Validating pathophysiological models of aging using clinical electronic medical records.
Bioinformatics methods that leverage the vast amounts of clinical data promises to provide insights into underlying molecular mechanisms that help explain human physiological processes. One of these processes is adolescent development. The utility of predictive aging models generated from cross-sectional cohorts and their applicability to separate populations, including the clinical population, has yet to be completely explored. In order to address this, we built regression models predictive of adolescent chronological age from 2001 to 2002 National Health and Nutrition Examination Survey (NHANES) data and validated them against independent 2003-2004 NHANES data and clinical data from an academic tertiary-care pediatric hospital. The results indicate distinct differences between male and female models with both alkaline phosphatase and creatinine as predictive biomarkers for both genders, hematocrit and mean cell volume for males, and total serum globulin for females. We also suggest that the models are generalizable, are clinically relevant, and imply underlying molecular and clinical differences between males and females that may affect prediction accuracy. The integration of both epidemiological and clinical data promises to create more robust models that shed new light on physiological processes.
Chen
David P
DP
Center for Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.
Morgan
Alexander A
AA
Butte
Atul J
AJ
D000293:Adolescent; D041923:Adolescent Development; D000375:Aging; D015415:Biomarkers / Q000737:chemistry; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D008297:Male; D008954:Models, Biological; D009749:Nutrition Surveys; D012044:Regression Analysis
D015415:Biomarkers