Investigating the risk of prediabetes among children in NZ: a cross-sectional study

Investigating the risk of prediabetes among children in NZ: a cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Investigating the risk of prediabetes among children in NZ: a cross-sectional study Ridvan Tupai-Firestone, Soo Cheng, Marine Corbin, Ngaire Lerwill, and 16 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5712832/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Prediabetes is a non-communicable disease (NCD) that are common in New Zealand (NZ) and it can lead to poor health. The aim of this study was to identify whether there is an increased risk of developing prediabetes among 11–13-year-olds, outside an organised screening programme. Methods Consenting school aged children and their parents completed a series of screening questionnaires including dietary patterns, anthropometrics and socio-economic characteristics. Adapted Australasian Paediatric Endocrinology Guidelines (APEG) criterion was used to identify children at risk of developing prediabetes or have new onset prediabetes. Results Of the 276 participants, significant differences between Pacific, Māori and non- Māori non-Pacific children were evident among those who: were obese (BMI > 95th percentile); lived in overcrowded homes and in deprived areas. A high proportion (35%) of children were at risk of developing prediabetes, and two dietary patterns of children consuming: (1) diverse range of food items but were especially high in snacks (sweet and savoury), takeaway food diversity, and drinks; and (2) a vegetarian and legume diet. Conclusion The study prevalence of prediabetes risk is indicative of childhood lifestyles, and we recommend early screening and better resourcing for promotion of healthy nutrition as preventative measures. BACKGROUND Non-communicable diseases (NCDs) that are predominantly diet-related, such as obesity, prediabetes, and Type 2 diabetes (T2DM) have an extensive impact on the New Zealand (NZ) health system. These NCDs collectively cost the health system over $ 3 billion per year[ 1 – 4 ]. Globally, NCDs are the leading cause of mortality and account for approximately 90% of deaths[ 5 ]. Of these NCDs, prediabetes and the risk factors of developing this condition are growing at an alarming rate, and urgent actions are needed to change the epidemic trajectory[ 6 ]. Furthermore, all of these conditions are the leading drivers for health inequalities, particularly among Pacific people living in NZ, as they experience the highest inequities with these NCDs[ 7 ]. A major independent risk factor for prediabetes and T2DM is obesity, which in NZ, 1 in 10 children (2–14 years) are considered obese (9.4%), based on children’s height and weight at, or above, the 95th percentile for age and sex adjusted body mass index (BMI). The prevalence of obesity among Pacific (29%) and Māori (13%) children is disproportionately higher than for European/Other (7%) and Asian (3%) children[ 8 ]. It has been well established that childhood obesity is a life-course predictor of being overweight in young adulthood[ 9 ], and it is associated with obesity related co-morbidities (e.g., T2DM and cardiovascular disease) in adulthood[ 10 ]. Further, it has been reported that persistent obesity is established in early childhood, before 11-years-old[ 11 ]. Wardle et al (2006) analysed data from over 5,800 English school children aged 11–12 years and found that obesity prevalence at the time of the study was approximately 25%. In their five-year follow-up, the obesity prevalence rose by approximately 7%, and for the most part this was because children who were overweight became obese. More recently, childhood obesity rates for children younger than 5 years of age has significantly reduced, but the increased rates for children older than this age group continue to remain high[ 12 ]. Regardless, it had been suggested that ‘obesity’ and ‘overweight’ are associated with a chronic low-grade inflammatory state[ 13 ], which may increase the risk of cardiovascular events, later in life. Therefore, a focus on early prevention in childhood will be critical for the prevention of poor health outcomes and other related risk factors in adulthood. Prediabetes defined as having a blood glucose level that is above normal, but below the threshold diagnostic criteria for T2DM. In NZ, the diagnostic criteria for prediabetes is based on having a haemoglobin A1C (HbA1c) between 41-49mmol/mol (5.8–6.7%), and no formal diagnosis of T2DM[ 14 ]. The prevalence of prediabetes is high among NZ adults (25%)[ 14 ], and it is elevated in Pacific adolescents and young adults. Among youth aged 15–24 years, 13% of Pacific youth have prediabetes (vs. 7% in NZ Europeans), and the prevalence increases with each life-course age group, with older adults (65–74 years) having the highest prevalence 56% (vs. 44% in NZ Europeans)[ 14 ]. A recent study of 451 Auckland school aged children, reported an overall prediabetes prevalence of 16% among their sample of 8–11-year-olds. Children of South-East Asian ethnicity had the highest prevalence (30%) and among Pacific Island children the prevalence was 27%[ 15 ]. With such a high study prevalence rate, the authors recommended the need for early identification of prediabetes and timely intervention at childhood, rather than adulthood. METHODS This paper provides recent findings from a feasible cross-sectional study undertaken in the wider Wellington region, NZ (2021–2022) to establish whether the risk of developing prediabetes was prevalent, when screening for prediabetes amongst young adolescents (aged 11-13-year-olds), using a two-phased process. This study involved recruiting 11–13-year-olds from primary schools in the Wellington, NZ, who fit the criteria (see below) for targeted screening for prediabetes risk. The targeted age group was selected because these children were due for their routine dental health check-ups which had been conducted yearly since the age of 2 years old until the end of their middle-school (year 8). This dental examination timeframe offered an ideal window-frame to undertake opportunistic screening for children at risk of developing prediabetes. Full ethical approval was obtained from the NZ national health ethics committee (HDEC: 2022-FULL-12212). Phase 1: Participant recruitment and enrollment: Thirty-three out of the 36 schools that were approached and invited to collaborate, had agreed to participate in the study. Prior to each child’s dental exam at each school, all parents were sent a study pack, inviting them and their child to participate by completing a consent form and screening questionnaire (phase 1). The study packs were distributed by the school office and the consent form sought parental/guardian permission to access their child’s, obtain an HbA1c blood test (if the child qualified for phase 2), and to access their family general practitioner (GP) if the research programme identified a new-onset of prediabetes or T2DM (ie. provide a letter of referral outlining the test results). Of note, a child assent form was also provided to each child (written in age-appropriate level) to provide an informed consent in parallel with their parents/guardians. To complete participation in phase 1, all completed (and non-completed) packs were returned to the school after a 7 day period (collected by our team). Within the study pack, parents and their child were to complete the Screening questionnaire. The questionnaire was composed of validated questions taken from the NZ national health survey[ 16 ] and other questions previously used in our research projects[ 17 , 18 ], such as: full name and address; ethnicity (self-identified); age of the child and family members living under the same household; parents’ perception of whether their child is overweight or obese; measured current weight (kilograms, kg) and height (centimeters, cm); maternal history of T2DM and or gestational diabetes of the mother whilst pregnant with the child; first degree relatives with T2DM and other symptoms of T2DM (eg. dark patches in body folds/creases); medications for mental health conditions (depression and anxiety); ever treated for other conditions (asthma, high blood pressure, heart trouble, diabetes, stroke, thyroid and sleep problems, and others); sleep health (snoring and apnoeas whilst sleeping); parents knowledge of the child’s dental status and history of treatment; how active the child is each week; and a 7-day record of food groups consumed (yes/no). We used a previously adapted dietary diversity questionnaire[ 18 ] to explore the individual consumption of foods and food groups in childrens’ home environment. The individual food groups included both nutritious and descretionary food items and food groups. The data consisted of 26 food groups groups (15 nutritious and 11 discretionary). However, we had combined or omitted food groups that were not consumed to 12 groups. This helped to determine the total percentage of items consumed within a food grouping, per person. The groupings were: Group 1 : meats, poultry, fish diversity; Group 2 : dairy products diversity; Group 3 : bread, cereals and starchy vegetable diversity; Group 4 : legumes and nut diversity; Group 5 : fruit diversity; Group 6 : vegetable diversity; Group 7 : oil and fat diversity; Group 8 : drinks diversity; Group 9 : alcohol diversity (omitted); Group 10 : sauces, spreads and flavouring diversity; Group 11 : sweets and sweet snacks diversity; Group 12 : savoury snacks diversity; and Group 13 : take way food diversity. For research purposes, phase 1 initial screening was based on the Australasian Pediatric Endocrinology Guidelines (APEG)[ 19 ] criterion, which endorsed screening among younger children aged 10 years and older, and among Indigenous peoples including Pacific peoples and Māori. Children who fit the APEG criterion and returned a signed parental consent and child assent forms with the screening questionnaire were eligible and therefore invited directly by the research team to participate in Phase 2. Phase 2: Targeted screening for prediabetes Following their consent and eligibility, the child and their parents received a confirmed appointment time and location for testing (usually at the child’s school) by the research team. Clinical data were collected by research assistants and nurses included: anthropometric measurements : body weight (kg) and height (cm) were measured from which BMI z-scores and percentiles were calculated using the NZ Ministry of Health’s BMI calculator for New Zealanders; waist and hip circumferences were measured to obtain the waist-to-hip ratio (WtHR) as an indicator of abdominal fatness; blood pressure; and a non-fasting blood glucose test (HbA1c) was taken specifically by a trained nurse. The HbA1c tests were delivered to a large pathology contracted provider in the region on the same day of the testing to analyse the samples. The results were emailed to the research lead and summary results were forwarded to parents within 24 hours of test interpretation. Initially, we used HbA1c cut-off points of 37-39mmol/mol plus established risk factors, as optimal cut-off points for screening undiagnosed prediabetes[ 19 , 20 ]. RESULTS A total of 322 children were recruited for the study, we retracted the following data from 46 participants, defined here as, non-respondents who provided questionnaires that were returned and designated as ‘declined, not eligible, or refused’ . Six respondents had provided consent, but their screening questionnaires were not received, and they were uncontactable. Therefore, the remaining 276 participants were eligible for phase 1. Table 1: Distribution frequency of characteristics of children with undiagnosed prediabetes and risk factors Māori n=41 (14.9%) Pacific n=66 (23.9%) nMnP n=169 (61.2%) Total n=276 (100%) Age-groups 10 years old 11 years old 12 years old 13 years old p-value 5 (12.2) 23 (56.1) 13 (31.7) 0 (0) 8 (12.1) 32 (48.5) 22 (33.3) 4 (6.1) 11 (6.5) 79 (46.8) 66 (39.1) 13 (7.7) 24 (8.7) 137 (48.6) 101 (36.6) 17 (6.2) 0.330 Gender Male Female p-value 23 (56.1) 18 (43.9) 25 (37.9) 41 (62.1) 95 (56.2) 74 (43.8) 143 (51.8) 133 (48.2) 0.034 Maternal history of T2DM, gestational diabetes Yes No Missing p-value 5 (13.2) 33 (86.8) 4 (6.3) 60 (93.3) 14 (8.4) 153 (91.6) 23 (8.6) 246 (91.5) 7 0.479 History of first-degree relatives with T2DM Yes No Missing p-value 10 (26.3) 28 (74.8) 25 (38.5) 40 (61.5) 32 (19.2) 135 (80.8) 67 (24.8) 203 (75.2) 6 0.009 Parent perception of child overweight/obese Overweight No Yes Missing p-value Obese No Yes Missing p-value 29 (82.9) 6 (17.1) 17 (48.6) 18 (51.4) 45 (80.4) 11 (19.6) 26 (46.4) 30 (53.6) 111 (88.8) 14 (11.2) 112 (89.6) 13 (10.4 155 (71.8) 61 (14.4) 60 0.285 155 (71.8) 61 (28.2) 60 <0.0001 BMI Categories Normal (BMI<85th% ile) Overweight (BMI≥85 th %ile and BMI≤95 th %ile) Obese (BMI ≥95th %ile) Missing p-value 11 (31.4) 6 (17.2) 18 (51.4) 15 (26.8) 11 (19.6) 30 (53.6) 98 (78.4) 14 (11.2) 13 (10.4) 124 (57.4) 31 (14.4) 61 (28.2) 60 <0.0001 Treatment for co-existing conditions (ever) Yes No Missing p-value 22 (55.0) 18 (45.0) 18 (28.1) 46 (71.9) 57 (34.3) 109 (65.7) 97 (35.9) 173 (64.1) 6 0.017 Live in crowded housing No Yes Missing p-value 36 (90.0) 4 (10.0) 43 (68.2) 20 (31.8) 158 (96.3) 6 (3.7) 237 (88.8) 30 (11.2) 9 <0.0001 Deprivation (quintiles) 1= least deprived 2 3 4 5= most deprived Missing p-value 6 (14.6) 4 (9.8) 3 (7.3) 8 (19.5) 20 (48.8) 7 (10.9) 6 (9.4) 11 (17.2) 11 (17.2) 29 (45.3) 58 (34.7) 37 (22.2) 33 (19.8) 20 (12.0) 19 (11.4) 71 (26.1) 47 (17.3) 47 (17.3) 39 (14.3) 68 (25.0) 4 <0.0001 Identified as being at moderate-high risk of prediabetes No Yes p-value 11 (26.8) 30 (73.2) 18 (27.3) 48 (72.7) 150 (88.8) 19 (11.2) 179 (64.9) 97 (35.1) <0.0001 Key: nMnP =non-Māori-non-Pacific; %ile = percentile; Deprivation = NZ IMD deprivation scale (2018) Table 1 highlights the key findings from phase 1 screening questionnaire, characterising the main risk factors for prediabetes, by ethnicity. Specifically, there were significant differences by ethnicity between gender groups (p=0.034); history of first-degree relatives with T2DM (p=0.009); perceived child obesity (p<0.0001) which was based on the parents/guardians self-reported observations; BMI categories (p<0.0001) using the childhood percentile rankings for young New Zealanders, where Pacific children (53.6%) were more than twice the proportion of nMnP children (10.4%) in the obesity percentile range; ever receiving treatment for co-existing conditions was evident (p=0.017) and this was especially for asthma (23.3%), and the presence of other conditions such as, Attention Deficit Disorder and Autism (11.9%), sleep problems, like snoring (5.2%), and psychological conditions (3.7%) were notable; living in a crowded home (p<0.0001) whereby the number of people exceeds the number of bedrooms in the house; and socio-economic deprivation status as measured by the NZ Index of Multiple Deprivation (2018) scale[21] measuring the level of deprivation for people in a small area based on the nine Census variables (measured in quintiles) (p<0.0001), whereby 1 is the least deprived and 5 is the most deprived areas. Also, based on the phase 1 screening questionnaire, the team identified 97 or 35% of children as being at moderate to high risk of developing prediabetes (p<0.0001). The majority of these children were Pacific (48/97), followed by Māori (30/97), and non-Māori-non-Pacific (19/97). These children were invited to the second phase of the study (see Table 3 ). The study also examined the dietary patterns for children in this study. Principal Axis Factor (PAF) method and Oblimin rotation was used for analysis of the 12 food data groups (described above). We excluded data that were identified as being major outliers (n=5 participants), and thus the analyses were based on 264 observations. Each dietary pattern was allocated weights for each food group, which were used to calculate a standardised mean score for each dietary pattern. Each factor was rotated and compared to observe the cross-loading to identify and improve interpretability of each dietary factor. Parallel analyses and scree plots were also used to check for data interpretability. Each of the dietary pattern