U.S. patent application number 13/824359 was filed with the patent office on 2013-07-11 for biomarkers associated with pre-diabetes, diabetes and diabetes related conditions.
This patent application is currently assigned to THE UNIVERSITY OF WESTERN AUSTRALIA. The applicant listed for this patent is Scott Bringans, Tammy Casey, Timothy Davis, Wendy Davis, Richard Lipscombe, Kirsten Peters, Thomas Stoll, Kaye Winfield. Invention is credited to Scott Bringans, Tammy Casey, Timothy Davis, Wendy Davis, Richard Lipscombe, Kirsten Peters, Thomas Stoll, Kaye Winfield.
Application Number | 20130177544 13/824359 |
Document ID | / |
Family ID | 45873304 |
Filed Date | 2013-07-11 |
United States Patent
Application |
20130177544 |
Kind Code |
A1 |
Stoll; Thomas ; et
al. |
July 11, 2013 |
BIOMARKERS ASSOCIATED WITH PRE-DIABETES, DIABETES AND DIABETES
RELATED CONDITIONS
Abstract
Biomarkers for pre-Diabetes, Diabetes and/or a Diabetes related
conditions, and methods of their use, including the biomarkers in
Tables 1 and 2 such as peroxiredoxin-2, complement C1q subcomponent
subunit B, sulfhydryl oxidase 1 and apolipoprotein A-IV.
Inventors: |
Stoll; Thomas; (Windorf,
DE) ; Bringans; Scott; (Harrisdale, AU) ;
Winfield; Kaye; (Bayswater, AU) ; Casey; Tammy;
(Mount Pleasant, AU) ; Davis; Wendy; (Cottesloe,
AU) ; Peters; Kirsten; (Wellard, AU) ; Davis;
Timothy; (Cottesloe, AU) ; Lipscombe; Richard;
(Floreat,, AU) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Stoll; Thomas
Bringans; Scott
Winfield; Kaye
Casey; Tammy
Davis; Wendy
Peters; Kirsten
Davis; Timothy
Lipscombe; Richard |
Windorf
Harrisdale
Bayswater
Mount Pleasant
Cottesloe
Wellard
Cottesloe
Floreat, |
|
DE
AU
AU
AU
AU
AU
AU
AU |
|
|
Assignee: |
THE UNIVERSITY OF WESTERN
AUSTRALIA
Crawley, Western Australia
AU
PROTEOMICS INTERNATIONAL PTY LTD
Crawley, Western Australia
AU
|
Family ID: |
45873304 |
Appl. No.: |
13/824359 |
Filed: |
September 20, 2011 |
PCT Filed: |
September 20, 2011 |
PCT NO: |
PCT/AU11/01212 |
371 Date: |
March 16, 2013 |
Current U.S.
Class: |
424/94.1 ;
435/23; 435/287.1 |
Current CPC
Class: |
C12Q 2600/158 20130101;
G01N 2333/908 20130101; A61P 3/10 20180101; G01N 2800/042 20130101;
A61P 13/12 20180101; G01N 2800/347 20130101; C12Q 1/6883 20130101;
G01N 2333/4716 20130101; G01N 33/6893 20130101; G01N 2333/90212
20130101; G01N 2333/775 20130101; G01N 2800/50 20130101; G01N
33/6848 20130101 |
Class at
Publication: |
424/94.1 ;
435/23; 435/287.1 |
International
Class: |
G01N 33/68 20060101
G01N033/68 |
Foreign Application Data
Date |
Code |
Application Number |
Sep 21, 2010 |
AU |
2010904249 |
Claims
1-42. (canceled)
43. A method of assessing a subject for diabetic nephropathy
comprising measuring at least one biomarker in a sample from the
subject, wherein said at least one biomarker is selected from the
list of biomarkers comprising: Peroxiredoxin-2, Protein AMBP,
Complement C1q, Insulin-like growth factor-binding protein,
Complement factor H-related protein 2, Apolipoprotein B-100,
Sulfhydryl oxidase 1 and Apolipoprotein A-IV.
44. A method according to claim 43 wherein the at least one
biomarker is at least two, three or four biomarkers selected from
the list of biomarkers comprising: Peroxiredoxin-2, Protein AMBP,
Complement C1q, Insulin-like growth factor-binding protein,
Complement factor H-related protein 2, Apolipoprotein B-100,
Sulfhydryl oxidase 1 and Apolipoprotein A-IV.
45. A method according to claim 43 wherein the at least one
biomarker comprises Apoliprotein A-IV, Insulin-like growth
factor-binding protein, Protein AMBP and Complement C1q.
46. A method according to claim 43 wherein the at least one
biomarker comprises Peroxiredoxin-2, Protein AMBP and Complement
C1q.
47. A method according to claim 43 wherein the at least one
biomarker is Peroxiredoxin-2, Protein AMBP and Complement C1q.
48. A method according to claim 43 wherein the step of measuring at
least one biomarker in a sample from the subject comprises the use
of mass spectrometry to measure a peptide fragment of the at least
one biomarker.
49. A method according to claim 48 where the peptide fragment is a
5-25 amino acid peptide of a protein selected from the list of
proteins comprising: Peroxiredoxin-2, Protein AMBP, Complement C1q,
Insulin-like growth factor-binding protein, Complement factor
H-related protein 2, Apolipoprotein B-100, Sulfhydryl oxidase 1 and
Apolipoprotein A-IV.
50. A method according to claim 48 wherein the peptide fragment
comprises at least two of: TABLE-US-00013 (SEQ ID NO: 9)
ATAVVDGAFK; (SEQ ID NO: 10) TVAACNLPIVR; (SEQ ID NO: 11)
EYCGVPGDGDEELLR; and (SEQ ID NO: 14) IAFSATR.
51. A method according to claim 48 wherein the peptide fragment
comprises at least two of: TABLE-US-00014 (SEQ ID NO: 17) FLNVLSPR
(SEQ ID NO: 3) ISASAEELR; (SEQ ID NO: 10) TVAACNLPIVR; (SEQ ID NO:
11) EYCGVPGDGDEELLR; (SEQ ID NO: 7) SVSLPSLDPASAK; (SEQ ID NO: 14)
IAFSATR; and (SEQ ID NO: 8) TEVIPPLIENR.
52. A method according to claim 43 wherein the subject is
asymptomatic for or only exhibits non-specific indicators of
diabetic nephropathy.
53. A method according to claim 43 wherein the subject has been
diagnosed with diabetic nephropathy.
54. A method according claim 43 wherein the subject has kidney
disease.
55. A method according to claim 43 wherein the subject has
microalbuminuria, macroalbuminuria or end stage renal disease.
56. The method according to claim 43 wherein the sample comprises a
blood sample.
57. A kit comprising reagents for measuring at least one biomarker
in a sample from a subject, wherein said at least one biomarker is
selected from the list of biomarkers comprising: Peroxiredoxin-2,
Protein AMBP, Complement C1q, Insulin-like growth factor-binding
protein, Complement factor H-related protein 2, Apolipoprotein
B-100, Sulfhydryl oxidase 1 and Apolipoprotein A-IV.
58. A computer readable medium having computer executable
instructions for assessing a subject for diabetic nephropathy, the
computer readable medium comprising: a routine, stored on the
computer readable medium and adapted to be executed by a processor,
to store biomarker measurement data representing at least one
biomarker selected from the list of biomarkers comprising:
Peroxiredoxin-2, Protein AMBP, Complement C1q, Insulin-like growth
factor-binding protein, Complement factor H-related protein 2,
Apolipoprotein B-100, Sulfhydryl oxidase 1 and Apolipoprotein
A-IV.
59. A method of assessing a treatment for diabetic nephropathy
comprising measuring at least one biomarker, in a sample from a
subject undergoing the treatment, selected from the list of
biomarkers comprising: Peroxiredoxin-2, Protein AMBP, Complement
C1q, Insulin-like growth factor-binding protein, Complement factor
H-related protein 2, Apolipoprotein B-100, Sulfhydryl oxidase 1 and
Apolipoprotein A-IV, at least twice over the course of the
treatment.
60. A test system comprising: means for obtaining test results data
representing levels of at least one biomarker selected from the
list of biomarkers comprising: Peroxiredoxin-2, Protein AMBP,
Complement C1q, Insulin-like growth factor-binding protein,
Complement factor H-related protein 2, Apolipoprotein B-100,
Sulfhydryl oxidase 1 and Apolipoprotein A-IV, in a sample from the
subject; means for collecting and tracking the test results data
generated in step (i); means for calculating a diabetic nephropathy
risk index value from the test results data, wherein said risk
index value is representative of the risk of an individual
developing or having diabetic nephropathy; and means for reporting
said risk index value.
61. A method of evaluating the risk of a subject developing
diabetic nephropathy comprising measuring at least one biomarker in
a sample from the subject, wherein said at least one biomarker is
selected from the list of biomarkers comprising: Peroxiredoxin-2,
Protein AMBP, Complement C1q, Insulin-like growth factor-binding
protein, Complement factor H-related protein 2, Apolipoprotein
B-100, Sulfhydryl oxidase 1 and Apolipoprotein A-IV.
62. A method of treating diabetic nephropathy in a subject
comprising: evaluating risk, for the subject, of developing
diabetic nephropathy using at least one biomarker selected from the
list of biomarkers comprising: Peroxiredoxin-2, Protein AMBP,
Complement C1q, Insulin-like growth factor-binding protein,
Complement factor H-related protein 2, Apolipoprotein B-100,
Sulfhydryl oxidase 1 and Apolipoprotein A-IV and treating the
subject when identified as being at elevated risk for diabetic
nephropathy related condition with a treatment regimen to delay or
prevent the onset of diabetic nephropathy.
63. A method of identifying or assessing an agent for treating or
reducing the risk of developing diabetic nephropathy comprising:
contacting cells expressing at least one biomarker selected from
the list of biomarkers comprising: Peroxiredoxin-2, Protein AMBP,
Complement C1q, Insulin-like growth factor-binding protein,
Complement factor H-related protein 2, Apolipoprotein B-100,
Sulfhydryl oxidase 1 and Apolipoprotein A-IV with a putative agent;
and comparing expression and/or levels of the at least one
biomarker in the cells prior to contact with the putative agent to
expression and/or levels of the at least one biomarker in the cells
after contact with the putative agent; wherein a change in the
level or expression identifies the agent as an agent for treating
diabetic nephropathy.
64. Use of at least one biomarker selected from the list of
biomarkers comprising: Peroxiredoxin-2, Protein AMBP, Complement
C1q, Insulin-like growth factor-binding protein, Complement factor
H-related protein 2, Apolipoprotein B-100, Sulfhydryl oxidase 1 and
Apolipoprotein A-IV as a drug target for diabetic nephropathy.
65. Use of an agent adapted to change the expression or level of at
least one biomarker selected from the list of biomarkers
comprising: Peroxiredoxin-2, Protein AMBP, Complement C1q,
Insulin-like growth factor-binding protein, Complement factor
H-related protein 2, Apolipoprotein B-100, Sulfhydryl oxidase 1 and
Apolipoprotein A-IV for preparing a medication for treating or
reducing the risk of developing diabetic nephropathy.
Description
FIELD OF THE INVENTION
[0001] The invention relates to biomarkers associated with
pre-Diabetes, Diabetes and Diabetes related conditions, such as
diabetic nephropathy, methods of using the biomarkers to determine
the risk that an individual will develop pre-Diabetes, Diabetes and
Diabetes related conditions, methods of screening a population to
identify persons at risk for developing pre-Diabetes, Diabetes and
Diabetes related conditions and drug targets for pre-Diabetes,
Diabetes and Diabetes.
BACKGROUND TO THE INVENTION
[0002] Diabetes mellitus is a chronic disease and one of the major
public health problems of our time. Worldwide there is an ever
increasing population of patients with diabetes that are imposing a
major financial burden on health systems. The prevalence of
diabetes for all age-groups worldwide was estimated to be 2.8% in
2000 and 4.4% by 2030. The total number of people with diabetes is
projected to rise from 171 million in 2000 to 366 million in 2030.
