Obstructive sleep apnea (OSA) is a condition extremely prevalent among hypertensive patients and evidence is also available indicating its relationship with incident hypertension. Nonetheless, whether OSA actually causes hypertension is… Click to show full abstract
Obstructive sleep apnea (OSA) is a condition extremely prevalent among hypertensive patients and evidence is also available indicating its relationship with incident hypertension. Nonetheless, whether OSA actually causes hypertension is a complex question, because both these conditions share several predisposing factors, the most important being obesity [1]. Moreover, the character of the relationship between OSA and blood pressure (BP) depends on the range of time wherein BP is assessed. On one hand there is little doubt that apneas cause acute nocturnal BP surges due to a combination of chemoreflex-mediated sympathetic activation and changes in ventricular filling caused by dramatic variations of intrathoracic pressure during obstructive events. In fact, particularly in patients with severe OSA in whom apneas may occur once a minute or even more frequently, an increase in average nocturnal BP level can be reasonably expected. Indeed, as observed by means of 24-h ambulatory blood pressure monitoring, patients with severe OSA tend to present blunted nocturnal fall of BP (so called non-dipping profile) [2]. On the other hand, patients with severe OSA frequently have a constantly increased basal sympathetic tone, which might at least in part explain the occurrence of elevated BP also during the day [1, 3]. The intermittent occurrence of a phenomenon with a major direct impact on BP such as obstructive apneas inevitably raises questions on the possible relationship between OSA and BP variations over time, i.e. BP variability (BPV). BPV is a multifaceted phenomenon with numerous determinants, which may be described by a number of different estimates and over different periods of time, ranging from seconds to years [4]. For a long time research on BPV had focused mainly on short-term BP variations, occurring within 24-h. A few years ago, however, Rothwell et al. published a study indicating that also long-term BPV, assessed on a visit-tovisit basis (visit-to-visit variability; VVV) is able to predict cardiovascular outcomes [5]. Although the understanding of the underlying mechanisms and pathophysiological consequences of VVV remains incomplete, its association with adverse outcomes was confirmed in a number of subsequent studies [6]. Consistent evidence indicates that the presence of OSA may be associated with increased short-term BPV but until now the information on its relationship with VVV was limited. Shiina et al. observed that patients with severe OSA (n= 35) had significantly higher systolic VVV than controls (n= 26) matched for age, BMI and systolic BP [7]. Moreover, in this study apnea-hypopnea index (AHI, the principal index of OSA severity) emerged as a significant predictor of VVV in multivariable analysis and VVV was significantly reduced in subjects treated with continuous positive airway pressure (CPAP) and adherent to this treatment. In a different study Pengo et al. also found a significant reduction in systolic BPV after 2 weeks of autotitrating CPAP treatment in OSA patients, but in this case BPV was estimated as standard deviations (SD) of values obtained during the same visit (within-visit variability) rather than on a visit-to-visit basis [8]. On this background the paper by Kansui et al. published in the current issue of Hypertension Research provides further interesting evidence on the association between OSA and VVV [9]. The study included employees of a transport company in Japan, who were systematically screened for the presence of OSA by means of nocturnal pulse oximetry, followed by polysomnography whenever the initial screening produced pathological results (4% oxygen desaturation index [ODI] ≥10/h). Out of 1653 initially screened participants 131 were found to have mildto-moderate OSA (5 ≤AHI < 30) and 108 had severe OSA (AHI ≥ 30). BPV was subsequently assessed by calculating SDs and coefficients of variation (CV) of BP values * Grzegorz Bilo [email protected]
               
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