U.S. patent application number 13/255568 was filed with the patent office on 2011-12-29 for predicting coronary artery disease and risk of cardiovascular events.
Invention is credited to Geoffrey S. Ginsburg, Elizabeth R. Hauser, William E. Kraus, L. Kristin Newby, Christopher B. Newgard, Svati H. Shah.
Application Number | 20110318726 13/255568 |
Document ID | / |
Family ID | 42728754 |
Filed Date | 2011-12-29 |
![](/patent/app/20110318726/US20110318726A1-20111229-D00000.png)
![](/patent/app/20110318726/US20110318726A1-20111229-D00001.png)
![](/patent/app/20110318726/US20110318726A1-20111229-D00002.png)
![](/patent/app/20110318726/US20110318726A1-20111229-D00003.png)
![](/patent/app/20110318726/US20110318726A1-20111229-D00004.png)
![](/patent/app/20110318726/US20110318726A1-20111229-D00005.png)
![](/patent/app/20110318726/US20110318726A1-20111229-D00006.png)
United States Patent
Application |
20110318726 |
Kind Code |
A1 |
Shah; Svati H. ; et
al. |
December 29, 2011 |
PREDICTING CORONARY ARTERY DISEASE AND RISK OF CARDIOVASCULAR
EVENTS
Abstract
Methods of assessing the risk of cardiovascular disease in a
subject by detecting the level of at least one metabolite in a
sample from the subject are disclosed herein. The level of the
metabolite is indicative of the risk of cardiovascular disease in
the subject. The metabolites may be acylcarnitines, amino acids,
ketones, free fatty acids or hydroxybutyrate. The cardiovascular
disease may be risk of a cardiovascular event, presence of coronary
artery disease or risk of development of coronary artery
disease.
Inventors: |
Shah; Svati H.; (Durham,
NC) ; Newgard; Christopher B.; (Durham, NC) ;
Kraus; William E.; (Durham, NC) ; Hauser; Elizabeth
R.; (Durham, NC) ; Ginsburg; Geoffrey S.;
(Durham, NC) ; Newby; L. Kristin; (Durham,
NC) |
Family ID: |
42728754 |
Appl. No.: |
13/255568 |
Filed: |
March 10, 2010 |
PCT Filed: |
March 10, 2010 |
PCT NO: |
PCT/US10/26845 |
371 Date: |
September 9, 2011 |
Current U.S.
Class: |
435/4 ; 250/282;
436/112; 436/128; 436/129; 436/71; 436/86 |
Current CPC
Class: |
G01N 21/78 20130101;
A61B 5/14546 20130101; H01J 49/0027 20130101; G01N 2800/50
20130101; G01N 2021/7786 20130101; G01N 2800/324 20130101; A61B
5/7275 20130101; Y10T 436/200833 20150115; G01N 33/6887 20130101;
Y10T 436/174614 20150115; A61B 5/00 20130101; A61B 5/4866 20130101;
Y10T 436/201666 20150115; G16H 50/30 20180101; A61B 5/14532
20130101 |
Class at
Publication: |
435/4 ; 436/86;
436/112; 436/128; 436/71; 436/129; 250/282 |
International
Class: |
C12Q 1/00 20060101
C12Q001/00; H01J 49/26 20060101 H01J049/26; G01N 33/64 20060101
G01N033/64; G01N 33/92 20060101 G01N033/92; G01N 33/68 20060101
G01N033/68; G01N 33/50 20060101 G01N033/50 |
Foreign Application Data
Date |
Code |
Application Number |
Mar 10, 2009 |
US |
61159077 |
Claims
1. A method for assessing risk of cardiovascular disease in a
subject comprising: a) detecting the level of a least one
metabolite in a sample from the subject, wherein the metabolite is
selected from the group consisting of acylcarnitines, amino acids,
ketones, free fatty acids and hydroxybutyrate; and b) comparing the
level of the metabolite in the sample to a standard, wherein the
level of the metabolite in the subject is indicative of the risk of
cardiovascular disease in the subject.
2. A method for assessing the risk of cardiovascular disease in a
subject, comprising: obtaining a sample from the subject; providing
the sample to a laboratory for detection of metabolite levels in
the sample, wherein the metabolite is selected from acylcarnitines,
amino acids, ketones, fatty acids and hydroxybutyrate; and
receiving from the laboratory a report indicating metabolite levels
in the sample, wherein the level of the metabolite is indicative of
the risk of cardiovascular disease in the subject.
3. The method of claim 1, wherein the cardiovascular disease is a
cardiovascular event and the level of the metabolite in the subject
is indicative of the risk of a cardiovascular event in the
subject.
4. The method of claim 3, wherein the metabolite detected in step
(a) is a short-chain dicarboxylacylcarnitine metabolite.
5. The method of claim 3, wherein the metabolite detected in step
(a) is selected from the group consisting of Gly, Ala, Ser, Pro,
Met, His, Phe, Tyr, Asx, Glx, Ornithine, Citrulline, arg, C2, C3,
C4:C14; C5:1, C5, C4:OH, C14-DC:C4DC, C5-DC, C6-DC, C10:3, C10,
C10-53 OH:C8DC, C12:1, C12, C12-OH:C10DC, C14:1-OH, C14-OH:C12-DC,
C16, C16-OH/C14-DC, C18:2, C18-OH/C16-DC, C20, C20:1-OH/C18:1-DC,
C20-OH/C18-DC, C8:1-OH/C6:1-DC, C8:1-DC, C16:1, C16:1-OH/C14:1-DC,
C20:4, FFA, HBUT, and Ket.
6. The method of claim 3, wherein the metabolite detected in step
(a) includes the metabolites of factor 8.
7. The method of claim 3, wherein the metabolites detected in step
(a) comprise citrulline, C5-DC, C6-DC, C8:1-OH/C6:1-DC, and
C8:1-DC.
8. The method of claim 3, wherein the metabolites detected in step
(a) comprise ornithine, citrulline, C5, C14-DC:C4DC, C5-DC, C6-DC,
C10-OH:C8DC, C8:1-OH/C6:1-DC, C8:1-DC, C20:4 and FFA.
9. The method of claim 1, wherein the cardiovascular disease is
coronary artery disease and the level of the metabolite in the
subject is indicative of the presence of coronary artery disease in
the subject.
10. The method of claim 9, wherein the metabolite detected in step
(a) is a medium-chain acylcarnitine, a branched chain amino acid or
associated metabolite, or a metabolite associated with the urea
cycle.
11. The method of claim 9, wherein the metabolite detected in step
(a) is selected from the group consisting of Pro, Leu/Ile, Met,
Val, Glx, Citrulline, C2, C3, C4:Ci4; C5, C8, C8:1-OH/C6:1-DC,
C10:1, C14:2, C14:1-OH, C16:2, C16:1, C16:2, C16:1,
C16:1-OH/C14:1-DC, C18-OH/C16-DC, HBUT, and Ket.
12. The method of claim 9, wherein the metabolite detected in step
(a) includes the metabolites of at least one of factor 1, factor 4
or factor 9.
13. The method of claim 9, wherein the metabolites detected in step
(a) comprise Leu/Ile, Glx, 014:1-OH and C16:1-OH/C14:1-DC.
14. The method of claim 13, wherein a level of Leu/Ile greater than
170 mM is indicative of coronary artery disease.
15. The method of claim 13, wherein a level of GLx greater than 128
mM is indicative of coronary artery disease.
16. The method of claim 13, wherein a level of 014:1-OH less than
0.013 uM is indicative of coronary artery disease.
17. The method of claim 13, wherein a level of C16:1-OH/C14:1-DC
less than 0.0089 uM is indicative of coronary artery disease.
18. The method of claim 13, wherein the metabolites detected in
step (a) further comprise C2, C5 and C18-OH/C16-DC.
19. The method of claim 1, wherein the cardiovascular disease is
coronary artery disease and the level of the metabolite in the
subject is indicative of the risk of developing coronary artery
disease in the subject.
20. The method of claim 19, wherein the metabolites measured
include the short- and medium-chain acylcarnitine metabolites,
branched chain amino acids and urea cycle related metabolites.
21. The method of claim 19, wherein the metabolites measured
include ketones. arg, ornithine, citrulline, glx, ala, val,
leu/ile, pro, C2, C14:1, C18:1, C5:1, C4-i4, C18, C10:1 and
FFA.
22. The method of claim 1, wherein the level of at least two
metabolites is detected in step (a).
23. The method of claim 1, wherein the sample is blood.
24. The method of claim 1, wherein the level of the metabolite is
detected using mass spectroscopy.
25. The method of claim 1, wherein the level of the metabolite is
detected using a colorimetric or fluorometric assay.
26. A method of developing a treatment plan for a subject
comprising using the comparison of step (b) of any of the preceding
claims to develop a treatment plan based on the risk of
cardiovascular disease in the subject.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the benefit of U.S. Provisional
Patent Application No. 61/159,077 filed Mar. 10, 2009, which is
incorporated herein by reference in its entirety.
BACKGROUND OF THE INVENTION
[0002] Coronary artery disease (CAD) is the leading cause of death
in industrialized countries, and in concert with the epidemic of
obesity and diabetes, is rapidly becoming the leading cause of
death in developing countries. The genetic predilection of CAD is
well-established; family history has been shown to be an
independent risk factor for CAD, especially in early onset forms.
Despite this, the genetic architecture of CAD remains largely
unknown.
[0003] Many accepted risk factors for CAD are metabolic. However,
there remains an incomplete mechanistic understanding of CAD risk,
and equally important, a need to refine our ability to identify
individuals at highest risk of cardiovascular events. Given the
complex nature of CAD, evaluation with more comprehensive tools may
improve risk stratification and enhance our understanding of the
disease process.
SUMMARY OF THE INVENTION
[0004] In one aspect, methods for assessing risk of cardiovascular
disease in a subject are provided. The risk assessment may include
predicting the likelihood a subsequent cardiovascular event such as
a myocardial infarction, predicting development of CAD, or
discriminating the presence of CAD in a subject. The methods
include detecting at least one metabolite in a sample from the
subject. The metabolite may be an acylcarnitine, an amino acid, a
ketone, a free fatty acid or .beta.-hydroxybutyrate. The levels of
metabolites are then compared to a standard or to control subjects
and can be used to determine the level of risk of a cardiovascular
event, the risk of development of CAD or the presence of CAD in the
subject.
[0005] In another aspect, methods of developing a treatment plan
for a subject with or at risk of developing CAD or a subject at
risk for a cardiovascular event are also provided. The methods
include using the level of detected metabolite in the subject to
develop a treatment plan based on the risk of cardiovascular
disease in the subject. The plan may include diet, exercise and
pharmaceutical treatment options.
[0006] In still another aspect, methods for assessing the risk of
cardiovascular disease in a subject are provided in which a sample
is obtained from the subject. The sample is provided to a
laboratory for detection of metabolite levels in the sample. The
metabolites detected may be acylcarnitines, amino acids, ketones,
fatty acids or hydroxybutyrate. The laboratory returns a report
indicating metabolite levels in the sample, which are indicative of
the risk of cardiovascular disease in the subject.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] FIG. 1 is a set of graphs showing the receiver operating
characteristic (ROC) curves for metabolite factors and CAD. ROC
curves and measures of model fit (c-statistic) are presented for
three models: a clinical model inclusive of traditional CAD risk
factors (diabetes, hypertension, dyslipidemia, smoking, BMI, family
history; and for the replication group, age, race and sex are also
included) (black line); a model inclusive of all traditional risk
factors plus metabolite factors 4 and 9 (gray line); and a model
inclusive of all traditional risk factors plus all metabolite
factors (dashed black line). The top graph shows the initial group
and the bottom graph shows the replication group.
[0008] FIG. 2 is a set of graphs showing the cox proportional
hazards model for predictive capability of metabolite Factor 8 for
cardiovascular events. Unadjusted (left panel) and adjusted (right
panel) survival curves (adjusted for BMI, severity of CAD,
hypertension, dyslipidemia, diabetes, smoking, family history,
ejection fraction, serum creatinine, subsequent CABG, age, race and
sex) are presented for metabolite factor 8.
[0009] FIG. 3 is a pedigree of the eight multiplex GENECARD
families. Black filled in symbols signify affected with premature
CAD; smaller gray circles signify blood profiling performed for
this study. Note that the majority of the family members profiled
are as-of-yet unaffected offspring of the original affected-sibling
pairs.
[0010] FIG. 4 is a graph showing the heritabilities of conventional
metabolites. The Y-axis is the negative log 10 of the p-value for
the heritability estimate (X-axis). Error bars around heritability
point estimates are in light grey.
[0011] FIG. 5 is a graph showing the heritabilities of amino acids
and free fatty acids. Displayed are heritabilities of amino acids
and free fatty acids. The Y-axis is the negative log 10 of the
p-value for the heritability estimate (X-axis). Error bars around
heritability point estimates are in light grey.
[0012] FIG. 6 is a graph showing the heritabilities of
acylcarnitines. The Y-axis is the negative log 10 of the p-value
for the heritability estimate (X-axis). Error bars around
heritability point estimates are in light grey.
DETAILED DESCRIPTION
[0013] Metabolomics, the study of small-molecule metabolites, may
be useful for diagnosis of human disease. Studies have demonstrated
heritability of metabolites in plants and mice. As described in the
Examples, metabolite profiles are heritable in human families with
early-onset CAD, suggesting that the known heritability of CAD may
be mediated at least in part through metabolic components
measurable in blood. The Examples describe quantitative profiling
of 69 metabolites, including acylcarnitine species (byproducts of
mitochondrial fatty acid, carbohydrate and amino acid oxidation),
amino acids and conventional metabolites such as free fatty acids,
ketones and .beta.-hydroxybutyrate, in participants enrolled in the
Duke CATHGEN biorepository and in families selected from the Duke
GENECARD study. The capability of metabolite profiles to assess the
risk of cardiovascular disease in a subject is provided herein. The
Examples demonstrate that the levels of particular metabolites,
alone or in combination, discriminate the likelihood of developing
CAD, the presence of CAD and the risk of subsequent cardiovascular
events.
[0014] Methods of assessing or predicting risk of cardiovascular
disease in a subject are provided. The methods include detecting
the level of at least one metabolite in a sample from the subject.
The amount or relative level of the metabolite may be detected. The
metabolites detected may be acylcarnitines, amino acids, ketones,
free fatty acids (FFA), or hydroxybutyrate. The level of the
metabolite in the sample from the subject is then compared to a
standard to assess the risk of cardiovascular disease. The standard
may be an empirically derived number for each metabolite indicating
a normal range and/or a range indicative of cardiovascular disease
or may be direct comparison to the levels of metabolite in
individuals with known cardiovascular disease status.
[0015] Methods for assessing the risk of cardiovascular disease in
a subject by obtaining a sample from the subject and providing the
sample to a laboratory for detection of metabolite levels in the
sample are also provided. As above, the metabolite detected by the
laboratory may include acylcarnitines, amino acids, ketones, fatty
acids and hydroxybutyrate. A report indicating metabolite levels in
the sample is then received from the laboratory. The report
indicates the level of the metabolite in the subject and the level
can be used to compare to standard values to indicate the risk of
cardiovascular disease in the subject.
[0016] The risk of cardiovascular disease includes assessing the
risk of a subject without CAD developing CAD over time due to
heritable factors, assessing the presence or absence of CAD in a
subject and assessing the risk of having a cardiovascular event.
