Cardiometabolic Risk Factors in Venezuela. the EVESCAM Study

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Primary Care Diabetes xxx (2020) xxx–xxx

Contents lists available at ScienceDirect

Primary Care Diabetes

j ournal homepage: http://www.elsevier.com/locate/pcd

Original research

Cardiometabolic risk factors in Venezuela. The EVESCAM study:

a national cross-sectional survey in adults

a,b,c,d e,b,d,∗ d,f

Ramﬁs Nieto-Martínez , Juan P. González-Rivas , Eunice Ugel ,

d g h i j

Maritza Duran , Eric Dávila , Ramez Constantino , Alberto García , Jeffrey I. Mechanick ,

María Inés Marulanda

LifeDoc Health, Memphis, TN, USA

Department of Global Health and Population. Harvard TH Chan School of Public Health, Harvard University, Boston, MA, USA

Department of Physiology, School of Medicine, University Centro-Occidental “Lisandro Alvarado” and Cardio-metabolic Unit 7, Barquisimeto, Venezuela

Foundation for Clinic, Public Health, and Epidemiology Research of Venezuela (FISPEVEN INC), Caracas, Venezuela

International Clinical Research Center (ICRC), St Anne’s University Hospital (FNUSA) Brno, Czech Republic

Public Health Research Unit, Department of Social and Preventive Medicine, School of Medicine, Universidad Centro-Occidental “Lisandro Alvarado”,

Barquisimeto, Venezuela

Department of Internal Medicine, School of Medicine “Dr. Luis Razetti”, Universidad Central de Venezuela (UCV), Caracas, Venezuela

Department of Internal Medicine, School of Medicine, Universidad de Carabobo, Valencia, Venezuela

Department of Physiology. School of Medicine “Dr. Luis Razetti”, Universidad Central de Venezuela (UCV), Caracas, Venezuela

The Marie-Josée and Henry R. Kravis Center for Cardiovascular Health at Mount Sinai Heart, and Division of Endocrinology, Diabetes and Bone Disease,

Icahn School of Medicine at Mount Sinai, New York, NY, USA

a r t i c l e i n f o a b s t r a c t

Article history: Background: No previous study in Venezuela and few in the Region of the Americas have reported national

Received 22 February 2020

cardiometabolic health data. Objectives: To determine the prevalence and distribution of cardiometabolic

Received in revised form 4 July 2020

risk factors (CMRF) in adults of Venezuela.

Accepted 16 July 2020

Methods: A population-based, cross-sectional, and randomized cluster sampling national study was

Available online xxx

designed to recruit 4454 adults with 20 years or older from the eight regions of the country from July

2014 to January 2017. Sociodemographic, clinical, physical activity, nutritional, and psychological ques-

Keywords:

tionnaires; anthropometrics, blood pressure, and biochemical measurements were obtained. The results

Venezuela

were weighted by gender, age, and regions.

Risk factors

Results: Data from 3414 participants (77% of recruited), 52.2% female, mean age of 41.2 ± 15.8 years,

Cardiovascular disease

Tobacco were analyzed. CMRF adjusted-prevalence were: diabetes (12.3%), prediabetes (34.9%), hypertension

Dyslipidemia (34.1%), obesity (24.6%), overweight (34.4%), abdominal obesity (47.6%), underweight (4.4%), hyperc-

Diabetes holesterolemia (19.8%), hypertriglyceridemia (22.7%), low HDL-cholesterol (63.2%), high LDL-c (20.5%),

Obesity

daily consumption of fruits (20.9%) and vegetables (30.0%), insufﬁcient physical activity (35.2%), anx-

iety (14.6%) and depression (3.2%) symptoms, current smoker (11.7%), and high (≥ 20%) 10-year fatal

cardiovascular risk (14.0%). CMRF prevalence varied according to gender, age and region of residence.

Conclusions: Cardiometabolic risk factors are highly prevalent in Venezuelan adults. This situation can be

affected by the severe socio-economic crisis in the country. The joint action of different stakeholders to

implement public health strategies for the prevention and treatment of these risk factors in Venezuela is

urgently needed.

1. Introduction

Cardiovascular disease (CVD) is the leading global cause of death

[1]. High adiposity, hypertension, high cholesterol, and hyper-

∗ glycemia are their most important risk factors. Between 1980 and

Corresponding author at: International Clinical Research Center, St Anne’s Uni-

2010, the mortality burden of cardiometabolic risk factors (CMRF)

versity, Hospital Brno Pekarska 53, 656 91 Brno, Czech Republic.

shifted from high-income to low and middle-income countries [2].

E-mail addresses: nietoramﬁ[email protected] (R. Nieto-Martínez),

[email protected] (J.P. González-Rivas). In Latin America, coronary heart disease and stroke cause 42.5%

https://doi.org/10.1016/j.pcd.2020.07.006

Please cite this article in press as: R. Nieto-Martínez, et al., Cardiometabolic risk factors in Venezuela. The EVESCAM study: a national

cross-sectional survey in adults, Prim. Care Diab. (2020), https://doi.org/10.1016/j.pcd.2020.07.006

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and 28.8% of the CVD mortality, respectively [3]. But, it is neces- participate in the study by not signing the informed consent. Then,

sary to identify the magnitude of the CMRF not only in each region identiﬁcation data and a social status questionnaire were collected

but in each country. Well-designed studies using sub-national sam- from each subject in their home. Finally, subjects were invited to

ples have reported the prevalence of CMRF in Peru [4] and South assist to the physical and metabolic evaluation in a nearby health

Cone (Argentina, Chile, and Uruguay) [5]. In the Americas, only 14 center and an instructive that includes a detailed explanation of

of 37 countries have reported national studies, and most of them the evaluation procedures was provided and explained. The evalu-

are based on self-reported data. ations were carried out in a total of 47 community health centers

Venezuela is a middle-income country privileged on geographic throughout the country.

localization, oil resources, and climate, but immersed in a politi- The main objective of the EVESCAM was to determine the preva-

cal turmoil, hyperinﬂation, and socio-economic changes that can lence cardiometabolic risk factors and diseases in Venezuela. The

inﬂuence the prevalence of non-communicable (NCD), especially target sample size was calculated in 2940 participants based on a

cardiometabolic and nutritional diseases. Previous data in diabetes previous report on diabetes (prevalence 7.7%, standard deviation

and CMRF in Venezuela has come from communities [6], a city 1.55%, and conﬁdence level 95%) [6]. Therefore, considering a mini-

[7], a state [8], and a sub-national study in three regions [9–13], mal expected response rate of 70%, the ﬁnal target sample size was

or from the calculation of weighted prevalence from available increased to 4200, representing the proportions of the country in

studies [14]. Therefore, the objective of the Venezuelan Study of terms of age, sex, and proportion of rural and urban populations. In

Cardiometabolic Health (EVESCAM, for the acronym in Spanish) each region, at least 525 subjects were recruited. The sampling also

is to determine the prevalence of cardio-metabolic risk factors in considered that is necessary to evaluate at least 70% of recruited

adults in a national sample of Venezuela. subjects in each region. Thus, if after recruiting 525 subjects the

evaluation of at least 70% has not been achieved, the recruitment

2. Methods was to continue until that response rate was reached in each region.

For this study, 4454 subjects were recruited (86.3% urban and 13.7%

2.1. Study design rural areas), of which 3414 were evaluated, corresponding to a net

response rate of 76.7%.

The study design, sampling, and implementation were The study protocol complied with the Helsinki declaration and

described previously [15,16]. In brief, the EVESCAM was a approved by the National Bioethics Committee (CENABI). Consent

population-based, observational, cross-sectional, and cluster sam- from all participants was obtained and ﬁled. The present report is

pling study, designed to evaluate cardiometabolic risk factors presented according to the Strengthening the Reporting of Obser-

among subjects aged ≥ 20 years in Venezuela from July 2014 to vational Studies in Epidemiology (STROBE) [17].

January 2017.

2.4. Clinical and biochemical measures

2.2. Population

A customized questionnaire was used to collect information

The Bolivarian Republic of Venezuela consists of 23 states, a on demographics, family and personal history, including type 2

capital district, federal entities, and 335 municipalities distributed diabetes (T2D) and CV risk, socioeconomic status (SES) [18], use

in 8 regions (Capital, Central, Western, Northeast, Guayana, of health care facilities, tobacco history, and depression and/or

Andeans, Zulia, and The Llanos). The population size reported by anxiety symptoms [19]. Dietary intake was ascertained using

the Venezuelan National Institute of Statistics (www.ine.gov.ve; both a food frequency questionnaire adapted to the Venezuelan

accessed on January 21, 2018) was 31,431,164 inhabitants in 2017, population. Questionnaires, anthropometrics, and other physical

of whom 65.3% were 20 years or older and 50% were female. measurements were obtained by trained and certiﬁed health per-

sonnel. Blood pressure was measured twice, with ﬁve minutes

2.3. Sampling and recruitment intervals, in the right arm, supported at heart level, in a sitting

position, after ﬁve minutes of rest, with a validated oscillometric

A multi-stage stratiﬁed sampling method was used to select sphygmomanometer (Omron HEM-705C Pint Omron Healthcare

a representative sample of the general population of Venezuela. CO., Kyoto/Japan) [20]. Weight was measured with the lightest

4454 women and men, aged 20 years and older, were recruited possible clothes, without shoes, using a calibrated scale (Tanita UM-

from randomly selected samples in the eight regions of Venezuela. 081 , Japan). Height was measured using a portable stadiometer

Initially, 23 cities (1st stage) from the eight regions – one to four (Seca 206 Seca GmbH & Co., Hamburg, Germany). Body mass index

cities per region – were chosen. Each selected city was stratiﬁed (BMI = kg/m ) was calculated for all subjects. Waist circumference

by municipalities. Two municipalities (2nd stage) in each city, then was measured twice with a measuring tape, at the iliac crest, in a

two parishes (3rd stage) in each municipality, and ﬁnally two loca- horizontal plane with the ﬂoor, at the end of expiration, and the

tions (4th stage) in each parish, were randomly selected. In the 5th average of both was used.

stage, mappings and censuses of each location delimited the streets Blood specimens were collected according to a standardized

or blocks (primary sampling units) and selected the households to protocol after at least 8-h of fasting. Samples were centrifuged,

◦

visit. Actual household visits were conducted in the 6th stage. The frozen, and shipped to the central laboratory to be stored at −40

visits to households started from number 1 onwards skipping every until assay. Blood tests included total cholesterol, triglycerides, and

two houses. That is, the household visited were 1, 4, 7, 10, 13, 16 HDL-c; LDL-c was calculated applying the Friedewald’s formula.

and so on. If the number of people required after covering all house- Fasting blood glucose and a 2-h oral glucose tolerance test (OGTT)

holds of this sequence was not achieved, the sampling continued using a 300 ml test solution containing 75 g anhydrous glucose was

on households 2, 5, 8, 11, and so on, until obtaining the number of performed.

subjects required to complete the sample from that sector.

