Elif I. Ekinci

Diabetes • March 28, 2025

Shinji Hagiwara, Jun Okabe, Mark Ziemann, Brian Drew, Maki Murakoshi, Karly Sourris, Aaron Mcclelland, Madhura Bose, Elif Ekinci, Melinda Coughlan, Adrienne Laskowski, Hiroko Sakuma, Tomohito Gohda, Yusuke Suzuki, Mark Cooper, Phillip Kantharidis

Pathological signaling via the receptor for advanced glycation end-products (RAGE) is critical in diabetic kidney disease (DKD) development, while RAGE deletion is reno-protective. Non-coding RNAs (ncRNAs), including microRNAs (miRs), also play key roles in DKD, including renal fibrosis. However, the involvement of ncRNAs in RAGE signaling remains unclear. This study investigated the regulation of ncRNAs by RAGE and assessed renal expression of ncRNAs, miRs, fibrotic/inflammatory markers in diabetic RAGE knockout (KO) and wild-type (WT) mice, as well as in mesangial cells (MCs) obtained from these mice. Diabetes induction in both RAGE-/- and WT mice exhibited elevated renal expression of miR-214 and its host ncRNA, Dnm3os. miR-214 and Dnm3os levels were remarkably higher in RAGE-/- MCs compared to WT MCs. Overexpression of miR-214 in WT MCs reduced fibrotic/inflammatory gene expression, while its inhibition increased these markers. Human DKD tissue demonstrated higher Dnm3os expression compared to controls. Notably, miR-214 targeted the RAGE signaling mediator diaphanous1 (DIAPH1), while Dnm3os had an opposing effect, enhancing fibrosis and inflammation. miR-214 administration in a DKD mouse model significantly reduced renal fibrosis. These findings propose a novel mechanism where miR-214 and Dnm3os act as negative and positive regulators of fibrosis via the RAGE-DIAPH1 axis.

BMJ Open • May 06, 2025

Jennifer Ngan, Yee Kong, Jenna Goad, Michael L Huang, Alicia Jenkins, Sara Vogrin, Steven Trawley, Adele Manzoney, Miyuki Nakano, Elif Ekinci, Adamandia Kriketos, Spiros Fourlanos, Lynelle Boisseau, Christopher Nolan, Pamela Taylor, Joanne Fenn, Stephen Stranks, David O'neal

Background: Sodium-glucose co-transporter inhibitors have potential glycaemic and non-glycaemic benefits in people with type 1 diabetes (T1D). However, the increased risk of diabetic ketoacidosis (DKA) limits their widespread use. We hypothesise that dapagliflozin 10 mg daily, combined with the use of continuous ketone monitoring (CKM) and education strategies to mitigate progression to DKA, will demonstrate improved glycaemic control without increasing DKA events. Methods: PARTNER is a multisite 6-month randomised crossover double-masked study involving Australian adults with T1D who have a Haemoglobin A1c (HbA1c) <85.8 mmol/mol (<10%), minimum total daily insulin dose ≥0.4 IU/kg, consume ≥100 g carbohydrates/day and have not had DKA in the last 3 months. All participants will undergo a 2-week run-in period wearing the Abbott FreeStyle Libre 2 Continuous Glucose Monitor (CGM) and Abbott CKM device. Following this, participants are randomised to receive dapagliflozin or placebo for 12 weeks, followed by crossover for a further 12 weeks separated by a 2-week washout period. The primary effectiveness outcome is the Abbott FreeStyle Libre 2 CGM time in range during the final 2 weeks of each stage. The primary safety outcome is the number of episodes of DKA requiring hospitalisation or emergency department presentation. 60 participants will be recruited across five sites. Background: The study has received ethical approval from the St Vincent's Hospital Melbourne Human Research Ethics Committee (HREC reference 302/23). The results will be published in peer-reviewed journals and presented at national and international diabetes conferences. Background: ACTRN12624000448549.

Diabetes Research And Clinical Practice • February 04, 2025

Rodney Kwok, Kartik Kishore, Tina Zafari, Digsu Koye, Mariam Hachem, Ian De Boer, Tae-dong Jeong, Won-ki Min, Esteban Porrini, Petter Bjornstad, Richard Macisaac, Leonid Churilov, Elif Ekinci

