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Review Article

Evidence for a perinatal origin of childhood obesity: a systematic review and meta-analysis of risk and protective factors

Andi Rispah Sulistianingsih1orcid, Nurjazuli Nurjazuli2orcid, Martha Irene Kartasurya3orcid, Syamsulhuda Budi Musthofa4orcid, Jauhari Oka Reuwpassa5orcid
Osong Public Health and Research Perspectives 2026;17(2):114-135.
Published online: March 27, 2026

1Doctoral Program of Public Health, Public Health Faculty, Universitas Diponegoro, Semarang, Indonesia

2Department of Environmental Health, Public Health Faculty, Universitas Diponegoro, Semarang, Indonesia

3Public Health Nutrition Department, Public Health Faculty, Universitas Diponegoro, Semarang, Indonesia

4Department of Health and Promotion, Public Health Faculty, Universitas Diponegoro, Semarang, Indonesia

5Dinoyo Primary Health Care Center, Malang District Health Office, Malang, Indonesia

Corresponding author: Andi Rispah Sulistianingsih Doctoral Program of Public Health, Faculty of Public Health, Universitas Diponegoro, Jl. Prof. Jacub Rais, Tembalang, Semarang 50275, Indonesia E-mail: andirispahris@gmail.com
• Received: September 2, 2025   • Revised: January 23, 2026   • Accepted: January 30, 2026

