Norcholic acid: a novel biomarker of early kidney injury in patients with metabolic dysfunction-associated fatty liver disease

doi:10.21203/rs.3.rs-2789347/v1

Background

Bile acids (BAs) are signaling molecules that regulate numerous metabolic processes in metabolic dysfunction-associated (MAFLD) and chronic kidney disease (CKD). Whether BAs are also associated with early abnormalities in renal function in MAFLD is uncertain.

Methods

We quantitatively measured plasma BA concentrations in biopsy-proven MAFLD patients with or without abnormal albuminuria (defined as albumin-to-creatinine ratio ≥ 30 mg/g) and in healthy controls, by using ultraperformance liquid chromatography coupled to tandem mass spectrometry.

Results

Plasma BA profiles (conjugated BAs, glycine-conjugated BAs, glycine-conjugated primary BAs, total conjugated primary BAs, and glycine-conjugated primary BAs) were up-regulated in MAFLD patients with abnormal albuminuria compared to their counterparts with normal albuminuria and healthy controls. In particular, we identified a distinct individual BA, i.e., norcholic acid (NorCA) that was markedly upregulated in MAFLD patients with abnormal albuminuria, and that was also positively correlated with albuminuria. Moreover, the combination of NorCA, tauro-deoxycholic acid, tauro-lithocholic acid and cholic acid, improved identification of abnormal albuminuria in MAFLD patients in a predictive model, that also included diabetes, hypertension, body mass index, and serum alanine aminotransferase levels (AUC = 0.80, 95%CI 0.740–0.863).

Conclusion

BA biomarkers are increased in patients with MAFLD and abnormal albuminuria and further investigation of their role in renal function is warranted.

Bile acids

NorCA

Risk

Albuminuria

Biomarker

Prediction

Diagnosis

Chronic kidney disease

Nonalcoholic fatty liver disease

Metabolic dysfunction-associated fatty liver disease

Nonalcoholic fatty liver disease (NAFLD) is one of the most common causes of chronic liver disease worldwide, affecting up to nearly a third of the global adult population[1]. NAFLD is a progressive spectrum of liver conditions, ranging from nonalcoholic fatty liver (NAFL) to nonalcoholic steatohepatitis (NASH), advanced fibrosis and cirrhosis[2, 3]. In 2020, international experts proposed that NAFLD be renamed and reclassified as metabolic dysfunction-associated fatty liver disease (MAFLD), which indicates the presence of hepatic steatosis, and the coexistence of overweight/obesity, or type 2 diabetes (T2D), or at least two other features of metabolic dysregulation[4]. Chronic kidney disease (CKD) is a general term used for defining heterogeneous diseases that affect both kidney structure and function. CKD has become a major public health issue, with a global prevalence estimated to be 8–16%[5]. To date, there is growing evidence that the increasing global prevalence of MAFLD will further increase the incidence of CKD worldwide[6–11].

Bile acids (BAs) are gaining increasing recognition as important metabolic signaling molecules that modulate lipid, glucose and energy metabolism. BAs are metabolites of cholesterol, which are generated in the liver, modified by gut microbiota, reabsorbed in the ileum, and excreted in faeces or urine, constituting the so-called enterohepatic circulation of BAs[12, 13]. Several cross-sectional studies have reported significant alterations in BA compositions in individuals with MAFLD, or in subjects with CKD, compared to healthy controls[14–18]. Our recent study has demonstrated that secondary unconjugated BAs (uncon SBAs), primary glycine conjugated BAs (Gcon PBAs), and primary unconjugated BAs (uncon PBAs) comprised more than 80% of the BA pool in serum, and the uncon SBAs were increased in patients with MAFLD and liver fibrosis[19].

To date, to our knowledge, no studies have investigated the association between plasma BA levels and early CKD stages (i.e., CKD stages 1–2 with coexisting abnormal albuminuria or overt proteinuria) in patients with MAFLD. This is potentially important because MAFLD is a significant risk factor for CKD[6], although yet the underlying mechanisms by which MAFLD may increase the risk of developing CKD are poorly understood[20]. BAs exert their effects on lipid and glucose homeostasis and energy metabolism, mainly by activation of BA-associated receptors, such as the nuclear receptor-farnesoid X receptor (FXR) and the membrane Takeda G-protein coupled receptor 5 (TGR5)[12, 21]. FXR and TGR5 play an important role in chronic inflammation, endoplasmic reticulum (ER) stress, oxidative stress and fibrosis, in both liver and kidney injury[22–25]. FXR can exert direct and indirect effects on the kidney, thereby mitigating key pathogenetic processes both in CKD development and in chemotherapy-induced nephrotoxicity. Moreover, FXR may also affect lipid metabolism within renal cells, which mediates inhibition of enzymes that contribute to diminish renal lipid accumulation[26].

