Real World Use of Antidiabetic Drugs in the Years 2011-2017: A Population-Based Study from Southern Italy

Pharmacological and Benefit-Risk Profile of Once-Weekly Basal Insulin Administration (Icodec): Addressing Patients' Unmet Needs and Exploring Future Applications

Diabetes mellitus (DM) is a chronic metabolic disease affecting over 500 million people worldwide, which leads to severe complications and to millions of deaths yearly. When therapeutic goals are not reached with diet, physical activity, or non-insulin drugs, starting/adding insulin treatment is recommended by international guidelines. A novel recombinant insulin is icodec, a once-weekly insulin that successfully completed phase III trials and that has recently obtained the marketing authorization approval from the European Medicines Agency. This narrative review aims to assess icodec pharmacological and clinical features concerning evidence on benefit-risk profile, as compared to other basal insulins, addressing the potential impact on patients' unmet needs. Icodec is a full agonist, recombinant human insulin analogue characterized by an ultra-long half-life (196 h), enabling its use in once-weekly administration. Phase III randomized clinical trials involving more than 4000 diabetic patients, mostly type 2 DM, documented non-inferiority of icodec, as compared to currently available basal insulins, in terms of estimated mean reduction of glycated hemoglobin levels; a superiority of icodec, compared to control, was confirmed in insulin-naïve patients (ONWARDS 1, 3, and 5), and in patients previously treated with basal insulin (ONWARDS 2). Icodec safety profile was comparable to the currently available basal insulins. Once-weekly icodec has the potential to improve patients' adherence, thus positively influencing patients' treatment satisfaction as well as quality of life, especially in type 2 DM insulin-naïve patients. An improved adherence might positively influence glycemic target achievement, reduce overall healthcare costs and overcome some of the unmet patients' needs. Icodec has the potential to emerge as a landmark achievement in the evolution of insulin therapy, with a positive impact also for the National Health Services and the whole society.

Estimating Type 2 Diabetes Prevalence: A Model of Drug Consumption Data

Observational, cross-sectional prevalence studies are costly and time-consuming. The development of indirect methods estimating prevalence used to obtain faster, less-expensive, and more robust results would be an advantage for several healthcare applications. This study aimed to use the drug dispensing data from community pharmacies to estimate the prevalence of Type 2 Diabetes mellitus (T2DM) in the Portuguese population. A cross-sectional study was conducted using a database of dispensed medicines with an indication for Diabetes mellitus in 2018 and 2021, stratified by geographic region. The methodology was based on a sequential method of acquiring prevalence estimates obtained through exposure to medicines using the daily doses defined per thousand inhabitants per day and adjusted to the rate of adherence to therapy, prescription patterns, and concomitance of antidiabetic drugs. The estimated overall T2DM prevalence in 2018 was 13.9%, and it was 14.2% for 2021. The results show the increased consumption of antidiabetic drugs, with fixed-dose combination antidiabetics and new antidiabetics being particularly important in 2021. This work allowed for the development of a model to obtain the estimated prevalence of T2DM based on drug consumption, using a simple, fast, and robust method that is in line with the available evidence. However, with the recent expanding indications for new antidiabetics, the inclusion of further data in the model needs to be studied.

Use of Concurrent Anti-diabetes Medications in Patients With Type 2 Diabetes in Clinical Practice in the United States

PURPOSE

This report describes the use of combination therapy in patients with type 2 diabetes mellitus (T2DM) who had been initially prescribed metformin in the United States.

METHODS

Retrospective claims data from a de-identified database were used to identify individuals aged ≥18 years with ≥1 claim for a metformin-containing regimen dated between January 1, 2018, and December 31, 2018. Demographics, insurance type, and prescriber type were compared among subgroups receiving two (dual) or three or more (triple+) anti-diabetes therapies. All analyses were descriptive; no formal comparisons were conducted.

FINDINGS

Data from 353,062 patients were included. Demographic and other baseline characteristics were similar between the groups receiving dual or triple+ therapy (n = 213,871 and 139,191, respectively). A small age difference was observed between patients receiving dual versus triple+ therapy (mean [SD], 66.5 [11.8] and 65.8 [10.8] years, respectively). Mean (SD) glycosylated hemoglobin levels were lower among patients receiving dual therapy versus triple+ therapy: 7.6% (1.7) versus 8.0% (1.7). The most frequent combination was metformin plus a sulfonylurea (33.4%). The percentage receiving combination therapy with newer treatments was relatively low, and slightly greater in younger patients. Total health care costs were similar with dual and triple+ therapies.

IMPLICATIONS

The current descriptive analysis demonstrated generally similar features, with regard to the evaluated factors, in cohorts receiving dual versus triple+ T2DM therapy. However, differences between unmeasured factors could exist and require further evaluation. These findings, based on data from a cohort of patients from clinical practice who had initially been prescribed metformin, provide a useful snapshot of current prescribing practices and can be used to inform future research and evidence-based policy decisions.

Network Theory and Switching Behaviors: A User Guide for Analyzing Electronic Records Databases

As part of studies that employ health electronic records databases, this paper advocates the employment of graph theory for investigating drug-switching behaviors. Unlike the shared approach in this field (comparing groups that have switched with control groups), network theory can provide information about actual switching behavior patterns. After a brief and simple introduction to fundamental concepts of network theory, here we present (i) a Python script to obtain an adjacency matrix from a records database and (ii) an illustrative example of the application of network theory basic concepts to investigate drug-switching behaviors. Further potentialities of network theory (weighted matrices and the use of clustering algorithms), along with the generalization of these methods to other kinds of switching behaviors beyond drug switching, are discussed.

Kidney Disease in Diabetic Patients: From Pathophysiology to Pharmacological Aspects with a Focus on Therapeutic Inertia

Diabetes mellitus represents a growing concern, both for public economy and global health. In fact, it can lead to insidious macrovascular and microvascular complications, impacting negatively on patients' quality of life. Diabetic patients often present diabetic kidney disease (DKD), a burdensome complication that can be silent for years. The average time of onset of kidney impairment in diabetic patients is about 7-10 years. The clinical impact of DKD is dangerous not only for the risk of progression to end-stage renal disease and therefore to renal replacement therapies, but also because of the associated increase in cardiovascular events. An early recognition of risk factors for DKD progression can be decisive in decreasing morbidity and mortality. DKD presents patient-related, clinician-related, and system-related issues. All these problems are translated into therapeutic inertia, which is defined as the failure to initiate or intensify therapy on time according to evidence-based clinical guidelines. Therapeutic inertia can be resolved by a multidisciplinary pool of healthcare experts. The timing of intensification of treatment, the transition to the best therapy, and dietetic strategies must be provided by a multidisciplinary team, driving the patients to the glycemic target and delaying or overcoming DKD-related complications. A timely nephrological evaluation can also guarantee adequate information to choose the right renal replacement therapy at the right time in case of renal impairment progression.