A kit for predicting the risk of occurrence of a delay in neurocognitive recovery and uses thereof

Abstract

The application discloses a kit for predicting the risk of neurocognitive recovery delay and application thereof. The kit comprises reagents for detecting the expression level of lipid metabolism markers in a sample, wherein the lipid metabolism markers comprise at least one of TG (58:7 / 22:5), TG (54:2 / 18:1), PE (O-16:0 / 18:1) and CL (72:3 / 18:2). The application finds through experiments that, compared with non-neurocognitive recovery delay (non-dNCR) patients, the expression level of TG (58:7 / 22:5) and TG (54:2 / 18:1) in dNCR patients is down-regulated, and the expression level of PE (O-16:0 / 18:1) and CL (72:3 / 18:2) in dNCR patients is up-regulated. By detecting the expression level of the aforementioned lipid metabolism markers, the risk of dNCR of a patient can be confirmed before and / or in an early stage after surgery, early intervention and treatment are facilitated, and postoperative repair is promoted.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of biomedicine, and in particular to a kit for predicting the risk of delayed neurocognitive recovery and application thereof. BACKGROUND

[0002] According to the recommendations on the nomenclature of postoperative cognitive changes proposed by the American Postoperative Cognitive Research Collaboration in 2018, delayed neurocognitive recovery (dNCR) is defined as a new objective cognitive function decline within 30 days after surgery. As an important subtype of perioperative neurocognitive disorders, dNCR has a high incidence (19.5%-41.4%), is associated with long-term cognitive impairment, mortality and quality of life, and has other characteristics.

[0003] At present, the clinical diagnosis of delayed neurocognitive recovery is mainly based on cognitive function scales, including MMSE (Mini-mmental state examination) evaluation scale, MoCA (Montreal Cognitive Assessment) evaluation scale, etc. In specific practice, the baseline data of cognitive function of patients are usually collected before surgery, and the scale evaluation is repeated at time points such as 1 day, 3 days, 7 days and 30 days after surgery, the changes in cognitive function are compared and analyzed, and the diagnosis is made after excluding delirium patients through 3-minute diagnostic interview for confusion assessment method (3D-CAM). However, this diagnosis method has the following significant limitations:

[0004] (1) Lack of predictive value and insufficient timeliness of diagnosis: the existing diagnosis method can only passively diagnose the dNCR that has occurred, and cannot early screen out high-risk groups, making it difficult to meet the clinical needs of preoperative risk stratification and individualized prevention program development; it also cannot quickly identify potential cases in the early postoperative period (such as within 24-48 hours), resulting in delayed intervention opportunity and missing the key period of neurocognitive function protection.

[0005] (2) Subjectivity and operability limitations: scale evaluation depends on patient compliance, and for patients who are agitated, have blurred consciousness or have language barriers after surgery, the accuracy of the results is easily affected; at the same time, the evaluation process is time-consuming and requires professional operation, which is difficult to implement efficiently in a busy clinical environment.

[0006] Therefore, there is an urgent need in the art to develop a method for screening high-risk groups of dNCR before or in the early postoperative period, so as to facilitate early intervention or early postoperative treatment, thereby promoting postoperative recovery. SUMMARY

[0007] The present application provides a kit for predicting the risk of delayed neurocognitive recovery and its application, which can identify the risk of delayed neurocognitive recovery in patients before and / or early after surgery, thereby helping early intervention or early postoperative treatment and avoiding greater harm due to untimely control and treatment.

[0008] Based on the above, the present application first provides a kit for predicting the risk of delayed neurocognitive recovery, comprising: reagents for detecting the expression level of lipid metabolism markers in a test sample, wherein the lipid metabolism markers comprise at least one of TG (58:7 / 22:5), TG (54:2 / 18:1), PE (O-16:0 / 18:1), and CL (72:3 / 18:2).

[0009] Preferably, the kit comprises reagents for detecting the expression level of TG (58:7 / 22:5), TG (54:2 / 18:1), PE (O-16:0 / 18:1), and CL (72:3 / 18:2) in a test sample.

