Application of reagent for detecting contents or expression levels of FABP3 and WIF1 in sample in preparation of reagent for diagnosing and / or predicting osteoporosis, kit and system

Abstract

The invention discloses application of a reagent for detecting the content or expression level of FABP3 and WIF1 in a sample in preparation of a reagent for diagnosing and / or predicting osteoporosis, a kit and a system. In the application, a sample comprises a serum sample. The application exemplarily provides the osteoporosis early noninvasive diagnosis kit based on serum protein marker FABP3 and WIF1 joint detection, so that accurate screening of high-risk groups is realized, and the deficiency of DXA in early diagnosis is made up.

Description

Technical Field

[0001] This application relates to the field of biomedical technology, to the development of biomedical detection and in vitro auxiliary diagnostic kits, and further to the application, kits, and systems of reagents for detecting the content or expression level of FABP3 and WIF1 in samples in the preparation of reagents for diagnosing and / or predicting osteoporosis. Background Technology

[0002] Osteoporosis in the elderly is a systemic bone disease characterized by low bone mass, damage to bone microstructure, increased bone fragility, and a high susceptibility to fractures. With the aging population, the prevalence of osteoporosis, especially in men, is rapidly increasing, becoming a significant public health issue. Osteoporosis patients are prone to fractures, with a mortality rate of approximately 20% and a disability rate of approximately 50%, resulting not only in reduced quality of life but also a heavy economic burden on society. Currently, the clinical diagnosis of osteoporosis mainly relies on clinical indications (lumbar spine, sacroiliac joint fractures, hip fractures) combined with bone mineral density testing (dual-energy X-ray absorptiometry, DXA). Quantitative computed tomography (QCT) is more accurate in detecting bone mineral density but is more expensive, and there is no internationally unified diagnostic standard for QCT. Osteocalcin, PINP, and CTX can also be tested in osteoporosis patients to determine the type of bone turnover and help predict fracture risk, but these cannot yet be used for the diagnosis of osteoporosis. In the absence of fractures, the internationally recognized standard for diagnosing osteoporosis is the result of a DXA (deep bone density) test. In the early stages of bone loss, a bone mineral density (BMD) result of -2.5 ≤ T-score ≤ -1.0 indicates a higher fracture risk than in healthy individuals. As age increases, bone mass further decreases, bone tissue structure deteriorates further, and the disease progresses to the osteoporosis stage, where the T-score ≤ -2.5, and the fracture risk intensifies. Currently, relying on BMD testing to diagnose osteoporosis still has the following limitations: BMD testing is not sensitive to early bone loss (<30%), potentially missing early-stage patients; BMD testing requires patients to endure a certain amount of radiation, and those without obvious clinical symptoms are often unwilling to undergo BMD testing; BMD testing is expensive (approximately 200 RMB per test), mostly used for those over 75-80 years old or patients with fractures, and high-risk individuals under 70 years old are often unwilling to undergo BMD testing; and primary care institutions lack understanding of osteoporosis and lack BMD testing equipment.

[0003] Therefore, there is an urgent need for a new, radiation-free, inexpensive, and equipment-free early screening and diagnostic kit for osteoporosis to be used in primary care settings. Summary of the Invention

[0004] Based on this, this application provides at least one application, kit, and system for a reagent for detecting the content or expression level of FABP3 and WIF1 in a sample in the preparation of reagents for diagnosing and / or predicting osteoporosis.

[0005] In a first aspect of this application, a reagent for detecting the content or expression level of FABP3 and WIF1 in a sample is provided for use in the preparation of products for diagnosing and / or predicting osteoporosis; said sample includes a serum sample.

[0006] In a second aspect of this application, a kit for diagnosing and / or predicting osteoporosis is provided, comprising reagents for detecting the content or expression level of FABP3 and WIF1 in a sample; said sample includes a serum sample;

[0007] The reagents include anti-FABP3 antibody and anti-WIF1 antibody.

[0008] In a third aspect of this application, a system for diagnosing and / or predicting osteoporosis is provided, the system comprising:

[0009] The data processing module is used to calculate the content or expression level of FABP3 and WIF1 in serum received or input data, and obtain the calculation results; and,

[0010] The judgment and output module is used to judge whether the calculation result meets the preset judgment conditions, so as to diagnose and / or predict individual osteoporosis, and output the prediction result;

[0011] In the judgment and output module, when the calculation result meets the preset judgment condition, the output prediction result is "the subject has osteoporosis or the probability level of having osteoporosis is high risk", and when the calculation result does not meet the preset judgment condition, the output prediction result is "the subject has no osteoporosis or the level of having osteoporosis is low risk".

[0012] The preset judgment criteria are "the content of FABP3 or its expression level is higher than that of the first negative control, and the content of WIF1 or its expression level is lower than that of the second negative control";

[0013] The first negative control is the content or expression level of FABP3 in the serum of healthy subjects or subjects who do not have osteoporosis, and the second negative control is the content or expression level of WIF1 in the serum of healthy subjects or subjects who do not have osteoporosis.

[0014] In a fourth aspect of this application, a computer-readable storage medium is provided that stores a computer program, which, when executed by a processor, enables the system for diagnosing and / or predicting osteoporosis as described in the third aspect.

[0015] A fifth aspect of this application provides a computer device including a memory and a processor, the memory storing a computer program and the processor executing the computer program to perform the functions of a system for diagnosing and / or predicting osteoporosis as described in the third aspect.

[0016] A sixth aspect of this application provides an apparatus for diagnosing and / or predicting osteoporosis, comprising one or more of the following: a system for diagnosing and / or predicting osteoporosis as described in the third aspect, a computer-readable storage medium as described in the fourth aspect, and a computer device as described in the fifth aspect.

