Use of glycochenodeoxycholic acid 7-sulfate in the preparation of a product for the detection of cognitive dysfunction in diabetes

Abstract

The application provides application of glycochenodeoxycholic acid 7-sulfate in preparation of a detection product of diabetic cognitive dysfunction, and belongs to the technical field of biological medicine detection. Based on the metabolomics technology of liquid chromatography-mass spectrometry, metabolite spectrum changes of different populations are analyzed, metabolite relative abundance data are obtained, inter-group difference analysis is carried out, and candidate differential metabolites are screened. It is found that the change of metabolic phenotype is accompanied by the appearance of cognitive dysfunction in type 2 diabetes patients, the level of metabolite glycochenodeoxycholic acid 7-sulfate is significantly increased, and the metabolite glycochenodeoxycholic acid 7-sulfate has good diagnostic value, can be used as a serum metabolite marker of diabetic cognitive dysfunction, and the diagnostic threshold is 25.8 ng / mL, and a matching detection method is established.

Description

Technical Field

[0001] This invention relates to the field of biomedical detection technology, and in particular to the application of glycochenodeoxycholic acid 7-sulfuric acid in the preparation of detection products for diabetic cognitive impairment. Background Technology

[0002] Diabetes mellitus is a common chronic metabolic disease. Long-term hyperglycemia and insulin resistance can lead to multi-system damage. Among these, central nervous system involvement can cause varying degrees of decline in cognitive abilities such as learning and memory, attention, and executive function, resulting in diabetic cognitive impairment. This type of cognitive impairment is characterized by insidious onset and gradual worsening. Early symptoms are often atypical and are frequently intertwined with factors such as age, education level, emotional state, sleep quality, and comorbidities, making early clinical identification and objective assessment difficult. On the other hand, cognitive decline reduces patients' self-management ability, which in turn affects medication and blood glucose control, creating a vicious cycle of metabolic disorders and nerve damage. Therefore, establishing objective indicators that can be used for screening, auxiliary diagnosis, and follow-up monitoring is of significant clinical importance.

[0003] Currently, the assessment methods for cognitive impairment in diabetes mainly include neuropsychological scales, imaging examinations, and some laboratory tests. While scale assessments are widely used, their results are easily influenced by the subject's education level, language comprehension, testing environment, and the tester's experience, resulting in subjectivity and variability. Furthermore, scale evaluations are typically time-consuming, making them difficult to meet the efficiency requirements of grassroots or large-scale screening, and their sensitivity to early, mild cognitive abnormalities is limited. Imaging examinations can indicate changes in brain structure or function, but they generally suffer from high costs, strong equipment dependence, insufficient accessibility, and high professional interpretation barriers, making them unsuitable as routine screening and high-frequency follow-up methods. Detections of cerebrospinal fluid and other samples closer to the central nervous system have some value, but due to their invasive sampling and limited patient acceptance, they are not suitable for widespread application. Therefore, from a clinical translation perspective, reproducible, quantifiable, and standardized biomarkers in peripheral body fluids (especially serum / plasma) have greater application potential.

[0004] With the development of omics technologies, metabolomics, which can reflect changes in the end-phenotype of the body's metabolic network, has been used to explore biomarkers related to diabetes and its complications. Liquid chromatography-mass spectrometry (LC-MS) offers advantages such as broad coverage of metabolite types and high sensitivity, providing a technological foundation for discovering peripheral metabolites associated with cognitive impairment. However, existing publicly available studies and protocols still have common shortcomings: First, some candidate indicators can only distinguish between diabetic and normal populations, failing to address the more critical clinical need for stratification within diabetic patients, i.e., differentiating between diabetic cognitive impairment and non-cognitive diabetes. Second, some studies lack independent sample validation or are affected by sample size and group heterogeneity, resulting in insufficient reproducibility and stability. Third, the detection process and quality control system are imperfect, and metabolite results are easily affected by sample collection and preservation, diet and medication, liver and kidney function, and matrix effects, leading to poor comparability between different platforms or batches. Fourth, many protocols remain at the discovery level, lacking quantitative detection methods and kit designs that can be directly used in clinical laboratories, making large-scale application difficult.

