SUPPLEMENTAL MATERIAL Materials and Methods Study design The ELSA-Brasil design and concepts have been detailed elsewhere 1. The ELSA-Brasil is a cohort study of active or retired 15,105 civil servants, aged 35 to 74 years, from six cities (São Paulo, Belo Horizonte, Porto Alegre, Salvador, Rio de Janeiro, and Vitória) in different Brazilian regions. Baseline assessment was a 7-hour onsite examination that included validated questionnaires, clinical and laboratory exams. This assessment took place from August 2008 to December 2010. We performed anthropometric measurements, including neck, waist and hip circumferences, using standard protocols. Bodymass index was calculated as weight (in kilograms) divided by the square of height (in meters). Blood samples, after an overnight fast, were collected in-site, frozen and sent for centralized analysis in the São Paulo ELSA-Brasil center. Oral glucose tolerance test was performed, two hours after a standard glucose oral intake (75g) only in previously non-diabetic participants. Approvals from institutional review boards of all centers were granted, and all individuals signed informed consent. Carotid IMT measurement A detailed protocol was published earlier 2,3. The same protocol was performed in all centers using a Toshiba (Aplio XG ) with a 7.5 MHz linear transducer. IMT was measured in the outer wall of a pre-defined carotid segment of 1 cm in length from 1 cm below carotid bifurcation, during three cardiac cycles. All participating centers obtained the carotid images and sent these acquisitions to the centralized core reading center in São Paulo. We considered as valid acquired images that clearly visualized on both the left and right sides (1) the anatomic guides for the common carotid arteries, (2) interfaces between the lumen and the vessel far wall and (3) interfaces between the media and the adventitia layers of the far vessel wall. We used MIA software to standardize the reading and interpretation of carotid scans. Study sample Of 15,105 ELSA-Brasil participants, 10,943 (72.4%) underwent IMT measurements and had adequate image quality for analysis for both CCA. We excluded 415 (2.7%) participants because of reported Asian or Indigenous race, as the low number would not allow accurate modelling, and 392 (2.6%) who reported previous myocardial infarction, stroke or revascularization. Additionally, 344 (2.3%) individuals were excluded due to any missing information on study variables. Therefore, our study sample consisted of 9,792 individuals with complete data. 1
Study variables In this paper, we defined CCA-IMT as the average of the mean left CCA- IMT and the mean right CCA-IMT values within an individual. CCA-IMT was used as the dependent variable in all models. Smoking status was self-reported. Hypertension was defined as the use of medications to treat hypertension, systolic blood pressure 140 mmhg or diastolic blood pressure 90 mmhg. Diabetes was defined as having a medical history of diabetes, use of medications to treat diabetes, a fasting glucose 126mg/dl, glycated hemoglobin (HbA1C) levels 6.5% or a 2h-oral glucose tolerance test 200mg/dl. Dyslipidemia was defined as use of lipid-lowering treatment or a LDL cholesterol level 130mg/dl. Family history of premature CVD refers to a diagnosis of myocardial infarction, stroke, revascularization or sudden death in a first-degree relative before age 60. 10-year coronary heart disease risk was calculated according to the Framingham Heart Study (FHS) risk score 4 and stratified as low (<10%) and intermediate-to-high ( 10%) 10-year coronary heart disease risk. Individuals with no traditional risk factors were defined as those without hypertension, diabetes or dyslipidemia diagnosis who did not take any cardiovascular medications, were not past nor current smokers, reported no family history of premature CVD, had a body-mass index < 30 kg/m 2 and a 10- year coronary heart disease risk < 10%. For this group with no traditional risk factors, to ensure maximum specificity, we opted also to exclude participants with reported angina pectoris. Statistical analysis Categorical variables are presented as proportions and compared using chi-squared tests. Except for age, we present continuous variables as mean ± standard deviations and they are compared using Student s t-test. ELSA-Brasil included individuals aged 35 to 74 years with inclusion goals according to age strata 5. Therefore, age has a marked non-normal distribution in the sample. We present medians and interquartile ranges for this variable within groups and compare them using the Kruskal-Wallis test. We built multiple linear regression models using CCA-IMT as the dependent variable to study how much of its variance was due to traditional cardiovascular risk factors. We used the coefficient of determination (Rsquared, R 2 ) as the main measurement for this purpose. All models were stratified by sex. We also analyze individuals stratified by 10-year coronary heart disease risk and, in separate, those with no traditional risk factors In regression models, all continuous variables except for CCA-IMT and age, were standardized to a mean at zero and standard deviation of one. Model 1 included age and race. Model 2 also included the diagnosis of hypertension, diabetes, dyslipidemia, smoking status and family history of premature CVD. In model 3 (main model), we added variables for blood pressure, glucose metabolism, lipid profile and adiposity that could further explain IMT variance (Supplemental Table I). Because of the expected high co-linearity among variables within each group, we built an iterative algorithm that chose the model with the highest R 2 among the possible combinations of only one variable from each group along with the variables already included in the previous model. We tested all models for heteroskedasticity using the Breusch-Pagan test. In all the 2