Cardiovascular events include myocardial infarction, stroke and
death.
[0017] Subjects may be any mammal, suitably the subject is human.
Subjects identified as having or at risk of developing CAD may be
further assessed to determine their risk of a cardiovascular event
using the methods provided herein. The methods may be used to help
diagnose the presence of CAD in a non-invasive fashion and/or to
develop a treatment plan for subjects identified as at risk for
CAD, having CAD or at risk for a cardiovascular event. The
treatment plan may include provision of dietary, exercise, and
pharmaceutical therapies to the subject. A cardiovascular event
includes, but is not limited to, myocardial infarction (MI), stroke
and death.
[0018] The metabolite may be detected using a variety of samples,
several of which will be apparent to those skilled in the art. In
the Examples, peripheral blood was obtained from the subject and
processed in order to detect the level of metabolites in the
subject. Other tissues or fluids from the subject may also be used,
including but not limited to blood, plasma, urine, serum, saliva,
and tissue biopsies.
[0019] Any method may be used to detect the metabolite. Suitably
the method is quantitative such that the level or amount of the
metabolite in the subject or a sample from the subject may be
determined. In the Examples, the level of the metabolites was
detected by mass spectrometry. Other methods of measurement may be
used, including nuclear magnetic resonance (NMR). The metabolites
may also be detected using colorimetric or fluorometric assays
based on detection of the metabolite by an assay such as a binding
or enzymatic assay. Any suitable assay method for the metabolites
may be used. Such methods will be apparent to those skilled in the
art. The level of the metabolite in the subject may be reported as
ng/ml of metabolite in blood or tissue, by the mM or .mu.M
concentration of the metabolite in the blood or tissue or by using
arbitrary units to show relative levels amongst subjects. In the
Examples, the mM or .mu.M of metabolite in the blood are
reported.
[0020] In some embodiments, detection of a single metabolite is
sufficient to assess risk of cardiovascular disease. In other
embodiments, 2, 3, 4, 5, 7, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55,
60 or 65 metabolites may be detected and used in the methods to
assess the risk of cardiovascular disease. The metabolites detected
may be related in a factor by principal component analysis of a
population of subjects. The factors, or groups of metabolites,
useful for assessing heritability of CAD and for the presence of
CAD or risk of having a cardiovascular event are presented in the
Examples below.
[0021] The level of metabolite in the subject is used to determine
whether the subject has CAD, risk of the subject developing CAD
and/or the risk of the subject experiencing a cardiovascular event
in the future. The level of risk determination may be based on a
standard level of the metabolite present in the blood. Such a
standard is used for the relationship between HDL and LDL
cholesterol measurements in which risk for CAD is predicted when
cholesterol levels reach certain level in the blood after fasting
and the ratio of HDL to LDL is beyond standard limits. Such
standards are generally developed based on a large population
study. Alternatively, the determination of risk may be based on
direct comparison to one or more control subjects. For example, a
set of control subjects lacking CAD and with no cardiovascular
events in the two years following sample procurement and a set of
control subjects with CAD and with or without a cardiovascular
event in the two years following sample procurement could be used
as a comparison.
[0022] The risk of cardiovascular disease in the subject may be
expressed in relative terms. For instance a normal level of a
metabolite may be referred to as 1.0 in subjects at low to average
risk for cardiovascular disease such as CAD or a cardiovascular
event. Any numbers below 1.0 could indicate the subject has a lower
risk than the general population risk. A number greater than 1.0
would indicate that the subject has a greater than average risk
level and the actual number could relate to the level of risk. For
example, a subject whose metabolite level is 2.0 may be two times
as likely to experience a cardiovascular event in the next two
years as compared to an average individual.
[0023] The assessment of risk of cardiovascular disease, including
CAD or a future cardiovascular event, includes but is not limited
to, developing a risk profile. The assessment or prediction may
indicate that the subject is 10%, 20%, 30%, 40%, 50%, 60%, 70%,
80%, 90%, 100%, 200%, 300%, 400%, 500%, 750% or 1000% more likely
to have or develop a cardiovascular disease, such as CAD or have
cardiovascular event, than a control subject. A control subject is
an individual that does not have CAD and possesses levels of the
metabolite that do not correlate with an increased risk of CAD or a
cardiovascular event.
[0024] The metabolites predictive of risk of developing
cardiovascular disease CAD include metabolites involved in many of
the major pathways of lipid, protein and carbohydrate metabolism.
Thus, the acylcarnitines include acetyl carnitine (C2), a
by-product of glucose, fatty acid and amino acid metabolism,
Propionyl carnitine (C3) and Isoveleryl carnitine (C5), which
provide information on amino acid catabolism, the dicarboxylated
acylcarnitine, which report on peroxisomal fatty acid metabolism,
and the medium-long chain acylcarnitines, which are intermediates
in long-chain fatty acid beta-oxidation. The amino acids serve as
important intermediates in protein turnover and catabolism and the
ketones are an index of fatty acid beta-oxidation. Table 1 below
shows the short and full-names of the metabolites tested in the
Examples. Table 2 shows the biological functions, if available, of
each of the tested metabolites.
TABLE-US-00001 TABLE 1 Nomenclature and Intra-lndividual
Variability of Metabolites. Measures of Intra-Individual
Variability Short Name Full Metabolite Name R.sup.2 CV C2 Acetyl
carnitine 0.84 5.16 C3 Propionyl carnitine 0.75 22.95 C4: Ci4
Butyryl carnitine or Isobutyryl carnitine 0.75 13.48 C5: 1 Tiglyl
carnitine 0.46 11.24 C5 Isovaleryl carnitine, 3-methylbutyryl
carnitine or 0.51 13.08 2-Methylbutyryl carnitine C4-OH
.beta.-Hydroxy-butyryl carnitine 0.40 15.46 Ci4-DC: C4DC
Methylmalonyl carnitine or Succinyl carnitine NA NA C8: 1 Octenoyl
carnitine 0.86 11.29 C8 Octanoyl carnitine 0.96 4.49 C8: 1-OH/
3-Hydroxy-cis-5-octenoyl carnitine or Hexenedioyl NA NA C6: 1-DC
carnitine C8: 1-DC Octenedioyl carnitine NA NA C5-DC Glutaryl
carnitine 0.60 10.33 C6-DC Adipoyl carnitine 0.54 13.33 C10: 3
Decatrienoyl carnitine 0.74 8.78 C10: 1 Decenoyl carnitine 0.99
3.57 C10 Decanoyl carnitine 0.90 8.3 C100H: C8DC 3-Hydroxy-decanoyl
carnitine or Suberoyl carnitine 0.91 6.47 C12: 1 Dodecenoyl
carnitine 0.91 8.07 C12 Lauroyl carnitine 0.48 12.54 C12OH: C10DC
3-Hydroxy-dodecanoyl carnitine or Sebacoyl 0.94 3.16 carnitine C14:
2 Tetradecadienoyl carnitine 0.89 4.25 C14: 1 Tetradecenoyl
carnitine 0.74 4.62 C14: 1-OH 3-Hydroxy-tetradecenoyl carnitine
0.74 4.8 C14-OH: 12-DC 3-Hydroxy-tetradecanoyl carnitine or 0.68
4.91 Dodecanedioyl carnitine C16 Palmitoyl carnitine 0.36 7.83
C16-OH: C14-DC 3-Hydroxy-hexadecanoyl carnitine or 0.95 3.98
Tetradecanedioyl carnitine C16: 2 Hexadecadienoyl carnitine NA NA
C16: 1 Palmitoleoyl carnitine NA NA C16: 1-OH/
3-Hydroxy-palmitoleoyl carnitine or NA NA C14: 1-DC
cis-5-Tetradecenedioyl carnitine C18: 2 Linoleyl carnitine 0.84
4.33 C18: 1 Oleyl carnitine 0.90 4.77 C18 Stearoyl carnitine 0.53
6.08 C18: 2-OH 3-Hydroxy-linoleyl carnitine NA NA C18: 1-OH
3-Hydroxy-octadecenoyl carnitine NA NA C18-OH: C16-DC
3-Hydroxy-octadecanoyl carnitine or 0.70 7.91 Hexadecanedioyl
carnitine, thapsoyl carnitine C20 Arachidoyl carnitine, eicosanoyl
carnitine 0.65 4.49 C20: 1-OH/ Octadecenedioyl carnitine 0.66 8.46
C18: 1-DC C20-OH/ 3-Hydroxy-eicosanoyl carnitine or Octadecanedioyl
0.39 9.42 C18-DC carnitine C20: 4 Arachidonoyl carnitine NA NA C22
Behenoyl carnitine, docosanoyl carnitine 0.84 5.16 GLY Glycine 0.85
1.36 ALA Alanine 0.97 0.49 SER Serine 0.79 2.14 PRO Proline 0.68
1.11 VAL Valine 0.84 1.25 LEU/ILE Leucine/lsoleucine 0.95 0.79 MET
Methionine 0.95 1.45 HIS Histidine 0.33 2.04 PHE Phenylalanine 0.95
0.72 TYR Tyrosine 0.94 1.61 ASX Aspartic acid/asparagine 0.88 1.55
GLX Glutamine/glutamate 0.55 2.33 ORN Ornithine 0.86 1.54 CIT
Citrulline 0.57 4.69 ARG Arginine 0.98 1.37 FFA Total free fatty
acids 0.99 14.1 HBUT .beta.-hydroxybutyrate 0.90 4.8 KET Ketones
0.91 3.7 Metabolite short names, full names, and measures of
intra-individual variability are presented.
TABLE-US-00002 TABLE 2 Biological functions of the metabolites
measured. By measuring conventional metabolites such as fatty
acids, ketones and beta-hydroxybutyrate, a large panel of
acylcarnitines and fifteen of the biologically relevant amino
acids, the panel of metabolites surveyed report on the major
pathways of lipid, protein and carbohydrate metabolism. This table
displays biological functions (when available) of each of the
individual metabolites measured. Biological function Branched
.beta.- .omega.- chain oxidation oxidataion Cholesterol Canonical
20 amino Glycolysis of of & chol amino acids of Urea acid &
Krebs Other Metabolite fatty acids fatty acids Ketogenesis
transport polypeptides cycle catabolism Cycle function(s)/related
conditions C2 x C3 x ? x Propionyl-CoA carboxylase deficiency
C4:C14 x x C5:1 x C5 x ? x C4-OH x x C14-DC:C4DC x x x C8:1 x C8 x
C8:1-OH/C6:1- x x DC C8:1-DC x x C5-DC x ? Glutaric acidermia type1
unable to break down completely the amino acids lyase,
hydroxylysine and tryptophan. C6-DC x 3-methylglutaryl
carnitine_HMGCo4 lyase deficiency C10:3 x C10:1 x C10 x C10OH:C8DC
x x C12:1 x C12 x C12OH:C10DC x x C14:2 x C14:1 x C14:1-OH
C14-OH:12-DC x x C16 x C16-OH:C14-DC x x C16:2 x C16:1 x
C16:1-OH/C14:1- x x DC C16:2 x C16:1 x C16 x C16:2-OH x C18:1-OH x
C18-OH:C16-DC x x C20 x C20:1-OH/C18:1- x x DC C20-OH/C18-DC x x
C20:4 x x Eicosanoids C22 x GLY x Neurotransmitter, glutathione ALA
x Transaminstiors SER x Purines, pyrimicines PRO x VAL x x LEU/ILE
x x MET x Carnitine, taurine, 1-carbon metabolism. phospholipids,
homocysteine HIS x Histamine PHE x Neurotransmitters TYR x
Neurotransmitters melanin ASX x x Neurotransmitters, nitrogen
transport GLX x x Transaminations, nitrogen transport, glutathione,
neurotransmitters ORN x Polyamines CIT x ARG x x Nitric oxide FFA x
x HBUT x KET x
[0025] Methods of predicting the risk of a cardiovascular event
(death or myocardial infarction) in a subject by detecting at least
one metabolite in the subject are also provided. The metabolites
predictive of risk of a cardiovascular event are presumed products
of peroxisomal fatty acid metabolism, in particular the short-chain
dicarboxyl acylcarnitines, and citrulline. The specific metabolites
are listed in Table 9 of the Examples and are identified as factor
8. Table 10 shows the individual metabolites within Factor 8 and
provides the Factor load data for each metabolite. The data in the
Examples demonstrate that citrulline and the short-chain dicarboxyl
acylcarnitines are predictive of the risk of a cardiovascular
event.
[0026] Individual metabolites may also be predictive of the risk of
a cardiovascular event. These metabolites include Gly, Ala, Ser,
Pro, Met, His, Phe, Tyr, Asx, Glx, Ornithine, Citrulline, arg, C2,
C3, C4:C14; C5:1, C5, C4:OH, C14-DC:C4DC, C5-DC, C6-DC, C10:3, C10,
C10-OH:C8DC, C12:1, C12, C12-OH:C10DC, C14:1-OH, C14-OH:C12-DC,
C16, C16-OH/C14-DC, C18:2, C18-OH/C16-DC, C20, C20:1-OH/C18:1-DC,
C20-OH/C18-DC, C8:1-OH/C6:1-DC, C8:1-DC, C16:1, C16:1-OH/C14:1-DC,
C20:4, FFA, HBUT, and Ket. In particular, the levels of citrulline,
C5-DC, C6-DC, C8:1-OH/C6:1-DC, and C8:1-DC are predictive of
cardiovascular events. In addition, the levels of ornithine,
citrulline, C5, C14-DC:C4DC, C5-DC, C6-DC, C10-OH:C8DC,
C8:1-OH/C6:1-DC, C8:1-DC, C20:4 and FFA are also useful for
assessing the risk of a cardiovascular event.
[0027] Methods of assessing the presence and/or extent of CAD in a
subject by detecting the level of at least one metabolite in a
sample from the subject are also provided. The metabolites useful
for assessing the presence of CAD are medium-chain acylcarnitine, a
branched chain amino acid or associated metabolite, or a metabolite
associated with the urea cycle.
[0028] The specific metabolites are listed in Table 6, 7 and 8 of
the Examples and are identified as factors 1, 4, and 9 in Table 9.
Table 10 shows the individual metabolites within Factors 1, 4 and 9
and provides the Factor load data for each metabolite. Only those
metabolites with factor loads greater than or equal to 0.04 are
included in the factor.
[0029] Individual metabolites may also be predictive of the risk of
a cardiovascular event. These metabolites include Pro, Leu/Ile,
Met, Val, Glx, Citrulline, C2, C3, C4:Ci4; C5, C8, C8:1-OH/C6:1-DC,
C10:1, C14:2, C14:1-OH, C16:2, C16:1, C16:2, C16:1,
C16:1-OH/C14:1-DC, C18-OH/C16-DC, HBUT, and Ket. In particular, the
levels of Leu/Ile, Glx, C2, C14:1-OH and C16:1-OH/C14:1-DC are
indicative of the presence of CAD in a subject. Increased levels of
Leu/Ile or Glx as compared to normal controls or a normal standard
are indicative of CAD in the subject. Decreases levels of C2,
C14:1-OH and C16:1-OH/C14:1-DC are indicative of the presence of
CAD in a subject. A level of Leu/Ile greater than 165 mM, 170 mM or
175 mM is indicative of coronary artery disease. A level of Glx
greater than 127 mM, 128 mM, 129 mM, 130 mM, 132 mM, 135 mM or 140
mM is indicative of coronary artery disease. A level of C14:1-OH
less than 0.014 .mu.M, 0.013 .mu.M or 0.012 .mu.M is indicative of
coronary artery disease. A level of C16:1-OH/C14:1-DC less than
0.009 .mu.M, 0.0089 .mu.M, or 0.0088 .mu.M is indicative of
coronary artery disease.
