Use of valproic acid in the prevention of progression of salt-sensitive hypertensive state

Abstract

The application provides application of valeryl carnitine in preventing and treating salt-sensitive hypertension state progression. The application provides application of acyl carnitine, especially valeryl carnitine in a sample from an individual as a target in screening and / or preparing a reagent and / or a drug for preventing and treating salt-sensitive hypertension, and application of acyl carnitine or a reagent for detecting acyl carnitine in a sample from an individual in preparing a product for evaluating blood pressure state progression of the individual, and application of acyl carnitine in preparing a product for improving, preventing and / or treating salt-sensitive hypertension. The application finds that acyl carnitine is significantly related to hypertension onset and blood pressure state progression, and is expected to become a potential dietary supplement or intervention target for preventing and treating hypertension.

Description

Technical Field

[0001] This invention relates to a diagnostic and therapeutic technique for salt-sensitive hypertension, specifically the use of acylcarnitine, particularly valerate, as a marker in the preparation of dietary supplements and / or medicines for the prevention and / or treatment of the progression of salt-sensitive hypertension. Background Technology

[0002] The Global Burden of Disease survey indicates that a high-sodium diet is the leading dietary risk factor for cardiovascular disease mortality. The effect of a high-sodium diet on the cardiovascular system is primarily related to sodium-induced increases in blood pressure. Studies have shown that blood pressure levels are significantly higher in people with high-sodium diets compared to those adhering to low-sodium diets. However, the response of blood pressure to sodium varies across populations; this phenomenon is called blood pressure salt sensitivity. Some individuals experience significant changes in blood pressure with changes in sodium intake; these individuals are called salt-sensitive. Others show a weaker response to sodium; these individuals are called salt-insensitive or salt-resistant. Hypertension caused by a high-sodium diet is called salt-sensitive hypertension. Compared to non-salt-sensitive individuals, salt-sensitive individuals have a higher risk of developing hypertension and cardiovascular disease. Previous studies have extensively explored the genetic and environmental factors associated with blood pressure salt sensitivity. For example, the Genetic Epidemiology Network of Salt Sensitivity (GenSalt) identified a series of genetic susceptibility loci associated with blood pressure salt sensitivity. However, the biological mechanisms by which high-sodium diets lead to salt-sensitive hypertension are not fully understood, and it remains to be revealed whether different molecular characteristics exist among salt-sensitive individuals with different blood pressure levels.

[0003] With the rise of multi-omics technologies, an increasing number of studies are utilizing proteomics, metabolomics, and gut microbiome to reveal the mechanisms of disease development and to uncover potential therapeutic and interventional targets. The gut is the first and largest site of sodium digestion and absorption; therefore, the gut microbiota may play a role in regulating blood pressure's response to sodium. Previous studies have also shown that high-sodium diets affect plasma metabolite concentrations. Therefore, it is reasonable to speculate that there may be some connection between high-sodium diets, gut microbiota, plasma metabolomics, and salt-sensitive hypertension. Revealing the effects of high-sodium diets on gut microbiota and metabolites can provide new insights into the biological mechanisms of salt-sensitive hypertension. Describing the gut microbiota and metabolomics characteristics of different blood pressure salt-sensitive populations can further elucidate the causes of blood pressure salt sensitivity and provide a basis for personalized and precise prevention. Summary of the Invention

[0004] One object of the present invention is to provide biomarkers related to blood pressure salt sensitivity.

[0005] Another object of the present invention is to provide the application of blood pressure salt sensitivity-related biomarkers.

[0006] The inventors of this invention studied the effects of a high-sodium diet on gut microbiota and plasma metabolites, depicting the differences in gut microbiota and metabolome characteristics among individuals with varying blood pressure salt sensitivity, and identifying potential intervention targets for the prevention of salt-sensitive hypertension. Specifically, this invention explored the association between blood pressure salt sensitivity and the response of gut microbiota and metabolites to salt, finding significant differences in the responses of 22 gut microbiota species and 9 metabolites across different blood pressure salt sensitivity groups. Furthermore, it constructed a gut microbiota-metabolite regulatory network influencing blood pressure salt sensitivity, and correlation analysis revealed that the gut-acylcarnitine axis may be a key pathway leading to differences in blood pressure salt sensitivity among individuals. This invention also found that acylcarnitines, particularly valerate carnitine, are significantly associated with the risk of hypertension and the progression of blood pressure status, and can serve as potential dietary supplements or intervention targets for the prevention and treatment of hypertension.

[0007] On the one hand, the present invention provides the use of acylcarnitine in samples from individuals as a target in screening and / or preparing reagents and / or drugs for the prevention and treatment of blood pressure salt sensitivity.

[0008] On the other hand, the present invention provides the use of acylcarnitine or reagents for detecting acylcarnitine in samples from an individual in the preparation of products for assessing an individual's risk of developing hypertension and / or the progression of blood pressure status.

[0009] According to a specific embodiment of the present invention, the acylcarnitine includes valerate carnitine.

[0010] According to some specific embodiments of the present invention, the acylcarnitine is valerate carnitine. The acylcarnitine in the sample refers to the concentration level of valerate carnitine in the sample.

[0011] According to a specific embodiment of the present invention, the acylcarnitine includes valerylcarnitine, and further includes one or more of isovalerylcarnitine and 3-hydroxyisovaleric acid. The acylcarnitine in the sample refers to the total concentration level of these acylcarnitines in the sample.