[0010] The present application also provides another aspect of the application, which is the use of reagents for detecting the expression level of lipid metabolism markers in a test sample in the preparation of a kit for predicting the risk of delayed neurocognitive recovery, wherein the lipid metabolism markers comprise at least one of TG (58:7 / 22:5), TG (54:2 / 18:1), PE (O-16:0 / 18:1), and CL (72:3 / 18:2). Compared with normal people, the expression level of TG (58:7 / 22:5) and TG (54:2 / 18:1) is down-regulated in patients with delayed neurocognitive recovery, and the expression level of PE (O-16:0 / 18:1) and CL (72:3 / 18:2) is up-regulated in patients with delayed neurocognitive recovery.

[0011] Preferably, the test sample is a preoperative sample, and the expression level of TG (58:7 / 22:5) and / or TG (54:2 / 18:1) in the preoperative sample of the subject is detected to determine the risk of delayed neurocognitive recovery in the subject after surgery.

[0012] Preferably, the test sample is a postoperative sample, and the expression level of at least one of TG (58:7 / 22:5), TG (54:2 / 18:1), PE (O-16:0 / 18:1), and CL (72:3 / 18:2) in the postoperative sample of the subject is detected to determine the risk of delayed neurocognitive recovery in the subject after surgery.

[0013] Preferably, the postoperative sample is a sample taken at 24 hours after operation.

[0014] Preferably, the sample to be detected comprises a blood sample.

[0015] Preferably, the method for detecting the expression level of the lipid metabolism marker in the sample to be detected comprises any one or a combination of more of chromatography, mass spectrometry, and chromatography-mass spectrometry.

[0016] Preferably, the chromatography comprises any one of gas chromatography, liquid chromatography, and high-performance liquid chromatography.

[0017] Compared with the prior art, the present application has at least the following beneficial effects:

[0018] 1. The present application first discovers, by the method of metabolomics, that the lipid metabolism difference biomarkers TG (58:7 / 22:5), TG (54:2 / 18:1), PE (O-16:0 / 18:1), and CL (72:3 / 18:2) can be used for preoperative and early postoperative (such as 24 hours after operation) evaluation of the risk of delayed recovery of neurocognitive function. Specifically, by detecting the expression level of (58:7 / 22:5) and / or TG (54:2 / 18:1) in preoperative serum, or the expression level of at least one of (58:7 / 22:5), TG (54:2 / 18:1), PE (O-16:0 / 18:1), and CL (72:3 / 18:2) in postoperative serum, the risk of postoperative delayed recovery of neurocognitive function can be effectively predicted, thereby facilitating early intervention and treatment and promoting postoperative repair.

[0019] 2. The kit provided by the present application can be used for early detection, diagnosis, and prediction of cognitive impairment, and has a small blood sample volume, is convenient and simple to sample, is simple to operate, has a relatively short time required for detection, and has a broad market application prospect and social benefits. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 An evaluation screening flowchart of the sample of the present application is shown.

[0021] Figure 2 Results of differential expression analysis based on preoperative or postoperative serum lipid metabolites are shown, wherein:

[0022] a-b are OPLS-DA score plots;

[0023] c-d are serum lipid metabolites ranked in the top 20 in terms of VIP values in the OPLS-DA model;

[0024] e-f are volcano plots for visualization of the results of differential analysis.

[0025] Figure 3 The a-d represent ROC curve figures for diagnosing and distinguishing the patient group with delayed neurocognitive recovery and the patient group without delayed neurocognitive recovery respectively using four different metabolites. DETAILED DESCRIPTION

[0026] The technical solutions of the present application are further described below in combination with the drawings and examples.

[0027] Explanation of terms

[0028] The "perioperative period" in the present application refers to the whole process around the operation, including the preoperative, intraoperative and postoperative three stages, covering the whole process from the patient's decision to accept the operation to the basic recovery of the body after the operation.

[0029] The "normal person" in the present application refers to a person without delayed neurocognitive recovery after operation.

[0030] "TG" represents the abbreviation of Triglyceride, TG as the most important glyceride is the main energy source of the brain, involving the energy supply, fat storage and body temperature regulation function of the body, but the mechanism of its effect on neurocognitive function is not clear.

