Methods for analysis of spectral data and their applications: atherosclerosis/coronary heart disease

• US 20040142496A1
• Filed: 10/22/2003
• Published: 07/22/2004
• Est. Priority Date: 04/23/2001
• Status: Abandoned Application

• Current Assignee: Metabometrix Limited
• Original Assignee: Metabometrix Limited
• Inventors: Brindle, Joanne Tracey, Nicholson, Jeremy Kirk, Grainger, David John, Holmes, Elaine, Lindon, John Christopher
• Application Number:
  US10/475,573
• Publication Number:
  US 20040142496A1
• Time in Patent Office: Days
• US Class Current: 436/536
• CPC Class Codes: A61B 5/055 involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• CPC Class Codes: A61B 5/412 Detecting or monitoring sepsis
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7232 involving compression of the physiological signal, e.g. to extend the signal recording period
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: A61P 19/08 for bone diseases, e.g. rachitism, Paget's disease
• CPC Class Codes: G01R 33/4625 Processing of acquired signals, e.g. elimination of phase errors, baseline fitting, chemometric analysis
• CPC Class Codes: G01R 33/465 applied to biological material, e.g. in vitro testing
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

This invention pertains to chemometric methods for the analysis of chemical, biochemical, and biological data, for example, spectral data, for example, nuclear magnetic resonance (NMR) spectra, and their applications, including, e.g., classification, diagnosis, prognosis, etc., especially in the context of atherosclerosis/coronary heart disease.

Claims

1. A method of classifying a sample, said method comprising the step of relating NMR spectral intensity at one or more predetermined diagnostic spectral windows for said sample with a predetermined condition associated with atherosclerosis/coronary heart disease.

2. A method, according to claim 1, of classifying a sample from a subject, said method comprising the step of relating NMR spectral intensity at one or more predetermined diagnostic spectral windows for said sample with a predetermined condition associated with atherosclerosis/coronary heart disease of said subject.

3. A method, according to claim 1, of classifying a sample, said method comprising the step of relating NMR spectral intensity at one or more predetermined diagnostic spectral windows for said sample with the presence or absence of a predetermined condition associated with atherosclerosis/coronary heart disease.

4. A method, according to claim 1, of classifying a sample from a subject, said method comprising the step of relating NMR spectral intensity at one or more predetermined diagnostic spectral windows for said sample with the presence or absence of a predetermined condition associated with atherosclerosis/coronary heart disease of said subject.

5. A method, according to claim 1, of classifying a sample, said method comprising the step of relating a modulation of NMR spectral intensity, relative to a control value, at one or more predetermined diagnostic spectral windows for said sample with a predetermined condition associated with atherosclerosis/coronary heart disease.

6. A method, according to claim 1, of classifying a sample from a subject, said method comprising the step of relating a modulation of NMR spectral intensity, relative to a control value, at one or more predetermined diagnostic spectral windows for said sample with a predetermined condition associated with atherosclerosis/coronary heart disease of said subject.

7. A method, according to claim 1, of classifying a sample, said method comprising the step of relating a modulation of NMR spectral intensity, relative to a control value, at one or more predetermined diagnostic spectral windows for said sample with the presence or absence of a predetermined condition associated with atherosclerosis/coronary heart disease.

8. A method, according to claim 1, of classifying a sample from a subject, said method comprising the step of relating a modulation of NMR spectral intensity, relative to a control value, at one or more predetermined diagnostic spectral windows for said sample with the presence or absence of a predetermined condition associated with atherosclerosis/coronary heart disease of said subject.

51. A method according to any one of claims 1 to 50, wherein said test sample is a test sample from a subject, and said predetermined condition is a predetermined condition of said subject.

52. A method according to any one of claims 1 to 50, wherein said “

a modulation of”

is “

an increase or decrease in.”

53. A method according to any one of claims 1 to 52, wherein said relating step involves the use of a predictive mathematical model.