© 2026 Korea Disease Control and Prevention Agency.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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  • Objectives
    This systematic review aimed to identify perinatal risk factors associated with obesity in children aged ≤5 years.
  • Methods
    This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Medline (via PubMed), Embase, and Cochrane CENTRAL were searched from inception, without language or date restrictions. In addition, gray literature sources, including LILACS and CNKI, were screened for comprehensive evidence synthesis. Meta-analyses were conducted to estimate pooled risk ratios. Study quality was independently assessed by 2 reviewers using the Joanna Briggs Institute critical appraisal tool.
  • Results
    A total of 24,643 articles were screened, and 39 cohort studies were included in the final analysis. Sample sizes ranged from 169 to 333,353 participants. Meta-analysis results indicated that high gestational weight gain (adjusted risk ratio [aRR], 1.46; 95% confidence interval [CI], 1.17–1.82), history of cesarean section (aRR, 1.25; 95% CI, 1.17–1.33), macrosomia (aRR, 1.88; 95% CI, 1.55–2.27), antibiotic use (aRR, 1.31; 95% CI, 1.14–1.51), pre-pregnancy obesity (aRR, 1.82; 95% CI, 1.21–2.73), and female sex (aRR, 1.46; 95% CI, 1.32–1.61) were associated with an increased risk of obesity in children aged ≤5 years. Exclusive breastfeeding (aRR, 0.74; 95% CI, 0.64–0.85) was identified as a protective factor.
  • Conclusion
    Perinatal factors and pre-pregnancy obesity played important roles in increasing the risk of obesity in children aged ≤5 years. Breastfeeding was associated with a protective effect against childhood obesity. Therefore, obesity prevention efforts should begin during pregnancy, and maintaining appropriate maternal weight before conception is equally essential.
  • Keywords: Breast feeding; Child; Obesity; Risk factors
Over the past 2 decades, the prevalence of overweight and obesity among children aged ≤5 years has shown a gradual yet consistent increase, rising from 5.4% in 2000 to 5.7% in 2022. Currently, 72 million children and adolescents aged 5–19 years are either overweight or obese, and this number is projected to increase to 101 million by 2035 [1]. Several studies have shown that approximately 25% of children with obesity and up to 80% of children aged 10–14 years with obesity remain obese into adulthood [2,3]. The increasing incidence of childhood overweight and obesity has been linked to a wide range of adverse health outcomes, including metabolic syndrome, cardiovascular disease, type 2 diabetes mellitus, respiratory symptoms, malignancies, and behavioral problems [4–6]. Each year, obesity is estimated to cause 4.72 million deaths worldwide, a figure comparable to the number of deaths attributed to air pollution [7].
The increasing incidence of childhood obesity is likely driven by a complex interaction of genetic factors, lifestyle behaviors, environmental exposures, dietary patterns, and socioeconomic conditions. Maternal factors, including high pre-pregnancy body mass index (BMI), excessive gestational weight gain, gestational diabetes mellitus, and maternal perinatal depression, have been associated with obesity in children aged 5 and 9 years [8,9]. In addition, nonexclusive breastfeeding, macrosomia, mode of delivery, and antibiotic use have been identified as risk factors for childhood obesity [10,11]. Antibiotic exposure and cesarean delivery may disrupt the composition of the gut microbiota, resulting in dysbiosis. The gut microbiota plays a critical role in energy extraction from food, appetite regulation, and maintenance of intestinal barrier function [12].
Several systematic reviews and umbrella reviews have examined early-life determinants of childhood obesity [13–16]. However, the available evidence remains inconsistent. For example, Weng et al. [15] reported a 15% protective effect of breastfeeding, whereas Woo et al. [13] concluded that the association between breastfeeding and obesity prevention is inconsistent across studies. These discrepancies are partly attributable to variations in follow-up duration, as many studies only monitor outcomes during the first few months of life, thereby limiting conclusions regarding long-term effects [13]. Conversely, studies with follow-up periods extending from conception to 18 or 26 years of age may be too prolonged to isolate early-life factors specific to childhood obesity [13,16]. In addition, the limited number of studies evaluating specific exposures, such as cesarean delivery, gestational weight gain, or infant temperament, restricts the ability to draw robust conclusions regarding these factors [15].
The heterogeneity of findings across previous reviews, along with variability in follow-up duration, underscores the need for a more comprehensive and up-to-date synthesis of the evidence. Such an evaluation is essential for identifying perinatal risk factors that remain relevant in the context of current childhood obesity trends. Focusing on children aged ≤5 years is particularly important, as this period represents a critical window for metabolic programming and the establishment of early dietary and sedentary behavior patterns. Accordingly, this systematic review and meta-analysis aimed to identify perinatal risk factors for childhood obesity in children aged ≤5 years. By concentrating on this sensitive developmental period, this study seeks to inform global policy frameworks and early intervention strategies designed to mitigate the long-term health consequences of childhood obesity.
This meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and was registered with PROSPERO (CRD42024513913).
Eligibility Criteria
We included cohort studies in this meta-analysis based on the population, exposure, comparison, and outcome (PECO) framework. Population: Perinatal or early infancy cohorts followed longitudinally. Specifically, we included prospective studies in which participants were recruited during the pre-pregnancy period or infancy and followed until age ≤5 years. Exposure: Early-life determinants, including preconception, prenatal, perinatal, and postnatal factors. There was no restricted time window for these exposures, which encompassed the entire developmental trajectory prior to the assessment of obesity status. Comparison: children not exposed to the specified risk factors. Outcome: obesity.
To minimize publication and language bias, no language or date restrictions were applied, and the search was expanded to include gray literature sources, such as Latin American and Caribbean Health Sciences Literature (LILACS) and China National Knowledge Infrastructure (CNKI). All identified non-English studies were assessed for eligibility using translation services when necessary. A priori exclusion criteria included non-cohort study designs, studies in which the final obesity assessment occurred after age 5 years, failure to assess obesity as a primary or secondary outcome, and studies focused on specific clinical populations.
Information Sources
Medline (via PubMed), Embase, Cochrane CENTRAL, LILACS, and CNKI were searched for articles relevant to the study objectives. These databases were searched from their inception (1973 for Medline, 1996 for Embase, 1990 for Cochrane CENTRAL, 1981 for LILACS, and 1994 for CNKI) through December 18, 2025.
Search Strategy
Keywords and related synonyms were applied by combining Medical Subject Headings (MeSH) and free text terms, as follows: (("child"[MeSH Terms] OR "infant"[MeSH Terms] OR "child, preschool"[MeSH Terms] OR "infant*"[Title/Abstract] OR "toddler*"[Title/Abstract] OR "preschool*"[Title/Abstract] OR "under five*"[Title/Abstract]) AND ("determinant*"[Title/Abstract] OR "risk factor*"[Title/Abstract] OR "protective factor*"[Title/Abstract] OR "predictor*"[Title/Abstract]) AND ("obesity"[MeSH Terms] OR "overweight"[MeSH Terms] OR "body mass index"[MeSH Terms] OR "obes*"[Title/Abstract] OR "overweight"[Title/Abstract] OR "BMI"[Title/Abstract] OR "weight-for-length"[Title/Abstract] OR "weight-for-height"[Title/Abstract] OR "z score*"[Title/Abstract] OR "WHZ"[Title/Abstract] OR "WFL"[Title/Abstract])).
Selection Process
The outcomes evaluated were the incidence of obesity in children aged ≤5 years and associated risk factors. After exporting records from the 5 databases into Mendeley, duplicate records were removed. Based on the eligibility criteria, 2 researchers (A.R.S. and J.O.R.) independently screened the titles and abstracts to identify studies relevant to the objectives of this review. Following this initial screening, full-text articles were retrieved and reviewed in detail to determine final eligibility. In cases of disagreement, 2 additional reviewers (N.N. and M.I.K.) were consulted to reach consensus.
Data Collection Process
Two researchers (A.R.S. and J.O.R.) independently extracted data into Microsoft Excel using a structured data extraction template. Extracted information included authors and year of publication, country, study design, sample size, exposure variables, operational definition of obesity (e.g., World Health Organization [WHO], Centers for Disease Control and Prevention [CDC], or International Obesity Task Force [IOTF]), timing of obesity assessment (reported in years), covariate adjustment, and adjusted risk ratios (aRRs) for obesity outcomes.
Data Items
The main outcome of interest in this study was obesity in children aged ≤5 years. Recognizing that included studies used different growth reference standards, outcome definitions were harmonized to ensure clinical and statistical comparability. Obesity was defined as a BMI-for-age z-score ≥+2 standard deviations according to the WHO Child Growth Standards, which is clinically equivalent to the ≥95th percentile on the CDC growth charts and the age- and sex-specific cut-offs defined by the IOTF. Overweight status was not analyzed, as this review focused exclusively on obesity. To ensure consistency across studies, a standardized harmonization protocol was applied to all exposure variables. For each determinant, minimum thresholds or binary classifications were defined based on clinical relevance. Mode of delivery was categorized as cesarean section or vaginal delivery. Antibiotic exposure was assessed based on the child’s history of antibiotic use (yes/no), rather than maternal antibiotic use during pregnancy. When studies reported multiple exposure levels (e.g., dosage or frequency), data corresponding to the highest exposure level were extracted. Macrosomia was defined as birth weight >4,000 g. Gestational weight gain was classified as excessive when it exceeded the upper recommended limit and as normal when it fell within recommended ranges according to the Institute of Medicine guidelines. Exclusive breastfeeding was defined as feeding with breast milk only, without formula, other milk, or complementary foods, until 6 months of age. Studies that did not align with these classifications were excluded from the meta-analysis to preserve statistical homogeneity but were retained in the study characteristics table for qualitative descriptive purposes.
Study Risk of Bias Assessment
Study quality was independently assessed by 2 reviewers (A.R.S. and J.O.R.) using the Joanna Briggs Institute (JBI) checklist for cohort studies [17]. This checklist comprises 11 criteria, including population similarity, consistency and validity of exposure measurement, identification and management of confounding factors, confirmation that participants were outcome-free at baseline, validity and reliability of outcome measurement, adequacy of follow-up duration and completeness, strategies for addressing incomplete follow-up, and appropriateness of statistical analyses. All included studies met the predefined quality threshold of ≥7 points, ensuring that only high-quality evidence contributed to the meta-analysis.
Effect Measures
The primary effect measure was the pooled aRR, representing the association between exposure and childhood obesity risk. When studies reported odds ratios (ORs), these were considered equivalent to aRRs when outcome incidence was low (<10%). Otherwise, ORs were converted to aRRs using the following formula: aRR=OR/[(1−P₀)+(P₀×OR)], where P₀ represents the incidence of the outcome in the unexposed group [18]. Review Manager (RevMan) version 5.3 software (The Cochrane Collaboration) was used to conduct meta-analyses. Statistical heterogeneity was assessed using the I2 statistic. A fixed-effects model was applied when I2<50%, whereas a random-effects model was used when I2≥50%.
Synthesis Methods
Studies meeting the inclusion criteria were grouped according to the perinatal risk factors evaluated. Only studies reporting obesity in children aged ≤5 years as an outcome were included in the quantitative synthesis. When ORs were reported, these were converted to aRRs using baseline risk estimates from the comparison group. Studies defining obesity using the 95th percentile were adjusted to align with the WHO definition. When summary data were incomplete, such as missing confidence intervals (CIs), calculations were derived from raw data when available. All quantitative analyses were performed using RevMan version 5.3 software, and results are presented using forest plots. Meta-analyses were conducted using a random-effects model to account for between-study variability. Statistical heterogeneity was evaluated using the I2 statistic, with values ≥50% indicating substantial heterogeneity. To assess the robustness of pooled estimates and determine whether any single study exerted disproportionate influence, a leave-one-out sensitivity analysis was conducted by sequentially excluding each study.
Study Selection
Database searches identified 24,890 records (13,971 from Medline via PubMed, 726 from Cochrane CENTRAL, 8,852 from Embase, 1,210 from LILACS, and 131 from CNKI). After removing 247 duplicate records, 24,643 records remained for title and abstract screening. Of these, 24,591 records were excluded because they did not meet the inclusion criteria. The full texts of 52 articles were assessed for eligibility. Twelve articles were excluded for the following reasons: obesity was measured beyond 5 years of age (n=3), the study design was not a cohort study (n=3), or results were not stratified for obesity outcomes (n=6). The complete process of study identification, screening, and inclusion is presented in Figure 1.
Study Characteristics
A total of 39 studies were included in this review. The included studies were conducted across diverse geographical regions, including China [19–22], Ireland [23–25], the United States [11,26–41], Japan [42], England [43–47], the Netherlands [48], Korea [10,49], Germany [50], Hawaii [51], Australia [52] and New Zealand [12,53,54]. Sample sizes ranged from 169 to 333,353 participants, with study populations generally comprising children aged ≤5 years. The detailed characteristics of the included studies are summarized in Table 1.
Risk of Bias in Studies
All studies included in the analysis demonstrated a low risk of bias, with quality assessment scores ≥7 (Tables 2, 3). Despite the overall high methodological quality, several studies did not report the number of participants lost to follow-up or the reasons for attrition. In addition, explicit strategies for addressing incomplete follow-up in the statistical analysis were often lacking. Another common limitation across the majority of studies was insufficient adjustment for potential confounding variables, particularly lifestyle-related factors such as diet, screen time, and physical activity.
Results of Syntheses
The synthesis of early-life determinants of childhood obesity (≤5 years) is visually summarized in Figure 2. Meta-analyses were performed for 7 key variables, as described below.

