U.S. patent application number 15/652446 was filed with the patent office on 2017-11-02 for method for determining risk of diabetes.
The applicant listed for this patent is True Health IP LLC. Invention is credited to Robert W. Gerwien, Michael P. McKenna, Edward J. Moler, JR., Michael Rowe.
Application Number | 20170316153 15/652446 |
Document ID | / |
Family ID | 43533151 |
Filed Date | 2017-11-02 |
United States Patent
Application |
20170316153 |
Kind Code |
A1 |
McKenna; Michael P. ; et
al. |
November 2, 2017 |
METHOD FOR DETERMINING RISK OF DIABETES
Abstract
A method of determining risk of diabetes is provided. In one
embodiment, the method comprises: a) measuring the levels of a
plurality of biomarkers in a blood samples obtained from a patient,
wherein the plurality of biomarkers comprises at least five of the
following biomarkers: glucose, adiponectin, CRP, IL2RA, ferritin,
insulin and HbA1c; b) calculating a diabetes risk score for the
patients using the levels and, optionally, patient age and/or
gender. Results obtained from performing the assay on a reference
population are similar or identical to those obtained using Formula
I.
Inventors: |
McKenna; Michael P.;
(Oakland, CA) ; Rowe; Michael; (Oakland, CA)
; Moler, JR.; Edward J.; (Walnut Creek, CA) ;
Gerwien; Robert W.; (Newington, CT) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
True Health IP LLC |
Frisco |
TX |
US |
|
|
Family ID: |
43533151 |
Appl. No.: |
15/652446 |
Filed: |
July 18, 2017 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13504478 |
Aug 15, 2012 |
9740819 |
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PCT/US10/54397 |
Oct 28, 2010 |
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15652446 |
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61256286 |
Oct 29, 2009 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G16B 40/00 20190201;
G16B 20/00 20190201 |
International
Class: |
G06F 19/24 20110101
G06F019/24 |
Claims
1-14. (canceled)
15. A method of preventing development of diabetes in a subject,
comprising: a) measuring levels of a plurality of biomarkers in a
blood sample obtained from the human subject, wherein said
plurality of biomarkers comprises at least five of the following
biomarkers: glucose, adiponectin, CRP, IL2RA, ferritin, insulin and
HbA1c; b) calculating a diabetes risk score for said subject as a
function of said measured levels and optionally, the subject's age
and/or gender; and c) applying the function of measured biomarker
levels and optional age and/or gender of the subject to measured
biomarker levels and optional age and/or gender of a human
reference population to generate a risk profile associated with the
reference population, the risk profile has a 95% confidence
interval of a Spearman rank correlation coefficient squared
(R.sup.2) that is entirely above or includes a correlation value of
0.5 with a comparative risk profile associated with the reference
population.
16. The method of claim 15, wherein said human reference population
comprises at least 25 subjects.
17. The method of claim 15, wherein the subjects of said human
reference population are randomly chosen from a larger population
of human subjects.
18. The method of claim 15, wherein a step of initiating a
therapeutic intervention or a treatment regimen to delay, reduce or
prevent the human subject's conversion to a diabetes disease state
is performed if the calculated diabetes risk score indicates a risk
that the subject has a high risk of developing diabetes.
19. A method of preventing a human subject from developing diabetes
if a categorical risk assessment associated with the human subject
falls within a high risk mutually exclusive ordered risk category
or a moderate risk mutually exclusive ordered risk category from
among a plurality of mutually exclusive ordered risk categories
consisting of high risk, moderate risk and low risk, comprising: a)
measuring levels of a plurality of biomarkers in a sample obtained
from the human subject, wherein said plurality of biomarkers
comprises at least five of the following biomarkers: glucose,
adiponectin, CRP, IL2RA, ferritin, insulin and HbA1c; and b)
generating a categorical risk assessment associated with the human
subject generated as a function of a diabetes risk score (D) using
said measured levels and optionally, the subject's age and/or
gender, wherein when a plurality of categorized risk assessments
from a plurality of human subjects is compared to a plurality of
comparative categorized risk assessments from a human reference
population each generated as a function of levels of at least five
of: glucose, adiponectin, CRP, IL2RA, ferritin, insulin and HbA1c
associated with each human reference population subject, and
optionally each human reference population subject's age and/or
gender, the plurality of comparative categorized risk assessments
from the human reference population: is not independent with 95%
confidence, using a chi-squared test, from the categorical risk
assessments, and each mutually exclusive ordered risk category
includes a range of diabetes risk scores (D) selected such that
each individual mutually exclusive ordered risk category includes
an identical number of human subjects as a number of human
reference population subjects included in a corresponding mutually
exclusive ordered risk category generated as a function of the
levels of at least five of: glucose, adiponectin, CRP, IL2RA,
ferritin, insulin and HbA1c associated with each human reference
population subject, and optionally each human reference population
subject's age and/or gender.
20. The method of claim 19, wherein said subject is categorized
into one of said risk categories using at least the levels of
glucose, adiponectin, CRP and HbA1c in the blood of said subject,
and subject age.
21. The method of claim 19, wherein said human reference population
comprises at least 25 subjects.
22. The method of claim 19, wherein the subjects of said human
reference population are randomly chosen from a larger population
of human subjects.
23. A method of preventing development of diabetes in a subject,
comprising: administering a therapeutic intervention or a treatment
regimen to delay, reduce or prevent the subject's conversion to a
diabetes disease state wherein the subject has been determined to
have a high risk of developing diabetes by a method comprising: a)
calculating a diabetes risk score for the subject as a function of
measured levels of a plurality of biomarkers comprising at least
five of: glucose, adiponectin, CRP, IL2RA, ferritin, insulin and
HbA1c from a blood sample obtained from the subject, and
optionally, the subject's age and/or gender; and b) applying the
function of measured biomarker levels and optional age and/or
gender of the subject to measured biomarker levels and optional age
and/or gender of a human reference population to generate a risk
profile associated with the reference population, the risk profile
has a 95% confidence interval of a Spearman rank correlation
coefficient squared (R.sup.2)that is entirely above or includes a
correlation value of 0.5 with a comparative risk profile associated
with the reference population generated from the formula:
D=X+0.062*Age-0.64*Gender+1.62*GLUCOSE-3.37*ADIPOQ+0.60*CRP+0.70*FTH1+1.3-
5*IL2RA+0.49*INSULIN+0.26*HBA1C, wherein: 0.062*Age is subject age
in years multiplied by 0.062; 0.64*Gender is subject gender,
wherein female=0 and male=1, multiplied by 0.64; 1.62*GLUCOSE is
the square root of the level of subject blood glucose in mg/dL,
multiplied by 1.62; 3.37*ADIPOQ is the log10 of the level of
subject blood adiponectin in .mu.g/mL, multiplied by 3.37; 0.60*CRP
is the log10 of level of subject blood CRP in mg/L, multiplied by
0.60; 0.70*FTH1 is the log10 of the level of subject blood level
ferritin in ng/mL, multiplied by 0.70; 1.35*IL2RA is the log10 of
the level of subject blood IL2RA in U/mL, multiplied by 1.35;
0.49*INSULIN is the log10 of the level of subject blood insulin in
ulU/mL, multiplied by 0.49; 0.26*HBA1C is the level of subject
blood Hb1Ac measured in as a percentage of total hemoglobin in
blood multiplied by 0.26; and X is any number.
24. The method of claim 23, wherein said human reference population
comprises at least 25 subjects.
25. The method of claim 23, wherein the subjects of said human
reference population are randomly chosen from a larger population
of human subjects.
Description
PRIORITY CLAIM
[0001] This application is a continuation application of U.S.
patent application Ser. No. 13/504,478 filed on Aug. 15, 2012,
which is a 371 U.S. National Stage Application of International PCT
Application No. PCT/US10/54397 filed on Oct. 28, 2010, which claims
priority to U.S. Provisional Application Ser. No. 61/256,286 filed
Oct. 29, 2009, the entire contents of which are incorporated by
herein reference and relied upon.
BACKGROUND
[0002] Diabetes mellitus is a serious illness characterized by a
loss of the ability to regulate blood glucose levels. The American
Diabetes Association addresses the diagnosis and classification of
Diabetes in Diabetes Care, 32 (Suppl. 1): S62-S67 (2009) and
Diabetes Care, 33 (Suppl. 1): S62-S69 (2010). The World Health
Organization (WHO) estimates that more than 180 million people
worldwide have Diabetes. This number is likely to more than double
by 2030. In 2005, an estimated 1.1 million people died from
Diabetes; this estimate likely undercounts deaths caused by
Diabetes, as Diabetes contributes to other diseases, such as heart
disease and kidney disease, that may be listed as the cause of
death.
[0003] There is a need for new methods for identifying persons at
risk of developing Diabetes.
SUMMARY
[0004] A method for calculating a diabetes risk score is provided.
In one embodiment, the method comprises: a) measuring the levels of
a plurality of biomarkers in a blood sample obtained from a human
patient, wherein the plurality of biomarkers comprises at least
five of the following biomarkers: glucose, adiponectin, CRP, IL2RA,
ferritin, insulin and HbA1c; b) calculating a numerical score for
the patient or categorizing the patient using the levels and,
optionally, patient age and/or gender. The method may be performed
using Formula I, or an alternative formula that provides results
that are similar or identical to those obtained using Formula I, as
determined by Spearman or chi-squared analysis on a human reference
population.
D=X+0.062*Age-0.636*Gender+1.621*GLUCOSE-3.370*ADIPOQ+0.600*CRP+0.699*FT-
H1+1.350*IL2RA+0.491*INSULIN+0.259*HBA1C, Formula I
wherein:
[0005] X is any number, including 0, of any sign, and may have 0,1,
2 or more than 2 decimal places, and in certain embodiments may be
-23.114;
[0006] 0.63*Gender is patient gender, wherein female=0 and male=1,
multiplied by 0.636;
[0007] 1.621*GLUCOSE is the square root of the level of patient
blood glucose in mg/dL, multiplied by 1.621;
[0008] 3.370*ADIPOQ is the log.sub.10 of the level of patient blood
adiponectin in .mu.g/mL, multiplied by 3.370;
[0009] 0.600*CRP is the log.sub.10 of level of patient blood CRP in
mg/L, multiplied by 0.600;
[0010] 0.699*FTH1 is the log.sub.10 of the level of patient blood
ferritin in ng/mL, multiplied by 0.699;
[0011] 1.350*IL2RA is the log.sub.10of the level of patient blood
IL2RA in U/mL, multiplied, by 1.350;
[0012] 0.491*INSULIN is the log.sub.10 of the level of patient
blood insulin in uIU/mL, multiplied 0.491; and
[0013] 0.259*HBA1C is the level of patient blood Hb1Ac measured as
a percentage of total hemoglobin in whole blood multiplied by
0.259.
[0014] In certain embodiments, the method may include: a) measuring
the levels of a plurality of biomarkers in a blood sample obtained
from the human subject, wherein said plurality of biomarkers
comprises at least five of the following biomarkers: glucose,
adiponectin, CRP, IL2RA, ferritin, insulin and HbA1c; and b)
calculating a diabetes risk score for said subject using the levels
and, optionally, subject age and/or gender, where the calculation
is performed by a method selected from the group consisting of:
[0015] i) a first method wherein the levels of all said biomarkers
are measured and calculating a diabetes risk score for the subjects
using the levels using a first formula that is identical to Formula
I; and [0016] ii) a second method comprising using measured levels
of said at least five biomarkers and optional age and/or gender are
used in calculating a diabetes risk score for a subject using a
second formula;
[0017] wherein, when the first and second formulas of the first and
second methods are applied to measured biomarker levels and
optional age and/or gender for human reference population to
generate first and second risk profiles, respectively, the second
risk profile has a 95% confidence interval of the Spearman rank
correlation coefficient squared (R.sup.2) which is entirely above
or includes a correlation value of 0.5 with the first risk
profile.
[0018] In alternative embodiments, a method of categorizing the
risk of developing a diabetic condition is provided. This method
may comprise: a) measuring the levels of a plurality of biomarkers
in a blood sample from a human subject, wherein the plurality of
biomarkers comprises at least five of the following biomarkers:
glucose, adiponectin, CRP, IL2RA, ferritin, insulin and HbA1c, and
optionally subject age and/or gender, and; b) categorizing the
subject into one of a plurality of mutually exclusive ordered risk
categories wherein placement into the ordered risk categories is
determined by a method selected from the group consisting of:
[0019] i) a first method comprising calculating a diabetes risk
score for the subject using the levels using the Formula I; and
categorizing the subject based on the calculated diabetes risk
score into one of the plurality of mutually exclusive ordered risk
categories that are each defined by a range of diabetes risk scores
to provide the categorical risk assessment for the subject; and
[0020] ii) a second method comprising using the measured levels of
the at least five biomarkers and optional age and/or gender to
categorize the subject into one of the plurality of mutually
exclusive ordered risk categories in accordance with a risk profile
to provide the categorical risk assessment for the subject, wherein
when a plurality of categorical risk assessments from a human
reference population calculated by the first method (first diabetes
risk categorization) is compared to a plurality of categorical risk
assessments from the human reference population calculated by the
second method (second diabetes risk categorization), the second
diabetes risk categorization is not independent with 95% confidence
from the first diabetes risk categorization using a chi-squared
test, and the ranges of the diabetes risk scores that define the
plurality of ordered risk categories are selected such that the
numbers of individuals from the human reference population in each
risk category for both the first diabetes risk categorization and
the second diabetes risk categorization are identical.
[0021] Computer readable medium comprising instructions for
execution of the above-described algorithm, as well as kits
containing the same, are also provided.
[0022] The foregoing summary is not intended to define every aspect
of the invention, and additional aspects are described in other
sections, such as the Detailed Description. The entire document is
intended to be related as a unified disclosure, and it should be
understood that all combinations of features described herein are
contemplated, even if the combination of features are not found
together in the same sentence, or paragraph, or section of this
document.
[0023] In addition to the foregoing, the invention includes, as an
additional aspect, all embodiments of the invention narrower in
scope in any way than the variations specifically mentioned above.
With respect to aspects of the invention described as a genus, all
individual species are individually considered separate aspects of
the invention. With respect to aspects described as a range, all
sub-ranges and individual values are specifically contemplated.
