This systematic review and meta-analysis investigated the effects of various exercise training (ExTr) programs on blood lipid levels, aiming to determine the magnitude of change, optimal exercise type, and sufficiency of existing data. Researchers analyzed 148 randomized controlled trials (RCTs) comprising 8673 participants, comparing aerobic, resistance, and combined training interventions to control groups. The study employed meta-analysis and trial sequence analysis (TSA) to synthesize data and assess the robustness of findings. Results showed modest but significant improvements in all five major lipid markers (total cholesterol (TC), high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL), triglycerides (TGD), and very low-density lipoprotein cholesterol (VLDL)), with combined training exhibiting the greatest benefit. The study's comprehensive approach and use of TSA provide strong evidence supporting exercise as a valuable tool in dyslipidemia management.
Description: These figures illustrate the distribution of true effect sizes for changes in TC, LDL, TGD, VLDL, and HDL following exercise training, including confidence intervals (CIs) and prediction intervals (PIs). They visually represent the magnitude and variability of effects, providing a clear picture of the range of potential outcomes.
Relevance: These figures are crucial for understanding the uncertainty associated with the mean effect estimates and the potential variability in individual responses to exercise. They emphasize that while the average effects are positive, there's a range of possible responses, and not everyone will experience the same benefits.
Description: This bar chart summarizes changes in lipid measures following different exercise types (aerobic, resistance, combined). It visually compares the effectiveness of different modalities, highlighting that combined training was most effective overall.
Relevance: This figure directly addresses the important question of which exercise type is optimal for managing dyslipidemia. It provides a clear visual representation of the comparative effectiveness of different training modalities, informing practical recommendations for exercise prescription.
This systematic review and meta-analysis, the most comprehensive to date, confirms that exercise training leads to small but statistically significant improvements in all five major lipid markers. The achievement of statistical futility through TSA strengthens these conclusions and suggests that further research is unlikely to change them. Combined aerobic and resistance training is the most effective modality, offering practical guidance for exercise prescription. While the observed lipid changes are modest, they may contribute meaningfully to primary CVD prevention. Future research should focus on quantifying the clinical significance of these changes, addressing the limitations posed by the generally poor quality of existing studies, and developing more specific exercise recommendations tailored to individual dyslipidemia profiles, considering factors like age, sex, and other health conditions. This would enhance the translation of these findings into effective, personalized exercise interventions for managing dyslipidemia and reducing cardiovascular disease risk.
This systematic review and meta-analysis investigated the effects of different exercise training (ExTr) types on blood lipid levels. Researchers analyzed 148 randomized controlled trials, finding that exercise training led to modest but significant improvements in total cholesterol (TC), high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL), triglycerides (TGD), and very low-density lipoprotein cholesterol (VLDL). Combined aerobic and resistance training (CT) proved most effective for managing dyslipidemia. Trial sequence analysis confirmed the findings' statistical significance, suggesting exercise training may be a valuable tool in managing dyslipidemia and potentially reducing the need for medication in primary prevention.
The study included a large number of RCTs and participants, making it a robust and comprehensive analysis of the effects of exercise on blood lipids. This strengthens the reliability and generalizability of the findings.
The use of TSA adds significant strength to the study. It helps to control for Type I and II errors and confirms that the available data is sufficient to draw firm conclusions, increasing confidence in the findings.
The abstract mentions CT as optimal but lacks specifics about the ideal frequency, intensity, and duration of both aerobic and resistance components. Providing more detail would make the findings more actionable for clinicians and individuals.
Rationale: This would enhance the practical applicability of the findings, allowing for more tailored exercise prescriptions.
Implementation: Include specific recommendations for the frequency, intensity, time, and type (FITT) principle for both aerobic and resistance training in CT.
While the abstract notes the modest effect sizes, it doesn't fully explain their clinical significance. Quantifying the potential impact on CVD risk reduction would strengthen the message.
