View Article PDF

Once considered conditions of adulthood, the increase in the rates of obesity and physical inactivity over the last few decades have seen an accompanying increase in the incidence of insulin resistance, pre-diabetes and type 2 diabetes mellitus (T2DM) in children around the world.1,2

New Zealand has one of the highest rates of obesity for adults and children globally, with over 30% of children aged 2 to 14 years now classified, using the International Obesity Taskforce (IOTF) cut-offs, as being overweight or obese.3,4 Although the absolute numbers of diagnoses of T2DM in children under 15 years are low, a steady increase has been observed.5 For pre-diabetes, only data for children 15 years and over has been reported, with a prevalence rate of 8.4% for those aged 15 to 24 years.6

The early onset of T2DM in children and young adults is of concern because the increased duration of exposure to elevated blood glucose levels is likely to lead to a greater cumulative risk of microvascular (retinopathy, nephropathy and neuropathy) and macrovascular complications as they age, and the increased personal, societal and healthcare costs.7,8 Further, there is some evidence emerging that T2DM developed in youth is more aggressive than that developing in adulthood with, for example, a more rapid deterioration of β cell function reported and an earlier introduction of insulin treatment required.1

Pre-diabetes is defined as having either impaired glucose tolerance and/or impaired fasting glucose. 9 People with pre-diabetes are at increased risk of developing T2DM and cardiovascular diseases (CVD).10 However, if a healthy lifestyle is followed at this stage, there is a chance to delay or prevent the future development of T2DM.

A glycated haemoglobin test is a recommended diagnostic test used in the diagnosis of T2DM and may also be used as an opportunistic screening tool for pre-diabetes in those who present with risk factors.11 The wider availability of point of care (PoC) meters to measure glycated haemoglobin A1c (HbA1c) levels provides the opportunity of reaching a greater proportion of at-risk individuals through screening in community settings.

However, there is some debate whether the same adult HbA1c cut-offs should be used in children. Some studies suggest that a lower cut-off should be applied.12–15 Even for adults there are differences in the cut-off criteria that are used between different countries. In New Zealand, an HbA1c level over 50mmol/mol indicates diabetes and a level between 41 to 49mmol/mol indicates pre-diabetes.11 This is in contrast to the American Diabetes Association (ADA), which recommends slightly lower cut-offs for diabetes (>48mmol/mol) and pre-diabetes (39mmol/mol).9

In New Zealand, there are currently limited guidelines as to when to screen a child for pre-diabetes. Screening should be done in children or adolescents if they are obese (body mass index (BMI) ≥30kg/m2 or ≥27kg/m2 in Indo-Asians), if there is a family history of early onset T2DM or if they are in an at-risk ethnic group (Māori, Pacific Island or Indo-Asian).11 The measurement of many recognised risk factors in adults (eg, BMI) is less reliable in children because the relationship between components of body composition changes throughout childhood due to periods of growth, such as with puberty.16,17 Furthermore, some studies suggest that waist circumference (WC)18,19 or waist to height ratio (WtHR)20 (as a measure of central obesity and fat distribution) are better predictors of T2DM than BMI or percentage body fat (%BF). However, others report that measures of both central and overall adiposity strongly and similarly are associated with T2DM.20 Evidence also suggests that, despite having a WC within the normal range, individuals might be at increased risk of pre-diabetes/T2DM due to the increased adiposity.21

The aim of this study was to investigate the relationship between HbA1c levels as a measure of glycaemia, and a number of recognised risk factors that are associated with the later development of T2DM, in a group of Auckland school children. Being able to identify at-risk children early could provide the opportunity to implement lifestyle modifications, which might delay the progression to T2DM.

Methods

The study was a cross-sectional study conducted between August and September 2016 and August and September 2017 using a subset of school students in years 5 and 6 (8–11 year olds) recruited from a wider study, the main aim of which was to investigate bone health in children of this age. Schools spanning a range of decile levels (a measure of socio-economic status, with a low-school decile representing a low socio-economic status) and ethnicities were approached to participate to ensure a diverse sample with oversampling of certain ethnic minority groups (Māori, Pacific Island and South Asian) known to be at higher risk of T2DM. This was to allow ethnic-specific analysis.

Schools that agreed to participate were provided with written study protocol information for the school, parents and children. This detailed the intent of the study and the anthropometric test procedures, and it included a link for a short video to explain the data collection procedures to the children. Informed written consent was obtained from the parent and child, alongside an option to decline the finger prick blood test but still participate in the wider bone health study. Children were eligible for the wider bone health study if they were healthy and fully mobile, and they were excluded if they had a history of any disease affecting calcium, and vitamin D metabolism (eg, cardiac, kidney or liver disease), gastrointestinal disorders, a history of any long-term medication (eg, corticosteroids, anticonvulsants and immune-suppressants) or had any surgical implants, metal screws or similar. The study was approved by the Massey University Human Ethics Committee: Southern A, Application 16/42.

Demographic and physical-activity questionnaire

A week prior to the school visit, the class teacher sent home a questionnaire to be completed by one parent or guardian for each child. The questionnaire included demographic questions, such as the date of birth and ethnicity of the child. Physical activity was evaluated using the short version of the International Physical Activity Questionnaire (IPAQ).22 Metabolic equivalent (MET) minutes/week of physical activity was calculated as the MET intensity (walking=3.3 METs, moderate physical activity=4.0 METs and vigorous physical activity=8.0 METs) multiplied by the minutes of each physical activity multiplied by the number of days each physical activity occurred.22 All questionnaires were returned to the teacher and were then checked for completeness by one of the research team on the day of the visit.

Auckland is an ethnically diverse city. The predominant ethnic groups, using Statistics New Zealand level 1 categories, are: (i) European; (ii) Māori; (iii) Pacific Peoples; (iv) Asian.23 However, as people of South Asian ethnicity are known to be at increased risk of T2DM, the Asian group was further subdivided based on the geographic origins into (i) South Asian: Indian, Pakistani, Sri Lankan, Bangladeshi; (ii) East Asian: Chinese, Taiwanese, Korean, Japanese and (iii) South-East Asian: Indonesian, Thai, Singaporean, Malaysian, Filipino, Laotian. Children of other ethnic groups such as Middle Eastern, Latin American and African were allocated to a separate ‘other’ group.

Anthropometric measures

Anthropometric measurements were collected by trained researchers in a designated room at the children’s school during the school day. Height without shoes was recorded with a portable stadiometer (Seca 213) to the nearest 0.1cm, with two measurements taken and averaged. Waist circumference was measured in duplicate using the landmarks for waist measurements24 with a Lufkin W606PM pocket tape positioned around the body over light clothing while standing and recorded to the nearest 0.1cm.

Percentage of body fat was assessed with the Biospace InBody 230 Bio-electric Impedance Analyser (BIA) with the whole body %BF used. The BIA has been validated against dual-energy x-ray absorptiometry for %BF in children.25 The statistical methods for the development of prediction equations based on body composition parameters have been described elsewhere.26 Weight without shoes and in light clothing was recorded to the nearest 0.1kg using the BIA. The BIA uses %BF and classifies children at risk versus normal/low risk based on their gender and age.

Body mass index and WtHR were calculated. Age and gender specific BMI were ascertained using the International Obesity Taskforce BMI cut-offs (also referred to as the IOTF cutoffs), applying the equivalent BMI values at 18 years and linking to child centiles.27–29 Waist-to-height ratio was categorised as <85th and ≥85th percentiles of study population.

Assessment of glycaemic status

A trained researcher collected a finger prick blood sample to measure HbA1c levels using the Roche Cobas b 101 POC meter. This has been validated and delivers comparable outcomes to venous results on reference laboratory platforms.30

The ADA cut-offs for increased risk 39–46mmol/mol (5.7–6.4%) in children were used to categorise children as being in the normoglycaemic or pre-diabetic range.9

Statistical analysis

Data was analysed using the IBM SPSS statistical program, Version 24.0 software (IBM Corporation, New York, USA).

Standard descriptive statistics, including means, standard deviations, frequencies and percentages, were used as appropriate to summarise the socio-demographic and anthropometric results across participants grouped by their HbA1c level. Between-group comparisons were made using Independent Student t-tests, or Pearson’s chi-square test for categorical data.

Factors associated with HbA1c levels were assessed employing binary logistic regression analysis (univariable and multivariable). Children with missing information were excluded (n=18; ethnicity, 9; WC, 9; WtHR, 9; BMI, 3; %BF, 3), which left 433 children for the binary logistic regression analysis. Waist circumference and %BF were included in the regression analysis as a clinical measure (easy to apply) and a research measure, respectively. To avoid the violation of multicollinearity, WtHR and BMI were not included in the regression analyses. Three regression analyses were run including WC and %BF together and either of the anthropometric variables (WtHR and BMI) each individually along with demographic and lifestyle variables. Also, because there was a strong relationship between ethnicity and school decile (a smaller proportion of European children were from low decile schools, in comparison to Māori, Pacific and South Asian ethnicities: 0.6% vs 36-76%, P<0.0001), we ran two regression analyses including either ethnicity or school decile to avoid multicollinearity. As the inclusion of either of these variables did not affect the results, the results with ethnicity are reported. Imbalanced data with binary outcome variables are associated with biases in the estimated probability of an event. We investigated the models to determine whether all the assumptions were met and which model had a better model fit (assessing -2 log likelihood). We also added interaction terms into the models to investigate for interaction effects between variables.

Results

Of the 741 children invited to participate in the wider Children’s Bone Study, 685 (92%) children consented to take part. Of these children, 451 who also consented to a finger prick blood sample were included in this study. The HbA1c group contained proportionally fewer European children than the total group, and they were significantly older, taller, heavier and had a greater WC. There were no significant differences between the two groups for gender, BMI, %BF or physical activity (data not shown).

Participant characteristics

Participant characteristics for the 451 children, (whose HbA1c level was available) stratified by glycaemic status, are shown in Table 1. Age, sex, school decile and physical activity information was available for 451 children; ethnicity, WC and WtHR information was available for 442 children; and BMI and %BF information was available for 448 children. The age of the children ranged from 8 to 12 years (mean 10±0.6 years). Approximately one third of children were New Zealand European, and 13%, 24% and 10% were Māori, Pacific Island and South Asian, respectively. The mean %BF was 23±10% (range 7.8% to 50%) and WC 63±11cm (range 41cm to 104cm).

Glycated haemoglobin levels ranged from 27 to 46mmol/mol. None of children had a HbA1c level in the diabetes range. However, 71 children (16%) had HbA1c levels indicative of pre-diabetes, with a greater proportion of South Asian (30%), Pacific Island (27%) and Māori (18%) children, and those from low decile schools (31%), classified into this group than the normoglycaemia group. In contrast, European and East Asian children and those from medium and high-decile schools were predominantly within the normoglycaemic range.

All anthropometric measures, except height, were significantly higher in the pre-diabetic group than the normoglycaemic group. There was also a significant difference in the reported hours of physical activity per week, with fewer hours reported in the pre-diabetic group.

Table 1: Participants’ characteristics.

Values are mean±SD unless otherwise stated.
BMI, body mass index; HbA1c, glycated haemoglobin A1c.
1 Independent Student t-test or Pearson’s chi-squared for categorical variables.
2 Indian, Pakistani, Sri Lankan and Bangladeshi
3 Chinese, Taiwanese, Korean and Japanese.
4 Indonesian, Thai, Singaporean, Malaysian, Pilipino and Laotian.
5 Middle Eastern, Latin American and African

Main analysis: associations of demographic and anthropometric factors with HbA1c

Due to the small number of children in the East Asian, South-East Asian and other ethnicity groups, these groups were combined for the regression analysis. Table 2 presents the odds ratios (95% CI) from the univariable and multivariable analysis. Model x2 (9)=54, P<0.0001.

Pacific and South Asian children had 3.5 and 5.8 times increased odds, respectively, of being in the pre-diabetic group, compared with European children. In the univariate analysis, those of Māori ethnicity also showed increased odds of being in the pre-diabetic group, but the significance was lost after controlling in the multivariable analysis.

Children whose self-reported physical activity was two hours or less a week had 2.0 times higher odds of being in the pre-diabetic group than those who reported doing more than two hours a week. If their WC was ≥85th percentile or %BF was above the normal range (normal: 17±4.0% vs above normal: 32±7.0%, applying children’s standards [30]), they had 2.6 and 2.3 times higher odds of being in the pre-diabetic group, respectively. Over 56% (37/66) and 66% (44/66) of children with an HbA1c>39mmol/mol had a BMI and %BF, respectively, within the overweight/obese range.

The associations of WtHR and BMI each individually with HbA1c are presented in Table 3. Children who had a BMI within overweight and obese ranges or a WtHR≥0.5cm had 2.3, 4.9 and 5.1 times higher odds (adjusted for demographic and lifestyle factors) of being in the pre-diabetes group, respectively. Inclusion of WtHR or BMI (each individually) in the analyses did not affect the association of ethnicity and physical activity with HbA1c (data not shown).

Follow up analysis: associations of socio-demographics, anthropometric factors and HbA1c with ethnicity and school decile

As a follow up analysis, the characteristics of children of different ethnicities were compared with New Zealand European children (Table 4). A larger proportion of Pacific Island, South Asian and Māori children were from low decile schools than New Zealand European children. Physical-activity levels were lower among Pacific Island and South Asian children and children of other ethnicities as compared to New Zealand European children. However, Māori children had physical-activity levels comparable to those of New Zealand European children. All anthropometric measures were higher in Māori and Pacific Island children, and only %BF was higher in South Asian children (while having comparable BMI and other anthropometric measures) than New Zealand European counterparts. New Zealand European children had the lowest HbA1c compared to all other ethnicities.

Children from low- and medium-decile schools (school decile was considered as a proxy measure of socioeconomic status) had lower physical-activity levels and higher BMI and %BF than children from high-decile schools. Children from low-, but not medium-, decile schools had also higher WC and WtHR than children from high-decile schools (Table 4).

Table 2: Factors associated with HbA1c (normoglycaemic vs pre-diabetic) in 433 children.

CI, confidence Interval; HbA1c, glycated haemoglobin A1c; OR, odds ratio.
1 Variables included in the multivariable model were: age, sex, ethnicity, physical activity, waist circumference (as a clinical measure), and %body fat (as a research measure); Model x2 (9) = 54, P<0.0001.
2 HbA1c was coded as 1=Normoglycaemic (HbA1c ≤39 mmol/mol or 5.7%) and 2=Pre-diabetic (HbA1c >39 mmol/mol or 5.7%).
3 Indian, Pakistani, Sri Lankan and Bangladeshi.
4 East Asian (including Chinese, Taiwanese, Korean, and Japanese), South-East Asian (including Indonesian, Thai, Singaporean, Malaysian, Pilipino and Laotian) and others (including Middle Eastern, Latin American and African).

Table 3: The association of waist-to-height ratio and BMI with HbA1c (normoglycaemic vs prediabetic) in 433 children.

BMI, body mass index; CI, confidence interval; HbA1c, glycated haemoglobin A1c; OR, odds ratio.
1 Variables included in the multivariate model 1 were: age, gender, ethnicity, physical activity and BMI; Model 1: x2 (9)=54, P<0.0001. BMI was categorised according to age and gender specific BMI was ascertained using the International BMI cut-offs (also referred to as the International Obesity Taskforce (IOTF) cut-offs), applying the equivalent BMI values at 18 years and linking to child centiles. 27–29
2 Variables included in the multivariate model 2 were: age, gender, ethnicity, physical activity, and waist to height ratio; Model 2: x2 (9)=62, P<0.0001. Waist-to-height ratio was categorised as <85th (<0.05 cm) and ≥85th (≥0.5 cm) percentiles of population.

Table 4: Participant characteristics across ethnic groups and school deciles.

