Pediatric obesity

Children from low socioeconomic households are at greater risk of obesity.

Based on a nationally representative, longitudinal study of early childhood, this study finds that healthy infant feeding practices, including predominant breastfeeding, mediates the negative association between social class and obesity at 24 months.

Children from low socioeconomic households are at greater risk of obesity. As breastfeeding can protect against child obesity, disadvantaged infants are less likely to breastfeed relative to more advantaged children. Whether infant feeding patterns, as well as other maternal characteristics mediate the association between social class and obesity has not been established in available research.

Examine the impact of infant feeding practices on child obesity and identify the mechanisms that link socioeconomic status (SES) with child obesity.

Based on a nationally representative longitudinal survey (ECLS‐B) of early childhood (n = 8030), we examine how breastfeeding practices, the early introduction of solid foods and putting an infant to bed with a bottle mediate the relationship between social class and early childhood obesity relative to the mediating influence of other maternal characteristics (BMI, age at birth, smoking, depression and daycare use).

Infants predominantly fed formula for the first 6 months were about 2.5 times more likely to be obese at 24 months of age relative to infants predominantly fed breast milk. The early introduction of solid foods (< 4 months) and putting the child to bed with a bottle also increased the likelihood of obesity. Unhealthy infant feeding practices were the primary mechanism mediating the relationship between SES and early childhood obesity. Results are consistent across measures of child obesity although the effect size of infant feeding practices varies.

The encouragement and support of breastfeeding and other healthy feeding practices are especially important for low socioeconomic children who are at increased risk of early childhood obesity. Targeting socioeconomically disadvantaged mothers for breastfeeding support and for infant‐led feeding strategies may reduce the negative association between SES and child obesity. The implications are discussed in terms of policy and practice.

There is emerging evidence suggesting that childhood obesity may influence the timing/tempo of puberty and growth patterns.

Obese boys and girls present an earlier onset of puberty and completion of puberty, with a shorter duration of puberty compared to normal‐weight peers.

There is emerging evidence suggesting that childhood obesity may influence the timing of puberty and growth patterns. However, there are scant and controversial data in this field.

To assess whether puberty and physical growth vary in obese when compared to normal‐weight children.

One hundred obese pre‐pubertal children (44 boys; mean age (±SD): 9.01 ± 0.62 years; 56 girls; 8.70 ± 0.57 years) were compared to 55 normal‐weight controls (27 boys; 9.17 ± 0.26 years; 28 girls; 8.71 ± 0.62 years). All study participants were followed prospectively with 6‐monthly follow‐up visits. At each study visit, height, weight, body mass index (BMI) and pubertal stage were assessed.

Obese children entered puberty and achieved later stages of puberty earlier than controls (onset of puberty: boys: 11.66 ± 1.00 vs. 12.12 ± 0.91 years, P = 0.049; girls: 9.90 ± 0.78 vs. 10.32 ± 1.70, P = 0.016; late puberty: boys: 13.33 ± 0.71 vs. 14.47 ± 1.00 years, P < 0.001; girls: 11.54 ± 0.99 vs. 12.40 ± 1.02, P = 0.001). Pre‐pubertal BMI standard deviation score (SDS) was inversely associated with both age at the onset of puberty (β = −0.506, P < 0.001) and age at late puberty (β = −0.514, P < 0.001). Obese children also showed an earlier age at peak height velocity (PHV) (boys: 12.62 ± 0.82 vs. 13.19 ± 0.96 years, P = 0.01; girls: 11.37 ± 0.89 vs. 12.77 ± 0.76, P < 0.001) and a lower PHV (boys: 7.74 ± 1.49 vs. 9.28 ± 1.64 cm year⁻¹, P < 0.001; girls: 7.60 ± 1.64 vs. 8.29 ± 1.03, P = 0.03). Height SDS progressively declined over the study period in the obese group (P for trend <0.001), whereas there were no significant changes in the control group (P for trend = 0.5).

Obese boys and girls presented an earlier onset of puberty and completion of puberty and an impaired height gain during puberty.

To assess the association between different macro‐ and micro‐level socioeconomic factors and childhood overweight.

Data from the IDEFICS baseline survey is used to investigate the cross‐sectional association between socioeconomic factors, like socioeconomic status (SES), and the prevalence of childhood overweight. Differences and similarities regarding this relationship in eight European regions (located in Belgium, Cyprus, Estonia, Germany, Hungary, Italy, Spain and Sweden) are explored. 11 994 children (50.9% boys, 49.1% girls) and their parents were included in the analyses.

In five of the eight investigated regions (in Belgium, Estonia, Germany, Spain and Sweden), the prevalence of childhood overweight followed an inverse SES gradient. In the other three regions (in Cyprus, Hungary and Italy), no association between SES and childhood overweight was found. The SES‐overweight association in a region was best explained by the country‐specific human development index and the centre‐specific mean income. For the investigated association between other socioeconomic factors and overweight, no clear pattern could be found in the different regions.

The association between socioeconomic factors and childhood overweight was shown to be heterogeneous across different European regions. Further research on nationwide European data is needed to confirm the results and to identify target groups for prevention.

Recent studies have identified distinct trajectories of obesity development in children, but more research is required to further explore these trajectories.

This study further demonstrates that there are distinct trajectories of body mass index in children.

A limited number of studies have demonstrated that there may be distinct developmental trajectories of obesity during childhood.

To identify distinct trajectories of body mass index (BMI) in a large sample of Australian children.

