Rationale and design of the predictive models of beryllium sensitisation and chronic beryllium disease (PROBe): study protocol
5 affiliations
- 1Division of Environmental and Occupational Health Sciences, National Jewish Health, Denver, Colorado, USA
- 2Division of Pulmonary and Critical Care Sciences, University of Colorado Anschutz, Aurora, Colorado, USA
- 3Biochemistry, Molecular Biology and Biophysics Biochemistry, College of Biological Sciences and University of Minnesota Medical School, University of Minnesota, Minneapolis, Minnesota, USA
- 4Department of Environmental and Occupational Health, Colorado School of Public Health, University of Colorado Anschutz, Aurora, Colorado, USA
- 5Division of Pulmonary, Critical Care and Sleep, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA
Abstract
Introduction: Beryllium exposure affects over one million individuals in the USA and remains an important cause of occupational lung disease. Up to 20% of exposed individuals develop beryllium sensitisation (BeS), a condition associated with an increased risk of progression to chronic beryllium disease (CBD), a granulomatous lung disorder with significant morbidity. Current diagnostic tools, including the beryllium lymphocyte proliferation test, have important limitations and cannot reliably distinguish BeS from CBD. There is therefore an unmet need for molecular biomarkers that enable accurate diagnosis and risk stratification. This study aims to develop and validate a multiprotein classifier from bronchoalveolar lavage (BAL) fluid to distinguish controls, BeS and CBD and to identify biomarkers predictive of progression.
Methods and analyses: This is a multicentre, observational study enrolling participants with BeS, CBD and healthy controls using established case definitions. Two independent cohorts will be studied: a Discovery Cohort (n=125) for feature selection and model development and a validation cohort (n=125) for external validation. BAL cells will undergo immunophenotyping, bulk and single-cell RNA sequencing and global proteomic profiling, while BAL fluid (BALF) will be analysed using untargeted mass spectrometry (MS) followed by targeted validation with parallel reaction monitoring-MS. Multiomic data will be integrated to identify disease-associated molecular pathways. Longitudinal samples collected at least 2 years apart will be used to evaluate classifier stability and to develop predictive models for progression from BeS to CBD.
Ethics and dissemination: The study has received approval from the Biomedical Research Alliance of New York, which serves as the single IRB (HS-4092). All participants will provide informed consent prior to enrolment. Results will be disseminated through peer-reviewed publications and scientific conferences. De-identified datasets and analytic workflows will be shared in accordance with institutional policies and data-sharing agreements to facilitate transparency, reproducibility and future collaborative research.
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BibTeX
@article{li2026rationale,
title = {{Rationale and design of the predictive models of beryllium sensitisation and chronic beryllium disease (PROBe): study protocol}},
author = {Li Li and Margaret M Mroz and Brian Vestal and Joshua D Macaluso and L Elaine Epperson and Timothy J Griffin and Pratik D Jagtap and Danielle O Weise and Lisa A Maier and Maneesh Bhargava},
journal = {BMJ Open},
year = {2026},
month = oct,
doi = {10.1136/bmjopen-2026-125477},
url = {https://bmjopen.bmj.com/lookup/doi/10.1136/bmjopen-2026-125477},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - Rationale and design of the predictive models of beryllium sensitisation and chronic beryllium disease (PROBe): study protocol
AU - Li Li
AU - Margaret M Mroz
AU - Brian Vestal
AU - Joshua D Macaluso
AU - L Elaine Epperson
AU - Timothy J Griffin
AU - Pratik D Jagtap
AU - Danielle O Weise
AU - Lisa A Maier
AU - Maneesh Bhargava
PY - 2026
DA - 2026/10/30
JO - BMJ Open
DO - 10.1136/bmjopen-2026-125477
UR - https://bmjopen.bmj.com/lookup/doi/10.1136/bmjopen-2026-125477
AB - Introduction: Beryllium exposure affects over one million individuals in the USA and remains an important cause of occupational lung disease. Up to 20% of exposed individuals develop beryllium sensitisation (BeS), a condition associated with an increased risk of progression to chronic beryllium disease (CBD), a granulomatous lung disorder with significant morbidity. Current diagnostic tools, including the beryllium lymphocyte proliferation test, have important limitations and cannot reliably distinguish BeS from CBD. There is therefore an unmet need for molecular biomarkers that enable accurate diagnosis and risk stratification. This study aims to develop and validate a multiprotein classifier from bronchoalveolar lavage (BAL) fluid to distinguish controls, BeS and CBD and to identify biomarkers predictive of progression. Methods and analyses: This is a multicentre, observational study enrolling participants with BeS, CBD and healthy controls using established case definitions. Two independent cohorts will be studied: a Discovery Cohort (n=125) for feature selection and model development and a validation cohort (n=125) for external validation. BAL cells will undergo immunophenotyping, bulk and single-cell RNA sequencing and global proteomic profiling, while BAL fluid (BALF) will be analysed using untargeted mass spectrometry (MS) followed by targeted validation with parallel reaction monitoring-MS. Multiomic data will be integrated to identify disease-associated molecular pathways. Longitudinal samples collected at least 2 years apart will be used to evaluate classifier stability and to develop predictive models for progression from BeS to CBD. Ethics and dissemination: The study has received approval from the Biomedical Research Alliance of New York, which serves as the single IRB (HS-4092). All participants will provide informed consent prior to enrolment. Results will be disseminated through peer-reviewed publications and scientific conferences. De-identified datasets and analytic workflows will be shared in accordance with institutional policies and data-sharing agreements to facilitate transparency, reproducibility and future collaborative research.
SN - 2044-6055
SN - 2044-6055
ER -