Rationale: This would help readers understand the real-world implications of the findings and the potential benefits of exercise even if the changes in lipid levels appear small.
Implementation: Provide context by relating the observed changes to established CVD risk reduction thresholds or by estimating the number needed to treat (NNT) to prevent one CVD event based on the observed effect sizes.
Dyslipidemia is a major risk factor for cardiovascular disease (CVD), and its management guidelines have evolved. While medication is crucial, lifestyle changes like diet and exercise are also important, especially for sub-clinical populations. This review focuses on the impact of exercise training (ExTr) on blood lipids. Previous research suggests exercise can modestly improve some lipid markers, but the findings are not always consistent and may be influenced by factors like exercise type, intensity, and energy expenditure. This review aims to provide a contemporary and comprehensive analysis of the effects of different ExTr types on dyslipidemia, including determining the expected changes in lipid markers, clarifying the impact on LDL, TGD, and VLDL, and using trial sequence analysis to assess the sufficiency of existing data.
The introduction effectively establishes the context of dyslipidemia as a CVD risk factor and the evolving nature of its management. It clearly explains the rationale for the review by highlighting gaps in the existing literature.
The introduction clearly outlines the specific aims of the review, including identifying expected changes in lipid markers, clarifying the impact on specific lipids, and using TSA. This provides a clear roadmap for the reader.
While TSA is mentioned, its importance could be further emphasized. Briefly explaining how TSA strengthens the review and addresses limitations of previous research would be beneficial.
Rationale: This would highlight the methodological rigor of the review and its contribution to the field.
Implementation: Add a sentence or two explaining the benefits of using TSA, such as reducing the risk of spurious findings due to random error and providing a more robust estimate of the true effect size.
The term "futility" is used but not explicitly defined. Providing a brief, accessible definition would improve clarity for readers unfamiliar with the concept.
Rationale: This would ensure all readers understand the key concept of futility and its implications for the review.
Implementation: Include a concise definition of futility, such as "Futility refers to the point at which further research is unlikely to change the conclusions of a study due to sufficient existing data."
Table 1 categorizes different types of dyslipidemia based on the levels of High Density Lipoprotein (HDL), Low Density Lipoprotein (LDL), and Triglycerides. Each dyslipidemia type (Hyperlipidemia, Hypoalphalipoproteinemia, Mixed hyperlipidemia, and Hypertriglyceridemia) is associated with either 'High' or 'Low' levels of these three lipid markers.
Text: "Dyslipidemia (DS) presents in different forms (see Table 1)"
Context: The introduction discusses dyslipidemia as a primary risk factor for cardiovascular disease and introduces a table to categorize its different forms.
Relevance: This table is relevant because it provides a clear classification of dyslipidemia types, which is crucial for understanding the specific lipid abnormalities targeted by exercise interventions. It helps to clarify the different ways lipid levels can be imbalanced, which is essential background for interpreting the study's findings.
This section details the methodology used in the systematic review and meta-analysis of exercise training's effects on blood lipids. The researchers systematically searched databases like PubMed, Web of Science, and the Cochrane Library for relevant randomized controlled trials (RCTs). They included studies that compared exercise training interventions (aerobic, resistance, or combined) to a control group and reported changes in TC, HDL, LDL, TGD, or VLDL levels. Data extracted from the included studies were analyzed using meta-analysis techniques in STATA V.18, including a random-effects model and trial sequence analysis (TSA) to assess the sufficiency of the data and control for errors. Meta-regression was used to explore the relationship between exercise training variables and lipid changes. The study quality was assessed using the TESTEX scale, and risk of bias was evaluated using the ROB 2.0 tool.
The search strategy included multiple databases and manual searches of reference lists, maximizing the chances of identifying all relevant studies. This reduces the risk of publication bias and strengthens the review's comprehensiveness.
Employing TSA is a significant strength, as it helps control for errors associated with repetitive testing in meta-analyses and provides a robust assessment of the sufficiency of the data.