In 2002 the prevalence of diabetes in the Australian population was
7.4% in those 25 years and older, and the number of Australians
with diabetes has trebled since 1981.
[0003] Type 2 diabetes is by far the most common, e.g. affecting 90
to 95% of the U.S. diabetes population. Diabetes mellitus
prevalence increases with age, and the numbers of older persons
with diabetes are expected to grow as the elderly population
increases in number. Along with the rising rate of diabetes there
is also a higher prevalence of impaired glucose metabolism, which
is associated with an increased risk of heart disease and diabetes.
Diabesity is a term which encompasses the prevalence of diabetes,
obesity, impaired glucose metabolism and the associated risk
factors of hypertension and abnormal plasma lipid profiles
(dyslipideamia). The "diabetes epidemic" will continue even if
levels of obesity remain constant. Given the increasing prevalence
of obesity, it is likely that these figures underestimate future
diabetes prevalence.
[0004] Diabetes mellitus is a condition where the body cannot
maintain normal blood glucose levels. Most cases of diabetes
mellitus fall into three broad categories: Type 1, Type 2 and
gestational diabetes. Type 1 diabetes results from the body's
failure to produce insulin, and presently requires the person to
inject insulin. Type 2 diabetes results from insulin resistance, a
condition in which cells fail to use insulin properly, sometimes
combined with an absolute insulin deficiency.
[0005] Type 2 diabetes can usually be controlled in the first
instance by regular exercise and diet. Tablets and eventually
insulin injections may be needed as the disease progresses. Over
time, high blood glucose levels may damage blood vessels and
nerves. These complications of diabetes can cause damage to eyes,
nerves and kidneys and increase the risk of heart attack, stroke,
impotence and foot problems. This damage can happen before an
individual knows that they have diabetes if left undetected for a
long time. Therefore, it is important to diagnose and control
diabetes and its complications at a very early stage.
[0006] Diabetes is also the largest cause of kidney disease
(nephropathy) in developed countries and is accountable for huge
costs in dialysis. 10% to 20% of people with diabetes will die of
kidney (renal) failure. The reasons behind the complication of
nephropathy in diabetes is complex, and includes the toxic effects
of high glucose levels; elevated blood pressure; abnormal lipid
levels and abnormalities of small blood vessels. The accumulative
result is that there is thickening of the glomeruli in the kidney
which allows protein (albumin) to be excreted in the urine.
[0007] Diabetes has become the single most common cause of end
stage renal failure (ESRF) at 40-50% of ESRD cases and annual
Australian Medicare expenditures are greatest for patients with
ESRF caused by diabetes compared with all other primary ESRD
diagnoses. Up to one-third of adults with newly diagnosed type 2
diabetes already have chronic renal disease, and data suggest that
in many of these patients it may have developed in the course of
the pre-diabetic state. The disease is progressive and affects more
men than women.
[0008] Diabetic nephropathy is detected primarily by measuring the
amount of albumin excreted in the urine (albuminuria). Albuminuria
is usually measured using the albumin creatinine ratio (ACR). This
is the ratio between the albumin and the creatinine in the urine.
The ratio considers the concentration of the albumin in relation to
the glomerular filtration rate, which is determined by the amount
of creatinine in the urine. Albuminuria is defined as: ACR>2.5
mg/mmol (men) or >3.5 mg/mmol (women).
[0009] Despite numerous studies and algorithms that have been used
to assess the risk of Diabetes and related conditions, there
remains a need for accurate methods of assessing such risks or
conditions that can be readily adopted by primary care physicians
who are most likely to initially encounter the pre-diabetic or
undiagnosed early diabetic.
[0010] Accordingly, there remains a need for relatively inexpensive
and convenient methods for screening persons at risk for developing
pre-Diabetes, Diabetes and/or a Diabetes related condition and for
monitoring patients with pre-Diabetes, Diabetes and/or a Diabetes
related condition. Such methods could be used for screening a large
population to identify persons at risk for Diabetes, for testing a
single person to determine that individual's risk of developing
Diabetes, for monitoring the health of diabetes patients and
assessing the efficacy of interventions designed to treat Diabetes,
pre-Diabetes and/or related conditions. There is also a need to
identify new drug targets for pre-Diabetes, Diabetes and/or
Diabetes related conditions including protein drug targets.
Identification of new drug targets will enable the development of
new interventions for pre-Diabetes, Diabetes and/or Diabetes
related conditions.
[0011] It is against this background and the problems and
difficulties associated therewith that the present invention has
been developed.
SUMMARY OF THE INVENTION
[0012] In one aspect, the present invention provides a method of
assessing a subject for pre-Diabetes, Diabetes and/or a Diabetes
related condition comprising measuring at least one biomarker in a
sample from the subject, wherein said at least one biomarker is
selected from the list of biomarkers in Table 1 or 2.
TABLE-US-00001 TABLE 1 Accession number (UniProt Protein Database)
Peroxiredoxin-2 P32119 Protein AMBP P02760 Complement C1q
subcomponent subunit P02746 Apolipoprotein A-IV P06727
Apolipoprotein C-III P02656 Insulin-like growth factor-binding
protein P17936 Adiponectin Q15848 Complement factor H-related
protein 2 P36980 Haemoglobin subunit beta P68871 CD5 antigen-like
O43866 Apolipoprotein B-100 P04114 Sulfhydryl oxidase 1 O00391
Complement component C8 beta chain P07358
TABLE-US-00002 TABLE 2 Accession number (UniProt Protein Database)
Peroxiredoxin-2 P32119 Protein AMBP P02760 Complement C1q
subcomponent subunit P02746 B Adiponectin Q15848 Complement factor
H-related protein 2 P36980 Apolipoprotein B-100 P04114 Sulfhydryl
oxidase 1 O00391 Apolipoprotein A-IV P06727
[0013] In another aspect the present invention provides a kit
comprising reagents for measuring at least one biomarker in a
sample from a subject, wherein said at least one biomarker is
selected from the list of biomarkers in Table 1 or 2.
[0014] In another aspect the present invention provides a computer
readable medium having computer executable instructions for
assessing a subject for pre-Diabetes, Diabetes and/or a Diabetes
related condition, the computer readable medium comprising: a
routine, stored on the computer readable medium and adapted to be
executed by a processor, to store biomarker measurement data
representing at least one biomarker selected from the list of
biomarkers in Table 1 or 2.
[0015] In another aspect the present invention provides a method of
assessing a treatment for pre-Diabetes, Diabetes and/or a Diabetes
related condition in a subject comprising measuring at least one
biomarker, in a sample from the subject undergoing the treatment,
selected from the list of biomarkers in Table 1 or 2, at least
twice over the course of the treatment.
[0016] In another aspect the present invention provides a method of
assessing the risk of a subject developing pre-Diabetes, Diabetes
and/or a Diabetes related condition comprising measuring at least
one biomarker, in a sample from the subject, selected from the list
of biomarkers in Table 1 or 2.
[0017] In another aspect the present invention provides a method of
monitoring pre-Diabetes, Diabetes and/or a Diabetes related
condition in a subject comprising measuring at least one biomarker,
in a sample from the subject, selected from the list of biomarkers
in Table 1 or 2 and comparing the measurement obtained with another
measure of the at least one biomarker.
[0018] In another aspect the present invention provides a method of
diagnosing or identifying pre-Diabetes, Diabetes and/or a Diabetes
related condition in a subject comprising measuring at least one
biomarker, in a sample from the subject, selected from the list of
biomarkers in Table 1 or 2.
[0019] In another aspect the present invention provides a method of
differentially diagnosing kidney disease from other conditions that
also cause proteinuria in a subject comprising measuring at least
one biomarker, in a sample from the subject, selected from the list
of biomarkers in Table 1 or 2.
[0020] In another aspect the present invention provides a method of
differentially diagnosing sub-classes or stages of pre-Diabetes,
Diabetes and/or a Diabetes related condition in a subject
comprising measuring at least one biomarker, in a sample from the
subject, selected from the list of biomarkers in Table 1 or 2.
[0021] In another aspect the present invention provides a test
system comprising: [0022] (i) means for obtaining test results data
representing levels of at least one biomarker selected from the
list of biomarkers in Table 1 or 2, in a sample from the subject;
[0023] (ii) means for collecting and tracking the test results data
generated in step (i); [0024] (iii) means for calculating a
pre-Diabetes, Diabetes and/or a Diabetes related condition risk
index value from the test results data, wherein said risk index
value is representative of the risk of an individual developing or
having pre-Diabetes, Diabetes and/or a Diabetes related condition;
and [0025] (iv) means for reporting said risk index value.
[0026] In another aspect the present invention provides a method of
ranking or grouping a population of individuals, comprising:
obtaining pre-Diabetes, Diabetes and/or a Diabetes related
condition risk index data for individuals in said population; and
ranking individuals within the population relative to the remaining
individuals in the population or dividing the population into at
least two groups, based on factors comprising said obtained risk
index data.
[0027] In another aspect the present invention provides a method of
evaluating a pre-Diabetes, Diabetes and/or a Diabetes related
condition surrogate endpoint in a subject, the method comprising:
measuring at least one biomarker from the list of biomarkers in
Table 1 or 2; and evaluating a pre-Diabetes, Diabetes and/or a
Diabetes related condition surrogate endpoint in the subject based
on said measure.
[0028] In another aspect the present invention provides a method of
evaluating the risk of a subject developing pre-Diabetes, Diabetes
and/or a Diabetes related condition comprising measuring at least
one biomarker in a sample from the subject, wherein said at least
one biomarker is selected from the list of biomarkers in Table 1 or
2.
[0029] In another aspect the present invention provides a method of
monitoring the risk of a subject developing pre-Diabetes, Diabetes
and/or a Diabetes related condition comprising measuring at least
one biomarker in a sample from the subject, wherein said at least
one biomarker is selected from the list of biomarkers in Table 1 or
2.
[0030] In another aspect the present invention provides a method of
diagnosing or identifying a subject with pre-Diabetes, Diabetes
and/or a Diabetes related condition comprising measuring at least
one biomarker in a sample from the subject, wherein said at least
one biomarker is selected from the list of biomarkers in Table 1 or
2.
[0031] In another aspect the present invention provides a method of
monitoring pre-Diabetes, Diabetes and/or a Diabetes related
condition therapy or intervention comprising measuring at least one
biomarker in a sample from the subject, wherein said at least one
biomarker is selected from the list of biomarkers in Table 1 or
2.
[0032] In another aspect the present invention provides a method of
differentially diagnosing a disease state or sub-class of
pre-Diabetes, Diabetes and/or a Diabetes related condition
comprising measuring at least one biomarker in a sample from the
subject, wherein said at least one biomarker is selected from the
list of biomarkers in Table 1 or 2.
[0033] In another aspect the present invention provides a method of
treating pre-Diabetes, Diabetes and/or a Diabetes related condition
in a subject comprising: evaluating risk, for the subject, of
developing pre-Diabetes, Diabetes and/or a Diabetes related
condition using at least one biomarker from Table 1 or 2 and
treating the subject when identified as being at elevated risk for
pre-Diabetes, Diabetes and/or a Diabetes related condition with a
treatment regimen to delay or prevent the onset of pre-Diabetes,
Diabetes and/or a Diabetes related condition.
[0034] In another aspect the present invention provides a method of
ranking or grouping a population of subjects, comprising: obtaining
data representing a pre-Diabetes, Diabetes and/or a Diabetes
related condition risk score for subjects comprised within said
population, wherein said risk score is calculated using at least
one biomarker from Table 1 or 2 and ranking subjects within the
population relative to the remaining individuals in the population
or dividing the population into at least two groups, based on
factors comprising said obtained risk score data.
[0035] In another aspect the present invention provides a method of
identifying or assessing an agent for treating or reducing the risk
of developing pre-Diabetes, Diabetes and/or a Diabetes related
condition comprising: [0036] (i) contacting cells expressing at
least one biomarker from Table 1 or 2 with a putative agent; and
[0037] (ii) comparing expression and/or levels of at least one
biomarker from Table 1 in the cells prior to contact with the
putative agent to expression and/or levels of at least one
biomarker from Table 1 or 2 in the cells after contact with the
putative agent; wherein a change in the level or expression
identifies the agent as an agent for treating pre-Diabetes,
Diabetes and/or a Diabetes related condition.
