論文

Studies examining long-term longitudinal metabolomic data and their reliability in large-scale populations are limited. Therefore, we aimed to evaluate the reliability of repeated measurements of plasma metabolites in a prospective cohort setting and to explore intra-individual concentration changes at three time points over a 6-year period. The study participants included 2999 individuals (1317 men and 1682 women) from the Tsuruoka Metabolomics Cohort Study, who participated in all three surveys—at baseline, 3 years, and 6 years. In each survey, 94 plasma metabolites were quantified for each individual and quality control (QC) sample. The coefficients of variation of QC, intraclass correlation coefficients, and change rates of QC were calculated for each metabolite, and their reliability was classified into three categories: excellent, fair to good, and poor. Seventy-six percent (71/94) of metabolites were classified as fair to good or better. Of the 39 metabolites grouped as excellent, 29 (74%) in men and 26 (67%) in women showed significant intra-individual changes over 6 years. Overall, our study demonstrated a high degree of reliability for repeated metabolome measurements. Many highly reliable metabolites showed significant changes over the 6-year period, suggesting that repeated longitudinal metabolome measurements are useful for epidemiological studies.

Currently, large-scale cohort studies for metabolome analysis have been launched globally. However, only a few studies have evaluated the reliability of urinary metabolome analysis. This study aimed to establish the reliability of urinary metabolomic profiling in cohort studies. In the Tsuruoka Metabolomics Cohort Study, 123 charged metabolites were identified and routinely quantified using capillary electrophoresis-mass spectrometry (CE-MS). We evaluated approximately 750 quality control (QC) samples and 6,720 participants’ spot urine samples. We calculated inter- and intra-batch coefficients of variation in the QC and participant samples and technical intraclass correlation coefficients (ICC). A correlation of metabolite concentrations between spot and 24-h urine samples obtained from 32 sub-cohort participants was also evaluated. The coefficient of variation (CV) was less than 20% for 87 metabolites (70.7%) and 20–30% for 19 metabolites (15.4%) in the QC samples. There was less than 20% inter-batch CV for 106 metabolites (86.2%). Most urinary metabolites would have reliability for measurement. The 96 metabolites (78.0%) was above 0.75 for the estimated ICC, and those might be useful for epidemiological analysis. Among individuals, the Pearson correlation coefficient of 24-h and spot urine was more than 70% for 59 of the 99 metabolites. These results show that the profiling of charged metabolites using CE-MS in morning spot human urine is suitable for epidemiological metabolomics studies.

In hepatic dysfunction, renal pharmacokinetic adaptation can be observed, although information on the changes in drug exposure and the interorgan regulation of membrane transporters in kidney in liver diseases is limited. This study aimed to clarify the effects of renal exposure to nephrotoxic drugs during cholestasis induced by bile duct ligation (BDL). Among the 11 nephrotoxic drugs examined, the tissue accumulation of imatinib and cisplatin in kidney slices obtained from mice 2 weeks after BDL operation was higher than that in sham-operated mice. The uptake of imatinib in the kidney slices of BDL mice was slightly higher, whereas its efflux from the slices was largely decreased compared to that in sham-operated mice. Proteomic analysis revealed a reduction in renal expression of the efflux transporter multidrug resistance-associated protein 6 (Mrp6/Abcc6) in BDL mice, and both imatinib and cisplatin were identified as Mrp6 substrates. Survival probability after cisplatin administration was reduced in BDL mice. In conclusion, the present study demonstrated that BDL-induced cholestasis leads to the downregulation of the renal basolateral efflux transporter Mrp6, resulting in drug accumulation in renal cells and promoting drug-induced renal injury.

Sodium-phosphate transporter NPT4 (SLC17A3) is a membrane transporter for organic anionic compounds localized on the apical membranes of kidney proximal tubular epithelial cells and plays a role in the urinary excretion of organic anionic compounds. However, its physiological role has not been sufficiently elucidated because its substrate specificity is yet to be determined. The present study aimed to comprehensively explore the physiological substrates of NPT4 in newly developed Slc17a3−/− mice using a metabolomic approach. Metabolomic analysis showed that the plasma concentrations of 11 biological substances, including 3-indoxyl sulfate, were more than two-fold higher in Slc17a3−/− mice than in wild-type mice. Moreover, urinary excretion of 3-indoxyl sulfate was reduced in Slc17a3−/− mice compared to that in wild-type mice. The uptake of 3-indoxyl sulfate by NPT4-expressing Xenopus oocytes was significantly higher than that by water-injected oocytes. The calculated Km and Vmax values for NPT4-mediated 3-indoxyl sulfate uptake were 4.52 ± 1.18 mM and 1.45 ± 0.14 nmol/oocyte/90 min, respectively. In conclusion, the present study revealed that 3-indoxyl sulfate is a novel substrate of NPT4 based on the metabolomic analysis of Slc17a3−/− mice, suggesting that NPT4 regulates systemic exposure to 3-indoxyl sulfate by regulating its urinary excretion.

