Abstract
Background Digital twins in healthcare enable the creation of accurate, real-time replicas of individual patients, allowing for personalized, data-driven diagnostics, treatment plans, and monitoring to enhance patient outcomes and healthcare efficiency. This transformative shift could significantly enhance our ability to comprehend and address individual well-being and health needs in a more personalized and preventive manner. This study aims to highlight the approach developed by BioTwin Inc., designed to assist and empower healthcare providers in their clinical practice. Methods Through the integration of dried blood samples (DBS), biometric data, untargeted metabolomics liquid chromatography and mass spectrometry (LC-MS) analysis, and a data-driven workflow, BioTwin Inc. holds the potential to generate insights into individual health, potentially catalyzing a transformative shift in healthcare. To achieve this, a cross-sectional study was conducted to collect DBS samples from 277 volunteers over 30 months across Canada and the United States of America. Samples were collected using standardized protocols and analyzed using LC-MS. Subsequently, a machine learning approach was employed for further analysis and refinement of prediction models. Results The results of the experiment demonstrate the dynamic nature of metabolism, revealing its variability within individuals over time and its uniqueness across different individuals. The precision for predicting sample ownership was 80% accuracy when users provided 5 samples and 92% accuracy when users provided 10 samples. These findings underscore the importance of understanding temporal variations and individuality in metabolomics research. Conclusions In conclusion, the use of digital twins in healthcare, coupled with untargeted metabolomics and advanced machine learning techniques, has the potential to revolutionize healthcare delivery. The emphasis on individuality, dynamic metabolic profiles, and precision in patient care opens new frontiers in personalized and preventive medicine. Moving forward, the integration of multiple data sources and the synergy between metabolic and biometric data will benefit both providers and patients.