Laura N. Anderson

Body Mass Index, Waist Circumference, and the Clustering of Cardiometabolic Risk Factors in Early Childhood

BACKGROUND Obesity has its origins in early childhood; however, there is limited evidence of the association between anthropometric indicators and cardiometabolic risk factors in young children. Our aim was to evaluate the associations between body mass index (BMI) and waist circumference (WC) in relation to cardiometabolic risk factors and to explore the clustering of these factors. METHODS A cross-sectional study was conducted in children aged 1-5 years through TARGet Kids! (n = 2917). Logistic regression was used to evaluate associations between BMI and WC z-scores and individual traditional and possible non-traditional cardiometabolic risk factors. The underlying clustering of these measures was evaluated using principal components analysis (PCA). RESULTS Child obesity (BMI z-score >2) was associated with high (>90th percentile) leptin [odds ratio (OR) 8.15, 95% confidence interval (CI) 4.56, 14.58] and insulin (OR = 1.76; 95% CI 1.05, 2.94). WC z-score >1 was associated with high insulin (OR 1.59, 95% CI 1.11, 2.28), leptin (OR 5.48, 95% CI 3.48, 8.63) and 25-hydroxyvitamin D < 75 nmol/L (OR 1.39, 95% CI 1.08, 1.79). BMI and WC were not associated with other traditional cardiometabolic risk factors, including non-High Density Lipoprotein (HDL) cholesterol, and glucose. Among children 3-5 years (n = 1035) the PCA of traditional risk factors identified three components: adiposity/blood pressure, metabolic, and lipids. The inclusion of non-traditional risk factors identified four additional components but contributed minimally to the total variation explained. CONCLUSIONS Anthropometric indicators are associated with selected cardiometabolic risk factors in early childhood, although the clustering of risk factors suggests that adiposity is only one distinct component of cardiometabolic risk. The measurement of other risk factors beyond BMI and WC may be important in defining cardiometabolic risk in early childhood.

BMI-for-Age and Weight-for-Length in Children 0 to 2 Years.

OBJECTIVES: To determine the agreement between weight-for-length and BMI-for-age in children 0 to <2 years by using research-collected data, examine factors that may affect agreement, and determine if agreement differs between research- and routinely collected data. METHODS: Cross-sectional data on healthy, term-born children (n = 1632) aged 0 to <2 years attending the TARGet Kids! practice-based research network in Toronto, Canada (December 2008–October 2014) were collected. Multiple visits for each child were included. Length (cm) and weight (kg) measurements were obtained by trained research assistants during research visits, and by nonresearch staff during all other visits. BMI-for-age z-scores were compared with weight-for-length z-scores (the criterion measure). RESULTS: The correlation between weight-for-length and BMI-for-age was strong (r = 0.986, P < .0001) and Bland-Altman plots revealed good agreement (difference = −0.08, SD = 0.20, P = .91). A small proportion (6.3%) of observations were misclassified and most misclassifications occurred near the percentile cutoffs. There were no differences by age and sex. Agreement was similar between research- and routinely collected data (r = 0.99, P < .001; mean difference −0.84, SD = 0.20, P = .67). CONCLUSIONS: Weight-for-length and BMI-for-age demonstrated high agreement with low misclassification. BMI-for-age may be an appropriate indicator of growth in the first 2 years of life and has the potential to be used from birth to adulthood. Additional investigation is needed to determine if BMI-for-age in children <2 years is associated with future health outcomes.

Objectively measured physical activity of young Canadian children using accelerometry