[0005] Therefore, there is an urgent need for existing technologies to provide a serum biomarker for cognitive impairment in diabetes and its supporting detection scheme, so that it can achieve objective stratification in the diabetic population, have good diagnostic efficacy and reproducibility, and further develop into a standardized in vitro diagnostic kit to meet the application needs of clinical screening, auxiliary diagnosis and follow-up monitoring. Summary of the Invention

[0006] The purpose of this invention is to provide a serum biomarker for the diagnosis of diabetic cognitive impairment—glycochenodeoxycholic acid 7-sulfate (GCDCAS) and its detection kit and detection method, in order to solve the problem of lack of objective serum diagnostic biomarkers for diabetic cognitive impairment and the difficulty in early identification in the prior art.

[0007] To achieve the above-mentioned objectives, the present invention provides the following technical solution: This invention provides the application of glycochenodeoxycholic acid 7-sulfuric acid as a serum biomarker in the preparation of a detection product for diabetic cognitive impairment.

[0008] Preferably, the detection product is a detection kit, and the diagnostic threshold of the detection kit is 25.8 ng / mL.

[0009] Preferably, liquid chromatography-mass spectrometry or liquid chromatography-tandem mass spectrometry is used to detect serum GCDCAS levels; The liquid chromatography conditions include: The chromatographic column was a UPLC BEH C18; The mobile phase is acetonitrile and water containing 0.1% (v / v) formic acid; Flow rate 0.4 mL / min, total elution time 4.5 min; The gradient elution program is as follows: 0 min - 0.3 min, acetonitrile 10% → 10%; 0.2 min - 1.2 min, acetonitrile 10% → 90%; 1.2 min - 3.5 min, acetonitrile 90% → 90%; 3.5 min - 3.6 min, acetonitrile 90% → 10%; 3.6 min–4.5 min, acetonitrile maintained at 10%; Mass spectrometry data were collected using electrospray ionization (ESI) in negative ion mode and quantitatively analyzed using multiple reaction monitoring (MRM) mode. Ion pair m / z 528.50→528.50, cone voltage 30 V, collision voltage 10 V.

[0010] This invention provides a kit for detecting cognitive impairment in diabetes, comprising glycochenodeoxycholic acid 7-sulfuric acid.

[0011] Preferably, the kit can be used with an LC-MS / MS detection platform.

[0012] This invention utilizes liquid chromatography-mass spectrometry (LC-MS) to analyze the serum of healthy volunteers, diabetic patients, and patients with diabetic cognitive impairment. The results showed that GCDCAS levels were significantly elevated in patients with diabetic cognitive impairment and possessed good diagnostic value, with an AUC of 0.898 in the discovery cohort. Further independent validation cohort analysis using LC-MS confirmed elevated GCDCAS levels in diabetic cognitive impairment patients, with an AUC of 0.803. This demonstrates that GCDCAS maintains stable diagnostic efficacy in independent samples and possesses potential for clinical translation.

[0013] Compared with the prior art, the present invention has at least the following beneficial effects: 1. Objective and quantifiable: GCDCAS is a serum metabolite indicator, suitable for standardized detection; 2. Excellent diagnostic performance: The AUC for diagnosing diabetic cognitive impairment was 0.898 in the discovery cohort and 0.803 in the independent validation cohort, demonstrating stable clinical value. 3. Easy to translate: The detection kit and quality control system can be established through a mature LC-MS / MS platform, which is conducive to clinical promotion; 4. Wide range of applications: It can be used for screening, auxiliary diagnosis, risk stratification and follow-up monitoring of people with diabetes. Attached Figure Description

[0014] Figure 1 Analysis of serum metabolic patterns and changes in GCDCAS levels in three population groups based on metabolomics technology (A).

[0015] Figure 2 The ROC curve (AUC=0.898) of GCDCAS used to diagnose DCD was found in the queue.

