A quantitative evaluation method and system based on senescence-related secretory phenotype in vitro detection data and application

Abstract

The present disclosure relates to the field of biomedical detection data processing, in particular to a kind of quantification evaluation method, system and application based on senescence-related secretory phenotype in vitro detection data. Including the detection data of IL-6, hs-CRP, GDF-15, IL-10, MMP3 and MCP-1 in serum or plasma sample and age and gender information;According to the age, call the segmented threshold value of GDF-15 and MCP-1, according to the gender, call the threshold value of MMP3, and respectively adopt one-way linear, age dynamic linear, gender difference linear and U type score model to obtain normalized single parameter;According to the preset weight weighting and according to the weight and normalization of the included index, SASP quantification evaluation parameter is obtained.The present disclosure also provides corresponding in vitro detection method, reagent combination, computing system, device and computer readable storage medium, which can be used for standardization characterization, longitudinal change monitoring and research purpose evaluation of detection data.

Description

Technical Field

[0001] This disclosure relates to the field of biomedical in vitro detection and detection data processing technology. Specifically, it relates to a method, in vitro detection method, reagent combination, computing system, device and computer-readable storage medium for generating quantitative evaluation parameters based on in vitro detection data of aging-associated secretory phenotype (SASP). Background Technology

[0002] Population aging is a major social and public health challenge facing the world today. According to UN projections, by 2050, the global population aged 60 and over will reach 2.1 billion, accounting for 22% of the total population. Aging is the leading risk factor for many chronic diseases, including cardiovascular disease, neurodegenerative diseases, metabolic syndrome, malignant tumors, and autoimmune diseases. Therefore, accurately assessing an individual's biological aging level is of great significance for disease prevention, health management, and anti-aging interventions.

[0003] Cellular senescence is one of the core mechanisms of aging in organisms. Although senescent cells lose their proliferative capacity, they retain metabolic activity and secrete a series of pro-inflammatory cytokines, chemokines, growth factors, and matrix remodeling enzymes, forming the so-called senescence-associated secretory phenotype (SASP). SASP not only reflects the degree of cellular senescence but also promotes the senescence of neighboring cells through paracrine and endocrine effects, thereby driving the decline of tissue and organ function and the occurrence of age-related diseases. The main components of SASP include pro-inflammatory cytokines (such as IL-6, IL-1β, and TNFα), chemokines (such as MCP-1 and IL-8), growth factors (such as GDF-15, TGF-β1, and VEGF), matrix metalloproteinases (such as MMP3), and anti-inflammatory factors (such as IL-10). Therefore, quantitative detection of SASP-related biomarkers is considered a "liquid biopsy" method for assessing biological aging.

[0004] Existing technologies have recognized the association between various SASP-related proteins and age, inflammatory burden, or age-related phenotypes, and schemes exist for inputting multiple biomarkers into statistical models to form comprehensive parameters. However, different analytes have different distribution patterns and reference ranges, and age, sex, sample type, and detection platform can introduce systematic biases. If a fixed linear mapping in the same direction is applied to all indicators, or if a fixed total score is used for comparison when indicators are missing, data from different age groups, sexes, or detection conditions may be incomparable. Furthermore, some indicators with bidirectional bias characteristics cannot be adequately characterized by a single upper limit threshold.

[0005] Therefore, a data processing scheme is needed that integrates with the in vitro detection process: applying matching mapping models and hierarchical thresholds to different indicators to perform bounded normalization of the detection data; normalizing according to the weights of effective indicators under clear quality control conditions; and enabling the output to be traceable to the detection values, thresholds, and individual parameters of each analyte, thereby improving the consistency, interpretability, and reproducibility of the detection data processing. Summary of the Invention

[0006] In view of the above-mentioned shortcomings of the prior art, the technical problems to be solved by this disclosure include: (1) How to integrate multiple SASP-related biomarkers to construct a comprehensive scoring system that reflects the aging-related inflammatory state; (2) How to dynamically adjust the threshold parameters based on age factors to improve the assessment accuracy for different age groups; (3) How to design a differential scoring model to accurately handle biomarkers with a U-shaped dose-response relationship; (4) How to output reliable scoring results even when some indicators are missing; (5) How to link the scoring results with intervention measures to achieve personalized anti-aging intervention; (6) How to incorporate the Body Mass Index (ABSI) into the SASP scoring system to improve the comprehensiveness of the assessment.

[0007] To achieve the above-mentioned technical objectives, the technical solution adopted in this disclosure is as follows: The first aspect of this disclosure provides a computer-based method for generating SASP quantitative evaluation parameters based on in vitro detection data of aging-related secretory phenotypes, comprising: acquiring the age and gender information of the subject, and the concentration values ​​of IL-6, hs-CRP, GDF-15, IL-10, MMP3, and MCP-1 obtained by converting the detection signals generated from the analyte-specific binding reaction of isolated serum or plasma samples into calibration curves corresponding to each analyte; determining age segments based on the age, and calling the GDF-15 threshold parameter and MCP-1 threshold parameter corresponding to the age segments; calling the MMP3 threshold parameter based on the gender; and converting the concentrations of IL-6 and hs-CRP... The concentration values ​​of GDF-15 are input into a one-way linear mapping model, the concentration values ​​of MMP3 are input into a sex-differentiated one-way linear mapping model, and the concentration values ​​of IL-10 and MCP-1 are input into a U-shaped four-threshold mapping model, respectively, to obtain the normalized single-item parameter q_i of each indicator in the range of 0 to 1; the SASP quantitative evaluation parameter S is calculated according to S=100×Σ(w_i×q_i) / Σw_i, where w_i is the preset weight of the i-th included indicator; and the SASP quantitative evaluation parameter S and at least one normalized single-item parameter are output; wherein, the output does not include a diagnostic conclusion to determine whether the subject has a specific disease.

[0008] In some implementations, the computer implementation method described in the first aspect of this disclosure further acquires waist circumference, BMI, and height information and calculates the body mass index (ABSI), inputs the ABSI into a U-shaped three-threshold mapping model to obtain the corresponding normalized individual parameters, and incorporates them into the calculation of the SASP quantitative evaluation parameter S.

[0009] In some embodiments, the computer-implemented method described in the first aspect of this disclosure further acquires in vitro detection data of at least one of TNFα, TGF-β1, IL-1β, IL-8, VEGF, and IFNγ, and after invoking a threshold parameter that matches the detection platform and sample type, normalizes the individual parameters and incorporates them into the calculation of the SASP quantitative evaluation parameter S; preferably, the included indicators include IL-6, hs-CRP, GDF-15, TNFα, IL-10, MMP3, MCP-1, TGF-β1, ABSI, IL-1β, IL-8, VEGF, and IFNγ.

[0010] In some embodiments, the preset weight of each index is: 24 for IL-6, 24 for hs-CRP, 10 for GDF-15, 8 for TNFα, 8 for IL-10, 8 for MMP3, 7 for MCP-1, 6 for TGF-β 1, 3 for ABSI, and 0.5 for each of IL-1β, IL-8, VEGF and IFNγ; when only the six indexes described in the first aspect of the present disclosure are included, Σw_i is 81; when ABSI is further included, Σw_i is 84; when all thirteen indexes are included, Σw_i is 100.

[0011] In some embodiments, the one-way linear mapping model, age-dynamic linear mapping model or gender-differentiated one-way linear mapping model determines the normalized single parameter q in the following manner: when x ≤ B, q = 0; when B < x < T, q = (x - B) / (T - B); when x ≥ T, q = 1; where x is the measured concentration value, B is the health reference value, and T is the upper threshold; for GDF-15, B and T are called according to age segments; for MMP3, B and T are called according to gender.

[0012] In some embodiments, the threshold parameters of the U-shaped four-threshold mapping model are successively the low-value warning L, the ideal lower limit A, the ideal upper limit B, and the high-value warning H, where L < A ≤ B ≤ H; when A ≤ x ≤ B, q = 0; when L ≤ x < A, q = (A - x) / (A - L); when B < x ≤ H and H > B, q = (x - B) / (H - B); when x < L or x > H, q = 1; when H = B, q is directly set to 1 for x > B.

[0013] In some embodiments, the age segments are less than 45 years old, 45 to 59 years old, 60 to 74 years old, and 75 years old or older; the (B, T) of GDF-15 in the above age segments are (500, 1000), (750, 1500), (1100, 2200) and (1100, 2200) respectively, with the unit of pg / mL; the (L, A, B, H) of MCP-1 in the above age segments are (18.02, 20, 350, 400), (20, 30, 380, 400), (25, 50, 390, 400) and (25, 50, 390, 400) respectively, with the unit of pg / mL; the (L, A, B, H) of IL-10 is (0.8, 1.0, 4.0, 6.0), with the unit of pg / mL; the (B, T) of MMP3 is (40, 100) ng / mL in males and (20, 50) ng / mL in females; the (B, T) of IL-6 is (1.5, 5.0) pg / mL, and the (B, T) of hs-CRP is (0, 3.0) mg / L.