scores was standardised to have a mean of zero and a variance of one. Each participant was assigned a score for each dietary pattern, since a typical person’s diet may include characteristics of more than one pattern. Thus, the dietary pattern scores are a constant measure of how closely the participant’s diet matches each type of diet. Based on the PAF approach, of all the participants’ dietary intake, standardised scores above 0.40 (i.e., threshold) for any given food grouping indicated a strong propensity matched to a particular dietary pattern (see Table 2 below). Thus, we have identified two distinctive dietary pattern groups (from 65 potential dietary groups). Our selection of the two-factor loadings was confirmed by parallel analyses. Dietary factor 1 consisted of a very diverse range of food groups, but particularly high in: Takeaways, sweets and savoury snack foods, drinks, meats and poultry and breads and cereals. Dietary factor 2 composed primarily of very high diversity of vegetarian and legume-based foods, and to a more limited extent sauces, spreads and flavourings. Additionally, the food groups 11-13 had negative standardised scores indicating that the children who consumed more food items under Dietary factor 2 ate a healthier range of food items within the food groups. Table 2: Dietary Diversity Patterns (7-days) for Pacific children Group Group items Dietary Factor1 Dietary Factor2 G01 Meat, poultry, fish diversity 0.66 0.08 G02 Dairy products diversity 0.63 0.16 G03 Bread, cereals and starchy vegetable diversity 0.66 0.30 G04 Legume and nut diversity 0.00 0.66 G06 Vegetable diversity 0.03 0.78 G07 Oil and fat diversity 0.42 0.27 G08 Drinks diversity 0.77 0.02 G10 Sauces, spreads and flavouring diversity 0.53 0.42 G11 Sweets and sweet snacks diversity 0.81 -0.10 G12 Savoury snacks diversity 0.73 -0.02 G13 Take away food diversity 0.89 -0.19 Key: Groups excluded: Fruit diversity (Group 5) and alcohol (Group 9) Out of the 97 children selected for phase 2, more than half were females (n=54) and 43 were males. In Table 3 (below), based on the median scores (Wilcoxon two-sample p-value test due to non-parametric data), across the three ethnic groups, Pacific children’s risk factor profile for prediabetes based on anthropometric measurements were significantly higher in: weight, hip circumference, BMI percentile rank, and blood glucose test results were significantly different, than for non-Māori-non-Pacific children. Table 3 Participants who completed Phase 2 Māori (n=30) Pacific (n=48) nMnP (n=19) Age (years) p-value 12.0 95% CI: 11.8-12.2 0.630 12.0 95% CI: 11.8-12.2 0.385 12.1 95% CI: 11.8-12.4 ref Height (cm) p-value 158 95% CI: 154.6-161.4 0.766 160.7 95% CI: 157.9-163.4 0.0124 157.3 95% CI: 152.9-163.4 ref Weight (kg) p-value 60.7 95% CI: 55.0-66.3 0.172 70.5 95% CI: 63.6-77.3 0.020 55.7 95% CI: 48.9-62.5 ref Waist (cm) p-value 80.6 95% CI: 75.7-85.5 0.538 84.4 95% CI: 80.1-88.7 0.142 78.3 95% CI: 72.7-84.0 Ref Hip (cm) p-value 94.9 95% CI: 90.5-99.3 0.361 101.6 95% CI: 97.3-105.8 0.012 91.8 95% CI: 86.9-96.8 Ref BP mmHg (systolic) p-value 117.1 95% CI: 112.3-121.9 0.406 117.7 95% CI: 113.2-122.2 0.280 112.3 95% CI: 105.6-119.0 Ref BP mmHg (diastolic) p-value 76.5 95% CI: 71.4-81.7 0.291 76.2 95% CI: 72.7-79.8 0.293 72.4 95% CI: 68.5-76.2 Ref BMI percentile rank p-value 88.6 95% CI: 81.6-95.5 0.090 91.2 95% CI: 86.7-95.7 0.010 78.1 95% CI: 66.0-90.3 ref HbA1c test results (mmol/mol) p-value 34.3 95% CI: 32.9-35.6 0.193 35.2 95% CI: 34.5-36.0 0.022 32.9 95% CI: 30.6-35.3 ref nMnP =non-Māori-non-Pacific ; BMI Percentile: Normal (BMI=85 th -=95 th percentile), Severely Obese (BMI>=99 th percentile); HbA1c test: blood sugar levels Out of the 97 children selected for phase 2, more than half were females (n=54) and 43 were males. In Table 3 (below), based on the median scores (Wilcoxon two-sample p-value test due to non-parametric data), across the three ethnic groups, Pacific children’s risk factor profile for prediabetes based on anthropometric measurements were significantly higher in: weight, hip circumference, BMI percentile rank, and blood glucose test results were significantly different, than for non-Māori-non-Pacific children. Table 4 provides multivariate regression analyses for each food group, by age, gender, ethnicity, BMI category, deprivation, and being risk of prediabetes. Median scores and the interquartile variance were used because the data was non-parametric, and the Wilcoxon two sample test was used for examining statistical relationships between co-variates. Of note, there were clear significant differences between gender groups for all food groups but not for food groups 4, 5, 6 and 11 (legume/nuts, fruits, vegetables, sweets and sweet snacks). Children were obese scored higher than their peers in the normal BMI percentile range across all food groups, but especially significant for groups 1, 8, 10,12, and 13 (meats/poultry/fish, drinks, sauces/spreads/ flavouring, savoury snacks, and take-away food). Furthermore, consumption of various food groups was significantly documented among those children living in the most ‘deprived’ areas for all groups, but not for food items listed under food groups: 2, 4, 5, 6, and 12 (dairy products, breads/cereals and starchy vegetables, legume/nuts, fruits, vegetables, and savour snacks). Furthermore, Pacific children reportedly ate greater amounts of food items in groups 3, 7, 10, 11 and 12, compared to their ethnic counter-parts. The factor scores (FS1, FS2) are the standardised mean scores for each dietary factor loading (as defined above), for that group. Positive standardised score counts indicate the number of deviations above or below the standardised mean score threshold (0.4). In our study, children who were of Pacific ethnicity, live in the most deprived area, and were in the highest BMI percentile (>95 th ), all scored above the mean score threshold, indicating a greater propensity for dietary factor 1, which as previously described was especially high in food groups 1, 3, 12 and 13. Also, children who were in the 12 year old age group score positively higher than the mean score for dietary factor 2, which consisted primarily of vegetarian and legume-based food. [INSERT TABLE 4 HERE] Table 4: Multivariate analyses of dietary habits and socio-demographic covariates G01 m(IQR) G02 m(IQR) G03 m(IQR) G04 m(IQR) G05 m(IQR) G06 m(IQR) G07 m(IQR) G08 m(IQR) G10 m(IQR) G11 m(IQR) G12 m(IQR) G13 m(IQR) FS 1 FS2 Age-groups 10 years 27.8 (16.7) 27.8 (16.7) 34.4 (12.5) 11.1 (33.3) 26.7 (11.7) 32.4 (18.9) 33.3 (22.2) 26.7 (13.3) 31.8 (18.2) 37.5 (33.3) 50.0 (10.0) 21.4 (32.1) -0.3 (1.4) -0.1 (1.0) 11 years 25.9 (11.1) 27.8 (16.7) 34.4 (12.5) 22.2 (22.2) 23.3 (16.7) 32.4 (21.6) 33.3 (22.2) 26.7 (20.0) 31.8 (18.2) 41.7 (25.0) 40.0 (30.0) 21.4 (21.4) -0.2 (0.9) -0.1 (1.3) 12 years 25.9 (18.5) 27.8 (16.7) 34.4 (15.6) 22.2 (33.3) 23.3 (23.3) 35.1 (24.3) 33.3 (22.2) 26.7 (13.3) 31.8 (22.7) 41.7 (33.3) 40.0 (20.0) 21.4 (28.6) -0.3 (0.9) -0.1 (1.5) 13 years 25.9 (18.5) 27.8 (11.1) 37.5 (9.4) 27.8 (33.3) 20.0 (10.0) 35.1 (13.5) 22.2 (11.1) 26.7 (13.3) 36.4 (13.6) 41.7 (25.0) 40.0 (20.0) 21.4 (21.4) -0.3 (0.9) 0.1 (1.1) p-value 0.876 0.607 0.402 0.244 0.770 0.513 0.589 0.900 0.601 0.542 0.801 0.662 0.967 0.554 Gender Male 25.9 (14.8) 27.8 (16.7) 31.3 (12.5) 11.1 (27.8) 23.3 (20.0) 32.4 (21.6) 33.3 (22.2) 23.3 (20.0) 31.8 (18.2) 41.7 (33.3) 40.0 (20.0) 21.4 (21.4) -0.4 (0.9) -0.2 (1.3) Female 29.6 (14.8) 33.3 (16.7) 37.5 (12.5) 22.2 (33.3) 26.7 (20.0) 32.4 (18.9) 33.3 (22.2) 26.7 (20.0) 31.8 (18.2) 50.0 (25.0) 45.0 (30.0) 21.4 (28.6) -0.1 (1.1) 0.0 (1.5) p-value 0.034 0.008 0.004 0.079 0.063 0.119 0.008 0.033 0.042 0.010 0.007 0.074 0.002 0.035 Ethnicity (3 groups) Māori 33.3 (11.1) 27.8 (16.7) 31.3 (18.8) 11.1 (33.3) 23.3 (23.3) 32.4 (24.3) 33.3 (22.2) 26.7 (20.0) 31.8 (27.3) 41.7 (25.0) 40.0 (20.0) 28.6 (35.7) -0.1 (1.0) -0.3 (1.5) Pacific 33.3 (18.5) 33.3 (16.7) 37.5 (17.2) 22.2 (33.3) 26.7 (20.0) 29.7 (27.0) 44.4 (33.3) 33.3 (20.0) 31.8 (18.2) 50.0 (33.3) 50.0 (30.0) 35.7 (35.7) 0.3 (1.8) -0.0 (1.2) Non-Maori non-Pacific 25.9 (13.0) 27.8 (11.1) 34.4 (12.5) 22.2 (27.8) 23.3 (16.7) 32.4 (21.6) 33.3 (22.2) 23.3 (13.3) 31.8 (18.2) 41.7 (33.3) 40.0 (20.0) 21.4 (21.4) -0.4 (0.7) -0.1 (1.3) p-value <0.000 0.025 0.049 0.042 0.150 0.573 <0.000 0.000 0.114 0.006 0.0009 <0.000 <0.000 0.416 BMI category Normal (BMI=85 th & BMI=95th percentile) 33.3 (18.5) 27.8 (16.7) 37.5 (21.9) 22.2 (44.4) 26.7 (23.3) 32.4 (23.0) 38.9 (22.2) 33.3 (20.0) 36.4 (22.7) 50.0 (37.5) 50.0 (40.0) 39.3 (32.1) 0.3 (1.6) -0.1 (1.7) p-value <0.0001 0.860 0.391 0.289 0.025 0.154 0.101 0.0005 0.009 0.069 0.034 <0.0001 0.002 0.646 Deprivation quintile 1= least deprived 25.9 (14.8) 27.8 (16.7) 34.4 (12.5) 22.2 (22.2) 23.3 (13.3) 32.4 (18.9) 22.2 (22.2) 20.0 (13.3) 31.8 (13.6) 41.7 (25.0) 40.0 (20.0) 14.3 (14.3) -0.5 (0.7) -0.1 (1.2) 2 25.9 (14.8) 27.8 (16.7) 34.4 (12.5) 33.3 (33.3) 23.3 (20.0) 35.1 (24.3) 33.3 (22.2) 23.3 (13.3) 36.4 (18.2) 41.7 (25.0) 40.0 (20.0) 21.4 (21.4) -0.1 (0.6) 0.3 (1.4) 3 25.9 (14.8) 27.8 (11.1) 31.3 (15.6) 11.1 (22.2) 23.3 (20.0) 32.4 (24.3) 33.3 (22.2) 26.7 (13.3) 31.8 (18.2) 41.7 (25.0) 40.0 (20.0) 21.4 (21.4) -0.4 (0.9) -0.3 (1.0) 4 29.6 (18.5) 30.6 (16.7) 34.4 (15.6) 22.2 (33.3) 21.7 (16.7) 27.0 (16.2) 33.3 (22.2) 26.7 (20.0) 36.4 (22.7) 58.3 (33.3) 40.0 (20.0) 28.6 (21.4) -0.1 (1.1) -0.2 (1.3) 5= most deprived 33.3 (20.4) 33.3 (22.2) 37.5 (21.9) 22.2 (44.4) 26.7 (26.7) 32.4 (25.7) 44.4 (22.2) 33.3 (20.0) 31.8 (27.3) 50.0 (37.5) 50.0 (35.0) 35.7 (32.1) 0.1 (1.8) 0.0 (1.6) p-value <0.0001 0.125 0.059 0.152 0.586 0.092 0.041 0.006 0.029 0.004 0.083 <0.0001 <0.0001 0.020 Identified as being at moderate-high risk of prediabetes No 25.9 (14.8) 27.8 (11.1) 34.4 (12.5) 22.2 (22.2) 23.3 (16.7) 32.4 (21.6) 33.3 (22.2) 20.0 (13.3) 31.8 (18.2) 41.7 (33.3) 40.0 (20.0) 21.4 (21.4) -0.4 (0.8) -0.1 (1.2) Yes 33.3 (14.8) 27.8 (16.7) 34.4 (15.6) 22.2 (33.3) 23.3 (20.0) 32.4 (24.3) 44.4 (22.2) 26.7 (20.0) 36.4 (22.7) 50.0 (33.3) 40.0 (30.0) 28.6 (28.6) 0.1 (1.4) -0.1 (1.4) p-value <0.0001 0.090 0.266 0.427 0.247 0.638 <0.0001 0.0005 0.0005 <0.0001 0.041 <0.0001 <0.0001 0.763 Key: m=median scores, IQR=interquartile range variance, FS1 and FS2=standardised factor scores DISCUSSION There are two major findings from this cross-sectional study. First, is the social-cultural-environmental determinants of health that highlight the long-term trajectory for poor health outcomes among children and those that are most vulnerable. Second, that children in their young adolescent years (aged 11–13 years old) have the high propensity to develop risk factors for prediabetes, which supports previous international studies[ 9 , 19 , 20 ], particularly for Indigenous population groups[ 22 – 25 ], and our study findings demonstrate the unique opportunistic window-frame for screening NZ children for prediabetes, a strategy recommended by international agencies[ 19 , 26 , 27 ]. The first major finding implicates that known risk factors for prediabetes are strongly established at an earlier age group, and that it is particularly strong for Pacific children. Social deprivation plays an important role in partially explaining these risk factors, particularly given that a quarter of the participants reside in the ‘most least’ and the same proportion of children live in the ‘most highly’ deprived areas . Added to this variable, are those children living in overcrowded households, defined as ‘an increased number of family members living in a home where the number of bedrooms are less than the total number of family members’. This latter variable was most prominent for Pacific children, and research has shown clear relationships between communicable diseases[ 28 ] and living in overcrowded homes among children, and its impact on social and health wellbeing (eg. higher rates of asthma among Pacific children due to cold and damp homes)[ 29 , 30 ]. Moreover, as prediabetes is a diet-related condition, the dietary diversity findings from the current study yielded two interesting patterns. One that was predominantly diverse but high in savoury snacks and takeaway food (dietary factor 1), and the other being almost entirely vegetarian and legume based (dietary factor 2). It is no surprise that children in our study, who live in the most deprived areas, are considered obese (> 95th percentile), and are of Pacific ethnicity, eat food that mostly fit with dietary factor 1, because the risk profile for type 2 diabetes and prediabetes among the youth and adult population is analogous to our study findings. Yet, effective and large-scale prevention programmes have not enabled better health outcomes for children (and the wider population) who are characteristic of type 2 diabetes and prediabetes risk, which from previous research cite known and established barriers (e.g., language, cultural appropriateness, financial support and transport) as inhibiting success and compliancy. Furthermore, it is possible that food groups and habits that are being consumed today are far more different compared to dietary patterns of the last two to three generations, because globalisation and technology has advanced food availability, costs and ultimately the impact from climate change. The latter is an interesting rationale for the dietary patterns, because dietary patterns have shown to contribute to the rising burden of diet related NCDs. For example, high consumption of processed food can assist to increase environmental sustainability by reducing food waste and extending shelf-life, but not reduce the NCD rates. In particular, new issues are emerging, where 45% of young people surveyed around the world report that climate change has negatively impacting on their daily functioning including eating, school, sleep health and relationships[ 31 ]. In the current study, we cannot show that climate change has had a direct impact on the dietary patterns illustrated, however, it is an important consideration for dietary behaviours, because as climate change advances, diet-related NCDs will also be exacerbated, and much of this will be determined by the habits and behaviours of dietary patterns in relation to the environment[ 32 ]. The second major finding from our study demonstrate the unique opportunistic window-frame for screening NZ children for prediabetes, a strategy recommended by international agencies[ 19 , 26 , 27 ]. Phase 2 of the study further highlighted the need for early screening for Indigenous children at risk of prediabetes. With more than a third (35%) of the total study population being identified as risk of prediabetes given the high presence of risk factors (p < 0.0001), particularly for obesity (≥ 99th percentile) where Pacific children had two times the proportion, compared to non-Māori-non-Pacific children (p < 0.0001). The blood glucose test results had demonstrated that on average, Pacific children had borderline moderate blood sugar levels that were higher than for non-Māori-non-Pacific children (p = 0.02). Invariably, researchers will recognise that the blood test results in the current study were not sufficiently high enough for defined prediabetes, we believe, our phase 1 screening questionnaire, followed by a lower HbA1c cutoff point of 35-39mmol/mol plus established risk factors, could be adequate to identify a potential risk of prediabetes. Several studies have identified that progression from prediabetes to T2DM in young adolescents can occur at an accelerated rate, compared to adults[ 33 , 34 ], and it is also important to note, that relying on HbA1c alone and using the cutoff points suggested by the ADA and APEG are based on adults. There are many studies that endorse the need for more research to be undertaken for the purpose of targeting ‘at-risk’ population groups as a preventative approach to establishing defined prediabetes and prevent the development of T2DM[ 35 ], particularly among more ethnically diverse children[ 36 ], and this is what our study findings is endorsing. Due to the small sample size and design of our study, we were not able to establish the actual prevalence of prediabetes in this age group, but our work does highlight a significant gap in screening and optimising on a lower HbA1c cutoff points, that requires further exploration. However, there