U.S. patent application number 14/553230 was filed with the patent office on 2015-05-28 for pc-o 44:4 - a biomarker for visceral adiposity.
The applicant listed for this patent is Nestec S.A.. Invention is credited to Philippe Alexandre Guy, Francois-Pierre Martin, Ivan Montoliu Roura, Serge Andre Dominique Rezzi.
Application Number | 20150147817 14/553230 |
Document ID | / |
Family ID | 48579077 |
Filed Date | 2015-05-28 |
United States Patent
Application |
20150147817 |
Kind Code |
A1 |
Martin; Francois-Pierre ; et
al. |
May 28, 2015 |
PC-O 44:4 - A BIOMARKER FOR VISCERAL ADIPOSITY
Abstract
The present invention generally relates to the field of
biomarkers. In particular, the present invention relates to
biomarkers such as PC-O 44:4 that can be used, for example for
detecting and/or quantifying visceral adiposity and/or changes in
visceral adiposity. This biomarker may also be used to diagnosing
the effect of a change in lifestyle on visceral adiposity in a
subject.
Inventors: |
Martin; Francois-Pierre;
(Vuisternens-devant-Romont, CH) ; Montoliu Roura;
Ivan; (Lausanne 26, CH) ; Guy; Philippe
Alexandre; (Lucens, CH) ; Rezzi; Serge Andre
Dominique; (Semsales, CH) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Nestec S.A. |
Vevey |
|
CH |
|
|
Family ID: |
48579077 |
Appl. No.: |
14/553230 |
Filed: |
November 25, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
PCT/EP2013/061876 |
Jun 10, 2013 |
|
|
|
14553230 |
|
|
|
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Current U.S.
Class: |
436/71 ;
554/80 |
Current CPC
Class: |
C07F 9/09 20130101; G01N
33/92 20130101; G01N 2405/04 20130101; G01N 2800/044 20130101; G01N
33/48 20130101 |
Class at
Publication: |
436/71 ;
554/80 |
International
Class: |
G01N 33/92 20060101
G01N033/92; C07F 9/09 20060101 C07F009/09 |
Foreign Application Data
Date |
Code |
Application Number |
Jun 12, 2012 |
EP |
12171570.0 |
Claims
1. A biomarker, wherein the biomarker is PC-O 44:4.
2. A method of diagnosing visceral adiposity in a subject,
comprising: determining the level of PC-O 44:4 in a body fluid
sample previously obtained from a subject to be tested, and
comparing the subject's PC-O 44:4 level to a predetermined
reference value, wherein the predetermined reference value is based
on an average PC-O 44:4 level in the same body fluid in a control
population, and wherein a decreased PC-O 44:4 level in the sample
compared to the predetermined reference value indicates an
increased visceral adiposity.
3. A method of diagnosing a change in visceral adiposity in a
subject, comprising: determining the level of PC-O 44:4 in a body
fluid sample previously obtained from a subject to be tested, and
comparing the subject's PC-O 44:4 level to a predetermined
reference value, wherein the predetermined reference value is based
on a PC-O 44:4 level in the same body fluid obtained from the same
subject previously, and wherein a decreased PC-O 44:4 level in the
sample compared to the predetermined reference value indicates
increased visceral adiposity.
4. A method of diagnosing the effect of a change in lifestyle on
visceral adiposity in a subject, comprising: determining the level
of PC-O 44:4 in a body fluid sample previously obtained from a
subject to be tested, and comparing the subject's PC-O 44:4 level
to a predetermined reference value, wherein the predetermined
reference value is based on a PC-O 44:4 level in the same body
fluid obtained from the same subject previously, and wherein an
increased PC-O 44:4 level in the sample compared to the
predetermined reference value indicates a positive effect of the
change in lifestyle on visceral adiposity.
5. The method according to claim 4, wherein the change in lifestyle
is a change in the diet.
6. The method according to claim 5, wherein the change in the diet
is the use of at least one nutritional product that was previously
not consumed or consumed in different amounts.
7. The method in accordance with claim 5, to test the effectiveness
of a new nutritional regimen.
8. The method in accordance with claim 2, further comprising the
steps of: determining the level of at least one further biomarker
selected from the group consisting of glutamine, tyrosine, PC-O
44:6, PC-O 42:4, PC-O 40:4, and PC-O 40:3 in the body fluid sample,
and comparing the subject's level of at least one of glutamine,
tyrosine, PC-O 44:6, PC-O 42:4, PC-O 40:4, and PC-O 40:3 to a
predetermined reference value, wherein the predetermined reference
value is based on average glutamine, tyrosine, PC-O 44:6, PC-O
42:4, PC-O 40:4, and/or PC-O 40:3 levels in a body fluid sample of
a normal healthy control population, or on glutamine, tyrosine,
PC-O 44:6, PC-O 42:4, PC-O 40:4, and/or PC-O 40:3 levels in the
same body fluid obtained from the same subject previously, and
wherein an increased glutamine and/or tyrosine level and/or a
decreased PC-O 44:6, PC-O 44:4, PC-O 42:4, PC-O 40:4, and/or PC-O
40:3 level in the body fluid sample compared to the predetermined
reference values indicates an increased visceral adiposity.
9. The method in accordance with claim 2, wherein the levels of the
biomarkers are determined by .sup.1H-NMR and/or mass spectrometry
in the sample and in the reference.
10. The method in accordance with claim 2, wherein the body fluid
is blood plasma or serum.
11. The method in accordance with claim 2, wherein the degree of
visceral adiposity is indicative for the likelihood to develop
disorders associated with excess visceral fat.
12. The method in accordance with claim 11, wherein the disorders
associated with excess visceral fat are cardio metabolic diseases
and/or metabolic deregulations.
13. The method in accordance with claim 2, to be carried out on
normal, overweight or in obese subjects.
14. The method in accordance with claim 2, wherein the subject is a
human or a companion animal such as a cat or a dog.
Description
[0001] The present application is a continuation of
PCT/EP2013/061876, filed Jun. 10, 2013, which application claims
priority to European Application No. 12171570.0, filed Jun. 12,
2012, the disclosures of which are hereby incorporated by reference
in their entirety for all purposes.
[0002] The present invention generally relates to the field of
biomarkers. In particular, the present invention relates to
biomarkers such as 1-O-alkyl-2-acylglycerophosphocholine (PC-O)
44:4 that can be used, for example for detecting and/or quantifying
visceral adiposity and/or changes in visceral adiposity. This
biomarker may also be used for diagnosing the effect of a change in
lifestyle on visceral adiposity in a subject.
[0003] The continuous rise in the overweight and obesity epidemic,
particularly among children, has made the deciphering of their
associated genome and metabolome phenotypes become one of the
greatest public health challenges. Although malnutrition and
obesity, as defined by body mass index (BMI), impose a substantial
toll on life expectancy, it is clear that EMI has considerable
limitations in the assessment of body composition and lack
sensitivity for assessing disease risks (Dulloo, A. G., et al.
