Next Article in Journal
Adipose Tissue Aging and Metabolic Disorder, and the Impact of Nutritional Interventions
Next Article in Special Issue
Determinants of Longitudinal Changes in Cardiometabolic Risk in Adolescents with Overweight/Obesity: The EVASYON Study
Previous Article in Journal
Alterations in Intestinal Brush Border Membrane Functionality and Bacterial Populations Following Intra-Amniotic Administration (Gallus gallus) of Nicotinamide Riboside and Its Derivatives
Previous Article in Special Issue
The Effects of Increasing Fruit and Vegetable Intake in Children with Asthma on the Modulation of Innate Immune Responses
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Nutrients
Volume 14
Issue 15
10.3390/nu14153131
nutrients-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The Combined Effect of Birth Weight and Lifestyle on Clustered Cardio-Metabolic Risk Factors in Children and Adolescents: A National School-Based Cross-Sectional Survey

by
Di Shi
1,2,
Jiajia Dang
1,2,
Ning Ma
1,2,
Yunfei Liu
1,2,
Panliang Zhong
1,2,
Shan Cai
1,2,
Yinghua Ma
1,2,
Zhiyong Zou
1,2,
Yanhui Dong
1,2,*,
Yi Song
1,2,* and
Jun Ma
1,2
1
Institute of Child and Adolescent Health, School of Public Health, Peking University, Beijing 100191, China
2
National Health Commission Key Laboratory of Reproductive Health, Peking University, Beijing 100191, China
*
Authors to whom correspondence should be addressed.
Nutrients 2022, 14(15), 3131; https://doi.org/10.3390/nu14153131
Submission received: 7 July 2022 / Revised: 24 July 2022 / Accepted: 27 July 2022 / Published: 29 July 2022
(This article belongs to the Special Issue Dietary, Lifestyle and Children Health)
Browse Figure
Versions Notes

Abstract

:
Background: Due to the adverse effects of cardio-metabolic risk factors (CMRFs) in children and adolescents on their current and later life health, and the growing evidence that birth weight and lifestyle have on CMRFs, we aimed to estimate the combined effect of birth weight and lifestyle on clustered CMRFs in children and adolescents. Methods: We enrolled 11,509 participants aged 7–18 years old in a national school-based cross-sectional study in seven provinces in China in 2013. Information on CMRFs was collected through anthropometric measurements and blood sample testing. Information on birth weight, lifestyle and other basic information were investigated through children and adolescents’ as well as parents’ questionnaires. The generalized linear mixed model was applied to estimate the odd ratio (OR) and 95% confidence interval (95% CI) for the associations between CMRFs, clustered CMRFs and birth weight, lifestyle, and the combinations of birth weight and lifestyle. Results: Overall, the prevalence of clustered CMRFs was 3.6% in children and adolescents aged 7–18 years, higher in boys (4.4%) than girls (2.9%). The combination of LBW/ideal lifestyle (OR = 2.00, 95% CI: 1.07–3.72) was associated with higher risk of clustered CMRFs, as well as in adolescents aged 13–18 years and in boys. The combination of HBW/poor lifestyle (OR = 1.74, 95% CI: 1.13–2.68) was related to elevated risk of clustered CMRFs, especially in children aged 7–12 years. Conclusions: CMRFs in Chinese children and adolescents is concerning, ideal lifestyle could weaken the association of birth weight with clustered CMRFs, especially in younger age, indicating that programs to prevent abnormal birth weight or poor lifestyle or both among children and adolescents may reduce CMRFs in China.

1. Introduction

Cardiovascular diseases have remained a major health burden in adults [1], and most could stem from childhood and adolescence. Specifically, risk factors and behaviors that accelerated the process of cardiovascular diseases begin in childhood and even in the perinatal period [2]. Considering that some children and adolescents have already had identifiable cardio-metabolic risk factors (CMRFs) including hypertension, impaired fasting glucose, dyslipidemia, abdominal obesity at a very early age [3,4,5,6], plus clustered CMRFs, more than three CMRF items, may strongly predict the occurrence of cardiovascular diseases in adulthood [7,8,9]. Identifying it in vulnerable groups and risk factors that could alter CMRFs status might help reduce the prevalence of cardiovascular disease in the future.
There is evidence that low birth weight (LBW) children are more likely to develop cardio-metabolic diseases such as obesity, hypertension, and insulin resistance [10,11,12,13,14,15] while other studies have only found an association between LBW and hypertension [16,17]. Furthermore, studies have shown that high birth weight (HBW) mainly caused by maternal gestational diabetes may result in increased risk of hypertension, abdominal obesity, and insulin resistance during puberty [18,19,20,21] while other studies have not observed such an association [22,23]. The potential impact of HBW on CMRFs in children and adolescents remains insufficient and controversial. In addition, the age and sex differences between birth weight and CMRFs are unclear [24,25,26,27,28]. Therefore, the association between birth weight and clustered CMRFs and how the association varies across age span and different sexes is still unclear.
It is known that poor lifestyle, such as lack of physical activity [29], unhealthy eating habits [8], screen time [30], and sleep deprivation [31], are closely associated with cardio-metabolic risk factors. The high prevalence of abnormal birth weight occurring in China, which was 5.2% of LBW, 7.4% of HBW in 2013 [32], is coupled with the low prevalence of children and adolescents meeting recommended levels of healthy lifestyles with the economic and dietary pattern transitions [33]. Considering that both birth weight and lifestyle have independent effects on CMRFs, and that birth weight is unlikely to affect the lifestyle of children and adolescents [34,35], to what extent the combinations of birth weight and lifestyle act on the occurrence of CMRFs is scarce, especially in China. We hypothesized that birth weight and lifestyle might influence CMRFs, whereby the effects would be cumulative, ideal lifestyle might weaken the association of birth weight with clustered CMRFs, and such an association might vary between age and sex groups. A national school-based healthy lifestyles investigation conducted in seven provinces provided us an opportunity to assess the effects [36]. In this large representative sample of Chinese children and adolescents, we examined overall CMRFs, and aimed to examine the strength of birth weight and lifestyle on clustered CMRFs alone and as combined effects and the age and sex differences in association.

2. Materials and Methods

2.1. Study Design and Participants

The data was extracted from the baseline of a nationwide school-based multi-centered study of 7–18 years children and adolescents which was performed in seven provinces (Tianjin, Shanghai, Chongqing, Liaoning, Hunan, Ningxia, and Guangdong) in China in 2013. The multistage cluster random sampling method was used in this study and more detailed information is published elsewhere [36]. Briefly, three to four districts from each province were randomly selected at first. Second, 12–16 schools were chosen from each district. Third, two to three classes per grade of each school were selected randomly, after excluding children and adolescents with serious organic diseases or those who refused to sign informed consent. Children and adolescents in those classes were invited for blood sample collection. In total, 15,732 participants aged between 7–18 years with blood sample information, while cases with missing data on anthropometric measurement (n = 664), missing data on birth weight (n = 2782), missing all information about lifestyle factors (n = 777) were excluded. A total of 11,509 participants aged 7–18 years were included in the final analysis (Figure 1). The primary sample and the final sample showed no statistical difference in age, residence, and parental education level. However, the proportion of boys in the final sample was less than the primary sample (Table S1). This study has been approved by the Ethical Committee of Peking University (No. IRB0000105213034). All participants and their parents provided signed informed consent.

2.2. Study Measurements

2.2.1. Questionnaire Survey

Self-filled questionnaires for children and adolescents were used to obtain basic characteristics of sex, age, residence of children and adolescents, and their lifestyle, such as dietary consumption behaviors (fruits, vegetables, meat, and sugar-sweetened beverages), physical activity level and time, screen time and sleep duration. We collected the frequency and amount of each food consumed in the past week to calculate the dietary behavior of the children and adolescents. Parents self-administered questionnaires were used to obtain birth weight information for children and adolescents. Parents were asked to fill out the questionnaire based on the child’s birth certificate or clinical record. If the documents were not available, they were required recall the information about the child’s early life accurately. Parents were also requested to report the information on single-child status, delivery mode, delivery time, breastfeeding status of the children and adolescents, and parental education level and health behaviors. The questionnaires used in this study were all tested for logic and completeness. An expert panel then reviewed the questionnaires and considered them feasible and acceptable for children and adolescents and their parents.

2.2.2. Anthropometric Measurement and Blood Sample Detection

All children and adolescents received a standardization physical examination [36]. We used a portable stadiometer to measure the height of the participants standing upright without shoes, accurate to 0.1 cm. The participants wore lightweight underwear and weight was measured using a lever scale to the nearest 0.1 kg. Waist circumference was measured 1 cm above the navel, to 0.1 cm. The values of height, weight and waist circumference were the average value of two consecutive measurements.
We used a mercury sphygmomanometer, a stethoscope and an appropriate cuff to measure blood pressure. Children and adolescents were required to quietly rest for 5 min before starting the first measurement. A second measurement was performed after a 5-min interval. We calculated the average systolic and diastolic blood pressure as a result the final measurement of blood pressure. Venous blood was collected by a professional nurse after a 12-h fasting period. Serum glucose, total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) were measured using an automatic biochemical analyzer by a qualified biological testing company.
Strict quality control has been carried out in the measurement of this study. All the measuring instruments have been corrected before use, and the measuring personnel have been trained in the standard process. Five percent of the subjects were retested daily, and failure to meet the criteria was considered a failure of the day’s results and required full retesting.

2.3. Definition and Classification

2.3.1. Birth Weight

Normal birth weight (NBW) was considered to be children and adolescents with a birth weight from 2500 g to 3999 g. LBW was considered as birth weight less than 2499 g, HBW was considered as birth weight greater than 4000 g [37].