is a concerning number of childhood participants with obesity and other risk factors that may be indicative that more research needs to be undertaken to establish a baseline since it is difficult to approximate the number of undiagnosed children. Current national proportions estimate approximately 13.5% of children (ages 2–14) were considered obese, indicating an increase over the last five years of 1.9%[ 25 ]. What we understand from previous research is that there can be difficulty detecting prediabetes in children where the cutoff rates for various glucose tests differ across organisations, but are also derived from studies that contained non-representative samples as described earlier[ 37 ]. This finding emphasises the need to act sooner with early interventions to mitigate the onset of prediabetes and prevent increasing rates of T2DM development in adolescents. Interventions of this calibre need to incorporate lifestyle changes as they are most effective when combined with pharmacological intervention [ 38 ]. It is also relevant to focus on other environmental and structural factors that may be barriers to access sustainable lifestyle changes[ 39 ] as highlighted from the first major finding, or an approach that includes health promotion and education programs that raise awareness and can be embedded in the school or home environment. CONCLUSIONS This study highlights that key risk factors for childhood prediabetes are firmly established before children reach youthhood (i.e., aged 15–24 years old), and that other factors (social-cultural-environmental) that are known to perpetuate these risk factors are not new findings when tackling diet related NCDs. However, a tailored approach to addressing the health inequalities faced by vulnerable population groups such as Pacific and Māori children are needed now more than ever. The need for early prevention methods to curb unhealthy eating habits and behaviours that contribute to the childhood obesity epidemic which increases the risk of prediabetes and T2DM is needed. More than likely, these programs should be culturally appropriate and tailored, particularly in the current study where Pacific and Māori children were overrepresented in poor health outcome statistics. If early and holistic programs can be employed to mitigate the risks of developing prediabetes amongst adolescents, we may see a shift in health and wellbeing amongst this age group, which could safeguard their wellbeing later in life and avoid early onset of adult NCDs. Acknowledging the risk of T2DM by focusing on prediabetes risk was also a major outcome of this study, alongside the need to explore opportunistic screening window-frames and a lower cutoff point for HbA1c that are age and culturally representative. Our study reported a high prediabetes risk proportion (35%), we believe that this is sufficient for health researchers and policymakers to discuss the potentiality of early intervention, through screening for prediabetes risk among children from age 10 years old, with a focus on those children in high-risk population groups. LIMITATIONS We have discussed above about the limitations of a small sample size. However, this study was conducted post-covid-19 pandemic lockdown, which placed a strained the education system, thus impacting effective recruitment strategies. The polarisation of the pandemic also meant distrust between families and health researchers, and the competing nature of other research projects targeting the same schools for their own research meant there was high attrition of schools, children and their families consenting to participate in our study. Moreover, the restrictions of the pandemic, funding period and the time allocated to recruit participants to carry out phases 1 and 2, and conduct data analyses, placed an enormous strain on our research team’s capability to complete this work within a 12-month timeline. Thus, the final study sample and findings may not be representative of the general population of NZ children of similar age and ethnicity. The timeline and funding resources also limited our ability to collect more accurate diagnostic measures, as a single point-in-time measurement of HbA1c was conducted, with no repeat testing (preferably in 6 months). FUTURE IMPLICATIONS Implement a scaled-up national-based cross-sectional study that involves a larger sample size that is more reflective of the children aged 10–15 years old (based on the APEG guidelines) with better resources, will improve our understanding of the magnitude of prediabetes risk among NZ children. Further research is needed to optimise on lower HbA1c cutoff points (plus established risk factors) that represent children aged 10–15 years old and is inclusive of cultural diversity. Health promotional programs that are inclusive of the social-cultural-environmental determinants of health, and that are culturally tailored to address the prediabetes risk for children and their families, be more widely available to vulnerable groups that need it the most. Declarations Ethics approval and consent to participate This project received full ethical approval from the Southern Health and Disability Ethics Committee: HDEC 2022 FULL 12212. Clinical Trial Number Not applicable. Consent for publication All participants provided written consent to participate and to publish with the knowledge that there will be no personally identifiable information that can identify any of our participants. The children in our study provided written assent and the parents provided written consent. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests We declare that the authors named in this manuscript have no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper. Funding The research was funded by the National Science Challenge: A Better Start, E Tipu e Rea, ABST2001/ 3719490. Authors' contributions RTF wrote the paper and assisted by HD. SC conducted the main analyses aided by MC, and assisted with data interpretations. All authors read the manuscript and provided input where necessary. Acknowledgements The authors would like to acknowledge the young participants and their parents for their time and effort to take part in this study. 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Tupai-Firestone","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIiWNgGAWjYLCCCgM4iyj1zAwMZ0Ba2ECcM0RrYYBqYWwjQoN5A/8BhgMF9+Tk5zcfe/Bx3jZ5+f7DxyQYauwYzIEy2IDMAaAtBwyKjQ2OsaUbztx223DDjbQ0CYZjyQyWDdi1SAAdxvzBICFxAxuPmTTvttuMGyR4zCQY2A4wGBxswKkFaEtC/fw2oJa/c27bz+8//02C4R9Qy2HsfoFpSWA4BtTC2HA7seFADpsEYxtQyzEcWpiZDQ4AtRhuOJaWJtlz7HYy0C/GFol9yTwGOEJcgr3x4YMDfxLk5ZuBAfWj5rbt/P7DD298+GYnZ3Aeu/fB7kIXY5FIYGDgwa4eB2D+QJLyUTAKRsEoGO4AAJOrWOR+KOjQAAAAAElFTkSuQmCC","orcid":"","institution":"Centre for Public Health Research, College of Health, Massey University","correspondingAuthor":true,"prefix":"","firstName":"Ridvan","middleName":"","lastName":"Tupai-Firestone","suffix":""},{"id":395875538,"identity":"bc1b5bb7-a8da-4e39-92b5-c468a591c4b5","order_by":1,"name":"Soo Cheng","email":"","orcid":"","institution":"Centre for Public Health Research, College of Health, Massey University","correspondingAuthor":false,"prefix":"","firstName":"Soo","middleName":"","lastName":"Cheng","suffix":""},{"id":395875539,"identity":"85eb9ac0-8ef1-48a3-9c8e-c9ef827f76c3","order_by":2,"name":"Marine Corbin","email":"","orcid":"","institution":"Centre for Public Health Research, College of Health, Massey University","correspondingAuthor":false,"prefix":"","firstName":"Marine","middleName":"","lastName":"Corbin","suffix":""},{"id":395875540,"identity":"f7814f6f-bde2-4aa6-bc82-dc861a9fefcb","order_by":3,"name":"Ngaire Lerwill","email":"","orcid":"","institution":"Centre for Public Health Research, College of Health, Massey University","correspondingAuthor":false,"prefix":"","firstName":"Ngaire","middleName":"","lastName":"Lerwill","suffix":""},{"id":395875541,"identity":"8e8ee3dc-f43b-49a6-9556-a80ef0cdea2d","order_by":4,"name":"Tupou Pulu","email":"","orcid":"","institution":"Centre for Public Health Research, College of Health, Massey 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University","correspondingAuthor":false,"prefix":"","firstName":"Veisinia","middleName":"","lastName":"Pulu","suffix":""},{"id":395875545,"identity":"37d394bb-93b7-47cb-8d2b-e31aecbea64e","order_by":8,"name":"Justice Firestone","email":"","orcid":"","institution":"Centre for Public Health Research, College of Health, Massey University","correspondingAuthor":false,"prefix":"","firstName":"Justice","middleName":"","lastName":"Firestone","suffix":""},{"id":395875546,"identity":"93c018ee-5fa0-4473-a7c2-e0a33ef3ffaf","order_by":9,"name":"Kathryn Fuge","email":"","orcid":"","institution":"Bee Healthy Regional Dental Service, Capital, Coast and Hutt Valley, Health NZ","correspondingAuthor":false,"prefix":"","firstName":"Kathryn","middleName":"","lastName":"Fuge","suffix":""},{"id":395875547,"identity":"8c2fa72c-ccf8-45ad-8479-18c7f8d1f40b","order_by":10,"name":"Sera Tapu-Ta'ala","email":"","orcid":"","institution":"IA MALU Consultancy, NZ","correspondingAuthor":false,"prefix":"","firstName":"Sera","middleName":"","lastName":"Tapu-Ta'ala","suffix":""},{"id":395875548,"identity":"574bbf8a-7e24-4917-a8c1-95b893f67c0a","order_by":11,"name":"Prachee Gokhale","email":"","orcid":"","institution":"Centre for Public Health Research, College of Health, Massey University","correspondingAuthor":false,"prefix":"","firstName":"Prachee","middleName":"","lastName":"Gokhale","suffix":""},{"id":395875549,"identity":"1e3c6d3c-d3a3-4b3d-bdd2-30428dfe73d5","order_by":12,"name":"Anna Matheson","email":"","orcid":"","institution":"School of Health, The Research Trust of Victoria University of Wellington","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Matheson","suffix":""},{"id":395875550,"identity":"69bf3a26-9a3e-45a7-a41a-024f68be4420","order_by":13,"name":"Deborah Reed","email":"","orcid":"","institution":"Centre for Public Health Research, College of Health, Massey University","correspondingAuthor":false,"prefix":"","firstName":"Deborah","middleName":"","lastName":"Reed","suffix":""},{"id":395875551,"identity":"4eb333e2-ce83-4e1d-905c-e9b98e219c2d","order_by":14,"name":"Barry Borman","email":"","orcid":"","institution":"Centre for Public Health Research, College of Health, Massey University","correspondingAuthor":false,"prefix":"","firstName":"Barry","middleName":"","lastName":"Borman","suffix":""},{"id":395875552,"identity":"1ad9f1ff-6643-412d-9fb3-991c3b5c635b","order_by":15,"name":"Akarere Henry","email":"","orcid":"","institution":"South Waikato Pacific Islands Community Services Trust","correspondingAuthor":false,"prefix":"","firstName":"Akarere","middleName":"","lastName":"Henry","suffix":""},{"id":395875553,"identity":"785ece5a-b02a-4859-8c48-b287ac97a13f","order_by":16,"name":"Jeremy Krebs","email":"","orcid":"","institution":"Department of Medicine, University of Otago","correspondingAuthor":false,"prefix":"","firstName":"Jeremy","middleName":"","lastName":"Krebs","suffix":""},{"id":395875554,"identity":"5c5d7877-0b7a-4f0c-ada6-25f927f06d23","order_by":17,"name":"Raynald Samoa","email":"","orcid":"","institution":"Department of Diabetes, Endocrinology and Metabolism","correspondingAuthor":false,"prefix":"","firstName":"Raynald","middleName":"","lastName":"Samoa","suffix":""},{"id":395875555,"identity":"f886969b-9deb-4cb1-b723-bcfb5632de5b","order_by":18,"name":"Te Kani Kingi","email":"","orcid":"","institution":"Te Whare Wananga o Awanuiorangi","correspondingAuthor":false,"prefix":"","firstName":"Te","middleName":"Kani","lastName":"Kingi","suffix":""},{"id":395875557,"identity":"812e535a-875e-4525-afca-64f6e2500637","order_by":19,"name":"Nia Aitaoto","email":"","orcid":"","institution":"National Association of Pasifika Organzations","correspondingAuthor":false,"prefix":"","firstName":"Nia","middleName":"","lastName":"Aitaoto","suffix":""}],"badges":[],"createdAt":"2024-12-25 23:08:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5712832/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5712832/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80026653,"identity":"b13ec872-4d20-400c-89de-47607333cf0e","added_by":"auto","created_at":"2025-04-07 06:31:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1719913,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5712832/v1/1e1f26b1-de01-4c1b-8d68-8430b05f5be4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigating the risk of prediabetes among children in NZ: a cross-sectional study","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eNon-communicable diseases (NCDs) that are predominantly diet-related, such as obesity, prediabetes, and Type 2 diabetes (T2DM) have an extensive impact on the New Zealand (NZ) health system. These NCDs collectively cost the health system over \u003cspan\u003e$\u003c/span\u003e3\u0026nbsp;billion per year[\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Globally, NCDs are the leading cause of mortality and account for approximately 90% of deaths[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Of these NCDs, prediabetes and the risk factors of developing this condition are growing at an alarming rate, and urgent actions are needed to change the epidemic trajectory[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Furthermore, all of these conditions are the leading drivers for health inequalities, particularly among Pacific people living in NZ, as they experience the highest inequities with these NCDs[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. A major independent risk factor for prediabetes and T2DM is obesity, which in NZ, 1 in 10 children (2\u0026ndash;14 years) are considered obese (9.4%), based on children\u0026rsquo;s height and weight at, or above, the 95th percentile for age and sex adjusted body mass index (BMI). The prevalence of obesity among Pacific (29%) and Māori (13%) children is disproportionately higher than for European/Other (7%) and Asian (3%) children[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. It has been well established that childhood obesity is a life-course predictor of being overweight in young adulthood[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and it is associated with obesity related co-morbidities (e.g., T2DM and cardiovascular disease) in adulthood[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Further, it has been reported that persistent obesity is established in early childhood, before 11-years-old[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Wardle et al (2006) analysed data from over 5,800 English school children aged 11\u0026ndash;12 years and found that obesity prevalence at the time of the study was approximately 25%. In their five-year follow-up, the obesity prevalence rose by approximately 7%, and for the most part this was because children who were overweight became obese. More recently, childhood obesity rates for children younger than 5 years of age has significantly reduced, but the increased rates for children older than this age group continue to remain high[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Regardless, it had been suggested that \u0026lsquo;obesity\u0026rsquo; and \u0026lsquo;overweight\u0026rsquo; are associated with a chronic low-grade inflammatory state[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], which may increase the risk of cardiovascular events, later in life. Therefore, a focus on early prevention in childhood will be critical for the prevention of poor health outcomes and other related risk factors in adulthood.\u003c/p\u003e \u003cp\u003ePrediabetes defined as having a blood glucose level that is above normal, but below the threshold diagnostic criteria for T2DM. In NZ, the diagnostic criteria for prediabetes is based on having a haemoglobin A1C (HbA1c) between 41-49mmol/mol (5.8\u0026ndash;6.7%), and no formal diagnosis of T2DM[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The prevalence of prediabetes is high among NZ adults (25%)[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and it is elevated in Pacific adolescents and young adults. Among youth aged 15\u0026ndash;24 years, 13% of Pacific youth have prediabetes (vs. 7% in NZ Europeans), and the prevalence increases with each life-course age group, with older adults (65\u0026ndash;74 years) having the highest prevalence 56% (vs. 44% in NZ Europeans)[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. A recent study of 451 Auckland school aged children, reported an overall prediabetes prevalence of 16% among their sample of 8\u0026ndash;11-year-olds. Children of South-East Asian ethnicity had the highest prevalence (30%) and among Pacific Island children the prevalence was 27%[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. With such a high study prevalence rate, the authors recommended the need for early identification of prediabetes and timely intervention at childhood, rather than adulthood.