[0031] "CL" represents the abbreviation of Cardiolipin, CL is a necessary phospholipid for mitochondrial energy production, mainly located in the inner membrane of mitochondria, which is essential for maintaining membrane integrity and crystalline form, and also involved in a wide range of mitochondrial processes, including the formation and maintenance of protein-protein and protein-membrane interactions. In terms of brain metabolism, CL also directly affects neural function by maintaining neuronal energy homeostasis, antioxidant defense and regulating neurotransmitter release.

[0032] "PE" represents the abbreviation of Phosphatidylethanolamine, PE is the second most abundant phospholipid in cells, and mitochondria membranes are also rich in PE. PE plays an important role in cell growth and maintaining mitochondrial dynamics. The imbalance of CL and PE in mitochondria destroys the mitochondrial-related membrane structure and disrupts the calcium ion homeostasis, thereby causing endoplasmic reticulum-mitochondria coupling disorders. At the same time, PE accounts for 45%-50% of total phospholipids in brain tissue, which can maintain brain homeostasis through antioxidant defense, synaptic plasticity regulation and energy metabolism integration.

[0033] As mentioned above, the current clinical diagnosis of delayed neurocognitive recovery mainly uses cognitive function scale, but this diagnosis method can only passively diagnose the occurrence of dNCR and cannot screen high-risk groups before or early after surgery, resulting in delayed intervention opportunity and missed key period of neurocognitive function protection. Therefore, developing a method for screening dNCR high-risk groups before or early after surgery has become an urgent need in the current field of research.

[0034] To solve the above technical problems, the present application has carried out a large number of researches and analyses. It has been found that lipid homeostasis imbalance is related to nervous system diseases and neurodegenerative diseases such as Alzheimer's disease. Therefore, the present application speculates that abnormal changes in lipid metabolism during the perioperative period may also be closely related to the development of dNCR.

[0035] Based on the above, in order to obtain a group of reliable lipid metabolism markers for predicting the risk of dNCR occurrence, the present application collects and analyzes the preoperative blood samples of dNCR patients and normal people (i.e. people who do not have delayed neurocognitive recovery after surgery) and the blood samples 24 hours after surgery, identifies the content of lipid metabolites in the blood samples by liquid chromatography-mass spectrometry method, and carries out differential expression and ROC curve analysis, and finally screens out four lipid metabolism markers with dNCR occurrence risk prediction value, which are TG (58:7 / 22:5), TG (54:2 / 18:1), PE (O-16:0 / 18:1) and CL (72:3 / 18:2). And verified by the verification population, the four lipid metabolism markers have good sensitivity and specificity in differential diagnosis of dNCR, have high prediction value for the risk of dNCR occurrence, and have good clinical application prospect.

[0036] Next, the research process of the present application will be described in detail through specific examples and drawings.

[0037] Unless otherwise specified, the experimental methods, detection methods, and preparation methods disclosed in the present application all use conventional biochemical, analytical chemistry, and related conventional techniques in the technical field. The materials, reagents, etc. used in the present application can be obtained from commercial channels unless otherwise specified.

[0038] Statistical analysis of metabolomics data to screen differential lipid metabolites

[0039] 1. Materials and methods

[0040] 1) General materials

[0041] This study was an observational study, and the research protocol was approved by the Medical Ethics Committee of the Ninth People's Hospital, School of Medicine, Shanghai Jiao Tong University (SH9H-2021-T120-7). All patients signed a paper informed consent form. Patients aged 65 years or older undergoing oral and maxillofacial surgery under general anesthesia were selected, and the American Society of Anesthesiologists (ASA) classification was grade I-III. Exclusion criteria: history of mental disorders, preoperative history of psychotropic drugs, diagnosis of Alzheimer's disease, preoperative anxiety or depression, postoperative delirium, and perioperative rescue history.

