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Evaluation of anthropometric measures for assessment of cardiometabolic risk in early childhood

Published online by Cambridge University Press:  17 April 2020

Nicole Coles
Affiliation:
Division of Endocrinology, The Hospital for Sick Children, University of Toronto, Toronto, Canada
Ravi Retnakaran
Affiliation:
Leadership Sinai Centre for Diabetes, Mount Sinai Hospital, Toronto, Canada Division of Endocrinology and Metabolism, University of Toronto, Toronto, Canada Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Canada
Anthony Hanley
Affiliation:
Leadership Sinai Centre for Diabetes, Mount Sinai Hospital, Toronto, Canada Division of Endocrinology and Metabolism, University of Toronto, Toronto, Canada Department of Nutritional Sciences, University of Toronto, Toronto, Canada
Catherine Birken
Affiliation:
Department of Nutritional Sciences, University of Toronto, Toronto, Canada Division of Paediatric Medicine, The Hospital for Sick Children, University of Toronto, Toronto, Canada
Jill Hamilton*
Affiliation:
Division of Endocrinology, The Hospital for Sick Children, University of Toronto, Toronto, Canada Department of Nutritional Sciences, University of Toronto, Toronto, Canada
*
*Corresponding author: Email [email protected]
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Abstract

Objective:

Waist-to-height ratio has been shown to be an important indicator of cardiometabolic risk. There are few studies evaluating this measure against existing measures of adiposity and cardiometabolic markers in early childhood. The objectives were: (i) to determine in young children the ability of waist-to-height ratio, BMI z-score, weight for length, and sum of skin fold thickness to predict cardiometabolic risk and (ii) to examine this association at ages 1, 3 and 5 years.

Design:

Prospective cohort study.

Setting:

A university hospital in Toronto, Ontario.

Participants:

Infants at 1 (n 406), 3 (n 112) and 5 years of age (n 94) born to mothers with and without gestational diabetes mellitus.

Results:

Weight for length and BMI z-score demonstrated the strongest correlations with biochemical measures compared to waist-to-height ratio, including leptin (at 5 years, weight for length z-score: ρ = 0·65, P < 0·001; BMI z-score: ρ = 0·67, P < 0·001) and measures of insulin resistance (at 3 years, weight for length z-score: ρ = 0·25, P = 0·02; BMI z-score: ρ = 0·24, P = 0·02). The magnitude of associations between anthropometric measures and biochemical measures strengthened over time. Weight for length and BMI z-scores were moderately correlated with overall measures of fat mass as measured by dual-energy X-ray absorptiometry (ρ = 0·65, P = 0·00; ρ = 0·61, P = 0·01).

Conclusions:

Waist-to-height ratio was not superior to existing measures in predicting cardiometabolic risk in young children. BMI z-score is a preferred measure of adiposity between birth and 5 years of age.

Type
Research paper
Copyright
© The Authors 2020

Health initiatives that target children at risk for cardiometabolic disease have demonstrated a modest impact on weight-related outcomes and quality of life(Reference Peirson, Fitzpatrick-Lewis and Morrison1,Reference De Onis2) . Accurate identification of ‘at risk’ youth in early childhood is, therefore, fundamental to health measures. Body composition in early childhood is important in determining future risk of cardiometabolic disease(Reference Wells, Chomtho and Fewtrell3), and monitoring of height and weight is a foundational aspect of paediatric care, which allows for the recognition of concerning weight trajectories prior to the onset of serious health consequences(Reference Geserick, Vogel and Gausche4,Reference Society, Summary and Society5) .

Measurement of adiposity in children can be accomplished using a variety of methods. Measurements such as weight for length and BMI z-scores are some of the most commonly used clinical techniques. Dual-energy X-ray absorptiometry (DXA), skin fold thickness measurements, air displacement plethysmography and bioelectrical impedance analysis are measures often used in a research setting to estimate adiposity. While providing a more comprehensive assessment of body composition, these measures can be technically challenging, expensive and not feasible to implement in routine care. Clinical measurements of adiposity should be simple, non-invasive, fast and inexpensive, while also offering diagnostic accuracy and precision. The measured parameter should also be associated with long-term cardiometabolic health. Currently, weight for length is the growth standard used to evaluate adiposity in children from birth to 2 years of age(Reference Society, Summary and Society57). However, there is increasing evidence to potentially support the utility of BMI in this age group(Reference Roy, Spivack and Faith8,Reference Furlong, Anderson and Kang9) . For children >2 years old, BMI z-score is the recommended measurement to assess for obesity and nutritional status(Reference Daniels and Hassink10,Reference Styne, Arslanian and Connor11) . However, both these measures have several inherent limitations as they do not specifically capture the amount of body fat or take into account its distribution(Reference Demerath, Schubert and Maynard12Reference Freedman, Wang and Maynard14).

