https://orcid.org/0000-0003-1520-9598
ABSTRACT
Background
Single nucleotide polymorphisms (SNPs) in the N-acetyltransferase 2 (NAT2) gene as well as several other clinical factors can contribute to the elevation of liver function test values in tuberculosis (TB) patients receiving antitubercular therapy (ATT).
Research design and methods
A prospective study involving dynamic monitoring of the liver function tests among 130 TB patients from baseline to 98 days post ATT initiation was undertaken to assess the influence of pharmacogenomic and clinical variables on the elevation of liver function test values. Genomic DNA was extracted from serum samples for the assessment of NAT2 SNPs. Further, within this study population, we conducted a case control study to identify the odds of developing ATT-induced drug-induced liver injury (DILI) based on NAT2 SNPs, genotype and phenotype, and clinical variables.
Results
NAT2 slow acetylators had higher mean [90%CI] liver function test values for 8–28 days post ATT and higher odds of developing DILI (OR: 2.73, 90%CI: 1.05–7.09) than intermediate acetylators/rapid acetylators.
Conclusion
The current study findings provide evidence for closer monitoring among TB patients with specific NAT2 SNPs, genotype and phenotype, and clinical variables, particularly between the period of more than a week to one-month post ATT initiation for better treatment outcomes.
1. Introduction
Antitubercular therapy (ATT) in tuberculosis (TB) patients could cause alterations in the liver function profile resulting in a spectrum of presentations ranging from an asymptomatic mild elevation of liver function enzymes to acute liver failure and mortality [Citation1–3]. Development of presentations such as ATT-induced hepatitis/antitubercular drug-induced liver injury (DILI) could lead to changes in medication administration, antitubercular drug(s) replacement, treatment interruption, discontinuation of the antitubercular drug(s), treatment prolongation, and poor treatment outcomes in TB patients [Citation4]. Periodic liver function test (LFT) monitoring is crucial for the prevention of antitubercular DILI during antitubercular treatment in all TB patients, regardless of the presence or absence of potential risk factors for liver injury. A delay in identifying abnormal LFTs post initiation of ATT resulted in an increased risk of developing DILI, liver failure, discontinuation of antitubercular drugs, and drug resistance [Citation5]. In a retrospective study, 53%, 72%, and 87.6% of TB patients developed DILI by 14, 28, and 56 days, respectively, suggesting that all TB patients on ATT should be considered for liver function monitoring, particularly during the first 8 weeks of treatment [Citation6].
rs1801279 (G > A), rs1041983 (282C > T), rs1801280 (341T > C), rs1799929 (481C > T), rs1799930 (590 G > A), rs1208 (803A > G), and rs1799931 (857 G > A) are the most frequently studied single nucleotide polymorphisms (SNPs) in the NAT2 gene [Citation7]. Previous studies exploring the missense variant, NAT2 191 G > A reported the absence of minor allele (A) among the Asian and Indian populations [Citation7–9]. Hence, NAT2 genotyping using the 6 SNP panel of NAT2 282C > T, 341T > C, 481C > T, 590 G > A, 803A > G, and 857 G > A among our TB patients could be used to classify the NAT2 acetylator status. Genetic polymorphisms in N-acetyltransferase 2 (NAT2) gene classify an individual as rapid acetylator, intermediate acetylator, and slow acetylator [Citation10,Citation11]. The wild-type allele NAT2*4 and the allele groups NAT2*11, NAT2*12, and NAT2*13 encode enzymatic variants with rapid acetylator phenotype, whereas the allele groups NAT2*5, NAT2*6, NAT2*7, and NAT2*14 encode slow acetylator variants. A slow acetylator is predicted if the genotype is comprised of two slow alleles; a rapid acetylator would consist of two rapid alleles, and an intermediate acetylator would be predicted if its genotype contained one slow and another rapid acetylator allele [Citation12–14]. Isoniazid and its metabolites have been associated with higher DILI related adverse event reports compared to other antitubercular drugs [Citation15–17]. Isoniazid is primarily metabolized via N-acetylation by NAT2 enzymes [Citation18]. NAT2 genotype/SNP is a significant variable influencing isoniazid plasma concentration in TB patients [Citation19]. NAT2 slow acetylators have a lower isoniazid clearance than intermediate and rapid acetylators with an increased risk of developing several adverse drug reactions [Citation19]. NAT2 slow acetylators has higher odds of developing both mild increases in liver enzymes as well as DILI during ATT in TB patients [Citation3,Citation20]. Time variations in development of DILI have been reported among Indian TB population [Citation21,Citation22]. Due to time variations occurring in the pattern of changes in liver enzymes and the development of DILI during ATT, an optimum monitoring strategy is unclear. Hence, scheduled monitoring is warranted for appropriate management, treatment interruption, and re-introduction strategies of antitubercular drugs [Citation23,Citation24]. Though many guidelines and studies suggest the need of regular LFT monitoring for preventing or alleviating antitubercular hepatoxicity/DILI, there is no consensus on the frequency of monitoring [Citation5,Citation23,Citation24]. Further there is an evidence gap showing the trends in liver enzyme changes in TB patients based on the NAT2 SNPs and phenotype, and other clinical variables. Hence, we extracted data from well-characterized TB patients undergoing isoniazid-containing ATT to understand the changes in the pattern of liver enzymes based on NAT2 phenotype and SNPs, and clinical variables through a time series analysis. To our knowledge, this is the first study exploring the pattern of changes in the liver function data in TB patients based on the NAT2 genotype and other clinical variables by a time series analysis.
