Ghodke Y, Joshi K, Patwardhan B. Traditional Medicine to Modern Pharmacogenomics: Ayurveda Prakriti Type and CYP2C19 Gene Polymorphism Associated with the Metabolic Variability.
EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2011;
2011:249528. [PMID:
20015960 PMCID:
PMC3135904 DOI:
10.1093/ecam/nep206]
[Citation(s) in RCA: 52] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2008] [Accepted: 11/10/2009] [Indexed: 12/25/2022]
Abstract
Traditional Indian medicine—Ayurveda—classifies the human population into three major constituents or Prakriti known as Vata, Pitta and Kapha types. Earlier, we have demonstrated a proof of concept to support genetic basis for Prakriti. The descriptions in Ayurveda indicate that individuals with Pitta Prakriti are fast metabolizers while those of Kapha Prakriti are slow metabolizers. We hypothesized that different Prakriti may have different drug metabolism rates associated with drug metabolizing enzyme (DME) polymorphism. We did CYP2C19 (Phase I DME) genotyping in 132 unrelated healthy subjects of either sex by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) technique. We observed significant association between CYP2C19 genotype and major classes of Prakriti types. The extensive metabolizer (EM) genotype (∗1/∗1, ∗1/∗2, ∗1/∗3) was found to be predominant in Pitta Prakriti (91%). Genotype (∗1/∗3) specific for EM group was present only in Pitta Prakriti. Poor metabolizer (PM) genotype (∗2/∗2, ∗2/∗3, ∗3/∗3) was highest (31%) in Kapha Prakriti when compared with Vata (12%) and Pitta Prakriti (9%). Genotype (∗2/∗3) which is typical for PM group was significant in Kapha Prakriti (odds ratio = 3.5, P = .008). We observed interesting correlations between CYP2C19 genotypes and Prakriti with fast and slow metabolism being one of the major distinguishing and differentiating characteristics. These observations are likely to have significant impact on phenotype-genotype correlation, drug discovery, pharmacogenomics and personalized medicine.
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