Abstract
The distribution of CYP2C19 polymorphisms and associated metabolizer phenotypes varies across populations. There is a lack of large scale pooled data on CYP2C19 polymorphisms and associated metabolizer phenotypes in Indian population. This systematic review and meta analysis aimed to determine the prevalence of CYP2C19 loss of function and gain of function polymorphisms (*2, *3, and *17) and their associated genotype predicted metabolizer phenotypes in the Indian population. A systematic literature search was conducted in electronic databases to identify relevant studies reporting CYP2C19 polymorphisms in individuals of Indian origin. Observational studies employing validated genotyping methods were included. Meta analysis was performed using a random effects model with double arcsine transformation to estimate pooled prevalence with 95% confidence intervals (CI). Subgroup analyses were conducted based on geographical regions of India, and heterogeneity was assessed using the I2 statistic. A total of 31 studies were included in the analysis. The pooled prevalence of the CYP2C19*2 allele was 32% (95% CI: 28%–37%), with the highest prevalence observed in South India. The CYP2C19*3 allele was rare, with a pooled prevalence of 1% (95% CI: 0%–1%). The gain of function allele CYP2C19*17 showed a prevalence of 17% (95% CI: 13%–21%), with the highest frequency in North India. The pooled prevalence of poor metabolizers was 16% (95% CI: 11%–22%), whereas ultra rapid metabolizers accounted for 12% (95% CI: 4%–24%) of the population. Substantial regional variability in allele frequencies and metabolizer phenotypes was observed. The distribution of CYP2C19 polymorphisms and metabolizer phenotypes shows considerable regional variability within the Indian population.
Keywords: CYP2C19, Indian population, metabolizer phenotypes, pharmacogenomics, polymorphism, precision medicine
Introduction
Cytochrome P450 family 2 subfamily C member 19 (CYP2C19) is an important hepatic enzyme involved in the metabolism of more than 10% of commonly prescribed drugs, including clopidogrel, proton-pump inhibitors, antidepressants, antiepileptics, and several anticancer agents.[1,2,3] The CYP2C19 gene, located on chromosome 10q23.33, encodes a 490-amino acid enzyme[4] and exhibits substantial genetic polymorphism that contributes to inter-individual variability in drug response.[5]
Among the identified variants, the single-nucleotide polymorphisms CYP2C19*2 (c.681G>A) and CYP2C19*3 (c.636G>A) result in loss-of-function due to defective or truncated proteins, whereas CYP2C19*17 (c.-806C>T) is a promoter variant associated with increased enzyme expression and activity.[6,7] These polymorphisms form the basis for the following genotype-predicted metabolizer phenotypes: *1/*1 (normal metabolizers [NM], two functional alleles); *1/*2 and*1/*3 (intermediate metabolizers [IM], one nonfunctional allele and one functional allele); *2/*2, *2/*3, and *3/*3 (poor metabolizers [PMs] and two nonfunctional alleles); *1/*17 (rapid metabolizers [RMs], one functional and one gain of function allele); *17/*17 (ultrarapid metabolizers [UMs] and gain of function alleles).[8] Each of these metabolizers would react differently to typical dosages of medications that are metabolized by CYP2C19 and can influence their pharmacokinetics and therapeutic response. PMs tend to have increased plasma concentrations of the prodrug and reduced formation of the active metabolite. This may lead to a therapeutic failure of prodrug, while high risk of toxicity due to the active drug. UM and RM may excessively convert or eliminate the drug, resulting in subtherapeutic levels and an increased risk of treatment failure.[9,10]
PMs with genotypes *2/*2, *2/*3, and*3/*3 are distributed variably in different groups. The highest prevalence is observed in East Asians (12.97%) and Asians (8.15%), followed by African Americans (4.05%) and Europeans (2.38%). A similar pattern is seen with IMs with East Asians having the highest frequency (45.92%), followed by Asians (40.80%), African-Americans (31.39%), and Europeans (26.10%).[11] The gain-of-function allele, CYP2C19*17, is more common in Europeans (16%–21%), Africans (16%), and Middle Eastern populations (~20%), as compared to Asians (3%–6%).
