Evaluation of Published Clinical Scores for the Prediction of
Cardiometabolic Risk in the SEMEOTICONS Project
Melania Gaggini
, Renata De Maria
Chiara Saponaro
, Emma Buzzigoli
, Demetrio Ciociaro
Sylvie Normand
, Giuseppe Coppini
, Martine Laville
, Paolo Marraccini
and Amalia Gastaldelli
Institute of Clinical Physiology CNR, Pisa, Italy
Centre Européen Nutrition Santé CENS, Lyon, France
Keywords: Cardiometabolic Risk, Abdominal Adiposity, Metabolic Syndrome.
Abstract: Cardiovascular and metabolic diseases are the major causes of morbidity and mortality in the Western
Countries. Metabolic syndrome (defined as 3 out of 5 factors among increased waist circumference,
hypertension, high blood glucose, high triglyceride and low high density lipoprotein (HDL) cholesterol
concentrations) is associated with increased cardiometabolic risk. Other indexes have been proposed and
validated, based on the measurement of plasma concentration of lipids, glucose and liver enzymes. In the
SEMEOTICONS project we plan to measure parameters related to increased cardiometabolic risk, e.g. skin
accumulation of cholesterol and advanced glycated end products, liver enzyme alteration by changing in
skin and eye color and obesity. The results will allow to evaluate cardiometabolic risk using non invasive
clinical parameters. The new score obtained will be compared with previously validated indexes. In this
paper we have evaluated the most common cardiometabolic risk scores i.e., VAI (Visceral Adiposity Index),
HTG- Waist (Hypertriglyceridemic Waist), FLI (Fatty Liver Index) and LAP (Lipid Accumulation Product),
that we will use during the project.
Obesity prevalence is rapidly increasing and
together with it there is a global increase in the risk
of developing related diseases, e.g., type 2 diabetes
mellitus (T2DM), atherosclerosis, hypertension,
hyperlipidemia, coronary artery disease and in
general cardiovascular diseases (CVD). The
assessment of risk of disease is becoming important
not only to prevent the development of co-related
diseases and complications through an early
intervention but also to maintain a good quality of
life. Moreover, risk reduction is important for
controlling the national health expenditure, through
prevention of people not at risk, intervention to
prevent complications, reduction of cases of
hospitalization of patients at risk.
It is important to become self-conscious of
potential risk factors. Technology can help in
evaluating individual risk and in promoting changes
in lifestyle that can reduce risk of disease.
Signs derivable from face observation are a
potential source of health information, e.g. for fat
accumulation, liver dysfunction, hyper-
cholesterolenemia, hyperlipidemia, cardiovascular
homeostasis and general psychophysical status.
Once properly mapped to computational descriptors,
systematic exploitation of face signs and their
change over time will allow to build an effective
self- monitoring system. In this perspective, in the
frame of the FP7 ICT project SEMEOTICONS
(SEMEiotic Oriented Technology for Individual’s
CardiOmetabolic risk self-assessmeNt and Self-
monitoring),, the most relevant face signs of
cardiometabolic risk are reviewed and analyzed so
as to drive their detection, quantification and
integration into a virtual individual model useful for
cardiometabolic risk prevention. In particular here
we are reviewing the most common used indexes
that will be also evaluated in the project and tested
against the data on face signs of cardiometabolic risk
obtained in the SEMEOTICONS project.
Gaggini M., De Maria R., Saponaro C., Buzzigoli E., Ciociaro D., Normand S., Coppini G., Laville M., Marraccini P. and Gastaldelli A..
Evaluation of Published Clinical Scores for the Prediction of Cardiometabolic Risk in the SEMEOTICONS Project.