[0014] In some embodiments, the calculation formula of ABSI is: ABSI equals waist circumference (meters) divided by (BMI to the power of 2 / 3 multiplied by height to the power of 1 / 2), that is, ABSI = Waist circumference (m) / (BMI^(2 / 3) × Height (m)^(1 / 2)), wherein the units of waist circumference and height are meters; the parameters of the U-shaped three-threshold mapping model are the low-value warning L, the ideal reference C, and the high-value warning H, where q=0 when x=C, q=(C-x) / (C-L) when L≤x<C, q=(x-C) / (H-C) when C<x≤H, and q=1 when x<L or x>H; the corresponding (L, C, H) for ages under 45 years old, 45 to 59 years old, 60 to 74 years old, and 75 years old or above are (0.060, 0.070, 0.073), (0.062, 0.070, 0.076), (0.064, 0.070, 0.078), and (0.065, 0.070, 0.080) respectively.

[0015] In some embodiments, in the quality control step, the sample type, detection platform, calibration status, data type of the concentration value, and whether the concentration value is within the reportable range of the detection method are verified; when any one of the six core indicators described in the first aspect of the present disclosure is missing or fails to pass the quality control, an insufficient data prompt is output without outputting the SASP quantitative evaluation parameter; when an optional indicator is missing, w_i×q_i and w_i corresponding to the optional indicator are excluded from the numerator and the denominator simultaneously.

[0016] In some embodiments, when different detection platforms or different sample types are used, platform-specific calibrators, quality control products or method comparison samples are first used to establish a conversion relationship, and threshold parameters matching the corresponding platform and sample type are invoked; data that has not undergone method comparison are not directly combined for calculation.

[0017] In some embodiments, the SASP quantitative evaluation parameter S is divided into a first interval S<30, a second interval 30≤S<60, and a third interval S≥60, and an interval identifier is output; optionally, at least one of q_i, w_i×q_i, data quality identifier, and historical detection change value of each indicator is also output.

[0018] A second aspect of the present disclosure provides an in vitro detection method, comprising: contacting an isolated serum or plasma sample with specific binding reagents targeting IL-6, GDF-15, IL-10, MMP3 and MCP-1 respectively, acquiring detection signals of each analyte, and converting them into concentration values through respective calibration curves; obtaining the hs-CRP concentration value in the sample by immunoturbidimetry or specific binding assay; and inputting the concentration values into the method according to any one of the descriptions in the first aspect of the present disclosure to generate a SASP quantitative evaluation parameter.

[0019] A third aspect of this disclosure provides a reagent combination for implementing the in vitro detection method described in the second aspect of this disclosure, comprising analyte-specific binding reagents for detecting IL-6, GDF-15, IL-10, MMP3 and MCP-1 respectively, a specific reagent for detecting hs-CRP, and calibrators or calibration information corresponding to each analyte.

[0020] In some embodiments, the reagent combination also includes quality control materials and machine-readable carriers, barcodes, QR codes, or data interface information recording any of the calculation methods described in the first aspect of this disclosure.

[0021] This disclosure provides a fourth aspect of a SASP quantitative evaluation calculation system, comprising: a data acquisition module for receiving in vitro detection data of the six core indicators described in the first aspect of this disclosure, as well as age and gender information; a quality control module for verifying sample type, detection platform, calibration status, and data validity; a parameter calling module for calling matching threshold parameters based on age, gender, sample type, and detection platform; a model matching module for matching unidirectional linear mapping models, age dynamic linear mapping models, gender-differentiated unidirectional linear mapping models, or U-shaped four-threshold mapping models for different indicators; a calculation module for calculating normalized individual parameters and SASP quantitative evaluation parameters; and an output module for outputting the SASP quantitative evaluation parameters and data quality identifiers.

[0022] In some implementations, the SASP quantitative evaluation calculation system described in the fourth aspect of this disclosure further includes at least one of an ABSI calculation module, an optional indicator processing module, a platform conversion module, a historical data storage module, and a visualization module; wherein the visualization module is used to generate a normalized single parameter graph, a longitudinal variation graph, or a detection quality graph.

[0023] The fifth aspect of this disclosure provides a SASP quantitative evaluation calculation device, including a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to implement any of the methods described in the first aspect of this disclosure.

[0024] A sixth aspect of this disclosure provides a non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, implements any of the methods described in the first aspect of this disclosure.

[0025] This disclosure has the following advantages: Compared with the prior art, this disclosure has the following beneficial effects: (1) Integration of multidimensional detection data: This disclosure uses six blood indicators with complete thresholds and mapping rules as the core combination, and can further incorporate ABSI and other SASP-related indicators. This hierarchical combination avoids treating any small number of indicators as equivalent to the complete combination, which is conducive to improving the consistency and traceability of the output.

[0026] (2) Age and gender matching: This disclosure uses age segmented thresholds for GDF-15 and MCP-1, and gender-differentiated thresholds for MMP3, and retains the source of the thresholds in the output, which helps to reduce systematic bias caused by directly applying a single fixed threshold.

[0027] (3) Distribution pattern adaptation: This disclosure sets up a U-shaped mapping model for the bidirectional deviation characteristics of IL-10, MCP-1 and ABSI, and adopts a bounded linear mapping for other indicators, so that the detection data of different dimensions and different directions are converted into normalized parameters of a unified range.

[0028] (4) Quality control and optional index tolerance: This disclosure checks the sample type, platform, calibration status and data validity before calculation. The results of 42 cases with missing ABSI in 194 samples are used to illustrate the feasibility of simultaneously adjusting the numerator and denominator when optional ABSI is missing; this result does not necessarily support that equivalent output can still be obtained when any core index is missing.

[0029] (5) Interpretable output: This disclosure simultaneously outputs the total parameter, the normalized individual parameters of each indicator, the weighted contribution and the data quality identifier, which facilitates tracing the source of changes in the total parameter and supports longitudinal comparison under the same detection conditions.

[0030] (6) Detection platform compatibility: This disclosure allows the use of detection platforms such as ELISA, flow cytometry microsphere array, chemiluminescence, mass spectrometry or immunoturbidimetry, but different platforms or sample types should establish conversion relationships through calibration or methodological comparison and use matching thresholds. It is not recommended that uncalibrated data be directly mutually recognized.

[0031] (7) Wide range of applications: This disclosure can be widely applied to multiple scenarios such as aging risk assessment, anti-aging intervention effect monitoring, health management, physical examination screening, insurance actuarial science, anti-aging drug screening and drug efficacy evaluation, and has significant social benefits and commercial value. Attached Figure Description

[0032] Technical Implementation and Alternative Implementation Methods of SASP Quantitative Evaluation Parameters In this disclosure, "in vitro detection data" refers to the raw signal generated by the analyte-specific reaction of an in vitro sample, the calibrated concentration value, and the sample type, detection platform, reagent batch number, calibration status, and quality control status associated with that concentration value. Preferably, only concentration values ​​that have passed quality control and are within the reportable range are input into the scoring engine.

[0033] For the i-th indicator, the scoring engine first calls the parameter set θ_i that matches age, gender, sample type, and detection platform, and then calculates q_i=f_i(x_i;θ_i) using the pre-assigned mapping function f_i. By limiting q_i to the range of 0 to 1, extreme detection values ​​can be avoided from causing the total parameters to increase indefinitely, and detection data of different dimensions can be converted to a uniform scale.

[0034] The core implementation method employs six blood indicators: IL-6, hs-CRP, GDF-15, IL-10, MMP3, and MCP-1. This core combination was chosen because existing literature already provides specific thresholds or segmented thresholds for each indicator, covering four types of mappings: unidirectional linear, age-dynamic, gender-differentiated, and U-shaped four-threshold mapping. Adding ABSI further covers the U-shaped three-threshold mapping.

[0035] The extended implementation may include one or more of TNFα, TGF-β1, IL-1β, IL-8, VEGF, and IFNγ. For the extended indicators, B and T can be established using preset percentiles in the training population, clinical reference intervals, or validated decision thresholds; however, without a specific threshold, the extended indicators are not considered essential features of the core combination.

[0036] In the platform adaptation implementation, at least 40 methodological comparison samples covering the measurement interval can be selected and tested by both the reference platform and the platform to be adapted. The conversion relationship is obtained using Passing-Bablok regression, Deming regression, or other suitable methods. If systematic bias exceeds a preset allowable range, a separate threshold is established for the platform to be adapted, instead of direct conversion. The sample size and statistical methods described above can be adjusted according to applicable testing specifications.

[0037] In the implementation where optional indicators are missing, 2 to 13 indicators can be randomly selected from the 13 indicators for combined evaluation.

[0038] In repeated testing implementations, longitudinal comparisons are preferably performed using reagents of the same sample type, the same testing platform, and traceable to the same calibration system. If the batch number or platform changes, bridging samples or quality control materials can be used to confirm the comparability of the results before and after. Repeated testing is used to reduce the impact of transient fluctuations, but the number of repetitions and the time interval can be adjusted according to the study protocol.

[0039] To more clearly illustrate the technical solutions in the specific embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. The accompanying drawings are incorporated in and constitute a part of this specification, illustrating embodiments consistent with this specification, and are used together with the specification to explain the principles of this specification.