As BAs have been implicated in the development and progression of MAFLD, we have therefore recruited a cohort of Chinese individuals with biopsy-proven MAFLD (most of whom had normal or near-normal kidney function) and stratified these subjects by presence of abnormal albuminuria in order to test differences in serum BA levels between these two subgroups of MAFLD patients. We then tested the predictive performance of serum BA levels for identifying abnormal albuminuria (i.e. a reliable marker of early CKD stages) in MAFLD.

Study populations

For this study, a total of 1,124 adult individuals, who had all undergone liver biopsy, were consecutively recruited at the First Affiliated Hospital of Wenzhou Medical University during a period of nearly 2 years (September 2017 to December 2020). The inclusion and exclusion criteria of the study have been described previously[27]. Briefly, we excluded subjects with: 1) excessive alcohol intake; 2) chronic liver diseases, such as viral hepatitis and autoimmune hepatitis; 3) chronic use of hepatotoxic or nephrotoxic drugs; 4) known causes of chronic or acute kidney diseases; and 5) fatty liver infiltration < 5% on liver histology or missing data. Then, according to the availability of serum samples, a total of 438 subjects with biopsy-proven MAFLD were included in the present study. A total of 15 healthy controls without any clinical, biochemical, or imaging evidence of fatty liver disease or metabolic dysfunctions or kidney injury at Hangzhou Normal University Affiliated Hospital were also enrolled in the study. The study was approved by the local ethics committee of our hospital. Written informed consent was obtained from all participants and person identifiable information was de-identified prior to statistical analysis.

Liver histology

An ultrasound-guided liver biopsy was performed under sedation using a 16-gauge Hepafix needle (Gallini, Modena, Italy). All liver biopsy specimens were placed in formalin solution for fixation, embedded in paraffin blocks and stained with hematoxylin-eosin and Masson’s trichrome. All biopsies were analyzed by an experienced liver pathologist, who was blinded to the clinical and laboratory data of participants. The histologic features of MAFLD were scored according to the NASH Clinical Research Network (NASH-CRN) classification[28]. The histology stage of hepatic fibrosis was quantified according to the Brunt’s criteria[29]. In these patients we have also estimated non-invasively the severity of liver fibrosis by using the fibrosis-4 (FIB-4) index and the NAFLD fibrosis score (NFS), according to widely used equations[30].

Targeted BA profiling

Blood samples were collected from all participants and serum BAs were measured targeted metabolomics approach according to standard laboratory methods, as reported previously[19]. Briefly, a 20 µL serum sample together with 180 µL of acetonitrile/methanol containing 10 internal standards was added into a 96-well plate. The metabolite extraction was centrifuged at 10°C and 1,500 rpm for 20 min. After centrifugation, the supernatant was transferred to a microcentrifuge tube for lyophilization using a FreeZone freeze dryer equipped with a stopping tray system (Labconco, Kansas City, MO, USA). The supernatant was transferred to a 96-well plate for ultra-performance liquid chromatography coupled to tandem mass spectrometry analysis (UPLC-MS/MS analysis, ACQUITY UPLC-Xevo TQ-S, Waters Corp., Milford, MA, USA). Then, raw data generated by UPLC-MS/MS analysis were processed using the TargetLynx software to perform peak integration, calibration, and quantitation for each BA metabolite. A total of 38 BAs were identified and quantified in serum samples. BAs were classified into 8 categories according to their chemical structures, including primary glycine or taurine conjugated BAs, unconjugated (uncon) PBAs, secondary glycine or taurine conjugated BAs, uncon SBAs, sulfated BAs, and glucuronidated BAs, respectively.