BRIEF DESCRIPTION OF THE DRAWINGS
[0024] FIG. 1 provides a table showing descriptive statistics for
the use of Algorithm A, as described in the Examples section of
this disclosure.
[0025] FIG. 2 show an ROC curve for Algorithm A.
DEFINITIONS
[0026] The term "biomarker" in the context of this disclosure
encompasses, without limitation, any measurable analyte, e.g., a
protein, nucleic acid, metabolite, including a lipid metabolite, in
a biological sample such as a bodily fluid, e.g., blood, obtained
from a subject. Biomarkers can also include mutated proteins,
mutated nucleic acids, splice variants, and modified proteins,
e.g., glycosylated or phosphorylated proteins. Adiponectin
(ADIPOQ), C-reactive protein (CRP); glucose (GLUCOSE);
glutamic-pyruvate transaminase (GPT or ALT); glycosylated
hemoglobin (HBA1C); heat shock 70 kDa protein 1B (HSPA1B);
insulin-like growth factor binding protein 1 (IGFBP1); insulin-like
growth factor binding protein 2 (IGFBP2); insulin (INS, INSULIN-M,
pro-insulin and SCp), leptin (LEP) and triglycerides (TRIG) are
examples of biomarkers. The biomarker GPT may be analyzed by
measuring the GPT protein level or measuring the enzymatic activity
as an alanine aminotransferase (ALT). The GPT enzymatic activity
(ALT activity) may be measured using conventional methods known in
the art. These markers are individually known; see US 2007/0218519
and US 2007/0259377, which are incorporated by reference herein in
their entirety, for descriptions of the individual markers.
[0027] The term "clinical parameter" or "CP" encompasses all
non-sample or non-analyte biomarkers of subject health status or
other characteristics, such as, without limitation, age (AGE), race
or ethnicity (RACE), gender (SEX), diastolic blood pressure (DBP)
and systolic blood pressure (SBP), family history (FHX, including
FHx1 for 1 parent and FHx2 for 2 parents), height (HT), weight
(WT), waist (Waist) and hip (Hip) circumference, Waist-Hip ratio
(WHr), body-mass index (BMI), past Gestational Diabetes Mellitus
(GDM), and resting heart rate.
[0028] The term "diabetes" in the context of this disclosure
encompasses Type 1 Diabetes, both autoimmune and idiopathic and
Type 2 Diabetes (referred to herein as "Diabetes" or "T2DM"). The
World Health Organization defined the diagnostic value of fasting
plasma glucose concentration to 7.0 mmol/l (126 mg/dl) and above
for Diabetes mellitus (whole blood 6.1 mmol/1 or 110 mg/dl), or
2-hour glucose level greater than or equal to 11.1 mmol/L (greater
than or equal to 200 mg/dL). It may also be possible to diagnose
Diabetes based on an HbA1e level of greater than 6%, for instance,
.gtoreq.6.5%. Other values suggestive of or indicating high risk
for Diabetes mellitus include elevated arterial pressure greater
than or equal to 140/90 mm Hg; elevated plasma triglycerides
(greater than or equal to 1.7 mmol/L; 150 mg/dL) and/or low
HDL-cholesterol (<0.9 mmol/L, 35 mg/dl for men; <1.0 mmol/L,
39 mg/dL women); central obesity (males: waist to hip ratio
>0.90; females; waist to hip ratio >0.85) and/or body mass
index exceeding 30 kg/m2; microalbuminuria, where the urinary
albumin excretion rate greater than or equal to 20 .mu.g/min or
albumin:creatinine ratio greater than or equal to 30 mg/g).
[0029] The oral glucose tolerance test (OGTT) is principally used
for diagnosis of Diabetes Mellitus when testing blood glucose
levels are equivocal, during pregnancy, or in epidemiological
studies (Definition, Diagnosis and Classification of Diabetes
Mellitus and its Complications, Part 1, World Health Organization,
1999). The OGTT should be administered in the morning after at
least 3 days of unrestricted diet (greater than 150 g of
carbohydrate daily) and usual physical activity. A reasonable
(30-50 g) carbohydrate-containing meal should be consumed on the
evening before the test. The test should be preceded by an
overnight fast of 8-14 hours during which water may be consumed. In
some embodiments, the test is preceded by an overnight fast of no
less than 10 hours After collection of the fasting blood sample,
the subject should drink 75 g of anhydrous glucose or 82.5 g of
glucose monohydrate in 250-300 ml of water over the course of 5
minutes. For children, the test load should be 1.75 g of glucose
per kg body weight up to a total of 75 g of glucose. Timing of the
test is from the beginning of the drink. Blood samples must be
collected 2 hours after the test load. Diabetes over such a period
unless otherwise enriched by other risk factors; in an unselected
general population, the rate of conversion over such periods is
typically estimated at 5-6%, or less than 1% per annum.
[0030] The term "gestational Diabetes" refers to glucose
intolerance during pregnancy. This condition results in high blood
sugar that starts or is first diagnosed during pregnancy.
[0031] "Diabetic condition" in the context of the present invention
comprises type I and type II Diabetes mellitus, and pre-Diabetes
(defined herein). It is also known in the art that Diabetic-related
conditions include Diabetes and the pre-diabetic condition (defined
herein).
[0032] The terms "formula," "algorithm," and "model" are used
interchangeably for any mathematical equation, algorithmic,
analytical or programmed process, or statistical technique that
takes one or more continuous or categorical inputs (herein called
"parameters") and calculates an output value, sometimes referred to
as an "index", "index value", "category" or "risk category".
Non-limiting examples of "formulas" include sums, ratios, and
regression operators, such as coefficients or exponents, biomarker
value transformations and normalizations (including, without
limitation, those normalization schemes based on clinical
parameters, such as gender, age, or ethnicity), decision trees,
rules and guidelines, statistical classification models, and neural
networks trained on historical populations. Of particular use for
the biomarkers are linear and non-linear equations and statistical
classification analyses to determine the relationship between
levels of biomarkers detected in a subject sample and the subject's
risk of Diabetes. In panel and combination construction, of
particular interest are structural and synactic statistical
classification algorithms, and methods of risk index construction,
utilizing pattern recognition features, including established
techniques such as cross-correlation, Principal Components Analysis
(PCA), factor rotation, Logistic Regression (LogReg), Linear
Discriminant Analysis (LDA), Eigengene Linear Discriminant Analysis
(ELDA), Support Vector Machines (SVM), Random Forest (RF),
Recursive Partitioning Tree (RPART), as well as other related
decision tree classification techniques, Shruken Centroids (SC),
StepAIC, Kth-Nearest Neighbor, Boosting, Decision Trees, Neural
Networks, Bayesian Networks, Support Vector Machines, and Hidden
Markov Models, Linear Regression or classification algorithms,
Nonlinear Regression or classification algorithms, analysis of
variants (ANOVA), hierarchical analysis or clustering algorithms;
hierarchical algorithms using decision trees; kernel based machine
algorithms such as kernel partial least squares algorithms, kernel
matching pursuit algorithms, kernel Fisher's discriminate analysis
algorithms, or kernel principal components analysis algorithms,
among others. Many of these techniques are useful either combined
with other selection techniques, such as forward selection,
backwards selection, or stepwise selection, complete enumeration of
all potential panels of a given size, genetic algorithms, or they
may themselves include biomarker selection methodologies in their
own technique. These may be coupled with information criteria, such
as Akaike's Information Criterion (AIC) or Bayes Information
Criterion (BIC), in order to quantify the tradeoff between
additional biomarkers and model improvement, and to aid in
minimizing overfit. The resulting predictive models may be
validated in other studies, or cross-validated in the study they
were originally trained in, using such techniques as Leave-One-Out
(LOO) and 10-Fold cross-validation (10-Fold CV) or correlated to
known predictive risk factors. A "DRS Formula" is a formula
developed used to calculate a Diabetes risk score from inputs
comprising the results from biomarker testing as described herein.
A DRS Formula can be used to calculate a Diabetes risk score.
[0033] "Measuring" or "measurement" means assessing the presence,
absence, quantity or amount (which can be an absolute or relative
amount) of either a given substance within a clinical or
subject-derived sample, including the derivation of qualitative or
quantitative concentration levels of such substances, or otherwise
evaluating the values or categorization of a subject's clinical
parameters.
[0034] A "negative predictive value" or "NPV" is calculated by
TN/(TN+FN) or the true negative fraction of all negative test
results. It also is inherently impacted by the prevalence of the
disease and pre-test probability of the population intended to be
tested. See, e.g., O'Marcaigh A S, Jacobson R M, "Estimating The
Predictive Value Of A Diagnostic Test, How To Prevent Misleading Or
Confusing Results," Clin. Ped. 1993, 32(8): 485-491, which
discusses specificity, sensitivity, and positive and negative
predictive values of a test, e.g., a clinical diagnostic test.
Often, for binary disease state classification approaches using a
continuous diagnostic test measurement, the sensitivity and
specificity is summarized by Receiver Operating Characteristics
(ROC) curves according to Pepe et al, "Limitations of the Odds
Ratio in Gauging the Performance of a Diagnostic, Prognostic, or
Screening Marker," Am. J. Epidemiol 2004,159 (9): 882-890, and
summarized by the Area Under the Curve (AUC) or c-statistic, an
indicator that allows representation of the sensitivity and
specificity of a test, assay, or method over the entire range of
test (or assay) cut points with just a single value. See also,
e.g., Shultz, "Clinical Interpretation Of Laboratory Procedures,"
chapter 14 in Teitz, Fundamentals of Clinical Chemistry, Burtis and
Ashwood (eds.), 4th edition 1996, W. B. Saunders Company, pages
192-199; and Zweig et al., "ROC Curve Analysis: An Example Showing
The Relationships Among Serum Lipid And Apolipoprotein
Concentrations In Identifying Subjects With Coronory Artery
Disease," Clin, Chem., 1992, 38(8): 1425-1428. An alternative
approach using likelihood functions, odds ratios, information
theory, predictive values, calibration (including goodness-of-fit),
and reclassification measurements is summarized according to Cook,
"Use and Misuse of the Receiver Operating Characteristic Curve in
Risk Prediction," Circulation 2007, 115: 928-935. Hazard ratios and
absolute and relative risk ratios within subject cohorts defined by
a test are a further measurement of clinical accuracy and utility.
In this last, multiple methods are frequently used to defining
abnormal or disease values, including reference limits,
discrimination limits, and risk thresholds as per Vasan,
"Biomarkers of Cardiovascular Disease: Molecular Basis and
Practical Considerations," Circulation 2006, 113: 2335-2362.
[0035] Analytical accuracy refers to the repeatability and
predictability of the measurement process itself, and may be
summarized in such measurements as coefficients of variation, and
tests of concordance and calibration of the same samples or
controls with different times, users, equipment and/or reagents.
These and other considerations in evaluating new biomarkers are
also summarized in Vasan, Circulation 2006, 113: 2335-2362.
[0036] "Normal glucose levels" is used interchangeably with the
term "normoglycemic" and "normal" and refers the definition
published by the American Diabetes Association, currently a fasting
venous plasma glucose concentration of less than 110 mg/dL.
Although this amount is arbitrary, such values have been observed
in subjects with proven normal glucose tolerance, although some may
have IGT as measured by oral glucose tolerance test (OGTT). Glucose
levels above normoglycemic are considered a pre-diabetic
condition.
[0037] "Performance" is a term that relates to the overall
usefulness and quality of a diagnostic or prognostic test,
including, among others, clinical and analytical accuracy, other
analytical and process characteristics, such as use characteristics
(e.g., stability, ease of use), health economic value, and relative
costs of components of the test. Any of these factors may be the
source of superior performance and thus usefulness of the test.
[0038] "Positive predictive value" or "PPV" is calculated by
TP/(TP+FP) or the true positive fraction of all positive test
results. It is inherently impacted by the prevalence of the disease
and pre-test probability of the population intended to be
tested.
[0039] "Pre-Diabetes" or "pre-Diabetic," in the context of this
disclosure indicates the physiological state, in an individual or
in a population, and absent any prescribed therapeutic intervention
(diet, exercise, pharmaceutical, or otherwise) of having a higher
than normal expected rate of disease conversion to Diabetes
Mellitus. Pre-Diabetes can also refer to those subjects or
individuals, or a population of subjects or individuals who will,
or are predicted to convert to Type 2 Diabetes Mellitus within a
given time period (e.g., 5, 7 or 10 years) or time horizon at a
higher rate than that of the general, unselected population. It may
also be stated in terms of a relative risk from normal between
quartiles of risk or as a likelihood ratio between differing
biomarker and index scores, including those described herein.
[0040] In an unselected individual population, pre-Diabetes
overlaps with, but is not necessarily a complete superset of, or
contained subset within, all those with "pre-diabetic conditions"
as many who will convert to Diabetes in a given time horizon are
now apparently healthy, and with no obvious pre-diabetic condition,
and many have pre-diabetic conditions but will not convert in a
given time horizon; such is the diagnostic gap and need to be
fulfilled by the invention.
[0041] "Diabetic condition" in the context of the present invention
comprises type I and type II Diabetes mellitus, and pre-Diabetes
(defined herein). It is also known in the art that Diabetic-related
conditions include Diabetes and the pre-diabetic condition (defined
herein).
[0042] "Pre-diabetic condition" refers to a metabolic state that is
intermediate between normal glucose homeostasis and metabolism and
states seen in frank Diabetes Mellitus. Pre-diabetic conditions
include, without limitation, Metabolic Syndrome ("Syndrome X"),
Impaired Glucose Tolerance (IGT), and Impaired Fasting Glycemia
(IFG). IGT refers to post-prandial or post-OGTT abnormalities of
glucose regulation, while IFG refers to abnormalities that are
measured in a fasting state. The American Diabetes Association
defines values for IFG as a fasting plasma glucose concentration of
4.4 mmol/L (100 mg/dL) or greater, but less than 7.0 mmol/L (126
mg/dL). Metabolic syndrome according to the National Cholesterol
Education Program (NCEP) criteria are defined as having at least
three of the following: blood pressure greater than or equal to
130/85 mm Hg; fasting plasma glucose greater than or equal to 6.1
mmol/L; waist circumference >102 cm (men) or >88 cm (women);
triglycerides greater than or equal to 1.7 mmol/L; and HDL
cholesterol <1.0 mmol/L (men) or 1.3 mmol/L (women). Many
individuals with pre-diabetic conditions will not convert to
T2DM.
