title: Principal component analysis reveals gender-specific predictors of cardiometabolic risk in 6th graders.
abstract: BACKGROUND
  The purpose of this study was to determine the sex-specific pattern of pediatric cardiometabolic risk with principal component analysis, using several biological, behavioral and parental variables in a large cohort (n = 2866) of 6th grade students.
  
  
  METHODS
  Cardiometabolic risk components included waist circumference, fasting glucose, blood pressure, plasma triglycerides levels and HDL-cholesterol. Principal components analysis was used to determine the pattern of risk clustering and to derive a continuous aggregate score (MetScore). Stratified risk components and MetScore were analyzed for association with age, body mass index (BMI), cardiorespiratory fitness (CRF), physical activity (PA), and parental factors.
  
  
  RESULTS
  In both boys and girls, BMI and CRF were associated with multiple risk components, and overall MetScore. Maternal smoking was associated with multiple risk components in girls and boys, as well as MetScore in boys, even after controlling for children's BMI. Paternal family history of early cardiovascular disease (CVD) and parental age were associated with increased blood pressure and MetScore for girls. Children's PA levels, maternal history of early CVD, and paternal BMI were also indicative for various risk components, but not MetScore.
  
  
  CONCLUSIONS
  Several biological and behavioral factors were independently associated with children's cardiometabolic disease risk, and thus represent a unique gender-specific risk profile. These data serve to bolster the independent contribution of CRF, PA, and family-oriented healthy lifestyles for improving children's health.
authors.affiliation: Laboratory for Physical Activity and Exercise Intervention Research, Department of Physical Medicine and Rehabilitation, University of Michigan, Ann Arbor, MI, USA.
authors.forename: Mark D
authors.initials: MD
authors.lastname: Peterson
authors.forename: Dongmei
authors.initials: D
authors.lastname: Liu
authors.forename: Heidi B
authors.initials: HB
authors.lastname: IglayReger
authors.forename: William A
authors.initials: WA
authors.lastname: Saltarelli
authors.forename: Paul S
authors.initials: PS
authors.lastname: Visich
authors.forename: Paul M
authors.initials: PM
authors.lastname: Gordon
mesh_terms: D000367:Age Factors; D001794:Blood Pressure; D015992:Body Mass Index; D002318:Cardiovascular Diseases / Q000453:epidemiology / Q000235:genetics; D016009:Chi-Square Distribution; D002648:Child; D016000:Cluster Analysis; D005080:Exercise Test; D005260:Female; D020022:Genetic Predisposition to Disease; D015438:Health Behavior; D006801:Humans; D008019:Life Style; D016014:Linear Models; D008297:Male; D008425:Maternal Behavior; D024821:Metabolic Syndrome / Q000175:diagnosis / Q000453:epidemiology* / Q000503:physiopathology / Q000523:psychology; D008824:Michigan / Q000453:epidemiology; D009043:Motor Activity; D015999:Multivariate Analysis; D010332:Paternal Behavior; D010375:Pedigree; D011237:Predictive Value of Tests; D025341:Principal Component Analysis; D018570:Risk Assessment; D012307:Risk Factors; D012737:Sex Factors; D012907:Smoking / Q000009:adverse effects / Q000453:epidemiology; D011795:Surveys and Questionnaires