2.3.2. Lifestyle

Dietary consumption, physical activity, screen time and sleep duration were used to construct lifestyle scores. Healthy dietary consumption was defined as daily intake of fruits of ≥3 servings (one serving is about 100 g), vegetables of ≥4 servings (one serving is about 100 g), meat products of 2–3 servings (one serving is about 50 g), and weekly intake of sugar-sweetened beverage of <1 serving (one serving is about 250 mL) [38]. Adequate physical activity was defined as at least one hour of moderate to high intensity physical activity per day. Screen time was defined as less than two hours per day. Healthy sleep duration was defined as sleeping for more than 9 h per day [39]. Participants were grouped in the ideal lifestyle group if they had at least two or more healthy lifestyle factors, or into the poor lifestyle group if they had only one health lifestyle factor or fewer.

2.3.3. Combination of Birth Weight and Lifestyle

According to the combination of birth weight and lifestyle, the participants were divided into six groups: NBW/ideal lifestyle, NBW/poor lifestyle, LBW/ideal lifestyle, LBW/poor lifestyle, HBW/ideal lifestyle, HBW/poor lifestyle.

2.3.4. Clustered CMRFs

Hypertension was defined as blood pressure ≥ 95th percentile of age-, sex-, and height-specific references recommended by American Academy of Pediatrics in 2017 [40]. Impaired fasting glucose was defined as fasting glucose ≥ 5.6 mmol/L [41]. Dyslipidemia was defined according to the National Heart, Lung, and Blood Institute guidelines for cardiovascular health and risk reduction in children and adolescents: elevated TC was defined as ≥5.2 mmol/L, elevated LDL-C was defined as ≥3.4 mmol/L, abnormal HDL-C defined as ≤1.0 mmol/L, and elevated TG defined as ≥1.1 mmol/L for children below 9 years and ≥1.5 mmol/L for those aged 10 years or older [2]. Participants who met any one of the above criteria were divided into dyslipidemia. Abdominal obesity was defined as waist circumference ≥ 90th percentile of age–sex specific references recommended by National Health Commission of the People’s Republic of China in 2018 [42]. Clustered CMRFs was defined as three or more factors being satisfied among all four items of hypertension, impaired fasting glucose, dyslipidemia, and abdominal obesity [43].

2.3.5. Confounding Variables

In this study, children and adolescents were divided into two age groups: 7–12 years and 13–18 years. Sex, residence, delivery mode, single-child status, parental smoking, and parental education were considered as confounding variables. Parental smoking was defined as either of parents smoked in the past week. Parental education was divided into junior high school and below and senior high school and above, with the highest education level of parents (either mother or father) being considered [44]. Missing values of the covariates (<8.6%) were imputed by multiple imputation. Sensitivity analysis results indicated that the direction and value of the findings before and after imputation were essentially unchanged, suggesting that the imputation variables would not affect the main results.

2.4. Statistics Analysis

Categorical variables were shown as number (percentage). Chi-square tests and Fisher’s exact test were used to compare the differences of categorical data between sex groups and the differences of CMRFs and clustered CMRFs in different groups. ANOVA and Dunnett-t test were used to compare the differences between combinations of birth weight and lifestyles. A generalized linear mixed model was used to estimate the odds ratio (OR) and 95% confidence interval (CI) for association between CMRFs, clustered CMRFs and birth weight, lifestyle, and their combinations, using the school’s name as the random-effect term to control the cluster effect of schools in different groups. Significance level was accepted at two-tailed p < 0.05. All data were analyzed by SPSS 26.0 and R 4.1.1.

3. Results

3.1. The Characteristics of Participants

The basic demographic characteristics of the total 11,509 participants from different sex groups were shown in Table 1. Children and adolescents with LBW accounted for 3.8% and HBW accounted for 9.0%, while poor lifestyle accounted for 46.2%. Girls tended to have unhealthier lifestyle (p = 0.005). Among the six combinations, the proportion of the number from high to low was NBW/ideal lifestyle (47.2%), NBW/poor lifestyle (40.1%), HBW/ideal lifestyle (4.7%), HBW/poor lifestyle (4.3%), LBW/ideal lifestyle (2.0%), LBW/poor lifestyle (1.8%). The prevalence of clustered CMRFs in children and adolescents aged 7–18 years was 3.6%, while 4.4% in boys and 2.9% in girls, the sex difference was statistically significant (p < 0.001).

3.2. CMRFs and Clustered CMRFs in Different Groups

As shown in Table 2, CMRFs and clustered CMRFs were differed in age and sex groups (p < 0.05). The proportion of hypertension, dyslipidemia and abdominal obesity differed significantly between urban and rural children and adolescents (p < 0.001). Birth weight and the combinations of birth weight and lifestyle were associated with dyslipidemia and abdominal obesity (p < 0.05). Lifestyle was associated with hypertension and dyslipidemia (p < 0.05). Higher risk of clustered CMRFs was related to meat intake, sugar-sweetened beverages, and sleep duration (p < 0.05).

3.3. Multivariate Associations between CMRFs, Clustered CMRFs and Its Risk Factors

The associations between CMRFs, clustered CMRFs and birth weight in mixed effect models are shown in Table 3. After adjusting for confounding factors, LBW was related to elevated risk of hypertension in children and adolescents aged 13–18 years (OR = 1.81, 95% CI: 1.19–2.75) and boys (OR = 1.54, 95% CI: 1.06–2.25). HBW was associated with higher risk of abdominal obesity (OR = 1.66, 95% CI: 1.43–1.92) and was associated with clustered CMRFs in children and adolescents aged 7–12 years (OR = 1.70, 95% CI: 1.12–2.58) and girls (OR = 1.83, 95% CI: 1.19–3.08) (Figures S1–S3). Table 4 shows the associations between CMRFs, clustered CMRFs and lifestyle. A higher risk of hypertension was found in the inadequate sleep duration group in total model (OR = 1.18, 95% CI: 1.02–1.36), children aged 7–12 years (OR = 1.25, 95% CI: 1.07–1.45) and girls (OR = 1.28, 95% CI: 1.03–1.59). Abdominal obesity was associated with excessive screen time (OR = 1.17, 95% CI: 1.06–1.30) and inadequate sleep duration (OR = 1.14, 95% CI: 1.01–1.29) in children and adolescents aged 7–18 years. Poor lifestyle was related to abdominal obesity in children aged 7–12 years (OR = 1.15, 95% CI: 1.02–1.29). Unhealthy dietary consumption was associated with a higher risk of clustered CMRFs in boys (OR = 1.40, 95% CI: 1.01–1.94) and lower risk of clustered CMRFs in girls (OR = 0.68, 95% CI: 0.47–0.97) (Figures S4–S6).
As shown in Table 5, a higher risk of hypertension was found in LBW/ideal lifestyle group compared with NBW/ideal lifestyle in adolescents aged 13–18 years (OR = 2.43, 95% CI: 1.15–5.17) and boys (OR = 2.45, 95% CI: 1.25–4.79). Boys with NBW/poor lifestyle had a higher risk of impaired fasting glucose (OR = 1.65, 95% CI: 1.02–2.67). Abdominal obesity was significant higher in HBW/ideal lifestyle (OR = 1.54, 95% CI: 1.26–1.90) and HBW/poor lifestyle (OR = 1.89, 95% CI: 1.53–2.33) groups in children and adolescents aged 7–18 years. The effect value of the NBW/ideal lifestyle and NBW/poor lifestyle groups in abdominal obesity were statistically different from that of the HBW/poor lifestyle group (p < 0.05). In the age group of 7–12 years, abdominal obesity in HBW/ideal lifestyle and HBW/poor lifestyle groups was significantly different (p < 0.05). In the age group of 13–18 years, dyslipidemia in the LBW/ideal lifestyle and LBW/poor lifestyle groups was significantly different (p < 0.05). Higher risk of clustered CMRFs was found in the HBW/poor lifestyle group in total model (OR = 1.74, 95% CI: 1.13–2.68) and children aged 7–12 years (OR = 2.37, 95% CI: 1.39–4.05). It was also found in the LBW/ideal lifestyle group in total model (OR = 2.00, 95% CI: 1.07–3.72), adolescents aged 13–18 years (OR = 3.57, 95% CI: 1.23–10.37) and boys (OR = 2.78, 95% CI: 1.02–7.62) (Figures S7–S9).

