A Longevity Biomarker Based on Serum Metabolomics and Its Application

Abstract

This invention discloses a longevity biomarker based on serum metabolomics and its application, belonging to the field of biomedical technology. The longevity biomarker disclosed in this invention, based on serum metabolomics, includes mandelic acid, glutethimide, fenmetrazine, hydroxyheptanoic acid, norfloxacin, N-acetylserotonin, 3,4-methylenedioxyphenylamine, palmitoleic acid, cinconidine, gulonose, 5-aminopentanal, 12,13-dihydroxy-9Z-octadecenoic acid, anserine, (3Z)-phytochrome bile, phytocyanin, and L-selenomethylselenocysteine. This invention provides new biomarkers for diagnosing healthy aging and predicting human lifespan, and can be used to prepare products that predict the likelihood of longevity, showing promising applications in the field of anti-aging and health.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, and more specifically to a longevity biomarker based on serum metabolomics and its application. Background Technology

[0002] Benefiting from improved living standards, advancements in medical technology, and improved living environments, the average life expectancy worldwide has increased significantly. Statistics show that by 2050, the global elderly population (over 60 years old) is projected to reach 2.1 billion. However, longer lifespan does not equate to healthy aging; older adults often suffer from one or more age-related diseases, placing a significant burden on the economy and healthcare systems. Therefore, identifying key biomarkers for healthy aging in centenarians and assessing the serum metabolite characteristics of healthy aging in the elderly population can serve as an early warning system for health risks and help save public health costs.

[0003] Centenarians represent a healthy aging population, living to over 90 years old and rarely suffering from age-related diseases. Current research on healthy aging focuses primarily on longevity genes, transcription factors, and epigenetic factors, but the information obtained so far remains very limited.

[0004] Metabolomics primarily obtains information on the dynamic changes of metabolic products in organisms over time and in pathophysiological processes by detecting changes in small molecule metabolites (MWK < 1000), including sugars, lipids, amino acids, and vitamins. As the final products of cellular physiological activities, metabolites can accurately and sensitively reflect the functional state of cells. Metabolomics has changed the traditional approach of single-marker detection, using a group of metabolites as "pattern markers" for diagnosis, which has unique advantages. Although metabolomics started relatively late, it has already shown significant advantages compared to traditional diagnostic methods and research tools. With age, characteristic changes in endogenous small molecule metabolites inevitably occur. Metabolomics, aided by advanced separation, analysis, and computational techniques, has the ability and advantage to distinguish characteristic metabolites under different age conditions. Using metabolomics to study the metabolites that distinguish centenarians from ordinary elderly people but are indistinguishable from those of younger people allows for a holistic exploration of this complex physiological process.

[0005] Serum metabolites play a vital role in human health and disease. Therefore, developing a highly specific and sensitive serum metabolic biomarker is crucial for assessing the healthy aging characteristics of the elderly population. However, currently, there is a lack of effective predictive serum metabolic biomarkers for long-lived individuals in clinical practice.

[0006] Therefore, providing a longevity biomarker based on serum metabolomics and its application is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0007] In view of this, the present invention provides a longevity biomarker based on serum metabolomics and its application.

[0008] This invention identifies changes in the relative abundance of serum metabolites associated with longevity in elderly individuals and provides a method for distinguishing longevity in elderly individuals from ordinary elderly populations based on the relative abundance of serum metabolic markers.

[0009] To achieve the above objectives, the present invention adopts the following technical solution:

[0010] A longevity biomarker based on serum metabolomics, wherein the longevity biomarker is mandelic acid, glumet, fenmetrazine, hydroxyheptanoic acid, norfloxacin, N-acetylserotonin, 3,4-methylenedioxyphenylamine, palmitoleic acid, cinconidine, gulose, 5-aminopentanal, 12,13-dihydroxy-9Z-octadecenoic acid, anserine, (3Z)-phytochrome bile, phytocyanin, and L-selenomethylselenocysteine.

[0011] Furthermore, the application of the aforementioned longevity biomarker based on serum metabolomics in the preparation of longevity prediction formulations.

