-A genome- wide study finds allelic differences between individuals that correlate with Ayurvedic body-types (Prakritis)
Snap-Shot of the study
Do the ayurvedic body types have genetic underpinnings? In a first step to answer this question, the authors evaluated differences between individuals whose body-type had been assigned by both Ayurvedic practitioners and a software. They found 52 variations across the genomes of 262 individuals which allowed them to be classified into ayurvedic Prakritis – Pitta, Kapha and Vata.
Introduction to Ayurvedic prakritis
In Ayurveda, according to the ancient text Charaka Samhita –the body and mind must be brought together to lead a harmonious existence. People can be classified into Prakritis or types on the basis of relative contribution of the three constituents Pitta, Kapha and Vata (roughly translating to – arising from movement, digestion and accumulation – of toxic metabolites for instance) to their body. According to Ayurvedic philosophy- an understanding of this body type and the ability to maintain a diet and lifestyle suited to that body type translate to balance and health. Prakriti or Ayurvedic body-types which define a person’s intrinsic physical abilities, mental states and also have implications for their susceptibility to disease and response to drugs (1,2).
Background for this study
In a recent study (3), researchers have identified genetic variations associated with the traditional classification of people into Ayurvedic Prakritis – specifically if small differences (SNPs, Single nucleotide polymorphisms) throughout the human genome correlate with the Prakriti classification. All humans are genetically very similar to each other, differences between us (populations, races, ethnic groups etc.) are captured in variations of nucleotides which make up the DNA – these are called single nucleotide polymorphisms. Many studies have shown particular SNP or group of SNPs to be correlated with risk of disease, whether a person will develop resistance to therapy, etc. forming the basis for personalized medicine (4,6).
What did they do?
3416 individuals were classified for their prakritis by Ayurvedic practitioners as well as a software. Of these, DNA isolated from blood samples of 262 individuals (male, healthy, between 20-30 years), who were reliably classified as having a clear dominance of one of the three constituents (Vata, Pitta or Kapha) representing the “extreme” Prakritis were used for the genome-wide study. A microarray consisting of 1 million positions / SNPs was used to identify the genotype of these individuals.
What did they find?
This study found 52 out of 1 million SNPs is sufficient to assign the Prakriti of individuals, irrespective of their ethnic background. One of the challenges in the study was that there was no control group – therefore each prakriti was compared to the other two. Subsequently, these 52 SNPs were able to cluster individuals into distinct groups by Principal Component Analysis. Interestingly, one of the SNPs in a PGM1 gene (Phosphoglucomutase 1), which codes for an important enzyme in sugar (glucose) metabolism, is significantly associated with Pitta dominant group that is known for efficient metabolism.
Things to keep in mind about the study:
It is to be noted that the three categories compared and defined here represent extremes –and according to Ayurvedic principles most people are a composite of all three- Kapha, Vata and Pitta, with the dominant element defining their type. There are some previous studies, which have suggested that the Ayurvedic Prakriti classification may have a genetic or metabolic basis (2,5). They were conducted on fewer subjects and looked at fewer genes/ phenotypes compared to this study, which rigorously recruited a large number of subjects, used multiple methods of classifications (software and Ayurveda practitioners) and a genome wide approach. There is much work to be done in understanding the scientific basis of the Ayurvedic classification system and whether we can independently and reliably assign people (independent of race, gender and ancestry) to a type.
What does this mean?
The 52 SNPs defined in this study can now be used independently in other populations and also provide a way of identifying new associations with metabolism and other phenotypes. For more than fifteen years now, we have been able to read our genome i.e – information in our genes but not been able to fully understand how it defines us as individuals. So on the one hand, this study by correlating phenotypes with genetic variations, helps us understand a little bit more about how genes make us who we are. On the other hand, by using modern genetic tools in the context of traditional knowledge, this study provides a rigorous way of assessing the framework of ayurvedic medicine.
3. Genome-wide analysis correlates Ayurveda Prakriti. Govindaraj P, Nizamuddin S, Sharath A, Jyothi V, Rotti H, Raval R, Nayak J, Bhat BK, Prasanna BV, Shintre P, Sule M, Joshi KS, Dedge AP, Bharadwaj R, Gangadharan GG, Nair S, Gopinath PM, Patwardhan B, Kondaiah P, Satyamoorthy K, Valiathan MV, Thangaraj K. Sci Rep. 2015 Oct 29;5:15786. d
5. Whole genome expression and biochemical correlates of extreme constitutional types defined in Ayurveda . Prasher B, Negi S, Aggarwal S, Mandal AK, Sethi TP, Deshmukh SR, Purohit SG, Sengupta S, Khanna S, Mohammad F, Garg G, Brahmachari SK; Indian Genome Variation Consortium, Mukerji M. J Transl Med. 2008 Sep 9;6:48.
