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Title:
THE SALIVARY MICROBIOME IN CARDIOVASCULAR DISEASE
Document Type and Number:
WIPO Patent Application WO/2023/219522
Kind Code:
A1
Abstract:
A method of treating a cardiovascular associated pathologic condition or disease in a subject in need thereof is provided. The method includes administering an effective amount of an active pharmaceutical ingredient approved for such treatment, wherein the subject has a microbiome dysbiosis that includes one or more of Clostridiaceae, Capnocytophaga, Lactobacillus, Rothia, Desulfobulbus, Prevotella, or Tissierellaceae.

Inventors:
AL KHODOR SOUHAILA (QA)
MURUGESAN SELVASANKAR (QA)
EL ANBARI MOHAMMED (QA)
TERRANEGRA ANNALISA (QA)
Application Number:
PCT/QA2023/050005
Publication Date:
November 16, 2023
Filing Date:
May 06, 2023
Export Citation:
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Assignee:
QATAR FOUND EDUCATION SCIENCE & COMMUNITY DEV (QA)
SIDRA MEDICAL AND RES CENTER (QA)
International Classes:
C12Q1/04; A61K35/741; C12Q1/6851; C12Q1/689; A61K45/06; A61P9/00
Domestic Patent References:
WO2013032538A12013-03-07
Foreign References:
US20180030516A12018-02-01
Other References:
MURUGESAN SELVASANKAR, ELANBARI MOHAMMED, BANGARUSAMY DHINOTH KUMAR, TERRANEGRA ANNALISA, AL KHODOR SOUHAILA: "Can the Salivary Microbiome Predict Cardiovascular Diseases? Lessons Learned From the Qatari Population", FRONTIERS IN MICROBIOLOGY, FRONTIERS MEDIA, LAUSANNE, vol. 12, Lausanne , XP093112284, ISSN: 1664-302X, DOI: 10.3389/fmicb.2021.772736
Attorney, Agent or Firm:
DEVSHI, Usha (QA)
Download PDF:
Claims:
CLAIMS

We claim:

1. A method of treating a cardiovascular associated pathologic condition or disease in a subject in need thereof, comprising: administering an effective amount of an active pharmaceutical ingredient approved for such treatment, wherein the subject comprises a microbiome dysbiosis that includes one or more of Clostridiaceae, Capnocytophaga, Lactobacillus, Rothia, Desulfobulbus, Prevotella, or Tissierellaceae.

2. The method of claim 1, wherein the microbiome dysbiosis is a salivary microbiome dysbiosis or a gut microbiome dysbiosis, or both.

3. The method of claim 1, wherein the subject comprises a microbiome dysbiosis that includes one or more of Desulfobulbus, Prevotella, or Tissierellaceae.

4. The method of claim 1, wherein the microbiome dysbiosis is determined by a method comprising analysis of a salivary sample of the subject.

5. The method of claim 4, wherein the analysis comprises gene sequencing.

6. The method of claim 1, wherein: a) the microbiome dysbiosis of Clostridiaceae is a decrease in the abundance of Clostridiaceae; b) the microbiome dysbiosis of Capnocytophaga is a decrease in the abundance of Capnocytophaga; c) the microbiome dysbiosis of Lactobacillus is an increase in the abundance of Lactobacillus; d) the microbiome dysbiosis of Rothia is an increase in the abundance of Rothia; e) the microbiome dysbiosis of Desulfobulbus is an increase in the abundance of Desulfobulbus; f) the microbiome dysbiosis of Prevotella is an increase in the abundance of Prevotella; g) the microbiome dysbiosis of Tissierellaceae is an increase in the abundance of Tissierellaceae; or h) a combination of a)-g).

7. The method of claim 1, wherein the microbiome dysbiosis comprises an about 4-6 log difference in microbiome abundance between Clostridiaceae, Capnocytophaga, or both, and Lactobacillus, Rothia, or both.

8. The method of claim 1, wherein the microbiome dysbiosis comprises 1) a microbiome relative abundance of about 4.3:43.6:43.5 of Proteobacteria:Firmicutes:Bacteriodetes; 2) an about 4-6 log difference in abundance between a) and b), wherein a) is Clostridiaceae, Capnocytophaga, or both, and b) is Lactobacillus, Rothia, or both; 3) a microbiome relative abundance of about 8.3:30.7 of Rothia : Prevotella, or 4) a combination of 1), 2), and 3).

9. A method of attenuating the risk of a cardiovascular associated pathologic condition or disease in a subject in need thereof, comprising administering a microbiota or promicrobiota to the subject, wherein: prior to administration of the microbiota or promicrobiota the subject comprises 1) a microbiome relative abundance of about 4.3:43.6:43.5 of Proteobacteria:Firmicutes:Bacteriodetes, 2) an about 4-6 log difference in abundance between a) and b), wherein a) is Clostridiaceae, Capnocytophaga, or both, and b) is Lactobacillus, Rothia, or both, 3) a microbiome relative abundance of about 8.3:30.7 of Rothia :Prevotella, or 4) a combination of 1), 2), and 3); and during, or in the 1-30 days subsequent to, administration of the microbiota or promicrobiota the subject comprises 1) a microbiome relative abundance of about 5.1- 5.8:39.7-40.8:47.4 to about 4.3:43.6:43.5 of Proteobacteria: Firmicutes:Bacteriodetes, or

2) a microbiome relative abundance of about 6.7-7.5:32.7-34.2 of Rothia : Prevotella, or

3) a combination of 1) and 2).

10. The method of claim 9, wherein the microbiome relative abundance is determined by a method comprising analysis of a salivary sample of the subject.

11. The method of claim 10, wherein the analysis comprises gene sequencing.

12. The method of claim 9, wherein the microbiome is a gut microbiome or a salivary microbiome.

13. A method of treating cardiovascular disease in a subject in need thereof, comprising administering a therapeutically effective amount of an approved cardiovascular disease medicament to the subject, wherein a salivary sample of the subject comprises an amount of one or more of Lactobacillus, Rothia, Desulfobulbus, Prevotella, or Tissierellaceae that is greater than the amount found in a subject that is substantially free of cardiovascular disease.

