Effectiveness of insulin therapy in people with Type 2 diabetes in the Hoorn Diabetes Care System

Efficacy and safety of once-weekly versus once-daily basal insulin analogues in the treatment of type 2 diabetes mellitus: A systematic review and meta-analysis

AIM

To summarize the evidence of recently published randomized controlled trials (RCTs) studying efficacy, in terms of glycaemic control, and safety of the newly developed once-weekly basal insulin analogues.

METHODS

A systematic literature search was conducted through Medline (via PubMed), Cochrane Library and Google Scholar until June 30, 2023. Double-independent study selection, data extraction and quality assessment were performed. Results were summarized with random-effects meta-analysis.

RESULTS

A total of 3962 patients with type 2 diabetes mellitus (T2DM) among nine RCTs were analysed. All RCTs had low risk of bias according to the Cochrane Collaboration risk-of-bias tool (RoB2). Once-weekly insulins demonstrated better efficacy in glycated haemoglobin (HbA1c) reduction (mean difference [MD] -0.13%, 95% confidence interval [CI] -0.23, -0.03; P = 0.08) and a significantly greater time in range compared with once-daily insulin analogues (MD 3.54%, 95% CI 1.56, 5.53; P = 0.005). Based on subgroup analyses, the reduction in HbA1c and the odds of achieving an end-of-treatment HbA1c <6.5% were significantly greater for icodec compared to the once-daily insulin (MD -0.18%, 95% CI -0.27, -0.09 [P < 0.001] and odds ratio [OR] 1.75, 95% CI 1.34, 2.29 [P < 0.001], respectively). Once-weekly insulins were associated with higher odds of level 1 hypoglycaemia during the 24-hour period (OR 1.3, 95% CI 1.04, 1.64; P = 0.02) but were safer in terms of level 2 or 3 nocturnal hypoglycaemic events (OR 0.74, 95% CI 0.56, 0.97; P = 0.03). No difference was observed regarding serious adverse events between the two groups.

CONCLUSION

The once-weekly basal insulin analogues seem to be at least equally efficient in glycaemic management and safe compared to once-daily injections in people with T2DM. Phase 4 RCTs are expected to shed further light on the effectiveness and safety of once-weekly insulin therapy over the long term.

Data-Driven Identification of Long-Term Glycemia Clusters and Their Individualized Predictors in Finnish Patients with Type 2 Diabetes

Purpose

To gain an understanding of the heterogeneous group of type 2 diabetes (T2D) patients, we aimed to identify patients with the homogenous long-term HbA1c trajectories and to predict the trajectory membership for each patient using explainable machine learning methods and different clinical-, treatment-, and socio-economic-related predictors.

Patients and Methods

Electronic health records data covering primary and specialized healthcare on 9631 patients having T2D diagnosis were extracted from the North Karelia region, Finland. Six-year HbA1c trajectories were examined with growth mixture models. Linear discriminant analysis and neural networks were applied to predict the trajectory membership individually.

Results

Three HbA1c trajectories were distinguished over six years: "stable, adequate" (86.5%), "improving, but inadequate" (7.3%), and "fluctuating, inadequate" (6.2%) glycemic control. Prior glucose levels, duration of T2D, use of insulin only, use of insulin together with some oral antidiabetic medications, and use of only metformin were the most important predictors for the long-term treatment balance. The prediction model had a balanced accuracy of 85% and a receiving operating characteristic area under the curve of 91%, indicating high performance. Moreover, the results based on SHAP (Shapley additive explanations) values show that it is possible to explain the outcomes of machine learning methods at the population and individual levels.

Conclusion

Heterogeneity in long-term glycemic control can be predicted with confidence by utilizing information from previous HbA1c levels, fasting plasma glucose, duration of T2D, and use of antidiabetic medications. In future, the expected development of HbA1c could be predicted based on the patient's unique risk factors offering a practical tool for clinicians to support treatment planning.

Gender differences in the blood glucose type 2 diabetes patients with combination rapid and long acting insulin therapy

OBJECTIVES

Previous research suggests that there may be intergender differences in the profile of glycemic control achievable during the treatment of type 2 diabetes mellitus. This preliminary study was conducted to determine differences in glycemic outcomes in type 2 diabetes mellitus patients amongst men and women in an Indonesian hospital.

METHODS

The study was conducted at the outpatient internal medicine polyclinic of Universitas Airlangga Teaching Hospital Surabaya. This observational prospective cohort study examining outcomes for 64 patients (32 men and 32 women) treated with insulin therapy. The primary outcome measure was the extent to which subjects achieved concordance with the target blood glucose parameters based on the American Diabetes Association (ADA) guidance.

RESULTS

After 3 months of combination basal-bolus insulin treatment, the proportion of subjects who had fasting blood glucose values in the target range did not increase for either gender. For women, there was a significantly higher proportion of subjects who achieved a postprandial glucose value within the target range (p=0.04).