[0030] Methods of assessing the likelihood of developing CAD in a
subject by detecting the level of at least one metabolite in a
sample from the subject are also provided. The metabolites useful
for assessing the likely development of CAD are the short- and
medium-chain acylcarnitine metabolites, branched chain amino acids
and urea cycle related metabolites.
[0031] The specific metabolites are listed in Table 14 of the
Examples. Table 15 shows the individual metabolites within the
identified Factors. Only those metabolites with factor loads
greater than or equal to 0.04 are included in the factor.
Individual metabolites may also be predictive of the risk of a
cardiovascular event. These metabolites include ketones, arg,
ornithine, citrulline, glx, ala, val, leu/Ile, pro, C2, C14:1,
C18:1, C5:1, C4-i4, C18, C10:1 and FFA.
[0032] The Examples below are meant to be illustrative and not to
limit the scope of the invention.
EXAMPLES
Example 1
Association of Metabolites with CAD and Risk of Cardiovascular
Events Methods
Study Sample
[0033] The CATHGEN biorepository consists of subjects recruited
sequentially through the cardiac catheterization laboratories at
Duke University Medical Center (Durham, N.C.). After informed
consent, blood was obtained from the femoral artery at time of
arterial access for catheterization, immediately processed to
separate plasma, and frozen at -80.degree. C. All subjects were
fasting for a minimum of six hours prior to collection. Clinical
data were provided by the DDCD, a database of patients undergoing
cardiac catheterization at Duke University since 1969. Medication
data were collected for medications used chronically, i.e.
medications at admission (inpatients) or from a clinic note within
one month prior (outpatients). Follow-up data, including occurrence
of myocardial infarction (MI) and death were collected at six
months after catheterization, then annually thereafter. Vital
status was confirmed through the National Death Index. The
indication for catheterization for all subjects was clinical
concern for ischemic heart disease. Patients with severe pulmonary
hypertension or organ transplant were excluded.
[0034] To evaluate the discriminative capability of metabolites for
CAD, two independent case-control groups were constructed:
`initial` (174 CAD cases and 174 CAD-free controls); and
`replication` (140 CAD cases and 140 CAD-free controls). For the
initial group, sequential cases meeting inclusion criteria were
selected: CADindex .gtoreq.32 (at least one coronary artery with
.gtoreq.95% stenosis) and age-of-onset .ltoreq.55 years. CADindex
is a numerical summary of angiographic data. (Smith et al.,
Circulation 1991; 84[5 Suppl], 111:245-253.) Age-of-onset was
defined as age at first MI, percutaneous coronary intervention
(PCI), coronary artery bypass grafting (CABG), or age at first
catheterization meeting CADindex threshold. Sex- and race-matched
controls meeting the following criteria were selected: CADindex
.ltoreq.23; no coronary artery with >50% stenosis;
age-at-catheterization .gtoreq.61 years; and no history of MI, PCI,
CABG, or transplant. Given the differences in age based on these
criteria, results could be confounded by age. Therefore, for the
replication group, sequential cases and controls meeting the same
inclusion criteria were selected, but the criterion of age-of-onset
(cases) or age-at-catheterization (controls) was removed and
cases/controls were not matched. This allowed generalizability of
findings to a representative population of patients referred for
catheterization. Analyses were also performed by constraining CAD
cases to those with a previous history of MI (N=86 cases in
initial, N=61 cases in replication).
[0035] To evaluate the capability of metabolites to predict risk of
subsequent cardiovascular events, an `event` group was constructed,
combining CAD cases from the initial and replication groups
(`event` group, N=314); of these, 74 individuals suffered death or
MI during follow-up. To validate findings for the association of
metabolites with risk of cardiovascular events, profiling was
performed in an independent cardiovascular event case-control group
(event-replication') composed of unique individuals from CATHGEN
meeting the following criteria: ejection fraction >40%; no
history of PCI or CABG; and no subsequent CABG. Among these, event
cases (N=63) suffered death or MI, or had PCI with acute coronary
syndrome within two years after catheterization; controls (N=66)
were event-free, with at least two years of follow-up, and were
matched to cases on age, race, sex and CADindex.
[0036] The Duke Institutional Review Board approved the protocols
for CATHGEN and the current study. Informed consent was obtained
from each subject.
Metabolite Measurements
[0037] Fasting plasma samples were used for quantitative
determination of targeted levels for 45 acylcarnitines, 15 amino
acids, five conventional analytes (total, low-density[LDL] and
high-density lipoprotein [HDL] cholesterol, triglycerides and
glucose), ketones, .beta.-hydroxybutyrate, total free fatty acids
and C-reactive protein [CRP] (Table 1). Methodology and
coefficients of variation for each assay have been reported. (Shah
et al., Mol Syst Biol 2009; 5:258 and Newgard et al., Cell Metab
2009; 9(4):311-26.) The laboratory (Sarah W. Stedman Nutrition and
Metabolism metabolomics/biomarker core laboratory) was blinded to
case-control status and cases/controls were randomly
distributed.
[0038] Standard clinical chemistry methods were used for
conventional metabolites with reagents from Roche Diagnostics
(Indianapolis, Ind.), and for free fatty acids (total) and ketones
(total and .beta.-hydroxybutyrate) with reagents from Wako. All
assays were performed on a Hitachi 911 clinical chemistry
analyzer.
[0039] For mass spectroscopy (MS)-profiled metabolites
(acylcarnitines, amino acids) the following protocol was used. (An
et al., Nat Med 2004; 10(3):268-74 and Chace et al., Clin Chem
1995; 41(1):62-8.) Proteins were first removed by precipitation
with methanol. Aliquoted supernatants were dried, and then
esterified with hot, acidic methanol (acylcarnitines) or n-butanol
(amino acids). Analysis was done using tandem MS with a Quattro
Micro instrument (Waters Corporation, Milford, Mass.).
Quantification of the "targeted" intermediary metabolites was
facilitated by addition of mixtures of known quantities of
stable-isotope internal standards (Table 3). Leucine/isoleucine
(LEU/ILE) are reported as a single analyte because they are not
resolved by our MS/MS method, and include contributions from
allo-isoleucine and hydroxyproline. Under normal circumstances
these isobaric amino acids contribute little to the signal
attributed to LEU/ILE. In addition, the acidic conditions used to
form butyl esters results in partial hydrolysis of glutamine to
glutamic acid and of asparagine to aspartate. Accordingly, values
that are reported as GLU/GLN (GLX) or ASP/ASN (ASX) are not meant
to signify the molar sum of glutamate and glutamine, or of
aspartate and asparagine, but rather measure the amount of
glutamate or aspartate plus the contribution of the partial
hydrolysis reactions of glutamine and asparagine, respectively.
Biological annotation is included in Table 2 above.
TABLE-US-00003 TABLE 3 Internal spiked standards for acylcarnitine
and amino acid measurements. Amino Acids Acylcarnitines Free Fatty
Acids .sup.15N.sub.1,.sup.13C.sub.1-glycine D.sub.3-acetyl
carnitine D.sub.3-octanoate D.sub.4-alanine D.sub.3-propionyl
carnitine D.sub.3-decanoate D.sub.8-valine D.sub.3-butyryl
carnitine D.sub.3-laurate D.sub.7-proline D.sub.9-isovaleryl
carnitine D.sub.3-myristate D.sub.3-serine D.sub.3-octanoyl
carnitine D.sub.3-palmitate D.sub.3-leucine D.sub.3-palmitoyl
carnitine .sup.13C.sub.1-oleate D.sub.3-methionine D.sub.3-stearate
D.sub.5-phenylalanine D.sub.4-tyrosine D.sub.3-aspartate
D.sub.3-glutamate D.sub.2-ornithine D.sub.2-citrulline
D.sub.5-arginine
Statistical Analysis
[0040] Metabolite levels reported as "0" (i.e., below the lower
limits of quantification (LOQ)) were given a value of LOQ/2.
Metabolites with >25% of values as "0" were not analyzed (five
acylcarnitines). All metabolites were natural log-transformed to
approximate a normal distribution. For analysis of CAD status,
generalized linear regression models were used to assess
differences in metabolite levels between CAD cases and controls,
both unadjusted and adjusted for traditional CAD risk factors not
constrained by matching: diabetes, hypertension, dyslipidemia,
body-mass-index (BMI), family history of CAD, and smoking Analyses
of the replication group were further adjusted for race, sex and
age. With log transformation, all significant metabolites showed a
normal distribution (Kolmogorov-Smirnov test P>0.01), except
valine, ketones, and C8, C8:1-OH/C6:1-DC, C10:1, C14:2, C16:1,
C16:1-OH/C14:1-DC, and C18-OH/C16-DC acylcarnitines. Visual
inspection of the distributions suggested a grossly normal
distribution. Regardless, we performed sensitivity analyses using
non-parametric Wilcoxon tests, showing similar results as the
semi-parametric linear models, except for valine and C14:2
acylcarnitine, both of which were not significant in linear
regressions (p=0.10 and p=0.06, respectively), but were significant
with these non-parametric tests (p=0.05 and p=0.008). Analyses were
also stratified by diabetes and smoking.
[0041] In exploratory analyses, multivariable models were further
adjusted for medication classes (beta-blockers, statins, diabetes
medications, aspirin, angiotensin-converting-enzyme inhibitors,
nitrates, clopidogrel and diuretics), use of pre-procedural
sedation, and continuous intravenous heparin use at time of
catheterization. The CATHGEN protocol requires sample collection
prior to supplemental heparin administration during
catheterization. Therefore, adjustment for continuous intravenous
heparin use at time of catheterization addresses differences
related to heparin. Only 66% of individuals had medication data,
hence medications were coded as a discrete variable: not on
medication, missing, and on medication.
[0042] Given that metabolites reside in overlapping pathways,
correlation of metabolites is expected. We used principal
components analysis (PCA) to reduce the large number of correlated
variables into uncorrelated factors. Factors with higher
"eigenvalues" account for larger amounts of variability within the
dataset. Factors with an eigenvalue .gtoreq.1.0 were identified and
varimax rotation performed to produce interpretable factors.
Metabolites with a factor load .gtoreq.|0.4| were reported as
composing a factor. See Table 10. Scoring coefficients were
constructed from the initial group and used to calculate factor
scores for each individual (weighted sum of the standardized
metabolites within that factor, weighted on the factor loading for
each metabolite), and were also applied to the replication group.
Generalized linear regression models were used to assess the
difference in factor scores between cases and controls. All factors
were normally distributed (Kolmogorov-Smirnov test P>0.01),
except for factors 7-9; visual inspection showed a grossly normal
distribution. Non-parametric Wilcoxon tests for these factors
showed the same results as linear models.
[0043] To further assess the independent capability of metabolite
profiles to discriminate CAD cases from controls, multivariable
logistic regression models were constructed; in these models, CAD
risk factors (BMI, dyslipidemia, hypertension, diabetes, family
history, smoking) were forced into the model, then metabolite
factors were added. Receiver operating curves (ROC) were
constructed and measures of model fit calculated. Nonparametric
analysis for comparison of the areas under these curves was
performed using previous methods. (DeLong et al., Biometrics 1988;
44(3):837-45.)
[0044] For analysis of subsequent cardiovascular events, cases from
initial and replication groups were pooled (`event` group). The
relationship between metabolite factors and time-to-occurrence of
death/MI was assessed using Cox proportional hazards (unadjusted
and adjusted for BMI, dyslipidemia, hypertension, diabetes, family
history, smoking, age, race, sex, creatinine, ejection fraction and
CADindex). The assumption of proportional hazards was met. For
replication in the `event-replication` group, scoring coefficients
from PCA-derived factors constructed in the initial CAD group were
used to calculate factor scores in the event-replication group;
logistic regression was used to assess the association between
factors and case/control status (unadjusted and adjusted for BMI,
dyslipidemia, hypertension, diabetes, family history, smoking,
creatinine, and ejection fraction).
[0045] As all analyses were exploratory in nature and given
co-linearity of the metabolites, two-sided p-values unadjusted for
multiple comparisons are presented; however, results interpreted in
the context of Bonferroni correction are reported. Nominal
statistical significance was defined as P<0.05. Bonferroni
corrected p-values were P<0.0007 (individual metabolites) and
P<0.004 (factors). Statistical analyses were performed by D.R.C.
and S.H.S. using SAS version 9.1 (Cary N.C.).
Results
Patient Populations
[0046] Population characteristics for the initial (174 early-onset
CAD cases, 174 matched controls) and replication groups (140 CAD
cases, 140 controls) are displayed in Table 4, and for the
event-replication group in Table 5.
TABLE-US-00004 TABLE 4 Baseline Clinical Characteristics of Initial
and Replication Groups. Initial Group Replication Group Cases
Controls Cases Controls (N = 174) (N = 174) (N = 140) (N = 140) Age
(mean [SD]) 48.7 (10.0) 67.8 (5.9) 61.1 (13.0) 60.3 (13.0)
Age-of-onset 45.8 (6.9) N/A 57.0 (10.8) N/A Sex (% male) 77.0%
75.3% 75.0% 51.4% Race (% white) 66.9% 67.8% 77.5% 76.9%
Hypertension 64.9% 68.4% 72.9% 64.3% Diabetes 32.2% 23.0% 28.6%
19.3% Family history of CAD 57.5% 22.4% 49.3% 32.9% Currently
smoking (%) 66.1% 47.1% 64.3% 39.3% Body mass index 31.1 (6.8) 29.3
(6.0) 29.1 (5.8) 31.4 (9.1) (mean [SD]) CADindex (mean [SD]) 56.5
(21.9) 6.2 (9.4) 58.6 (21.8) 4.6 (8.6) No. of coronary arteries
w/.gtoreq.75% stenosis 0 0% 100% 0% 100% 1 27.0% 0% 22.1% 0% 2
29.3% 0% 35.0% 0% 3 43.7% 0% 42.9% 0% Ejection fraction (mean 51.8
(13.0) 59.4 (13.0) 53.5 (15.1) 63.4 (9.5) [SD]) History of MI 49.4%
0% 42.9% 0% History of dyslipidemia 73.6% 44.8% 60.7% 49.3% Total
cholesterol (mean 178.5 (55.1) 176.1 (39.2) 177.9 (44.4) 169.9
(38.7) [SD]) LDL cholesterol (mean 105.3 (39.3) 104.2 (32.1) 105.9
(36.8) 101.1 (32.5) [SD]) HDL cholesterol (mean 35.6 (10.8) 48.0
(16.0) 39.3 (12.2) 39.0 (12.6) [SD]) Triglycerides (mean 157.7
(170.4) 93.2 (60.7) 128.1 (82.4) 119.8 (91.1) [SD])
TABLE-US-00005 TABLE 5 Baseline Clinical Characteristics of the
Event Replication Group. Overall Event Cases No Event Controls (N =
129) (N = 63) (N = 66) P-value* Age (mean [SD)) 62.9 (10.4) 63.2
(10.7) 62.7 (10.1) 0.81 Sex (% male) 54.3% 54.0% 54.6% 0.95 Race (%
white) 74.2% 72.1% 76.2% 0.57 Hypertension 72.1% 77.8% 66.7% 0.16
Diabetes 31.8% 33.3% 30.3% 0.71 Family History of CAD 32.6% 27.0%
37.9% 0.19 Currently smoking (%) 51.2% 44.4% 53.0% 0.33 Body mass
index 30.0 (7.6) 30.5 (8.5) 29.4 (6.6) 0.42 (mean [SD]) CADindex
(mean [SD]) 28.6 (12.4) 28.9 (12.0) 26.3 (12.8) 0.81 No. of
coronary arteries 0.96 w/.gtoreq.75% stenosis 0 15.1% 15.0% 15.2% 1
63.5% 63.3% 63.6% 2 19.1% 20.0% 18.2% 3 2.4% 1.7% 3.0% Ejection
fraction 61.0 (9.2) 61.8 (8.8) 0.63 (mean [SD]) History of MI 0% 0%
NA History of dyslipidemia 55.6% 54.6% 0.91 Total cholesterol 166.6
(44.2) 168.7 (49.0) 0.81 (mean [SD]) LDL cholesterol (mean 104.5
(34.2) 108.0 (28.6) 0.70 [SD]) HDL cholesterol (mean 49.0 (16.4)
51.6 (22.2) 0.53 [SD]) Triglycerides (mean 117.0 (94.9) 108.5
(113.9) 0.36 [SD]) *p-value for difference between subjects with
events and event-free controls.
