Metabolic spectrum detection kit based on three differential metabolites and application thereof

Abstract

The invention discloses a metabolic spectrum detection kit based on three differential metabolites and application of the metabolic spectrum detection kit, and belongs to a biomedical detection technology. The invention provides a kit for detecting three metabolites of glycerol, 3-hydroxyisovaleric acid and methionine. The kit comprises a calibrator unit, a sample treatment reagent unit, a derivatization reagent unit and a detection unit, the calibrator unit comprises a glycerol standard substance, a 3-hydroxyisovaleric acid standard substance and a methionine standard substance with concentration gradients; the sample treatment reagent unit comprises a protein precipitator, a metabolite extractant and a stabilizer; the derivatization reagent unit comprises a silanization reagent for gas chromatography-mass spectrometry analysis; the detection unit comprises a specific ion pair and an internal standard substance which are used for liquid chromatography-tandem mass spectrometry. By utilizing the kit, comprehensive evaluation of mitochondrial function, amino acid metabolism and energy metabolism can be realized, and the kit can be clinically applied to early screening of perinatal depression.

Description

Technical Field

[0001] This invention relates to the field of biomedical detection technology, and in particular to a metabolic profile detection kit based on three differential metabolites and its application. Background Technology

[0002] Among numerous metabolic pathways, lipid oxidation, branched-chain amino acid (BCAA) breakdown, and one-carbon unit cycling are widely recognized as the "crossroads" connecting the metabolism of carbohydrates, lipids, and amino acids. Specifically: 1. Glycerol In patients with obesity, diabetes, non-alcoholic fatty liver disease (NAFLD), and long-chain fatty acid oxidation deficiency (LC-FAOD), fasting glycerol levels are often significantly elevated and positively correlated with the insulin resistance index (HOMA-IR).

[0003] 2. 3-Hydroxyisovalerate (3-HIV) Inhibition of mitochondrial oxidative phosphorylation (OXPHOS), hypoxia, or a high-fat diet can also lead to elevated 3-HIV levels. Recent studies have found that 3-HIV can inhibit histone deacetylase (HDAC), thereby regulating the expression of adipogenesis genes, suggesting that it has a dual role as both a "metabolic abnormality signal" and an "epiggenetic regulatory molecule."

[0004] 3. Methionine (Met) The methionine cycle is highly intertwined with the folate cycle, the choline pathway, and the nicotinamide N-methyltransferase (NNMT) pathway. Disequilibrium in these pathways can be observed in various pathological conditions, including hyperhomocysteinemia, Alzheimer's disease, tumors, and neural tube defects during pregnancy. It is noteworthy that Met is an amphoteric ion with high polarity and weak UV absorption. Traditional chromatographic-UV / fluorescence detection requires pre-column derivatization, a cumbersome procedure with fluctuating recovery rates.

[0005] Although the clinical and research value of the above three metabolites has been widely recognized, the current technology still faces the following prominent bottlenecks: (1) Limitations of single-index detection Currently, clinical laboratories mostly use enzymatic methods (Glycerol), gas chromatography-mass spectrometry (GC-MS, 3-HIV), or amino acid analyzers (Met) for individual measurements. These indicators are scattered across different testing platforms, lacking standardized calibrators and a unified quality control system, making cross-sectional comparisons difficult. More importantly, the concentration of a single metabolite is significantly affected by food intake, exercise, and circadian rhythms, making it impossible to distinguish between two distinct biological scenarios: "pathway activation" and "substrate accumulation." For example, measuring elevated blood triglycerides alone cannot determine whether it is due to enhanced fat mobilization (e.g., starvation, exercise) or impaired hepatic gluconeogenesis (e.g., cortisol deficiency); similarly, detecting elevated 3-HIV alone cannot rule out a secondary cause of biotin deficiency rather than a hereditary MCC defect.

[0006] (2) Complex sample preprocessing Both glycerol and 3-HIV are highly polar and volatile. Glycerol has a boiling point of 290℃ but is hygroscopic, while 3-HIV can be lactone-formed into 3-methyl-γ-butyrolactone under acidic conditions, causing a 20-40% decrease in recovery rate in GC-MS analysis. Methionine, due to its sulfur group, is easily oxidized to methionine sulfoxide at alkaline pH, leading to peak tailing or false positives. Traditional liquid-liquid extraction (LLE) requires multiple lyophilization-reconstitution steps, taking >2 hours. Derivatization reagents (such as BSTFA+TMCS) are extremely sensitive to moisture; differences in laboratory humidity can cause batch-to-batch CV >15%. In addition, dried blood spot (DBS) samples from infants and young children suffer from severe matrix interference; hemoglobin and phospholipid co-extractants inhibit electrospray ionization (ESI) efficiency, reducing detection sensitivity.

