Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2024-12-25T19:21:30.454Z Has data issue: false hasContentIssue false

A growth curve model to estimate longitudinal effects of parental BMI on Indonesian children’s growth patterns

Published online by Cambridge University Press:  26 September 2024

Yoseph Leonardo Samodra
Affiliation:
School of Public Health, Taipei Medical University, New Taipei City, Taiwan
Ying-Chih Chuang*
Affiliation:
School of Public Health, Taipei Medical University, New Taipei City, Taiwan
*
Corresponding author: Ying-Chih Chuang; Email: yingchih@tmu.edu.tw.
Rights & Permissions [Opens in a new window]

Abstract

The global surge in childhood obesity is also evident in Indonesia. Parental body mass index (BMI) values were found to be one of the major determinants of the increasing prevalence of childhood obesity. It is uncertain if parental BMI during their offspring’s childhood significantly affects their children’s BMI trajectories into adulthood. We aimed to investigate the influence of parental BMI Z-scores on BMI trajectories of Indonesian school-aged children, with a focus on sex-specific effects. This study utilized data from the Indonesian Family Life Survey and tracked the same respondents over four time points, from wave 2 (1997–1998) to wave 5 (2014–2015). The sample of this study consisted of children aged 5–12 years in wave 2 for whom height and weight data were available. We utilized a two-level growth curve model to account for the hierarchical structure of the data, with time nested within individual children. Fathers’ BMI Z-scores in wave 2 had a pronounced influence (β = 0.31) on female children’s BMI Z-scores compared to the influence of mothers’ BMI Z-scores (β = 0.17). Mothers’ BMI Z-scores in wave 2 showed a stronger positive association with male children’s BMI Z-scores (β = 0.22) than did the father’s BMI Z-scores (β = 0.19). A significant interaction of fathers’ BMI Z-scores and years of follow-up was found for male children. As male children’s BMI Z-scores increased by year, this effect was stronger in those whose fathers’ BMI Z-scores were at a higher level. In conclusion, we found that parental BMI values profoundly influenced their children’s BMI trajectories.

Type
Original Article
Copyright
© The Author(s), 2024. Published by Cambridge University Press in association with The International Society for Developmental Origins of Health and Disease (DOHaD)

Introduction

Childhood obesity is on the rise worldwide, and overweight children and adolescents are more likely to develop problems later in life. Reference Togbo1 Between 1993 and 2014, prevalences of overweight children aged 6–12 and 13–18 years in Indonesia, respectively, grew from 5.1% to 15.6% and 7.1% to 14.1%. Reference Oddo, Maehara and Rah2 A full array of risk factors was identified for the rise in children’s obesity. Reference Endalifer and Epidemiology3 One major risk factor is the parental body mass index (BMI). Reference Sutherland4 Having one overweight parent raised the likelihood of being overweight by almost twice for boys and girls aged 3–8 years and for boys aged 11–16 years. However, only when both parents were overweight did the likelihood of being overweight increase over two times in girls aged 11–16 years. Reference Parikka, Mäki, Levälahti, Lehtinen-Jacks, Martelin and Laatikainen5 In addition to the significant relationship between parents’ BMI and their child’s BMI assessed at a single point of time, an increasing number of studies focused on how parental BMI can affect children’s BMI trajectories. Reference Mattsson, Maher, Boland, Fitzgerald, Murray and Biesma6 Parental BMI values were shown to be associated to increased children’s BMI Z-score trajectories at the ages of 6–12 years. Reference Fan, Lee, Yang, Chien, Chao and Chen7 Fathers and mothers seem to contribute differently to the development of a child’s BMI trajectory. While an increase in the father’s BMI was associated with an increase in BMI for both male and female children, an increase in the mother’s BMI was associated with an increase in BMI for male children only. Reference Litchford, Savoie Roskos and Wengreen8,Reference Garden, Marks, Simpson and Webb9 From a longitudinal study, the risk of being overweight at 20 years old was influenced by parental BMI values during one’s childhood. Reference Magarey, Daniels, Boulton and Cockington10 Additionally, another study, utilizing self-reported parental weight and height, revealed that the impact of parental BMI values on childhood obesity became more pronounced as the child progressed into adolescence. Reference Svensson, Jacobsson and Fredriksson11

Many hypotheses have been proposed to explain why parental BMI values in their offspring’s childhood predict their children’s BMI trajectories even until early adulthood. Reference Power, Kuh and Morton12,Reference Eberle, Kirchner, Herden and Stichling13 Researchers indicated that genetic and fetal environments can increase the risk of children’s obesity. Reference Cisse, Lioret and de Lauzon-Guillain14 Many genetic loci were proven to be associated with BMI, but studies suggested that genetics alone cannot be the sole explanation for either the prevalence or the degree of obesity. Reference Sandholt, Grarup, Pedersen and Hansen15 Another important influential factor explaining the parent–child BMI association is the family environment. Reference Lee, Jin and Lee16 Studies have consistently shown that many family environmental factors can moderate expressions of obesogenic genetic tendencies. Reference Warkentin, Mais, do RD de Latorre, Carnell and de Taddei J.A.17 Parenting styles, the parental socioeconomic status (SES), and parental health behaviors, such as parents’ diet and physical activity patterns, all have strong impacts on the food and exercise choices of the child even until adulthood. Reference Niermann, Spengler and Gubbels18,Reference Vinciguerra, Tumminia and Roppolo19 In addition to parental factors, other risk factors contributing to one’s increased BMI trajectory include poor sleep quality, smoking, disadvantaged community settings, and urbanicity. Reference Thamrin, Arsyad, Kuswanto, Lawi and Nasir20Reference Nurwanti, Hadi and Chang22