Clinical and laboratory data

Demographic data, anthropometry, clinical parameters and comorbidities were recorded for all participants. Urinary albumin excretion was measured on a morning spot urine sample and expressed as urinary albumin-to-creatinine ratio (ACR); abnormal albuminuria was defined as ACR ≥ 30 mg/g creatinine. Blood samples were collected after an overnight fast. Biochemical parameters included measurements of serum glucose, insulin, blood urea nitrogen, creatinine, alanine aminotransferase (ALT), aspartate aminotransferase (AST), albumin, uric acid, lipids, as well as complete blood count. All these parameters were measured by an automated analyzer (Abbott AxSYM), using standard laboratory methods. MAFLD was defined by evidence of hepatic steatosis on liver biopsy in addition to one of the following three criteria, namely overweight/obesity, presence of T2D, or evidence of metabolic dysregulation. The presence of metabolic dysregulation among lean/normal weight individuals with hepatic steatosis who did not have T2D was defined as the presence of two or more of the following metabolic risk abnormalities: 1) waist circumference ≥ 102 cm in men and 88 cm in women, 2) blood pressure ≥ 130/85 mmHg or specific drug treatment, 3) serum triglycerides (TG) ≥ 1.70 mmol/L or specific drug treatment, 4) high-density lipoprotein cholesterol (HDL-C) < 1.0 mmol/L for men and < 1.3 mmol/L for women, 5) prediabetes, 6) homeostasis model assessment (HOMA-IR) score ≥ 2.5, and 7) a plasma C-reactive protein level > 2 mg/L. Estimated glomerular filtration rate (eGFR) was calculated using the CKD-Epidemiology Collaboration (CKD-EPI) equation[31]. HOMA-IR score was used for estimating insulin resistance. Body mass index (BMI) was calculated as weight (kg) divided by height (m²). Blood pressure (BP) was measured using an automated sphygmomanometer with the subject sitting in a quiet environment for at least 10 min. Hypertension was defined as BP ≥ 130/85 mmHg or use of any anti-hypertensive drugs. Diabetes was defined as a fasting glucose level ≥ 126 mg/dL (≥ 7.0 mmol/L) or hemoglobin A1c (HbA1c) ≥ 6.5% (≥ 48 mmol/mol), or use of any glucose-lowering agents.

Statistical analysis

R software (version 3.6.3, R Foundation for statistical Computing, Vienna, Austria) was applied for statistical analysis and visualization. The normal distribution of variables was initially tested using the Shapiro Wilk test. Baseline clinical and biochemical characteristics of the study population were compared using the one-way analysis of variance (ANOVA) for normally distributed continuous variables, the Mann-Whitney U test or the Kruskal Wallis test for non-normally distributed continuous variables and the chi-squared test for categorical variables. Spearman’s rank correlation coefficients were calculated to examine the associations between albuminuria, BAs and other clinical parameters. The association between NorCA levels (per 10-unit increase) and presence of abnormal albuminuria was tested using logistic regression analyses. In addition, discrimination was examined in the context of logistic regression analyses using the area under the receiver operating characteristic curve (AUROC).

Clinical and biochemical characteristics of participants

Among 438 patients with biopsy-confirmed MAFLD were included in the analysis, mean age was 41±14 years, 72.6% were men, mean eGFR_CKD-EPIwas 114.4±20 mL/min/1.73 m², and 11.2% had abnormal albuminuria (defined as ACR ≥30 mg/g). According to their eGFR_CKD-EPIlevels, 11.2% of enrolled patients had CKD stage 1 (defined as eGFR ³90 mL/min/1.73 m²), and 2.5% had CKD stage 2 (eGFR 89-60 mL/min/1.73 m²). Only two patients had CKD stage ≥3 (eGFR <60 mL/min/1.73 m²). Among 15 healthy controls who were also included, age was 40±3 years and 60% were men, and none of them had abnormal albuminuria and kidney injury.

Patients with MAFLD were further divided into two groups according to the presence of abnormal albuminuria. As shown in Supplementary Figure 1, healthy controls were defined as HC, MAFLD patients with normal albuminuria were defined as MAFLD-ACR0, while the MAFLD patients with abnormal albuminuria were defined as MAFLD-ACR1. Compared with HC, all MAFLD patients had significantly higher levels of blood pressure, serum transaminases, LDL-C and lower HDL-C concentrations. Conversely, the three groups were similar for age and sex.

