Adherence behavior assessment of oral antidiabetic medication use: a study of patient decisions in long-term disease management in primary health care centers in Surabaya

Sustained linagliptin administration: superior glycemic control and less pancreatic injury in diabetic rats

While linagliptin is the most potent dipeptidyl peptidase 4 inhibitor, its use is limited due to poor bioavailability and the potential risk of pancreatic injury. Here, we investigated whether the sustained weekly administration of linagliptin could provide better effect compared to frequent daily oral administration. Type 2 diabetes was induced by feeding rats a high fructose/fat/salt diet followed by STZ injection. Compared to the partial glycemic control achieved with daily oral linagliptin, a weekly subcutaneous injection containing about one-fourth of the oral dose produced superior glycemic control, as evidenced by the 4-week postprandial glucose follow-up and oral glucose tolerance test. This was confirmed by the significant increase in serum insulin in the case of the sustained linagliptin administration. Higher levels of the anti-inflammatory cytokine adiponectin and lower triglyceride levels were observed after sustained linagliptin administration compared with daily oral linagliptin. In addition, sustained linagliptin displayed a significant increase in β-cells' insulin immunoreactivity when compared with daily linagliptin. More reduction in collagen deposition and caspase-3 immunoreactivity in pancreatic tissue were observed in sustained linagliptin compared with oral linagliptin. In conclusion, sustained linagliptin administration provided superior glycemic control, which seems to be mediated by more reduction in pancreatic injury.

Adherence to Oral Antidiabetic Drugs in Patients with Type 2 Diabetes: Systematic Review and Meta-Analysis

Poor adherence to oral antidiabetic drugs (OADs) in patients with type 2 diabetes (T2D) can lead to therapy failure and risk of complications. The aim of this study was to produce an adherence proportion to OADs and estimate the association between good adherence and good glycemic control in patients with T2D. We searched in MEDLINE, Scopus, and CENTRAL databases to find observational studies on therapeutic adherence in OAD users. We calculated the proportion of adherent patients to the total number of participants for each study and pooled study-specific adherence proportions using random effect models with Freeman-Tukey transformation. We also calculated the odds ratio (OR) of having good glycemic control and good adherence and pooled study-specific OR with the generic inverse variance method. A total of 156 studies (10,041,928 patients) were included in the systematic review and meta-analysis. The pooled proportion of adherent patients was 54% (95% confidence interval, CI: 51-58%). We observed a significant association between good glycemic control and good adherence (OR: 1.33; 95% CI: 1.17-1.51). This study demonstrated that adherence to OADs in patients with T2D is sub-optimal. Improving therapeutic adherence through health-promoting programs and prescription of personalized therapies could be an effective strategy to reduce the risk of complications.

Development and assessment of novel machine learning models to predict medication non-adherence risks in type 2 diabetics

Background

Medication adherence is the main determinant of effective management of type 2 diabetes, yet there is no gold standard method available to screen patients with high-risk non-adherence. Developing machine learning models to predict high-risk non-adherence in patients with T2D could optimize management.

Methods

This cross-sectional study was carried out on patients with T2D at the Sichuan Provincial People's Hospital from April 2018 to December 2019 who were examined for HbA1c on the day of the survey. Demographic and clinical characteristics were extracted from the questionnaire and electronic medical records. The sample was randomly divided into a training dataset and a test dataset with a radio of 8:2 after data preprocessing. Four imputing methods, five sampling methods, three screening methods, and 18 machine learning algorithms were used to groom data and develop and validate models. Bootstrapping was performed to generate the validation set for external validation and univariate analysis. Models were compared on the basis of predictive performance metrics. Finally, we validated the sample size on the best model.

Results

This study included 980 patients with T2D, of whom 184 (18.8%) were defined as medication non-adherence. The results indicated that the model used modified random forest as the imputation method, random under sampler as the sampling method, Boruta as the feature screening method and the ensemble algorithms and had the best performance. The area under the receiver operating characteristic curve (AUC), F1 score, and area under the precision-recall curve (AUPRC) of the best model, among a total of 1,080 trained models, were 0.8369, 0.7912, and 0.9574, respectively. Age, present fasting blood glucose (FBG) values, present HbA1c values, present random blood glucose (RBG) values, and body mass index (BMI) were the most significant contributors associated with risks of medication adherence.

Conclusion

We found that machine learning methods could be used to predict the risk of non-adherence in patients with T2D. The proposed model was well performed to identify patients with T2D with non-adherence and could help improve individualized T2D management.

