Body Weight at Age Four Years and Readiness to Start School: A Prospective Cohort Study

Abstract

Background:

Adolescents with obesity have lower academic performance, but little is known about the association between body weight in early childhood and school readiness. The objective was to examine the association between age- and sex-standardized body mass index (zBMI) and body weight status and school readiness in young children.

Methods:

A prospective cohort study in Toronto, Canada, was conducted in young children enrolled in TARGet Kids!. Children's weight and height were measured before the start of kindergarten. Children's school readiness was measured by the Early Development Instrument (EDI), a validated teacher-completed instrument that assesses children's skills and behaviors in five developmental domains in kindergarten. Generalized estimating equations, adjusted for relevant confounders, were used in the analysis.

Results:

The study included 1015 children (1217 observations): 52% were male and mean age at zBMI was 4.2 years [50 months (SD 12.1)] and school readiness was 5.2 years [62.7 months (SD 6.9)]. There was no evidence found that zBMI was associated with school readiness. However, in a post hoc analysis, being classified as overweight or with obesity in kindergarten was associated with twofold higher odds of vulnerability in school readiness and a lower social competence score compared with their normal weight peers.

Conclusions:

Being classified as overweight or with obesity was associated with poor school readiness in year 2 of kindergarten. Early interventions to promote healthy growth before school entry may help promote development and school readiness in young children. www.clinicaltrials.gov (NCT01869530).

Introduction

Children with overweight and obesity is a global public health concern. According to the 2020 data from the World Health Organization (WHO), 39 million children younger than 5 years are classified as overweight or with obesity worldwide.¹ Childhood body weight is commonly presented using age- and sex-adjusted body mass index scores (zBMI).¹ A higher zBMI in childhood often tracks throughout the life course, and children with increased zBMI in early childhood are at higher risk of overweight and obesity in adulthood, as well as chronic diseases including type 2 diabetes, cardiovascular disease, depression, and metabolic syndrome.^2,3

Previous research has suggested that adolescents with obesity, compared with adolescents with normal body weight, have lower academic outcomes,^4,5 but little is known about the association between body weight in early childhood and school readiness. Dietary lipids play an important role in brain growth and cerebral energy metabolism in early childhood, which may impact cognitive development.^6–8 Research on the relationship between adiposity and cognitive deficits has focused on potential mechanisms, including poor diet, poor sleep, obesity-related inflammation, or social competence.^9–14 School readiness is an important construct that holistically describes children's preparedness to learn at school,¹⁵ a pivotal transition between early development to school age, and is associated with a child's future academic achievement^16–18 and well-being.¹⁶ It is important to understand the relationship between body weight and school readiness to develop and target early interventions at this developmental transition to prepare children for success in school.

In 2016, an Australian study found that children with obesity, compared with their normal weight peers, were at higher risk of being vulnerable on a validated school readiness measure.¹⁹ However, the study did not control for important confounders known to affect school readiness, including family income or nutrition. Understanding the relationship between body weight and school readiness in young children is key to developing and targeting interventions aimed at improving children's school readiness, which may have lasting effects on children's learning trajectories.

The primary objective of this study was to determine if body weight in early childhood, as measured by zBMI and weight status, was associated with overall vulnerability in school readiness in kindergarten (4–6 years). Secondary objectives were to determine if body weight in early childhood was associated with individual school readiness domains, namely: physical health and well-being, social competence, emotional maturity, language and cognitive development, and communication skills and general knowledge. We hypothesized that higher body weight would be associated with higher vulnerability in school readiness.

Methods

A prospective cohort study was conducted in healthy children enrolled in The Applied Research Group for Kids (TARGet Kids! www.targetkids.ca) between 2015 and 2020. The mission of TARGet Kids! is to partner with community health care providers, families, and children and create knowledge to raise healthy children for lifelong health. Children were recruited at their well-child health visits between 0 and 5 years of age from primary care pediatrician's and family physician's offices in Toronto, Canada, and followed annually.

Children enrolled in the TARGet Kids! cohort, ages 4–6 years [typically, in Canada, children enter year 1 of kindergarten (K1) the year they turn 4 and year 2 of kindergarten (K2) the year they turn 5], were also invited to provide the names of their teacher, school, and school district (methods described elsewhere²⁰), so that teachers could be contacted through a secure electronic platform and complete the Early Development Instrument (EDI).¹⁵ Children were included if they had valid teacher-completed EDI data in kindergarten and weight and height measurements before an EDI collection. In the analysis, we had 1015 children and 1217 observations (202 children had repeated EDI data in K1 and K2). We used data from both K1 and K2 as this increased the power of our analysis.

Children were excluded if they had health conditions affecting growth; chronic conditions except for asthma; were born <32 weeks of gestation; or had parents/guardians who were not able to complete the consent and/or questionnaires in English or French. We excluded zBMI observations if they were <−5 and >5, as these were deemed implausible according to the WHO cut points.¹ This study was approved by the Research Ethics Boards at the Hospital for Sick Children, Unity Health, and the school districts where children were enrolled. Informed consent was provided by the parents and/or guardians of participants for primary data collection, and collection of EDI data was completed voluntarily by their teachers. All data were stored within The Applied Health Research Centre (AHRC) at Unity Health, Toronto, Canada, using a secure electronic data repository. These methods and study protocols have been previously described and the study is registered at www.clinicaltrials.gov.^20,21

Measurements

Participants' parents or guardians completed the questionnaires at each annual TARGet Kids! visit, including information on child and family characteristics and age-specific health and behavioral information. Physical measurements including weight and height were measured at each visit by trained research assistants. The EDI was completed in the second half of K1 and/or K2 by each child's teacher.