2. Patients and methods
2.1. Data collection
A prospective study design was followed, in which a total of 130 adult TB patients receiving isoniazid containing antitubercular treatment during December 2021 to January 2023, in Kasturba Medical College, Manipal were included using convenient sampling method. The flow chart of participant enrollment is described in the Supplementary Figure S1. Patients with liver diseases such as hepatitis B (n = 2), chronic liver disease (n = 2), autoimmune hepatitis (n = 1), and decompensated cirrhosis (n = 1) was included for weekly and time series analysis of LFT but were excluded for case control study. The demographic, clinical, and laboratory data were extracted from medical records and patient interviews. The study had received institutional ethics committee approval (Kasturba Hospital and Kasturba Medical College IEC No. 243/2019), and informed consent was obtained for all patients.
2.2. NAT2 genotyping
Genomic DNA was extracted from the serum samples of TB patients using QIAamp DNA blood mini kit (Qiagen, Hilden, Germany) as per the kit instruction manual and quantitated on NanoDrop 2000 spectrophotometer (ThermoFisher Scientific, U.S.A.). Six SNPs in the NAT2 gene 282C > T, 341T > C, 481C > T, 590 G > A, 803A > G, and 857 G > A was analyzed using predesigned TaqMan drug metabolism genotyping assays (Applied Biosystems, U.S.A.) by QuantStudio™ 5 Real-Time PCR System (Applied Biosystems, U.S.A.). The polymerase chain reaction (PCR) consisted of pre-PCR read at 60°C for 30 seconds; holding stage at 95°C for 10 minutes; and 45 denaturing cycles at 95°C for 15 seconds and annealing/extension at 65°C for 1 minute, and post-PCR read at 60°C for 30 seconds. A total of 12.5 µl reaction mixture consisting of 20ng of genomic DNA was used for each assay/per well. All assays were performed using TaqMan™ Genotyping Master Mix (Applied Biosystems, U.S.A.) on MicroAmp™ Optical 96-well reaction plate (Applied Biosystems, U.S.A.). Allelic discrimination was carried using QuantStudio™ Design and Analysis Software v1.5.2 (Applied Biosystems, U.S.A.). The TaqMan fluorogenic probes are labeled with the reporter dyes VIC and FAM, which are specific for one of the two possible bases at the NAT2 SNPs positions investigated. Classification of NAT2 phenotype was according to the consensus nomenclature of the human NAT2 alleles haplotypes database [Citation25].
2.3. LFT monitoring and analysis
LFT reports available from baseline to 98 days (14 weeks) were recorded for the patients along with the day of visit since the initiation of ATT. The LFT values of intermediate and rapid acetylators were combined for plots and data analysis due to lesser proportion of RA. A total of 308 (n = 97 and 211 for NAT2 slow acetylators and intermediate acetylator/rapid acetylator respectively) observations each for ALP, ALT, AST, direct bilirubin, and total bilirubin were utilized for plotting violin and time series plots. Violin plots were generated using LFT values segregated into weekly categories of baseline (n = 117), 2–3 weeks (n = 58), 4–6 weeks (n = 62), 7–10 weeks (n = 37), and 11–14 weeks (n = 34) based on the NAT2 phenotypes (NAT2 slow acetylators versus intermediate and rapid acetylators) for understanding the pattern change of LFT values over different periods of time. TB patients without both baseline and any follow-up LFT report till 14 weeks were excluded. Patients who developed DILI were not further followed up for LFT due to treatment interruption, change of antitubercular drug, or its dose. Further a time series analysis for all the LFTs were performed based on various NAT2 phenotype, SNPs, and demographic and clinical variables.