High gestational weight gain

The meta-analysis of 5 studies examining the association between excessive gestational weight gain and childhood obesity demonstrated a significantly increased risk. The pooled aRR was 1.46 (95% CI, 1.17–1.82; I2=52%), indicating that children born to mothers with excessive gestational weight gain had a 46% higher risk of obesity compared with those born to mothers with adequate gestational weight gain. Heterogeneity across studies was moderate (I2=52%, χ2=8.31, degrees of freedom [df]=4, p=0.08). Detailed results are presented in Figure 3.

Cesarean section

Cesarean section was associated with a 25% increased risk of obesity in children aged ≤5 years (Figure 4), with a pooled aRR of 1.25 (95% CI, 1.17–1.33; I2=55%). Most studies reported positive associations, although 2 studies did not observe statistically significant results. Despite this variability, the overall pooled effect remained highly significant, supporting cesarean delivery as a risk factor for childhood obesity.

Macrosomia

Children with a birth weight greater than 4,000 g demonstrated an increased risk of obesity, with a pooled aRR of 1.88 (95% CI, 1.55–2.27; I2=76%). All included studies reported positive associations, with effect sizes ranging from moderate to strong (Figure 5).

Antibiotic use

The pooled aRR for antibiotic exposure was 1.31 (95% CI, 1.14–1.51; Z=3.75, p<0.00001), indicating that children exposed to antibiotics had a 31% higher risk of developing obesity compared with those without antibiotic exposure. Heterogeneity across studies was very high (I2=86%; χ2=33.08, df=4, p<0.00001), reflecting substantial variability among studies. Despite this heterogeneity, all included studies consistently demonstrated a positive association between antibiotic exposure and childhood obesity (Figure 6).

Exclusive breastfeeding

Exclusive breastfeeding was associated with a 26% reduction in the risk of childhood obesity among children aged ≤5 years, with a pooled aRR of 0.74 (95% CI, 0.64–0.85; I2=90%). Heterogeneity among studies was very high (I2=90%, χ2=80.27, df=8, p<0.00001). Despite this variability, the overall pooled effect consistently indicated that exclusive breastfeeding acts as a protective factor against childhood obesity (Figure 7). Subgroup analysis revealed that age at obesity assessment was a significant source of heterogeneity (p=0.02). In the subgroup of children aged ≤3 years, heterogeneity was completely eliminated (I2=0%).

Sex

Female sex was associated with a higher risk of obesity, with a pooled aRR of 1.46 (95% CI, 1.32–1.61; I2=0%). These results are illustrated in Figure 8.

