Inconsistent results across association studies including Genome-wide association, have posed a major challenge in complex disease genetics. Of the several factors which contribute to this, phenotypic heterogeneity is a serious limitation encountered in modern medicine. On the other hand, Ayurveda, a holistic Indian traditional system of medicine, enables subgrouping of individuals into three major categories namely Vata, Pitta and Kapha, based on their physical and mental constitution, referred to as Prakriti. We hypothesised that conditioning association studies on prior risk, predictable in Ayurveda, will uncover much more variance and potentially open up more predictive health.
Identification of genetic susceptibility markers by combining the prakriti based subgrouping of individuals with genetic analysis tools was attempted in a Rheumatoid arthritis (RA) cohort. Association of 21 markers from commonly implicated inflammatory and oxidative stress pathways was tested using a case-control approach in a total cohort comprising 325 cases and 356 controls and in the three subgroups separately. We also tested few postulates of Ayurveda on the disease characteristics in different prakriti groups using clinico-genetic data.
Inflammatory genes like IL1β (C-C-C haplotype, p=0.0005, OR=3.09) and CD40 (rs4810485 allelic, p=0.04, OR=2.27) seem to be the determinants in Vata subgroup whereas oxidative stress pathway genes are observed in Pitta (SOD3 rs699473, p=0.004, OR=1.83; rs2536512 p=0.005; OR=1.88 and PON1 rs662, p=0.04, OR=1.53) and Kapha (SOD3 rs2536512, genotypic, p=0.02, OR=2.39) subgroups. Fixed effect analysis of the associated markers from CD40, SOD3 and TNFα with genotype X prakriti interaction terms suggests heterogeneity of effects within the subgroups. Further, disease characteristics such as severity was most pronounced in Vata group.
This exploratory study suggests discrete causal pathways for RA etiology in prakriti based subgroups, thereby, validating concepts of prakriti and personalized medicine in Ayurveda. Ayurgenomics approach holds promise for biomarker discovery in complex diseases.