14. The method of claim 13, wherein the salivary sample of the subject comprises amounts of two or more of Lactobacillus, Rothia, Desulfobulbus, Prevotella, or Tissierellaceae that are greater than the amounts found in a subject that is substantially free of cardiovascular disease.

15. The method of claim 13, wherein the salivary sample of the subject comprises amounts of three or more of Lactobacillus, Rothia, Desulfobulbus, Prevotella, or Tissierellaceae that are greater than the amounts found in a subject that is substantially free of cardiovascular disease.

16. The method of claim 13, wherein the salivary sample of the subject comprises amounts of four or more of Lactobacillus, Rothia, Desulfobulbus, Prevotella, or Tissierellaceae that are greater than the amounts found in a subject that is substantially free of cardiovascular disease.

17. The method of claim 13, wherein the salivary sample of the subject comprises amounts of all five of Lactobacillus, Rothia, Desulfobulbus, Prevotella, or Tissierellaceae that are greater than the amounts found in a subject that is substantially free of cardiovascular disease.

18. The method of claim 13, wherein the presence or absence of Lactobacillus, Rothia, Desulfobulbus, Prevotella, or Tissierellaceae in the salivary sample of the subject is determined by screening a 16S-rDNA library.

19. The method of claim 13, wherein the approved cardiovascular disease medicament comprises a statin, a beta-blocker, an Angiotensin Converting Enzyme (ACE) inhibitor, a calcium channel blocker, a diuretic, an antiplatelet agent, nitroglycerin, digitalis or a combination thereof.

20. A method of diagnosing an oral or systemic disease, including a cardiovascular associated pathologic condition or disease, in a subject, comprising: collecting a saliva sample from the subject and determining a most significantly abundant salivary microbiome genera in the saliva sample from the subject, wherein the most significantly abundant salivary microbiome genera is determined by a process including gene sequencing, optionally, including determining whether at least one of Clostridiaceae or Capnocytophaga genera is the most significantly abundant salivary microbiome genera in the subject, optionally, including determining the relative abundance of at least one of Desulfobulbus, Prevotella, or Tissierellaceae genera in the salivary microbiome in the subject as compared to the abundance of the same genera in the general population in which the subject is a member.

21. A kit, comprising a salivary collection vessel, for use in the method of one of claims 1-20.

Description:
THE SALIVARY MICROBIOME IN CARDIOVASCULAR DISEASE

RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional patent application 63/339,698 filed May 9, 2022, the entire contents of which are incorporated by reference herein.

FIELD

[0002] The present disclosure relates to determining the risk of cardiovascular disease in a subject.

BACKGROUND

[0003] Non-communicable diseases (NCD) are a global threat to the health sector, and according to the World health organization, the burden is around 82%. Cardiovascular diseases (CVD) are the leading cause of death, accounting for 17.9 million per year worldwide. In the Qatari population, CVD is the number one cause of death among NCD. The most common NCDs are cardiovascular diseases (CVD), cancer, respiratory disorders, and diabetes. CVD comprises coronary heart disease, heart failure, stroke, rheumatic heart disease, and cardiomyopathies among others. In Qatar, NCDs are the leading cause of death for the past 10 years with the CVD mortality rates reaching 8.3 per 100000 Qatari males. In addition, the 2006-World-Health-Survey revealed that the Qatari population suffers from various predisposing factors to CVD such as obesity (28.8%), high cholesterol (24.7%), diabetes (16.7%), and hypertension (14.4%). Since the last decade, multiomics technologies have emerged to discover biomarkers to diagnose diseases. However, currently available blood-based biomarkers are invasive, and there is a need for a simple non-invasive tool to develop disease biomarkers. Many studies have linked dysbiosis of the gut microbiome to the development of cardiovascular diseases (CVD). In addition, machine learning (ML), a vital branch of artificial intelligence, has enabled the development of several predictive biomarkers for diseases, including arthritis, diabetes, and bowel disease using blood parameters, though very few studies using gut microbiome profiles. In addition, the advance in Next-Generation Sequencing platforms (NGS) has enabled assessment of the human microbiome with an unprecedented resolution and depth.

[0004] Commercially available products, many of which involve invasive tests that use blood, include the following:

Access BNP Assay (Beckmann Coulter CVD diagnostic solutions): Access BNP (B-type natriuretic peptide) is used to diagnose heart failure and left ventricular dysfunction;

HsCRP (Roche diagnostics): High sensitive C-reactive protein to predict CVD. Roche Cardiac Troponin T sensitive test (Roche diagnostics): To aid in the rapid diagnosis of myocardial infarction;

Myeloperoxidase (Siemens Healthineers): It provides prognostic information in addition to troponin and hsCRP testing; and

Electrocardiograms, echocardiograms, nuclear stress tests, carotid ultrasound and Holter monitor to monitor the cardiac activity.

[0005] However, studies assessing the association between the salivary microbiome (SM) and CVD risk on a large cohort remain sparse.

