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Evaluation of urinary volatile organic compounds as a novel metabolomic biomarker to assess chronic kidney disease progression

BMC Nephrology volume 25, Article number: 352 (2024) Cite this article

Abstract

Background

There is a need to develop accurate and reliable non-invasive methods to evaluate chronic kidney disease (CKD) status and assess disease progression. Given it is recognized that dysregulation in metabolic pathways occur from early CKD, there is a basis in utilizing metabolomic biomarkers to monitor CKD progression. Volatile Organic Compounds (VOCs), a form of metabolomic biomarker, are gaseous products of metabolic processes in organisms which are typically released with greater abundance in disease conditions when there is dysregulation in metabolism. How urinary VOCs reflect the abnormal metabolic profile of patients with CKD status is unknown. Our study aimed to explore this.

Methods

Individuals aged 18–75 years undergoing kidney biopsy were included. Pre-biopsy urine samples were collected. All biopsy samples had an interstitial fibrosis and tubular atrophy (IFTA) grade scored by standardized assessment. Urine supernatant was extracted from residue and sampled for stir bar sorptive extraction followed by Gas chromatography–mass spectrometry (GC-MS) analysis. Post-processing of GC-MS data separated complex mixtures of VOCs based on their volatility and polarity. Mass-to-charge ratios and fragment patterns were measured for individual VOCs identification and quantification. Linear discriminant analysis (LDA) was performed to assess the ability of urinary VOCs in discriminating between IFTA 0 (‘no or minimal IFTA’ i.e. <10%, IFTA), IFTA 1 (‘mild IFTA’ i.e. 10–25% IFTA) and IFTA ≥ 2 (‘moderate or severe IFTA’ i.e. >25% IFTA). Linear regression analysis adjusting for age, sex, estimated glomerular filtration rate, diabetes mellitus (DM) status, and albuminuria was conducted to determine significantly regulated urinary VOCs amongst the groups.

Results

64 study participants (22 individuals IFTA 0, 15 individuals IFTA 1, 27 individuals IFTA ≥ 2) were included. There were 34 VOCs identified from GC-MS which were statistically associated with correct classification between the IFTA groups, and LDA demonstrated individuals with IFTA 0, IFTA 1 and IFTA ≥ 2 could be significantly separated by their urinary VOCs profile (p < 0.001). Multivariate linear regression analysis reported 4 VOCs significantly upregulated in the IFTA 1 compared to the IFTA 0 group, and 2 VOCs significantly upregulated in the IFTA ≥ 2 compared to the IFTA 1 group (p < 0.05). Significantly upregulated urinary VOCs belonged to one of four functional groups - aldehydes, ketones, hydrocarbons, or alcohols.

Conclusions

We report novel links between urinary VOCs and tubulointerstitial histopathology. Our findings suggest the application of urinary VOCs as a metabolomic biomarker may have a useful clinical role to non-invasively assess CKD status during disease progression.

Peer Review reports

Background

An increase in life expectancy and an increasing prevalence of diabetes mellitus and obesity has amounted to a greater number of individuals affected by chronic kidney disease (CKD), with more than 10% of the global population affected by this condition currently [1]. By 2040, CKD is projected to emerge as the fifth-leading cause of mortality worldwide [2]. Early diagnosis is important to allow for timely intervention which may reduce the excess morbidity and mortality in patients with CKD.

Histopathological evaluation of kidney biopsy tissue remains the gold standard approach which accurately reflects any presence of kidney pathology. Serially performing kidney biopsies to monitor kidney status is not ideal however, as it is invasive and costly. Traditional serum and urine-based tests such as estimated glomerular filtration rate (eGFR) and urinary albumin, whilst considered convenient routinely performed tests to determine kidney function, do have limitations when aiming to accurately assess kidney disease status [3,4,5]. There remains a need to develop reliable non-invasive methods to evaluate kidney disease status. To this end, the emergence of novel proteomic and metabolomic techniques to determine specific biomarkers which inform on the metabolic and kidney disease status of an individual has taken significant strides [6,7,8].

Utilization of volatile organic compounds (VOCs) as non-invasive metabolomic biomarkers to evaluate metabolic and kidney status has received growing interest over recent years [9,10,11,12]. VOCs are gaseous products of metabolic processes in organisms which are conventionally released with greater abundance in disease conditions when there is dysregulation in metabolism [13]. Due to the kidneys’ extraction of soluble wastes from the bloodstream and pre-concentration capabilities, urine has considerable value as a source of VOCs which may reflect the state and function of the kidneys, as well as other organs and pathologies. More than 400 human urinary VOCs – ranging across different organic chemistry functional groups (e.g. alcohols, benzenes, ketones, hydrocarbons, pyrroles, furans, aldehydes, terpenes, sulfur-containing compounds (isocyanates, sulfides), and O- and N-heterocyclic compounds – have been previously identified in normal physiological conditions and in various pathological conditions [14]. Whether the expression levels of VOCs in human urine can play a considerable role in accurately assessing CKD status remains unknown to date. Our study aimed to evaluate whether urinary expression levels of VOCs are significantly associated with the degree of tubulointerstitial fibrosis in the kidney, as reported by kidney biopsy.

Methods

Study participant recruitment and ethical considerations

Adult individuals of either sex aged between 18 and 75 years of age under the care of the Department of Renal Medicine at Royal North Shore Hospital or North Shore Private Hospital, Sydney, Australia referred for kidney biopsy were included in this study. Individuals receiving kidney replacement therapy were excluded from this study. Informed consent was obtained from all study participants. Data collection in this study was carried out in accordance with relevant local guidelines and regulations, and collection of human data was approved by the human ethics committee at Royal North Shore Hospital (Ref: HREC/17/HAWKE/471).