As shown in Table 1, compared to the MAFLD-ACR0 group, patients with MAFLD and abnormal albuminuria (MAFLD-ACR1) were more likely to be men, older, and to have higher blood pressure and hemoglobin A1c, and lower eGFR levels (that were within the normal range in most of our patients, 8.5% had CKD stage 1 and 0.5% had CKD stage 2 in the MAFLD-ACR0 group). Patients with MAFLD and abnormal albuminuria also had a higher prevalence of pre-existing type 2 diabetes and hypertension compared to those belonging to the MAFLD-ACR0 group. Liver histology features (steatosis, hepatocyte ballooning, and lobular inflammation) were not significantly different according to albuminuria group. Interestingly, there was a trend toward a higher proportion of subjects with liver fibrosis in the MAFLD-ACR1 group (Table 1).

Overall BA profiles in MAFLD patients stratified by abnormal albuminuria

Overall, glycine conjugated primary BAs (Gcon PBAs), unconjugated secondary BAs (uncon SBAs), unconjugated primary BAs (uncon PBAs), and glycine conjugated secondary BAs (Gcon SBAs) comprised more than 80% of BA pool in serum (Figure 1A). Compared to the HC group, BA profiles changed significantly in MAFLD patients, exhibiting higher plasma levels of conjugated BAs (con BAs), glycine conjugated BAs (Gcon BAs), conjugated primary BAs (Con PBAs), Gcon PBAs, and taurine-conjugated primary BAs (Tcon PBAs). To further investigate the differences in BA profiles, the circulating levels of these BAs, con BAs, Gcon BAs, Con PBAs (including both Tcon and Gcon PBAs) were markedly up-regulated in the MAFLD-ACR1 group compared with the MAFLD-ACR0 group. In addition, we found significantly higher ratios of con BAs to total BAs (con BAs/TBAs), Gcon BAs to TBAs (Gcon BAs/TBAs), and con BAs to unconjugated BAs (con BAs/ Uncon BAs) in the MAFLD-ACR1 group compared with the MAFLD-ACR0 group (Figure 1).

Association between NorCA levels and risk of abnormal albuminuria in MAFLD

The circus plot showed differential BAs and their fold changes by comparing MAFLD-ACR0 vs. MALFD-ACR1 in Figure 2A. Compared to the MAFLD-ACR0 group, we found that the MAFLD-ACR1 group had significantly higher levels of plasma GCDCA, TCDCA, and NorCA (Figure 2B-D).

Next, we analysed 38 BA biomarkers and 7 clinical biomarkers (i.e., age, sex, BMI, diabetes, hypertension, eGFR and ACR) in the heatmap (Figure 3A). Firstly, 6 primary BAs (i.e., HCA, THCA, TCA, GHCA, GCA and GCDCA) showed significant inverse associations with age, while 7 secondary BAs (6-ketoLCA, UDCA, HDCA, LCA, 7-ketoLCA, 12-ketoLCA and TLCA) and a sulfated BA, the LCA-3S, showed positive associations with age. Nine BAs (TCA, TCDCA, GCA, GCDCA, 6-ketoLCA, TLCA, TDCA, CDCA-3Glu and NorCA) were significantly associated with BMI. As regards to the presence of type 2 diabetes, we found that GHCA was inversely associated with diabetes, while 8 BAs (CA, CDCA, GCDCA, 6-ketoLCA, UDCA, 7-DHCA, 7-ketoLCA and NorCA) were positively associated with diabetes. The presence of hypertension was also associated with 8 BAs (CA, UCA, 6-ketoLCA, UDCA, 7-DHCA, 7-ketoLCA, TLCA and NorCA). Finally, we showed that 5 primary BAs (HCA, TCA, GHCA, GCA and GCDCA) were positively associated with eGFR levels. More importantly, 2 primary BAs (THCA, GHCA), 9 secondary BAs (βUCA, UCA, 6-ketoLCA, UDCA, 7-DHCA, βCA, dehydroLCA, 7-ketoLCA, TLCA) and NorCA showed significant associations with ACR, respectively. Further, the Spearman’s correlation analysis showed that NorCA levels markedly increased with increasing ACR values (Figure 3B).

