Association between area under the C‐peptide curve during an oral glucose tolerance test and diabetic retinopathy in type 2 diabetes patients

• Published in: Journal of diabetes investigation 2024-03, Vol.15 (3), p.315-325
• Main Authors: Zhang, Cong, Ma, Xuefei, Lin, Wenjian, Xu, Qian, Li, Hongxue, Xu, Chengye, Hao, Ming, Kuang, Hongyu
• Format: Article
• Language: English
• Subjects: Age; Area under the C‐peptide curve; Beta cells; Beta‐cell function; Blood pressure; Body mass index; C-Peptide; Cholesterol; Correlation analysis; Creatinine; Diabetes; Diabetes mellitus (non-insulin dependent); Diabetes Mellitus, Type 2 - complications; Diabetic retinopathy; Diabetic Retinopathy - diagnosis; Diabetic Retinopathy - etiology; Disease; Endocrinology; Glucose; Glucose tolerance; Glucose Tolerance Test; High density lipoprotein; Humans; Insulin; Neutrophils; Normal distribution; Peptides; Retinopathy; Retrospective Studies; Risk assessment; Sociodemographics; Triglycerides; Uric acid; Variables
• ISSN: 2040-1116; 2040-1124
• DOI: 10.1111/jdi.14109

Aims/Introduction To evaluate the relative contributions of the area under the C‐peptide curve (AUCC) in diabetic retinopathy (DR) during an oral glucose tolerance test and C‐peptide release test in patients with type 2 diabetes. Materials and Methods We retrospectively analyzed the data of 969 patients. Their general characteristics were retrieved. A series of parameters for assessing pancreatic β‐cells function, such as the AUCC for six time periods: 0–60 min (AUCC0–60), 0–120 min (AUCC0–120), 0–180 min (AUCC0–180), 60–120 min (AUCC60–120), 60–180 min (AUCC60–180) and 120–180 min (AUCC120–180); the area under the glucose‐time curve for six time periods: 0–60 min (AUCG0–60), 0–120 min (AUCG0–120), 0–180 min (AUCG0–180), 60–120 min (AUCG60–120), 60–180 min (AUCG60–180) and 120–180 min (AUCG120–180) and their related indexes, were calculated through 0–180 min oral glucose tolerance test and C‐peptide release test. We used univariate analysis to examine the potential factors affecting DR. Spearman's correlation was used to analyze the correlation between AUCC‐related indexes and DR. The logistic regression model was used to investigate AUCC and its related indexes’ contribution to incidence DR. A smooth curve fitting model was used to determine the correlation, non‐linear relationship, and threshold effect between AUCC and DR. Results Of the 969 patients with type 2 diabetes, 469 (48.40%) and 500 (51.60%) were classified as the DR group and non‐DR group. Compared with the non‐DR group, the DR patients had lower AUCC and AUCC/AUCG. Spearman's correlation analysis showed that AUCC‐related indexes were all negatively correlated with DR. The logistic regression analysis determined that there were associations between AUCC and DR in the adjusted models. The odds ratio values of AUCC0–60, AUCC0–120, AUCC0–180, AUCC0–60/AUCG0–60, AUCC0–120/AUCG0–120, AUCC0–180/AUCG0–180, AUCC60–120, AUCC60–180, AUCC120–180, AUCC60–120/AUCG60–120, AUCC60–180/AUCG60–180 and AUCC120–180/AUCG120–180 were 0.817 (0.750, 0.890), 0.925 (0.895, 0.955), 0.951 (0.932, 0.970), 0.143 (0.060, 0.340), 0.194 (0.093, 0.406), 0.223 (0.116, 0.427), 0.886 (0.842, 0.933), 0.939 (0.915, 0.963), 0.887 (0.846, 0.930), 0.253 (0.133, 0.479), 0.282 (0.160, 0.497) and 0.355 (0.220, 0.573), respectively. AUCC showed a non‐linear relationship with DR, with an inflection point. The inflection points of AUCC180/AUCG180, AUCC60–120, AUCC60–180, AUCC120–180, AUCC60–120/AUCG60–120, AUCC60–180/AUCG60–180, AUCC120–180