(2010) Int. J. Obes. (Lond) 34 Suppl 2, S4-17. Dullo et al.
recently reviewed recent advances in concepts about health risks
related to body composition phenotypes, including (i) the
partitioning of BMI into a fat mass (FM) index (FM/H2) and a
fat-free-mass (FFM) index (FFM/H2), (ii) the partitioning of FFM
into organ mass and skeletal muscle mass, (iii) the partitioning of
FM into hazardous fat and protective fat and (iv) the interplay
between adipose tissue expandability and ectopic fat deposition
within or around organs/tissues that constitute the lean body mass
(Dulloo, A. G., et al. (2010) Int. J. Obes. (Lond) 34 Suppl 2,
S4-17)
[0004] During the last decades, numerous investigations using state
of the art technologies have identified genes and transcription
factors associated with fat storage and obesity (Viguerie, N., et
al. (2005) Diabetologia 48, 123-131; Viguerie, N., et al. (2005)
Biochimie 87, 117-123; Sorensen, T. I. et al. (2006) PLoS. Clin.
Trials 1, e12; Klannemark, M., et al. (1998) Diabetologia 41,
1516-1522; Clement, K. et al. (2007) J. Intern. Med. 262, 422-430),
genetic inheritability (Teran-Garcia, M. Et al. (2007) Appl.
Physiol Nutr. Metab 32, 89-114) and have suggested an influence of
the human gut microbiota on obesity incidence (Backhed, F., et al.
(2007) Proc. Natl. Acad. Sci. U.S.A. 104, 979-984; Ley, R., et al.
(2006) Nature 444, 1022-1023; Turnbaugh, P., et al. (2006) Nature
444, 1027-1031).
[0005] However, under similar obesogenic and diabetogenic
environments, many individuals still remain metabolically healthy
and resistant to adiposity-associated cardiovascular disease (CVD)
risks. In addition to the awareness that disease risks associated
with obesity may not be uniform (Wildman, R. P., et al., (2008)
Arch. Intern. Med. 168, 1617-1624), an increasing number of
individuals with normal weight (body mass index, BMI<25) express
cardiometabolic abnormalities previously thought to be specific to
overweight and obese states. Most recent evidence indicates how
regio-specific body composition may determine individual
predisposition to metabolic disease, with body fat and in
particular visceral fat distribution being correlated with
increased risk of cardiometabolic disorders, diabetes,
hypertension, nonalcoholic fatty liver disease, and mortality.
[0006] Visceral adiposity is been clinically monitored using waist
and hip measurements, (e.g. >0.9 for men and >0.85 for women,
which however suffers from similar limitations especially in obese
populations. Gold standard imaging technologies, including magnetic
resonance imaging (MRI) and computed tomography (CT) scans, now
generate regio-specific and highly accurate quantification of
visceral fat depots. However, the assessment of the metabolism
associated with visceral fat remains particularly challenging due
to the lack of non-invasive, fast and reliable biomarkers that can
be used in epidemiological studies and due to limitations of
conventional analytical approaches that are not suited for the
holistic analysis of the metabolism.
[0007] Excess fat stored in the trunk or android regions could be
metabolically less healthy than fat stored in the gynoid area, with
insulin resistance as a key causal mechanism. Therefore, patient
stratification is necessary for personalized nutritional and
therapeutical management, yet its application is challenged when
subjects have similar waist-to-hipp ratio and access to imaging
facilities is limited. There is therefore and urgent need for
biomarkers that allow assessing the presence of visceral fat, the
metabolism associated with visceral fat and/or changes therein in a
simple and reliable way.
[0008] The present inventors have addresses this need.
[0009] It was therefore the objective of the present invention to
improve the state of the art and to provide biomarkers that meet
the objective of the present invention and/or that allow overcoming
at least one of the disadvantages of the present state of the
art.
[0010] To identify appropriate biomarkers the inventors have used a
metabonomic approach.
[0011] Metabonomics is considered today a well-established system
approach to characterize the metabolic phenotype, that comprises
the influence of various factors such as environment, drugs,
dietary, lifestyle, genetics, and microbiome factors. Unlike gene
expression and proteomic data that indicate the potential for
physiological changes, metabolites and their dynamic concentration
changes within cells, tissues and organs, represent the real
end-points of physiological regulatory processes.
[0012] Recently, metabolomics and lipidomics-based discoveries have
been accelerating our understanding of disease processes, and will
provide novel avenues for prevention and nutritional management of
the sub-clinical disorders associated to metabolic syndrome.
[0013] The present inventors have aimed to provide a comprehensive
metabolic phenotype of a regio-specific body composition: visceral
adiposity. This has allowed the identification of biological
markers specific of visceral adiposity.
[0014] In the present study, the metabolism associated with
visceral adiposity was investigated in a cohort of 40 healthy obese
women using the measurement of various metabolic endpoints in
combination with in vivo quantification of body composition using
Dual energy X-ray Absorptiometry (DXA) and abdominal fat
distribution using computerized tomography (CT) scan.
[0015] Using a combination of proton nuclear magnetic resonance
(.sup.1H NMR) spectroscopy and targeted LC-MS/MS profiles of plasma
and 24 hour urine samples collected overtime, the inventors have
identified novel metabolic biomarkers of visceral fat distribution
in this well defined obese cohort with different visceral fat
deposition patterns.
[0016] As such, the inventors have identified a novel biomarker,
PC-O 44:4.
[0017] PC-O is 1-O-alkyl-2-acylglycerophosphocholine.
[0018] The individual lipid species were annotated as follows:
[lipid class] [total number of carbon atoms]:[total number of
double bonds]. For example, PC 34:1 reflects a phosphatidylcholine
species comprising 34 carbon atoms and 1 double bond.
[0019] PC-O 44:4 is therefore 1-O-alkyl-2-acylglycerophosphocholine
44:4.
[0020] The inventors have further found that PC-O 44:4 may be used
as a biomarker for detecting and/or quantifying visceral adiposity
and/or changes in visceral adiposity. This diagnostic method is
practised outside of the human or animal body.
[0021] This detection and/or quantification of the biomarker may be
carried out in a body liquid. The body liquid may be blood, blood
plasma, blood serum or urine, for example.
[0022] Typically, the biomarker detection and/or quantification
step is carried out in a body fluid sample that was previously
obtained from the subject to be tested.
[0023] Visceral fat is also known as abdominal fat, organ fat or
intra-abdominal fat, and is located inside the abdominal cavity in
between organs.
[0024] Visceral fat may be composed of several adipose depots,
including mesenteric, epididymal white adipose tissue (EWAT), and
perirenal depots, as well as epicardial adipose tissue and fat
around liver and stomach. Typically, fat in the abdomen is mostly
visceral, often resulting in the famous "beer belly".
[0025] Too much visceral fat results in central obesity, which in
turn is linked to cardiovascular disorders, type 2 diabetes,
insulin resistance, or inflammatory diseases, for example.
[0026] These are examples of disorders associated with excess
visceral fat.
[0027] The present invention relates also to a method of diagnosing
visceral adiposity in a subject, comprising determining the level
of 1-O-alkyl-2-acylglycerophosphocholine (PC-O) 44:4 in a body
fluid sample previously obtained from a subject to be tested, and
comparing the subject's PC-O 44:4 level to a predetermined
reference value, wherein the predetermined reference value is based
on an average PC-O 44:4 level in the same body fluid in a control
population, and wherein a decreased PC-O 44:4 level in the sample
compared to the predetermined reference value indicates an
increased visceral adiposity.
[0028] Visceral adiposity may include mesenteric, epididymal white
adipose tissue and/or perirenal fat, as well as epicardial adipose
tissue and fat around liver and stomach.
[0029] The body fluid may be blood, blood serum, blood plasma, or
urine, for example.
[0030] Blood serum and/or blood plasma have the advantage that the
signal to noise ratio for the biomarker to be tested is
particularly high.
