Biomarker related to bone fragility and / or fracture of diabetic patient and kit thereof

Abstract

The invention discloses a biomarker related to bone fragility and / or fracture of a diabetic patient and a kit of the biomarker. The biomarker related to the bone fragility and / or fracture of the diabetic patient is maltol. Along with the increase of the content of maltol in the body of a diabetic patient, the possibility of fracture is also increased. When a diabetic patient is treated, the content of maltol in the body can be used as a basis for judging whether treatment is effective or not and / or whether bone quality is improved or not.

Description

Technical Field

[0001] This invention relates to the field of biomarkers, and more particularly to a biomarker and its kit associated with bone fragility and / or fractures in diabetic patients. Background Technology

[0002] Diabetes mellitus (DM) is a metabolic disease with a rising global incidence rate, and its chronic complications severely impact patients' quality of life. Among these complications, osteoporosis and increased fracture risk are serious and lead to high disability rates. Traditionally, it has been believed that while diabetic patients sometimes have higher bone mineral density than normal individuals, their fracture risk is significantly increased—a phenomenon known as the "diabetic osteoporosis paradox." This suggests that other key factors influencing bone fragility exist besides bone mineral density. Current clinical methods for fracture diagnosis and prediction, such as X-ray imaging, bone mineral density testing, and quantitative CT scans, primarily rely on structural indicators. These methods often only reveal abnormalities after significant bone loss or fracture has occurred, lacking the ability to predict bone fragility and fracture risk in diabetic patients early on.

[0003] In recent years, metabolomics has demonstrated unique advantages in disease mechanism research and biomarker discovery. Through holistic analysis of metabolite profiles, potential molecular-level changes in tissues or blood can be captured, thus providing new tools for disease diagnosis and prognosis.

[0004] Therefore, there is an urgent need to find a biomarker that can sensitively and specifically reflect changes in the intrinsic bone mass of diabetic patients and can be used for early warning of fracture risk. Summary of the Invention

[0005] The purpose of this invention is to provide a biomarker associated with bone fragility and / or fractures in diabetic patients.

[0006] To achieve the above objectives, the present invention provides a biomarker associated with bone fragility and / or fractures in diabetic patients, characterized in that the biomarker is maltol.

[0007] Furthermore, as the maltol content in the bodies of diabetic patients increases, their likelihood of fractures also increases.

[0008] Furthermore, for every standard deviation increase in maltol content, the likelihood of fracture increases by more than 140%.

[0009] Furthermore, for every 11.1 μmol / L increase in maltol content, the likelihood of fracture increases by 149%.

[0010] Furthermore, the maltol content in the body of diabetic patients during treatment can serve as an indicator of the effectiveness of the treatment and / or whether bone quality has improved.

[0011] The present invention also provides a kit, characterized in that it contains the biomarkers associated with bone fragility and / or fractures in diabetic patients.

[0012] This invention is the first to utilize rigorous metabolomics analysis (as shown in the appendix) Figure 1 , 2 As shown in Figures 3 and 4, a direct link between maltol and abnormal bone tissue metabolism and fracture risk in diabetic patients was discovered and verified. Maltol is not a traditional marker of blood glucose or bone turnover; as a small molecule metabolite, it directly reflects the abnormal metabolic state of bone tissue in diabetic patients, providing a new biochemical perspective and material basis for understanding the "diabetic osteoporosis paradox".

[0013] This invention is based on solid omics data, including significant intergroup separation, greater than 2-fold upregulation of expression, a p-value of less than 0.05, and a high VIP value, which collectively demonstrate the reliability, specificity, and importance of maltol as a biomarker. The data support is sufficient.

[0014] Existing technologies largely rely on invasive bone biopsies or delayed imaging examinations. This invention assesses fracture risk by detecting maltol levels in peripheral serum, achieving non-invasive, convenient, and repeatable early screening, and possesses significant advantages for clinical translation.

[0015] The marker of this invention has: High specificity and early warning: Maltol specifically increases in diabetic patients with fractures, effectively identifying diabetic patients at high risk of fractures, compensating for the shortcomings of bone mineral density testing, and achieving early warning.

[0016] Directly reflects the pathological process: This biomarker originates directly from the target tissue of the lesion and can more intuitively reflect the direct damage of diabetes to bone quality.