BMI, body mass index; HbA1c, glycated haemoglobin A1c.
Values are mean±SD and median (25th, 75th percentiles) unless otherwise stated.
I1ndian, Pakistani, Sri Lankan and Bangladeshi.
2 East Asian (including Chinese, Taiwanese, Korean and Japanese), South East Asian (including Indonesian, Thai, Singaporean, Malaysian, Pilipino and Laotian) and others (including Middle Eastern, Latin American and African).
3 One-way ANOVA for normally distributed data and Kruskal–Wallis non-parametric test for not-normally distributed data. Adjusted for multiple comparison. Ethnic groups were compared vs New Zealand European. Data in bold are indicative of a significant difference (significant at P<0.0125).
4 One-way ANOVA for normally distributed data and Kruskal-Wallis non-parametric test for not-normally distributed data. Adjusted for multiple comparison. Lowe and medium decile schools were compared vs. high decile school, Data in bold are indicative of a significant difference (significant at P<0.025).

Discussion

The present study showed a high prevalence of pre-diabetes (HbA1c levels >39mmol/mol or 5.7%)9 in New Zealand school-aged children. Being of South Asian or Pacific Island ethnicity, doing less than two hours of exercise a week and having a WC≥85th percentiles and an above normal %BF (normal: 17±4.0% vs above normal: 32±7.0%, applying children’s standards)31 were associated with an increased odds of having pre-diabetes in these children.

In line with the increasing prevalence of overweight and obesity in children (by 47% during the period 1980–2013), 32 T2DM has become increasingly prevalent in children and adolescents.33–36 The participants in this study were New Zealand children aged 8–11 years. The prevalence of pre-diabetes was 16% in this study, which was higher than the prevalence reported in Chinese children and adolescents aged 6–17 years (1.9%),37 Indian children aged 5–10 years (3.7%)38 and Vietnamese children aged 11–14 years (6.1%),39 and it is closer to the prevalence reported in American adolescents aged 12–18 years (18%)40 and New Zealand adults aged 15 years and older (19%).6 This difference may be due to the different screening methods used in these studies (eg, fasting blood glucose (FPG), oral glucose tolerance (OGT) and HbA1c) and participants’ characteristics (eg, age and ethnicity).

Okosun et al (2015) compared the efficacy of individual blood glucose tests in screening for pre-diabetes in adolescents aged 12–19 years and reported a prevalence rate that substantially differed across different screening methods (a prevalence rate of 6.4, 12 and 1.8% using HbA1c, FPG and OGT, respectively).41 Furthermore, the difference was more pronounced for some ethnic groups; the prevalence of pre-diabetes was greater in non-Hispanic Black adolescents using HbA1c than FPG and OGT tests. The higher prevalence of pre-diabetes using HbA1c may be attributed to the racial/ethnic differences in haemoglobin glycation or red cell survival and vitamin and medication use.42,43 It is important to note that these blood glucose tests might measure different aspects of blood glucose metabolism. Using a combination of HbA1c and FPG test has been suggested to provide both the benefits of individual test and decreases the risk of systemic bias inherent using only the HbA1c test.41,44

Ethnicity/race has been recognised as a well-established risk factor for pre-diabetes and T2DM.9,45,46 Although the high prevalence of pre-diabetes was evident across all ethnic groups in our study, the greatest prevalence was observed in South Asian (30%), Pacific Island (27%) and Māori children (18%), rather than New Zealand European children (6%), which is similar to older population patterns in New Zealand.6 Coppell et al (2013) reported a higher prevalence of pre-diabetes among Pacific Island and Māori youths and adults (aged 15–24 years) than New Zealand European and others (13–14% vs 7%, respectively).6 Similarly, the prevalence of T2DM has been reported to be higher among Pacific Island and South Asian adults aged 25 years and older than New Zealand Europeans.47 Reports from other countries (eg, the US and UK) also show a higher prevalence of pre-diabetes in children and adolescents of some ethnic groups (eg, South Asian and Black African–Caribbean children as compared to whites48 and non-Hispanic Black adolescents as compared to their non-Hispanic white counterparts).40

We speculated that the difference of overweight/obesity measures between children of these ethnicities (South Asian, Pacific Island and Māori children) and New Zealand European children might partially contribute to differences in pre-diabetes prevalence. All measures of overweight/obesity were positively associated with pre-diabetes (Tables 2 and 3), which is consistent with previous studies showing obesity as a risk factor for pre-diabetes and T2DM.6,40,41 We also showed that %BF above normal was a better factor associated with pre-diabetes than BMI (eight more pre-diabetic children). It is important to note that although anthropometric measures are recommended methods of assessing pre-diabetes and T2DM risk, they do not necessarily capture all those at risk. We found that 9%, 10%, 10% and 13% of children with normal %BF, BMI, WtHR and WC, respectively, were in the pre-diabetic group, suggesting that other risk factors are involved.

In agreement with other studies,49–53 Pacific Island and Māori children had higher rates of overweight/obesity, but South Asian children had comparable rates, when compared to New Zealand European children. However, in line with previous reports,54–57 we showed South Asian children to have a higher amount of total body fat for a given BMI compared to New Zealand European children; the mean %BF of South Asian and New Zealand European children for BMIs within the normal range were 20±6.8% and 18±5.2% (P=0.03, not adjusted for multiple comparison), and within the overweight and obese range they were 37±6.0% and 32±8.3% (P=0.08, not adjusted for multiple comparisons), respectively. It has been suggested that South Asians, while anthropometrically thin, are metabolically obese, which is evident even from infancy.58 The mechanism behind the association of overweight/obesity and %BF and diabetes may be linked with the adverse effect of excess visceral and hepatic fat on endocrine function and the body’s inflammatory system (the release of pro-inflammatory cytokines, such as C-reactive protein),59 which leads to insulin resistance and compensatory hyperinsulinemia and T2DM. 59

Our study showed that physical activity was negatively associated with pre-diabetes prevalence, and physical-activity levels were lower in Pacific Island and South Asian children and children of other ethnicities than New Zealand European children. However, Māori children in our study had a physical-activity level comparable to that of New Zealand European children, and this may partly explain why Māori children had lower prevalence of pre-diabetes than Pacific Island and South Asian children. Consistent with our findings, previous cross-sectional and longitudinal studies and meta-analyses have shown that physical activity reduces the risk of T2DM in people with pre-diabetes and the effect is independent of dietary factors and weight loss.60–62 The mechanism behind the association of physical activity and diabetes may be linked with physical-activity induced energy deficits and improvements in glucose homoeostasis that occur through acute responses and chronic adaptations.63,64

Ethnic differences in pre-diabetes risk in the present study could be partially explained by differences in socio-economic status. Ethnic groups in our study were disproportionally distributed across different school deciles, with a larger proportion of Pacific Island (76%), South Asian (50%) and Māori (36%), rather than European children (0.6%), being from low-decile schools. In comparison to those from high-decile schools, children from low-decile schools had more adiposity (as was shown by higher WC, WtHR, BMI and %BF) and less physical activity, and they had increased odds of being in the pre-diabetes group. The findings of the present study are consistent with those of other studies showing that social disadvantage is a determinant of T2DM and its risk factors.65–67 Social disadvantage influences people's attitudes, experiences and behaviours and exposure to several health risk factors and therefore may lead to chronic diseases, including T2DM.68

The present study had several limitations. Firstly, only HbA1c, rather than a combination of glucose tests, was measured. Studies found the sensitivity of individual glucose tests as a screening method for pre-diabetes could be relatively less than when a combination of tests (eg, fasting blood glucose and HbA1c) are performed, and thus this method may misclassify some individuals as pre-diabetic due to differences in haemoglobin glycation or red cell survival. However, using HbA1c is associated with some advantages that include being less invasive and having no requirement for fasting (which can be problematic for a paediatric population),69 a longer-term of glycaemia than plasma glucose43 and less analytical variability than FPG and the OGT methods.11 Secondly, we did not measure the fasting insulin concentration and could not assess the insulin resistance, such as homoeostasis model assessment-insulin resistance. Thirdly, we did not collect information about family history of diabetes, gestational diabetes, pre-term birth, birth weight and stage of puberty, all of which are considered risk factors for diabetes.70–73 We also did not collect information about diet (including components, quality and behaviours), which is considered an important risk factor for diabetes.37,74,75 Finally, a self-completed questionnaire was used to estimate time spent engaging in physical activities (including walking to school). This meant that the accuracy of the data was dependent on the literacy and willingness of those providing it. Additionally, since the study conducted over the winter period, seasonality may have had an effect. The use of accelerometers for energy expenditure would provide a more accurate assessment of physical-activity levels.

Despite the limitations, the present study had several strengths. This study used a large sample with broad representation of New Zealand’s ethnic distribution. Furthermore, with the relatively recent emergence of T2DM in children and adolescents, HbA1c testing has not been used as extensively in these groups. The present study, to the best of authors’ knowledge, is the first to investigate selected factors associated with pre-diabetes using HbA1c in children in New Zealand. Finally, as our cohort was not recruited on any specific weight status, we were able to assess a range of body types, whereas paediatric studies using HbA1c as a measure have tended to focus on overweight or obese children only. The observation that even some of children who had normal anthropometric measures (eg, WC, %BF, BMI and WtHR) were pre-diabetic was an advantage of this approach.

Conclusions

Using a large sample with broad representation of ethnic distribution in New Zealand, the present study found a high prevalence of pre-diabetes in children. Being of South Asian and Pacific Island ethnicities, doing less physical activity and having high WC or %BF were significantly associated with pre-diabetes in children. Ethnic disparities in overweight/obesity and physical-activity level were observed. The prevalence of elevated HbA1c in children of these ethnicities suggests that the risk is present early in life, which supports the need for appropriate and timely approaches to halt the progression to T2DM. However, it should be noted that neither WC, %BF, BMI nor WtHR were associated with high HbA1c in all children. Using these factors, a proportion of children would not have been identified as being at risk, which highlights the importance of other risk factors including ethnicity as an independent risk in the assessment of children.

Author contributions

Conceptualisation, PRvH and CAC. Data curation, DL. Formal analysis, HM, CG and DL. Funding acquisition, PRvH. Investigation, CAC and KLB. Project administration, DL. Supervision, PRvH and CG. Writing (original draft), HM, CG and DL. Writing (review & editing), HM, CG, CAC, KLB and PRvH.

Summary

Abstract

AIMS: The incidence of pre-diabetes and type 2 diabetes mellitus (T2DM) is increasing in children. Early identification of pre-diabetes is an important first step in preventing the progression to T2DM. The aim was to investigate the association of selected factors with pre-diabetes in children. METHODS: This data were from a subset of the 685 children recruited for the Children’s Bone Study, a cross-sectional study of children aged 8–11 years in Auckland, New Zealand. Glycated haemoglobin (HbA1c) was measured from a finger-prick blood test. Children were classified as normoglycaemic (HbA1c≤39mmol/mol) and pre-diabetic (HbA1c>39mmol/mol). Anthropometry included weight, height, waist circumference (WC) and percentage body fat (%BF) measured using bioelectrical impedance analysis. Information about age, gender, ethnicity and physical activity was collected by questionnaires. RESULTS: HbA1c was measured in 451 children (10.4±0.6years, 45% male). Pre-diabetes was present in 71 (16%) children and was greatest in South Asian (n=13, 30%), Pacific Island (n=29, 27%) and Māori (n=10, 18%) children, compared with European children (n=10, 6.0%) (P< 0.001). South Asian and Pacific Island ethnicity, high WC, high %BF and low physical activity were associated with pre-diabetes. CONCLUSIONS: Factors associated with pre-diabetes in children were ethnicity, anthropometric measures and physical-activity levels. The prevalence of pre-diabetes in children of South Asian and Pacific Island ethnicities suggests the need for appropriate and timely identification and intervention to halt the progression to T2DM.

Aim

Method

Results

Conclusion

Author Information

Hajar Mazahery: School of Sport, Exercise, and Nutrition, College of Health, Massey University, Auckland 0745, New Zealand. Cheryl S Gammon: School of Sport, Exercise, and Nutrition, College of Health, Massey University, Auckland 0745, New Zealand. Donna Lawgun: School of Sport, Exercise, and Nutrition, College of Health, Massey University, Auckland 0745, New Zealand. Cathryn A Conlon: School of Sport, Exercise, and Nutrition, College of Health, Massey University, Auckland 0745, New Zealand. Kathryn L Beck: School of Sport, Exercise, and Nutrition, College of Health, Massey University, Auckland 0745, New Zealand. Pamela R von Hurst: School of Sport, Exercise, and Nutrition, College of Health, Massey University, Auckland 0745, New Zealand.

Acknowledgements

Correspondence

Pamela R von Hurst, School of Sport, Exercise, and Nutrition, College of Health, Massey University, Auckland 0745, New Zealand, +64-9-213-6657

Correspondence Email

p.r.vonhurst@massey.ac.nz

Competing Interests

Nil.

1. Hannon TS, Arslanian SA, The changing face of diabetes in youth: lessons learned from studies of type 2 diabetes. Ann NY Acad Sci, 2015. 1353: p. 113-37.

2. World Health Organisation, Global report on diabetes. 2016, World Health Organisation: France.

3. Ministry of Health. Annual update of key results 2017/18: New Zealand health survey. 2018; Available from: https://www.health.govt.nz/publication/annual-update-key-results-2017-18-new-zealand-health-survey.

4. Ng M, Fleming T, Robinson M, et al., Global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet, 2014. 384(9945): p. 766-81.

5. Sjardin N, Reed P, Albert B, et al. Increasing incidence of type 2 diabetes in New Zealand children <15 years of age in a regional-based diabetes service, Auckland, New Zealand. J Paediatr Child Health, 2018. 54(9): p. 1005-1010.

6. Coppell KJ, Mann JI, Williams SM, et al. Prevalence of diagnosed and undiagnosed diabetes and prediabetes in New Zealand: findings from the 2008/09 Adult Nutrition Survey. NZ Med J, 2013. 126(1370): p. 23-42.

7. Gregg EW, Sattar N, Ali MK. The changing face of diabetes complications. Lancet Diabetes Endocrinol, 2016. 4(6): p. 537-47.

8. Zheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev. Endocrinol, 2018. 14(2): p. 88-98.

9. American Diabetes Association, Classification and diagnosis of diabetes. Diabetes Care, 2015. 38(Supplement 1): p. S8-S16.

10. Weiss R, Santoro N, Giannini C, et al. Prediabetes in youth - mechanisms and biomarkers. Lancet Child Adolesc Health, 2017. 1(3): p. 240-248.

11. New Zealand Society for the Study of Diabetes. NZSSD position statement on the diagnosis of, and screening for, type 2 diabetes. 2011; Available from: http://www.nzssd.org.nz/HbA1c/1.

12. Hosking J, Metcalf BS, Jeffery AN, et al. Divergence between HbA1c and fasting glucose through childhood: implications for diagnosis of impaired fasting glucose (Early Bird 52). Pediatr Diabetes, 2014. 15(3): p. 214-9.

13. Lee JM, Wu EL, Tarini B, et al. Diagnosis of diabetes using hemoglobin A1c: should recommendations in adults be extrapolated to adolescents? J Pediatr, 2011. 158(6): p. 947-952 e1-3.

14. Nam HK, Cho WK, Kim JH, et al. HbA1c cutoff for prediabetes and diabetes based on oral glucose tolerance test in obese children and adolescents. J. Korean Med Sci, 2018. 33(12): p. e93.

15. Nowicka P, Santoro N, Liu H, et al. Utility of hemoglobin A(1c) for diagnosing prediabetes and diabetes in obese children and adolescents. Diabetes Care, 2011. 34(6): p. 1306-11.

16. Cole TJ, Freeman JV, Preece MA. Body mass index reference curves for the UK, 1990. Arch Dis Child, 1995. 73(1): p. 25-9.

17. Wells JC, Hattori A. Chart analysis of body mass index in infants and children. Int J Obes Relat Metab Disord, 2000. 24(3): p. 325-9.

18. Dervaux N, Wubuli M, Megnien JL, et al. Comparative associations of adiposity measures with cardiometabolic risk burden in asymptomatic subjects. Atheroscler, 2008. 201(2): p. 413-7.

19. Shen W, Punyanitya M, Chen J, et al. Waist circumference correlates with metabolic syndrome indicators better than percentage fat. Obesity (Silver Spring), 2006. 14(4): p. 727-36.

20. MacKay MF, Haffner SM, Wagenknecht LE, et al. Prediction of type 2 diabetes using alternate anthropometric measures in a multi-ethnic cohort: the insulin resistance atherosclerosis study. Diabetes Care, 2009. 32(5): p. 956-8.