[0031] Urine has the advantage that the body fluid sample can be
obtained non-invasively.
[0032] Irrespective of the chosen body fluid, the method of the
present invention has the advantage that obtaining such body fluids
from a subject is a well established procedure.
[0033] The actual diagnosis method is then carried out in a body
fluid sample outside the body.
[0034] The level of PC-O 44:4 in the sample can be detected and
quantified by any means known in the art. For example, mass
spectroscopy, e.g, UPLC-ESI-MS/MS, may be used. Other methods, such
as other spectroscopic methods, chromatographic methods, labeling
techniques, or quantitative chemical methods may be used as
well.
[0035] Ideally, the PC-O 44:4 level in the sample and the reference
value are determined by the same method.
[0036] The predetermined reference value may be based on an average
PC-O 44:4 level in the tested body fluid in a control population.
The control population can be a group of at least 3, preferably at
least 10, more preferred at least 50 people with a similar genetic
background, age and average health status.
[0037] The control population can also be the same person, so that
the predetermined reference value is obtained previously from the
same subject. This will allow a direct comparison of the effect of
a present lifestyle to a previous lifestyle on visceral adiposity,
for example, and improvements can be directly assessed.
[0038] The determination of visceral fat adiposity allows
concluding on the presence of visceral fat adiposity and on the
risk to acquire associated disorders.
[0039] The subject matter of the present invention also relates to
a method of diagnosing a change in visceral adiposity in a subject,
comprising determining the level of PC-O 44:4 in a body fluid
sample previously obtained from a subject to be tested, and
comparing the subject's PC-O 44:4 level to a predetermined
reference value, wherein the predetermined reference value is based
on a PC-O 44:4 level in the same body fluid obtained from the same
subject previously, and wherein a decreased PC-O 44:4 level in the
sample compared to the predetermined reference value indicates
increased visceral adiposity.
[0040] This method allows following the build-up or reduction of
visceral fat over time, and consequently allows conclusions on
increased or decreased risks to develop disorders associated with
visceral adiposity.
[0041] This has for example the advantage that immediate results
are available, long before an actual increase or decrease of
visceral fat can be determined. This is in particular good for the
motivation of people that aim to reduce visceral fat. Notably, the
reduction of visceral fat is a difficult task often requiring
intensive exercise. Ohkawara et al. suggests at least 10 metabolic
equivalent of task (MET)-hours per week of aerobic exercise are
required for effective visceral fat reduction (Ohkawara, K.; et
al., (2007), International journal of obesity (2005) 31 (12):
1786-1797).
[0042] The present invention also relates to a method of diagnosing
the effect of a change in lifestyle on visceral adiposity in a
subject, comprising determining the level of PC-O 44:4 in a body
fluid sample previously obtained from a subject to be tested, and
comparing the subject's PC-O 44:4 level to a predetermined
reference value, wherein the predetermined reference value is based
on a PC-O 44:4 level in the same body fluid obtained from the same
subject previously, and wherein a decreased PC-O 44:4 level in the
sample compared to the predetermined reference value indicates a
positive effect of the change in lifestyle on visceral
adiposity.
[0043] This method has the effect that it allows monitoring the
effect of lifestyle changes on visceral fat mass and on risks for
associated disorders.
[0044] The change in lifestyle may be any change, such as a new
job, a different stress level, a new relationship, increases or
decreases in physical activity, and/or a change in overall
wellbeing.
[0045] For example, the change in lifestyle may be a change in the
diet.
[0046] The change in diet may be an increase or decrease in
carbohydrate, lipid and/or protein content. It may be the switch to
a different regional diet, such as the Mediterranean diet, for
example. It may also be a change in total caloric intake.
[0047] As such the method of the present invention may be used to
test the effectiveness of a new nutritional regimen, of nutritional
products and/or of medicaments.
[0048] Nutritional products may be for example products that claim
to have an effect on body fat, weight management and/or visceral
fat.
[0049] Typically, nutritional products may be food products,
drinks, pet food products, food supplements, nutraceuticals, food
additives or nutritional formulas.
[0050] For example, the change in the diet may be the use of at
least one nutritional product that was previously not consumed or
consumed in different amounts.
[0051] As such, the method of the present invention may be used to
test the effectiveness of a new nutritional regimen and/or a
nutritional product.
[0052] PC-O 44:4 may be used as the only marker for the purpose of
the present invention.
[0053] While PC-O 44:4 as sole marker is effective as a tool for
the diagnosis method of the present invention, the quality and/or
the predictive power of said diagnosis will be improved, if the
diagnosis relies on more than just one marker.
[0054] Hence one or more other markers for diagnosing visceral
adiposity and/or the risk for associated disorders in a subject may
be used in combination with PC-O 44:4.
[0055] The inventors were surprised to see that also other
biomarkers can be used to detect diagnosing visceral adiposity
and/or the risk for associated disorders.
[0056] As such the inventors have identified that decreased body
fluid concentrations of PC-O 44:6, PC-O 44:4, PC-O 42:4, PC-O 40:4,
and/or PC-O 40:3; and/or increased body fluid concentrations of
tyrosine and/or glutamine allow diagnosing an increase in visceral
fat amounts and/or an increased risk for developing disorders
associated with excess visceral fat.
[0057] Conversely, increased body fluid concentrations of PC-O
44:6, PC-O 44:4, PC-O 42:4, PC-O 40:4, and/or PC-O 40:3; and/or
decreased body fluid concentrations of tyrosine and/or glutamine
allow diagnosing a decrease in visceral fat amounts and/or a
reduced risk for developing disorders associated with excess
visceral fat.
[0058] The methods of the present invention may, hence, further
comprise the steps of determining the level of at least one further
biomarker selected from the group consisting of glutamine, and/or
tyrosine, PC-O 44:6, PC-O 42:4, PC-O 40:4, and/or PC-O 40:3 in the
body fluid sample, and comparing the subject's level of at least
one of glutamine, and/or tyrosine, PC-O 44:6, PC-O 42:4, PC-O 40:4,
and/or PC-O 40:3 to a predetermined reference value, wherein the
predetermined reference value is based on average glutamine,
tyrosine, PC-O 44:6, PC-O 42:4, PC-O 40:4, and/or PC-O 40:3 levels
in a body fluid sample of a normal healthy control population, or
on glutamine, tyrosine, PC-O 44:6, PC-O 42:4, PC-O 40:4, and/or
PC-O 40:3 levels in the same body fluid obtained from the same
subject previously, and wherein an increased glutamine and/or
tyrosine level and/or a decreased PC-O 44:6, PC-O 44:4, PC-O 42:4,
PC-O 40:4, and/or PC-O 40:3 level in the body fluid sample compared
to the predetermined reference values indicates an increased
visceral adiposity.
[0059] The method of the present invention may comprise the
assessment of at least 2, at least 3, at least 4, at least 5, at
least 6, or at least 7 biomarkers.
[0060] For example, PC-O 44:4 may be assessed together with PC-O
44:6.
[0061] PC-O 44:4 may also be assessed together with PC-O 42:4.
[0062] PC-O 44:4 may also be assessed together with PC-O 40:4.
[0063] PC-O 44:4 may also be assessed together with PC-O 40:3.
[0064] PC-O 44:4 may also be assessed together with PC-O 44:6 and
PC-O 42:4.