The Methods section mentions meta-regression but doesn't specify all the variables included in the models. Listing these variables would enhance transparency and allow readers to better understand the analysis.
Rationale: This would improve the clarity and reproducibility of the analysis.
Implementation: List all variables considered in the meta-regression models, including both exercise-related variables (e.g., frequency, intensity, duration) and participant characteristics (e.g., age, sex, baseline lipid levels).
The Methods section briefly mentions separating data for multiple intervention groups but doesn't fully explain how the control group data were handled in these cases. Providing more detail would improve the transparency of the analysis.
Rationale: This would ensure clarity regarding the statistical handling of studies with multiple intervention arms and prevent potential misinterpretations.
Implementation: Explain the specific method used to divide the control group data, such as whether it was a simple division by the number of intervention arms or a more complex weighting approach. Also, justify the chosen method and its implications for the analysis.
Figure 1 is a PRISMA flow diagram that illustrates the process of selecting studies for inclusion in the systematic review. It starts with the initial identification of studies from databases and other sources, then details the number of studies excluded at each stage, along with the reasons for exclusion. The diagram visually represents the step-by-step process, making it easy to understand how the researchers arrived at the final set of included studies.
Text: "see PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) diagram Fig. 1"
Context: This quote appears in the Methods section when describing the study selection process. It refers readers to the PRISMA flow diagram for a visual representation of the study selection process.
Relevance: This flow diagram is crucial for understanding the study selection process and assessing the potential for bias. It provides transparency and allows readers to evaluate the rigor of the systematic review. By outlining the number of studies excluded at each stage and the reasons for exclusion, the diagram helps to ensure the reliability and validity of the review's findings.
This section presents the findings of the systematic review and meta-analysis on the effects of exercise training on blood lipids. The analysis included 148 randomized controlled trials (RCTs) with a total of 8673 participants. The results showed that exercise training led to modest but statistically significant reductions in total cholesterol (TC), low-density lipoprotein cholesterol (LDL), triglycerides (TGD), and very low-density lipoprotein cholesterol (VLDL), and a significant increase in high-density lipoprotein cholesterol (HDL). Trial sequence analysis (TSA) confirmed that the available data were sufficient to support these findings. Further analyses explored the effects of different exercise types (aerobic, resistance, combined) and study quality on lipid changes.
The results are presented in a clear and organized manner, with key findings summarized effectively. This makes it easy for readers to understand the main outcomes of the analysis.
The use of TSA strengthens the study by confirming the sufficiency of the data and controlling for errors associated with multiple testing. This increases confidence in the reported findings.
While the Results section mentions figures, they are not included in the main text. Including key figures, such as forest plots or summary figures for the main outcomes, would enhance the presentation and make the results more accessible.
Rationale: Visualizations can effectively communicate complex data and improve reader understanding.
Implementation: Consider creating simplified forest plots or other summary figures that clearly display the effect sizes and confidence intervals for each lipid outcome. If font size is an issue, explore alternative presentation formats or software that allow for better control over font size and layout.
While the results highlight the statistical significance of the findings, they could be strengthened by discussing the clinical implications of the modest effect sizes. Relating the observed changes to established clinical thresholds or providing context on the potential impact on CVD risk would be beneficial.
Rationale: This would help readers understand the real-world implications of the findings and their relevance for clinical practice.
Implementation: Discuss the clinical significance of the observed changes in lipid levels, potentially by referencing established clinical guidelines or by estimating the number needed to treat (NNT) to prevent one CVD event based on the observed effect sizes.
Figure 2 presents the distribution of true effect sizes for the change in total cholesterol (TC) after exercise training. It uses a normal distribution curve to illustrate the range of likely effects. The x-axis represents the difference in means (change in TC levels) in mg/dL. The peak of the curve is around -5.90 mg/dL, which is the mean effect size. The 95% confidence interval (CI) is represented by a horizontal bar, indicating the range within which the true mean effect is likely to fall 95% of the time. The wider prediction interval (PI) shows the range where the true effect size would fall in 95% of similar studies.
