A combination of diagnostic biomarkers for age-related frailty and its application

Abstract

This invention discloses a diagnostic biomarker combination for frailty in the elderly and its applications, relating to the field of biomedical technology. The diagnostic biomarker combination includes GDF15 protein, FABP4 protein, ITGA11 protein, LEP protein, REN protein, and CTSB protein. Addressing the urgent medical problem of the lack of a globally unified diagnostic standard for frailty in the elderly, and the gap in blood-based diagnostic indicators posing serious obstacles to community screening, clinical decision-making, and health management, this invention, based on research into sample screening and validation, utilizes systems epidemiology methods to identify biomarkers at multiple levels and in a comprehensive manner. It has developed a diagnostic biomarker combination for frailty in the elderly, as well as biomolecular-based diagnostic products, for application in community screening and clinical diagnosis, laying a crucial foundation for disease prevention and clinical application.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, and in particular to a combination of diagnostic biomarkers for frailty in the elderly and their applications. Background Technology

[0002] With the accelerating aging of the population and the changing disease spectrum among the elderly, new geriatric syndromes such as frailty are emerging and developing, continuously increasing with the extension of life expectancy and showing a significant upward trend with the aging population. These new geriatric syndromes, including frailty, have high prevalence and severe harm; at the same time, their pathogenesis is complex, lacking reliable, stable, and objective diagnostic criteria, and different countries and regions have varying understandings of the diseases, making precise global prevention and control difficult. Frailty syndromes in the elderly are dynamic and reversible, therefore prevention and intervention are crucial, especially in the early stages, where timely identification is essential to maximizing intervention opportunities. Clinically, frailty may amplify the impact of traditional risk factors on outcomes. Identifying and treating frail elderly individuals may be effective in addressing poor prognoses or those requiring preoperative rehabilitation, potentially aiding in risk prediction and decision-making in clinical settings and contributing to healthy aging. Therefore, the molecular biological mechanisms, prevention, diagnosis, and intervention technologies of important geriatric diseases such as frailty, which arise during population aging, have become cutting-edge research topics in geriatric medicine and public health. This invention aims to address the challenges of healthy aging and frailty in the elderly by analyzing the biological framework and developing a novel biological diagnostic system. It also seeks to proactively establish a paradigm for preventing diseases during the aging process, laying a crucial foundation for achieving my country's goals of human health and longevity.

[0003] Addressing key scientific questions regarding the biological framework, definition, diagnostic criteria, mechanism analysis, and community and clinical applications of frailty in the elderly, this invention aims to develop a biological diagnostic and assessment tool, establishing the first blood diagnostic technology for frailty in my country and even globally. Summary of the Invention

[0004] The purpose of this invention is to provide a combination of diagnostic biomarkers for frailty in the elderly and their applications, thereby addressing the problems existing in the prior art. This combination of diagnostic biomarkers can effectively diagnose frailty in the elderly, thus providing technical support for community screening and clinical diagnosis.

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

[0006] This invention provides a diagnostic biomarker combination for frailty in the elderly, the biomarker combination including GDF15 protein, FABP4 protein, ITGA11 protein, LEP protein, REN protein and CTSB protein.

[0007] This invention also provides a diagnostic model for frailty in the elderly, wherein the diagnostic model uses the plasma concentration of diagnostic biomarkers as input variables, and the diagnostic biomarkers are GDF15 protein, FABP4 protein, ITGA11 protein, LEP protein, REN protein, and CTSB protein; the diagnostic model calculates a risk score using 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 reagents for detecting the above-described combination of diagnostic biomarkers in the preparation of diagnostic products for age-related frailty.

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

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

[0012] The present invention also provides a diagnostic product for frailty in the elderly, comprising a reagent for detecting the combination of the above-mentioned diagnostic markers.

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

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

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

[0016] This invention addresses the urgent medical problem of the lack of a globally unified diagnostic standard for frailty in the elderly, the gap in blood-based diagnostic indicators, and the serious obstacles it poses to community screening, clinical decision-making, and health management. Based on research into sample screening and validation, this invention employs systems epidemiology methods to identify biomarkers at multiple levels and in a comprehensive manner, developing a combination of diagnostic biomarkers for frailty in the elderly, as well as biomolecular-based diagnostic products for application in community screening and clinical diagnosis, laying a crucial foundation for disease prevention and clinical application. Attached Figure Description

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. 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 any creative work.