Pre-pregnancy obesity

Figure 9 presents the pooled analysis of maternal pre-pregnancy obesity. Children born to mothers with obesity prior to pregnancy were 1.82 times more likely to develop obesity. Maternal pre-pregnancy obesity was significantly associated with an increased risk of childhood obesity (aRR, 1.82; 95% CI, 1.21–2.73; I2=94%). Age at obesity assessment significantly explained between-study variability (p=0.02 for subgroup differences). After stratification, heterogeneity was substantially reduced: the subgroup aged ≤3 years showed an I2 of 49%, whereas the subgroup aged >3 years demonstrated low heterogeneity (I2=17%).
Our findings indicate that several perinatal and early-life factors are significantly associated with the risk of obesity in children aged ≤5 years. Specifically, high gestational weight gain, sex, pre-pregnancy obesity, history of cesarean section, macrosomia, and early-life antibiotic exposure were associated with an increased risk of obesity, whereas exclusive breastfeeding demonstrated a protective effect. Excessive maternal weight gain during pregnancy was associated with a 46% higher risk of obesity in offspring. Eating behavior–related factors and genetic predispositions that contribute to excessive weight gain during pregnancy are likely to be transmitted to children who develop obesity early in life [10,43,53]. Children born to mothers who experienced high gestational weight gain during their first pregnancy remained at increased risk of obesity, even when maternal pre-pregnancy BMI was within the normal range [43,48]. Similarly, children of mothers who experienced excessive weight gain during both the first and second pregnancies were at increased risk of overweight, regardless of whether the mother was normal weight or obese prior to pregnancy [43,48]. These findings underscore the importance of weight control during pregnancy, as gestational weight gain may exert long-term effects on child obesity risk.
Our results were consistent with findings from a study conducted in Japan, which reported that excessive gestational weight gain increased the risk of offspring overweight by 20% to 27% at age 3 years [24]. Similarly, Voerman et al. [55], using pooled data from more than 160,000 mother–child dyads in an individual participant data meta-analysis, demonstrated that excessive gestational weight gain was associated with an increased risk of childhood overweight. A dysmetabolic intrauterine environment resulting from excessive gestational weight gain, particularly during the first trimester, may expose the fetus to excess energy substrates, reflecting increased maternal fat accumulation [53]. Excessive gestational weight gain may therefore represent nutritional overload and altered maternal metabolism, leading to elevated fetal exposure to glucose and lipids. This exposure may stimulate increased fetal insulin production, thereby promoting greater infant adiposity [40,53].
Cesarean section was associated with a 25% increased risk of obesity in children aged ≤5 years. Cesarean delivery limits neonatal exposure to maternal vaginal microbiota, which is a primary source of early gut colonization in newborns [25]. Alterations in gut microbiota composition may affect energy metabolism, as microbial fermentation of indigestible carbohydrates contributes to additional energy extraction. Moreover, the gut microbiota plays a role in regulating circulating lipopolysaccharide levels, which are implicated in long-term systemic inflammation and metabolic dysregulation, including obesity and diabetes [12]. Zhang et al. [56]conducted a meta-analysis of 9 studies and reported that cesarean delivery was associated with a 10% increased risk of obesity, particularly among children aged 3–18 years. In contrast, the present study focused exclusively on children aged ≤5 years and identified a comparable effect size, reinforcing the relevance of cesarean-related microbial alterations during early childhood.
Cesarean delivery has been associated with reduced abundance of Bacteroidetes and increased abundance of Firmicutes, microbial patterns frequently observed in obese children. These bacterial groups can extract energy from indigestible colonic polysaccharides, thereby increasing caloric availability [12,57]. In addition, pregnancy- or early childhood–related antibiotic exposure has been shown to increase obesity risk. Previous studies reported a 4% increased risk of obesity in children aged 5 years following antibiotic exposure, whereas our meta-analysis identified a substantially higher pooled risk. This difference likely reflects the heightened vulnerability of children aged ≤5 years to disruptions of gut microbiota during critical periods of development. Antibiotics exert broad-spectrum antibacterial effects that can substantially alter the composition and diversity of the child’s intestinal microbiota [58].
High birth weight (>4,000 g) was associated with nearly double the risk of childhood obesity. Macrosomia emerged as a key perinatal factor, corroborating findings from prior studies. For example, previous research reported that children with macrosomia at birth had a 70%–130% higher likelihood of being overweight or obese [48]. A survey by Adebile et al. [59] similarly demonstrated that macrosomia (birth weight ≥4,000 g) significantly increased the risk of childhood obesity. Our findings align with this literature, emphasizing birth weight as a strong predictor of early childhood obesity. Potential mechanisms underlying the association between macrosomia and childhood obesity include shared genetic predispositions and intrauterine programming effects that influence early hormonal responses and energy balance regulation [35]. Maternal hyperglycemia is a central contributor to the development of macrosomia. Elevated levels of pregnancy-related hormones, such as cortisol, human placental lactogen, and prolactin, particularly during the second trimester, increase maternal insulin resistance. Enhanced placental glucose transfer results in fetal hyperglycemia, leading to increased fetal insulin secretion and excessive fetal growth [11,35]. In this meta-analysis, macrosomia—particularly in Asian populations—appeared to be strongly influenced by maternal obesity prior to pregnancy, suggesting a genetic and metabolic predisposition to obesity in offspring. In addition, behavioral factors such as food preferences and early eating patterns increasingly influence obesity risk by age 5 years. These postnatal behavioral influences may attenuate the effect of macrosomia on obesity at age 5, although its impact remains more pronounced at younger ages.
Exclusive breastfeeding demonstrated a protective effect against childhood obesity. Subgroup analysis indicated that the age at obesity assessment significantly influenced result consistency. Among children assessed at ≤3 years of age, the protective effect of breastfeeding was highly consistent. As children grow older (>3 years), exposure to increasingly diverse obesogenic environments intensifies. Variations in complementary feeding practices, consumption of sugar-sweetened beverages, and physical activity levels accumulate over time. These external influences vary substantially across study populations and geographical regions, likely contributing to the persistently high heterogeneity observed in older age groups. In contrast, among children aged ≤3 years, the direct biological effects of breastfeeding—such as modulation of gut microbiota, insulin responses, and leptin regulation—appear to remain the dominant determinants of weight status. At this early developmental stage, the influence of external environmental factors is relatively limited.
Breastfeeding influences infant gut microbiota development and may mitigate the effects of antibiotic exposure and cesarean delivery [26,50]. Multiple reviews have reported a protective association between exclusive breastfeeding and obesity. For instance, Horta et al. [60] estimated a 26% reduction in obesity risk among breastfed individuals aged 1–9 years. Breast milk serves as the primary source of infant nutrition and contains numerous bioactive components that support early gut microbial balance and protect against pathogenic bacteria [37,61]. Human milk oligosaccharides promote the growth of Bifidobacteria and Bacteroidetes, microbial groups that are typically reduced in obese children [57]. Consistent with this, Wang et al. [57] reported reduced Bacteroidetes abundance in obese children, whereas other studies have demonstrated increased Firmicutes abundance in obese children and adults [62]. These microbial shifts enhance energy extraction from indigestible polysaccharides in the colon, thereby increasing caloric availability [63]. Collectively, these findings highlight breastfeeding as a critical postnatal factor in shaping early gut microbial colonization and reducing obesity risk. The results of the present meta-analysis are consistent with previous evidence supporting the protective role of breastfeeding against childhood obesity.
Beyond the factors included in the meta-analysis, several additional determinants were identified in the systematic review but could not be quantitatively synthesized because of the limited number of available studies. These determinants spanned a broad range of sociodemographic, maternal, behavioral, and psychosocial domains, including maternal education, sedentary behaviors, smoking during pregnancy, feeding practices, insecure attachment, and infant temperament.
