Note: Descriptions are shown in the official language in which they were submitted.
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TITLE
CHARACTERIZATION OF BIOLOGICAL SAMPLES
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] The present application claims priority to U.S. Provisional Patent
Application Serial
No. 61/061,509 filed June 13, 2008 by Henriquez et al. and entitled
"Characterization of Biological
Samples," which is incorporated herein by reference as if reproduced in its
entirety.
STATEMENT REGARDING FEDERALLY SPONSORED
RESEARCH OR DEVELOPMENT
[0002] Not applicable.
REFERENCE TO A MICROFICHE APPENDIX
[0003] Not applicable.
BACKGROUND
[0004] Cardiovascular disease or cardiovascular diseases refers to the class
of diseases that
involve the heart or blood vessels (e.g., arteries, capillaries, and veins).
Cardiovascular disease
refers to any disease affecting the cardiovascular system, although it is
commonly used to refer to
those related to atherosclerosis (e.g., arterial disease).
[0005] The American Heart Association estimates that, for the year 2006,
80,000,000 people in
the United States had one or more forms of CVD, including 73,600,000 with high
blood pressure,
16,800,000 with coronary heart disease, 7,900,000 having suffered a myocardial
infarction,
9,800,000 cases of angina, 6,500,000 strokes, and 5,700,000 cases of heart
failure. Cardiovascular
diseases claimed 864,480 lives in 2005 (final mortality) (35.3 percent of all
deaths or 1 of every 2.8
deaths).
[0006] Conventionally, biomarkers have been thought to offer a more detailed
risk of
cardiovascular disease. For example, biomarkers which may tend to reflect a
higher risk for
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cardiovascular disease include elevated fibrinogen and PAI-I blood
concentrations, elevated
homocysteine levels; elevated blood levels of asymmetric dimethylarginine;
high inflammation
as measured by C-reactive protein; and elevated blood levels of brain
natriuretic peptide (also
known as B-type) (BNP). However, use of these biomarkers can be inconclusive
and difficult to
apply in a clinical setting. Further, some individuals develop CVD despite
lacking any one or
more of the conventional biomarkers.
[0007] Determination that an individual is at risk for the development of a
disease such as
CVD or is in some stage of development of a disease such as CVD would allow
for either
treatment of the disease state or the implementation of measures to prevent
the onset of a disease
state or dysfunction (e.g., CVD). Thus, there exists a need for rapid,
accurate, and clinically
implementable means of assessing an individual's risk for the development of a
disease state or
dysfunction such as CVD.
SUMMARY
[0008] Disclosed herein is a method of characterizing a biological sample
comprising:
separating the biological sample into constituents; observing the separated
constituents; applying
statistical classification modeling to the observed constituents; deriving
quantifiable data from the
applied statistical classification modeling; and analyzing the data from the
applied statistical
classification modeling to assess a donor of the biological compounds' health.
The biological
sample may comprise blood, serum, proteins, lipoproteins, cells, cell
constituents, microorganisms,
DNA, or combinations thereof. Separation of the biological sample into
constituents may be
effected by density gradient ultracentrifugation, gradient gel
electrophoresis, capillary
electrophoresis, ultracentrifugation-vertical auto profile, nuclear magnetic
resonance, tube gel
electrophoresis, chromatography, or combinations thereof. The constituents may
be separated
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according to size (rate zonal), density (isopycnic), or combinations thereof.
The centrifugation
may be via centrifuges comprising fixed angle rotors, vertical tube rotors,
swinging bucket rotors,
or combinations thereof. The biological sample may be suspended in media
comprising inorganic
salts (cesium chloride, potassium bromide, sodium chloride), sucrose, a
synthetic polysaccharide
made by crosslinking sucrose, a suspension of silica particles coated with
polyviynlpyrrolidone,
derivatives of metrizoic acid, dimers of metrizoic acid, Optiprep , and metal
ion chelate
complexes. The metal ion chelate complexes may comprise metal ions and
chelating agents. The
metal ions may comprise copper, iron, bismuth, zinc cadmium, calcium, thorium,
manganese,
lithium, sodium, potassium, cesium, magnesium, calcium, ammonium, ammonium
complexes,
tetrabutylammonium, or combinations thereof. The chelating agents may comprise
polydentate
ligands. The polydentate ligands may comprise oxalate, ethylenediamine,
diethylenetriamine,
1,3,5 triminocyclohexane, ethlylenediaminetertaacetic acid (EDTA), or
combinations thereof. The
metal ion chelate complexes may comprise CsBiEDTA, NaCuEDTA, NaFeEDTA,
NaBiEDTA,
Cs2CdEDTA, Na2CdEDTA, or combinations thereof. The observed constituents may
comprise
low density lipoprotein (LDL), very low density lipoprotein (VLLP),
intermediate density
lipoprotein (IDL), high density lipoprotein (HDL), lipoprotein(a) (Lp(a)),
bTRL, dTRL, LDL-1,
LDL-2, LDL-3, LDL-4, LDL-5, HDL-2b, HDL-2a, HDL-3b, HDL-3c, APOCIHDL, TC, LDL-
C,
HDL-C, TG, or combinations thereof. Observation of the constituents may
comprise
photography, videography, microscopy, nuclear magnetic resonance imaging,
computer scanning,
human visualization, or combinations thereof. The observation of the
constituents may further
comprise the utilization of dyes, stains, fluorescent markers, Rayleigh
scattering, computer
enhancement, or combinations thereof. The observation of the constituents may
comprise
confirming and qualifying the presence of constituent components. The
statistical classification
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modeling may comprise linear discrimination analysis (LDA), recursive
partitioning (RP), sliced
average variance estimation (SAVE), sliced mean variance covariance (SMVCIR),
or
combinations thereof. The statistical classification modeling may further
comprise consideration
of age, hypertension, hyperlipidemia, family history, gender, tobacco use,
alcohol use, other health
related factors, or combinations thereof. The quantifiable data may
TC HDL .29LDL . 9TG .11 HDL s9LDL .49TG0.03
comprise HDL0.35LDL0.25TG .04 TC TC
HDL-3b x LDL-50.77HDL 55 HDL-2b x LDL-40.43
HDL-2b 0.93HDL-3c0.77 HDL-2a 0.87LDL-50.65
HDL-3b x HDL-3c0.75HDL-2a0.61LDL-2041 HDL-3b x LDL-20.60
LDL-3 51LDL-50.42 HDL-3c 83LDL-3 .56
or combinations thereof.
Analyzing the data from the applied statistical classification modeling may
comprise comparing
the data to a benchmark value. Assessment of the donor's health may be greater
than 80%
effective in identifying issues concerning the donor's health. Assessment of
the donor's health
may be greater than 85% effective in identifying issues concerning the donor's
health. Assessment
of the donor's health may be greater than 90% effective in identifying issues
concerning the
donor's health. Assessment of the donor's health may be greater than 95%
effective in identifying
issues concerning the donor's health. Assessment of the donor's health may be
greater than 99%
effective in identifying issues concerning the donor's health. Assessment of
the donor's health
may comprise identifying cardiovascular disease, genetic disorders, coronary
heart disease, a
disease that influences lipoproteins, or combinations thereof.
[0009] Also disclosed herein is a system for characterizing a biological
sample comprising: a
biological sample separator, wherein the biological sample separator functions
to separate the
biological sample into constituents; a constituent observer, wherein the
constituent observer
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functions to confirm and qualify the presence of the constituent; a
constituent statistical processor,
wherein the constituent statistical processor functions to apply statistical
classification modeling to
the observed constituent to derive representative data; and a statistical
analyzer, wherein the
statistical analyzer functions to compare the representative data to benchmark
values to derive a
predictor for a health concern. The biological sample separator may comprise a
centrifuge, a gel
electrophoresis system, a chromatography system, a capillary electrophoresis
system, or
combinations thereof. The constituent observer may comprise a photography
device, a
videography device, a microscopy device, a nuclear magnetic resonance imaging
device, a
computer scanning device, a human visualization device, or combinations
thereof. The constituent
statistical processor may comprise a computer, software, a mathematical
computation device, or
combinations thereof. The statistical analyzer may comprise a computer,
software, a mathematical
computation device, or combinations thereof.
[0010] Also disclosed herein is a method of determining a benchmark for health
assessment
comprising: separating a biological sample into constituents; observing the
separated constituents;
applying statistical classification modeling to the observed constituents;
correlating the observed
constituents to a health concern; and performing an amount of correlations of
components to health
concerns to achieve a statistically significant predictor.
[0011] Also disclosed herein is a method of assessing a individual's health
comprising:
applying statistical classification modeling to an individual's assessment
sample; deriving
quantifiable data from the applied statistical classification modeling; and
analyzing the data from
the applied statistical classification modeling to assess the individual's
health. The statistical
classification modeling may comprise linear discrimination analysis (LDA),
recursive partitioning
(RP), sliced average variance estimation (SAVE), sliced mean variance
covariance (SMVCIR), or
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combinations thereof. The quantifiable data may comprise TC
Y HDL 35LDLo.25TGo.04
HDL0.29LDL . 9TG0.11 HDL0-59LDLo.49TG0.03 HDL-3b x LDL-50.7HDLo.55
TC ' TC ' HDL-2b 0.93HDL-3c0.77
HDL-2b x LDL-4043 HDL-3b x HDL-3c0.75HDL-2a0.61LDL-2041 HDL-3b x LDL-20.60
HDL-2a 0.17 LDL-5 0.65 LDL-3 0.51LDL-5 0.42 HDL-3co.13 LDL-3 0.56 ' or
combinations thereof. Analyzing the data from the applied statistical
classification modeling may
comprise comparing the data to a benchmark value. Assessment of the
individual's health may be
greater than 80% effective in identifying issues concerning the individual's
health. Assessment of
the individual's health may be greater than 85, effective in identifying
issues concerning the
individual's health. Assessment of the individual's health may be greater than
90% effective in
identifying issues concerning the individual's health. Assessment of the
individual's health may be
greater than 95% effective in identifying issues concerning the individual's
health. Assessment of
the individual's health may be greater than 99% effective in identifying
issues concerning the
individual's health. Assessment of the individual's health may comprise
identifying cardio
vascular disease, genetic disorders, coronary heart disease, a disease that
influences lipoproteins, or
combinations thereof.
