CN118169274A - Metabolic marker for detecting staphylococcus aureus blood flow infection and application thereof - Google Patents
Metabolic marker for detecting staphylococcus aureus blood flow infection and application thereof Download PDFInfo
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Abstract
The invention relates to a metabolic marker for detecting staphylococcus aureus blood flow infection and application thereof, belonging to the technical field of inspection medicine. The invention provides a metabolic marker, which comprises at least one of kynurenine and decanoyl carnitine. The invention takes kynurenine (Kynurenine) and decanoyl carnitine (Decanoylcarnitine) as markers, has good diagnosis efficacy on staphylococcus aureus blood flow infection, wherein when the kynurenine and the decanoyl carnitine are used as markers in combination, the AUC value is as high as 0.9299.
Description
Technical Field
The invention relates to the technical field of inspection medicine, in particular to a metabolic marker for detecting staphylococcus aureus blood flow infection and application thereof.
Background
Staphylococcus aureus (Staphylococcus aureus) is a common clinical pathogen, particularly in hospitals and other medical settings. Such gram positive bacteria can cause infections ranging from mild skin infections to severe blood flow infections such as sepsis and septic shock, and even death when severe. Because of the risk of staphylococcus aureus in its ability to proliferate rapidly and its resistance to certain antibiotics, diagnosis and treatment time are key factors affecting the treatment of staphylococcus aureus blood stream infections (SaB). Studies have shown that delay per hour of antibiotic can reduce patient survival by an average of 7.6%. If not properly treated within 24 hours, patient survival may drop to only 10%. Therefore, it is important to early diagnosis and timely treatment of staphylococcus aureus blood flow infection.
Currently, methods for determining pathogenic bacteria of bacterial blood stream infection are generally based on blood culture or high throughput sequencing techniques. Blood culture is a "gold standard" for diagnosis, typically requiring several days to one week to give a result, and in some cases, the sensitivity of blood culture may be insufficient, low levels of bacterial presence cannot be detected, leading to misdiagnosis or missed diagnosis, and improper treatment of certain antibiotics or samples may lead to false negative results, thereby delaying treatment. This delay is particularly dangerous for staphylococcus aureus blood stream infections, as the patient's condition may deteriorate rapidly while waiting for the result. Traditional bacterial infection diagnosis indexes, including leucocytes, C-reactive protein, procalcitonin and the like, cannot judge infectious pathogens, and have limited clinical guidance value.
While high throughput sequencing (NGS), while faster than traditional culture methods, is limited by its high cost, limited in clinical popularity, interpretation of NGS data may be ambiguous in the face of unknown or rare pathogens; and NGS has high requirements on the quality of the samples. Any contamination during sample preparation may lead to inaccurate results, and special handling methods may be required for blood samples to extract enough pathogen DNA for sequencing, which is not suitable for rapid assessment of acute infection.
Recent researches show that after bacterial blood flow infection, human serum is subjected to remarkable metabolic change, and certain metabolites are closely related to the type and prognosis of bacterial infection, so that early diagnosis and prognosis evaluation indexes of bacterial blood flow infection can be developed based on serometabonomics. However, there is no research or product application to predict or diagnose blood flow infection of staphylococcus aureus by using serum metabolite. Therefore, the development of novel staphylococcus aureus blood stream infection markers has extremely important significance for early diagnosis and treatment of patients of this type and reduction of mortality.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a metabolic marker for detecting staphylococcus aureus blood flow infection, which has better diagnosis efficiency, high sensitivity and shorter detection time, and application thereof.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
In a first aspect, the invention provides a metabolic marker for detecting a blood flow infection by staphylococcus aureus, the metabolic marker comprising at least one of kynurenine, decanoyl carnitine.
Preferably, kynurenine is L-kynurenine, which is a metabolic intermediate of tryptophan, the CAS number is 2922-83-0, the chemical formula of the kynurenine is C 10H12N2O3, and the molecular formula is shown as follows:
preferably, the CAS number of the decanoyl carnitine is 1492-27-9, the chemical formula is C 17H33NO4, and the molecular formula of the decanoyl carnitine is shown as follows:
As a preferred embodiment of the metabolic marker according to the present invention, the metabolic marker is a combination of kynurenine and decanoyl carnitine.
