Large Waist but Low Body Mass Index: The Metabolic Syndrome in Australian Aboriginal Children

Large Waist but Low Body Mass Index: The Metabolic Syndrome in Australian Aboriginal Children ELIZABETH A.C. SELLERS, MD, MSC, GURMEET R. SINGH, MD, MPH&TM,

AND

SUSAN M. SAYERS, FRACP, PHD

Objective To describe the prevalence and clinical characteristics of the metabolic syndrome (MetS) in a cohort of Australian Aboriginal children. Study design Body mass index (BMI), waist circumference, skin fold thickness, body fat percentage, insulin resistance, and the prevalence of MetS were evaluated in 486 children age 9 to 14 years from the Darwin Health Region, Northern Territory, Australia. Results Using an age- and sex- specific definition, 14% of the children in the cohort had MetS, 6.4% were overweight, 4.9% were obese, and 26.2% had an elevated waist circumference. The mean percentage of body fat was 30.2%. The children with MetS had higher BMI and waist z-scores, percent body fat, Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) score, and skin fold thickness compared with those without MetS (P < .001); however, >50% of those with MetS were neither overweight nor obese. Waist circumference was significantly associated with insulin resistance as measured by the HOMA-IR (P < .001). Conclusions MetS is common in our cohort despite low rates of overweight and obesity. A tendency for central adiposity is already evident in these young children. Measurement of waist circumference may help identify Aboriginal children at high risk for MetS. (J Pediatr 2008;153:222-7) he Aboriginal people of the Northern Territory in Australia have high rates of cardiovascular disease (CVD)1 and type 2 diabetes (T2DM),2 both of which have increased over the last 3 decades.3 This accounts for, at least in part, the significantly lower average lifespan of Aboriginal Australians compared with non-Aboriginal Australians.4 CVD is a multifactorial disease process.5 The metabolic syndrome (MetS) is a cluster of CVD risk factors associated with an increased risk of cardiovascular morbidity and mortality, exceeding that associated with the individual components of hypertension, glucose intolerance, elevated triglycerides, low high-density lipoprotein cholesterol (HDL-c), and abdominal obesity.6 In adults, MetS increases the risk of and mortality from CVD and T2DM.7 Insulin resistance and compensatory hyperinsulinemia have been proposed as the common etiologic mechanism to the components of MetS,8 From the Department of Pediatrics and although other mechanisms may be important as well.9,10 MetS and its components are Child Health, University of Manitoba, Winhighly prevalent in the adult Australian Aboriginal population.9,11 nipeg, Manitoba, Canada (E.S.) and Menzies 5 School of Health Research, Charles Darwin Obesity is a predisposing risk factor for CVD and is closely linked to the risk of University, Darwin, Northern Territory, MetS and T2DM. The prevalence of MetS is much higher in obese populations.12,13 In Australia (G.S., S.S.). 2 Australian Aboriginal adults, a body mass index (BMI) ⱖ 22 kg/m is associated with an Supported by the National Health and Medical Research Council of Australia, the increased risk of T2DM.2 This value lies within the typically defined “healthy” adult BMI Colonial Foundation Trust and the Channel range of 20 to 25 kg/m2. 7 Research Foundation of SA Inc. The atherosclerotic process begins in early life. Obesity and the components of The authors have no conflicts of interest to declare. MetS are known to track from childhood into adulthood;14 therefore, the detection of Submitted for publication Jun 5, 2007; last MetS in childhood may be fundamental to early intervention and development of primary revision received Nov 19, 2007; accepted prevention strategies for T2DM and CVD in at-risk populations. The objective of this Feb 1, 2008. study was to determine the prevalence and clinical characteristics of MetS in a cohort of Reprint requests: Elizabeth A.C. Sellers, MD, MSc, Department of Pediatrics and Australian Aboriginal children.

