Validation of the revised Diabetes Self-Management Questionnaire (DSMQ-R) in the primary care setting

Background Available tools measuring self-management in diabetes are often improperly validated or do not correlate with glucose metabolism. The Diabetes Self-Management Questionnaire (DSMQ-R) is a valid tool, that showed strong relationship with glucose metabolism in tertiary care among people with mostly type 1 diabetes. Aim of the study To validate the translated DSMQ-R questionnaire in a Hungarian sample of people with predominantly type 2 diabetes in primary care. Methods We enrolled 492 adults from 38 practices in this cross-sectional cohort study, who filled out the self-administered questionnaire, consisting of DSMQ-R and the Summary of Diabetes Self-Care Activities (SDSCA) questionnaires. Family doctors provided clinical data. The translation process was performed in six steps, reaching the expert committee appraisal. The validity of the questionnaire was evaluated by assessing reliability and construct validity. Results Cronbach’s alpha showed the questionnaire to reach good reliability (α = 0.845), although subscales had lower alphas. Contrary to the SDSCA questionnaire, the DSMQ-R sum scale differed significantly between persons on target vs not on target (median (interquartile range): 7.23 (6.17–8.44) vs 6.91 (5.91–8.02), and the DSMQ-R sum scale correlated significantly with BMI, HbA1c and SDSCA sum scale. In multivariate analysis higher DSMQ-R scores were significant predictor of achieving glycemic target goal. Conclusion The Hungarian translation of the DSMQ-R is a comprehensible tool to assess self-management of persons with diabetes. The questionnaire is valid and reliable in family practice, although its association with achievement of diabetes HbA1c target is weaker in primary than in tertiary care.

Optimal Data Collection Period for Continuous Glucose Monitoring to Assess Long-Term Glycemic Control: Revisited

Background and Objective: It is not clear how the short-term continuous glucose monitoring (CGM) sampling time could influence the bias in estimating long-term glycemic control. A large bias could, in the worst case, lead to incorrect classification of patients achieving glycemic targets, nonoptimal treatment, and false conclusions about the effect of new treatments. This study sought to investigate the relation between sampling time and bias in the estimates. Methods: We included a total of 329 type 1 patients (age 14-86 years) with long-term CGM (90 days) data from three studies. The analysis calculated the bias from estimating long-term glycemic control based on short-term sampling. Time in range (TIR), time above range (TAR), time below range (TBR), correlation, and glycemic target classification accuracy were assessed. Results: A sampling time of ten days is associated with a high bias of 10% to 47%, which can be reduced to 4.9% to 26.4% if a sampling time of 30 days is used ( P < .001). Correct classification of patients archiving glycemic targets can also be improved from 81.5% to 91.9 to 90% to 95.2%. Conclusions: Our results suggest that the proposed 10-14 day CGM sampling time may be associated with a high correlation with three-month CGM. However, these estimates are subject to large intersubject bias, which is clinically relevant. Clinicians and researchers should consider using assessments of longer durations of CGM data if possible, especially when assessing time in hypoglycemia or while testing a new treatment.

A multi-professional approach for transition of care at discharge in hyperglycemic inpatients with covid-19: a single center study

Background: The discharge from hospital of insulin-treated hyperglycemic patients is always challenging. This is even more so in patients requiring glucocorticoid treatment, such as those with COVID-19. Patients and method: A retrospective monocentric study of 23 inpatients with newly diagnosed or already known diabetes mellitus (DM) who were naïve to insulin treatment, , and who were hospitalized with COVID-19 in non-critical settings and then discharged. Patients were followed-up for one month after discharge for the management of insulin treatment by a multi-professional team through phone consultations. Results: Insulin prescriptions at discharge were 24.6 ± 14 U/day injected in 2 ± 1.5 daily shots. A mean of three phone consultations were required. One month later, the mean insulin reduction was 1.5 ± 1.3 shots and 6 ± 5 U/day. All patients reached their glycemic target without hypoglycemic events, drop-outs, or readmissions. Conclusion: This study demonstrates the feasibility, efficacy, and safety of a multi-professional approach through telemedicine for managing DM patients after discharge during COVID-19.

Associated Factors and Pharmacists’ Role in Medication Adherence and Glycemic Control : A Study in Outpatients With Diabetes at Hue University Hospital, Vietnam

Objective: To evaluate medication adherence, associated factors, and the role of pharmacists in adherence and outcome treatments in outpatients with diabetes at Hue University Hospital. Type 2 diabetes (T2DM) is a chronic illness that requires daily treatment. Poor adherence to antidiabetic medication can have negative consequences for patients. Data on medication adherence and programs to improve adherence for patients with diabetes in Vietnam are lacking. Methods: A pre-post study was conducted on 354 outpatients diagnosed with T2DM at Hue University Hospital. Participants were interviewed, counseled, and educated by a pharmacist once. The researchers assessed medication adherence levels and glycemic outcomes before and around three months after the intervention. Results: The prevalence of achieving adherence before the intervention was 65.0%. Factors associated with achieving medication adherence were medication regimen (P = 0.001) and controlled glycemic target (P < 0.001). The most common nonadherence behavior was forgetting to take antidiabetic medication. After the intervention, the prevalence of achieving adherence rose to 74.6%, and patients reported that they were more likely to remember to take antidiabetic medications (with statistical significance). The prevalence of achieving the glycemic target (both glycated hemoglobin and fasting plasma glucose) rose from 21.8% (before the intervention) to 31.1% (after the intervention). Conclusions: A significant proportion of patients did not achieve medication adherence. Medication adherence is associated with better glycemic outcomes. The role of pharmacists in patient education, medication counseling, and reminding is beneficial in terms of improving adherence levels and glycemic outcomes.