Waist-to-height ratio is another anthropometric tool that may help stratify obese individuals at an increased risk of health sequelae(Reference Savva, Lamnisos and Kafatos15). Since the presence of visceral fat is strongly associated with cardiometabolic risk in adults, a measure that captures body fat distribution has additional discriminatory value(Reference Fox, Massaro and Hoffmann16). Compared to waist circumference, waist-to-height ratio has additional advantage accounting for the impact of height and relative body proportion on obesity-related risk. In adults, a waist-to-height ratio >0·5 is associated with an increased risk of CVD and metabolic syndrome across different ethnicities(Reference Ashwell and Hsieh17). Studies evaluating waist-to-height ratio in the paediatric age range are largely limited to older children and adolescents, with very few studies exploring the use of waist-to-height ratio in preschool-aged children. Some studies in children aged 3–7 years have shown that this measure may help identify those with risk factors for metabolic syndrome, such as insulin resistance, and is also associated with important indices of body composition such as body fat percentage(Reference Aristizabal, Barona and Hoyos18Reference Campagnolo, Hoffman and Vitolo20). However, other studies in this same age group have demonstrated that it is inferior to other clinical measures of adiposity and cardiometabolic health(Reference Whitrow, Moore and Davies21Reference Taylor, Williams and Grant23). To our knowledge, there are no studies comparing waist-to-height ratio with existing measures of adiposity in children under 3 years of age. Accordingly, the objectives of this study were: (i) to determine in young children the ability of waist-to-height ratio, BMI z-score, weight for length, and sum of skin fold thickness to predict cardiometabolic risk and (ii) to examine this association at ages 1, 3 and 5 years.

Methods

We included children from an ongoing cohort of mother–infant dyads in Toronto, Canada. The study design and protocol have been previously described in detail(Reference Retnakaran, Hanley and Raif24). The study cohort consists of mother–infant dyads with and without a history of maternal gestational diabetes mellitus (GDM). Study participants were recruited at the time of their antepartum screening for GDM at the Mount Sinai Hospital outpatient laboratory. All subjects who presented for screening were approached. According to routine practice, pregnant women are universally screened during the second trimester with 50 g glucose challenge test (GCT). Any abnormalities identified during this initial screening (defined as a 1-h plasma glucose ≥7·8 mmol/l) are subsequently referred for a formal oral glucose tolerance test (3 h 100 g). All participants were recruited before or after the GCT, and underwent a diagnostic glucose tolerance test for the determination of GDM status, irrespective of the GCT outcome. This was done to enrich the study population for offspring of women with GDM and cardiometabolic risk. Baseline maternal and paternal data were collected at the time of study recruitment and included demographic data from an established questionnaire. Parental ethnicity was self-reported, and the ethnicity of the offspring was determined by considering both maternal and paternal ethnicity. As the number of participants in each ethnic group was small, participants were classified as either ‘Caucasian/White/European’ and ‘Other’ for the analysis.

Participants and their offspring were followed during pregnancy and postpartum period, evaluating maternal markers of glucose metabolism and adiposity, as well as metabolic changes in the offspring, including growth, adiposity and insulin resistance.

In this analysis, the offspring had their anthropometry measured at 1, 3 and 5 years of age. Subjects were excluded if they were a product of twin pregnancy, if they were delivered prematurely (defined as <32 weeks gestation), if they were low birth weight (defined as a birth weight <2500 g) or if they had a known diagnosis impacting growth. Measurements of adiposity were not collected at birth and, therefore, were not included in the analysis.

Anthropometry

Offspring anthropometry, including waist-to-height ratio, sum of skin fold thickness, BMI z-score and weight for length z-score, was obtained at 1, 3 and 5 years of age. All measurements were obtained in triplicate by trained research staff following standardised procedures(25). A digital paediatric scale was used to obtain weight, rounded to the nearest gram. Up until 2 years of age, recumbent length was obtained using a digital board, rounded to the nearest 0·5 mm. After 2 years of age, standing height was measured using a stadiometer, rounded to the nearest 0·5 mm. Waist circumference was estimated by measuring the circumferential distance between the lowest rib and the top of the lilac crest. Skin fold thickness measurements were obtained in triplicate using a Harpenden Caliper(Reference Pierson, Wang and Heymsfield26) at the triceps, biceps, supra-iliac and subscapular regions. A sum of skin fold thickness was calculated to provide an aggregate measure of adiposity(Reference Schmelzle and Fusch27). These measurements have been shown to correlate well with estimates of body fat, as determined by DXA(Reference Schmelzle and Fusch27,Reference Freedman, Wang and Ogden28) . Supra-iliac and subscapular regions, in particular, are thought to act as a proxy for central adiposity.