[0040] Figure 1 This is a flowchart of the overall SASP risk scoring method disclosed herein; Figure 2 This is a flowchart of the age segmentation and dynamic threshold acquisition process disclosed in this publication; Figure 3 This is a schematic diagram of the scoring curves for the five scoring models disclosed herein, where Figure 3 (a) is the standard one-way linear scoring model. Figure 3 (b) is an age-dynamic linear scoring model. Figure 3 (c) represents a gender-differentiated one-way linear scoring model. Figure 3 (d) is a U-shaped 4-tuple scoring model. Figure 3 (e) is a U-shaped triplet scoring model; Figure 4 This is a module architecture diagram of the SASP scoring calculation system disclosed herein; Figure 5 This is a flowchart of the closed-loop management process for anti-aging interventions. Figure 6 This is a distribution chart of SASP scores for 194 clinical samples disclosed in this publication; Figure 7 This is a distribution chart of SASP risk ratios for each age group disclosed in this publication. Detailed Implementation

[0041] Definitions and Explanations To facilitate understanding of this disclosure, certain technical and scientific terms are specifically defined below. In this disclosure, unless otherwise stated, the scientific and technical terms used herein have meanings commonly understood by those skilled in the art. Furthermore, the cell and tissue culture, microbiology-related terms, and laboratory procedures used herein are all widely used terms and routine procedures in their respective fields. Meanwhile, to better understand this disclosure, definitions and explanations of relevant terms are provided below. It should be understood that this disclosure is not limited to specific methods, reagents, compounds, compositions, or biological systems, and variations thereof are certainly possible. It should also be understood that the terminology used in this application is for describing specific embodiments only and is not intended to be limiting.

[0042] As used herein, the terms “an,” “a,” and “the” mean one or more, or one or more kinds.

[0043] As used herein, the term "and / or" describes the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone. The character " / " usually indicates an "or" relationship between the related objects; the character "+" usually indicates an "and" relationship between the related objects, unless the context clearly indicates otherwise.

[0044] As used herein, the terms “some embodiments,” “some implementations,” or “some implementation schemes” refer to a subset of all possible implementations. However, it is understood that different “some embodiments,” “some implementations,” or “some implementation schemes” may be the same subset or different subsets of all possible implementations and may be combined with each other without conflict.

[0045] As used herein, the terms “optional,” “optionally,” “any,” “arbitrary,” or “any one” indicate that the event, condition, component, step, or feature described below may or may not be present; may or may not occur. This statement includes the presence or absence, occurrence or non-occurrence of the event, condition, component, step, or feature.

[0046] As used herein, the terms “about,” “approximately,” “substantially,” or “similar to” mean within a range of error acceptable to a person skilled in the art. This range of error may depend in part on the method of measurement or determination of the value, limitations of the measurement system, sample variability, or differences in experimental conditions. For example, for parameters such as concentration, time, temperature, proportion, fluorescence intensity, average fluorescence intensity, and percentage of positive cells, “about” can include a range of variation of ±20%, ±15%, ±10%, ±5%, ±3%, or ±1%, unless the context explicitly limits it.

[0047] As used herein, the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or device that includes a series of steps is not limited to the steps or modules listed, but may optionally include steps not listed, or may optionally include other steps inherent to such process, method, product, or device.

[0048] As used herein, when describing a numerical range, such as “1-50 μM,” “0.5-6 h,” or “0.5-2 mm³,” it should be understood that the range includes the endpoints as well as any subranges and specific values ​​within that range. For example, “1-50 μM” includes 1 μM, 5 μM, 10 μM, 20 μM, 50 μM, and any values ​​or subranges thereof, unless the context clearly indicates otherwise.

[0049] As used herein, the term "biomarker" in the core scoring scheme of this disclosure refers to a target protein or its specific corresponding immune response signal that is quantitatively measured in an in vitro sample. Unless a nucleic acid detection method, calibration relationship, and reference threshold are established separately, the thresholds given for protein concentration in this document are not directly applicable to RNA concentration.

[0050] As used herein, the term "biological sample" refers to ex vivo material that may contain the target analyte. The specific concentration thresholds given in the examples are applicable to serum or plasma and their corresponding detection methods. Whole blood, urine, saliva, cerebrospinal fluid, interstitial fluid, cell culture supernatant, or other sample types may be used as alternative samples, but pretreatment, matrix effects, calibration relationships, and reference thresholds should be validated separately, and serum or plasma thresholds should not be applied directly.

[0051] In a first aspect, this disclosure provides a computer-implemented method for generating quantitative evaluation parameters based on SASP in vitro detection data. The method receives concentration values ​​of at least IL-6, hs-CRP, GDF-15, IL-10, MMP3, and MCP-1 in an isolated serum or plasma sample, along with age and sex information; the concentration values ​​are obtained by converting analyte-specific reaction signals using corresponding calibration curves.

[0052] The method calls matching threshold parameters based on age, gender, sample type, and detection platform, and matches different mapping models according to the data distribution characteristics of the indicators: IL-6 and hs-CRP use a one-way linear mapping model; GDF-15 uses an age-dynamic linear mapping model; MMP3 uses a gender-differentiated one-way linear mapping model; and IL-10 and MCP-1 use a U-shaped four-threshold mapping model. Each model converts detection values ​​of different dimensions into normalized single-item parameters q_i within the range of 0 to 1.

[0053] The SASP quantitative evaluation parameters are calculated according to S=100×Σ(w_i×q_i) / Σw_i, where w_i is the preset weight of the i-th effective indicator. This normalization structure ensures that effective indicators of different combinations have a unified output scale; however, to ensure the comparability of results, it is preferred that all six core indicators pass quality control, and optional indicators are only excluded from the numerator and denominator simultaneously when they are missing.

[0054] In some implementations, ABSI is further incorporated into the six core indicators. ABSI is calculated from waist circumference, BMI, height, and gender, and normalized individual parameters are obtained using a U-shaped three-threshold mapping model. In other implementations, at least one of TNFα, TGF-β1, IL-1β, IL-8, VEGF, and IFNγ may also be included; for newly added indicators, reference thresholds matching the sample type and detection platform should be established in advance.

[0055] In a second aspect, this disclosure provides an in vitro detection method, which includes using an analyte-specific binding reagent to detect the aforementioned core indicators in an isolated serum or plasma sample, converting the detection signal into a concentration value via a calibration curve, and then using the computer-implemented method to generate SASP quantitative evaluation parameters.

[0056] In a third aspect, this disclosure provides a reagent combination for the above-described in vitro detection method. The reagent combination includes analyte-specific binding reagents for each core indicator and corresponding calibrators or calibration information; optionally, it also includes quality control materials and machine-readable information carrying the computational method.

[0057] In a fourth aspect, this disclosure provides a SASP quantitative evaluation calculation system, a computing device, and a non-transitory computer-readable storage medium. The system includes at least a data acquisition module, a quality control module, a parameter calling module, a model matching module, a calculation module, and an output module.

[0058] In some implementations, data from different testing platforms are not assumed to be inherently interchangeable. When using another testing platform or sample type, conversion relationships can be established by comparing samples using platform-specific calibrators, quality controls, or methodologies, and matching threshold parameters can be invoked. Data that has not undergone methodological comparison is not directly merged.

[0059] The SASP quantitative evaluation parameters generated in this disclosure are a quantitative representation of the degree to which in vitro test data deviates from a preset reference range. They can be used for longitudinal monitoring of changes under the same testing conditions, stratification for research purposes, or comparison of in vitro test data. These parameters themselves are not equivalent to a specific disease diagnosis and cannot replace a physician's diagnostic or treatment decision based on clinical data.

[0060] Example To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, not all embodiments. The following is merely a further description of this disclosure, and the scope of protection of this disclosure is not limited thereto. In the embodiments and experimental examples of this disclosure, the technical means or methods, etc., not specifically described, are conventional technical means or methods in the art. Unless otherwise specified, the materials, reagents, experimental equipment, etc., used in the embodiments and experimental examples of this disclosure can all be obtained commercially.

[0061] Example 1: SASP Biomarker Combinations and Weights This embodiment describes in detail the combination scheme, weighting, and biological significance of the SASP-related biomarkers used in this disclosure.

[0062] Based on extensive literature review and clinical data analysis, this disclosure selects 13 of the most representative and complementary biomarkers from dozens of SASP-related biomarkers, categorized into seven dimensions. This disclosure uses six blood markers—IL-6, hs-CRP, GDF-15, IL-10, MMP3, and MCP-1—as the core combination, and can optionally add ABSI and other SASP-related markers to form extended combinations. The weighting principle is as follows: IL-6 and hs-CRP each account for 24 points (total 48 points), serving as the core indicators of the SASP score, as they are the most fundamental and stable biomarkers of age-related inflammation; GDF-15 accounts for 10 points, serving as a core biomarker of aging stress; TNFα, IL-10, and MMP3 each account for 8 points (total 24 points), representing the three dimensions of acute inflammation, anti-inflammatory capacity, and matrix remodeling, respectively; MCP-1 accounts for 7 points, TGF-β1 for 6 points, and ABSI for 3 points; IL-1β, IL-8, VEGF, and IFNγ each account for 0.5 points (total 2 points), serving as supplementary indicators to enhance the comprehensiveness of the score. The total weight of the 13 indicators is 100 points.

[0063] Scientific basis for weight allocation: The weight allocation in this disclosure is based on the following three principles: (1) the strength of the causal association between biomarkers and aging. IL-6 and hs-CRP, as the core effector molecules of SASP, have the clearest causal relationship with aging, and are therefore given the highest weight; (2) the detection stability and clinical accessibility of biomarkers. The detection methods for indicators such as IL-6, hs-CRP, and GDF-15 are mature and the results are stable, making them suitable as backbone indicators; (3) the independent information contribution of biomarkers. Based on the analysis of 194 clinical samples, IL-6 and hs-CRP have the largest contribution to the total SASP score (average scores of 5.19 and 8.86, respectively), while IL-1β, IL-8, VEGF, and IFNγ have smaller contributions (average scores of less than 0.2), and are therefore given lower weights. The specific values ​​of the weight allocation can be optimized and adjusted according to different populations and application scenarios.