BMI Z-scores have consistently been used as an indicator of children’s nutritional status. Reference Must and Anderson23 They can indicate longitudinal changes in children and are comparable across different ages. Reference Salahuddin, Pérez, Ranjit, Hoelscher and Kelder24 Although BMI Z-scores are commonly used to assess children’s overweight and obese status, the number of studies analyzing risk factors for BMI Z-score trajectories has been relatively small compared to the studies using raw BMI values. Reference Phelan, Foster and Schwandt25,Reference Zhang, Huang and Zhao26 Additionally, previous studies on the influence of parental BMI on children’s long-term BMI using BMI Z-scores as measures showed conflicting findings. Reference Caixeta and Amato27,Reference Caleyachetty, Stafford and Cooper28 The present study aimed to examine the influence of Indonesian parental BMI Z-scores on the BMI Z-score trajectories of their children from ages 5–12 to 21–30 years. We further examined the effects separately by sex of the child. The hypothesis is that Indonesian parental BMI during their offspring’s childhood significantly influences their children’s BMI trajectories into adulthood, with differential effects by child’s sex.

Methods

Study population

The Indonesian Family Life Survey (IFLS) is an extensive longitudinal survey that investigates social, economic, and health-related behaviors at individual, household, and community levels. The inaugural IFLS, conducted in 1993 and 1994, covered 83% of the Indonesian population. The first wave encompassed 13 of 27 provinces, and surveyed 22,000 individuals from 7244 households, using a stratified random sampling technique. The sample represented population heterogeneity and covered the population that resided on four of the five largest Indonesian islands (viz., Sumatra, Java, Kalimantan, and Sulawesi). There have been five waves of data collection in the IFLS: wave 1 (1993–1994), wave 2 (1997–1998), wave 3 (2000), wave 4 (2007–2008), and wave 5 (2014–2015). Face-to-face interviews, were conducted with one or two household members aged 12 years and older utilizing a structured questionnaire. For children under the age of 12 years, parents or caregivers were interviewed. In wave 5, the data collection method transitioned from pen-and-paper personal interviews to computer-assisted personal interviews. Further details on the sampling frame and data collection procedures were discussed elsewhere. Reference Strauss, Witoelar and Sikoki29

This panel study followed the same respondents across four time points from waves 2 to 5. The main target sample was children aged 5–12 years in wave 2 for whom height and weight data were available. When a family had multiple children, only the eldest was chosen in order to avoid a clustering effect within a family. Reference McAlister, Yan, Roos and Lix30 Both children and parents with extreme BMI Z-scores (below -5 or above 5) were excluded. In wave 2, there were 2031 children aged 5–12 years whose parents’ heights and weights were measured. Sample sizes in waves 3, 4, and 5 were 1959, 1461, and 1083 children, respectively (see Fig. 1). The IFLS underwent institutional review board reviews, gaining approval from the RAND Corporation and Indonesian institutions, that thoroughly assessed the study’s human subject concerns and adhered to ethical standards. Reference Strauss, Witoelar and Sikoki29,Reference Frankenberg and Thomas31 Ethical approval for this study was obtained from the Research Ethic Committee of Universitas Ahmad Dahlan, Indonesia (no.: 022302023).

Figure 1. Flow diagram of sample selection by data wave.

Measures

Dependent variables

The dependent outcome variable was children’s BMI Z-scores based on their height and weight measurements. Regarding the measures of height and weight, heights were measured using a Seca plastic height board, model 213, which measures to the nearest millimeter. Weights were recorded using a Camry model EB1003 scale, accurate to the nearest tenth of a kilogram. Reference Strauss, Witoelar and Sikoki29,Reference Wang, Chen and Preedy32 The anthropometric measurements for both children and parents were collected by nurses or doctors in waves 2–4 and trained interviewers in wave 5. Reference Strauss, Witoelar and Sikoki29,Reference Frankenberg and Thomas31 The BMI Z-score, which served as an indicator of general adiposity, was calculated in accordance with the World Health Organization (WHO) growth standards, using the age- and sex-specific WHO Anthro plugin. Reference Aguilar-Morales, Colin-Ramirez, Rivera-Mancía, Vallejo and Vázquez-Antona33

Independent variables

Parental BMI Z-score data from waves 2 and 5, calculated from heights and weights, Reference Strauss, Witoelar and Sikoki29 were used as time-invariant predictors in the models. Parental BMI Z-scores in wave 2 served as the main independent variables, while parental BMI Z-scores from wave 5 served as control variables. Other predictors of children’s BMI trajectories included child sociodemographic characteristics and behaviors that were previously suggested to be related to children’s BMI. Reference Oddo, Maehara and Rah2,Reference Bella, Dartanto and Nurshadrina34Reference Barus, Sudharta and Suswanti36 These time-invariant predictors were the child’s sex (wave 2) and child’s health behavioral variables (wave 5) including smoking behavior, self-rated health, physical activity, sleep quality, and green vegetable consumption. This study also included random effects of years of follow-up to address differences in children’s BMI trajectories throughout the four waves.

