Abstract
Background: Metabolic syndrome (MS) and its components increase the risk of a number of chronic diseases. Evidence regarding its prevalence among health professionals, particularly in Latin America, is limited. The purpose of this study was to assess the overall prevalence of MS and its components among health professionals and students from five Latin American countries.
Methods: A cross-sectional multicenter study entitled LATIN America METabolic Syndrome (LATINMETS) was conducted on five groups of apparently healthy volunteer subjects. Sociodemographic factors, lifestyle variables (smoking and physical activity), anthropometric measurements (weight, height, and waist circumference), standard biochemical analyses [triglycerides, glucose, and high-density lipoprotein cholesterol (HDL-C)], and blood pressure measurements were assessed. MS was diagnosed based on internationally harmonized criteria. Associations between MS components and sociodemographic, lifestyle, and anthropometric variables were analyzed using multivariate logistic regression.
Results: A total of 1,032 volunteers (n = 316-Mexico, n = 285-Colombia, n = 223-Brazil, n = 132-Paraguay, and n = 76-Argentina) were recruited. The majority of them were women (71.9%), students (55.4%), and younger than 28 years (67.2%). The overall prevalence of age-standardized MS was 15.5% (23.1% men and 12.2% women). The majority (59.3%) presented at least one MS component, mainly abdominal obesity (29.7%) and low HDL-C levels (27.5%). After adjusting for age and sex, MS and its components were positively associated with being overweight or obese.
Conclusions: MS prevalence in this study was similar to that generally found among young populations in Latin-American countries. More than half of the sample had at least one MS component, suggesting that preventive measures and treatments aimed at achieving low-risk health status are essential in this population.
Keywords: metabolic syndrome, health professionals, Latin America, dyslipidemia, abdominal obesity
Introduction
Several Latin American countries are undergoing a demographic, epidemiological and nutritional transition that has affected the region's disease profile.1,2
Noncommunicable diseases—particularly cardiovascular diseases (CVDs), cancer, and type 2 diabetes (DM2)—are now among the leading causes of death (accounting for 70%–80% of all deaths) in most Latin-American countries, including Colombia,3 Brazil,4 Mexico,5 Paraguay,6 and Argentina.7
Metabolic syndrome (MS) increases the risk of developing CVD and DM2.8,9 It is considered to exist when an individual presents three or more of the following risk factors: abdominal obesity (AO), high fasting plasma glucose (High-FPG), hypertriglyceridemia, low levels of high-density lipoprotein cholesterol (HDL-C; low HDL-C), and high blood pressure (High-BP).10 The etiology of MS is multifactorial and attributable to an interaction between genetic, metabolic, and environmental factors.11 MS prevalence varies in accordance with the age, sex, and ethnic profiles of the populations studied, as well as the criteria used to diagnose it.11–13 The populations of several Latin American countries (Chile, Mexico, Venezuela, Ecuador, Puerto Rico, Colombia, Brazil, Peru, Argentina, and Uruguay) have exhibited a high prevalence of MS (>20%),11,12,14–23 as assessed by a range of diagnostic criteria. This high prevalence coupled with the condition's health consequences makes MS a significant public health problem in these countries.24
Data on MS prevalence among health professionals, particularly in Latin America, are limited, and data on this population based on uniform MS diagnostic criteria are even more scarce. Studies in Mexico suggest that the prevalence of MS within this population (mainly in the fields of medicine and nursing) may be comparable to its prevalence within the general population (>20%).25–29 Among health professionals in Brazil30 and medical students in Mexico,31 Venezuela,32 and Ecuador,33 prevalence rates under 15% have been reported based on a variety of diagnostic criteria.
Health professionals are an important segment of the population because of the role they play in promoting health. A congruence between what they know and what they do could thus be expected, as could an expectation for this population to maintain a low-risk health status. Evaluating cardiometabolic risk factors among health professionals is important because unhealthy behaviors within this population could negatively influence the health of the general population. Moreover, healthy lifestyles among health professionals have been associated with their patients having positive attitudes toward preventive recommendations34 as well as a higher likelihood of engaging in preventive health practices.35
Assessments of cardiometabolic risk factors among health professionals could lead to recommendations for the treatment and/or prevention of MS complications that would benefit both this group and the general population.
The purpose of this multicenter study was to evaluate the overall prevalence of MS and its components in a sample of young health professionals from five Latin American countries (Mexico, Colombia, Brazil, Paraguay, and Argentina).
Materials and Methods
Study population
A cross-sectional multicenter study entitled LATIN America METabolic Syndrome (LATINMETS) was conducted. The LATINMETS project was coordinated by Universitat Rovira i Virgili in Reus, Spain, and included research groups in Mexico, Colombia, Brazil, Paraguay, and Argentina.
The LATINMETS study population was made up of a nonrandom or convenience sample of individuals between 20 and 59 years of age in apparent good health, who lived in five different cities in five countries. They were either health professionals who worked at a health facility and/or a higher education institution or university students in health-related fields (medicine, nursing, nutrition, dentistry, psychology, pharmaceutical biochemistry, and physical education), who were in their final semesters of coursework. Volunteers were recruited from one or two health care facilities and/or health education institutions in each of the following cities: Buenos Aires (Argentina), Guadalajara (Mexico), Viçosa (Brazil), Medellín (Colombia), and Asunción (Paraguay) in the period from October 2010 to July 2013. Excluded were the following: (1) pregnant or breastfeeding women; (2) people taking steroids; (3) people suffering from illnesses requiring hospitalization at the time of the study; (4) cancer patients or individuals who had had cancer within 3 years before the study; and (5) subjects who did not complete the entire assessment [BP, blood collection, and waist circumference (WC)].