[0064] Example 2: Age Segmentation and Dynamic Threshold Mechanism This embodiment describes in detail the four-segment age segmentation scheme and the dynamic threshold parameters of four age sensitivity indicators disclosed herein.

[0065] This disclosure divides the age of the subjects into four segments: youth (<45 years), middle age (45 to 59 years), young old age (60 to 74 years), and old age (≥75 years). This segmentation scheme is based on the WHO age segmentation standard and optimized to take into account the aging characteristics of the Chinese population.

[0066] For the three age-related indicators GDF-15, MCP-1, and ABSI, as well as IL-10 which uses a fixed U-shaped threshold, this disclosure sets differentiated threshold parameters for different age segments: Method for determining dynamic threshold parameters: The dynamic threshold parameters disclosed herein are determined based on the following methods: (1) collecting biomarker detection data from healthy individuals and individuals with aging-related diseases in each age group; (2) performing a normality test on each group of data, and using the percentile method to determine the health baseline value (25th percentile) and the aging threshold (95th percentile); (3) for the U-shaped threshold model, using the interquartile range method to determine the ideal interval, and using the 1st and 99th percentiles to determine the warning threshold; (4) calibrating with reference to published literature on the normal reference range of age-related biomarkers. The specific threshold parameters for each age group are as follows: GDF-15 (Age-Dynamic Threshold) The sample sizes for each age group were as follows: <45 years old: 96 cases; 45-59 years old: 61 cases; 60-74 years old: 31 cases; ≥75 years old: 6 cases. Due to the limited sample size, the same threshold was used for the ≥75 years old group as for the 60-74 years old group.

[0067] IL-10 (U-shaped 4-tuple threshold) 2.1 GDF-15 Age Dynamic Linear Threshold The baseline level of GDF-15 increases significantly with age, and this disclosure uses an age-dynamic linear threshold: The same threshold for GDF-15 is used in both the younger and older age groups because GDF-15 levels tend to plateau in people over 75 years of age.

[0068] 2.2 MCP-1 Age Dynamic U-Shaped 4-Tube Threshold MCP-1 exhibits a U-shaped dose-response relationship; both excessively low and excessively high levels indicate risk, and the ideal range varies with age. The ideal lower limit of MCP-1 increases with age (from 20 to 50), reflecting the physiological increase in baseline MCP-1 levels in the elderly population.

[0069] 2.3 ABSI Age Dynamic U-Shaped Triple Threshold The ABSI (Abdominal Abdominal Index) reflects the degree of abdominal obesity; both excessively low (lean) and excessively high (abdominal obesity) indicate risk. The high ABSI warning threshold increases with age (from 0.073 to 0.080), reflecting the increased risk of abdominal obesity in the older population.

[0070] 2.4 IL-10 Fixed U-shaped 4-tuple threshold As an anti-inflammatory factor, IL-10 has an ideal level of 1.0-4.0 pg / mL. Levels below 0.8 pg / mL indicate insufficient anti-inflammatory capacity, while levels above 6.0 pg / mL indicate immune dysregulation. The threshold for IL-10 remains consistent across all age groups because IL-10 levels are less affected by age.

[0071] Example 3: Standard One-Way Linear Scoring Model This embodiment details the calculation method of the standard one-way linear scoring model, applicable to eight indicators: IL-6, hs-CRP, TNFα, TGF-β1, IL-1β, IL-8, VEGF, and IFNγ. Although MMP3 uses one-way linear scoring logic, its thresholds are set according to gender differences, which are explained in detail in Example 7.

[0072] The standard one-way linear scoring model is suitable for biomarkers where "lower is better," meaning lower values ​​indicate better health, and higher values ​​indicate greater risk. The scoring formula is as follows: When the measured value is less than or equal to the health baseline, the score for a single item is 0. When the health baseline < measured value < aging threshold, the individual score = (measured value - health baseline) / (aging threshold - health baseline) × weight; When the measured value is greater than or equal to the aging threshold, the individual score is equal to the weight (the normalized individual parameter reaches 1).

[0073] Taking IL-6 as an example (weight 24, healthy baseline 1.5 pg / mL, aging threshold 5.0 pg / mL): Taking hs-CRP as an example (weight 24, healthy baseline 0 mg / L, aging threshold 3.0 mg / L): The healthy baseline for hs-CRP is set to 0, indicating that any detectable level of hs-CRP suggests an inflammatory response, and the higher the measured value, the more points are deducted. When hs-CRP = 1.5 mg / L, the deviation = 1.5 / 3.0 = 0.5, and the score = 0.5 × 24 = 12 points.

[0074] Taking TGF-β1 as an example (weight 6, healthy baseline 0 pg / mL, aging threshold 19 pg / mL): The healthy baseline for TGF-β1 is set to 0. Low levels in a healthy state and elevated levels indicate risk. Higher measured values ​​result in more deductions. When TGF-β1 = 38 mg / L, the deviation is 38 / 19 = 2, and the score is 2 × 6 = 12 points.

[0075] Taking IL-1β as an example (weight 0.5, healthy baseline 0 pg / mL, aging threshold 2 pg / mL): IL-1β is a core marker of NLRP3 inflammasome activation, and its level is extremely low in healthy individuals; any increase indicates risk. When IL-1β = 1.0 pg / mL, the deviation = 1.0 / 2.0 = 0.5, and the score = 0.5 × 0.5 = 0.25 points.

[0076] Example 4: Age-based dynamic linear scoring model (GDF-15) This embodiment describes in detail the age-dynamic linear scoring model, which is applicable to the GDF-15 index.

[0077] The GDF-15 scoring logic is the same as the standard unidirectional linear scoring model, but the health baseline and aging threshold are dynamically adjusted according to age segments. The scoring steps are as follows: Step 1: Determine the age segment based on the age of the subjects (youth segment / middle-aged segment / young-old segment / old segment). Step 2: Obtain the GDF-15 health benchmark and aging threshold corresponding to this age segment from the dynamic threshold database; Step 3: Calculate the individual score using the standard one-way linear formula.

[0078] Taking a 70-year-old subject (young old age group, healthy baseline 1100 pg / mL, aging threshold 2200 pg / mL) as an example: In contrast, if the 70-year-old subject had used a fixed threshold for youth (healthy baseline 500, aging threshold 1000), a GDF-15 of 800 pg / mL would have been judged as a deviation of 0.6 with a score of 6.0, significantly overestimating the risk. However, with an age-dynamic threshold, a GDF-15 of 800 pg / mL was lower than the healthy baseline of 1100 for the elderly, and the normalized individual parameter was 0, which reduced the impact of physiological baseline differences in GDF-15 in this age group on the quantitative parameters.

[0079] Example 5: U-shaped 4-tuple scoring model (MCP-1 and IL-10) This embodiment describes in detail the U-shaped 4-tuple scoring model, which is applicable to MCP-1 and IL-10, two indicators with a U-shaped dose-response relationship.

[0080] The U-shaped 4-tuple scoring model uses four threshold parameters: (low-value warning, ideal lower limit, ideal upper limit, and high-value warning). The scoring logic is as follows: When the lower limit of the ideal is less than or equal to the measured value, and the upper limit of the ideal is less than or equal to the upper limit of the ideal, the score of a single item is 0 (the ideal interval and the normalized single item parameter is 0). When the low value warning is less than or equal to the measured value but less than the ideal lower limit, the score for a single item is calculated as (ideal lower limit - measured value) / (ideal lower limit - low value warning) × weight, and shall not exceed the weight. When the ideal upper limit < the measured value ≤ the high value warning, the single item score = (measured value - ideal upper limit) / (high value warning - ideal upper limit) × weight, and shall not exceed the weight; When the measured value is less than the low value warning or the measured value is greater than the high value warning, the individual score is equal to the weight (the normalized individual parameter reaches 1).

[0081] 5.1 MCP-1 scoring example Taking a 50-year-old subject (middle-aged, ideal range 30-380, low value warning 20, high value warning 400, weight 7) as an example: 5.2 IL-10 scoring example Taking IL-10 (ideal range 1.0-4.0, low value warning 0.8, high value warning 6.0, weight 8) as an example: The U-shaped 4-tuple scoring model is used to characterize bidirectional deviations. Taking IL-10 = 0.5 pg / mL as an example, if only a unidirectional model of "the higher the value, the larger the parameter" is used, this low value might correspond to 0. However, using the U-shaped model of this disclosure, this value is below the low-value warning, with the normalized single-item parameter q = 1 and the weighted score being 8 points. Similarly, when IL-10 = 7.0 pg / mL is above the high-value warning, q = 1, and the weighted score is also 8 points. The above example illustrates the mapping difference between the two types of models; the association strength between low and high IL-10 values ​​and the target aging phenotype still needs further verification with independent data.