The measure for smoking was based on a binary variable of smoking status, by asking the respondents, “Do you have the habit of smoking self-rolled cigarettes, commercial cigarettes, or cigars?” Self-rated health was assessed by asking the respondents, “What is your current general health condition?” Answers were categorized into “very unhealthy,” “sometimes unhealthy,” “sometimes healthy,” and “very healthy.” Respondents were considered to have good self-perceived health if they responded with “sometimes healthy” or “very healthy,” while they were considered to have poor self-perceived health if they responded with “sometimes unhealthy” or “very unhealthy.” Respondents were asked about the type, duration, and frequency of physical activities they had engaged in over the past week. The cumulative duration of activities across various aspects of life – work, home, and exercise – was converted into metabolic equivalents of task (MET)-minutes and aggregated. This aggregation provided an overall assessment of physical activity in the past week, which was subsequently categorized as “low physical activity” for < 600 MET-min. Reference Defianna, Santosa, Probandari and Dewi37 Sleep quality was assessed by asking respondents “During the past week, how would you assess your sleep quality?” Response categories were “very bad,” “bad,” “moderate,” “good,” and “very good.” Respondents were considered to have adequate sleep quality if they responded “moderate,” “good,” or “very good.” Reference Idris and Tuzzahra38 For dietary habits, participants were asked about their food intake in the previous week. Specifically, they were asked whether they had eaten certain types of food and the intake frequency. For this study, we focused on the daily consumption of green leafy vegetables. Reference Pristyna, Mahmudiono, Rifqi and Indriani39 The SES level was measured by asking respondents to subjectively rate their economic situation using the question, “If you visualize a six-step ladder, with the first step representing the poorest and the sixth step representing the richest, which step do you think you are currently on?” Respondents perceiving themselves as being on the fourth, fifth, or sixth step were classified as having a high SES level. Reference Kim and Radoias40 Geographical setting was measured by asking respondents if they resided in Java-Bali Provinces or elsewhere and whether they lived in an urban or rural area.

Statistical analyses

A two-level growth curve model with an unrestricted covariance matrix for dependence across time was employed to analyze the data. This approach accounted for the hierarchical nature of the dataset, where time (level 1) was nested within children (level 2). This method allowed us to understand the longitudinal progression of children’s BMI Z-scores from waves 2 through 5. At level 1, the model estimated individual growth trajectories based on BMI Z-scores over time. At level 2, the model captured the variability in initial BMI Z-scores between children and variations in growth rates among children. In this model, a time indicator called “year” was utilized, which respectively indicated 0, 3, 10, and 17 years of follow-up from waves 2 to 5. Selection of the appropriate functional form for incorporating the personal-level predictor was based on the Akaike information criterion (AIC). Specifically, linear, quadratic, and cubic models were ranked based on their AIC values. The linear model was finally selected because it had the smallest AIC value. Conditional growth curve models were conducted by adding parental BMI Z-scores, family environmental covariates, personal health behavioral factors, year, and parental BMI Z-scores × year interaction terms as covariates. To ensure accurate interpretation of child growth trajectories, BMI z-scores, which are age- and sex-specific according to WHO standards, were employed to allow consistent comparisons over time. Reference de Onis, Onyango, Borghi, Siyam, Nishida and Siekmann41

The growth curve model was tested using the following equations.

Level 1:

$$ \rm {Y_{ti}=\pi _{0i}+\pi _{1i}\left(year\right)_{ti}+e{ti}} $$

Level 2:

$$\rm {\pi _{0i}=\beta _{00}+\sum _{k=0}^{17}\beta _{0k}\left({covariates}\right)_i+r_{0i}}; $$
$$\rm{ \pi _{1i}=\beta 10+\sum _{k=0}^{17}\beta _{1k}\left({covariates}\right)_i+r_{1i}.} $$

The level 1 equation specifies the level 1 model, where Yti is the BMI Z-score at time t for child i, π0i is the BMI Z-score of child i at the baseline (wave 2), π1i is the linear growth rate of the BMI Z-score over time for child i, and eti is the unexplained level-1 residual. The first level-2 equation specifies initial BMI Z-scores across children (π0ij), which were modeled as a function of intercept (β00), covariates (β0k), and a residual (r 0i) for the random intercept. The second level-2 equation specifies that linear growth rates (π1i) were modeled as a function of intercepts (β10), covariates (β1k), and a residual for linear growth (r1i).

Before growth curve modeling, we conducted univariate and bivariate analyses. In the univariate analyses, continuous variables were displayed as the mean ± standard deviation, while categorical variables were shown as frequencies along with their respective percentages. Bivariate analyses were conducted using either a chi-squared or Kruskal–Wallis test, with only significant variables later being included in growth curve modeling. All analyses were executed in Stata 14.2 (Stata Corp., College Station, TX, USA). A p value of < 0.05 was considered statistically significant.

Results

Table 1 shows descriptive statistics of the sample by wave. Both males and females had a nearly equal representation across all waves. The average age of participants increased in each wave, beginning at 9.33 years in wave 2 and reaching 26.33 years in wave 5. Around 40.93% of participants in wave 2 had a high SES, which had increased to 60.67% in wave 5. Regarding health behaviors, about 36% of participants reported having smoking behaviors across all waves. Around 84% rated their health as healthy, and this pattern remained consistent across waves. Approximately 36% of participants had a low level of physical activity, and around 85% of participants rated their sleep quality as adequate across waves. Nearly 40% of participants had daily green leafy vegetable consumption. In terms of residence, around 43% of participants in wave 2 lived in urban areas, and this had increased to 51.62% in wave 5. A majority of participants (62%) were from the Java-Bali region. Fathers had slightly negative BMI Z-scores in wave 2 (Z = −0.31), which hovered around zero by wave 5. Mothers, on the other hand, had slightly positive BMI Z-scores in wave 2 (Z = 0.30), which had increased to 0.93 in wave 5.

Table 1. Characteristics of the sample population by data wave

SES, socioeconomic status; BMI, body mass index.

a n (%).

b mean ± standard deviation.

* p < 0.05.

Table 2 provides an overview of the progression of total and sex-specific children’s BMI Z-scores over time. Regarding BMI Z-scores for the total sample, the Z-score began at −0.66 in wave 2 and increased to 0.15 in wave 5. Males had a Z-score of −0.71 in wave 2, which had increased to −0.22 in wave 5. In contrast, females began with a Z-score of −0.61 in wave 2 and had a more significant increase of BMI Z-scores to 0.50 in wave 5.