Association of Individual Metabolites with CAD
[0047] Levels of several amino acids were different between cases
and controls in the initial group (Table 6), including the
branched-chain amino acids leucine/isoleucine (P<0.0001) and
valine (P=0.007), glutamate/glutamine (P<0.0001), proline
(P=0.04) and methionine (P=0.05). Levels of several acylcarnitines
were also different between cases and controls in the initial
group, including the C16 acylcarnitines (C16:1, P=0.006;
C16:1-OH/C14:1-DC, P=0.004; C16:2, P=0.05; and C18-OH/C16-DC,
P=0.003), and C4:Ci4 (P=0.009), C8 (P=0.009), C8:1-OH/C6:1-DC
(P=0.003), and C10:1 (P=0.002) acylcarnitine (Table 6). For most
metabolites, these differences persisted after adjustment for CAD
risk factors.
TABLE-US-00006 TABLE 6 Association of Individual Metabolites with
CAD. Means and standard deviations for metabolites significantly
different between cases and controls in the initial group are
presented. Results for these analytes in the replication group are
also presented. All values are in millimolar for amino acids and
micromolar for acylcarnitines. Analytes in bold show consistent
association across both datasets (with consistent direction of
effect). Initial Group Replication Group Metabolite CAD Cases
Controls Unadj p Adj p* Cases Controls Unadj p Adj p.dagger. Amino
Acids PRO 190.2 (56.4) 177.5 (41.3) 0.04 0.13 197.0 173.9 0.001
0.03 (75.9) (44.3) LEU/ILE 175.1 (39.5) 158.7 (36.3) <0.0001
0.004 183.6 162.3 <0.0001 0.002 (52.7) (35.5) VAL 259.1 (58.1)
242.2 (54.9) 0.007 0.26 256.8 266.2 0.10 0.05 (63.7) (51.5) MET
25.8 (5.6) 24.6 (4.8) 0.05 0.14 26.6 24.0 0.003 0.03 (7.7) (5.2)
GLX 151.1 (41.7) 125.5 (39.0) <0.0001 <0.0001 129.7 120.3
0.005 0.02 (30.5) (31.4) CIT 36.4 (12.2) 39.9 (11.2) 0.002 0.003
39.7 37.8 0.21 0.86 (12.0) (10.8) Acylcarnitines C2 9.10 (4.25)
9.90 (3.98) 0.02 0.01 10.73 8.76 <0.0001 <0.0001 (4.86)
(3.83) C4:Ci4 0.22 (0.12) 0.18 (0.10) 0.009 0.03 0.22 0.20 0.06
0.15 (0.11) (0.11) C5 0.104 (0.095) 0.087 (0.047) 0.01 0.08 0.092
0.101 0.05 0.004 (0.045) (0.045) C8 0.107 (0.057) 0.123 (0.076)
0.009 0.04 0.129 0.124 0.54 0.17 (0.121) (0.106) C8:1-OH/C6:1-
0.030 (0.027) 0.032 (0.019) 0.003 0.005 0.026 0.028 0.47 0.56 DC
(0.013) (0.012) C10:1 0.174 (0.097) 0.193 (0.083) 0.002 0.002 0.200
0.198 0.87 0.47 (0.096) (0.096) C14:2 0.039 (0.027) 0.044 (0.028)
0.02 0.02 0.039 0.047 0.06 0.42 (0.028) (0.025) C14:1-OH 0.012
(0.006) 0.014 (0.007) 0.04 0.03 0.012 0.015 0.002 0.006 (0.006)
(0.007) C16:2 0.0088 (0.0065) 0.0092 (0.0053) 0.05 0.03 0.0098
0.0117 0.03 0.11 (0.0059) (0.007) C16:1 0.0258 (0.0171) 0.0262
(0.0124) 0.006 0.07 0.0286 0.0321 0.03 0.24 (0.0154) (0.0138)
C16:1- 0.0087 (0.0036) 0.0091 (0.0031) 0.004 0.01 0.0088 0.0096
0.008 0.01 OH/C14:1-DC (0.0040) (0.0040) C18-OH/C16- 0.008 (0.009)
0.007 (0.004) 0.003 0.02 0.007 0.008 0.005 0.03 DC (0.003) (0.004)
Ketones 289.8 (345.0) 324.3 (286.0) 0.04 0.14 319.2 313.1 0.87 0.44
(289.9) (279.0) .beta.- 199.7 (271.6) 237.1 (235.3) 0.01 0.05 211.5
202.9 0.63 0.29 hydroxybutyrate (205.7) (199.8) *adjusted for
diabetes, hypertension, smoking, dyslipidemia, family history of
CAD, BMI. .dagger.adjusted for age, race, sex, diabetes,
hypertension, smoking, dyslipidemia, family history of CAD, BMI
[0048] Several of these metabolites were also significant in the
replication group in adjusted analyses (with similar direction of
effect), including the amino acids leucine/isoleucine and
glutamate/glutamine, and the long-chain acylcarnitines C14:1-OH and
C16:1-OH/C14:1-DC (Table 6). In unadjusted analyses, these
metabolites, amino acids methionine and proline, and C16:2 and
C16:1 acylcarnitine were significant in both groups.
[0049] Further adjustment for lipids (total, LDL, and HDL
cholesterol and triglycerides) resulted in similar results,
although with attenuation of association for LEU/ILE in the initial
group (Tables 7 and 8). Analyses stratified by diabetes suggested
some heterogeneity of association by diabetes. For example, LEU/ILE
and C16:1-OH/C14:1-DC showed stronger association in non-diabetics.
Analyses stratified by smoking suggested no difference in smokers
and non-smokers.
TABLE-US-00007 TABLE 7 Association of Individual Metabolites with
CAD in the Initial Group. Means and standard deviations for all
individual metabolites for the overall initial group, as well as
stratified by CAD, are presented. All values are in millimolar for
amino acids and micromolar for acylcarnitines. P-values are for the
association between metabolites and CAD in unadjusted and adjusted
analyses. Analytes in bold show consistent association across both
datasets in adjusted analyses (with consistent direction of
effect). Overall CAD Cases CAD Controls Mean SD Mean SD Mean SD
Unadjusted Adjusted* Adjusted p.dagger. Amino Acids GLY 311.48
77.87 308.33 78.16 316.65 79.53 0.23 0.57 0.20 ALA 324.98 87.49
333.56 94.24 316.19 79.76 0.12 0.51 0.37 SER 93.59 20.53 100.16
20.30 97.21 20.64 0.15 0.09 0.39 PRO 183.78 49.65 190.17 56.43
177.58 41.28 0.04 0.13 0.56 VAL 250.59 57.02 259.08 58.33 242.16
54.94 0.007 0.26 0.84 LEU/ILE 166.94 38.70 175.12 39.47 158.74
36.32 <0.0001 0.004 0.30 MET 25.17 5.26 25.75 5.58 24.56 4.83
0.05 0.14 0.92 HIS 60.95 13.09 60.57 13.76 61.10 12.43 0.71 0.63
0.30 PHE 65.36 12.93 66.57 13.28 64.12 12.52 0.09 0.29 0.60 TYR
61.65 15.05 62.56 15.72 60.95 14.18 0.37 0.47 0.16 ASX 108.58 23.06
110.70 23.54 108.89 22.30 0.12 0.54 0.94 GLX 138.17 42.23 151.07
41.68 125.46 39.02 <0.0001 <0.0001 0.02 ORN 75.14 23.31 77.53
24.08 72.75 22.41 0.08 0.15 0.92 CIT 38.23 11.85 38.44 12.17 39.86
11.21 0.002 0.003 0.003 ARG 70.54 20.74 71.25 22.16 89.58 19.01
0.84 0.29 0.86 Acylcamitines C2 9.499 4.123 9.105 4.249 9.900 3.976
0.02 0.01 0.01 C3 0.469 0.243 0.496 0.270 0.438 0.210 0.06 0.63
0.72 C4:C14 0.200 0.113 0.216 0.116 0.183 0.104 0.009 0.03 0.81
C5.1 0.057 0.034 0.057 0.035 0.056 0.031 0.90 0.62 0.97 C5 0.096
0.075 0.104 0.094 0.067 0.047 0.01 0.08 0.88 C4:OH 0.056 0.039
0.058 0.045 0.054 0.032 0.83 0.62 0.70 C14-DC:C4DC 0.052 0.033
0.055 0.039 0.049 0.024 0.53 0.48 0.43 C8:1 0.262 0.145 0.271 0.164
0.253 0.123 0.69 0.50 0.12 C8 0.115 0.068 0.107 0.067 0.123 0.076
0.009 0.04 0.04 C5-DC 0.044 0.045 0.045 0.062 0.043 0.019 0.29 0.53
0.15 C6-DC 0.085 0.131 0.084 0.181 0.075 0.042 0.86 0.73 0.07 C10:3
0.125 0.076 0.125 0.063 0.125 0.067 0.37 0.24 0.11 C10:1 0.154
0.090 0.174 0.095 0.193 0.083 0.002 0.002 0.0002 C10 0.223 0.164
0.200 0.154 0.245 0.207 0.05 0.14 0.00 C10-OH:C8DC 0.035 0.026
0.034 0.028 0.035 0.023 0.11 0.37 0.11 C12:1 0.120 0.069 0.117
0.075 0.122 0.062 0.12 0.48 0.05 C12 0.075 0.053 0.074 0.059 0.075
0.047 0.32 0.84 0.11 C12-OH:C10DC 0.009 0.007 0.008 0.007 0.009
0.007 0.10 0.23 0.07 C14:2 0.043 0.026 0.041 0.025 0.046 0.027 0.02
0.02 0.01 C14:1 0.051 0.050 0.078 0.049 0.085 0.051 0.53 0.07 0.05
C14:1-OH 0.014 0.007 0.013 0.006 0.015 0.007 0.04 0.03 0.009
C14-OH/C12-DC 0.010 0.005 0.010 0.006 0.010 0.005 0.78 0.20 0.10
C16 0.086 0.025 0.087 0.026 0.085 0.024 0.59 0.43 0.98
C16-OH/C14-DC 0.007 0.005 0.006 0.005 0.007 0.005 0.40 0.25 0.07
C18:2 0.074 0.038 0.072 0.038 0.075 0.039 0.40 0.24 0.21 C18:1
0.154 0.063 0.150 0.058 0.157 0.069 0.30 0.32 0.24 C18 0.044 0.015
0.043 0.014 0.046 0.015 0.22 0.34 0.25 C18:1-OH/C16:1-DC 0.010
0.006 0.010 0.006 0.010 0.007 0.46 0.04 0.03 C18-OH/C16-DC 0.009
0.008 0.010 0.009 0.008 0.005 0.003 0.02 0.09 C20 0.009 0.007 0.008
0.007 0.009 0.009 0.22 0.20 0.13 C20:1-OH/C18:1-DC 0.010 0.009
0.011 0.011 0.009 0.009 0.65 0.54 0.89 C20-OH/C18-DC 0.010 0.010
0.011 0.014 0.009 0.005 0.26 0.76 0.92 C22 0.009 0.008 0.009 0.008
0.006 0.007 0.24 0.69 0.00 C8:1-OH/C6:1-DC 0.031 0.021 0.029 0.025
0.032 0.017 0.003 0.005 <.0001 C8:1-DC 0.029 0.022 0.029 0.027
0.026 0.014 0.24 0.97 0.23 C16:2 0.012 0.007 0.012 0.007 0.013
0.009 0.05 0.03 0.09 C16:1 0.029 0.015 0.027 0.016 0.030 0.015
0.006 0.07 0.05 C16:1-OH/C14:1-DC 0.010 0.054 0.009 0.005 0.010
0.004 0.004 0.01 0.003 C18:2-OH 0.014 0.009 0.014 0.008 0.014 0.006
0.82 0.42 0.81 C20:4 0.011 0.007 0.011 0.007 0.011 0.006 0.91 0.92
0.78 Other Total free fatty acids 1.13 0.59 1.09 0.61 1.18 0.56
0.11 0.08 0.28 Ketones 306.49 315.4 289.82 345.01 324.29 285.96
0.04 0.14 0.73 .beta.-hydroxybutyrate 218.01 254.14 199.72 271.84
237.06 235.30 0.01 0.05 0.54 *adjusted for diabetes, hypertension,
smoking, dyslipidemia, family history of CAD, BMI. .dagger.adjusted
for diabetes, hypertension, smoking, dyslipidemia, family history
of CAD, BMI, and total, LDL, HDL cholesterol and triglycerides.