54. A method according to any one of claims 1 to 52, wherein said modelling method is a multivariate statistical analysis modelling method.

55. A method according to any one of claims 1 to 52, wherein said modelling method is a multivariate statistical analysis modelling method which employs a pattern recognition method.

56. A method according to any one of claims 1 to 52, wherein said modelling method is, or employs PCA.

57. A method according to any one of claims 1 to 52, wherein said modelling method is, or employs PLS.

58. A method according to any one of claims 1 to 52, wherein said modelling method is, or employs PLS-BA.

59. A method according to any one of claims 1 to 58, wherein said modelling method includes a step of data filtering.

60. A method according to any one of claims 1 to 58, wherein said modelling method includes a step of orthogonal data filtering.

61. A method according to any one of claims 1 to 58, wherein said modelling method includes a step of OSC.

62. A method according to any one of claims 1 to 61, wherein said model takes account of one or more diagnostic species.

63. A method according to any one of claims 1 to 62, wherein said modelling data comprise spectral data.

64. A method according to any one of claims 1 to 62, wherein said modelling data comprise both spectral data and non-spectral data.

65. A method according to any one of claims 1 to 62, wherein said modelling data comprise NMR spectral data.

66. A method according to any one of claims 1 to 62, wherein said modelling data comprise both, NMR spectral data and non-NMR spectral data.

67. A method according to any one of claims 1 to 62, wherein said NMR spectral data comprises ¹H NMR spectral data and/or ¹³C NMR spectral data.

68. A method according to any one of claims 1 to 62, wherein said NMR spectral data comprises ¹H NMR spectral data.

69. A method according to any one of claims 1 to 62, wherein said modelling data comprise spectra.

70. A method according to any one of claims 1 to 62, wherein said modelling data are spectra.

71. A method according to any one of claims 1 to 70, wherein said modelling data comprises a plurality of data sets for modelling samples of known class.

72. A method according to any one of claims 1 to 70, wherein said modelling data comprises at least one data set for each of a plurality of modelling samples.

73. A method according to any one of claims 1 to 70, wherein said modelling data comprises exactly one data set for each of a plurality of modelling samples.

74. A method according to any one of claims 1 to 70, wherein said using step is:

using said model with a data set for said test sample to classify said test sample as being a member of one class selected from said class group.

75. A method according to any one of claims 1 to 74, wherein each of said data sets comprises spectral data.

76. A method according to any one of claims 1 to 74, wherein each of said data sets comprises both spectral data and non-spectral data.

77. A method according to any one of claims 1 to 74, wherein each of said data sets comprises NMR spectral data.

78. A method according to any one of claims 1 to 74, wherein each of said data sets comprises both NMR spectral data and non-NMR spectral data.

79. A method according to any one of claims 1 to 74, wherein said NMR spectral data comprises ¹H NMR spectral data and/or ¹³C NMR spectral data.

80. A method according to any one of claims 1 to 74, wherein said NMR spectral data comprises ¹H NMR spectral data.

81. A method according to any one of claims 1 to 74, wherein each of said data sets comprises a spectrum.

82. A method according to any one of claims 1 to 74, wherein each of said data sets comprises a ¹H NMR spectrum and/or ¹³C NMR spectrum.

83. A method according to any one of claims 1 to 74, wherein each of said data sets comprises a ¹H NMR spectrum.

84. A method according to any one of claims 1 to 74, wherein each of said data sets is a spectrum.

85. A method according to any one of claims 1 to 74, wherein each of said data sets is a ¹H NMR spectrum and/or ¹³C NMR spectrum.

86. A method according to any one of claims 1 to 74, wherein each of said data sets is a ¹H NMR spectrum.

87. A method according to any one of claims 1 to 86, wherein said non-spectral data is non-spectral clinical data.

88. A method according to any one of claims 1 to 86, wherein said non-NMR spectral data is non-spectral clinical data.

89. A method according to any one of claims 1 to 88, wherein said class group comprises classes associated with said predetermined condition.

