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.

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 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.

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.

Non-polarized cytokine profile of a long-term non-progressor HIV infected patient.

The HIV-1 initial viral infection may present diverse clinical and laboratory course and lead to rapid, intermediate, or long-term progression. Among the group of non-progressors, the elite controllers are those who control the infection most effectively, in the absence of antiretroviral therapy (ART). In this paper, the TH1, TH2 and TH17 cytokines profiles are described, as well as clinical and laboratory aspects of an HIV-infected patient with undetectable viral load without antiretroviral therapy.