TABLE-US-00008 TABLE 8 Association of Individual Metabolites with
CAD in the Replication Group. Means and standard deviations for all
individual metabolites for the overall replication group, as well
as stratified by CAD, are presented. All values are in millimolar
for amino acids and micromolar for acylcarnitines. P-values are for
the association between metabolites and CAD in unadjusted and
adjusted analyses. Analytes in bold show consistent association
across both datasets in adjusted analyses (with consistent
direction of effect). Overall CAD Cases CAD controls P-values Mean
SD Mean SD Mean SD Unadjusted Adjusted* Adjusted p.dagger. Amino
Acids GLY 315.81 88.31 320.30 73.59 311.33 82.54 0.34 0.58 0.57 ALA
322.45 101.97 338.60 112.14 314.31 90.34 0.25 0.29 0.70 SER 103.65
26.15 104.12 29.34 103.18 22.81 0.99 0.52 0.34 PRO 185.45 63.10
197.04 75.89 173.67 44.28 0.001 0.03 0.04 VAL 281.47 58.00 258.77
53.88 266.17 51.49 0.10 0.05 0.04 LEU/ILE 172.94 46.10 163.63 52.68
152.26 35.50 <.0001 0.002 0.002 MET 25.31 8.88 26.58 7.73 24.03
5.16 0.003 0.53 0.04 HIS 65.53 13.86 66.25 14.26 67.41 13.47 0.42
0.55 0.14 PHE 66.18 13.46 69.20 14.70 63.16 11.40 0.0003 0.002
0.0006 TYR 64.58 15.17 65.72 17.11 63.41 12.90 0.39 0.45 0.34 ASX
98.44 26.06 114.16 23.81 82.64 17.67 <.0001 <.0001 <.0001
GLX 125.01 31.25 129.72 30.46 128.38 31.43 0.005 0.02 0.04 ORN
80.90 23.67 83.07 23.25 78.74 23.96 0.88 0.29 0.46 CIT 38.76 11.40
39.74 11.95 37.78 10.77 0.21 0.66 0.86 ARG 72.73 22.56 71.66 24.54
73.80 20.42 0.20 0.09 0.14 Acylcarnitines C2 9.743 4.481 10.726
4.863 8.756 3.834 <.0001 <.0001 <.0001 C3 0.439 0.230
0.525 0.253 0.352 0.164 <.0001 <.0001 <.0001 C4:C14 0.209
0.114 0.222 0.115 0.197 0.112 0.05 0.15 0.13 C5:1 0.072 0.030 0.058
0.023 0.087 0.025 <.0001 <.0001 <.0001 C5 0.097 0.045
0.092 0.045 0.101 0.044 0.05 0.004 0.003 C4:OH 0.068 0.046 0.060
0.038 0.975 0.051 0.006 0.11 0.27 C14-DC:C4DC 0.056 0.034 0.065
0.042 0.048 0.019 <.0001 <.0001 <.0001 C8:1 0.267 0.137
0.254 0.126 0.279 0.145 0.25 0.53 0.55 C8 0.126 0.113 0.129 0.121
0.124 0.106 0.54 0.17 0.08 C5-DC 0.042 0.025 0.044 0.031 0.040
0.019 0.29 0.85 0.43 C6-DC 0.053 0.080 0.094 0.101 0.552 0.049 0.48
0.62 0.29 C10:3 0.139 0.062 0.144 0.099 0.133 0.074 0.67 0.46 0.61
C10:1 0.199 0.096 0.200 0.099 0.198 0.096 0.87 0.47 0.33 C10 0.272
0.267 0.280 0.307 0.264 0.220 0.61 0.94 0.49 C10-OH:C8DC 0.034
0.021 0.036 0.024 0.032 0.017 0.15 0.12 0.05 C12:1 0.124 0.059
0.125 0.062 0.123 0.055 0.93 0.66 0.21 C12 0.079 0.052 0.066 0.065
0.071 0.033 0.003 0.002 0.0005 C12-OH:C10DC 0.008 0.006 0.008 0.005
0.009 0.086 0.05 0.11 0.20 C14:2 0.045 0.025 0.043 0.025 0.048
0.025 0.06 0.42 0.51 C14:1 0.087 0.052 0.084 0.054 0.090 0.050 0.12
0.53 0.81 C14:1-OH 0.015 0.006 0.013 0.005 0.016 0.007 0.002 0.006
0.005 C14-OH/C12-DC 0.011 0.005 0.010 0.006 0.011 0.005 0.30 0.58
0.53 C16 0.088 0.031 0.089 0.037 0.066 0.022 0.85 0.99 0.20
C16-OH/C14-DC 0.007 0.006 0.006 0.006 0.005 0.007 0.006 0.07 0.10
C18:2 0.074 0.040 0.072 0.048 0.077 0.031 0.05 0.05 0.005 C18:1
0.160 0.089 0.157 0.108 0.163 0.064 0.15 0.24 0.05 C18 0.046 0.022
0.845 0.027 0.047 0.013 0.03 0.02 <.0001 C18:1-OH/C16:1-DC 0.009
0.005 0.008 0.005 0.010 0.005 0.0055 0.01 0.02 C18-OH/C16-DC 0.003
0.005 0.008 0.005 0.009 0.005 0.005 0.03 0.009 C20 0.007 0.005
0.007 0.006 0.007 0.004 0.22 0.60 0.89 C20:1-OH/C18:1-DC 0.011
0.007 0.011 0.008 0.011 0.006 0.19 0.21 0.37 C20-OH/C18-DC 0.009
0.005 0.009 0.006 0.010 0.005 0.02 0.13 0.19 C22 0.008 0.008 0.009
0.010 0.007 0.006 0.73 0.99 0.88 C8:1-OH/C6:1-DC 0.026 0.012 0.027
0.012 0.028 0.012 0.47 0.56 0.67 C8:1-DC 0.027 0.015 0.029 0.018
0.025 0.010 0.10 0.06 0.04 C16:2 0.012 0.007 0.011 0.006 0.013
0.007 0.03 0.11 0.13 C16:1 0.051 0.014 0.029 0.015 0.032 0.014 0.03
0.24 0.25 C16:1-OH/C14:1-DC 0.010 0.005 0.009 0.004 0.011 0.004
0.008 0.01 0.004 C18:2-OH 0.011 0.008 0.012 0.009 0.011 0.007 0.45
0.72 0.50 C20:4 0.010 0.005 0.010 0.007 0.010 0.005 0.20 0.22 0.12
Other Total free fatty acids 1.16 0.70 1.22 0.79 1.10 0.80 0.26
0.12 0.09 Ketones 316.12 283.99 319.20 269.88 313.06 279.02 0.87
0.44 0.10 .beta.-hydroxybutyrate 207.21 202.44 211.52 205.69 202.94
199.81 0.63 0.29 0.08 *adjusted for age, race, sex, diabetes,
hypertension, smoking, dyslipidemia, family history of CAD, BMI.
.dagger.adjusted for age, race, sex, diabetes, hypertension,
smoking, dyslipidemia, family history of CAD, BMI, and total, LDL,
HDL cholesterol and triglycerides.
Unbiased Principal Components Analysis
[0050] PCA identified 12 factors comprised of collinear metabolites
(Table 9), grouping in biologically plausible factors. Three
factors were significantly different between cases and controls in
the initial group in adjusted analyses: factor 1 (medium-chain
acylcarnitines), factor 4 (branched-chain amino acids and related
metabolites), and factor 9 (arginine, histidine, citrulline,
Ci4-DC:C4DC). Of these factors, two factors (4 and 9) remained
significant in the replication group. Factor 1 was only weakly
significant in the replication group (unadjusted P=0.15, adjusted
P=0.03). The factor load for each metabolite is presented in Table
10.
TABLE-US-00009 TABLE 9 Principal Components Analysis. Results of
unbiased principal components analysis (PCA) are presented. Factors
were constructed using the initial group; scoring coefficients from
this PCA were used to calculate factor scores for the initial and
replication groups. P-values for the difference in the mean value
of the factors between cases and controls for the initial and
replication groups are presented. Initial Group Replication Group
Individual Eigen- CAD MI CAD MI Factor Name Components* value Var**
Unadj Adj.dagger. Unadj Adj.dagger. Unadj Adj.dagger. Unadj
Adj.dagger. 1 Medium Chain C8, C10:1, 12.45 0.21 0.001 0.01 0.01
0.06 0.15 0.03 0.59 0.18 Acyl-carnitines C12, C10, C12:1, C10-
OH:C8DC, C6-DC, C8:1-DC, C14:1, C14:2, C8:1- OH/C6:1- DC, C2
acylcarnitines 2 Long Chain C18:1, 5.78 0.10 0.28 0.34 0.21 0.14
0.03 0.01 0.08 0.05 Acyl-carnitines C18:2, C18, C16, C16:1, C20:4,
C14:1, C14:2, C16:2, C14:1-OH 3 Long Chain C18- 4.75 0.08 0.10 0.36
0.04 0.13 <0.0001 0.004 0.03 0.21 Dicarboxyl/ OH/C16- Hydroxyl
Acyl- DC, C20- carnitines OH/C18- DC, C20:1- OH/C18:1- DC, C16-
OH/C14- DC, C18:1- OH/C16:1- DC, C14- OH/C12- DC, C12- OH:C10- DC,
C14:1-OH, C20 4 BCAA Related Phe, Tyr, 2.87 0.05 0.002 0.02 0.0002
0.01 0.01 0.03 0.006 0.005 leu/Ile, Met, Val, C5, Ala 5 Ketone
Related Ket, Hbut, 2.24 0.04 0.18 0.33 0.02 0.12 0.54 0.41 0.07
0.06 Ala (-), C2, C4:OH, C14:1 6 Various C8:1, 1.92 0.03 0.56 0.75
0.92 0.28 0.79 0.92 0.76 0.89 C10:3 7 Amino Acids Ser, Gly, 1.71
0.03 0.19 0.13 0.59 0.42 0.04 0.18 0.28 0.60 FFA (-) 8 Dicarboxyls
C5-DC, 1.41 0.02 0.73 0.34 0.59 0.25 0.05 0.57 0.002 0.04 C8:1-
OH/C6:1- DC, Cit, C8:1-DC, C6-DC 9 Urea Cycle Arg, His, 1.33 0.02
0.0004 0.004 0.0006 0.01 0.01 0.01 0.003 0.006 Related Cit, Ci4-
DC:C4DC (-) 10 Short Chain C3, 1.22 0.02 0.02 0.19 0.03 0.23 0.72
0.92 0.27 0.48 Acyl-carnitines C4:Ci4, C5 11 Various C5:1, 1.15
0.02 0.62 0.13 0.95 0.13 0.03 0.01 0.13 0.12 C18:2-OH (-), C22 (-)
12 Various Asx, C22 1.08 0.02 0.12 0.83 0.15 0.80 <.0001
<.0001 0.01 0.05 *Analytes with a factor load .gtoreq.|0.4| for
that factor are listed, in order of magnitude of load for that
factor; analytes with a negative factor load for that factor are
annotated with a (-). **Proportion of variance explained by that
factor. .dagger.adjusted for diabetes, hypertension, smoking,
dyslipidemia, family history of CAD, BMI; replication group results
are additionally adjusted for age, race and sex.
TABLE-US-00010 TABLE 10 Factor Loads of Individual Metabolites on
Factors Identified from PCA on the Initial Group. FACTOR Metabolite
1 2 3 4 5 6 7 8 9 10 11 12 GLY 0.25 0.03 -0.16 0.03 -0.06 0.06 0.74
0.12 0.06 -0.08 0.09 0.09 ALA 0.09 0.01 0.06 0.43 -0.67 0.13 0.00
-0.16 0.12 0.12 0.08 0.17 SER 0.01 0.19 0.02 0.16 0.07 -0.07 0.76
-0.14 0.17 0.04 -0.06 0.09 PRO 0.24 0.02 -0.17 0.36 -0.37 0.12 0.09
0.17 0.24 0.06 0.05 0.37 VAL -0.24 0.00 0.02 0.71 0.07 -0.06 -0.12
-0.04 0.06 0.28 -0.11 0.36 LEU/ILE -0.06 -0.05 -0.12 0.79 0.15
-0.06 0.09 0.00 -0.08 0.30 0.03 0.19 MET 0.06 -0.07 0.00 0.74 -0.14
-0.10 0.28 -0.06 0.24 0.02 0.05 -0.04 HIS -0.13 0.02 0.08 0.26
-0.13 -0.07 0.28 -0.07 0.58 0.09 -0.21 -0.03 PHE -0.01 0.06 0.17
0.85 -0.01 -0.02 -0.03 0.07 0.10 0.02 -0.07 -0.03 TYR -0.04 0.15
0.07 0.80 -0.22 0.10 0.03 -0.06 0.05 -0.05 0.02 -0.13 ASX -0.15
0.00 0.12 0.07 0.01 -0.08 0.15 0.06 0.05 0.06 0.08 0.69 GLX -0.25
0.11 0.27 0.29 -0.23 0.28 -0.07 0.08 -0.01 0.06 0.00 0.29 ORN 0.11
0.38 -0.27 0.38 -0.14 0.14 0.26 0.33 -0.02 0.09 -0.05 0.03 CIT 0.14
0.11 -0.20 0.06 -0.06 0.24 0.13 0.46 0.54 -0.08 0.06 0.18 ARG -0.08
-0.19 -0.02 0.26 -0.07 -0.09 0.18 0.07 0.68 0.07 0.13 0.04 C2 0.44
0.33 0.08 0.00 0.60 0.16 0.11 -0.13 0.02 0.22 0.11 0.07 C3 0.00
0.01 -0.16 0.23 -0.15 0.06 0.04 -0.09 0.11 0.72 0.03 0.05 C4:C14
0.30 -0.06 -0.01 0.19 -0.13 0.14 0.03 0.18 -0.03 0.50 0.13 0.01
C5:1 0.14 -0.05 -0.07 -0.03 -0.04 -0.09 0.02 -0.05 0.01 0.30 0.69
0.10 C5 0.13 0.03 0.04 0.44 -0.06 0.06 -0.14 0.20 -0.01 0.49 0.02
0.10 C4:OH 0.32 0.05 0.07 0.00 0.56 0.24 0.23 -0.07 -0.19 0.19 0.29
0.00 C14-DC:C4DC 0.39 -0.01 -0.12 0.18 0.05 0.12 0.22 0.32 -0.43
0.08 0.06 -0.21 C8:1 0.27 0.04 0.12 0.01 0.06 0.85 -0.02 0.03 -0.02
0.10 -0.09 0.05 C8 0.80 0.16 0.15 -0.01 0.10 0.09 0.06 -0.02 0.01
0.11 -0.13 -0.03 C5-DC 0.34 -0.03 0.28 0.02 -0.04 -0.11 -0.10 0.62
0.11 0.05 -0.08 0.07 C6-DC 0.60 0.06 0.29 -0.05 0.04 0.14 -0.02
0.41 -0.15 -0.03 0.11 0.07 C10:3 0.30 0.07 0.10 -0.04 0.05 0.82
0.01 0.07 -0.04 0.04 -0.04 -0.10 C10:1 0.76 0.16 0.12 -0.03 0.04
0.31 0.01 0.03 0.00 0.09 -0.18 -0.04 C10 0.72 0.06 0.00 -0.09 0.14
-0.05 0.09 -0.06 -0.04 0.03 0.04 0.04 C10-OH:C8DC 0.65 0.16 0.39
0.00 0.19 0.14 -0.08 0.22 -0.08 0.03 0.11 -0.01 C12:1 0.88 0.26
0.25 0.10 0.23 0.23 -0.09 0.10 0.04 -0.06 0.15 -0.11 C12 0.74 0.13
0.00 0.01 0.02 0.03 0.12 0.16 -0.06 -0.01 0.24 -0.12 C12-OH:C10DC
0.33 0.09 0.46 -0.02 0.12 0.05 -0.11 0.12 0.14 -0.07 -0.06 0.20
C14:2 0.47 0.48 0.38 0.04 0.37 0.14 -0.10 0.02 0.12 -0.07 -0.21
0.00 C14:1 0.52 0.49 0.39 0.02 0.40 0.05 -0.12 -0.01 0.07 -0.04
-0.10 0.02 C14:1-OH 0.34 0.42 0.44 0.04 0.20 0.11 0.01 0.06 0.01
0.01 -0.02 -0.08 C14-OH/C12-DC 0.16 0.29 0.51 0.09 -0.03 0.09 0.09
-0.10 -0.19 0.01 -0.02 0.12 C16 0.23 0.71 0.30 0.08 0.15 -0.05 0.02
-0.15 -0.14 0.03 0.14 0.11 C16-OH/C14-DC 0.18 0.18 0.57 -0.01 0.07
0.00 -0.23 -0.09 0.02 0.02 0.05 0.09 C18:2 0.16 0.79 0.16 0.07 0.11
0.16 0.03 0.05 -0.02 -0.10 -0.22 0.04 C18:1 0.22 0.83 0.24 0.04
0.19 0.01 0.06 0.00 -0.08 -0.09 -0.03 0.00 C18 0.08 0.75 0.25 -0.07
0.01 -0.02 0.14 0.01 -0.02 0.08 0.03 0.01 C18:1-OH/C16:1-DC 0.15
0.11 0.52 0.07 0.19 0.01 -0.07 0.01 -0.01 -0.20 -0.17 0.24
C18-OH/C16-DC -0.05 0.19 0.69 0.06 0.00 0.09 -0.01 0.08 -0.10 -0.03
-0.10 0.09 C20 -0.04 0.26 0.42 -0.07 0.05 0.00 -0.15 0.04 0.34 0.12
-0.07 -0.06 C20:1-OH/C18:1-DC 0.11 0.24 0.62 0.01 0.10 0.12 0.01
0.15 0.08 -0.06 -0.17 -0.12 C20-OH/C18-DC 0.15 0.15 0.62 0.00 -0.01
-0.01 0.14 0.07 0.03 0.00 0.05 -0.14 C22 0.04 0.03 0.00 0.06 0.01
0.05 -0.07 -0.03 0.00 0.03 -0.46 0.41 C8:1-OH/C6:1-DC 0.46 0.02
0.12 -0.09 -0.11 0.31 0.02 0.46 -0.03 0.13 -0.06 0.12 C8:1-DC 0.56
0.08 0.10 -0.03 -0.07 0.30 0.10 0.44 -0.06 0.01 0.18 -0.09 C16:2
0.17 0.47 0.34 0.09 0.30 0.05 -0.24 0.03 0.22 -0.11 -0.26 0.03
C16:1 0.39 0.62 0.27 0.00 0.32 -0.03 -0.14 -0.09 0.06 -0.17 -0.02
-0.07 C16:1-OH/C14:1-DC 0.22 0.36 0.32 -0.02 0.19 0.02 0.02 -0.06
0.18 -0.08 0.23 -0.08 C18:2-OH -0.02 0.24 0.29 -0.06 -0.06 0.00
0.09 -0.03 0.01 0.17 -0.51 -0.08 C20:4 -0.24 0.58 0.15 0.07 -0.13
0.06 0.03 0.22 0.02 0.16 -0.25 -0.01 FFA 0.10 0.23 -0.13 0.05 0.19
-0.01 -0.44 -0.36 -0.22 -0.33 0.02 0.22 HBUT 0.17 0.24 0.15 -0.05
0.85 0.02 -0.09 -0.08 -0.03 -0.22 -0.04 0.05 KET 0.17 0.20 0.15
-0.04 0.87 0.01 -0.07 -0.06 -0.03 -0.20 -0.01 0.04
[0051] Further adjustment for lipids showed continued association
with CAD, although Factor 4 was not significant in the initial
group (initial group: factor 1, P=0.0002; factor 4, P=0.59; factor
9, P=0.02; replication group: factor 1, P=0.01; factor 4, P=0.02;
factor 9, P=0.004). Although we adjusted for diabetes, given
studies showing relationships between metabolites with insulin
resistance, we further adjusted the base multivariable model for
fasting glucose. These analyses revealed a continued significant
association with CAD (initial group: factor 1, P=0.02; factor 4,
P=0.02; factor 9, P=0.003; replication group: factor 1, P=0.03;
factor 4, P=0.05; factor 9, P=0.02).