DOI: 10.5220/0004939005990605
In Proceedings of the International Conference on Health Informatics (SUPERHEAL-2014), pages 599-605
ISBN: 978-989-758-010-9
2014 SCITEPRESS (Science and Technology Publications, Lda.)
Atherosclerotic cardiovascular diseases (CVDs),
including heart disease and stroke, are the leading
causes of mortality worldwide (World Health
Organization, 2008). Clinical manifestations of
atherosclerotic disease are detected only when they
are advanced stages. One of the major risk factor for
atherosclerosis is related to impaired in glucose
metabolism, especially high postprandial glucose
even in non diabetic subjects (Andreozzi et al.,
2013). Moreover alterations in glucose and lipid
metabolism are also associated with an increased
risk of endothelial dysfunction, decreased diastolic
function, but also increased risk of myocardial
infarction and stroke. Altogether, frequently, the
major events, such as serious health complications,
disability and death can occur between 40 and 60
years of age. CVDs represent one of the major
challenges to the health systems since the majority
of patients who survive a myocardial infarction do
not fully recover the ventricular function, and many
stroke survivors have physical limitation in the daily
activities. This explains the importance to prevent
and treat even early clinical manifestations of CVDs.
Among the most common risk scores for
coronary heart disease (CHD) and CVD there are the
Framingham score (Grundy et al., 1998) derived in
the general population in the USA that estimates the
10 year CHD risk and the SCORE (Systematic
COronary Risk Evaluation for High & Low
cardiovascular Risk) proposed by the European
Society of Cariology (Perk et al., 2012) tha evaluates
the 10 year fatal CVD event. The SCORE uses age,
smoking, total cholesterol and systolic blood
pressure. The Framingham score uses the same
parameters of the SCORE plus diastolic blood
pressure, high density lipoprotein (HDL) cholesterol
and presence of diabetes. Both scores use different
cut off for males and females. The SCORE has the
advantage of having different charts for different
countries to better estimate the risk. These scores
need the use of charts to build up the score or the use
of online calculators. However, neither the
Framingham nor the SCORE can be used to predict
the metabolic risk but only the CVD risk.
Main cardiometabolic risk factors are listed in Table
1 and divided among modifiable and non-
modifiable. Among the strongest modifiable risk
factors for cardiometabolic diseases there are obesity
(defined as a BMI>30 kg/m2), hypertension
(increased systolic and/or diastolic blood pressure),
hyperlipidemia (ie, increased concentrations of
triglyceride and/or total cholesterol and/or decreased
concentrations of HDL cholesterol). (Kissebah et al.,
1989); (Despres et al., 1990); (Pouliot et al., 1992);
(Carey et al., 1997); (Turkoglu et al., 2003). The risk
is even higher in subjects with abdominal obesity
(i.e., with increased waist circumference), and it is
independently associated with increased age-
adjusted risk of CVD, even after adjusting for BMI
and other cardiovascular risk factors (Carey et al.,
1997); (Rexrode et al., 1998); (Rexrode et al., 2001).
Table 1: Main cardiometabolic risk factors.
Modifiable factors
High LDL cholesterol
Low HDL cholesterol
High triglycerides
High blood glucose
Family History
3.1 Metabolic Syndrome (MS)
It has been recognized that subjects at risk to
develop type 2 diabetes and CVD tended to have not
only high blood pressure, fasting glucose and low
HDL, as recognized by the Framingham study, but
also increased waist circumference and triglyceride
concentration. Since these parameters cluster
together it has been proposed to define this clinical
condition as metabolic syndrome (MS) when at least
3 out of 5 factors are above the normal range
(NCEP-ATPIII, 2001); (Alberti et al., 2006);
(Alberti et al., 2009) (Table 2) and identify the
subjects at risk of developing type 2 diabetes and/or
CVD. After the initial general ranges proposed in
2001 (NCEP-ATPIII, 2001) different cut offs for the
parameters described have been proposed (see Table
2). More importantly the IDF has recognized that
different cut offs for waist circumference identify
the risk in the European compared to the American
or Asian subjects. Although widely criticized these
definitions are widely used (Ferrannini, 2007).