2.4. Association of NAT2 phenotype, genotype and SNPs, and clinical variables with DILI
Further, within this study, the odds of association of NAT2 phenotype, genotype, and SNPs, and clinical variables among DILI and patients who did not develop DILI (controls) were assessed. Cases (n = 17) were defined as TB patients undergoing ATT who have developed DILI/ATT-induced hepatitis and met either of the DILI criteria laid by the American Thoracic Society [Citation26,Citation27] or NIH Drug-Induced Liver Injury Network (DILIN) during first 8 weeks after ATT initiation was collected prospectively [Citation28]. Controls (n = 48) were defined as TB patients under the same treatment protocol, who did not develop DILI during first 8 weeks after ATT initiation.
2.5. Statistical analysis
All the data were entered into Microsoft Excel. Statistical analysis and data visualization was performed with R software v4.3.0 [Citation29] using tidyverse v2.0.0 [Citation30]. Violin plot was created to visualize the pattern change in LFT values over a period with respect to different NAT2 phenotypes. The test of normality for LFT values was performed using Shapiro-Wilk test. The statistical difference among the different time periods was assessed using Kruskal-Wallis test. The statistical significance of difference in LFT values between baseline and each of the different periods for both NAT2 slow acetylators and intermediate acetylators/rapid acetylators was assessed using Pairwise Wilcoxon rank-sum test with adjusted p values using Bonferroni correction. Time series plots were created with mean LFT values (90%CI) and compared against different NAT2 phenotype, SNPs, demographic, and clinical variables. Odds ratio for genomic and clinical variables were performed using epitools package v0.5–10.1 [Citation31] based on Wald test approach with a confidence interval of 90% and was visualized using forest plot using forestplot packages v3.1.1 [Citation32].
3. Results
3.1. Demographic, clinical and NAT2 pharmacogenomic data
The median (interquartile range [IQR]) age of the TB patients in the current study was 47 (33–57) years. About two-thirds of the patients had pulmonary TB. The median [IQR] weight (n = 128) and BMI (n = 115) were 51.68 (44.00–59.25) kg and 19.76 (16.49–22.82) kg/m2, respectively. About 55% (n = 66 out of 120 patients with baseline albumin values) of the TB patients had a baseline albumin level lesser than 3.5 g/dL. NAT2 intermediate acetylators (63%) were the most frequently occurring NAT2 acetylator group, followed by slow acetylators (30.7%) and rapid acetylators (6.1%). The minor allelic frequency (MAF) of NAT2 SNPs 282C > T, 341T > C, 481C > T, 590 G > A, 803A > G, and 857 G > A were 0.45, 0.31, 0.28, 0.38, 0.33, and 0.13, respectively. All the studied NAT2 SNPs were in Hardy–Weinberg equilibrium (HWE), except for NAT2 857 G > A (p < .05) as shown in the Supplementary Table S2. The detailed demographical, social, clinical, and NAT2 SNPs, genotype and phenotype data is shown in .
Table 1. Demographic, clinical and NAT2 phenotype, genotype, and SNP summarization of the study population.
