Nontraditional cardiovascular risk factors in pediatric metabolic syndrome

Article Outline

• Abstract
• Methods
  • Statistical Analysis
• Results
• Discussion
• Acknowledgment
• References
• Copyright

Objective

To study the relationships between nontraditional cardiovascular (CV) risk factors and components of the metabolic syndrome in Native Canadian children, a population at risk of future CV disease.

Study design

CV risk factors were evaluated in a population-based study of a Canadian Oji-Cree community, involving 236 children aged 10 to 19 years.

Results

Using an age- and sex-specific case definition, 18.6% of the children met criteria for pediatric metabolic syndrome. As the number of metabolic syndrome component criteria increased, C-reactive protein, leptin, and ratio of apolipoprotein B to apolipoprotein A1 levels rose (all P < .0001) and adiponectin concentration decreased (P = .0006). Principal factor analysis using both traditional and nontraditional CV risk factors revealed 5 underlying core traits, defined as follows: adiposity, lipids/adiponectin, inflammation, blood pressure, and glucose.

Conclusions

Nontraditional CV risk factors accompany the accrual of traditional risk factors early in the progression to pediatric metabolic syndrome. Furthermore, inclusion of these factors in factor analysis suggests that 5 core traits underlie the early development of an enhanced CV risk factor profile in Native children.

ANOVA, Analysis of variance , apoB:A1, Ratio of apolipoprotein B to apolipoprotein A1 , BMI, Body mass index , CRP, C-reactive protein , CV, Cardiovascular , CVD, Cardiovascular disease , DM, Diabetes mellitus , HDL, High-density lipoprotein , HOMA-IR, Homeostasis model assessment index , IL-6, Interleukin-6 , LDL, Low-density lipoprotein , NHANES-III, Third National Health and Nutritional Survey , SAA, Serum amyloid A

The metabolic syndrome identifies a patient population at high risk of future development of cardiovascular disease (CVD) and type 2 diabetes (type 2 DM).¹ The risk of atherosclerotic disease in affected patients, however, is not fully reconciled by the cluster of traditional cardiovascular (CV) risk factors that define this syndrome (central obesity, hyperglycemia, hypertension, hypertriglyceridemia, decreased high-density-lipoprotein [HDL] cholesterol).¹ Thus the relationship between the metabolic syndrome and novel nontraditional CV risk factors, including the inflammatory bio-markers C-reactive protein (CRP), serum amyloid A (SAA), and interleukin-6 (IL-6); the adipocyte-derived cytokines adiponectin and leptin; and the ratio of apolipoprotein B to apolipoprotein A1 (apoB:A1) is of interest. These nontraditional CV risk factors have shown independent associations with CVD, after adjustment for traditional risk factors.², ³, ⁴, ⁵ Furthermore, elevated CRP concentration and hypoadiponectinemia, in particular, have both emerged as independent predictors of incident CVD.², ³

To study the pathophysiology of CVD, numerous investigators have used the multivariate correlation technique of factor analysis to reduce the cluster of interrelated CV risk factors observed in the metabolic syndrome to a set of discrete underlying core traits.⁶ Focusing on traditional CV risk factors, these studies have typically identified a set of 2 to 4 fundamental traits underlying the CV risk factor profile.⁶ Importantly, however, the contribution of nontraditional factors in this context has received limited attention to date.

Native North American populations are experiencing high prevalence rates of both traditional and nontraditional CV risk factors, as well as vascular and metabolic disease.⁷, ⁸, ⁹ Indeed, although overall rates of CVD and associated mortality have been declining in North America, Native populations have exhibited the opposite trend.⁹, ¹⁰ Particularly of concern, with respect to the future, is the growing prevalence of CV risk factors, including obesity and early-onset type 2 diabetes mellitus (DM), in Native children.¹¹

Since CV risk factors in childhood track into adulthood and can predict future CVD,¹² evaluation of the metabolic syndrome in Native children offers a potential model for studying early events in the development of vascular disease. While studies of metabolic syndrome in childhood have traditionally been hampered by the lack of a standard definition, de Ferranti et al recently proposed a definition of pediatric metabolic syndrome based on extrapolation from Adult Treatment Panel-III (ATP-III) criteria.¹³

In this report, we evaluate the prevalence of metabolic syndrome in Native Canadian children participating in a population-based study. We hypothesized that nontraditional CV risk factors would be associated with pediatric metabolic syndrome and that inclusion of these variables in factor analysis would provide novel insights into the pathophysiological underpinnings of CVD.

