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BACKGROUND Blood pressure (BP) is associated with carotid intima-media thickness (CIMT), but few studies have explored the association between BP variability and CIMT. We aimed to investigate this association in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) baseline. METHODS We analyzed data from 7,215 participants (56.0% women) without overt cardiovascular disease (CVD) or antihypertensive use. We included 10 BP readings in varying positions during a 6-hour visit. We defined BP variability as the SD of these readings. We performed a 2-step analysis. We first linearly regressed the CIMT values on main and all-order interaction effects of the variables age, sex, body mass index, race, diabetes diagnosis, dyslipidemia diagnosis, family history of premature CVD, smoking status, and ELSA-Brasil site, and calculated the residuals (residual CIMT). We used partial least square path analysis to investigate whether residual CIMT was associated with BP central tendency and BP variability. RESULTS Systolic BP (SBP) variability was significantly associated with residual CIMT in models including the entire sample (path coefficient [PC]: 0.046; P < 0.001), and in women (PC: 0.046; P = 0.007) but not in men (PC: 0.037; P = 0.09). This loss of significance was probably due to the smaller subsample size, as PCs were not significantly different according to sex. CONCLUSIONS We found a small but significant association between SBP variability and CIMT values. This was additive to the association between SBP central tendency and CIMT values, supporting a role for high short-term SBP variability in atherosclerosis.

Faced with the lack of reliability and reproducibility in omics studies, more careful and robust methods are needed to overcome the existing challenges in the multi-omics analysis. In conventional omics data analysis, signal intensity values (denoted by M and values) are estimated neglecting pixel-level uncertainties, which may reflect noise and systematic artifacts. For example, intensity values from two-color microarray data are estimated by taking the mean or median of the pixel intensities within the spot and then subjected to a within-slide normalization by LOWESS. Thus, focusing on estimation and normalization of gene expression profiles, we propose a spot quantification method that takes into account pixel-level variability. Also, to preserve relevant variation that may be removed in LOWESS normalization with poorly chosen parameters, we propose a parameter selection method that is parsimonious and considers intrinsic characteristics of microarray data, such as heteroskedasticity. The usefulness of the proposed methods is illustrated by an application to real intestinal metaplasia data. Compared with the conventional approaches, the analysis is more robust and conservative, identifying fewer but more reliable differentially expressed genes. Also, the variability preservation allowed the identification of new differentially expressed genes. Using the proposed approach, we have identified differentially expressed genes involved in pathways in cancer and confirmed some molecular markers already reported in the literature.