The distribution of CYP2C19 polymorphisms varies considerably across ethnic populations. The literature suggests loss-of-function alleles are more prevalent (~one out 10 individuals) in Asian populations, whereas the gain-of-function allele CYP2C19*17 (~one out of 20 individuals) is more commonly observed in European and African populations.[9,10] Indian literature on the prevalence of CYP2C19 polymorphisms and their associated metabolizer phenotypes is scattered across individual studies. They have limited generalizability due to limited sample size or confined to one geographic location in India. Only one Pan-India population study is on the prevalence of CYP2C19 polymorphisms is available. However, it did not assess the genotype-predicted metabolizer phenotypes.[12] Given the substantial genetic diversity within India and the increasing importance of pharmacogenomics in precision medicine, understanding population-specific genetic variability is essential for optimizing drug therapy and guiding genotype-based prescribing strategies.
Therefore, this systematic review was aimed to estimate the pooled prevalence of CYP2C19 polymorphisms (*2, *3, and *17) and the distribution of genotype-predicted metabolizer phenotypes in the Indian population.
Methods
This systematic review and meta-analysis were conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines, and the study protocol was prospectively registered in the PROSPERO International Prospective Register of Systematic Reviews (CRD420250652737).
Search strategy
A systematic literature search was conducted in PubMed, Embase, Scopus, and Google Scholar from database inception to the date of search to identify relevant studies. The search strategy was developed based on the Population–Exposure–Outcome (PEO) framework. The search terms included combinations of the keywords: “CYP2C19,” “polymorphism,” “genetic variant,” “India,” and “Indian population.” Reference lists of relevant articles were also screened to identify additional eligible studies. Only studies published in the English language were considered.
Eligibility criteria
Studies were included if they met the following criteria: conducted in individuals of Indian origin; reported the frequency of **CYP2C19 polymorphisms (*2, 3, or 17) and/or genotype-predicted metabolizer phenotypes including PMs, intermediate metabolizers (IM), NM, RMs, or UMs; employed observational study designs such as cross-sectional, case–control, or cohort studies; and used validated genotyping techniques including polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP), real-time PCR, sequencing, or global screening assay (GSA).
Studies were excluded if they were nonhuman studies, lacked sufficient data on CYP2C19 allelic or genotype frequencies, did not provide adequate methodological details for quality assessment, or were review articles, editorials, commentaries, or conference abstracts without primary data.
Study selection
Two investigators (SS and TKP) independently screened studies in two stages. First, titles, and abstracts were reviewed to identify potentially eligible studies. Subsequently, full-text articles were assessed against the predefined inclusion and exclusion criteria. Any disagreements between reviewers were resolved through discussion and consensus.
Data extraction
The following data were extracted in excel sheet: author details, study design, study population, sample size, genotyping methods (PCR-RFLP, real-time PCR, GSA), allele frequencies (*1, *2, *3, and *17), population region (North India, South India, East India, and West India), and genotype-predicted metabolizer phenotypes (PM: *2/*2, *2/*3, *3/*3; IM: *1/*2, *1/*3, *2/*17; RM: *1/*17; and UM: *17/*17). Data were extracted by one investigator (SS) and its accuracy by another investigator (TKP/CKB).
Quality assessment
The included studies were assessed using a modified Newcastle–Ottawa Scale (NOS) for this systematic review. The NOS evaluated studies based on selection, comparability, and outcome criteria, with a maximum score of 10 points to ensure methodological quality [Table 1]. The studies are rated on factors such as sample size, metabolizer status definition, exposure ascertainment, statistical methods, and completeness of data reporting. A total score reflects the overall quality and reliability of each study in assessing genetic variability in drug response.
Table 1.