[0065] PC-O 44:4 may also be assessed together with PC-O 44:6, PC-O
42:4, and PC-O 40:4.
[0066] PC-O 44:4 may also be assessed together with PC-O 44:6, PC-O
42:4, and PC-O 40:3.
[0067] PC-O 44:4 may also be assessed together with PC-O 44:6, PC-O
42:4, PC-O 40:3, and PC-O 40:4.
[0068] PC-O 44:4 may also be assessed together with PC-O 44:6, PC-O
42:4, PC-O 40:3, PC-O 40:4, and glutamine.
[0069] PC-O 44:4 may also be assessed together with PC-O 44:6, PC-O
42:4, PC-O 40:3, PC-O 40:4, glutamine and tyrosine.
[0070] The advantage of assessing more than one biomarker is that
the more biomarkers are evaluated the more reliable the diagnosis
will become. If, e.g., more than 1, 2, 3, 4, 5, 6, or 7 biomarkers
exhibit the elevations or decreases in concentration as described
above, the predictive power for the presence or absence and the
degree of visceral obesity as well as the risk for associated
disorders is stronger.
[0071] In accordance with this the inventors have identified even
further biomarkers that can be used to predict visceral adiposity
and the risk to develop associated disorders.
[0072] For example, it was found that increased concentrations of
phenylalanine, Leucine, Isoleucine, palmitoylcarnitine (C16),
caproylcarnitine (C10) octenoylcarnitine
(C8:1)lysophospatidylcholine (LPC) 24:0, phosphatidylcholine (PC)PC
30:0, and/or PC 34:4 in body fluids or decreased concentrations of
PC-O 34:1, PC-O 34:2, PC-O 36:2, PC-O 36:3, PC-O 40:6, PC-O 42:2,
PC-O 42:3, PC-O 44:3, PC-O 44:5, PC 42:0, and/or PC 42:2 in body
fluids compared to corresponding reference values previously
obtained indicates an increased visceral adiposity and an increased
risk for associated disorders.
[0073] PC is phosphatidylcholine. LPC is lysophospatidylcholine. C
is acyl carnitine.
[0074] Conversely, decreased concentrations of phenylalanine,
Leucine, Isoleucine, C10 (decanoyl carnitine), C16
(Palmitoylcarnitine), C8:1 (Octenoyl-Carnitine) LPC 24:0, PC 30:0,
and/or PC 34:4 in body fluids or increased concentrations of PC-O
34:1, PC-O 34:2, PC-O 36:2, PC-O 36:3, PC-O 40:6, PC-O 42:2, PC-O
42:3, PC-O 44:3, PC-O 44:5, PC 42:0, and/or PC 42:2 in body fluids
compared to corresponding reference values previously obtained
indicates a decreased visceral adiposity and a reduced risk for
associated disorders.
[0075] Hence, the methods of the present invention may, further
comprise the steps of determining the level of at least one further
biomarker selected from the group consisting of phenylalanine,
Leucine, Isoleucine, C10 (decanoyl carnitine), C16
(Palmitoylcarnitine), C8:1 (Octenoyl-Carnitine), LPC 24:0, PC 30:0,
PC 34:4, PC-O 34:1, PC-O 34:2, PC-O 36:2, PC-O 36:3, PC-O 40:6,
PC-O 42:2, PC-O 42:3, PC-O 44:3, PC-O 44:5, PC 42:0, and/or PC 42:2
in the body fluid sample, and comparing the subject's level of at
least one of phenylalanine, Leucine, Isoleucine, C10, C16, C8:1,
carnitine, LPC 24:0, PC 30:0, PC 34:4, PC-O 34:1, PC-O 34:2, PC-O
36:2, PC-O 36:3, PC-O 40:6, PC-O 42:2, PC-O 42:3, PC-O 44:3, PC-O
44:5, PC 42:0, and/or PC 42:2 to a predetermined reference value,
wherein the predetermined reference value is based on average
phenylalanine, Leucine, Isoleucine, C10, C16, C8:1, carnitine, LPC
24:0, PC 30:0, PC 34:4, PC-O 34:1, PC-O 34:2, PC-O 36:2, PC-O 36:3,
PC-O 40:6, PC-O 42:2, PC-O 42:3, PC-O 44:3, PC-O 44:5, PC 42:0,
and/or PC 42:2 levels in a body fluid sample of a normal healthy
control population, or on phenylalanine, Leucine, Isoleucine, C10
(decanoyl carnitine), C16 (Palmitoylcarnitine), C8:1
(Octenoyl-Carnitine), LPC 24:0, PC 30:0, PC 34:4, PC-O 34:1, PC-O
34:2, PC-O 36:2, PC-O 36:3, PC-O 40:6, PC-O 42:2, PC-O 42:3, PC-O
44:3, PC-O 44:5, PC 42:0, and/or PC 42:2 levels in the same body
fluid obtained from the same subject previously, and wherein an
increased phenylalanine, Leucine, Isoleucine, C10, C16, C8:1,
carnitine, LPC 24:0, PC 30:0, and/or PC 34:4 level in the body
fluid and/or a decreased PC-O 34:1, PC-O 34:2, PC-O 36:2, PC-O
36:3, PC-O 40:6, PC-O 42:2, PC-O 42:3, PC-O 44:3, PC-O 44:5, PC
42:0, and/or PC 42:2 level in the body fluid sample compared to the
predetermined reference values indicates an increased visceral
adiposity.
[0076] An increased visceral adiposity increases the risk to
develop disorders associated with excess visceral fat.
[0077] Consequently, in the methods of the present invention the
degree of visceral adiposity may be used as indication for the
likelihood to develop disorders associated with excess visceral
fat.
[0078] Also, changes in visceral adiposity may be used as
indication for an increased or decreased likelihood to develop
disorders associated with excess visceral fat.
[0079] Disorders associated with visceral adiposity are for example
cardio metabolic disorders.
[0080] Hence, the methods of the present invention may be used to
determine the risk to develop cardio metabolic disorders.
[0081] Further disorders associated with visceral adiposity are for
example metabolic deregulations. Typical metabolic deregulations
are the following obesity, insulin resistance, Type 2 diabetes,
metabolic syndrome, vascular diseases (hypertension, coronary heart
disease), steatohepatitis in metabolic liver disease,
lipodystrophies, pulmonary function, inflammatory disorders and
other obesity related disorders.
[0082] The methods of the present invention can be carried out with
any subject.
[0083] Advantageously, the method of the present invention may be
carried out on subjects at risk of developing visceral adiposity
and/or disorders associated with visceral adiposity.
[0084] For example the methods of the present invention may be to
be carried out with normal, overweight or obese subjects.
[0085] "Overweight" is defined for an adult human as having a BMI
between 25 and 30. "Body mass index" or "BMI" means the ratio of
weight in kg divided by the height in metres, squared. "Obesity" is
a condition in which the natural energy reserve, stored in the
fatty tissue of animals, in particular humans and other mammals, is
increased to a point where it is associated with certain health
conditions or increased mortality. "Obese" is defined for an adult
human as having a BMI greater than 30.
[0086] The reference value for PC-O 44:4 and optionally for the
other biomarkers is preferably measured using the same units used
to characterize the level of PC-O 44:4 and optionally the other
biomarkers obtained from the test subject. Thus, if the level of
the PC-O 44:4 and optionally the other biomarkers is an absolute
value such as the units of PC-O 44:4 in .mu.mol/l (.mu.M) the
reference value is also based upon the units of PC-O 44:4 in
.mu.mol/l (.mu.M) in individuals in the general population or a
selected control population of subjects.