Cardiometabolic function

A fasting blood sample was collected from the infants at 1, 3 and 5 years. Fasting duration was defined as the typical length of time the child normally passes overnight without eating. Ideally, this would be a period of 10 h, but a minimum of 4 h was required(Reference Anderson, Maguire and Lebovic29). Subjects were offered the option of a topical anaesthetic cream to minimise any potential pain associated with the procedure. The following measurements were included: insulin (Roche Diagnostics, Laval, Canada; electrochemiluminescence immunoassay range 21–118 pmol/l); glucose (enzymatic reference method with hexokinase); leptin (Millipore, Linco, St Charles, MO; ELISA range 0·5–100 ng/ml); lipids (cholesterol, TAG, HDL, LDL [derived, not measured directly]; Roche, Indianapolis, IN; fluorometric assay).

Insulin resistance was determined using the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR). HOMA-IR is a surrogate measure of insulin resistance that has been validated against gold-standard clamp procedures in non-diabetic young children under 5 years(Reference Gungor, Saad and Janosky30). It is calculated using the following formula: (fasting plasma glucose [mmol/l] × fasting plasma insulin [mU/l])/22·5(Reference Matthews, Hosker and Rudenski31).

Dual-energy X-ray absorptiometry

DXA scans were used to determine fat mass, fat-free mass and percent body fat at the age of 3 years. A Lunar Prodigy whole-body scanner (GE Medical Systems, Madison, WI) was used in conjunction with Encore 2002 software. Results from all scans were interpreted by a trained radiologist. Subjects did not require any preparation prior to the scan, and caregivers were present during the study for comfort. Each scan lasted approximately 3 min, during which time the subject lay supine. DXA scans provided estimates of percent body fat, percent truncal fat and overall fat mass (<1 % CV).

Statistical analysis

Descriptive statistics were used to characterise the study participants at birth and at different measurement intervals. Data are presented as means and standard deviations for continuous variables, and percentages for categorical variables. Continuous variables were assessed for normality of distribution. Natural log transformation was performed for skewed variables when required. Not all subjects were the exact same age at the time of study measurements; and given the age-related variation in anthropometric and biochemical variables, adjustment for age at study visit was performed. When age adjustment was performed, means and standard errors were reported. z-Scores were calculated for weight for length and BMI using the WHO calculator(32). P values <0·05 (two-tailed) were considered statistically significant. The prevalence of obesity was determined in the overall cohort and was defined by the WHO classification criteria(Reference de Onis and Lobstein33).

To assess the relationship between different anthropometric measures, body composition indices and fasting biochemical measures of cardiometabolic risk, Pearson correlation coefficients were used. Log transformation was performed for skewed variables, and Spearman correlation was also performed for variables that remained skewed after transformation. As there were no differences between GDM and non-GDM offspring with respect to waist-to-height ratio, a further stratified analysis was not performed. Coefficient values of 0·0–0·3, 0·3–0·5, 0·5–0·7, 0·7–0·9 represented negligible, weak, moderately strong and strong correlations, respectively(Reference Mukaka34). All statistical analysis was performed with SAS, version 9·4.

Results

Cohort characteristics

A total of 406 subjects had measurements at 1 year; 112 had measurements at 3 years; and 94 had measurements at 5 years of age (Tables 1 and 2). Offspring in the cohort were born at an average of 39 ± 1·3 weeks, with a mean birth weight of 3416 ± 432 g. The offspring cohort was ethnically diverse, with 55·6% identifying as ‘White/European/Caucasian’ and the remaining 44·4% as ‘Other’. ‘Other’ encompassed multiple ethnic groups, including South Asian, Asian, Black, Filipino, Latin American and Arab. Over time, the relative percentage of GDM offspring within the cohort fluctuated between 29·1 % at 1 year to 39·2% at 3 years and 31·9% at 5 years as the sample size changed over time.

Table 1 Cohort characteristics

GDM, gestational diabetes mellitus.

* Values are expressed as mean and standard deviation or as estimate ± standard error if age-adjusted.

Table 2 Prevalence of weight classes

Values are expressed as number of subjects (percentage); definition for weight status based on WHO classification(Reference Martin-Calvo, Moreno-Galarraga and Martinez-Gonzalez35).