3.2. Weekly analysis of LFT
NAT2 slow acetylators had relatively higher median values than intermediate acetylators/rapid acetylators for the all the LFTs during the 8–21 days, 22–42 days, and 43–70 days post ATT as shown in . In particular, when compared to NAT2 intermediate acetylators/rapid acetylators, slow acetylators had comparatively higher ALT values (median [IQR] of NAT2 slow acetylators = 53 [28–115] IU/L and intermediate acetylators/rapid acetylators = 23 [15–66] IU/L) and AST values (median [IQR] of NAT2 slow acetylators = 44 [29–119] IU/L and intermediate acetylators/rapid acetylators = 35 [22–85] IU/L) during the 8–21 days of post ATT initiation (n = 23 and 35 for NAT2 slow acetylators and intermediate acetylators/rapid acetylators respectively). Subsequent normalization of both ALT and AST values from the 8–21 days period was observed for both the NAT2 slow and intermediate/rapid acetylators during the 22–42 days period (median [IQR] ALT values of NAT2 slow acetylators = 22 [19–56] IU/L and intermediate acetylators/rapid acetylators = 15 [13–22] IU/L and median [IQR] AST values of NAT2 slow acetylators = 29 [24–49] IU/L and intermediate acetylators/rapid acetylators = 24 [18–30] IU/L) and 43–70 days period (median [IQR] ALT values of NAT2 slow acetylators = 21 [16–38] IU/L and intermediate acetylators/rapid acetylators = 14 [12–1 8] IU/L) days period and median [IQR] AST values of NAT2 slow acetylators = 31 [28–40] IU/L and intermediate acetylators/rapid acetylators = 21 [19–27] IU/L. Reduction of ALP values also from 8–21 days (median [IQR] of NAT2 slow acetylators = 124 [95–191] U/L and intermediate acetylators/rapid acetylators = 100 [86–143) U/L] were observed during the 22–42 days (median [IQR] of NAT2 slow acetylators = 114 [89–140] U/L and intermediate acetylators/rapid acetylators = 98 [80–137] U/L) and further over the 43–70 days period (median [IQR] of NAT2 slow acetylators = 92 [87–113] U/L and intermediate acetylators/rapid acetylators = 81 [68–102] U/L) post ATT initiation.
Figure 1. Violin plot showing the liver function tests (ALP, ALT, AST, direct bilirubin, and total bilirubin) values based on NAT2 phenotype status at baseline and over different periods of post ATT initiation.
Statistically significant difference was observed among most of the LFT values when compared between different periods by Kruskal-Wallis test as shown in , except for ALP values of NAT2 intermediate acetylators/rapid acetylators (p = 0.063), and total bilirubin values of NAT2 intermediate acetylators/rapid acetylators (p = 0.081) and NAT2 slow acetylators (p = 0.4). NAT2 slow acetylators had a statistically significant increase in ALT (p < 0.001), AST (p = 0.001) and direct bilirubin (p = 0.001) values during the 8–21 days period, and AST (p = 0.041) values for 22–42 days period. Whereas, NAT2 intermediate acetylators/rapid acetylators had statistically significant increase in direct bilirubin (p = 0.01) for 8–21 days period post ATT initiation compared to their respective baseline values using Pairwise Wilcoxon rank-sum test with adjusted p values using Bonferroni correction ().
Table 2. Liver function profiles over different periods of time among the NAT2 phenotype groups.
3.3. Time series analysis
The mean [90%CI] LFT values of all the 130 TB patients during the 8–28 days post ATT period were 136.2 [120.4–152.1] U/L for ALP, 58.6 [43.2–74.0] IU/L for ALT, 69.4 [47.0–91.8] IU/L for AST, 1.0 [0.7–1.3] mg/dL for direct bilirubin, and 1.2 [0.9–1.6] mg/dL for total bilirubin (Supplementary Figure S2). From the time series plot, NAT2 slow acetylators had a relatively higher mean [90%CI] LFT values compared to intermediate acetylators/rapid acetylators during the 8–28 days post ATT period, particularly with respect to ALT (SA = 96.7 [56.5–136.8] IU/L; intermediate acetylators/rapid acetylators = 35.8 [23.0–48.6] IU/L), and AST (slow acetylators = 91.9 [53.7–130.1] IU/L; intermediate acetylators/rapid acetylators = 51.6 [29.8–73.5] IU/L) as shown in . Among the 6 NAT2 SNPs investigated, patients with NAT2 341T > C, NAT2 481C>T, and NAT2 803A>G polymorphisms had higher mean [90%CI] for all the LFT values compared to their respective wildtypes, as shown in . The mean [90%CI] ALT values were relatively higher for all the 6 NAT2 SNPs compared to their respective wildtypes during the 8–28 days post ATT period. Except for the NAT2 857 G>A, patients with NAT2 SNP 282C>T, 341T>C, 481C>T, 590 G>A, and 803A>G had higher mean [90%CI] AST values as compared to wildtypes during the 8–28 days post ATT period.
Figure 2. Time series plot comparing liver function tests (ALP, ALT, AST, direct bilirubin, and total bilirubin) values from baseline to 98 days post ATT initiation based on NAT2 phenotype.
Figure 3. Time series plot comparing liver function tests (ALP, ALT, AST, direct bilirubin, and total bilirubin) values from baseline to 98 days post ATT initiation based on NAT2 SNP status.