[0087] Moreover, the reference value can be a single cut-off value,
such as a median or mean. Reference values of PC-O 44:4 and
optionally the other biomarkers in obtained body fluid samples,
such as mean levels, median levels, or "cut-off" levels, may be
established by assaying a large sample of individuals in the
general population or the selected population and using a
statistical model such as the predictive value method for selecting
a positivity criterion or receiver operator characteristic curve
that defines optimum specificity (highest true negative rate) and
sensitivity (highest true positive rate) as described in Knapp, R.
G., and Miller, M. C. (1992). Clinical Epidemiology and
Biostatistics. William and Wilkins, Harual Publishing Co. Malvern,
Pa., which is incorporated herein by reference.
[0088] Skilled artesians will know how to assign correct reference
values as they will vary with gender, race, genetic heritage,
health status or age, for example.
[0089] As an example the inventors have determined typical
reference values in obese adult human subjects and in normal adult
human subjects in a body fluid such as blood plasma, for
example.
[0090] Consequently, the predetermined mean reference values for
obese subjects may be about [0091] 68.71 .mu.M body fluid for
tyrosine, [0092] 662.67 .mu.M body fluid for glutamine, [0093] 1.47
.mu.M body fluid for PC-O 44:6, [0094] 0.84 .mu.M body fluid for
PC-O 44:4, [0095] 1.27 .mu.M body fluid for PC-O 42:4, [0096] 2.65
.mu.M body fluid for PC-O 40:4, [0097] 1.37 .mu.M body fluid for
PC-O 40:3, [0098] 52.97 .mu.M body fluid for phenylalanine, [0099]
193.56 .mu.M body fluid for Leucine+Isoleucine, [0100] 0.19 .mu.M
body fluid for C10, [0101] 0.06 .mu.M body fluid for C16, [0102]
0.03 .mu.M body fluid for C8:1, [0103] 0.25 .mu.M body fluid for
LPC 24:0, [0104] 4.18 .mu.M body fluid for PC 30:0, [0105] 1.11
.mu.M body fluid for PC 34:4, [0106] 9.84 .mu.M body fluid for PC-O
34:1, [0107] 11.49 .mu.M body fluid for PC-O 34:2, [0108] 11.79
.mu.M body fluid for PC-O 36:2, [0109] 7.20 .mu.M body fluid for
PC-O 36:3, [0110] 3.68 .mu.M body fluid for PC-O 40:6, [0111] 0.56
.mu.M body fluid for PC-O 42:2, [0112] 0.89 .mu.M body fluid for
PC-O 42:3, [0113] 0.21 .mu.M body fluid for PC-O 44:3, [0114] 2.17
.mu.M body fluid for PC-O 44:5, [0115] 0.56 .mu.M body fluid for PC
42:0, [0116] 0.22 .mu.M body fluid for PC 42:2.
[0117] In normal subjects the predetermined mean reference values
may be about [0118] 75.00 .mu.M body fluid for tyrosine, [0119]
748.27 .mu.M body fluid for glutamine, [0120] 1.21 .mu.M body fluid
for PC-O 44:6, [0121] 0.50 .mu.M body fluid for PC-O 44:4, [0122]
1.12 .mu.M body fluid for PC-O 42:4, [0123] 3.24 .mu.M body fluid
for PC-O 40:4, [0124] 2.10 .mu.M body fluid for PC-O 40:3, [0125]
49.17 .mu.M body fluid for phenylalanine, [0126] 197.52 .mu.M body
fluid for Leucine+Isoleucine, [0127] 0.29 .mu.M body fluid for C10,
[0128] 0.09 .mu.M body fluid for C16, [0129] 0.04 .mu.M body fluid
for C8:1, [0130] 0.77 .mu.M body fluid for LPC 24:0, [0131] 4.10
.mu.M body fluid for PC 30:0, [0132] 1.42 .mu.M body fluid for PC
34:4, [0133] 8.20 .mu.M body fluid for PC-O 34:1, [0134] 9.26 .mu.M
body fluid for PC-O 34:2, [0135] 12.67 .mu.M body fluid for PC-O
36:2, [0136] 5.83 .mu.M body fluid for PC-O 36:3, [0137] 4.45 .mu.M
body fluid for PC-O 40:6, [0138] 0.82 .mu.M body fluid for PC-O
42:2, [0139] 1.08 .mu.M body fluid for PC-O 42:3, [0140] 0.22 .mu.M
body fluid for PC-O 44:3, [0141] 1.82 .mu.M body fluid for PC-O
44:5, [0142] 0.65 .mu.M body fluid for PC 42:0, [0143] 0.35 .mu.M
body fluid for PC 42:2.
[0144] The subjects to be tested with the method of the present
invention may be a human or an animal, in particular a mammal, for
example. Typical animals may be companion animals, such as cats or
dogs of farm animals, for example.
[0145] Those skilled in the art will understand that they can
freely combine all features of the present invention described
herein, without departing from the scope of the invention as
disclosed. In particular, features described for the methods of the
present invention may be applied to other methods and to the use of
the present invention and vice versa.
[0146] Further advantages and features of the present invention are
apparent from the following Examples and Figures.
[0147] Table 1 shows clinical characteristics of the recruited
cohort as stratified in four quartiles based on their visceral fat
content as assessed by the log.sub.10 value of the
intraperitoneal/abdominal fat ratio measured by computerized
tomography.
[0148] Values are presented as mean (.+-.SD). ALAT=alanine
aminotransferase, ASAT=aspartate aminotransferase, BMI=body mass
index, GGT=gamma-glutamyl transpeptidase, HDL-C=high density
lipoprotein cholesterol, HOMA-IR=homeostasis model assessment of
insulin resistance, LDL-C=low density lipoprotein cholesterol,
MAP=mean arterial blood pressure, NEFAs=non esterified fatty acids,
TG=triglycerides.
[0149] Table 2 shows metabolite concentrations presented as mean
(.+-.SD) for each of the four quartiles based on their visceral fat
content as assessed by the log.sub.10 value of the
intraperitoneal/abdominal fat ratio measured by computerized
tomography.
[0150] FIG. 1 shows the statistical reconstruction of .sup.1H NMR
blood plasma profiles using random forest analysis to identify
metabolic patterns associated with visceral adiposity (as
identified with squared boxes). GPCs=glycerophospholipids,
PUFAs=polyunsaturated fatty acids, UFAs=unsaturated fatty
acids.
[0151] FIG. 2 shows metabolite importance and robustness in
predicting visceral fat adiposity as assessed by Random forest
analysis. Pooled mean decrease in accuracy after n=10000 random
forest generations. Higher variable importance corresponds to
higher values of pooled mean decrease in accuracy.
[0152] FIGS. 3-1, 3-2 and 3-3 show metabolite differences between
high and low visceral fat subjects for each selected metabolite.
Data are plotted for each quartile based on their visceral fat
content as assessed by the log.sub.10 value of the intraperitoneal
fat volume measured by computerized tomography, 1=quartile 1,
2=quartile 2, 3=quartile 3, 4=quartile 4.