The average waist-to-height ratio (estimate and standard error), adjusted for age, was 0·59 ± 0·00, 0·52 ± 0·00 and 0·47 ± 0·00 at 1, 3 and 5 years, respectively. The average BMI z-scores were 0·28 ± 1·06, 0·29 ± 1·05 and 0·15 ± 0·98 at 1, 3 and 5 years, respectively. The average weight for length z-scores were 0·30 ± 1·01, 0·25 ± 1·08 and 0·05 ± 0·97 at 1, 3 and 5 years, respectively. The prevalence of obesity in the overall cohort was determined using the WHO criteria(Reference de Onis and Lobstein33) and defined as BMI z-score >3 SD. The proportion of children with obesity was 1·5 % at 1 year and 2·1 % at 5 years. The prevalence of overweight was 3·0 % at 1 year and 3·1 % at 5 years.

Correlation between anthropometric measures and biochemical cardiometabolic markers

The correlations of anthropometric measures with contemporaneous fasting biochemical measures of cardiometabolic risk are shown in Table 3. Weight for length and BMI z-score demonstrated correlations with the greatest magnitude. They were also most consistently associated with biochemical measures of cardiometabolic risk at each time point. At 3 years of age, BMI z-score and weight for length were positively associated with leptin (ρ = 0·52, P < 0·0001; ρ = 0·51, P < 0·0001) and HOMA-IR (ρ = 0·24, P = 0·02; ρ = 0·25, P = 0·02), and both measures were also positively correlated with fasting insulin and leptin at 3 and 5 years. Only weight for length was positively associated with fasting insulin at 1 year of age (ρ = 0·18, P = 0·02). Conversely, waist-to-height ratio was only moderately positively associated with leptin. The correlation coefficients for leptin and waist-to-height ratio increased in strength from 1 to 5 years (ρ = 0·42, P < 0·0001 at 1 year; ρ = 0·51, P < 0·0001 at 3 years; ρ = 0·65, P < 0·0001 at 5 years). In general, the associations between all of the anthropometric measures and biochemistry were greater over time; however, as a result of subject attrition and ongoing study follow-up, the groups at different time points did not comprise the same population. None of the anthropometric measures were consistently associated with serum lipids.

Table 3 Pearson correlation between anthropometrics and biochemical markers of metabolic risk at 1, 3 and 5 years

HOMA-IR, Homeostatic Model Assessment of Insulin Resistance; TAG, triacylglycerides.

* Correlations are adjusted for age at the time of visit.

Correlation between anthropometric measures and body composition

Correlation between anthropometric measures and fat mass at 3 years of age, as estimated by DXA, was determined (Table 4). Waist-to-height ratio was only weakly positively correlated with overall fat mass (ρ = 0·41, P = 0·03). Sum of skin fold thickness, weight for length z-score and BMI z-score were weakly or moderately positively correlated with all three measures of adiposity, including percent body fat, percent truncal fat and overall fat mass. All measures were most strongly positively correlated with overall fat mass and not truncal measures of adiposity.

Table 4 Pearson correlation between anthropometrics and body composition assessed by dual-energy X-ray absorptiometry

Correlation between anthropometric measures at 1 year and BMI at 5 years

The relationships between measures of adiposity at 1 year and BMI z-score at 5 years of age were evaluated to determine the correlation between early childhood anthropometrics and future weight status (Fig. 1). BMI z-score and weight for length z-score measured at 1 year were most strongly predictive of future weight status at 5 years (ρ = 0·53, P < 0·0001; ρ = 0·56, P < 0·0001). Waist-to-height ratio and sum of skin fold thickness were less strongly associated with BMI z-score at 5 years (ρ = 0·28, P < 0·007; ρ = 0·34, P < 0·0001).

Fig. 1 (a) Pearson correlation between anthropometrics at 1 year and future BMI z-score at 5 years. (b) Scatterplots of Pearson correlations between anthropometrics at 1 year and future BMI z-score at 5 years

Discussion

This study was the first to compare existing measures of adiposity with waist-to-height ratio in young children within a large longitudinal cohort. Importantly, our study illustrates that, unlike in adolescents and adults, waist-to-height ratio, a measure of central adiposity(Reference Martin-Calvo, Moreno-Galarraga and Martinez-Gonzalez35), has a weaker association with biochemical metabolic risks and indices of adiposity compared to BMI z-score or weight for length z-score. Secondly, our study lends further support to the use of weight for length and BMI z-score in early childhood. It adds to the growing literature that endorses the use of BMI z-score as an anthropometric measure for predicting future obesity and cardiometabolic risk, which can be used continuously throughout early childhood(Reference Roy, Spivack and Faith36). In a cross-sectional study, Anderson et al. also described a significant relationship between BMI z-score and biochemical measures of traditional cardiometabolic risk, including leptin and insulin(Reference Anderson, Lebovic and Hamilton37). However, they did not evaluate BMI z-score against other alternative anthropometric measures such as waist-to-height ratio and sum of skin fold thickness. Furthermore, our study analysed fasting blood samples, which more accurately reflects insulin sensitivity compared to random insulin and glucose levels.

