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Fulltext Self-reported hypoglycaemia in insulin-treated patients with diabetes mellitus: Results from the Singapore cohort of the International Operations Hypoglycaemia Assessment Tool study

Tan NC, Goh SY, Khoo EY, Dalan R, Koong A, Khoo CM, et al.

Singapore Med J, 2019 Jul 22.
PMID: 31328239 DOI: 10.11622/smedj.2019081

INTRODUCTION: Hypoglycaemia constitutes a significant barrier to achieving glycaemic control with insulin in both Type 1 (T1DM) and Type 2 diabetes mellitus (T2DM). The International Operations Hypoglycaemia Assessment Tool (IO HAT) study was designed to determine the incidence of hypoglycaemia in insulin-treated patients with T1DM and T2DM.

METHODS: The IO HAT study retrospectively and prospectively assessed the incidence of hypoglycaemia in patients with insulin-treated diabetes mellitus in nine countries. This sub-analysis included patients from Singapore with T1DM or T2DM who were aged ≥ 21 years and had completed two self-assessment questionnaires (SAQ1 and SAQ2).

RESULTS: Of the 50 T1DM and 320 T2DM patients who completed the SAQ1, 39 T1DM and 265 T2DM patients completed SAQ2; 100% and 90.9%, respectively, experienced at least one hypoglycaemic event prospectively. The incidence rates of any hypoglycaemia were 49.5 events per patient-year (EPPY) and 16.1 EPPY for T1DM and T2DM patients, respectively, in the four-week prospective period. Hypoglycaemia rate did not differ in terms of HbA1c level. The vast majority of T1DM or T2DM patients (92.0% and 90.7%, respectively) knew the overall definition of hypoglycaemia before study participation, although over half of the patients (T1DM 54.0%, T2DM 51.9%) defined hypoglycaemia based only on symptoms.

CONCLUSION: High proportions of insulin-treated patients with diabetes mellitus in Singapore reported hypoglycaemic events prospectively, showing that they had underreported hypoglycaemic episodes retrospectively. Patient education can help in improving hypoglycaemia awareness and its management in the region.
Effectiveness, safety, initial optimal dose, and optimal maintenance dose range of basal insulin regimens for type 2 diabetes: A systematic review with meta-analysis

Luo Y, Xia J, Zhao Z, Chang Y, Bee YM, Nguyen KT, et al.

J Diabetes, 2023 Apr 10.
PMID: 37038616 DOI: 10.1111/1753-0407.13381

AIMS: To investigate the effectiveness, safety, optimal starting dose, optimal maintenance dose range, and target fasting plasma glucose of five basal insulins in insulin-naïve patients with type 2 diabetes mellitus.
METHODS: MEDLINE, EMBASE, Web of Science, and the Cochrane Library were searched from January 2000 to February 2022. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed and the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach was adopted. The registration ID is CRD42022319078 in PROSPERO.
RESULTS: Among 11 163 citations retrieved, 35 publications met the planned criteria. From meta-analyses and network meta-analyses, we found that when injecting basal insulin regimens at bedtime, the optimal choice in order of most to least effective might be glargine U-300 or degludec U-100, glargine U-100 or detemir, followed by neutral protamine hagedorn (NPH). Injecting glargine U-100 in the morning may be more effective (ie, more patients archiving glycated hemoglobin
Fulltext Use of basal insulin in the management of adults with type 2 diabetes: An Asia-Pacific evidence-based clinical practice guideline

Ji L, Luo Y, Bee YM, Xia J, Nguyen KT, Zhao W, et al.

J Diabetes, 2023 Jun;15(6):474-487.
PMID: 37088916 DOI: 10.1111/1753-0407.13392

The objective of this study was to provide recommendations regarding effectiveness, safety, optimal starting dose, optimal maintenance dose range, and target fasting plasma glucose of five basal insulins (glargine U-300, degludec U-100, glargine U-100, detemir, and insulin protamine Hagedorn) in insulin-naïve adult patients with type 2 diabetes in the Asia-Pacific region. Based on evidence from a systematic review, we developed an Asia-Pacific clinical practice guideline through comprehensive internal review and external review processes. We set up and used clinical thresholds of trivial, small, moderate, and large effects for different critical and important outcomes in the overall certainty of evidence assessment and balancing the magnitude of intervention effects when making recommendations, following GRADE methods (Grading of Recommendations, Assessment, Development, and Evaluation). The AGREE (Appraisal of Guidelines, Research and Evaluation) and RIGHT (Reporting Items for practice Guidelines in HealThcare) guideline reporting checklists were complied with. After the second-round vote by the working group members, all the recommendations and qualifying statements reached over 75% agreement rates. Among 44 contacted external reviewers, we received 33 clinicians' and one patient's comments. The overall response rate was 77%. To solve the four research questions, we made two strong recommendations, six conditional recommendations, and two qualifying statements. Although the intended users of this guideline focused on clinicians in the Asia-Pacific region, the eligible evidence was based on recent English publications. We believe that the recommendations and the clinical thresholds set up in the guideline can be references for clinicians who take care of patients with type 2 diabetes worldwide.
Fulltext Device-supported automated basal insulin titration in adults with type 2 diabetes: a systematic review and meta-analysis of randomized controlled trials

Luo Y, Chang Y, Zhao Z, Xia J, Xu C, Bee YM, et al.

