将胰岛素转化为c肽或c肽指数以及将c肽转化为HOMA2-IR：单中心回顾性观察研究

IF 2.9 3区医学 Q2 MEDICAL LABORATORY TECHNOLOGY

Clinica Chimica Acta Pub Date : 2025-09-02 DOI:10.1016/j.cca.2025.120585

Yuichiro Iwamoto, Tomohiko Kimura, Toshitomo Sugisaki, Kazunori Dan, Hideyuki Iwamoto, Junpei Sanada, Yoshiro Fushimi, Masashi Shimoda, Shuhei Nakanishi, Tomoatsu Mune, Kohei Kaku, Hideaki Kaneto

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引用次数: 0

摘要

在本研究中，我们利用机器学习开发了一种转换方法，使用标准化方法比较内源性胰岛素分泌能力指标免疫反应性胰岛素（IRI）和c肽免疫反应性胰岛素（CPR）。方法对我院住院治疗的449例2型糖尿病（T2D）患者和63例门诊T2D患者进行单中心回顾性观察研究，重点研究IRI和CPR同时测量的患者。结果对住院患者构建的梯度增强决策树（GBDT）模型采用IRI等7个特征，对CPR指数（CPI）进行非线性变换后，准确率为R2 = 0.641, MSE = 0.247 ng/mL。实际CPI与预测CPI的相关系数r = 0.943。使用包括CPR在内的7个特征对HOMA2-IR进行非线性变换的GBDT模型的精度为R2 = 0.615, MSE = 0.268。实际homa - ir与预测homa - ir的相关系数r = 0.943。将模型应用于门诊患者时，CPI和HOMA2-IR与实际值显著相关（r = 0.820, r = 0.812）。结论无论是IRI还是CPR，都可以用相同的标准评价内源性胰岛素分泌能力和胰岛素抵抗，有望在今后的临床研究和多机构数据整合中作为辅助指标。

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Assay-specific machine-learning models converting insulin to C-peptide or C-peptide index and C-peptide to HOMA2-IR: Single-center retrospective observational study

Background

In this study, we employed machine learning to develop a conversion method for comparing immunoreactive insulin (IRI) and C-peptide immunoreactivity (CPR), which are indicators of endogenous insulin secretion capacity, using a standardized approach.

Methods

This is a single-center retrospective observational study of 449 patients with type 2 diabetes (T2D) who were hospitalized at our hospital and 63 patients with T2D who were treated as outpatients, focusing on patients in whom IRI and CPR were measured simultaneously.

Results

The gradient boosting decision tree (GBDT) model constructed for hospitalized patients used seven features, including IRI, and showed an accuracy of R² = 0.641 and MSE = 0.247 ng/mL after applying a nonlinear transformation to the CPR index (CPI). The correlation coefficient between actual CPI and predicted CPI was r = 0.943. The accuracy of the GBDT model, which nonlinearly transforms HOMA2-IR using seven features, including CPR, was R² = 0.615 and MSE = 0.268. The correlation coefficient between the actual HOMA2-IR and the predicted HOMA2-IR was r = 0.943. When the model was applied to outpatients, CPI and HOMA2-IR were significantly correlated with actual values (r = 0.820 and r = 0.812, respectively).

Conclusions

If either IRI or CPR is measured, it will be possible to evaluate endogenous insulin secretion capacity and insulin resistance using the same standards, and it is expected to be used as an auxiliary indicator in future clinical research and when integrating data from multiple institutions.

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来源期刊

Clinica Chimica Acta 医学-医学实验技术

CiteScore

10.10

自引率

2.00%

发文量

1268

审稿时长

23 days

期刊介绍： The Official Journal of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Clinica Chimica Acta is a high-quality journal which publishes original Research Communications in the field of clinical chemistry and laboratory medicine, defined as the diagnostic application of chemistry, biochemistry, immunochemistry, biochemical aspects of hematology, toxicology, and molecular biology to the study of human disease in body fluids and cells. The objective of the journal is to publish novel information leading to a better understanding of biological mechanisms of human diseases, their prevention, diagnosis, and patient management. Reports of an applied clinical character are also welcome. Papers concerned with normal metabolic processes or with constituents of normal cells or body fluids, such as reports of experimental or clinical studies in animals, are only considered when they are clearly and directly relevant to human disease. Evaluation of commercial products have a low priority for publication, unless they are novel or represent a technological breakthrough. Studies dealing with effects of drugs and natural products and studies dealing with the redox status in various diseases are not within the journal''s scope. Development and evaluation of novel analytical methodologies where applicable to diagnostic clinical chemistry and laboratory medicine, including point-of-care testing, and topics on laboratory management and informatics will also be considered. Studies focused on emerging diagnostic technologies and (big) data analysis procedures including digitalization, mobile Health, and artificial Intelligence applied to Laboratory Medicine are also of interest.