The relationship between various measures of obesity and arterial stiffness in morbidly obese patients

<p>Abstract</p> <p>Background</p> <p>Obesity is associated with increased risk of cardiovascular disease. Arterial stiffness assessed by carotid femoral pulse wave velocity (PWV) is an independent predictor of cardiovascular morbidity and mortality. We aimed to investigate how various measures of body composition affect arterial stiffness.</p> <p>Methods</p> <p>This is an analysis of cross-sectional baseline data from a controlled clinical trial addressing changes in arterial stiffness after either surgery or lifestyle intervention in a population of morbidly obese patients. High-fidelity applanation tonometry (Millar^®, Sphygmocor^®) was used to measure pulse wave velocity (PWV). Carotid femoral PWV is a direct measure of arterial stiffness and is considered to be the gold standard method. The Inbody 720 Body Composition Analyzer was used for bioelectrical impedance analysis (BIA). Spearman's correlation, independent samples <it>t</it>-test, chi-square tests, Fisher's exact test and multiple linear regression analyses were used as statistical methods.</p> <p>Results</p> <p>A total of 133 patients (79 women), with a mean (SD) age of 43 (11) years were included in the study. Men had a significantly higher prevalence of obesity related comorbidities and significantly higher PWV, 9.1 (2.0) m/s vs. 8.1 (1.8) m/s, p = 0.003, than women. In the female group, PWV was positively correlated with WC, WHtR, BMI and visceral fat area. In the male group, PWV was negatively correlated with BMI. Multiple linear regression analysis showed that increasing BMI, WC, WHtR, visceral fat area and fat mass were independently associated with higher PWV in women, but not in men, after adjustment for age, hypertension and type 2 diabetes.</p> <p>Conclusion</p> <p>Most measures of general and abdominal obesity were predictors of arterial stiffness in female morbidly obese patients.</p> <p>Trial registration</p> <p>ClinicalTrials.gov Identifier <a href="http://www.clinicaltrials.gov/ct2/show/NCT00626964">NCT00626964</a></p

Product and process innovation in manufacturing firms: a 30-year bibliometric analysis

Built upon a thirty-year dataset collected from the Web of Science database, the present research aims to offer a comprehensive overview of papers, authors, streams of research, and the most influential journals that discuss product and process innovation in the manufacturing environment. The dataset is composed of 418 papers from more than 150 journals from the period between 1985 and 2015. Homogeneity analysis by means of alternating least squares (HOMALS) and Social Network Analysis (SNA) are used to accomplish the objectives listed above through the keywords given by authors. Initially, the paper highlights and discusses the similarity between the topics debated by the main journals in this field. Subsequently, a wide-range map of topics is presented highlighting five main areas of interests; namely, performance, patent, small firm, product development, and organization. A SNA is also performed in order to validate the results that emerged from HOMALS. Finally, several insights about future research avenues in the manufacturing field are provided

The poly-omics of ageing through individual-based metabolic modelling

Abstract Background Ageing can be classified in two different ways, chronological ageing and biological ageing. While chronological age is a measure of the time that has passed since birth, biological (also known as transcriptomic) ageing is defined by how time and the environment affect an individual in comparison to other individuals of the same chronological age. Recent research studies have shown that transcriptomic age is associated with certain genes, and that each of those genes has an effect size. Using these effect sizes we can calculate the transcriptomic age of an individual from their age-associated gene expression levels. The limitation of this approach is that it does not consider how these changes in gene expression affect the metabolism of individuals and hence their observable cellular phenotype. Results We propose a method based on poly-omic constraint-based models and machine learning in order to further the understanding of transcriptomic ageing. We use normalised CD4 T-cell gene expression data from peripheral blood mononuclear cells in 499 healthy individuals to create individual metabolic models. These models are then combined with a transcriptomic age predictor and chronological age to provide new insights into the differences between transcriptomic and chronological ageing. As a result, we propose a novel metabolic age predictor. Conclusions We show that our poly-omic predictors provide a more detailed analysis of transcriptomic ageing compared to gene-based approaches, and represent a basis for furthering our knowledge of the ageing mechanisms in human cells