Incidence of diabetes mellitus in Italy in year 2018. A nationwide population-based study of the ARNO Diabetes Observatory

Adherence to GLP1-RA and SGLT2-I affects clinical outcomes and costs in patients with type 2 diabetes

AIMS

To evaluate the impact of adherence to glucagon like peptide-1 receptor agonists (GLP1-RA) and sodium-glucose transporter two inhibitors (SGLT2-I) on clinical outcomes and costs in patients with type 2 diabetes mellitus (T2DM).

MATERIALS AND METHODS

The 121,115 residents of the Lombardy Region (Italy) aged ≥40 years newly treated with metformin during 2007-2015 were followed to identify those who started therapy with GLP1-RA or SGLT2-I. Adherence to drug therapy over the first year was defined as the proportion of days covered >80%. Within each drug class, for each adherent patient, one non-adherent patient was matched for age, sex, duration, adherence to metformin treatment and propensity score. The primary clinical outcome was a composite of insulin initiation, hospitalisation for micro- and macrovascular complications and all-cause mortality after the first year of drug treatment. Costs were evaluated based on reimbursements from the national healthcare system.

RESULTS

After matching, 1182 pairs of adherent and non-adherent GLP1-RA users and 1126 pairs of adherent and non-adherent SGLT2-I users were included. In both groups, adherent patients experienced a significantly lower incidence of the primary outcome (HR: 0.85, 95% CI 0.72-0.98 for GLP1-RA and HR: 0.69, 95% CI 0.55-0.87 for SGLT2-I). A significant reduction in hospitalizations was found for adherent patients in the GLP1-RA group but not for the SGLT2-I group. Results were consistent when analyses were stratified by age and sex. While higher drug-related costs in the adherent group were counterbalanced by decreased hospitalisation costs in SGLT2-I treated patients, this was not the case for GLP1-RA.

CONCLUSIONS

Higher adherence to drug treatment with GLP1-RA and SGLT2-I during the first year of the drug intake is associated with a lower incidence of adverse clinical outcomes in a real-world setting.

Genetic, lifestyle and metabolic factors contributing to cardiovascular disease in the Italian population: a literature review

Cardiovascular diseases (CVD) represent a major health problem worldwide. In Italy, despite the decline in CVD mortality and disability-adjusted life years recently observed, CVD remains the leading cause of death. The development of CVD has a complex and multifactorial etiology that involves environmental, lifestyle/behavioral (e.g., unhealthy diet, physical inactivity, smoking, and alcohol abuse), metabolic, and genetic factors. Although a large number of CVD susceptibility genetic variants have been identified, some seem to confer risk according to the genetic background or ethnicity of the population. Some CVD-associated polymorphisms with appreciable frequency in the Italian population may be important contributors to the development and progression of the most prevalent CVD in the population. This literature review aims to provide an overview of the epidemiology of CVD in Italy, as well as to highlight the main genetic, lifestyle/behavioral, and metabolic factors contributing to CVD risk in this population.

The multidimensional prognostic index (MPI) predicts long-term mortality in old type 2 diabetes mellitus patients: a 13-year follow-up study

PURPOSE

The Multidimensional Prognostic Index (MPI) is a tool capable of holistically frame older patients in different settings and affected by different pathologies, establishing a risk of adverse events. Among them, type 2 diabetes mellitus (T2DM), a common metabolic disease in the elderly, is responsible for complications and deaths. Few previous works have focused specifically on MPI and DM, and none have followed up the patients for more than 3 years. The aim of the present study is to analyze MPI accuracy in predicting mortality in a cohort of T2DM patients followed-up for 13 years.

METHODS

The enrolled subjects were evaluated with MPI, identifying three levels of risk: MPI1 (low risk, 0.0-0.33), MPI2 (moderate risk, 0.34-0.66), and MPI3 (severe risk, 0.67-1.0), and with glycated hemoglobin, and years since T2DM diagnosis.