[0017] This application provides an exemplary non-invasive diagnostic kit for early osteoporosis based on the combined detection of serum protein markers FABP3 and WIF1, which enables accurate screening of high-risk groups and makes up for the shortcomings of DXA in early diagnosis. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments and examples of this application, and to more completely understand this application and its beneficial effects, the drawings used in the description of the embodiments or examples will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of this application. Those skilled in the art can obtain other drawings based on these drawings without creative effort. It should also be noted that the drawings are all drawn in a simplified form and are only used to conveniently and clearly assist in illustrating this application.

[0019] Figure 1 A schematic diagram illustrating that WIF1 is an osteoblast-specific secreted factor in one embodiment of this application; A: Comparison of WIF1 expression levels in various tissues throughout the body, with expression levels normalized to TPM; B: Pie chart showing the percentage of average WIF1 expression in the epiphysis of long bones, diaphysis, spine, skull, and all other extraosseous tissues; C: Single-cell sequencing showing the expression levels of WIF1 in various cells of the epiphyseal tissue.

[0020] Figure 2 This is a schematic diagram illustrating the significant downregulation of WIF1 expression and secretion in the epiphyses of aged osteoporotic mice and in humans, as shown in one embodiment of this application; A: Wif1 mRNA expression is significantly reduced in the long bone epiphyses of a microgravity mouse model with a suspended tail; B: Wif1 mRNA expression is significantly reduced in the long bone epiphyses of aged mice (19 months old) compared to young mice (3 months old); C: ELISA detection shows that serum Wif1 secretion levels are significantly downregulated in aged mice (19 months old) compared to young mice (3 months old); D: ELISA detection shows that serum WIF1 levels are significantly reduced in a population (n=200) with reduced bone mass (including osteoporosis). P<0.01, p<0.001, , P<0.0001.

[0021] Figure 3 This is the WIF1 standard curve constructed in one embodiment of this application.

[0022] Figure 4 This is the FABP3 standard curve constructed in one embodiment of this application.

[0023] Figure 5 This is a schematic diagram of the AUC in one embodiment of this application.

[0024] Figure 6 This is a bar chart illustrating the effects of other factors on the diagnosis of osteoporosis in one embodiment of this application. Detailed Implementation

[0025] To facilitate understanding of this application, a more complete description will be provided below with reference to the accompanying drawings. Preferred embodiments of this application are shown in the drawings. However, this application can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a thorough and complete understanding of the disclosure of this application.

[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0027] In this application, unless otherwise specified, "one or more" means any one of the listed items or any combination of the listed items. Similarly, "one or more" and other instances that otherwise indicate "one or more" shall be understood in the same way unless otherwise specified.

[0028] The terms “combinations thereof,” “any combination thereof,” and “any combination thereof” as used in this application include all suitable combinations of any two or more of the listed items.

[0029] In this application, the word "suitable" in "suitable combination", "suitable method", "any suitable method" etc., shall be defined as being able to implement the technical solution of this application, solve the technical problem of this application, and achieve the expected technical effect of this application.

[0030] In this application, terms such as "further," "even more," "particularly," "for example," "like," "example," and "exemplary" are used for descriptive purposes to indicate that different technical solutions preceding and following each other are related in terms of their coverage, but should not be construed as limiting the preceding technical solution or restricting the scope of protection of this application. In this application, unless otherwise specified, A (e.g., B) indicates that B is a non-limiting example of A, and it can be understood that A is not limited to B.

[0031] The terms “containing,” “comprising,” and “including” as used in this application are synonyms and are inclusive or open-ended, not excluding additional, uncited members or features. Members or features include, for example, materials or components, structures, elements, instruments, etc.; non-limiting examples of members or features include actions, conditions under which actions occur, timing, states, etc.

[0032] In this application, the technical features or solutions described in open-ended language include both closed-ended technical features or solutions consisting of the listed contents and open-ended technical features or solutions that include the listed contents.

[0033] In this application, the exemplary descriptions such as "in some implementations (or embodiments)" and "in one implementation (or embodiment)" may cover, but are not limited to, the following meanings: these solutions can be combined with other solutions in a suitable manner to form new technical solutions.

[0034] In this application, the terms "first aspect," "second aspect," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or quantity, nor should they be construed as implicitly indicating the importance or quantity of the indicated technical features. Moreover, "first aspect," "second aspect," etc., serve only as a non-exhaustive enumeration and should be understood not to constitute a closed limitation on quantity.

[0035] In this application, when numerical intervals (i.e., numerical ranges) are involved, unless otherwise specified, the distribution of selectable numerical values ​​within the numerical interval is considered continuous, and includes the two endpoints of the numerical interval (i.e., the minimum and maximum values), as well as every numerical value between these two endpoints. Unless otherwise specified, when a numerical interval refers only to integers within that numerical interval, it includes the two endpoint integers of the numerical range, as well as every integer between the two endpoints, which is equivalent to directly listing every integer. When multiple numerical ranges are provided to describe features or characteristics, these numerical ranges can be merged. In other words, unless otherwise specified, the numerical ranges disclosed herein should be understood to include any and all subranges included therein. The "numerical value" in the numerical interval can be any quantitative value, such as a number, percentage, ratio, etc. The term "numerical interval" can be broadly included to include numerical interval types such as percentage intervals, ratio intervals, and proportion intervals.

[0036] In this application, where the method flow involves multiple steps, unless otherwise explicitly stated herein, there is no strict order restriction on the execution of these steps; they can be executed in any order other than those described. Moreover, any step may include multiple sub-steps or multiple stages, which are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or simultaneously with other steps or parts of the sub-steps or stages of other steps.

[0037] Previous work found a significant positive correlation between serum fatty acid-binding protein 3 (Fabp3) secretion levels and age (r=0.8979, P<0.0001), but a significant negative correlation with femoral neck bone mineral density (r=-0.7572, P=0.002). However, further expansion of the sample size, combined with patient history and drug treatment history, revealed that serum Fapp3 levels are easily affected by osteoporosis treatment drugs. Furthermore, it was found that Wnt's endogenous inhibitory factor (WIF1) is a proprietary osteoblast-secreting factor. Serum WIF1 levels are significantly reduced in patients with decreased bone mass and osteoporosis, and WIF1 is less affected by drugs. Therefore, the development of a dual FABP3 and WIF1 kit for diagnosing osteoporosis and patients with decreased bone mass holds potential.

