We live in an era of medical advancements where sequencing of the human genome and its subsequent applications in personalised medicine, offer to completely revolutionise the diagnosis, treatment and even prevention of various diseases. Personalised or precision medicine is an approach that strives to move away from the ‘one-size-fits-all’ philosophy of Western medicine. It tries to cater to an individual’s disease condition, genetic predispositions as well as local environmental factors. Surprisingly, the concept of personalised medicine isn’t a brand new one. The ancient system of Ayurveda, well over five thousand years old, has always advocated the classification of patients into broad constitution types based on their physiological and behavioural traits.
In Ayurveda, there are seven constitution types are called “Prakriti” (Sanskrit: ‘nature’), which comprises of three distinct constitutions called “Vata” (V), “Pitta” (P) and “Kapha” (K), and 4 non-distinct or intermediate constitutions which are a combination of the above three, namely VP, VK, PK and VPK. Practitioners of Ayurveda have been relying on this system of classification of patients for ages. Interestingly enough, previous research has confirmed that there are molecular differences and phenotypic diversity among people belonging to different Prakriti types, even when they belong to a genetically homogenous population. A modern framework for standardizing the detection of different Prakriti types, however was still largely missing, until recently.
Now, researchers from the CSIR-Institute of Genomics and Integrative Biology, New Delhi, KEM Hospital Research Centre, Pune and Indian Statistical Institute, Kolkata, have come up with a computational framework for predicting Prakriti classes from phenotypic attributes of patients, using machine learning approaches. They were able to train a computer program to identify and classify patient records into the three distinct Prakriti types, and by extension, also predict the patients belonging to the non-distinct Prakritis, when they showed characteristics of multiple distinct Prakritis. Such a standardization of the clinical methods of Prakriti evaluation could help in bringing the benefits of personalised medicine to more and more people.