It is thought that the model, devised by researchers from the State University of New York at Buffalo (UB), could support the assessment of long-term chronic drug therapies and help clinicians develop more effective treatments for complex diseases.
Described in the Journal of Pharmacokinetics and Pharmacodynamics, the model assesses measurable biological processes such as cholesterol levels, body mass index, glucose and blood pressure to calculate health status and disease risks across a patient’s lifespan.
“There is an unmet need for scalable approaches that can provide guidance for pharmaceutical care across the lifespan in the presence of ageing and chronic co-morbidities,” said lead author Professor Murali Ramanathan, from the UB School of Pharmacy and Pharmaceutical Sciences. “This knowledge gap may be potentially bridged by innovative disease progression modelling.”
The research was underpinned by data from three case studies within the third National Health and Nutrition Examination Survey (NHANES) that assessed the metabolic and cardiovascular biomarkers of nearly 40,000 people in the United States. The team examined seven metabolic biomarkers (body mass index, waist-to-hip ratio, total cholesterol, high-density lipoprotein cholesterol, triglycerides, glucose and glycohemoglobin) whilst the cardiovascular biomarkers examined included systolic and diastolic blood pressure, pulse rate and homocysteine.
By analysing changes in metabolic and cardiovascular biomarkers, the model “learns” how aging affects these measurements. With machine learning, the system uses a memory of previous biomarker levels to predict future measurements, which ultimately reveal how metabolic and cardiovascular diseases progress over time.
The model could facilitate the assessment of long-term chronic drug therapies, and help clinicians monitor treatment responses for conditions such as diabetes, high cholesterol and high blood pressure, which become more frequent with age, said Ramanathan.