[0038] The inventors have for the first time discovered the synergistic changes in FABP3 (conditional factor) and WIF1 (constant factor) in the serum of patients with osteopenia. FABP3 is defined as a serum factor for diagnosing conditional osteoporosis, while WIF1 is defined as a serum factor for diagnosing constant osteoporosis. Using machine learning methods such as logistic regression and decision trees, different diagnostic weights are assigned to FABP3 and WIF1, ultimately adjusting the osteopenia-early osteoporosis screening-diagnosis protocol to a combined detection of serum levels of both FABP3 and WIF1.

[0039] One aspect of this application provides the use of reagents for detecting the content or expression level of FABP3 and WIF1 in a sample in the preparation of products for diagnosing and / or predicting osteoporosis; said sample includes serum samples.

[0040] In some embodiments, the sample may also include whole blood, plasma, etc.

[0041] The levels of FABP3 and WIF1, or their expression levels, were found to be differentially present in biological samples obtained from subjects with osteoporosis compared to “normal” subjects. For example, a difference in the expression levels of an mRNA biomarker between samples is determined to be statistically significant if such a difference is determined to be statistically significant. Common tests for statistical significance include, but are not limited to: t-test, ANOVA, Kniskal-Wallis, Wilcoxon, Mann-Whitney, and odds ratio.

[0042] The expression levels of FABP3 and WIF1, such as mRNA content or protein expression levels.

[0043] In some implementations, mRNA biomarkers are used to determine osteoporosis: these can be used in diagnostic tests to assess a subject's osteoporosis status. Disease status includes, but is not limited to, the presence or absence of osteoporosis. Disease status may also include monitoring the progression of osteoporosis, for example, monitoring disease progression. Based on a subject's osteoporosis status, other procedures may be indicated, including, for example, other diagnostic tests or treatments.

[0044] The ability of a diagnostic test to correctly predict disease states is typically measured by its accuracy, sensitivity, specificity, or area under the curve (AUC) (e.g., the area under a receiver operating characteristic (ROC) curve). Accuracy, as used herein, is a measure of the proportion of samples that are misclassified. Accuracy can be calculated as the total number of correctly classified samples divided by the total number of samples (e.g., in the test population). Sensitivity is a measure of “true positives” predicted by the test and can be calculated as the number of correctly identified osteoporosis samples divided by the total number of osteoporosis samples. Specificity is a measure of “true negatives” predicted by the test and can be calculated as the number of correctly identified normal samples divided by the total number of normal samples. AUC is a measure of the area under the receiver operating characteristic curve, which is a graph of sensitivity versus the false positive rate (specificity). The higher the AUC, the more effective the test's predictions. Other useful measures of test utility include “positive predictive value” (which is the percentage of actual positives when the test is positive) and “negative predictive value” (which is the percentage of actual negatives when the test is negative).

[0045] It should be understood that the reagents mentioned refer to any reagents capable of detecting, for example, the content or expression level of FABP3 and WIF1 in serum samples, including but not limited to specific probes, gene chips, specific primers, and anti-FABP3 and anti-WIF1 antibodies.

[0046] Unless otherwise specified, the term "primer" in this application refers to a nucleic acid sequence capable of forming a base pair complementary to the template strand and serving as a starting point for template strand replication; for example, its length may be 7-50 bases. Primers are usually synthesized, but naturally occurring nucleic acids may also be used. The primer sequence does not necessarily need to be completely identical to the template sequence, as long as it is sufficiently complementary to the template and can hybridize.

[0047] Unless otherwise specified, the term "probe" in this application refers to a nucleic acid fragment, such as RNA or DNA, ranging from a few to hundreds of bases in length, which can specifically bind to mRNA and can determine the presence of a specific mRNA through labeling. Probes can be prepared in the form of oligonucleotide probes, single-stranded DNA probes, double-stranded DNA probes, and RNA probes.

[0048] It should be noted that other technical means can also be used to detect the expression level of biomarkers, such as RNA sequencing, immunohistochemistry, flow cytometry, and in situ hybridization.

[0049] There are no special requirements for anti-FABP3 and anti-WIF1 antibodies that are suitable for detecting the content or expression level of FABP3 and WIF1 in samples, as long as they can specifically bind to FABP3 and WIF1 respectively.

[0050] In some embodiments, the anti-FABP3 antibody is, for example, a commercially available antibody with catalog number PA5-92386.

[0051] In some embodiments, the anti-WIF1 antibody is, for example, a commercially available antibody with catalog number PA5-117011.

[0052] In some embodiments, detecting the content or expression level of FABP3 and WIF1 in the sample includes comparing the content or expression level of FABP3 and WIF1 in the sample with a control.

[0053] If the FABP3 content or expression level is higher than that of the first negative control, and the WIF1 content or expression level is lower than that of the second negative control, the subject corresponding to the sample detected by the reagent is considered to have osteoporosis or to have a high risk of osteoporosis; the first negative control is the FABP3 content or expression level in the serum of a healthy subject or at least a subject without osteoporosis, and the second negative control is the WIF1 content or expression level in the serum of a healthy subject or at least a subject without osteoporosis.

[0054] If the content or expression level of FABP3 is consistent with or lower than that of the first negative control, and / or the content or expression level of WIF1 is consistent with or higher than that of the second negative control, then the subject corresponding to the sample detected by the reagent is considered to be at low risk of osteoporosis.

[0055] Another aspect of this application provides a kit for diagnosing and / or predicting osteoporosis, comprising reagents for detecting the content or expression level of FABP3 and WIF1 in a sample; said sample includes a serum sample.

