Anthropometric Indicators Are Associated With Cardiovascular Risk Measured by Framingham Risk Score in Men Living With Hiv, but Not in Women.

Anthropometric Indicators Are Associated With Cardiovascular Risk Measured by Framingham Risk Score in Men Living With Hiv, but Not in Women. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Anthropometric Indicators Are Associated With Cardiovascular Risk Measured by Framingham Risk Score in Men Living With Hiv, but Not in Women. Marcilene Glay Pessoa, Luciana Melo, Fabiana Moura, Diego Silva, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4004802/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction: People living with HIV (PLHIV) present metabolic and morphological changes that increase cardiovascular risk due to infection and antiretroviral therapy (ART). Early detection of cardiovascular risk using anthropometric indicators is crucial, given the low cost and feasibility of this technique. Objective: To analyze the association between anthropometric indicators and cardiovascular risk in PLHIV. Methods: Cross-sectional study with cis-gender PLHIV, ≥18 years old of both sexes. Sociodemographic, clinical, personal information and anthropometric measurements (body mass, height and neck, waist and hip circumferences) were collected and 11 anthropometric indicators were calculated. Cardiovascular risk was determined by the Framingham risk score. Multivariable regression analyses adjusted for confounding factors and stratified by sex were conducted using STATA® v. 13.0, p<0.05. Results: 354 PLHIV participated, 41.2% (n=146) female, with a mean age of 42.7 ± 13.0 years. Among the participants, 70.1% (n=248), 16.7% (n=59) and 13.3% (n=47) have low, moderate, and high cardiovascular risk, respectively. Among the indicators analyzed, conicity index (CI), waist-to-hip ratio (WHR), body shape index (BSI), waist-to-height ratio (WHtR) and body roundness index (BRI) present significant association with cardiovascular risk, only in men (β*=0.4985; β*=0.4861; β*=0.4645; β*=0.4320; β*=0.4204 [β*=standardized betas]), adjusted for education, level of physical activity, T-CD4+ lymphocytes, income and ART. The analyzes did not demonstrate significant associations ​​for women. Conclusion: The anthropometric indicators, notedly CI and WHR, are associated with cardiovascular risk independent of clinical factors in men living with HIV. Health sciences/Diseases/Infectious diseases/HIV infections Health sciences/Diseases/Cardiovascular diseases HIV Anthropometry Body fat distribution Lipodystrophy Cardiovascular risk. Figures Figure 1 Figure 2 Figure 3 INTRODUCTION The infection with the Human Immunodeficiency Virus (HIV) causes progressive deterioration of the immune system, leading to Acquired Immunodeficiency Syndrome (AIDS) when there is a sharp drop in T-CD4 + lymphocytes 1 , in the absence of treatment. However, the implementation of Antiretroviral Therapy (ART) has increased the survival and quality of life of people living with HIV (PLHIV) due to its effectiveness in inhibit viral replication, preserving the immune function 2 . The prolonged use of ART combined to effects of HIV itself causes metabolic and morphological changes, such as hypertriglyceridemia, hyperglycemia, insulin resistance, dyslipidemia and increased visceral adiposity. 3,4 In this condition, there is anincreased risk of early cardiovascular events. Therefore, to determine body composition, particularly body fat, is important to the management of PLHIV in the clinical setting 4,15 . Body composition can be evaluated through the reference methods as dual-energy x-ray absorptiometry (DXA), air displacement plethysmography (ADP), magnetic resonance imaging, computed tomography and by practical methos as bioelectrical impedance, ultrasound and anthropometry 16 . However, anthropometry is the most used technique in clinical practice capable of estimating body composition or calculating surrogate anthropometric indicators, obtaining clinical diagnoses and prognoses 17,18 . Studies suggest to evaluate body fat changes associated with HIV using low cost methods, more accessible option for both clinical practice and research 19,20 and encourage the use of anthropometric measurements, such as waist circumference (WC), waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR) as predictors of cardiometabolic risk in PLHIV 21 . The assessment of cardiovascular risk in PLWH continues to be a challenge in clinical practice, since there are several methods for screening it, such as Pooled Cohort Equations (PCE), Systematic Coronary Risk Assessment (SCORE), the American College of Cardiology/American Heart Association (ACC/AHA), the Framingham risk score, among others 23,24 . However, they are not validated in populations with HIV. Despite this, the Clinical Protocol and Therapeutic Guidelines of the Ministry of Health in Brazil 25 recommends the application of the Framingham Risk Score in the assessment of cardiovascular risk in PLHIV. Although previous studies have evaluated the use of anthropometric indicators in the general population and in PLHIV 8,14,21,26–33 , no study has addressed a large number of anthropometric indicators at the same study in the HIV population and tested their association with cardiovascular risk. Thus, the objective of this study was to test the association of eleven anthropometric indicators with cardiovascular risk in PLHIV. METHODS Study design Cross-sectional study. The university’s Institutional Review Board approved the study (no. 4.864.332). All participants signing the writing informed consent. The present study is reported in accordance with the guidelines STROBE Statement for cross-sectional studies (supplementary file 1). Setting The study was carried out in Maceió-AL (northeast of Brazil) from November, 2021 to September, 2022. Maceió has a Human Development Index (HDI) of 0.721 below the Brazilian average of 0.727. Participants Sampling was carried out for convenience in the waiting room following the medical care routine and the sample size was established at 354 participants (calculation carried out in OpenEpi®, Dean, Sullivan and Soe 34 assuming confidence intervals of 95% and 5% margin of error). Participants were included if they met the eligibility criteria: age 18 and above; cisgender; both sexes; diagnosis of HIV infection; assisted at the outpatient level; and who had clinical and laboratory records. Participants were excluded if they had neurological disorders affecting the motor system; psychiatric disorders preventing them from completing the questionnaires; diagnosed with type I diabetes mellitus; genetic metabolic diseases; cancer; and if they were pregnant or postpartum. Variables and Data Collection Instruments The researchers involved in the collection process underwent prior training to reduce bias and a pilot study was carried out. Cardiovascular Risk (Outcome) The Framingham Risk Score was used to estimate cardiovascular risk 35 through the following variables: sex, age, measurement of systolic blood pressure (SBP), diagnosis of diabetes and/or arterial hypertension, smoking habit, total cholesterol and high-density lipoprotein (HDL-c) of cholesterol. The score calculation was conducted with parameters of cardiovascular risk prediction scores plotted in formulas in Excel® spreadsheets, for women and men 35 . Individuals were classified into categories: Low, Moderate and High Risk of a cardiovascular events in the next 10 years, assuming a chance of 20%, respectively. Furthermore, the continuous value of the percentage of cardiovascular risk was used. To analyze isolated cardiovascular risk factors, the following reference values ​​were used: for abdominal obesity, waist circumference > 88cm for women and > 102cm for men; for hypertriglyceridemia ≥ 150mg/dL; for low-density lipoprotein (LDL-c) high > 160mg/dL; for low HDL < 50mg/dL for women and 240mg/dL; elevated SBP ≥ 130mmHg; High age, > 55 years for women and > 45 years for men 36,37 . Blood pressure was measured with the individual in a seated state, using a sphygmomanometer (PREMIUM®) and stethoscope (MEDICATE®) calibrated following international standards for determining SBP and diastolic blood pressure (DBP) 38 . Capillary blood sample was collected with a 35µl pipette carried out by transferring the blood to the enzymatic cholesterol strip and inserted into the lipid profile monitor (MISSION®) to read the lipid profile (total cholesterol, HDL-c and triglycerides). LDL-c was obtained through calculation using the Friedewald, Levy and Fredrickson Eq. 3 9 . Blood glucose was assessed using a ACCU-CHEK® with blood glucose strips (ACCU-CHEK®). Blood collection was waived when volunteers presented a complete lipid profile from up to 3 months ago, counting from the date of their evaluation. Anthropometric Indicators (Exposure) To assess the anthropometric profile of the volunteers, measurements were taken of height (meters), body mass (kilograms), circumferences (centimeters) of the waist (WC), hip (HC) and neck (NC). Using a scale (MULTILASER®) and a stadiometer (AVANUTRI®), measurements of body mass and height, respectively, were taken using standardized procedures 40 . The individual remained barefoot, in the orthostatic position on the platform. For height measurement, the participant remained standing, erect, with arms extended along the body, head raised looking at a fixed point at eye level, feet together at 90º angle with the legs. The height was taken after a maximal inspiration 40 . Using an inelastic measuring tape (CESCORF®), the WC was measured, following the recommendations described in the 1st Brazilian Guideline for Diagnosis and Treatment of Metabolic Syndrome 41 , at the midpoint between the lower rib margin and the iliac crest. To collect data regarding the HC, the measuring tape was positioned on the line of the trochanters. The measurement of NC was conducted with the individual with their head raised and gaze fixed at eye level, the tape was positioned around the central region of the neck 40 . From the measured anthropometric variables, body fat anthropometric surrogate indicators were calculated: Body Mass Index (BMI); Waist Circumference (WC); Neck Circumference (NC); Body Adiposity Index (BAI) using the formula proposed by Bergman et al. 42 ; Fat Mass Index (FMI); Waist-to-Hip Ratio (WHR); Waist-to-Height Ratio (WHtR); Conicity Index (CI); Abdominal Volume Index (AVI); Body Shape Index (BSI); Body Roundness Index (BRI) 43 . The anthropometric indicators of body fat were calculated by formulas availablein supplementary file 2. Covariables Through questionnaires and interviews sociodemographic (i.e. age, education, income) and behavior variables (i.e. physical activity) were collected. Income was assessed by completing the questionnaire from the Brazilian Association of Research Companies – ABEP 44 for socioeconomic classification in the following economic classes: class D-E (0–16 points), class C2 (17–22 points), C1 (23–28 points), B2 (29–37 points), B1 (38–44 points), and class A (> 45 points). The International Physical Activity Questionnaire - Short Form 45,46 , was used to classify the volunteers as inactive, irregularly active or active. Information related to ART, lymphocytes T-CD4, and viral load were obtained from the completion of the therapeutic form conducted at the end of all daily assessment with one of the physicians accessing the database in the system. Statistical analysis The data were tabulated in Excel® spreadsheet. Descriptive statistics were performed using percentages, measures of central tendency (mean and median) and dispersion (standard deviation and interquartile range). The Kolmogorov-Smirnov normality test was employed, with kurtosis analyses, skewness, and histograms to check the gaussian distribution. Assuming the disparities in body composition between the sexes described in the literature, the data was stratified between men and women 12,13 . The Student's T-test and Mann-Whitney U test were applied to test the differences between sexes. Additionally, Pearson's and Spearman's linear correlation analyses were used to assess bivariate correlations between exposure and outcome. Correlation values ​​between 0.00 and 0.30 indicate a non-existent or very weak correlation, values ​​greater than 0.30 indicate weak correlation, values ​​greater than 0.50, moderate correlation, and values ​​greater than 0.70 may be interpreted as signs of high correlation 47 . Simple and multivariable linear regression was used to test the relationship between cardiovascular risk and each anthropometric indicator in crude and adjusted for covariates (education, physical activity level, T-CD4 lymphocytes, income, and ART) analysis. For multivariable regression analysis, the variables were insert into the model was stepwise approach, from which the covariates were included all together (the order was according to their power to predict the output variable). The model diagnosis was performed using the variance inflation factor (VIF), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). Moreover, regression coefficients (adjusted β*), 95% confidence interval, p-value, adjusted coefficient of determination (R²adj), and effect size (f²) were also estimated. All statistical analyses were conducted using Stata® 13.0 software, and the graphs were created using GraphPad Prism® 8.0 software. A significance level of p < 0.05 was considered statistically significant. RESULTS The study included 367 volunteers as illustrated in Fig. 1 ; however, 13 were excluded due to insufficient data to calculate cardiovascular risk. Final sample comprised 354 PLHIV, 58.8% male, 42.7 (13.0) years, mean (standard deviation). Regarding socioeconomic and demographic information, 26.7% completed elementary education, 49.6% (n = 172) resided in the state capital, and 64.4% (n = 228) belonged to social classes D and E. Biochemical, hemodynamic variables and anthropometric indicators were presented in Table 1. Table 1 Characteristics of People Living with HIV, according to sex. Data expressed as Mean ± SD / Median (IQR). Feminine Masculine N Mean ± SD Median (IQR) N Mean ± SD Median (IQR) U / t critical p-value Age (years) a 146 43.98 ± 11.72 208 41.81 ± 13.77 1.5518 0.1216 SBP (mmHg) b 146 120 (110; 130) 208 120 (110; 120) -0.205 0.8375 DBP (mmHg) b 146 80 (70; 90) 208 80 (70; 90) 0.335 0.7380 Glucose (mg/dL) b 146 100 (91; 112) 208 96.5 (88; 107) 1.926 0.0540 Total Cholesterol (mg/dL) b 146 190 (162; 233) 208 166 (139; 200.5) 4.519 < 0.0001 HDL (mg/dL) b 146 39 (31; 50) 208 32 (25; 42) 4.493 < 0.0001 LDL (mg/dL) b 143 119 (93.6; 159.4) 208 105 (84.1; 126) 3.323 0.0009 Triglycerides (mg/dL) b 146 147 (109; 202) 208 131 (95; 205) 2.192 0.0284 BMI (kg/m²) b 146 26.8 (23.7; 31) 205 24.2 (22; 27.1) 4.976 < 0.0001 WC (cm) a 146 90.77 ± 13.32 207 88.19 ± 11.46 1.9479 0.0522 NC (cm) b 146 33.7 ( 32 , 36 ) 208 37.4 (35.5; 39) -9.845 < 0.0001 BAI * b 146 33.88 (29.8; 38.2) 203 25.83 (23.9; 28.5) 12.537 < 0.0001 FMI (kg/m²) b 146 9.33 (7.1; 11.6) 203 6.28 (5.4; 7.7) 9.243 < 0.0001 WHR * a 146 0.89 ± 0.07 206 0.90 ± 0.08 -1.8283 0.0684 WHtR * b 146 0.57 (0.52; 0.64) 204 0.51 (0.47; 0.56) 6.961 < 0.0001 CI * a 146 1.27 ± 0.08 204 1.25 ± 0.09 2.3696 0.0184 AVI (L) b 146 16.05 (13.4; 20.1) 207 15.37 (12.9; 18.1) 1.844 0.0651 BSI * a 146 0.08 ± 0.00 204 0.08 ± 0.00 0.0962 0.9234 BRI * b 146 4.83 (3.8; 6.3) 204 3.62 (2.8; 4.6) 6.961 < 0.0001 CVR (%) b 146 3.9 (2; 10) 208 6.7 (2.8; 13.2) -2.616 0.0089 IQR = interquartile range; SD = standard deviation; SBP = systolic blood pressure; DBP = diastolic blood pressure; HDL = high-density lipoprotein; LDL = low-density lipoprotein; BMI = body mass index; WC = waist circumference; NC = neck circumference; BAI = body adiposity index; FMI = fat mass index; WHR = waist-hip ratio; WHtR = waist-to-height ratio; CI = conicity index; AVI = abdominal volume index; BSI = body shape index; BRI = body roundness index; CVR = cardiovascular risk. * Dimensionless measurements. a Variables with normal distribution, used Student's T test for independent samples. b Variables without normal distribution, using the Mann-Whitney U test. From the 346 participants with ART information, 23.4% (n = 81) used protease inhibitors (ART-PI), 66.2% (n = 229) followed therapeutic regimens without PI, and 10.4% (n = 36) had not yet initiated treatment. Data regarding HIV infection show 31.6% (n = 107) of participants were detectable viral load (≥ 20 copies/mL) and 24.3% (n = 79) presented immunosuppression with T-CD4 < 350 48 cells/mm³. Among lifestyle habits and the presence of chronic diseases, 