Patients with age greater than 60 years had a higher mean [90%CI] ALT (age > 60 years = 73.3 [36–110.5] IU/L, age ≤ 60 years = 54.5 [35.5–73.5] IU/L), and AST (age >60 years = 93 [45.8–140.3] IU/L, age ≤60 years = 60.8 [40–81.6] IU/L) values as compared to those with ≤60 years of age during the 8–28 days post ATT period as shown in Supplementary Figure S3. Female patients had higher mean [90%CI] ALT levels (females = 62.4 [38.7–86.2] IU/L; males = 52.7 [35.5–69.8] IU/L), whereas males had a higher mean values for direct bilirubin (males = 1.3 [0.7–1.9] mg/dL; females = 0.6 [0.3–0.9] mg/dL) and total bilirubin (males = 1.6 [0.9–2.3] mg/dL; female = 0.8 [0.4–1.1] mg/dL) during the 8–28 days of post ATT period (). Higher mean [90%CI] ALP (BMI <18.5 kg/m2 = 157.3 [120.9–193.8] U/L versus BMI ≥18.5 kg/m2 = 136.9 [100.0–173.7] U/L) and ALT (BMI <18.5 kg/m2 = 73.1 [40.7–105.5] IU/L versus BMI ≥18.5 kg/m2= 52.3 [38.3–66.3] IU/L) were observed among patients who had lower BMI (<18.5 kg/m2) as compared to those with BMI ≥18.5 kg/m2 during the 8–28 days of post ATT period, as shown in Supplementary Figure S4. Higher mean [90%CI] ALP (albumin <3.5 g/dL = 156.9 [132.8–181] U/L versus albumin ≥3.5 g/dL = 111.4 [93.8–128.9] U/L), direct bilirubin (albumin <3.5 g/dL = 1.3 [0.8–1.7] mg/dL versus albumin ≥3.5 g/dL = 0.4 [0.2–0.7]) mg/dL and total bilirubin (albumin <3.5 g/dL = 1.6 [1.0–2.1] mg/dL versus albumin ≥3.5 g/dL = 0.6 [0.3–0.9] mg/dL) levels were also observed among patients who had an hypoalbuminemia (<3.5 g/dL) compared to those patients with albumin levels ≥3.5 g/dL during the 8–28 days of post ATT period (Supplementary Figure S5). Time series analysis relating to the influence of other variables such as globulin, sodium, potassium, creatinine, and comorbidities such as hypertension to LFT values did not show any significant differences. Influence of random blood sugar and diabetes to LFT values also did not show any significant differences and is shown in Supplementary Figures S6 and S7.
Figure 4. Time series plot comparing liver function tests (ALP, ALT, AST, direct bilirubin, and total bilirubin) values from baseline to 98 days post ATT initiation between male and female TB patients.
3.4. Association of NAT2 phenotype, genotype and SNPs, and clinical variables with DILI
Among 17 cases of DILI, eight (47%) and nine (53%) patients developed DILI within 14 days and between 15–30 days respectively. NAT2 slow acetylators had higher odds of developing DILI than intermediate acetylators/rapid acetylators (OR: 2.73, 90%CI: 1.05–7.09). NAT2*5B was found to be in higher frequency among DILI cases (n = 12, 70.5%) compared to controls (n = 19, 39.5%). NAT2*6A was found among 11 (64.7%) DILI cases and 34 (70.8%) controls, whereas 2 (11.7%) DILI cases and 7 (14.5%) controls had NAT2*7B haplotype. Among the NAT2 genotypes, the slow NAT2*5A/6B genotype had higher risk (OR: 3.03, 90%CI: 1.10–8.35) for developing DILI compared to all the other genotypes combined. Patients with NAT2 SNPs 341T>C (OR: 4.96, 90%CI: 1.72–14.29), 481T>C (OR: 3.66, 90%CI: 1.34–9.96) and 803A>G (OR: 4.17, 90%CI: 1.45–12.00) had increased odds of developing DILI as shown in . Among the clinical variables, TB patients who had age above 60 years (OR: 3.19, 90%CI: 1.09–9.33) had higher odds of developing DILI. Higher MAF was observed for the NAT2 SNPs 341T>C (MAF among DILI = 0.47, MAF among controls = 0.26), 481C>T (MAF among DILI = 0.44, MAF among controls = 0.26) and 803A> G (MAF among DILI = 0.47, MAF among controls = 0.29) among DILI cases compared to controls (Supplementary Figure S8). An increased frequency of NAT2*5A/6B genotype was also observed among DILI cases compared to controls (Supplementary Table S1).