[0052] Stratified analyses suggested stronger association between
factors 4 and 9 with CAD in non-diabetics as compared with
diabetics (Table 11), with minimal or no discernable signal in
diabetics, but no consistent differences in association with CAD by
smoking (Table 12).
TABLE-US-00011 TABLE 11 Association of PCA Derived Metabolomic
Factors with CAD, Stratified by Diabetes. P-values for the
association of PCA-derived metabolomic factors with CAD, stratified
by a medical history of diabetes, are presented. Unadjusted
p-values and p-values adjusted for hypertension, smoking,
dyslipidemia, family history and BMI (and also for age, race and
sex in the Replication Group) are presented. Initial Group
Replication Group Diabetics Non-Diabetics Diabetics Non-Diabetics
Factor Name Unadj Adj Unadj Adj Unadj Adj Unadj Adj 1 Medium Chain
Acylcarnitines 0.007 0.01 0.03 0.19 0.15 0.09 0.42 0.26 2 Long
Chain Acylcarnitines 0.70 0.65 0.12 0.46 0.06 0.08 0.23 0.15 3 Long
Chain Dicarboxyl/Hydroxyl Acylcarnitines 0.85 0.77 0.04 0.18 0.53
0.54 <0.0001 0.002 4 BCAA Related 0.12 0.09 <0.0001 0.0003
0.86 0.75 0.005 0.0004 5 Ketone Related 0.06 0.11 0.56 0.97 0.13
0.23 0.95 0.94 6 Various 0.01 0.07 0.17 0.10 0.33 0.88 0.25 0.69 7
Amino Acids 0.41 0.32 0.38 0.27 0.61 0.69 0.03 0.08 8 Dicarboxyis
0.18 0.11 0.76 0.82 0.80 0.88 0.06 0.28 9 Urea Cycle Related 0.69
0.98 0.0005 0.0002 0.53 0.44 0.02 0.002 10 Short Chain
Acylcarnitines 0.09 0.25 0.16 0.35 0.90 0.87 0.89 0.85 11 Various
0.14 0.14 0.99 0.27 0.19 0.04 0.08 0.19 12 Various 0.11 0.10 0.61
0.52 0.06 0.11 <0.0001 <0.0001
TABLE-US-00012 TABLE 12 Association of PCA Derived Metabolomic
Factors with CAD, Stratified by Smoking. P-values for the
association of PCA-derived metabolomic factors with CAD, stratified
by smoking (currently smoking or not), are presented. Unadjusted
p-values and p- values adjusted for diabetes, hypertension,
dyslipidemia, family history and BMI (and also for age, race and
sex for the Replication Group) are presented. Initial Group
Replication Group Smokers NonSmokers Smokers Non-Smokers Factor
Name Unadj Adj Unadj Adj Unadj Adj Unadj Adj 1 Medium Chain
Acylcarnitines 0.03 0.09 0.009 0.05 0.38 0.18 0.14 0.16 2 Long
Chain Acylcarnitines 0.15 0.05 0.87 0.64 0.24 0.15 0.04 0.07 3 Long
Chain Dicarhoxyl/Hydroxyl Acylcarnitines 0.08 0.33 0.77 0.81 0.48
0.50 <0.0001 0.0005 4 BCAA Related 0.003 0.07 0.24 0.23 0.27
0.12 0.02 0.005 5 Ketone Related 0.14 0.84 0.76 0.46 0.24 0.71 0.83
0.70 6 Various 0.82 0.32 0.46 0.62 0.89 0.87 0.96 0.77 7 Amino
Acids 0.16 0.18 0.69 0.41 0.02 0.10 0.84 0.79 8 Dicarhoxyls 0.39
0.12 0.42 0.79 0.66 0.90 0.05 0.16 9 Urea Cycle Related 0.14 0.58
0.0001 0.0004 0.04 0.05 0.02 0.03 10 Short Chain Acylcarnitines
0.14 0.41 0.08 0.27 0.37 0.39 0.48 0.58 11 Various 0.83 0.94 0.40
0.08 0.23 0.11 0.07 0.15 12 Various 0.15 0.47 0.97 0.60 0.0004
0.002 0.0002 0.0001
[0053] Additional adjustment for ten classes of medications had
minimal influence on the relationship between factors and CAD in
the initial group (factor 1, adjusted P=0.009; factor 4, P=0.03;
factor 9, P=0.003), but were no longer significant in the
replication group (factor 1, P=0.02; factor 4, P=0.19; factor 9,
P=0.14). We also performed similar analyses restricted to those
individuals with available medication data, in the combined
datasets to optimize power (N=416). These results showed continued
association between factors 4 and 9 with CAD, although attenuated
(factor 4: unadjusted model, p=0.0009; model adjusted for CAD risk
factors, P=0.03; model adjusted for CAD risk factors and
medications, P=0.05; factor 9: unadjusted model, P=0.0003; model
adjusted for CAD risk factors, P=0.002; model adjusted for CAD risk
factors and medications, P=0.007).
[0054] Results presented are unadjusted for multiple comparisons.
We used PCA to account for co-linearity of metabolites. Of the
individual metabolites, only glutamate/glutamine would survive
Bonferroni correction. Factors 4 and 9 would survive Bonferroni
correction at the level of factors (P<0.004).
Association of Metabolite Profiles with Prevalent Myocardial
Infarction
[0055] To examine association of these metabolites with a more
severe phenotype, we evaluated the relationship of the PCA-derived
factors in cases with a prior history of MI compared with controls
free of CAD (initial group N=86 MI cases, replication group N=61 MI
case). The two factors (4 and 9) that were associated with CAD were
also associated with prior MI in both groups (Table 9).
Assessment of Model Fit and ROC Curves for CAD
[0056] To further quantify the independent association of
metabolite factors with CAD, logistic regression models were
constructed: (1) clinical model; (2) clinical model plus factors 4
and 9; and (3) clinical model plus all metabolite factors. Factors
4 and 9 were independently associated with CAD in both the initial
group (factor 4: odds ratio [OR] 1.42; 95% CI, 1.09 to 1.84,
P=0.01; factor 9: OR 0.69, 95% CI, 0.53 to 0.90, P=0.006) and the
replication group (factor 4: OR1.42; 95% CI 1.06 to 1.89, P=0.02;
factor 9: OR 0.67; 95% CI 0.48 to 0.92, P=0.01). Measure of model
fit and ROC curves (FIG. 1) in the initial group showed modestly
greater discriminative capability for models containing factors 4
and 9 (c-statistic 0.778), with some improvement with addition of
all factors (c-statistic 0.804), above the model containing only
clinical variables (c-statistic 0.756; P=0.06 for comparison of
clinical model to clinical model plus factors 4 and 9; P=0.003 for
comparison of clinical model to clinical model plus all factors).
In the replication group, there was a slightly higher c-statistic
with the addition of factors 4 and 9 to the clinical model
(c-statistic 0.773) than for the clinical model alone (c-statistic
0.743), but more dramatic improvement with addition of all factors
(c-statistic 0.874; P=0.04 for comparison of clinical model to
clinical model plus factors 4 and 9; and P<0.0001 for comparison
of clinical model to clinical model plus all factors).
[0057] Given it is standard of care to measure lipids in patients
in whom a diagnosis of CAD is being considered, and that CRP is a
recognized biomarker of cardiovascular disease, we reconstructed
these models including lipids and CRP. These analyses revealed a
higher clinical model fit in both initial and replication groups
(c-statistic 0.842 and 0.778, respectively). The addition of
factors 4 and 9 to the clinical model inclusive of lipids and CRP
resulted in no improvement in the discriminative ability of the
model in the initial group (c-statistic 0.848, P=0.31 for
comparison with clinical model), with some improvement with
addition of all factors (c-statistic 0.865, P=0.01 for comparison
with clinical model). However, the magnitude of improvement in the
clinical model with addition of metabolite factors remained similar
and large in the replication group (c-statistics: clinical model
inclusive of lipids and CRP, 0.778; clinical model+factors 4 and 9,
0.799, P=0.08; clinical model+all metabolite factors, 0.900,
P=0.0001 for comparison with clinical model).
Metabolite Factors and Risk of Subsequent Cardiovascular Events
[0058] During a median of 2.72 years of follow-up, 74 of 314 CAD
cases had an incident cardiovascular event. In unadjusted
comparisons, factor 8 (short-chain dicarboxylacylcarnitines) was
highly associated with occurrence of death or MI (FIG. 2; highest
versus lowest tertile hazard ratio [HR] 2.50; 95% CI, 1.47 to 4.17;
P=0.0008; highest versus middle tertile HR 2.33; 95% CI, 1.39 to
3.85; P=0.002). The strength of this association was somewhat
attenuated after adjustment for CAD risk factors, CADindex, age,
race, sex, ejection fraction, creatinine and treatment with CABG
after catheterization (highest versus lowest tertile: HR1.67; 95%
CI, 0.88 to 3.13; P=0.11; highest versus middle tertile: HR1.89;
95% CI 1.09 to 3.33; P=0.03). Factor 1 was also associated with the
occurrence of death/MI in unadjusted comparisons (highest versus
lowest tertile HR1.85; 95% CI, 1.06 to 3.23; P=0.03; highest versus
middle tertile HR1.79; 95% CI, 1.02 to 3.03; P=0.04), but was no
longer significant after adjustment (P=0.14 and 0.05,
respectively).
[0059] To validate these findings, we performed metabolomic
profiling in an independent case-control dataset
(`event-replication` group). In this group, factor 8 was associated
with cardiovascular events (unadjusted OR1.52; 95% CI, 1.08 to
2.14; P=0.01; adjusted OR1.82; 95% CI, 1.08 to 3.50; P=0.03), with
higher scores in cases who suffered subsequent cardiovascular
events versus event-free controls. Individual metabolites within
the factor were also significantly different (P<0.05) between
cases and controls, with a similar direction of effect as observed
in the original `event` dataset.
[0060] This example demonstrates that peripheral blood metabolite
profiles are independently associated with the presence of CAD, and
add to the discriminative capability for CAD compared with models
containing only clinical variables. Further, we report a specific
metabolite cluster that independently predicts subsequent
cardiovascular events in individuals with CAD.
Example 2
Heritability of CAD
Materials and Methods
[0061] Study Population. The GENECARD study enrolled 920 families
to perform affected-sibling-pair linkage for identification of
genes for early-onset CAD (before age 51 for men, age 56 for women)
(Hauser et al., 2003, Am Heart J, 145, 602-613). Families with at
least two siblings each of whom met the criteria for early-onset
CAD (before age 51 for men, age 56 for women) were recruited.
Unaffected family members were defined as no clinical evidence of
CAD and age greater than 55 years for men (greater than 60 years
for women). From this cohort, we selected eight representative
families we believed would be particularly informative, based on
availability of a relatively large number of family members and a
heavy burden of CAD in the proband and surrounding generations
(FIG. 3). These families were recontacted; the
affected-sibling-pair and family members not previously enrolled
were ascertained regardless of CAD, focused on offspring of the
affected-sibling-pair. This ascertainment strategy was based on the
hypothesis that if abnormalities in metabolic profiles preceded
development of CAD in these families, that significant concordance
of metabolite levels within families would be evident even in the
absence of overt CAD in the offspring. Sample collections within a
given family were done at several different times and at different
locations, by a single experienced phlebotomist. Blood samples were
promptly processed after collection via peripheral venous
phlebotomy (within minutes), frozen as soon as possible thereafter
(at most within 12 hours with the majority of samples being frozen
within 1-2 hours of collection), and stored as plasma samples in
EDTA-treated tubes at -80.degree. C. Samples were collected as
often as possible in a fasting state; however, the consistency of
this could not be determined. Institutional Review Boards approved
study protocols; informed consent was obtained from each
subject.
[0062] Biochemical measurements. Frozen plasma samples were used to
quantitatively measure targeted metabolites, including 37
acylcarnitine species, 15 amino acids, nine free fatty acids and
conventional analytes, ketones and C-reactive protein (CRP). Sample
preparation and coefficients of variation have been reported (Haqq
et al., 2005 Contemp Clin Trials, 26, 616-625). The laboratory was
blinded to family identifiers and case-control status. Assay ranges
are 0.05-40 micromolar (.mu.M) (acylcarnitines); 5-1000 .mu.M
(amino acids); and 1-1000 mmol/L (fatty acids). For simplicity, the
clinical shorthand of metabolites is used (Table 1).
