Aging biomarker detection system and application thereof

Abstract

The invention provides an aging biomarker detection system and application thereof, and belongs to the technical field of biomedicine. The senescence biomarker detection system comprises senescence biomarker detection and senescence evaluation model establishment, has the characteristics of comprehensiveness, accuracy and high efficiency, and can provide powerful support for senescence research and anti-senescence intervention. The construction method of the senescence biomarker detection system is scientific and reasonable, the screened 12 senescence biomarkers have high specificity and sensitivity, the senescence degree is comprehensively evaluated through the integral senescence evaluation model, and the senescence biomarker detection system is comprehensive, accurate and efficient. The senescence biomarker detection system is wide in application, can be applied to multiple fields of senescence degree evaluation, disease risk prediction, anti-senescence intervention effect evaluation, drug research, development and screening and the like, provides a new tool and method for anti-senescence research and clinical practice, and has important application value and wide market prospects.

Description

Technical Field

[0001] The present invention relates to the field of biomedical technology, and in particular to an aging biomarker detection system and application thereof. Background Art

[0002] With the aging of the population, research on aging has received increasing attention. Accurate detection of aging biomarkers is of great significance for assessing the degree of aging of individuals, predicting disease risks, and developing anti-aging interventions. However, most of the existing aging biomarker detection indicators reflect the aging status from one or several aspects. For example, telomere length mainly reflects the decline of cell division ability, and gene expression detection only targets the expression changes of specific genes. The aging process is extremely complex and involves changes in multiple systems and levels. It is difficult for a single indicator to comprehensively and accurately assess the overall degree of aging. In addition, there are some aging biomarker detection indicators that are clinical disease diagnostic indicators and are not specific to aging, which may lead to misjudgment and inaccurate interpretation of the test results.

[0003] Therefore, there are currently no unified standards and specifications for the detection of aging biomarkers and the evaluation of the degree of aging. Different detection methods and evaluation criteria may produce different results, which makes the interpretation and comparison of the test results difficult and is not conducive to their widespread application in clinical practice and scientific research. Most anti-aging testing institutions rely mainly on clinical test indicators to assess the degree of aging. Clinical test indicators have a lag and cannot detect potential health problems as early as possible. Moreover, most clinical test indicators mainly reflect disease characteristics, have no obvious correlation with age, and cannot reflect the degree of aging. There are also some anti-aging testing institutions that rely on telomere length testing to assess aging. Although telomere length is related to aging, aging is related to multiple factors, and one indicator cannot fully reflect the aging state of the entire body. Summary of the invention

[0004] The purpose of the present invention is to provide an aging biomarker detection system and its application, which has the advantages of being more comprehensive, accurate and efficient.

[0005] In order to achieve the above-mentioned object of the invention, the present invention provides the following technical solutions:

[0006] The present invention provides an aging biomarker detection system, including an aging biomarker detection module and an aging assessment model establishment module;

[0007] The aging biomarker detection module includes the following units: aging biomarker screening unit, detection method establishment unit, detection condition optimization unit and aging biomarker detection unit:

[0008] The aging biomarker screening unit screens aging biomarkers through the following aging markers:

[0009] Genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, macroautophagy dysfunction, dysregulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, altered intercellular communication, chronic inflammation, and gut microbiota dysbiosis.

[0010] Preferably, the following aging biomarker combinations are obtained through the aging biomarker screening unit:

[0011] Genomic instability: 8-hydroxydeoxyguanosine;

[0012] Telomere attrition: Telomere length;

[0013] Epigenetic changes: DNA methylation levels;

[0014] Loss of protein homeostasis: heat shock protein Hsp70;

[0015] Macroautophagy dysfunction: microtubule-associated protein light chain 3B-II;

[0016] Dysregulated nutrient sensing: Nicotinamide adenine dinucleotide;

[0017] Mitochondrial dysfunction: Growth differentiation factor 15;

[0018] Cellular senescence: soluble urokinase-type plasminogen activator receptor;

[0019] Stem cell exhaustion: circulating osteoblast progenitor cells;

[0020] Altered intercellular communication: α-klotho;

[0021] Chronic inflammation: interleukin-11;

[0022] Intestinal flora imbalance: lipopolysaccharide.