90. A method according to any one of claims 1 to 88, wherein said class group comprises exactly two classes.

91. A method according to any one of claims 1 to 88, wherein said class group comprises exactly two classes:

presence of said predetermined condition; and

absence of said predetermined condition.

92. A method according to any one of claims 1 to 91, wherein said sample is an in vivo sample.

93. A method according to any one of claims 1 to 91, wherein said sample is an ex vivo sample.

94. A method according to any one of claims 1 to 91, wherein said sample is a blood sample or a blood-derived sample.

95. A method according to any one of claims 1 to 91, wherein said sample is a blood sample.

96. A method according to any one of claims 1 to 91, wherein said sample is a blood plasma sample.

97. A method according to any one of claims 1 to 91, wherein said sample is a blood serum sample.

98. A method according to any one of claims 1 to 97, wherein said subject is an animal.

99. A method according to any one of claims 1 to 97, wherein said subject is a mammal.

100. A method according to any one of claims 1 to 97, wherein said subject is a human.

101. A method according to any one of claims 1 to 100, wherein said one or more predetermined diagnostic spectral windows is:

a single predetermined diagnostic spectral window.

102. A method according to any one of claims 1 to 100, wherein said one or more predetermined diagnostic spectral windows is:

a plurality of predetermined diagnostic spectral windows.

103. A method according to any one of claims 1 to 100, wherein

said one or more predetermined diagnostic spectral windows is:

a plurality of diagnostic spectral windows, and,

said NMR spectral intensity at one or more predetermined diagnostic spectral windows is;

a combination of a plurality of NMR spectral intensities, each of which is NMR spectral intensity for one of said plurality of predetermined diagnostic spectral windows.

104. A method according to claim 103, wherein said combination is a linear combination.

105. A method according to any one of claims 1 to 104, wherein said one or more predetermined diagnostic spectral windows are associated with one or more diagnostic species.

106. A method according to any one of claims 1 to 104, wherein at least one of said one or more predetermined diagnostic spectral windows encompasses a chemical shift value for an NMR resonance of a diagnostic species.

107. A method according to any one of claims 1 to 104, each of a plurality of said one or more predetermined diagnostic spectral windows encompasses a chemical shift value for an NMR resonance of a diagnostic species.

108. A method according to any one of claims 1 to 104, each of said one or more predetermined diagnostic spectral windows encompasses a chemical shift value for an NMR resonance of a diagnostic species.

109. A method according to any one of claims 106 to 108, wherein said NMR resonance is a ¹H NMR resonance.

110. A method according to any one of claims 1 to 109, wherein said one or more diagnostic species are endogenous diagnostic species.

111. A method according to any one of claims 1 to 1109, wherein said one or more diagnostic species are associated with NMR spectral intensity at predetermined diagnostic spectral windows.

112. A method according to any one of claims 1 to 111, said one or more diagnostic species are a plurality of diagnostic species.

113. A method according to any one of claims 1 to 111, said one or more diagnostic species is a single diagnostic species.

114. A method according to any one of claims 1 to 113, wherein said classification is performed on the basis of an amount, or a relative amount, of a single diagnostic species.

115. A method according to any one of claims 1 to 113, wherein said classification is performed on the basis of an amount, or a relative amount, of a plurality of diagnostic species.

116. A method according to any one of claims 1 to 113, wherein said classification is performed on the basis of an amount, or a relative amount, of each of a plurality of diagnostic species.

117. A method according to any one of claims 1 to 113, wherein said classification is performed on the basis of a total amount, or a relative total amount, of a plurality of diagnostic species.

118. A method according to any one of claims 1 to 113, wherein:

said one or more diagnostic species is;

a plurality of diagnostic species; and

,

said amount of, or relative amount of one or more diagnostic species is;

a combination of a plurality of amounts, or relative amounts, each of which is the amount of, or relative amount of one of said plurality of diagnostic species.

