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      Changes in Circulating Stem and Progenitor Cell Numbers Following Acute Exercise in Healthy Human Subjects: a Systematic Review and Meta-analysis

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          Despite of the increasing number of investigations on the effects of acute exercise on circulating stem and progenitor cell (SC) numbers, and in particular on respective subgroups, i.e. endothelial (ESC), hematopoietic (HSC), and mesenchymal (MSC) stem and progenitor cells, a consensus regarding mechanisms and extent of these effects is still missing. The aim of this meta-analysis was to systematically evaluate the overall-effects of acute exercise on the different SC-subgroups and investigate possible subject- and intervention-dependent factors affecting the extent of SC-mobilization in healthy humans. Trials assessing SC numbers before and at least one timepoint after acute exercise, were identified in a systematic computerized search. Compared to baseline, numbers were significantly increased for early and non-specified SCs (enSCs) until up to 0.5 h after exercise (0–5 min: +0.64 [Standardized difference in means], p < 0.001; 6–20 min: +0.42, p < 0.001; 0.5 h: +0.29, p = 0.049), for ESCs until 12–48 h after exercise (0–5 min: +0.66, p < 0.001; 6–20 min: +0.43 p < 0.001; 0.5 h: +0.43, p = 0.002; 1 h: +0.58, p = 0.001; 2 h: +0.50, p = 0.002; 3–8 h: +0.70, p < 0.001; 12–48 h: +0.38, p = 0.003) and for HSCs at 0–5 min (+ 0.47, p < 0.001) and at 3 h after exercise (+ 0.68, p < 0.001). Sex, intensity and duration of the intervention had generally no influence. The extent and kinetics of the exercise-induced mobilization of SCs differ between SC-subpopulations. However, also definitions of SC-subpopulations are non-uniform. Therefore, finding a consensus with a clear definition of cell surface markers defining ESCs, HSCs and MSCs is a first prerequisite for understanding this important topic.

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          Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement.

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            Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range

            Background In systematic reviews and meta-analysis, researchers often pool the results of the sample mean and standard deviation from a set of similar clinical trials. A number of the trials, however, reported the study using the median, the minimum and maximum values, and/or the first and third quartiles. Hence, in order to combine results, one may have to estimate the sample mean and standard deviation for such trials. Methods In this paper, we propose to improve the existing literature in several directions. First, we show that the sample standard deviation estimation in Hozo et al.’s method (BMC Med Res Methodol 5:13, 2005) has some serious limitations and is always less satisfactory in practice. Inspired by this, we propose a new estimation method by incorporating the sample size. Second, we systematically study the sample mean and standard deviation estimation problem under several other interesting settings where the interquartile range is also available for the trials. Results We demonstrate the performance of the proposed methods through simulation studies for the three frequently encountered scenarios, respectively. For the first two scenarios, our method greatly improves existing methods and provides a nearly unbiased estimate of the true sample standard deviation for normal data and a slightly biased estimate for skewed data. For the third scenario, our method still performs very well for both normal data and skewed data. Furthermore, we compare the estimators of the sample mean and standard deviation under all three scenarios and present some suggestions on which scenario is preferred in real-world applications. Conclusions In this paper, we discuss different approximation methods in the estimation of the sample mean and standard deviation and propose some new estimation methods to improve the existing literature. We conclude our work with a summary table (an Excel spread sheet including all formulas) that serves as a comprehensive guidance for performing meta-analysis in different situations. Electronic supplementary material The online version of this article (doi:10.1186/1471-2288-14-135) contains supplementary material, which is available to authorized users.
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              A basic introduction to fixed-effect and random-effects models for meta-analysis.

              There are two popular statistical models for meta-analysis, the fixed-effect model and the random-effects model. The fact that these two models employ similar sets of formulas to compute statistics, and sometimes yield similar estimates for the various parameters, may lead people to believe that the models are interchangeable. In fact, though, the models represent fundamentally different assumptions about the data. The selection of the appropriate model is important to ensure that the various statistics are estimated correctly. Additionally, and more fundamentally, the model serves to place the analysis in context. It provides a framework for the goals of the analysis as well as for the interpretation of the statistics. In this paper we explain the key assumptions of each model, and then outline the differences between the models. We conclude with a discussion of factors to consider when choosing between the two models. Copyright © 2010 John Wiley & Sons, Ltd. Copyright © 2010 John Wiley & Sons, Ltd.
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                Author and article information

                Contributors
                michelle.schmid@hest.ethz.ch
                julia.kroepfl@hest.ethz.ch
                christina.spengler@hest.ethz.ch
                Journal
                Stem Cell Rev Rep
                Stem Cell Rev Rep
                Stem Cell Reviews and Reports
                Springer US (New York )
                2629-3269
                2629-3277
                2 January 2021
                2 January 2021
                2021
                : 17
                : 4
                : 1091-1120
                Affiliations
                [1 ]GRID grid.5801.c, ISNI 0000 0001 2156 2780, Exercise Physiology Lab, Institute of Human Movement Sciences and Sport, , ETH Zurich, ; Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
                [2 ]GRID grid.7400.3, ISNI 0000 0004 1937 0650, Zurich Center for Integrative Human Physiology (ZIHP), , University of Zurich, ; Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
                Author information
                http://orcid.org/0000-0001-5669-9517
                Article
                10105
                10.1007/s12015-020-10105-7
                8316227
                33389632
                0dd8427c-8038-41d4-a076-e17e9c9e12e7
                © The Author(s) 2021, corrected publication 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 1 December 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100012652, ETH Zürich Foundation;
                Award ID: Grant ETH-43 15-2
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                © Springer Science+Business Media, LLC, part of Springer Nature 2021

                stem and progenitor cells,acute exercise,meta-analysis,systematic review,stem cell mobilization,endothelial,hematopoietic,mesenchymal,kinetics,moderator variables

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