School Readiness

School readiness refers to whether a child has the competencies to successfully transition into school and can be measured using the valid and reliable teacher-completed EDI. School readiness is measured during kindergarten, which in Canada means ages 4–6 years. The EDI is used internationally and includes the following five developmental domains: physical health and well-being, social competence, emotional maturity, language and cognitive development, and communication skills and general knowledge.¹⁵ School readiness can be broadly viewed as a developmental outcome of the early years and has been shown to be highly predictive of academic achievement and later well-being.^{15–18,22,23}

The EDI data are mean scores on each of the five domains, vulnerability on each domain, as well as overall vulnerability, which is defined as vulnerability in at least one of the five domains. Vulnerability indicates a percentage of children who are struggling (i.e., not ready for school) in comparison with the Ontario normative baseline data and score below the 10th percentile cutoff.^24,25 The normative distribution scores for children in K1 were recently established (year 2021),²⁵ and thus, we decided to conduct the analysis by kindergarten grade level.

Overall vulnerability in school readiness was dichotomized as yes vulnerable or not vulnerable on the EDI. The continuous scores on each of the five domains (ranging from 0 = lowest score/low school readiness to 10 = highest score/high school readiness) were analyzed individually in the analysis.

zBMI and Body Weight Status

Body weight in early childhood was determined using the WHO child growth standards and was measured using both continuous and ordinal data (zBMI and body weight status).^1,26 Trained research assistants embedded within each TARGet Kids! primary care practice measured each child's weight and height following standardized WHO protocols.²⁷ For children <2 years, weight was measured using an infant scale and length using a length board. For children ≥2 years, weight was measured using a calibrated precision digital scale and standing height using a stadiometer (Seca, Hamburg, Germany). Weight in kilograms was divided by length/height in meters squared to calculate BMI (kg/m²), and the WHO growth standards were used to determine the zBMI.^1,26 Body weight status was defined according to the WHO cutoffs for children.²⁷

We combined normal weight and underweight as the reference group, as only 7 children were classified as underweight (i.e., zBMI<−2), and they were categorized as weight-for-height ≤1 standard deviation (SD) below the WHO child growth standards median (i.e., zBMI ≤1); overweight was categorized as weight-for-height >1 and ≤2 SD (i.e., zBMI >1 and ≤2); and obesity was categorized as weight-for-height >2 SD (i.e., zBMI >2). All weights and heights were measured before the child's school readiness measure and if there were multiple weight and height measurements available for a child, the data obtained closest in time to the date that the teacher completed the EDI were used for the child's zBMI.

Covariates

Parent-completed, standardized questionnaires were used to collect annual data on child/family characteristics and potential confounders. All potential confounders were included in the analysis and were identified a priori from the literature. All confounders were measured at the same time as the zBMI except for special needs status and child's first language concordant with the language of school instruction, which were measured at the same time as the school readiness measure. Confounders were as follows: child birthweight (<2.5; ≥2.5 and <4; and ≥4 kg),²⁸ gestational age (full term ≥37 weeks; and late preterm ≥32 to <37 weeks),²⁹ maternal age (in years),³⁰ self-reported family income,^31,32 and special needs designation on the EDI (yes/no).^33,34

We have recently published data showing that nutritional risk was associated with zBMI and therefore added nutritional risk as a confounder in a fully adjusted model.³⁵ Predictors of school readiness included child age at the time of EDI completion (in years),³⁰ maternal education (college/university; high school; and public school),³⁰ and whether a child's first language is concordant with the language of school instruction (yes/no).³⁶ Potential effect modifiers included child sex³¹ and self-reported family income.^31,32 Language was considered nonconcordant if it was not English in English-language schools, or not French in French-language schools. Nutritional risk was defined using the total score of the parent-completed Nutrition Screening for Toddlers and Preschoolers questionnaires (NutriSTEP^®).^37,38 The NutriSTEP are validated parent-completed questionnaires designed to identify children with characteristics or risk factors that might lead to impaired nutritional status and includes 17 questions related to dietary intake, eating behaviors, parental concerns about food and activity levels, screen time duration, and the use of vitamin/mineral supplements.^37,38

Statistical Analysis

Descriptive statistics were performed separately for nontime-sensitive and time-sensitive characteristics for all children based on their body weight status. School readiness was presented for the total cohort and by grade level (K1 and K2, separately) for children based on their body weight status. To avoid bias that can result from omitting observations with missing data, we used the MICE package in R to perform multiple imputations (all covariates had <15% missing data).²³ We used the variance inflation factor to test for multicollinearity for all predictors in all models. We reported 95% confidence intervals (CIs) and p-values for all models, and R version 3.5.0 (www.R-project.org) was used to conduct analyses.³⁹

For the primary analysis, generalized estimating equations (GEEs) with a logistic link function were used to test the associations between zBMI and body weight status in early childhood and school readiness accounting for within-person and within-family correlation. For the secondary analysis, we used Gaussian GEE models to test the association between zBMI and body weight status in early childhood and the five EDI domain scores separately, accounting for within-person and within-family correlation. Nonlinearity was explored for each association using restricted cubic splines with 5 knots on zBMI in separate models, and analysis of variance (ANOVA) was used to determine the best fit of the data.