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Methods 

The methodology of the Sandy Lake Health and Diabetes Project has previously been described in detail.⁷, ⁸, ¹¹ In brief, 728 of 1018 eligible residents of Sandy Lake, an Oji-Cree community in northwestern Ontario, participated in a population-based cross-sectional survey to determine the prevalence of type 2 diabetes and associated risk factors. There were 236 participants aged 10 to 19 years, representing participation of 72.6 % of the eligible population in this age range. Signed, informed consent was obtained from all participants or their parents or guardians. The study was approved by the Sandy Lake First Nation Band Council and the University of Toronto Ethics Review Committee. Interviews and examinations were conducted by trained community members.

Participants underwent oral glucose tolerance testing, with measurement of blood pressure and body anthropometry (height, weight, waist circumference, percentage body fat), as described previously.⁷, ⁸, ¹¹ Fasting insulin, CRP, IL-6, SAA, adiponectin, leptin, total cholesterol, triglycerides, HDL, and low-density-lipoprotein (LDL) cholesterol were measured as previously described.⁷, ⁸, ¹⁴, ¹⁵ Apolipoprotein B (apoB) and apolipoprotein A1 (apoA1) were measured using the Behring BN100 nephelometer and Behring reagents.¹⁶ Insulin resistance was estimated using the homeostasis model assessment index (HOMA-IR).¹⁷ Diabetes was diagnosed according to 1985 World Health Organization criteria.¹⁸

As recently proposed, the definition of metabolic syndrome in children aged 10-19 years was determined based on extrapolation from ATP-III criteria.¹³ Pediatric metabolic syndrome was defined by the presence of 3 or more of the following 5 criteria: (1) triglycerides ≥1.1 mmol/L; (2) HDL < 1.2 mmol/L in boys aged 15 to 19 years or HDL < 1.3 mmol/L in all other children; (3) fasting blood glucose ≥ 6.1 mmol/L; (4) waist circumference ≥90th percentile for age and sex; and (5) blood pressure ≥ 90th percentile for age, sex, and height. The triglyceride and HDL thresholds were originally derived from pediatric percentiles and have been previously used in the definition of pediatric metabolic syndrome.¹³ The hyperglycemia threshold is based on the ATP-III cut point and has also been previously used in the definition of pediatric metabolic syndrome.¹³ The 90th percentile of waist circumference for age and sex was determined from the Third National Health and Nutrition Examination Survey (NHANES-III).¹⁹ The 90th percentile of systolic and diastolic blood pressure for age, sex, and height was determined from the recommendations of the National High Blood Pressure Education Program Working Group on High Blood Pressure in Children and Adolescents.²⁰ Because waist circumference and blood pressure percentiles were available up to the ages of 17 and 18 years, respectively, the thresholds of the highest available age group were carried forward for older children (18- and 19-year-olds for waist and 19-year-olds for blood pressure). Results were not significantly different if ATP-III criteria for waist and blood pressure were used instead in these older children.

Statistical Analysis 

All analyses were conducted using the Statistical Analysis System (SAS 8.02; SAS Institute, Cary, NC). The distributions of continuous variables were assessed for normality, and the natural log transformations of skewed variables (fasting insulin, HOMA-IR, fasting and 2-hr pc blood glucose, CRP, IL-6, SAA, leptin) were used in subsequent analyses. Analysis of variance (ANOVA) and χ² tests were used to assess univariate differences between continuous and categorical variables, respectively. Given the potential confounding effect of diabetes, univariate associations between traditional and nontraditional CV risk factors were assessed by Spearman correlation analysis restricted to nondiabetic participants (n = 231). Principal factor analysis was conducted with the FACTOR procedure of SAS. The number of factors to be retained was determined on the basis of scree plot analysis (retaining factors above the break in the curve), the proportion of common variance explained (>5%), and established factor interpretability criteria, described and recommended elsewhere.²¹ Oblique (promax) rotation was used to obtain a set of underlying interpretable factors. The resultant factor pattern was interpreted using |factor loadings|≥ 0.3.

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Results 

Demographic, clinical, and metabolic characteristics of the 236 study participants are shown in Table I. Mean age was 14.9 years (range 10-19), and 61% of participants were female. The vast majority (92.9%) of the children had normal glucose tolerance, whereas 11 participants exhibited impaired glucose tolerance and 5 had diabetes. Fifty-six percent reported a history of smoking cigarettes. Overall, 18.6% of the children met criteria for diagnosis of the metabolic syndrome, and 66.5% exhibited at least 1 component of the metabolic syndrome. The most common component abnormality was low HDL, followed by hypertriglyceridemia and central obesity.

