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1. WO2020157762 - PRÉDICTION DE MÉTABOLITES SANGUINS

Note: Texte fondé sur des processus automatiques de reconnaissance optique de caractères. Seule la version PDF a une valeur juridique

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WHAT IS CLAIMED IS:

1. A method of predicting the quantity of a metabolite in the blood of a subject, the method comprising:

accessing a computer readable medium storing a library of trained machine learning procedures, each being associated with a different metabolite;

searching said library for a trained machine learning procedure associated with the metabolite;

feeding said selected procedure with amount of a plurality of microbes of a microbiome of the subject; and

receiving from said selected procedure an output indicative of the quantity of the metabolite in the blood.

2. The method of claim 1 , further comprising measuring the amount of microbes of said microbiome of the subject prior to said analyzing.

3. The method according to any of claims 1 and 2, wherein said microbiome is a fecal microbiome.

4. The method according to any of claims 1-3, wherein said plurality of microbes comprises more than 20 microbes.

5. The method according to any of claims 1-4, of claim 1, wherein said metabolite is set forth in Table 2.

6. The method according to any of claims 1-4, wherein said metabolite is other than glucose and other than cholesterol.

7. The method according to claim 5, wherein said metabolite is other than glucose and other than cholesterol

8. The method according to any of claims 1-5, wherein at least some of said trained machine learning procedures in said library comprises a set of decision trees.

9. The method according to claim 8, wherein each set of decision trees comprises at least 1000 decision trees.

10. The method according to any of claims 1-5, wherein said selected machine learning procedure comprises a set of decision trees, each decision tree comprises a plurality of nodes associated with a respective plurality of decision rules, each decision rule relating to at least one microbe of said mierobiome, and wherein a number of decision rules relating to microbes listed in Table 1 is larger than a number of decision rules relating to other microbes of said mierobiome.

11. A method of predicting the quantity of a metabolite set forth in Table 1 , the method comprising:

accessing a computer readable medium storing a trained machine learning procedure associated with the metabolite;

feeding said trained procedure with an amount of N of the corresponding microbes set forth in Table 1 , said N being at most 50; and

receiving from said procedure an output indicative of the quantity of the metabolite in the blood, thereby predicting the quantity of the metabolite in the blood.

12. The method of claim 1 1 , further comprising measuring the amount of microbes of said fecal mierobiome of the subject prior to said analyzing.

13. The method according to any of claims 11 and 12, wherein said metabolite is other than glucose and other than cholesterol.

14. A method of predicting the quantity of a metabolite in the blood of a subject that consumes a diet of a plurality of food types, the method comprising:

accessing a computer readable medium storing a library of trained machine learning procedures, each being associated with a different metabolite;

searching said library for a trained machine learning procedure associated with the metabolite;

feeding said selected procedure with a frequency of consumption of at least 5 of said food types over at least one month and/or a daily mean consumption of at least 5 of said food types; and receiving from said selected procedure an output indicative of the quantity of the metabolite in the blood.

15. The method of claim 14, wherein said metabolite is set forth in Table 4.

16. The method according to claim 14, wherein said metabolite is other than glucose and other than cholesterol.

17. The method according to claim 15, wherein said metabolite is other than glucose and other than cholesterol.

18. The method according to any of claims 14-17, wherein at least some of said trained machine learning procedures in said library comprises a set of decision trees

19. The method according to claim 18, wherein each set of decision trees comprises at least 1000 decision trees.

20. The method according to any of claims 14 and 15, wherein said selected machine learning procedure comprises a set of decision trees, each decision tree comprises a plurality of nodes associated with a respective plurality of decision rules, each decision rule relating to at least one food type, and wherein a number of decision rules relating to food types listed in Table 3 is larger than a number of decision rules relating to other food types.

21. A method of predicting the quantity of a metabolite set forth in Table 3, the method comprising:

accessing a computer readable medium storing a trained machine learning procedure associated with the metabolite;

feeding said selected procedure with a daily mean consumption and/or frequency of consumption over at least one month of N of the corresponding food types set forth in Table 3 of the subject; and

receiving from said selected procedure an output indicative of the quantity of the metabolite in the blood, thereby predicting the quantity of the metabolite in the blood.

O Ί The method of claim 21, wherein said hi is at most 50.

23. The method according to any of claims 21 and 22, wherein said metabolite is other than glucose and other than cholesterol.

24. The method according to any one of claims 1-23, further comprising corroborating the quantity of the metabolite by measuring the amount of said metabolite in a blood sample of the subject.

25. A method of diagnosing a disease of a subject comprising predicting the quantity of at least one metabolite which is indicative of the disease, wherein said predicting is carried out according to any one of claims 1-21, thereby diagnosing the disease

26. The method of claim 25, wherein the disease is selected from the group consisting of a metabolic disease, a cardiovascular disease and kidney disease.

27. A method of altering the quantity of a metabolite in the blood of the subject, the method comprising:

predicting the quantity of the metabolite; and

administering to the subject at least one agent which specifically increases or decreases at least one microbe, wherein the agent is selected based on the quantity of the metabolite;

wherein said predicting the quantity of the metabolite comprises:

accessing a computer readable medium storing a library of trained machine learning procedures, each being associated with a different metabolite;

searching said library for a trained machine learning procedure associated with the metabolite;

feeding said selected procedure with an amount of a plurality of microbes; and

receiving from said selected procedure an output indicative of the quantity of the metabolite in the blood.

28. A method of altering the amount of a metabolite in the blood of the subject, the method comprising:

accessing a computer readable medium storing a library of trained machine learning procedures, each being associated with a different metabolite;

searching said library for a trained machine learning procedure associated with the metabolite;

feeding said selected procedure with a predetermined quantity of the metabolite;

receiving from said selected procedure an output indicative of at least one microbe; and administering to the subject at least one agent which specifically increases or decreases the amount of said at least one microbe,

thereby altering the amount of the metabolite in the blood of the subject.

29. The method of claim 28, further comprising predicting the amount of the metabolite using another trained machine learning procedure.

30. The method of claims 27 or 28, wherein said agent which increases said microbe is a probiotic.

31. The method of claims 27 or 28, wherein said agent which decreases said microbe is an antibiotic or a phage directed to said microbe.

32. A method of providing dietary advice to a subject, the method comprising predicting the quantity of a metabolite in the blood by carrying out the method according to claim 14-22, wherein when said metabolite is above or below the recommended quantity of said metabolite, recommending consumption of at least one food type that alters the quantity of said metabolite

33. The method of claim 32, wherein said metabolite is set forth in Table 4

34. The method of claim 33, wherein said food type is the corresponding food type set forth in Table 4.

35. A method of altering the amount of a metabolite set forth in Table 3 in the blood of the subject, the method comprising:

accessing a computer readable medium storing a library of trained machine learning procedures, each being associated with a different metabolite;

searching said library for a trained machine learning procedure associated with the metabolite;

feeding said selected procedure with a predetermined quantity of the metaboli te, receiving from said selected procedure an output indicative of a list of food types; and providing dietary advice to the subject, based on said output.

36. The method of claim 35, further comprising predicting the amount of the metabolite using another trained machine learning procedure.