3.2 Ectopic Fat and Cardio-metabolic
Cardiometabolic risk is associated not only to
Table 2: Criteira for diagnosis of Metabolic Syndrome.
general obesity but also to abdominal obesity, i.e.,
the preferential fat accumulation as visceral fat
ectopic fat in liver, heart, pancreas and muscle. Both
visceral and ectopic fat accumulations are associated
to the presence of insulin resistance, increased
lipolysis, release of free fatty acid, very low density
lipoprotein (VLDL), proinflammatory factors as
cytokines, C reactive protein and fibrinogen,
decreased cardiac function, atherosclerosis,
endothelial dysfunction and decreased carotid
elasticity (Figure 1).
Visceral, cardiac and ectopic fat accumulation
can be evaluated using imaging techniques such as
magnetic resonance imaging (MRI) and computed
tomography (CT) but this is feasible only in a
research setting. Surrogate measures for abdominal
fat is waist circumference that is linearly related to
the amount of visceral fat. Ectopic fat is associated
with lipotoxicity and organ dysfunction, so for
example alteration in liver enzymes are often
associated with triglyceride accumulation in the
liver, high cholesterol is a major risk factor for
atherosclerosis, increased triglyceride is associated
with both alteration in liver and cardiac metabolism.
(Morelli et al., 2013)
Although the MS recognizes the importance of
abdominal obesity in the stratification of
cardiometabolic risk it does not include any
parameter related to liver function, i.e., alanine
aminotransferase (ALT), aspartate aminotransferase
(AST) and gamma glutamyl transferase (GGT).
Several studies have shown that not only abdominal
obesity but also hepatic steatosis clusters with the
parameters of CVD and is a recognized risk factor
for the development of cardiometabolic diseases
(Anstee et al., 2013); (Morelli et al., 2013).
Figure 1: Accumulation of visceral fat, hepatic and cardiac
fat as well as atherosclerosis and plaque formation are
major risk factors for cardiovascular and metabolic
Assessment of hepatic steatosis and of
cardiometabolic risk associated could be done using
scores recently developed, i.e., Fatty liver index
(FLI), visceral adiposity index (VAI),
hypertriglyceridemic waist (HTG-Waist) or lipid
accumulation product (LAP).
Impaired glucose metabolism is associated with
the formation of advanced glycated end-products,
known also as AGEs, that are believed to play a
causative role in the blood-vessel complications of
diabetes mellitus by speeding up oxidative damage
to cells and in altering their normal behaviour. These
harmful compounds can affect nearly every type of
cell and molecule in the body and are thought to be
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3.3 Indexes of Cardiometabolic Risk
There are several indexes that have been validated as
score for risk of cardiometabolic disease. VAI
(visceral adiposity index) (Amato et al., 2010);
(Petta et al., 2010); (Petta et al., 2012), HTG-Waist
(Hypertriglyceridemic Waist Phenotype) (Lemieux
et al., 2007); (Blackburn et al., 2009); (de Graaf et
al., 2010), LAP (Lipid Accumulation Product)
(Kahn, 2005); (Bedogni et al., 2010); (Ioachimescu
et al., 2010), FLI (Fatty Liver Index) (Bedogni et al.,
2006); (Gastaldelli et al., 2009); (Balkau et al.,
2010); (Kozakova et al., 2012).
Figure 2: Variables used in the most reliable
cardiometabolic risk scores cluster together and resembled
factors of metabolic syndrome.
These scores have the advantage of using
continuous values of clinical variables (except the
HTG-Waist index) instead of cut offs to asses risk
for CVD, T2DM or in general cardiometabolic
diseases and they return a continuous value. They
are based on clinical parameters generally acquired
during medical routine control visits (e.g., weight,
height, BMI, waist circumference, blood pressure,
plasma concentrations of triglyceride, cholesterol
and glucose).