In each household, all members were eligible to enter the study 2.5. Deﬁnitions

and were invited to participate if met the criteria. Inclusion criteria

were all those subjects with 20 or older years of age living in the A questionnaire validated in the Venezuelan population and

house selected for more than six months. Exclusion criteria were based on four variables (source of income, profession of house-

current pregnancy, inability to stand or communicate, or refusal to holder, educational level, and housing conditions), was used to

Please cite this article in press as: R. Nieto-Martínez, et al., Cardiometabolic risk factors in Venezuela. The EVESCAM study: a national

cross-sectional survey in adults, Prim. Care Diab. (2020), https://doi.org/10.1016/j.pcd.2020.07.006

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determine SES [18]. A Likert-type scale ranging from 4 to 20 points

rated each item from 1 to 5, being 1 the best situation and 5 the

worst. Thus, the population was categorized by four strata: SES I —

high class (4–6 points); SES II — middle-high class (7–9 points); SES

III — middle class (10–12 points); SES IV — relative poverty (working

class, 13–16 points); and SES V — extreme poverty (marginal class,

17–20 points). Education was categorized as: (a) illiterate, subjects

with no reading or writing skills; (b) primary education, those who

attended or ﬁnished primary school; (c) secondary education, those

who attended or ﬁnished high school; and (d) higher education,

those who attended or ﬁnished university including techniques.

Diabetes was established if fasting plasma glucose was ≥ 126

mg/dL or 2-h after 75 g oral glucose tolerance test ≥ 200 mg/dL

or personal history of diabetes [21]. Prediabetes was established

if fasting plasma glucose ≥ 100 mg/dL and <126 mg/dL or glucose

level after a 75 g oral glucose tolerance test ≥ 140 mg/dL and ≤ 199

≥

mg/dL [21]. Hypertension was deﬁned as systolic blood pressure Fig. 1. Final sample for analysis to estimate the prevalence of cardiometabolic risk

factors.

140 mmHg or diastolic blood pressure ≥ 90 mmHg, or self-report

of hypertension or antihypertensive medication use [22]. BMI was

categorized as underweight < 18.5 kg/m , normal weight 18.5–24.9

2 2 2 were obtained from the Venezuelan 2011 census. All continu-

kg/m , overweight 25–29.9 kg/m , and obesity ≥ 30 kg/m [23].

ous variables were initially tested for normality (Q–Q plots). All

Abdominal obesity was deﬁned as a waist circumference ≥ 94 cm in

variables had normal distribution and were presented as mean ±

men or ≥ 90 cm in women [24]. Dyslipidemia was deﬁned according

standard deviation (SD), except the metabolic equivalents (METs),

to the American Association of Clinical Endocrinologist and Ameri-

with skewed distribution, which was presented as median and

can College of Endocrinology (AACE/ACE) 2017 [25] cut-off values:

interquartile range (IR). Differences between parametric variables

hypercholesterolemia (≥200 mg/dL of total cholesterol); high Low

were assessed by the Student t-test or analysis of variance (ANOVA).

Density Lipoprotein cholesterol (LDL-c ≥ 130 mg/dL) low High

Differences between nonparametric variables (e.g., METs) were

Density Lipoprotein cholesterol (HDL-c < 40 mg/dL HDL-c); and

assessed by the Mann–Whitney U test Proportions were pre-

hypertriglyceridemia (≥150 mg/dL of triglycerides) or prescription

sented as prevalence and 95% conﬁdence intervals (95% CI) and

of lipid lowering medications.

compared using the Chi-square test. A p-value < 0.05 was con-

Physical activity was assessed using the International Question-

sidered signiﬁcant. Crude results are presented in Supplementary

naire of Physical Activity (IPAQ) short version which evaluates the

Appendix.

frequency of physical activity and time spent in the last 7 days in

vigorous, moderate activity, walking and seated [26]. Subjects were

categorized as “minimally active” if they participated in: (a) 3 or 3. Results

more days of vigorous activity of at least 20 min per day, or (b) 5 or

more days of moderate-intensity activity or (c) walking of at least 3.1. Subjects characteristics

30 min per day, or (d) 5 or more days of any combination of walking,

moderate- or vigorous intensity activities achieving a minimum of Out of 3445 subjects who completed all stages of data collection,

at least 600 MET-min/week. Subjects were categorized as “inactive” 31 did not complete the evaluation. The ﬁnal sample comprised

if they did not reach any of the above criteria [26]. Current smoker 3414 participants (Fig. 1).

was deﬁned as those individuals who report using more than 100

cigarettes, 20 tobaccos, or 20 pipes throughout his life, and reported

3.1.1. Sociodemographic

using it in the past twelve months. Daily consumption of fruits

Fifty-two percent of the study subjects were female. Almost 48%

and/or vegetables was reported. Anxiety and depression symptoms

of the population was categorized as poor; only 21.2% were consid-

were determined using the Hospital Anxiety and Depression Scale

ered high and middle-upper class (Table 1). Eight out 10 subjects

(HADS), a self-report questionnaire with 14 items (7 for depres-

were mixed race and lived in urban locations. Only 3.3% of the

sion and 7 for anxiety), with each item completed on a Likert scale

population was illiterate, 39.1% not completed high school, 31.6%

from 0 to 3 points, which categorizes subjects as normal (<8 points),

had technique education or high school and 25.9% had a univer-

mild symptoms (8–10 points), or moderate/severe symptoms (≥11

sity degree. When a health service was required, 67.4% attended

points), in each of the two domains [19]. A laboratory-based risk

to public health care centers (53.2% the traditional hospital and

score (Globorisk) derived from a prediction model that includes

ambulatory network and 14.2% the “Barrio Adentro” parallel Cuban

age, sex, smoking, blood pressure, diabetes, and total cholesterol

mission network) and 21% used private centers (almost 12.5% with

was used to estimate the 10-year risk of fatal and non-fatal car-

insurance coverage and 8.5% out of pocket).

diovascular disease [27]. As is suggested for low-middle income

countries, a score ≥ 20% was considered high risk [27].

3.1.2. Anthropometric and cardiometabolic risk factors

The mean age was 41.2 ± 15.8 years (Table 2). It is noteworthy

2.6. Data analysis

that the mean of some CMRF (BMI, fasting blood glucose, HDL-

cholesterol in both genders, and waist circumference in women)

All calculations were performed using SPSS 20 software (IBM

were outside the normal range. Men presented higher age, weight,

corp. Released 2011; Armonk, NY, USA). Data were weighted to

height, values of metabolic syndrome components (higher blood

address any imbalance in the distribution of variables in the sam-

pressure, fasting blood glucose, triglycerides, and lower HDL-c), and

ple compared with the whole Venezuelan population. Sampling

10-year high fatal and non-fatal CVD risk score than women (p <

weights were created using standardized population weights for

0.01); instead women presented higher 2h-post 75 g blood glucose,

gender in combination with a second set of weights based on the

total cholesterol, and LDL-c and less METS than men (p < 0.01).

region and age distribution in Venezuela. Population distributions

Please cite this article in press as: R. Nieto-Martínez, et al., Cardiometabolic risk factors in Venezuela. The EVESCAM study: a national

cross-sectional survey in adults, Prim. Care Diab. (2020), https://doi.org/10.1016/j.pcd.2020.07.006

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Table 1

Sociodemographic characteristics.

Total Men Women P

n (%) 3414 (100.0) 1631 (47.8) 1783 (52.2)

Socioeconomic Status (%) % 95% CI % 95% CI % 95% CI

I — high 2.5 1.8 – 3.4 1.5 1.0 - 2.2 2.0 1.5 – 2.5 0.843

II — middle upper class 19.8 17.9 – 21.8 18.7 16.9 – 20.5 19.2 17.9 -20.6 0.871

III — middle class 31.0 29.5−32.6 30.8 28.2−33.0 31.3 29.1−33.4 0.76

IV — relative poverty 42.1 40.5−43.8 40.6 38.2−43.1 43.5 41.2−45.8 0.08

V — extreme poverty 5.7 4.8−6.4 6.3 5.1−7.5 5.1 4.0−6.1 0.13

Ethnicity % 95% CI % 95% CI % 95% CI

Mixed 78.4 77.0−79.8 76.7 74.6−78.7 80.0 78.1−81.8 0.02

− − −

Hispanic White 14.8 13.6 16.0 16.1 14.3 18.8 13.6 12.0 15.1 0.05

− −

Afro-Venezuelan 5.5 4.7 6.3 5.7 4.6 6.8 5.4 4.3−6.4 0.68

Amerindian 1.3 0.9−1.7 1.5 0.9−2.1 1.1 0.6−1.5 0.22

Location % 95% CI % 95% CI % 95% CI

Urban 80.8 79.3−82.0 82.4 80.5−84.2 79.1 77.2−80.9 0.01

Rural 19.4 18.1−20.1 17.6 15.8−19.5 20.9 19.0−22.8 0.01

Education % 95% CI % 95% CI % 95% CI

Illiterate 2.0 1.4–2.7 2.0 1.4–2.7 2.0 1.6–2.5 0.90

Primary 17.0 15.2–18.8 16.1 14.4–17.8 16.5 15.3–17.8 0.73

Secondary 45.0 42.4–47.2 39.8 37.6–42.1 42.3 40.7–44.0 0.03

University or more 36.0 33.5–38.1 42.1 39.8–44.4 39.2 37.6–40.8 0.01

Health services % 95% CI % 95% CI % 95% CI

Public hospital or ambulatory 53.2 51.6−55.0 51.2 48.8−53.6 55.1 52.7−57.3 0.02

¨Barrio AdentroNetwork¨ 14.2 13.0−15.3 14.0 12.3−15.7 14.3 12.7−15.9 0.82

Private centers out of pocket 8.5 7.6−9.4 8.9 7.5−10.3 8.1 6.9−9.4 0.39

−

Private centers with insurance 12.5 11.4 13.6 15.0 13.2−17.6 10.3 8.8−11.6 0.001

Both public and private 9.0 8.0−10.0 7.8 6.5−9.1 10.0 8.6−11.3 0.02

Proportions are presented as percent (95% CI). Chi-square test was used to determine differences between categorical variables.