Objective: This study assessed the concordance and misclassification of chronic kidney disease (CKD) stages between directly measured glomerular filtration rate (mGFR) and estimates of GFR (eGFR) using the creatinine-based CKD-EPI-2009 and the CKD-EPI-2021 equations in individuals with diabetes. Methods: Data from 5,177 individuals across six international diabetes cohorts included mGFR measurements using exogenous filtration markers. We calculated an intra-class correlation coefficient (ICC), bias, precision and accuracy between mGFR and CKD-EPI estimates using a four-level mixed-effect linear variance component model. Results: The pooled cohort included people with type 1 (n = 1,748, median age: 33 years [IQR: 27, 40], mGFR = 104.2 ml/min per 1.73 m2) and type 2 diabetes (n = 3,429, median age: 66 years [IQR: 58, 73], mGFR = 58.4 ml/min per 1.73 m2). Both CKD-EPI equations showed good agreement (2009 ICC: 0.90; 2021 ICC: 0.87) but substantial bias (2009: 3.7 ml/min/1.73 m2; 2021: 8.6 ml/min/1.73 m2), low precision (2009: 12.4 ml/min/1.73 m2; 2021: 13.91 ml/min/1.73 m2), and limited accuracy (2009 p30: 77 %; 2021 p30: 70 %) compared to mGFR. Conclusions: The use of CKD-EPI equations has the potential for misdiagnosis and suboptimal CKD management in people with diabetes. Alternative methods of estimating kidney function for people with diabetes are needed to optimally manage diabetes-related kidney disease.

Theranostics • December 10, 2024

Charumathi Sabanayagam, Riswana Banu, Cynthia Lim, Yih Tham, Ching-yu Cheng, Gavin Tan, Elif Ekinci, Bin Sheng, Gareth Mckay, Jonathan Shaw, Kunihiro Matsushita, Navdeep Tangri, Jason Choo, Tien Wong

Rationale: Chronic kidney disease (CKD) is a major public health problem worldwide associated with cardiovascular disease, renal failure, and mortality. To effectively address this growing burden, innovative solutions to management are urgently required. We conducted a scoping review to identify key use cases in which artificial intelligence (AI) could be leveraged for improving management of CKD. Additionally, we examined the challenges faced by AI in CKD management, proposed potential solutions to overcome these barriers. Methods: We reviewed 41 articles published between 2014-2024 which examined various AI techniques including machine learning (ML) and deep learning (DL), unsupervised clustering, digital twin, natural language processing (NLP) and large language models (LLMs) in CKD management. We focused on four areas: early detection, risk stratification and prediction, treatment recommendations and patient care and communication. Results: We identified 41 articles published between 2014-2024 that assessed image-based DL models for early detection (n = 6), ML models for risk stratification and prediction (n = 14) and treatment recommendations (n = 4), and NLP and LLMs for patient care and communication (n = 17). Key challenges in integrating AI models into healthcare include technical issues such as data quality and access, model accuracy, and interpretability, alongside adoption barriers like workflow integration, user training, and regulatory approval. Conclusions: There is tremendous potential of integrating AI into clinical care of CKD patients to enable early detection, prediction, and improved patient outcomes. Collaboration among healthcare providers, researchers, regulators, and industries is crucial to developing robust protocols that ensure compliance with legal standards, while minimizing risks and maintaining patient safety.

The Medical Journal Of Australia • November 01, 2024

Sunita De Sousa, Timothy Davis, James Harraway, Mark Greenslade, Kathy Wu, Ryan Paul, Juliet Taylor, Aleena Ali, Elif Ekinci, Rinki Murphy, Jerry Greenfield

Background: Monogenic diabetes accounts for 2-5% of diabetes. Although its identification has substantial therapeutic implications, more than 80% of affected individuals are undiagnosed or misdiagnosed as having type 1 or 2 diabetes. This consensus statement reviews genetic testing for monogenic diabetes in adults and provides evidence-based recommendations. With representation from the Australian Diabetes Society (ADS), Endocrine Society of Australia (ESA), Human Genetics Society of Australasia (HGSA), New Zealand Society for the Study of Diabetes (NZSSD) and Royal College of Pathologists of Australasia (RCPA), the writing group: (i) defined questions to be addressed, (ii) conducted critical literature reviews, (iii) graded the evidence, and (iv) generated recommendations that were refined until consensus was achieved. All contemporary literature was considered, with a focus on Australian and New Zealand data, where available. Conclusions: Indications for genetic testing for monogenic diabetes in adults include: (i) diabetes onset before 12 months of age, (ii) glucokinase (GCK)-hyperglycaemia phenotype, (iii) diabetes onset before 30 years of age without markers of type 1 or 2 diabetes, (iv) syndromic monogenic diabetes phenotype, or (v) high probability of monogenic diabetes using validated screening tools. Individuals undergoing genetic testing should be provided with comprehensive pre- and post-test counselling. Genetic testing typically involves next-generation sequencing, and should include classically syndromic genetic variants (eg, m.3243A>G, HNF1B variants) even in individuals with isolated diabetes. A molecular diagnosis facilitates gene-specific treatment, surveillance, reproductive planning and cascade testing of relatives. In pregnancies of individuals with GCK-hyperglycaemia, maternal treatment can be individualised to known or assumed fetal genotype. Individuals with monogenic diabetes variants of uncertain significance or negative results may be considered for further phenotype or genotype assessment and recruitment into research studies. This consensus statement aims to raise awareness of monogenic diabetes among clinicians involved in the care of patients with diabetes, and to improve genetic testing rates across Australia and New Zealand.