14.3% (n = 50) were considered inactive, 43.7% (n = 153) and 42.0% (n = 147) insufficiently active and active, respectively; 17.8% (n = 63) were smokers, and 21.2% (n = 75) were ex-smokers; 19.2% (n = 68) were hypertensive, and 9.9% (n = 35) diabetic. The prevalence of high cardiovascular risk was 8.9% (n = 13) for women and 16.3% (n = 34) for men (Fig. 2 ). Furthermore, more than 50% of individuals, regardless of sex, had low HDL, which is one of the cardiovascular risk factors according to the Framingham Risk Score. Heat map of correlations between anthropometric indicators and biochemical, hemodynamic, and cardiovascular risk variables showed non-existent or very weak correlations (r = 0.00 and 0.30) for all anthropometric indicators for females (Fig. 3 ). For males, WHR, WHtR, CI, and BRI presented moderates or strong correlations (r > 0.50); BMI, WC, FMI, AVI, and BSI exhibited weak or moderate correlations (r between 0.30 and 0.70); and only two indicators, NC and BAI, showed non-existent or very weak correlations (r < 0.30). Table 2 presents data from simple and multivariable linear regression between anthropometric indicators and cardiovascular risk in women living with HIV. None of the tested associations were significant, the standardized β* coefficients presented low (non-significant) values, and the adjusted R² explained little the variance of the Framingham Risk Score. These results did not provide sufficient statistical evidence to assert associations between anthropometric indicators and cardiovascular risk in women living with HIV in this sample. Table 2 Linear regression analysis between anthropometric indicators and cardiovascular risk defined by the Framingham risk score (continuous value), in woman living with HIV. Gross Adjusted** Variables β* (CI − 95%) SE P R 2 adj β* (CI − 95%) SE P R 2 adj VIF AIC BIC BMI (kg/m²) 0.0857 (-0.1023; 0.3258) 0.1083 0.304 0.0004 -0.0039 (-0.2407; 0.2302) 0.1189 0.965 0.1007 1.07 845.1936 864.8789 WC (cm) 0.0848 (-0.0446; 0.1399) 0.0467 0.309 0.0003 0.0290 (-0.0838; 0.1171) 0.0507 0.743 0.1015 1.06 845.0814 864.7667 NC (cm) -0.0042 (-0.3861; 0.3670) 0.1905 0.960 -0.0069 -0.0353 (-0.4679; 0.3076) 0.1958 0.683 0.1020 1.01 845.0181 864.7033 BAI * 0.1101 (-0.0669; 0.3416) 0.1033 0.186 0.0053 0.0172 (-0.2032; 0.2465) 0.1135 0.849 0.1010 1.10 845.1571 864.8424 FMI (kg/m²) 0.0758 (-0.1729; 0.4691) 0.1624 0.363 -0.0012 -0.0103 (-0.3705; 0.3295) 0.1767 0.908 0.1008 1.08 845.1815 864.8668 WHR * 0.1203 (-4.9188; 32.2948) 9.4137 0.148 0.0076 0.1081 (-7.7825; 33.2636) 10.3619 0.221 0.1123 1.06 843.603 863.2883 WHtR * 0.1271 (-3.2619; 26.1084) 7.4296 0.126 0.0093 0.0555 (-11.1915; 21.4194) 8.2325 0.536 0.1037 1.09 844.7872 864.4725 CI * 0.0910 (-6.5933; 23.0345) 7.4947 0.275 0.0014 0.0986 (-7.3310; 26.2293) 8.4721 0.267 0.1102 1.07 843.8837 863.569 AVI (L) 0.0644 (-0.1472; 0.3366) 0.1224 0.440 -0.0028 0.0166 (-0.2370; 0.2871) 0.1323 0.850 0.1010 1.05 845.1578 864.8431 BSI * 0.0394 (-195.8451; 319.1106) 130.2647 0.637 -0.0054 0.1066 (-117.3419; 488.0127) 152.819 0.228 0.1119 1.06 843.6459 863.3312 BRI * 0.1071 (-0.2202; 1.0532) 0.3221 0.198 0.0046 0.0406 (-0.5414; 0.8652) 0.3551 0.649 0.1023 1.08 844.9755 864.6608 β* = standardized beta regression coefficient; CI-95% = 95% confidence interval; SE = standard error; p = p value (statistical significance, p < 0.05); R 2 adj = adjusted R 2 of the model; VIF = variance inflation factor; AIC = Akaike information draws; BIC = Bayesian Information Criterion, de Schwarz; BMI = body mass index; WC = waist circumference; NC = neck circumference; BAI = body adiposity index; FMI = fat mass index; WHR = waist-hip ratio; WHtR = waist-to-height ratio; CI = conicity index; AVI = abdominal volume index; BSI = body shape index; BRI = body roundness index. f² of the respective indicators, gross analysis = 0.0004; 0.0003; -0.0069; 0.0053; -0.0012; 0.0077; 0.0094; 0.0014; -0.0028; -0.0054; 0.0046. f² of the respective indicators, adjusted analysis = 0.1120; 0.1130; 0.1136; 0.1123; 0.1121; 0.1265; 0.1157; 0.1238; 0.1123; 0.1260; 0.1140. f² = size of association effects. * Dimensionless measurements. ** The linear regression model was adjusted for education, level of physical activity, lymphocytes T-CD4, income, ART (antiretroviral therapy). In Table 3 , concerning simple and multivariable linear regression between anthropometric indicators and cardiovascular risk in men living with HIV, all indicators demonstrated significant associations with cardiovascular risk, even after adjusted for covariates. Notably, WHR, WHtR, CI, BSI, and BRI (β*=0.4861; β*=0.4320; β*=0.4985; β*=0.4645; β*=0.4204; respectively) are the indicators that maintained the most robust associations (analyzed by p-value, β*, R² adj , and f 2 ), showing that these anthropometric measurements better reflect cardiovascular risk in men living with HIV. Table 3 Linear regression analysis between anthropometric indicators and cardiovascular risk defined by the Framingham risk score (continuous value), in men living with HIV. Gross Adjusted** Variables β* (CI − 95%) SE P R 2 adj β* (CI − 95%) SE P R 2 adj VIF AIC BIC BMI (kg/m²) 0.2374 (0.2302; 0.8314) 0.1524 0.001 0.0517 0.2299 (0.2095; 0.8138) 0.1531 0.001 0.1897 1.09 1330.917 1353.572 WC (cm) 0.3966 (0.2147; 0.4155) 0.0509 < 0.0001 0.1532 0.3713 (0.1936; 0.3945) 0.0507 < 0.0001 0.2768 1.09 1336.672 1359.438 NC (cm) 0.2243 (0.2452; 0.9710) 0.1840 0.001 0.0457 0.2796 (0.4279; 1.1683) 0.1876 < 0.0001 0.2222 1.06 1350.592 1373.358 BAI * 0.2382 (0.2534; 0.9169) 0.1682 0.001 0.0521 0.2158 (0.1888; 0.8643) 0.1712 0.002 0.1848 1.12 1320.862 1343.48 FMI (kg/m²) 0.2471 (0.5151; 1.7511) 0.3134 < 0.0001 0.0564 0.2340 (0.4426; 1.6837) 0.3145 0.001 0.1931 1.10 1318.934 1341.551 WHR * 0.5409 (47.5649; 73.5671) 6.5940 < 0.0001 0.2891 0.4861 (41.6184; 68.0967) 6.7101 < 0.0001 0.3761 1.07 1297.789 1320.518 WHtR * 0.4641 (43.1727; 74.2748) 7.8868 < 0.0001 0.2115 0.4320 (38.6386; 70.2790) 8.0177 < 0.0001 0.3144 1.10 1299.481 1322.136 CI * 0.5396 (42.7812; 66.4190) 5.9940 < 0.0001 0.2877 0.4985 (38.5878; 62.6493) 6.0972 < 0.0001 0.3770 1.08 1281.508 1304.163 AVI (L) 0.3918 (0.5770; 1.1283) 0.1398 < 0.0001 0.1494 0.3705 (0.5274; 1.0763) 0.1391 < 0.0001 0.2764 1.08 1336.795 1359.561 BSI * 0.5079 (694.5838; 1121.964) 108.3743 < 0.0001 0.2543 0.4645 (618.8836; 1054.705) 10.4375 < 0.0001 0.3469 1.08 1290.372 1313.027 BRI * 0.4512 (2.0197; 3.5471) 0.3873 < 0.0001 0.1997 0.4204 (1.8035; 3.3531) 0.3927 < 0.0001 0.3052 1.10 1301.997 1324.652 β* = standardized beta regression coefficient; CI-95% = 95% confidence interval; SE = standard error; p = p value (statistical significance, p < 0.05); R 2 adj = adjusted R 2 of the model; VIF = variance inflation factor; AIC = Akaike information draws; BIC = Bayesian Information Criterion, de Schwarz; BMI = body mass index; WC = waist circumference; NC = neck circumference; BAI = body adiposity index; FMI = fat mass index; WHR = waist-hip ratio; WHtR = waist-to-height ratio; CI = conicity index; AVI = abdominal volume index; BSI = body shape index; BRI = body roundness index. f² of the respective indicators, gross analysis = 0.0545; 0.1809; 0.0479; 0.0550; 0.0598; 0.4067; 0.2682; 0.4039; 0.1756; 0.3410; 0.2495. f² of the respective indicators, adjusted analysis = 0.2341; 0.3827; 0.2857; 0.2267; 0.2393; 0.6028; 0.4586; 0.6051; 0.3820; 0.5312; 0.4393. f² = size of association effects. * Dimensionless measurements. ** The linear regression model was adjusted for education, level of physical activity, lymphocytes T-CD4, income, ART (antiretroviral therapy). In order to facilitate understanding of the conducted regression analyses, a frequency table of the covariates has been created and is available in Supplementary File 3. DISCUSSION The main finding of this study highlights the association of of anthropometric indicators and single cardiovascular risk factors and Framingham Risk Score in men living with HIV, while this result was not observed in women. Furthermore, the correlation of these indicators with isolated biochemical and hemodynamic variables seems to contribute to its composition. CI, WHR, WHtR, BSI and BRI stand out as indicators that maintained more robust associations for men. Despite being simple (single measurement) and practical for assessing abdominal fat in adults, showing a substantial relationship with the percentage of body fat 43 , the WC alone was not associated with cardiovascular risk in this study. On the other hand, this measurement was inserted in formulas to calculate anthropometric indicators with largest association with cardiovascular risk factors.,. Espírito Santo 49 studied BMI, CI, WC, WHR, and WHtR as discriminators of cardiovascular risk, defined by the Framingham Risk Score in ART-naïve PLHIV. Of these, the indicators that were best associated with high cardiovascular risk were WHR and WC, where CI was the indicator with the lowest discriminatory power. Beraldo 4 studied WC, HC, thigh circumference, BMI, BAI, WHR, and waist-to-thigh ratio with the aim of analyzing their associations with metabolic syndrome in PLHIV, observing that WC was the indicator that presented the best performance to identify there. This was not found in our study, since the CI was the indicator that best explained the variance in risk. The divergence between study results can be justified by the different cutoff points used for WC. Oliveira et al. 32 found that the poor performance of NC in ROC curve analysis for predicting cardiometabolic risk in women with HIV, compared to men, can be attributed to variations in the distribution of body fat. In the present study, although a cardiovascular risk prediction analysis was not conducted, none anthropometric indicator, including NC, revealed a significant association with cardiovascular risk factors in women. Women tend to have subcutaneous fat, while men have a more centralized distribution of body fat 32 . These differences, together with factors such as hormonal variations, body composition and ethnic characteristics, can negatively influence the discriminatory capacity of NC in predicting cardiometabolic risk in women 32 . A significant difference was also observed between men and women for total cholesterol, HDL-c, LDL-c and triglycerides. Previous studies, such Oh and Hegele 50 , indicate that the disparity in cholesterol levels between men and women can be attributed to hormonal, genetic and behavioral factors. Furthermore, it has been observed that men have slightly higher levels of total cholesterol compared to women. However, LDL and total cholesterol are less predominant in predicting cardiovascular risk compared to HDL and triglycerides, highlighting the need for a more individualized approach in women for effective heart health management 51 . The Framingham Risk Score stratifies the sample by sex due to characteristic differences in risk (for example, high levels of HDL-c in women, as well as the hormonal protective factor before menopause, and a higher prevalence of diabetes in men). Stratification allows the creation of specific risk profiles, taking into account physiological and health differences between the sexes 35 , and is supported by differences in body composition between the sexes 12,13,14 . Our findings reflect the sex-stratified cardiovascular risk in PLHIV. Although women present more metabolic dysfunctions, such as abdominal obesity and dyslipidemia, men exhibited a higher percentage of elevated risk – using Framingham Risk Score for this definition. Furthermore, the justification may be the score calculation itself, where there are differences in the points attributed to risk factors stratified by sex, such as age, treated and untreated SBP and smoking habit, considering differences in the rates of cardiovascular events between the sexes 35 . Analyzing the characteristics of the sample in the present study, it is possible to observe a higher prevalence for the male sex, with a predominance of advanced age 36 , this was the main factor that possibly influenced a high score on the Framingham Risk Score in male gender. Previous research has highlighted metabolic and morphological changes that may be due to HIV infection, including marked dyslipidemia (increased triglycerides and reduced HDL-c) that are strongly related to increased cardiovascular risk 3,8,52,53 . This evidence demonstrates that these factors may have influenced the results of the present study (Fig. 2 ). Among the variables that increased cardiovascular risk, the one with the highest prevalence, regardless of sex, was low HDL. Oh and Hegele 50 associate low HDL concentration with immune activation in initial HIV infection. The decrease observed in HDL in patients with HIV, treated by antiretrovirals or not, results from impaired cholesterol efflux from macrophages due to HIV interference in ATP binding, contributing to low HDL concentration in plasma 3,50 . Inflammation stimulates endothelial lipases and phospholipases A2, reducing availability of plasma HDL; Additionally, hypertriglyceridemia enriches triglycerides in HDL, making it more susceptible to hepatic removal by hepatic lipase 3,50 . Other metabolic changes have been associated with prolonged use of ART 4 , especially in combination with PI and nucleoside analogue reverse transcriptase inhibitors, which inhibit the mitochondrial enzyme DNA polymerase γ, resulting in mitochondrial DNA depletion and respiratory chain dysfunction, leading to reduced energy production in mitochondria. This mitochondrial dysfunction can affect different cell types, causing lipoatrophy in adipocytes and insulin resistance in skeletal muscle 4 . Therefore, secondary dyslipidemia occurs characterized by increase in plasma triglycerides, total cholesterol and LDL, and reduction in HDL, causing an elevated cardiovascular risk in HIV patients undergoing treatment 3,50 . The limitations of this study include an absence of technical measurement error calculation intra and inter-evaluators of anthropometric measurements; however, anthropometric measurement training and a pilot study were done prior to data collection. Another limitation of the study is the impossibility of predicting the cardiovascular outcome due to the type of study and the analyzes carried out that do not allow such inference. We also assume the limitation of having chosen Framingham as a predictor of cardiovascular risk, although it is indicated by Brazilian institutions. The strengths of study include the address cardiovascular risk and a wide range the anthropometric indicators of AVI, BRI, BSI and FMI in PLHIV, which highlights the urgent need for in-depth research. This gap in the literature highlights the importance of specifically addressing the intersection between these indicators and cardiovascular health in this population, providing crucial insights for clinical practice and preventive interventions. CONCLUSION In men living with HIV, all 11 anthropometric indicators evaluated showed an association with cardiovascular risk measured by Framingham Risk Score, independent of confounding variables, with emphasis on CI, WHR, WHtR, BSI and BRI. Assuming practical and low-cost indicators the WHR and WHtR may be more feasible to clinical and outpatient practice evaluation as easy-to-implement tools at all levels of health care. For women, additional investigations are needed to identify cardiovascular risk early, as none of the analyzed measures proved suitable for this population. The results of this study emphasize the importance of special attention to PLHIV, especially regarding the control of NCDs. Although ART has represented a significant advancement in HIV infection control, the increased lifespan in this population exposes chronic health challenges that require careful management. It is essential to implement appropriate strategies and tolls (.i.e anthropometric indicators) for health promotion and improving quality of life, aiming to effectively address chronic health conditions that may arise over time. References Centers for Disease Control and Prevention (CDC). HIV Basics. Disponível em: . Acesso em: 10 agosto 2023. NUNES JUNIOR, Sebastiao Silveira; e CIOSAK, Suely Itsuko. Terapia antirretroviral para hiv/aids: o estado da arte. Revista de Enfermagem UFPE on line, [s. l.], v. 12, n. 4, p. 1103, 4 abr. 2018. ISSN 1981–8963. DOI 10.5205/1981-8963-v12i4a231267p1103-1111-2018 . BORATO, Danielle Cristyane Kalva et al. Changes of metabolic and inflammatory markers in HIV infection: glucose, lipids, serum Hs-CRP and myeloperoxidase. Metabolism, [s. l.], v. 61, n. 10, p. 1353–1360, out. 2012. ISSN 00260495. DOI 10.1016/j.metabol.2012.03.003 . BERALDO, Rebeca Antunes et al. Comparing the Ability of Anthropometric Indicators in Identifying Metabolic Syndrome in HIV Patients. PloS one, United States, v. 11, n. 2, p. e0149905, 2016. ISSN 1932–6203. DOI 10.1371/journal.pone.0149905 . GUPTA, V.; BISWAS, A.; e SHARMA, S. K. Metabolic and body composition changes after six months of highly active antiretroviral therapy in northern Indian patients. International journal of STD & AIDS, England, v. 22, n. 1, p. 46–49, jan. 2011. ISSN 1758 – 1052 0956–4624. DOI 10.1258/ijsa.2010.010193 . CALZA, L. et al. Prevalence of diabetes mellitus, hyperinsulinaemia and metabolic syndrome among 755 adult patients with HIV-1 infection. International Journal of STD & AIDS, [s. l.], v. 22, n. 1, p. 43–45, jan. 2011. ISSN 0956–4624, 1758 – 1052. DOI 10.1258/ijsa.2010.010256 . CAPILI, Bernadette; ANASTASI, Joyce K.; e OGEDEGBE, Olugbenga. HIV and General Cardiovascular Risk. Journal of the Association of Nurses in AIDS Care, [s. l.], v. 22, n. 5, p. 362–375, set. 2011. ISSN 10553290. DOI 10.1016/j.jana.2010.12.002 . ERLANDSON, Kristine M.; e LAKE, Jordan E. Fat Matters: Understanding the Role of Adipose Tissue in Health in HIV Infection. Current HIV/AIDS reports, United States, v. 13, n. 1, p. 20–30, fev. 2016. ISSN 1548–3576 1548–3568. DOI 10.1007/s11904-016-0298-8 . GIRALT, Marta; DOMINGO, Pere; e VILLARROYA, Francesc. Adipose tissue biology and HIV-infection. Best Practice & Research Clinical Endocrinology & Metabolism, [s. l.], v. 25, n. 3, p. 487–499, jun. 2011. ISSN 1521690X. DOI 10.1016/j.beem.2010.12.001 . SPRINZ, Eduardo; ALVES, Marcelle D. e BRITES, Carlos. HIV-associated lipodystrophy: a review from a Brazilian perspective. Therapeutics and clinical risk management, New Zealand, v. 10, p. 559–566, 2014. ISSN 1176–6336 1178-203X. DOI 10.2147/TCRM.S35075 . MASENGA, Sepiso K. et al. Hypertension and Metabolic Syndrome in Persons with HIV. Current Hypertension Reports, [s. l.], v. 22, n. 10, p. 78, out. 2020. ISSN 1522–6417, 1534–3111. DOI 10.1007/s11906-020-01089-3 . DOS SANTOS, André P. et al. Lipodystrophy diagnosis in people living with HIV/AIDS: prediction and validation of sex-specific anthropometric models. BMC Public Health, [s. l.], v. 18, n. 1, p. 806, dez. 2018. ISSN 1471–2458. DOI 10.1186/s12889-018-5707-z . ABRAHAMS, Zulfa; MAARTENS, Gary; LEVITT, Naomi; e DAVE, Joel. Anthropometric definitions for antiretroviral-associated lipodystrophy derived from a longitudinal South African cohort with serial dual-energy X-ray absorptiometry measurements. International Journal of STD & AIDS, [s. l.], v. 29, n. 12, p. 1194–1203, out. 2018. ISSN 0956–4624, 1758 – 1052. DOI 10.1177/0956462418778649 . ABELMAN, Rebecca A. et al. Body Composition Changes Over the Menopausal Transition in Women With and Without Human Immunodeficiency Virus. Clinical Infectious Diseases, [s. l.], v. 77, n. 2, p. 265–271, 26 jul. 2023. ISSN 1058–4838, 1537–6591. DOI 10.1093/cid/ciad165 . BERALDO, Rebeca Antunes et al. Redistribuição de gordura corporal e alterações no metabolismo de lipídeos e glicose em pessoas vivendo com HIV/AIDS. Revista Brasileira de Epidemiologia, [s. l.], v. 20, n. 3, p. 526–536, jul. 2017. ISSN 1415-790X. DOI 10.1590/1980-5497201700030014 . PONTI, Federico et al. Aging and Imaging Assessment of Body Composition: From Fat to Facts. Frontiers in Endocrinology, [s. l.], v. 10, p. 861, 14 jan. 2020. ISSN 1664–2392. DOI 10.3389/fendo.2019.00861 . LICHTENSTEIN, Kenneth; BALASUBRAMANYAM, Ashok; SEKHAR, Rajagopal; e FREEDLAND, Eric. HIV-associated adipose redistribution syndrome (HARS): definition, epidemiology and clinical impact. AIDS Research and Therapy, [s. l.], v. 4, n. 1, p. 16, dez. 2007. ISSN 1742–6405. DOI 10.1186/1742-6405-4-16 . RUMBO-RODRÍGUEZ, Lorena; SÁNCHEZ-SANSEGUNDO, Miriam; FERRER-CASCALES, Rosario; GARCÍA-D’URSO, Nahuel; HURTADO-SÁNCHEZ, Jose A.; e ZARAGOZA-MARTÍ, Ana. Comparison of Body Scanner and Manual Anthropometric Measurements of Body Shape: A Systematic Review. International Journal of Environmental Research and Public Health, [s. l.], v. 18, n. 12, p. 6213, 8 jun. 2021. ISSN 1660–4601. DOI 10.3390/ijerph18126213 . SCHWENK, Achim. Methods of assessing body shape and composition in HIV-associated lipodystrophy: Current Opinion in Infectious Diseases, [s. l.], v. 15, n. 1, p. 9–16, fev. 2002. ISSN 0951–7375. DOI 10.1097/00001432-200202000-00003 . FINKELSTEIN, Julia L.; GALA, Pooja; ROCHFORD, Rosemary; GLESBY, Marshall J.; e MEHTA, Saurabh. HIV/AIDS and lipodystrophy: Implications for clinical management in resource-limited settings. Journal of the International AIDS Society, [s. l.], v. 18, n. 1, p. 19033, jan. 2015. ISSN 1758–2652, 1758–2652. DOI 10.7448/IAS.18.1.19033 . DIMALA, Christian Akem; NGU, Roland Cheofor; KADIA, Benjamin Momo; TIANYI, Frank-Leonel; e CHOUKEM, Simeon Pierre; WILLIG, Amanda (ed.). Markers of adiposity in HIV/AIDS patients: Agreement between waist circumference, waist-to-hip ratio, waist-to-height ratio and body mass index. PLOS ONE, [s. l.], v. 13, n. 3, p. e0194653, 22 mar. 2018. ISSN 1932–6203. DOI 10.1371/journal.pone.0194653 . COFRANCESCO, Joseph Jr; FREEDLAND, Eric; e MCCOMSEY, Grace. Treatment options for HIV-associated central fat accumulation. AIDS patient care and STDs, United States, v. 23, n. 1, p. 5–18, jan. 2009. ISSN 1557–7449 1087–2914. DOI 10.1089/apc.2008.0067 . ACHHRA, Amit C. et al. Assessing Cardiovascular Risk in People Living with HIV: Current Tools and Limitations. Current HIV/AIDS Reports, [s. l.], v. 18, n. 4, p. 271–279, ago. 2021. ISSN 1548–3568, 1548–3576. DOI 10.1007/s11904-021-00567-w . PINTO NETO, Lauro Ferreira Da Silva; DIAS, Fernanda Rezende; BRESSAN, Flavia Feres; e SANTOS, Carolina Rocio Oliveira. Comparison of the ACC/AHA and Framingham algorithms to assess cardiovascular risk in HIV-infected patients. The Brazilian Journal of Infectious Diseases, [s. l.], v. 21, n. 6, p. 577–580, nov. 2017. ISSN 14138670. DOI 10.1016/j.bjid.2017.06.007 . BRASIL. Protocolo clínico e diretrizes terapêuticas para o manejo da infecção pelo HIV em adultos. Revista de Enfermagem UFPE on line, v. 12, n. 12, p. 3533, 2 dez. 2018. Disponível em: https://doi.org/10.5205/1981-8963-v12i12a238453p3533-3534-2018 . Acesso em: 14 ago. 2023. SMITH, Sidney C. et al. Principles for National and Regional Guidelines on Cardiovascular Disease Prevention: A Scientific Statement From the World Heart and Stroke Forum. Circulation, [s. l.], v. 109, n. 25, p. 3112–3121, 29 jun. 2004. ISSN 0009-7322, 1524–4539. DOI 10.1161/01.CIR.0000133427.35111.67 . OLIVEIRA, Mirele Arruda Michelotto De; FAGUNDES, Regina Lúcia Martins; MOREIRA, Emília Addison Machado; TRINDADE, Erasmo Benício Santos De Moraes; e CARVALHO, Tales De. Relação de indicadores antropométricos com fatores de risco para doença cardiovascular. Arquivos Brasileiros de Cardiologia, [s. l.], v. 94, n. 4, p. 478–485, abr. 2010. ISSN 0066-782X. DOI 10.1590/S0066-782X2010005000012 . NOGUEIRA, Amanda et al. Fat Mass Ratio in Brazilian HIV-infected Patients Under Antiretroviral Therapy and Its Relationship With Anthropometric Measurents. Journal of Clinical Densitometry, [ s. l. ], v. 23, n. 4, p. 623–629, out. 2020. ISSN 10946950. DOI 10.1016/j.jocd.2018.07.013 . KOETHE, John R. et al. HIV and antiretroviral therapy-related fat alterations. Nature Reviews Disease Primers, [s. l.], v. 6, n. 1, p. 48, 18 jun. 2020. ISSN 2056-676X. DOI 10.1038/s41572-020-0181-1 . OLAWEPO, John O.; PHARR, Jennifer R.; CROSS, Chad L.; KACHEN, Axenya; OLAKUNDE, Babayemi O.; e SY, Francisco S. Changes in body mass index among people living with HIV who are new on highly active antiretroviral therapy: a systematic review and meta-analysis. AIDS Care, [s. l.], v. 33, n. 3, p. 326–336, 4 mar. 2021. ISSN 0954 – 0121, 1360 – 0451. DOI 10.1080/09540121.2020.1770181 . MAVARANI, Laven et al. Changes in body mass index, weight, and waist-to-hip ratio over five years in HIV-positive individuals in the HIV Heart Aging Study compared to the general population. Infection, Germany, v. 51, n. 4, p. 1081–1091, ago. 2023. ISSN 1439 – 0973 0300–8126. DOI 10.1007/s15010-023-02009-8 . OLIVEIRA, Iara Katrynne Fonsêca et al. Neck circumference as a predictor of cardiometabolic risk and truncal obesity in people living with HIV. Nutricion hospitalaria, Spain, 11 maio 2023. ISSN 1699–5198 0212–1611. DOI 10.20960/nh.04402 . GUARALDI, Giovanni et al. Evidence gaps on weight gain in people living with HIV: a scoping review to define a research agenda. BMC Infectious Diseases, [s.l.], v. 23, n. 1, p. 230, 14 abr. 2023. ISSN 1471–2334. DOI 10.1186/s12879-023-08174-3 . Dean, Andrew G.; Pezzulo, John C.; Sullivan, Kevin M. Universidade Emory baseado no código de John C. Pezzullo / Interface: Andrew G. Dean, EpiInformatics.com, e Roger A. Mir (2013). Acesso: https://www.openepi.com/SampleSize/SSPropor.htm D’AGOSTINO, Ralph B. et al. General Cardiovascular Risk Profile for Use in Primary Care: The Framingham Heart Study. Circulation, [s. l.], v. 117, n. 6, p. 743–753, 12 fev. 2008. ISSN 0009-7322, 1524–4539. DOI 10.1161/CIRCULATIONAHA.107.699579 . PESCATELLO, L. S. Diretrizes do ACSM: para os testes de esforço e sua prescrição (9a. ed). [S. l.]: Grupo Gen - Guanabara Koogan, 2000. GRUNDY, S. M.; BREWER, H. B.; CLEEMAN, J. I.; SMITH, S. C.; LENFANT, C. Definition of Metabolic Syndrome: Report of the National Heart, Lung, and Blood Institute/American Heart Association Conference on Scientific Issues Related to Definition. Circulation, v. 109, n. 3, p. 433–438, 27 jan. 2004. https://doi.org/10.1161/01.CIR.0000111245.75752.C6 . WHO EXPERT COMMITTEE ON HYPERTENSION CONTROL (ed.). Hypertension control: report of a WHO expert committee. Geneva: World Health Organization, 1996. 83 p. (WHO technical report series, 862). ISBN 978-92-4-120862-8. FRIEDEWALD, W. T.; LEVY, R. I.; e FREDRICKSON, D. S. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clinical Chemistry, [s. l.], v. 18, n. 6, p. 499–502, jun. 1972. ISSN 0009–9147. Associação Brasileira para o Estudo da Obesidade e da Síndrome Metabólica. Diretrizes brasileiras de obesidade 2016 / ABESO - Associação Brasileira para o Estudo da Obesidade e da Síndrome Metabólica – 4.ed. - São Paulo, SP. I Diretriz Brasileira de Diagnóstico e Tratamento da Síndrome Metabólica. Rev Soc Bras Hipert 2004;17(4). BERGMAN, Richard N. et al. A Better Index of Body Adiposity. Obesity, [s. l.], v. 19, n. 5, p. 1083–1089, maio 2011. ISSN 19307381. DOI 10.1038/oby.2011.38 . PIQUERAS, Paola; BALLESTER, Alfredo; DURÁ-GIL, Juan V.; MARTINEZ-HERVAS, Sergio; REDÓN, Josep; e REAL, José T. Anthropometric Indicators as a Tool for Diagnosis of Obesity and Other Health Risk Factors: A Literature Review. Frontiers in Psychology, [s. l.], v. 12, p. 631179, 9 jul. 2021. ISSN 1664 – 1078. DOI 10.3389/fpsyg.2021.631179 . CRITÉRIO DE CLASSIFICAÇÃO ECONÔMICA BRASIL. Associação Brasileira de Empresas de Pesquisa – ABEP. 2016. CRAIG, Cora L. et al. International Physical Activity Questionnaire: 12-Country Reliability and Validity: Medicine & Science in Sports & Exercise, [s. l.], v. 35, n. 8, p. 1381–1395, ago. 2003. ISSN 0195–9131. DOI 10.1249/01.MSS.0000078924.61453.FB . LEE, Paul H.; MACFARLANE, Duncan J.; LAM, Th; e STEWART, Sunita M. Validity of the international physical activity questionnaire short form (IPAQ-SF): A systematic review. International Journal of Behavioral Nutrition and Physical Activity, [s. l.], v. 8, n. 1, p. 115, dez. 2011. ISSN 1479–5868. DOI 10.1186/1479-5868-8-115 . RUMSEY, Deborah. Statistics for dummies. 2nd edition ed. Hoboken, NJ: Wiley Publishing, Inc, 2016. 388 p. (Learning made easy). ISBN 978-1-119-29352-1. BRASIL. Protocolo clínico e diretrizes terapêuticas para o manejo da infecção pelo hiv em adultos. Revista de Enfermagem UFPE on line, v. 12, n. 12, p. 3533, 2 dez. 2023. Disponível em: https://doi.org/10.5205/1981-8963-v12i12a238453p353 3-3534-2018. Acesso em: 14 ago. 2023 ESPIRITO, Santo Raquel. Avaliação do risco cardiovascular através dos indicadores de obesidade e de distribuição da gordura corporal em portadores de HIV virgens de tratamento antirretroviral. 2013. reponame:Repositório Institucional da FIOCRUZ, [s. l.], 2013. Disponível em: https://www.arca.fiocruz.br/handle/icict/12491 . Acesso em: 14 ago. 2023. OH, Jisun; HEGELE, Robert A. HIV-associated dyslipidaemia: pathogenesis and treatment. The Lancet Infectious Diseases, v. 7, n. 12, p. 787–796, dez. 2007. Disponível em: https://doi.org/10.1016/s1473-3099(07)70287-6 . Acesso em: 15 ago. 2023. PHAN, Binh An. e TOTH, Peter. Dyslipidemia in women: etiology and management. International Journal of Women’s Health, [ s. l. ], p. 185, fev. 2014. ISSN 1179–1411. DOI 10.2147/IJWH.S38133 . KOETHE, John R. Adipose Tissue in HIV Infection. In: TERJUNG, Ronald (ed.). Comprehensive Physiology. 1. ed. [S. l.]: Wiley, 12 set. 2017. p. 1339–1357. ISBN 978-0-470-65071-4. DOI 10.1002 /cphy.c160028. Disponível em: https://onlinelibrary.wiley.com/doi/10.1002/cphy.c160028 . Acesso em: 10 jan. 2024. BAILIN, Samuel S.; GABRIEL, Curtis L.; WANJALLA, Celestine N.; e KOETHE, John R. Obesity and Weight Gain in Persons with HIV. Current HIV/AIDS Reports, [s. l.], v. 17, n. 2, p. 138–150, abr. 2020. ISSN 1548–3568, 1548–3576. DOI 10.1007/s11904-020-00483-5 . Additional Declarations There is NO conflict of interest to disclose. Supplementary Files Supplementaryfile1.docx Supplementary file 1 STROBE Statement—Checklist of items that should be included in reports of cross-sectional studies Supplementaryfile2.docx Supplementary file 2 Supplementary Table 1 – Formulas for calculating Anthropometric Indicators of body fat. Supplementaryfile3.docx Supplementary file 3 Supplementary Table 2 – Frequency distribution of covariates used in multivariate regression analyses. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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SAE=serviço de atendimento especializado; HD=hospital dia.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4004802/v1/0ce83af92157e2618cc2bb02.png"},{"id":60171095,"identity":"6dba6e30-2a19-44e6-93a4-00f43fa2208c","added_by":"auto","created_at":"2024-07-12 15:08:47","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":196358,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of variables that increase cardiovascular risk and prevalence of cardiovascular risk (CVR) classified by the Framingham Risk Score. Stratified by sex.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4004802/v1/7fe4c4ec1c6bf05f9f3e3dd8.jpg"},{"id":60171098,"identity":"a97d925d-bdae-44ae-9c10-249276e26892","added_by":"auto","created_at":"2024-07-12 15:08:47","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":294092,"visible":true,"origin":"","legend":"\u003cp\u003eHeat map of correlations between anthropometric indicators and cardiovascular risk factors isolated and grouped into the Framingham risk score, in PLHIV stratified by sex. The values represent the correlation coefficient r or ρ.\u003c/p\u003e\n\u003cp\u003eTG = triglycerides; LDL = low-density lipoprotein; HDL = high-density lipoprotein; TC = total cholesterol; SBP = systolic blood pressure; FRS = Framingham risk score; BMI = body mass index; WC = waist circumference; NC = neck circumference; BAI = body adiposity index; FMI = fat mass index; WHR = waist-hip ratio; WHtR = waist-to-height ratio; CI = conicity index; AVI = abdominal volume index; BSI = body shape index; BRI = body roundness index; CVR = cardiovascular risk.\u003c/p\u003e\n\u003cp\u003e* Dimensionless measurements.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Variables with normal distribution, using Pearson Correlation.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003e Variables without normal distribution, using Spearman Correlation.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4004802/v1/9496af0eb0314846f04e5e59.jpg"},{"id":67293029,"identity":"aa309bcb-de14-4464-8d00-779455260062","added_by":"auto","created_at":"2024-10-23 10:33:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1259830,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4004802/v1/36deacb1-4823-471a-8470-ee958664cb7b.pdf"},{"id":60172353,"identity":"95be1cb9-bc78-44dd-ac5a-500267ba0f5a","added_by":"auto","created_at":"2024-07-12 15:16:47","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":25296,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary file 1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSTROBE Statement—Checklist of items that should be included in reports of \u003cem\u003e\u003cstrong\u003ecross-sectional studies\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Supplementaryfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4004802/v1/5da1471506ae1ee5dcc5b709.docx"},{"id":60172355,"identity":"1ef48e6e-59d4-49b2-9d5b-937a83f594aa","added_by":"auto","created_at":"2024-07-12 15:16:47","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":41120,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary file 2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary Table 1 – Formulas for calculating Anthropometric Indicators of body fat.