T

BMI CVD HDL-c HOMA-IR

222

Body mass index Cardiovascular disease High-density lipoprotein cholesterol Homeostasis Model Assessment of Insulin Resistance

LDL-c MetS NCEP III T2DM

Low-density lipoprotein cholesterol Metabolic syndrome Third National Cholesterol Education Program Type 2 diabetes mellitus

Child Health, University of Manitoba FE325 685 William Avenue, Winnipeg, MB, Canada R3E 0Z2. E-mail: esellers@exchange. hsc.mb.ca. 0022-3476/$ - see front matter Copyright © 2008 Mosby Inc. All rights reserved. 10.1016/j.jpeds.2008.02.006

METHODS The recruitment and follow-up of this Aboriginal Birth Cohort has been described in detail presviously.15 In brief, 686 of 1238 Aboriginal children born at the Royal Darwin Hospital between January 1987 and March 1990 were recruited into the cohort. There were no significant differences in the mean birth weight, birth weight frequencies, or sex ratio between those recruited and not recruited.15 The Royal Darwin Hospital is the designated hospital for routine newborn deliveries within the Darwin Health Region. The present study is restricted to children from the Darwin Health Region (570 of the 686 children in the original cohort). Cohort participants were reexamined between December 1998 and March 2001. Each child was assessed while wearing light clothing and barefoot. Height was measured to the nearest millimeter with a portable stadiometer. Weight was measured to the nearest 0.1 kg using a digital electronic scale (model TBF-521; Tanita Corp, Arlington Heights, IL). Sitting blood pressure was measured using an automatic unit and an appropriate cuff size (Lifesigns BP monitor; Welch Allyn, Skaneanteles Falls, NY). Mid-upper arm and waist circumferences were measured to the nearest millimeter using a flexible tape measure. Waist circumference was measured at the midpoint between the lowest rib and iliac crest along the mid-axillary line at the end of expiration. Triceps and subscapular skin folds were measured using Harpenden calipers; these measurements were repeated in triplicate and averaged. Body composition (fat %) was measured using a bioelectric impedance analyzer (model TBF-521; Tanita Corp). Pubertal stage was determined by physical examination.16,17 The children was instructed to fast starting at midnight, and blood samples were obtained in the morning after application of a topical anesthetic cream (EMLA; AstraZeneca, London, UK). Breakfast was provided after the study visit. Plasma glucose concentration, cholesterol, triglycerides, and HDL-c were measured by a routine enzymatic method using an autoanalyzer (model 917; Hitachi, Tokyo, Japan) autoanalyzer using Roche reagents (Roche Diagnostics, Basel Switzerland) at Western Diagnostic Pathology, Darwin, Australia. Low-density lipoprotein cholesterol (LDL-c) was calculated from Friedewald’s equation: LDL-c ⫽ total cholesterol ⫺ [HDL-c ⫺ (triglycerides/2.2)]. Plasma insulin was measured by a 2-site AIA-Pack immunoenzymometric assay using a Tosoh AIA-600 immunoanalyzer (Tosoh, Tokyo, Japan), with no cross-reactivity with proinsulin, at Royal Perth Hospital Laboratory, Perth, Australia. Insulin resistance was estimated using the homeostasis model assessment of insulin resistance (HOMA-IR).18 We adopted a pediatric definition of MetS modified from that of the Third National Cholesterol Education Program (NCEP III),12 comprising at least 3 of the following features: TG ⱖ 1.24 mmol/L, HDL-c ⱕ 1.03 mmol/L, waist circumference ⬎ 90th percentile for age and sex, fasting glucose ⱖ 6.1 mmol/L, and systolic or diastolic blood pressure ⱖ 90th percentile for age, height, and sex. For comparative purposes, MetS also was defined using the lower glucose

cutoff value of ⱖ5.6 mmol/L recently adopted to define impaired glucose tolerance.19 BMI was calculated as weight divided by height squared (kg/m2). Overweight was defined as a BMI ⱖ 85th percentile for age and sex; obese, as a BMI ⱖ 95th percentile for age and sex (corresponding to BMI z-scores of 1.04 and 1.64, respectively).20 Waist circumference was compared with published pediatric standards derived using on the same measurement technique.21 No standards are available for Australian Aboriginal children. Blood pressure was defined according to published pediatric standards.22 Descriptive statistics were expressed as means and 95% confidence intervals. The unpaired t-test was used to compare means between groups. The 2-tailed ␹2 test was used to compare categorical variables. Pearson’s correlation coefficient (r) was used to determine associations between continuous variables of interest, and partial correlation coefficients were calculated. All analyses were conducted using Stata 9.0 software (StataCorp, College Station, TX). Prevalence of MetS was based on participants for whom all data were available. Statistical significance for all tests was set at P ⬍ .05. The Joint Institutional Ethics Committee of the Royal Darwin Hospital and the Menzies School of Health Research, including the Aboriginal Ethical Subcommittee (which has veto power), approved the study design. Written consent was obtained from a caregiver of each participating child.