Table 2. Body mass index (BMI) Z-scores of children by sex and data wave

Data are the mean ± standard deviation.

Using two-level growth curve modeling, we examined whether parental BMI Z-scores in wave 2 influenced children’s BMI Z-score trajectories (Table 3). In the total sample, fathers’ (β = 0.25) and mothers’ (β = 0.19) BMI Z-scores in wave 2 were significantly and positively associated with children’s BMI Z-scores across waves, indicating a potential genetic or environmental influence from parents during early life stages. As the years of follow-up increased, there was a notable rise in BMI Z-scores for all participants. Other factors, such as urban residence and a high SES level, showed no associations with the children’s BMI Z-score trajectories when taking into account parental BMI Z-scores.

Table 3. Growth curve modeling of children’s body mass index (BMI) Z-score trajectories on parental BMI Z-scores and sociodemographic characteristics

SES, socioeconomic status.

*p < 0.05.

Study results showed different patterns of influence from parents to children by sex. Specifically, fathers’ BMI Z-scores in wave 2 had a pronounced influence (β = 0.31) on female children’s BMI Z-scores compared to the influence of mothers’ BMI Z-scores (β = 0.17). Conversely, mothers’ BMI Z-scores in wave 2 showed a stronger positive association with male children’s BMI Z-scores (β = 0.22) than did fathers’ BMI Z-scores (β = 0.19). Interestingly, in wave 5, mothers’ BMI Z-scores were positively associated with children’s BMI Z-scores only for females (β = 0.1), suggesting that mothers still had an influence on their daughters even when they had become adults.

Table 3 shows the results of significant interaction effects of parental BMI and years of follow-up. A significant interaction effect of fathers’ BMI Z-scores and years of follow-up was found for male children. To demonstrate the interaction effect, in Fig. 2, we divided fathers’ BMI Z-scores into tertiles. The figure shows that the slope of the trajectory increased when fathers’ BMI Z-scores increased from the third to the first tertile. This suggests that as BMI Z-scores of male children increased by year, this effect was more pronounced when their fathers had higher BMI Z-scores.

Figure 2. Trajectory of male children’s body mass index (BMI) Z-scores by levels of fathers’ BMI Z-scores.

Discussion

This study examined the impacts of parental BMI values during childhood on BMI Z-score trajectories of Indonesian school-aged children and separately estimated the effect by sex using a 17-year longitudinal dataset. We utilized a two-level growth curve model to account for the hierarchical structure of the data, with time nested within individual children. Results showed a consistent increase of children’s BMI Z-scores throughout the survey period. This upward trend was evident in both boys and girls, although girls experienced a more pronounced rise in their BMI Z-scores compared to boys. Other studies also reported similar results of a more salient increase in BMI for girls over the years such as that in a longitudinal study conducted in the United States. Reference Crimmins, Dolan and Martin42 This study reported that while the overall cohort showed consistent BMI Z-scores, individuals with a BMI Z-score of < 0 in the first year had experienced significant increases by the fourth year, with girls having a more pronounced gain than boys. Reference Crimmins, Dolan and Martin42 The higher BMI Z-score gains in girls compared to boys might be in part influenced by hormonal effects of menarche. Reference Gavela-Pérez, Garcés, Navarro-Sánchez, López Villanueva and Soriano-Guillén43

Our findings revealed positive associations between both parents’ BMI Z-scores at the baseline and their children’s BMI Z-scores throughout the study. This finding aligns with previous research which suggested that children’s health behaviors and outcomes are often influenced by their parents, either through genetics, a shared environment, or a combination of these, especially in the early stages of life. Reference Feng, Wang and Chen44,Reference Abrego Del Castillo, Dennis and Wamithi45 The association between parental and children’s BMI values underscores the hereditary aspect of weight-related traits. Reference Smith, Fu and Kobayashi46 A stronger genetic predisposition to obesity was correlated with an earlier onset of adiposity rebound, which is associated with an increased risk of being overweight or obese in later life. Reference Cisse, Lioret and de Lauzon-Guillain14

In addition to hereditary influences, our study suggested that the family environment in the early stages of life may significantly shape one’s BMI trajectory from childhood to adulthood. Families share common environments, including dietary habits, physical activity routines, and socioeconomic conditions. Reference Kader, Sundblom and Elinder47 Family dietary and physical activity habits play crucial roles in nutritional similarities between parents and their children. Reference Nyberg, Sundblom, Norman, Bohman, Hagberg and Elinder48 Families often have shared eating behaviors influenced by shared preferences and the household food environment. Reference Hebestreit, Intemann and Siani49 In addition, one’s physical activity habits are influenced by social support from family and friends, and by the physical environment near the household, including the availability and accessibility of facilities. Reference Deliens, Deforche, De Bourdeaudhuij and Clarys50 Since children often model their behaviors after their parents, parents with unhealthy eating habits or a sedentary lifestyle can inadvertently influence their children to adopt similar behaviors Reference Mado, Sirajuddin, Muis, Maria, Darmawansyah and Arifin51Reference Reicks, Banna and Cluskey53

Our study showed sex-specific trends of parental BMI influences. While both parents can affect their children’s BMI trajectories, the extent of this influence varied based on the child’s sex. Specifically, fathers’ BMI Z-scores at the baseline had a greater impact on girls, while mothers’ BMI Z-scores had more influence on boys. This possibly suggests specific sex-related roles and family dynamics in Indonesia regarding health behaviors and nutrition outcomes. Reference Sule, Uthman and Olamijuwon54 For instance, a mother might be more attentive to her son’s dietary needs, while a father might encourage more physical activities from his daughter. Reference Roche, Goto, Zhao and Wolff55 Findings of this study also possibly hint at potential genetic factors that differently influence weight trajectories for males and females. Reference Cifkova, Pitha, Krajcoviechova and Kralikova56 In a genome-wide association meta-analysis, the genetic basis of obesity was explored, and 20 of 49 genetic loci that showed sex-specific differences play roles in regulating adipose tissues and insulin biology. Reference Shungin, Winkler and Croteau-Chonka57