[0056] In some embodiments, the reagents include, but are not limited to, specific probes, gene chips, specific primers, and antibodies.

[0057] In some embodiments, the reagent is used to detect mRNA biomarkers.

[0058] Determining the level of mRNA biomarkers in a sample: The level of mRNA biomarkers in a biological sample can be determined by any suitable method. Any reliable method for measuring the level or amount of mRNA in a sample can be used. Typically, mRNA can be detected and quantified from a sample, such as a sample of RNA isolated by various methods known about mRNA, including, for example, amplification-based methods (e.g., polymerase chain reaction (PCR), real-time polymerase chain reaction (RT-PCR), quantitative polymerase chain reaction (qPCR), rolling circle amplification, etc.).

[0059] In some embodiments, the reagents include anti-FABP3 antibody and anti-WIF1 antibody.

[0060] In some embodiments, the anti-FABP3 antibody is, for example, a commercially available antibody with catalog number PA5-92386, and the anti-WIF1 antibody is, for example, a commercially available antibody with catalog number PA5-117011.

[0061] In some embodiments, the kit further includes a positive control and / or a negative control; wherein: the positive control comprises or is whole blood, mRNA, or cDNA of a subject with known osteoporosis; and the negative control comprises or is whole blood, mRNA, or cDNA of a healthy subject or at least a subject without osteoporosis.

[0062] In some embodiments, the kit further includes primers and / or antibodies for detecting the content or expression level of an internal reference gene.

[0063] One embodiment provides a dual ELISA diagnostic kit for FABP3 and WIF1, which can simultaneously detect FABP3 and WIF1, simplifying the operation process. The kit achieves an AUC of 0.9692 for diagnosing bone loss (including osteoporosis in the elderly) (WIF1 <= 0.34 ng / mL and FABP3 > 2.33 ng / mL). In comparison, the optimal diagnostic AUC for the FABP3 ELISA kit is 0.77, and for the WIF1 ELISA kit it is 0.81, both significantly lower than 0.9. Table 1 below provides an exemplary comparison of the detection methods.

[0064] Table 1

[0065]

[0066] In short, the combined detection of osteoporosis based on serum protein biomarkers FABP3 and WIF1 is suitable for screening at the grassroots level, helping to achieve the "hierarchical diagnosis and treatment" goal proposed in the "Guidelines for the Diagnosis and Treatment of Primary Osteoporosis (2022)"; it also provides key technical support for the primary prevention of osteoporotic fractures; it is suitable for grassroots medical institutions, does not require large equipment, and has a lower cost than DXA; and it provides a technical basis for the subsequent development of colloidal gold test strips.

[0067] Another aspect of this application provides a system for diagnosing and / or predicting osteoporosis, the system comprising:

[0068] The data processing module is used to calculate the content or expression level of FABP3 and WIF1 in serum received or input data, and obtain the calculation results; and,

[0069] The judgment and output module is used to judge whether the calculation result meets the preset judgment conditions, so as to diagnose and / or predict individual osteoporosis, and output the prediction result;

[0070] In the judgment and output module, when the calculation result meets the preset judgment condition, the output prediction result is "the subject has osteoporosis or the probability level of having osteoporosis is high risk", and when the calculation result does not meet the preset judgment condition, the output prediction result is "the subject has no osteoporosis or the level of having osteoporosis is low risk".

[0071] The preset judgment criteria are "the content of FABP3 or its expression level is higher than that of the first negative control, and the content of WIF1 or its expression level is lower than that of the second negative control";

[0072] The first negative control is the content or expression level of FABP3 in the serum of healthy subjects or subjects who do not have osteoporosis, and the second negative control is the content or expression level of WIF1 in the serum of healthy subjects or subjects who do not have osteoporosis.

[0073] In some implementations, the determination and output module includes a machine learning model that performs the calculation;

[0074] The machine learning model is selected from one or more of the following: generalized linear model, random forest, decision tree, and support vector machine.

[0075] In another aspect of this application, a computer-readable storage medium is provided that stores a computer program, which, when executed by a processor, enables the system described above for diagnosing and / or predicting osteoporosis.

[0076] In another aspect of this application, a computer device is provided, comprising a memory and a processor, the memory storing a computer program and the processor executing the computer program to perform the functions of a system for diagnosing and / or predicting osteoporosis as described above.

[0077] The computer device can be a terminal. The computer device includes a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface of the computer device is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a method for determining the battery performance of an energy storage system. The display screen of the computer device can be an LCD screen or an e-ink display screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad located on the computer device's casing, or an external keyboard, touchpad, or mouse, etc.

[0078] Those skilled in the art will understand that all or part of the processes in the above-described method embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0079] Another aspect of this application provides an apparatus for diagnosing and / or predicting osteoporosis, comprising one or more of the following: a system for diagnosing and / or predicting osteoporosis as described above, a computer-readable storage medium as described above, and a computer device as described above.

[0080] The following are some examples.

[0081] The embodiments of this application will be described in detail below with reference to examples. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of this application. For experimental methods in the following embodiments where conditions are not specified, reference should be made to the guidelines given in this application, or to experimental manuals or conventional conditions in the art, or to the conditions recommended by the manufacturer, or to experimental methods known in the art.

[0082] Example 1: Elevated serum fatty acid-binding protein 3 (Fabp3) levels in elderly individuals with osteoporosis.

[0083] 1. Bioinformatics Analysis

[0084] Building upon previous work, to further validate the concordance of the identified age-related osteoporosis-related serum factors (S100A8 / A9, FABP3) across different populations, we further analyzed proteomics data from the UK Biobank (UKB) public database, examining the correlation between increased or decreased secretion of serum factors and bone mineral density. Using a set of human serum proteomics data from UKKB, we conducted preliminary machine learning analysis, predicting bone mineral density based on serum protein expression.

