Buscar en
Clínica e Investigación en Arteriosclerosis (English Edition)
Toda la web
Inicio Clínica e Investigación en Arteriosclerosis (English Edition) Population and methodology of the SIMETAP study: Prevalence of cardiovascular ri...
Journal Information
Vol. 30. Issue 5.
Pages 197-208 (September - October 2018)
Visits
764
Vol. 30. Issue 5.
Pages 197-208 (September - October 2018)
Original article
Full text access
Population and methodology of the SIMETAP study: Prevalence of cardiovascular risk factors, cardiovascular diseases, and related metabolic diseases
Población y metodología del estudio SIMETAP: Prevalencia de factores de riesgo cardiovascular, enfermedades cardiovasculares y enfermedades metabólicas relacionadas
Visits
764
Antonio Ruiz-Garcíaa,
Corresponding author
antoniodoctor@gmail.com

Corresponding author.
, Ezequiel Arranz-Martínezb, Juan Carlos García-Álvarezc, Luis Enrique Morales-Cobosd, María Eugenia García-Fernándeze, Nuria de la Peña-Antónf, María Carmen Martín Calleg, M. Cruz Díez Pérezh, Alejandra Montero Costai, David Palacios Martínezj, M. Paloma García Villasurk, M.a Dolores García Granadol, M. Soledad Lorenzo Bordad, Juan A. Cique Herráinzm, Nuria Marañón Henrichn, Nieves Zarzuelo Martínn, M. del Camino Baltuille Allern, Teresa Fátima Casaseca Calvol, Pilar Arribas Álvaroo, Ana Isabel Macho del Barriop..., Juan Carlos Moreno Fernándezd, Carlos Ribot Cataláq, Mercedes Capitán Caldasr, Cristina Ciria de Pablos, Carmelina Sanz Velascoj, Paula Simonaggio Stancampianot, Concepción Vargas-Machuca Cabañerou, M. Teresa Sarriá Sánchezv, Esther Gómez Díazn, Blanca Sanz Pozod, Enrique Rodríguez de Mingod, M. Pilar Cabello Igualw, M. Rosario Rico Péreza, Alfonso Migueláñez Valerot, Amelia Alcaraz Bethencourtx, Concepción Zafra Urangox, Paula Chao Escuere, Sonia Redondo de Pedroc, Nuria Escamilla Guijarrod, Beatriz López Uriartey, Montserrat Rivera Teijidox, on behalf of Grupo de investigación del Estudio SIMETAP Ver más
a Unidad de Lípidos y Prevención Cardiovascular, Centro de Salud Universitario Pinto, Pinto, Madrid, Spain
b Centro de Salud San Blas, Parla, Madrid, Spain
c Centro de Salud Universitario Dr. Mendiguchía Carriche, Leganés, Madrid, Spain
d Centro de Salud Universitario Las Américas, Parla, Madrid, Spain
e Centro de Salud Griñón, Griñón, Madrid, Spain
f Centro de Salud El Restón, Valdemoro, Madrid, Spain
g Centro de Salud Ntra. Sra. de Fátima, Madrid, Spain
h Centro de Salud Los Cármenes, Madrid, Spain
i Centro de Salud Fuencarral, Madrid, Spain
j Centro de Salud Universitario Sector III, Getafe, Madrid, Spain
k Centro de Salud C.S. María Montessori, Leganés, Madrid, Spain
l Centro de Salud Casa de Campo, Madrid, Spain
m Centro de Salud Torito, Madrid, Spain
n Centro de Salud Las Olivas, Aranjuez, Madrid, Spain
o Centro de Salud Campamento, Madrid, Spain
p Centro de Salud Vicente Soldevilla, Madrid, Spain
q Centro de Salud Jaime Vera, Leganés, Madrid, Spain
r Centro de Salud Las Ciudades, Getafe, Madrid, Spain
s Centro de Salud Hoyo de Manzanares, Torrelodones, Madrid, Spain
t Centro de Salud San Martín de la Vega, San Martín de la VegaMadrid, Madrid, Spain
u Centro de Salud Guayaba, Madrid, Spain
v Centro de Salud Baviera, Madrid, Spain
w Centro de Salud Parque Europa, Pinto, Madrid, Spain
x Centro de Salud Isabel II, Parla, Madrid, Spain
y Centro de Salud Humanes, Madrid, Spain
Ver más
This item has received
Article information
Abstract
Full Text
Bibliography
Download PDF
Statistics
Figures (1)
Tables (4)
Table 1. Definitions of metabolic syndrome for the European population.
Table 2. Age distribution of the study and reference populations.
Table 3. Quantitative characteristicsa of the study population.
Table 4. Qualitative characteristicsa of the study population.
Show moreShow less
Abstract

The prevention of cardiovascular disease is based on the detection and control of cardiovascular risk factors (CVRF). In Spain there are important geographical differences both in the prevalence and in the level of control of the CVRF. In the last decade there has been an improvement in the control of hypertension and dyslipidaemia, but a worsening of cardio-metabolic risk factors related to obesity and diabetes.

The SIMETAP study is a cross-sectional descriptive, observational study being conducted in 64 Primary Care Centres located at the Community of Madrid. The main objective is to determine the prevalence rates of CVRF, cardiovascular diseases, and metabolic diseases related to cardiovascular risk. A report is presented on the baseline characteristics of the population, the study methodology, and the definitions of the parameters and diseases under study. A total of 6631 study subjects were selected using a population-based random sample. The anthropometric variables, lifestyles, blood pressure, biochemical parameters, and pharmacological treatments were determined.

The highest crude prevalences were detected in smoking, physical inactivity, obesity, prediabetes, diabetes, hypertension, dyslipidaemias, and metabolic syndrome. A detailed analysis needs to be performed on the prevalence rates, stratified by age groups, and prevalence rates adjusted for age and sex to assess the true epidemiological dimension of these CVRF and diseases.

Keywords:
Dyslipidaema
Cardiovascular disease
Risk factors
Prevalence
Metabolic syndrome
Abbreviations:
ACR
ADA
AHA
AIP
ALT
AST
ATPIII
BMI
CDA
CHD
CI
CKD
CKD-EPI
COPD
CVA
CVD
CVR
CVRF
DBP
DM
eGFR
FHpCVD
FPG
GGT
HbA1c
HDL-C
LDL-C
HTG
HTN
ICD-9
ICPC-2
IDF
INE
IQR
IR
KDIGO
MetSyn
NCEP
NHLBI
OR
PAD
SBP
SCORE
SD
SERMAS
TC
TG
UAE
VLDL-C
WHtR
Resumen

La prevención de la enfermedad cardiovascular se fundamenta en la detección y control de los factores de riesgo cardiovascular (FRCV). En España existen importantes diferencias territoriales tanto en la prevalencia como en el grado de control de los FRCV. En la última década ha habido una mejora del control de la hipertensión y la dislipidemia, pero un empeoramiento de los factores de riesgo cardiometabólicos relacionados con la obesidad y la diabetes.

El estudio SIMETAP es un estudio observacional descriptivo transversal realizado en 64 centros de atención primaria de la Comunidad de Madrid. El objetivo principal es determinar las tasas de prevalencia de FRCV, de las enfermedades cardiovasculares y de las enfermedades metabólicas relacionadas con el riesgo cardiovascular. El presente artículo informa sobre las características basales de la población, la metodología del estudio, y las definiciones de los parámetros y enfermedades en estudio. Se seleccionaron 6.631 sujetos de estudio mediante una muestra aleatoria base poblacional. Se determinaron variables antropométricas, estilos de vida, presión arterial, parámetros bioquímicos, y tratamientos farmacológicos.