21. Gómez-Ambrosi J, Silva C, Galofré JC, et al. Body adiposity and type 2 diabetes: Increased risk with a high body fat percentage even having a normal BMI. Obesity, 2011. 19(7): p. 1439-1444.

22. Patterson E. Guidelines for data processing and analysis of the International Physical Activity Questionnaire (IPAQ)-short and long forms. 2005, Available from: http://www.ipaq.ki.se/

23. Statistics New Zealand. 2013 Census QuickStats about culture and identity. 2014; Available from: http://www.stats.govt.nz

24. Gibson RS. Principles of nutritional assessment. 2005, New York, USA: Oxford University Press.

25. Delshad M, Beck KL, Conlon CA, et al. Validity of quantitative ultrasound and bioelectrical impedance analysis for measuring bone density and body composition in children. Eu J Clin Nutr. 2020. DOI : 10.1038/s41430-020-00711-6.

26. Guo SS, Chumlea WC, Cockram DB. Use of statistical methods to estimate body composition. Am J Clin Nutr, 1996. 64(3 Suppl): p. 428s-435s.

27. Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: international survey. Br Med J, 2000. 320(7244): p. 1240-3.

28. Cole TJ, Lobstein T. Extended international (IOTF) body mass index cut-offs for thinness, overweight and obesity. Pediatr Obes, 2012. 7(4): p. 284-294.

29. Pan H, Cole TJ. LMS growth, a Microsoft Excel add-in to access growth references based on the LMS method. 2012.

30. Allan K, Taylor F. Little b makes big impact. 2014, Canterbury Health Laboratories: Christchurch, NZ.

31. BioSpace InBody, InBody 720 Results interpretation and application. 2017, BioSpace InBody.

32. World Health Organisation, Interim report of the commission on ending childhood obesity. 2015, World Health Organisation: Geneva, Switzerland.

33. Dabelea D, Mayer-Davis EJ, Saydah S, et al. Prevalence of type 1 and type 2 diabetes among children and adolescents from 2001 to 2009. J Am Med Assoc, 2014. 311(17): p. 1778-1786.

34. Chen L, Magliano DJ, Zimmet PZ. The worldwide epidemiology of type 2 diabetes mellitus - Present and future perspectives. Nat Rev Endocrinol, 2012. 8(4): p. 228-236.

35. Zimmet PZ, Magliano DJ, Herman WH, Shaw JE. Diabetes: a 21st century challenge. Lancet Diabetes Endocrinol, 2013. 2(1): p. 56-64.

36. Al-Saeed AH, Constantino MI, Molyneaux L, et al. An inverse relationship between age of type 2 diabetes onset and complication risk and mortality: The impact of youth-onset type 2 diabetes. Diabetes Care, 2016. 39(5): p. 823.

37. Wang Z, Zou Z, Wang H, et al. Prevalence and risk factors of impaired fasting glucose and diabetes among Chinese children and adolescents: a national observational study. Br J Nutr, 2018. 120(7): p. 813-819.

38. Narayanappa D, Hs R, Mahendrappa K, Prabhakar AK. Prevalence of prediabetes in school-going children. Indian Pediatr, 2010. 48: p. 295-9.

39. Phan DH, Do VV, Khuong, LQ, et al. Pevalence of diabetes and prediabetes among children aged 11-14 years old in Vietnam. J Diabetes Res, 2020. 2020: p. 7573491.

40. Andes LJ, Cheng YJ, Rolka DB, et al. Prevalence of prediabetes among adolescents and young adults in the United States, 2005-2016. JAMA Pediatr, 2020. 174(2): p. e194498-e194498.

41. Okosun IS, Seale JP, Lyn R, Davis-Smith YM. Improving detection of prediabetes in children and adults: Using combinations of blood glucose tests. Front Public Health, 2015. 3(260).

42. Rohlfing CL, Wiedmeyer HM, Little RR, et al. Defining the relationship between plasma glucose and HbA(1c): Analysis of glucose profiles and HbA(1c) in the diabetes control and complications trial. Diabetes Care, 2002. 25(2): p. 275-278.

43. Saudek CD, Herman WH, Sacks DB, et al. A new look at screening and diagnosing diabetes mellitus. J Clin Endocrinol Metab, 2008. 93(7): p. 2447-2453.

44. The International Expert Committee, International expert committee report on the role of the A1c assay in the diagnosis of diabetes. Diabetes Care, 2009. 32(7): p. 1327-1334.

45. Pinhas-Hamiel O, Zeitler P. The global spread of type 2 diabetes mellitus in children and adolescents. J Pediatr, 2005. 146(5): p. 693-700.

46. Goran MI, Ball GDC, Cruz ML. Obesity and risk of type 2 diabetes and cardiovascular disease in children and adolescents. J Clin Endocrinol Metab, 2003. 88(4): p. 1417-1427.

47. Health quality and comission. Diabetes. Atlas of healthcare vairation, 2020 [cited 2020 06/04/2020].

48. Whincup PH, Nightingale CM, Owen CG, et al. Early emergence of ethnic differences in type 2 diabetes precursors in the UK: The child heart and health study in england (CHASE Study). PLOS Med, 2010. 7(4): p. e1000263.

49. Goulding A, Grant AM, Taylor RW, et al. Ethnic differences in extreme obesity. J Pediatr, 2007. 151(5): p. 542-544.

50. Ministry of Health. Annual update of key results 2015/16: New Zealand Health Survey. 2016 [cited 2016 19 December]; Available from: http://www.health.govt.nz/publication/annual-update-key-results-2015-16-new-zealand-health-survey.

51. Duncan JS, Duncan EK, Schofield G. Accuracy of body mass index (BMI) thresholds for predicting excess body fat in girls from five ethnicities. Asia Pacific J Clin Nutr, 2009. 18(3): p. 404-11.

52. Tyrrell VJ, Ichards GER, Hofman P, et al. Obesity in Auckland school children: A comparison of the body mass index and percentage body fat as the diagnostic criterion. Int J Obes, 2001. 25(2): p. 164-169.

53. Ministry of Health, New Zealand Health Survey 2018-19: Annual data explore, MoH, 2019, Ministry of Health.

54. Deurenberg-Yap M, Schmidt G, van Staveren WA, Deurenberg P. The paradox of low body mass index and high body fat percentage among Chinese, Malays and Indians in Singapore. Int J Obes Relat Metab Disord, 2000. 24(8): p. 1011-7.

55. Eyre ELJ, Duncan MJ, Nevill A. South Asian children have increased body fat in comparison to white children at the same body mass index. Children (Basel, Switzerland), 2017. 4(11): p. 102.

56. Deurenberg P, Deurenberg-Yap M, Guricci S. Asians are different from Caucasians and from each other in their body mass index/body fat per cent relationship. Obes Rev, 2002. 3(3): p. 141-146.

57. Tillin T, Sattar N, Godsland IF, Hughes AD, et al. Ethnicity-specific obesity cut-points in the development of Type 2 diabetes – A prospective study including three ethnic groups in the United Kingdom. Diabet Med, 2015. 32(2): p. 226-234.

58. Lawlor DA, West J, Fairley L, et al. Pregnancy glycaemia and cord-blood levels of insulin and leptin in Pakistani and white British mother-offspring pairs: findings from a prospective pregnancy cohort. Diabetologia, 2014. 57(12): p. 2492-500.

59. Bays HE. Adiposopathy is "sick fat" a cardiovascular disease? J Am Coll Cardiol, 2011. 57(25): p. 2461-73.

60. Manson JE, Nathan DM, Krolewski AS, et al. A prospective study of exercise and incidence of diabetes among US male physicians. Jama, 1992. 268(1): p. 63-7.

61. Hu FB, Manson JE, Stampfer MJ, et al. Diet, lifestyle, and the risk of type 2 diabetes mellitus in women. N Engl J Med, 2001. 345(11): p. 790-7.

62. Yates T, Khunti K, Bull F, et al. The role of physical activity in the management of impaired glucose tolerance: a systematic review. Diabetologia, 2007. 50(6): p. 1116-1126.

63. Goodyear LJ, Kahn BB. Exercise, glucose transport, and insulin sensitivity. Annu Rev Med, 1998. 49: p. 235-61.

64. Schleh MW, Pitchford LM, Gillen JB, Horowitz JF. Energy deficit required for exercise-induced improvements in glycemia the next day. Med Sci Sports Exerc, 2020. 52(4): p. 976-982.

65. Nadeau, KJ, Anderson BJ, Berg EG, et al. Youth-onset type 2 diabetes consensus report: Current status, challenges, and priorities. Diabetes care, 2016. 39(9): p. 1635-1642.

66. Drenowatz C, Eisenmann JC, Pfeiffer KA, et al. Influence of socio-economic status on habitual physical activity and sedentary behavior in 8- to 11-year old children. BMC Public Health, 2010. 10: p. 214.

67. Jones-Smith JC, Dieckmann MG, Gottlieb L, et al. Socioeconomic status and trajectory of overweight from birth to mid-childhood: the Early Childhood Longitudinal Study-Birth Cohort. PloS one, 2014. 9(6): p. e100181.

68. Hill J, Nielsen M, Fox MH. Understanding the social factors that contribute to diabetes: a means to informing health care and social policies for the chronically ill. Perm J, 2013. 17(2): p. 67-72.

69. Bennett CM, Guo M, Dharmage SC. HbA1c as a screening tool for detection of Type 2 diabetes: A systematic review. Diabet Med, 2007. 24: p. 333-343.

70. Halipchuk J, Temple B, Dart A, et al. Prenatal, Obstetric and perinatal factors associated with the development of childhood-onset type 2 diabetes. Can J Diabetes, 2018. 42(1): p. 71-77.

71. Reinehr T, Wabitsch M, Kleber M, et al. Parental diabetes, pubertal stage, and extreme obesity are the main risk factors for prediabetes in children and adolescents: A simple risk score to identify children at risk for prediabetes. Pediatr Diabetes, 2009. 10(6): p. 395-400.

72. Mi D, Fang H, Zhao Y, Zhong L. Birth weight and type 2 diabetes: A meta-analysis. Exp Ther Med, 2017. 14(6): p. 5313-5320.

73. Crump C, Sundquist J, Sundquist K. Preterm birth and risk of type 1 and type 2 diabetes: A national cohort study. Diabetologia, 2020. 63(3): p. 508-518.

74. McNaughton SA, Dunstan DW, Ball K, et al. Dietary quality is associated with diabetes and cardio-metabolic risk factors. J Nutr, 2009. 139(4): p. 734-42.

75. Gingras V, Rifas-Shiman SL, Taveras EM, et al. Dietary behaviors throughout childhood are associated with adiposity and estimated insulin resistance in early adolescence: a longitudinal study. Int J Behav Nutr Phys Act, 2018. 15(1): p. 129.

For the PDF of this article,
contact nzmj@nzma.org.nz

View Article PDF

Once considered conditions of adulthood, the increase in the rates of obesity and physical inactivity over the last few decades have seen an accompanying increase in the incidence of insulin resistance, pre-diabetes and type 2 diabetes mellitus (T2DM) in children around the world.1,2

New Zealand has one of the highest rates of obesity for adults and children globally, with over 30% of children aged 2 to 14 years now classified, using the International Obesity Taskforce (IOTF) cut-offs, as being overweight or obese.3,4 Although the absolute numbers of diagnoses of T2DM in children under 15 years are low, a steady increase has been observed.5 For pre-diabetes, only data for children 15 years and over has been reported, with a prevalence rate of 8.4% for those aged 15 to 24 years.6

The early onset of T2DM in children and young adults is of concern because the increased duration of exposure to elevated blood glucose levels is likely to lead to a greater cumulative risk of microvascular (retinopathy, nephropathy and neuropathy) and macrovascular complications as they age, and the increased personal, societal and healthcare costs.7,8 Further, there is some evidence emerging that T2DM developed in youth is more aggressive than that developing in adulthood with, for example, a more rapid deterioration of β cell function reported and an earlier introduction of insulin treatment required.1

Pre-diabetes is defined as having either impaired glucose tolerance and/or impaired fasting glucose. 9 People with pre-diabetes are at increased risk of developing T2DM and cardiovascular diseases (CVD).10 However, if a healthy lifestyle is followed at this stage, there is a chance to delay or prevent the future development of T2DM.

A glycated haemoglobin test is a recommended diagnostic test used in the diagnosis of T2DM and may also be used as an opportunistic screening tool for pre-diabetes in those who present with risk factors.11 The wider availability of point of care (PoC) meters to measure glycated haemoglobin A1c (HbA1c) levels provides the opportunity of reaching a greater proportion of at-risk individuals through screening in community settings.

However, there is some debate whether the same adult HbA1c cut-offs should be used in children. Some studies suggest that a lower cut-off should be applied.12–15 Even for adults there are differences in the cut-off criteria that are used between different countries. In New Zealand, an HbA1c level over 50mmol/mol indicates diabetes and a level between 41 to 49mmol/mol indicates pre-diabetes.11 This is in contrast to the American Diabetes Association (ADA), which recommends slightly lower cut-offs for diabetes (>48mmol/mol) and pre-diabetes (39mmol/mol).9

In New Zealand, there are currently limited guidelines as to when to screen a child for pre-diabetes. Screening should be done in children or adolescents if they are obese (body mass index (BMI) ≥30kg/m2 or ≥27kg/m2 in Indo-Asians), if there is a family history of early onset T2DM or if they are in an at-risk ethnic group (Māori, Pacific Island or Indo-Asian).11 The measurement of many recognised risk factors in adults (eg, BMI) is less reliable in children because the relationship between components of body composition changes throughout childhood due to periods of growth, such as with puberty.16,17 Furthermore, some studies suggest that waist circumference (WC)18,19 or waist to height ratio (WtHR)20 (as a measure of central obesity and fat distribution) are better predictors of T2DM than BMI or percentage body fat (%BF). However, others report that measures of both central and overall adiposity strongly and similarly are associated with T2DM.20 Evidence also suggests that, despite having a WC within the normal range, individuals might be at increased risk of pre-diabetes/T2DM due to the increased adiposity.21

The aim of this study was to investigate the relationship between HbA1c levels as a measure of glycaemia, and a number of recognised risk factors that are associated with the later development of T2DM, in a group of Auckland school children. Being able to identify at-risk children early could provide the opportunity to implement lifestyle modifications, which might delay the progression to T2DM.

Methods

The study was a cross-sectional study conducted between August and September 2016 and August and September 2017 using a subset of school students in years 5 and 6 (8–11 year olds) recruited from a wider study, the main aim of which was to investigate bone health in children of this age. Schools spanning a range of decile levels (a measure of socio-economic status, with a low-school decile representing a low socio-economic status) and ethnicities were approached to participate to ensure a diverse sample with oversampling of certain ethnic minority groups (Māori, Pacific Island and South Asian) known to be at higher risk of T2DM. This was to allow ethnic-specific analysis.

Schools that agreed to participate were provided with written study protocol information for the school, parents and children. This detailed the intent of the study and the anthropometric test procedures, and it included a link for a short video to explain the data collection procedures to the children. Informed written consent was obtained from the parent and child, alongside an option to decline the finger prick blood test but still participate in the wider bone health study. Children were eligible for the wider bone health study if they were healthy and fully mobile, and they were excluded if they had a history of any disease affecting calcium, and vitamin D metabolism (eg, cardiac, kidney or liver disease), gastrointestinal disorders, a history of any long-term medication (eg, corticosteroids, anticonvulsants and immune-suppressants) or had any surgical implants, metal screws or similar. The study was approved by the Massey University Human Ethics Committee: Southern A, Application 16/42.

Demographic and physical-activity questionnaire

A week prior to the school visit, the class teacher sent home a questionnaire to be completed by one parent or guardian for each child. The questionnaire included demographic questions, such as the date of birth and ethnicity of the child. Physical activity was evaluated using the short version of the International Physical Activity Questionnaire (IPAQ).22 Metabolic equivalent (MET) minutes/week of physical activity was calculated as the MET intensity (walking=3.3 METs, moderate physical activity=4.0 METs and vigorous physical activity=8.0 METs) multiplied by the minutes of each physical activity multiplied by the number of days each physical activity occurred.22 All questionnaires were returned to the teacher and were then checked for completeness by one of the research team on the day of the visit.

