Fasting C-peptide and Related Parameters Characterizing Insulin Secretory Capacity for Correctly Classifying Diabetes Type and for Predicting Insulin Requirement in Patients with Type 2 Diabetes

2016 ◽  
Vol 124 (03) ◽  
pp. 148-156 ◽  
Author(s):  
F. Becht ◽  
K. Walther ◽  
E. Martin ◽  
M. Nauck
Keyword(s):  
Type 2 Diabetes ◽  
Insulin Requirement ◽  
Diabetes Type ◽  
C Peptide ◽  
Insulin Secretory Capacity ◽  
Secretory Capacity

scholarly journals The relationship between serum uric acid within the normal range and β-cell function in Chinese patients with type 2 diabetes: differences by body mass index and gender

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6666 ◽  
Author(s):  
Xing Zhong ◽  
Deyuan Zhang ◽  
Lina Yang ◽  
Yijun Du ◽  
Tianrong Pan
Keyword(s):  
Type 2 Diabetes ◽  
Cell Function ◽  
Normal Weight ◽  
Normal Range ◽  
Β Cell ◽  
C Peptide ◽  
Β Cell Function ◽  
Insulin Secretory Capacity ◽  
Secretory Capacity

Background Elevated serum uric acid (SUA) has a positive correlation with insulin secretion and insulin resistance indexes. However, whether weight- and gender-specific differences regarding the relationship between SUA within the normal range and β-cell function and insulin resistance exist is unknown in type 2 diabetes mellitus (T2DM) patients. Methods A total of 380 patients with type 2 diabetes were divided into two groups as overweight/obesity (n = 268) and normal weight (n = 112). Each group were again divided into low (LSUA) and high normal SUA (HSUA). The HbA1c, C-peptide, SUA, creatinine, and lipids profiles were measured. HOMA2IR and HOMA%2B were estimated using fasting glucose and C-peptide by homeostasis model assessment (HOMA). Pearson’s correlations and multiple linear regression analyses were conducted to assess the associations between SUA levels and islet function indexes. Results In overweight/obesity subgroup, the levels of body mass index, fasting C-peptide (FCP), P2hCP, fasting CPI (FCPI), postprandial CPI (PPCPI), ΔC-peptide, HOMA2%B, and HOMA2IR were higher in HSUA group than in LSUA group. In contrast, the HbA1c, FBS, and P2hBS were lower in HSUA than in LSUA. In normal weight subgroup, there were no differences between the HSUA than LSUA group in terms of clinical characteristics. Pearson’s correlations indicated that there were no significant correlations between SUA and insulin secretory capacity in normal weight group, but in overweight/obesity group, SUA had positive significant correlations with P2hCP, FCPI, PPCPI, ΔC-peptide, and HOMA2%B. In the female group, there were no significant correlations between SUA and insulin secretory capacity. However, in the male group, SUA had positive significant correlations with insulin secretory capacity include P2hCP, FCPI, PPCPI, ΔC-peptide, and HOMA2%B. Multiple linear regression showed that SUA was significantly associated with HOMA2%B, but not with HOMA2IR in overweight/obesity and male group. Conclusions Our study shows that SUA levels within normal range were associated with β-cell function in T2DM patients with overweight/obesity or male. This finding supports that the association between SUA within normal range and insulin secretion ability differs by weight and sex.

scholarly journals Association between Pancreatic Atrophy and Loss of Insulin Secretory Capacity in Patients with Type 2 Diabetes Mellitus

2019 ◽  
Vol 2019 ◽  
pp. 1-6 ◽  
Author(s):  
Jun Lu ◽  
Meixiang Guo ◽  
Hongtao Wang ◽  
Haibin Pan ◽  
Liang Wang ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Multiple Logistic Regression Analysis ◽  
Insulin Deficiency ◽  
Cross Sectional ◽  
Pancreatic Atrophy ◽  
Insulin Secretory Capacity ◽  
Absolute Insulin Deficiency ◽  
Secretory Capacity

Aims. To examine pancreatic volume (PV) changes among patients with different duration of type 2 diabetes and whether pancreatic atrophy was associated with loss of insulin secretory capacity. Methods. This cross-sectional study (203 patients with type 2 diabetes, 93 controls without diabetes) was conducted from January 2016 to December 2017. Patients with type 2 diabetes were divided into 3 groups: recently diagnosed (duration≤2 years), midterm (duration 3-9 years), and long term (duration≥10 years). All the patients were scanned with upper abdominal computerized tomography; PV was then calculated by an experienced technician. Absolute insulin deficiency was defined as fasting C−peptide<0.9 ng/mL. Results. Compared with PV (cm3) in the controls, the mean PV was similar in patients with recently diagnosed type 2 diabetes (68.8 versus 71.0, P=0.56) but significantly reduced in patients with midterm (68.8 versus 60.8, P<0.05) and long-term (68.8 versus 53.1, P<0.001) type 2 diabetes. A similar trend was observed for the PV index (PV adjusted for body surface area and body mass index). Furthermore, rates of pancreatic atrophy and absolute insulin deficiency increased with duration of diabetes. Multiple logistic regression analysis indicated that pancreatic atrophy was associated with higher likelihood of absolute insulin deficiency (odds ratio=4.47, 95%confidence interval=1.45‐13.8). Conclusions. PV was reduced in those with midterm and long-term type 2 diabetes compared to individuals without type 2 diabetes. Overall, pancreatic atrophy was associated with the loss of insulin secretory capacity in patients with type 2 diabetes.