To further study the positive association between levels of NorCA and albuminuria, we performed logistic regression analyses. As shown in Table 2, in univariable logistic regression, there was a significant association between higher NorCA levels and risk of abnormal albuminuria (unadjusted-OR=1.580, 95%CI 1.21-2.06; P=0.001). After adjustment for age, diabetes, hypertension, this association remained statistically significant (adjusted-OR=1.534, 95%CI 1.16-2.03). Notably, the significant association between higher NorCA levels and risk of abnormal albuminuria was not attenuated after further adjustment for sex, BMI, eGFR, presence of steatohepatitis, and histologic stage of liver fibrosis (adjusted-OR=1.525, 95%CI 1.14-2.04; P=0.004).

Biomarker discovery and prediction for abnormal albuminuria in MAFLD

Through BA and clinical biomarkers selection by predictive modelling in logistic regression analysis, the combination of serum BAs and clinical biomarkers enabled us to obtain optimal models for predicting the presence of abnormal albuminuria in patients with MAFLD. Firstly, we conducted 7 predictive models for BA biomarkers including GCDCA, NorCA, TLCA, 7-ketoLCA, TDCA, CA, TCDCA, with AUC values of 0.62 (95%CI 0.54-0.70), 0.61(95%CI 0.52-0.69), 0.53(95%CI 0.44-0.62), 0.51(95%CI 0.42-0.61), 0.49(95%CI 0.39-0.58), 0.46(95%CI 0.39-0.54) and 0.39(95%CI 0.32-0.46), respectively (Figure 4A). Furthermore, 7 clinical biomarkers, and the presence of type 2 diabetes and hypertension, CysC, eGFR, age, BMI and ALT, yielded an AUC of 0.67 (95%CI 0.61-0.74), 0.61 (95%CI 0.55-0.67), 0.66 (95%CI 0.58-0.74), 0.63 (95%CI 0.56-0.71), 0.59 (95%CI 0.52-0.67), 0.58 (95%CI 0.50-0.67) and 0.55 (95%CI 0.47-0.63), respectively (Figure 4B). Next, we tested widely known existing non-invasive fibrosis scores that are used in clinical practice for diagnosing high probability of advanced liver fibrosis, i.e., NAFLD fibrosis score (NFS), FIB-4 index, and fibrosis stage with AUCs of 0.65 (95%CI 0.57-0.72), 0.60 (95%CI 0.52-0.68), and 0.57 (95%CI 0.49-0.66) respectively, in predicting the presence of abnormal albuminuria in patients with MAFLD. However, univariable logistic regression analyses failed to show good performance in most of MAFLD patients. Furthermore, we added BA biomarkers to clinical biomarkers and obtained a predictive model for NorCA plus age, diabetes, and hypertension. This predictive model showed good diagnostic performance for predicting the presence of abnormal albuminuria in MAFLD, with an AUC of 0.74 (95%CI 0.67-0.81) (Figure 4D). More importantly, another predictive model including 4 BA biomarkers (NorCA, TLCA, TDCA and CA) plus 3 clinical biomarkers (diabetes, hypertension and BMI) had even better performance for predicting abnormal albuminuria in patients with MAFLD, with AUC of 0.80 (95%CI 0.74-0.86) (Figure 4E).

Our cross-sectional study has shown for the first time that the circulating levels of specific BAs may efficiently predict abnormal albuminuria, as a reliable marker of early stages of CKD, in a large sample of Chinese adult individuals with biopsy-proven MAFLD (most of whom had normal or near-normal kidney function). Our predictive models achieved better performances in predicting abnormal albuminuria, when combining BA biomarkers (plasma levels of NorCA, TLCA, TDCA, CA) and key clinical risk factors for renal disease (BMI, type 2 diabetes, and hypertension).