[0012] Also disclosed herein is a method of determining a benchmark for health
assessment
comprising: applying statistical classification modeling to observed
assessment factors; correlating
the observed assessment factors to a health concern; and performing an amount
of correlations of
components to health concerns to achieve a statistically significant
predictor.
[0013] Also disclosed herein is a system that analyzes a biological sample
comprising: a
separator; a device for measuring/profiling the distribution of constituents
in a separated sample;
and a program that statistically analyzes the distribution/profile and
classifies the sample according
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to the output of the analysis. The classification may comprise a diagnosis.
The classification may
comprise a diagnosis related to CVD. The device may permit observation of a
profile of a
distribution of constituents in a separated sample.
[0014] Also disclosed herein is a method comprising diagnosing CVD with at
least 50, 60, 70,
80, 85, 90, 95, 99, or 99.9 percent certainty. Diagnosing may comprise
operating on a blood
sample having normal or better than normal HDL-c levels, normal or better than
normal LDL-c
levels, or combinations thereof. Operating may comprise: calculating certain
ratios of lipoproteins;
executing LDA; executing SAVE; or combinations thereof.
[0015] Also disclosed herein is a method of diagnosing CVD comprising
analyzing a
biological sample via: ratios of lipoproteins, LDA, SAVE, or combinations
thereof. Analyzing a
biological sample may comprise operating on a lipoprotein distribution profile
with LDA, SAVE,
or combinations thereof.
[0016] Also disclosed herein is a method or system according to any foregoing
embodiment,
wherein the embodiments are combined in any desirable or operable arrangement
to provide
multiple embodiments.
[0017] Also disclosed herein is a system for characterizing a biological
sample comprising:
means for separating the biological sample into constituents; means for
observing the separated
constituents; means for applying statistical classification modeling to the
observed constituents;
means for deriving quantifiable data from the applied statistical
classification modeling; and means
for analyzing the data from the applied statistical classification modeling to
assess a donor of the
biological compounds' health.
[0018] Also disclosed herein is a system for determining a benchmark for
health assessment
comprising: means for separating a biological sample into constituents; means
for observing the
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separated constituents; means for applying statistical classification modeling
to the observed
constituents; means for correlating the observed constituents to a health
concern; and means for
performing a number of correlations of components to health concerns to
achieve a statistically
significant predictor.
[0019] Also disclosed herein is a system for assessing an individual's health
comprising:
means for applying statistical classification modeling to an individual's
assessment sample; means
for deriving quantifiable data from the applied statistical classification
modeling; and means for
analyzing the data from the applied statistical classification modeling to
assess the individual's
health.
[0020] Also disclosed herein is a system for determining a benchmark for
health assessment
comprising: means for applying statistical classification modeling to observed
assessment factors;
means for correlating the observed assessment factors to a health concern; and
means for
performing an amount of correlations of components to health concerns to
achieve a statistically
significant predictor.
[0021] Also disclosed herein is a patient therapy strategy comprising all or a
portion of any
foregoing method or system.
[0022] Also disclosed herein is a method of diagnosing and/or treating a
patient comprising all
or a portion of any foregoing method or system.
[0023] Also disclosed herein is a computerized implementation, instantiation,
or embodiment
of all or a portion of any foregoing system or method.
[0024] Also disclosed herein is a method comprising: selecting a general
population; selecting
a disease or interest; selecting a population subset whose membership is based
on a known shared
characteristic; obtaining a biological sample from members of the population
subset; deriving at
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least one attribute of the biological samples; using a statistical correlation
method to calculate a
correlative between the at least one attribute and the disease of interest;
selecting a subject wherein
it is unknown if the subject has the known shared characteristic; obtaining a
biological sample
from the subject; deriving at least one attribute of the biological sample
from the subject wherein
the sample and attribute are the same as the samples and attributes derived
for the population
subset; using a statistical correlation method to calculate a correlative
between the attribute of the
biological sample from the subject and the disease of interest; and
determining the membership of
the subject in the population subset wherein the subject is a member of the
population subset if the
correlative derived based on the attributes of the subject's biological sample
approximates the
correlative derived based on the attributes of the population subset's
biological samples.
BRIEF DESCRIPTION OF THE DRAWINGS
[0025] Figure 1 is a schematic of a method of developing a correlative between
an attribute of
a biological sample and a disease.
[0026] Figure 2 is a method of assessing a risk for the development of CVD.
[0027] Figure 3 is a plot of the ratio of two correlatives for the samples
from Example 1.
[0028] Figure 4 is a plot of two linear combinations derived from analysis of
the samples from
Example 2.
[0029] Figure 5 is a plot of the ratio of two correlatives obtained for the
samples from Example
3.
[0030] Figure 6 is a plot of the two linear combinations derived from analysis
of the samples
from Example 3.
[0031] Figure 7A is an illustration of an image of the liprotein-metal ion
chelate complex (far
left panel), a liproprotein profile (middle panel) and the integrated
intensities (far right panel).
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[0032] Figure 7B is a plot of the ratio of two correlatives for the samples
from Example 8.
[0033] Figure 8 is a plot of the two dimensional SAVE analysis for the samples
from Example
8.
[0034] Figure 9 is a plot of LDA and SAVE for the samples from Example 8.
DETAILED DESCRIPTION
[0035] It should be understood at the outset that although illustrative
implementations of one
or more embodiments are illustrated below, the disclosed systems and methods
may be
implemented using any number of techniques, whether currently known or in
existence. The
disclosure should in no way be limited to the illustrative implementations,
drawings, and
techniques illustrated below, but may be modified within the scope of the
appended claims along
with their full scope of equivalents.
[0036] In an embodiment, one or more methods disclosed herein may be suitable
for
developing a correlative. Such a correlative may be useful for identifying
individuals who present
with or are at an elevated risk for a development of a disease state,
dysfunction, or disorder
(hereinafter collectively termed a disease) such as CVD. In an embodiment, the
correlative
functions to relate an attribute of a biological sample obtained from an
individual to a disease. For
example the correlative may relate one or more of attributes of a biological
sample (e.g.,
lipoprotein profile) to the existence or potential occurrence of CVD. In an
embodiment, a
correlative of the type described herein may be utilized to assess the
presence or absence of a
disease and/or an individual's risk for the onset or occurrence of a disease.
In an embodiment, one
or more methods disclosed herein may be suitable for predicting the occurrence
and/or risk of
occurrence of CVD in an individual.
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[0037] Referring to Figure 1, a method of developing a correlative between an
attribute of a
biological sample obtained from an individual and a disease, referred to
herein as a "disease
correlative development method" (DCD) 1000, is illustrated schematically. In
an embodiment, the
DCD 1000 generally comprises selecting a general population, selecting a
population subset where
the members of the population subset comprise at least one shared
characteristic, obtaining a
biological sample from members of the population subset, deriving at least one
attribute for each
biological sample, and calculating a correlative between the attributes of the
biological sample and
the shared characteristic. Herein the general population refers to that
collection of organisms for
which a correlative between an attribute of a biological sample obtained from
members of the
general population and a disease is to be made.
[0038] Referring to Figure 2, in an embodiment, a method of assessing a risk
for the
development of CVD, referred to herein as a "disease risk assessment method"
(DRA) 2000 is
illustrated. In an embodiment, the DRA 2000 generally comprises selecting a
population of
organisms, identifying a population subset, obtaining biological samples from
members of the
population subset, and developing a correlative between at least one attribute
of the biological
sample and a disease wherein the correlative is indicative of and/or
predictive for a disease. The
method may further comprise obtaining a biological sample from a member of the
general
population, determining the attributes of the biological sample and predicting
whether the subject
is a member of the population subset.
[0039] In an embodiment, the DCD 1000 and the DRA 2000 comprise selecting a
general
population 100. The general population will comprise any suitable collection
of organisms sharing
a biological relationship and whose disease and/or risk for disease is to be
assessed. Nonlimiting
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examples of such a population include mammals, humans, dogs, cats, horses,
cattle, and chickens.
In a particular embodiment, the general population will comprise humans.
[0040] In an embodiment, the DCD 1000 and the DRA 2000 comprise selecting a
population
subset 200. As used herein, "population subset" refers a subset the general
population, wherein all
members of the population subset comprise at least one shared characteristic.
[0041] In an embodiment, the shared characteristic comprises the disease which
is to be
correlated to the one or more attributes of the biological sample, referred to
herein as the disease of
interest (DOI). For example, the population subset may comprise members of the
general
population symptomatic for the DOI. Alternatively, the population subset may
comprise members
of the general population asymptomatic for the DOI. Alternatively, the
population subset
comprises members of the general population having risk factors (e.g.,
genetic, lifestyle) for the
DOI. Alternatively, the population subset may comprise members of the general
population for
which the presence of the DOI is established by alternative methodologies.
Alternatively, the
population subset may comprise members of the general population for which the
absence of the
DOI is established by alternative methodologies.
[0042] In a particular embodiment, the population subset comprises humans
known to have
CVD. In an alternative embodiment, the population subset comprises humans
known to not have
CVD.