[0007] (3) Insufficient technology integration Glycerol is a neutral small molecule, 3-HIV is an acidic organic acid, and methionine is an amphoteric amino acid. These three molecules differ significantly in polarity, pKa, and protonation efficiency, making it difficult for conventional reversed-phase C18 chromatography to balance retention and peak shape. Using HILIC mode carries the risk of high organic phase ratios and buffer salt precipitation that could clog the system. Regarding mass spectrometry parameters, glycerol has only one characteristic fragment (m / z 57), making it susceptible to background interference; 3-HIV requires monitoring in negative ion mode, while methionine responds more strongly in positive ion mode, leading to a prolonged "positive-negative" switching cycle and reduced throughput. Therefore, existing literature / patents mostly focus on single or similar structural metabolites (e.g., CN119000968A only measures homocysteine; CN201711409132.X only measures ketone bodies such as hydroxybutyric acid and acetoacetic acid), and there are no reports on the feasibility of integrating "neutral polyols, volatile organic acids, and amphoteric sulfur-containing amino acids" into a single analytical system.

[0008] (4) Limited clinical application scenarios Existing commercial reagent kits are mostly based on enzyme colorimetric methods or immunochromatography, which have poor anti-interference capabilities and can only be used for plasma / serum samples, and cannot be compatible with trace samples such as urine, dried blood spots, and saliva. For newborn screening for inherited metabolic diseases, point-of-care testing (POCT), or large-scale physical examinations, there is an urgent need for a high-throughput solution that can achieve simultaneous quantification of multiple indicators with just "a drop of blood / a drop of urine".

[0009] (5) The main technical difficulties faced by the simultaneous detection of three metabolites in the existing technology Achieving simultaneous, efficient, and accurate detection of three metabolites with vastly different physicochemical properties—Glycerol (a highly polar small molecule), 3-Hydroxyisovalerate (a thermally unstable organic acid), and Methionine (a sulfur-containing amino acid)—faces the following major technical challenges, which prevent simple reagent combinations from achieving the technical effects of this invention: a. Conflicts in extraction efficiency during sample preprocessing: Challenges: Glycerol is highly hydrophilic and has low solubility in organic phases; the extraction of 3-Hydroxyisovalerate and Methionine requires organic solvents with a certain polarity to achieve efficient protein precipitation and extraction. Using high proportions of organic solvents (such as methanol and acetonitrile) will significantly reduce the extraction recovery rate of Glycerol; while using an aqueous environment is not conducive to the extraction and subsequent derivatization of the other two metabolites.

[0010] Consequence: It is difficult to find a universal extraction method that can simultaneously achieve high and stable recoveries of all three metabolites (typically requiring >85%).

[0011] b. Incompatibility of conditions for derivatization reactions: Challenges: To meet the requirements of GC-MS analysis, silanization derivatization is typically necessary. However, Glycerol contains three hydroxyl groups, 3-Hydroxyisovalerate contains one carboxyl group and one hydroxyl group, and Methionine contains one carboxyl group and one amino group; their functional group reactivity and optimal reaction conditions differ. Strong reaction conditions may lead to the thermal decomposition of 3-Hydroxyisovalerate or the destruction of the sulfur-containing side chain of Methionine; while mild conditions may result in incomplete derivatization of Glycerol.

[0012] Consequences: Incomplete derivatization reaction, excessive byproducts, or decomposition of the target analyte can lead to decreased detection sensitivity, poor chromatographic peak shape, and inaccurate quantification.

[0013] c. Interference between chromatographic separation and mass spectrometry response: Challenges: Even with successful derivatization, the three metabolite derivatives exhibit significantly different retention behaviors on chromatography, making baseline separation difficult to achieve in a short time. Poor separation can lead to significant ion suppression during mass spectrometry detection, where co-eluting substances interfere with each other's ionization efficiency, severely impacting the accuracy and precision of quantification.

[0014] Consequences: overlapping chromatographic peaks, unstable detection signals, poor linearity of the standard curve, increased intra- and inter-batch coefficient of variation (CV%), and inability to meet clinical testing requirements.

[0015] The combined detection of these three metabolites is not a simple matter of stacking reagent kits; it requires overcoming a series of complex technical obstacles and involves systematic optimization and innovation in multiple aspects such as sample pretreatment, chemical derivatization, and instrumental analysis.

[0016] In summary, there is an urgent need in this field for a multi-metabolite combined detection kit that features "one-step sample processing, integrated detection mode, and standardized result interpretation" to break through the technological silos of single indicators and single platforms and meet the needs for early screening, classification, dynamic monitoring, and individualized intervention of perinatal depression. Summary of the Invention

[0017] The purpose of this invention is to provide a metabolic profile detection kit based on three differential metabolites and its application, in order to solve the problems existing in the prior art. This invention provides a kit that can simultaneously detect three metabolites: glycerol, 3-hydroxyisovalerate, and methionine, enabling a comprehensive assessment of mitochondrial function, amino acid metabolism, and energy metabolism, and can be applied to clinical screening for early perinatal depression.