Table 2

Anthropometric and cardiometabolic risk factors by gender.

Total Men Women p

Age (years) 41.2 ± 15.8 41.9 ± 16.5 40.5 ± 15.1 0.007

Weight (kg) 72.5 ± 17.9 77.7 ± 17.7 67.7 ± 16.7 0.001

Height (m) 1.64 ± 0.09 1.70 ± 0.07 1.58 ± 0.06 0.001

Body mass index (kg/m ) 26.8 ± 5.9 26.7 ± 5.4 27.0 ± 6.3 0.092

Waist circumference (cm) 91.6 ± 14.1 92.9 ± 14.0 90.5 ± 514.1 0,001

Systolic blood pressure (mmHg) 126.7 20.7 130.6 ± 19.4 123.1 ± 21.3 0.001

Diastolic blood pressure (mmHg) 75.2 ± 11.6 75.8 ± 11.7 74.7 ± 11.4 0.007

Fasting blood glucose (mg/dL) 102.2 ± 30.2 105.3 ± 33.7 99.3 ± 26.3 0.001

2h-post 75 g blood glucose (mg/dL) 112.3 ± 36.1 110.8 ± 38.2 113.6 ± 34.1 0.034

Total cholesterol (mg/dL) 155.5 ± 39.8 153.8 ± 39.4 156.9 ± 40.1 0.020

± ±

Triglycerides (mg/dL) 108.4 64.4 116.6 72.7 100.8 54.6 0.001

LDL-c (mg/dL) 96.8 ± 31.0 95.8 ± 30.9 97.7 ± 31.1 0.070

HDL-c (mg/dL) 36.9 ± 11.2 34.9 ± 10.5 38.7 ± 11.2 0.001

METS (min/week) 1597 (495−4788) 2014 (669−5939) 1356(396−3810) 0.001

Personal cardiovascular heart disease (%) 1.4 (1.1−1.9) 1.3 (0.8−2.0) 1.6 (1.1−2.3) 0.501

Family history of stroke (%) 32.9 (31.4−34.5) 32.4 (30.2−34.7) 33.4 (31.2−35.6) 0.533

Family history of premature CVD (%) 19.4 (18.1−20.7) 18.0 (16.1−19.8) 20.8 (18.9−22.7) 0.033

10-year high fatal CVD risk score 10.3 ± 9.3 12.4 ± 9.6 8.3 ± 7.3 0.001

Continues variables are presented as mean ± standard deviation of the mean. Except METS which is presented as median and IQR. Proportions are presented as percent

(95% CI). Student t-test and Chi-square test were used to determine differences between continues and categorical variables respectively. (p < 0.01) Abbreviations: CVD —

Cardiovascular diseases; HDL-c — High density lipoprotein cholesterols; LDL-c — Low density lipoprotein cholesterol.

3.2. Prevalence of cardiometabolic risk factors 3.2.1. By gender

Diabetes, hypertension, overweight, hypertriglyceridemia, low

The most prevalent CMRF in Venezuela were non-daily con- HDL-c, current smokers, non-daily intake both fruits and vegeta-

sumption of fruits (79.1%) and vegetables (70.0%), low HDL-c bles, and 10-year high CVD risk were more prevalent in men than

(63.2%), and abdominal obesity (47.6%) (Table 3). Hypertension, women (p < 0.05). Underweight, obesity, anxiety and depression

overweight, prediabetes, and physical inactivity affected around symptoms, and physical inactivity were more prevalent in women

35% of the population, almost 25% was affected by obesity, 12.3% by than men (p < 0.05) (Table 3).

diabetes, and 4.4% by underweight. Other dyslipidemias (hyperc-

holesterolemia, hypertriglyceridemia, and high LDL-c) ranged from

3.2.2. By age and gender

19.8% to 22.7%. (Table 3). Approximately, 12% were current smok-

Most of CMRF were affected by age, but their distribution was

ers and 14.6% and 3.2% reported anxiety and depression symptoms,

heterogeneous (Table 4). Traditional risk factors, such as diabetes,

respectively. According to the Globorisk score, 14% of the popula-

hypertension, abdominal obesity, hypercholesterolemia, hyper-

tion had a 10-year high risk of a fatal or non-fatal cardiovascular

triglyceridemia, high LDL-c, and physical inactivity increased with

event.

age in both genders. Hypertension and 10-year high CVD risk

Please cite this article in press as: R. Nieto-Martínez, et al., Cardiometabolic risk factors in Venezuela. The EVESCAM study: a national

cross-sectional survey in adults, Prim. Care Diab. (2020), https://doi.org/10.1016/j.pcd.2020.07.006

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Table 3

Prevalence of cardiometabolic risk factors by gender.

Total Men Women p

Diabetes 12.3 (11.2−13.4) 14.5 (12.7−16.2) 10.3 (8.9−11.7) 0.001

Prediabetes 34.9 33.3−36.5 36.9 34.5−39.2 33.1 30.9−35.3 0.023

Hypertension 34.1 (32.5−36.6) 36.4 (34.0−38.7) 32.0 (29.8−34.1) 0.007

Underweight 4.4 (3.7−5.1) 3.6 (2.7−4.5) 5.1 (4.1−6.1) 0.016

Normal weight 36.6 (35.0−38.2) 36.2 (33.9−38.6) 36.9 (34.7−39.2) 0.686

Overweight 34.4 (32.8−36.0) 38.0 (35.9−40.3) 31.2 (29.0−33.3) 0.001

Obesity 24.6 (23.1−26.0) 22.2 (20.2−24.2) 26.7 (24.7−28.8) 0.002

Abdominal obesity 47.6 45.9−49.3 46.7 44.3−49.1 48.5 46.1−50.8 0.302

− −

Hypercholesterolemia 19.8 (18.5 21.1) 17.9 (11.6 19.8) 21.4 (19.6−23.4) 0.001

Hypertriglyceridemia 22.7 (21.3−24.1) 24.6 (22.6−26.8) 20.9 (19.0−22.8) 0.001

Low HDL-c 63.2 (61.5−64.8) 70.9 (68.7−72.1) 56.0 (45.1−66.5) 0.001

High LDL-c 20.5 (19.2−21.9) 18.9 (17.0−21.9) 22.0 (20.1−24.0) 0.020

Anxiety symptoms 14.6 (13.4−15.8) 9.6 (8.1−11.1) 19.2 17.4−21.1 0.001

Depression symptoms 3.2 (2.5−3.7) 2.1 (1.4−2.8) 4.1 3.1−5.0 0.001

Daily fruits intake 20.9 (19.5−22.3) 19.1 (17.2−21.0) 22.6 (20.8−24.6) 0.016

Daily vegetables intake 30.0 (28.5−31.5) 28.2 (26.0−30.4) 31.7 (29.5−33.8) 0.023

Physical inactivity 35.2 (33.5−36.9) 29.9 (27.5−32.2) 39.9 (37.5−42.2) 0.001

−

Current smoker 11.7 (10.6 12.7) 17.0 (15.1 18.8) 6.8 (5.6−8.0) 0.001

10-year high fatal CVD risk 14.0 (12.4−15.9) 19.2 (16.5−22.3) 9.1 (7.2−11.3) 0.001

Data are percent and (95% CI). Chi-square test was used to determine differences by gender.

Calculated by Globorisk.

Table 4

Prevalence of cardiometabolic risk factors by age and gender.

−

20–34 years 35–49 years 50 64 years 65 and more years p

Men

Diabetes 3.5 (2.3−5.2) 16.7 (13.8−20.2) 26.5 (21.7−32.0) 26.7 (20.9−33.3) 0.001

Prediabetes 35.1 31.5−38.9 36.5 32.5−40.7 39.4 33.8−42.5 40.5 33.8−47.2 0.433

Hypertension 13.9 (11.4−16.8) 36.1 (33.1−40.3) 59.5 (53.6−65.1) 76.8 (70.4−82.2) 0.001

Underweight 4.5 (3.1−6.4) 4.0 (2.6−6.1) 1.4 (0.6−3.6) 2.6 (1.1−5.9) 0.011

Normal weight 45.9 (42.0−49.8) 22.5 (19.1−26.3) 32.7 (27.5−38.4) 46.9 (40.0−53.9) 0.001

Overweight 31.2 (27.7−34.9) 42.7 (38.6−47.0) 46.8 (41.0−53.6) 34.4 (28.0−41.4) 0.001

Obesity 18.5 (15.6−21.7) 30.7 (26.9−34.8) 19.1 (14.9−24.1) 16.1 (11.6−22.0) 0.001

Abdominal obesity 33.3 29.8−37.1 53.6 49.3−57.8 59.1 53.3−64.7 53.6 46.6−60.5 0.001

Hypercholesterolemia 9.7 (7.6−12.2) 18.1 (15.0−21.6) 28.4 (23.4−33.9) 29.2 (23.3−35.9) 0.0001

Hypertriglyceridemia 17.5 (14.7−20.6) 26.2 (22.6−30.1) 35.4 (30.0−41.1) 28.2 (22.4−34.9) 0.0001

Low HDL-c 71.0 (67.4−77.4) 73.0 (69.1−76.6) 71.8 (66.3−77.8) 63.9 (56.9−70.3) 0.119

High LDL-c 11.1 (8.8−13.8) 20.0 (16.8−23.7) 28.6 (23.5−34.3) 27.2 (21.4−33.9) 0.0001