The domain scores were all negatively skewed, with many scores being closer to 10 (i.e., highest score/high school readiness) compared with 0 (i.e., lowest score/low school readiness). To estimate the 95% CI and p-values for each of the skewed domain scores, we used bootstrap resampling from the imputed data set; we iteratively performed bootstrapping and multivariate imputation on each bootstrap sample (n = 500 times), performing the analysis on each bootstrap/imputation combination, and then pooled the results.⁴⁰ We used likelihood ratio tests for interaction terms, to determine whether child sex and self-reported family income modified the association of zBMI and body weight status with school readiness. We compared parameter estimates for the models including and excluding children classified as underweight from the normal weight comparison group.

In a post hoc analysis, we combined children classified as overweight and with obesity and compared this group with the reference group. In a post hoc analysis, we added nutritional risk total score to the fully adjusted model.

Results

A total of 1015 children (1217 observations) were included in the analysis. Of the 1217 observations, 591 and 626 observations were collected from children in K1 and K2, respectively, and 202 children had EDI data from both K1 and K2. (Fig. 1). Of the 1015 children included, 52% were male, 13% were born late preterm (between 32 and 37 weeks of gestation), 93% had mothers with college/university education, 47% had self-reported family income <$150,000/year, and 70% had European ethnicity.

Figure 1.

Sample size flowchart.

The mean age at zBMI measurement was 4.2 years (50 months, SD 12.1) [3.8 years (45.2 months, SD 9.7) and 4.5 years (54.5 months, SD 12.4) in the K1 and K2 cohorts, respectively] and school readiness measure was 5.2 years (62.7 months, SD 6.9) [4.7 years (56.8 months, SD 3.8) and 5.8 years (69.4 months, SD 3.7) in the K1 and K2 cohorts, respectively]. The mean BMI in K1 was 15.7 (SD 1.3) [zBMI 0.2 (SD 0.9)] and in K2 was 15.6 (SD 1.4) [zBMI 0.1 (SD 1.0)] (Table 1 ). There were 1041 observations of children with normal weight or underweight (zBMI ≤1) [n = 7 children were underweight zBMI < −2]; 138 observations of children with overweight (1 < zBMI ≤2); and 38 observations of children with obesity (zBMI >2). Two hundred and two children (17%) were overall vulnerable in school readiness, of which 86 (15%) were in K1 and 116 (19%) were in K2 (Table 2).

Table 1.

Participant Characteristics

N = 1015 children	N (missing%)	Mean (SD) or N (%)
N = 1015 children	N (missing%)	All children N = 1015	Children with zBMI ≤1^a N = 865	Children with 1 < zBMI ≤2^a N = 115	Children with zBMI >2^a N = 35
Nontime-sensitive variables (n = 1015 subjects)
Child sex^b	1015 (0.0%)
Female		483 (47.6%)	421 (48.7%)	45 (39.1%)	17 (48.6%)
Male		532 (52.4%)	444 (51.3%)	70 (60.9%)	18 (51.4%)
Birthweight (kg)^b	959 (5.5%)	3.2 (0.7)	3.2 (0.7)	3.4 (0.6)	3.5 (0.7)
<2.5 kg		102 (10.6%)	91 (11.1%)	8 (7.5%)	3 (8.8%)
≥ 2.5 and <4 kg		763 (79.6%)	658 (80.3%)	82 (77.4%)	23 (67.6%)
≥4 kg		94 (9.8%)	70 (8.5%)	16 (15.1%)	8 (23.5%)
Maternal education^b	998 (1.7%)
College/University		930 (93.2%)	801 (93.9%)	99 (88.4%)	30 (90.9%)
High school		52 (5.2%)	37 (4.3%)	12 (10.7%)	3 (9.1%)
Public school		16 (1.6%)	15 (1.8%)	1 (0.9%)	0 (0.0%)
Self-reported family income^b	975 (3.9%)
$0 to $39,999		49 (5.0%)	40 (4.8%)	6 (5.5%)	3 (9.4%)
$40,000 to $79,999		112 (11.5%)	92 (11.0%)	12(10.9%)	8 (25.0%)
$80,000 to $149,999		292 (29.9%)	247 (29.7%)	30 (27.3%)	15 (46.9%)
$150,000+		522 (53.5%)	454 (54.5%)	62 (56.4%)	6 (18.8%)
Maternal ethnicity^b	883 (13.0%)
European		621 (70.3%)	522 (69.4%)	79 (79.0%)	20 (64.5%)
East Asian		55 (6.2%)	51 (6.8%)	3 (3.0%)	1 (3.2%)
South and Southeast Asian		67 (7.6%)	61 (8.1%)	4 (4.0%)	2 (6.5%)
African		45 (5.1%)	34 (4.5%)	8 (8.0%)	3 (9.7%)
Latin American		28 (3.2%)	25 (3.3%)	0 (0.0%)	3 (9.7%)
Mixed ethnicity		54 (6.1%)	50 (6.6%)	4 (4.0%)	0 (0.0%)
Other		13 (1.5%)	9 (1.2%)	2 (2.0%)	2 (6.5%)
Gestational age^b	917 (9.6%)
Preterm (32–37 weeks)		122 (13.3%)	103 (13.1%)	16 (15.5%)	3 (10.0%)
Full term (≥37 weeks)		795 (86.7%)	681 (86.9%)	87 (84.5%)	27 (90.0%)
Special needs^c	1012 (0.3%)
No		979 (96.7%)	836 (96.8%)	111 (96.5%)	32 (97.0%)
Yes		33 (3.3%)	28 (3.2%)	4 (3.5%)	1 (3.0%)
Child's first language concordant with child's school language^c	962 (5.2%)
Yes		917 (95.3%)	778 (95.2%)	107 (95.5%)	32 (97.0%)
No		45 (4.7%)	39 (4.8%)	5 (4.5%)	1 (3.0%)
NutriSTEP total score	883 (13.0%)	12.8 (5.9)	12.7 (5.9)	12.6 (5.7)	14.9 (6.0)
Child age at the start of kindergarten^d	1015 (0.0%)	49.6 (3.4)	49.6 (3.4)	49.1 (3.4)	50.2 (3.5)
Time-sensitive variables (n = 1217 observations)
EDI outcome measured in K1 (n = 591 observations)
Child age (months)^e	591	56.8 (3.8)
Height (cm)^f	591	101.3 (7.7)
zHeight^f	591	0.007 (1.0)
Weight (kg)^f	591	16.2 (2.5)
zWeight^f	591	0.1 (0.8)
BMI (kg/m²)^f	591	15.7 (1.3)
zBMI^f	591	0.2 (0.9)
Maternal age (years)^e	591	37.8 (4.5)
EDI outcome measured in K2 (n = 626 observations)
Child age (months)^e	626	68.4 (3.7)
Height (cm)^f	626	106.7 (8.9)
zHeight^f	626	0.09 (1.0)
Weight (kg)^f	626	17.9 (3.4)
zWeight^f	626	0.2 (0.9)
BMI (kg/m²)^f	626	15.6 (1.4)
zBMI^f	626	0.1 (1.0)
Maternal age (years)^f	626	38.7 (4.6)