[0016] Figure 3 ROC curve (AUC=0.803) of GCDCAS in an independent validation cohort for the diagnosis of DCD, and changes in serum GCDCAS levels in patients with T2D and DCD.

[0017] Figure 4 Schematic diagram of LC-MS chromatographic peaks of GCDCAS standard (A) and GCDCAS in human serum (B), and standard curve of GCDCAS (C). Detailed Implementation

[0018] The technical solutions provided by the present invention will be described in detail below with reference to the embodiments, but they should not be construed as limiting the scope of protection of the present invention.

[0019] Example 1: Screening of serum biomarkers GCDCAS based on metabolomics

[0020] (1) Sample source and grouping: A total of 72 serum samples were collected and divided into: 24 normal volunteers (CTRL); 24 patients with type 2 diabetes (T2D); Twenty-four patients with type 2 diabetes and cognitive impairment (DACD) were included.

[0021] All diabetic patients had type 2 diabetes and had not taken antibiotics or bile acid medications within the past 7 days.

[0022] Sampling: Fasting is required after 8:00 PM the night before sampling. Blood is drawn on an empty stomach the following morning, allowed to stand for 15 minutes, and the whole blood volume is between 3,000 g and 4 g. o Centrifuge at C for 15 min to separate serum samples, and store at -80°C. o C, pending.

[0023] (2) Detection method: We used metabolomics technology based on liquid chromatography-mass spectrometry to analyze changes in serum metabolite profiles in three groups of people, obtained relative abundance data of metabolites, and performed inter-group difference analysis to screen candidate differential metabolites.

[0024] (3) Results: Figure 1 A showed a clear distinction in serum metabolic patterns among the three groups, indicating that cognitive impairment in patients with type 2 diabetes is accompanied by changes in metabolic phenotype. Compared to the normal population (CTRL) and patients with type 2 diabetes (T2D), the level of the metabolite GCDCAS was significantly elevated in patients with diabetic cognitive impairment (DACD). Figure 1 B), and it has good diagnostic value in differentiating between DCD and non-cognitive impairment in diabetic patients, with an area under the ROC curve (AUC) of 0.898, a sensitivity of 0.79 (0.65-0.92), and a specificity of 0.88 (0.75-0.98). Figure 2 ).

[0025] (4) Conclusion: GCDCAS can be used as a serum metabolite marker for DACD.

[0026] Example 2: Independent Sample Validation of the Clinical Diagnostic Performance of GCDCAS

[0027] (1) Sample source and grouping: In addition, 150 independent serum samples were collected: 61 patients with type 2 diabetes (T2D); Eighty-nine patients with type 2 diabetes and cognitive impairment (DACD) were included.

[0028] All diabetic patients were type 2 diabetes patients who had not taken antibiotics or bile acid medications within 7 days. The sample processing method was the same as in Example 1.

[0029] (2) Detection method: Serum GCDCAS levels were quantitatively / relatively determined using liquid chromatography-tandem mass spectrometry.

[0030] A Waters ACQUITY H-Class UPLC and an XEVO TQ-S micro triple quadrupole tandem mass spectrometer were used; the column was an UPLC BEH C18 (50 mm × 2.1 mm, 1.7 μm). Mobile phase A: Water (containing 0.1% v / v formic acid); Mobile phase B: Acetonitrile; Gradient elution was performed at a flow rate of 0.4 mL / min for 4.5 min. 0 min - 0.3 min, acetonitrile 10% → 10%; 0.2 min - 1.2 min, acetonitrile 10% → 90%; 1.2 min - 3.5 min, acetonitrile 90% → 90%; 3.5 min - 3.6 min, acetonitrile 90% → 10%; 3.6 min-4.5 min, acetonitrile maintained at 10%.

[0031] Data were collected using electrospray ionization (ESI) negative ion mode and quantitatively analyzed using multiple reaction monitoring (MRM) mode. Ion pair m / z 528.50→528.50, cone voltage 30 V, collision voltage 10 V.