[0042] 2) Anesthesia and perioperative management

[0043] All patients fasted for 8 hours before surgery. After the patient entered the room, peripheral venous access was established routinely, and electrocardiogram, non-invasive and invasive blood pressure, transcutaneous finger oxygen saturation, bispectral index, end-tidal carbon dioxide, airway pressure, and tidal volume were monitored. All operations were performed under tracheal intubation general anesthesia. Preoperative routine use of dolasetron 12.5 mg and penehyclidine hydrochloride 0.5 mg. Anesthesia induction: midazolam 2 mg, dezocine 3-5 mg, sufentanil 10-20 μg, propofol 1-2 mg / kg, rocuronium 0.6 mg / kg. After tracheal intubation, the anesthesia machine was connected using volume control ventilation, maintaining a tidal volume of 6-8 mL / kg, an oxygen flow of 2-6 L / min, an oxygen concentration of 60-80%, a respiratory rate of 10-16 f / min, and an end-tidal carbon dioxide of 35-45 mmHg. Anesthesia maintenance drugs included sevoflurane 1.5%-2.5%, propofol 2-6 mg / kg / h, and remifentanil 0.05-1 μg / kg / min, and rocuronium and sufentanil could be supplemented intermittently as needed. Ephedrine, phenylephrine, or norepinephrine were used to treat hypotension when necessary. Fluid therapy used sodium acetate Ringer's solution, sodium lactate Ringer's solution, and hydroxyethyl starch. Arterial blood gas analysis was performed every 2 hours, and blood transfusion was considered when hemoglobin was <80 g / L. Postoperative routine administration of pentazocine 90 mg for analgesia.

[0044] 3) Perioperative evaluation

[0045] Preoperative data collection included systemic history, laboratory tests and examination results. All patients were assessed with self-rating depression scale (SDS), self-rating anxiety scale (SAS), mini-mental state examination (MMSE) and montreal cognitive assessment (MoCA) before operation. Intraoperative observation indicators included vital signs, general anesthetic medication, fluid replacement volume, blood transfusion volume, blood loss volume, urine volume, operation duration and anesthesia duration.

[0046] MMSE and MoCA scales were used to assess postoperative cognitive function of the patients at 1st, 3rd, 7th and 30th day after operation, and 3D-CAM was used to exclude patients with postoperative delirium. The main endpoint of the present study was the occurrence of dNCR, which was defined as the decrease of MMSE and MoCA scores by more than or equal to 1 standard deviation compared with preoperative scores in any one of the postoperative assessments.

[0047] 4) Sample processing and analysis

[0048] a. Sample collection

[0049] Before anesthesia induction and 24 hours after operation, 5 mL of arterial blood was drawn through an arterial catheter. The sample was placed at 25°C for 30 minutes, then centrifuged at 4°C and 3000 rpm for 15 minutes. The supernatant was frozen in a -80°C refrigerator for standby.

[0050] b. Sample pretreatment

[0051] After adding -30°C pre-cooled methanol to the sample and vortexing, -30°C pre-cooled methyl tert-butyl ether (MTBE) was added and vortexed again. 4°C pre-cooled ultrapure water was added, vortexed (4°C, 2000 rpm) for 5 minutes, and then centrifuged (4°C, 17000 g) for 10 minutes. 200 μL of supernatant was taken into a 1.5 mL centrifuge tube, vacuum dried, and 50 μL of acetonitrile: water = 95:5 (v / v) solution was added, vortexed (4°C, 2000 rpm) for 5 minutes, and then re-dissolved. Finally, centrifuged (4°C, 17000 g) for 10 minutes, and 2 μL of supernatant was taken for metabolomics analysis.

[0052] c. Sample analysis

[0053] Lipid metabolites were detected using a liquid chromatograph mass spectrometer (LC-MS). The chromatographic conditions were as follows: the chromatographic column was BEH Amide (1.8 μm, 100 x 2.1 mm); the mobile phase A was water:acetonitrile = 50:50 (v / v) (containing: 10 mM ammonium acetate + 0.2% ammonia water), and the mobile phase B was water:acetonitrile = 5:95 (v / v) (containing: 10 mM ammonium acetate + 0.2% ammonia water). Gradient elution was used, the flow rate was 0.30 mL / min, the injection volume was 2 μL, and the column temperature was 45°C. Mass spectrometry was performed using a TSQ Altis (Thermo Scientific), and the ion source settings were as follows: sheath gas flow 40 Arb, auxiliary gas flow 10 Arb, purge gas flow 1 Arb, ion transfer tube temperature 320°C, and evaporation temperature 325°C. The positive and negative spray voltages were 3.5 KV and 2.8 KV, respectively. The Q1 and Q3 resolutions were 0.7 and 1.2 Da, respectively. The CID gas pressure was 1.5 mTorr.