Auckland is an ethnically diverse city. The predominant ethnic groups, using Statistics New Zealand level 1 categories, are: (i) European; (ii) Māori; (iii) Pacific Peoples; (iv) Asian.23 However, as people of South Asian ethnicity are known to be at increased risk of T2DM, the Asian group was further subdivided based on the geographic origins into (i) South Asian: Indian, Pakistani, Sri Lankan, Bangladeshi; (ii) East Asian: Chinese, Taiwanese, Korean, Japanese and (iii) South-East Asian: Indonesian, Thai, Singaporean, Malaysian, Filipino, Laotian. Children of other ethnic groups such as Middle Eastern, Latin American and African were allocated to a separate ‘other’ group.

Anthropometric measures

Anthropometric measurements were collected by trained researchers in a designated room at the children’s school during the school day. Height without shoes was recorded with a portable stadiometer (Seca 213) to the nearest 0.1cm, with two measurements taken and averaged. Waist circumference was measured in duplicate using the landmarks for waist measurements24 with a Lufkin W606PM pocket tape positioned around the body over light clothing while standing and recorded to the nearest 0.1cm.

Percentage of body fat was assessed with the Biospace InBody 230 Bio-electric Impedance Analyser (BIA) with the whole body %BF used. The BIA has been validated against dual-energy x-ray absorptiometry for %BF in children.25 The statistical methods for the development of prediction equations based on body composition parameters have been described elsewhere.26 Weight without shoes and in light clothing was recorded to the nearest 0.1kg using the BIA. The BIA uses %BF and classifies children at risk versus normal/low risk based on their gender and age.

Body mass index and WtHR were calculated. Age and gender specific BMI were ascertained using the International Obesity Taskforce BMI cut-offs (also referred to as the IOTF cutoffs), applying the equivalent BMI values at 18 years and linking to child centiles.27–29 Waist-to-height ratio was categorised as <85th and ≥85th percentiles of study population.

Assessment of glycaemic status

A trained researcher collected a finger prick blood sample to measure HbA1c levels using the Roche Cobas b 101 POC meter. This has been validated and delivers comparable outcomes to venous results on reference laboratory platforms.30

The ADA cut-offs for increased risk 39–46mmol/mol (5.7–6.4%) in children were used to categorise children as being in the normoglycaemic or pre-diabetic range.9

Statistical analysis

Data was analysed using the IBM SPSS statistical program, Version 24.0 software (IBM Corporation, New York, USA).

Standard descriptive statistics, including means, standard deviations, frequencies and percentages, were used as appropriate to summarise the socio-demographic and anthropometric results across participants grouped by their HbA1c level. Between-group comparisons were made using Independent Student t-tests, or Pearson’s chi-square test for categorical data.

Factors associated with HbA1c levels were assessed employing binary logistic regression analysis (univariable and multivariable). Children with missing information were excluded (n=18; ethnicity, 9; WC, 9; WtHR, 9; BMI, 3; %BF, 3), which left 433 children for the binary logistic regression analysis. Waist circumference and %BF were included in the regression analysis as a clinical measure (easy to apply) and a research measure, respectively. To avoid the violation of multicollinearity, WtHR and BMI were not included in the regression analyses. Three regression analyses were run including WC and %BF together and either of the anthropometric variables (WtHR and BMI) each individually along with demographic and lifestyle variables. Also, because there was a strong relationship between ethnicity and school decile (a smaller proportion of European children were from low decile schools, in comparison to Māori, Pacific and South Asian ethnicities: 0.6% vs 36-76%, P<0.0001), we ran two regression analyses including either ethnicity or school decile to avoid multicollinearity. As the inclusion of either of these variables did not affect the results, the results with ethnicity are reported. Imbalanced data with binary outcome variables are associated with biases in the estimated probability of an event. We investigated the models to determine whether all the assumptions were met and which model had a better model fit (assessing -2 log likelihood). We also added interaction terms into the models to investigate for interaction effects between variables.

Results

Of the 741 children invited to participate in the wider Children’s Bone Study, 685 (92%) children consented to take part. Of these children, 451 who also consented to a finger prick blood sample were included in this study. The HbA1c group contained proportionally fewer European children than the total group, and they were significantly older, taller, heavier and had a greater WC. There were no significant differences between the two groups for gender, BMI, %BF or physical activity (data not shown).

Participant characteristics

Participant characteristics for the 451 children, (whose HbA1c level was available) stratified by glycaemic status, are shown in Table 1. Age, sex, school decile and physical activity information was available for 451 children; ethnicity, WC and WtHR information was available for 442 children; and BMI and %BF information was available for 448 children. The age of the children ranged from 8 to 12 years (mean 10±0.6 years). Approximately one third of children were New Zealand European, and 13%, 24% and 10% were Māori, Pacific Island and South Asian, respectively. The mean %BF was 23±10% (range 7.8% to 50%) and WC 63±11cm (range 41cm to 104cm).

Glycated haemoglobin levels ranged from 27 to 46mmol/mol. None of children had a HbA1c level in the diabetes range. However, 71 children (16%) had HbA1c levels indicative of pre-diabetes, with a greater proportion of South Asian (30%), Pacific Island (27%) and Māori (18%) children, and those from low decile schools (31%), classified into this group than the normoglycaemia group. In contrast, European and East Asian children and those from medium and high-decile schools were predominantly within the normoglycaemic range.

All anthropometric measures, except height, were significantly higher in the pre-diabetic group than the normoglycaemic group. There was also a significant difference in the reported hours of physical activity per week, with fewer hours reported in the pre-diabetic group.

Table 1: Participants’ characteristics.

Values are mean±SD unless otherwise stated.
BMI, body mass index; HbA1c, glycated haemoglobin A1c.
1 Independent Student t-test or Pearson’s chi-squared for categorical variables.
2 Indian, Pakistani, Sri Lankan and Bangladeshi
3 Chinese, Taiwanese, Korean and Japanese.
4 Indonesian, Thai, Singaporean, Malaysian, Pilipino and Laotian.
5 Middle Eastern, Latin American and African

Main analysis: associations of demographic and anthropometric factors with HbA1c

Due to the small number of children in the East Asian, South-East Asian and other ethnicity groups, these groups were combined for the regression analysis. Table 2 presents the odds ratios (95% CI) from the univariable and multivariable analysis. Model x2 (9)=54, P<0.0001.

Pacific and South Asian children had 3.5 and 5.8 times increased odds, respectively, of being in the pre-diabetic group, compared with European children. In the univariate analysis, those of Māori ethnicity also showed increased odds of being in the pre-diabetic group, but the significance was lost after controlling in the multivariable analysis.

Children whose self-reported physical activity was two hours or less a week had 2.0 times higher odds of being in the pre-diabetic group than those who reported doing more than two hours a week. If their WC was ≥85th percentile or %BF was above the normal range (normal: 17±4.0% vs above normal: 32±7.0%, applying children’s standards [30]), they had 2.6 and 2.3 times higher odds of being in the pre-diabetic group, respectively. Over 56% (37/66) and 66% (44/66) of children with an HbA1c>39mmol/mol had a BMI and %BF, respectively, within the overweight/obese range.

The associations of WtHR and BMI each individually with HbA1c are presented in Table 3. Children who had a BMI within overweight and obese ranges or a WtHR≥0.5cm had 2.3, 4.9 and 5.1 times higher odds (adjusted for demographic and lifestyle factors) of being in the pre-diabetes group, respectively. Inclusion of WtHR or BMI (each individually) in the analyses did not affect the association of ethnicity and physical activity with HbA1c (data not shown).

Follow up analysis: associations of socio-demographics, anthropometric factors and HbA1c with ethnicity and school decile

As a follow up analysis, the characteristics of children of different ethnicities were compared with New Zealand European children (Table 4). A larger proportion of Pacific Island, South Asian and Māori children were from low decile schools than New Zealand European children. Physical-activity levels were lower among Pacific Island and South Asian children and children of other ethnicities as compared to New Zealand European children. However, Māori children had physical-activity levels comparable to those of New Zealand European children. All anthropometric measures were higher in Māori and Pacific Island children, and only %BF was higher in South Asian children (while having comparable BMI and other anthropometric measures) than New Zealand European counterparts. New Zealand European children had the lowest HbA1c compared to all other ethnicities.

Children from low- and medium-decile schools (school decile was considered as a proxy measure of socioeconomic status) had lower physical-activity levels and higher BMI and %BF than children from high-decile schools. Children from low-, but not medium-, decile schools had also higher WC and WtHR than children from high-decile schools (Table 4).

Table 2: Factors associated with HbA1c (normoglycaemic vs pre-diabetic) in 433 children.

CI, confidence Interval; HbA1c, glycated haemoglobin A1c; OR, odds ratio.
1 Variables included in the multivariable model were: age, sex, ethnicity, physical activity, waist circumference (as a clinical measure), and %body fat (as a research measure); Model x2 (9) = 54, P<0.0001.
2 HbA1c was coded as 1=Normoglycaemic (HbA1c ≤39 mmol/mol or 5.7%) and 2=Pre-diabetic (HbA1c >39 mmol/mol or 5.7%).
3 Indian, Pakistani, Sri Lankan and Bangladeshi.
4 East Asian (including Chinese, Taiwanese, Korean, and Japanese), South-East Asian (including Indonesian, Thai, Singaporean, Malaysian, Pilipino and Laotian) and others (including Middle Eastern, Latin American and African).

Table 3: The association of waist-to-height ratio and BMI with HbA1c (normoglycaemic vs prediabetic) in 433 children.

BMI, body mass index; CI, confidence interval; HbA1c, glycated haemoglobin A1c; OR, odds ratio.
1 Variables included in the multivariate model 1 were: age, gender, ethnicity, physical activity and BMI; Model 1: x2 (9)=54, P<0.0001. BMI was categorised according to age and gender specific BMI was ascertained using the International BMI cut-offs (also referred to as the International Obesity Taskforce (IOTF) cut-offs), applying the equivalent BMI values at 18 years and linking to child centiles. 27–29
2 Variables included in the multivariate model 2 were: age, gender, ethnicity, physical activity, and waist to height ratio; Model 2: x2 (9)=62, P<0.0001. Waist-to-height ratio was categorised as <85th (<0.05 cm) and ≥85th (≥0.5 cm) percentiles of population.

Table 4: Participant characteristics across ethnic groups and school deciles.

BMI, body mass index; HbA1c, glycated haemoglobin A1c.
Values are mean±SD and median (25th, 75th percentiles) unless otherwise stated.
I1ndian, Pakistani, Sri Lankan and Bangladeshi.
2 East Asian (including Chinese, Taiwanese, Korean and Japanese), South East Asian (including Indonesian, Thai, Singaporean, Malaysian, Pilipino and Laotian) and others (including Middle Eastern, Latin American and African).
3 One-way ANOVA for normally distributed data and Kruskal–Wallis non-parametric test for not-normally distributed data. Adjusted for multiple comparison. Ethnic groups were compared vs New Zealand European. Data in bold are indicative of a significant difference (significant at P<0.0125).
4 One-way ANOVA for normally distributed data and Kruskal-Wallis non-parametric test for not-normally distributed data. Adjusted for multiple comparison. Lowe and medium decile schools were compared vs. high decile school, Data in bold are indicative of a significant difference (significant at P<0.025).

Discussion

The present study showed a high prevalence of pre-diabetes (HbA1c levels >39mmol/mol or 5.7%)9 in New Zealand school-aged children. Being of South Asian or Pacific Island ethnicity, doing less than two hours of exercise a week and having a WC≥85th percentiles and an above normal %BF (normal: 17±4.0% vs above normal: 32±7.0%, applying children’s standards)31 were associated with an increased odds of having pre-diabetes in these children.

In line with the increasing prevalence of overweight and obesity in children (by 47% during the period 1980–2013), 32 T2DM has become increasingly prevalent in children and adolescents.33–36 The participants in this study were New Zealand children aged 8–11 years. The prevalence of pre-diabetes was 16% in this study, which was higher than the prevalence reported in Chinese children and adolescents aged 6–17 years (1.9%),37 Indian children aged 5–10 years (3.7%)38 and Vietnamese children aged 11–14 years (6.1%),39 and it is closer to the prevalence reported in American adolescents aged 12–18 years (18%)40 and New Zealand adults aged 15 years and older (19%).6 This difference may be due to the different screening methods used in these studies (eg, fasting blood glucose (FPG), oral glucose tolerance (OGT) and HbA1c) and participants’ characteristics (eg, age and ethnicity).

Okosun et al (2015) compared the efficacy of individual blood glucose tests in screening for pre-diabetes in adolescents aged 12–19 years and reported a prevalence rate that substantially differed across different screening methods (a prevalence rate of 6.4, 12 and 1.8% using HbA1c, FPG and OGT, respectively).41 Furthermore, the difference was more pronounced for some ethnic groups; the prevalence of pre-diabetes was greater in non-Hispanic Black adolescents using HbA1c than FPG and OGT tests. The higher prevalence of pre-diabetes using HbA1c may be attributed to the racial/ethnic differences in haemoglobin glycation or red cell survival and vitamin and medication use.42,43 It is important to note that these blood glucose tests might measure different aspects of blood glucose metabolism. Using a combination of HbA1c and FPG test has been suggested to provide both the benefits of individual test and decreases the risk of systemic bias inherent using only the HbA1c test.41,44

Ethnicity/race has been recognised as a well-established risk factor for pre-diabetes and T2DM.9,45,46 Although the high prevalence of pre-diabetes was evident across all ethnic groups in our study, the greatest prevalence was observed in South Asian (30%), Pacific Island (27%) and Māori children (18%), rather than New Zealand European children (6%), which is similar to older population patterns in New Zealand.6 Coppell et al (2013) reported a higher prevalence of pre-diabetes among Pacific Island and Māori youths and adults (aged 15–24 years) than New Zealand European and others (13–14% vs 7%, respectively).6 Similarly, the prevalence of T2DM has been reported to be higher among Pacific Island and South Asian adults aged 25 years and older than New Zealand Europeans.47 Reports from other countries (eg, the US and UK) also show a higher prevalence of pre-diabetes in children and adolescents of some ethnic groups (eg, South Asian and Black African–Caribbean children as compared to whites48 and non-Hispanic Black adolescents as compared to their non-Hispanic white counterparts).40

We speculated that the difference of overweight/obesity measures between children of these ethnicities (South Asian, Pacific Island and Māori children) and New Zealand European children might partially contribute to differences in pre-diabetes prevalence. All measures of overweight/obesity were positively associated with pre-diabetes (Tables 2 and 3), which is consistent with previous studies showing obesity as a risk factor for pre-diabetes and T2DM.6,40,41 We also showed that %BF above normal was a better factor associated with pre-diabetes than BMI (eight more pre-diabetic children). It is important to note that although anthropometric measures are recommended methods of assessing pre-diabetes and T2DM risk, they do not necessarily capture all those at risk. We found that 9%, 10%, 10% and 13% of children with normal %BF, BMI, WtHR and WC, respectively, were in the pre-diabetic group, suggesting that other risk factors are involved.