In a second aspect, the invention provides the use of a metabolic marker for the manufacture of a product for the detection, diagnosis or prognosis of a bacterial blood flow infection.
As a preferred embodiment of the use according to the invention, the bacterial blood-stream infection is a Staphylococcus aureus blood-stream infection.
Blood flow infections often begin at other sites of infection, such as wounds, surgical incisions, or respiratory tract infections. Staphylococcus aureus enters the whole body through the blood stream, triggering an abnormal response of the immune system. This process may lead to Systemic Inflammatory Response Syndrome (SIRS) and Multiple Organ Dysfunction Syndrome (MODS), which in severe cases can be life threatening. The invention provides kynurenine, decanoyl carnitine or the combination of kynurenine and decanoyl carnitine as metabolic markers, and the staphylococcus aureus blood stream infection detection product prepared by the invention has good diagnosis efficacy: by ROC analysis, kynurenine (Kynurenine), decanoyl carnitine (Decanoylcarnitine) was found to diagnose staphylococcus aureus blood stream infections (SaB) with areas under the curve (AUC) as high as 0.8531 and 0.8452, respectively, whereas when two metabolites were combined as metabolic markers, the diagnostic efficacy was higher with AUC values as high as 0.9299.
As a preferred embodiment of the use according to the invention, the product comprises a chip, a reagent or a kit for detection of a blood stream infection by Staphylococcus aureus.
In a fourth aspect, the present invention provides the use of a reagent for detecting at least one of kynurenine, decanoyl carnitine in a sample for the preparation of a product for diagnosing a yellow blood stream infection of staphylococcus aureus.
As a preferred embodiment of the use according to the invention, the sample is a biological sample of a subject; the biological sample includes, but is not limited to, plasma, serum, whole blood, urine, or sweat; preferably, the biological sample is serum.
In a fourth aspect, the invention provides a kit for detecting a blood flow infection by staphylococcus aureus, comprising reagents for detecting the metabolic markers; the metabolic marker is kynurenine, decanoyl carnitine or a combination of kynurenine and decanoyl carnitine.
The kit for detecting staphylococcus aureus blood flow infection provided by the invention comprises the reagent for detecting the metabolic markers of kynurenine, decanoyl carnitine or the combination of kynurenine and decanoyl carnitine, has good diagnosis efficiency, and has shorter detection time and higher detection rate compared with the traditional blood culture identification method and high-throughput sequencing method.
As a preferred embodiment of the kit, the kit further comprises sampling materials, separation reagents, quality control substances and standard substances. The quality control or standard comprises kynurenine and/or decanoyl carnitine in known concentrations.
As a preferred embodiment of the kit according to the invention, the kit further comprises a control sample comprising a biological sample from a healthy individual or a biological sample from a patient suffering from a non-staphylococcus aureus blood stream infection.
As a preferred embodiment of the kit according to the invention, the kit judges the pathogenic bacteria of the bacterial blood stream infection as Staphylococcus aureus by detecting the abundance level or relative content of at least one metabolic marker in the biological sample of the subject.
Further, the biological sample of the subject has an elevated level or relative content of kynurenine and/or decanoyl carnitine in comparison to a control sample. More preferably, the level of abundance of kynurenine and decanoyl carnitine is increased simultaneously or the relative content is increased simultaneously.
As a preferred embodiment of the kit of the present invention, the method for detecting the abundance level or relative content of a metabolic marker by the kit includes, but is not limited to, liquid chromatography-mass spectrometry or gas chromatography-mass spectrometry.
As a preferred embodiment of the kit of the invention, the kit further comprises other reagents for clinical use in the selection, monitoring and prognostic evaluation of a treatment regimen for Staphylococcus aureus blood stream infection.
Further, the kit further comprises a control sample comprising a biological sample from a patient dying from a staphylococcus aureus blood stream infection or a biological sample from a patient surviving a staphylococcus aureus blood stream infection.
As a preferred embodiment of the kit, the kit can be applied to prognosis evaluation of staphylococcus aureus blood flow infection. The kit can be used in combination with a critical illness score, acute physiology and chronic health score, APACHE II. Further, the disease processes of staphylococcus aureus blood stream infections include, but are not limited to bacteremia, sepsis and septic shock.