zBMI was measured at the closest time point before the EDI measurement. The reference group combined children with normal weight and underweight and is classified as zBMI ≤1, overweight is classified as 1 < zBMI ≤2, and obesity is classified as zBMI >2 according to the WHO body weight status cutoffs for children.

Collected at the time of zBMI measurement using a standardized parent-completed questionnaire adapted from the Canadian Community Health Survey.⁵⁰

Collected at the time of EDI measurement.

In Canada, children start K1 the year they turn 4 years.

Collected at the time of EDI measurement.

Measured at the closest time point before the EDI measurement.

EDI, Early Development Instrument; K1, kindergarten year 1; K2, kindergarten year 2.

Table 2.

Overall Vulnerability in School Readiness by zBMI Categories

	N (%)
Overall vulnerability	All observations	Observations with zBMI ≤1^a Normal weight and underweight	Observations with 1 < zBMI ≤2^a Overweight	Observations with zBMI >2^a Obesity
Total cohort	N = 1217	N = 1041	N = 138	N = 38
Yes	202 (16.6%)	162 (15.6%)	30 (21.7%)	10 (26.3%)
No	1015 (83.4%)	879 (84.4%)	108 (78.3%)	28 (73.7%)
K1 cohort	N = 591	N = 501	N = 74	N = 16
Yes	86 (14.6%)	69 (13.8%)	12 (16.2%)	5 (31.2%)
No	505 (85.4%)	432 (86.2%)	62 (83.8%)	11 (68.8%)
K2 cohort	N = 626	N = 540	N = 64	N = 22
Yes	116 (18.5%)	93 (17.2%)	18 (28.1%)	5 (22.7%)
No	510 (81.5%)	447 (82.8%)	46 (71.9%)	17 (77.3%)

Overall vulnerability is represented by vulnerability in at least one of the five domains. Vulnerable describes the children who score below the 10th percentile cutoff of the Ontario baseline population.

In total, 16% of children with normal weight, 22% of children with overweight, and 26% of children with obesity were overall vulnerable in school readiness. On average, the EDI collection occurred 382 days (SD 340 days) after BMI measurements.

For the primary analysis, there was no evidence of an association between zBMI and overall vulnerability in school readiness in the total cohort (adj OR: 1.14, 95% CI: 0.94–1.38, p = 0.19), children in K1 (adj OR: 1.08, 95% CI: 0.81–1.43, p = 0.61) or children in K2 (adj OR: 1.21, 95% CI: 0.94–1.55, p = 0.14) (Table 3). There was no evidence of higher odds of being overall vulnerable in school readiness for children classified as overweight or with obesity in K1. In contrast, among children in K2, the odds of children with overweight being overall vulnerable in school readiness were 2.07 times higher than those of children with normal weight (adj OR: 2.07, 95% CI: 1.09–3.90, p = 0.03) (Table 4).

Table 3.

Association Between zBMI and Overall Vulnerability in School Readiness in the Whole Cohort, and in Kindergarten Year 1 and Year 2

	Overall vulnerability in school readiness
	Unadjusted			Adjusted^b			Post hoc fully adjusted^c
	OR	95% CI	p-value	OR	95% CI	p-value	OR	95% CI	p-value
zBMI^a	Total (N = 1217)
	1.08	0.91–1.28	0.39	1.14	0.94–1.38	0.19	1.16	0.96–1.39	0.12
	K1 (N = 591)
	1.08	0.83–1.41	0.55	1.08	0.81–1.43	0.61	1.07	0.80–1.43	0.63
	K2 (N = 626)
	1.08	0.87–1.35	0.49	1.21	0.94–1.55	0.14	1.25	0.97–1.61	0.08

Logistic GEEs were used to examine the association between zBMI and overall vulnerability.

zBMI was measured at the closest time point before the EDI measurement.

Adjusted for child's age at the time of EDI measure, child sex, special needs, gestational age, maternal age, self-reported family income, maternal education, child's first language concordance with school language, and birthweight.

In a post hoc fully adjusted analysis, we added the NutriSTEP total score to the abovementioned covariates.

GEEs, generalized estimating equations.

Table 4.