In agreement with other studies,49–53 Pacific Island and Māori children had higher rates of overweight/obesity, but South Asian children had comparable rates, when compared to New Zealand European children. However, in line with previous reports,54–57 we showed South Asian children to have a higher amount of total body fat for a given BMI compared to New Zealand European children; the mean %BF of South Asian and New Zealand European children for BMIs within the normal range were 20±6.8% and 18±5.2% (P=0.03, not adjusted for multiple comparison), and within the overweight and obese range they were 37±6.0% and 32±8.3% (P=0.08, not adjusted for multiple comparisons), respectively. It has been suggested that South Asians, while anthropometrically thin, are metabolically obese, which is evident even from infancy.58 The mechanism behind the association of overweight/obesity and %BF and diabetes may be linked with the adverse effect of excess visceral and hepatic fat on endocrine function and the body’s inflammatory system (the release of pro-inflammatory cytokines, such as C-reactive protein),59 which leads to insulin resistance and compensatory hyperinsulinemia and T2DM. 59

Our study showed that physical activity was negatively associated with pre-diabetes prevalence, and physical-activity levels were lower in Pacific Island and South Asian children and children of other ethnicities than New Zealand European children. However, Māori children in our study had a physical-activity level comparable to that of New Zealand European children, and this may partly explain why Māori children had lower prevalence of pre-diabetes than Pacific Island and South Asian children. Consistent with our findings, previous cross-sectional and longitudinal studies and meta-analyses have shown that physical activity reduces the risk of T2DM in people with pre-diabetes and the effect is independent of dietary factors and weight loss.60–62 The mechanism behind the association of physical activity and diabetes may be linked with physical-activity induced energy deficits and improvements in glucose homoeostasis that occur through acute responses and chronic adaptations.63,64

Ethnic differences in pre-diabetes risk in the present study could be partially explained by differences in socio-economic status. Ethnic groups in our study were disproportionally distributed across different school deciles, with a larger proportion of Pacific Island (76%), South Asian (50%) and Māori (36%), rather than European children (0.6%), being from low-decile schools. In comparison to those from high-decile schools, children from low-decile schools had more adiposity (as was shown by higher WC, WtHR, BMI and %BF) and less physical activity, and they had increased odds of being in the pre-diabetes group. The findings of the present study are consistent with those of other studies showing that social disadvantage is a determinant of T2DM and its risk factors.65–67 Social disadvantage influences people's attitudes, experiences and behaviours and exposure to several health risk factors and therefore may lead to chronic diseases, including T2DM.68

The present study had several limitations. Firstly, only HbA1c, rather than a combination of glucose tests, was measured. Studies found the sensitivity of individual glucose tests as a screening method for pre-diabetes could be relatively less than when a combination of tests (eg, fasting blood glucose and HbA1c) are performed, and thus this method may misclassify some individuals as pre-diabetic due to differences in haemoglobin glycation or red cell survival. However, using HbA1c is associated with some advantages that include being less invasive and having no requirement for fasting (which can be problematic for a paediatric population),69 a longer-term of glycaemia than plasma glucose43 and less analytical variability than FPG and the OGT methods.11 Secondly, we did not measure the fasting insulin concentration and could not assess the insulin resistance, such as homoeostasis model assessment-insulin resistance. Thirdly, we did not collect information about family history of diabetes, gestational diabetes, pre-term birth, birth weight and stage of puberty, all of which are considered risk factors for diabetes.70–73 We also did not collect information about diet (including components, quality and behaviours), which is considered an important risk factor for diabetes.37,74,75 Finally, a self-completed questionnaire was used to estimate time spent engaging in physical activities (including walking to school). This meant that the accuracy of the data was dependent on the literacy and willingness of those providing it. Additionally, since the study conducted over the winter period, seasonality may have had an effect. The use of accelerometers for energy expenditure would provide a more accurate assessment of physical-activity levels.

Despite the limitations, the present study had several strengths. This study used a large sample with broad representation of New Zealand’s ethnic distribution. Furthermore, with the relatively recent emergence of T2DM in children and adolescents, HbA1c testing has not been used as extensively in these groups. The present study, to the best of authors’ knowledge, is the first to investigate selected factors associated with pre-diabetes using HbA1c in children in New Zealand. Finally, as our cohort was not recruited on any specific weight status, we were able to assess a range of body types, whereas paediatric studies using HbA1c as a measure have tended to focus on overweight or obese children only. The observation that even some of children who had normal anthropometric measures (eg, WC, %BF, BMI and WtHR) were pre-diabetic was an advantage of this approach.

Conclusions

Using a large sample with broad representation of ethnic distribution in New Zealand, the present study found a high prevalence of pre-diabetes in children. Being of South Asian and Pacific Island ethnicities, doing less physical activity and having high WC or %BF were significantly associated with pre-diabetes in children. Ethnic disparities in overweight/obesity and physical-activity level were observed. The prevalence of elevated HbA1c in children of these ethnicities suggests that the risk is present early in life, which supports the need for appropriate and timely approaches to halt the progression to T2DM. However, it should be noted that neither WC, %BF, BMI nor WtHR were associated with high HbA1c in all children. Using these factors, a proportion of children would not have been identified as being at risk, which highlights the importance of other risk factors including ethnicity as an independent risk in the assessment of children.

Author contributions

Conceptualisation, PRvH and CAC. Data curation, DL. Formal analysis, HM, CG and DL. Funding acquisition, PRvH. Investigation, CAC and KLB. Project administration, DL. Supervision, PRvH and CG. Writing (original draft), HM, CG and DL. Writing (review & editing), HM, CG, CAC, KLB and PRvH.

Summary

Abstract

AIMS: The incidence of pre-diabetes and type 2 diabetes mellitus (T2DM) is increasing in children. Early identification of pre-diabetes is an important first step in preventing the progression to T2DM. The aim was to investigate the association of selected factors with pre-diabetes in children. METHODS: This data were from a subset of the 685 children recruited for the Children’s Bone Study, a cross-sectional study of children aged 8–11 years in Auckland, New Zealand. Glycated haemoglobin (HbA1c) was measured from a finger-prick blood test. Children were classified as normoglycaemic (HbA1c≤39mmol/mol) and pre-diabetic (HbA1c>39mmol/mol). Anthropometry included weight, height, waist circumference (WC) and percentage body fat (%BF) measured using bioelectrical impedance analysis. Information about age, gender, ethnicity and physical activity was collected by questionnaires. RESULTS: HbA1c was measured in 451 children (10.4±0.6years, 45% male). Pre-diabetes was present in 71 (16%) children and was greatest in South Asian (n=13, 30%), Pacific Island (n=29, 27%) and Māori (n=10, 18%) children, compared with European children (n=10, 6.0%) (P< 0.001). South Asian and Pacific Island ethnicity, high WC, high %BF and low physical activity were associated with pre-diabetes. CONCLUSIONS: Factors associated with pre-diabetes in children were ethnicity, anthropometric measures and physical-activity levels. The prevalence of pre-diabetes in children of South Asian and Pacific Island ethnicities suggests the need for appropriate and timely identification and intervention to halt the progression to T2DM.

Aim

Method

Results

Conclusion

Author Information

Hajar Mazahery: School of Sport, Exercise, and Nutrition, College of Health, Massey University, Auckland 0745, New Zealand. Cheryl S Gammon: School of Sport, Exercise, and Nutrition, College of Health, Massey University, Auckland 0745, New Zealand. Donna Lawgun: School of Sport, Exercise, and Nutrition, College of Health, Massey University, Auckland 0745, New Zealand. Cathryn A Conlon: School of Sport, Exercise, and Nutrition, College of Health, Massey University, Auckland 0745, New Zealand. Kathryn L Beck: School of Sport, Exercise, and Nutrition, College of Health, Massey University, Auckland 0745, New Zealand. Pamela R von Hurst: School of Sport, Exercise, and Nutrition, College of Health, Massey University, Auckland 0745, New Zealand.

Acknowledgements

Correspondence

Pamela R von Hurst, School of Sport, Exercise, and Nutrition, College of Health, Massey University, Auckland 0745, New Zealand, +64-9-213-6657

Correspondence Email

p.r.vonhurst@massey.ac.nz

Competing Interests

Nil.

1. Hannon TS, Arslanian SA, The changing face of diabetes in youth: lessons learned from studies of type 2 diabetes. Ann NY Acad Sci, 2015. 1353: p. 113-37.

2. World Health Organisation, Global report on diabetes. 2016, World Health Organisation: France.

3. Ministry of Health. Annual update of key results 2017/18: New Zealand health survey. 2018; Available from: https://www.health.govt.nz/publication/annual-update-key-results-2017-18-new-zealand-health-survey.

4. Ng M, Fleming T, Robinson M, et al., Global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet, 2014. 384(9945): p. 766-81.

5. Sjardin N, Reed P, Albert B, et al. Increasing incidence of type 2 diabetes in New Zealand children <15 years of age in a regional-based diabetes service, Auckland, New Zealand. J Paediatr Child Health, 2018. 54(9): p. 1005-1010.

6. Coppell KJ, Mann JI, Williams SM, et al. Prevalence of diagnosed and undiagnosed diabetes and prediabetes in New Zealand: findings from the 2008/09 Adult Nutrition Survey. NZ Med J, 2013. 126(1370): p. 23-42.

7. Gregg EW, Sattar N, Ali MK. The changing face of diabetes complications. Lancet Diabetes Endocrinol, 2016. 4(6): p. 537-47.

8. Zheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev. Endocrinol, 2018. 14(2): p. 88-98.

9. American Diabetes Association, Classification and diagnosis of diabetes. Diabetes Care, 2015. 38(Supplement 1): p. S8-S16.

10. Weiss R, Santoro N, Giannini C, et al. Prediabetes in youth - mechanisms and biomarkers. Lancet Child Adolesc Health, 2017. 1(3): p. 240-248.

11. New Zealand Society for the Study of Diabetes. NZSSD position statement on the diagnosis of, and screening for, type 2 diabetes. 2011; Available from: http://www.nzssd.org.nz/HbA1c/1.

12. Hosking J, Metcalf BS, Jeffery AN, et al. Divergence between HbA1c and fasting glucose through childhood: implications for diagnosis of impaired fasting glucose (Early Bird 52). Pediatr Diabetes, 2014. 15(3): p. 214-9.

13. Lee JM, Wu EL, Tarini B, et al. Diagnosis of diabetes using hemoglobin A1c: should recommendations in adults be extrapolated to adolescents? J Pediatr, 2011. 158(6): p. 947-952 e1-3.

14. Nam HK, Cho WK, Kim JH, et al. HbA1c cutoff for prediabetes and diabetes based on oral glucose tolerance test in obese children and adolescents. J. Korean Med Sci, 2018. 33(12): p. e93.

15. Nowicka P, Santoro N, Liu H, et al. Utility of hemoglobin A(1c) for diagnosing prediabetes and diabetes in obese children and adolescents. Diabetes Care, 2011. 34(6): p. 1306-11.

16. Cole TJ, Freeman JV, Preece MA. Body mass index reference curves for the UK, 1990. Arch Dis Child, 1995. 73(1): p. 25-9.

17. Wells JC, Hattori A. Chart analysis of body mass index in infants and children. Int J Obes Relat Metab Disord, 2000. 24(3): p. 325-9.

18. Dervaux N, Wubuli M, Megnien JL, et al. Comparative associations of adiposity measures with cardiometabolic risk burden in asymptomatic subjects. Atheroscler, 2008. 201(2): p. 413-7.

19. Shen W, Punyanitya M, Chen J, et al. Waist circumference correlates with metabolic syndrome indicators better than percentage fat. Obesity (Silver Spring), 2006. 14(4): p. 727-36.

20. MacKay MF, Haffner SM, Wagenknecht LE, et al. Prediction of type 2 diabetes using alternate anthropometric measures in a multi-ethnic cohort: the insulin resistance atherosclerosis study. Diabetes Care, 2009. 32(5): p. 956-8.

21. Gómez-Ambrosi J, Silva C, Galofré JC, et al. Body adiposity and type 2 diabetes: Increased risk with a high body fat percentage even having a normal BMI. Obesity, 2011. 19(7): p. 1439-1444.

22. Patterson E. Guidelines for data processing and analysis of the International Physical Activity Questionnaire (IPAQ)-short and long forms. 2005, Available from: http://www.ipaq.ki.se/

23. Statistics New Zealand. 2013 Census QuickStats about culture and identity. 2014; Available from: http://www.stats.govt.nz

24. Gibson RS. Principles of nutritional assessment. 2005, New York, USA: Oxford University Press.

25. Delshad M, Beck KL, Conlon CA, et al. Validity of quantitative ultrasound and bioelectrical impedance analysis for measuring bone density and body composition in children. Eu J Clin Nutr. 2020. DOI : 10.1038/s41430-020-00711-6.

26. Guo SS, Chumlea WC, Cockram DB. Use of statistical methods to estimate body composition. Am J Clin Nutr, 1996. 64(3 Suppl): p. 428s-435s.

27. Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: international survey. Br Med J, 2000. 320(7244): p. 1240-3.

28. Cole TJ, Lobstein T. Extended international (IOTF) body mass index cut-offs for thinness, overweight and obesity. Pediatr Obes, 2012. 7(4): p. 284-294.

29. Pan H, Cole TJ. LMS growth, a Microsoft Excel add-in to access growth references based on the LMS method. 2012.

30. Allan K, Taylor F. Little b makes big impact. 2014, Canterbury Health Laboratories: Christchurch, NZ.

31. BioSpace InBody, InBody 720 Results interpretation and application. 2017, BioSpace InBody.

32. World Health Organisation, Interim report of the commission on ending childhood obesity. 2015, World Health Organisation: Geneva, Switzerland.

33. Dabelea D, Mayer-Davis EJ, Saydah S, et al. Prevalence of type 1 and type 2 diabetes among children and adolescents from 2001 to 2009. J Am Med Assoc, 2014. 311(17): p. 1778-1786.

34. Chen L, Magliano DJ, Zimmet PZ. The worldwide epidemiology of type 2 diabetes mellitus - Present and future perspectives. Nat Rev Endocrinol, 2012. 8(4): p. 228-236.

35. Zimmet PZ, Magliano DJ, Herman WH, Shaw JE. Diabetes: a 21st century challenge. Lancet Diabetes Endocrinol, 2013. 2(1): p. 56-64.

36. Al-Saeed AH, Constantino MI, Molyneaux L, et al. An inverse relationship between age of type 2 diabetes onset and complication risk and mortality: The impact of youth-onset type 2 diabetes. Diabetes Care, 2016. 39(5): p. 823.

37. Wang Z, Zou Z, Wang H, et al. Prevalence and risk factors of impaired fasting glucose and diabetes among Chinese children and adolescents: a national observational study. Br J Nutr, 2018. 120(7): p. 813-819.

38. Narayanappa D, Hs R, Mahendrappa K, Prabhakar AK. Prevalence of prediabetes in school-going children. Indian Pediatr, 2010. 48: p. 295-9.

39. Phan DH, Do VV, Khuong, LQ, et al. Pevalence of diabetes and prediabetes among children aged 11-14 years old in Vietnam. J Diabetes Res, 2020. 2020: p. 7573491.

40. Andes LJ, Cheng YJ, Rolka DB, et al. Prevalence of prediabetes among adolescents and young adults in the United States, 2005-2016. JAMA Pediatr, 2020. 174(2): p. e194498-e194498.

41. Okosun IS, Seale JP, Lyn R, Davis-Smith YM. Improving detection of prediabetes in children and adults: Using combinations of blood glucose tests. Front Public Health, 2015. 3(260).

42. Rohlfing CL, Wiedmeyer HM, Little RR, et al. Defining the relationship between plasma glucose and HbA(1c): Analysis of glucose profiles and HbA(1c) in the diabetes control and complications trial. Diabetes Care, 2002. 25(2): p. 275-278.

43. Saudek CD, Herman WH, Sacks DB, et al. A new look at screening and diagnosing diabetes mellitus. J Clin Endocrinol Metab, 2008. 93(7): p. 2447-2453.

44. The International Expert Committee, International expert committee report on the role of the A1c assay in the diagnosis of diabetes. Diabetes Care, 2009. 32(7): p. 1327-1334.

45. Pinhas-Hamiel O, Zeitler P. The global spread of type 2 diabetes mellitus in children and adolescents. J Pediatr, 2005. 146(5): p. 693-700.

46. Goran MI, Ball GDC, Cruz ML. Obesity and risk of type 2 diabetes and cardiovascular disease in children and adolescents. J Clin Endocrinol Metab, 2003. 88(4): p. 1417-1427.

47. Health quality and comission. Diabetes. Atlas of healthcare vairation, 2020 [cited 2020 06/04/2020].

48. Whincup PH, Nightingale CM, Owen CG, et al. Early emergence of ethnic differences in type 2 diabetes precursors in the UK: The child heart and health study in england (CHASE Study). PLOS Med, 2010. 7(4): p. e1000263.

49. Goulding A, Grant AM, Taylor RW, et al. Ethnic differences in extreme obesity. J Pediatr, 2007. 151(5): p. 542-544.

50. Ministry of Health. Annual update of key results 2015/16: New Zealand Health Survey. 2016 [cited 2016 19 December]; Available from: http://www.health.govt.nz/publication/annual-update-key-results-2015-16-new-zealand-health-survey.