Quality assessment criteria for Cytochrome P450 family 2 subfamily C member 19 Genotyping Studies
| Criteria | Max. stars | Assessment points |
|---|---|---|
| Selection | 5 stars | |
| Representativeness of the sample | 1 | Was the study population representative of the general Indian population? |
| Sample size | 1 | Was the sample size justified and adequate for prevalence estimation? |
| Non-respondents | 1 | Were response rates and missing data properly addressed? |
| Ascertainment of exposure | 1 | Were validated genotyping methods (PCR-RFLP, RT-PCR, sequencing) used? |
| Definition of metabolizer status | 1 | Were CYP2C19 metabolizer phenotypes (PM, IM, NM, RM, UM) clearly defined? |
| Comparability | 2 stars | |
| Adjustment for confounding factors | 1 | Did the study account for potential confounders such as age, sex, and ethnicity in the analysis? |
| Standardization across studies | 1 | Were comparisons between studies made using similar methodologies, genotype classifications, and statistical analyses? |
| Outcome | 3 stars | |
| Measurement of outcome | 1 | Were CYP2C19 polymorphism prevalence and metabolizer phenotypes assessed using reliable and standardized laboratory methods? |
| Statistical analysis | 1 | Were appropriate statistical methods used, such as prevalence with 95% CI and/or significance testing? |
| Data completeness and transparency | 1 | Was the data on allele frequencies and metabolizer distribution fully reported and free from selective reporting bias? |
CI=Confidence intervals, PM=Poor metabolizers, IM=Intermediate metabolizers, NM=Normal metabolizers, RM=Rapid metabolizers, UM=Ultra-rapid metabolizers, RFLP=Restriction fragment length polymorphism, RT-PCR=Reverse transcription polymerase chain reaction, CYP2C19=Cytochrome P450 family 2 subfamily C member 19
Outcome assessment
The primary outcome of this study was to estimate the pooled prevalence (95% confidence interval [CI]) of CYP2C19 polymorphisms (*2, *3, and *17) in the Indian population. The secondary outcome was to estimate the pooled prevalence (95% CI) of genotype-predicted metabolizer phenotypes (PM, IM, NM, RM, and UM) in the Indian population. The proportion of individuals with each genotype or phenotype was used as the numerator, whereas the total study population served as the denominator. Prevalence estimates were calculated as proportions with standard errors, using Excel for initial data processing.
The pooled prevalence (95% CI) was estimated using the double arcsine transformation with a random-effects model. The statistical heterogeneity was assessed using the I² statistic. I² >50% was considered to have high heterogeneity. Subgroup analyses of primary and secondary outcomes were performed according to geographical regions of India (North, South, East, West, and Pan-India studies). The publication bias was assessed through visually inspection of asymmetry of the Funnel plot and statistically using Egger’s regression test. P < 0.05 indicated potential small-study effects.
The meta-analyses were performed using MetaXL version 5.3, and publication bias was assessed using JASP version 0.19.3.
Results
Literature search
As shown in Figure 1, from the literature search, a total of 75 full-text articles were fully evaluated, and 31 observational studies were included as per the selection criteria.[12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40] Two included studies reported separate data for healthy individuals and diseased populations. They were considered as independent datasets in the analysis while citing the same reference.[17,25]
Figure 1.
PRISMA flow diagram of study selection. The figure illustrates the systematic process of identification, screening, eligibility assessment, and final inclusion of studies
Study characteristics
A characteristic of the included studies is shown in Table 2. It outlines their study design, study population and region, sample size, genotyping methods, CYP2C19 variants assessed, genotype distributions, and corresponding phenotype classifications. Of the 31 studies reviewed, 10 were conducted in North India, 14 in South India, 5 in West India, 1 in East India, and 1 represented a Pan-India population. Fifteen studies were conducted on healthy populations, whereas 16 studies included patients with specific clinical conditions, including cardiovascular diseases (n = 11), cancer (n = 2), central nervous system disorders (n = 2), and pulmonary or extrapulmonary tuberculosis (n = 1). Regarding methodology, 20 studies employed PCR-RFLP, 8 utilized RT-PCR, 1 used GSA, and 2 used conventional PCR techniques for genotyping.
Table 2.
