Diagnostic marker combination for senility asthenia and application of diagnostic marker combination

Abstract

The invention discloses a diagnostic marker combination for senile weakness and application thereof, and relates to the technical field of biological medicines. The diagnostic marker combination comprises a GDF15 protein, an FABP4 protein, an ITGA11 protein, an LEP protein, an REN protein and a CTSB protein. Aiming at the urgent medical problems that no unified elderly weakness diagnosis standard exists in the world at present, a blank exists on the basis of blood diagnosis indexes, and serious obstacles are brought to community screening, clinical decision making and health management, on the basis of sample screening and sample verification research, a systematic epidemiology method is applied, and the elderly weakness diagnosis standard is established. The biomarkers are identified in a multi-level and omnibearing manner, and a diagnostic marker combination for senile weakness and a diagnostic product based on biomolecules are developed so as to be applied to community screening and clinical diagnosis and lay a key foundation for disease prevention and treatment and clinical application.

Description

Technical Field

[0001] The present invention relates to the field of biomedical technology, and in particular to a diagnostic marker combination for senile frailty and an application thereof. Background Art

[0002] With the acceleration of population aging, the spectrum of diseases in the elderly population has changed, and new geriatric syndromes such as frailty have occurred and developed. They have continued to increase with the extension of life expectancy, and have shown a clear upward trend as the elderly population ages. New geriatric syndromes such as frailty have a high prevalence and serious harm; at the same time, the pathogenesis is complex, there is a lack of reliable, stable and objective diagnostic criteria, and the understanding of the disease varies in different countries and regions, making it difficult to prevent and treat them globally. Frailty syndrome in the elderly population is dynamically reversible, so preventive intervention is very important, especially in the early stages, and timely identification is crucial to maximize the opportunity for intervention. In clinical practice, frailty may amplify the impact of traditional risk factors on outcomes. Identifying and treating frail elderly people may be effective in treating those with poor prognosis or who may need pre-rehabilitation before surgery, and may help predict risks and make decisions in clinical settings, which is more conducive to achieving healthy aging. Therefore, the molecular biological mechanisms, prevention, diagnosis and intervention technologies of important geriatric diseases such as frailty derived from the process of population aging have become the frontier hotspots in the fields of geriatric medicine and public health. The present invention plans to analyze the biological framework for the healthy aging of the population and senile frailty, develop a new biological diagnosis system, and actively build a disease paradigm for preventing the aging of the population, laying an important foundation for my country to achieve the goal of human health and longevity.

[0003] Aiming at key scientific issues such as biological framework, definition and diagnostic criteria, mechanism analysis, and community and clinical applications of frailty in the elderly, the present invention intends to develop a biological-based diagnostic and assessment tool to establish the first blood diagnostic technology for frailty in the elderly in my country and even in the world. Summary of the invention

[0004] The purpose of the present invention is to provide a diagnostic marker combination for senile frailty and its application to solve the problems existing in the above-mentioned prior art. The diagnostic marker combination can effectively diagnose senile frailty, thereby providing technical support for community screening and clinical diagnosis.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] The present invention provides a diagnostic marker combination for senile frailty, which comprises GDF15 protein, FABP4 protein, ITGA11 protein, LEP protein, REN protein and CTSB protein.

[0007] The present invention also provides a diagnostic model for senile frailty, wherein the diagnostic model uses the content of diagnostic markers in plasma as input variables, wherein the diagnostic markers are GDF15 protein, FABP4 protein, ITGA11 protein, LEP protein, REN protein and CTSB protein; the diagnostic model calculates the risk score by the following formula:

[0008] Risk score = 0.26582768 + 1.139 × GDF15 protein content + 0.257 × FABP4 protein content + 0.153 × LEP protein content + 0.687 × REN protein content - 0.619 × ITGA11 protein content - 0.390 × CTSB protein content.

[0009] The present invention also provides the use of a reagent for detecting the above-mentioned diagnostic marker combination in the preparation of a diagnostic product for senile frailty.

[0010] Furthermore, the diagnostic product is a kit.

[0011] Furthermore, the diagnostic product is a chip.

[0012] The present invention also provides a diagnostic product for senile frailty, comprising a reagent for detecting the above-mentioned diagnostic marker combination.