4. Discussion

To the best of our knowledge, this was the first nationwide study addressing the effects of the combination of birth weight and lifestyle on clustered CMRFs and age and sex differences among children and adolescents in China. Our findings showed that the combined effect of LBW and ideal lifestyle was associated with clustered CMRFs, especially for those adolescents aged 13–18 years and boys. In addition, the significant combined effect of HBW and poor lifestyle on the clustered CMRFs in children and adolescents was found, especially for those children aged 7–12 years.
Previous studies have shown the association between CMRFs and birth weight in children and adolescents [10,11,12,13,14,15,45,46]. However, few studies focused on clustered CMRFs, possibly due to its low prevalence among children and adolescents. However, our study found that HBW had significant effects on clustered CMRFs in children aged 7–12 years and girls. In addition, the disease risk of the HBW group was higher than the corresponding NBW group regardless of lifestyle in children and adolescents aged 7–18 years, suggesting that HBW might be a health risk population that needs to be concentrated on, especially in younger children and girls. Currently it is believed that the cause of HBW is genetic factors [45] and uterine environmental factors. Maternal intermittent hyperglycemia leads to fetal hyperglycemia and fetal release of insulin, insulin-like growth factor and growth hormone which cause an increase in fetal fat deposition and weight gain [47,48,49]. Maternal abnormal lipid levels might also affect the placental nutrition supply, which prompts fetal overgrowth in early life [50]. Studies have shown that 16% of HBW children develop hypoglycemia within 24 h of life due to a placental glucose supply interruption [51,52]. All these factors might affect adipose tissue and pancreatic cell development, leading to increased BMI and impaired glucose metabolism, thus increasing the risk of obesity and metabolic complications in later life [53,54,55]. Furthermore, many studies support the evidence that LBW has an effect on hypertension in children and adolescents [16,17,56], which are consistent with our findings. For this phenomenon, the most reasonable hypothesis based on trial and clinical evidence is “congenital nephron reduction” [57,58], which means impaired intrauterine development can lead to impaired kidney development. A decrease in the number of nephrons could lead to compensatory hypertrophy, associated intraglomerular hypertension could then lead to glomerulosclerosis and hypertension within years [59,60]. In addition, HBW was confirmed to be associated with a higher risk of abdominal obesity in children and adolescents, which was consistent with the results of multiple other studies [61,62]. Early identification of abnormal birth weight children at health risk and the feasible prevention of CMRFs through lifestyle intervention are of great public health significance for the prevention of cardiovascular disease.
This study showed that the combination of LBW/ideal lifestyle was associated with elevated risk of clustered CMRFs. Due to the limitation of LBW sample size, this association was not found in LBW/poor lifestyle. However, it could be seen that the association between LBW and clustered CMRFs was mainly due to its association with hypertension, and studies have shown that the effect of LBW on hypertension can be weakened by a healthy lifestyle [22]. Therefore, maintaining a healthy lifestyle among low-birth-weight children and adolescents might also help to reduce their adverse health effects in current and later life. We also found that children and adolescents in the group of HBW/poor lifestyle had a greater risk than the group of HBW/ideal lifestyle, indicating the need to maintain a healthy lifestyle in children and adolescents with early-life adverse events to avoid cardiovascular metabolism-related diseases. Since the self-reported lifestyle of the latest week could not represent the health effect of long-term lifestyle in children and adolescents and other possible confounding variables, our study only found the association between poor lifestyle and abdominal obesity in children aged 7–12 years, the effect of sleep duration on abdominal obesity and hypertension and the role of screen time on abdominal obesity. Previous longitudinal studies have shown that healthy lifestyle was associated with CMRFs in children and adolescents. Accumulating evidence showed physical activity could affect C-reactive protein and had beneficial effects on the CMRFs in children [63]. In addition, a high-fat diet or deficiencies of specific nutrients could increase the risk of cardiovascular disease [64]. Furthermore, research has shown that screen time and poor sleep were important factors related to cardio-metabolic risk indicators in children [30,65]. However, considering that it is difficult for children and adolescents to achieve a completely healthy lifestyle, our research showed that as long as there were more than two of those healthy lifestyle factors, the risk of clustered CMRFs could be reduced, although significant results were only found in subgroups. This might be due to the small sample size of the subgroups. But prior studies have shown that ideal lifestyle had a positive effect on obesity in children and adolescents with abnormal birth weight [66,67], which could be referred by CMRFs [46].
We also found age and sex differences in clustered CMRFs. The prevalence of clustered CMRFs was higher in adolescents aged 13–18 years than those in children aged 7–12 years, higher in boys than in girls, which was consistent with previous findings [38,68,69,70]. However, a previous longitudinal study showed that birth weight had adverse effects on cardiometabolic metabolism in childhood, but further intensified in adolescence [55], which was not consistent with the conclusions of this study. We found the adverse effect of HBW on clustered CMRFs in the 7–12-year-old group and LBW on hypertension in the 13–18-year-old group, but we did not observe this association in other groups, which indicates that the earlier intervention is conducted, the bigger benefit obtained. Clustered CMRFs in adolescents might mean the track from childhood and the long-term accumulation of adverse outcomes of CMRFs, and simple lifestyle intervention might not work enough, and multiple and fortified measures are needed [27]. In addition, we observed an interesting phenomenon that unhealthy dietary consumption may lead to increased risk of clustered CMRFs in boys but may lead to decreased risk of clustered CMRFs in girls. This may be related to the different weight distribution between sex, with the proportion of overweight and obesity in boys is higher than that in girls, and the proportion of thinness in girls is higher than that in boys [71].
The sample size of this study was obtained from seven provinces in China and could be used as a representative national data. Furthermore, this study was the first one to date addressing the combined effects of birth weight and lifestyle on clustered CMRFs in children and adolescents, providing a strong rationale for promoting cardiovascular and metabolic health in children and adolescents. Moreover, the outcomes measured were based on blood samples, which were more accurate and objective. However, this study still has some limitations. Firstly, our data came from a cross-sectional survey, preventing us from making causal inferences. Secondly, the birth weight and lifestyle factors were collected according to questionnaires and recall bias might exist. However, as important information from a child’s early life, parents were unlikely to forget a child’s birth weight information, and children and adolescents surveyed could recall the lifestyle of the latest week clearly. Thirdly, residual confounding factors existed given that this study did not investigate maternal pre-pregnancy weight, pubertal development in children and adolescents, and other potentially unknown confounding information, which might have some impact on the result. Finally, according to the MCAR test, there were more boys with missing data, which might lead to the underestimation of the results due to higher clustered CMRFs prevalence in boys than girls.

5. Conclusions

In summary, we found that the combination of abnormal birth weight and poor lifestyle had a greater impact on clustered CMRFs in children and adolescents. This suggested that avoiding high birth weight through prenatal health education on nutritious diet and exercise guidelines and maintaining a healthy lifestyle during childhood and adolescents or both, such as sufficient physical activity, sleep duration, favorable diet consumption and proper screen time, might reduce the prevalence of CMRFs, especially in younger children.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/nu14153131/s1, Table S1: Missing completely at random (MCAR) test of the sample. Figure S1: Associations between hypertension, impaired fasting glucose and birth weight stratified by age and sex groups; Figure S2: Associations between dyslipidemia, abdominal obesity and birth weight stratified by age and sex groups; Figure S3: Associations between clustered CMRFs and birth weight stratified by age and sex groups; Figure S4: Associations between hypertension, impaired fasting glucose and lifestyle stratified by age and sex groups; Figure S5: Associations between dyslipidemia, abdominal obesity and lifestyle stratified by age and sex groups; Figure S6: Associations between clustered CMRFs and lifestyle stratified by age and sex groups; Figure S7: Associations between hypertension, impaired fasting glucose and combinations of birth weight and lifestyle stratified by age and sex groups; Figure S8: Associations between dyslipidemia, abdominal obesity and combinations of birth weight and lifestyle stratified by age and sex groups; Figure S9: Associations between clustered CMRFs and combinations of birth weight and lifestyle stratified by age and sex groups.

Author Contributions

Conceptualization, D.S. and Y.S.; data curation, J.D., N.M., Y.L., P.Z. and S.C.; formal analysis, D.S.; funding acquisition, Y.S.; investigation, Z.Z., Y.M. and J.M.; methodology, D.S., Y.D. and Y.S.; project administration, Y.D., Z.Z., Y.M., Y.S. and J.M.; resources, Y.D., Z.Z., Y.M., Y.S. and J.M.; software, D.S., J.D. and S.C.; supervision, Y.D., Y.S. and J.M.; validation, Y.D., Z.Z. and Y.S.; visualization, N.M., Y.L. and P.Z.; writing—original draft, D.S. and J.D.; writing—review and editing, N.M., Y.L., P.Z., S.C., Z.Z., Y.M., J.M., Y.D. and Y.S. All authors have read and agreed to the published version of the manuscript.

Funding

The present study was supported by the grants from Capital’s Funds for Health Improvement and Research (2022-1G-4251 to Y.S.), the Natural Science Foundation of Beijing (Grant No. 7222247 to Y.S.; NO. 7222244 to Y.D.) and the National Natural Science Foundation (82103865 to Y.D.).

Institutional Review Board Statement

This study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving students were approved by the Ethics Committee of Peking University (NO. IRB0000105213034).

Informed Consent Statement

All participants and their parents voluntarily signed informed consent forms.

Data Availability Statement

The data supporting the conclusions of this article can be made available from the corresponding author upon request.