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eThis paper provides recent findings from a feasible cross-sectional study undertaken in the wider Wellington region, NZ (2021\u0026ndash;2022) to establish whether the risk of developing prediabetes was prevalent, when screening for prediabetes amongst young adolescents (aged 11-13-year-olds), using a two-phased process. This study involved recruiting 11\u0026ndash;13-year-olds from primary schools in the Wellington, NZ, who fit the criteria (see below) for targeted screening for prediabetes risk. The targeted age group was selected because these children were due for their routine dental health check-ups which had been conducted yearly since the age of 2 years old until the end of their middle-school (year 8). This dental examination timeframe offered an ideal window-frame to undertake opportunistic screening for children at risk of developing prediabetes. Full ethical approval was obtained from the NZ national health ethics committee (HDEC: 2022-FULL-12212).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePhase 1: Participant recruitment and enrollment:\u003c/h2\u003e \u003cp\u003e Thirty-three out of the 36 schools that were approached and invited to collaborate, had agreed to participate in the study. Prior to each child\u0026rsquo;s dental exam at each school, all parents were sent a study pack, inviting them and their child to participate by completing a consent form and screening questionnaire (phase 1). The study packs were distributed by the school office and the consent form sought parental/guardian permission to access their child\u0026rsquo;s, obtain an HbA1c blood test (if the child qualified for phase 2), and to access their family general practitioner (GP) if the research programme identified a new-onset of prediabetes or T2DM (ie. provide a letter of referral outlining the test results). Of note, a child assent form was also provided to each child (written in age-appropriate level) to provide an informed consent in parallel with their parents/guardians. To complete participation in phase 1, all completed (and non-completed) packs were returned to the school after a 7 day period (collected by our team). Within the study pack, parents and their child were to complete the \u003cb\u003eScreening questionnaire.\u003c/b\u003e The questionnaire was composed of validated questions taken from the NZ national health survey[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and other questions previously used in our research projects[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], such as: full name and address; ethnicity (self-identified); age of the child and family members living under the same household; parents\u0026rsquo; perception of whether their child is overweight or obese; measured current weight (kilograms, kg) and height (centimeters, cm); maternal history of T2DM and or gestational diabetes of the mother whilst pregnant with the child; first degree relatives with T2DM and other symptoms of T2DM (eg. dark patches in body folds/creases); medications for mental health conditions (depression and anxiety); ever treated for other conditions (asthma, high blood pressure, heart trouble, diabetes, stroke, thyroid and sleep problems, and others); sleep health (snoring and apnoeas whilst sleeping); parents knowledge of the child\u0026rsquo;s dental status and history of treatment; how active the child is each week; and a 7-day record of food groups consumed (yes/no).\u003c/p\u003e \u003cp\u003eWe used a previously adapted \u003cb\u003edietary diversity\u003c/b\u003e questionnaire[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] to explore the individual consumption of foods and food groups in childrens\u0026rsquo; home environment. The individual food groups included both nutritious and descretionary food items and food groups. The data consisted of 26 food groups groups (15 nutritious and 11 discretionary). However, we had combined or omitted food groups that were not consumed to 12 groups. This helped to determine the total percentage of items consumed within a food grouping, per person. The groupings were: \u003cb\u003eGroup 1\u003c/b\u003e: meats, poultry, fish diversity; \u003cb\u003eGroup 2\u003c/b\u003e: dairy products diversity; \u003cb\u003eGroup 3\u003c/b\u003e: bread, cereals and starchy vegetable diversity; \u003cb\u003eGroup 4\u003c/b\u003e: legumes and nut diversity; \u003cb\u003eGroup 5\u003c/b\u003e: fruit diversity; \u003cb\u003eGroup 6\u003c/b\u003e: vegetable diversity; \u003cb\u003eGroup 7\u003c/b\u003e: oil and fat diversity; \u003cb\u003eGroup 8\u003c/b\u003e: drinks diversity; \u003cb\u003eGroup 9\u003c/b\u003e: alcohol diversity (omitted); \u003cb\u003eGroup 10\u003c/b\u003e: sauces, spreads and flavouring diversity; \u003cb\u003eGroup 11\u003c/b\u003e: sweets and sweet snacks diversity; \u003cb\u003eGroup 12\u003c/b\u003e: savoury snacks diversity; and \u003cb\u003eGroup 13\u003c/b\u003e: take way food diversity.\u003c/p\u003e \u003cp\u003eFor research purposes, phase 1 initial screening was based on the Australasian Pediatric Endocrinology Guidelines (APEG)[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] criterion, which endorsed screening among younger children aged 10 years and older, and among Indigenous peoples including Pacific peoples and Māori. Children who fit the APEG criterion and returned a signed parental consent and child assent forms with the screening questionnaire were eligible and therefore invited directly by the research team to participate in Phase 2.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePhase 2: Targeted screening for prediabetes\u003c/h3\u003e\n\u003cp\u003e Following their consent and eligibility, the child and their parents received a confirmed appointment time and location for testing (usually at the child\u0026rsquo;s school) by the research team. Clinical data were collected by research assistants and nurses included: \u003cem\u003eanthropometric measurements\u003c/em\u003e: body weight (kg) and height (cm) were measured from which BMI z-scores and percentiles were calculated using the NZ Ministry of Health\u0026rsquo;s BMI calculator for New Zealanders; waist and hip circumferences were measured to obtain the waist-to-hip ratio (WtHR) as an indicator of abdominal fatness; blood pressure; and a non-fasting blood glucose test (HbA1c) was taken specifically by a trained nurse. The HbA1c tests were delivered to a large pathology contracted provider in the region on the same day of the testing to analyse the samples. The results were emailed to the research lead and summary results were forwarded to parents within 24 hours of test interpretation. Initially, we used HbA1c cut-off points of 37-39mmol/mol plus established risk factors, as optimal cut-off points for screening undiagnosed prediabetes[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eA total of 322 children were recruited for the study, we retracted the following data from 46 participants, defined here as, non-respondents who provided questionnaires that were returned and designated as \u003cem\u003e\u0026lsquo;declined, not eligible, or refused\u0026rsquo;\u003c/em\u003e. \u0026nbsp;Six respondents had provided consent, but their screening questionnaires were not received, and they were uncontactable. \u0026nbsp;Therefore, the remaining 276 participants were eligible for phase 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003eTable 1: Distribution frequency of characteristics of children with undiagnosed prediabetes and risk factors\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1431%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMāori\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=41 (14.9%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1431%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePacific\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=66 (23.9%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1431%;\"\u003e\n \u003cp\u003e\u003cstrong\u003enMnP\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=169 (61.2%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.1414%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=276 (100%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAge-groups\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e10 years old\u003c/p\u003e\n \u003cp\u003e11 years old\u003c/p\u003e\n \u003cp\u003e12 years old\u003c/p\u003e\n \u003cp\u003e13 years old\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1431%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5 (12.2)\u003c/p\u003e\n \u003cp\u003e23 (56.1)\u003c/p\u003e\n \u003cp\u003e13 (31.7)\u003c/p\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1431%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8 (12.1)\u003c/p\u003e\n \u003cp\u003e32 (48.5)\u003c/p\u003e\n \u003cp\u003e22 (33.3)\u003c/p\u003e\n \u003cp\u003e4 (6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1431%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e11 (6.5)\u003c/p\u003e\n \u003cp\u003e79 (46.8)\u003c/p\u003e\n \u003cp\u003e66 (39.1)\u003c/p\u003e\n \u003cp\u003e13 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.1414%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e24 (8.7)\u003c/p\u003e\n \u003cp\u003e137 (48.6)\u003c/p\u003e\n \u003cp\u003e101 (36.6)\u003c/p\u003e\n \u003cp\u003e17 (6.2)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.330\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eGender\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1431%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e23 (56.1)\u003c/p\u003e\n \u003cp\u003e18 (43.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1431%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e25 (37.9)\u003c/p\u003e\n \u003cp\u003e41 (62.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1431%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e95 (56.2)\u003c/p\u003e\n \u003cp\u003e74 (43.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.1414%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e143 (51.8)\u003c/p\u003e\n \u003cp\u003e133 (48.2)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.034\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eMaternal history of T2DM, gestational diabetes\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1431%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5 (13.2)\u003c/p\u003e\n \u003cp\u003e33 (86.8)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1431%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4 (6.3)\u003c/p\u003e\n \u003cp\u003e60 (93.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1431%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e14 (8.4)\u003c/p\u003e\n \u003cp\u003e153 (91.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.1414%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e23 (8.6)\u003c/p\u003e\n \u003cp\u003e246 (91.5)\u003c/p\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.479\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eHistory of first-degree relatives with T2DM\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1431%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10 (26.3)\u003c/p\u003e\n \u003cp\u003e28 (74.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1431%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e25 (38.5)\u003c/p\u003e\n \u003cp\u003e40 (61.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1431%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e32 (19.2)\u003c/p\u003e\n \u003cp\u003e135 (80.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.1414%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e67 (24.8)\u003c/p\u003e\n \u003cp\u003e203 (75.2)\u003c/p\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.009\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eParent perception of child overweight/obese\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eOverweight\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eObese\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1431%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e29 (82.9)\u003c/p\u003e\n \u003cp\u003e6 (17.1)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e17 (48.6)\u003c/p\u003e\n \u003cp\u003e18 (51.4)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1431%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e45 (80.4)\u003c/p\u003e\n \u003cp\u003e11 (19.6)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e26 (46.4)\u003c/p\u003e\n \u003cp\u003e30 (53.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1431%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e111 (88.8)\u003c/p\u003e\n \u003cp\u003e14 (11.2)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e112 (89.6)\u003c/p\u003e\n \u003cp\u003e13 (10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.1414%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e155 (71.8)\u003c/p\u003e\n \u003cp\u003e61 (14.4)\u003c/p\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.285\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e155 (71.8)\u003c/p\u003e\n \u003cp\u003e61 (28.2)\u003c/p\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.0001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eBMI Categories\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNormal (BMI\u0026lt;85th% ile)\u003c/p\u003e\n \u003cp\u003eOverweight (BMI\u0026ge;85\u003csup\u003eth\u003c/sup\u003e %ile and BMI\u0026le;95\u003csup\u003eth\u003c/sup\u003e %ile)\u003c/p\u003e\n \u003cp\u003eObese (BMI \u0026ge;95th %ile)\u003c/p\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1431%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e11 (31.4)\u003c/p\u003e\n \u003cp\u003e6 (17.2)\u003c/p\u003e\n \u003cp\u003e18 (51.4)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1431%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e15 (26.8)\u003c/p\u003e\n \u003cp\u003e11 (19.6)\u003c/p\u003e\n \u003cp\u003e30 (53.6)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1431%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e98 (78.4)\u003c/p\u003e\n \u003cp\u003e14 (11.2)\u003c/p\u003e\n \u003cp\u003e13 (10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.1414%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e124 (57.4)\u003c/p\u003e\n \u003cp\u003e31 (14.4)\u003c/p\u003e\n \u003cp\u003e61 (28.2)\u003c/p\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.0001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eTreatment for co-existing conditions (ever)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1431%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e22 (55.0)\u003c/p\u003e\n \u003cp\u003e18 (45.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1431%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e18 (28.1)\u003c/p\u003e\n \u003cp\u003e46 (71.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1431%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e57 (34.3)\u003c/p\u003e\n \u003cp\u003e109 (65.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.1414%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e97 (35.9)\u003c/p\u003e\n \u003cp\u003e173 (64.1)\u003c/p\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.017\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eLive in crowded housing\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1431%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e36 (90.0)\u003c/p\u003e\n \u003cp\u003e4 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1431%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e43 (68.2)\u003c/p\u003e\n \u003cp\u003e20 (31.