Purpose: Radiotherapy is an integral treatment for non-small cell lung cancer (NSCLC); however, radiation-induced toxicities such as radiation pneumonitis (RP) present a considerable challenge. Herein, we aimed to evaluate the potential of salivary metabolomics as an independent risk factor for predicting RP. Methods: This study included 62 consecutive patients with NSCLC who underwent thoracic radiotherapy at Tokyo Medical University between September 2016 and December 2018. The median age of the patients was 75 years (range: 41–89), comprising 47 (75.8%) males and 15 (24.2%) females. Patients with stage I NSCLC received 75 Gy in 30 fractions, whereas those with stage II and III NSCLC received 66 Gy in 33 fractions. Saliva samples were collected before treatment and at 2 weeks, 1 month, 3 months, and 1 year after initiating radiotherapy. Clinical RP was defined as grade 2 according to the Common Toxicity Criteria for Adverse Events. Salivary metabolomics were analyzed using capillary electrophoresis-mass spectrometry. Salivary metabolites were evaluated as potential predictors of RP. Results: Clinical RP was observed in 11 patients (17.7%); no RP-related deaths were observed. Clinical RP developed at a median of 4 months (range: 2–6 months) after initiating radiotherapy. Three metabolites, butyrate, propionate, and hexanoate, collected before radiotherapy exhibited predictive ability for clinical RP. Multivariate logistic analysis indicated butyrate (P = 0.033) as a predictive factor, along with the previously known factor of lung volume irradiated with > 20 Gy (P = 0.045). Conclusion: Salivary metabolite butyrate was an independent risk factor for clinical RP.

Purpose: Saliva is often used as a tool for identifying systemic diseases because of the noninvasive nature of its collection. Moreover, salivary metabolites can be potential predictive factors for postoperative survival. We conducted the present study to establish whether salivary metabolites can function as predictive biomarkers for lung surgery complications. Methods: Unstimulated salivary samples were collected from 412 patients before lung surgery. Salivary metabolites were analyzed comprehensively by capillary electrophoresis mass spectrometry. Clinical data with the discriminatory ability of biomarkers were assessed to predict lung surgery complications using multivariate logistic regression analysis. The primary endpoint was the risk factors for postoperative complications of Clavien–Dindo grade ≥ III. Results: Postoperative complications of Clavien–Dindo grade ≥ III developed in 36 patients (8.7%). There was no postoperative 30-day mortality. Male sex (odds ratio [OR], 3.852; 95% confidence interval CI 1.455–10.199; p = 0.007) and salivary gamma-butyrobetaine (OR, 0.809; 95% CI 0.694–0.943; p = 0.007) were identified as significant risk factors for postoperative complications of Clavien–Dindo grade ≥ III. Conclusion: Salivary metabolites are potential noninvasive biomarkers for predicting postoperative complications of lung surgery.

Early detection of esophageal and gastric cancers is essential for patients' prognosis; however, optimal noninvasive screening tests are currently not available. Saliva is a biofluid that is readily available, allowing for frequent screening tests. Thus, we explored salivary diagnostic biomarkers for esophageal and gastric cancers using metabolomic analyses. Saliva samples were collected from patients with esophageal (n = 50) and gastric cancer (n = 63), and patients without cancer as controls (n = 20). Salivary metabolites were analyzed by liquid chromatography–mass spectrometry to identify salivary biomarkers. We also examined the metabolic profiles of gastric cancer tissues and compared them with the salivary biomarkers. The sensitivity of the diagnostic models based on salivary biomarkers was assessed by comparing it with that of serum tumor markers. Additionally, using postoperative saliva samples collected from patients with gastric cancer, we analyzed the changes in the biomarkers' concentrations before and after surgery. Cytosine was detected as a salivary biomarker for gastric cancer, and cytosine, 2-oxoglutarate, and arginine were detected as salivary biomarkers for esophageal cancer. Cytidine, a cytosine nucleotide, showed decreased concentrations in gastric cancer tissues. The sensitivity of the diagnostic models for esophageal and gastric cancers was 66.0% and 47.6%, respectively, while that of serum tumor markers was 40%. Salivary cytosine concentration increased significantly postoperatively relative to the preoperative value. In summary, we identified salivary biomarkers for esophageal and gastric cancers, which showed diagnostic sensitivity at least comparable to that of serum tumor markers. Salivary metabolomic tests could be promising screening tests for these types of cancer.

