The use of prednisolone during Ramadan fasting in patients with adrenal insufficiency.

OBJECTIVE: Once daily prednisolone taken at predawn has been proposed to be the glucocorticoid replacement of choice in patients with adrenal insufficiency (AI) who intend to fast for the month of Ramadan. However, the effects of prednisolone on metabolic parameters and quality of life during fasting for Ramadan are unknown. DESIGN, PATIENTS AND MEASUREMENTS: Patients with AI on twice-daily hydrocortisone, who had low or moderate risk and intended to fast, were recruited. Patients were converted to prednisolone 5 mg once daily taken at sahur (predawn) and Ramadan education given. Weight, sleep duration, biochemical parameters and quality of life measures (SF-36 questionnaire) were analysed at the end of Ramadan and compared against baseline. RESULTS: Twenty patients (13 men) were recruited, with a mean age of 59.9 ± 15.0 years. All patients were on hydrocortisone 15 mg daily (in divided doses) as pre-Ramadan glucocorticoid replacement. Half had type 2 diabetes with low IDF-DAR risk. Eighty-five percent of patients completed the full 29 days of fasting with no complications. There was a significant reduction in weight (-1.1 ± 1.6 kg, p = .005), with no significant change in blood pressure or sleep duration. There was a significant increase in urea (0.80 ± 1.1 mmol/L, p = .005) and haematocrit, (0.011 ± 0.019 L/L, p = .019) and decrease in serum sodium (-1.6 ± 3.0 mmol/L, p = .028), with no change in serum creatinine or liver function. Quality of life measures were preserved in all domains with significant improvement in role limitation due to physical health (15.3 ± 21.6, p = .005) and bodily pain (8.8 ± 16.3, p = .031). CONCLUSIONS: This study has demonstrated that converting patients with AI who are fasting for Ramadan from twice-daily hydrocortisone to prednisolone 5 mg daily at sahur was safe, with no major short-term adverse effects. Despite the higher equivalent glucocorticoid doses, patients experienced weight loss and no clinically significant change in blood pressure, sleep, biochemical parameters or quality of life. This study paves the way to trial even lower doses of prednisolone once daily in patients fasting for Ramadan with AI.

Risk Prediction and Management of Chronic Kidney Disease in People Living with Type 2 Diabetes Mellitus.

People with type 2 diabetes mellitus have increased risk of chronic kidney disease and atherosclerotic cardiovascular disease. Improved care delivery and implementation of guideline-directed medical therapy have contributed to the declining incidence of atherosclerotic cardiovascular disease in high-income countries. By contrast, the global incidence of chronic kidney disease and associated mortality is either plateaued or increased, leading to escalating direct and indirect medical costs. Given limited resources, better risk stratification approaches to identify people at risk of rapid progression to end-stage kidney disease can reduce therapeutic inertia, facilitate timely interventions and identify the need for early nephrologist referral. Among people with chronic kidney disease G3a and beyond, the kidney failure risk equations (KFRE) have been externally validated and outperformed other risk prediction models. The KFRE can also guide the timing of preparation for kidney replacement therapy with improved healthcare resources planning and may prevent multiple complications and premature mortality among people with chronic kidney disease with and without type 2 diabetes mellitus. The present review summarizes the evidence of KFRE to date and call for future research to validate and evaluate its impact on cardiovascular and mortality outcomes, as well as healthcare resource utilization in multiethnic populations and different healthcare settings.

Integrated image-based deep learning and language models for primary diabetes care.

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 < 0.05). For patients with referral DR, those in the PCP+DeepDR-LLM arm were more likely to adhere to DR referrals (P < 0.01). Additionally, DeepDR-LLM deployment improved the quality and empathy level of management recommendations. Given its multifaceted performance, DeepDR-LLM holds promise as a digital solution for enhancing primary diabetes care and DR screening.