Background: Insulin therapy is crucial for managing type 2 diabetes mellitus, with its use steadily increasing in Indonesia and its effectiveness well established. However, prescribing insulin poses various challenges that can impact the effectiveness of insulin. Patient education is crucial for the successful implementation of insulin therapy. Proper insulin use remains insufficient in Indonesia.
Objective: This study aims to investigate physicians' knowledge and practice in providing education on insulin use to patients with type 2 diabetes mellitus in Indonesia.
Methods: This study recruited potential participants (all physicians in Indonesia) through the internet using a convenience sampling method. The participants were asked to fill out a questionnaire. The questionnaire had 32 questions divided into 4 sections: demographics and clinical practice, practice of insulin education, the Indonesian insulin injection technique guideline, and knowledge of insulin injection techniques. The instrument used in this study was developed based on the Pedoman Teknik Menyuntik Insulin Indonesia, which was adapted from the international consensus by the Forum for Injection Technique and Therapy Expert Recommendations. The survey lasted from February 2021 to March 2021. Data were analyzed using the Kruskal-Wallis tests.
Results: A total of 823 participants were included in the analysis. Out of 823 participants, 680 (82.6%) had given insulin education to patients at least once during the last 30 days. However, out of 823 participants, only 479 (58.2%) used specific guidelines in their practice, with only 280 (34.0%) aware of the Indonesian guidelines. Out of 823 participants, 815 (99.1%) agreed that insulin injection techniques would affect clinical results. The median score of knowledge about insulin injection techniques was 7 (IQR 2) among the study participants, indicating good knowledge. Profession was the only variable significantly associated with knowledge scores, with consultants in endocrinology, metabolism, and diabetes achieving the highest median scores, and other physicians the lowest (P<.001).
Conclusions: Most physicians in this study reported providing education to their patients. However, there was still a gap between the guidelines and the practice of insulin education, as indicated by the lack of awareness and a fair level of knowledge about the Indonesian guidelines.
Unlabelled: We developed an innovative bilingual toolkit comprising a personalized action plan and educational videos to encourage insulin dose self-titration by adults living with type 2 diabetes.
Background: Basal rate (BR) adjustment is crucial for managing type 1 diabetes mellitus, accounting for 30% to 50% of total daily insulin needs. All current closed-loop systems revert to the user's usual pump BR (known as manual mode) in the event of closed loop failure. Furthermore, access to closed-loop systems remains relatively low in low- and middle-income countries and among those without suitable health insurance. Accurately adjusting the BR remains challenging, leading to hypo- or hyperglycemia, and research on optimizing the BR is limited.
Objective: This study proposed an adaptive algorithm that uses continuous glucose monitoring data to identify BR inaccuracies without requiring meal intake information.
Methods: The OhioT1DM dataset formed the basis for implementing this methodology. Each composite day was generated by excluding bolus insulin profiles lacking meal intake information and by calculating hourly blood glucose (BG) relative levels along with their corresponding reliability measures, enabling assessment of deviations from the recommended BR (ie, a BG relative change of 0 mg/dL). Both a noninferiority analysis and a classification precision metric were used to assess the practicality of this approach compared to using meal data.
Results: Data from 12 participants showed noninferiority of the no-meal method: using a 20% noninferiority margin on absolute BG relative change, 9 of 12 participants met the criterion (1-sided P<.05). Classification precision was 73.9% (139/188) of meals correctly classified on average per participant (SD 11.8%; 95% CI 67.2%-79.7%). The daily cumulative BG average was 200.6 mg/dL (SD 61.7 mg/dL; 11.1 mmol/L, SD 3.4 mmol/L; 95% CI 161.4-239.8 mg/dL), with peak values reaching 270.15 mg/dL (14.99 mmol/L). Furthermore, 99.3% (286/288) of the BG relative values (SD 0.5%; 95% CI 97.5%-99.8%) that were unaffected by external factors were associated with incorrect BR settings, with deviations ranging from -25.5 to 46 mg/dL (-1.58 to 2.59 mmol/L).
Conclusions: Current strategies to optimize BR settings are inadequate, and our approach of a personalized basal tuner (PBT) helps better analyze BR without relying on meal intake information. Indeed, without an optimally set BR, in the event of the closed loop reverting to manual mode, patients may be exposed to persistent hypo- or hyperglycemia, leading to safety and efficacy issues. Future work will focus on generating BR recommendations through the application of this algorithm in clinical practice to assist clinicians in setting BR in low- and middle-income countries, where closed-loop systems are not prevalent, to help increase time in range.