EXAMPLES
Subjects and Experimental Design
[0153] The clinical trial was an observational study conducted on
40 healthy obese Caucasian women at the Centre Hospitalier
Universitaire Vaudois (CHUV, Lausanne, Switzerland). The study
protocol was approved by an independent Ethical Committee located
at the CHUV. The participants had a BMI between 28 and 40, aged
between 25 and 45 years old, and showed no metabolic disease traits
(diabetes type 2, cardiovascular disease or metabolic syndrome).
The resulting biological samples (blood plasma and 24 hours urine
samples) were stored at -80.degree. C. until metabolomic
analysis.
[0154] Body Composition Assessment
[0155] Full body scan was performed to determine both abdominal fat
distribution and total body composition. Total body scans were made
on a GE Lunar iDXA system (software version: enCORE version
12.10.113) with scan mode automatically determined by the device.
For the DXA measurement, all subjects were wearing a hospital gown
and had all metal artifacts removed. The iDXA unit was calibrated
daily using the GE Lunar calibration phantom. A trained operator
performed all scans following the operator's manual for patient
positioning and data acquisition. During the one-hour appointment,
total body scans of each subject were performed twice with
repositioning between scans. Scans were analyzed with the enCORE
software (version 14.00.207). The ROIs were automatically
determined by the enCORE software (Auto ROI) for total body, arms,
legs, trunk, android, and gynoid regions. An experienced DXA
operator also verified and, when indicated, repositioned the ROI
placements (Expert ROI). In addition to iDXA scan, waist and hip
measurements were performed.
[0156] The CT measures were performed on 64 multi-detector CT
scanner (VCT Lightspeed, GE Medical Systems, Milwaukee, USA).
Subjects lied in the supine position with their arms above their
head and legs elevated with a cushion. A single scan (10 mm) of the
abdomen is acquired at the level of L4-L5 vertebrae and analyzed
for a cross-sectional area of adipose tissue, expressed in square
centimeters. The following acquisition parameters were used: 120
Kv, 100-200 mA with z-axis dose modulation and a field of view 500
mm. Axial transverse images of 5 mm slice thickness are
reconstructed using a standard kernel. The quantification process
uses a semi interactive commercially available algorithm for
segmentation of subcutaneous and intra-abdominal fat on the
Advantage Window workstation (GE Medical Systems).
Clinical Chemistry.
[0157] Plasma total, HDL and LDL cholesterol, triglycerides,
urates, creatinine, sodium and potassium concentrations, ALAT,
ASAT, GGT, glucose, non-esterified fatty acids, insulin and mean
arterial blood pressure (MAP) were determined using routine
laboratory methods. Insulin resistance status was assessed as
homeostasis model assessment of insulin resistance (HOMA-IR)
according to the previously described formula (Mathews et al.,
1985): insulin (.mu.U/mL).times.glucose (mmol/L)/22.5.
Sample Preparation and .sup.1H-NMR Spectroscopic Analysis
[0158] Heparin blood plasma samples (400 .mu.L) were introduced
into 5 mm NMR tubes with 200 .mu.L of deuterated phosphate buffer
solution (KH2PO4 with a final concentration of 0.2M). Deuterium was
employed as locking substance. Metabolic profiles were measured on
a Bruker Avance III 600 MHz spectrometer equipped with an inverse 5
mm cryogenic probe at 300 K (Bruker Biospin, Rheinstetten,
Germany). Standard .sup.1H-NMR one-dimensional pulse sequence with
water suppression (RD-4s), Carr-Purcell-Meiboom-Gill (CPMG)
spin-echo sequence with water suppression (RD-4s), and
diffusion-edited sequence (RD-4s) where acquired for each plasma
sample. For each one dimensional experiment 32 scans were collected
using 98 K data points. .sup.1H-NMR spectra were processed using
TOPSPIN (version 2.1, Bruker, Germany) software package prior to
Fourier transformation. The acquired NMR spectra were manually
phased and baseline corrected, and referenced to the chemical shift
of the anomeric proton of .alpha.-glucose at .delta. 5.2396 for
plasma spectra. The assignment of the .sup.1H-NMR resonances to
specific metabolites was achieved by matching our in-house
developed NMR database of pure compounds and using literature data
(Fan, T. W. (1996) Progress in Nuclear Magnetic Resonance
Spectroscopy 28, 161-219; Nicholson, J. K., et al. (1995) Anal.
Chem. 67, 793-811). Metabolite identification was confirmed by 2D
.sup.1H-.sup.1H COrrelation SpectroscopY (COSY) (Hurd, R. E. (1990)
J. Magn. Reson. 87, 422-428), .sup.1H-.sup.1H TOtal Correlation
SpectroscopY (TOCSY) (Bax, A. & Davis (1985) J. Magn. Reson.
65, 355-360) and .sup.1H-.sup.13C Heteronuclear Single Quantum
Correlation (HSQC) (Bodenhausen, G. & Ruben (1980) Chemical
Physics Letters 69, 185-189) NMR techniques.
Sample Preparation for Biocrates Life Sciences Absolute IDQ.TM. Kit
Analysis
[0159] The Biocrates Life Sciences AbsolutelDQ.TM. kit was used for
EDTA plasma samples as previously published (Romisch-Margl, W. C.
Prehn, R. Bogumil, C. Rohring, K. Suhre, J. Adamski, Procedure for
tissue sample preparation and metabolite extraction for
high-throughput targeted metabonomics. Metabonomics, 2011. Online
First). Well plate preparation and sample application and
extraction were carried out according to the manufacturer's
instructions. A final volume of 10 .mu.l of plasma was loaded onto
the provided 96-well plate, containing isotopically labeled
internal standards. Liquid chromatography was realized on a Dionex
Ultimate 3000 ultra high pressure liquid chromatography (UHPLC)
system (Dionex AG, Olten, Switzerland) coupled to a 3200 Q TRAP
mass spectrometer (AB Sciex; Foster City, Calif., USA) fitted with
a TurboV ion source operating in electrospray ionization (ESI)
mode. Sample extracts (20 .mu.l) were injected two times (in
positive and negative ESI modes) via direct infusion using a
gradient flow rate of 0-2.4 min: 30 .mu.l/min, 2.4-2.8 min: 200
.mu.l/min, 2.9-3 min: 30 .mu.l/min. MS source parameters were set
at: desolvation temperature (TEM): 200.degree. C., high voltage:
-4500 V (ESI-), 5500 V (ESI+), curtain (CUR) and nebuliser (GS1 and
GS2) gases: nitrogen; 20, 40, and 50 psi; respectively, nitrogen
collision gas pressure: 5 mTorr. MS/MS acquisition was realised in
scheduled reaction monitoring (SRM) mode with optimised
declustering potential values for the 163 metabolites screened in
the assay. Raw data files (Analyst software, version 1.5.1; AB
Sciex, Foster City, Calif., USA) were imported into the provided
analysis software MetIQ to calculate metabolite concentrations.
List of all detectable metabolites is available from Biocrates Life
Sciences, Austria (http://biocrates.com).
Multivariate Data Analysis
[0160] The plasma NMR spectra were converted into 22K data points
over the range of .delta. 0.2-10.0 using an in-house developed
MATLAB routine excluding the water residue signal between
.delta.4.68-5.10. Chemical shift intensities were normalized to the
sum of all intensities within the specified range prior to
chemometric analysis. Chemometric analysis was performed using the
software package SIMCA-P+ (version 12.0.1, Umetrics AB, Umea,
Sweden) and in-house developed MATLAB (The MathWorks Inc., Natick,
Mass., USA) routines. In order to detect the presence of
similarities between metabolic profiles, Principal Component
Analysis (PCA) (Wold, S., et al. (1987) Chemom. Intell. Lab. Syst.
