Abstract
Purpose
CYP3A4 and CYP3A5 are major drug‑metabolizing enzymes that require electrons supplied by P450 oxidoreductase (POR) for catalytic cycle. CYP3A expression is regulated by the nuclear receptors; constitutive androstane receptor (CAR) and pregnane X receptor (PXR). Single-nucleotide polymorphisms (SNPs) of CYP3A5, CYP3A4, POR, CAR, and PXR have been documented to modulate CYP3A enzyme activity and/or expression. We performed a targeted candidate-gene-based analysis of selected CYP3A-related SNPs associated with variability in CYP3A activity in a relatively large sample of general Japanese population.
Methods
The study population was sampled from the general population participating in the J-MICC Study, and comprised 351 adults who underwent health checkups at Kyoto Prefectural University of Medicine, with genotyping of one SNP in CYP3A5, one SNP in CYP3A4, one SNP in POR, two SNPs in CAR, and six SNPs in PXR. 4β-hydroxycholesterol (4β-OHC) was used as endogenous biomarker of CYP3A activity. Plasma 4β-OHC levels were measured using ultra-high-performance liquid chromatography coupled to tandem mass spectrometry, and normalized to total cholesterol (TC).
Results
In univariate analyses, CYP3A5 expressors (*1/*1, *1/*3) exhibited approximately 25% higher median 4β-OHC/TC ratio than non-expressors (*3/*3) (p < 0.001). Carriers of the rs2242480 variant and POR*28 showed significantly higher 4β-OHC/TC ratios than their respective non-carriers (p < 0.001 and p = 0.044, respectively), whereas no significant differences were observed for CAR or PXR SNPs. Multiple linear regression analysis with demographic and clinical covariates as independent variables identified CYP3A5*3 and sex as independent predictors of 4β-OHC/TC ratio.
Conclusion
These findings suggest that when assessing CYP3A activity using 4β-OHC/TC ratio as biomarker, only the CYP3A5*3 genotype may need to be considered.
Supplementary Information
The online version contains supplementary material available at 10.1007/s00228-026-04114-7.
Keywords: Single-nucleotide polymorphisms, CYP3A activity, Endogenous biomarker, 4β-hydroxycholesterol
Introduction
The cytochrome P450 (CYP) 3 A subfamily plays a pivotal role in metabolizing a wide variety of endogenous and exogenous substrates, mediating the biotransformation of approximately 40% of clinically used drugs [1]. Representative isoforms include CYP3A4, CYP3A5, CYP3A7, and CYP3A43. Among these, CYP3A4 and CYP3A5 are the predominant adult isoforms, contributing substantially to the metabolic clearance of diverse pharmacologic agents. CYP3A activity exhibits considerable interindividual variability caused by genetic polymorphisms, physiological conditions, pathological states, drug-drug interactions, and environmental factors such as dietary habits [2]. This variability has significant impact on the pharmacokinetics of CYP3A substrates, leading to differences in therapeutic efficacy and adverse event profiles [2].
Numerous single-nucleotide polymorphisms (SNPs) that influence the expression or catalytic function of CYP3A4 and CYP3A5 have been characterized. The CYP3A4*22 polymorphism (Intron 6, 15389 C > T, rs35599367) alters transcript splicing, leading to reduced CYP3A4 expression [3]. This variant is associated with altered pharmacokinetics and therapeutic responses [4], although the allele frequency of CYP3A4*22 has been reported to be extremely low in the Japanese population [5]. The rs2242480 polymorphism (Intron 10, 20230G > A) has been reported to modulate CYP3A4 intron 10 enhancer and promoter activity in an allelic-dependent manner [6]. The A allele of rs2242480 has been associated with increased promoter activity relative to the G allele, suggesting that A-allele carriers may exhibit increased CYP3A4 activity in certain contexts. Indeed, rs2242480 has been associated with altered pharmacokinetics of several CYP3A substrates, including fentanyl [7], atorvastatin [8], and tacrolimus [9].
Among CYP3A5 variants, the CYP3A5*3 allele (Intron 3, 6986 A > G, rs776746) is the most prevalent nonfunctional variant, resulting in markedly decreased enzyme expression [10]. The CYP3A5*6 (Exon 7, 14690G > A, rs10264272) and CYP3A5*7 (27,131-27132insT, rs41303343) alleles also confer reduced enzymatic activity relative to the wild-type allele [10]; however, these variants are predominantly observed in African populations and are not found among Asians [11]. Patients carrying one of the nonfunctional CYP3A5 alleles (*3, *6, or 7) demonstrated decreased clearance of CYP3A5-metabolized drugs such as tacrolimus [12] and sirolimus [13], compared with those carrying the CYP3A5*1 allele. Moreover, recipients expressing at least one CYP3A5*1 allele required higher daily tacrolimus doses to achieve equivalent trough concentrations compared with patients homozygous for nonfunctional alleles (*3/*3, *6/*6, or *7/*7) [11, 14].
