Search

Fasting during Ramadan is mandatory for all adult healthy Muslims. International studies found that most Muslims with diabetes mellitus fast during Ramadan. The main risk factors are hypoglycemia, Hyperglycemia, diabetic ketoacidosis, and dehydration during fasting. Therefore, stratification of the risks for severe acute diabetes complications needs to be considered for each individual and strategies personalized to advert these complications. The advent of new diabetes medications which are effective yet with a better safety profile and monitoring of blood glucose levels during the day are important to reduce the risk of untoward effects of hypoglycemia and hyperglycemia during Ramadan fasting. Here we review the safety and effectiveness of the newer diabetes medications for Ramadan fasting and whether it is safe to perform fasting after bariatric surgery.An extensive literature search using PubMed and Google Scholar was done using different search terms. The eligible studies were 48 randomized controlled trials, prospective observational studies, and reviews from January 2008 to June 2022 which were conducted in individuals living with diabetes.The newer diabetes medications such as GLP-1 agonists, DPP-4 inhibitors, SGLT-2 inhibitors, and new Insulin therapy are thought to be safe and effective during fasting of Ramadan. These medications are associated with a reduction in HbA1c, body weight, systolic blood pressure and risk of hypoglycemia during Ramadan fasting. However, further studies with larger sample size are needed to confirm the efficacy and safety of these newer medications during Ramadan fasting. Individuals with Bariatric surgery should seek advice and approval to fast from the bariatric dietician, physician, and surgeon before the beginning of the month of Ramadan.

Background Population segmentation permits the division of a heterogeneous population into relatively homogenous subgroups. This scoping review aims to summarize the clinical applications of data driven and expert driven population segmentation among Type 2 diabetes mellitus (T2DM) patients. Methods The literature search was conducted in Medline®, Embase®, Scopus® and PsycInfo®. Articles which utilized expert-based or data-driven population segmentation methodologies for evaluation of outcomes among T2DM patients were included. Population segmentation variables were grouped into five domains (socio-demographic, diabetes related, non-diabetes medical related, psychiatric / psychological and health system related variables). A framework for PopulAtion Segmentation Study design for T2DM patients (PASS-T2DM) was proposed. Results Of 155,124 articles screened, 148 articles were included. Expert driven population segmentation approach was most commonly used, of which judgemental splitting was the main strategy employed (n = 111, 75.0%). Cluster based analyses (n = 37, 25.0%) was the main data driven population segmentation strategies utilized. Socio-demographic (n = 66, 44.6%), diabetes related (n = 54, 36.5%) and non-diabetes medical related (n = 18, 12.2%) were the most used domains. Specifically, patients’ race, age, Hba1c related parameters and depression / anxiety related variables were most frequently used. Health grouping/profiling (n = 71, 48%), assessment of diabetes related complications (n = 57, 38.5%) and non-diabetes metabolic derangements (n = 42, 28.4%) were the most frequent population segmentation objectives of the studies. Conclusions Population segmentation has a wide range of clinical applications for evaluating clinical outcomes among T2DM patients. More studies are required to identify the optimal set of population segmentation framework for T2DM patients.

