Key Points Summary We report how blood pressure, cardiac output and vascular resistance are related to height, weight, body surface area (BSA), and body mass index (BMI) in healthy young adults at supine rest and standing.Much inter-subject variability in young adult's blood pressure, currently attributed to health status, may actually result from inter-individual body size differences.Each cardiovascular variable is linearly related to height, weight and/or BSA (more than to BMI).When supine, cardiac output is positively related, while vascular resistance is negatively related, to body size. Upon standing, the change in vascular resistance is positively related to size.The height/weight relationships of cardiac output and vascular resistance to body size are responsible for blood pressure relationships to body size.These basic components of blood pressure could help distinguish normal from abnormal blood pressures in young adults by providing a more effective scaling mechanism.Introduction: Effects of body size on inter-subject blood pressure (BP) variability are not well established in adults. We hypothesized that relationships linking stroke volume (SV), cardiac output (CO), and total peripheral resistance (TPR) with body size would account for a significant fraction of inter-subject BP variability.Methods: Thirty-four young, healthy adults (19 men, 15 women) participated in 38 stand tests during which brachial artery BP, heart rate, SV, CO, TPR, and indexes of body size were measured/calculated.Results: Steady state diastolic arterial BP was not significantly correlated with any index of body size when subjects were supine. However, upon standing, the more the subject weighed, or the taller s/he was, the greater the increase in diastolic pressure. Systolic pressure strongly correlated with body weight and height both supine and standing. Diastolic and systolic BP were more strongly related to height, weight and body surface area than to body mass index. When supine: lack of correlation between diastolic pressure and body size, resulted from the combination of positive SV correlation and negative TPR correlation with body size. The positive systolic pressure vs. body size relationship resulted from a positive SV vs. height relationship. In response to standing: the positive diastolic blood pressure vs. body size relationship resulted from the standing-induced, positive increase in TPR vs. body size relationship. The relationships between body weight or height with SV and TPR contribute new insight into mechanisms of BP regulation that may aid in the prediction of health in young adults by providing a more effective way to scale BP with body size.
Count regression models maintain a steadfast presence in modern applied statistics as highlighted by their usage in diverse areas like biometry, ecology, and insurance. However, a common practical problem with observed count data is the presence of excess zeros relative to the assumed count distribution. The seminal work of Lambert (1992) was one of the first articles to thoroughly treat the problem of zero-inflated count data in the presence of covariates. Since then, a vast literature has emerged regarding zero-inflated count regression models. In this first of two review articles, we survey some of the classic and contemporary literature on parametric zero-inflated count regression models, with emphasis on the utility of different univariate discrete distributions. We highlight some of the primary computational tools available for estimating and assessing the adequacy of these models. We concurrently emphasize the diverse data problems to which these models have been applied.
The invasive liana Euonymus fortunei (Turcz.) Hand.-Maz. (wintercreeper) is an emerging invader that through monodominance of woodlands can drastically reduce native species diversity and alter nutrient cycling. We studied how the vegetation and soils of invaded (INV), “native” (NAT), and wintercreeper removal (REM) site treatments influenced seed germination and seedling survival of this invader. The effect of aril (with vs. without) was also tested for wintercreeper seeds under field and in vitro conditions as a proxy for gravity vs. animal dispersal of seed, respectively. Germination was significantly delayed for seeds sown with an aril (vs. without), as well as those sown in INV soils (vs. NAT or REM), but neither site nor aril affected total germination. The proportion of germinated seedlings that survived after the first winter was significantly different based on site (p = 0.054) and aril (p = 0.071) treatments, with lower survival resulting from seeds sown without arils, and for seeds sown in INV sites. Magnesium (Mg) concentrations were significantly higher among INV soils (vs. NAT) and provide further support that wintercreeper is a driver of soil nutrient change. Our findings that aril-enclosed (gravity-dispersed) seeds yielded greater survival, despite being locally dispersed within invaded sites (where survival was lowest), support the historically slow rate of spread for this species.
The prequel to this review provided an extensive treatment of classic zero‐inflated count regression models where a univariate discrete distribution is used for the count regression component of the model. The treatment of zero inflation beyond the classic univariate count regression setting has seen a substantial increase in recent years. This second review paper surveys some of this recent literature and focuses on important developments in handling zero inflation for correlated count settings, discrete time series models, spatial models, and multivariate models. We discuss some of the available computational tools for performing estimation in these settings, while again highlighting the diverse data problems that have been addressed using these methods. This article is categorized under: Statistical Models > Multivariate Models Statistical Models > Generalized Linear Models Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory
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