Utilizing serum and urine metabolomics for diagnosing acute intra- abdominal infection in the early stage

doi:10.21203/rs.3.rs-4968285/v1

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Utilizing serum and urine metabolomics for diagnosing acute intra- abdominal infection in the early stage

https://doi.org/10.21203/rs.3.rs-4968285/v1

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Background:

Acute intra-abdominal infection (IAI), a normal disease of general surgery, is an important reason for patients’ death. However, owing to the defects of medical laboratory science and imaging tests, we can’t identify the patients with IAI timely. So, the metabolomics is applied to explore early biomarkers for IAI.

Methods:

A total of 30 IAI patients and 20 healthy volunteers are analyzed by liquid chromatography-mass spectrometry (LC-MS). First, we utilize total ion chromatography (TIC) and principal component analysis (PCA) to judge whether differential metabolites exist. Then student t test, partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) are performed to identify differential metabolites. We further use fisher discriminant analysis and hierarchical clustering analysis to observe the distinguishing effect of chosen metabolites. last, we select most eligible biomarkers based on receiver operating characteristic curve (ROC).

Results:

Finally, we identify 6 and 2 metabolites as biomarkers for IAI in serum and urine respectively.

Conclusions:

There are differential metabolites between healthy people and IAI patients. We could identify IAI patients more conveniently by detecting these biomarkers regularly.

Liquid chromatography–mass spectrometry

Acute intra-abdominal infection

Metabolomics

Biomarkers

IAI, an infectious disease, is often caused by bacteria invading peritoneum, retroperitoneum or visceromegaly. IAI is often secondary to some diseases such as acute appendicitis [1] (AA), acute gastrointestinal perforation (AGP), acute cholecystitis and so on. The typical symptoms of IAI include rapid-onset abdominal pain and local or systemic inflammation [2]. If it can’t be controlled effectively, IAI will lead to the inflammation storm and even sepsis. There are 50 million septic cases and 11 million deaths related with sepsis in the word, which brings a huge medical burden, and IAI is the second pathogen. [3] Currently, effective treatment is defined as the combination of early recognition, controlling inflammation source, utilizing antibiotics and fluid resuscitation for serious patients, which emphasizes the importance of early diagnosis.

Although C-reactive protein (CRP) and procalcitonin (PCT) is applied broadly to identify infection in the clinic, the sensitivity and specificity for CRP diagnosing AA is only 67%-97.8% and 31.9%-80% respectively [4], meanwhile PCT tends to be insensitive to local inflammation [5]. Ultrasonic (UC) is recommended as the first choice to detect IAI. UC possesses high sensitivity and specificity, but the outcomes are easily influenced by free gas, obesity and the sonographer experience.[4] Compare with UC, computed tomography (CT) performs more perfect. However, CT increases the expo-sure for radiation and needs more expensive price [6], which limits the broad application of item in some degree. So, exploring specific biomarkers for identifying IAI early is paramount.

Metabolomics, a new branch of science after genome, has enormous potential to dis-cover the complex pathogenesis by measuring the low-molecular-weight compounds in samples [7]. We choose metabolomics as a main method for this study because it has played an essential role in diagnosing disease like gastric cancer, colorectal cancer [8], breast cancer [9] and so on. By shedding light on biomarkers from serum and urine, we are committed to identifying IAI early, with the final hope to improve the patients’ prognosis.