Cytochrome P450 oxidoreductase (POR) is the sole electron donor to cytochrome P450 enzymes, including the CYP3A subfamily [15]. It plays a critical role in the catalytic activity of these enzymes by facilitating electron transfer from NADPH to the heme moiety [16]. This electron transfer is indispensable for CYP3A-mediated metabolism of various xenobiotics and endogenous compounds. A common POR variant, POR*28 (Exon 12, 1508 C > T, rs1057868), alters electron transfer efficiency and thereby modulates CYP3A activity. Carriers of the POR*28 allele, particularly those expressing CYP3A5 (CYP3A5*1/*1 or *1/*3), exhibit lower tacrolimus trough concentrations, reflecting enhanced CYP3A5 activity and necessitating higher dosing to achieve therapeutic windows [17–19]. In addition, POR*28 homozygosity has been associated with increased CYP3A4 activity in CYP3A5 nonexpressers (CYP3A5*3/*3) and reduced cyclosporine trough levels [18].
Pregnane X receptor (PXR) and the constitutive androstane receptor (CAR), encoded by the NR1I2 and NR1I3 gene, are ligand-activated transcription factors of the nuclear receptor superfamily that sense xenobiotics, including drugs, and enhance their clearance by upregulating genes encoding drug-metabolizing enzymes (e.g., CYP3A) and transporters [20]. Several SNPs have been documented to potentially influence PXR or CAR expression and/or activity. Major NR1I2 variants include rs2276706 (UTR-5’, 24113G > A) [21, 22], rs2472677 (Intron 1, 63396 C > T) [23, 24], rs3814055 (UTR-5’, 25385 C > T) [21, 23, 25, 26], rs3814057 (UTR-3’, 11156 A > C) [22, 26], rs6785049 (Intron 5, 7635G > A) [21, 22], and rs7643645 (Intron 1, 69789 A > G) [23, 24], while key NR1I3 variants comprise rs2307424 (Exon 5, 540 C > T) [21, 23, 26] and rs2502815 (Intron 3, 99 C > T) [26].
While numerous SNPs capable of modulating CYP3A metabolic activity have been identified, the specific variants influencing enzyme function vary depending on the substrate and study design. In vivo human CYP3A activity can be quantified by administering probe drugs (e.g., midazolam), measuring urinary 6β-hydroxycortisol/cortisol ratio, assessing cortisol 6β-hydroxylation clearance, or determining plasma 4β-hydroxycholesterol (4β-OHC) levels [27]. Among these biomarkers, 4β-OHC has the advantages of reflecting CYP3A activity from a single blood sample and being unaffected by renal function [28, 29]. Because 4β-OHC is produced via CYP3A-mediated cholesterol metabolism, its plasma concentration may be influenced by conditions such as hyperlipidemia and treatment with hydroxymethylglutaryl-CoA reductase inhibitors. Therefore, normalization to total plasma cholesterol (TC) is recommended [30]. Based on this background, the present study aimed to evaluate selected candidate SNPs in CYP3A-related genes associated with CYP3A activity by measuring TC-normalized plasma 4β-OHC levels (4β-OHC/TC ratio) in a relatively large general adult population. One SNP in CYP3A5 (CYP3A5*3), one SNP in CYP3A4 (rs2242480), one SNP in POR (POR*28), two SNPs in CAR (NR1I3 rs2307424, NR1I3 rs2502815), and six SNPs in PXR (NR1I2 rs2276706, NR1I2 rs2472677, NR1I2 rs3814055, NR1I2 rs3814057, NR1I2 rs6785049, and NR1I2 rs7643645) were analyzed.
Methods
Study design and subjects
This study was a candidate-gene-based analysis of selected CYP3A-related SNPs in a relatively large sample of the general Japanese population. The study data were obtained from the Japan Multi-institutional Collaborative Cohort for the Kyoto area (Kyoto J-MICC) [31]. Kyoto J-MICC Study was a part of J-MICC Study, which was designed to identify gene–environment interactions associated with lifestyle-related diseases using genetic and clinical data collected from the general Japanese population [31]. The study protocol of Kyoto J-MICC Study was approved by the ethics committee of the Kyoto Prefectural University of Medicine (RBMR-E-267–10), and all participants provided written informed consent. A previous study randomly selected 500 general adults from the Kyoto J-MICC Study and evaluated their CYP3A activities [32]. Among them, 352 individuals who had measurements for all the SNPs analyzed in the present study were enrolled. Exclusion criteria were estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2, total bilirubin > 1.5 mg/dL, and alanine aminotransferase (ALT) > 100 U/L. Only one individual was excluded from analysis, and the analysis cohort consisted of 351 adults. The demographic, anthropometric, and laboratory parameters obtained at the health check-ups comprised sex, age, body weight, height, body mass index, albumin, total bilirubin, aspartate aminotransferase (AST), ALT, blood urea nitrogen (BUN), serum creatinine, and TC. eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation adapted for the Japanese population [33].
