Evidence map›Paper›PMID 42862337›Full record

ArticleActa diabetologica2026

Prognostic value of diabetes-related clinical indices in patients with non-small cell lung cancer and comorbid type 2 diabetes receiving immune checkpoint inhibitor therapy.

Qiang Ren, Hengwei Zhang, Delong Huang, Haoshu Zhong, Junhao Wu, Yiren Wang, Yangkai Zhao, Shaojie Luo, Yuying Li

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Article in Acta diabetologica, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

9 authors.

Qiang Ren *Department of Respiratory and Critical Care Medicine, The Affiliated Hospital, Southwest Medical University, No. 25 Taiping Street, Luzhou, Sichuan, PR China.
Hengwei Zhang *Department of Urology, The Affiliated Hospital, Southwest Medical University, Luzhou, Sichuan, China.
Delong Huang *Department of Neurosurgery, The Second Affiliated Hospital of Soochow University, Suzhou, China.
Haoshu ZhongDepartment of Hematology, Huashan Hospital, Fudan University, Shanghai, China.
Junhao WuDepartment of Nuclear Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Yiren WangDepartment of Oncology, The Affiliated Hospital, Southwest Medical University, Luzhou, Sichuan, China.
Yangkai ZhaoSchool of Graduate, Jinzhou Medical University, Jinzhou, Liaoning, China.
Shaojie LuoSchool of Clinical Medicine, Southwest Medical University, Luzhou, Sichuan, China.
Yuying LiDepartment of Respiratory and Critical Care Medicine, The Affiliated Hospital, Southwest Medical University, No. 25 Taiping Street, Luzhou, Sichuan, PR China. lzhlyyhy@126.com.ORCID http://orcid.org/0000-0002-3370-6179

Funding

Tianfu Emei Program CW202204
6 · The paper itself

Abstract

backgroundImmune checkpoint inhibitors (ICIs) targeting programmed cell death protein 1 (PD-1) or programmed death-ligand 1 (PD-L1) have transformed the treatment of non-small cell lung cancer (NSCLC). However, diabetes-induced immune dysfunction may attenuate ICI efficacy. Clinical evidence further suggests poorer outcomes in patients with NSCLC and comorbid type 2 diabetes mellitus (T2DM). Reliable prognostic tools for this subgroup remain lacking.

methodsWe conducted a three-part study. First, a two-sample Mendelian randomization (MR) analysis was performed to assess the causal relationship between T2DM and PD-L1. Second, Cox regression was applied to evaluate associations between diabetes-related clinical indices and progression-free survival (PFS) and overall survival (OS). Third, machine learning models were developed to predict prognosis using diabetes-related features.

resultsMR analysis supported a causal association between T2DM and PD-L1. Cox regression identified HbA1c as significantly associated with PFS. Among the machine learning models, the support vector machine (SVM) achieved the best performance for predicting PFS (AUC = 0.935, 95% CI: 0.872-0.997; C-index: 0.763). For OS prediction, the best-performing model was a random forest model based on HbA1c (AUC = 0.863, 95% CI: 0.736-0.990; C-index: 0.618).

conclusionDiabetes-related indices-particularly HbA1c-are meaningful prognostic markers in patients with NSCLC and comorbid T2DM receiving ICI therapy. The proposed machine learning models may support individualized outcome prediction and clinical decision-making.

Indexed as

DiabetesImmune checkpoint inhibitorsLung cancerMachine learningPredictive model

Identifiers

PMID42862337

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