Low-density lipoproteins cause atherosclerotic cardiovascular disease. 1. Evidence from genetic, epidemiologic, and clinical studies. A consensus statement fromthe European Atherosclerosis Society Consensus Panel

Table of Contents

Overall Summary

Overview

This paper provides a comprehensive analysis of the causal relationship between low-density lipoprotein cholesterol (LDL-C) and atherosclerotic cardiovascular disease (ASCVD), utilizing evidence from genetic studies, prospective epidemiologic cohort studies, Mendelian randomization studies, and randomized controlled trials. A consistent dose-dependent log-linear association between LDL-C and ASCVD risk is demonstrated across all study types. For instance, meta-analyses of randomized statin trials indicated a 22% reduction in major cardiovascular events per 1 mmol/L reduction in LDL-C over 5 years. Mendelian randomization studies suggested that long-term exposure to lower LDL-C is associated with up to a 54.5% reduction in ASCVD risk per 1 mmol/L lower LDL-C. The paper also highlights that the effect of LDL-C on ASCVD risk is cumulative over time, suggesting that earlier and more sustained LDL-C lowering could lead to greater reductions in lifetime risk of ASCVD.

Key Points

Comprehensive Evidence Synthesis (written-content)
The abstract effectively summarizes a vast amount of evidence from diverse study designs, including genetic, epidemiologic, Mendelian randomization, and randomized controlled trials, providing a strong foundation for its conclusion.
Section: Abstract
Consistent Dose-Response Relationship (written-content)
The paper highlights a remarkably consistent dose-dependent log-linear association between LDL-C and ASCVD risk across all study types, strengthening the causal argument.
Section: Abstract
Unified Mechanism of Action (written-content)
The paper effectively conveys that various LDL-lowering mechanisms consistently reduce ASCVD risk, supporting the central role of LDL in disease pathogenesis.
Section: Abstract
Explicitly State Population Scope (written-content)
This medium-impact improvement would enhance the clarity and applicability of the research findings. The Abstract section particularly needs this detail as it provides the first impression of the study's scope and relevance to different populations.
Section: Abstract
Quantify Key Findings (written-content)
This high-impact improvement would significantly enhance the informativeness and impact of the abstract. As the first point of contact for most readers, the Abstract section should provide specific quantitative data to convey the magnitude of the effects observed.
Section: Abstract
Clear Definition of Scope (written-content)
The introduction clearly defines the scope of the paper by focusing on the role of LDL in ASCVD, while acknowledging the contribution of other apoB-containing lipoproteins. This provides a clear framework for the subsequent discussion.
Section: Introduction
Emphasis on Totality of Evidence (written-content)
The authors explicitly state their commitment to evaluating the totality of evidence, which enhances the credibility and objectivity of their analysis.
Section: Introduction
Elaborate on the Implications of the Consensus (written-content)
This high-impact improvement would significantly enhance the introduction's ability to engage readers and underscore the importance of the research. Currently, the Introduction section briefly mentions the need for a consensus to inform treatment guidelines and regulatory agency guidance, but it does not fully explore the potential impact of establishing LDL as a causal factor in ASCVD.
Section: Introduction
Clarity and Organization (graphical-figure)
Table I is well-organized and clearly presents the criteria for causality, the evidence grade, and the summary of the evidence. The layout is logical and easy to follow, enhancing the reader's understanding of the complex relationship between LDL and ASCVD.
Section: Introduction
Comprehensive Criteria for Causality (graphical-figure)
Table I adapts widely recognized criteria for establishing causality, originally derived from the Bradford Hill criteria, and is appropriately modified for the context of cardiovascular disease.
Section: Introduction
Clear Focus on Early Events (written-content)
The section effectively focuses on the initial steps of atherogenesis, particularly emphasizing the retention of apoB-containing lipoproteins as a critical initiating event.
Section: Pathophysiology of atherosclerosis
Elaborate on Post-Retention Mechanisms (written-content)
This high-impact improvement would provide a more complete understanding of atherogenesis, bridging the gap between lipoprotein retention and plaque formation. While the Pathophysiology section effectively describes the initial retention of apoB-containing lipoproteins, it only briefly touches upon the subsequent events that lead to atherosclerotic plaque development.
Section: Pathophysiology of atherosclerosis
Clear Definition of Terms (written-content)
The section clearly defines and differentiates between cholesterol, LDL, and LDL-C, which is crucial for understanding the subsequent discussion on their roles in cardiovascular disease.
Section: Cholesterol, LDL, and LDL-C
Expand on the Pathophysiological Implications of Discordance (written-content)
This high-impact improvement would significantly enhance the section's ability to bridge the gap between basic science and clinical practice. While the section mentions that discordance between LDL-C and LDL particle number can occur, it does not fully explore the pathophysiological implications of this phenomenon.
Section: Cholesterol, LDL, and LDL-C
Clarity of the Pie Chart (graphical-figure)
Figure 1 effectively communicates the relative proportions of ApoB in different lipoproteins. The visual representation is clear and easy to understand, making it accessible to a broad scientific audience.
Section: Cholesterol, LDL, and LDL-C
Missing legend (graphical-figure)
Figure 1 is missing a legend that explains what lipoprotein each color represents. This will hinder the understanding of readers who are not experts in this specific field.
Section: Cholesterol, LDL, and LDL-C
Clear Definition of FH (written-content)
The section clearly defines Familial Hypercholesterolemia (FH) and its genetic basis, providing a solid foundation for understanding its role as evidence for LDL causality in ASCVD.
Section: Evidence from inherited disorders of lipid metabolism
Expand on the Role of Other Genes (written-content)
This medium-impact improvement would provide a more comprehensive understanding of the genetic basis of FH. While the section primarily focuses on LDLR mutations, it briefly mentions APOB and PCSK9, but does not fully explore their roles.
Section: Evidence from inherited disorders of lipid metabolism
Robust Meta-Analyses (written-content)
The section relies on large-scale meta-analyses, such as the Emerging Risk Factors Collaboration and the Prospective Studies Collaboration, which provide robust evidence due to their large sample sizes and consistent findings across multiple studies.
Section: Evidence from prospective epidemiologic studies
Address Potential Confounding Factors (written-content)
This medium-impact improvement would enhance the section's rigor by acknowledging and discussing potential confounding factors. While the section mentions the inherent limitations of observational studies, it does not specifically address potential confounders that could influence the relationship between LDL-C and ASCVD.
Section: Evidence from prospective epidemiologic studies
Visual Clarity (graphical-figure)
Figure 2 effectively communicates the log-linear relationship between LDL-C and cardiovascular risk. The use of different colors and symbols for each study type enhances visual clarity. The trend lines for each study type are clearly distinguishable and effectively illustrate the increasingly steeper slope with increasing follow-up time.
Section: Evidence from prospective epidemiologic studies
Strong Methodological Approach (written-content)
The section effectively utilizes the Mendelian randomization approach, which is a robust method for assessing causality in observational settings by leveraging naturally occurring genetic variation.
Section: Evidence from Mendelian randomization studies
Elaborate on Potential Limitations (written-content)
This medium-impact improvement would enhance the critical evaluation of the Mendelian randomization approach by acknowledging its limitations. While the section mentions the strengths of this method, it does not discuss potential limitations, which is crucial for a balanced scientific assessment.
Section: Evidence from Mendelian randomization studies
Clarity of Panel Structure (graphical-figure)
The use of two separate panels in Figure 3 for genetic variants and therapies is effective in organizing the information and facilitating comparison. The panels are clearly labeled and visually distinct, making it easy to understand the different aspects being presented.
Section: Evidence from Mendelian randomization studies
Comprehensive Meta-Analyses (written-content)
The section effectively utilizes meta-analyses of randomized controlled trials, which provide a high level of evidence by synthesizing data from multiple studies, thereby increasing the statistical power and generalizability of the findings.
Section: Evidence from randomized controlled trials
Discuss Limitations of Individual Trials More Explicitly (written-content)
This medium-impact improvement would enhance the critical evaluation of the evidence by providing a more balanced perspective on the limitations of individual randomized controlled trials.
Section: Evidence from randomized controlled trials
Clarity and Simplicity of the Schematic (graphical-figure)
Figure 4 is clear, simple, and effectively communicates the main message to a scientific audience. The use of a simplified representation of the human body, with a focus on the liver and intestine, is appropriate for illustrating the primary sites of action of the mentioned therapies.
Section: Evidence from randomized controlled trials
Clarity of Panel Structure (graphical-figure)
The use of two separate panels in Figure 5 is effective in organizing the information and facilitating comparison between clinical event rates and atherosclerosis progression.
Section: Evidence from randomized controlled trials
Comprehensive Evidence Synthesis (written-content)
This section effectively synthesizes evidence from multiple study types, including prospective epidemiologic studies, Mendelian randomization studies, and randomized controlled trials, to support the causal link between LDL-C and ASCVD.
Section: Criteria for causality
Expand on the Limitations of Each Study Type (written-content)
This medium-impact improvement would enhance the critical evaluation of the evidence by providing a more balanced perspective on the limitations of each study type. While the section effectively summarizes the evidence supporting causality, it does not fully explore the potential biases and limitations associated with each type of study.
Section: Criteria for causality
Strong Integration of Evidence (written-content)
This section effectively integrates findings from Mendelian randomization studies and randomized controlled trials to support the concept of a cumulative effect of LDL-C on ASCVD risk.
Section: Evidence for the cumulative effect of exposure to LDL on ASCVD
Quantify the Increased Risk More Precisely (written-content)
This high-impact improvement would significantly enhance the section's impact by providing more precise quantitative estimates of the increased risk associated with long-term LDL-C exposure. While the section mentions a "three-fold greater proportional reduction" in risk, providing a more detailed and nuanced quantification would strengthen the paper's overall message.
Section: Evidence for the cumulative effect of exposure to LDL on ASCVD
Clear Connection to Previous Sections (written-content)
This section effectively connects the evidence presented in previous sections to provide clear recommendations for treatment, logically building upon the established causal relationship between LDL-C and ASCVD.
Section: Recommendations for treatment
Elaborate on Specific Treatment Strategies (written-content)
This high-impact improvement would significantly enhance the section's practical utility by providing more concrete guidance on specific treatment strategies. While the section effectively establishes the importance of LDL-C lowering and provides estimates of risk reduction, it does not delve into the details of how to achieve these reductions in different patient populations.
Section: Recommendations for treatment
Clarity of Presentation (graphical-figure)
Table 2 is well-organized and presents the information in a clear and logical manner. The use of separate columns for different durations of therapy makes it easy to compare the expected proportional risk reduction over time.
Section: Recommendations for treatment
Clarity of Presentation (graphical-figure)
Table 3 is well-organized and presents the information in a clear and logical manner. The use of separate columns for baseline risk, LDL-C levels, absolute risk reduction, and NNT makes it easy to compare the expected benefits of treatment across different risk categories and LDL-C levels.
Section: Recommendations for treatment
Clarity of Presentation (graphical-figure)
Table 4 is well-organized and presents the information in a clear and logical manner. The use of separate columns for baseline risk, LDL-C levels, absolute risk reduction, and NNT makes it easy to compare the expected benefits of treatment across different risk categories and LDL-C levels.
Section: Recommendations for treatment
Logical Connection to Previous Sections (written-content)
This section effectively builds upon the evidence presented in earlier sections, logically extending the argument for LDL-C's causal role in ASCVD to address the influence of other risk factors.
Section: Impact of other exposures on the casual effect of LDL on ASCVD
Quantify the Impact of Other Risk Factors (written-content)
This medium-impact improvement would enhance the section's informativeness by providing quantitative data on the impact of other risk factors. While the section states that the proportional effect of LDL-C lowering is consistent across different risk factor levels, it does not quantify how the absolute risk reduction varies depending on the specific combination and severity of these other factors.
Section: Impact of other exposures on the casual effect of LDL on ASCVD
Strong and Definitive Conclusion (written-content)
The Conclusions section provides a clear, concise, and definitive statement that LDL is a causal factor in the development of ASCVD, effectively summarizing the key findings of the paper.
Section: Conclusions
Summarize Key Evidence More Explicitly (written-content)
This medium-impact improvement would enhance the clarity and impact of the Conclusions section by providing a more explicit summary of the key evidence supporting the causal link between LDL and ASCVD.
Section: Conclusions