2.1 Patients

Our study got an approval from Ethics Committee of the first hospital of Jilin univer-sity and all research methods satisfied ethical criteria (ethical number: 24K181-001). Only after patients themselves or legal guardians signed informed consent forms did we collect serum and urine from patients. Samples were collected from pathologically supported IAI patients who accessed to treatment at the Department of Gastrointesti-nal surgery, The First Hospital of Jilin University during July and August, 2024. We collected serum and urine samples from 30 IAI patients and 20 healthy volunteers. Inclusion criteria for 30 patients: (1) patients with AA or AGP confirmed by postoperative pathology; (2) no history of tumor, hormone treatment, radiotherapy or chemotherapy; (3) patients with good bone marrow function, liver function, renal function and heart function; (4) absence of evident acute lung inflammation. Inclusion criteria for 20 volunteers: peo-ple with normal results in blood routine examination, urine routine examination, liver function, renal function and electrocardiogram. Exclusion criteria for 20 healthy vol-unteers: (1) people with congenital disease; (2) people with metabolic diseases (e.g., diabetes) or mental diseases; (3) pregnant or lactating women; (4) Individuals with drunkenness, drug abuse containing proton pump inhibit, hormone or nonsteroidal anti-inflammatory drugs; (5) people with acute or chronic inflammation; (6) people with major stress response(e.g., burn or psychic trauma) in the past two weeks; (7) people with blood disease including leukaemia or anaemia.

2.2 Sample collection

The serum and urine samples of patients are collected at one hour before emergency surgery, while the ones of volunteers are obtained from fasting subjects in the morning. We centrifuged all samples at 3500rpm for 10min at 4℃, and the upper serum was ex-tracted and stored at -80℃ for subsequent analysis. For analysis, 30ul plasma from every tube was mixed to use as quality control (QC) samples. Then we drew 100ul plasma into 1.5ml EP tube and mixed it with 300ul methanol: acetonitrile (1:1, V/V). The mixture was vortexed for 1min and kept at refrigerator (4℃) for 20 min. After-ward, the mixture was centrifuged at 13500rpm for 10min at 4℃ and we drew 100ul plasma filtered by 0.22um filtration membrane for LC-MS. For urine samples, we just retained midstream specimen of urine and the rest of procedure is same as serum sam-ples.

2.3 LC-MS

Triple TOF 5600TM system (AB SCIEX, America) and the EXION UHPLC system (Shimadzu, Japan) is utilized for LC-MS. We use Vortex 3 from Germany (IKA) to mix and the H165R centrifuge (4℃) from Xiangyi Centrifuge Instrument Co., Ltd. (China) to centrifuge. The solvents include acetonitrile (HPLC grade, Sigma-Aldrich, America), formic acid (HPLC grade, Sigma-Aldrich, America), methanol (HPLC grade, Merk, Germany) and deionized water (Watson, China). APCI Positive Calibration Solution (AB SCIEX) and APCI Negative Calibration Solution (AB SCIEX) are applied for ion calibration. The EXION UHPLC System serves as liquid chromatography system. We set column, [ACQUITY UPLC HSS T3 (2.1×100 mm, 1.8µm, waters, USA)], temperature at 35℃. The mobile phase consists of solvent A (0.1% formic acid in water) and solvent B (pure acetonitrile). Before analysis, we carry out a 5-minute equilibration with the initial mobile phase to make sure the stableness of system. At first, we put ten QCs into analysis to make experimental results more accurate. We maintain the flow rate at 0.35 ml/min and injection volume at 0.5ul. The gradient time is shown as below: 0.5 min (A phase 98%+B phase 2%); 1.5 min (A phase 80%+ B phase 20%); 4 min (A phase 35%+ B phase 65%); 11 min (A phase 5%+ B phase 95%); 15 min (A phase 5%+ B phase 95%); 15.1 min (A phase 98%+ B phase 2%); at 20 min (A phase 98%+ B phase 2%).

2.4 Mass spectrometry

Under both negative and positive ion models, we all utilize Triple TOF 5600TM system (AB SCIEX) with an electrospray ionization source. The first-level mass spectrometry parameters are shown: in positive model, ion spray voltage (V) is set to 5500; temperature (℃) to 550; gas 1 (psi) to 55; gas 2 (psi) to 55; curtain gas (psi) to 30; declustering potential (DP) to 100; collision energy (CE) to 10, while in negative model, ion spray voltage (V) is changed to -5500; DP to -100; CE to -10 and the remainders is same.