\u003c/p\u003e","description":"","filename":"Supplementaryfile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-4004802/v1/b2f5506f052a0c39268b670c.docx"},{"id":60173671,"identity":"386e11e4-b0bf-45c0-809f-90aca8a9e35e","added_by":"auto","created_at":"2024-07-12 15:24:47","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":22601,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary file 3\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary Table 2 – Frequency distribution of covariates used in multivariate regression analyses.\u003c/p\u003e","description":"","filename":"Supplementaryfile3.docx","url":"https://assets-eu.researchsquare.com/files/rs-4004802/v1/8a6654169b9a5859c57a001f.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"\u003cp\u003eAnthropometric Indicators Are Associated With Cardiovascular Risk Measured by Framingham Risk Score in Men Living With Hiv, but Not in Women.\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe infection with the Human Immunodeficiency Virus (HIV) causes progressive deterioration of the immune system, leading to Acquired Immunodeficiency Syndrome (AIDS) when there is a sharp drop in T-CD4\u0026thinsp;+\u0026thinsp;lymphocytes\u003csup\u003e1\u003c/sup\u003e, in the absence of treatment. However, the implementation of Antiretroviral Therapy (ART) has increased the survival and quality of life of people living with HIV (PLHIV) due to its effectiveness in inhibit viral replication, preserving the immune function\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe prolonged use of ART combined to effects of HIV itself causes metabolic and morphological changes, such as hypertriglyceridemia, hyperglycemia, insulin resistance, dyslipidemia and increased visceral adiposity.\u003csup\u003e3,4\u003c/sup\u003e In this condition, there is anincreased risk of early cardiovascular events.\u003c/p\u003e \u003cp\u003eTherefore, to determine body composition, particularly body fat, is important to the management of PLHIV in the clinical setting \u003csup\u003e4,15\u003c/sup\u003e. Body composition can be evaluated through the reference methods as dual-energy x-ray absorptiometry (DXA), air displacement plethysmography (ADP), magnetic resonance imaging, computed tomography and by practical methos as bioelectrical impedance, ultrasound and anthropometry\u003csup\u003e16\u003c/sup\u003e. However, anthropometry is the most used technique in clinical practice capable of estimating body composition or calculating surrogate anthropometric indicators, obtaining clinical diagnoses and prognoses\u003csup\u003e17,18\u003c/sup\u003e. Studies suggest to evaluate body fat changes associated with HIV using low cost methods, more accessible option for both clinical practice and research\u003csup\u003e19,20\u003c/sup\u003e and encourage the use of anthropometric measurements, such as waist circumference (WC), waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR) as predictors of cardiometabolic risk in PLHIV\u003csup\u003e21\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe assessment of cardiovascular risk in PLWH continues to be a challenge in clinical practice, since there are several methods for screening it, such as Pooled Cohort Equations (PCE), Systematic Coronary Risk Assessment (SCORE), the American College of Cardiology/American Heart Association (ACC/AHA), the Framingham risk score, among others\u003csup\u003e23,24\u003c/sup\u003e. However, they are not validated in populations with HIV. Despite this, the Clinical Protocol and Therapeutic Guidelines of the Ministry of Health in Brazil\u003csup\u003e25\u003c/sup\u003e recommends the application of the Framingham Risk Score in the assessment of cardiovascular risk in PLHIV.\u003c/p\u003e \u003cp\u003eAlthough previous studies have evaluated the use of anthropometric indicators in the general population and in PLHIV\u003csup\u003e8,14,21,26\u0026ndash;33\u003c/sup\u003e, no study has addressed a large number of anthropometric indicators at the same study in the HIV population and tested their association with cardiovascular risk. Thus, the objective of this study was to test the association of eleven anthropometric indicators with cardiovascular risk in PLHIV.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e \u003cb\u003eStudy design\u003c/b\u003e \u003c/p\u003e \u003cp\u003eCross-sectional study. The university\u0026rsquo;s Institutional Review Board approved the study (no. 4.864.332). All participants signing the writing informed consent. The present study is reported in accordance with the guidelines STROBE Statement for cross-sectional studies (supplementary file 1).\u003c/p\u003e \u003cp\u003e \u003cb\u003eSetting\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe study was carried out in Macei\u0026oacute;-AL (northeast of Brazil) from November, 2021 to September, 2022. Macei\u0026oacute; has a Human Development Index (HDI) of 0.721 below the Brazilian average of 0.727.\u003c/p\u003e \u003cp\u003e \u003cb\u003eParticipants\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSampling was carried out for convenience in the waiting room following the medical care routine and the sample size was established at 354 participants (calculation carried out in OpenEpi\u0026reg;, Dean, Sullivan and Soe\u003csup\u003e34\u003c/sup\u003e assuming confidence intervals of 95% and 5% margin of error).\u003c/p\u003e \u003cp\u003eParticipants were included if they met the eligibility criteria: age 18 and above; cisgender; both sexes; diagnosis of HIV infection; assisted at the outpatient level; and who had clinical and laboratory records. Participants were excluded if they had neurological disorders affecting the motor system; psychiatric disorders preventing them from completing the questionnaires; diagnosed with type I diabetes mellitus; genetic metabolic diseases; cancer; and if they were pregnant or postpartum.\u003c/p\u003e \u003cp\u003e \u003cb\u003eVariables and Data Collection Instruments\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe researchers involved in the collection process underwent prior training to reduce bias and a pilot study was carried out.\u003c/p\u003e \u003cp\u003eCardiovascular Risk (Outcome)\u003c/p\u003e \u003cp\u003eThe Framingham Risk Score was used to estimate cardiovascular risk\u003csup\u003e35\u003c/sup\u003e through the following variables: sex, age, measurement of systolic blood pressure (SBP), diagnosis of diabetes and/or arterial hypertension, smoking habit, total cholesterol and high-density lipoprotein (HDL-c) of cholesterol. The score calculation was conducted with parameters of cardiovascular risk prediction scores plotted in formulas in Excel\u0026reg; spreadsheets, for women and men\u003csup\u003e35\u003c/sup\u003e. Individuals were classified into categories: Low, Moderate and High Risk of a cardiovascular events in the next 10 years, assuming a chance of \u0026lt;\u0026thinsp;10%, 10 to 20% and \u0026gt;\u0026thinsp;20%, respectively. Furthermore, the continuous value of the percentage of cardiovascular risk was used.\u003c/p\u003e \u003cp\u003eTo analyze isolated cardiovascular risk factors, the following reference values ​​were used: for abdominal obesity, waist circumference\u0026thinsp;\u0026gt;\u0026thinsp;88cm for women and \u0026gt;\u0026thinsp;102cm for men; for hypertriglyceridemia\u0026thinsp;\u0026ge;\u0026thinsp;150mg/dL; for low-density lipoprotein (LDL-c) high\u0026thinsp;\u0026gt;\u0026thinsp;160mg/dL; for low HDL\u0026thinsp;\u0026lt;\u0026thinsp;50mg/dL for women and \u0026lt;\u0026thinsp;40mg/dL for men; for classification of hypercholesterolemia\u0026thinsp;\u0026gt;\u0026thinsp;240mg/dL; elevated SBP\u0026thinsp;\u0026ge;\u0026thinsp;130mmHg; High age, \u0026gt;\u0026thinsp;55 years for women and \u0026gt;\u0026thinsp;45 years for men\u003csup\u003e36,37\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBlood pressure was measured with the individual in a seated state, using a sphygmomanometer (PREMIUM\u0026reg;) and stethoscope (MEDICATE\u0026reg;) calibrated following international standards for determining SBP and diastolic blood pressure (DBP)\u003csup\u003e38\u003c/sup\u003e. Capillary blood sample was collected with a 35\u0026micro;l pipette carried out by transferring the blood to the enzymatic cholesterol strip and inserted into the lipid profile monitor (MISSION\u0026reg;) to read the lipid profile (total cholesterol, HDL-c and triglycerides). LDL-c was obtained through calculation using the Friedewald, Levy and Fredrickson Eq.\u0026nbsp;3\u003csup\u003e9\u003c/sup\u003e. Blood glucose was assessed using a ACCU-CHEK\u0026reg; with blood glucose strips (ACCU-CHEK\u0026reg;). Blood collection was waived when volunteers presented a complete lipid profile from up to 3 months ago, counting from the date of their evaluation.\u003c/p\u003e \u003cp\u003eAnthropometric Indicators (Exposure)\u003c/p\u003e \u003cp\u003eTo assess the anthropometric profile of the volunteers, measurements were taken of height (meters), body mass (kilograms), circumferences (centimeters) of the waist (WC), hip (HC) and neck (NC).\u003c/p\u003e \u003cp\u003eUsing a scale (MULTILASER\u0026reg;) and a stadiometer (AVANUTRI\u0026reg;), measurements of body mass and height, respectively, were taken using standardized procedures\u003csup\u003e40\u003c/sup\u003e. The individual remained barefoot, in the orthostatic position on the platform. For height measurement, the participant remained standing, erect, with arms extended along the body, head raised looking at a fixed point at eye level, feet together at 90\u0026ordm; angle with the legs. The height was taken after a maximal inspiration\u003csup\u003e40\u003c/sup\u003e. Using an inelastic measuring tape (CESCORF\u0026reg;), the WC was measured, following the recommendations described in the 1st Brazilian Guideline for Diagnosis and Treatment of Metabolic Syndrome\u003csup\u003e41\u003c/sup\u003e, at the midpoint between the lower rib margin and the iliac crest. To collect data regarding the HC, the measuring tape was positioned on the line of the trochanters. The measurement of NC was conducted with the individual with their head raised and gaze fixed at eye level, the tape was positioned around the central region of the neck\u003csup\u003e40\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFrom the measured anthropometric variables, body fat anthropometric surrogate indicators were calculated: Body Mass Index (BMI); Waist Circumference (WC); Neck Circumference (NC); Body Adiposity Index (BAI) using the formula proposed by Bergman et al.\u003csup\u003e42\u003c/sup\u003e; Fat Mass Index (FMI); Waist-to-Hip Ratio (WHR); Waist-to-Height Ratio (WHtR); Conicity Index (CI); Abdominal Volume Index (AVI); Body Shape Index (BSI); Body Roundness Index (BRI)\u003csup\u003e43\u003c/sup\u003e. The anthropometric indicators of body fat were calculated by formulas availablein supplementary file 2.\u003c/p\u003e \u003cp\u003eCovariables\u003c/p\u003e \u003cp\u003eThrough questionnaires and interviews sociodemographic (i.e. age, education, income) and behavior variables (i.e. physical activity) were collected. Income was assessed by completing the questionnaire from the Brazilian Association of Research Companies \u0026ndash; ABEP\u003csup\u003e44\u003c/sup\u003e for socioeconomic classification in the following economic classes: class D-E (0\u0026ndash;16 points), class C2 (17\u0026ndash;22 points), C1 (23\u0026ndash;28 points), B2 (29\u0026ndash;37 points), B1 (38\u0026ndash;44 points), and class A (\u0026gt;\u0026thinsp;45 points). The International Physical Activity Questionnaire - Short Form\u003csup\u003e45,46\u003c/sup\u003e, was used to classify the volunteers as inactive, irregularly active or active. Information related to ART, lymphocytes T-CD4, and viral load were obtained from the completion of the therapeutic form conducted at the end of all daily assessment with one of the physicians accessing the database in the system.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe data were tabulated in Excel\u0026reg; spreadsheet. Descriptive statistics were performed using percentages, measures of central tendency (mean and median) and dispersion (standard deviation and interquartile range). The Kolmogorov-Smirnov normality test was employed, with kurtosis analyses, skewness, and histograms to check the gaussian distribution. Assuming the disparities in body composition between the sexes described in the literature, the data was stratified between men and women\u003csup\u003e12,13\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe Student's T-test and Mann-Whitney U test were applied to test the differences between sexes. Additionally, Pearson's and Spearman's linear correlation analyses were used to assess bivariate correlations between exposure and outcome. Correlation values ​​between 0.00 and 0.30 indicate a non-existent or very weak correlation, values ​​greater than 0.30 indicate weak correlation, values ​​greater than 0.50, moderate correlation, and values ​​greater than 0.70 may be interpreted as signs of high correlation\u003csup\u003e47\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSimple and multivariable linear regression was used to test the relationship between cardiovascular risk and each anthropometric indicator in crude and adjusted for covariates (education, physical activity level, T-CD4 lymphocytes, income, and ART) analysis. For multivariable regression analysis, the variables were insert into the model was stepwise approach, from which the covariates were included all together (the order was according to their power to predict the output variable). The model diagnosis was performed using the variance inflation factor (VIF), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). Moreover, regression coefficients (adjusted β*), 95% confidence interval, p-value, adjusted coefficient of determination (R\u0026sup2;adj), and effect size (f\u0026sup2;) were also estimated.\u003c/p\u003e \u003cp\u003eAll statistical analyses were conducted using Stata\u0026reg; 13.0 software, and the graphs were created using GraphPad Prism\u0026reg; 8.0 software. A significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThe study included 367 volunteers as illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e; however, 13 were excluded due to insufficient data to calculate cardiovascular risk. Final sample comprised 354 PLHIV, 58.8% male, 42.7 (13.0) years, mean (standard deviation). Regarding socioeconomic and demographic information, 26.7% completed elementary education, 49.6% (n\u0026thinsp;=\u0026thinsp;172) resided in the state capital, and 64.4% (n\u0026thinsp;=\u0026thinsp;228) belonged to social classes D and E. Biochemical, hemodynamic variables and anthropometric indicators were presented in Table \u003cspan class=\"InternalRef\"\u003e1.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of People Living with HIV, according to sex. Data expressed as Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD / Median (IQR).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eFeminine\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eMasculine\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eU / t critical\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.98\u0026thinsp;\u0026plusmn;\u0026thinsp;11.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.81\u0026thinsp;\u0026plusmn;\u0026thinsp;13.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.5518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1216\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSBP (mmHg) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120 (110; 130)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120 (110; 120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8375\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDBP (mmHg) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80 (70; 90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80 (70; 90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7380\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlucose (mg/dL) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100 (91; 112)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.5 (88; 107)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0540\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Cholesterol (mg/dL) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e190 (162; 233)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e166 (139; 200.