A team of researchers from the University of Buffalo has created a revolutionary method that uses artificial intelligence to model the progression of chronic diseases as individuals age.
The study has been published in the ‘Journal of Pharmacokinetics and Pharmacodynamics’.
The model assessed metabolic and cardiovascular biomarkers – 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.
The findings are critical due to the increased risk of developing metabolic and cardiovascular diseases with ageing, a process that has adverse effects on cellular, psychological and behavioural processes.
“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 Murali Ramanathan, PhD, professor of pharmaceutical sciences in the UB School of Pharmacy and Pharmaceutical Sciences.
“This knowledge gap may be potentially bridged by innovative disease progression modelling,” he added.
“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.
Additional investigators included first author and UB School of Pharmacy and Pharmaceutical Sciences alumnus Mason McComb, PhD; Rachael Hageman Blair, PhD, associate professor of biostatistics in the UB School of Public Health and Health Professions; and Martin Lysy, PhD, associate professor of statistics and actuarial science at the University of Waterloo.
The research examined 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.
Biomarkers also included measurements such as temperature, body weight and height, which are used to diagnose, treat and monitor the overall health and numerous diseases.
The researchers examined seven metabolic biomarkers: body mass index, waist-to-hip ratio, total cholesterol, high-density lipoprotein cholesterol, triglycerides, glucose and glycohemoglobin. The cardiovascular biomarkers examined include systolic and diastolic blood pressure, pulse rate and homocysteine.
By analyzing changes in metabolic and cardiovascular biomarkers, the model “learned” how ageing affected these measurements. With machine learning, the system used memory of previous biomarker levels to predict future measurements, which ultimately revealed how metabolic and cardiovascular diseases progressed over time.
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