The present study adhered to the ethical standards of our institute and the tenets of Helsinki Declaration of 1975, as revised in 2013. This retrospective study was initiated after obtaining approval by the Ethics Committees of Oita University Faculty of Medicine (review reference number: 2838), Meiji Pharmaceutical University (approval number: 202430), and Kyoto Prefectural University of Medicine (approval number: ERB-G-166). Informed consent was obtained via an opt-out system on the website of each institution.
Genotyping procedures
A total of 14,539 J-MICC participants from 12 regions across Japan, including the Kyoto J-MICC Study, were genotyped at the RIKEN Center for Integrative Medicine using the HumanOmniExpressExome-8 version 1.2 BeadChip array (Illumina Inc., San Diego, CA, USA) [34]. Quality control filtering of samples and SNPs followed previously established procedures [34]. Briefly, exclusion criteria were discordant sex information, one sample of each pair of closely related pairs identified by identity-by-descent method, and inferred non-Japanese ancestry. At the variant level, SNPs with a genotype call rate below 0.98 and/or failing the Hardy–Weinberg equilibrium exact test (p < 1 × 10–6) were excluded. As a result, 873,254 variants with genetic polymorphisms were detected. After detection of genetic polymorphisms, variants with minor allele frequency < 0.01 were eliminated. Consequently, 14,087 samples and 570,162 genetic polymorphisms were eligible for analysis.
Candidate SNPs in CYP3A-related genes were selected based on previous reports suggesting associations with pharmacokinetics of CYP3A substrates or CYP3A-related phenotypes, as described in Introduction. The rs776746 (CYP3A5, intron 3, 6986 A > G; CYP3A5*3), rs1057868 (POR, exon 12, 1508 C > T; POR*28), rs2502815 (NR1I3, intron 3, 99 C > T), rs3814055 (NR1I2, UTR-5’, 25385 C > T), rs3814057 (NR1I2, UTR-3’, 11156 A > C) and rs6785049 (NR1I2, intron 5, 7635G > A) SNP data after quality control filtering were obtained as directly genotyped data. Genotype imputation was performed using SHAPIT [35] and Minimac3 [36] software based on the cosmopolitan reference panel of the 1000 Genomes Project (phase 3) [37]. The rs2242480 (CYP3A4, intron 10, 20230G > A), rs2307424 (NR1I3, exon 5, 540 C > T), rs2276706 (NR1I2, UTR-5’, 24113G > A), rs2472677 (NR1I2, intron 1, 63396 C > T) and rs7643645 (NR1I2, intron 1, 69789 A > G) SNP data were obtained as imputed genotype data. The imputation quality scores of these SNPs were r2 ≥ 0.8.
Linkage disequilibrium among the studied SNPs of CYP3A, NR1I3, and NR1I2
Linkage disequilibrium among the SNPs of CYP3A, NR1I3, and NR1I2 SNPs was assessed in the studypopulation using Phase 3 data (JPT) from the 1000 Genomes Project. Pairwise linkage disequilibrium (r2) was calculated using PLINK2 and visualized as heatmaps using ggplot2 in R [38].
Measurement of plasma 4β-OHC levels
Plasma 4β-OHC levels were quantified using a previously developed ultra-high performance liquid chromatography coupled to tandem mass spectrometry method [39]. Briefly, the sample was pretreated with saponification using sodium methoxide, followed by two-step liquid–liquid extraction with n-hexane and derivatization with picolinic acid. The assay was calibrated over a range of 0.5–50 ng/mL, with a lower limit of quantification of 0.5 ng/mL. Within-batch accuracy ranged from 92.1% to 106.6%, and precision (% coefficient of variation) was ≤ 9.9%.
Statistical analysis
Statistical analyses and visualization were conducted using GraphPad Prism version 10.4.2 (GraphPad Software, Dotmatics, Boston, MA) and IBM SPSS Statistics version 27 (SPSS Inc., Chicago, IL). Enrolled patients were classified as wild-type, heterozygote, or homozygote for each SNP. Demographic and laboratory parameters were summarized as n (%) for categorical variables and median [interquartile range] for continuous variables. Normality of the 4β-OHC/TC ratio data was assessed using the Shapiro–Wilk test. 4β‑OHC/TC ratios were compared across genotypes using Kruskal–Wallis test with post hoc Dunn’s tests. For demographic and laboratory data, comparisons among genotypes were analyzed using chi‑squared test for categorical variables and Kruskal–Wallis test for continuous variables.