[0012] Furthermore, the longevity biomarkers were used as quality control samples for gas chromatography-mass spectrometry.

[0013] Furthermore, the method for screening longevity biomarkers based on serum metabolomics includes the following steps:

[0014] (1) Collected serum samples were used as analytical samples;

[0015] (2) Non-targeted metabolomics analysis was performed on each sample using GC-MS to obtain the raw metabolic data of each serum sample;

[0016] (3) Perform data analysis on the raw metabolic data obtained in step (2) and screen for differential metabolites;

[0017] (4) The AUC value was 0.9343 obtained by ROC curve analysis, and the longevity biomarker was determined.

[0018] As can be seen from the above technical solution, compared with the prior art, this invention discloses a longevity biomarker based on serum metabolomics and its application. By analyzing the serum metabolomes of centenarians, their immediate family members, ordinary elderly people, and young people, differential metabolites are analyzed to identify potential predictive biomarkers. This invention provides new biomarkers for diagnosing healthy aging and predicting human lifespan, and can be used to prepare products that predict the likelihood of longevity, showing promising applications in the field of anti-aging and health. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 The attached figure is an experimental flowchart of the present invention for screening and validating longevity biomarkers based on serum metabolomics;

[0021] Figure 2 The attached figure is a Venn diagram showing the intersection of the screening of potential longevity-related candidate metabolites, the differential metabolites between centenarians and ordinary elderly people, and the differential metabolites between the immediate family members of centenarians and ordinary elderly people in this invention.

[0022] Figure 3 The attached figure shows a bee colony diagram of the 16 differential metabolites screened by this invention;

[0023] Figure 4 The attached figure is a heatmap of the 16 differential metabolites screened by this invention;

[0024] Figure 5 The attached figure shows the ROC curves of the 16 differential metabolite test sets selected by this invention.

[0025] Figure 6 The attached figure shows the ROC curves of the validation set of 16 differential metabolites selected in this invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] Example 1

[0028] The experimental flowchart for screening and validating longevity biomarkers based on serum metabolomics is shown below. Figure 1 .

[0029] (I) Sample collection and information gathering

[0030] Serum samples were collected from healthy individuals in Guangdong Province, China, ranging in age from 20 to 100+ years. These individuals were divided into four groups: centenarians (CE group, ≥100 years, 101 people), lineal relatives of centenarians (CE-L group, 65-89 years, 51 people), the elderly (65-89 years, 64 people), and the young (20-50 years, 62 people). The immediate relatives of centenarians and the elderly included in the study belonged to the same age group, and the elderly population was surveyed to ensure that none of their parents or siblings were over 90 years old.

[0031] Serum sample collection: Fasting blood samples were collected from the subjects by medical personnel via intravenous puncture. The samples were left to stand at room temperature for 30 minutes, and then transported to the laboratory at 4°C for centrifugation (3500 rpm, 10 minutes). The separated serum samples were then stored in an ultra-low temperature freezer at -80°C for later use.

[0032] (II) Serum Non-target Metabolomics Test

[0033] 1) Extraction of metabolites: (1) Thaw the experimental sample at 4℃, vortex the sample for 1 min after thawing, and mix well; (2) Accurately transfer 100 μL of serum sample into a 2 mL centrifuge tube; (3) Add 400 μL of methanol (stored at -20℃) and vortex for 1 min; (4) Centrifuge at 12,000 rpm at 4℃ for 10 min, take all the supernatant, transfer it to a new 2 mL centrifuge tube, concentrate and dry; (5) Accurately add 150 μL of 2-chloro-L-phenylalanine (4 ppm) solution (stored at 4℃) prepared with 80% methanol aqueous solution to reconstitute the sample, filter the reconstituted solution through a 0.22 μm membrane, add the filtrate to the detection bottle for LC-MS detection; (6) Transfer 20 μL of filtrate from each sample, mix them and use as quality control sample.

