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Three Systemic Comorbidity Axes in Chronic Obstructive Pulmonary Disease: A Comparative Bibliometric Analysis Based
Lili Zhong1, Zhaoshuang Zhong2, Wenhui Ma1
1Department of Respiratory Medicine, The Second Affiliated Hospital of Shenyang Medical College, Shenyang, Liaoning, People's Republic of China.
Background:
Multisystem comorbidities contribute to the burden of chronic obstructive pulmonary disease (COPD), but the comparative maturity, frontier evolution, and evidence gaps of major COPD comorbidity domains remain unclear.
Objective:
To map and compare three systemic COPD comorbidity axes: cardiometabolic, neuropsychiatric/cognitive, and musculoskeletal/nutritional/frailty.
Methods:
: English-language Articles and Reviews published from 2005 to 2026 were retrieved from the Web of Science Core Collection using common COPD and axis-specific terms. After field checking and deduplication, we analyzed publication trends, contributors, normalized Author Keywords, thematic structures, co-occurrence networks, temporal frontiers, and an exploratory evidence-gap map. A random sample of 100 records per axis was independently assessed for topical relevance, and annual axis-specific publication shares were calculated against a background COPD corpus. Records from 2026 were excluded from temporal comparisons because 2026 was incomplete.
Results:
Datasets included 5386 cardiometabolic, 1671 neuropsychiatric/cognitive, and 2124 musculoskeletal/nutritional/frailty records. The cardiometabolic axis had the largest literature base and a mature disease-risk factor-outcome structure. The neuropsychiatric/cognitive axis centered on depression, anxiety, cognitive impairment, and quality of life. The musculoskeletal/nutritional/frailty axis showed the highest within-axis growth ratio (1.88) and the largest increase in background-normalized publication share, from 2.41% to 3.17%. Random validation identified 266/300 records as topically relevant, with 94.3% inter-reader agreement. Excluding pulmonary-hypertension-only records did not change the cardiometabolic temporal pattern. Recent frontiers included machine learning, Mendelian randomization, prediction models, and NHANES-based analyses.
Conclusion:
These findings describe publication activity and knowledge structure rather than clinical comorbidity frequency, severity, or evidence certainty. The three axes showed distinct patterns of scale, expansion, thematic maturity, and methodological development. This framework may inform topic selection and study design, while the exploratory research directions can be evaluated in future longitudinal, causal, interventional, and implementation studies.
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