Computer-assisted means for assessing lifestyle risk factors

• US 7,054,758 B2
• Filed: 01/30/2001
• Issued: 05/30/2006
• Est. Priority Date: 01/30/2001
• Status: Expired due to Fees

• Current Assignee: Bodysync, Inc.
• Original Assignee: Sciona, Limited
• Inventors: Martin, Christopher J., Sanchez-Felix, Manuel V., Gill-Garrison, Rosalynn D.
• Primary Examiner(s): Moran, Marjorie A.
• Assistant Examiner(s): Clow, Lori A.
• Application Number:
  US09/771,933
• Publication Number:
  US 20030023387A1
• Time in Patent Office: 1,946 Days
• Field of Search: 702/19, 702/20, 128/920, 128/923
• US Class Current: 702/20
• CPC Class Codes: G09B 19/0092 Nutrition
• CPC Class Codes: G09B 5/02 with visual presentation of the material to be studied, e.g. using film strip
• CPC Class Codes: G16B 25/00 ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
• CPC Class Codes: G16B 25/10 Gene or protein expression profiling; Expression-ratio estimation or normalisation
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16B 40/10 Signal processing, e.g. from mass spectrometry [MS] or from PCR
• CPC Class Codes: G16H 10/40 for data related to laboratory analysis, e.g. patient specimen analysis
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 20/70 relating to mental therapies, e.g. psychological therapy or autogenous training
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment

Abstract

The present invention relates to methods of assessing disease susceptibility associated with dietary and lifestyle risk factors. The invention provides for analysis of alleles at loci of genes associated with lifestyle risk factors, and the disease susceptibility profile of an individual is determined by reference to datasets which further match the risk factor with lifestyle recommendations in order to produce a personalized lifestyle advice plan.

Claims

1. A computer assisted method of providing a personalized lifestyle advice plan for a human subject comprising:

(i) providing a first dataset on a data processing device, said first dataset comprising information correlating the presence of individual alleles known to be associated with increased or decreased disease susceptibility, with a lifestyle risk factor;

(ii) providing a second dataset on a data processing device, said second dataset comprising information matching each said risk factor with at least one lifestyle recommendation;

(iii) inputting a third dataset identifying alleles present in said subject, wherein said alleles are one or more of the alleles of said first dataset;

(iv) determining the risk factors associated with said alleles present in said human subject by correlating said alleles with risk factors provided by said first dataset;

(v) determining at least one lifestyle recommendation based on each identified risk factor from step (iv) by matching said risk factor with a lifestyle recommendation from said second dataset; and

(vi) generating a personalized lifestyle advice plan based comprising at least one lifestyle recommendation determined in step (v), wherein said personalized lifestyle advice plan includes recommended minimum and/or maximum amounts of food subtypes.

2. The method according to claim 1 wherein the method comprises the step of delivering the plan to the client.

3. The method according to claim 2 wherein the plan is delivered via the Internet and accessible via a unique identifier code.

4. The method according to claim 3 wherein the plan comprises hyperlinks to one or more Web pages.

5. The method according to claim 1 wherein said first dataset comprises information relating to two or more alleles of a gene selected from the group comprising:

(a) a gene that encodes an enzyme responsible for detoxification of xenobiotics in Phase I metabolism;

(b) a gene that encodes an enzyme responsible for conjugation reactions in Phase II metabolism;

(c) a gene that encodes an enzyme that helps cells to combat oxidative stress;

(d) a gene associated with micronutrient deficiency; and

(e) a gene that encodes an enzyme responsible for metabolism of alcohol.

(f) a gene that encodes an enzyme involved in lipid and/or cholesterol metabolism;

(g) a gene that encodes an enzyme involved in clotting;

(h) a gene that encodes a trypsin inhibitor;

(i) a gene that encodes an enzyme related to susceptibility to metal toxicity;

(j) a gene which encodes a protein required for normal cellular metabolism and growth;

(k) a gene which encodes a HLA Class 2 molecule.

6. The method according to claim 5 wherein said first dataset comprises information relating to two or more alleles of a gene selected from each member of the group comprising:

(a) a gene that encodes an enzyme responsible for detoxification of xenobiotics in Phase I metabolism;

(b) a gene that encodes an enzyme responsible for conjugation reactions in Phase II metabolism;

(c) a gene that encodes an enzyme that helps cells to combat oxidative stress;

(d) a gene associated with micronutrient deficiency; and

(e) a gene that encodes an enzyme responsible for metabolism of alcohol.

(f) a gene that encodes an enzyme involved in lipid and/or cholesterol metabolism;

(g) a gene that encodes an enzyme involved in clotting;

(h) a gene that encodes a trypsin inhibitor;

(i) a gene that encodes an enzyme related to susceptibility to metal toxicity;

(j) a gene which encodes a protein required for normal cellular metabolism and growth;

(k) a gene which encodes a HLA Class 2 molecule.

7. The method according to claim 5 wherein said first dataset comprises information relating to two or more alleles of a gene encoding an enzyme selected from the group comprising:

cytochrome P450 monooxygenase, N-acetyltransferase 1, N-acetyltransferase 2, glutathione-S-transferase, manganese superoxide dismutase, 5,10-methylenetetrahydrofolatereductase and alcohol dehydrogenase 2.

8. The method according to claim 7 wherein said first dataset comprises information relating to two or more alleles of each of the genes encoding cytochrome P450 monooxygenase, N-acetyltransferase 1, N-acetyltransferase 2, glutathione-S-transferase, manganese superoxide dismutase, 5,10-methylene-tetrahydrofolatereductase and alcohol dehyd rogenase 2.