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDL (mg/dL) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39 (31; 50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (25; 42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDL (mg/dL) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119 (93.6; 159.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105 (84.1; 126)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTriglycerides (mg/dL) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e147 (109; 202)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e131 (95; 205)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0284\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u0026sup2;) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.8 (23.7; 31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.2 (22; 27.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWC (cm) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.77\u0026thinsp;\u0026plusmn;\u0026thinsp;13.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.19\u0026thinsp;\u0026plusmn;\u0026thinsp;11.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.9479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0522\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNC (cm) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.7 (\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.4 (35.5; 39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-9.845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBAI * \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.88 (29.8; 38.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.83 (23.9; 28.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFMI (kg/m\u0026sup2;) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.33 (7.1; 11.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.28 (5.4; 7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWHR * \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.8283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0684\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWHtR * \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57 (0.52; 0.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51 (0.47; 0.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCI * \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.3696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0184\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAVI (L) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.05 (13.4; 20.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.37 (12.9; 18.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0651\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBSI * \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9234\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBRI * \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.83 (3.8; 6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.62 (2.8; 4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCVR (%) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.9 (2; 10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.7 (2.8; 13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0089\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eIQR\u0026thinsp;=\u0026thinsp;interquartile range; SD\u0026thinsp;=\u0026thinsp;standard deviation; SBP\u0026thinsp;=\u0026thinsp;systolic blood pressure; DBP\u0026thinsp;=\u0026thinsp;diastolic blood pressure; HDL\u0026thinsp;=\u0026thinsp;high-density lipoprotein; LDL\u0026thinsp;=\u0026thinsp;low-density lipoprotein; BMI\u0026thinsp;=\u0026thinsp;body mass index; WC\u0026thinsp;=\u0026thinsp;waist circumference; NC\u0026thinsp;=\u0026thinsp;neck circumference; BAI\u0026thinsp;=\u0026thinsp;body adiposity index; FMI\u0026thinsp;=\u0026thinsp;fat mass index; WHR\u0026thinsp;=\u0026thinsp;waist-hip ratio; WHtR\u0026thinsp;=\u0026thinsp;waist-to-height ratio; CI\u0026thinsp;=\u0026thinsp;conicity index; AVI\u0026thinsp;=\u0026thinsp;abdominal volume index; BSI\u0026thinsp;=\u0026thinsp;body shape index; BRI\u0026thinsp;=\u0026thinsp;body roundness index; CVR\u0026thinsp;=\u0026thinsp;cardiovascular risk.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e* Dimensionless measurements.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003csup\u003ea\u003c/sup\u003e Variables with normal distribution, used Student\u0026apos;s T test for independent samples.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003csup\u003eb\u003c/sup\u003e Variables without normal distribution, using the Mann-Whitney U test.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eFrom the 346 participants with ART information, 23.4% (n\u0026thinsp;=\u0026thinsp;81) used protease inhibitors (ART-PI), 66.2% (n\u0026thinsp;=\u0026thinsp;229) followed therapeutic regimens without PI, and 10.4% (n\u0026thinsp;=\u0026thinsp;36) had not yet initiated treatment. Data regarding HIV infection show 31.6% (n\u0026thinsp;=\u0026thinsp;107) of participants were detectable viral load (\u0026ge;\u0026thinsp;20 copies/mL) and 24.3% (n\u0026thinsp;=\u0026thinsp;79) presented immunosuppression with T-CD4\u0026thinsp;\u0026lt;\u0026thinsp;350\u003csup\u003e48\u003c/sup\u003e cells/mm\u0026sup3;.\u003c/p\u003e\n\u003cp\u003eAmong lifestyle habits and the presence of chronic diseases, 14.3% (n\u0026thinsp;=\u0026thinsp;50) were considered inactive, 43.7% (n\u0026thinsp;=\u0026thinsp;153) and 42.0% (n\u0026thinsp;=\u0026thinsp;147) insufficiently active and active, respectively; 17.8% (n\u0026thinsp;=\u0026thinsp;63) were smokers, and 21.2% (n\u0026thinsp;=\u0026thinsp;75) were ex-smokers; 19.2% (n\u0026thinsp;=\u0026thinsp;68) were hypertensive, and 9.9% (n\u0026thinsp;=\u0026thinsp;35) diabetic. The prevalence of high cardiovascular risk was 8.9% (n\u0026thinsp;=\u0026thinsp;13) for women and 16.3% (n\u0026thinsp;=\u0026thinsp;34) for men (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Furthermore, more than 50% of individuals, regardless of sex, had low HDL, which is one of the cardiovascular risk factors according to the Framingham Risk Score.\u003c/p\u003e\n\u003cp\u003eHeat map of correlations between anthropometric indicators and biochemical, hemodynamic, and cardiovascular risk variables showed non-existent or very weak correlations (r\u0026thinsp;=\u0026thinsp;0.00 and 0.30) for all anthropometric indicators for females (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). For males, WHR, WHtR, CI, and BRI presented moderates or strong correlations (r\u0026thinsp;\u0026gt;\u0026thinsp;0.50); BMI, WC, FMI, AVI, and BSI exhibited weak or moderate correlations (r between 0.30 and 0.70); and only two indicators, NC and BAI, showed non-existent or very weak correlations (r\u0026thinsp;\u0026lt;\u0026thinsp;0.30).\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents data from simple and multivariable linear regression between anthropometric indicators and cardiovascular risk in women living with HIV. None of the tested associations were significant, the standardized \u0026beta;* coefficients presented low (non-significant) values, and the adjusted R\u0026sup2; explained little the variance of the Framingham Risk Score. These results did not provide sufficient statistical evidence to assert associations between anthropometric indicators and cardiovascular risk in women living with HIV in this sample.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLinear regression analysis between anthropometric indicators and cardiovascular risk defined by the Framingham risk score (continuous value), in woman living with HIV.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGross\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdjusted**\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026beta;* (CI \u0026minus;\u0026thinsp;95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e \u003csub\u003eadj\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026beta;* (CI \u0026minus;\u0026thinsp;95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e \u003csub\u003eadj\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0857 (-0.1023; 0.3258)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0039 (-0.2407; 0.2302)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e845.1936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e864.8789\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWC (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0848 (-0.0446; 0.1399)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0290 (-0.0838; 0.1171)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e845.0814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e864.7667\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNC (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0042 (-0.3861; 0.3670)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0353 (-0.4679; 0.3076)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e845.0181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e864.7033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBAI *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1101 (-0.0669; 0.3416)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0172 (-0.2032; 0.2465)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e845.1571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e864.8424\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFMI (kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0758 (-0.1729; 0.4691)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0103 (-0.3705; 0.3295)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e845.1815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e864.8668\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWHR *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1203 (-4.9188; 32.2948)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.4137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1081 (-7.7825; 33.2636)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.3619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e843.603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e863.2883\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWHtR *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1271 (-3.2619; 26.1084)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.4296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0555 (-11.1915; 21.4194)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.2325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e844.7872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e864.4725\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCI *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0910 (-6.5933; 23.0345)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.4947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0986 (-7.3310; 26.2293)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.4721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e843.8837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e863.569\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAVI (L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0644 (-0.1472; 0.3366)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0166 (-0.2370; 0.2871)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e845.1578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e864.8431\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBSI *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0394 (-195.8451; 319.1106)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130.2647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1066 (-117.3419; 488.0127)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e152.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e843.6459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e863.3312\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBRI *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1071 (-0.2202; 1.0532)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0406 (-0.5414; 0.8652)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e844.9755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e864.6608\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\"\u003e\u0026beta;* = standardized beta regression coefficient; CI-95% = 95% confidence interval; SE\u0026thinsp;=\u0026thinsp;standard error; p\u0026thinsp;=\u0026thinsp;p value (statistical significance, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05); R\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eadj\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;adjusted R\u003csup\u003e2\u003c/sup\u003e of the model; VIF\u0026thinsp;=\u0026thinsp;variance inflation factor; AIC\u0026thinsp;=\u0026thinsp;Akaike information draws; BIC\u0026thinsp;=\u0026thinsp;Bayesian Information Criterion, de Schwarz; BMI\u0026thinsp;=\u0026thinsp;body mass index; WC\u0026thinsp;=\u0026thinsp;waist circumference; NC\u0026thinsp;=\u0026thinsp;neck circumference; BAI\u0026thinsp;=\u0026thinsp;body adiposity index; FMI\u0026thinsp;=\u0026thinsp;fat mass index; WHR\u0026thinsp;=\u0026thinsp;waist-hip ratio; WHtR\u0026thinsp;=\u0026thinsp;waist-to-height ratio; CI\u0026thinsp;=\u0026thinsp;conicity index; AVI\u0026thinsp;=\u0026thinsp;abdominal volume index; BSI\u0026thinsp;=\u0026thinsp;body shape index; BRI\u0026thinsp;=\u0026thinsp;body roundness index.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\"\u003ef\u0026sup2; of the respective indicators, gross analysis\u0026thinsp;=\u0026thinsp;0.0004; 0.0003; -0.0069; 0.0053; -0.0012; 0.0077; 0.0094; 0.0014; -0.0028; -0.0054; 0.0046.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\"\u003ef\u0026sup2; of the respective indicators, adjusted analysis\u0026thinsp;=\u0026thinsp;0.1120; 0.1130; 0.1136; 0.1123; 0.1121; 0.1265; 0.1157; 0.1238; 0.1123; 0.1260; 0.1140.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\"\u003ef\u0026sup2; = size of association effects.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\"\u003e* Dimensionless measurements.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\"\u003e** The linear regression model was adjusted for education, level of physical activity, lymphocytes T-CD4, income, ART (antiretroviral therapy).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIn Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, concerning simple and multivariable linear regression between anthropometric indicators and cardiovascular risk in men living with HIV, all indicators demonstrated significant associations with cardiovascular risk, even after adjusted for covariates. Notably, WHR, WHtR, CI, BSI, and BRI (\u0026beta;*=0.4861; \u0026beta;*=0.4320; \u0026beta;*=0.4985; \u0026beta;*=0.4645; \u0026beta;*=0.4204; respectively) are the indicators that maintained the most robust associations (analyzed by p-value, \u0026beta;*, R\u0026sup2;\u003csub\u003eadj\u003c/sub\u003e, and f\u003csup\u003e2\u003c/sup\u003e), showing that these anthropometric measurements better reflect cardiovascular risk in men living with HIV.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" style=\"width: 1003px;\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLinear regression analysis between anthropometric indicators and cardiovascular risk defined by the Framingham risk score (continuous value), in men living with HIV.