[0023] Preferably, the detection method establishment unit includes the following detection scheme:

[0024] Peripheral blood was collected, serum was separated, and the 8-OHdG content in serum was determined by double antibody sandwich enzyme-linked immunosorbent assay;

[0025] DNA was isolated from whole blood, and the average absolute telomere length of the sample DNA was determined using the absolute telomere length PCR method;

[0026] DNA was isolated from whole blood and analyzed for methylation levels at specific CpG sites using the next-generation sequencing-based MethylTarget sequencing method;

[0027] Peripheral blood was collected, serum was separated, and the Hsp70 content in serum was determined by double antibody sandwich enzyme-linked immunosorbent assay;

[0028] Peripheral blood was collected, and the whole blood samples were treated with lysosomal inhibitors first, and then peripheral blood mononuclear cells were isolated, and the autophagy mechanism protein LC3B-II of peripheral blood mononuclear cells was measured by Western blotting;

[0029] Peripheral blood was collected, plasma was separated, and the NAD+ content in plasma was determined using the MTT enzyme cycle method;

[0030] Peripheral blood was collected, serum was separated, and the GDF15 content in serum was determined by double antibody sandwich enzyme-linked immunosorbent assay;

[0031] Peripheral blood was collected, serum was separated, and the suPAR content in serum was determined by double antibody sandwich enzyme-linked immunosorbent assay;

[0032] Peripheral blood was collected, peripheral blood mononuclear cells were isolated, and flow cytometry was used to determine the number of circulating osteoblast progenitor cells with specific phenotypes;

[0033] Peripheral blood was collected, serum was separated, and the α-klotho content in serum was determined by double antibody sandwich enzyme-linked immunosorbent assay;

[0034] Peripheral blood was collected, serum was separated, and the IL-11 content in serum was determined by double antibody sandwich enzyme-linked immunosorbent assay;

[0035] Peripheral blood was collected, serum was separated, and double antibody sandwich enzyme-linked immunosorbent assay was used to determine the lipopolysaccharide content in serum.

[0036] Preferably, in the detection condition optimization unit, the detection conditions include sample processing conditions, sample storage methods, reaction systems or detection instrument parameters.

[0037] Preferably, the aging biomarker detection unit comprises the following steps: sample collection, aging biomarker detection and data recording;

[0038] The sample collection also includes collecting disease-related data, which includes the patient's basic information, lifestyle factors, clinical examination indicators, past medical history and anti-aging intervention conditions.

[0039] Preferably, the aging assessment model building module comprises the following steps:

[0040] S1. Correlation analysis: Conduct correlation analysis on the collected data;

[0041] S2. Set scoring criteria for aging biomarkers: Determine the reference value range for each aging biomarker based on medical standards or large-scale population studies, and set scoring criteria;

[0042] S3. Assigning weight values: assigning a weight value to each aging biomarker according to the correlation coefficient between the aging biomarker and age;

[0043] S4. Calculate the aging risk value: multiply the score of each aging biomarker by its corresponding weight value, and then add the weighted scores of all aging biomarkers to obtain the aging risk value;

[0044] S5. Determine the risk level: divide the aging risk value into three intervals, corresponding to low risk, medium risk and high risk levels;

[0045] According to the calculated aging risk value, the aging risk level of the individual is determined.

[0046] The present invention also provides the use of the above-mentioned aging biomarker detection system in the preparation of aging degree assessment products.

[0047] The present invention also provides the use of the above-mentioned aging biomarker detection system in the preparation of disease risk prediction products.

[0048] The present invention also provides the use of the above-mentioned aging biomarker detection system in the preparation of anti-aging intervention effect evaluation products.