51. Duncan JS, Duncan EK, Schofield G. Accuracy of body mass index (BMI) thresholds for predicting excess body fat in girls from five ethnicities. Asia Pacific J Clin Nutr, 2009. 18(3): p. 404-11.

52. Tyrrell VJ, Ichards GER, Hofman P, et al. Obesity in Auckland school children: A comparison of the body mass index and percentage body fat as the diagnostic criterion. Int J Obes, 2001. 25(2): p. 164-169.

53. Ministry of Health, New Zealand Health Survey 2018-19: Annual data explore, MoH, 2019, Ministry of Health.

54. Deurenberg-Yap M, Schmidt G, van Staveren WA, Deurenberg P. The paradox of low body mass index and high body fat percentage among Chinese, Malays and Indians in Singapore. Int J Obes Relat Metab Disord, 2000. 24(8): p. 1011-7.

55. Eyre ELJ, Duncan MJ, Nevill A. South Asian children have increased body fat in comparison to white children at the same body mass index. Children (Basel, Switzerland), 2017. 4(11): p. 102.

56. Deurenberg P, Deurenberg-Yap M, Guricci S. Asians are different from Caucasians and from each other in their body mass index/body fat per cent relationship. Obes Rev, 2002. 3(3): p. 141-146.

57. Tillin T, Sattar N, Godsland IF, Hughes AD, et al. Ethnicity-specific obesity cut-points in the development of Type 2 diabetes – A prospective study including three ethnic groups in the United Kingdom. Diabet Med, 2015. 32(2): p. 226-234.

58. Lawlor DA, West J, Fairley L, et al. Pregnancy glycaemia and cord-blood levels of insulin and leptin in Pakistani and white British mother-offspring pairs: findings from a prospective pregnancy cohort. Diabetologia, 2014. 57(12): p. 2492-500.

59. Bays HE. Adiposopathy is "sick fat" a cardiovascular disease? J Am Coll Cardiol, 2011. 57(25): p. 2461-73.

60. Manson JE, Nathan DM, Krolewski AS, et al. A prospective study of exercise and incidence of diabetes among US male physicians. Jama, 1992. 268(1): p. 63-7.

61. Hu FB, Manson JE, Stampfer MJ, et al. Diet, lifestyle, and the risk of type 2 diabetes mellitus in women. N Engl J Med, 2001. 345(11): p. 790-7.

62. Yates T, Khunti K, Bull F, et al. The role of physical activity in the management of impaired glucose tolerance: a systematic review. Diabetologia, 2007. 50(6): p. 1116-1126.

63. Goodyear LJ, Kahn BB. Exercise, glucose transport, and insulin sensitivity. Annu Rev Med, 1998. 49: p. 235-61.

64. Schleh MW, Pitchford LM, Gillen JB, Horowitz JF. Energy deficit required for exercise-induced improvements in glycemia the next day. Med Sci Sports Exerc, 2020. 52(4): p. 976-982.

65. Nadeau, KJ, Anderson BJ, Berg EG, et al. Youth-onset type 2 diabetes consensus report: Current status, challenges, and priorities. Diabetes care, 2016. 39(9): p. 1635-1642.

66. Drenowatz C, Eisenmann JC, Pfeiffer KA, et al. Influence of socio-economic status on habitual physical activity and sedentary behavior in 8- to 11-year old children. BMC Public Health, 2010. 10: p. 214.

67. Jones-Smith JC, Dieckmann MG, Gottlieb L, et al. Socioeconomic status and trajectory of overweight from birth to mid-childhood: the Early Childhood Longitudinal Study-Birth Cohort. PloS one, 2014. 9(6): p. e100181.

68. Hill J, Nielsen M, Fox MH. Understanding the social factors that contribute to diabetes: a means to informing health care and social policies for the chronically ill. Perm J, 2013. 17(2): p. 67-72.

69. Bennett CM, Guo M, Dharmage SC. HbA1c as a screening tool for detection of Type 2 diabetes: A systematic review. Diabet Med, 2007. 24: p. 333-343.

70. Halipchuk J, Temple B, Dart A, et al. Prenatal, Obstetric and perinatal factors associated with the development of childhood-onset type 2 diabetes. Can J Diabetes, 2018. 42(1): p. 71-77.

71. Reinehr T, Wabitsch M, Kleber M, et al. Parental diabetes, pubertal stage, and extreme obesity are the main risk factors for prediabetes in children and adolescents: A simple risk score to identify children at risk for prediabetes. Pediatr Diabetes, 2009. 10(6): p. 395-400.

72. Mi D, Fang H, Zhao Y, Zhong L. Birth weight and type 2 diabetes: A meta-analysis. Exp Ther Med, 2017. 14(6): p. 5313-5320.

73. Crump C, Sundquist J, Sundquist K. Preterm birth and risk of type 1 and type 2 diabetes: A national cohort study. Diabetologia, 2020. 63(3): p. 508-518.

74. McNaughton SA, Dunstan DW, Ball K, et al. Dietary quality is associated with diabetes and cardio-metabolic risk factors. J Nutr, 2009. 139(4): p. 734-42.

75. Gingras V, Rifas-Shiman SL, Taveras EM, et al. Dietary behaviors throughout childhood are associated with adiposity and estimated insulin resistance in early adolescence: a longitudinal study. Int J Behav Nutr Phys Act, 2018. 15(1): p. 129.

For the PDF of this article,
contact nzmj@nzma.org.nz

View Article PDF

Once considered conditions of adulthood, the increase in the rates of obesity and physical inactivity over the last few decades have seen an accompanying increase in the incidence of insulin resistance, pre-diabetes and type 2 diabetes mellitus (T2DM) in children around the world.1,2

New Zealand has one of the highest rates of obesity for adults and children globally, with over 30% of children aged 2 to 14 years now classified, using the International Obesity Taskforce (IOTF) cut-offs, as being overweight or obese.3,4 Although the absolute numbers of diagnoses of T2DM in children under 15 years are low, a steady increase has been observed.5 For pre-diabetes, only data for children 15 years and over has been reported, with a prevalence rate of 8.4% for those aged 15 to 24 years.6

The early onset of T2DM in children and young adults is of concern because the increased duration of exposure to elevated blood glucose levels is likely to lead to a greater cumulative risk of microvascular (retinopathy, nephropathy and neuropathy) and macrovascular complications as they age, and the increased personal, societal and healthcare costs.7,8 Further, there is some evidence emerging that T2DM developed in youth is more aggressive than that developing in adulthood with, for example, a more rapid deterioration of β cell function reported and an earlier introduction of insulin treatment required.1

Pre-diabetes is defined as having either impaired glucose tolerance and/or impaired fasting glucose. 9 People with pre-diabetes are at increased risk of developing T2DM and cardiovascular diseases (CVD).10 However, if a healthy lifestyle is followed at this stage, there is a chance to delay or prevent the future development of T2DM.

A glycated haemoglobin test is a recommended diagnostic test used in the diagnosis of T2DM and may also be used as an opportunistic screening tool for pre-diabetes in those who present with risk factors.11 The wider availability of point of care (PoC) meters to measure glycated haemoglobin A1c (HbA1c) levels provides the opportunity of reaching a greater proportion of at-risk individuals through screening in community settings.

However, there is some debate whether the same adult HbA1c cut-offs should be used in children. Some studies suggest that a lower cut-off should be applied.12–15 Even for adults there are differences in the cut-off criteria that are used between different countries. In New Zealand, an HbA1c level over 50mmol/mol indicates diabetes and a level between 41 to 49mmol/mol indicates pre-diabetes.11 This is in contrast to the American Diabetes Association (ADA), which recommends slightly lower cut-offs for diabetes (>48mmol/mol) and pre-diabetes (39mmol/mol).9

In New Zealand, there are currently limited guidelines as to when to screen a child for pre-diabetes. Screening should be done in children or adolescents if they are obese (body mass index (BMI) ≥30kg/m2 or ≥27kg/m2 in Indo-Asians), if there is a family history of early onset T2DM or if they are in an at-risk ethnic group (Māori, Pacific Island or Indo-Asian).11 The measurement of many recognised risk factors in adults (eg, BMI) is less reliable in children because the relationship between components of body composition changes throughout childhood due to periods of growth, such as with puberty.16,17 Furthermore, some studies suggest that waist circumference (WC)18,19 or waist to height ratio (WtHR)20 (as a measure of central obesity and fat distribution) are better predictors of T2DM than BMI or percentage body fat (%BF). However, others report that measures of both central and overall adiposity strongly and similarly are associated with T2DM.20 Evidence also suggests that, despite having a WC within the normal range, individuals might be at increased risk of pre-diabetes/T2DM due to the increased adiposity.21

The aim of this study was to investigate the relationship between HbA1c levels as a measure of glycaemia, and a number of recognised risk factors that are associated with the later development of T2DM, in a group of Auckland school children. Being able to identify at-risk children early could provide the opportunity to implement lifestyle modifications, which might delay the progression to T2DM.

Methods

The study was a cross-sectional study conducted between August and September 2016 and August and September 2017 using a subset of school students in years 5 and 6 (8–11 year olds) recruited from a wider study, the main aim of which was to investigate bone health in children of this age. Schools spanning a range of decile levels (a measure of socio-economic status, with a low-school decile representing a low socio-economic status) and ethnicities were approached to participate to ensure a diverse sample with oversampling of certain ethnic minority groups (Māori, Pacific Island and South Asian) known to be at higher risk of T2DM. This was to allow ethnic-specific analysis.

Schools that agreed to participate were provided with written study protocol information for the school, parents and children. This detailed the intent of the study and the anthropometric test procedures, and it included a link for a short video to explain the data collection procedures to the children. Informed written consent was obtained from the parent and child, alongside an option to decline the finger prick blood test but still participate in the wider bone health study. Children were eligible for the wider bone health study if they were healthy and fully mobile, and they were excluded if they had a history of any disease affecting calcium, and vitamin D metabolism (eg, cardiac, kidney or liver disease), gastrointestinal disorders, a history of any long-term medication (eg, corticosteroids, anticonvulsants and immune-suppressants) or had any surgical implants, metal screws or similar. The study was approved by the Massey University Human Ethics Committee: Southern A, Application 16/42.

Demographic and physical-activity questionnaire

A week prior to the school visit, the class teacher sent home a questionnaire to be completed by one parent or guardian for each child. The questionnaire included demographic questions, such as the date of birth and ethnicity of the child. Physical activity was evaluated using the short version of the International Physical Activity Questionnaire (IPAQ).22 Metabolic equivalent (MET) minutes/week of physical activity was calculated as the MET intensity (walking=3.3 METs, moderate physical activity=4.0 METs and vigorous physical activity=8.0 METs) multiplied by the minutes of each physical activity multiplied by the number of days each physical activity occurred.22 All questionnaires were returned to the teacher and were then checked for completeness by one of the research team on the day of the visit.

Auckland is an ethnically diverse city. The predominant ethnic groups, using Statistics New Zealand level 1 categories, are: (i) European; (ii) Māori; (iii) Pacific Peoples; (iv) Asian.23 However, as people of South Asian ethnicity are known to be at increased risk of T2DM, the Asian group was further subdivided based on the geographic origins into (i) South Asian: Indian, Pakistani, Sri Lankan, Bangladeshi; (ii) East Asian: Chinese, Taiwanese, Korean, Japanese and (iii) South-East Asian: Indonesian, Thai, Singaporean, Malaysian, Filipino, Laotian. Children of other ethnic groups such as Middle Eastern, Latin American and African were allocated to a separate ‘other’ group.

Anthropometric measures

Anthropometric measurements were collected by trained researchers in a designated room at the children’s school during the school day. Height without shoes was recorded with a portable stadiometer (Seca 213) to the nearest 0.1cm, with two measurements taken and averaged. Waist circumference was measured in duplicate using the landmarks for waist measurements24 with a Lufkin W606PM pocket tape positioned around the body over light clothing while standing and recorded to the nearest 0.1cm.

Percentage of body fat was assessed with the Biospace InBody 230 Bio-electric Impedance Analyser (BIA) with the whole body %BF used. The BIA has been validated against dual-energy x-ray absorptiometry for %BF in children.25 The statistical methods for the development of prediction equations based on body composition parameters have been described elsewhere.26 Weight without shoes and in light clothing was recorded to the nearest 0.1kg using the BIA. The BIA uses %BF and classifies children at risk versus normal/low risk based on their gender and age.

Body mass index and WtHR were calculated. Age and gender specific BMI were ascertained using the International Obesity Taskforce BMI cut-offs (also referred to as the IOTF cutoffs), applying the equivalent BMI values at 18 years and linking to child centiles.27–29 Waist-to-height ratio was categorised as <85th and ≥85th percentiles of study population.

Assessment of glycaemic status

A trained researcher collected a finger prick blood sample to measure HbA1c levels using the Roche Cobas b 101 POC meter. This has been validated and delivers comparable outcomes to venous results on reference laboratory platforms.30

The ADA cut-offs for increased risk 39–46mmol/mol (5.7–6.4%) in children were used to categorise children as being in the normoglycaemic or pre-diabetic range.9

Statistical analysis

Data was analysed using the IBM SPSS statistical program, Version 24.0 software (IBM Corporation, New York, USA).

Standard descriptive statistics, including means, standard deviations, frequencies and percentages, were used as appropriate to summarise the socio-demographic and anthropometric results across participants grouped by their HbA1c level. Between-group comparisons were made using Independent Student t-tests, or Pearson’s chi-square test for categorical data.

Factors associated with HbA1c levels were assessed employing binary logistic regression analysis (univariable and multivariable). Children with missing information were excluded (n=18; ethnicity, 9; WC, 9; WtHR, 9; BMI, 3; %BF, 3), which left 433 children for the binary logistic regression analysis. Waist circumference and %BF were included in the regression analysis as a clinical measure (easy to apply) and a research measure, respectively. To avoid the violation of multicollinearity, WtHR and BMI were not included in the regression analyses. Three regression analyses were run including WC and %BF together and either of the anthropometric variables (WtHR and BMI) each individually along with demographic and lifestyle variables. Also, because there was a strong relationship between ethnicity and school decile (a smaller proportion of European children were from low decile schools, in comparison to Māori, Pacific and South Asian ethnicities: 0.6% vs 36-76%, P<0.0001), we ran two regression analyses including either ethnicity or school decile to avoid multicollinearity. As the inclusion of either of these variables did not affect the results, the results with ethnicity are reported. Imbalanced data with binary outcome variables are associated with biases in the estimated probability of an event. We investigated the models to determine whether all the assumptions were met and which model had a better model fit (assessing -2 log likelihood). We also added interaction terms into the models to investigate for interaction effects between variables.

Results

Of the 741 children invited to participate in the wider Children’s Bone Study, 685 (92%) children consented to take part. Of these children, 451 who also consented to a finger prick blood sample were included in this study. The HbA1c group contained proportionally fewer European children than the total group, and they were significantly older, taller, heavier and had a greater WC. There were no significant differences between the two groups for gender, BMI, %BF or physical activity (data not shown).

Participant characteristics

Participant characteristics for the 451 children, (whose HbA1c level was available) stratified by glycaemic status, are shown in Table 1. Age, sex, school decile and physical activity information was available for 451 children; ethnicity, WC and WtHR information was available for 442 children; and BMI and %BF information was available for 448 children. The age of the children ranged from 8 to 12 years (mean 10±0.6 years). Approximately one third of children were New Zealand European, and 13%, 24% and 10% were Māori, Pacific Island and South Asian, respectively. The mean %BF was 23±10% (range 7.8% to 50%) and WC 63±11cm (range 41cm to 104cm).

Glycated haemoglobin levels ranged from 27 to 46mmol/mol. None of children had a HbA1c level in the diabetes range. However, 71 children (16%) had HbA1c levels indicative of pre-diabetes, with a greater proportion of South Asian (30%), Pacific Island (27%) and Māori (18%) children, and those from low decile schools (31%), classified into this group than the normoglycaemia group. In contrast, European and East Asian children and those from medium and high-decile schools were predominantly within the normoglycaemic range.

All anthropometric measures, except height, were significantly higher in the pre-diabetic group than the normoglycaemic group. There was also a significant difference in the reported hours of physical activity per week, with fewer hours reported in the pre-diabetic group.

