WO2008058332A1

WO2008058332A1 - Diagnostic protocols for diabetes

Info

Publication number: WO2008058332A1
Application number: PCT/AU2007/001752
Authority: WO
Inventors: Gregory Royce Collier; Kenneth Russell Walder; Jeremy Bryan Mark Jowett; Katherine Anne Shields; Joanne Elizabeth Curran; John Blangero; Eric Keith Moses
Original assignee: Autogen Research Pty Ltd
Current assignee: Autogen Research Pty Ltd
Priority date: 2006-11-14
Filing date: 2007-11-14
Publication date: 2008-05-22
Anticipated expiration: 2009-05-14

Abstract

The present invention relates generally to the field of diagnostic and prognostic protocols. More particularly, the subject invention provides methods for diagnosing or prognosing a subject having or developing diabetes, or developing a condition associated with diabetes based on selected biomarkers.

Description

DIAGNOSTIC PROTOCOLS FOR DIABETES

FIELD

BACKGROUND

Bibliographic details of references provided in the subject specification are listed at the end of the specification.

Reference to any prior art in this specification is not, and should not be taken as, an acknowledgment or any form of suggestion that this prior art forms part of the common general knowledge in any country.

The increasing sophistication of recombinant DNA technology is greatly facilitating research and development in the field of human health management. This is particularly the case in the investigation of the genetic bases involved in the etiology of certain disease conditions. Diseases of particular concern include disorders associated with diabetes, complications associated with diabetes, such as blindness, nephropathy and/or cardiovascular disease, and mitochondrial dysfunction as well as myopathies, genetic disorders and cancers and in modulating apoptosis, signal transduction and/or nuclear targeting.

Diabetes represents a significant and debilitating disease. The incidence of diabetes is increasing rapidly. It has been estimated that there were about 700,000 persons with diabetes in Australia in 1995 while in the US, the prevalence of diabetes increased from 4.9% in 1990 to 6.9% in 1999 (Mokdad Diabetes Care 24(2) All, 2001). There are two main types of diabetes referred to as Type 1 and Type 2 diabetes.

Type 1 diabetes, also known as insulin-dependent diabetes mellitus (IDDM), results from an inability to produce insulin. It can develop at any age, although it usually develops in children and young adults and is also referred to as juvenile-onset diabetes. Once it has developed, Type 1 diabetes is a life-long condition.

Type 2 diabetes occurs later in life and is sometimes known as late-onset diabetes or non- insulin-dependent diabetes mellitus (NIDDM), because insulin treatment is not always needed. Type 2 diabetes develops when the body becomes resistant to insulin. This happens when the body's tissues, such as muscle, do not respond fully to the actions of insulin, so cannot make use of glucose in the blood. The pancreas responds by producing more insulin. In addition, the liver, where glucose is stored, releases more glucose to try to increase the amount of glucose available. Eventually, the pancreas becomes less able to produce enough insulin and the tissues become more resistant to insulin. As a result, blood glucose levels slowly start to rise.

Due to the ever increasing incidence of diabetes in society, there is an ever more urgent need to develop a diagnostic or prognostic method which is capable of not only diagnosing diabetes, but also predicting i.e. prognosing, the likelihood of developing diabetes or the probability of developing a condition associated with diabetes, such as blindness, nephropathy and/or cardiovascular disease.

SUMMARY

The present invention provides biomarkers which are useful in diagnosing, prognosing, or otherwise stratifying a subject with diabetes, or a predisposition to develop diabetes or the probability of a subject developing a condition associated with diabetes, such as blindness, nephropathy and/or cardiovascular disease. In one aspect, the biomarkers of the present invention are differentially expressed in subjects with a predisposition to develop diabetes, subjects with diabetes or subjects at risk of developing secondary conditions associated with diabetes relative to a control population. Diagnosis or prognosis of diabetes, a pre- disposition for diabetes or a probability of developing a condition associated with diabetes may include, but is not limited to, the measurement of specific biomarkers or a determination of a profile or ranking of biomarkers in a biological sample. The ability to diagnose or prognose diabetes, a pre-disposition for diabetes or a probability of developing a condition associated with diabetes has important implications for the treatment and/or management of a subject's condition, i.e. in the monitoring of a therapeutic regime.

Reference to a "biomarker" includes a marker of diabetes, a pre-disposition for diabetes or a probability of developing a condition associated with diabetes, or a predisposition for developing diabetes. The biomarker may be proteinaceous or genetic in nature and hence the present invention extends to proteomic and genomic indicators of diabetes, a predisposition of developing diabetes or a probability of developing a condition associated with diabetes. A "genomic marker" includes a "nucleomic marker" which includes RNA and in particular niRNA.

The present invention particularly identifies 51 diabetes-specific biomarkers. The nucleotide sequences defining the biomarkers are disclosed in SEQ ID NOs: 1 to 51 (see Table 1). One biomarker (CXS-2038) is also defined by an amino acid sequence (SEQ ID NO:52). The 51 biomarkers represent a profile or ranking of predicative markers of diabetes, a pre-disposition of developing diabetes or a probability of developing a condition associated with diabetes. The presence, absence or profile of the biomarkers assists in stratifying a subject or group of subjects for levels of health or ill health. The present invention contemplates the use of these sequences or mammalian homologs or equivalents or their expression products in the manufacture of diagnostic or prognostic agents for diabetes, a pre-disposition of developing diabetes or a probability of developing a condition associated with diabetes.

Accordingly, one aspect of the present invention contemplates a method for the diagnosis or prognosis of diabetes or a predisposition for development of diabetes or a complication associated with diabetes in a subject, the method comprising determining the level of one or more biomarkers listed in Table 3 in a biological sample from the subject and comparing the level of the one or more biomarkers to a statistically validated threshold, wherein a difference in the level of one or more biomarkers is predicative of the subject having or developing diabetes or a complication of same.

The present invention may be conducted in situ or on a biological sample from the subject. Hence, the present invention further provides a method for the diagnosis or prognosis of diabetes or a predisposition for the development of diabetes or a complication associated with diabetes in a subject, the method comprising: (a) obtaining a biological sample from a subject; (b) determining the level of one or more biomarkers listed in Table 3 in the biological sample; and (c) comparing the level of the one or more biomarkers in the biological sample to a statistically validated threshold, wherein a difference in the level of one or more biomarkers in comparison step (c) is predicative of the subject developing diabetes.

A further aspect of the present invention is directed to a diagnostic or prognostic agent for use in monitoring or diagnosing or prognosing diabetes or a condition associated with diabetes or predicting the likelihood of a subject developing diabetes, the diagnostic or prognostic agent selected from an antibody specific for a polypeptide disclosed in SEQ ID

NO: 52 or a polypeptide encoded by a nucleic acid molecule having a nucleotide sequence selected from the group consisting of SEQ ID NOs :1 to 51, or a derivative or homolog of said polypeptide. The agent may also be a genetic sequence comprising or capable of annealing to a nucleotide sequence selected from the group consisting of SEQ ID NOs: 1 to

51 or a complementary form thereof, useful inter alia in PCR, qRT-PCR, RT PCR, Northern hybridization, hybridization, RFLP analysis or AFLP analysis.

The present invention provides, therefore, a profile of biomarkers comprising from 1 to 51 markers, the levels of one or more of which are instructive as to the presence or absence or the likelihood of development of diabetes in a subject.

TABLE 1 Summary of Sequence Identifiers

DETAILED DESCRIPTION

Throughout this specification, unless the context requires otherwise, the word "comprise", or variations such as "comprises" or "comprising", will be understood to imply the inclusion of a stated element or integer or group of elements or integers but not the exclusion of any other element or integer or group of elements or integers.

Nucleotide and amino acid sequences are referred to by a sequence identifier number (SEQ ID NO:). The SEQ ID NO: correspond numerically to the sequence identifiers <400>l (SEQ ID NO:1), <400>2 (SEQ ID NO:2), etc. A summary of the sequence identifiers is provided in Table 1. A sequence listing is provided after the claims.

All scientific citations, patents, patent applications and manufacturer's technical specifications referred to hereinafter are incorporated herein by reference in their entirety.

Unless otherwise indicated, the subject invention is not limited to specific diagnostic or prognostic components or agents, assay methods, or the like, and as such, may vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting.

As used in the subject specification, the singular forms "a", "an" and "the" include plural aspects unless the context clearly dictates otherwise. Thus, for example, reference to "a biomarker" includes a single biomarker, as well as two or more biomarkers; reference to "an agent" includes a single agent, as well as two or more agents; reference to "the invention" includes one or more aspects of the invention; and so forth.

A "biological sample" includes a biological fluid sample such as but not limited to whole blood, blood plasma, serum, mucus, urine, isolated peripheral blood mononuclear cells, lymphocytes, semen, faecal matter, bile, cellular extracts, respiratory fluid, lavage fluid, lymph fluid, saliva and other tissue secretions or fluid. The preferred biological fluid is whole blood, blood plasma and serum. The biological sample may, therefore, be a fluid- based sample or cells including cells captured to solid support. It is not necessary for a biological sample to be physically removed from a subject, although removal and subsequent analysis of biomarkers in a biological sample is the most convenient method for conducting the instant methods. The biological fluid may undergo an enrichment process or high abundance molecules which might interfere in the assay may be removed.

The present invention is predicated in part on the identification of biomarkers associated inter alia with diabetes, complications associated with diabetes, such as blindness, nephropathy and/or cardiovascular disease or the probability of developing diabetes.

Reference to "identification" includes ranking, stratifying, or profiling selected biomarkers indicative of diabetes, or a complication arising therefrom. Reference to "diabetes" includes type I and type II diabetes.

Hence, the present invention provides biomarkers which are useful in diagnosing, prognosing, or otherwise stratifying a subject with diabetes, or a predisposition to develop diabetes or the probability of a subject developing a condition associated with diabetes, such as blindness, nephropathy and/or cardiovascular disease. In one aspect, the biomarkers of the present invention are differently expressed in subjects with a predisposition to develop diabetes, subjects with diabetes or subjects at risk of developing secondary conditions associated with diabetes relative to a control population. Diagnosis or prognosis of diabetes, a pre-disposition for diabetes or a probability of developing a condition associated with diabetes may include, but is not limited to, the measurement of specific biomarkers or a determination of a profile or ranking of biomarkers in a biological sample such as blood, serum, urine or saliva, including provided on or by the cells within the biological sample. The ability to diagnose or prognose diabetes, a pre-disposition for diabetes or a probability of developing a condition associated with diabetes has important implications for the treatment and/or management of a subject's condition.

More particularly, the present invention contemplates a method for diagnosing diabetes complications associated with diabetes or a predisposition of a subject to develop diabetes, the method comprising: (a) obtaining a biological sample from a subject; (b) determining the level of one or more biomarkers listed in Table 3 in the biological sample; and

(c) comparing the level of the one or more biomarkers in the biological sample to a statistically validated threshold, wherein detecting a difference in the level of one or more biomarkers in the comparison step (c) is predicative of the subject having diabetes, or a complication associated with diabetes or developing diabetes.

In an aspect, the method of the present invention relies upon determining the level of one of the biomarkers listed in Table 3. In a related aspect, the method uses 2, 3, 4, 5, 6, 7, 8, 9, 10, H₅ 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50 or 51 of the biomarkers in combination for diagnosing diabetes, or a complication associated with diabetes or a predisposition of a subject to develop diabetes. The list of biomarkers in Table 1 and Table 3 should not be taken as a rank of the most sensitive or important to test. Reference to the biomarkers includes a profile of 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, 16 or more, 17 or more, 18 or more, 19 or more, 20 or more, 21 or more, 22 or more, 23 or more, 24 or more, 25 or more, 26 or more, 27 or more, 28 or more, 29 or more, 30 or more, 31 or more, 32 ormore, 33 or more, 34 or more, 35 or more, 36 or more, 37 or more, 38 or more, 39 or more, 40 or more, 41 or more, 42 or more, 43 or more, 44 or more, 45 or more, 46 or more, 47 or more, 48 or more, 49 or more, 50 or more, 51 or more.