Compared with the prior art, the invention has the beneficial effects that:
1. The metabolic marker provided by the invention has excellent diagnosis efficacy, and the area under the ROC curve of the kynurenine or decanoyl carnitine or the combination thereof in the metabolic marker optimally reaches 0.9299 when diagnosis is carried out on blood flow infection of staphylococcus aureus. Compared with the traditional blood culture or blood high-throughput sequencing technology, the metabolic marker provided by the invention has the advantages that the detection time of the staphylococcus aureus blood infection is only 20.07+/-3.06 hours, and is shortened by 62% compared with the traditional blood culture method, and is shortened by 28% compared with the high-throughput sequencing method. The detection rate of the three metabolic markers is 86.67% -100%.
2. The metabolic marker provided by the invention can evaluate and prognose the blood-flow infection condition of staphylococcus aureus, can judge the bacteremia-sepsis-septic shock condition of blood-flow infection of staphylococcus aureus or evaluate the death risk caused by blood-flow infection of staphylococcus aureus according to the relative abundance level, and shows that kynurenine and decanoyl carnitine or the combination of kynurenine and decanoyl carnitine has good guiding value in the blood-flow infection evaluation and prognosis of staphylococcus aureus.
Drawings
FIG. 1 is a graph of metabolite analysis of Staphylococcus aureus in a patient suffering from a blood stream infection and in a healthy population;
FIG. 2 is a graph of metabolite analysis of patients with S.aureus blood stream infections versus non-S.aureus blood stream infections;
FIG. 3 is a diagram of a key metabolite screen for diagnosis of Staphylococcus aureus blood stream infection;
FIG. 4 metabolic markers are graphs of the outcome of evaluation of S.aureus blood stream infection.
Detailed Description
For a better description of the objects, technical solutions and advantages of the present invention, the present invention will be further described with reference to the following specific examples.
Example 1 acquisition of serum Metabolic marker samples
1. Study object and group
85 Of the populations were co-included, 11 of which were healthy physical examination populations (Ctrl group), 15 of which were staphylococcus aureus blood stream infected patients (SaB patients), and 59 of which were non-staphylococcus aureus blood stream infected patients (NSaB patients). The ages of the three groups of people are 55.54+/-16.04 years old, 52.07+/-19.91 years old and 57.69 +/-14.42 years old, the male-female ratios are 6:5, 8:7 and 31:28 respectively, and the ages and the sexes of the three groups are not different.
2. Serum sample treatment for screening metabolic markers
2ML of the extracted elbow vein blood of each subject was extracted in a dry tube, and the blood samples were centrifuged at 4000rpm for 10min, and 200. Mu.l of serum was extracted per sample to 1.5mL of EP tube, and the tube was placed in a-80℃refrigerator for cryopreservation.
Serum samples were thawed at 4℃and 400. Mu.l acetonitrile + 400. Mu.l methanol were added as metabolic extracts, 10. Mu.l internal standard. The precipitated protein was shaken, centrifuged at 12000rpm for 10min, 500. Mu.l supernatant was transferred to a 1.5mL EP tube, and lyophilized in vacuo. Accurately adding 150 μl of methanol for redissolution, and transferring 100 μl to a lining tube for testing.
3. Data acquisition of serum metabolome
Analysis of serum metabolites was performed using LC-MS method, obtaining metabolite raw data. The LC-MS instrument is an ultra-high performance liquid chromatography-four-level rod-electrostatic field orbitrap high-resolution mass spectrometer UHPLC-MS (Thermo Scientific), and the chromatographic separation column is an acquisition BEH C18 column, and the specific analysis conditions are as follows:
adopting a positive ion and negative ion electrospray ionization mode, wherein:
positive ion detection: with 0.1% formic acid (A) and 0.1% formic acid-acetonitrile (B) as mobile phases, the initial mobile phase ratio was 2% B, and after 10min was linearly changed to 98% B.
And (3) negative ion detection: linear separation gradient with ammonium bicarbonate-water solution (a) and ammonium bicarbonate-acetonitrile/methanol solution (B) as mobile phases: 0min,2% B;10min,100% B. The scanning range is 70-1000m/z respectively.