[0013] Furthermore, the diagnostic product is a kit.

[0014] Furthermore, the diagnostic product is a chip.

[0015] The present invention discloses the following technical effects:

[0016] In response to the urgent medical problem that there is currently no unified diagnostic standard for frailty in the elderly in the world, there is a gap in blood-based diagnostic indicators, and it brings serious obstacles to community screening, clinical decision-making and health management. Based on the research of screening samples and verification samples, the present invention uses a systematic epidemiological method to identify biomarkers at multiple levels and in all directions, and develops a diagnostic marker combination for frailty in the elderly and a diagnostic product based on biomolecules for application in community screening and clinical diagnosis, laying a key foundation for disease prevention and control and clinical application. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 This is the differential protein analysis diagram of the screening samples;

[0019] Figure 2 To screen the importance ranking diagram of differentially expressed proteins in samples;

[0020] Figure 3 It is a statistical chart of the expression of 6 core proteins in the screening samples; among them, AE are GDF15, FABP4, LEP, REN, ITGA11 and CTSB;

[0021] Figure 4 This is a statistical chart of the expression of six core proteins in the validation samples; among them, AE are GDF15, FABP4, LEP, REN, ITGA11 and CTSB;

[0022] Figure 5 This is a statistical chart of the expression of 6 core proteins in the samples validated by the Elisa method; among them, AE are GDF15, FABP4, LEP, REN, ITGA11 and CTSB;

[0023] Figure 6 The receiver operating characteristic curves of the six core proteins in the screening samples are shown;

[0024] Figure 7 The receiver operating characteristic curve of the combination of six core proteins (diagnostic model) in clinical samples;

[0025] Figure 8 The receiver operating characteristic curves of the six core proteins in the validation samples are shown;

[0026] Fig. 9 This is the receiver operating characteristic curve for the combination of 6 core proteins (diagnostic model) in the validation sample. DETAILED DESCRIPTION

[0027] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as limiting the present invention, but should be understood as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0028] It should be understood that the terms described in the present invention are only for describing a particular embodiment and are not intended to limit the present invention. In addition, for the numerical range in the present invention, it should be understood that each intermediate value between the upper and lower limits of the scope is also specifically disclosed. The intermediate value in any stated value or stated range, and each smaller range between any other stated value or intermediate value in the described range is also included in the present invention. The upper and lower limits of these smaller ranges can be independently included or excluded in the scope.

[0029] Unless otherwise indicated, all technical and scientific terms used herein have the same meanings as those generally understood by those skilled in the art. Although the present invention describes only preferred methods and materials, any methods and materials similar or equivalent to those described herein may also be used in the implementation or testing of the present invention. All documents mentioned in this specification are incorporated by reference to disclose and describe the methods and / or materials associated with the documents. In the event of a conflict with any incorporated document, the content of this specification shall prevail.

[0030] It will be apparent to those skilled in the art that various modifications and variations may be made to the specific embodiments of the present invention description without departing from the scope or spirit of the present invention. Other embodiments derived from the present invention description will be apparent to those skilled in the art. The present invention description and examples are exemplary only.

[0031] The words “include,” “including,” “have,” “contain,” etc. used in this document are open-ended terms, meaning including but not limited to.

[0032] Terminology Note:

[0033] GDF15 refers to growth differentiation factor 15, which is a stress-induced cytokine produced primarily by activated macrophages.

[0034] FABP4 refers to fatty acid binding protein 4, which belongs to the lipid binding protein superfamily. FABP4 is mainly expressed in adipocytes, so it is also called adipocyte fatty acid binding protein 4.

[0035] ITGA11 refers to integrin α11, an important extracellular matrix protein.

[0036] LEP stands for Leptin, a protein encoded by the obesity gene (ob), also known as Leptin.

[0037] REN stands for renin, an enzyme secreted by the kidneys.

[0038] CTSB refers to cathepsin B, an enzymatic protein belonging to the peptidase C1 family.