[0018] To achieve the above objectives, the present invention provides the following solution: This invention provides a kit for detecting three metabolites: glycerol, 3-hydroxyisovalerate, and methionine. The kit comprises: a calibrator unit, a sample processing reagent unit, a derivatization reagent unit, and a detection unit. The calibrator unit contains glycerol, 3-hydroxyisovalerate, and methionine standards at concentration gradients. The sample processing reagent unit contains a protein precipitant, a metabolite extractant, and a stabilizer. The derivatization reagent unit contains a silanizing reagent for gas chromatography-mass spectrometry (GC-MS) analysis. The detection unit contains specific ion pairs and an internal standard for liquid chromatography-tandem mass spectrometry (LC-MS) analysis.

[0019] Preferably, the concentration of the glycerol standard is 0.1-100 μg / mL, the concentration of the 3-hydroxyisovaleric acid standard is 0.05-50 μg / mL, and the concentration of the methionine standard is 0.05-50 μg / mL.

[0020] Preferably, the volume ratio of the protein precipitant, metabolite extractant, and stabilizer is 5:4:1; the protein precipitant is an acetonitrile solution, the metabolite extractant is a methanol solution, and the stabilizer is an ascorbic acid solution.

[0021] Preferably, the derivatizing reagent unit is a mixed solution of N-methyl-N-trimethylsilyltrifluoroacetamide and trimethylchlorosilane; the volume ratio of N-methyl-N-trimethylsilyltrifluoroacetamide to trimethylchlorosilane is 9:1.

[0022] Preferably, the detection unit comprises the following specific ion pairs: Glycerin: m / z 116.9→75.0; 3-Hydroxyisovaleric acid: m / z 131.0→87.0; Methionine: m / z 150.0→104.0; Internal standards: d5-glycerol, 13C-3-hydroxyisovaleric acid and d3-methionine.

[0023] The present invention also provides the use of the kit in the preparation of products for assessing mitochondrial dysfunction, amino acid metabolism disorders, and energy metabolism disorders.

[0024] The present invention also provides the use of the kit in the preparation of products for early screening of perinatal depression.

[0025] The present invention also provides a method for detecting the concentrations of glycerol, 3-hydroxyisovaleric acid and methionine in a biological sample, comprising the following steps using the kit described above: (1) Sample pretreatment: Take the sample to be tested, add the protein precipitant, vortex mix and centrifuge to collect the supernatant; (2) Metabolite extraction: Add the metabolite extractant and the stabilizer to the supernatant above, mix thoroughly and centrifuge to obtain the extract; (3) Derivatization treatment: The derivatization reagent is added to the above extract and reacted; (4) Instrumental analysis: Gas chromatography-mass spectrometry or liquid chromatography-tandem mass spectrometry was used for analysis; (5) Data analysis: Calculate the concentrations of the three metabolites based on the standard curve.

[0026] Preferably, in the derivatization step (3), the reaction temperature is 70°C and the reaction time is 30 minutes.

[0027] Preferably, the instrumental analysis in (4) adopts a multi-reaction monitoring mode, the chromatographic separation uses a C18 column with a column temperature of 40°C, and the mobile phase is a methanol-water gradient system.

[0028] The present invention discloses the following technical effects: Compared with the prior art, the present invention has the following main advantages: (1) Multi-index joint detection: For the first time, the simultaneous detection of three metabolites with different properties, namely glycerol, 3-hydroxyisovalerate and methionine, is realized, providing more comprehensive metabolic profile information.

[0029] (2) High sensitivity and specificity: By optimizing derivatization conditions and mass spectrometry parameters, the detection sensitivity reaches the pg level, which is far higher than that of conventional clinical detection methods. The limits of detection for the three metabolites are: Glycerol 0.02 μg / mL, 3-Hydroxyisovalerate 0.01 μg / mL, and Methionine 0.01 μg / mL.

[0030] (3) Simple and quick operation: The unified sample pretreatment process is adopted, and the entire detection process only takes 2.5 hours, which greatly improves the detection efficiency.

[0031] (4) Wide range of applications: It can be applied to various biological samples such as serum and plasma, and is suitable for multiple fields such as clinical diagnosis, basic research and health screening.

[0032] This invention provides a kit and detection method for the simultaneous quantitative detection of these three key metabolites, which can achieve a comprehensive assessment of mitochondrial function, amino acid metabolism and energy metabolism. When applied in clinical practice, it can be used for early screening of perinatal depression. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 The ranking results of the importance of differentially expressed metabolites to be tested in the early pregnancy group; Figure 21. Multivariate ROC curve prediction model for the first trimester group; 2: prediction model Var.2; 3: prediction model Var.3; 5: prediction model Var.5; 10: prediction model Var.10; 20: prediction model Var.20; 42: prediction model Var.42; Figure 3 The prediction rate of the multi-model combination for the early pregnancy group; Figure 4 TIC overlay plot for QC samples; QC1-2 to QC1-9: QC samples; Figure 5 The single ROC curve is the best model for the early pregnancy group. Detailed Implementation

[0035] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as a limitation of the present invention, but rather as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0036] It should be understood that the terminology used in this invention is merely for describing particular embodiments and is not intended to limit the invention. Furthermore, with respect to numerical ranges in this invention, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Any stated value or intermediate value within a stated range, as well as each smaller range between any other stated value or intermediate value within said range, is also included in this invention. The upper and lower limits of these smaller ranges may be independently included or excluded from the range.

