The effects of whey protein supplementation on indices of cardiometabolic health: A systematic review and meta-analysis of randomized controlled trials

Table of Contents

Overall Summary

Study Background and Main Findings

This meta-analysis investigated the effects of whey protein supplementation on cardiometabolic health markers in adults. The study found that whey protein supplementation significantly reduced total cholesterol (mean difference [MD]: -6.35 mg/dL, 95% CI: -11.10 to -1.61, p = 0.007) and LDL cholesterol in subgroups of individuals younger than 50 years and those who combined whey protein with exercise. Additionally, a significant reduction in triglycerides was observed after 12 weeks of supplementation. However, no significant effects were found on systolic or diastolic blood pressure, HDL cholesterol, or HOMA-IR overall.

Research Impact and Future Directions

The meta-analysis provides evidence that whey protein supplementation may reduce total cholesterol and LDL cholesterol, particularly in individuals under 50 and when combined with exercise. It also suggests a potential benefit for reducing triglycerides after 12 weeks of supplementation. However, no significant effects were found on blood pressure or insulin resistance (HOMA-IR). While the study demonstrates a correlation between whey protein supplementation and improvements in certain cardiometabolic markers, it does not establish a direct causal relationship. The observed associations could be influenced by other factors, such as lifestyle modifications or underlying health conditions.

The practical utility of the findings is promising, particularly for individuals seeking to improve their cholesterol levels through dietary interventions. The results align with existing research on the benefits of exercise and dietary modifications for cardiometabolic health. However, the lack of effect on blood pressure and insulin resistance suggests that whey protein may not be a comprehensive solution for all aspects of cardiometabolic health.

Healthcare practitioners can consider recommending whey protein supplementation as part of a broader strategy for managing cholesterol levels, especially in younger adults and those engaged in regular exercise. However, it is crucial to emphasize that whey protein is not a substitute for a healthy diet and lifestyle. The heterogeneity observed in some outcomes highlights the need for personalized approaches, considering individual factors like age, health status, and exercise habits. Further research is needed to determine the optimal dosage, duration, and long-term effects of whey protein supplementation.

Critical unanswered questions remain regarding the mechanisms underlying the observed effects, particularly the specific pathways involved in cholesterol and triglyceride reduction. Additionally, the high heterogeneity in some outcomes suggests the need for further investigation into the factors influencing individual responses to whey protein. While the methodological limitations, such as the variability in exercise protocols and the lack of a protein control group in some studies, do not fundamentally undermine the overall conclusions, they highlight the need for more standardized research approaches in future studies. Future research should also focus on long-term effects and potential side effects of whey protein supplementation to provide a more comprehensive understanding of its safety and efficacy.

Critical Analysis and Recommendations

Comprehensive search strategy (written-content)
The search strategy is comprehensive, covering multiple databases (PubMed, Scopus, Web of Science, and Cochrane Library) and spanning from each database's inception until June 2024. This thorough approach minimizes the risk of missing relevant studies and strengthens the reliability of the meta-analysis.
Section: Methods
Clear and systematic presentation of results (written-content)
The results are presented clearly and systematically, following the order established in the Methods section. This logical flow makes it easy for the reader to follow the analysis and interpret the findings.
Section: Results
Appropriate statistical methods (written-content)
The study uses appropriate statistical methods for a meta-analysis, including a random-effects model and the inverse-variance approach. The use of established statistical methods ensures that the analysis is robust and appropriate for the data, enhancing the reliability and validity of the findings.
Section: Results
Comprehensive subgroup analyses (written-content)
The study conducts subgroup analyses to explore the potential influence of various factors (age, exercise, treatment duration, dose, BMI, and control type) on the effects of whey protein. This approach allows for a more nuanced understanding of the results and identifies specific populations or conditions where whey protein may be most effective.
Section: Results
Provide full search strategies (written-content)
The Methods section should provide enough detail for another researcher to replicate the search independently. Providing the full search strategy for each database, including any filters applied, is crucial for reproducibility and transparency.
Section: Methods
State the knowledge gap (written-content)
While the introduction discusses dietary interventions, it doesn't fully articulate the specific gap in knowledge that this meta-analysis addresses. Clearly stating this gap would enhance the justification for the study and highlight its unique contribution to the field.
Section: Introduction
Provide more statistical details (written-content)
The Results section should provide sufficient detail about the statistical analyses performed, allowing other researchers to understand and potentially replicate the findings. Providing more information about the statistical methods, including the specific tests used and any adjustments made, would strengthen the transparency and reproducibility of the results.
Section: Results
Elaborate on the mechanisms of action (written-content)
While the discussion mentions potential mechanisms, it doesn't delve into the specific pathways or biological processes involved in whey protein's effects on cardiometabolic markers. A more in-depth exploration of these mechanisms would strengthen the discussion's scientific contribution and provide a more comprehensive understanding of the observed effects.
Section: Discussion
Discuss clinical implications (written-content)
While the conclusion mentions the potential benefits of whey protein for specific outcomes, it doesn't explicitly discuss the implications for dietary recommendations or clinical practice. A dedicated paragraph outlining these implications would provide valuable guidance for healthcare professionals and individuals interested in using whey protein to improve their cardiometabolic health.
Section: Conclusions

