Elsevier

Preventive Medicine

Volume 49, Issue 6, December 2009, Pages 473-475
Preventive Medicine

Impact of progressive resistance training on lipids and lipoproteins in adults: Another look at a meta-analysis using prediction intervals

https://doi.org/10.1016/j.ypmed.2009.09.018Get rights and content

Abstract

Objective

Given recently developed prediction intervals (PIs) in which a random mean effect for a new study is estimated from meta-analytic data, we used the results from our previously published meta-analysis to calculate PIs for changes in lipids and lipoproteins as a result of progressive resistance training (PRT) in adults.

Methods

Twenty-nine studies representing 1329 men and women (676 exercise, 653 control) were included. The primary outcomes included total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), ratio of total cholesterol to high-density lipoprotein cholesterol (TC/HDL-C), non-high-density lipoprotein cholesterol (non-HDL-C,) low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG). Separate PIs (95%) were calculated for all lipids and lipoproteins.

Results

The expected outcomes of a new study on this topic were as follows: TC, − 5.5 (− 24.0, 13.0) mg/dl; HDL-C, 0.7 (− 8.9, 10.4) mg/dl; TC/HDL-C, − 0.5 (− 1.8, 0.8); non-HDL-C, − 8.7 (− 35.7, 18.3) mg/dl; LDL-C, − 6.1 (− 28.9, 16.4) mg/dl; TG, − 8.1 (− 34.5, 18.3) mg/dl.

Conclusions

Caution may be warranted in recommending that PRT improves TC, HDL-C, TC/HDL-C, non-HDL-C, LDL-C, and TG in adults. Future research should continue to examine the effects of PRT on lipids and lipoproteins in adults so as to determine optimal programs and populations in which PRT may have a positive effect.

Introduction

Cardiovascular disease affects an estimated 80 million American adults (about 1 in 3) and is the number one cause of mortality in the United States, accounting for an estimated 864,480 deaths annually (Lloyd-Jones et al., 2009). In terms of expenditures, the annual total direct and indirect costs associated with cardiovascular disease in 2009 have been estimated to be $475.3 billion (Lloyd-Jones et al., 2009). Less than optimal lipid and lipoprotein levels, a common problem among more than 98.6 million (45.3%) American adults, have been closely associated with the development of atherosclerotic coronary artery disease and subsequent mortality (Lloyd-Jones et al., 2009).

In a previous issue of Preventive Medicine, we reported statistically significant average treatment effect reductions in total cholesterol (TC), the ratio of total cholesterol to high-density lipoprotein cholesterol (TC/HDL-C), non-high-density lipoprotein cholesterol (non-HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG), with no statistically significant changes in high-density lipoprotein cholesterol (HDL-C), as a result of progressive resistance training (PRT) in adult humans (Kelley and Kelley, 2009). These results were based on the preferred random-effects model, an approach that is especially appropriate when statistically significant between-study heterogeneity exists (Hunter and Schmidt, 2000). While our results were encouraging with respect to the potential benefit of PRT for improving lipids and lipoproteins in adults, they were based on the traditional underlying mean treatment effect and their corresponding 95% confidence intervals. However, this approach does not provide any information regarding how treatment effects from new individual trials are distributed around the summary effect (Higgins et al., 2009). Consequently, this may be problematic since the treatment effect is assumed to vary between studies when a random-effects model is used. Since the time that our work was completed, a prediction intervals (PIs) approach has been developed and recommended for determining how treatment effects from new individual trials are distributed about the mean in a random-effects meta-analysis (Higgins et al., 2009). The calculation of PI is an approximation of the expected treatment effect in a new trial and is probably more practically relevant (Higgins et al., 2009).

Given that (1) our previous meta-analytic work was based on random-effects models (Kelley and Kelley, 2009), (2) the use of PIs for meta-analysis was not well-established at the time, and (3) the possibility that PIs may be the most appropriate and comprehensive statistical inferences to be derived from random-effects meta-analyses (Higgins et al., 2009), the purpose of this brief article was to calculate PIs for a random mean effect in a new (future) study using data from our previously published research dealing with the effects of PRT training on TC, HDL-C, TC/HDL-C, non-HDL-C, LDL-C, and TG in adult humans (Kelley and Kelley, 2009). To the best of our knowledge, this is the first time that PIs have been used in an exercise meta-analysis.

Section snippets

Data source

We used data from our previous meta-analytic work dealing with the effects of PRT on lipids and lipoproteins in adults, which have been described in detail elsewhere (Kelley and Kelley, 2009). Briefly, randomized controlled trials > 4 weeks dealing with the effects of PRT on lipids and lipoproteins in adult humans > 18 years of age and published between January 1, 1955, and July 12, 2007, were included. Primary outcomes included TC, HDL-C, TC/HDL-C, non-HDL-C, LDL-C, and TG. Treatment effects for

Results

The results for underlying mean treatment effects, their 95% confidence intervals, as well as 95% PIs for changes in lipids and lipoproteins are shown in Table 1. As can be seen, our random-effects meta-analysis resulted in statistically significant improvements, on average, for TC, TC/HDL-C, non-HDL-C, LDL-C, and TG, but not HDL-C. In contrast, the approximate PIs for the true effect in a new study crossed zero (0) for all lipid and lipoprotein outcomes.

Discussion

The purpose of this brief article was to calculate PIs from our previously published research dealing with the effects of PRT on TC, HDL-C, TC/HDL-C, non-HDL-C, LDL-C, and TG in adult humans (Kelley and Kelley, 2009). Our current analysis resulted in PIs for the true effects in a new study overlapping zero for all outcomes. From a practical standpoint, the results derived from PIs may be more practically relevant than those derived from confidence intervals. Consequently, caution may be

Conflict of interest statement

The authors declare that there are no conflicts of interest.

Acknowledgments

This brief report was funded by Grant-in-Aid 0755207B from the American Heart Association (G.A. Kelley, Principal Investigator).

The authors would like to thank Gerry Hobbs, PhD, Department of Statistics, West Virginia University, for his assistance in the preparation of this article.

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