OBJECTIVE With rising health care costs and finite health care resources, understanding the population needs of different type 2 diabetes mellitus (T2DM) patient subgroups is important. Sparse data exist for the application of population segmentation on health care needs among Asian T2DM patients. We aimed to segment T2DM patients into distinct classes and evaluate their differential health care use, diabetes-related complications, and mortality patterns. RESEARCH DESIGN AND METHODS Latent class analysis was conducted on a retrospective cohort of 71,125 T2DM patients. Latent class indicators included patient’s age, ethnicity, comorbidities, and duration of T2DM. Outcomes evaluated included health care use, diabetes-related complications, and 4-year all-cause mortality. The relationship between class membership and outcomes was evaluated with the appropriate regression models. RESULTS Five classes of T2DM patients were identified. The prevalence of depression was high among patients in class 3 (younger females with short-to-moderate T2DM duration and high psychiatric and neurological disease burden) and class 5 (older patients with moderate-to-long T2DM duration and high disease burden with end-organ complications). They were the highest tertiary health care users. Class 5 patients had the highest risk of myocardial infarction (hazard ratio [HR] 12.05, 95% CI 10.82–13.42]), end-stage renal disease requiring dialysis initiation (HR 25.81, 95% CI 21.75–30.63), stroke (HR 19.37, 95% CI 16.92–22.17), lower-extremity amputation (HR 12.94, 95% CI 10.90–15.36), and mortality (HR 3.47, 95% CI 3.17–3.80). CONCLUSIONS T2DM patients can be segmented into classes with differential health care use and outcomes. Depression screening should be considered for the two identified classes of patients.

Background: With the rising prevalence of diabetes, machine learning (ML) models have been increasingly used for prediction of diabetes and its complications, due to their ability to handle large complex data sets. This study aims to evaluate the quality and performance of ML models developed to predict microvascular and macrovascular diabetes complications in an adult Type 2 diabetes population. Methods: A systematic review was conducted in MEDLINE®, Embase®, the Cochrane® Library, Web of Science®, and DBLP Computer Science Bibliography databases according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist. Studies that developed or validated ML prediction models for microvascular or macrovascular complications in people with Type 2 diabetes were included. Prediction performance was evaluated using area under the receiver operating characteristic curve (AUC). An AUC >0.75 indicates clearly useful discrimination performance, while a positive mean relative AUC difference indicates better comparative model performance. Results: Of 13 606 articles screened, 32 studies comprising 87 ML models were included. Neural networks (n = 15) were the most frequently utilized. Age, duration of diabetes, and body mass index were common predictors in ML models. Across predicted outcomes, 36% of the models demonstrated clearly useful discrimination. Most ML models reported positive mean relative AUC compared with non-ML methods, with random forest showing the best overall performance for microvascular and macrovascular outcomes. Majority (n = 31) of studies had high risk of bias. Conclusions: Random forest was found to have the overall best prediction performance. Current ML prediction models remain largely exploratory, and external validation studies are required before their clinical implementation. Protocol Registration: Open Science Framework (registration number: 10.17605/OSF.IO/UP49X).

Ramadan fasting, a month-long annual practice for Muslims, can be challenging for those who have diabetes mellitus with or without associated complications or pre-existing comorbidities, as well as healthcare providers involved in their care. Inadequate preparation for this fasting period can result in increased complications. We reviewed the current practice of Muslims with diabetes mellitus in Singapore who intend to fast during Ramadan, with particular attention on locally available evidence. Adequate preparation for Ramadan fasting, including pre-Ramadan assessment, optimisation of glycaemic control, structured Ramadan-focused diabetes education, medication adjustment, glucose monitoring and test fasting, can lead to benefits in terms of improvements in metabolic control and reduced risk of fasting-related complications in people with diabetes mellitus. While there are ongoing efforts to reduce risk during this period, larger-scale national programmes are needed to avert complications and assess the long-term effects of Ramadan fasting in the local population.

Introduction This study aimed to explore the level of knowledge of Muslim patients with diabetes mellitus (DM) regarding DM and the self-management of DM when fasting during Ramadan. Methods Muslim patients with DM attending the Diabetes Centre of Singapore General Hospital, Singapore, were surveyed on their knowledge of DM and self-management of DM when fasting during Ramadan. Data on patient demographics, DM history and management of DM during the previous Ramadan was also collected. Results A total of 92 patients (34 male, 58 female) were surveyed. The mean age of the patients was 53.4 ± 13.3 years. The patients were either Malay (91.3%) or Indian (8.7%), and most (66.3%) had at least a secondary school education. Most (89.1%) had Type 2 DM. The mean duration of DM was 8.7 ± 5.1 years and mean pre-Ramadan haemoglobin A1c was 8.4% ± 1.8%. DM treatment consisted of insulin therapy (37.0%), oral glucose-lowering drugs (35.9%) and combined therapy (22.8%). The mean DM knowledge score was 58.8% for general knowledge and 75.9% for fasting knowledge. During the previous Ramadan, although 71.4% of the patients consulted their physicians, 37.3% did not monitor their blood glucose levels and 47.0% had hypoglycaemic episodes. Among those who had hypoglycaemia, 10.8% continued to fast. Conclusion Unsafe self-management practices were observed among DM patients who fasted during Ramadan. Efforts should be made to bridge the gap between knowledge of DM and self-management of DM.