Section Analysis

Abstract

Key Aspects

Strengths

Suggestions for Improvement

Introduction

Key Aspects

Strengths

Suggestions for Improvement

Methods

Key Aspects

Strengths

Suggestions for Improvement

Results

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

Fig. 1. Flowchart of the employed literature search.
Figure/Table Image (Page 4)
Fig. 1. Flowchart of the employed literature search.
First Reference in Text
Finally, 21 RCTs were included in the systematic review and meta-analysis [28-48] (Fig. 1).
Description
  • Identification: The flowchart visually depicts the literature search process, starting with the identification stage where searches were performed across multiple databases (PubMed, Cochrane Library, Web of Science, and Scopus). Each database search yielded a specific number of initial records.
  • Duplicate Removal: After identifying records from databases, duplicate records were removed.
  • Screening: After removing duplicate records, the remaining records were screened for eligibility based on titles and abstracts.
  • Retrieval: Full-text articles were sought for the records that passed the initial screening.
  • Eligibility Assessment: The full-text articles were assessed to determine if they met pre-defined inclusion criteria for the review. Studies that met all inclusion criteria were included in the systematic review and meta-analysis; those that did not were excluded, with reasons for exclusion documented.
  • Inclusion: This stage represents the final set of studies included for analysis. The flowchart specifies the final number (21) of randomized controlled trials (RCTs) that met all inclusion criteria and were used in the meta-analysis.
  • Randomized Controlled Trials (RCTs): Randomized controlled trials (RCTs) are a type of scientific study where participants are randomly assigned to different intervention groups. This design helps minimize bias and allows for stronger causal inferences between the intervention and outcomes.
Scientific Validity
  • Transparency and Reproducibility: The transparent presentation of the literature search process is crucial for evaluating the comprehensiveness and potential biases of the review. The flowchart facilitates this by clearly outlining the search strategy, databases used, and the number of studies included and excluded at each stage. This allows readers and peer reviewers to critically assess the search methodology and its potential impact on the review's conclusions.
  • Comprehensiveness of Search: The use of multiple databases (PubMed, Cochrane Library, Web of Science, Scopus) strengthens the validity of the search strategy by minimizing the risk of missing relevant studies. Each database has its own coverage and indexing protocols, so searching across multiple platforms increases the chance of capturing a broader range of relevant literature.
  • Justification of Exclusions: The explicit documentation of exclusion criteria is essential for scientific rigor. The flowchart indicates the reasons for excluding studies, allowing readers to evaluate the appropriateness and objectivity of these decisions. This transparency minimizes the risk of selection bias, ensuring that the included studies represent a fair and unbiased sample of the relevant literature.
Communication
  • Clarity and Transparency: The flowchart clearly presents the step-by-step process of the literature search, which is crucial for transparency and reproducibility in systematic reviews. The visual representation enhances the communication of the search strategy and the number of studies included/excluded at each step.
  • Adherence to PRISMA Guidelines: The flowchart adheres to PRISMA guidelines, which are widely accepted standards for reporting systematic reviews and meta-analyses. This strengthens the credibility and rigor of the presented research.
  • Specificity of Search Terms: While the flowchart effectively communicates the overall search process, providing more specific search terms within each database could further enhance transparency. For instance, indicating whether the search was limited to specific publication types (e.g., clinical trials) or date ranges would be beneficial.
Fig. 2. Effects of whey protein supplementation on systolic blood pressure.
Figure/Table Image (Page 6)
Fig. 2. Effects of whey protein supplementation on systolic blood pressure.
First Reference in Text
Our meta-analysis revealed no statistically significant effects of whey protein (n = 326) on SBP (n = 371) (k = 12; MD: -1.52, 95% CI: -3.51 0.47, I2 = 76 %, P = 0.14) (Fig. 2) or DBP (n = 371) (k = 12; MD: 1.05, 95% CI: -2.58 0.49, I2 = 81 %, P = 0.18) (Fig. 3) vs. placebo or carbohydrate-based control.
Description
  • Individual Study Results: The forest plot displays the effect of whey protein supplementation on systolic blood pressure (SBP) across multiple studies. Each horizontal line represents a single study. The square on each line indicates the mean difference (MD) in SBP between the whey protein group and the control group in that specific study. The size of the square is proportional to the weight of the study in the meta-analysis, reflecting its sample size and precision.
