# Association between platelet-to-neutrophil ratio and asthma–COPD overlap: a cross-sectional study in China

**Authors:** Lei Yang, TingTing Zeng, Na Li, DePeng Jiang

PMC · DOI: 10.3389/fmed.2026.1729278 · Frontiers in Medicine · 2026-03-18

## TL;DR

This study finds that lower platelet-to-neutrophil ratio is linked to higher risk of asthma-COPD overlap in a Chinese population.

## Contribution

The study identifies a non-linear relationship and threshold value for PNR in predicting asthma-COPD overlap.

## Key findings

- Patients with asthma-COPD overlap had significantly lower platelet-to-neutrophil ratio (PNR) than healthy controls.
- A non-linear association was found with a threshold PNR of 61.17, below which ACO risk increases significantly.

## Abstract

The platelet-to-neutrophil ratio (PNR) has emerged as a valuable biomarker that reflects both systemic inflammatory activity and overall nutritional status. Asthma–chronic obstructive pulmonary disease (COPD) overlap (ACO) is a clinical syndrome characterized by persistent airway inflammation and recurrent episodes of respiratory deterioration. Although inflammation represents a common underlying pathological mechanism, the relationship between PNR and the incidence or severity of ACO has not yet been fully clarified. Elucidating this association is therefore of considerable clinical and scientific importance.

This cross-sectional retrospective study used multivariable logistic regression analyses to examine the association between PNR and ACO, after adjusting for key covariates, including age, sex, body mass index (BMI), hemoglobin, white blood cell count, eosinophil count, creatinine, alanine aminotransferase, aspartate aminotransferase, albumin levels, smoking history, and alcohol consumption. Restricted cubic spline (RCS) models assessed linear and non-linear relationships, while Spearman’s correlation analysis evaluated the strength and direction of associations. In addition, subgroup analyses were performed to investigate potential differences across specific population groups.

A total of 1,025 participants were included in the analysis, with a median age of 61 years; 72.78% of the cohort were male. The study population comprised 685 healthy controls (HCs), 348 individuals with asthma, 372 with chronic obstructive pulmonary disease (COPD), and 340 with asthma–COPD overlap (ACO). Patients with ACO exhibited significantly lower PNR values than HCs (p < 0.05). After adjusting for potential confounders, PNR remained independently associated with a reduced risk of ACO (odds ratio [OR] = 0.964, 95% confidence interval [CI]: 0.954–0.975; p < 0.0001). RCS analyses confirmed a dose–response relationship between PNR and ACO risk. A non-linear association was identified, with a threshold inflection point at a PNR value of 61.17. Above this threshold, higher PNR levels remained significantly associated with a lower risk of ACO (OR = 0.926, 95% CI: 0.905–0.948; p < 0.001). Spearman’s correlation analysis demonstrated a moderate negative correlation between PNR and ACO (r = −0.447, p < 0.001). Receiver operating characteristic (ROC) analysis yielded an area under the curve (AUC) of 0.774 (95% CI: 0.742–0.806), indicating acceptable discriminatory performance.

This study demonstrates a significant inverse association between PNR and ACO, particularly when PNR values fall below the identified threshold of 61.17. These findings suggest that PNR may serve as a potentially valuable biomarker for assessing ACO risk. Further prospective and validation studies are warranted to confirm its diagnostic performance and clinical applicability.

## Linked entities

- **Diseases:** asthma (MONDO:0004979), chronic obstructive pulmonary disease (MONDO:0005002)

## Full-text entities

- **Genes:** ALB (albumin) [NCBI Gene 213] {aka FDAHT, HSA, PRO0883, PRO0903, PRO1341}
- **Diseases:** ACO (MESH:D000080445), COPD (MESH:D029424), airway inflammation (MESH:D007249), Asthma (MESH:D001249)
- **Chemicals:** alcohol (MESH:D000438)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Full text

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## Figures

6 figures with captions in the complete paper: https://tomesphere.com/paper/PMC13039030/full.md

## References

56 references — full list in the complete paper: https://tomesphere.com/paper/PMC13039030/full.md

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Source: https://tomesphere.com/paper/PMC13039030