Predicting cardiovascular risk in young adulthood from the metabolic syndrome, its component risk factors, and a cluster score in childhood.
OBJECTIVE
The value of metabolic syndrome (MetS) in childhood and adolescence and its stability into young adulthood have been questioned. This study compared the MetS in late childhood (mean age 13) versus a cluster score of the MetS components as predictors of young adult (mean age 22) cardiovascular risk.


METHODS
Anthropometrics, blood pressure, lipid profile, and insulin resistance (insulin clamp) were obtained in 265 individuals at mean ages 13 and 22. The MetS was defined dichotomously by current pediatric and adult criteria. The MetS cluster score used the average of deviates of the MetS components standardized to their means and standard deviations at mean age 13.


RESULTS
The MetS was rarely present at mean age 13 and did not predict MetS at mean age 22 but identified individuals who continued to have adverse levels of risk factors at mean age 22. In contrast to the standard MetS definition, the MetS cluster score tracked strongly and at mean age 22 was significantly higher in the individuals with MetS at mean age 13 (0.78 ± 0.71) than those without MetS at mean age 13 (0.09 ± 0.70, p <0.0001).


CONCLUSIONS
Although the MetS at mean age 13, using the conventional definition, is not a reliable method for predicting the MetS at mean age 22, it does predict adverse levels of cardiovascular risk factors. A cluster score, using the MetS components as continuous variables, is more reliable in predicting young adult risk from late childhood.
Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN, USA. kelly105@umn.edu
Aaron S
AS
Kelly
Julia
J
Steinberger
David R
DR
Jacobs
Ching-Ping
CP
Hong
Antoinette
A
Moran
Alan R
AR
Sinaiko
D000293:Adolescent; D000367:Age Factors; D000375:Aging; D000886:Anthropometry; D015415:Biomarkers / Q000097:blood; D001794:Blood Pressure; D015992:Body Mass Index; D002318:Cardiovascular Diseases / Q000097:blood / Q000453:epidemiology* / Q000503:physiopathology; D002648:Child; D016000:Cluster Analysis; D005260:Female; D015309:Glucose Clamp Technique; D006801:Humans; D007328:Insulin / Q000097:blood; D007333:Insulin Resistance; D016014:Linear Models; D008055:Lipids / Q000097:blood; D016015:Logistic Models; D008137:Longitudinal Studies; D008297:Male; D024821:Metabolic Syndrome / Q000097:blood / Q000175:diagnosis / Q000453:epidemiology* / Q000503:physiopathology; D008910:Minnesota / Q000453:epidemiology; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors; D055105:Waist Circumference; D055815:Young Adult
D015415:Biomarkers; D007328:Insulin; D008055:Lipids