Kit for predicting occurrence risk of neurocognitive recovery delay and application thereof

Abstract

The invention discloses a kit for predicting the occurrence risk of neurocognitive recovery delay and application of the kit. The kit comprises a reagent for detecting the expression level of a lipid metabolism marker in a sample to be detected, wherein 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). It is found through experiments that compared with non-dNCR patients, the expression levels of TG (58: 7 / 22: 5) and TG (54: 2 / 18: 1) in the dNCR patients are reduced, the expression levels of PE (O-16: 0 / 18: 1) and CL (72: 3 / 18: 2) in the dNCR patients are increased, the risk of dNCR occurrence of the patients can be confirmed in the early stage before and / or after an operation by detecting the expression levels of the lipid metabolism markers, early intervention and treatment are facilitated, and the risk of dNCR occurrence of the patients is reduced. The postoperative repair is promoted.

Description

Technical Field

[0001] This invention relates to the field of biomedicine, specifically to a kit for predicting the risk of delayed neurocognitive recovery and its application. Background Technology

[0002] According to the 2018 recommendations from the Postoperative Cognitive Research Collaboration Group in the United States regarding the nomenclature of perioperative cognitive changes, delayed neurocognitive recovery (dNCR) is defined as a new-onset objective decline in cognitive function within 30 days postoperatively. As an important subtype of perioperative neurocognitive impairment, dNCR is characterized by a high incidence (19.5%-41.4%) and is associated with long-term cognitive impairment, mortality, and quality of life.

[0003] Currently, clinical diagnosis of delayed neurocognitive recovery primarily relies on cognitive function scales, including the MMSE (Mini-Mmental State Examination) and MoCA (Montreal Cognitive Assessment). In practice, baseline cognitive function data is typically collected preoperatively, and the scales are repeated at 1, 3, 7, and 30 days post-surgery. Changes in cognitive function are compared and analyzed, and a diagnosis is made after excluding patients with delirium using the 3-minute diagnostic interview for confusion assessment method (3D-CAM). However, this diagnostic approach has the following significant limitations: (1) Lack of predictive value and insufficient diagnostic timeliness: Existing diagnostic methods can only passively diagnose dNCR that has already occurred, and cannot screen out high-risk groups in the early stage, making it difficult to meet the clinical needs of preoperative risk stratification and personalized prevention plan formulation; it also cannot quickly identify potential cases in the early postoperative period (such as within 24-48 hours), resulting in delayed intervention and missing the critical period for neurocognitive function protection.

[0004] (2) Subjectivity and operational limitations: The scale assessment depends on the patient's cooperation. For patients who are agitated, confused or have language disorders after surgery, the accuracy of the results is easily affected. At the same time, the assessment process is time-consuming and requires professional personnel to operate, making it difficult to implement efficiently in a busy clinical environment.

[0005] Therefore, there is an urgent need in this field to develop a method that can screen high-risk individuals for dNCR before or early after surgery, so as to facilitate early intervention or early postoperative treatment and thus promote postoperative recovery. Summary of the Invention

[0006] This invention addresses the problem that existing diagnostic methods can only passively diagnose dNCR that has already occurred and cannot screen high-risk individuals in the early stages. It provides a kit for predicting the risk of delayed neurocognitive recovery and its application. This kit can confirm the risk of patients developing dNCR before and / or early after surgery, thereby facilitating early intervention or early postoperative treatment and avoiding greater harm caused by untimely control and treatment.

[0007] Based on the above, the present invention first provides a kit for predicting the risk of delayed neurocognitive recovery. The kit includes: a reagent for detecting the expression level of lipid metabolism biomarkers in the sample to be tested. The lipid metabolism biomarkers include 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).

[0008] Preferably, the kit comprises: reagents for detecting the expression levels 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 sample to be tested.