[0054] 5) Metabolite differential analysis and structure annotation

[0055] Differential analysis: The lipid metabolite data was extracted using Xcalibur software. Lipids with a loss greater than 50% were removed, and the missing values were filled with 1 / 5 of the minimum value. The mass control sample was used to correct the batch effect, and MetaboAnalyst6.0 software was used for lipid metabolite analysis. After standardizing the data, orthogonal partial least-squares discriminant analysis (OPLS-DA) was used for dimensionality reduction analysis to screen the differential lipid metabolites between the two groups and calculate the variable importance in projection (VIP). Student-t test was performed using SPSS 26.0 software to calculate the false discovery rate (FDR) to correct the P value; and the fold change (FC) was calculated. When the lipid metabolite met the conditions of VIP>2.0, FDR<0.05, and FC>1.2 or <0.83, it was considered to be different between the two groups, and was included in further analysis.

[0056] Structure annotation: For the lipid molecules with clear structure, the Lipid Maps standard annotation is used, that is, the lipid classification abbreviation (carbon chain composition / modification information), wherein the carbon chain composition is the number of carbon atoms: the number of double bonds, and ether lipids are distinguished by O- (vinyl ether bond) or P- (alkyl ether) prefix. However, it is known in the art that in the existing technology of lipid metabolomics liquid chromatography-mass spectrometry (LC-MS) analysis, there are some cases where the structure of the lipid molecules is not clear. For the lipid molecules with unclear structure, the annotation form is: lipid classification abbreviation (total carbon chain composition / measured carbon chain composition) or lipid classification abbreviation (total carbon chain composition).

[0057] Four differential metabolites are obtained by subsequent co-screening of the application, including one lipid molecule with clear structure and three lipid molecules with unclear structure, and the specific information is shown in Table 1 and as follows:

[0058] Table 1: Information related to four differential metabolites

[0059]

[0060] PE (O-16:0 / 18:1), the basic structure of which is: phosphatidylethanolamine (PE) is composed of glycerol, phosphate and ethanolamine, glycerol is the skeleton, the hydroxyl groups thereof are connected with phosphate and two fatty acids through ester bonds, and the phosphate is combined with ethanolamine. The fatty acid composition is: the sn-1 position (i.e. the first carbon atom on the glycerol skeleton) is a carbon chain containing 16 carbon atoms and 0 double bonds connected by an ethylene ether bond (-O-); the sn-2 position is an ester bond (-COO-) connected with a carbon chain containing 18 carbon atoms and 1 double bond (i.e. the carbon chain length is 18 carbon atoms and contains 1 double bond).

[0061] TG (54:2 / 18:1), the basic structure of which is: triglyceride (TG) is composed of glycerol and fatty acid, and glycerol is the skeleton. The fatty acid composition is: composed of three groups of carbon chains, the total carbon chain has 54 carbon atoms, and there are 2 double bonds, and it is measured that at least one 18:1 carbon chain (i.e. the carbon chain length is 18 carbon atoms and contains 1 double bond) is contained.

[0062] TG (58:7 / 22:5), the basic structure of which is: triglyceride (TG) is composed of glycerol and fatty acid, and glycerol is the skeleton. The fatty acid composition is: composed of three groups of carbon chains, the total carbon chain has 58 carbon atoms, and there are 7 double bonds, and it is measured that at least one 22:5 carbon chain (i.e. the carbon chain length is 22 carbon atoms and contains 5 double bonds) is contained.