Our study found a significant interaction effect of fathers’ BMI values and years of follow-up on male children’s BMI values. This result suggests that fathers’ BMI during their offspring’s childhood is important in determining an increasing BMI trajectory in male children. Lasting effects of fathers’ BMI values, either because of genetic influences or early family environmental influences, were also found in prior studies. A longitudinal Canadian study revealed that paternal preconception BMI affected growth rates and mean BMI Z-scores in boys, but not in girls. Boys with a father with a higher BMI were more likely to be overweight or obese. Reference Deveci, Keown-Stoneman and Maguire58

Our study findings showed that the number of participants with a high SES tended to grow over different waves of data collection. While many studies have explored the relationship between SES and childhood obesity, the results were mixed. Reference Mech, Hooley, Skouteris and Williams59,Reference Vazquez and Cubbin60 Some research suggested that children from higher SES backgrounds are at a lower risk of obesity due to better access to health-promoting resources, while others argued the opposite. Reference Fruhstorfer, Mousoulis, Uthman and Robertson61,Reference Assari62 In this study, a high SES level showed no associations with children’s BMI Z-score trajectories when accounting for parental BMI Z-scores. This suggests that in the current Indonesian context, where rapid changes in lifestyles are taking place, other risky behavioral factors may play more significant roles in children’s BMI and obesity, which should be explored in future studies. The increasing urbanization observed in the study, with more participants from waves 2 to 5 living in urban areas, mirrors global trends. Reference Pirgon and Aslan63,Reference Kuddus, Tynan and McBryde64 Previous research often linked urban living with higher obesity rates due to sedentary lifestyles and increased access to unhealthy foods. Reference de Bont, Márquez and Fernández-Barrés65,Reference Janzon, Namusaazi and Bolmsjö66 However, this study did not find a direct association between urban residence and children’s BMI Z-score trajectories when parental BMI Z-scores were considered.

The main strength of this study is the use of a 17-year longitudinal dataset, which allowed us to track BMI Z-score trajectories over an extended period providing insights into long-term trends and changes. Additionally, by employing a two-level growth curve model, we accounted for the hierarchical structure of the data (time nested within individuals), enhancing the robustness of our findings. The IFLS provided a comprehensive and representative sample of the Indonesian population, which ensures the generalizability of our findings.

The main limitation of the study is the reliance on self-reported data for certain variables, such as smoking behavior, self-rated health, physical activity, and sleep quality, which may introduce potential recall bias and social desirability bias. Reference Althubaiti67 Despite controlling for several confounding variables, there remains the possibility of unmeasured covariates influencing the observed associations. Future studies should consider additional factors such as cultural practices, local infrastructure for healthy living, and local nutrition and physical activity-related policies. Additionally, although we highlighted the significant role of parental BMI values, our study primarily focused on parents, which potentially overlooks the influences of siblings and other family members on a child’s health behaviors and BMI. Reference Li, Adab and Cheng68,Reference Park and Cormier69 Future research could explore the influence of siblings, especially in families with multiple children, to provide a more comprehensive understanding of family dynamics in children’s growth patterns. Finally, while the study emphasized the influence of parental BMI values on children’s BMI trajectories, the underlying mechanisms (i.e., genetic, environmental, behavioral, etc.) remain unclear and warrant further investigation.

Conclusions

The escalating trend of childhood obesity is a pressing concern worldwide, and Indonesia is no exception. This study has shed light on the intricate dynamics of childhood obesity within the Indonesian context, emphasizing the profound influence of parental BMI values on children’s BMI trajectories. The findings underscore that the roots of childhood obesity are not just individual but are deeply intertwined with familial and environmental factors. Parental BMI values, as highlighted, serve as a significant predictor of a child’s BMI trajectory. This association underscores the genetic, environmental, and behavioral influences that parents impart to their children. As children often mirror the habits, behaviors, and lifestyles of their parents, it is imperative to view the challenge of childhood obesity not just as an individual concern but as a familial one.

Acknowledgments

The authors are thankful for RAND which provided access to IFLS data at http://www.rand.org/labor/FLS/IFLS/download.html.

Funding statement

None.

Competing interests

None.

