Big data and machine learning to tackle diabetes management.

BACKGROUND: Type 2 Diabetes (T2D) diagnosis is based solely on glycaemia, even though it is an endpoint of numerous dysmetabolic pathways. Type 2 Diabetes complexity is challenging in a real-world scenario; thus, dissecting T2D heterogeneity is a priority. Cluster analysis, which identifies natural clusters within multidimensional data based on similarity measures, poses a promising tool to unravel Diabetes complexity. METHODS: In this review, we scrutinize and integrate the results obtained in most of the works up to date on cluster analysis and T2D. RESULTS: To correctly stratify subjects and to differentiate and individualize a preventive or therapeutic approach to Diabetes management, cluster analysis should be informed with more parameters than the traditional ones, such as etiological factors, pathophysiological mechanisms, other dysmetabolic co-morbidities, and biochemical factors, that is the millieu. Ultimately, the above-mentioned factors may impact on Diabetes and its complications. Lastly, we propose another theoretical model, which we named the Integrative Model. We differentiate three types of components: etiological factors, mechanisms and millieu. Each component encompasses several factors to be projected in separate 2D planes allowing an holistic interpretation of the individual pathology. CONCLUSION: Fully profiling the individuals, considering genomic and environmental factors, and exposure time, will allow the drive to precision medicine and prevention of complications.

Metabolic Footprint, towards Understanding Type 2 Diabetes beyond Glycemia.

Type 2 diabetes (T2D) heterogeneity is a major determinant of complications risk and treatment response. Using cluster analysis, we aimed to stratify glycemia within metabolic multidimensionality and extract pathophysiological insights out of metabolic profiling. We performed a cluster analysis to stratify 974 subjects (PREVADIAB2 cohort) with normoglycemia, prediabetes, or non-treated diabetes. The algorithm was informed by age, anthropometry, and metabolic milieu (glucose, insulin, C-peptide, and free fatty acid (FFA) levels during the oral glucose tolerance test OGTT). For cluster profiling, we additionally used indexes of metabolism mechanisms (e.g., tissue-specific insulin resistance, insulin clearance, and insulin secretion), non-alcoholic fatty liver disease (NAFLD), and glomerular filtration rate (GFR). We found prominent heterogeneity within two optimal clusters, mainly representing normometabolism (Cluster-I) or insulin resistance and NAFLD (Cluster-II), at higher granularity. This was illustrated by sub-clusters showing similar NAFLD prevalence but differentiated by glycemia, FFA, and GFR (Cluster-II). Sub-clusters with similar glycemia and FFA showed dissimilar insulin clearance and secretion (Cluster-I). This work reveals that T2D heterogeneity can be captured by a thorough metabolic milieu and mechanisms profiling-metabolic footprint. It is expected that deeper phenotyping and increased pathophysiology knowledge will allow to identify subject's multidimensional profile, predict their progression, and treat them towards precision medicine.

Insulin: Trigger and Target of Renal Functions.

Kidney function in metabolism is often underestimated. Although the word "clearance" is associated to "degradation", at nephron level, proper balance between what is truly degraded and what is redirected to de novo utilization is crucial for the maintenance of electrolytic and acid-basic balance and energy conservation. Insulin is probably one of the best examples of how diverse and heterogeneous kidney response can be. Kidney has a primary role in the degradation of insulin released in the bloodstream, but it is also incredibly susceptible to insulin action throughout the nephron. Fluctuations in insulin levels during fast and fed state add another layer of complexity in the understanding of kidney fine-tuning. This review aims at revisiting renal insulin actions and clearance and to address the association of kidney dysmetabolism with hyperinsulinemia and insulin resistance, both highly prevalent phenomena in modern society.