Characteristics of insulin-Naïve people with type 2 diabetes who successfully respond to insulin glargine U100 after 24 weeks of treatment: a meta-analysis of individual participant data from 3 randomized clinical trials

Time for Using Machine Learning for Dose Guidance in Titration of People With Type 2 Diabetes? A Systematic Review of Basal Insulin Dose Guidance

BACKGROUND

Real-world studies of people with type 2 diabetes (T2D) have shown insufficient dose adjustment during basal insulin titration in clinical practice leading to suboptimal treatment. Thus, 60% of people with T2D treated with insulin do not reach glycemic targets. This emphasizes a need for methods supporting efficient and individualized basal insulin titration of people with T2D. However, no systematic review of basal insulin dose guidance for people with T2D has been found.

OBJECTIVE

To provide an overview of basal insulin dose guidance methods that support titration of people with T2D and categorize these methods by characteristics, effect, and user experience.

METHODS

The review was conducted according to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. Studies about basal insulin dose guidance, including adults with T2D on basal insulin analogs published before September 7, 2022, were included. Joanna Briggs Institute critical appraisal checklists were applied to assess risk of bias.

RESULTS

In total, 35 studies were included, and three categories of dose guidance were identified: paper-based titration algorithms, telehealth solutions, and mathematical models. Heterogeneous reporting of glycemic outcomes challenged comparison of effect between the three categories. Few studies assessed user experience.

CONCLUSIONS

Studies mainly used titration algorithms to titrate basal insulin as telehealth or in paper format, except for studies using mathematical models. A numerically larger proportion of participants seemed to reach target using telehealth solutions compared to paper-based titration algorithms. Exploring capabilities of machine learning may provide insights that could pioneer future research while focusing on holistic development.

Recent Progress in the Diagnosis and Management of Type 2 Diabetes Mellitus in the Era of COVID-19 and Single Cell Multi-Omics Technologies

Type 2 diabetes mellitus (T2DM) is one of the world’s leading causes of death and life-threatening conditions. Therefore, we review the complex vicious circle of causes responsible for T2DM and risk factors such as the western diet, obesity, genetic predisposition, environmental factors, and SARS-CoV-2 infection. The prevalence and economic burden of T2DM on societal and healthcare systems are dissected. Recent progress on the diagnosis and clinical management of T2DM, including both non-pharmacological and latest pharmacological treatment regimens, are summarized. The treatment of T2DM is becoming more complex as new medications are approved. This review is focused on the non-insulin treatments of T2DM to reach optimal therapy beyond glycemic management. We review experimental and clinical findings of SARS-CoV-2 risks that are attributable to T2DM patients. Finally, we shed light on the recent single-cell-based technologies and multi-omics approaches that have reached breakthroughs in the understanding of the pathomechanism of T2DM.

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.

Predictors of response to insulin therapy in youth with poorly-controlled type 2 diabetes in the TODAY trial

OBJECTIVE

To understand the factors associated with glycemic control after starting insulin in youth with type 2 diabetes following glycemic failure (persistent HbA1c ≥8%) with metformin alone, metformin + rosiglitazone or metformin + lifestyle in the TODAY study.

METHODS

Change in HbA1c after add-on insulin therapy and the factors predictive of glycemic response were evaluated. At 1-year postinsulin initiation, 253 youth had a mean of 3.9 ± 1.0 visits since the time of insulin initiation. Participants were divided into three groups according to glycemic control: consistent decrease in HbA1c by ≥0.5%, change <0.5%, or consistent increase in HbA1c ≥0.5%, at 75% or more of the visits.

RESULTS

Within 1-year postinsulin initiation, 33.2% of participants had a consistent HbA1c decrease of ≥0.5%, 46.2% changed HbA1c <0.5%, and 20.6% had an increase ≥0.5%. At randomization into TODAY and at time of insulin initiation, the three glycemia groups were similar in age, sex, race-ethnicity, pubertal stage, BMI z-score, diabetes duration, and insulin secretion indices. Consistent HbA1c improvement was associated with higher insulin sensitivity (1/fasting insulin) at randomization and at time of failure, higher adiponectin at randomization, and was not associated with indices of β-cell function.

CONCLUSIONS

Response to add-on insulin was highly variable among youth in TODAY. Greater insulin sensitivity and higher adiponectin concentrations at randomization were associated with improved glycemic control after initiation of insulin. Due to limited information on adherence to insulin injections, the roles of adherence to the prescribed insulin regimen or psychosocial factors are unknown.

Commencing insulin glargine 100 U/mL therapy in individuals with type 2 diabetes: Determinants of achievement of HbA1c goal less than 7.0

AIMS

To identify factors associated with achievement of glycated haemoglobin A1c (HbA1c) target at 24 weeks after commencing basal insulin therapy in individuals with type 2 diabetes mellitus (T2DM).

MATERIALS AND METHODS

Post-hoc pooled analysis of 16 randomized, treat-to-target trials involving individuals with T2DM inadequately controlled with oral anti-hyperglycaemic drugs (n = 3415) initiated on once-daily insulin glargine 100 U/mL (Gla-100). Clinical outcomes were assessed by HbA1c response at 24 weeks and individuals were classified as "good responders" with HbA1c <7.0% (<53 mmol/mol) or as "poor responders" with HbA1c ≥7.0% (≥53 mmol/mol). Univariable and multivariable stepwise logistic regression analyses were performed to identify predictive factors for attaining HbA1c <7.0%.

RESULTS

Lower levels of baseline HbA1c, fasting plasma glucose (FPG) and post-prandial plasma glucose (PPG), higher body mass index (BMI), shorter diabetes duration and male sex were associated with a good glycaemic response, but not age or baseline C-peptide levels. Gla-100 dose (U/kg) was highest in the poor-responder group, which had the fewest hypoglycaemia episodes. Univariable analysis for achievement of HbA1c <7.0% confirmed these observations. Multivariable analysis retained baseline HbA1c, body weight, BMI, sex, 2-hours PPG and diabetes duration as predictors of a good response. Continued use of sulfonylureas, hypoglycaemia and change in body weight were indicative of poor response.

CONCLUSIONS

Baseline HbA1c was the strongest determinant for achieving target HbA1c <7.0% by supplementary Gla-100 therapy, while sex and BMI were also useful indicators. However, age and C-peptide levels at baseline did not predict glycaemic response to the introduction of basal insulin.