Las prevalencias crudas más elevadas se detectaron en tabaquismo, inactividad física, obesidad, prediabetes, diabetes, hipertensión, dislipidemias y síndrome metabólico. Para valorar la verdadera dimensión epidemiológica de estas enfermedades y FRCV, es necesario realizar un análisis pormenorizado de tasas de prevalencia estratificadas por grupos etarios y de las tasas de prevalencia ajustadas por edad y sexo.

Palabras clave:
Dislipidemia
Enfermedad cardiovascular
Factores de riesgo
Prevalencia
Síndrome metabólico
Full Text
Introduction

Atherosclerotic cardiovascular disease (CVD) can be considered the result of a pathogenic continuum involving an unhealthy lifestyle and multiple cardiovascular risk factors (CVRF). The clinical manifestations of CVD are the leading cause of death in the western world. It is therefore a priority that we implement high-impact healthcare interventions to reduce cardiovascular risk (CVR).

The Spanish adaptation1 of the European Guidelines on cardiovascular disease prevention in clinical practice2 recommend prior screening to assess CVR, promoting healthy lifestyles and intervening in CVRF and CVD-related syndromes and disorders with an intensity proportional to the pre-established CVR.

These considerations can also be applied in clinical conditions related to insulin resistance (IR) such as prediabetes, diabetes mellitus (DM), chronic kidney disease (CKD), familial combined hyperlipidaemia, metabolic syndrome (MetSyn) and obesity. The European guidelines2,3 place great emphasis on the fact that patients with DM are at high or very high CVR, and the consensus of American endocrinology associations4 considers CVR to be extreme if CVD is associated with DM or CKD.

Continued action of the most important CVRF, such as smoking, hypertension and hypercholesterolaemia, causes deterioration of the vascular endothelium, favouring the formation, oxidation and vulnerability of atherosclerotic plaque, until clinical manifestation of CVD in the form of coronary heart disease (CHD), stroke or cerebrovascular accidents (CVA), or peripheral arterial disease (PAD).

Patients with IR-related conditions have a particularly high CVR because they have a characteristic lipid profile which accelerates the atherosclerosis process known as atherogenic dyslipidaemia. This lipid phenotype is characterised by low levels of high-density lipoprotein cholesterol (HDL-C), hypertriglyceridaemia (HTG) and non-high levels of low-density lipoprotein cholesterol (LDL-C), although with a high concentration of LDL particles with apolipoprotein B, which are typically small and dense.5 Given its importance in aetiopathogenic terms, we need to assess the different phenotypic expressions of lipid profiles and IR in patients suffering from clinical or metabolic manifestations of CVD or DM.

The concept of MetSyn6 emerged almost 40 years ago to define a non-coincidental grouping of factors associated with IR observed in clinical practice: central obesity, dyslipidaemia, abnormalities in glucose metabolism and hypertension. MetSyn is of particular importance because it increases the risk of DM,7,8 CVD,8 and CKD.9 MetSyn increases all-cause mortality rates and doubles the risk of suffering CVD.8 Even if the most commonly used definitions of MetSyn10–13 excluded DM from among the diagnostic criteria, the increased risk of suffering from CVD would be maintained.

Probably the best thing we can do is accept that MetSyn covers a group of individuals in whom some of the criteria may be absent, but who have a high CVR, and that this would not be detected if we did not consider the overall view. Hence the dual importance of MetSyn. On the one hand, it serves to alert the physician to look for other CVRF in patients with a particular cardiometabolic risk factor. While on the other, it can identify a large number of subjects with high CVR who need intervention with both medical and public health strategies. The WHO experts14 consider that the key action on MetSyn needs to focus on health policies based in primary healthcare.

However, studies on prevalence have encountered several conceptual issues. One is understanding that MetSyn is a cluster of factors which are associated with CVRF, CVD and the related morbidity of IR. Another is the fact that there are a number of definitions of MetSyn that include classic CVRF and other cardiometabolic risk factors which are considered differently depending on the scientific society.10–13 Moreover, as the definitions of MetSyn do not currently appear in the episode coding of the International Classification of Primary Care (ICPC-2)15 or in the International Classification of Diseases (ICD-9),16 it is not easy to record an episode of MetSyn in the patient's medical records. Nor do the codes in the ICPC-2 (K22) or the ICD-9 (277.7; 277.9) specifically refer to any of the existing definitions of MetSyn. This causes difficulties in terms of detecting MetSyn, assessing the patient's overall CVR, and comprehensive intervention on all cardiovascular and cardiometabolic risk factors related to MetSyn.

CVD prevention requires prior assessment of the epidemiology of all the factors that can influence the problem to be evaluated. In turn, to make an adequate health diagnosis, we need to know the prevalence in all age groups of the study population. Following this strategy of action, the aim of the SIMETAP study was to determine the prevalence rates, adjusted for age and gender, of CVRF, CKD, CVD and CVD-related metabolic diseases in the adult population of the Madrid Region.

Material and methodsStudy design and population

The SIMETAP study was an observational, descriptive, cross-sectional study conducted by 121 general practitioners from 64 primary care centres run by the Public Health Service for the Madrid Region (SERMAS). The study was carried out according to the principles established by the 18th World Medical Assembly (Helsinki, 1964) and its amendments, and by the International Conference on Harmonization and guidelines for good clinical practice. The Research Commission of the Madrid Region Primary Care Management Department for Planning and Quality issued a favourable opinion for the study to be conducted.

The population assigned to SERMAS was 5,144,860 adults (99% of the population census), whose healthcare was provided in 260 primary care centres. A representative sample (10,579 adults) was obtained based on the population assigned to general practitioners aged 18 or over, with no upper age limit (194,073 adults). The sample size was calculated with this finite population for a confidence level of 95% (α error), 0.024 for the confidence interval (margin of error of 1.2%), p=0.5 for the expected proportion, and considering 25% for lack of response and 14% for losses and dropouts.

To obtain the sample population, we used tables of random numbers generated by the Microsoft© Excel© 2013 program. These were applied to randomly sort the lists of patients assigned to general practitioners and to select the subjects for the study.

Institutionalised and terminally ill patients, pregnant women and patients with cognitive impairment were excluded from the study as per protocol. Through an active search of the subjects in the resulting sample, the investigators made up to five telephone calls to contact them and invite them to take part in the study.

Data collection

After obtaining the signed consent, the investigators conducted a clinical interview with the study subjects to gather information on age, gender, healthy lifestyles, anthropometric measurements, systolic and diastolic blood pressure, CVRF, history of metabolic disorders, chronic obstructive pulmonary disease, cardiovascular disease (CHD, stroke, PAD, heart failure, atrial fibrillation), erectile dysfunction, CKD, albuminuria, drug treatments and most recent biochemical parameters determined from blood and urine tests over the previous year. The investigators collected the study data from January to December 2015. Double entry of data guaranteed registration in the database.

Definition of variables

For the purposes of this study, patients were considered to suffer from the syndromes and diseases under study if their respective diagnoses or their related ICPC-215 or ICD-916 codes were registered in their medical records, considering the following definitions and diagnostic criteria:

Smoking: Consumption of any number of cigarettes or tobacco in the last month.

Former smoker: Patient who has not smoked for over a year.

Alcoholism: Regular consumption of alcohol >21 units (210g) per week in males and >14 units (140g) per week in females.