[0012] According to a specific embodiment of the present invention, the sample may be blood or plasma. The concentration of acylcarnitine in the sample from an individual can be detected using any feasible reagent or method in the prior art.

[0013] According to a specific embodiment of the present invention, the individual is of East Asian race, preferably Chinese.

[0014] According to a specific embodiment of the present invention, the level of valerocarnitine in samples from individuals is significantly negatively correlated with the progression of blood pressure status.

[0015] According to a specific embodiment of the present invention, the level of valeroylcarnitine in samples from individuals is significantly negatively correlated with the incidence of hypertension.

[0016] According to a more specific embodiment of the invention, for every SD increase in the logarithmic concentration of valerate in a sample from an individual, the individual's risk of progressing from ideal blood pressure to prehypertension or hypertension is reduced by 12.7% (HR = 0.873, 95% CI: 0.772, 0.987), or by 13.1% (HR = 0.869, 95% CI: 0.768, 0.984), or by 13.2% (HR = 0.868, 95% CI: 0.778, 0.970).

[0017] According to a specific embodiment of the present invention, the valeroylcarnitine may be used independently for the application (as a target in screening and / or preparing reagents and / or drugs for preventing and treating blood pressure salt sensitivity, or valeroylcarnitine or a reagent for detecting valeroylcarnitine in samples from an individual for assessing an individual's risk of developing hypertension and / or the progression of blood pressure status), or in combination with other salt sensitivity markers for the application.

[0018] According to a specific embodiment of the present invention, the other salt-sensitive markers include one or more of salt-sensitive gut bacteria and salt-sensitive metabolites.

[0019] According to some specific embodiments of the present invention, the salt-sensitive intestinal bacteria include *Alistipes ihumii*, *Anaerotruncus colihominis*, *Clostridiales bacterium* 42_27, *Clostridiales bacterium* 52_15, *Clostridium sp. CAG:389*, *Clostridium sp. CAG:413*, *Clostridium sp. CAG:780*, *Dialister sp. CAG:357*, *Eubacterium sp. CAG:180*, *Firmicutes bacterium* CAG:124*, *Firmicutes bacterium* CAG:129_59_24*, *Firmicutes bacterium* CAG:137*, *Firmicutes bacterium* CAG:170*, *Firmicutes bacterium* CAG:176*, *Firmicutes bacterium* CAG:24053_14*, and *Firmicutes bacterium*. One or more of the following: CAG:555, Firmicutes bacterium CAG:83, Intestinimonas butyriciproducens, Oscillibacter sp. CAG:241, Oscillibacters sp.ER4, Pseudoflavonifractor capillosus, and Ruminococcus sp. CAG:177.

[0020] According to some specific embodiments of the present invention, the salt-sensitive metabolite includes one or more of methylcysteine, histidine, phenylpyruvic acid, L-carnitine, isovalerylcarnitine, 2-hydroxybutyric acid, α-hydroxyisobutyric acid, and 3-hydroxyisovaleric acid.

[0021] According to some preferred embodiments of the present invention, the other salt-sensitive markers include one or more of 3-hydroxyisovaleric acid and isovaleric carnitine.

[0022] On the other hand, the present invention also provides the use of valerylcarnitine in the preparation of products for improving, preventing and / or treating salt-sensitive hypertension.

[0023] According to some specific embodiments of the present invention, for individuals suffering from or at risk of salt-sensitive hypertension, the dosage of valerate carnitine may be 4-8 μg / kg body weight / day.

[0024] According to some specific embodiments of the present invention, the valeroylcarnitine is an active ingredient for reducing systolic and / or diastolic blood pressure in patients with salt-sensitive hypertension.

[0025] According to some specific embodiments of the present invention, the products for improving, preventing, and / or treating salt-sensitive hypertension are dietary supplements or medicines. More specifically, the products for improving salt-sensitive hypertension are typically dietary supplements, and the products for preventing and / or treating salt-sensitive hypertension are typically medicines.

[0026] According to a specific embodiment of the present invention, this invention identifies key biomarkers based on the MetaSalt dietary salt intervention trial. This trial, conducted in 2019, enrolled 528 participants and included a 23-day intervention period, comprising 3 days of baseline observation, 10 days of low-salt intervention (3 g salt / day), and 10 days of high-salt intervention (18 g salt / day). Based on the response of mean arterial pressure to sodium intervention, the population was divided into salt-resistant, moderately salt-sensitive, and extremely salt-sensitive groups. Further, based on a prospective Chinese cohort population (n=3907) with a median follow-up of 5.5 years, the association between key biomarkers and the prevalence, onset, and progression of hypertension was explored. This invention uses shotgun metagenomic sequencing for fecal gut microbiota detection and ultra-high performance liquid chromatography-tandem mass spectrometry for plasma targeted metabolomics detection. Results showed that 85 (16%) gut microbiota species were identified in the MetaSalt population (P<9.42×10⁻⁶). -5 ) and 71 kinds (31.98%) of metabolites (P<2.25×10) -4 The study investigated the effects of high sodium intake. From these salt-related biomarkers, changes in 22 gut microbiota and 9 metabolites were further identified as significantly correlated with blood pressure salt sensitivity (P<0.05). Correlation analysis suggested that the gut-acylcarnitine axis is a key pathway affecting blood pressure salt sensitivity, with valerylcarnitine, isovalerylcarnitine, and 3-hydroxyisovaleric acid as core metabolites. Further focusing on valerylcarnitine revealed a significant negative correlation between its activity and the risk of hypertension and the progression of hypertension (P<0.05). For example, for every standard deviation increase in the logarithmic concentration of valerylcarnitine, the risk of hypertension decreased by 13.2% (hazard ratio = 0.868, 95% confidence interval: 0.778, 0.970). Further research in this invention found that valerylcarnitine significantly reduced high-salt-induced blood pressure elevation in animal experiments.