Identifying novel biomarkers for early detection of lung cancer is crucial. Non-invasively available saliva is an ideal biofluid for biomarker exploration; however, the rationale underlying biomarker detection from organs distal to the oral cavity in saliva requires clarification. Therefore, we analyzed metabolomic profiles of cancer tissues compared with those of adjacent non-cancerous tissues, as well as plasma and saliva samples collected from patients with lung cancer (n = 109 pairs). Additionally, we analyzed plasma and saliva samples collected from control participants (n = 83 and 71, respectively). Capillary electrophoresis–mass spectrometry and liquid chromatography–mass spectrometry were performed to comprehensively quantify hydrophilic metabolites. Paired tissues were compared, revealing 53 significantly different metabolites. Plasma and saliva showed 44 and 40 significantly different metabolites, respectively, between patients and controls. Of these, 12 metabolites exhibited significant differences in all three comparisons and primarily belonged to the polyamine and amino acid pathways; N1-acetylspermidine exhibited the highest discrimination ability. A combination of 12 salivary metabolites was evaluated using a machine learning method to differentiate patients with lung cancer from controls. Salivary data were randomly split into training and validation datasets. Areas under the receiver operating characteristic curve were 0.744 for cross-validation using training data and 0.792 for validation data. This model exhibited a higher discrimination ability for N1-acetylspermidine than that for other metabolites. The probability of lung cancer calculated using this model was independent of most patient characteristics. These results suggest that consistently different salivary biomarkers in both plasma and lung tissues might facilitate non-invasive lung cancer screening.

The development of low-or non-invasive screening tests for cancer is crucial for early detection. Saliva is an ideal biofluid containing informative components for monitoring oral and systemic diseases. Metabolomics has frequently been used to identify and quantify numerous metabolites in saliva samples, serving as novel biomarkers associated with various conditions, including cancers. This review summarizes the recent applications of salivary metabolomics in biomarker discovery in oral cancers. We discussed the prevalence, epidemiologic characteristics, and risk factors of oral cancers, as well as the currently available screening programs, in India and Japan. These data imply that the development of biomarkers by itself is inadequate in cancer detection. The use of current diagnostic methods and new technologies is necessary for efficient salivary metabolomics analysis. We also discuss the gap between biomarker discovery and nationwide screening for the early detection of oral cancer and its prevention.

Purpose: Oral leukoplakia (OL) is a common potentially malignant oral disorder. Therefore, there is a need for simple screening methods for OL before its transformation into oral cancer. Furthermore, because invasive open biopsy is the sole method to determine if an OL lesion is dysplastic, there is also a clinical need for non-invasive methods to differentiate dysplastic OL from non-dysplastic OL. This study aimed to identify salivary metabolites that can help differentiate patients with OL from healthy controls (HC) and also dysplastic OL from non-dysplastic OL. Material & methods: Whole unstimulated saliva samples were collected from patients with OL (n = 30) and HCs (n = 29). The OL group included nine patients with dysplastic OL and 20 with non-dysplastic OL. Hydrophilic metabolites in the saliva samples were comprehensively analyzed through capillary electrophoresis mass spectrometry. To evaluate the discrimination ability of a combination of multiple markers, a multiple logistic regression (MLR) model was developed to differentiate patients with OL from HCs and dysplastic OL from non-dysplastic OL. Results: Twenty-eight metabolites were evidently different between patients with OL and HCs. Finally, three metabolites (guanine, carnitine, and N-acetylputrescine) were selected to develop the MLR model, which resulted in a high area under curve (AUC) of the receiver operating characteristic (ROC) to differentiate patients with OL from HCs (AUC = 0.946, p < 0.001, 95% confidential interval [CI] = 0.889– 1.000). Similarly, two metabolites were evidently different between patients with dysplastic and non-dysplastic OL. Finally, only one metabolite (7-methylguanine) was selected in the MLR model, which revealed a moderate discrimination ability for dysplastic and non-dysplastic OL (AUC = 0761, p = 0.027, 95% CI = 0.551–0.972). Conclusion: Our candidate salivary metabolites showed potential not only to discriminate OL from HC, but also to discriminate dysplastic OL from non-dysplastic OL.