RESULTS A total of 489 of the original cohort of 570 from the Darwin Health Region (86%) were seen in follow-up examination. The remaining children were either not traced or not available for reexamination at the time of follow-up. Demographic, clinical, and metabolic characteristics of the cohort participants are given in Table I. The cohort’s mean age was 11.5 years (range, 8.8 to 13.8 years), and it was 52% male. Fasting lipid profile and insulin and glucose levels were available for 258 participants (55%). There were no significant differences in mean age, BMI z-score, waist circumference, birth weight, gestational age, or sex distribution between those who underwent fasting blood work and those who did not. The mean BMI z-score of the cohort was ⫺0.73 (⫺0.83 for boys; ⫺0.62 for girls). Overall, 6.4% and 4.9% of the cohort were either overweight or obese, respectively. Waist circumference was increased (⬎90th percentile) in 26.3% of the participants. All of the children classified as overweight or obese based on BMI had an elevated waist circumference; however, 58.7% of those children with an elevated waist circumference were not classified as either overweight or obese based on BMI. BMI and waist circumference z-scores are shown in Figure 1. The discrepancy between BMI and waist circumference seen in the cohort as a whole is also present in those with MetS, in which the mean BMI z-score was below the definition of overweight (1.04) while waist circumference z-score was grossly increased (2.69). Some 70% of the participants had at least 1 metabolic abnormality associated with MetS. The prevalences of the

Large Waist but Low Body Mass Index: The Metabolic Syndrome in Australian Aboriginal Children

223

Table I. Demographic, clinical, and metabolic characteristics of cohort participants

Demographic Age, years Sex, M/F Clinical BMI z-score Overweight, % Obese, % Total body fat, % Waist circumference z-score Birth weight, g Gestational age, weeks Metabolic Cholesterol, mmol/L Triglyceride, mmol/L HDL-c, mmol/L LDL-c, mmol/L Fasting glucose, mmol/L Fasting insulin, ␮M/L HOMA-IR score

Total

Boys

Girls

11.4 (11.3, 11.5) (489) —

11.5 (11.4, 11.7) (255) 52% (254)

11.3 (11.1, 11.4) (234) 48% (235)

⫺0.73 (⫺0.85, 0.61) (488) 6.4% (31) 4.9% (24) 21.2 (20.3, 22.0) (439) 0.61 (0.46, 0.75) (459) 3112 (3061, 3163) (489) 39.0 (38.9, 39.2) (442) 4.11 (4.03, 4.20) (292) 1.12 (1.04,1.18) (288) 1.24 (1.20, 1.28) (286) 2.37 (2.29, 2.45) (290) 4.5 (4.4, 4.6) (290) 8.5 (7.4, 9.6) (277) 1.8 (1.5, 2.0) (271)

⫺0.83 (⫺1.01, ⫺0.65) (254) 5.1% (3.0, 9.0) (13) 5.9% (4.0,10.0) (15) 17.1 (16.0, 18.3) (229) 0.4 (0.20, 0.60) (242) 3204 (3131, 3277) (255) 39.0 (38.8, 39.3) (224) 4.20 (4.07, 4.32) (154) 1.03 (0.94, 1.14) (153) 1.29 (1.23, 1.35) (152) 2.44 (2.32, 2.55) (153) 4.5 (4.4, 4.6) (153) 7.2 (6.0, 8.3) (145) 1.5 (1.3, 1.8) (142)