Lack of physical exercise: Less than 150min physical exercise in one week.

High blood pressure (ICPC-2: K85): Systolic blood pressure ≥130mmHg and/or diastolic blood pressure ≥85mmHg without a diagnosis of hypertension.17

Hypertension (ICPC-2: K86, K87. ICD-9: 401–404): Systolic blood pressure ≥140mmHg and/or diastolic blood pressure ≥90mmHg,17 or if the patient was taking medication to reduce blood pressure.

Marked hypertension: Blood pressure ≥180/110mmHg.

Prediabetes (ADA: American Diabetes Association)18 (ICPC-2: A91. ICD-9: 271.3): Without criteria for DM, fasting plasma glucose (FPG) from 100mg/dl (5.6mmol/l) to 125mg/dl (6.9mmol/l) both inclusive, or glycosylated haemoglobin A1c (HbA1c) from 5.7% (39mmol/mol) to 6.4% (47mmol/mol), both inclusive.

Prediabetes (CDA: Canadian Diabetes Association)19 (ICPC-2: A91. ICD-9: 271.3): Without criteria for DM, FPG from 110mg/dl (6.1mmol/l) to 125mg/dl (6.9mmol/l) both inclusive, or HbA1c from 6% (42mmol/mol) to 6.4% (47mmol/mol), both inclusive.

DM Type 1 (ICPC-2: T89. ICD-9: 250.01). ADA18 or CDA19 criteria.

DM Type 2 (ICPC-2: T90. ICD-9: 249; 250.02): ADA18 or CDA19 criteria. FPG ≥126mg/dl (7mmol/l) or HbA1c ≥6.5% (48mmol/mol).

Hypercholesterolaemia (ICPC-2: T93. ICD-9: 272.0; 272.2; 272.4): Serum total cholesterol (TC) ≥200mg/dl (5.17mmol/l), or if the patient was taking medication to reduce their cholesterol.

Marked hypercholesterolaemia: Serum TC ≥300mg/dl (7.76mmol/l), or LDL-C ≥190mg/dl (4.91mmol/l), or if the diagnosis of familial hypercholesterolaemia or familial combined hyperlipidaemia was registered in the patient's medical records.

HTG10 (ICD-9: 272.1; 272.3): Serum triglycerides (TG) ≥150mg/dl (1.69mmol/l), or if the patient was taking medication to reduce their TG.

High HDL-C: Serum HDL-C ≥60mg/dl (1.55mmol/l).

Low HDL-C: Serum HDL-C<40mg/dl (1.03mmol/l) in males or<45mg/dl (1.16mmol/l) in females.5 HDL-C<50mg/dl (1.29mmol/l) was also considered in females.10–13 Non-HDL cholesterol: Difference between serum TC and HDL-C.

LDL-C: Determined with the Friedewald formula (LDL-C=TC - [HDL-C] - TG/5) if TG were<400mg/dl (<4.52mmol/l).20

Very low-density lipoprotein cholesterol: TC(HDL-C)(LDL-C).

Atherogenic dyslipidaemia5: HTG with low HDL-C.

Castelli index-I21: TC/HDL-C.

Castelli index-II21: LDL-C/HDL-C

Atherogenic non-HDL cholesterol/HDL-C coefficient.

TG/HDL-C ratio.

Atherogenic index of plasma22: log (TG/HDL-C).

TG and glucose index23: Ln [TG (mg/dl)×FPG (mg/dl)/2]

Overweight24 (ICPC-2: T83. ICD-9: 278.02): body mass index (BMI) from 25.0 to 29.9kg/m2.

Obesity24 (ICPC-2: T82; ICD-9: 278.00; 278.01): BMI (weight/height2) ≥30kg/m2.

Abdominal or central obesity: Waist circumference ≥102cm in males or ≥88cm in females,13 and ≥94cm in males and ≥80cm in females,12 measured with the subject in a standing position using a flexible tape measure tightened without compressing the skin, at the end of a normal expiration, locating the upper edge of the iliac crests and, above that point, surrounding the waist parallel to the floor.24

Waist-to-height ratio25 (WHtR): Waist circumference/height (cm/cm).

Hypothyroidism (ICPC-2: T86; ICD-9: 244.9) on replacement therapy.

Hepatic steatosis (ICD-9: 571.8) registered in medical records.

CHD (ICPC-2: K74, K75, K76. ICD-9: 410–414): Includes ischaemic heart disease, prior acute myocardial infarction, acute coronary syndromes, coronary artery bypass and other arterial revascularisation procedures.

CVA (ICPC-2: K89, K90; K91. ICD-9: 430; 431–436): It includes cerebrovascular accident, cerebral ischaemia or intracranial haemorrhages and transient ischaemic attack.

PAD (ICPC-2: K92. ICD-9: 440; 443.9; 444): Also includes intermittent claudication and an ankle/brachial index ≤0.9.

CVD: Includes CHD, CVA and PAD.

Heart failure (ICPC-2: K77. ICD-9: 428).

Atrial fibrillation (ICPC-2: K78. ICD-9: 427.3).

MetSyn (ICPC-2: K22. ICD-9: 277.7; 277.9): Three concepts were considered: The National Cholesterol Education Program-Adult Treatment Panel III10 definition (NCEP-ATPIII), maintained by the National Heart, Lung, and Blood Institute/American Heart Association11 (NHLBI/AHA); the definition of the International Diabetes Federation (IDF) for the European population12; and the harmonised consensus13 definition of MetSyn for the European population established by the following scientific societies: IDF, Task Force on Epidemiology and Prevention; NHLBI; AHA; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity (Table 1).

Table 1.

Definitions of metabolic syndrome for the European population.

  NCEP-ATPIII 2001
NHLBI/AHA 2004 
IDF 2005  2009 Consensus 
Factors required  At least 3 factors  At least 3 factors including central obesity  At least 3 factors 
Central obesity (waist circumference)  Males: >102cm
Females: >88cm 
Males: ≥94cm
Females: ≥80cm 
Males: ≥102cm
Females: ≥88cm 
Fasting blood glucose  ≥110mg/dl
(≥6.1mmol/l) 
≥100mg/dl
(≥5.6mmol/l)
or previous diagnosis of type 2 diabetes 
≥100mg/dl
(≥5.6mmol/l)
or lipid-lowering treatment 
Triglycerides  ≥150mg/dl
(≥1.7mmol/l) 
≥150mg/dl (≥1.7mmol/l)
or specific treatment
HDL Cholesterol  Males: <40mg/dl
(<1.03mmol/l)
Females: <50mg/dl
(<1.29mmol/l) 
Males: <40mg/dl (<1.03mmol/l)
Females: <50mg/dl (<1.29mmol/l)
or specific treatment
Blood pressure  Systolic ≥130mmHg or diastolic ≥ 85mmHg  Systolic ≥130mmHg
or diastolic ≥85mmHg
or anti-hypertensive treatment

IDF: International Diabetes Federation; NCEP-ATPIII: National Cholesterol Education Program-Adult Treatment Panel III; NHLBI/AHA: National Heart, Lung, and Blood Institute/American Heart Association.

Morbid MetSyn: MetSyn with DM or CVD.

Premorbid MetSyn: MetSyn without DM or CVD.

Chronic obstructive pulmonary disease (ICPC-2: R95. ICD-9: 491.2; 492; 496): Criteria of the GOLD guidelines.26

Urinary albumin excretion and albumin/creatinine ratio (ICPC-2: U90; U98. ICD-9: 593.6; 791): Criteria of the KDIGO guidelines.27

Albuminuria: albumin/creatinine ratio ≥30mg/g.

