In vivo and in vitro studies of GAD-antibody positive subjects with Type 2 diabetes: A distinct sub-phenotype

Precision subclassification of type 2 diabetes: a systematic review

BACKGROUND

Heterogeneity in type 2 diabetes presentation and progression suggests that precision medicine interventions could improve clinical outcomes. We undertook a systematic review to determine whether strategies to subclassify type 2 diabetes were associated with high quality evidence, reproducible results and improved outcomes for patients.

METHODS

We searched PubMed and Embase for publications that used 'simple subclassification' approaches using simple categorisation of clinical characteristics, or 'complex subclassification' approaches which used machine learning or 'omics approaches in people with established type 2 diabetes. We excluded other diabetes subtypes and those predicting incident type 2 diabetes. We assessed quality, reproducibility and clinical relevance of extracted full-text articles and qualitatively synthesised a summary of subclassification approaches.

RESULTS

Here we show data from 51 studies that demonstrate many simple stratification approaches, but none have been replicated and many are not associated with meaningful clinical outcomes. Complex stratification was reviewed in 62 studies and produced reproducible subtypes of type 2 diabetes that are associated with outcomes. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into clinically meaningful subtypes.

CONCLUSION

Critical next steps toward clinical implementation are to test whether subtypes exist in more diverse ancestries and whether tailoring interventions to subtypes will improve outcomes.

Systematic review of precision subclassification of type 2 diabetes

Heterogeneity in type 2 diabetes presentation, progression and treatment has the potential for precision medicine interventions that can enhance care and outcomes for affected individuals. We undertook a systematic review to ascertain whether strategies to subclassify type 2 diabetes are associated with improved clinical outcomes, show reproducibility and have high quality evidence. We reviewed publications that deployed 'simple subclassification' using clinical features, biomarkers, imaging or other routinely available parameters or 'complex subclassification' approaches that used machine learning and/or genomic data. We found that simple stratification approaches, for example, stratification based on age, body mass index or lipid profiles, had been widely used, but no strategy had been replicated and many lacked association with meaningful outcomes. Complex stratification using clustering of simple clinical data with and without genetic data did show reproducible subtypes of diabetes that had been associated with outcomes such as cardiovascular disease and/or mortality. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into meaningful groups. More studies are needed to test these subclassifications in more diverse ancestries and prove that they are amenable to interventions.

An Analysis of Glucose Effectiveness in Subjects With or Without Type 2 Diabetes via Hierarchical Modeling

Glucose effectiveness, defined as the ability of glucose itself to increase glucose utilization and inhibit hepatic glucose production, is an important mechanism maintaining normoglycemia. We conducted a minimal modeling analysis of glucose effectiveness at zero insulin (GEZI) using intravenous glucose tolerance test data from subjects with type 2 diabetes (T2D, n=154) and non-diabetic (ND) subjects (n=343). A hierarchical statistical analysis was performed, which provided a formal mechanism for pooling the data from all study subjects, to yield a single composite population model that quantifies the role of subject specific characteristics such as weight, height, age, sex, and glucose tolerance. Based on the resulting composite population model, GEZI was reduced from 0.021 min-1 (standard error - 0.00078 min-1) in the ND population to 0.011 min-1 (standard error - 0.00045 min-1) in T2D. The resulting model was also employed to calculate the proportion of the non-insulin-dependent net glucose uptake in each subject receiving an intravenous glucose load. Based on individual parameter estimates, the fraction of total glucose disposal independent of insulin was 72.8% ± 12.0% in the 238 ND subjects over the course of the experiment, indicating the major contribution to the whole-body glucose clearance under non-diabetic conditions. This fraction was significantly reduced to 48.8% ± 16.9% in the 30 T2D subjects, although still accounting for approximately half of the total in the T2D population based on our modeling analysis. Given the potential application of glucose effectiveness as a predictor of glucose intolerance and as a potential therapeutic target for treating diabetes, more investigations of glucose effectiveness in other disease conditions can be conducted using the hierarchical modeling framework reported herein.