Conclusion

This paper presents a compelling and comprehensive argument for the causal role of LDL-C in the development of ASCVD. The synthesis of evidence from genetic studies, prospective epidemiologic cohort studies, Mendelian randomization studies, and randomized controlled trials provides strong support for this conclusion. The consistent dose-dependent log-linear association between LDL-C and ASCVD risk across diverse study designs and populations, along with the demonstration of a cumulative effect of LDL-C exposure, further strengthens the causal inference. While the paper effectively highlights the strengths of the evidence, it could be improved by more explicitly addressing the limitations of each study type and by providing more specific quantitative data on the impact of other risk factors. The findings have significant implications for clinical practice, underscoring the importance of early and sustained LDL-C lowering, particularly in high-risk individuals. The paper provides clear guidance for clinicians regarding the potential benefits of LDL-C lowering therapies, although more specific recommendations on treatment strategies and a discussion of potential barriers to treatment adherence would further enhance its practical utility. Future research should focus on identifying individuals who are most likely to benefit from LDL-C-lowering therapies and on further elucidating the complex interplay between LDL-C and other risk factors. Overall, this paper makes a substantial contribution to the field by providing a robust and well-supported argument for the causal role of LDL-C in ASCVD, which should inform clinical guidelines and public health policies aimed at reducing the burden of cardiovascular disease. The identified limitations, particularly regarding the need for more detailed discussion of potential biases in different study designs and the need for more precise quantification of risk in the context of multiple risk factors, do not fundamentally undermine the paper's main conclusions but highlight areas for further research and refinement.

Section Analysis

Abstract

Key Aspects

Strengths

Suggestions for Improvement

Introduction

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

Table I Criteria for causality: low-density lipoprotein (LDL) and...
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Table I Criteria for causality: low-density lipoprotein (LDL) and atherosclerotic cardiovascular disease (ASCVD)

First Reference in Text
Together these studies provide remarkably consistent and unequivocal evidence that LDL causes ASCVD as summarized in Table 1.
Description
  • Purpose of the table: This table outlines the criteria used to determine whether something, in this case, low-density lipoprotein (LDL), can be said to cause something else, which is atherosclerotic cardiovascular disease (ASCVD). In other words, it's a checklist to see if LDL is just hanging around when ASCVD happens or if it actually makes ASCVD happen. LDL is a type of lipoprotein that transports cholesterol in the blood, and is often referred to as "bad" cholesterol. ASCVD is a disease where plaque builds up inside arteries, making it difficult for blood to flow through.
  • Structure and organization of the table: The table is organized with three columns. The first column lists the criteria used to establish causality. These criteria are modified from guidelines adopted by the European Society of Cardiology. The second column, labeled 'Evidence Grade', indicates the strength of evidence supporting each criterion. The grading is explained below the table, with 'Class 1' being the strongest evidence and 'Class 3' being the weakest. The third column provides a summary of the scientific evidence that supports each criterion. This includes references to various types of studies such as monogenic lipid disorders, prospective cohort studies, Mendelian randomization studies, and randomized intervention trials.
  • Explanation of key terms: The table uses several technical terms. 'Low-density lipoprotein (LDL)' is a type of fat-carrying particle in the blood. 'Atherosclerotic cardiovascular disease (ASCVD)' is a disease where plaque builds up in the arteries, leading to heart attacks and strokes. 'Monogenic lipid disorders' are genetic conditions affecting fat metabolism. 'Prospective cohort studies' are studies that follow a group of people over time to see who develops a disease. 'Mendelian randomization studies' are a way to use genetic information to understand cause-and-effect relationships. 'Randomized intervention trials' are experiments where people are randomly assigned to different treatments to see which works best. 'Apolipoprotein (apo) B-containing lipoproteins' refers to particles in the blood that carry cholesterol and have a protein called apoB. These include LDL but also very low-density lipoprotein (VLDL) and their remnants, intermediate-density lipoprotein (IDL), and lipoprotein(a) [Lp(a)]. They are involved in the development of ASCVD. 'Log-linear association' means that as one variable increases, the other increases in a way that looks like a straight line when plotted on a logarithmic scale. 'Unconfounded randomized evidence' refers to evidence from studies where participants are randomly assigned to different groups, reducing bias.
Scientific Validity
  • Comprehensive Criteria for Causality: The table adapts widely recognized criteria for establishing causality, originally derived from the Bradford Hill criteria, and is appropriately modified for the context of cardiovascular disease. The inclusion of criteria such as plausibility, strength, biological gradient, temporal sequence, specificity, consistency, coherence, and reduction in risk with intervention provides a robust framework for evaluating the causal relationship between LDL and ASCVD. The grading system, adopted from the European Society of Cardiology, adds further rigor to the assessment.
  • Evidence Summary and Referencing: The evidence summarized in the table is comprehensive, drawing from over 200 studies, including genetic studies, prospective cohort studies, Mendelian randomization studies, and randomized controlled trials. The citations provided are appropriate and support the claims made. The breadth and depth of the evidence presented lend strong support to the assertion that LDL causes ASCVD.
  • Consideration of Potential Biases: The authors acknowledge the limitations of observational studies and emphasize the use of Mendelian randomization studies and randomized intervention trials to minimize confounding and reverse causation. This methodological rigor strengthens the scientific validity of the conclusions drawn in the table.
Communication
  • Clarity and Organization: The table is well-organized and clearly presents the criteria for causality, the evidence grade, and the summary of the evidence. The layout is logical and easy to follow, enhancing the reader's understanding of the complex relationship between LDL and ASCVD.
  • Use of Technical Language: The table appropriately uses technical language suitable for a scientific audience. Key terms are adequately defined either within the table or in the accompanying text. The use of specific study types and methodologies is precise and reflects the scientific rigor of the analysis.
  • Conciseness and Precision: The table effectively summarizes a large body of evidence in a concise and precise manner. The summaries in the 'Summary of the evidence' column are informative and accurately reflect the findings of the cited studies. The table's ability to synthesize complex information into a digestible format is a significant strength.
  • Clarity of Grading System: The grading system is clearly explained in the footnote, enhancing the reader's ability to interpret the strength of the evidence presented. However, it would be beneficial to include a brief explanation of the Bradford Hill criteria in the main text to provide further context for readers unfamiliar with these criteria.