[0038] Thus, another aspect of the present invention provides for
the use of at least one biomarker in Table 1 or 2 as a drug target
for pre-Diabetes, Diabetes and/or a Diabetes related condition.
[0039] In another aspect the present invention provides a method of
treating or reducing the risk of developing pre-Diabetes, Diabetes
and/or a Diabetes related condition in a subject comprising
administering to the subject an effective amount of an agent
adapted to change the expression or level of at least one biomarker
in Table 1 or 2.
[0040] In another aspect the present invention provides for the use
of an agent adapted to change the expression or level of at least
one biomarker in Table 1 or 2 for preparing a medication for
treating or reducing the risk of developing pre-Diabetes, Diabetes
and/or a Diabetes related condition.
BRIEF DESCRIPTION OF DRAWINGS
[0041] The following Detailed Description of the Invention, given
by way of example, but not intended to limit the invention to
specific embodiments described, may be understood in conjunction
with the accompanying Figures, in which:
[0042] FIG. 1 is a table listing biomarker protein data obtained
from three studies with respect to the presence of diabetic
nephropathy in diabetes patients measured by multiple reaction
monitoring (MRM);
[0043] FIG. 2 is a series of box-whisker plots for each biomarker
listed in FIG. 1 from the FDS 1 study (left boxplot: diabetic
group; right boxplot: diabetic group with severe nephropathy;
x-axis: protein/peptide; y-axis: Relative abundance ratio; peptide
sequences: ATA=ATAVVDGAFK; TVA=TVAACNLPIVR; EYC=EYCGVPGDGDEELLR;
LEP=LEPYADQLR; and ISA=ISASAEELR)
[0044] FIG. 3 is a series of box-whisker plots for each biomarker
listed in FIG. 1 from the FDS2 study (left boxplot: diabetic group;
right boxplot: diabetic group with severe nephropathy; x-axis:
protein/peptide; y-axis: Relative abundance ratio; peptide
sequences: IAF=IAFSATR; LEP=LEPYADQLR; ISA=ISASAEELR;
ALA=ALAQCAPPPAVCAELVR; and FLN=FLNVLSPR; DAL=DALSSVQESQVAQQAR;
TVA=TVAACNLPIVR; EYC=EYCGVPGDGDEELLR; GDI=GDIGETGVPGAEGPR;
TGD=TGDIVEFVCK; LVY=LVYPSCEEK);
[0045] FIG. 4 is a series of box-whisker plots for each biomarker
listed in FIG. 1 from the BDS study (left boxplot: diabetic group;
right boxplot: diabetic group with severe nephropathy; x-axis:
protein/peptide; y-axis: Relative abundance ratio; peptide
sequences: LVG=LVGGDNLCSGR; IWL=IWLDNVR; SVS=SVSLPSLDPASAK; and
TEV=TEVIPPLIENR); and
[0046] FIG. 5 is a table listing biomarker protein data obtained
from the BDS study with respect to patients with diabetic
nephropathy and healthy patients measured by MRM.
DETAILED DESCRIPTION OF THE INVENTION
[0047] The present invention relates to the identification of
biomarkers associated with pre-Diabetes, Diabetes and/or Diabetes
related conditions, such as diabetic nephropathy. Accordingly, the
present invention features methods for identifying subjects who are
at risk of developing pre-Diabetes, Diabetes and/or Diabetes
related conditions, including those subjects who are asymptomatic
or only exhibit non-specific indicators of pre-Diabetes, Diabetes
and/or Diabetes related conditions by detection of the biomarkers
disclosed herein. These biomarkers are also useful for monitoring
subjects undergoing treatments and therapies for pre-Diabetes,
Diabetes and/or Diabetes related conditions, and for selecting or
modifying therapies and treatments that would be efficacious in
subjects having pre-Diabetes, Diabetes and/or Diabetes related
conditions, wherein selection and use of such treatments and
therapies slow the progression of pre-Diabetes, Diabetes and/or
Diabetes related conditions, or prevent their onset. The present
invention also features new drug targets for pre-Diabetes, Diabetes
and/or Diabetes related conditions comprising at least one of the
biomarkers in Table 1 or 2.
Definitions
[0048] "Agents for treating or reducing the risk of developing
pre-Diabetes, Diabetes and/or a Diabetes related condition"
include: insulin such as mature insulin, pro-insulin and soluble
c-peptide (SCp), rapid acting forms of insulin, regular insulin,
intermediate-acting insulin and long-acting forms of insulin;
hypoglycaemic agents; anti-inflammatory agents; lipid reducing
agents; anti-hypertensives such as calcium channel blockers,
beta-adrenergic receptor blockers, cyclooxygenase-2 inhibitors
including prodrugs of COX-2 inhibitors, angiotensin system
inhibitors including angiotensin II receptor blockers (ARBs), ACE
inhibitors and rennin inhibitors including amino acids and
derivatives thereof, peptides and derivatives thereof, and
antibodies to renin.
[0049] "Angiotensin II antagonists" are compounds which interfere
with the activity of angiotensin II by binding to angiotensin II
receptors and interfering with its activity and include peptide
compounds and non-peptide compounds. Most angiotensin II
antagonists are slightly modified congeners in which agonist
activity is attenuated by replacement of phenylalanine in position
8 with some other amino acid. Examples of angiotensin II
antagonists include: peptidic compounds (e.g., saralasin,
angiotensin-(1-8) octapeptide and related analogs); N-substituted
imidazole-2-one; imidazole acetate derivatives including
2-N-butyl-4-chloro-1-(2-chlorobenzile) imidazole-5-acetic acid;
4,5,6,7-tetrahydro-1H-imidazo[4,5-c]pyridine-6-carboxylic acid and
analog derivatives; N2-tetrazole beta-glucuronide analogs;
substituted pyrroles, pyrazoles, and tryazoles; phenol and
heterocyclic derivatives such as 1,3-imidazoles; imidazo-fused
7-member ring heterocycles; antibodies to angiotensin II; and
aralkyl imidazole compounds such as biphenyl-methyl substituted
imidazoles; ES8891 (N-morpholino acetyl-(-1-naphthyl)-L-alany-1-(4,
thiazolyl)-L-alanyl
(35,45)-4-amino-3-hydroxy-5-cyclo-hexapentanoyl-N-hexylamide);
SKF108566 (E-alpha-2-[2-butyl-1-(carboxy phenyl)methyl]
1H-imidazole-5-yl[methylan-e]-2-thiophenepropanoic acid); Losartan
(DUP753/MK954); and Remikirin.
[0050] "Angiotensin converting enzyme (ACE) inhibitors" include
amino acids and derivatives thereof, peptides, including di- and
tri-peptides and antibodies to ACE which intervene in the
renin-angiotensin system by inhibiting the activity of ACE thereby
reducing or eliminating the formation of pressor substance
angiotensin II. Classes of compounds known to be useful as ACE
inhibitors include acylmercapto and mercaptoalkanoyl prolines such
as captopril and zofenopril, carboxyalkyl dipeptides such as
enalapril, lisinopril, quinapril, ramipril, and perindopril,
carboxyalkyl dipeptide mimics such as cilazapril and benazapril,
phosphinylalkanoyl prolines such as fosinopril and
trandolopril.
[0051] "Anti-inflammatory" agents include Alclofenac; Alclometasone
Dipropionate; Algestone Acetonide; Alpha Amylase; Amcinafal;
Amcinafide; Amfenac Sodium; Amiprilose Hydrochloride; Anakinra;
Anirolac; Anitrazafen; Apazone; Balsalazide Disodium; Bendazac;
Benoxaprofen; Benzydamine Hydrochloride; Bromelains; Broperamole;
Budesonide; Carprofen; Cicloprofen; Cintazone; Cliprofen;
Clobetasol Propionate; Clobetasone Butyrate; Clopirac; Cloticasone
Propionate; Cormethasone Acetate; Cortodoxone; Deflazacort;
Desonide; Desoximetasone; Dexamethasone Dipropionate; Diclofenac
Potassium; Diclofenac Sodium; Diflorasone Diacetate; Diflumidone
Sodium; Diflunisal; Difluprednate; Diftalone; Dimethyl Sulfoxide;
Drocinonide; Endrysone; Enlimomab; Enolicam Sodium; Epirizole;
Etodolac; Etofenamate; Felbinac; Fenamole; Fenbufen; Fenclofenac;
Fenclorac; Fendosal; Fenpipalone; Fentiazac; Flazalone; Fluazacort;
Flufenamic Acid; Flumizole; Flunisolide Acetate; Flunixin; Flunixin
Meglumine; Fluocortin Butyl; Fluorometholone Acetate; Fluquazone;
Flurbiprofen; Fluretofen; Fluticasone Propionate; Furaprofen;
Furobufen; Halcinonide; Halobetasol Propionate; Halopredone
Acetate; Ibufenac; Ibuprofen; Ibuprofen Aluminum; Ibuprofen
Piconol; Ilonidap; Indomethacin; Indomethacin Sodium; Indoprofen;
Indoxole; Intrazole; Isoflupredone Acetate; Isoxepac; Isoxicam;
Ketoprofen; Lofemizole Hydrochloride; Lornoxicam; Loteprednol
Etabonate; Meclofenamate Sodium; Meclofenamic Acid; Meclorisone
Dibutyrate; Mefenamic Acid; Mesalamine; Meseclazone;
Methylprednisolone Suleptanate; Morniflumate; Nabumetone; Naproxen;
Naproxen Sodium; Naproxol; Nimazone; Olsalazine Sodium; Orgotein;
Orpanoxin; Oxaprozin; Oxyphenbutazone; Paranyline Hydrochloride;
Pentosan Polysulfate Sodium; Phenbutazone Sodium Glycerate;
Pirfenidone; Piroxicam; Piroxicam Cinnamate; Piroxicam Olamine;
Pirprofen; Prednazate; Prifelone; Prodolic Acid; Proquazone;
Proxazole; Proxazole Citrate; Rimexolone; Romazarit; Salcolex;
Salnacedin; Salsalate; Salycilates; Sanguinarium Chloride;
Seclazone; Sermetacin; Sudoxicam; Sulindac; Suprofen; Talmetacin;
Talniflumate; Talosalate; Tebufelone; Tenidap; Tenidap Sodium;
Tenoxicam; Tesicam; Tesimide; Tetrydamine; Tiopinac; Tixocortol
Pivalate; Tolmetin; Tolmetin Sodium; Triclonide; Triflumidate;
Zidometacin; Glucocorticoids; Zomepirac Sodium, aspirin, cytokine
inhibitors such as cytokine antagonists (e.g., IL-6 receptor
antagonists), aza-alkyl lysophospholipids (AALP), and Tumor
Necrosis Factor-alpha (TNF-alpha) inhibitors, such as
anti-TNF-alpha antibodies, soluble TNF receptor, TNF-alpha,
anti-sense nucleic acid molecules, multivalent guanylhydrazone
(CNI-1493), N-acetylcysteine, pentoxiphylline, oxpentifylline,
carbocyclic nucleoside analogues, Dexanabinol and TNF-alpha
inhibitors such as Etanercept and Infliximab.
[0052] "Beta-adrenergic receptor blocking agents" antagonize the
cardiovascular effects of catecholamines in angina pectoris,
hypertension, and cardiac arrhythmias and include atenolol,
acebutolol, alprenolol, befunolol, betaxolol, bunitrolol,
carteolol, celiprolol, hydroxalol, indenolol, labetalol,
levobunolol, mepindolol, methypranol, metindol, metoprolol,
metrizoranolol, oxprenolol, pindolol, propranolol, practolol,
practolol, sotalolnadolol, tiprenolol, tomalolol, timolol,
bupranolol, penbutolol, trimepranol,
2-(3-(1,1-dimethylethyl)-amino-2-hydroxypropoxy)-3-pyridenecarbonitrilHCl-
-, 1-butylamino-3-(2,5-dichlorophenoxy-)-2-propanol,
1-isopropylamino-3-(4-(2-cyclopropylmethoxyethyl)phenoxy)-2-propanol,
3-isopropylamino-1-(7-methylindan-4-yloxy)-2-butanol,
2-(3-t-butylamino-2-hydroxy-propylthio)-4-(5-carbamoyl-2-thienyl)thiazole
and 7-(2-hydroxy-3-t-butylaminpropoxy)phthalide.