Seven subjects (Mexico n = 3, Brazil n = 3, and Argentina n = 1) were excluded from the analysis due to a lack of data in relationship to MS components. These participants were women with an average age of 31 years (standard deviation 10.4), who presented normal body mass index (BMI). With respect to the assessed components, only one of these participants had AO, and another had low HDL-C (both from Brazil).
The study was designed in accordance with Declaration of Helsinki guidelines and approved by the respective ethics committees of the universities in each country (Colombia: Medical Research Institute Bioethics Committee, Faculty of Medicine, University of Antioquia, certificate 008-29, in addition to approval by the Research Development Committee, certificate 580-23; Mexico: University Center for Health Sciences Committee of Ethics and Research, University of Guadalajara, registry number CI-13909; Argentina: Provisional Committee on Human Ethics, Faculty of Medicine, University of Buenos Aires, Resolución Consejo Directivo 2862; Brazil: Ethics Committee on Research with Human Beings, Federal University of Viçosa, registry number 005/2011; and Paraguay: Research Ethics Committee, Faculty of Medical Sciences, National University of Asuncion). All subjects signed an informed consent, and the confidentiality of their personal data was guaranteed.
Sociodemographic and lifestyle assessment
By means of interviews, data were collected in the following categories: age, sex, country, occupational status (student/professional), health sector, personal medical history, and medication use. Smoking habits were recorded, and data on physical activity (PA) levels were collected by means of a Spanish-language version of the Minnesota Leisure-Time Physical Activity Questionnaire.36 Based on reported frequencies and minutes spent per day on PA together with the metabolic equivalents for each activity, daily PA energy expenditures were estimated and categorized in tertiles to facilitate their interpretation. The number of minutes of PA per week was then calculated and classified according to World Health Organization recommendations (cutoff point 150 min/week).
Anthropometric assessment
Measurements of body weight (scale, 0.1 kg) and height (stadiometer, 0.1 cm) were taken. BMI was calculated (in kg/m2), and each subject was classified according to World Health Organization criteria.37 WC was measured at the midpoint between the lowermost rib and the upper portion of the iliac crest (fiberglass measuring tape, 0.1 cm). All measurements were performed by the study's research team according to ISAK (International Society for the Advancement of Kinanthropometry) standards.
BP assessment
Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured on both the left and right arms (semiautomatic oscillometer) according to the recommendations of the European Society of Hypertension and the European Society of Cardiology.38 Data from the arm that produced the higher SBP and DBP readings were used.38
Biochemical assessments
Blood samples were collected from all participants after a 12-hr overnight fast, and they were distributed in serum and plasma extractions bottles (two of each) labeled with the participant's code. Subsequently, blood samples were centrifuged (2,500 rpm, 4°C, and 10 min) and immediately stored at −80°C. Analyses were carried out in local laboratories. FPG was determined by the glucose oxidase method, and HDL-C and triglyceride concentrations were assessed using the enzymatic colorimetric method.
Diagnostic criteria for MS
MS was defined based on a consensus statement on diagnostic criteria issued by number of prominent institutions.10 Individuals were diagnosed with MS if they had three or more of the following abnormal conditions: AO (WC ≥80 cm in women and ≥90 cm in men); high-BP (SBP ≥130 mmHg and/or DBP ≥85 mmHg); high-FPG (plasma glucose ≥100 mg/dL); low HDL-C (HDL-C <40 mg/dL in men and <50 mg/dL in women); and hypertriglyceridemia (triglycerides ≥150 mg/dL). Taking pharmacological drugs to treat any of these disorders (antihypertensive drug, hypoglycemic drug, nicotinic acid, or fibrates) was also used as an indicator of the corresponding component, with the exception of AO component.10
Statistical analysis
Qualitative variables were reported as numbers, percentages, and 95% confidence intervals (CIs) for a proportion (using a normal distribution or an exact method based on the binomial distribution, as applicable). Comparisons between proportions were carried out using the chi-square or Fisher's exact test. The frequency of MS was standardized by age. The associations between MS and its components and sociodemographic factors and lifestyle variables were determined by logistic regression analysis. P < 0.05 was considered statistically significant. Most calculations were performed using SPSS version 25 statistical software for Windows, while the Stata program (version 15) was used for the calculation, which produced the 95% CI for a proportion using an exact method based on the binomial distribution.
Results
General characteristics of the sample
A total of 1,032 volunteers were analyzed. The sample consisted of women (71.9%), students (55.4%), and subjects younger than 28 years (67.2%). Overweight (BMI ≥25) was observed in 30.7% of subjects, and 7.5% were smokers. The presence of pathologies such as DM2 (1.0%), dyslipidemia (7.9%), and hypertension (3.0%) was rare, as was taking medication to treat such disorders (<2%). Most participants performed PA more than 150 min/week (94.5%). Significant differences between countries (P < 0.05) were observed for all of the previously described variables. A description of general variables by country is shown in Table 1.
Table 1.