This study aimed to identify salivary metabolomic biomarkers for predicting the prognosis of oral squamous cell carcinoma (OSCC) based on comprehensive metabolomic analyses. Quantified metabolomics data of unstimulated saliva samples collected from patients with OSCC (n = 72) were randomly divided into the training (n = 35) and validation groups (n = 37). The training data were used to develop a Cox proportional hazards regression model for identifying significant metabolites as prognostic factors for overall survival (OS) and disease-free survival. Moreover, the validation group was used to develop another Cox proportional hazards regression model using the previously identified metabolites. There were no significant between-group differences in the participants’ characteristics, including age, sex, and the median follow-up periods (55 months [range: 3–100] vs. 43 months [range: 0–97]). The concentrations of 5-hydroxylysine (p = 0.009) and 3-methylhistidine (p = 0.012) were identified as significant prognostic factors for OS in the training group. Among them, the concentration of 3-methylhistidine was a significant prognostic factor for OS in the validation group (p = 0.048). Our findings revealed that salivary 3-methylhistidine is a prognostic factor for OS in patients with OSCC.

Background: If skin cancer (SC) can be screened using saliva, it would be extremely important for improving the prognosis of skin cancer. However, there is currently no available data on salivary screening for SC. Objective: This study aimed to identify salivary metabolomic biomarkers for SC screening by comparing tissue and saliva samples. Methods: This single-center study involved SC patients from Yamagata University Hospital, enrolled between May 2017 and April 2020. For the healthy controls (HCs), our database comprising salivary metabolomics data of HCs was used. Whole unstimulated saliva samples were collected from patients with SC (n = 75) and HCs (n = 77). Paired tumor and control tissues after SC resection were obtained from the same patients who donated saliva. Hydrophilic metabolites in the tissue and saliva samples were comprehensively analyzed using capillary electrophoresis–mass spectrometry. Using these candidate metabolites, a multiple logistic regression (MLR) model was developed to differentiate SC from HCs Results: Sixty-six and 12 metabolites showed significant differences in tissues and saliva, respectively. Of these, six metabolites were commonly different between SC and HCs. Spermidine, 2-aminobutyric acid, and isoleucine were selected to develop the MLR model. The model exhibited a large area under the receiver operating characteristic curve (0.802, 95 % confidence interval: 0.731–0.874, P < 0.0001). Additionally, no significant differences were shown in candidate salivary metabolites between stages in both malignant melanoma and squamous cell carcinoma. Conclusions: The salivary metabolomic profiles between SC and HCs differed clearly. This combination of salivary metabolites could serve as non-invasive biomarkers for screening SC.

Introduction: Oral squamous cell carcinoma (OSCC) represents the most prevalent form of oral cancer. Potentially malignant disorders of oral mucosa exhibit an elevated propensity for malignant progression. A substantial proportion of cases are discerned during advanced stages, significantly impacting overall survival. This investigation aims to ascertain salivary metabolites with potential utility in the early detection of OSCC. Methods: A search encompassing PubMed, EMBASE, Scopus, Ovid, Science Direct, and Web of Science databases was conducted to identify eligible articles. The search strategy employed precise terms. The quality assessment of the included studies was executed using the QUADAS 2 ROB tool. This was registered with PROSPERO CRD42021278217. Results: Upon removing duplicate articles and publications that didn’t satisfy the inclusion criteria, seven articles were included in the current study. The Random Effects Maximum Likelihood (REML) model adopted for quantitative synthesis identified Nacetyl glucosamine as the sole metabolite in two studies included in this metaanalysis. The pathways significantly influenced by these identified metabolites were delineated. Conclusion: This study highlights Nacetyl glucosamine as a distinctive metabolite with the potential to serve as an early diagnostic marker for OSCC. Nevertheless, further research is warranted to validate its clinical utility.