−

Anxiety symptoms 9.2 7.1 11.7 8.0 5.9−10.7 13.2 9.6−17.7 8.7 5.4−13.6 0.357

Depression symptoms 0.5 0.1−1.4 2.3 1.3−4.0 4.2 2.3−7.3 4.4 2.2−8.4 0.001

Daily fruits intake 16.9 (14.2−20.0) 21.0 (17.7−24.6) 15.5 (11.7−20.3) 25.7 (20.0−32.3) 0.010

Daily vegetables intake 28.1 (24.7−31.7) 27.8 (24.2−31.8) 28.7 (23.7−34.2) 28.6 (22.7−35.4) 0.990

Physical inactivity 23.1 (20.0−26.8) 29.6 (25.6−33.8) 34.5 (28.9−40.6) 45.6 (38.5−52.8) 0.000

Current smoker 17.1 (14.3−20.2) 19.0 (15.9−22.5) 16.2 (21.3−21.0) 11.8 (8.0−17.1) 0.140

10-year high fatal CVD risk – – 3.9 (2.3−6.6) 22.7 (18.1−27.9) 53.8 (44.8−62.5) 0.000

Women

Diabetes 3.9 (2.7−5.6) 9.4 (7.3−12.0) 17.5 (13.8−22.0) 30.7 (23.7−38.8) <0.001

Prediabetes 22.1 19.3−25.3 38.7 34.8−42.7 44.0 37.8−49.4 42.9 35.0−51.0 <0.001

Hypertension 11.7 (9.6−14.2) 31.1 (27.5−35.0) 58.2 (52.7−63.4) 82.1 (75.0−87.6) <0.001

Underweight 8.6 (6.8−10.8) 2.4 (1.5−4.0) 1.9 (0.8−4.0) 5.1 (2.5−10.1) <0.001

Normal weight 46.8 (43.3−50.4) 30.8 (27.2−34.7) 27.2 (22.7−32.2) 31.4 (24.2−39.6) <0.001

Overweight 23.6 (20.6−26.7) 36.1 (32.2−40.0) 35.9 (30.8−41.2) 40.9 (33.0−49.2) <0.001

Obesity 21.0 (18.2−24.0) 30.7 (27.0−34.5) 35.0 (30.0−40.0) 22.6 (16.4−30.3) <0.001

Abdominal obesity 33.7 30.4−37.1 55.0 50.8−59.0 64.9 59.6−69.1 62.4 54.2−70.0 <0.001

Hypercholesterolemia 7.6 (5.9−9.7) 19.3 (16.2−27.2) 43.2 (38.2−49.8) 51.4 (43.2−56.5) <0.001

Hypertriglyceridemia 8.4 (6.6−10.6) 20.5 (17.4−24.0) 38.6 (33.4−44.0) 47.1 (33.4−43.9) <0.001

Low HDL-c 57.7 (54.1−61.6) 60.3 (56.2−64.2) 49.1 (43.7−54.5) 45.7 (37.7−54.0) 0.001

High LDL-c 7.8 (6.0−9.9) 21.0 (17.8−24.5) 44.1 (38.7−49.5) 50.4 (42.1−58.5) <0.001

Anxiety symptoms 20.9 18.0−24.0 18.4 15.4−21.8 18.5 14.6−23.1 15.6 10.4−22.6 0.382

Depression symptoms 2.5 1.6−4.0 4.5 3.0−6.5 5.4 3.4−8.5 6.7 4.0−12.0 0.097

Daily fruits intake 20.5 (17.7−26.3) 20.2 (17.1−23.6) 29.1 (24.4−34.3) 28.4 (21.6−36.3) 0.002

Daily vegetables intake 27.3 (24.9−30.6) 33.5 (29.8−37.5) 38.1 (32.9−43.5) 32.6 (25.4−40.7) 0.001

Physical inactivity 36.5 (33.0−40.2) 40.8 (36.7−45.0) 38.9 (33.5−44.5) 56.5 (47.9−64.8) <0.001

Current smoker 4.0 (2.8−5.7) 7.1 (5.3−9.5) 12.6 (9.4−16.6) 7.8 (4.4−13.4) <0.001

10-year high fatal CVD risk – – 1.4 (0.6−3.3) 9.3 (6.6−12.9) 37.8 (28.5−48.1) <0.001

Data are percent and (95% CI). Chi-square test was used to determine differences.

in both genders and diabetes in women were the only condi- vegetable intake, and current smoker) and in women (anxiety

tions that increase in each age category with no overlap between and depression symptoms) were present in the youngest (20–34

conﬁdence intervals. Some unfavorable risk conditions in men (pre- years) individuals in a similar proportion to its older counter-

diabetes, underweight, low-HDL-c, anxiety symptoms, non-daily parts.

Please cite this article in press as: R. Nieto-Martínez, et al., Cardiometabolic risk factors in Venezuela. The EVESCAM study: a national

cross-sectional survey in adults, Prim. Care Diab. (2020), https://doi.org/10.1016/j.pcd.2020.07.006

G Model

PCD-925; No. of Pages 9 ARTICLE IN PRESS

6 R. Nieto-Martínez et al. / Primary Care Diabetes xxx (2020) xxx–xxx 22.2) 50.4) 52.0) 44.3) 37.5) 45.2) 38.1) 37.7) 51.4) 20.8) 28.7) 25.0) 43.7) 55.9) 25.5) 18.9) 26.8) 18.5) 22.7) 19.8) 48.0) 28.4) 31.1) 37.0) 57.2) 36.4) 35.3) 27.7) 22.9) 37.3) 16.6) 39.1) 39.6) 38.2) 33.3) 51.5) 28.8) 52.7) 25.1) 49.5) 46.8) 72.5) 30.1) 16.7) 17.0) 18.8) 14.7) 13.2) − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − 404) 19.0)

6.4) 10.3) 6.5) 4.0) 10.7) 7.5) 3.) − − − − − 12.5) = −

− − − − − − − − (n

(12.4 (10.4 (37.9 (42.9 (31.6 (25.8 (30.4 (32.4 (26.3 (30.8 (26.0 (8.2 (25.9 (25.0 (38.4 (39.0 (11.2 (17.5 (18.0 (13.8 (60.0 (31.4 (46.7 (8.2 (15.3 (12.9 (16.5 (11.9 (20.0 (13.1 (13.1 (40.1 (38.9 (17.1 (22.8 (9.7 (19.1 (14.0 (6.9 (44.1 (24.7 (26.8 (40.4 (17.1 (15.6 (34.0 (25.3 (7.6

(1.5 (3.9 (1.7 (1.2 (4.2 (3.4 (0.3

Zulia 16.7 10.2 13.2 50.7 44.1 47.5 37.7 31.4 34.6 6.4 5.0 38.6 31.9 35.1 31.8 30.9 31.5 44.8 54.9 45.0 15.4 22.0 19.0 22.6 22.9 22.8 66.5 37.3 51.3 11.9 19.9 16.1 8.1(5.2 21.2 14.9 0.9 3.4 2.2 14.9 17.4 16.2 40.3 46.4 43.4 22.2 31.0 26.8 13.6 6.8 10.1 27.2 11.2(6.4 18.9

425)

= 23.9) 17.8) 49.7) 43.9) 38.7) 34.5) 34.3) 38.7) 37.1) 41.7) 30.2 35.9) 26.4 33.8) 28.9 58.2) 22.8) 29.3) 31.7) 80.5) 80.5) 29.1) 30.7) 23.8) 17.7) 29.1) 30.9) 33.4) 52.1) 40.6) 34.9) 23.9) 21.) 30.4) 43.2) 42.5) 39.2) 62.4) 25.1) 39.2) 83.7) 46.5) 21.1) 44.1) 37.0) 57.0) 26.5)

17.0) 17.2) 16.0) − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − (n 5.8) 3.9) 13.3) 7.5) 7.5) 9.3) 12.8) 15.8) 4.6) 3.2 6.2) − − −

− − − − − − − − − − (13.1 (8.0 (11.3 (35.8 (30.9 (34.8 (25.5 (23.3 (25.7 (26.0 (27.1 (28.3 (29.8 (23.1 (25.2 (50.1 (48.8 (14.0 (15.5 (26.0 (18.7 (22.4 (72.0 (69.8 (72.6 (18.5 (22.3 (14.1 (11.1 (7.8 (18.5 (18.8 (22.2 (39.5 (37.0 (10.8 (10.0 (8.2 (30.5 (31.6 (23.6 (13.3 (24.0 (14.6 (22.1 (29.3 (42.9

(0.5 (1.4 (1.1 (5.3 (1.8 (2.3 (3.4 (7.2 (4.7 (2.5

Western 17.9 11.4 14.3 42.6 36.7 39.3 31.7 28.6 29.9 2.9 2.1 32.3 32.7 32.6 37.0 36.0 29.0 50.0 56.3 53.6 18.9 18.0 18.9 32.3 23.6 27.4 78.4 75.5 76.8 30.1 23.4 26.2 8.5 18.4 14.1 3.7 4.2 4.0 11.6 23.4 18.0 24.3 27.5 26.0 36.0 45.8 41.6 15.3 5.7 9.7 16.7 8.7 12.2

436)

31.1) 35.1) 36.5) 62.7) 28.4) 35.5 21.3)38.9) 29.3 38.5) 25.4) 20.5) 59.7) 23.7) 19.9) 27.2) 30.5) 26.3) 28.4) 46.6) 23.5) 32.7) 27.3) 45.4) 50.2) 37.0) 29.3) 33.7) 56.0) 41.8) 23.9) 20.0) 21.7) 27.1) 26.6) 53.3) 32.5) 35.6) 28.5) (n

18.3) 29.1 19.4) 19.6) 15.4) 15.1) 15.0) 19.5) 14.3) 17.2) 23.0) − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − 9.6) 10.8) 9.5)10.8) 1.6 9.5) 7.1) 4.3) 10.8) 17.8) 3.8) − − − − − − − − − − − − − − − − − − − − (22.3 (22.7 (26.3 (48.7 (40.0 (20.3 (16.9 (8.7 (16.0 (13.8 (25.7 (29.2 (9.6 (14.2 (13.1 (45.5 (32.0 (40.4 (9.7 (13.1 (12.6 (19.5 (18.4 (18.1 (16.2 (32.4 (12.7 (8.9 (6.5 (24.1 (17.7 (21.2 (6.6 (46.1 (20.0 (28.7 (13.3 (12.7 (9.9 (11.6 (8.1 (15.6 (20.4 (15.5 (26.0 (7.0 (6.9

