C-Peptide Index may Predict endogenous insulin secretory capacity under non-fasting conditions: Study

Written By :  Dr Riya Dave
Medically Reviewed By :  Dr. Kamal Kant Kohli
Published On 2024-05-04 04:30 GMT   |   Update On 2024-05-04 06:33 GMT
Advertisement

Researchers have found that the C-peptide index (CPI) at 2 hours post-meal is a significant indicator of endogenous insulin secretory capacity in patients with type 2 diabetes, according to a study published in Diabetes Obesity & Metabolism. This measure can help predict the likelihood of patients being able to withdraw from insulin therapy, providing valuable insights for personalized diabetes management. The study was conducted by Yuichiro Iwamoto and colleagues.

Advertisement

Type 2 diabetes is a chronic metabolic disorder characterized by insulin resistance and pancreatic beta-cell dysfunction. Managing the condition often involves insulin therapy; however, the ability to withdraw from insulin therapy can improve the quality of life for patients. Identifying reliable indicators for withdrawal can guide treatment plans and optimize patient outcomes.

The study was a single-center retrospective analysis involving 147 patients with type 2 diabetes admitted to a hospital. Participants 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. Researchers evaluated the correlation between CPI at 2 hours post-meal and diabetes-related parameters. Machine learning was employed to create clinical models to predict the possibility of withdrawal from insulin therapy.

The key findings of the study were:

• The CPI at 2 hours post-meal was significantly higher in the withdrawal group (2.97 ± 2.07) compared to the non-withdrawal group (1.93 ± 1.28) with a p-value of <0.001.

• This suggests that a higher post-meal CPI is associated with a greater likelihood of withdrawing from insulin therapy.

• CPI at 2 hours post-meal was an independent predictor of withdrawal from insulin therapy.

• It was also found to be 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 hours post-meal) were used to create two clinical models using machine learning.

• The accuracy of the generated clinical models ranged from 78.3% to 82.6%.

The study concludes that the CPI at 2 hours post-meal is a clinically useful measure of endogenous insulin secretory capacity under non-fasting conditions. This measure can serve as a valuable tool for predicting the possibility of withdrawal from insulin therapy in patients with type 2 diabetes. This finding can help tailor treatment plans to individual patients' needs, potentially improving their overall quality of life.

Reference:

Iwamoto, Y., Kimura, T., Shimoda, M., Morimoto, Y., Watanabe, Y., Itoh, T., Sasaki, T., Mori, S., Kubo, M., Takenouchi, H., Dan, K., Iwamoto, H., Sanada, J., Fushimi, Y., Katakura, Y., Nakanishi, S., Mune, T., Kaku, K., & Kaneto, H. (2024). 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. Diabetes, Obesity & Metabolism. https://doi.org/10.1111/dom.15595



Tags:    
Article Source : Diabetes Obesity & Metabolism

Disclaimer: This website is primarily for healthcare professionals. The content here does not replace medical advice and should not be used as medical, diagnostic, endorsement, treatment, or prescription advice. Medical science evolves rapidly, and we strive to keep our information current. If you find any discrepancies, please contact us at corrections@medicaldialogues.in. Read our Correction Policy here. Nothing here should be used as a substitute for medical advice, diagnosis, or treatment. We do not endorse any healthcare advice that contradicts a physician's guidance. Use of this site is subject to our Terms of Use, Privacy Policy, and Advertisement Policy. For more details, read our Full Disclaimer here.

NOTE: Join us in combating medical misinformation. If you encounter a questionable health, medical, or medical education claim, email us at factcheck@medicaldialogues.in for evaluation.

Our comments section is governed by our Comments Policy . By posting comments at Medical Dialogues you automatically agree with our Comments Policy , Terms And Conditions and Privacy Policy .

Similar News