9. The method of claim 8 wherein said alleles are alleles of genes selected from the group consisting of the MTHFR gene, the MnSOD gene, the CYP1A1 gene, the GSTμ

gene, GSTπ

gene, the GSTΘ

θ

gene and the ALDH2 gene.

10. The method according to claim 1 including the step determining the presence of individual alleles in a DNA sample of said human subject, and constructing the dataset used in step (iii) using results of said determination.

11. The method according to claim 10 wherein said presence of said individual alleles is determined by hybridisation with allele-specific oligonucleotides.

12. The method according to claim 11 wherein said allele specific oligonucleotides are selected from oligonucleotides each specific for one of the genes selected from the group comprising the CYP1A1 gene, the GSTμ

gene, the GSTπ

gene, the GSTΘ

θ

gene, the NAT1 gene, the NAT2 gene, the MnSOD gene, the MTHFR gene and the ALDH2 gene.

13. The method of claim 1 wherein said first dataset and said second dataset are provided on the same data processing device.

14. A computer assisted method of providing a personalized lifestyle advice plan for a human subject comprising:

(i) providing a first dataset on a data processing device, said first dataset comprising information correlating the presence of individual alleles known to be associated with increased or decreased disease susceptibility, with a lifestyle risk factor;

(ii) providing a second dataset on a data processing device, said second dataset comprising information matching each said risk factor with at least one lifestyle recommendation;

(iii) inputting a third dataset identifying alleles present in said subject, wherein said alleles are two or more of the alleles of said first;

(iv) determining the risk factors associated with said alleles present in said human subject by correlating said alleles with risk factors provided by said first dataset;

(v) determining at least one lifestyle recommendation based on each identified risk factor from step (iv) by matching said risk factor with a lifestyle recommendation from said second dataset; and

(vi) generating a personalized lifestyle advice plan based comprising at least one lifestyle recommendation determined in step (v), wherein said personalized lifestyle advice plan includes recommended minimum and/or maximum amounts of food subtypes.

15. The method according to claim 14 wherein the method comprises the step of delivering the plan to the client.

16. The method according to claim 15 wherein the plan is delivered via the Internet and accessible via a unique identifier code.

17. The method according to claim 16 wherein the plan comprises hyperlinks to one or more Web pages.

18. The method according to claim 14 wherein said first dataset comprises information relating to two or more alleles of two or more genes selected from the group comprising:

(a) a gene that encodes an enzyme responsible for detoxification of xenobiotics in Phase I metabolism;

(b) a gene that encodes an enzyme responsible for conjugation reactions in Phase II metabolism;

(c) a gene that encodes an enzyme that help cells to combat oxidative stress;

(d) a gene associated with micronutrient deficiency; and

(e) a gene that encodes an enzyme responsible for metabolism of alcohol.

(f) a gene that encodes an enzyme involved in lipid and/or cholesterol metabolism;

(g) a gene that encodes an enzyme involved in clotting;

(h) a gene that encodes a trypsin inhibitor;

(i) a gene that encodes an enzyme related to susceptibility to metal toxicity;

(j) a gene which encodes a protein required for normal cellular metabolism and growth;

(k) a gene which encodes a HLA Class 2 molecule.

19. The method according to claim 18 wherein said first dataset comprises information relating to two or more alleles of a gene selected from each member of the group comprising:

(a) a gene that encodes an enzyme responsible for detoxification of xenobiotics in Phase I metabolism;

(b) a gene that encodes an enzyme responsible for conjugation reactions in Phase II metabolism;

(c) a gene that encodes an enzyme that help cells to combat oxidative stress;

(d) a gene associated with micronutrient deficiency; and

(e) a gene that encodes an enzyme responsible for metabolism of alcohol.

(f) a gene that encodes an enzyme involved in lipid and/or cholesterol metabolism;

(g) a gene that encodes an enzyme involved in clotting;

(h) a gene that encodes a trypsin inhibitor;

(i) a gene that encodes an enzyme related to susceptibility to metal toxicity;

(j) a gene which encodes a protein required for normal cellular metabolism and growth;

(k) a gene which encodes a HLA Class 2 molecule.

20. The method according to claim 18 wherein said first dataset comprises information relating to two or more alleles of of a gene encoding an enzyme selected from the group comprising:

cytochrome P450 monooxygenase, N-acetyltransferase 1, N-acetyltransferase 2, glutathione-S-transferase, manganese superoxide dismutase, 5,10-methylenetetrahydrofolatereductase and alcohol dehydrogenase 2.

21. The method according to claim 20 wherein said first dataset comprises information relating to two or more alleles of each of the genes encoding cytochrome P450 monooxygenase, N-acetyltransferase 1, N-acetyltransferase 2, glutathione-S-transferase, manganese superoxide dismutase, 5,10-methylenetetrahydrofolatereductase and alcohol dehydrogenase 2.

22. The method of claim 21 wherein said alleles are alleles of genes selected from the group consisting of the MTHFR gene, the MnSOD gene, the CYP1A1 gene, the GSTμ

gene, GSTTπ

gene, the GSTΘ

θ

gene and the ALDH2 gene.

23. The method according to claim 14 including the step determining the presence of individual alleles in a DNA sample of said human subject, and constructing the dataset used in step (iii) using results of said determination.

24. The method according to claim 23 wherein said presence of said individual alleles is determined by hybridisation with allele-specific oligonucleotides.

25. The method according to claim 24 wherein said allele specific oligonucleotides are selected from oligonucleotides each specific for two of the genes selected from the group comprising the CYP1A1 gene, the GSTμ

gene, the GSTπ

gene, the GSTΘ

θ

gene, the NAT1 gene, the NAT2 gene, the MnSOD gene, the MTHFR gene and the ALDH2 gene.

26. The method of claim 14 wherein said first dataset and said second dataset and provided on the same data processing device.