Acknowledgments

The authors gratefully acknowledge all the schoolteachers and doctors for their assistance in data collection and all students and their guardians for participating in the study.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Wang, Z.; Du, A.; Liu, H.; Wang, Z.; Hu, J. Systematic Analysis of the Global, Regional and National Burden of Cardiovascular from 1990 to 2017. J. Epidemiol. Glob. Health 2022, 12, 92–103. [Google Scholar] [CrossRef] [PubMed]
  2. Lung Expert Panel on Integrated Guidelines for Cardiovascular Health and Risk Reduction in Children and Adolescents; National Heart, and Blood Institute. Expert panel on integrated guidelines for cardiovascular health and risk reduction in children and adolescents: Summary report. Pediatrics 2011, 128 (Suppl. 125), S213–S256. [Google Scholar] [CrossRef] [Green Version]
  3. Jackson, S.L.; Zhang, Z.; Wiltz, J.L.; Loustalot, F.; Ritchey, M.D.; Goodman, A.B.; Yang, Q. Hypertension among Youths—United States, 2001–2016. MMWR Morb. Mortal. Wkly Rep. 2018, 67, 758–762. [Google Scholar] [CrossRef] [PubMed]
  4. Kit, B.K.; Kuklina, E.; Carroll, M.D.; Ostchega, Y.; Freedman, D.S.; Ogden, C.L. Prevalence of and Trends in Dyslipidemia and Blood Pressure among US Children and Adolescents, 1999–2012. JAMA Pediatr. 2015, 169, 272–279. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. May, A.L.; Kuklina, E.V.; Yoon, P.W. Prevalence of Cardiovascular Disease Risk Factors among US Adolescents, 1999−2008. Pediatrics 2012, 129, 1035–1041. [Google Scholar] [CrossRef] [Green Version]
  6. Perak, A.M.; Ning, H.; Kit, B.K.; de Ferranti, S.D.; Van Horn, L.V.; Wilkins, J.T.; Lloyd-Jones, D.M. Trends in Levels of Lipids and Apolipoprotein B in US Youths Aged 6 to 19 Years, 1999–2016. JAMA 2019, 321, 1895–1905. [Google Scholar] [CrossRef] [Green Version]
  7. Berenson, G.S.; Srinivasan, S.R.; Bao, W.; Newman, W.P., 3rd; Tracy, R.E.; Wattigney, W.A. Association between multiple cardiovascular risk factors and atherosclerosis in children and young adults. The Bogalusa Heart Study. N. Engl. J. Med. 1998, 338, 1650–1656. [Google Scholar] [CrossRef]
  8. Juonala, M.; Viikari, J.S.; Kähönen, M.; Taittonen, L.; Laitinen, T.; Hutri-Kähönen, N.; Lehtimäki, T.; Jula, A.; Pietikäinen, M.; Jokinen, E.; et al. Life-time risk factors and progression of carotid atherosclerosis in young adults: The Cardiovascular Risk in Young Finns study. Eur. Heart J. 2010, 31, 1745–1751. [Google Scholar] [CrossRef] [Green Version]
  9. Li, S.; Chen, W.; Srinivasan, S.R.; Bond, M.G.; Tang, R.; Urbina, E.M.; Berenson, G.S. Childhood cardiovascular risk factors and carotid vascular changes in adulthood: The Bogalusa Heart Study. JAMA 2003, 290, 2271–2276. [Google Scholar] [CrossRef]
  10. Phillips, D.I.W.; Barker, D.J.P.; Hales, C.N.; Hirst, S.; Osmond, C. Thinness at birth and insulin resistance in adult life. Diabetologia 1994, 37, 150–154. [Google Scholar] [CrossRef] [Green Version]
  11. Davies, A.A.; Smith, G.D.; May, M.T.; Ben-Shlomo, Y. Association between Birth Weight and Blood Pressure Is Robust, Amplifies with Age, and May Be Underestimated. Hypertension 2006, 48, 431–436. [Google Scholar] [CrossRef] [Green Version]
  12. Barker, D.J.P.; Hales, C.N.; Fall, C.H.D.; Osmond, C.; Phipps, K.; Clark, P.M.S. Type 2 (non-insulin-dependent) diabetes mellitus, hypertension and hyperlipidaemia (syndrome X): Relation to reduced fetal growth. Diabetologia 1993, 36, 62–67. [Google Scholar] [CrossRef] [Green Version]
  13. Stettler, N.; Stallings, V.A.; Troxel, A.B.; Zhao, J.; Schinnar, R.; Nelson, S.E.; Ziegler, E.E.; Strom, B.L. Weight gain in the first week of life and overweight in adulthood: A cohort study of European American subjects fed infant formula. Circulation 2005, 111, 1897–1903. [Google Scholar] [CrossRef] [Green Version]
  14. Vohr, B.R.; Allan, W.; Katz, K.H.; Schneider, K.C.; Ment, L.R. Early predictors of hypertension in prematurely born adolescents. Acta Paediatr. 2010, 99, 1812–1818. [Google Scholar] [CrossRef]
  15. Singhal, A.; Lucas, A. Early origins of cardiovascular disease: Is there a unifying hypothesis? Lancet 2004, 363, 1642–1645. [Google Scholar] [CrossRef]
  16. Fonseca, M.J.; Severo, M.; A Lawlor, D.; Barros, H.; Santos, A. Direct and BMI-mediated effect of birthweight on childhood cardio-metabolic health—A birth cohort study. Int. J. Obes. 2019, 43, 1923–1931. [Google Scholar] [CrossRef] [Green Version]
  17. Hovi, P.; Vohr, B.; Ment, L.R.; Doyle, L.W.; McGarvey, L.; Morrison, K.M.; Evensen, K.A.; van der Pal, S.; Grunau, R.E.; Brubakk, A.M.; et al. Blood Pressure in Young Adults Born at Very Low Birth Weight: Adults Born Preterm International Collaboration. Hypertension 2016, 68, 880–887. [Google Scholar] [CrossRef]
  18. Sørensen, H.T.; Sabroe, S.; Rothman, K.J.; Gillman, M.; Fischer, P.; Sørensen, T.I. Relation between weight and length at birth and body mass index in young adulthood: Cohort study. BMJ 1997, 315, 1137. [Google Scholar] [CrossRef]
  19. Seidman, D.S.; Laor, A.; Gale, R.; Stevenson, D.K.; Danon, Y.L. A longitudinal study of birth weight and being overweight in late adolescence. Am. J. Dis. Child. 1991, 145, 782–785. [Google Scholar] [CrossRef]
  20. Darendeliler, F.; Poyrazoglu, S.; Sancakli, O.; Bas, F.; Gokcay, G.; Aki, S.; Eskiyurt, N. Adiponectin is an indicator of insulin resistance in non-obese prepubertal children born large for gestational age (LGA) and is affected by birth weight. Clin. Endocrinol. 2009, 70, 710–716. [Google Scholar] [CrossRef]
  21. Giapros, V.; Evagelidou, E.; Challa, A.; Kiortsis, D.; Drougia, A.; Andronikou, S. Serum adiponectin and leptin levels and insulin resistance in children born large for gestational age are affected by the degree of overweight. Clin. Endocrinol. 2007, 66, 353–359. [Google Scholar]
  22. Dong, B.; Dong, Y.-H.; Yang, Z.-G.; Wang, X.-J.; Zou, Z.-Y.; Wang, Z.; Ma, J. Healthy Body Weight may Modify Effect of Abnormal Birth Weight on Metabolic Syndrome in Adolescents. Obesity 2019, 27, 462–469. [Google Scholar] [CrossRef]
  23. Fan, J.; Shi, X.; Jia, X.; Wang, Y.; Zhao, Y.; Bao, J.; Zhang, H.; Yang, Y. Birth weight, childhood obesity and risk of hypertension: A Mendelian randomization study. J. Hypertens. 2021, 39, 1876–1883. [Google Scholar] [CrossRef]
  24. Hirschler, V.; Edit, S.; Miorin, C.; Guntsche, Z.; Maldonado, N.; Garcia, C.; Lapertosa, S.; Gonzalez, C.D.; Calzia, V.; Di Firma, R.; et al. Association between High Birth Weight and Later Central Obesity in 9-Year-Old Schoolchildren. Metab. Syndr. Relat. Disord. 2021, 19, 213–217. [Google Scholar] [CrossRef]
  25. Kuciene, R.; Dulskiene, V.; Medzioniene, J. Associations between high birth weight, being large for gestational age, and high blood pressure among adolescents: A cross-sectional study. Eur. J. Nutr. 2018, 57, 373–381. [Google Scholar] [CrossRef] [Green Version]
  26. Zhang, Y.; Li, H.; Liu, S.-J.; Fu, G.-J.; Zhao, Y.; Xie, Y.J.; Zhang, Y.; Wang, Y.-X. The associations of high birth weight with blood pressure and hypertension in later life: A systematic review and meta-analysis. Hypertens. Res. 2013, 36, 725–735. [Google Scholar] [CrossRef]
  27. Pocobelli, G.; Dublin, S.; Enquobahrie, D.A.; Mueller, B.A. Birth Weight and Birth Weight for Gestational Age in Relation to Risk of Hospitalization with Primary Hypertension in Children and Young Adults. Matern. Child Health J. 2016, 20, 1415–1423. [Google Scholar] [CrossRef] [Green Version]
  28. Luetic, G.G.; Menichini, M.L.; Deri, N.; Steinberg, J.; Carrá, A.; Cristiano, E.; Patrucco, L.; Curbelo, M.C.; Rojas, J.I. High birth weight and risk of multiple sclerosis: A multicentre study in Argentina. Mult. Scler. Relat. Disord. 2021, 47, 102628. [Google Scholar] [CrossRef]