119. A method according to claim 118, wherein said combination is a linear combination.

120. A method according to any one of claims 1 to 119, wherein said predetermined diagnostic spectral windows are defined by one or more index values, δ

_r, corresponding to the bucket regions listed in Table 4-CHD.

121. A method according to any one of claims 1 to 119, wherein at least one of said one or more predetermined diagnostic species is a species described in Table 4-CHD.

122. A method according to any one of claims 1 to 119, wherein each of a plurality of said one or more predetermined diagnostic species is a species described in Table 4-CHD.

123. A method according to any one of claims 1 to 119, wherein each of said one or more predetermined diagnostic species is a species described in Table 4-CHD.

148. A computer system or device, such as a computer or linked computers, operatively configured to implement a method according to any one of claims 1 to 147.

149. Computer code suitable for implementing a method according to any one of claims 1 to 147 on a suitable computer system.

150. A computer program comprising computer program means adapted to perform a method according to according to any one of claims 1 to 147, when said program is run on a computer.

151. A computer program according to claim 150, embodied on a computer readable medium.

152. A data carrier which carries computer code suitable for implementing a method according to any one of claims 1 to 147 on a suitable computer.

153. Computer code and/or computer readable data representing a predictive mathematical model as described in any one of claims 1 to 147.

154. A data carrier which carries computer code and/or computer readable data representing a predictive mathematical model as described in any one of claims 1 to 147.

155. A computer system or device, such as a computer or linked computers, programmed or loaded with computer code and/or computer readable data representing a predictive mathematical model as described in any one of claims 1 to 147.

156. A system comprising:

(a) a first component comprising a device for obtaining NMR spectral intensity data for a sample; and

,

(b) a second component comprising computer system or device, such as a computer or linked computers, operatively configured to implement a method according to any one of claims 1 to 147, and operatively linked to said first component.

163. A method of therapeutic monitoring of a subject undergoing therapy which employs a method of classification according to any one of claims 1 to 123.

164. A method of evaluating drug therapy and/or drug efficacy which employs a method of classification according to any one of claims 1 to 123.

9. A method of classifying a subject, said method comprising the step of relating NMR spectral intensity at one or more predetermined diagnostic spectral windows for a sample from said subject with a predetermined condition associated with atherosclerosis/coronary heart disease of said subject.

10. A method, according to claim 9, of classifying a subject, said method comprising the step of relating NMR spectral intensity at one or more predetermined diagnostic spectral windows for a sample from said subject with the presence or absence of a predetermined condition associated with atherosclerosis/coronary heart disease of said subject.

11. A method, according to claim 9, of classifying a subject, said method comprising the step of relating a modulation of NMR spectral intensity, relative to a control value, at one or more predetermined diagnostic spectral windows for a sample from said subject with a predetermined condition associated with atherosclerosis/coronary heart disease of said subject.

12. A method, according to claim 9, of classifying a subject, said method comprising the step of relating a modulation of NMR spectral intensity, relative to a control value, at one or more predetermined diagnostic spectral windows for a sample from said subject with the presence or absence of a predetermined condition associated with atherosclerosis/coronary heart disease of said subject.

13. A method of diagnosing a predetermined condition associated with atherosclerosis/coronary heart disease of a subject, said method comprising the step of relating NMR spectral intensity at one or more predetermined diagnostic spectral windows for a sample from said subject with said predetermined condition of said subject.

14. A method, according to claim 13, of diagnosing a predetermined condition associated with atherosclerosis/coronary heart disease of a subject, said method comprising the step of relating NMR spectral intensity at one or more predetermined diagnostic spectral windows for a sample from said subject with the presence or absence of said predetermined condition of said subject.

15. A method, according to claim 13, of diagnosing a predetermined condition associated with atherosclerosis/coronary heart disease of a subject, said method comprising the step of relating a modulation of NMR spectral intensity, relative to a control value, at one or more predetermined diagnostic spectral windows for a sample from said subject with said predetermined condition of said subject.