2, 37-52), Projection to Latent Structures (PLS) (Wold, S., et al.
(1987) PLS Meeting, Frankfurt) and the Orthogonal Projection to
Latent Structures (O-PLS) (Trygg, J. & Wold (2003) J. Chemom.
17, 53-64) were used. Seven-fold cross validation was used to
assess the validity of the model (Cloarec, O., et al. (2005) Anal.
Chem. 77, 517-526). The classification accuracy of the O-PLS-DA
model was established from the predicted samples in the 7-fold
cross-validation cycle.
[0161] Targeted MS data was analyzed by Random Forests by using the
package `randomForest` (A. Liaw and M. Wiener (2002).
Classification and Regression by randomForest. R News 2(3), 18-22.)
running in the R environment (R Development Core Team (2011). R: A
language and environment for statistical computing. R Foundation
for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL
http://www.R-project.org/.). Univariate significance tests for
confirmation were also performed in R.
[0162] Due to the non-normal distribution of the visceral
adiposity, the following parameters were employed for the
subsequent metabolomics analysis: log-transform value of the
visceral fat content, of the intraperitoneal/subcutaneous fat ratio
(ratio 1), or of the intraperitoneal/abdominal fat ratio (ratio
2).
[0163] Anthropometric and biochemical characteristics of the cohort
are shown in Table 1, as per stratification in four quartiles
(Q1-4, n=10) based on the log.sub.10 value of the
intraperitoneal/abdominal fat ratio (ratio 2) measured using CT.
Fasting glucose (p<0.10) and insulin (p<0.05), as well as
HOMA-IR (p<0.05) were higher in the subjects with the highest
visceral adiposity (Q4) compared to (Q1). Log-transform values of
the intraperitoneal/subcutaneous fat ratio or of the
intraperitoneal/abdominal fat ratio were used preferably to
stratify subjects according to their visceral adiposity, since
these parameters were shown to be independent of BMI, Hipp, Waist,
ALAT, MAP, and calorimetric indices, which was not the case for
log-transform value of intraperitoneal fat volume. Interestingly in
his cohort, HDL, LDL, and total cholesterol were not statistically
different between groups.
[0164] In order to identify phenotypic signatures of visceral fat
deposition, plasma samples were analyzed using .sup.1H-NMR and
targeted LC-MS/MS metabolomic approach. Analyses were conducted on
the fasting plasma samples. OPLS analysis of samples collected
showed some subtle but significant associations between blood
plasma lipids and visceral fat deposition (R.sup.2X: 0.68;
R.sup.2Y: 0.506; Q.sup.2Y: 0.167). Random forest analysis was also
employed to confirm the statistical association between specific
plasma lipids and visceral fat status (FIG. 1), which suggested a
specific plasma lipid remodeling marked by changes in
glycerophospholipids and the fatty acid saturation pattern.
[0165] Therefore, targeted LC-MS/MS metabonomics was employed to
provide structural information and quantitative measures of 163
metabolites, including amino acids, sugars, acyl-carnitines,
sphingolipids, and glycerophospholipids. Using OPLS analysis, it
was possible to determine a metabolic signature of visceral fat
adiposity (R2X: 0.29; R2Y: 0.68; Q2Y: 0.32).
[0166] To select the more robust markers, there was used the % Mean
decrease accuracy of `out-of-bag` data as variable importance
feature. In this way, it was possible to determine the variables
that better discriminate subjects according to their visceral fat
status (Q1 versus Q4). Both Q1, Q4 were assessed using either
log-transform value of the intra peritoneal fat volume, ratio 1 and
ratio 2. The modeling was also tested for assessing inter-days
metabolic variations, by considering each visit separately (data
not shown). Ultimately, 26 metabolites were retained as of
importance to classify subjects according to their visceral fat
adiposity (FIGS. 2, 3-1, 3-2, 3-3. Visceral adiposity was
associated with increasing concentrations of circulating amino
acids, including glutamine, leucine/isoleucine, phenylalanine and
tyrosine. These patterns were associated with higher conentrations
of acylcarnitines, including palmitoylcarnitine (016),
caproylcarnitine (C10) octenoylcarnitine (C8:1), and
lysophospatidylcholine LPC 24:0 and diacyl phospholipids, including
PC 30:0, PC 34:4. In addition, visceral adiposity was marked by a
depletion in acyl ethers PC-O 36:3, PC-O 40:3, PC-O 40:4, PC-O
40:6, PC-O 42:2, PC-O 42:3, PC-O 42:4, PC-O 44:3, PC-O 44:4, PC-O
44:6, and two diacyl phosphopcholines (PC 42:0 and PC 42:2). To
assess the individual discriminant ability of each component of the
signature, Wilcoxon Rank sum tests among the visceral fat adiposity
groups were performed (all metabolites are listed in Table 2
according to the tested descriptor, namely log 10 value of ratio
2).
[0167] FIG. 2 summarizes the selected biomarkers together with
their weight in the classification of visceral adiposity, using log
10 value of visceral fat content, log 10 value of ratio 1 or log 10
value of ratio 2. These markers showed sensitivity and specificity
of 0.90 for visceral fat in cross-validation mode (Sencv,
Specv).
TABLE-US-00001 TABLE 1 Mann-Whitney P First Quartile Second
Quartile Third Quartile Fourth Quartile values Factor Q1 Q2 Q3 Q4
(Q1-Q4) Age, years 33.90 .+-. 4.89 32.80 .+-. 3.58 38.00 .+-. 4.42
37.60 .+-. 5.82 0.13897 BMI, kg/m2 34.01 .+-. 3.27 36.34 .+-. 3.62
37.00 .+-. 2.95 34.59 .+-. 4.42 0.93969 Log10 intraperitoneal/
-0.70 .+-. 0.05 -0.61 .+-. 0.02 -0.52 .+-. 0.02 -0.40 .+-. 0.06
1.25506E-09 abdominal fat ratio Hipp, cm 122 .+-. 5.47 128 .+-.
7.48 127.34 .+-. 6.29 122.28 .+-. 9.65 0.56498 Waist, cm 97.28 .+-.
8.28 103.39 .+-. 8.7 108.72 .+-. 11.71 104.73 .+-. 13.84 0.45838
Waist/Hipp ratio 0.80 .+-. 0.07 0.81 .+-. 0.06 0.85 .+-. 0.07 0.84
.+-. 0.09 0.35104 Na, mmol/L 140.40 .+-. 1.35 140.80 .+-. 1.32
141.50 .+-. 1.58 139.9 .+-. 1.10 0.32894 K, mmol/L 4.05 .+-. 0.18
4.10 .+-. 0.18 3.99 .+-. 0.25 4.04 .+-. 0.18 0.87656 Glucose,
mmol/L 4.95 .+-. 0.35 5.17 .+-. 0.52 5.41 .+-. 0.49 5.37 .+-. 0.5
0.05716 Creatinine, mmol/L 65.60 .+-. 9.45 65.2 .+-. 11.2 64.78
.+-. 9.28 70.3 .+-. 6.53 0.2563 Cholesterol, mmol/L 5.52 .+-. 1.01
5.58 .+-. 0.85 5.31 .+-. 0.68 5.48 .+-. 0.97 0.90965 HDL, mmol/L
1.54 .+-. 0.43 1.32 .+-. 0.29 1.38 .+-. 0.25 1.32 .+-. 0.24 0.18104
HDL/Chol ratio 3.77 .+-. 1.07 4.42 .+-. 1.22 3.99 .+-. 0.97 4.24
.+-. 0.95 0.28901 LDL, mmol/L 3.50 .+-. 0.97 3.56 .+-. 0.88 3.34
.+-. 0.61 3.47 .+-. 0.79 1 TG, mmol/L 1.04 .+-. 0.43 2.25 .+-. 2.1
1.28 .+-. 0.45 1.52 .+-. 0.57 0.09354 Urates, .mu.mol/L 275.20 .+-.