Evaluation of kidney biopsy tissue for interstitial fibrosis and tubular atrophy grading to determine study participant groups

The procurement of kidney biopsy tissue was performed in the Medical Day Procedure Unit at Royal North Shore Hospital. Prior to commencing the procedure, written consent was obtained from study participants to collect the pre-biopsy urine sample for purposes of this study, and to obtain access to the kidney biopsy tissue, which was otherwise performed for clinical indications. Tissue obtained from kidney biopsies were subsequently transferred to the histopathology department and assessed as per standard protocols to determine interstitial fibrosis and tubular atrophy (IFTA) grading. Kidney biopsy samples were processed for light microscopic evaluation via paraffin-embedded sections, supplemented by special and immune histochemical (IHC) stains. Some samples were reserved for immunofluorescence and electron microscopic studies if indicated. Light microscopy assessment included a minimum of two hematoxylin and eosin (H&E), two periodic acid-Schiff (PAS), two Masson’s trichrome (trichrome), and two Jones methenamine silver (silver) stains in complementary fashion. H&E stains provided a general overview of all structures, cytoplasmic and nuclear features, PAS stains highlighted tubular and glomerular basement membranes, trichrome stains accentuated fibrous tissue and fibrin, if present, and silver stains highlighted the glomerular and tubular basement membranes, and also sclerosis. The biopsy assessment was conducted blindly by three accreditated pathologists from the NSW Health Pathology Laboratory, Department of Anatomical Pathology, Northern Sydney Local Health District, Sydney, Australia. Kidney biopsy tissue was assessed as having IFTA 0 (‘no or minimal IFTA’ i.e. <10%, IFTA), IFTA 1 (‘mild IFTA’ i.e. 10–25% IFTA) and IFTA ≥ 2 (‘moderate or severe IFTA’ i.e. >25% IFTA).

Study participants’ demographic alongside clinical and biochemical data were acquired from the Royal North Shore Hospital PowerChart Database, summarized using appropriate descriptive statistics and compared between the three groups. For demographic and clinical variables with symmetric normal distributions, the mean and standard deviation were reported. For variables that were skewed or ordinal, the median and interquartile range were used for statistical purposes. Proportions were also presented for categorical variables. Continuous variables between the groups were compared using the Analysis of Variance (ANOVA) test (if normally distributed) or Kruskal-Wallis test (if the distribution was non-parametric). Categorical variables were compared using the Chi-square test or Freeman-Halton extension of the Fisher’s exact test accounting for sparsely distributed data.

Collection of urine samples and transferring sample for stir bar sorptive extraction

Using urine bottles with capacity of up to 100 ml, spot urine samples were collected from adult individuals who fulfilled the study criteria. Each collected urine sample was placed on ice immediately after collection for transportation to the Renal Research Laboratory, Kolling Institute of Medical Research and were centrifuged for 20 min at 4℃ to isolate urine supernatant from residue. Urine supernatant were stored at -80 °C and defrosted overnight at 4 °C before further sampling. 5 ml of urine was transferred to a 20 ml headspace vial and 3 µl of 15ppm bromobenzene in methanol internal standard (IS) was added along with a conditioned polydimethylsiloxane phase stir bar (Twister, 10 mm x 0.5 mm film thickness; Gerstel, Mülheim an der Ruhr, Germany). The headspace vial was capped and Stir Bar Sorptive Extraction (SBSE) proceeded, with the stir bar spun at 800 rpm for 2 h. The stir bar was then removed, rinsed with double distilled water and patted dry with a lint free tissue before analysis. Two blank samples consisting of 5 ml of double-distilled water, spiked with the same IS were run with each cohort of samples. To determine retention indices, 1 µl of C8-C20 homologous n-alkanes (containing approximately 40 mg/l of each alkane) was injected onto separate, conditioned SBSE. All reagents were sourced from Sigma-Aldrich (Sydney, Australia).

Gas chromatography–mass spectrometry analysis

Thermal desorption (TD) of the stir bars was done using a Gerstel Thermal Desorption Unit (TDU; Gerstel, Mülheim an der Ruhr, Germany). SBSE stir bars were placed into glass thermal desorption liners that were inserted into the TDU for analysis. Upon insertion into the TDU, the samples were purged with ultra-high purity helium (BOC Ltd, North Ryde, NSW, Australia) at 35 °C for 1 min to eliminate air from the sample and inlet. Samples were then heated by the TDU at 12 °C/s to 250 °C with a helium flow of 50 ml/min. TD products were carried by the helium through to a programmed temperature vaporization (PTV) inlet (CIS-4; Gerstel) installed in an Agilent 7890GC (Agilent Technologies Pty Ltd, Mulgrave, Australia), which was used in solvent mode during the TD. The PTV inlet, containing a glass liner filled with Tenax TA, was held at 30 °C during the TD using liquid CO2 (BOC Ltd) as the cryogen. After 5 min of TD, the CIS-4 was heated at 12 °C/s to 250 °C and held at that temperature for 3 min while the TD products were injected into the GC without splitting. TD products were separated on a HP-5ms capillary column (30 m x 0.25 mm, 0.25 μm film thickness; Agilent), which was connected to a mass selective detector (Model 5975 C; Agilent). Ultra-high purity helium was used as carrier gas (flow rate through the HP-5ms column was 2.3 ml/min). The initial oven temperature of the GC was 40 °C, held for 2 min, then heated at a rate of 4 °C/min to 250 °C and held for 5 min. The temperature of the Gas chromatography-mass spectrometry (GC-MS) interface was 280 °C, the MS ion source 230 °C and the quadrupole 150 °C. The detector, in electron impact mode (70 eV), scanned the range of 35–300 m/z. Operation of the GC-MS was controlled by Agilent Chemstation (version E.02.01.117) and the TDU by Maestro (version 1.4.36.16; Gerstel).

Quality Control

Pooled urine quality control (QC) samples were generated for each of the three cohorts (IFTA 0; IFTA 1; IFTA ≥ 2) by mixing an equal volume of urine of each study sample to make a total of 30 ml of urine. This allowed for 6 × 5 ml QC samples for each cohort. These were extracted and analyzed as described for the study samples.