Llanos

(3.1 (5.5 (2.9 (5.5 (2.9 (0.3 (1.7 (1.2 (3.8 (4.5

The 5.5 7.7 30.2 23.0 26.5 27.0 28.5 27.8 10.1 7.7 55.9 46.7 51.0 26.1 24.0 21.1 31.9 35.0 33.7 13.8 18.0 16.0 19.1 16.5 52.7 38.5 45.2 13.9 17.8 15.9 5.3 16.0 10.8 1.1 3.5 2.3 20.7 26.0 23.5 23.9 20.5 21.9 21.7 39.3 17.5 6.5 11.8 13.0 9.2 11.0

409)

20.6)38.2) 40.7) 10.1 37.6) 45.0) 41.8) 41.2) 43.8) 43.7) 42.3)41.1) 25.1) 19.4) 22.1 41.1) 17.3 22.5) 22.5) 22.9)40.2) 14.0 22.1) 30.0) 25.8) 29.9) 25.9) 31.0) 21.8) 26.8) 30.6 30.5) 21.0) 52.7) 42.6) 47.2) 44.8) 26.1) 44.8) 24.2) 26.4) 24.3) 25.8) 23.7) 33.5) 23.5) 53.2) 19.3) 16.4) 15.3) 15.7) 15.1) 14.6) − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − 17.3) 12.8 13.9)11.8) 11.5) 5.3 10.1) 16.2) 4.5) 4.8) 3.6) − − − − − − − − − − − − − (11.4 (26.5 (28.1 (28.9 (32.6 (32.8 (31.3 (34.8 (31.9 (29.5 (32.2 (14.5 (12.6 (28.9 (40.0 (19.0 (18.7 (12.9 (12.8 (14.0 (40.1 (27.6 (12.4 (18.6 (10.0 (9.2 (15.1 (18.5 (18.2 (9.6 (15.9 (11.9 (6.9 (10.2 (29.8 (34.7 (35.8 (15.8 (35.8 (16.7 (15.9 (16.8 (15.5 (13.4 (21.3 (13.6 (6.8

(6.3 (4.7 (6.3 (0.7 (0.8 (0.9 6(18.8 (3.6 (4.8

North-Eastern 10.3 13.0 22.2 34.1 33.1 38.6 37.2 7.5 8.5 40.8 37.6 37.3 36.5 19.2 34.8 46.5 40.3 20.5 24.3 22.4 17.2 17.2 46.4 33.6 40.3 16.7 23.8 20.2 10.0 13.9 11.9 1.9 1.8 20.7 19.9 20.3 20.2 23.7 21.8 18.0 27.0 18.0 6.1 12.3 22.6 9.0 16.3

445)

= 41.8) 42.8) 47.6) 47.9)45.0) 36.0 66.3) 54.1) 32.6) 22.4)34.6) 15.7 29.1) 22.1) 26.0) 25.0) 67.4) 53.6) 23.4) 29.1) 31.1) 32.5) 40.7) 22. 49.7) 39.0)33.0) 35.7 39.0) 33.7) 26.8) 27.8) 25.8) 57.4) 39.2 30.0) 17.3 57.9) 30.3) 27.2) 30.4) 54.7)

20.2) 22.6) 19.1) 17.3) 27.8) 23.2) 12.4(8.8 18.7) 20.8) − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − (n 15.3)14.5) 15.5 11.7)17.1) 11.2) 9.4 17.6) 12.8) 22.5) 6.9)8.9) 5.5) 1.8

− − − − − − − − − − − − − − − − − − − (30.7 (22.6 (29.1 (28.8 (28.1 (31.2 (46.8 (33.7 (15.8 (20.3 (20.4 (9.0 (11.6–27.8) (11.7 (17.7 (20.3 (21.0 (7.5 (12.6 (11.5 (11.1 (13.8 (48.3 (33.3 (8.7 (12.5 (10.1 (8.5 (14.6.38.0) (12.6 (14.8 (16.0 (15.2 (26.7 (11.0 (7.9 (6.7 (5.6 (43.1 (13.3 (43.6 (13.5 (15.5 (13.5 (33.9 (6.7

(4.2 (6.2 (2.3 (4.7 (4.2 (4.5 (0.2 (0.7 (0.6 (2.5 (3.9 0.05.

Guayana 10.5 9.7 39.8 31.4 35.7 37.8 37.8 9.2 7.1 56.8 43.7 23.2 26.2 18.4 25.3 28.7 26.9 12.2 19.5 16.1 17.3 18.8 58.2 43.2 50.8 14.3 20.7 9.1 16.9 12.9 2.6 1.9 21.5 19.5 20.8 21.9 20.7 21.3 22.7 44.0 17.2 5.7 11.8 14.3 9.8 12.0 *p

0.01;

< 467)

p =

†

17.0) 43.4) 41.2) 41.4)40.1) 37.5 25.6) 36.6) 32.8)55.1) 50.3 32.8)36.5) 14.7 59.6) 19.8) 22.3) 19.6) 26.9)96.9) 20.5 85.4) 90.0) 21.5) 26.4) 26.7)26.3) 17.3 18.9) 18.5) 32.8) 44.4) 38.8) 50.1) 23.9) 34.6) 18.8)18.1) 8.2 39.6) 28.7 57.2) 19.3) 22.6) 45.0) 34.5)66.1) 16.4 36.6) 29.8) 20.6) 43.3) 33.3 32.7) 50.6) 42.3) 15.3) 16.6) − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − 3.9) 13.5) 5.6)7.8) 5.8) 1.3 10.4) 5.6) 5.3 6.4) − − − 14(n − − − − − − − −

(10.3 (10.4 (11.3 (32.5 (29.6 (31.6 (32.2 (16.2 (26.2 (25.3 (43.5 (29.0 (4.3 (22.4 (26.2 (46.0 (51.4 (11.5 (13.6 (13.5 (17.6 (91.4 (76.6 (84.6 (12.3 (17.1 (16.1 (16.9 (12.8 (10.7 (12.5 (22.7 (16.6 (26.7 (38.4 (14.4 (9.8 (18.4 (13.0 (36.9 (26.8 (54.2 (25.4 (22.5 (12.2 (22.0 (34.7 (6.0

regions. (2.0 (1.4 (6.2 (1.3 (2.8 (2.5 (4.6 (1.5

Central 13.8 13.9 44.5(38.6 37.8 35.2 36.1 3.6 2.3 26.2 31.1 43.4 38.2 31.1 60.3 51.6 55.5 14.9 17.6 16.3 30.7 26.0 94.9 81.4 87.6 16.4 21.4 9.2 21.2 15.7 4.7 3.8 14.5 16.0 15.2 27.0 27.2 32.4 44.2 18.8 6.9 12.3 25.7 10.2 17.3

between

416) 21.7) 14.1 37.1) 39.0) 47.1) 39.0)41.1) 36.8 44.0) 55.1) 34.3)42.3) 34.1 32.8) 29.7 35.4) 30.5 59.4) 61.7) 24.4) 23.1) 23.2) 19.5)18.9) 21.9 87.7) 69.7) 28.7) 26.3) 25.9) 19.2 23.1) 43.3) 42.9)25.0) 39.0 25.6) 34.0) 28.9 35.2) 67.5) 24.9) 35.8) 31.7) 36.6) 38.8) 46.6) 16.4) 36.5) 40.9 43.5) 27.5 15.4) 77.0) 29.6) 30.0)

18.3) 20.6) 14.4) 14.7) 13.7) = − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − −

5.6)4.2) 0.8 8.3) 6.1) 13.2) 4.4) 5.2) 2.8 − − − − − (n − − − − − − −

(12.9 (11.0 (26.4 (28.8 (29.0 (35.6 (28.8 (33.4 (33.5 (43.5 (24.5 (34.6 (22.4 (25.2 (48.7 (53.9 (15.2 (16.7 (14.2 (11.7 (13.8 (79.1 (59.4 (70.0 (18.8 (17.5 (19.3 (9.5 (20.0 (16.4 (20.0 (33.0 (32.5 (34.8 (11.9 (10.1 (9.4 (7.8 (16.2 (26.7 (25.3 (56.2 (16.2 (25.8 (24.6 (25.7 (31.3 (34.4 (7.6 (7.2

differences (1.5 (1.0 (1.6 (1.0 (3.1 (2.7 (4.3

Capital 10.6 13.5 31.5 33.7 41.2 37.2 2.1 2.6 20.5 38.7 49.3 38.4 30.1 62.0 54.1 55.5 19.4 20.1 19.7 18.3 16.6 83.9 64.7 73.7 23.4 21.6 13.3 24.4 19.5 5.1 3.8 24.7 30.5 28.0 30.9 35.0 40.3 37.7 15.8 10.0 12.5 14.1 7.6 10.6

determine

418)

= 28.1) 47.0) 48.9) 51.2) 38.5)22.6) 29.1 30.3) 24.4) 27.3 57.8) 28.7 54.4) 42.0) 43.8)43.7) 79.1) 15.2 29.6) 35.8) 30.6)22.8) 22.4 33.0) 33.1) 40.7) 50.1)43.2) 52.2) 38.1 27.9) 28.9)36.0) 33.7 46.2) 30.2 60.4) 36.4) 48.6) 65.3) 30.8) 56.4) 38.8 22.2) 32.7 42.4) 55.4) 18.5) 63.6) 27.1)

33.7) 20.4) 17.5) 17.2) 19.3) 16.6) 16.8 47.5) − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − (n 12.8) 12.9) 8.4) 4.7) 7.0)10.8) 7.4) 2.3 9.9) 17.0) 3.4) 2.9 regions. − − − − −

−

used − − − − − − − − − −

(15.6 (9.8 (14.0 (32.2 (34.1 (36.1 (24.6 (32.0 (11.3 (17.6 (15.8 (37.8 (43.8 (27.7 (29.4 (33.7 (65.7 (40.5 (16.6 (22.1 (21.1 (7.6 (11.5 (10.9 (19.7 (19.8 (26.4 (35.3 (33.8 (36.6 (48.0 (15.6 (9.9 (10.3 (8.4 (7.0 (16.4 (26.1 (35.6 (45.4 (25.8 (33.7 (54.8 (21.4 (44.9

(4.6 (6.6 (2.1 (1.2 (1.3 (3.3 (2.8 (2.8 (3.9 (0.1

was

10.9 0.6 16.1 3.1 Andeans 7.7 9.3 21.2 14.3 39.4 22.0 30.8 4.2 2.4 39.832.5 41.3 43.5 37.1 23.4 45.2 53.0 49.1 26.5(20.4 34.5 30.5 41.0 38.4 72.9 47.9 60.1 22.4 28.5 11.7 16.4 14.3 6.1 4.6 25.8 25.9 20.3 33.1 37.9 44.8 55.9 21.1 5.4 13.1 17.1 8.3 12.9 42.5 50.7 17.7 40.7 31.1 19.8 36.3 25.5 test factors

† risk

† † † † † † † † † † † † † † † riskF

fatal

Chi-square

† † † † † † † † † † † † †

† † † † † † † † † † † † M M M M M M M M M M M M M M M* M F Total Total Total F F Total Total F Total Total F Total F F F F Total F Total F Total F Total F Total F F Total F Total Total Total F Total Total F CVD CI).