  • Confidence Intervals: The horizontal lines extending from each square represent the 95% confidence intervals (CIs) for the MD. The CI indicates the range within which the true effect is likely to lie with 95% certainty. If the CI crosses the vertical line of no effect (MD = 0), the study result is considered statistically non-significant.
  • Pooled Effect Estimate: The diamond at the bottom represents the overall pooled effect estimate from the meta-analysis. The center of the diamond indicates the overall MD, and its width represents the 95% CI for the pooled effect.
  • Heterogeneity: The figure also presents heterogeneity statistics (I² = 76%) which quantifies the variability in effect sizes between studies. A high I² value suggests substantial heterogeneity, indicating that the studies are not all measuring the same underlying effect.
Scientific Validity
  • Appropriateness of Meta-Analysis: The meta-analysis methodology appears appropriate for synthesizing results from multiple RCTs. The use of a random-effects model is justified given the high heterogeneity (I² = 76%), acknowledging potential variations in true effect sizes between studies.
  • Addressing Heterogeneity: The high heterogeneity (I² = 76%) warrants further investigation. Subgroup analyses or meta-regression could explore potential sources of heterogeneity (e.g., study design, participant characteristics, intervention protocols). Simply reporting the I² value without exploring the reasons for heterogeneity limits the interpretability of the pooled effect estimate.
  • Interpretation of Non-Significance: The confidence interval for the pooled effect (-3.51 to 0.47) includes zero, indicating that the overall effect is not statistically significant. This conclusion is supported by the p-value (0.14). However, the authors should avoid overinterpreting non-significance as evidence of no effect. The observed MD of -1.52 mmHg suggests a potential, albeit non-significant, reduction in SBP with whey protein supplementation.
Communication
  • Effective Visualization: The forest plot effectively visualizes the results of the meta-analysis, displaying individual study results and the overall pooled effect. The use of graphical elements like confidence intervals and the summary diamond enhances understanding.
  • Clarity of Caption: The figure caption clearly describes the outcome measure (systolic blood pressure) and the intervention (whey protein supplementation). However, explicitly stating the comparator (placebo or carbohydrate control) in the caption itself would enhance clarity. Currently, this information is only present in the main text.
  • Accessibility to Non-Experts: The use of standard meta-analysis conventions (forest plot, summary diamond, heterogeneity statistics) facilitates interpretation for a scientific audience. Non-experts, however, may find the plot challenging to fully grasp without prior knowledge of meta-analysis principles.
Fig. 3. Effects of whey protein supplementation on diastolic blood pressure.
Figure/Table Image (Page 6)
Fig. 3. Effects of whey protein supplementation on diastolic blood pressure.
First Reference in Text
Our meta-analysis revealed no statistically significant effects of whey protein (n = 326) on SBP (n = 371) (k = 12; MD: -1.52, 95% CI: -3.51 0.47, I2 = 76 %, P = 0.14) (Fig. 2) or DBP (n = 371) (k = 12; MD: 1.05, 95% CI: -2.58 0.49, I2 = 81 %, P = 0.18) (Fig. 3) vs. placebo or carbohydrate-based control.
Description
  • Individual Study Effects: The forest plot visually summarizes the effects of whey protein supplementation on diastolic blood pressure (DBP) from multiple studies. Each horizontal line corresponds to a single study, and the square on each line marks the mean difference (MD) in DBP between the whey protein group and the control group in that study. The size of the square reflects the study's weight in the meta-analysis, which is determined by its sample size and precision.
  • Confidence Intervals: The horizontal lines extending from each square depict the 95% confidence intervals (CIs) for the MD. The CI provides a range within which the true effect is estimated to lie with 95% confidence. If a CI crosses the vertical line of no effect (MD=0), the result is not statistically significant.
  • Overall Pooled Effect: The diamond at the bottom of the plot represents the overall pooled effect estimate from all included studies. The center of the diamond indicates the pooled MD, while its width corresponds to the 95% CI for this pooled estimate.
  • Heterogeneity: The figure includes the I² statistic (81%), which measures the heterogeneity or inconsistency between the studies' results. A high I² value suggests substantial variability in effect sizes across the studies.
Scientific Validity
  • Meta-Analysis Model: The use of a random-effects model for the meta-analysis is appropriate, considering the substantial heterogeneity (I² = 81%) observed between studies. This model accounts for variation in true effect sizes across different studies, providing a more robust estimate of the overall effect.
  • Heterogeneity Investigation: The high heterogeneity (I² = 81%) raises concerns about the comparability of the included studies. Further exploration of potential sources of heterogeneity through subgroup analysis or meta-regression would strengthen the validity of the findings.