General Characteristics of the Sample by Country
| Total, n = 1,032 |
Mexico, n = 316 |
Colombia, n = 285 |
Brazil, n = 223 |
Paraguay, n = 132 |
Argentina, n = 76 |
Pa | |
|---|---|---|---|---|---|---|---|
| n (%) | n (%) | n (%) | n (%) | n (%) | n (%) | ||
| Sex | |||||||
| Male | 290 (28.1) | 92 (29.1) | 83 (29.1) | 58 (26.0) | 52 (39.4) | 5 (6.6) | 0.001b |
| Female | 742 (71.9) | 224 (70.9) | 202 (70.9) | 165 (74.0) | 80 (60.6) | 71 (93.4) | |
| Age (years) | |||||||
| ≤23 | 401 (38.9) | 179 (56.6) | 91 (31.9) | 46 (20.6) | 37 (28.0) | 48 (63.2) | 0.001b |
| 24–28 | 292 (28.3) | 75 (23.7) | 63 (22.1) | 86 (38.6) | 50 (37.9) | 18 (23.7) | |
| ≥29 | 339 (32.8) | 62 (19.6) | 131 (46.0) | 91 (40.8) | 45 (34.1) | 10 (13.2) | |
| Occupational status | |||||||
| Student | 572 (55.4) | 188 (59.5) | 163 (57.2) | 65 (29.1) | 80 (60.6) | 76 (100.0) | 0.001b |
| Professional | 460 (44.6) | 128 (40.5) | 122 (42.8) | 158 (70.9) | 52 (39.4) | 0 (0.0) | |
| Health sector | |||||||
| Nutrition | 351 (34.1) | 78 (24.8) | 69 (24.2) | 115 (51.6) | 15 (11.4) | 74 (100.0) | 0.001b |
| Medicine | 237 (23.0) | 41 (13.0) | 84 (29.5) | 10 (4.5) | 102 (77.3) | 0 (0) | |
| Nursing | 134 (13.0) | 50 (15.9) | 56 (19.6) | 13 (5.8) | 15 (11.4) | 0 (0) | |
| Other areas | 307 (29.8) | 146 (46.3) | 76 (26.7) | 85 (38.1) | 0 (0) | 0 (0) | |
| BMI | |||||||
| Underweight | 52 (5.0) | 22 (7.0) | 9 (3.2) | 11 (4.9) | 5 (3.8) | 5 (6.6) | 0.001b |
| Normal weight | 664 (64.3) | 189 (59.8) | 188 (66.0) | 164 (73.5) | 59 (44.7) | 64 (84.2) | |
| Overweight | 237 (23.0) | 78 (24.7) | 73 (25.6) | 40 (17.9) | 39 (29.5) | 7 (9.2) | |
| Obese | 79 (7.7) | 27 (8.5) | 15 (5.3) | 8 (3.6) | 29 (22.0) | 0 (0.0) | |
| Smoking status | |||||||
| Nonsmoker | 888 (87.4) | 267 (84.8) | 253 (88.8) | 200 (95.7) | 105 (80.2) | 63 (82.9) | 0.001b |
| Smoker | 76 (7.5) | 33 (10.5) | 11 (3.9) | 6 (2.9) | 16 (12.2) | 10 (13.2) | |
| Former smoker | 52 (5.1) | 15 (4.8) | 21 (7.4) | 3 (1.4) | 10 (7.6) | 3 (3.9) | |
| PA (min/week) | |||||||
| <150 | 57 (5.5) | 6 (1.9) | 17 (6.0) | 6 (2.7) | 23 (17.4) | 5 (6.6) | 0.001b |
| >150 | 975 (94.5) | 310 (98.1) | 268 (94.0) | 217 (97.3) | 109 (82.6) | 71 93.4 | |
| PA energy expenditure (kcal/day) | |||||||
| ≤317 | 344 (33.3) | 94 (29.7) | 96 (33.7) | 50 (22.4) | 68 (51.5) | 36 (47.4) | 0.001b |
| 318–596 | 344 (33.3) | 114 (36.1) | 92 (32.3) | 82 (36.8) | 33 (25.0) | 23 (30.3) | |
| ≥597 | 344 (33.3) | 108 (34.2) | 97 (34.0) | 91 (40.8) | 31 (23.5) | 17 (22.4) | |
Qualitative data are expressed by number (n) and percentage (%). The total n in the variable “smoking status” is 1016 and the total n in the variable “health sector” is 1029.
Comparisons between proportions were carried out using the chi-square.
p < 0.05.
BMI, body mass index; PA, physical activity.
Prevalence of MS
The overall prevalence of age-standardized MS was 15.5% (23.1% in men, 12.2% in women). The prevalence of MS increased with age and BMI (P < 0.001). Age-standardized MS was most frequent in participants from Paraguay (28.3%) and less frequent in those from Colombia (15.7%), Mexico (13.9%), Brazil (10.4%), and Argentina (3.6%). Likewise, age-standardized MS was more prevalent among health professionals (14.6%), medical professionals and students (24.2%), and those who performed PA <150 min/week (28%) than it was among health students (8.0%), nutrition professionals and students (7.6%), and those whose per-week PA frequency was higher (14.5%). Data for non-age-standardized MS prevalence are shown in Table 2.
Table 2.