[0082] U-shaped dose-response relationship between IL-10 and MCP-1: IL-10 is a major anti-inflammatory cytokine secreted by Treg cells, macrophages, and other cells. When its level is too low, the body's anti-inflammatory capacity is insufficient to suppress SASP-driven chronic low-grade inflammation (inflammaging), leading to cumulative tissue damage. When its level is too high, it may reflect a persistently activated immune system (immune exhaustion) or indicate uncontrolled compensatory anti-inflammatory responses, which are also associated with adverse clinical outcomes. Therefore, IL-10 levels should be maintained within an appropriate range (e.g., 1.0-4.0 pg / mL). Deviations from this range (whether too low or too high) indicate an increased risk of aging.

[0083] MCP-1 (CCL2) is a monocyte chemotactic factor responsible for recruiting monocytes to sites of inflammation. When its level is too low, immune surveillance is insufficient, making it difficult to clear senescent cells (of SASP origin); when its level is too high, it reflects chronic inflammation leading to persistent monocyte recruitment and tissue infiltration, driving age-related pathological changes such as atherosclerosis and fibrosis. Therefore, the ideal range for MCP-1 is a specific lower to upper limit for each age group; deviations from this range indicate an increased risk of aging.

[0084] Key literature on bidirectional association of MCP-1 Literature evidence of a bidirectional association between IL-10 and IL-10 Example 6: U-shaped triplet scoring model (ABSI) This embodiment describes in detail the U-shaped triplet scoring model, which is applicable to the ABSI index.

[0085] The U-shaped relationship of ABSI differs slightly from that of MCP-1 / IL-10. ABSI has an ideal baseline value (rather than an ideal range), and deviations from this baseline value (whether too low or too high) indicate risk. Therefore, a ternary threshold is used: (low value warning, ideal baseline, high value warning). The scoring logic is as follows: When the measured value equals the ideal baseline, the individual score is 0. When the low value warning is less than or equal to the measured value but less than the ideal baseline, the individual score is calculated as (ideal baseline - measured value) / (ideal baseline - low value warning) × weight, and shall not exceed the weight. When the ideal benchmark < measured value ≤ high value warning, the individual score = (measured value - ideal benchmark) / (high value warning - ideal benchmark) × weight, and shall not exceed the weight; When the measured value is less than the low value warning or the measured value is greater than the high value warning, the individual score is equal to the weight (the normalized individual parameter reaches 1).

[0086] Taking a 60-year-old male subject (young old age group, ideal baseline 0.070, low warning 0.064, high warning 0.078, weight 3) as an example: Example 7: MMP3 Gender-Differentiated One-Way Linear Scoring Model This embodiment describes in detail the gender-differentiated unidirectional linear scoring model for the MMP3 index.

[0087] There are significant sex differences in the normal reference range for MMP3 (matrix metalloproteinase 3), with males typically having higher baseline MMP3 levels than females. Using a uniform threshold could lead to an overestimation of false positives in men or false negatives in women. This disclosure establishes sex-differentiated health benchmarks and aging thresholds: The scoring logic is the same as the standard one-way linear scoring model, but the health benchmark and aging threshold are dynamically obtained based on gender: When the measured value is less than or equal to the health benchmark corresponding to gender, the individual score is 0; When the gender-corresponding health benchmark < measured value < gender-corresponding aging threshold, the individual score = (measured value - health benchmark) / (aging threshold - health benchmark) × weight; When the measured value is greater than or equal to the aging threshold corresponding to gender, the individual score is equal to the weight (the normalized individual parameter reaches 1).

[0088] 7.1 Example of Male MMP3 Score Taking male subjects (health baseline 40, aging threshold 100, weight 8) as an example: 7.2 Example of MMP3 scoring for women Taking female subjects (health baseline 20, aging threshold 50, weight 8) as an example: 7.3 Advantages of gender differentiation For example, comparing MMP3 = 45 ng / mL: Using a uniform threshold (health benchmark 30, aging threshold 50): Deviation = (45-30) / (50-30) = 0.75, Score = 0.75 × 8 = 6.0 points.

[0089] Using gender-differentiated thresholds: Male deviation = (45-40) / (100-40) = 0.083, score = 0.67; Female deviation = (45-20) / (50-20) = 0.833, score = 6.67.

[0090] It is evident that an MMP3 value of 45 represents a slight deviation for men (0.67 points) and a significant deviation for women (6.67 points), indicating that gender-differentiated scoring more accurately reflects the risk levels of different genders. In an internal comparison of 194 cases, after applying the gender-differentiated threshold, the number of men exceeding the threshold (as defined by the threshold) decreased from 62 to 20. This result demonstrates that threshold selection significantly affects stratification results, but in the absence of an independent clinical gold standard, changes in the number of participants should not be directly equated with a reduction in the false positive rate.

[0091] Example 8: ABSI Calculation Formula This embodiment describes in detail the calculation method of ABSI.

[0092] ABSI (A Body Shape Index) is a body shape index that reflects abdominal obesity, calculated from waist circumference, BMI, and height. The calculation formula disclosed here is: ABSI equals waist circumference (meters) divided by (BMI^2 / 3 multiplied by height^1 / 2), that is, ABSI = waist circumference (m) / (BMI^(2 / 3) × height (m)^(1 / 2)), which is gender-neutral and applicable to both men and women.

[0093] Among them, waist circumference is in meters (in centimeters when entered, need to be divided by 100 to convert), BMI is in kg / m², and height is in meters.

[0094] Calculation example: A 50-year-old male, 1.75m tall, with a waist circumference of 85cm and a BMI of 24.0.

[0095] ABSI=(85 / 100) / (24.0^(2 / 3)×1.75^(1 / 2))=0.85 / (8.3203×1.3229)=0.85 / 11.0069=0.0772 Error tolerance handling for ABSI calculation: If any of the waist circumference, BMI, or height information is missing, ABSI cannot be calculated. The system will automatically skip the ABSI indicator and not participate in the scoring, and the theoretical maximum score will be reduced by 3 points accordingly.

[0096] Example 9: Risk Classification Standard This embodiment describes in detail the SASP risk classification standard of this disclosure.

[0097] Based on the hazard ratio distribution of a sample of 194 cases, the hazard ratios corresponding to each percentile were calculated. The results showed that the 30% cutoff value corresponds exactly to the median (P51), and the 60% cutoff value corresponds to P93 (the 93rd percentile). This means that the current three-part risk stratification is actually equivalent to: low risk (lower half, approximately 51%), medium risk (upper middle half, approximately 42%), and high risk (upper half, approximately 7%). This distribution is highly consistent with the "Pareto Principle" in clinical practice (approximately 20% of individuals require close monitoring, while approximately 80% can be routinely managed).

[0098] This disclosure uses the risk ratio (the ratio of the total SASP score to the theoretical highest score) as the basis for risk classification, dividing SASP risk into 3 levels: The advantage of using risk ratios instead of absolute total scores as the basis for grading is that when some indicators are missing, the theoretical maximum score will decrease accordingly. In this case, using absolute total scores for grading would lead to a lower rating. Using risk ratios (relative values) helps to eliminate the impact of missing indicators and makes risk levels comparable under different missing conditions.

[0099] For example: One subject has all 13 indicators, a total SASP score of 35, a theoretical maximum score of 100, and a hazard ratio of 35%, thus being classified as medium risk. Another subject lacks the ABSI indicator, has a total SASP score of 34, a theoretical maximum score of 97, and a hazard ratio of 35.1%, also classified as medium risk. The risk levels of both subjects are consistent, reflecting the actual situation.

[0100] Example 10: Missing Fault Tolerance Mechanism This embodiment describes in detail the missing fault tolerance mechanism of this disclosure.

[0101] In actual clinical testing, some indicators may be undetectable due to insufficient sample size, limitations of testing kits, cost control, or subject cooperation. The missing data tolerance mechanism disclosed herein can automatically adjust the scoring logic when some indicators are missing, helping to maintain the reliability of the scoring results.

[0102] The process for handling missing fault tolerance mechanisms: Step 1: Iterate through the 13 biomarkers and identify missing indicators (indicators with empty or non-numerical values). Step 2: Skip missing indicators and do not include them in the individual score calculation; Step 3: When calculating the theoretical maximum score, only the weights of the detected indicators are added, and the weights of the missing indicators are not included. Step 4, SASP total score = sum of individual scores for all detected indicators; Step 5, Risk Ratio = Total SASP Score / Theoretical Maximum Score; Step 6: Determine the risk level based on the risk ratio.

[0103] Validation of the missing data tolerance mechanism: In 194 clinical samples, 42 subjects could not have their ABSI calculated due to missing waist circumference or height data. After adopting the missing data tolerance mechanism, the risk stratification distribution of the 42 missing ABSI samples was as follows: low risk 22 cases (52.4%), medium risk 16 cases (38.1%), and high risk 4 cases (9.5%), which is consistent with the distribution trend of samples with complete ABSI in the same age group (low risk 51.0%, medium risk 41.8%, and high risk 7.2%), indicating that the missing data tolerance mechanism helps maintain the reliability of the scoring results.

[0104] Example 11: Internal consistency and executability analysis of 194 samples This embodiment describes the computational feasibility, data distribution, and internal consistency of the method disclosed herein in a sample of 194 cases. Since the existing data do not establish independent gold standards for biological aging or clinical outcomes, the following results are not used to calculate sensitivity, specificity, or clinical diagnostic accuracy.