[0049] The present invention also provides the use of the above-mentioned aging biomarker detection system in the preparation of drug development and screening products.

[0050] Technical effects and advantages of the present invention:

[0051] 1. The aging biomarker detection system of the present invention includes two major sections: aging biomarker detection and aging assessment model establishment. It is comprehensive, accurate and efficient, and can provide strong support for aging research and anti-aging intervention.

[0052] 2. The construction method of the aging biomarker detection system of the present invention is scientific and reasonable. The 12 aging biomarkers screened out have high specificity and sensitivity. The aging degree is comprehensively evaluated through the integral aging assessment model. It is a comprehensive, accurate and efficient aging biomarker detection system.

[0053] 3. The detection method of the present invention is accurate and reliable. All test samples are peripheral blood. The peripheral blood samples are relatively easy to obtain and cause less trauma to the patients.

[0054] 4. The aging biomarker detection system of the present invention has a wide range of applications and can be used in many fields such as aging degree assessment, disease risk prediction, anti-aging intervention effect evaluation, drug development and screening, etc.

[0055] 5. The present invention provides a new tool and method for anti-aging research and clinical practice, which has important application value and broad market prospects. DETAILED DESCRIPTION

[0056] The technical solutions provided by the present invention are described in detail below in conjunction with the embodiments, but they should not be construed as limiting the protection scope of the present invention.

[0057] Example

[0058] This embodiment provides an aging biomarker detection system, including: an aging biomarker detection module, an aging assessment model establishment module, and an aging biomarker detection system application module.

[0059] The aging biomarker detection module includes:

[0060] (1) Aging biomarker screening unit:

[0061] Based on the twelve currently recognized hallmarks of aging, namely genomic instability, telomere loss, epigenetic changes, loss of protein homeostasis, macroautophagy dysfunction, nutrient sensing disorders, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, altered intercellular communication, chronic inflammation and intestinal flora imbalance, we creatively proposed a combination of aging biomarkers for the early detection of potential aging-related problems and for timely and targeted prevention and intervention.

[0062] The following is a panel of aging biomarkers for the twelve hallmarks of aging:

[0063] ①Genomic instability: 8-hydroxydeoxyguanosine (8-OHdG)

[0064] ②Telomere loss: Telomere length

[0065] ③Epigenetic changes: DNA methylation levels

[0066] ④ Loss of protein homeostasis: heat shock protein Hsp70

[0067] ⑤ Macroautophagy dysfunction: microtubule-associated protein light chain 3B-II (LC3B-II)

[0068] ⑥Nutrient sensing disorders: Nicotinamide adenine dinucleotide (NAD + )

[0069] ⑦ Mitochondrial dysfunction: Growth differentiation factor 15 (GDF15)

[0070] ⑧Cellular senescence: soluble urokinase-type plasminogen activator receptor (suPAR)

[0071] ⑨ Stem cell exhaustion: circulating osteoblast progenitor cells (COP)

[0072] ⑩Changes in intercellular communication: α-klotho

[0073] Chronic inflammation: interleukin-11 (IL-11)

[0074] Intestinal flora imbalance: lipopolysaccharide

[0075] (2) Detection method establishment unit:

[0076] ①Genomic instability: 8-hydroxydeoxyguanosine (8-OHdG)

[0077] Peripheral blood was collected, serum was separated, and double antibody sandwich enzyme-linked immunosorbent assay was used to determine the 8-OHdG content in serum.

[0078] ②Telomere loss: Telomere length

[0079] DNA was isolated from whole blood, and the absolute telomere length PCR method was used to determine the average absolute telomere length of the sample DNA.

[0080] ③Epigenetic changes: DNA methylation levels

[0081] DNA was isolated from whole blood and analyzed for methylation levels at specific CpG sites using the next-generation sequencing-based MethylTarget sequencing method.