Pathophysiology of atherosclerosis

Key Aspects

Strengths

Suggestions for Improvement

Cholesterol, LDL, and LDL-C

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

Figure I Relative concentration of apolipoprotein B (ApoB) contained in...
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Figure I Relative concentration of apolipoprotein B (ApoB) contained in circulating lipoproteins in normolipidaemic individuals. ApoB content was calculated in nanomoles per litre using 500 000 as the defined molecular mass [i.e. low-density lipoprotein (LDL) 100 mg/dL or 2000 nmol/L, very low-density lipoprotein (VLDL) 5 mg/dL or 100 nmol/L, intermediate density lipoprotein (IDL) remnants 5 mg/dL or 100 nmol/L and lipoprotein (a) 10 nmol/l*]. *Based on population median.

First Reference in Text
In most people, LDL particles constitute ~90% of circulating apoB-containing lipoproteins in fasting blood (Figure 1).
Description
  • Type of the figure: This figure is a pie chart. It is used to show the different parts that make up a whole. In this case, the 'whole' is the total amount of a substance called apolipoprotein B (ApoB) found in the blood of people with normal lipid levels. 'Normal lipid levels' means that these people have healthy amounts of fats, like cholesterol, in their blood.
  • What is ApoB and lipoproteins: Apolipoprotein B (ApoB) is a protein that helps carry fats around the body. It's like a delivery truck for fats. Lipoproteins are combinations of fats (lipids) and proteins. Think of them as packages that transport fats through the bloodstream because fats can't travel on their own in the blood, just like oil can't mix with water. The figure shows four types of lipoproteins that carry ApoB: LDL (low-density lipoprotein), VLDL (very low-density lipoprotein), IDL (intermediate-density lipoprotein) remnants, and lipoprotein(a).
  • What is shown in the pie chart: The pie chart displays how much of the total ApoB is found in each of the four types of lipoproteins. Each slice of the pie represents a different type of lipoprotein. The size of the slice shows the relative amount of ApoB in that lipoprotein compared to the others. For example, a bigger slice means more ApoB is found in that type of lipoprotein. 'Relative concentration' means how much there is of one type compared to the others.
  • Explanation of the calculation in the caption: The caption mentions that the ApoB content was calculated in 'nanomoles per litre'. This is a unit of measurement, like saying 'grams' or 'kilograms'. It tells us how many particles of ApoB are in a certain volume of blood (one litre). The calculation used a 'molecular mass' of 500,000, which is a way to estimate the weight of the ApoB protein. They then use examples to explain how they made the calculation, for example, they measured LDL as 100 mg/dL and converted it to 2000 nmol/L.
Scientific Validity
  • Relevance of ApoB in Cardiovascular Risk Assessment: The focus on ApoB is scientifically valid and increasingly recognized as a crucial marker for cardiovascular risk assessment. ApoB provides a direct measure of the number of atherogenic particles, which is a better predictor of cardiovascular risk than traditional lipid measures like LDL-C alone, especially in certain metabolic conditions.
  • Methodology for Calculating ApoB Content: The methodology for calculating ApoB content, as described in the caption, is based on established practices in lipidology. Using a defined molecular mass of 500,000 for ApoB is a standard approach for converting between mass and molar concentrations. However, the specific values used for LDL, VLDL, IDL remnants, and lipoprotein(a) should be verified for accuracy and consistency with current literature.
  • Representation of 'Normolipidaemic Individuals': The representation of ApoB content in 'normolipidaemic individuals' is crucial for establishing a baseline for comparison with individuals with dyslipidemia. However, the authors should ensure that the population median used is representative of a well-defined and relevant population group. The specific characteristics of this population should be clearly described to allow for appropriate interpretation of the data.
Communication
  • Clarity of the Pie Chart: The pie chart effectively communicates the relative proportions of ApoB in different lipoproteins. The visual representation is clear and easy to understand, making it accessible to a broad scientific audience.
  • Use of Color and Labeling: The use of different colors for each lipoprotein enhances the visual distinction between the different components. The labels are clear and concise, providing essential information without cluttering the figure.
  • Caption Clarity and Detail: The caption provides a detailed explanation of the figure, including the methodology for calculating ApoB content. However, the use of technical terms like 'nanomoles per litre' and 'molecular mass' may be challenging for some readers. While these terms are appropriate for a scientific audience, the authors could consider providing a brief explanation of these concepts in the main text to enhance accessibility.
  • Missing legend: The figure is missing a legend that explains what lipoprotein each color represents. This will hinder the understanding of readers who are not experts in this specific field.

