Is LDL cholesterol associated with long-term mortality among primary prevention adults? A retrospective cohort study from a large healthcare system

Table of Contents

Overall Summary

Study Background and Main Findings

This retrospective cohort study analyzed data from 177,860 primary prevention patients aged 50-89 years without diabetes and not on statin therapy, followed for a mean of 6.1 years. The study found a U-shaped relationship between LDL-C and all-cause mortality, with the lowest risk observed in the LDL-C range of 100-189 mg/dL (adjusted HRs 0.87, 0.88, and 0.91 for 100-129, 130-159, and 160-189 mg/dL, respectively, compared to the referent group of 80-99 mg/dL). The highest mortality risk was found in the lowest LDL-C category (30-79 mg/dL, HR 1.23) and the highest category (≥190 mg/dL, HR 1.19). Secondary lipid measures, T-C/HDL-C and triglycerides/HDL-C ratios, were more strongly associated with mortality than LDL-C.

Research Impact and Future Directions

The study provides compelling evidence challenging the "lower is better" LDL-C paradigm in primary prevention, particularly for older adults without diabetes and not on statin therapy. The observed U-shaped relationship between LDL-C and mortality, along with the stronger predictive value of secondary lipid measures, suggests a need to re-evaluate current clinical guidelines. However, it is crucial to distinguish between correlation and causation. While the study effectively mitigates reverse causation, it cannot definitively establish a causal link between LDL-C levels and mortality. The findings primarily demonstrate an association, and other factors may contribute to the observed relationship.

The practical utility of these findings lies in their potential to inform a more personalized approach to cardiovascular risk assessment in primary prevention. The study suggests that focusing solely on LDL-C may be insufficient and that a broader assessment incorporating secondary lipid measures and other risk factors could be more informative. This aligns with the growing recognition of the complex interplay of factors contributing to cardiovascular disease.

Clinicians should consider these findings when counseling patients about cardiovascular risk, particularly older adults without diabetes who are not on statin therapy. While aggressive LDL-C lowering may not be universally beneficial in this population, emphasizing established risk reduction strategies, such as lifestyle modifications and management of other risk factors, remains crucial. The potential role of secondary lipid measures in risk assessment warrants further investigation and may offer a more refined approach. However, it is essential to acknowledge the uncertainties that remain. The lack of cause-specific mortality data limits our understanding of the specific mechanisms involved.

Future research should focus on elucidating the mechanisms underlying the observed associations, particularly the role of inflammation and other potential mediators. Investigating the utility of secondary lipid measures and coronary artery calcium scoring in primary prevention populations is also warranted. While the study's methodological limitations, such as the potential for selection bias and residual confounding, are acknowledged, they do not fundamentally undermine the main conclusions. The large sample size, long-term follow-up, and comprehensive data analysis provide a robust foundation for the findings. However, further research, including prospective studies and randomized controlled trials, is needed to confirm these findings and establish definitive clinical guidelines.

