Document Detail

Effects of Diet, Aerobic Exercise, or Both on Non-HDL-C in Adults: A Meta-Analysis of Randomized Controlled Trials.
Jump to Full Text
MedLine Citation:
PMID:  23198142     Owner:  NLM     Status:  PubMed-not-MEDLINE    
Purpose. To use the meta-analytic approach to examine the effects of diet (D), aerobic exercise (E), or both (DE) on non-high-density lipoprotein cholesterol (non-HDL-C) in adults. Methods. Randomized controlled trials in adults ≥18 years of age were included. A mixed-effect model was used to combine effect size (ES) results within each subgroup and to compare subgroups (Q(b)). Heterogeneity was examined using the Q and I(2) statistics, and 95% confidence intervals (CI) were also calculated. Statistical significance was set at P ≤ 0.05, while a trend for statistical significance was set between P > 0.05, and ≤0.10. Results. A statistically significant exercise minus control group decrease in non-HDL-C was found for DE (7 ESs, 389 participants, [Formula: see text] mg/dL, 95%  CI = -21.7 to -0.6, P = 0.04, Q = 2.4, P = 0.88, I(2) = 0%), a trend for the D group (7 ESs, 402 participants, [Formula: see text] mg/dL, 95%  CI = -18.6 to 1.6, P = 0.10, Q = 0.76, P = 0.99, I(2) = 0%), and no change for the E group (7 ESs, 387 participants, [Formula: see text] mg/dL, 95% CI = -7.1 to 13.1, P = 0.56, Q = 0.78, P = 0.99, I(2) = 0%). Overall, no statistically significant between-group differences were found (Q(b) = 4.1, P = 0.12). Conclusions. Diet combined with aerobic exercise may reduce non-HDL-C among adults in some settings.
George A Kelley; Kristi S Kelley
Publication Detail:
Type:  Journal Article     Date:  2012-11-08
Journal Detail:
Title:  Cholesterol     Volume:  2012     ISSN:  2090-1291     ISO Abbreviation:  Cholesterol     Publication Date:  2012  
Date Detail:
Created Date:  2012-11-30     Completed Date:  2012-12-03     Revised Date:  2013-03-21    
Medline Journal Info:
Nlm Unique ID:  101540641     Medline TA:  Cholesterol     Country:  United States    
Other Details:
Languages:  eng     Pagination:  840935     Citation Subset:  -    
Meta-Analytic Research Group, Department of Biostatistics, School of Public Health, Robert C. Byrd Health Sciences Center, West Virginia University, Morgantown, WV 26506-9190, USA.
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

Full Text
Journal Information
Journal ID (nlm-ta): Cholesterol
Journal ID (iso-abbrev): Cholesterol
Journal ID (publisher-id): CHOL
ISSN: 2090-1283
ISSN: 2090-1291
Publisher: Hindawi Publishing Corporation
Article Information
Download PDF
Copyright © 2012 G. A. Kelley and K. S. Kelley.
Received Day: 18 Month: 8 Year: 2012
Accepted Day: 26 Month: 9 Year: 2012
Print publication date: Year: 2012
Electronic publication date: Day: 8 Month: 11 Year: 2012
Volume: 2012E-location ID: 840935
PubMed Id: 23198142
ID: 3502755
DOI: 10.1155/2012/840935

Effects of Diet, Aerobic Exercise, or Both on Non-HDL-C in Adults: A Meta-Analysis of Randomized Controlled Trials
George A. KelleyI1*
Kristi S. KelleyI1
Meta-Analytic Research Group, Department of Biostatistics, School of Public Health, Robert C. Byrd Health Sciences Center, West Virginia University, Morgantown, WV 26506-9190, USA
Correspondence: *George A. Kelley:
[other] Academic Editor: Jan Wouter Jukema

1. Introduction

Cardiovascular disease is a major public health problem affecting an estimated 82.6 million adults in the United States (USA) [1]. In terms of mortality, heart disease is the leading cause of death in the USA, affecting 616,628 people (25% of all deaths) in 2008 [2]. Not surprisingly, the economic costs associated with cardiovascular disease are also high. In 2008, the annual direct and indirect costs of cardiovascular disease in the USA were estimated to be $297.7 billion [1].