Association Between Body Weight Status and Overall Vulnerability in School Readiness

Body weight status^a	Overall vulnerability in school readiness
	Unadjusted			Adjusted^b			Post hoc fully adjusted^c
	OR	95% CI	p-value	OR	95% CI	p-value	OR	95% CI	p-value
Total (N = 1217)
Overweight N = 138	1.53	0.97–2.40	0.06	1.48	0.91–2.40	0.11	1.45	0.90–2.35	0.13
Obesity N = 38	1.92	0.87–4.25	0.11	2.44	0.99–6.04	0.05	2.25	0.89–5.70	0.09
Overweight and obesity^d N = 176	1.61	1.07–2.41	0.02	1.65	1.06–2.56	0.03	1.60	1.02–2.50	0.04
K1 (N = 591)
Overweight N = 74	1.21	0.62–2.37	0.57	1.04	0.49–2.20	0.91	1.05	0.49–2.23	0.91
Obesity N = 16	2.85	0.96–8.46	0.06	2.91	0.92–8.25	0.07	2.71	0.85–8.63	0.09
Overweight and obesity^d N = 90	1.46	0.81–2.62	0.21	1.29	0.67–2.48	0.44	1.30	0.67–2.51	0.43
K2 (N = 626)
Overweight N = 64	1.88	1.04–3.39	0.04	2.07	1.09–3.90	0.03	2.29	1.20–4.38	0.01
Obesity N = 22	1.41	0.51–3.94	0.51	2.47	0.78–7.80	0.12	2.09	0.62–7.07	0.45
Overweight and obesity^d N = 86	1.75	1.03–2.97	0.04	2.15	1.21–3.82	0.009	2.25	1.24–4.08	0.008

p-values <0.05 are bolded.

Logistic GEEs were used to examine the association between zBMI and overall vulnerability.

In a post hoc fully adjusted analysis, we added the NutriSTEP total score to the abovementioned covariates.

In a post hoc analysis, we combined children classified as overweight and with obesity groups to look at zBMI >1.

In a post hoc analysis, we combined children classified as overweight and with obesity (zBMI >1) and found higher odds of being overall vulnerable in school readiness among those classified as overweight or with obesity compared with their normal weight peers (adj OR: 1.65, 95% CI: 1.06–2.56, p = 0.03). Stratification by kindergarten grade level revealed an association between overweight and obesity and overall vulnerability in school readiness among children in K2 but not in K1 (Table 4).

For the secondary analyses, there was no evidence of an association between zBMI and each of the 5 EDI domain scores in the total cohort, or for children in K1, or in K2 (Supplementary Table S1). We found evidence that the nonlinear model improved the fit of the data for zBMI and physical health and well-being (p = 0.03), as well as zBMI and social competence (p = 0.04) (Supplementary Figs. S1 and S2 and Supplementary Tables S2 and S3). Children classified as overweight or with obesity had lower scores on all five domains compared with their normal weight peers; however, these relationships were attenuated in the adjusted models (Table 5).

Table 5.

Association Between Body Weight Status and Scores in Each of the Early Development Instrument Developmental Domains

Body weight status^a	Unadjusted			Adjusted^b
Body weight status^a	β	95% CI	p-value	β	95% CI	p-value
Physical health and well-being score
Overweight N = 138	−0.22	−0.47 to −0.01	0.04	−0.11	−0.34 to 0.07	0.26
Obesity N = 38	−0.25	−0.66 to 0.29	0.28	−0.32	−0.67 to 0.26	0.13
Overweight and obesity^c N = 176	−0.23	−0.44 to −0.03	0.03	−0.17	−0.39 to −0.002	0.08
Social competence score
Overweight N = 138	−0.38	−0.70 to −0.07	0.01	−0.27	−0.59 to 0.04	0.08
Obesity N = 38	−0.43	−0.95 to 0.12	0.11	−0.49	−0.95 to 0.12	0.08
Overweight and obesity^c N = 176	−0.39	−0.65 to −0.13	<0.001	−0.32	−0.61 to −0.07	0.02
Emotional maturity score
Overweight N = 138	−0.16	−0.39 to 0.17	0.29	−0.07	−0.30 to 0.26	0.65
Obesity N = 38	−0.05	−0.52 to 0.46	0.84	−0.11	−0.55 to 0.47	0.61
Overweight and obesity^c N = 176	−0.13	−0.36 to 0.13	0.26	−0.08	−0.36 to 0.17	0.49
Language and cognitive development score^d
Overweight N = 138	−0.35	−0.66 to −0.08	0.02	−0.22	−0.60 to 0.06	0.09
Obesity N = 38	−0.37	−0.99 to 0.67	0.37	−0.41	−0.86 to 0.56	0.28
Overweight and obesity^c N = 176	−0.35	−0.63 to −0.06	0.02	−0.26	−0.57 to −0.05	0.05
Communication skills and general knowledge score
Overweight N = 138	−0.13	−0.53 to 0.24	0.51	0.02	−0.39 to 0.39	0.94
Obesity N = 38	−0.28	−1.05 to 0.59	0.49	−0.37	−0.93 to 0.49	0.33
Overweight and obesity^c N = 176	−0.16	−0.49 to 0.24	0.42	−0.06	−0.43 to 0.28	0.71

p-values <0.05 are bolded.

Linear GEEs were used to examine the association between zBMI and each EDI domain score.

zBMI was measured at the closest time point before the EDI measurement. Normal weight is classified as zBMI ≤1, overweight is classified as 1 < zBMI ≤2, and obesity is classified as zBMI >2 according to the WHO body weight status cutoffs for children.

Adjusted for child's age at the time of EDI measure, child sex, special needs, gestational age, maternal age, self-reported family income, maternal education, and birthweight.