Critical Analysis and Recommendations

Large Sample Size and Long-Term Follow-Up (written-content)
The study included a large sample of 177,860 patients with a mean follow-up of 6.1 years, providing robust data for analysis. This large sample size and extended follow-up increase the statistical power and enhance the ability to detect meaningful associations between LDL-C and mortality.
Section: Abstract
Mitigation of Reverse Causation (written-content)
The study design addresses potential reverse causation by excluding patients who died within one year of baseline cholesterol measurement or had exceptionally low T-C or LDL-C levels. This strengthens the causal inference by reducing the likelihood that the observed associations are due to underlying illness affecting both cholesterol levels and mortality.
Section: Abstract
Comprehensive Data Sources (written-content)
The study leverages a robust data infrastructure, integrating multiple clinical and administrative data sources within the UPMC system, enhancing data completeness and validity. This comprehensive data collection allows for a more thorough assessment of potential confounders and reduces the risk of bias due to missing information.
Section: Methods
Comprehensive Reporting of Mortality Outcomes (written-content)
The section thoroughly reports the primary outcome of all-cause mortality, including cumulative mortality rates, adjusted hazard ratios, and subgroup analyses, providing a comprehensive picture of the relationship between LDL-C and mortality. This detailed reporting allows for a nuanced understanding of the findings and facilitates comparisons with other studies.
Section: Results
Emphasis on Secondary Lipid Measures (written-content)
The section highlights the importance of secondary lipid measures, such as T-C/HDL-C and triglycerides/HDL-C ratios, in predicting mortality risk. This emphasis expands the focus beyond LDL-C alone and provides a more holistic view of lipid profiles and their association with cardiovascular health, potentially leading to improved risk assessment strategies.
Section: Discussion
Clarify Cause-Specific Mortality (written-content)
The study was unable to assess cause-specific mortality due to data limitations. Including this information would enhance the study's clinical relevance and impact on patient care guidelines by providing a more detailed understanding of the relationship between LDL-C and specific causes of death.
Section: Abstract
Expand on Clinical Implications (written-content)
The Discussion section mentions clinical implications but lacks a detailed discussion of how these findings might influence current guidelines and patient counseling. Providing a more thorough discussion would further strengthen the section's contribution to clinical practice and help translate the findings into actionable recommendations.
Section: Discussion
Clarify Covariate Selection (written-content)
The Methods section does not provide a list of the specific covariates included in the adjusted Cox regression model. Providing this list would enhance the transparency of the statistical analysis and allow readers to better understand the potential confounders considered, increasing confidence in the adjusted hazard ratios.
Section: Methods
Address Potential for Selection Bias (written-content)
The authors acknowledge that excluding patients who died within the first year might have led to a population less likely to initiate lipid-lowering therapy, but they do not fully discuss the potential impact of this selection bias on the generalizability of the findings. Further discussing this limitation would provide a more nuanced understanding of the study's limitations and help readers assess the applicability of the findings to different populations.
Section: Discussion

Section Analysis

Abstract

Key Aspects

Strengths

Suggestions for Improvement

Introduction

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

Supplement Table 1. ASCVD 10-Year Risk Calculations for Primary Prevention* by...
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Supplement Table 1. ASCVD 10-Year Risk Calculations for Primary Prevention* by Age, Race, and Sex