RESULTS

One hundred and seven patients met the inclusion criteria. MPI3 was excluded by further analysis since it was made up of only three patients. Overall, cognitive performances, autonomies in daily living, nutritional status, risk of pressure injuries, comorbidities, and taken drugs were better (p ≤ 0.0077) in MPI1 than MPI2; moreover, the story of T2DM was shorter (p = 0.0026). Cox model showed an overall 13-year survival of 51.9%, and survival rates were significantly smaller in MPI2 (HR: 4.71, p = 0.0007). Finally, increased age (HR: 1.15), poorer cognitive abilities (HR: 1.26), vascular (HR: 2.15), and kidney (HR: 2.17) diseases were independently associated with death.

CONCLUSION

Our results prove that MPI predicts short-, mid-, and even long-term mortality in T2DM patients, whose death seems to be related to age and cognitive status, and even more to vascular and kidney diseases.

La pandemia diabete in Italia

In Italia vivono circa 4 milioni di persone con il diabete e ogni anno ci sono circa 350 mila nuove diagnosi. Le persone con diabete ricevono prescrizioni di farmaci, esami di laboratorio e strumentali e richiedono ricoveri ospedalieri più spesso delle persone senza il diabete. Il diabete è gravato da aumentata mortalità e accorcia la vita, soprattutto nelle persone di media età. La qualità della cura nelle persone con diabete è subottimale, soprattutto fra chi non è assistito nei centri diabetologici (che, peraltro, rappresentano dei luoghi “salvavita”). Il diabete contribuisce fortemente alla spesa sanitaria e comporta ingenti spese, non tutte specificamente sanitarie. Nonostante tutto questo e nonostante leggi nazionali e regionali, documenti di indirizzo nazionali e regionali, PDTA regionali e locali, promesse e proponimenti di varia provenienza, la pandemia diabete non riceve le attenzioni che merita.

Inhibition of α-glucosidase by trilobatin and its mechanism: kinetics, interaction mechanism and molecular docking

α-Glucosidase is related to the increase in postprandial blood glucose in vivo. Inhibition of α-glucosidase is supposed to be an effective approach to treat type 2 diabetes mellitus (T2DM). Trilobatin, a member of the dihydrochalcone family, shows anti-oxidant, anti-inflammatory and anti-diabetic activities. In this study, the inhibitory activity and mechanism of trilobatin on α-glucosidase were investigated using multispectroscopic and molecular docking techniques. The kinetic analysis showed that trilobatin reversibly inhibited α-glucosidase in a noncompetitive-type manner and the value of IC₅₀ was 0.24 ± 0.02 mM. The analysis of fluorescence spectra demonstrated that the formation of the trilobatin-α-glucosidase complex was driven mainly by hydrogen bonding and van der Waals forces, resulting in the conformational changes of α-glucosidase. Fourier transform infrared spectroscopy (FT-IR) and circular dichroism (CD) measurements suggested that the interaction could change the micro-environment and conformation of α-glucosidase affected by trilobatin. Molecular docking analysis determined the exact binding sites of trilobatin on α-glucosidase. These results indicated that trilobatin is a strong α-glucosidase inhibitor, thus it could be conducive to ameliorate T2DM.

The Application of Wearable Glucose Sensors in Point-of-Care Testing

Diabetes and its complications have become a worldwide concern that influences human health negatively and even leads to death. The real-time and convenient glucose detection in biofluids is urgently needed. Traditional glucose testing is detecting glucose in blood and is invasive, which cannot be continuous and results in discomfort for the users. Consequently, wearable glucose sensors toward continuous point-of-care glucose testing in biofluids have attracted great attention, and the trend of glucose testing is from invasive to non-invasive. In this review, the wearable point-of-care glucose sensors for the detection of different biofluids including blood, sweat, saliva, tears, and interstitial fluid are discussed, and the future trend of development is prospected.