Text: "TC (4542 exercise/3073 controls) was lower by - 5.90 mg/dL or 0.15 mmol/L (95% CI - 8.14, - 3.65), see Fig. 2"
Context: This figure is first mentioned in the Results section when discussing the overall pooled analysis of 95% confidence intervals and prediction intervals for total cholesterol.
Relevance: This figure is highly relevant as it visually represents the impact of exercise training on total cholesterol. It provides not only the mean effect size but also the uncertainty around this estimate (CI) and the variability of the effect across different studies (PI). This information is crucial for understanding the potential benefits of exercise on TC and for interpreting the clinical significance of the findings.
Figure 3 illustrates the distribution of true effect sizes for changes in low-density lipoprotein cholesterol (LDL) following exercise training. Similar to Figure 2, it uses a normal distribution curve with the x-axis representing the difference in means (change in LDL) in mg/dL. The peak of the curve is around -7.22 mg/dL, the mean effect size. The horizontal bar indicates the 95% confidence interval (CI), showing the range where the true mean effect is likely to fall. The prediction interval (PI) represents the range where the true effect size is expected in 95% of similar studies.
Text: "LDL was reduced by - 7.22 mg/dL or 0.19 mmol/L (95% CI - 9.08, - 5.35), see Fig. 3"
Context: This figure is introduced in the Results section following the discussion of total cholesterol, as part of the overall pooled analysis of lipid changes.
Relevance: This figure is important because it visually represents the effect of exercise training on LDL cholesterol, another key lipid marker for CVD risk. It provides the mean effect size, the CI, and the PI, which are essential for understanding the magnitude and variability of LDL reduction with exercise.
Figure 4 presents the distribution of the true effect size of exercise training on triglyceride (TGD) levels. The figure uses a normal distribution curve to illustrate the range of possible true effects across different populations. The mean effect size, representing the average change in TGD due to exercise, is -8.01 mg/dL. The 95% confidence interval (CI) is -10.44 to -5.58 mg/dL, indicating that we can be 95% confident that the true mean effect lies within this range. The 95% prediction interval (PI) is -23.13 to 7.11 mg/dL, suggesting that in 95% of similar populations, the true effect of exercise on TGD would fall within this broader range.
Text: "TGD was reduced by – 8.01 mg/dL or 0.09 mmol/L (95% CI – 10.45, – 5.58), see Fig. 4"
Context: This figure is mentioned in the Results section when discussing the overall pooled analysis of the effects of exercise training on various lipid measures. It is presented alongside the results for other lipid markers like TC, LDL, VLDL, and HDL.
Relevance: This figure is relevant because it provides a visual representation of the uncertainty associated with the estimated effect of exercise on TGD. The CI helps us understand the precision of the mean effect estimate, while the PI gives a broader picture of the potential variability of the effect across different populations. This information is crucial for interpreting the clinical significance of the findings.
Figure 5 illustrates the distribution of the true effect size of exercise training on very low-density lipoprotein cholesterol (VLDL). Similar to Figure 4, it uses a normal distribution curve to show the range of possible true effects. The mean effect size is -3.85 mg/dL, indicating an average reduction in VLDL due to exercise. The 95% CI is -5.48 to -2.22 mg/dL, meaning we are 95% confident that the true mean effect lies within this range. The 95% PI is -7.37 to -0.33 mg/dL, suggesting that in 95% of similar populations, the true effect of exercise on VLDL would fall within this interval.
Text: "VLDL was reduced by – 3.85 mg/dL or 0.10 mmol/L (95% CI – 5.49, – 2.22), see Fig. 5"
Context: This figure is presented in the Results section as part of the overall pooled analysis, alongside the results for other lipid markers and following the presentation of Figure 4.