In a post hoc analysis, we combined children classified as overweight and with obesity groups to look at zBMI >1.

Additionally adjusted for child's first language concordance with school language in the adjusted model.

In a post hoc analysis, we found that the combined group of children classified as overweight or with obesity (zBMI >1) had lower physical health and well-being, social competence, and language and cognitive development scores; after adjustment, the social competence score had the strongest association with zBMI (p < 0.05) (Table 5). There were no significant interaction effects by child sex and self-reported family income, and these interaction terms were not included in the final models. Nutritional risk total score was 12.7, 12.6, and 14.9 for children with normal weight, overweight, and obesity, respectively (Table 2). The addition of nutritional risk total score as a covariate did not change the relationship observed between zBMI and school readiness or body weight status and school readiness. The model removing children classified as underweight from the normal weight comparison group was not significantly different from the model including these children (p = 0.96).

Discussion

In this prospective cohort study including 1015 young healthy urban children (1217 observations), there was no evidence of an association between zBMI and school readiness in the total cohort. Being classified as overweight or with obesity in K2, at a mean age of 4.5 years (54.5 months), was associated with poor school readiness at a mean age of 5.8 years (69.4 months).

In a post hoc analysis, we combined children classified as overweight or with obesity (zBMI >1) and found that children in K2 classified as overweight or with obesity had >2 times higher odds of being overall vulnerable in school readiness compared with children classified as normal weight, but this was not observed for children in K1. It may take time for overweight or obesity to have a negative impact on school readiness and the impact of weight bias in the classroom may increase as a child gets older, hence the relationship in K2 and not in K1.^41,42 A larger sample of children would be beneficial to differentiate associations in different body weight status groups.

Generally, children in K1 had a lower frequency of being vulnerable overall on the EDI compared with children in K2 (15% vs. 19%). In addition, the percentages of children in both K1 and K2 who were classified as vulnerable on the EDI in our sample were lower compared with the provincial vulnerability rates in Ontario, Canada, where ∼26% and 27% of children are vulnerable in K1 and K2, respectively.^25,43 These differences are expected, given that the sample included an urban population with higher annual family income and higher maternal education levels compared with the provincial averages. Furthermore, there were only 38 (3%) children in this study with obesity (16 in K1 and 22 in K2), and thus, we may not have had enough power to detect differences between those with obesity and normal weight.

Children classified as overweight or with obesity had lower scores on all the five developmental domains of the EDI compared with their normal weight peers (zBMI ≤1), with the strongest association observed for the social competence domain.

School readiness is broadly viewed as the multidimensional skills required for children to successfully transition to school.^44,45 The EDI measures a child's early capacities, which have been shown to be good predictors of their later school achievement.^46,47 The strongest predictors of school readiness are predominantly nonmodifiable,³⁰ and thus, identifying factors that could be modified in early life to improve school readiness is important. BMI is commonly measured in the primary care setting and is a widely acknowledged marker of overnutrition as it reflects whether a child's diet is meeting, or exceeding, the nutritional needs over an extended period.⁴⁸ It is important to understand the relationship between a child's body weight status and school readiness to develop and target early lifestyle interventions to prevent overweight and obesity that may have a great return on investment to promote school readiness and help children succeed later in life.

In previous studies, the relationship between adiposity and cognitive deficits has been demonstrated while controlling for socioeconomic factors as well as associated medical conditions including cardiovascular and depression.¹⁰ The onset of obesity often occurs during early childhood, which is also when rapid brain development is occurring, and thus, understanding the association between obesity and the developing brain is important.⁹ Literature suggests that the possible brain development-related mechanisms driven by obesity include obesity-related inflammatory cytokines and obesity-related gut hormones, which have been associated with learning, memory, and general cognitive function in animal studies and studies in older adults.^9,10 Other data suggest it might be diet rather than adiposity that could be responsible for the cognitive deficits.^11,12 There is also some evidence that lower cognitive function in childhood can predict future increases in BMI, suggesting a possible bidirectional relationship.⁴⁹

Previous studies have looked at the relationship between child body weight status and the specific dimensions of child development including social functioning and physical health. Some studies found that children with overweight or obesity had increased risk of poor mental health, social functioning, and physical health.^50,51 Other studies with enough power to look at children with overweight and obesity separately found that obesity had a greater adverse association with social functioning and physical health.^19,52,53

In our adjusted analysis, there does not appear to be an association between body weight status and the physical health and well-being domain, despite previous research finding this association. A child with overweight/obesity would not necessarily score higher on the physical health and well-being domain as it includes the following seven subdomains: (1) physical readiness for school (i.e., dressed appropriately, not hungry/tired/late); (2) physical independence (i.e., independent in looking after their needs); (3) gross and fine motor skills (i.e., ability to physically tackle the school day); (4) overall social competence (i.e., good social development, cooperative, and self-confident); (5) responsibility and respect (i.e., show respect and follow rules); (6) approaches to learning (i.e., ability to work independently, solve problems, and follow class rules/routines); and (7) readiness to explore new things (i.e., curiosity about the surrounding world and eagerness to explore new toys, books, and games).

Further research should explore children with overweight and obesity separately and the physical health and well-being domains.

