Distinct pathoclinical clusters among patients with uncontrolled type 2 diabetes: results from a prospective study in rural India.

INTRODUCTION: Inadequate glycemic control among patients with type 2 diabetes mellitus (T2DM) poses an enormous challenge. Whether this uncontrolled T2DM population is a heterogenous mix of disease subtypes remains unknown. Identification of these subtypes would result in a customized T2DM management protocol thereby paving the way toward personalized therapy. RESEARCH DESIGN AND METHODS: Electronic health records of 339 patients with uncontrolled T2DM patients followed up for a median period of 14 months were analyzed using Uniform Manifold Approximation and Projection followed by density-based spatial clustering of applications with noise. Baseline clinical features and final diagnoses with drug combinations were selected in the analysis. A 30 min oral glucose tolerance test was next performed for assessing the underlying insulin resistance and ß cell dysfunction. RESULTS: Three major clusters were identified. The first cluster characterized by recent onset T2DM had moderately preserved ß cell function. The second cluster with a longer duration of T2DM and associated hypertension showed the best glycemic control with dual antidiabetic therapy. The third cluster with the longest history of T2DM and no history of hypertension had the worst glycemic control in spite of the highest percentage of patients on triple therapy (34.58%) and quadruple therapy (8.41%). CONCLUSIONS: Uncontrolled T2DM comprises a heterogeneous population with respect to disease duration, presence of co-morbidities and ß cell function without significant difference in insulin resistance. Stratifying them on the basis of pathoclinical features is the first step toward a personalized management in T2DM.

Clustering Based Parameter Estimation of Thyroid Hormone Pathway.

An ordinary differential equation (ODE) model of the working of the thyroid system for euthyroidism has been presented. As clinical data for thyroid hormones is relatively scarce, such modelling offers potential benefits over wet lab procedures. Genetic algorithms developed for determining of parameters of the ODE system using the available data have been presented and evaluated. This approach enables subject specific parameter estimation towards characterisation of individual thyroid operation. Initially, a simple steady state model was used. Later a cosinor model for the circadian variation of thyroid hormones was used to obtain more reliable results, as indicated through sensitivity analysis in conjunction with other statistical methods. Our parameter determination method has been tested on groups of patients with similar observed values of thyroid stimulating hormone (TSH), free T 3 and free T 4 (identified through clustering) to determine their parameter values jointly. This approach appears to produce parameter sets with lower variation than parameters determined independently, thus leading to better parameter determination.