  • Interpretation of Results: While the overall effect on DBP is not statistically significant (MD: 1.05, 95% CI: -2.58 to 0.49, p = 0.18), the authors should refrain from concluding that there is "no effect." The observed MD suggests a slight increase in DBP, and the wide confidence interval indicates considerable uncertainty around this estimate. Further research with larger sample sizes may be needed to definitively determine the effect of whey protein on DBP.
Communication
  • Clear Presentation of Results: The forest plot effectively communicates the results of the meta-analysis on diastolic blood pressure (DBP). The visual representation of individual study results and the overall pooled effect, along with confidence intervals and heterogeneity statistics, provides a clear overview of the findings.
  • Completeness of Caption: While the figure caption mentions diastolic blood pressure, it lacks explicit mention of the comparator (placebo or carbohydrate control). Including this information directly in the caption would improve clarity and make the figure more self-explanatory.
  • Accessibility to Broader Audience: The figure assumes familiarity with forest plots and meta-analysis terminology. While appropriate for a scientific audience, non-experts may find it challenging to interpret without additional context or explanation.
Fig. 4. Effects of whey protein supplementation on HDL-cholesterol levels.
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Fig. 4. Effects of whey protein supplementation on HDL-cholesterol levels.
First Reference in Text
No statistically significant effects of whey protein were observed (n = 300) on HDL-cholesterol (n = 327) (k = 13; MD: -0.82, 95% CI: -2.17 0.53, I2 = 43 %, P = 0.23) (Fig. 4) or LDL-cholesterol (n = 318) (k = 12; MD: −2.07, 95% CI: -4.10 to −0.03, I2 = 6 %, P = 0.05) (Fig. 5) profiles vs. placebo or carbohydrate-based control.
Description
  • Individual Study Results: The forest plot shows the effect of whey protein supplementation on HDL cholesterol levels across multiple studies. Each horizontal line represents an individual study. The square box on each horizontal line represents the mean difference (MD) between the whey protein group and the control group in that study. The size of the square corresponds to the weight given to each study in the meta-analysis, reflecting factors like sample size and precision. Larger squares indicate greater influence on the pooled result.
  • Confidence Intervals: The horizontal lines extending from each square represent the 95% confidence intervals (CIs) for the MD. The CI provides a range of values within which the true effect is likely to fall 95% of the time. If a CI crosses the vertical line at zero MD, it indicates that the effect is not statistically significant.
  • Overall Pooled Effect: The diamond at the bottom represents the pooled effect estimate across all studies. Its center marks the overall MD, and its width represents the 95% CI for the pooled effect. In this figure, the diamond overlaps the zero MD line, suggesting no statistically significant overall effect.
  • Heterogeneity: The I² value (43%) quantifies the heterogeneity, or inconsistency, among the included studies' results. This value suggests moderate heterogeneity in the effects of whey protein on HDL cholesterol.
Scientific Validity
  • Meta-Analysis Model: A random-effects model is likely appropriate for the meta-analysis due to the observed moderate heterogeneity (I² = 43%). This approach acknowledges potential variations in true effect sizes across studies.
  • Exploration of Heterogeneity: While the I² value suggests moderate heterogeneity, investigating potential sources of this variability (e.g., study design, participant characteristics, whey protein dose) through subgroup analyses or meta-regression would provide a more nuanced understanding of the results.
  • Interpretation of Non-Significance: The lack of statistical significance (p = 0.23) should not be interpreted as evidence of *no* effect. The confidence interval (-2.17 to 0.53) includes a range of clinically relevant changes in HDL. Further research with larger sample sizes or longer intervention durations may be needed to clarify the effect of whey protein on HDL cholesterol.
Communication
  • Appropriate Use of Forest Plot: The forest plot visually represents the meta-analysis results clearly, showing the effect of whey protein on HDL cholesterol. The use of a forest plot is standard for meta-analyses and effectively communicates the individual study results and the overall pooled effect.
  • Clarity of Caption: The figure caption clearly states the analyzed outcome (HDL-cholesterol levels) and intervention (whey protein supplementation). However, it would benefit from explicitly mentioning the comparator/control group (e.g., placebo or carbohydrate) within the caption for enhanced clarity.
  • Effective Visual Elements: The visual elements, such as the summary diamond and confidence intervals, are well-presented and aid in understanding the data. The I² value provides a quick assessment of heterogeneity. Labeling the x-axis with units (mg/dL) improves interpretability.