Estimated glomerular filtration rate (eGFR): The following Chronic Kidney Disease Epidemiology collaboration (CKD-EPI)28 equations were used, expressed in ml/min/1.73 m2: Females with creatinine ≤0.7mg/dl=144×(creatinine)−0.329×(0.993)age; females with creatinine>0.7mg/dl=144×(creatinine)−1.209×(0.993)age; males with creatinine ≤0.9mg/dl=141×(creatinine)−0.411×(0.993)age; males with creatinine>0.9mg/dl=141×(creatinine)−1.209×(0.993)age.

CKD (ICPC-2: U99. ICD-9: 585): eGFR<60ml/min/1.73m2 or albumin/creatinine ratio ≥30mg/g (KDIGO).27

Erectile dysfunction: Inability to get and maintain an erection that is sufficient for satisfactory sexual intercourse.29

CVRF: Risk factor that predicts the likelihood of developing a CVD: Age (>55 in males;>65 in females), hypertension, hypercholesterolaemia, smoking.

Very high CVRF: Marked hypercholesterolaemia or marked hypertension.

CVR modifier: Factor with possible potential for CVR reclassification when the SCORE (systematic coronary risk evaluation)2 score is close to the decision threshold. Lack of physical exercise, a history of premature CVD (males<55 or females<65) in a first-degree relative, low HDL-C, obesity.

CVR: Risk of death due to cardiovascular disease at 10 years. For the population aged 40 to 65, the CVR categories were assigned using the SCORE system2 for low-risk European countries. Low CVR: Population aged<40 without two or more CVRF; SCORE=0%. Moderate CVR: Population aged<40 with two or more CVRF; DM<40; MetSyn; SCORE 1–4%. High CVR: Very high CVRF; DM without CVRF; moderate CKD (eGFR 30–59ml/min/1.73m2); SCORE 5–9%. Very high CVR: Clinical CVD or CVD documented by imaging; DM with target organ damage or with CVRF; severe CKD (eGFR<30ml/min/1.73m2); SCORE ≥10%. Extreme CVR4: CVD associated with DM or CKD. The assigning of CVR in the population aged>65 was determined with the SCORE OP criteria.30

Statistical analysis

Statistical analysis was carried out using the Statistical Package for the Social Sciences program (IBM® SPSS® Statistical release 20.0, Armonk, NY, United States). We determined the range, median and interquartile range (IQR) (25th percentile, 75th percentile) of the age variable. The mean and the standard deviation (±SD) were determined for the descriptive statistical analysis. Quantitative variables were compared using the two-tailed Levene's and Student's t tests for two variables or the one-way ANOVA for more than two variables. The variables analysed had normal distribution (skewness and kurtosis between −2 and +2). The qualitative variables were analysed by prevalence rates and percentages in each category, presented with lower and upper limits of the 95% confidence interval (CI). The inferential analysis of the qualitative data was performed with the Chi-square test, and of the quantitative data with Fisher's test. The odds ratios were determined with a 95% CI. All tests were considered statistically significant if the odds ratio estimates were greater than 1 or the 2-tail p value was less than 0.05. The prevalence rates were determined as gross rates and adjusted for age and gender. The adjustment of rates was carried out using the direct method31 using as standard populations the distributions by five-year and ten-year age brackets of the male and female populations of Madrid Region and Spain; information obtained from the database of the Instituto Nacional de Estadística32 [Spanish National Institute of Statistics] in January 2015.

Results

The population aged over 18 of the Madrid Region and Spain was 5,196,828 and 38,102,546, respectively. The distribution by five-year age segments of the study and reference populations are shown in Table 2. The distribution of the ethnic origin of the inhabitants of the Madrid Region was as follows32: Spanish (88.9%); other European (4.9%); Central and South American (3.4%); North American (0.2%); African (1.5%); Asian (1.1%); and Oceanian (0.01%).

Table 2.

Age distribution of the study and reference populations.

  Male population N (%)Female population N (%)
Age (years)  Spain  Madrid Region  Study subjects  Spain  Madrid Region  Study subjects 
18–24  1,621,285 (9)  212,006 (9)  103 (4)  1,553,370 (8)  209,934 (8)  154 (4) 
25–29  1,321,886 (7)  181,252 (7)  124 (4)  1,318,453 (7)  189,349 (7)  188 (5) 
30–34  1,642,803 (9)  233,340 (9)  147 (5)  1,627,061 (8)  241,791 (9)  217 (6) 
35–39  2,011,297 (11)  288,141 (12)  241 (8)  1,938,493 (10)  292,360 (11)  302 (8) 
40–44  1,982,454 (11)  281,920 (11)  266 (9)  1,907,214 (10)  286,175 (10)  342 (9) 
45–49  1,861,331 (10)  253,561 (10)  295 (10)  1,829,678 (9)  263,036 (10)  334 (9) 
50–54  1,698,889 (9)  221,702 (9)  307 (10)  1,710,108 (9)  238,303 (9)  349 (9) 
55–59  1,467,132 (8)  185,035 (8)  284 (10)  1,511,242 (8)  207,951 (8)  316 (9) 
60–64  1,219,621 (7)  151,984 (6)  270 (9)  1,288,216 (7)  176,697 (6)  341 (9) 
65–69  1,120,627 (6)  140,455 (6)  297 (10)  1,236,887 (6)  168,286 (6)  292 (8) 
70–74  902,510 (5)  112,073 (5)  204 (7)  1,046,518 (5)  138,030 (5)  257 (7) 
75–79  673,645 (4)  78,652 (3)  156 (5)  879,421 (4)  109,891 (4)  223 (6) 
80–84  573,317 (3)  65,954 (3)  141 (5)  852,248 (4)  104,151 (4)  223 (6) 
85–89  301,428 (2)  35,055 (1)  73 (2)  553,996 (3)  70,373 (3)  113 (3) 
90–94  105,675 (1)  12,553 (1)  22 (1)  251,486 (1)  33,786 (1)  38 (1) 
95–102  22,029 (0.1)  2696 (0.1)  4 (0.1)  72,226 (0.4)  10,336 (0.4)  8 (0.2) 

N: population sizes; (%): percentage of the total.

Of the initial sample, 7.6% refused to take part in the study or did not sign the informed consent form, 13.8% of the subjects had personal data errors or could not be contacted after an active search, and 4.7% met exclusion criteria. We also excluded 7.1% of the study subjects for whom there was no clinical data or whose medical records lacked relevant clinical information. The response rate was 74%. Losses, dropouts or study subjects who did not attend the clinical interview amounted to 4.2% (Fig. 1).

Figure 1.

Diagram showing the sampling process for the SIMETAP study.

(0.23MB).

The study population consisted of 6631 subjects, 44.25% of them male (CI: 43.05; 45.45) (Table 1). The mean age (±SD) of the study population was 55.03 (±17.54 years), the median age was 54.56, and the range (IQR) was from 18.01 to 102.80 (41.55, 67.98).

The mean age (±SD) of the male population was 55.06 (±16.90 years), the median was 54.75, and the range (IQR) from 18.01 to 102.12 (42.01; 67.35). The mean age (±SD) of the female population was 55.01 (±18.04 years), the median was 54.36, and the range (IQR) from 18.01 to 102.80 (40.94; 68.77). The difference in the mean ages (0.05 [CI: −0.80, 0.90] years) between the male and female populations was not significant (p=0.908).