Precision health care strategies for older adults with diabetes in Indonesia: a Delphi consensus study

BACKGROUND Studies on precision health care for older adults with diabetes in Indonesia are still limited. This study was aimed to reach the experts consensus on the suitable precision health care strategies for older adults with diabetes. METHODS A total of 10 experts (4 physicians, 4 nurses, and 2 dietitians) agreed to participate in the 3-round interview using Delphi technique. The experts should have at least 5 years of experience in teaching or working as health professionals in a hospital. RESULTS Consensus was reached that precision health care consisted of eight elements: self-management, interdisciplinary collaborative practice, personalized genetic or lifestyle factors, glycemic target, patient preferences, glycemic control, patient priority-directed care, and biodata- or evidence-based practice. The strategies of precision health care for diabetes were divided into seven steps: conducting brief deducting teaching; assessing self-management level and risk of cardiovascular disease; organizing a brainstorming session among patients to exchange experiences on glycemic target and specific target behavior; making a list of patients’ needs and ranking the priorities; setting a goal and writing action; doing follow-up; and reporting the goal attempts. CONCLUSIONS The eight elements of precision health care provided the basis of precision health care strategies for diabetic older adults, which are the real and measurable strategies for precision health care implementation in clinical settings.

Circulating microRNAs Signature for Predicting Response to GLP1-RA Therapy in Type 2 Diabetic Patients: A Pilot Study

Type 2 diabetes (T2D) represents one of the major health issues of this century. Despite the availability of an increasing number of anti-hyperglycemic drugs, a significant proportion of patients are inadequately controlled, thus highlighting the need for novel biomarkers to guide treatment selection. MicroRNAs (miRNAs) are small non-coding RNAs, proposed as useful diagnostic/prognostic markers. The aim of our study was to identify a miRNA signature occurring in responders to glucagon-like peptide 1 receptor agonists (GLP1-RA) therapy. We investigated the expression profile of eight T2D-associated circulating miRNAs in 26 prospectively evaluated diabetic patients in whom GLP1-RA was added to metformin. As expected, GLP1-RA treatment induced significant reductions of HbA1c and body weight, both after 6 and 12 months of therapy. Of note, baseline expression levels of the selected miRNAs revealed two distinct patient clusters: “high expressing” and “low expressing”. Interestingly, a significantly higher percentage of patients in the high expression group reached the glycemic target after 12 months of treatment. Our findings suggest that the evaluation of miRNA expression could be used to predict the likelihood of an early treatment response to GLP1-RA and to select patients in whom to start such treatment, paving the way to a personalized medicine approach.

Dynamic Insulin Basal Needs Estimation and Parameters Adjustment in Type 1 Diabetes

Technology advances have made possible improvements such as Continuous Glucose Monitors, giving the patient a glucose reading every few minutes, or insulin pumps, allowing more personalized therapies. With the increasing number of available closed-loop systems, new challenges appear regarding algorithms and functionalities. Several of the analysed systems in this paper try to adapt to changes in some patients’ conditions and, in several of these systems, other variables such as basal needs are considered fixed from day to day to simplify the control problem. Therefore, these systems require a correct adjustment of the basal needs profile which becomes crucial to obtain good results. In this paper a novel approach tries to dynamically determine the insulin basal needs of the patient and use this information within a closed-loop algorithm, allowing the system to dynamically adjust in situations of illness, exercise, high-fat-content meals or even partially blocked infusion sites and avoiding the need for setting a basal profile that approximately matches the basal needs of the patient. The insulin sensitivity factor and the glycemic target are also dynamically modified according to the situation of the patient. Basal insulin needs are dynamically determined through linear regression via the decomposition of previously dosed insulin and its effect on the patient’s glycemia. Using the obtained value as basal insulin needs and other mechanisms such as basal needs modification through its trend, ISF and glycemic targets modification and low-glucose-suspend threshold, the safety of the algorithm is improved. The dynamic basal insulin needs determination was successfully included in a closed-loop control algorithm and was simulated on 30 virtual patients (10 adults, 10 adolescent and 10 children) using an open-source python implementation of the FDA-approved (Food and Drug Administration) UVa (University of Virginia)/Padova Simulator. Simulations showed that the proposed system dynamically determines the basal needs and can adapt to a partial blockage of the insulin infusion, obtaining similar results in terms of time in range to the case in which no blockage was simulated. The proposed algorithm can be incorporated to other current closed-loop control algorithms to directly estimate the patient’s basal insulin needs or as a monitoring channel to detect situations in which basal needs may differ from the expected ones.