Overall Prevalence of Metabolic Syndrome in Accordance with Sociodemographic Characteristics
| Total |
MS prevalence |
||
|---|---|---|---|
| n | n | % (95% CI) | |
| Totala | 1032 | 102 | 9.9 (8.1–11.7) |
| Mexico | 316 | 22 | 7.0 (4.1–9.8) |
| Colombia | 285 | 37 | 13.0 (9.1–16.9) |
| Brazil | 223 | 11 | 4.9 (2.1–7.8) |
| Paraguay | 132 | 30 | 22.7 (15.5–30.0) |
| Argentina | 76 | 2 | 2.6 (0.3–9.2) |
| Sexa | |||
| Male | 290 | 52 | 17.9 (13.5–22.3) |
| Female | 742 | 50 | 6.7 (4.9–8.5) |
| Age (years)a,b | |||
| ≤28 | 693 | 38 | 5.5 (3.8–7.2) |
| ≥29 | 339 | 64 | 18.9 (14.7–23.1) |
| Occupational status | |||
| Student | 572 | 38 | 6.6 (4.6–8.7) |
| Professional | 460 | 64 | 13.9 (10.7–17.1) |
| Health sectora | |||
| Nutrition | 351 | 11 | 3.1 (1.3–5.0) |
| Medicine | 237 | 43 | 18.1 (13.2–23.1) |
| Nursing | 134 | 19 | 14.2 (8.2–20.2) |
| Other areas | 307 | 29 | 9.4 (6.2–12.7) |
| BMIa,b | |||
| Normal weight | 716 | 11 | 1.5 (0.6–2.4) |
| Overweight-obese | 316 | 91 | 28.8 (23.8–33.8) |
| Smoking statusb | |||
| Nonsmoker | 940 | 91 | 9.7 (7.8–11.6) |
| Smoker | 76 | 10 | 13.2 (5.4–20.9) |
| PA (min/week)c | |||
| <150 | 57 | 11 | 19.3 (8.7–29.9) |
| >150 | 975 | 91 | 9.3 (7.5–11.2) |
| PA energy expenditure (kcal/day)b | |||
| ≤596 | 688 | 70 | 10.2 (7.9–12.4) |
| ≥597 | 344 | 32 | 9.3 (6.2–12.4) |
Presence of three or more of the following risk factors: AO, high-BP, high-FPG, high-TG, or low HDL-C (medication for one of these conditions should be included as a criterion, even when the measurements taken show adequate values). The total n in the variable “smoking status” is 1016 and the total n in the variable “health sector” is 1029. The prevalence rates reported in this table are not standardized by age. Data are expressed as numbers (n), percentages (%), and 95% CI. Comparisons between proportions were calculated using the chi-square or Fisher's exact test, in accordance with testing conditions.
P < 0.001.
Variables were recategorized into two categories due to the low frequency of MS in some categories.
P < 0.05.
AO, abdominal obesity; BP, blood pressure; CI, confidence interval; high-TG, hypertriglyceridemia; MS, metabolic syndrome.
Prevalence of MS components
AO (29.7%) and low HDL-C (27.5%) were the most common MS components. AO was significantly more frequent in subjects from Paraguay (56.8%), men (36.9%), smokers (42.1%), medical professionals and students (47.7%), and those who reported a daily PA energy expenditure of <317 kcal (36.3%), and who spent fewer than 150 min/week performing PA (43.9%). Low HDL-C was most prevalent among subjects in Paraguay (65.2%) and medical professionals and students (41.8%); no significant differences were observed according to age and sex. The frequencies of hypertriglyceridemia (15.8%) and high-FPG (10.7%) were highest in Colombia (22.5% and 26.7%, respectively), among men (25.5% and 18.3%, respectively), and among medical professionals and students (21.5% and 16.5%, respectively). In addition, hypertriglyceridemia was more common among former smokers (30.8%). High-BP was most prevalent in subjects from Paraguay (18.9%) and in men (30.0%). AO, hypertriglyceridemia, high-FPG, and high-BP increased with age (P < 0.05). All MS components significantly increased with BMI (Table 3).
Table 3.
Prevalence of Metabolic Syndrome Components in the Presence of Different Variables (n = 1,032)
| Total |
AOa |
Low HDL-Cb |
High-TGc |
High-BPd |
High-FPGe |
||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| n | n | % (95% CI) | n | % (95% CI) | n | % (95% CI) | n | % (95% CI) | n | % (95% CI) | |
| Total | 1032 | 306 | 29.7 (26.9–32.4) | 284 | 27.5 (24.8–30.2) | 163 | 15.8 (13.6–18.0) | 143 | 13.9 (11.7–16.0) | 110 | 10.7 (8.8–12.5) |
| Country | |||||||||||
| Mexico | 316 | 86 | 27.2f (22.3–32.1) | 84 | 26.6f (21.7–31.5) | 36 | 11.4f (7.9–14.9) | 52 | 16.5g (12.3–20.6) | 3 | 0.9f (0.2–2.7) |