The present invention extends to derivatives and homologs of the nucleotide sequences of Table 1 or amino acid sequences encoded thereby or of the amino acid defining CXS-2038. Hence, the biomarkers of the present invention include those listed in Table 3, as well as nucleotide sequences having 90% identity thereto or capable of hybridising to the sequence or their complementary forms under high stringency conditions or encoding an amino acid sequence having at least 90% similarity to the amino acid sequence encoded by the sequences in Table 1. Reference herein to similarity or identity is generally at a level of comparison of at least 15 consecutive or substantially consecutive nucleotides (or corresponding amino acids) such as at least 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399 or 400 consecutive or substantially consecutive nucleotides (or amino acids). Preferred percentage similarities or identities have at least about 80%, at least about 90%, or at least about 95%. Examples include 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 and 100%.

The term "similarity" as used herein includes exact identity between compared sequences at the nucleotide or amino acid level. Where there is non-identity at the nucleotide level, "similarity" includes differences between sequences which result in different amino acids that are nevertheless related to each other at the structural, functional, biochemical and/or conformational levels. Where there is non-identity at the amino acid level, "similarity" includes amino acids that are nevertheless related to each other at the structural, functional, biochemical and/or conformational levels. In a particularly preferred embodiment, nucleotide and sequence comparisons are made at the level of identity rather than similarity.

Terms used to describe sequence relationships between two or more polynucleotides or polypeptides include "reference sequence", "comparison window", "sequence similarity", "sequence identity", "percentage of sequence similarity", "percentage of sequence identity", "substantially similar" and "substantial identity". A "reference sequence" is at least 12 but frequently 15 to 18 and often at least 25 or above, such as 30 monomer units, inclusive of nucleotides and amino acid residues, in length, examples include 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24 and 25. Because two polynucleotides may each comprise (1) a sequence (i.e. only a portion of the complete polynucleotide sequence) that is similar between the two polynucleotides, and (2) a sequence that is divergent between the two polynucleotides, sequence comparisons between two (or more) polynucleotides are typically performed by comparing sequences of the two polynucleotides over a "comparison window" to identify and compare local regions of sequence similarity. A "comparison window" refers to a conceptual segment of typically 12 contiguous residues that is compared to a reference sequence. The comparison window may comprise additions or deletions (i.e. gaps) of about 20% or less as compared to the reference sequence (which does not comprise additions or deletions) for optimal alignment of the two sequences. Optimal alignment of sequences for aligning a comparison window may be conducted by computerized implementations of algorithms (GAP, BESTFIT₅ FASTA, and TFASTA in the Wisconsin Genetics Software Package Release 7.0, Genetics Computer Group, 575 Science Drive Madison, WI, USA) or by inspection and the best alignment (i.e. resulting in the highest percentage homology over the comparison window) generated by any of the various methods selected. Reference also may be made to the BLAST family of programs as for example disclosed by Altschul et al. (Nucl Acids Res 25:3389, 1997). A detailed discussion of sequence analysis can be found in Unit 19.3 of Ausubel et al. ("Current Protocols in Molecular Biology" John Wiley & Sons Inc, Chapter 15, 1994- 1998). By "high stringency conditions", is meant conditions under which the probe specifically hybridizes to a target sequence in an amount that is detectably stronger than non-specific hybridization. High stringency conditions, then, would be conditions which would distinguish a polynucleotide with an exact complementary sequence, or one containing only a few scattered mismatches from a random sequence that happened to have a few small regions (3-10 bases, for example) that matched the probe. Such small regions of complementarity, are more easily melted than a full length complement of 14-17 or more bases and high stringency hybridization makes them easily distinguishable. Relatively high stringency conditions would include, for example, low salt and/or high temperature conditions, such as provided by about 0.02 M to about 0.10 M NaCl or the equivalent, at temperatures of about 50°C to about 70° C. Such high stringency conditions tolerate little, if any, mismatch between the probe and the template or target strand, and would be particularly suitable for detecting expression of specific biomarkers. It is generally appreciated that conditions can be rendered more stringent by the addition of increasing amounts of formamide .

Reference herein to a high stringency includes and encompasses from at least about 0 to at least about 15% v/v formamide and from at least about 1 M to at least about 2 M salt for hybridization, and at least about 31% v/v to at least about 50% v/v formamide, such as 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49 and 50% v/v formamide and from at least about 0.01 M to at least about 0.15 M salt, such as 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.10, 0.11, 0.12, 0.13, 0.14 and 0.15 M for hybridization, and at least about 0.01 M to at least about 0.15 M salt, such as 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.10, 0.11, 0.12, 0.13, 0.14 and 0.15 M for washing conditions. In general, washing is carried out T_m = 69.3 + 0.41 (G+C)% (Marmur and Doty, J. MoI Biol. 5: 109, 1962). However, the T_m of a duplex DNA decreases by 1°C with every increase of 1% in the number of mismatch base pairs (Bonner and Laskey, Eur J Biochem 46: S3, 1974). Formamide is optional in these hybridization conditions. Accordingly, high stringency is defined as 0.1 x SSC buffer, 0.1% w/v SDS at a temperature of at least 65°C.

In another embodiment, the present invention provides a method for diagnosing diabetes or a complication arising from diabetes in a subject or a predisposition of a subject to develop diabetes, said method comprising screening for levels of protein or mRNA encoding said protein or a homolog thereof wherein the protein is a biomarker listed in Table 3 in a biological sample from said subject, wherein a difference in the level of the protein of compared to a statistically validated threshold is indicative of diabetes or a complication arising therefrom or a predisposition to develop same.

The present invention, in certain aspects, is directed to the diagnosis or prognosis of diabetes, or a complication associated therewith or a predisposition for developing diabetes by comparing levels of the biomarkers in the biological sample obtained from the subject may compared to a statistically validated threshold. The statistically validated threshold is based upon levels of biomarkers, in comparable samples obtained from a control population, e.g., the general population or a select population of human subjects. For example, the select population may be comprised of apparently healthy subjects. "Apparently healthy", as used herein, means individual who have not previously had any signs or symptoms indicating the presence of diabetes, including one or more of a family history of diabetes, evidence of factors associated with diabetes, including one or more of low activity level, poor diet, excess body weight (especially around the waist), over 45 years old, high blood pressure, high blood levels of triglycerides, HDL cholesterol of less than 35, previously identified impaired glucose tolerance by doctor, previous diabetes during pregnancy or baby weighing more than nine pounds. Apparently healthy individuals also do not otherwise exhibit symptoms of disease. In other words, such individuals, if examined by a medical professional, would be characterized as healthy and free of symptoms of disease. In another example, the control value can be derived from a genetically related group of individuals, such as the San Antonio Family Heart Study. Accordingly, the control values selected may take into account the category into which the test subject falls. Appropriate categories can be selected with no more than routine experimentation by those of ordinary skill in the art.

The statistically validated threshold is related to the value used to characterize the level of the biomarker, be it a nucleic acid or polypeptide obtained from the subject. Thus, if the level of the biomarker nucleotide or polypeptide is an absolute value, such as the number of copies of a particular transcript or level of a protein per ml of blood, or cell number then the control value is also based upon the number of copies of a particular transcriptor level of a protein per ml of blood, or cell number.

The statistically validated threshold can take a variety of forms. The statistically validated threshold can be a single cut-off value, such as a median or mean. The statistically validated threshold can be established based upon comparative groups such as where the risk in one defined group is double the risk in another defined group. The statistically validated threshold can be divided equally (or unequally) into groups, such as a low risk group, a medium risk group and a high-risk group, or into quadrants, the lowest quadrant being individuals with the lowest risk the highest quadrant being individuals with the highest risk, and the subject's risk of having diabetes or a predisposition to develop diabetes can be based upon which group his or her test value falls.

Statistically validated threshold of the biomarkers obtained, such as for example, mean levels, median levels, or "cut-off" levels, are established by assaying a large sample of individuals in the general population or the select population and using a statistical model such as the predictive value method for selecting a positivity criterion or receiver operator characteristic curve that defines optimum specificity (highest true negative rate) and sensitivity (highest true positive rate) as described in Knapp, R. G., and Miller, M.C. (1992). Clinical Epidemiology and Biostatistics. William and Wilkins, Harual Publishing

Co. Malvern, Pa., which is specifically incorporated herein by reference. A "cutoff value can be determined for each biomarker that is assayed.

Levels of each select biomarker nucleic acid (nucleomic marker) or polypeptide (proteomic marker) in the subject's biological sample may be compared to a single control value or to a range of control values. If the level of the biomarker in the subject's biological sample is different than the statistically validated threshold, the test subject is at greater risk of developing or having diabetes or a condition associated with diabetes or a predisposition of a subject to develop diabetes than individuals with levels comparable to the statistically validated threshold. The extent of the difference between the subject's biomarker(s) levels and statistically validated threshold is also useful for characterizing the extent of the risk and thereby, determining which individuals would most greatly benefit from certain aggressive therapies. In those cases, where the statistically validated threshold ranges are divided into a plurality of groups, such as the statistically validated threshold ranges for individuals at high risk, average risk and low risk, the comparison involves determining into which group the subject's level of the relevant risk predictor falls.

For some biomarkers, when the level is higher (i.e. a positive correlation co-efficient; see Table 3) in the subject than for the statistically validated threshold, then the subject has an increased chance of having diabetes, a condition associated with diabetes or developing diabetes. Conversely, for other biomarkers, when the level of the biomarker is lower (i.e. negative correlation co-efficient; see Table 3) in the subject than for the statistically validated threshold, then the subject has an increased chance of having or developing diabetes. A list of the biomarkers and their relative expression levels compared to a statistically validated thresholds obtained from a "control group" is shown in Table 3.

The present predictive tests are useful for determining if and when therapeutic agents that are targeted at preventing diabetes or for slowing the progression of diabetes or for treating a condition associated with diabetes should and should not be prescribed for an individual. For example, individuals with values of one or more biomarkers different from a statistically validated threshold, or that are in the higher tertile or quartile of a "normal range," could be identified as those in need of more aggressive intervention with diabetic therapies, life style changes, etc.

In the practice of this embodiment, one may use a nucleic acid segment that is complementary to the full length of the mRNA specific for a biomarker listed in Table 3, or one may use a smaller segment that is complementary to a portion of the mRNA. Such smaller segments may be from about 14, about 15, about 16, about 17, about 18, about 19, about 20, about 21, about 22, about 23, about 24, about 25, about 25, about 30, about 50, about 75, about 100 or even several hundred bases in length and may be contained in larger segments that provide other functions such as promoters, restriction enzyme recognition sites, or other expression or message processing or replication functions. In certain embodiments such probes are designed to selectively hybridize to a biomarker listed in Table 3 or product thereof. A product thereof would include a DNA or RNA strand that is complementary to the mRNA and thus a useful probe would include both the sense and antisense orientations of a particular sequence. Also preferred are the use of probes or primers that are designed to selectively hybridize to a nucleic acid segment having a sequence selected from the group consisting of SEQ ID NOs: 1 to 51 or the complements thereof.

The methods of the present invention may also include determining the amount of hybridized product. Such determination may be by direct detection of a labeled hybridized probe, such as by use of a radioactive, fluorescent or other tag on the probe, or it may be by use of an amplification of a target sequence, and quantification of the amplified product. A preferred method of amplification is a reverse transcriptase polymerase chain reaction (RT-PCR) as described herein. In the practice of such a method, amplification may comprise contacting the target ribonucleic acids with a pair of amplification primers designed to amplify mRNA of a biomarker listed in Table 3, or even contacting the ribonucleic acids with a pair of amplification primers designed to amplify a nucleic acid segment comprising the nucleic acid sequence or complement thereof of a sequence selected from the group consisting of SEQ ID NOs: 1 to 51 or the complement thereof.

Diagnostic and prognostic methods may be based upon the steps of obtaining a biological sample from a subject or patient, contacting nucleic acids from the biological sample with an isolated nucleic acid segment specific for a biomarker listed in Table 3 under conditions effective to allow hybridization of substantially complementary nucleic acids, and detecting, and optionally further characterizing, the hybridized complementary nucleic acids thus formed.

The methods may involve in situ detection of sample nucleic acids located within the cells of the sample. The sample nucleic acids may also be separated from the cell prior to contact. The sample nucleic acids may be DNA or RNA.

The methods may involve the use of isolated nucleic acid segments from biomarkers listed in Table 3 that comprises a radio, enzymatic or fluorescent detectable label, wherein the hybridized complementary nucleic acids are detected by detecting the label. In certain embodiments, such probes are designed to selectively hybridize to the mRNA or product thereof of one or more biomarkers listed in Table 3. A product thereof would include a DNA or RNA strand that is complementary to the mRNA and thus a useful probe would include both the sense and antisense orientations of a particular sequence. Also contemplated are the use of probes or primers that are designed to selectively hybridize to a nucleic acid segment having a sequence selected from the group consisting of SEQ ID NOs: 1 to 51 or the complements thereof.