OBJECTIVE With rising health care costs and finite health care resources, understanding the population needs of different type 2 diabetes mellitus (T2DM) patient subgroups is important. Sparse data exist for the application of population segmentation on health care needs among Asian T2DM patients. We aimed to segment T2DM patients into distinct classes and evaluate their differential health care use, diabetes-related complications, and mortality patterns. RESEARCH DESIGN AND METHODS Latent class analysis was conducted on a retrospective cohort of 71,125 T2DM patients. Latent class indicators included patient’s age, ethnicity, comorbidities, and duration of T2DM. Outcomes evaluated included health care use, diabetes-related complications, and 4-year all-cause mortality. The relationship between class membership and outcomes was evaluated with the appropriate regression models. RESULTS Five classes of T2DM patients were identified. The prevalence of depression was high among patients in class 3 (younger females with short-to-moderate T2DM duration and high psychiatric and neurological disease burden) and class 5 (older patients with moderate-to-long T2DM duration and high disease burden with end-organ complications). They were the highest tertiary health care users. Class 5 patients had the highest risk of myocardial infarction (hazard ratio [HR] 12.05, 95% CI 10.82–13.42]), end-stage renal disease requiring dialysis initiation (HR 25.81, 95% CI 21.75–30.63), stroke (HR 19.37, 95% CI 16.92–22.17), lower-extremity amputation (HR 12.94, 95% CI 10.90–15.36), and mortality (HR 3.47, 95% CI 3.17–3.80). CONCLUSIONS T2DM patients can be segmented into classes with differential health care use and outcomes. Depression screening should be considered for the two identified classes of patients.

Context: We report a 59 year old Chinese woman in pheochromocytoma multisystem crisis and was initially stabilised with ECMO; subsequently she manifested rapid cyclical fluctuation of blood pressure precipitated by explantation of ECMO. Case Description: Madam L presented with chest pain and giddiness. She developed cardiogenic shock refractory to double inotropes and intra-aortic balloon pump (IABP); but responded to ExtraCorporal Membrane Oxygenation (ECMO). Subsequently rapid cyclical blood pressure fluctuations from 230 mmHg to 50mmHg systolic occurred after explantation of ECMO. Biochemically urine metanephrines and normetanephrines were more than seven times above upper limit of normal. Imaging confirmed the presence of right adrenal mass likely pheochromocytoma. She was started on alpha blockade and underwent an uneventful laparotomy with removal of a 10 cm adrenal mass. Histology confirmed a large cystic pheochromocytoma with haemorrhagic components. Conclusions: Pheochromocytoma crisis should be suspected in cases of unexplained shock or Takutsubo cardiomyopathy; early recognition and ECMO support can be life-saving. In addition, ECMO explantation can trigger rapid cyclical blood pressure fluctuations and should be closely monitored for. Case Study

Infundibuloneurohypophysitis is a rare disorder which involves inflammation of the pituitary stalk and posterior pituitary gland. We describe the case of a woman in her late postpartum period presenting with acute onset central diabetes insipidus. Magnetic resonance imaging (MRI) showed nodular enhancement of the pituitary infundibulum and ectopia of the posterior pituitary gland. The treatment was hormone replacement. The role of steroid therapy for this condition is controversial.