Multivariate analysis was conducted using forced entry method. Dichotomization of all the SNPs was adopted to avoid overparameterization through introducing additional dummy variables for each genotype category. Subjects were dichotomized by genotype into two groups: CYP3A5 expressors (CYP3A5*1/*1 and *1/*3) versus non-expressors (CYP3A5*3/*3) for CYP3A5*3, and wild-type versus variants (both heterozygotes and homozygotes) for all other SNPs. The CYP3A5 classification was based on the established functional distinction between CYP3A5 expressors and non-expressors widely used in clinical pharmacology studies. Differences in 4β‑OHC/TC ratio between the two dichotomized groups were assessed using Mann–Whitney U test. Multiple linear regression analysis was performed to identify SNPs independently associated with 4β-OHC/TC ratio. Residual normality was evaluated using the Anderson–Darling test, and the dependent variable was log-transformed to satisfy this assumption. Variance inflation factors (VIFs) were calculated to assess collinearity among independent variables, with a VIF > 10 indicating potential multicollinearity. All variables that potentially influence CYP3A activity—comprising the SNPs studied, sex, age, total bilirubin, and eGFR—were entered into the regression model by forced entry. Two-sided p < 0.05 was considered statistically significant.
Results
Patient characteristics
Table 1 summarizes the demographic, anthropometric, and laboratory parameters of the 351 enrolled subjects. Table 2 summarizes the genotype results and allele frequencies of the eleven SNPs analyzed in this study. The cohort was predominantly female, with a female to male ratio of approximately 2.5:1. Median age was 55 years, and median levels of all clinical laboratory parameters were within the respective normal ranges. Median plasma 4β‑OHC level and 4β‑OHC/TC ratio were 33.3 µg/mL and 0.15 µg/mL/mg/dL, respectively. Based on the 1000 Genomes data from the ClinPGx database, the allele frequencies of the eleven SNPs were generally consistent with those reported for East Asian populations.
Table 1.
Subject demographic, anthropometric, and laboratory parameters
| Characteristics | Value |
|---|---|
| Sex; male/female | 98 (27.9)/253 (72.1) |
| Age (year) | 55 [47 − 65] |
| Height (cm) | 159.4 [154.7 − 165.1] |
| Weight (kg) | 54.4 [49.1 − 63.5] |
| Body mass index (kg/m2) | 21.6 [19.5 − 23.6] |
| Albumin (mg/dL) | 4.4 [4.2 − 4.5] |
| Total bilirubin (mg/dL) | 0.7 [0.6 − 0.9] |
| Aspartate aminotransferase (U/L) | 20 [17 − 24] |
| Alanine aminotransferase (U/L) | 15 [12 − 20] |
| Blood urea nitrogen (mg/dL) | 14.4 [11.9 − 17.0] |
| Serum creatinine (mg/dL) | 0.67 [0.60 − 0.76] |
| Estimated glomerular filtration rate (mL/min/1.73 m2) | 80.2 [75.2 − 86.4] |
| Total cholesterol (mg/dL) | 214 [188 − 237] |
| 4β-hydroxycholesterol (µg/mL) | 33.3 [24.9 − 43.4] |
| 4β-hydroxycholesterol/total cholesterol (µg/mL/mg/dL) | 0.15 [0.12 − 0.21] |
Data are expressed as numbers (%) for categorical variables and median [interquartile range] for continuous variables
Table 2.
Genotypes and allele frequencies of the eleven SNPs analyzed in this study
| SNP | Ref/Alt | Genotype counts (%) | Alt allele frequency | |
|---|---|---|---|---|
| Wild-type/heterozygote/homozygote | This study | ClinPGx† | ||
| CYP3A5 (rs776746) | *1/*3 | 18 (5.1)/138 (39.3)/195 (55.6) | 0.752 | 0.713 |
| CYP3A4 (rs2242480) | G/A | 204 (58.1)/128 (36.5)/19 (5.4) | 0.236 | 0.268 |
| POR (rs1057868) | *1/*28 | 118 (33.6)/165 (47.0)/68 (19.4) | 0.429 | 0.374 |
| NR1I3 (rs2307424) | C/T | 81 (23.1)/178 (50.7)/92 (26.2) | 0.516 | 0.519 |
| NR1I3 (rs2502815) | C/T | 108 (30.8)/180 (51.3)/63 (17.9) | 0.436 | 0.436 |
| NR1I2 (rs2276706) | G/A | 198 (56.4)/126 (35.9)/27 (7.7) | 0.256 | 0.205 |
| NR1I2 (rs2472677) | C/T | 53 (15.1)/174 (49.6)/124 (35.3) | 0.601 | 0.623 |
| NR1I2 (rs3814055) | C/T | 196 (55.8)/128 (36.5)/27 (7.7) | 0.259 | 0.215 |
| NR1I2 (rs3814057) | A/C | 105 (29.9)/175 (49.9)/71 (20.2) | 0.452 | 0.471 |
| NR1I2 (rs6785049) | G/A | 114 (32.5)/161 (45.9)/76 (21.7) | 0.446 | 0.404 |
| NR1I2 (rs7643645) | A/G | 136 (38.7)/170 (48.4)/45 (12.8) | 0.370 | 0.431 |
†Alt allele frequencies were obtained from the ClinPGx database that used East Asian population data from the 1000 Genomes Project. SNP single-nucleotide polymorphism, Ref reference, Alt alternative, CYP cytochrome P450, POR cytochrome P450 oxidoreductase, NR nuclear receptor
4β‑OHC/TC ratios among wild-type, heterozygous, and homozygous genotypes
Data of the 4β-OHC/TC ratio were not normally distributed (p < 0.001); therefore, non-parametric methods were used. Figure 1 shows 4β-OHC/TC ratios stratified by wild-type, heterozygous, and homozygous genotypes for the eleven SNPs. The CYP3A5*3/*3 carriers exhibited a significantly lower 4β-OHC/TC ratio compared with CYP3A5*1/*3 carriers (median [interquartile range]: 0.143 [0.110–0.176] vs. 0.178 [0.139–0.227] µg/mL/mg/dL), corresponding to an approximately 25% higher median value in CYP3A5*1/*3 carriers (Fig. 1A). In addition, rs2242480 GA carriers showed higher 4β-OHC/TC ratio than rs2242480 GG carriers (Fig. 1B). No significant differences in any demographic, anthropometric, or laboratory parameters were observed among the genotypes for CYP3A5*3 and rs2242480 (Tables S1 and S2). For the POR*28, NR1I3, and NR1I2 SNPs, no significant differences in 4β-OHC/TC ratio were observed among wild-type, heterozygous, and homozygous genotypes (Fig. 1C-K).