These indexes are widely used in research
settings individually but, to the best of our
knowledge, there isn’t an independent comparison of
their ability to predict cardiometabolic risk or
incidence of type 2 diabetes or cardiovascular of
cerebrovascular events. For this reason we decided
to evaluate all of them in the SEMEOTICONS
project. It is clear looking at figure 2 that there is
quite an overlap in the calculation of the indexes and
that they all use TG and waist circumference. If the
addition of BMI and/or GGT and/or HDL is adding
power to the ability of prediction of risk it is not
In the following paragraphs we report the way to
calculate the indexes and their ability in determining
cardiometabolic risk. It is important to remark that
all indexes have been developed in the general or
healthy population and their use in patients with type
2 diabetes or other diseases has to be confirmed. In
the SEMEOTICONS project we will also evaluate
subjects without known disease either cardiovascular
or metabolic.
3.3.1 VAI (Visceral Adiposity Index)
The VAI (visceral adiposity index) was developed in
a population of over 300 healthy non obese subjects
and then validated in a risk population (Amato et al.,
2010). It is calculated using different formulas for
men and women and is based on waist
circumference (WC in cm), body mass index (BMI),
triglyceride concentration (TG in mg/dl) and HDL
cholesterol concentration (in mg/dl).
male: (WC/39.68 + (1.88 x BMI))x TG/1.03x
female: (WC/36.58 + (1.89 x BMI))x TG/0.81
x1.52/ HDL
Normal values are VAI< 1 in healthy nonobese
subjects with normal adipose distribution and
normal TG and HDL cholesterol levels.
The VAI has been shown to be related to
increased insulin resistance, cardiovascular and
cerebrovascular events, liver fibrosis and steatosis in
patients with NAFLD and hepatitis C (Amato et al.,
2010); (Petta et al., 2010); (Petta et al., 2012).
3.3.2 HTG-Waist (Hypertriglyceridemic
Waist Phenotype)
It is calculated using different values for men and
women with a cut off for waist circumference (WC
in cm), and triglyceride concentration (TG in mg/dl)
(i.e., it is not a continuos score).
3 groups with different risk have been identified:
Group 1 (low waist circumference and low
triglycerides): waist 90 cm in men or85 cm in
women and triglyceride < 177 mg/dl
Group 2 (high waist circumference and low
triglycerides): waist circumference >90 cm in men
or >85 cm in women and triglycerides <177 mg/dl
Group 3 (high waist circumference and high
triglycerides): waist circumference >90 cm in men
or >85 cm in women and triglycerides 177 mg/dl
This index has been found associated not only with a
deteriorated cardiometabolic risk profile but also
with an increased risk for coronary artery disease
(Arsenault, Lemieux et al. 2010).
3.3.3 LAP (Lipid Accumulation Product)
This index has been developed in the general
population (i.e., the third National Health and
Nutrition Examination Survey) (Kahn, 2005) and
identifies cardiometabolic disorders and depends on
the measurement of WC and fasting triglycerides
(expressed in mmol/l). The formulas are different for
men and women:
men = (WC [cm] - 65) × (triglycerides
women = (WC [cm] - 58) × (triglycerides
The diagnosis of enlarged waist elevated TG
syndrome can be evaluated by considering the cut
off utilized for the definition of MS (see table 2) that
for Europe will become
Europeans: Men=LAP>50, Women = LAP>38
Americans: Men=LAP>63, Women = LAP>51
Considering the different cut offs for waist
The index has been validated in the Dionysos
population (the same used to develop the Fatty liver
index FLI) as a marker of steatosis (Bedogni et al.,
2010) and has been shown to be associated with
increased cardiometabolic risk and metabolic
syndrome (Taverna et al., 2011) and predicted
mortality in a population of nondiabetic patients at
high risk for cardiovascular diseases (Ioachimescu et
al., 2010).