Less than optimal levels of lipids and lipoproteins are a major risk factor for cardiovascular morbidity and mortality in adults [1]. According to recent estimates, 33.6 million USA adults have total cholesterol (TC) levels ≥240 mg/dL, 71.3 million have low-density-lipoprotein cholesterol (LDL-C) levels ≥130 mg/dL and 41.8 million have high-density lipoprotein cholesterol (HDL-C) levels ≤40 mg/dL [1]. Currently, the primary target of lipid-lowering therapy in adults is low-density lipoprotein cholesterol (LDL-C) with non-high-density lipoprotein cholesterol (non-HDL-C) recommended as a secondary target of therapy in adults with triglyceride levels ≥200 mg/dL [3]. However, it has been suggested that non-HDL-C may be a more relevant target for lipid-lowering therapy because it contains all the lipids and lipoproteins considered to be atherogenic (low-density lipoprotein cholesterol, lipoprotein (a), intermediate-density lipoprotein, very-low-density lipoprotein) [4, 5]. Indeed, previous meta-analytic research has shown that non-HDL-C is a better predictor than LDL-C for future cardiovascular risk. For example, Boekholdt et al. found that non-HDL-C was a better predictor than LDL-C for future risk of cardiovascular events in statin-treated patients [6]. In addition, another meta-analysis found that over a 10-year period, a focus on lowering non-HDL-C versus LDL-C would prevent 300,000 more fatal or nonfatal ischemic cardiovascular events [7]. From a practical perspective, the assessment of non-HDL-C versus LDL-C may be preferred because (1) it can be assessed in the nonfasting state, (2) incurs no additional costs because it is calculated as the difference between TC and HDL-C, and (3) has well-documented benefits [5].

Aerobic exercise and diet are first-line lifestyle interventions recommended for improving lipids and lipoproteins, including LDL-C, in adults [3]. Recently, aggregate data meta-analytic research of randomized controlled trials addressing the effects of diet (D), aerobic exercise (E), or both (DE) on lipids and lipoproteins in adults were reported by the authors [8]. Interventions had to last at least 4 weeks with diet including any type previously considered to improve lipids and lipoproteins in adults (low saturated fat, caloric restriction, etc.) [3]. For both the D and DE groups, statistically significant intervention minus control (C) group improvements were observed for TC, LDL-C, and triglycerides (TG), but not HDL-C. For the E groups, improvements were limited to TG. When between-group comparisons were conducted, reductions in TC and LDL-C were greater in both the D and DE groups versus E group (P < 0.05). No other between-group differences were observed. Unfortunately, none of the studies reported data for non-HDL-C, including dispersion data. In this brief paper, we use an existing method for estimating measures of dispersion for non-HDL-C based on data reported for TC and HDL-C [9] in order to conduct a meta-analysis on the effects of D, E, or both on non-HDL-C in adult humans.

2. Methods
2.1. Study Eligibility, Data Sources, Data Extraction, and Risk of Bias Assessment

Study eligibility, data sources, data extraction, and risk of bias assessment have been previously described in detail elsewhere [8]. Briefly, studies in any language were included if they were randomized controlled trials ≥4 weeks that included D, E, DE, and C groups in adults ≥18 years of age and in which mean and dispersion data for TC and HDL-C were available for calculating non-HDL-C. Data sources included searching nine electronic databases, cross-referencing, and expert review. Dual data extraction occurred using predeveloped codebooks. Risk of bias was assessed by both authors, independent of each other, using the Cochrane Risk of Bias Assessment instrument [10].