The main parameters for mass spectrometry detection were set as follows:
The electrospray ion source is heated, the positive ion ionization voltage is 4.0KV, the negative ion ionization voltage is 3.5KV, the auxiliary air flow is 10arb, the heating temperature is 350 ℃, the ion transmission capillary temperature is 320 ℃, and the S-lens radio frequency percentage is 50%.70000FWHM resolution captures metabolic profiles, automatic gain of 3e6, maximum ion implantation time of 200ms. When the information depends on the acquisition mode, the full scanning resolution of the primary mass spectrum is 70000FWHM, and the resolution of the secondary mass spectrum is 17500FWHM. The parent ion isolation mass number window is 1.0Da, ultra-pure nitrogen is used as collision induced dissociation gas, and the normalized collision energy is set to be 30% +/-15%.
EXAMPLE 2 serum Metabolic marker screening of patients with Staphylococcus aureus blood stream infection and healthy people
The metabolome raw data obtained in example 1 were subjected to peak alignment, peak identification and peak extraction in Compound Discoveryer software and TRACEFINDER software. The Human Metabolome Database (HMDB), kyoto genes and genome encyclopedia (KEGG), mzCloud spectral library (mzCloud. Org) were retrieved for metabolite annotation. Data correction is carried out according to the peak area of the internal standard, and then normalization processing is carried out by adopting the total peak area of each sample.
And (3) carrying out T test and the like on the normalized data to obtain differential metabolites, further carrying out multivariate statistical analysis in SIMCA14.1 software, including Principal Component Analysis (PCA) of an unsupervised learning method and orthogonal least squares discriminant analysis (OPLS-DA) of a supervised learning method, and screening target substances with high contribution efficiency based on VIP values (VIP > 1).
To determine metabolic profile and differential metabolites in staphylococcus aureus blood stream infected patients (SaB patients), 15 SaB patients were compared to 11 healthy people (Ctrl group) in serometabonomics.
By T-test, saB patients identified 56 different metabolites in total and plotted their differential metabolic heat patterns compared to healthy humans, and the results are shown in fig. 1 (a). According to the metabolic heat diagram, saB patients and healthy people respectively gather different metabolites of the two groups of people. Analysis of differential metabolites in two groups of SaB and healthy individuals using PCA showed that SaB and healthy individuals were also significantly divided into two groups as shown in fig. 1 (B). It is shown that among serum metabolites of patients suffering from Staphylococcus aureus blood stream infection (SaB patients), there are characteristic serum metabolites that are distinguished from healthy people.
In order to obtain key differential metabolites for staphylococcus aureus blood stream infected patients (SaB patients) that are distinct from healthy humans, 22 key differential metabolites were obtained by OPLS-DA analysis, the results are shown in fig. 1 (C), 17 of which were in an up-regulation trend in SaB patients, and 5 metabolites were in a down-regulation trend. Serum metabolic markers with the greatest contribution efficiency in the statistical analysis are screened according to VIP values, and 11 metabolites with VIP values greater than 1 are screened by taking VIP > 1 as a standard, as shown in fig. 1 (D). Wherein the serum metabolic markers at position 5 before the excretion are auxin (Auxin), 3-hydroxyglutarate (3-Hydroxyglutaric acid), decanoyl carnitine (Decanoylcarnitine), 2-naphthoic acid (2-Naphthoic acid) and kynurenine (Kynurenine) respectively.
Combining the data from the differential metabolite OPLS-DA analysis of fig. 1 (C) and the differential metabolite VIP analysis of fig. 1 (D) shows that 10 common differential metabolites of auxin (Auxin), 3-hydroxyglutarate (3-Hydroxyglutaric acid), decanoyl carnitine (Decanoylcarnitine), 2-naphthacene (2-Naphthoic acid), kynurenine (Kynurenine), inositol (Inositol), glycocholic acid (Glycocholic acid), estradiol valerate (Estradiol valerate), lactulose (Lactulose), butyric acid (Butyric acid) are of great value for the identification of SaB patients from healthy humans.
EXAMPLE 3 serum Metabolic marker screening of patients with Staphylococcus aureus blood stream infection and other bacteria blood stream infection
The metabolome raw data obtained in example 1 were subjected to peak alignment, peak identification and peak extraction in Compound Discoveryer software and TRACEFINDER software. The Human Metabolome Database (HMDB), kyoto genes and genome encyclopedia (KEGG), mzCloud spectral library (mzCloud. Org) were retrieved for metabolite annotation. Data correction is carried out according to the peak area of the internal standard, and then normalization processing is carried out by adopting the total peak area of each sample.