⫺0.62 (⫺0.78, ⫺0.46) (234) 7.7% (5, 12) (18) 3.8% (2.0,7.0) (9) 25.6 (24.6, 26.6) (210) 0.83 (0.62, 1.05) (217) 3012 (2941, 3082) (234) 39.0 (38.8, 39.2) (218) 4.03 (3.91, 4.15) (138) 1.20 (1.10, 1.31) (135) 1.18 (1.13, 1.22) (134) 2.30 (2.19, 2.41) (137) 4.5 (4.4, 4.6) (137) 9.9 (8.0, 11.8) (132) 2.0 (1.5, 2.5) (129)

P .008 .42 .09 .40 .27 ⬍.001 .004 ⬍.001 .68 .06 .03 .003 .09 .52 .01 .08

Data are presented as mean (95% confidence interval) (n) or percent (95% confidence interval) (n).

Figure 1. BMI and waist circumference z-scores for all participants and for participants with MetS.

individual components of MetS in this cohort are shown in Figure 2. MetS was diagnosed in 14% of the participants overall (13.3% of the boys and 15.2% of the girls). The prevalence of MetS did not change when we lowered the threshold for impaired fasting glucose from ⱖ6.1 mmol/L to ⱖ5.6 mmol/L. The children with MetS had significantly greater BMI z-scores, HOMA-IR scores, insulin levels, percent body fat, mid-arm circumferences, and skin fold thicknesses compared with those without MetS (P ⬍ .05). Gestational age, birth weight, onset of puberty, and mean age did not differ between the 2 groups (Table II). The prevalence of MetS did not differ between rural or urban place of residence either at birth or at the time of the study (P ⬎ .05). 224

Sellers, Singh, and Sayers

Figure 2. Prevalence of MetS and individual components of the syndrome using the definitions modified from the NCEP III.12

The incidence of T2DM increases significantly at a BMI of 22 kg/m2 in the adult Aboriginal population; thus, this may be a more appropriate cutoff to define overweight in this population.2 This value corresponds to BMI z-scores of ⫺0.39 in boys and 0.07 in girls; in our cohort, 36.9% of the boys and 30.8% of the girls had a BMI z-score above these thresholds. The prevalence of MetS in boys with a BMI z-score ⱖ ⫺0.39 and in girls with a z-score ⱖ 0.07 was increased to 23% and 35%, respectively. The prevalance of MetS in the overweight or obese children was 54.8% overall (47.1% in boys and 64.3% in girls). In terms of waist circumference, 94% of the girls with a BMI z-score ⱖ 0.07 had a waist circumference ⬎90th percentile for age and sex, and 55% of the boys with a BMI z-score ⱖ ⫺0.39 had an elevated waist circumference. No boy or girl with an elevated waist circumference had a BMI z-score below these thresholds. The Journal of Pediatrics • August 2008

Table II. Characteristics of the children with and without MetS

BMI z-score Waist circumference z-score Mid-arm circumference, cm Triceps skinfold, mm Subscapular skinfold, mm Triceps/subscapular skinfold ratio Insulin, ␮mol/L HOMA-IR score Body fat, % Birth weight, g Gestational age, weeks Age, years Pubertal, %

Children with MetS (n ⴝ 36)

Children without MetS (n ⴝ 217)

P

0.67 (0.22, 1.13) 2.69 (1.92, 3.47) 25.0 (23.3, 26.6) 17.6 (14.2, 21.0) 23.2 (18.4, 28.0) 1.3 (1.2, 1.4) 16.7 (10.1, 23.2) 3.7 (1.7, 5.5) 30.2 (26.0, 34.5) 3344 (3130, 3559) 39.2 (38.8, 39.6) 11.6 (11.2, 12.0) 52.7

⫺0.89 (⫺1.06, ⫺0.73) 0.27 (0.11, 0.43) 21.1 (20.6, 21.6) 9.5 (8.9, 10.0) 10.0 (9.2, 10.7) 1.0 (1.0, 1.1) 7.2 (6.4, 8.1) 1.5 (1.3, 1.6) 19.7 (18.6, 20.8) 3111 (3034, 3188) 38.9 (38.7, 39.1) 11.4 (11.3, 11.6) 48.1

⬍.001 ⬍.001 ⬍.001 ⬍.001 ⬍.001 ⬍.001 ⬍.001 ⬍.001 ⬍.001 .06 .64 .50 .61

Data are presented as mean (95% confidence interval).