Previous cross-sectional studies have reported significant changes in BA profiles in subjects with NAFLD or CKD compared with healthy controls[14, 15, 18, 32–35]. For example, Nimer et al. reported that compared to healthy controls, subjects with biopsy-proven NAFLD had higher circulating levels of the majority of BA molecular species[14]. In addition, Puri et al. reported that Con PBAs, conjugated UDCA and DCA were significantly higher in patients with NASH compared to those with NAFL and healthy controls[15]. In our recent study involving NAFLD patients, we showed that BA profiles changed more significantly in patients with mild fibrosis than in those with significant fibrosis; in particular, secondary BAs (HDCA, UCA, βCA, 7-DHCA, βUCA, dehydroLCA, 6-ketoLCA and TLCA) were significantly up-regulated in NAFLD patients with mild fibrosis compared to those who did not have fibrosis[19]. In addition, in patients with end-stage renal disease, Li R et al. reported that circulating levels of Con BAs, Con PBAs, Gcon BAs, Tcon BAs and Con BAs/Uncon BAs (specifically GCDCA and TCDCA) were significantly higher compared to those observed in healthy controls[18]. In the present study, we have shown that Con BAs were significantly increased in MAFLD patients with abnormal albuminuria, which is consistent with other studies. In contrast to other studies, in our study we subdivided Con BAs and Con PBAs into glycine- or tauro-conjugated BAs, in order to better discriminate possible differences in BA profile between healthy controls and MAFLD patients, with or without abnormal albuminuria. In doing this, we found that Con BAs/TBAs and Gcon BAs/TBAs were significantly increased in MAFLD patients with abnormal albuminuria.

In particular, compared with MAFLD patients with normal albuminuria, we found that three distinct BAs (NorCA, GCDCA and TCDCA) were markedly increased in patients with MAFLD and abnormal albuminuria. To further understand the association between distinct BAs and albuminuria in MAFLD patients, we showed that only plasma NorCA levels were positively associated with albuminuria. NorCA is a 23-hydroxylated bile alcohol, and is different from regular widely known bile acid, such as cholic acid[36]. It has been reported that nor-bile acids are generated from natural BAs by degradation of the side chain, removing a methyl group, that include organic compounds and have a common robust motif in their crystal structures based on a cyclic hydrogen bond network and steric complementarity in the lipophilic parts[37, 38]. Studies have demonstrated that in the drug-induced liver injury (DILI) group, plasma NorCA level was significantly increased in those with DILI compared to controls, and NorCA level was also a strong predictor for differentiating patients with severe DILI[39]. In addition, NorCA was a major trihydroxy bile acid in the urine and one study showed that NorCA was also a major component of the urine in cirrhotic patients, suggesting that unusual cleavage of the cholesterol side chain can occur in cirrhosis[40]. A recent study also suggested that NorCA promoted the migration and invasion of hepatocellular carcinoma (HCC) cells, inducing upregulation of PD-L1 expression mainly through the FXR/SHP signaling pathway[41]. However, the precise mechanisms and significance of NorCA remain poorly defined and further research is needed.

Non-invasive diagnostic indices, such as the NAFLD fibrosis score (NFS) and FIB-4 index, have been regarded as reliable markers for diagnosing high probability of advanced liver fibrosis[3, 42, 43]. Moreover, several cross-sectional studies have reported an association between MAFLD severity and increased CKD prevalence[7, 43–45]. By combining NorCA levels, age and the presence of type 2 diabetes and hypertension, we found that this predictive model performed well in identifying abnormal albuminuria (AUC = 0.74, 95%CI 0.67–0.81). Interestingly, by combining NorCA, TLCA, TDCA, CA with BMI, ALT and the presence of type 2 diabetes and hypertension, our predictive model achieved even a better diagnostic performance with AUC of 0.80 (95%CI 0.74–0.86).

There are some important limitations of our study that should be mentioned. Firstly, the cross-sectional design of the study does not allow for establishing the temporality and causality of the observed associations. Secondly, in this single-center study we enrolled adult individuals with biopsy-proven MAFLD belonging to a single Asian ethnicity. Hence, additional large studies are needed to further validate these findings in other ethnic populations.

In conclusion, the results of this large cross-sectional study, involving patients with biopsy proven-MAFLD, showed for the first time that a specific BA, plasma NorCA levels, may improve the identification of MAFLD patients with abnormal albuminuria. Further mechanistic studies are now needed to better elucidate the complex but existing link between altered BA metabolism and risk of developing CKD in MAFLD.