3.3.4 FLI (Fatty Liver Index)
This index has been developed in the general
population (Bedogni et al., 2006). Compared to the
previous indexes here reviewed the FLI does not
discriminate by gender and it is calculated using
waist circumference (WC in cm), body mass index
(BMI), triglyceride concentration (TG in mg/dl) and
GGT concentration (in mg/dl) using the formula:
(0.953xlnTG+0.139xBMI+0.718xlnGGT+0.053xWC -15.745)
The score is varying between 0 and 100. Three
groups with different risk have been identified:
Group 1 =FLI 20 probability not to have FL >90%
Group 2 = FLI: 21-59, intermediate group
Group 3 = FLI 60 probability to have FL > 78%.
Although GGT is used in the calculation of FLI, the
index was not dependent on alcohol intake
(Gastaldelli et al., 2009). High FLI values have been
associated also to early carotid plaques (Kozakova et
al., 2012) and increased mortality (Calori et al.,
The central idea SEMEOTICONS project is to
exploit the face as a major indicator of individual’s
wellbeing for the prevention of cardio-metabolic risk
and cardiovascular diseases. In accordance to a
semeiotics viewpoint, face signs will be mapped to
measures and computational descriptors,
automatically assessed. Detection and integration of
signs derived from the semeiotics of the face can be
used to build sensitive equipment to self-monitor the
physical state, evaluate the risk of disease and
elaborate suggestions useful for optimizing life style
through personalized changes. SEMEOTICONS’s
main technological objective is to develop a
multisensory system hosted into a hardware platform
having the exterior aspect of a conventional mirror
(Wize Mirror). The latter will be equipped with
cameras and depth sensors to analyse face
morphology. That will provide, among other things,
descriptors of facial physiognomy with respect to
obesity traits. In addition, the Wize Mirror will
include multispectral cameras working with a
dedicated lighting system for image acquisition, UV
light to stimulate fluorescence mechanisms, and
thermal lamps for heat testing of endothelial
function. Beside morphological description of the
face, the implemented system will allow obtaining
data on the cardio-respiratory system (heart rate,
blood-oxygen saturation, endothelial function,
respiratory rate), on the presence of products of
glucose and lipid metabolism in the skin, and the
evaluation of colour of face and eyes that could be
related to alteration in liver enzymes (Hentges and
Huerter, 2001); (Pejic and Lee, 2006).
In the Wize Mirror, integration of multisensory
data will exploit an innovative Virtual Individual
Model (VIM) whose development is a main
methodological objective in SEMEOTICONS. A set
of objective signs, closely related to cardiometabolic
risk profile assessed from unobtrusive examination
of the face will drive the temporal evolution of VIM
defining a subject’s well-being status. To track the
evolution VIM status, we will implement a so-called
Well Being Index (WBI), which is conceived as a
multidimensional score mapping the VIM status to
different risk’s components (i.e. cardiovascular risk,
metabolic risk, lifestyle-habits risks).
To reach methodological and technological
objectives, clinical scores of cardiometabolic risk
have a twofold role: they provide a path to develop
the new indices for cardiometabolic risk and offer a
well-established basis to validate the system.
It is worth noting that several signs observed by
the Wize Mirror are related to the parameters used in
the risk scores shown in Figure 2.
During the validation part of the project we plan
to evaluate the association of metabolic parameters
with the measured clinical parameters in order to
evaluate the new cardiometabolic risk scores (i.e.
WBI components) made available by the Wize
Mirror platform.
In recent years, self-monitoring and self-training
approaches to personalized strategies for the
cardiometabolic risk prevention have experienced
growing interest from both the scientific community
and health care systems.
In this context, medical semeiotics offers a sound
methodological frame to build new computational
tools also exploiting innovative multi-sensing
devices. The rich variety of signs detectable in an
individual’s face is particularly attractive to
implement effective methods for self-assessment of
individuals’ health status. The integration of
computational descriptors of well-established face
signs (e.g. expressive traits, morphometric and
colorimetric features) with new measurements of
physiological quantities (e.g. skin cholesterol, AGE
concentration, heart and respiratory rates, analysis of
exhaled gases) is an important step towards digital
semeiotics. In view of that, the existing charts of
cardio metabolic risk offer significant clues and
provide meaningful indications to researchers and
system developers. At the same time, they remain
essential tools to validate self-monitoring activity.