Table I. Demographic, clinical and metabolic characteristics of study population^⁎

	Overall (n = 236)	Boys (n = 91)	Girls (n = 145)	P value⁎⁎
Demographic
Age (y)	14.9 [2.9]	15.0 [3.0]	14.7 [2.9]	.4369
Sex (M/F)	39%/61%	—	—
BMI (kg/m2)	23.4 [5.6]	22.1 [4.6]	24.2 [6.0]	.0062
Waist circumference (cm)	81.1 [12.6]	80.1 [11.7]	81.7 [13.2]	.3252
% body fat (%)	29.6% [13.4]	20.1% [8.9]	35.4% [12.4]	<.0001
Traditional CV risk factors
Ever smoker (% Yes)	56%	52%	59%	.2937
Systolic BP (mm Hg)	109.0 [11.4]	112.2 [11.8]	106.9 [10.8]	.0005
Diastolic BP (mm Hg)	60.2 [10.0]	60.8 [11.3]	59.8 [9.0]	.4310
HDL (mmol/L)	1.27 [0.25]	1.31 [0.26]	1.24 [0.25]	.0543
Triglycerides (mmol/L)	1.12 [0.56]	0.98 [0.57]	1.20 [0.55]	.0046
LDL (mmol/L)	2.10 [0.56]	2.01 [0.58]	2.16 [0.54]	.0546
Fasting insulin (pmol/L)	95.0 [67-139.5]	84.0 [52-118]	101.0 [74-162]	0.0002
HOMA-IR	3.1 [2.1 4.6]	2.6 [1.7 3.9]	3.2 [2.4 5.5]	.0008
FBG (mmol/L)	5.3 [5.0 5.6]	5.4 [5.0 5.7]	5.3 [5.0 5.6]	.6901
2-hr pc BG (mmol/L)	5.3 [4.2 6.2]	4.9 [4.0 5.7]	5.5 [4.5 6.3]	.0099
Non-traditional Bio-markers
CRP (mg/L)	0.54 [0.19 1.94]	0.48 [0.18 1.65]	0.66 [0.2 2.14]	.5516
IL-6 (ng/L)	0.67 [0.36-1.08]	0.58 [0.31-0.97]	0.70 [0.42-1.2]	.3707
SAA (mg/L)	5.8 [3.7-9.3]	5.4 [3.6-9.6]	5.8 [3.7-9.0]	.6225
Adiponectin (μg/mL)	17.5 [8.0]	17.1 [7.1]	17.9 [8.6]	.4727
Leptin (ng/mL)	10.5 [4.8-18.6]	4.5 [2.0-8.9]	15.7 [8.6-25.1]	<.0001
apoB (mg/dL)	0.87 [0.21]	0.84 [0.22]	0.90 [0.20]	.0381
apoA1 (g/L)	1.45 [0.20]	1.46 [0.20]	1.44 [0.20]	.6302
apoB:A1	0.61 [0.17]	0.58 [0.17]	0.63 [0.16]	.0290
Metabolic syndrome components
Low HDL (%)	54.7%	42.9%	62.1%	.0039
Hypertriglyceridemia (%)	38.1%	27.5%	44.8%	.0075
Central obesity (%)	22.9%	14.3%	28.3%	.0128
Hypertension (%)	8.5%	12.1%	6.2%	.1143
Hyperglycemia (%)	6.4%	6.6%	6.2%	.9057
Metabolic syndrome	18.6%	14.3%	21.4%	.1732

BP, Blood pressure; FBG, fasting blood glucose; BG, blood glucose.

When comparing girls and boys (Table I), the girls were noted to have significantly higher mean body mass index (BMI) and percent body fat, although there were no significant sex-related differences with respect to age or waist circumference. The girls also exhibited higher insulin resistance (HOMA-IR), 2-hour blood glucose, triglycerides, leptin, apoB and apoB:A1. Although the component abnormalities of low HDL, hypertriglyceridemia, and central obesity were each more common in girls, the overall prevalence of the metabolic syndrome was not significantly different between girls and boys (21.4% and 14.3%, respectively, P = 0.1732).