Evidence from inherited disorders of lipid metabolism

Key Aspects

Strengths

Suggestions for Improvement

Evidence from prospective epidemiologic studies

Key Aspects

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Suggestions for Improvement

Non-Text Elements

Figure 2 Log-linear association per unit change in low-density lipoprotein...
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Figure 2 Log-linear association per unit change in low-density lipoprotein cholesterol (LDL-C) and the risk of cardiovascular disease as reported in meta-analyses of Mendelian randomization studies, prospective epidemiologic cohort studies, and randomized trials. The increasingly steeper slope of the log-linear association with increasing length of follow-up time implies that LDL-C has both a causal and a cumulative effect on the risk of cardiovascular disease. The proportional risk reduction (y axis) is calculated as 1-relative risk (as estimated by the odds ratio in Mendelian randomization studies, or the hazard ration in the prospective epidemiologic studies and randomized trials) on the log scale, then exponentiated and converted to a percentage. The included meta-analyses were identified from (i) MEDLINE and EMBASE using the search terms meta-analysis, LDL, and 'cardiovascular or coronary'; (ii) the reference lists of the identified meta-analyses; (iii) public data from GWAS consortia; and (iv) by discussion with members of the EAS Consensus Panel. We included the most updated meta-analyses available, giving preference to meta-analyses that used individual participant data. Trial acronyms: AF/TexCAPS, Air Force/Texas Coronary Atherosclerosis Prevention Study; ALERT, Assessment of LEscol in Renal Transplantation; ALLHAT-LLT, Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial Lipid Lowering Trial; ALLIANCE, Aggressive Lipid-Lowering Initiation Abates New Cardiac Events; ASPEN, Atorvastatin Study for Prevention of Coronary Heart Disease Endpoints in non-insulin-dependent diabetes mellitus; ASCOT LLA, Anglo Scandinavian Cardiac Outcomes Trial Lipid Lowering Arm; AURORA, A Study to Evaluate the Use of Rosuvastatin in Subjects on Regular Hemodialysis: An Assessment of Survival and Cardiovascular Events; CARE, Cholesterol and Recurrent Events; CARDS, Collaborative Atorvastatin Diabetes Study; CHGN, Community Health Global Network; 4D Deutsche Diabetes Dialyse Studies; ERFC, Emerging Risk Factors Collaboration; GISSI, Gruppo Italiano per lo Studio della Sopravvivenza nell'Infarto Miocardico; HOPE, Heart Outcomes Prevention Evaluation Study; HPS, Heart Protection Study; IDEAL, Incremental Decrease in End Points Through Aggressive Lipid Lowering; IMPROVE-IT, Examining Outcomes in Subjects With Acute Coronary Syndrome: Vytorin (Ezetimibe/Simvastatin) vs Simvastatin; JUPITER, Justification for the Use of Statins in Primary Prevention: An Intervention Trial Evaluating Rosuvastatin trial; LIPID,, Long-Term Intervention with Pravastatin in Ischemic Disease; LIPS, Lescol Intervention Prevention Study; MEGA, Management of Elevated Cholesterol in the Primary Prevention Group of Adult Japanese; POST-CABG, Post Coronary Artery Bypass Graft; PROSPER, Pravastatin in elderly individuals at risk of vascular disease; PROVE-IT, Pravastatin or Atorvastatin Evaluation and Infection Therapy; SHARP, Study of Heart and Renal Protection; TNT, Treating to New Targets; WOSCOPS, West of Scotland Coronary Prevention Study.

First Reference in Text
Together, these meta-analyses of prospective epidemiologic cohort studies provide coherent and consistent evidence that plasma LDL-C concentration is strongly and log-linearly associated with a dose-dependent increase in the risk of incident ASCVD events (Figure 2).
Description
  • What the graph shows: This graph shows the relationship between changes in low-density lipoprotein cholesterol (LDL-C) and the risk of developing cardiovascular disease. LDL-C is a type of cholesterol, often called 'bad cholesterol', that can build up in the arteries and lead to heart disease. The graph displays data from three different types of studies: Mendelian randomization studies, prospective epidemiologic cohort studies, and randomized controlled trials. Each dot on the graph represents the results of one of these studies. The studies had differing follow-up times: 5 years for randomized controlled trials, 12 years for prospective cohort studies, and 52 years for Mendelian randomization studies.
  • Explanation of axes: The horizontal axis (x-axis) shows the magnitude of exposure to lower LDL-C, measured in millimoles per liter (mmol/L). This indicates how much the LDL-C levels were reduced. The vertical axis (y-axis) shows the proportional reduction in the risk of coronary heart disease (CHD). The y-axis is calculated as '1 - relative risk', which is then converted to a percentage. For example, if a study found that lowering LDL-C by a certain amount reduced the risk of CHD by 20%, the y-axis would show a 20% proportional risk reduction.
  • Log-linear association and its implication: The graph shows a 'log-linear' association between LDL-C and cardiovascular disease risk. This means that when the data is plotted on a logarithmic scale, the relationship forms a straight line. The slope of this line represents the change in risk for each unit change in LDL-C. The graph shows that the slope of the line becomes steeper as the length of the study increases (from randomized controlled trials to prospective studies to Mendelian randomization studies). This increasingly steeper slope suggests that lowering LDL-C not only causes a reduction in cardiovascular disease risk but also that this effect is cumulative over time. In simpler terms, the longer a person has lower LDL-C levels, the lower their risk of developing cardiovascular disease.
  • Sources of data and included studies: The data for this graph came from meta-analyses, which are studies that combine the results of multiple individual studies. The authors identified these meta-analyses by searching medical databases (MEDLINE and EMBASE), looking at the reference lists of other meta-analyses, using public data from genome-wide association studies (GWAS), and discussing with members of the European Atherosclerosis Society (EAS) Consensus Panel. They included the most up-to-date meta-analyses available, particularly those that used individual participant data. The graph includes data from various studies, each represented by an acronym, for example, AF/TexCAPS, ALLHAT-LLT, etc. These acronyms are explained at the end of the caption.
Scientific Validity
  • Comprehensive Data Sources: The figure synthesizes data from multiple meta-analyses derived from three distinct study types: Mendelian randomization studies, prospective cohort studies, and randomized controlled trials. This approach strengthens the scientific validity by providing a comprehensive view of the relationship between LDL-C and cardiovascular risk from different methodological perspectives. The inclusion criteria for the meta-analyses are clearly defined, ensuring transparency and reproducibility.
  • Methodological Rigor: The methodology for calculating the proportional risk reduction is appropriate and consistent with standard practices in epidemiological research. The use of the odds ratio for Mendelian randomization studies and the hazard ratio for prospective studies and randomized trials is appropriate given the nature of these study designs. The conversion of these measures to a proportional risk reduction allows for a standardized comparison across different study types.
  • Interpretation of Log-Linear Association: The interpretation of the increasingly steeper slope of the log-linear association with increasing length of follow-up time is scientifically sound. This observation supports the hypothesis that LDL-C has a causal and cumulative effect on cardiovascular risk, which is a key finding of the study. The authors appropriately acknowledge the importance of long-term exposure to lower LDL-C in reducing cardiovascular risk.
Communication
  • Visual Clarity: The figure effectively communicates the log-linear relationship between LDL-C and cardiovascular risk. The use of different colors and symbols for each study type enhances visual clarity. The trend lines for each study type are clearly distinguishable and effectively illustrate the increasingly steeper slope with increasing follow-up time.
  • Axis Labels and Units: The axis labels are clear and informative, providing the necessary information to interpret the data. The units of measurement are appropriate and consistent with standard practice in the field.
  • Caption Detail: The caption provides a detailed explanation of the figure, including the methodology for calculating the proportional risk reduction, the sources of data, and the inclusion criteria for the meta-analyses. The detailed list of trial acronyms is helpful for readers who want to delve deeper into the individual studies.
  • Complexity of Data Presentation: The figure presents a large amount of data from multiple studies, which may be overwhelming for some readers. The authors could consider simplifying the figure by presenting the data in separate panels for each study type or by using different symbols or colors to highlight key findings.
  • Use of Acronyms: While the caption provides a comprehensive list of trial acronyms, the use of numerous acronyms in the figure itself may be confusing for some readers. The authors could consider using a combination of acronyms and study names or providing a separate table that lists the full names of each study.

Evidence from Mendelian randomization studies

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Figure 3 Effect of exposure to lower low-density lipoprotein cholesterol...
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Figure 3 Effect of exposure to lower low-density lipoprotein cholesterol (LDL-C) by mechanism of LDL-C lowering. Panel A shows the effect of genetic variants or genetic scores combining multiple variants in the genes that encode for the targets of currently available LDL-C-lowering therapies, adjusted for a standard decrement of 0.35 mmol/L lower LDL-C, in comparison with the effect of lower LDL-C mediated by variants in the LDL receptor gene. Panel B shows the effect of currently available therapies that act to primarily lower LDL-C through the LDL receptor pathway, adjusted per millimole per litre lower LDL-C. Both the naturally randomized genetic data in Panel A and the data from randomized trials in Panel B suggest that the effect of LDL-C on the risk of cardiovascular events is approximately the same per unit change in LDL-C for any mechanism that lowers LDL-C via up-regulation of the LDL receptor where the change in LDL-C (which is used in clinical medicine to estimate the change in LDL particle concentration) is likely to be concordant with changes in LDL particle concentration.

