The Maracaibo City Metabolic Syndrome Prevalence Study (MMSPS) is a descriptive and cross-sectional study carried out by our research group in Maracaibo, Venezuela, with the main goal to determine the prevalence of metabolic syndrome in this population and it´s methodology was described previously ¹⁷ . For the purpose of the present sub-study, individuals with no determination of fasting insulin level were excluded; thus, a total of 2026 individuals older than 18 years old were included for this investigation. The study was approved by the Bioethics Committee of the Endocrine and Metabolic Diseases Research Center – University of Zulia (approval number: BEC-006-0305). This ethical approval included all future studies that used the data from the MMSPS. All participants signed written consent before being questioned and physically examined by a trained team.

All individuals underwent a full history and physical exam by trained personnel. During the initial interview, personal and family history of premature ASCVD, endocrine and metabolic diseases were explored. Age, gender, as well as social and economic stratus using Graffar’s scale modified by Mendez-Castellano ¹⁸ , were recorded. Smoking history was categorized in three different classes: a) current smoker (smoked >100 cigarettes in a lifetime, current smoking, and chronic smoker who stopped for <1 year; b) ex-smoker (smoker who stopped smoking for >1 year); c) non-smoker (never smoked or who smoked <100 cigarettes in a lifetime). Current drinkers were considered to be those having drunk >1 gram a day ²⁰ .

Physical activity was assessed by the Long Form of the International Physical Activity Questionnaire (IPAQ) ²¹ . This instrument quantifies the amount of minutes invested in transportation, work, homework (gardening, cleaning), and leisure time. The participants were divided into quintiles based on total Metabolic Equivalents (METs)/min/week scores considering a sedentary person those with a MET score of 0 and those individuals with some degree of physical activity (≥1 MET) were stratified into five groups: very low (Q1), low (Q2), moderate (Q3), high (Q4) and very high (Q5) for a total of six categories. Leisure time was classified as follows: Q1 or very low PA in men<296.999 METs and women <230.999 METs; b) Q2 or low PA in men 297.000–791.999 METs and women 231.000–445.499 METs; c) Q3 or moderate PA in men 792.000–1532.399 METs and in women 445.500–742.499 METs; d) Q4 high PA in men 1532.400–2879.999 METs and in women 742.500–1798.499 METs; and e) Q5 or very high PA in men ≥2879.000 METs and women ≥1798.500 METs.

Blood pressure was measured by manual methods using a sphygmomanometer and stethoscope to detect 1 ^st and 5 ^th Korotkoff’s sounds for systolic and diastolic blood pressure, respectively. Participants had a 15 minute resting period before BP determination, they were seating with both feet on the ground. Measurements were repeated three times in 15 minute intervals. Joint National Committee 7 (JNC7) was used to classify BP as normal BP <120/80 mmHg, prehypertension in those with systolic blood pressure (SBP) 120–139 mmHg and/or diastolic blood pressure (PAD) between 80–89 mmHg, and hypertension when BP is ≥140/90 mmHg ²² .

Height was determined using a calibrated stadiometer placed on a flat surface. Weight was determined using a digital scale (Tanita, TBF-310 GS Body Composition Analyzer, Tokyo – Japan), with the patient wearing light clothing and barefoot. Body mass index (BMI) was determined using Quetelec´s equation [weight/height ²], and using World Health Organization criteria participants were deemed normal weight (BMI <25 kg/m ²), overweight (25.0 – 29.9 kg/m ²), and obese (≥30.0 kg/m ²) ²³ .

Waist circumference was measured using a standardized metric belt using the metric system in centimeters and millimeters. An anatomic reference was used to measure waist circumference an equidistant point between the lower border of the ribs and the antero-superior iliac spine, according to the National Institutes of Health of the United States ²⁴ . Central obesity was considered if waist circumference was ≥91 cm in women and ≥98 cm in men, according to the specific cut off values proposed for the population of Maracaibo, Venezuela ²⁵ .

Antecubital venous sampling was performed after an eight hour period of fasting. Samples were centrifuged and serum was obtained. Levels of glucose, total cholesterol, and TGs were determined using commercial enzymatic and colorimetric ELISA kits (Human Gesellshoft Biochemica and Diagnostica MBH). Glucose levels were interpreted according to the American Diabetes Association 2017 diagnostic criteria as follows: normal glucose <100 md/dL, impaired fasting glucose when fasting glucose is 100–125 mg/dL, and diabetes mellitus when glucose was ≥126 mg/dL ²⁶ . Before diagnosing diabetes, a confirmatory test was repeated on a different day. Levels of high sensitive C reactive protein (hs-CRP) were determined using immunoturbidimetric analyses (Human Gesellshoft Biochemica and Diagnostica MBH), and the cut off point for an elevated hs-CRP was ≥0.765 mg/L ²⁷ .