[0034] 2) LC-MS detection:

[0035] (1) Chromatographic conditions: ThermoVanquish ultra-high performance liquid chromatography system, using ACQUITY An HSST3 (2.1 x 150 mm, 1.8 μm) column was used at a flow rate of 0.25 mL / min and a column temperature of 40 °C. The injection volume was 2 μL. Positive ion mode was used. The mobile phase consisted of 0.1% formic acid acetonitrile (B2) and 0.1% formic acid aqueous solution (A2). The gradient elution program was as follows: 0–1 min, 2% B2; 1–9 min, 2%–50% B2; 9–12 min, 50%–98% B2; 12–13.5 min, 98% B2; 13.5–14 min, 98%–2% B2; 14–20 min, 2% B2. In negative ion mode, the mobile phase consisted of acetonitrile (B3) and 5 mM ammonium formate aqueous solution (A3). The gradient elution program was as follows: 0–1 min, 2% B3; 1–9 min, 2%–50% B3; 9–12 min, 50%–98% B3; 12–13.5 min, 98% B3; 13.5–14 min, 98%–2% B3; 14–17 min, 2% B3.

[0036] (2) Mass spectrometry conditions: A Thermo Orbitrap Exploris 120 mass spectrometer (Thermo Fisher Scientific, USA) was used with an electrospray ionization (ESI) source. Data was acquired in both positive and negative ion modes. The positive ion spray voltage was 3.50 kV, the negative ion spray voltage was -2.50 kV, the sheath gas was 30 alb, and the auxiliary gas was 10 alb. The capillary temperature was 325 °C. A first-stage full scan was performed at a resolution of 60,000, with a first-stage ion scan range of 100–1000 m / z. Second-stage fragmentation was performed using an HCD with a collision energy of 30% and a second-stage resolution of 15,000. The first four ions acquired were fragmented, and unnecessary MS / MS information was removed using dynamic exclusion. Quality control (QC) samples were prepared by mixing equal volumes of extracts from all samples. Each QC sample had the same volume as the sample and was processed and detected using the same methods as the analytical samples. During the instrument analysis, one QC sample was inserted every 10 samples to examine the stability of the entire detection process.

[0037] 3) Metabolomics Data Processing: After the mass spectrometry run, the raw mass spectrometry files were converted to mzXML file format using the MSConvert tool in the Proteowizard software package (v3.0.8789). Peak detection, peak filtering, and peak alignment were performed using the RXCMS software package to obtain a final list of quantified substances. The parameters were set as follows: bw=2, ppm=15, peakwidth=c(5,30), mzwid=0.015, mzdif-0.01, method="centWave". Substances were identified using public databases HMDB, massbank, LipidMaps, mzcloud, KEGG, and a self-built material library, with the parameter set to ppm<30ppm. Data correction was performed using the LOESS signal correction method based on QC samples to eliminate systematic errors. Substances with RSD>30% in Q samples were filtered out during data quality control.

[0038] (III) Differential Metabolite Analysis

[0039] First, based on the analysis of the raw LC-MS data, 12,809 and 12,137 metabolites were captured in positive and negative ion modes, respectively. Further analysis using precise molecular weights and MS / MS fragmentation patterns confirmed annotations in databases such as HMDB, Massbank, LiqidMaps, and mzcloud, identifying 808 secondary metabolites.

[0040] First, to screen for differentially expressed metabolites between centenarians and the general elderly population, differential metabolite analysis was performed, yielding 322 differentially expressed metabolites (p < 0.0001). Furthermore, serum differentially expressed metabolites were compared between immediate family members of centenarians and the general elderly population, resulting in 213 differentially expressed metabolites (p < 0.0001).

[0041] To identify metabolites common to centenarians and their immediate family members that differ from those in the general elderly population, 322 differentially expressed metabolites and 213 metabolites were analyzed, resulting in a set of 187 metabolites. Figure 2 ).

[0042] Subsequently, to eliminate age-related differences among metabolites, an age-related analysis was performed on these 187 metabolites (Table 1). 109 metabolites were age-related, while 78 were not. The 78 age-independent metabolites were used for subsequent screening of longevity biomarkers.