A study conducted in Germany among children ages 2–3 years (N = 444), looking at a composite measure of skill attainment in early life (verbal skills, activities of daily living, motor skills, and social skills)⁵⁴ found that children with obesity had lower skills compared with their normal weight peers.⁵⁴ However, both the height and weight and skill attainment measures were based on parental report and subject to reporting and recall bias.⁵⁴ This limitation is overcome in the present study, which uses directly measured height and weight, and teacher-reported school readiness. A study found that children with low body fat had a higher likelihood of poor development specifically for the cognitive, language, and socioemotional development domains.⁸

A large study of more than 7500 children, conducted in Australia¹⁹ by Pearce et al. in 2016, found that children with obesity were more likely to be vulnerable in school readiness compared with their normal weight peers and this relationship was primarily driven by the physical health and well-being and social competence domains. Their height and weight measurements were collected at a mean age of 4.8 years, which is slightly older than our cohort (mean of 4.2 years), but the school readiness data were collected at similar times (both at a mean age of 5.2 years). They used a tool called the Australian Early Development Census (AEDC), which was adapted from the Canadian EDI.¹⁹ Pearce et al. were unable to control for important sociodemographic variables including family income or nutrition, both of which we were able to control for in this study.

Previous research has shown that income accounts for a large percentage of the variability in school readiness.^25,32,55 We did not find a statistically significant interaction effect for zBMI or body weight status and self-reported family income for the adjusted models, but included this variable as a covariate. A previous study by our team found that children in K2 at high nutritional risk in early life were 4 times more likely to be vulnerable on the EDI.³⁵ In this study, we found that nutritional risk increased with increasing body weight categories; however, we did not find that the addition of this variable as a covariate in our post hoc fully adjusted models changed the observed relationships.

This prospective cohort study had several strengths. Valid and reliable zBMI and school readiness measures while controlling for many potential confounders including individual sociodemographic measures and accounting for the correlation within each subject. Children's weight and height were measured by trained research assistants using valid and reliable equipment and methods.^1,26,27 The EDI was completed by the children's kindergarten teacher and was at low risk of reporting error, recall, and social desirability biases.¹⁵ However, there is a body of evidence that suggests that weight bias (i.e., social marginalization and stigmatization of obesity) exists in the classroom, with BMI being associated with worsening teacher perception of academic ability in childhood.^41,56,57

Studies have found teachers' weight bias in kindergarten had a significant mediating effect on the evaluation of children with overweight or obesity and their cognitive and noncognitive skills.^42,58 It remains possible that a teacher's perceptions of a child's body weight status may have impacted the assessment on the EDI.⁴¹ In this study, body weight status but not zBMI was related to school readiness; this may be due to distinctive differences in body size and the impact this may have on teachers' perception of school readiness. Future work should examine the relationship between body weight status and objectively measured ability using standardized academic test scores and body weight status and school readiness while considering weight bias as a potential mediator.

The limitations of this study include that this was an observational study, and thus, causality cannot be inferred. More than 50% of the sample had a self-reported family income greater than $150,000/year, and since it is known that both family and neighborhood-level socioeconomic statuses (SES) account for a large percentage of the variability in school readiness, our sample may have underrepresented children in low SES circumstances.^25,32,55 Further investigation into the relationship between body weight status and school readiness among children in low-income families is warranted to optimally target future interventions. The modest number of children with overweight and obesity (n = 150) in this sample may have underrepresented children with overweight and obesity in this analysis.

Future studies with larger sample sizes would allow us to determine whether there is a dose–response relationship between body weight status and school readiness in children with severe obesity. Unmeasured confounders including physical activity, sleep, and weight bias may be accounting for the relationships observed in this analysis. Future longitudinal studies should explore whether the relationship persists into later childhood or changes throughout childhood while controlling for behavioral variables. Finally, this sample was primarily urban children living in Southern Ontario with highly educated mothers, and thus, the results may not be generalizable to other populations.

Conclusions

In summary, there was no evidence of an association between zBMI and overall vulnerability in school readiness in the total cohort. Overweight and obesity in early life were associated with poor school readiness in children in K2. The ability to meet age-appropriate developmental expectations at school entry is important for setting children up for future success in school and life. Early interventions to promote healthy growth before school entry may help promote development and school readiness.

Impact Statement

This study showed no evidence of an association between zBMI and school readiness; however, children with overweight or obesity were associated with poor school readiness. Understanding the relationship between body weight and school readiness is key to developing interventions that may have lasting effects on children's learning trajectories.

Footnotes

Acknowledgments

We thank all the participating families for their time and involvement in TARGet Kids! We are grateful to all the teachers who completed the EDI and the practitioners who are currently involved in the TARGet Kids! practice-based research network.

Members of the TARGet Kids! Collaboration:

Coleads: Catherine S. Birken and Jonathon L. Maguire.

Executive Committee and Core Research Team: Laura N. Anderson, PhD; Imaan Bayoumi, MD, MSc; Cornelia M. Borkhoff, PhD; Mateenah Jaleel, BSc; Charles Keown-Stoneman, PhD; Patricia Li, MD, MSc; Natricha Levy McFarlane, PhD; Jessica A. Omand RD, PhD; Pat Parkin, MD; Nav Persaud, MD, MSc; and Peter Wong, MD, PhD.

Research Staff: Natricha Levy McFarlane, MSc; Mateenah Jaleel, BSc; Dana Arafeh, MHI; Xuedi Li, MSc; Michelle Mitchell, BA; Hakimat Shaibu, MSc; and Yulika Yoshida-Montezuma, MPH.

Clinical Site Research Staff: Marivic Bustos, RPN; Pamela Ruth Flores, MD; Martin Ogwuru, MBBS; Sharon Thadani, MLT; Julia Thompson, SSRP; Laurie Thompson, MLT; and Kardelen Kurt, BSc; Ataat Malick, MD.