[0053] "Calcium channel blockers" belong to one of three major
chemical groups of drugs, the dihydropyridines, such as nifedipine,
the phenyl alkyl amines, such as verapamil, and the
benzothiazepines, such as diltiazem. Other calcium channel blockers
useful according to the invention include aminone, amlodipine,
bencyclane, felodipine, fendiline, flunarizine, isradipine,
nicardipine, nimodipine, perhexylene, gallopamil, tiapamil and
tiapamil analogues, phenyloin, barbiturates, and the peptides
dynorphin, omega-conotoxin, and omega-agatoxin.
[0054] "Diabetes" includes Type 1 Diabetes, both autoimmune and
idiopathic, Type 2 Diabetes and gestational Diabetes. Diabetes can
be characterised by recurrent and persistent hyperglycaemia and may
be diagnosed by increased blood glucose levels and glycated
haemoglobin (.gtoreq.6.5%). According to the current definition,
two fasting glucose measurements above 126 mg/dL (7.0 mmol/L) is
considered diagnostic for Diabetes Mellitus.
[0055] "Diabetes related condition" includes any condition or
disease that is a result or complication of or is otherwise
correlated or associated with Diabetes including a condition caused
by higher than normal blood glucose levels and a condition selected
from the list consisting of: hypoglycaemia, diabetic ketoacidosis,
diabetic neuropathy, kidney disease including diabetic nephropathy,
cardiovascular disease, stroke and diabetic retinopathy and
arteriovascular disease.
[0056] "Biomarker" in the context of the present invention
encompasses, without limitation, the proteins in Table 1 or 2 and
de facto measures thereof; nucleic acids encoding the proteins in
Table 1 or 2; metabolites and degradation products of the proteins
in Table 1 or 2; polymorphisms, mutations, variants, modifications,
subunits, peptides (such as those in Table 3) and fragments of the
proteins in Table 1 or 2; and protein-ligand complexes including
the proteins in Table 1 or 2. Biomarkers can also include proteins
with at least 50%, 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98% or 99%
identity or similarity with the proteins in Table 1 or 2 as well as
mutated forms of the proteins in Table 1 or 2 and nucleic acids
encoding such mutations. The Biomarkers can be used to calculate
mathematical indices or other measurements, including temporal
trends and differences that are useful with respect to the present
invention.
[0057] "Gestational Diabetes" refers to glucose intolerance during
pregnancy. This condition results in high blood sugar that starts
or is first diagnosed during pregnancy.
[0058] "Hypoglycaemic" agents include oral hypoglycaemic agents and
include, without limitation, first-generation sulfonylureas:
Acetohexamide, Chlorpropamide, Tolbutamide; second-generation
sulfonylureas: Glipizide, Glyburide, Glimepiride; Biguanides:
Metformin; Alpha-glucosidase inhibitors: Acarbose, Miglitol,
Thiazolidinediones: Rosiglitazone, Pioglitazone, Troglitazone;
Meglitinides: Repaglinide; and other hypoglycemics such as
Acarbose; Buformin; Butoxamine Hydrochloride; Camiglibose;
Ciglitazone; Englitazone Sodium; Darglitazone Sodium; Etoformin
Hydrochloride; Gliamilide; Glibomuride; Glicetanile Gliclazide
Sodium; Gliflumide; Glucagon; Glyhexamide; Glymidine Sodium;
Glyoctamide; Glyparamide; Linogliride; Linogliride Fumarate; Methyl
Palmoxirate; Palmoxirate Sodium; Pirogliride Tartrate; Proinsulin
Human; Seglitide Acetate; Tolazamide; Tolpyrramide;
Zopolrestat.
[0059] "Impaired fasting glucose" (IFG) is a pre-Diabetic condition
associated with a blood glucose level that is higher than normal,
but not high enough to be classified as Diabetes. A subject with
IFG may have a fasting blood sugar (glucose) level below or equal
to 125 mg/L, between 100 and 125 mg/dL or between 105 and 125
mg/dL.
[0060] The term "identity," used herein refers to a relationship
between the sequences of two or more molecules, as determined by
comparing the sequences. "Identity" also means the degree of
sequence relatedness between polypeptide or nucleic acid molecule
sequences, as the case may be, as determined by the match between
strings of nucleotide or amino acid sequences. "Identity" measures
the percent of identical matches between two or more sequences with
gap alignments addressed by a particular mathematical model of
computer programs.
[0061] "Impaired glucose tolerance" (IGT) is a pre-Diabetic
condition associated with a blood glucose level that is higher than
normal, but not high enough to be classified as Diabetes. A subject
with IGT may have two-hour glucose levels of 140 to 199 mg/dL (7.8
to 11.0 mmol) on the 75-g oral glucose tolerance test.
[0062] "Lipid reducing agents" include gemfibrozil, cholystyramine,
colestipol, nicotinic acid, and HMG-CoA reductase inhibitors such
as simvastatin, lovastatin, pravastatin sodium, fluvastatin,
atorvastatin and cerivastatin.
[0063] The term "measuring" and variants such as "measure" as used
herein in relation to the biomarkers described herein refers to
determining the presence and/or quantity of a given biomarker.
[0064] "pre-Diabetes" is a state in which some but not all of the
diagnostic criteria for Diabetes are met. It includes conditions
where subjects display blood sugar levels between normal and
diabetic levels, conditions where subjects suffer from impaired
glucose tolerance (IGT), impaired fasting glucose (IFG) and/or
glycated haemoglobin between 5.7 and 6.4%.
[0065] A "sample" in the context of the present invention is a
biological sample isolated from a subject and can include, by way
of example and not limitation, whole blood, blood fraction, serum,
plasma, blood cells, endothelial cells, tissue biopsies, lymphatic
fluid, ascites fluid, interstitial fluid (also known as
"extracellular fluid" and encompasses the fluid found in spaces
between cells, including, inter alia, gingival crevicular fluid),
bone marrow, cerebrospinal fluid (CSF), saliva, mucous, sputum,
sweat, urine, or any other secretion, excretion, or other bodily
fluids.
[0066] The term "similarity" is a related concept to "identity",
but in contrast refers to a measure of similarity which includes
both identical matches and conservative substitution matches. Since
conservative substitutions apply to polypeptides and not nucleic
acid molecules, similarity only deals with polypeptide sequence
comparisons. If two polypeptide sequences have, for example, 10 out
of 20 identical amino acids, and the remainder are all
non-conservative substitutions, then the percent identity and
similarity would both be 50%. If in the same example, there are 5
more positions where there are conservative substitutions, then the
percent identity remains 50%, but the percent similarity would be
75% (15 out of 20). Therefore, in cases where there are
conservative substitutions, the degree of similarity between two
polypeptide sequences will be higher than the percent identity
between those two sequences.
[0067] The term "conservative amino acid substitution" refers to a
substitution of a native amino acid residue with a normative
residue such that there is little or no effect on the polarity or
charge of the amino acid residue at that position. For example, a
conservative substitution results from the replacement of a
non-polar residue in a polypeptide with any other non-polar
residue. Furthermore, any native residue in the polypeptide may
also be substituted with alanine. General rules for conservative
amino acid substitutions are set forth in the table hereunder:
TABLE-US-00003 Original Preferred Residues Exemplary Substitutions
Substitutions Ala Val, Leu, Ile Val Arg Lys, Gln, Asn Lys Asn Gln,
His, Lys, Arg Gln Asp Glu Glu Cys Ser Ser Gln Asn Asn Glu Asp Asp
Gly Pro, Ala Ala His Asn, Gln, Lys, Arg Arg Ile Leu, Val, Met, Ala,
Phe, Leu Norleucine Leu Norleucine, Ile, Val, Met, Ala, Phe Ile Lys
Arg, Gln, Asn Arg Met Leu, Phe, Ile Leu Phe Leu, Val, Ile, Ala, Tyr
Leu Pro Ala Ala Ser Thr Thr Thr Ser Ser Trp Tyr, Phe Tyr Tyr Trp,
Phe, Thr, Ser Phe Val Ile, Met, Leu, Phe, Ala, Norleucine Leu
[0068] Conservative amino acid substitutions also encompass
non-naturally occurring amino acid residues that are typically
incorporated by chemical peptide synthesis rather than by synthesis
in biological systems. These include peptidomimetics, and other
reversed or inverted forms of amino acid moieties. Conservative
modifications to the amino acid sequence (and the corresponding
modifications to the encoding nucleotides) are expected to produce
polypeptides having functional and chemical characteristics similar
to those of the biomarkers in Table 1. In contrast, substantial
modifications in the functional and/or chemical characteristics of
the biomarkers in Table 1 may be accomplished by selecting
substitutions that differ significantly in their effect on
maintaining (a) the structure of the molecular backbone in the area
of the substitution, for example, as a sheet or helical
conformation, (b) the charge or hydrophobicity of the molecule at
the target site, or (c) the bulk of the side chain. Naturally
occurring residues may be divided into groups based on common side
chain properties: [0069] 1) hydrophobic: norleucine, Met, Ala, Val,
Leu, Ile; [0070] 2) neutral hydrophilic: Cys, Ser, Thr; [0071] 3)
acidic: Asp, Glu; [0072] 4) basic: Asn, Gln, His, Lys, Arg; [0073]
5) residues that influence chain orientation: Gly, Pro; and [0074]
6) aromatic: Trp, Tyr, Phe.
[0075] Preferred methods to determine identity and/or similarity
are designed to give the largest match between the sequences
tested. Methods to determine identity and similarity are codified
in publicly available computer programs. Preferred computer program
methods to determine identity and similarity between two sequences
include the GCG program package, including GAP (Devereux et al.,
Nuc. Acids Res. 12:387 (1984); Genetics Computer Group, University
of Wisconsin, Madison, Wis.), BLASTP, BLASTN, and FASTA (Atschul et
al., J. Mol. Biol. 215:403-10 (1990)). The BLAST X program is
publicly available from the National Center for Biotechnology
Information (NCBI) and other sources (Altschul et al., BLAST Manual
(NCB NLM NIH, Bethesda, Md.); Altschul et al., 1990, supra). The
well-known Smith Waterman algorithm may also be used to determine
identity.
[0076] A "subject" in the context of the present invention is
preferably a mammal. The mammal can be a human, non-human primate,
mouse, rat, dog, cat, horse, or cow. A subject can be one who has
been previously diagnosed or identified as having Diabetes,
pre-Diabetes, or a Diabetes related condition, and optionally has
already undergone, or is undergoing, a therapeutic intervention for
the Diabetes, pre-Diabetes, or Diabetes related condition.
Alternatively, a subject can also be one who has not been
previously diagnosed as having pre-Diabetes, Diabetes and/or a
Diabetes related condition. For example, a subject can be one who
exhibits one or more risk factors for pre-Diabetes, Diabetes and/or
a Diabetes related condition, or a subject who does not exhibit any
such risk factors or a subject who is asymptomatic for
pre-Diabetes, Diabetes and/or a Diabetes related condition. A
subject can also be one who is suffering from or at risk of
developing pre-Diabetes, Diabetes and/or a Diabetes related
condition.
Diagnostics and Prognostics
[0077] The invention provides improved diagnosis and prognosis of
pre-Diabetes, Diabetes or a Diabetes related condition. The risk of
developing pre-Diabetes, Diabetes or a Diabetes related condition
can be assessed by measuring one or more of the biomarkers
described herein, and comparing the measured values to reference or
index values. Such a comparison can be undertaken with mathematical
algorithms or formula in order to combine information from results
of multiple individual biomarkers and other parameters into a
single measurement or index. Subjects identified as having an
increased risk of pre-Diabetes, Diabetes or a Diabetes related
condition can optionally be selected to receive treatment regimens,
such as administration of prophylactic or therapeutic compounds or
implementation of exercise regimens or dietary supplements to
prevent, treat or delay the onset of pre-Diabetes, Diabetes or a
Diabetes related condition.