CONCLUSIONS

In this study, patients achieved postprandial glycemic outcomes for women but not men. More research is required to elucidate the possible intergender difference in results for subjects treated with basal-bolus insulin for type 2 diabetes mellitus.

The burden of type 2 diabetes in Europe: Current and future aspects of insulin treatment from patient and healthcare spending perspectives

Due to the progressive nature of type 2 diabetes (T2DM), initiation of insulin therapy is very likely in the disease continuum. This article aims at highlighting the current situation with regard to insulin therapy in people with T2DM in Europe and at presenting the associated unmet need. Challenges for both people with T2DM and healthcare professionals include clinical inertia also derived from fear of hypoglycaemia, weight gain and injections as well as increased need for a comprehensive diabetes management. We compare national and international guidelines and recommendations for the initiation and intensification of insulin therapy with the real-world situation in six European countries, demonstrating that glycaemic targets are only met in a minority of people with T2DM on insulin therapy. Furthermore, this work evaluates currently recorded numbers of people with T2DM treated with insulin in Europe, the proportion not achieving the stated glycaemic targets and thus in need to enhance insulin therapy e.g. by a change in means of insulin delivery including, but not limited to, insulin pens, wearable mealtime insulin delivery patches, patch pumps, and conventional insulin pumps with continuous subcutaneous insulin infusion.

Predicting short- and long-term glycated haemoglobin response after insulin initiation in patients with type 2 diabetes mellitus using machine-learning algorithms

AIM

To assess the potential of supervised machine-learning techniques to identify clinical variables for predicting short-term and long-term glycated haemoglobin (HbA1c) response after insulin treatment initiation in patients with type 2 diabetes mellitus (T2DM).

MATERIALS AND METHODS

We included patients with T2DM from the Groningen Initiative to Analyse Type 2 diabetes Treatment (GIANTT) database who started insulin treatment between 2007 and 2013 and had a minimum follow-up of 2 years. Short- and long-term responses at 6 (±2) and 24 (±2) months after insulin initiation, respectively, were assessed. Patients were defined as good responders if they had a decrease in HbA1c ≥ 5 mmol/mol or reached the recommended level of HbA1c ≤ 53 mmol/mol. Twenty-four baseline clinical variables were used for the analysis and an elastic net regularization technique was used for variable selection. The performance of three traditional machine-learning algorithms was compared for the prediction of short- and long-term responses and the area under the receiver-operating characteristic curve (AUC) was used to assess the performance of the prediction models.

RESULTS

The elastic net regularization-based generalized linear model, which included baseline HbA1c and estimated glomerular filtration rate, correctly classified short- and long-term HbA1c response after treatment initiation, with AUCs of 0.80 (95% CI 0.78-0.83) and 0.81 (95% CI 0.79-0.84), respectively, and outperformed the other machine-learning algorithms. Using baseline HbA1c alone, an AUC = 0.71 (95% CI 0.65-0.73) and 0.72 (95% CI 0.66-0.75) was obtained for predicting short-term and long-term response, respectively.

CONCLUSIONS

Machine-learning algorithm performed well in the prediction of an individual's short-term and long-term HbA1c response using baseline clinical variables.

Distinct trajectories of HbA1c in newly diagnosed Type 2 diabetes from the DPV registry using a longitudinal group-based modelling approach

AIM

To identify groups of heterogeneous HbA_1c trajectories over time in newly diagnosed Type 2 diabetes.

METHODS

The study comprised 6355 adults with newly diagnosed Type 2 diabetes (55% men, median age 62 years, baseline BMI 31 kg/m² ) from the Diabetes Patienten Verlaufsdokumentation (DPV) prospective multicentre diabetes registry (Germany, Austria). Individuals were assessed during the first 5 years after diabetes diagnosis if they had ≥ 3 aggregated HbA_1c measurements during follow-up. Latent class growth modelling was used to determine distinct subgroups that followed similar longitudinal HbA_1c patterns (SAS: Proc Traj). Multinomial logistic regression models were used to investigate which variables were associated with the respective HbA_1c trajectory groups.

RESULTS

Four distinct longitudinal HbA_1c trajectory (glycaemic control) groups were found. The largest group (56% of participants) maintained stable good glycaemic control (HbA_1c 42-45 mmol/mol). Twenty-six percent maintained stable moderate glycaemic control (HbA_1c 57-62 mmol/mol). A third group (12%) initially showed severe hyperglycaemia (HbA_1c 97 mmol/mol) but reached good glycaemic control within 1 year. The smallest group (6%) showed stable poor glycaemic control (HbA_1c 79-88 mmol/mol). Younger age at diabetes diagnosis, male sex, and higher BMI were associated with the stable moderate or poor glycaemic control groups. Insulin therapy was strongly associated with the highly improved glycaemic control group.