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1431%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e158 (96.3)\u003c/p\u003e\n \u003cp\u003e6 (3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.1414%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e237 (88.8)\u003c/p\u003e\n \u003cp\u003e30 (11.2)\u003c/p\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.0001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eDeprivation (quintiles)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1= least deprived\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e5= most deprived\u003c/p\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1431%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e6 (14.6)\u003c/p\u003e\n \u003cp\u003e4 (9.8)\u003c/p\u003e\n \u003cp\u003e3 (7.3)\u003c/p\u003e\n \u003cp\u003e8 (19.5)\u003c/p\u003e\n \u003cp\u003e20 (48.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1431%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7 (10.9)\u003c/p\u003e\n \u003cp\u003e6 (9.4)\u003c/p\u003e\n \u003cp\u003e11 (17.2)\u003c/p\u003e\n \u003cp\u003e11 (17.2)\u003c/p\u003e\n \u003cp\u003e29 (45.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1431%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e58 (34.7)\u003c/p\u003e\n \u003cp\u003e37 (22.2)\u003c/p\u003e\n \u003cp\u003e33 (19.8)\u003c/p\u003e\n \u003cp\u003e20 (12.0)\u003c/p\u003e\n \u003cp\u003e19 (11.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.1414%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e71 (26.1)\u003c/p\u003e\n \u003cp\u003e47 (17.3)\u003c/p\u003e\n \u003cp\u003e47 (17.3)\u003c/p\u003e\n \u003cp\u003e39 (14.3)\u003c/p\u003e\n \u003cp\u003e68 (25.0)\u003c/p\u003e\n \u003cp\u003e4\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.0001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eIdentified as being at moderate-high risk of prediabetes\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1431%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e11 (26.8)\u003c/p\u003e\n \u003cp\u003e30 (73.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1431%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e18 (27.3)\u003c/p\u003e\n \u003cp\u003e48 (72.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1431%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e150 (88.8)\u003c/p\u003e\n \u003cp\u003e19 (11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.1414%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e179 (64.9)\u003c/p\u003e\n \u003cp\u003e97 (35.1)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.0001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eKey: nMnP\u003c/strong\u003e =non-Māori-non-Pacific; \u003cstrong\u003e%ile\u003c/strong\u003e = percentile; \u003cstrong\u003eDeprivation =\u0026nbsp;\u003c/strong\u003eNZ IMD deprivation scale (2018)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e highlights the key findings from phase 1 screening questionnaire, characterising the main risk factors for prediabetes, by ethnicity. \u0026nbsp;Specifically, there were significant differences by ethnicity between gender groups (p=0.034); history of first-degree relatives with T2DM (p=0.009); perceived child obesity (p\u0026lt;0.0001) which was based on the parents/guardians self-reported observations; BMI categories (p\u0026lt;0.0001) using the childhood percentile rankings for young New Zealanders, where Pacific children (53.6%) were more than twice the proportion of nMnP children (10.4%) in the obesity percentile range; ever receiving treatment for co-existing conditions was evident (p=0.017) and this was especially for asthma (23.3%), and the presence of other conditions such as, Attention Deficit Disorder and Autism (11.9%), sleep problems, like snoring (5.2%), and psychological conditions (3.7%) were notable; living in a crowded home (p\u0026lt;0.0001) whereby the number of people exceeds the number of bedrooms in the house; and socio-economic deprivation status as measured by the NZ Index of Multiple Deprivation (2018) scale[21] measuring the level of deprivation for people in a small area based on the nine Census variables (measured in quintiles) (p\u0026lt;0.0001), whereby 1 is the least deprived and 5 is the most deprived areas. \u0026nbsp; Also, based on the phase 1 screening questionnaire, the team identified 97 or 35% of children as being at moderate to high risk of developing prediabetes (p\u0026lt;0.0001). The majority of these children were Pacific (48/97), followed by Māori (30/97), and non-Māori-non-Pacific (19/97). \u0026nbsp;These children were invited to the second phase of the study (see \u003cstrong\u003eTable 3\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study also examined the dietary patterns for children in this study. \u0026nbsp;Principal Axis Factor (PAF) method and Oblimin rotation was used for analysis of the 12 food data groups (described above). We excluded data that were identified as being major outliers (n=5 participants), and thus the analyses were based on 264 observations. \u0026nbsp;Each dietary pattern was allocated weights for each food group, which \u0026nbsp;were used to calculate a standardised mean score for each dietary pattern. \u0026nbsp;Each factor was rotated and compared to observe the cross-loading to identify and improve interpretability of each dietary factor. \u0026nbsp;Parallel analyses and scree plots were also used to check for data interpretability. \u0026nbsp;Each of the dietary pattern scores was standardised to have a mean of zero and a variance of one. \u0026nbsp;Each participant was assigned a score for each dietary pattern, since a typical person\u0026rsquo;s diet may include characteristics of more than one pattern. Thus, the dietary pattern scores are a constant measure of how closely the participant\u0026rsquo;s diet matches each type of diet. \u0026nbsp; Based on the PAF approach, of all the participants\u0026rsquo; dietary intake, standardised scores above 0.40 (i.e., threshold) for any given food grouping indicated a strong propensity matched to a particular dietary pattern (see \u003cstrong\u003eTable 2\u003c/strong\u003e below). \u0026nbsp;Thus, we have identified two distinctive dietary pattern groups (from 65 potential dietary groups). \u0026nbsp;Our selection of the two-factor loadings was confirmed by parallel analyses. \u0026nbsp;\u003cstrong\u003eDietary factor\u003c/strong\u003e 1 consisted of a very diverse range of food groups, but particularly high in: Takeaways, sweets and savoury snack foods, drinks, meats and poultry and breads and cereals. \u003cstrong\u003eDietary factor 2\u003c/strong\u003e composed primarily of very high diversity of vegetarian and legume-based foods, and to a more limited extent sauces, spreads and flavourings. \u0026nbsp; Additionally, the food groups 11-13 had negative standardised scores indicating that the children who consumed more food items under Dietary factor 2 ate a healthier range of food items within the food groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eTable 2: Dietary Diversity Patterns (7-days) for Pacific children\u0026nbsp;\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"482\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.6424%;\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62.7859%;\"\u003e\n \u003cp\u003eGroup items\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.8503%;\"\u003e\n \u003cp\u003eDietary Factor1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.7214%;\"\u003e\n \u003cp\u003eDietary Factor2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.6424%;\"\u003e\n \u003cp\u003eG01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62.7859%;\"\u003e\n \u003cp\u003eMeat, poultry, fish diversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.8503%;\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.7214%;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.6424%;\"\u003e\n \u003cp\u003eG02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62.7859%;\"\u003e\n \u003cp\u003eDairy products diversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.8503%;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.7214%;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.6424%;\"\u003e\n \u003cp\u003eG03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62.7859%;\"\u003e\n \u003cp\u003eBread, cereals and starchy vegetable diversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.8503%;\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.7214%;\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.6424%;\"\u003e\n \u003cp\u003eG04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62.7859%;\"\u003e\n \u003cp\u003eLegume and nut diversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.8503%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.7214%;\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.6424%;\"\u003e\n \u003cp\u003eG06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62.7859%;\"\u003e\n \u003cp\u003eVegetable diversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.8503%;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.7214%;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.6424%;\"\u003e\n \u003cp\u003eG07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62.7859%;\"\u003e\n \u003cp\u003eOil and fat diversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.8503%;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.7214%;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.6424%;\"\u003e\n \u003cp\u003eG08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62.7859%;\"\u003e\n \u003cp\u003eDrinks diversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.8503%;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.7214%;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.6424%;\"\u003e\n \u003cp\u003eG10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62.7859%;\"\u003e\n \u003cp\u003eSauces, spreads and flavouring diversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.8503%;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.7214%;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.6424%;\"\u003e\n \u003cp\u003eG11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62.7859%;\"\u003e\n \u003cp\u003eSweets and sweet snacks diversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.8503%;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.7214%;\"\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.6424%;\"\u003e\n \u003cp\u003eG12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62.7859%;\"\u003e\n \u003cp\u003eSavoury snacks diversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.8503%;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.7214%;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.6424%;\"\u003e\n \u003cp\u003eG13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62.7859%;\"\u003e\n \u003cp\u003eTake away food diversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.8503%;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.7214%;\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eKey:\u003c/strong\u003e Groups excluded: Fruit diversity (Group 5) and alcohol (Group 9)\u003c/p\u003e\n\u003cp\u003eOut of the 97 children selected for phase 2, more than half were females (n=54) and 43 were males. \u0026nbsp; In \u003cstrong\u003eTable 3\u003c/strong\u003e (below), based on the median scores (Wilcoxon two-sample p-value test due to non-parametric data), across the three ethnic groups, Pacific children\u0026rsquo;s risk factor profile for prediabetes based on anthropometric measurements were significantly higher in: weight, hip circumference, BMI percentile rank, and blood glucose test results were significantly different, than for non-Māori-non-Pacific children. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003eTable 3 Participants who completed Phase 2\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.4291%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2514%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMāori (n=30)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6597%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePacific (n=48)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6597%;\"\u003e\n \u003cp\u003e\u003cstrong\u003enMnP (n=19)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.4291%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep-value\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2514%;\"\u003e\n \u003cp\u003e12.0\u003c/p\u003e\n \u003cp\u003e95% CI: 11.8-12.2\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.630\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6597%;\"\u003e\n \u003cp\u003e12.0\u003c/p\u003e\n \u003cp\u003e95% CI: 11.8-12.2\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.385\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6597%;\"\u003e\n \u003cp\u003e12.1\u003c/p\u003e\n \u003cp\u003e95% CI: 11.8-12.4\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eref\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.4291%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeight (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2514%;\"\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003cp\u003e95% CI: 154.6-161.4\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.766\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6597%;\"\u003e\n \u003cp\u003e160.7\u003c/p\u003e\n \u003cp\u003e95% CI: 157.9-163.4\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.0124\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6597%;\"\u003e\n \u003cp\u003e157.3\u003c/p\u003e\n \u003cp\u003e95% CI: 152.9-163.4\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eref\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.4291%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeight (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2514%;\"\u003e\n \u003cp\u003e60.7\u003c/p\u003e\n \u003cp\u003e95% CI: 55.0-66.3\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.172\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6597%;\"\u003e\n \u003cp\u003e70.5\u003c/p\u003e\n \u003cp\u003e95% CI: 63.6-77.3\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.020\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6597%;\"\u003e\n \u003cp\u003e55.7\u003c/p\u003e\n \u003cp\u003e95% CI: 48.9-62.5\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eref\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.4291%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWaist (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2514%;\"\u003e\n \u003cp\u003e80.6\u003c/p\u003e\n \u003cp\u003e95% CI: 75.7-85.5\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.538\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6597%;\"\u003e\n \u003cp\u003e84.4\u003c/p\u003e\n \u003cp\u003e95% CI: 80.1-88.7\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.142\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6597%;\"\u003e\n \u003cp\u003e78.3\u003c/p\u003e\n \u003cp\u003e95% CI: 72.7-84.0\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eRef\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.4291%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHip (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2514%;\"\u003e\n \u003cp\u003e94.9\u003c/p\u003e\n \u003cp\u003e95% CI: 90.5-99.3\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.361\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6597%;\"\u003e\n \u003cp\u003e101.6\u003c/p\u003e\n \u003cp\u003e95% CI: 97.3-105.8\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.012\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6597%;\"\u003e\n \u003cp\u003e91.8\u003c/p\u003e\n \u003cp\u003e95% CI: 86.9-96.8\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eRef\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.4291%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBP mmHg (systolic)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2514%;\"\u003e\n \u003cp\u003e117.1\u003c/p\u003e\n \u003cp\u003e95% CI: 112.3-121.9\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.406\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6597%;\"\u003e\n \u003cp\u003e117.7\u003c/p\u003e\n \u003cp\u003e95% CI: 113.2-122.2\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.280\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6597%;\"\u003e\n \u003cp\u003e112.3\u003c/p\u003e\n \u003cp\u003e95% CI: 105.6-119.0\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eRef\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.4291%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBP mmHg (diastolic)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2514%;\"\u003e\n \u003cp\u003e76.5\u003c/p\u003e\n \u003cp\u003e95% CI: 71.4-81.7\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.291\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6597%;\"\u003e\n \u003cp\u003e76.2\u003c/p\u003e\n \u003cp\u003e95% CI: 72.7-79.8\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.293\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6597%;\"\u003e\n \u003cp\u003e72.4\u003c/p\u003e\n \u003cp\u003e95% CI: 68.5-76.2\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eRef\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.4291%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eBMI percentile rank\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2514%;\"\u003e\n \u003cp\u003e88.6\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e95% CI: 81.6-95.5\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.090\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6597%;\"\u003e\n \u003cp\u003e91.2\u003c/p\u003e\n \u003cp\u003e95% CI: 86.7-95.7\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.010\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6597%;\"\u003e\n \u003cp\u003e78.1\u003c/p\u003e\n \u003cp\u003e95% CI: 66.0-90.3\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eref\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.4291%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHbA1c test results (mmol/mol)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2514%;\"\u003e\n \u003cp\u003e34.3\u003c/p\u003e\n \u003cp\u003e95% CI: 32.9-35.6\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e0.193\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6597%;\"\u003e\n \u003cp\u003e35.2\u003c/p\u003e\n \u003cp\u003e95% CI: 34.5-36.0\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.022\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6597%;\"\u003e\n \u003cp\u003e32.9\u003c/p\u003e\n \u003cp\u003e95% CI: 30.6-35.3\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eref\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003enMnP\u003c/strong\u003e =non-Māori-non-Pacific\u003cstrong\u003e; BMI Percentile:\u003c/strong\u003e Normal (BMI\u0026lt;85\u003csup\u003eth\u003c/sup\u003e percentile), Overweight (BMI\u0026gt;=85\u003csup\u003eth\u0026nbsp;\u003c/sup\u003e-\u0026lt;95\u003csup\u003eth\u003c/sup\u003e percentile), Obese (BMI\u0026gt;=95\u003csup\u003eth\u003c/sup\u003e percentile), Severely Obese (BMI\u0026gt;=99\u003csup\u003eth\u003c/sup\u003e percentile); \u003cstrong\u003eHbA1c test:\u003c/strong\u003e blood sugar levels\u003cstrong\u003e\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOut of the 97 children selected for phase 2, more than half were females (n=54) and 43 were males. \u0026nbsp; In \u003cstrong\u003eTable 3\u003c/strong\u003e (below), based on the median scores (Wilcoxon two-sample p-value test due to non-parametric data), across the three ethnic groups, Pacific children\u0026rsquo;s risk factor profile for prediabetes based on anthropometric measurements were significantly higher in: weight, hip circumference, BMI percentile rank, and blood glucose test results were significantly different, than for non-Māori-non-Pacific children. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e provides multivariate regression analyses for each food group, by age, gender, ethnicity, BMI category, deprivation, and being risk of prediabetes. Median scores and the interquartile variance were used because the data was non-parametric, and the Wilcoxon two sample test was used for examining statistical relationships between co-variates. \u0026nbsp;Of note, there were clear significant differences between gender groups for all food groups but not for food groups 4, 5, 6 and 11 (legume/nuts, fruits, vegetables, sweets and sweet snacks). Children were obese scored higher than their peers in the normal BMI percentile range across all food groups, but especially significant for groups 1, 8, 10,12, and 13 (meats/poultry/fish, drinks, sauces/spreads/ flavouring, savoury snacks, and take-away food). \u0026nbsp; Furthermore, consumption of various food groups was significantly documented among those children living in the most \u0026lsquo;deprived\u0026rsquo; areas for all groups, but not for food items listed under food groups: 2, 4, 5, 6, and 12 (dairy products, breads/cereals and starchy vegetables, legume/nuts, fruits, vegetables, and savour snacks). Furthermore, Pacific children reportedly ate greater amounts of food items in groups 3, 7, 10, 11 and 12, compared to their ethnic counter-parts. The factor scores (FS1, FS2) are the standardised mean scores for each dietary factor loading (as defined above), \u0026nbsp;for that group. Positive standardised score counts indicate the number of deviations above or below the standardised mean score threshold (0.4). In our study, children who were of Pacific ethnicity, live in the most deprived area, and were in the highest BMI percentile (\u0026gt;95\u003csup\u003eth\u003c/sup\u003e), all scored above the mean score threshold, indicating a greater propensity for dietary factor 1, which as previously described was especially high in food groups 1, 3, 12 and 13. Also, children who were in the 12 year old age group score positively higher than the mean score for dietary factor 2, which consisted primarily of vegetarian and legume-based food.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[INSERT TABLE 4 HERE]\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e\u003cu\u003eTable 4: Multivariate analyses of dietary habits and socio-demographic covariates\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"973\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003eG01\u003c/p\u003e\n \u003cp\u003em(IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eG02\u003c/p\u003e\n \u003cp\u003em(IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003eG03\u003c/p\u003e\n \u003cp\u003em(IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003eG04 m(IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003eG05 m(IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003eG06 m(IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003eG07 m(IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003eG08 m(IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003eG10\u0026nbsp;\u003c/p\u003e\n \u003cp\u003em(IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003eG11 m(IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003eG12 m(IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003eG13 m(IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003eFS 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003eFS2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"15\" valign=\"bottom\" style=\"width: 973px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAge-groups\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e10 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e27.8 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e27.8 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e34.4 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e11.1 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e26.7 (11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e32.4 (18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e33.3 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e26.7 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e31.8 (18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e37.5 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e50.0 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e21.4 (32.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.3 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e11 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e25.9 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e27.8 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e34.4 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e22.2 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e23.3 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e32.4 (21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e33.3 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e26.7 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e31.8 (18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e41.7 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e40.0 (30.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e21.4 (21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.2 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e12 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e25.9 (18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e27.8 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e34.4 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e22.2 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e23.3 (23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e35.1 (24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e33.3 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e26.7 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e31.8 (22.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e41.7 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e40.0 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e21.4 (28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.3 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e13 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e25.9 (18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e27.8 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e37.5 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e27.8 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e20.0 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e35.1 (13.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e22.2 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e26.7 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e36.4 (13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e41.7 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e40.0 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e21.4 (21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.3 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.876\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.607\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.402\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.244\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.770\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.513\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.589\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.900\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.601\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.542\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.801\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.662\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.967\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.554\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"15\" valign=\"bottom\" style=\"width: 973px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eGender\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e25.9 (14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e27.8 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e31.3 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e11.1 (27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e23.3 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e32.4 (21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e33.3 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e23.3 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e31.8 (18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e41.7 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e40.0 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e21.4 (21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.4 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.2\u003c/p\u003e\n \u003cp\u003e(1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e29.6 (14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e33.3 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e37.5 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e22.2 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e26.7 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e32.4 (18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e33.3 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e26.7 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e31.8 (18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e50.0 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e45.0 (30.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e21.4 (28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.1 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.034\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.008\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.004\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.079\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.063\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.119\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.008\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.033\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.042\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.010\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.007\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.074\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.002\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.035\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"15\" valign=\"bottom\" style=\"width: 973px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthnicity (3 groups)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003eMāori\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e33.3 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e27.8 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e31.3 (18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e11.1 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e23.3 (23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e32.4 (24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e33.3 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e26.7 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e31.8 (27.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e41.7 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e40.0 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e28.6 (35.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.1 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003ePacific\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e33.3 (18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e33.3 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e37.5 (17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e22.2 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e26.7 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e29.7 (27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e44.4 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e33.3 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e31.8 (18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e50.0 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e50.0 (30.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e35.7 (35.