[0078] The amount of the biomarker can be measured in a test sample
and compared to a reference or normal level, utilizing techniques
such as reference limits, discrimination limits, or risk defining
thresholds to define cut-off points and abnormal values for
pre-Diabetes, Diabetes or a Diabetes related condition. The normal
control level is the level of one or more biomarkers or combined
biomarker indices typically found in a subject not suffering from
pre-Diabetes, Diabetes or a Diabetes related condition. The normal
and abnormal levels and cut-off points may vary based on whether a
biomarker is used alone or in a formula combining with other
biomarkers into an index. Alternatively, the normal or abnormal
level can be a database of biomarker patterns or "signatures" from
previously tested subjects who did or did not develop or convert to
pre-Diabetes, Diabetes or a Diabetes related condition over a
clinically relevant time horizon.
[0079] The present invention may be used to make continuous or
categorical measurements of the risk of develop or convert to
pre-Diabetes, Diabetes or a Diabetes related condition, thus
diagnosing and defining the risk spectrum of a category of subjects
with a defined clinical status. In the categorical scenario, the
methods of the present invention can be used to discriminate
between normal cohorts and cohort with pre-Diabetes, Diabetes or a
Diabetes related condition. In other embodiments, the present
invention may be used so as to discriminate pre-Diabetes from
Diabetes, Diabetes from normal, different Diabetes related
conditions or different Diabetes conditions from normal. Such
differing use may require different biomarker combinations in
individual panels, mathematical algorithms, and/or cut-off points,
but subject to the same aforementioned measurements of accuracy for
the intended use.
[0080] Identifying a subject before they develop pre-Diabetes,
Diabetes or a Diabetes related condition enables the selection and
initiation of various therapeutic interventions or treatment
regimens in order to delay, reduce or prevent that subject's
conversion to a disease state. Monitoring the levels of at least
one biomarker also allows for the course of treatment of
pre-Diabetes, Diabetes or a Diabetes related condition to be
monitored. For example, a sample can be provided from a subject
undergoing treatment regimens or therapeutic interventions, e.g.,
drug treatments, for pre-Diabetes, Diabetes or a Diabetes related
condition. Such treatment regimens or therapeutic interventions can
include exercise regimens, dietary modification, dietary
supplementation, bariatric surgical intervention, administration of
pharmaceuticals, and treatment with therapeutics or prophylactics
used in subjects diagnosed or identified with pre-Diabetes,
Diabetes or a Diabetes related condition. Samples can be obtained
from the subject at various time points before, during, or after
treatment.
[0081] The present invention can also be used to screen populations
in a variety of settings. For groups of subjects can be screened:
to identify those requiring interventions; for the collection of
epidemiological data; to assess them for health insurance purposes.
Data obtained through population screens will be particularly
valuable when correlated with clinical measures of pre-Diabetes,
Diabetes or a Diabetes related condition and can be stored in data
arrays or other collections in machine-readable media for
convenient use by healthcare service providers and the allied
health industry to improve service delivery and efficiency and
hence improve patient outcomes.
[0082] A machine-readable storage medium includes any data storage
material encoded with machine readable data or data arrays which,
when using a machine programmed with instructions for using said
data, is capable of use for a variety of purposes, such as, without
limitation, providing or generating subject information relating to
pre-Diabetes, Diabetes or a Diabetes related condition risk factors
over time or in response to interventions or therapies and drug
discovery. Assessment or measurement of the biomarkers of the
invention and/or the corresponding risk determined therefrom may be
implemented in computer programs executing on programmable
computers, comprising, inter alia, a processor, a data storage
system (including volatile and non-volatile memory and/or storage
elements), at least one input device, and at least one output
device. Program code or software can be applied to input data to
perform the functions required to generate the required output.
[0083] The program code or software can perform one or more of the
functions in relation to data concerning the biomarkers including:
determining normal or abnormal levels of a biomarker and comparing
a level of a biomarker to a reference value, e.g. a control subject
or population whose pre-Diabetes, Diabetes or a Diabetes related
condition state is known or an index value or baseline value. The
reference sample or index value or baseline value may be taken or
derived from one or more subjects who have been exposed to a
treatment, or may be taken or derived from one or more subjects who
are at low risk of developing pre-Diabetes, Diabetes or a Diabetes
related condition, or may be taken or derived from subjects who
have shown improvements in one or more risk factors associated with
pre-Diabetes, Diabetes or a Diabetes related condition (including
established clinical parameters) as a result of exposure to a
treatment. The reference sample or index value or baseline value
may also be taken or derived from one or more subjects who have not
been exposed to the treatment. For example, samples may be
collected from subjects who have received initial treatment for
pre-Diabetes, Diabetes or a Diabetes related condition and
subsequent treatment for pre-Diabetes, Diabetes or a Diabetes
related condition to monitor the progress of the treatment. A
reference value can also comprise a value derived from a risk
prediction algorithm or computed indices from population
studies.
[0084] The biomarkers of the present invention can thus be used to
generate a biomarker profile or signature of subjects: (i) who do
not have and are not expected to develop pre-Diabetes, Diabetes or
a Diabetes related condition and/or (ii) who have or expected to
develop pre-Diabetes, Diabetes or a Diabetes related condition. The
biomarker profile of a subject can be compared to a predetermined
or reference biomarker profile to diagnose or identify subjects at
risk for developing pre-Diabetes, Diabetes or a Diabetes related
condition, to monitor the progression of disease, as well as the
rate of progression of disease, and to monitor the effectiveness of
pre-Diabetes, Diabetes or a Diabetes related condition treatments.
Biomarker profiles of the present invention are preferably
contained in a machine-readable medium and are "live" insofar as
they can be updated with further data that comes to hand, thus
improving the strength and clinical significance of the biomarkers.
Data concerning the biomarkers of the present invention can also be
combined or correlated with other data or test results, such as,
without limitation, measurements of clinical parameters or other
algorithms for pre-Diabetes, Diabetes or a Diabetes related
condition. Other data includes age, ethnicity, body mass index
(BMI), total cholesterol levels, blood glucose levels, blood
pressure, LDL and HDL levels. The machine-readable media can also
comprise subject information such as medical history and any
relevant family history.
[0085] The present invention also provides methods for identifying
agents for treating pre-Diabetes, Diabetes or a Diabetes related
condition that are appropriate or otherwise customised for a
specific subject. In this regard, a test sample from a subject,
exposed to a therapeutic agent or a drug, can be taken and the
level of one or more biomarkers can be determined. The level of one
or more biomarkers can be compared to a sample derived from the
subject before and after treatment, or can be compared to samples
derived from one or more subjects who have shown improvements in
risk factors as a result of such treatment or exposure.
Tests
[0086] The biomarkers and panels thereof of the present invention
can be implemented in a range of test systems. Typically, test
systems include a means for obtaining test results from a sample, a
means for collecting, storing, processing and/or tracking test
results for the sample, usually in a database and a means for
reporting test results. The means for obtaining test results can
include a module adapted for automatic testing utilising one or
more of biochemical, immunological and nucleic acid detection
assays. Some test systems can process multiple samples and can run
multiple tests on a given sample. The means for collecting,
storing, processing and/or tracking test results may comprise a
physical and/or electronic data storage device such as a hard drive
or flash memory or paper print-outs. The means for reporting test
results can include a visible display, a link to a data structure
or database, or a printer. In this regard, the reporting means may
simply be a data link that is adapted to send results to another
device such as a database, visual display, or printer.
[0087] Thus, the present invention provides a test system adapted
to aid in the identification of individuals at risk of developing
pre-Diabetes, Diabetes or a Diabetes related condition or diagnose
pre-Diabetes, Diabetes or a Diabetes related condition, the test
system comprising a means that uses data relating to at least one
of the biomarkers described herein. Typically, test results from
system of the present invention serve as inputs to a computer or
microprocessor programmed with a machine code or software that
takes the data relating to at least one of the biomarkers described
herein and determines the risk of developing or already having
pre-Diabetes, Diabetes or a Diabetes related condition.
Biomarker Selection
[0088] The biomarkers in Table 1 have been identified as being
found to have altered or modified presence or concentration levels
in subjects who have Diabetes and or diabetic nephropathy. Thus,
the biomarkers and methods of the present invention allow one of
skill in the art to identify, diagnose, or otherwise assess
subjects who do not exhibit any symptoms of pre-Diabetes, Diabetes
or a Diabetes related condition, but who nonetheless may have or be
at risk for developing pre-Diabetes, Diabetes or a Diabetes related
condition.
[0089] One or more of the biomarkers in Table 1 or 2 can be
selected to form a panel of markers. For example, one embodiment of
the invention is a method of evaluating the risk of developing
pre-Diabetes, Diabetes or a Diabetes related condition, comprising
the step of measuring the levels of at least 2, 3, 4, 5, 6, 7, 8,
9, 10, 11, 12 or 13 biomarkers from Table 1 or 2. Preferably, the
panel includes at least one of: Peroxiredoxin-2 (P32119), Protein
AMBP (P02760); Apolipoprotein A-IV (P06727) and Complement C1q
subcomponent subunit B (P02746); at least one of Adiponectin
(Q15848), Complement factor H-related protein 2 (P36980),
Haemoglobin subunit beta (P68871), Apolipoprotein B-100 (P04114)
and Sulfhydryl oxidase 1 (000391) or; at least one of
Apolipoprotein C-III (P02656), Insulin-like growth factor-binding
protein 3 (P17936), CD5 antigen-like (043866) and Complement
component C8 beta chain (P07358).
Clinical Algorithms
[0090] Results obtained using the biomarkers of the present
invention can be combined into indices useful in the practice of
the invention using any one or more formulae. As indicated above,
and without limitation, such indices may indicate, among the
various other indications, the probability, likelihood, absolute or
relative risk, time to or rate of conversion from one to another
disease states, or make predictions of future biomarkers
measurements of pre-Diabetes, Diabetes or a Diabetes related
condition. This may be for a specific time period or horizon, or
for remaining lifetime risk, or simply be provided as an index
relative to another reference subject population.
[0091] Preferred formulas include the broad class of statistical
classification algorithms such as relative operating characteristic
(ROC), the use of discriminant analysis e.g. linear discriminant
analysis (LDA). Features can be identified for LDA using an
eigengene based approach with different thresholds (ELDA) or a
stepping algorithm based on a multivariate analysis of variance
(MANOVA). Forward, backward, and stepwise algorithms can be
performed that minimize the probability of no separation based on
the Hotelling-Lawley statistic. Other formulas include a support
vector machine (SVM), a random forest or recursive partitioning can
also be used separately or in combination to identify biomarker
combinations that are most important.
[0092] Other formula may be used in order to pre-process the
results of individual biomarker measurements into more valuable
forms of information, prior to further processing. Pre-processing
includes inverse and square root transformations, normalisation of
biomarker results, using mathematical transformations such as
logarithmic or logistic functions. Normalisations based on clinical
parameters such as age, gender, race, BMI or sex are particularly
preferred.
[0093] One or more clinical parameters may be used in the practice
of the invention in combination with the biomarkers of the present
invention as an input to a formula or as pre-selection criteria
defining a relevant population to be measured using a particular
biomarker panel and formula. Clinical parameters may also be useful
in the biomarker normalization and pre-processing, or in biomarker
selection, panel construction, formula type selection and
derivation, and formula result post-processing.
[0094] The biomarker panels of the present invention may be
tailored to the population and end point or use that is intended.
