Potential of Ayurgenomics Approach in Complex Trait Research: Leads from a Pilot Study on Rheumatoid Arthritis

<div><h3>Background</h3><p>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 <em>Vata</em>, <em>Pitta</em> and <em>Kapha,</em> based on their physical and mental constitution, referred to as <em>Prakriti</em>. We hypothesised that conditioning association studies on prior risk, predictable in Ayurveda, will uncover much more variance and potentially open up more predictive health.</p> <h3>Objectives and Methods</h3><p>Identification of genetic susceptibility markers by combining the <em>prakriti</em> 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 <em>prakriti</em> groups using clinico-genetic data.</p> <h3>Results</h3><p>Inflammatory genes like <em>IL1β</em> (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 <em>Vata</em> subgroup whereas oxidative stress pathway genes are observed in <em>Pitta (SOD3</em> rs699473<b>,</b> p = 0.004, OR = 1.83; rs2536512 p<b> = </b>0.005; OR = 1.88 and <em>PON1</em> rs662, p = 0.04, OR = 1.53) and <em>Kapha</em> (<em>SOD3</em> rs2536512, genotypic, p = 0.02, OR = 2.39) subgroups. Fixed effect analysis of the associated markers from <em>CD40, SOD3</em> and <em>TNFα</em> with genotype X <em>prakriti</em> interaction terms suggests heterogeneity of effects within the subgroups. Further, disease characteristics such as severity was most pronounced in <em>Vata</em> group.</p> <h3>Conclusions</h3><p>This exploratory study suggests discrete causal pathways for RA etiology in <em>prakriti</em> based subgroups, thereby, validating concepts of <em>prakriti</em> and personalized medicine in Ayurveda. Ayurgenomics approach holds promise for biomarker discovery in complex diseases.</p> </div

Significant allelic/genotypic associations [p value; OR (95%CI)] of genes/markers tested in total sample set and <i>prakriti</i>-wise subgroups.

<p>NS = not significant.</p>#<p>was previously referred to as rs6954345.</p><p>‘*’stands significant at FDR <0.05.</p

Regression analysis of covariates showing significance (p<0.2) in different <i>prakriti</i> subgroups.

<p>Significant associations are indicated in bold.</p

<i>Prakriti</i>-wise distribution and demography of the study samples.

<p><i>Prakriti</i>-wise distribution and demography of the study samples.</p

Significant haplotypic# associations [p value; OR (95%CI)] of genes/markers tested in total sample set and <i>prakriti</i>-wise subgroups.

#<p>Order of markers constituting the haplotypes are indicated in parentheses after the genes.</p><p>NS = not significant.</p><p>‘*’adjusted p = 0.02.</p><p>‘**’adjusted p = 0.004.</p

Comparison of clinical parameters between <i>prakriti</i> subgroups.

*<p>Values not available for all the samples in the study, thus there are varying numbers in the group under the different parameters.</p>**<p>‘+’ and ‘−’ indicate higher and lower means of parameter between respective comparison groups; these are shown only for those comparisons showing significant differences.</p><p>Significant associations are indicated in bold.</p