[0037] Unless otherwise stated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. While only preferred methods and materials have been described herein, any methods and materials similar or equivalent to those described herein may be used in the implementation or testing of this invention. All references to this specification are incorporated by way of citation to disclose and describe methods and / or materials associated with those references. In the event of any conflict with any incorporated reference, the content of this specification shall prevail.

[0038] Various modifications and variations can be made to the specific embodiments described in this specification without departing from the scope or spirit of the invention, as will be apparent to those skilled in the art. Other embodiments derived from this specification will also be readily apparent to those skilled in the art. This specification and embodiments are merely exemplary.

[0039] The terms “include,” “including,” “have,” “contain,” etc., used in this article are all open-ended terms, meaning that they include but are not limited to.

[0040] Example 1: Screening and Prediction Model Determination for Differential Metabolites in Early Pregnancy Group 1. Ranking of the importance of differentially metabolites to be tested in the early pregnancy group, see [link to relevant documentation]. Figure 1 .

[0041] like Figure 1 As shown, the three metabolites Glycerol, 3-Hydroxyisovalerate, and Methionine are of high importance in early pregnancy.

[0042] Predictive Model Design: Based on machine learning methods and metabolite profile data from the first trimester, multiple predictive models were constructed through feature importance ranking and model optimization. The numbers in the model names (e.g., Var.2, Var.3, etc.) indicate the number of metabolites included in the model. Figure 1 The importance ranking results shown were used to filter the metabolites included in each model. The following is a list of specific metabolites included in each prediction model: Predictive model Var.2: Glycerol, 3-Hydroxyisovalerate; Predictive model Var.3: Glycerol, 3-Hydroxyisovalerate, Methionine; Predictive model Var.5: Glycerol, 3-Hydroxyisovalerate, Methionine, Acetate, Arginine; Predictive model Var.10: Glycerol, 3-Hydroxyisovalerate, Methionine, Acetate, Arginine, Trimethylamine, 2-Hydroxybutyrate, Acetoacetate, Carnitine, 3-Hydroxybutyrate; Predictive model Var.20: Glycerol, 3-Hydroxyisovalerate, Methionine, Acetate, Arginine, Trimethylamine, 2-Hydroxybutyrate, Acetoacetate, Carnitine, 3-Hydroxybutyrate, Betaine, Alanine, Ethanol, Glutamine, Citric acid, Lactate, Proline, Pyruvate, Glycine, Glucose; Predictive model Var.42 includes all 42 screened differentially expressed metabolites. In addition to the 20 metabolites mentioned above, it also includes various amino acids, organic acids, carbohydrates, and lipid metabolites, specifically: phenylalanine, tryptophan, histidine, ornithine, citrulline, creatine, creatinine, uric acid, fructose, galactose, triglycerides, cholesterol, lactate dehydrogenase, acetone, acetyl-CoA, taurine, pyruvate kinase, and malate dehydrogenase. The following are listed: dehydrogenase, α-ketoglutarate, succinyl-CoA, oxaloacetate, and phosphoenolpyruvate (PEP).

[0043] like Figure 2 As shown, based on the multifactor ROC curve prediction model for the early pregnancy group, prediction model 3 has the highest prediction success rate, meaning that prediction model 3, composed of the three metabolites Glycerol, 3-Hydroxyisovalerate, and Methionine, is the best model.

[0044] 2. Experimental design and methods for predicting the multi-model combination prediction rate in the early pregnancy group. 1) Sample source and grouping A total of 58 serum samples were collected from pregnant women in the first, second, and third trimesters of pregnancy. Among them, there were 30 healthy control subjects (Control) and 28 patients in the perinatal depression risk group (Risk) (based on clinical diagnosis and with a score of ≥10 on the Edinburgh Postnatal Depression Scale). All samples were collected in a fasting state. After centrifugation to separate the serum, the samples were aliquoted and stored at -80℃ to avoid repeated freeze-thaw cycles.

[0045] 2) Metabolite detection and data preprocessing The concentrations of 42 candidate metabolites in all samples were detected using the kit of this invention. The data were preprocessed as follows after detection: Missing value handling: Use K-Nearest Neighbor (KNN) method to complete the missing values; Data standardization: Pareto Scaling method adopted; Outlier detection: Hotelling's T² test based on principal component analysis was used.