Study participants were stratified into metabolic risk groups defined by the number of metabolic syndrome component criteria exhibited (Table II). There were no significant age differences between the groups. Measures of adiposity (BMI, waist circumference, % body fat) all increased with increasing numbers of metabolic abnormalities (all P < .0001). Furthermore, as expected, the traditional CV risk factors of blood pressure (systolic and diastolic), lipids (HDL, LDL, triglycerides),insulin resistance, and glucose intolerance (fasting and 2-hour blood glucose) all worsened across the groups as the number of metabolic syndrome component abnormalities increased (all P < .005). Importantly, the nontraditional CV risk factors showed a similar pattern as CRP, leptin, apoB, and apoB:A1 all increased (all P < .0001), and adiponectin decreased (P = .0006) across the groups.

Table II. Clinical and metabolic characteristics by metabolic risk group (defined by number of metabolic syndrome components exhibited)^⁎

	No. of metabolic syndrome factors				P value⁎⁎
	0 (n = 79)	1 (n = 61)	2 (n = 52)	>3 (n = 44)
Demographic
Age (yrs)	15.3 [2.8]	14.8 [3.0]	14.2 [3.0]	14.9 [2.9]	.1594
Sex (M/F)	54%/46 %	30%/70%	33%/67%	30%/70%	.0052
BMI (kg/m2)	20.0 [3.0]	21.7 [3.7]	24.9 [5.8]	30.0 [4.6]	<.0001
Waist circumference (cm)	74.0 [7.7]	76.9 [9.0]	83.3 [11.9]	97.2 [9.8]	<.0001
% body fat (%)	20.8 [10.4]	28.2 [10.9]	33.1 [13.0]	42.9 [9.1]	<.0001
Traditional CV risk factors
Ever smoker (% Yes)	57%	61%	50%	55%	0.7142
Systolic BP (mm Hg)	105.5 [10.3]	108.0 [10.0]	108.4 [10.9]	117.2 [12.2]	<.0001
Diastolic BP (mm Hg)	58.3 [8.6]	59.7 [8.7]	60.1 [9.9]	64.2 [12.8]	.0030
HDL (mmol/L)	1.49 [0.18]	1.24 [0.23]	1.15 [0.18]	1.04 [0.12]	<.0001
Triglycerides (mmol/L)	0.74 [0.17]	0.98 [0.34]	1.36 [0.54]	1.73 [0.68]	<.0001
LDL (mmol/L)	1.91 [0.54]	2.11 [0.55]	2.14 [0.48]	2.38 [0.56]	<.0001
Fasting insulin (pmol/L)	67.0 [47-93]	87.0 [68-109]	120.5 [91-167]	185.0 [133-272]	<0.0001
HOMA-IR	2.1 [1.6-2.9]	2.7 [2.3-3.6]	4.0 [2.7-5.3]	6.9 [4.4-9.9]	<.0001
FBG (mmol/L)	5.2 [4.9-5.4]	5.2 [4.9-5.5]	5.4 [5.1-5.7]	5.6 [5.3-6.0]	<.0001
2-hr pc BG (mmol/L)	4.6 [3.8-5.5]	5.3 [4.4-5.8]	5.6 [4.9-6.3]	6.4 [4.8-7.5]	<.0001
Nontraditional bio-markers
CRP (mg/L)	0.24 [0-0.9]	0.51 [0.2-2.4]	0.59 [0.3-3.6]	1.45 [0.9-4.0]	<.0001
IL-6 (ng/L)	0.54 [0.3-0.9]	0.60 [0.4-1.1]	0.84 [0.4-1.2]	0.77 [0.5-1.3]	.0005
SAA (mg/L)	5.1 [3.2-8.6]	5.8 [3.4-8.8]	5.7 [4.1-10.2]	7.1 [4.6-11.6]	.0458
Adiponectin (μg/mL)	19.6 [9.2]	18.2 [7.5]	16.0 [7.0]	14.9 [6.8]	.0006
Leptin (ng/mL)	3.7 [2.1-9.9]	10.7 [6.2-15.9]	11.8 [7.2-25.5]	25.4 [17-32.5]	<.0001
apoB (mg/dL)	0.78 [0.17]	0.84 [0.18]	0.92 [0.17]	1.04 [0.24]	<.0001
apoA1 (g/L)	1.55 [0.16]	1.42 [0.23]	1.39 [0.20]	1.36 [0.17]	<.0001
apoB:A1	0.50 [0.10]	0.60 [0.14]	0.68 [0.15]	0.77 [0.17]	<.0001

BP, Blood pressure; FBG, fasting blood glucose; BG, blood glucose.