Precision Health Care Elements, Definitions and Strategies for Patients with Diabetes: A Literature Review

Diabetes is a prevalent disease with a high risk of complications. The number of people with diabetes worldwide was reported to increase every year. However, new integrated individualized health care related to diabetes is insufficiently developed. Purpose: The objective of this study was to conduct a literature review and discover precision health care elements, definitions, and strategies. Methods: This study involved a 2-stage process. The first stage comprised a systematic literature search, evidence evaluation, and article extraction. The second stage involved discovering precision health care elements and defining and developing strategies for the management of patients with diabetes. Results: Of 1337 articles, we selected 35 relevant articles for identifying elements and definitions of precision health care for diabetes, including personalized genetic or lifestyle factors, biodata- or evidence-based practice, glycemic target, patient preferences, glycemic control, interdisciplinary collaboration practice, self-management, and patient priority direct care. Moreover, strategies were developed to apply precision health care for diabetes treatment based on eight elements. Conclusions: We discovered precision health care elements and defined and developed strategies of precision health care for patients with diabetes. precision health care is based on team foundation, personalized glycemic target, and control as well as patient preferences and priority, thus providing references for future research and clinical practice.

Efficacy of Dulaglutide Expanded Doses by Baseline A1C Categories: Post Hoc Analysis of AWARD-11

The AWARD-11 trial demonstrated the safety and efficacy of dulaglutide (DU) once weekly doses of 3 mg and 4.5 mg compared to DU 1.5 mg in patients with type 2 diabetes (T2D) inadequately controlled with metformin monotherapy. This exploratory post hoc analysis of AWARD-11 assessed the effect of dulaglutide on A1C reduction by clinically-relevant baseline A1C subgroups (&lt;8%; 8%-&lt;9%; 9%-&lt;10%; ≥10%) and the proportion of patients achieving A1C &lt;7% in these subgroups through 36 and 52 weeks. Patients were randomized to once weekly DU 1.5 mg (n=612), 3 mg (n=616), or 4.5 mg (n=614). All patients initiated once weekly DU 0.75 mg for 4 weeks, followed by stepwise dose escalation every 4 weeks to the randomized dose. A mixed effects model for repeated measures was used within the A1C subgroups to assess the change in A1C from baseline at 36 and 52 weeks. A longitudinal logistic regression model was used within subgroups to analyze the proportion of patients achieving A1C &lt;7% at 36 and 52 weeks. Efficacy analyses used data collected up to initiation of rescue medication or premature treatment discontinuation, if either occurred. DU 1.5 mg reduced A1C across all baseline A1C categories at 36 weeks (range, -1.0 to -2.2%) and effects were sustained through 52 weeks (range, -1.0 to -2.1%). A1C reductions were greater in patients randomized to DU 3 mg or 4.5 mg versus 1.5 mg in each A1C subgroup, with greater dose-related improvements in patients with higher baseline A1C through 36 weeks (A1C subgroup, least-squares mean change in A1C [%] with 1.5 mg, 3 mg, and 4.5 mg, respectively: A1C&lt;8%, -1.0, -1.2, -1.2; A1C 8-&lt;9%, -1.4, -1.6, -1.8; A1C 9-&lt;10%, -2.1, -2.2, -2.3; A1C ≥10%, -2.2, -2.5, -3.2; interaction p&lt;0.001). More patients randomized to 3 mg or 4.5 mg achieved A1C &lt;7% versus those on 1.5 mg at 36 weeks regardless of baseline A1C, but the difference across dose groups was greater at higher baseline A1Cs. Over half of patients randomized to DU 4.5 mg achieved A1C &lt;7% in every baseline A1C category (A1C&lt;8%, 75%, 87%, 83%; A1C 8-&lt;9%, 61%, 64%, 73%; A1C 9-&lt;10%, 46%, 51%, 64%; A1C ≥10%, 19%, 33%, 55% for DU 1.5 mg, 3 mg, and 4.5 mg, respectively; interaction p=0.096). Similar patterns of dose-related improvement in A1C and proportions of patients achieving A1C &lt;7% across baseline A1C categories were observed at 52 weeks. Gastrointestinal adverse events were similar between A1C subgroups. Glycemic control as measured by A1C and proportion of patients achieving A1C &lt;7% was improved with DU dose escalation from 1.5 mg to 3 mg or 4.5 mg across a spectrum of clinically relevant baseline A1C categories without increasing incidence of GI adverse events. Patients at higher baseline A1Cs (9%-&lt;10% and ≥10%) had larger dose-related improvements in glycemic control than those at lower baseline A1Cs (&lt;8% and 8%-&lt;9%). The majority of patients randomized to DU 4.5 mg achieved glycemic target across all categories of baseline A1C.

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.