[0043] "Risk" in the context of the present disclosure, relates to
the probability that an event will occur over a specific time
period, as in the conversion to frank Diabetes, and can mean a
subject's "absolute" risk or "relative" risk. Absolute risk can be
measured with reference to either actual observation
post-measurement for the relevant time period, or with reference to
index values developed from historical cohorts that have been
followed for the relevant time period. Relative risk refers to the
ratio of absolute risks of a subject compared either to the
absolute risks of low risk cohorts or an average population risk,
which can vary by how clinical risk factors are assessed. Odds
ratios, the proportion of positive events to negative events for a
given test result, are also commonly used (odds are according to
the formula p/(1-p) where p is the probability of event and (1-p)
is the probability of no event) to no-conversion. Alternative
continuous measures which may be assessed in the context of the
present invention include time to Diabetes conversion and
therapeutic Diabetes conversion risk reduction ratios.
[0044] "Risk evaluation," or "evaluation of risk" in the context of
the present invention encompasses estimating the probability, odds,
or likelihood that an event or disease state may occur, the rate of
occurrence of the event or conversion from one disease state to
another, i.e., from a normoglycemic condition to a pre-diabetic
condition or pre-Diabetes, or from a pre-diabetic condition to
pre-Diabetes or Diabetes. Risk evaluation can also comprise
prediction of future glucose, HBA1c scores or other indices of
Diabetes, either in absolute or relative terms in reference to a
previously measured population. The methods of the present
invention may be used to make continuous or categorical
measurements of the risk of conversion to Type 2 Diabetes. In the
categorical scenario, the invention can be used to discriminate
between normal and pre-Diabetes subject cohorts. In other
embodiments, the present invention may be used so as to
discriminate pre-Diabetes from Diabetes, or Diabetes from normal.
Such differing use may require different biomarker combinations in
individual panels, mathematical algorithm, and/or cut-off points,
but be subject to the same aforementioned measurements of accuracy
for the intended use.
[0045] 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, serum, plasma, blood
cells, endothelial cells, tissue biopsies, lymphatic fluid, ascites
fluid, interstitital 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. "Blood sample" refers
to whole blood or any fraction thereof, including blood cells,
serum and plasma.
[0046] "Sensitivity" is calculated by TP/(TP+FN) or the true
positive fraction of disease subjects.
[0047] "Specificity" is calculated by TN/(TN+FP) or the true
negative fraction of non-disease or normal subjects.
[0048] By "statistically significant", it is meant that the
alteration is greater than what might be expected to happen by
chance alone (which could be a "false positive"). Statistical
significance can be determined by any method known in the art.
Commonly used measures of significance include the p-value, which
presents the probability of obtaining a result at least as extreme
as a given data point, assuming the data point was the result of
chance alone. A result is often considered highly significant at a
p-value of 0.05 or less.
[0049] The Spearman's rank correlation coefficient is calculated
using known statistical procedures, e.g., using the formula:
.rho. = 1 - 6 d i 2 n ( n 2 - 1 ) ##EQU00001## [0050] where
d.sub.i=x.sub.i-y.sub.i=the difference between the ranks of
corresponding values X.sub.i and Y.sub.i, and n=the number of
values in each data set (same for both sets). Spearman correlation
coefficient is a standard statistical method and described in C.
Spearman ("The proof and measurement of association between two
things" Amer. J. Psychol., 15 (1904) pp. 72-101) and Corder
("Nonparametric Statistics for Non-Statisticians: A Step-by-Step
Approach", Wiley, 2009).
[0051] Chi squared analysis is performed using known statistical
procedures, such as any described in the following: Abramowitz et
al ("Chapter 26", Handbook of Mathematical Functions with Formulas,
Graphs, and Mathematical Tables, New York: Dover, 1965 ISBN
0-486-61272-4), NIST (Engineering Statistics Handbook--Chi-Square
Distribution 2006), Johnson et al (Continuous Univariate
Distributions (Second Ed., Vol. 1, Chapter 18). John Willey and
Sons. 1994 ISBN 0-471-58495-9), Mood et al (Introduction to the
Theory of Statistics 1974 Third Edition, p. 241-246, McGraw-Hill.
ISBN 0-07-042864-6).
[0052] A "subject" or "patient" in the context of the present
disclosure is a mammal. The mammal can be a human, non-human
primate, mouse, rat, dog, cat, horse, or cow, but are not limited
to these examples. Mammals other than humans can be used as
subjects that represent animal models of Diabetes Mellitus,
pre-Diabetes, or pre-diabetic conditions. A subject can be male or
female. A subject can be one who has been previously diagnosed or
identified as having Diabetes, pre-Diabetes, or a pre-diabetic
condition, and optionally has already undergone, or is undergoing,
a therapeutic intervention for the Diabetes, pre-Diabetes, or
pre-diabetic condition. Alternatively, a subject can also be one
who has not been previously diagnosed as having Diabetes,
pre-Diabetes, or a pre-diabetic condition. For example, a subject
can be one who exhibits one or more risk factors for Diabetes,
pre-Diabetes, or a pre-diabetic condition, or a subject who does
not exhibit Diabetes risk factors, or a subject who is asymptomatic
for Diabetes, pre-Diabetes, or pre-diabetic conditions. A subject
can also be one who is diagnosed, diagnosed or suffering from or at
risk of developing Diabetes, pre-Diabetes, or a pre-diabetic
condition.
[0053] "Traditional laboratory risk factors" or "TLRFs" correspond
to biomarkers isolated or derived from subject samples and which
are currently evaluated in the clinical laboratory and used in
traditional global risk assessment algorithms, such as Stern,
Framingham, Finland Diabetes Risk Score, ARIC Diabetes, and
Archimedes. Traditional laboratory risk factors commonly tested
from subject blood samples include, but are not limited to, total
cholesterol (CHOL), LDL (LDL/LDLC), HDL (HDL/HDLC), VLDL (VLDLC),
triglycerides (TRIG), glucose (including, without limitation, the
fasting plasma glucose (Glucose) and the oral glucose tolerance
test (OGTT)) and HBA1c (HBA1C) levels.
[0054] The oral glucose tolerance test (OGTT) is principally used
for diagnosis of Diabetes Mellitus or pre-diabetic conditions when
testing blood glucose levels are equivocal, during pregnancy, or in
epidemiological studies (Definition, Diagnosis and Classification
of Diabetes Mellitus and its Complications, Part 1, World Health
Organization, 1999). The OGTT should be administered in the morning
after at least 3 days of unrestricted diet (greater than 150 g of
carbohydrate daily) and usual physical activity. A reasonable
(30-50 g) carbohydrate-containing meal should be consumed on the
evening before the test. The test should be preceded by an
overnight fast of 8-14 hours, during which water may be consumed.
After collection of the fasting blood sample, the subject should
drink 75 g of anhydrous glucose or 82.5 g of glucose monohydrate in
250-300 ml of water over the course of 5 minutes. For children, the
test load should be 1.75 g of glucose per kg body weight up to a
total of 75 g of glucose. Timing of the test is from the beginning
of the drink. Blood samples must be collected 2 hours after the
test load. As previously noted, a diagnosis of impaired glucose
tolerance (IGT) has been noted as being only 50% sensitive, with a
>10% false positive rate, for a 7.5 year conversion to Diabetes
when used at the WHO cut-off points. This is a significant problem
for the clinical utility of the test, as even relatively high risk
ethnic groups have only a 10% rate of conversion to Diabetes over
such a period unless otherwise enriched by other risk factors; in
an unselected general population, the rate of conversion over such
periods is typically estimated at 5-6%, or less than 1% per
annum.
DETAILED DESCRIPTION
[0055] In general terms, the method described herein provides
diabetes risk scores that are very similar or identical to those
obtained by the use of Formula I, where the similarity between
scores are evaluated using a Spearman test or a chi-squared test on
a human reference population, as described in greater detail
below.
[0056] Formula I is as follows:
D=X+0.062*Age-0.636*Gender+1.621*GLUCOSE-3.370*ADIPOQ+0.600*CRP+0.699*FT-
H1+1.350*IL2RA0.491*INSULIN+0.259*HBA1C
[0057] wherein:
[0058] X is any number, including 0, of any sign, and may have 0,
1, 2 or more than 2 decimal places, and in certain embodiments may
be -23.114;
[0059] 0.062*Age is patient age in years multiplied by 0.062;
[0060] 0.636*Gender is patient gender, wherein female=0 and male=1,
multiplied by 0.636;
[0061] 1.621*GLUCOSE is the square root of the level of patient
blood glucose in mg/dL, multiplied by 1.621;
[0062] 3.370*ADIPOQ is the log.sub.10 of the level of patient blood
adiponectin in .mu.g/mL, multiplied by 3.370;
[0063] 0.600*CRP is the log.sub.10 of level of patient blood CRP in
mg/L, multiplied by 0.600;
[0064] 0.699*FTH1 is the log.sub.10 of the level of patient blood
ferritin in ng/mL, multiplied by 0.699;
[0065] 1.350*IL2RA is the log.sub.10 of the level of patient blood
IL2RA in U/mL, multiplied, by 1.350;
[0066] 0.491*INSULIN is the log.sub.10 of the level of patient
blood insulin in uIU/mL, multiplied 0.491; and
[0067] 0.259*HBA1C is the level of patient blood Hb1Ac measured as
a percentage of total hemoglobin in whole blood multiplied by
0.259.
[0068] In general terms, execution of Formula I produces a linear
predictor, lp, that is related to group membership of a sample
(e.g. case or controls), assuming a 50% prior probability of
belonging to a group of converters being a case. This lp can be
converted to a convenient score for an individual subject (DRS) on
a 0-10 scale using the following equation:
DRS=10*e.sup.lp/(1+e.sup.lp)
[0069] This score correlates with the absolute risk of conversion
at a specified prior probability (assuming a specified probability
of 50%). Changing the prior probability that was used to construct
the algorithm to a probability that reflects the actual percentage
of "cases" in the population (based on epidemiology data of that
population) effectively shifts the linear model by changing the
intercept term, a, as follows:
a'=a+ln(p.sub.1/p.sub.0)
[0070] Where a* is the new intercept, a is the intercept assuming a
50% prior, p.sub.1 is the prior probability of being a case and
p.sub.0 is the prior probability of being a control. The remaining
coefficients stay the same and a new linear predictor, lp', is
computed. From this Risk (is computed as follows:
Risk=e.sup.lp'/(1+e.sup.lp')
[0071] The Risk is the probability that a subject would become a
case (a converter). For example, a risk of 25% indicates that 25%
of the people with a similar DRS will convert to a diabetic, within
5 years.
[0072] In certain embodiments, the method may include: a) measuring
the levels of a plurality of biomarkers in a blood sample obtained
from the human subject, wherein said plurality of biomarkers
comprises at least five of the following biomarkers: glucose,
adiponectin, CRP, IL2RA, ferritin, insulin and HbA1c; and b)
calculating a diabetes risk score for said subject using the levels
and, optionally, subject age and/or gender, where the calculation
is performed by a method selected from the group consisting of:
[0073] i) a first method wherein the levels of all said biomarkers
are measured and calculating a diabetes risk score for the subjects
using the levels using a first formula that is identical to Formula
I; and [0074] ii) a second method wherein measured levels of said
at least five biomarkers and optional age and/or gender are used in
calculating a diabetes risk score for a subject using a second
formula;
[0075] wherein, when the first and second formulas of the first and
second methods are applied to measured biomarker levels and
optional age and/or gender for human reference population to
generate first and second risk profiles, respectively, the second
risk profile has a 95% confidence interval of the Spearman rank
correlation coefficient squared (R.sup.2) which is entirely above
or includes a correlation value of 0.5 with the first risk
profile.
[0076] In one embodiment, in order to determine if the first method
provides results that are similar to those of the second method, a
human reference population may be selected and two assays may be
performed on the subjects of the population. In general terms, if
the diabetes risk scores are expressed numerically (e.g., as a
continuous variable) each subject will have two scores, then, for
example, the scores for each method may be ranked across the
population and compared using a Spearman test as described below.
If the patients are categorized into one of a plurality of risk
categories, then, for example, the patients may categorized into
categories so that the ranges of the risk scores that define the
second plurality of ordered risk categories are mutually exclusive
relative to one another and cover the entire range of the second
diabetes risk scores, b. the number of the second plurality of
ordered risk categories is equal to the number of the first
plurality of ordered risk categories, and c. the ranges of the risk
scores that define the second plurality of ordered risk categories
are selected such that the numbers of the patients in each risk
category is identical to the numbers of the patients in each of the
corresponding risk categories, in order of increasing risk, in the
first plurality of ordered risk categories. The categorization may
then be analyzed using a chi-squared test, as described below.
Categorization may be done by first calculating a risk score, or in
the absence of such a calculation.
[0077] In certain embodiments, the method may include: a) measuring
the levels of a plurality of biomarkers in a blood sample obtained
from a human patient, wherein the plurality of biomarkers comprises
at least five of the following biomarkers: glucose, adiponectin,
CRP, IL2RA, ferritin, insulin and HbA1c; b) calculating a diabetes
risk score for the patient using the levels and, optionally,
patient age and/or gender; and c) providing the diabetes risk score
to the patient or the patient's healthcare practitioner in the form
of a paper or electronic report; wherein steps a) and b), when
performed on a human referece population, provide a first profile
of diabetes risk scores having an absolute value of the 95%
confidence interval of the Spearman correlation coefficient which
is entirely above or includes a correlation value of 0.5 with a
second profile of diabetes risk scores obtained from the plurality
of human blood samples by: i. measuring the levels of glucose,
adiponectin, CRP, IL2RA, ferritin, insulin and HbA1c in blood
samples obtained from the plurality of human patients; and ii.
calculating a second diabetes risk score for each of the patients
using the levels using Formula I.