SUMMARY

[0006] It has been discovered that saliva, which is easy to collect and rich in microbial diversity, can serve as a provider of biomarkers to diagnose CVD early. The present disclosure identifies the predictive salivary microbiome biomarkers linked to Qataris with the risk of developing CVD. This present disclosure identifies whether a predictive salivary microbiome signature is associated with a high risk of developing CVD in the Qatari population, and by extension, humans generally. Saliva samples from 2974 Qatar Genome Project (QGP) participants were collected and were classified into low-risk (LR<10) (n=2491), moderate-risk (MR=10-20) (n=320) and high-risk (HR>30) (n = 163). The assessment of SM composition followed by a Machine Learning (ML) approach revealed that Desulfobulbus, Prevotella, and Tissierellaceae were the common predictors of increased risk to CVD. The inventors believe that this disclosure is the first to apply ML- based prediction modeling using the SM to predict CVD in an Arab population. Saliva samples from 2974 Qatar Genome Project (QGP) participants were collected from Qatar Biobank (QBB). Based on the CVD score, subjects were classified into low-risk (LR<10) (n=2491), moderate-risk (MR=10-20) (n=320) and high-risk (HR>30) (n = 163). To assess the salivary microbiome (SM) composition, 16S-rDNA libraries were sequenced and analyzed using QIIME-pipeline. Machine Learning (ML) strategies were used to identify SM-based predictors of CVD risk. The present disclosure predicts CVD in the Arab population using SM signatures. The present disclosure provides specific SM signatures, including Desulfobulbus, Prevotella, and Tissierellaceae to predict the risk of CVD- associated pathologic condition or disease in a human using ML based approach. It was surprisingly found that the salivary Desulfobulbus, Prevotella, and Tissierellaceae abundances were increased with CVD risk score in Qatari population. Further, the abundance of members of the Desulfobulbus, Prevotella, and Tissierellaceae is associated with an increase in CVD risk score at least 80% of the time among the 50-random splits of the data and the four feature selection techniques (Least Absolute Shrinkage and Selection Operator (Lasso), Smoothly Clipped Absolute Deviation Penalty (Zou and Li, 2008) (Scad), Elastic Net (Zou, 2005) (Enet), and Minimax concave penalty (Zhang, 2010) (Mcp)) using binary and arcsin transformations. This can predict the early diagnosis of CVD and associated co-morbidities.

[0007] It is expected that a test assessing the presence of one or all these bacteria in the saliva of human subjects can indicate their predisposition to CVD. Moreover, a targeted therapy towards these microbes can shape the SM community, which contributes to the attenuation of CVD and other risks, including hypertension and hypercholesterolemia. Thus, it can reduce risk of, prevention of and/or treatment of CVD and associated comorbidities.

[0008] Firmicutes and Bacteroidetes were the predominant phyla among all the subjects included. LefSe analysis revealed that Clostridiaceae and Capnocytophaga were the most significantly abundant genera in the LR group, while Lactobacillus and Rothia were significantly abundant in the HR group. ML based prediction models revealed that Desulfobulbus, Prevotella, and Tissierellaceae were the common predictors of increased risk to CVD.

[0009] The present disclosure identified significant differences in the SM composition in HR and LR CVD subjects. The human microbiome (HM) comprises trillions of bacteria, viruses, protozoa, and fungi that reside in and on our body surfaces. The HM is complex, dynamic, ubiquitous, and shows striking variability from one individual to another and between various body sites. The HM has a wide array of roles ranging from digestion, protection from pathogens, immune-regulation, and metabolites production. The oral cavity harbors more than 700 diverse microorganisms and is considered the second most diverse site after the gut. In healthy subjects, the core salivary microbiome (SM) includes genera Streptococcus, Veillonella, Neisseria, and Actinomyces. In a large-scale populationbased Japanese study, it was shown that the SM is dominated by Streptococcus, Neisseria, Rothia, Prevotella, Actinomyces, Granulicatella, Haemophilus, and Porphyromonas. Previous studies aiming to characterize the salivary microbiome composition in the Qatari population showed that Bacteroidetes, Firmicutes, Actinobacteria, and Proteobacteria were the common phyla, with Bacteroidetes being the most predominant. Dysbiosis in the SM is associated with oral diseases and systemic diseases like obesity, diabetes, and CVD.

[0010] Advances in Machine Learning (ML) technologies, an essential branch of artificial intelligence, have enabled researchers to build prediction biomarker models for various diseases such as arthritis, diabetes, and inflammatory bowel disease. In contrast, few studies have trained ML models using the gut microbiome profiles to identify predictors of atherosclerosis and CVD and none have used the SM so far. [0011] In this disclosure, the SM composition of 2974 Qatari participants randomly selected from the cohort of Qatar Genome Project (QGP) was analyzed. The phenotypic, clinical, and microbiome data were integrated and SM-biomarkers associated with an increased risk to CVD using ML models were identified.

BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The following figures are included to illustrate certain aspects of the present disclosure and should not be viewed as exclusive embodiments. The subject matter disclosed is capable of considerable modifications, alterations, combinations, and equivalents in form and function, as will occur to one having ordinary skill in the art and having the benefit of this disclosure.

[0013] FIG. 1A-B. Overall study design from participant recruitment to SM-based CVD marker selection. (FIG. 1A) the study workflow; (FIG. IB) Strategies applied in Supervised machine learning (ML) to select pertinents.

[0014] FIG. 2A-B show the salivary microbiome composition of CVD risk groups. Y- axis shows % of relative abundance of the microbiome; X-axis indicates the microbial abundance in LR, MR, HR groups; (FIG. 2A) phylum level; (FIG. 2B) genus level.

[0015] FIG. 3A-C show graphs of linear discriminant analysis (LDA) scores for differentially enriched bacterial genera among the groups. (FIG. 3A) LR vs. HR groups; (FIG. 3B) MR vs. LR groups; (FIG. 3C) MR vs HR groups.

[0016] FIG. 4A-G. Machine learning models. Barplots representing the selection percentages of the microbes selected at least 80% of the time by the four methods over the 50 random splits of the data. (FIG. 4A) binary transformation. (FIG. 4B) Arcsin transformation. (FIG. 4C) Venn Diagram showing the number of microbes. (FIG. 4D) Heatmap [presence /absence] of selected microbes using Binary and Arcsin transformations. (FIG. 4E) Balloon plot representing sign counts of the regression coefficients: Binary transformation (FIG. 4F) Arcsin transformation. The size of circles represents the number of splits. The color represents the number of counts. (FIG. 4G) Box plots of the Mse for the four-methods and the two transformations applied to the microbiome abundance data. Each point of the boxplot represents the Mse on the test-set.

[0017] FIG. 5A-B. (FIG. 5A) Alpha diversity measures for the LR, MR and HR groups. (FIG. 5B) Principal Coordinates Analysis (PCoA) based on Bray-Curtis distances of SM.

DETAILED DESCRIPTION

[0018] Aspects of the present disclosure are directed to methods of treating a cardiovascular associated pathologic condition or disease m a subject in need thereof. The method includes administering an effective amount of an active pharmaceutical ingredient approved for such treatment, wherein the subject has a microbiome dysbiosis that includes one or more of Ciostridiaceae, Capnocytophaga, Lactobacillus, Rothia, Desulfobulbus, Prevoteila, or Tissiereliaceae.