[0018] Figure 1 A differential protein analysis diagram for screening samples;

[0019] Figure 2 A ranking plot of the importance of differentially expressed proteins in a sample;

[0020] Figure 3 A statistical graph showing the expression of six core proteins in the sample; where AE represents GDF15, FABP4, LEP, REN, ITGA11, and CTSB, respectively.

[0021] Figure 4 A statistical graph to verify the expression of six core proteins in the sample; where AE represents GDF15, FABP4, LEP, REN, ITGA11, and CTSB, respectively.

[0022] Figure 5 A statistical graph showing the expression of six core proteins in the sample validated by ELISA; where AE represents GDF15, FABP4, LEP, REN, ITGA11, and CTSB, respectively.

[0023] Figure 6 Receiver operating characteristic (ROC) curves for the six core proteins in the screened samples;

[0024] Figure 7 Receiver operating characteristic curves for a combination of six core proteins (diagnostic model) in clinical samples;

[0025] Figure 8 Receiver operating characteristic (ROC) curves for the six core proteins in the validation sample;

[0026] Figure 9 Subject operating characteristic curves for a combination of six core proteins (diagnostic model) in validation samples. Detailed Implementation

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

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

[0029] Unless otherwise stated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. While only preferred methods and materials have been described herein, any methods and materials similar or equivalent to those described herein may be used in the implementation or testing of this invention. All references to this specification are incorporated by way of citation to disclose and describe methods and / or materials associated with those references. In the event of any conflict with any incorporated reference, the content of this specification shall prevail.

[0030] Various modifications and variations can be made to the specific embodiments described in this specification without departing from the scope or spirit of the invention, as will be apparent to those skilled in the art. Other embodiments derived from this specification will also be readily apparent to those skilled in the art. This specification and embodiments are merely exemplary.

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

[0032] Terminology Explanation:

[0033] GDF15 refers to growth differentiation factor 15, which is a cytokine induced by stress response and mainly produced 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, and is therefore also known as adipocyte-type fatty acid-binding protein 4.

[0035] ITGA11 refers to integrin α11, which is 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] I. Materials and Methods

[0041] 1. Sample selection (database samples)

[0042] UK Biobank is a large population-based prospective cohort study with extensive and detailed proteomic and phenotypic data. More than half a million participants aged between 39 and 73 were recruited between 2006 and 2010 at 22 assessment centers in England, Wales, and Scotland in the UK. 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 = 24560), and without Rockwood FI at baseline (n = 9896); the analysis of the present invention was limited to individuals of white British ethnic background and frail individuals, so non-white British individuals (n = 6740) and pre-frail individuals (0.1 < FI < 0.21, n = 3205) were also excluded, resulting in an analysis cohort of 8258 participants. This cohort obtained ethical approval from the North West Research Centre 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 of Dongguan City, the project further carried out longitudinal follow-up of the cohort by adding a skeletal muscle function evaluation project.

[0045] All subjects underwent questionnaire surveys. Combining with the regular physical examinations 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; informed consent from the person himself and his family, and voluntarily signed the consent form. Exclusion criteria: Alzheimer's disease patients; bedridden and disabled elderly people; elderly people who cannot communicate normally due to hearing and vision impairments; temporary residents; patients with 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 Extended Analysis (PEA) (Assarsson et al., 2014), coupled with next-generation sequencing (NGS) readouts. This analysis uses 2.8 μL of serum / plasma and can simultaneously detect up to 1536 proteins in 90 samples. A pair of oligonucleotide-tagged antibody probes is designed for each protein to bind to their target, bringing complementary oligonucleotides close together and allowing hybridization. The addition of DNA polymerase causes the oligonucleotides in the hybridized strand to extend, generating a unique protein recognition “barcode.” Next, the library is prepared by adding sample identification indicators and the nucleotides required for Illumina sequencing. Previous sequencing used… NovaSeq TM 6000 / NextSeq TM 550 / NextSeq TM In 2000, the library was purified using a bead-based purification process and evaluated using an Agilent 2100 Quality Bioanalyzer (Agilent Technologies, Palo Alto, CA). Raw output data underwent quality control, normalization, and conversion to normalized protein expression (NPX), Olink's proprietary unit of relative abundance. Data normalization was performed using an internal extended control and an external board control to accommodate variations in internal and external runs. All data analysis validations (limits of detection, intra- and inter-assay precision data, predefined values, etc.) are available on the manufacturer's website (www.olink.com).