[0078] In alternative embodiments, the method may comprise: a)
measuring the levels of a plurality of biomarkers in a blood sample
from a human subject, wherein the plurality of biomarkers comprises
at least five of the following biomarkers: glucose, adiponectin,
CRP, IL2RA, ferritin, insulin and HbA1c, and optionally subject age
and/or gender, and; b) categorizing the subject into one of a
plurality of mutually exclusive ordered risk categories wherein
placement into the ordered risk categories is determined by a
method selected from the group consisting of:
[0079] i) a first method comprising calculating a diabetes risk
score for the subject using the levels using the Formula I; and
categorizing the subject based on the calculated diabetes risk
score into one of the plurality of mutually exclusive ordered risk
categories that are each defined by a range of diabetes risk scores
to provide the categorical risk assessment for the subject; and
[0080] ii) a second method comprising using the measured levels of
the at least five biomarkers and optional age and/or gender to
categorize die subject into one of the plurality of mutually
exclusive ordered risk categories in accordance with a risk profile
to provide the categorical risk assessment for the subject, wherein
when a plurality of categorical risk assessments from a human
reference population calculated by the first method (first diabetes
risk categorization) is compared to a plurality of categorical risk
assessments from the human reference population calculated by the
second method (second diabetes risk categorization), the second
diabetes risk categorization is not independent with 95% confidence
from the first diabetes risk categorization using a chi-squared
test, and the ranges of the diabetes risk scores that define the
plurality of ordered risk categories are selected such that the
numbers of individuals from the human reference population in each
risk category for both the first diabetes risk categorization and
the second diabetes risk categorization are identical.
[0081] In some embodiments, the method may include: a) measuring
the levels of a plurality of biomarkers in a blood samples obtained
from a human patient, wherein the plurality of biomarkers comprises
at least five of the following biomarkers: glucose, adiponectin,
CRP, IL2RA, ferritin, insulin and HbA1c, and; b) categorizing the
patient into one of a first plurality of ordered risk categories
using the levels and, optionally, patient age and/or gender to
provide a categorical risk assessment for the patient; and c)
providing the categorical risk assessment for the patient to the
patient's healthcare practitioner in the form of a paper or
electronic report; wherein steps a) and b), when performed on a
human reference population, categorize the subjects of the human
referece population among the ordered risk categories in a way that
is not independent using a valid chi-squared test with 95%
confidence from the categorization of the patients by: i. measuring
the levels of glucose, adiponectin, CRP, IL2RA, ferritin, insulin
and HbA1c in blood samples obtained from the plurality of human
patients; and ii. calculating a second diabetes risk score for each
of the patients using the levels using Formula I; and iii.
categorizing each of the patients into one of a plurality of
ordered risk categories that are each defined by a range of the
risk scores to provide a second categorical risk assessment for
each patient, as described above. In certain cases, the chi-sqared
analysis is performed with the following conditions: a. the ranges
of the risk scores that define the second plurality of ordered risk
categories are mutually exclusive relative to one another and cover
the entire range of the second diabetes risk scores, b. the number
of the second plurality of ordered risk categories is equal to the
number of the first plurality of ordered risk categories, and c.
the ranges of the risk scores that define the second plurality of
ordered risk categories are selected such that the numbers of the
patients in each risk category is identical to the numbers of the
patients in each of the corresponding risk categories, in order of
increasing risk, in the first plurality of ordered risk
categories.
[0082] Methods for producing diabetes risk scores that are
effectively very similar or identical to those provided by the use
of Formula I (as evaluated by a Spearman or chi-squared test)
without employing Formula I, include, for example: a) methods that
measure the levels of one or more of the same biomarkers or
clinical parameters using different units than those required by
Formula I, e.g., by measuring any one or more of the following
biomarkers: glucose, adiponectin, CRP, ferritin, IL2RA, insulin,
and Hb1Ac in pounds/pint, moles/liter or some other unit of
concentration, or by measuring age in days, months, or some other
unit of time, for example; b) methods that multiply the levels of
one or more of the same biomarkers by a coefficient that is similar
to but not the same as the coefficients recited in Formula I (e.g.,
multiplying the age of a patient by 0.063 rather than 0.062, as
required by Formula 1); c) use of the same markers and clinical
parameters as those required by Formula I, except that one or more
of the markers is measured by a different method (e.g., using a
different assay kit) and/or different instrumentation (e.g., using
a different analyzer or chromatography system; d) methods that
measure the levels of the same biomarkers using different
normalization controls to those used by the kits described in the
Examples section herein; e) use of the same markers and clinical
parameters as those required by Formula I, except that one or more
(e.g., one or two) of the markers recited in Formula I is
substituted with another marker of equal prognostic value; f)
methods that transform the levels or score non-linearly; g) use of
the more markers and clinical parameters than those required by
Formula I, where the additional markers have little or no
prognostic value; h) use of a different value for X, the intercept
that normalizes the other variables; i) using of a formula I that
is otherwise identical to that of Formula I, except the resultant
risk score is on a different scale (e.g., on a scale of 1-100 as
opposed to a scale of 1-10, etc.). The scale of the score may be
derived using well known mathematical procedures, e.g., using the
formula:
DRS=exp(D)/(1+exp(D))*Y,
[0083] where DRS is the diabetes risk score, D is the output of the
formula, and Y is the upper limit of the scale (e.g., 5, 10, 100,
1,000, etc).
[0084] In particular embodiments, a marker or clinical parameter
not listed in Formula I may be employed in the method, either to
substitute one or more markers or clinical parameters of Formula I,
or in addition to the markers and clinical parameters listed in
Formula I. Exemplary clinical parameters and biomarkers that could
be employed in the method are set forth in the table that follows
below, and in Table 1 of US20090012716, which Table is incorporated
by reference for disclosure of those biomarkers and clinical
parameters.
TABLE-US-00001 Clinical Core Core Additional. Additional Parameters
Biomarkers Biomarkers I Biomarkers II Biomarkers I Biomarkers II
Age (AGE) Cholesterol Adiponectin Advanced Chemokine Angiotensin-
Body Mass (CHOL) (ADIPOQ) Glycosylation (C-C motif) Converting
Index (BMI) Glucose C-Reactive End Product- ligand 2 aka Enzyme
Diastolic (fasting Protein Specific monocyte (ACE) Blood plasma
(CRP) Receptor chemoattractant Complement Pressure glucose
Fibrinogen (AGER) protein- Component (DBP) (FPG/Glucose) alpha
chain Alpha-2-HS- 1 (CCL2) C4 (C4A) Family or with (FGA)
Glycoprotein Cyclin- Complement History oral glucose Insulin, Pro-
(AHSG) dependent Factor D (FHX) tolerance test insulin, and
Angiogenin kinase 5 (Adipsin) Gestational (OGTT)) soluble C- (ANG)
(CDK5) (CFD) Diabetes HBA1c Peptide (any Apolipoprotein Complement
Dipeptidyl- Mellitus (Glycosylated and/or all of E (APOE) Component
Peptidase 4 (GDM), Past Hemoglobin which, INS) CD14 3 (C3) (CD26)
Height (HT) (HBA1/HBA1C) Leptin molecule Fas aka (DPP4) Hip High
Density (LEP) (CD14) TNF Haptoglobin Circumference Lipoprotein
Ferritin receptor (HP) (Hip) (HDL/HDLC) (FTH1) superfamily,
Interleukin 8 Race Low Density Insulin-like member 6 (IL8) (RACE)
Lipoprotein growth factor (FAS) Matrix Sex (SEX) (LDL/LDLC) binding
Hepatocyte Metallopeptidase 2 Systolic Very Low protein 1 Growth
(MMP2) Blood Density (IGFBP1) Factor Selectin E Pressure
Lipoprotein Interleukin 2 (HGF) (SELE) (SBP) (VLDLC) Receptor,
Interleukin Tumor Waist Triglycerides Alpha 18 (IL18) Necrosis
Circumference (TRIG) (IL2RA) Inhibin, Factor (TNF- (Waist) Vascular
Cell Beta A aka Alpha) (TNF) Weight Adhesion Activin-A Tumor (WT)
Molecule 1 (INHBA) Necrosis (VCAM1) Resistin Factor Vascular (RETN)
Superfamily Endothelial Selectin-P Member 1A Growth (SELP)
(TNFRSF1A) Factor Tumor (VEGF) Necrosis Von Factor Willebrand
Receptor Factor (VWF) Superfamily, member 1 B (TNFRSF1B)
[0085] In certain embodiments, the method may include measuring the
blood levels of at least 4 of the following biomarkers: glucose,
adiponectin, CRP, IL2RA, ferritin, insulin and HbA1c, e.g.,
measuring the levels glucose, adiponectin, CRP and HbA1c, and also
measuring the levels of 1 or more other biomarkers which markers
may be selected from the table shown above or Table 1 of
US20090012716, for example. The total number of biomarkers measured
in the method may be 4, 5, 6, 7, 8, 9, 10, 11, 12 or more then 12,
more than 15, up to 20 or more. Likewise, the method may optionally
employ the age and/or gender of the patient and, in certain cases,
1, 2, 3, 4, 5, or 6 or more, up to 10 or 20 clinical parameters
such as the clinical parameters listed in the table shown
above.
[0086] In certain cases, the method may employ coefficients in a
range of coefficients and/or "adjusted coefficients" (i.e.,
coefficients that relative to the coefficients of Formula I, are
adjusted to neutralize the effects of measuring biomarkers using
units that are different to those recited in Formula I), As such,
in certain cases, the method may comprise calculating a risk scores
using Formula II:
D=b+(a1*glucose)-(a2*adiponectin)+(a3*CRP)+(a4*ferritin)+(a5*IL2RA)+(a6*-
insulin)+(a7*Hb1Ac);
[0087] where b is in the interval of -32.865 to -13.363;
[0088] a1*glucose is the square root of the level of blood glucose
in mg/dL multiplied by a coefficient in the interval of 0.911 to
2.331 or an adjusted coefficient if the level of blood glucose is
not measured in mg/dL;
[0089] a2*adiponectin is the log.sub.10 of the level of blood
adiponectin in .mu.g/mL multiplied by a coefficient in the interval
of -5.419 to -1.321, or an adjusted coefficient if the level of
blood adiponectin is not measured in .mu.g/mL;
[0090] a3*CRP is the log.sub.10 of the level of blood CRP in mg/L
multiplied by a coefficient in the interval of -0.094 to 1.294, or
an adjusted coefficient if the level of blood adiponectin is not
measured in mg/L;
[0091] a4*ferritin is the log.sub.10 of the level of blood Ferritin
in ng/mL multiplied by a coefficient in the interval of -0.077 to
1.475, or an adjusted coefficient if the level of blood Ferritin is
not measured in ng/mL;
[0092] a5*IL2RA is the log.sub.10 of the level of blood IL2RA in
U/mL, multiplied by a coefficient in the interval of -1.132 to
3.832, or an adjusted coefficient if the level of blood IL2RA is
not measured in U/mL;
[0093] a6*insulin is the log.sub.10 of the level of blood insulin
in uIU/mL multiplied by a coefficient in the interval of -0.772 to
1.754, or an adjusted coefficient if the level of blood insulin is
not measured in uIU/mL; and
[0094] a7*Hb1Ac is the level of blood Hb1Ac measured in as a
percentage of Hemoglobin in whole blood multiplied by a coefficient
in the interval of -0.415 to 0.933, or an adjusted coefficient if
the level of blood Hb 1 Ac is not measured as a percentage.
[0095] In certain cases, patient age may also be used as an input
to Formula II, where the formula may further comprise the
term+(a8*AGE), where a8*AGE is the age of the patient in years,
multiplied by a coefficient in the interval of 0.071 to 1.107.
Likewise, patient age may also be used as an input to Formula II,
where the formula may further comprise the term+(a9*GENDER), where
a9*GENDER is the gender of the patient, where a male=1 and
female=0, multiplied by a coefficient in the interval of -1,353 to
0.081.
[0096] As noted above, the similarity between the risk scores
obtained by the subject method and a method that employs Formula I
for a human reference population may be evaluated using a Spearman
test or a chi-squared test. In each of these tests (i.e., the
Spearman and chi-squared tests), results obtained using the subject
method are compared to the results obtained using a method that
employs Formula I on the same patients. A human reference
population is a population of human subjects of a size that allows
the results to be significant to the required standard (e.g., at
least 10, at least 25, at least 50, at least 100, at least 200, at
least 500, at least 1000, at least 5000, at least 10,000 or more
subjects). In certain cases, the subjects of the human reference
population may be selected from a larger number of human subjects
(e.g., at least 500, at least 1000, at least 5000, at least 10,000,
at least 10,000, at least 100,000, or more subjects). In certain
embodiments, the subjects of the human reference population may be
may be randomly selected from the larger number of patients in
order to remove bias from the test.
[0097] As noted above, in embodiments in which similarity between
two methods in a human reference population is evaluated using a
Spearman test, the diabetes risk scores for a number of subjects
that is sufficient to provide results that are significant to the
desired confidence level (e.g., risk scores for at least 25, at
least 50, at least 100, at least 500, at least 200, at least 1,000,
at least 10,000 or more patients) may be expressed as a continuous
variable (e.g., a number with 0, 1, 2 or more decimal points), and
the profile of the first diabetes risk scores (i.e., the profile of
the risk scores obtained by use of Formula I) for the human
reference population may have a 95% confidence interval of the
Spearman rank correlation coefficient squared (R2) which is
entirely above or includes a correlation value of 0.5 (e.g., a
Spearman rank correlation coefficient squared (R2) which is
entirely above or includes a correlation value of 0.55, a Spearman
rank correlation coefficient squared (R2) which is entirely above
or includes a correlation value of 0.60, a Spearman rank
correlation coefficient squared (R2) which is entirely above or
includes a correlation value of 0.70, a Spearman rank correlation
coefficient squared (R2) which is entirely above or includes a
correlation value of 0:75, a Spearman rank correlation coefficient
squared (R2) which is entirely above or includes a correlation
value of 0.80, a Spearman rank correlation coefficient squared (R2)
which is entirely above or includes a correlation value of 0.85, a
Spearman rank correlation coefficient squared (R2) which is
entirely above or includes a correlation value of 0.90, a Spearman
rank correlation coefficient squared (R2) which is entirely above
or includes a correlation value of 0.95, a Spearman rank
correlation coefficient squared (R2) which is entirely above or
includes a correlation value of 0,97, a Spearman rank correlation
coefficient squared (R2) which is entirely above or includes a
correlation value of 0.98, a Spearman rank correlation coefficient
squared (R2) which is entirely above or includes a correlation
value of 0.99, a Spearman rank correlation coefficient squared (R2)
which is entirely above or includes a correlation value of 1.0)
with a profile of second diabetes risk scores obtained from the
reference population, where the second diabetes risk scores are
obtained from the same subjects as the first diabetes risk score
using an alternative but similar method.