First Reference in Text
Furthermore, when adjusted for a standard decrement in LDL-C, each of the genetic variants associated with LDL-C has a remarkably similar effect on the risk of CHD per unit lower LDL-C, including variants in the genes that encode the targets of pharmacological agents commonly used to lower LDL-C [i.e. 3-hydroxy-3-methyl-glutaryl-coenzyme A reductase (HMGCR), the target of statins; Niemann-Pick C1-like 1 (NPC1L1), the target of ezetimibe; and proprotein convertase subtilisin/kexin type 9 (PCSK9), the target of the monoclonal antibodies alirocumab and evolocumab; see Figure 3], with no evidence of any heterogeneity of effect (12 = 0%).
Description
  • Overall purpose of the figure: This figure explores how different methods of lowering low-density lipoprotein cholesterol (LDL-C), also known as 'bad cholesterol', affect the risk of cardiovascular events. It uses two panels to compare the effects of genetic variations (Panel A) and medical treatments (Panel B) on LDL-C lowering.
  • What Panel A shows: Panel A is a forest plot that displays the impact of genetic differences on LDL-C levels and consequently on the risk of cardiovascular issues. It looks at specific genes that are targeted by current LDL-C-lowering medications. Each gene is represented by a horizontal line, and a square on the line represents the effect of that gene on cardiovascular risk. The position of the square indicates whether the genetic variant in that gene increases or decreases risk. These genetic effects are compared to the effect of variations in the LDL receptor gene, which is a key protein involved in removing LDL-C from the bloodstream. All these effects are adjusted to a standard decrease in LDL-C of 0.35 mmol/L, which allows for a direct comparison between the different genes. A genetic score is a number calculated by combining information from multiple genetic variants. It gives an overall estimate of how a person's genes affect their LDL-C levels.
  • What Panel B shows: Panel B is another forest plot that focuses on the effects of different LDL-C-lowering therapies on cardiovascular risk. It includes medications like statins, ezetimibe, and PCSK9 inhibitors. Similar to Panel A, each therapy is represented by a horizontal line, and a square shows the effect of that therapy on the risk. The effects are adjusted to a standard decrease in LDL-C of 1.0 mmol/L, allowing for a fair comparison between different treatments. All these therapies primarily work by increasing the activity of the LDL receptor, which helps remove LDL-C from the blood.
  • Main takeaway from both panels: The main point of the figure is that both genetic variations (Panel A) and medical treatments (Panel B) that lower LDL-C through the LDL receptor pathway have a similar effect on reducing the risk of cardiovascular events for each unit decrease in LDL-C. In other words, it doesn't matter how you lower LDL-C, as long as it's done by affecting the LDL receptor. The reduction in cardiovascular risk will be about the same per unit of LDL-C lowered.
Scientific Validity
  • Comparison of Genetic and Therapeutic Effects: The comparison of genetic and therapeutic effects on LDL-C lowering and cardiovascular risk is a scientifically valid and powerful approach. By examining both genetic variants and pharmacological interventions that affect the same pathway (LDL receptor pathway), the authors provide strong evidence for the causal role of LDL-C in cardiovascular disease. The use of Mendelian randomization principles in Panel A strengthens the causal inference by minimizing confounding and reverse causation.
  • Adjustment for Standard Decrement in LDL-C: The adjustment for a standard decrement in LDL-C (0.35 mmol/L in Panel A and 1.0 mmol/L in Panel B) is crucial for allowing a direct comparison between different genetic variants and therapies. This standardization ensures that the observed effects are comparable and not simply due to differences in the magnitude of LDL-C lowering. The choice of specific decrement values should be justified based on typical changes observed with these variants and therapies.
  • Focus on LDL Receptor Pathway: The focus on the LDL receptor pathway is appropriate given its central role in LDL-C metabolism and its relevance to the mechanism of action of many LDL-C-lowering therapies. The conclusion that the effect of LDL-C on cardiovascular risk is approximately the same per unit change in LDL-C for any mechanism that lowers LDL-C via up-regulation of the LDL receptor is well-supported by the data presented.
  • Heterogeneity Assessment: The reference text mentions that there is no evidence of heterogeneity of effect (I2 = 0%). The authors should explicitly state this finding in the figure or caption to further support the claim of a consistent effect across different mechanisms of LDL-C lowering.
Communication
  • Clarity of Panel Structure: The use of two separate panels for genetic variants and therapies is effective in organizing the information and facilitating comparison. The panels are clearly labeled and visually distinct, making it easy to understand the different aspects being presented.
  • Use of Forest Plots: Forest plots are an appropriate and effective way to present the results of studies that estimate an effect size, such as the effect of a genetic variant or a therapy on a risk. They allow for a quick visual assessment of the direction, magnitude, and precision of the effects. Each horizontal line represents a study or a genetic variant, and the square on the line indicates the point estimate of the effect, while the horizontal line indicates the confidence interval. The diamond at the bottom represents the overall effect across all studies or variants.
  • Caption Detail: The caption provides a detailed explanation of the figure, including the specific adjustments made for each panel. However, the caption could be improved by explicitly stating the main conclusion drawn from the figure, namely that the effect of LDL-C on cardiovascular risk is approximately the same per unit change in LDL-C for any mechanism that lowers LDL-C via up-regulation of the LDL receptor.
  • Use of Technical Terms: The caption appropriately uses technical terms such as 'genetic score,' 'standard decrement,' and 'LDL receptor pathway.' While these terms are suitable for a scientific audience, the authors should ensure that they are adequately defined in the main text.
  • Visual Appeal and Accessibility: The figure is visually appealing and relatively easy to understand, although the density of information may be challenging for some readers. The use of different colors or symbols to distinguish between different types of genetic variants or therapies could further enhance visual clarity.

Evidence from randomized controlled trials

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

Figure 4 Schematic figure showing that all therapies that act predominantly to...
Full Caption

Figure 4 Schematic figure showing that all therapies that act predominantly to lower low-density lipoprotein (LDL) act via the LDL receptor pathway to up-regulate LDL receptors and thus increase LDL clearance.

First Reference in Text
Perhaps the most compelling clinical evidence for causality is provided by randomized clinical trials evaluating the effect of therapies that reduce LDL-C on the risk of cardiovascular events. (For reference, Figure 4 shows the principal sites of action of therapeutic interventions that act mainly to lower LDL.)
Description
  • Type of figure and what it depicts: This figure is a schematic diagram, which is like a simplified drawing or a visual explanation of how something works. In this case, it illustrates how different treatments lower a type of cholesterol called low-density lipoprotein (LDL), often referred to as 'bad cholesterol'. The diagram shows a simplified representation of the human body, focusing on the liver, intestine, and bloodstream.
  • Explanation of the LDL receptor pathway: The main idea being shown is that all the treatments primarily work through a common mechanism called the 'LDL receptor pathway'. LDL receptors are special proteins on the surface of liver cells that act like 'catchers' for LDL particles circulating in the blood. When these receptors 'catch' LDL, they remove it from the bloodstream, thus lowering LDL levels. This process is called 'LDL clearance'. The diagram shows that these therapies increase or 'up-regulate' the number of LDL receptors on liver cells, which leads to more LDL being removed from the blood.
  • Specific therapies shown and their mechanism: The diagram shows three main types of treatments: bile acid sequestrants (resins), ezetimibe, and ileal bypass surgery. It also mentions statins and PCSK9 inhibitors. Bile acid sequestrants work in the intestine to bind bile acids, which are made from cholesterol. This leads to the liver making more bile acids, using up more cholesterol, and increasing LDL receptors. Ezetimibe also works in the intestine to block the absorption of cholesterol, which has a similar effect. Ileal bypass surgery is a procedure that shortens the small intestine, reducing cholesterol absorption. Statins work in the liver to reduce cholesterol production, while PCSK9 inhibitors increase the number of LDL receptors available to remove LDL from the blood.
  • Overall message of the figure: The main takeaway from the figure is that even though these treatments may work at different locations in the body (liver or intestine), they all ultimately lead to an increase in LDL receptors on liver cells. This common mechanism is how they achieve the goal of lowering LDL cholesterol levels in the blood.
Scientific Validity
  • Accuracy of the Depicted Mechanisms: The schematic accurately depicts the primary mechanisms of action of the mentioned therapies. Bile acid sequestrants, ezetimibe, and ileal bypass surgery all reduce cholesterol absorption in the intestine, leading to increased LDL receptor activity in the liver. Statins inhibit HMG-CoA reductase, the rate-limiting enzyme in cholesterol synthesis, also leading to increased LDL receptor expression. PCSK9 inhibitors prevent the degradation of LDL receptors, thus increasing their availability for LDL clearance. The convergence of these mechanisms on the LDL receptor pathway is well-established in the scientific literature.
  • Simplification of Complex Processes: While the schematic provides a simplified view of complex biological processes, it appropriately conveys the essential information without significant loss of accuracy. The simplification is necessary for a schematic representation and does not detract from the overall scientific validity of the figure. However, it is important to recognize that other factors and pathways may also contribute to the LDL-lowering effects of these therapies.
  • Support for the Claim of a Common Mechanism: The figure effectively supports the claim that all therapies that act predominantly to lower LDL do so via the LDL receptor pathway. This is a crucial point in understanding the causal relationship between LDL-C lowering and cardiovascular risk reduction. By demonstrating a common mechanism, the figure strengthens the argument that LDL-C lowering, regardless of the specific intervention, leads to a reduction in cardiovascular events.
Communication
  • Clarity and Simplicity of the Schematic: The schematic is clear, simple, and effectively communicates the main message to a scientific audience. The use of a simplified representation of the human body, with a focus on the liver and intestine, is appropriate for illustrating the primary sites of action of the mentioned therapies. The arrows and labels clearly indicate the direction of flow and the effects of each intervention.
  • Use of Color and Visual Cues: The use of color and visual cues, such as the arrows indicating the direction of cholesterol and bile acid movement, enhances the clarity and understanding of the figure. The distinct colors for each therapy help to differentiate their mechanisms of action.
  • Caption Clarity: The caption accurately describes the figure and its main message. It clearly states that all therapies shown act via the LDL receptor pathway to increase LDL clearance.
  • Potential for Misinterpretation: While the schematic is generally clear, there is a potential for misinterpretation regarding the role of statins. The figure shows statins acting solely within the liver to increase LDL receptors, which is accurate. However, the text also mentions that statins inhibit cholesterol synthesis. To avoid confusion, the authors could consider adding a visual cue or a brief explanation in the caption to indicate that statins primarily work by inhibiting cholesterol synthesis, which subsequently leads to increased LDL receptor expression.
Figure 5 Linear association between achieved low-density lipoprotein...
Full Caption