[0017] Therefore, this invention functionally achieves a shift from "structural diagnosis" to "molecular-level prediction," and structurally distinguishes itself from existing technologies by establishing a technical link of metabolomics screening + quantitative detection. The detection methods are not limited to HPLC, liquid chromatography-tandem mass spectrometry, enzyme-linked immunosorbent assay (ELISA), chemical colorimetry, or test strips, etc.

[0018] These biomarkers can be used for prognostic assessment of fracture risk and monitoring of treatment efficacy in diabetic patients. For example, a decrease in serum maltol levels after drug treatment may indicate effective treatment and potential improvement in bone quality. Attached Figure Description

[0019] Figure 1 This is a PLS-DA score graph of femoral head tissue metabolites in diabetic and non-diabetic groups.

[0020] Figure 2 It is a volcano diagram of differential metabolites.

[0021] Figure 3 This is a PLS-DA analysis of the VIP score distribution (where A-T818: maltol).

[0022] Figure 4 This is a box plot of maltol content in bone tissue.

[0023] Figure 5 The heatmap analysis of differentially expressed metabolites across all samples clearly demonstrates a consistent high expression pattern of maltol in the diabetic group (where A-T818 is maltol).

[0024] Figure 6 This is a comparison chart of weight and blood glucose levels between the control group and the insulin treatment group in Example 3.

[0025] Figure 7 This is a comparative graph showing the decrease in serum maltol levels after insulin treatment in Example 4.

[0026] Figure 8 This is a diagram showing the improvement in bone health after insulin therapy.

[0027] above Figure 1-3 , Figure 5 In the middle, A: non-diabetic group, B: diabetic group; Figure 4 In the text, non-CD indicates the non-diabetic group, and CD indicates the diabetic group. Detailed Implementation

[0028] The embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention. Where specific techniques or conditions are not specified in the embodiments, they are performed according to the techniques or conditions described in the literature in the art or according to the product instructions. Reagents or instruments used, unless otherwise specified, are all conventional products that can be obtained commercially.

[0029] In the following examples: According to the World Health Organization definition, the diagnostic criteria for type 2 diabetes mellitus (T2DM) are a fasting plasma glucose (FPG) ≥7.0 mmol / L or a 2-hour postprandial glucose (2-hPG) ≥11.1 mmol / L. Osteoporotic fractures, also known as fragility fractures, are fractures that occur due to minor trauma or daily activities. The International Classification of Diseases, 10th Revision (ICD-10) codes for osteoporotic fractures are as follows: femoral neck (S72.x), vertebrae (M48.50XA, S12.x, S14.x, S22.x, S24.x, T08.x), radius / ulna (S52.x), pelvis (S32.x), and humerus (S42.2-S42.4, S52.x). The following experiments excluded patients under 50 years of age and patients diagnosed with multiple fractures or traumatic fractures. At the same time, patients with diseases that may affect bone metabolism (such as malignant tumors, multiple myeloma, hyperparathyroidism, and rheumatic diseases) were excluded.

[0030] Example 1: Screening for differentially expressed metabolites in bone tissue Sample Collection: Femoral head tissue was collected from patients with femoral neck fractures. Based on preoperative diagnosis, patients were divided into a diabetic group (n=11) and a non-diabetic group (n=11). All samples were obtained from the First Affiliated Hospital of Xiamen University, and all participants provided written informed consent before participating in the study.

[0031] Non-targeted metabolomics detection and analysis: Metabolite extraction was performed on the above bone tissue samples. The extraction method was as follows: after rewarming the samples at 4°C, extraction was carried out using a pre-cooled extraction solution (80% methanol) at a ratio of 500 μL per 100 mg of tissue. The samples were homogenized using a cryogenic homogenizer with steel balls, followed by ultrasonic extraction (frequency 40 kHz, temperature 4°C). The samples were centrifuged at 14,000 g for 20 minutes at 4°C. The resulting supernatant was dried under low-temperature vacuum and then reconstituted with 100 μL of acetonitrile / water solution (1:1, v / v) using vortexing.

[0032] Then, the results were obtained by detection using a chromatography-mass spectrometry system. Figure 1-5 .

[0033] Mass spectrometry data were annotated by alignment with the Human Metabolome Database (HMDB, http: / / www.hmdb.ca / ) and the Metlin database (https: / / metlin.scripps.edu / ). Metabolite enrichment and pathway analyses of differentially abundant metabolites (DAMs) were performed using the MetaboAnalyst 6.0 platform (https: / / www.metaboanalyst.ca / ) based on the KEGG database.