[0009] In another aspect, this invention provides the application of a reagent for detecting the expression levels of lipid metabolism biomarkers in a test sample in the preparation of a kit for predicting the risk of delayed neurocognitive recovery. The lipid metabolism biomarkers include 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 to normal individuals, the expression levels of TG (58:7 / 22:5) and TG (54:2 / 18:1) are downregulated in patients with delayed neurocognitive recovery, while the expression levels of PE (O-16:0 / 18:1) and CL (72:3 / 18:2) are upregulated in patients with delayed neurocognitive recovery.

[0010] Preferably, the sample to be tested is a preoperative sample. By detecting the expression levels of TG (58:7 / 22:5) and / or TG (54:2 / 18:1) in the preoperative sample of the subject, the risk of delayed neurocognitive recovery after surgery can be determined.

[0011] Preferably, the sample to be tested is a postoperative sample. The risk of delayed neurocognitive recovery in the 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 (0-16:0 / 18:1), and CL (72:3 / 18:2) in the subject's postoperative sample.

[0012] Preferably, the postoperative sample is a sample taken 24 hours after the operation.

[0013] Preferably, the sample to be tested includes a blood sample.

[0014] Preferably, the method for detecting the expression level of lipid metabolism markers in the sample to be tested includes any one or more combinations of chromatography, mass spectrometry, and chromatography-mass spectrometry.

[0015] Preferably, the chromatographic method includes any one of gas chromatography, liquid chromatography, and high-performance liquid chromatography.

[0016] Compared with the prior art, the beneficial effects of the present invention include at least the following: 1. This invention, through metabolomics, for the first time discovers that the differential lipid metabolism 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 to assess the risk of delayed neurocognitive recovery before and in the early postoperative period (e.g., 24 hours postoperatively). Specifically, by detecting the expression levels of (58:7 / 22:5) and / or TG (54:2 / 18:1) in preoperative serum, or the expression levels 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 neurocognitive recovery can be effectively predicted, thereby facilitating early intervention and treatment and promoting postoperative repair.

[0017] 2. The kit provided by this invention can be used for the early detection, diagnosis and prediction of cognitive impairment. The test sample is serum, which requires a small amount of blood. The sampling is convenient and simple, the operation is simple, and the test results are obtained in a short time. It has broad market application prospects and social benefits. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the evaluation and screening process for including samples in this invention.

[0019] Figure 2 This represents the results of differential expression analysis based on preoperative or postoperative serum lipid metabolites, where: ab is the OPLS-DA score graph; cd represents the top 20 serum lipid metabolites by VIP value in the OPLS-DA model; ef is a volcano plot that visualizes the results of the differential analysis.

[0020] Figure 3 The ad represents the ROC curves used to diagnose and differentiate between patients with delayed neurocognitive recovery and those without, based on four differentially expressed metabolites. Detailed Implementation

[0021] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0022] Terminology Explanation The "perioperative period" as described in this invention refers to the entire process surrounding the surgery, including the three stages of preoperative, intraoperative, and postoperative surgery, covering the entire process from when the patient decides to undergo surgery to when the body has basically recovered after the surgery.

[0023] The term "normal person" in this invention refers to a person who does not experience delayed neurocognitive recovery after surgery.

[0024] "TG" is an abbreviation for triglyceride. As the most important type of triglyceride, TG is the main energy source for the brain and is involved in the body's energy supply, fat storage, and body temperature regulation. However, its mechanism of action on neurocognitive function is still unclear.

[0025] "CL" is an abbreviation for cardiolipin. CL is an essential phospholipid for mitochondrial energy production, mainly located in the inner mitochondrial membrane. It is crucial for maintaining membrane integrity and crystalline morphology, and also participates in a wide range of mitochondrial processes, including the formation and maintenance of protein-protein and protein-membrane interactions. In brain metabolism, CL also directly affects neuronal function by maintaining neuronal energy homeostasis, providing antioxidant defense, and regulating neurotransmitter release.

[0026] "PE" is an abbreviation for phosphatidylethanolamine. PE is the second most abundant phospholipid in cells, and it is also rich in mitochondrial membranes. PE plays an important role in cell growth and maintaining mitochondrial dynamics. An imbalance of CL and PE in mitochondria disrupts mitochondrial membrane structures, causes calcium ion homeostasis disorder, and leads to endoplasmic reticulum-mitochondrial coupling impairment. Meanwhile, PE accounts for 45%-50% of total phospholipids in brain tissue and maintains brain homeostasis through antioxidant defense, synaptic plasticity regulation, and energy metabolism integration.