Overview of Cytochrome P450 family 2 subfamily C member 19 genetic polymorphism studies conducted across different regions of India
| Authors | Study Design | Subject/Region of India | Sample size | Method | CYP2C19 genotype (%) | Phenotype | ||||
|---|---|---|---|---|---|---|---|---|---|---|
|
| ||||||||||
| PM | IM | UM | RM | NM | ||||||
| Adithan 2003[13] | Observational, cross-sectional study | Healthy/South | 112 | PCR-RFLP | *1 (59.8) *2 (37.9) *3 (2.2) |
9.8 | 60.7 | NS | NS | 29.5 |
| Anichavezhi 2012[14] | Observational, cross-sectional study | Healthy/South | 206 | PCR-RFLP | *1 (65) *17 (19.2) |
NS | NS | 31.5 | 68.5 | NS |
| Bhat 2015[15] | Prospective cohort study. | cardiovascular disorders/North | 100 | PCR-RFLP | *1 (73) *2 (31) *3 (8) |
19 | 50 | NS | NS | 56 |
| Bhat 2023_1[17] | observational case control study | cardiovascular disorders/North | 645 | RT-PCR | *2 (32.4) *3 (0.01) *17 (16.2) |
10.39 | 44.34 | 18.14 | NS | 27.13 |
| Bhat 2023_2[17] | observational case control study | Healthy/North | 546 | RT-PCR | *2 (17) *3 (0.01) *17 (29.4) |
2 | 31.14 | 38 | NS | 28.75 |
| Deshpande 2016[16] | Prospective cohort study. | Healthy/South | 560 | Rt-PCR | *2 (41) *3 (1) *17 (19.7) |
18.03 | 49.1 | 2.85 | 15.35 | 14.64 |
| Erathi 2018[18] | prospective, observational study | cardiovascular disorders/South | 151 | PCR-RFLP | *2 (49) | 49 | NS | NS | NS | NS |
| Garg 2016[19] | observational case control study | CNS Disorder/North | 142 | RT-PCR | *1 (66.67) *3 (0) |
0 | 0 | NS | NS | 66 |
| George 2021[20] | Prospective cohort study. | cardiovascular disorders/South | 60 | PCR-RFLP | *2 (41) | 41 | NS | NS | NS | NS |
| Ghodke 2007[21] | observational case control study | Healthy/West | 139 | PCR-RFLP | *1 (57.1) *2 (41.7) *3 (1.2) |
19.4 | 46.7 | NS | NS | 33.8 |
| Gudur 2024[22] | Observational study | Cancer/West | 200 | PCR-RFLP | *2 (9) | NS | NS | NS | NS | NS |
| Gulati 2014[23] | Observational, cross-sectional study. | Healthy/North | 308 | PCR-RFLP | *2 (22) *3 (0) |
4.9 | 34.4 | NS | NS | 60.7 |
| Jose 2004[24] | Observational, cross-sectional study | Healthy/South | 906 | PCR-RFLP | *1 (64) *2 (35) *3 (1) |
NS | NS | NS | NS | NS |
| Joshi 2022_1[25] | Observational, cross-sectional study | Cancer/North | 103 | PCR-RFLP | *2 (13.59) *3 (0) |
NS | NS | NS | NS | NS |
| Joshi 2022_2[25] | Observational, cross-sectional study | Healthy/North | 103 | PCR-RFLP | *2 (4.58) *3 (0) |
NS | NS | NS | NS | NS |
| Kumar 2024[26] | prospective, open label, observational study | CNS Disorder/South | 119 | PCR-RFLP | *1 (24.5) *2 (27.35) *17 (48.05) |
27.35 | NS | 48.05 | NS | 24.5 |
| Lamba 1998[27] | Observational, cross-sectional study | Healthy/North | 121 | PCR | *1 (70.1) *2 (29.8) *3 (0) |
7.4 | 44.6 | NS | NS | 47.9 |
| Mahadevan 2014[28] | Observational, cross-sectional study. | cardiovascular disorders/South | 100 | PCR-RFLP | *2 (66) | 9 | 57 | NS | NS | NS |
| Mehta 2022[29] | cross-sectional study | cardiovascular disorders/West | 100 | Rt-PCR | *2 (46) | NS | NS | NS | NS | NS |
| Naushad 2021[12] | Observational study | Healthy/PAN | 2000 | GSA | *2 (32) *3 (0.425) *17 (13.95) |
NS | NS | NS | NS | NS |
| Panchabhai 2006[30] | Observational, cross-sectional study. | Healthy/West | 170 | RT-PCR | *1 (89.6) *2 (10.36) |
10.36 | NS | NS | NS | 89.63 |
| Patel 2019[31] | A Pilot Study | cardiovascular disorders/North | 57 | PCR-RFLP | *2 (36) | 36 | NS | NS | NS | NS |
| Prasanthi 2014[32] | Observational, cross-sectional study. | cardiovascular disorders/East | 60 | PCR-RFLP | *2 (40) *3 (0) *17 (0) |
NS | NS | NS | NS | NS |
| Rath 2015[33] | Observational study | cardiovascular disorders/Sout h | 200 | PCR-RFLP | *1 (45.5) *2 (39.5) *3 (0.5) *17 (14.5) |
16.5 | 47 | 2.5 | 14.5 | 19.5 |
| Satyanarayana 2009[34] | Observational, cross-sectional study. | Healthy/South | 50 | PCR | *1 (60) *2 (38) *3 (2) |
68 | NS | NS | NS | 32 |
| Shalia 2013[35] | Observational study | Healthy/West | 102 | PCR-RFLP | *2 (35.2) *17 (8.97) |
NS | NS | NS | NS | NS |
| Shetkar 2014[36] | prospective cohort study | cardiovascular disorders/North | 110 | PCR-RFLP | *1 (23.64) *2 (40.9) *3 (0) *17 (35.45) |
NS | NS | NS | NS | NS |
| Sridharan 2016[37] | Observational study | Healthy/South | 87 | RT-PCR | *1 (65.5) *2 (33.3) *3 (1.2) |
9.2 | 50.6 | NS | NS | 40.2 |
| Subraja 2012[38] | Observational study | cardiovascular disorders/South | 149 | PCR-RFLP | *1 (39.3) *2 (44.97) *3 (1.01) *17 (16.8) |
20.8 | 50.32 | 2.01 | 10.06 | 16.77 |
| Xavier 2016[39] | Observational study | pulmonary and extra-pulmonary tuberculosis/South | 80 | PCR-RFLP | *1 (41.02) *2 (33.6) *3 (0.8) *17 (24.4) |