[0034] Sample analysis was performed using the Rapid LC-MS Analysis for DeepMarkerMT (LC-MS) metabolomics platform. A total of 36 data points were collected via LC-MS (33 sample data points and 3 quality control sample data points). After data acquisition and export, all data were uploaded to IsoMS Pro 1.2.19 for data processing and analysis. After format conversion and quality checks, the data were processed and analyzed. A three-tiered metabolite identification method was applied to identify the structures of the detected metabolites: Tier 1: Matching to the derivatized standard database (CILLibrary) based on precise molecular weight and retention time. Tier 2: Identifying the remaining metabolites using the Related Metabolites Library (LI Library). Tier 3: Matching the remaining metabolites to the MyCompoundID (MCID) database based on precise molecular weight. A total of 2280 small metabolic molecules were identified, of which 80 metabolites met the criteria of FC > 1.2 and p < 0.05, and 46 metabolites met the criteria of FC < 0.83 and p < 0.05. A-T818 represents maltol (Fold Change = 2.37, p-value = 0.016).

[0035] Multivariate statistical analysis: as attached Figure 1 As shown, the PLS-DA score map clearly shows a significant separation in the bone tissue metabolic profiles between the diabetic group and the non-diabetic group, indicating that there are essential differences in the metabolic profiles between the two groups.

[0036] Screening for differentially expressed metabolites: as attached Figure 2 As shown in the volcano plot analysis, maltol was significantly upregulated in bone tissue of the diabetic group, with a Fold Change of 2.37 and a p-value of 0.016, indicating a significant statistical and biological difference. Variable importance projection analysis is attached. Figure 3 As shown, maltol ranks high in the VIP Score distribution of the PLS-DA model, indicating that it makes a significant contribution to distinguishing between diabetic and non-diabetic bone tissue and is a key differential metabolite.

[0037] Metabolite expression pattern validation: as attached Figure 4 (non-CD indicates the non-diabetic group, CD indicates the diabetic group) and appendix Figure 5 As shown, both box plots and heatmaps clearly demonstrate that the content of maltol in bone tissue of the diabetic group was significantly higher than that of the non-diabetic group.

[0038] Example 2: Validation experiment on the association between serum maltol content and fracture risk Serum sample collection: Serum samples were collected from patients with type 2 diabetes (with fractures, 17 cases) and non-diabetic patients (with fractures, 49 cases). All samples were obtained from the First Affiliated Hospital of Xiamen University, and all participants provided written informed consent before participating in the study.

[0039] Serum maltol content detection: Establish and optimize an HPLC method for the detection of maltol in serum.

[0040] Sample pretreatment: Take an appropriate amount of serum, add acetonitrile as a protein precipitant, vortex mix and centrifuge, and take the supernatant for analysis.

[0041] HPLC conditions: Chromatographic column: C18 reversed-phase column; mobile phase: acetonitrile-0.1% formic acid aqueous solution, gradient elution; flow rate: 1.0 mL / min; detector: UV detector 275 nm; column temperature: 40 ℃; injection volume: 10 μL.

[0042] Data Analysis: Differences in serum maltol levels were compared among different groups. Results showed that serum maltol levels were significantly higher in patients with type 2 diabetes compared to non-diabetic patients (Table 1). For categorical variables, frequencies and counts were used for data aggregation; for continuous variables, means and standard deviations were used. Patients were further stratified according to clinical diagnosis for association analysis. In Model 1, a multivariate logistic regression model was used to calculate the odds ratio (OR) and 95% confidence intervals (CIs) for the association between serum maltol levels and fractures in patients with type 2 diabetes after adjusting for sex, current smoking status, and weekly alcohol consumption. In Model 2, age was further adjusted. A two-sided P-value < 0.05 was considered statistically significant. The odds ratio for fractures in patients with type 2 diabetes compared to non-diabetic patients was 2.49 (95% confidence intervals: 1.07–5.84). Furthermore, each standard deviation increase in serum maltol levels (11.1 μmol / L) was associated with a 149% increase in the likelihood of fractures in patients with type 2 diabetes (Table 2). Receiver operating characteristic analysis demonstrated that serum maltol has good discriminatory and predictive abilities in determining whether type 2 diabetic patients will develop fractures.