[0027] As mentioned earlier, current clinical practice primarily uses cognitive function scales to diagnose delayed neurocognitive recovery (dNCR). However, this diagnostic method can only passively diagnose existing dNCR and cannot screen high-risk individuals before or early after surgery, leading to delayed intervention and missing the critical period for neurocognitive function protection. Therefore, developing a method to screen high-risk individuals for dNCR before or early after surgery has become an urgent research need in this field.

[0028] To address the aforementioned technical problems, this invention has conducted extensive research and analysis. Studies have revealed that lipid homeostasis imbalance is associated with neurological diseases and neurodegenerative diseases such as Alzheimer's disease. Therefore, this invention hypothesizes that abnormal changes in lipid metabolism during the perioperative period may also be closely related to the development of dNCR.

[0029] Based on the above, to obtain a set of reliable lipid metabolism biomarkers for predicting the risk of dNCR, this invention collected and analyzed preoperative blood samples from dNCR patients and normal individuals (i.e., those who did not experience delayed neurocognitive recovery after surgery), as well as blood samples 24 hours postoperatively. The content of lipid metabolites in the blood samples was identified using liquid chromatography-mass spectrometry (LC-MS / MS), and differential expression and ROC curve analysis were performed. Ultimately, four lipid metabolism biomarkers with predictive value for dNCR risk were selected: TG (58:7 / 22:5), TG (54:2 / 18:1), PE (O-16:0 / 18:1), and CL (72:3 / 18:2). Validation in a validation population showed that these four lipid metabolism biomarkers have good sensitivity and specificity in the differential diagnosis of dNCR, and possess high predictive value for the risk of dNCR, indicating promising clinical application prospects.

[0030] The research process of the present invention will now be described in detail with reference to specific embodiments and accompanying drawings.

[0031] Unless otherwise stated, the experimental methods, detection methods, and preparation methods disclosed in this invention all employ conventional techniques in the fields of biochemistry, analytical chemistry, and related areas. Unless otherwise specified, all materials and reagents used in this invention are commercially available.

[0032] Metabolomics data statistical analysis to screen differentially expressed lipid metabolites 1. Materials and Methods 1) General materials This was an observational study, and the protocol was approved by the Medical Ethics Committee of the Ninth People's Hospital affiliated with Shanghai Jiao Tong University School of Medicine (SH9H-2021-T120-7). All patients signed written informed consent forms. Patients aged 65 years and older scheduled for elective oral and maxillofacial surgery under general anesthesia, meeting the American Society of Anesthesiologists (ASA) classification of I-III, were selected. Exclusion criteria included: history of mental disorders, preoperative use of psychotropic medications, diagnosis of Alzheimer's disease, preoperative anxiety or depression, postoperative delirium, and history of perioperative resuscitation.

[0033] 2) Anesthesia and perioperative management All patients fasted for 8 hours preoperatively. Upon arrival at the operating room, peripheral intravenous access was routinely established, and electrocardiogram (ECG), non-invasive and invasive blood pressure, percutaneous pulse oximetry, bispectral index (BPI), end-tidal carbon dioxide (UTC), airway pressure, and tidal volume were monitored. All surgeries were performed under general anesthesia with endotracheal intubation. Preoperatively, dolasetron 12.5 mg and pentoxyverine hydrochloride 0.5 mg were routinely administered. Anesthesia induction was performed with midazolam 2 mg, dezocine 3-5 mg, sufentanil 10-20 μg, propofol 1-2 mg / kg, and rocuronium bromide 0.6 mg / kg. After endotracheal intubation, machine-controlled ventilation was used, maintaining a tidal volume of 6-8 mL / kg, oxygen flow rate of 2-6 L / min, oxygen concentration of 60-80%, respiratory rate of 10-16 f / min, and end-tidal CO2 of 35-45 mmHg. Maintenance anesthesia medications include sevoflurane 1.5%-2.5%, propofol 2-6 mg / kg / h, and remifentanil 0.05-1 μg / kg / min. Rocuronium and sufentanil may be intermittently administered as needed. Ephedrine, phenylephrine, or norepinephrine may be used to treat hypotension if necessary. Fluid therapy consists of sodium acetate Ringer's solution, lactated Ringer's solution, and hydroxyethyl starch. Arterial blood gas analysis is performed every 2 hours; blood transfusion is considered when hemoglobin is <80 g / L. Postoperatively, pentazocine 90 mg is routinely administered for analgesia.

