Evidence map›Paper›PMID 42192339›Full record

ArticleBMC infectious diseases2026

Multi-dimensional risk prediction and genetic architecture of aspiration pneumonia: a population-based analysis.

Chaoyuan Jin, Qingxia Dai, Xingxing Ren, Jie Shen

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Article in BMC infectious diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Chaoyuan JinCenter of Emergency & Intensive Care Unit, Jinshan Hospital, Fudan University, Shanghai, 201508, China.
Qingxia DaiCenter of Emergency & Intensive Care Unit, Jinshan Hospital, Fudan University, Shanghai, 201508, China.
Xingxing RenDepartment of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, 200032, China. ren.xingxing@zs-hospital.sh.cn.
Jie ShenCenter of Emergency & Intensive Care Unit, Jinshan Hospital, Fudan University, Shanghai, 201508, China. shen2018jie@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAspiration pneumonia represents a significant but understudied cause of morbidity and mortality, particularly among aging populations with neurological comorbidities. Current risk stratification tools rely on single-dimensional clinical data, limiting their predictive accuracy and population health utility. This study aimed to develop comprehensive risk prediction models and identify genetic determinants of aspiration pneumonia using integrated epidemiological, computational, and genomic approaches.

methodsWe conducted a nested case-control study within the UK Biobank (n = 501,968), identifying 3,659 aspiration pneumonia cases (ICD-10 J69.0) and selecting 1:4 controls initially matched on age, sex, and assessment center. Propensity scores were estimated using logistic regression based on demographic, lifestyle, functional, biomarker, and comorbidity variables. A 1:4 nearest-neighbor propensity score matching without replacement was then performed using a caliper of 0.2 standard deviations of the logit of the propensity score. We performed multivariable logistic regression and sensitivity analyses for comorbidity profiling, machine-learning risk prediction with model interpretation, and genome-wide association analysis using REGENIE.

resultsNeurological conditions were the strongest risk factors, including dysphagia (OR 11.71, 95% CI 10.52-13.03; P < 0.001), Parkinson's disease (OR 10.94, 95% CI 9.20-13.02; P < 0.001), and dementia (OR 9.45, 95% CI 8.35-10.69; P < 0.001). In joint comorbidity models, dysphagia, stroke, Parkinson's disease, and dementia remained independently associated with aspiration pneumonia. The LightGBM model showed high discrimination (AUC 0.867, 95% CI 0.854-0.880) with balanced sensitivity (75.3%) and specificity (78.9%). GWAS identified genome-wide significant signals at the APOE locus, with rs429358 as the lead variant (P = 2.29e-13; OR 2.22, 95% CI 2.09-2.36).

conclusionsThis population-based analysis shows that aspiration pneumonia is closely associated with neurological morbidity, functional vulnerability, inflammatory markers, and an APOE-centered genetic signal. The prediction model demonstrated strong discriminative performance. Because dementia-excluded and comorbidity-stratified genotype-level analyses were not performed, the APOE association should be interpreted as a neurodegeneration-related susceptibility signal rather than definitive evidence of an effect independent of dementia. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Pneumonia, AspirationAgedAged, 80 and overCase-Control StudiesComorbidityFemaleGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansLogistic ModelsMaleMiddle AgedRisk AssessmentRisk FactorsUK BiobankUnited KingdomAspiration pneumoniaGenome-wide association studyMachine learningNeurological disordersPopulation healthRisk prediction

Identifiers

PMID42192339
PMCPMC13393677

What Socratic holds

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LicenceCC BY-NC-ND
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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 Socratic graph.