Using the area under the curve to reduce measurement error in predicting young adult blood pressure from childhood measures

Stat Med. 2004 Nov 30;23(22):3421-35. doi: 10.1002/sim.1921.


Tracking correlations of blood pressure, particularly childhood measures, may be attenuated by within-person variability. Combining multiple measurements can reduce this error substantially. The area under the curve (AUC) computed from longitudinal growth curve models can be used to improve the prediction of young adult blood pressure from childhood measures. Quadratic random-effects models over unequally spaced repeated measures were used to compute the area under the curve separately within the age periods 5-14 and 20-34 years in the Bogalusa Heart Study. This method adjusts for the uneven age distribution and captures the underlying or average blood pressure, leading to improved estimates of correlation and risk prediction. Tracking correlations were computed by race and gender, and were approximately 0.6 for systolic, 0.5-0.6 for K4 diastolic, and 0.4-0.6 for K5 diastolic blood pressure. The AUC can also be used to regress young adult blood pressure on childhood blood pressure and childhood and young adult body mass index (BMI). In these data, while childhood blood pressure and young adult BMI were generally directly predictive of young adult blood pressure, childhood BMI was negatively correlated with young adult blood pressure when childhood blood pressure was in the model. In addition, racial differences in young adult blood pressure were reduced, but not eliminated, after controlling for childhood blood pressure, childhood BMI, and young adult BMI, suggesting that other genetic or lifestyle factors contribute to this difference.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Adolescent
  • Adult
  • Age Factors
  • Area Under Curve
  • Blacks
  • Blood Pressure / physiology*
  • Blood Pressure Determination / methods*
  • Child
  • Child, Preschool
  • Cross-Sectional Studies
  • Female
  • Humans
  • Longitudinal Studies
  • Male
  • Models, Biological*
  • Models, Statistical*
  • Sex Factors
  • Whites