CONCLUSIONS

Four subgroups with distinct HbA_1c trajectories were determined in newly diagnosed Type 2 diabetes using a group-based modelling approach. Approximately one-third of people with newly diagnosed Type 2 diabetes need either better medication adherence or earlier intensification of glucose-lowering therapy.

Longitudinal trends in HbA1c patterns and association with outcomes: A systematic review

BACKGROUND

This study aimed to review studies that identified patterns of longitudinal HbA1c trends in patients with diabetes and to summarize factors and outcomes associated with distinct trajectory patterns.

METHODS

PubMed and Web of Science were systematically searched for studies examining HbA1c trends among patients with diabetes from database inception through September 2017. Articles were included if they met the following inclusion criteria: (a) longitudinal study of subjects with diabetes only, (b) use of serial measurements of HbA1c , and (c) analysis of the trend of HbA1c using group-based trajectory approaches.

RESULTS

Twenty studies were included, 11 on type 1 diabetes and 9 on type 2 diabetes. These studies identified 2 to 6 HbA1c trajectory patterns. The most commonly identified patterns included stable HbA1c around 7.0% and at levels between 8.0% and 9.9%, which usually captured the HbA1c pattern among the majority of subjects in the study population. Unstable patterns identified included increasing HbA1c trend, decreasing HbA1c trend, and non-linear patterns. These patterns were associated with differential risk of disease outcomes, over and beyond single-point HbA1c measures. Age, gender, ethnicity, diabetes duration, disease management frequency, cardiovascular risk factors, insulin treatment, family environment, and psychosocial factors were the most frequently reported factors associated with membership of specific HbA1c pattern groups.

CONCLUSION

Common patterns of longitudinal HbA1c trends were identified despite heterogeneity among the studies. A better understanding of what underlies these different patterns may provide opportunities to tailor therapies and care for these patients to reduce adverse outcomes.

HbA1c response after insulin initiation in patients with type 2 diabetes mellitus in real life practice: Identifying distinct subgroups

AIMS

To identify subgroups of patients with type 2 diabetes mellitus (T2DM) following distinct trajectories of HbA1c after insulin initiation and explore underlying differences in clinical characteristics.

MATERIALS AND METHODS

A cohort study was conducted in patients with T2DM initiating insulin in 2007-2013 with a follow-up of 2 to 4 years. Data were collected from the Groningen Initiative to Analyze Type 2 Diabetes Treatment (GIANTT) database. The primary outcome was subgroups with different trajectories of HbA1c patterns after insulin initiation, as identified by latent class growth modeling. Differences between subgroups were tested using one-way ANOVA, Kruskal-Wallis or chi-square tests, where appropriate.

RESULTS

From 1459 patients, three subgroups with distinct HbA1c patterns were identified. Group 1 (8%) initially showed a moderate decrease followed by an increase in HbA1c 2 years later, despite receiving more comedication. Group 2 (84%) showed a stable decrease. Group 3 (8%) had a high initial level of HbA1c and a rapid decline within the first year, followed by a slow increase thereafter. Group 1 patients were on average 6-7 years younger than patients in groups 2 and 3 and were more likely to receive sulfonylureas than Group 3 patients. Group 3 patients had a shorter diabetes duration and were less well-controlled for HbA1c, systolic blood pressure and LDL-cholesterol at insulin initiation.

CONCLUSIONS

Most patients showed a stable HbA1c response, but one out of six patients showed either a poor response, or a rapid initial response only after insulin initiation. Response patterns were associated with age, diabetes duration and risk-factor controls at the time of insulin initiation.

Investigating the relationship between quality of life with lipid and glucose levels in Iranian diabetic patients

BACKGROUND

Metabolic disorders including obesity, diabetes and hyperlipidemia are the most important human health problems.

AIMS

This study aimed to determine the relationship between quality of life (QOL) with lipid and glucose levels in diabetic patients of Hamadan, Iran in 2016.

METHODS

This was a cross-sectional study. The research sample consisted of 112 patients with type II diabetes (56 men and 56 women) who were referred to the public hospitals and diabetes research center in Hamadan, Iran. The samples were selected randomly from the list patients. Data were collected using a questionnaire that consisting of three parts: demographic characteristics, quality of life and the laboratory results of Fasting Blood Sugar (FBS), blood lipid profiles and HbA₁c. Data were analyzed using SPSS 20 software.

RESULTS

The average age of men was 12/4±57/8years and the average age of women was 15/2±55/1years. This study showed that the QOL in 53.6% of people was undesirable and 46.4% were desirable. HbA₁c level was significantly higher in men than women. Whereas, HDL levels were significantly higher in women than men (p>0/05). There was no significant correlation between blood lipids, FBS and quality of life of patients. The individual characteristics such as education, economic status and duration of diabetes had significant correlation with quality of life (p<0.05).

CONCLUSION

Due to the unsatisfactory quality of life in diabetes patients, it is recommended that implementation of training programs for diabetes patients and application suitable care approaches to enhance diabetes QOL.