[0098] In embodiments in which similarity between two methods in a
human reference population is evaluated using a chi-squared test,
the diabetes risk scores for a number of subjects that is
sufficient to provide results that are significant to the desired
confidence level (e.g., risk scores for at least 25, at least 50,
at least 100, at least 500, at least 200, at least 1,000, at least
10,000 or more patients) are used to categorize the patients into a
plurality of ordered risk categories (where in certain embodiments
there are: a) two ordered risk categories such as "high" and "low"
risk categories; b) three ordered risk categories such as "high",
"medium" and "low" risk categories;, c) four ordered risk
categories such as "high", "medium-high", "medium-low" and "low"
risk categories; or d) five or more ordered risk categories) such
that each patient is assigned a categorical risk assessment (i.e.,
"high", "medium" or "low", etc.). In this embodiment, the
categorization of the reference population among the ordered risk
categories by the first diabetes risk scores is not independent
using a valid chi-squared test with 95% confidence (e.g., not
independent with 96% confidence, not independent with 97%
confidence, not independent with 98% confidence, not independent
with 99% confidence, or not independent with 100% confidence) from
the categorization of the same subjects using Formula I, and then
categorizing each of the patients into one of a second plurality of
ordered risk categories that are each defined by a range of the
risk scores to provide a second categorical risk assessment for
each patient, wherein: a. the ranges of the risk scores that define
the second plurality of ordered risk categories are mutually
exclusive (i.e., non-overlapping) relative to one another and cover
the entire range of the second diabetes risk scores, b, the number
of the second plurality of ordered risk categories is equal to the
number of the first plurality of ordered risk categories, and c.
the ranges of the risk scores that define the second plurality of
ordered risk categories are selected such that the numbers of the
patients in each risk category is identical to the numbers of the
patients in each of the corresponding risk categories, in order of
increasing risk, in the first plurality of ordered risk categories.
In other words, use of the subject method may provide a plurality
of patients in each risk category, where the identities of the
patients in each risk category are the same or very similar to the
identities of the patients categorized into equivalent risk
categories using Formula I.
[0099] The method may be performed on asymptomatic patients who may
or may not be known to be at risk of diabetes, where risks include
increased age, body mass index (BMI), family history, hypertension,
and dyslipidemia, including patients that are insulin resistant,
have altered beta cell function or are at risk of developing
Diabetes based upon known clinical parameters or traditional
laboratory risk factors, such as family history of Diabetes, low
activity level, poor diet, excess body weight (especially around
the waist), age greater than 45 years, high blood pressure, high
levels of triglycerides, HDL cholesterol of less than 35,
previously identified impaired glucose tolerance, previous Diabetes
during pregnancy (Gestational Diabetes Mellitus or GDM) or giving
birth to a baby weighing more than nine pounds, and ethnicity.
Measurement of Biomarkers
[0100] Methods for measuring the levels of the individual
biomarkers employed in the subject method are either known or
readily adapted from known methods. For example, blood glucose may
be measured using conventional method using any of several
commercially available kits. Adiponectin can be measured by any of
several commercially available kits, including kits sold by Cayman
Chemical (Ann Arbor, Mich.), Abnova Corporation (Taiwan) R&D
systems (Minneapolis, Minn.). CRP can be measured by any of several
commercially available kits, including kits from ALPCO (Salem,
N.H.), Immuno-Biological Laboratories (Minneapolis, Minn.) and
USBIO (Swampscott, Mass.). FTH1 can be measured by any of several
commercially available kits, including a kit sold by
Immuno-Biological Laboratories (Minneapolis, Minn.). IL2RA can be
measured by any of several commercially available kits including
kits sold by ALPCO (Salem, NH) and Bender MedSystems (Vienna,
Austria). HBA1C can be measured by any any of several commercially
available kits including kits sold by Afinion (Oslo, Norway) and
Diazyme (Poway, Calif.).
[0101] Biomarkers may be general measured in using several
techniques designed to achieve predictable subject and analytical
variability. On subject variability, many of the above biomarkers
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.
[0102] The actual measurement of levels of the markers can be
determined at the protein level using any method known in the art.
Such methods are well known in the art and include, e.g.,
immunoassays based on antibodies to proteins encoded by the genes,
aptamers or molecular imprints, or other affinity reagents. Any
biological material can be used for the detection/quantification of
the protein or its activity. Alternatively, a suitable method can
be selected to determine the activity of proteins encoded by the
biomarker genes according to the activity of each protein
analyzed.
[0103] The biomarkers can be detected in any suitable manner, and
in certain embodiments may be detected by contacting a sample from
the subject with an antibody which binds the biomarker and then
detecting the presence or absence of a reaction product. The
antibody may be monoclonal, polyclonal, chimeric, or a fragment of
the foregoing, as discussed in detail above, and the step of
detecting the reaction product may be carried out with any suitable
immunoassay. The sample from the subject may be biological fluid,
e.g., blood, as described above, and may be the same sample of
biological fluid used to conduct the method described above.
[0104] Immunoassays may be homogeneous or heterogeneous. In a
homogeneous assay the immunological reaction usually involves the
specific antibody (e.g., anti-biomarker antibody), a labeled
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 labeled analyte. Both the immunological reaction
and detection of the extent thereof can be carried out in a
homogeneous solution. Immunochemical labels which may be employed
include free radicals, radioisotopes, fluorescent dyes, enzymes,
bacteriophages, or coenzymes.
[0105] 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, enzyme labels, or reporter reactions
that produce a measureable signal. 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, but are not limited to oligonucleotides,
immunoblotting, immunoprecipitation, immunofluorescence methods,
chemiluminescence methods, electrochemiluminescence (ECL) or
enzyme-linked immunoassays.
[0106] Those skilled in the art will be familiar with numerous
specific immunoassay formats and variations thereof which may be
useful for carrying out the method disclosed herein. See generally
E. Maggio, Enzyme-Immunoassay, (1980) (CRC Press, Inc., Boca Raton,
Fla.); see also U.S. Pat. No. 4,727,022 to Skold et al. titled
"Methods for Modulating Ligand-Receptor Interactions and their
Application," U.S. Pat. No. 4,659,678 to Forrest et al. titled
"Immunoassay of Antigens," U.S. Pat. No. 4,376,110 to David et al.,
titled "Immunometric Assays Using Monoclonal Antibodies," U.S. Pat.
No. 4,275,149 to Litman et al., titled "Macromolecular Environment
Control in Specific Receptor Assays," U.S. Pat. No. 4,233,402 to
Maggio et al., titled "Reagents and Method Employing Channeling,"
and U.S. Pat. No. 4,230,767 to Boguslaski et al., titled
"Heterogeneous Specific Binding Assay Employing a Coenzyme as
Label."
[0107] Antibodies can be conjugated to a solid support suitable for
a diagnostic assay (e.g., beads such as protein A or protein G
agarose, microspheres, plates, slides or wells formed from
materials such as latex or polystyrene) in accordance with known
techniques, such as passive binding. Antibodies as described herein
may likewise be conjugated to detectable labels or groups such as
radiolabels (e.g., 35S, 1251, 1311), enzyme labels (e.g.,
horseradish peroxidase, alkaline phosphatase), and fluorescent
labels (e.g., fluorescein, Alexa, green fluorescent protein,
rhodamine) in accordance with known techniques.
[0108] Antibodies can also be useful for detecting
post-translational modifications of biomarkers, such as tyrosine
phosphorylation, threonine phosphorylation, serine phosphorylation,
glycosylation (e.g., O-GlcNAc). Such antibodies specifically detect
the phosphorylated amino acids in a protein or proteins of
interest, and can be used in immunoblotting, immunofluorescence,
and ELISA assays described herein. These antibodies are well-known
to those skilled in the art, and commercially available.
Post-translational modifications can also be determined using
metastable ions in reflector matrix-assisted laser desorption
ionization-time of flight mass spectrometry (MALDI-TOF) (Wirth, U.
et al. (2002) Proteomics 2(10): 1445-51).
[0109] For biomarkers known to have enzymatic activity, the
activities can be determined in vitro using enzyme assays known in
the art. Such assays include, without limitation, kinase assays,
phosphatase assays, reductase assays, among many others. Modulation
of the kinetics of enzyme activities can be determined by measuring
the rate constant KM using known algorithms, such as the Hill plot,
Michaelis-Menten equation, linear regression plots such as
Lineweaver-Burk analysis, and Scatchard plot.
[0110] Tests to measure biomarkers can be implemented on a wide
variety of diagnostic test systems. Diagnostic test systems are
apparatuses that typically include means for obtaining test results
from biological samples. Examples of such means include modules
that automate the testing (e.g., biochemical, immunological,
nucleic acid detection assays). Some diagnostic test systems are
designed to handle multiple biological samples and can be
programmed to run the same or different tests on each sample.
Diagnostic test systems typically include means for collecting,
storing and/or tracking test results for each sample, usually in a
data structure or database. Examples include well-known physical
and electronic data storage devices (e.g., hard drives, flash
memory, magnetic tape, paper print-outs). It is also typical for
diagnostic test systems to include means for reporting test
results. Examples of reporting means include visible display, a
link to a data structure or database, or a printer. The reporting
means can be nothing more than a data link to send test results to
an external device, such as a data structure, data base, visual
display, or printer.
[0111] One embodiment of the present invention comprises a
diagnostic test system that has been adapted to aide in the
identification of individuals at risk of developing Diabetes. The
test system employs means to apply a formula to inputs that include
the levels of biomarkers measured from a biomarker panel in
accordance with the description herein. In certain cases, test
results from a biomarker panel of the present invention serve as
inputs to a computer or microprocessor programmed with the formula.
When the inputs include all the measurements of relevant biomarkers
for a Diabetes risk score, then the diagnostic test system can
include the score in the reported test results. If some factors
apart from the biomarkers tested in the system are used to
calculate the final risk score, then these factors can be supplied
to the diagnostic test system so that it can complete the risk
score calculation, or the formula can produce an index score that
will be reported and externally combined with the other data to
calculate a final risk score.
[0112] A number of diagnostic test systems are available for use in
implementing the present invention and exemplify further means for
carrying out the invention. One such device is the Abbott
Architect.RTM. System, a high throughput, fully automated, clinical
chemistry analyzer (ARCHITECT is a registered trademark of Abbott
Laboratories, Abbott Park, Ill. 60064 United States of America, for
data management and laboratory automation systems comprised of
computer hardware and software for use in the field of medical
diagnostics). The Architect.RTM. system is described at URL
World-Wide-Web.abbottdiagnostics.com/pubs/2006/2006_AACC_Wilson_c
16000.pdf (Wilson, C. et al., "Clinical Chemistry Analyzer
Sub-System Level Performance," American Association for Clinical
Chemistry Annual Meeting, Chicago, Ill., Jul. 23-27, 2006, and in
Kisner H J, "Product development: the making of the Abbott
ARCHITECT," Clin Lab Manage Rev. 1997
November-December;11(6):419-21; Ognibene A et al., "A new modular
chemiluminescence immunoassay analyser evaluated," Clin Chem Lab
Med. 2000 March;38(3):251-60; Park J W et al., "Three-year
experience in using total laboratory automation system," Southeast
Asian J Trop Med Public Health. 2002;33 Suppl 2:68-73; Pauli D et
al., "The Abbott Architect c8000: analytical performance and
productivity characteristics of a new analyzer applied to general
chemistry testing," Clin Lab. 2005;51(1-2):31-41. Another useful
system is the Abbott AxSYM.RTM. and AxSYM.RTM. Plus systems, which
is described, along with other Abbott systems, at URL
World-Wide-Web.abbbttdiagnostics.com/Products/Instruments_by_Platform/.
[0113] Other devices useful for implementation of the tests to
measure biomarkers are the Johnson & Johnson Vitros.RTM. system
(VITROS is a registered trademark of Johnson & Johnson Corp.,
New Brunswick, N.J., United States of America, for medical
equipment, namely, chemistry analyzer apparatus used to generate
diagnostic test results from blood and other body fluids by
professionals in hospitals, laboratories, clinics and doctor's
offices), see URL
World-Wide-Web.jnjgateway.com/home.jhtmlloc=USENG&page=menu&nodekey=/Prod-
_Info/Specialty/Diagnostics/Laboratory_and_Transfusion_Medicine/Chemistry_-
Immunodiagnostics; and the Dade-Behring Dimension.RTM. system
(DIMENSION is a registered trademark of Dade Behring Inc.,
Deerfield Ill., United States of America for medical diagnostic
analyzers for the analysis of bodily fluids, and computer hardware
and computer software for use in operating the analyzers and for
use in analyzing the data generated by the analyzers), see URL
diagnostics.siemens.com/webapp/wcs/stores/servlet/PSGenericDisplay.about.-
q_catalogId.about.e_-111.about.a_langId.about.e_-111.about.a_pageId.about.-
e_94489.about.a_storeId.about.e_10001.htm.
[0114] The biomarker tests can be carried out by laboratories such
as those which are certified under the Clinical Laboratory
Improvement Amendments of the United States (42 U.S.C.
.sctn.263(a)), or other federal, national, state, provincial, or
other law of any country, state, or province governing the
operation of laboratories which analyze samples for clinical
purposes. Such laboratories include, for example, Laboratory
Corporation of America, with headquarters at 358 South Main Street,
Burlington, N.C. 27215, United States of America; Quest
Diagnostics, with corporate headquarters at 3 Giralda Farms,
Madison, N.J. 07940, United States of America; and hospital-based
reference laboratories and clinical chemistry laboratories.
Suitable laboratories also include point of care laboratories.
