Evidence map›Paper›PMID 42466883›Full record

Observational studyMicrobiology spectrum2026

Diabetes-associated

Honghao Han, Qian Qian, Weiwei Wu, Jiangnan Yang, Ji Zhou, Wenkui Sun

Abstract readObservational Study
In one paragraph

Observational study in Microbiology spectrum, 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

6 authors.

Honghao Han *Department of Respiratory Medicine, Jiangsu Province Hospital/The First Affiliated Hospital with Nanjing Medical University, Nanjing, Jiangsu, China.
Qian Qian *Jiangsu Health Vocational College, Nanjing, Jiangsu, China.
Weiwei Wu *Dinfectome Medical Technology Nanjing Co., Ltd, Nanjing, Jiangsu, China.
Jiangnan YangDinfectome Medical Technology Nanjing Co., Ltd, Nanjing, Jiangsu, China.
Ji ZhouDepartment of Respiratory Medicine, Jiangsu Province Hospital/The First Affiliated Hospital with Nanjing Medical University, Nanjing, Jiangsu, China.
Wenkui SunDepartment of Respiratory Medicine, Jiangsu Province Hospital/The First Affiliated Hospital with Nanjing Medical University, Nanjing, Jiangsu, China.ORCID 0000-0002-2992-9783

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The homeostasis of pulmonary microbiota is crucial in maintaining human health and modulating disease progression. The stability of pulmonary microbial flora may be associated with diabetes, yet the specific alterations remain poorly characterized. This retrospective observational study aims to analyze the profiles in pulmonary microbiota between individuals with and without diabetes, using metagenomic next-generation sequencing (mNGS). A total of 632 patients were sequentially enrolled, including 77 patients with both pneumonia and diabetes, 46 patients without either pneumonia or diabetes, 499 patients with pneumonia but without diabetes, and 10 diabetic patients without pneumonia. Pathogens in bronchoalveolar lavage fluid (BALF) specimens were detected using mNGS (DNA). The lung microbiota of diabetic individuals significantly differs from that of non-diabetic individuals in the non-lower respiratory tract infection (non-LRTI) cohort.

Indexed as

Diabetes ComplicationsDiabetes MellitusLungMicrobiotaRespiratory Tract InfectionsAgedBacteriaBronchoalveolar Lavage FluidFemaleHigh-Throughput Nucleotide SequencingHumansMaleMetagenomeMetagenomicsMiddle AgedPneumoniadiabeteslung microbiotametagenomic next-generation sequencingParvimonaspneumonia

Identifiers

PMID42466883
PMCPMC13436127

What OpenQuestion holds

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LicenceCC BY
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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.