[0027] In summary, this invention is the first to explore the effects of a high-sodium diet on the gut microbiota and plasma metabolome of the Chinese population: based on dietary salt intervention trials, metagenomic sequencing and plasma targeted metabolomics technologies were used to discover that a high-sodium diet can significantly affect the levels of many gut microbiota species and plasma metabolites. This invention further characterizes the gut microbiota and plasma metabolome of blood pressure salt sensitivity in the Chinese population: for the first time, the association between blood pressure salt sensitivity and the response of gut microbiota and metabolites to salt was explored, revealing significant differences in the responses of 22 gut microbiota species and 9 metabolites across different blood pressure salt sensitivity groups. This indicates that gut microbiota and metabolomics can partially explain population-specific differences in blood pressure salt sensitivity, providing clues for precise population-based prevention. This invention also constructs a gut microbiota-metabolite regulatory network affecting blood pressure salt sensitivity: through correlation analysis, it was found that the gut-acylcarnitine axis may be a key pathway leading to population-specific differences in blood pressure salt sensitivity. This invention discovers that acylcarnitine holds promise as an intervention target for preventing salt-sensitive hypertension: further focusing on valerylcarnitine, it was found to be significantly associated with the risk of hypertension onset and the progression of blood pressure status, potentially becoming a dietary supplement or intervention target for the prevention and treatment of hypertension. This invention reveals the "black box" of high-sodium diets leading to salt-sensitive hypertension, providing new insights into the biological mechanisms of salt-sensitive hypertension and offering new intervention targets for the prevention and treatment of hypertension. Attached Figure Description

[0028] Figure 1 This section presents the mean arterial pressure (MAP) response trajectory for different blood pressure salt sensitivity groups. The differences between the MAP values ​​on days 2, 8, 9, and 10 of the low-salt phase and the baseline mean MAP were calculated for all subjects, as were the differences between the MAP values ​​on days 2, 8, 9, and 10 of the high-salt phase and the low-salt phase mean MAP (the last three days). The average MAP change values ​​at each time point were calculated for each salt sensitivity group, and then the trajectory was plotted. MAP: Mean Arterial Pressure.

[0029] Figure 2A and Figure 2B Volcano plot for salt-related biomarkers. A linear mixture model was used to compare the differences in the relative abundance of gut microbiota and plasma levels of metabolites between the low-salt and high-pressure phases. Species and metabolites that reached Bonferroni significance were defined as salt-related species and salt-related metabolites.

[0030] Figure 3 This section displays the adjusted mean change in salt-sensitive gut microbiota across different blood pressure salt sensitivity groups. The changes in salt-associated gut microbiota from low to high salt levels were calculated, and a linear mixed model was used to analyze the association between these changes and blood pressure salt sensitivity. The adjusted mean change for each salt sensitivity group was then estimated.

[0031] Figure 4This displays the adjusted mean changes in salt-sensitive metabolites across different blood pressure salt sensitivity groups. Changes in salt-related metabolites were calculated from low to high salt levels, and a linear mixed model was used to analyze the association between these changes and blood pressure salt sensitivity. The adjusted mean changes for each salt sensitivity group were then estimated.

[0032] Figure 5 Displaying a salt-sensitive gut microbiota-metabolite correlation network.

[0033] Figure 6 This study demonstrates the association between valeroylcarnitine and the prevalence, incidence, and progression of hypertension. Logistic regression and Cox proportional hazards models were used to analyze the association between valeroylcarnitine and the prevalence, incidence, and progression of hypertension, estimating the odds ratio (OR) and risk ratio (HR) for each standard deviation increase in the logarithmic concentration of valeroylcarnitine, along with their corresponding confidence intervals (CIs). CI: confidence interval; HR: hazard ratio; OR: odds ratio.

[0034] Figure 7 This shows the results of animal experiments. DBP: Diastolic blood pressure; SBP: Systolic blood pressure. Detailed Implementation

[0035] Before further describing specific embodiments of the present invention, it should be understood that the scope of protection of the present invention is not limited to the specific embodiments described below; it should also be understood that the terminology used in the embodiments of the present invention is for describing specific embodiments and not for limiting the scope of protection of the present invention.

[0036] When numerical ranges are given in the embodiments, it should be understood that, unless otherwise stated in the present invention, both endpoints of each numerical range and any value between the two endpoints may be selected. Unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. In addition to the specific methods, equipment, and reagents used in the embodiments, based on the knowledge of the prior art possessed by one of ordinary skill in the art and the description of this invention, any prior art methods, equipment, and materials similar to or equivalent to those described in the embodiments of this invention may be used to implement this invention.