[0043] Table 1 compares serum maltol levels between diabetic patients with fractures and non-diabetic patients.

[0044] Table 2 is a prediction table of fracture risk associated with maltol and type 2 diabetes.

[0045] In addition to HPLC for detecting serum maltol content, liquid chromatography-tandem mass spectrometry (LC-MS / MS) can also be used, which has higher sensitivity and specificity and can be used as the gold standard. Enzyme-linked immunosorbent assay (ELISA) can also be used: by preparing specific antibodies against maltol, ELISA kits can be developed, which are more suitable for large-scale clinical screening. Chemical colorimetric methods or test strips can also be used: based on the unique chemical properties of maltol, rapid and inexpensive point-of-care testing products can be developed.

[0046] The detection results of the biomarkers can be integrated into a fracture risk prediction system or model, and combined with data such as the patient's age, gender, disease duration, and bone density to output a comprehensive risk assessment report.

[0047] Example 3: Experimental animals: Males aged 6-8 weeks, C57BLKS / J-LepR. db / LepR db (abbreviation) db / db Genotype mice were used as a diabetes model, and maltol (MA) was administered via drinking water (maltol was dissolved in the mice's drinking water to a concentration of 48 μM). db / db + MA.

[0048] Grouping: ctrl group (i.e., control group): 6 animals db / db Mice were treated with maltol only; Insulin group (i.e., insulin treatment group): 6 animals db / db Mice were treated with maltol while receiving insulin therapy.

[0049] Experimental protocol: The ctrl group received intraperitoneal injection of Saline as a control; the insulin group received intraperitoneal injection of 192 U / kg of insulin once a day.

[0050] Mice in the ctrl group and the Insulin group were weighed before the start of the experiment, and 1 week, 2 weeks, 3 weeks and 4 weeks after the start of the experiment. Serum was collected from the mice, and the maltol content in the serum was detected in the same way as in Example 2.

[0051] See results Figure 6-8 .in Figure 6This is a comparison chart of body weight and blood glucose levels between the control group and the insulin treatment group in Example 3. Chart A shows the weight comparison between the control group and the insulin treatment group, and chart B shows the blood glucose level comparison between the control group and the insulin treatment group. Saline represents the control group (ctrl group), and INS represents the insulin treatment group (insulin group). It can be seen that insulin treatment did not significantly affect mouse weight, but it significantly reduced blood glucose levels in the mice.

[0052] Figure 7 This is a comparative graph showing the decrease in serum maltol levels after insulin treatment in Example 4. ctrl represents the control group, and Insulin represents the insulin treatment group. It can be seen that the maltol level in the blood was significantly reduced after insulin treatment.

[0053] Figure 8 This is a graph showing the improvement in bone health after insulin treatment. A is a cross-sectional image of bone mineral density in mice, B is a coronal image of bone mineral density in mice, C is a bone mineral density value image, D is a bone volume fraction image, E is a trabecular bone thickness image, F is a trabecular bone number image, and G is a trabecular separation image. Figure 7 It can be seen that insulin treatment significantly improved bone density in diabetic mice.

[0054] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention without departing from the principles and spirit of the present invention.

Claims

1. A biomarker associated with bone fragility and / or fractures in diabetic patients, characterized in that, The biomarker is maltol.

2. The biomarkers associated with bone fragility and / or fractures in diabetic patients as described in claim 1, characterized in that, As the maltol content in the body of diabetic patients increases, their likelihood of fractures also increases.

3. The biomarkers associated with bone fragility and / or fractures in diabetic patients as described in claim 2, characterized in that, For every standard deviation increase in maltol content, the likelihood of fracture increases by more than 140%.

4. The biomarkers associated with bone fragility and / or fractures in diabetic patients as described in claim 3, characterized in that, For every 11.1 μmol / L increase in maltol content, the likelihood of fracture increases by 149%.

5. The biomarkers associated with bone fragility and / or fractures in diabetic patients as described in claim 1, characterized in that, In diabetic patients, the maltol content in their bodies can serve as an indicator of the effectiveness of treatment and / or whether bone quality has improved.

6. A reagent kit, characterized in that, Contains the biomarkers associated with bone fragility and / or fractures in diabetic patients as described in any of claims 1-5.

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