The interplay between family socioeconomic status and childhood obesity operates through multiple complex mechanisms, ranging from nutritional access to environmental stressors. Household income and food security serve as primary determinants; families with low income often face limited access to nutrient-dense foods and may rely disproportionately on energy-dense, low-cost processed foods that increase obesity risk. Moreover, children living in food-insecure households are paradoxically more vulnerable to obesity due to the hunger–obesity cycle, in which irregular food availability promotes overeating when food becomes accessible [26]. Maternal education and related characteristics also play a pivotal role in shaping child health outcomes. Lower maternal educational attainment is frequently associated with limited nutritional literacy, which may contribute to suboptimal complementary feeding practices following the cessation of exclusive breastfeeding [21].
With respect to maternal pre-pregnancy obesity, our meta-analysis demonstrated substantial heterogeneity. However, subgroup analyses stratified by age at obesity assessment markedly reduced statistical inconsistency, particularly in older children. The influence of the intrauterine environment and maternal metabolic status may follow a sleeper effect or a progressive trajectory. Although infant growth (≤3 years) is highly dynamic and sensitive to rapid changes in feeding practices, epigenetic programming associated with maternal obesity may become increasingly evident and stable as the child’s physiological systems mature.
Maternal pre-pregnancy obesity functions as both a biological and environmental predictor, as elevated maternal BMI is often associated with higher child BMI at age 5 through a combination of genetic susceptibility and shared household lifestyle patterns [52]. Emerging evidence further indicates that maternal overweight and obesity pre-pregnancy are independent risk factors for congenital heart defects (CHDs) in offspring, with risk increasing in proportion to the severity of maternal adiposity [64]. The underlying mechanisms likely involve chronic inflammation, oxidative stress, and disrupted glucose metabolism, which collectively interfere with molecular signaling essential for normal cardiac development during the first trimester. Severe CHD phenotypes, including tetralogy of Fallot, pulmonary valve stenosis, and atrial septal defects, have been reported more frequently among offspring of mothers with severe obesity, with odds of major malformations up to 1.8-fold higher in this group [64]. These findings highlight maternal pre-pregnancy weight as a critical and modifiable risk factor that warrants prioritization in preconception counseling to improve neonatal cardiovascular outcomes. As highlighted in recent prediction models, early identification of high-risk infants through maternal and neonatal data is crucial for timely and effective obesity prevention strategies. Specifically, the integration of factors such as maternal pre-pregnancy BMI, paternal BMI, and infant birth weight into screening protocols allows for more precise risk stratification during the first years of life, which is a critical window for mitigating long-term metabolic and cardiovascular complications [65].
Family structure and geographical context further modulate obesity risk in early childhood. In some regions, larger household size may strain financial resources, potentially diminishing the quality of individual caregiving and dietary intake [21]. Certain occupation-related risks have also been reported; for example, in rural Shaanxi, children of mothers employed in agriculture exhibited a higher risk of obesity, possibly reflecting differences in childcare arrangements or daily physical activity patterns [21]. Social disadvantage and ethnic background frequently intersect with healthcare access. Children from socially disadvantaged or underserved communities, such as those residing in the South Bronx in New York City, experience cumulative exposures to environmental stressors and reduced access to preventive healthcare services [26]. Similarly, in the Korean context, children from lower-income groups within the national health insurance system showed a significantly higher prevalence of obesity by age 5, underscoring economic stability as a consistent protective factor against early childhood weight gain [10].
The development of childhood obesity is increasingly understood to involve early emotional and behavioral regulation processes, beginning with the security of mother–child attachment [27]. Insecure attachment has been hypothesized to elevate obesity risk by disrupting stress-response systems, leading to maladaptive physiological and behavioral reactions to psychological stress. Children with insecure attachment may have diminished capacity for emotional regulation, increasing susceptibility to behaviors such as eating in the absence of hunger as a coping mechanism. Infant temperament and appetite-related traits also serve as early precursors shaping long-term weight trajectories [10]. Innate characteristics, such as heightened food responsiveness or strong appetite signals identified through temperament assessments, may result in excessive energy intake when responsive feeding practices are lacking. Behavioral patterns established as early as age 3, including rapid eating and preferences for energy-dense, sweet, or oily foods, have been identified as significant predictors of obesity by age 5 [10,27].
A key strength of this systematic review is the exclusive inclusion of cohort studies, ensuring that exposure preceded obesity outcomes. Additionally, ORs were converted to aRRs to minimize overestimation or underestimation of associations. Nevertheless, several limitations should be acknowledged. The included studies exhibited considerable methodological heterogeneity, particularly in variable definitions and measurement approaches across populations. This limitation was addressed by applying a random-effects model and conducting leave-one-out sensitivity analyses, which demonstrated that pooled estimates remained stable and statistically significant despite study-level variability. Furthermore, many studies provided limited adjustment for important confounding factors, including maternal BMI, socioeconomic status, and postnatal lifestyle behaviors such as child diet, physical activity, and screen time. As a result, residual confounding may contribute to variability in findings and restrict causal interpretation; thus, observed relationships should be interpreted as statistically significant associations rather than definitive causal effects. In addition, although antibiotic exposure was evaluated, variations in dosage and clinical indications could not be assessed and may have influenced the observed associations.
Another limitation is the exclusion of 12 studies during the full-text assessment due to methodological inconsistencies, such as the use of combined 'overweight/obesity' categories, ineligible study designs (cross-sectional or case-control), and outcome definitions that did not align with our inclusion criteria [65–76]. Detailed justifications for each excluded study are systematically documented in Table S1 [65–76].
These findings have several implications. For practice, promoting appropriate gestational weight gain, encouraging exclusive breastfeeding, and using antibiotics cautiously in early life may help reduce obesity risk. At the policy level, public health strategies should prioritize maternal nutrition, safe delivery practices, and early-life interventions targeting modifiable risk factors. Future research should further investigate the biological mechanisms linking perinatal exposures to childhood obesity and evaluate intervention effectiveness across diverse populations to strengthen causal inference.
Excessive gestational weight gain, pre-pregnancy obesity, female sex, cesarean section, macrosomia, and early-life antibiotic exposure were associated with an increased risk of obesity in children aged ≤5 years. In contrast, exclusive breastfeeding demonstrated a protective effect against early childhood obesity. Accordingly, promoting and supporting exclusive breastfeeding is particularly important for children at elevated risk of obesity. At the same time, it is essential to recognize that these perinatal factors do not operate in isolation but interact dynamically with postnatal environmental influences and lifestyle behaviors. Obesity prevention should therefore be conceptualized as a continuum that begins before conception. Maintaining optimal maternal weight prior to pregnancy is a critical component of this continuum, as it provides a foundational benefit for offspring growth, metabolic regulation, and long-term health.
• High gestational weight gain, pre-pregnancy obesity, sex, cesarean delivery, macrosomia, and early antibiotic exposure were identified as risk factors for obesity in children aged ≤5 years.
• Exclusive breastfeeding was identified as a protective factor against obesity in children aged ≤5 years.
• These findings highlight the perinatal period as a critical window for the implementation of obesity prevention strategies.