As the worldwide prevalence of colorectal cancer (CRC) increases, it is vital to reduce its morbidity and mortality through early detection. Saliva-based tests are an ideal noninvasive tool for CRC detection. Here, we explored and validated salivary biomarkers to distinguish patients with CRC from those with adenoma (AD) and healthy controls (HC). Saliva samples were collected from patients with CRC, AD, and HC. Untargeted salivary hydrophilic metabolite profiling was conducted using capillary electrophoresis–mass spectrometry and liquid chromatography–mass spectrometry. An alternative decision tree (ADTree)-based machine learning (ML) method was used to assess the discrimination abilities of the quantified metabolites. A total of 2602 unstimulated saliva samples were collected from subjects with CRC (n = 235), AD (n = 50), and HC (n = 2317). Data were randomly divided into training (n = 1301) and validation datasets (n = 1301). The clustering analysis showed a clear consistency of aberrant metabolites between the two groups. The ADTree model was optimized through cross-validation (CV) using the training dataset, and the developed model was validated using the validation dataset. The model discriminating CRC + AD from HC showed area under the receiver-operating characteristic curves (AUC) of 0.860 (95% confidence interval [CI]: 0.828-0.891) for CV and 0.870 (95% CI: 0.837-0.903) for the validation dataset. The other model discriminating CRC from AD + HC showed an AUC of 0.879 (95% CI: 0.851-0.907) and 0.870 (95% CI: 0.838-0.902), respectively. Salivary metabolomics combined with ML demonstrated high accuracy and versatility in detecting CRC.

Objectives: This study aimed to evaluate salivary monoacetylated polyamines as noninvasive biomarkers for the detection and staging of oral squamous cell carcinoma (OSCC). Design: Unstimulated saliva samples were collected from healthy controls (n = 15), oral leukoplakia (OLK) patients (n = 15), and OSCC patients stratified into early (n = 28) and advanced stages (n = 46). Seven polyamines were quantified using liquid chromatography–tandem mass spectrometry (LC–MS/MS). Diagnostic performance was assessed via receiver operating characteristic (ROC) analysis, logistic regression modeling, and multivariate clustering. Results: N1-acetylspermidine and N1-acetylspermine showed progressive elevation from OLK to advanced OSCC, with AUCs of 0.87 and 0.81, respectively. A two-marker logistic model achieved a cross-validated AUC of 0.85, demonstrating good discriminatory performance between malignant and non-malignant states. Multivariate analysis confirmed their contribution to disease stratification, while calibration plots supported model reliability. Conclusions: Salivary N1-acetylspermidine and N1-acetylspermine are promising biomarkers for noninvasive OSCC detection and staging.

Pancreatic cancer is one of the most malignant cancer types and has a poor prognosis. It is often diagnosed at an advanced stage because of the absence of typical symptoms. Therefore, it is necessary to establish a screening method for the early detection of pancreatic cancer in high-risk individuals. This is a prospective validation study conducted in a cohort of 1033 Japanese individuals (male, n = 467, age = 63.3 ± 11.5 years; female, n = 566, age = 64.2 ± 10.6 years) to evaluate the use of salivary polyamines for screening pancreatic diseases and cancers. Patients with pancreatic cancer were not included; however, other pancreatic diseases were treated as positive cases for accuracy verification. Of the 135 individuals with elevated salivary polyamine markers, 66 had pancreatic diseases, such as chronic pancreatitis and pancreatic cysts, and 1 had gallbladder cancer. These results suggest that the salivary polyamine panel is a useful noninvasive pancreatic disease screening tool.

Differential expression (DE) analysis between cell types for scRNA-seq data by capturing its complicated features is crucial. Recently, different methods have been developed for targeting the scRNA-seq data analysis based on different modeling frameworks, assumptions, strategies and test statistic in considering various data features. The scDEA is an ensemble learning-based DE analysis method developed recently, yielding p-values using Lancaster's combination, generated by 12 individual DE analysis methods, and producing more accurate and stable results than individual methods. The objective of our study is to propose a new ensemble learning-based DE analysis method, scHD4E, using top performers in only 4 separate methods. The top performer 4 methods have been selected through an evaluation process using six real scRNA-seq data sets. We conducted comprehensive experiments for five experimental data sets to evaluate our proposed method based on the sample size effects, batch effects, type I error control, gene ontology enrichment analysis, runtime, identified matched DE genes, and semantic similarity measurement between methods. We also perform similar analyses (except the last 3 terms) and compute performance measures like accuracy, F1 score, Mathew's correlation coefficient etc. for a simulated data set. The results show that scHD4E is performs better than all the individual and scDEA methods in all the above perspectives. We expect that scHD4E will serve the modern data scientists for detecting the DEGs in scRNA-seq data analysis. To implement our proposed method, a Github R package scHD4E and its shiny application has been developed, and available in the following links: https://github.com/bbiswas1989/scHD4E and https://github.com/bbiswas1989/scHD4E-Shiny.

