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322,707 results on '"Respiratory tract diseases"'

27. Extremely Preterm Respiratory Outcome Cohort

Author

Asan Medical Center, Samsung Medical Center, Seoul National University Bundang Hospital, Chonnam National University Hospital, Severance Hospital, Yonsei University College of Medicine, Ajou University Medical Center, and Seung Han Shin, Professor

Published
2024

30. A protein risk score for all-cause and respiratory-specific mortality in non-Hispanic white and African American individuals who smoke

Author

Moll, Matthew, Pratte, Katherine A, Debban, Catherine L, Liu, Congjian, Belinsky, Steven A, Picchi, Maria, Konigsberg, Iain, Tern, Courtney, Rijhwani, Heena, Hobbs, Brian D, Silverman, Edwin K, Tesfaigzi, Yohannes, Rich, Stephen S, Manichaikul, Ani, Rotter, Jerome I, Bowler, Russel P, and Cho, Michael H

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Epidemiology, Public Health, Health Sciences, Health Disparities, Clinical Research, Prevention, Precision Medicine, Minority Health, Lung, Respiratory, Good Health and Well Being, Aged, Female, Humans, Male, Middle Aged, Biomarkers, Black or African American, Cardiovascular Diseases, Proteomics, Risk Factors, Smoking, White, Mortality, Respiratory Tract Diseases
Abstract

Protein biomarkers are associated with mortality in cardiovascular disease, but their effect on predicting respiratory and all-cause mortality is not clear. We tested whether a protein risk score (protRS) can improve prediction of all-cause mortality over clinical risk factors in smokers. We utilized smoking-enriched (COPDGene, LSC, SPIROMICS) and general population-based (MESA) cohorts with SomaScan proteomic and mortality data. We split COPDGene into training and testing sets (50:50) and developed a protRS based on respiratory mortality effect size and parsimony. We tested multivariable associations of the protRS with all-cause, respiratory, and cardiovascular mortality, and performed meta-analysis, area-under-the-curve (AUC), and network analyses. We included 2232 participants. In COPDGene, a penalized regression-based protRS was most highly associated with respiratory mortality (OR 9.2) and parsimonious (15 proteins). This protRS was associated with all-cause mortality (random effects HR 1.79 [95% CI 1.31-2.43]). Adding the protRS to clinical covariates improved all-cause mortality prediction in COPDGene (AUC 0.87 vs 0.82) and SPIROMICS (0.74 vs 0.6), but not in LSC and MESA. Protein-protein interaction network analyses implicate cytokine signaling, innate immune responses, and extracellular matrix turnover. A blood-based protein risk score predicts all-cause and respiratory mortality, identifies potential drivers of mortality, and demonstrates heterogeneity in effects amongst cohorts.

Published
2024

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