Whole Exome Sequencing in Healthy Individuals of Extreme Constitution Types Reveals Differential Disease Risk: A Novel Approach towards Predictive Medicine

Abstract

Precision medicine aims to move from traditional reactive medicine to a system where risk groups can be identified before the disease occurs. However, phenotypic heterogeneity amongst the diseased and healthy poses a major challenge for identification markers for risk stratification and early actionable interventions. In Ayurveda, individuals are phenotypically stratified into seven constitution types based on multisystem phenotypes termed “Prakriti”. It enables the prediction of health and disease trajectories and the selection of health interventions. We hypothesize that exome sequencing in healthy individuals of phenotypically homogeneous Prakriti types might enable the identification of functional variations associated with the constitution types. Exomes of 144 healthy Prakriti stratified individuals and controls from two genetically homogeneous cohorts (north and western India) revealed differential risk for diseases/traits like metabolic disorders, liver diseases, and body and hematological measurements amongst healthy individuals. These SNPs differ significantly from the Indo-European background control as well. Amongst these we highlight novel SNPs rs304447 (IFIT5) and rs941590 (SERPINA10) that could explain differential trajectories for immune response, bleeding or thrombosis. Our method demonstrates the requirement of a relatively smaller sample size for a well powered study. This study highlights the potential of integrating a unique phenotyping approach for the identification of predictive markers and the at-risk population amongst the healthy.

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“… Can we validate the molecular biomarkers of body constitution or Prakriti using genomic or epigenomic expression profiles? (See recent publication for efforts in this area [ 64 ]. Can we add Prakriti assessment to evaluate existing genomics, epigenomics, metabolomics, and immunophenotyping data bases to correlate these genotypes and phenotypes to the Prakriti of the patients?…”

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“…Prakriti types have been found to have unique genetic variability that could address phenotypic heterogeneity of patients and understand their disease susceptibility, conditioning, and predictive health outcomes (55,56). Different trajectories of immune response, thrombosis, and bleeding are reported among the Prakriti types based on genetic variations in IFIT5 and SERPINA10 genes (57).…”

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“…These display contrasting phenotyping attributes comprise 10% of the population and have predominance of either of dosha s. Prakriti of individuals is assessed using a compendium of composite phenotypes that encompass anatomical, physiological, response, and activity-associated attributes. Nearly two decades of exploratory research carried out by different groups have provided molecular correlates of Prakriti from different functional hierarchies – phenomic, genetic, genomic, epigenomic, transcriptomic, metagenomic as well as at biochemical, immunological, and cellular levels, and most recently, at the physiological level (Bhushan et al, 2005 ; Prasher et al, 2008 ; Aggarwal et al, 2010 , 2015 ; Joshi et al, 2011 ; Rotti et al, 2014 , 2015 ; Satyamoorthy et al, 2014 ; Govindaraj et al, 2015 ; Tiwari et al, 2017 ; Chauhan et al, 2018 ; Chaudhari et al, 2019 ; Abbas et al, 2020 , 2022 ; Mobeen et al, 2020 ; Chakraborty et al, 2021 ; Shalini et al, 2021 ; Rani et al, 2022 ). These studies have been primarily conducted on healthy individuals of extreme and contrasting constitution types that are most easily discernible at the level of composite phenotypes, and who also have contrasting responses and vulnerability (Prasher et al, 2017 ).…”

“…Exome sequencing of healthy individuals of extreme Prakriti types has provided some interesting insights (Abbas et al, 2020 , 2022 ). Extreme Prakriti types in a population are rare and are often the most vulnerable to specific types of diseases.…”

“…These are also supported by genetic and exome studies that demonstrate risk alleles that associate with different diseases are partitioned differently amongst the healthy individual of different constitutions. In a population, the frequency of these risk alleles is masked due to extensive phenotypic heterogeneity (Aggarwal et al, 2010 ; Abbas et al, 2022 ).…”