Spearman univariate correlation analysis was performed to study the relationships among traditional CV risk factors and nontraditional CV risk factors. Given the potential confounding effects of diabetes, this analysis was restricted to the non-diabetic children (n = 231) in the study. CRP was most strongly associated with measures of adiposity (BMI and waist circumference; both r = 0.44), leptin (r = 0.39), and fasting insulin (r = 0.36) (all P < .0005). Leptin exhibited strongly positive correlations with adiposity (BMI and waist; r ≥ 0.63), triglycerides (r = 0.53), and fasting insulin (r = 0.39) (all P < .0005). Similarly, ApoB:A1 was positively associated with adiposity (BMI and waist; r ≥ 0.41), triglycerides (r = 0.55), and fasting insulin (r = 0.50) and inversely correlated with HDL (r = −0.63) (all P < .0005). On the other hand, adiponectin was inversely associated with adiposity (BMI and waist; r ≤ −0.31), triglycerides (r = −0.26), and apoB:A1 (r = −0.28) and positively correlated with HDL (r = 0.25) (all P < .0005).

Consistent with previous observations in other datasets,⁶ principal factor analysis of the traditional CV risk factors associated with the metabolic syndrome identified 3 underlying traits, interpreted as follows: (1) an “adiposity” factor, based on positive loadings of percentage body fat, BMI, waist circumference, fasting insulin and triglycerides; (2) a “metabolic” parameter, with positive loadings of fasting insulin, fasting glucose, 2-hour glucose and triglycerides and an inverse loading of HDL; and (3) a “blood pressure” variable, based on positive loadings of systolic and diastolic blood pressure (Table III, A). These 3 factors explained 38.0%, 8.4%, and 4.3% of the total variance in the dataset, respectively (cumulative 50.7%).

Table III, A. Principal factor analysis of traditional metabolic syndrome parameters in non-diabetic children (|loading| >0.30 in bold)

In Table III, B, principal factor analysis was repeated with both traditional and nontraditional CV risk factors. This analysis revealed 5 core traits underlying the CV risk factor profile: (1) an “adiposity” trait, with positive loadings of percentage body fat, BMI, waist circumference, and leptin; (2) a “lipids/adiponectin” trait, with inverse loadings of HDL and adiponectin and positive loadings of apoB:A1 and triglycerides; (3) an “inflammation” component, based on positive loadings of CRP, SAA and IL-6; (4) a “blood pressure” factor, with positive loadings of systolic blood pressure, waist, and BMI; and (5) a “glucose” trait, based on positive loadings of fasting glucose, 2-hour glucose, and fasting insulin. These 5 factors explained 33.2%, 7.0%, 6.5%, 3.9%, and 3.3% of the total variance in the dataset, respectively (cumulative 53.9%).

Table III, B. Principal factor analysis of traditional and non-traditional metabolic syndrome parameters in non-diabetic children (|loading| >0.30 in bold)

BP, Blood pressure.

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Discussion 

In a comprehensive evaluation of CV risk factors in this high-risk population, the nontraditional factors CRP, adiponectin, leptin, and apoB:A1 are found to accompany pediatric metabolic syndrome and correlate with traditional CV risk factors. Furthermore, inclusion of these emerging metabolic parameters in factor analysis suggests that an expanded set of 5 core traits underlies the early development of an enhanced CV risk factor profile in Native children at risk of future CVD.

This study provides a relatively conservative estimate of the prevalence of pediatric metabolic syndrome in Sandy Lake. Nevertheless, the suitability of the current definition for this population is potentially supported by the fact that the relative frequency of component metabolic abnormalities was similar to that observed by de Ferranti et al¹³ in their study of children aged 12 to 19 years participating in NHANES-III (low HDL > hypertriglyceridemia > central obesity > hypertension > hyperglycemia). Furthermore, in both studies, the same proportion of children (2/3) exhibited at least 1 component abnormality. Finally, in this study, the gender-specific prevalence of metabolic syndrome in preadolescents (age 10 to 12) was identical to that observed in adolescents (age 13 to 19), consistent with previous reports in other populations.²², ²³

Although likely a conservative estimate, the 18.6% prevalence of pediatric metabolic syndrome in Sandy Lake exceeds rates described in other populations. De Ferranti et al¹³ reported a prevalence of 9.2% in the NHANES-III population. Other reports have suggested prevalence rates between 4% and 11.5% in pediatric populations.²², ²³ Although definitional differences preclude direct comparison of these studies, the current findings are nevertheless consistent with an excessive burden of traditional CV risk factors in Native Canadian children.