[0037] Unless otherwise stated, the experimental methods, detection methods and preparation methods disclosed in this invention all adopt conventional techniques in this technical field.

[0038] Unless otherwise expressly defined herein, the technical and scientific terms used herein have the meanings commonly understood by those skilled in the art.

[0039] Example 1. Screening and identification of salt-related biomarkers in gut microbiota and metabolites

[0040] I. Experimental Methods

[0041] 1. Study population and study design

[0042] 1.1 MetaSalt Research

[0043] This invention's study on the effects of dietary salt on gut microbiota and metabolism (MetaSalt) aims to explore the responses of gut microbiota and metabolites to a high-sodium diet and their impact on blood pressure and blood pressure salt sensitivity. In 2019, the study enrolled 528 participants from four rural areas in northern China and conducted a 23-day trial, including 3 days of baseline observation (no sodium intake control), 10 days of low-salt intervention (3 grams of salt or 51.3 mmol of sodium per day), and 10 days of high-salt intervention (18 grams of salt or 307.8 mmol of sodium per day). Ultimately, 512 participants (96.97%) completed the low-salt intervention, and 503 participants (95.27%) completed the entire trial.

[0044] During the baseline phase, a standard questionnaire was used to collect data on general demographic characteristics, medical history, medication history, dietary factors, and lifestyle of all participants. Blood pressure was measured three times on days 1-3 of baseline, and on days 2, 8, 9, and 10 of the low-salt and high-salt phases. The mean blood pressure for each phase was the average blood pressure over the last three days of each phase. Participants with a baseline systolic blood pressure (SBP) ≥140 mmHg and / or a baseline diastolic blood pressure (DBP) ≥90 mmHg were defined as hypertensive patients. Fasting blood samples were collected from participants on day 1 of baseline and day 9 of the low-salt and high-salt intervention phases for biochemical markers and plasma targeted metabolomics analysis. One stool sample was collected from each participant within three days of baseline and within the last two days of each intervention phase for metagenomic analysis. Three urine samples were collected from participants within the last three days of each phase, and 24-hour urinary sodium and potassium levels were measured and estimated.

[0045] This invention refers to the definition of blood pressure salt sensitivity in previous literature to classify salt-sensitive individuals and salt-resistant individuals. First, the difference in mean arterial pressure (MAP) before and after the intervention was calculated for 503 participants who completed the entire trial; that is, MAP during the low-salt phase minus MAP at baseline (ΔMAP). L-B ) and the decrease in MAP during the high-salt phase and the decrease in MAP during the low-salt phase (ΔMAP) H-L Next, the study subjects were divided into three groups according to a certain threshold: extreme salt sensitivity individuals (ΔMAP). L-B ≥-10mmHg or ΔMAPH-L ≥10 mmHg; Moderately salt-sensitive individuals: ΔMAP L-B -5 to -10 mmHg or ΔMAP H-L : 5-10 mmHg; Salt resistant: Remaining study subjects.

[0046] 1.2 Forward-looking cohort

[0047] This invention explores the association between key metabolites and the prevalence, incidence, and progression of hypertension in an independent prospective cohort population. The study population consisted of participants from the International Collaborative Study of Cardiovascular Disease in Asia (InterASIA) cohort and the China Multi-Center Collaborative Study of Cardiovascular Epidemiology 1998 (China MUCA 1998) cohort. The China MUCA 1998 cohort, conducted in 1998, included 11,480 participants; the InterASIA cohort, conducted from 2000 to 2001, included 15,540 participants. Both cohorts underwent their first follow-up in 2007–2008 and their second follow-up in 2012–2015. Blood pressure and antihypertensive medication use were collected at baseline and at each follow-up stage. Hypertensive patients were defined as those with SBP ≥ 140 mmHg and / or DBP ≥ 90 mmHg and / or those who had been taking antihypertensive medication in the past two weeks. In individuals not taking antihypertensive medication, ideal blood pressure was defined as SBP <120 mmHg and DBP <80 mmHg; prehypertension was defined as SBP between 120 and 139 mmHg and / or DBP between 80 and 89 mmHg; and normal blood pressure was defined as both ideal and prehypertension. Progression in blood pressure status was defined as an increase of at least one grade in the individual's blood pressure status during the follow-up period compared to the previous stage (e.g., from ideal blood pressure to prehypertension, from stage 1 hypertension to stage 2 hypertension, etc.).

[0048] This invention uses a survey conducted in 2007–2008 as a baseline, randomly selecting 3907 participants with blood samples for plasma targeted metabolomics analysis to explore the association between metabolites and the prevalence of hypertension, as well as the onset and progression of hypertension during the follow-up period from 2012 to 2015. First, the association between metabolites and the prevalence of hypertension was analyzed in all participants. Then, participants with missing blood pressure information during the 2012–2015 follow-up period (n=1181) were removed, leaving 2726 participants for analysis of the association between metabolites and the progression of hypertension. Finally, participants with hypertension during the 2007–2008 survey period (n=1404) were removed, leaving 1322 participants for analysis of the association between metabolites and prehypertension or the onset of hypertension.