The means (±SD) of the anthropometric, blood pressure and biochemical variables of the study population and the significance of the differences between the male and female populations are shown in Table 3. The means of the highest parameters were: overweight (BMI: 27.5kg/m2), waist circumference (97.9cm in men and 89.7cm in women), WHtR (0.57), and HbA1c (5.6%). All the parameters mentioned were significantly higher in the male population, except for eGFR, TC, LDL-C and HDL-C, which were significantly higher in the female population.

Table 3.

Quantitative characteristicsa of the study population.

  Study population  Male population  Female population  p value 
Weight (kg)  6631  74.2 (15.6)  81.7 (14.5)  68.2 (13.8)  <0.001 
Height (cm)  6631  164.2 (9.8)  171.1 (7.8)  158.8 (7.4)  <0.001 
BMI (kg/m26631  27.5 (5.1)  27.9 (4.5)  27.1 (5.6)  <0.001 
Waist circumference (cm)  6631  93.3 (14.1)  97.9 (12.6)  89.7 (14.1)  <0.001 
WHtR (cm/cm)  6631  0.57 (0.09)  0.57 (0.08)  0.57 (0.10)  0.002 
FPG (mg/dl)  6506  96.1 (26.1)  99.9 (28.7)  93.2 (23.4)  <0.001 
HbA1c (%)  5201  5.64 (0.90)  5.71 (0.94)  5.58 (0.86)  <0.001 
SBP (mmHg)  6631  121.9 (15.4)  124.0 (14.1)  120.2 (16.2)  <0.001 
DBP (mmHg)  6631  73.3 (9.8)  74.9 (9.4)  72.1 (9.9)  <0.001 
Creatinine (mg/dl)  6506  0.84 (0.29)  0.96 (0.30)  0.75 (0.26)  <0.001 
eGFR (ml/min/1.73m26506  90.5 (20.6)  89.8 (19.8)  91.1 (21.1)  0.014 
UAE (mg/dl)  4402  15.1 (65.4)  19.6 (81.8)  11.5 (47.7)  <0.001 
ACR (mg/g)  4402  16.0 (74.6)  19.6 (92.0)  13.0 (56.3)  <0.001 
Uric acid (mg/dl)  6088  4.96 (1.49)  5.68 (1.44)  4.39 (1.26)  <0.001 
AST (U/l)  4749  23.1 (43.5)  25.7 (47.4)  22.0 (40.0)  <0.001 
ALT (U/l)  6330  24.9 (17.0)  29.2 (19.2)  21.5 (14.0)  <0.001 
GGT (U/l)  6001  33.4 (51.0)  42.3 (65.3)  26.5 (34.5)  <0.001 
TC (mg/dl)  6496  192.7 (39.4)  188.1 (39.0)  196.3 (39.2)  <0.001 
HDL-C (mg/dl)  6496  54.8 (14.7)  49.2 (12.6)  59.2 (14.7)  <0.001 
Non-HDL-C (mg/dl)  6496  137.9 (38.5)  138.9 (38.8)  137.1 (38.2)  0.618 
LDL-C (mg/dl)  6434  114.1 (34.5)  112.4 (34.4)  115.4 (34.6)  <0.01 
VLDL-C (mg/dl)  6434  23.2 (12.2)  25.5 (13.2)  21.3 (11.0)  <0.001 
TG (mg/dl)  6496  120.6 (83.4)  135.8 (100.9)  108.7 (64.1)  <0.001 
TC/HDL-C  6496  3.72 (1.13)  4.03 (1.23)  3.47 (0.98)  <0.001 
Non-HDL-C/HDL-C  6496  2.72 (1.13)  3.03 (1.23)  2.47 (0.98)  <0.001 
TG/HDL-C  6496  2.53 (2.53)  3.13 (3.24)  2.06 (1.70)  <0.001 
LDL-C/HDL-C  6434  2.21 (0.88)  2.41 (0.96)  2.06 (0.79)  <0.001 
AIP  6496  -0.07 (0.29)  0.03 (0.29)  -0.14 (0.27)  <0.001 
TyG index  6496  8.49 (0.61)  8.64 (0.61)  8.38 (0.58)  <0.001 

ACR: albumin/creatinine ratio; AIP: atherogenic index of plasma; ALT: alanine aminotransferase; AST: aspartate aminotransferase; BMI: body mass index (weight/height2); DBP: diastolic blood pressure; eGFR: estimated glomerular filtration rate according to CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration); FPG: fasting plasma glucose; GGT: gamma-glutamyl transferase; HbA1c: glycosylated haemoglobin A1c; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; n: number of cases; p value: of the difference in means between the male and female populations; SBP: systolic blood pressure; TC: total blood cholesterol; TG: blood triglycerides; TyG: TG and glucose index; UAE: urinary albumin excretion; VLDL-C: very low-density lipoprotein cholesterol; WHtR: waist-to-height ratio.

a

Mean (±standard deviation).

The crude prevalence rates of the qualitative characteristics and morbidity of the study population and the significance of the differences between the male and female populations are shown in Table 4. The highest overall crude prevalence rates were: smoking (22%); physical inactivity (47%); overweight (38%); obesity (28%); abdominal obesity (65%); WHtR ≥0.55 (56%); prediabetes (22%); DM (16%); hypertension (38%); hypercholesterolaemia (61%); HTG (29%); atherogenic dyslipidaemia (14%); MetSyn (43%); and premorbid MetSyn (25%). There were no differences in crude prevalence rates between male and female populations in the following CVRF and disorders: history of premature CVD in first-degree relative; obesity; type 1 DM; hypercholesterolaemia; premorbid MetSyn; heart failure; atrial fibrillation; and CKD. The rest of the crude prevalence rates were significantly higher in the male population, except for physical inactivity, abdominal obesity, hypothyroidism and HDL-C ≥60mg/dl, which were significantly higher in the female population.

Table 4.

Qualitative characteristicsa of the study population.