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.0\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003eNon-Maori non-Pacific\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e25.9 (13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e27.8 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e34.4 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e22.2 (27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e23.3 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e32.4 (21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e33.3 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e23.3 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e31.8 (18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e41.7 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e40.0 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e21.4 (21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.4 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.000\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.025\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.049\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.042\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.150\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.573\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.000\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.000\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.114\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.006\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.0009\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.000\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.000\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.416\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"15\" valign=\"bottom\" style=\"width: 973px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eBMI category\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003eNormal (BMI\u0026lt;85th %tile)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e25.9 (14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e27.8 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e34.4 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e22.2 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e25.0 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e32.4 (24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e33.3 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e26.7 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e31.8 (18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e41.7 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e40.0 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e21.4 (21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.4 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.0\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003eOverweight (BMI\u0026gt;=85\u003csup\u003eth\u003c/sup\u003e \u0026amp; BMI\u0026lt;95\u003csup\u003eth\u003c/sup\u003e ile%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e25.9 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e30.6 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e34.4 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e11.1 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e16.7 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e28.4 (24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e33.3 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e20.0 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e31.8 (13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e41.7 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e40.0 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e21.4 (21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.3 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003eObese (BMI \u0026gt;=95th percentile)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e33.3 (18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e27.8 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e37.5 (21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e22.2 (44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e26.7 (23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e32.4 (23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e38.9 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e33.3 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e36.4 (22.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e50.0 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e50.0 (40.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e39.3 (32.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.0001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.860\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.391\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.289\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.025\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.154\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.101\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.0005\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.009\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.069\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.034\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.0001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.002\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.646\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"15\" valign=\"bottom\" style=\"width: 973px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eDeprivation quintile\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e1= least deprived\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e25.9 (14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e27.8 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e34.4 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e22.2 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e23.3 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e32.4 (18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e22.2 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e20.0 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e31.8 (13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e41.7 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e40.0 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e14.3 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.5 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.1\u003c/p\u003e\n \u003cp\u003e(1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e25.9 (14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e27.8 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e34.4 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e33.3 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e23.3 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e35.1 (24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e33.3 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e23.3 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e36.4 (18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e41.7 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e40.0 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e21.4 (21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.1 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e25.9 (14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e27.8 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e31.3 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e11.1 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e23.3 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e32.4 (24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e33.3 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e26.7 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e31.8 (18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e41.7 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e40.0 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e21.4 (21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.4 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.3\u003c/p\u003e\n \u003cp\u003e(1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e29.6 (18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e30.6 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e34.4 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e22.2 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e21.7 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e27.0 (16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e33.3 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e26.7 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e36.4 (22.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e58.3 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e40.0 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e28.6 (21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.1 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.2\u003c/p\u003e\n \u003cp\u003e(1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e5= most deprived\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e33.3 (20.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e33.3 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e37.5 (21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e22.2 (44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e26.7 (26.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e32.4 (25.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e44.4 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e33.3 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e31.8 (27.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e50.0 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e50.0 (35.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e35.7 (32.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003cp\u003e(1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.0001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.125\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.059\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.152\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.586\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.092\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.041\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.006\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.029\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.004\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.083\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.0001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.0001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.020\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"15\" valign=\"bottom\" style=\"width: 973px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eIdentified as being at moderate-high risk of prediabetes\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e25.9 (14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e27.8 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e34.4 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e22.2 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e23.3 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e32.4 (21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e33.3 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e20.0 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e31.8 (18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e41.7 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e40.0 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e21.4 (21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.4 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.1\u003c/p\u003e\n \u003cp\u003e(1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e33.3 (14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e27.8 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e34.4 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e22.2 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e23.3 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e32.4 (24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e44.4 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e26.7 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e36.4 (22.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e50.0 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e40.0 (30.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e28.6 (28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003cp\u003e(1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.1\u003c/p\u003e\n \u003cp\u003e(1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.0001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.090\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.266\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.427\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.247\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.638\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.0001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.0005\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.0005\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.0001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.041\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.0001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.0001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.763\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eKey: m=median scores, IQR=interquartile range variance, FS1 and FS2=standardised factor scores\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThere are two major findings from this cross-sectional study. First, is the social-cultural-environmental determinants of health that highlight the long-term trajectory for poor health outcomes among children and those that are most vulnerable. Second, that children in their young adolescent years (aged 11\u0026ndash;13 years old) have the high propensity to develop risk factors for prediabetes, which supports previous international studies[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], particularly for Indigenous population groups[\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and our study findings demonstrate the unique opportunistic window-frame for screening NZ children for prediabetes, a strategy recommended by international agencies[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe first major finding implicates that known risk factors for prediabetes are strongly established at an earlier age group, and that it is particularly strong for Pacific children. Social deprivation plays an important role in partially explaining these risk factors, particularly given that a quarter of the participants reside in the \u003cem\u003e\u0026lsquo;most least\u0026rsquo;\u003c/em\u003e and the same proportion of children live in the \u003cem\u003e\u0026lsquo;most highly\u0026rsquo; deprived areas\u003c/em\u003e. Added to this variable, are those children living in overcrowded households, defined as \u0026lsquo;an increased number of family members living in a home where the number of bedrooms are less than the total number of family members\u0026rsquo;. This latter variable was most prominent for Pacific children, and research has shown clear relationships between communicable diseases[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] and living in overcrowded homes among children, and its impact on social and health wellbeing (eg. higher rates of asthma among Pacific children due to cold and damp homes)[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Moreover, as prediabetes is a diet-related condition, the dietary diversity findings from the current study yielded two interesting patterns. One that was predominantly diverse but high in savoury snacks and takeaway food (dietary factor 1), and the other being almost entirely vegetarian and legume based (dietary factor 2). It is no surprise that children in our study, who live in the most deprived areas, are considered obese (\u0026gt;\u0026thinsp;95th percentile), and are of Pacific ethnicity, eat food that mostly fit with dietary factor 1, because the risk profile for type 2 diabetes and prediabetes among the youth and adult population is analogous to our study findings. Yet, effective and large-scale prevention programmes have not enabled better health outcomes for children (and the wider population) who are characteristic of type 2 diabetes and prediabetes risk, which from previous research cite known and established barriers (e.g., language, cultural appropriateness, financial support and transport) as inhibiting success and compliancy.