Age acceleration in survivors of breast cancer is a critical issue because cancer and its treatment can increase structural and numerical chromosomal aberrations, while simultaneously shortening telomere length and changing ageing phenotype. Therefore, the current study will be using machine learning architectures to accurately predict the factors that contribute to age acceleration among survivors of breast cancer.

The Cancer Survivors’ Trajectories of Ageing Research (C*STAR) is a hospital-based cross-sectional study involving multi-ethnic Malaysian survivors of breast cancer and a non-breast cancer control group, frequency-matched by age group (±5 years), sex and ethnicity. The three main stages of this study will be conducted in the predictive model development. First, a set of validated questionnaires will be used to collect the data on modifiable factors of ageing phenotypes and behavioural determinants of health. Second, 3 mL non-fasting blood samples will be collected, and lymphocytes will be isolated to determine telomere length using real-time PCR as a biomarker of age acceleration. Lastly, a machine learning architecture will be deployed to identify modifiable factors that may contribute to age acceleration in survivors of breast cancer and controls, with these factors used as input and ageing biomarkers of telomere length as output. The study outcomes may serve as guidance to enhance the quality of life of survivors of breast cancer and hinder the recurrence of cancer while ageing successfully.

Ethical approval was obtained from the Research Ethics Committee, Universiti Kebangsaan Malaysia (JEP-2022-700) to carry out this study. Written informed consent will be obtained from each survivor of breast cancer and each cancer-free woman prior to participation. The results of this study will be published for future research and clinical applications.

To examine the distribution and frequency of International Classification of Diseases, 10th Revision (ICD-10), codes in emergency departments (EDs) across Malaysia, providing insights into the most common diagnoses. The aim is to support the development of a principal diagnosis short list for implementing ED-specific diagnosis-related groups (DRGs) to enhance resource allocation and healthcare efficiency.

A cross-sectional study conducted as part of a functional exercise by the Ministry of Health Malaysia, with systematic retrospective data collection over a 6-week period in 2022.

13 public emergency hospitals across Malaysia, representing state, major specialist, minor specialist and non-specialist hospitals, including facilities from Sabah and Sarawak for geographical diversity.

10 247 ED visit records were collected through systematic random sampling, of which 9942 complete and valid records were retained for analysis after the exclusion of incomplete or erroneous entries.

The study included 9942 ED patient records from 13 public hospitals across Malaysia over a 6-week period. Of these, 54.4% were male, and 45.5% were female. Malaysian citizens comprised 96.1% of the study population. The most frequently reported diagnoses were respiratory diseases (21.2%), followed by injuries and poisoning (13.2%) and digestive system disorders (8.4%). A total of 946 unique ICD-10 codes were identified, with 73.7% used fewer than five times. The top 20 diagnoses accounted for 42.9% of all records. Acute upper respiratory infection (J06.9) was the most commonly reported diagnosis (961 cases), followed by COVID-19 (U07.1, 608 cases) and gastroenteritis of unspecified origin (A09.9, 313 cases). The data demonstrated variation in the distribution of ICD-10 diagnoses across participating hospitals, highlighting key diagnostic categories relevant for future DRG development.

This study highlights the diversity of diagnoses in Malaysian EDs and underscores the need for tailored DRGs to optimise healthcare resource allocation. The findings suggest that a principal diagnosis short list may support future efforts to improve classification consistency and inform resource planning, although its effect warrants empirical evaluation. Given the concentration of diagnoses within a limited number of ICD-10 codes, implementing DRGs in emergency care is both feasible and necessary. Future research should expand data collection to capture seasonal trends and refine the principal diagnosis list to further support DRG categorisation and ensure its applicability across varying healthcare demands.

Uncontrolled type 2 diabetes mellitus (T2DM) causes microvascular and macrovascular issues that hike healthcare costs and threaten global health. Previous studies have suggested meal replacement (MR) therapy for T2DM, but there were inconsistencies in the results. Thus, a randomised controlled trial is proposed to determine the efficacy of a diabetes-specific MR product on weight loss, glycaemic control, satiety, quality of life, metabolic gene expression and cost benefit in overweight and obese patients with T2DM.

164 overweight and obese participants with T2DM will be randomly assigned to either an intervention group (n=82) or a control group (n=82). All participants will receive dietary counselling; however, only the intervention group will receive MR for 12 weeks. Glycaemic control and obesity indicators are the study’s main outcomes. Secondary outcomes include cardiovascular disease risk markers, metabolic risk, metabolic gene profile analysis, dietary data, physical activity, satiety level, quality of life and cost analysis. Baseline data will include sociodemographics, anthropometry, blood pressure, diet, physical activity, satiety level, quality of life, blood profile, metabolic gene expression profile and cost-benefit analysis. A follow-up is planned at intervention weeks 6 and 12. Week 6 will assess only anthropometry, blood pressure, diet, physical activity and satiety level. For compliance assessment, intervention group participants will bring their MR container. Week 12 will measure the same baseline parameter, except sociodemographic data. Individuals who consume less than 80% of the MR will be deemed non-compliant. All parameter modifications will be documented and analysed for comparison. All statistical analyses will be conducted using IBM SPSS V.29.0 software, with a significance level of p

This research protocol was approved by the Ethical Committee of the National University of Malaysia (JEP-2024-695) and registered on International Standard Randomised Controlled Trial Number. Study findings will be disseminated in peer-reviewed journals, conference presentations and social media.

ISRCTN57040303).