This work was partly supported by the EU FP7
Project SEMEOTICONS - SEMEiotic Oriented
Technology for Individual’s CardiOmetabolic risk
self-assessmeNt and Self-monitoring (Grant
agreement no: 611516).
Alberti, K. G., R. H. Eckel, et al., 2009. "Harmonizing the
metabolic syndrome: a joint interim statement of the
International Diabetes Federation Task Force on
Epidemiology and Prevention; National Heart, Lung,
and Blood Institute; American Heart Association;
World Heart Federation; International Atherosclerosis
Society; and International Association for the Study of
Obesity." Circulation 120(16): 1640-1645.
Alberti, K. G., P. Zimmet, et al., 2006. "Metabolic
syndrome--a new world-wide definition. A Consensus
Statement from the International Diabetes Federation."
Diabet Med 23(5): 469-480.
Amato, M. C., C. Giordano, et al., 2010. "Visceral
Adiposity Index: a reliable indicator of visceral fat
function associated with cardiometabolic risk."
Diabetes Care 33(4): 920-922.
Andreozzi, F., A. Gastaldelli, et al., 2013. "Increased
carotid intima-media thickness in the physiologic
range is associated with impaired postprandial glucose
metabolism, insulin resistance and beta cell
dysfunction." Atherosclerosis 229(2): 277-281.
Anstee, Q. M., G. Targher, et al., 2013. "Progression of
NAFLD to diabetes mellitus, cardiovascular disease or
cirrhosis." Nat Rev Gastroenterol Hepatol 10(6): 330-
Arsenault, B. J., I. Lemieux, et al., 2010. "The
hypertriglyceridemic-waist phenotype and the risk of
coronary artery disease: results from the EPIC-Norfolk
prospective population study." CMAJ 182(13): 1427-
Balkau, B., C. Lange, et al., 2010. "Nine-year incident
diabetes is predicted by fatty liver indices: the French
D.E.S.I.R. study." BMC Gastroenterol 10: 56.
Bedogni, G., S. Bellentani, et al., 2006. "The Fatty Liver
Index: a simple and accurate predictor of hepatic
steatosis in the general population." BMC
Gastroenterol 6: 33.
Bedogni, G., H. S. Kahn, et al., 2010. "A simple index of
lipid overaccumulation is a good marker of liver
steatosis." BMC Gastroenterol 10: 98.
Blackburn, P., I. Lemieux, et al., 2009. "The
hypertriglyceridemic waist phenotype versus the
National Cholesterol Education Program-Adult
Treatment Panel III and International Diabetes
Federation clinical criteria to identify high-risk men
with an altered cardiometabolic risk profile."
Metabolism 58(8): 1123-1130.
Calori, G., G. Lattuada, et al., 2011. "Fatty liver index and
mortality: the Cremona study in the 15th year of
follow-up." Hepatology 54(1): 145-152.
Carey, V. J., E. E. Walters, et al., 1997. "Body fat
distribution and risk of non-insulin-dependent diabetes
mellitus in women. The Nurses' Health Study." Am J
Epidemiol 145(7): 614-619.
de Graaf, F. R., J. D. Schuijf, et al., 2010. "Usefulness of
hypertriglyceridemic waist phenotype in type 2
diabetes mellitus to predict the presence of coronary
artery disease as assessed by computed tomographic
coronary angiography." Am J Cardiol 106(12): 1747-
Despres, J. P., S. Moorjani, et al., 1990. "Regional
distribution of body fat, plasma lipoproteins, and
cardiovascular disease." Arteriosclerosis 10(4): 497-
Ferrannini, E., 2007. "Metabolic syndrome: a solution in
search of a problem." J Clin Endocrinol Metab 92(2):
Gastaldelli, A., M. Kozakova, et al., 2009. "Fatty liver is
associated with insulin resistance, risk of coronary
heart disease, and early atherosclerosis in a large
European population." Hepatology 49(5): 1537-1544.