Classification of Type 1 and Type 2 Diabetes Mellitus from Studies of ICA, HLA and Insulin Secretory Capacity

1987 ◽  
Vol 29 (3) ◽  
pp. 340-344
Author(s):  
Nobuo Matsuura ◽  
Kenji Fujieda ◽  
Yuhei Mikami ◽  
Hiroko Fujita ◽  
Shohei Harada ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Insulin Secretory Capacity ◽  
Secretory Capacity

scholarly journals Preserved Insulin Secretory Capacity and Weight Loss Are the Predominant Predictors of Glycemic Control in Patients With Type 2 Diabetes Randomized to Roux-en-Y Gastric Bypass

Diabetes ◽  
2015 ◽  
Vol 64 (9) ◽  
pp. 3104-3110 ◽  
Author(s):  
Kim T. Nguyen ◽  
Charles J. Billington ◽  
Adrian Vella ◽  
Qi Wang ◽  
Leaque Ahmed ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Weight Loss ◽  
Gastric Bypass ◽  
Glycemic Control ◽  
Insulin Secretory Capacity ◽  
Secretory Capacity

scholarly journals Pancreatic β-Cell Rest Replenishes Insulin Secretory Capacity and Attenuates Diabetes in an Extreme Model of Obese Type 2 Diabetes

Diabetes ◽  
2018 ◽  
Vol 68 (1) ◽  
pp. 131-140 ◽  
Author(s):  
Brandon B. Boland ◽  
Charles Brown ◽  
Michelle L. Boland ◽  
Jennifer Cann ◽  
Michal Sulikowski ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Pancreatic Β Cell ◽  
Β Cell ◽  
Extreme Model ◽  
Insulin Secretory Capacity ◽  
Secretory Capacity

scholarly journals Morphology of the pancreas in type 2 diabetes: effect of weight loss with or without normalisation of insulin secretory capacity

Diabetologia ◽  
2016 ◽  
Vol 59 (8) ◽  
pp. 1753-1759 ◽  
Author(s):  
Ahmad Al-Mrabeh ◽  
Kieren G. Hollingsworth ◽  
Sarah Steven ◽  
Roy Taylor
Keyword(s):  
Type 2 Diabetes ◽  
Weight Loss ◽  
Insulin Secretory Capacity ◽  
Secretory Capacity

scholarly journals Replication and cross-validation of type 2 diabetes subtypes based on clinical variables: an IMI-RHAPSODY study

Diabetologia ◽  
2021 ◽  
Author(s):  
Roderick C. Slieker ◽  
Louise A. Donnelly ◽  
Hugo Fitipaldi ◽  
Gerard A. Bouland ◽  
Giuseppe N. Giordano ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Cross Validation ◽  
Hdl Cholesterol ◽  
Insulin Requirement ◽  
Lower Sensitivity ◽  
C Peptide ◽  
Clinical Variables ◽  
Age Related ◽  
Mild Diabetes

Abstract Aims/hypothesis Five clusters based on clinical characteristics have been suggested as diabetes subtypes: one autoimmune and four subtypes of type 2 diabetes. In the current study we replicate and cross-validate these type 2 diabetes clusters in three large cohorts using variables readily measured in the clinic. Methods In three independent cohorts, in total 15,940 individuals were clustered based on age, BMI, HbA1c, random or fasting C-peptide, and HDL-cholesterol. Clusters were cross-validated against the original clusters based on HOMA measures. In addition, between cohorts, clusters were cross-validated by re-assigning people based on each cohort’s cluster centres. Finally, we compared the time to insulin requirement for each cluster. Results Five distinct type 2 diabetes clusters were identified and mapped back to the original four All New Diabetics in Scania (ANDIS) clusters. Using C-peptide and HDL-cholesterol instead of HOMA2-B and HOMA2-IR, three of the clusters mapped with high sensitivity (80.6–90.7%) to the previously identified severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD) and mild obesity-related diabetes (MOD) clusters. The previously described ANDIS mild age-related diabetes (MARD) cluster could be mapped to the two milder groups in our study: one characterised by high HDL-cholesterol (mild diabetes with high HDL-cholesterol [MDH] cluster), and the other not having any extreme characteristic (mild diabetes [MD]). When these two milder groups were combined, they mapped well to the previously labelled MARD cluster (sensitivity 79.1%). In the cross-validation between cohorts, particularly the SIDD and MDH clusters cross-validated well, with sensitivities ranging from 73.3% to 97.1%. SIRD and MD showed a lower sensitivity, ranging from 36.1% to 92.3%, where individuals shifted from SIRD to MD and vice versa. People belonging to the SIDD cluster showed the fastest progression towards insulin requirement, while the MDH cluster showed the slowest progression. Conclusions/interpretation Clusters based on C-peptide instead of HOMA2 measures resemble those based on HOMA2 measures, especially for SIDD, SIRD and MOD. By adding HDL-cholesterol, the MARD cluster based upon HOMA2 measures resulted in the current clustering into two clusters, with one cluster having high HDL levels. Cross-validation between cohorts showed generally a good resemblance between cohorts. Together, our results show that the clustering based on clinical variables readily measured in the clinic (age, HbA1c, HDL-cholesterol, BMI and C-peptide) results in informative clusters that are representative of the original ANDIS clusters and stable across cohorts. Adding HDL-cholesterol to the clustering resulted in the identification of a cluster with very slow glycaemic deterioration. Graphical abstract

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