[0085] 2. Clinical sample validation

[0086] Human serum extraction: Serum is separated from whole blood using centrifugation. Venous blood is collected by a professional using sterile vacuum blood collection tubes or a syringe. The collected blood sample is placed in the blood collection tube and allowed to stand at room temperature (approximately 22-25°C) for 30-60 minutes. After standing, the blood is centrifuged at 2000-3000 rpm for 10-15 minutes. After serum separation, the supernatant is carefully aspirated using a sterile pipette, diluted with sample diluent, and then tested, or aliquoted into cryovials and stored at -80°C. Repeated freeze-thaw cycles should be avoided during serum storage.

[0087] Serum samples were collected from 200 patients with decreased bone mass (including osteoporosis patients) and 50 healthy controls. The serum FABP3 protein content in the experimental group and the control group was detected by ELISA.

[0088] 3. Experimental Results

[0089] Serum Fap3 secretion levels were significantly positively correlated with age (r=0.8979, P<0.0001), but significantly negatively correlated with femoral neck bone mineral density (r=-0.7572, P=0.002). However, it is easily affected by osteoporosis treatment drugs. If patients have recently received osteoblast-osteoblast-targeting drugs such as teriparatide, denosumab, or romosuzumab to treat osteoporosis before serum collection, the increase in serum Fap3 levels in osteoporosis patients is no longer significant.

[0090] Example 2: Serum levels of Wnt's endogenous inhibitory factor (WIF1) were significantly reduced in patients with decreased bone mass and osteoporosis.

[0091] 1. Single-cell sequencing results

[0092] WIF1 is a proprietary osteoblast-secreted factor, and its expression level in cancellous bone tissue is higher than the total expression level in all extraosseous tissues (including 21 tissues such as key organs like the heart, liver, spleen, lungs, kidneys, and brain, as well as adipose tissue) (see [link to relevant documentation]). Figure 1 (A and B), and single-cell sequencing results show that WIF1 in bone tissue is mainly secreted by osteoblasts (see A and B). Figure 1 (C)

[0093] 2. Establishment of a decompression mouse model

[0094] The deload mouse model, often called the tail suspension model, is an experimental method that simulates weightlessness or reduced physical activity by removing the weight-bearing capacity of a mouse's hind limbs. The core principle of this model is to suspend the mouse's tail, raising its hind limbs so that its body forms an angle of approximately 30° with the horizontal plane. This causes the skeletal muscles and bones of the hind limbs to no longer bear the pressure of normal body weight, thus simulating the pathological changes such as muscle atrophy and osteoporosis caused by the weightlessness of space travel or prolonged bed rest. Two- to three-month-old C57BL / 6 mice are selected, as this strain shows relatively consistent responses to suspension stress with little individual variability, especially in terms of stable hind limb bone loss. Medical tape, breathable adhesive tape, or a specially designed suspension kit are used, and fishing line or a thin chain can be used for connection, ensuring sufficient strength yet lightweight. The key adjustment parameter is to maintain an angle of approximately 30° between the mouse's long axis and the horizontal plane. This angle ensures that the hind limbs are completely deloaded without causing excessive discomfort to the animal. The device design must ensure that the mouse can move freely along the sliding bar and easily access food and water. Some improved devices also include a soft pad at the bottom of the cage to prevent injury if the mice occasionally fall. The smoothness of the pulley system also needs to be tested to avoid unnecessary resistance during suspension. The suspension period is 2-4 weeks, maintaining the suspension while providing normal care for the mice.

[0095] 3. Establishment of an aged mouse model

[0096] Aged mouse models are important tools for studying aging mechanisms and age-related diseases. This study used a natural aging model, selecting 2-3 month old C57BL / 6 mice and allowing them to age naturally through normal feeding for 18 to 24 months.

[0097] 4. mRNA extraction from mouse cancellous bone tissue, and RT-qPCR verification of mRNA levels.

[0098] After all equipment was treated with RNase-free materials, mouse cancellous bone tissue was obtained and immersed in liquid nitrogen. The tissue was placed in an enzyme-free collection tube, and lysis buffer and enzyme-free ceramic beads were added. The tissue was then ground using a ball mill to ensure thorough disruption, or frozen at 80°C for subsequent RNA extraction experiments. The lysis buffer supernatant was mixed with ethanol and transferred to a specific adsorption column. The column was centrifuged at 12,000 rpm for 1 minute, the separation buffer was discarded, and the column was washed with a prepared wash buffer (usually containing ethanol). The column was centrifuged at 12,000 rpm for 1 minute, the separation buffer was discarded, and elution buffer was added. After incubation at room temperature for 1-2 minutes, the column was centrifuged at 12,000 rpm for 1 minute to obtain the purified total RNA solution. Subsequent RT-qPCR analysis of mRNA levels was performed.

[0099] 5. Clinical sample validation

[0100] Following the FABP3 clinical sample validation procedure, serum samples were collected from 200 patients with decreased bone mass (including patients with osteoporosis) and 50 healthy controls. The serum WIF1 protein content in both groups was detected using the ELISA method.

[0101] 6. Experimental Results

[0102] Among the various factors leading to osteoporosis, the mRNA expression level of Wif1 in the cancellous bone tissue of tail-suspended mice is decreased, as is the mRNA expression level of Wif1 in the cancellous bone tissue of aged mice, and serum secretion is also reduced (see [link]). Figure 2 (A, B, and C). Serum levels of WIF1 were significantly reduced in patients with decreased bone mass and osteoporosis (see A, B, and C). Figure 2 In patients with normal bone mass and elevated FABP3 serum levels (D), FABP3 serum levels showed no significant change, while WIF1 was significantly reduced. Conversely, in patients with normal bone mass and elevated FABP3 serum levels, WIF1 levels were within the normal range. In conclusion, WIF1 is a relatively specific serological marker for skeletal hypoplasia.