[0019] In some embodiments, the microbiome dysbiosis is a salivary microbiome dysbiosis or a gut microbiome dysbiosis, or both.

[0020] In some embodiments, the subject comprises a microbiome dysbiosis that includes one or more of Desulfobulbus, Prevoteila, or Tissiereliaceae.

[0021] In some embodiments, the subject comprises a microbiome dysbiosis that includes one or more of Ciostridiaceae or Capnocytophaga.

[0022] In some embodiments, the microbiome dysbiosis is determined by a method comprising analysis of a salivary sample of the subject.

[0023] In some embodiments, the analysis includes gene sequencing.

[0024] In some embodiments, a) the microbiome dysbiosis of Ciostridiaceae is a decrease in the abundance of Ciostridiaceae; b) the microbiome dysbiosis of Capnocytophaga is a decrease in the abundance of Capnocytophaga; c) the microbiome dysbiosis of Lactobacillus is an increase in the abundance of Lactobacillus; d) the microbiome dysbiosis of Rothia is an increase in the abundance of Rothia; e) the microbiome dysbiosis of Desuifobulbus is an increase in the abundance of Desulfobulbus; f) the microbiome dysbiosis of Prevoteila is an increase in the abundance of Prevoteila; g) the microbiome dysbiosis of Tissiereliaceae is an increase in the abundance of Tissiereliaceae; h) or a combination of a)-g).

[0025] In some embodiments, the microbiome dysbiosis includes an about 4-6 log difference in microbiome abundance between Ciostridiaceae, Capnocytophaga, or both, and Lactobacillus, Rothia, or both.

[0026] In some embodiments, the microbiome dysbiosis comprises 1) a microbiome relative abundance of about 4.3:43.6:43.5 of Proteobacteria:Firmicutes:Bacteriodetes; 2) an about 4-6 log difference in abundance between a) and b), wherein a) is Ciostridiaceae, Capnocytophaga, or both, and b) is Lactobacillus, Rothia, or both; 3) a microbiome relative abundance of about 8.3:30.7 of Rothia: Prevoteila, or 4) a combination of 1), 2), and 3).

[0027] In some embodiments, provided herein are methods of diagnosing an oral or systemic disease, including a cardiovascular associated pathologic condition or disease, in a subject, comprising: collecting a saliva sample from the subject and determining a most significantly abundant salivary microbiome genera in the saliva sample from the subject, wherein the most significantly abundant salivary microbiome genera is determined by a process including gene sequencing, optionally, including determining whether at least one of Clostridiaceae or Capnocytophaga genera is the most significantly abundant salivary microbiome genera in the subject, optionally, including determining the relative abundance of at least one of Desulfobulbus, Prevotella, or Tissierellaceae genera in the salivary microbiome in the subject as compared to the abundance of the same genera in the general population in which the subject is a member.

[0028] In some embodiments, when at least one of Clostridiaceae or Capnocytophaga genera are not the most significantly abundant salivary microbiome genera in the subject, the subject may be considered as being at high risk for an oral or systemic disease, such as, but not limited to, a cardiovascular disease.

[0029] In some embodiments, when at least one of Desulfobulbus, Prevotella, or Tissierellaceae genera in the salivary microbiome in the subject significantly (e.g., mathematically) increases as compared to the abundance of the same genera in the general population (e.g., nationality, sex, ethnicity, or a combination thereof) in which the subject is a member, the subject may be considered as being at high risk for an oral or systemic disease, such as, but not limited to, a cardiovascular disease.

[0030] In some embodiments, provided herein are methods of attenuating the risk of a cardiovascular associated pathologic condition or disease in a subject in need thereof. The method includes administering a microbiota or promicrobiota to the subject, wherein: prior to administration of the microbiota or promicrob^ota the subject comprises 1) a microbiome relative abundance of about 4.3:43.6:43.5 of Proteobacteria:Firmicutes:Bacteriodetes, 2) an about 4-6 log difference in abundance between a) and b), wherein a) is Clostridiaceae, Capnocytophaga, or both, and b) is Lactobacillus, Rothia, or both, 3) a microbiome relative abundance of about 8.3:30.7 of Rothia: Prevotella, or 4) a combination of 1), 2), and 3); and during, or in the 1-30 days subsequent to, administration of the microbiota or promicrobiota the subject comprises 1) a microbiome relative abundance of about 5.1-5.8:39.7-40.8:47.4 to about 4.3:43.6:43.5 of Proteobacteria:Firmicutes:Bacteriodetes, or 2) a microbiome relative abundance of about 6.7-7.5:32.7-34.2 of Rothia: Prevotella, or 3) a combination of 1) and 2).

[0031] In some embodiments, the microbiome relative abundance is determined by a method that includes analysis of a salivary sample of the subject.

[0032] In some embodiments the analysis includes gene sequencing. [0033] In some embodiments, the microbiome is a gut microbiome or a salivary microbiome.

[0034] Another aspect of the present disclosure is directed to methods of treating cardiovascular disease in a subject in need thereof. The methods include administering a therapeutically effective amount of an approved cardiovascular disease medicament to the subject, wherein a salivary sample of the subject includes an amount of one or more of Lactobacillus, Rothia, Desulfobulbus, Prevotella, or Tissierellaceae that is greater than the amount found in a subject that is substantially free of cardiovascular disease.

[0035] In some embodiments, the salivary sample of the subject includes amounts of two or more of Lactobacillus, Rothia, Desulfobulbus, Prevotella, or Tissierellaceae that are greater than the amounts found in a subject that is substantially free of cardiovascular disease.

[0036] In some embodiments, the salivary sample of the subject includes amounts of three or more of Lactobacillus, Rothia, Desulfobulbus, Prevotella, or Tissierellaceae that are greater than the amounts found in a subject that is substantially free of cardiovascular disease.