Gas chromatography–mass spectrometry post processing of urinary volatile organic compound data

Post-processing of GC-MS data to separate complex mixtures of VOCs based on their volatility and polarity, and measuring mass-to-charge ratios and fragment patterns for individual VOC identification and quantification was performed. Chromatograms were batch processed by metaMS (version 2.1.1) [15], hosted on the Workflow4Metabolomics Galaxy Server [16]. metaMS outputs a data matrix of aligned mass spectra with their corresponding peak area and a mass spectral pattern file. The maximum peak area of aligned mass spectra of the two water blanks run in every batch of samples were subtracted before further analysis. Mass spectra were considered reproducible if they were present in four out of six QC samples, the presence in the QC samples had a coefficient of variation < 30% and the dispersion ratio (a measure of variance in the QC samples to those of the urine samples) was less than 50% [17]. The mass spectra were identified against the NIST14 mass spectral library in NIST MS Search (NIST MS Search v.2.2; NIST, Gaithersburg, MD) using a match factor threshold of 700, and closeness to available retention index value (using non-isothermal Kovats’ Retention Indices from the definition of van den Dool and Kratz, for a semi-standard non-polar column) [18].

Statistical analysis of post-processed urinary volatile organic compound data

Expression levels of identified VOCs were compared across the 3 study participant groups. To determine the importance of VOCs and their presence to differentiate IFTA status, linear discriminant analysis (LDA), a supervised learning technique, was used to distinguish the groups. The Mahalanobis distance between each group was calculated to validate the LDA model. Leave-one-out (LOO) cross validation was performed to determine the classification correctness rate of the VOCs across the 3 IFTA groups.

A number of statistical methods were used, including descriptive statistics, one-way ANOVA with post hoc Bonferroni correction, and Kruskal–Wallis test according to the data types and distributions. Associations between the expression levels of identified urinary VOCs and IFTA grading were then evaluated by linear regression analyses. Linear relationships between the dependent and independent variables, multivariate normality (via Q-Q plots of the residuals), and multicollinearity were checked before implementing the regression models. For eligible VOCs, two linear regression models were performed – the univariate model and a multivariate model adjusting for age, sex, estimated glomerular filtration rate (eGFR), diabetes mellitus (DM) status (i.e. no DM or DM), and albuminuria (i.e. no albuminuria, microalbuminuria or macroalbuminuria) of study participants. Covariates were selected a priori. In the multivariate model, a secondary analysis evaluating between the expression levels of identified urinary VOCs and covariates was also completed. Coefficient values, standard error (SE) values and 95% confidence intervals (95%CI) were reported for each model. All statistical tests were 2-sided, and p < 0.05 was considered statistically significant. Statistical analyses were performed using Stata 16 (StataCorp MP, College Station, TX, USA).

Results

Characteristics of study participants

The relevant demographic, clinical and biochemistry characteristics of study participants are presented in Table 1. The three study groups included 22 individuals diagnosed with IFTA 0, 15 individuals diagnosed with IFTA 1, and 27 individuals diagnosed with IFTA ≥ 2 upon evaluation of kidney biopsy. There were statistically significant differences in age, level of eGFR and albuminuria among the three groups (both p < 0.05), while sex and the presence of diabetes displayed no statistically significant differences between the three groups. As such, study participants with more severe IFTA were older, had lower eGFR and more severe albuminuria, as expected, compared to the other two groups.

Table 1 Relevant characteristics of the study participants by IFTA status (n = 64)
Full size table

Characteristics of post-processed urinary volatile organic compound data

There were 34 urinary VOCs which were identified following GC-MS post-processing. A summary of the expression levels in relation to each identified urinary VOC across the three IFTA groups is described in Table 2. The expression levels of 29 urinary VOCs have appeared with a ‘zero’ value in one or two IFTA groups, and 5 urinary VOCs had mean values different from a ‘zero’ value for all three IFTA groups. These 5 urinary VOCs are Benzeneacetaldehyde, α-methyl-; Benzaldehyde, 4-propyl-; Phenol, 2,5-bis(1,1-dimethylethyl)-; Hexamethylene diacrylate; and 2(3 H)-Furanone, dihydro-5-(2-octenyl)-, (Z)-. Amongst these 5 urinary VOCs, there were statistically significant differences in the Phenol, 2,5-bis(1,1-dimethylethyl)- levels between the three IFTA groups. Compared to study participants with IFTA 0, those with IFTA 1 and IFTA ≥ 2 had statistically significantly higher Phenol, 2,5-bis(1,1-dimethylethyl)- levels. The Phenol, 2,5-bis(1,1-dimethylethyl)- level among people with IFTA ≥ 2 was significantly higher than those with IFTA 1 (all p < 0.05).

Table 2 Characteristics of post-processed urinary volatile organic compound data by IFTA status (GC-MS peak area; n = 34)
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Evaluating the separation of the groups of urinary volatile organic compounds by linear discriminant analysis and leave-one-out cross validation

LDA results demonstrated three well-separated groups (i.e. individuals with IFTA 0, individuals with IFTA 1, and individuals with IFTA ≥ 2) (Fig. 1). This finding indicates the three IFTA groups are easily separable by their urinary VOC profile. LDA confirmed the pre-identified 34 urinary VOCs were statistically associated with the correct classification of study participants with IFTA 0, study participants with IFTA 1, or study participants with IFTA ≥ 2 (p < 0.001).

Fig. 1
figure 1

Linear discriminant analysis demonstrating individuals with IFTA 0, IFTA 1 and IFTA ≥ 2 could be significantly separated by their urinary VOCs profile. IFTA: Interstitial fibrosis and tubular atrophy

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The Mahalanobis distance values were 176, 24, and 162 respectively between study participants with IFTA 0 and IFTA 1; between study participants with IFTA 0 and IFTA ≥ 2, and between study participants with IFTA 0 and IFTA ≥ 2 (all p < 0.001). Therefore, the current model displayed a very good discrimination of the three groups, particularly between individuals with IFTA 0 and IFTA 1, and between individuals with IFTA 1 and those with IFTA ≥ 2.

