C‐peptide index at 2 h post‐meal is a useful predictor of endogenous insulin secretory capacity and withdrawal from insulin therapy in subjects with type 2 diabetes
Aims To evaluate the correlation between C‐peptide index (CPI) at 2 h post‐meal and endogenous insulin secretory capacity and to develop clinical models to predict the possibility of withdrawal from insulin therapy in patients with type 2 diabetes. Method This was a single‐centre retrospective study...
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Published in | Diabetes, obesity & metabolism Vol. 26; no. 7; pp. 2761 - 2773 |
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Main Authors | , , , , , , , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Oxford, UK
Blackwell Publishing Ltd
01.07.2024
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Abstract | Aims
To evaluate the correlation between C‐peptide index (CPI) at 2 h post‐meal and endogenous insulin secretory capacity and to develop clinical models to predict the possibility of withdrawal from insulin therapy in patients with type 2 diabetes.
Method
This was a single‐centre retrospective study of patients with type 2 diabetes admitted to our hospital. Patients were divided into a withdrawal group (n = 72) and a non‐withdrawal group (n = 75) based on whether they were able to withdraw from insulin therapy at discharge, and the correlation between CPI at 2 h after meal and diabetes‐related parameters was evaluated. In addition, we created two clinical models to predict the possibility of withdrawal from insulin therapy using machine learning.
Results
The glycated haemoglobin values of the study participants were 87.8 ± 22.6 mmol/mo. The CPI at 2 h post‐meal was 1.93 ± 1.28 in the non‐withdrawal group and 2.97 ± 2.07 in the withdrawal group (p < 0.001). CPI at 2 h post‐meal was an independent predictor of withdrawal from insulin therapy. In addition, CPI at 2 h post‐meal was a better predictor than fasting CPI. Six factors associated with insulin therapy withdrawal (age, duration of diabetes, creatinine, alanine aminotransferase, insulin therapy until hospitalization, and CPI at 2 h post‐meal) were used to generate two clinical models by machine learning. The accuracy of the generated clinical models ranged from 78.3% to 82.6%.
Conclusion
The CPI at 2 h post‐meal is a clinically useful measure of endogenous insulin secretory capacity under non‐fasting conditions. |
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AbstractList | To evaluate the correlation between C-peptide index (CPI) at 2 h post-meal and endogenous insulin secretory capacity and to develop clinical models to predict the possibility of withdrawal from insulin therapy in patients with type 2 diabetes.AIMSTo evaluate the correlation between C-peptide index (CPI) at 2 h post-meal and endogenous insulin secretory capacity and to develop clinical models to predict the possibility of withdrawal from insulin therapy in patients with type 2 diabetes.This was a single-centre retrospective study of patients with type 2 diabetes admitted to our hospital. Patients were divided into a withdrawal group (n = 72) and a non-withdrawal group (n = 75) based on whether they were able to withdraw from insulin therapy at discharge, and the correlation between CPI at 2 h after meal and diabetes-related parameters was evaluated. In addition, we created two clinical models to predict the possibility of withdrawal from insulin therapy using machine learning.METHODThis was a single-centre retrospective study of patients with type 2 diabetes admitted to our hospital. Patients were divided into a withdrawal group (n = 72) and a non-withdrawal group (n = 75) based on whether they were able to withdraw from insulin therapy at discharge, and the correlation between CPI at 2 h after meal and diabetes-related parameters was evaluated. In addition, we created two clinical models to predict the possibility of withdrawal from insulin therapy using machine learning.The glycated haemoglobin values of the study participants were 87.8 ± 22.6 mmol/mo. The CPI at 2 h post-meal was 1.93 ± 1.28 in the non-withdrawal group and 2.97 ± 2.07 in the withdrawal group (p < 0.001). CPI at 2 h post-meal was an independent predictor of withdrawal from insulin therapy. In addition, CPI at 2 h post-meal was a better predictor than fasting CPI. Six factors associated with insulin therapy withdrawal (age, duration of diabetes, creatinine, alanine aminotransferase, insulin therapy until hospitalization, and CPI at 2 h post-meal) were used to generate two clinical models by machine learning. The accuracy of the generated clinical models ranged from 78.3% to 82.6%.RESULTSThe glycated haemoglobin values of the study participants were 87.8 ± 22.6 mmol/mo. The CPI at 2 h post-meal was 1.93 ± 1.28 in the non-withdrawal group and 2.97 ± 2.07 in the withdrawal group (p < 0.001). CPI at 2 h post-meal was an independent predictor of withdrawal from insulin therapy. In addition, CPI at 2 h post-meal was a better predictor than fasting CPI. Six factors associated with insulin therapy withdrawal (age, duration of diabetes, creatinine, alanine