Grundy, S. M., G. J. Balady, et al., 1998. "Primary
prevention of coronary heart disease: guidance from
Framingham: a statement for healthcare professionals
from the AHA Task Force on Risk Reduction.
American Heart Association." Circulation 97(18):
Hentges, P. P. and C. J. Huerter, 2001. "Eruptive
xanthomas and chest pain in the absence of coronary
artery disease." Cutis 67(4): 299-302.
Ioachimescu, A. G., D. M. Brennan, et al., 2010. "The
lipid accumulation product and all-cause mortality in
patients at high cardiovascular risk: a PreCIS database
study." Obesity (Silver Spring) 18(9): 1836-1844.
Kahn, H. S., 2005. "The "lipid accumulation product"
performs better than the body mass index for
recognizing cardiovascular risk: a population-based
comparison." BMC Cardiovasc Disord 5: 26.
Kissebah, A. H., D. S. Freedman, et al., 1989. "Health
risks of obesity." Med Clin North Am 73(1): 111-138.
Kozakova, M., C. Palombo, et al., 2012. "Fatty liver
index, gamma-glutamyltransferase, and early carotid
plaques." Hepatology 55(5): 1406-1415.
Lemieux, I., P. Poirier, et al., 2007. "Hypertriglyceridemic
waist: a useful screening phenotype in preventive
cardiology?" Can J Cardiol 23 Suppl B: 23B-31B.
Morelli, M., M. Gaggini, et al., 2013. "Ectopic fat: the true
culprit linking obesity and cardiovascular disease?"
Thromb Haemost 110(4): 651-660.
NCEP-ATPIII, a., 2001. "Executive Summary of The
Third Report of The National Cholesterol Education
Program (NCEP) Expert Panel on Detection,
Evaluation, And Treatment of High Blood Cholesterol
In Adults (Adult Treatment Panel III)." JAMA
285(19): 2486-2497.
Pejic, R. N. and D. T. Lee, 2006. "Hypertriglyceridemia."
J Am Board Fam Med 19(3): 310-316.
Perk, J., G. De Backer, et al., 2012. "European Guidelines
on cardiovascular disease prevention in clinical
practice (version 2012). The Fifth Joint Task Force of
the European Society of Cardiology and Other
Societies on Cardiovascular Disease Prevention in
Clinical Practice (constituted by representatives of
nine societies and by invited experts)." Eur Heart J
33(13): 1635-1701.
Petta, S., M. Amato et al., 2010. "Visceral adiposity index
is associated with histological findings and high viral
load in patients with chronic hepatitis C due to
genotype 1." Hepatology 52(5): 1543-1552.
Petta, S., M. C. Amato, et al., 2012. "Visceral adiposity
index is associated with significant fibrosis in patients
with non-alcoholic fatty liver disease." Aliment
Pharmacol Ther 35(2): 238-247.
Pouliot, M. C., J. P. Despres, et al., 1992. "Visceral
obesity in men. Associations with glucose tolerance,
plasma insulin, and lipoprotein levels." Diabetes
41(7): 826-834.
Rexrode, K. M., J. E. Buring, et al., 2001. "Abdominal and
total adiposity and risk of coronary heart disease in
men." Int J Obes Relat Metab Disord 25(7): 1047-
Rexrode, K. M., V. J. Carey, et al., 1998. "Abdominal
adiposity and coronary heart disease in women."
JAMA 280(21): 1843-1848.
Taverna, M. J., M. T. Martinez-Larrad, et al., 2011. "Lipid
accumulation product: a powerful marker of metabolic
syndrome in healthy population." Eur J Endocrinol
164(4): 559-567.
Turkoglu, C., B. S. Duman, et al., 2003. "Effect of
abdominal obesity on insulin resistance and the
components of the metabolic syndrome: evidence
supporting obesity as the central feature." Obes Surg
13(5): 699-705.