| Argentina | 76 | 1 | 1.3 (0.03–7.1) | 0 | 0.0 | 10 | 13.2 (5.4–20.9) | 13 | 17.1 (8.4–25.8) | 3 | 3.9 (0.8–11.1) |
| Brazil | 223 | 55 | 24.7 (19.0–30.4) | 42 | 18.8 (13.7–24.0) | 25 | 11.2 (7.0–15.4) | 28 | 12.6 (8.2–16.9) | 16 | 7.2 (3.8–10.6) |
| Colombia | 285 | 89 | 31.2 (25.8–36.6) | 72 | 25.3 (20.2–30.3) | 64 | 22.5 (17.6–27.3) | 25 | 8.8 (5.5–12.1) | 76 | 26.7 (21.5–31.8) |
| Paraguay | 132 | 75 | 56.8 (48.3–65.4) | 86 | 65.2 (56.9–73.4) | 28 | 21.2 (14.1–28.3) | 25 | 18.9 (12.2–25.7) | 12 | 9.1 (4.1–14.1) |
| Sex | |||||||||||
| Male | 290 | 107 | 36.9h (31.3–42.5) | 77 | 26.6 (21.4–31.7) | 74 | 25.5f (20.5–30.6) | 87 | 30.0f (24.7–35.3) | 53 | 18.3f (13.8–22.7) |
| Female | 742 | 199 | 26.8 (23.6–30.0) | 207 | 27.9 (24.7–31.1) | 89 | 12.0 (9.6–14.3) | 56 | 7.5 (5.6–9.4) | 57 | 7.7 (5.8–9.6) |
| Age (years) | |||||||||||
| ≤23 | 401 | 71 | 17.7f (14.0–21.5) | 106 | 26.4 (22.1–30.8) | 41 | 10.2f (7.2–13.2) | 45 | 11.2f (8.1–14.3) | 25 | 6.2f (3.9–8.6) |
| 24–28 | 292 | 75 | 25.7 (20.6–30.7) | 78 | 26.7 (21.6–31.8) | 40 | 13.7 (9.7–17.7) | 25 | 8.6 (5.3–11.8) | 25 | 8.6 (5.3–11.8) |
| ≥29 | 339 | 160 | 47.2 (41.9–52.5) | 100 | 29.5 (24.6–34.4) | 82 | 24.2 (19.6–28.8) | 73 | 21.5 (17.1–25.9) | 60 | 17.7 (13.6–21.8) |
| Health sector | |||||||||||
| Nutrition | 351 | 48 | 13.7f (10.1–17.3) | 70 | 19.9f (15.7–24.1) | 43 | 12.3g (8.8–15.7) | 28 | 8.0f (5.1–10.8) | 31 | 8.8f (5.8–11.8) |
| Medicine | 237 | 113 | 47.7 (41.3–54.1) | 99 | 41.8 (35.4–48.1) | 51 | 21.5 (16.2–26.8) | 36 | 15.2 (10.6–19.8) | 39 | 16.5 (11.7–21.2) |
| Nursing | 134 | 51 | 38.1 (29.7–46.4) | 48 | 35.8 (27.6–44.0) | 19 | 14.2 (8.2–20.2) | 21 | 15.7 (9.4–21.9) | 19 | 14.2 (8.2–20.2) |
| Other areas | 307 | 94 | 30.6 (25.4–35.8) | 67 | 21.8 (17.2–26.5) | 49 | 16.0 (11.8–20.1) | 57 | 18.6 (14.2–22.9) | 20 | 6.5 (3.7–9.3) |
| BMI | |||||||||||
| Underweight | 52 | 0 | 0.0f | 9 | 17.3f (6.7–27.9) | 1 | 1.9f (0.05–10.2) | 2 | 3.8f (0.5–13.2) | 2 | 3.8f (0.5–13.2) |
| Normal weight | 664 | 75 | 11.3 (8.9–13.7) | 152 | 22.9 (19.7–26.1) | 70 | 10.5 (8.2–12.9) | 54 | 8.1 (6.0–10.2) | 47 | 7.1 (5.1–9.0) |
| Overweight | 237 | 152 | 64.1 (58.0–70.3) | 80 | 33.8 (27.7–39.8) | 60 | 25.3 (19.7–30.9) | 53 | 22.4 (17.0–27.7) | 49 | 20.7 (15.5–25.9) |
| Obese | 79 | 79 | 100.0 | 43 | 54.4 (43.2–65.7) | 32 | 40.5 (29.4–51.6) | 34 | 43.0 (31.9–54.2) | 12 | 15.2 (7.1–23.3) |
| Smoking status | |||||||||||
| Nonsmoker | 888 | 251 | 28.3g (25.3–31.2) | 248 | 27.9 (25.0–30.9) | 133 | 15.0h (12.6–17.3) | 113 | 12.7 (10.5–14.9) | 92 | 10.4 (8.3–12.4) |
| Smoker | 76 | 32 | 42.1 (30.7–53.5) | 22 | 28.9 (18.5–39.4) | 13 | 17.1 (8.4–25.8) | 15 | 19.7 (10.6–28.9) | 7 | 9.2 (2.6–15.9) |
| Former smoker | 52 | 18 | 34.6 (21.2–48.0) | 13 | 25.0 (12.8–37.2) | 16 | 30.8 (17.8–43.7) | 11 | 21.2 (9.7–32.6) | 10 | 19.2 (8.1–30.3) |
| PA (min/week) | |||||||||||
| >150 | 975 | 281 | 28.8g (26.0–31.7) | 263 | 27.0 (24.2–29.8) | 153 | 15.7 (13.4–18.0) | 133 | 13.6 (11.5–15.8) | 101 | 10.4 (8.4–12.3) |
| <150 | 57 | 25 | 43.9 30.6–57.1 | 21 | 36.8 (23.9–49.8) | 10 | 17.5 (7.4–27.7) | 10 | 17.5 (7.4–27.7) | 9 | 15.8 (6.0–25.5) |
| PA energy expenditure (kcal/day) | |||||||||||
| ≤317 | 344 | 125 | 36.3h (31.2–41.4) | 96 | 27.9 (23.1–32.7) | 64 | 18.6 (14.5–22.7) | 52 | 15.1 (11.3–18.9) | 40 | 11.6 (8.2–15.0) |
| 318–596 | 344 | 95 | 27.6 (22.9–32.4) | 89 | 25.9 (21.2–30.5) | 42 | 12.2 (8.7–15.7) | 39 | 11.3 (8.0–14.7) | 38 | 11.0 (7.7–14.4) |
| ≥597 | 344 | 86 | 25.0 (20.4–29.6) | 99 | 28.8 (24.0–33.6) | 57 | 16.6 (12.6–20.5) | 52 | 15.1 (11.3–18.9) | 32 | 9.3 (6.2–12.4) |
Taking pharmacological drugs to treat any of these last four disorders was also used as an indicator of this component. Data are expressed as number (n), percentage (%), and 95% CI. The total n in the variable “smoking status” is 1016 and the total n in the variable “health sector” is 1029. The distribution of cases across qualitative variables was analyzed with a chi-squared or Fisher's exact test, according to testing conditions.