Fig. 1.
4β-OHC/total cholesterol ratios in wild-type, heterozygous, and homozygous genotypes of eleven SNPs: (A) CYP3A5*3, (B) CYP3A4 rs2242480, (C) POR*28, (D) NR1I3 rs2307424, (E) NR1I3 rs2502815, (F) NR1I2 rs2276706, (G) NR1I2 rs2472677, (H) NR1I2 rs3814055, (I) NR1I2 rs3814057, (J) NR1I2 rs6785049, and (K) NR1I2 rs7643645. 4β-OHC/total cholesterol ratios among genotypes of each SNP were compared using Kruskal–Wallis test with post hoc Dunn’s test. Open circles, squares, and triangles indicate wild‑type, heterozygous, and homozygous genotypes, respectively. Red horizontal lines denote median values in each genotype. SNP; single-nucleotide polymorphism, 4β‑OHC, 4β‑hydroxycholesterol; CYP, cytochrome P450; POR, cytochrome P450 oxidoreductase; NR, nuclear receptor
Multiple linear regression analysis to identify SNPs associated with 4β‑OHC/TC ratio
For multivariate analyses, subjects were dichotomized by phenotype: CYP3A5 expressors versus non‑expressors for CYP3A5*3, and wild‑type versus variants (heterozygous or homozygous) for all the other SNPs. Univariate comparisons using Mann–Whitney U test demonstrated significantly lower 4β‑OHC/TC ratio in CYP3A5 non-expressors than in CYP3A5 expressors (p < 0.001), and higher 4β‑OHC/TC ratio in carriers of rs2242480 and POR*28 than in the corresponding wild-type genotypes (p < 0.001 and p = 0.044) (Figure S1). Evaluation of linkage disequilibrium between the two SNPs in NR1I3, among the six SNPs in NR1I2, and between CYP3A5*3 and rs2242480 in the CYP3A locus revealed near-complete linkage disequilibrium between rs2307424 and rs2502815 in NR1I3 (r2 = 0.98), and between rs3814055 and rs2276706 in NR1I2 (r2 = 0.93), as well as substantial linkage disequilibrium between CYP3A5*3 and rs2242480 (r2 = 0.76) (Fig. 2). Therefore, rs2502815 and rs2276706, and rs2242480 were excluded from the multiple regression analysis. Multiple linear regression analysis using the eight SNPs, age, sex, total bilirubin, and eGFR as independent variables identified CYP3A5*3 and sex as independent factors significantly associated with 4β‑OHC/TC ratio (p < 0.001 and p < 0.001, respectively) (Table 3). Similar findings were obtained in a sensitivity analysis using backward elimination, in which CYP3A5*3 and sex remained significant independent factors associated with the 4β-OHC/TC ratio (Table S3).
Fig. 2.
Linkage disequilibrium plot of (A) the two SNPs of NR1I3 and (B) six SNPs of NR1I2 in Japanese population from the 1000 Genomes Project Phase 3 JPT. Pairwise linkage disequilibrium between SNPs was assessed using r2, with values shown in each diamond. The shading intensity reflects the strength of linkage disequilibrium (darker shade indicates higher r2). SNP; single-nucleotide polymorphism, NR, nuclear receptor
Table 3.