12.8 | 43.2 | 1.6 | 25.6 | 16.8 |
| Yadav 2023[40] | cross-sectional | Healthy/South | 300 | RT-PCR | *2 (36.5) *3 (0.3) *17 (18) |
12.67 | 46.67 | 5.67 | 35 | NS |
It includes details on study design, setting, population, genotyping methods, sample sizes, and key genetic variants analysed. The data highlights the distribution of CYP2C19 alleles (*1, *2, *3, *17) and metabolizer phenotypes (PM, IM, UM, and RM) in various health conditions and populations. PM=Poor metabolizers, IM=Intermediate metabolizers, NM=Normal metabolizers, RM=Rapid metabolizers, UM=Ultra-rapid metabolizers, RFLP=Restriction fragment length polymorphism, RT-PCR=Reverse transcription polymerase chain reaction, NS=Not significant, CNS=Central nervous system, GSA=Global Screening Assay, CYP2C19=Cytochrome P450 family 2 subfamily C member 19
Quality of included studies
The methodological quality of the included studies based on predefined criteria is presented in Table 3. Four studies scored 7, nineteen scored 8, whereas eight studies scored 9. Only 5 studies were considered to have adequate sample sizes (n ≥ 5). Five Studies employed robust statistical methods.
Table 3.
Quality assessment of Cytochrome P450 family 2 subfamily C member 19 genotyping studies
| Author, Year | Selection | Comparability | Outcome | Total (10) | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|
| |||||||||||
| Representa tiveness of the sample |
Sample size | Non- respondents |
Definition of metabolizer status | Ascertainment of exposure | Standardization across studies | Adjustment for confounding factors | Assessment of outcome | Statistical methods | Completeness of data reporting | ||
| Adithan 2003[13] | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 9 |
| Anichavezhi 2012[14] | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 9 |
| Bhat 2015[15] | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 8 |
| Bhat 2023_1[17] | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 9 |
| Bhat 2023_2[17] | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 9 |
| Deshpande 2016[16] | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 9 |
| Erathi 2018[18] | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 8 |
| Garg 2016[19] | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 8 |
| George 2021[20] | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 8 |
| Ghodke 2007[21] | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 8 |
| Gudur 2024[22] | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 9 |
| Gulati 2014[23] | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 8 |
| Jose 2004[24] | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 9 |
| Joshi 2022_1[25] | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 7 |
| Joshi 2022_2[25] | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 7 |
| Kumar 2024[26] | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 8 |
| Lamba 1998[27] | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 9 |
| Mahadevan 2014[28] | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 8 |
| Mehta 2022[29] | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 8 |
| Naushad 2021[12] | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 8 |
| Panchabhai 2006[30] | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 8 |
| Patel 2019[31] | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 8 |
| Prasanthi 2014[32] | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 8 |
| Rath 2015[33] | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 8 |
| Satyanarayana 2009[34] | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 7 |
| Shalia 2013[35] | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 8 |
| Shetkar 2014[36] | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 7 |
| Sridharan 2016[37] | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 8 |
| Subraja 2012[38] | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 8 |
| Xavier 2016[39] | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 8 |
| Yadav 2023[40] | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 8 |
Prevalence of CYP2C19 polymorphisms
As shown in Figure 2, the CYP2C19*2 allele was reported in 29 studies (n = 7738) with an overall pooled prevalence of 32% (95% CI: 28%–37%; I2 = 93%). Subgroup analysis revealed regional variation, with the highest prevalence observed in South India (40%), followed by East India (40%), Pan-India studies (32%), West India (27%), and North India (24%). Prevalence estimates were broadly consistent between studies involving healthy individuals and patient populations. No significant publication bias was observed [Supplementary Figure 1 (64.6KB, tif) ].