Figure 5. The odds ratio for NAT2 phenotype, genotype, SNPs, and clinical variables associated with the development of DILI.
4. Discussion
Isoniazid is a hydrazine derivative. In general, hydrazines are hepatotoxic since they are readily oxidized to chemically reactive species. Three metabolites of isoniazid, acetylhydrazine, hydrazine, and a reactive metabolite resulting from the bioactivation of isoniazid itself are considered to be hepatotoxic [Citation33,Citation34]. The mechanism of isoniazid-induced hepatotoxicity could be best explained by the covalent binding of these reactive metabolites to hepatocytes and the immune response to drug-modified liver proteins [Citation34,Citation35]. An Indian study reported that DILI occurred at the highest frequency in the first 15 days of ATT initiation, followed by the next 15 days of treatment, with an overall 76.8% of cases occurring within the first 2 months of ATT initiation [Citation2]. Previous reports from India reported the average duration and median time for development of DILI among TB patients as 20 days and 10 days respectively after ATT initiation [Citation21,Citation22]. About one-half of the patients developed DILI within 2 weeks of ATT initiation and another half developed during 15–30 days of post ATT initiation, suggesting the need for aggressive LFT monitoring during these intervals of ATT. The standard biomarkers for evaluating hepatoxicity/DILI have been ALT, AST, ALP, and total bilirubin for several decades [Citation36]. The time of visits of the patients for LFT monitoring post ATT initiation varied.
We found that NAT2 IA were found to be the most frequently occurring NAT2 acetylator phenotype assessed by the six SNP NAT2 panel of 282C>T, 341T>C, 481C>T, 590 G>A, 803A>G and 857 G>A. NAT2 IA were identified to be most frequent acetylator group in Mexican and Rwandan TB patients by same NAT2 SNP panel [Citation37,Citation38]. Lower MAF for NAT2 857 G>A was observed in our study, consistent to other global and Indian population reports [Citation7,Citation8,Citation39]. A metanalysis of 37 studies in 2018 reported that the overall odds ratio (OR) of ATT induced DILI associated with NAT2 slow acetylators compared to non-slow NAT2 acetylators (i.e. NAT2 intermediate acetylators and rapid acetylators) was 3.15 (95% CI: 2.58–3.84) [Citation20]. We observed that NAT2 slow acetylators had 2.73 times the risk for developing DILI in our study compared to intermediate acetylators and rapid acetylators combined. Higher odds of developing ATT induced hepatoxicity/DILI were reported among NAT2 slow acetylators among several other population such as in Western Indian (OR: 2.3; 95%CI: 1.2–4.6), Indonesian (OR: 3.64, 95%CI: 2.21–6.00), Thai (8.80, 95%CI: 4.01–19.31), Tunisian (OR = 4.30; 95%CI: 1.51–18), Portuguese (OR:2.46; 95%CI:1.25–4.84), and Brazilian (OR: 3.05, 95%CI 1.07–8.64) TB populations [Citation40–45]. A recent study among Iranian PTB patients reported that the rapid acetylator NAT2*4/*4 genotypes were not found among DILI patients, similar to our results [Citation46]. The NAT2*4/*4 genotype achieve the lowest plasma concentration of isoniazid at 2 hours after drug administration compared to intermediate and fast NAT2 genotypes [Citation47]. Tunisian TB patients with NAT2*5B haplotype was found to have higher risk of developing ATT induced hepatoxicity (OR: 2.51, 95%CI: 0.92–10) [Citation43]. Higher odds of developing DILI were reported among Japanese TB population with the haplotype, NAT2*6A (OR: 1.77, 95%CI: 1.15–2.72, p = 1.19 × 10−2), and diplotypes, NAT2*5B/*6A (OR: 4.06, 95%CI: 0.25–65.6, p = 0.36), NAT2*6A/*6A (OR: 6.47, 95%CI: 1.78–23.6, p = 5.58 × 10−3), NAT2*6A/*7B (OR: 3.34, 95%CI: 0.87–12.8, p = 0.08) and NAT2*7B/*7B (OR: 2.02, 95%CI: 0.18–22.6, p = 0.48) [Citation48]. Several other studies have reported NAT2*6A haplotype as a strong genetic risk factor for DILI among TB population on ATT [Citation41,Citation49]. The slow NAT2*5B/6A genotype was found in high frequency (41.1%) among the DILI patients (Supplementary Table S1) and had higher odds for developing DILI in our study. Results from a metanalysis have also reported NAT2*5B/6A genotype patients to have significant risk of developing ATT induced DILI (OR: 2.12, 95%CI: 1.40–3.22) [Citation50]. One-fifth of our TB patients had this genotype. Previous NAT2 genotyping studies from India have reported similar high proportion of 15.4% and 25% with NAT2*5B/6A genotype [Citation9,Citation51]. The low prevalent NAT2*6A/*7B genotype in our population was reported to have higher odds of developing DILI among Thai TB patients (OR: 16.38, 95%CI: 3.57–75.24) [Citation42].