In the practice of the subject invention, some methods may involve detection of expression of a polypeptide product and particularly the expression product of a polypeptide disclosed in SEQ ID NO: 52 or a polypeptide encoded by a nucleic acid selected from the group consisting of SEQ ID NOs: 1 to 51. Such detection may be by any means known in the art and may include an immunoassay, an immunoaffinity purification or detection, an ELISA, or a radioimmunoassay, for example.

The expression pattern of one or more of the biomarkers listed in Table 3 has been determined, inter alia, to indicate an involvement in the regulation of one or more processes associated with one or more of diabetes, or the complications associated with diabetes or a predisposition of developing diabetes.

The biomarkers listed in Table 3 and their derivatives and homologs may be in isolated or purified form and/or may be ligated to a vector such as an expression vector. Expression may be in a eukaryotic cell line (e.g. mammalian, insect or yeast cells) or in microbial cells (e.g. E. coli) or both.

A homolog is considered to be a biomarker gene from another animal species. The present invention extends to the homologous gene, as determined by nucleotide sequence and/or amino acid sequences and/or function, from primates, including humans, marmosets, orangutans and gorillas, livestock animals (e.g. cows, sheep, pigs, horses, donkeys), laboratory test animals (e.g. mice, rats, guinea pigs, hamsters, rabbits), companion animals (e.g. cats, dogs) and captured wild animals (e.g. rodents, foxes, deer, kangaroos). The present invention also contemplates deimmunized forms of the expression products from one species relative to another species. In one preferred embodiment, the deimmunized form of the expression product is a mammalianized form relative to a particular target animal. In a most preferred embodiment where the target mammal is a human, the present invention contemplates use of a humanized form of a non-human expression product.

The nucleic acid molecule may be ligated to an expression vector capable of expression in a prokaryotic cell (e.g. E. coli) or a eukaryotic cell (e.g. yeast cells, fungal cells, insect cells, mammalian cells or plant cells). The nucleic acid molecule may be ligated or fused or otherwise associated with a nucleic acid molecule encoding another entity such as, for example, a signal peptide. It may also comprise additional nucleotide sequence information fused, linked or otherwise associated with it either at the 3' or 5' terminal portions or at both the 3' and 5' terminal portions. The nucleic acid molecule may also be part of a vector, such as an expression vector.

The derivatives of the nucleic acid molecule of the present invention include oligonucleotides, PCR primers, antisense molecules, molecules suitable for use in co- suppression (e.g. RNAi) and fusion nucleic acid molecules. Ribozymes and DNA enzymes are also contemplated by the present invention directed to a biomarker listed in Table 3 or their mRNA. Derivatives and homologs of biomarkers are conveniently encompassed by those nucleotide sequences capable of hybridizing to a sequence selected from the group consisting of SEQ ID NOs: 1 to 51 or their complementary form under high stringency conditions.

The present invention extends to expression products of the biomarkers listed in Table 3. The preferred expression products are proteins or mutants, derivatives or homologs thereof as well as a range of RNA molecules such as a mRNA transcript. Some genes are nonprotein encoding genes and produce mRNA or other RNA type molecules and are involved in regulation by RNA:DNA, RNA:RNA or RNA:protein interaction. The RNA (e.g. mRNA) may act directly or via the induction of other molecules such as RNAi or via products mediated from splicing events (e.g. exons or introns). Other genes encode mRNA transcripts which are then translated into proteins. A protein includes a polypeptide. The differentially expressed nucleic acid molecules, therefore, may encode mRNAs only or, in addition, proteins. Both mRNAs and proteins are forms of "expression products". Hence, Table 1 comprises nucleomic biomarkers with reference to the markers including corresponding proteomic biomarkers or derivatives or homologs thereof.

Another aspect of the present invention provides an isolated protein or other expression product or a derivative, homolog or mimetic thereof which is associated with one or more of diabetes, complications associated with diabetes, a predisposition for developing diabetes.

Derivatives include fragments, parts, portions, mutants, variants and mimetics from natural, synthetic or recombinant sources including fusion proteins. Parts or fragments include, for example, active regions of the biomarkers listed in Table 3. Derivatives may be derived from insertion, deletion or substitution of amino acids. Amino acid insertional derivatives include amino and/or carboxylic terminal fusions as well as intrasequence insertions of single or multiple amino acids. Insertional amino acid sequence variants are those in which one or more amino acid residues are introduced into a predetermined site in the protein although random insertion is also possible with suitable screening of the resulting product. Deletional variants are characterized by the removal of one or more amino acids from the sequence. Substitutional amino acid variants are those in which at least one residue in the sequence has been removed and a different residue inserted in its place. An example of substitutional amino acid variants are conservative amino acid substitutions. Conservative amino acid substitutions typically include substitutions within the following groups: glycine and alanine; valine, isoleucine and leucine; aspartic acid and glutamic acid; asparagine and glutamine; serine and threonine; lysine and arginine; and phenylalanine and tyrosine. Additions to amino acid sequences include fusions with other peptides, polypeptides or proteins.

The derivatives include fragments having particular epitopes or parts of the entire protein fused to peptides, polypeptides or other proteinaceous or non-proteinaceous molecules.

In a further aspect of the present invention, there is provided an isolated protein or derivative, homolog, fragment or mimetic thereof for use in the diagnostic and prognostic methods of the present invention wherein the protein or polypeptide comprises an amino acid sequence disclosed in SEQ ID NO:52 or a polypeptide encoded by a sequence selected from the group consisting of SEQ ID NOs: 1 to 51 or an amino acid sequence having at least 90% similarity to all or part thereof and wherein the protein or expression product is associated with one or more of diabetes, complications associated with diabetes, such as blindness, nephropathy and/or cardiovascular disease, and/or the probability of developing diabetes.

Reference herein to a biomarker includes reference to isolated or purified naturally occurring biomarker protein or expression product molecules as well as any derivatives, homologs and mimetics thereof. Derivatives include parts, fragments and portions of a biomarker listed in Table 3, as well as single and multiple amino acid substitutions, deletions and/or additions to a biomarker listed in Table 3. A derivative of a biomarker listed in Table 3 is conveniently encompassed by molecules encoded by a nucleotide sequence capable of hybridizing to a sequence selected from the group consisting of SEQ ID NOs:l to 51, respectively under high stringency conditions at a specified temperature.

Also contemplated are analogs of the biomarkers which have been stabilized by modifications. Such stabilized forms may be useful in biomarker arrays, or the like.

Other derivatives of a biomarker listed in Table 3 include chemical analogs. Analogs of a biomarker listed in Table 3 contemplated herein include, but are not limited to, modifications to side chains, incorporation of unnatural amino acids and/or their derivatives during peptide, polypeptide or protein synthesis and the use of crosslinkers and other methods which impose confirmational constraints on the proteinaceous molecule or their analogs.

Examples of side chain modifications contemplated by the present invention include modifications of amino groups such as by reductive alkylation by reaction with an aldehyde followed by reduction with NaBH₄; amidination with methylacetimidate; acylation with acetic anhydride; carbamoylation of amino groups with cyanate; trinitrobenzylation of amino groups with 2, 4, 6-trinitrobenzene sulphonic acid (TNBS); acylation of amino groups with succinic anhydride and tetrahydrophthalic anhydride; and pyridoxylation of lysine with pyridoxal-5-phosphate followed by reduction with NaBH₄.

The guanidine group of arginine residues may be modified by the formation of heterocyclic condensation products with reagents such as 2,3-butanedione, phenylglyoxal and glyoxal.

The carboxyl group may be modified by carbodiimide activation via O-acylisourea formation followed by subsequent derivitization, for example, to a corresponding amide.

Sulphydryl groups may be modified by methods such as carboxymethylation with iodoacetic acid or iodoacetamide; performic acid oxidation to cysteic acid; formation of a mixed disulphides with other thiol compounds; reaction with maleimide, maleic anhydride or other substituted maleimide; formation of mercurial derivatives using 4- chloromercuribenzoate, 4-chloromercuriphenylsulphonic acid, phenylmercury chloride, 2- chloromercuri-4-nitrophenol and other mercurials; carbamoylation with cyanate at alkaline pH.

Tryptophan residues may be modified by, for example, oxidation with N- bromosuccinimide or alkylation of the indole ring with 2-hydroxy-5-nitrobenzyl bromide or sulphenyl halides. Tyrosine residues on the other hand, may be altered by nitration with tetranitromethane to form a 3-nitrotyrosine derivative.

Modification of the imidazole ring of a histidine residue may be accomplished by alkylation with iodoacetic acid derivatives or N-carbethoxylation with diethylpyrocarbonate.

Examples of incorporating unnatural amino acids and derivatives during peptide synthesis include, but are not limited to, use of norleucine, 4-amino butyric acid, 4-amino-3- hydroxy-5-phenylpentanoic acid, 6-aminohexanoic acid, t-butylglycine, norvaline, phenylglycine, ornithine, sarcosine, 4-amino-3-hydroxy-6-methylheptanoic acid, 2-thienyl alanine and/or D-isoniers of amino acids. A list of unnatural amino acid, contemplated herein is shown in Table 2.

TABLE 2 Codes for non-conventional amino acids

Non-conventional Code Non-conventional Code amino acid amino acid

α-aminobutyric acid Abu L-N-methylalanine Nniala α-amino-α-methylbutyrate Mgabi L-N-methylarginine Nmarg aminocyc lopropane- Cpro L-N-methylasparagine Nmasn carboxylate L-N-methylaspartic acid Nmasp aminoisobutyric acid Aib L-N-methylcysteine Nmcys aminonorbornyl- Norb L-N-methylglutamine Nmgln carboxylate L-N-methylglutamic acid Nmglu cyclohexylalanine Chexa L-Nmethylhistidine Nmhis cyclopentylalanine Cpen L-N-methylisolleucine Nmile

D-alanine Dal L-N-methylleucine Nmleu

D-arginine Darg L-N-methyllysine Nmlys

D-aspartic acid Dasp L-N-methylmethionine Nmmet

D-cysteine Dcys L-N-methylnorleucine Nmnle

D-glutamine DgIn L-N-methylnorvaline Nmnva

D-glutamic acid DgIu L-N-methylornithine Nmorn

D-histidine Dhis L-N-methylphenylalanine Nmphe

D-isoleucine DiIe L-N-methylproline Nmpro

D-leucine Dleu L-N-methylserine Nmser

D-lysine Dlys L-N-methylthreonine Nmthr

D-methionine Dmet L-N-methyltryptophan Nmtrp

D-ornithine Dorn L-N-methyltyrosine Nmtyr

D-phenylalanine Dphe L-N-methylvaline Nmval

D-proline Dpro L-N-methylethylglycine Nmetg

D-serine Dser L-N-methyl-t-butylglycine Nmtbug

D-threonine Dthr L-norleucine NIe Non-conventional Code Non-conventional Code amino acid amino acid

D-tryptophan Dtrp L-norvaline Nva

D-tyrosine Dtyr α-methyl-aminoisobutyrate Maib

D-valine Dval α-methyl-γ-aminobutyrate Mgabu

D-α-methylalanine Dmala α-methylcyclohexylalanine Mchexa

D-α-methylarginine Dmarg α-methylcylcopentylalanine Mcpen

D-α-methylasparagine Dmasn α-methyl-α-napthylalanine Manap

D-α-methylaspartate Dmasp α-methylpenicillamine Mpen

D-α-methylcysteine Dmcys N-(4-aminobutyl)glycine NgIu

D-α-methylglutamine Dmgln N-(2-aminoethyl)glycine Naeg

D-α-methylhistidine Dmhis N-(3 -aminopropyl)glycine Norn

D-α-methylisoleucine Dmile N-amino-α-methylbutyrate Nmaabu

D-α-methylleucine Dmleu α-napthylalanine Anap

D-α-methyllysine Dmlys N-benzylglycine Nphe

D-α-methylmethionine Dmmet N-(2-carbamylethyl)glycine NgIn

D-α-methylornithine Dmorn N-(carbamylmethyl)glycine Nasn

D-α-methylphenylalanine Dmphe N-(2-carboxyethyl)glycine NgIu

D-α-methylproline Dmpro N-(carboxymethyl)glycine Nasp

D-α-methylserine Dmser N-cyclobutylglycine Ncbut

D-α-methylthreonine Dmthr N-cycloheptylglycine Nchep

D-α-methyltryptophan Dmtrp N-cyclohexylglycine Nchex

D-α-methyltyrosine Dmty N-cyclodecylglycine Ncdec

D-α-methylvaline Dmval N-cylcododecylglycine Ncdod

D-N-methylalanine Dnnαala N-cyclooctylglycine Ncoct

D-N-methylarginine Dnmarg N-cyclopropylglycine Ncpro

D-N-methylasparagine Dnmasn N-cycloundecylglycine Ncund

D-N-methylaspartate Dnmasp N-(2,2-diphenylethyl)glycine Nbhm

D-N-methylcysteine Dnmcys N-(3,3-diphenylpropyl)glycine Nbhe Non-conventional Code Non-conventional Code amino acid amino acid

