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Peer-reviewedCC BY 4.0Respirology (Carlton, Vic.)

Three Spirometry‐Based At‐Risk Phenotypes Predict Incident Airflow Limitation: A Population‐Based Study

  • Hendrik Pott1,2
  • Roman Martin3
  • Wilhelm Bertrams2
  • Mustafa Abdo4
  • Margit Heier5,6
  • Annette Peters5,7,8,9,10,11
  • Beate Stubbe12
  • Max Kutzinski1,2
  • Anne Obst12
  • Ralf Ewert12
  • Dominik Heider3
  • Henry Völzke13
5 more authors
  • Gernot Rohde14,15
  • Claus F. Vogelmeier14,15
  • Bernd Schmeck1,2,14,15,16,17
  • Stefan Karrasch5,9,10
  • the KORA‐study group
17 affiliations
  1. 1Department of Medicine, Pulmonary and Critical Care Medicine Clinic for Airway Infections, University Medical Centre Marburg, Philipps‐University Marburg Marburg Germany
  2. 2Institute for Lung Research, Universities of Giessen and Marburg Lung Centre, Philipps‐University Marburg Marburg Germany
  3. 3Institute of Medical Informatics, University of Münster Münster Germany
  4. 4LungenClinic Grosshansdorf and Airway Research Center North Grosshansdorf Germany
  5. 5Institute of Epidemiology, Helmholtz Zentrum München ‐ German Research Center for Environmental Health (GmbH) Neuherberg Germany
  6. 6KORA Study Centre, University Hospital Augsburg Augsburg Germany
  7. 7Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Faculty of Medicine, LMU Munich Munich Germany
  8. 8German Centre for Cardiovascular Research (DZHK), Partner Site: Munich Heart Alliance Munich Germany
  9. 9Institute and Clinic for Occupational, Social and Environmental Medicine, LMU University Hospital, LMU Munich Munich Germany
  10. 10Comprehensive Pneumology Center Munich (CPC‐M), Member of the German Center for Lung Research (DZL) Munich Germany
  11. 11Chair of Epidemiology, Institute for Medical Information Processing, Biometry and Epidemiology, Medical Faculty, Ludwig‐Maximilians‐Universität München Munich Germany
  12. 12Department of Internal Medicine B University Medicine Greifswald Greifswald Germany
  13. 13Institute for Community Medicine, University Medicine Greifswald Greifswald Germany
  14. 14Department of Medicine, Pulmonary and Critical Care Medicine University Medical Centre Marburg, Philipps‐University Marburg Marburg Germany
  15. 15German Centre for Lung Research (DZL) Marburg Germany
  16. 16Institute for Lung Health Giessen Germany
  17. 17German Centre for Infectious Disease Research (DZIF) Germany
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Abstract

Background and Objective: Incident airflow limitation is frequently diagnosed at advanced stages. Identifying individuals at risk through primary care spirometry may enable earlier intervention, yet validated, pragmatic frameworks remain lacking.

Methods: We applied a framework of three mutually exclusive spirometric at‐risk phenotypes, termed Three‐Phenotype Spirometry‐Based Identification (hereafter TriSpi), to 6123 participants from two population‐based cohorts: KORA (n = 1973, 3‐year Follow‐up, derivation) and SHIP (n = 4150, 5‐year Follow‐up, validation). TriSpi+ individuals were defined as meeting criteria for one of the three phenotypes: Early airflow limitation (EAL, FEV1/FVC > 0.7 and < 10th percentile or < 0.7 and > 5th percentile), small airway dysfunction (SAD) defined using FEF50‐ or FEF75‐based thresholds; and preserved‐ratio impaired‐spirometry (PRISm). Associations with incident airflow limitation were tested using Firth's regression.

Results: EAL, PRISm, and SAD (defined using either FEF50 or FEF75‐based thresholds) were significantly associated with incident airflow limitation across cohorts and follow‐ups. TriSpi+ individuals accounted for 26%–36% of the population, identifying 74%–93% of future cases, while TriSpi+ was associated with a 10–25‐fold increase in risk. Negative predictive values exceeded 95% across definitions, and the number needed to screen among TriSpi+ individuals ranged from 7 to 13. EAL showed the strongest individual association (OR up to 53.2), while SAD was more common in younger adults.