And (3) carrying out T test and the like on the normalized data to obtain differential metabolites, further carrying out multivariate statistical analysis in SIMCA14.1 software, including Principal Component Analysis (PCA) of an unsupervised learning method and orthogonal least squares discriminant analysis (OPLS-DA) of a supervised learning method, and screening target substances with high contribution efficiency based on VIP values (VIP > 1).
To determine metabolic profile and differential metabolites in staphylococcus aureus blood stream infected patients (SaB patients), a serometabonomic comparison was performed on 15 SaB patients versus 59 non-staphylococcus aureus blood stream infected patients (NSaB patients), respectively.
By T-test, saB patients identified 41 different metabolites in total and plotted their differential metabolic heat patterns compared to NSaB patients, the results are shown in fig. 2 (a); analysis of both SaB and NSaB patient populations was further performed using PCA analysis, and the results are shown in fig. 2 (B), which shows that SaB patient has a characteristic serum metabolite that is significantly different from NSaB patient.
In order to obtain key differential metabolites for staphylococcus aureus blood stream infected patients (SaB patients) as distinguished from non-staphylococcus aureus blood stream infected patients (NSaB patients), 6 key differential metabolites were obtained by OPLS-DA analysis, the results are shown in fig. 3 (C); of these, 3 showed an up-regulation trend in SaB patients and 3 showed a down-regulation trend. Screening serum metabolic markers with maximum contribution efficacy in statistical analysis according to VIP values, and screening 9 metabolites with VIP > 1 as standard, as shown in fig. 2 (D), wherein key metabolites at 5 positions before the excretion are decanoyl carnitine (Decanoylcarnitine), kynurenine (Kynurenine), uridine (Uridine), lactulose (Lactulose) and PI (18:0/18:0), respectively.
Combining the data from the differential metabolite OPLS-DA analysis of fig. 2 (C) and the differential metabolite VIP analysis of fig. 2 (D) shows that decanoyl carnitine (Decanoylcarnitine), kynurenine (Kynurenine), PI (18:0/18:0) and palmitoyl stearate sterol (Palmitoylstigmasterol) are found.
Example 4 screening of key metabolites for diagnosis of Staphylococcus aureus blood stream infection
Intersection of key differential metabolites from healthy and SaB patients screened in example 2 and key differential metabolites from SaB and NSaB patients screened in example 3 were obtained, with different statistical methods yielding different results:
The key differential metabolite intersection results obtained by OPLS-DA analysis are shown in fig. 3 (a). Of these, only 2 metabolites of decanoyl carnitine (Decanoylcarnitine) and kynurenine (Kynurenine) are different from SaB patients at the same time and are both increasing in trend, relative to healthy humans or NSaB patients.
Key differential metabolite intersection results obtained by VIP values are shown in fig. 3 (B). Among them, the 5 different metabolites were found to be different in SaB patients-healthy humans, and SaB patients-NSaB patients, with respect to healthy humans or NSaB patients, namely decanoyl carnitine (Decanoylcarnitine), kynurenine (Kynurenine), uridine (Uridine), lactulose (Lactulose), estradiol valerate (Estradiol valerate), respectively. Detecting the abundance level of the plurality of critical bad foreign body healthy people, NSaB and SaB patients; among them, fig. 3 (C) shows that only the abundance levels of decanoyl carnitine (Decanoylcarnitine) and kynurenine (Kynurenine) are sequentially increased in healthy people-NSaB patients-SaB patients, suggesting that decanoyl carnitine and kynurenine have good value in diagnosing staphylococcus aureus blood stream infections.
ROC analysis was further performed on the above 6 different metabolites or combinations of different metabolites, and as a result, as shown in fig. 3 (D), the area under the curve (AUC) for diagnosis SaB of kynurenine was 0.8531, and the area under the curve (AUC) for diagnosis SaB of decanoyl carnitine was 0.8452, which were higher than the AUC values of uridine, lactulose, and estradiol valerate. ROC analysis was performed using decanoyl carnitine in combination with kynurenine (the product of the abundance of two markers, which is an indicator of kynurenine in combination with decanoyl carnitine as a marker), as a metabolic indicator, and AUC values were found to be as high as 0.9299. The above results demonstrate that kynurenine, decanoyl carnitine or a combination of both are key metabolic markers for detection of blood flow infections in staphylococcus aureus.