Fig. 5. Effects of whey protein supplementation on LDL-cholesterol levels.
Figure/Table Image (Page 7)
Fig. 5. Effects of whey protein supplementation on LDL-cholesterol levels.
First Reference in Text
No statistically significant effects of whey protein were observed (n = 300) on HDL-cholesterol (n = 327) (k = 13; MD: -0.82, 95% CI: -2.17 0.53, I2 = 43 %, P = 0.23) (Fig. 4) or LDL-cholesterol (n = 318) (k = 12; MD: −2.07, 95% CI: -4.10 to −0.03, I2 = 6 %, P = 0.05) (Fig. 5) profiles vs. placebo or carbohydrate-based control.
Description
  • Individual Study Results: This forest plot illustrates the effects of whey protein supplementation on LDL-cholesterol levels across multiple studies. Each horizontal line corresponds to a separate study. The small square on each line indicates the mean difference (MD) in LDL-cholesterol between the whey protein group and the control group in that specific study. The size of the square reflects the weight assigned to that study in the meta-analysis, which is based on factors like sample size and precision of the study. Larger squares contribute more to the overall pooled effect.
  • Confidence Intervals: The horizontal lines emanating from each square represent the 95% confidence intervals (CIs) associated with each study's MD. A CI gives a range of values within which we can be 95% confident that the true effect lies. If a CI crosses the vertical line at MD=0, it means the result is not statistically significant, as it suggests the possibility of no true effect (i.e., the difference between groups could be zero).
  • Overall Pooled Effect: The diamond at the bottom of the plot summarizes the pooled effect estimate derived from combining all the individual study results. The center of the diamond represents the overall MD in LDL cholesterol, and the width of the diamond spans the 95% CI for this pooled estimate. The diamond nearly touches the zero MD line, indicating a borderline statistically significant reduction.
  • Heterogeneity: The I² value of 6% indicates low heterogeneity, suggesting that the results across the included studies are relatively consistent. This means the studies are generally pointing towards a similar effect of whey protein on LDL-cholesterol, though not all effects are necessarily statistically significant.
Scientific Validity
  • Low Heterogeneity: The low heterogeneity (I² = 6%) suggests that the studies are relatively homogeneous, strengthening the validity of pooling the results in a meta-analysis. The choice of a random-effects model is still generally preferred, though a fixed-effects model might also be justifiable in this case. The authors should ideally justify their model choice explicitly.
  • Interpretation of P-value: Although the p-value is close to the significance threshold (p=0.05), it is crucial to interpret the results with caution. The confidence interval (-4.10 to -0.03) just barely excludes zero, indicating a borderline statistically significant reduction in LDL cholesterol. Emphasizing the magnitude of the effect (MD = -2.07 mg/dL) and the clinical relevance of this change is more informative than solely relying on the p-value.
  • Power Analysis: Given the borderline significance, a power analysis would have been beneficial to assess whether the included studies were adequately powered to detect a clinically meaningful change in LDL cholesterol. This would inform whether the observed non-significance is due to a lack of a true effect or simply insufficient power.
Communication
  • Clear Visual Representation: The forest plot effectively presents the results of the meta-analysis on LDL-cholesterol. The visual representation of individual study results, the overall pooled effect, confidence intervals, and heterogeneity statistics makes the findings easy to comprehend.
  • Specificity of Caption: The caption clearly indicates that the figure pertains to LDL-cholesterol levels and whey protein supplementation. However, specifying the comparator (e.g., placebo or carbohydrate) in the caption itself, rather than solely in the main text, would enhance clarity and make the figure self-contained.
  • Target Audience: The figure adheres to standard meta-analysis visualization conventions, making it readily understandable for a scientific audience familiar with forest plots. Non-experts, though, might require additional context to fully grasp the information presented.
Fig. 6. Effects of whey protein supplementation on total cholesterol levels.
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Fig. 6. Effects of whey protein supplementation on total cholesterol levels.
First Reference in Text
This meta-analysis revealed an effect of whey protein (n = 300) on total cholesterol concentration vs. placebo or carbohydrate-based control (n = 327) (k = 13; MD: -6.35, 95% CI: -10.96 to -1.74, I² = 71%, P = 0.007) (Fig. 6).
Description
  • Individual Study Results: This forest plot displays the effects of whey protein supplementation on total cholesterol levels across multiple studies. Each horizontal line represents a single study, and the square on each line signifies the mean difference (MD) in total cholesterol between the whey protein and control groups in that study. The size of the square corresponds to the weight of each study in the meta-analysis. Larger squares indicate studies with greater influence (typically due to larger sample sizes or lower variability).