According to the LOO cross-validation results (Table 3), 86.4% of study participants (19 of 22 people) with IFTA 0 were classified correctly by their urinary VOCs profile; 86.7% of study participants (13 of 15 people) with IFTA 1 were classified correctly by their urinary VOCs profile; and 74.1% of study participants with IFTA ≥ 2 (20 of 27 people) were classified correctly by their urinary VOCs profile.

Table 3 Correct classification rate based on the LOO cross validation method
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Associations between individual urinary volatile organic compounds with IFTA status and covariates

Results from linear regression analysis evaluating associations between IFTA grading amongst the three study participant groups and expression levels of identified urinary VOCs are presented in Table 4. There were 5 VOCs from the univariate model and 4 VOCs from the multivariate model which were significantly upregulated in the IFTA 1 compared to the IFTA 0 group (p < 0.05), of which 2-heptanone; Benzene, 1,2,4-trimethyl; Benzeneacetaldehyde; and 2(3 H)-furanone, 5-hexyldihydro were significantly upregulated VOCs in both the univariate and multivariate analyses. There were 12 VOCs from the univariate model and 2 VOCs from the multivariate model which were significantly upregulated in the IFTA ≥ 2 compared to the IFTA 1 group (p < 0.05), of which 2-heptanone, 4-methyl and Benzaldehyde, 4-methyl were significantly upregulated VOCs in both the univariate and multivariate analyses. There are 2 VOCs (Benzene (isothiocyantomethyl) and Benzaldehyde, 2,5-dimethyl) in the univariate model which were positively associated with IFTA progression across all stages (p < 0.05), while no VOCs in the multivariate model displayed such statistical association.

Table 4 Associations between identified individual urinary volatile organic compounds and IFTA status & adjusted covariates as determined by linear regression analyses
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On evaluating associations between identified urinary VOCs and adjusted covariates within the multivariate linear regression model (Table 4), there were 2 VOCs (4-Hepatanone; and Benzoic acid, 4-ethoxy, ethyl ester) which were downregulated and 2 VOCs (Benzene (isothiocyantomethyl); and 1-Napthalenecarboxaldeyde) which were upregulated with the male sex. There were 3 VOCs (Benzaldehyde, 4-propyl; 2-heptanone; and Benzaldehyde, 4-methyl) which were positively associated with decline in eGFR levels. There were 3 VOCs ( 2,3-butanedione; Benzeneacetaldehyde; and 2(3 H)-Furanone, 5-hexyldihydro) which were positive associated with DM status. Benzeneacetaldehyde was positively associated with albuminuria status.

Discussion

This study is the first that has evaluated the associations between expression levels of urinary VOCs and kidney tubulointerstitial histopathology. It is particularly significant in a CKD context, given IFTA is the hallmark of CKD. Overall, our results identified 34 VOCs which enabled classification between individuals with no tubulointerstitial disease, mild tubulointerstitial disease and moderate/severe tubulointerstitial disease. Our multivariate regression analysis model evaluating the association between expression levels of urinary VOCs and CKD adjusted for age, sex, eGFR, diabetic and albuminuria status, given these covariates were determined to be significantly associated with VOCs expression and CKD progression from previous studies [19,20,21]. In the multivariate analysis, we identified 4 VOCs significantly upregulated in the mild IFTA compared to the no IFTA group and 2 VOCs significantly upregulated in the moderate/severe IFTA compared to the mild IFTA group.

Metabolic dysregulation that occurs with CKD progression is primarily characterized by oxidative stress and inflammation [22]. Increased production of reactive oxygen species (ROS) results in oxidative damage to lipids, proteins and DNA through their reactive properties [23]. Emerging evidence suggests ROS also function as important secondary messengers in cellular signalling pathways [24, 25]. For one, cytoplasmic ROS induces the activity of AMP-activated protein kinase, which has a crucial role in glucose and lipid metabolism, cell survival, growth, and inflammation, all of which are affected in CKD [24, 26]. Oxidative stress can also activate the transcription factor NF-κB, which induces the expression of cytokines and chemokines to regulate inflammatory responses in the kidneys [27]. The inflammatory cascade in CKD is characterized by the generation and/or accumulation of proinflammatory cytokines (e.g. tumour necrosis factor-α and interleukin-1) from intrinsic and/or extrinsic kidney damage not limited to uraemia, dyslipidaemia, malnutrition, infection and gut microbiota, resulting in increased blood flow, upregulation of chemical mediators and leukocyte infiltration [28]. Prior investigations established physiological links between VOCs and oxidative stress, lipid and amino acid metabolism, and inflammation [29, 30]. Hence, there is a basis in CKD for utilizing metabolomic markers such as VOCs to capture the extent of oxidative stress and inflammation, and translationally inform on the degree of CKD progression.

The majority of the 34 identified urinary VOCs in our study, and all of the significantly upregulated urinary VOCs belonged to one of four key organic chemistry functional groups - aldehydes, ketones, hydrocarbons, and alcohols. Urinary aldehydes can be exogenous or endogenous in origin. They can be produced during lipid peroxidation via the beta-cleavage reaction of lipid alkoxyl radicals [31]. It is well-known that there are lipid metabolic disturbances in patients with kidney disease [32]. Therefore, abnormal urinary aldehyde levels in these conditions may be explained by the lipid peroxidation damage that occurs. Ketones typically originate from exogenous sources and from the decarboxylation of oxo-acids [33, 34]. In healthy humans, ketones are mainly formed in hepatocytes from acetoacetate during the decarboxylation of excess acetyl-CoA [34]. Human breath, blood and urine all contain ketones in the form of acetone [34]. Heptanone in urine is supposedly the product of beta-oxidation of 2-ethylhexanoic acid, a metabolic product of the plasticizer di-(2-ethylhexyl)-phthalate [10]. Impairment of kidney function may reduce the filtration of ketones, leading to decreased concentration of ketones detected in the urine of kidney disease patients [35]. There is emerging evidence nevertheless, which observed increased urinary ketone (2-pentanone) levels in kidney disease aetiologies such as idiopathic membranous nephropathy (IMN) [36]. Further study is needed to delineate the intricacies that are linked between kidney pathology and ketone physiology. Hydrocarbons are thought to be the by-product of cholesterol biosynthesis [37, 38]. Change in levels of urinary VOCs stemming from the hydrocarbon group (i.e. benzaldehydes and carbonyl groups) in kidney disease may indicate disorders in tryptophan metabolism and alterations in pyruvate, glycine, serine, and threonine metabolisms, respectively [39]. Alcohols originate from aliphatic alcohol in human tissue fluids, and various processes formed from acetaldehyde metabolism or exogenous intake [40]. Its role in oxidative stress and inflammation pathways in kidney disease is well-established [41].