[0039] Example 1

[0040] 1. Materials and Methods

[0041] 1. Screening samples (database samples)

[0042] UK Biobank is a large population-based prospective cohort study with extensive and in-depth proteomic and phenotypic data. More than half a million participants aged between 39 and 73 years were recruited from 22 assessment centers in England, Wales, and Scotland in the UK from 2006 to 2010. The present invention focuses on the population aged 60 - 75 years. Combining the analysis objectives, the present invention excluded subjects with missing proteomic data (n = 343), outside the age range (n = 24,560), and without Rockwood FI at baseline (n = 9,896); the analysis of the present invention was limited to individuals of white British ethnic background and frail individuals, so non-white British (n = 6,740) and pre-frail individuals (0.1 < FI < 0.21, n = 3,205) were also excluded, resulting in an analysis cohort of 8,258 participants. This cohort obtained ethical approval from the North West Research Ethics Committee (11 / NW / 0382). And an electronic consent form signed by each participant was obtained at the baseline assessment.

[0043] 2. Validation samples (clinical samples)

[0044] Based on the follow-up of the elderly population and frailty screening carried out by the project team in Dalang Town and Guancheng, Dongguan City, the project further carried out longitudinal follow-up of the cohort by adding the evaluation project of skeletal muscle function.

[0045] All subjects underwent a questionnaire survey. Combining with the regular physical examination of community elderly people (regular annual physical examinations have been carried out for the elderly 65 years old and above), blood sample specimens were collected. The present invention obtained the informed consent of the elderly or their families and the approval of the Ethics Committee of Guangdong Medical University. Inclusion and exclusion criteria: Inclusion criteria: aged 65 years old and above; conscious; both the subject and their family members were informed and consented and voluntarily signed the consent form. Exclusion criteria: Alzheimer's disease patients; bedridden and disabled elderly; elderly with hearing or vision impairments who cannot communicate normally; temporary residents; those suffering from immune system diseases.

[0046] 3. Assessment methods for elderly frailty

[0047] The present invention uses the Rockwood Frailty Index (FI) to assess elderly frailty. See references PMID: 31951595, PMID: 16129869, PMID: 31609229, and PMID: 39115063.

[0048] 4. Proteomic detection

[0049] Protein measurement uses Explore [1536, 4 × 384 panel used] (Olink Proteomics AB, Uppsala, Sweden). The technology behind the Olink protocol is based on proximity extension analysis (PEA) (Assarsson et al., 2014), coupled with readout via next-generation sequencing (NGS). The assay uses 2.8 μL of serum / plasma and can simultaneously detect up to 1536 protein samples in 90 samples. For each protein, a pair of oligonucleotide-labeled antibody probes are designed to bind to their targets, bringing the complementary oligonucleotides into close proximity and allowing their hybridization. The addition of DNA polymerase causes the oligonucleotides to extend the hybridized strand, generating a unique protein identification “barcode”. Next, the library is prepared by adding sample identification indicators and the nucleotides required for Illumina sequencing. Previously sequenced using NovaSeq TM 6000 / NextSeq TM 550 / NextSeq TM In 2000, libraries passed through a bead purification step and quality was assessed using an Agilent 2100 Bioanalyzer (Agilent Technologies, PaloAlto, CA). Raw output data were quality controlled, normalized, and converted to normalized protein expression (NPX), Olink's proprietary unit of relative abundance. Data normalization was performed using an internal expansion control and an external plate control to adjust for intra- and inter-run variations. All data analysis validations (detection limits, intra- and inter-assay precision data, predefined values, etc.) can be performed on the manufacturer's website (www.olink.com).

[0050] 5. Data Analysis

[0051] Of the 1459 plasma proteomic biomarkers tested in the screening samples, four areas were analyzed using the PEA platform based on Olink analysis: cardiometabolic, inflammatory, oncological, and neurological. Using machine learning methods, 132 proteins were found to be significantly associated with frailty status (Table 1). After adjustment for demographic characteristics (sex, education level) and lifestyle (smoking, alcohol consumption, and physical activity) in a binary logistic regression model, 128 proteins remained significantly associated, of which 89 proteins were significantly upregulated in frail individuals and 43 proteins were downregulated ( Figure 1). GDF15 was most strongly associated with frailty risk. When evaluating subgroups stratified by sex, most frailty-associated proteins had similar correlations in men (92.97%) and women (97.5%). Using a set of significant frailty-associated proteins, the present invention performed biological pathway analysis to determine which biological processes and molecular functions were altered in frail individuals. The pathways associated with frailty mainly involved immune inflammation (complement and coagulation cascades, antigen processing and presentation, chemokine signaling pathways), endocrine (regulation of adipocyte lipolysis, renin secretion, prolactin signaling pathway), lipid metabolism, signal transduction (neuroactive ligand-receptor interaction, cytokine-cytokine receptor interaction, nf-κB signaling pathway), infection, cancer, amino acid metabolism, cell growth and death (apoptosis, p53 signaling pathway, cell cycle), cell movement (actin skeleton regulation), transport and catabolism (lysosomes, phagosomes), sensory system, energy metabolism (nitrogen metabolism), digestive system (protein digestion and absorption, cholesterol metabolism, vitamin digestion and absorption), see Table 2.