While there are several studies that have shown some utility of waist-to-height ratio in predicting metabolic risk among younger school-aged children (3–7 years)(Reference Sijtsma, Bocca and L’abée19,Reference Campagnolo, Hoffman and Vitolo20,Reference Aristizabal, Barona and Hoyos38) , our findings are in line with other studies that found no superiority compared to other measures. A study of 439 healthy children by Whitrow et al. sought to determine the association between waist-to-height ratio and systolic blood pressure. They demonstrated that this measure of central adiposity is not predictive of hypertension(Reference Whitrow, Moore and Davies21). Similarly, Corvalan et al. found a statistically significant correlation between waist-to-height ratio and other indices of body fat in 4-year-olds; however, it was only weakly associated with biochemical cardiometabolic markers(Reference Corvalán, Uauy and Kain22). Finally, Taylor et al. reported that waist-to-height ratio is correlated with trunk fat mass as measured by DXA in 301 children aged 3–5 years; however, waist circumference was overall more strongly correlated(Reference Taylor, Williams and Grant23).

Waist-to-height ratio may not offer the same predictive utility in young children as in adolescents or adults, for several reasons. There are significant changes in body proportion that occur between birth and the first few years of life, related to differential growth of different body segments. As a result, rapid growth and significant changes in height may invalidate the relationship between waist circumference and length. In contrast, in adults and adolescents, height is a more stable measure by which to standardise changes in waist circumference. Other studies in neonates have demonstrated that waist circumference is not strongly correlated with measures of truncal and overall adiposity(Reference Chen, Tint and Fortier39). This may be, in part, due to the technical difficulties that accompany this measurement, including movement, variations in feeding status, and accurate land marking of the ribs and iliac crest. Furthermore, signs of metabolic dysfunction in early childhood are subtle. While abnormalities in patterns of insulin resistance have been identified in early infancy(Reference Ong, Petry and Emmett40,Reference Lawlor, Riddoch and Page41) , accumulation of central adiposity that occurs in adolescents and adults may not have had time to develop. This may reduce the relevance of waist-to-height ratio as a predictor of cardiometabolic risk in early childhood.

One of the challenges of obesity prevention efforts stems from the fact that there is no consensus regarding the optimal way to define obesity in early childhood. At present, weight for length is the recommended anthropometric measure for the evaluation of growth in young children under 2 years of age(Reference Daniels and Hassink42). However, recent evidence suggests that BMI z-score may be more predictive of future obesity risk(Reference Roy, Spivack and Faith36). In our study, both indices demonstrated comparable associations between fasting biochemical measures and fat mass as measured by DXA. The use of BMI z-score is advantageous, as it would simplify growth monitoring for clinicians, allowing for a consistent measure of adiposity throughout childhood.

Interestingly, at 1 year of age, weight for length demonstrated a negligible association with fasting insulin and a weak association with leptin, while BMI z-score was only weakly associated with leptin. As the cohort grew older, both BMI z-score and weight for length showed a correlation with either fasting insulin or HOMA-IR as well as leptin. This suggests that weight for length may begin to correlate with insulin resistance at an earlier age compared to BMI z-score However, at 1 year, both measures were equally correlated with future BMI z-score at 5 years. While the sum of skin fold thickness has consistently shown an association with leptin, the cumbersome technique required to obtain this measurement limits its use in practice. These findings also serve to highlight that alterations in leptin and insulin resistance may be the earliest signs of cardiometabolic dysfunction in young children.

This study has several strengths. It is the first to evaluate waist-to-height ratio in children under the age of 3 years in a prospective and longitudinal design. This measure has been primarily studied in school-aged children with little focus on younger children. The analysis also included multiple measures of anthropometric and fasting cardiometabolic outcomes in very young children. One of the primary limitations of this longitudinal study relates to subject attrition in our cohort, resulting in a smaller number of measurements over time. In the overall study, the drop-out rate remained higher from birth to 1 year, with relatively stable rates of attrition after this time. Many of the mother–infant dyads in this cohort are first-time parents with otherwise healthy children. The childbirth period represents a difficult time for parents and families, rendering follow-up visits more challenging. Furthermore, parents of healthy children may not be as motivated to participate in follow-up visits. Importantly, there was no differential attrition in the proportion of GDM and non-GDM offspring.