Although there were no previous studies which evaluated associations between expression of urinary VOCs and CKD as defined by tubulointerstitial pathology, urinary VOCs have been previously studied for their potential as biomarkers in multiple glomerular diseases such as mesangial proliferative glomerulonephritis, Immunoglobulin A nephropathy, IMN and minimal change disease [36, 42,43,44]. In the preliminary studies that were conducted, a different panel of significantly upregulated (or downregulated) VOCs with progressing disease severity were identified, in comparison to the identified VOCs from our study [36, 42,43,44]. Wang et al. [42] evaluated urine samples in 15 mesangial proliferative glomerulonephritis (MPGN) patients, 21 Immunoglobulin A nephropathy (IgAN) patients and 15 healthy controls. Five VOCs (Carbamic acid, monoammonium salt; Carbon disulfide; Silanediol, dimethyl-; 2 H-1,4-Benzodiazepin-2-one, 7-chloro-1,3-dihydro-5-phenyl-1-(trimethylsilyl)- and Butylated Hydroxytoluene) had significantly elevated expression levels in the MPGN group compared with the control group, whilst 3 VOCs (Carbamic acid, monoammonium salt; Carbon disulfide and 2 H-1,4-Benzodiazepin-2-one,7-chloro-1,3-dihydro-5-phenyl-1-(trimethylsilyl)-) were found at increased expression levels in the IgAN group compared to normal controls. In addition, 5 VOCs (Tartronic acid; Carbamic acid; Sulfide, allyl methyl; Hydrogen azide and Benzeneethanamine, N-[(pentafluorophenyl)methylene]-.beta,4-bis[(trimethylsilyl)oxy]-) were significantly increased in IgAN patients compared with MPGN patients, suggesting these urinary VOCs may be specific biomarkers which differentiate between the two conditions. 4-heptanone, 2-pentanone and pyrrole were identified at decreased urinary levels in IgAN and MPGN patients compared to the control groups. Wang et al. [43] also aimed to detect urinary VOCs which could distinguish between patients with idiopathic membranous nephropathy (IMN) and normal controls. The investigators assessed the urine collected from 63 IMN patients and 15 normal controls, in which 6 VOCs (Carbamic acid, monoammonium salt; 2-pentanone; 2,4-dimethyl-pentanal; Hydrogen azide; Thiourea and 4-heptanone) displayed significantly higher expression levels in IMN patients compared to normal controls. The same investigator group [36] also collected urine samples from 38 minimal change disease (MCD) patients and 15 healthy controls. They identified 6 VOCs (Trans-2,2-dimethyl-4-decene; Pyrrole; Carbamic acid, monoammonium salt; 1-butyne, 3,3-dimethyl-; Diisopropylamine and 4-heptanone) that are present at reduced urinary expression levels in MCD patients. Further work is needed to validate the use of these urinary VOCs as biomarkers to predict MCD status and disease progression. A more recently conducted study by Ligor et al. [44], which separated and identified urinary VOCs via gas chromatography time-of-flight mass spectrometry, aimed to determine urinary VOC profiles between 27 patients diagnosed with glomerular diseases and 20 healthy controls. Amongst those diagnosed with glomerular disease, there were 4 VOCs (Methyl hexadecanoate; 9-hexadecen-1-ol; 6,10-dimethyl-5,9-undecadien-2-one and 2-pentanone) found to be at elevated urinary expression levels.

Otherwise, links between exhaled air VOCs from human breath with CKD were recently investigated. Romani et al. [45] examined the utility of selected ion flow tube-mass spectrometry (SIFT-MS) to measure breath VOCs in CKD patients and healthy subjects, and evaluated the possible correlation between breath VOC expression levels with the presence of CKD and CKD progression as determined by the Kidney Disease Improving Global Outcomes guideline diagnostic criteria [46]. The investigators enrolled 68 Stage I-IV CKD patients (all were receiving conservative therapy) and 54 healthy subjects. Analysis of the VOCs from exhaled air of the enrolled subjects was performed by SIFT-MS. They observed increased breath VOCs expression levels for numerous VOCs in CKD compared to healthy subjects and with progressing CKD severity, albeit these were different VOCs from the ones identified in our study. The most relevant results by receiver operating characteristic curves were observed for trimethylamine (TMA), acetone, ammonia, and dimethyl sulfide. Romani et al. [45] noted that an individual’s breath TMA concentration superior to 26 parts per billion by volume characterizes a 6.11 times greater risk of having CKD, compared to those with lower levels of breath TMA concentration. Moreover, they detected an increased concentration of acetone and ammonia in CKD patients compared to healthy subjects. SIFT-MS is considered a superior mass spectrometry option for measuring nitrogen- and sulfur-containing VOCs, which are more challenging to measure when using other mass spectrometry modalities. Future studies evaluating urinary VOCs within a CKD context using SIFT-MS is anticipated.