ACR = albumin to creatinine ratio; ANOVA = analysis of variance; BAs = bile acids; BMI = body mass index; CKD = chronic kidney disease; CI = confidence interval; CDCA = chenodeoxycholic acid; CA = cholic acid; DBP = diastolic blood pressure; DILI = drug-induced liver injury; DCA = deoxycholic acid; eGFR = estimated glomerular filtration rate; ESRD = end-stage renal disease; ER = endoplasmic reticulum; FXR = farnesoid X receptor; GCDCA = glycochenodeoxycholic acid; GDCA = glycodeoxycholic acid; GCA = glycocholic acid; GHCA = glycohyocholic aicd; HCC = hepatic cell carcinoma; HCA = hyocholic aicd; HDCA = hyodeocholic aicd; LCA = lithocholic acid; NAFLD = nonalcoholic fatty liver disease; NASH = nonalcoholic steatohepatitis; NFS = NAFLD fibrosis score; NorCA = norcholic acid; SD = standard derivation; SBP = systolic blood pressure; TGR5 = takeda G-protein coupled receptor 5; TLCA, taurolithocholic acid; TUDCA = tauroursodeoxycholic acid; TDCA = taurodeoxycholic acid; TCA = taurocholic acid; TCDCA = taurochenodeoxycholic acid; THCA = taurohyocholic aicd; UCA = ursocholic acid; UDCA = ursodeoxycholic acid; 7-DHCA = 7-dehydrocholic aicd; uncon SBAs = unconjugated secondary BAs; Gcon PBAs = glycine conjugated primary BAs; uncon PBAs = unconjugated primary BAs

Acknowledgements

The author would like to thanks Mingming Su and Ana Liu for the targeted identification and analysis of bile acids; Jun-Ping Shi for providing the data of the healthy controls; Cui-Fang Xu for performing the statistical analysis. Giovanni Targher and Christopher D Byrne for contribution to writing and proof reading the manuscript. Fu-Qiang Yuan and Jing Zhao for reviewing the results, interpreted data, and Wen-Ying Chen for drafting the manuscript.

Funding

This work was supported by grants from the National Natural Science Foundation of China (82070588, 82000690, 82170583, and 81900510), National Key Research and Development Program of China (2021YFC2701904), High Level Creative Talents from Department of Public Health in Zhejiang Province, Project of New Century 551 Talent Nurturing in Wenzhou. Dan-Qin Sun is supported in part by grants from Top-notch Talents from Young and Middle-Age Health Care in Wuxi. GT is supported in part by grants from the University School of Medicine of Verona, Verona, Italy. CDB is supported in part by the Southampton NIHR Biomedical Research Center (NIHR 203319), UK.

Author contributions

Dan-Qin Sun, Ming-Hua Zheng, and Yan Ni designed the study and prepared the figures. Mingming Su and Ana Liu performed the targeted identification and analysis of bile acids. Jun-Ping Shi provided the data of the healthy controls. Cui-Fang Xu performed the statistical analysis. Giovanni Targher and Christopher D Byrne contributed to writing and proof reading the manuscript. Fu-Qiang Yuan and Jing Zhao reviewed the results, interpreted data, and Wen-Ying Chen draft the manuscript.

Data Availability Statement

The data underlying this article will be shared on reasonable request to the corresponding author.

Conflict of interest

The authors Dan-Qin Sun, Cuifang Xu, Wen-Ying Chen, Fuqiang Yuan, Giovanni Targher, Christopher D. Byrne, Jing Zhao, Ana Liu, Mingming Su, Jun-Ping Shi, Yan Ni and Ming-Hua Zheng declare no conflict of interest.

Ethical approval

The study was approved by the local ethics committee of the First Affiliated Hospital of Wenzhou Medical University and Hangzhou Normal University Affiliated Hospital. Written informed consent was obtained from all participants and person identifiable information was de-identified prior to statistical analysis.

Informed consent

All enrolled patients gave written informed consent.

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Table 1. Clinical and biochemical characteristics of patients with MAFLD stratified by abnormal albuminuria.