Figure 2.
Forest plot showing pooled prevalence of CYP2C19*2 by region
The CYP2C19*3 allele was rare and reported in 21 studies (n = 6821) with an overall pooled prevalence of 1% (95% CI: 0%–1%) and moderate heterogeneity (I2 = 62%) [Supplementary Figure 2 (140.2KB, tif) ]. This allele was absent in studies from North India and Pan-India datasets, whereas very low frequencies were observed in South and West India. Funnel plot asymmetry suggested potential publication bias (Z = 5.311; P < 0.001) [Supplementary Figure 3 (57.4KB, tif) ].
The CYP2C19*17 allele was reported in 13 studies (n = 5077) with an overall pooled prevalence of 17% (95% CI: 13%–21%) and high heterogeneity (I2 = 91%) [Supplementary Figure 4 (103.5KB, tif) ]. The highest prevalence was observed in North India (26%), followed by South India (19%), Pan-India studies (14%), and West India (9%). No significant publication bias was observed (Z = 3.523; P = 0.001) [Supplementary Figure 5 (56.2KB, tif) ].
Prevalence of genotype-predicted metabolizer phenotypes
A total of 21 studies (n = 4196) reported the prevalence of PMs with a pooled estimate of 16% (95% CI: 11%–22%) and high heterogeneity (I2 = 96%) [Figure 3]. Regional subgroup analysis showed the highest prevalence in South India (23%), followed by West India (15%), whereas North India showed the lowest prevalence (8%). Funnel plot asymmetry suggested potential publication bias (Z = 6.763; P < 0.001) [Supplementary Figure 6 (70.3KB, tif) ].
Figure 3.
Forest plot showing the pooled prevalence of poor metabolizer by region
UMs were reported in 9 studies (n = 2805) with a pooled prevalence of 12% (95% CI: 4%–24%) and substantial heterogeneity (I2 = 98%). The prevalence was higher in North India (27%) compared with South India (9%) [Supplementary Figure 9 (75.5KB, tif) ].
The prevalence of IM was reported in 14 studies (n = 3589) with a pooled prevalence of 42% (95% CI: 34%–50%) and high heterogeneity (I2 = 96%) [Supplementary Figure 7 (127.8KB, tif) ]. The highest prevalence was observed in South India (50%), followed by West India (47%), whereas North India had the lowest prevalence (30%). Funnel plot asymmetry suggested potential publication bias (Z = 6.763; P < 0.001) [Supplementary Figure 8 (65.8KB, tif) ].
RMs were reported in 6 studies (n = 1495) with a pooled prevalence of 26% (95% CI: 11%–44%). These were reported exclusively in South Indian populations [Supplementary Figure 10 (66.8KB, tif) ].
Discussion
This systematic review and meta-analysis synthesized evidence from 31 observational studies (n = 8086) to estimate the prevalence of CYP2C19 polymorphisms and genotype-predicted metabolizer phenotypes in the Indian population. CYP2C19*2 was the most prevalent allele (32%) with regional variations (24%–40%), whereas CYP2C19*3 was rare (1%) and absent in several regions of India. The gain-of-function allele CYP2C19*17 was also observed to have notable regional variation (9%–26%). We also observed variable distribution of metabolizer phenotypes across India, with a relatively higher prevalence of PMs in South India (23%) and UMs in North India (27%). To the best of our knowledge, this study provides the first comprehensive pooled estimation of the prevalence of CYP2C19 polymorphism and metabolizer phenotypes across different regions of India.