Secondly, due to the relatively smaller numbers present at 3 and 5 years, we were unable to analyse the results based on ethnicity, which may have moderated the strength of association between waist-to-height ratio and cardiometabolic outcomes. In this study, 55·6 % of the cohort were classified as ‘White/European/Caucasian’. The remaining 44·4 % were classified as ‘Other’, which encompassed multiple ethnic groups, including South Asian, Asian, Black, Filipino, Latin American and Arab. Importantly, the cohort’s ethnic composition was similar to that of the city of Toronto, with 47·7 % of the city’s population identifying as Caucasian(43). The generalisability of study results to other multicultural cities is hence possible, but may not be universally relevant.

Finally, the rates of overweight and obesity were comparable to other cohorts recruited from the same geographic area(Reference Anderson, Carsley and Lebovic44), but are lower compared to cohorts from previous studies(Reference Sijtsma, Bocca and L’abée19,Reference Corvalán, Uauy and Kain22,Reference Taylor, Williams and Grant23) . The studied measures are likely to be more accurate among children with excess fat mass, as there may be less variation from changes in lean mass with greater adiposity. Enriching our study population with a higher proportion of overweight and obesity would have likely strengthened the relationship between anthropometric and cardiometabolic measures. Our cohort was, however, enriched for the offspring of GDM mothers. While in utero exposure to GDM may be a risk factor for metabolic dysfunction later in adult life, most studies have not found significant differences in anthropometric or metabolic profiles until later adolescence and adulthood(Reference Philipps, Santhakumaran and Gale45,Reference Kim, England and Sharma46) . This is consistent with our study in that we found no differences between GDM and non-GDM offspring with respect to waist-to-height ratio (data not shown).

Taken together, these results demonstrate that in early childhood, BMI z-score and weight for length z-score are superior anthropometric measures in terms of their association with both adiposity and cardiometabolic markers compared to waist-to-height ratio and sum of skin fold thickness. Conversely, waist-to-height ratio is a less useful measure of cardiometabolic health in young children. As BMI z-score may be used as a consistent measure throughout a child’s life time, this further supports its use as a proxy for cardiometabolic health and future weight status in early childhood. Future direction should focus on the relationship between these two measures in early childhood and longer-term cardiometabolic outcomes in adulthood.

Acknowledgements

Acknowledgements: We would like to acknowledge the work of Shiyi Cheng, the biostatistician who contributed to the statistical analysis of the manuscript. Financial support: This work was supported by the Heart and Stroke Foundation of Canada. Conflict of interest: J.H. is supported by the Mead Johnson Chair in Nutritional Science. R.R. holds the Boehringer Ingelheim Chair in Beta-cell Preservation, Function and Regeneration at Mount Sinai Hospital, and his research programme is supported by the Sun Life Financial Program to Prevent Diabetes in Women. The other authors have no disclosures. Authorship: N.C. and J.H. contributed to the design of the study and collection of data. All authors contributed to data analysis and interpretation. N.C. and J.H. participated in the drafting of the manuscript. All authors contributed to manuscript revision and approved the final version. Ethics of human subject participation: This study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving study participants were approved by the Institutional Research Ethics Board at Mount Sinai Hospital and the Hospital for Sick Children. Written consent was obtained from all participants, and mothers provided informed consent on behalf of their infant.