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003cth style=\"width: 79.1597px; height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth style=\"width: 139.84px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eGross\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 73px; height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth style=\"width: 66px; height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth style=\"width: 54px; height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth style=\"width: 6px; height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth style=\"width: 146px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eAdjusted**\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 64px; height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth style=\"width: 66px; height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth style=\"width: 54px; height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth style=\"width: 35px; height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth style=\"width: 73px; height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth style=\"width: 73px; height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr style=\"height: 40px;\"\u003e\n \u003ctd style=\"width: 79.1597px; height: 40px;\" align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139.84px; height: 40px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026beta;* (CI \u0026minus;\u0026thinsp;95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px; height: 40px;\" align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px; height: 40px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px; height: 40px;\" align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e \u003csub\u003eadj\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px; height: 40px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 146px; height: 40px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026beta;* (CI \u0026minus;\u0026thinsp;95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px; height: 40px;\" align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px; height: 40px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px; height: 40px;\" align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e \u003csub\u003eadj\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px; height: 40px;\" align=\"left\"\u003e\n \u003cp\u003eVIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px; height: 40px;\" align=\"left\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px; height: 40px;\" align=\"left\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"width: 79.1597px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139.84px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.2374 (0.2302; 0.8314)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.1524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.0517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px; height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 146px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.2299 (0.2095; 0.8138)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.1531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.1897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e1330.917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e1353.572\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"width: 79.1597px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eWC (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139.84px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.3966 (0.2147; 0.4155)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.0509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.1532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px; height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 146px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.3713 (0.1936; 0.3945)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.0507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.2768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e1336.672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e1359.438\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"width: 79.1597px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eNC (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139.84px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.2243 (0.2452; 0.9710)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.1840\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.0457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px; height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 146px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.2796 (0.4279; 1.1683)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.1876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.2222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e1350.592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e1373.358\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"width: 79.1597px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eBAI *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139.84px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.2382 (0.2534; 0.9169)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.1682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.0521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px; height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 146px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.2158 (0.1888; 0.8643)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.1712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.1848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e1320.862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e1343.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"width: 79.1597px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eFMI (kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139.84px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.2471 (0.5151; 1.7511)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.3134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.0564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px; height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 146px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.2340 (0.4426; 1.6837)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.3145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.1931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e1318.934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e1341.551\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 48px;\"\u003e\n \u003ctd style=\"width: 79.1597px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003eWHR *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139.84px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e0.5409 (47.5649; 73.5671)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e6.5940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e0.2891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px; height: 48px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 146px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e0.4861 (41.6184; 68.0967)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e6.7101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e0.3761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e1297.789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e1320.518\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 48px;\"\u003e\n \u003ctd style=\"width: 79.1597px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003eWHtR *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139.84px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e0.4641 (43.1727; 74.2748)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e7.8868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e0.2115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px; height: 48px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 146px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e0.4320 (38.6386; 70.2790)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e8.0177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e0.3144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e1299.481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e1322.136\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 48px;\"\u003e\n \u003ctd style=\"width: 79.1597px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003eCI *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139.84px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e0.5396 (42.7812; 66.4190)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e5.9940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e0.2877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px; height: 48px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 146px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e0.4985 (38.5878; 62.6493)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e6.0972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e0.3770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e1281.508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e1304.163\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"width: 79.1597px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eAVI (L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139.84px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.3918 (0.5770; 1.1283)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.1398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.1494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px; height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 146px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.3705 (0.5274; 1.0763)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.1391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.2764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e1336.795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e1359.561\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 48px;\"\u003e\n \u003ctd style=\"width: 79.1597px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003eBSI *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139.84px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e0.5079 (694.5838; 1121.964)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e108.3743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e0.2543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px; height: 48px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 146px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e0.4645 (618.8836; 1054.705)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e10.4375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e0.3469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e1290.372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px; height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e1313.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"width: 79.1597px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eBRI *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139.84px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.4512 (2.0197; 3.5471)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.3873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.1997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px; height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 146px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.4204 (1.8035; 3.3531)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.3927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e0.3052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e1301.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px; height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e1324.652\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr style=\"height: 58px;\"\u003e\n \u003ctd style=\"width: 929px; height: 58px;\" colspan=\"13\"\u003e\u0026beta;* = standardized beta regression coefficient; CI-95% = 95% confidence interval; SE\u0026thinsp;=\u0026thinsp;standard error; p\u0026thinsp;=\u0026thinsp;p value (statistical significance, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05); R\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eadj\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;adjusted R\u003csup\u003e2\u003c/sup\u003e of the model; VIF\u0026thinsp;=\u0026thinsp;variance inflation factor; AIC\u0026thinsp;=\u0026thinsp;Akaike information draws; BIC\u0026thinsp;=\u0026thinsp;Bayesian Information Criterion, de Schwarz; BMI\u0026thinsp;=\u0026thinsp;body mass index; WC\u0026thinsp;=\u0026thinsp;waist circumference; NC\u0026thinsp;=\u0026thinsp;neck circumference; BAI\u0026thinsp;=\u0026thinsp;body adiposity index; FMI\u0026thinsp;=\u0026thinsp;fat mass index; WHR\u0026thinsp;=\u0026thinsp;waist-hip ratio; WHtR\u0026thinsp;=\u0026thinsp;waist-to-height ratio; CI\u0026thinsp;=\u0026thinsp;conicity index; AVI\u0026thinsp;=\u0026thinsp;abdominal volume index; BSI\u0026thinsp;=\u0026thinsp;body shape index; BRI\u0026thinsp;=\u0026thinsp;body roundness index.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 13px;\"\u003e\n \u003ctd style=\"width: 929px; height: 13px;\" colspan=\"13\"\u003ef\u0026sup2; of the respective indicators, gross analysis\u0026thinsp;=\u0026thinsp;0.0545; 0.1809; 0.0479; 0.0550; 0.0598; 0.4067; 0.2682; 0.4039; 0.1756; 0.3410; 0.2495.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 13px;\"\u003e\n \u003ctd style=\"width: 929px; height: 13px;\" colspan=\"13\"\u003ef\u0026sup2; of the respective indicators, adjusted analysis\u0026thinsp;=\u0026thinsp;0.2341; 0.3827; 0.2857; 0.2267; 0.2393; 0.6028; 0.4586; 0.6051; 0.3820; 0.5312; 0.4393.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 13px;\"\u003e\n \u003ctd style=\"width: 929px; height: 13px;\" colspan=\"13\"\u003ef\u0026sup2; = size of association effects.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 13px;\"\u003e\n \u003ctd style=\"width: 929px; height: 13px;\" colspan=\"13\"\u003e* Dimensionless measurements.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 13px;\"\u003e\n \u003ctd style=\"width: 929px; height: 13px;\" colspan=\"13\"\u003e** The linear regression model was adjusted for education, level of physical activity, lymphocytes T-CD4, income, ART (antiretroviral therapy).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIn order to facilitate understanding of the conducted regression analyses, a frequency table of the covariates has been created and is available in Supplementary File 3.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe main finding of this study highlights the association of of anthropometric indicators and single cardiovascular risk factors and Framingham Risk Score in men living with HIV, while this result was not observed in women. Furthermore, the correlation of these indicators with isolated biochemical and hemodynamic variables seems to contribute to its composition.\u003c/p\u003e \u003cp\u003eCI, WHR, WHtR, BSI and BRI stand out as indicators that maintained more robust associations for men. Despite being simple (single measurement) and practical for assessing abdominal fat in adults, showing a substantial relationship with the percentage of body fat\u003csup\u003e43\u003c/sup\u003e, the WC alone was not associated with cardiovascular risk in this study. On the other hand, this measurement was inserted in formulas to calculate anthropometric indicators with largest association with cardiovascular risk factors.,.\u003c/p\u003e \u003cp\u003eEsp\u0026iacute;rito Santo\u003csup\u003e49\u003c/sup\u003e studied BMI, CI, WC, WHR, and WHtR as discriminators of cardiovascular risk, defined by the Framingham Risk Score in ART-na\u0026iuml;ve PLHIV. Of these, the indicators that were best associated with high cardiovascular risk were WHR and WC, where CI was the indicator with the lowest discriminatory power. Beraldo\u003csup\u003e4\u003c/sup\u003e studied WC, HC, thigh circumference, BMI, BAI, WHR, and waist-to-thigh ratio with the aim of analyzing their associations with metabolic syndrome in PLHIV, observing that WC was the indicator that presented the best performance to identify there. This was not found in our study, since the CI was the indicator that best explained the variance in risk. The divergence between study results can be justified by the different cutoff points used for WC.\u003c/p\u003e \u003cp\u003eOliveira et al.