For example, biomarker panels and formulas may be used for
assessment of subjects for primary prevention and diagnosis and for
secondary prevention and management. For primary assessment, the
panels and formulas may be used for prediction and risk
stratification for conditions, for the diagnosis of diabetic
conditions, for the prognosis of glucose level and rate of change
and for indication for future diagnosis. For secondary prevention
and management, the panels and formulas may be used for prognosis
and risk stratification for Diabetes complications. The panels and
formulas may be used for clinical decision support, such as
determining whether to defer intervention to next visit, to
recommend normal preventive check-ups, to recommend increased visit
frequency, to recommend increased testing and to recommend
therapeutic intervention. The panels and formulas may also be
useful for intervention in subjects with diabetic conditions, such
as therapeutic selection and response, adjustment and dosing of
therapy, monitoring ongoing therapeutic efficiency and indication
for change in therapeutic intervention.
[0095] The disease endpoints of the invention include pre-Diabetes,
Diabetes or a Diabetes related condition. The panels and formulas
herein may be used to evaluate current status of the disease
endpoints by aiding in the diagnosis and/or the determination of
severity of the pre-Diabetes, Diabetes or a Diabetes related
condition and/or determination of the subclass of the disease or
condition. The panels and formulas herein are also useful for
determining the future status of intervention such as determining
the prognosis of future pre-Diabetes, Diabetes or a Diabetes
related condition with therapy, intervention and drug therapy. The
invention may be tailored to a specific intervention, drug class,
therapeutic class or therapy or drug therapy or a combination
thereof.
[0096] The surrogate endpoints of the invention include measuring
HBAIc, glucose (I7PG and OGTT), and glucose class (normal glucose
tolerance (NGT), IGT, IFG and T2DM). The panels and formulas herein
are useful for determining the current status of the surrogate
endpoints by diagnosing glucose class with or without fasting. The
future status of surrogate endpoints may be determined using the
biomarker panels herein such as determination of the prognosis of
future glucose class. The biomarker panels and formulas are also
useful for determining the future status of intervention such as
determination of prognosis of future glucose class with drug
therapy.
[0097] The complication endpoints of diabetic conditions include
the Diabetes related conditions herein such as kidney disease, eye
retinopathy, microvascular damage, liver damage, limb amputation
and cardiovascular complications. The biomarker panels and formulas
may be used to evaluate the current status of the disease endpoints
by aiding in the diagnosis of pre-Diabetes, Diabetes or a Diabetes
related condition. The future status of complication endpoints may
be determined using the biomarker panels and formulas such as
determination of the prognosis of future pre-Diabetes, Diabetes or
a Diabetes related condition. The panels and formulas are also
useful for determining the future status of intervention such as
determining the prognosis of future pre-Diabetes, Diabetes or a
Diabetes related condition with therapy.
Agents for Treating or Reducing the Risk of Developing
Pre-Diabetes, Diabetes or a Diabetes Related Condition
[0098] The biomarkers of the present invention can also be used to
identify and assess agents for treating or reducing the risk of
developing pre-Diabetes, Diabetes or a Diabetes related condition.
Thus, the present invention also provides a method of identifying
or assessing an agent for treating or reducing the risk of
developing pre-Diabetes, Diabetes and/or a Diabetes related
condition comprising: [0099] (i) contacting cells expressing at
least one biomarker from Table 1 or 2 with a putative agent; and
[0100] (ii) comparing expression or level of at least one biomarker
from Table 1 or 2 in the cells prior to contact with the putative
agent to expression of at least one biomarker from Table 1 or 2 in
the cells after contact with the putative agent; wherein a change
in expression or level identifies the agent as an agent for
treating pre-Diabetes, Diabetes and/or a Diabetes related
condition.
[0101] The cells may be contacted with the putative agent in vivo,
such as in an animal model, or in vitro, such as in a cell culture
or line. The expression or level may be compared using a computer
driven program or software.
[0102] The present invention also provides a method of treating or
reducing the risk of developing pre-Diabetes, Diabetes and/or a
Diabetes related condition in a subject comprising administering an
effective amount of an agent adapted to change the expression or
level of at least one biomarker in Table 1 or 2 to the subject.
[0103] The agent may be administered according to any one of the
known methods as selected by a suitably qualified practitioner. The
agents may be administered as part of a composition comprising an
effective amount of the agent in admixture with a pharmaceutically
acceptable agent such as a pharmaceutically acceptable carrier. The
carrier material may be water for injection, preferably
supplemented with other materials common in solutions for
administration to mammals. Standard pharmaceutically acceptable
agents such as carriers, diluents, and excipients may be included
as desired. Other exemplary compositions comprise Tris buffer of
about pH 7.0-8.5, or acetate buffer of about pH 4.0-5.5, which may
further include sorbitol or a suitable substitute therefor.
[0104] The optimal formulation of the agent will be determined by
one skilled in the art depending upon the intended route of
administration, delivery format and desired dosage. See, e.g.,
Remington's Pharmaceutical Sciences, 1435-1712 (18th Ed., A. R.
Gennaro, ed., Mack Publishing Company 1990). Such compositions may
influence the physical state, stability, rate of in vivo release,
and rate of in vivo clearance.
[0105] Thus, the present invention also provides for the use of an
agent adapted to change the expression or level of at least one
biomarker in Table 1 or 2 for preparing a medicament for treating
or reducing the risk of developing pre-Diabetes, Diabetes and/or a
Diabetes related condition.
[0106] Preferably the agent adapted to change the expression or
level of at least one biomarker in Table 1 or 2 is an agent for
treating or reducing the risk of developing pre-Diabetes, Diabetes
and/or a Diabetes related condition as defined herein. Other Agents
for treating or reducing the risk of developing pre-Diabetes,
Diabetes and/or Diabetes related conditions include, lipase
inhibitors such as cetilistat; synthetic amylin analogs such as
Symlin pramlintide with or without recombinant leptin;
sodium-glucose cotransporter 2 inhibitors like sergliflozin, YM543,
dapagliflozin, dual adipose triglyceride lipase and PI3 kinase
activators like Adyvia; antagonists of neuropeptide Y2, Y4, and Y5
receptors, synthetic analog of human hormones PYY3-36 and
pancreatic polypeptide; cannabinoid CB 1 receptor antagonists such
as rimonabant, taranabant, CP-945,598, hormones like
oleoyl-estrone; inhibitors of serotonin, dopamine, and
norepinephrine (also known in the art as "triple monoamine reuptake
inhibitors") like tesofensine; inhibitors of norepinephrine and
dopamine reuptake, like Contrave (bupropion plus opioid antagonist
naltrexone) and Excalia (bupropion plus anticonvulsant
zonisaminde); inhibitors of 111.beta.-hydroxysteroid dehydrogenase
type 1 (11b-HSD1); inhibitors of cortisol synthesis such as
ketoconazole; inhibitors of gluconeogenesis; glucokinase
activators; antisense inhibitors of protein tyrosine
phosphatase-1B; as well as other agents like injections of gastrin
and epidermal growth factor (EGF) analogs such as Islet Neogenesis
Therapy (E1-I.N.T.); and betahistine.
Biomarker Measurement
[0107] Biomarkers may be measured using one or more of a range of
techniques. Preferably the biomarkers are measured in a way that
minimises subject variability. For example, they may be measured in
a fasting state, and most commonly in the morning, providing a
reduced level of subject variability due to both food consumption
and metabolism and diurnal variation. Any fasting or temporal-based
sampling procedure can be used in the present invention.
[0108] The actual measurement of levels of the biomarkers herein
can be determined at the protein or nucleic acid level using any
method known in the art. For example, at the nucleic acid level,
Northern and Southern hybridization analysis, as well as
ribonuclease protection assays using probes which specifically
recognize one or more of these sequences can be used to determine
gene expression. Biomarker levels can also be measured using
reverse-transcription-based PCR assays (RT-PCR), e.g., using
primers specific for the differentially expressed sequence of
genes. Preferably, biomarker levels are determined at the protein
level, e.g., by measuring the levels of peptides encoded by the
gene products described herein, or activities thereof. Such methods
include, e.g., immunoassays based on antibodies to proteins encoded
by the genes, aptamers or molecular imprints.
[0109] The biomarkers in Table 1 or 2, polypeptides, peptides,
mutations, and polymorphisms thereof can be detected in any
suitable manner, but are typically detected by contacting a sample
from the subject with an antibody which binds the biomarker
protein, polypeptide, mutation, or polymorphism and then detecting
the presence or absence of a reaction product. Antibodies can be
monoclonal, polyclonal, chimeric, or a fragment of the foregoing,
and the step of detecting the reaction product may be carried out
with any suitable immunoassay.
[0110] Immunoassays carried out in accordance with the present
invention may be homogeneous assays or heterogeneous assays. In a
homogeneous assay the immunological reaction usually involves the
specific antibody to the biomarker, a labelled analyte, and the
sample of interest. The signal arising from the label is modified,
directly or indirectly, upon the binding of the antibody to the
labelled analyte. Both the immunological reaction and detection of
the extent thereof can be carried out in a homogeneous
solution.
[0111] Immunochemical labels which may be employed include free
radicals, radioisotopes, fluorescent dyes, enzymes, bacteriophages,
or coenzymes.
[0112] In a heterogeneous assay approach, the reagents are usually
the sample, the antibody, and means for producing a detectable
signal. Samples as described above may be used. The antibody can be
immobilized on a support, such as a bead (such as protein A and
protein G agarose beads), plate or slide, and contacted with the
specimen suspected of containing the antigen in a liquid phase. The
support is then separated from the liquid phase and either the
support phase or the liquid phase is examined for a detectable
signal employing means for producing such signal. The signal is
related to the presence of the analyte in the sample. Means for
producing a detectable signal include the use of radioactive
labels, fluorescent labels, or enzyme labels. For example, if the
antigen to be detected contains a second binding site, an antibody
which binds to that site can be conjugated to a detectable group
and added to the liquid phase reaction solution before the
separation step. The presence of the detectable group on the solid
support indicates the presence of the antigen in the test sample.
Examples of suitable immunoassays include oligonucleotides,
immunoblotting, immunoprecipitation, immunofluorescence methods,
chemiluminescence methods, electrochemiluminescence (ECL) or
enzyme-linked immunoassays.
[0113] Using sequence information provided by the database entries
for the biomarkers in Table 1, expression of the biomarker
sequences can be detected (if present) and measured using
techniques well known to one of ordinary skill in the art such as
Northern blot hybridization analyses or methods which specifically,
and, preferably, quantitatively amplify specific nucleic acid
sequences. As another example, the sequences can be used to
construct primers for specifically amplifying the biomarker
sequences in, e.g., amplification-based detection methods such as
reverse-transcription based polymerase chain reaction (RT-PCR).
When alterations in gene expression are associated with gene
amplification, deletion, polymorphisms, and mutations, sequence
comparisons in test and reference populations can be made by
comparing relative amounts of the examined DNA or RNA sequences in
the test and reference cell populations.
[0114] Biomarker protein and/or nucleic acid metabolites can also
be measured using one or more of a variety of ways known to one of
skill in the art, including the refractive index spectroscopy (RI),
ultra-violet spectroscopy (UV), fluorescence analysis,
radiochemical analysis, near-infrared spectroscopy (near-IR),
nuclear magnetic resonance spectroscopy (NMR), light scattering
analysis (LS), mass spectrometry including multiple reaction
monitoring (MRM) mass spectrometry, pyrolysis mass spectrometry,
nephelometry, dispersive Raman spectroscopy, gas chromatography
combined with mass spectrometry, liquid chromatography combined
with mass spectrometry, matrix-assisted laser desorption
ionization-time of flight (MALDI-TOF) combined with mass
spectrometry, ion spray spectroscopy combined with mass
spectrometry, capillary electrophoresis, NMR and IR detection.
[0115] When the biomarkers are measured using mass spectrometry
they may be measured via a peptide selected from the list of:
[0116] (i) a 5-25 amino acid peptide of a protein from Table 1 or
2; [0117] (ii) a 5-20 amino acid peptide of a protein from Table 1
or 2; [0118] (iii) a 10-20 amino acid peptide of a protein from
Table 1 or 2; [0119] (iv) a 10-15 amino acid peptide of a protein
from Table 1 or 2; or [0120] (v) a peptide in Table 3.
