Search

Background: Medications targeting the glucagon-like peptide-1 (GLP-1) pathway are an important therapeutic class currently used for the treatment of Type 2 diabetes (T2D). However, there is not enough known about which subgroups of patients would receive the most benefit from these medications., Objective: The goal of this study was to develop a predictive model for patient responsiveness to medications, here collectively called GLP-1 M, that include GLP-1 receptor agonists and dipeptidyl peptidase-4 (DPP4) inhibitors (that normally degrade endogenously-produced GLP-1). Such a model could guide clinicians to consider certain patient characteristics when prescribing second line medications for T2D., Methods: We analyzed de-identified electronic health records of 7856 subjects with T2D treated with GLP-1 M drugs at Vanderbilt University Medical Center from 2003-2019. Using common clinical features (including commonly ordered lab tests, demographic information, other T2D medications, and diabetes-associated complications), we compared four different models: logistic regression, LightGBM, artificial neural network (ANN), and support vector classifier (SVC)., Results: Our analysis revealed that the traditional logistic regression model outperforms the other machine learning models, with an area under the Receiver Operating Characteristic curve (auROC) of 0.77.Our model showed that higher pre-treatment HbA1C is a dominant feature for predicting better response to GLP-1 M, while features such as use of thiazolidinediones or sulfonylureas is correlated with poorer response to GLP-1 M, as assessed by lowering of hemoglobin A1C (HbA1C), a standard marker of glycated hemoglobin used for assessing glycemic control in individuals with diabetes. Among female subjects under 40 taking GLP-1 M, the simultaneous use of non-steroidal anti-inflammatory drugs (NSAIDs) was associated with a greater reduction in HbA1C (0.82 ± 1.72% vs 0.28 ± 1.70%, p = 0.008)., Conclusion: These findings indicate a thorough analysis of real-world electronic health records could reveal new information to improve treatment decisions for the treatment of T2D. The predictive model developed in this study highlights the importance of considering individual patient characteristics and medication interactions when prescribing GLP-1 M drugs., Competing Interests: Declarations. Ethics approval and consent to participate: All methods were carried out in accordance with relevant guidelines and regulations. All experimental protocols were approved by the Vanderbilt University Medical Center IRB Committee/Committee on Human Research Protection. All of the data used in the current study was obtained from a fully deidentified synthetic derivative of the electronic health record from Vanderbilt University Medical Center. Individual informed consent for participation in the current study was waived since subject information is deidentified and specific patients cannot be identified. However, at the time of their visit, patients can opt out of being included in the database. Thus, all subjects in the database chose to be included. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Type 2 diabetes mellitus (DM2) is characterized by excessive hepatic gluconeogenesis, increased insulin resistance and a progressive inability of pancreatic beta cells to produce sufficient insulin. DM2 evolves as a progression from normal glucose tolerance, to impaired glucose tolerance (IGT) to frank diabetes mellitus, reflecting the establishment of insulin resistance and beta cell dysfunction. Insulin resistance not only contributes to impaired glycemic control in DM2, but to the development of hypertension, dyslipidemia and endothelial dysfunction. Cardiovascular disease is the primary morbidity for patients with DM2. The onset of insulin resistance and cardiovascular insult likely occurs well before the onset of IGT is detected clinically. Biguanides and thiazolidinediones (TZDs) are two classes of oral agents for the management of DM2 that improve insulin resistance, and thus have potential cardiovascular benefits beyond glycemic control alone. Metformin additionally inhibits hepatic gluconeogenesis. The combined use of two of these agents targets key pathophysiologic defects in DM2. Single pill combinations of rosiglitazone/metformin and pioglitazone/metformin have recently been approved for use in the US and Europe. This article reviews the clinical data behind the use of metformin in combination with TZDs for the management of diabetes, its impact on vascular health, side effects and potential mechanisms of action for combined use.

Pathway-selective insulin resistance where insulin fails to suppress hepatic glucose production but promotes liver fat storage may underlie glucose and lipid abnormalities after menopause. We tested the mechanisms by which estrogen treatment may alter the impact of a high-fat diet (HFD) when given at the time of ovariectomy (OVX) in mice. Female C57BL/6J mice underwent sham operation, OVX, or OVX with estradiol (E2) treatment and were fed an HFD. Hyperinsulinemic-euglycemic clamps were used to assess insulin sensitivity, tracer incorporation into hepatic lipids, and liver triglyceride export. OVX mice had increased adiposity that was prevented with E2 at the time of OVX. E2 treatment increased insulin sensitivity with OVX and HFD. In sham and OVX mice, HFD feeding induced fatty liver, and insulin reduced hepatic apoB100 and liver triglyceride export. E2 treatment reduced liver lipid deposition and prevented the decrease in liver triglyceride export during hyperinsulinemia. In mice lacking the liver estrogen receptor α, E2 after OVX limited adiposity but failed to improve insulin sensitivity, to limit liver lipid deposition, and to prevent insulin suppression of liver triglyceride export. In conclusion, estrogen treatment may reverse aspects of pathway-selective insulin resistance by promoting insulin action on glucose metabolism but limiting hepatic lipid deposition. [ABSTRACT FROM AUTHOR]