	NAFLD with normal albuminuria (ACR<30 mg/g), n=389	NAFLD with abnormal albuminuria (ACR≥30 mg/g), n=49	P-value
Demographic parameters
Age (years)	41 (31, 52)	44 (37, 54)	0.03
Men, n (%)	287 (74%)	31 (63%)	0.07
BMI (kg/m²)	26.30 (24.1, 28.7)	27.06 (25.2, 29.7)	0.06
WHR	0.92 (0.88, 0.95)	0.92 (0.9, 0.97)	0.186
Clinical parameters
Systolic BP (mmHg)	126 (115, 135)	134 (122,146)	<0.05
Diastolic BP (mmHg)	81 (74, 88)	83 (75, 90)	<0.05
Hypertension, n (%)	229 (58.6%)	40 (81.6%)	0.02
Type 2 diabetes, n (%)	128 (32.9%)	33 (67.3%)	<0.05
Laboratory parameters
Hemoglobin A1c (%)	5.7 (5.4, 6.5)	6.6 (5.8, 8.0)	<0.05
ACR (mg/g)	7.49 (3.6, 9.7)	112.68 (38.4, 104.8)	<0.05
Creatinine (μmol/L)	67 (54, 81)	66 (54, 81)	0.88
Uric acid (μmol/L)	383 (318, 458)	367 (305, 449)	0.49
Triglycerides (mmol/L)	1.9 (1.32, 2.68)	2.2 (1.33, 2.81)	0.53
Total cholesterol (mmol/L)	5.06 (4.38, 5.76)	4.85 (4.12, 5.97)	0.59
HDL cholesterol (mmol/L)	0.99 (0.87, 1.11)	0.99 (0.86, 1.14)	0.90
LDL cholesterol (mmol/L)	3.01 (2.40, 3.59)	2.67 (2.14, 3.74)	0.26
Albumin (g/L)	46.2 (43.0, 48.7)	44.9 (42.8, 47.9)	0.21
eGFR (mL/min/1.73 m²)	115.6 (103.1, 129.4)	109.9 (92.1, 118.2)	<0.05
CKD stages, n (%)			0.001
stage 1	33 (8.5%)	16 (32.7%)
stage 2	2 (0.5%)	9 (18.4%)
stage ≥3	2 (0.5%)	0 (0%)
HOMA-IR score	3.57 (2.26, 5.10)	4.1 (2.60, 6.93)	0.13
Liver histology features
Steatosis, n (%)			0.55
1	128 (33%)	16 (33%)
2	69 (18%)	7 (14%)
3	120 (31%)	15 (31%)
Hepatocyte ballooning, n (%)			0.59
0	83 (22%)	14 (29%)
1	207 (54%)	17 (35%)
2	94 (24%)	18 (37%)
Lobular inflammation, n (%)			0.59
0	22 (6%)	6 (12%)
1	285 (74%)	28 (57%)
2	72 (19%)	14 (29%)
3	5 (1%)	1 (2%)
Fibrosis, n (%)			0.07
0	81 (21%)	8 (16%)
1	208 (54%)	23 (47%)
2	70 (18%)	11 (22%)
3	23 (6%)	4 (8%)
4	2 (1%)	3 (6%)

Abbreviations: ACR = albumin to creatinine ratio, BMI = body mass index, BP = blood pressure, CKD = chronic kidney disease, HOMA-IR = homeostasis model assessment score, HDL-C = high-density lipoprotein cholesterol, LDL-C = low-density lipoprotein cholesterol, WHR = waist-to-hip ratio

Table 2. Association between NorCA levels（per 10-unit increase）and abnormal albuminuria in patients with biopsy-proven MAFLD.

	B coefficient	Wald	Odds ratio^a	95% CI	P-value
Unadjusted model 1	0.457	11.4	1.580	1.21-2.06	0.001
Adjusted model 2	0.428	8.76	1.534	1.16-2.03	0.003
Adjusted model 3	0.422	8.17	1.525	1.14-2.04	0.004

The presence of abnormal albuminuria (i.e., urinary ACR ≥30 mg/g) was included as the dependent variable in all logistic regression models.

Model 1: univariable regression model;

Model 2: adjusted for age, type 2 diabetes, and hypertension;

Model 3: adjusted for model 2’s covariates plus sex, BMI, eGFR, presence of steatohepatitis, and histologic stage of fibrosis.

^a Represents the probability of risk of abnormal albuminuria in MAFLD patients per 10-unit change in serum NorCA measurement.

Supplemataryfigure1.tif
Supplementary figure 1. Comparison of clinical parameters in different subgroups of individuals.

Norcholic acid: a novel biomarker of early kidney injury in patients with metabolic dysfunction-associated fatty liver disease

Status:

Version 1

Abstract

Figures

Introduction

Materials and Methods

Study populations

Liver histology

Targeted BA profiling

Clinical and laboratory data

Statistical analysis

Results

Discussion

Abbreviations

Declarations

References

Tables

Supplementary Files

Status:

Version 1