescents who had participated in the KNHANES and defined overtime as exceeding the legal weekly work hours (53 hours). Future studies with larger sample sizes may yield more conclusive results. Kachi et al. [27] in 2021 observed that irregular maternal work schedules were significantly associated with child obesity only in the high-income group; whereas Miller and Han [28] in 2008 showed an association between mothers with irregular maternal work schedules and child obesity. The inconsistent findings might be due to variations in age distribution. Further research with a larger sample size and children whose mothers worked 1–19 hours a week is necessary.
In our analysis of the presence of MetS according to sex, age, household income, and maternal BMI, the association was more pronounced when the mother was overweight or obese. This finding is consistent with those of previous reports showing that maternal BMI is associated with MetS in children [29].
Our study had several limitations. A small MetS sample size was noted, as many participants were excluded because of missing data. Although there were 3,554 youth participants in the 5 years (2016–2020) in which the KNHANES data were obtained, many were excluded from this study owing to missing data for other study variables. Moreover, the lower prevalence of MetS in young individuals than that in adults has led to a restricted number of children and adolescents with MetS, thus constraining statistical analyses. Second, our reliance on cross-sectional KNHANES data further limited the scope. This dataset solely encompasses maternal work-hour information within the collection period, lacking insight into the duration of their employment or the age of their children when they began working. Another limitation is our inability to assess health-related habits among children and adolescents. Finally, the 2020 data may have been influenced by factors such as changes in eating habits and decreased physical activity owing to the impact of social distancing during coronavirus disease 2019 (COVID-19). However, the low prevalence of MetS in this study limits further analysis. It would be valuable to integrate data from 2020, 2021, and 2022 and compare the association between maternal working hours and the presence of MetS before and after COVID-19 as a new research topic.
In conclusion, as the mothers’ working hours increased, the likelihood of their children developing abdominal obesity increased. In female children, as the mothers’ working hours increased, the likelihood of developing MetS significantly increased. Extended maternal working hours can influence the dietary and physical activity patterns of children and adolescents. Given that the lifestyles of children and adolescents may contribute to the development of MetS, additional research and analyses are warranted.

Notes

CONFLICT OF INTEREST

No potential conflict of interest relevant to this article was reported.

Table 1.
Baseline characteristics of study participants
Characteristic Metabolic syndrome
Total P-value
Yes No
Total 78 (3.1) 2,520 (96.9) 2,598 (100.0)
Age (y) 14.41±0.31 14.33±0.06 14.33±0.06 0.808
Maternal age (y) 43.40±0.56 44.34±0.12 44.31±0.12 0.105
Maternal BMI (kg/m2) 25.45±0.61 23.25±0.10 23.32±0.10 <0.001
Sex male 38 (45.9) 1,341 (52.5) 1,379 (52.3) 0.278
BMI percentile group <0.001
 Underweight (<5) 0 201 (8.1) 201 (7.8)
 Normal (5–84.99) 0 1,834 (73.1) 1,834 (70.9)
 Overweight (85–94.99) 10 (13.5) 242 (9.0) 252 (9.1)
 Obese (≥95) 68 (86.5) 243 (9.8) 311 (12.2)
Household income, lowest quartile 7 (10.1) 191 (7.8) 198 (7.9) 0.763
Maternal education level 0.081
 ≤Elementary school 1 (1.0) 29 (1.1) 30 (1.1)
 Middle school 2 (3.9) 71 (2.9) 73 (2.9)
 High school 44 (57.2) 1,021 (41.4) 1,065 (41.9)
 ≥College 31 (37.8) 1,399 (54.6) 1,430 (54.1)
Maternal marital status 0.553
 Married 69 (90.7) 2,353 (92.6) 2,422 (92.5)
 Unmarried 9 (9.3) 167 (7.4) 176 (7.5)
Maternal smoking 0.320
 Yes 7 (7.7) 124 (4.9) 131 (5.0)
 No 71 (92.3) 2,396 (95.1) 2,467 (95.0)
Maternal working hour 29.50±2.84 26.33±0.61 26.43±0.61 0.266
Maternal working hour group 0.087
 None 20 (26.7) 690 (26.0) 710 (26.0)
 1–19 3 (3.1) 271 (11.6) 274 (11.3)
 20–39 20 (26.6) 601 (24.6) 621 (24.6)
 40–52 26 (30.0) 799 (31.2) 825 (31.2)
≥53 9 (13.7) 159 (6.6) 168 (6.9)