Ethics Approval

Not applicable.

Conflicts of Interest

The authors have no conflicts of interest to declare.

Funding

None.

Availability of Data

All data generated or analyzed during this study are included in this published article. Other data may be requested through the corresponding author.

Authors’ Contributions

Conceptualization: ARS, NN, MIK, SBM; Data curation: ARS, MIK, JOR; Formal analysis: ARS, MIK, JOR; Funding acquisition: None; Investigation: all authors; Methodology: ARS, NN, MIK, JOR; Project administration: all authors; Resources: all authors; Software: all authors; Supervision: NN, MIK, SBM; Validation: ARS, SBM, JOR; Visualization: ARS, NN, MIK; Writing–original draft: ARS; Writing–review & editing: all authors. All authors read and approved the final manuscript.

Supplementary data are available at https://doi.org/10.24171/j.phrp.2025.0351.
Table S1.
List of studies excluded during full-text screening with reasons for exclusion.
j-phrp-2025-0351-Supplementary-Table-S1.pdf
Figure 1.
Preferred Reporting Items for Systematic Reviews and Meta-Analyses flow diagram for study identification and selection. a)Databases searched include Cochrane, Embase, Medline (via PubMed), LILACS, and CNKI. b)Records excluded after title and abstract screening due to irrelevance or not meeting inclusion criteria.
LILACS, Latin American and Caribbean Health Sciences Literature; CNKI, China National Knowledge Infrastructure.
Figure 1. Preferred Reporting Items for Systematic Reviews and Meta-Analyses flow diagram for study identification and selection. a)Databases searched include Cochrane, Embase, Medline (via PubMed), LILACS, and CNKI. b)Records excluded after title and abstract screening due to irrelevance or not meeting inclusion criteria.  
Figure 2.
Conceptual framework of perinatal/early determinants of childhood obesity. BMI, body mass index.
Figure 2. Conceptual framework of perinatal/early determinants of childhood obesity. BMI, body mass index.  
Figure 3.
Forest plot of the association between high gestational weight gain and the risk of childhood obesity.
SE, standard error; IV, inverse variance; CI, confidence interval; df, degrees of freedom.
Figure 3. Forest plot of the association between high gestational weight gain and the risk of childhood obesity.  
Figure 4.
Forest plot of the association between cesarean section and the risk of childhood obesity.
SE, standard error; IV, inverse variance; CI, confidence interval; df, degrees of freedom.
Figure 4. Forest plot of the association between cesarean section and the risk of childhood obesity.  
Figure 5.
Forest plot of the association between macrosomia and the risk of childhood obesity.
SE, standard error; IV, inverse variance; CI, confidence interval; df, degrees of freedom.
Figure 5. Forest plot of the association between macrosomia and the risk of childhood obesity.  
Figure 6.
Forest plot of the association between antibiotic use and the risk of childhood obesity.
SE, standard error; IV, inverse variance; CI, confidence interval; df, degrees of freedom.
Figure 6. Forest plot of the association between antibiotic use and the risk of childhood obesity.  
Figure 7.
Forest plot of the association between exclusive breastfeeding and the risk of childhood obesity.
SE, standard error; IV, inverse variance; CI, confidence interval; df, degrees of freedom.
Figure 7. Forest plot of the association between exclusive breastfeeding and the risk of childhood obesity.  
Figure 8.
Forest plot of the association between female sex and the risk of childhood obesity.
SE, standard error; IV, inverse variance; CI, confidence interval; df, degrees of freedom.
Figure 8. Forest plot of the association between female sex and the risk of childhood obesity.  
Figure 9.
Forest plot of the association between pre-pregnancy obesity and the risk of childhood obesity.
SE, standard error; IV, inverse variance; CI, confidence interval; df, degrees of freedom.
Figure 9. Forest plot of the association between pre-pregnancy obesity and the risk of childhood obesity.  
Evidence for a perinatal origin of childhood obesity: a systematic review and meta-analysis of risk and protective factors
Table 1.
Descriptive summary of the studies
Table 1.
Study Year Country Study design Sample size Obesity definition (standard) Timing of obesity assessment (y) Adjusted variables Factors aRR
Anderson et al. [34] 2020 USA Cohort 260,935 Sex-specific BMI 95th percentile for age 4 Children’s age, sex, race/ethnicity, family income Breastfeeding (female) 0.67
Breastfeeding (male) 0.78
Awujoola et al. [26] 2024 USA Retrospective 1,447 Sex-specific BMI 95th percentile for age 5 Maternal age at delivery, ethnicity, pregravid maternal weight, mode of delivery, intrapartum prophylaxis against group B streptococcal disease, birth weight, neonatal sex, type of feeding, mother and child comorbidities Neonatal antibiotic 1.27a)
cohort
Badon et al. [35] 2020 USA Cohort 96,289 Sex-specific BMI 95th percentile for age 3 Maternal age, race/ethnicity, parity, education, insurance, smoking High gestational weight gain 1.30
Bailey et al. [36] 2014 USA Cohort 65,480 Sex-specific BMI 95th percentile for age 2–5 Gender, race, ethnicity, age at first primary care visit, location at first visit, insurance, clinical diagnoses, steroid use, antireflux medication use Antibiotic use 1.11
Butler et al. [54] 2021 New Zealand Cohort 1,731 Sex-specific BMI 95th percentile for age 4–5 Paternal BMI, maternal smoking, infant weight gain Birth weight 3.81
Chaparro et al. [37] 2020 USA Cohort 9,129 Sex-specific BMI 95th percentile for age 4 Child sex, race/ethnicity, family poverty, maternal education Breastfeeding (female) 1.10
Breastfeeding (male) 1.00
Chiasson et al. [39] 2016 USA Cohort 50,589 Sex-specific BMI 95th percentile for age 3 Race, residence, birth weight, breastfeeding package, screen time, healthy food daily, fruit and vegetable consumption Macrosomia 1.66a)
Exclusive breastfeeding 0.55a)
Screen time >2 h daily 1.15a)
Choi et al. [10] 2022 Korea Cohort 26,047 Sex-specific BMI 95th percentile for age 5 Birth weight, breastfeeding, income level, dietary behaviors, physical activity Macrosomia 1.42a)
Female 1.44a)
Maternal BMI 2.02a)
Middle income 1.15a)
Good appetite 1.51a)
Heavy intake of sweet food 1.24a)
Diesel et al. [38] 2015 USA Cohort 609 Sex-specific BMI 95th percentile for age 3 Race, marital status, employment status, household income, education, parity, mental health, smoking, alcohol, pre-pregnancy BMI, breastfed status High gestational weight gain 2.20
Gaillard et al. [48] 2013 The Netherlands Cohort 4,571 IOTF cut-offs for BMI, which are age and sex-specific 4 Maternal age, education, ethnicity, parity, folic acid supplementation, smoking habit, alcohol consumption High gestational weight gain 0.93
Hawkins et al. [11] 2019 USA Cohort 55,058 Sex-specific BMI 95th percentile for age 5 Child sex, race/ethnicity, maternal education, maternal age, marital status, sibling order, child year of birth Cesarean section 1.26
Breastfeeding 0.80
Smoking during pregnancy 1.54
Hu et al. [40] 2019 USA Cohort 1,425 Sex-specific BMI 95th percentile for age 4 Maternal age, race, marital status, education, insurance, energy intake during pregnancy, alcohol, tobacco use, parity, child sex, birth weight, gestational age, breastfed status High gestational weight gain 1.46
Pre-pregnancy obesity 2.24
Gestational diabetes 2.14
Kelly et al. [24] 2019 Ireland Cohort 8,186 Sex-specific BMI 98th percentile for age 5 Gender, creche, breastfed, food energy intake, level of exercise, having chronic illness, maternal BMI, birth weight of child, social class of household, maternal smoking, maternal education, and ethnicity Antibiotics 1.60
use
Malihi et al. [53] 2021 New Zealand Cohort 5,598 IOTF cut-offs for BMI, which are age and sex-specific 4.5 Child sex, ethnicity, birth weight, household income, maternal age, maternal education Macrosomia 1.40