16. A method, according to claim 13, of diagnosing a predetermined condition associated with atherosclerosis/coronary heart disease of a subject, said method comprising the step of relating a modulation of NMR spectral intensity, relative to a control value, at one or more predetermined diagnostic spectral windows for a sample from said subject with the presence or absence of said predetermined condition of said subject.

17. A method of classifying a sample, said method comprising the step of relating the amount of, or relative amount of one or more diagnostic species present in said sample with a predetermined condition associated with atherosclerosis/coronary heart disease.

18. A method, according to claim 17, of classifying a sample from a subject, said method comprising the step of relating the amount of, or relative amount of one or more diagnostic species present in said sample with a predetermined condition associated with atherosclerosis/coronary heart disease of said subject.

19. A method, according to claim 17, of classifying a sample, said method comprising the step of relating the amount of, or relative amount of one or more diagnostic species present in said sample with the presence or absence of a predetermined condition associated with atherosclerosis/coronary heart disease.

20. A method, according to claim 17, of classifying a sample from a subject, said method comprising the step of relating the amount of, or the relative amount of, one or more diagnostic species present in said sample with the presence or absence of a predetermined condition associated with atherosclerosis/coronary heart disease of said subject.

21. A method, according to claim 17, of classifying a sample, said method comprising the step of relating a modulation of the amount of, or relative amount of one or more diagnostic species present in said sample, as compared to a control sample, with a predetermined condition associated with atherosclerosis/coronary heart disease.

22. A method, according to claim 17, of classifying a sample from a subject, said method comprising the step of relating a modulation of the amount of, or relative amount of one or more diagnostic species present in said sample, as compared to a control sample, with a predetermined condition associated with atherosclerosis/coronary heart disease of said subject.

23. A method, according to claim 17, of classifying a sample, said method comprising the step of relating a modulation of the amount of, or relative amount of one or more diagnostic species present in said sample, as compared to a control sample, with the presence or absence of a predetermined condition associated with atherosclerosis/coronary heart disease.

24. A method, according to claim 17, of classifying a sample from a subject, said method comprising the step of relating a modulation of the amount of, or relative amount of one or more diagnostic species present in said sample, as compared to a control sample, with the presence or absence of a predetermined condition associated with atherosclerosis/coronary heart disease of said subject.

25. A method of classifying a subject, said method comprising the step of relating the amount of, or relative amount of one or more diagnostic species present in a sample from said subject with a predetermined condition associated with atherosclerosis/coronary heart disease of said subject.

26. A method, according to claim 25, of classifying a subject, said method comprising the step of relating the amount of, or relative amount of one or more diagnostic species present in a sample from said subject with the presence or absence of a predetermined condition associated with atherosclerosis/coronary heart disease of said subject.

27. A method, according to claim 25, of classifying a subject, said method comprising the step of relating a modulation of the amount of, or relative amount of one or more diagnostic species present in a sample from said subject, as compared to a control sample, with a predetermined condition associated with atherosclerosis/coronary heart disease of said subject.

28. A method, according to claim 25, of classifying a subject, said method comprising the step of relating a modulation of the amount of, or relative amount of one or more diagnostic species present in a sample from said subject, as compared to a control sample, with the presence or absence of a predetermined condition associated with atherosclerosis/coronary heart disease of said subject.

29. A method of diagnosing a predetermined condition associated with atherosclerosis/coronary heart disease of a subject, said method comprising the step of relating the amount of, or relative amount of one or more diagnostic species present in a sample from said subject with said predetermined condition of said subject.

30. A method, according to claim 29, of diagnosing a predetermined condition associated with atherosclerosis/coronary heart disease of a subject, said method comprising the step of relating the amount of, or relative amount of one or more diagnostic species present in a sample from said subject with the presence or absence of said predetermined condition of said subject.