[0034] 3) Perioperative assessment Preoperative data collected included systemic medical history, laboratory test results, and examination findings. All patients underwent preoperative assessments using the Self-Rating Depression Scale (SDS), Self-Rating Anxiety Scale (SAS), Mini-Mental State Examination (MMSE), and Montreal Cognitive Assessment (MoCA). Intraoperative monitoring indicators included vital signs, administration of general anesthetic medications, fluid resuscitation, blood transfusions, blood loss, urine output, and the duration of surgery and anesthesia.

[0035] Postoperative cognitive function was assessed using the MMSE and MoCA scales on days 1, 3, 7, and 30 postoperatively. Patients experiencing postoperative delirium were excluded using 3D-CAM. The primary endpoint of this study was the occurrence of dNCR, defined as a decrease of ≥1 standard deviation in both MMSE and MoCA scores at any postoperative assessment compared to preoperative scores.

[0036] 4) Sample processing and analysis a. Sample collection Arterial blood was drawn 5 mL via arterial catheter before anesthesia induction and again 24 hours post-anesthesia. The samples were incubated at 25°C for 30 minutes, then centrifuged at 3000 rpm for 15 minutes at 4°C. The supernatant was frozen at -80°C for later use.

[0037] b. Sample preprocessing After adding pre-chilled methanol (-30°C) to the sample and vortexing thoroughly, pre-chilled methyl tert-butyl ether (MTBE) (-30°C) was added and vortexed again. Pre-chilled ultrapure water (4°C) was added, and the mixture was vortexed (4°C, 2000 rpm) for 5 minutes, then centrifuged for 10 minutes (4°C, 17000 g). 200 μL of the supernatant was transferred to a 1.5 mL centrifuge tube, vacuum dried, and then reconstituted with 50 μL of acetonitrile:water = 95:5 (v / v) solution by vortexing for 5 minutes (4°C, 2000 rpm). Finally, the mixture was centrifuged for 10 minutes (4°C, 17000 g), and 2 μL of the supernatant was collected for metabolomics analysis.

[0038] c. Sample Analysis Lipid metabolites were detected using liquid chromatography-mass spectrometry (LC-MS). Chromatographic conditions were as follows: BEH Amide column (1.8 μm, 100 × 2.1 mm); mobile phase A was water:acetonitrile = 50:50 (v / v) (containing 10 mM ammonium acetate + 0.2% ammonia), and mobile phase B was water:acetonitrile = 5:95 (v / v) (containing 10 mM ammonium acetate + 0.2% ammonia). Gradient elution was used at a flow rate of 0.30 mL / min, an injection volume of 2 μL, and a column temperature of 45°C. Mass spectrometry was performed using a TSQ Altis (Thermo Scientific) ion source with the following settings: sheath gas flow rate 40 Arb, auxiliary gas flow rate 10 Arb, purge gas flow rate 1 Arb, ion transfer tube temperature 320°C, and evaporation temperature 325°C. The positive and negative injection voltages were 3.5 kV and 2.8 kV, respectively. The resolutions of Q1 and Q3 are 0.7 and 1.2 Da, respectively. The CID gas pressure is 1.5 mTorr.