[0043] In an embodiment, the DCD 1000 and the DRA 2000 further comprise
obtaining a
biological sample from members of the population subset 300. Nonlimiting
examples of biological
samples include proteins, lipoproteins, cells, cell constituents,
microorganisms, DNA, or
combinations thereof.
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[0044] In an embodiment, the biological sample is obtained by any method known
to one of
ordinary skill in the art and compatible with the methodologies described
herein. For example, a
blood sample may be obtained from a member of the population subset.
[0045] In a specific embodiment, the biological sample is a blood sample. A
blood sample
may be obtained from a subject according to methods well known in the art. In
some
embodiments, a drop of blood is collected from a pin prick made in the skin of
a subject. Blood
may be drawn from a subject from any part of the body (e.g., a finger, a hand,
a wrist, an arm, a
leg, a foot, an ankle, a stomach, and a neck) using techniques known to one of
skill in the art, in
particular methods of phlebotomy known in the art. In a specific embodiment,
venous blood is
obtained from a subject and utilized in accordance with the methods of this
disclosure. In another
embodiment, arterial blood is obtained and utilized in accordance with the
methods of this
disclosure. The composition of venous blood varies according to the metabolic
needs of the area
of the body it is servicing. In contrast, the composition of arterial blood is
consistent throughout
the body.
[0046] Venous blood can be obtained from the basilic vein, cephalic vein, or
median vein.
Arterial blood can be obtained from the radial artery, brachial artery or
femoral artery. A vacuum
tube, a syringe or a butterfly may be used to draw the blood. Typically, the
puncture site is
cleaned, a tourniquet is applied approximately 3-4 inches above the puncture
site, a needle is
inserted at about a 15-45 degree angle, and if using a vacuum tube, the tube
is pushed into the
needle holder as soon as the needle penetrates the wall of the vein. When
finished collecting the
blood, the needle is removed and pressure is maintained on the puncture site.
Heparin or another
type of anticoagulant may be in the tube or vial that the blood is collected
in so that the blood does
not clot.
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[0047] In some embodiments, the collected blood is stored prior to being
subjected to further
processing as will be described herein. In one embodiment, the collected blood
is stored at room
temperature (i.e., approximately 22 C). In another embodiment, the collected
blood is stored at
refrigerated temperatures, such as 4 C, prior to use. In some embodiments, a
portion of the blood
sample is used in accordance with this disclosure at a first instance of time
whereas one or more
remaining portions of the blood sample is stored for a period of time for
later use. This period of
time can be an hour or more, a day or more, a week or more, a month or more, a
year or more, or
indefinitely. For long term storage, storage methods well known in the art,
such as storage at cryo
temperatures (e.g., below -60 C) can be used.
[0048] Those of skill in the art will further appreciate that the size of the
biological sample
obtained will vary depending upon the type of biological sample to be
obtained, the method by
which it is obtained, and other variables. In an embodiment, the biological
sample comprises
blood and the sample size may be about 25 mL, alternatively, less than about
25 mL alternatively,
less than about 5mL, alternatively, less than about 1 mL, alternatively, less
than about 0.5 mL,
alternatively, less than about 0.1 mL. In an embodiment, the biological sample
comprises blood
and a sample size of approximately 50 l is collected onto a substrate (e.g.,
filter paper) which may
be treated (prior and/or subsequent to) contact with the biological sample.
Treatment may include
materials and/or processes that inhibit the degradation of the biological
sample.
[0049] In another embodiment, the biological sample comprises serum and/or
plasma. Serum
and/or plasma may be derived from the blood sample by methods well known to
those of skill in
the art. For example, blood may be clotted, and the serum or plasma isolated
by low speed
centrifugation.
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[0050] In an embodiment, the DCD 1000 and the DRA 2000 further comprise
deriving at least
one attribute of the biological sample 400. Such attributes generally comprise
various quantitative
or qualitative characteristics. For example, the attribute may be the nature
and amount of one or
more components of the biological sample.
[0051] In an embodiment, the attribute of the biological sample derived is the
nature and
amount of lipoproteins present in the biological sample. Herein a lipoprotein
refers to a
biochemical assembly that contains both protein and lipids. The lipids or
their derivatives may be
covalently or non-covalently bound to the proteins. Lipoproteins differ in the
ratio of protein to
lipids, and in the particular apoproteins and lipids that they contain.
Lipoproteins are typically
divided into classes based on differences in density and composition. Such
classes include very
low density lipoprotein (VLDL), low density lipoproteins (LDL), intermediate
density lipoproteins
(IDL), high density lipoproteins (HDL) and lipoprotein(a) (Lp(a)).
Lipoproteins may be further
divided in subclasses based on differences in density and composition. Such
subclasses include
buoyant triglyceride-rich lipoprotein (bTRL), dense triglyceride-rich
lipoprotein (dTRL), LDL-1,
LDL-2, LDL-3, LDL-4, LDL-5, HDL-2b, HDL-2a, HDL-3a, HDL-3b, and HDL3c.
[0052] In an embodiment, deriving the attribute of the biological sample
comprises
determining the lipoprotein profile of the biological sample. Herein the
lipoprotein profile refers to
information on the amount and type of lipoprotein subclasses derived from the
biological sample.
[0053] The lipoprotein profile of the biological sample may be determined by
first separating
the biological sample into lipoprotein subclasses. Separation of the
biological sample into
lipoprotein subclasses may be carried using any methodology compatible with
the currently
disclosed process. For example, the biological sample may be separated into
lipoprotein
subclasses by an isopycnic and/or zonal gradient ultracentrifugation process.
Processes of utilizing
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isopycnic gradient ultracentrifugation process to obtain lipoprotein
subclasses are discussed in
greater detail in U.S. Patent No. 7,320,893, U.S. Patent No. 6,753,185, and
U.S. Patent No.
6,593,145, each of which is incorporated herein in its entirety.
[0054] In an embodiment, the biological sample comprises a blood sample,
alternatively the
biological sample comprises serum. Although the following embodiments are
discussed with
reference to serum, one or more of the embodiments disclosed herein may be
similarly applicable
to other biological samples.
[0055] Separation of the serum into lipoprotein subclasses may involve
staining the serum with
a visualization dye and subjecting the stained serum to ultracentrifugation.
The visualization dye
may be chosen to interact with the surface of the lipoproteins and exhibit
saturation kinetics such
that uptake of the visualization dye by the lipoprotein is proportional to the
lipoprotein
concentration. In various embodiments, the visualization dye is a visible or
fluorescent dye.
Examples of such dyes include but are not limited to NBD C6-ceramide (6-[N-(7-
nitrobenz-2-oxa-
1,3-diazole-4-yl) amino] hexanoyl-D-erythro-sphingosine and Sudan Black B.
Further, the
visualization dye may be a lipophilic or protein stain. Nonlimiting examples
of a lipophilic or
protein stain suitable for use in this disclosure include Sudan Black B and
Coomassie Brilliant
Blue R. The visualization dye can also be a fluorescent membrane probe.
Nonlimiting examples
of such probes suitable for use in this disclosure include NBD, Dil (3,3'-
dioctadecylindocarbocyanine) (D-282), DiA (N,N-
dipentadecylaminostyrilpyridinium), (D-3883),
BODIPY (dipyrrometheneboron difluoride) C5-HPC (D-3795).
[0056] In embodiments, a method for separating a biological sample into
lipoprotein
subclasses comprises suspending the stained serum in a density gradient
forming solution (DGFS).
The mixture of stained serum and DGFS is termed the unseparated solution.
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[0057] The DGFS may comprise inorganic salts (cesium chloride, potassium
bromide, sodium
chloride), sucrose, a synthetic polysaccharide made by crosslinking sucrose, a
suspension of silica
particles coated with polyviynlpyrrolidone, derivatives of metrizoic acid,
dimers of metrizoic acid,
Optiprep , metal ion chelate complexes, or combinations thereof.
[0058] In an embodiment, the DGFS comprises one or more metal ion chelate
complexes. As
used herein, the term "metal ion chelate complex" refers to a complex formed
between a metal ion
and a chelating agent. The metal ion can generally be any metal ion. Examples
of metal ions
which may be employed in the present disclosure include, but are not limited
to ions of copper,
iron, bismuth, zinc, cadmium, calcium, thorium and manganese.
[0059] As will be appreciated by one of skill in the art, the term "chelating
agent" refers to a
particular type of ligand that can form a complex with a metal ion, wherein
the ligand comprises
more than one atom having unshared pairs of electrons that form bonds or
associations with the
same metal ion. Chelating agents are also referred to as polydentate ligands.
Examples of
chelating agents suitable for use in the present disclosure include, but are
not limited to oxalate,
ethylenediamine, diethlyenetriamine, 1,3,5-triaminocyclohexane and
ethylenediaminetetraacetic
acid (EDTA). In an embodiment, the chelating agent comprises EDTA.
[0060] Metal ion chelate complexes may further comprise one or more positively
charged
counter-ions to balance the overall charge of the complex. Examples of counter-
ions include, but
are not limited to lithium, sodium, potassium, cesium, magnesium, calcium and
ammonium as well
as counter-ions such as ammonium complexes, for example tetrabutylammonium. In
an
embodiment where more than one counter-ion is used to balance the overall
charge, the counter-
ions can be mixed. For example, a metal ion chelate complex requiring two
positive charges may
have one positive charge supplied by sodium and the other by potassium.