Lower food security during infancy 1.32
Screen time >1 h/d 1.22
Shorter sleep duration 1.30
Weekly to daily consumption soft drink 1.25
Female 1.26
Masukume et al. [25] 2018 Ireland Cohort 11,134 IOTF cut-offs for BMI, which are age and sex-specific 3 Maternal age, education, ethnicity, marital status, region, infant sex, gestational age, pre-eclampsia, gestational diabetes, parity, birth weight Cesarean section (elective) 1.32
Masukume et al. [12] 2019 New Zealand Cohort 6,599 IOTF cut-offs for BMI, which are age and sex-specific 2–4.5 Maternal age, maternal ethnicity, education, marital status, pre-pregnancy BMI, maternal smoking during pregnancy, infant sex, gestational age at delivery, birth weight, parity, diabetes mellitus. Cesarean section (planned) 1.42
Pan et al. [19] 2019 China Cohort 1,767 Sex-specific BMI 95th percentile for age 3 Maternal age, gestational age, parity, infant sex, education, anemia at first antenatal visit, pre-pregnancy BMI, gestational weight gain, breastfeeding Macrosomia 2.40a)
Pei et al. [50] 2014 Germany Cohort 1,734 Sex-specific BMI 95th percentile for age 2 Parental education, birth weight, duration of gestation, head circumference, maternal age, maternal pre-pregnancy BMI, maternal smoking Cesarean section 1.59a)
Ralphs et al. [46] 2021 UK Cohort 6,410 Sex-specific BMI 95th percentile for age 4–5 Maternal age, maternal BMI, maternal education, alcohol consumption, maternal smoking, parity, gestational diabetes, child sex, birth weight, gestational period, maternal job status, maternal house tenure Cesarean section 0.98a)
Stark et al. [41] 2019 USA Retrospective 333,353 Sex-specific BMI 95th percentile for age 2 Maternal smoking, parental relationship, mode of delivery, birth weight, birth length. Antibiotics use 1.42
Cohort Cesarean section 1.26
Terashita et al. [42] 2023 Japan Cohort 60,769 IOTF cut-offs for BMI, which are age and sex-specific 3 Maternal age, pre-pregnancy BMI, education, household income, history of smoking, alcohol consumption, pregnancy complication, parity, child sex, birth term, birth weight Cesarean section 1.16
Ziauddeen et al. [43] 2022 UK Cohort 4,789 Sex-specific BMI 95th percentile for age 4–5 Maternal age, maternal education, smoking, employment status, gestational diabetes, birth weight, gestational age, breastfeeding High gestational weight gain 1.87
Anderson et al. [27] 2011 USA Cohort 6,650 Sex-specific BMI 95th percentile for age 4.5 Parenting practices, maternal BMI, sociodemographic characteristics, the quality of mother-child interaction Insecure attachment 1.24a)
Taveras et al. [28] 2010 USA Cohort prospective 826 Sex-specific BMI 95th percentile for age 4 Maternal age, education, parity, household income, pre-pregnancy BMI, paternal BMI Higher rates of maternal depression 1.51
Infancy rapid weight gain 2.27
Introduce solid food before 4 months of age 2.14
Higher rates of maternal restrictive feeding practice 2.99
Television in bedroom 2.00
Higher intake of sugar-sweetened beverages 4.58
Higher intake of fast food 2.00
Yang et al. [21] 2025 China Cohort prospective 8,201 BMI for age ≥+2 SD 3 Parental sociodemographic characteristics (education, occupation, household size, and household income); maternal health factors (delivery mode and gestational illness); and child-level variables (birth year, birth weight, gestational age, sex, anemia, breastfeeding, and primary caregiver) Low economic status 2.15
Mother work in agriculture 2.30
Small for gestational age 1.63
Okihiro et al. [51] 2012 Hawaii Retrospective cohort 389 Sex-specific BMI 95th percentile for age 4–5 Ethnicity, cohort and sex Severe rapid gain from 12 to 23 months 2.64a)
Brophy et al. [47] 2009 UK Cohort prospective 17,561 IOTF cut-offs for BMI, which are age and sex-specific 5 Socioeconomic status Ethnic group Asian 1.6
Ethnic group African 2.5
Watch more time 3 hours of TV a day 1.3
Solid food before 3 months 1.2
Smoking near child 1.3
Mothers pre-pregnancy weight 1.9
Goodwin et al. [44] 2025 UK Cohort prospective 10,446 BMI z-score≥95th percentile 4–5 Gestational diabetes, socioeconomic position, smoking during pregnancy, maternal pre-pregnancy obesity, low birth weight, prenatal antibiotic use Early life antibiotic use 1.36
Janjua et al. [29] 2012 USA Cohort prospective 740 BMI for age and sex ≥95th percentile 5 Maternal pre-pregnancy BMI, birth weight, number of children at home, sex, smoking during pregnancy Maternal pre-pregnancy BMI (obese) 2.53
Birth weight 2.04
Number of children at home (<2) 1.64
Sex (female) 1.67
Smoking during pregnancy (1–12 cigarette) 1.42
Lee et al. [49] 2024 Korea Cohort 16,866 BMI for age and sex ≥95th percentile 3–5 Prematurity, prolonged breastfeeding, late complementary food, sugar-sweetened beverages At 3 years, meat in complementary food 1.27
Prolonged breastfeeding 1.14
Consumption of sweetened beverages 1.37
At 4 years, meat in complementary food 1.24
Consumption of sweetened beverages 1.58
At 5 years, meat in complementary food 1.16
Consumption of sweetened beverages 1.514
Wojcicki et al. [30] 2015 USA Retrospective 833 BMI for age and sex ≥95th percentile 3 Maternal and family sociodemographic, prenatal and postnatal health, infant delivery and feeding, maternal and child psychosocial, child dietary and lifestyle Low income 3.94
Maternal pre-pregnancy obesity 1.77
Longer duration of breastfeeding 0.95
Sun et al. [22] 2017 China Cohort 1,949 BAZ >+2 2 Gender, delivery mode, maternal age, birth weight, duration of breastfeeding and sleeping Higher BMI magnitude 2.69
Later timing of infant BMI peak 1.35
Zhang et al. [20] 2013 China Cohort 1,098 BMI for age and sex ≥95th percentile 2 Child sex, delivery type, gestational age, number of siblings, and parental social demographic and economic Birth weight 1.85
Pre-pregnancy maternal BMI 1.09
Paternal BMI 1.06
Exclusive breastfeeding 0.53
Bottle emptying by encouragement 1.35
Verstraete et al. [31] 2014 USA Cohort 169 BMI for age and sex ≥95th percentile 4 Maternal obesity, maternal marriage status, maternal education, maternal country of origin, years mother has lived in the United States Breastfeeding 0.29
Peacock-Chambers et al. [33] 2017 USA Cohort 5,750 BMI for age and sex ≥95th percentile 4 Child race, birth weight, maternal age, maternal pre-pregnancy weight, maternal education, child gestational age, parent marital status, maternal employment, smoking in pregnancy. Infants with ITSC scores ≥6 1.25a)
Goisis et al. [45] 2016 UK Cohort prospective 11,965 IOTF cut-offs for BMI, which are age and sex-specific 5 child’s sex, mother smoking during pregnancy, length of breastfeeding, introduction to solid foods before months, frequency of sport per week, frequency of active playing with a parent per week, frequency of TV, watching, frequency of PC use, bed time, frequency child is taken to the playground, fruit portion per day, eating breakfast every day, maternal BMI and sweet drinks consumption Bottom income quintile 1.3
Huh et al. [32] 2012 USA Cohort prospective 1,255 BMI for age and sex ≥95th percentile 3 Maternal age, maternal education, race/ethnicity, child age, sex, maternal pre-pregnancy BMI, birth weight Cesarean delivery 2.10
Layte et al. [23] 2014 Ireland Cohort prospective 9,057 IOTF cut-offs for BMI, which are age and sex-specific 3 Maternal age, child sex, gestation, birth weight, birth order, weight gain in pregnancy and multiple status Female 1.44
Birth weight 1.75
Breastfeeding (6+mo) 0.51
Smoking in pregnancy (6–10 daily) 1.93
Maternal obesity 2.83
O'Callaghan et al. [52] 1997 Australia Cohort 4,062 BMI for age and sex ≥95th percentile 5 Birth weight (≥95 percentiles) 1.70
Female 1.40
Maternal BMI (≥95 percentiles) 3.90
Maternal education (primary) 1.90
Income (5 y) 1.20
Paternal BMI (≥95 percentiles) 2.00