[0046] 3) Differential metabolite screening methods Differential metabolites were screened using multivariate statistical analysis.

[0047] Principal component analysis (PCA): Preliminary observation of the separation trend between groups; Partial Least Squares Discriminant Analysis (PLS-DA): Constructing a classification model and calculating the variable importance projection (VIP) value; T-test and Fold Change analysis: Metabolites with VIP>1.0, p<0.05, and |log2FC|>0.5 were selected as differential metabolites.

[0048] After screening, a total of 42 differential metabolites were obtained. Figure 1 (as shown), and sorted by importance.

[0049] 4) Predictive model construction and evaluation methods 4.1 Model Building Strategy Based on the aforementioned differential metabolites, multiple prediction models were constructed using machine learning methods: Algorithm selection: Logistic regression was used for binary classification outcomes. Predicted probabilities were obtained by fitting a multivariate logistic regression model. The ROC curve is plotted using this probability as a continuous indicator.

[0050] Random forest is chosen as a machine learning algorithm because it handles nonlinear relationships and interactions and outputs probabilistic predictions.

[0051] Feature selection methods: 1. Prior screening: Based on literature, clinical guidelines, and pathophysiological mechanisms, the core variable glycerol was forcibly included.

[0052] 2. Multifactor screening uses stepwise regression backward elimination: starting from the full model, insignificant variables are eliminated one by one.

[0053] Model partitioning: All metabolites are sorted by importance and randomly combined for multi-factor ROC curve prediction. For ease of presentation, only the 6 prediction models with the highest prediction reliability are shown.

[0054] 4.2 Model Training and Validation Dataset partitioning: Divide the dataset into training and test sets in a 7:3 ratio; Cross-validation: Five-fold cross-validation is used to optimize model parameters; Model evaluation metrics: accuracy, sensitivity, specificity, and AUC value.

[0055] 5) Multi-model combined prediction rate evaluation method To evaluate the predictive efficacy of each model in the first trimester group, the following steps were performed: 5.1 Model Predictive Rate Calculation and Internal Validation To evaluate the classification efficacy and stability of each prediction model in the early pregnancy group, the Bootstrap internal validation method (500 repetitions) was used for model validation and prediction rate calculation. The specific steps are as follows: a model was built for each sample, and the corrected AUC was calculated.

[0056] To assess the model's ability to distinguish between different models.

[0057] (1) Bootstrap sampling Random sampling with replacement is performed from the original training set (n=70). Each time, the same number of samples as the training set are drawn to form a Bootstrap sample set. This process is repeated 500 times to generate 500 Bootstrap sample sets.

[0058] (2) Model training and prediction The prediction model is trained using the same machine learning algorithm (random forest) on each Bootstrap sample set; the model is then used to make predictions on the original training set and the corresponding out-of-bag (OOB) samples, and the prediction accuracy is calculated.

[0059] (3) Calculation of Predictive Rate and Corrected AUC The formula for calculating the predictability is as follows: ; Based on 500 Bootstrap results, the average prediction rate and its 95% confidence interval (CI) were calculated. Simultaneously, the ROC curve for each Bootstrap iteration is plotted, and the corrected AUC is calculated using the following formula: ; Where: Original AUC: AUC calculated based on the complete training set; Bootstrap Average AUC: The average AUC of 500 Bootstrap validations.

[0060] (4) Stability assessment The stability of the model was assessed using the coefficient of variation (CV%) of the Bootstrap results; Typically, a CV% < 10% indicates that the model has good repeatability and robustness.

[0061] Example results: Taking the Var.3 model (glycerol, 3-hydroxyisovalerate, methionine) as an example: average prediction rate: 92.3% (95% CI: 89.8%-100%); corrected AUC: 0.971; bootstrap AUC standard deviation: 0.012, CV% = 1.24%, indicating that the model has good stability.

[0062] 5.2 Model Comparison and Optimization Compare the AUC, sensitivity, specificity and prediction rate of each model, and select the best model (Var.3).

[0063] 6) Results Explanation like Figure 3 As shown, the Var.3 model exhibited the highest predictive rate (92%) in the early pregnancy group, with an AUC value of 0.971, indicating that this combination of three metabolites has excellent potential for early screening.

[0064] Example 2: Composition and configuration of the reagent kit 1. The reagent kit of this invention consists of the following parts: (1) Calibration unit: containing three metabolite standards with concentration gradients (according to...) Figure 3 The multifactor ROC curve prediction model selected Model 3 as the differential metabolite combination, namely Glycerol, 3-Hydroxyisovalerate, and Methionine.

[0065] Glycerol standard: concentration range 0.1-100 μg / mL.

[0066] 3-Hydroxyisovalerate: Concentration range 0.05-50 μg / mL.

[0067] Methionine standard: concentration range 0.05-50 μg / mL.