[0050] 5. Data Analysis

[0051] Among the 1459 plasma proteomic biomarkers screened for testing, analysis was performed using the PEA platform based on Olink analysis across four dimensions: cardiometabolism, inflammation, oncology, and neurology. Machine learning methods identified 132 proteins significantly associated with frailty (Table 1). After adjusting for demographic characteristics (sex, education level) and lifestyle factors (smoking, alcohol consumption, and physical activity) in a binary logistic regression model, 128 proteins remained significantly associated, with 89 proteins significantly upregulated and 43 proteins downregulated in frail individuals. Figure 1GDF15 was most strongly associated with frailty risk. When assessing subgroups stratified by sex, most frailty-related proteins showed similar associations in males (92.97%) and females (97.5%). Using a significant set of frailty-related proteins, this invention performed biological pathway analysis to identify which biological processes and molecular functions are altered in frail individuals. Frailty-related pathways primarily involve immune inflammation (complement and coagulation cascades, antigen processing and presentation, chemokine signaling pathways), endocrine function (regulation of adipocyte lipolysis, renin secretion, prolactin signaling pathways), lipid metabolism, signal transduction (neuroactive ligand-receptor interactions, cytokine-cytokine receptor interactions, NF-κB signaling pathways), infection, cancer, amino acid metabolism, cell growth and death (apoptosis, p53 signaling pathway, cell cycle), cell movement (actin cytoskeleton regulation), transport and catabolism (lysosomes, phagosomes), sensory systems, energy metabolism (nitrogen metabolism), and the digestive system (protein digestion and absorption, cholesterol metabolism, vitamin digestion and absorption), as shown in Table 2.

[0052] Table 1. 132 age-related frailty proteins identified in the screening cohort based on machine learning models.

[0053]

[0054]

[0055]

[0056]

[0057] Table 21 shows the biological pathway enrichment of 32 proteins.

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[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066]

[0067]

[0068]

[0069] 6. Screening of core proteins

[0070] This invention used a recursive elimination method to ultimately identify six core proteins: GDF15, FABP4, ITGA11, LEP, REN, and CTSB. Figure 2 For specific expression levels, see Figure 3 .

[0071] A diagnostic model for age-related frailty was constructed using these six core proteins. This model uses the plasma concentration of these core proteins as input variables and calculates a 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 screened sample, the operating characteristic curves of six core proteins for elderly and frail subjects are shown below. Figure 6 The operating characteristic curves of the above diagnostic model for elderly frail subjects are shown in the figure. Figure 7 Therefore, this diagnostic model has high diagnostic value in frailty in the elderly (AUC = 0.80).

[0074] 7. Validation of the core protein

[0075] The statistical results of the specific expression levels of the 6 core proteins in 176 validation samples are shown below. Figure 4 The trend was the same as that of protein expression in the screened samples.

[0076] A diagnostic model for age-related frailty was used, and validation was performed on a sample of 176 participants. In this validation sample, the operating characteristic curves for the six core proteins against age-related frailty are shown in the table below. Figure 8 The operating characteristic curves of the above diagnostic model for elderly frail subjects are shown in the figure. Figure 9 Therefore, this diagnostic model has high diagnostic value in frailty in the elderly (AUC = 0.83).

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

[0078] The embodiments described above are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by persons skilled in the art should fall within the scope of protection defined by the claims of the present invention.

Claims

1. A method for constructing a diagnostic model for frailty in the elderly, characterized in that, The diagnostic model was constructed using six diagnostic biomarkers; The diagnostic model uses the plasma concentrations of diagnostic biomarkers as input variables. These biomarkers are GDF15 protein, FABP4 protein, ITGA11 protein, LEP protein, REN protein, and CTSB protein. The diagnostic model calculates the risk score using 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.

2. The application of reagents for detecting combinations of diagnostic biomarkers in the preparation of diagnostic products for age-related frailty, characterized in that, The diagnostic biomarker combination consists of GDF15 protein, FABP4 protein, ITGA11 protein, LEP protein, REN protein, and CTSB protein.

3. The application according to claim 2, characterized in that, The diagnostic product is a reagent kit.

4. The application according to claim 2, characterized in that, The diagnostic product is a chip.

5. A diagnostic product for senile frailty, characterized in that, Reagents that include combinations of diagnostic biomarkers; The diagnostic biomarker combination consists of GDF15 protein, FABP4 protein, ITGA11 protein, LEP protein, REN protein, and CTSB protein.

6. The diagnostic product according to claim 5, characterized in that, The diagnostic product is a reagent kit.

7. The diagnostic product according to claim 5, characterized in that, The diagnostic product is a chip.

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