Exploring of Determinants Factors of Anti-Diabetic Medication Adherence in Several Regions of Asia - A Systematic Review

BACKGROUND

The determinants of medication adherence in people with diabetes may differ between populations of an area due to social environment, cultural beliefs, socioeconomic conditions, education, and many other factors differences.

OBJECTIVE

Therefore, this study aims to explore, identify and classify the determinants of medication adherence in several Asian regions.

METHODS

A systematic literature review was conducted to gain insight into the determinants of medication adherence. Seven relevant databases (EBSCO, ProQuest, PubMed, ScienceDirect, Scopus, Wiley, dan Taylor and Francis) and hand searching methods were conducted from January 2011 to December 2020. Keywords were compiled based on the PICO method. The selection process used the PRISMA guidelines based on inclusion, and the quality was assessed using Crowe's critical assessment tool. Textual summaries and a conceptual framework model of medication adherence were proposed to aid in the understanding of the factors influencing medication adherence.

RESULTS

Twenty-six articles from countries in several Asian regions were further analyzed. Most studies on type 2 diabetes patients in India used the MMAS-8 scale, and cross-sectional study is the most frequently used research design. The medication adherence rate among diabetic patients was low to moderate. Fifty-one specific factors identified were further categorized into twenty-three subdomains and six domains. Furthermore, the determinants were classified into four categories: inconsistent factors, positively related factors, negatively related factors, and non-associated factors. In most studies, patient-related factors dominate the association with medication adherence. This domain relates to patient-specific demographics, physiological feelings, knowledge, perceptions and beliefs, comorbidities, and other factors related to the patient. Several limitations in this review need to be considered for further research.

CONCLUSION

Medication adherence to diabetic therapy is a complex phenomenon. Most determinants produced disparate findings in terms of statistical significance. The identified factors can serve various goals related to medication adherence. Policymakers and health care providers should consider patient-related factors.

Social media health interventions to improve diabetes mellitus patient outcome: a systematic review

OBJECTIVES

The use of modern technology and social media has revolutionized the way health information is distributed to diabetes mellitus patients. Social media can be used as a medium of providing health interventions to improve patient health outcomes. Social media is able to provide a more intensive communication facility between healthcare professionals and patients. We aim to systematically review and describe the effect of social media interventions on health outcomes of patients with diabetes mellitus.

METHODS

A systematic review was carried out from three electronic databases (Pubmed, Scopus, and Medline). Eligible publications are studies that describe the application of social media interventions on the health outcomes of patients with diabetes mellitus.

RESULTS

Fourteen studies were selected for this systematic review, 10 studies with a randomized controlled trial design, and 4 studies with a nonrandomized controlled trial design. Six studies only used interventions using social media, A blend of face-to-face social media intervention was used in 6 studies, 2 studies used a combination of telephone and social media intervention. One study had treatment behavior outcomes with improvement in treatment behavior, 6 studies had clinical outcomes (an improvement in HbA1c values in the four studies), 6 studies had treatment behavior outcomes and clinical outcomes (1 study had improved treatment behavior and clinical outcomes, 3 studies had improved treatment behavior outcome only), and 1 study had medication adherence outcome (no improvement in medication adherence).

CONCLUSIONS

These findings indicate that the intervention using social media can improve the health outcomes of diabetes mellitus patients.

Efficacy of Cinnamon as an Adjuvant in Reducing the Glycemic Biomarkers of Type 2 Diabetes Mellitus: A Three-Month, Randomized, Triple-Blind, Placebo-Controlled Clinical Trial

The major aim of this randomized, placebo-controlled, triple-blind clinical trial was to evaluate the efficacy of cinnamon as an adjuvant treatment in reducing glycemic levels in people with type 2 diabetes, compared to a placebo. The study was conducted between August and December 2019, with 160 people with type 2 diabetes, in five Primary Health Units, in Parnaíba, Brazil. Inclusion criteria were: persons of both genders using oral antidiabetic agents, with glycated hemoglobin ≥ 6.0%, and between 18 and 80 years of age. The primary outcome was change in glycated hemoglobin levels after 90 days of intervention. Other biomarkers evaluated were fasting blood glucose, insulin level, and HOMA-IR index. Participants were divided equally into two groups of 80 individuals each, and were given 3 g capsules of either cinnamon or placebo to be taken in combination with their usual oral antidiabetic agents. After 90 days, participants in the cinnamon group had statistically significant reductions of 0.2% of glycated hemoglobin and 0.55 mmol/L of fasting venous glucose, when compared with the placebo group. Cinnamon reduced the glycemic measures of persons with type 2 diabetes, albeit with modest reductions. TRIAL: RBR-2KKB6D.