Higher MAF among DILI patients as well as odds of developing DILI () was observed for the NAT2 SNPs 341T>C, 481C>T and 803A>G in our study. Reports from Brazilian TB population also have reported high MAF, and odds of developing DILI for NAT2 SNPs 341T>C and 481C>T [Citation52]. Higher MAF for NAT2 481C>T were observed among Tunisian and western Indian TB patients who developed ATT induced hepatoxicity, and homozygous point mutation at NAT2 position 481 was associated with increased odds of developing ATT induced hepatotoxicity in both the studies [Citation40,Citation43]. Among the clinical variables, TB patients who had age above 60 years were found to have increased odds of developing DILI in our study. Previous report had identified age above 60 years as a risk factor for development of DILI among Indian TB patients [Citation21]. Mean values of ALP, direct and total bilirubin were higher for male patients whereas ALT was higher in female patients during 8–28 days of ATT. Female patients have been identified as a risk factor for the development and severity of ATT induced DILI [Citation21,Citation53]. Hypoalbuminemia is considered as a risk factor for mortality and development of DILI among TB patients on ATT [Citation54–56]. We observed high prevalence of TB patients with lower baseline albumin levels in our study to be associated with DILI consistent with previous reports from India as well as other countries [Citation57,Citation58]. Earlier reports had identified diabetes as a risk factor for development of ATT induced DILI on univariate analysis [Citation21]. However, a recent case-control study in TB patients reported that diabetes did not increase the odds of DILI in TB patients [Citation59]. We did not identify diabetes patients to have higher mean values of LFTs compared to non-diabetics during 8–28 days of post ATT initiation by time series analysis.
The risk of DILI and liver failure was reported to be significantly higher among TB patients who were identified to have abnormal LFT after 8 weeks compared to those identified at 4- and 8-weeks post ATT [Citation5]. Several studies have recommended a 2-week post ATT LFT monitoring strategy to reduce incidence of liver injury, treatment interruption and improvement of treatment outcomes [Citation24,Citation60]. However, abnormal LFTs were also detected after 2-week post ATT initiation, indicating that if LFTs were measured only at 2-weeks, identification of liver injury could be missed in some patients [Citation5]. A previous expert opinion had recommended LFT monitoring prior to ATT initiation and further at every 2-weeks until first two months post ATT initiation, and monthly thereafter [Citation61]. The risk of developing a late DILI (after 2 weeks of ATT initiation) among TB patients was 2.1-fold greater for every 30 U/L increment in ALT gradient at a 2-weeks post ATT initiation period (OR 2.06, 95% CI: 1.52–2.76, p < 0.001) [Citation60]. As shown in our , higher transaminase levels are particularly seen among NAT2 slow acetylators when compared to intermediate acetylators/rapid acetylators during the 8–21 days (>1 week-3 weeks). Subsequent reduction/normalization of the LFT values such as ALP, ALT, and AST were observed for both NAT2 slow and intermediate/rapid acetylator TB patients during the period of 22–42- and 43–70-days. The median values of all the LFTs were within the normal ranges during the 71–98 days period post ATT initiation. Time series analysis () also shows NAT2 slow acetylators having higher mean [90%CI] transaminase values compared to intermediate acetylators/rapid acetylators between >1 week and 42 days post ATT initiation period. Several NAT2 SNPs were also shown to have higher LFT values during this phase of ATT. Our results reinforce the need for preemptive NAT2 genotyping and developing a NAT2 phenotype, genotype and SNP, and clinical based risk factor monitoring plan in TB patients. A NAT2 genotype-guided regimen was reported to lower the incidence of isoniazid induced DILI compared to conventional standard regimen [Citation62]. Hence, NAT2 genotype-based risk stratification and isoniazid dose adjustment among TB patients is warranted for preventing the risk of elevation of LFT values/DILI.