[0105] The applicant collected SASP-related biomarker detection data from 194 subjects and used the method disclosed in this publication to calculate the SASP score: 11.1 Sample Collection The collection and processing of blood samples shall strictly follow the "Collection and Processing of Blood Specimens for Clinical Chemistry Laboratory Tests" (WS / T 225-2024); the collection and processing of urine samples shall strictly follow the "Collection and Processing of Urine Specimens" (WS / T 348-2024); the collection and processing of cerebrospinal fluid and saliva samples shall also be carried out in accordance with the relevant standard documents.

[0106] The inclusion criteria for the subjects are as follows: (1) Adults aged ≥18 years, covering four age groups: youth (<45 years), middle-aged (45-59 years), young-old (60-74 years) and old (≥75 years); (2) Willing to cooperate in completing blood sample collection and physical measurements (height, weight, waist circumference); The core principle of the exclusion criteria for subjects is that "there is no disease state that affects the level of SASP index before the test". Specific exclusion conditions include: (1) a history of strenuous exercise within 24-48 hours before blood collection (strenuous exercise can lead to a transient increase in acute phase reactants such as IL-6, hs-CRP, and TNFα); (2) the presence of acute infectious diseases (such as upper respiratory tract infection, acute gastroenteritis, etc., which can lead to a significant increase in systemic inflammatory markers); (3) the presence of active autoimmune diseases (such as rheumatoid arthritis, systemic lupus erythematosus, etc., with persistently abnormal SASP index); (4) the presence of malignant tumors or currently receiving anti-tumor treatment; (5) recent (within 3 months) major surgery or severe trauma; (6) pregnant or lactating women; (7) those who cannot cooperate to complete the physical measurement (such as severe spinal deformity affecting height measurement).

[0107] 11.2 Marker Detection The tests were conducted using test reagents manufactured by Vovent Biotechnology Co., Ltd. The test reagents were as follows: Interleukin-6 Assay Kit (Chemiluminescence Method) Interleukin-1β Assay Kit (Chemiluminescence Method) Interleukin-8 Assay Kit (Chemiluminescence Method) Interleukin-10 Assay Kit (Chemiluminescence Method) Alpha tumor necrosis factor assay kit (chemiluminescence method) Interferon-gamma assay kit (chemiluminescence method) Transforming growth factor β1 assay kit (chemiluminescence method) Vascular endothelial growth factor assay kit (chemiluminescence method) Growth Differentiation Factor-15 Assay Kit (Chemiluminescence Method) Monocyte Chemoattractant Protein-1 Assay Kit (Chemiluminescence Method) Matrix metalloproteinase 3 assay kit (chemiluminescence method) High-sensitivity C-reactive protein assay kit (latex-enhanced immunoturbidimetric assay) The chemiluminescence method involves 11 parameters (interleukin-6, interleukin-1β, interleukin-8, interleukin-10, α-tumor necrosis factor, γ-interferon, transforming growth factor β1, vascular endothelial growth factor, growth differentiation factor-15, monocyte chemoattractant protein-1, and matrix metalloproteinase 3), and can be used in conjunction with the Wovent Bio-Automated Chemiluminescence Immunoassay Analyzer (LA2000, LA3000, LA3000S), Beckman Coulter Automated Immunoassay Analyzer (81600N (Access2)), and Automated Chemiluminescence Immunoassay Analyzer (UniCel DxI 800) manufactured by Wovent Biotechnology Co., Ltd.

[0108] The specific testing steps are as follows: a. Before use, please follow the kit instruction manual and refer to the chemiluminescence analyzer operation guide.

[0109] b. Before using a new kit for the first time, carefully invert it several times to ensure the magnetic beads are mixed. Do not invert an already opened kit.

[0110] c. Calibrators and quality control samples must be equilibrated to room temperature before testing.

[0111] d. Place the reagent bottle into the instrument's reagent compartment. The instrument scans the barcode on the reagent bottle, or you can manually enter the barcode information.

[0112] e. Calibrate using the reagent-compatible calibrators. Vavent models use two-point or six-point calibration, while Beckman models use six-point calibration.

[0113] f. The instrument obtains the project parameters, main calibration curve, and calibrator target value information by scanning the barcode on the luminescent calibration information card or manually entering the barcode information, and then starts the calibration procedure.

[0114] g. By measuring the calibrator, each calibration point on the predefined master curve will be adjusted (recalibrated) to a new, instrument-specific working curve that can then be used to measure samples.

[0115] h. Place the quality control sample / sample to be tested in the instrument's sample compartment. After setting the information for the quality control sample / sample to be tested according to the instrument's operation manual, start the running program. The instrument will automatically complete the measurement and output the test results.

[0116] The other methodology involves one item (high-sensitivity C-reactive protein), which can be used on multiple models from manufacturers such as Hitachi, Roche, Beckman Coulter, and Wovent. For details on compatible models, please refer to the instruction manual.

[0117] The specific testing steps are as follows: a. Before use, please follow the kit instruction manual and refer to the chemiluminescence analyzer operation guide.

[0118] b. The reagent is a ready-to-use liquid reagent that can be directly placed into the instrument for use.

[0119] c. Set the parameters on the applicable biochemical analyzer according to the basic parameters. For specific analytical parameter settings, refer to the basic operation guide of the relevant instrument.

[0120] d. Follow the calibration procedure in the biochemical analyzer's operation manual, using two-point / multi-point calibration, and let the instrument automatically generate the calibration curve.

[0121] e. Follow the testing procedures for quality control samples / samples to be tested in the biochemical analyzer's operation manual, and the instrument will automatically output the test results.

[0122] 11.3 Calculate the overall risk distribution based on the test results. 11.4 Gender Grouping Analysis The average risk ratio for men (38.90%) was significantly higher than that for women (27.00%), with 13.3% of men and 3.4% of women at high risk, consistent with literature reports of higher levels of age-related inflammation in men.

[0123] 11.5 Age Group Analysis Distribution of the 194 participants: The hazard ratio increased with age, reaching an average of 51.38% in the ≥75-year-old group, which is consistent with the biological expectations of aging and shows that the calculated parameter changes in the same direction as the age group, which can serve as evidence of internal consistency; however, this result does not constitute a verification of the accuracy of clinical outcomes.

[0124] Missing rate for each indicator: The logic for handling missing indicators in the "dynamic theoretical maximum score" mechanism is as follows: (1) When an indicator is missing, the indicator does not participate in the scoring, and the SASP total score is only accumulated with the scores of the detected indicators; (2) The theoretical maximum score is reduced accordingly and equal to the sum of the weights of all detected biomarkers (instead of a fixed 100 points); (3) Risk ratio = SASP total score / theoretical maximum score × 100%, still expressed as a percentage, consistent with the calculation method when all indicators are complete.

[0125] 11.6 Ranking of average scores for each indicator: hs-CRP had the highest average score and was the main contributor to the population inflammatory burden.

[0126] Summary of outlier handling procedures: Complete calculation example from raw detection values ​​to final parameters Example 1: A 42-year-old male with all 13 indicators met. Basic information of the subject: male, 42 years old (youth, <45 years old), height 1.75m, weight 78kg, waist circumference 85cm, BMI=25.5.

[0127] AB calculation: ABSI = waist circumference (m) / [BMI^(2 / 3) × height (m)^(1 / 2)] = 0.85 / [25.5^(2 / 3) × 1.75^(1 / 2)] = 0.85 / [8.66 × 1.3229] = 0.85 / 11.45 = 0.0742. This value is higher than the ideal benchmark for the youth group (0.070) but lower than the high-value warning (0.073) (Note: Ideal benchmark for the youth group = 0.070, high-value warning = 0.073, therefore 0.0742 > 0.073, the deviation is capped at 1.0, and the score = 3 points).

[0128] Calculation results: SASP total score = 54.15 points, theoretical maximum score = 100 points (all 13 indicators are complete), hazard ratio = 54.15 / 100×100% = 54.15%, classified as medium risk (30%≤hazard ratio<60%). The main risk contributors for this subject were hs-CRP (16.0 points) and IL-6 (11.66 points), totaling 27.66 points, accounting for 51.08% of the total score, suggesting that inflammatory burden was a major risk factor for aging.

[0129] Example 2: A 68-year-old female with ABSI deficiency. Basic information of the subject: female, 68 years old (young elderly group, 60-74 years old), height 1.58m, weight 62kg, waist circumference data missing (physical measurement not completed), BMI=24.8.

[0130] Calculation results: SASP total score = 74.46 (ABSI missing, not included in scoring), theoretical maximum score = 100 - 3 = 97 (sum of weights of the 12 tested indicators), hazard ratio = 74.46 / 97 × 100% = 76.76%, classified as high risk (hazard ratio ≥ 60%). The main risk contribution of this subject came from hs-CRP (22.40 points) and IL-6 (20.57 points), totaling 42.97 points, accounting for 57.69% of the total score.

[0131] Example 3: Comparison of MMP3 gender difference thresholds for a 52-year-old male. Subject basic information: Male, 52 years old (middle age, 45-59 years old), MMP3 detection value = 45 ng / mL. The study focuses on demonstrating the impact of the MMP3 gender-differentiated threshold on the scoring results, comparing the calculation differences between the uniform threshold and the gender-differentiated threshold.

[0132] Analysis: For the same MMP3 detection value (45 ng / mL), if a uniform threshold (healthy baseline 30, aging threshold 50) is used regardless of gender, the deviation is (45-30) / (50-30) = 0.75, and the score is 0.75 × 8 = 6.0 points, indicating that the risk is overestimated for men. After using a gender-differentiated threshold, the deviation for men is (45-40) / (100-40) = 0.083, and the score is 0.67 points, which is more consistent with the characteristic of men having higher physiological levels of MMP3.