aminotransferase, insulin therapy until hospitalization, and CPI at 2 h post-meal) were used to generate two clinical models by machine learning. The accuracy of the generated clinical models ranged from 78.3% to 82.6%.The CPI at 2 h post-meal is a clinically useful measure of endogenous insulin secretory capacity under non-fasting conditions.CONCLUSIONThe CPI at 2 h post-meal is a clinically useful measure of endogenous insulin secretory capacity under non-fasting conditions. Aims To evaluate the correlation between C‐peptide index (CPI) at 2 h post‐meal and endogenous insulin secretory capacity and to develop clinical models to predict the possibility of withdrawal from insulin therapy in patients with type 2 diabetes. Method This was a single‐centre retrospective study of patients with type 2 diabetes admitted to our hospital. Patients were divided into a withdrawal group (n = 72) and a non‐withdrawal group (n = 75) based on whether they were able to withdraw from insulin therapy at discharge, and the correlation between CPI at 2 h after meal and diabetes‐related parameters was evaluated. In addition, we created two clinical models to predict the possibility of withdrawal from insulin therapy using machine learning. Results The glycated haemoglobin values of the study participants were 87.8 ± 22.6 mmol/mo. The CPI at 2 h post‐meal was 1.93 ± 1.28 in the non‐withdrawal group and 2.97 ± 2.07 in the withdrawal group (p < 0.001). CPI at 2 h post‐meal was an independent predictor of withdrawal from insulin therapy. In addition, CPI at 2 h post‐meal was a better predictor than fasting CPI. Six factors associated with insulin therapy withdrawal (age, duration of diabetes, creatinine, alanine aminotransferase, insulin therapy until hospitalization, and CPI at 2 h post‐meal) were used to generate two clinical models by machine learning. The accuracy of the generated clinical models ranged from 78.3% to 82.6%. Conclusion The CPI at 2 h post‐meal is a clinically useful measure of endogenous insulin secretory capacity under non‐fasting conditions. To evaluate the correlation between C-peptide index (CPI) at 2 h post-meal and endogenous insulin secretory capacity and to develop clinical models to predict the possibility of withdrawal from insulin therapy in patients with type 2 diabetes. This was a single-centre retrospective study of patients with type 2 diabetes admitted to our hospital. Patients were divided into a withdrawal group (n = 72) and a non-withdrawal group (n = 75) based on whether they were able to withdraw from insulin therapy at discharge, and the correlation between CPI at 2 h after meal and diabetes-related parameters was evaluated. In addition, we created two clinical models to predict the possibility of withdrawal from insulin therapy using machine learning. The glycated haemoglobin values of the study participants were 87.8 ± 22.6 mmol/mo. The CPI at 2 h post-meal was 1.93 ± 1.28 in the non-withdrawal group and 2.97 ± 2.07 in the withdrawal group (p < 0.001). CPI at 2 h post-meal was an independent predictor of withdrawal from insulin therapy. In addition, CPI at 2 h post-meal was a better predictor than fasting CPI. Six factors associated with insulin therapy withdrawal (age, duration of diabetes, creatinine, alanine aminotransferase, insulin therapy until hospitalization, and CPI at 2 h post-meal) were used to generate two clinical models by machine learning. The accuracy of the generated clinical models ranged from 78.3% to 82.6%. The CPI at 2 h post-meal is a clinically useful measure of endogenous insulin secretory capacity under non-fasting conditions. |
Author | Morimoto, Yuichi Katakura, Yukino Fushimi, Yoshiro Iwamoto, Hideyuki Watanabe, Yuki Sanada, Junpei Kaku, Kohei Kubo, Masato Iwamoto, Yuichiro Nakanishi, Shuhei Mune, Tomoatsu Takenouchi, Haruka Kimura, Tomohiko Kaneto, Hideaki Mori, Shigehito Dan, Kazunori Shimoda, Masashi Sasaki, Taku Itoh, Takashi |
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To evaluate the correlation between C‐peptide index (CPI) at 2 h post‐meal and endogenous insulin secretory capacity and to develop clinical models to... To evaluate the correlation between C-peptide index (CPI) at 2 h post-meal and endogenous insulin secretory capacity and to develop clinical models to predict... |
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SubjectTerms | Aged C-Peptide - blood C‐peptide index Diabetes Mellitus, Type 2 - blood Diabetes Mellitus, Type 2 - drug therapy endogenous insulin secretory capacity Female Glycated Hemoglobin - analysis Glycated Hemoglobin - metabolism Humans Hypoglycemic Agents - therapeutic use Insulin - administration & dosage Insulin - therapeutic use Insulin Secretion insulin therapy Machine Learning Male Meals Middle Aged Postprandial Period Retrospective Studies retrospective study type 2 diabetes Withholding Treatment - statistics & numerical data |
Title | C‐peptide index at 2 h post‐meal is a useful predictor of endogenous insulin secretory capacity and withdrawal from insulin therapy in subjects with type 2 diabetes |
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