Figure 5 Linear association between achieved low-density lipoprotein cholesterol (LDL-C) level and absolute coronary heart disease (CHD) event rate or progression of atherosclerosis. Panel A shows absolute cardiovascular event rates in randomized statin trials and Panel B shows progression of atherosclerosis as measured by intravascular ultrasound. In Panel A, achieved LDL-C in primary prevention trials and secondary prevention trials in stable CHD patients was related to the end point of CHD events (fatal plus non-fatal myocardial infarction, sudden CHD death) proportioned to 5 years assuming linear rates with time. Trendlines for primary and secondary prevention associations are virtually superimposable. Key: p, placebo; a, active treatment arm, except for IDEAL, where s, simvastatin and a, atorvastatin; and HOPE-3, where r, rosuvastatin; and TNT where reference is made to atorvastatin 10 and 80 mg dose. Trial acronyms: AFCAPS, Air Force Coronary Atherosclerosis Prevention Study; ASCOT, Anglo Scandinavian Cardiac Outcomes Trial; ASTEROID, A Study To Evaluate the Effect of Rosuvastatin on Intravascular Ultrasound-Derived Coronary Atheroma Burden; CARE, Cholesterol and Recurrent Events; CAMELOT, Comparison of Amlodipine vs. Enalapril to Limit Occurrence of Thrombosis; HOPE, Heart Outcomes Prevention Evaluation Study; HPS, Heart Protection Study; IDEAL, Incremental Decrease in End Points Through Aggressive Lipid Lowering; ILLUSTRATE, Investigation of Lipid Level Management Using Coronary Ultrasound To Assess Reduction of Atherosclerosis by CETP Inhibition and HDL Elevation; JUPITER, Justification for the Use of Statins in Primary Prevention: An Intervention Trial Evaluating Rosuvastatin trial; LIPID, Long-Term Intervention with Pravastatin in Ischemic Disease; PRECISE IVUS, Plaque REgression with Cholesterol absorption Inhibitor or Synthesis inhibitor Evaluated by IntraVascular UltraSound; PROSPER, Pravastatin in elderly individuals at risk of vascular disease; REVERSAL, Reversal of Atherosclerosis With Aggressive Lipid Lowering; 4S Scandinavian Simvastatin Survival Study; SATURN, Study of Coronary Atheroma by Intravascular Ultrasound: Effect of Rosuvastatin vs. Atorvastatin; STRADIVARIUS, Strategy To Reduce Atherosclerosis Development Involving Administration of Rimonabant-the Intravascular Ultrasound Study; TNT, Treating to New Targets; WOSCOPS, West Of Scotland Coronary Prevention Study.

First Reference in Text
Notably, in the statin trials, the absolute yearly event rate observed in each randomized treatment arm was strongly and linearly associated with the absolute achieved LDL-C level (Figure 5A).
Description
  • Overall Description of the Figure: This figure presents two separate panels (Panel A and Panel B) that illustrate the relationship between low-density lipoprotein cholesterol (LDL-C) levels and coronary heart disease (CHD). LDL-C is often called "bad cholesterol" because high levels can lead to the buildup of plaque in the arteries, increasing the risk of heart disease. Panel A focuses on the rate of cardiovascular events, while Panel B examines the progression of atherosclerosis, which is the underlying process of plaque buildup in the arteries.
  • Panel A: Absolute Cardiovascular Event Rates in Statin Trials: Panel A is a scatter plot that shows the relationship between achieved LDL-C levels and the absolute rate of cardiovascular events in various randomized clinical trials that used statins. Statins are medications that lower LDL-C levels. Each dot on the graph represents the results of one arm of a clinical trial. The horizontal position of the dot indicates the achieved LDL-C level in that group, while the vertical position indicates the absolute rate of cardiovascular events (like heart attacks or death from CHD) over a 5-year period. The data points are divided into primary prevention trials (people who have not had a cardiovascular event before) and secondary prevention trials (people who have already had a cardiovascular event). The graph shows two trend lines, one for each type of trial. These lines indicate the general relationship between achieved LDL-C and event rates.
  • Panel B: Progression of Atherosclerosis Measured by Intravascular Ultrasound: Panel B is a scatter plot that displays the relationship between achieved LDL-C levels and the change in the amount of plaque in the arteries, as measured by intravascular ultrasound. Intravascular ultrasound is a technique that uses sound waves to create images of the inside of blood vessels. Each dot represents a different study. The horizontal position of the dot indicates the achieved LDL-C level, while the vertical position indicates the percentage change in atheroma volume (the volume of plaque in the arteries) over time. A positive value indicates an increase in plaque volume (progression of atherosclerosis), while a negative value indicates a decrease (regression).
  • Main takeaway: The key takeaway from both panels is that there is a linear relationship between achieved LDL-C levels and both the rate of cardiovascular events and the progression of atherosclerosis. This means that lower achieved LDL-C levels are associated with lower event rates and slower progression (or even regression) of atherosclerosis. In simpler terms, the lower the LDL-C, the better the outcome for heart health.
Scientific Validity
  • Appropriateness of Data Presentation: The presentation of data in two separate panels, one focusing on clinical event rates and the other on atherosclerosis progression, is appropriate and scientifically valid. This approach allows for a comprehensive evaluation of the relationship between LDL-C lowering and cardiovascular outcomes, encompassing both clinical events and the underlying disease process. The use of scatter plots with trend lines is suitable for visualizing the relationship between two continuous variables.
  • Validity of Panel A Findings: The findings in Panel A, demonstrating a linear association between achieved LDL-C levels and cardiovascular event rates in randomized statin trials, are consistent with a large body of evidence supporting the efficacy of statins in reducing cardiovascular risk. The distinction between primary and secondary prevention trials is important, and the observation that the trend lines are virtually superimposable strengthens the conclusion that the relationship between LDL-C and event rates is consistent across different patient populations.
  • Validity of Panel B Findings: The findings in Panel B, showing a linear association between achieved LDL-C levels and the progression of atherosclerosis as measured by intravascular ultrasound, are also scientifically valid. Intravascular ultrasound is a well-established technique for assessing plaque burden, and the observed relationship between LDL-C lowering and changes in atheroma volume provides mechanistic support for the clinical findings in Panel A. The inclusion of studies using different statins and other LDL-lowering therapies strengthens the generalizability of the findings.
  • Assumption of Linear Rates with Time (Panel A): The assumption of linear rates with time in Panel A is a simplification, as event rates may not be perfectly linear over a 5-year period. However, this assumption is commonly used in clinical trial analysis and is unlikely to significantly affect the overall interpretation of the data, especially given the strong linear association observed. The authors should acknowledge this assumption as a potential limitation.
Communication
  • Clarity of Panel Structure: The use of two separate panels is effective in organizing the information and facilitating comparison between clinical event rates and atherosclerosis progression. The panels are clearly labeled and visually distinct, making it easy to understand the different aspects being presented.
  • Effectiveness of Scatter Plots: Scatter plots are an effective way to visualize the relationship between two continuous variables. The use of different symbols or colors for different types of trials (primary vs. secondary prevention in Panel A) and different therapies (Panel B) enhances the clarity of the presentation.
  • Caption Detail: The caption provides a detailed explanation of the figure, including the specific endpoints used, the methodology for calculating event rates, and the key to interpreting the symbols. The extensive list of trial acronyms is helpful for readers who want to delve deeper into the individual studies.
  • Complexity of Data Presentation: The figure presents a large amount of data from multiple studies, which may be overwhelming for some readers. The authors could consider simplifying the figure by presenting the data in separate panels for different types of therapies or by using different symbols or colors to highlight key findings.
  • Use of Acronyms: While the caption provides a comprehensive list of trial acronyms, the use of numerous acronyms in the figure itself may be confusing for some readers. The authors could consider using a combination of acronyms and study names or providing a separate table that lists the full names of each study.

