Nonlinear Mixed Effects Models to Study Determinants of Local Airway Inflammation Using Multiple Flow Exhaled Nitric Oxide Data

• Activity Number: 145
• Type: Contributed
• Date/Time: Monday, August 5, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Sandrah Eckel and Kiros Berhane and Meng Liu and Linn S William and Muhammad T. Salam and Edward B. Rappaport and Frank D. Gilliland
• Companies: University of Southern California and University of Southern California and University of Southern California and University of Southern California and University of Southern California and University of Southern California and University of Southern California
• Keywords: Air pollution ; Biomarkers ; Environmental epidemiology ; Mixed models

Abstract

The analysis of biomarkers in exhaled breath is an important and emerging field. One such biomarker, the fractional concentration of exhaled nitric oxide (FeNO) is a marker of airway inflammation that is of growing interest for clinical and epidemiological applications (e.g., air pollution health effects studies). FeNO levels are highly flow rate dependent. Mathematical models have been developed to describe FeNO as a function of flow and parameters representing airway NO flux (J) and alveolar NO concentration (C). Measurements of FeNO at multiple flow rates can be used to estimate J and C. Multiple flow data originated in small experimental studies, but is now available in more than 1500 children in the Southern California Children's Health Study (CHS). Statistical methods for effectively modeling this data have not been developed. We propose nonlinear mixed effects models that estimate individual-level physiologic parameters (J and C) and relate these to potential determinants, such as air pollution. We discuss and evaluate this approach, which has considerably greater power to detect determinant effects than existing methods, and demonstrate applications in the CHS.