Lancet Reg Health West Pac, 2023 Jun;35:100746.
PMID: 37424694 DOI: 10.1016/j.lanwpc.2023.100746

BACKGROUND: Technological advances make it possible to use device-supported, automated algorithms to aid basal insulin (BI) dosing titration in patients with type 2 diabetes.
METHODS: A systematic review and meta-analysis of randomized controlled trials were performed to evaluate the efficacy, safety, and quality of life of automated BI titration versus conventional care. The literature in Medline, Embase, Web of Science, and the Cochrane databases from January 2000 to February 2022 were searched to identify relevant studies. Risk ratios (RRs), mean differences (MDs), and their 95% confidence intervals (CIs) were calculated using random-effect meta-analyses. Certainty of evidence was assessed using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach.
FINDINGS: Six of the 7 eligible studies (889 patients) were included in meta-analyses. Low- to moderate-quality evidence suggests that patients who use automated BI titration versus conventional care may have a higher probability of reaching a target of HbA1c <7.0% (RR, 1.82 [95% CI, 1.16-2.86]); and a lower level of HbA1c (MD, -0.25% [95% CI, -0.43 to -0.06%]). No statistically significant differences were detected between the two groups in fasting glucose results, incidences of hypoglycemia, severe or nocturnal hypoglycemia, and quality of life, with low to very low certainty for all the evidence.
INTERPRETATION: Automated BI titration is associated with small benefits in reducing HbA1c without increasing the risk of hypoglycemia. Future studies should explore patient attitudes and the cost-effectiveness of this approach.
FUNDING: Sponsored by the Chinese Geriatric Endocrine Society.
Large language models for diabetes care: Potentials and prospects

Sheng B, Guan Z, Lim LL, Jiang Z, Mathioudakis N, Li J, et al.

Sci Bull (Beijing), 2024 Jan 04.
PMID: 38220476 DOI: 10.1016/j.scib.2024.01.004
Artificial intelligence for diabetes care: current and future prospects

Sheng B, Pushpanathan K, Guan Z, Lim QH, Lim ZW, Yew SME, et al.

Lancet Diabetes Endocrinol, 2024 Aug;12(8):569-595.
PMID: 39054035 DOI: 10.1016/S2213-8587(24)00154-2

Artificial intelligence (AI) use in diabetes care is increasingly being explored to personalise care for people with diabetes and adapt treatments for complex presentations. However, the rapid advancement of AI also introduces challenges such as potential biases, ethical considerations, and implementation challenges in ensuring that its deployment is equitable. Ensuring inclusive and ethical developments of AI technology can empower both health-care providers and people with diabetes in managing the condition. In this Review, we explore and summarise the current and future prospects of AI across the diabetes care continuum, from enhancing screening and diagnosis to optimising treatment and predicting and managing complications.
Integrated image-based deep learning and language models for primary diabetes care

Li J, Guan Z, Wang J, Cheung CY, Zheng Y, Lim LL, et al.

Nat Med, 2024 Jul 19.
PMID: 39030266 DOI: 10.1038/s41591-024-03139-8

Primary diabetes care and diabetic retinopathy (DR) screening persist as major public health challenges due to a shortage of trained primary care physicians (PCPs), particularly in low-resource settings. Here, to bridge the gaps, we developed an integrated image-language system (DeepDR-LLM), combining a large language model (LLM module) and image-based deep learning (DeepDR-Transformer), to provide individualized diabetes management recommendations to PCPs. In a retrospective evaluation, the LLM module demonstrated comparable performance to PCPs and endocrinology residents when tested in English and outperformed PCPs and had comparable performance to endocrinology residents in Chinese. For identifying referable DR, the average PCP's accuracy was 81.0% unassisted and 92.3% assisted by DeepDR-Transformer. Furthermore, we performed a single-center real-world prospective study, deploying DeepDR-LLM. We compared diabetes management adherence of patients under the unassisted PCP arm (n = 397) with those under the PCP+DeepDR-LLM arm (n = 372). Patients with newly diagnosed diabetes in the PCP+DeepDR-LLM arm showed better self-management behaviors throughout follow-up (P