[0049] 2. Metagenomic Detection

[0050] This invention utilizes shotgun metagenomic sequencing technology to detect gut microbiota in fecal samples from three phases of the MetaSalt study population. First, DNA was extracted from the fecal samples using a Tiangen Magnetic Bead Kit (Tiangen Biotech Co., Ltd., Beijing). Then, the purity and integrity of the DNA were analyzed using 1% agarose gel electrophoresis. Finally, [the process was repeated in the original text]. The Double-Stranded DNA Quantitative Detection Kit (Life Technologies, CA, USA) accurately quantifies DNA concentration. 1 μg of genomic DNA is used for each sample. Ultra TMDNA library preparation kit (NEB, USA) was used to construct sequence libraries. After library construction, the effective concentration of the library was accurately quantified using real-time quantitative PCR, and then the indexed samples were clustered using the cBot clustering generation system. After clustering, sequencing was performed on the Illumina HiSeq platform to generate paired end reads. The raw data obtained from the Illumina HiSeq platform was quality controlled to generate valid data for subsequent analysis. The specific steps included: (1) removing reads containing low-quality bases; (2) removing reads with N-terminal bases reaching 10 bp; (3) removing reads that overlapped with adapter sequences by more than 15 bp. Next, Bowtie2 software (version 2.2.4) was used for genome alignment to exclude host DNA. Metagenome assembly was performed using SOAPdenova software (version 2.04). Open reading frames were predicted using MetaGeneMark software (version 2.10). Redundancy was removed and a unique initial gene catalog was obtained using CD-HIT software (version 4.5.8). Bowtie2 was used to map the valid data of each sample to an initial gene catalog, obtaining the absolute abundance of the mapped genes (unigenes) in each sample. Microbial sequences were extracted from the MicroNR database (January 2, 2018) and compared with the sample microbial unigenes using DIAMOND software (version 0.9.9). The alignment results were applied to MEGAN software for systematic classification. Finally, a table of gene quantity and absolute abundance information for each sample at each taxonomic level (kingdom, phylum, class, order, family, genus, species) was obtained. A relative abundance table was calculated based on the absolute abundance.

[0051] Ultimately, a total of 14,533 gut microbiota species were detected in fecal samples from the MetaSalt study population. This invention retains 531 species with a minimum relative abundance of 0.01% in at least 10% of the samples for subsequent analysis. For observations with a relative abundance of 0, a minimum value (1 × 10⁻⁶) was used. -11 The data was then filled in. Subsequently, a rank-based inverse normal transformation was performed on the relative abundance of all bacterial species.

[0052] 3. Plasma targeted metabolomics detection

[0053] Plasma samples from three phases of the MetaSalt population and baseline plasma samples from a prospective cohort were subjected to targeted metabolomics analysis. The assays used were the Q300 kit and the trimethylamine N-oxide (TMAO) kit, both provided by MetaBio (Shanghai). The Q300 kit can detect approximately 300 metabolites, encompassing 12 major metabolite classes. The TMAO kit can detect seven TMAO-related metabolites: TMAO, trimethylamine, choline, L-carnitine, betaine, creatinine, and imidazole propionic acid. The two kits together detect 13 major metabolite classes. Plasma samples were thawed in an ice bath and prepared by adding internal standards and stock solutions. Metabolite determination was performed using ultra-performance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS / MS) (ACQUITY UPLCXevo TQ-S, Waters Corp., Milford, MA, 570 USA). For the Q300 kit, an ACQUITY UPLC BEH C18 (1.7 μM, 2.1 × 100 mm) column was used; for the TMAO kit, an ACQUITY UPLC HILIC (1.7 μM, 2.1 × 100 mm) column was used. During the assay, an internal standard was used to monitor analytical biases in sample processing and analysis. Mixed biological samples were used as quality control samples, inserted into the injection sequence at intervals of 10 samples to objectively evaluate intra-batch repeatability and correct for inter-batch errors. Raw data generated by UPLC-MS / MS were imported into the analytical system for peak integration, calibration, and quantification. A linear relationship between the analytical signal and concentration was constructed using standards of different concentrations. The concentration of the analyte metabolite was calculated by substituting its analytical signal into the formula.

[0054] The MetaSalt cohort and prospective cohort populations detected 294 and 288 metabolites, respectively. After removing metabolites with a detection rate below 80%, 222 and 214 metabolites remained in the two populations, respectively. The K-nearest neighbor algorithm was used to select the five observations with the closest Euclidean distance to the missing observations, and their mean was used to fill in the missing values ​​of the metabolites. A rank-based inverse normal transformation was performed on the metabolites in the MetaSalt cohort population, and a logarithmic transformation and standardization were performed on the metabolites in the prospective cohort population.

[0055] 4. Validation of drug administration in rats

[0056] An in vivo intervention experiment was conducted using 8-week-old SD rats (n=6) to verify the effects of key metabolites. After weighing, anesthesia was induced with 4.5% isoflurane, followed by endotracheal intubation. The isoflurane concentration was adjusted to 1.5% to maintain anesthesia. Warming pads were used to maintain the rats' body temperature throughout the experiment. After immobilization, cannulas were inserted into the right femoral artery and left femoral vein. The arterial cannula was connected to a BL-420S biofunctional experimental system for continuous real-time blood pressure monitoring, while the intravenous cannula was connected to a drug delivery system. Blood pressure was monitored for 15 minutes until it reached a stable state. Physiological saline (0.5 ml) was administered intravenously, and blood pressure changes were monitored. After blood pressure stabilized, valerylcarnitine (3.3 μg / kg) was administered intravenously, and blood pressure changes and the recovery process were observed. Monitoring was conducted for at least 20 minutes after drug administration. The animal housing environment was specific pathogen-free, with light from 8:00 to 20:00 and darkness from 20:00 to 8:00. Animals were allowed free access to sufficient food and water. The animal experimental protocol was approved by the Laboratory Animal Management and Use Committee of Fuwai Hospital, Chinese Academy of Medical Sciences.