References

Peirson, L, Fitzpatrick-Lewis, D, Morrison, Ket al. (2015) Prevention of overweight and obesity in children and youth: a systematic review and meta-analysis. CMAJ Open 3, E23E33.CrossRefGoogle ScholarPubMed
De Onis, M (2004) The use of anthropometry in the prevention of childhood overweight and obesity. Int J Obes 28, S81S85.CrossRefGoogle ScholarPubMed
Wells, JCK, Chomtho, S & Fewtrell, MS (2007) Programming of body composition by early growth and nutrition. Proc Nutr Soc 66, 423434.10.1017/S0029665107005691CrossRefGoogle ScholarPubMed
Geserick, M, Vogel, M, Gausche, Ret al. (2018) Acceleration of BMI in early childhood and risk of sustained obesity. N Engl J Med 379, 13031312.CrossRefGoogle ScholarPubMed
Society, CP, Summary, E, Society, CPet al. (2010) Promoting optimal monitoring of child growth in Canada: using the new World Health Organization growth charts – executive summary. Paediatr Child Health 15, 7779.Google Scholar
Barlow, SE (2007) Expert committee recommendations regarding the prevention, assessment, and treatment of child and adolescent overweight and obesity: summary report. Pediatrics 120, Suppl., S164S192.CrossRefGoogle ScholarPubMed
WHO (2006) WHO child growth standards based on length/height, weight and age. Acta Paediatr Suppl 450, 7685.Google Scholar
Roy, SM, Spivack, JG, Faith, MSet al. (2016) Infant BMI or weight-for-length and obesity risk in early childhood. Pediatrics 137, e20153492.CrossRefGoogle ScholarPubMed
Furlong, KR, Anderson, LN, Kang, Het al. (2016) BMI-for-age and weight-for-length in children 0 to 2 years. Pediatrics 138, e20153809e20153809.CrossRefGoogle ScholarPubMed
Daniels, SR, Hassink, SG & Committee on Nutrition (2015) The role of the pediatrician in primary prevention of obesity. Pediatrics 136, e275e292.CrossRefGoogle ScholarPubMed
Styne, DM, Arslanian, SA, Connor, ELet al. (2017) Pediatric obesity-assessment, treatment, and prevention: an endocrine society clinical practice guideline. J Clin Endocrinol Metab 102, 709757.CrossRefGoogle Scholar
Demerath, EW, Schubert, CM, Maynard, LMet al. (2006) Do changes in body mass index percentile reflect changes in body composition in children? data from the Fels Longitudinal Study. Pediatrics 117, e487e495.CrossRefGoogle ScholarPubMed
Telford, RD, Cunningham, RB & Abhayaratna, WP (2014) Temporal divergence of percent body fat and body mass index in pre-teenage children: the LOOK longitudinal study. Pediatr Obes 9, 448454.CrossRefGoogle ScholarPubMed
Freedman, DS, Wang, J, Maynard, LMet al. (2005) Relation of BMI to fat and fat-free mass among children and adolescents. Int J Obes 29, 18.CrossRefGoogle ScholarPubMed
Savva, SC, Lamnisos, D & Kafatos, AG (2013) Predicting cardiometabolic risk: waist-to-height ratio or BMI. A meta-analysis. Diabetes Metab Syndr Obes 6, 403419.CrossRefGoogle ScholarPubMed
Fox, CS, Massaro, JM, Hoffmann, Uet al. (2007) Abdominal visceral and subcutaneous adipose tissue compartments: association with metabolic risk factors in the Framingham Heart Study. Circulation 116, 3948.CrossRefGoogle ScholarPubMed
Ashwell, M & Hsieh, SD (2005) Six reasons why the waist-to-height ratio is a rapid and effective global indicator for health risks of obesity and how its use could simplify the international public health message on obesity. Int J Food Sci Nutr 56, 303307.CrossRefGoogle ScholarPubMed
Aristizabal, JC, Barona, J, Hoyos, Met al. (2015) Association between anthropometric indices and cardiometabolic risk factors in pre-school children. BMC Pediatr 15, 170.CrossRefGoogle ScholarPubMed
Sijtsma, A, Bocca, G, L’abée, Cet al. (2014) Waist-to-height ratio, waist circumference and BMI as indicators of percentage fat mass and cardiometabolic risk factors in children aged 3–7 years. Clin Nutr 33, 311315.CrossRefGoogle ScholarPubMed
Campagnolo, PDB, Hoffman, DJ & Vitolo, MR (2011) Waist-to-height ratio as a screening tool for children with risk factors for cardiovascular disease. Ann Hum Biol 38, 265270.CrossRefGoogle ScholarPubMed
Whitrow, MJ, Moore, VM & Davies, MJ (2011) Waist-to-height ratio is not a predictor of systolic blood pressure in 3-year-old children. J Pediatr 159, 501503.CrossRefGoogle Scholar
Corvalán, C, Uauy, R, Kain, Jet al. (2010) Obesity indicators and cardiometabolic status in 4-y-old children. Am J Clin Nutr 91, 166174.CrossRefGoogle ScholarPubMed
Taylor, RW, Williams, SM, Grant, AMet al. (2008) Waist circumference as a measure of trunk fat mass in children aged 3 to 5 years. Int J Pediatr Obes 3, 226233.CrossRefGoogle ScholarPubMed
Retnakaran, R, Hanley, AJG, Raif, Net al. (2003) C-reactive protein and gestational diabetes: the central role of maternal obesity. J Clin Endocrinol Metab 88, 35073512.CrossRefGoogle ScholarPubMed