Offord Centre for Child Studies Collaboration: Principal Investigator: Magdalena Janus, PhD; Coinvestigator: Eric Duku, PhD; Research Team: Caroline Reid-Westoby, PhD; Patricia Raso, MSc; and Amanda Offord, MSc.

Site Investigators: Murtala Abdurrahman, MD; Kelly Anderson, MD; Gordon Arbess, MD; Jillian Baker, MD; Tony Barozzino, MD; Sylvie Bergeron, MD; Gary Bloch, MD; Joey Bonifacio, MD; Ashna Bowry, MD; Caroline Calpin, MD; Douglas Campbell, MD; Sohail Cheema, MD; Elaine Cheng, MD; Brian Chisamore, MD; Evelyn Constantin, MD; Karoon Danayan, MD; Paul Das, MD; Mary Beth Derocher, MD; Anh Do, MD; Kathleen Doukas, MD; Anne Egger, BScN; Allison Farber, MD; Amy Freedman, MD; Sloane Freeman, MD; Sharon Gazeley, MD; Charlie Guiang, MD; Dan Ha, MD; Curtis Handford, MD; Laura Hanson, MD; Leah Harrington, MD; Sheila Jacobson, MD; Lukasz Jagiello, MD; Gwen Jansz, MD; Paul Kadar, MD; Tara Kiran, MD; Holly Knowles, MD; Bruce Kwok, MD; Sheila Lakhoo, MD; Margarita Lam-Antoniades, MD; Eddy Lau, MD; Denis Leduc, MD; Fok-Han Leung, MD; Alan Li, MD; Patricia Li, MD; Jessica Malach, MD; Roy Male, MD; Aleks Meret, MD.

Elise Mok, MD; Rosemary Moodie, MD; Katherine Nash, MD; Sharon Naymark, MD; James Owen, MD; Michael Peer, MD; Marty Perlmutar, MD; Navindra Persaud, MD; Andrew Pinto, MD; Michelle Porepa, MD; Vikky Qi, MD; Noor Ramji, MD; Danyaal Raza, MD; Alana Rosenthal, MD; Katherine Rouleau, MD; Caroline Ruderman, MD; Janet Saunderson, MD; Vanna Schiralli, MD; Michael Sgro, MD; Hafiz Shuja, MD; Susan Shepherd, MD; Barbara Smiltnieks, MD; Cinntha Srikanthan, MD; Carolyn Taylor, MD; Stephen Treherne, MD; Suzanne Turner, MD; Fatima Uddin, MD; Meta van den Heuvel, MD; TheaWeisdorf, MD; PeterWong, MD; John Yaremko, MD; Ethel Ying, MD; Elizabeth Young, MD; and Michael Zajdman, MD.

Applied Health Research Centre: Christopher Allen, BSc; Peter Juni, MD; Gurpreet Lakhanpal, MSc; Gerald Lebovic, PhD; Audra Stitt, MSc; Kevin Thorpe, MMath; Ifeayinchukwu (Shawn) Nnorom, BSc; and Esmot ara Begum, PhD.

Mount Sinai Services Laboratory: Rita Kandel, MD; Michelle Rodrigues, PhD; Andrea Djolovic; Raya Assan; and Homa Bondar.

Authors' Contributions

J.A.O., M.J., and C.S.B. conducted the literature search, conceptualized and designed the study, drafted the initial article, and reviewed and revised the article.

X.L. and C.D.G.K.S. carried out the initial analyses and reviewed and revised the article.

C.M.B., E.D., G.L., J.L.M., M.M.M., P.C.P., J.R.S., and M.S.T. contributed to the study design, critically reviewed the article for important intellectual content, and reviewed and revised the article.

C.R.W. contributed to the collection of data and reviewed and revised the article.

All authors approved the final article as submitted and agree to be accountable for all aspects of the work.

Funding Information

Funding to support TARGet Kids! was provided by multiple sources including the Canadian Institutes for Health Research (CIHR), namely the Institute of Human Development, Child and Youth Health and the Institute of Nutrition, Metabolism and Diabetes (CIHR; FRN-115059), the St. Michael's Hospital Foundation (2012-0051-GF), and the Hospital for Sick Children Foundation for a grant to the Paediatric Outcomes Research Team (SP-5-602). Funding to support the first author, Dr. Jessica Omand, was provided by the Canadian Child Health Clinician Scientist Program, Restracomp through the Clinician Scientist Training Program (CSTP) at the Hospital for Sick Children, and Thor E. and Nicole Eaton Family Charitable Foundation Fellowship at the Hospital for Sick Children's Centre for Brain & Mental Health. The funding agencies had no role in the design, collection, analyses or interpretation of the results of this study, or in the preparation, review, or approval of the article.

Author Disclosure Statement

J.L.M. received an unrestricted research grant for a completed investigator-initiated study from the Dairy Farmers of Canada (2011–2012), and Ddrops provided nonfinancial support (vitamin D supplements) for an investigator-initiated study on vitamin D and respiratory tract infections (2011–2015). PCP received unrestricted research grants for completed investigator-initiated studies from Danone Institute of Canada (2002–2004 and 2006–2009), Dairy Farmers of Ontario (2008–2010). Mead Johnson Nutrition provided nonfinancial support (Fer-In-Sol^® liquid iron supplement) (2011–2017) for a completed investigator-initiated trial of iron deficiency in young children that was funded by the Canadian Institutes of Health Research (FRN # 115059). CSB received a research grant from the Centre for Addiction and Mental Health Foundation (CAMH 2017–2020). These agencies had no role in the design, collection, analyses or interpretation of the results of this study, or in the preparation, review, or approval of the article.

All other authors declare no conflicts of interest.

Supplementary Material

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