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[0061] The properties of the density gradient may be modified by choosing
different
combinations of metal ions, chelating agents and counter ions. Examples of
metal ion chelate
complexes suitable for use in the present disclosure include, but are not
limited to NaCuEDTA,
NaFeEDTA, NaBiEDTA, Cs2CdEDTA and CsBiEDTA. It is contemplated that solutions
of more
than one metal ion chelate complex can also be used to form density gradients.
The concentration
of the metal ion chelate complex may generally be any suitable concentration.
In the embodiment
of Figure 1, the concentration of the metal ion chelate complex solution is
from about 0.01 M to
about 0.7 M, alternatively, from about 0.1 M to about 0.3 M. In an embodiment,
a lower
concentration may generally result in a lower density range while a more
concentrated solution
may generally yield a higher density range.
[0062] In an embodiment, the unseparated solution further comprises a buffer.
Examples of
suitable buffers include but are not limited to phosphate, acetate, and
tris(hydroxymethyl)aminomethane ("Tris"). The buffers used herein may be
chosen by one of
ordinary skill in the art so as to be compatible with the methodolgies
disclosed herein. In an
embodiment, the buffer is chosen so as to provide a pH in the range of from
about 3.5 to about 8.5,
alternatively from about 4.0 to about 8.0, alternatively from about 4.5 to
about 7.5. Additional
disclosure on metal ion chelate complexes suitable for use in this disclosure
can be found in US
Patent Nos 6,753,185; 6,593,145 and 7,320,893 each of which is incorporated by
reference herein
in its entirety.
[0063] In an embodiment, the unseparated solution is separated by forming a
density gradient.
In an embodiment, separating serum into lipoprotein subclasses comprises
mixing the serum with
the DGFS in a centrifuge tube or other container and applying a centrifugal
force. It is
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contemplated that different rotor sizes, shapes, and compositions as well as
sample tubes of
different sizes, shapes, and compositions may be employed.
[0064] A centrifugal force may be applied to the solution by spinning the tube
in a rotor. Any
of the suitable rotor/tube configurations known in the art may be used with
the density gradients of
the present disclosure. Examples include, but are not limited to fixed angle
rotors, vertical tube
rotors and swinging bucket rotors. In a particular embodiment, the rotor
configuration comprises a
fixed angle ranging from about 15 degrees to about 45 degrees, alternatively,
about 30 degrees.
[0065] The centrifugal force may generally be any strength sufficient to
separate the
lipoprotein subclasses. In various embodiments, the force field is at least
about 400,000 x g,
alternatively, about 400,000 x g to about 600,000 X g, alternatively, about
500,000 x g to about
550,000 x g. As will be appreciated by those of skill in the art, the spin
rate may affect the speed at
which the density gradient is formed, a faster spin rate typically resulting
in faster gradient
formation. In embodiments, rapid gradient formation may be desirable where a
reduced time for
separation is desired. However, too rapid of a gradient formation may
adversely affect particle
separation in that the particles do not have a chance to find their isopycnic
point before the gradient
becomes too steep.
[0066] The properties of the density gradient are a function of a variety of
factors such as the
particular metal ion chelate complex employed, the concentration of the
solution, temperature and
the magnitude of the centrifugal field. The density gradient formed may be an
essentially
exponential density gradient. By exponential density gradient, it is meant
that the density of the
solution varies essentially exponentially as a function of position from one
end of the tube to the
other. Generally, exponential geometry of a density gradient is an indication
that the gradient is at
equilibrium. A density gradient is a suitable means of attaining isopycnic
mode separations
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wherein the particles migrate through the gradient until they reach a position
that is equal to their
own density. In embodiments, isopycnic mode separations are desirable because
they reflect the
true equilibrium densities of the particles. Isopycnic mode separations are
particularly suitable for
the analysis of lipoproteins because the isopycnic mode yields substantial
information about the
equilibrium density of the lipoproteins. This information may be relevant as a
clinical diagnostic
for CVD.
[0067] Without wishing to be limited by theory, separation of the biological
sample into
lipoprotein subclasses as described herein is accomplished by the subjecting
the biological sample
to a diffusive force. The diffusive force arises due to the density gradient
and is always directed
towards the center of the rotor (i.e., centrifuge). The sedimenting particles
(i.e., various subclasses
of lipoproteins) will sediment away from the rotor until their density is
equivalent to the local
density of the density gradient which was formed, at which point the diffusive
force is equivalent
to the centrifugal force. As such, the separated solution will comprise one or
more "bands," each
band comprising a lipoprotein subclass separated from other lipoprotein
subclasses on the basis of
density. The relative densities of these lipoprotein subclasses are shown in
Table 1:
Table 1
LIPOPROTEIN DENSITY KG/L
bTRL < 1.00
dTRL 1.000-1.019
LDL-1 1.019-1.023
LDL-2 1.023-1.029
LDL-3 1.029-1.039
LDL-4 1.039-1.050
LDL-5 1.050-1.063
HDL-2b 1.063-1.091
HDL-2a 1.091-1.110
HDL-3a 1.110-1.133
HDL-3b 1.133-1.156
HDL-3c 1.156-1.179
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[0068] The biological samples can generally comprise any lipoprotein subclass.
A separated
mixture may contain one or more of these subclasses, depending on the
composition of the
biological sample. The biological samples may also comprise remnant
lipoproteins, oxidized
lipoproteins, and inflammatory markers. Such components may also be detectable
by the
methodologies described herein.
[0069] In an alternative embodiment, separating the lipoprotein subclasses of
the serum may
be carried out using any suitable process. Such processes are known in the art
and include, but are
not limited to gradient gel eletrophoresis, capillary electrophoresis,
ultracentrifugation-vertical auto
profiling, tube gel electrophoresis, chromatography, or combinations thereof.
The unseparated
solution having been subjected to the separation processes disclosed herein is
termed the
"separated solution."
[0070] In an embodiment, the lipoprotein profile of the biological sample is
determined by
identifying and quantifying the lipoprotein subclasses present in the
separated solution. In an
embodiment, lipoprotein subclasses are observed using any suitable technique
such as dyes, stains,
fluorescent markers, Rayleigh scattering, computer enhancement, differential
density profiling,
fluorescent antibodies, and natural fluorescence or combinations thereof.
Further visualization of
markers associated with a lipoprotein subclass may comprise the use of any
excitation source
including halide lamps, lasers, etc and any modification to said source.
Quantification of the
lipoprotein subclasses may be made using any suitable methodology such as
photography,
videography, microscopy, nuclear magnetic resonance imaging, computer
scanning, human
visualization, or combinations thereof.
[0071] In an embodiment, a method of quantifying the lipoprotein subclasses
comprises
imaging the separated solution. Generally, the separated solution may be
imaged using any
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suitable means, examples of which include but are not limited to scanning a
spectrophotometer
image of the separated mixture or photographing the separated mixture.
[0072] The photograph or scanned image (hereinafter the image) may be
digitized in a
computer and analyzed. For example analysis of the image may comprise
converting the image
into a particle density profile. Methods of converting the image into a
particle density profile are
known to one of ordinary skill in the art and any suitable method may be
employed. For example,
converting the image into a particle density profile may comprise determining
the intensity of one
or more of the bands within the separated solution (e.g., using refractive
index, gravimetry, and/or
UV-absorbance of the band). The particle density profile may express the
intensity of at least a
portion of the bands occurring on the image. Not seeking to be bound by theory
the intensity of a
given band as compared to the cumulative intensity of all bands in the
separated solution will be
approximately proportionate to the quantity of the lipoprotein within the
individual band as
compared to the total quantity of lipoproteins present in the separated
solution. As such, by
ascertaining the relative intensity of a band (e.g., as via imaging the
separated solution) the relative
concentration of lipoprotein represented by that band may be calculated.
Consequently, if the total
quantity of lipoprotein in the biological sample is known, the quantity of
lipoprotein in a given
band may be calculated.
[0073] In an embodiment, particular regions of interest may be selected and
isolated from the
ultracentrifuge tube using a freeze, cut, and thaw method. For example, the
VLDL, LDL, and/or
HDL fractions can be isolated and analyzed for cholesterol and triglyceride
levels using standard
analytical assays. Alternatively, aliquots of the fractions can be withdrawn
by pipetting at specific
density locations in the ultracentrifuge tube.
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[0074] In embodiments, isolated lipoprotein subclasses may subsequently be
analyzed by a
variety of methods nonlimiting examples of which include capillary
electrophoresis, solid phase
extraction, mass spectrometry, thin layer chromatography, electron
paramagnetic resonance,
immobilized pH gradient isoelectric focusing, matrix assisted laser
desorption/ionization (MALDI)
mass spectrometry, electrospray ionization mass spectrometry (ESI-MS), and two
dimensional gel
electrophoresis.
[0075] In an embodiment, the lipoprotein profile may be determined as
described herein for a
biological sample under a plurality of conditions. For example, a plurality of
biological samples
may be obtained from a member of the population subset as a function of time
to monitor changes
in the lipoprotein profile. The processes disclosed herein may afford the
monitoring of a member
of the population subset occurrence and/or risk for CVD due to various factors
such as medication,
exercise, diet, age, or combinations thereof.
[0076] In an embodiment, the DCD 1000 and the DRA 2000 further comprise
determining a
correlative between the one or more attributes of the biological sample and
the DOI 500. The
correlative may be determined using a statistical method to define a
mathematical relationship
between the one or more attributes derived from the biological sample and the
DOI. In an
embodiment, the statistical method is employed to define an approximately
linear relationship
between the one or more attributes derived from the biological sample and the
DOI. In an
embodiment, the statistical method employed is a statistical classification
modeling method.
Nonlimiting examples of statistical classification modeling methods suitable
for use in this
disclosure include linear discrimination analysis (LDA), recursive
partitioning (RP), sliced average
variance estimation (SAVE), sliced mean variance covariance (SMVCIR), or
combinations
thereof.
