Identification and validation of gestational diabetes subgroups by data-driven cluster analysis.

AIMS/HYPOTHESIS: Gestational diabetes mellitus (GDM) is a heterogeneous condition. Given such variability among patients, the ability to recognise distinct GDM subgroups using routine clinical variables may guide more personalised treatments. Our main aim was to identify distinct GDM subtypes through cluster analysis using routine clinical variables, and analyse treatment needs and pregnancy outcomes across these subgroups. METHODS: In this cohort study, we analysed datasets from a total of 2682 women with GDM treated at two central European hospitals (1865 participants from Charité University Hospital in Berlin and 817 participants from the Medical University of Vienna), collected between 2015 and 2022. We evaluated various clustering models, including k-means, k-medoids and agglomerative hierarchical clustering. Internal validation techniques were used to guide best model selection, while external validation on independent test sets was used to assess model generalisability. Clinical outcomes such as specific treatment needs and maternal and fetal complications were analysed across the identified clusters. RESULTS: Our optimal model identified three clusters from routinely available variables, i.e. maternal age, pre-pregnancy BMI (BMIPG) and glucose levels at fasting and 60 and 120 min after the diagnostic OGTT (OGTT0, OGTT60 and OGTT120, respectively). Cluster 1 was characterised by the highest OGTT values and obesity prevalence. Cluster 2 displayed intermediate BMIPG and elevated OGTT0, while cluster 3 consisted mainly of participants with normal BMIPG and high values for OGTT60 and OGTT120. Treatment modalities and clinical outcomes varied among clusters. In particular, cluster 1 participants showed a much higher need for glucose-lowering medications (39.6% of participants, compared with 12.9% and 10.0% in clusters 2 and 3, respectively, p<0.0001). Cluster 1 participants were also at higher risk of delivering large-for-gestational-age infants. Differences in the type of insulin-based treatment between cluster 2 and cluster 3 were observed in the external validation cohort. CONCLUSIONS/INTERPRETATION: Our findings confirm the heterogeneity of GDM. The identification of subgroups (clusters) has the potential to help clinicians define more tailored treatment approaches for improved maternal and neonatal outcomes.

Glucagon kinetics assessed by mathematical modelling during oral glucose administration in people spanning from normal glucose tolerance to type 2 diabetes.

Background/Objectives: Glucagon is important in the maintenance of glucose homeostasis, with also effects on lipids. In this study, we aimed to apply a recently developed model of glucagon kinetics to determine the sensitivity of glucagon variations (especially, glucagon inhibition) to insulin levels ("alpha-cell insulin sensitivity"), during oral glucose administration. Subjects/Methods: We studied 50 participants (spanning from normal glucose tolerance to type 2 diabetes) undergoing frequently sampled 5-hr oral glucose tolerance test (OGTT). The alpha-cell insulin sensitivity and the glucagon kinetics were assessed by a mathematical model that we developed previously. Results: The alpha-cell insulin sensitivity parameter (named SGLUCA; "GLUCA": "glucagon") was remarkably variable among participants (CV=221%). SGLUCA was found inversely correlated with the mean glycemic values, as well as with 2-hr glycemia of the OGTT. When stratifying participants into two groups (normal glucose tolerance, NGT, N=28, and impaired glucose regulation/type 2 diabetes, IGR_T2D, N=22), we found that SGLUCA was lower in the latter (1.50 ± 0.50·10-2 vs. 0.26 ± 0.14·10-2 ng·L-1 GLUCA/pmol·L-1 INS, in NGT and IGR_T2D, respectively, p=0.009; "INS": "insulin"). Conclusions: The alpha-cell insulin sensitivity is highly variable among subjects, and it is different in groups at different glucose tolerance. This may be relevant for defining personalized treatment schemes, in terms of dietary prescriptions but also for treatments with glucagon-related agents.

Glucometabolism in Kidney Transplant Recipients with and without Posttransplant Diabetes: Focus on Beta-Cell Function.

Posttransplant diabetes mellitus (PTDM) is a common complication after kidney transplantation. Pathophysiologically, whether beta-cell dysfunction rather than insulin resistance may be the predominant defect in PTDM has been a matter of debate. The aim of the present analysis was to compare glucometabolism in kidney transplant recipients with and without PTDM. To this aim, we included 191 patients from a randomized controlled trial who underwent oral glucose tolerance tests (OGTTs) 6 months after transplantation. We derived several basic indices of beta-cell function and insulin resistance as well as variables from mathematical modeling for a more robust beta-cell function assessment. Mean ± standard deviation of the insulin sensitivity parameter PREDIM was 3.65 ± 1.68 in PTDM versus 5.46 ± 2.57 in NON-PTDM. Model-based glucose sensitivity (indicator of beta-cell function) was 68.44 ± 57.82 pmol∙min-1∙m-2∙mM-1 in PTDM versus 143.73 ± 112.91 pmol∙min-1∙m-2∙mM-1 in NON-PTDM, respectively. Both basic indices and model-based parameters of beta-cell function were more than 50% lower in patients with PTDM, indicating severe beta-cell impairment. Nonetheless, some defects in insulin sensitivity were also present, although less marked. We conclude that in PTDM, the prominent defect appears to be beta-cell dysfunction. From a pathophysiological point of view, patients at high risk for developing PTDM may benefit from intensive treatment of hyperglycemia over the insulin secretion axis.

Artificial Intelligence Methodologies Applied to Technologies for Screening, Diagnosis and Care of the Diabetic Foot: A Narrative Review.