2.2. Statistical Analysis
2.2.1. Calculation of Treatment Effects from Each Study

The primary outcome for this meta-analysis was treatment effect changes in non-HDL-C. First, each intervention (D, E, DE) and Control (C) group result was calculated as the change outcome difference in TC minus the change outcome difference in HDL-C. Second, the variance for non-HDL-C for each result from each group (D, E, DE, C) was calculated by pooling the variances of the change outcome differences for TC and HDL-C. Third, treatment effect changes in non-HDL-C were calculated as the intervention (D, E, DE) minus the C result. Variances for these changes were calculated by pooling intervention (D, E, DE) and C results [9].

2.2.2. Pooling of Treatment of Effects

A mixed effects model was used to pool non-HDL-C treatment effects (intervention minus control) for each group (D, E, DE) from each study and to compare results across the three groups. This consisted of a random-effects model to combine studies within each group (D, E, DE) and a fixed-effect model to compare results between groups (Qb). Study-to-study variance (tau-squared) was not assumed to be equal for all subgroups. A z-score alpha value of ≤0.05 (two-tailed) was considered statistically significant while alpha values >0.05 but ≤0.10 were considered as a trend. Precision of treatment effects estimates for non-HDL-C was determined using two-tailed 95% confidence intervals (CIs) based on z. Estimation of treatment effects for non-HDL-C in a new trial was calculated using 95% prediction intervals (PI) [1113]. Any statistically significant outliers (P ≤ 0.05) were deleted from the model.

Heterogeneity of results for each group was examined using the Q and I2 statistics [14, 15]. The alpha value for statistical significance for Q was set at P ≤ 0.10. For I2, values of 25% to <50% were considered small, 50% to <75% medium, and ≥75% large [15]. Potential bias due to small-study effects was examined using a funnel plot along with the data imputation approach of Duval and Tweedie [16, 17]. Simple, mixed-effects meta-regression was conducted to examine the effects of age, baseline non-HDL-C, and intervention minus control group changes in body weight on changes in non-HDL-C in each group (D, E, DE). A two-tailed alpha value of ≤0.05 was considered as a statistically significant association while alpha values >0.05 and ≤0.10 were considered as a trend.

All data were analyzed using Comprehensive Meta-Analysis (version 2.2) [18], Microsoft Excel 2007 [19], and SSC-Stat (version 2.18) [20].

3. Results

Six studies representing 788 men and women (D = 207, E = 192, DE = 194, C = 195) from 28 groups (7 groups each for D, E, DE, and C) met all eligibility criteria [2126] and have been described in detail elsewhere [8]. The baseline between-study range for all groups combined was 34 to 57 years for age (x-±SD=46.5±6.5 years), 63 to 100 kg for bodyweight (x-±SD=80.8±13.4 kg), 180 to 254 mg/dL for TC (x-±SD=213.6±22.0 mg/dL), and 36 to 63 mg/dL for HDL-C (x-±SD=48.3±7.7 mg/dL).

Baseline values for non-HDL-C are shown in Table 1, group changes in Table 2, and study-level changes in (Figure 1). As can be seen, there was a statistically significant intervention minus control group decrease for non-HDL-C in the DE group, a trend for a statistically significant decrease in the D group, and no statistically significant change in the E group. Nonoverlapping 95% confidence intervals were also observed for the DE group. However, for all groups, the 95% PI for changes in non-HDL-C included zero (0). Changes in non-HDL-C were equivalent to −6.5%, −5.6% and 0.8%, respectively, for DE, D, and E groups. No outliers or heterogeneity were observed. In addition, no small-study effects were found as the funnel plot was generally symmetrical and no data points had to be imputed (Figure 2). When between-group changes in non-HDL-C were calculated, no statistically significant difference was observed (Qb = 4.2, P = 0.13). No statistically significant or trend for a statistical association was found between changes in non-HDL-C and age, initial non-HDL-C, and changes in bod y weight (P > 0.10 for all).