[0039] 5) Metabolite differential analysis and structural annotation Analysis of Differences: Lipid metabolite data were extracted using Xcalibur software. Lipids with a loss greater than 50% were removed, and missing values ​​were filled with 1 / 5 of the minimum value. Batch effects were corrected using quality control samples, and lipid metabolite analysis was performed using MetaboAnalyst 6.0 software. After data standardization, orthogonal partial least-squares discriminant analysis (OPLS-DA) was used for dimensionality reduction to screen for differentially expressed lipid metabolites between the two groups and calculate their variable importance inprojection (VIP). The Student-t test was performed using SPSS 26.0 software to calculate the false discovery rate (FDR) to correct for p-values ​​and to calculate the fold change (FC). Lipid metabolites were considered differentially expressed between the two groups if they met the following criteria: VIP > 2.0, FDR < 0.05, and FC > 1.2 or < 0.83, and were included in further analysis.

[0040] Structural labeling: For lipid molecules with well-defined structures, the Lipid Maps standard labeling is used, i.e., lipid classification abbreviation (carbon chain composition / modification information), where the carbon chain composition is the number of carbon atoms: number of double bonds. Ether lipids are distinguished by the prefix O- (ethylene ether bond) or P- (alkyl ether). However, it is well known in the art that in existing technologies for lipid metabolomics liquid chromatography-mass spectrometry (LC-MS) analysis, there are cases where the structure of some lipid molecules is unclear. For lipid molecules with unclear structures, the labeling format is: lipid classification abbreviation (total carbon chain composition / measured carbon chain composition) or lipid classification abbreviation (total carbon chain composition).

[0041] Subsequent screening in this invention yielded four differentially expressed metabolites, including one lipid molecule with a defined structure and three lipid molecules with undefined structures. Specific information is shown in Table 1 and below: Table 1. Information on four differentially metabolites

[0042] PE (O-16:0 / 18:1) has the following basic structure: Phosphatidylethanolamine (PE) is composed of glycerol, phosphoric acid, and ethanolamine. Glycerol forms the backbone, with its hydroxyl groups linked to phosphoric acid and two fatty acids via ester bonds. The phosphoric acid then combines with ethanolamine. The fatty acid composition is as follows: at the sn-1 position (i.e., the first carbon atom on the glycerol backbone), there is an ethylene ether bond (-O-) connecting a carbon chain containing 16 carbon atoms and 0 double bonds; at the sn-2 position, there is an ester bond (-COO-) connecting 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).

[0043] TG (54:2 / 18:1) has the following basic structure: Triglycerides (TG) are composed of glycerol and fatty acids, with glycerol forming the backbone. The fatty acid composition consists of three carbon chains, with a total of 54 carbon atoms and two double bonds. It was found that at least one 18:1 carbon chain (i.e., a carbon chain length of 18 carbon atoms and containing one double bond) is present.

[0044] TG (58:7 / 22:5) has the following basic structure: Triglycerides (TG) are composed of glycerol and fatty acids, with glycerol forming the backbone. The fatty acid composition consists of three carbon chains, with a total of 58 carbon atoms and 7 double bonds. It was found that at least one 22:5 carbon chain (i.e., a carbon chain length of 22 carbon atoms and containing 5 double bonds) is present.

[0045] CL (72:3 / 18:2) has the following basic structure: Cardiolipin (CL) consists of a glycerol backbone, with two phosphatidic acid (PA) molecules attached to its sn-1 and sn-2 positions respectively. Each PA has a fatty acid chain attached to its sn-1 and sn-2 positions (a total of four fatty acid chains). The fatty acid side chains consist of 72 carbon atoms in total, with 3 double bonds, and at least one 18:2 carbon chain (i.e., a carbon chain length of 18 carbon atoms with 2 double bonds).

[0046] 6) Statistical analysis Sample size was calculated using PASS 15.0 software. Based on the definition of dNCR, assuming α = 0.05 and 1 - α = 0.90, it was calculated that a baseline size of 22 cases per group would detect a difference greater than or equal to one standard deviation. Assuming a loss to follow-up rate of 10%, the sample size per group was 25 cases. Since the dNCR incidence rate is approximately 20%-35%, a total sample size greater than 75 cases is required to meet the study requirements, based on the incidence rate calculation.