Table 1: Participants’ characteristics.

Values are mean±SD unless otherwise stated.
BMI, body mass index; HbA1c, glycated haemoglobin A1c.
1 Independent Student t-test or Pearson’s chi-squared for categorical variables.
2 Indian, Pakistani, Sri Lankan and Bangladeshi
3 Chinese, Taiwanese, Korean and Japanese.
4 Indonesian, Thai, Singaporean, Malaysian, Pilipino and Laotian.
5 Middle Eastern, Latin American and African

Main analysis: associations of demographic and anthropometric factors with HbA1c

Due to the small number of children in the East Asian, South-East Asian and other ethnicity groups, these groups were combined for the regression analysis. Table 2 presents the odds ratios (95% CI) from the univariable and multivariable analysis. Model x2 (9)=54, P<0.0001.

Pacific and South Asian children had 3.5 and 5.8 times increased odds, respectively, of being in the pre-diabetic group, compared with European children. In the univariate analysis, those of Māori ethnicity also showed increased odds of being in the pre-diabetic group, but the significance was lost after controlling in the multivariable analysis.

Children whose self-reported physical activity was two hours or less a week had 2.0 times higher odds of being in the pre-diabetic group than those who reported doing more than two hours a week. If their WC was ≥85th percentile or %BF was above the normal range (normal: 17±4.0% vs above normal: 32±7.0%, applying children’s standards [30]), they had 2.6 and 2.3 times higher odds of being in the pre-diabetic group, respectively. Over 56% (37/66) and 66% (44/66) of children with an HbA1c>39mmol/mol had a BMI and %BF, respectively, within the overweight/obese range.

The associations of WtHR and BMI each individually with HbA1c are presented in Table 3. Children who had a BMI within overweight and obese ranges or a WtHR≥0.5cm had 2.3, 4.9 and 5.1 times higher odds (adjusted for demographic and lifestyle factors) of being in the pre-diabetes group, respectively. Inclusion of WtHR or BMI (each individually) in the analyses did not affect the association of ethnicity and physical activity with HbA1c (data not shown).

Follow up analysis: associations of socio-demographics, anthropometric factors and HbA1c with ethnicity and school decile

As a follow up analysis, the characteristics of children of different ethnicities were compared with New Zealand European children (Table 4). A larger proportion of Pacific Island, South Asian and Māori children were from low decile schools than New Zealand European children. Physical-activity levels were lower among Pacific Island and South Asian children and children of other ethnicities as compared to New Zealand European children. However, Māori children had physical-activity levels comparable to those of New Zealand European children. All anthropometric measures were higher in Māori and Pacific Island children, and only %BF was higher in South Asian children (while having comparable BMI and other anthropometric measures) than New Zealand European counterparts. New Zealand European children had the lowest HbA1c compared to all other ethnicities.

Children from low- and medium-decile schools (school decile was considered as a proxy measure of socioeconomic status) had lower physical-activity levels and higher BMI and %BF than children from high-decile schools. Children from low-, but not medium-, decile schools had also higher WC and WtHR than children from high-decile schools (Table 4).

Table 2: Factors associated with HbA1c (normoglycaemic vs pre-diabetic) in 433 children.

CI, confidence Interval; HbA1c, glycated haemoglobin A1c; OR, odds ratio.
1 Variables included in the multivariable model were: age, sex, ethnicity, physical activity, waist circumference (as a clinical measure), and %body fat (as a research measure); Model x2 (9) = 54, P<0.0001.
2 HbA1c was coded as 1=Normoglycaemic (HbA1c ≤39 mmol/mol or 5.7%) and 2=Pre-diabetic (HbA1c >39 mmol/mol or 5.7%).
3 Indian, Pakistani, Sri Lankan and Bangladeshi.
4 East Asian (including Chinese, Taiwanese, Korean, and Japanese), South-East Asian (including Indonesian, Thai, Singaporean, Malaysian, Pilipino and Laotian) and others (including Middle Eastern, Latin American and African).

Table 3: The association of waist-to-height ratio and BMI with HbA1c (normoglycaemic vs prediabetic) in 433 children.

BMI, body mass index; CI, confidence interval; HbA1c, glycated haemoglobin A1c; OR, odds ratio.
1 Variables included in the multivariate model 1 were: age, gender, ethnicity, physical activity and BMI; Model 1: x2 (9)=54, P<0.0001. BMI was categorised according to age and gender specific BMI was ascertained using the International BMI cut-offs (also referred to as the International Obesity Taskforce (IOTF) cut-offs), applying the equivalent BMI values at 18 years and linking to child centiles. 27–29
2 Variables included in the multivariate model 2 were: age, gender, ethnicity, physical activity, and waist to height ratio; Model 2: x2 (9)=62, P<0.0001. Waist-to-height ratio was categorised as <85th (<0.05 cm) and ≥85th (≥0.5 cm) percentiles of population.

Table 4: Participant characteristics across ethnic groups and school deciles.

BMI, body mass index; HbA1c, glycated haemoglobin A1c.
Values are mean±SD and median (25th, 75th percentiles) unless otherwise stated.
I1ndian, Pakistani, Sri Lankan and Bangladeshi.
2 East Asian (including Chinese, Taiwanese, Korean and Japanese), South East Asian (including Indonesian, Thai, Singaporean, Malaysian, Pilipino and Laotian) and others (including Middle Eastern, Latin American and African).
3 One-way ANOVA for normally distributed data and Kruskal–Wallis non-parametric test for not-normally distributed data. Adjusted for multiple comparison. Ethnic groups were compared vs New Zealand European. Data in bold are indicative of a significant difference (significant at P<0.0125).
4 One-way ANOVA for normally distributed data and Kruskal-Wallis non-parametric test for not-normally distributed data. Adjusted for multiple comparison. Lowe and medium decile schools were compared vs. high decile school, Data in bold are indicative of a significant difference (significant at P<0.025).

Discussion

The present study showed a high prevalence of pre-diabetes (HbA1c levels >39mmol/mol or 5.7%)9 in New Zealand school-aged children. Being of South Asian or Pacific Island ethnicity, doing less than two hours of exercise a week and having a WC≥85th percentiles and an above normal %BF (normal: 17±4.0% vs above normal: 32±7.0%, applying children’s standards)31 were associated with an increased odds of having pre-diabetes in these children.

In line with the increasing prevalence of overweight and obesity in children (by 47% during the period 1980–2013), 32 T2DM has become increasingly prevalent in children and adolescents.33–36 The participants in this study were New Zealand children aged 8–11 years. The prevalence of pre-diabetes was 16% in this study, which was higher than the prevalence reported in Chinese children and adolescents aged 6–17 years (1.9%),37 Indian children aged 5–10 years (3.7%)38 and Vietnamese children aged 11–14 years (6.1%),39 and it is closer to the prevalence reported in American adolescents aged 12–18 years (18%)40 and New Zealand adults aged 15 years and older (19%).6 This difference may be due to the different screening methods used in these studies (eg, fasting blood glucose (FPG), oral glucose tolerance (OGT) and HbA1c) and participants’ characteristics (eg, age and ethnicity).

Okosun et al (2015) compared the efficacy of individual blood glucose tests in screening for pre-diabetes in adolescents aged 12–19 years and reported a prevalence rate that substantially differed across different screening methods (a prevalence rate of 6.4, 12 and 1.8% using HbA1c, FPG and OGT, respectively).41 Furthermore, the difference was more pronounced for some ethnic groups; the prevalence of pre-diabetes was greater in non-Hispanic Black adolescents using HbA1c than FPG and OGT tests. The higher prevalence of pre-diabetes using HbA1c may be attributed to the racial/ethnic differences in haemoglobin glycation or red cell survival and vitamin and medication use.42,43 It is important to note that these blood glucose tests might measure different aspects of blood glucose metabolism. Using a combination of HbA1c and FPG test has been suggested to provide both the benefits of individual test and decreases the risk of systemic bias inherent using only the HbA1c test.41,44

Ethnicity/race has been recognised as a well-established risk factor for pre-diabetes and T2DM.9,45,46 Although the high prevalence of pre-diabetes was evident across all ethnic groups in our study, the greatest prevalence was observed in South Asian (30%), Pacific Island (27%) and Māori children (18%), rather than New Zealand European children (6%), which is similar to older population patterns in New Zealand.6 Coppell et al (2013) reported a higher prevalence of pre-diabetes among Pacific Island and Māori youths and adults (aged 15–24 years) than New Zealand European and others (13–14% vs 7%, respectively).6 Similarly, the prevalence of T2DM has been reported to be higher among Pacific Island and South Asian adults aged 25 years and older than New Zealand Europeans.47 Reports from other countries (eg, the US and UK) also show a higher prevalence of pre-diabetes in children and adolescents of some ethnic groups (eg, South Asian and Black African–Caribbean children as compared to whites48 and non-Hispanic Black adolescents as compared to their non-Hispanic white counterparts).40

We speculated that the difference of overweight/obesity measures between children of these ethnicities (South Asian, Pacific Island and Māori children) and New Zealand European children might partially contribute to differences in pre-diabetes prevalence. All measures of overweight/obesity were positively associated with pre-diabetes (Tables 2 and 3), which is consistent with previous studies showing obesity as a risk factor for pre-diabetes and T2DM.6,40,41 We also showed that %BF above normal was a better factor associated with pre-diabetes than BMI (eight more pre-diabetic children). It is important to note that although anthropometric measures are recommended methods of assessing pre-diabetes and T2DM risk, they do not necessarily capture all those at risk. We found that 9%, 10%, 10% and 13% of children with normal %BF, BMI, WtHR and WC, respectively, were in the pre-diabetic group, suggesting that other risk factors are involved.

In agreement with other studies,49–53 Pacific Island and Māori children had higher rates of overweight/obesity, but South Asian children had comparable rates, when compared to New Zealand European children. However, in line with previous reports,54–57 we showed South Asian children to have a higher amount of total body fat for a given BMI compared to New Zealand European children; the mean %BF of South Asian and New Zealand European children for BMIs within the normal range were 20±6.8% and 18±5.2% (P=0.03, not adjusted for multiple comparison), and within the overweight and obese range they were 37±6.0% and 32±8.3% (P=0.08, not adjusted for multiple comparisons), respectively. It has been suggested that South Asians, while anthropometrically thin, are metabolically obese, which is evident even from infancy.58 The mechanism behind the association of overweight/obesity and %BF and diabetes may be linked with the adverse effect of excess visceral and hepatic fat on endocrine function and the body’s inflammatory system (the release of pro-inflammatory cytokines, such as C-reactive protein),59 which leads to insulin resistance and compensatory hyperinsulinemia and T2DM. 59

Our study showed that physical activity was negatively associated with pre-diabetes prevalence, and physical-activity levels were lower in Pacific Island and South Asian children and children of other ethnicities than New Zealand European children. However, Māori children in our study had a physical-activity level comparable to that of New Zealand European children, and this may partly explain why Māori children had lower prevalence of pre-diabetes than Pacific Island and South Asian children. Consistent with our findings, previous cross-sectional and longitudinal studies and meta-analyses have shown that physical activity reduces the risk of T2DM in people with pre-diabetes and the effect is independent of dietary factors and weight loss.60–62 The mechanism behind the association of physical activity and diabetes may be linked with physical-activity induced energy deficits and improvements in glucose homoeostasis that occur through acute responses and chronic adaptations.63,64

Ethnic differences in pre-diabetes risk in the present study could be partially explained by differences in socio-economic status. Ethnic groups in our study were disproportionally distributed across different school deciles, with a larger proportion of Pacific Island (76%), South Asian (50%) and Māori (36%), rather than European children (0.6%), being from low-decile schools. In comparison to those from high-decile schools, children from low-decile schools had more adiposity (as was shown by higher WC, WtHR, BMI and %BF) and less physical activity, and they had increased odds of being in the pre-diabetes group. The findings of the present study are consistent with those of other studies showing that social disadvantage is a determinant of T2DM and its risk factors.65–67 Social disadvantage influences people's attitudes, experiences and behaviours and exposure to several health risk factors and therefore may lead to chronic diseases, including T2DM.68

The present study had several limitations. Firstly, only HbA1c, rather than a combination of glucose tests, was measured. Studies found the sensitivity of individual glucose tests as a screening method for pre-diabetes could be relatively less than when a combination of tests (eg, fasting blood glucose and HbA1c) are performed, and thus this method may misclassify some individuals as pre-diabetic due to differences in haemoglobin glycation or red cell survival. However, using HbA1c is associated with some advantages that include being less invasive and having no requirement for fasting (which can be problematic for a paediatric population),69 a longer-term of glycaemia than plasma glucose43 and less analytical variability than FPG and the OGT methods.11 Secondly, we did not measure the fasting insulin concentration and could not assess the insulin resistance, such as homoeostasis model assessment-insulin resistance. Thirdly, we did not collect information about family history of diabetes, gestational diabetes, pre-term birth, birth weight and stage of puberty, all of which are considered risk factors for diabetes.70–73 We also did not collect information about diet (including components, quality and behaviours), which is considered an important risk factor for diabetes.37,74,75 Finally, a self-completed questionnaire was used to estimate time spent engaging in physical activities (including walking to school). This meant that the accuracy of the data was dependent on the literacy and willingness of those providing it. Additionally, since the study conducted over the winter period, seasonality may have had an effect. The use of accelerometers for energy expenditure would provide a more accurate assessment of physical-activity levels.

Despite the limitations, the present study had several strengths. This study used a large sample with broad representation of New Zealand’s ethnic distribution. Furthermore, with the relatively recent emergence of T2DM in children and adolescents, HbA1c testing has not been used as extensively in these groups. The present study, to the best of authors’ knowledge, is the first to investigate selected factors associated with pre-diabetes using HbA1c in children in New Zealand. Finally, as our cohort was not recruited on any specific weight status, we were able to assess a range of body types, whereas paediatric studies using HbA1c as a measure have tended to focus on overweight or obese children only. The observation that even some of children who had normal anthropometric measures (eg, WC, %BF, BMI and WtHR) were pre-diabetic was an advantage of this approach.

Conclusions

Using a large sample with broad representation of ethnic distribution in New Zealand, the present study found a high prevalence of pre-diabetes in children. Being of South Asian and Pacific Island ethnicities, doing less physical activity and having high WC or %BF were significantly associated with pre-diabetes in children. Ethnic disparities in overweight/obesity and physical-activity level were observed. The prevalence of elevated HbA1c in children of these ethnicities suggests that the risk is present early in life, which supports the need for appropriate and timely approaches to halt the progression to T2DM. However, it should be noted that neither WC, %BF, BMI nor WtHR were associated with high HbA1c in all children. Using these factors, a proportion of children would not have been identified as being at risk, which highlights the importance of other risk factors including ethnicity as an independent risk in the assessment of children.

Author contributions

Conceptualisation, PRvH and CAC. Data curation, DL. Formal analysis, HM, CG and DL. Funding acquisition, PRvH. Investigation, CAC and KLB. Project administration, DL. Supervision, PRvH and CG. Writing (original draft), HM, CG and DL. Writing (review & editing), HM, CG, CAC, KLB and PRvH.

Summary

Abstract

AIMS: The incidence of pre-diabetes and type 2 diabetes mellitus (T2DM) is increasing in children. Early identification of pre-diabetes is an important first step in preventing the progression to T2DM. The aim was to investigate the association of selected factors with pre-diabetes in children. METHODS: This data were from a subset of the 685 children recruited for the Children’s Bone Study, a cross-sectional study of children aged 8–11 years in Auckland, New Zealand. Glycated haemoglobin (HbA1c) was measured from a finger-prick blood test. Children were classified as normoglycaemic (HbA1c≤39mmol/mol) and pre-diabetic (HbA1c>39mmol/mol). Anthropometry included weight, height, waist circumference (WC) and percentage body fat (%BF) measured using bioelectrical impedance analysis. Information about age, gender, ethnicity and physical activity was collected by questionnaires. RESULTS: HbA1c was measured in 451 children (10.4±0.6years, 45% male). Pre-diabetes was present in 71 (16%) children and was greatest in South Asian (n=13, 30%), Pacific Island (n=29, 27%) and Māori (n=10, 18%) children, compared with European children (n=10, 6.0%) (P< 0.001). South Asian and Pacific Island ethnicity, high WC, high %BF and low physical activity were associated with pre-diabetes. CONCLUSIONS: Factors associated with pre-diabetes in children were ethnicity, anthropometric measures and physical-activity levels. The prevalence of pre-diabetes in children of South Asian and Pacific Island ethnicities suggests the need for appropriate and timely identification and intervention to halt the progression to T2DM.