The second-level mass spectrometry parameters are shown: in positive model, ion spray voltage (V) is set to 5500; temperature (℃) to 550; gas 1 (psi) and gas 2 (psi) to 55; curtain gas (psi) to 30; DP to 100; CE to 35; collision energy spread (CES) to 15; ion re-lease delay (IRD) to 67; ion release width (IRW) to 25, while in negative model, ion spray voltage (V) is changed to -4500; DP to -100; CE to -35 and the remainder is same.

2.5 Statistical analysis

First, the Analyst TF1.7.1 software (AB SCIEX) is applied to transform raw data obtained from LC-MS into file at the form of wiff and we use PeakView2.2 (AB SCIEX) to get TIC. After data undergoing process of standardization, normalization, noise filtering and peak alignment, Markerview (AB SCIEX) could provide PCA for us. Next, we perform t test (p < 0.05) and PLS-DA [variable importance in projection (VIP) > 1] at MetaboAnalyst 5.0. VIP indicates the correlation between metabolite and sample classification. Then, we use BRB-array software to perform SVM to select those metabolites whose weight function for sample classification accesses to 100%. HMDB, a public website for exploring small molecule metabolites, is applied to inquire structures and names according to metabolites’ mass-to-charge ratio and we exclude ones without specific information. We also utilize R studio 4.3.1 to protract hierarchical clustering analysis. What’s more, SPSS 27.0 is used to finish ROC and we choose metabolites with high area under curve (AUC) as biomarkers. Finally, we plot scatterplots based on the mean level of metabolites to show metabolites’ change trend within two groups.

3.1 The feature of subjects

The age for 30 IAI patients is 58.87 ± 24.45 (mean ± standard deviation), while age for 20 healthy volunteers is 53.05 ± 15.65. IAI group consists of 10 women and 20 men, while healthy group is composed of 10 women and 10 men. The causes of IAI cases include 4 stomach ulcer, 2 duodenum ulcer, 2 small intestinal trauma, 2 colon diverticular perforation, 6 acute pure appendix, 5 acute suppurative appendix, 4 gangrene perforation appendix, 3 acute cholecystitis and 2 acute pancreatitis.

3.2 Serum sample

The TIC of IAI patients (Fig. 1A, C) and healthy volunteers (Fig. 1B, D) is analyzed. TIC pictures show the difference in peak intensities of many metabolites be-tween two groups in positive model and negative model, suggesting the existence of differential metabolites. We perform unsupervised PCA for serum samples from patients, volunteers and quality controls in positive (Fig. 1E) and negative model (Fig. 1F). In PCA, every plot represents a sample. The larger difference of the metabolites in samples is, the greater distance between plots is, meanwhile the smaller difference, the closer distance. In PCA, the distance among samples within each group is close, suggesting the difference of each group is small and the distance among three groups is great, indicating the difference between groups is large. In addition, the aggregation of QC is good, proving the credibility of experimental data. Comparing IAI patients with volunteers by t test (p < 0.05), we find great difference for 431 metabolites in positive mode and 216 ones in negative mode (supplementary sheet1). We employ PLS-DA for further sifting and identify 50 metabolites (VIP > 1) in positive and negative models. (Fig. 2, B) (supplementary sheet2). The PLS-DA plot reveals that two groups could be separated clearly. (Fig. 2, A)

According to 16 metabolites, hierarchical clustering analysis is used to access the similarity of differential metabolites (Dendritic structure on picture’s left) and sample (Dendritic structure above picture). (Fig. 2, C) The color intensity represents the content of metabolites in samples and red means the most content. This picture shows that 16 metabolites have enough ability to divide 50 samples into infect group and health group.