  • Confidence Intervals: The horizontal lines extending from the squares represent the 95% confidence intervals (CIs) for each study's MD. The CI indicates a range of values within which we can be 95% confident that the true effect lies. If a CI crosses the vertical line at zero MD, the result is not considered statistically significant.
  • Pooled Effect Size: The diamond at the bottom represents the overall, pooled effect size from the meta-analysis. The center of the diamond corresponds to the overall MD, and its horizontal width shows the 95% CI of the pooled effect. Here, the diamond is entirely to the left of the zero MD line, indicating a statistically significant reduction in total cholesterol.
  • Heterogeneity: The I² statistic (71%) quantifies the heterogeneity, or inconsistency, of effect sizes across the included studies. A value of 71% suggests considerable heterogeneity, meaning the studies vary quite a bit in terms of the observed effect of whey protein on total cholesterol.
Scientific Validity
  • Justification of Random-Effects Model: The choice of a random-effects model seems justified given the substantial heterogeneity (I² = 71%) across the included studies. This model assumes true effect sizes vary between studies and provides a more conservative estimate of the overall effect compared to a fixed-effects model.
  • Addressing Heterogeneity: The substantial heterogeneity (I² = 71%) warrants further investigation. Subgroup analyses or meta-regression should be conducted to explore potential sources of this heterogeneity, such as differences in study design, participant characteristics (age, health status), whey protein dosage, or intervention duration. Addressing the heterogeneity would increase confidence in the overall pooled effect.
  • Clinical Significance: While the overall pooled effect is statistically significant (p=0.007), it's important to consider the clinical significance of the observed MD (-6.35 mg/dL). Relating this reduction to established clinical guidelines for cholesterol management would strengthen the interpretation and practical implications of the findings.
Communication
  • Effective Data Visualization: The forest plot effectively communicates the meta-analysis results for total cholesterol. The visual presentation of individual study results, pooled effect size, confidence intervals, and heterogeneity statistics enhances understanding.
  • Clarity and Completeness of Caption: The caption clearly states the outcome (total cholesterol) and intervention (whey protein). Adding the comparator (placebo or carbohydrate control) directly to the caption would improve clarity and make the figure more self-explanatory.
  • Accessibility for Diverse Audiences: The figure uses standard conventions for presenting meta-analysis results (forest plot, summary diamond), which makes it readily accessible to a scientific audience. However, non-experts might find it challenging to interpret without background knowledge of meta-analysis.
Fig. 7. Effects of whey protein supplementation on triglyceride levels.
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Fig. 7. Effects of whey protein supplementation on triglyceride levels.
First Reference in Text
In addition, our main analysis revealed no effects of whey protein (n = 300) on triglyceride concentrations vs. placebo or carbohydrate-based control (n = 327) (k = 13; MD: -3.20, 95% CI: -8.03 1.62, I2 = 78 %, P = 0.19) (Fig. 7).
Description
  • Individual Study Results: This forest plot illustrates the effects of whey protein supplementation on triglyceride levels across multiple studies. Each horizontal line represents an individual study. The square on each line indicates the mean difference (MD) in triglyceride levels between the whey protein group and the control group (e.g., placebo or carbohydrate) in that study. The size of the square corresponds to the weight given to that study in the meta-analysis, with larger squares representing studies that have a greater influence on the pooled effect (usually due to larger sample size or lower variability).
  • Confidence Intervals: The horizontal lines extending from each square are the 95% confidence intervals (CIs) for the MD. The CI provides a range of values within which we are 95% confident that the true effect lies. If a CI crosses the vertical line at zero MD, it suggests that the observed difference might not be statistically significant (the true effect could be zero).
  • Pooled Effect Size: The diamond at the bottom represents the overall, combined effect estimate from all the included studies. The center of the diamond indicates the pooled MD, and its width represents the 95% CI for the pooled effect. In this figure, the diamond slightly overlaps the vertical line at zero, indicating a non-significant reduction in triglycerides.