  29. Ekelund, U.; Luan, J.; Sherar, L.B.; Esliger, D.W.; Griew, P.; Cooper, A. Moderate to vigorous physical activity and sedentary time and cardiometabolic risk factors in children and adolescents. JAMA 2012, 307, 704–712. [Google Scholar] [CrossRef] [Green Version]
  30. Danielsen, Y.; Júlíusson, P.; Nordhus, I.; Kleiven, M.; Meltzer, H.; Olsson, S.; Pallesen, S. The relationship between life-style and cardio-metabolic risk indicators in children: The importance of screen time. Acta Paediatr. 2010, 100, 253–259. [Google Scholar] [CrossRef]
  31. Owens, J.A.; Weiss, M.R. Insufficient sleep in adolescents: Causes and consequences. Minerva Pediatr. 2017, 69, 326–336. [Google Scholar] [CrossRef]
  32. Shen, L.; Wang, J.; Duan, Y.; Yang, Z. Prevalence of low birth weight and macrosomia estimates based on heaping adjustment method in China. Sci. Rep. 2021, 11, 15016. [Google Scholar] [CrossRef] [PubMed]
  33. Chen, P.; Wang, D.; Shen, H.; Yu, L.; Gao, Q.; Mao, L.; Jiang, F.; Luo, Y.; Xie, M.; Zhang, Y.; et al. Physical activity and health in Chinese children and adolescents: Expert consensus statement (2020). Br. J. Sports Med. 2020, 54, 1321–1331. [Google Scholar] [CrossRef] [PubMed]
  34. Hildebrand, M.; Øglund, G.P.; Wells, J.; Ekelund, U. Prenatal, birth and early life predictors of sedentary behavior in young people: A systematic review. Int. J. Behav. Nutr. Phys. Act. 2016, 13, 63. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Ridgway, C.; Brage, S.; Sharp, S.J.; Corder, K.; Westgate, K.; van Sluijs, E.; Goodyer, I.M.; Hallal, P.C.; Anderssen, S.A.; Sardinha, L.; et al. Does Birth Weight Influence Physical Activity in Youth? A Combined Analysis of Four Studies Using Objectively Measured Physical Activity. PLoS ONE 2011, 6, e16125. [Google Scholar] [CrossRef] [PubMed]
  36. Chen, Y.; Ma, L.; Ma, Y.; Wang, H.; Luo, J.; Zhang, X.; Luo, C.; Wang, H.; Zhao, H.; Pan, D.; et al. A national school-based health lifestyles interventions among Chinese children and adolescents against obesity: Rationale, design and methodology of a randomized controlled trial in China. BMC Public Health 2015, 15, 210. [Google Scholar] [CrossRef] [Green Version]
  37. Barth, J.R.; William, H.; Jackson, R. Macrosomia: ACOG Practice Bulletin, Number 216. Obstet. Gynecol. 2020, 135, e118–e135. [Google Scholar]
  38. Dang, J.; Chen, T.; Ma, N.; Liu, Y.; Zhong, P.; Shi, D.; Dong, Y.; Zou, Z.; Ma, Y.; Song, Y.; et al. Associations between Breastfeeding Duration and Obesity Phenotypes and the Offsetting Effect of a Healthy Lifestyle. Nutrients 2022, 14, 1999. [Google Scholar] [CrossRef]
  39. Hirshkowitz, M.; Whiton, K.; Albert, S.M.; Alessi, C.; Bruni, O.; DonCarlos, L.; Hazen, N.; Herman, J.; Adams Hillard, P.J.; Katz, E.S.; et al. National Sleep Foundation’s updated sleep duration recommendations: Final report. Sleep Health. 2015, 1, 233–243. [Google Scholar] [CrossRef]
  40. Flynn, J.T.; Kaelber, D.C.; Baker-Smith, C.M.; Blowey, D.; Carroll, A.E.; Daniels, S.R.; de Ferranti, S.D.; Dionne, J.M.; Falkner, B.; Flinn, S.K.; et al. Clinical Practice Guideline for Screening and Management of High Blood Pressure in Children and Adolescents. Pediatrics 2017, 140, e20171904. [Google Scholar] [CrossRef] [Green Version]
  41. Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Report of the expert committee on the diagnosis and classification of diabetes mellitus. Diabetes Care 2003, 26 (Suppl. 21), S5–S20. [Google Scholar] [CrossRef] [Green Version]
  42. National Health Commission of the People’s Republic of China. Boundary Value of High Waist Circumference Screening for Children and Adolescents Aged 7–18 Years. Available online: http://www.nhc.gov.cn/wjw/pqt/201807/417de6982ab8493b91aba925b51a8a19.shtml (accessed on 13 June 2018).
  43. Dou, Y.; Jiang, Y.; Yan, Y.; Chen, H.; Zhang, Y.; Chen, X.; Wang, Y.; Cheng, H.; Zhao, X.; Hou, D.; et al. Waist-to-height ratio as a screening tool for cardiometabolic risk in children and adolescents: A nationwide cross-sectional study in China. BMJ Open 2020, 10, e037040. [Google Scholar] [CrossRef]
  44. Fernández-Alvira, J.M.; Mouratidou, T.; Bammann, K.; Hebestreit, A.; Barba, G.; Sieri, S.; Reisch, L.; Eiben, G.; Hadjigeorgiou, C.; Kovacs, E.; et al. Parental education and frequency of food consumption in European children: The IDEFICS study. Public Health Nutr. 2013, 16, 487–498. [Google Scholar] [CrossRef] [Green Version]
  45. Renom Espineira, A.; Fernandes-Rosa, F.L.; Bueno, A.C.; de Souza, R.M.; Moreira, A.C.; de Castro, M.; Barbieri, M.A.; Bettiol, H.; Antonini, S.R. Postnatal growth and cardiometabolic profile in young adults born large for gestational age. Clin. Endocrinol. 2011, 75, 335–341. [Google Scholar] [CrossRef]
  46. Araújo, J.; Severo, M.; Barros, H.; Mishra, G.D.; Guimarães, J.T.; Ramos, E. Developmental trajectories of adiposity from birth until early adulthood and association with cardiometabolic risk factors. Int. J. Obes. 2015, 39, 1443–1449. [Google Scholar] [CrossRef]
  47. Ornoy, A. Growth and neurodevelopmental outcome of children born to mothers with pregestational and gestational diabetes. Pediatr Endocrinol. Rev. 2005, 3, 104–113. [Google Scholar]
  48. Ornoy, A.; Wolf, A.; Ratzon, N.; Greenbaum, C.; Dulitzky, M. Neurodevelopmental outcome at early school age of children born to mothers with gestational diabetes. Arch. Dis. Child Fetal Neonatal Ed. 1999, 81, F10–F14. [Google Scholar] [CrossRef] [Green Version]
  49. Silverman, B.L.; Rizzo, T.A.; Cho, N.H.; Metzger, B.E. Long-term effects of the intrauterine environment. The Northwestern University Diabetes in Pregnancy Center. Diabetes Care 1998, 21, B142. [Google Scholar]
  50. DeVader, S.R.; Neeley, H.L.; Myles, T.D.; Leet, T.L. Evaluation of gestational weight gain guidelines for women with normal prepregnancy body mass index. Obstet. Gynecol. 2007, 110, 745–751. [Google Scholar] [CrossRef]
  51. Groenendaal, F.; Elferink-Stinkens, P.M.; Registry, T.N.P. Hypoglycaemia and seizures in large-for-gestational-age (LGA) full-term neonates. Acta Paediatr. 2007, 95, 874–876. [Google Scholar] [CrossRef]
  52. Schaefer-Graf, U.M.; Rossi, R.; Bührer, C.; Siebert, G.; Kjos, S.L.; Dudenhausen, J.W.; Vetter, K. Rate and risk factors of hypoglycemia in large-for-gestational-age newborn infants of nondiabetic mothers. Am. J. Obstet. Gynecol. 2002, 187, 913–917. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Touger, L.; Looker, H.C.; Krakoff, J.; Lindsay, R.S.; Cook, V.; Knowler, W.C. Early growth in offspring of diabetic mothers. Diabetes Care 2005, 28, 585–589. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Clausen, T.D.; Mathiesen, E.R.; Hansen, T.; Pedersen, O.; Jensen, D.M.; Lauenborg, J.; Schmidt, L.; Damm, P. Overweight and the Metabolic Syndrome in Adult Offspring of Women with Diet-Treated Gestational Diabetes Mellitus or Type 1 Diabetes. J. Clin. Endocrinol. Metab. 2009, 94, 2464–2470. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  55. Chiavaroli, V.; Marcovecchio, M.L.; De Giorgis, T.; Diesse, L.; Chiarelli, F.; Mohn, A. Progression of Cardio-Metabolic Risk Factors in Subjects Born Small and Large for Gestational Age. PLoS ONE 2014, 9, e104278. [Google Scholar] [CrossRef] [Green Version]
  56. Raaijmakers, A.; Zhang, Z.Y.; Claessens, J.; Cauwenberghs, N.; van Tienoven, T.P.; Wei, F.F.; Jacobs, L.; Levtchenko, E.; Pauwels, S.; Kuznetsova, T.; et al. Does Extremely Low Birth Weight Predispose to Low-Renin Hypertension? Hypertension 2017, 69, 443–449. [Google Scholar] [CrossRef]