[0082] ④ Loss of protein homeostasis: heat shock protein Hsp70

[0083] Peripheral blood was collected, serum was separated, and the Hsp70 content in serum was determined by double antibody sandwich enzyme-linked immunosorbent assay.

[0084] ⑤ Macroautophagy dysfunction: microtubule-associated protein light chain 3B-II (LC3B-II)

[0085] Peripheral blood was collected and the whole blood samples were treated with lysosomal inhibitors. Then peripheral blood mononuclear cells were separated and the autophagy mechanism protein LC3B-II of peripheral blood mononuclear cells was measured by Western blotting.

[0086] ⑥Nutrient sensing disorders: Nicotinamide adenine dinucleotide (NAD + )

[0087] Peripheral blood was collected, plasma was separated, and NAD in plasma was determined using MTT enzyme cycling method. + content.

[0088] ⑦ Mitochondrial dysfunction: Growth differentiation factor 15 (GDF15)

[0089] Peripheral blood was collected, serum was separated, and the GDF15 content in serum was determined by double antibody sandwich enzyme-linked immunosorbent assay.

[0090] ⑧Cellular senescence: soluble urokinase-type plasminogen activator receptor (suPAR)

[0091] Peripheral blood was collected, serum was separated, and the suPAR content in serum was determined by double antibody sandwich enzyme-linked immunosorbent assay.

[0092] ⑨ Stem cell exhaustion: circulating osteoblast progenitor cells (COP)

[0093] Peripheral blood was collected, peripheral blood mononuclear cells were isolated, and flow cytometry was used to determine the number of circulating osteoblast progenitor cells with specific phenotypes.

[0094] ⑩Changes in intercellular communication: α-klotho

[0095] Peripheral blood was collected, serum was separated, and the α-klotho content in serum was determined by double antibody sandwich enzyme-linked immunosorbent assay.

[0096] Chronic inflammation: interleukin-11 (IL-11)

[0097] Peripheral blood was collected, serum was separated, and the IL-11 content in serum was determined by double antibody sandwich enzyme-linked immunosorbent assay.

[0098] Intestinal flora imbalance: lipopolysaccharide

[0099] Peripheral blood was collected, serum was separated, and double antibody sandwich enzyme-linked immunosorbent assay was used to determine the lipopolysaccharide content in serum.

[0100] (3) Detection condition optimization unit:

[0101] Optimize the established detection methods, including sample processing conditions, sample storage methods, reaction systems, detection instrument parameters, etc., and establish standard operating procedures to improve the accuracy and repeatability of detection.

[0102] (4) Aging biomarker detection unit:

[0103] ① Sample collection: Collect a large number of peripheral blood samples from healthy people of different ages, and collect various disease-related data, including but not limited to: basic information of patients (such as age, gender, weight, height, family medical history, etc.), lifestyle factors (such as smoking status, drinking status, exercise, eating habits, etc.), clinical examination indicators (such as blood pressure, blood sugar, blood lipids, cholesterol levels, etc.), previous medical history (such as whether they have suffered from certain specific diseases or received specific treatments), anti-aging interventions (such as whether they have received cell therapy, daily nutrient supplements, etc.). When collecting samples, attention should be paid to following aseptic operation specifications to ensure the quality and stability of the samples.

[0104] ② Detection of aging biomarkers: Use the selected detection method to analyze the samples and obtain the detection data of aging biomarkers. Corresponding quality control standards should be established, and experimental conditions should be strictly controlled during the detection process to ensure the accuracy and reliability of the data.

[0105] ③Data recording: Record the test data in detail in the database, including the various disease-related data mentioned above.

[0106] The aging assessment model building module includes:

[0107] (1) Correlation analysis

[0108] The collected data were subjected to correlation analysis to observe the relationship between aging biomarkers and age. If the correlation coefficient is high, it means that the marker has a strong correlation with the degree of aging.