41.93 263.22 .+-. 71.45 303.40 .+-. 75.28 285.00 .+-. 31.70 0.35268
ASAT, U/L 21.40 .+-. 3.24 21.4 .+-. 4.48 24.00 .+-. 6.94 24.50 .+-.
6.7 0.40157 ALAT, U/L 18.40 .+-. 6.11 19.20 .+-. 5.07 23.50 .+-.
8.34 27.10 .+-. 13.28 0.13971 ALAT/ASAT ratio 0.86 .+-. 0.25 0.91
.+-. 0.21 0.99 .+-. 0.3 1.08 .+-. 0.34 0.12066 MAP, mmHg 57.80 .+-.
18.6 71.10 .+-. 19.75 62.40 .+-. 20.97 57.80 .+-. 14.15 0.8796 GGT,
U/L 20.00 .+-. 11.86 17.50 .+-. 6.88 21.10 .+-. 4.84 25.44 .+-.
11.26 0.19122 Calorimetry, kcal/24 h 1357.00 .+-. 191.78 1434.00
.+-. 142.61 1469.00 .+-. 152.49 1433.00 .+-. 210.82 0.36362 Insulin
18.60 .+-. 9.21 22.12 .+-. 6.32 24.36 .+-. 7.22 25.44 .+-. 4.62
0.01468 HOMA-IR 4.24 .+-. 2.02 4.95 .+-. 1.49 6.06 .+-. 1.87 6.12
.+-. 1.23 0.01149 NEFAs, .mu.mol/L 544.50 .+-. 201.51 580.60 .+-.
301.38 596.20 .+-. 185.79 585.10 .+-. 188.62 0.66426
TABLE-US-00002 TABLE 2 Mann-Whitney P First Quartile Second
Quartile Third Quartile Fourth Quartile values Metabolites Q1 Q2 Q3
Q4 (Q1-Q4) Glutamine, .mu.mol/L 615.56 .+-. 107.95 748 .+-. 193.49
792.1 .+-. 260.61 .sup. 714 .+-. 94.03 0.02468 Tyrosine, .mu.mol/L
61.97 .+-. 11.02 80.54 .+-. 22.21 75.91 .+-. 21.83 80.99 .+-. 24.69
0.05347 Caproylcarnitine, .mu.mol/L 0.22 .+-. 0.1 0.2 .+-. 0.09
0.14 .+-. 0.06 0.3 .+-. 0.19 0.40018 Palmitoylcarnitine, .mu.mol/L
0.07 .+-. 0.02 0.07 .+-. 0.03 0.07 .+-. 0.03 0.1 .+-. 0.04 0.12065
Octenoylcarnitine, .mu.mol/L 0.04 .+-. 0.02 0.05 .+-. 0.02 0.05
.+-. 0.04 0.05 .+-. 0.02 0.25258 LPC 24:0, .mu.mol/L 0.36 .+-. 0.25
0.51 .+-. 0.24 0.52 .+-. 0.36 0.46 .+-. 0.31 0.21613 PC 30:0,
.mu.mol/L 4.43 .+-. 1.48 5.17 .+-. 2.35 5.75 .+-. 1.98 5.57 .+-.
1.76 0.1564 PC 34:4, .mu.mol/L 1.3 .+-. 0.46 1.53 .+-. 1.14 1.41
.+-. 0.52 1.55 .+-. 0.75 0.31537 PC 42:0, .mu.mol/L 0.65 .+-. 0.23
0.48 .+-. 0.16 0.47 .+-. 0.08 0.48 .+-. 0.14 0.07889 PC 42:2,
.mu.mol/L 0.2 .+-. 0.06 0.19 .+-. 0.11 0.13 .+-. 0.05 0.17 .+-.
0.08 0.40018 PC--O 34:1, .mu.mol/L 9.94 .+-. 2.22 9.78 .+-. 3.84
8.48 .+-. 2.15 8.53 .+-. 0.99 0.17752 PC--O 34:2, .mu.mol/L 10.66
.+-. 3.5 9.31 .+-. 3.51 9.38 .+-. 4.57 8.77 .+-. 1.76 0.21102 PC--O
36:2, .mu.mol/L 11.29 .+-. 2.64 11.86 .+-. 2.68 10.38 .+-. 3.08
9.17 .+-. 2.09 0.07865 PC--O 36:3, .mu.mol/L 7.04 .+-. 1.68 6.7
.+-. 2.61 7.11 .+-. 1.82 5.5 .+-. 1.24 0.02792 PC--O 40:3,
.mu.mol/L 1.41 .+-. 0.27 1.46 .+-. 0.38 1.27 .+-. 0.3 0.86 .+-.
0.42 0.00421 PC--O 40:4, .mu.mol/L 2.79 .+-. 0.56 2.9 .+-. 0.73
2.47 .+-. 0.69 2.02 .+-. 0.83 0.01784 PC--O 40:6, .mu.mol/L 3.81
.+-. 0.86 3.27 .+-. 1.09 2.8 .+-. 0.84 2.74 .+-. 1.09 0.06525 PC--O
42:2, .mu.mol/L 0.66 .+-. 0.23 0.56 .+-. 0.14 0.53 .+-. 0.16 0.45
.+-. 0.18 0.05347 PC--O 42:3, .mu.mol/L 0.89 .+-. 0.2 0.92 .+-.
0.14 0.9 .+-. 0.27 0.63 .+-. 0.26 0.06525 PC--O 42:4, .mu.mol/L
1.34 .+-. 0.33 1.09 .+-. 0.28 1.09 .+-. 0.37 0.82 .+-. 0.22 0.00298
PC--O 44:3, .mu.mol/L 0.21 .+-. 0.06 0.2 .+-. 0.04 0.15 .+-. 0.06
0.17 .+-. 0.05 0.1564 PC--O 44:4, .mu.mol/L 0.8 .+-. 0.3 0.67 .+-.
0.24 0.63 .+-. 0.19 0.51 .+-. 0.18 0.01721 PC--O 44:5, .mu.mol/L
2.29 .+-. 0.74 2.03 .+-. 0.55 1.9 .+-. 0.74 1.71 .+-. 0.7 0.04113
PC--O 44:6, .mu.mol/L 1.52 .+-. 0.56 1.22 .+-. 0.3 1.11 .+-. 0.5
1.03 .+-. 0.32 0.01013 Phenylalanine, .mu.mol/L 49.9 .+-. 14.16
50.47 .+-. 8.45 62.82 .+-. 22.17 56.42 .+-. 8.38 0.04113 Leucine +
Isoleucine, .mu.mol/L 181.44 .+-. 53.02 214.2 .+-. 56.71 202.4 .+-.
27.39 228.8 .+-. 33.83 0.04536
* * * * *
References