Multiple linear regression analysis to identify significant genotypes independently associated with 4β-hydroxycholesterol normalized to total cholesterol
| Independent variable | Estimate | 95% CI | P value | VIF | Adjusted R2 |
|---|---|---|---|---|---|
| Intercept | − 0.747 | − 1.140 to − 0.353 | < 0.001 | 0.235 | |
| CYP3A5*3 | − 0.103 | − 0.136 to − 0.070 | < 0.001 | 1.017 | |
| POR*28 | 0.011 | − 0.024 to 0.047 | 0.526 | 1.048 | |
| NR1I3 (rs2307424) | − 0.004 | − 0.043 to 0.035 | 0.851 | 1.025 | |
| NR1I2 (rs2472677) | 0.038 | − 0.011 to 0.086 | 0.129 | 1.138 | |
| NR1I2 (rs3814055) | − 0.014 | − 0.049 to 0.021 | 0.438 | 1.138 | |
| NR1I2 (rs3814057) | − 0.017 | − 0.077 to 0.043 | 0.576 | 2.148 | |
| NR1I2 (rs6785049) | 0.000 | − 0.052 to 0.052 | 0.990 | 2.228 | |
| NR1I2 (rs7643645) | 0.008 | − 0.028 to 0.045 | 0.647 | 1.165 | |
| Sex (male) | − 0.128 | − 0.166 to − 0.090 | < 0.001 | 1.093 | |
| Age | − 0.002 | − 0.004 to 0.001 | 0.142 | 1.974 | |
| Total bilirubin | 0.042 | − 0.028 to 0.111 | 0.239 | 1.075 | |
| eGFR | 0.001 | − 0.002 to 0.004 | 0.584 | 2.050 |
Residual normality was assessed using Anderson–Darling test, and the dependent variable was log-transformed to satisfy this assumption. VIFs were calculated to evaluate collinearity among independent variables. CI confidence interval, VIF variance inflation factor, CYP cytochrome P450, POR cytochrome P450 oxidoreductase, NR nuclear receptor, eGFR estimated glomerular filtration rate
Impact of SNPs on 4β‑OHC/TC ratio in CYP3A5 expressors and non-expressor
To examine the possibility that other SNPs, including POR*28, may influence 4β-OHC/TC ratios differently depending on CYP3A5 expression status, subjects were divided into CYP3A5 expressor and non-expressor subgroups. In each subgroup, 4β-OHC/TC ratios were compared across wild-type, heterozygous, and homozygous genotypes for the remaining ten SNPs. In CYP3A5 expressors, no significant differences in 4β-OHC/TC ratio across genotypes were observed in all the SNPs (Fig. 3). This pattern was similarly noted in CYP3A5 non-expressors (Figure S2). In addition, focused analyses were conducted to compare 4β-OHC/TC ratios between carriers and non-carriers of rs2242480 and POR*28 in CYP3A5 expressor and non-expressor subgroups. The analyses showed no significant differences between SNP carrier status in both CYP3A5 expressors and non-expressors (Figure S3).
Fig. 3.
Subgroup analysis of CYP3A5 expressors (CYP3A5*1*1 or *1*3) showing 4β‑OHC/total cholesterol ratios in wild-type, heterozygous, and homozygous genotypes of ten SNPs: (A) CYP3A4 rs2242480, (B) POR*28, (C) NR1I3 rs2307424, (D) NR1I3 rs2502815, (E) NR1I2 rs2276706, (F) NR1I2 rs2472677, (G) NR1I2 rs3814055, (H) NR1I2 rs3814057, (I) NR1I2 rs6785049, and (J) NR1I2 rs7643645. 4β-OHC/total cholesterol ratios among genotypes of each SNP were compared using Kruskal–Wallis test with post hoc Dunn’s test. Open circles, squares, and triangles indicate wild‑type, heterozygous, and homozygous genotypes, respectively. Red horizontal lines denote median values in each genotype. SNP; single-nucleotide polymorphism, 4β‑OHC, 4β‑hydroxycholesterol; CYP, cytochrome P450; POR, cytochrome P450 oxidoreductase; NR, nuclear receptor
Discussion
This study evaluated the impact of eleven SNPs on CYP3A metabolic activity using the 4β-OHC/TC ratio as endogenous CYP3A biomarker in 351 general Japanese adults. In univariate analyses, CYP3A5*3 homozygotes (non-expressors) exhibited significantly lower 4β-OHC/TC ratios, whereas carriers (both homozygotes and heterozygotes) of rs2242480 and POR*28 variants showed significantly higher ratios. However, in multiple linear regression analysis, only CYP3A5*3 remained a significant independent predictor of 4β-OHC/TC ratio, with all other polymorphisms losing statistical significance. Further subgroup analysis of CYP3A5 expression status confirmed that none of the other ten SNPs, including rs2242480 and POR*28, significantly influenced 4β-OHC/TC ratio.