D-N-methylglutamine Dnmgln N-(3-guanidinopropyl)glycine Narg

D-N-methylglutamate Dnmglu N-(I -hydroxyethyl)glycine Nthr

D-N-methylhistidine Dnmhis N-(hydroxyethyl))glycine Nser

D-N-methylisoleucine Dnmile N-(imidazolylethyl))glycine Nhis

D-N-niethylleucine Dnmleu N-(3 -indoly Iy ethyl)gly cine Nhtrp

D-N-methyllysine Dnmlys N-methyl-γ-aminobutyrate Nmgabu

N-methylcyclohexylalanine Nmchexa D-N-methylmethionine Dnmmet

D-N-methylornithine Dnmorn N-methylcyclopentylalanine Nmcpen

N-methylglycine NaIa D-N-methylphenylalanine Dnmphe

N-methylaminoisobutyrate Nmaib D-N-methylproline Dnmpro

N-( 1 -methylpropyl)glycine Nile D-N-methylserine Dnmser

N-(2-methylpropyl)glycine Nleu D-N-methylthreonine Dnmthr

D-N-methyltryptophan Dnmtrp N-(I -methy lethyl)glycine Nval

D-N-methyltyrosine Dnmtyr N-methyla-napthylalanine Nmanap

D-N-methylvaline Dnmval N-methylpenicillamine Nmpen γ-aminobutyric acid Gabu N-(p-hydroxyphenyl)glycine Nhtyr

L-t-buty 1 glycine Tbug N-(thiomethyl)glycine Ncys

L-ethylglycine Etg penicillamine Pen

L-homophenylalanine Hphe L-α-methylalanine Mala

L-α-methylarginine Marg L-α-methylasparagine Masn

L-α-methylaspartate Masp L-α-methyl-t-butylglycine Mtbug

L-α-methylcysteine Mcys L-methylethylglycine Metg

L-α-methylglutamine MgIn L-α-methylglutamate MgIu

L-α-methylhistidine Mhis L-α-methylhomophenylalanine Mhphe

L-α-methylisoleucine Mile N-(2-methylthioethyl)glycine Nmet

L-α-methylleucine Mleu L-α-methyllysine Mlys

L-α-methylmethionine Mmet L-α-methylnorleucine MnIe Non-conventional Code Non-conventional Code amino acid amino acid

L-α-methylnorvaline Mnva L-α-methylornithine Morn

L-α-methylphenylalanine Mphe L-α-methylproline Mpro

L-α-methylserine Mser L-α-methylthreonine Mthr

L-α-methyltryptophan Mtrp L-α-methyltyrosine Mtyr

L-α-methylvaline Mval L-N-methylhomophenylalanine Nmhphe

N-(N-(2,2-diphenylethyl) Nnbhm N-(N-(3,3-diphenylpropyl) Nnbhe carbamylmethyl)glycine carbamylmethyl)glycine

1 -carboxy- 1 -(2,2-diphenyl- Nmbc ethylamino)cyclopropane

Crosslinkers can be used, for example, to stabilize 3D conformations, using homo- bifunctional crosslinkers such as the bifunctional imido esters having (CH₂)_n spacer groups with n=l to n=6, glutaraldehyde, N-hydroxysuccinimide esters and hetero-bifunctional reagents which usually contain an amino-reactive moiety such as N-hydroxysuccinimide and another group specific-reactive moiety such as maleimido or dithio moiety (SH) or carbodiimide (COOH). In addition, peptides can be conformationally constrained by, for example, incorporation of C_α and N _α-methylamino acids, introduction of double bonds between C_α and C_β atoms of amino acids and the formation of cyclic peptides or analogs by introducing covalent bonds such as forming an amide bond between the N and C termini, between two side chains or between a side chain and the N or C terminus.

All such modifications may also be useful in stabilizing the biomarker molecule for use in diagnostic and prognostic purposes.

As stated above, the expression product may be an RNA or protein. The term "protein" should be understood to encompass peptides, polypeptides and proteins. The protein may be glycosylated or unglycosylated and/or may contain a range of other molecules fused, linked, bound or otherwise associated to the protein such as amino acids, lipids, carbohydrates or other peptides, polypeptides or proteins. Reference hereinafter to a "protein" includes a protein comprising a sequence of amino acids as well as a protein associated with other molecules such as amino acids, lipids, carbohydrates or other peptides, polypeptides or proteins.

In a related embodiment, the expression product is encoded by a sequence of nucleotides as set forth in a sequence selected from the group consisting of SEQ ID NOs :1 to 51 or a derivative or homolog thereof including a nucleotide sequence having at least about 90% identity to a sequence selected from the group consisting of SEQ ID NOs: 1 to 51.

Higher similarities are also contemplated by the present invention such as greater than 90% or 95% or 96% or 97% or 98% or 99% or above. Further examples include 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 and 100%.

The proteins used in the diagnostic and prognostic methods of the present invention can be in isolated form. By "isolated" is meant a protein having undergone at least one purification step and this is conveniently defined, for example, by a composition comprising at least about 10% subject protein, preferably at least about 20%, more preferably at least about 30%, still more preferably at least about 40-50%, even still more preferably at least about 60-70%, yet even still more preferably 80-90% or greater, such as 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 61, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 and 100% of subject protein relative to other components as determined by molecular weight, amino acid sequence or other convenient means. The protein of the present invention may also be considered, in a preferred embodiment, to be biologically pure.

The nucleotide sequence or amino acid sequence of the present invention may correspond to exactly the same sequence of the naturally occurring gene (or corresponding cDNA) or protein or may carry one or more nucleotide or amino acid substitutions, additions and/or deletions. The nucleotide sequences set forth in SEQ ID NOs: 1 to 51 correspond to the genes referred to Table 3. Reference to the genes in Table 1 or Table 3 includes, where appropriate, reference to the genomic gene or cDNA as well as any naturally occurring or induced derivatives. Apart from the substitutions, deletions and/or additions to the nucleotide sequence, the present invention further encompasses mutants, fragments, parts and portions of the nucleotide sequence disclosed in SEQ ID NOs: 1 to 51.

Still another aspect of the present invention is directed to antibodies to the biomarkers disclosed in Table 3 and their derivatives and homologs. Such antibodies may be monoclonal or polyclonal and may be selected from naturally occurring antibodies to the biomarkers listed in Table 3 or may be specifically raised to the biomarkers listed in Table 3 or derivatives or homologs thereof. In the case of the latter, the biomarkers or their derivatives or homologs may first need to be associated with a carrier molecule. The antibodies and/or recombinant biomarkers or their derivatives of the present invention are particularly useful as diagnostic or prognostic agents.

For example, the biomarkers listed in Table 3 and their derivatives can be used to screen for naturally occurring antibodies to the biomarkers listed in Table 3, the presence of which is indicative of a subject having diabetes or diseases associated therewith or the likelihood of developing diabetes. Techniques for screening for the presence of the biomarkers such assays are well known in the art include, for example, sandwich assays and ELISA.

Antibodies to the biomarkers listed in Table 3 may be monoclonal or polyclonal and may be selected from naturally occurring antibodies to the biomarkers or may be specifically raised to the biomarkers or their derivatives. In the case of the latter, the proteins specific for the biomarkers may first need to be associated with a carrier molecule. Alternatively, fragments of antibodies may be used such as Fab fragments. Furthermore, the present invention extends to recombinant and synthetic antibodies and to antibody hybrids. A "synthetic antibody" is considered herein to include fragments and hybrids of antibodies. The antibodies of this aspect of the present invention are particularly useful as a diagnostic or prognostic tools for diabetes, a predisposition to develop diabetes, or for a condition associated with diabetes.

For example, specific antibodies can be used to screen for biomarker proteins. The latter is important, for example, as a means for screening for levels of one or more of the biomarkers in a cell extract or other biological fluid such as serum, blood, urine or saliva. Techniques for the assays contemplated herein are known in the art and include, for example, sandwich assays and ELISA.

Immunoassays, in their most simple and direct sense, are binding assays. Certain preferred immunoassays are the various types of enzyme linked immunosorbent assays (ELISAs) and radioimmunoassays (RIA) known in the art. Immunohistochemical detection using tissue sections is also particularly useful. However, it will be readily appreciated that detection is not limited to such techniques, and Western blotting, dot blotting, FACS analyses, and the like may also be used.

In one exemplary ELISA, antibodies binding to the encoded proteins of the invention are immobilized onto a selected surface exhibiting protein affinity, such as a well in a polystyrene microtiter plate. Then, a test composition suspected of containing the diabetes biomarker antigen, such as a clinical sample, is added to the wells. After binding and washing to remove non-specifically bound immunocomplexes, the bound antigen may be detected. Detection is generally achieved by the addition of a second antibody specific for the target protein, that is linked to a detectable label. This type of ELISA is a simple "sandwich ELISA". Detection may also be achieved by the addition of a second antibody, followed by the addition of a third antibody that has binding affinity for the second antibody, with the third antibody being linked to a detectable label.

In another exemplary ELISA, the samples suspected of containing the biomarker antigen are immobilized onto the well surface and then contacted with the antibodies of the invention. After binding and washing to remove non-specifically bound immunocomplexes, the bound antigen is detected. Where the initial antibodies are linked to a detectable label, the inimunocomplexes may be detected directly. Again, the immunocomplexes may be detected using a second antibody that has binding affinity for the first antibody, with the second antibody being linked to a detectable label.

Another ELISA in which the proteins or peptides are immobilized, involves the use of antibody competition in the detection. In this ELISA, labelled antibodies are added to the wells, allowed to bind to the biomarker protein, and detected by means of their label. The amount of marker antigen in an unknown sample is then determined by mixing the sample with the labelled antibodies before or during incubation with coated wells. The presence of marker antigen in the sample acts to reduce the amount of antibody available for binding to the well and thus reduces the ultimate signal. This is appropriate for detecting antibodies in an unknown sample, where the unlabeled antibodies bind to the antigen-coated wells and also reduces the amount of antigen available to bind the labeled antibodies.

Irrespective of the format employed, ELISAs have certain features in common, such as coating, incubating or binding, washing to remove non-specifically bound species, and detecting the bound immunocomplexes. These are described as follows:

In coating a plate with either antigen or antibody, one will generally incubate the wells of the plate with a solution of the antigen or antibody, either overnight or for a specified period of hours. The wells of the plate will then be washed to remove incompletely adsorbed material. Any remaining available surfaces of the wells are then "coated" with a nonspecific protein that is antigenically neutral with regard to the test antisera. These include bovine serum albumin (BSA), casein and solutions of milk powder. The coating allows for blocking of nonspecific adsorption sites on the immobilizing surface and thus reduces the background caused by nonspecific binding of antisera onto the surface.

In ELISAs, it is probably more customary to use a secondary or tertiary detection means rather than a direct procedure. Thus, after binding of a protein or antibody to the well, coating with a non-reactive material to reduce background, and washing to remove unbound material, the immobilizing surface is contacted with the control sample and/or clinical or biological sample to be tested under conditions effective to allow immunecomplex (antigen/antibody) formation. Detection of the immunecomplex then requires a labeled secondary binding ligand or antibody, or a secondary binding ligand or antibody in conjunction with a labeled tertiary antibody or third binding ligand.

"Under conditions effective to allow immunecomplex (antigen/antibody) formation" means that the conditions preferably include diluting the antigens and antibodies with solutions such as BSA, bovine gamma globulin (BGG) and phosphate buffered saline (PBS)/Tween. These added agents also tend to assist in the reduction of nonspecific background.