[0057] 5. Statistical Analysis Methods

[0058] A linear mixed model was used to compare differences in gut microbiota and metabolites between low-salt and high-salt phases in the MetaSalt population to identify salt-related biomarkers. The model adjusted for individual and family random effects. Because a self-controlled pre- and post-controlled design was used, comparisons were made across different interventions for individuals, eliminating the need for additional adjustment of other covariates. Gut microbiota and metabolites with Bonferroni significance (P < 9.42 × 10⁻⁶) were defined as salt-related microbiota. -5 =0.05 / 531) and salt-related metabolites (P<2.25×10) -4 =0.05 / 222).

[0059] The association between gut microbiota or metabolite responses and blood pressure salt sensitivity was analyzed. First, the changes in microbiota and metabolites from the low-salt stage to the high-salt stage were calculated, i.e., the marker levels in the high-salt stage decreased the marker levels in the low-salt stage. Next, blood pressure salt sensitivity was used as an ordinal variable, and a linear mixed model was used to analyze the association between blood pressure salt sensitivity and marker changes. The model was adjusted for random effects of age, sex, body mass index (BMI), study location, total cholesterol, hypertension, smoking, and family. Gut microbiota and metabolites were defined as salt-sensitive microbiota or salt-sensitive metabolites if they met the following conditions: (1) the association between marker changes and blood pressure salt sensitivity was P < 0.05; (2) the association between marker changes and blood pressure salt sensitivity was in the same direction as the effect of high salt on the marker. Furthermore, blood pressure salt sensitivity was used as a categorical variable for trend testing, and the least squares estimation method was used to obtain the adjusted mean changes in salt sensitivity markers in salt-resistant, moderately salt-sensitive, and extremely salt-sensitive individuals. Pearson partial correlation analysis was performed on salt-sensitive bacterial species and metabolites, adjusting for age, sex, BMI, study location, total cholesterol, hypertension, and smoking. Associations with P < 0.05 were retained, and a correlation network was constructed using Cytoscape (version 3.8.2) to identify the gut microbiota-metabolite regulatory network associated with blood pressure salt sensitivity.

[0060] The association between salt-sensitive metabolites and hypertension and the progression of hypertension was analyzed in a prospective cohort population. Logistic regression was used to analyze the association between salt-sensitive metabolites and the prevalence of hypertension, while Cox proportional hazards models were used to analyze the association between salt-sensitive metabolites and the progression of hypertension and the onset of hypertension. The odds ratio (OR) and hazard ratio (HR) for each standard deviation (SD) increase in the logarithmic value of the metabolite concentration, along with their corresponding 95% confidence intervals (CI), were estimated. All models were adjusted for age, sex, BMI, work-related physical activity, urban / rural location, north / south orientation, smoking, alcohol consumption, education level, dietary score, dyslipidemia, diabetes, and L-carnitine levels.

[0061] Paired t-tests were used to compare the differences in blood pressure before and after intervention in rats.

[0062] II. Experimental Results

[0063] 1. General characteristics of MetaSalt users

[0064] Among the 503 participants who completed the entire trial, the mean age was 48.11 ± 9.26 years, and the mean BMI was 26.30 ± 3.52 kg / m². 2Of the participants, 317 (63.02%) were women. At baseline, the mean SBP and DBP of these participants were 129.32 ± 13.64 mmHg and 80.23 ± 9.75 mmHg, respectively, and 139 (27.63%) had hypertension. The mean 24-hour urinary sodium and potassium levels at baseline were 204.70 ± 50.24 mmol and 37.01 ± 9.02 mmol, respectively. Based on the MAP response to low- or high-salt interventions, the 503 participants were divided into three groups: 247 salt-resistant (49.10%), 168 moderately salt-sensitive (33.40%), and 88 extremely salt-sensitive (17.50%). The mean MAP response of the three groups showed three different trajectories throughout the study period. Figure 1 ).

[0065] 2. Salt-related markers

[0066] This invention found that, compared with the low-salt phase, the relative abundance of 85 (16.00%) gut microbiota species changed significantly after high-salt intervention (P<9.42×10⁻⁶). -5 These bacteria were defined as salt-associated species. Among them, the relative abundance of 79 species significantly decreased after high-salt intervention, while only 6 showed a significant increase. For example, compared to the low-salt stage, the standardized relative abundance of *Anaerotruncus colihominis* decreased by 0.13 after high-salt intervention (95% CI: 0.07, 0.20, P = 6.48 × 10⁻⁶). -5 ()( Figure 2A (See Table 1). These bacterial species mainly come from five major phyla, of which 60 species belong to Firmicutes (Table 1).

[0067] Table 1. Salt-associated gut microbiota

[0068]

[0069]

[0070]

[0071] A linear mixed model was used to compare the differences in relative abundance of gut microbiota during the low-salt and high-salt phases, retaining P < 9.42 × 10⁻⁶. -5 Bacterial species with a value of (0.05 / 531) are defined as salt-associated species. CI: confidence interval.