To date, there has been limited study of nontraditional factors in pediatric metabolic syndrome. As in adults, CRP in children is known to correlate with several traditional CV risk factors including obesity.²⁴ In a study of predominantly obese children, Weiss et al²⁵ found that CRP levels increased with the number of metabolic syndrome components present, but that the trend did not reach statistical significance. Because obesity is associated with a chronic inflammatory response, the lack of heterogeneity in body mass within their study population may have masked the relationship between CRP and pediatric metabolic syndrome. By contrast, in this population-based study (a more heterogeneous sample), a significant association between CRP and the number of component abnormalities is apparent. Furthermore, the current analysis extends this concept to other nontraditional CV risk factors by demonstrating that the number of pediatric metabolic syndrome component abnormalities is positively correlated with leptin and apoB:A1 and inversely associated with adiponectin concentration. Thus nontraditional risk factors appear to accompany the accrual of traditional CV risk factors early in the progression to metabolic syndrome in Native children.

Elucidation of the pathophysiological relationships among traditional and nontraditional CV risk factors is complicated by physiological complexity and statistical intercorrelation. Factor analysis has been proposed as a means for identifying underlying patterns or structure among these highly intercorrelated variables. In adults, factor analysis of traditional CV risk factors has typically identified 2 to 4 underlying core traits, variously interpreted as an obesity trait, a blood pressure trait, and a metabolic trait (sometimes identified separately as lipid and glucose factors).⁶ Measures of adiposity and fasting insulin, a surrogate marker of insulin resistance, have both frequently correlated with more than 1 trait. In children, studies of factor analysis of traditional risk factors have yielded similar patterns to those observed in adults.²³, ²⁵ This study now confirms these patterns in a pediatric Native population at high risk of future CVD, insofar as the factor analysis of traditional CV risk factors identified a similar set of core traits, with insulin loading on 2 of these factors.

Unlike traditional factors, however, the clustering of nontraditional CV risk factors has not been extensively studied to date with factor analysis. In a study of the clustering of hemostatic and inflammatory variables with traditional risk factors in elderly subjects, Sakkinen et al²⁶ identified 7 core factors, consisting of the typical traits of body mass, insulin/glucose, blood pressure, and lipids and 3 novel factors, interpreted as inflammation, vitamin K–dependent proteins, and procoagulation.²⁶ Two recent studies involving measures of inflammation in adults both also noted a separate inflammation factor, in addition to a “metabolic” trait (insulin/lipids/obesity) and a blood pressure trait.²⁷, ²⁸ The current factor analysis represents an important extension of these earlier observations in 2 ways: (1) a broad array of novel nontraditional CV risk factors was analyzed in the context of a population-based study and (2) the analysis was performed in children, thereby potentially providing insights into the early pathophysiology of CVD. Importantly, inclusion of the nontraditional factors provided superior resolution of core traits, with the metabolic factor identified in Table III, A, resolved into separate adiponectin/lipid and glucose tolerance traits (Table III, B). Moreover, as observed in adults, inflammation emerged as a separate trait in this pediatric population. Finally, in the expanded factor analysis, waist circumference, a measure of visceral obesity in children,²⁹ correlated with 2 traits (adiposity, blood pressure) whereas insulin showed significant loading on the glucose tolerance trait and moderate loading on both the adiposity and lipids/adiponectin factors. As such, visceral obesity and insulin resistance are potentially implicated as key factors in CV risk factor clustering and the early pathophysiology of CVD in this pediatric population.

The cross-sectional design of this study limits our ability to comment on causal relationships among the factors described herein. Nevertheless, these findings are consistent with emerging concepts regarding the pathophysiology of CVD and should lead to further prospective studies. A second limitation to consider is that the applicability of diagnostic thresholds for central obesity derived from the NHANES-III dataset may be questioned, particularly given the relative underrepresentation of Native peoples in that study. It is nevertheless encouraging that findings with the current definition were consistent with previous observations in other pediatric populations. Third, the lack of pubertal staging in this study limits the interpretation of any sex-related differences in CV risk factors, because differences in sexual maturation between male and female participants may underlie these observations. Finally, the relatively small sample size should be noted, although the population-based context of the study likely supports the internal validity of the findings.

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We gratefully acknowledge the efforts of the Sandy Lake First Nation Band Council, community members and community surveyors (Tina Noon, Elda Anishinabie, Madeliene Kakegamic, Mary Mamakeesick), whose co-operation was essential in the design and implementation of this project.

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