The "suitable" conditions also mean that the incubation is at a temperature and for a period of time sufficient to allow effective binding. Incubation steps are typically from about 1 to 2 to 4 hours, at temperatures preferably on the order of 25° to 27° C, or may be overnight at about 4°C or so.

Following all incubation steps in an ELISA, the contacted surface is washed so as to remove non-complexed material. A preferred washing procedure includes washing with a solution such as PBS/Tween, or borate buffer. Following the formation of specific immunocomplexes between the test sample and the originally bound material, and subsequent washing, the occurrence of even minute amounts of immunocomplexes may be determined.

To provide a detecting means, the second or third antibody will have an associated label to allow detection. Preferably, this will be an enzyme that will generate color development upon incubating with an appropriate chromogenic substrate. Thus, for example, one will desire to contact and incubate the first or second immunecomplex with a urease, glucose oxidase, alkaline phosphatase or hydrogen peroxidase-conjugated antibody for a period of time and under conditions that favor the development of further immunecomplex formation (e.g., incubation for 2 hours at room temperature in a PB S -containing solution such as PBS-Tween).

After incubation with the labeled antibody, and subsequent to washing to remove unbound material, the amount of label is quantified, e.g., by incubation with a chromogenic substrate such as urea and bromocresol purple or 2,2'-azido-di-3-ethyl-benzthiazoline-6- sulfonic acid [ABTS] and H₂O₂, in the case of peroxidase as the enzyme label. Quantitation is then achieved by measuring the degree of color generation, e.g., using a visible spectra spectrophotometer.

The antibodies of this invention will be used to quantify and localize the expression of the encoded marker proteins. The antibody, for example, will be labeled by any one of a variety of methods and used to visualize the localized concentration of the cells producing the encoded protein.

The invention also relates to an in vivo method of imaging diabetes or pre-clinical manifestations of diabetes using the above-described monoclonal antibodies. Specifically, this method involves administering to a subject an imaging-effective amount of a detectably-labeled biomarker monoclonal antibody or fragment thereof and a pharmaceutically effective carrier and detecting the binding of the labeled monoclonal antibody to the diseased, or in the case of down regulated marker genes, healthy tissue. The term "in vivo imaging" refers to any method which permits the detection of a labeled monoclonal antibody of the present invention or fragment thereof that specifically binds to a diseased tissue located in the subject's body. A "subject" is a mammal, preferably a human. An "imaging effective amount" means that the amount of the detectably-labeled monoclonal antibody, or fragment thereof, administered is sufficient to enable detection of binding of the monoclonal antibody or fragment thereof to the diseased tissue, or the binding of the monoclonal antibody or fragment thereof in greater proportion to healthy tissue relative to diseased tissue.

A factor to consider in selecting a radionuclides for in vivo diagnosis or prognosis is that the half-life of a nuclide be long enough so that it is still detectable at the time of maximum uptake by the target, but short enough so that deleterious radiation upon the host, as well as background, is minimized. Ideally, a radionuclides used for in vivo imaging will lack a particulate emission, but produce a large number of photons in a 140-2000 keV range, which may be readily detected by conventional gamma cameras. Radionuclides may be bound to an antibody either directly or indirectly by using an intermediary functional group. Intermediary functional groups which are often used to bind radioisotopes which exist as metallic ions to antibody are diethylenetriaminepentaacetic acid (DTPA) and ethylene diaminetetracetic acid (EDTA). Examples of metallic ions suitable for use in this invention are ^99mTc, ¹²³I₃ ¹³¹1, ¹¹¹In, ¹³¹1, ⁹⁷Ru, ⁶⁷Cu, ⁶⁷Ga, ¹²⁵1, ⁶⁸Ga, ⁷²As, ⁸⁹Zr, and ²⁰¹Tl.

In accordance with the present invention, the monoclonal antibody or fragment thereof may be labeled by any of several techniques known to the art. The methods of the present invention may also use paramagnetic isotopes for purposes of in vivo detection. Elements particularly useful in Magnetic Resonance Imaging ("MRI") include ¹⁵⁷Gd, ⁵⁵Mn, ¹⁶²Dy, ⁵²Cr, and ⁵⁶Fe.

Administration of the labeled antibody may be local or systemic and accomplished intravenously, intraarterially, via the spinal fluid or the like. Administration may also be intradermal or intracavitary, depending upon the body site under examination. After a sufficient time has lapsed, for example 30 minutes to 48 hours, for the monoclonal antibody or fragment thereof to bind with the target tissue, either diseased and/or healthy tissue, the area of the subject under investigation is examined by routine imaging techniques such as MRI, SPECT, planar scintillation imaging and emerging imaging techniques, as well. The exact protocol will necessarily vary depending upon factors specific to the patient, as noted above, and depending upon the body site under examination, method of administration and type of label used; the determination of specific procedures would be routine to the skilled artisan. The distribution of the bound radioactive isotope and its increase or decrease with time is then monitored and recorded.

By comparing the results with data obtained from studies of clinically normal individuals, the presence and extent of the diseased tissue may be determined.

It will be apparent to those of skill in the art that a similar approach may be used to radio- image the production of the encoded biomarker proteins in human patients. The present invention provides methods for the in vivo diagnosis or prognosis of diabetes in a patient.

Such methods generally comprise administering to a patient an effective amount of a biomarker specific antibody, which antibody is conjugated to a marker, such as a radioactive isotope or a spin-labeled molecule, that is detectable by non-invasive methods. The antibody-marker conjugate is allowed sufficient time to come into contact with reactive antigens that are present within the tissues of the patient, and the patient is then exposed to a detection device to identify the detectable marker.

It is within the scope of this invention to include any second antibodies (monoclonal, polyclonal or fragments of antibodies) directed to the first mentioned antibodies discussed above. Both the first and second antibodies may be used in detection assays or a first antibody may be used with a commercially available anti-immunoglobulin antibody. An antibody as contemplated herein includes any antibody specific to any region of a biomarker listed in Table 3.

Both polyclonal and monoclonal antibodies are obtainable by immunization with the enzyme or protein and either type is utilizable for immunoassays. The methods of obtaining both types of sera are well known in the art. Polyclonal sera are less preferred but are relatively easily prepared by injection of a suitable laboratory animal with an effective amount of a biomarker listed in Table 3, or antigenic parts thereof, collecting serum from the animal, and isolating specific sera by any of the known immunoadsorbent techniques. Although antibodies produced by this method are utilizable in virtually any type of immunoassay, they are generally less favoured because of the potential heterogeneity of the product.

The use of monoclonal antibodies in an immunoassay is particularly preferred because of the ability to produce them in large quantities and the homogeneity of the product. The preparation of hybridoma cell lines for monoclonal antibody production derived by fusing an immortal cell line and lymphocytes sensitized against the immunogenic preparation can be done by techniques which are well known to those who are skilled in the art. (See, for example, Douillard and Hoffman Compendium of Immunology Vol. II, ed. by Schwartz, 1981; Kohler and Milstein Nature 256:495-499, 1975; Kohler and Milstein European

Journal of Immunology 6:511 -519, 1976). Another aspect of the present invention contemplates a method for detecting one or more biomarkers listed in Table 3 or a derivative or homolog thereof in a biological sample from a subject, the method comprising obtaining a biological sample from a subject and determining the level of one or more biomarker in the biological sample using one or more antibodies specific for one ore more biomarkers listed in Table 3 or their antigenic derivatives or homologs, then comparing the level of the biomarker to that of a statistically validated threshold.

The presence of the complex is indicative of the presence of a biomarker. This assay may be quantitated or semi-quantitated to determine a propensity to develop diabetes or to monitor a therapeutic regimen for treating diabetes.

The presence of one or more biomarkers listed in Table 3 may be detected in a number of ways such as by Western blotting and ELISA procedures. A wide range of immunoassay techniques are available as can be seen by reference to U.S. Patent Nos. 4,016,043, 4,424,279 and 4,018,653. These, of course, includes both single-site and two-site or "sandwich" assays of the non-competitive types, as well as in the traditional competitive binding assays. These assays also include direct binding of a labeled antibody to a target.

Sandwich assays are among the most useful and commonly used assays. A number of variations of the sandwich assay technique exist, and all are intended to be encompassed by the present invention. Briefly, in a typical forward assay, an unlabeled antibody is immobilized on a solid substrate and the sample to be tested brought into contact with the bound molecule. After a suitable period of incubation, for a period of time sufficient to allow formation of an antibody-biomarker complex, a second antibody specific to the biomarker, labeled with a reporter molecule capable of producing a detectable signal, is then added and incubated, allowing time sufficient for the formation of another complex of antibody-biomarker labeled antibody. Any unreacted material is washed away, and the presence of the biomarker(s) is determined by observation of a signal produced by the reporter molecule. The results may either be qualitative, by simple observation of the visible signal, or may be quantitated by comparing with a control sample containing known amounts of hapten. Variations on the forward assay include a simultaneous assay, in which both sample and labeled antibody are added simultaneously to the bound antibody. These techniques are well known to those skilled in the art, including any minor variations as will be readily apparent. In accordance with the present invention, the sample is any sample which might contain a biomolecular polypeptide, including cell extract, tissue biopsy or possibly serum, saliva, mucosal secretions, lymph, tissue fluid and respiratory fluid. The sample is, therefore, generally a biological sample comprising biological fluid but also extends to fermentation fluid and supernatant fluid such as from a cell culture.

The solid surface is typically glass or a polymer, the most commonly used polymers being cellulose, polyacrylamide, nylon, polystyrene, polyvinyl chloride or polypropylene. The solid supports may be in the form of tubes, beads, discs of microplates, or any other surface suitable for conducting an immunoassay. The binding processes are well-known in the art and generally consist of cross-linking covalently binding or physically adsorbing, the polymer-antibody complex is washed in preparation for the test sample. An aliquot of the sample to be tested is then added to the solid phase complex and incubated for a period of time sufficient (e.g. 2-40 minutes or overnight if more convenient) and under suitable conditions (e.g. from room temperature to about 37°C) to allow binding of any subunit present in the antibody. Following the incubation period, the antibody subunit solid phase is washed and dried and incubated with a second antibody specific for a portion of a biomarker. The second antibody is linked to a reporter molecule which is used to indicate the binding of the second antibody to the biomarker.

An alternative method involves immobilizing the target molecules in the biological sample and then exposing the immobilized target to specific antibody which may or may not be labeled with a reporter molecule. Depending on the amount of target and the strength of the reporter molecule signal, a bound target may be detectable by direct labeling with the antibody. Alternatively, a second labeled antibody, specific to the first antibody is exposed to the target-first antibody complex to form a target-first antibody-second antibody tertiary complex. The complex is detected by the signal emitted by the reporter molecule. By "reporter molecule" as used in the present specification, is meant a molecule which, by its chemical nature, provides an analytically identifiable signal which allows the detection of antigen-bound antibody. Detection may be either qualitative or quantitative. The most commonly used reporter molecules in this type of assay are either enzymes, fluorophores or radionuclide containing molecules (i.e. radioisotopes) and chemiluminescent molecules.

In the case of an enzyme immunoassay, an enzyme is conjugated to the second antibody, generally by means of glutaraldehyde or periodate. As will be readily recognized, however, a wide variety of different conjugation techniques exist, which are readily available to the skilled artisan. Commonly used enzymes include horseradish peroxidase, glucose oxidase, β-galactosidase and alkaline phosphatase, amongst others. The substrates to be used with the specific enzymes are generally chosen for the production, upon hydrolysis by the corresponding enzyme, of a detectable colour change. Examples of suitable enzymes include alkaline phosphatase and peroxidase. It is also possible to employ fluorogenic substrates, which yield a fluorescent product rather than the chromogenic substrates noted above. In all cases, the enzyme-labeled antibody is added to the first antibody hapten complex, allowed to bind, and then the excess reagent is washed away. A solution containing the appropriate substrate is then added to the complex of antibody-antigen- antibody. The substrate will react with the enzyme linked to the second antibody, giving a qualitative visual signal, which may be further quantitated, usually spectrophotometrically, to give an indication of the amount of hapten which was present in the sample. A "reporter molecule" also extends to use of cell agglutination or inhibition of agglutination such as red blood cells on latex beads, and the like.

