Introduction: Statistical methods such as Principal Component Analysis (PCA) and Factor Analysis (FA) are increasingly popular in Nutritional Epidemiology studies. However, misunderstandings regarding the choice and application of these methods have been observed. Objectives: This study aims to compare and present the main differences and similarities between FA and PCA, focusing on their applicability to nutritional studies. Methods: PCA and FA were applied on a matrix of 34 variables expressing the mean food intake of 1,102 individuals from a population-based study. Results: Two factors were extracted and, together, they explained 57.66% of the common variance of food group variables, while five components were extracted, explaining 26.25% of the total variance of food group variables. Among the main differences of these two methods are: normality assumption, matrices of variance-covariance/correlation and its explained variance, factorial scores, and associated error. The similarities are: both analyses are used for data reduction, the sample size usually needs to be big, correlated data, and they are based on matrices of variance-covariance. Conclusion: PCA and FA should not be treated as equal statistical methods, given that the theoretical rationale and assumptions for using these methods as well as the interpretation of results are different.

This study aimed to investigate the effects of factor rotation methods on interpretability and construct validity of dietary patterns derived in a representative sample of 1,102 Brazilian adults. Dietary patterns were derived from exploratory factor analysis. Orthogonal (varimax) and oblique rotations (promax, direct oblimin) were applied. Confirmatory factor analysis assessed construct validity of the dietary patterns derived according to two factor loading cut-offs (≥ |0.20| and ≥ |0.25|). Goodness-of-fit indexes assessed the model fit. Differences in composition and in interpretability of the first pattern were observed between varimax and promax/oblimin at cut-off ≥ |0.20|. At cut-off ≥ |0.25|, these differences were no longer observed. None of the patterns derived at cut-off ≥ |0.20| showed acceptable model fit. At cut-off ≥ |0.25|, the promax rotation produced the best model fit. The effects of factor rotation on dietary patterns differed according to the factor loading cut-off used in exploratory factor analysis.

Background: Self-reported hypertension is an important piece of information for public health that is available in epidemiological studies. For proper use of this information, such studies should be validated. Objective: To validate self-reported hypertension and associated factors in adults and elderly individuals in Sao Paulo, Brazil. Methods: Participants were selected from the sample of a population-based cross-sectional health survey carried out in Sao Paulo (ISA Capital-2008). Their age was 20 years or older, they were from both genders, and had their blood pressure measured (n = 535). Hypertension was defined as blood pressure >= 140/90 mmHg and/or use of medication for hypertension. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and Kappa coefficient were calculated. Poisson regression was used to identify factors associated with sensitivity of self-reported hypertension. Results: Sensitivity of self-reported hypertension was 71.1% (95%CI: 64.8 to 76.9), specificity 80.5% (95%CI: 75.6 to 84.8), PPV 73.7% (95%CI: 67.4 to 79.3), and NPV 78.5% (95%CI: 73.5 to 82.9). There was moderate agreement between self-reported hypertension and hypertension as diagnosed by blood pressure measurement (kappa = 0.52, 95%CI: 0.45 to 0.59). Body mass index and level of education were independently associated with sensitivity (body mass index >= 25 kg/m(2): PR = 1.42, 95% CI: 1.15 to 1.76; schooling >= 9 years: PR = 0.71 95% CI: 0.54-0.94). Conclusion: Self-reported hypertension was shown to be valid in adults and the elderly in the city of Sao Paulo, and is thus an appropriate indicator for the surveillance of hypertension prevalence in the absence of blood pressure measurement. Overweight was positively associated with validity of self-reported hypertension. Further studies are needed to elucidate the inverse association between the validity of self-reported hypertension and level of education. (Arq Bras Cardiol. 2013;100(1):52-59)

The association between dietary patterns and metabolic cardiovascular risk factors has long been addressed but there is a lack of evidence towards the effects of the overall diet on the complex net of biological inter-relationships between risk factors. This study aimed to derive dietary patterns and examine their associations with metabolic cardiovascular risk factors following a theoretic model for the relationship between them. Participants included 417 adults of both sexes, enrolled to the cross-sectional population-based study performed in Brazil. Body weight, waist circumference, high-sensitivity C-reactive protein, blood pressure, total cholesterol:HDL-cholesterol ratio, TAG:HDL-cholesterol ratio, fasting plasma glucose and serum leptin were evaluated. Food consumption was assessed by two non-consecutive 24-h dietary recalls adjusted for the within-person variation of intake. A total of three dietary patterns were derived by exploratory structural equation modelling: ‘Traditional’, ‘Prudent’ and ‘Modern’. The ‘Traditional’ pattern had a negative and direct effect on obesity indicators (serum LEP, body weight and waist circumference) and negative indirect effects on total cholesterol:HDL-cholesterol ratio, TAG:HDL-cholesterol ratio and fasting plasma glucose. The ‘Prudent’ pattern had a negative and direct effect on systolic blood pressure. No association was observed for the ‘Modern’ pattern and metabolic risk factors. In conclusion, the ‘Traditional’ and ‘Prudent’ dietary patterns were negatively associated with metabolic cardiovascular risk factors among Brazilian adults. Their apparent protective effects against obesity and high blood pressure may be important non-pharmacological strategies for the prevention and control of obesity-related metabolic disorders and CVD.