Intra-individual variability was assessed in samples from five
individuals for which repeat profiling was performed on the same
sample on five separate days. Coefficients-of-variation and
correlation confirmed minimal inter-assay variability (Table
1).
[0063] Conventional metabolite analysis. Standard clinical
chemistry methods were used for conventional metabolites, including
glucose, total cholesterol, high-density-lipoprotein (HDL)- and
low-density-lipoprotein (LDL) cholesterol, and triglycerides with
reagents from Roche Diagnostics (Indianapolis, Ind.); and free
fatty acids (total) and ketones (total and 3-hydroxybutyrate) with
reagents from Wako (Richmond, Va.). All measurements were performed
using a Hitachi 911 clinical chemistry analyzer.
[0064] Acylcarnitines and amino acids. Proteins were first removed
by precipitation with methanol. Aliquoted supernatants were dried,
and then esterified with hot, acidic methanol (acylcarnitines) or
n-butanol (amino acids). Acylcarnitines and amino acids were
analyzed by tandem MS with a Quattro Micro instrument (Waters
Corporation, Milford, Mass.). Thirty-seven acylcarnitine species
and 15 amino acids in plasma were assayed by our previously
described methods (Millington et al., 1990, J Inherit Metab Dis,
13, 321-324; An et al., 2004, Nat Med, 10, 268-274; Wu et al.,
2004, J Clin Invest, 113, 434-440). Leucine/isoleucine (LEU/ILE)
are reported as a single analyte because they are not resolved by
our MS/MS method, and include contributions from alto-isoleucine
and hydroxyproline. Under normal circumstances these isobaric amino
acids contribute little to the signal attributed to LEU/ILE. In
addition, the acidic conditions used to form butyl esters results
in partial hydrolysis of glutamine to glutamic acid and of
asparagine to aspartate. Accordingly, values that are reported as
GLU/GLN or ASP/ASN are not meant to signify the molar sum of
glutamate and glutamine, or of aspartate and asparagine, but rather
measure the amount of glutamate or aspartate plus the contribution
of the partial hydrolysis reactions of glutamine and asparagine,
respectively.
[0065] Free fatty acids. Free fatty acids were gently methylated
using iodomethane and purified by solid-phase extraction (Patterson
et al., 1999, J Lipid Res, 40, 2118-2124). Derivatized fatty acids
were analyzed by capillary gas chromatography/mass spectrometry
(GC/MS) using a Trace DSQ instrument (Thermo Electron Corporation,
Austin, Tex.). Due to sample volume considerations, only 80 of the
117 individuals (five out of eight families) had free fatty acid
measurements performed.
[0066] All mass-spectrometric analyses employed
stable-isotope-dilution. Quantification of the foregoing "targeted"
intermediary metabolites was facilitated by addition of mixtures of
known quantities of stable-isotope internal standards to samples,
from Isotec (St. Louis, Mo.), Cambridge Isotope Laboratories
(Andover, Mass.) and CDN Isotopes (Pointe-Claire, Quebec, CN)
(Table 3).
[0067] Heritability analysis. Heritabilities were calculated using
the Sequential Oligogenic Linkage Analysis Routines (SOLAR)
software version 4.0.7 (Almasy and Blangero, 1998, Am J Hum Genet,
62, 1198-1211), which uses maximum-likelihood methods to estimate
variance components, allowing incorporation of fixed effects for
known covariates and variance components for genetic effects. This
approach appropriately accounts for correlation between all family
members and allows incorporation of extended pedigrees such as is
present in the current study. The total variation is partitioned
into components for additive genetic variance and environmental
variance, as well as a residual (unexplained) variability. The
program uses the pedigree covariance matrix
.OMEGA.=2.PHI..sup..sigma..sup.g.sup.2+I.sup..sigma..sup.e.sup.2
where .OMEGA. is the covariance matrix, .PHI. is the matrix of
kinship values, i.sup..sigma..sup.g.sup.2 is the additive genetic
variance, I represents the identity matrix, and .sigma..sub.e.sup.2
is the random environmental variance (Almasy et al., 1998, supra).
This model allows for complex pedigree data (i.e. beyond
parent-offspring pairs) and hence, the resulting heritability
estimates are more accurate than those obtained using only nuclear
family members. For the current study, all sampled individuals from
the pedigree were entered into the variance components models,
including unaffected offspring, cousins, and married-in family
members. Incorporation of married-in family members (i.e.
genetically unrelated but with shared environment) allows for
better estimation of the environmental component of intrafamilial
clustering of traits.
[0068] Values considered outliers were excluded from heritability
analyses, defined as values falling outside of the mean.+-.4SD
(one-two outliers for each of 24 of the metabolites). Metabolite
measurements below the lower limits of quantification (LOQ) were
given a value of LOQ/2. Four metabolites having >25% of samples
below LOQ were not further analyzed (C6, C5-OH:C3-DC, C4DC, and
C10:2 acylcarnitines). All measurements were natural
log-transformed prior to analysis, resulting in most metabolites
approximating a normal distribution, an important consideration for
variance components analysis. Eighteen metabolites did not meet
this criterion, and therefore, linear regression models adjusted
for body-mass index (BMI), age, sex, CAD, diabetes mellitus (DM
(yes/no), hypertension (yes/no), and dyslipidemia (yes/no) were
constructed for each of these metabolites, and the residuals were
used for heritability estimates. Given occasional low trait
standard deviations for metabolites (<0.5), all log transformed
metabolites were multiplied by a factor of 4.7 prior to
analysis.
[0069] Polygenic heritability models were then constructed. For the
normally distributed metabolites (the majority of metabolites),
polygenic heritability models were calculated using the
log-transformed values, adjusting for age, sex, BMI, DM,
dyslipidemia, hypertension and CAD. The proband and family members
were not selected based on any metabolite values; however, the
potential for ascertainment bias exists. Therefore, analyses were
corrected based on which of the family members (proband) was the
index member for ascertainment of the family for early-onset CAD.
To account for factors such as diet (which are shared in households
but are presumably not genetic) an additional variance component
parameter corresponding to the fraction of variance associated with
the effect of a common household (included in the model by a marker
for residential address), was added to each model. All residual
kurtoses for the final polygenic model were within normal range
(i.e. <0.8), except for two amino acids (serine and
phenylalanine), eleven acylcarnitines (C5, C10, C10:1, C10:3,
C12:1, C14, C14-OH:C12-DC, C16-OH:C14-DC, C18:1-OH, C18:1-DC, and
C18-DC:C20-OH) and three free fatty acids (FAC14:0, FAC16:1,
FAC18:1). For these metabolites, removal of 1-4 of the most extreme
values was necessary, which then resulted in a normal residual
kurtosis. Two acylcarnitines required removal of a larger number of
outliers to achieve a normal residual kurtosis (C16-OH:C14-DC and
C12-OH:C10-DC), and hence, these results should be interpreted
accordingly. For the eighteen non-normally distributed metabolites,
standardized residuals from adjusted regression models were used to
estimate heritabilities using SOLAR, but since the normalized
deviates were already adjusted for relevant covariates heritability
models using these residuals were not further adjusted. Estimates
of the proportion of variance explained by clinical covariates are
reported for these non-normally distributed metabolites as
estimated using the adjusted polygenic model constructed from the
log-transformed crude values.
[0070] For understanding quantitative differences in metabolites
between families, multivariable generalized linear models adjusted
for sex, age, BMI, CAD, DM, dyslipidemia and hypertension, were
used to compare mean metabolite levels between families.
[0071] Unsupervised principal components analyses. Given that many
metabolites reside in overlapping pathways, correlation of
metabolites is expected. To understand the correlation, we used
principal components analysis (PCA) to reduce the large number of
correlated variables into clusters of fewer uncorrelated factors
using raw metabolite values without removal of outliers. The factor
with the highest "eigenvalue" accounts for the largest amount of
the variability within the dataset. Standardized residuals
calculated for each metabolite from linear regression models
adjusted for age, sex, BMI, DM, and CAD, were used as input for
PCA. PCA using residuals is recommended when, as in this case, the
units for each variable vary significantly in magnitude (Johnson
and Wichern D. W., 1988, Applied Multivariate Statistical Analysis.
Prentice Hall, Englewood Cliffs, N.J.). Factors with an eigenvalue
.gtoreq.1.0 were identified based on the commonly employed Kaiser
criterion (Kaiser, 1960, Educational and Psychological Measurement,
20, 141-151). Varimax rotation was then performed to produce
interpretable factors. Metabolites with a factor load .gtoreq.|0.4|
are reported as composing a given factor, as is commonly used as an
arbitrary threshold (Lawlor et al., 2004, Am J Epidemiol, 159,
1013-1018). Scoring coefficients were then used to compute factor
scores for each individual (consisting of a weighted sum of the
values of the standardized metabolites within that factor, weighted
on the factor loading calculated for each individual metabolite).
These factor scores were then used to calculate heritabilities for
each factor with SOLAR as detailed above, using a polygenic model
not further adjusted for covariables. Removal of 1-4 of the most
extreme values for several of the factors was necessary to achieve
a normal residual kurtosis.
[0072] As all analyses were exploratory in nature and given
collinearity of the metabolites, nominal two-sided p-values
unadjusted for multiple comparisons are presented, however results
interpreted in the context of a conservative Bonferroni correction
are reported. Nominal statistical significance was defined as
p-value<0.05. Statistical analyses used SAS version 9.1 (SAS
Institute, Cary N.C.), other than for heritability estimates which
used SOLAR (Almasy et al., 1998, supra).
RESULTS AND DISCUSSION
[0073] Heritability Analysis. Metabolic profiling was performed on
117 individuals within eight multiplex Caucasian families (FIG. 3)
from the GENECARD study of premature CAD. Of note, the majority of
family members sampled for this study were as-yet-unaffected
offspring of the original affected-sibling-pair, but who, as
members of these families, were at high risk for development of
premature CAD. As expected, there was a high burden of CAD risk
factors, although the prevalence differed between families (Table
13).
TABLE-US-00013 TABLE 13 Clinical characteristics of GENECARD
families. The overall baseline clinical characteristics of the
GENECARD cohort are presented, as well as baseline characteristics
within each family. Overall Family 1 Family 2 Family 3 Family 4
Family 5 Family 6 Family 7 Family 8 Variable (N = 117) (N = 22) (N
= 3) (N = 22) (N = 9) (N = 18) (N = 27) (N = 9) (N = 7) Age (SD)
45.62 49.77 39.00 39.27 49.22 49.33 44.04 46.33 49.29 (15.82)
(16.12) (18.25) (15.85) (11.12) (17.82) (15.38) (15.00) (15.96) Sex
(% female) 48.7% 36.4% 66.7% 59.1% 66.7% 44.4% 48.2% 44.4% 42.9%
Diabetes (%) 9.4% 13.6% 0.0% 9.1% 22.2% 16.7 0.0% 11.1% 0.0%
Hypertension (%) 36.8% 36.4% 66.7% 36.4% 33.3% 44.4% 22.2 33.3%
71.4% Dyslipidemia (%) 35.0% 45.5% 0.0% 31.8% 44.4% 44.4% 22.2
44.4% 28.6% BMI (SD) 28.68 28.77 32.02 26.73 35.59 30.19 27.43
27.98 25.81 (5.70) (5.86) (7.13) (5.90) (7.18) (5.38) (4.05) (3.30)
(4.00) Total cholesterol 191.12 180.68 192.33 187.86 211.78 197.22
181.56 234.67 171.86 mean (SD) (43.20) (32.64) (23.18) (60.56)
(43.42) (46.90) (34.30) (25.23) (8.32) (mg/dL) HDL cholesterol
45.34 39.70 51.67 56.10 48.20 45.54 40.56 39.97 47.66 mean (SD)
(15.18) (13.13) (3.70) (20.69) (15.18) (13.35) (11.46) (10.77)
(9.32) (mg/dL) LDL cholesterol 117.67 102.48 138.30 116.10 137.46
124.76 116.80 140.11 92.16 mean (SD) (35.94) (22.55) (30.58)
(46.91) (35.12) (41.79) (26.99) (38.36) (17.15) (mg/dL)
Triglycerides 161.63 176.91 91.67 90.23 120.78 141.94 188.26 270.11
229.00 mean (SD) (115.02) (99.71) (19.66) (67.75) (54.97) (81.08)
(125.14) (170.20) (154.39) (mg/dL) C-reactive protein 3.42 2.49
2.47 2.36 4.92 5.27 2.99 1.68 7.66 mean (SD) (3.61) (1.60) (1.69)
(2.16) (5.37) (4.88) (2.29) (2.33) (6.70) (mg/L) HDL: high-density
lipoprotein; LDL: low-density lipoprotein; BMI: body-mass-index
[0074] We found high heritabilities for conventional risk factors
such as lipids and BMI (FIG. 4). Total ketones (h.sup.2 0.75,
p=3.8.times.10.sup.-8) had the highest heritability among the
metabolites analyzed by non-mass spectrometry-based methods, with
similarly high heritability of the individual ketone
.beta.-hydroxybutyrate (h.sup.2 0.51, p=0.004). Among analytes
measured by mass spectrometry, several amino acids had high
heritability (FIG. 5, Table 14). Arginine (ARG) had the highest
score (h.sup.2 0.80, p=1.9.times.10.sup.-16), with strong
heritabilities also for glutamine/glutamate (GLX; h.sup.2 0.73,
p=0.00006), alanine (ALA; h.sup.2 0.55, p=0.00002), proline (PRO;
h.sup.2 0.52, p=0.00004), ornithine (ORN, h.sup.2 0.48,
p=0.000005), phenylalanine (PHE; h.sup.2 0.46, p=0.0001), and the
branched-chain amino acids leucine/isoleucine (LEU/ILE; h.sup.2
0.39, p=0.00005) and valine (VAL; h.sup.2 0.44, p=0.00006). Of the
free fatty acids (FIG. 5), FA-C20:4 (arachidonic acid, a key
component in inflammatory pathways) was the most heritable (h.sup.2
0.59, p=0.00005), as well as FA-C18:2 (linoleic acid, precursor to
arachidonic acid, h.sup.2 0.48, p=0.002). Many acylcarnitines also
had high heritabilities (FIG. 6, Table 14), the highest being the
C18 acylcarnitines (C18, C18:1, and C18:2, h.sup.2 0.39-0.82,
p=0.0000007-0.004); C14:1 (h.sup.2 0.79, p=0.0000002); C5:1
(h.sup.2 0.67, p=0.000003); the C10s (C10-OH:C8-DC, C10 and C10:1,
h.sup.2 0.35-0.57, p=0.00003-0.02); C16 (h.sup.2 0.57, (p=0.0002);
C4:Ci4 (h.sup.2 0.56, p=0.00003); short chain
dicarboxylacylcarnitines (C5-DC, C6-DC, h.sup.2 0.45-0.51,
p=0.003-0.004); and C2 acylcarnitine (h.sup.2 0.50, p=0.00008).
Interestingly, estimates for the genetic component of the
variability of each metabolite often exceeded the proportion of
variance explained by clinical covariates (Table 14).
