All-cause and cause-specific mortality in respiratory symptom clusters: a population-based multicohort studyShow others and affiliations
2025 (English)In: Respiratory Research, ISSN 1465-9921, E-ISSN 1465-993X, Vol. 26, no 1, article id 150
Article in journal (Refereed) Published
Abstract [en]
Background: Respiratory symptoms are common in the general adult population. Increased burden of respiratory symptoms may increase the risk of mortality. We assessed the association between respiratory symptom clusters and mortality.
Methods: Participants were derived from two population-based Swedish adult cohorts (N = 63,060). Cluster analysis was performed with Locality Sensitive Hashing (LSH)-k-prototypes in subjects with ≥ 1 self-reported respiratory symptom. Linked mortality register data (up to 21 years of follow-up, > 600,000 person-years) were used. Associations between clusters and all-cause/cause-specific mortality were assessed using asymptomatic subjects as reference.
Results: Over 60% reported ≥ 1 respiratory symptom and ~ 30% reported ≥ 5 respiratory symptoms. Five clusters were identified, partly overlapping with established respiratory disease phenotypes but many individuals were undiagnosed: (1) "low-symptomatic" (30.3%); (2) "allergic nasal symptoms" (10.7%); (3) "allergic nasal symptoms, wheezing, and dyspnea attacks" (4.7%); (4) "wheezing and dyspnea attacks" (6.6%); (5) "recurrent productive cough and wheezing" (4.1%). All but Cluster 2 were associated with all-cause mortality, highest risk for Cluster 3 (hazard ratio 1.4, 95% confidence interval 1.13–1.73) and Cluster 5 (1.4, 1.22–1.61). Comparable associations were seen for cardiovascular mortality. For respiratory mortality, Cluster 4 (2.02, 1.18–3.46) and Cluster 5 (1.89, 1.1–3.25) were most strongly associated.
Conclusions: Respiratory symptoms are common in the general adult population, with identifiable clusters. These clusters have clinical relevancy as they are differentially associated with mortality and relatively weakly correlated with diagnosed respiratory disease.
Place, publisher, year, edition, pages
BioMed Central (BMC), 2025. Vol. 26, no 1, article id 150
Keywords [en]
Cluster analysis, Cough, Dyspnea, Machine learning, Mortality, Respiratory symptoms, Wheezing
National Category
Respiratory Medicine and Allergy
Identifiers
URN: urn:nbn:se:umu:diva-238198DOI: 10.1186/s12931-025-03224-7ISI: 001470206000003PubMedID: 40241067Scopus ID: 2-s2.0-105002993949OAI: oai:DiVA.org:umu-238198DiVA, id: diva2:1956468
Funder
Swedish Asthma and Allergy AssociationSwedish Heart Lung FoundationSwedish Research CouncilForte, Swedish Research Council for Health, Working Life and Welfare2025-05-062025-05-062025-05-06Bibliographically approved