[0037] In some embodiments, the salivary sample of the subject includes amounts of four or more of Lactobacillus, Rothia, Desulfobulbus, Prevotella, or Tissierellaceae that are greater than the amounts found in a subject that is substantially free of cardiovascular disease.

[0038] In some embodiments, the salivary sample of the subject includes amounts of all five of Lactobacillus, Rothia, Desulfobulbus, Prevotella, or Tissierellaceae that are greater than the amounts found in a subject that is substantially free of cardiovascular disease.

[0039] In some embodiments, the presence or absence of Lactobacillus, Rothia, Desulfobulbus, Prevotella, or Tissierellaceae is determined by screening a 16S-rDNA library.

[0040] In some embodiments, the approved cardiovascular disease medicament includes a statin, a beta-blocker, an Angiotensin Converting Enzyme (ACE) inhibitor, a calcium channel blocker, a diuretic, an antiplatelet agent, nitroglycerin, digitalis or a combination thereof.

[0041] Another aspect of the present disclosure is directed to methods for determining the risk of cardiovascular disease in a subject. The methods include taking a salivary sample from the subject; quantifying the amount of one or more of Clostridiaceae, Capnocytophaga, Lactobacillus, Rothia, Desulfobulbus, Prevotella, or Tissierellaceae; comparing the amount of one or more of Clostridiaceae, Capnocytophaga, Lactobacillus, Rothia, Desulfobulbus, Prevotella, or Tissierellaceae to a reference value; and determining the risk of cardiovascular disease. The reference value may be an amount found in a subject that is substantially free of cardiovascular disease.

[0042] In some embodiments, the presence or absence of Clostridiaceae, Capnocytophaga, Lactobacillus, Rothia, Desulfobulbus, Prevotella, or Tissierellaceae is determined by screening a 16S-rDNA library.

[0043] In some embodiments, if the amount of one or more of Lactobacillus, Rothia, Desulfobulbus, Prevotella, or Tissierellaceae is greater than a reference value, then the subject is at a greater risk of having cardiovascular disease than if the amount is not greater than the reference value. The reference value may be an amount found in a subject that is substantially free of cardiovascular disease.

[0044] In some embodiments, if the amounts of two or more of Lactobacillus, Rothia, Desulfobulbus, Prevotella, or Tissierellaceae are greater than a reference value, then the subject is at greater risk of having cardiovascular disease than if the amounts are not greater than the reference value. The reference value may be amounts found in a subject that is substantially free of cardiovascular disease.

[0045] In some embodiments, if the amounts of three or more of Lactobacillus, Rothia, Desulfobulbus, Prevotella, or Tissierellaceae are greater than a reference value, then the subject is at a greater risk of having cardiovascular disease than if the amounts are not greater than the reference value. The reference value may be amounts found in a subject that is substantially free of cardiovascular disease.

[0046] In some embodiments, if the amounts of four or more of Lactobacillus, Rothia, Desulfobulbus, Prevotella, or Tissierellaceae are greater than the reference value, then the subject is at greater risk of having cardiovascular disease than if the amounts are not greater than the reference value. The reference value may be amounts found in a subject that is substantially free of cardiovascular disease.

[0047] In some embodiments, if the amounts of all five of Lactobacillus, Rothia, Desulfobulbus, Prevotella, or Tissierellaceae are greater than the reference value, then the subject is at a greater risk of having cardiovascular disease than if the amounts are not greater than the reference value. The reference value may be amounts found in a subject that is substantially free of cardiovascular disease.

[0048] In some embodiments, if the amount of one or more of Clostridiaceae or Capnocytophaga is greater than a reference value, then the subject is at a lesser risk of having cardiovascular disease than if the amount is not greater than the reference value. The reference value may be an amount found in a subject that is substantially free of cardiovascular disease.

[0049] In some embodiments, if the amounts of both Clostridiaceae and Capnocytophaga are greater than a reference value, then the subject is at a lesser risk of having cardiovascular disease than if the amounts are not greater than the reference value. The reference value may be amounts found in a subject that is substantially free of cardiovascular disease.

[0050] In some embodiments, the salivary sample is stored at about -80 °C until analyzed.

[0051] In some embodiments, provided herein are kits useful for carrying out the methods described herein, including the treatment or diagnostic methods. Thus, in some embodiments, provided herein are kits, comprising one or more salivary collection containers, and instructions for use, optionally further including one or more accessory kits, including, without limitation, gene sequencing kit components.

METHODS AND RESULTS

Ciinicai data

[0052] The following were collected: de-identified saliva samples, phenotypic and clinical data from a total of 2974 Qatari participants that were selected randomly. All participants were 18 years old and above and no exclusion-criteria were applied. The cohort consisted of 1432 males and 1542 females (Tabie 1). Each subject's anthropometric and blood parameters were established by analyzing body mass index (BMI), total protein content, hemoglobin, albumin, ferritin, calcium, iron, vitamin-D, high or low density lipoprotein cholesterol (HDL, LDL), triglycerides, and glucose levels.

[0053] Tabie 1

[0054] Calculation of CVD Kssk score. Cox proportional-hazards regression has been used to evaluate the risk of developing CVD over 10-years. The CVD-risk score for 2974 patients was estimated using sex-specific multivariable factors consisting of age, total- Cholesterol, HDL, systolic blood pressure (BP), hypertension treatment, smoking, and diabetes status (HbA1C>6.5%, and participants who confirmed having diabetes). In some embodiments, the methods herein use the following equation for analysis: where Soft): baseline survival at follow-up time t (here t=10 years); B,: estimated regression coefficient (log hazard ratio that is measured for all risk functions and sexspecific); xi: log-transformed value of the I th risk factor; i: corresponding mean, p: number of risk factors.

[0055] Sahva sample coiiection and DNA extraction. Saliva samples were collected in QBB and stored at -80 °C until further analysis. Then, the total salivary DMA was extracted using automated QIAsymphony protocol (Qiagen, Hilden, Germany), foilowing the manufacturer's instructions.

