Current progress in pharmacogenomics of Type 2 diabetes: A systemic overview

Profiling of patients with type 2 diabetes based on medication adherence data

Introduction

Type 2 diabetes mellitus (T2DM) is a complex, chronic disease affecting multiple organs with varying symptoms and comorbidities. Profiling patients helps identify those with unfavorable disease progression, allowing for tailored therapy and addressing special needs. This study aims to uncover different T2DM profiles based on medication intake records and laboratory measurements, with a focus on how individuals with diabetes move through disease phases.

Methods

We use medical records from databases of the last 20 years from the Department of Endocrinology and Diabetology of the University Medical Center in Maribor. Using the standard ATC medication classification system, we created a patient-specific drug profile, created using advanced natural language processing methods combined with data mining and hierarchical clustering.

Results

Our results show a well-structured profile distribution characterizing different age groups of individuals with diabetes. Interestingly, only two main profiles characterize the early 40-50 age group, and the same is true for the last 80+ age group. One of these profiles includes individuals with diabetes with very low use of various medications, while the other profile includes individuals with diabetes with much higher use. The number in both groups is reciprocal. Conversely, the middle-aged groups are characterized by several distinct profiles with a wide range of medications that are associated with the distinct concomitant complications of T2DM. It is intuitive that the number of profiles increases in the later age groups, but it is not obvious why it is reduced later in the 80+ age group. In this context, further studies are needed to evaluate the contributions of a range of factors, such as drug development, drug adoption, and the impact of mortality associated with all T2DM-related diseases, which characterize these middle-aged groups, particularly those aged 55-75.

Conclusion

Our approach aligns with existing studies and can be widely implemented without complex or expensive analyses. Treatment and drug use data are readily available in healthcare facilities worldwide, allowing for profiling insights into individuals with diabetes. Integrating data from other departments, such as cardiology and renal disease, may provide a more sophisticated understanding of T2DM patient profiles.

Identification of Transporter Polymorphisms Influencing Metformin Pharmacokinetics in Healthy Volunteers

For patients with type 2 diabetes, metformin is the most often recommended drug. However, there are substantial individual differences in the pharmacological response to metformin. To investigate the effect of transporter polymorphisms on metformin pharmacokinetics in an environment free of confounding variables, we conducted our study on healthy participants. This is the first investigation to consider demographic characteristics alongside all transporters involved in metformin distribution. Pharmacokinetic parameters of metformin were found to be affected by age, sex, ethnicity, and several polymorphisms. Age and SLC22A4 and SLC47A2 polymorphisms affected the area under the concentration-time curve (AUC). However, after adjusting for dose-to-weight ratio (dW), sex, age, and ethnicity, along with SLC22A3 and SLC22A4, influenced AUC. The maximum concentration was affected by age and SLC22A1, but after adjusting for dW, it was affected by sex, age, ethnicity, ABCG2, and SLC22A4. The time to reach the maximum concentration was influenced by sex, like half-life, which was also affected by SLC22A3. The volume of distribution and clearance was affected by sex, age, ethnicity and SLC22A3. Alternatively, the pharmacokinetics of metformin was unaffected by polymorphisms in ABCB1, SLC2A2, SLC22A2, or SLC47A1. Therefore, our study demonstrates that a multifactorial approach to all patient characteristics is necessary for better individualization.

Development and validation of a model that predicts the risk of diabetic retinopathy in type 2 diabetes mellitus patients

AIMS

Diabetic retinopathy is the leading cause of blindness in people with type 2 diabetes. To enable primary care physicians to identify high-risk type 2 diabetic patients with diabetic retinopathy at an early stage, we developed a nomogram model to predict the risk of developing diabetic retinopathy in the Xinjiang type 2 diabetic population.

METHODS

In a retrospective study, we collected data on 834 patients with type 2 diabetes through an electronic medical record system. Stepwise regression was used to filter variables. Logistic regression was applied to build a nomogram prediction model and further validated in the training set. The c-index, forest plot, calibration plot, and clinical decision curve analysis were used to comprehensively validate the model and evaluate its accuracy and clinical validity.

RESULTS

Four predictors were selected to establish the final model: hypertension, blood urea nitrogen, duration of diabetes, and diabetic peripheral neuropathy. The model displayed medium predictive power with a C-index of 0.781(95%CI:0.741-0.822) in the training set and 0.865(95%CI:0.807-0.923)in the validation set. The calibration curve of the DR probability shows that the predicted results of the nomogram are in good agreement with the actual results. Decision curve analysis demonstrated that the novel nomogram was clinically valuable.

CONCLUSIONS

The nomogram of the risk of developing diabetic nephropathy contains 4 characteristics. that can help primary care physicians quickly identify individuals at high risk of developing DR in patients with type 2 diabetes, to intervene as soon as possible.