Alternately, fluorescent compounds, such as fluorecein and rhodamine, may be chemically coupled to antibodies without altering their binding capacity. When activated by illumination with light of a particular wavelength, the fluorochrome-labeled antibody adsorbs the light energy, inducing a state to excitability in the molecule, followed by emission of the light at a characteristic colour visually detectable with a light microscope. As in the EIA, the fluorescent labeled antibody is allowed to bind to the first antibody- hapten complex. After washing off the unbound reagent, the remaining tertiary complex is then exposed to the light of the appropriate wavelength the fluorescence observed indicates the presence of the hapten of interest. Immunofluorescene and EIA techniques are both very well established in the art and are particularly preferred for the present method. However, other reporter molecules, such as radioisotope, chemiluminescent or bioluminescent molecules, may also be employed.

The present invention also contemplates genetic assays such as involving PCR analysis to detect one or more biomarkers or their derivatives. Further assays, include quantitative reverse transcriptase PCR (qRT-PCR), northern blot analysis, real time PCR, array technology.

The assays of the present invention may also extend to measuring the biomarkers in association with another gene or molecule.

The present invention may also be described in certain embodiments as a kit for use in detecting diabetes or a condition associated therewith or the likelihood of developing diabetes through testing of a biological sample. A representative kit may comprise one or more nucleic acid segments as described above that selectively hybridize to one or more of the biomarkers listed in Table 3 and a container for each of the one or more nucleic acid segments. In certain embodiments the nucleic acid segments may be combined in a single tube. In certain embodiments the nucleic acid segments would be designed to selectively hybridize to a nucleic acid segment that includes the sequence or complement of a sequence selected from SEQ ID NOs: 1 to 51. In further embodiments, the nucleic acid segments may also include a pair of primers for amplifying the target mRNA. Such kits may also include any buffers, solutions, solvents, enzymes, nucleotides, or other components for hybridization, amplification or detection reactions. Preferred kit components include reagents for RT-PCR, in situ hybridization, Northern analysis and/or

RPA.

In certain embodiments the kit for use in detecting diabetes or a condition associated therewith or the likelihood of developing diabetes in a biological sample may comprise an antibody which immunoreacts with a polypeptide specific for a biomarker listed in Table 3 and a container for the antibody. Such an antibody may be a polyclonal or a monoclonal antibody and may be included in a kit with reagents, secondary antibodies, labeling means, or other components for polypeptide detection including, but not limited to an ELISA kit.

The present invention further comprises the prognosis and/or diagnosis of diabetes or a condition associated therewith or the likelihood of developing diabetes by measuring the amounts of nucleic acid amplification products formed as above. The amounts of nucleic amplification products identified in an individual patient may be compared with groups of normal individuals or individuals with an identified disease state. Diagnosis or prognosis may be accomplished by finding that the subject's level of one or more of the biomarkers falls within the normal range, or within the range observed in individuals with the disease state. Further comparison with groups of individuals of varying disease state progression may provide a prognosis for the individual patient. The present invention further broadly comprises kits for performing the above-mentioned procedures, containing amplification primers and/or hybridization probes.

Another aspect of the present invention comprises the detection and diagnosis or prognosis of diabetes or a condition associated therewith or a predisposition of developing diabetes by combining measurement of levels of biomarkers of diabetes or a condition associated therewith or the likelihood of developing diabetes. An embodiment of the invention comprises combining measurement of one or more of the biomarkers listed in Table 3 with other markers associated with diabetes or a condition associated therewith or the likelihood of developing diabetes, such as BMI, weight, waste to hip ratio, fasting glucose, fasting insulin and percent body fat. Yet another aspect of the present invention comprises kits for detection and measurement of the levels of two or more disease state biomarkers in biological samples. The skilled practitioner will realize that such kits may incorporate a variety of methodologies for detection and measurement of disease state markers, including but not limited to oligonucleotide probes, primers for nucleic acid amplification, antibodies which bind specifically to protein products of disease state marker genes, and other proteins or peptides which bind specifically to disease state marker gene products. In other aspects, the present invention provides kits for the detection and measurement of the levels of 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51 or more biomarkers in a biological sample.

In still further embodiments, the present invention concerns immunodetection kits for use with the immunodetection methods described above. As the encoded marker proteins or peptides may be employed to detect antibodies and the corresponding antibodies may be employed to detect encoded proteins or peptides, either or both of such components may be provided in the kit. The immunodetection kits thus comprise, in suitable container means, an encoded protein or peptide and/or a first antibody that binds to an encoded protein or peptide, and an immunodetection reagent.

In certain embodiments, the encoded protein or peptide, or the first antibody that binds to the encoded protein or peptide, may be bound to a solid support, such as a column matrix or well of a microtiter plate.

The immunodetection reagents of the kit may take any one of a variety of forms, including those detectable labels that are associated with or linked to the given antibody or antigen, and detectable labels that are associated with or attached to a secondary binding ligand. Exemplary secondary ligands are those secondary antibodies that have binding affinity for the first antibody or antigen, and secondary antibodies that have binding affinity for a human antibody.

Further suitable immunodetection reagents for use in the present kits include the two- component reagent that comprises a secondary antibody that has binding affinity for the first antibody or antigen, along with a third antibody that has binding affinity for the second antibody, the third antibody being linked to a detectable label.

The kits may further comprise a suitably aliquoted composition of the encoded protein or polypeptide antigen, whether labeled or unlabeled, as may be used to prepare a standard curve for a detection assay. The kits may contain antibody-label conjugates either in fully conjugated form, in the form of intermediates, or as separate moieties to be conjugated by the user of the kit. The components of the kits may be packaged either in aqueous media or in lyophilized form.

The container means of the kits will generally include at least one vial, test tube, flask, bottle, syringe or other container means, into which the antibody or antigen may be placed, and preferably, suitably aliquoted. Where a second or third binding ligand or additional component is provided, the kit will also generally contain a second, third or other additional container into which this ligand or component may be placed. The kits of the present invention will also typically include a means for containing the antibody, antigen, and any other reagent containers in close confinement for commercial sale. Such containers may include injection or blow-molded plastic containers into which the desired vials are retained.

The present invention is further described by the following non-limiting Examples.

EXAMPLE 1 Human Sample Collection

San Antonio Family Heart Study

Existing DNA samples and data from the San Antonio Family Heart Study (SAFHS) were utilized in this project. The study began in 1991 and was designed to primarily investigate the genetics of cardiovascular disease and its risk factors in Mexican Americans. The SAFHS, previously described in detail in MacCluer et al. (1999), included 1,431 individuals in 42 extended families at baseline. Ascertainment occurred by way of a single adult Mexican American proband selected at random, without regard to presence or absence of disease and almost exclusively from Mexican American census tracts in San Antonio. To ensure large, multigenerational pedigrees, probands had to have at least 6 age- eligible offspring and/or siblings living in San Antonio. All first, second, and third degree relatives of the proband and of the proband's spouse, aged 16 years or above, were eligible to participate in the study. Subsequently, approximately 850 participants were recalled for a five-year follow-up, and these participants have been recalled again for a ten-year follow-up as part of the ongoing SAFHS investigations.

Lymphocyte samples were available from 1,280 Mexican American individuals. Of the 1,280 samples analyzed, acceptable transcriptional profiles were obtained from 1,240 individuals. The sample consisted of 1,154 individuals from 46 pedigrees and an additional 86 singletons. There are 734 females and 506 males in the sample, with a mean age of 39.3 years (SD=I 6.7 years). Ages range between 15.5 and 94.2 years.

Isolation of Lymphocytes from Fresh Blood

A 1OmL blood sample, collected in an EDTA tube, was diluted 1:1 with RPMI-C and the 2OmL volume then carefully overlaid with 1OmL of Histopaque (Sigma Chemical Co., St. Louis, MO). Tubes were immediately centrifuged for 30 minutes at 25OOrpm. The upper plasma layer was carefully removed using a Pasteur pipette, leaving the lymphocyte layer undisturbed at the interface. This lymphocyte layer was transferred to a 5OmL tube with 4OmL of RPMI-C and centrifuged at 1500rpm for 5 minutes. Following centrifugation, the supernatant was removed and the lymphocyte pellet was washed with 1OmL of RPMI-C in a new 15mL tube, centrifuged at 1300rpm for 5 minutes. The supernatant decanted and the washed lymphocytes frozen down in ImI of RPMI-C containing 30% FBS and 10% DMSO and stored at -80°C for overnight and then transferred into liquid nitrogen tanks.

Total RNA Isolation

Total RNA was isolated from 1,280 lymphyocyte samples housed in RPMI-C freeze medium using a modified procedure of the QIAGEN RNeasy^® 96 protocol for isolation of total RNA from animal cells using spin technology (QIAGEN Inc.; Valencia CA). Samples (within single tubes) were centrifuged at 60OxG for 5 minutes at room temperature to precipitate the buffy coat pellet from the RPMI-C freeze medium solution. The freeze medium supernatant solution was aspirated and the pelleted buffy coat samples were resuspended in 250μl of QIAGEN' s QIAzol RNase inactivating cell lysis reagent solution. Samples were rigourously vortexed and left to incubate at room temperature for 10 minutes. To the lysed cell solution and additional 50μl of chloroform (Sigma-Aldrich; St. Louis, MO) was added, vortexed for 15 seconds and then left to stand at room temperature for 3 minutes. Samples were then centrifuged at 13,00OxG for 15 minutes at a temperature of 4°C to isolate a clear aqueous cell lysate. A total of 140μl of the clear aqueous cell lysate was available for transfer into a clean nuclease free 96 well plate (NUNC; Germany). An equal volume of 100% ethanol was added to each well of the 96 well plate to provide appropriate binding conditions prior to the transfer of samples to QIAGEN' s RNeasy 96 plate placed on top of QIAGEN' s square-well collection block. The addition of ethanol assists in the selective binding of total RNA to the RNeasy 96 plate silica membrane whilst contaminants are washed away. The remainder of the total RNA isolation procedure continues from Step 6 of QIAGEN's RNeasy 96 protocol for isolation of total RNA from animal cells using spin technology. The optional on-column DNase digestion treatment was not performed. Total RNA was eluted with 50μl of RNase free water followed by a second elution with the initial eluate (-47 μl) for the complete recovery of total RNA. To guarantee all total RNA had been eluted a fresh 50μl volume of RNase free water was added to the RNeasy 96 plate and eluted into a new elution microtube rack and archived at -80°C. Using the QIAGEN RNeasy 96 procedure all RNA molecules >200 nucleotides in length will be isolated thus providing a concentrated eluate of mRNA molecules. Total RNA yield (μg) and purity (260nm:280nm) were determined spectrophotometrically using the NanoDrop ND- 1000 (Wilmington, DE). Integrity of resuspended total RNA was determined by electrophoretic separation and subsequent laser induced florescence detection using the RNA 6000 Nano Assay Chip Kit on the Bioanalyzer 2100 using the 2100 Expert software (Agilent Technologies, Germany). Total RNA integrity was scored by an RNA Integrity Number (RIN) which provides a standardized score on a scale of 1 to 10. A score of 1 is interpretative of complete RNA degradation whilst a score of 10 denotes completely intact total RNA. This score eliminates user observational subjectiveness and provides a more unifed measure of RNA integrity. A total of 500ng total RNA was dried down using an Eppendorf Vacufuge Concentrator 5301 (Eppendorf, Germany) and stored at -20⁰C prior to anti-sense RNA (aRNA) synthesis, amplification and purification.

EXAMPLE 2

Anti-sense RNA synthesis, amplification and purification

Anti-sense RNA (aRNA) was synthesized, amplified and purified using the Ambion MessageAmp II Amplification Kit (Ambion; Austin TX) following the Illumina Sentrix Array Matrix 96-well expression protocol (Illumina Inc.; San Diego CA). First strand cDNA synthesis was reverse transcribed by a two-step mastermix process by firstly resuspending 500ng of total RNA with a T7 Oligo(dT) primer in a final volume of 12μl and incubation for 10 minutes at 70°C using an Applied Biosystems 9700 thermal cycler (Applied Biosystems; Foster City CA). Secondly, addition of a reverse transcription master mix using Array Script™ was added to each sample and then incubated at 42⁰C for 2 hours. This two-step first strand cDNA synthesis produces a subtle yield advantage and the use of Array Script™ catalyzes the synthesis of near full-length cDNA molecules, thus enhancing reproducibility of expression samples. Second strand cDNA synthesis was performed using DNA Polymerase and RNase H by adding a second strand cDNA master mix to the existing first strand cDNA master mix and incubating at 16°C for a further 2 hours. Synthesised cDNA samples were purified using QIAGEN's QIAquick 96 PCR purification supplementary protocol for spin technology (QIAGEN document QQOl. doc, October 2001). Purified cDNA samples were eluted with 80μl of RNase free water into a new 96 well RNase free PCR plate. Samples were dried down using an Eppendorf Vacufuge Concentrator 5301 prior to in vitro transcription to synthesise biotin-labeled aRNA.