[0056] 16S rRNA gene sequencing and data analysis. The V1-V3 regions of the

16S rRNA gene were amplified using Illumina Nextera XT library preparation Kit (FC-131- 1002). The 16S amplicons were prepared, purified, and sequenced using MiSeq. The sequence data were analyzed using QIIME1.9.0 pipeline. Operational taxonomic units (OTUs) were generated by aligning against the Greengenes database (Version: 13 .8) with a confidence threshold of 97%.

[0057] Statistical Taxonomic and diversity analyses. Linear Discriminant Analysis Effect Size (LEfSe) was used to find differentially abundant taxa between the studied categories. Alpha diversity measures including Chaol, Observed, Shannon, and Simpson indices were calculated with R-phyioseq package. The alpha diversity statistical significance was calculated using Mann-Whitney test through MINITAB-17. P-values less than 0.05 were considered statistically significant. Differences in the beta diversity were presented as principal coordinate analysis using QIIME. ANOSIM (Analysis of similarities) was used to calculate the distance matrix difference between the categories using Bray- Curtis metric.

[0058] Supervised ML Modeling. Four statistical ML methods for regularization and feature selection based on penalized least squares were applied (FIG. 4A-G). The methods are the Least Absolute Shrinkage and Selection Operator (Lasso), Smoothly Clipped Absolute Deviation Penalty (Scad), Elastic Net (Enet), and Mmimax concave penalty (Mcp). The methods differ by the mathematical properties of the corresponding penalties: Lasso and Enet use convex penalties, while Mcp and Scad use concave penalties. Two transformations to the OUT-counts as in: a binary transformation (Binary), and a variance-stability transformation (Arcsin) were applied, while the CVD-score outcome was log-transformed. Analyses were performed using the R-packages glmnet and ncvreg. The graphics were generated using the R-packages ggplot2, RVenn, and ggpubr. The data was randomly spilt 50-times into a training set (80%) on which the predictive-models were build and a test-set (20%) on which tested the performance of each model was tested. Optimal tuning parameters were chosen via 10-fold cross-validation.

[0059] Demographic and ci mica! parameters of the study population. The studypopulation was composed of 2974 Qatari participants. The cohort was classified into three CVD groups as Low-risk (LR) (CVD score<10), Moderate-risk (MR) (CVD score: 10-20), and High-risk (HR) (>20), as described in the methods section. A total of 2491 participants were LR, 320 were MR, and 163 were HR (Table 1). The average participant's age in the HR group (55.87±8.14years) was significantly higher than those in the MR (50.89--t-7. ISyears) and LR (35. II-AIO.22years) groups (Table 1). Moreover, the BMI was significantly higher in the HR group than in the MR and LR groups (Table 1). In addition, among the blood parameters tested, Alkaline phosphatase, Calcium, Total-Cholesterol, LDL, Creatinine, Ferritin, Fibrinogen, Folate, Glucose, HbAlC, Urea, and Triglycerides were significantly higher in the HR group (Table 1).

[0060] The salivary microbiome composition reveals signatures for CVD. After stratifying the study cohort based on the CVD risk score, the SM composition m all subjects was assessed. Then, the compositional changes between different study groups was compared. A diagram that summarizes the study design is shown in (FIG. 1A-B). The microbial sequence data generated from ail the participants revealed 22 bacterial phyla, 46 classes, 87 orders, 173 families, and 390 genera. Bacteroidetes, Firmicutes, Actinobacteria, and Proteobacteria were the most abundant phyla observed in the saliva samples collected from the Qatari subjects which covered approximately 90% of total microbial abundance (FIG. 2A). The data showed that Streptococcus, Prevotella, Porphyromonas, Granulicatella, and Veillonelia represent the salivary core microbiome members at the genus level (FIG. 2B).

[0061] Differential Salivary microbial taxa Between the HR and LR-CVD Groups. After assessing the study cohort's SM, LefSE analysis compared the salivary microbiome compositions in the LR, MR, and HR (FIG. 3A-C). Our data indicated that Capnocytophaga and Ciostridiaceae were significantly abundant in the LR group compared to HR group (pcO.OOOl). In contrast, Lactobacillus and Rothia were significantly enriched in the HR group (pcO.OOOl) (FIG. 3A) in comparison to LR group.

[0062] Alpha and beta diversity measures were calculated to assess the changes in diversity among groups (FIG. 5A-B). Alpha diversity parameters revealed no significant differences observed between ail groups (FIG. 5A). We then performed beta diversity analysis to assess the divergence in the community composition between the groups using the Bray-Curtis distance metric (FIG. 5B). It was shown that the salivary microbiome in HR and MR were not significantly dissimilar from the LR group (p -0.085) (FIG. 5B).

[0063] Identification of Pertinent salivary microbial markers associated with the CVD score using ML models. The apparent differences between the study groups using alpha and beta diversity measures were not identified due to the large sample size differences and imbalance among the groups. Since the subjects were selected randomly, it was decided to use ML-based selection of pertinent SM biomarkers. The data were split 50-times randomly, using the four feature selection techniques to avoid bias based on the size, and the whole dataset was used without any exclusion.

[0064] To search for pertinent variables, we focused on the abundances of SM selected at least 80% of the time among the 50-random splits of the data and the four feature selection techniques as described in the methods section. Ther results are shown in FIG. 4. Seven microbes were selected at least 80% of the time using the binary and Arcsin transformations by all the ML methods (Lasso, Scad, ENet, Mcp) (FIG. 4A-B). Three microbes presented at ali the tested models and both transformations (FIG. 4C-D), while four microbes were specific to the binary transformation and four were particular to the Arscin transformation (FIG. 4D). The common microbes were Prevotella, Tissierellaceae, and Desuifobulbus (FIG. 4D). To better understand how these microbes affect the CVD- score, we counted the sign of the regression coefficients number of times, which was Positive, Negative, or Zero (FIG. 4E). From this analysis, it was inferred that the three microbes mentioned above contributing to an increase in the CVD-score (FIG. 4E), while our data showed that an increase in Clostridiaceae level contributed to a decrease in CVD- score (FIG. 4F). Assessment using the Mean squared error (Mse) method disclosed that binary transformation has better prediction accuracy than arcsin. (FIG. 4G).