[0047] Simultaneously, this invention plans to individually assess the relationship between each metabolite and dNCR using logistic regression, and correct for any potential confounding factors. Based on previous metabolomics studies, this invention selects to correct for gender, age, and education level; therefore, each logistic regression plan will include four relevant factors, with each factor requiring approximately 10 samples from the study group and control group. The required total dNCR sample size and control group sample size are approximately 40 samples each to meet the requirements.

[0048] Data were analyzed using SPSS 26.0. For normally distributed continuous data, mean ± standard deviation (x ± s) was used, and independent samples t-tests were employed for between-group analysis. For non-normally distributed continuous data, median (interquartiles) [M(Q1, Q3)] was used, and Wilcoxon tests were employed for between-group analysis. For categorical data, percentage (%) was used, and chi-square tests were employed for between-group analysis. P < 0.05 was considered statistically significant.

[0049] Receiver operating characteristic (ROC) curve analysis was performed on the screened differentially expressed metabolites, and the area under the curve (AUC) was calculated. The cutoff point was determined based on the maximum value of the Youden index, thereby calculating sensitivity and specificity. Logistic regression analysis was used to determine the correlation between lipid molecules and dNCR occurrence, and adjustments were made for age, sex, and education level.

[0050] 2. Experimental Results 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.

[0051] 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.

[0052] 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).

[0053] 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).

[0054] The above results indicate that the expression levels of lipid metabolites TG (58:7 / 22:5) and TG (54:2 / 18:1) in preoperative serum samples are significant indicators of the occurrence of dNCR; and 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) in postoperative serum samples are significant indicators of the occurrence of dNCR.

[0055] (ii) ROC analysis and logistic regression analysis of differential metabolites ROC and logistic regression analyses were performed on the differentially metabolites selected above to evaluate their performance in predicting the risk of dNCR.

[0056] Figure 3'a' represents the ROC curve used by TG (58:7 / 22:5) to differentiate between the dNCR group and the non-dNCR group based on preoperative or postoperative samples. The results show that when using TG (58:7 / 22:5) to diagnose dNCR based on preoperative samples, the accuracy (AUC) reached 0.81, the sensitivity was 0.75, and the specificity was 0.74. When using TG (58:7 / 22:5) to diagnose dNCR based on postoperative samples, the accuracy (AUC) reached 0.75, the sensitivity was 0.87, and the specificity was 0.50. After adjustment for multivariate model, logistic regression analysis showed that lower serum TG levels (58:7 / 22:5) before and after surgery were positively correlated with the risk of dNCR (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).

[0057] Figure 3 'b' represents the ROC curve used to differentiate between the dNCR group and the non-dNCR group based on preoperative or postoperative samples using TG (54:2 / 18:1). The results show that when using TG (54:2 / 18:1) to diagnose dNCR based on preoperative samples, the accuracy (AUC) reached 0.77, the sensitivity was 0.65, and the specificity was 0.78. When using TG (54:2 / 18:1) to diagnose dNCR based on postoperative samples, the accuracy (AUC) reached 0.75, the sensitivity was 0.71, and the specificity was 0.66. After adjustment for multivariate model, logistic regression analysis showed that lower serum TG levels (54:2 / 18:1) before and after surgery were 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).

[0058] Figure 3 The 'c' represents the ROC curve used to differentiate between the dNCR group and the non-dNCR group based on postoperative samples using PE (O-16:0 / 18:1). The results show that when using PE (O-16:0 / 18:1) to diagnose dNCR based on postoperative samples, the accuracy (AUC) reached 0.77, the sensitivity was 0.62, and the specificity was 0.90. After multivariate model correction, logistic regression analysis showed that higher levels of PE (O-16:0 / 18:1) in postoperative serum were positively correlated with the risk of dNCR (postoperative OR = 5.085, 95% CI = 2.146–12.048, P < 0.001).

