Evidence map›Paper›PMID 42855483›Full record

ReviewNature reviews. Microbiology2026

The role of the respiratory microbiome in acute and chronic respiratory diseases.

Lauren R Heinzinger, Leopoldo N Segal, Robert P Dickson

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Lauren R HeinzingerDepartment of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, USA.ORCID http://orcid.org/0000-0003-4890-9216
Leopoldo N SegalDivision of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA.ORCID http://orcid.org/0000-0003-3559-9431
Robert P DicksonDepartment of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, USA. rodickso@med.umich.edu.ORCID http://orcid.org/0000-0002-6875-4277

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The lungs, once considered sterile, are now known to contain diverse, dynamic microbial communities that are metabolically active and immunologically consequential even in health. In respiratory disease, these communities undergo quantitative and compositional changes that predict clinical trajectories and may contribute to pathogenesis. The past decade of research has advanced our understanding of the complex bidirectional relationship between the respiratory tract and the microbial communities it encounters. In health, the lungs are nutrient poor and defended by robust host defences, limiting local microbial growth and keeping bacterial populations sparse and transient. Yet, in respiratory disease, the ecological conditions of the lungs permit and promote the outgrowth of select microorganisms. Recent in vivo and ex vivo discoveries explain how this respiratory dysbiosis is both a cause and a consequence of airway and alveolar injury. To leverage the respiratory microbiome for therapeutic benefit, we must interrogate both how disease-altered microbial communities shape immune responses, treatment efficacy and outcomes, and how host and environmental factors, in turn, shape the respiratory microbiome. In this Review, we synthesize current insights into how the respiratory microbiome contributes to chronic and acute lung diseases and explore its clinical implications for diagnosis, prognosis and therapy.

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.