Unsupervised Phenotyping of Asthma: Integrating Serum Periostin with Clinical and Inflammatory Profiles

<b>Background/Objectives:</b> Asthma is a heterogeneous inflammatory airway disease. Periostin, a matricellular protein induced by interleukin-13, contributes to airway inflammation and remodeling. This study evaluated serum periostin as a diagnostic biomarker and explored multidimensional phenotypes in adult asthma. <b>Methods:</b> A cross-sectional study included 76 adults, with 25 healthy controls, 25 moderate, and 26 severe asthma patients, classified per Global Initiative for Asthma (GINA)-2020 guidelines. Serum periostin was measured using an enzyme-linked immunosorbent assay (ELISA). Diagnostic accuracy was assessed using receiver operating characteristic (ROC) analysis, Firth-penalized logistic regression, bootstrap calibration (1000 resamples), decision curve analysis (DCA), and gradient boosting machine (GBM) validation. Principal component analysis (PCA) followed by k-means clustering identified distinct phenotypes based on clinical, functional, and inflammatory variables. <b>Results:</b> Asthma patients had higher serum periostin than controls (median 52.9 vs. 32.5 pg/mL; <i>p</i> < 0.01), with excellent diagnostic accuracy (AUC = 0.987; sensitivity = 94.1%, specificity = 100%). Firth regression identified periostin as the only independent predictor of asthma diagnosis (β = 0.387; OR = 1.47; 95% CI 1.23-2.08; <i>p</i> < 0.001). Calibration showed minimal error (MAE = 0.042) and DCA demonstrated clear net benefit. GBM confirmed periostin as the dominant diagnostic predictor. PCA revealed three clusters: Cluster 1: younger, lower periostin, preserved lung function, good symptom control; Cluster 2: intermediate periostin, greater airflow limitation, poorer control; and Cluster 3: highest periostin, elevated systemic inflammation (NLR, PLR, SII), with moderate functional impairment. <b>Conclusions:</b> Serum periostin is a reliable diagnostic biomarker for asthma. Multidimensional clustering highlights clinically relevant phenotypes linked to periostin, inflammatory burden, and lung function, supporting its role in personalized asthma management.

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Journal
Diagnostics
Published
2025-11-27
DOI
https://doi.org/10.3390/diagnostics15233028
Primary Topic
Asthma and respiratory diseases
Type
article
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0.00
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article

Unsupervised Phenotyping of Asthma: Integrating Serum Periostin with Clinical and Inflammatory Profiles

Padukudru Anand Mahesh, SubbaRao V. Madhunapantula, Sachith Srinivas, Malavika Shankar et al.
Diagnostics
Asthma and respiratory diseases
article

Unsupervised Phenotyping of Asthma: Integrating Serum Periostin with Clinical and Inflammatory Profiles

Padukudru Anand Mahesh, SubbaRao V. Madhunapantula, Sachith Srinivas, Malavika Shankar, Sindaghatta Krishnarao Chaya, Medha Karnik, Ashwaghosha Parthasarathi, Jayaraj Biligere Siddaiah, Mandya Venkateshmurthy Greeshma, Sukanya Ravindran, N. Bansal, Mohammed Kaleem Ullah, Rekha Vaddarahalli ShankaraSetty, Komarla Sundararaja Lokesh, Shreedhar Kulkarni, Shashi K. Ramaiah, Vikhnesh Padmakaran
article en

Abstract

<b>Background/Objectives:</b> Asthma is a heterogeneous inflammatory airway disease. Periostin, a matricellular protein induced by interleukin-13, contributes to airway inflammation and remodeling. This study evaluated serum periostin as a diagnostic biomarker and explored multidimensional phenotypes in adult asthma. <b>Methods:</b> A cross-sectional study included 76 adults, with 25 healthy controls, 25 moderate, and 26 severe asthma patients, classified per Global Initiative for Asthma (GINA)-2020 guidelines. Serum periostin was measured using an enzyme-linked immunosorbent assay (ELISA). Diagnostic accuracy was assessed using receiver operating characteristic (ROC) analysis, Firth-penalized logistic regression, bootstrap calibration (1000 resamples), decision curve analysis (DCA), and gradient boosting machine (GBM) validation. Principal component analysis (PCA) followed by k-means clustering identified distinct phenotypes based on clinical, functional, and inflammatory variables. <b>Results:</b> Asthma patients had higher serum periostin than controls (median 52.9 vs. 32.5 pg/mL; <i>p</i> < 0.01), with excellent diagnostic accuracy (AUC = 0.987; sensitivity = 94.1%, specificity = 100%). Firth regression identified periostin as the only independent predictor of asthma diagnosis (β = 0.387; OR = 1.47; 95% CI 1.23-2.08; <i>p</i> < 0.001). Calibration showed minimal error (MAE = 0.042) and DCA demonstrated clear net benefit. GBM confirmed periostin as the dominant diagnostic predictor. PCA revealed three clusters: Cluster 1: younger, lower periostin, preserved lung function, good symptom control; Cluster 2: intermediate periostin, greater airflow limitation, poorer control; and Cluster 3: highest periostin, elevated systemic inflammation (NLR, PLR, SII), with moderate functional impairment. <b>Conclusions:</b> Serum periostin is a reliable diagnostic biomarker for asthma. Multidimensional clustering highlights clinically relevant phenotypes linked to periostin, inflammatory burden, and lung function, supporting its role in personalized asthma management.

DiagnosticsVol. 15(23)
Rutgers, The State University of New Jersey (US), JSS Academy of Higher Education and Research (IN), Bridgeport Hospital (US), Barnsley Hospital NHS Foundation Trust (GB), West Virginia University Hospitals (US)
Openalex Percentile: Top 54%
Asthma and respiratory diseases
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