Q. What was the overlap between the prediction by the software, by different Ayurvedic practitioners? Have you tried to estimate if this classification is robust enough to suggest underlying genetic differences?
The first assessment was by Ayurvedic physicians and classified using their knowledge. To double check what they have done, we have used a computer software. The software is also based on various parameters specified by the Ayurveda physicians.There were many questions which the individual had to answer to be assigned a Prakriti. Across the three centres – Bangalore, Pune and Udupi, on average, 75% of the individuals were in concordance.
Q. Why not perform an enzyme profile or measure transcriptional differences for metabolic enzymes? Is there an advantage in taking an SNP approach?
The advantage is that the SNP does not changes, it is there from birth till the individual dies. Whereas transcription profiles may change at different times, depending on time of day, tissue type to tissue type etc. We have looked into that also. This is the very first step. We can extend this across countries, across ethnic groups and cluster them.
Q. Ayurvedic medicine believes in holistic changes including those of lifestyle, dietary along with medicines and does not rely overly on mechanistic explanations beyond the classification into types. There is less emphasis, if you will on dissection of cause-effect and more on restoring the overall balance. Do you agree with this? If yes, then what are the challenges with respect to the study design when you tried to apply the modern framework of science- which relies on a reductionist approach, finding a cause and targeting only that specifically, to the ayurvedic system?
I agree that there is a holistic approach, but the basic approach is to classify the individual. Based on the prakriti, they will make the changes to the diet, prescribe treatment etc., so this is a very important stage. The challenge is the following- I am a geneticist looking at diseases,looking at case-control studies is very easy – for every marker we can ask if the mutation is present or if the prevalence is higher in the patients versus the control. In this case all the individuals are normal and within the same age-group. The only difference is the prakiti. So we wanted to see how to differentiate these individuals- as there are three groups, not two. Then we decided to compare one prakriti against the other two types, and try to see if there is genetic variation between the groups. Then there was a lot of statistical analysis. We used 1 million markers, this has many advantages, the disadvantage is the robustness and having to come up with statistical analysis ourselves. (MT: So, by using 1 million regions, you may increase the chances of false associations, is that the worry?) Not, really false associations. For example we may not have information about a particular SNP in an individual. We need to use markers that are consistent between all the individuals. So, when data is not available, we need ways of retrieving the data. In that process we need to use genetic panel of markers which are Indian specific. We developed our own panel of markers – Dravidian, Indo-European and used as a reference and to extract what is the possible marker in a given position.
Q. Have you tried to validate your classification using the 52 SNP panel with an independent population? For instance, if you knew what a person’s SNP state is, how reliably can you assign their Prakriti? Would it be useful to perform a blind study in which both the SNP panel ayurvedic practitioners and the software performed the classification, with the aim of determining how often they match?
That is very interesting. What we did was, we has more than 300 samples analyzed for these 1 million genetic markers, from our initial studies on population genetics. We used some of those samples, as these are all populations specific – a very endogamous population. We tried to project some of those individuals into these three clusters, we did find that although the individuals have come from the same ethnic background (more homogeneous), they were falling in 2 or 3 different prakritis. The same ethinic background can be placed into different prakriti. This we tried to do with our own data, this is not as detailed as you suggest. Independent blind studies need to be done.
Q. You have excluded women from your study and restricted yourself to the Indian population, does this limit the applicability of your results?
Yes, of course. At this point we selected only males because we did not want any confounding effects, in the females there are many hormonal changes and so on.
Q. You have started a way of examining traditional medicine in the framework of modern science. What are the challenges and the future of this approach?
(The future of this approach) This has paved the way to do many more things. For example, the discovery of PGM1 has given the clue that you can take the phenotype of the particular prakriti and correlate it with the gene, this gene is involved in metabolism and individuals with the pitta prakirti have high metabolism. We can now use the characteristic feature of the prakriti and look into those genes in a detailed way. These are some of the futuristic aspects, one can take the study further with. We did try to look at the network of all the metabolic pathways genes, the problem is this will need transcription or metabolic profiles from tissues of these normal individuals.