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Peer-reviewedCC BY-NC-NDBMC cardiovascular disorders

Inflammation-based phenotyping and prognostic implications in patients with non-obstructive coronary artery disease.

  • Yutao Liu1
  • Shuyi Hu1
  • Ruobin Wu2
  • Tao Fu1
  • Zhiyong Li1
  • Jingyi Zhang3
  • Ming Zhang4
4 affiliations
  1. 1Department of Cardiology, Hubei No.3 People's Hospital of Jianghan University, Wuhan, Hubei, 430033, P.R. China.
  2. 2Department of Anesthesiology, Hubei No.3 People's Hospital of Jianghan University, Master's Degree Training Base of Hubei University of Medicine, Shiyan, Hubei, P.R. China.
  3. 3Department of Cardiology, Hubei No.3 People's Hospital of Jianghan University, Wuhan, Hubei, 430033, P.R. China. [email protected].
  4. 4Department of Cardiology, Hubei No.3 People's Hospital of Jianghan University, Wuhan, Hubei, 430033, P.R. China. [email protected].
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Abstract

Background

Non-obstructive coronary artery disease (NOCAD) represents an increasing proportion of patients presenting with angina, yet these patients face significant cardiovascular risk. This study aimed to explore distinct classes in NOCAD patients based on demographic characteristics, clinical comorbidities, and inflammatory biomarkers, and to investigate their associations with prognosis.Methods

We retrospectively analyzed patients diagnosed with NOCAD by coronary angiography for angina between January 1, 2023, and December 1, 2024, with follow-up until August 1, 2025. Latent Class Analysis (LCA) was employed to identify distinct classes based on demographic features, inflammatory markers, including 8 composite inflammatory indices. The primary endpoint was major adverse cardiovascular events (MACEs). Cox proportional hazards models were used to assess the associations between different classes and individual inflammatory markers with clinical outcomes after multivariate adjustment. Prognostic discrimination compared with traditional risk factors was quantified using the area under the receiver operating characteristic curve.Results

Among 342 NOCAD patients, LCA identified three distinct classes: low-inflammation with few comorbidities (n = 156, 45.6%), elderly with multiple comorbidities (n = 126, 36.8%), and high-inflammation subgroup (n = 60, 17.5%). Over a median follow-up of 363 days (interquartile range, 193-613 days), 89 patients (26.0%) experienced MACEs. In fully adjusted models, the high-inflammation class was the strongest predictor of MACEs (HR 3.090, 95% CI: 1.614-5.919), followed by the elderly with multiple comorbidities class (HR 2.181, 95% CI: 1.224-3.886). Among 8 composite inflammatory indices evaluated, three demonstrated significant associations with MACEs: the C-reactive protein-albumin-lymphocyte (CALLY) index (per unit increase in Z-score, HR 0.402, 95% CI: 0.230-0.703), neutrophil-to-albumin ratio (NAR) index (per unit increase in Z-score, HR 1.234, 95% CI: 1.002-1.522), and aggregate index of systemic inflammation (AISI) index (Z-score > 0, HR 1.635, 95% CI: 1.007-2.655). Kaplan-Meier survival analysis demonstrated significant differences in MACE-free survival across the three classes (log-rank p = 0.001), with CALLY, NAR, and AISI also showing significant prognostic discrimination. Component analysis revealed the high-inflammation phenotype was independently associated with a 2.44-fold increased risk of hospitalization for unstable angina or heart failure (HR = 2.44, 95% CI 1.230-4.846), accounting for 60.67% of total MACEs. These findings were consistently reproduced in a sensitivity analysis restricted to patients without a history of myocardial infarction.Conclusion

Inflammatory and clinical phenotyping identifies distinct risk subgroups in NOCAD. While the high-inflammation phenotype is associated with the highest overall MACEs risk, this prognostic signal is predominantly driven by hospitalizations for unstable angina or heart failure. These findings suggest that inflammation-based phenotyping could serve as an exploratory complementary tool for risk stratification, requiring further external validation before any routine clinical application.