Whilst our study findings provide novel evidence into the associations between urinary VOCs and CKD, there remain important gaps in our knowledge base which require evaluation. For one, the exact mechanisms for the generation of most urinary VOCs is unclear at a molecular level, and they could be perturbed in many physiological and pathological states outside of tubulointerstitial disease alone, Although we adjusted for several potential confounding factors in our analyses, there may be other factors challenging to control, not limited to dietary habits, physical stress and environmental exposure to toxins, which could affect the accuracy of urinary VOCs profiling [47]. Hence, further studies with larger clinical cohorts are required to validate our data, adjusting for other potential covariates that may be relevant to kidney disease. Another issue relates to the vast quantity of urinary VOCs that were found to be potentially useful biomarkers of CKD across different IFTA stages, also considering there may be other clinically significant urinary VOCs that remain unidentified currently. Further evidence to specify and narrow towards the key urinary VOCs that could be confidently applied in clinical practice to predict CKD progression is required. While most urinary VOCs and other metabolomic studies reported to date used GC-MS as the analytical method, complementary analysis could be performed by reversed-phase liquid chromatography-mass spectrometry (RP-LC-MS), hydrophilic interaction liquid chromatography-mass spectrometry (HILIC-LC-MS), and capillary electrophoresis-mass spectrometry (CE-MS) methods as well [48]. This would broaden the range of potential disease markers that could be investigated. Alternative types of mass spectrometry analysis approaches could also be considered to improve sensitivity of metabolite detection but this must be balanced against their increasing price, operating costs and complicated operation in a clinical setting [49]. Hence, improving biosensing software platforms to detect clinically useful urinary VOCs is an attractive proposition where ongoing technological developments are foreseeable. For one, the feasibility of metal oxide biosensor platforms to determine urinary VOCs with significant predictive capability for detecting genitourinary cancers (i.e. renal cell carcinoma, transitional cell carcinoma and prostate cancer) has been recently demonstrated to good levels of accuracy. Future studies could perhaps consider extending its use for this purpose in CKD [50]. Furthermore, a mass spectrometry-based electronic nose (MS-EN) approach possesses tremendous potential but has been seldomly applied for urinary VOCs and so far, has not been explored within in CKD yet though it has been trialled within the context of kidney cancer [51, 52]. This is also a potential avenue of further research to be considered.

Conclusions

Our study demonstrated that the urinary expression levels of various aldehydes, ketones, hydrocarbons and alcohols are significantly associated with tubulointerstitial histopathology, which suggests urinary VOCs may indeed have a clinically useful role in CKD as a metabolomic biomarker. Additional studies are required to validate our findings in a larger cohort and examine the potential of utilizing urinary VOCs to reliably assess CKD progression in clinical practice.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

ANOVA:

Analysis of Variance

CKD:

Chronic Kidney Disease

DM:

Diabetes Mellitus

eGFR:

Estimated Glomerular Filtration Rate

GC:

Gas Chromatography

GC-MS:

Gas Chromatography–Mass Spectrometry

IFTA:

Interstitial Fibrosis and Tubular Atrophy

IMN:

Idiopathic Membranous Nephropathy

IS:

Internal Standard

LDA:

Linear Discriminant Analysis

LOO:

Leave-one-out

MS-EN:

Mass Spectrometry-based Electronic Nose

PTV:

Programmed Temperature Vaporization

QC:

Quality Control

ROS:

Reactive Oxygen Species

SBSE:

Stir Bar Sorptive Extraction

SIFT-MS:

Selected Ion Flow Tube-Mass Spectrometry

VOCs:

Volatile Organic Compounds

TD:

Thermal Desorption

TDU:

Thermal Desorption Unit

TMA:

Trimethylamine

95%CI:

95% Confidence Intervals

References

  1. Kalantar-Zadeh K, Jafar TH, Nitsch D, Neuen BL, Perkovic V. Chronic kidney disease. Lancet. 2021;398:786–802.

    Article  PubMed  Google Scholar 

  2. Foreman KJ, Marquez N, Dolgert A, Fukutaki K, Fullman N, McGaughey M, et al. Forecasting life expectancy, years of life lost, and all-cause and cause-specific mortality for 250 causes of death: reference and alternative scenarios for 2016–40 for 195 countries and territories. Lancet. 2018;392:2052–90.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Inker LA, Coresh J, Levey AS, Tonelli M, Muntner P, Estimated GFR. Albuminuria, and complications of chronic kidney disease. J Am Soc Nephrol. 2011;22:2322–31.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Alaini A, Malhotra D, Rondon-Berrios H, Argyropoulos CP, Khitan ZJ, Raj DS, et al. Establishing the presence or absence of chronic kidney disease: uses and limitations of formulas estimating the glomerular filtration rate. World J Methodol. 2017;7:73–92.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Houlihan CA, Tsalamandris C, Akdeniz A, Jerums G. Albumin to creatinine ratio: a screening test with limitations. Am J Kidney Dis. 2002;39:1183–9.

    Article  PubMed  Google Scholar 

  6. Wu HHL, Goldys EM, Pollock CA, Saad S. Exfoliated kidney cells from urine for early diagnosis and prognostication of CKD: the way of the future? Int J Mol Sci. 2022;23:7610.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Bidin MZ, Shah AM, Stanslas J, Seong CL. Blood and urine biomarkers in chronic kidney disease: an update. Clin Chim Acta. 2019;495:239–50.

    Article  PubMed  Google Scholar 

  8. Rende U, Guller A, Goldys EM, Pollock C, Saad S. Diagnostic and prognostic biomarkers for tubulointerstitial fibrosis. J Physiol. 2023;601:2801–26.

    Article  PubMed  Google Scholar 

  9. Meinardi S, Jin KB, Barletta B, Blake DR, Vaziri ND. Exhaled breath and fecal volatile organic biomarkers of chronic kidney disease. Biochim Biophys Acta. 2013;1830:2531–7.

    Article  PubMed  Google Scholar 

  10. Mochalski P, King J, Haas M, Unterkofler K, Amann A, Mayer G. Blood and breath profiles of volatile organic compounds in patients with end-stage renal disease. BMC Nephrol. 2014;15:43.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Schönermarck U, Dengler C, Gmeinwieser A, Praun S, Schelling G, Fischereder M, et al. Exhaled breath volatile organic and inorganic compound composition in end-stage renal disease. Clin Nephrol. 2016;86:132–40.