Centers for Disease Control and Prevention (2003) National Health and Nutrition Examination (NHANES III) Anthropometric Procedures Video. Atlanta: Centers for Disease Control and Prevention.Google Scholar
Pierson, RN, Wang, J, Heymsfield, SBet al. (1991) Measuring body fat: calibrating the rulers. Intermethod comparisons in 389 normal Caucasian subjects. Am J Physiol 261, E103E108.Google ScholarPubMed
Schmelzle, HR & Fusch, C (2002) Body fat in neonates and young infants: validation of skinfold thickness versus dual-energy X-ray absorptiometry. Am J Clin Nutr 76, 10961100.CrossRefGoogle Scholar
Freedman, DS, Wang, J, Ogden, CLet al. (2007) The prediction of body fatness by BMI and skinfold thicknesses among children and adolescents. Ann Human Biol 34, 183194.CrossRefGoogle ScholarPubMed
Anderson, LN, Maguire, JL, Lebovic, Get al. (2017) Duration of fasting, serum lipids, and metabolic profile in early childhood. J Pediatr 180, 4752.e1.CrossRefGoogle ScholarPubMed
Gungor, N, Saad, R, Janosky, Jet al. (2004) Validation of surrogate estimates of insulin sensitivity and insulin secretion in children and adolescents. J Pediatr 144, 4755.CrossRefGoogle ScholarPubMed
Matthews, DR, Hosker, JP, Rudenski, ASet al. (1985) Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28, 412419.CrossRefGoogle ScholarPubMed
World Health Organization (2011) WHO Anthro V3.2.2 Macros. Geneva: World Health Organization.Google Scholar
de Onis, M & Lobstein, T (2010) Defining obesity risk status in the general childhood population: which cut-offs should we use? Int J Pediatr Obes 5, 458460.CrossRefGoogle ScholarPubMed
Mukaka, MM (2012) Statistics corner: a guide to appropriate use of correlation coefficient in medical research. Malawi Med J 24, 6971.Google ScholarPubMed
Martin-Calvo, N, Moreno-Galarraga, L & Martinez-Gonzalez, MA (2016) Association between body mass index, waist-to-height ratio and adiposity in children: A systematic review and meta-analysis. Nutrients 8, 512.CrossRefGoogle ScholarPubMed
Roy, SM, Spivack, JG, Faith, MSet al. (2016) Infant BMI or weight-for-length and obesity risk in early childhood. Pediatrics 137, e20153492e20153492.CrossRefGoogle ScholarPubMed
Anderson, LN, Lebovic, G, Hamilton, Jet al. (2016) Body mass index, waist circumference, and the clustering of cardiometabolic risk factors in early childhood. Paediatr Perinat Epidemiol 30, 160170.CrossRefGoogle ScholarPubMed
Aristizabal, JC, Barona, J, Hoyos, Met al. (2015) Association between anthropometric indices and cardiometabolic risk factors in pre-school children. BMC Pediatr 15, 170.CrossRefGoogle ScholarPubMed
Chen, LW, Tint, MT, Fortier, M Vet al. (2018) Which anthropometric measures best reflect neonatal adiposity? Int J Obes 42, 501506.CrossRefGoogle ScholarPubMed
Ong, KK, Petry, CJ, Emmett, PMet al. (2004) Insulin sensitivity and secretion in normal children related to size at birth, postnatal growth, and plasma insulin-like growth factor-I levels. Diabetologia 47, 10641070.CrossRefGoogle ScholarPubMed
Lawlor, DA, Riddoch, CJ, Page, ASet al. (2005) The association of birthweight and contemporary size with insulin resistance among children from Estonia and Denmark: findings from the European Youth Heart Study. Diabet Med 22, 921930.CrossRefGoogle ScholarPubMed
Daniels, SR, Hassink, SG & Committee on Nutrition (2015) The role of the pediatrician in primary prevention of obesity. Pediatrics 136, e275e92.CrossRefGoogle ScholarPubMed
Statistics Canada (2017) Toronto, C [Census Subdivision], Ontario And Toronto, CDR [Census Division]. 98–316-X2016001. Ottawa, Canada: Statistics Canada.Google Scholar
Anderson, LN, Carsley, S, Lebovic, Get al. (2017) Misclassification of child body mass index from cut-points defined by rounded percentiles instead of Z-scores. BMC Res Notes 10, 639.CrossRefGoogle ScholarPubMed
Philipps, LH, Santhakumaran, S, Gale, Cet al. (2011) The diabetic pregnancy and offspring BMI in childhood: a systematic review and meta-analysis. Diabetologia 54, 19571966.CrossRefGoogle ScholarPubMed
Kim, SY, England, JL, Sharma, JAet al. (2011) Gestational diabetes mellitus and risk of childhood overweight and obesity in offspring: a systematic review. Exp Diabet Res 2011, 541308.CrossRefGoogle ScholarPubMed
Figure 0

Table 1 Cohort characteristics

Figure 1

Table 2 Prevalence of weight classes

Figure 2

Table 3 Pearson correlation between anthropometrics and biochemical markers of metabolic risk at 1, 3 and 5 years

Figure 3

Table 4 Pearson correlation between anthropometrics and body composition assessed by dual-energy X-ray absorptiometry

Figure 4

Fig. 1 (a) Pearson correlation between anthropometrics at 1 year and future BMI z-score at 5 years. (b) Scatterplots of Pearson correlations between anthropometrics at 1 year and future BMI z-score at 5 years