Criteria for causality

Key Aspects

Strengths

Suggestions for Improvement

Evidence for the cumulative effect of exposure to LDL on ASCVD

Key Aspects

Strengths

Suggestions for Improvement

Recommendations for treatment

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

Table 2 Expected proportional risk reduction based on pre-treatment low-density...
Full Caption

Table 2 Expected proportional risk reduction based on pre-treatment low-density lipoprotein cholesterol (LDL-C), absolute magnitude of LDL-C reduction, and total duration of therapy

First Reference in Text
Tables 2-4 provide estimates of the potential clinical benefit that can be achieved by lowering plasma LDL-C concentration based on a person's baseline risk of ASCVD, baseline LDL-C, and the duration of lipid-lowering therapy, expressed as both expected proportional and absolute risk reductions.
Description
  • Purpose of the table: This table shows how much a person can reduce their risk of developing heart disease by lowering their low-density lipoprotein cholesterol (LDL-C), often called "bad cholesterol." The table provides estimates based on three factors: the person's starting LDL-C level, how much they reduce it, and for how long they maintain that reduction.
  • Organization of the table: The table is organized into rows and columns. Each row represents a different starting LDL-C level. The first column shows the baseline LDL-C levels. The second column shows the absolute reduction in LDL-C. The next five columns show the expected proportional risk reduction for different durations of therapy: 5 years, 10 years, 20 years, 30 years, and 40 years. The last column indicates whether treatment is recommended based on current guidelines.
  • Key concepts: Proportional risk reduction: "Proportional risk reduction" means the percentage decrease in the risk of developing heart disease compared to not lowering LDL-C. For example, if a person has a 10% risk of developing heart disease in the next 10 years, and a treatment reduces that risk to 8%, the proportional risk reduction is 20% (a 2% decrease divided by the original 10% risk). The table estimates this reduction based on the starting LDL-C level, the amount of LDL-C reduction, and the duration of treatment.
  • How the estimates were calculated: The estimates in the table are based on data from clinical trials and studies that used genetic information to understand the long-term effects of lower LDL-C levels. The calculations assume that the longer a person maintains lower LDL-C levels, the greater the reduction in their risk of heart disease. For short-term risk (5 years), the proportional risk reduction is based on an expected 22% reduction in risk per 1 mmol/L reduction in LDL-C. For long-term risk, the calculation is based on an expected 54% reduction in risk per 1 mmol/L reduction in LDL-C over 40 years.
Scientific Validity
  • Basis for Estimates: The estimates provided in the table are based on a combination of data from randomized controlled trials and Mendelian randomization studies. This approach is scientifically sound, as it combines the strengths of interventional data with long-term observational data from genetic studies. The use of Mendelian randomization studies, which leverage naturally occurring genetic variation to estimate causal effects, strengthens the validity of the long-term risk reduction estimates.
  • Assumptions and Calculations: The calculations of proportional risk reduction are based on established methods and are consistent with the findings of previous research on the relationship between LDL-C lowering and cardiovascular risk. The assumption that the effect of LDL-C lowering is cumulative over time is supported by both clinical trial data and Mendelian randomization studies. The specific formulas used for calculating short-term and long-term risk reduction are clearly stated in the footnote, ensuring transparency and reproducibility.
  • Generalizability: The table provides estimates for a wide range of baseline LDL-C levels and absolute reductions, enhancing the generalizability of the findings. However, it is important to note that these are estimates and individual responses to LDL-C lowering may vary. The authors appropriately acknowledge that these estimates are intended to guide treatment decisions but should not replace individualized risk assessment and clinical judgment.
Communication
  • Clarity of Presentation: The table is well-organized and presents the information in a clear and logical manner. The use of separate columns for different durations of therapy makes it easy to compare the expected proportional risk reduction over time. The color-coding or shading of cells could further enhance the visual distinction between different levels of risk reduction.
  • Caption Detail: The caption accurately describes the content and purpose of the table. It clearly states that the table provides estimates of expected proportional risk reduction based on pre-treatment LDL-C, absolute magnitude of LDL-C reduction, and total duration of therapy.
  • Explanation of Calculations: The footnote provides a clear and concise explanation of the calculations used to derive the estimates. This transparency enhances the reader's understanding of the table and allows for critical evaluation of the methodology.
  • Use of Technical Terms: The table and caption use appropriate technical terms such as "proportional risk reduction," "absolute magnitude," and "Mendelian randomization." While these terms are suitable for a scientific audience, the authors could consider providing brief definitions or explanations in the main text to enhance accessibility for readers who may be less familiar with these concepts.
Table 3 Expected short-term absolute risk reduction and number needed to treat...
Full Caption

Table 3 Expected short-term absolute risk reduction and number needed to treat based on baseline absolute risk of cardiovascular disease and pre-treatment low-density lipoprotein cholesterol (LDL-C) with 5 years of treatment to lower LDL-C