Figure/Table Image (Page 15)
Supplement Table 1. ASCVD 10-Year Risk Calculations for Primary Prevention* by Age, Race, and Sex
First Reference in Text
Risk of ASCVD is often estimated using the online ACC-ASCVD Risk Estimator, and as seen in online supplemental table 1, all males ages 59 and older even in the presence of 'normal' ASCVD risk factors (lipids included) may be classified at intermediate or high risk of ASCVD, and thus candidates for LDL-C lowering therapy.
Description
  • Table purpose: This table shows the estimated 10-year risk of developing ASCVD for different groups of people, categorized by age, race, and sex. ASCVD stands for atherosclerotic cardiovascular disease, which includes conditions like heart attacks and strokes caused by plaque buildup in the arteries. 'Primary prevention' means that these calculations are for people who have not yet had an ASCVD event. It's like trying to predict the chances of someone getting these diseases in the next 10 years, based on their current characteristics.
  • Table structure: The table is organized with rows representing different age groups and columns representing different combinations of race and sex (e.g., White Male, Black (AA) Male, White Female, Black (AA) Female). Each cell in the table likely contains a percentage value, which represents the estimated 10-year risk of ASCVD for that particular group. For example, one cell might show the risk for white males aged 50-59, while another might show the risk for black females aged 60-69.
  • Risk categories: The table also categorizes the estimated risk into different levels, such as 'Low,' 'Borderline,' 'Intermediate,' and 'High.' These categories provide a way to interpret the percentage values and understand the relative risk for each group. For example, a risk of less than 5% might be considered 'Low,' while a risk greater than 20% might be considered 'High.'
  • Risk factors used: The footnote to the table specifies that the risk calculations are based on certain assumptions about the individuals' characteristics, including total cholesterol, LDL cholesterol, HDL cholesterol, systolic blood pressure, diastolic blood pressure, smoking history, and medication use. These are all factors known to influence the risk of ASCVD. It's like saying, 'these risk estimates are based on people with these specific characteristics, which are considered 'normal' or average values.'
  • Source of calculations: The reference text mentions that the risk estimates are based on the ACC-ASCVD Risk Estimator, which is a widely used online tool developed by the American College of Cardiology and the American Heart Association. This tool uses a specific formula, or algorithm, to calculate the 10-year risk of ASCVD based on an individual's characteristics.
Scientific Validity
  • Validity of the ACC-ASCVD Risk Estimator: The ACC-ASCVD Risk Estimator is a well-established tool based on data from large cohort studies. However, its accuracy may vary depending on the population being assessed. The tool has been validated primarily in US populations, and its applicability to other populations may be limited.
  • Appropriateness of 'normal' values: The use of 'normal' values for risk factors in the table footnote is a simplification. While these values may represent average or guideline-recommended levels, they do not necessarily reflect the actual distribution of risk factors in the population. The specific values used should be clearly defined and justified.
  • Limitations of risk prediction: It is important to recognize that risk prediction models are not perfect and have limitations. They provide estimates based on population averages and may not accurately predict individual risk. Other factors not included in the model may also influence an individual's risk of ASCVD.
  • Relevance to the study's research question: The table provides context for the study by illustrating how the ACC-ASCVD Risk Estimator categorizes individuals based on their risk factors. However, the direct relevance to the study's main research question about the relationship between LDL-C and mortality may be limited, as the table does not specifically address this relationship.
Communication
  • Clarity of caption: The caption clearly states the content and purpose of the table, including the outcome being assessed (ASCVD 10-year risk), the target population (primary prevention), and the variables used for categorization (age, race, and sex).
  • Organization and layout: The table is well-organized, with clear headings for each column and row. The use of separate columns for different race and sex groups facilitates comparison. The specific layout and formatting details are not provided in the text but should be designed for optimal readability.
  • Use of abbreviations: The table uses abbreviations (e.g., ASCVD, AA). These abbreviations are defined in either the caption or the footnote.
  • Accessibility to non-experts: The table presents relatively straightforward data that can be understood by a broad audience. However, a brief explanation of the concept of ASCVD risk and the purpose of risk estimation in the introduction or discussion section could enhance comprehension for non-experts.
  • Completeness of information: The footnote provides important information about the assumptions used in the risk calculations. However, it could be further improved by specifying the exact values used for each risk factor (e.g., total cholesterol = 190 mg/dL). The source of the risk estimation formula (ACC-ASCVD Risk Estimator) should also be explicitly stated in the table or the accompanying text.
Supplement Table 2. Maximum/Range of Total Cholesterol (T-C) Values Along with...
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Supplement Table 2. Maximum/Range of Total Cholesterol (T-C) Values Along with T-C to HDL-C Cholesterol Ratios for Different Life Insurance Underwriting Categories