31. A method, according to claim 29, of diagnosing a predetermined condition associated with atherosclerosis/coronary heart disease of a subject, said method comprising the step of relating a modulation of the amount of, or relative amount of one or more diagnostic species present in a sample from said subject, as compared to a control sample, with said predetermined condition of said subject.

32. A method, according to claim 29, of diagnosing a predetermined condition associated with atherosclerosis/coronary heart disease of a subject, said method comprising the step of relating a modulation of the amount of, or relative amount of one or more diagnostic species present in a sample from said subject, as compared to a control sample, with the presence or absence of said predetermined condition of said subject.

33. A method of classification, said method comprising the steps of:

(a) forming a predictive mathematical model by applying a modelling method to modelling data;

(b) using said model to classify a test sample.

34. A method, according to claim 33, of classifying a test sample, said method comprising the steps of:

(a) forming a predictive mathematical model by applying a modelling method to modelling data;

wherein said modelling data comprises a plurality of data sets for modelling samples of known class;

(b) using said model to classify said test sample as being a member of one of said known classes.

35. A method, according to claim 33, of classifying a test sample, said method comprising the steps of:

(a) forming a predictive mathematical model by applying a modelling method to modelling data;

wherein said modelling data comprises at least one data set for each of a plurality of modelling samples;

wherein said modelling samples define a class group consisting of a plurality of classes;

wherein each of said modelling samples is of a known class selected from said class group; and

,

(b) using said model with a data set for said test sample to classify said test sample as being a member of one class selected from said class group.

36. A method of classification, said method comprising the step of:

using a predictive mathematical model;

wherein said model is formed by applying a modelling method to modelling data;

to classify a test sample.

37. A method, according to claim 36, of classifying a test sample, said method comprising the step of:

using a predictive mathematical model;

wherein said model is formed by applying a modelling method to modelling data;

wherein said modelling data comprises a plurality of data sets for modelling samples of known class;

to classify said test sample as being a member of one of said known classes.

38. A method, according to claim 36, of classifying a test sample, said method comprising the step of:

using a predictive mathematical model;

wherein said model is formed by applying a modelling method to modelling data;

wherein said modelling data comprises at least one data set for each of a plurality of modelling samples;

wherein said modelling samples define a class group consisting of a plurality of classes;

wherein each of said modelling samples is of a known class selected from said class group;

with a data set for said test sample to classify said test sample as being a member of one class selected from said class group.

39. A method of classification, said method comprising the steps of:

(a) forming a predictive mathematical model by applying a modelling method to modelling data;

(b) using said model to classify a subject.

40. A method, according to claim 39, of classifying a subject, said method comprising the steps of:

(a) forming a predictive mathematical model by applying a modelling method to modelling data;

wherein said modelling data comprises a plurality of data sets for modelling samples of known class;

(b) using said model to classify a test sample from said subject as being a member of one of said known classes, and thereby classify said subject.

41. A method, according to claim 39, of classifying a subject, said method comprising the steps of:

(a) forming a predictive mathematical model by applying a modelling method to modelling data;

wherein said modelling data comprises at least one data set for each of a plurality of modelling samples;

wherein said modelling samples define a class group consisting of a plurality of classes;

wherein each of said modelling samples is of a known class selected from said class group; and

,

(b) using said model with a data set for a test sample from said subject to classify said test sample as being a member of one class selected from said class group, and thereby classify said subject.

42. A method of classification, said method comprising the step of:

using a predictive mathematical model;

wherein said model is formed by applying a modelling method to modelling data;

to classify a subject.

43. A method, according to claim 42, of classifying a subject, said method comprising the step of:

using a predictive mathematical model wherein said model is formed by applying a modelling method to modelling data;

wherein said modelling data comprises a plurality of data sets for modelling samples of known class;

to classify a test sample from said subject as being a member of one of said known classes, and thereby classify said subject.