Calculating corresponding about log indexes by using the obtained ROC curves of kynurenine, decanoyl carnitine or the combination of the two, determining the optimal judgment threshold value under different metabolic marker diagnosis models by using the about log indexes, establishing a metabolic marker diagnosis model by using the optimal judgment threshold value as a detection standard for diagnosing blood flow infection of staphylococcus aureus, wherein the greater the about log index is = (sensitivity + specificity) -1, the greater the about log index is, the greater the authenticity of the result is.
Wherein, when kynurenine is used as a single metabolic marker, the optimal judgment threshold value is 6677249 at the maximum dengue index, the sensitivity of diagnosing staphylococcus aureus blood stream infection is 100%, and the specificity is 74.58%; when decanoyl carnitine is used as a single metabolic marker, a threshold 5312447 is taken at the maximum about dengue index, the sensitivity of diagnosing staphylococcus aureus blood stream infection is 73.33%, and the specificity is 83.05%; when kynurenine and decanoyl carnitine are used as the joint metabolic markers, the optimal judgment threshold is 34006150000000, and the sensitivity of diagnosing staphylococcus aureus blood stream infection is 86.67 percent and the specificity is 88.14 percent.
Example 5 comparison of detection Effect by Metabolic marker diagnostic model with conventional detection means
For 11 of 85 samples, healthy samples and 74 bacterial blood flow infected patients (including 15 staphylococcus aureus blood flow infected patients and 59 non-staphylococcus aureus blood flow infected patients), the best judgment threshold for each metabolite marker in example 4 was compared with the blood culture, blood high throughput sequencing (NGS) technique: when blood culture or blood NGS find bacteria, two clinicians comprehensively judge and determine the pathogenic bacteria.
And (5) respectively carrying out diagnosis time statistics on a metabolic marker method, a blood culture method and NGS diagnosis staphylococcus aureus blood stream infection. The statistical method is as follows:
(1) Metabolic markers: starting calculation after peripheral blood is extracted, wherein the time from obtaining the peak area of the marker metabolite is the time required by the metabolome method diagnosis SaB;
(2) Blood culture: starting calculation after blood culture is extracted, and the time from the start of the calculation to the start of the calculation after the pathogen is obtained through bacterial culture and identification is the time required by blood culture diagnosis SaB;
(3) NGS: starting calculation after peripheral blood is extracted, and determining the time for infection pathogen as the time required by NGS diagnosis SaB by NGS data analysis and comparison.
Kynurenine is taken as a single metabolic marker, and when the optimal judgment threshold value is 6677249, the detection rate of the kynurenine on SaB is 100%; when decanoyl carnitine is used as a single metabolic marker, the detection rate of SaB is 73.33% when a threshold 5312447 is taken at the maximum approximate dengue index; when kynurenine and decanoyl carnitine are taken as the joint metabolism markers, the optimal judgment threshold is 34006150000000, and the detection rate of SaB is 86.67%.
When blood culture or blood NGS find bacteria, two clinicians comprehensively judge and determine the pathogenic bacteria, the result is as follows: the results of 6 patients with blood culture only were positive, and the results of 13 patients with NGS only were positive, with 4 blood cultures and NGS both positive and staphylococcus aureus. 59 non-Staphylococcus aureus blood stream infected patients (NSaB patients) were judged, of which 20 were infected with E.coli, 13 were infected with P.aeruginosa, 10 were infected with Staphylococcus hemolyticus, 9 were infected with Klebsiella pneumoniae, 4 were infected with Staphylococcus aureus, and 3 were infected with Acinetobacter baumannii.
By the blood culture method, only 6 SaB patients showed positive blood culture, i.e., the detection rate of blood culture was only 40.0%. Detection of blood flow infected patients by NGS showed that 13 samples showed SaB positives, with a detection rate of 86.67%.
Statistics of the time of the method show that the average detection time of the metabolite marker pair SaB is 20.07+/-3.06 hours, the detection time of the blood culture is 53.67 +/-9.69 hours, and the detection time of the NGS is 28.15 +/-6.97 hours. The detection of SaB based on the metabolic markers is superior to blood culture in terms of detection rate and detection time; in addition, the detection rate is similar to that of NGS, but the detection time is better than that of NGS. This demonstrates that diagnosis SaB based on kynurenine, decanoyl carnitine or a combination of both is of advantage in persons suspected of having a blood flow infection.