Figure/Table Image (Page 16)
Supplement Table 2. Maximum/Range of Total Cholesterol (T-C) Values Along with T-C to HDL-C Cholesterol Ratios for Different Life Insurance Underwriting Categories
First Reference in Text
As seen in online supplemental table 2, T-C and HDL-C are used jointly in policy underwriting, whereas LDL-C is not used, and lipid-lowering therapy is not emphasised.
Description
  • Table purpose: This table shows the acceptable ranges of total cholesterol and the ratio of total cholesterol to HDL cholesterol for different categories used in life insurance underwriting. Life insurance underwriting is the process by which insurance companies assess the risk of insuring an individual, which then determines the premium (the cost of the insurance). In this case, the table is showing how cholesterol levels are used to help determine that risk.
  • Cholesterol measures: The table focuses on two main cholesterol measures: Total Cholesterol (T-C) and the ratio of Total Cholesterol to HDL Cholesterol (T-C/HDL-C). Total cholesterol is a measure of all the cholesterol in your blood, while HDL cholesterol is often called 'good' cholesterol because it helps remove other forms of cholesterol from your bloodstream. The ratio between the two provides an indication of cardiovascular risk. It's like looking at both the total amount of something and the proportion that is considered beneficial.
  • Underwriting categories: The table presents different underwriting categories, such as 'Elite Plus,' 'Preferred Plus,' and 'Standard Plus.' These categories likely represent different levels of risk as assessed by the insurance company, with 'Elite Plus' being the lowest risk and 'Standard' or 'Standard Plus' being higher risk. Each category has its own set of acceptable cholesterol ranges, which are probably stricter for the lower-risk categories.
  • Table structure: The table is organized with rows representing different age groups (e.g., '54 and younger,' '55 to 69') and columns representing the different underwriting categories. Each cell in the table shows the maximum acceptable total cholesterol value and the corresponding T-C/HDL-C ratio for that age group and underwriting category. For example, for individuals aged 54 and younger in the 'Elite Plus' category, the maximum total cholesterol might be 220 mg/dL and the maximum T-C/HDL-C ratio might be 4.5.
  • Implication for LDL-C: The reference text points out that LDL cholesterol, often called 'bad' cholesterol, is not used in these underwriting guidelines, while total cholesterol and the T-C/HDL-C ratio are used together. This suggests that, at least for the insurance company that created these guidelines, overall cholesterol levels and the balance between total and 'good' cholesterol are considered more important indicators of risk than LDL-C alone.
Scientific Validity
  • Relevance to the study: This table provides an interesting perspective on how cholesterol is assessed in a real-world setting outside of clinical practice. It highlights the potential difference between clinical guidelines, which often emphasize LDL-C, and the criteria used by insurance companies, which may focus on other lipid measures. However, it is important to note that these are specific underwriting guidelines from one company and may not be representative of the entire insurance industry. The connection between life insurance underwriting practices and the relationship between LDL-C and mortality (the main focus of the study) may be considered tangential.
  • Validity of underwriting criteria: The specific cholesterol ranges and ratios used in the table are based on the insurance company's internal risk assessment models. The scientific validity of these models is not directly assessed in the study. It is possible that these models are based on actuarial data and internal analyses, but the specific data and methods used are not provided.
  • Emphasis on T-C and HDL-C: The emphasis on total cholesterol and the T-C/HDL-C ratio in the underwriting guidelines is noteworthy, given the current clinical focus on LDL-C. This could reflect a difference in the timeframes considered (long-term mortality risk for insurance vs. shorter-term cardiovascular risk in clinical settings) or a difference in the specific outcomes being assessed.
  • Source of the guidelines: The footnote indicates that the source of the table is a specific life insurance company's underwriting guide. The generalizability of these guidelines to other insurance companies or to clinical practice is unknown. It would be helpful to know if these guidelines are based on industry standards or are specific to this particular company.
Communication
  • Clarity of caption: The caption clearly states the content of the table, including the lipid measures presented (total cholesterol and T-C/HDL-C ratio) and the context (life insurance underwriting categories).
  • Organization and layout: The table is well-organized, with clear headings for each column (underwriting categories) and row (age groups). The use of separate rows for different age groups allows for an assessment of how cholesterol criteria may vary with age. However, the specific layout and formatting details are not provided in the text and would need to be assessed directly from the table itself.
  • Use of abbreviations: The table uses abbreviations (T-C, HDL-C). These abbreviations are clearly defined in the caption.
  • Accessibility to non-experts: The table presents relatively straightforward data, but the concept of life insurance underwriting and the specific categories used may be unfamiliar to some readers. Providing a brief explanation of these concepts in the introduction or discussion section could enhance comprehension for a broader audience.
  • Completeness of information: The footnote provides the source of the underwriting guidelines, which is important for transparency. However, it could be further improved by providing more details about the specific criteria used for each underwriting category, beyond just cholesterol levels. Additionally, explaining why certain age ranges were chosen would add clarity.