  57. Hoy, W.E.; Hughson, M.D.; Bertram, J.F.; Douglas-Denton, R.; Amann, K. Nephron number, hypertension, renal disease, and renal failure. J. Am. Soc. Nephrol. 2005, 16, 2557–2564. [Google Scholar] [CrossRef]
  58. Samuel, T.; Hoy, W.E.; Douglas-Denton, R.; Hughson, M.D.; Bertram, J.F. Determinants of Glomerular Volume in Different Cortical Zones of the Human Kidney. J. Am. Soc. Nephrol. 2005, 16, 3102–3109. [Google Scholar] [CrossRef] [Green Version]
  59. Lopes, A.A.S.; Port, F.K. The low birth weight hypothesis as a plausible explanation for the black/white differences in hypertension, non-insulin-dependent diabetes, and end-stage renal disease. Am. J. Kidney Dis. 1995, 25, 350–356. [Google Scholar] [CrossRef]
  60. Eriksson, J.; Forsén, T.; Tuomilehto, J.; Osmond, C.; Barker, D. Fetal and Childhood Growth and Hypertension in Adult Life. Hypertension 2000, 36, 790–794. [Google Scholar] [CrossRef] [Green Version]
  61. Gomes, K.B.A.; Leal, V.S.; Oliveira, J.S.; Pereira, C.G.D.S.; Gonçalves, F.C.L.D.S.P.; Andrade, I.S.; Eickmann, S.H.; Lira, P.I.C.; Lima, M.C. Birth weight and overweight in adolescents: The erica project in the city of recife, pernambuco. Rev. Paul. Pediatr. 2021, 39, e2019380. [Google Scholar] [CrossRef]
  62. Skilton, M.R.; Siitonen, N.; Würtz, P.; Viikari, J.S.; Juonala, M.; Seppälä, I.; Laitinen, T.; Lehtimäki, T.; Taittonen, L.; Kähönen, M.; et al. High birth weight is associated with obesity and increased carotid wall thickness in young adults: The cardiovascular risk in young Finns study. Arterioscler. Thromb Vasc. Biol. 2014, 34, 1064–1068. [Google Scholar] [CrossRef] [Green Version]
  63. Andersen, L.B.; Riddoch, C.; Kriemler, S.; Hills, A.P. Physical activity and cardiovascular risk factors in children. Br. J. Sports Med. 2011, 45, 871–876. [Google Scholar] [CrossRef] [Green Version]
  64. Kalea, A.Z.; Drosatos, K.; Buxton, J.L. Nutriepigenetics and cardiovascular disease. Curr. Opin. Clin. Nutr. Metab. Care 2018, 21, 252–259. [Google Scholar] [CrossRef]
  65. Lissak, G. Adverse physiological and psychological effects of screen time on children and adolescents: Literature review and case study. Environ. Res. 2018, 164, 149–157. [Google Scholar] [CrossRef]
  66. Qiao, Y.; Zhang, T.; Liu, H.; Katzmarzyk, P.T.; Chaput, J.-P.; Fogelholm, M.; Johnson, W.D.; Kuriyan, R.; Kurpad, A.; Lambert, E.; et al. Joint association of birth weight and physical activity/sedentary behavior with obesity in children ages 9–11 years from 12 countries. Obesity 2017, 25, 1091–1097. [Google Scholar] [CrossRef] [Green Version]
  67. Deng, J.R.; Tan, W.Q.; Yang, S.Y.; Ao, L.P.; Liang, J.P.; Li, L.X.; Gao, Y.H.; Yang, Y.; Liu, L. High birth weight and its interaction with physical activity influence the risk of obesity in early school-aged children. World J. Pediatr. 2020, 16, 385–392. [Google Scholar] [CrossRef]
  68. Baker, J.L.; Olsen, L.W.; Sørensen, T.I. Childhood body mass index and the risk of coronary heart disease in adulthood. Ugeskr Laeger 2008, 170, 2434–2437. [Google Scholar] [CrossRef] [Green Version]
  69. Skinner, A.C.; Perrin, E.M.; Moss, L.A.; Skelton, J.A. Cardiometabolic Risks and Severity of Obesity in Children and Young Adults. N. Engl. J. Med. 2015, 373, 1307–1317. [Google Scholar] [CrossRef]
  70. Liu, C.; Wu, S.; Pan, X. Clustering of cardio-metabolic risk factors and pre-diabetes among U.S. adolescents. Sci. Rep. 2021, 11, 5015. [Google Scholar] [CrossRef]
  71. Wang, L.; Zhou, B.; Zhao, Z.; Yang, L.; Zhang, M.; Jiang, Y.; Li, Y.; Zhou, M.; Wang, L.; Huang, Z.; et al. Body-mass index and obesity in urban and rural China: Findings from consecutive nationally representative surveys during 2004–18. Lancet 2021, 398, 53–63. [Google Scholar] [CrossRef]
Nutrients 14 03131 g001 550
Figure 1. Flow diagram of participants’ inclusion.
Figure 1. Flow diagram of participants’ inclusion.
Nutrients 14 03131 g001
Table
Table 1. Demographic characteristics of eligible children and adolescents and their parents stratified by sex.
Table 1. Demographic characteristics of eligible children and adolescents and their parents stratified by sex.
VariablesTotalBoysGirlsχ2/Fp
Age 8.9080.003
7–12 years70903609 (63.0)3481 (60.3)
13–18 years44192123 (37.0)2296 (39.7)
Residence 0.9360.333
Rural45232278 (39.7)2245 (38.9)
Urban69863454 (60.3)3532 (61.1)
Single-child status 82.888<0.001
Yes77714099 (71.5)3672 (63.6)
No37381633 (28.5)2105 (36.4)
Parental education level 3.8410.050
Junior high school and below44992292 (40.0)2207 (38.2)
Senior high school and above70103440 (60.0)3570 (61.8)
Birth weight 58.484<0.001
NBW10,0444913 (85.7)5131 (88.8)
LBW432191 (3.3)241 (4.2)
HBW1033628 (11.0)405 (7.0)
Fruits intake 1.4650.226
Inadequate10,4825202 (90.8)5280 (91.4)
Adequate1027530 (9.2)497 (8.6)
Vegetables intake 7.8650.005
Inadequate10,5335204 (90.8)5329 (92.2)
Adequate976528 (9.2)448 (7.8)
Meat intake 113.261<0.001
Improper95434538 (79.2)5005 (86.6)
proper19661194 (20.8)772 (13.4)
Sugar-sweetened beverage 168.492<0.001
Excessive1316877 (15.3)439 (7.6)
Proper10,1934855 (84.7)5338 (92.4)
Dietary consumption 30.410<0.001
Unhealthy86674189 (73.1)4478 (77.5)
Healthy28421543 (26.9)1299 (22.5)
Physical activity 33.352<0.001
Inadequate60312849 (49.7)3182 (55.1)
Adequate54782883 (50.3)2595 (44.9)
Screen time 70.682<0.001
Excessive32121802 (31.4)1410 (24.4)
Proper82973930 (68.6)4367 (75.6)
Sleep duration 1.9510.163
Inadequate91964550 (79.4)4646 (80.4)
Adequate23131182 (20.6)1131 (19.6)
Lifestyle 7.8880.005
Poor lifestyle53172573 (44.9)2744 (47.5)
Ideal lifestyle61923159 (55.1)3033 (52.5)
Blood pressure 55.597<0.001
Normal blood pressure94784568 (79.7)4910 (85.0)
Hypertension20311164 (20.3)867 (15.0)
Fasting glucoses 39.274<0.001
Normal fasting glucoses11,2855574 (97.2)5711 (98.9)
Impaired fasting glucose224158 (2.8)66 (1.1)
Blood lipids 8.6050.003
Normal blood lipids82224166 (72.7)4056 (70.2)
Dyslipidemia32871566 (27.3)1721 (29.8)
Waist circumference 4.4080.036
Normal waist circumference88704465 (77.9)4405 (76.3)
Abdominal obesity26391267 (22.1)1372 (23.7)
CMRFs scores <0.001
057252868 (50.0)2857 (49.4)
138151825 (31.8)1990 (34.5)
21553791 (13.8)762 (13.2)
3406244 (4.3)162 (2.8)
4104 (0.1)6 (0.1)
Combinations of birth weight and lifestyle a 13.794<0.001
NBW/Ideal lifestyle54302720 (47.5)2710 (46.9) Ref
NBW/Poor lifestyle46142193 (38.3)2421 (41.9) 0.050
LBW/Ideal lifestyle225109 (1.9)116 (2.0) 0.992
LBW/Poor lifestyle20782 (1.4)125 (2.2) 0.015
HBW/Ideal lifestyle537330 (5.8)207 (3.6) <0.001
HBW/Poor lifestyle496298 (5.2)198 (3.4) <0.001
Total11,50957325777
Note: Chi square test was used to compare the two categorical variables. a ANOVA and Dunnett-t test were used to compare the differences between subgroups and NBW/Ideal lifestyle was used as reference group. Abbreviations: NBW, normal birth weight; LBW, low birth weight, HBW, high birth weight, CMRFs, cardio-metabolic risk factors, Ref, the reference group of Dunnett-t test.
Table
Table 2. Prevalence of CMRFs and clustered CMRFs in different groups.
Table 2. Prevalence of CMRFs and clustered CMRFs in different groups.
VariablesTotalHypertensionImpaired Fasting GlucoseDyslipidemiaAbdominal ObesityClustered CMRFs
n (%)pn (%)pn (%)pn (%)pn (%)p
Sex <0.001 <0.001 0.003 0.036 <0.001
Boys57321164 (20.3) 158 (2.8) 1566 (27.3) 1267 (22.1) 248 (4.3)
Girls5777867 (15.0) 66 (1.1) 1721 (29.8) 1372 (23.7) 168 (2.9)
Age 0.020 <0.001 0.027 0.011 0.022
7–12 years70901205 (17.0) 105 (1.5) 2077 (29.3) 1570 (22.1) 234 (3.3)
13–18 years4419826 (18.7) 119 (2.7) 1210 (27.4) 1069 (24.2) 182 (4.1)
Residence <0.001 0.787 <0.001 <0.001 0.331
Rural4523963 (21.3) 90 (2.0) 1096 (24.2) 930 (20.6) 173 (3.8)
Urban69861068 (15.3) 134 (1.9) 2191 (31.4) 1709 (24.5) 243 (3.5)
Birth weight 0.406 0.297 0.034 <0.001 0.528