\u003csup\u003e32\u003c/sup\u003e found that the poor performance of NC in ROC curve analysis for predicting cardiometabolic risk in women with HIV, compared to men, can be attributed to variations in the distribution of body fat. In the present study, although a cardiovascular risk prediction analysis was not conducted, none anthropometric indicator, including NC, revealed a significant association with cardiovascular risk factors in women. Women tend to have subcutaneous fat, while men have a more centralized distribution of body fat\u003csup\u003e32\u003c/sup\u003e. These differences, together with factors such as hormonal variations, body composition and ethnic characteristics, can negatively influence the discriminatory capacity of NC in predicting cardiometabolic risk in women\u003csup\u003e32\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA significant difference was also observed between men and women for total cholesterol, HDL-c, LDL-c and triglycerides. Previous studies, such Oh and Hegele\u003csup\u003e50\u003c/sup\u003e, indicate that the disparity in cholesterol levels between men and women can be attributed to hormonal, genetic and behavioral factors. Furthermore, it has been observed that men have slightly higher levels of total cholesterol compared to women. However, LDL and total cholesterol are less predominant in predicting cardiovascular risk compared to HDL and triglycerides, highlighting the need for a more individualized approach in women for effective heart health management\u003csup\u003e51\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe Framingham Risk Score stratifies the sample by sex due to characteristic differences in risk (for example, high levels of HDL-c in women, as well as the hormonal protective factor before menopause, and a higher prevalence of diabetes in men). Stratification allows the creation of specific risk profiles, taking into account physiological and health differences between the sexes\u003csup\u003e35\u003c/sup\u003e, and is supported by differences in body composition between the sexes\u003csup\u003e12,13,14\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur findings reflect the sex-stratified cardiovascular risk in PLHIV. Although women present more metabolic dysfunctions, such as abdominal obesity and dyslipidemia, men exhibited a higher percentage of elevated risk \u0026ndash; using Framingham Risk Score for this definition. Furthermore, the justification may be the score calculation itself, where there are differences in the points attributed to risk factors stratified by sex, such as age, treated and untreated SBP and smoking habit, considering differences in the rates of cardiovascular events between the sexes\u003csup\u003e35\u003c/sup\u003e. Analyzing the characteristics of the sample in the present study, it is possible to observe a higher prevalence for the male sex, with a predominance of advanced age\u003csup\u003e36\u003c/sup\u003e, this was the main factor that possibly influenced a high score on the Framingham Risk Score in male gender.\u003c/p\u003e \u003cp\u003ePrevious research has highlighted metabolic and morphological changes that may be due to HIV infection, including marked dyslipidemia (increased triglycerides and reduced HDL-c) that are strongly related to increased cardiovascular risk\u003csup\u003e3,8,52,53\u003c/sup\u003e. This evidence demonstrates that these factors may have influenced the results of the present study (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong the variables that increased cardiovascular risk, the one with the highest prevalence, regardless of sex, was low HDL. Oh and Hegele\u003csup\u003e50\u003c/sup\u003e associate low HDL concentration with immune activation in initial HIV infection. The decrease observed in HDL in patients with HIV, treated by antiretrovirals or not, results from impaired cholesterol efflux from macrophages due to HIV interference in ATP binding, contributing to low HDL concentration in plasma\u003csup\u003e3,50\u003c/sup\u003e. Inflammation stimulates endothelial lipases and phospholipases A2, reducing availability of plasma HDL; Additionally, hypertriglyceridemia enriches triglycerides in HDL, making it more susceptible to hepatic removal by hepatic lipase\u003csup\u003e3,50\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOther metabolic changes have been associated with prolonged use of ART\u003csup\u003e4\u003c/sup\u003e, especially in combination with PI and nucleoside analogue reverse transcriptase inhibitors, which inhibit the mitochondrial enzyme DNA polymerase γ, resulting in mitochondrial DNA depletion and respiratory chain dysfunction, leading to reduced energy production in mitochondria. This mitochondrial dysfunction can affect different cell types, causing lipoatrophy in adipocytes and insulin resistance in skeletal muscle\u003csup\u003e4\u003c/sup\u003e. Therefore, secondary dyslipidemia occurs characterized by increase in plasma triglycerides, total cholesterol and LDL, and reduction in HDL, causing an elevated cardiovascular risk in HIV patients undergoing treatment\u003csup\u003e3,50\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe limitations of this study include an absence of technical measurement error calculation intra and inter-evaluators of anthropometric measurements; however, anthropometric measurement training and a pilot study were done prior to data collection. Another limitation of the study is the impossibility of predicting the cardiovascular outcome due to the type of study and the analyzes carried out that do not allow such inference. We also assume the limitation of having chosen Framingham as a predictor of cardiovascular risk, although it is indicated by Brazilian institutions.\u003c/p\u003e \u003cp\u003eThe strengths of study include the address cardiovascular risk and a wide range the anthropometric indicators of AVI, BRI, BSI and FMI in PLHIV, which highlights the urgent need for in-depth research. This gap in the literature highlights the importance of specifically addressing the intersection between these indicators and cardiovascular health in this population, providing crucial insights for clinical practice and preventive interventions.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn men living with HIV, all 11 anthropometric indicators evaluated showed an association with cardiovascular risk measured by Framingham Risk Score, independent of confounding variables, with emphasis on CI, WHR, WHtR, BSI and BRI. Assuming practical and low-cost indicators the WHR and WHtR may be more feasible to clinical and outpatient practice evaluation as easy-to-implement tools at all levels of health care. For women, additional investigations are needed to identify cardiovascular risk early, as none of the analyzed measures proved suitable for this population.\u003c/p\u003e \u003cp\u003eThe results of this study emphasize the importance of special attention to PLHIV, especially regarding the control of NCDs. Although ART has represented a significant advancement in HIV infection control, the increased lifespan in this population exposes chronic health challenges that require careful management. It is essential to implement appropriate strategies and tolls (.i.e anthropometric indicators) for health promotion and improving quality of life, aiming to effectively address chronic health conditions that may arise over time.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCenters for Disease Control and Prevention (CDC). HIV Basics. Dispon\u0026iacute;vel em: \u0026lt;\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/hiv/basics/index.html\u0026gt;\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/hiv/basics/index.html%3E\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Acesso em: 10 agosto 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNUNES JUNIOR, Sebastiao Silveira; e CIOSAK, Suely Itsuko. Terapia antirretroviral para hiv/aids: o estado da arte. Revista de Enfermagem UFPE on line, [s. l.], v. 12, n. 4, p. 1103, 4 abr. 2018. ISSN 1981\u0026ndash;8963. DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5205/1981-8963-v12i4a231267p1103-1111-2018\u003c/span\u003e\u003cspan address=\"10.5205/1981-8963-v12i4a231267p1103-1111-2018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBORATO, Danielle Cristyane Kalva et al. Changes of metabolic and inflammatory markers in HIV infection: glucose, lipids, serum Hs-CRP and myeloperoxidase. Metabolism, [s. l.], v. 61, n. 10, p. 1353\u0026ndash;1360, out. 2012. ISSN 00260495. DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.metabol.2012.03.003\u003c/span\u003e\u003cspan address=\"10.1016/j.metabol.2012.03.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBERALDO, Rebeca Antunes et al. Comparing the Ability of Anthropometric Indicators in Identifying Metabolic Syndrome in HIV Patients. PloS one, United States, v. 11, n. 2, p. e0149905, 2016. ISSN 1932\u0026ndash;6203. DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0149905\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0149905\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGUPTA, V.; BISWAS, A.; e SHARMA, S. K. Metabolic and body composition changes after six months of highly active antiretroviral therapy in northern Indian patients. International journal of STD \u0026amp; AIDS, England, v. 22, n. 1, p. 46\u0026ndash;49, jan. 2011. ISSN 1758\u0026thinsp;\u0026ndash;\u0026thinsp;1052 0956\u0026ndash;4624. DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1258/ijsa.2010.010193\u003c/span\u003e\u003cspan address=\"10.1258/ijsa.2010.010193\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCALZA, L. et al. Prevalence of diabetes mellitus, hyperinsulinaemia and metabolic syndrome among 755 adult patients with HIV-1 infection. International Journal of STD \u0026amp; AIDS, [s. l.], v. 22, n. 1, p. 43\u0026ndash;45, jan. 2011. ISSN 0956\u0026ndash;4624, 1758\u0026thinsp;\u0026ndash;\u0026thinsp;1052. DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1258/ijsa.2010.010256\u003c/span\u003e\u003cspan address=\"10.1258/ijsa.2010.010256\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCAPILI, Bernadette; ANASTASI, Joyce K.; e OGEDEGBE, Olugbenga. HIV and General Cardiovascular Risk. Journal of the Association of Nurses in AIDS Care, [s. l.], v. 22, n. 5, p. 362\u0026ndash;375, set. 2011. ISSN 10553290. DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jana.2010.12.002\u003c/span\u003e\u003cspan address=\"10.1016/j.jana.2010.12.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eERLANDSON, Kristine M.; e LAKE, Jordan E. Fat Matters: Understanding the Role of Adipose Tissue in Health in HIV Infection. Current HIV/AIDS reports, United States, v. 13, n. 1, p. 20\u0026ndash;30, fev. 2016. ISSN 1548\u0026ndash;3576 1548\u0026ndash;3568. DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11904-016-0298-8\u003c/span\u003e\u003cspan address=\"10.1007/s11904-016-0298-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGIRALT, Marta; DOMINGO, Pere; e VILLARROYA, Francesc. Adipose tissue biology and HIV-infection. Best Practice \u0026amp; Research Clinical Endocrinology \u0026amp; Metabolism, [s. l.], v. 25, n. 3, p. 487\u0026ndash;499, jun. 2011. ISSN 1521690X. DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.beem.2010.12.001\u003c/span\u003e\u003cspan address=\"10.1016/j.beem.2010.12.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSPRINZ, Eduardo; ALVES, Marcelle D. e BRITES, Carlos. HIV-associated lipodystrophy: a review from a Brazilian perspective. Therapeutics and clinical risk management, New Zealand, v. 10, p. 559\u0026ndash;566, 2014. ISSN 1176\u0026ndash;6336 1178-203X. DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2147/TCRM.S35075\u003c/span\u003e\u003cspan address=\"10.2147/TCRM.S35075\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMASENGA, Sepiso K. et al. Hypertension and Metabolic Syndrome in Persons with HIV. Current Hypertension Reports, [s. l.], v. 22, n. 10, p. 78, out. 2020. ISSN 1522\u0026ndash;6417, 1534\u0026ndash;3111. DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11906-020-01089-3\u003c/span\u003e\u003cspan address=\"10.1007/s11906-020-01089-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDOS SANTOS, Andr\u0026eacute; P. et al. Lipodystrophy diagnosis in people living with HIV/AIDS: prediction and validation of sex-specific anthropometric models. BMC Public Health, [s. l.], v. 18, n. 1, p. 806, dez. 2018. ISSN 1471\u0026ndash;2458. DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12889-018-5707-z\u003c/span\u003e\u003cspan address=\"10.1186/s12889-018-5707-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eABRAHAMS, Zulfa; MAARTENS, Gary; LEVITT, Naomi; e DAVE, Joel. Anthropometric definitions for antiretroviral-associated lipodystrophy derived from a longitudinal South African cohort with serial dual-energy X-ray absorptiometry measurements. International Journal of STD \u0026amp; AIDS, [s. l.], v. 29, n. 12, p. 1194\u0026ndash;1203, out. 2018. ISSN 0956\u0026ndash;4624, 1758\u0026thinsp;\u0026ndash;\u0026thinsp;1052. DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/0956462418778649\u003c/span\u003e\u003cspan address=\"10.1177/0956462418778649\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eABELMAN, Rebecca A. et al. Body Composition Changes Over the Menopausal Transition in Women With and Without Human Immunodeficiency Virus. Clinical Infectious Diseases, [s. l.], v. 77, n. 2, p. 265\u0026ndash;271, 26 jul. 2023. ISSN 1058\u0026ndash;4838, 1537\u0026ndash;6591. DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/cid/ciad165\u003c/span\u003e\u003cspan address=\"10.1093/cid/ciad165\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBERALDO, Rebeca Antunes et al. Redistribui\u0026ccedil;\u0026atilde;o de gordura corporal e altera\u0026ccedil;\u0026otilde;es no metabolismo de lip\u0026iacute;deos e glicose em pessoas vivendo com HIV/AIDS. Revista Brasileira de Epidemiologia, [s. l.], v. 20, n. 3, p. 526\u0026ndash;536, jul. 2017. ISSN 1415-790X. DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1590/1980-5497201700030014\u003c/span\u003e\u003cspan address=\"10.1590/1980-5497201700030014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePONTI, Federico et al. Aging and Imaging Assessment of Body Composition: From Fat to Facts. Frontiers in Endocrinology, [s. l.], v. 10, p. 861, 14 jan. 2020. ISSN 1664\u0026ndash;2392. DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fendo.2019.00861\u003c/span\u003e\u003cspan address=\"10.3389/fendo.2019.00861\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLICHTENSTEIN, Kenneth; BALASUBRAMANYAM, Ashok; SEKHAR, Rajagopal; e FREEDLAND, Eric. HIV-associated adipose redistribution syndrome (HARS): definition, epidemiology and clinical impact. AIDS Research and Therapy, [s. l.], v. 4, n. 1, p. 16, dez. 2007. ISSN 1742\u0026ndash;6405. DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1742-6405-4-16\u003c/span\u003e\u003cspan address=\"10.1186/1742-6405-4-16\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRUMBO-RODR\u0026Iacute;GUEZ, Lorena; S\u0026Aacute;NCHEZ-SANSEGUNDO, Miriam; FERRER-CASCALES, Rosario; GARC\u0026Iacute;A-D\u0026rsquo;URSO, Nahuel; HURTADO-S\u0026Aacute;NCHEZ, Jose A.; e ZARAGOZA-MART\u0026Iacute;, Ana. Comparison of Body Scanner and Manual Anthropometric Measurements of Body Shape: A Systematic Review. International Journal of Environmental Research and Public Health, [s. l.], v. 18, n. 12, p. 6213, 8 jun. 2021. ISSN 1660\u0026ndash;4601. DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijerph18126213\u003c/span\u003e\u003cspan address=\"10.3390/ijerph18126213\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSCHWENK, Achim. Methods of assessing body shape and composition in HIV-associated lipodystrophy: Current Opinion in Infectious Diseases, [s. l.], v. 15, n. 1, p. 9\u0026ndash;16, fev. 2002. ISSN 0951\u0026ndash;7375. DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/00001432-200202000-00003\u003c/span\u003e\u003cspan address=\"10.1097/00001432-200202000-00003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFINKELSTEIN, Julia L.; GALA, Pooja; ROCHFORD, Rosemary; GLESBY, Marshall J.; e MEHTA, Saurabh. HIV/AIDS and lipodystrophy: Implications for clinical management in resource-limited settings. Journal of the International AIDS Society, [s. l.], v. 18, n. 1, p. 19033, jan. 2015. ISSN 1758\u0026ndash;2652, 1758\u0026ndash;2652. DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7448/IAS.18.1.19033\u003c/span\u003e\u003cspan address=\"10.7448/IAS.18.1.19033\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDIMALA, Christian Akem; NGU, Roland Cheofor; KADIA, Benjamin Momo; TIANYI, Frank-Leonel; e CHOUKEM, Simeon Pierre; WILLIG, Amanda (ed.). Markers of adiposity in HIV/AIDS patients: Agreement between waist circumference, waist-to-hip ratio, waist-to-height ratio and body mass index. PLOS ONE, [s. l.], v. 13, n. 3, p. e0194653, 22 mar. 2018. ISSN 1932\u0026ndash;6203. DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0194653\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0194653\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCOFRANCESCO, Joseph Jr; FREEDLAND, Eric; e MCCOMSEY, Grace. Treatment options for HIV-associated central fat accumulation. AIDS patient care and STDs, United States, v. 23, n. 1, p. 5\u0026ndash;18, jan. 2009. ISSN 1557\u0026ndash;7449 1087\u0026ndash;2914. DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1089/apc.2008.0067\u003c/span\u003e\u003cspan address=\"10.1089/apc.2008.0067\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eACHHRA, Amit C. et al. Assessing Cardiovascular Risk in People Living with HIV: Current Tools and Limitations. Current HIV/AIDS Reports, [s. l.], v. 18, n. 4, p. 271\u0026ndash;279, ago. 2021. ISSN 1548\u0026ndash;3568, 1548\u0026ndash;3576. DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11904-021-00567-w\u003c/span\u003e\u003cspan address=\"10.1007/s11904-021-00567-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePINTO NETO, Lauro Ferreira Da Silva; DIAS, Fernanda Rezende; BRESSAN, Flavia Feres; e SANTOS, Carolina Rocio Oliveira. Comparison of the ACC/AHA and Framingham algorithms to assess cardiovascular risk in HIV-infected patients. The Brazilian Journal of Infectious Diseases, [s. l.], v. 21, n. 6, p. 577\u0026ndash;580, nov. 2017. ISSN 14138670. DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.bjid.2017.06.007\u003c/span\u003e\u003cspan address=\"10.1016/j.bjid.2017.06.