[0103] Example 3

[0104] 1. Reagent kit design:

[0105] A dual ELISA diagnostic kit for FABP3 and WIF1 was developed. Half of the ELISA plate is coated with FABP3 antibody (PA5-92386), and the other half with WIF1 antibody (PA5-117011), allowing simultaneous detection of FABP3 and WIF1 secretion levels in human serum. An internal control standard is added to ensure stability. Sample type: human serum (stored at -80℃ after collection to avoid repeated freeze-thaw cycles). The kit has an intra-assay coefficient of variation of <10% and an inter-assay coefficient of variation of <15%.

[0106] 2. Reagent kit composition and storage

[0107] The components of the kit are shown in Table 2.

[0108] Table 2

[0109]

[0110] Partial Product Composition Description:

[0111] Concentrated HRP enzyme conjugate (100×) and substrate solution (TMB) should be stored away from light. All reagent bottle caps must be tightly closed to prevent evaporation and microbial contamination. Reagent volumes are as per the actual shipped instructions. Some reagents may be dispensed in slightly larger quantities than indicated on the label; please measure the correct volume before use, rather than pouring it out directly.

[0112] 3. Items to be brought by the applicant for the experiment

[0113] 1) Microplate reader (450 nm wavelength filter);

[0114] 2) High-precision pipettes, EP tubes, and four sizes of disposable pipette tips: 0.5-10μL, 2-20μL, 20-200μL, and 200-1000μL;

[0115] 3) 37℃ constant temperature chamber;

[0116] 4) Double-distilled water or deionized water;

[0117] 5) Absorbent paper;

[0118] 6) Sample addition tank.

[0119] 4. Preparations before testing

[0120] 1) Remove the kit from the refrigerator 20 minutes in advance and allow it to equilibrate to room temperature (18-25℃). If the kit is to be used multiple times, only remove the ELISA strips and reagents required for this experiment. Store the remaining strips and reagents according to the specified conditions.

[0121] 2) Washing solution: Dilute the concentrated washing solution with double-distilled water (1:24). Note: Concentrated washing solution taken from the refrigerator may have crystals, which is normal. You can gently heat it in a 40℃ water bath to completely dissolve the crystals before preparing the washing solution. Use on the same day.

[0122] 3) Standard Working Solution: Centrifuge the standard at 10000×g for 1 minute. Add 1 mL of standard & sample diluent to the lyophilized standard, tighten the cap, let stand for 10 minutes, invert several times until fully dissolved, then gently mix, avoiding foaming, to prepare a 10 ng / mL standard working solution (or add 1 mL of standard & sample diluent, let stand for 1-2 minutes, and mix thoroughly using a low-speed vortex mixer. Air bubbles generated during vortexing can be removed by low-speed centrifugation). Then perform serial dilutions as needed. It is recommended to prepare the following concentrations: 10, 5, 2.5, 1.25, 0.63, 0.32, 0.16, 0 ng / mL. Serial dilution method: Take 7 EP tubes, add 500 μL of standard & sample diluent to each tube. Pipette 500 μL of the 10 ng / mL standard working solution into the first EP tube and mix well to prepare a 5 ng / mL standard working solution. Repeat this process for the remaining tubes. See the diagram on the next page. Note: The last tube is used as a blank well; it is not necessary to draw liquid from the second-to-last tube. After reconstitution, aliquot the 10 ng / mL standard working solution and store at -20°C. Use within two weeks and avoid repeated freeze-thaw cycles. Serially diluted standard working solutions should be prepared and used immediately.

[0123] 4) Biotinylated antibody working solution: Calculate the required volume for the experiment beforehand (based on 100 μL / well). Prepare an additional 100-200 μL. 15 minutes before use, centrifuge the concentrated biotinylated antibody at 800×g for 1 minute. Dilute the 100× concentrated biotinylated antibody to a 1× working concentration using biotinylated antibody diluent (e.g., 10 μL concentrate + 990 μL diluent). Prepare fresh before use.

[0124] 5) HRP Enzyme Conjugate Working Solution: The HRP enzyme conjugate is HRP-binding avidin. Calculate the required volume for the experiment beforehand (based on 100 μL / well). Prepare an additional 100-200 μL. 15 minutes before use, centrifuge the concentrated HRP enzyme conjugate at 800×g for 1 minute. Dilute the 100× concentrated HRP enzyme conjugate to a 1× working concentration using enzyme conjugate diluent (e.g., 10 μL concentrate + 990 μL diluent). Prepare fresh before use.

[0125] 5. Testing steps:

[0126] Step A. Set up standard wells, blank wells, and sample wells separately. Add 100 μL of serially diluted standard to the standard wells, 100 μL of standard and sample diluent to the blank wells, and 100 μL of the sample to be tested to the remaining wells (it is recommended to set up duplicate wells for all samples and standards in the test). Cover the ELISA plate and incubate at 37°C for 90 minutes. Tip: When adding samples, add them to the bottom of the ELISA plate, trying not to touch the well walls, and gently shake to mix, avoiding the formation of air bubbles. The sample addition time should be controlled within 10 minutes.

[0127] Step B. Discard the liquid from the wells; no washing is required. Add 100 μL of biotinylated antibody working solution to each well, cover the plate with a membrane, and incubate at 37°C for 1 hour.

[0128] Step C. Discard the liquid from the wells and pat dry on clean absorbent paper. Add 350 μL of washing buffer to each well, soak for 1 minute, then aspirate or shake off the liquid from the microplate and pat dry. Repeat this washing step 3 times. Note: A plate washer can be used for this and other washing steps (refer to the parameters of the Beijing Top DEM-3 plate washer: 2-point aspiration, 350 μL of washing buffer per well, shake for 5 seconds, aspirate for 0.5 seconds). After washing, proceed to the next step immediately; do not allow the microplate to dry.

[0129] Step D. Add 100 μL of HRP enzyme conjugate working solution to each well, cover the microplate with a membrane, and incubate at 37°C for 30 minutes.

[0130] Step E. Shake off all the liquid in the hole and wash the plate 5 times, using the same method as in Step 3.

