Abstract

BackgroundInconsistent 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.Objectives and MethodsIdentification 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.ResultsInflammatory 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.ConclusionsThis 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.

Highlights

  • Challenges in identifying clinically relevant genetic determinants underlying complex diseases include phenotypic and genetic heterogeneity; gene-gene interactions and allelic spectrum which include both common and rare genetic variants

  • Inflammatory genes like IL1b (C-C-C haplotype, p = 0.0005, odds ratio (OR) = 3.09) and CD40 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

  • A total of 350 Amavata (RA) patients and 376 control subjects were recruited for this study

Read more

Summary

Introduction

Challenges in identifying clinically relevant genetic determinants underlying complex diseases include phenotypic and genetic heterogeneity; gene-gene interactions and allelic spectrum which include both common and rare genetic variants. Stratifying the cohort and conducting genetic analysis or functional studies within strata may facilitate the biomedical researchers to search for ‘‘the needle’’ in a haystack It is at this juncture that the insights from Ayurveda, the traditional holistic Indian system of medicine, seem promising [1]. This system of medicine and healthcare enables subgrouping of individuals into three contrasting phenotypic categories namely Vata predominant, Pitta predominant and Kapha predominant This classification is based on the body constitution termed as prakriti in Ayurveda lexicon [2,3]. 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

Objectives
Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call