Entelos develops mathematical formula to predict weight gain for diabetics

By Elaine Rigoli

Tampa, Fla., June 12 - Entelos, Inc. presented data Monday that revealed the first mathematical means for predicting not only the amount of weight gain expected for patients receiving type 2 diabetes treatments - a common side effect - but also the decrease in food intake necessary to prevent it.

Entelos said such information can help doctors counsel type 2 diabetes patients on ways to minimize weight gain during treatment, which should enhance drug efficacy and improve their control of blood sugar levels.

Increases in body weight can accompany treatment with many type 2 diabetes drugs, preventing them from achieving full efficacy.

This increase reflects a shift in energy balance, with urinary glucose excretion (glycosuria) playing an important and underappreciated role in the energy balance shift, according to a news release.

To investigate the impact of common type 2 diabetes drugs on glycosuria and weight gain, 117 clinically representative virtual patients with normal glomerular filtration rates and renal function were treated with nateglinide (N), pioglitazone (P) or insulin (I) for six to 12 months, and body weight, A1C and urinary glucose loss were measured.

Food intake and energy expenditure were maintained at pre-treatment levels, the release said.

Results show a strong correlation between the calories gained from decreased renal glucose loss and the increase in body weight, with r2 = 0.93, 0.94, 0.92 for N, P and I treatment, respectively.

Additional correlations were found between pre-treatment A1C and increased body weight, with r2 = 0.63, 0.65, 0.61 for N, P and I, respectively.

Based on these relationships, a mathematical formula was developed that accurately predicts not only the amount of weight patients can expect to gain, but also the quantitative modifications in energy balance necessary to prevent it.

Entelos, a Foster City, Calif.-based life sciences company, applies engineering principles and mathematical modeling approaches to simulate human biology.

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