CVD risk factors were common in those children with an elevated waist circumference (⬎90th percentile for age and sex) but a BMI z-score less than the threshold for overweight (⬍1.04). Of this group of normal-weight but metabolically obese children, 2% had impaired fasting glucose, 53.3% had elevated triglycerides, 40% had systolic hypertension, 18% had diastolic hypertension, 48.9% had total cholesterol ⬎4.4 mmol/L and 68.75% had a total body fat percentage (measured by bioelectric impedance analysis) ⬎95th percentile for age and sex. None of these children had an LDL-c ⬎4.14 mmol/L. Both waist circumference z-score and BMI z-score were significantly correlated with HOMA-IR score (P ⬍ .001); however, the strength of this relationship was strongest for waist circumference z-score (r ⫽ 0.37 vs r ⫽ 0.29).

DISCUSSION We found a high rate of MetS (14%) in our cohort of Australian Aboriginal children. This finding is unexpected in view of the relative underweight nature of this cohort, as indicated by a mean BMI z-score ⬍0. The prevalence of overweight (6.4%) and obesity (4.9%) also was low in this cohort. This contrasts with an estimated prevalence of 4% for MetS and 15% for obesity in US adolescents using the same definitions as we used in the present study.12 Some 70% of the cohort participants had at least 1 metabolic abnormality. It is possible that the criteria for defining a metabolic abnormality may be too stringent. Alternatively, this finding suggests that this young population is at significant risk for early onset of complications associated with MetS, including CVD and T2DM. Given that high and increasing rates of T2DM and CVD have been documented in Australian Aboriginal adults, the latter explanation seems more likely. Recently, a high prevalence of MetS in adolescents from 2 Canadian First Nation populations (18% and 40%) was reported, based on similar diagnostic criteria as we used.23,24 Like adult Aboriginal Australians, adult First Nation Canadians are disproportionably affected by T2DM and

CVD. Both of the Canadian studies reported a mean BMI unadjusted for age and sex, precluding assessment of overweight and obesity as defined by BMI in their populations. Previous reports, however, have demonstrated very high rates of obesity in First Nation children,25 differing significantly from that in our cohort. In the present study, despite a relatively low mean BMI z-score, the children with MetS had a high body fat percentage (mean, 30.2%). Both waist circumference and triceps/ subscapular skinfold ratio, indicators of central fat distribution, also were significantly higher in those with MetS. The combination of a relatively low BMI, high body fat percentage, and elevated waist circumference suggests a predominantly central adiposity distribution. This body habitus is similar to that described primarily in adults as a metabolically obese, normal-weight individual.26 In our cohort, the normalweight (based on BMI) individuals with an elevated waist circumference had a high frequency of CVD risk factors and bore a striking resemblance to the metabolically obese, normal weight phenotype. A propensity for central adiposity has been found in the adult Australian Aboriginal population.11 In children, as in adults, central adiposity is associated with a less favorable lipid profile and increased insulin levels, blood pressure, and left ventricular mass.27,28 In our cohort, waist circumference was more strongly correlated with insulin resistance (as measured by HOMA-IR) compared with BMI. Recently, a prospective study in Japanese-American adults demonstrated that intra-abdominal fat is an independent predictor for the development of MetS.29 In African-American and Caucasian children, waist circumference has been shown to be a good anthropometric measure of central fat distribution compared with dual-energy x-ray absorptiometry. Of all the measurement techniques evaluated, waist circumference has been found to be the least affected by sex, race, and overall adiposity.30 In adult Australian Aboriginals, waist circumference has been shown to be the best body size measurement for the prediction of diabetes.31

Large Waist but Low Body Mass Index: The Metabolic Syndrome in Australian Aboriginal Children