Relevance: This figure is important because it visually represents the uncertainty and variability associated with the estimated effect of exercise on VLDL. The CI and PI provide a more complete understanding of the potential range of true effects, which is essential for interpreting the clinical significance of the findings and for designing future research.
Figure 6 presents the distribution of the true effect size of exercise training on High-Density Lipoprotein Cholesterol (HDL). It uses a normal distribution curve to illustrate the range within which the true effect is likely to fall in 95% of similar populations. The mean effect size is an increase of 2.11 mg/dL, with a 95% Confidence Interval (CI) ranging from 1.43 to 2.79 mg/dL. The broader prediction interval (PI) spans from -4.66 to 8.88 mg/dL, indicating the potential variability of the effect in future studies.
Text: "HDL was significantly higher by 2.11 mg/dL or 0.05 mmol/L (95% CI 1.43, 2.79), see Fig. 6"
Context: This figure is mentioned in the Results section when discussing the overall pooled analysis of 95% confidence intervals and prediction intervals for the change in HDL levels following exercise training.
Relevance: This figure is relevant as it visually represents the impact of exercise training on HDL cholesterol, a key marker of cardiovascular health. It provides a clear picture of the average effect size and the range of possible true effects, which is essential for understanding the potential benefits of exercise on HDL. The inclusion of both CI and PI helps to distinguish between the precision of the estimated effect and the potential variability in future studies.
Figure 7 is a bar chart summarizing the changes in various lipid outcome measures (HDL, TC, LDL, TGD, and VLDL) following different types of exercise training (Overall, Aerobic, Resistance, and Combined). Each bar represents the mean change in the respective lipid measure, and error bars likely indicate the variability (e.g., standard deviation or standard error). Asterisks mark statistically non-significant changes (p > 0.05). The figure aims to compare the effectiveness of different exercise modalities on lipid profiles.
Text: "RT only improved HDL (see Fig. 7)."
Context: This figure is first referenced in the Results section when summarizing the effects of different exercise types (aerobic, resistance, and combined) on lipid outcomes. It highlights that resistance training only showed significant improvement in HDL cholesterol.
Relevance: This figure is highly relevant as it directly addresses one of the study's main objectives: comparing the effects of different exercise training types on lipid markers. It provides a visual overview of the changes in each lipid measure following different exercise modalities, allowing for easy comparison and identification of the most effective training type for managing dyslipidemia.
This is the most comprehensive analysis of exercise training for dyslipidemia management to date, demonstrating statistical futility for all five lipid outcome measures using trial sequence analysis (TSA). Exercise training showed modest but favorable benefits (3.5-11.7%) across 148 randomized controlled trials (RCTs). These changes may contribute to primary cardiovascular disease prevention and potentially reduce or delay medication needs. Combined training (CT) was found to be the optimal exercise type. However, the study quality was generally poor, with most studies having low TESTEX scores.
The study is the most comprehensive to date and the first to demonstrate statistical futility for all five lipid outcomes using TSA. This strengthens the conclusions and provides a valuable contribution to the field.
The discussion effectively summarizes the key findings, including the modest benefits, the optimal exercise type, and the limitations related to study quality. This makes it easy for readers to understand the main takeaways.
While the discussion mentions the potential clinical relevance of the modest benefits, it could be strengthened by providing more specific examples or quantifying the potential impact on CVD risk reduction. This would make the findings more impactful for clinicians.
Rationale: This would enhance the practical implications of the findings and provide more concrete guidance for clinical practice.
Implementation: Provide specific examples of how the observed changes in lipid levels might translate into reduced CVD risk, or estimate the number needed to treat (NNT) to prevent one CVD event based on the observed effect sizes.
The discussion acknowledges the poor study quality but doesn't fully explore its potential impact on the findings. Discussing the potential biases introduced by low TESTEX scores and how these might affect the interpretation of the results would strengthen the analysis.