[0032] (3) Results: Serum GCDCAS levels were also significantly elevated in the validation cohort of patients with DCD, with an AUC of 0.803 for diagnosing DCD, indicating that GCDCAS maintains stable diagnostic efficacy in independent samples and has clinical translational potential; the sensitivity was 0.71 (0.63-0.81), the specificity was 0.72 (0.61-0.84), and the judgment threshold was 25.8 ng / mL. Figure 3 ).

[0033] (4) Conclusion: GCDCAS maintained stable diagnostic efficacy in independent samples and has potential for clinical translation. A serum GCDCAS level higher than 25.8 ng / mL in subjects suggests either DACD or a higher risk of DACD.

[0034] Example 3

[0035] (1) A GCDCAS test kit for the diagnosis of diabetic cognitive impairment, comprising: 1. GCDCAS standards (used for calibration and quality control, standard curve plotting and quantitative analysis, purchased from Tianjin Alta Technology Co., Ltd.); 2. Sample pretreatment reagents: protein precipitant / extraction solution (chromatographic grade acetonitrile, purchased from Merck); 3. Mobile phase A: Water (containing 0.1% formic acid); 4. Mobile phase B: Acetonitrile; 5. User Manual: Includes sample processing, gradient program, ESI negative ion mode acquisition, and result interpretation suggestions.

[0036] (2) Detection

[0037] The target population for screening is individuals with type 2 diabetes who have not taken antibiotics or bile acid medications within the past 7 days.

[0038] Sample processing: Fasting was required after 8:00 PM the night before sampling. Blood was collected on an empty stomach the following morning, allowed to stand for 15 minutes, and the whole blood sample was taken at a concentration of 3,000 g and 4 g / mL. o Centrifuge at C for 15 min to separate serum samples, and store at -80°C. o C, pending.

[0039] The level of GCDCAS in serum was quantitatively determined by liquid chromatography-tandem mass spectrometry, and the detection method was the same as in Example 2. Results interpretation suggestion: When the serum GCDCAS level of the subject is higher than 25.8 ng / mL, it suggests that the subject has DACD or has a high risk.

[0040] Figure 4 A shows the chromatogram of the GCDCAS standard, with good peak shape and a retention time of 1.88 min. Figure 4 B shows the chromatogram of GCDCAS in a serum sample, demonstrating that this method can effectively separate and determine GCDCAS. Figure 4 C shows the standard curve of GCDCAS, R 2 A value of 0.998 is achieved, which is sufficient for the quantitative analysis of GCDCAS.

[0041] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. Application of glycochenodeoxycholic acid 7-sulfuric acid as a serum biomarker in the preparation of detection products for diabetic cognitive impairment.

2. The application according to claim 1, characterized in that, The detection product is a detection kit, and the diagnostic threshold of the detection kit is 25.8 ng / mL.

3. The application according to claim 1, characterized in that, Serum GCDCAS levels were detected using liquid chromatography-mass spectrometry or liquid chromatography-tandem mass spectrometry.

4. The application according to claim 1, characterized in that, The conditions for the liquid chromatography include: a UPL C18 column; The mobile phase is acetonitrile and water containing 0.1% (v / v) formic acid; Flow rate 0.4 mL / min, total elution time 4.5 min; The gradient elution program is as follows: 0 min - 0.3 min, acetonitrile 10% → 10%; 0.2 min - 1.2 min, acetonitrile 10% → 90%; 1.2 min - 3.5 min, acetonitrile 90% → 90%; 3.5 min - 3.6 min, acetonitrile 90% → 10%; 3.6 min–4.5 min, acetonitrile maintained at 10%; The mass spectrometry data were collected using an electrospray source in negative ion mode and quantitatively analyzed using multiple reaction monitoring mode. The ion pair m / z was 528.50→528.50, the cone voltage was 30 V, and the collision voltage was 10 V.

5. A kit for detecting cognitive impairment in diabetes, characterized in that, It contains glycine chenodeoxycholic acid 7-sulfuric acid.

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