(95%

cardiometabolic

and

sex

symptoms M

obesity

inactivity

high symptoms smoker

weight M

percent

LDL-c

HDL-c 5

are

Diabetes Obesity Hypertriglyceridemia Hypercholesterolemia Prediabetes Hypertension Underweight Overweight Abdominal Low High Anxiety Depression Vegetables Physical Current Fruits Normal 10-year Table Data

Prevalence

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cross-sectional survey in adults, Prim. Care Diab. (2020), https://doi.org/10.1016/j.pcd.2020.07.006

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R. Nieto-Martínez et al. / Primary Care Diabetes xxx (2020) xxx–xxx 7

In men, diabetes increased until the 50th decade, whereas of cardiovascular risk factors in a city of Venezuela. Prevalence

hypercholesterolemia, hypertriglyceridemia, and high LDL- of diabetes was 6% in Barquisimeto [7]. The CARMELA also stud-

c increased until that age in women. In women, low HDL-c ied 6 other capitals of Latin America (n = 11,550) with a global

decreased after the 50th decade. In men, some CMRF were similar prevalence of 7% [7]. In Maracaibo (2007–2009), the second large

in all age categories but lowest before the 50th decade (hyper- city of Venezuela, prevalence of diabetes of 8.8% was obtained

cholesterolemia, hypertriglyceridemia, high LDL-c, and physical in 2026 subjects evaluated [28]. During 2006–2010, 1392 adults

inactivity). Nutritional status and current smokers also varied with were evaluated in three regions of the country in the Venezuelan

age. Current smoker was less prevalent in the oldest group in men Metabolic Syndrome, Obesity, and Lifestyle Study (VEMSOLS), age-

and in the youngest group in women. In men, the prevalence of standardized prevalence of diabetes was 8.0% [12], consistent with

underweight was lower at the groups with 50–64 years (1.4%) and a weighted prevalence of eight studies in the country of 7.0% from

65 or more years (2.6%) compared with younger ages (∼4.3%) (p < 2006 to 2010 [14]. This represents around a 75% increase in dia-

0.01), and prevalence of obesity was higher at the35–49 years old betes prevalence in Venezuela in less than a decade. Our results in

group. In women, the prevalence of underweight was very high at diabetes prevalence are similar to those obtained in 7524 subjects

the30–34 years old group (8.6%), followed by the 65 years or older from 3 subnational samples from Argentina, Uruguay, and Chile

group (5.1%) (p < 0.01) (Table 4). (12.4%) in the Centro de Excelencia en Salud Cardiovascular para

el Cono Sur (CESCAS) I study (2010–2011) [5] and higher than that

obtained in the baseline data of 3238 adults studied in 4 settings of

3.2.3. By regions

Peru (7%) [4] in the Cohort Study CRONICAS (2010–2011). World-

Prevalence of CMRF varied among regions (Table 5). This het-

wide, diabetes prevalence is continually growing, the number of

erogeneity goes from the Andes region with 5 of the worst values

subjects with diabetes increased from 108 million in 1980 (4.5%) to

of CMRF, highest dyslipidemia, hypercholesterolemia (30.5%) and

422 million in 2014% (8.4%) [29]. This increase is primarily based

hypertriglyceridemia (38.4%), physical inactivity (50.7%), current

on a longer life expectancy and the obesity epidemic [29].

smoker (13.1%) and depressive symptoms (4.6%) to Los Llanos

A striking ﬁnding of our study is the high prevalence of pre-

region with the least adverse cardiometabolic proﬁle. The preva-

diabetes (34.9%), which was similar in all age categories. In the

lence of diabetes in the Llanos (7.7%) was half that observed in the

EVESCAM, an OGTT was performed to all participants which let us

Western region (14.3%), and the prevalence of obesity (17.3%) was

detect a higher proportion of the population at risk to develop dia-

nearly half that observed in the Central region (30.5%) (p < 0.01).

betes and the prediabetes subtypes (impaired fasting glucose and

Although the highest prevalence (43.4%) of daily consumption of

impaired glucose tolerance). The heterogeneous distribution across

vegetables (p < 0.01) and the second lowest proportion of high LDL-

the regions ranged from 17.7% in Andeans region to 47.5% in Zulia

c (16.1%) was found, Zulia region showed the highest composite

region imposes the challenge of determining the geospatial compo-

10-year high CVD risk (18.9%) compared with the lowest one in

nents and determinants of these differences, and thus implement

the Capital region (10.6%) and almost half of the population (47.5%)

more effective prevention strategies. This number represent about

in Zulia was in a prediabetic state which is more than double the

7 million adults with prediabetes, potentially 2 million of new cases

lowest prediabetes proportion in the country found in the Andeans

with T2D in the next three to ﬁve years. This high prevalence was

region (17.7%).

similar than the observed in the US in 2017 (33.9%) [30].

Western region also showed a high number of CMRF elevated

Hypertension prevalence in our study (34.1%) was also higher

(diabetes, obesity, low HDL-c, high LDL-c, low consumption of

than reported previous reports in Barquisimeto (CARMELA, 24.7%)

fruits, and higher physical inactivity), but also the lowest preva-

[7], Maracaibo (23.1%) [28], the three regions evaluated in the

lence of current smokers compared with other regions. Central

VEMSOLS (31.3%) [13], and Peru (19.7%) [4] but lower than South-

region presented the highest prevalence of low HDL-c (87.6%) and

ern Cone countries (40.8%) [5]. Worldwide, high blood pressure

the lowest prevalence of hypercholesterolemia (16.3%). Capital

(≥140/90 mmHg) prevalence is getting lower in high and middle-

region presented the highest prevalence of daily consumption of

income countries but is continually growing in low-income and

fruits (28.0%). North-Eastern region presented the highest preva-

in some middle-income countries [31]. High and middle-income

lence of underweight (8.5%) and the lowest prevalence of obesity

(15.7%). countries are more exposed to advances in prevention and treat-

ment that could account, in part, for these blood pressure trends

[32]. However, blood pressure is also inﬂuenced by genetic, nutri-

4. Discussion

tional, behavioral, emotional, and social drivers that are difﬁcult to

Cardio-metabolic diseases are a global problem and information determine once speciﬁc interventions are already in place [33–35].

on their distribution and determinants in low- and middle-income Contrary to the observed with diabetes and hypertension, obe-

countries is needed. The EVESCAM is contributing relevant data sity prevalence (24.6%) in this study was lower than previously

regarding the prevalence and distribution of cardiometabolic dis- reported within Venezuela, Maracaibo (33.8%) [28] and VEMSOLS

ease and its risk factors. This is the ﬁrst report presenting the (29.3%) [36], and also in the Region, Peru (26.9%) [4] and South-

prevalence of CMRF from a nationally representative evaluation of ern Cone (35.7%) [5]. Conversely, underweight prevalence changed

adults in Venezuela. The sample was obtained randomly from the from 1.1% in VEMSOLS [36] to 4.4% in this report. These changes

eight regions and then a weighted analysis was made to obtain rep- suggest a population weight reduction trend, contrary to the world-

resentative data of the whole country. The population was mostly wide tendency where the number of subjects with obesity has

mixed race and from urban areas (∼80%), with a high proportion increased from 105 million (4.8%) in 1975 to 641 million (12.8%) in

of poverty (47.8%), high educational degree, a 64.9% had completed 2014 [37]. The current Venezuelan sociopolitical crisis which gen-

high school and 27.7% had reached university degree. Most of the erates a large inﬂation rate (over 1 million), shortage of foods and

population is attended in public health care centers (67.4% in pub- transportation deﬁcit could explain, in part, these results by mod-

lic centers exclusively, and 9% in both public and private centers). iﬁcation of the energy balance. The pathological distribution of fat

CMRF prevalence varied by gender, age, and region evaluated. has decreased but in a lower proportion. The percentage of subjects

In the EVESCAM, the diabetes prevalence (12.3%) was very high with abdominal obesity in our study (47.6%, cutoff ≥ 94 cm in men

compared with previous reports. The Cardiovascular Risk Factor and ≥ 90 cm in women) was higher to data obtained 20 years ago in

Multiple Evaluation in Latin America (CARMELA) study (2008) was Zulia state (42.9%, cutoff ≥ 120 cm in men and ≥ 88 cm in women)

the ﬁrst to obtain data representative (n = 1848) of the prevalence but using a lower cutoff and lower than obtained in Maracaibo 10

Please cite this article in press as: R. Nieto-Martínez, et al., Cardiometabolic risk factors in Venezuela. The EVESCAM study: a national

cross-sectional survey in adults, Prim. Care Diab. (2020), https://doi.org/10.1016/j.pcd.2020.07.006

G Model

PCD-925; No. of Pages 9 ARTICLE IN PRESS

8 R. Nieto-Martínez et al. / Primary Care Diabetes xxx (2020) xxx–xxx

years ago (50.7%) [28] with a similar cutoff. In CESCAS (Argentina, geneity in the proﬁle of CMRF is described. Notoriously, compared

Uruguay, and Chile) a higher prevalence of abdominal obesity was with previous reports, some risk factors were highest (diabetes,

reported (52.9%) [5]. hypertension and underweight), similar (Low HDL-c) and lowest