References

Togbo, IDR. Obesogenic factors influencing overweight among asian children and youth. J Heal Res Rev. 2018; 5(1), 111116.Google Scholar
Oddo, VM, Maehara, M, Rah, JH. Overweight in Indonesia: an observational study of trends and risk factors among adults and children. BMJ Open. 2019; 9(9), 114.CrossRefGoogle ScholarPubMed
Endalifer, ML, Epidemiology, Diress G. Predisposing factors, biomarkers, and prevention mechanism of obesity: a systematic review. J Obes. 2020; 2020(6134362), 18.CrossRefGoogle ScholarPubMed
Sutherland, ME. Prevalence of overweight and obesity among African American children and adolescents: risk factors, health outcomes, and prevention/Intervention strategies. J Racial Ethn Heal Disparities. 2021; 8(5), 12811292.CrossRefGoogle ScholarPubMed
Parikka, S, Mäki, P, Levälahti, E, Lehtinen-Jacks, S, Martelin, T, Laatikainen, T. Associations between parental BMI, socioeconomic factors, family structure and overweight in Finnish children: A path model approach disease epidemiology - chronic. BMC Public Health. 2015; 15(1), 110.CrossRefGoogle Scholar
Mattsson, M, Maher, GM, Boland, F, Fitzgerald, AP, Murray, DM, Biesma, R. Group-based trajectory modelling for BMI trajectories in childhood: a systematic review. Obes Rev. 2019; 20(7), 9981015.CrossRefGoogle ScholarPubMed
Fan, HY, Lee, YL, Yang, SH, Chien, YW, Chao, JCJ, Chen, YC. Comprehensive determinants of growth trajectories and body composition in school children: a longitudinal cohort study. Obes Res Clin Pract. 2018; 12(3), 270276.CrossRefGoogle ScholarPubMed
Litchford, A, Savoie Roskos, MR, Wengreen, H. Influence of fathers on the feeding practices and behaviors of children: a systematic review. Appetite. 2020; 147(104558), 19.CrossRefGoogle ScholarPubMed
Garden, FL, Marks, GB, Simpson, JM, Webb, KL. Body mass index (BMI) trajectories from birth to 11.5 years: relation to early life food intake. Nutrients. 2012; 4(10), 13821398.CrossRefGoogle ScholarPubMed
Magarey, AM, Daniels, LA, Boulton, TJ, Cockington, RA. Predicting obesity in early adulthood from childhood and parental obesity. Int J Obes. 2003; 27(4), 505513.CrossRefGoogle ScholarPubMed
Svensson, V, Jacobsson, JA, Fredriksson, R, et al. Associations between severity of obesity in childhood and adolescence, obesity onset and parental BMI: a longitudinal cohort study. Int J Obes. 2011; 35(1), 4652.CrossRefGoogle ScholarPubMed
Power, C, Kuh, D, Morton, S. From developmental origins of adult disease to life course research on adult disease and aging: insights from birth cohort studies. Annu Rev Public Health. 2013; 34(1), 728.CrossRefGoogle ScholarPubMed
Eberle, C, Kirchner, MF, Herden, R, Stichling, S. Paternal metabolic and cardiovascular programming of their offspring: a systematic scoping review. PLoS One [Internet]. 2020; 15(12), 119.Google ScholarPubMed
Cisse, AH, Lioret, S, de Lauzon-Guillain, B, et al. Association between perinatal factors, genetic susceptibility to obesity and age at adiposity rebound in children of the EDEN mother-child cohort. Int J Obes. 2021; 45(8), 18021810.CrossRefGoogle ScholarPubMed
Sandholt, CH, Grarup, N, Pedersen, O, Hansen, T. Genome-wide association studies of human adiposity: zooming in on synapses. Mol Cell Endocrinol. 2015; 418, 90100.CrossRefGoogle Scholar
Lee, JS, Jin, MH, Lee, HJ. Global relationship between parent and child obesity: a systematic review and meta-analysis. Clin Exp Pediatr. 2022; 65(1), 3546.CrossRefGoogle ScholarPubMed
Warkentin, S, Mais, LA, do RD de Latorre, MO, Carnell, S, de Taddei J.A., AC. Parents matter: associations of parental BMI and feeding behaviors with child BMI in Brazilian preschool and school-aged children. Front Nutr. 2018; 5(8), 112.CrossRefGoogle ScholarPubMed
Niermann, CYN, Spengler, S, Gubbels, JS. Physical activity, screen time, and dietary intake in families: a cluster-analysis with mother-father-child triads. Front Public Heal. 2018; 6(9), 112.Google ScholarPubMed
Vinciguerra, F, Tumminia, A, Roppolo, F, et al. Impact of unhealthy childhood and unfavorable parents’ characteristics on adiposity in schoolchildren. Diabetes Metab Res Rev. 2019; 35(8), 17.CrossRefGoogle ScholarPubMed
Thamrin, SA, Arsyad, DS, Kuswanto, H, Lawi, A, Nasir, S. Predicting obesity in adults using machine learning techniques: an analysis of Indonesian basic health research 2018. Front Nutr. 2021; 8(669155), 115.CrossRefGoogle ScholarPubMed
Fatima, Y, Doi, SAR. Mamun AA.Sleep quality and obesity in young subjects: a meta-analysis. Obes Rev. 2016; 17(11), 11541166.CrossRefGoogle ScholarPubMed
Nurwanti, E, Hadi, H, Chang, JS, et al. Rural-urban differences in dietary behavior and obesity: results of the riskesdas study in 10-18-year-old Indonesian children and adolescents. Nutrients. 2019; 11(11), 114.CrossRefGoogle ScholarPubMed
Must, A, Anderson, SE. Body mass index in children and adolescents: considerations for population-based applications. Int J Obes. 2006; 30(4), 590594.CrossRefGoogle ScholarPubMed
Salahuddin, M, Pérez, A, Ranjit, N, Hoelscher, DM, Kelder, SH. The associations of large-for-gestational-age and infant feeding practices with children’s body mass index z-score trajectories: the early childhood longitudinal study, birth cohort. Clin Obes. 2017; 7(5), 307315.CrossRefGoogle ScholarPubMed