[0133] Example 12: Comparison and verification of dynamic threshold and fixed threshold This embodiment describes the advantages of dynamic thresholding over fixed thresholding.

[0134] The applicant used 194 clinical samples to calculate SASP scores using both fixed thresholds (uniform thresholds for all age groups) and dynamic thresholds (differentiated thresholds for four age groups), and compared and analyzed the differences in risk grading between the two schemes at different age groups.

[0135] Comparative analysis showed that with a fixed threshold scheme, the proportion of elderly subjects (≥60 years old) classified as intermediate-to-high risk was significantly higher due to elevated baseline levels of biomarkers such as GDF-15 and MCP-1, leading to false positives. With a dynamic threshold scheme, the risk stratification for the elderly more accurately reflected their actual aging state, and the proportion of intermediate-to-high risk became more reasonable. Meanwhile, the proportion of low-risk subjects in the younger age group (<45 years old) under the dynamic threshold scheme better reflected the expected aging state of the younger population.

[0136] Mechanism analysis of the superiority of dynamic thresholds: Baseline levels of biomarkers such as GDF-15 and MCP-1 physiologically increase with age, a factor that fixed thresholds do not consider, leading to a higher false-positive rate in the elderly population. Dynamic thresholds, by setting differentiated thresholds based on the actual baseline levels of each age group, help distinguish between physiological and pathological increases, thus improving assessment accuracy.

[0137] Expected Results: Based on the above mechanism analysis, the dynamic threshold scheme is expected to achieve the following effects: (1) reduce the false positive rate in the elderly population, reducing unnecessary medical interventions and psychological burden; (2) improve the assessment sensitivity in the young population, avoiding false negatives; (3) make the risk classification of each age group more consistent with the actual aging state. The above expected results are derived from the known biological mechanisms of the age-related correlation of SASP biomarkers, and the specific quantitative improvement needs to be verified through subsequent clinical studies.

[0138] Note: The comparative analysis in this embodiment is based on the SASP score calculation results of 194 clinical samples and does not involve comparative verification with the clinical diagnostic gold standard. The accuracy of the risk stratification method disclosed herein needs to be further verified through subsequent follow-up studies.

[0139] Example 13: Application of anti-aging intervention This embodiment describes the application process of the method disclosed herein in anti-aging intervention.

[0140] This disclosed method implements tiered interventions based on SASP risk levels, simplifying the intervention process and improving operability. The specific tiered intervention plan is as follows: 13.1 Tiered Intervention Program The basic interventions include at least one of the following: Mediterranean diet (increasing intake of fruits, vegetables, and fish, and reducing refined carbohydrates), Omega-3 fatty acid supplementation (EPA+DHA 1-2g / day), aerobic exercise (150 minutes / week at moderate intensity), resistance training (2-3 times / week), and nutritional supplementation (vitamin D 1000-2000 IU / day).

[0141] Note: The interventions described in this disclosure are all lifestyle and nutritional interventions and do not involve prescription drugs. If a participant has a clinical condition requiring drug treatment, they should be referred to a specialist physician who will develop a medication plan based on clinical guidelines. This disclosure can be used to evaluate the efficacy of drug treatment, but it does not directly guide clinical drug use.

[0142] 13.2 Evaluation of Intervention Effectiveness Intervention effectiveness was assessed by comparing changes in SASP scores between baseline and retest, with the following indicators: (1) SASP total score change rate = baseline risk ratio % - retest risk ratio % (2) Changes in risk level: Compare the risk level between the baseline and the retest to see if it has improved.

[0143] If the change rate of the total SASP score is greater than or equal to 10% or the risk level improves by at least one level, the intervention is deemed effective and the current plan continues; otherwise, the intervention plan is adjusted.

[0144] Lifestyle and nutritional interventions were implemented for different risk groups according to different risk levels. The intervention period was one month. The risk changes before and after the intervention are shown in the table below: 13.3 Expected Results Based on the biological characteristics of SASP-related biomarkers and the design principles of the scoring model disclosed herein, the expected effects of the method disclosed herein in anti-aging intervention are as follows: (1) Expected changes in total SASP score: After the implementation of tiered intervention, the total SASP score of the medium- and high-risk population is expected to decline.

[0145] (2) Expected risk level change: For medium- or high-risk groups, the expected risk level can be improved by at least one level after effective intervention.

[0146] (3) Expected value of dynamic monitoring: By regularly retesting the SASP score, it is expected that the trend of age-related inflammatory status changes in the subjects can be dynamically monitored, providing an objective basis for timely adjustment of the intervention plan.

[0147] The above expected results are derived based on the known biological mechanisms of SASP-related biomarkers and the design principles of the scoring model disclosed herein, and are theoretical predictions. Actual intervention effects may vary due to individual differences, intervention adherence, intervention duration, and other factors; specific effect data need to be verified through actual clinical studies.

[0148] Example 14: Application of Health Management System This embodiment describes in detail the application of the method disclosed herein in a health management system.

[0149] This disclosed method can be integrated into a health management platform to provide SASP risk assessment services for physical examination centers, health management institutions, and corporate health benefits.

[0150] 14.1 Application in physical examination screening The SASP score is offered as an added-value item in the health checkup package. After completing the routine health checkup, participants have additional blood samples collected for testing 13 SASP biomarkers. The system automatically calculates the SASP score and generates an assessment report. The report includes: total SASP score, hazard ratio, risk level, ranking of scores for each indicator, analysis of major risk factors, and personalized health recommendations.

[0151] 14.2 Health Management Applications We provide dynamic monitoring services for SASP scores for health management members, and recommend testing every 6-12 months. The system automatically generates SASP score trend charts to help members and health managers understand the changing trends of aging risks and adjust health management plans in a timely manner.

[0152] 14.3 Actuarial Applications The SASP score is used as a risk assessment factor for health insurance and retirement insurance, and premium rates are adjusted according to the SASP risk level. Low-risk individuals enjoy premium discounts, while high-risk individuals need to pay additional premiums or receive health management interventions. The SASP score provides actuarial science with an objective biological indicator of aging, which is superior to traditional age and medical history assessments.

[0153] 14.4 Application of Corporate Health and Benefits Companies can incorporate SASP assessments into their employee health and wellness programs, providing annual SASP assessments for employees aged 35 and older. Based on the assessment results, they can offer personalized health management recommendations to reduce the risk of age-related diseases among employees and decrease corporate healthcare expenditures and productivity losses.

[0154] Example 15: Application of Anti-aging Drug Screening and Efficacy Evaluation This embodiment describes the application process of the method disclosed herein in the screening of anti-aging drugs and the evaluation of drug efficacy.

[0155] This disclosed method can be used for screening and evaluating the efficacy of anti-aging drugs. The basic procedure is as follows: detect the baseline SASP score; administer the candidate drug or the drug to be evaluated; detect the SASP score again; evaluate the anti-aging effect or efficacy of the drug by comparing the changes in the SASP score before and after the treatment.

[0156] 15.1 Drug Screening Applications In the drug screening stage, aging models with high SASP expression can be constructed in cells or animals. After detecting the baseline SASP score, the candidate drug is administered, and the SASP score is detected again. By comparing the changes in the total SASP score before and after treatment, the anti-aging effect of the candidate drug can be evaluated. The greater the decrease in the total SASP score, the higher the anti-aging potential of the candidate drug.

[0157] 15.2 Application in drug efficacy evaluation During the drug efficacy evaluation phase, the SASP score of subjects receiving drug treatment can be monitored periodically. By comparing the changes in SASP scores between baseline and retest, the drug efficacy can be objectively assessed. Specific evaluation indicators include the rate of change in the total SASP score, changes in risk level, and changes in individual indicator scores.

[0158] Note: This embodiment only describes the method and process of using the SASP score for drug screening and efficacy evaluation, and does not involve specific drug recommendations or prescriptions, nor does it involve specific trial data. The clinical use of any drug must be carried out under the guidance of a licensed physician, in accordance with the drug's instructions and clinical guidelines. This disclosed method serves as an evaluation tool to provide objective reference for drug development and clinical decision-making, but it does not replace the clinical judgment of physicians.

[0159] Example 16: SASP Scoring Calculation System This embodiment describes in detail the SASP scoring calculation system that implements the method of this disclosure.

[0160] This system includes the following 11 functional modules: The system supports multiple deployment methods: cloud deployment (suitable for sharing among multiple institutions), local deployment (suitable for hospitals with high data security requirements), edge deployment (suitable for real-time processing by testing equipment), and hybrid deployment (the core engine is deployed locally, and the threshold database is updated in the cloud).

[0161] Example 17: Computer-readable storage medium This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the SASP risk scoring method described in this disclosure.

[0162] The computer-readable storage media include, but are not limited to, USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0163] The computer program includes the following functional modules: data acquisition program code, age segmentation program code, threshold acquisition program code, ABSI calculation program code, score calculation program code, missing data tolerance program code, risk grading program code, and result output program code.