Kits
[0121] The invention also includes a biomarker-detection reagent,
e.g., an antibody specific for a biomarker protein in Table 1 or 2
or peptide in Table 3 or a nucleic acid that specifically
identifies or binds to one or more nucleic acids encoding a
biomarker protein in Table 1 or 2 or a peptide in Table 3 by having
homologous nucleic acid sequences, such as oligonucleotide
sequences or aptamers, complementary to a portion of the nucleic
acid packaged together in the form of a kit. The kit may contain in
separate containers a nucleic acid or antibody (either already
bound to a solid matrix or packaged separately with reagents for
binding them to the matrix), control formulations (positive and/or
negative), and/or a detectable label such as fluorescein, green
fluorescent protein, rhodamine, cyanine dyes, Alexa dyes,
luciferase, radiolabels, among others. Instructions for carrying
out the assay may also be included in the kit. The assay may for
example be in the form of a Northern hybridization, sandwich ELISA
or protein antibody array.
[0122] Reagents for detecting biomarkers of the present invention
can be immobilized on a solid matrix such as a porous strip to form
at least one biomarker detection site. The measurement or detection
region of the porous strip may include a plurality of sites
containing an antibody or nucleic acid. A test strip may also
contain sites for negative and/or positive controls. Alternatively,
control sites can be located on a separate strip from the test
strip. Optionally, the different detection sites may contain
different amounts of immobilized antibodies or nucleic acids, e.g.,
a higher amount in the first detection site and lesser amounts in
subsequent sites. Upon the addition of test sample, the number of
sites displaying a detectable signal provides a quantitative
indication of the amount of biomarker present in the sample. The
detection sites may be configured in any suitably detectable shape
and are typically in the shape of a bar or dot spanning the width
of a test strip.
[0123] Alternatively, the kit contains a nucleic acid substrate
array comprising one or more nucleic acid sequences. The nucleic
acids on the array specifically identify one or more nucleic acid
sequences adapted to bind a nucleic acid sequence encoding a
biomarker in Table 1 or 2. The substrate array can be on, e.g., a
solid substrate or "chip". Alternatively, the substrate array can
be a solution array.
EXAMPLES
Example 1
Identification and Validation of Diabetes Biomarkers
1. Materials/Methods
A. Cohort Description
A.1. Fremantle Diabetes Study (Phase 1)
[0124] Rational: The FDS1 cohort comprised 1294 patients who had
type 2 diabetes. Diabetic subjects with and without diabetic
nephropathy were selected to give markedly different phenotypic
presentations enabling the greatest difference in protein
expression.
[0125] The Fremantle Diabetes Study (FDS) Phase I was a
longitudinal observational study of diabetes care, control,
complications and cost in patients from a stable postcode-defined
urban community of 120,097 people. When Phase I was conceived in
1991, there were few published diabetes natural history data.
TABLE-US-00004 Study groups: Adults type 2 diabetics, Anglo-Celts
Group 1: Normoalbuminuria (ACR: 0.57-1.53 mg/mmol) Group 2:
Macroalbuminuia (ACR: 49.0-300.0 mg/mmol) Ages: 33-84 years Number
of subjects: 20 per group; 40 in total Selection range: 1294
Protocol: EDTA tube, centrifuged within 4 hours, separated &
stored at-80.degree. C. Specimens: Plasma, urine
A.2. Fremantle Diabetes Study (Phase 2)
[0126] Rational: The FDS2 cohort recruited diabetics referred by
clinicians in the Fremantle locality and those from the FDS1 cohort
database. Diabetic subjects with and without diabetic nephropathy
were selected to give markedly different phenotypic presentations
enabling the greatest difference in protein expression.
[0127] Phase II was conceived in 2007 for improved and extended
data collection in order to characterise the nature of diabetes in
contemporary urban Australia.
TABLE-US-00005 All adults type 2 diabetics, undifferentiated for
Study groups: race Group 1: Normoalbuminuria (ACR; 0.3-2.3 mg/mmol)
Group 2: Macroalbuminuia (ACR 34.1-405.0 mg/mmol) Group 3:
Microalbuminuria (ACR 3.5-18.3 mg/mmol) Ages: 44-85 years Consent
for genetics: blood collected and stored Number of subjects: 20 per
group; 60 in total Selection range: 2000 Recruited from: Fremantle
Diabetes Study Phase 13 (680) + new recruits in Fremantle area
Protocol: Proteomics International standard protocol Specimens:
Plasma, serum, whole blood & urine
A.3. Busselton Diabetes Study
[0128] Rational: Expand information on diabetic patients from a
rural community. Complement information obtained from the FDS1
& FDS2 urban studies. Includes matched non-diabetic control
subjects.
[0129] The Busselton Health Study is one of the longest running
epidemiological research programs in the world. The residents of
the town of Busselton, a coastal community in the south-west of
Western Australia, have been involved in a series of health surveys
since 1966. To date over 16,000 men, women & children of all
ages have taken part in the surveys and have helped contribute to
the understanding of many common diseases and health
conditions.
TABLE-US-00006 All adults with/without diabetes, undifferentiated
for race, not age- Study groups: matched Group 1: Diabetic with
worst albuminuria (ACR 17.5-408 mg/mmol) Group 2: Diabetic with
normoalbuminuria (ACR 0.4-2.6 mg/mmol) Group 3: Controls with
normoalbuminuria (ACR 0.2-1.7 mg/mmol) Ages: 41-94 years Consent
for genetics: blood collected and stored Number of subjects: 20 per
group; 60 in total Selection range: 250 from 329 adults with
diabetes, 250 controls from 2595 non-diabetic Recruited from:
Busselton Health Survey Protocol: Proteomics International standard
protocol Specimens: Plasma, serum, whole blood & urine
B. Protein Biomarker Discovery Using iTRAQ and 2D LC MALDI
TOF/TOF
[0130] This discovery methodology involves chemically labelling the
plasma of different groups of patients (e.g. diabetic nephropathy
vs. diabetic with no nephropathy) and determining by mass
spectrometry the relative ratio of the presence of a particular
protein. Proteins with significantly altered concentrations after
analysis indicate a change in the biochemistry of one group of
patients versus another. This technique was used to measure the
relative concentrations of 130-200 proteins per sample. Proteins of
significantly different concentration between groups were
identified, and these were selected for further examination by MRM
methodology (section C below).
B.1. Sample Preparation
[0131] Plasma samples (N=10 or 20) were pooled before
immunodepletion of the 14 most abundant proteins using a MARS 14
HPLC column (Agilent Technologies). Immunodepleted samples were
buffer exchanged using 10 kDa cut-off spin filters (Sartorius) into
1M Triethylammonium bicarbonate. The protein samples were reduced,
alkylated, trypsin digested and labelled according to the iTRAQ
protocol (Applied Biosystems).
B.2. Instrumental Analysis
[0132] Peptides were desalted on a Strata-X 33 .mu.M polymeric
reversed phase column (Phenomenex) before separation by strong
cation exchange liquid chromatography (SCX) on an Agilent 1100 HPLC
using a PolySulfoethyl column (4.6.times.100 mm, 5 .mu.m, 300
.ANG.). Peptides were eluted with a linear gradient of 0-400 mM
KCl. SCX fractions were desalted and loaded onto an Ultimate 3000
nano HPLC system (Dionex C18, PepMap 100, 3 .mu.m) and separated
with a gradient of 10-40% acetonitrile (0.1% formic acid) with
spotting using a ProBot (LC Packings) robotic spotter. The
resultant spots were analysed on a 4800 MALDI TOF/TOF Analyzer.
B.3. Data Analysis
[0133] Data analysis was performed using ProteinPilot.TM. 2.0.1
software (Applied Biosystems). False discovery rates were
calculated using the PSPEP algorithm that works in conjunction with
ProteinPilot.TM. 2.0.1 and only proteins with a global false
discovery rate (FDR) from fit of <5% were accepted.
C. Biomarker Candidate Validation Using Multiple Reaction
Monitoring (MRM)
[0134] Multiple Reaction Monitoring (MRM) is a mass
spectrometry-based approach to specifically target transitions
(precursor-fragment ion pairs) for a signature peptide, which
represents a surrogate for the whole biomarker candidate protein.
For each candidate one or two peptides unique to that protein (when
compared to the SwissProt Human Database ver 57.1) were used. This
high-throughput approach was used to validate biomarkers from the
discovery phase (see Section B above) in a larger number of
individual patient plasma samples.
C.1. Sample Preparation
[0135] Pooled samples identical to previous iTRAQ experiments as
well as individual samples (N=10 per group) different from previous
iTRAQ pools (validation samples) were prepared. Samples were
immunodepleted of the 14 most abundant proteins using a MARS 14
HPLC column. Immunodepleted samples were buffer exchanged using 10
kDa cut-off spin filters. The protein samples were reduced,
alkylated, trypsin digested and desalted. In addition, one plasma
reference sample (pool of healthy individuals) was .sup.18O
labelled and finally spiked into each cohort sample (1:1) prior to
LC-MRM/MS analysis.
C.2. Translating Biomarker Lists into MRM Transition Lists
[0136] Preliminary MRM transition lists were generated by a series
of steps which included downloading protein sequences, digesting
proteins in silico in conjunction with a filter (e.g. 7-21 amino
acids, 0 missed cleavage) and selecting a minimum of 4 transitions
per peptide (usually precursor charge z2, product charge z1).
Useful information on proteotypic peptides from literature and
repositories (PeptideAtlas, MRMaid) was also incorporated and the
selection of transitions was supported by spectral libraries (ISB,
NIST, GPM, BiblioSpec). An open-source software called Skyline
(MacCoss laboratory, University of Washington, Seattle, Wash., USA)
was used to generate and refine MRM transitions as well as to
analyse MRM transition data.
[0137] An aliquot of lug of plasma digest was directly loaded onto
a nano column (Dionex C18, PepMap 100, 3 .mu.m) and peptides were
eluted with a 100 min gradient of 2-30% acetonitrile (0.1% formic
acid) into a 4000 QTrap equipped with a nanoelectrospray ionisation
source. A maximum of 200 MRMs were acquired per run with a dwell
time of 20 ms and a cycle of 5 s. Runs were analysed (i.e. peptides
without reasonable transitions were deleted) and a refined list of
peptides and transitions was subjected to an MRM triggered MS/MS
experiment to validate peptide assignment. Since peptide assignment
for low abundant proteins is a quite challenging task without
standards, product ion scan (EPI) settings varied e.g. scan rate
(1000-4000), LIT fill time (20-300 ms). The two most intense
transitions per peptide were selected for validation and were sent
for MS/MS (mass range 200-1200) when a transition exceeded a
threshold of 1000 cps. In total 40 MRMs per run were used with a
dwell time of 20 ms and a cycle of .about.7 s. Acquired MS/MS data
were searched against a current SwissProt database with human
taxonomy filter using MASCOT. Peptides identified were matched
against MRM data (peptide sequence, retention time). Finally,
validated peptides were tested for their suitability to be used
with the MRM .sup.18O labelling method. The final transition list
for each cohort study consisted of 1-2 peptides (see Table 3) per
candidate protein (see Table 1) and 3 transitions per peptide. If
possible, peptide sequences which were not unique to the candidate
protein and peptides with amino acids M, W, N-terminal Q or E, etc.
were excluded.
