Predicting susceptibility to inflammaging using a genetic inflammatory index through Prakriti-based phenotypic...

Inflammaging is chronic low-grade inflammation arising from a progressive imbalance between pro-inflammatory and anti-inflammatory immune networks. The rate of inflammaging exhibits marked interindividual variation owing to differences in the pace of biological aging. Within the two-hit theory of inflammaging, such variability involves both genetic and epigenetic components. Stratification models derived from quantification of the genetic component allow classification of individuals according to their baseline genetic inflammatory tone. Such phenotypic stratification could facilitate informed decision-making in prioritizing medical care and vaccination strategies during public healthcare emergencies, including epidemics and pandemics. To this end, this paper develops a novel conceptual framework that predicts susceptibility to inflammaging based on an individual's unique constitutional phenotype, termed Prakriti. Based on the Prakriti-based phenotypic stratification rooted in Ayurveda (Indian traditional medicine) and emerging ayurgenomics evidence suggesting genotype-phenotype correlations, we hypothesize that Prakriti constitutes a phenotypic biomarker of genetic inflammatory tone. Specifically, we introduce a mathematical model that quantifies the baseline inflammatory tone as the Genetic Inflammatory Index (GII), a measure of constitutional inflammatory architecture derived from cytokine single nucleotide polymorphism (SNP) scores. Based on the sign of GII, we hypothesize that Vata and Pitta dominant constitutions carry a pro-inflammatory (GII > 0) genetic predisposition, while Kapha dominant constitutions are hypothesized to possess an anti-inflammatory (GII < 0) genetic resilience. Accordingly, within age-matched cohorts exposed to broadly comparable environmental conditions, Vata and Pitta Prakriti individuals are predicted to exhibit greater epigenetic age acceleration than Kapha Prakriti individuals. Finally, we outline experimental strategies to test these hypotheses using epigenetic clocks in Prakriti-stratified cohort studies.