Glucose Effectiveness from Short Insulin-Modified IVGTT and Its Application to the Study of Women with Previous Gestational Diabetes Mellitus

BACKGROUND

This study aimed to design a simple surrogate marker (i.e., predictor) of the minimal model glucose effectiveness (S_G), namely calculated S_G (CS_G), from a short insulin-modified intravenous glucose tolerance test (IM-IVGTT), and then to apply it to study women with previous gestational diabetes mellitus (pGDM).

METHODS

CS_G was designed using the stepwise model selection approach on a population of subjects (n=181) ranging from normal tolerance to type 2 diabetes mellitus (T2DM). CS_G was then tested on a population of women with pGDM (n=57). Each subject underwent a 3-hour IM-IVGTT; women with pGDM were observed early postpartum and after a follow-up period of up to 7 years and classified as progressors (PROG) or non-progressors (NONPROG) to T2DM. The minimal model analysis provided a reference S_G.

RESULTS

CS_G was described as CS_G=1.06×10⁻²+5.71×10⁻²×K_G/G_peak, K_G being the mean slope (absolute value) of loge glucose in 10-25- and 25-50-minute intervals, and G_peak being the maximum of the glucose curve. Good agreement between CS_G and S_G in the general population and in the pGDM group, both at baseline and follow-up (even in PROG and NONPROG subgroups), was shown by the Bland-Altman plots (<5% observations outside limits of agreement), and by the test for equivalence (equivalence margin not higher than one standard deviation). At baseline, the PROG subgroup showed significantly lower S_G and CS_G values compared to the NONPROG subgroup (P<0.03).

CONCLUSION

CS_G is a valid S_G predictor. In the pGDM group, glucose effectiveness appeared to be impaired in women progressing to T2DM.

Islet cell antibody-positive versus -negative phenotypic type 2 diabetes in youth: does the oral glucose tolerance test distinguish between the two?

OBJECTIVE Using the clamp technique, youths with a clinical diagnosis of type 2 diabetes (CDx-type 2 diabetes) and positive pancreatic autoantibodies (Ab(+)) were shown to have severe impairment in insulin secretion and less insulin resistance than their peers with negative antibodies (Ab(-)). In this study, we investigated whether oral glucose tolerance test (OGTT)-derived indexes of insulin secretion and sensitivity could distinguish between these two groups. RESEARCH DESIGN AND METHODS A total of 25 Ab(-), 11 Ab(+) CDx-type 2 diabetic, and 21 obese control youths had an OGTT. Fasting and OGTT-derived indexes of insulin sensitivity (including the Matsuda index, homeostasis model assessment [HOMA] of insulin resistance, quantitative insulin sensitivity check index, and glucose-to-insulin ratio) and insulin secretion (HOMA of insulin secretion and 30-min insulogenic and C-peptide indexes) were used. Glucagon and glucagon-like peptide (GLP)-1 responses were assessed. RESULTS Fasting C-peptide and C-peptide-to-glucose ratio, and C-peptide area under the curve (AUC) were significantly lower in the Ab(+) CDx-type 2 diabetic patients. Other OGTT-derived surrogate indexes of insulin sensitivity and secretion were not different between the Ab(+) versus Ab(-) patients. GLP-1 during the OGTT was highest in the Ab(+) youths compared with the other two groups, but this difference disappeared after adjusting for BMI. Ab(+) and Ab(-) CDx-type 2 diabetes had relative hyperglucagonemia compared with control subjects. CONCLUSIONS The clinical measures of fasting and OGTT-derived surrogate indexes of insulin sensitivity and secretion, except for fasting C-peptide and C-peptide AUC, are less sensitive tools to distinguish metabolic/pathopysiological differences, detected by the clamp, between Ab(+) and Ab(-) CDx-type 2 diabetic youths. This underscores the importance of using more sensitive methods and the importance of determining antibody status in obese youths with CDx-type 2 diabetes.