Background: Managing type 1 diabetes (T1D) requires maintaining target blood glucose levels through precise diet and insulin dosing. Predicting postprandial glycemic responses (PPGRs) based solely on carbohydrate content is limited by factors such as meal composition, individual physiology, and lifestyle. Continuous glucose monitors provide insights into these responses, revealing significant individual variability. The statistical clustering method proposed here balances the number of clusters formed and the glycemic variability of the PPGRs within each cluster to offer a clustering technique on which treatment decisions could be based.
Objective: This study aims to develop and evaluate a PPGR clustering method that identifies reproducible meal-specific glucose patterns in people with type 1 diabetes.
Methods: Blood glucose data from the OhioT1DM dataset were used to assess clustering of PPGR based on the coefficient of variability (CV) of glucose. Clustering was performed using statistical clustering, with each PPGR isolated into 48 data points per event. A CV threshold of <36% was used to define clinically similar clusters. This aimed to cluster PPGRs with minimal glycemic variability. The approach aims to enhance precision in analyzing postprandial glycemic dynamics, assessing cluster cohesion via standard deviation and CV within meal categories.
Results: The analysis revealed a reproducible set of PPGR clusters specific to meal types and individuals (mean [SD], 2.4 [1.8] for breakfast, 2.7 [0.9] for lunch, and 3.1 [1.0] for dinner), with the number of clusters varying across participants and meals in the dataset. Carbohydrate intake alone did not affect cluster formation, suggesting a complex relationship between meal composition and PPGR variability. However, certain individuals showed significant associations between carbohydrate intake and cluster formation for specific meals.
Conclusions: The meal-based glycemic clustering algorithm provides a promising framework for predicting PPGRs in people with type 1 diabetes, independent of carbohydrate intake. It emphasizes the need for personalized prediction models to optimize time in range and enhance diabetes management. Efforts to refine treatment strategies are crucial in reducing T1D-related complications.
Unlabelled: Diabetes self-management plays a major role in controlling blood sugar levels and avoiding chronic complications. In this report, we investigate the strengths and limitations of artificial intelligence chatbots in supporting patients with type 1 diabetes and their families. With the growing accessibility of these constantly evolving tools, front-line providers must advocate for their responsible use.
Background: The escalating rates of obesity and type 2 diabetes mellitus (T2DM) in Saudi Arabia highlight the impending burden of metabolic dysfunction-associated steatotic liver disease (MASLD) and nonalcoholic steatohepatitis.
Objective: This study aimed to identify MASLD among patients with T2DM at King Saud Medical City family medicine clinics, Riyadh, and explore associated factors to facilitate early intervention and prevention strategies.
Methods: This cross-sectional study identified patients with T2DM who attended King Saud Medical City, Riyadh, underwent an abdominal ultrasound, and were diagnosed with MASLD. The study data were collected by a peer-reviewed validated data extraction sheet and analyzed by SPSS (version 26.0; IBM Corp).
Results: Our study included 292 participants, with 47.3% (n=138) males and 52.7% (n=154) females. Notably, the prevalence of MASLD was 54.5% (n=159). Prevalent comorbidities included dyslipidemia (218/292, 74.7%) and hypertension (209/292, 71.6%). Most participants were nonsmokers (218/292, 74.7%). Higher waist circumference was significantly associated with MASLD (P=.02), with >80 cm among females (85/141, 60.3%) and >94 cm among males (60/141, 54.5%) affected across different stages of MASLD. Obesity (BMI>30 kg/m2) also significantly correlated with MASLD (P<.001). Individuals taking aspirin had half the odds of MASLD development (odds ratio [OR] 0.523, 95% CI 0.331-0.844; P=.007). Biochemical analysis revealed significant associations between MASLD and elevated alanine aminotransferase (P=.009), aspartate aminotransferase (P=.01), and homeostatic model assessment of insulin resistance (P=.001). Total cholesterol (P=.01), triglycerides (P=.03), and low-density lipoprotein (P=.04) were significantly elevated in patients with MASLD. Insulin exhibited a significant positive correlation with MASLD (r=0.24; P=.001). Glucose levels showed no significant association (r=0.03; P=.63).
Conclusions: Our study highlights significant associations between MASLD and various factors, including waist circumference, obesity, and certain biochemical markers. Furthermore, the protective effect of aspirin against MASLD warrants further investigation. These findings underscore the importance of early intervention and targeted preventive strategies.
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