AO: waist circumference ≥80 cm in women and ≥90 cm in men.
Low HDL-C: HDL-C <40 mg/dL in men and <50 mg/dL in women.
High-TG: triglycerides ≥150 mg/dL.
High-BP: SBP ≥130 mmHg and/or DBP ≥85 mmHg.
High-FPG: plasma glucose ≥100 mg/dL.
P < 0.001.
P < 0.05.
P < 0.01.
DBP, diastolic blood pressure; FPG, fasting plasma glucose; HDL-C, high-density lipoprotein cholesterol; SBP, systolic blood pressure.
More than half of participants showed one or more MS components (59.3%). The majority presented one (34.7%) or two (14.7%) components, while subjects presenting three (6.6%), four (2.9%), or five (0.4%) components were observed with less frequency.
Association between MS and its components with sociodemographic characteristics and lifestyle variables
After adjusting for age, being female was negatively associated with MS and all of its components, except low HDL-C. In addition, after adjusting for sex, being older than 29 years was positively associated with MS and every one of its components, except low HDL-C.
After adjusting for age and sex, being a professional was negatively associated with high-BP; being a smoker was positively associated with AO; and being a medical or nursing student or professional was positively associated with MS, AO, and low HDL-C. More generally, being either overweight or obese was positively associated with MS and all of its components (Table 4).
Table 4.
Association Between Metabolic Syndrome and Its Components with Sociodemographic Characteristics and Lifestyle Variables
| MSa |
AO |
Low HDL-C |
High-TG |
High-BP |
High-FPG |
|
|---|---|---|---|---|---|---|
| OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | |
| Sex | ||||||
| Male | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| Female | 0.3 (0.2–0.5b) | 0.6 (0.5–0.8b) | 1.1 (0.8–1.4) | 0.4 (0.3–0.6b) | 0.2 (0.1–0.3b) | 0.4 (0.2–0.6b) |
| 0.3 (0.2–0.5b)c | 0.7 (0.5–0.9b)c | 1.1 (0.8–1.5)c | 0.4 (0.3–0.6b)c | 0.2 (0.1–0.3b)c | 0.4 (0.3–0.6b)c | |
| Age (years) | ||||||
| ≤28 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| ≥29 | 4.0 (2.6–6.1b) | 3.3 (2.5–4.4b) | 1.2 (0.9–1.5) | 2.4 (1.7–3.4b) | 2.4 (1.7–3.5b) | 2.8 (1.8–4.1b) |
| 3.9 (2.6–6.1b)c | 3.3 (2.5–4.4b)c | 1.2 (0.9–1.5)c | 2.4 (1.7–3.3b)c | 2.4 (1.7–3.6b)c | 2.7 (1.8–4.1b)c | |
| Occupational status | ||||||
| Student | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| Professional | 2.3 (1.5–3.5b) | 1.9 (1.5–2.5b) | 0.9 (0.7–1.3) | 1.9 (1.3–2.6b) | 1.5 (1.0–2.1b) | 1.8 (1.2–2.8b) |
| 0.6 (0.3–1.0)c | 0.8 (0.5–1.1)c | 0.8 (0.6–1.2)c | 1.0 (0.6–1.6)c | 0.6 (0.3–0.9b)c | 0.8 (0.5–1.4)c | |
| Health sector | ||||||
| Nutrition | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| Medicine | 6.8 (3.4–13.6b) | 5.7 (3.9–8.6b) | 2.9 (2.0–4.2b) | 1.9 (1.3–3.1b) | 2.1 (1.2–3.5b) | 2.0 (1.2–3.4b) |
| 4.2 (2.0–8.7b)c | 5.4 (3.5–8.3b)c | 3.1 (2.1–4.5b)c | 1.3 (0.8–2.1)c | 0.9 (0.5–1.6)c | 1.1 (0.6–1.9)c | |
| Nursing | 5.1 (2.4–11.0b) | 3.9 (2.4–6.2b) | 2.2 (1.4–3.5b) | 1.2 (0.7–2.1) | 2.1 (1.2–3.9b) | 1.7 (0.9–3.1) |
| 3.4 (1.5–7.7b)c | 3.0 (1.8–4.9b)c | 2.2 (1.4–3.4b)c | 0.9 (0.5–1.6)c | 1.6 (0.9–3.0)c | 1.2 (0.7–2.3)c | |
| Other areas | 3.2 (1.6–6.6b) | 2.8 (1.9–4.1b) | 1.1 (0.8–1.6) | 1.4 (0.9–2.1) | 2.6 (1.6–4.3b) | 0.7 (0.4–1.3) |
| 1.7 (0.8–3.7)c | 2.3 (1.5–3.5b)c | 1.2 (0.8–1.8)c | 0.9 (0.5–1.4)c | 1.2 (0.7–2.1)c | 0.4 (0.2–0.7b)c | |
| BMI | ||||||
| Normal weight | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| Overweight-obese | 25.9 (13.6–49.3b) | 23.2 (16.4–32.8b) | 2.2 (1.6–2.9b) | 3.7 (2.6–5.3b) | 4.5 (3.1–6.5b) | 3.3 (2.2–4.9b) |
| 17.0 (8.8–33.0b)c | 24.3 (16.4–35.8b)c | 2.5 (1.8–3.4b)c | 2.5 (1.8–3.7b)c | 2.5 (1.7–3.8b)c | 2.0 (1.3–3.1b)c | |
| Smoking status | ||||||
| Nonsmoker | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| Smoker | 1.4 (0.7–2.8) | 1.8 (1.1–2.9b) | 1.1 (0.6–1.8) | 1.1 (0.6–2.0) | 1.6 (0.9–2.9) | 0.8 (0.4–1.9) |
| 1.2 (0.6–2.6)c | 1.7 (1.0–2.8b)c | 1.1 (0.6–1.8)c | 0.9 (0.5–1.8)c | 1.3 (0.7–2.5)c | 0.7 (0.3–1.6)c | |
| PA (min/week) | ||||||
| >150 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| <150 | 2.3 (1.2–4.6b) | 1.9 (1.1–3.3b) | 1.6 (0.9–2.7) | 1.1 (0.6–2.3) | 1.3 (0.7–2.7) | 1.6 (0.8–3.4) |
| 1.8 (0.8–3.9)c | 1.7 (0.9–3.0)c | 1.5 (0.9–2.7)c | 0.9 (0.4–1.9)c | 1.0 (0.5–2.2)c | 1.3 (0.6–2.8)c | |
| PA energy expenditure (kcal/day) | ||||||
| ≤596 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| ≥597 | 0.9 (0.6–1.4) | 0.7 (0.5–0.9b) | 1.1 (0.8–1.5) | 1.1 (0.8–1.5) | 1.2 (0.8–1.7) | 0.8 (0.5–1.2) |
| 0.9 (0.6–1.6)c | 0.7 (0.5–1.0)c | 1.1 (0.8–1.5)c | 1.1 (0.7–1.6)c | 1.1 (0.7–1.6)c | 0.8 (0.5–1.2)c | |
Data are expressed as OR and 95% CI.
Presence of three or more of the following risk factors: AO, high-BP, high-FPG, high-TG, or low HDL-C.
P < 0.05 was considered significant.
Adjusted for age and sex (age was adjusted for sex and sex was adjusted for age). Associations between variables and MS components were calculated using logistic regression.
OR, odds ratio.
Discussion