[0068] (2) Sample processing reagent unit: Protein precipitant: Acetonitrile solution (containing 1% formic acid); Metabolite extractant: Methanol-water mixture (volume ratio 4:1); Stabilizer: 0.1% ascorbic acid solution.

[0069] (3) Derivatization reagent unit: Silanization reagent: A mixed solution of N-methyl-N-trimethylsilyltrifluoroacetamide (MSTFA) and trimethylchlorosilane (TMCS) (volume ratio 9:1).

[0070] (4) Detection unit: Internal standards: d5-glycerol, 13C-3-hydroxyisovalerate, and d3-methionine.

[0071] The list of specific ion pairs used for mass spectrometry analysis is shown in Table 1.

[0072] Table 1. Mass spectrometry detection parameters of the three metabolites and internal standard. 2. This embodiment details the specific configuration method of the reagent kit. 2.1 Preparation of Calibration Units Accurately weigh high-purity Glycerol, 3-Hydroxyisovalerate, and Methionine standards, and prepare stock solutions with ultrapure water. Then, dilute with phosphate buffer to prepare working solutions with six concentration gradients as shown in Table 2, aliquot into brown ampoules, and store at -80°C.

[0073] Table 2 Calibrator Concentration Gradient Design 2.2 Preparation of Sample Processing Reagents Protein precipitant: acetonitrile and 1% formic acid solution are mixed at a volume ratio of 95:5.

[0074] Metabolite extractant: Methanol and ultrapure water are mixed at a volume ratio of 4:1.

[0075] Stabilizer: 0.1% ascorbic acid aqueous solution, prepared fresh before use.

[0076] 2.3. Preparation of Derivatization Reagents Under anhydrous conditions, N-methyl-N-trimethylsilyltrifluoroacetamide and trimethylchlorosilane were mixed at a volume ratio of 9:1 and stored under nitrogen atmosphere in a sealed container.

[0077] 2.4. Preparation of Internal Standard Working Solution Accurately weigh d5-Glycerol, 13C-3-Hydroxyisovalerate, and d3-Methionine respectively, and prepare a stock solution of 1 mg / mL with methanol. Dilute with methanol to the working concentration (100 ng / mL) before use.

[0078] Example 3: Clinical Sample Testing The specific operating procedure for testing clinical samples using the kit of this invention is as follows.

[0079] 1. Sample pretreatment Take 100 μL of serum sample, add 300 μL of protein precipitant, vortex for 3 minutes, and centrifuge at 15000 rpm for 10 minutes. Take 200 μL of supernatant, add 500 μL of metabolite extractant and 50 μL of stabilizer, vortex for 2 minutes, and centrifuge at 12000 rpm for 5 minutes. Transfer the supernatant to a derivatization tube and dry it under nitrogen.

[0080] 2. Derivatization treatment Add 100 μL of derivatization reagent to the dried sample, seal, and react at 70°C for 30 minutes. After cooling to room temperature, perform GC-MS / MS analysis directly.

[0081] GC-MS / MS analysis procedure: DB-5MS capillary column (30 m × 0.25 mm × 0.25 μm, Agilent J&W Scientific, Folsom, CA, USA), carrier gas high-purity helium (purity not less than 99.999%), flow rate 1.0 mL / min, injection port temperature 260℃. Injection volume 1 μL, splitless injection, solvent delay 4.8 min. Temperature program: initial oven temperature 60℃, hold for 0.5 min; ramp to 125℃ at 8℃ / min; ramp to 210℃ at 8℃ / min; ramp to 270℃ at 15℃ / min; ramp to 305℃ at 20℃ / min, hold for 5 min.

[0082] 3. Instrumental analysis conditions 3.1 Glycerol and 3-hydroxyisovaleric acid were detected using gas chromatography-mass spectrometry (GC-MS). GC-MS instrument: Agilent 8890-5977B. Product description: Detection limit as low as 1 fg IDL, high sensitivity, and strong reliability.

[0083] Chromatographic conditions: DB-5MS capillary column (30 m × 0.25 mm × 0.25 μm); injection port temperature 250℃; carrier gas was high-purity helium, flow rate 1.0 mL / min; temperature program: initial temperature 80℃, hold for 1 minute, increase to 280℃ at 15℃ / min, hold for 5 minutes.

[0084] Mass spectrometry conditions: Electron impact ionization (EI); Ion source temperature 230 °C; Transfer line temperature 280 °C; Multiple reaction monitoring (MRM) mode.

[0085] 3.2 Methionine was detected by high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS / MS). HPLC system (according to the ACQUITY UPLC I-Class PLUS system user manual): operating pressure limit 18,000 psi; pH range... 1–12.5; operating flow rate range up to 2.0 mL / min; accuracy up to 1 fg / mL.