TABLE-US-00007 TABLE 3 Protein name Accession number
Position/Peptide (see Table 1) (Uniprot) sequences (SEQ ID NO) Key
Sulfhydryl sp|O00391|QS0X1_ 257-265 SFYTAYLQR L oxidase 1 HUMAN
(SEQ ID NO: 1) Apolipoprotein sp|P06727|APOA4_ 135-143 LEPYADQLR
APOA4/LEP A-IV HUMAN (SEQ ID NO: 2) sp|P06727|APOA4_ 256-264
ISASAEELR APOA4/ISA HUMAN (SEQ ID NO: 3) CD5 antigen-
sp|O43866|CD5L_ 246-256 LVGGDNLCSGR CDSL/LVG like HUMAN (SEQ ID NO:
4) sp|O43866|CD5L_ 308-314 IWLDNVR CDSL/IWL HUMAN (SEQ ID NO: 5)
Complement sp|P07358|CO8B_ 122-132 CEGFVCAQTGR M component C8 HUMAN
(SEQ ID NO: 6) beta chain Apolipoprotein sp|P04114|APOB_ 642-654
SVSLPSLDPASAK APOB/SVS B-100 HUMAN (SEQ ID NO: 7) sp|P04114|APOB_
950-960 TEVIPPLIENR APOB/TEV HUMAN (SEQ ID NO: 8) Peroxiredoxin-2
sp|P32119|PRDX2_ 17-26 ATAVVDGAFK PRDX2/ATA HUMAN (SEQ ID NO: 9)
Protein AMBP sp|P02760|AMBP_ 283-293 TVAACNLPIVR AMBP/TVA HUMAN
(SEQ ID NO: 10) 335-349 AMBP/EYC sp|P02760|AMBP_ EYCGVPGDGDEELLR
HUMAN (SEQ ID NO: 11) Hemoglobin sp|P68871|HBB_ 10-18 SAVTALWGK I1
subunit beta HUMAN (SEQ ID NO: 12) sp|P68871|HBB_ 19-31
VNVDEVGGEALGR I2 HUMAN (SEQ ID NO: 13) Complement sp|P02746|C1QB_
122-128 IAFSATR C1QB/IAF C1q HUMAN (SEQ ID NO: 14) subcomponent
subunit B Apolipoprotein sp|P02656|APOC3_ 45-60 APOC3/DAL C-III
HUMAN DALSSVQESQVAQQAR (SEQ ID NO: 15) Insulin-like sp|P17936|IBP3_
47-63 IBP3/ALA growth factor- HUMAN ALAQCAPPPAVCAELVR binding
protein sp|P17936|IBP3_ (SEQ ID NO: 16) 3 HUMAN 226-233 FLNVLSPR
IBP3/FLN (SEQ ID NO: 17) Adiponectin sp|Q15848|ADIPO_ 78-92
ADIPO/GDI HUMAN GDIGETGVPGAEGPR (SEQ ID NO: 18) Complement
sp|P36980|FHR2_ 233-242 TGDIVEFVCK FHR2/TGD factor H-related HUMAN
(SEQ ID NO: 19) protein 2 sp|P36980|FHR2_ 262-270 LVYPSCEEK
FHR2/LVY HUMAN (SEQ ID NO: 20)
C.3. Instrumental Analysis
[0138] All samples were reconstituted and spiked 1:1 with an
.sup.18O labelled reference plasma (pool of healthy individuals)
prior to LC-MRM/MS analysis to correct for spray efficiency and
ionization differences between runs. Each sample was injected in
duplicate directly onto a nano column (Dionex C18, PepMap 100, 3
.mu.m) and peptides were eluted in a 100 min gradient of 2-30%
acetonitrile (0.1% formic acid) into a 4000 QTrap equipped with a
nanoelectrospray ionisation source. The scheduled MRM option was
used for all data acquisition with a target scan time of 4 s (at
least 8 data points across a peak) and a 6-8 min MRM detection
window which resulted in minimum dwell times of 50-60 ms.
C.4. Data Analysis
[0139] All transitions were integrated and for each peptide a
(weighted) ratio of area of unlabelled peptide to area of labelled
peptide was calculated. Ratios were normalised for population-based
differences based on an invariate set of proteins. Finally, a
Mann-Whitney test for non-parametric data was applied to the
normalised ratios and a p value was calculated, which defines a
protein as significantly differentially expressed between two
subject groups, e.g. healthy vs. diseased.
[0140] The sensitivity, or true positive rate, vs. false positive
rate (Relative Operating Characteristic curves) were also plotted
for a range of markers (univariate and multivariate). A number of
statistical transformations were used to improve power including
natural logarithm (ln), inverse (inv) and square root ( ).
2. Results
D. Biomarkers
D1. Biomarkers for Diabetic Nephropathy in Diabetic Patients
[0141] The table in FIG. 1 shows biomarker protein data from the
Busselton and both Fremantle Diabetes Studies with respect to the
presence of diabetic nephropathy where all subjects had diabetes.
The question that is addressed is `What are the biomarkers for
diabetic nephropathy in diabetic patients?`
[0142] The results of the table in FIG. 1 are illustrated as
box-whisker plots in FIG. 2 (Study FDS1), FIG. 3 (Study FDS2) and
FIG. 4 (Study BDS). For each biomarker candidate one to two
signature peptides per protein were measured by MRM. (left boxplot:
diabetic group; right boxplot: diabetic group with severe
nephropathy; x-axis: protein/peptide; y-axis: Relative abundance
ratio).
[0143] The ROC data in Tables 4-8 further illustrate that the
biomarker(s) can be used as a diagnostic for diabetic
nephropathy.
TABLE-US-00008 TABLE 4 Univariate analysis Peptide (Key--see Odds
ROC Mean Table 3) Ratio Sensitivity specificity P AUC control case
n PRDX2/ATA 17.7 80.0 80.0 0.040 0.860 0.245 0.507 20 (ln) AMBP/TVA
22.6 70.0 70.0 0.034 0.840 2.00 2.63 20 AMBP/EYC 1.42 60.0 80.0
0.061 0.850 10.6 14.1 20 C1QB/IAF 0.002 90.0 87.5 0.088 0.950 1.08
0.363 18 (ln)
TABLE-US-00009 TABLE 5 Multivariate analysis (Model 3) Peptide
(Key--see Odds ROC Mean Table 3) Ratio Sensitivity specificity P
AUC control case n PRDX2/ATA 6.06 0.678 0.265 0.61 (ln) AMBP/TVA
0.109 0.697 2.04 2.68 AMBP/EYC 2.10 0.426 11 14.3 C1QB/IAF 0.0004
90.0 87.5 0.287 0.9625 1.2 0.41 18 (ln)
TABLE-US-00010 TABLE 6 Multivariate analysis (Model FDS1) Peptide
(Key--see Odds ROC Mean Table 3) Ratio Sensitivity specificity P
AUC control case n PRDX2/ATA 6.52 0.254 -1.41 -0.68 (ln) AMBP/TVA
6.61 0.355 2.04 2.67 AMBP/EYC 1.01 90 90 0.97 0.89 10.98 14.34
20
TABLE-US-00011 TABLE 7 Multivariate analysis (Model FDS2) Peptide
(Key--see Odds ROC Mean Table 3) Ratio Sensitivity Specificity P
AUC control Case n C1QB/IAF 0.016 0.167 1.08 0.363 (ln) AMBP/EYC
0.044 0.218 2.78 2.28 ADIPO/GDI 0.281 0.589 0.797 0.394 (ln)
FHR2/LVY 0.342 88.9 100 0.842 0.958 1.46 1.08 17 ( )
TABLE-US-00012 TABLE 8 Multivariate analysis (Model BDS) Peptide
(Key--see Odds ROC Mean Table 3) Ratio Sensitivity Specificity P
AUC control Case n CD5L/LVG 34679 0.209 0.796 0.890 ( ) CD5L/IWL
0.0009 0.151 3.11 1.65 (ln) APOB/SVS 5.42e.sup.25 0.076 0.146 0.308
APOB/TEV 1.95e.sup.-40 66.7 80 0.092 0.922 0.130 0.237 19
D2. Biomarkers for Diabetics with Nephropathy Versus Healthy
Patients
[0144] The table in FIG. 5 describes the biomarkers discovered for
patients with diabetic nephropathy versus a healthy control group
without diabetes. This data is derived from the Busselton
study.
[0145] As would be apparent, various alterations and equivalent
forms may be provided without departing from the spirit and scope
of the present invention. This includes modifications within the
scope of the appended claims along with all modifications,
alternative constructions and equivalents.
[0146] In the present specification, the presence of particular
features does not preclude the existence of further features. The
words "comprising", "including" and "having" are to be construed in
an inclusive rather than an exclusive sense.
Sequence CWU 1
1
2019PRTHomo sapiensSOURCE1..9/mol_type="protein"/organism="Homo
sapiens" 1Ser Phe Tyr Thr Ala Tyr Leu Gln Arg 1 5 29PRTHomo
sapiensSOURCE1..9/mol_type="protein"/organism="Homo sapiens" 2Leu
Glu Pro Tyr Ala Asp Gln Leu Arg 1 5 39PRTHomo
sapiensSOURCE1..9/mol_type="protein"/organism="Homo sapiens" 3Ile
Ser Ala Ser Ala Glu Glu Leu Arg 1 5 410PRTHomo
sapiensSOURCE1..10/mol_type="protein"/organism="Homo sapiens" 4Leu
Val Gly Gly Asp Asn Leu Cys Ser Gly 1 5 1057PRTHomo
sapiensSOURCE1..7/mol_type="protein"/organism="Homo sapiens" 5Ile
Trp Leu Asp Asn Val Arg 1 5 611PRTHomo
sapiensSOURCE1..11/mol_type="protein"/organism="Homo sapiens" 6Cys
Glu Gly Phe Val Cys Ala Gln Thr Gly Arg 1 5 10 713PRTHomo
sapiensSOURCE1..13/mol_type="protein"/organism="Homo sapiens" 7Ser
Val Ser Leu Pro Ser Leu Asp Pro Ala Ser Ala Lys 1 5 10 811PRTHomo
sapiensSOURCE1..11/mol_type="protein"/organism="Homo sapiens" 8Thr
Glu Val Ile Pro Pro Leu Ile Glu Asn Arg 1 5 10 910PRTHomo
sapiensSOURCE1..10/mol_type="protein"/organism="Homo sapiens" 9Ala
Thr Ala Val Val Asp Gly Ala Phe Lys 1 5 101011PRTHomo
sapiensSOURCE1..11/mol_type="protein"/organism="Homo sapiens" 10Thr
Val Ala Ala Cys Asn Leu Pro Ile Val Arg 1 5 10 1115PRTHomo
sapiensSOURCE1..15/mol_type="protein"/organism="Homo sapiens" 11Glu
Tyr Cys Gly Val Pro Gly Asp Gly Asp Glu Glu Leu Leu Arg 1 5 10
15129PRTHomo sapiensSOURCE1..9/mol_type="protein"/organism="Homo
sapiens" 12Ser Ala Val Thr Ala Leu Trp Gly Lys 1 5 1313PRTHomo
sapiensSOURCE1..13/mol_type="protein"/organism="Homo sapiens" 13Val
Asn Val Asp Glu Val Gly Gly Glu Ala Leu Gly Arg 1 5 10 147PRTHomo
sapiensSOURCE1..7/mol_type="protein"/organism="Homo sapiens" 14Ile
Ala Phe Ser Ala Thr Arg 1 5 1516PRTHomo
sapiensSOURCE1..16/mol_type="protein"/organism="Homo sapiens" 15Asp
Ala Leu Ser Ser Val Gln Glu Ser Gln Val Ala Gln Gln Ala Arg 1 5 10
15 1618PRTHomo sapiensSOURCE1..18/mol_type="protein"/organism="Homo
sapiens" 16Ala Leu Ala Gln Cys Ala Pro Pro Pro Ala Val Cys Ala Glu
Leu Val 1 5 10 15 Arg Arg 178PRTHomo
sapiensSOURCE1..8/mol_type="protein"/organism="Homo sapiens" 17Phe
Leu Asn Val Leu Ser Pro Arg 1 5 1815PRTHomo
sapiensSOURCE1..15/mol_type="protein"/organism="Homo sapiens" 18Gly
Asp Ile Gly Glu Thr Gly Val Pro Gly Ala Glu Gly Pro Arg 1 5 10
151910PRTHomo sapiensSOURCE1..10/mol_type="protein"/organism="Homo
sapiens" 19Thr Gly Asp Ile Val Glu Phe Val Cys Lys 1 5 10209PRTHomo
sapiensSOURCE1..9/mol_type="protein"/organism="Homo sapiens" 20Leu
Val Tyr Pro Ser Cys Glu Glu Lys 1 5
* * * * *