SVM is performed to filter further and we identify 20 metabolites with 100% weight in positive (11) and negative (9) models. Based on the HMDB (http://www.hmdb.ca), we identify 16 metabolites meeting our requirement, including Myristic acid, PGP(18:0/18:3(6Z,9Z,12Z)), TG(14:1(9Z)/16:1(9Z)/20:3n6), Decanoylcholine, MG(i-15:0/0:0/0:0), TG(14:0/i-16:0/i-12:0), DG(22:5n6/0:0/22:5n6), PS(18:0/20:0), PE(16:0/20:2(11Z:14Z)), FAHFA(18:1(9Z)/6-O-18:0), 8,9 Epoxyeicosatrienoic acid, 5a-Dihydrotestosterone sulfate, LysoPC(0:0/16:0), LysoPC(0:0/18:2(9Z,12Z)), Plate-let-activating factor, PS(22:1(13Z)/15:0). (Table1) Based on 16 metabolites, we utilized fisher distinguish analysis to classify 28/30 IAI into IAI and 20/20 health people into health people, therefore it has sensitivity of 93.3% and specificity of 100%. (Table2)

Table 1

16 metabolites identified that differentiate IAI and health people
Ionization	mass-to-charge ratio	Chemical formula	Metabolite
ESI+	246.2441	C14H28O2	Myristic acid
ESI+	285.1718	C44H72O15P2	PGP(18:0/18:3(6Z,9Z,12Z))
ESI+	301.2031	C53H92O6	TG(14:1(9Z)/16:1(9Z)/20:3n6)
ESI+	318.3017	C15H32NO2+	Decanoylcholine
ESI+	334.2961	C18H36O4	MG(i-15:0/0:0/0:0)
ESI+	362.3278	C49H94O6	TG(14:0/i-16:0/i-12:0)
ESI+	758.5726	C47H72O5	DG(22:5n6/0:0/22:5n6)
ESI+	784.587	C44H80NO10P	PS(18:0/20:0)
ESI+	806.5728	C41H78NO8P	PE(16:0/20:2(11Z:14Z))
ESI-	281.2498	C36H68O4	FAHFA(18:1(9Z)/6-O-18:0)
ESI-	339.2343	C20H32O3	8,9-Epoxyeicosatrienoic acid
ESI-	369.1748	C19H30O5S	5a-Dihydrotestosterone sulfate
ESI-	540.3326	C24H50NO7P	LysoPC(0:0/16:0)
ESI-	564.3316	C26H50NO7P	LysoPC(0:0/18:2(9Z,12Z))
ESI-	568.3637	C26H54NO7P	Platelet-activating factor
ESI-	802.5634	C43H82NO10P	PS(22:1(13Z)/15:0)

Table 2

Performance of 16 identified metabolites in differentiating IAI and healthy people
predict outcome	actual conditions
	IAI	health	summation	sensitivity	specificity
IAI	28	2	30
health	0	20	20	93.30%	100%
summation	28	22

ROCs involving 16 metabolites are drawn and we choose 9 metabolites with AUC > 0.85 or AUC < 0.15, including, PGP (18:0/18:3(6Z,9Z,12Z)), 8,9-Epoxyeicosatrienoic acid, 5a-Dihydrotestosterone sulfate, LysoPC(0:0/16:0), LysoPC(0:0/18:2(9Z,12Z)), Platelet activating factor (Fig. 3,A) (Table3). PCT, a biomarker for systematic inflammation, is a protein produced by C cells of the thyroid gland and a precursor of calcitonin. We collect the PCT results of 50 subjects and make the ROC whose AUC is 0.89, while the maximum AUC among 16 metabolite is 0.96 (LysoPC(0:0/18:2(9Z,12Z))) that possesses better predicting performance. (Fig. 3, C)