Biotin-16-UTP (Roche, Germany) labeled aRNA was synthesized using Ambion's proprietary MEGAscript^® in vitro transcription (IVT) technology and T7 RNA Polymerase. A total volume of lOμl of aRNA synthesis IVT master mix was added to the dried purified cDNA samples and incubated at 37°C for 14 hours. This proprietary technology results in thousands of aRNA copies being generated for each mRNA molecule in a single sample. The optional second round of aRNA amplification was not performed. Purification of aRNA samples was performed using QIAGEN's RNeasy^® 96 protocol for RNA cleanup using spin technology. The optional DNase digestion step was not performed. Changes were made to the two elution steps of QIAGEN's aRNA purification protocol (Steps 11 & 12). In place of using 70μl of RNase free water for the second elution, the first eluate was used, resulting in a more concentrated sample. Anti-sense RNA total yield (μg) and purity (260nm:280nm) were determined spectrophotometrically using the NanoDrop ND-1000 and a total of 1.5μg of aRNA was dried down and stored at -2O⁰C prior to sample hybridization.

EXAMPLE 3 Sample Hybridization to Illumina BeadChip

Hybridization of aRNA to Illumina^® Sentrix^® Human Whole Genome (WG-6) BeadChips and subsequent washing, blocking and detecting were performed using Illumina' s BeadChip 6x2 protocol. Purified aRNA (1.5μg) was resuspended in RNase free water and then added to a hybridization mix of pre-warmed Hyb El buffer (Illumina; San Diego CA) and formamide. aRNA samples (with hybridization mix) were "denatured" by heating at 65°C for 5 minutes prior to dispensing them onto Illumina's Sentrix Human WG-6 (6x2) BeadChip which were housed within specifically designed Hybridization (Hyb) Cartridges. "Denaturing" the aRNA samples enhances the binding affinity of the aRNA sample to the BeadChip oligo substrate. Hybridization cartridges, with BeadChips were placed on a rotary wheel for 16-20 hours at 55⁰C to allow hybridization of aRNA to the oligo substrate. The Illumina^® Sentrix Human WG-6 BeadChips were washed (ElBC Solution Buffer), blocked (El Buffer), signal detected, washed again and then dried prior to scanning. Sample signal detection was developed with the addition of streptavidin-Cy3 (Roche, Germany).

EXAMPLE 4

Sample Scanning and Detection

Samples were scanned on the Illumina^® BeadArray™ 500GX Reader using Illumina^® BeadScan image data acquisition software (ver. 2.3.0.13). Illumina^® BeadStudio software (ver. 1.5.0.34) was used for preliminary data analysis, with a standard background normalization, to generate an output file for statistcial analysis. To assess quality metrics of each days run, several quality control procedures were implemented. A control RNA sample, supplied by Ambion, was analyzed with each daily run. The Illumina^® BeadStudio software was used to view control summary reports, scatter plots of the Ambion control RNA day-to-day and scatter plots of daily run samples. The scatter plots compared control to control or sample to sample and calculated an r² correlation coefficient value. Viewing the scatter plots allowed us to determine if controls across days varied with quality, indicating a reduction in assay performance and also highlight those samples that were of lesser quality. The control summary report is a report generated by the BeadStudio software that evaluates the performance of the built-in controls of the beadchips across a particular days run. This allows the user to look for variations in signal intensity, hybridization signal, background signal and the background to noise ratio level for all samples analyzed that day. EXAMPLE 5 Statistical Analysis

Identification of transcripts significantly expressed in lymphocytes In order to identify transcripts that exhibited sufficient quantitative expression in lymphocytes, differences between the distribution of expression values for a given transcript and that of the control values that are imbedded within each Illumina assay were tested. For each transcript, a chi-square based "tail" test that tested whether there was a signficant excess of individuals with values above the 95%-tile of the control null distribution were tested. This test was formulated to allow for the detection of even those transcripts that may generally have no to little expression in most individuals but that are reliably increased in a subset of individuals. Using a false discovery rate of 0.05, 20,413 transcripts were identified which successfully exibited signfϊciant expression.

Standardization of expression values

After identification of the 20,413 transcripts that exhibited sufficient quantitative variation for retention, a series of standardization procedures were performed to allow comparison of expression scores across individuals. In order to minimize the effect of overall signal levels on expression values, sets of transcripts were grouped by their average log raw signals for standardization. Transcripts were grouped in sets based on the percentile of their average log raw signals. 10%-tile groupings were utilised and then standardized expression values for genes within a group within an individual using z-scores. After this initial within-individual standardization, an additional standardization to remove any residual effects of total expression signal via transcript-specific regression analysis in which we calculated residual expression values corrected for individual-specific average log raw signal was performed. Finally, for each transcript, these residual expression scores were directly normalised by employing an inverse gaussian transformation across individuals. This conservative procedure results in a normalized expression phenotype that is comparable between individuals and across transcripts. Identification of cis-regulated transcripts

In ascertaining genes whose expression is causally involved in disease risk, it was decided to focus on genes that are highly likely to have sequence variants within (or near) their structural locus that influence their quantitative expression. Such cis-regulated transcripts are more likely to directly involved in disease risk than transcripts whose expression levels may be due to unknown trans-acting factors. In order to identify transcripts that are likely to be cis-regulated, quantitative trait linkage analysis was performed on each transcript. These analyses were limited to the examination of linkage at the genetic location of the structural locus for each transcript. Genetic locations for each gene were imputed based on available STR marker-based linkage maps and known physical locations. For the data set utilized, existing STR genetic marker data at an approximate 8 cM density was used. These STR markers were used to estimate multipoint location-specific identity-by-descent probability matrices at 1 cM intervals using the Monte Carlo method employed in the computer program, LOKI. These IBD matrices were then utilized in our computer package, SOLAR, to perform variance component-based quantitative trait linkage analysis for each transcript at the location of its structural gene. For each transcript, no linkage at the structural locus utlizing standard likelihood based methods was tested. The resulting test statistic, the LOD score, was calculated for each transcript. Employing a false discovery rate of 0.25 (so that on average 75% of positive results will be true), 2,772 potentially cis-regulated transcripts were identified.

Identification of cis-regulated transcripts that correlate with diabetes

For each of the previously identified cis-regulated transcripts, formal tests of association with a variety of diabetes-related phenotypes including diabetes affection status, diabetes risk index, fasting glucose levels, fasting insulin levels, 2 hour glucose levels, 2 hour insuling levels, HOMA-B index, HOMA-IR index, body mass index, HDL cholesterol and relative fat (as measured by biompedence) were tested. Using regression-based association tests, all transcripts that were significantly associated with a given target phenotype were searched for. False discovery rate methods were employed to account for multiple testing. Transcripts that are identified in this way are likely to be directly involved in disease risk and also are more likely to represent reliable biomarkers of risk. Transcriptional Correlates of diabetes and condition associated therewith Risk Factors

To identify potential candidate genes that influence HDL-C or TG levels, the correlations between expression measures of cis-regulated transcripts versus these two phenotypes was examined (controlling for sex and age). Using this approach, 51 cis-regulated transcripts were identified which were significantly (corrected for multiple testing) associated with one or more of these traits. The candidate transcripts are those that are both highly significant for cis-effects and for correlations with the risk factors.

The nucleic acid molecules specific to the markers identified as being associated with diabetes, a predisposition for developing diabetes or a complication associated with diabetes are disclosed in SEQ ID NOs: 1 to 51, and are described in detail in Table 3.

TABLE 3 Diabetes Risk

EXAMPLE 6 VNNl as a marker of diabetes risk

VNNl gene (SEQ ID NO:51) expression shows extremely strong evidence for cis- regulation (p = 1.2 x 10^"11) and for a correlation with both HDL-C (p - 4 x 10^"9) and triglyceride levels (p = 0.002).

Resequencing VNNl Yields Significant Associations

In order to confirm whether genetic variation within VNNl influences its expression levels approximately 2kb of putative VNNl promoter using the ATG start site as the terminus was resequenced in 96 Mexican American founder individuals, that is individuals who contribute new genetic information to the family cohorts. Part of exon 1 was also sequenced. These regions are known for most genes to influence expression levels. Resequencing revealed a total of 25 SNPs. Association analysis to test for correlation between the promoter SNPs and VNNl transcript expression levels. Eight SNPs showed evidence for association with VNNl transcripts levels including SNPs at positions: -1026 (p = 0.032), -708 (p = 2.3 x 10^"7), -667 (p = 8.9 x 10^'8), -623 (p = 0018), -612 (p = 0.024), -587 (p = 3.1 x 10^"8), -137 (p = 3.1 x 10^"8), and a coding variant at position 91 (p = 8.1 x 10^"8). Positions are given relative to position 1501 of SEQ ID NO: 53. To summarize the evidence for association between promoter SNPs within VNNl and its quantitative transcription level, a global (or omnibus) p-value was calculated that has been adjusted for the effective number of SNPs. The global association with VNNl SNP variation yielded a p-value of p = 6.8 x 10^"7. Given that these results are based on a small sample of 96 individuals, the association results are striking evidence for strong promoter-based regulatory effects on VNNl transcript levels. Thus genetic variation within VNNl influences expression levels of VNNl.

The six most strongly associated promoter SNPs in the rest of the sample of 1,240 individuals with transcriptional profiles (not just the founders of the cohort as above) was typed, and found overwhelming evidence for association. The promoter SNP at position - 587 was highly correlated with VNNl expression levels (p = 8.7 x 10^"77), as were SNPs at positions, -137 (p = 3.2 x lO^"71), -612(p = 3.4 x 10^"35), -708 (p = 7.8 x lO^"35), and -667 (p = 1.5 x 10^"8). Since genetic variants can be inherited together especially when they occur close to each other on the genome, it was determined whether each of these variants was likely to be acting in concert (high linkage disequilibirum 'LD') or independently (low LD) to influence expression levels. It was found that these SNPs are not all in high linkage disequlibrium with one another and multivariant analysis suggests at least two independent functional sites.

VNNl Promoter Variants Correlate With HDL-C

After finding promoter variants contributing to VNNl expression levels, the association between these SNPs and HDL-C levels was determined in light of the earlier showing that expression levels of VNNl were correlated with HDL-cholesterol. Two of the promoter variants showed significant correlations with HDL-C. All five SNPs that exhibited significant correlations with expression were also correlated with HDL-C levels, with observed p-values ranging from 0.0004 to 0.021. The likely functional promoter variant at -137 exhibits a strong association with HDL-C levels (p = 0.002).

VNNl Promoter Variants and the Causal Network for CVD Risk

Given the strong association between VNNl promoter variants and both VNNl gene expression and HDL-C levels, the network of transcription that is influenced by this obligately causal anchor were examined. Associations between VNNl promoter variants were calculated for every transcript, that is, genes from the entire dataset that showed an association with the genetic variation of VNNl were determined. A total of 3,048 transcripts showed nominal association (that is, P-value < 0.05 prior to adjustment for multiple testing). By chance, one would only expect approximately 1,000. Restricting the examination to this set of genes that are downstream of VNNl, a global pathway analysis was performed to better understand the functional network.

The connectivity information was used to create gene networks as graphical representations of the interactions between genes downstream of VNNl and correlated with HDL-cholesterol. All of the depicted genes have expression levels that are at least nominally correlated with VNNl promoter genetic variants and thus must be at least partially causally downstream of VNNl. This is because genetic variation within VNNl is inherited and fixed within an individual. Red colored symbols indicate positive relationships with HDL-C levels, while green symbols depict negative relationships. The VNNl gene (bottom center) is vividly red to symbolize the strong positive relationship between expression levels and HDL-C. Notable players in lipid metabolism abound in this network providing further support for the role of this gene in HDL cholesterol modulation.