[0131] Step F. Add 90 μL of substrate solution (TMB) to each well, cover the plate with a membrane, and incubate at 37°C in the dark for approximately 15 minutes. Note: Adjust the incubation time according to the actual color development, but do not exceed 30 minutes. Stop incubation when a clear gradient appears in the standard wells (a clear blue gradient appears in the first four wells). Preheat the microplate reader 15 minutes beforehand.

[0132] Step G. Add 50 μL of stop solution to each well to terminate the reaction. Note: The order of adding the stop solution should ideally be the same as the order of adding the substrate solution.

[0133] Step H. Immediately measure the optical density (OD value) of each well using a microplate reader at a wavelength of 450 nm.

[0134] 6. Result Judgment

[0135] 1) Calculate the average OD value of the standard and sample replicates and subtract the OD value of the blank wells as the correction value. Plot concentration on the x-axis and OD value on the y-axis, and fit a standard curve of a four-parameter logarithmic function on a double logarithmic coordinate system.

[0136] 2) If the OD value of the sample is higher than the upper limit of the standard curve, it should be diluted appropriately and retested, and the sample concentration should be multiplied by the corresponding dilution factor when calculating the sample concentration.

[0137] For the WIF1 standard curve and FABP3 standard curve, please refer to [links to be inserted here]. Figure 3 and Figure 4 .

[0138] Preliminary research foundation:

[0139] This study, based on previous research by our team on aged osteoporosis mouse models and multicenter cohort studies of healthy and aged osteoporotic individuals, analyzed the dynamic changes in the secretory factor profiles of bone tissue, bone marrow supernatant, and serum in aged osteoporotic mice (Cell Metabolism, 2024). It then summarized the changing patterns of secretion of two key protein factors—FABP3 and WIF1—in aging osteoblasts. Based on multi-omics analysis, the following findings were made:

[0140] WIF1: It is highly expressed in osteoblasts (accounting for 91.9% of the total in the body). Compared with healthy controls, the serum WIF1 secretion level in people with reduced bone mass (DXA bone mineral density test T value ≤ -1.0) is significantly reduced, and WIF1 expression and secretion are significantly reduced during aging, which is positively correlated with bone mineral density.

[0141] FABP3: Serum FABP3 secretion levels were significantly elevated in individuals with reduced bone mass (DXA bone mineral density test T value ≤ -1.0), which was positively correlated with age (r=0.8979) and negatively correlated with femoral neck bone mineral density (r=-0.7572), but was greatly affected by medications.

[0142] Patients with a history of osteoporosis can also be tested for biochemical markers of bone turnover such as osteocalcin, PINP, and CTX to determine the type of bone turnover and help predict fracture risk. However, these indicators can only reflect the real-time bone turnover status and cannot be used for the diagnosis of osteoporosis.

[0143] Using FABP3 and WIF1 alone is easily affected by other factors. For example, although senescent osteoblasts secrete more FABP3, serum FABP3 levels are significantly affected after patients take anti-osteoporosis drugs such as denosumab or teriparatide. While WIF1 is less affected by drugs, its linear range is also narrower. Therefore, this method, based on the changing trends of serum FABP3 and WIF1 levels in people with decreased bone mass, uses machine learning methods such as random forest and decision tree to assign different diagnostic weights to FABP3 and WIF1. Ultimately, the scheme of bone mass decrease-early screening-diagnosis of osteoporosis in the elderly is adjusted to jointly detect serum levels of FABP3 and WIF1, and the optimal diagnostic model is fitted. FABP3 and WIF1 were measured in 200 patients with osteopenia (T-score ≤ -1.0) using ELISA. More than 93% of these patients had serum FABP3 and WIF1 concentrations within a specific range (WIF1 <= 0.34 ng / mL and FABP3 > 2.33 ng / mL), with an AUC of 0.9692 for diagnosing osteopenia (including osteoporosis in the elderly) (WIF1 <= 0.34 ng / mL and FABP3 > 2.33 ng / mL). In comparison, the optimal diagnostic AUC for the FABP3 ELISA kit was 0.77, while the optimal diagnostic AUC for the WIF1 ELISA kit was 0.85 (see [link to ELISA kit]). Figure 5 Therefore, the FABP3 and WIF1 dual reagent kit can more accurately diagnose whether a patient has reduced bone mass.

[0144] Relevant experimental data

[0145] Case 1 of decreased bone mass: an 85-year-old male with WIF1: 0.15 ng / mL, FABP3: 3.31 ng / mL, and a T value of -3.2;

[0146] Case 2 of decreased bone mass: A 72-year-old male with WIF1: 0.18 ng / mL, FABP3: 3.01 ng / mL, and a T value of -3.3;

[0147] Case 3 of decreased bone mass: a 75-year-old male with WIF1: 0.20 ng / mL, FABP3: 5.63 ng / mL, and a T value of -2.5;

[0148] Case 4 of decreased bone mass: a 67-year-old female with WIF1: 0.22 ng / mL, FABP3: 3.79 ng / mL, and a T value of -1.1;

[0149] Case 5 of decreased bone mass: a 73-year-old female with WIF1: 0.23 ng / mL, FABP3: 2.64 ng / mL, and a T value of -3.4;

[0150] Case 6 of decreased bone mass: A 62-year-old male with WIF1: 0.19 ng / mL, FABP3: 3.71 ng / mL, and a T value of -1.7;

[0151] Case 7 of decreased bone mass: a 63-year-old male with WIF1: 0.25 ng / mL, FABP3: 3.81 ng / mL, and a T value of -1.7;

[0152] Case 8 of decreased bone mass: A 79-year-old male with WIF1: 0.24 ng / mL, FABP3: 7.39 ng / mL, and a T value of -2.5;

[0153] Case 9 of decreased bone mass: A 74-year-old female with WIF1: 0.24 ng / mL, FABP3: 5.38 ng / mL, and a T value of -3.6;