[0065] The need for practical, non-mvasive tools for predicting and preventing CVD- risk has led to concerted research efforts in recent years to identify and characterize biomarkers associated with the disease as a step forward towards precision medicine. In addition, recent studies on the microbiome have enlightened its role in human health and disease. Despite that, the diversity of the gut microbiome is affected by several factors like gender, ethnicity, age, and environmental factors; it was found to be associated with many diseases, including CVD and IBD using ML-models. However, the potential use of the SM composition in assessing CVD is still lacking.

[0066] This disclosure evaluated whether the SM composition can predict a high risk for developing CVD in a diverse Qatari population. Using a large cohort of 2974 Qatari participants and based on the CVD risk score, we showed for the first time that the SM composition in LR and HR individuals is different (LefSe analysis). A significant SM alteration was observed between LR, MR, and HR groups (FIG. 3A-C). Furthermore, Capnocytophaga and Clostridiaceae were significantly enriched in LR group (FIG. 3A). While there are no studies addressing the role of Capnocytophaga in health and disease, a study among Japanese patients showed that non-ischemic heart failure is associated with lower levels of Clostridiaceae. In line with the present findings, a significant reduction of Clostiridiaceae was observed in the HR-CVD group in the Qatari population (FIG. 3A).

[0067] Moreover, this disclosure shows that Lactobacillus and Rothia were enriched in the HR group compared to the LR group (FIG. 3A). Similarly, a study aiming to utilize the gut microbiome as a diagnostic marker of coronary artery disease (CAD) in the Japanese population has revealed that Lactobacilli were more abundant in patients with CAD than their matching controls. In contrast, Rothia, a nitrate-reducing bacterium, was enriched in hypertensive patients. [0068] Next, an approach of regression-based machine learning was employed bycombining the entire dataset of ISrDNA sequencing data with ML models to identify the potential predictors of HR CVD without stratifying the cohort to mask the bias due to sample size difference among groups. We found that three microbes (Prevotella, Tissiereilaceae, and Desulfobuibus) were represented by both the binary and arcsin transformations and different training model techniques. Those were associated with high CVD-score (FIG. 4A-G). The Bogalusa Heart Study aimed to associate the lifetime CVD risk among the participants using the gut microbes revealed that the genus Prevotella was significantly enriched in the CVD HR participants. Also, the role of gut microbiome in Chinese CVD patients with cardiac valve calcification revealed that Prevotella is a potential pathogen that is positively correlated with LDL, Moreover, hypertensive rats had a significant increase of Tissiereilaceae in the gut microbiome. Furthermore, Tissierella Soehngenia was highly abundant in rats with acute myocardial infarction compared to the control groups. Tissiereilaceae produces trimethyl amino N-oxide (TMAO), a known microbial metabolite associated with heart attack, stroke, and chronic kidney disease. Our study showed that Desulfobuibus - suifidogenic bacterium has a positive regression coefficient with CVD scores in both trained models (FIG. 4C-D). The elevated level of Desulfobuibus is known to trigger proinfiammatory cytokines in patients with rheumatoid arthritis and periodontitis. Moreover, its abundance is positively correlated with age rendering it an excellent predictor to diagnose systemic diseases like diabetes and CVD.

[0069] This disclosure is believed to be the first to demonstrate the promising potential of artificial intelligence via ML modeling for a convenient prediction screening of CVD based on the SM composition in the Arab population. While most ML strategies based on the health records (including age, sex, smoking habit, systolic BP, total cholesterol, HDL, cholesterol, BP treatment, and diabetes), fewer studies used gut microbiome profiles to predict IBD and CVD with an of ~0.7C> and 0.90 respectively. A pilot study of Japanese patients with atherosclerotic cardiovascular disease (ACVD) revealed that SM can be used as an optimal marker of ACVD with an AUC of 0.933. It is a promising finding to enable the discovery of non-invasive biomarkers that can predict the risk of the disease before it occurs.

[0070] As used herein, the term "cardiovascular disease (CVD)" refers broadly to any disease of the heart and circulatory system (arteries and veins). Cardiovascular disease generally refers to conditions that involve narrowed or blocked blood vessels that can lead to a heart attack, chest pain (angina), or stroke. Other heart conditions, such as those that affect the heart muscle, valves, or rhythm, are also considered forms of heart disease. Examples of CVD include, but are not limited to, coronary artery disease (blockage of blood vessels that serve the heart), acute coronary syndrome (symptoms such as pain, weakness, and tiredness caused by coronary artery disease), angina Pectoris (pain resulting from coronary artery disease or other causes), myocardial infarction (heart attack, with damage to the heart muscle caused by coronary artery disease).

[0071] As used herein, the term "microbiome" preferably describes a community of living microorganisms that typically inhabits a bodily organ or part. The most dominant members of the SM include microorganisms of the phyla of Firmicutes, Bacteroidetes, Actinobacteria, Proteobacteria. At genus level of Streptococcus, Prevotella, Rothia and Haemophilus are the dominant members.

[0072] Microbiome dysbiosis refers to an imbalance or disruption of the normal microbial community in a particular environment, such as the gut, skin, or oral cavity. This may result from a variety of factors, including changes in diet, antibiotic use, stress, and illness.

[0073] The terms "microbiota" and "probiotics" are commonly used in the field of microbiology and refer to different aspects of the microbial world. Microbiota refers to the collection of microorganisms that inhabit a particular environment, such as the human gut, skin, or oral cavity. These microorganisms can include bacteria, viruses, fungi, and other microorganisms. Probiotics or "promicrobiota", in contrast, are living microorganisms that are consumed as dietary supplements or added to food products with the goal of providing a health benefit to the host. Probiotics are thought to work by promoting the growth of beneficial bacteria in the gut and supporting a healthy balance of the gut microbiota.

[0074] As used herein, the term "attenuating" refers to the process of weakening or reducing the strength, potency, likelihood, or virulence of something

[0075] As used herein, the term "individual", "patient", or "subject" used interchangeably, refers to any animal, including mammals, preferably mice, rats, other rodents, rabbits, dogs, cats, swine, cattle, sheep, horses, or primates, and most preferably humans.

