Dietary patterns and markers for the metabolic syndrome in Australian adolescents.
OBJECTIVE
Overweight and other risk factors for cardiovascular disease (CVD) as well as their clustering, are increasingly prevalent among adolescents. We examined dietary patterns, CVD risk factors, and the clustering of these risk factors in 1139 14-year-olds living in Western Australia.


RESULTS
Usual dietary intake was assessed using a food frequency questionnaire. Two dietary patterns, 'Western' and 'Healthy', were identified using factor analysis. Associations between these dietary patterns and BMI, waist circumference, systolic blood pressure, fasting levels of serum glucose, insulin, total cholesterol, HDL-C, LDL-C, triglycerides and insulin resistance were assessed using ANOVA. Cluster analysis identified a high risk group (the 'high risk metabolic cluster') with features akin to adult metabolic syndrome. Belonging to the 'high risk metabolic cluster' was examined in relation to dietary patterns using logistic regression, adjusting for aerobic fitness and socio-demographic factors. Higher 'Western' dietary pattern scores were associated with greater odds for the 'high risk metabolic cluster' (p for trend=0.02) and greater mean values for total cholesterol (p for trend=0.03), waist circumference (p for trend=0.03) and BMI (p for trend=0.02) in girls, but not boys. Scores for the 'Healthy' dietary pattern were not related to the 'high risk metabolic cluster' but were inversely associated with serum glucose in boys and girls (p for trend=0.01 and 0.04, respectively) and were positively associated with HDL-C in boys (p for trend=0.02).


CONCLUSIONS
Dietary patterns are associated with CVD risk factors and the clustering of these risk factors in adolescence.
Ambrosini
G L
GL
School of Public Health, Curtin University of Technology, Western Australia, Australia.
Huang
R-C
RC
Mori
T A
TA
Hands
B P
BP
O'Sullivan
T A
TA
de Klerk
N H
NH
Beilin
L J
LJ
Oddy
W H
WH
D000293:Adolescent; D000886:Anthropometry; D015415:Biomarkers; D001786:Blood Glucose / Q000378:metabolism; D001794:Blood Pressure; D015992:Body Mass Index; D002318:Cardiovascular Diseases / Q000453:epidemiology; D002648:Child; D016000:Cluster Analysis; D004032:Diet; D005163:Factor Analysis, Statistical; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D007328:Insulin / Q000097:blood; D007333:Insulin Resistance; D008055:Lipids / Q000097:blood; D008137:Longitudinal Studies; D024821:Metabolic Syndrome / Q000097:blood* / Q000453:epidemiology*; D011247:Pregnancy; D012307:Risk Factors; D012959:Socioeconomic Factors; D055105:Waist Circumference; D014904:Western Australia / Q000453:epidemiology
D015415:Biomarkers; D001786:Blood Glucose; D007328:Insulin; D008055:Lipids