DNA extracted from serum could be used for reliable and accurate SNP analysis using single SNP TaqMan genotyping assay and multiplex SNP genotyping approaches, although the extracted DNA may not be of the high quality/quantity compared to those extracted from other types of biological samples, such as whole blood or buffy coats [Citation63–66]. Smaller sample size, irregular and varying duration of the post ATT LFT monitoring, limited duration of LFT monitoring (only till 98 days post ATT), and correlation with only NAT2 phenotype, genotype, and SNPs, which is associated with only isoniazid metabolism (rifampicin and pyrazinamide have also hepatotoxic potential), are few limitations of our study. Several other gene polymorphisms in CYP2E1, HLA, GST, SLCO1B1, and UGT1A1 have been reported to be associated with antitubercular drug-induced hepatotoxicity/DILI that may have to be investigated in future studies [Citation67–70]. Another limitation of the study is that we used convenient sampling, leading to selection bias as majority of the TB patients who enrolled in the study were from few districts having proximity to our tertiary care hospital. As other genetic, environmental, dietary, and cultural factors may impact the reproducibility of our results in populations with distinct characteristics, we recommend future investigations to validate these results in diverse multicentric populations to enhance the generalizability of our conclusions and for laying out guidelines for NAT2 genotype-based risk stratification and monitoring of TB patients for antitubercular hepatotoxicity/DILI. Further, long-term cost-efficiency of LFT monitoring and NAT2 genotyping needs to be assessed. A uniform policy of LFT assessment at two time periods of between 1-week to 2-week and 3-week to 4-week post ATT initiation, particularly for NAT2 slow acetylators could be useful for prompt identification of a subgroup who develop early DILI and may offer to rule out late DILI.
5. Conclusion
A proactive approach to monitoring the LFT values at 2 weeks interval for the first one month after ATT initiation might benefit TB patients, especially who have NAT2 slow acetylator phenotype. To ensure the broader applicability of our findings, further validation and replication in diverse population are warranted. A personalized liver function monitoring approach based on preemptive assessment of NAT2 SNPs and genotype and phenotype status with other clinical variables may aid in the identification of asymptomatic liver damage, early identification, and treatment for DILI, reducing treatment interruptions, and improving compliance of ATT in TB patients.
Declaration of interests
The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants, or patents received or pending, or royalties.
Reviewer disclosures
Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.
Ethics statement
The study had received institutional ethics committee approval (Kasturba Hospital and Kasturba Medical College IEC No. 243/2019), and informed consent was obtained for all patients.
Author contributions
M Rao was involved in funding acquisition for the project. M Rao, L Thomas, and AP Raju were involved in the conceptualization of the study. Chaithra was involved in patient recruitment, data collection and sample processing. Chaithra and L Thomas were involved in data curation. L Thomas, S Kulavalli, and M Rao were involved in laboratory experiments. Analysis and interpretation of data were done by AP Raju and L Thomas. L Thomas wrote the first draft of the paper. AP Raju, Chaithra, M Varma, CS SV, M Banerjee, K Saravu, S Mallayasamy and M Rao critically revised the manuscript. All authors have read and approved the final manuscript.
Supplemental Material
Download MS Word (4.1 MB)Acknowledgments
L Thomas thanks the Indian Council of Medical Research for providing Senior Research Fellowship (No.45/25/2020/PHA/BMS) and contingency grant from Manipal Academy of Higher Education (MAHE). AP Raju and S Kulavalli are thankful to the Dr. TMA Pai PhD scholarship from MAHE. Chaithra is thankful to the Junior Research Fellowship from ICMR (F.No.5/8/5/45/multicentric study/2019/ECD-1). M Rao is also thankful to the Department of Science & Technology- Fund for Improvement of S&T Infrastructure in Universities and Higher Educational Institutions (DST-FIST) (TPN: 32196) for providing Quant 5 Studio real time PCR (Applied Biosystems, USA).
Supplementary material
Supplemental data for this article can be accessed online at https://doi.org/10.1080/17512433.2024.2311314
Data availability statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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