These findings highlight CYP3A5*3 as a major genetic determinant of interindividual variability in CYP3A activity in humans. CYP3A5*3 is a nonfunctional allele caused by a 6986 A > G splice site variant, which markedly reduces CYP3A5 protein expression and is observed at high frequency in many populations including the Japanese [10]. The lower 4β‑OHC/TC ratios observed in CYP3A5*3 homozygotes in this study can be interpreted as reflecting diminished CYP3A5‑dependent biosynthesis of 4β‑OHC, consistent with reports from other groups [40, 41]. The approximately 25% higher median 4β-OHC/TC ratio observed in CYP3A5 expressors was also comparable to previous reports evaluating plasma 4β-OHC concentrations across different ethnic populations. In contrast, Hole et al. [42] evaluated the explanatory power of genetic and non‑genetic factors for interindividual variability in 4β‑OHC levels in a large naturalistic patient cohort, including variant alleles of CYP3A5*3, CYP3A4*22, and POR*28 in the multiple regression model. Unlike the present study, they analyzed non-normalized plasma 4β-OHC concentrations instead of 4β-OHC/TC ratios and identified only POR*28, but not CYP3A5*3, as a genetic factor associated with variability in 4β-OHC concentrations. By contrast, our study was designed to focus on the impact of genetic factors, by selecting a sample of the general population with minimal confounding effects from drug-drug interactions or comorbidities. Differences in biomarker normalization, study populations, and evaluated CYP3A biomarkers between the previous and current studies may explain the discrepancies in findings and limit direct comparison of effect sizes.
In our multiple linear regression analysis, in addition to genetic factors, we included sex, age, total bilirubin, and eGFR as covariates potentially influencing individual CYP3A activity. In general, metabolic capacity declines with advancing age. In cirrhosis exhibiting elevated total bilirubin levels, CYP3A expression decreases in parallel with reduced hepatic functional reserve [43]. Furthermore, in chronic kidney disease (CKD) with reduced eGFR, increased accumulation of the uremic toxin indoxyl sulfate downregulates CYP3A expression [44], and plasma indoxyl sulfate concentrations have been shown to correlate negatively with plasma 4β‑OHC levels in CKD patients [45]. However, in the primary multiple regression analysis using the forced-entry method, none of the above factors were identified as significant determinants of 4β-OHC/TC ratio. In a sensitivity analysis using backward elimination, age was additionally retained in the reduced model, although the estimated association of age with 4β-OHC/TC ratio was relatively small. In contrast, sex emerged as a significant factor influencing 4β-OHC/TC ratio (p < 0.001), with higher median [interquartile range] values in females (0.17 [0.13–0.22] µg/mL/mg/dL) compared to males (0.12 [0.10–0.16] µg/mL/mg/dL), corresponding to an approximately 37% higher median value in females. Female sex hormones, such as estrogen and progesterone, are known to stimulate nuclear receptors including PXR and CAR, which activate CYP3A transcription [46]. Previous reports have also demonstrated higher midazolam clearance and elevated 4β‑OHC levels in females [41, 42, 45, 47], which corroborate our findings.
In univariate analysis, POR*28 or rs2242480 carriers showed significantly higher 4β-OHC/TC ratio compared with non-carriers. Both variants have been associated with increased CYP3A activities [6, 17], inferring increased CYP3A‑mediated 4β‑OHC production. However, multiple linear regression analysis revealed no significant association between POR*28 and 4β-OHC/TC ratio, suggesting that its contribution is modest compared to sex or CYP3A5*3. In addition, substantial linkage disequilibrium was observed between CYP3A5*3 and rs2242480 (r2 = 0.76). Hence, rs2242480 was excluded from the multiple regression analysis. This finding suggests that the association observed for rs2242480 in the univariate analysis may partly reflect the collinearity with CYP3A5*3 rather than an independent effect of rs2242480 itself. POR modulates electron transfer from NADPH to CYP enzymes, but its influence may have minimal impact on CYP3A5 activity in CYP3A5 non-expressors. This is supported by a systematic review and meta-analysis of adult kidney transplant recipients, showing that POR*28 carriers had significantly lower dose-normalized tacrolimus trough levels than non-carriers only in CYP3A5 expressors, and had no effect in CYP3A5 non-expressors [17]. While 4β‑OHC is produced by both CYP3A5 and CYP3A4 [48], Elen et al. [4] reported lower levels of cyclosporine, a CYP3A4 substrate, in POR*28 homozygotes among CYP3A5 non-expressors, suggesting that POR*28 may enhance CYP3A4 activity in this subgroup. Based on these findings, we performed a subgroup analysis based on CYP3A5 expression status, and found no significant effect of POR*28 on 4β-OHC/TC ratio in either CYP3A5 expressors or non-expressors.
