Validating pathophysiological models of aging using clinical electronic medical records

Abstract

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 2001–2002 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 all 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.

Predicting age by mining electronic medical records with deep learning characterizes differences between chronological and physiological age

“…For instance, DeepPatient uses denoising Autoencoder (dAE) to perform unsupervised feature extraction from EMR [21]. Most relevant to the work presented here is a study that implemented a Least Angle Regression (LARS) model to predict the age of adolescents using 39 biomarkers from a blood test [5]. The model identified alkaline phosphatase and creatinine levels as the most predictive biomarkers for both male and female age; hematocrit and mean-cell-volume levels were found to be the most predictive markers for males, whereas total serum globulin was the most predictive marker for females.…”

Latent physiological factors of complex human diseases revealed by independent component analysis of clinarrays

“…In previous studies, we demonstrated the utility for patient clinical biomarker vectors, which we termed clinarrays, in distinguishing between different severities of disease [ 7 ]; in integrative analysis with gene expression measurements to elucidate genes related to maturation and aging [ 8 ]; and to build a model of pediatric aging [ 9 ]. Clinarrays are a vector representation of a person’s physiological biomarker state across all visits at a hospital.…”

Current methodologies for translational bioinformatics

“…In this special issue, D. Chen et al present customary molecular clinical measurements - such as creatinine and alkaline phosphatase - as new predictive biomarkers for aging[3]. These regression models, conducted for chronological ages of adolescents in the 2001-2002 National Health and Nutrition Examination Survey (NHANES) datasets [4] and validated using the 2003-2004 data, satisfactorily match the results seen in the aging process using cohorts collected from a hospital-based electronic health record system, and holds the potential to identify adolescent development-related disorders.…”