ABSTRACT

Objective: Metabolic Syndrome (MetS) is the leading cause to develop type 2 diabetes worldwide. We examined associations of MetS components early in life, and their use as risk factors of acquiring MetS. Method: We used an international definition of MetS. Subjects were categorized into “Healthy”/“Not Healthy”, altered parameters are low HDL-cholesterol, large waist circumference (WC), hypertriacylglycerolemia, hypertension, and hyperglycemia, in 32 combinations (2^5) with two values (altered/not altered). MetS was identified with three or more altered parameters. Results: A total of 3424 students (ages 17 - 24 years) participated in the survey, and 2475 were “Not Healthy” showing at least 1 parameter altered; from them 49.6% showed low blood HDL either alone or combined, 38.2% had altered waist circumference either alone or combined; while 18.1% showed hypertriacylglycerolemia either alone or combined. Hypertension and hyperglycemia were the lowest in frequency. Conclusion: We propose that the binomium HDL/ Waist Circumference is the main prevalence factor to develop MetS in the asymptomatic young population, followed by hypertriacylglycerolemia which together define MetS; while hypertension and hyperglycemia seem to occur later in MetS.

1. INTRODUCTION

Metabolic syndrome (MetS) has become a pandemic, it accounts for 35% of adult population (i.e., ~47 million persons) in US, whereas it is about 30% in many developing countries [1-4]. MetS is a set of metabolic impairments that includes dyslipidemia (low HDL-cholesterol and high triacylglycerols), abnormal waist circumference (overweight and obesity), high blood pressure and high fasting glucose in blood, in this study we considered metabolic alterations as those reported by Alberti et al., that define MetS, which is quite similar to the AHA definition (Table 1) [5,6]. MetS predisposes the individual to more serious complications, such as diabetes and cardiovascular diseases [7-11]. Abdominal obesity and central adiposity are involved in cardiometabolic impairment, and are commonly associated to insulin resistance and other MetS disorders [12].

The diagnostic value of MetS remains controversial: even though it provides a measurable and reliable set of criteria that help physicians to a better focused clinical data on the underlying causes, to reinforce interventions both in lifestyle changes and clinical; in contrast, those that claim that MetS is not well defined and is a poorly understood entity [9-13]. However, physicians diagnose MetS once a person has 3 or more of the cited alterations, i.e., once the person is already ill.

We hypothesized that MetS, as a multifactorial process, is a continuum between “Healthy” and “Not Healthy” status, thus, it is possible to elucidate early events of that process that might predict progression to future chronic disease. In support of this contention, a recent paper

suggests that physicians should treat individual risk factors, instead than MetS [14]. We aimed to determine risk factors in previous stages of MetS, and their use as predictors in the population.

2. METHODS

2.1. Participants

The study sample, 3424 first year university alumni, was representative of Mexican university students, aged 17 to 24 years, of the metropolitan area. Data were obtained by the Multidisciplinary Group to Investigate Health and Academic Performance (GMISARA), from Facultad de Estudios Superiores Iztacala, Universidad Nacional Autónoma de México (U.N.A.M.). GMISARA surveys a complex, multistage, and geographic area design for collecting data from public universities of Mé- xico City metropolitan area (U.N.A.M. and Universidad Autónoma de la Ciudad de México, U.A.C.M.); all students signed an informed consent, the protocol was approved by the ethical committee of Facultad de Estudios Superiores Iztacala.

Before the study, none of the students perceived themselves as ill, and no metabolic disorder was diagnosed by a physician. Data of the study sample were collected over a 3-year survey (2008-2010). GMISARA uses trained personnel to conduct interviews for collecting reliable data that include demographic, socioeconomic, dietary, and health related information. Medical personnel obtained medical and physiological measurements, and CARPERMOR, S.A., an internationally certified laboratory, obtained laboratory data.

2.2. Data for Diagnosing of Metabolic Disorders

Students were required to come to the university, either U.N.A.M. or U.A.C.M., between 7 - 10 AM after fasting for at least 9 hours. Waist circumference was measured to the nearest 0.1 cm at minimal respiration at the end of normal expiration, with a flexible measuring tape placed at the high point of the iliac crest, when the student was in a standing position. Diastolic and systolic blood pressure (BP) values were obtained after replicated measurements. After resting quietly in a sitting position for 5 minutes and determination of the maximum inflation level, up to 4 consecutive BP readings were obtained with a standard aneroid sphygmomanometer (Model DS44, WelchAllyn). The maximum values of replicate systolic and diastolic measurements provided estimates of current BP values. Glucose, triacylglycerols and HDL concentration were determined by standard methods (CARPERMOR, S.A. de C.V.).

2.3. Metabolic Disorder and MetS Characterization

In this work, students were classified as having metabolic disorders (i.e., “Not Healthy”) if they had altered values in any one of the five parameters: HDL-cholesterol, waist circumference, triacylglycerols, blood pressure, and glucose (Table 1), or “Healthy” if they showed none of them. According to Alberti et al. definition, MetS was diagnosed if tree or more of those parameters were altered [5,6].

3. RESULTS

3.1. Prevalence of MetS and Individual Risk Factors

There were 3424 first year university students in the sample, where 68.4% were women and 31.7% men (Table 2). There were between 1.6% (24 years-old) to 33.2%  (18 years-old) in each age group (range 17 - 24 yearsold), with an average of 19 years-old.

An estimated 14.4% of students in the survey had MetS (494/3424, Table 2). Overall, the observed frequency order was low HDL > WC > higher triacylglycerols > hypertension > hyperglycemia (Table 2).

3.2. Prevalence of Risk Factor Combinations

Table 3 shows the 32 combinations (2^5) of five parameters with two possible values (altered/not altered), with prevalence estimates for the diagnostic components of MetS. The case with none altered parameter (“Healthy”) had a frequency of 27.7% (949/3424); while the remaining 72.3% are those cases with one to five altered parameters. Table 3 also shows the frequencies of “Not Healthy” students, i.e., taking 2475 cases as 100%. The cases are sorted by frequency in descending order. Notice that cases 2, 3 and 4, represent 57% of all “Not Healthy” students, and involve HDL-cholesterol or Waist Circumference, or both. Thus, the binomium HDL/Waist Circumference is likely to be the main association that could