Phelan, H, Foster, NC, Schwandt, A, et al. Longitudinal trajectories of BMI z-score: an international comparison of 11,513 Australian, American and german/Austrian/Luxembourgian youth with type 1 diabetes. Pediatr Obes. 2020; 15(2), 19.CrossRefGoogle Scholar
Zhang, L, Huang, L, Zhao, Z, et al. Associations between delivery mode and early childhood body mass index Z-score trajectories: a retrospective analysis of 2,685 children from mothers aged 18 to 35 years at delivery. Front Pediatr. 2020; 8(12), 19.CrossRefGoogle ScholarPubMed
Caixeta, HCV, Amato, AA. Factors associated with overweight and abdominal obesity in Brazilian school-aged children: a comprehensive approach. Arch Endocrinol Metab. 2020; 64(4), 445453.Google ScholarPubMed
Caleyachetty, R, Stafford, M, Cooper, R, et al. Exposure to multiple childhood social risk factors and adult body mass index trajectories from ages 20 to 64 years. Eur J Public Health. 2021; 31(2), 385390.CrossRefGoogle ScholarPubMed
Strauss, J, Witoelar, F, Sikoki, B. The 5th Wave of the Indonesia Family Life Survey (IFLS): Overview and Field Report. 2016. RAND, Santa Monica, CA.Google Scholar
McAlister, FA, Yan, L, Roos, LL, Lix, LM. Parental atrial fibrillation and stroke or atrial fibrillation in young adults: a population-based cohort study. Stroke. 2019; 50(9), 23222328.CrossRefGoogle ScholarPubMed
Frankenberg, E, Thomas, D. The Indonesia Family Life Survey (IFLS): Study Design and Results from Waves 1 and 2. 2000. RAND, Santa Monica, CA.Google Scholar
Wang, Y, Chen, HJ. Use of percentiles and Z-scores in anthropometry. In Handbook of Anthropometry: Physical Measures of Human Form in Health and Disease (eds. Preedy, VR), 2012; pp. 2948. Springer, New York.CrossRefGoogle Scholar
Aguilar-Morales, I, Colin-Ramirez, E, Rivera-Mancía, S, Vallejo, M, Vázquez-Antona, C. Performance of waist-to-height ratio, waist circumference, and body mass index in discriminating cardio-metabolic risk factors in a sample of school-aged Mexican children. Nutrients. 2018; 10(12), 114.CrossRefGoogle Scholar
Bella, A, Dartanto, T, Nurshadrina, DS, et al. Do parental smoking behaviors affect children’s thinness, stunting, and overweight status in Indonesia? Evidence from a large-scale longitudinal survey. J Fam Econ Issues. 2023; 44(3), 714726.CrossRefGoogle Scholar
Astutik, E, Hidajah, AC, Tama, TD, Efendi, F, Li, CY. Prevalence and determinants of depressive symptoms among adults in Indonesia: a cross-sectional population-based national survey. Nurs Forum. 2020; 56(1), 3744.CrossRefGoogle ScholarPubMed
Barus, JFA, Sudharta, H, Suswanti, I. Associations of sociodemographic and psychosocial factors with headache symptom among Indonesian adolescents based on the 5th wave of the Indonesian family life survey (IFLS-5). J Res Health Sci. 2023; 23(2), 17.CrossRefGoogle ScholarPubMed
Defianna, SR, Santosa, A, Probandari, A, Dewi, FST. Gender differences in prevalence and risk factors for hypertension among adult populations: a cross-sectional study in indonesia. Int J Environ Res Public Health. 2021; 18(12), 112.CrossRefGoogle ScholarPubMed
Idris, H, Tuzzahra, F. Factors associated with depressive symptoms among adolescents in Indonesia: a cross-sectional study of results from the Indonesia family life survey. Malaysian Fam Physician. 2023; 18, 19.Google ScholarPubMed
Pristyna, G, Mahmudiono, T, Rifqi, MA, Indriani, D. The relationship between big five personality traits, eating habits, physical activity, and obesity in Indonesia based on analysis of the 5th wave Indonesia family life survey, 2014. Front Psychol. 2022; 13(8), 117.CrossRefGoogle ScholarPubMed
Kim, Y, Radoias, V. Subjective socioeconomic status, health, and early-life conditions. J Health Psychol. 2021; 26(4), 595604.CrossRefGoogle ScholarPubMed
de Onis, M, Onyango, AW, Borghi, E, Siyam, A, Nishida, C, Siekmann, J. Development of a WHO growth reference for school-aged children and adolescents. Bull World Health Organ. 2007; 85(9), 660667.CrossRefGoogle ScholarPubMed
Crimmins, NA, Dolan, LM, Martin, LJ, et al. Stability of adolescent body mass index during three years of follow-up. J Pediatr. 2007; 151(4), 383387.CrossRefGoogle ScholarPubMed
Gavela-Pérez, T, Garcés, C, Navarro-Sánchez, P, López Villanueva, L, Soriano-Guillén, L. Earlier menarcheal age in spanish girls is related with an increase in body mass index between pre-pubertal school age and adolescence. Pediatr Obes. 2015; 10(6), 410415.CrossRefGoogle ScholarPubMed
Feng, A, Wang, L, Chen, X, et al. Developmental origins of health and disease (DOHaD): implications for health and nutritional issues among rural children in China. Biosci Trends. 2015; 9(2), 8287.CrossRefGoogle ScholarPubMed
Abrego Del Castillo, KY, Dennis, CL, Wamithi, S, et al. Maternal BMI, breastfeeding and perinatal factors that influence early childhood growth trajectories: a scoping review. J Dev Orig Health Dis. 2022; 13(5), 541549.CrossRefGoogle ScholarPubMed
Smith, JD, Fu, E, Kobayashi, MA. Prevention and management of childhood obesity and its psychological and health comorbidities. Annu Rev Clin Psychol. 2020; 16(1), 351378.CrossRefGoogle ScholarPubMed
Kader, M, Sundblom, E, Elinder, LS. Effectiveness of universal parental support interventions addressing children’s dietary habits, physical activity and bodyweight: a systematic review. Prev Med (Baltim). 2015; 77, 5267.CrossRefGoogle ScholarPubMed