[0063] CL (72:3 / 18:2), the basic structure of which is: cardiolipin (CL) is composed of one glycerol as a backbone, and two phosphatidic acid (PA) molecules are connected to the sn-1 and sn-2 positions, respectively, and each PA has one fatty acid chain (a total of four fatty acid chains) connected to the sn-1 and sn-2 positions. The fatty acid side chain composition is: there are 72 carbon atoms in the total carbon chain, and there are 3 double bonds, and it is measured that at least one 18:2 carbon chain (i.e. the carbon chain length is 18 carbon atoms, containing 2 double bonds) is contained.

[0064] 6) Statistical analysis

[0065] The sample size was calculated using PASS 15.0 software. According to the definition of dNCR, assuming α = 0.05, 1-β = 0.90, it is calculated that the basic amount of each group is 22 cases to find a difference greater than or equal to 1 standard deviation. Estimate the amount of loss to be 10%, so the sample size of each group is 25 cases. Since the incidence of dNCR is about 20%-35%, the total sample size of more than 75 cases can meet the research needs according to the incidence.

[0066] At the same time, the present application plans to evaluate the relationship between each metabolite and dNCR according to logistic regression, and correct the possible confounding factors. According to previous metabolomics research, the present application selects to correct gender, age, and education level, so each logistic regression plans to include 4 related factors, and about 10 cases of study group and control group samples are needed for each factor, so the total dNCR sample size and control group sample size of about 40 cases can meet the demand.

[0067] SPSS 26.0 was used to analyze the data. For normally distributed continuous data, mean ± standard deviation (x±s) was used, and independent sample t test was used for intergroup analysis. For non-normally distributed continuous data, median (quartile) [M (Q1, Q3)] was used, and Wilcoxon test was used for intergroup analysis. For count data, cases (%) were used, and chi-square test was used for intergroup analysis, P<0.05 was statistically significant.

[0068] The receiver operating characteristic (ROC) curve analysis was performed on the screened differential metabolites, and the area under the curve (AUC) was calculated. According to the maximum value of the Youden index, the cut-off point was determined, and the sensitivity and specificity were calculated. Logistic regression analysis was used to determine the correlation between lipid molecules and the occurrence of dNCR, and age, gender and education level were corrected.

[0069] 2、Experimental results

[0070] like Figure 1 As shown, this invention ultimately included 160 patients for metabolomics analysis. Among them, 52 patients were identified as the delayed neurocognitive recovery group (dNCR group) and 108 patients were identified as the non-delayed neurocognitive recovery group (non-dNCR group) based on cognitive function assessment.

[0071] This invention performed metabolomics analysis on preoperative serum samples and postoperative 24-hour serum samples from two groups of people. The metabolomics data contained rich information on 658 lipid metabolites.

[0072] Preoperative serum lipid metabolites of the two groups were analyzed using OPLS-DA, and VIP value, FDR, and FC were calculated. Results showed that, according to the OPLS-DA score plot, there was a significant separation between preoperative serum metabolites in the dNCR group and the non-dNCR group. Figure 2 (a), while also showcasing the top 20 preoperative serum lipid metabolites by VIP value ( Figure 2 (c) Finally, under the conditions of VIP>2.0, FDR<0.05, FC>1.2 or <0.83, differentially expressed metabolites between the preoperative groups were obtained, namely TG (58:7 / 22:5) and TG (54:2 / 18:1). Compared with the non-dNCR group, TG (58:7 / 22:5) and TG (54:2 / 18:1) were significantly downregulated in the dNCR group. Figure 2 (e).

[0073] Similarly, OPLS-DA analysis was performed on serum lipid metabolites in both groups 24 hours post-surgery, and VIP values, FDR, and FC were calculated. The results showed that, according to the OPLS-DA score plot, there was a significant separation between postoperative serum metabolites in the dNCR group and the non-dNCR group. Figure 2 (b), while also showcasing the top 20 preoperative serum lipid metabolites by VIP value ( Figure 2 Finally, under the conditions of VIP>2.0, FDR<0.05, FC>1.2 or <0.83, differentially expressed metabolites between the groups after surgery were screened and identified as TG (58:7 / 22:5), TG (54:2 / 18:1), PE (O-16:0 / 18:1), and CL (72:3 / 18:2). Compared with the non-dNCR group, TG (58:7 / 22:5) and TG (54:2 / 18:1) were significantly downregulated in the dNCR group, while PE (O-16:0 / 18:1) and CL (72:3 / 18:2) were significantly upregulated. Figure 2 f).