[0072] Compared with the low-salt phase, the plasma levels of 71 metabolites (31.98%) changed significantly after the high-salt intervention (P<2.25×10⁻⁶). -4These metabolites were defined as salt-related metabolites. Of these, 66 metabolites significantly decreased after high-salt intervention, while only 5 metabolites significantly increased (serine, methylcysteine, arginine, glutamine, and maltotriose). For example, compared to the low-salt phase, the standardized concentration of valerate carnitine decreased by 0.20 in the high-salt phase (95% CI: 0.12, 0.28, P = 9.10 × 10⁻⁶). -7 The standardized concentration of methylcysteine ​​increased by 0.82 (95% CI: 0.74, 0.90, P = 3.71 × 10⁻⁶). -68 ()( Figure 2B (See Table 2). These salt-related metabolites come from 13 major categories, including 17 amino acids, 15 fatty acids, 8 carbohydrates, 7 organic acids, 7 carnitines, and others such as benzene ring compounds, indoles, and bile acids (Table 2).

[0073] Table 2. Salt-related metabolites

[0074]

[0075]

[0076]

[0077] A linear mixed model was used to compare the differences in plasma metabolite levels between the low-salt and high-salt phases, retaining P < 2.25 × 10⁻⁶. -4 Metabolites with a value of (0.05 / 222) are defined as salt-related metabolites. CI: Confidence interval.

[0078] 3. Salt-sensitive markers

[0079] This invention further analyzes the correlation between changes in metabolites from low-salt to high-salt phases and blood pressure salt sensitivity. From 85 salt-associated bacterial species, 22 salt-sensitive species were identified. The changes in these species were all significantly negatively correlated with blood pressure salt sensitivity (P<0.05), of which 21 belong to Firmicutes (…). Figure 3 (and Table 3). For example, the mean changes in Anaerotruncus colihominis in the salt-resistant, moderately salt-sensitive, and extremely salt-sensitive groups were -0.03 (95% CI: -0.15, 0.09), -0.15 (95% CI: -0.27, -0.03), and -0.23 (95% CI: -0.40, 0.06), respectively (P < 0.09). 趋势 =0.0322).

[0080] Table 3. Salt-sensitive gut microbiota

[0081]

[0082]

[0083] A linear mixed model was used to analyze the association between changes in gut microbiota and blood pressure salt sensitivity. Bacteria with P < 0.05 and an association direction consistent with the direction of the effect of high salt were defined as salt-sensitive bacteria. CI: confidence interval.

[0084] Of 71 salt-related metabolites, this invention identified 9 metabolites whose changes were significantly correlated with blood pressure salt sensitivity (P<0.05), including methylcysteine, 2-hydroxybutyric acid, carnitine, phenylpyruvic acid, isovalerylcarnitine, 3-hydroxyisovaleric acid, valerylcarnitine, histidine, and α-hydroxyisobutyric acid. Figure 4 (See Table 4). Among these, only changes in methylcysteine ​​were positively correlated with blood pressure and salt sensitivity. For example, the mean differences in valerate carnitine between the salt-resistant group, moderately salt-sensitive group, and extremely salt-sensitive group were -0.14 (95% CI: -0.28, 0.01), -0.34 (95% CI: -0.49, -0.19), and -0.38 (95% CI: -0.59, -0.17), respectively (P < 0.01). 趋势 =0.0236).

[0085] Table 4. Salt-sensitive metabolites

[0086]

[0087]

[0088] A linear mixed model was used to analyze the association between changes in metabolites and blood pressure salt sensitivity. Metabolites with P < 0.05 and an association direction consistent with the direction of the effect of high salt on blood pressure were defined as salt-sensitive metabolites. CI: confidence interval.

[0089] Correlation analysis was performed on 22 salt-sensitive bacterial species and 9 salt-sensitive metabolites, revealing 52 pairs of relationships with P < 0.05, including 5 metabolites and 20 bacterial species (Table 5). By constructing a correlation network, this invention identified a gut-acylcarnitine axis (…). Figure 5 Among them, valerylcarnitine, isovalerylcarnitine, and 3-hydroxyisovaleric acid (acylcarnitine-related precursor metabolites) play a central role, and changes in these three metabolites are significantly correlated with changes in at least 10 bacterial species. Many of these species are known short-chain fatty acid producers, such as *Pseudoflavonifractor capillosus*, *Intestinimonasbutyriciproducens*, and *Anaerotruncus colihominis*.

[0090] Table 5. Correlation between salt-sensitive bacterial species and salt-sensitive metabolites

[0091]

[0092]

[0093] Pearson partial correlation analysis was used to calculate the correlation between salt-sensitive bacterial species and salt-sensitive metabolites.

[0094] 4. The association between acylcarnitine and the onset and progression of hypertension.

[0095] This invention further investigates the association between valerylcarnitine and the prevalence, onset, and progression of hypertension in a prospective cohort population (n=3907). The baseline (2007–2008) mean age of this population was 57.01 ± 8.80 years, including 2036 women (52.11%), with mean SBP and DBP of 136.46 ± 21.07 mmHg and 82.91 ± 11.84 mmHg, respectively.