Nyberg, G, Sundblom, E, Norman, Å., Bohman, B, Hagberg, J, Elinder, LS. Effectiveness of a universal parental support programme to promote healthy dietary habits and physical activity and to prevent overweight and obesity in 6-year-old children: the healthy school start study, a cluster-randomised controlled trial. PLoS One. 2015; 10(2), 119.CrossRefGoogle ScholarPubMed
Hebestreit, A, Intemann, T, Siani, A, et al. Dietary patterns of european children and their parents in association with family food environment: results from the i.family study. Nutrients. 2017; 9(2), 117.CrossRefGoogle ScholarPubMed
Deliens, T, Deforche, B, De Bourdeaudhuij, I, Clarys, P. Determinants of physical activity and sedentary behaviour in university students: a qualitative study using focus group discussions. BMC Public Health. 2015; 15(1), 19.CrossRefGoogle Scholar
Mado, FG, Sirajuddin, S, Muis, M, Maria, IL, Darmawansyah, D, Arifin, MA. Intervention empowerment of families in preventing and controlling overweight and obesity in children: a systematic review. J Public Health Res. 2021; 10(2), 267273.CrossRefGoogle ScholarPubMed
Hesketh, KR, Lakshman, R, van Sluijs, EMF. Barriers and facilitators to young children’s physical activity and sedentary behaviour: a systematic review and synthesis of qualitative literature. Obes Rev. 2017; 18(9), 9871017.CrossRefGoogle ScholarPubMed
Reicks, M, Banna, J, Cluskey, M, et al. Influence of parenting practices on eating behaviors of early adolescents during independent eating occasions: implications for obesity prevention. Nutrients. 2015; 7(10), 87838801.CrossRefGoogle ScholarPubMed
Sule, FA, Uthman, OA, Olamijuwon, EO, et al. Examining vulnerability and resilience in maternal, newborn and child health through a gender lens in low-income and middle-income countries: a scoping review. BMJ Glob Heal. 2022; 7(4), 111.Google ScholarPubMed
Roche, A, Goto, K, Zhao, Y, Wolff, C. Bonding and bridging social and cultural capitals: perceived factors associated with family eating practices among Hmong, Latino, and white mothers and fathers. J Nutr Educ Behav. 2015; 47(6), 540547.e1.CrossRefGoogle ScholarPubMed
Cifkova, R, Pitha, J, Krajcoviechova, A, Kralikova, E. Is the impact of conventional risk factors the same in men and women? Plea for a more gender-specific approach. Int J Cardiol. 2019; 286, 214219.CrossRefGoogle ScholarPubMed
Shungin, D, Winkler, T, Croteau-Chonka, DC, et al. New genetic loci link adipose and insulin biology to body fat distribution. Nature. 2015; 518(7538), 187196.CrossRefGoogle ScholarPubMed
Deveci, AC, Keown-Stoneman, CDG, Maguire, JL, et al. Paternal BMI in the preconception period, and the association with child zBMI. Int J Obes. 2023; 47(4), 280287.CrossRefGoogle ScholarPubMed
Mech, P, Hooley, M, Skouteris, H, Williams, J. Parent-related mechanisms underlying the social gradient of childhood overweight and obesity: a systematic review. Child Care Health Dev. 2016; 42(5), 603624.CrossRefGoogle ScholarPubMed
Vazquez, CE, Cubbin, C. Socioeconomic status and childhood obesity: a review of literature from the past decade to inform intervention research. Curr Obes Rep. 2020; 9(4), 562570.CrossRefGoogle ScholarPubMed
Fruhstorfer, BH, Mousoulis, C, Uthman, OA, Robertson, W. Socio-economic status and overweight or obesity among school-age children in sub-saharan Africa - a systematic review. Clin Obes. 2016; 6(1), 1932.CrossRefGoogle ScholarPubMed
Assari, S. Family income reduces risk of obesity for white but not black children. Children. 2018; 5(6), 113.CrossRefGoogle Scholar
Pirgon, Ö., Aslan, N. The role of urbanization in childhood obesity. J Clin Res Pediatr Endocrinol. 2015; 7(3), 163167.CrossRefGoogle ScholarPubMed
Kuddus, MA, Tynan, E, McBryde, E. Urbanization: a problem for the rich and the poor? Public Health Rev. 2020; 41(1), 14.CrossRefGoogle ScholarPubMed
de Bont, J, Márquez, S, Fernández-Barrés, S, et al. Urban environment and obesity and weight-related behaviours in primary school children. Environ Int. 2021; 155, 114.CrossRefGoogle ScholarPubMed
Janzon, E, Namusaazi, S, Bolmsjö, I. Increasing obesity in Ugandan women due to transition from rural to urban living conditions? a qualitative study on traditional body image, changed lifestyles and unawareness of risk for heart disease. J Res Obes. 2015; 2015, 113.Google Scholar
Althubaiti, A. Information bias in health research: definition, pitfalls, and adjustment methods. J Multidiscip Healthc. 2016; 9, 211217.CrossRefGoogle ScholarPubMed
Li, B, Adab, P, Cheng, KK. The role of grandparents in childhood obesity in China - evidence from a mixed methods study. Int J Behav Nutr Phys Act. 2015; 12(1), 19.CrossRefGoogle Scholar
Park, SH, Cormier, E. Influence of siblings on child health behaviors and obesity: a systematic review. J Child Fam Stud. 2018; 27(7), 20692081.CrossRefGoogle Scholar
Figure 0

Figure 1. Flow diagram of sample selection by data wave.

Figure 1

Table 1. Characteristics of the sample population by data wave

Figure 2

Table 2. Body mass index (BMI) Z-scores of children by sex and data wave

Figure 3

Table 3. Growth curve modeling of children’s body mass index (BMI) Z-score trajectories on parental BMI Z-scores and sociodemographic characteristics

Figure 4

Figure 2. Trajectory of male children’s body mass index (BMI) Z-scores by levels of fathers’ BMI Z-scores.