Objective: To establish the relationship between cardiovascular (CV) risk profile and detected risk of Obstructive Sleep Apnea (OSA) in two questionnaires - STOP-BANG (SB) and Epworth Sleepiness Scale (ESS), in a young population of adults registered in a Primary Health Care unit in Rio de Janeiro, Brazil. Design and method: This cross-sectional population study enrolled adults between 20–50 years old, registered in a primary healthcare unit in Rio de Janeiro. A database is being developed including sociodemographic and anthropometric data, and CV risk factors. Office blood pressure (BP) and Home Blood Pressure Monitoring (HBPM) (7-day protocol) (Omron-705CP). Moreover, OSA was investigated by SB and ESS. Patients with a high risk for OSA in either of these two questionnaires were subsequently assigned for polysomnography (PSG). Results: A total of 562 subjects were evaluated [40% males, 38.9 ± 8.8 years], where 151 (26.9%) were identified as high risk for OSA by the SB questionnaire and 210 (37.4%) by ESS. The most common CV risk factor was physical inactivity (43%), followed by dyslipidemia (38%) and obesity (28%). By OBP, the prevalence of hypertension was 13.4% while by HBPM was 18.6%, with a low concordance between them (kappa = 0.472). Subjects with a high risk at SB are older, with a higher prevalence of obesity, hypertension and higher office BP and HBPM. On the other hand, individuals with high-risk by ESS were more obese, with increased waist circumference, higher prevalence of dyslipidemia and metabolic syndrome. Nevertheless, there was no difference in BP levels. Among the subjects submitted to PSG, 46% had a diagnosis of OSA (AHI higher 5/hour) and 23% of moderate/severe OSA (AHI higher 15/hour). The best predictor of AOS was SB, positive in 100% of subjects with moderate/severe OSA, while ESS was positive in only 20% of them. Conclusions: This young and apparently healthy population presented a high prevalence and risk for OSA. The SB had a higher association with higher BP levels, while ESE was associated with a worse metabolic profile. SB questionnaire seems to be the best predictor for moderate/severe OSA in this young adult population.

Objective: To evaluate concordance between diagnosis of hypertension by Office Blood Pressure (OBP) and Home Blood Pressure Monitoring (HBPM) in a young adult population. Design and method: A cross-sectional population study enrolled adults between 20 and 50 years registered in a primary healthcare unit in the city of Rio de Janeiro, Brazil. Socio-demographic and anthropometric characteristics were registered at the entrance of the study. Also, the presence of CV risk factors was evaluated. OBP was determined by calculating the mean value of 2 consecutive measurements (Omron-705CP) while the HBPM followed a 7-day protocol with 2 measurements in the morning and 2 in the evening (28 measurements). Measurements of the first day were discarded and the average of the other 6 days was calculated. It was considered normal a Home BP < 135 x 85 mmHg and OBP < 140 x 90 mmHg. Patients were classified into 4 groups: normotension (controlled OBP and HBPM); white coat hypertension (uncontrolled OBP and controlled HBPM); masked hypertension (controlled OBP and uncontrolled HBPM) and sustained hypertension (uncontrolled OBP and HBPM). Results: A total of 462 individuals were evaluated [37.7% males with mean age 37.4 ± 8.8 years]. Sedentary lifestyle (43%), dyslipidemia (38%) and obesity (28%) were the main CV risk factors. By OBP, the prevalence of hypertension was 13.4% while by HBPM was 18.6%. Kappa coefficient demonstrated a low concordance between the two diagnostic methods (kappa = 0.472). After HBPM, 68 individuals (16.9%) changed the diagnosis, being 21 (5.6%) with white coat hypertension and 45 (11.3%) with masked hypertension. The variables that were independently associated with hypertension diagnosed by OBP were male gender (OR 1.83, CI95%, 1.01–3.33, p = 0.04) and increased neck circumference (OR 3.77, CI95%:1.59–8.93, p = 0.003). Hypertension diagnosed by HBPM was associated with obesity (OR 2.18, CI95%, 1.27–3.76, p = 0.005) and increased neck circumference (OR 2.37, CI95%, 1.05–5.33, p = 0.04). Conclusions: Concordance between office BP and HBPM was low in this young adult population. Thus, If the diagnosis was based only in the office BP values, 17.5% of the subjects would have an erroneous diagnosis of hypertension.

Background: Obesity is increasing in younger populations, and is associated with a high cardiovascular (CV) risk, however, it is not clear whether metabolically healthy obesity (MHO) may have a lower CV risk or if it is just an earlier stage of the disease. Objective: To evaluate the prevalence and CV risk factors associated with MHO in a young adult population provided by a Primary Healthcare Center in a large urban area of Brazil. Methods: A cross-sectional population study for CV risk assessment in adults aged 20-50 years old provided by a Primary Healthcare Center in Rio de Janeiro, Brazil. Demographic, anthropometric data and CV risk factors were recorded. All underwent office blood pressure (OBP) measurements, laboratory evaluation (lipid and glycemic profile). Obesity was defined as a BMI ≥ 30 kg/m2 and MHO are those who have less than 3 of the following criteria: hypertension, diabetes, total cholesterol ≥ 200 mg/dL, HDL<40 mg/dL (men) and 50 mg/dL (women), triglycerides>150 mg/dL and increased waist circumference. Results: A total of 632 individuals were evaluated (60% female; mean age 37 ± 9 years). The prevalence of obesity was 25% (161 of 632 individuals), of which 73% (117 of 161 individuals) were classified as MHO. Obese individuals are older, with a higher prevalence of physical inactivity (51% vs 41%, p=0.03), hypertension (44% vs 19%, p<0.001), dyslipidemia (50% vs 36%, p=0.002) and diabetes (7% vs 2%, p=0.001) with higher systolic OBP. MHO compared to unhealthy ones are significantly younger and smoke less. Despite being obese, they have lower BMI (33.6 vs 35.2 kg/m2, p=0.02) and abdominal circumference (102 vs 110 cm, p=0.03), with lower diastolic BP. Conclusions: MHO was more prevalent in this young adult population and seems to have a lower CV risk, however it is not clear whether these younger and less obese individuals are only at an earlier stage of the disease. Perhaps the CV diseases onset is postponed for a few years. Even so, these individuals should not be excluded from public health policies as a form of primary prevention.

Objective: To evaluate the relationship between the main CV risk factors and socioeconomic indicators in a population of adults registered in a Family Health Care (FHC) unit in the center of Rio de Janeiro. Design and method: Cross-sectional population study that included adults aged between 20 and 50 years living in the area covered by the FHC in Rio de Janeiro. Demographic data (gender and age), socioeconomic data (education level, profession, employment), CV risk factors (smoking, sedentary lifestyle, obesity, hypertension, diabetes, dyslipidemia) were recorded. The metabolic profile is evaluated through laboratory tests. Those who studied up to high school were considered poorly educated. Results: 604 individuals were enrolled [39% male, mean age: 38.8 ± 8,9 years] The median of schooling was 12 years. 288 individuals had high schooling, 44.5% were male. A total of 130 individuals did not study or work. Women with low education had a higher risk of smoking, obesity and hypertension with no difference regarding labor or study activities. Otherwise, men with low education had higher risk of sedentary lifestyle and hypertension. Among men, not working or studying increased the risk of smoking and hypertension. Conclusions: We found an inverse association between socioeconomic conditions and the prevalence of CV risk factors. Women are more affected by low schooling, while men are more affected by their working occupation. The study suggests that socioeconomic factors influence the CV risk, affecting men and women differently, pointing to the need for public policies to reverse this situation.