Such as obesity, dyslipidemias prevalence, except low HDL- (obesity, abdominal obesity, dyslipidemia, and current smoker).

c, were also lower than previous reports. Comparing VEMSOLS An alarming number of subjects with prediabetes, as well as, low

and the EVESCAM, the change is represented as follow: hyper- intake of vegetables and fruits and high physical inactivity, are an

triglyceridemia (39.7% and 22.7%); hypercholesterolemia (22.2% urgent call to implement structured lifestyle plans to prevent dia-

and 19.8%); high LDL-c (23.3% and 20.5%); low HDL-c (58.6% and betes and cardiovascular disease and reduce their negative burden

63.2%), respectively [9]. These differences are present despite the on mortality and costs of healthcare systems. Considering the cur-

cut-off values to deﬁne dyslipidemias in EVESCAM were lower than rent complex humanitarian emergency context in Venezuela with a

those used in VEMSOLS, which was based on the Adult Treatment shortage of medications, electric service, gasoline, food insecurity,

Panel (ATP) III [38] and the use of lipid lowering drugs was not con- and hyperinﬂation, allostatic overload as a measure of popula-

sidered in the VEMSOLS to deﬁne dyslipidemia. Higher values of tion stress can worsen the outcomes. From a resource-constrained

triglycerides (32.3%) and low HDL-c (65.3%) also were reported in setting perspective, these ﬁndings will serve for purposes of estab-

a study made between 1999 and 2001 that represented the popu- lishing priority in vulnerable groups and regions to overcome the

lation of the Zulia State of Venezuela [8]. A change in Venezuelan’s crisis and appropriately implement interventions to tackle the bur-

nutritional habits could drive these differences, but this analysis is den of cardiometabolic diseases.

beyond the scope of this paper.

Several cardiometabolic risk behaviors were evaluated in the

Authors contributions

EVESCAM. The prevalence of physical inactivity was signiﬁcant

(35.2%) and higher in women (∼ 40%) than men (∼ 30%). The pro-

RNM and MIM conceived the idea of this study. RNM conceived

portion of physical inactivity in our report is higher than reported

and designed the overall study with support from EU, JPGR, MD and

worldwide (31.1%) [39] in adults, lower than reported in the Amer-

MIM. RNM and JPGR contributed with data analysis and wrote the

icas (43%) [39] and similar than reported in Southern Cone (35.2%)

draft of the manuscript. EU and JPGR made the statistical analysis.

[5]. Daily consumption of fruits and vegetables has been estab-

MIM, EU, MD, JPGR, ED, RC, AG and RNM coordinated and super-

lished as a cardioprotective behavior [40]. Few previous studies

vised ﬁeldwork activities and recollected data. MIM, EU, MD and

have reported fruits and vegetable intake in Venezuela. A study

JM contributed to the discussion and review.

using food frequency questionnaire informed twice more intake of

fruits (40% vs. 20.9%) and a half intake of vegetables (14% vs. 30%)

Funding

in adult women compared with this study [41].

Tobacco smoking in Venezuela was previously assessed in Bar-

The EVESCAM was partially funded by a grant of Novartisand

quisimeto city as part of the CARMELA study. The prevalence of

donations.

current smokers was 21.8%, 32.2% in men and 14.9% in women [7],

duplicating the observed in this report, 11.7% total, 17.0% in men

Conﬂicts of interest

and 6.8% in women. These values were also lower than reported

in Maracaibo (14.9%, 2007–2009) [28]. This is in concordance with

JM has received honoraria from Abbott Nutrition International

the global tendency. Worldwide, the age-standardized prevalence

for lectures and program development. The other authors declare

of daily smoking was 25.0% in men and 5.4% in women, represent-

no conﬂict of interest.

ing 28.4% and 34.4% reductions, respectively, since 1990 [42]. In

Venezuela, this is probably resulting from the strong tobacco con-

trol policies implemented and the continuous increasing cost of Acknowledgments

tobacco due to the rising inﬂation rate. This study is the ﬁrst on

using Globorisk score to report of 10-year fatal and non-fatal CVD We thank the Venezuelan Society of Internal Medicine directive

risk in Venezuela. 14% of the population, twice in men (19.2%) than and members and the EVESCAM research team for their continued

women (9.1%) had more than 20% risk of CVD. These results are collaboration and support.

higher than Mexico (7.5% in men and 6.9% in women) and Jamaica

(8.0% in men and 6.9% in women) [27].

Appendix A. Supplementary data

The impact of the current sociopolitical crisis on the CMRF

prevalence limits the interpretation of this study. All the regions

Supplementary material related to this article can be found,

were not assessed during the same year; in consequence, some

in the online version, at doi:https://doi.org/10.1016/j.pcd.2020.07.

regions were more exposed to the crisis than others. This situa- 006.

tion of complex humanitarian emergency causes food insecurity

and is probably related to obesity reduction and can be driving

References

the improvement of some CMRF. However, other CMRF’s could

be worsening like the increasing the prevalence of underweight

[1] WHO, Global Health Estimates 2016: Deaths by Cause, Age, Sex, by Country and

which is related to undernutrition and increased risk for com- by Region, 2000–2016, World Health Organization, Geneva, 2018 http://www.

who.int/healthinfo/global burden disease/estimates/en/.

municable diseases. Despite this limitation, the study has many

[2] Cardiovascular disease, chronic kidney disease, and diabetes mortality bur-

strengths, including the national representativeness, the multi-

den of cardiometabolic risk factors from 1980 to 2010: a comparative risk

stage stratiﬁed sampling, the standardization of the data collection assessment, Lancet Diabetes Endocrinol. 2 (2014) 634–647.

and laboratory process including OGTT in all participants, and the [3] L. Fernando, S. Pamela, L. Alejandra, Cardiovascular disease in Latin America:

the growing epidemic, Prog. Cardiovasc. Dis. 57 (2014) 262–267.

weighing analysis, ensuring the result’s external validation.

[4] A. Bernabe-Ortiz, R.M. Carrillo-Larco, R.H. Gilman, W. Checkley, L. Smeeth, J.J.

Miranda, Contribution of modiﬁable risk factors for hypertension and type-2

diabetes in Peruvian resource-limited settings, J. Epidemiol. Community Health

5. Conclusion

70 (2016) 49–55.

[5] A.L. Rubinstein, V.E. Irazola, M. Calandrelli, N. Elorriaga, L. Gutierrez, F. Lanas,

The ﬁrst national study in Venezuela reporting the prevalence

J.A. Manfredi, N. Mores, H. Olivera, R. Poggio, J. Ponzo, P. Seron, C.S. Chen, L.A.

and distribution of CMRF in adults is presented. An evident hetero- Bazzano, J. He, Multiple cardiometabolic risk factors in the Southern Cone of

Please cite this article in press as: R. Nieto-Martínez, et al., Cardiometabolic risk factors in Venezuela. The EVESCAM study: a national

cross-sectional survey in adults, Prim. Care Diab. (2020), https://doi.org/10.1016/j.pcd.2020.07.006

G Model

PCD-925; No. of Pages 9 ARTICLE IN PRESS

R. Nieto-Martínez et al. / Primary Care Diabetes xxx (2020) xxx–xxx 9

Latin America: a population-based study in Argentina, Chile, and Uruguay, Int. [26] The IPAQ Group, Guidelines for Data Processing and Analysis of the Interna-

J. Cardiol. 183 (2015) 82–88. tional Physical Activity Questionnaire (IPAQ) - Short Form, Version 2.0 April

[6] R. Nieto-Martínez, O. Hamdy, D. Marante, M. Marulanda, A. Marchetti, R. Hegazi, 2004 — Google Search [cited 2020 June 13]. Available from:, 2004 https://sites.

J. Mechanick, Transcultural diabetes nutrition algorithm (tDNA): Venezuelan google.com/site/theipaq/.

application, Nutrients 6 (2014) 1333–1363. [27] P. Ueda, M. Woodward, Y. Lu, K. Hajifathalian, R. Al-Wotayan, C.A. Aguilar-

[7] H. Schargrodsky, R. Hernandez-Hernandez, B.M. Champagne, H. Silva, R. Vin- Salinas, A. Ahmadvand, F. Azizi, J. Bentham, R. Cifkova, M. Di Cesare, L. Eriksen,

ueza, L.C. Silva Aycaguer, P.J. Touboul, C.P. Boissonnet, J. Escobedo, F. Pellegrini, F. Farzadfar, T.S. Ferguson, N. Ikeda, D. Khalili, Y.H. Khang, V. Lanska, L. Leon-

A. Macchia, E. Wilson, CARMELA: assessment of cardiovascular risk in seven Munoz, D.J. Magliano, P. Margozzini, K.P. Msyamboza, G. Mutungi, K. Oh,

Latin American cities, Am. J. Med. 121 (2008) 58–65. S. Oum, F. Rodriguez-Artalejo, R. Rojas-Martinez, G. Valdivia, R. Wilks, J.E.

[8] H. Florez, E. Silva, V. Fernandez, E. Ryder, T. Sulbaran, G. Campos, G. Calmon, Shaw, G.A. Stevens, J.S. Tolstrup, B. Zhou, J.A. Salomon, M. Ezzati, G. Danaei,

E. Clavel, S. Castillo-Florez, R. Goldberg, Prevalence and risk factors associated Laboratory-based and ofﬁce-based risk scores and charts to predict 10-year

with the metabolic syndrome and dyslipidemia in White, Black, Amerindian risk of cardiovascular disease in 182 countries: a pooled analysis of prospective

and Mixed Hispanics in Zulia State, Venezuela, Diabetes Res. Clin. Pract. 69 cohorts and health surveys, Lancet Diabetes Endocrinol. 5 (2017) 196–213.