  Study population
n=6631 
Male population
n=2934 
Female population
n=3697 
p value 
Smoking  1431 (21.6)  756 (25.8)  675 (18.3)  <0.001 
COPD  246 (3.7)  182 (6.2)  64 (1.7)  <0.001 
Alcoholism  610 (9.2)  503 (17.1)  107 (2.9)  <0.001 
Lack of physical exercise  3092 (46.6)  1259 (42.9)  1833 (49.6)  <0.001 
Normal weight  2152 (32.5)  771 (26.3)  1381 (37.4)  <0.001 
Overweight  2530 (38.2)  1307 (44.5)  1223 (33.1)  <0.001 
Obesity  1835 (27.7)  834 (28.4)  1001 (27.1)  0.224 
Central obesity  2928 (44.2)  1106 (37.7)  1822 (49.3)  <0.001 
Central obesity (IDF)  4329 (65.3)  1744 (59.4)  2585 (69.9)  <0.001 
WHtR ≥0.55  3705 (55.9)  1785 (60.8)  1920 (51.9)  <0.001 
Prediabetes (CDA)  519 (7.8)  252 (8.6)  267 (7.2)  0.043 
Prediabetes (ADA)  1447 (21.8)  701 (23.9)  746 (20.2)  <0.001 
DM  1033 (15.6)  568 (19.4)  465 (12.6)  <0.001 
DM Type 1  58 (0.9)  30 (1.0)  28 (0.8)  0.288 
DM Type 2  975 (14.7)  538 (18.3)  437 (11.8)  <0.001 
Hypertension  2548 (38.4)  1223 (41.7)  1325 (35.8)  <0.001 
Hypercholesterolaemia  4063 (61.3)  1800 (61.3)  2263 (61.2)  0.919 
HDL-C <40/45mg/dl  1346 (20.3)  839 (28.6)  507 (13.7)  <0.001 
HDL-C <40/50mg/dl  1808 (27.3)  839 (28.6)  969 (26.2)  0.030 
HDL-C ≥60mg/dl  2174 (32.8)  541 (18.4)  1633 (44.2)  <0.001 
Hypertriglyceridaemia  1936 (29.2)  1063 (36.2)  873 (23.6)  <0.001 
Atherogenic dyslipidaemia (45)  764 (11.5)  508 (17.3)  256 (6.9)  <0.001 
Atherogenic dyslipidaemia (50)  934 (14.1)  508 (17.3)  426 (11.5)  <0.001 
Premorbid MetSyn (AHA)  1412 (20.6)  623 (21.2)  789 (21.3)  0.928 
MetSyn (AHA)  2618 (39.5)  1279 (43.6)  1339 (36.2)  <0.001 
Premorbid MetSyn (IDF)  1650 (24.9)  724 (24.7)  926 (25.0)  0.732 
MetSyn (IDF)  2623 (41.0)  1266 (43.1)  1456 (39.4)  0.002 
Premorbid MetSynb  1629 (24.6)  728 (24.8)  901 (24.4)  0.688 
MetSynb  2847 (42.9)  1392 (47.4)  1455 (39.4)  <0.001 
Hypothyroidism  648 (9.8)  102 (3.5)  546 (14.8)  <0.001 
Hepatic steatosis  580 (8.7)  384 (13.1)  196 (5.3)  <0.001 
Coronary heart disease  319 (4.8)  210 (7.2)  109 (2.9)  <0.001 
CVA  250 (3.8)  128 (4.4)  122 (3.3)  0.027 
PAD  150 (2.3)  94 (3.2)  56 (1.5)  <0.001 
CVD  613 (9.2)  369 (12.6)  244 (6.6)  <0.001 
DM or CVD  1407 (21.2)  784 (26.7)  623 (16.9)  <0.001 
FHpCVD  387 (5.8)  169 (5.8)  218 (5.9)  0.833 
Heart failure  183 (2.8)  80 (2.7)  103 (2.8)  0.940 
Atrial fibrillation  247 (3.7)  108 (3.7)  139 (3.8)  0.896 
UAE ≥30mg/dl (n=4402)  347 (7.9)  198 (10.0)  149 (6.2)  <0.001 
ACR ≥30mg/g (n=4402)  369 (8.4)  206 (10.4)  163 (6.8)  <0.001 
eGFR <60ml/min/1.73m2 (n=6506)  522 (8.0)  215 (7.5)  307 (8.4)  0.183 
CKD (n=6506)  748 (11.5)  335 (11.7)  413 (11.3)  0.667 
Low CVR  2186 (33.0)  728 (24.8)  1458 (39.4)  <0.001 
Moderate CVR  1395 (21.0)  624 (21.9)  753 (20.4)  0.133 
High CVR  1010 (15.2)  480 (16.4)  530 (14.3)  0.023 
Very high CVR  1694 (25.5)  890 (30.3)  804 (21.7)  <0.001 
Extreme CVR  346 (5.2)  194 (6.6)  152 (4.1)  <0.001 

ACR: albumin-creatinine ratio; ADA: American Diabetes Association; AHA: American Heart Association; ATPIII: Adult Treatment Panel III; BMI: body mass index (weight/height2); CDA: Canadian Diabetes Association; CKD: chronic kidney disease; COPD: chronic obstructive pulmonary disease; CVA: cerebrovascular accident or stroke; CVD: cardiovascular disease; CVR: cardiovascular risk; DM: diabetes mellitus; eGFR: glomerular filtration rate estimated according to CKD-EPI (Chronic Kidney Disease Epidemiology collaboration); FHpCVD: Family history of premature CVD (in a first-degree relative); HDL-C: high-density lipoprotein cholesterol; IDF: International Diabetes Federation; MetSyn: metabolic syndrome; NCEP-ATPIII: National Cholesterol Education Program-Adult Treatment Panel III; NHLBI: National Heart, Lung, and Blood Institute; p value: of the difference in means between the male and female populations; PAD: peripheral arterial disease; UAE: urinary albumin excretion; WHtR: waist-to-height ratio.

Normal weight24: BMI from 18.5 to 24.9kg/m2. Overweight24: BMI from 25.0 to 29.9kg/m2. Obesity24: BMI ≥30kg/m2. Central obesity13: Waist circumference ≥102cm (males) or ≥88cm (females). Central obesity (IDF)12: ≥94cm (males) or ≥80cm (females). Prediabetes (CDA)19: blood glucose from 110 to 125mg/dl, or HbA1c from 6% to 6.4%. Prediabetes (ADA)18: blood glucose from 100 to 125mg/dl, or HbA1c from 5.7% to 6.4%. HDL-C<40/45: high-density lipoprotein cholesterol<40mg/dl (males) or<45mg/dl (females). HDL-C<40/50: idem<50mg/dl (females). Atherogenic dyslipidaemia (45): hypertriglyceridaemia and HDL-C<45mg/dl (females). Atherogenic dyslipidaemia (50): hypertriglyceridaemia and HDL-C<50mg/dl (females). MetSyn (AHA)10,11: metabolic syndrome according to NCEP-ATPIII/NHLBI/AHA. MetSyn (IDF)12: Metabolic syndrome according to IDF. eGFR: glomerular filtration rate estimated according to CKD-EPI.28

a

No. cases (%).

b

Metabolic syndrome harmonised consensus criteria 2009.13

Discussion

CVD caused 119,778 deaths in 2016 and was the leading cause of death (30%) in the Spanish population.33 Cardiovascular mortality has been reduced in recent years thanks to intervention on CVRF such as smoking, hypertension and hypercholesterolaemia. However, the intervention has not had such positive effects34 on eating habits, physical inactivity, obesity and DM. The preliminary information provided in this article describes the epidemiological situation of a population with a sustained high prevalence of lifestyle-related CVRF and metabolism-related morbidity. The study population had quantitative anthropometric and metabolic characteristics that predispose to the diagnosis of MetSyn. The mean BMI indicated grade ii overweight or obesity; waist circumferences exceeded the limits established by the IDF,12 and the HbA1c was on the border of the lower limit for prediabetes according to the IDF.12 However, the population averages showed good control of blood pressure, eGFR, FPG and lipid profile.

The highest overall crude prevalence rates were cardiometabolic CVRF: smoking, physical inactivity, overweight, obesity, abdominal obesity, increased WHtR, prediabetes, DM, hypertension, hypercholesterolaemia, HTG, atherogenic dyslipidaemia, MetSyn and premorbid MetSyn. The high prevalence of cardiometabolic CVRF suggests that 21% of the population could go on to suffer from CVD or DM, with 31% having very high or extreme CVR.

However, to assess the true epidemiological magnitude and to compare prevalence rates between different populations, it is important to bear in mind that both age and gender are factors directly related to CVRF, CVD and cardiometabolic morbidity. We therefore need to analyse not only the crude prevalence rates, but also the specific prevalence rates stratified by age groups, and the overall prevalence rates adjusted for age and gender.

In Spain, there are large regional differences in both the prevalence and the degree of control of CVRF.35 Population prevention strategies have been shown to be highly beneficial,1 and it is therefore essential to make a population health diagnosis that includes all the factors related to cardiovascular pathology.