Table 3

the AUC of 16 metabolites and PCT between two groups
Variable	AUC	standard error	sig	95% confidence intervals
				lower	upper
Myristic acid	0.203	0.062	0.000	0.082	0.325
MG(i-15:0/0:0/0:0)	0.838	0.055	0.000	0.730	0.947
PGP(18:0/18:3(6Z,9Z,12Z))	0.092	0.043	0.000	0.007	0.176
Decanoylcholine	0.238	0.069	0.002	0.103	0.373
TG(14:1(9Z)/16:1(9Z)/20:3n6)	0.798	0.068	0.000	0.666	0.931
TG(14:0/i-16:0/i-12:0)	0.195	0.062	0.000	0.074	0.316
PS(18:0/20:0)	0.242	0.068	0.002	0.109	0.375
PE(16:0/20:2(11Z:14Z))	0.785	0.064	0.001	0.660	0.910
FAHFA(18:1(9Z)/6-O-18:0)	0.798	0.068	0.000	0.664	0.933
8,9-Epoxyeicosatrienoic acid	0.127	0.060	0.000	0.008	0.245
5a-Dihydrotestosterone sulfate	0.063	0.032	0.000	0.000	0.127
LysoPC(0:0/16:0)	0.077	0.037	0.000	0.004	0.149
LysoPC(0:0/18:2(9Z,12Z))	0.040	0.023	0.000	0.000	0.085
Platelet activating factor	0.083	0.040	0.000	0.006	0.161
DG(22:5n6/0:0/22:5n6)	0.177	0.067	0.000	0.046	0.307
PS(22:1(13Z)/15:0)	0.270	0.074	0.006	0.125	0.415
PCT	0.890	0.053	0.000	0.786	0.994

3.3 Urine sample

The calculate procedure of urine samples is same as serum ones. TIC suggests content difference of some metabolites in urine sample at same retention time and different ion model. (Fig. 4, A, B, C, D) In PCA, we observe that difference within group is small, meanwhile variance between groups is large. The position of QC is consistent, revealing that test data is believable. (Fig. 4E, F) After t test, we select significant difference for 269 metabolites in positive model and 409 ones in negative model. (supplementary sheet3). We utilize PLS-DA for further screening and choose 63 metabolites whose VIP > 1 in positive and negative mode (supplementary sheet4). (Fig. 5, B) In PLS-DA plot, two groups overlap partly, but they could be divided generally. (Fig. 5, A) The hierarchical clustering analysis exhibits 11 metabolites’ discrimination is excel-lent. (Fig. 5, C)

SVM is applied to select 12 metabolites whose weight accesses to 100%. Then, we im-port mass-to-charge ratio of the metabolites into HMDB and 11 metabolites meet requirements, including Tryptamine, Etiocholanolone, Deoxycholic acid 3-glucuronide, Margaroylglycine, Norepinephrine, Etiocholanolone glucuronide, 4-Hydroxyproline, Dopamine glucuronide, Riboflavin, Pregnanetriol, 5-alpha-Dihydrotestosterone glucuronide (Table4). Based on 11 metabolites, we utilize SVMDA to distinguish samples and the result indicates that they have specificity of 85%, sensitivity of 86.7%. Finally, we protract ROC about 11 metabolites and metabolites with AUC > 0.9 or AUC < 0.1 are chosen (Fig. 3, B), including Deoxycholic acid 3-glucuronide and Margaroylglycine (Table5). The mean Plots of 8 differential metabolites is made to observe the change trend of metabolites’ content between two groups. (Fig. 6)

Table 4

Metabolites identified that differentiate IAI and health people
Ionization	mass-to-charge ratio	Chemical formula	Metabolite
ESI+	221.1656	C10H12N2	Tryptamine
ESI+	273.2219	C19H30O2	Etiocholanolone
ESI+	285.1716	C30H48O10	Deoxycholic acid 3-glucuronide
ESI+	334.296	C19H37NO3	Margaroylglycine
ESI+	152.0709	C8H11NO3	Norepinephrine
ESI+	484.2907	C25H38O8	Etiocholanolone glucuronide
ESI-	150.0567	C5H9NO3	4-Hydroxyproline
ESI-	350.0884	C14H19NO8	Dopamine glucuronide
ESI-	375.1307	C17H50N4O6	Riboflavin
ESI-	449.2552	C21H36O3	Pregnanetriol
ESI-	931.5049	C25H38O8	5-alpha-Dihydrotestosterone glucuronide

