# Predictive Molecular Markers of Lung Function Decline

> **NIH NIH R01** · CINCINNATI CHILDRENS HOSP MED CTR · 2021 · $436,073

## Abstract

ABSTRACT
 The identification of molecules that reflect and/or predict disease progression and can be easily measured
in chronic lung disease is highly desirable. Such biomarkers that can be rapidly and reliably measured to
establish change in the course of the disease over time or in response to therapy would significantly improve
care. For example, measurement of short term markers that track with or predict long term changes in FEV1
(a clinical measure of lung function) may shorten the length, significantly reduce the cost of clinical trials, and
accelerate the examination of potential therapies allowing faster delivery to the patients. Furthermore, the
behavior of molecular biomarkers can inform therapy and increase success of alleviating disease progression.
In preliminary studies, we analyzed differences in pathways in serum samples from 44 children with mild lung
disease (FEV1>80th percentile) and 44 with more severe lung disease (FEV1<45th percentile). We found
significant differences in a number of proteins and pathways associated with disease. The top markers in our
analysis correlated with disease progression and significantly improved novel disease progression prediction
models. Here we propose to integrate the longitudinal behavior of novel disease markers with novel
Functional Data (FD) analysis of FEV1 and other clinical data to develop an algorithm that models lung function
decline. To achieve this we propose to; 1) identify and validate serum proteome changes in banked samples
collected from patients with stable and declining FEV1, 2) develop a dynamic prediction model that integrates
validated proteomic biomarkers with Functional Data (FD) analysis of longitudinal FEV1 values to produce a
novel diagnostic algorithm that identifies individuals at risk of lung function decline, and 3) test the capacity of
dynamic prediction modeling to identify modulator treated CF patients who demonstrate rapid pulmonary
decline compared to highly responsive individuals in banked longitudinal samples. If we are successful, we will
clinically translate a novel lung function prediction model that will significantly improve therapeutic intervention
and accelerate clinical studies.

## Key facts

- **NIH application ID:** 10192809
- **Project number:** 5R01HL142210-03
- **Recipient organization:** CINCINNATI CHILDRENS HOSP MED CTR
- **Principal Investigator:** Assem G Ziady
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $436,073
- **Award type:** 5
- **Project period:** 2019-05-01 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10192809

## Citation

> US National Institutes of Health, RePORTER application 10192809, Predictive Molecular Markers of Lung Function Decline (5R01HL142210-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10192809. Licensed CC0.

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