Conclusion: Combining definitions for EAL, PRISm, and SAD enables robust prediction of incident airflow limitation. TriSpi may serve as a scalable, pragmatic approach for early risk stratification in primary care, in absence of post‐BD spirometry.

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Cite

BibTeXRIS
BibTeX
@article{pott2026three,
  title = {{Three Spirometry‐Based At‐Risk Phenotypes Predict Incident Airflow Limitation: A Population‐Based Study}},
  author = {Hendrik Pott and Roman Martin and Wilhelm Bertrams and Mustafa Abdo and Margit Heier and Annette Peters and Beate Stubbe and Max Kutzinski and Anne Obst and Ralf Ewert and Dominik Heider and Henry Völzke and Gernot Rohde and Claus F. Vogelmeier and Bernd Schmeck and Stefan Karrasch and the KORA‐study group},
  journal = {Respirology (Carlton, Vic.)},
  year = {2026},
  month = aug,
  doi = {10.1002/resp.70296},
  url = {https://doi.org/10.1002/resp.70296},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Three Spirometry‐Based At‐Risk Phenotypes Predict Incident Airflow Limitation: A Population‐Based Study
AU  - Hendrik Pott
AU  - Roman Martin
AU  - Wilhelm Bertrams
AU  - Mustafa Abdo
AU  - Margit Heier
AU  - Annette Peters
AU  - Beate Stubbe
AU  - Max Kutzinski
AU  - Anne Obst
AU  - Ralf Ewert
AU  - Dominik Heider
AU  - Henry Völzke
AU  - Gernot Rohde
AU  - Claus F. Vogelmeier
AU  - Bernd Schmeck
AU  - Stefan Karrasch
AU  - the KORA‐study group
PY  - 2026
DA  - 2026/08/11
JO  - Respirology (Carlton, Vic.)
DO  - 10.1002/resp.70296
UR  - https://doi.org/10.1002/resp.70296
AB  - Background and Objective: Incident airflow limitation is frequently diagnosed at advanced stages. Identifying individuals at risk through primary care spirometry may enable earlier intervention, yet validated, pragmatic frameworks remain lacking.  Methods: We applied a framework of three mutually exclusive spirometric at‐risk phenotypes, termed Three‐Phenotype Spirometry‐Based Identification (hereafter TriSpi), to 6123 participants from two population‐based cohorts: KORA (n = 1973, 3‐year Follow‐up, derivation) and SHIP (n = 4150, 5‐year Follow‐up, validation). TriSpi+ individuals were defined as meeting criteria for one of the three phenotypes: Early airflow limitation (EAL, FEV1/FVC > 0.7 and < 10th percentile or < 0.7 and > 5th percentile), small airway dysfunction (SAD) defined using FEF50‐ or FEF75‐based thresholds; and preserved‐ratio impaired‐spirometry (PRISm). Associations with incident airflow limitation were tested using Firth's regression.  Results: EAL, PRISm, and SAD (defined using either FEF50 or FEF75‐based thresholds) were significantly associated with incident airflow limitation across cohorts and follow‐ups. TriSpi+ individuals accounted for 26%–36% of the population, identifying 74%–93% of future cases, while TriSpi+ was associated with a 10–25‐fold increase in risk. Negative predictive values exceeded 95% across definitions, and the number needed to screen among TriSpi+ individuals ranged from 7 to 13. EAL showed the strongest individual association (OR up to 53.2), while SAD was more common in younger adults.  Conclusion: Combining definitions for EAL, PRISm, and SAD enables robust prediction of incident airflow limitation. TriSpi may serve as a scalable, pragmatic approach for early risk stratification in primary care, in absence of post‐BD spirometry.
SN  - 1323-7799
SN  - 1440-1843
ER  -