Aim

Method

Results

Conclusion

Author Information

Hajar Mazahery: School of Sport, Exercise, and Nutrition, College of Health, Massey University, Auckland 0745, New Zealand. Cheryl S Gammon: School of Sport, Exercise, and Nutrition, College of Health, Massey University, Auckland 0745, New Zealand. Donna Lawgun: School of Sport, Exercise, and Nutrition, College of Health, Massey University, Auckland 0745, New Zealand. Cathryn A Conlon: School of Sport, Exercise, and Nutrition, College of Health, Massey University, Auckland 0745, New Zealand. Kathryn L Beck: School of Sport, Exercise, and Nutrition, College of Health, Massey University, Auckland 0745, New Zealand. Pamela R von Hurst: School of Sport, Exercise, and Nutrition, College of Health, Massey University, Auckland 0745, New Zealand.

Acknowledgements

Correspondence

Pamela R von Hurst, School of Sport, Exercise, and Nutrition, College of Health, Massey University, Auckland 0745, New Zealand, +64-9-213-6657

Correspondence Email

p.r.vonhurst@massey.ac.nz

Competing Interests

Nil.

1. Hannon TS, Arslanian SA, The changing face of diabetes in youth: lessons learned from studies of type 2 diabetes. Ann NY Acad Sci, 2015. 1353: p. 113-37.

2. World Health Organisation, Global report on diabetes. 2016, World Health Organisation: France.

3. Ministry of Health. Annual update of key results 2017/18: New Zealand health survey. 2018; Available from: https://www.health.govt.nz/publication/annual-update-key-results-2017-18-new-zealand-health-survey.

4. Ng M, Fleming T, Robinson M, et al., Global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet, 2014. 384(9945): p. 766-81.

5. Sjardin N, Reed P, Albert B, et al. Increasing incidence of type 2 diabetes in New Zealand children <15 years of age in a regional-based diabetes service, Auckland, New Zealand. J Paediatr Child Health, 2018. 54(9): p. 1005-1010.

6. Coppell KJ, Mann JI, Williams SM, et al. Prevalence of diagnosed and undiagnosed diabetes and prediabetes in New Zealand: findings from the 2008/09 Adult Nutrition Survey. NZ Med J, 2013. 126(1370): p. 23-42.

7. Gregg EW, Sattar N, Ali MK. The changing face of diabetes complications. Lancet Diabetes Endocrinol, 2016. 4(6): p. 537-47.

8. Zheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev. Endocrinol, 2018. 14(2): p. 88-98.

9. American Diabetes Association, Classification and diagnosis of diabetes. Diabetes Care, 2015. 38(Supplement 1): p. S8-S16.

10. Weiss R, Santoro N, Giannini C, et al. Prediabetes in youth - mechanisms and biomarkers. Lancet Child Adolesc Health, 2017. 1(3): p. 240-248.

11. New Zealand Society for the Study of Diabetes. NZSSD position statement on the diagnosis of, and screening for, type 2 diabetes. 2011; Available from: http://www.nzssd.org.nz/HbA1c/1.

12. Hosking J, Metcalf BS, Jeffery AN, et al. Divergence between HbA1c and fasting glucose through childhood: implications for diagnosis of impaired fasting glucose (Early Bird 52). Pediatr Diabetes, 2014. 15(3): p. 214-9.

13. Lee JM, Wu EL, Tarini B, et al. Diagnosis of diabetes using hemoglobin A1c: should recommendations in adults be extrapolated to adolescents? J Pediatr, 2011. 158(6): p. 947-952 e1-3.

14. Nam HK, Cho WK, Kim JH, et al. HbA1c cutoff for prediabetes and diabetes based on oral glucose tolerance test in obese children and adolescents. J. Korean Med Sci, 2018. 33(12): p. e93.

15. Nowicka P, Santoro N, Liu H, et al. Utility of hemoglobin A(1c) for diagnosing prediabetes and diabetes in obese children and adolescents. Diabetes Care, 2011. 34(6): p. 1306-11.

16. Cole TJ, Freeman JV, Preece MA. Body mass index reference curves for the UK, 1990. Arch Dis Child, 1995. 73(1): p. 25-9.

17. Wells JC, Hattori A. Chart analysis of body mass index in infants and children. Int J Obes Relat Metab Disord, 2000. 24(3): p. 325-9.

18. Dervaux N, Wubuli M, Megnien JL, et al. Comparative associations of adiposity measures with cardiometabolic risk burden in asymptomatic subjects. Atheroscler, 2008. 201(2): p. 413-7.

19. Shen W, Punyanitya M, Chen J, et al. Waist circumference correlates with metabolic syndrome indicators better than percentage fat. Obesity (Silver Spring), 2006. 14(4): p. 727-36.

20. MacKay MF, Haffner SM, Wagenknecht LE, et al. Prediction of type 2 diabetes using alternate anthropometric measures in a multi-ethnic cohort: the insulin resistance atherosclerosis study. Diabetes Care, 2009. 32(5): p. 956-8.

21. Gómez-Ambrosi J, Silva C, Galofré JC, et al. Body adiposity and type 2 diabetes: Increased risk with a high body fat percentage even having a normal BMI. Obesity, 2011. 19(7): p. 1439-1444.

22. Patterson E. Guidelines for data processing and analysis of the International Physical Activity Questionnaire (IPAQ)-short and long forms. 2005, Available from: http://www.ipaq.ki.se/

23. Statistics New Zealand. 2013 Census QuickStats about culture and identity. 2014; Available from: http://www.stats.govt.nz

24. Gibson RS. Principles of nutritional assessment. 2005, New York, USA: Oxford University Press.

25. Delshad M, Beck KL, Conlon CA, et al. Validity of quantitative ultrasound and bioelectrical impedance analysis for measuring bone density and body composition in children. Eu J Clin Nutr. 2020. DOI : 10.1038/s41430-020-00711-6.

26. Guo SS, Chumlea WC, Cockram DB. Use of statistical methods to estimate body composition. Am J Clin Nutr, 1996. 64(3 Suppl): p. 428s-435s.

27. Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: international survey. Br Med J, 2000. 320(7244): p. 1240-3.

28. Cole TJ, Lobstein T. Extended international (IOTF) body mass index cut-offs for thinness, overweight and obesity. Pediatr Obes, 2012. 7(4): p. 284-294.

29. Pan H, Cole TJ. LMS growth, a Microsoft Excel add-in to access growth references based on the LMS method. 2012.

30. Allan K, Taylor F. Little b makes big impact. 2014, Canterbury Health Laboratories: Christchurch, NZ.

31. BioSpace InBody, InBody 720 Results interpretation and application. 2017, BioSpace InBody.

32. World Health Organisation, Interim report of the commission on ending childhood obesity. 2015, World Health Organisation: Geneva, Switzerland.

33. Dabelea D, Mayer-Davis EJ, Saydah S, et al. Prevalence of type 1 and type 2 diabetes among children and adolescents from 2001 to 2009. J Am Med Assoc, 2014. 311(17): p. 1778-1786.

34. Chen L, Magliano DJ, Zimmet PZ. The worldwide epidemiology of type 2 diabetes mellitus - Present and future perspectives. Nat Rev Endocrinol, 2012. 8(4): p. 228-236.

35. Zimmet PZ, Magliano DJ, Herman WH, Shaw JE. Diabetes: a 21st century challenge. Lancet Diabetes Endocrinol, 2013. 2(1): p. 56-64.

36. Al-Saeed AH, Constantino MI, Molyneaux L, et al. An inverse relationship between age of type 2 diabetes onset and complication risk and mortality: The impact of youth-onset type 2 diabetes. Diabetes Care, 2016. 39(5): p. 823.

37. Wang Z, Zou Z, Wang H, et al. Prevalence and risk factors of impaired fasting glucose and diabetes among Chinese children and adolescents: a national observational study. Br J Nutr, 2018. 120(7): p. 813-819.

38. Narayanappa D, Hs R, Mahendrappa K, Prabhakar AK. Prevalence of prediabetes in school-going children. Indian Pediatr, 2010. 48: p. 295-9.

39. Phan DH, Do VV, Khuong, LQ, et al. Pevalence of diabetes and prediabetes among children aged 11-14 years old in Vietnam. J Diabetes Res, 2020. 2020: p. 7573491.

40. Andes LJ, Cheng YJ, Rolka DB, et al. Prevalence of prediabetes among adolescents and young adults in the United States, 2005-2016. JAMA Pediatr, 2020. 174(2): p. e194498-e194498.

41. Okosun IS, Seale JP, Lyn R, Davis-Smith YM. Improving detection of prediabetes in children and adults: Using combinations of blood glucose tests. Front Public Health, 2015. 3(260).

42. Rohlfing CL, Wiedmeyer HM, Little RR, et al. Defining the relationship between plasma glucose and HbA(1c): Analysis of glucose profiles and HbA(1c) in the diabetes control and complications trial. Diabetes Care, 2002. 25(2): p. 275-278.

43. Saudek CD, Herman WH, Sacks DB, et al. A new look at screening and diagnosing diabetes mellitus. J Clin Endocrinol Metab, 2008. 93(7): p. 2447-2453.

44. The International Expert Committee, International expert committee report on the role of the A1c assay in the diagnosis of diabetes. Diabetes Care, 2009. 32(7): p. 1327-1334.

45. Pinhas-Hamiel O, Zeitler P. The global spread of type 2 diabetes mellitus in children and adolescents. J Pediatr, 2005. 146(5): p. 693-700.

46. Goran MI, Ball GDC, Cruz ML. Obesity and risk of type 2 diabetes and cardiovascular disease in children and adolescents. J Clin Endocrinol Metab, 2003. 88(4): p. 1417-1427.

47. Health quality and comission. Diabetes. Atlas of healthcare vairation, 2020 [cited 2020 06/04/2020].

48. Whincup PH, Nightingale CM, Owen CG, et al. Early emergence of ethnic differences in type 2 diabetes precursors in the UK: The child heart and health study in england (CHASE Study). PLOS Med, 2010. 7(4): p. e1000263.

49. Goulding A, Grant AM, Taylor RW, et al. Ethnic differences in extreme obesity. J Pediatr, 2007. 151(5): p. 542-544.

50. Ministry of Health. Annual update of key results 2015/16: New Zealand Health Survey. 2016 [cited 2016 19 December]; Available from: http://www.health.govt.nz/publication/annual-update-key-results-2015-16-new-zealand-health-survey.

51. Duncan JS, Duncan EK, Schofield G. Accuracy of body mass index (BMI) thresholds for predicting excess body fat in girls from five ethnicities. Asia Pacific J Clin Nutr, 2009. 18(3): p. 404-11.

52. Tyrrell VJ, Ichards GER, Hofman P, et al. Obesity in Auckland school children: A comparison of the body mass index and percentage body fat as the diagnostic criterion. Int J Obes, 2001. 25(2): p. 164-169.

53. Ministry of Health, New Zealand Health Survey 2018-19: Annual data explore, MoH, 2019, Ministry of Health.

54. Deurenberg-Yap M, Schmidt G, van Staveren WA, Deurenberg P. The paradox of low body mass index and high body fat percentage among Chinese, Malays and Indians in Singapore. Int J Obes Relat Metab Disord, 2000. 24(8): p. 1011-7.

55. Eyre ELJ, Duncan MJ, Nevill A. South Asian children have increased body fat in comparison to white children at the same body mass index. Children (Basel, Switzerland), 2017. 4(11): p. 102.

56. Deurenberg P, Deurenberg-Yap M, Guricci S. Asians are different from Caucasians and from each other in their body mass index/body fat per cent relationship. Obes Rev, 2002. 3(3): p. 141-146.

57. Tillin T, Sattar N, Godsland IF, Hughes AD, et al. Ethnicity-specific obesity cut-points in the development of Type 2 diabetes – A prospective study including three ethnic groups in the United Kingdom. Diabet Med, 2015. 32(2): p. 226-234.

58. Lawlor DA, West J, Fairley L, et al. Pregnancy glycaemia and cord-blood levels of insulin and leptin in Pakistani and white British mother-offspring pairs: findings from a prospective pregnancy cohort. Diabetologia, 2014. 57(12): p. 2492-500.

59. Bays HE. Adiposopathy is "sick fat" a cardiovascular disease? J Am Coll Cardiol, 2011. 57(25): p. 2461-73.

60. Manson JE, Nathan DM, Krolewski AS, et al. A prospective study of exercise and incidence of diabetes among US male physicians. Jama, 1992. 268(1): p. 63-7.

61. Hu FB, Manson JE, Stampfer MJ, et al. Diet, lifestyle, and the risk of type 2 diabetes mellitus in women. N Engl J Med, 2001. 345(11): p. 790-7.

62. Yates T, Khunti K, Bull F, et al. The role of physical activity in the management of impaired glucose tolerance: a systematic review. Diabetologia, 2007. 50(6): p. 1116-1126.

63. Goodyear LJ, Kahn BB. Exercise, glucose transport, and insulin sensitivity. Annu Rev Med, 1998. 49: p. 235-61.

64. Schleh MW, Pitchford LM, Gillen JB, Horowitz JF. Energy deficit required for exercise-induced improvements in glycemia the next day. Med Sci Sports Exerc, 2020. 52(4): p. 976-982.

65. Nadeau, KJ, Anderson BJ, Berg EG, et al. Youth-onset type 2 diabetes consensus report: Current status, challenges, and priorities. Diabetes care, 2016. 39(9): p. 1635-1642.

66. Drenowatz C, Eisenmann JC, Pfeiffer KA, et al. Influence of socio-economic status on habitual physical activity and sedentary behavior in 8- to 11-year old children. BMC Public Health, 2010. 10: p. 214.

67. Jones-Smith JC, Dieckmann MG, Gottlieb L, et al. Socioeconomic status and trajectory of overweight from birth to mid-childhood: the Early Childhood Longitudinal Study-Birth Cohort. PloS one, 2014. 9(6): p. e100181.

68. Hill J, Nielsen M, Fox MH. Understanding the social factors that contribute to diabetes: a means to informing health care and social policies for the chronically ill. Perm J, 2013. 17(2): p. 67-72.

69. Bennett CM, Guo M, Dharmage SC. HbA1c as a screening tool for detection of Type 2 diabetes: A systematic review. Diabet Med, 2007. 24: p. 333-343.

70. Halipchuk J, Temple B, Dart A, et al. Prenatal, Obstetric and perinatal factors associated with the development of childhood-onset type 2 diabetes. Can J Diabetes, 2018. 42(1): p. 71-77.

71. Reinehr T, Wabitsch M, Kleber M, et al. Parental diabetes, pubertal stage, and extreme obesity are the main risk factors for prediabetes in children and adolescents: A simple risk score to identify children at risk for prediabetes. Pediatr Diabetes, 2009. 10(6): p. 395-400.

72. Mi D, Fang H, Zhao Y, Zhong L. Birth weight and type 2 diabetes: A meta-analysis. Exp Ther Med, 2017. 14(6): p. 5313-5320.

73. Crump C, Sundquist J, Sundquist K. Preterm birth and risk of type 1 and type 2 diabetes: A national cohort study. Diabetologia, 2020. 63(3): p. 508-518.

74. McNaughton SA, Dunstan DW, Ball K, et al. Dietary quality is associated with diabetes and cardio-metabolic risk factors. J Nutr, 2009. 139(4): p. 734-42.

75. Gingras V, Rifas-Shiman SL, Taveras EM, et al. Dietary behaviors throughout childhood are associated with adiposity and estimated insulin resistance in early adolescence: a longitudinal study. Int J Behav Nutr Phys Act, 2018. 15(1): p. 129.

Contact diana@nzma.org.nz
for the PDF of this article

Subscriber Content

The full contents of this pages only available to subscribers.
Login, subscribe or email nzmj@nzma.org.nz to purchase this article.

LOGINSUBSCRIBE