Methods

Key Aspects

Strengths

Suggestions for Improvement

Results

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

Figure 1 Plot of cumulative mortality rates in 6-month intervals over 12 years...
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Figure 1 Plot of cumulative mortality rates in 6-month intervals over 12 years of follow-up by baseline low-density lipoprotein cholesterol (LDL-C) category. Dashed lines depict the three lowest LDL-C categories (30–79, 80–99 and 100–129mg/dL) and solid lines depict the highest LDL-C categories (130–159, 160–189 and ≥190 mg/dL).

Figure/Table Image (Page 7)
Figure 1 Plot of cumulative mortality rates in 6-month intervals over 12 years of follow-up by baseline low-density lipoprotein cholesterol (LDL-C) category. Dashed lines depict the three lowest LDL-C categories (30–79, 80–99 and 100–129mg/dL) and solid lines depict the highest LDL-C categories (130–159, 160–189 and ≥190 mg/dL).
First Reference in Text
In ascending order from lowest LDL-C category (30-79 mg/dL) to highest LDL-C category (≥190mg/ dL), 10-year cumulative mortality rates were U-shaped at 19.8%, 14.7%, 11.7%, 10.7%, 10.1% and 14.0% (table 2, figures 1 and 2).
Description
  • Type of plot: This is a line graph, a visual representation where data points are connected by lines to show trends over time.
  • X-axis: The horizontal axis (x-axis) represents time, spanning 12 years and divided into 6-month intervals. This means each tick mark on the axis corresponds to a half-year period, allowing us to see how something changes over the course of a dozen years.
  • Y-axis: The vertical axis (y-axis) represents the cumulative mortality rate. 'Cumulative' means the total percentage of people who have died up to a given point in time. So, as you move up the y-axis, the percentage of deaths increases.
  • Data groups: The graph shows six different lines, each representing a group of people categorized by their baseline LDL-C levels. LDL-C stands for low-density lipoprotein cholesterol, often referred to as 'bad' cholesterol because high levels are associated with an increased risk of heart disease. The categories range from low (30-79 mg/dL) to very high (≥190 mg/dL), with 'mg/dL' meaning milligrams per deciliter, a unit used to measure the concentration of a substance in a liquid (in this case, cholesterol in blood).
  • Line styles: The lines are differentiated by style: dashed lines for the three lowest LDL-C categories and solid lines for the three highest. This visual distinction helps to quickly compare the trends between groups with lower versus higher initial cholesterol levels.
  • Overall trend: The graph illustrates how the percentage of deaths within each LDL-C category changes over the 12-year period. By following each line, we can see whether mortality increases steadily, levels off, or shows other patterns over time.
Scientific Validity
  • Relevance of LDL-C categorization: The categorization of LDL-C is consistent with clinical guidelines, although the specific cut-off points might be debated. The use of standard categories allows for comparison with other studies and clinical practice.
  • Suitability of cumulative mortality rate: Cumulative mortality rate is an appropriate measure for assessing the long-term risk associated with different LDL-C levels. It reflects the overall impact of LDL-C on survival over the study period.