[0109] (2) Setting the scoring criteria for aging biomarkers

[0110] According to medical standards or large-scale population studies, the reference value range of each aging biomarker is determined and the scoring standard is set. Generally, the closer to the normal range, the lower the score; the further away from the normal range, the higher the score. For example, each aging biomarker is divided into four levels, with a normal range of 0 points, a mild abnormality of 1 point, a moderate abnormality of 2 points, and a severe abnormality of 3 points.

[0111] (3) Assigning weight values

[0112] According to the correlation coefficient between aging biomarkers and age, a weight value is assigned to each aging biomarker. The weight value is usually between 0 and 1, and the sum of the weight values ​​of all indicators is 1.

[0113] (4) Calculation of aging risk value

[0114] First, the score of each aging biomarker is multiplied by its corresponding weight value, and then the weighted scores of all aging biomarkers are added together to obtain the aging risk value.

[0115] The calculation formula is: Aging risk value = Σ(indicator score × weight value).

[0116] (5) Determine the risk level

[0117] The aging risk value is divided into three intervals, corresponding to low risk, medium risk and high risk. According to the calculated aging risk value, the aging risk level of the individual is determined.

[0118] Application modules of aging biomarker detection system:

[0119] (1) Assessment of aging degree:

[0120] According to the aging assessment model, the degree of aging of the individual is determined, providing a basis for personalized anti-aging intervention.

[0121] For low-risk individuals, maintaining a healthy lifestyle and having regular medical checkups may be recommended.

[0122] For individuals at medium risk, more detailed examinations, lifestyle adjustments, and possibly some preventive treatments may be recommended. For example, preventive measures can be taken based on the test results of each aging biomarker. + Low levels can limit calorie intake and supplement key NAD + Precursor, inhibits NAD + Increase NAD by consuming enzyme activity + level and improve the body's nutritional sensing state.

[0123] For high-risk individuals, more intensive diagnosis and treatment, as well as close monitoring of health, may be necessary.

[0124] (2) Disease risk prediction:

[0125] Studies have shown that aging is closely related to the occurrence and development of a variety of diseases. The aging biomarker detection system can be used to predict an individual's risk of developing certain diseases, such as cardiovascular disease, diabetes, cancer, etc., and provide individuals with disease prevention advice and early intervention measures.

[0126] (3) Evaluation of the effect of anti-aging intervention:

[0127] The aging biomarker detection system can be used to evaluate the effects of anti-aging interventions. Before and after the anti-aging intervention, the changes in the levels of aging biomarkers in individual samples are detected to evaluate the impact of the intervention on the aging process. Based on the evaluation results, the anti-aging intervention plan can be adjusted to improve the intervention effect.

[0128] (4) Drug development and screening:

[0129] The aging biomarker detection system can provide support for the development and screening of anti-aging drugs. By detecting the effect of drugs on aging biomarkers, the anti-aging activity of drugs can be evaluated. Drugs with potential anti-aging effects can be screened out, providing new ideas and methods for the development of anti-aging drugs.

[0130] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A system for detecting aging biomarkers, characterized in that: It includes an aging biomarker detection module and an aging assessment model establishment module; The aging biomarker detection module includes the following units: aging biomarker screening unit, detection method establishment unit, detection condition optimization unit and aging biomarker detection unit: The aging biomarker screening unit screens aging biomarkers through the following aging markers: Genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, macroautophagy dysfunction, dysregulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, altered intercellular communication, chronic inflammation, and gut microbiota dysbiosis.

2. The aging biomarker detection system according to claim 1, characterized in that: The following aging biomarker combination is obtained through the aging biomarker screening unit: Genomic instability: 8-hydroxydeoxyguanosine; Telomere attrition: Telomere length; Epigenetic changes: DNA methylation levels; Loss of protein homeostasis: heat shock protein Hsp70; Macroautophagy dysfunction: microtubule-associated protein light chain 3B-II; Dysregulated nutrient sensing: Nicotinamide adenine dinucleotide; Mitochondrial dysfunction: Growth differentiation factor 15; Cellular senescence: soluble urokinase-type plasminogen activator receptor; Stem cell exhaustion: circulating osteoblast progenitor cells; Altered intercellular communication: α-klotho; Chronic inflammation: interleukin-11; Intestinal flora imbalance: lipopolysaccharide.