44. A method, according to claim 42, of classifying a subject, said method comprising the step of:

using a predictive mathematical model,

wherein said model is formed by applying a modelling method to modelling data;

wherein said modelling data comprises at least one data set for each of a plurality of modelling samples;

wherein said modelling samples define a class group consisting of a plurality of classes;

wherein each of said modelling samples is of a known class selected from said class group;

with a data set for a test sample from said subject to classify said test sample as being a member of one class selected from said class group, and thereby classify said subject.

45. A method of diagnosis, said method comprising the steps of:

(a) forming a predictive mathematical model by applying a modelling method to modelling data;

(b) using said model to diagnose a subject.

46. A method, according to claim 45, of diagnosing a predetermined condition associated with atherosclerosis/coronary heart disease of a subject, said method comprising the steps of:

(a) forming a predictive mathematical model by applying a modelling method to modelling data;

wherein said modelling data comprises a plurality of data sets for modelling samples of known class;

(b) using said model to classify a test sample from said subject as being a member of one of said known classes, and thereby diagnose said subject.

47. A method, according to claim 45, of diagnosing a predetermined condition associated with atherosclerosis/coronary heart disease of a subject, said method comprising the steps of:

(a) forming a predictive mathematical model by applying a modelling method to modelling data;

wherein said modelling data comprises at least one data set for each of a plurality of modelling samples;

wherein said modelling samples define a class group consisting of a plurality of classes;

wherein each of said modelling samples is of a known class selected from said class group; and

,

(b) using said model with a data set for a test sample from said subject to classify said test sample as being a member of one class selected from said class group, and thereby diagnose said subject.

48. A method of diagnosis, said method comprising the step of:

using a predictive mathematical model;

wherein said model is formed by applying a modelling method to modelling data;

to diagnose a subject.

49. A method, according to claim 48, of diagnosing a predetermined condition associated with atherosclerosis/coronary heart disease of a subject, said method comprising the step of:

using a predictive mathematical model;

wherein said model is formed by applying a modelling method to modelling data;

wherein said modelling data comprises a plurality of data sets for modelling samples of known class;

to classify a test sample from said subject as being a member of one of said known classes, and thereby diagnose said subject.

50. A method, according to claim 48, of diagnosing a predetermined condition associated with atherosclerosis/coronary heart disease of a subject, said method comprising the step of:

using a predictive mathematical model;

wherein said model is formed by applying a modelling method to modelling data;

wherein said modelling data comprises at least one data set for each of a plurality of modelling samples;

wherein said modelling samples define a class group consisting of a plurality of classes;

wherein each of said modelling samples is of a known class selected from said class group;

with a data set for a test sample from said subject to classify said test sample as being a member of one class selected from said class group, and thereby diagnose said subject.

124. A method of identifying a diagnostic species, or a combination of a plurality of diagnostic species, for a predetermined condition associated with atherosclerosis/coronary heart disease, said method comprising the steps of:

(a) applying a multivariate statistical analysis method to experimental data;

wherein said experimental data comprises at least one data comprising experimental parameters measured for each of a plurality of experimental samples;

wherein said experimental samples define a class group consisting of a plurality of classes;

wherein at least one of said plurality of classes is a class associated with said predetermined condition, e.g., a class associated with the presence of said predetermined condition;

wherein at least one of said plurality of classes is a class not associated with said predetermined condition, e.g., a class associated with the absence of said predetermined condition;

wherein each of said experimental samples is of known class selected from said class group;

and;

(b) identifying one or more critical experimental parameters;

wherein each of said critical experimental parameters is statistically significantly different for classes of said class group, e.g., is statistically significant for discriminating between classes of said class group; and

,

(c) matching each of one or more of said one or more critical experimental parameters with said diagnostic species;

or;

(b) identifying a combination of a plurality of critical experimental parameters;

wherein said combination of a plurality of critical experimental parameters is statistically significantly different for classes of said class group, e.g., is statistically significant for discriminating between classes of said class group; and

,

(c) matching each of one or more of said plurality of critical experimental parameters with said combination of a plurality of diagnostic species.

