Impact of baseline body mass index status on glucose lowering and weight change during sitagliptin treatment for type 2 diabetics

Impact of Weight Change in Adults with Type 2 Diabetes Mellitus: A Literature Review and Critical Analysis

Objective

Weight reduction is a key component of diabetes management in adults with type 2 diabetes mellitus (T2DM), yet the benefits of weight loss in T2DM patients have been difficult to quantify. We examined the medical literature regarding the relationships between weight change and 1) glycemic control and 2) cost and resource use.

Methods

Systematic searches were conducted in the electronic databases Embase, MEDLINE, and the Cochrane Database of Systematic Reviews to identify publications regarding the impact of weight change on T2DM outcomes from 2007 onward. Identified publications were screened for relevance against predefined eligibility criteria, and methodological approaches and results were extracted. Evidence for the impact of weight change on outcomes was evaluated and used to identify strengths, limitations, and gaps in the current literature.

Results

The number of studies meeting eligibility criteria for each outcome was: glycemic control (n=38) and cost and resource use (n=11). The relationship between weight change and glycemic control was dependent on the interplay of multiple factors, eg, the weight loss interventions employed, the antidiabetic medication classes used, the time horizon, and baseline BMI and glycemic control. With respect to cost and resource use, the review indicated that savings were associated with weight loss, and increased costs were associated with weight gain.

Conclusion

Studies regarding weight change in T2DM patients demonstrated varying effects on glycemic control and a positive association with costs and resource use, where weight loss was associated with decreased costs and resource use. Future studies may be able to clarify these relationships.

Multispectral and computational probing of the interactions between sitagliptin and serum albumin

The binding of sitagliptin (SIT), an anti-diabetic drug, to human and bovine serum albumin (HSA and BSA; main serum transport proteins) was investigated using various spectroscopic and molecular docking techniques. The fluorescence data demonstrated that SIT quenched inherent fluorescence of these proteins through the formation of SIT-HSA/BSA complexes. The number of binding sites was obtained (~1) and binding constant (K_b) and effective quenching constant (K_a) were calculated as 10⁴ for both systems. Based on thermodynamic parameters, the van der Waals forces and hydrogen bonding were the most important forces in the interactions between HSA/BSA and SIT, and the complex formation processes were spontaneous. The results of UV-vis absorption and FT-IR spectroscopic revealed that SIT induces small conformational changes in the structure of the proteins (HSA/BSA). The synchronous fluorescence (SF) spectroscopy demonstrated that the binding of SIT with HSA/BSA had no effect on the polarity around Trp and Tyr residues. The CD spectra showed changes in the secondary and tertiary structures of both proteins with a decrease in α-helices contents and an increase in β-turn structures. The molecular docking and spectroscopic data verified the binding mechanisms between SIT and HSA/BSA, and revealed that SIT completely fits into the hydrophobic cavity between domain II and domain III of these proteins.

Secure Communication through MultiAgent System-Based Diabetes Diagnosing and Classification

The main objective of the research is to provide a multi-agent data mining system for diagnosing diabetes. Here, we use multi-agents for diagnosing diabetes such as user agent, connection agent, updation agent, and security agent, in which each agent performs their own task under the coordination of the connection agent. For secure communication, the user symptoms are encrypted with the help of Elliptic Curve Cryptography and Optimal Advanced Encryption Standard. In Optimal Advanced Encryption Standard algorithm, the key values are optimally selected by means of differential evaluation algorithm. After receiving the encrypted data, the suggested method needs to find the diabetes level of the user through multiple kernel support vector machine algorithm. Based on that, the agent prescribes the drugs for the corresponding user. The performance of the proposed technique is evaluated by classification accuracy, sensitivity, specificity, precision, recall, execution time and memory value. The proposed method will be implemented in JAVA platform.