[0154] Case 10 of osteopenia: A 74-year-old male with WIF1: 0.23 ng / mL, FABP3: 4.01 ng / mL, and a T value of -1.3;

[0155] Control Case 1: A 44-year-old male, WIF1: 0.75 ng / mL, FABP3: 0.87 ng / mL;

[0156] Control Example 2: A 52-year-old male, WIF1: 0.61 ng / mL, FABP3: 1.79 ng / mL, T value: -0.9;

[0157] Control Case 3: A 23-year-old male, WIF1: 1.19 ng / mL, FABP3: 0.38 ng / mL;

[0158] Control Case 4: A 37-year-old male, WIF1: 1.31 ng / mL, FABP3: 0.49 ng / mL;

[0159] Control Case 5: 38-year-old male, WIF1: 1.60 ng / mL, FABP3: 0.77 ng / mL;

[0160] Control Case 6: A 21-year-old female, WIF1: 7.91 ng / mL, FABP3: 0.14 ng / mL;

[0161] Control Case 7: A 22-year-old male, WIF1: 0.8 ng / mL, FABP3: 0.08 ng / mL;

[0162] Control Case 8: A 24-year-old male, WIF1: 1.25 ng / mL, FABP3: 0.08 ng / mL;

[0163] Control Case 9: A 27-year-old male, WIF1: 3.08 ng / mL, FABP3: 0.16 ng / mL;

[0164] Control case 10: 31-year-old male, WIF1: 0.74 ng / mL, FABP3: 0.24 ng / mL.

[0165] Based on the above research findings, a FABP3-WIF1 dual ELISA kit was developed for early screening of decreased bone mass and early warning of the risk of osteoporotic fractures.

[0166] In addition, the inventors also tried other factors, such as VCAM-1 (vascular cell adhesion molecule-1), Angptl3 (angiopoietin-like protein 3), IGFBP-4 (insulin-like growth factor binding protein-4), TNF (tumor necrosis factor), PINP (type I procollagen N-terminal propeptide), and CTX (type I collagen C-terminal crosslinking telopeptide), for the diagnosis of osteoporosis, but the results were not significant (see [link to relevant documentation]). Figure 6 The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0167] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims, and the specification and drawings can be used to interpret the content of the claims.

Claims

1. The use of reagents for detecting the content or expression level of FABP3 and WIF1 in samples in the preparation of products for diagnosing and / or predicting osteoporosis; said samples include serum samples.

2. The application as described in claim 1, characterized in that, The reagents are selected from one or more of the following: specific probes, gene chips, specific primers, anti-FABP3 antibodies, and anti-WIF1 antibodies.

3. The application as described in claim 1 or 2, characterized in that, The detection of the content or expression level of FABP3 and WIF1 in the sample includes: comparing the content or expression level of FABP3 and WIF1 in the sample with the control. If the FABP3 content or expression level is higher than that of the first negative control, and the WIF1 content or expression level is lower than that of the second negative control, the subject corresponding to the sample detected by the reagent is considered to have osteoporosis or to have a high risk of osteoporosis; the first negative control is the FABP3 content or expression level in the serum of a healthy subject or at least a subject without osteoporosis, and the second negative control is the WIF1 content or expression level in the serum of a healthy subject or at least a subject without osteoporosis. If the content or expression level of FABP3 is consistent with or lower than that of the first negative control, and / or the content or expression level of WIF1 is consistent with or higher than that of the second negative control, then the subject corresponding to the sample detected by the reagent is considered to be at low risk of osteoporosis.

4. A kit for diagnosing and / or predicting osteoporosis, characterized in that, It contains reagents for detecting the content or expression level of FABP3 and WIF1 in a sample; the sample includes a serum sample; The reagents include anti-FABP3 antibody and anti-WIF1 antibody.

5. The kit according to claim 4, characterized in that, The kit also includes a positive control and / or a negative control; wherein: the positive control includes or is whole blood, mRNA or cDNA of a subject with known osteoporosis; the negative control includes or is whole blood, mRNA or cDNA of a healthy subject or at least a subject without osteoporosis; And / or, the kit may also include primers and / or antibodies for detecting the content or expression level of an internal reference gene.

6. A system for diagnosing and / or predicting osteoporosis, characterized in that, The system includes: The data processing module is used to calculate the content or expression level of FABP3 and WIF1 in serum received or input data, and obtain the calculation results; and, The judgment and output module is used to judge whether the calculation result meets the preset judgment conditions, so as to diagnose and / or predict individual osteoporosis, and output the prediction result; In the judgment and output module, when the calculation result meets the preset judgment condition, the output prediction result is "the subject has osteoporosis or the probability level of having osteoporosis is high risk", and when the calculation result does not meet the preset judgment condition, the output prediction result is "the subject does not have osteoporosis or the level of having osteoporosis is low risk". The preset judgment conditions are "the content of FABP3 or its expression level is higher than that of the first negative control, and the content of WIF1 or its expression level is lower than that of the second negative control"; The first negative control is the content or expression level of FABP3 in the serum of healthy subjects or subjects who do not have osteoporosis, and the second negative control is the content or expression level of WIF1 in the serum of healthy subjects or subjects who do not have osteoporosis.

7. The system for diagnosing and / or predicting osteoporosis as described in claim 6, characterized in that, The judgment and output module includes a machine learning model that performs the calculation; The machine learning model is selected from one or more of the following: generalized linear model, random forest, decision tree, and support vector machine.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it can perform the functions of the system for diagnosing and / or predicting osteoporosis as described in claim 6 or 7.

9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor is used to execute the computer program to implement the functions of the system for diagnosing and / or predicting osteoporosis as described in claim 6 or 7.

10. An apparatus for diagnosing and / or predicting osteoporosis, comprising one or more of the system for diagnosing and / or predicting osteoporosis as claimed in claim 6 or 7, the computer-readable storage medium as claimed in claim 8, and the computer device as claimed in claim 9.

Images