NBW10,0441756 (17.5) 203 (2.0) 2877 (28.6) 2233 (22.2) 356 (3.5)
LBW43285 (19.7) 7 (1.6) 141 (32.6) 89 (20.6) 19 (4.4)
HBW1033190 (18.4) 14 (1.4) 269 (26.0) 317 (30.7) 41 (4.0)
Fruits intake 0.428 0.479 0.79 0.293 0.614
Inadequate10,4821859 (17.7) 207 (2.0) 2990 (28.5) 2390 (22.8) 376 (3.6)
Adequate1027172 (16.7) 17 (1.7) 297 (28.9) 249 (24.2) 40 (3.9)
Vegetables intake 0.212 0.029 0.154 0.226 0.897
Inadequate10,5331873 (17.8) 214 (2.0) 2989 (28.4) 2400 (22.8) 380 (3.6)
Adequate976158 (16.2) 10 (1.0) 298 (30.5) 239 (24.5) 36 (3.7)
Meat intake <0.001 0.397 0.002 0.732 0.017
Improper95431738 (18.2) 181 (1.9) 2781 (29.1) 2194 (23.0) 363 (3.8)
proper1966293 (14.9) 43 (2.2) 506 (25.7) 445 (22.6) 53 (2.7)
Sugar-sweetened beverage 0.149 0.001 0.117 0.035 0.006
Excessive1316251 (19.1) 42 (3.2) 400 (30.4) 332 (25.2) 65 (4.9)
Proper10,1931780 (17.5) 182 (1.8) 2887 (28.3) 2307 (22.6) 351 (3.4)
Dietary consumption <0.001 0.193 0.749 0.824 0.312
Unhealthy86671603 (18.5) 177 (2.0) 2482 (28.6) 1983 (22.9) 322 (3.7)
Healthy2842428 (15.1) 47 (1.7) 805 (28.3) 656 (23.1) 94 (3.3)
Physical activity 0.166 0.724 0.066 0.531 0.424
Inadequate60311036 (17.2) 120 (2.0) 1678 (27.8) 1397 (23.2) 210 (3.5)
Adequate5478995 (18.2) 104 (1.9) 1609 (29.4) 1242 (22.7) 206 (3.8)
Screen time 0.248 0.326 <0.001 0.415 0.585
Excessive3212588 (18.3) 56 (1.7) 845 (26.3) 753 (23.4) 121 (3.8)
Proper82971443 (17.4) 168 (2.0) 2442 (29.4) 1886 (22.7) 295 (3.6)
Sleep duration 0.010 0.004 0.075 0.003 0.028
Inadequate91961665 (18.1) 196 (2.1) 2661 (28.9) 2163 (23.5) 350 (3.8)
Adequate2313366 (15.8) 28 (1.2) 626 (27.1) 476 (20.6) 66 (2.9)
Lifestyle 0.004 0.263 0.002 0.226 0.545
Poor lifestyle5317978 (18.4) 113 (2.1) 1474 (27.7) 1258 (23.7) 200 (3.8)
Ideal lifestyle61921053 (17.0) 111 (1.8) 1813 (29.3) 1381 (22.3) 216 (3.5)
Combinations of birth weight and lifestyle a 0.277 0.414 0.046 <0.001 0.077
NBW/Ideal lifestyle5430921 (17.0)Ref102 (1.9)Ref1589 (29.3)Ref1183 (21.8)Ref189 (3.5)Ref
NBW/Poor lifestyle4614835 (18.1)0.514101 (2.2)0.7761288 (27.9)0.5121050 (22.8)0.754167 (3.6)0.998
LBW/Ideal lifestyle22541 (18.2)0.9922 (0.9)0.81780 (35.6)0.18543 (19.1)0.87913 (5.8)0.302
LBW/Poor lifestyle20744 (21.3)0.4415 (2.4)0.98761 (29.5)1.00046 (22.2)1.0006 (2.9)0.995
HBW/Ideal lifestyle53791 (16.9)1.0007 (1.3)0.886144 (26.8)0.724155 (28.9)0.00114 (2.6)0.828
HBW/Poor lifestyle49699 (20.0)0.3837 (1.4)0.956125 (25.2)0.244162 (32.7)<0.00127 (5.4)0.117
Total11,5092031 (17.6) 224 (1.9) 3287 (28.6) 2639 (22.9) 416 (3.6)
Note: a ANOVA and Dunnett-t test were used to compare the differences between subgroups and NBW/Ideal lifestyle was used as reference group. Abbreviations: NBW, normal birth weight; LBW, low birth weight, HBW, high birth weight, CMRFs, cardio-metabolic risk factors, Ref, the reference group of Dunnett-t test.
Table
Table 3. Associations between CMRFs, clustered CMRFs and birth weight stratified by age and sex groups.
Table 3. Associations between CMRFs, clustered CMRFs and birth weight stratified by age and sex groups.
Birth WeightHypertensionImpaired Fasting GlucoseDyslipidemiaAbdominal ObesityClustered CMRFs
OR (95% CI)pOR (95% CI)pOR (95% CI)pOR (95% CI)pOR (95% CI)p
Total aNBWRef Ref Ref Ref Ref
LBW1.21 (0.92–1.60)0.1760.99 (0.46–2.17)0.9851.13 (0.88–1.44)0.3480.98 (0.76–1.27)0.8971.43 (0.87–2.36)0.155
HBW1.02 (0.85–1.22)0.8640.68 (0.39–1.19)0.1710.99 (0.84–1.17)0.8981.66 (1.43–1.92)<0.0011.18 (0.84–1.67)0.333
7–12 years bNBWRef Ref Ref Ref Ref
LBW0.94 (0.64–1.37)0.7370.80 (0.25–2.59)0.7061.21 (0.89–1.66)0.2180.82 (0.58–1.17)0.2811.24 (0.62–2.49)0.541
HBW1.08 (0.85–1.36)0.5370.60 (0.26–1.41)0.2441.06 (0.86–1.30)0.5851.74 (1.44–2.10)<0.0011.70 (1.12–2.58)0.013
13–18 years bNBWRef Ref Ref Ref Ref
LBW1.81 (1.19–2.75)0.0061.27 (0.44–3.65)0.6520.96 (0.63–1.45)0.8431.25 (0.86–1.83)0.2451.82 (0.88–3.75)0.106
HBW0.92 (0.69–1.23)0.5670.74 (0.35–1.57)0.4340.84 (0.63–1.12)0.2351.54 (1.21–1.96)<0.0010.65 (0.35–1.21)0.176
Boys cNBWRef Ref Ref Ref Ref
LBW1.54 (1.06–2.25)0.0241.07 (0.42–2.73)0.8831.18 (0.82–1.71)0.3691.13 (0.77–1.65)0.5341.47 (0.75–2.89)0.257
HBW0.98 (0.78–1.23)0.8510.81 (0.45–1.48)0.4960.98 (0.79–1.22)0.8521.48 (1.21–1.81)<0.0010.92 (0.58–1.45)0.713
Girls cNBWRef Ref Ref Ref Ref
LBW0.96 (0.63–1.46)0.8330.95 (0.45–2.01)0.8921.07 (0.76–1.49)0.7000.87 (0.62–1.23)0.4421.44 (0.68–3.03)0.342
HBW1.02 (0.75–1.38)0.8940.85 (0.47–1.54)0.5910.99 (0.77–1.28)0.9431.99 (1.59–2.48)<0.0011.83 (1.19–3.08)0.022
Note: Model a was adjusted for age, sex, residence, delivery mode, single-child status, parental smoking, parental education, and school effect. Model b was adjusted for sex, residence, delivery mode, single-child status, parental smoking, parental education, and school effect. Model c was adjusted for age, residence, delivery mode, single-child status, parental smoking, parental education, and school effect. NBW was used as reference group. NBW, normal birth weight; LBW, low birth weight; HBW, high birth weight; CMRFs, cardio-metabolic risk factors; OR, odds ratio; CI, confidence interval, Ref, the reference group.
Table
Table 4. Associations between CMRFs, clustered CMRFs and lifestyles stratified by age and sex group.
Table 4. Associations between CMRFs, clustered CMRFs and lifestyles stratified by age and sex group.
LifestylesHypertensionImpaired Fasting
Glucose		DyslipidemiaAbdominal ObesityClustered CMRFs
OR (95% CI)pOR (95% CI)pOR (95% CI)pOR (95% CI)pOR (95% CI)p
Total a Unhealthy dietary consumption1.10 (0.97–1.25)0.1321.30 (0.93–1.82)0.1290.95 (0.85–1.06)0.3600.98 (0.88–1.09)0.6951.02 (0.80–1.29)0.905
Inadequate physical activity1.01 (0.91–1.12)0.8741.06 (0.80–1.40)0.6831.00 (0.91–1.10)0.9530.99 (0.91–1.09)0.8821.01 (0.82–1.24)0.940
Excessive screen time1.04 (0.92–1.17)0.5720.99 (0.72–1.36)0.9450.98 (0.88–1.09)0.7581.17 (1.06–1.30)0.0031.16 (0.92–1.45)0.209
Inadequate sleep duration1.18 (1.02–1.36)0.0271.11 (0.72–1.71)0.6510.97 (0.86–1.10)0.6731.14 (1.01–1.29)0.0351.17 (0.87–1.56)0.293
Poor lifestyle1.08 (0.97–1.20)0.1811.14 (0.86–1.50)0.3680.97 (0.88–1.06)0.5001.08 (0.99–1.19)0.0851.09 (0.89–1.33)0.431
7–12 years bUnhealthy dietary consumption1.00 (0.72–1.37)0.9801.51 (0.90–2.54)0.1160.94 (0.83–1.08)0.4011.02 (0.89–1.17)0.7561.02 (0.80–1.29)0.905
Inadequate physical activity1.00 (0.88–1.15)0.9700.95 (0.63–1.41)0.7821.02 (0.91–1.15)0.7111.02 (0.90–1.14)0.7931.05 (0.80–1.38)0.727
Excessive screen time1.01 (0.87–1.18)0.8591.08 (0.69–1.68)0.7470.98 (0.86–1.12)0.7981.17 (1.03–1.34)0.0181.07 (0.79–1.44)0.682
Inadequate sleep duration1.25 (1.07–1.45)0.0051.27 (0.78–2.09)0.3350.99 (0.87–1.12)0.8241.19 (1.04–1.36)0.0101.24 (0.90–1.70)0.181
Poor lifestyle1.12 (0.98–1.29)0.0981.05 (0.70–1.58)0.8070.95 (0.84–1.07)0.3531.15 (1.02–1.29)0.0231.03 (0.78–1.35)0.852
13–18 years bUnhealthy dietary consumption1.07 (0.87–1.31)0.5421.15 (0.74–1.81)0.5320.96 (0.80–1.15)0.6750.97 (0.82–1.15)0.7431.10 (0.75–1.60)0.636
Inadequate physical activity1.04 (0.88–1.24)0.6391.18 (0.79–1.76)0.4170.97 (0.83–1.13)0.6650.96 (0.83–1.11)0.6000.98 (0.71–1.34)0.879
Excessive screen time1.03 (0.85–1.25)0.7640.83 (0.51–1.33)0.4290.97 (0.81–1.16)0.7081.16 (0.99–1.37)0.0751.18 (0.83–1.67)0.364
Inadequate sleep duration1.01 (0.68–1.49)0.9730.75 (0.31–1.83)0.5320.95 (0.65–1.38)0.7760.96 (0.68–1.37)0.8221.10 (0.54–2.27)0.792
Poor lifestyle1.03 (0.87–1.22)0.7211.19 (0.81–1.75)0.3841.01 (0.86–1.18)0.9371.02 (0.88–1.18)0.7841.17 (0.85–1.60)0.338
Boys cUnhealthy dietary consumption1.11 (0.94–1.31)0.2131.31 (0.88–1.96)0.1841.00 (0.86–1.16)0.9991.03 (0.89–1.20)0.6671.40 (1.01–1.94)0.046
Inadequate physical activity1.00 (0.87–1.15)0.9881.07 (0.76–1.49)0.7040.95 (0.83–1.09)0.4220.96 (0.84–1.10)0.5511.12 (0.86–1.46)0.411