[0076] As used herein, the phrase "effective amount" or "therapeutically effective amount" refers to the amount of active compound or pharmaceutical agent that elicits the biological or medicinal response in a tissue, system, animal, individual or human that is being sought by a researcher, veterinarian, medical doctor or other clinician.

[0077] The terms "effective amount" or "therapeutically effective amount" refer to an amount, i.e. a dosage, of therapeutic agent administered to a subject (e.g., a mammalian subject, i.e. a human subject), either as a single dose or as part of a series of doses, which is effective to produce a desired therapeutic effect (e.g., effective for influencing, reducing or inhibiting the activity of or preventing activation of a kinase, or effective at bringing about a desired in vivo effect in an animal, preferably, a human, such as reduction in intraocular pressure).

[0078] As used herein the term "treating" or "treatment" refers to 1) inhibiting the disease; for example, inhibiting a disease, condition or disorder in an individual who is experiencing or displaying the pathology or symptomatology of the disease, condition or disorder (i.e., arresting further development of the pathology and/or symptomatology), or 2) ameliorating the disease; for example, ameliorating a disease, condition or disorder in an individual who is experiencing or displaying the pathology or symptomatology of the disease, condition or disorder (i.e., reversing the pathology and/or symptomatology).

[0079] The term "treatment" may refer to the application of one or more specific procedures used for the amelioration of a disease. In certain embodiments, the specific procedure is the administration of one or more pharmaceutical agents. "Treatment" of an individual (e.g. a mammal, such as a human) or a cell is any type of intervention used in an attempt to alter the natural course of the individual or cell. Treatment includes, but is not limited to, administration of a therapeutic agent or a pharmaceutical composition, and may be performed either prophylactically or subsequent to the initiation of a pathologic event or contact with an etiologic agent. Treatment includes any desirable effect on the symptoms or pathology of a disease or condition, and may include, for example, minimal changes or improvements in one or more measurable markers of the disease or condition being treated. Also included are "prophylactic" treatments, which can be directed to reducing the rate of progression of the disease or condition being treated, delaying the onset of that disease or condition, or reducing the severity of its onset.

[0080] As used herein, the term "preventing" or "prevention" of a disease, condition or disorder refers to decreasing the risk of occurrence of the disease, condition or disorder in a subject or group of subjects (e.g., a subject or group of subjects predisposed to or susceptible to the disease, condition or disorder). In some embodiments, preventing a disease, condition or disorder refers to decreasing the possibility of acquiring the disease, condition or disorder and/or its associated symptoms. In some embodiments, preventing a disease, condition or disorder refers to completely or almost completely stopping the disease, condition or disorder from occurring.

[0081] The term "approved" when applied to a therapy or drug refers to one that a licensed physician is allowed to prescribe for its intended use.

[0082] Unless otherwise indicated, all numbers expressing quantities of ingredients, properties such as molecular weight, reaction conditions, and so forth used in the specification and claims are to be understood as being modified in all instances by the term "about." As used herein the terms "about" and "approximately" means within 10 to 15%, preferably within 5 to 10%. Accordingly, unless indicated to the contrary, the numerical parameters set forth in the specification and attached claims are approximations that may vary depending upon the desired properties sought to be obtained by the present disclosure. At the very least, and not as an attempt to limit the application of the doctrine of equivalents to the scope of the claims, each numerical parameter should at least be construed in light of the number of reported significant digits and by applying ordinary rounding techniques. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the disclosure are approximations, the numerical values set forth in the specific examples are reported as precisely as possible. Any numerical value, however, inherently contains certain errors necessarily resulting from the standard deviation found in their respective testing measurements.

[0083] The terms "a," "an," "the" and similar referents used in the context of describing the disclosure (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. Recitation of ranges of values herein is merely intended to serve as a shorthand method of referring individually to each separate value falling within the range. Unless otherwise indicated herein, each individual value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., "such as") provided herein is intended merely to better illuminate the disclosure and does not pose a limitation on the scope of the disclosure otherwise claimed. No language in the specification should be construed as indicating any non-claimed element essential to the practice of the disclosure.

[0084] Groupings of alternative elements or embodiments of the disclosure disclosed herein are not to be construed as limitations. Each group member may be referred to and claimed individually or in any combination with other members of the group or other elements found herein. It is anticipated that one or more members of a group may be included in, or deleted from, a group for reasons of convenience and/or patentability. When any such inclusion or deletion occurs, the specification is deemed to contain the group as modified thus fulfilling the written description of all Markush groups used in the appended claims.

[0085] Certain embodiments of this disclosure are described herein, including the best mode known to the inventors for carrying out the disclosure. Of course, variations on these described embodiments will become apparent to those of ordinary skill in the art upon reading the foregoing description. The inventor expects skilled artisans to employ such variations as appropriate, and the inventors intend for the disclosure to be practiced otherwise than specifically described herein. Accordingly, this disclosure includes all modifications and equivalents of the subject matter recited in the claims appended hereto as permitted by applicable law. Moreover, any combination of the above-described elements in all possible variations thereof is encompassed by the disclosure unless otherwise indicated herein or otherwise clearly contradicted by context.

[0086] Specific embodiments disclosed herein may be further limited in the claims using consisting of or consisting essentially of language. When used in the claims, whether as filed or added per amendment, the transition term "consisting of" excludes any element, step, or ingredient not specified in the claims. The transition term "consisting essentially of" limits the scope of a claim to the specified materials or steps and those that do not materially affect the basic and novel characteristic(s). Embodiments of the disclosure so claimed are inherently or expressly described and enabled herein.

[0087] Furthermore, numerous references have been made to patents and printed publications throughout this specification. Each of the above-cited references and printed publications are individually incorporated herein by reference in their entirety.

[0088] In closing, it is to be understood that the embodiments of the disclosure disclosed herein are illustrative of the principles of the present disclosure. Other modifications that may be employed are within the scope of the disclosure. Thus, by way of example, but not of limitation, alternative configurations of the present disclosure may be utilized in accordance with the teachings herein. Accordingly, the present disclosure is not limited to that precisely as shown and described.