Objective A significant number of patients experience early recurrence after surgical resection for pancreatic ductal adenocarcinoma (PDAC), negating the benefit of surgery. The present study conducted clinicopathologic and metabolomic analyses to explore the factors associated with the early recurrence of PDAC. Materials and Methods Patients who underwent pancreatectomy for PDAC at Kagawa University Hospital between 2011 and 2020 were enrolled. Tissue samples of PDAC and nonneoplastic pancreas were collected and frozen immediately after resection. Charged metabolites were quantified by capillary electrophoresis-mass spectrometry. Patients who relapsed within 1 year were defined as the early recurrence group. Results Frozen tumor tissue and nonneoplastic pancreas were collected from 79 patients. The clinicopathologic analysis identified 11 predictive factors, including preoperative carbohydrate antigen 19-9 levels. The metabolomic analysis revealed that only hypotaurine was a significant risk factor for early recurrence. A multivariate analysis, including clinical and metabolic factors, showed that carbohydrate antigen 19-9 and hypotaurine were independent risk factors for early recurrence (P = 0.045 and P = 0.049, respectively). The recurrence-free survival rate 1 year after surgery with both risk factors was only 25%. Conclusions Our results suggested that tumor hypotaurine is a potential metabolite associated with early recurrence. Carbohydrate antigen 19-9 and hypotaurine showed a vital utility for predicting early recurrence.

BACKGROUND The role of the apoptosis inhibitor of macrophages (AIM) in human lipid metabolism remains unclear. OBJECTIVE This study aimed to investigate the cross-sectional associations between serum AIM and plasma metabolites and to determine if baseline AIM concentrations predict incident dyslipidemia. METHODS This study used a community-based cohort of 3139 participants for cross-sectional metabolomic analysis. Among them, 1292 participants without dyslipidemia at baseline were followed prospectively for up to 8.5 years. Cox proportional hazards models were used to estimate hazard ratios for incident high low-density lipoprotein cholesterol (LDL-C) and metabolic dyslipidemia (high triglycerides and/or low high-density lipoprotein cholesterol [HDL-C]) across AIM tertiles. RESULTS Cross-sectionally, higher AIM concentrations were associated with elevated acylcarnitines and tricarboxylic acid cycle intermediates. Longitudinally, higher baseline AIM was associated with an increased risk of developing metabolic dyslipidemia in women (highest vs lowest tertile hazard ratio, 1.84) and showed a similar but smaller nonsignificant trend in men. The risk for high LDL-C was significantly increased in women but not in men. These associations remained robust after multivariable adjustment. CONCLUSION Elevated baseline AIM concentrations were prospectively associated with the development of metabolic dyslipidemia in women, with a similar but nonsignificant trend in men, and with high LDL-C in women. The related metabolomic profile suggests enhanced lipolysis and altered lipid metabolism. Therefore, AIM may serve as a biomarker for future dyslipidemia, particularly for triglyceride and HDL-C dysregulation.

The liver metabolizes a variety of substances that sometimes interact and regulate each other. The modeling of a single cell or a single metabolic pathway does not represent the complexity of the organ, including metabolic zonation (heterogeneity of functions) along with liver sinusoids. Here, we integrated multiple metabolic pathways into a single numerical liver zonation model, including drug and glucose metabolism. The model simulated the time-course of metabolite concentrations by the combination of dynamic simulation and metabolic flux analysis and successfully reproduced metabolic zonation and localized hepatotoxicity induced by acetaminophen (APAP). Drug metabolism was affected by nutritional status as the glucuronidation reaction rate changed. Moreover, sensitivity analysis suggested that the reported metabolic characteristics of obese adults and healthy infants in glucose metabolism could be associated with the metabolic features of those in drug metabolism. High activities of phosphoenolpyruvate carboxykinase (PEPCK) and glucose-6-phosphate phosphatase in obese adults led to increased APAP oxidation by cytochrome P450 2E1. In contrast, the high activity of glycogen synthase and low activities of PEPCK and glycogen phosphorylase in healthy infants led to low glucuronidation and high sulfation rates of APAP. In summary, this model showed the effects of glucose metabolism on drug metabolism by integrating multiple pathways into a single liver metabolic zonation model.