TABLE-US-00014 TABLE 14 Heritabilities, clinical covariates and
household effects for individual metabolites. Results for
individual metabolites are presented, including: heritability point
estimates, standard error for the heritability estimate, clinical
covariates found to be significant in the polygenic model, the
proportion of variance explained by household effects, the p-value
for the household effects, the proportion of variance in the
metabolite explained by those clinical covariates, and the p-value
for the heritabilities. Proportion House- Proportion Var hold
Variance Heritability Short Name Heritability SE Covariates*
Household p-value Covariates.dagger. p-value** C2 0.50 0.17 Age
0.06 0.3 0.18 0.00008 C3 0.35 0.13 HTN, Sex 0.08 0.06 0.18 0.0003
C4:Ci4 0.56 0.17 CAD, Age, 0.01 0.4 0.29 0.00003 HTN, Dys, Sex C5:1
0.67 0.14 None 0.02 0.4 N/A 0.000003 C5 0.34 0.16 Sex 0.00 N/A 0.22
0.003 C4-OH 0.37 0.16 Age 0.04 0.2 0.05 0.001 C8:1 0.27 0.18 BMI,
Age 0.00 N/A 0.20 0.03 C8 0.45 0.23 Age 0.09 0.2 0.10 0.01 C5-DC
0.45 0.18 Age 0.00 N/A 0.05 0.003 C6-DC 0.51 0.20 HTN, Age, 0.08
0.2 0.20 0.004 Sex C10:3 0.16 0.13 Age 0.00 N/A 0.14 0.08 C10:1
0.57 0.16 Age, Sex 0.04 0.3 0.12 0.00003 C10 0.35 0.22 None 0.20
0.09 N/A 0.02 C10OH:C8DC 0.43 0.19 Age, Sex 0.00 N/A 0.09 0.004
C12:1 0.44 0.22 DM 0.00 0.5 0.003 0.005 C12 0.34 0.22 Sex 0.17 0.01
0.04 0.02 C12OH:C10DC 0.23 0.16 Age, Dys, Sex 0.00 N/A 0.11 0.04
C14:2 0.40 0.17 None 0.00 N/A N/A 0.003 C14:1 0.79 0.15 DM, Age
0.00 N/A 0.03 <0.0001 C14 0.25 0.19 Dys 0.18 0.05 0.04 0.06
C14:1-OH 0.23 0.19 None 0.01 0.4 N/A 0.08 C14-OH:12- 0.48 0.25 BMI
0.02 0.4 0.003 0.03 DC C16 0.57 0.20 BMI, Age 0.00 N/A 0.15 0.0003
C16- 0.06 0.18 None 0.04 0.3 N/A 0.36 OH:C14-DC C18:2 0.39 0.15 BMI
0.23 0.0007 0.11 0.0004 C18:1 0.82 0.17 BMI, Age 0.02 0.4 0.06
0.0000007 C18 0.55 0.15 BMI, Age, 0.03 0.3 0.09 0.00006 Sex
C18:1-OH 0.00 -- None 0.00 N/A N/A 0.50 C18- 0.00 -- None 0.007 0.5
N/A 0.50 OH:C16-DC C20 0.05 0.12 None 0.00 N/A N/A 0.33 C18:1-DC
0.15 0.23 Age 0.00 N/A 0.04 0.23 C18- 0.40 0.25 None 0.00 N/A N/A
0.04 DC:C20-OH C22 0.00 -- None 0.08 0.2 N/A 0.50 GLY 0.33 0.14
Sex, Dys 0.09 0.09 0.15 0.005 ALA 0.55 0.16 BMI 0.00 N/A 0.09
0.00002 SER 0.25 0.17 BMI 0.19 0.02 0.13 0.06 PRO 0.52 0.16 Age,
Sex 0.03 0.4 0.10 0.00004 VAL 0.44 0.14 Age, Sex, 0.04 0.2 0.18
0.00006 BMI LEU/ILE 0.39 0.13 Sex 0.13 0.1 0.15 0.00005 MET 0.35
0.17 Sex 0.02 0.4 0.11 0.008 HIS 0.35 0.18 HTN 0.02 0.4 0.04 0.03
PHE 0.46 0.16 Sex, BMI, 0.00 N/A 0.20 0.0001 HTN TYR 0.38 0.20 Sex,
BMI 0.08 0.1 0.13 0.02 ASX 0.15 0.14 None 0.32 0.01 N/A 0.09 GLX
0.73 0.21 BMI, HTN, 0.00 N/A 0.23 0.00006 Sex ORN 0.48 0.13 Age,
BMI, 0.04 0.3 0.16 0.000005 Dys CIT 0.39 0.18 CAD, Age 0.00 N/A
0.26 0.01 ARG 0.80 0.11 DM 0.13 0.004 0.003 1.9 .times. 10.sup.-16
FA-C14:0 0.51 0.24 DM 0.00 N/A 0.03 0.01 FA-C16:1 0.42 0.20 DM, Age
0.00 N/A 0.11 0.01 FA-C16:0 0.45 0.17 None 0.05 0.3 N/A 0.0008
FA-C18:3 0.41 0.27 DM, CAD, 0.00 N/A 0.08 0.06 Dys FA-C18:2 0.48
0.18 None 0.00 N/A N/A 0.002 FA-C18:1 0.40 0.19 Age, DM 0.00 N/A
0.13 0.01 FA-C18:0 0.39 0.20 CAD, Age 0.08 0.3 0.06 0.01 FA-C20:4
0.59 0.19 None 0.00 N/A N/A 0.00005 FA-C22:6 0.16 0.16 None 0.00
N/A N/A 0.15 FFA 0.45 0.16 CAD, Age, 0.14 0.11 0.09 0.0003 DM GLU
0.47 0.18 DM, Dys, Sex 0.02 0.4 0.23 0.001 TC 0.51 0.16 Age, CAD,
0.09 0.08 0.14 0.00007 DM, Dys HDL 0.35 0.17 BMI, Sex 0.00 N/A 0.19
0.004 LDL 0.37 0.16 DM, BMI 0.11 0.02 0.08 0.004 TG 0.49 0.14 BMI
0.00 N/A 0.05 0.00001 Ket 0.75 0.13 None 0.00 0.5 N/A 3.8 .times.
10.sup.-8 HBut 0.51 0.21 None 0.04 0.3 N/A 0.004 CRP 0.16 0.17 BMI
0.09 0.2 0.20 0.13 BMI 0.51 0.16 Age, HTN 0.06 0.2 0.14 0.0004
*Clinical covariates (age, sex, BMI, hypertension, diabetes,
dyslipidemia, CAD status) significant in polygenic model.
.dagger.Proportion of variance in metabolite levels accounted for
by clinical covariates significant in the model. **P-value for
heritability estimate. DM: diabetes mellitus; HTN: hypertension;
BMI: body-mass-index; CAD: affected with premature CAD; DYS:
dyslipidemia.
[0075] Metabolomic Profiles within Families. Given these strong
findings, we sought to understand quantitative differences in
metabolites between families. Multivariable linear models were used
to test for differences in metabolites between families. Of the
amino acids, glutamate, ornithine, arginine, proline, histidine,
phenylalanine, alanine and methionine (all p<0.0001),
leucine/isoleucine (p<0.0001) and valine (p=0.003) best
differentiated families. Of the acylcarnitines, the C18 (C18,
C18:1, and C18:2) and the C14 acylcarnitines (C14, C14:1) (all
p<0.0001), along with C5:1 (p<0.0001), and C2 (p<0.0001)
acylcarnitines best differentiated families. Many free fatty acids
differentiated families, the strongest being arachidonic and
palmitic acid (both p<0.0001). Of the conventional metabolites,
ketones (p<0.0001) and .beta.-hydroxybutyrate (p=0.0001) best
differentiated families.
[0076] Principal Components Analysis. Given correlation of
metabolites in biological pathways, we performed PCA to understand
which clusters of metabolites were correlated and to identify
factors that were most heritable. Fifteen factors were identified,
demonstrating biologically consistent relationships (Table 15).
Factors accounting for the largest amount of variance within the
dataset were Factor 1 (short- and medium-chain acylcarnitines);
Factor 2 (long-chain free fatty acids); Factor 3 (long-chain
acylcarnitines and amino acids [arginine, glutamate/glutamine, and
ornithine] possibly reporting on mitochondrial function); Factor 4
(ketones, .beta.-hydroxybutyrate, C2 and C4-OH
[.beta.-hydroxybutryl] acylcarnitines; all markers of terminal
steps of fatty acid oxidation); and Factor 5 (amino acids,
including branched-chain amino acids, and C3 and C5 acylcarnitines
[by-products of branched-chain amino acid catabolism]). As
expected, given results for individual metabolites, many factors
were heritable.
TABLE-US-00015 TABLE 15 Principal components analysis in GENECARD.
Results of PCA in the dataset are presented, including the key
metabolites within each factor (i.e. those with a factor load
.gtoreq.|0.4|); an overall biochemical description of the key
metabolites within each factor; and the eigenvalue, total and
cumulative variance, heritability and p-value for the heritability
point estimate for each factor. Overall Metabolites Description of
Eigen- Total Cum Factor within Factor* Factor value Var Var
Heritability (SD) p-value 1 C2, C6-DC, C8, Short- and 11.88 0.20
0.20 0.39 (0.16) 0.0006 C8:1, C10, C10:1, medium-chain C10:3, C10-
acylcarnitines OH:C8-DC, C12, C12:1, C14, C14:1, C14:2, C14:1-OH,
C14- OH:C12-DC 2 Total FFA, FA- Free fatty acids 7.55 0.13 0.32
0.35 (0.20) 0.02 C14:0, FA-C16:0, FA-C16:1, FA- C18:0, FA-C18:1,
FA-C18:2, FA- C18:3 3 ARG, GLX, Amino acids, 5.89 0.10 0.42 0.40
(0.18) 0.002 ORN, C16, C18, long-chain C18:1, C18:2 acylcarnitines
(markers of overall mitochondrial function) 4 C2, C4-OH, FFA
oxidation 3.51 0.06 0.48 0.61 (0.17) 0.00004 C14:1, C14:2,
byproducts C14:1-OH, Ket, Hbut 5 ALA, LEU/ILE, Metabolites 2.98
0.05 0.53 0.27 (0.15) 0.01 MET, PRO, TYR, involved in VAL, PHE, C5,
amino acid C3, C20 catabolism 6 CIT, C5-DC, C8:1, Various 2.36 0.04
0.57 0.51 (0.17) 0.0008 C10:3 7 SER, GLY, CIT, Amino Acids 2.04
0.03 0.60 0.44 (0.28) 0.09 MET 8 C14-OH:C12-DC, Various 1.89 0.03
0.64 0.40 (0.18) 0.003 C18:1-OH, C22 9 C12-OH:C10-DC, Various 1.86
0.03 0.67 0.51 (0.17) 0.0003 C14, C14:1-OH, C20 10 C3, C4:Ci4, C22
Various 1.67 0.03 0.69 0.46 (0.19) 0.002 11 ASX, HIS Amino Acids
1.48 0.02 0.72 0.33 (0.17) 0.005 12 FAC22:6, Long chain free 1.37
0.02 0.74 0.36 (0.17) 0.007 FAC20:4, C20 fatty acids 13
C16-OH:C14-DC Various 1.24 0.02 0.76 0.46 (0.20) 0.002 14 PRO, ALA,
Various 1.18 0.02 0.78 0.54 (0.16) 0.0001 C18:1-DC 15 C18-DC:C20-OH
Various 1.06 0.02 0.80 0.45 (0.19) 0.006 *Factor load .gtoreq.|0.4;
FFA: free fatty acids; Tot Var: total variance; Cum Var: cumulative
variance
[0077] A comprehensive set of analytical tools was applied to gain
a better understanding of the biochemical and physiologic
underpinnings of cardiovascular disease, and how metabolomic
profiles may relate to the known genetic component of CAD risk.
Targeted, quantitative metabolic profiling was performed in
multiplex families burdened with premature CAD, the majority
representing offspring of the affected generation that had not yet
developed CAD, but in whom we hypothesized similar metabolic
profiles as their affected family members, if such profiles were
heritable. High heritabilities were found for many metabolites,
many with higher heritabilities than for conventional risk factors.
These high heritabilities suggest a strong correlation between
genotype and phenotype, implying a strong genetic component to
clustering of these metabolic signatures in families burdened with
CAD.
[0078] In addition, several individual metabolites distinguished
families, the most prominent being, among the amino acids,
arginine, ornithine, and glutamate/glutamine; and among the
lipid-derived metabolites, the long-chain acylcarnitines C18:0,
C18:1, and C18:2. These findings suggest fundamental differences in
mitochondrial function in these families, consistent with prior
studies showing relationships between impaired mitochondrial
function and insulin resistance.
[0079] Given our studies were hypothesis-generating, we did not
adjust for multiple comparisons. However, with a Bonferroni
correction at the level of the factors, nine factors remain
significant (p<0.003). We did not account for dietary pattern
(known influence on metabolites), renal function, or medications
(unknown influence). To help minimize these "non-genetic" effects,
we incorporated a household effect and included married-in
individuals, partially controlling for shared nutritional and other
environmental effects. The measures of household effects suggest
minimal influence on heritability estimates with high
heritabilities despite adjustment. Therefore, we believe our
results reflect both underlying genetic and environmental effects,
similar to traditional cholesterol parameters. Accordingly, we
found a significant household effect for LDL cholesterol
(proportion of variance due to household 0.11, p=0.02), but with a
significant heritability despite adjustment for this environmental
effect (h.sup.2 0.37, p=0.004).
[0080] Similarly, results could reflect differences in essential
versus non-essential metabolites. However, we found similar
heritabilities for the essential (h.sup.2=0.40, p=0.0004) and
non-essential (h.sup.2=0.63, p=0.00002) amino acids when analyzed
as groups, and for the essential (h.sup.2=0.50, p=0.003) compared
with the non-essential (h.sup.2=0.33, p=0.03) fatty acids. Although
underpowered for such analyses, we also examined the relationship
of age with heritabilities related to these groups. Age was a
significant covariate on heritability estimates for both essential
(valine) and nonessential (proline, ornithine, citrulline) amino
acids (Table 14). For the free fatty acids, age was a covariate
only for nonessential fatty acids (palmitoleic, oleic and stearic
acid). We also examined correlations of metabolites with age and
found that both essential (tyrosine, linoleic acid) and
non-essential (glutamine, ornithine, citrulline, oleic acid)
metabolites were significantly correlated with age (data not
shown). Therefore, there does not seem to be a consistent variation
of metabolites with age, nor with heritability estimates, based on
essential/non-essential groups. This may indicate that fundamental
and genetically controlled metabolic processes (e.g. mitochondrial
or microsomal catabolic pathways) are influencing the levels of
both essential and non-essential metabolites that utilize these
common elements of the metabolic machinery.
[0081] Other factors that could impact heritability estimates
include variability in sample collection or processing. We used a
standardized protocol to limit this type of variability,
intra-individual variation was low in a set of repeated assays, and
family members were collected at different locations and times.
[0082] A major strength of the study is the use of a very accurate,
targeted, quantitative approach to metabolomic profiling, allowing
us to dissect biological mechanisms underlying CAD pathophysiology.
In addition to furthering the understanding of CAD pathophysiology,
these results may have significant implications for risk
prediction.
[0083] Each of the references cited herein is hereby incorporated
by reference in its entirety.
* * * * *