Table 5

the AUC of 11 metabolites and PCT between two groups
Variable	AUC	standard error	sig	95% confidence intervals
				lower	upper
4-Hydroxyproline	0.725	0.081	0.005	0.567	0.883
Riboflavin	0.195	0.073	0	0.052	0.338
Pregnanetriol	0.155	0.069	0	0.019	0.291
5-alpha-Dihydrotestosterone glucuronide	0.19	0.069	0	0.054	0.326
Dopamine glucuronide	0.2	0.065	0	0.073	0.327
Tryptamine	0.817	0.06	0	0.699	0.934
Etiocholanolone	0.127	0.059	0	0.012	0.241
Deoxycholic acid 3-glucuronide	0.968	0.02	0	0.929	1.008
Margaroylglycine	0.007	0.007	0	-0.007	0.021
Norepinephrine	0.706	0.076	0.007	0.557	0.855
Etiocholanolone glucuronide	0.155	0.067	0	0.023	0.287
PCT	0.890	0.053	0.000	0.786	0.994

To our acknowledge, this study is the first to explore possible serum and urine biomarkers for IAI patients by the method of metabolomics. Finally, we find 6 metabolites in serum and 2 metabolites in urine utilized to identify IAI patients early. These biomarkers have high specificity and sensitivity, in which the content of Margaroylglycine increases, while residual 7 metabolites decreases, including PGP (18:0/18:3(6Z,9Z,12Z)), 8,9-Epoxyeicosatrienoic acid, 5a-Dihydrotestosterone sulfate, LysoPC(0:0/16:0), LysoPC(0:0/18:2(9Z,12Z)), Platelet-activating factor and Deoxycholic acid 3-glucuronide.

Next, we will discuss the correlation between these metabolites and inflammation. Margaroylglycine is an acylglycine with C-17 fatty acid group as the acyl moiety. First, the receptor and transporters of glycine are found in Macrophages which plays an important role in inflammation response[1]. Glycinergic system could change macrophage signaling pathways (NRF2, Akt and miRNA) to influence the immune function of macrophage and possible mechanism also includes altering cellular metabolism (glycolysis)[2]. A review thinks that glycine could inhibit inflammation by adjusting the expression level nuclear factor kappa B.[3] Second, PGP (18:0/18:3(6Z,9Z,12Z)) is a glycerophospholipid in which a phosphoglycerol moiety occupies a glycerol substitution site followed by another phosphate moiety. phospholipids are potential inflammatory mediators. Glycerophospholipid, an inflammation factor, often participates in systematic inflammation reaction[4]. An animal study finds that isoflavone consumption reduces inflammation through inhibiting the expression the glycerophospholipid, which suggests that glycerophospholipid is an inflammation mediator[5]. The other animal experiment holds that isosteviol sodium can alleviate sepsis by managing glycerophospholipid metabolism in mouse body, revealing that glycerophospholipid is a part of inflammation[6]. Third, 8,9-Epoxyeicosatrienoic acid is an epoxyeicosatrienoic acid eicosanoid, a metabolite of arachidonic acid. Arachidonic acid (AA) is important for the initiation and maintenance of inflammation. AA is a double-edged sword for inflammation, which stimulates inflammation by generating interferon-gamma and interleukin-2 and minimizes inflammation by producing interleukin-4,5,10,13.[7] Researchers also find that AA could inhibit macrophage M2 polarization, meanwhile metabolite prostaglandin E2 (PGE2), a derivative of AA, could increase macrophage function by regulating oxidative phosphorylation (OXPHOS) and peroxisome proliferator-activated receptor gamma (PPAR)[8]. Fourth, 5a-Dihydrotestosterone sulfate is a sulfate derivative of 5a-Dihydrotestosterone produced through phase II metabolism. In mouse, 5a-Dihydrotestosterone could prevent gastritis through reducing production of interleukin-13 and CSF-2 in type 2 innate lymphoid cells (ILC2s), suggesting 5a-Dihydrotestosterone may be essential for inflammation.[9, 10] Firth, LysoPC(0:0/16:0) and LysoPC(0:0/18:2(9Z,12Z)) belongs to the family of lysophospholipid. Lysophosphatidylcholine(LPC) is an endogenous lysophospholipid.[11] LPC could induce neutrophils to produce superoxide and NADPH oxidase which have the effect of anti-infection. When LPC is combined with CD1d-restricted T cells, interleukin-13 will increase and prompt aggregated inflammation.[12] Lysophospholipid could regulate vasodilation, vasoconstriction, and vascular leak and cause sense of nerve pain during inflammation.[13] Lysophosphatidic acid is important for the existence of macrophages, initiates inflammation cascades and increases the release of interleukin-1 and reactive oxygen species (ROS) in macrophages.[14] Sixth, Platelet-activating factor (PAF) is a ubiquitous, potent phospholipid activator and mediator of inflammation that has an important role in the pathogenesis of inflammatory disorders.[15] [16] During inflammation, signal transduction of PAF is influenced by acyl and alkyl.[17] Seventh, Deoxycholic acid 3-glucuronide is a natural human metabolite of Deoxycholic acid (DCA) generated in the liver by UDP glucuonyltransferase. A study exhibits that high-fat diet increases the content of DCA which encourages the infiltration of macrophages and prompts the inflammation of colon. This effect is mediated partly by M2 muscarinic acetylcholine receptor (M2-mAchR)/Src pathway.[18] DCA also prompt the differentiation of T helper cell 17 by regulating cholesterol biosynthesis, thus influences inflammation diseases.[19]