225

The current BMI threshold levels used to define overweight and obesity (and to predict associated risks) did not identify those children at risk for the metabolic consequences of obesity in our cohort. More than 50% of the children with an elevated waist circumference or MetS were neither overweight nor obese based on BMI. This finding suggests that the currently used BMI thresholds may not be appropriate for this population. This is supported by the findings of Daniel et al,2 who demonstrated an increasing risk for the development of T2DM in adult Aboriginal populations at a BMI of 22 kg/m2. The typical Aboriginal body shape, with a relatively low sitting height-to-stature ratio, may contribute to the lower BMI found in this population, further illustrating that BMI cutoffs derived from other populations may not be appropriate for this cohort.32 Longitudinal follow-up of this cohort may allow the definition of BMI thresholds based on the ability to predict negative health outcomes. As expected, the children with MetS were more insulin-resistant, as demonstrated by their significantly higher insulin levels and HOMA-IR scores compared with those without MetS. The girls had a slightly higher mean fasting insulin value than the boys, likely due to their more advanced pubertal stage. BMI z-score and all other measures of adiposity also were significantly higher in those with MetS. The prevalence of MetS was not affected by place of residence (urban or rural) either at birth or at the time of study followup. This finding was unexpected, because the degree of acculturalization is likely greater in urban centers, and higher BMI and waist circumferences have been reported in urban dwellers.33 We predict that as our cohort ages, significantly higher rates of MetS may develop in the urban-dwelling participants. No significant relationship between birth weight and the presence of absence of MetS was found in this cohort at a mean age of 11 years. This finding does not appear to be in agreement with previous large epidemiologic studies demonstrating an association between low birth weight and cardiovascular mortality34 and T2DM in adulthood.35 Longterm follow-up of this cohort may help better define the relative influences of prenatal and postnatal growth patterns on the subsequent development of CVD and its related comorbidities. The significance of MetS is currently under debate.36 The controversy will continue until we have better agreement on the definition of MetS in children, as well as evidence to support (or refute) that the diagnosis is more important than individual assessment of risk factors in children. Regardless of the outcome of this debate, we have demonstrated a high frequency of CVD risk factors at an early age in our cohort from a population that is disproportionately affected by CVD morbidity and mortality. Intervention strategies should be aimed at early childhood or perhaps even earlier stages of development. We thank Robyn Liddle and Susan Mott for their assistance with data management, Joseph McDonnell for his help with the statistical analysis, and especially all of the mothers and children who participated in this study. 226