This study also evaluated several NR1I2 and NR1I3 polymorphisms potentially affecting drug pharmacokinetics. None of the SNPs were associated with 4β-OHC/TC ratio in either univariate or multivariate analyses. NR1I2 rs3814055 (C > T) is the most extensively studied SNP with respect to the pharmacokinetics of CYP3A substrates. In a study of 240 White kidney transplant recipients, carriers of the CT or TT genotypes had significantly higher tacrolimus blood levels and required lower doses than CC carriers [21]. In 42 healthy volunteers genotyped using the DMET Plus microarray, NR1I2 rs3814055 was associated with increased tacrolimus exposure, and the area under the concentration–time curve was 3.42‑fold higher in homozygotes with CYP3A5*3/*3 and NR1I2 TT genotype than in wild‑type individuals [25]. Conversely, in 42 patients receiving risperidone long‑acting injection, NR1I2 rs7643645 (A > G) influenced risperidone exposure [24]. Furthermore, a study of 36 Japanese patients identified the NR1I2 rs7643645 GG genotype and NR1I2 rs3814055 TT genotype as independent determinants of voriconazole concentrations [23]. Contrary to these previous reports, our findings suggest that these variants do not significantly influence CYP3A activity as measured by 4β-OHC. Differences in sample size, patient characteristics, and the relative contributions of CYP3A4 and CYP3A5 to metabolism of the index substrates may account for the inconsistencies. To our knowledge, this is the first study to evaluate NR1I2 and NR1I3 polymorphisms in relation to 4β‑OHC/TC ratio. Based on the present results, NR1I2 and NR1I3 genotyping is probably not required when assessing CYP3A activity using 4β‑OHC as a biomarker.
This study has several limitations. First, although we attempted to minimize the impact of non-genetic factors by enrolling general adults and excluding patients with advanced or overt diseases through applying exclusion criteria of impaired renal and hepatic function, residual confounding could not be completely eliminated because detailed information regarding concomitant medications, lifestyle factors, and comorbidities was not fully available in the Kyoto J-MICC database used in this study. Hence, some participants may have had comorbidities such as hypertension, diabetes mellitus, or hyperlipidemia; or may have been receiving concomitant medications, including CYP3A inducers, inhibitors, or statins, which could potentially influence CYP3A activity and the plasma 4β-OHC/TC ratio. In addition, lifestyle and environmental factors that may affect CYP3A activity were not evaluated. Second, since all participants were Japanese, generalization of our findings to other ethnic groups and populations remains to be confirmed. As an example, the CYP3A4*22 variant, which reduces CYP3A4 activity, is absent in Japanese populations and could not be evaluated in this study [5]. Third, the present study used a targeted candidate-gene-based approach and therefore did not evaluate all potentially relevant coding or regulatory variants in CYP3A-related genes. In addition, the current sample size is likely underpowered for a genome-wide association study. Future research using broader, genome-wide strategies may be more effective to comprehensively characterize the genetic determinants of CYP3A activity. Fourth, several SNPs analyzed in the present study were imputed rather than directly genotyped. Although genotype imputation may introduce uncertainty compared with direct genotyping, all imputed SNPs showed high imputation quality scores (r2 ≥ 0.8), suggesting acceptable reliability of genotype assignment.
In conclusion, this study was a targeted evaluation of eleven selected CYP3A-related polymorphisms in a relatively large sample of the Japanese general population, using 4β-OHC/TC ratio as a surrogate marker of CYP3A activity. Our study identified CYP3A5*3 as a major genetic determinant of CYP3A activity. Given the broad range of drugs predominantly metabolized by CYP3A enzymes and the variable contributions of CYP3A5 and CYP3A4 across substrates, caution is warranted when extrapolating these findings to all CYP3A-metabolized drugs. Nevertheless, when using the 4β-OHC/TC ratio as a biomarker for CYP3A activity, genotyping for SNPs other than CYP3A5*3 may provide limited additional information in the Japanese population.
Supplementary Information
Below is the link to the electronic supplementary material.
Author contributions
R. Tanaka, Y. S., T. K., and T. S. participated in research design. R. Tanaka, Y. S., T. K., T. S., A. O., H. S., E. O., R. Tatsuta, Y. Y., M. N., Y. M., K. O., N. T., K. M., and H. I. conducted experiments. R. Tanaka performed data analysis and wrote the manuscript. All authors reviewed and approved the final manuscript.
Funding
Open Access funding provided by Oita University. This study was supported by Grants-in-Aid for Scientific Research in Priority Areas of Cancer (No. 17015018) and Innovative Areas (No. 221S0001); and by the Japan Society for the Promotion of Science (JSPS) KAKENHI Grant (Nos. 16H06277, 22H04923 [CoBiA], and 24K18313) from the Japanese Ministry of Education, Culture, Sports, Science and Technology. This work was also supported in part by funding for the BioBank Japan Project from the Ministry of Education, Culture, Sports, Science and Technology from April 2003 to March 2015; and the Japan Agency for Medical Research and Development since April 2015.
Data availability
The data presented in this study are available upon written and reasonable request from the corresponding authors.
Declarations
Human ethics and consent to participate
The present study adhered to the ethical standards of our institute and the tenets of Helsinki Declaration of 1975, as revised in 2013. This retrospective study was initiated after obtaining approval by the Ethics Committees of Oita University Faculty of Medicine (review reference number: 2838), Meiji Pharmaceutical University (approval number: 202430), and Kyoto Prefectural University of Medicine (approval number: ERB-G-166). Informed consent was obtained via an opt-out system on the website of each institution.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
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Data Availability Statement
The data presented in this study are available upon written and reasonable request from the corresponding authors.