aRR, adjusted risk ratio; BMI, body mass index; IOTF, International Obesity Task Force; ITSC, Infant–Toddler Social and Emotional Assessment.

a)aRR converted from odds ratio.

Table 2.
Risk of bias assessment of each study included in the systematic review (Joanna Briggs Institute)
Table 2.
A1 A2 B1 B2 B3 C1 C2 C3 D G H1 H2 K M1 M2 M3 P1 P2 R S T Z
Were the 2 groups similar and recruited from the same population? Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Were the exposures measured similarly to assign people to both exposed and unexposed groups? Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Was the exposure measured in a valid and reliable way? Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes No Yes No Yes Yes Yes Yes Yes Yes Yes
Were confounding factors identified? Yes Yes Yes No Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Were strategies to deal with confounding factors stated? Yes Yes Yes No Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Were the groups/participants free of the outcome at the start of the study (or at the moment of exposure)? Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Were the outcomes measured in a valid and reliable way? Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Was the follow-up time reported and sufficient to be long enough for outcomes to occur? Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes No No Yes
Was follow-up complete, and if not, were the reasons to loss to follow-up described and explored? No No ? Yes No No Yes No Yes No No No Yes No Yes Yes Yes Yes Yes No No Yes
Were strategies to address incomplete follow-up utilized? No No ? Yes No No Yes No No No No No No No Yes Yes Yes Yes Yes No No Yes
Was appropriate statistical analysis used Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Score 9 9 9 9 9 9 11 9 10 9 9 9 9 9 10 11 11 11 11 8 8 11

A1=Anderson 2020, B3=Butler 2021, D=Diesel 2015, K=Kelly 2019, P1=Pan 2019, T=Terashita 2022, A2=Awujoola 2023, C1=Chappar o 2019, G=Gaillard 2013, M1=Malihi 2021, P2=Pei 2014, Z=Ziauddeen 2022, B1=Badon 2020, C2=Chiasson 2016, H1=Hawkins 2019, M2=Masukume 2018, R=Ralphs 2021, B2=Bailey 2024, C3=Choi 2022, H2=Hu 2019, M3=Masukume 2019, S=Stark 2018. Quality assessment was rated as yes, no, unclear (indicated by ?), or not applicable.

Table 3.
Risk of bias assessment of each study included in the systematic review (Joanna Briggs Institute) continued
Table 3.
A1 A2 B1 B2 B3 C1 C2 C3 D G H1 H2 K M1 M2 M3
Were the 2 groups similar and recruited from the same population? Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Were the exposures measured similarly to assign people to both exposed and unexposed groups? Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Was the exposure measured in a valid and reliable way? Yes ? ? Yes ? ? ? Yes Yes ? Yes ? Yes Yes Yes ?
Were confounding factors identified? Yes Yes Yes No Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Were strategies to deal with confounding factors stated? Yes Yes Yes No Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Were the groups/participants free of the outcome at the start of the study (or at the moment of exposure)? Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Were the outcomes measured in a valid and reliable way? Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Was the follow-up time reported and sufficient to be long enough for outcomes to occur? Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Was follow-up complete, and if not, were the reasons to loss to follow-up described and explored? ? ? ? ? No ? ? ? ? ? ? ? Yes No Yes ?
Were strategies to address incomplete follow-up utilized? No No No ? No No No No No No No No No No Yes No
Was appropriate statistical analysis used Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Score 9 8 8 7 8 8 8 9 9 8 9 8 10 9 11 8

A1=Anderson 2011, B3=Brophy 2009, D=Wojicicki 2015, K= Peacock-Chambers 2017, A2=Taveras 2010, C1=Goodwin 2025, G=Sun 2017, M1=Hu 2012, B1=Yang 2025, C2=Janjua 2012, H1=Zhang 2013, M2=Layte 2014, B2=Okihiro 2012, C3=Lee 2024, H2=Verstaete 2014, M=O’Callaghan.

Quality assessment was rated as yes, no, unclear (indicated by ?), or not applicable.

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Evidence for a perinatal origin of childhood obesity: a systematic review and meta-analysis of risk and protective factors
Osong Public Health Res Perspect. 2026;17(2):114-135.   Published online March 27, 2026
DOI: https://doi.org/10.24171/j.phrp.2025.0351
Download Citation

Download a citation file in RIS format that can be imported by all major citation management software, including EndNote, ProCite, RefWorks, and Reference Manager.
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Evidence for a perinatal origin of childhood obesity: a systematic review and meta-analysis of risk and protective factors
Osong Public Health Res Perspect. 2026;17(2):114-135.   Published online March 27, 2026
DOI: https://doi.org/10.24171/j.phrp.2025.0351
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