4. Discussion

To the best of the authors' knowledge, this is the first meta-analytic study to examine the effects of D, E, and DE on changes in non-HDL-C in adult humans. The overall findings suggest that DE reduces non-HDL-C in adults, while there was a trend for statistically reductions in the D group. No statistically significant reductions were found for the E group. The observed changes in non-HDL-C for the DE and D groups were almost entirely the result of statistically significant decreases in TC and little or no change in HDL-C [8].

The approximate 7% decrease observed in the DE group may be clinically important. A recent meta-analysis by Robinson et al. found that most lipid-modifying drugs used as monotherapy have an approximate one to one relationship between percent non-HDL-C lowering and reduction in coronary heart disease [27]. Assuming that the same benefits could be achieved as a result of the current interventions, this would result in an approximate 7% reduction in coronary heart disease in the DE groups and an approximate 6% reduction in the D group. The National Cholesterol Education Program currently recommends a target for non-HDL-C, that is, 30 mg/dL higher than the target for LDL-C [3]. Given the current findings, it appears that DE, and possibly D, may contribute to achieving that goal.

No statistically significant associations were found between changes in non-HDL-C and age, initial non-HDL-C and changes in body weight. While these results are interesting, they should be interpreted with caution since studies in a meta-analysis are not randomly assigned to predictors [28]. Therefore, these potential predictors should be tested in large, randomized controlled trials.

While the results of this study suggest that DE, and possibly D, reduce non-HDL-C in adults, they should be interpreted with respect to the following. First, the 95% PI included zero (0) for all groups. This suggests that reductions in non-HDL-C may not occur in every setting. Second, given the small number of studies included as well as missing data, a determination of the optimal diet and dose of aerobic exercise needed to reduce non-HDL-C in adults could not be elucidated. Given the need to determine such, it is suggested that future randomized controlled trials address this issue. Third, because none of the studies reported non-HDL-C, variances were estimated based on the data reported for TC and HDL-C. This could have possibly led to results that are different than if the original variance statistics had been available. Given the former, it is strongly suggested that future studies report non-HDL-C, including the variance statistics for such. Fourth, the results of this study, like most studies, should not be generalized beyond the characteristics of the participants included.

5. Conclusions

Combined diet and aerobic exercise may reduce non-HDL-C among adults in some settings. However, future randomized controlled trials are needed before any final recommendations can be made.


This study was supported by Grant R01 HL069802 from the National Institutes of Health, National Heart, Lung, and Blood Institute (G. A. Kelley, Principal Investigator).