  • Follow-up duration: A 12-year follow-up is substantial for this type of study and allows for the observation of long-term trends. However, it's important to note that even longer follow-up periods might reveal different patterns.
  • Clarity of methodology in figure: While the figure itself does not detail the methodology used to collect or analyze the data, it is implied that standard survival analysis techniques were employed. The reference text confirms this, and the methods section of the paper should provide further details. The specific statistical methods used to generate the cumulative mortality rates are not detailed in the figure or the provided reference text, which could be a limitation for readers trying to replicate or critically evaluate the analysis.
Communication
  • Visual clarity: The use of different line styles (dashed vs. solid) for different LDL-C categories is effective in visually distinguishing between the groups. However, the specific colors used for each line are not described in the provided information and should be clearly indicated in the figure legend for optimal clarity.
  • Labeling: The axes are labeled with appropriate units (years for the x-axis and presumably percentage for the y-axis, although the specific unit is not stated in the caption). The LDL-C categories are clearly labeled in the caption.
  • Caption completeness: The caption provides a concise description of the figure's content, including the meaning of the different line styles and the LDL-C categories. However, it could be improved by explicitly stating the unit for the y-axis (e.g., 'cumulative mortality rate (%)').
  • Interpretation guidance: The caption does not provide any interpretation of the observed trends, leaving that to the results section of the paper. This is appropriate for a figure caption, which should primarily describe what is shown rather than interpret its meaning.
Figure 2 Plot of mortality HRs (filled circles) and 95% Cls (vertical lines)...
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Figure 2 Plot of mortality HRs (filled circles) and 95% Cls (vertical lines) across categories of LDL cholesterol (top), total cholesterol to HDL cholesterol ratio (middle) and triglycerides to HDL cholesterol ratio (bottom). The left side of the graph is for patients aged 50-69 years; the right side is for patients aged 70-89 years. The dashed line reflects the referent group null value (1.0) for the HR. Q: quintile. Each model is adjusted for age, race, sex, BMI, current smoker, former smoker and history of the following in the past year: hypertension, atrial fibrillation, arrhythmia, congestive heart failure, cancer, chronic obstructive pulmonary disease, chronic kidney disease, baseline systolic and diastolic blood pressure, glucose and the following medications in the past year: ACE inhibitors, beta-blockers, calcium blockers, any SBP lowering medication, diuretics, aspirin, DOACs, antidepressants, opioids and statin initiation >1 year after baseline cholesterol measurement. BMI, Body Mass Index; DOAC, direct oral anticoagulant; HDL, high-density lipoprotein; LDL, low-density lipoprotein; SBP, systolic blood pressure.