    Article  PubMed  Google Scholar 

  12. Hüppe T, Klasen R, Maurer F, Meiser A, Groesdonk HV, Sessler DI, et al. Volatile organic compounds in patients with acute kidney injury and changes during dialysis. Crit Care Med. 2019;47:239–46.

    Article  PubMed  Google Scholar 

  13. Djago F, Lange J, Poinot P. Induced volatolomics of pathologies. Nat Rev Chem. 2021;5:183–96.

    Article  PubMed  Google Scholar 

  14. Drabińska N, Flynn C, Ratcliffe N, Belluomo I, Myridakis A, Gould O, et al. A literature survey of all volatiles from healthy human breath and bodily fluids: the human volatilome. J Breath Res. 2021;15:034001.

    Article  Google Scholar 

  15. Wehrens R, Weingart G, Mattivi F, metaMS. An open-source pipeline for GC–MS-based untargeted metabolomics. J Chromatogr B Biomed Appl. 2014;966:109–16.

    Article  Google Scholar 

  16. Giacomoni F, Le Corguille G, Monsoor M, Landi M, Pericard P, Pétéra M, et al. Workflow4Metabolomics: a collaborative research infrastructure for computational metabolomics. Bioinformatics. 2015;31:1493–5.

    Article  PubMed  Google Scholar 

  17. Broadhurst D, Goodacre R, Reinke SN, Kuligowski J, Wilson ID, Lewis MR, et al. Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies. Metabolomics. 2018;14:72.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Van Den Dool HA, Kratz PD. A generalization of the retention index system including linear temperature programmed gas-liquid partition chromatography. J Chromatogr. 1963.

  19. Lee J, Oh S, Kang H, Kim S, Lee G, Li L, et al. Environment-wide association study of CKD. Clin J Am Soc Nephrol. 2020;15:766–75.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Seong SH, Kim HS, Lee YM, Kim JS, Park S, Oh J. Exploration of potential breath biomarkers of chronic kidney disease through Thermal desorption–gas Chromatography/Mass Spectrometry. Metabolites. 2023;13:837.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Zhou Q, Wang Q, Chen B, Han Y, Cheng L, Shen Y, et al. Factors influencing breath analysis results in patients with diabetes mellitus. J Breath Res. 2019;13:046012.

    Article  PubMed  Google Scholar 

  22. Ruiz S, Pergola PE, Zager RA, Vaziri ND. Targeting the transcription factor Nrf2 to ameliorate oxidative stress and inflammation in chronic kidney disease. Kidney Int. 2013;83:1029–41.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Kishi S, Nagasu H, Kidokoro K, Kashihara N. Oxidative stress and the role of redox signalling in chronic kidney disease. Nat Rev Nephrol. 2024;20:101–19.

    Article  PubMed  Google Scholar 

  24. Forrester SJ, Kikuchi DS, Hernandes MS, Xu Q, Griendling KK. Reactive oxygen species in metabolic and inflammatory signaling. Circ Res. 2018;122:877–902.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Martínez-Reyes I, Diebold LP, Kong H, Schieber M, Huang H, Hensley CT, et al. TCA cycle and mitochondrial membrane potential are necessary for diverse biological functions. Mol Cell. 2016;61:199–209.

    Article  PubMed  Google Scholar 

  26. Qu L, Jiao B. The interplay between immune and metabolic pathways in kidney disease. Cells. 2023;12:1584.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Oliveira-Marques V, Marinho HS, Cyrne L, Antunes F. Role of hydrogen peroxide in NF-κB activation: from inducer to modulator. Antioxid Redox Signal. 2009;11:2223–43.

    Article  PubMed  Google Scholar 

  28. Kadatane SP, Satariano M, Massey M, Mongan K, Raina R. The role of inflammation in CKD. Cells. 2023;12:1581.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Pal VK, Li AJ, Zhu H, Kannan K. Diurnal variability in urinary volatile organic compound metabolites and its association with oxidative stress biomarkers. Sci Total Env. 2022;818:151704.

    Article  Google Scholar 

  30. Ogbodo JO, Arazu AV, Iguh TC, Onwodi NJ, Ezike TC. Volatile organic compounds: a proinflammatory activator in autoimmune diseases. Front Immunol. 2022;13:928379.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Moradi H, Vaziri ND. Molecular mechanisms of disorders of lipid metabolism in chronic kidney disease. Front Biosci (Landmark). 2018;23:146–61.

    Article  Google Scholar 

  32. Mills GA, Walker V. Headspace solid-phase microextraction profiling of volatile compounds in urine: application to metabolic investigations. J Chromatogr B Biomed Sci Appl. 2001;753:259–68.

    Article  PubMed  Google Scholar 

  33. Cotter DG, Schugar RC, Crawford PA. Ketone body metabolism and cardiovascular disease. Am J Physiol Heart Circ. 2013;304:H1060–76.

    Article  Google Scholar 

  34. Wahl HG, Hong Q, Hildenbrand S, Risler T, Luft D, Liebich H. 4-Heptanone is a metabolite of the plasticizer di (2-ethylhexyl) phthalate (DEHP) in haemodialysis patients. Nephrol Dial Transpl. 2004;19:2576–83.

    Article  Google Scholar 

  35. Hyspler R, Crhova S, Zadak Z, Gasparic J. Breath isoprene as a measure of the depression in cholesterol synthesis in intensive care patients. Atherosclerosis Suppl. 2001;2:102.

    Article  Google Scholar 

  36. Liu D, Zhao N, Wang M, Pi X, Feng Y, Wang Y, et al. Urine volatile organic compounds as biomarkers for minimal change type nephrotic syndrome. Biochem Biophys Res Commun. 2018;496:58–63.