The lack of consensus data on the prevalence of MS and its components among health professionals has limited the number of initiatives aimed at preventing metabolic disorders within this population. In this regard, the results of this study will contribute by helping to describe a part of that reality using standardized criteria. The LATINMETS study showed an overall prevalence of age-standardized MS of 15.5%. Almost 60% of its subjects presented one or more MS components, mainly AO and low HDL-C. After adjusting for age and sex, MS and all of its components were positively associated with being overweight or obese.
The overall prevalence of age-standardized MS observed in the LATINMETS study (15.5%) was similar to the prevalence rates found in other studies conducted on young people (18–39 years) in the United States (20.3%)39 and in Latin American countries (<22%).14,17,18,40 One of those studies was the CARMELA (Cardiovascular Risk Factor Multiple Evaluation in Latin America) multicenter study that evaluated general populations in seven Latin American cities.14 The MS prevalence found in this study was close to the prevalence rates reported in a sample of health professionals in Brazil (12.8%)30 and among medical students in Mexico (14.5%)31 and Ecuador (7.5%).33 Nonetheless, the overall MS frequency found in this study was lower than the frequencies observed in young people (20–39 years) within the general population (23.8%)16,20 and in health professionals who worked at health institutions in Mexico (>30%),25–29 and lower than the 25.8% reported in another multicenter study on South American cities (subjects 35–44 years old).15
Similarities between the overall prevalence of MS in this study and the prevalence rates found in the above-mentioned studies may be attributable to the fact that most of the subjects in this study were younger than 28 years (67.2%) and presented low obesity rates (7.7%). As found in other published studies, MS frequency in this analysis was shown to increase with age14–18,27,30,39 and BMI.16,17,19,30,39
The population group evaluated in this study is exposed to cardiometabolic risk, as the majority (59.3%) presented one or more MS components, the most frequent of which were AO and low HDL-C. The risk of CVD and DM2 increases as the number of components increases,9 and the number of components can increase with age. A prospective study on Mexican medical students showed that, based on a 6-year follow-up, their rates of AO and MS had increased significantly.31 In addition, AO and low HDL-C are independent risk factors for the development of CVD.41,42 These two components were also the most frequently reported MS components in other studies on general populations in Latin American countries such as Mexico,16,19 Venezuela,17 Argentina, Chile, and Uruguay,15 in addition to university students in the United States,43 medical students in Ecuador33 and Mexico,31 and health professionals in Brazil30 and Mexico.26–28 However, the frequency of AO observed in this study is higher than that reported in university students in the United States (22%)43 and in medical students from Mexico (17.8%).31 Moreover, the frequency of AO in this study was less than the frequencies observed in medical students from Ecuador (43.2%)33 and in young health professionals (74.6% younger than 40 years) in Brazil (55.4%).30
However, the frequency of low HDL-C in our study was higher than that observed in university students from the United States (12.6%)43 and in young Brazilian health professionals (23.8%)30; and it was lower than the frequency reported in Ecuadorian medical students (31.8%)33 and in medical students from Mexico (59.1%).31
The higher prevalence of AO may be attributable to the fact that Latin American populations are more susceptible to abdominal fat accumulation and to the development of insulin resistance and fatty liver than non-Hispanic white populations.10,11 In addition, HDL-C concentrations have been shown to depend on genetic factors.41 Another factor that may explain the presence of MS components in young adults (18–30 years of age) may be a lack of healthy lifestyle practices.44,45 A review showed that the lifestyle habits of most university students, including those enrolled in health care-related fields, leave much to be desired. More specifically, they have unbalanced and high-calorie diets that often include fast food, they do not perform PA frequently, and they consume high amounts of alcohol, tobacco, and other drugs (mainly marijuana).44,45 In other words, a high percentage of students do not apply the knowledge that one might assume their university education would give them.45 All of these unhealthy behaviors combined with a genetic predisposition and the presence of overweight are risk factors for the development of MS components.11,41 In addition, the food environment to which one is exposed may contribute to either positive or negative health outcomes.46,47
The early detection and monitoring of metabolic disorders in young people favor the implementation of preventive measures and treatments aimed at achieving low-risk health status before risk factors accumulate and trigger the development of MS or cardiometabolic diseases. We must be aware that the absence of apparent disease in young adults does not mean absence of risk factors.