Figure/Table Image (Page 8)
Figure 2 Plot of mortality HRs (filled circles) and 95% Cls (vertical lines) across categories of LDL cholesterol (top), total cholesterol to HDL cholesterol ratio (middle) and triglycerides to HDL cholesterol ratio (bottom). The left side of the graph is for patients aged 50-69 years; the right side is for patients aged 70-89 years. The dashed line reflects the referent group null value (1.0) for the HR. Q: quintile. Each model is adjusted for age, race, sex, BMI, current smoker, former smoker and history of the following in the past year: hypertension, atrial fibrillation, arrhythmia, congestive heart failure, cancer, chronic obstructive pulmonary disease, chronic kidney disease, baseline systolic and diastolic blood pressure, glucose and the following medications in the past year: ACE inhibitors, beta-blockers, calcium blockers, any SBP lowering medication, diuretics, aspirin, DOACs, antidepressants, opioids and statin initiation >1 year after baseline cholesterol measurement. BMI, Body Mass Index; DOAC, direct oral anticoagulant; HDL, high-density lipoprotein; LDL, low-density lipoprotein; SBP, systolic blood pressure.
First Reference in Text
For the two different age groups, the three LDL-C catego- ries within the range of 100–189mg/dL showed relatively similar and slightly lower mortality risk compared with the referent group of LDL-C 80–99 mg/dL (table 2, figure 2).
Description
  • Overall structure: This figure is a complex graph divided into six sub-graphs arranged in a 2x3 grid. It compares the risk of death across different categories of cholesterol levels and ratios, separated by age group.
  • Data presentation: Each sub-graph uses a dot-and-whisker plot. The 'dot' (filled circle) represents the Hazard Ratio (HR), which is a measure of how often a particular event (in this case, death) happens in one group compared to another group over a certain period. The 'whiskers' (vertical lines) represent the 95% Confidence Interval (CI) for each HR. The 95% CI gives a range of values within which we are 95% confident that the true HR lies.
  • X-axis of sub-graphs: The horizontal axis (x-axis) of each sub-graph represents different categories of cholesterol levels or ratios. For example, in the top sub-graphs, it shows categories of LDL cholesterol (often called 'bad' cholesterol). The middle sub-graphs show categories of the ratio of total cholesterol to HDL cholesterol ('good' cholesterol), and the bottom sub-graphs show categories of the ratio of triglycerides (another type of fat in the blood) to HDL cholesterol.
  • Y-axis of sub-graphs: The vertical axis (y-axis) represents the Hazard Ratio (HR). A dashed horizontal line is drawn at HR = 1.0. This line represents the 'null value,' meaning no difference in risk between the groups being compared. If a dot is above this line, it suggests a higher risk of death in that category compared to the reference group. If a dot is below the line, it suggests a lower risk.
  • Age groups: The left side of the entire graph shows data for patients aged 50-69 years, while the right side shows data for patients aged 70-89 years. This allows for a comparison of how cholesterol levels and ratios affect mortality risk in different age groups.
  • Statistical adjustments: The caption states that each 'model' (each sub-graph) is 'adjusted' for a long list of factors, including age, race, sex, Body Mass Index (BMI), smoking status, various health conditions, and medication use. This means that the researchers have used statistical methods to account for the potential influence of these factors on the relationship between cholesterol and mortality. This is important because these factors could independently affect the risk of death.
  • Quintiles: The abbreviation 'Q' in the caption likely refers to 'quintile.' This means that for the ratios (total cholesterol to HDL and triglycerides to HDL), the data has been divided into five equal groups based on the distribution of these ratios in the study population.
Scientific Validity
  • Appropriateness of Hazard Ratios: Hazard ratios are an appropriate measure for comparing mortality risk between groups in a cohort study. They provide a relative measure of risk, which is useful for understanding the impact of different cholesterol levels.
  • Validity of statistical adjustments: The extensive list of covariates adjusted for in the analysis is commendable. This helps to isolate the specific effect of cholesterol on mortality, reducing the risk of confounding. However, the specific methods used for adjustment are not described in the caption and should be detailed in the methods section.
  • Choice of reference group: The choice of the 80-99 mg/dL LDL-C category as the reference group is justified in the paper as a group with putatively lower risk than the lowest category, but may still be debated. The rationale for this choice should be clearly explained in the methods section.
  • Generalizability: The generalizability of the findings may be limited by the specific characteristics of the study population (e.g., patients from a particular healthcare system). The demographics and clinical characteristics of the cohort should be thoroughly described to allow readers to assess the applicability of the results to other populations.
Communication
  • Complexity: The figure is highly complex, presenting a large amount of information in a condensed format. This may make it challenging for readers to fully grasp the findings without careful study.
  • Clarity of labeling: The labeling of the axes and categories within each sub-graph is not described in the provided caption, but should be clear and consistent across all sub-graphs. The use of abbreviations (BMI, DOAC, HDL, LDL, SBP) is acceptable given that they are defined in the caption.
  • Use of visual cues: The use of filled circles for HRs and vertical lines for 95% CIs is standard and effective. The dashed line at HR = 1.0 is a helpful visual aid for interpreting the results.
  • Caption completeness: The caption provides a detailed description of the figure's content, including the variables plotted, the age groups represented, and the statistical adjustments made. However, it could be improved by explicitly stating what is plotted on the x-axis of each subgraph (e.g., categories of LDL cholesterol).
  • Interpretation guidance: The caption does not provide any interpretation of the observed trends, leaving that to the results section. This is appropriate for a figure caption.
Table 1 Baseline characteristics of study population by baseline LDL...
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Table 1 Baseline characteristics of study population by baseline LDL cholesterol value