[0086] Pretreatment: Remove the samples stored at -80℃, thaw at room temperature, and transfer 150 μL of sample to a 1.5 mL EP tube; add 600 μL of protein precipitant methanol-acetonitrile (V:V=2:1, containing L-2-chlorophenylalanine, 2 μg / mL), vortex for 1 min; sonicate in an ice-water bath for 10 min, and incubate at -40℃ for 2 hours; centrifuge for 10 min (12000 rpm, 4℃), and use a syringe to aspirate 150 μL of the supernatant. Filter through a 0.22 μm organic phase pinhole filter and transfer to an LC vial, storing at -80℃ until LC-MS analysis. Quality control (QC) samples were prepared by mixing equal volumes of extracts from all samples.

[0087] Note: All extraction reagents were pre-cooled at -20℃ before use.

[0088] Chromatographic conditions: Column: ACQUITY UPLC HSS T3 (100 mm × 2.1 mm, 1.8 μm); Column temperature: 45℃; Mobile phase: A-water (containing 0.1% formic acid), B-acetonitrile; Flow rate: 0.35 mL / min; Injection volume: 5 μL.

[0089] 4. Result Calculation Quantification was performed using the internal standard method, and the concentrations of the three metabolites in the sample were calculated using a standard curve.

[0090] Non-targeted detection only uses the relative concentration compared to the internal reference.

[0091] 5. Performance evaluation of reagent kits and detection methods 5.1 Precision evaluation of the detection method Precision is assessed using the intra-assay and inter-assay coefficients of variation (CV). This invention uses three different concentrations of quality control samples (QC samples)—low, medium, and high—and measures them five times consecutively on the same day (intra-assay precision), and then measures them once a day for five consecutive days (inter-assay precision).

[0092] 5.2 Evaluation of the accuracy (spike recovery) of the detection method Accuracy was assessed using a spiked recovery experiment. Known levels of standards (low, medium, and high) were added to serum samples with a known baseline concentration, with each level measured in triplicate. The recovery rate (Recovery%) was calculated as: (Total concentration measured - Baseline concentration) / Spiked concentration × 100%.

[0093] 5.3 Evaluation of the Limit of Quantitative Detection (LOQ) of the Detection Method The limit of quantitation (LOQ) is defined as the lowest concentration that can be reliably quantified while meeting the requirements of precision and accuracy (typically CV% < 20% and recovery rate between 80% and 120%). This invention determines the LOQ by continuously measuring a series of low-concentration standard solutions. The signal-to-noise ratio (S / N) is read directly from the instrument software.

[0094] 5.4 Evaluation of the stability and repeatability of the detection method Sample stability: The same mixed serum sample was placed at room temperature (25℃), refrigerated at 4℃ and frozen at -80℃, and tested at different time points (0, 6, 12, 24 hours). Compared with the results at time 0, the concentration change rate was within ±10%, indicating that the sample before treatment was stable for at least 12 hours at room temperature and 24 hours at 4℃.

[0095] Post-treatment sample stability: The derivatized sample was placed in an autosampler (10°C) and analyzed every 6 hours over 24 hours. The rate of change of the response values ​​of the three metabolites was less than ±8%, indicating that the derivatized sample was stable at 10°C for at least 24 hours.

[0096] Reagent stability: The calibrators and working solutions of this invention were stored at -80°C and tested periodically over 3 months. The response values ​​and the linearity of the standard curve showed no significant changes (R²>0.995), demonstrating that the reagents have good stability.

[0097] 6. Results Analysis The total ion chromatograms of the QC samples were overlaid for comparison. Equal amounts of all samples were mixed to prepare QC samples (1-2 to 1-9). During mass spectrometry, QC samples were interspersed between samples, with one QC needle inserted every 10 samples to evaluate the stability of the mass spectrometry platform throughout the experiment. Results are shown below. Figure 4 .

[0098] like Figure 4 As shown, the TIC curves of all QC samples almost perfectly overlap. The elution times of each chromatographic peak are consistent (stable retention times). The peak heights and shapes of each chromatographic peak are consistent (stable response intensities). This indicates that the experimental process is stable and the data is highly reliable.

[0099] like Figure 5 As shown in the single ROC curve of the best model in the early pregnancy group, AUC=0.971, indicating a good model fit.

[0100] The limits of detection for the three metabolites were: Glycerol 0.02 μg / mL, 3-Hydroxyisovalerate 0.01 μg / mL, and Methionine 0.01 μg / mL.

[0101] Application Example 1: Clinical Application Case Thirty patients with clinically diagnosed metabolic diseases (including organic acidemia, hypermethionineemia, etc.) and 30 healthy individuals were selected as the control group, and the kit of this invention was used for detection and analysis.

[0102] Table 3 Comparison of the levels of three metabolites between the disease group and the healthy control group (μg / mL, x̄ ± s) Note: Compared with the healthy control group P<0.01.

[0103] The results showed that the levels of the three metabolites in the disease group were significantly higher than those in the healthy control group (P<0.01), proving that the kit of the present invention can effectively distinguish between patients with metabolic diseases and healthy people, and has important clinical application value.