The objective of the study was to describe objectively measured physical activity (PA) and sedentary time of infants, toddlers, and preschoolers and determine the proportion meeting Canadian age-specific PA guidelines. Ninety children (47 girls, 43 boys; mean age 32 (range, 4-70) months) attending scheduled health supervision visits and in the TARGet Kids! (The Applied Research Group for Kids) cohort wore an Actical accelerometer for 7 days. Participants with 4 or more valid days were included in the analysis. Time, in mean minutes per day (min/day), spent sedentary and in light PA, moderate to vigorous PA (MVPA), and total PA was determined using published cut-points; age groups were compared using ANOVA. Twenty-three percent of children <18 months (n = 28) and 76% of children aged 18-59 months (n = 45) met the guideline of 180 min/day of total PA; 13% of children ≥60 months (n = 17) met the guideline of 60 min/day of MVPA. Children <18 months spent more of their waking time per day engaged in sedentary behaviours (79%; ∼7.3 h) compared with children aged 18-59 months (63%; ∼6.6 h) and children ≥60 months (58%; ∼6.6 h). In conclusion, most children aged 18-59 months met the Canadian PA guidelines for children aged 0-4 years, whereas few younger than 18 months met the same guidelines. Only 13% of children ≥5 years met their age-specific PA guidelines. Further research is needed to develop, test, and implement effective strategies to promote PA and reduce sedentary behaviour in very young children.

Duration of Fasting, Serum Lipids, and Metabolic Profile in Early Childhood

Objectives To evaluate the association between fasting duration and lipid and metabolic test results. Study design A cross‐sectional study was conducted in healthy children aged 0‐6 years from The Applied Research Group for Kids! (TARGet Kids!) primary care practice network, Toronto, Canada, 2008‐2013. The associations between duration of fasting at blood collection and serum lipid tests and metabolic tests were evaluated using linear regression. Results Among 2713 young children with blood tests the fasting time ranged from 0 to 5 hours (1st and 99th percentiles). Fasting duration was not significantly associated with total cholesterol (&bgr; = 0.006; P = .629), high‐density lipoprotein (HDL) (&bgr; = 0.002; P = .708), low‐density lipoprotein (&bgr; = 0.0013; P = .240), non‐HDL (&bgr; = 0.004; P = .744), or triglycerides (&bgr; = −0.016; P = .084) adjusted for age, sex, body mass index, maternal ethnicity, and time of blood draw. Glucose, insulin, and homeostasis model assessment of insulin resistance were significantly associated with fasting duration, and the average percent change between 0 and 5 hours was −7.2%, −67.1%, and −69.9%, respectively. The effect of fasting on lipid or metabolic test results did not differ by age or sex; HDL and triglycerides may differ by weight status. Conclusions In this cohort of healthy young children, we found little evidence to support the need for fasting prior to measurement of lipids. The effect of fasting on glucose was small and may not be clinically important. When measuring serum lipid tests in early childhood, fasting makes a very small difference. Trial registration ClinicalTrials.gov: NCT0186953.

Prospective cohort study of vitamin D and autism spectrum disorder diagnoses in early childhood

Several studies have suggested an association between vitamin D in childhood and autism spectrum disorder. No prospective studies have evaluated whether lower vitamin D levels precede ASD diagnoses – a necessary condition for causality. The objective of this study was to prospectively evaluate whether vitamin D serum levels in early childhood was associated with incident physician diagnosed ASD. A prospective cohort study was conducted using data from preschool-aged children in the TARGet Kids! practice-based research network in Toronto, Canada, from June 2008 to July 2015. 25-hydroxyvitamin D concentration was measured through blood samples and vitamin D supplementation from parent report. Autism spectrum disorder diagnosis was determined from medical records at follow-up visits. Covariates included age, sex, family history of autism spectrum disorder, maternal ethnicity, and neighborhood household income. Unadjusted and adjusted relative risks and 95% confidence intervals were estimated using Poisson regression with a robust error variance. In this study, 3852 children were included. Autism spectrum disorder diagnosis was identified in 41 children (incidence = 1.1%) over the observation period (average follow-up time = 2.5 years). An association between 25-hydroxyvitamin D concentration and autism spectrum disorder was not identified in the unadjusted (relative risk = 1.04, 95% confidence interval: 0.97, 1.11 per 10 nmol/L increase in 25-hydroxyvitamin D concentration) or adjusted models (adjusted relative risk = 1.06; 95% confidence interval: 0.95, 1.18). An association between vitamin D supplementation in early childhood and autism spectrum disorder was also not identified (adjusted relative risk = 0.86, 95% confidence interval: 0.46, 1.62). Vitamin D in early childhood may not be associated with incident physician diagnoses of autism spectrum disorder.