[0164] When the computer program is executed on the processor, it performs the following steps: receiving basic information of the subject and measured values ​​of 13 biomarkers; determining age segments and obtaining dynamic thresholds based on age; obtaining gender-differentiated thresholds based on gender; calculating the ABSI value; calculating individual scores for each indicator using 5 scoring models; obtaining the SASP total score through weighted summation; calculating the theoretical maximum score and risk ratio; determining the risk level; and outputting the scoring results and assessment report.

[0165] The foregoing description of specific exemplary embodiments of this disclosure is for illustrative and explanatory purposes. These descriptions are not intended to limit this disclosure to the precise forms disclosed, and it will be apparent that many changes and variations can be made in accordance with the foregoing teachings. The exemplary embodiments were chosen and described in order to explain the specific principles of this disclosure and their practical application, thereby enabling those skilled in the art to implement and utilize various different exemplary embodiments of this disclosure, as well as various different choices and variations. The scope of this disclosure is intended to be defined by the claims and their equivalents.

Claims

1. A computer-based method for generating SASP quantitative evaluation parameters based on in vitro detection data of aging-related secretion phenotypes, characterized in that, include: The study obtains the subject's age and gender information, as well as the concentrations of IL-6, hs-CRP, GDF-15, IL-10, MMP3, and MCP-1, calculated from the detection signals generated by the analyte-specific binding reaction of isolated serum or plasma samples and converted using the corresponding calibration curves for each analyte. Age segments are determined based on the age, and the corresponding GDF-15 and MCP-1 threshold parameters are retrieved. The MMP3 threshold parameter is retrieved based on the gender. The concentrations of IL-6 and hs-CRP are input into a one-way linear mapping model, and the concentration of GDF-15 is input into... An age-dynamic linear mapping model is used, inputting the concentration value of MMP3 into a sex-differentiated unidirectional linear mapping model, and inputting the concentration values ​​of IL-10 and MCP-1 into a U-shaped four-threshold mapping model respectively, to obtain the normalized single-item parameter q_i of each indicator in the range of 0 to 1; the SASP quantitative evaluation parameter S is calculated according to S=100×Σ(w_i×q_i) / Σw_i, where w_i is the preset weight of the i-th included indicator; and the SASP quantitative evaluation parameter S and at least one normalized single-item parameter are output; wherein, the output does not include a diagnostic conclusion to determine whether the subject has a specific disease.

2. The method according to claim 1, characterized in that, It also acquires waist circumference, BMI, and height information and calculates the body mass index (ABSI). The ABSI is then input into a U-shaped three-threshold mapping model to obtain the corresponding normalized individual parameters, which are then incorporated into the calculation of the SASP quantitative evaluation parameter S.

3. The method according to claim 1 or 2, characterized in that, In vitro detection data for at least one of TNFα, TGF-β1, IL-1β, IL-8, VEGF, and IFNγ are also obtained, and after calling threshold parameters that match the detection platform and sample type, the normalized individual parameters are incorporated into the calculation of the SASP quantitative evaluation parameter S; preferably, the included indicators include IL-6, hs-CRP, GDF-15, TNFα, IL-10, MMP3, MCP-1, TGF-β1, ABSI, IL-1β, IL-8, VEGF, and IFNγ.

4. The method according to claim 3, characterized in that, The preset weights for each indicator are as follows: IL-6 is 24, hs-CRP is 24, GDF-15 is 10, TNFα is 8, IL-10 is 8, MMP3 is 8, MCP-1 is 7, TGF-β1 is 6, ABSI is 3, and IL-1β, IL-8, VEGF, and IFNγ are each 0.

5. When only the six indicators of claim 1 are included, Σw_i is 81; when ABSI is further included, Σw_i is 84; when all thirteen indicators are included, Σw_i is 100.

5. The method according to claim 1, characterized in that, The one-way linear mapping model, age dynamic linear mapping model or gender-differentiated one-way linear mapping model determines the normalized individual parameter q in the following manner: when x≤B, q=0; when B<x<T, q=(x-B) / (T-B); when x≥T, q=1; where x is the measured concentration value, B is the health reference value, and T is the upper threshold; for GDF-15, B and T are invoked according to age groups; for MMP3, B and T are invoked according to gender.

6. The method according to claim 1, characterized in that, The threshold parameters of the U-shaped four-threshold mapping model are sequentially the low-value warning L, ideal lower limit A, ideal upper limit B and high-value warning H, where L<A≤B≤H; when A≤x≤B, q=0; when L≤x<A, q=(A-x) / (A-L); when B<x≤H and H>B, q=(x-B) / (H-B); when x<L or x>H, q=1; when H=B, q is directly set to 1 for x>B.

7. The method according to claim 1, 5, or 6, characterized in that, The age groups are under 45 years old, 45 to 59 years old, 60 to 74 years old, and 75 years old and above; the (B, T) of GDF-15 in the above age groups are (500, 1000), (750, 1500), (1100, 2200) and (1100, 2200) respectively, with the unit of pg / mL; the (L, A, B, H) of MCP-1 in the above age groups are (18.02, 20, 350, 400), (20, 30, 380, 400), (25, 50, 390, 400) and (25, 50, 390, 400) respectively, with the unit of pg / mL; the (L, A, B, H) of IL-10 is (0.8, 1.0, 4.0, 6.0), with the unit of pg / mL; the (B, T) of MMP3 is (40, 100) ng / mL in males and (20, 50) ng / mL in females; the (B, T) of IL-6 is (1.5, 5.0) pg / mL, and the (B, T) of hs-CRP is (0, 3.0) mg / L.

8. The method according to claim 2, characterized in that, The calculation formula of ABSI is: ABSI equals waist circumference (in meters) divided by (BMI to the power of 2 / 3 multiplied by height to the power of 1 / 2), that is, ABSI = waist circumference (m) / (BMI^(2 / 3) × height (m)^(1 / 2)), where the units of waist circumference and height are meters; the parameters of the U-shaped three-threshold mapping model are the low-value warning L, ideal reference C and high-value warning H, when x=C, q=0, when L≤x<C, q=(C-x) / (C-L), when C<x≤H, q=(x-C) / (H-C), when x<L or x>H, q=1; the (L, C, H) corresponding to under 45 years old, 45 to 59 years old, 60 to 74 years old, and 75 years old and above are (0.060, 0.070, 0.073), (0.062, 0.070, 0.076), (0.064, 0.070, 0.078) and (0.065, 0.070, 0.080) respectively.

9. The method according to any one of claims 1 to 8, characterized in that, In the quality control steps, verify the sample type, detection platform, calibration status, data type of concentration value, and whether the concentration value is within the reportable range of the detection method; when any of the six core indicators described in claim 1 is missing or fails quality control, output a data insufficiency prompt instead of outputting the SASP quantitative evaluation parameter; when an optional indicator is missing, simultaneously exclude w_i×q_i and w_i corresponding to the optional indicator from both the numerator and denominator.

10. The method according to any one of claims 1 to 9, characterized in that, When using different testing platforms or different sample types, first establish a conversion relationship by comparing samples using platform-specific calibrators, quality control samples, or methodologies, and then call the threshold parameters that match the corresponding platform and sample type; data that have not been compared by methodology are not directly merged for calculation.

11. The method according to any one of claims 1 to 10, characterized in that, The SASP quantitative evaluation parameter S is divided into a first interval S<30, a second interval 30≤S<60, and a third interval S≥60, and the interval identifiers are output. Optionally, at least one of the following is also output: q_i, w_i×q_i, data quality identifier, and change value of each indicator in previous tests.

12. An in vitro detection method, characterized in that, include: The isolated serum or plasma sample is contacted with specific binding reagents for IL-6, GDF-15, IL-10, MMP3, and MCP-1, respectively, to obtain the detection signal of each analyte, and convert it into a concentration value using its respective calibration curve; the hs-CRP concentration value in the sample is obtained by immunoturbidimetry or specific binding detection; and the concentration value is input into the method of any one of claims 1 to 11 to generate SASP quantitative evaluation parameters.

13. A reagent combination for implementing the in vitro detection method of claim 12, characterized in that, This includes analyte-specific binding reagents for detecting IL-6, GDF-15, IL-10, MMP3, and MCP-1, specific reagents for detecting hs-CRP, and calibrators or calibration information corresponding to each analyte.

14. The reagent combination according to claim 13, characterized in that, It also includes quality control materials and machine-readable carriers, barcodes, QR codes, or data interface information recording the calculation method described in any one of claims 1 to 11.

15. A SASP quantitative evaluation calculation system, characterized in that, include: The data acquisition module is used to receive in vitro detection data of the six core indicators described in claim 1, as well as age and gender information; The quality control module is used to verify sample type, detection platform, calibration status, and data validity; the parameter calling module is used to call matching threshold parameters based on age, gender, sample type, and detection platform; the model matching module is used to match unidirectional linear mapping models, age dynamic linear mapping models, gender-differentiated unidirectional linear mapping models, or U-shaped four-threshold mapping models for different indicators. The calculation module is used to calculate normalized individual parameters and SASP quantitative evaluation parameters; And an output module, used to output the SASP quantitative evaluation parameters and data quality identifiers.

16. The system according to claim 15, characterized in that, It also includes at least one of the following: ABSI calculation module, optional indicator processing module, platform conversion module, historical data storage module, and visualization module; wherein, the visualization module is used to generate normalized single parameter plots, longitudinal variation plots, or detection quality plots.

17. A SASP quantitative evaluation calculation device, characterized in that, It includes a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to implement the method of any one of claims 1 to 11.

18. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 11.

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