[0074] The above results show that in the preoperative serum samples, the expression levels of lipid metabolites TG (58:7 / 22:5) and TG (54:2 / 18:1) are important indicators of the occurrence of dNCR; in the postoperative serum samples, the expression levels of lipid metabolites TG (58:7 / 22:5), TG (54:2 / 18:1), PE (O-16:0 / 18:1) and CL (72:3 / 18:2) are important indicators of the occurrence of dNCR.

[0075] (II) ROC analysis and logistic regression analysis of differential metabolites

[0076] ROC analysis and logistic regression analysis were performed on the differential metabolites screened above to evaluate the performance of the differential metabolites for predicting the risk of dNCR occurrence.

[0077] Figure 3 The a indicates the ROC curve for diagnosing and distinguishing dNCR group and non-dNCR group using TG (58:7 / 22:5) based on preoperative or postoperative samples. The results show that when diagnosing dNCR based on preoperative samples using TG (58:7 / 22:5), the accuracy (AUC) reaches 0.81, the sensitivity is 0.75, and the specificity is 0.74. When diagnosing dNCR based on postoperative samples using TG (58:7 / 22:5), the accuracy (AUC) reaches 0.75, the sensitivity is 0.87, and the specificity is 0.50. After multivariate model correction, logistic regression analysis showed that lower levels of TG (58:7 / 22:5) in preoperative and postoperative serum were positively correlated with the risk of dNCR occurrence (preoperative OR = 0.018, 95% CI = 0.003-0.124, P < 0.001; postoperative OR = 0.067, 95% CI = 0.015-0.308, P < 0.001).

[0078] Figure 3b indicates the ROC curve figure for diagnosing dNCR based on TG (54:2 / 18:1) in preoperative or postoperative samples. The results show that the accuracy (AUC) reaches 0.77, the sensitivity is 0.65, and the specificity is 0.78 when diagnosing dNCR based on TG (54:2 / 18:1) in preoperative samples. The accuracy (AUC) reaches 0.75, the sensitivity is 0.71, and the specificity is 0.66 when diagnosing dNCR based on TG (54:2 / 18:1) in postoperative samples. After multivariate model correction, logistic regression analysis shows that lower levels of TG (54:2 / 18:1) in preoperative and postoperative serum are positively correlated with the risk of dNCR (preoperative OR = 0.053, 95% CI = 0.011-0.257, P < 0.001; postoperative OR = 0.035, 95% CI = 0.007-0.178, P < 0.001).

[0079] Figure 3 c indicates the ROC curve figure for diagnosing dNCR based on PE (O-16:0 / 18:1) in postoperative samples. The results show that the accuracy (AUC) reaches 0.77, the sensitivity is 0.62, and the specificity is 0.90 when diagnosing dNCR based on PE (O-16:0 / 18:1) in postoperative samples. After multivariate model correction, logistic regression analysis shows that higher levels of PE (O-16:0 / 18:1) in postoperative serum are positively correlated with the risk of dNCR (postoperative OR = 5.085, 95% CI = 2.146-12.048, P < 0.001).

[0080] Figure 3 d indicates the ROC curve figure for diagnosing dNCR based on CL (72:3 / 18:2) in postoperative samples. The results show that the accuracy (AUC) reaches 0.74, the sensitivity is 0.50, and the specificity is 0.92 when diagnosing dNCR based on CL (72:3 / 18:2) in postoperative samples. After multivariate model correction, logistic regression analysis shows that CL (72:3 / 18:2) in postoperative serum is not positively correlated with the risk of dNCR.

[0081] The application subsequently collected 20 patients (including 13 dNCRs and 7 non-dNCRs) for evaluating the prediction evaluation performance of the above-mentioned four lipid metabolism markers for predicting the risk of dNCR occurrence. It is found that when the four lipid metabolism markers TG (58:7 / 22:5), TG (54:2 / 18:1), PE (O-16:0 / 18:1), and CL (72:3 / 18:2) are diagnosed alone, the accuracy (AUC) is all above 0.75; when the four lipid metabolism markers are used in combination, the accuracy (AUC) is 0.85.

