Sodium glucose transporter protein 2 inhibitors: focusing on the kidney to treat type 2 diabetes

Type 2 diabetes mellitus (T2DM) is increasing worldwide. Treatment of T2DM continues to present challenges, with a significant proportion of patients failing to achieve and maintain glycemic targets. Despite the availability of many oral antidiabetic agents, therapeutic efficacy is also offset by side effects such as weight gain and hypoglycemia. Therefore, the search for novel therapeutic agents with an improved benefit–risk profile continues. In the following review we focus on a novel class of oral antidiabetic drugs, the sodium glucose transporter protein 2 (SGLT2) inhibitors, which have unique characteristics. SGLT2 inhibitors focus on the kidney as a therapeutic target, where they inhibit the reabsorption of glucose in the proximal tubule, causing an increase in urinary glucose excretion. Doing this, they reduce plasma glucose independently of the β-cell function of the pancreas. SGLT2 inhibitors are effective at lowering hemoglobin A1c, but also induce weight loss and reduce blood pressure, with a low risk of hypoglycemia. In general, the SGLT2 inhibitors are well tolerated, with the most frequent adverse events being mild urinal and genital infections. Since their primary site of effect is the kidney, these drugs are less effective in patients with impaired kidney function but evidence is emerging that these drugs may also have a protective effect against diabetic nephropathy. This review focuses on the most extensively studied SGLT2 inhibitors dapagliflozin, canagliflozin and empagliflozin. Dapagliflozin and canagliflozin have already been approved for marketing by the US Food and Drug Administration. The European Medicines Agency has accepted all three drugs for marketing

Insulin degludec + liraglutide: a complementary combination

The treatment of patients with type 2 diabetes mellitus remains challenging, as it goes beyond adequate glycemic control, in particular addressing weight, blood pressure and other contributors to cardiovascular disease. In addition, the progressive nature of type 2 diabetes mellitus demands the intensification and combination of glucose lowering therapies. In many patients, there is a clinical inertia for the initiation of insulin therapy, leading to failure in reaching glycemic targets in many patients.peerreview_statement: The publishing and review policy for this title is described in its Aims & Scope. aims_and_scope_url: http://www.tandfonline.com/action/journalInformation?show=aimsScope&journalCode=iebt20status: publishe

Two Distinct Chronic Obstructive Pulmonary Disease (COPD) Phenotypes Are Associated with High Risk of Mortality

In COPD patients, mortality risk is influenced by age, severity of respiratory disease, and comorbidities. With an unbiased statistical approach we sought to identify clusters of COPD patients and to examine their mortality risk.status: publishe

Two Distinct Chronic Obstructive Pulmonary Disease (COPD) Phenotypes Are Associated with High Risk of Mortality

<div><h3>Rationale</h3><p>In COPD patients, mortality risk is influenced by age, severity of respiratory disease, and comorbidities. With an unbiased statistical approach we sought to identify clusters of COPD patients and to examine their mortality risk.</p> <h3>Methods</h3><p>Stable COPD subjects (n = 527) were classified using hierarchical cluster analysis of clinical, functional and imaging data. The relevance of this classification was validated using prospective follow-up of mortality.</p> <h3>Results</h3><p>The most relevant patient classification was that based on three clusters (phenotypes). Phenotype 1 included subjects at very low risk of mortality, who had mild respiratory disease and low rates of comorbidities. Phenotype 2 and 3 were at high risk of mortality. Phenotype 2 included younger subjects with severe airflow limitation, emphysema and hyperinflation, low body mass index, and low rates of cardiovascular comorbidities. Phenotype 3 included older subjects with less severe respiratory disease, but higher rates of obesity and cardiovascular comorbidities. Mortality was associated with the severity of airflow limitation in Phenotype 2 but not in Phenotype 3 subjects, and subjects in Phenotype 2 died at younger age.</p> <h3>Conclusions</h3><p>We identified three COPD phenotypes, including two phenotypes with high risk of mortality. Subjects within these phenotypes may require different therapeutic interventions to improve their outcome.</p> </div

Description of the 527 COPD patients based on spirometric GOLD classification.

<p>BMI : body mass index; FEV1: forced expiratory volume in 1 sec, FVC: forced vital capacity, RV: residual volume, TLC: total lung capacity, TGV: thoracic gas volume, Raw: airway resistance, Sgaw: specific airway conductance, DLCO: diffusing capacity of the lung for carbon monoxide, KCO: ratio of DLCO to alveolar volume, mMRC: modified Medical Research Council Scale.</p>*<p>, % missing data: GOLD I 83%, GOLD II 28%.</p

Kaplan-Meier analysis of mortality between Phenotypes.

<p>Subjects in Phenotype 2 and 3 were at higher risk of mortality than subjects in Phenotype 1 (each comparison, <i>P</i><0.0001; log-rank test). However, no significant difference was observed between Phenotype 2 and 3, indicating that during the period of observation both group had comparable mortality.</p

Dendrogram illustrating the results of the cluster analysis in 527 COPD subjects.

<p>Subjects were classified using agglomerative hierarchical cluster analysis based on the main axes identified by principal component analysis (PCA) and multiple correspondence analyses (MCA, see Methods section). Each vertical line represents an individual subject and the length of vertical lines represents the degree of similarity between subjects. The horizontal lines identify possible cut-off for choosing the optimal number of clusters in the data. When choosing 3 clusters (upper line) the 3 groups (labelled 1 to 3) have differential mortality rates (0.5%, 20.6% and 14.3% for Phenotype 1, 2, and 3, respectively). When choosing 5 clusters (lower line, labelled 1′ to 5′), subjects in clusters 1′ and 2′ had comparable mortality rates (0.7% and 0%, respectively) and subjects in clusters 4′ and 5′ had similar mortality rates (14.3% in each group), suggesting that grouping in 5 phenotypes would not improve patient classification.</p

Cox model analysis of mortality between phenotypes.

<p>CI: confidence interval.</p

Description of the 527 COPD patients based on phenotypes identified by cluster analysis.

*<p>% missing data: Phenotype 1∶67%; Phenotype 2∶1%, Phenotype 3∶4%.</p><p>P values correspond to comparisons between the 3 phenotypes using Kruskal-Wallis or Chi-square tests, as appropriate.</p

Flow chart.

<p>Abbreviations: BMI: body mass index; mMRC: modified Medical Research Council; CCQ: clinical COPD questionnaire; TGV: thoracic gas volume and DLCO: diffusing capacity of the lung for carbon monoxide.</p