cases in which the test rejected the null hypothesis, we calculated standard errors and p-values using White-corrected covariance matrices. We examined the diagnostic plots and performed a systematic search for potential influential points in the main models described in the article using the following four measurements: DFFITS, Cook s distance, leverage values and covariance ratio. We performed a sensitivity analysis excluding the observations identified as potential influential points in any of these procedures. To support our decision to include only one variable from each group in supplemental table I in main models, and to quantify the impact of such decision in R 2 values, we further explored the problem of multi-collinearity in our data. We fitted post-hoc models including all possible variables to verify the highest R 2 obtained with this strategy and the highest increase in R 2 compared to the correspondent main model. We also calculated the variance inflation factor for these post-hoc models, excluding mean blood pressure and pulse pressure, already known as linear combinations of systolic and diastolic blood pressures. In another posthoc analysis, after observing that neck circumference was the main adiposity measurement contributing to CCA-IMT, we fitted models including CCA vessel diameters and body-mass index as independent variables to determine whether the association between CCA-IMT and neck circumference remained significant after adjusting for these variables. As a complementary analysis, we present as supplemental material the results of a simulated scenario where new information (as the emergence of novel risk factors) may rise the proportion of explained variance of CCA-IMT to 0.5. We compared the performance of main models and simulated models to correctly classify individuals as above (or equal) the 75 th percentile of CCA-IMT (P75) or below P75 in each subsample. For this purpose, we aimed to calculate point estimate predictions, 95% [95%PI] and 99% [99%PI] prediction intervals for each individual, for both real and simulated models. We considered that prediction intervals were correct only if all the predicting range would correctly classify the subject. We adopted the following procedure, for each subsample: (1) We calculated the point estimate prediction and prediction intervals for each subject in the subsample according to the main models. (2) We calculated P75 for the subsample. (3) We indicate the number of individuals with a predicted value, 95%PI and 99%PI above P75. (4) We calculate the proportions of correct classifications for individuals with CCA-IMT above P75, below P75 and overall. In this case, we considered as correct classifications for the 95%PI and 99%PI when the entire interval range indicated a correct classification. (5) To simulate the scenario where novel risk factor(s) increase the proportion of explained variance to approximately 50%, we built a new variable calculated as the actual CCA-IMT value for each individual plus an error with normal distribution, expected value at zero and variance V. We expected with the inclusion of the new variable that, when V equals zero, R 2 would equal 1.0; for very high values of V, R 2 would be substantially unchanged from the observed in the main real model (since the new variable would be only weakly associated with CCA- IMT). To select V, we built an iterative algorithm that simulated models with the new variable 100 times, from a starting value V=1, and aiming a mean R 2 value between 0.498 and 0.502. If mean R 2 for these 100 simulations was not within this range, V value was changed accordingly. (6) We simulated the new variable and included it in the model. We calculated the same indicators of 3
performance described for the main model. We repeated this simulation 100 times and report the mean values as the measurements of performance for the simulated model. (7) We also calculate the difference in overall accuracy (proportion of correct predictions) from the main model to the simulated model (point estimate, 95%PI and 99%PI). Significance level was set at 0.05. We used R software version 3.1.2 for analyses 6. References 1. Schmidt MI, Duncan BB, Mill JG, Lotufo PA, Chor D, Barreto SM, Aquino EM, Passos VM, Matos SM, Molina MD, Carvalho MS, Bensenor IM. Cohort profile: Longitudinal Study of Adult Health (ELSA-Brasil). Int J Epidemiol. 2014 2. Santos IS, Bittencourt MS, Oliveira IR, Souza AG, Meireles DP, Rundek T, Foppa M, Bezerra DC, Freire CM, Roelke LH, Carrilho S, Benseñor IM, Lotufo PA. Carotid intima-media thickness value distributions in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Atherosclerosis. 2014;237:227-235 3. Mill JG, Pinto K, Griep RH, Goulart A, Foppa M, Lotufo PA, Maestri MK, Ribeiro AL, Andreão RV, Dantas EM, Oliveira I, Fuchs SC, Cunha ReS, Bensenor IM. [Medical assessments and measurements in ELSA-Brasil]. Rev Saude Publica. 2013;47 Suppl 2:54-62 4. Wilson PW, D'Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation. 1998;97:1837-1847 5. Aquino EM, Barreto SM, Bensenor IM, Carvalho MS, Chor D, Duncan BB, Lotufo PA, Mill JG, Molina MeC, Mota EL, Passos VM, Schmidt MI, Szklo M. Brazilian Longitudinal Study of Adult Health (ELSA-Brasil): Objectives and design. Am J Epidemiol. 2012;175:315-324 6. R Development Core Team. R: A language and environment for statistical computing. 2011 4
Supplemental table I. Blood pressure, glucose metabolism, lipid profile and adiposity variables included in linear regression models. Blood pressure Systolic blood pressure Diastolic blood pressure Mean blood pressure Pulse pressure Lipid profile LDL-cholesterol HDL-cholesterol Triglycerides LDL/HDL ratio Triglycerides/HDL ratio Glucose metabolism Fasting plasma glucose Glycated hemoglobin Adiposity Body-mass index Waist circumference Hip circumference Neck circumference Waist-to-hip ratio Waist-to-height ratio 5