The pooled prevalence of CYP2C19*2 observed in this study (32%) is consistent with reports from Chinese populations (31%) and higher than that reported in American populations (15%).[41,42,43,44] The relatively higher prevalence of this loss-of-function allele in our study suggests that a substantial proportion of Indian populations may exhibit reduced CYP2C19 enzymatic activity. This could potentially affect the pharmacokinetics and therapeutic response of multiple medications. In contrast, CYP2C19*3 was observed at a very low frequency (1%). This is in accordance with the previous reports indicating that this variant is relatively uncommon outside Asian populations.[10,11] The CYP2C19*17 showed a pooled prevalence of 17% in the Indian population, which is slightly lower than that reported in Europeans (22.27%) and Africans (23.52%) populations.[9,10]
The prevalence of genotype-predicted metabolizer phenotypes observed in this study further highlights the clinical relevance of CYP2C19 polymorphisms in the Indian population. Our findings suggest approximately one-sixth of the Indian population belonged to the PM phenotype of CYP2C19, which is higher than that reported in many Western populations.[11] A notable proportion of individuals were also categorized as rapid (26%) or UMs (12%). This suggests increased CYP2C19 activity in certain subgroups. Such metabolic phenotype variability is likely to affect the efficacy and safety of drugs metabolized by CYP2C19.[9,10]
From the clinical implication point of view, reduced CYP2C19 activity in PMs could impair the bioactivation of prodrugs such as clopidogrel. This may lead to inadequate antiplatelet effects and an increased risk of thrombotic events in patients receiving clopidogrel.[45] Similarly, alteration of metabolism of certain drugs such as phenytoin, diazepam, proton pump inhibitors, and antidepressants could affect therapeutic response and risk of occurrence of adverse effects.[3,46,47,48] Conversely, patients having rapid or UM phenotypes could experience reduced therapeutic response due to accelerated drug metabolism.[45,47,48,49] Our study’s findings highlight the value of pharmacogenetic testing in managing medications having strong dependence on CYP2C19 for metabolism, especially for drugs with a limited therapeutic index, ensuring better patient safety and therapeutic effectiveness.[9,47]
Our findings suggest ethnic and regional genetic variability should be considered while developing pharmacogenetic guidelines. Such variability could influence population-level responses to commonly prescribed drugs and emphasize the need for region-specific pharmacogenetic data to implement personalized medicine strategies.[1,2]
This systematic review and meta-analysis have several limitations. Substantial heterogeneity was observed in the estimation of the pooled prevalence of polymorphisms and their associated genotype-predicted metabolizer phenotypes. This could be due to differences in study populations, sample sizes, genotyping methods, and regional representation among included studies. Several studies included study populations with specific diseases. This could have influenced the allele frequency estimation compared with the general population. The number of studies from certain regions of India was limited. This may potentially affect the precision of regional estimates.
Conclusion
There is a significant regional variation in CYP2C19 polymorphisms and genotype-predicted metabolizer phenotypes in the Indian population. Our findings emphasize the clinical relevance of CYP2C19 genotyping for optimizing drug therapy. These findings have major implications for precision medicine, necessitating pharmacogenetic testing to improve therapeutic outcomes. Due to the high heterogeneity, large-scale, standardized pharmacogenetic studies are needed to validate the findings of this study.
Conflicts of interest
There are no conflicts of interest.
Funnel plot of CYP2C19*2 in the Indian population
Pooled prevalence of CYP2C19*3 in the Indian population. CI = Confidence intervals
Funnel plot of CYP2C19*3 in Indian population
Pooled prevalence of CYP2C19*17 in the Indian population. CI = Confidence intervals
Funnel plot of CYP2C19*17 in Indian population
Funnel plot of poor metabolizer of CYP2C19 in the Indian population
Pooled prevalence of Intermediate metabolizer of CYP2C19 in the Indian population
Funnel plot of Intermediate metabolizer of CYP2C19 in the Indian population
Pooled prevalence of ultra-rapid metabolizer of CYP2C19 in the Indian population. CI = Confidence intervals
Pooled prevalence of rapid metabolizer of CYP2C19 in Indian population. CI = Confidence intervals
Funding Statement
Nil.
References
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Funnel plot of CYP2C19*2 in the Indian population
Pooled prevalence of CYP2C19*3 in the Indian population. CI = Confidence intervals
Funnel plot of CYP2C19*3 in Indian population
Pooled prevalence of CYP2C19*17 in the Indian population. CI = Confidence intervals
Funnel plot of CYP2C19*17 in Indian population
Funnel plot of poor metabolizer of CYP2C19 in the Indian population
Pooled prevalence of Intermediate metabolizer of CYP2C19 in the Indian population
Funnel plot of Intermediate metabolizer of CYP2C19 in the Indian population
Pooled prevalence of ultra-rapid metabolizer of CYP2C19 in the Indian population. CI = Confidence intervals
Pooled prevalence of rapid metabolizer of CYP2C19 in Indian population. CI = Confidence intervals