Rationale: This would enhance the transparency and rigor of the analysis by acknowledging and addressing potential limitations.
Implementation: Discuss the specific types of bias that might be introduced by low TESTEX scores (e.g., selection bias, performance bias, detection bias) and how these biases could potentially influence the observed effect sizes. Consider conducting sensitivity analyses to assess the robustness of the findings to variations in study quality.
Table 2 presents the meta-regression models used to predict changes in lipid levels (TC, HDL, and LDL) based on various factors related to aerobic exercise training. For TC, the model includes session frequency, program duration in weeks, and participant number. For HDL, the model includes session time. For LDL, the model includes age and participant number. The table also provides the R-squared (R2) values, p-values, and I2 values for each model.
Text: "Meta-regression showed every extra weekly aerobic session reduced TC – 7.68 mg/dL and for every extra week of training by – 0.5 mg/dL. Each minute of session time produced an additional 2.11 mg/dL HDL increase."
Context: This is the first mention of the meta-regression results in the abstract, highlighting the key findings related to the impact of aerobic exercise variables on TC and HDL changes.
Relevance: This table is highly relevant as it provides a deeper understanding of the factors influencing changes in lipid levels with aerobic exercise. It goes beyond simply reporting the overall effects of exercise and explores the relationships between specific exercise variables (session frequency, program duration, session time, participant number, and age) and changes in TC, HDL, and LDL. This information is crucial for developing tailored exercise prescriptions for managing dyslipidemia.
Table 3 provides exercise program recommendations based on the type of dyslipidemia. It lists five types: General dyslipidemia, Hyperlipidemia, Hypoalphalipoproteinemia, Mixed hyperlipidemia, and Hypertriglyceridemia. For each type, it indicates whether HDL, LDL, and Triglycerides are high or low and recommends either aerobic and resistance training, or aerobic training alone.
Text: "future work should focus on optimal exercise programming for different types of DS, such as those shown in Table 3"
Context: This quote from the Discussion section suggests future research directions and refers to Table 3 for specific exercise program recommendations based on dyslipidemia type.
Relevance: This table is relevant because it translates the study's findings into practical recommendations for exercise prescriptions. It provides tailored advice based on the specific type of dyslipidemia, which is valuable for clinicians and individuals seeking to manage their lipid profiles through exercise.
Exercise training leads to small but positive changes in lipid profiles. There's enough data to confidently say exercise improves lipid levels. Combined training (aerobic and resistance) seems to be the most effective. These lipid changes may help prevent cardiovascular disease, regardless of whether someone is also taking medication. Different types of dyslipidemia might require slight adjustments to the exercise program.
The section provides a clear and concise summary of the main findings regarding the effects of exercise training on lipid profiles. This allows readers to quickly grasp the key takeaways of the study.
The section highlights the clinical relevance of the findings by emphasizing the potential for primary CVD prevention, independent of medical therapy. This makes the findings more impactful for clinicians and patients.
While the section mentions "small, but favourable, changes," it doesn't quantify these changes. Providing specific numbers or percentages would strengthen the conclusion and give readers a clearer understanding of the magnitude of the effects.
Rationale: This would make the conclusions more concrete and allow readers to better assess the clinical significance of the findings.
Implementation: Include specific numbers or percentages for the changes in each lipid marker (TC, HDL, LDL, TGD, VLDL) observed with exercise training. Refer back to the results section for these values.
The section suggests that different types of dyslipidemia may require adjustments to the exercise program but doesn't provide specific recommendations. Offering more tailored guidance would enhance the practical applicability of the findings.
Rationale: This would make the conclusions more actionable for clinicians and individuals seeking to manage dyslipidemia through exercise.
Implementation: Provide more specific exercise recommendations based on the type of dyslipidemia. Consider including information on the FITT (frequency, intensity, time, type) principle for each dyslipidemia category, drawing on the findings from the study and existing exercise guidelines.