\u003c/p\u003e \u003cp\u003eFurthermore, it is possible that food groups and habits that are being consumed today are far more different compared to dietary patterns of the last two to three generations, because globalisation and technology has advanced food availability, costs and ultimately the impact from climate change. The latter is an interesting rationale for the dietary patterns, because dietary patterns have shown to contribute to the rising burden of diet related NCDs. For example, high consumption of processed food can assist to increase environmental sustainability by reducing food waste and extending shelf-life, but not reduce the NCD rates. In particular, new issues are emerging, where 45% of young people surveyed around the world report that climate change has negatively impacting on their daily functioning including eating, school, sleep health and relationships[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In the current study, we cannot show that climate change has had a direct impact on the dietary patterns illustrated, however, it is an important consideration for dietary behaviours, because as climate change advances, diet-related NCDs will also be exacerbated, and much of this will be determined by the habits and behaviours of dietary patterns in relation to the environment[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe second major finding from our study demonstrate the unique opportunistic window-frame for screening NZ children for prediabetes, a strategy recommended by international agencies[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePhase 2 of the study further highlighted the need for early screening for Indigenous children at risk of prediabetes. With more than a third (35%) of the total study population being identified as risk of prediabetes given the high presence of risk factors (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), particularly for obesity (\u0026ge;\u0026thinsp;99th percentile) where Pacific children had two times the proportion, compared to non-Māori-non-Pacific children (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The blood glucose test results had demonstrated that on average, Pacific children had borderline moderate blood sugar levels that were higher than for non-Māori-non-Pacific children (p\u0026thinsp;=\u0026thinsp;0.02). Invariably, researchers will recognise that the blood test results in the current study were not sufficiently high enough for \u003cem\u003edefined\u003c/em\u003e prediabetes, we believe, our phase 1 screening questionnaire, followed by a lower HbA1c cutoff point of 35-39mmol/mol \u003cem\u003eplus\u003c/em\u003e established risk factors, could be adequate to identify a potential risk of prediabetes. Several studies have identified that progression from prediabetes to T2DM in young adolescents can occur at an accelerated rate, compared to adults[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], and it is also important to note, that relying on HbA1c alone and using the cutoff points suggested by the ADA and APEG are based on adults. There are many studies that endorse the need for more research to be undertaken for the purpose of targeting \u0026lsquo;at-risk\u0026rsquo; population groups as a preventative approach to establishing defined prediabetes and prevent the development of T2DM[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], particularly among more ethnically diverse children[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], and this is what our study findings is endorsing. Due to the small sample size and design of our study, we were not able to establish the actual prevalence of prediabetes in this age group, but our work does highlight a significant gap in screening and optimising on a lower HbA1c cutoff points, that requires further exploration. However, there is a concerning number of childhood participants with obesity and other risk factors that may be indicative that more research needs to be undertaken to establish a baseline since it is difficult to approximate the number of undiagnosed children. Current national proportions estimate approximately 13.5% of children (ages 2\u0026ndash;14) were considered obese, indicating an increase over the last five years of 1.9%[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. What we understand from previous research is that there can be difficulty detecting prediabetes in children where the cutoff rates for various glucose tests differ across organisations, but are also derived from studies that contained non-representative samples as described earlier[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This finding emphasises the need to act sooner with early interventions to mitigate the onset of prediabetes and prevent increasing rates of T2DM development in adolescents. Interventions of this calibre need to incorporate lifestyle changes as they are most effective when combined with pharmacological intervention [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. It is also relevant to focus on other environmental and structural factors that may be barriers to access sustainable lifestyle changes[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] as highlighted from the first major finding, or an approach that includes health promotion and education programs that raise awareness and can be embedded in the school or home environment.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThis study highlights that key risk factors for childhood prediabetes are firmly established before children reach youthhood (i.e., aged 15\u0026ndash;24 years old), and that other factors (social-cultural-environmental) that are known to perpetuate these risk factors are not new findings when tackling diet related NCDs. However, a tailored approach to addressing the health inequalities faced by vulnerable population groups such as Pacific and Māori children are needed now more than ever. The need for early prevention methods to curb unhealthy eating habits and behaviours that contribute to the childhood obesity epidemic which increases the risk of prediabetes and T2DM is needed. More than likely, these programs should be culturally appropriate and tailored, particularly in the current study where Pacific and Māori children were overrepresented in poor health outcome statistics. If early and holistic programs can be employed to mitigate the risks of developing prediabetes amongst adolescents, we may see a shift in health and wellbeing amongst this age group, which could safeguard their wellbeing later in life and avoid early onset of adult NCDs. Acknowledging the risk of T2DM by focusing on prediabetes risk was also a major outcome of this study, alongside the need to explore opportunistic screening window-frames and a lower cutoff point for HbA1c that are age and culturally representative. Our study reported a high prediabetes risk proportion (35%), we believe that this is sufficient for health researchers and policymakers to discuss the potentiality of early intervention, through screening for prediabetes risk among children from age 10 years old, with a focus on those children in high-risk population groups.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eLIMITATIONS\u003c/h2\u003e \u003cp\u003eWe have discussed above about the limitations of a small sample size. However, this study was conducted post-covid-19 pandemic lockdown, which placed a strained the education system, thus impacting effective recruitment strategies. The polarisation of the pandemic also meant distrust between families and health researchers, and the competing nature of other research projects targeting the same schools for their own research meant there was high attrition of schools, children and their families consenting to participate in our study. Moreover, the restrictions of the pandemic, funding period and the time allocated to recruit participants to carry out phases 1 and 2, and conduct data analyses, placed an enormous strain on our research team\u0026rsquo;s capability to complete this work within a 12-month timeline. Thus, the final study sample and findings may not be representative of the general population of NZ children of similar age and ethnicity. The timeline and funding resources also limited our ability to collect more accurate diagnostic measures, as a single point-in-time measurement of HbA1c was conducted, with no repeat testing (preferably in 6 months).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFUTURE IMPLICATIONS\u003c/h3\u003e\n\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e Implement a scaled-up national-based cross-sectional study that involves a larger sample size that is more reflective of the children aged 10\u0026ndash;15 years old (based on the APEG guidelines) with better resources, will improve our understanding of the magnitude of prediabetes risk among NZ children.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eFurther research is needed to optimise on lower HbA1c cutoff points (plus established risk factors) that represent children aged 10\u0026ndash;15 years old and is inclusive of cultural diversity.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eHealth promotional programs that are inclusive of the social-cultural-environmental determinants of health, and that are culturally tailored to address the prediabetes risk for children and their families, be more widely available to vulnerable groups that need it the most.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project received full ethical approval from the Southern Health and Disability Ethics Committee: HDEC 2022 FULL 12212.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants provided written consent to participate and to publish with the knowledge that there will be no personally identifiable information that can identify any of our participants. The children in our study provided written assent and the parents provided written consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare that the authors named in this manuscript have no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research was funded by the National Science Challenge: A Better Start, E Tipu e Rea, ABST2001/ 3719490.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRTF wrote the paper and assisted by HD. SC conducted the main analyses aided by MC, and assisted with data interpretations. \u0026nbsp; All authors read the manuscript and provided input where necessary.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to acknowledge the young participants and their parents for their time and effort to take part in this study. \u0026nbsp;We would also like to acknowledge all the schools in the region by allowing our research team members to come into the school to promote our study and to collect data (for phase 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' information (optional)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNational Diabetes Work Programme. 2014/15 (pdf). In. 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Diabetes Care. 2021;44:S180\u0026ndash;199.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThornley S, Marshall RJ, Bach K, Koopu P, Reynolds G, Sundborn G, Ei WL. Sugar, dental caries and the incidence of acute rheumatic fever: a cohort study of Māori and Pacific children. J Epidemiol Community Health. 2017;71(4):364\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEllison-Loschmann L, Pattemore PK, Asher MI, Clayton TO, Crane J, Ellwood P, Mackay RJ, Mitchell EA, Moyes C, Pearce N, et al. Ethnic differences in time trends in asthma prevalence in New Zealand: ISAAC Phases I and III. Int J Tuberc Lung Dis. 2009;13(6):775\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNoonan CW, Brown BD, Bentley B, et al. Variability in childhood asthma and body mass index across Northern Plains American Indian communities. J Asthma. 2010;47:496\u0026ndash;500.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClayton SD, Pihkala P, Wray B, Marks E. Psychological and Emotional Responses to Climate Change among Young People Worldwide: Differences Associated with Gender, Age, and Country. Sustainability. 2023;15(4):3540.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHunter R, Garcia L, Dagless S, Haines A, Penney T, Astbury C, Whiting S, Wickramasinghe K, Racioppi F, Galea G et al. The emerging syndemic of climate change and non-communicable diseases. Lancet - Planet Health 2024, 8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDart AB, Martens PJ, Rigatto C, Brownell MD, Dean HJ, Sellers EA. Earlier onset of complications in youth with type 2 diabetes. Diabetes Care. 2014;37(2):436\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDabelea D, Stafford JM, Mayer-Davis EJ, D'Agostino R Jr., Dolan L, Imperatore G, Linder B, Lawrence JM, Marcovina SM, Mottl AK, et al. Association of Type 1 Diabetes vs Type 2 Diabetes Diagnosed During Childhood and Adolescence With Complications During Teenage Years and Young Adulthood. JAMA. 2017;317(8):825\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEsquivel Zuniga R, DeBoer MD. Prediabetes in Adolescents: Prevalence, Management and Diabetes Prevention Strategies. Diabetes Metab Syndr Obes. 2021;14:4609\u0026ndash;19.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNg HY, Chan LTW. Prediabetes in children and adolescents: An updated review. World J Clin Pediatr. 2023;12(5):263\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHak Yung Ng LTWC. Prediabetes in children and adolescents: an updated review. World J Clin Pediatr. 2023;12(5):263.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGillies CL, Abrams KR, Lambert PC, Cooper NJ, Sutton AJ, Hsu RT, Khunti K. Pharmacological and lifestyle interventions to prevent or delay type 2 diabetes in people with impaired glucose tolerance: systematic review and meta-analysis. BMJ. 2007;334(7588):299.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWare LJ, Prioreschi A, Bosire E, Cohen E, Draper CE, Lye SJ, Norris SA. Environmental, Social, and Structural Constraints for Health Behavior: Perceptions of Young Urban Black Women During the Preconception Period-A Healthy Life Trajectories Initiative. J Nutr Educ Behav. 2019;51(8):946\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5712832/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5712832/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePrediabetes is a non-communicable disease (NCD) that are common in New Zealand (NZ) and it can lead to poor health. The aim of this study was to identify whether there is an increased risk of developing prediabetes among 11\u0026ndash;13-year-olds, outside an organised screening programme.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eConsenting school aged children and their parents completed a series of screening questionnaires including dietary patterns, anthropometrics and socio-economic characteristics. Adapted Australasian Paediatric Endocrinology Guidelines (APEG) criterion was used to identify children at risk of developing prediabetes or have new onset prediabetes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOf the 276 participants, significant differences between Pacific, Māori and non- Māori non-Pacific children were evident among those who: were obese (BMI\u0026thinsp;\u0026gt;\u0026thinsp;95th percentile); lived in overcrowded homes and in deprived areas. A high proportion (35%) of children were at risk of developing prediabetes, and two dietary patterns of children consuming: (1) diverse range of food items but were especially high in snacks (sweet and savoury), takeaway food diversity, and drinks; and (2) a vegetarian and legume diet.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe study prevalence of prediabetes risk is \u003cem\u003eindicative\u003c/em\u003e of childhood lifestyles, and we recommend early screening and better resourcing for promotion of healthy nutrition as preventative measures.\u003c/p\u003e","manuscriptTitle":"Investigating the risk of prediabetes among children in NZ: a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-01 08:54:32","doi":"10.21203/rs.3.rs-5712832/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6fdfb6d0-d024-45d5-a907-87f900f2fd52","owner":[],"postedDate":"January 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-07T06:23:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-01 08:54:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5712832","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5712832","identity":"rs-5712832","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}