125. A method, according to claim 124, wherein:

one or more of said critical experimental parameters is a spectral parameter, and said identifying and matching steps are;

(b) identifying one or more critical experimental spectral parameters; and

,

(c) matching each of one or more of said one or more critical experimental spectral parameters with a spectral feature, e.g., a spectral peak;

and matching one or more of said spectral peaks with said diagnostic species;

or;

(b) identifying a combination of a plurality of critical experimental spectral parameters; and

,

(c) matching each of a plurality of said plurality of critical experimental spectral parameters with a spectral feature, e.g., a spectral peak;

and matching one or more of said spectral peaks with said combination of a plurality of diagnostic species.

126. A method according to any one of claims 124 to 125, wherein said multivariate statistical analysis method is a multivariate statistical analysis method which employs a pattern recognition method.

127. A method according to any one of claims 124 to 126, wherein said multivariate statistical analysis method is, or employs PCA.

128. A method according to any one of claims 124 to 126, wherein said multivariate statistical analysis method is, or employs PLS.

129. A method according to any one of claims 124 to 126, wherein said multivariate statistical analysis method is, or employs PLS-DA.

130. A method according to any one of claims 124 to 129, wherein said multivariate statistical analysis method includes a step of data filtering.

131. A method according to any one of claims 124 to 129, wherein said multivariate statistical analysis method includes a step of orthogonal data filtering.

132. A method according to any one of claims 124 to 129, wherein said multivariate statistical analysis method includes a step of OSC.

133. A method according to any one of claims 124 to 132, wherein said experimental parameters comprise spectral data.

134. A method according to any one of claims 124 to 132, wherein said experimental parameters comprise both spectral data and non-spectral data.

135. A method according to any one of claims 124 to 132, wherein said experimental parameters comprise NMR spectral data.

136. A method according to any one of claims 124 to 132, wherein said experimental parameters comprise both NMR spectral data and non-NMR spectral data.

137. A method according to any one of claims 124 to 136, wherein said NMR spectral data comprises ¹H NMR spectral data and/or ¹³C NMR spectral data.

138. A method according to any one of claims 124 to 136, wherein said NMR spectral data comprises ¹H NMR spectral data.

139. A method according to any one of claims 124 to 138, wherein said non-spectral data is non-spectral clinical data.

140. A method according to any one of claims 124 to 138, wherein said non-NMR spectral data is non-spectral clinical data.

141. A method according to any one of claims 124 to 140, wherein said critical experimental parameters are spectral parameters.

142. A method according to any one of claims 124 to 141, wherein said class group comprises classes associated with said predetermined condition.

143. A method according to any one of claims 124 to 142, wherein said class group comprises exactly two classes.

144. A method according to any one of claims 124 to 142, wherein said class group comprises exactly two classes:

presence of said predetermined condition; and

absence of said predetermined condition.

145. A method according to any one of claims 124 to 142, wherein said class associated with said predetermined condition is a class associated with the presence of said predetermined condition.

146. A method according to any one of claims 124 to 142, wherein said class not associated with said predetermined condition is a class associated with the absence of said predetermined condition.

147. A method according to any one of claims 124 to 146, said method further comprising the additional step of:

(d) confirming the identity of said diagnostic species.

157. A diagnostic species identified by a method according to any one of claims 124 to 147.

158. A diagnostic species identified by a method according to any one of claims 124 to 147 for use in a method of classification.

159. A method of classification which employs or relies upon one or more diagnostic species identified by a method according to any one of claims 124 to 147.

160. Use of one or more diagnostic species identified by a method of classification according to any one of claims 124 to 147.

161. An assay for use in a method of classification, which assay relies upon one or more diagnostic species identified by a method according to any one of claims 124 to 147.

162. Use of an assay in a method of classification, which assay relies upon one or more diagnostic species identified by a method according to any one of claims 124 to 147.