Serum IGFBP2 Level Is a New Candidate Biomarker of Severe Malnutrition in Advanced Lung Cancer

Abstract

Objectives: This study aimed to analyze and evaluate serum insulin-like growth factor-binding protein 2 (IGFBP2) levels as a new biomarker of severe malnutrition in patients with advanced lung cancer.

Design and methods: This prospective study involved 59 patients with advanced lung cancer. We detected serum IGFBP2 level by using enzyme-linked immunosorbent assay and analyzed its relationship to clinical characteristics, nutritional status, Glasgow prognostic score (GPS), and survival. Serum albumin and C-reactive protein (CRP) levels were measured, and nutritional status was assessed using Patient-Generated Subjective Global Assessment (PG-SGA). The best cutoff point value for serum IGFBP2 level was established using receiver operating characteristic analysis. Kaplan–Meier method was utilized to analyze the survival curves.

Results: Serum IGFBP2 levels were elevated in patients with advanced lung cancer and severe malnutrition. The best cutoff value for serum IGFBP2 level was determined at 363 ng/ml, which could diagnose severe malnutrition with 73.3% sensitivity and 70.5% specificity and was found to be related to albumin, CRP, and GPS. Patients whose serum IGFBP2 levels were higher than 363 ng/ml had poor survival outcome.

Conclusion: This study demonstrates the remarkably association between higher serum level of IGFBP2 and severe malnutrition, albumin, CRP, GPS, and survival. Hence, serum IGFBP2 level can be used as a potential biomarker for diagnosis of severe malnutrition in patients with advanced lung cancer.

Disclosure Statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The study received grants from the Tianjin Medical University Cancer Institute and Hospital Research Funding (no. 1710).

Notes on contributors

Kun Wang

Kun Wang and Jie Dong substantially contributed to conception and design. Yaqi Zeng and Ping Zhang contributed to acquisition of data. Chunlei Li, Yajun Chen, and Yueying Li contributed to analysis and interpretation of data. Jie Dong and Kun Wang contributed to drafting the article or revising it critically for important intellectual content. Jie Dong contributed to the final approval of the version to be published.