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[0077] LDA is a statistical method by which Linear classification algorithms,
which represent
a large portion of the available techniques, are based on fitting a linear
discriminant function to the
data. This linear decision is of the form f (x) = wx + b where b is the bias
and w the normal vector
to the decision boundary f (x) - 0. One approach to fit this linear function
to the data is Linear
Discriminant Analysis which amounts to fitting a Gaussian probabilistic model
to the data. In its
simplest formulation, assuming the feature vector x has a Gaussian
distribution with different class
conditional means [Xj and \i2 for class 1 and 2 respectively and the same
covariance matrix Hl -
22 = 2 in the two cases, the optimal decision function in a Bayesian framework
is of the form:
[0078] SAVE is a statistical method which does not require the specification
of a model in
order to estimate a linear combination of attributes. SAVE is the most
inclusive among dimension
reduction methods as it gains information from both the inverse mean function
and the differences
of the inverse covariances.
[0079] SMVCIR is a statistical method based on searching for linear
combinations of the
predictor variables which best separate the groups in terms of mean, variance
and covariance of
these linear combinations.
[0080] Recursive partitioning (RP) is a data mining tool. When RP makes the
first sweep
through attributes (e.g., lipoprotein subclasses) it tries to identify those
attributes that result in a
significant association with the DOI (e.g., CVD). The data are then divided
into subgroups
corresponding to selected attribute categories, and the association test is
repeated in the subgroups
using the remaining attributes. At this stage the interaction can be detected.
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[0081] In an embodiment, the methodologies disclosed herein establish a
correlative between
an attribute of the biological sample and the DOI using a statistical
classification modeling method.
As used herein, the "correlative" refers to the combination of attributes that
distinguish one group
from another. In other words the correlative serves as a mechanism of
classification and utilizes
the attributes of the biological sample to assign membership of the individual
to a particular group.
[0082] In an embodiment, the population comprises humans and the population
subset
comprises organisms having CVD. The lipoprotein profile of the population
subset may be
subjected to a statistical classification methodology of the type previously
described herein. The
resulting correlative is an approximately linear combination between two or
more lipoprotein
subclasses and the DOI. The correlative may be predictive for an individual's
membership within
the population subset as the correlative may be determined to be valid for a
majority of members of
the population subset.
[0083] In an embodiment, the correlative comprises a relationship between a
plurality of
lipoprotein subclasses and CVD. In an additional embodiment, the correlative
further comprises a
relationship between a plurality of lipoprotein subclasses and one or more
other characteristics.
Nonlimiting examples of such other characteristics includes, age,
hypertension, hyperlipidemia,
family history, gender, tobacco use, alcohol use, other health related
factors, or combinations
thereof. Correlatives defining an approximately linear relationship between
lipoprotein profiles
and occurrence and/or risk for developing CVD are described in greater detail
below.
[0084] In an embodiment, the calculated correlative (C) comprises a
mathematical relationship
between a TC fraction, an HDL fraction, an LDL fraction, and a TG fraction. It
is to be understood
in the equations to follow the exponents are given a two symbol alpha-numeric
designation. The
numeric portion is not to be considered as a multiplier of the value denoted
by the alphabetic
CA 02727818 2010-12-13
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portion. In other words, the exponent 5m is given values as described in the
specification. It is not
to be construed as the value obtained by multiplying the variable m by five.
Further, the
mathematical relationship may lead to quantifiable data which can be used to
determine an
individual's membership in a given population subset.
[0085] In an embodiment, C comprises the mathematical relationship expressed
in Equation
(1), or approximations of the same:
~.~la
Equation (1): Ci = HDLib XLDLl XTGId
where la is about 1; where lb is about 0.30 to about 0.40, alternatively about
0.33 to about 0.37,
alternatively, about 0.35; where lc is 0 to about 0.35, alternatively, 0.23 to
about 0.27,
alternatively, about 0.25; and where Id is about 0 to about 0.08,
alternatively, about .02 to about
0.06, alternatively, about 0.04. In a particular embodiment, the correlative
comprises the
mathematical relationship expressed in Equation (1 p) or approximations of the
same:
Equation (lp): C1 = TC HDL0.35 XLDL0.25XTG .04
[0086] In an additional embodiment, the correlative (C) comprises a simplified
expression of
the mathematical relationship of Equation (1), or approximations of the same,
expressed as
Equation (1y):
TC
Equation (ly): Ciy = HDL0.35
[0087] In an alternative embodiment, the correlative (C) comprises the
mathematical
relationship expressed in Equation (2), or approximations of the same:
Equation (2): C2 = HDL2a XLDL2b XTG2a
TC2d
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where 2a is about 0.24 to about 0.39, alternatively about 0.27 to about 0.31,
alternatively, about
0.29; where 2b is 0 to about 0.14, alternatively, 0.07 to about 0.11,
alternatively, about 0.09; where
2c is 0 to about 0.16, alternatively, about .09 to about 0.13, alternatively,
about 0.1 1; and where 2d
is about 1. In a particular embodiment, the correlative comprises the
mathematical relationship
expressed in Equation (2a) or approximations of the same:
HDL0.29 xLDL0.09 xTGo.ii
Equation (2a): C2a, = TC
[0088] In an additional embodiment, the correlative (C) comprises a simplified
expression of
the mathematical relationship of Equation (2), or approximations of the same,
expressed as
Equation (2p):
HDL0.29
Equation (2p): C20 = TC
[0089] In an alternative embodiment, the correlative (C) comprises the
mathematical
relationship expressed in Equation (3), or approximations of the same:
HDL3a xLDL3b xTG3C
Equation (3): C3 = TC3d
where 3a is about 0.49 to about 0.69, alternatively about 0.57 to about 0.61,
alternatively, about
0.59; where 3b is about 0.44 to about 0.54, alternatively, 0.47 to about 0.51,
alternatively, about
0.49; where 3c is 0 to about 0.06, alternatively, about .02 to about 0.04,
alternatively, about 0.11;
and where 3d is about 1. In a particular embodiment, the correlative comprises
the mathematical
relationship expressed in Equation (3a,) or approximations of the same:
HDL0.59 xLDL0.49 xTG0.03
Equation (3a,): C3a, = TC
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[0090] In an additional embodiment, the correlative (C) comprises a simplified
expression of
the mathematical relationship of Equation (3), or approximations of the same,
expressed as
Equation (3p):
HDL0.59 XLDL0.49
Equation (3p): C30 = TC
[0091] In an alternative embodiment, the correlative (C) comprises an
expression of a
mathematical relationship between an HDL fraction, an LDL-5 fraction, an HDL-
2b fraction, and
an HDL-3c fraction. In an embodiment, the correlative comprises the
mathematical relationship
expressed in Equation (4), or approximations of the same:
Equation (4): C4 =
HDL-3b 4a XLDL-541 XHDL4c XLDL-34d XHDL-2a 4e XLDL-24f
HDL-2b 4g XHDL-3C41 XHDL-3a4' XLDL-441 xdTRL4m XbTRL4n XLDL-14p XAge4q
where 4a is about 1; where 4b is about 0.72 to about 0.82, alternatively about
0.75 to about 0.79,
alternatively, about 0.77; where 4c is about 0.50 to about 0.60,
alternatively, 0.53 to about 0.57,
alternatively, about 0.55; where 4d is 0 to about 0.37, alternatively, about
0.32; where 4e is 0 to
about 0.14, alternatively, about 0.09; where 4f is from 0 to about 0.12,
alternatively, about 0.07,
where 4g is about 0.88 to about 0.98, alternatively, about 0.91 to about 0.95,
alternatively, about
0.93; and where 4h is about 0.72 to about 0.82, alternatively about 0.75 to
about 0.79, alternatively,
about 0.77; where 4j is 0 to about 0.43, alternatively, about 0.37; where 4k
is 0 to about 0.43,
alternatively, about 0.37; where 4m is 0 to about 0.22, alternatively, about
0.17; where 4n is 0 to
about 0.22, alternatively, about 0.17; where 4p is 0 to about 0.08,
alternatively, about 0.03; and
where 4q is 0 to about 0.05, alternatively, about 0.01. In a particular
embodiment, the correlative
comprises the mathematical relationship expressed in Equation (4A) or
approximations of the
same:
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Equation (4a,): C40. _
HDL-3b XLDL-5077 XHDL0.55 XLDL-3032 XHDL-2a ' 9 XLDL-2 .07
HDL-2b0.93 XHDL-30.77 XHDL-30.37 XLDL-40.37 xdTRL0'17 XbTRL0-17 XLDL-10.03
XAge.01
[0092] In an additional embodiment, the correlative (C) comprises a simplified
expression of
the mathematical relationship of Equation (4), or approximations of the same,
expressed as
Equation (4p):
HDL-3b XLDL-5077 XHDL0.55
Equation (4p) : Cop = HDL-2b0.93 XHDL-3c .77
[0093] In an alternative embodiment, the C comprises an expression of a
mathematical
relationship between an HDL-2b fraction, an LDL-4 fraction, an HDL-2a
fraction, an LDL-5
fraction, and an HDL fraction. In an embodiment, the C comprises the
mathematical relationship
expressed in Equation (5), or approximations of the same:
Equation (5): C5 =
HDL-2b 5a XLDL-45b XHDL5c XLDL-25d XLDL-15e XHDL-3C5f XbTRL5g
HDL-2a5h XLDL-55' XLDL-351 XAgesm XdTRL5i XHDL-3c5p XHDL-3b 5q
where 5a is about 1; where 5b is about 0.38 to about 0.48, alternatively about
0.41 to about 0.45,
alternatively, about 0.43; where 5c is 0 to about 0.23, alternatively, about
0.18; where 5d is 0 to
about 0.18, alternatively, about 0.13; where 5d is 0 to about 0.18,
alternatively, about 0.13; where
5f is 0 to about 0.13, alternatively, about 0.08; where 5g is 0 to about 0.08,
alternatively, about
0.03; where 5h is about 0.82 to about 0.92, alternatively, 0.85 to about 0.89,
alternatively, about
0.87; where 5j is about 0.60 to about 0.70, alternatively, about 0.63 to about
0.67, alternatively,
about 0.65; 5k is 0 to about 0.24, alternatively, about 0.19; where 5m is 0 to
about 0.21,
alternatively, about 0.16; where 5n is 0 to about 0.16, alternatively, about
0.11; where 5p is 0 to
about 0.14, alternatively, about 0.09; and where 5q is 0 to about 0.09,
alternatively, about 0.04. In
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a particular embodiment, the correlative comprises the mathematical
relationship expressed in
Equation (5A) or approximations of the same:
Equation (5a,): C5u, =
HDL-2b XLDL-4043 XHDL018 XLDL-2013 XLDL-1013 XHDL-3c .O8 XbTRL0.03
HDL-2a .87 XLDL-5065 XLDL-3019 XAge.16 XdTRL -11 XHDL-3c - 9 XHDL-3b0.04
[0094] In an additional embodiment, the correlative (C) comprises a simplified
expression of
the mathematical relationship of Equation (5), or approximations of the same,
expressed as
Equation (5p):
HDL-2b XLDL-40.43
Equation (5p): C50 = HDL-2a0-87 XDL-50.65
[0095] In an alternative embodiment, the correlative (C) comprises an
expression of a
mathematical relationship between an HDL-3b fraction, an HDL-3c fraction, an
HDL-2a fraction,
an LDL-2a fraction, an LDL-2 fraction, LDL-3 fraction, and an LDL-5 fraction.