3. The aging biomarker detection system according to claim 1, characterized in that: The detection method establishment unit includes the following detection schemes: Peripheral blood was collected, serum was separated, and the 8-OHdG content in serum was determined by double antibody sandwich enzyme-linked immunosorbent assay; DNA was isolated from whole blood, and the average absolute telomere length of the sample DNA was determined using the absolute telomere length PCR method; DNA was isolated from whole blood and analyzed for methylation levels at specific CpG sites using the next-generation sequencing-based MethylTarget sequencing method; Peripheral blood was collected, serum was separated, and the Hsp70 content in serum was determined by double antibody sandwich enzyme-linked immunosorbent assay; Peripheral blood was collected, and the whole blood samples were treated with lysosomal inhibitors first, and then peripheral blood mononuclear cells were isolated, and the autophagy mechanism protein LC3B-II of peripheral blood mononuclear cells was measured by Western blotting; Peripheral blood was collected, plasma was separated, and NAD in plasma was determined using MTT enzyme cycling method. + content; Peripheral blood was collected, serum was separated, and the GDF15 content in serum was determined by double antibody sandwich enzyme-linked immunosorbent assay; Peripheral blood was collected, serum was separated, and the suPAR content in serum was determined by double antibody sandwich enzyme-linked immunosorbent assay; Peripheral blood was collected, peripheral blood mononuclear cells were isolated, and flow cytometry was used to determine the number of circulating osteoblast progenitor cells with specific phenotypes; Peripheral blood was collected, serum was separated, and the α-klotho content in serum was determined by double antibody sandwich enzyme-linked immunosorbent assay; Peripheral blood was collected, serum was separated, and the IL-11 content in serum was determined by double antibody sandwich enzyme-linked immunosorbent assay; Peripheral blood was collected, serum was separated, and double antibody sandwich enzyme-linked immunosorbent assay was used to determine the lipopolysaccharide content in serum.

4. The aging biomarker detection system according to claim 2, characterized in that: In the detection condition optimization unit, the detection conditions include sample processing conditions, sample storage methods, reaction systems or detection instrument parameters.

5. The aging biomarker detection system according to claim 2, characterized in that: The aging biomarker detection unit includes the following steps: sample collection, aging biomarker detection and data recording; The sample collection also includes collecting disease-related data, which includes the patient's basic information, lifestyle factors, clinical examination indicators, past medical history and anti-aging intervention conditions.

6. The aging biomarker detection system according to any one of claims 1 to 5, characterized in that: The aging assessment model establishment module comprises the following steps: S1. Correlation analysis: Conduct correlation analysis on the collected data; S2. Set scoring criteria for aging biomarkers: Determine the reference value range for each aging biomarker based on medical standards or large-scale population studies, and set scoring criteria; S3. Assigning weight values: assigning a weight value to each aging biomarker according to the correlation coefficient between the aging biomarker and age; S4. Calculate the aging risk value: multiply the score of each aging biomarker by its corresponding weight value, and then add the weighted scores of all aging biomarkers to obtain the aging risk value; S5. Determine the risk level: divide the aging risk value into three intervals, corresponding to low risk, medium risk and high risk levels; According to the calculated aging risk value, the aging risk level of the individual is determined.

7. Use of the aging biomarker detection system according to any one of claims 1 to 6 in the preparation of an aging degree assessment product.

8. Use of the aging biomarker detection system according to any one of claims 1 to 6 in the preparation of disease risk prediction products.

9. Use of the aging biomarker detection system according to any one of claims 1 to 6 in the preparation of an anti-aging intervention effect evaluation product.

10. Use of the aging biomarker detection system according to any one of claims 1 to 6 in the preparation of drug development and screening products.