The aim of the SIMETAP study was to provide an update on the epidemiological dimension of CVRF, CVD, MetSyn and associated metabolic morbidity. Our intention was that this update should facilitate comparison between different populations, stimulate health intervention and encourage health authorities to intensify population prevention strategies by more efficiently applying available resources. This article is the preamble to further more detailed analyses of prevalence rates both by stratified age groups and adjusted for age and gender with reference populations from the Madrid Region and from Spain.

Conclusions

This study found population averages of good control of blood pressure, eGFR, FPG and lipid profile. However, the anthropometric characteristics of the population, which are overweight, central obesity (according to IDF criteria), and a mean HbA1c of 5.6%, suggest a tendency towards the diagnosis of prediabetes or MetSyn.

The highest overall crude prevalence rates were recorded in inadequate lifestyles (smoking, physical inactivity, obesity) and cardiometabolic morbidity (prediabetes, DM, hypertension, dyslipidaemia and MetSyn). This could signify that a large percentage of the population has high or very high CVR.

To determine the true epidemiological dimension and be able to compare populations, we need to analyse prevalence rates stratified by age groups and adjusted for age and gender.

Funding

The Agencia “Pedro Laín Entralgo” de Formación, Investigación y Estudios Sanitarios [“Pedro Laín Entralgo” Agency for Training, Research and Health Studies] for the Madrid Region (grant No. RS05/2010) provided funding for conducting this study. The Research Commission of the Madrid Region Primary Care Management Department for Planning and Quality issued a favourable opinion for the study to be conducted.

Conflicts of interest

For this study, the authors declare that there was no interference in the attaining or interpretation of the results and that they therefore have no conflicts of interest.

Acknowledgements

We would like to thank the following physicians who have participated in the SIMETAP Study Research Group for their assistance and collaboration:

Abad Schilling C., Adrián Sanz M., Aguilera Reija P., Alcaraz Bethencourt A., Alonso Roca R., Álvarez Benedicto R., Arranz Martínez E., Arribas Álvaro P., Baltuille Aller M.C., Barrios Rueda E., Benito Alonso E., Berbil Bautista M.L., Blanco Canseco J.M., Caballero Ramírez N., Cabello Igual P., Cabrera Vélez R., Calderín Morales M.P., Capitán Caldas M., Casaseca Calvo T.F., Cique Herráinz J.A., Ciria de Pablo C., Chao Escuer P., Dávila Blázquez G., de la Peña Antón N., de Prado Prieto L., del Villar Redondo M.J., Delgado Rodríguez S., Díez Pérez M.C., Durán Tejada M.R., Escamilla Guijarro N., Escrivá Ferrairó R.A., Fernández Vicente T., Fernández-Pacheco Vila D., Frías Vargas M.J., García Álvarez J.C., García Fernández M.E., García García Alcañiz M.P., García Granado M.D., García Pliego R.A., García Redondo M.R., García Villasur M.P., Gómez Díaz E., Gómez Fernández O., González Escobar P., González-Posada Delgado J.A., Gutiérrez Sánchez I. Hernández Beltrán M.I., Hernández de Luna M.C., Hernández López R.M., Hidalgo Calleja Y., Holgado Catalán M.S., Hombrados Gonzalo M.P., Hueso Quesada R., Ibarra Sánchez A.M., Iglesias Quintana J.R., Íscar Valenzuela I., Iturmendi Martínez N., Javierre Miranda A.P., López Uriarte B., Lorenzo Borda M.S., Luna Ramírez S., Macho del Barrio A.I., Magán Tapia P., Marañón Henrich N., Mariño Suárez J.E., Martín Calle M.C., Martín Fernández A.I., Martínez Cid de Rivera E., Martínez Irazusta J., Migueláñez Valero A., Minguela Puras M.E., Montero Costa A., Mora Casado C., Morales Cobos L.E., Morales Chico M.R., Moreno Fernández J.C., Moreno Muñoz M.S., Palacios Martínez D., Pascual Val T., Pérez Fernández M., Pérez Muñoz R., Plata Barajas M.T., Pleite Raposo R., Prieto Marcos M., Quintana Gómez J.L., Redondo de Pedro S., Redondo Sánchez M., Reguillo Díaz J., Remón Pérez B., Revilla Pascual E., Rey López A.M., Ribot Catalá C., Rico Pérez M.R., Rivera Teijido M., Rodríguez Cabanillas R., Rodríguez de Cossío A., Rodríguez de Mingo E., Rodríguez Rodríguez A.O., Rosillo González A., Rubio Villar M., Ruiz Díaz L., Ruiz García A., Sánchez Calso A., Sánchez Herráiz M., Sánchez Ramos M.C., Sanchidrián Fernández P.L., Sandín de Vega E., Sanz Pozo B., Sanz Velasco C., Sarriá Sánchez M.T., Simonaggio Stancampiano P., Tello Meco I., Vargas-Machuca Cabañero C., Velazco Zumarrán J.L., Vieira Pascual M.C., Zafra Urango C., Zamora Gómez M.M., Zarzuelo Martín N.