In an
embodiment, the correlative comprises the mathematical relationship expressed
in Equation (6), or
approximations of the same:
Equation (6): C6 =
HDL-3b XHDL-3c61 XHDL-2a6' XLDL-26d XbTRL6e XHDL61 XLDL-46g xHDL-3a61 XdTRL6'
LDL-361 XLDL-56m XAge6i XHDL-2b 6p XLDL-16q
where 6a is about 1; where 6b is about 0.70 to about 0.80, alternatively about
0.73 to about 0.77,
alternatively, about 0.75; where 6c is about 0.56 to about 0.66,
alternatively, 0.59 to about 0.63,
alternatively, about 0.61; where 6d is about 0.36 to about 0.46,
alternatively, about 0.39 to about
0.43, alternatively, about 0.41; where 6e is 0 to about 0.25, alternatively,
about 0.20; where 6f is 0
to about 0.24, alternatively, about 0.19; where 6g is 0 to about 0.21,
alternatively, about 0.16;
where 6h is 0 to about 0.14, alternatively, about 0.09; where 6j is 0 to about
0.07, alternatively,
about 0.02; where 6k is about 0.46 to about 0.56, alternatively, about 0.49 to
about 0.53,
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alternatively, about 0.51; where 6m is about 0.37 to about 0.47,
alternatively, about 0.40 to about
0.44, alternatively, about 0.42; where 6n is 0 to about 0.44, alternatively,
about 0.39; where 6p is 0
to about 0.42, alternatively, about 0.37; and where 6q is 0 to about 0.34,
alternatively, about 0.29.
In a particular embodiment, the correlative comprises the mathematical
relationship expressed in
Equation (6a,) or approximations of the same:
Equation (6a,): C6a, =
HDL-3b XHDL-30.75 XHDL-20.61 XLDL-2041 XbTRL0.20 XHDL -19 XLDL-4016 XHDL-3a '
9 XdTRL0.02
LDL-30.51 XLDL-5042 XAge.39 XHDL-2b0.37 XLDL-10.29
[0096] In an additional embodiment, the correlative (C) comprises a simplified
expression of
the mathematical relationship of Equation (6), or approximations of the same,
expressed as
Equation (6p):
HDL-3b X HDL-3c0.75 x HDL-2a0.61 x LDL-20.41
Equation (6p): C60 = LDL-30.51 X LDL-50.42
[0097] In an alternative embodiment, the correlative (C) comprises an
expression of a
mathematical relationship between an HDL-3b fraction, an LDL-2 fraction, an
HDL-3c fraction,
and LDL-3 fraction. In an embodiment, the correlative comprises the
mathematical relationship
expressed in Equation (7), or approximations of the same:
Equation (7): C7 =
HDL-3b 7a XLDL-271 XLDL-47a XAge7d XLDL-l7e XHDL7f
HDL-3c7g XLDL-37h XHDL-3a71 XbTRLT` XdTRL7m XHDL-2b 7i XLDL-57p XHDL-2a 7q
where 7a is about 1; where 7b is about 0.55 to about 0.65, alternatively about
0.58 to about 0.62,
alternatively, about 0.60; where 7c is 0 to about 0.44, alternatively about
0.39; where 7d is 0 to
about 0.22, alternatively, about 0.17; where 7e is 0 to about 0.09,
alternatively, about 0.04; where
7f is 0 to about 0.08, alternatively, about 0.03; where 7g is about 0.78 to
about 0.88, alternatively,
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0.81 to about 0.85, alternatively, about 0.83; where 7h is about 0.51 to about
0.61, alternatively,
about 0.54 to about 0.58, alternatively, about 0.56; where 7j is 0 to about
0.42, alternatively, about
0.37; where 7k is 0 to about 0.27, alternatively, about 0.22; where 7m is 0 to
about 0.16,
alternatively, about 0.11; where 7n is 0 to about 0.14, alternatively, about
0.09; where 7p is 0 to
about 0.10, alternatively, about 0.05; and where 7q is 0 to about 0.06,
alternatively, about 0.01. In
a particular embodiment, the correlative comprises the mathematical
relationship expressed in
Equation (7a,) or approximations of the same:
Equation (7a,): C. =
HDL-3b XLDL-20.60 XLDL-4 39 XAge .17 XLDL-10.04 XHDL0.03
HDL-3c0.83 XLDL-30.56 XHDL-3a0.37 XbTRL0.22 xdTRL0.11 XHDL-2b0.09 XLDL-50.05
XHDL-2a ' '
[0098] In an additional embodiment, the correlative (C) comprises a simplified
expression of
the mathematical relationship of Equation (7), or approximations of the same,
expressed as
Equation (7p):
HDL-3b X LDL-20.60
Equation (7p): C70 = HDL-3c0.83 X LDL-30.56
[0099] Returning to Figure 2, in an embodiment, the DRA 2000 comprises
obtaining a
biological sample from a subject 600. In an embodiment, the subject is a human
whose risk for the
development of CVD is to be assessed. The biological sample may be of the type
previously
described herein. In an embodiment, the biological sample obtained from the
subject will be the
same type of biological sample obtained from the members of the population
subset used to
determine the correlative. Further, the biological sample may be obtained from
the subject via any
suitable means or process as previously disclosed herein.
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[00100] In an embodiment, the DRA 2000 comprises deriving one or more
attributes for the
subject biological sample 700. For example, the lipoprotein profile of the
subject may be
determined as described previously herein.
[00101] In an embodiment, the DRA 2000 comprises predicting whether the
subject is a
member of the population subset 800. Predicting whether the subject is a
member of the
population subset 800 generally comprises employing a correlative to ascertain
whether one or
more attributes of the subject biological sample bear the same or about the
same relationship as the
attributes derived from biological samples obtained from the population
subset.
[00102] Not intending to be limited by theory, those of skill in the art may
theorize that where
the relationship between one or more of the attributes obtained from a
biological sample obtained
from a subject is approximated by a correlative, the subject would be expected
to be a member of
the group used to derive that correlative. Alternatively, where the
relationship between one or
more of the attributes derived from a biological sample obtained from a
subject is not
approximated by a correlative, the subject would not be expected to be a
member of the group used
to derive that correlative.
[00103] In an embodiment, a subject has a relationship among the lipoprotein
subclasses which
is approximated by the correlative determined for the population subset
comprising humans
established to have CVD. In such an embodiment, the subject would be
characterized as having
CVD. Alternatively, a subject having a relationship among the lipoprotein
subclasses which is
approximated by the correlative determined for the population subset
comprising humans not
having CVD may be characterized as not having CVD.
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[00104] In an embodiment, a subject having a relationship among the
lipoprotein subclasses
which is approximated by the correlative determined for the population subset
comprising humans
at risk for developing CVD may be characterized as at risk for developing CVD.
[00105] In an embodiment, the methodologies disclosed herein are equal to or
greater than
about 85% effective in classifying membership of an individual subject in a
population subset.
Alternatively equal to or greater than about 90, 95, 97.5, 99, 99.5 or 100%
effective in classifying
membership of an individual subject in a population subset. In an embodiment,
classification of a
subject comprises determining the membership of the subject in a group wherein
membership in
the group is based on a health related issue. For example, the classification
of the subject as
described herein may result in the subject being classified as a member of a
population subset at
risk for developing CVD. In an embodiment, the methodologies disclosed herein
are employed to
develop methods of classifying subjects into groups wherein membership is
based on the presence,
absence, or risk for development of a disease. In such an embodiment, the
methodologies disclose
herein are equal to or greater than about 85% effective in identifying issues
concerning the
individual's health.