[0104] Comparative Example 1: Single Sample Extractant Test Solution: To simplify the process, try using a single, commonly used protein precipitant / extractant.

[0105] Group A: Only a high proportion of methanol (90%) is used as the extraction agent.

[0106] Group B: Only a high proportion of acetonitrile (90%) was used as the extraction agent.

[0107] Group C: Only acidic acetonitrile (containing 1% formic acid) was used as the extraction agent.

[0108] The results are shown in Table 4 below. A single extractant could not simultaneously achieve the extraction efficiency of all three metabolites. Methanol and acetonitrile showed extremely low recoveries of Glycerol; while acidic conditions, although beneficial for the extraction of some substances, may lead to instability of 3-Hydroxyisovalerate or Methionine.

[0109] Table 4 Comparison of the recovery rates of three metabolites by different single extractants (n=5) Note: Recovery rates below 80% or relative standard deviations (RSD) greater than 5% are generally considered unacceptable for bioanalysis.

[0110] It is evident that the strategy employed in this invention, combining stepwise precipitation (first precipitating proteins with acidic acetonitrile) with composite extraction (methanol-water mixture), is the optimal solution selected through extensive experimental screening to balance the recovery rates of the three metabolites. The technical advantages are manifested in the following aspects: Improved detection efficiency: Traditional methods require separate detection of three metabolites, which takes about 6-8 hours. This invention can complete simultaneous detection in just 2.5 hours, improving efficiency by about 2-3 times.

[0111] Reduced sample volume: Traditional methods require 300 μL of serum for three separate tests, while this invention only requires 100 μL of serum to complete the simultaneous analysis of three metabolites, making it particularly suitable for the detection of trace samples such as pediatric samples.

[0112] This invention provides a powerful tool for the early diagnosis, efficacy monitoring, and prognostic assessment of perinatal depression, and has promising clinical application prospects and socioeconomic benefits.

[0113] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A kit for detecting three metabolites: glycerol, 3-hydroxyisovaleric acid, and methionine, characterized in that, It includes: The system comprises a calibrator unit, a sample processing reagent unit, a derivatization reagent unit, and a detection unit; the calibrator unit includes glycerol standards, 3-hydroxyisovaleric acid standards, and methionine standards at concentration gradients; the sample processing reagent unit includes protein precipitants, metabolite extractants, and stabilizers; the derivatization reagent unit includes silanizing reagents for gas chromatography-mass spectrometry analysis; and the detection unit includes specific ion pairs and internal standards for liquid chromatography-tandem mass spectrometry analysis.

2. The kit according to claim 1, characterized in that, The concentration of the glycerol standard is 0.1-100 μg / mL, the concentration of the 3-hydroxyisovaleric acid standard is 0.05-50 μg / mL, and the concentration of the methionine standard is 0.05-50 μg / mL.

3. The kit according to claim 1, characterized in that, The volume ratio of the protein precipitant, metabolite extractant, and stabilizer is 5:4:1; the protein precipitant is an acetonitrile solution, the metabolite extractant is a methanol solution, and the stabilizer is an ascorbic acid solution.

4. The kit according to claim 1, characterized in that, The derivatization reagent unit is a mixed solution of N-methyl-N-trimethylsilyltrifluoroacetamide and trimethylchlorosilane; the volume ratio of N-methyl-N-trimethylsilyltrifluoroacetamide to trimethylchlorosilane is 9:

1.

5. The kit according to claim 1, characterized in that, The detection unit contains the following specific ion pairs: Glycerin: m / z 116.9→75.0; 3-Hydroxyisovaleric acid: m / z 131.0→87.0; Methionine: m / z 150.0→104.0; Internal standards: d5-glycerol, 13C-3-hydroxyisovaleric acid and d3-methionine.

6. The use of the kit according to any one of claims 1-5 in the preparation of products for assessing mitochondrial dysfunction, amino acid metabolism disorders, and energy metabolism disorders.

7. The use of the kit according to any one of claims 1-5 in the preparation of a product for early screening of perinatal depression.

8. A method for detecting the concentrations of glycerol, 3-hydroxyisovaleric acid, and methionine in a biological sample, characterized in that, It includes the following steps performed using the kit as described in any one of claims 1-5: (1) Sample pretreatment: Take the sample to be tested, add the protein precipitant, vortex mix and centrifuge to collect the supernatant; (2) Metabolite extraction: Add the metabolite extractant and the stabilizer to the supernatant above, mix thoroughly and centrifuge to obtain the extract; (3) Derivatization treatment: The derivatization reagent is added to the above extract and reacted; (4) Instrumental analysis: Gas chromatography-mass spectrometry or liquid chromatography-tandem mass spectrometry was used for analysis; (5) Data analysis: Calculate the concentrations of the three metabolites based on the standard curve.

9. The method as described in claim 8, characterized in that, In the derivatization step (3), the reaction temperature is 70°C and the reaction time is 30 minutes.

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