Current barriers to initiating insulin therapy in individuals with type 2 diabetes.

Insulin is an essential drug in the treatment of diabetes, often necessary for managing hyperglycemia in type 2 diabetes mellitus (T2DM). It should be considered in cases of severe hyperglycemia requiring hospitalization, after the failure of other treatments, in advanced chronic kidney disease, liver cirrhosis, post-transplant diabetes, or during pregnancy. Moreover, in specific patient subgroups, early initiation of insulin is crucial for hyperglycemia control and prevention of chronic complications. Clinical guidelines recommend initiating insulin when other treatments fail, although there are barriers that may delay its initiation. The timing of initiation depends on individual patient characteristics. Typically, insulinization starts by adding basal insulin to the patient's existing treatment and, if necessary, progresses by gradually introducing prandial insulin. Several barriers have been identified that hinder the initiation of insulin, including fear of hypoglycemia, lack of adherence, the need for glucose monitoring, the injection method of insulin administration, social rejection associated with the stigma of injections, weight gain, a sense of therapeutic failure at initiation, lack of experience among some healthcare professionals, and the delayed and reactive positioning of insulin in recent clinical guidelines. These barriers contribute, among other factors, to therapeutic inertia in initiating and intensifying insulin treatment and to patients' non-adherence. In this context, the development of once-weekly insulin formulations could improve initial acceptance, adherence, treatment satisfaction, and consequently, the quality of life for patients. Currently, two once-weekly basal insulins, insulin icodec and basal insulin BIF, which are in different stages of clinical development, may help. Their longer half-life translates to lower variability and reduced risk of hypoglycemia. This review addresses the need for insulin in T2DM, its positioning in clinical guidelines under specific circumstances, the current barriers to initiating and intensifying insulin treatment, and the potential role of once-weekly insulin formulations as a potential solution to facilitate timely initiation of insulinization, which would reduce therapeutic inertia and achieve better early control in people with T2DM.

Clinical Practice Experiences Using a Professional Diabetes Management Ecosystem During COVID.

BACKGROUND: Challenges of patient care in diabetes were exacerbated by COVID, undermining the ability of patients to engage in-person with health care professionals (HCPs). To combat this, there has been accelerated adoption of telemedicine to support patient and provider connectivity. METHODS: We collated survey information regarding telemedicine from 21 European clinical institutions. Health care professionals joined virtual meetings focusing on the OneTouch Reveal (OTR) ecosystem and its utility for conducting telemedicine. Selected HCPs provided clinical case studies to explain how the OTR ecosystem supported patient care. RESULTS: Remote consultations increased by nearly 50% in 21 European clinics during the pandemic (Belgium [24%], Iberia [65%], Germany [34%], Italy [54%]). In all, 52% of people with diabetes using OTR app to connect remotely with HCPs had type 1 diabetes and 48% had type 2 diabetes. Remote connection methods included telephone (60%), email (19%), video chat (10%), text only (3%), or a mix of these methods (8%). Health care professionals usually reviewed patient data during consultations (45%) rather than before consultations (25%). Fifty-five percent of HCPs indicated digital ecosystems like OTR ecosystem would become their standard of care for diabetes management. In-depth conversations with HCPs provided a deeper understanding of how a digital ecosystem integrated into clinical practice and population management. In addition, five patient case studies using OTR ecosystem were provided by a selection of our HCPs. CONCLUSION: Diabetes management solutions, such as OTR ecosystem, supported telemedicine during the pandemic and will continue to play a valuable role in patient care beyond the pandemic.

Riesgo de mortalidad total y cardiovascular según la clasificación propuesta por las guías KDIGO en pacientes con diabetes tipo 2 / Total and cardiovascular mortality risk according to KDIGO guidelines classification in type 2 diabetic patients