[0096] This invention observed no significant association between valeroylcarnitine and the risk of hypertension. After a median follow-up of 5.5 years, blood pressure was measured in 2726 participants, of whom 733 experienced progression of hypertension. Valeroylcarnitine (HR = 0.923, 95% CI: 0.857, 0.993) was significantly negatively correlated with progression of hypertension. Among 1322 participants without baseline hypertension, 286 progressed from ideal blood pressure to prehypertension or hypertension, 269 progressed from prehypertension to hypertension, and 345 progressed from normal blood pressure to hypertension. For every standard deviation (SD) increase in the logarithmic concentration of valerate, the risk of progression from ideal blood pressure to prehypertension or hypertension decreased by 12.7% (HR = 0.873, 95% CI: 0.772, 0.987), the risk of progression from prehypertension to hypertension decreased by 13.1% (HR = 0.869, 95% CI: 0.768, 0.984), and the risk of progression from normal blood pressure to hypertension decreased by 13.2% (HR = 0.868, 95% CI: 0.778, 0.970). Figure 6 ).

[0097] 5. Animal experiment verification results

[0098] There were no significant changes in SBP and DBP in rats before and after injection of saline (P>0.05). After injection of valerate, SBP decreased by an average of 10.80 mmHg (P=0.0038), and DBP decreased by an average of 9.13 mmHg (P=0.0149). Figure 7 ).

[0099] Based on the above animal experiments, this invention hypothesizes that administering 432 μg of valerate daily (144 μg three times a day) to individuals with a high-salt diet and blood pressure greater than 120 / 80 mmHg could achieve a similar significant reduction in SBP and DBP.

[0100] The above embodiments are only used to illustrate the present invention and are not intended to limit the present invention. Those skilled in the art can make various changes and modifications without departing from the essence and scope of the present invention. Therefore, all equivalent technical solutions also fall within the protection scope of the present invention.

Claims

1. The use of acylcarnitine from individual samples as a target in screening and / or preparing reagents and / or drugs for the prevention and treatment of salt-sensitive hypertension; preferably, the acylcarnitine includes valerate carnitine.

2. The use of acylcarnitine or reagents for detecting acylcarnitine in samples from an individual in the preparation of products for assessing an individual's risk of developing hypertension and / or the progression of their blood pressure status; preferably, the acylcarnitine includes valerate carnitine.

3. The application according to claim 1 or 2, wherein, The sample is blood or plasma.

4. The application according to claim 1 or 2, wherein, The individuals in question are of East Asian descent, preferably Chinese.

5. The application according to claim 1 or 2, wherein: Levels of valerylcarnitine in samples from individuals were significantly negatively correlated with progression of blood pressure status; and / or Levels of valerocarnitine in samples from individuals were significantly negatively correlated with the risk of developing hypertension. Preferably, for every standard deviation increase in the logarithmic concentration of valerate in an individual's sample, the individual's risk of progressing from ideal blood pressure to prehypertension or hypertension is reduced by 12.7% (HR = 0.873, 95% CI: 0.772, 0.987), or by 13.1% (HR = 0.869, 95% CI: 0.768, 0.984), or by 13.2% (HR = 0.868, 95% CI: 0.778, 0.970).

6. The application according to any one of claims 1-5, wherein, The valeroylcarnitine is used independently or in combination with other salt-sensitive markers in the application; Preferably, the other salt-sensitive markers include one or more of salt-sensitive gut microbiota and salt-sensitive metabolites; More preferably, the salt-sensitive intestinal bacteria include Alistipes ihumii, Anaerotruncuscolihominis, Clostridiales bacterium 42_27, Clostridiales bacterium52_15, Clostridium sp.CAG:389, Clostridiumsp.CAG:413, Clostridium sp.CAG:780, Dialistersp.CAG:357, Eubacteriumsp.CAG:180, Firmicutes bacterium CAG:124, Firmicutes bacterium CAG:129_59_24, Firmicutes bacterium CAG:137, Firmicutes bacteriumCAG:170, Firmicutes bacterium CAG:176, Firmicutes bacterium CAG:24053_14, Firmicutes bacterium One or more of the following: CAG:555, Firmicutes bacterium CAG:83, Intestinimonasbutyriciproducens, Oscillibacter sp.CAG:241, Oscillibacter sp.ER4, Pseudoflavonifractor capillosus, and Ruminococcus sp.CAG:177; More preferably, the salt-sensitive metabolite includes one or more of methylcysteine, histidine, phenylpyruvic acid, L-carnitine, isovalerylcarnitine, 2-hydroxybutyric acid, α-hydroxyisobutyric acid, and 3-hydroxyisovaleric acid; More preferably, the other salt-sensitive markers include one or more of 3-hydroxyisovaleric acid and isovaleric carnitine.

7. Application of valerylcarnitine in the preparation of products for improving, preventing and / or treating salt-sensitive hypertension.

8. The application according to claim 7, wherein, The dosage of valerate carnitine is 4-8 μg / kg body weight / day.

9. The application according to claim 7, wherein, The valeroylcarnitine is the active ingredient used to reduce systolic and / or diastolic blood pressure in patients with salt-sensitive hypertension.

10. The application according to any one of claims 7-9, wherein, The products mentioned for improving, preventing, and / or treating salt-sensitive hypertension are dietary supplements or medications.

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