[0052] Table 1 132 proteins related to frailty found in the screening cohort based on machine learning models

[0053]

[0054]

[0055]

[0056]

[0057] Table 2132 biological pathway enrichment of proteins

[0058]

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066]

[0067]

[0068]

[0069] 6. Screening of core proteins

[0070] The present invention finally determined the six core proteins GDF15, FABP4, ITGA11, LEP, REN and CTSB by recursive elimination method. Figure 2 For specific expression, see Figure 3 .

[0071] These six core proteins were used to construct a diagnostic model for frailty in the elderly: the diagnostic model used the content of core proteins in plasma as input variables and calculated the risk score using the following formula:

[0072] Risk score = 0.26582768 + 1.139 × GDF15 protein content + 0.257 × FABP4 protein content + 0.153 × LEP protein content + 0.687 × REN protein content - 0.619 × ITGA11 protein content - 0.390 × CTSB protein content.

[0073] In the screening samples, the receiver operating characteristic curves of the six core proteins for elderly frailty were Figure 6 ; The receiver operating characteristic curve of the above diagnostic model for elderly frailty is shown in Figure 7 It can be seen that the diagnostic model has a high diagnostic value in elderly frailty (AUC = 0.80).

[0074] 7. Verification of core protein

[0075] The statistical results of the specific expression levels of the six core proteins in 176 validation samples are shown in Figure 4 , which is consistent with the protein expression trend in the screened samples.

[0076] The diagnostic model of frailty was used to verify the results in 176 validation samples. In the validation samples, the receiver operating characteristic curves of the six core proteins for frailty were shown in Figure 2. Figure 8 ; The receiver operating characteristic curve of the above diagnostic model for elderly frailty is shown in Fig. 9 It can be seen that the diagnostic model has a high diagnostic value in elderly frailty (AUC = 0.83).

[0077] The present invention further uses the ELISA method to verify the expression of the six core proteins in another independent population (validation sample, derived from a Chinese cohort population), and the results are consistent with the protein sequencing data (see Figure 5 ).

[0078] The embodiments described above are only descriptions of the preferred modes of the present invention, and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A diagnostic marker combination for senile frailty, characterized in that: The diagnostic marker combination includes GDF15 protein, FABP4 protein, ITGA11 protein, LEP protein, REN protein and CTSB protein.

2. A diagnostic model for senile frailty, characterized in that: The diagnostic model uses the content of diagnostic markers in plasma as input variables, and the diagnostic markers are GDF15 protein, FABP4 protein, ITGA11 protein, LEP protein, REN protein and CTSB protein; the diagnostic model calculates the risk score by the following formula: Risk score = 0.26582768 + 1.139 × GDF15 protein content + 0.257 × FABP4 protein content + 0.153 × LEP protein content + 0.687 × REN protein content - 0.619 × ITGA11 protein content - 0.390 × CTSB protein content.

3. Use of a reagent for detecting the diagnostic marker combination according to claim 1 in the preparation of a diagnostic product for senile frailty.

4. The use according to claim 3, characterized in that: The diagnostic product is a kit.

5. The use according to claim 3, characterized in that: The diagnostic product is a chip.

6. A diagnostic product for senile frailty, characterized in that: A reagent for detecting the diagnostic marker combination according to claim 1.

7. The diagnostic product according to claim 6, characterized in that The diagnostic product is a kit.

8. The diagnostic product according to claim 6, characterized in that The diagnostic product is a chip.

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