[0082] The above results show that the four lipid metabolism markers screened by the application can accurately predict the risk of dNCR occurrence before or early after surgery, and have clinical diagnostic application value. If a single biomarker is found to be differentially changed in the blood of a patient, it is necessary to be vigilant about the possibility of delayed postoperative neurocognitive recovery in such patients, and if four biomarkers are differentially changed, it is necessary to be highly vigilant about the possibility of delayed postoperative neurocognitive recovery in such patients, so as to carry out early intervention or early postoperative treatment in a targeted manner to promote postoperative recovery.

[0083] In summary, the application first finds that the lipid metabolism differential biomarkers TG (58:7 / 22:5), TG (54:2 / 18:1), PE (O-16:0 / 18:1), and CL (72:3 / 18:2) can be used for preoperative and early postoperative (such as 24 h after surgery) evaluation of the risk of delayed neurocognitive recovery. And verified by the verification population, the four lipid metabolism markers have good sensitivity and specificity in differential diagnosis of dNCR, and have high prediction value for the risk of dNCR occurrence, and have a promising clinical application prospect.

[0084] Although the content of the application has been described in detail through the above preferred embodiments, it should be recognized that the above description should not be considered as limiting the application. After reading the above content, various modifications and alternatives of the application will be apparent to those skilled in the art. Therefore, the protection scope of the application should be defined by the appended claims.

Claims

1. A kit for predicting the risk of occurrence of a delay in neurocognitive recovery, characterized in that, The kit comprises: reagents for detecting the expression level of a lipid metabolism marker in a sample to be tested, the lipid metabolism marker comprising: at least one of TG (58:7 / 22:5), TG (54:2 / 18:1), PE (O-16:0 / 18:1), and CL (72:3 / 18:2).

2. The kit of claim 1, wherein The kit comprises: reagents for detecting the expression level of TG (58:7 / 22:5), TG (54:2 / 18:1), PE (O-16:0 / 18:1), and CL (72:3 / 18:2) in a sample to be tested.

3. Use of a reagent for detecting the expression level of a lipid metabolism marker in a test sample in the manufacture of a kit for predicting the risk of delayed occurrence of neurocognitive recovery, characterized in that, The lipid metabolism marker comprises: at least one of TG (58:7 / 22:5), TG (54:2 / 18:1), PE (O-16:0 / 18:1), and CL (72:3 / 18:2).

4. Use according to claim 3, wherein the compound is ###0002### Compared with normal people, the expression levels of TG (58:7 / 22:5) and TG (54:2 / 18:1) are down-regulated in patients with delayed recovery of neurocognition, and the expression levels of PE (O-16:0 / 18:1) and CL (72:3 / 18:2) are up-regulated in patients with delayed recovery of neurocognition.

5. The use according to claim 4, wherein the compound is ###0002### The sample to be tested is a preoperative sample, and the risk of delayed recovery of neurocognition of a subject after surgery is determined by detecting the expression level of TG (58:7 / 22:5) and / or TG (54:2 / 18:1) in the preoperative sample of the subject.

6. Use according to claim 4, wherein the compound is ###0002### The sample to be tested is a postoperative sample, and the risk of delayed recovery of neurocognition of a subject after surgery is determined by detecting the expression level of at least one of TG (58:7 / 22:5), TG (54:2 / 18:1), PE (O-16:0 / 18:1), and CL (72:3 / 18:2) in the postoperative sample of the subject.

7. Use according to claim 6, wherein The postoperative sample is a sample taken 24 hours after surgery.

8. The use according to claim 3, wherein the compound is ###0002### The sample to be tested comprises: a blood sample.

9. The use according to claim 3, wherein the compound is ###00003### 3 The method for detecting the expression level of a lipid metabolism marker in a sample to be tested comprises: any one or a combination of more than one of chromatography, mass spectrometry, and chromatography-mass spectrometry.

10. Use according to claim 9, wherein The chromatography comprises any one of gas chromatography, liquid chromatography, and high-performance liquid chromatography.

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