The main limitation of this study is that the study sample was not obtained at random or stratified by age and sex, despite the presence of these stipulations in protocols. For logistical reasons and due to the reluctance of some health professionals to be assessed, our sample was made up of volunteer subjects. Nonetheless, we believe that this study represents an important step forward in the assessment of MS and its components in young people working or studying in health-related fields in Latin American countries, a population that is and will continue to play a key role in the health of the general population. One of our study's strengths is the limited number of previous studies on MS and cardiovascular risk factors among health professionals. The need for more cross-sectional studies to assess cardiovascular risk factors in college students has already been identified.44 Another strength is the fact that MS was assessed using diagnostic criteria issued by prominent institutions in a consensus statement10 that included specific cutoff values for the diagnosis of AO for Central and South American populations. In addition, this study is the first to provide data on metabolic disorders on a young population from these five countries using the same MS criteria.
Acknowledgments
The authors would like to thank the volunteer subjects in all five countries, the researchers involved in fieldwork for the study, and the entire LATINMETS team. We also thank others who collaborated in all five countries, including undergraduate and graduate students. Other LATINMETS researchers: special thanks to the following researchers for their work in gathering, obtaining, and validating data or for their help with database and statistical analyses: University of Guadalajara, México: Salazar-Ruiz E, Fierro-Galvéz M, Vázquez-Valencia R, Estrada-Alcalá L, Preciado-Saldaña A, Ramírez-Ortiz K, Gómez L, Alcaraz A, Plascencia I, Morán Y, Orozo-Reus D, López-Áviles L, Narváez-Altamirano O, Plascencia-Aguirre M, Miramontes M, Fregoso-Ascencio I, Godoy-Mejía L, and Márquez-Sandoval C. University of Antioquia, Medellín-Colombia: Deossa-Restrepo GC, Díaz-García J, and Estrada-Restrepo A. Federal University of Viçosa, Brazil: Carraro JCC and Chaves LO. National University of Asunción, Paraguay: Cáceres M, Arguello R, Noguera S, Romero MJ, Guillen I, Alborno R, and Echague G. University of Buenos Aires, Argentina: Montero J, Chevallier C, Manuzza M, García K, Weisstaub A, and Palenque P. University of Rovira i Virgili, Spain: Fernández-Ballart J. Las Palmas de Gran Canaria University, Spain: Serra-Majem L. Special thanks to Silanes Laboratories (Mexico), which assisted with organizational meetings in Spain, Mexico, and Paraguay and also contributed laboratory materials.
Author Disclosure Statement
No competing financial interests exist.
Funding Information
Mexico: “Programa de Apoyos Complementarios para la Consolidación Institucional de Grupos de Investigación, modalidad Repatriación,” registry number 120696, year 2010; and Programa para el Desarrollo Profesional Docente, modalidad Apoyo a la Incorporación de Profesores de Tiempo Completo, year 2011, number PROMEP103.5/11/3653 (PRODEP; www.dgesu.ses.sep.gob.mx/PRODEP.htm). Colombia: Comité para el Desarrollo de la Investigación (CODI) 2010; Escuela de Nutrición y Dietética, Universidad de Antioquia (www.udea.edu.co/wps/portal/udea/web/inicio/investigacion/convocatoriasfondos-etiqueta/convocatorias-codi); Corporación Interuniversitaria de Servicios (CIS, www.cis.org.co); and Universidad Rovira i Virgili in Spain. Brazil: Conselho Nacional de Desenvolvimento Científico e Tecnológico, registry number 481518/2011-8, year 2011; registry number 481019/1890/2012-0, year 2012; and registry number: 444519/2014-9, year 2014 (CNPq Foundation; http://cnpq.br). Paraguay: Programa de Apoyo a Investigaciones, year 2010. Argentina: YSONUT SRL Laboratories. None of the funders had any role in the study's design, data collection, or data analysis, nor did they take part in the decision to publish or in the writing of this article.
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