[0043] Table 1. Statistical analysis of the age-related correlations of 187 candidate metabolites.

[0044] No correlation with age (p>0.05) It was correlated with age (p<0.05) Number of metabolites 78 109

[0045] Note: Correlation was calculated using Spearman's rank correlation method.

[0046] "Rejuvenating" metabolites were screened as longevity biomarkers. These metabolites are those that show no difference between centenarians and younger people, but differ between the general elderly and younger groups. These metabolites can distinguish centenarians from the general elderly population and also indicate the youthful state of centenarians. Therefore, 16 metabolites were further screened from 78 metabolites that showed no difference between centenarians and younger people, but differed between the general elderly and younger people. Figure 3 and Figure 4 Compared to the average elderly person, the abundance of 15 of these metabolites was downregulated: mandelic acid, glutethimide, fenmetrazine, hydroxyheptanoic acid, norfloxacin, N-acetylserotonin, 3,4-methylenedioxyphenylamine, palmitoleic acid, cinconidine, gulose, 5-aminopentanal, 12,13-dihydroxy-9Z-octadecenoic acid, anserine, (3Z)-phytochrome bile, and phytocyanin. The abundance of one metabolite was upregulated: L-selenomethylselenocysteine. These trends in metabolite abundance indicate the body's health status.

[0047] Therefore, the 16 differentially expressed metabolites (Table 2) ultimately selected are those shared by centenarians and their immediate family members with ordinary elderly individuals, unaffected by age, and showing no difference between centenarians and younger individuals, while differing between older and younger individuals. To evaluate the potential of these 16 differentially expressed metabolites in distinguishing centenarians from ordinary elderly individuals, a ROC curve was constructed, and the combined diagnostic AUC value was 0.9343 (…). Figure 5 ).

[0048] Table 2: Differential metabolites of "rejuvenation"

[0049] Serial Number Chinese name English name 1 Mandelic acid Mandelicacid 2 Grumit Glutethimide 3 fenmetrazine Phenmetrazine 4 Hydroxyheptanoic acid 6-Hydroxyhexanoic acid 5 Norfloxacin Norfloxacin 6 N-acetylserotonin N-Acetylserotonin 7 3,4-Methylenedioxyamphetamine 3,4-Methylenedioxyamphetamine 8 Palmitoleic acid Palmitoleicacid 9 Cincontin Cinchonidine 10 Gulu sugar L-Gulose 11 5-Aminopentanal 5-Aminopentanal 12 12,13-Dihydroxy-9Z-octadecenoic acid 12,13-DHOME 13 Goose muscle peptide Anserine 14 (3Z)-Phytochrome Bile (3Z)-Phytochromobilin 15 Pale blue fungus Cerulenin 16 L-Selenomethylselenocysteine Se-Methylselenocysteine

[0050] Validation of longevity biomarkers

[0051] To verify the reliability and stability of the longevity biomarkers identified above, an additional 44 samples from centenarians and 28 samples from ordinary elderly individuals were collected as a validation set. The AUC value of the ROC curves for the 16 metabolites was 0.9602. Figure 6 The results indicate that the 16 metabolites can effectively distinguish between centenarians and ordinary elderly people, and have a good discriminative effect on longevity, and can be used as longevity biomarkers.

[0052] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. The application of a longevity biomarker based on serum metabolomics in the preparation of longevity prediction formulations, characterized in that, The longevity biomarkers are mandelic acid, grumite, fenmetrazine, hydroxyheptanoic acid, norfloxacin, N-acetylserotonin, 3,4-methylenedioxyphenylamine, palmitoleic acid, cinconidine, gulose, 5-aminopentanal, 12,13-dihydroxy-9Z-octadecenoic acid, anserine, (3Z)-phytochrome bile, phytocyanin, and L-selenomethylselenocysteine.

2. The application according to claim 1, characterized in that, The longevity biomarkers were used as quality control samples for gas chromatography-mass spectrometry.

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