  • Heterogeneity: The I² value (78%) represents the percentage of variation across studies that is due to heterogeneity rather than chance. A high I² value, such as 78%, suggests substantial heterogeneity, meaning the studies show quite different effects of whey protein on triglyceride levels.
Scientific Validity
  • Appropriate Use of Random-effects model: Given the high heterogeneity (I² = 78%), the use of a random-effects model is justified. This model assumes that the true effect of whey protein varies across studies and provides a more conservative pooled effect estimate.
  • Addressing Heterogeneity: The substantial heterogeneity warrants further exploration. Subgroup analyses or meta-regression should be performed to identify potential sources of this heterogeneity (e.g., differences in participant characteristics, intervention protocols, or study quality). Understanding the reasons for the variability in effects is essential for interpreting the overall findings.
  • Interpretation of Non-Significance: While the overall effect is not statistically significant (p = 0.19), the relatively wide confidence interval (-8.03 to 1.62) indicates uncertainty. It is premature to conclude a definitive lack of effect. Further research with larger sample sizes is needed to better estimate the true effect of whey protein on triglyceride levels.
Communication
  • Effective Visual Representation: The forest plot provides a clear visual summary of the meta-analysis results for triglyceride levels. The graphical presentation, including individual study results, the overall pooled effect, confidence intervals, and heterogeneity statistics, enhances understanding and facilitates interpretation.
  • Clarity and Specificity of Caption: While the caption clearly states the outcome measure (triglyceride levels), it would benefit from explicitly mentioning the control/comparator group (placebo or carbohydrate) in the caption itself. This would improve clarity and make the figure more self-contained.
  • Accessibility to Diverse Audiences: The use of standard meta-analysis visualization (forest plot, summary diamond) is appropriate for a scientific audience. However, readers unfamiliar with meta-analysis may require additional explanation to fully grasp the presented information.
Fig. 8. Effects of whey protein supplementation on HOMA-IR.
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Fig. 8. Effects of whey protein supplementation on HOMA-IR.
First Reference in Text
Our meta-analysis revealed no effect of whey protein (n = 171) on HOMA-IR vs. placebo or carbohydrate-based control (n = 232) (k = 8; MD: 0.24, 95% CI: −0.27 – 0.76, I² = 82 %, P = 0.35) (Fig. 8).
Description
  • Individual Study Results: The forest plot summarizes the effects of whey protein supplementation on HOMA-IR, a measure of insulin resistance, across several studies. Each horizontal line represents an individual study. The square box on the line marks the mean difference (MD) in HOMA-IR between the whey protein group and the control group (placebo or carbohydrate) in that study. The size of the square reflects the weight of the study in the meta-analysis, which considers factors like the study's sample size and precision. Larger squares indicate studies with more weight.
  • Confidence Intervals: The horizontal lines extending from each square represent the 95% confidence intervals (CIs) of the MD for each study. A CI provides a range of values within which we can be 95% certain that the true effect of the intervention lies. If a CI crosses the vertical line at MD = 0, it suggests that the observed effect may not be statistically significant (the true effect could be zero).
  • Overall Pooled Effect: The diamond at the bottom summarizes the pooled effect across all included studies. Its center indicates the MD in HOMA-IR from combining all studies, and its width represents the 95% CI of this overall effect. In this case, the diamond overlaps the zero MD line, signifying that the pooled effect is not statistically significant.
  • Heterogeneity: The I² value (82%) quantifies the inconsistency or heterogeneity among the results of the individual studies. A high I², like the one observed here, indicates substantial heterogeneity, meaning the studies don't all agree on the direction or size of the effect of whey protein on HOMA-IR.
  • HOMA-IR: HOMA-IR (Homeostatic Model Assessment of Insulin Resistance) is a method for assessing insulin resistance, a condition where the body's cells don't respond effectively to insulin. Higher HOMA-IR values indicate greater insulin resistance.
Scientific Validity
  • High Heterogeneity: While a random-effects model is generally appropriate for meta-analyses with substantial heterogeneity (I² = 82%), the very high heterogeneity raises concerns. Exploring potential sources of this heterogeneity (e.g., participant characteristics, intervention protocols, study quality) through subgroup analysis or meta-regression would be crucial. Without investigating the reasons for such high heterogeneity, the pooled effect estimate should be interpreted cautiously.
  • Interpretation of Non-Significance: Given the high heterogeneity and non-significant result, it's essential to avoid concluding that there's "no effect" of whey protein on HOMA-IR. The relatively small number of included studies (k=8) could also limit the power to detect a true effect. Further research with larger sample sizes and strategies to minimize heterogeneity (e.g., standardized protocols) are needed.