Values are presented as mean±standard error for continuous variable or number (weighted %) for categorical variable. P-values are from Rao-scott χ2 test or Student t-test.

BMI, body mass index.

Table 2.
Multiple logistic regression analysis of the odds ratios of the presence of MetS and each MetS components according to maternal working hours
Model Outcome Maternal working hour group
P for trend
None 1–19 20–39 40–52 ≥53
Unadjusted High WC 1 (Reference) 0.697 (0.377–1.288) 1.127 (0.729–1.742) 1.175 (0.763–1.810) 2.399 (1.352–4.257)* 0.012
High TG 1 (Reference) 0.679 (0.380–1.212) 0.940 (0.602–1.468) 0.881 (0.582–1.334) 1.641 (0.916–2.942) 0.124
Low HDL-C 1 (Reference) 1.170 (0.791–1.730) 1.090 (0.832–1.428) 1.157 (0.887–1.509) 1.328 (0.854–2.065) 0.707
High BP 1 (Reference) 0.387 (0.140–1.068) 1.497 (0.816–2.748) 1.357 (0.754–2.441) 0.942 (0.309–2.870) 0.142
High FPG 1 (Reference) 0.931 (0.579–1.495) 0.905 (0.629–1.301) 0.900 (0.642–1.261) 1.161 (0.638–2.114) 0.909
MetS 1 (Reference) 0.257 (0.076–0.864)* 1.055 (0.540–2.063) 0.935 (0.465–1.882) 2.004 (0.765–5.252) 0.083
Adjusted High WC 1 (Reference) 0.733 (0.379–1.419) 1.015 (0.650–1.586) 1.117 (0.720–1.731) 2.267 (1.210–4.246)* 0.041
High TG 1 (Reference) 0.722 (0.401–1.297) 0.946 (0.608–1.474) 0.921 (0.601–1.411) 1.634 (0.892–2.993) 0.192
Low HDL-C 1 (Reference) 1.132 (0.760–1.686) 1.147 (0.860–1.531) 1.211 (0.912–1.608) 1.362 (0.877–2.115) 0.585
High BP 1 (Reference) 0.362 (0.129–1.016) 1.225 (0.653–2.299) 1.154 (0.629–2.116) 0.798 (0.261–2.437) 0.262
High FPG 1 (Reference) 0.983 (0.610–1.583) 0.973 (0.672–1.409) 1.017 (0.716–1.445) 1.270 (0.690–2.339) 0.947
MetS 1 (Reference) 0.291 (0.086–0.988)* 1.025 (0.521–2.019) 0.931 (0.457–1.898) 1.927 (0.676–5.493) 0.156

Values are presented as odds ratio (95% confidence interval). Logistic regression analysis was done after adjustment for age, sex, household income, maternal age, body mass index, education level, marital status, and smoking.

MetS, metabolic syndrome; WC, waist circumference; TG, triglycerides; HDL-C, high density lipoprotein cholesterol; BP, blood pressure; FPG, fasting plasma glucose.

* P<0.05.