[0115] Suitable sources for antibodies for the detection of
biomarkers include commercially available sources such as, for
example, Abazyme, Abnova, Affinity Biologicals, AntibodyShop,
Biogenesis, Biosense Laboratories, Calbiochem, Cell Sciences,
Chemicon International, Chemokine, Clontech, Cytolab, DAKO,
Diagnostic BioSystems, eBioscience, Endocrine Technologies, Enzo
Biochem, Eurogentec, Fusion Antibodies, Genesis Biotech,
GloboZymes, Haematologic Technologies, HyTest Ltd., Immunodetect,
Immunodiagnostik, Immunometrics, Immunostar, Immunovision,
Biogenex, Invitrogen, Jackson ImmunoResearch Laboratory, KMI
Diagnostics, Koma Biotech, Lab Frontier Life Science Institute, Lee
Laboratories, Lifescreen, Maine Biotechnology Services, Mediclone,
Mercodia, MicroPharm Ltd., ModiQuest, Molecular Innovations,
Molecular Probes, Neoclone, Neuromics, New England Biolabs,
Novocastra, Novus Biologicals, Oncogene Research Products, Orbigen,
Oxford Biotechnology, Panvera, PerkinElmer Life Sciences,
Pharmingen, Phoenix Pharmaceuticals, Pierce Chemical Company,
Polymun Scientific, Polysiences, Inc., Promega Corporation,
Proteogenix, Protos Immunoresearch, QED Biosciences, Inc., R&D
Systems, Repligen, Research Diagnostics, Roboscreen, Santa Cruz
Biotechnology, Seikagaku America, Serological Corporation, Serotec,
SigmaAldrich, StemCell Technologies, Synaptic Systems GmbH,
Technopharm, Terra Nova Biotechnology, TiterMax, Trillium
Diagnostics, Upstate Biotechnology, US Biological, Vector
Laboratories, Wako Pure Chemical Industries, and Zeptometrix.
However, the skilled artisan can routinely make antibodies against
any of the biomarkers employed in the method.
Reports
[0116] The methods of the present disclosure are suited for the
preparation of a report that provides a risk score resulting from
the method of the present disclosure, A "report," as described
herein, is an electronic or tangible document which includes report
elements that provide information of interest relating to a risk
assessment and its results. A subject report includes at least a
risk assessment, e.g., an indication as to the likelihood that a
patient will become diabetic. A subject report can be completely or
partially electronically generated, e.g., presented on an
electronic display (e.g., computer monitor). A report can further
include one or more of: 1) information regarding the testing
facility; 2) service provider information; 3) patient data; 4)
sample data; 5) an interpretive report, which can include various
information including: a) indication; b) test data, where test data
can include a normalized level of one or more genes of interest,
and 6) other features.
[0117] The present disclosure thus provides for methods of creating
reports and the reports resulting therefrom. The report may include
a summary of the levels of biomarkers in the patient's blood. The
report may include a score within a range of scores that indicate
the risk of diabetes. The report may be presented in electronic
format or on paper, and may be provided to the patient or the
patient's healthcare provider.
[0118] In certain embodiments, the method disclosed herein can
further include a step of generating or outputting a report
providing the results of a subject response likelihood assessment,
which report can be provided in the form of an electronic medium
(e.g., an electronic display on a computer monitor), or in the form
of a tangible medium (e.g., a report printed on paper or other
tangible medium).
[0119] A report that includes information regarding the likelihood
that a patient will develop diabetes may be provided to a user,
e.g., a patient or a healthcare provider A person or entity who
prepares a report ("report generator") may also perform the risk
assessment. The report generator may also perform one or more of
sample gathering, sample processing, and data generation, e.g., the
report generator may also perform one or more of: a) sample
gathering; b) sample processing; c) measuring a level of a test
biomarker; d) measuring a level of a reference biomarkers; and e)
determining a normalized level of a test biomarker. Alternatively,
an entity other than the report generator can perform one or more
sample gathering, sample processing, and data generation.
[0120] In certain embodiments, e.g., where the methods are
completely executed on a single computer, the user or client
provides for data input and review of data output. A "user" can be
a health professional (e.g., a clinician, a laboratory technician,
a physician (e.g., an oncologist, surgeon, pathologist), etc.),
[0121] In embodiments where the user only executes a portion of the
method, the individual who, after computerized data processing
according to the methods of the invention, reviews data output
(e.g., results prior to release to provide a complete report, a
complete, or reviews an "incomplete" report and provides for manual
intervention and completion of an interpretive report) is referred
to herein as a "reviewer." The reviewer may be located at a
location remote to the user (e.g., at a service provided separate
from a healthcare facility where a user may be located).
[0122] Where government regulations or other restrictions apply
(e.g., requirements by health, malpractice, or liability
insurance), all results, whether generated wholly or partially
electronically, may be subjected to a quality control routine prior
to release to the user,
[0123] The methods provided by the present disclosure may also be
automated in whole or in part.
Computer-Based Systems and Methods
[0124] The methods and systems described herein can be implemented
in numerous ways. In one embodiment of particular interest, the
methods involve use of a communications infrastructure, for example
the internet. Several embodiments are discussed below. It is also
to be understood that the present method may be implemented in
various forms of hardware, software, firmware, processors, or a
combination thereof, The methods and systems described herein can
be implemented as a combination of hardware and software. The
software can be implemented as an application program tangibly
embodied on a program storage device, or different portions of the
software implemented in the user's computing environment (e.g., as
an applet) and on the reviewer's computing environment, where the
reviewer may be located at a remote site associated (e.g., at a
service provider's facility),
[0125] For example, during or after data input by the user,
portions of the data processing can be performed in the user-side
computing environment. For example, the user-side computing
environment can be programmed to provide for defined test codes to
denote a likelihood "score," where the score is transmitted as
processed or partially processed responses to the reviewer's
computing environment in the form of test code for subsequent
execution of one or more algorithms to provide a results and/or
generate a report in the reviewer's computing environment. The
score can be a numerical score (representative of a numerical
value) or a non-numerical score representive of a numerical value
or range of numerical values (e.g,. "A" representative of a 90-95%
likelihood of an outcome; "high" representative of a greater than
50% chance of response (or some other selected threshold of
likelihood); "low" representative of a less than 50% chance of
response (or some other selected threshold of likelihood); and the
like.
[0126] The application program for executing the algorithms
described herein may be uploaded to, and executed by, a machine
comprising any suitable architecture. In general, the machine
involves a computer platform having hardware such as one or more
central processing units (CPU), a random access memory (RAM), and
input/output (I/O) interface(s). The computer platform also
includes an operating system and microinstruction code. The various
processes and functions described herein may either be part of the
microinstruction code or part of the application program (or a
combination thereof) which is executed via the operating system. In
addition, various other peripheral devices may be connected to the
computer platform such as an additional data storage device and a
printing device.
[0127] As a computer system, the system generally includes a
processor unit. The processor unit operates to receive information,
which can include test data (e.g., level of a response indicator
gene product(s); level of a reference gene product(s); normalized
level of a response indicator gene product(s)); and may also
include other data such as patient data. This information received
can be stored at least temporarily in a database, and data analyzed
to generate a report as described above.
[0128] Part or all of the input and output data can also be sent
electronically; certain output data (e.g., reports) can be sent
electronically or telephonically (e.g., by facsimile, e.g., using
devices such as fax back). Exemplary output receiving devices can
include a display element, a printer, a facsimile device and the
like. Electronic forms of transmission and/or display can include
email, interactive television, and the like. In an embodiment of
particular interest, all or a portion of the input data and/or all
or a portion of the output data (e.g., usually at least the final
report) are maintained on a web server for access, preferably
confidential access, with typical browsers. The data may be
accessed or sent to health professionals as desired. The input and
output data, including all or a portion of the final report, can be
used to populate a patient's medical record which may exist in a
confidential database at the healthcare facility.
[0129] A system for use in the methods described herein generally
includes at least one computer processor (e.g., where the method is
carried out in its entirety at a single site) or at least two
networked computer processors (e.g., where data is to be input by a
user (also referred to herein as a "client") and transmitted to a
remote site to a second computer processor for analysis, where the
first and second computer processors are connected by a network,
e.g., via an intranet or internet). The system can also include a
user component(s) for input; and a reviewer component(s) for review
of data, generated reports, and manual intervention. Additional
components of the system can include a server component(s); and a
database(s) for storing data (e.g., as in a database of report
elements, e.g., interpretive report elements, or a relational
database (RDB) which can include data input by the user and data
output. The computer processors can be processors that are
typically found in personal desktop computers (e.g., IBM, Dell,
Macintosh), portable computers, mainframes, minicomputers, or other
computing devices.
[0130] The networked client/server architecture can be selected as
desired, and can be, for example, a classic two or three tier
client server model. A relational database management system
(RDMS), either as part of an application server component or as a
separate component (RDB machine) provides the interface to the
database,
[0131] In one example, the architecture is provided as a
database-centric client/server architecture, in which the client
application generally requests services from the application server
which makes requests to the database (or the database server) to
populate the report with the various report elements as required,
particularly the interpretive report elements, especially the
interpretation text and alerts. The servers) (e.g., either as part
of the application server machine or a separate RDB/relational
database machine) responds to the client's requests.
[0132] The input client components can be complete, stand-alone
personal computers offering a full range of power and features to
run applications. The client component usually operates under any
desired operating system and includes a communication element
(e.g., a modem or other hardware for connecting to a network), one
or more input devices (e.g., a keyboard, mouse, keypad, or other
device used to transfer information or commands), a storage element
(e.g., a hard drive or other computer-readable, computer-writable
storage medium), and a display element (e.g., a monitor,
television, LCD, LED, or other display device that conveys
information to the user). The user enters input commands into the
computer processor through an input device. Generally, the user
interface is a graphical user interface (GUI) written for web
browser applications.
[0133] The server component(s) can be a personal computer, a
minicomputer, or a mainframe and offers data management,
information sharing between clients, network administration and
security. The application and any databases used can be on the same
or different servers.
[0134] Other computing arrangements for the client and server(s),
including processing on a single machine such as a mainframe, a
collection of machines, or other suitable configuration are
contemplated. In general, the client and server machines work
together to accomplish the processing and reporting of the present
method.
[0135] Where used, the database(s) is usually connected to the
database server component and can be any device which will hold
data. For example, the database can be a any magnetic or optical
storing device for a computer (e.g., CDROM, internal hard drive,
tape drive). The database can be located remote to the server
component (with access via a network, modem, etc.) or locally to
the server component.
[0136] Where used in the system and methods, the database can be a
relational database that is organized and accessed according to
relationships between data items. The relational database is
generally composed of a plurality of tables (entities). The rows of
a table represent records (collections of information about
separate items) and the columns represent fields (particular
attributes of a record). In its simplest conception, the relational
database is a collection of data entries that "relate" to each
other through at least one common field.
[0137] Additional workstations equipped with computers and printers
may be used at point of service to enter data and, in some
embodiments, generate appropriate reports, if desired. The
computer(s) can have a shortcut (e.g., on the desktop) to launch
the application to facilitate initiation of data entry,
transmission, analysis, report receipt, etc. as desired.
Computer-readable storage media
[0138] The present disclosure also contemplates an accessible
computer-readable storage medium (e.g. a physical medium such as a
CD-ROM, memory key, flash memory card, diskette, etc.) having
stored thereon a program which, when executed in a computing
environment, provides for implementation of algorithms to carry out
all or a portion of the results of a method described herein. Where
the computer-readable medium contains a complete program for
carrying out a method described herein, the program includes
program instructions for collecting, analyzing and generating
output, and generally includes computer readable code devices for
interacting with a user as described herein, processing that data
in conjunction with analytical information, and generating unique
printed or electronic media for that user. A file containing
information may be "stored" on computer readable medium, where
"storing" means recording information such that it is accessible
and retrievable at a later date by a computer.
[0139] In certain embodiments, the computer readable medium may
contain programming for execution of Formula I or an alternative
formula that provides results that are similar or identical to
those obtained using Formula I, as described above, after input of
the variables,
[0140] Where the storage medium provides a program which provides
for implementation of a portion of the methods described herein
(e.g., the user-side aspect of the methods (e.g., data input,
report receipt capabilities, etc.)), the program provides for
transmission of data input by the user (e.g., via the internet, via
an intranet, etc.) to a computing environment at a remote site.
Processing or completion of processing of the data is carried out
at the remote site to generate a report. After review of the
report, and completion of any needed manual intervention, to
provide a complete report, the complete report is then transmitted
back to the user as an electronic document or printed document
(e.g., fax or mailed paper report). The storage medium containing a
program according to the invention can be packaged with
instructions (e.g., for program installation, use, etc.) recorded
on a suitable substrate or a web address where such instructions
may be obtained. The computer-readable storage medium can also be
provided in combination with one or more reagents for carrying out
response likelihood assessment (e.g., antibodies, supports,
primers, probes, arrays, or other such kit components).
[0141] With respect to computer readable media, "permanent memory"
refers to memory that is permanent. Permanent memory is not erased
by termination of the electrical supply to a computer or processor.
Computer hard-drive ROM (i.e. ROM not used as virtual memory),
CD-ROM, floppy disk and DVD are all examples of permanent memory.
Random Access Memory (RAM) is an example of non-permanent memory. A
file in permanent memory may be editable and re-writable.
[0142] A "computer-based system" refers to the hardware means,
software means, and data storage means used to analyze the
information of the present invention. The minimum hardware of the
computer-based system embodiment described herein contains a
central processing unit (CPU), input means, output means, and data
storage means. A skilled artisan can readily appreciate that any
one of the currently available computer-based system are suitable
for use in the present invention. The data storage means may
comprise any manufacture comprising a recording of the present
information as described above, or a memory access means that can
access such a manufacture.
[0143] A "processor" references any hardware and/or software
combination which will perform the functions required of it. For
example, any processor herein may be a programmable digital
microprocessor such as available in the form of a electronic
controller, mainframe, server or personal computer (desktop or
portable). Where the processor is programmable, suitable
programming can be communicated from a remote location to the
processor, or previously saved in a computer program product (such
as a portable or fixed computer readable storage medium, whether
magnetic, optical or solid state device based). For example, a
magnetic medium or optical disk may carry the programming, and can
be read by a suitable reader communicating with each processor at
its corresponding station.
[0144] In certain embodiments, the processor will be in operable
linkage, i.e., part of or networked to, the aforementioned/device,
and capable of directing its activities.
Kits
[0145] Kits for use in practicing certain methods described herein
are also provided. In certain embodiments, a kit may include
reagents for measuring the level of biomarkers, e.g., antibodies
that may or may not be bound to a solid support, positive controls,
negative controls, labeling reagents, and/or test strips, etc.,
and, in certain cases, a computer-readable medium as described
above. In certain embodiments, the kits will further include
instructions for practicing the subject method or means for
obtaining the same (e.g., a website URL directing the user to a
webpage which provides the instructions), where these instructions
may be printed on a substrate, where substrate may be one or more
of: a package insert, the packaging, reagent containers and the
like. In the subject kits, the one or more components are present
in the same or different containers, as may be convenient or
desirable.