[00106] Although specific embodiments related to classification of subjects
based on
relationships between lipoprotein profile and CVD have been described, it is
contemplated the
disclosed methodologies may be employed for classification of subjects based
on relationships
between lipoprotein profile and other DOIs. In an embodiment, the
methodologies disclosed
herein may be utilized for any disease (e.g., genetic disorders, coronary
heart disease) wherein
lipoprotein type and amount may influence the onset, progression and/or
outcome of the disease.
In an embodiment, the methodologies disclosed may be used to identify subjects
at risk or
experiencing a form of diabetes and/or suffering from metabolic syndrome. In
another
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WO 2009/152437 PCT/US2009/047211
embodiment, the methodologies disclosed herein may be employed for
establishing relationships
between attributes of a biological sample obtained from a subject and a DOI.
[00107] In an embodiment, all or some portion of a DCD or a DRA may be
automated. In an
embodiment, all or some portion of such a DCD or DRA is implemented in
software on a
computer or other computerized component having a processor, user interface,
microprocessor,
memory, and other associated hardware and operating software. Software
implementing the
preparation of a theoretical diffraction pattern may be stored in tangible
media and/or may be
resident in memory on the computer. Likewise, input and/or output from the
software, for example
ratios, comparisons and results may, be stored in a tangible media, computer
memory, hardcopy
such a paper printout, or other storage device.
[00108] Alternatively, in an embodiment, all or some portion of a DCD or a DRA
may be
performed manually, for example, as by a clinician or other person.
Alternatively, some portion of
a DCD or a DRA is performed manually and some portion is automated.
EXAMPLES
[00109] These examples describe the analysis of data on a number of variables
that were
provided for 18 control patients and 14 patients with CVD. An aim of this
analysis was to
understand how attributes of these variables differ across the two groups of
patients. In these
examples, LDA, SAVE, RP, and SMVCIR was employed as a classification models.
The SAVE
and SMVCIR approaches to classification are based on searching for linear
combinations of the
predictor variables which best separate the groups in terms of mean, variance
and covariance of
these linear combinations.
CA 02727818 2010-12-13
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EXAMPLE 1
[00110] The lipoprotein profile with respect to TC, HDL, LDL, TG were analyzed
using LDA
and SAVE. Specifically the relative amounts of these lipoprotein subclasses
were determined
using the methodologies described herein and are denoted the attributes of the
sample. All
attributes were log-transformed; that is, the attributes used in LDA and SAVE
were log-
transformed versions of the attributes.
[00111] In a two group problem, LDA seeks to find a linear combination of the
predictor
variables which best separates the two groups in terms of the mean of this
linear
combination. Given below are the results obtained from LDA using these four
variables as
predictors. Based on cross-validation, we find that LDA based on these two
predictors correctly
classifies 81.3% of the 32 cases as either CVD or control. For the current
data set, the most
important variable in the LDA linear combination was found to be log(TC).
[00112] The linear combination obtained from LDA is expressed as:
= 8.2llog(TC) - 2.871og(HDL) - 2.05log(LDL) - 0.3281og(TG)
= 8.21 [log(TC) -log(HDL)2s7/8.21 - log(LDL)2.05/8.21 - log(TG)0.328/8.21]
= 8.21 log TC
HDL0.35 XLDL0.25XTG0.04
[00113] Thus, the relevant term in LDA is proportional to: TC
HDL0.35 XLDL0.25XTG0.04
[00114] Figure 3 provides a plot of this ratio for these 32 data points. Thus,
this
straightforward ratio provides a rule by which to discriminate between the CVD
and control
groups. The most significant terms in this expression are: TC
HDL0.35
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[00115] Referring to Figure 3, the LDA ratio is on average smaller for
patients with CVD than
it is for patients without CVD. Thus, the previous ratio is generally smaller
for patients with CVD
than it is for patients without CVD.
EXAMPLE 2
[00116] In a two group problem, SAVE1 seeks to find linear combinations of the
predictor
variables which best separates the two groups in terms of the mean, variance
and covariance of
these linear combinations. Figure 2 shows a plot of the first two linear
combinations produced
by SAVE for the log-transformed attributes. It is apparent from Figure 4 that
the variability of
SAVE1 differs across the two groups. On the other hand, SAVE2 splits the two
groups in
terms of location or means. In fact, SAVE2 and LDA are somewhat similar.
[00117] The first SAVE dimension can be approximated by
= -7.661og(TC) +4.521og(HDL) +3.75log(LDL) +0.2251og(TG)
= 7.66[-log(TC) +log(HDL)4.52"7 66 + log(LDL)3.75n.66 - log(TG)o.225/7.66]
= -7.66 log I HDL0.59 xLDL0.49 XT 003
TC
[00118] Thus, the relevant term in SAVE1 is proportional to: HDL059 xLDL049
xTGo.o3
TC
[00119] The most significant terms in this ratio are: HDL0.59 xLDL0.49
TC
[00120] Referring to Figure 4, we see that SAVE1 varies less across patients
with CVD
than it does across patients without CVD. Thus, the previous ratio is less
variable for patient
with CVD than it is for patients without CVD.
EXAMPLE 3
[00121] The second SAVE dimension can be approximated by:
_ -5.761og(TC) +1.21log(HDL)- 0.505log(LDL) - 0.6431og(TG)
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= 5.76[-log(TC) +log(HDL)1.21/5.76 - log(LDL) 0.505/5.76 - log(TG)0.643/5.76]
= -7.76 to HDL0.29 XLDL0.09 XTGo.II
TC
[00122] The most important terms in this ratio are: HDL0.29
TC
[00123] Looking back at Figure 4, we see that SAVE2 is on average larger for
patients with
CVD than it is for patients without CVD. Thus, the previous ratio is generally
larger for patients
with CVD than it is for patients without CVD.
EXAMPLE 4
[00124] The linear combination obtained from LDA is given by:
= -5.11 log(HDL-3b) +4.751og(HDL-2b) + 3.951og(HDL-3c) -3.91log(LDL-5)-
2.831og(HDL)
+1.891og(HDL-3a) +1.88 log(LDL-4) -1 .63 log(LDL-3) +0.881og(dTRL) +0.
871og(bTRL)
-0.441og(HDL-2a) -0.341og(LDL-2) +0.15 log(LDL-1) +0.051og(Age)
= -5.11 [log(HDL3-b) -log(HDL-2b)o.93 -log(HDL-3c) .77 +log(LDL-5)077
+log(HDL)0.55 -
log(HDL-3a) .37-log(LDL-4)037+log(LDL-3) .32-log(dTRL)0*17-log(bTRL)0.17
+log(HDL-
2a)0.09 +log(LDL-2)- . 7 -log(LDL- 1 )0.03 -log(Age)o.01]
= -5.11 log
HDL-3b XLDL-5 0.77 XHDL 0.55 XLDL-3 0.32 XHDL-2a0-09 XLDL-2 0.07
HDL-20.93 XHDL-3c .77 XHDL-3a .37 XLDL-4037 XdTRLO'17 XbTRL0.17 XLDL-10.03
XAge.01
[00125] Thus, the relevant term in LDA is proportional to:
HDL-3b XLDL-5077 XHDL0.55 XLDL-3032 XHDL-2a ' 9 XLDL-20.07
HDL-20.93 XHDL-3c .77 XHDL-3a .37 XLDL-4037 XdTRLO'17 XbTRL0.17 XLDL-10.03
XAge-01
[00126] Figure 5 provides a plot of this ratio for the 32 data points. Thus,
this ratio provides a
rule to discriminate between the CVD and control groups. The most significant
terms in this
ratio are: HDL-3b XLDL-5077 XHDL0.55
HDL-2b 93 XHDL-3c .77
[00127] In a two group problem, SAVE seeks to find linear combinations of the
predictor
variables which best separates the two groups in terms of the mean, variance
and covariance
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of these linear combinations. Figure 6 shows a plot of the first two linear
combinations
produced by SAVE for the log-transformed data. It is apparent from Figure 4
that the
variability of SAVE1 and SAVE2 differs across the two groups. On the other
hand, the plots
SAVE3 and SAVE1 and SAVE3 and SAVE2 each produce points which lie close to two
lines
with different slopes. In other words, SAVE3 has found two linear combinations
of the
predictors whose covariance (or relationship) differs across the control and
CVD groups.
EXAMPLE 5
[00128] The first SAVE dimension can be approximated by:
= 10. 64 1 og(HDL-2b) -9.211 og(HDL-2a) -6.971og(LDL-5) +4.5 6 1 og(LDL-4)-
2.06log(LDL-3)
+1.91log(HDL)-1.71log(Age) +1.44 log(LDL-2)+1.421og(LDL-1)-i.14log(dTRL) -
0.971og(HDL-3c) +0.87Iog(HDL-3a)-0.40log(HDL-31:)+0.3 1 log(bTRL)
= 10.64 [log(HDL-2b) -log(HDL-2a)0.87 -log(LDL-5) .65+1og(LDL-4) .43-log(LDL-
3) 19
+log(HDL)018 -log(Age)0*16 +log(LDL-2) 13 +log(LDL-1)0'13-1og(dTRL)0'111 -
log(HDL-
3c) . 9+log(HDL-3a) '08 -log(HDL-3b)o.04 +log(bTRL) . 4]
=10.64log HDL-2b XLDL-4 0.43 XHDL0'18 XLDL-20.13 xLDL- 10.13 XHDL-3c0'08 XbTRL
003
L HDL-2a '87 XLDL-50.65 XLDL-30.19 XAge'16 XdTRL0.11 XHDL-3c . 9 XHDL-3b0.04
[00129] Thus, the relevant term in SAVE 1 is proportional to:
HDL-2b XLDL-4 .43 XHDL '18 XLDL-2 .13 XLDL-10.13 XHDL-3c ' 8 XbTRL0.03
HDL-2a .87 XLDL-50.65 XLDL-30.19 XAge'16 XdTRL0.11 XHDL-3c-09 XHDL-3b0.04 .