Sellers, Singh, and Sayers

REFERENCES 1. Zhao Y, Guthridge S, Magnus A, Vos T. Burden of disease and injury in Aboriginal and non-Aboriginal populations in the Northern Territory. Med J Aust 2004;180:498-502. 2. Daniel M, Rowley KG, McDermott R, O’Dea K. Diabetes and impaired glucose tolerance in Aboriginal Australians: prevalence and risk. Diabetes Res Clin Pract 2002;57:23-33. 3. Thomas DP, Condon JR, Anderson IP, Li SQ, Halpin S, Cunningham J, et al. Long-term trends in indigenous deaths from chronic diseases in the Northern Territory: a foot on the brake, a foot on the accelerator. Med J Aust 2006;185:145-9. 4. Cunningham J, Condon JR. Premature mortality in Aboriginal adults in the Northern Territory, 1979-1991. Med J Aust 1996;165:309-12. 5. Grundy SM, Pasternak R, Greenland P, Smith S Jr, Fuster V. Assessment of cardiovascular risk by use of multiple-risk-factor assessment equations: a statement for healthcare professionals from the American Heart Association and the American College of Cardiology. Circulation 1999;100:1481-92. 6. Isomaa B, Almgren P, Tuomi T, Forsen B, Lahti K, Nissen M, et al. Cardiovascular morbidity and mortality associated with the metabolic syndrome. Diabetes Care 2001;24:683-9. 7. Malik S, Wong ND, Franklin SS, Kamath TV, L’Italien GJ, Pio JR, et al. Impact of the metabolic syndrome on mortality from coronary heart disease, cardiovascular disease, and all causes in United States adults. Circulation 2004;110:1245-50. 8. Reaven G. Metabolic syndrome: pathophysiology and implications for management of cardiovascular disease. Circulation 2002;106:286-8. 9. O’Dea K, Rowley KG. Macrovascular disease risk factors and insulin resistance in Aboriginal and Torres Strait Islander people. J Diabetes Complications 2002; 16:9-16. 10. Carr DB, Utzschneider KM, Hull RL, Kodama K, Retzlaff BM, Brunzell JD, et al. Intra-abdominal fat is a major determinant of the National Cholesterol Education Program Adult Treatment Panel III criteria for the metabolic syndrome. Diabetes 2004;53:2087-94. 11. Schutte AE, Shemesh T, Rowley K, Best JD, McDermott R, O’Dea K. The metabolic syndrome and changing relationship between blood pressure and insulin with age, as observed in Aboriginal and Torres Strait Islander peoples. Diabetes Med 2005;22:1589-97. 12. Cook S, Weitzman M, Auinger P, Nguyen M, Dietz WH. Prevalence of a metabolic syndrome phenotype in adolescents: findings from the Third National Health and Nutrition Examination Survey, 1988-1994. Arch Pediatr Adolesc Med 2003; 157:821-7. 13. Smith SC Jr. Multiple risk factors for cardiovascular disease and diabetes mellitus. Am J Med 2007;120(3 Suppl 1):S3-S11. 14. Bao W, Srinivasan SR, Wattigney WA, Berenson GS. Persistence of multiple cardiovascular risk clustering related to syndrome X from childhood to young adulthood. The Bogalusa Heart Study. Arch Intern Med 1994;154:1842-7. 15. Sayers SM, Mackerras D, Singh G, Bucens I, Flynn K, Reid A. An Australian Aboriginal birth cohort: a unique resource for a life course study of an indigenous population. A study protocol. BMC Int Health Human Rights 2003;3:1. 16. Marshall WA, Tanner JM. Variations in the pattern of pubertal changes in boys. Arch Dis Child 1970;45:13-23. 17. Marshall WA, Tanner JM. Variations in pattern of pubertal changes in girls. Arch Dis Child 1969;44:291-303. 18. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985;28: 412-9. 19. Genuth S, Alberti KG, Bennett P, Buse J, Defronzo R, Kahn R, et al. Follow-up report on the diagnosis of diabetes mellitus. Diabetes Care 2003;26:3160-7. 20. Kuczmarski RJ, Ogden CL, Grummer-Strawn LM, Flegal KM, Guo SS, Wei R, et al. CDC growth charts: United States. Adv Data 2000:1-27. 21. McCarthy HD, Jarrett KV, Crawley HF. The development of waist circumference percentiles in British children aged 5.0-16.9 y. Eur J Clin Nutr 2001;55:902-7. 22. National High Blood Pressure Education Program Working Group on High Blood Pressure in Children and Adolescents. The fourth report on the diagnosis, evaluation, and treatment of high blood pressure in children and adolescents. Pediatrics 2004;114(2 Suppl 4th Report):555-76. 23. Retnakaran R, Zinman B, Connelly PW, Harris SB, Hanley AJ. Nontraditional cardiovascular risk factors in pediatric metabolic syndrome. J Pediatr 2006; 148:176-82. 24. Kaler SN, Ralph-Campbell K, Pohar S, King M, Laboucan CR, Toth EL. High rates of the metabolic syndrome in a First Nations community in western Canada: prevalence and determinants in adults and children. Int J Circumpolar Health 2006;65:389-402. 25. Young TK, Dean HJ, Flett B, Wood-Steiman P. Childhood obesity in a population at high risk for type 2 diabetes. J Pediatr 2000;136:365-9.

The Journal of Pediatrics • August 2008

26. Ruderman N, Chisholm D, Pi-Sunyer X, Schneider S. The metabolically obese, normal-weight individual revisited. Diabetes 1998;47:699-713. 27. Daniel M, Rowley KG, McDermott R, Mylvaganam A, O’Dea K. Diabetes incidence in an Australian Aboriginal population: an 8-year follow-up study. Diabetes Care 1999;22:1993-8. 28. Freedman DS, Serdula MK, Srinivasan SR, Berenson GS. Relation of circumferences and skinfold thicknesses to lipid and insulin concentrations in children and adolescents: the Bogalusa Heart Study. Am J Clin Nutr 1999;69:308-17. 29. Tong J, Boyko EJ, Utzschneider KM, McNeely MJ, Hayashi T, Carr DB, et al. Intra-abdominal fat accumulation predicts the development of the metabolic syndrome in non-diabetic Japanese-Americans. Diabetologia 2007. 30. Daniels SR, Khoury PR, Morrison JA. Utility of different measures of body fat distribution in children and adolescents. Am J Epidemiol 2000;152:1179-84.