Utility
[0146] The method described herein may be used to make continuous
or categorical measurements of the risk of conversion to Diabetes,
thus diagnosing and defining the risk spectrum of a category of
subjects defined as pre-diabetic.
[0147] Identifying the pre-diabetic subject 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 diabetes disease state. Levels of an effective
amount of biomarkers also allows for the course of treatment of
Diabetes, pre-Diabetes or a pre-diabetic condition to be monitored.
In this method, a biological sample can be provided from a subject
undergoing treatment regimens or therapeutic interventions, e.g.,
drug treatments, for Diabetes. Such treatment regimens or
therapeutic interventions can include, but are not limited to,
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 Diabetes, pre-Diabetes, or a
pre-diabetic condition. If desired, biological samples are obtained
from the subject at various time points before, during, or after
treatment.
[0148] The method can also be used to screen patient or subject
populations in any number of settings. For example, a health
maintenance organization, public health entity or school health
program can screen a group of subjects to identify those requiring
interventions, as described above, or for the collection of
epidemiological data. Insurance companies (e.g., health, life, or
disability) may screen applicants in the process of determining
coverage or pricing, or existing clients for possible intervention.
Data collected in such population screens, particularly when tied
to any clinical progession to conditions like Diabetes, will be of
value in the operations of, for example, health maintenance
organizations, public health programs and insurance companies. Such
data arrays or collections can be stored in machine-readable media
and used in any number of health-related data management systems to
provide improved healthcare services, cost effective healthcare,
improved insurance operation, etc. See, for example, U.S. Patent
Application No.; U.S. Patent Application No. 2002/0038227; U.S.
Patent Application No. US 2004/0122296; U.S. Patent Application No.
US 2004/ 0122297; and U.S. Pat. No. 5,018,067. Such systems can
access the data directly from internal data storage or remotely
from one or more data storage sites as further detailed herein.
Thus, in a health-related data management system, wherein risk of
developing a diabetic condition for a subject or a population
comprises analyzing Diabetes risk factors, the present invention
provides an improvement comprising use of a data array encompassing
the biomarker measurements as defined herein and/or the resulting
evaluation of risk from those biomarker measurements.
EXAMPLES
[0149] The following examples are set forth so as to provide those
of ordinary skill in the art with a complete disclosure and
description of how to make and use the present invention, and are
not intended to limit the scope of what the inventors regard as
their invention nor are they intended to represent that the
experiments below are all or the only experiments performed.
Efforts have been made to ensure accuracy with respect to numbers
used (e.g. amounts, temperature, etc.) but some experimental errors
and deviations should be accounted for. Unless indicated otherwise,
parts are parts by weight, molecular weight is weight average
molecular weight, temperature is in degrees Celsius, and pressure
is at or near atmospheric.
Example 1
Workflow and Sample Collection
[0150] The Diabetes Risk Test described below is a quantitative
diagnostic test intended to aid in the assessment of a patient's
risk for developing Type 2 diabetes within five years. The test is
performed on a blood sample for patients at risk for diabetes.
[0151] The information provided by the Diabetes Risk Test may be
used by a physician in conjunction with other clinical indicators
to develop an effective diabetes prevention program. The dDiabetes
Risk Test may be indicated for use as an adjunctive test to
complement, not replace, other diagnostic and clinical
procedures.
[0152] The Diabetes Risk Test may be recommended for use in
individuals who are known to be at risk of diabetes. Risks include
increased age, body mass index (BMI), family history, hypertension,
and dyslipidemia. Baseline samples from individuals 30 to 60 years
of age who developed diabetes within 5 years and a random selection
of controls were used to develop and independently validate the
Diabetes Risk Score.
[0153] The Diabetes Risk Test requires fasting for a minimum of 10
hours prior to blood collection.
[0154] Blood is collected in an 8-10 mL red top serum tube or serum
separator tube (SST). Allow to clot and separate serum within one
hour of collection. Serum for the Diabetes Risk Test is stable for
up to 7 days at 2-8.degree. C.
[0155] Whole blood specimens are collected in a non-breakable
collection tube containing EDTA. Whole blood samples for the
Diabetes Risk Test are stable for up to 7 days at 2-8.degree.
C.
[0156] Sample Volume (preferred): 4-6 mL whole EDTA blood tube and
3-5 mL serum
[0157] Sample Volume (minimum): 2.0 mL whole EDTA blood tube and
1.0 mL serum
[0158] Samples should be shipped on the day of collection, using
overnight delivery. Samples should be maintained at 2-8.degree. C.
or colder during shipping and storage. To ensure samples can be
tested within the 7 days stability term, samples should be shipped
overnight Monday through Thursday, and will be accepted for testing
Monday through Friday during working hours (8 am to 5 pm Pacific
Time).
[0159] The following individual tests are described in greater
detail below.
TABLE-US-00002 INSTRUMENT ASSAY(S) Randox Daytona Glucose, hsCRP
Immulite 1000 IL2Ra, Ferritin, Insulin Bio-Rad D-10 Hemoglobin A1c
SpectraMax ELISA Adiponectin Algorithm calculation DP-PreDx
Example 2
Glucose Assay Protocol
[0160] This example describes the procedure for testing patient
samples for glucose using the Randox Daytona automated chemistry
analyzer. The glucose test is intended for the in vitro
determination of glucose concentration in serum.
[0161] The measurement of glucose in serum is enzymatic using both
hexokinase (HK) and glucose-6-phosphate dehydrogenase (G6P-DH).
##STR00001##
[0162] NADH is measured at 340 nm and is directly proportional to
the amount of glucose in the sample.
Specimen Collection and Handling
[0163] Patient preparation: For fasting glucose, patient must fast
for a minimum of 10 hours.
[0164] Collect blood in a red top serum tube or serum separator
tube (SST). Allow to clot and separate serum within one hour of
collection. Store and ship serum at 2-8.degree. C. Serum glucose is
stable for up to 7 days at 2-8.degree. C. and up to 1 year frozen
at -20--60.degree. C.
TABLE-US-00003 Sample volume (preferred) 0.5 mL Samples volume
(minimum) 0.2 mL
Procedure
[0165] Blood glucose is measured using the Glucose (GLUC-HK)
hexokinase method kit Cat. No. GL 3816 (Randox Laboratories Ltd,
Oceanside, Calif.), according to GL 3816 Instructions for Use
Revised 9 Oct. 2006 and Randox Daytona Operator Manual Version 1.6
Rev. May 2005
Results
[0166] Reference Range of blood glucose is 70-125 mg/dL
[0167] Critical high threshold of 500 mg/dL will trigger an alert
in LIMS. Glucose results of 500 mg/dL or greater will be phoned
immediately to provider and documented in the Orchard Harvest
LIMS.
[0168] Critical low threshold of 45 mg/dL will trigger an alert in
LIMS. Glucose results of 45 mg/dL or lower will be phoned
immediately to provider and documented in the Orchard Harvest
LIMS.
[0169] Reporting units: mg/dL
[0170] Reportable Range (linear range) 27 mg/dL to 630 mg/dL
Example 3
Adiponectin Assay Protocol
[0171] Adiponectin may be assayed using a kit supplied by Cayman
Chemical (Ann Arbor, Mich.), Abnova Corporation (Taiwan), R&D
systems (Minneapolis, Minn.), Mercodia (Sweden), or others.
[0172] Adiponectin is an adipocyte-secreted hormone, containing 244
amino acids with a molecular weight of approximately 30 kDa (28-30
kDa). It is one of the most abundant proteins in human blood, with
circulating concentrations of 0.5-30 .mu.g/ml, which accounts for
approximately 0.01% of total plasma protein. Several manufacturers
provide a method for the quantitative determination of human
adiponectin in serum or plasma.
[0173] The Adiponectin ELIS A used is a solid phase two-site enzyme
immunoassay. It is based on the sandwich technique in which two
monoclonal antibodies are directed against separate antigenic
determinants on the adiponectin molecule. During incubation,
adiponectin in the sample reacts with anti-adiponectin antibodies
bound to the microtiter plate well. After washing, peroxidase
conjugated anti-adiponectin antibodies are added and after the
second incubation and a simple washing step that removes unbound
enzyme labelled antibody, the bound conjugate is detected by
reaction with 3,3',5'-tetramethylbenzidine (TMB). The reaction is
stopped by adding acid to give a colorimetric endpoint that is read
spectrophotometrically. The concentration of adiponectin in the
sample is determined from the calibration curve run with the
samples.
Specimen Collection and Handling
[0174] Collect blood in a red top serum tube or serum separator
tube (SST). Allow to clot and separate serum within one hour of
collection. Store and ship serum at 2-8.degree. C. Serum
Adiponectin is stable for up to 14 days at 2-8.degree. C. For long
term storage keep at -20.degree. C. or below.
TABLE-US-00004 Sample volume (preferred) 0.2 mL Sample volume
(minimum) 0.1 mL
Procedure
[0175] Adiponectin is measured using the following protocol. [0176]
a. Prepare Enzyme Conjugate working solution by diluting the Enzyme
Conjugate 11X with Enzyme Conjugate Buffer according to table 1
below. Mix gently. Diluted Enzyme Conjugate can be stored at
2-8.degree. C. for two months.
TABLE-US-00005 [0176] TABLE 1 Enzyme Conjugate Dilution Volume
Enzyme Number Volume Enzyme Conjugate Buffer of strips Conjugate
11X (.mu.L) (mL) 12 1 vial 1 vial 8 700 7 6 500 5 4 400 4
[0177] b. Prepare Wash Buffer working solution by adding 800 mL
deionized water to 40 mL Wash Buffer 21X, mix well. Diluted Wash
Buffer can be stored at 2-8.degree. C. for two months. [0178] c.
Prepare Sample Buffer working solution by adding 50 mL deionized
water to 50 mL Sample Buffer 2X, mix well. Diluted Sample Buffer
can be stored at 2-8.degree. C. for two months. [0179] d. Pipette
0.5 mL Sample Buffer into required number of 8-strip microtiter
tubes or equivalent according to plate map. [0180] e. Prepare a
1:101 dilution of samples and controls as follows: Add 5 .mu.L
sample to each well or tube containing 0.5 mL Sample Buffer
according to the platemap (1:101 dilution). Seal plates and mix at
1350 rpm for 15 seconds on the Eppendorf thermomixer to thoroughly
mix. Diluted samples can be sealed and stored at 2-8.degree. C. up
to 14 days, [0181] f. Pipette 25 .mu.L Calibrators and blanks into
duplicate wells according to plate map. [0182] g. Pipette 25 .mu.L
diluted samples and controls into duplicate wells according to
plate map. [0183] h. Pipette 100 .mu.L Assay Buffer into each well.
Seal plate with a plate sealer. [0184] i. Transfer plate to plate
shaker and adjust to 700 rpm. Incubate plate at room temperature
(18-30.degree. C.) while shaking for one hour. [0185] j. During
incubation, prepare BioTek plate washer by priming with Wash
Buffer. [0186] k. At end of one hour incubation, remove sealer from
plate and transfer to BioTek plate washer. Select the Wash program:
ELISA WASH 6X. Ensure that sufficient Wash Buffer is in the correct
container. Press START to begin the BioTek wash cycle. [0187] l.
Pipette 100 .mu.L Enzyme Conjugate into each well. Seal plate with
a plate sealer. [0188] m. Transfer plate to plate shaker and adjust
to 700 rpm. Incubate plate at room temperature (C while shaking for
one hour. [0189] n. At end of one hour incubation, remove sealer
from plate and transfer to BioTek plate washer. Select the Wash
program: ELISA WASH 6X. Ensure that sufficient Wash Buffer is in
the correct container. Press START to begin the BioTek wash cycle.
[0190] o. Pipette 200 .mu.L Substrate TMB into each well. Seal
plate and incubate for 15 minutes at room temperature
(18-25.degree. C.). [0191] p. Remove sealer from plate and pipette
50 .mu.L Stop Solution into each well. Shake plate gently by hand
for 5 seconds to mix. Do not allow contents of wells to
intermingle. [0192] q. Transfer plate to Molecular Devices plate
reader and read Optical Density (OD) at 450 nm within 30 minutes.
Refer to TP-018: SpectraMax Operation and Maintenance for
SpectraMax plate reader instructions. [0193] r. The SpectraMax
plate reader will calculate the concentration of adiponectin in the
sample(s) in .mu.g/mL.
Results
[0194] Results are reported in .mu.g/mL. The reportable range
(linear range) is 1.4 .mu.g/mL to 33.2 .mu.g/mL
Example 4
CRP Assay Protocol
[0195] This example describes the procedure for testing patient
samples for high-sensitivity C-reactive protein (hs-CRP) using the
Randox Daytona automated chemistry analyzer.
[0196] The hs-CRP test system is intended for the quantitative in
vitro determination of C-reactive protein (CRP) in serum.
C-reactive protein is present in the serum of normal individuals at
levels between 0-5 mg/L. CRP levels at or near normal levels can be
used for the assessment of cardiovascular event risk. CRP levels
within or near the normal range may be affected by a number of
different factors and should be interpreted along with clinical
history.
[0197] Sample is reacted with a buffer and anti-CRP coated latex
particles. The formation of the antibody-antigen complex results in
an increase in turbidity, the extent of which is measured as the
amount of light absorbed at 570 nm. By constructing a standard
curve from the absorbance of the standards, CRP concentration of
sample can be determined.
Specimen Collection and Handling
[0198] Patient preparation: For fasting hsCRP, patient must fast
for a minimum of 10 hours. For non-fasting hsCRP no preparation is
necessary.
[0199] Collect blood in a red top serum tube or serum separator
tube (SST). Allow to clot and separate serum within one hour of
collection. Store and ship serum at 2-8.degree. C. Serum hsCRP is
stable for up to 7 days at 2-8.degree. C. and up to 6 months frozen
at -10--30.degree. C. Do not refreeze.
TABLE-US-00006 Sample volume (preferred) 0.5 mL Samples volume
(minimum) 0.2 mL
Procedure
[0200] CRP is measured using the hsCRP (GLUC-HK)
Immunoturbidimetric method kit Cat. No. CP 3885 (Randox
Laboratories Ltd,Ocean side, Calif.), using a minimum volume of 150
.mu.L.
