Circadian regulation modulates metabolic and hormonal processes throughout the day, yet it remains unclear whether these diurnal fluctuations are reflected in exhaled volatile organic compound (VOC) profiles and whether such temporal patterns differ between individuals with and without diabetes. Previous breath analysis studies in diabetes have shown heterogeneous results, which may reflect differences in analytical approaches and the lack of standardised sampling times.

This prospective, single-centre observational study examines daytime VOC dynamics from 08:00 to 16:00 among adults without diabetes, and individuals with type 1 diabetes or type 2 diabetes. 60 participants will complete one in-person visit with repeated breath measurements using a BreathSpec^® gas chromatography–ion mobility spectrometry system (GC-IMS) device, capillary glucose testing, body composition assessment, questionnaires, and oral and stool microbiota sampling. A standardised breakfast is provided; subsequent meals follow structured timing but are not standardised. The primary outcome is temporal variation in VOC intensities. Secondary outcomes include between-group differences and associations with glucose levels, body composition and microbiota composition. Analyses will use established GC–IMS tools and exploratory multivariate approaches.

Ethics approval was granted by the Ethics Committee of the Canton of Bern (BASEC 2023-01143). Results will be shared via peer-reviewed publications, conferences and lay summaries.

NCT05984979.

Atrial Fibrillation (AF) is the most common arrhythmia worldwide affecting an estimated 5% of people over the age of 65 and is a leading cause of stroke and heart failure. Identification of patients at risk allows preventative measures and treatment before these complications occur. Conventional risk prediction models are static, do not have flexibility to incorporate dynamic risk factors and possess only modest predictive value. Artificial intelligence and machine learning-powered health virtual twin technology offer transformative methods for risk prediction and guiding clinical decisions.

In this prospective observational study, 1200 patients will be recruited in two tertiary centres. Patients hospitalised with acute illnesses (sepsis, heart failure, respiratory failure, stroke or critical illness) and patients having undergone high-risk surgery (major vascular surgery, upper gastrointestinal surgery and emergency surgery) will be monitored with a patch-based remote wireless monitoring system for up to 14 days. Clinical and electrocardiographic data will be used for modelling the risk of new-onset AF. The primary outcome is episodes of AF >30 s and will be described as ratio of episodes/patient and as percentage of patients having episodes of AF. Secondary outcomes include 30-day and 90-day readmission rates and complications of AF.

The aim of this study is to generate data for the development and validation of health virtual twins predicting onset of AF in an at-risk population. The intelligent monitoring to predict atrial fibrillation (NOTE-AF) study is part of the TARGET project, a Horizon Europe funded programme which includes risk prediction, diagnosis and management of AF-related stroke (https://target-horizon.eu/).

The study has received approval by the Health Research Authority and the National Research Ethics Service (REC reference 24/NW/0170, IRAS project ID: 342528) in the UK and has been registered on clinicaltrials.gov (NCT06600620). Results will be disseminated as outlined in the TARGET protocol to communicate project ideas, activities and results to diverse audiences.

NCT06600620.