Participants included 4601 children aged 4–5 years at baseline, who were followed up at ages 6–7 years, 8–9 years and 10–11 years. Height and weight were measured at each of these time points, and used to calculate BMI. Growth Mixture Modelling was used to identify the presence of distinct BMI trajectories.

Four distinct trajectories were identified (i) High Risk Overweight; (ii) Early Onset Overweight; (iii) Later Onset Overweight and (iv) Healthy Weight. Further analyses indicated that factors such as parental overweight, parent education, parent smoking and child birth weight were significant predictors of these trajectories.

These findings indicate that different patterns of BMI development exist in children, which may require tailored interventions.

Childhood obesity has increased 3 to 4 fold.

Total energy expenditure increases almost linearly with fat‐free mass.

Recent data report that the youth experience greater weight gain during summer than during school months. We tested the hypothesis that a difference in total energy expenditure (TEE) between school and summer months exists and may contribute to summer weight gain.

A secondary analysis was performed on cross‐sectional TEE data from school‐age, sedentary African–American and Caucasian youth based in or near the District of Columbia who were at‐risk for adult obesity because they had body mass index (BMI) ≥ 85th percentile or had overweight parents. TEE was estimated from 18‐O and deuterium measurements during 1‐week intervals using urine samples collected after ingestion of doubly labelled water. Differences in summer‐ and school‐time TEE were assessed using analysis of covariance. The data were adjusted for fat‐free mass (FFM) as determined by deuterium dilution to adjust for the effect of body size on TEE.

Data were collected from 162 youth (average age 10 ± 2 years, BMI 28 ± 8 kg m⁻² and BMI z‐score 1.96 + 0.96). Of these, 96 youth had TEE measured during the school year (September–June); 66 different youths had TEE measured during summer months (June–August). After adjustment for FFM, average summertime TEE was 2450 ± 270 kcal d⁻¹ and average school‐time TEE was 2510 ± 350 kcal d⁻¹ (P = 0.26).

No difference in TEE was detected between the school year and the summer months. These data suggest that seasonal differences in youth weight gain are not necessarily due to differences in energy expenditures.

The capacity to correctly assess insulin resistance and its role in further obesity‐associated metabolic derangement in children is under debate, and its determinants remain largely unknown.

We investigated the association of the insulin secretion profile with other metabolic derangements and anthropometric features in children and adolescents with obesity, exploring the role of ethnicity.

Growth and metabolic features, including fasting insulin levels and insulin secretory profile in an oral glucose tolerance test (OGTT), were analyzed according to ethnicity in 1300 patients with obesity (75.8% Caucasians/19.0% Latinos).

Height and bone age were influenced by sex, ethnicity, and insulinemia. Latino patients had higher insulin (P < .001), but similar glycemia both prepubertally and postpubertally, compared with Caucasians. Type 2 diabetes was uncommon (0.1%). Impaired glucose tolerance was associated to higher age, BMI, uric acid, and triglyceride levels (all P < .05), as was fasting hyperinsulinism. Impaired fasting glucose or HbA1c 5.7% to 6.4% showed no association with further metabolic derangement. A delayed insulin peak in the OGTT was associated to more severe metabolic disturbances.

Obesity‐associated hyperglycemia is unusual in our environment whereas fasting and late postprandial hyperinsulinemia are highly prevalent, with this being influenced by race and closely related with lipid metabolism impairment.

This study aimed to project the societal cost and benefit of an expansion of a water access intervention that promotes lunchtime plain water consumption by placing water dispensers in New York school cafeterias to all schools nationwide.

A decision model was constructed to simulate two events under Markov chain processes – placing water dispensers at lunchtimes in school cafeterias nationwide vs. no action. The incremental cost pertained to water dispenser purchase and maintenance, whereas the incremental benefit was resulted from cases of childhood overweight/obesity prevented and corresponding lifetime direct (medical) and indirect costs saved.

Based on the decision model, the estimated incremental cost of the school‐based water access intervention is $18 per student, and the corresponding incremental benefit is $192, resulting in a net benefit of $174 per student. Subgroup analysis estimates the net benefit per student to be $199 and $149 among boys and girls, respectively. Nationwide adoption of the intervention would prevent 0.57 million cases of childhood overweight, resulting in a lifetime cost saving totalling $13.1 billion. The estimated total cost saved per dollar spent was $14.5.

The New York school‐based water access intervention, if adopted nationwide, may have a considerably favourable benefit–cost portfolio.

The role of eating frequency on relative weight in childhood is not well understood.

To clarify this relationship by assessing the cross‐sectional and prospective relationships of weekday eating frequency with BMI z‐score (BMIz) and change in BMIz in a sample of schoolchildren.

Eating frequency, the average number of reported daily eating occasions, was assessed using two weekday 24‐h diet recalls. BMIz was measured at baseline, 6 months and 1 year in 155 urban schoolchildren, ages 9–15 years. Multiple linear regression models were used.

Cross‐sectional analyses at baseline suggest that BMIz was 0.23 units lower for each additional reported eating occasion (regression coefficient = −0.23; 95% confidence interval [CI]: −0.44, −0.07). From baseline to 6 months, BMIz increased by 0.03 units for each additional reported eating occasion (regression coefficient = 0.03; 95% CI: 0.01, 0.05). This relationship was no longer statistically significant at 1 year (regression coefficient = 0.01; 95% CI: −0.01, 0.03).

The findings suggest that the relationship of eating frequency with BMIz differs from that of change in BMIz. This difference may be due to methodological deficiencies of cross‐sectional studies, challenges of dietary assessment or differences in eating patterns among normal and overweight youth. Controlled trials are needed to further clarify this relationship.