Results
[0201] Manufacturer's Reference Range is 0-5 mg/L for adults.
Results are reported in mg/L, and the reportable (linear) range is
0.1 mg/L to 9.9 mg/L.
Example 5
Ferritin Assay Protocol
[0202] This example describes the procedure for testing patient
serum samples for Ferritin using the Immulite 1000 automated
chemistry analyzer.
[0203] Immulite 1000 Ferritin is a solid phase, two site
chemiluminescent immunometric assay. Sample is added to a Test Unit
containing one bead coated with murine monoclonal anti-Ferritin
antibody. After incubation, alkaline phosphatase conjugated to goat
polyclonal anti-Ferritin is added. Following incubation and washes,
chemiluminescent substrate is added and light output is measured.
The amount of light measured is directly proportional to the
concentration of Ferritin in the sample.
[0204] This assay is intended for the quantitative measurement of
Ferritin in serum as an aid in the clinical diagnosis of iron
deficiency and overload.
[0205] The Ferritin molecule contains a protein shell (MW 450,000)
and a core of iron. High concentrations are found in liver cells
and in erythrocyte recycling centers (RE cells) of the liver,
spleen and bone marrow. In these tissues, Ferritin serves as the
body's principal storehouse for surplus iron, protecting against
the toxic effects of excess and maintaining a readily mobilized
reserve for erythropoieses.
Specimen Collection and Handling
[0206] Collect blood in a red top serum tube or serum separator
tube (SST). Allow to clot and separate serum within one hour of
collection. Store and ship serum at 2-8.degree. C. Serum Ferritin
is stable for up to 7 days at 2-8.degree. C and up to 2 weeks
stored at -10.degree. C. to -30.degree. C.
[0207] Sample volume (preferred) 0.5 mL
[0208] Samples volume (minimum) 0.2 mL
Procedure
[0209] Ferritin is measured using the Immulite/Immulite 1000
Ferritin Cat. No. LKFE1 (100 tests) or LKFES (500 tests)
(PILKFE-8,2006-12-29; Siemens Medical Solutions Diagnostics Los
Angeles, Calif.) assay on an Immulite 1000 analyzer (Siemens
Medical Solutions Diagnostics Los Angeles, Calif.).
Results
[0210] Manufacturers Reference Range: Adult Male: 28-397 ng/mL,
Adult Female: 6-159 ng/mL.
[0211] Reporting units are in ng/mL, and the Reportable Range
(linear range) is 1.5 ng/mL to 1,500 ng/mL.
Example 6
IL2RA Assay Protocol
[0212] This example describes the procedure for testing patient
serum samples for Interleukin-2 Receptor alpha (IL2Ra or IL2RA)
using the Immulite 1000 automated chemistry analyzer.
[0213] Immulite 1000 IL2Ra is a solid-phase, two site
chemiluminescent immunometric assay. Sample is added to a Test Unit
containing one bead coated with murine monoclonal anti-IL2Ra
antibody. After incubation, alkaline phosphatase conjugated to
rabbit polyclonal anti-IL2Ra is added. Following incubation and
washes, chemiluminescent substrate is added and light output is
measured. The amount of light measured is directly proportional to
the concentration of IL2Ra in the sample.
[0214] The receptor of the cytokine interleukin 2 (IL-2) plays a
crucial role in the regulation of the immune response. Binding of
11-2 to its receptor (IL2R) on the surface of T-lymphocytes
triggers a series of intracellular signaling events that results in
the activation and proliferation of resting T cells and ultimately
in the generation of helper, suppressor and cytotoxic T cells which
mediate immune reactions.
[0215] The IL-2 receptor is made up of at least three distinct
membrane components: the a chain (IL2R.alpha.), the .beta. chain
(IL2R.beta.), and the .gamma. chain (IL2R.gamma.). Different
combinations of these three components give rise to the generation
of various forms of the IL2R, each of which manifests different
binding affinities to IL2.
[0216] Most resting T cells, B cells, large granular lymphocytes
and monocytes do not express significant numbers of this receptor
on their surfaces. Upon activation, receptor molecules are
expressed on the surface of the cells, and a soluble form (sIL2Ra)
is released, which is about 10 kDa smaller than the membrane bound
protein.
Specimen Collection and Handling
[0217] Collect blood in a red top serum tube or serum separator
tube (SST). Allow to clot and separate serum within one hour of
collection. Store and ship serum frozen. Serum IL2R is stable for
up to 2 days at 2-8.degree. C., for long term storage keep at
-20.degree. C. or below.
TABLE-US-00007 Sample volume (preferred) 0.5 mL Samples volume
(minimum) 0.2 mL
Procedure
[0218] IL2Ra is measured using an Immulite/Immulite 1000 IL2R assay
Cat. No. LKIPZ (50 tests) LKIP1 (100 tests) LKIP5 (500 tests)
(Immulite/Immulite 1000 1L2R) (PILKIP-16, 2007-04-10; Siemens
Medical Solutions Diagnostics Los Angeles, Calif.) using an
Immulite 1000 analyzer (Siemens Medical Solutions Diagnostics Los
Angeles, Calif.).
Results
[0219] Results are reported in: U/mL, in the Reportable Range
(linear range) of 50 U/mL to 7,500 U/mL.
Example 7
Insulin Assay Protocol
[0220] The example describes the procedure for testing patient
serum samples for Insulin using the Immulite 1000 automated
immunoassay system.
[0221] Immulite 1000 Insulin is a solid phase, two site
chemiluminescent immunometric assay. This assay is intended for the
quantitative measurement of Insulin in serum for the management of
diabetes
[0222] Sample is added to a Test Unit containing at least one bead
coated with monoclonal murine anti-insulin. After incubation,
alkaline phosphatase conjugated to polyclonal sheep anti-insulin is
added. Following incubation and washes, chemiluminescent substrate
is added and light output is measured. The amount of light measured
is directly proportional to the concentration of Insulin in the
sample.
[0223] Human insulin is a polypeptide hormone originating in the
beta cells of the pancreas and serving as a principal regulator for
the storage and production of carbohydrates. Its secretion is
normally stimulated by increases in the amount of glucose in
circulation. This leads to a higher insulin levels and more rapid
tissue assimilation of glucose followed by a decline in the insulin
level as the glucose level subsides.
Specimen Collection and Handling
[0224] Collect blood in a red top serum tube or serum separator
tube (SST). Allow to clot and separate serum within one hour of
collection. Store and ship serum at 2-8.degree. C. Serum Insulin is
stable for up to 7 days at 2-8.degree. C. and 3 months at
-20.degree. C.
TABLE-US-00008 Sample volume (preferred) 1.0 mL Samples volume
(minimum) 0.5 mL
Procedure
[0225] Insulin is measured using an immulite 1000 Insulin assay
Cat. No, LKIN1 (100 tests) or LKIN5 (500 tests) (Siemens Medical
Solutions Diagnostics Los Angeles, Calif.) using an Immulite 1000
analyzer (Siemens Medical Solutions Diagnostics Los Angeles,
Calif.).
Results
[0226] The Manufacturers Reference Range is 8.9 .mu.IU/mL to 28.4
.mu.IU/mL.
[0227] Results are reported in: .mu.IU/mL, and the Reportable Range
(linear range) is 2 to 300 .mu.IU/mL.
Example 8
HBA1C Assay Protocol
[0228] This example describes the procedure for testing patient
samples for Hemoglobin A1c (HbA1c) using the Bio-Rad D-10 automated
high-performance liquid chromatography (HPLC) analyzer.
[0229] The D-10 Hemoglobin Ale program utilizes principles of
ion-exchange high-performance chromatography (HPLC). Samples are
automatically diluted on the D-10 and injected into the analytical
cartridge. The D-10 delivers a programmed buffer gradient of
increasing ionic strength to the cartridge, where the hemoglobins
are separated based on their ionic interactions with the cartridge
material. The separated hemoglobins then pass through the flow cell
of the filter photometer, where changes in the absorbance at 415 nm
are measured.
[0230] The D-10 software performs reduction of raw data from each
analysis. Two-level calibration is used for quantitation of the
HbA1c values. The A1c area is calculated using an exponentially
modified Gaussian (EMG) algorithm that excludes the labile A1c and
carbamylated peak areas from the A1c peak area.
[0231] The Bio-Rad D-10 Hemoglobin A1c Program is used for the
determination of the percent of hemoglobin A1c in human whole
blood.
[0232] The level of HbA1c is proportional to both the average
glucose concentration and the life span of the red blood cell in
the circulation.
Specimen Collection and Handling
[0233] Collect whole blood specimens in a non-breakable collection
tube containing EDTA. Store and ship whole blood at 2-820 C. Whole
blood samples may be stored for up to 7 days at 2-8.degree. C.
[0234] Sample Volume (preferred): 4-6 mL whole EDTA blood tube
[0235] Sample Volume (minimum): 2.0 mL whole EDTA blood tube
Procedure
[0236] HBA1C is assayed using an Biorad D-10 Hemoglobin Ale assay,
Cat. No. 220-0101 (Bio-Rad Laboratories, Hercules, Calif.)
Results
[0237] Manufacturers reference range in EDTA whole blood
(non-pregnant individuals):
TABLE-US-00009 HbA1c (%) Glucose Control >8 Action suggested
<7 Goal (American Diabetes Association) <6 Non-diabetic
level
Example 9
Validation of Diabetes Risk Score Algorithm
[0238] This example the results of the testing and analysis used to
validate a Diabetes Risk Score (DRS) algorithm
Study Objective
[0239] The primary objective of the CLIA-001 study is to develop
and validate an algorithm capable of estimating the five-year risk
of developing diabetes from a panel of biomarkers in individuals.
The algorithm includes concentration values of biomarkers and
individual data (such as age and gender) demonstrating a
significantly improved fit over a model with glucose alone and
validated on a sequestered data set.
Summary of Validation
[0240] The algorithm were evaluated in the validation study. For
the algorithm, markers were selected because their coefficients
were statistically different from zero at the 90% confidence level
(estimated using bootstrap resampling) in the training portion of
the study. These markers are: age, gender, fasting plasma glucose,
C-reactive protein (CRP), adiponectin (ADIPOQ) and ferritin (FTHI),
glycated hemoglobin (HbA1c), insulin and interleukin receptor 2
alpha (IL2Ra).
[0241] The ability of the algorithm to predict risk of diabetes
conversion was compared to the ability of the fasting glucose
alone. For the final validation, the primary endpoint was improved
fit as assessed by the likelihood ratio test. The secondary
endpoint was improved discrimination as assessed by a Receiver
Operator Characteristic (ROC) curve.
[0242] Algorithm A for the validation was:
D=-23.114+0.062*Age-0.636*Gender+1.621*GLUCOSE-3.370*ADIPOQ+0.600*CRP+0.-
699*FTH1+1.350*IL2RA+0.491*INSULIN+0.259*HBA1C
[0243] (For Gender, female=0 and male=1)
DRS=(exp(D)/(1+exp(D))*10
[0244] The above model was compared to a model based on fasting
glucose alone using a likelihood ratio test. Deviance for the
validation data was calculated with the following model estimated
from 10,000 bootstrap replicates of the training data:
Glucose_Score=-23.227+2.291*GLUCOSE
[0245] The algorithm must fit the data significantly better
(p<0.025) than the glucose-alone model based on a likelihood
ratio test adjusted for the degrees of freedom in the model.
Summary of Results
[0246] The algorithms met both the primary and secondary endpoints.
The primary endpoint was superior fit to glucose alone based on the
Likelihood Ratio test. The secondary endpoint was a comparison of
ROC curves (by the method of DeLong, DeLong and Clarke-Pearson, as
implemented in the ucR package for the R statistical computer
language).
Detailed Results
[0247] a. Data Exclusions
[0248] A total of 686 measurements of ALT were below the limit of
quantification of the assay. ALT was removed from subsequent
analysis. All 800 samples were quantitatively detected across the
remaining 4 assays with the exception of 34 out of range in CRP and
1 out of range in FTH1. Values at the limit of detection were set
to the limit value. All samples except sample ID 89992477 (PID
097075) and sample ID 89992478 (PID 097145) were included in
subsequent analysis.
[0249] b. Algorithm Validation
[0250] Data Preparation
[0251] Predictor values were transformed by taking the log, square
root or square of the raw concentrations if the distribution of the
transformed values more closely approximated the normal
distribution. The independence of the predictors was assessed based
on their correlation to each other; none were highly correlated as
defined in the statistical analysis plan (R>0.7). In addition, a
linearity evaluation was performed and all quantitative metrics
appeared linearly and significantly related to outcome.
[0252] Determination of Model Parameters
[0253] A weighted (prior probability of conversion=50%) logistic
regression model was used to balance sensitivity and specificity.
Coefficients were estimated for the selected markers on 10,000
bootstrap replicates. The algorithm utilizes the median value of
these replicates.
[0254] Score is calculated as DRS=exp(lp)/(1+exp(lp))*10 (where lp
is the linear sum of the products of each biomarker and its
respective coefficient
[0255] Risk is calculated as follows
[0256] Ip is adjusted from a 50% prior to the test population prior
using the equation:
lp'=lp+log(p/(1-p))
where p is the proportion of expected 5 year converters in the test
population
Risk=exp(lp')/(1+exp(lp')
D=lp.
Likelihood Ratio Test
[0257] The DRS Algorithm was compared to a model of fasting glucose
alone using a likelihood ratio test.
Comparison of ROC Curves
[0258] ROC curves were calculated for each algorithm and for
Fasting Glucose alone. These results are shown in FIG. 2
Data Transformation
[0259] To improve the symmetry of the distributions Log.sub.10
transformations were used for insulin, IL2Ra, ADIPOQ, CRP, and
FTH1. Glucose was transformed with a square root while age was left
raw. Gender was coded as 0=female, 1=male.
Conclusions
[0260] Algorithm A, as shown below:
D=-23.114+0.062*Age-0.636*Gender+1.621*GLUCOSE-3.370*ADIPOQ+0.600*CRP+0.-
699*FTH1+1.350*IL2RA+0.491*INSULIN+0.259*HBA1C [0261] met all
acceptance criteria. Specifically, Algorithm A performed better
(p<le-5) than fasting glucose alone, based on the results of a
likelihood ratio test. Algorithm A will provide an accurate
prediction of risk of diabetes in people between thirty and sixty
years of age.
* * * * *