    Article  PubMed  Google Scholar 

  37. Stein RA, Mead JF. Small hydrocarbons formed by the peroxidation of squalene. Chem Phys Lipids. 1988;46:117–20.

    Article  PubMed  Google Scholar 

  38. Janfaza S, Khorsand B, Nikkhah M, Zahiri J. Digging deeper into volatile organic compounds associated with cancer. Biol Methods Protoc. 2019;4:bpz014.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Caballería J. Current concepts in alcohol metabolism. Ann Hepatol. 2003;2:60–8.

    Article  PubMed  Google Scholar 

  40. Ozbek E. Induction of oxidative stress in kidney. Int J Nephrol. 2012;2012:465897.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Thorn RM, Greenman J. Microbial volatile compounds in health and disease conditions. J Breath Res. 2012;6:024001.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Wang C, Feng Y, Wang M, Pi X, Tong H, Wang Y, et al. Volatile organic metabolites identify patients with mesangial proliferative glomerulonephritis, IgA nephropathy and normal controls. Sci Rep. 2015;5:14744.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Wang M, Xie R, Jia X, Liu R. Urinary volatile organic compounds as potential biomarkers in idiopathic membranous nephropathy. Med Princ Pract. 2017;26:375–80.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Ligor T, Zawadzka J, Strączyński G, González Paredes RM, Wenda-Piesik A, Ratiu IA, et al. Searching for potential markers of glomerulopathy in urine by HS-SPME-GC×GC TOFMS. Molecules. 2021;26:1817.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Romani A, Marrone G, Celotto R, Campo M, Vita C, Chiaramonte C, et al. Utility of SIFT-MS to evaluate volatile organic compounds in nephropathic patients’ breath. Sci Rep. 2022;12:10413.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Kopple JD. National kidney foundation K/DOQI clinical practice guidelines for nutrition in chronic renal failure. Am J Kidney Dis. 2001;37:S66–70.

    Article  PubMed  Google Scholar 

  47. Ramos ÁG, Antón AP, del Nogal Sánchez M, Pavón JL, Cordero BM. Urinary volatile fingerprint based on mass spectrometry for the discrimination of patients with lung cancer and controls. Talanta. 2017;174:158–64.

    Article  PubMed  Google Scholar 

  48. Danilova EY, Maslova AO, Stavrianidi AN, Nosyrev AE, Maltseva LD, Morozova OL. CKD urine metabolomics: modern concepts and approaches. Pathophysiology. 2023;30:443–66.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Li C, Chu S, Tan S, Yin X, Jiang Y, Dai X, et al. Towards higher sensitivity of mass spectrometry: a perspective from the mass analyzers. Front Chem. 2021;9:813359.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Suthat Na Ayutaya V, Tantisatirapoon C, Aekgawong S, Anakkamatee W, Danjittrong T, Kreepala C. Urinary cancer detection by the target urine volatile organic compounds biosensor platform. Sci Rep. 2024;14:3551.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Jin X, Liu C, Xu T, Su L, Zhang X. Artificial intelligence biosensors: challenges and prospects. Biosens Bioelectron. 2020;165:112412.

    Article  PubMed  Google Scholar 

  52. Costantini M, Filianoti A, Anceschi U, Bove AM, Brassetti A, Ferriero M, et al. Human urinary volatilome analysis in renal cancer by electronic nose. Biosensors. 2023;13:427.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors would like to acknowledge Prof. Anthony Gill, A/Prof. Angela Chou, Dr. Kirsty McIlroy and Dr. Jason Chen from the New South Wales Health Pathology Department for their contributions in providing the Interstitial Fibrosis and Tubular Atrophy scoring in this study.

Funding

The study and manuscript is not supported by an external funding source.

Author information

Authors and Affiliations

  1. Renal Research Laboratory, Kolling Institute of Medical Research, Royal North Shore Hospital & The University of Sydney, Sydney, Australia

    Henry H. L. Wu, Long The Nguyen, Carol A. Pollock & Sonia Saad

  2. Centre for Carbon, Water and Food, School of Life and Environmental Sciences, The University of Sydney, Sydney, Australia

    Malcolm Possell

  3. School of Public Health, Faculty of Health, University of Technology Sydney, Sydney, Australia

    Wenbo Peng

  4. Department of Renal Medicine, Royal North Shore Hospital, Northern Sydney Local Health District, Sydney, Australia

    Carol A. Pollock

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  1. Henry H. L. Wu
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  3. Long The Nguyen
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  4. Wenbo Peng
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  5. Carol A. Pollock
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  6. Sonia Saad
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Contributions

HHLW conceptualized the study, collected the urine samples, obtained the patient demographic and clinical data, prepared the original manuscript version and revised the manuscript; MP processed the urine samples including Stir Bar Sorptive Extraction and Gas Chromatography-Mass Spectrometry to obtain the raw data for further analysis, and was involved in revising the manuscript; LTN was involved in urine sample collection and obtained the patient demographic and clinical data; WP was involved in formal statistical analysis of the post-processed data obtained from Gas Chromatography-Mass Spectrometry and prepared the data presented in the results section of this manuscript; CAP conceptualized the study and revised the manuscript; SS conceptualized the study, provided the resources for the study, revised the manuscript, and was in charge of the project administration and supervision. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Henry H. L. Wu.

Ethics declarations

Ethics approval and consent to participate

Data collection in this study was carried out in accordance with relevant local guidelines and regulations, and collection of human data was approved by the human ethics committee at Royal North Shore Hospital (Ref: HREC/17/HAWKE/471). Informed consent was obtained from all study participants.

Consent for publication

No individual patient data has been disclosed in this manuscript. Individual consent obtained from all study participants in this study included consent for publication of study results.

Competing interests

HHLW is a member of the editorial board in BMC Nephrology. The other authors have no competing interests to declare in relation to the contents of this manuscript.

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Wu, H.H.L., Possell, M., Nguyen, L.T. et al. Evaluation of urinary volatile organic compounds as a novel metabolomic biomarker to assess chronic kidney disease progression. BMC Nephrol 25, 352 (2024). https://doi.org/10.1186/s12882-024-03819-0

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BMC Nephrology

ISSN: 1471-2369

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