References
[1]
M.Á. Royo Bordonada, P. Armario García, J.M. Lobos Bejarano, P.J. Pedro-Botet Montoya, F. Villar Álvarez, R. Elosua, et al.
Adaptación española de las guías europeas de 2016 sobre prevención de la enfermedad cardiovascular en la práctica clínica.
Clin Investig Arterioscler, 29 (2017), pp. 69-85
[2]
M.F. Piepoli, A.W. Hoes, S. Agewall, C. Albus, C. Brotons, A.L. Catapano, et al.
2016 European Guidelines on cardiovascular disease prevention in clinical practice. The Sixth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of 10 societies and by invited experts). Developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation (EACPR).
Eur Heart J, 37 (2016), pp. 2315-2381
[3]
A.L. Catapano, I. Graham, G. de Backer, O. Wiklund, M.J. Chapman, H. Drexel, et al.
Guía ESC/EAS 2016 sobre el tratamiento de las dislipemias.
Rev Esp Cardiol, 70 (2017),
115.e1–1160.ee64
[4]
A.J. Garber, M.J. Abrahamson, J.I. Barzilay, L. Blonde, Z.T. Bloomgarden, M.A. Bush, et al.
Consensus statement by the American Association of Clinical Endocrinologists and American College of Endocrinology on the comprehensive type 2 diabetes management algorithm – 2017 Executive summary.
Endocr Pract, 23 (2017), pp. 207-238
[5]
J.F. Ascaso, J. Millán, A. Hernández-Mijares, M. Blasco, A. Brea, A. Díaz, et al.
Documento de consenso sobre el manejo de la dislipemia aterogénica de la Sociedad Española de Arteriosclerosis.
Clin Investig Arterioscler, 29 (2017), pp. 86-91
[6]
M. Hanefeld, W. Leonhardt.
Das metabolische Syndrom.
DT Gesundh Wesen, 36 (1981), pp. 545-551
[7]
E.S. Ford.
Risks for all-cause mortality, cardiovascular disease, and diabetes associated with the metabolic syndrome: a summary of the evidence.
Diabetes Care, 28 (2005), pp. 1769-1778
[8]
S. Mottillo, K.B. Filion, J. Genest, L. Joseph, L. Pilote, P. Poirier, et al.
The metabolic syndrome and cardiovascular risk a systematic review and meta-analysis.
J Am Coll Cardiol, 56 (2010), pp. 1113-1132
[9]
G. Thomas, A.R. Sehgal, S.R. Kashyap, T.R. Srinivas, J.P. Kirwan, S.D. Navaneethan.
Metabolic syndrome and kidney disease: a systematic review and meta-analysis.
Clin J Am Soc Nephrol, 6 (2011), pp. 2364-2373
[10]
Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol In Adults.
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 (2001), pp. 2486-2497
[11]
S.M. Grundy, H.B. Brewer, J.I. Cleeman, S.C. Smith, C. Lenfant.
Definition of metabolic syndrome: report of the National Heart, Lung, and Blood Institute/American Heart Association. Conference on Scientific Issues Related to Definition.
Circulation, 109 (2004), pp. 433-438
[12]
K.G.M.M. Alberti, P. Zimmet, J. Shaw.
Metabolic syndrome—a new world-wide definition. A Consensus Statement from the International Diabetes Federation.
Diabet Med, 23 (2006), pp. 469-480
[13]
K.G.M.M. Alberti, R.H. Eckel, S.M. Grundy, P.Z. Zimmet, J.I. Cleeman, K.A. Donato, et al.
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 (2009), pp. 1640-1645
[14]
R.K. Simmons, K.G. Alberti, E.A. Gale, S. Colagiuri, J. Tuomilehto, Q. Qiao, et al.
The metabolic syndrome: useful concept or clinical tool? Report of a WHO Expert Consultation.
Diabetologia, 53 (2010), pp. 600-605
[15]
Comité Internacional de Clasificación de la WONCA. Clasificación Internacional de la Atención Primaria. 2.a ed. Barcelona: Masson; 1999.
[16]
Ministerio de Sanidad, Servicios Sociales e Igualdad. Clasificación Internacional de Enfermedades: 9.a Revisión. Modificación Clínica. CIE-9-MC. 9.a ed. 2014.
[17]
G. Mancia, R. Fagard, K. Narkiewicz, J. Redon, A. Zanchetti, M. Böhm, et al.
2013 ESH/ESC Guidelines for the management of arterial hypertension.
Eur Heart J, 34 (2013), pp. 2159-2219
[18]
American Diabetes Association.
Classification and diagnosis of diabetes. Standards of medical care in diabetes – 2018.
Diabetes Care, 41 (2018), pp. S13-S27
[19]
R. Goldenberg, Z. Punthakee, Canadian Diabetes Association Clinical Practice Guidelines Expert Committee.
Definition, classification and diagnosis of diabetes, prediabetes and metabolic syndrome.
Can J Diabetes, 37 (2013), pp. S8-S11
[20]
A.L. Catapano, I. Graham, G. de Backer, O. Wiklund, M.J. Chapman, H. Drexel, et al.
2016 ESC/EAS Guidelines for the Management of Dyslipidaemias.
Eur Heart J, 37 (2016), pp. 2999-3058
[21]
W.P. Castelli, R.D. Abbott, P.M. McNamara.
Summary estimates of cholesterol used to predict coronary heart disease.
Circulation, 67 (1983), pp. 730-734
[22]
M. Dobiasova.
AIP-atherogenic index of plasma as a significant predictor of cardiovascular risk: from research to practice.
Vnitr Lek, 52 (2006), pp. 64-71
[23]
L.E. Simental-Mendia, M. Rodriguez-Moran, F. Guerrero-Romero.
The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects.
Metab Syndr Relat Disord, 6 (2008), pp. 299-304
[24]
J. Salas-Salvadó, M.A. Rubio, M. Barbany, B. Moreno, Grupo colaborativo de la SEEDO.
Consenso SEEDO 2007 para la evaluación del sobrepeso y la obesidad y el establecimiento de criterios de intervención terapéutica.
Med Clin (Barc), 128 (2007), pp. 184-196
[25]
H.J. Schneider, J. Klotsche, S. Silber, G.K. Stalla, H.U. Wittchen.
Measuring abdominal obesity: effects of height on distribution of cardiometabolic risk factors risk using waist circumference and waist-to-height ratio.
Diabetes Care, 34 (2011), pp. e7
[26]
J. Vestbo, S.S. Hurd, A.G. Agustí, P.W. Jones, C. Vogelmeier, A. Anzueto, et al.
Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Pulmonary Disease: GOLD Executive Summary.
Am J Respir Crit Care Med, 187 (2013), pp. 347-365
[27]
Improving Global Outcomes (KDIGO) CKD Work Group.
KDIGO Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease.
Kidney Int Suppl, 3 (2013), pp. S6-S308
[28]
A.S. Levey, J. Coresh, T.H. Greene, L.A. Stevens, Y. Zhang, S. Hendriksen, Chronic Kidney Disease Epidemiology Collaboration.
Using standardized serum creatinine values in the Modification of Diet in Renal Disease study equation for estimating glomerular filtration rate.
Ann Intern Med, 145 (2006), pp. 247-254
[29]
NIH Consensus Conference: Impotence.
NIH Consensus Development Panel on Impotence.
JAMA, 270 (1993), pp. 83-90
[30]
M.T. Cooney, R. Selmer, A. Lindman, A. Tverdal, A. Menotti, T. Thomsen, et al.
Cardiovascular disease risk estimation in older persons: SCORE O.P..
Eur J Prev Cardiol, 23 (2016), pp. 1093-1103
[31]
R. Armitage, G. Berry, J.N.S. Matthews.
Statistical methods in medical research.
4th ed., Blackwell, (2002), pp. 659-667
[32]
Instituto Nacional de Estadística. INEbase. Demografía y población. Cifras de población y Censos demográficos. Cifras de población. Available from: http://www.ine.es/dynt3/inebase/es/index.htm?padre=1894∩sel=1895 [accessed 27.02.18].
[33]
Instituto Nacional de Estadística. Defunciones según la Causa de Muerte 2016. Resultados Nacionales. Defunciones por causas (lista reducida), sexo y edad. Available from: http://www.ine.es/jaxi/Datos.htm?path=/t15/p417/a2016/l0/&file=01001.px [accessed 27.02.18].
[34]
G. Flores-Mateo, M. Grau, M. O’Flaherty, R. Ramos, R. Elosua, C. Violan-Fors, et al.
Análisis de la disminución de la mortalidad por enfermedad coronaria en una población mediterránea: España 1998–2005.
Rev Esp Cardiol, 64 (2011), pp. 988-996
[35]
M. Grau, R. Elosua, A. Cabrera de León, M.J. Guembe, J.M. Baena-Díez, T. Vega Alonso, et al.
Factores de riesgo cardiovascular en España en la primera década del siglo xxi: análisis agrupado con datos individuales de 11 estudios de base poblacional, estudio DARIOS.
Rev Esp Cardiol, 64 (2011), pp. 295-304

Please cite this article as: Ruiz-García A, Arranz-Martínez E, García-Álvarez JC, Morales-Cobos LE, García-Fernández ME, de la Peña-Antón N, et al. Población y metodología del estudio SIMETAP: Prevalencia de factores de riesgo cardiovascular, enfermedades cardiovasculares y enfermedades metabólicas relacionadas. Clin Investig Arterioscler. 2018;30:197–208.

Copyright © 2018. Sociedad Española de Arteriosclerosis
Article options
Tools
es en pt

¿Es usted profesional sanitario apto para prescribir o dispensar medicamentos?

Are you a health professional able to prescribe or dispense drugs?

Você é um profissional de saúde habilitado a prescrever ou dispensar medicamentos