Table 3.
Logistic regression analysis of the presence of metabolic syndrome according to sex, age, household income, maternal BMI, and maternal working hours
Predictor Crude OR (95% CI) P-value Adjusted OR (95% CI) P-value
Sex
 Male 0.845 (0.503–1.418) 0.522 0.766 (0.473–1.242) 0.279
 Female 1 (Reference) 1 (Reference)
Age
 Prepubertal 0.560 (0.254–1.236) 0.151 0.494 (0.239–1.023) 0.058
 Postpubertal 1 (Reference) 1 (Reference)
Household income
 Low 1.092 (0.389–3.065) 0.867 1.441 (0.496–4.192) 0.502
 Mid-low 0.808 (0.382–1.709) 0.576 0.956 (0.455–2.005) 0.904
 Mid-high 1.207 (0.671–2.172) 0.529 1.268 (0.691–2.328) 0.443
 High 1 (Reference) 1 (Reference)
Maternal BMI (kg/m2)
 Normal (<23) 1 (Reference) 1 (Reference)
 Overweight (23–24.9) 1.353 (0.636–2.877) 0.432 1.427 (0.680–2.996) 0.346
 Obese (≥25) 2.704 (1.543–4.739) <0.001 2.739 (1.557–4.817) <0.001
Maternal working hours
 None 1 (Reference) 1 (Reference)
 1–19 0.257 (0.076–0.864) 0.025 0.291 (0.086–0.988) 0.028
 20–39 1.055 (0.540–2.063) 0.829 1.025 (0.521–2.019) 0.874
 40–52 0.935 (0.465–1.882) 0.688 0.931 (0.457–1.898) 0.850
 ≥53 2.004 (0.765–5.252) 0.253 1.927 (0.676–5.493) 0.157

Multiple linear regression was done after adjustment for other variables such as age, sex, household income, maternal age, maternal education level, marital status, maternal smoking, and maternal BMI.

BMI, body mass index; OR, odds ratio; CI, confidence interval.

Table 4.
Stratified analysis of the association between the maternal working hours and the presence of metabolic syndrome according to sex, age, maternal BMI, and household income
Variable Maternal working hour group
P for trend
None 1–19 20–39 40–52 ≥53
Sex
 Male 1 (Reference) 0.657 (0.190–2.268) 1.118 (0.398–3.139) 0.815 (0.305–2.176) 0.868 (0.202–3.730) 0.931
 Female 1 (Reference) 1.404 (0.532–3.706) 1.622 (0.640–4.110) 6.065 (1.954–18.822) 0.016
Age
 Prepubertal (F<11 y, M<13 y) 1 (Reference) 3.159 (0.491–20.313) 3.077 (0.552–17.158) 1.757 (0.312–9.889) 3.314 (0.329–33.347) 0.645
 Postpubertal (F≥11 y, M≥13 y) 1 (Reference) 0.107 (0.014–0.821) 0.930 (0.459–1.884) 0.892 (0.426–1.866) 1.873 (0.707–4.964) 0.124
Maternal BMI (kg/m2)
 <25 1 (Reference) 0.312 (0.072–1.359) 0.724 (0.247–2.127) 1.079 (0.423–2.751) 2.020 (0.575–7.088) 0.273
 ≥25 1 (Reference) 0.192 (0.023–1.631) 1.130 (0.415–3.080) 0.642 (0.219–1.888) 2.376 (0.486–11.614) 0.236
Household income
 Lower 1 (Reference) 1.062 (0.341–3.305) 0.476 (0.107–2.121) 2.381 (0.550–10.311) 0.372
 Upper 1 (Reference) 1.408 (0.621–3.196) 1.516 (0.614–3.744) 2.403 (0.609–9.474) 0.627

Values are presented as odds ratio (95% confidence interval). Logistic regression analysis was done after adjustment for age, sex, household income, maternal age, BMI, marital status, and smoking. Logistic regression analysis was done after adjustment for age, sex, household income, maternal age, BMI, marital status, and smoking.

Adjustment variables that overlap with each stratification variable were excluded from the analysis; for example, the age variable was excluded from the age stratification analysis. In the stratification analysis according to sex and household income, as the number of participants in the maternal working group 1–19 hours and metabolic syndrome was 0, they were excluded from the analysis.

BMI, body mass index.

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