Figure/Table Image (Page 4)
Table 1 Baseline characteristics of study population by baseline LDL cholesterol value
First Reference in Text
The median age of patients was 59 years and mean age ranged nominally across the six LDL-C categories from 60.7 to 61.7 years. There was a general indication of overall higher baseline risk in the group of patients with LDL-C from 30 to 79mg/dL (table 1) (consistent with the stated concern of potential reverse causation).
Description
  • Table purpose: This table presents the characteristics of the study participants at the beginning of the study, categorized by their baseline LDL cholesterol values. Think of it like a detailed description of the different groups of people being studied, before any interventions or treatments are applied. This is important to understand the composition of each group and whether they differ in ways other than their cholesterol levels.
  • Table structure: The table is organized with rows representing different characteristics of the participants (like age, sex, race, medical history, etc.), and columns representing different categories of LDL cholesterol levels. LDL cholesterol, or low-density lipoprotein cholesterol, is often referred to as 'bad' cholesterol. These categories likely range from low to high, allowing for a comparison of characteristics across different levels of LDL cholesterol.
  • Data presented: For each characteristic, the table shows either the median and interquartile range (IQR) for continuous variables (like age or blood pressure) or counts and percentages for categorical variables (like sex or history of a certain disease). The median is the middle value when all values are arranged in order, and the IQR gives an idea of the spread of the data around the median. Counts and percentages show how many participants in each LDL-C category have a particular characteristic.
  • Baseline characteristics: The characteristics listed in the table are called 'baseline characteristics' because they describe the participants at the start of the study. These include demographic information (age, sex, race), medical history (e.g., history of obesity, hypertension, diabetes), and lifestyle factors (e.g., smoking status). They provide a snapshot of the health status and other relevant factors of the participants before any changes that might occur during the study.
  • Importance of baseline data: Comparing baseline characteristics across different LDL-C categories is crucial for several reasons. First, it helps to identify any potential confounding factors, which are factors other than LDL-C that might influence the outcome of the study. For example, if one LDL-C group has a much higher percentage of smokers, this could affect their risk of death independently of their cholesterol levels. Second, it allows researchers to assess whether the groups are comparable at the start of the study, which is important for drawing valid conclusions about the effects of LDL-C.
Scientific Validity
  • Comprehensiveness of characteristics: The table appears to include a comprehensive list of relevant demographic, clinical, and lifestyle characteristics. This allows for a thorough assessment of potential confounders and baseline differences between the LDL-C groups.
  • Appropriateness of statistical measures: The use of median and IQR for continuous variables and counts/percentages for categorical variables is appropriate for describing the distribution of baseline characteristics.
  • Potential for residual confounding: While the table includes many important characteristics, there is always a possibility of residual confounding due to unmeasured or unknown factors. The authors should acknowledge this limitation in the discussion section.
  • Relevance to reverse causation: The reference text highlights a higher baseline risk in the lowest LDL-C group, which could be indicative of reverse causation (i.e., underlying illness leading to low LDL-C rather than low LDL-C causing illness). The table provides the necessary data to further investigate this possibility by examining the prevalence of specific illnesses in this group.
Communication
  • Clarity of caption: The caption clearly states the purpose and content of the table.
  • Organization and layout: The table is well-organized, with clear headings for each column (LDL-C categories) and row (characteristic). The use of bold font for subheadings within the table enhances readability. However, the specific layout and formatting details are not provided in the text and would need to be assessed directly from the table itself.
  • Use of abbreviations: The table likely uses abbreviations for some characteristics (e.g., BMI, HDL, LDL). These abbreviations should be clearly defined in a footnote or legend accompanying the table.
  • Accessibility to non-experts: While the table presents complex data, the clear labeling and organization make it relatively accessible to readers with some background knowledge. However, a brief explanation of the importance of baseline characteristics and the concept of confounding in the results or discussion section could further enhance understanding for a broader audience.
Table 2 Risks and HRs for death by LDL cholesterol level at baseline
Figure/Table Image (Page 6)