We notice most of metabolites showing a downward trend, which may be related with great consuming of inflammation factors and negative feedback of inflammation system. There are also several limitations in our study. First, we include limited sample size. Second, our sample don’t cover all etiology for IAI, just focusing on common dis-eases. Last, we can’t carry out experiments stratified by sex and different age group. Before it is used in clinic, the results still need massive basic studies to prove.

We seek out 6 metabolites in serum and 2 metabolites in urine. By detecting these biomarkers for emergency case, we aim to identify IAI patients as soon as possible, with the final goal to improve prognosis.

Ethics approval and consent to participate

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board (protocol code: 24K181-001 and date of approval: July 2024). All participants have signed informed consent forms.

Consent for publication

For this article, all anthors consent for publication.

Competing interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Funding

This work was supported by the Major Research Program of the National Natural Science Foundation of China (52072142).

Author Contribution

ZHD: Study design, literature search and manuscript writing. SPZ: Study selection and data analysis. HWZ and KY : Data collection. DGW: Article Guidance. All authors revised the manuscript and approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

Acknowledgement

Thank for all the patients in this research, thank for all the scholars in this article. Thank for all the teammates for supporting this research. We are also particularly grateful to our colleagues in The First Affiliated Hospital of Jilin University for their contributions.

Data Availability

Data is provided within the manuscript or supplementary information files

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No competing interests reported.

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Utilizing serum and urine metabolomics for diagnosing acute intra- abdominal infection in the early stage

Status:

Version 1

Abstract

Background:

Methods:

Results:

Conclusions:

Figures

1. Introduction

2. Materials and methods

2.1 Patients

2.2 Sample collection

2.3 LC-MS

2.4 Mass spectrometry

2.5 Statistical analysis

3. Results

3.1 The feature of subjects

3.2 Serum sample

3.3 Urine sample

4. Discussion

5. Conclusion

Declarations

Competing interests

Publisher’s note

Funding

Author Contribution

Acknowledgement

Data Availability

References

Additional Declarations

Supplementary Files

Status:

Version 1