[00130] The most significant terms in this ratio are: HDL-2b XLDL-40.43
HDL-2a0.87 XDL-50.65
[00131] Referring to Figure 6, we see that SAVE1 varies more across patients
with CVD than it
does across patients without CVD. Thus, the previous ratio is more variable
for patients with CVD
than it is for patients without CVD.
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EXAMPLE 6
[00132] The second SAVE dimension can be approximated by
= 4.461 og(HDL-3b) +3.351 og(HDL-3 c) +2.7 1 log(HDL-2a) -2.291 og(LDL-3) -
1.881og(LDL-5) +1.821og(LDL-2) -1.761og(Age) -1.631og(HDL-2b) -1.28log(LDL-
1)+0.891og(bTRL) +0.871og(HDL) +0.701og(LDL-4) +0.41log(HDL-3a)
+0.091og(dTRL)
= 4.46[log(HDL-3b) +log(HDL-3c)0.75 +log(HDL-2a)0.61 -log(LDL-3)051-log(LDL-
5)0.42 +log(LDL-2)041 -log(Age)039 - log(HDL-2b)0.37 -log(LDL-1)029
+log(bTRL)0.20
+l og(HDL)0.19 +log(LDL-4)0.16 +log(HDL-3 a)0.09 +log(dTRL)0.02]
= 4.46 log
HDL-3b XHDL-3c 0.75 XHDL-2a 0.61 XLDL-2 0.41 XbTRL0 .20 XHDL0-19 XLDL-40- 16
XHDL-3a0'09 XdTRL 002
LDL-30.51 XLDL-5042 XAge.39 XHDL-2b0.37 XLDL-10.29
[00133] Thus, the relevant term in SAVE2 is proportional to:
HDL-3b XHDL-3c0.75 XHDL-2a0.61 XLDL-2041 XbTRL0.20 XHDL0.19 XLDL-40.16 XHDL-3a
' 9 XdTRL0.02
LDL-30.51 XLDL-5042 XAge.39 XHDL-2b0.37 XLDL-10.29
[00134] The most important terms in this ratio are:
HDL-3b X HDL-3c0.75 x HDL-2a0.61 x LDL-20.41
LDL-30.51 x LDL-50.42
[00135] Referring to Figure 6, SAVE2 varies more across patients with CVD than
it does across
patients without CVD. Thus, the previous ratio is more variable for patients
with CVD than it is
for patients without CVD.
EXAMPLE 7
[00136] The third SAVE dimension can be approximated by:
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= 6.481og(HDL-3b) -5.40 log(HDL-3e) +3.891og(LDL-2) -3.61log(LDL-
3)+2.521og(LDL-4)
-2.381og(HDL-3a) -1.43log(bTRL)+1.1llog(Age) -0.741og(dTRL) -0.6llog(HDL-2b) -
0.291og(LDL-5) +0.281og(LDL-1) +0.21log(HDL) -0.07log(HDL-2a)
= 6.48 [log(HDL-3b) -log(HDL-3c) 83 +log(LDL-2)0.60 -log(LDL-3)0.56 +log(LDL-
4) .39 -
log(HDL-3a)0.37 -log(bTRL)0*22 +log(Age)0*17 -log(dTRL) -11-log(HDL-2b) ' 9 -
log(LDL-5) . 5
+log(LDL-1) . 4_log(HDL) . 3 -log(HDL-2a) . 1]
= 6.481og
HDL-3b XLDL-20.60 XLDL-4039 XAge0-17 XLDL-10.04 XHDL . 3
L HDL-3c0.83 XLDL-30.56 XHDL-3a0.37 XbTRL .22 XdTRL .11 XHDL-2bo-09 XLDL-50.05
XHDL-2a . 1
[00137] Thus, the relevant term in SAVE3 is proportional to:
HDL-3b XLDL-2 .60 XLDL-4039 XAge .17 XLDL-10.04 XHDL . 3
HDL-3c .83 XLDL-30.56 XHDL-30.37 XbTRL0.22 xdTRL .11 XHDL-2b -09 XLDL-50.05
XHDL-2a . 1
[00138] The most important terms in this ratio are: HDL-3b X LDL-20.60
HDL-3c0 83 X LDL-30s6
[00139] Referring to Figure 6, the relationship between SAVE 3 and SAVE1 and
the
relationship between SAVE 3 and SAVE 2 varies across the two groups of
patients. For patients
with CVD, SAVE3 is essentially the same for all values of SAVE1, while for
patients without
CVD, SAVE3 varies widely while SAVE1 is essentially constant. For patients
without CVD,
SAVE3 is essentially the same for all values of SAVE2, while for patients
without CVD, SAVE3
varies widely while SAVE2 is essentially constant.
EXAMPLE 8
[00140] A pilot clinical study used 15 control subjects (or donors) and 15
disease group
subjects. All donors had normal LDL-c and normal to elevated HDL-c. Individual
serum samples
were separated into major lipoprotein subclasses using metal ion chelates of
EDTA as described
previously herein. The resulting separation was then imaged as shown in the
far left panel of
Figure 7A. The image was then processed to produce the lipoprotein profile
shown in the center
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panel of Figure 7A. Form this profile integrated intensities of the major
lipoprotein subclasses
were measured and are shown in the far right panel of Figure 7A.
[00141] The groups were classified using the integrated fluorescence
intensities of the 12
lipoprotein subclasses described previously herein. The integrated intensities
were then subjected
to LDA and SAVE. Two correlatives found by LDA were log [LDL-3] intensity and
log [HDL-
2b] intensity. Back transformation of the logs resulted in the relevant ratio
of the two predictors
being LDL-3/HDL-2b. Utilizing just this relationship, LDA separates 83.3% of
the 30 cases
correctly as either CVD or control. This is graphically depicted in Figure 7B.
[00142] The data was also subjected to analysis using the statistical
correlation method SAVE.
SAVE seeks to find linear combinations of the predictor variables which best
separates the two
groups in terms of the mean, variance and covariance of these linear
combinations. This makes
SAVE a multidimensional separation. Figure 8 shows a graphical representation
of the two
dimensions produced by SAVE for the data set in the log transformation. It is
readily apparent that
the slopes of the two lines in Figure 8 are very different. Therefore, the
covariance or relationship
differs across the control and CVD group. Thus, SAVE has found two linear
combinations
capable of classifying these two groups. Interestingly, the values of SAVE1
for the control group
are relatively constant while they change dramatically for the CVD group. The
most significant
variables were found to be an associated HDL-5a and HDL-3b comparison of
exponential power.
Then by back transforming the logs, the relevant ratio of these variables is
HDL-3a/HDL-3b0 86
Similarly the second dimension of the SAVE analysis was reviewed. In the
second SAVE
dimension the SAVE-2 values for the CVD subjects is relatively constant and
varies dramatically
for the control subjects. From this linear combination we found the relevant
ratio of these variables
was HDL-3b/HDL-5c078.
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[00143] The classification power of the disclosed methodologies was improved
by combining
SAVE and LDA to produce a three-dimensional plot of LDA, SAVE 1, and SAVE 2,
Figure 9. It is
clear from this plot that the two groups of points are close to being disjoint
(i.e., have very little
overlap). The fact that the groups are so well separated is evidence that this
analysis is highly
effective for classifying cohorts.
[00144] At least one embodiment is disclosed and variations, combinations,
and/or
modifications of the embodiment(s) and/or features of the embodiment(s) made
by a person having
ordinary skill in the art are within the scope of the disclosure. Alternative
embodiments that result
from combining, integrating, and/or omitting features of the embodiment(s) are
also within the
scope of the disclosure. Where numerical ranges or limitations are expressly
stated, such express
ranges or limitations should be understood to include iterative ranges or
limitations of like
magnitude falling within the expressly stated ranges or limitations (e.g.,
from about 1 to about 10
includes, 2, 3, 4, etc.; greater than 0.10 includes 0.11, 0.12, 0.13, etc.).
For example, whenever a
numerical range with a lower limit, R1, and an upper limit, R, is disclosed,
any number falling
within the range is specifically disclosed. In particular, the following
numbers within the range are
specifically disclosed: R=R1 +k* (Re-Ri), wherein k is a variable ranging from
1 percent to 100
percent with a 1 percent increment, i.e., k is 1 percent, 2 percent, 3
percent, 4 percent, 5 percent,
50 percent, 51 percent, 52 percent, ....., 95 percent, 96 percent, 97 percent,
98 percent, 99
percent, or 100 percent. Moreover, any numerical range defined by two R
numbers as defined in
the above is also specifically disclosed. Use of the term "optionally" with
respect to any element
of a claim means that the element is required, or alternatively, the element
is not required, both
alternatives being within the scope of the claim. Use of broader terms such as
comprises, includes,
and having should be understood to provide support for narrower terms such as
consisting of,
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consisting essentially of, and comprised substantially of. Accordingly, the
scope of protection is
not limited by the description set out above but is defined by the claims that
follow, that scope
including all equivalents of the subject matter of the claims. Each and every
claim is incorporated
as further disclosure into the specification and the claims are embodiment(s)
of the present
invention. The discussion of a reference in the disclosure is not an admission
that it is prior art,
especially any reference that has a publication date after the priority date
of this application. The
disclosure of all patents, patent applications, and publications cited in the
disclosure are hereby
incorporated by reference, to the extent that they provide exemplary,
procedural or other details
supplementary to the disclosure.
44