(2005) 63–77. [28] V. Bermudez, J. Salazar, Prevalence and associated factors of insulin resistance

[9] J. González-Rivas, I. Brajkovich, E. Ugel, A. Risquez, R. Nieto-Martínez, Preva- in adults from Maracaibo City, Venezuela 2016 (2016), 9405105.

lence of dyslipidemias in three regions from Venezuela: the VEMSOLS study [29] Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-

results, Arq. Bras. Cardiol. 110 (2018) 30–35. based studies with 4.4 million participants. NCD Risk Factor Collaboration

[10] I. Brajkovich, J. González-Rivas, E. Ugel, A. Risquez, R. Nieto-Martínez, Preva- (NCD-RisC), Lancet (London, England) 387 (2016) 1513–1530.

lence of metabolic syndrome and in three regions from Venezuela: the [30] Centers for Disease Control and Prevention, National Diabetes Statis-

VEMSOLS study results, Epub Int. J. Cardiovasc. Sci. 31 (2018) 603–609. tics Report, 2017: Estimates of Diabetes and Its Burden in the, U.S.

[11] R. Nieto-Martínez, J. González-Rivas, E. Ugel, Prevalence of cardiometabolic risk Department of Health and Human Services, Centers for Disease Con-

factors in three populations from Venezuela: the VEMSOLS Study 2006–2010, trol and Prevention, United States. Atlanta, GA, 2017, Available from

Rev. Méd. UIS 31 (2018) 15–22. https://www.cdc.gov/diabetes/data/statistics/statistics-report.html.

[12] R. Nieto-Martinez, J.I. Mechanick, I. Brajkovich, E. Ugel, A. Risques, H. Florez, (Accessed 14 May 2020).

J.P. Gonzalez-Rivas, Prevalence of diabetes in three regions of Venezuela. The [31] Worldwide trends in blood pressure from 1975 to 2015: a pooled analysis

VEMSOLS study results, Prim. Care Diabetes 12 (2018) 126–132. of 1479 population-based measurement studies with 19·1 million partici-

[13] J. González-Rivas, R. García Santiago, E. Ugel, I. Brajkovich, A. Risquez, R. Nieto- pants. NCD Risk Factor Collaboration (NCD-RisC), Lancet (London, England) 389

Martínez, High prevalence and poor control of hypertension in ﬁve populations (2016) 37–55.

from Venezuela: the VEMSOLS study, Invest. Clin. 57 (2016) 237–245. [32] C.K. Chow, K.K. Teo, S. Rangarajan, S. Islam, R. Gupta, A. Avezum, A. Bahonar,

[14] R. Nieto-Martínez, J.P. González, M. Lima-Martínez, V. Stepenka, A. Rísquez, J. Chifamba, G. Dagenais, R. Diaz, K. Kazmi, F. Lanas, L. Wei, P. Lopez-Jaramillo,

J.I. Mechanick, Diabetes care in Venezuela, Ann. Glob. Health 81 (2015) L. Fanghong, N.H. Ismail, T. Puoane, A. Rosengren, A. Szuba, A. Temizhan, A.

776–791. Wielgosz, R. Yusuf, A. Yusufali, M. McKee, L. Liu, P. Mony, S. Yusuf, Preva-

[15] R. Nieto-Martínez, M.I. Marulanda, E. Ugel, M. Duran, J. González-Rivas, M. lence, awareness, treatment, and control of hypertension in rural and urban

Patino,˜ L. López-Gómez, P. Monsalve, H. Marcano, N. Barengo, P. Aschner, communities in high-, middle-, and low-income countries, JAMA 310 (2013)

H. Flórez, Venezuelan study of cardio-metabolic health (EVESCAM): general 959–968.

description and sampling, Med. Interna 31 (2015) 102–111. [33] Q. Chan, J. Stamler, L.M.O. Griep, M.L. Daviglus, L.V. Horn, P. Elliott, An update

[16] R. Nieto-Martínez, M.I. Marulanda, J.P. González-Rivas, E. Ugel, M. Durán, N. on nutrients and blood pressure. Summary of INTERMAP study ﬁndings, J.

Barengo, P. Aschner, M. Patino,˜ L.-G. L, P. Monsalve, H. Marcano, H. Florez, Atheroscler. Thromb. 23 (2016) 276–289.

Cardio-metabolic health Venezuelan study (EVESCAM): design and implemen- [34] Y. Cuffee, C. Ogedegbe, N.J. Williams, G. Ogedegbe, A. Schoenthaler, Psychoso-

tation, Invest. Clin. 58 (2017) 56–69. cial risk factors for hypertension: an update of the literature, Curr. Hypertens.

[17] E. von Elm, D.G. Altman, M. Egger, S.J. Pocock, P.C. Gotzsche, J.P. Vandenbroucke, Rep. 16 (2014) 483.

The strengthening the Reporting of Observational Studies in Epidemiology [35] C. Trudel-Fitzgerald, P. Gilsanz, M.A. Mittleman, L.D. Kubzansky, Dysregulated

(STROBE) statement: guidelines for reporting observational studies, PLoS Med. blood pressure: can regulating emotions help? Curr. Hypertens. Rep. 17 (2015)

4 (2007) e296. 92.

[18] H. Méndez-Castellano, M.C. Méndez, Estratiﬁcación social y humana. Método [36] R. Nieto-Martínez, J. González-Rivas, E. Ugel, I. Brajkovich, A. Risquez, W.T. Gar-

de Graffar modiﬁcado, Arch. Venez. Puer. Pediatr. 49 (1986) 93–104. vey, J.I. Mechanick, Application of the AACE/ACE advanced framework for the

[19] A.S. Zigmond, R.P. Snaith, The hospital anxiety and depression scale, Acta Psy- diagnosis of obesity and cardiometabolic disease staging in a general popula-

chiatr. Scand. 67 (1983) 361–370. tion from three regions of Venezuela: the VEMSOLS study results, Endocr. Pract.

[20] L.M. Vera-Cala, M. Orostegui, L.I. Valencia-Angel, N. Lopez, L.E. Bautista, Accu- 24 (2018) 6–13.

racy of the Omron HEM-705 CP for blood pressure measurement in large [37] Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled

epidemiologic studies, Arq. Bras. Cardiol. 96 (2011) 393–398. analysis of 1698 population-based measurement studies with 19.2 million par-

[21] 2. Classiﬁcation and diagnosis of diabetes: standards of medical care in ticipants. NCD Risk Factor Collaboration (NCD-RisC), Lancet (London, England)

diabetes—2019, Diabetes Care 42 (2019) S13–s28. 387 (2016) 1377–1396.

[22] G. Mancia, R. Fagard, K. Narkiewicz, J. Redon, A. Zanchetti, M. Bohm, T. Chris- [38] Executive summary of the third report of the national cholesterol educa-

tiaens, R. Cifkova, G. De Backer, A. Dominiczak, M. Galderisi, D.E. Grobbee, T. tion program (NCEP) expert panel on detection, evaluation, and treatment of

Jaarsma, P. Kirchhof, S.E. Kjeldsen, S. Laurent, A.J. Manolis, P.M. Nilsson, L.M. high blood cholesterol in adults (adult treatment panel III), JAMA 285 (2001)

Ruilope, R.E. Schmieder, P.A. Sirnes, P. Sleight, M. Viigimaa, B. Waeber, F. Zan- 2486–2497.

nad, 2013 ESH/ESC Guidelines for the management of arterial hypertension: [39] P.C. Hallal, L.B. Andersen, F.C. Bull, R. Guthold, W. Haskell, U. Ekelund, Global

the Task Force for the management of arterial hypertension of the European physical activity levels: surveillance progress, pitfalls, and prospects, Lancet

Society of Hypertension (ESH) and of the European Society of Cardiology (ESC), (London, England) 380 (2012) 247–257.

J. Hypertens. 31 (2013) 1281–1357. [40] M. Dehghan, A. Mente, X. Zhang, S. Swaminathan, W. Li, V. Mohan, R. Iqbal, R.

[23] Clinical guidelines on the identiﬁcation, evaluation, and treatment of over- Kumar, E. Wentzel-Viljoen, A. Rosengren, L.I. Amma, A. Avezum, J. Chifamba, R.

weight and obesity in adults—the evidence report. National institutes of health, Diaz, R. Khatib, S. Lear, P. Lopez-Jaramillo, X. Liu, R. Gupta, N. Mohammadifard,

Obes. Res. 6 (Suppl 2) (1998) 51S–209S. N. Gao, A. Oguz, A.S. Ramli, P. Seron, Y. Sun, A. Szuba, L. Tsolekile, A. Wielgosz,

[24] P. Aschner, R. Buendia, I. Brajkovich, A. Gonzalez, R. Figueredo, X.E. Juarez, R. Yusuf, A. Hussein Yusufali, K.K. Teo, S. Rangarajan, G. Dagenais, S.I. Bangdi-

F. Uriza, A.M. Gomez, C.I. Ponte, Determination of the cutoff point for waist wala, S. Islam, S.S. Anand, S. Yusuf, Associations of fats and carbohydrate intake

circumference that establishes the presence of abdominal obesity in Latin with cardiovascular disease and mortality in 18 countries from ﬁve continents

American men and women, Diabetes Res. Clin. Pract. 93 (2011) 243–247. (PURE): a prospective cohort study, Lancet 390 (2017) 2050–2062.

[25] P.S. Jellinger, Y. Handelsman, P.D. Rosenblit, Z.T. Bloomgarden, V.A. Fonseca, A.J. [41] M. Montilva, Y. Berné, J. Papale, M.N. García-Casal, Y. Ontiveros, L. Durán, Perﬁl

Garber, G. Grunberger, C.K. Guerin, D.S.H. Bell, J.I. Mechanick, R. Pessah-Pollack, de alimentación y nutrición de mujeres en edad fértil de un Municipio del Cen-

K. Wyne, D. Smith, E.A. Brinton, S. Fazio, M. Davidson, American association of troccidente de Venezuela, Anales Venezolanos de Nutrición 23 (2010) 67–74.

clinical endocrinologists and american college of endocrinology guidelines for [42] Smoking prevalence and attributable disease burden in 195 countries and ter-

management of dyslipidemia and prevention of cardiovascular disease, Endocr. ritories, 1990–2015: a systematic analysis from the Global Burden of Disease

Pract. 23 (2017) 1–87. Study 2015, Lancet 389 (2017) 1885–1906.

Please cite this article in press as: R. Nieto-Martínez, et al., Cardiometabolic risk factors in Venezuela. The EVESCAM study: a national

cross-sectional survey in adults, Prim. Care Diab. (2020), https://doi.org/10.1016/j.pcd.2020.07.006