For example, LPL positively related to HDL-C), LCAT (positively related), LRP 3 (positively related), and ACAT2 (negatively related) are represented. All three of these genes have transcript levels that are significantly influenced by VNNl sequence variation with p-values of 0012, 0.018, 0.020, and 0.030 for ACAT2, LRP3, LPL, and LCAT respectively. VNNl is known to interact with PPARG, a major metabolic regulator, which appears in the network and may form the hub through which VNNl acts. PPARG levels are also significantly influenced (p = 0.033) by sequence variation within the VNNl gene. However, this examination is merely meant to show the potential value of identifying promoter variants that can anchor the causal chain of a system related to disease risk.

EXAMPLE 7 BiomarkerdLlRΛP

ILlRAP was one of a group of novel biomarkers discovered using a large scale gene expression profiling study in extensively phenotyped Mexican American subjects in an extended family pedigree structure (as described above).

Interleukin 1 (IL-I) induces synthesis of acute phase and proinflammatory proteins during infection, tissue damage, or stress, by forming a complex at the cell membrane with an interleukin 1 receptor and an accessory protein. This gene encodes an interleukin 1 receptor accessory protein, the amino acid sequence of which is disclosed in SEQ ID

NO:52. It potentially acts as stress/inflammation regulator as soluble IL-IRAcP (sIL-

IRAcP) has been reported as an inhibitor of IL-I signaling. sIL-lRAcP mRNA levels increase 16-fold in response to phorbol esters in the human hepatoma cell line HepG2

(PMID: 12781872). T AU2007/001752

- 56 -

IL-IRAP - ELISA

Using a commercially available antibody to IL-IRAP, a sandwich ELISA was developed to measure IL-IRAP levels in serum.

Using the IL-IRAP ELISA, a longitudinal analysis was performed. Samples from 230 human subjects were analysed for IL-IRAP levels. The 230 individuals were all stratified as normal glucose tolerant (NGT) at the time of the samples being taken (1992). The case who went onto develop type 2 diabetes (T2DM) by 1998 were matched for ethnicity, age, sex, fasting glucose, fasting insulin, BMI, waist circumference and HDL cholesterol to control subjects.

IL-IRAP levels were significantly different (p value = 0.020) between those who progressed to T2DM as compared to control subjects who did not over the same period (see Table 4).

TABLE 4

Those skilled in the art will appreciate that the invention described herein is susceptible to variations and modifications other than those specifically described. It is to be understood that the invention includes all such variations and modifications. The invention also includes all of the steps, features, compositions and compounds referred to or indicated in this specification, individually or collectively, and any and all combinations of any two or more of said steps or features. BIBLIOGRAPHY

Altschul et al. Nucl Acids Res 25:3389, 1997

Ausubel et al. "Current Protocols in Molecular Biology" John Wiley & Sons Inc, Chapter 15, 1994-1998

Bonner and Laskey, Eur J Biochem 46:83, 1974

Douillard and Hoffman Compendium of Immunology Vol. II, ed. by Schwartz, 1981

Knapp and Miller, Clinical Epidemiology and Biostatistics. William and Wilkins, Harual Publishing Co. Malvern, Pa., 1992

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U.S. Patent No. 4,016,043

U.S. Patent No. 4,018,653

U.S. Patent No. 4,424,279

Claims

CLAIMS:

1. A method for diagnosing diabetes, complications associated with diabetes or a predisposition of a subject to develop diabetes, said method comprising:

(a) obtaining a biological sample from a subject;

(b) determining the level of one or more biomarkers listed in Table 3 in the biological sample;

(c) comparing the level of the one or more biomarkers in the biological sample to a statistically validated threshold, wherein detecting a difference in the level of one or more biomarkers in the comparison step (c) is predicative of said subject having diabetes or developing diabetes.

2. The method of Claim 1, wherein the biomarker is a nucleic acid molecule selected from the group consisting of:

(a) SEQ ID NOs:l to 51;

(b) a sequence which hybridizes under high stringency to SEQ ID NOs: 1 to 51, or a fragment thereof or a complementary form thereof; and

(c) a sequence having 90% similarity to SEQ ID NOs : 1 to 51.

3. The method of Claim 1, wherein the diabetes is type II diabetes.

4. The method of Claim 1, wherein the biological sample is selected from the group consisting of: blood, serum, urine, isolated peripheral blood mononuclear cells, lymphocytes, urine, tissue, faecal matter, bile, cellular extracts, respiratory fluid, saliva and mucosal secretions.

5. The method of Claim 1, further comprising the step of isolating DNA, RNA or niRNA from the biological sample.

6. The method of Claim 4, wherein RNA is isolated from the biological sample.

7. The method of Claim 1, wherein the step of determining the level of the one or more biomarkers comprises quantitative reverse transcriptase PCR (RT-PCR).

8. The method of Claim 7, wherein said RT-PCR comprises primers which hybridize to mRNA specific for said one or more biomarkers listed in Table 3 or the complement thereof.

9. The method of Claim 8, wherein the primers are 15-25 nucleotides in length.

10. The method according to Claim 5, wherein the RNA is detected by Northern Blot analysis by hybridizing RNA from the biological sample with one or more probes specific for one or more of the biomarkers listed Table 3.

11. The method according to Claim 5, wherein the level of one or more biomarkers listed in Table 3 is measured using real time PCR.

12. The method of Claim 1, wherein the step of determining the level of the one or more biomarkers listed in Table 3 comprises hybridizing a first plurality of nucleic acid molecules isolated from the biological sample to an array comprising nucleic acid molecules specific for said one or more biomarkers.

13. The method of Claim 12, wherein said first plurality of isolated nucleic acid molecules is selected from DNA, RNA, cDNA, mRNA, PCR products or ESTs.

14. The method of Claim 1, wherein the one or more biomarkers are in the form of a secreted protein.

15. The method of Claim I₃ wherein the biomarker is a polypeptide selected from the group consisting of:

(a) SEQ ID NO:52;

(b) a polypeptide encoded by a nucleic acid molecule selected from SEQ ID NOs: 1 to 51 or the complement thereof;

(c) a polypeptide encoded by a nucleic acid molecule which hybridizes under high stringency to one or more of SEQ ID NOs: 1 to 51, or a fragment thereof or a complementary for thereof; and

(d) a polypeptide encoded by a nucleic acid molecule having 90% similarity to SEQ ID NOs: l to 51.

16. The method of any one of Claims 14 and 15, wherein the secreted proteins are detected using antibodies.

17. The method of any one of Claims 14 and 15, wherein the antibody is selected from the group consisting of a polyclonal antibody, a monoclonal antibody, a humanised antibody, a single side chain antibody, a chimeric antibody, and a fab antibody fragment.

18. The method of any one of Claims 14 and 17, wherein the secreted proteins are detected using a technique selected from western blot, ELISA, radioimmunoassy, immunohistochemistry, mass spectrometry, proteomics, and HPLC.

19. The method of any one of Claims 14 and 15, wherein the secreted proteins are detected using an ELISA.

20. The method of Claim 1, wherein the step of determining the level of the one or more biomarkers listed in Table 3 comprises hybridizing proteins isolated from the biological sample to an array comprising antibodies specific for said one or more biomarkers.

21. A method of diagnosing diabetes or a complication arising from diabetes or a predisposition for developing diabetes in a subject comprising:

(a) obtaining a serum sample from said subject;

(b) contacting said serum sample with one or more antibodies immunoreactive with one or more biomarkers listed in Table 3 to form an immunoconiplex;

(c) detecting said immunocomplex; and

(d) comparing the quantity of said immunocomplex to the quantity of immunocomplex formed under identical conditions with the same antibody and a control sereum from one or more controls, wherein a difference quantity of said immunocomplex(es) in serum from said subject relative to said control serum is indicative of diabetes or a complication arising from diabetes or a predispposition for developing diabetes.

22. The method of Claim 21 wherein said immunocomplex is detected in a Western blot assay.

23. The method of Claim 21 , wherein said immunocomplex is detected in an ELISA.

24. The method of Claim 1, wherein the level of two or more biomarkers are determined.

25. The method of Claim 1, wherein the level of three or more biomarkers are determined.

26. The method of Claim 1, wherein the level of four or more biomarkers are determined.

27. The method of Claim 1, wherein the level of five or more biomarkers are determined.

28. The method of Claim 1, wherein the level of six or more biomarkers are determined.

29. The method of Claim 1, wherein the level of seven or more biomarkers are determined.

30. The method of Claim 1, wherein the level of eight or more biomarkers are determined.

31. The method of Claim 1, wherein the level of nine or more biomarkers are determined.

32. The method of Claim 1, wherein the level of ten or more biomarkers are determined.

33. The method of Claim 1, wherein the level of eleven or more biomarkers are determined.

34. The method of Claim 1, wherein the level of twelve or more biomarkers are determined.

35. The method of Claim 1, wherein the level of thirteen or more biomarkers are determined.

36. The method of Claim 1, wherein the level of fourteen or more biomarkers are determined.

37. The method of Claim 1, wherein the level of fifteen or more biomarkers are determined.

38. The method of Claim 1, wherein the level of sixteen or more biomarkers are determined.

39. The method of Claim 1, wherein the level of seventeen or more biomarkers are determined.

40. The method of Claim 1, wherein the level of eighteen or more biomarkers are determined.

41. The method of Claim 1, wherein the level of nineteen or more biomarkers are determined.

42. The method of Claim 1, wherein the level of twenty or more biomarkers are determined.

43. The method of Claim I₅ wherein the level of twenty-one or more biomarkers are determined.

44. The method of Claim 1, wherein the level of twenty-two or more biomarkers are determined.

45. The method of Claim 1, wherein the level of twenty-three or more biomarkers are determined.

46. The method of Claim 1, wherein the level of twenty-four or more biomarkers are determined.

47. The method of Claim 1, wherein the level of twenty-five or more biomarkers are determined.

48. The method of Claim 1, wherein the level of twenty-six or more biomarkers are determined.

49. The method of Claim 1, wherein the level of twenty-seven or more biomarkers are determined.

50. The method of Claim 1, wherein the level of twenty-eight or more biomarkers are determined.

51. The method of Claim 1, wherein the level of twenty-nine or more biomarkers are determined.

52. The method of Claim 1, wherein the level of thirty or more biomarkers are determined.

53. The method of Claim 1, wherein the level of thirty-one or more biomarkers are determined.

54. The method of Claim 1, wherein the level of thirty-two or more biomarkers are determined.

55. The method of Claim 1, wherein the level of thirty-three or more biomarkers are determined.

56. The method of Claim 1, wherein the level of thirty-four or more biomarkers are determined.

57. The method of Claim 1, wherein the level of thirty-five or more biomarkers are determined.

58. The method of Claim 1, wherein the level of thirty-six or more biomarkers are determined.

59. The method of Claim 1, wherein the level of thirty-seven or more biomarkers are determined.

60. The method of Claim 1, wherein the level of thirty-eight or more biomarkers are determined.

61. The method of Claim 1, wherein the level of thirty-nine or more biomarkers are determined.

62. The method of Claim 1, wherein the level of forty or more biomarkers are determined.

63. The method of Claim 1, wherein the level of forty-one or more biomarkers are determined.

64. The method of Claim 1, wherein the level of forty-two or more biomarkers are determined.

65. The method of Claim 1, wherein the level of forty-three or more biomarkers are determined.

66. The method of Claim 1, wherein the level of forty-four or more biomarkers are determined.

67. The method of Claim 1, wherein the level of forty-five or more biomarkers are determined.

68. The method of Claim 1, wherein the level of forty-six or more biomarkers are determined.

69. The method of Claim 1, wherein the level of forty-seven or more biomarkers are determined.

70. The method of Claim 1, wherein the level of forty-eight or more biomarkers are determined.

71. The method of Claim 1, wherein the level of forty-nine or more biomarkers are determined.

72. The method of Claim 1, wherein the level of fifty or more biomarkers are determined.

73. The method of Claim 1, wherein the level of fifty-one or more biomarkers are determined.

74. A kit for diagnosing diabetes or a predisposition for developing diabetes, said kit comprising: (a) one or more nucleic acid segments that selectively hybridise to a sequence selected from the group consisting of SEQ ID NOs: 1 to 51; and (b) a container for each of said one or more nucleic acid segments.

75. The kit of Claim 74, wherein one or more pairs of nucleic acid segments are used as primers for amplifying said one or more biomarkers.

76. A kit for use in diagnosing diabetes or a predisposition for developing diabetes, comprising (a) one or more antibodies which immunoreact with a protein disclosed in SEQ ID NO: 52 or a protein encoded by a sequence selected from the group consisting of SEQ ID NOs :1 to 51; and (b) a container for said antibodies.