EXAMPLE 6 use of kynurenine, decanoyl carnitine or a combination thereof for assessing SaB conditions and prognosis
Further analysis was performed on 15 patients with staphylococcus aureus blood stream infection (SaB patients), 7 patients with bacteremia (Bacteremia), 5 patients with sepsis (Sepsis), and 3 patients with septic shock (Septic shock) as the course of the disease progresses. The differences in abundance of kynurenine, decanoyl carnitine, and combinations of the two were compared in three different degrees of SaB patients, respectively, and the results are shown in fig. 4 (a). The results show that the abundance levels of the corresponding metabolic markers are sequentially increased in the order of progression of bacteremia-sepsis-septic shock in the three groups of kynurenine, decanoyl carnitine and combinations of both.
Critical disease scoring-acute physiological and chronic health scoring (APACHE II) was performed on 15 patients with staphylococcus aureus blood stream infection, and kynurenine, decanoyl carnitine and the combination of both metabolic markers were analyzed by Spearman for correlation with the two factors of the APACHE II score, and significant differences were found with P < 0.05, as shown in fig. 4 (B). The correlation between kynurenine and the APACHE II score was shown to be 0.7567, the correlation between decanoyl carnitine and the APACHE II score was shown to be 0.7682, and the correlation between the combined marker and the APACHE II score was shown to be 0.8628.
To further analyze the prognostic value of kynurenine, decanoyl carnitine or combinations thereof for SaB patients, serum data from more than 200 of SaB patients were further analyzed using raw data from Wozniak J M, mills R H, olson J, et al, CELL,2020, wherein the data samples included 25 normal, 99 of SaB surviving patients and 76 of SaB dead patients; its average age is 58.7±15.5 years old; 49.1% of SaB patients were male and 33.2% were infected with methicillin-resistant staphylococcus aureus.
Analysis of serum samples of SaB surviving patients and SaB dead patients for kynurenine, decanoyl carnitine or a combination thereof, respectively, showed that the abundance levels of decanoyl carnitine and decanoyl carnitine + kynurenine were significantly increased in SaB dead patients, as shown in fig. 4 (C), with the presence of inter-group differences.
Further, the result of ROC analysis for death determination using three metabolic markers or combinations of metabolic markers is shown in fig. 4 (D), wherein the area under the curve AUC for kynurenine determination death is 0.63934, the area under the curve AUC for decanoyl carnitine determination death is 0.7043, and the area under the curve AUC for decanoyl carnitine+kynurenine determination death is 0.7180. Indicating that kynurenine, decanoyl carnitine or a combination thereof can well assess the condition and prognosis of SaB patients.
Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the scope of the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted equally without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. A metabolic marker for detecting a staphylococcus aureus blood stream infection, wherein the metabolic marker comprises at least one of kynurenine, decanoyl carnitine.
2. The metabolic marker of claim 1, wherein said metabolic marker is a combination of kynurenine and decanoyl carnitine.
3. Use of a metabolic marker according to any one of claims 1-2 for the preparation of a product for the detection, diagnosis or prognosis of a bacterial blood flow infection.
4. The use according to claim 3, wherein the bacterial blood flow infection is a staphylococcus aureus blood flow infection.
5. The use of claim 4, wherein the product comprises a chip, a reagent or a kit.
6. Use of a reagent for detecting at least one of kynurenine, decanoyl carnitine in a sample for the preparation of a product for diagnosing a golden yellow balloon blood flow infection.
7. The use of claim 6, wherein the sample is a biological sample of a subject; the biological sample includes, but is not limited to, plasma, serum, whole blood, urine, or sweat; preferably, the biological sample is serum.
8. A kit for detecting a staphylococcus aureus blood stream infection, comprising reagents for detecting the metabolic marker of any one of claims 1-2.
9. The kit of claim 8, wherein the kit determines that the pathogen of the bacterial blood stream infection is staphylococcus aureus by detecting the level or relative amount of the metabolic marker abundance in the biological sample of the subject.
10. The kit of claim 9, wherein the method of detecting the abundance level or relative content of a metabolic marker in the kit includes, but is not limited to, liquid chromatography-mass spectrometry or gas chromatography-mass spectrometry.
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