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Cite

BibTeXRIS
BibTeX
@article{liu2026inflammationbased,
  title = {{Inflammation-based phenotyping and prognostic implications in patients with non-obstructive coronary artery disease.}},
  author = {Yutao Liu and Shuyi Hu and Ruobin Wu and Tao Fu and Zhiyong Li and Jingyi Zhang and Ming Zhang},
  journal = {BMC cardiovascular disorders},
  year = {2026},
  month = jul,
  doi = {10.1186/s12872-026-06337-z},
  url = {https://europepmc.org/article/MED/42907779},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - Inflammation-based phenotyping and prognostic implications in patients with non-obstructive coronary artery disease.
AU  - Yutao Liu
AU  - Shuyi Hu
AU  - Ruobin Wu
AU  - Tao Fu
AU  - Zhiyong Li
AU  - Jingyi Zhang
AU  - Ming Zhang
PY  - 2026
DA  - 2026/07/01
JO  - BMC cardiovascular disorders
DO  - 10.1186/s12872-026-06337-z
UR  - https://europepmc.org/article/MED/42907779
AB  - Background  Non-obstructive coronary artery disease (NOCAD) represents an increasing proportion of patients presenting with angina, yet these patients face significant cardiovascular risk. This study aimed to explore distinct classes in NOCAD patients based on demographic characteristics, clinical comorbidities, and inflammatory biomarkers, and to investigate their associations with prognosis.Methods  We retrospectively analyzed patients diagnosed with NOCAD by coronary angiography for angina between January 1, 2023, and December 1, 2024, with follow-up until August 1, 2025. Latent Class Analysis (LCA) was employed to identify distinct classes based on demographic features, inflammatory markers, including 8 composite inflammatory indices. The primary endpoint was major adverse cardiovascular events (MACEs). Cox proportional hazards models were used to assess the associations between different classes and individual inflammatory markers with clinical outcomes after multivariate adjustment. Prognostic discrimination compared with traditional risk factors was quantified using the area under the receiver operating characteristic curve.Results  Among 342 NOCAD patients, LCA identified three distinct classes: low-inflammation with few comorbidities (n = 156, 45.6%), elderly with multiple comorbidities (n = 126, 36.8%), and high-inflammation subgroup (n = 60, 17.5%). Over a median follow-up of 363 days (interquartile range, 193-613 days), 89 patients (26.0%) experienced MACEs. In fully adjusted models, the high-inflammation class was the strongest predictor of MACEs (HR 3.090, 95% CI: 1.614-5.919), followed by the elderly with multiple comorbidities class (HR 2.181, 95% CI: 1.224-3.886). Among 8 composite inflammatory indices evaluated, three demonstrated significant associations with MACEs: the C-reactive protein-albumin-lymphocyte (CALLY) index (per unit increase in Z-score, HR 0.402, 95% CI: 0.230-0.703), neutrophil-to-albumin ratio (NAR) index (per unit increase in Z-score, HR 1.234, 95% CI: 1.002-1.522), and aggregate index of systemic inflammation (AISI) index (Z-score > 0, HR 1.635, 95% CI: 1.007-2.655). Kaplan-Meier survival analysis demonstrated significant differences in MACE-free survival across the three classes (log-rank p = 0.001), with CALLY, NAR, and AISI also showing significant prognostic discrimination. Component analysis revealed the high-inflammation phenotype was independently associated with a 2.44-fold increased risk of hospitalization for unstable angina or heart failure (HR = 2.44, 95% CI 1.230-4.846), accounting for 60.67% of total MACEs. These findings were consistently reproduced in a sensitivity analysis restricted to patients without a history of myocardial infarction.Conclusion  Inflammatory and clinical phenotyping identifies distinct risk subgroups in NOCAD. While the high-inflammation phenotype is associated with the highest overall MACEs risk, this prognostic signal is predominantly driven by hospitalizations for unstable angina or heart failure. These findings suggest that inflammation-based phenotyping could serve as an exploratory complementary tool for risk stratification, requiring further external validation before any routine clinical application.
SN  - 1471-2261
ER  -