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.

1 INTRODUCTION

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.

599

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.)

2 CARDIOVASCULAR RISK

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.

3 CARDIOMETABOLIC 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

Non-modifiable

factors

Overweight/Obesity

High LDL cholesterol

Low HDL cholesterol

High triglycerides

hyper-coagulation

High blood glucose

Hypertension

Inflammation

Smoking

Unhealthy

eating

Health

disparities

Physical

inactivity

Psychosocial

issues

Age

Race/Ethnicity

Gender

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

Risk

Cardiometabolic risk is associated not only to

HEALTHINF2014-InternationalConferenceonHealthInformatics

600

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

diseases.

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

HEART



Diastolic

Fct

↑Epicardial

fat

VISCERAL

FAT

↑Insulin

Resistance

↑Portal

FFA

→

NAFLD

↑Cytokine

release

Adiponec n

ATHEROSCLEROSIS

Endothelial

dysfunc on

Plaque

forma on

Caro d

Elas city

LIVER

↑Insulin

Resistance

↑

VLDL

produc on

↑

CRP,

Fibrinogen

EvaluationofPublishedClinicalScoresforthePredictionofCardiometabolicRiskintheSEMEOTICONSProject

601

one factor in aging and in some age-related chronic

diseases.

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

known.

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

1.31/HDL

(1)

female: (WC/36.58 + (1.89 x BMI))x TG/0.81

x1.52/ HDL

(2)

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 or≤85 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

Waist

BM I

HDL

GGT

HTG-Waist

LAP

FLI

HEALTHINF2014-InternationalConferenceonHealthInformatics

602

(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

[mmol/L])

(4)

women = (WC [cm] - 58) × (triglycerides

[mmol/L])

(5)

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

circumference.

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)

x100

(1+e

(0.953xlnTG+0.139xBMI+0.718xlnGGT+0.053xWC -15.745)

)

(3)

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.,

2011).

4 SEMEOTICONS PROJECT

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,

EvaluationofPublishedClinicalScoresforthePredictionofCardiometabolicRiskintheSEMEOTICONSProject

603

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.

5 CONCLUSIONS

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.

ACKNOWLEDGEMENTS

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).

REFERENCES

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-

344.

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-

1432.

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

HEALTHINF2014-InternationalConferenceonHealthInformatics

604

coronary angiography." Am J Cardiol 106(12): 1747-

1753.

Despres, J. P., S. Moorjani, et al., 1990. "Regional

distribution of body fat, plasma lipoproteins, and

cardiovascular disease." Arteriosclerosis 10(4): 497-

511.

Ferrannini, E., 2007. "Metabolic syndrome: a solution in

search of a problem." J Clin Endocrinol Metab 92(2):

396-398.

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):

1876-1887.

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-

1056.

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.

EvaluationofPublishedClinicalScoresforthePredictionofCardiometabolicRiskintheSEMEOTICONSProject

605