  • HOMA-IR Assessment Methods: It would be valuable to provide more details about the studies included in the meta-analysis regarding the specific HOMA-IR assessment methods used. Variations in fasting durations or assay protocols across studies could contribute to heterogeneity and influence the overall result.
Communication
  • Visual Presentation: The forest plot adequately presents the results of the meta-analysis for HOMA-IR. The visualization of individual study data, the pooled effect, confidence intervals, and the heterogeneity statistic provides a reasonable overview of the findings.
  • Completeness of Caption: The figure caption clearly states the outcome measure (HOMA-IR) and mentions whey protein supplementation. However, it would be beneficial to explicitly state the comparator (placebo or carbohydrate control) within the caption itself for improved clarity. Currently, this information is only present in the accompanying text.
  • Accessibility for Broader Audience: While the presentation is suitable for a scientific audience, non-experts might struggle to interpret the forest plot and the HOMA-IR outcome without additional context. A brief explanation of HOMA-IR and its relevance to insulin resistance in the caption or figure legend could broaden accessibility.
Table 1 Characteristics of the included studies.
Figure/Table Image (Page 5)
Table 1 Characteristics of the included studies.
First Reference in Text
A detailed description of the characteristics of the included studies is presented in Table 1.
Description
  • Table Structure: Table 1 provides a summary of the key features of each study included in the meta-analysis. It is organized in rows, where each row represents a single study, and columns, where each column represents a specific characteristic of that study.
  • Study Characteristics: The table lists the first author's last name, the publication year, the country of origin, the study design (e.g., randomized controlled trial), participant details (population type, number of female/male participants), the type of intervention and control conditions used (including dose and duration), and the age of the intervention and control groups. The 'Intervention' column specifies whether whey protein (WP) was provided alone or in combination with other interventions like resistance training (RT).
  • Abbreviations: Abbreviations are used for common terms like resistance training (RT), high-intensity interval training (HIIT), and type 2 diabetes (T2D). These abbreviations help to condense information within the table.
Scientific Validity
  • Transparency and Reproducibility: The detailed characterization of included studies is essential for transparency and allows readers to assess the quality and relevance of the studies included in the meta-analysis. Providing specific information on study design, populations, interventions, and comparators enables evaluation of potential sources of heterogeneity and bias.
  • Detailed Intervention Description: The table provides sufficient detail to understand the interventions employed in each study, including the dose and duration of whey protein supplementation, which are critical factors for evaluating the effectiveness of the intervention. This detailed reporting is essential for interpreting the results of the meta-analysis.
  • Study Quality Assessment: While Table 1 provides a good overview, it would enhance scientific rigor to include information on study quality assessment (e.g., risk of bias) directly within the table or by referencing a supplementary table. This would allow readers to quickly assess the methodological quality of the included studies and its potential influence on the meta-analysis results.
Communication
  • Clear Presentation of Study Characteristics: Table 1 effectively presents key characteristics of the included studies in a structured format. This clear organization allows for easy comparison of study designs, populations, interventions, and other relevant factors. The use of abbreviations is generally helpful, although ensuring all are defined in a footnote would enhance clarity.
  • Comprehensive Information: The table includes a comprehensive set of variables relevant to the meta-analysis, such as study design, participant demographics, intervention details (dose, duration), and comparators. This level of detail enables readers to assess the characteristics of the individual studies and their potential contribution to the overall findings.
  • Readability and Visual Appeal: While the information provided is valuable, the table's density and the use of abbreviations may make it challenging for readers to quickly grasp key differences between studies. Consider highlighting key variables (e.g., intervention type, dose, duration) using bolding or color-coding to improve readability and draw attention to critical information.

Discussion

Key Aspects

Strengths

Suggestions for Improvement

Conclusions

Key Aspects

Strengths

Suggestions for Improvement

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