[0059] Figure 3 'd' represents the ROC curve used to differentiate between the dNCR group and the non-dNCR group based on postoperative samples using CL (72:3 / 18:2). The results show that when using CL (72:3 / 18:2) to diagnose dNCR based on postoperative samples, the accuracy (AUC) reached 0.74, the sensitivity was 0.50, and the specificity was 0.92. After multivariate model correction, logistic regression analysis did not show a positive correlation between postoperative serum CL (72:3 / 18:2) and the risk of dNCR.

[0060] In this study, an additional 20 patients (including 13 with dNCR and 7 without dNCR) were collected to evaluate the predictive performance of the four lipid metabolism biomarkers in predicting the risk of dNCR. The results showed that when the four lipid metabolism biomarkers TG (58:7 / 22:5), TG (54:2 / 18:1), PE (O-16:0 / 18:1), and CL (72:3 / 18:2) were used individually, their accuracy (AUC) was above 0.75; when the four lipid metabolism biomarkers were used in combination, the accuracy (AUC) reached 0.85.

[0061] The above results demonstrate that the four lipid metabolism biomarkers screened in this invention can accurately predict the risk of dNCR before or early after surgery, and have clinical diagnostic value. If a single biomarker shows differential changes in a patient's blood, it is necessary to be alert to the possibility of delayed neurocognitive recovery after surgery. If all four biomarkers show differential changes, it is also necessary to be highly alert to the possibility of delayed neurocognitive recovery after surgery, thus enabling targeted early intervention or early postoperative treatment to promote postoperative recovery.

[0062] In summary, this invention, through metabolomics, for the first time discovered that the differential lipid metabolism 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 preoperatively and in the early postoperative period (e.g., 24 hours postoperatively) to assess the risk of delayed neurocognitive recovery. Validation in a population demonstrated that these four lipid metabolism biomarkers exhibit good sensitivity and specificity in the differential diagnosis of dNCR, and possess high predictive value for the risk of dNCR, showing promising clinical application prospects.

[0063] Although the present invention has been described in detail through the preferred embodiments above, it should be understood that the above description should not be considered as a limitation of the present invention. Various modifications and substitutions to the present invention will be apparent to those skilled in the art after reading the above description. Therefore, the scope of protection of the present invention should be defined by the appended claims.

Claims

1. A kit for predicting the risk of delayed neurocognitive recovery, characterized in that, The kit includes: a reagent for detecting the expression level of lipid metabolism biomarkers in the sample to be tested, wherein the lipid metabolism biomarkers include 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 according to claim 1, characterized in that, The kit contains reagents for detecting the expression levels 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 sample to be tested.

3. The application of reagents for detecting the expression levels of lipid metabolism markers in test samples in the preparation of kits for predicting the risk of delayed neurocognitive recovery, characterized in that... The lipid metabolism markers include 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. The application as described in claim 3, characterized in that, Compared with normal individuals, the expression levels of TG (58:7 / 22:5) and TG (54:2 / 18:1) were downregulated in patients with delayed neurocognitive recovery, while the expression levels of PE (O-16:0 / 18:1) and CL (72:3 / 18:2) were upregulated in patients with delayed neurocognitive recovery.

5. The application as described in claim 4, characterized in that, The test sample is a preoperative sample. By detecting the expression levels of TG (58:7 / 22:5) and / or TG (54:2 / 18:1) in the preoperative sample, the risk of delayed neurocognitive recovery in the subject after surgery can be determined.

6. The application as described in claim 4, characterized in that, The test sample is a postoperative sample. 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, the risk of delayed neurocognitive recovery in the subject after surgery can be determined.

7. The application as described in claim 6, characterized in that, The postoperative sample was taken 24 hours after the surgery.

8. The application as described in claim 3, characterized in that, The samples to be tested include: blood samples.

9. The application as described in claim 3, characterized in that, Methods for detecting the expression levels of lipid metabolism biomarkers in a sample include: chromatography, mass spectrometry, and any combination of one or more of the following: chromatography, mass spectrometry, and chromatography-mass spectrometry.

10. The application as described in claim 9, characterized in that, The chromatographic method includes any one of gas chromatography, liquid chromatography, and high-performance liquid chromatography.

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