Diabetic foot syndrome is a multifactorial pathology with at least three main etiological factors, i.e., peripheral neuropathy, peripheral arterial disease, and infection. In addition to complexity, another distinctive trait of diabetic foot syndrome is its insidiousness, due to a frequent lack of early symptoms. In recent years, it has become clear that the prevalence of diabetic foot syndrome is increasing, and it is among the diabetes complications with a stronger impact on patient's quality of life. Considering the complex nature of this syndrome, artificial intelligence (AI) methodologies appear adequate to address aspects such as timely screening for the identification of the risk for foot ulcers (or, even worse, for amputation), based on appropriate sensor technologies. In this review, we summarize the main findings of the pertinent studies in the field, paying attention to both the AI-based methodological aspects and the main physiological/clinical study outcomes. The analyzed studies show that AI application to data derived by different technologies provides promising results, but in our opinion future studies may benefit from inclusion of quantitative measures based on simple sensors, which are still scarcely exploited.

TyGIS: improved triglyceride-glucose index for the assessment of insulin sensitivity during pregnancy.

BACKGROUND: The triglyceride-glucose index (TyG) has been proposed as a surrogate marker of insulin resistance, which is a typical trait of pregnancy. However, very few studies analyzed TyG performance as marker of insulin resistance in pregnancy, and they were limited to insulin resistance assessment at fasting rather than in dynamic conditions, i.e., during an oral glucose tolerance test (OGTT), which allows more reliable assessment of the actual insulin sensitivity impairment. Thus, first aim of the study was exploring in pregnancy the relationships between TyG and OGTT-derived insulin sensitivity. In addition, we developed a new version of TyG, for improved performance as marker of insulin resistance in pregnancy. METHODS: At early pregnancy, a cohort of 109 women underwent assessment of maternal biometry and blood tests at fasting, for measurements of several variables (visit 1). Subsequently (26 weeks of gestation) all visit 1 analyses were repeated (visit 2), and a subgroup of women (84 selected) received a 2 h-75 g OGTT (30, 60, 90, and 120 min sampling) with measurement of blood glucose, insulin and C-peptide for reliable assessment of insulin sensitivity (PREDIM index) and insulin secretion/beta-cell function. The dataset was randomly split into 70% training set and 30% test set, and by machine learning approach we identified the optimal model, with TyG included, showing the best relationship with PREDIM. For inclusion in the model, we considered only fasting variables, in agreement with TyG definition. RESULTS: The relationship of TyG with PREDIM was weak. Conversely, the improved TyG, called TyGIS, (linear function of TyG, body weight, lean body mass percentage and fasting insulin) resulted much strongly related to PREDIM, in both training and test sets (R2 > 0.64, p < 0.0001). Bland-Altman analysis and equivalence test confirmed the good performance of TyGIS in terms of association with PREDIM. Different further analyses confirmed TyGIS superiority over TyG. CONCLUSIONS: We developed an improved version of TyG, as new surrogate marker of insulin sensitivity in pregnancy (TyGIS). Similarly to TyG, TyGIS relies only on fasting variables, but its performances are remarkably improved than those of TyG.

Temporal Patterns of Glucagon and Its Relationships with Glucose and Insulin following Ingestion of Different Classes of Macronutrients.

BACKGROUND: glucagon secretion and inhibition should be mainly determined by glucose and insulin levels, but the relative relevance of each factor is not clarified, especially following ingestion of different macronutrients. We aimed to investigate the associations between plasma glucagon, glucose, and insulin after ingestion of single macronutrients or mixed-meal. METHODS: thirty-six participants underwent four metabolic tests, based on administration of glucose, protein, fat, or mixed-meal. Glucagon, glucose, insulin, and C-peptide were measured at fasting and for 300 min following food ingestion. We analyzed relationships between time samples of glucagon, glucose, and insulin in each individual, as well as between suprabasal area-under-the-curve of the same variables (ΔAUCGLUCA, ΔAUCGLU, ΔAUCINS) over the whole participants' cohort. RESULTS: in individuals, time samples of glucagon and glucose were related in only 26 cases (18 direct, 8 inverse relationships), whereas relationship with insulin was more frequent (60 and 5, p < 0.0001). The frequency of significant relationships was different among tests, especially for direct relationships (p ≤ 0.006). In the whole cohort, ΔAUCGLUCA was weakly related to ΔAUCGLU (p ≤ 0.02), but not to ΔAUCINS, though basal insulin secretion emerged as possible covariate. CONCLUSIONS: glucose and insulin are not general and exclusive determinants of glucagon secretion/inhibition after mixed-meal or macronutrients ingestion.

Stem cell factor affects tumour progression markers in metastatic melanoma cells.

Stem cell factor (SCF), next to various relevant biological effects exerted on many cell types, is able to keep melanocyte homeostasis through its receptor c-kit. Only a minority of metastatic melanoma cells (MMC) express c-kit receptor, but c-kit positive MMC move more slowly towards tumour progression and have a more natural tendency to undergo apoptosis. In our study c-kit positive MMC from human melanoma metastases and a c-kit positive human melanoma cell line-SK-MEL-28-showed a clear-cut reduction of cytokines normally up-regulated along melanoma progression after SCF stimulation. SCF was also able to maintain all MMC and SK-MEL-28 cells in a well differentiated status with an increase in organellogenesis and in particular of melanosomes in various degree of differentiation, but it did not induce apoptosis as observed in other in vitro models. The increase of melanosomes matched an increase of tyrosinase production. SCF did not modify the expression of NOS while it enhanced the expression of HLA-DR molecules on MMC membranes. Taken altogether these data stress the biological activity of SCF as a cytokine which is able to maintain MMC in a well differentiated status, and suggest a more in depth evaluation of possible effects of SCF on melanoma cells.