First Reference in Text
Tables 2-4 provide estimates of the potential clinical benefit that can be achieved by lowering plasma LDL-C concentration based on a person's baseline risk of ASCVD, baseline LDL-C, and the duration of lipid-lowering therapy, expressed as both expected proportional and absolute risk reductions.
Description
  • Purpose of the table: This table provides estimates of how much a person can reduce their risk of developing cardiovascular disease (like a heart attack or stroke) in the short term (over 5 years) by lowering their low-density lipoprotein cholesterol (LDL-C), often called "bad cholesterol." It also estimates the "number needed to treat" (NNT), which is a way of expressing how many people need to be treated to prevent one event.
  • Organization of the table: The table is organized into rows and columns. Each row represents a different level of a person's initial 10-year risk of cardiovascular disease. The first column shows this risk. The second column shows the starting LDL-C level. The third column shows what the LDL-C level would be after a 50% reduction. The fourth column shows the proportional risk reduction expected with a 50% reduction in LDL-C. The fifth column displays the absolute risk reduction. The sixth column shows the number needed to treat (NNT). The last column indicates whether treatment is recommended based on current guidelines.
  • Key concepts: Absolute risk reduction: Absolute risk reduction is the difference between the risk of an event (like a heart attack) in the untreated group and the risk of an event in the treated group. For example, if a person has a 20% risk of having a heart attack in the next 10 years, and treatment reduces that risk to 15%, the absolute risk reduction is 5% (20% - 15%).
  • Key concepts: Number needed to treat (NNT): The number needed to treat (NNT) is the number of people who need to be treated for a certain period (in this case, 5 years) to prevent one additional bad outcome (like a heart attack). For example, if the NNT is 10, it means that 10 people need to be treated for 5 years to prevent one additional heart attack. A lower NNT indicates a more effective treatment. The NNT is calculated as 1 divided by the absolute risk reduction.
  • How the estimates were calculated: The estimates in the table are based on data from clinical trials that evaluated the effects of LDL-C-lowering treatments. The calculations assume a 22% reduction in the risk of cardiovascular events for every 1 mmol/L reduction in LDL-C over 5 years. The table also takes into account the person's baseline risk of cardiovascular disease, as the benefits of treatment are greater for people with a higher initial risk.
Scientific Validity
  • Basis for Estimates: The estimates provided in the table are based on data from randomized controlled trials of LDL-C-lowering therapies, primarily statins. This is a scientifically sound approach, as randomized trials provide the highest level of evidence for evaluating the effectiveness of interventions. The assumption of a 22% reduction in risk per 1 mmol/L reduction in LDL-C over 5 years is consistent with the findings of major meta-analyses of statin trials.
  • Calculation of Absolute Risk Reduction and NNT: The calculations of absolute risk reduction and NNT are appropriate and follow standard epidemiological methods. The use of a 50% LDL-C reduction as a benchmark is somewhat arbitrary but provides a useful illustration of the potential benefits of treatment. The authors acknowledge that the actual LDL-C reduction achieved may vary depending on the specific therapy and individual patient characteristics.
  • Consideration of Baseline Risk: The table appropriately takes into account the baseline risk of cardiovascular disease, as the absolute benefits of LDL-C lowering are greater for individuals at higher risk. This is consistent with current guidelines, which recommend risk-based approaches to treatment decisions. The inclusion of different baseline risk categories enhances the clinical relevance of the table.
  • Generalizability: The table provides estimates for a range of baseline risk categories and LDL-C levels, enhancing the generalizability of the findings. However, it is important to note that these are estimates based on population averages, and individual responses to treatment may vary. The authors should emphasize that these estimates are intended to guide treatment decisions but should not replace individualized risk assessment and clinical judgment.
Communication
  • Clarity of Presentation: The table is well-organized and presents the information in a clear and logical manner. The use of separate columns for baseline risk, LDL-C levels, absolute risk reduction, and NNT makes it easy to compare the expected benefits of treatment across different risk categories and LDL-C levels.
  • Caption Detail: The caption accurately describes the content and purpose of the table. It clearly states that the table provides estimates of short-term absolute risk reduction and NNT based on baseline risk, pre-treatment LDL-C, and 5 years of treatment.
  • Explanation of Calculations: The footnote provides a clear and concise explanation of the calculations used to derive the estimates, including the formula for calculating absolute risk reduction and NNT. This transparency enhances the reader's understanding and allows for critical evaluation of the methodology.
  • Use of Technical Terms: The table and caption use appropriate technical terms such as "absolute risk reduction" and "number needed to treat." While these terms are suitable for a scientific audience, the authors could consider providing brief definitions or explanations in the main text to enhance accessibility for readers who may be less familiar with these concepts.
Table 4 Expected long-term absolute risk reduction and number needed to treat...
Full Caption

Table 4 Expected long-term absolute risk reduction and number needed to treat based on baseline absolute risk of cardiovascular disease and pre-treatment low-density lipoprotein cholesterol (LDL-C) with 30 years of treatment (or exposure) to lower LDL-C

First Reference in Text
Tables 2-4 provide estimates of the potential clinical benefit that can be achieved by lowering plasma LDL-C concentration based on a person's baseline risk of ASCVD, baseline LDL-C, and the duration of lipid-lowering therapy, expressed as both expected proportional and absolute risk reductions.
Description
  • Purpose of the table: This table is similar to Table 3, but instead of looking at short-term risk reduction, it focuses on the long-term benefits of lowering low-density lipoprotein cholesterol (LDL-C), or "bad cholesterol." It estimates how much a person can reduce their risk of developing cardiovascular disease over 30 years by maintaining lower LDL-C levels. Like Table 3, it also provides the "number needed to treat" (NNT), but for a longer time frame.
  • Organization of the table: The table is organized into rows and columns. Each row represents a different level of a person's initial 30-year risk of cardiovascular disease. The first column shows this risk. The second column shows the starting LDL-C level. The third column shows what the LDL-C level would be after a 50% reduction. The fourth column shows the proportional risk reduction. The fifth column displays the absolute risk reduction. The sixth column shows the number needed to treat (NNT). The last column indicates whether treatment is recommended based on current guidelines.
  • Key concepts: Long-term absolute risk reduction: Similar to Table 3, this table shows the absolute risk reduction, which is the difference in the risk of an event (like a heart attack) between the untreated group and the treated group. However, in this case, the risk is calculated over a 30-year period. For example, if a person has a 40% risk of having a heart attack in the next 30 years, and treatment reduces that risk to 30%, the absolute risk reduction is 10% (40% - 30%).
  • Key concepts: Long-term number needed to treat (NNT): The number needed to treat (NNT) in this table represents the number of people who need to be treated for 30 years to prevent one additional bad outcome (like a heart attack). For instance, if the NNT is 5, it means that 5 people need to be treated for 30 years to prevent one additional heart attack. A lower NNT indicates a more effective treatment. The NNT is calculated as 1 divided by the absolute risk reduction.
  • How the estimates were calculated: The estimates in this table are based on data from studies that used genetic information to understand the long-term effects of lower LDL-C levels (Mendelian randomization studies). The calculations assume that the longer a person maintains lower LDL-C levels, the greater the reduction in their risk of heart disease. The table uses an expected 54% reduction in risk per 1 mmol/L reduction in LDL-C over 40 years of exposure, which is based on these genetic studies.
Scientific Validity
  • Basis for Estimates: The estimates provided in the table are primarily based on data from Mendelian randomization studies, which leverage naturally occurring genetic variation to estimate the causal effects of long-term exposure to lower LDL-C. This approach is scientifically sound, as it helps to minimize confounding and reverse causation, which can be limitations of traditional observational studies. The use of genetic data to estimate long-term effects is particularly valuable, as long-term randomized controlled trials are often not feasible.
  • Calculation of Absolute Risk Reduction and NNT: The calculations of absolute risk reduction and NNT are appropriate and follow standard epidemiological methods. The use of a 50% LDL-C reduction as a benchmark is somewhat arbitrary but provides a useful illustration of the potential benefits of treatment. The authors acknowledge that the actual LDL-C reduction achieved may vary depending on the specific therapy and individual patient characteristics.
  • Consideration of Baseline Risk: The table appropriately takes into account the baseline risk of cardiovascular disease, as the absolute benefits of LDL-C lowering are greater for individuals at higher risk. This is consistent with current guidelines, which recommend risk-based approaches to treatment decisions. The inclusion of different baseline risk categories enhances the clinical relevance of the table.
  • Generalizability: The table provides estimates for a range of baseline risk categories and LDL-C levels, enhancing the generalizability of the findings. However, it is important to note that these are estimates based on population averages, and individual responses to treatment may vary. The authors should emphasize that these estimates are intended to guide treatment decisions but should not replace individualized risk assessment and clinical judgment.
Communication
  • Clarity of Presentation: The table is well-organized and presents the information in a clear and logical manner. The use of separate columns for baseline risk, LDL-C levels, absolute risk reduction, and NNT makes it easy to compare the expected benefits of treatment across different risk categories and LDL-C levels.
  • Caption Detail: The caption accurately describes the content and purpose of the table. It clearly states that the table provides estimates of long-term absolute risk reduction and NNT based on baseline risk, pre-treatment LDL-C, and 30 years of treatment or exposure to lower LDL-C.
  • Explanation of Calculations: The footnote provides a clear and concise explanation of the calculations used to derive the estimates, including the formula for calculating absolute risk reduction and NNT. This transparency enhances the reader's understanding and allows for critical evaluation of the methodology.
  • Use of Technical Terms: The table and caption use appropriate technical terms such as "absolute risk reduction," "number needed to treat," and "Mendelian randomization." While these terms are suitable for a scientific audience, the authors could consider providing brief definitions or explanations in the main text to enhance accessibility for readers who may be less familiar with these concepts.

Impact of other exposures on the casual effect of LDL on ASCVD

Key Aspects

Strengths

Suggestions for Improvement

Conclusions

Key Aspects

Strengths

Suggestions for Improvement

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