31. Wang Z, Hoy WE. Body size measurements as predictors of type 2 diabetes in aboriginal people. Int J Obes Relat Metab Disord 2004;28:1580-4. 32. Norgan NG. Interpretation of low body mass indices: Australian aborigines. Am J Phys Anthropol 1994;94:229-37. 33. Mackerras DE, Reid A, Sayers SM, Singh GR, Bucens IK, Flynn KA. Growth and morbidity in children in the Aboriginal Birth Cohort Study: the urban-remote differential. Med J Aust 2003;178:56-60. 34. Barker DJ, Osmond C. Infant mortality, childhood nutrition, and ischaemic heart disease in England and Wales. Lancet 1986;1(8489):1077-81. 35. Ravelli AC, van der Meulen JH, Michels RP, Osmond C, Barker DJ, Hales CN, et al. Glucose tolerance in adults after prenatal exposure to famine. Lancet 1998;351:173-7. 36. Jones KL. The dilemma of the metabolic syndrome in children and adolescents: disease or distraction? Pediatr Diabetes 2006;7:311-21.

50 Years Ago in The Journal of Pediatrics HYDRAMNIOS

AS A SIGNAL TO THE PHYSICIAN RESPONSIBLE FOR NEWBORN PATIENTS

DeYoung VR. J Pediatr 1958;53:277-84

When DeYoung published his observations on hydramnios (polyhydramnios) in The Journal, accurate prenatal diagnosis was rarely, if ever, available at the time of delivery. Prenatal ultrasound, fetal echocardiography, and fetal magnetic resonance imaging (MRI) were many decades away, leaving the primary responsibility for diagnosis to the physician caring for the newborn during the first few days of life. But physical signs and symptoms leading to a prompt diagnosis of a pathological condition, such as bowel obstruction, may remain nonspecific, until it is too late for effective treatment. DeYoung presented an observational study of 8 infants with proximal bowel obstruction culled from a nursery service of more than 12 000 deliveries to demonstrate the diagnostic significance of polyhydramnios at the time of delivery. He used available scientific knowledge regarding amniotic fluid circulation to link excess intrauterine fluid accumulation with impaired absorption through the fetal gastrointestinal tract. His observations were buttressed by results of the Farber test, a microsopic examination of meconium for swallowed lanugo hair and squamous epithelium. Absence of these elements in meconium along with polyhydramnios and clinical feeding intolerance suggested a high index of suspicion for a proximal bowel obstruction and supported prompt diagnosis and treatment. Thus, DeYoung emphasized the power of prenatal as well as neonatal signs to clarify an obscure, difficult diagnosis. Over the ensuing years, prenatal diagnostic testing has become commonplace, with improving sensitivity and specificity.1 A newborn presenting with a proximal bowel obstruction without advance notice through prenatal diagnosis is now unusual, even exotic in some settings. This presents a new risk: Clinicians may assume that prenatal diagnostic studies are uniformly accurate and forget to look at the patient. The key lesson of DeYoung’s article lies in remembering the value of critical thinking. The article emphasizes the importance of correlating hydramnios with neonatal symptoms and testing, still a pretty good idea in 2008. Even with such remarkable tools as prenatal ultrasound, fetal echocardiography, and MRI, the neonatal physician still has a responsibility to carefully evaluate the patient to verify the prenatal diagnosis. James M. Greenberg, MD Division of Neonatology Cincinnati Children’s Hospital Medical Center Department of Pediatrics University of Cincinnati College of Medicine Cincinnati, Ohio 10.1016/j.jpeds.2008.03.024

REFERENCE 1. Gottliebson WM, Border WL, Franklin CM, Meyer RA, Michelfelder EC. Accuracy of fetal echocardiography: a cardiac segment-specific analysis. Ultrasound Obstet Gynecol 2006;28:15-21.

Large Waist but Low Body Mass Index: The Metabolic Syndrome in Australian Aboriginal Children

227