Fasting insulin concentration was determined using a commercial kit based on ELISA (DRG International. Inc. USA. New Jersey), with a detection limit of <1 mU/L. Resistance to insulin was calculate by the software HOMA-Calculator v2.2.2 provided by the Oxford Centre for Diabetes Endocrinology and Metabolism. Cutoff value for HOMA2-IR was 2.00 ²⁸ .

Non-HDL cholesterol levels were calculated with the following formula:

Non-HDL-c = total cholesterol – HDL-c

LDL-c were determined using Friedwald formula ²⁹ . Cutoff points for non-HDL-c: a) <130 mg/dL; b) 130–159 mg/dL; and c) ≥160 mg/dL. Cutoff points for LDL-c: a) <100 mg/dL; b) 100–129 mg/dL; and c) ≥130 mg/dL ³⁰ .

The aggregation of multiple risk factors was considered when one individual presented with two or more of the following:

Qualitative variables were shown as absolute and relative frequencies. Associations between these variables were explored using χ ² (Chi square) testing and differences with Z test. Quantitative variables were shown as arithmetic mean ± standard deviation after normality testing was performed using the Geary test. Non-normal distribution variables were logarithmically transformed and analyzed as with parametric testing when normality was achieved. When these variables remained non-normal they were shown as median with interquartile ranges (p25–p75 ^th). U Mann Whitney test and Kruskal-Wallis test were used for comparisons between two groups and three or more groups, respectively.

A multivariate regression model was created to estimate odds ratio and confidence intervals for prediction of composite of multiple risk factors. The first model was adjusted for age, sex, age group, ethnic group, socio-economic status, literacy, employment status, smoking, alcohol consumption, physical activity during leisure time, hypertension, hs-CRP, LDL-c and non-HDL cholesterol.

SPSS v.21 for Windows (IBM Chicago, IL) was used for statistical analyses and data gathering. We considered results statistically significant at p<0.05.

From the 2026 participants, 52.1% (n=1056) were female and 47.9% were male (n=846). The mean age was 40.79±15.76 years. Other general features are presented in Table 1. Median non-HDL-c was 143 mg/dL (114–174) mg/dL, with 144 (115–174) mg/dL among females and 143 (114–174) mg/dL in males; p=0.740.

Table 2 shows the epidemiology of non-HDL-c according to social and demographic features. Non-HDL-c levels showed an increasing trend with age, from 118 (97–143) mg/dL in those <30 years old, 151 (124–175) mg/dL among those from 30–49 years old and 166 (137–196) mg/dL in >50 years old; p<0.001. On the other hand, indigenous Venezuelan populations showed lower non-HDL-c levels (127; 97–151 mg/dL) compared with mixed race (145; 116–175 mg/dL) and white Hispanics (145; 114–176 mg/dL; p<0.001).

Higher levels of non-HDL-c were found among smokers (151; 118–183 mg/dL) compared with non-smokers or ex-smokers, p=0.001. Subjects with very high physical activity exhibited lower non-HDL-c levels 124 (98–160) mg/dL when compared with sedentary subjects [147 (118–175) mg/dL; p<0.001]. No significant differences were found when comparing alcohol drinkers and non-drinkers.

Table 3 shows non-HDL-c levels according to clinical, metabolic, and anthropometric variables. Non-HDL-c were significantly higher among those with hypertension compared to those with normal blood pressure (159 vs. 132 mg/dL, respectively; p<0.001). This behavior was also observed when comparing obese and normal weight individuals (155 vs. 124 mg/dL, respectively; p<0.001), type 2 diabetes and non-diabetic individuals (161 vs. 137 mg/dL; p<0.001), abdominal obesity and persons with normal waist circumference (154 vs. 132 mg/dL; p<0.001), and elevated hs-CRP vs. normal hs-CRP (156 vs. 140 mg/dL; p<0.001). Tertile distribution according non-HDL-c and both, clinical and anthropometric variables are shown in Table 4.

Figure 1 shows levels of non-HDL-c according to the number of risk factors for ASCVD. Those with any risk factor had a non-HDL-c of 122 (98–146) mg/dL, and 161 (131–192) mg/dL in those with three criteria and 159 (137–195) mg/dL in those with four criteria; p<0.001. Table 5 shows a multivariate logistic regression model where levels of non-HDL-c between 130 – 159 mg/dL, (OR=2.59; CI95%: 1.62-4.13; p<0.001) and ≥160 mg/dL (OR=3.75; CI95%=2.04-6.91; p<0.001), had an inverse probability of presenting a composite of multiple risk factors, while LDL-C was not significantly associated (OR=0.42; CI95%: 0.23-0,95; p=0.035 ).