Antecedentes y objetivo: El objetivo del estudio fue comprobar la validez de la clasificación de riesgo KDIGO 2012 para predecir mortalidad total (MT) y cardiovascular (MCV) en diabetes mellitus tipo 2 (DM2). Materiales y métodos: Estudio de cohortes prospectivo incluyendo pacientes con DM2. Los puntos finales clínicos fueron MT y MCV. La principal variable predictora fue la clasificación KDIGO, una variable que recoge 4 niveles de riesgo en dependencia de una combinación de la tasa de filtración glomerular y la excreción de albúmina urinaria. La evaluación del poder predictivo se realizó con el índice de mejora de discriminación integrada (IDI). Resultados: Se incluyeron 453 pacientes (39,3% varones, edad 64,9 [DE 9,3] años y evolución de DM2 de 10,4 [DE 7,5] años). Durante una mediana de 13 años de seguimiento, hubo incremento significativo de la tasa/1000 pacientes-año de MT (26,5 vs. 45,1 vs. 79,2 vs. 109,8; p<0,001) y de MCV (8,1 vs. 17,4 vs. 24,7 vs. 57,5; p<0,001) en las sucesivas categorías de riesgo KDIGO. En análisis multivariante también hubo incremento de riesgo de MT (HR[riesgo moderado]=1,29; HR[riesgo alto]=1,83; HR[riesgo muy alto]=2,15; p=0,016) y MCV (HR[riesgo moderado]=1,73; HR[riesgo alto]=2,27; HR[riesgo muy alto]=4,22; p=0,007) en las sucesivas categorías. La clasificación KDIGO mejoró la predicción de MT (IDI=0,00888; p=0,047) y MCV (IDI=0,01813; p=0,035). Conclusiones: La clasificación de riesgo según guías KDIGO 2012 puede estratificar eficazmente el riesgo de MT y MCV en pacientes con DM2

Background and aims: Our aim was to assess the usefulness of KDIGO 2012 risk classification to predict total and cardiovascular mortality in type 2 diabetes mellitus (DM2). Material and methods: Prospective cohort study that included DM2 patients. Clinical end-points were total and cardiovascular mortality. The main predictive variable was KDIGO risk classification, which is a combination of urinary albumin excretion and glomerular filtration rate. The predictive value was evaluated by the integrated discrimination improvement (IDI) index. Results: 453 patients (39.3% males, aged 64.9 [SD 9.3] and with a mean diabetes duration of 10.4 [SD 7.5] years) were included. During a median follow-up of 13 years, mortality rates per 1000 patients/year (26.5 vs. 45.1 vs. 79,2 vs. 109,8; p<0,001) and cardiovascular mortality (8.1 vs. 17.4 vs. 24.7 vs. 57.5; p<0,001) were progressively increased in successive KDIGO categories. In the multivariate analysis, there was also a progressive increase of mortality risk (HR[moderate risk]=1.29; HR[high risk])=1.83; HR[very high risk]=2.15; p=.016) and cardiovascular mortality risk (HR[moderate risk]=1.73; HR[high risk]=2.27; HR[very high risk]=4.22; p=.007) in the successive categories. KDIGO classification was able to improve the mortality risk prediction (IDI=0.00888; p=.047) and cardiovascular mortality risk prediction (IDI=0.01813; p=.035). Conclusions: KDIGO risk classification can effectively stratify total and cardiovascular mortality risk in DM2 patients

Total and cardiovascular mortality risk according to KDIGO guidelines classification in type 2 diabetic patients. / Riesgo de mortalidad total y cardiovascular según la clasificación propuesta por las guías KDIGO en pacientes con diabetes tipo 2.

BACKGROUND AND AIMS: Our aim was to assess the usefulness of KDIGO 2012 risk classification to predict total and cardiovascular mortality in type 2 diabetes mellitus (DM2). MATERIAL AND METHODS: Prospective cohort study that included DM2 patients. Clinical end-points were total and cardiovascular mortality. The main predictive variable was KDIGO risk classification, which is a combination of urinary albumin excretion and glomerular filtration rate. The predictive value was evaluated by the integrated discrimination improvement (IDI) index. RESULTS: 453 patients (39.3% males, aged 64.9 [SD 9.3] and with a mean diabetes duration of 10.4 [SD 7.5] years) were included. During a median follow-up of 13 years, mortality rates per 1000 patients/year (26.5 vs. 45.1 vs. 79,2 vs. 109,8; p<0,001) and cardiovascular mortality (8.1 vs. 17.4 vs. 24.7 vs. 57.5; p<0,001) were progressively increased in successive KDIGO categories. In the multivariate analysis, there was also a progressive increase of mortality risk (HR[moderate risk]=1.29; HR[high risk])=1.83; HR[very high risk]=2.15; p=.016) and cardiovascular mortality risk (HR[moderate risk]=1.73; HR[high risk]=2.27; HR[very high risk]=4.22; p=.007) in the successive categories. KDIGO classification was able to improve the mortality risk prediction (IDI=0.00888; p=.047) and cardiovascular mortality risk prediction (IDI=0.01813; p=.035). CONCLUSIONS: KDIGO risk classification can effectively stratify total and cardiovascular mortality risk in DM2 patients.