Liisa Hakaste

Nonsuppressed glucagon after glucose challenge as a potential predictor for glucose tolerance

Glucagon levels are classically suppressed after glucose challenge. It is still not clear as to whether a lack of suppression contributes to hyperglycemia and thus to the development of diabetes. We investigated the association of postchallenge change in glucagon during oral glucose tolerance tests (OGTTs), hypothesizing that higher postchallenge glucagon levels are observed in subjects with impaired glucose tolerance (IGT). Glucagon levels were measured during OGTT in a total of 4,194 individuals without diabetes in three large European cohorts. Longitudinal changes in glucagon suppression were investigated in 50 participants undergoing a lifestyle intervention. Only 66–79% of participants showed suppression of glucagon at 120 min (fold change glucagon120/0 <1) during OGTT, whereas 21–34% presented with increasing glucagon levels (fold change glucagon120/0 ≥1). Participants with nonsuppressed glucagon120 had a lower risk of IGT in all cohorts (odds ratio 0.44–0.53, P < 0.01). They were also leaner and more insulin sensitive and had lower liver fat contents. In the longitudinal study, an increase of fold change glucagon120/0 was associated with an improvement in insulin sensitivity (P = 0.003). We characterize nonsuppressed glucagon120 during the OGTT. Lower glucagon suppression after oral glucose administration is associated with a metabolically healthier phenotype, suggesting that it is not an adverse phenomenon.

Diabetes and Prediabetes Classification Using Glycemic Variability Indices From Continuous Glucose Monitoring Data

Background: Tens of glycemic variability (GV) indices are available in the literature to characterize the dynamic properties of glucose concentration profiles from continuous glucose monitoring (CGM) sensors. However, how to exploit the plethora of GV indices for classifying subjects is still controversial. For instance, the basic problem of using GV indices to automatically determine if the subject is healthy rather than affected by impaired glucose tolerance (IGT) or type 2 diabetes (T2D), is still unaddressed. Here, we analyzed the feasibility of using CGM-based GV indices to distinguish healthy from IGT&T2D and IGT from T2D subjects by means of a machine-learning approach. Methods: The data set consists of 102 subjects belonging to three different classes: 34 healthy, 39 IGT, and 29 T2D subjects. Each subject was monitored for a few days by a CGM sensor that produced a glucose profile from which we extracted 25 GV indices. We used a two-step binary logistic regression model to classify subjects. The first step distinguishes healthy subjects from IGT&T2D, the second step classifies subjects into either IGT or T2D. Results: Healthy subjects are distinguished from subjects with diabetes (IGT&T2D) with 91.4% accuracy. Subjects are further subdivided into IGT or T2D classes with 79.5% accuracy. Globally, the classification into the three classes shows 86.6% accuracy. Conclusions: Even with a basic classification strategy, CGM-based GV indices show good accuracy in classifying healthy and subjects with diabetes. The classification into IGT or T2D seems, not surprisingly, more critical, but results encourage further investigation of the present research.

A bayesian network for probabilistic reasoning and imputation of missing risk factors in type 2 diabetes

We propose a novel Bayesian network tool to model the probabilistic relations between a set of type 2 diabetes risk factors. The tool can be used for probabilistic reasoning and for imputation of missing values among risk factors.

Genetic determinants of circulating GIP and GLP-1 concentrations

The secretion of insulin and glucagon from the pancreas and the incretin hormones glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic peptide (GIP) from the gastrointestinal tract is essential for glucose homeostasis. Several novel treatment strategies for type 2 diabetes (T2D) mimic GLP-1 actions or inhibit incretin degradation (DPP4 inhibitors), but none is thus far aimed at increasing the secretion of endogenous incretins. In order to identify new potential therapeutic targets for treatment of T2D, we performed a meta-analysis of a GWAS and an exome-wide association study of circulating insulin, glucagon, GIP, and GLP-1 concentrations measured during an oral glucose tolerance test in up to 7,828 individuals. We identified 6 genome-wide significant functional loci associated with plasma incretin concentrations in or near the SLC5A1 (encoding SGLT1), GIPR, ABO, GLP2R, F13A1, and HOXD1 genes and studied the effect of these variants on mRNA expression in pancreatic islet and on metabolic phenotypes. Immunohistochemistry showed expression of GIPR, ABO, and HOXD1 in human enteroendocrine cells and expression of ABO in pancreatic islets, supporting a role in hormone secretion. This study thus provides candidate genes and insight into mechanisms by which secretion and breakdown of GIP and GLP-1 are regulated.

A Bayesian Network analysis of the probabilistic relations between risk factors in the predisposition to type 2 diabetes.

In order to better understand the relations between different risk factors in the predisposition to type 2 diabetes, we present a Bayesian Network analysis of a large dataset, composed of three European population studies. Our results show, together with a key role of metabolic syndrome and of glucose after 2 hours of an Oral Glucose Tolerance Test, the importance of education, measured as the number of years of study, in the predisposition to type 2 diabetes.

Glycaemic variability-based classification of impaired glucose tolerance vs. type 2 diabetes using continuous glucose monitoring data

Many glycaemic variability (GV) indices extracted from continuous glucose monitoring systems data have been proposed for the characterisation of various aspects of glucose concentration profile dynamics in both healthy and non-healthy individuals. However, the inter-index correlations have made it difficult to reach a consensus regarding the best applications or a subset of indices for clinical scenarios, such as distinguishing subjects according to diabetes progression stage. Recently, a logistic regression-based method was used to address the basic problem of differentiating between healthy subjects and those affected by impaired glucose tolerance (IGT) or type 2 diabetes (T2D) in a pool of 25 GV-based indices. Whereas healthy subjects were classified accurately, the distinction between patients with IGT and T2D remained critical. In the present work, by using a dataset of CGM time-series collected in 62 subjects, we developed a polynomial-kernel support vector machine-based approach and demonstrated the ability to distinguish between subjects affected by IGT and T2D based on a pool of 37 GV indices complemented by four basic parameters-age, sex, BMI, and waist circumference-with an accuracy of 87.1%.

HAPT2D: high accuracy of prediction of T2D with a model combining basic and advanced data depending on availability

OBJECTIVE Type 2 diabetes arises from the interaction of physiological and lifestyle risk factors. Our objective was to develop a model for predicting the risk of T2D, which could use various amounts of background information. RESEARCH DESIGN AND METHODS We trained a survival analysis model on 8483 people from three large Finnish and Spanish data sets, to predict the time until incident T2D. All studies included anthropometric data, fasting laboratory values, an oral glucose tolerance test (OGTT) and information on co-morbidities and lifestyle habits. The variables were grouped into three sets reflecting different degrees of information availability. Scenario 1 included background and anthropometric information; Scenario 2 added routine laboratory tests; Scenario 3 also added results from an OGTT. Predictive performance of these models was compared with FINDRISC and Framingham risk scores. RESULTS The three models predicted T2D risk with an average integrated area under the ROC curve equal to 0.83, 0.87 and 0.90, respectively, compared with 0.80 and 0.75 obtained using the FINDRISC and Framingham risk scores. The results were validated on two independent cohorts. Glucose values and particularly 2-h glucose during OGTT (2h-PG) had highest predictive value. Smoking, marital and professional status, waist circumference, blood pressure, age and gender were also predictive. CONCLUSIONS Our models provide an estimation of patients risk over time and outweigh FINDRISC and Framingham traditional scores for prediction of T2D risk. Of note, the models developed in Scenarios 1 and 2, only exploited variables easily available at general patient visits.