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBRASIL. Protocolo cl\u0026iacute;nico e diretrizes terap\u0026ecirc;uticas para o manejo da infec\u0026ccedil;\u0026atilde;o pelo HIV em adultos. Revista de Enfermagem UFPE on line, v. 12, n. 12, p. 3533, 2 dez. 2018. Dispon\u0026iacute;vel em: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5205/1981-8963-v12i12a238453p3533-3534-2018\u003c/span\u003e\u003cspan address=\"10.5205/1981-8963-v12i12a238453p3533-3534-2018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Acesso em: 14 ago. 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSMITH, Sidney C. et al. Principles for National and Regional Guidelines on Cardiovascular Disease Prevention: A Scientific Statement From the World Heart and Stroke Forum. Circulation, [s. l.], v. 109, n. 25, p. 3112\u0026ndash;3121, 29 jun. 2004. ISSN 0009-7322, 1524\u0026ndash;4539. DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/01.CIR.0000133427.35111.67\u003c/span\u003e\u003cspan address=\"10.1161/01.CIR.0000133427.35111.67\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOLIVEIRA, Mirele Arruda Michelotto De; FAGUNDES, Regina L\u0026uacute;cia Martins; MOREIRA, Em\u0026iacute;lia Addison Machado; TRINDADE, Erasmo Ben\u0026iacute;cio Santos De Moraes; e CARVALHO, Tales De. Rela\u0026ccedil;\u0026atilde;o de indicadores antropom\u0026eacute;tricos com fatores de risco para doen\u0026ccedil;a cardiovascular. Arquivos Brasileiros de Cardiologia, [s. l.], v. 94, n. 4, p. 478\u0026ndash;485, abr. 2010. ISSN 0066-\u0026lt;background-color:#D279AA;\u0026gt;782X\u0026lt;/background-color:#D279AA;\u0026gt;. DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1590/S0066-782X2010005000012\u003c/span\u003e\u003cspan address=\"10.1590/S0066-782X2010005000012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNOGUEIRA, Amanda et al. Fat Mass Ratio in Brazilian HIV-infected Patients Under Antiretroviral Therapy and Its Relationship With Anthropometric Measurents. Journal of Clinical Densitometry, [\u003cem\u003es. l.\u003c/em\u003e], v. 23, n. 4, p. 623\u0026ndash;629, out. 2020. ISSN 10946950. DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jocd.2018.07.013\u003c/span\u003e\u003cspan address=\"10.1016/j.jocd.2018.07.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKOETHE, John R. et al. HIV and antiretroviral therapy-related fat alterations. Nature Reviews Disease Primers, [s. l.], v. 6, n. 1, p. 48, 18 jun. 2020. ISSN 2056-676X. DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41572-020-0181-1\u003c/span\u003e\u003cspan address=\"10.1038/s41572-020-0181-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOLAWEPO, John O.; PHARR, Jennifer R.; CROSS, Chad L.; KACHEN, Axenya; OLAKUNDE, Babayemi O.; e SY, Francisco S. Changes in body mass index among people living with HIV who are new on highly active antiretroviral therapy: a systematic review and meta-analysis. AIDS Care, [s. l.], v. 33, n. 3, p. 326\u0026ndash;336, 4 mar. 2021. ISSN 0954\u0026thinsp;\u0026ndash;\u0026thinsp;0121, 1360\u0026thinsp;\u0026ndash;\u0026thinsp;0451. DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/09540121.2020.1770181\u003c/span\u003e\u003cspan address=\"10.1080/09540121.2020.1770181\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMAVARANI, Laven et al. Changes in body mass index, weight, and waist-to-hip ratio over five years in HIV-positive individuals in the HIV Heart Aging Study compared to the general population. Infection, Germany, v. 51, n. 4, p. 1081\u0026ndash;1091, ago. 2023. ISSN 1439\u0026thinsp;\u0026ndash;\u0026thinsp;0973 0300\u0026ndash;8126. DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s15010-023-02009-8\u003c/span\u003e\u003cspan address=\"10.1007/s15010-023-02009-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOLIVEIRA, Iara Katrynne Fons\u0026ecirc;ca et al. Neck circumference as a predictor of cardiometabolic risk and truncal obesity in people living with HIV. Nutricion hospitalaria, Spain, 11 maio 2023. ISSN 1699\u0026ndash;5198 0212\u0026ndash;1611. DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.20960/nh.04402\u003c/span\u003e\u003cspan address=\"10.20960/nh.04402\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGUARALDI, Giovanni et al. Evidence gaps on weight gain in people living with HIV: a scoping review to define a research agenda. BMC Infectious Diseases, [s.l.], v. 23, n. 1, p. 230, 14 abr. 2023. ISSN 1471\u0026ndash;2334. DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12879-023-08174-3\u003c/span\u003e\u003cspan address=\"10.1186/s12879-023-08174-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDean, Andrew G.; Pezzulo, John C.; Sullivan, Kevin M. Universidade Emory baseado no c\u0026oacute;digo de John C. Pezzullo / Interface: Andrew G. Dean, EpiInformatics.com, e Roger A. Mir (2013). Acesso: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.openepi.com/SampleSize/SSPropor.htm\u003c/span\u003e\u003cspan address=\"https://www.openepi.com/SampleSize/SSPropor.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD\u0026rsquo;AGOSTINO, Ralph B. et al. General Cardiovascular Risk Profile for Use in Primary Care: The Framingham Heart Study. Circulation, [s. l.], v. 117, n. 6, p. 743\u0026ndash;753, 12 fev. 2008. ISSN 0009-7322, 1524\u0026ndash;4539. DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/CIRCULATIONAHA.107.699579\u003c/span\u003e\u003cspan address=\"10.1161/CIRCULATIONAHA.107.699579\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePESCATELLO, L. S. Diretrizes do ACSM: para os testes de esfor\u0026ccedil;o e sua prescri\u0026ccedil;\u0026atilde;o (9a. ed). [S. l.]: Grupo Gen - Guanabara Koogan, 2000.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGRUNDY, S. M.; BREWER, H. B.; CLEEMAN, J. I.; SMITH, S. C.; LENFANT, C. Definition of Metabolic Syndrome: Report of the National Heart, Lung, and Blood Institute/American Heart Association Conference on Scientific Issues Related to Definition. Circulation, v. 109, n. 3, p. 433\u0026ndash;438, 27 jan. 2004. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/01.CIR.0000111245.75752.C6\u003c/span\u003e\u003cspan address=\"10.1161/01.CIR.0000111245.75752.C6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO EXPERT COMMITTEE ON HYPERTENSION CONTROL (ed.). Hypertension control: report of a WHO expert committee. Geneva: World Health Organization, 1996. 83 p. (WHO technical report series, 862). ISBN 978-92-4-120862-8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFRIEDEWALD, W. T.; LEVY, R. I.; e FREDRICKSON, D. S. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clinical Chemistry, [s. l.], v. 18, n. 6, p. 499\u0026ndash;502, jun. 1972. ISSN 0009\u0026ndash;9147.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAssocia\u0026ccedil;\u0026atilde;o Brasileira para o Estudo da Obesidade e da S\u0026iacute;ndrome Metab\u0026oacute;lica. Diretrizes brasileiras de obesidade 2016 / ABESO - Associa\u0026ccedil;\u0026atilde;o Brasileira para o Estudo da Obesidade e da S\u0026iacute;ndrome Metab\u0026oacute;lica \u0026ndash; 4.ed. - S\u0026atilde;o Paulo, SP.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eI Diretriz Brasileira de Diagn\u0026oacute;stico e Tratamento da S\u0026iacute;ndrome Metab\u0026oacute;lica. Rev Soc Bras Hipert 2004;17(4).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBERGMAN, Richard N. et al. A Better Index of Body Adiposity. Obesity, [s. l.], v. 19, n. 5, p. 1083\u0026ndash;1089, maio 2011. ISSN 19307381. DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/oby.2011.38\u003c/span\u003e\u003cspan address=\"10.1038/oby.2011.38\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePIQUERAS, Paola; BALLESTER, Alfredo; DUR\u0026Aacute;-GIL, Juan V.; MARTINEZ-HERVAS, Sergio; RED\u0026Oacute;N, Josep; e REAL, Jos\u0026eacute; T. Anthropometric Indicators as a Tool for Diagnosis of Obesity and Other Health Risk Factors: A Literature Review. Frontiers in Psychology, [s. l.], v. 12, p. 631179, 9 jul. 2021. ISSN 1664\u0026thinsp;\u0026ndash;\u0026thinsp;1078. DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpsyg.2021.631179\u003c/span\u003e\u003cspan address=\"10.3389/fpsyg.2021.631179\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCRIT\u0026Eacute;RIO DE CLASSIFICA\u0026Ccedil;\u0026Atilde;O ECON\u0026Ocirc;MICA BRASIL. Associa\u0026ccedil;\u0026atilde;o Brasileira de Empresas de Pesquisa \u0026ndash; ABEP. 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCRAIG, Cora L. et al. International Physical Activity Questionnaire: 12-Country Reliability and Validity: Medicine \u0026amp; Science in Sports \u0026amp; Exercise, [s. l.], v. 35, n. 8, p. 1381\u0026ndash;1395, ago. 2003. ISSN 0195\u0026ndash;9131. DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1249/01.MSS.0000078924.61453.FB\u003c/span\u003e\u003cspan address=\"10.1249/01.MSS.0000078924.61453.FB\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLEE, Paul H.; MACFARLANE, Duncan J.; LAM, Th; e STEWART, Sunita M. Validity of the international physical activity questionnaire short form (IPAQ-SF): A systematic review. International Journal of Behavioral Nutrition and Physical Activity, [s. l.], v. 8, n. 1, p. 115, dez. 2011. ISSN 1479\u0026ndash;5868. DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1479-5868-8-115\u003c/span\u003e\u003cspan address=\"10.1186/1479-5868-8-115\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRUMSEY, Deborah. Statistics for dummies. 2nd edition ed. Hoboken, NJ: Wiley Publishing, Inc, 2016. 388 p. (Learning made easy). ISBN 978-1-119-29352-1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBRASIL. Protocolo cl\u0026iacute;nico e diretrizes terap\u0026ecirc;uticas para o manejo da infec\u0026ccedil;\u0026atilde;o pelo hiv em adultos. Revista de Enfermagem UFPE on line, v. 12, n. 12, p. 3533, 2 dez. 2023. Dispon\u0026iacute;vel em: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5205/1981-8963-v12i12a238453p353\u003c/span\u003e\u003cspan address=\"10.5205/1981-8963-v12i12a238453p353\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e 3-3534-2018. Acesso em: 14 ago. 2023\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eESPIRITO, Santo Raquel. Avalia\u0026ccedil;\u0026atilde;o do risco cardiovascular atrav\u0026eacute;s dos indicadores de obesidade e de distribui\u0026ccedil;\u0026atilde;o da gordura corporal em portadores de HIV virgens de tratamento antirretroviral. 2013. reponame:Reposit\u0026oacute;rio Institucional da FIOCRUZ, [s. l.], 2013. Dispon\u0026iacute;vel em: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.arca.fiocruz.br/handle/icict/12491\u003c/span\u003e\u003cspan address=\"https://www.arca.fiocruz.br/handle/icict/12491\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Acesso em: 14 ago. 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOH, Jisun; HEGELE, Robert A. HIV-associated dyslipidaemia: pathogenesis and treatment. The Lancet Infectious Diseases, v. 7, n. 12, p. 787\u0026ndash;796, dez. 2007. Dispon\u0026iacute;vel em: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/s1473-3099(07)70287-6\u003c/span\u003e\u003cspan address=\"10.1016/s1473-3099(07)70287-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Acesso em: 15 ago. 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePHAN, Binh An. e TOTH, Peter. Dyslipidemia in women: etiology and management. International Journal of Women\u0026rsquo;s Health, [\u003cem\u003es. l.\u003c/em\u003e], p. 185, fev. 2014. ISSN 1179\u0026ndash;1411. DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2147/IJWH.S38133\u003c/span\u003e\u003cspan address=\"10.2147/IJWH.S38133\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKOETHE, John R. Adipose Tissue in HIV Infection. In: TERJUNG, Ronald (ed.). Comprehensive Physiology. 1. ed. [S. l.]: Wiley, 12 set. 2017. p. 1339\u0026ndash;1357. ISBN 978-0-470-65071-4. DOI \u003cdiv class=\"ExternalRefDOI\"\u003e10.1002\u003c/div\u003e/cphy.c160028. Dispon\u0026iacute;vel em: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://onlinelibrary.wiley.com/doi/10.1002/cphy.c160028\u003c/span\u003e\u003cspan address=\"https://onlinelibrary.wiley.com/doi/10.1002/cphy.c160028\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Acesso em: 10 jan. 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBAILIN, Samuel S.; GABRIEL, Curtis L.; WANJALLA, Celestine N.; e KOETHE, John R. Obesity and Weight Gain in Persons with HIV. Current HIV/AIDS Reports, [s. l.], v. 17, n. 2, p. 138\u0026ndash;150, abr. 2020. ISSN 1548\u0026ndash;3568, 1548\u0026ndash;3576. DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11904-020-00483-5\u003c/span\u003e\u003cspan address=\"10.1007/s11904-020-00483-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"HIV, Anthropometry, Body fat distribution, Lipodystrophy, Cardiovascular risk.","lastPublishedDoi":"10.21203/rs.3.rs-4004802/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4004802/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cu\u003eIntroduction: \u003c/u\u003ePeople living with HIV (PLHIV) present metabolic and morphological changes that increase cardiovascular risk due to infection and antiretroviral therapy (ART). Early detection of cardiovascular risk using anthropometric indicators is crucial, given the low cost and feasibility of this technique.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eObjective: \u003c/u\u003eTo analyze the association between anthropometric indicators and cardiovascular risk in PLHIV.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eMethods:\u003c/u\u003e Cross-sectional study with cis-gender PLHIV, ≥18 years old of both sexes. Sociodemographic, clinical, personal information and anthropometric measurements (body mass, height and neck, waist and hip circumferences) were collected and 11 anthropometric indicators were calculated. Cardiovascular risk was determined by the Framingham risk score. Multivariable regression analyses adjusted for confounding factors and stratified by sex were conducted using STATA® v. 13.0, p\u0026lt;0.05.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eResults:\u003c/u\u003e 354 PLHIV participated, 41.2% (n=146) female, with a mean age of 42.7 ± 13.0 years. Among the participants, 70.1% (n=248), 16.7% (n=59) and 13.3% (n=47) have low, moderate, and high cardiovascular risk, respectively. Among the indicators analyzed, conicity index (CI), waist-to-hip ratio (WHR), body shape index (BSI), waist-to-height ratio (WHtR) and body roundness index (BRI) present significant association with cardiovascular risk, only in men (β*=0.4985; β*=0.4861; β*=0.4645; β*=0.4320; β*=0.4204 [β*=standardized betas]), adjusted for education, level of physical activity, T-CD4+ lymphocytes, income and ART. The analyzes did not demonstrate significant associations ​​for women.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eConclusion:\u003c/u\u003e The anthropometric indicators, notedly CI and WHR, are associated with cardiovascular risk independent of clinical factors in men living with HIV.\u003c/p\u003e","manuscriptTitle":"Anthropometric Indicators Are Associated With Cardiovascular Risk Measured by Framingham Risk Score in Men Living With Hiv, but Not in Women.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-12 15:08:42","doi":"10.21203/rs.3.rs-4004802/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f95d4049-6da0-43b9-b98c-f6427cba3d1c","owner":[],"postedDate":"July 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":34151371,"name":"Health sciences/Diseases/Infectious diseases/HIV infections"},{"id":34151372,"name":"Health sciences/Diseases/Cardiovascular diseases"}],"tags":[],"updatedAt":"2024-10-23T10:25:14+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-12 15:08:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4004802","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4004802","identity":"rs-4004802","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}