1. Roger VL,Go AS,Lloyd-Jones DM,et al. Heart disease and stroke statistics—2012 update: a report from the American Heart AssociationCirculationYear: 2012125e2e22022179539
2. Heron M. Deaths: leading causes for 2008National Vital Statistics ReportsYear: 201260619422827019
3. National Cholesterol Education ProgramThird report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III) final reportCirculationYear: 20021063143342112485966
4. Milani RV,Lavie CJ. Another step forward in refining risk stratification: moving past low-density lipoprotein cholesterolJournal of the American College of CardiologyYear: 20115854644662-s2.0-7996059176121777741
5. Rana JS,Boekholdt SM,Kastelein JJP,Shah PK. The role of non-HDL cholesterol in risk stratification for coronary artery diseaseCurrent Atherosclerosis ReportsYear: 20121421301342-s2.0-8275519856922203405
6. Boekholdt SM,Arsenault BJ,Mora S,et al. Association of LDL cholesterol, non-HDL cholesterol, and apolipoprotein B levels with risk of cardiovascular events among patients treated with statins: a meta-analysisThe Journal of the American Medical AssociationYear: 201230712130213092-s2.0-84860193210
7. Sniderman AD,Williams K,Contois JH,et al. A meta-analysis of low-density lipoprotein cholesterol, non-high-density lipoprotein cholesterol, and apolipoprotein b as markers of cardiovascular riskCirculationYear: 2011433373452-s2.0-7995969469621487090
8. Kelley GA,Kelley KS,Roberts S,Haskell W. Comparison of aerobic exercise, diet or both on lipids and lipoproteins in adults: a meta-analysis of randomized controlled trialsClinical NutritionYear: 20123115616722154987
9. Hedges LV,Olkin I. Statistical Methods for Meta-AnalysisYear: 1985San Diego, Calif, USAAcademic Press
10. Higgins JPT,Green S. Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0Year: 2011The Cochrane Collaboration
11. Higgins JPT,Thompson SG,Spiegelhalter DJ. A re-evaluation of random-effects meta-analysisJournal of the Royal Statistical Society AYear: 200917211371592-s2.0-57849168342
12. Kelley GA,Kelley KS. Impact of progressive resistance training on lipids and lipoproteins in adults: another look at a meta-analysis using prediction intervalsPreventive MedicineYear: 20094964734752-s2.0-7074912550819804794
13. Graham PL,Moran JL. Robust meta-analytic conclusions mandate the provision of prediction intervals in meta-analysis summariesJournal of Clinical EpidemiologyYear: 20126555035102-s2.0-8486111406422265586
14. Cochran WG. The combination of estimates from different experimentsBiometricsYear: 195410101129
15. Higgins JPT,Thompson SG,Deeks JJ,Altman DG. Measuring inconsistency in meta-analysesBritish Medical JournalYear: 200332774145575602-s2.0-004187613312958120
16. Duval S,Tweedie R. Trim and fill: a simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysisBiometricsYear: 20005624554632-s2.0-003393494910877304
17. Duval S,Tweedie R. A Nonparametric “Trim and Fill” method of accounting for publication bias in meta-analysisJournal of the American Statistical AssociationYear: 20009544989982-s2.0-0442309557
18. BiostatComprehensive meta-analysis. (2.2) Englewood Cliffs, NJ, USA, 2006.
19. Microsoft Excel 2007 Microsoft Corporation, Redmond, Wash, USA, 2007.
20. Statistical Services CenterSSC-Stat. (2.18) University of Reading, Statistical Services Center, UK, 2007.
21. Hellenius ML,Faire UD,Berglund B,Hamsten A,Krakau I. Diet and exercise are equally effective in reducing risk for cardiovascular disease. Results of a randomized controlled study in men with slightly to moderately raised cardiovascular risk factorsAtherosclerosisYear: 1993103181912-s2.0-00274581248280188
22. Hopewell R. The effect of fiber and exercise on weight loss and blood lipids in moderately overweight women [Dissertation]Year: 1989West Virginia University
23. Nieman DC,Brock DW,Butterworth D,Utter AC,Nieman CC. Reducing diet and/or exercise training decreases the lipid and lipoprotein risk factors of moderately obese womenJournal of the American College of NutritionYear: 20022143443502-s2.0-003602253612166532
24. Stefanick ML,Mackey S,Sheehan M,Ellsworth N,Haskell WL,Wood PD. Effects of diet and exercise in men and postmenopausal women with low levels of HDL cholesterol and high levels of LDL cholesterolNew England Journal of MedicineYear: 1998339112202-s2.0-00324746829647874
25. Vetro VL. The effects of aerobic dance exercise and nutrition intervention on cholesterol levels [M.S. thesis]Year: 1990Springfield College
26. Wing RR,Venditti E,Jakicic JM,Polley BA,Lang W. Lifestyle intervention in overweight individuals with a family history of diabetesDiabetes CareYear: 19982133503592-s2.0-00319098819540015
27. Robinson JG,Wang S,Smith BJ,Jacobson TA. Meta-analysis of the relationship between non-high-density lipoprotein cholesterol reduction and coronary heart disease riskJournal of the American College of CardiologyYear: 20095343163222-s2.0-5824908641719161879
28. Littell JH,Corcoran J,Pillai V. Systematic Reviews and Meta-AnalysisYear: 2008New York, NY, USAOxford University Press

Article Categories:
  • Research Article

Previous Document:  Traditional herbal management of sickle cell anemia: lessons from Nigeria.
Next Document:  Role of electroencephalography in presurgical evaluation of temporal lobe epilepsy.