Excessive screen time1.05 (0.90–1.23)0.5071.06 (0.73–1.53)0.7690.94 (0.81–1.09)0.4351.17 (1.01–1.35)0.0341.10 (0.83–1.48)0.504
Inadequate sleep duration1.12 (0.93–1.36)0.2361.08 (0.65–1.79)0.7641.05 (0.88–1.25)0.6131.15 (0.97–1.37)0.1131.20 (0.81–1.76)0.369
Poor lifestyle1.08 (0.93–1.24)0.3041.22 (0.87–1.70)0.2440.97 (0.85–1.11)0.6771.09 (0.95–1.24)0.2131.28 (0.98–1.67)0.070
Girls cUnhealthy dietary consumption1.11 (0.91–1.35)0.3041.04 (0.74–1.47)0.8170.90 (0.77–1.05)0.1920.96 (0.82–1.11)0.5510.68 (0.47–0.97)0.034
Inadequate physical activity1.02 (0.87–1.20)0.8030.99 (0.74–1.32)0.9291.04 (0.91–1.19)0.5581.02 (0.89–1.16)0.8010.85 (0.62–1.17)0.327
Excessive screen time1.00 (0.83–1.21)0.9730.93 (0.66–1.31)0.6761.03 (0.88–1.20)0.7381.18 (1.02–1.36)0.0311.21 (0.84–1.74)0.313
Inadequate sleep duration1.28 (1.03–1.59)0.0281.06 (0.71–1.57)0.7890.91 (0.77–1.08)0.2701.11 (0.93–1.32)0.2631.18 (0.76–1.82)0.456
Poor lifestyle1.09 (0.93–1.28)0.3040.97 (0.73–1.30)0.8570.95 (0.84–1.09)0.4861.08 (0.95–1.23)0.2370.85 (0.61–1.17)0.323
Note: Model a was adjusted for age, sex, residence, delivery mode, single-child status, parental smoking, parental education, the interaction of sex and birth weight and school effect. Model b was adjusted for sex, residence, delivery mode, single-child status, parental smoking, parental education, and school effect. Model c was adjusted for age, residence, delivery mode, single-child status, parental smoking, parental education, and school effect. Healthy dietary consumption, adequate physical activity, proper screen time and adequate sleep duration were used as reference groups. NBW, normal birth weight; LBW, low birth weight; HBW, high birth weight; CMRFs, cardio-metabolic risk factors; OR, odds ratio; CI, confidence interval.
Table
Table 5. Associations between CMRFs, clustered CMRFs and combinations of birth weight and lifestyle in mixed effect models stratified by age and sex groups.
Table 5. Associations between CMRFs, clustered CMRFs and combinations of birth weight and lifestyle in mixed effect models stratified by age and sex groups.
CombinationsHypertensionImpaired Fasting
Glucose		DyslipidemiaAbdominal ObesityClustered CMRFs
OR (95% CI)pOR (95% CI)pOR (95% CI)pOR (95% CI)pOR (95% CI)p
Total aNBW/Ideal lifestyleRef Ref Ref Ref * Ref
NBW/Poor lifestyle1.06 (0.95–1.19)0.3021.12 (0.83–1.49)0.4590.98 (0.89–1.08)0.6831.06 (0.96–1.17) *0.2371.04 (0.83–1.29)0.754
LBW/Ideal lifestyle1.25 (0.84–1.85)0.2690.56 (0.14–2.34)0.4281.35 (0.97–1.88)0.0740.94 (0.66–1.36)0.7592.00 (1.07–3.72)0.029
LBW/Poor lifestyle1.25 (0.84–1.84)0.2741.59 (0.62–4.10)0.3330.89 (0.61–1.29)0.5401.08 (0.76–1.54)0.6690.95 (0.41–2.19)0.896
HBW/Ideal lifestyle0.94 (0.73–1.22)0.6520.70 (0.32–1.55)0.3840.98 (0.78–1.22)0.8411.54 (1.26–1.90)<0.0010.76 (0.43–1.33)0.327
HBW/Poor lifestyle1.16 (0.90–1.49)0.2490.72 (0.33–1.58)0.4110.99 (0.78–1.25)0.8981.89 (1.53–2.33)<0.0011.74 (1.13–2.68)0.012
7–12 years bNBW/Ideal lifestyleRef Ref Ref Ref * Ref
NBW/Poor lifestyle1.11 (0.96–1.29)0.1591.12 (0.74–1.71)0.5920.92 (0.81–1.05)0.2221.10 (0.97–1.25) *0.1340.89 (0.66–1.21)0.470
LBW/Ideal lifestyle0.83 (0.49–1.43)0.5041.03 (0.24–4.37)0.9741.21 (0.80–1.83)0.3670.74 (0.45–1.21)0.2281.02 (0.36–2.84)0.977
LBW/Poor lifestyle1.16 (0.68–1.99)0.5800.61 (0.08–4.59)0.6321.14 (0.72–1.80)0.5831.01 (0.62–1.66)0.9631.38 (0.54–3.53)0.506
HBW/Ideal lifestyle1.06 (0.78–1.44)0.7200.79 (0.28–2.24)0.6530.97 (0.74–1.27)0.7981.55 (1.20–2.00) *0.0011.07 (0.56–2.04)0.838
HBW/Poor lifestyle1.21 (0.86–1.72)0.2730.46 (0.11–1.92)0.2841.11 (0.82–1.49)0.5082.20 (1.67–2.88)<0.0012.37 (1.39–4.05)0.002
13–18 years bNBW/Ideal lifestyleRef Ref Ref Ref * Ref
NBW/Poor lifestyle1.10 (0.86–1.39)0.4581.26 (0.78–2.05)0.3431.07 (0.87–1.31)0.5500.90 (0.74–1.10) *0.3161.18 (0.77–1.81)0.442
LBW/Ideal lifestyle2.43 (1.15–5.17)0.0211.00 (0.66–1.62)0.9811.69 (0.86–3.31) #0.1291.38 (0.72–2.65)0.3383.57 (1.23–10.37)0.019
LBW/Poor lifestyle1.32 (0.58–2.99)0.5062.03 (0.44–9.43)0.3660.51 (0.21–1.24)0.1370.98 (0.49–1.96)0.9501.00 (0.62–1.48)0.970
HBW/Ideal lifestyle0.64 (0.34–1.19)0.1571.00 (0.64–1.52)0.9701.04 (0.64–1.69)0.8791.76 (1.16–2.67)0.0080.36 (0.08–1.58)0.177
HBW/Poor lifestyle1.32 (0.78–2.23)0.2961.58 (0.58–4.34)0.3720.64 (0.35–1.16)0.1391.70 (1.09–2.65)0.0191.06 (0.40–2.83)0.907
Boys cNBW/Ideal lifestyleRef Ref Ref Ref * Ref
NBW/Poor lifestyle1.15 (0.92–1.43)0.2151.65 (1.02–2.67)0.0431.03 (0.85–1.24)0.7571.07 (0.90–1.28) *0.4511.33 (0.91–1.95)0.135
LBW/Ideal lifestyle2.45 (1.25–4.79)0.0090.92 (0.12–7.10)0.9351.14 (0.61–2.12)0.6821.03 (0.55–1.95)0.9202.78 (1.02–7.62)0.047
LBW/Poor lifestyle1.36 (0.64–2.91)0.4281.02 (0.13–7.99)0.9880.68 (0.31–1.50)0.3421.08 (0.54–2.17)0.8290.56 (0.08–4.23)0.576
HBW/Ideal lifestyle0.93 (0.58–1.50)0.7750.31 (0.04–2.31)0.2511.31 (0.90–1.90)0.1551.56 (1.10–2.19)0.0120.76 (0.29–1.98)0.574
HBW/Poor lifestyle1.03 (0.63–1.67)0.9112.03 (0.79–5.21)0.1390.88 (0.57–1.37)0.5692.49 (1.73–3.57)<0.0011.19 (0.51–2.74)0.689
Girls cNBW/Ideal lifestyleRef Ref Ref Ref * Ref
NBW/Poor lifestyle1.01 (0.79–1.29)0.9281.00 (0.68–1.49)0.9910.98 (0.81–1.17)0.7880.96 (0.81–1.15) *0.6890.68 (0.41–1.13) *0.135
LBW/Ideal lifestyle1.24 (0.59–2.62)0.5720.80 (0.20–3.30)0.7581.40 (0.80–2.46)0.2440.91 (0.50–1.68)0.7711.60 (0.46–5.55)0.462
LBW/Poor lifestyle1.36 (0.58–3.20)0.4771.04 (0.27–4.08)0.9561.12 (0.59–2.15)0.7230.90 (0.47–1.73)0.7531.19 (0.26–5.41)0.818
HBW/Ideal lifestyle0.86 (0.48–1.54)0.6000.91 (0.33–2.47)0.8481.06 (0.68–1.65)0.7892.00 (1.37–2.93)<0.0011.38 (0.52–3.68)0.520
HBW/Poor lifestyle1.53 (0.86–2.72)0.1510.80 (0.24–2.70)0.7151.02 (0.61–1.70)0.9502.08 (1.34–3.20)0.0011.92 (0.72–5.10)0.191
Note: Model a was adjusted for age, sex, residence, delivery mode, single-child status, parental smoking, parental education, and school effect. Model b was adjusted for sex, residence, delivery mode, single-child status, parental smoking, parental education, and school effect. Model c was adjusted for age, residence, delivery mode, single-child status, parental smoking, parental education, and school effect. NBW/Ideal lifestyle was used as reference group. NBW, normal birth weight; LBW, low birth weight; HBW, high birth weight; CMRFs, cardio-metabolic risk factors; OR, odds ratio; CI, confidence interval, Ref, the reference group. #, the odds ratio difference from the group of LBW/Poor lifestyle was statistically significant, LBW/Poor lifestyle was used as reference group. * The odds ratio difference from the group of HBW/Poor lifestyle was statistically significant, HBW/Poor lifestyle was used as reference group.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Shi, D.; Dang, J.; Ma, N.; Liu, Y.; Zhong, P.; Cai, S.; Ma, Y.; Zou, Z.; Dong, Y.; Song, Y.; et al. The Combined Effect of Birth Weight and Lifestyle on Clustered Cardio-Metabolic Risk Factors in Children and Adolescents: A National School-Based Cross-Sectional Survey. Nutrients 2022, 14, 3131. https://doi.org/10.3390/nu14153131

AMA Style

Shi D, Dang J, Ma N, Liu Y, Zhong P, Cai S, Ma Y, Zou Z, Dong Y, Song Y, et al. The Combined Effect of Birth Weight and Lifestyle on Clustered Cardio-Metabolic Risk Factors in Children and Adolescents: A National School-Based Cross-Sectional Survey. Nutrients. 2022; 14(15):3131. https://doi.org/10.3390/nu14153131

Chicago/Turabian Style

Shi, Di, Jiajia Dang, Ning Ma, Yunfei Liu, Panliang Zhong, Shan Cai, Yinghua Ma, Zhiyong Zou, Yanhui Dong, Yi Song, and et al. 2022. "The Combined Effect of Birth Weight and Lifestyle on Clustered Cardio-Metabolic Risk Factors in Children and Adolescents: A National School-Based Cross-Sectional Survey" Nutrients 14, no. 15: 3131. https://doi.org/10.3390/nu14153131

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop