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

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

• 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.

• 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.

• 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 T-C to HDL-C Cholesterol Ratios for Different Life Insurance Underwriting Categories

• 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.

• 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.

• 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.

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).

• 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.

• 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.

• 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) 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.

• 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.

• 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.

• 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 cholesterol value

• 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.

• 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.

• 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 purpose: This table shows the risk of death associated with different levels of LDL cholesterol, often called 'bad' cholesterol, measured at the beginning of the study. It's like comparing the chances of dying within a certain period for people grouped by their initial LDL cholesterol levels.
• Table structure: The table likely has columns representing different categories of LDL cholesterol levels, ranging from low to high. The rows likely present information about the risk of death within each category, including the number of people in each category, the number of deaths, and statistical measures like cumulative incidence, hazard ratios, and confidence intervals.
• Cumulative incidence: Cumulative incidence is the percentage of people within each LDL-C category who died during the study's follow-up period. For example, a 10-year cumulative incidence of 20% in a particular category means that 20 out of every 100 people in that group died within 10 years.
• Hazard Ratio (HR): The Hazard Ratio (HR) compares the risk of death in one LDL-C category to the risk in a reference category. An HR of 1.0 means there's no difference in risk. An HR greater than 1.0 means the risk is higher in that category compared to the reference, while an HR less than 1.0 means the risk is lower. For example, an HR of 1.2 would indicate a 20% higher risk of death compared to the reference group. It's like saying, 'people in this group are 20% more likely to die during the study period than people in the reference group'.
• Confidence Interval (CI): The Confidence Interval (CI), usually presented as a 95% CI, gives a range of values within which we are fairly certain the true HR lies. It's like saying, 'we're 95% confident that the true HR is between these two numbers.' A narrower CI indicates more precise estimate of the HR.
• Baseline LDL cholesterol: The term 'baseline' refers to the measurements taken at the start of the study. So, 'baseline LDL cholesterol level' means the amount of LDL cholesterol in a person's blood at the beginning of the study, before any interventions or changes that might occur later.

• Appropriateness of outcome measure: Mortality is a relevant and objective outcome measure for assessing the impact of LDL cholesterol.
• Validity of HRs: Hazard ratios are a standard statistical tool for comparing risks between groups in survival analysis. Their validity depends on the correct application of the underlying statistical model (e.g., Cox proportional hazards regression).
• Consideration of confounding factors: The reference text does not specify whether the HRs presented in Table 2 are adjusted for potential confounding factors. Adjustment for factors such as age, sex, comorbidities, and medication use is crucial for isolating the specific effect of LDL-C on mortality. The methods section should clearly state which, if any, covariates were included in the statistical models.
• Choice of LDL-C categories: The rationale for the specific LDL-C categories used in the table should be provided. While the categories used in the study are stated elsewhere, the reasoning for these specific cut-points should be justified based on clinical relevance or previous research.
• Potential for reverse causation: As mentioned in relation to Table 1, the possibility of reverse causation (where underlying illness leads to low LDL-C rather than vice versa) should be considered when interpreting the results, particularly for the lowest LDL-C category.

• Clarity of caption: The caption clearly states the content and purpose of the table.
• Organization and layout: The table is presumably well-organized, with clear headings for each column and row. 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 (e.g., HR, CI, LDL-C). These abbreviations should be clearly defined in a footnote or legend accompanying the table.
• Accessibility to non-experts: The table presents complex statistical data that may be challenging for non-experts to fully understand. Providing a clear explanation of HRs and CIs in the results or discussion section could enhance comprehension for a broader audience.
• Completeness of information: The table should include the number of participants and the number of deaths in each LDL-C category to provide context for the risk estimates.

Table 3 Risks and HRs for ASCVD and ASCVD/mortality by LDL cholesterol levels at baseline

• Table purpose: This table expands on the previous table by examining not only the risk of death but also the risk of ASCVD events and a combined outcome of ASCVD and death, all grouped by different levels of LDL cholesterol ('bad' cholesterol) measured at the start of the study. ASCVD stands for atherosclerotic cardiovascular disease, which refers to conditions like heart attacks and strokes caused by the buildup of plaque in the arteries. Essentially, it's comparing the chances of experiencing these events or dying within a certain period for people grouped by their initial LDL cholesterol levels.
• Table structure: Similar to Table 2, this table likely has columns representing different categories of LDL cholesterol levels, ranging from low to high. The rows likely present information about the risk of ASCVD, the risk of death, and the combined risk of ASCVD or death within each category. This probably includes the number of people in each category, the number of events (ASCVD or death), and statistical measures like cumulative incidence, hazard ratios, and confidence intervals.
• Two outcome types: The table presents data on two main types of outcomes: ASCVD alone and a composite outcome of ASCVD and mortality. This means that the researchers looked at the occurrence of heart attacks, strokes, and other related events separately, as well as combined with deaths from any cause. This allows for a more comprehensive assessment of the impact of LDL cholesterol on cardiovascular health.
• Hazard Ratio (HR) for ASCVD: Similar to Table 2, this table likely presents Hazard Ratios (HRs) to compare the risk of ASCVD in different LDL-C categories to the risk in a reference category. An HR greater than 1.0 indicates a higher risk of ASCVD in that category compared to the reference group, while an HR less than 1.0 indicates a lower risk. The reference text specifies that the reference group here is the LDL-C category of 80-99 mg/dL.
• Hazard Ratio (HR) for ASCVD/mortality: The table also presents HRs for the composite outcome of ASCVD and mortality. This means that it compares the risk of either experiencing an ASCVD event or dying from any cause in different LDL-C categories to the risk in the reference category. This provides a broader picture of the overall impact of LDL cholesterol on health outcomes.
• Baseline LDL cholesterol: As in the previous tables, 'baseline' refers to the measurements taken at the start of the study. So, 'baseline LDL cholesterol level' means the amount of LDL cholesterol in a person's blood at the beginning of the study.

• Relevance of ASCVD as an outcome: ASCVD is a clinically relevant outcome that complements mortality in assessing the impact of LDL cholesterol on cardiovascular health. It provides a more comprehensive picture of the disease process.
• Validity of composite outcome: The use of a composite outcome (ASCVD/mortality) can be useful for increasing statistical power and capturing the overall impact of LDL-C on health. However, it is important to ensure that the components of the composite outcome are clinically meaningful and have similar importance.
• Adjustment for confounding factors: The reference text indicates that the HRs presented in Table 3 are adjusted for potential confounding factors. This is crucial for isolating the specific effect of LDL-C on ASCVD and mortality. The methods section should clearly state which covariates were included in the statistical models.
• Consistency of findings with Table 2: The reference text highlights the similarity of the findings for ASCVD and the composite outcome, suggesting a consistent effect of LDL-C across different endpoints. This strengthens the overall conclusions of the study.
• Choice of reference group: The use of the 80-99 mg/dL LDL-C category as the reference group is consistent with the approach taken in Table 2 and should be justified based on clinical or scientific rationale.

• Clarity of caption: The caption clearly states the content and purpose of the table, specifying the outcomes assessed (ASCVD and ASCVD/mortality) and the variable used for grouping (LDL cholesterol levels at baseline).
• Organization and layout: The table is presumably well-organized, with clear headings for each column and row. The specific layout and formatting details are not provided in the text but should be designed for optimal readability. Separating the results for ASCVD and ASCVD/mortality into distinct sections (as suggested by the reference text) enhances clarity.
• Use of abbreviations: The table likely uses abbreviations (e.g., HR, CI, LDL-C, ASCVD). These abbreviations should be clearly defined in a footnote or legend accompanying the table.
• Accessibility to non-experts: The table presents complex statistical data that may be challenging for non-experts to fully understand. Providing a clear explanation of HRs, CIs, and the concept of composite outcomes in the results or discussion section could enhance comprehension for a broader audience.
• Completeness of information: The table should include the number of participants and the number of events (ASCVD or death) in each LDL-C category to provide context for the risk estimates. The specific statistical methods used to calculate the HRs and CIs should be described in the methods section.

Supplement Table 3. Risks and Hazard Ratios of Death by LDL Cholesterol Levels at Baseline Stratified by Sex at Baseline

• Table purpose: This table presents the risk of death associated with different levels of LDL cholesterol, separately for men and women. LDL cholesterol is often called 'bad' cholesterol. By stratifying the analysis by sex, the researchers are examining whether the relationship between LDL cholesterol and the risk of death differs between males and females. It's like comparing the chances of dying within a certain period for men and women with different levels of LDL cholesterol measured at the beginning of the study.
• Table structure: The table likely has columns representing different categories of LDL cholesterol levels, ranging from low to high. The rows likely present information about the risk of death within each category, separately for men and women. This probably includes the number of individuals in each category, the number of deaths, and statistical measures like cumulative incidence, hazard ratios, and confidence intervals.
• Hazard Ratio (HR): The table likely presents Hazard Ratios (HRs), which compare the risk of death in one LDL-C category to the risk in a reference category, separately for men and women. An HR of 1.0 means there's no difference in risk. An HR greater than 1.0 means the risk is higher in that category compared to the reference, while an HR less than 1.0 means the risk is lower. The reference text indicates that the reference group is the LDL-C category of 80-99 mg/dL.
• Confidence Interval (CI): The table likely also presents Confidence Intervals (CIs), usually 95% CIs, for each HR. The CI gives a range of values within which we are fairly certain the true HR lies. A narrower CI indicates a more precise estimate of the HR. If the CI includes 1.0, it suggests that there may not be a statistically significant difference in the risk of death between that category and the reference category.
• Baseline LDL cholesterol: The term 'baseline' refers to the measurements taken at the start of the study. So, 'baseline LDL cholesterol level' means the amount of LDL cholesterol in a person's blood at the beginning of the study.
• Stratified by sex: 'Stratified by sex' means that the data is divided into separate groups for males and females. This allows the researchers to see if the relationship between LDL cholesterol and mortality risk is different for men and women. It's like looking at two separate tables, one for males and one for females, to see if the patterns are the same or different.

• Importance of sex-stratified analysis: Stratifying by sex is important because there can be physiological differences between men and women that affect the relationship between LDL-C and mortality. This allows for a more nuanced understanding of the role of LDL-C in different populations.
• Appropriateness of statistical methods: The use of hazard ratios and confidence intervals is appropriate for assessing the risk of death associated with different LDL-C levels. The validity of these measures depends on the correct application of the underlying statistical models (e.g., Cox proportional hazards regression).
• Adjustment for confounding factors: The reference text mentions an 'adjusted HR,' which implies that the analysis has accounted for potential confounding factors. This is crucial for isolating the specific effect of LDL-C on mortality. The methods section should clearly state which covariates were included in the statistical models.
• Choice of reference group: The use of the 80-99 mg/dL LDL-C category as the reference group is consistent with other analyses in the study. However, the rationale for this choice should be clearly explained based on clinical or scientific grounds.
• Statistical significance: The reference text highlights the lack of a statistically significant association between LDL-C ≥190 mg/dL and mortality in males, as indicated by the 95% CI including 1.0. This suggests that in this particular study, very high LDL-C levels in men were not associated with a significantly increased risk of death compared to the reference group. However, the wide confidence interval (0.85 to 1.32) indicates considerable uncertainty in the estimate, possibly due to a smaller sample size in this subgroup.

• Clarity of caption: The caption clearly states the content and purpose of the table, including the outcome being assessed (risks and HRs of death), the variable used for grouping (LDL cholesterol levels), and the stratification factor (sex).
• Organization and layout: The table is presumably well-organized, with clear headings for each column and row. The specific layout and formatting details are not provided in the text, but should be designed for optimal readability. Separating the results for males and females into distinct sections (either separate tables or clearly demarcated sections within a single table) is essential for clarity.
• Use of abbreviations: The table likely uses abbreviations (e.g., HR, CI, LDL-C). These abbreviations should be clearly defined in a footnote or legend accompanying the table.
• Accessibility to non-experts: The table presents complex statistical data that may be challenging for non-experts to fully understand. Providing a clear explanation of HRs, CIs, and the concept of stratification in the results or discussion section could enhance comprehension for a broader audience.
• Completeness of information: The table should include the number of participants and the number of deaths in each LDL-C category for both men and women to provide context for the risk estimates. The specific statistical methods used to calculate the HRs and CIs should be described in the methods section. The adjusted covariates should be listed either in the table footnote or the methods section.

Supplement Table 4. Risks and Hazard Ratios of Death by LDL Cholesterol Levels at Baseline Stratified by ASCVD Risk Classification

• Table purpose: This table examines the risk of death associated with different levels of LDL cholesterol ('bad' cholesterol), but this time it divides the participants into groups based on their estimated 10-year risk of ASCVD. ASCVD stands for atherosclerotic cardiovascular disease, which includes conditions like heart attacks and strokes. By doing this, the researchers are trying to see if the relationship between LDL cholesterol and the risk of death is different for people who are already at high risk of ASCVD compared to those at lower risk. It's like comparing the chances of dying within a certain period for people with different LDL cholesterol levels, but also taking into account their overall risk of heart disease.
• Table structure: The table likely has columns representing different categories of LDL cholesterol levels, ranging from low to high. The rows likely present information about the risk of death within each LDL-C category, separately for different levels of ASCVD risk (e.g., low, intermediate, high). This probably includes the number of individuals in each category, the number of deaths, and statistical measures like cumulative incidence, hazard ratios, and confidence intervals.
• Hazard Ratio (HR): The table likely presents Hazard Ratios (HRs), which compare the risk of death in one LDL-C category to the risk in a reference category, separately for different ASCVD risk groups. An HR of 1.0 means there's no difference in risk. An HR greater than 1.0 means the risk is higher in that category compared to the reference, while an HR less than 1.0 means the risk is lower. The reference text indicates that the reference group is the LDL-C category of 80-99 mg/dL.
• Confidence Interval (CI): The table likely also presents Confidence Intervals (CIs), usually 95% CIs, for each HR. The CI gives a range of values within which we are fairly certain the true HR lies. A narrower CI indicates a more precise estimate of the HR. If the CI includes 1.0, it suggests that there may not be a statistically significant difference in the risk of death between that category and the reference category.
• Baseline LDL cholesterol: The term 'baseline' refers to the measurements taken at the start of the study. So, 'baseline LDL cholesterol level' means the amount of LDL cholesterol in a person's blood at the beginning of the study.
• Stratified by ASCVD risk: 'Stratified by ASCVD risk classification' means that the data is divided into separate groups based on the participants' estimated 10-year risk of developing ASCVD. This is usually calculated using a risk assessment tool that takes into account factors like age, sex, blood pressure, and cholesterol levels. By doing this, the researchers can examine whether the relationship between LDL-C and mortality differs across different levels of overall cardiovascular risk.

• Importance of risk stratification: Stratifying by ASCVD risk is important because it allows for a more nuanced understanding of the relationship between LDL-C and mortality. It helps to determine whether the effect of LDL-C varies across different levels of baseline cardiovascular risk.
• Validity of ASCVD risk assessment: The validity of the risk stratification depends on the accuracy and appropriateness of the ASCVD risk assessment tool used. The specific tool used should be clearly stated and justified in the methods section.
• Appropriateness of statistical methods: The use of hazard ratios and confidence intervals is appropriate for assessing the risk of death associated with different LDL-C levels within each risk stratum. The validity of these measures depends on the correct application of the underlying statistical models.
• Adjustment for confounding factors: While stratification by ASCVD risk implicitly adjusts for some confounding, further adjustment for other potential confounders within each risk stratum may be necessary. The methods section should clearly describe the adjustment process.
• Consistency with other findings: The reference text highlights the consistency of the findings with other analyses in the study, suggesting that the lower mortality risk associated with LDL-C levels between 100-189 mg/dL is observed across different levels of ASCVD risk. This strengthens the overall conclusions.

• Clarity of caption: The caption clearly states the content and purpose of the table, including the outcome being assessed (risks and HRs of death), the variable used for grouping (LDL cholesterol levels), and the stratification factor (ASCVD risk classification).
• Organization and layout: The table is presumably well-organized, with clear headings for each column and row. The specific layout and formatting details are not provided in the text, but should be designed for optimal readability. Separating the results for different ASCVD risk groups into distinct sections is essential for clarity.
• Use of abbreviations: The table likely uses abbreviations (e.g., HR, CI, LDL-C, ASCVD). These abbreviations should be clearly defined in a footnote or legend accompanying the table.
• Accessibility to non-experts: The table presents complex statistical data that may be challenging for non-experts to fully understand. Providing a clear explanation of HRs, CIs, and the concept of risk stratification in the results or discussion section could enhance comprehension for a broader audience.
• Completeness of information: The table should include the number of participants and the number of deaths in each LDL-C category for each ASCVD risk group to provide context for the risk estimates. The specific statistical methods used to calculate the HRs and CIs, as well as the method used to determine ASCVD risk, should be described in the methods section. The specific criteria used to define the ASCVD risk categories should also be provided.

Supplement Table 5. Risks and Hazard Ratios of Death by Total Cholesterol to HDL Cholesterol Ratio at Baseline

• Table purpose: This table examines the relationship between the ratio of total cholesterol to HDL cholesterol and the risk of death. Total cholesterol represents 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 (T-C/HDL-C) provides an indication of cardiovascular risk. This table compares the chances of dying within a certain period for people grouped by their T-C/HDL-C ratio measured at the beginning of the study.
• Table structure: The table likely has columns representing different categories of T-C/HDL-C ratios, such as ≤3.0, >3.0 to 4.0, >4.0 to 5.0, >5.0 to 6.0, and >6.0. The rows likely present information about the risk of death within each category, including the number of individuals in each category, the number of deaths, and statistical measures like cumulative incidence, hazard ratios, and confidence intervals.
• Hazard Ratio (HR): The table likely presents Hazard Ratios (HRs), which compare the risk of death in one T-C/HDL-C category to the risk in a reference category. An HR of 1.0 means there's no difference in risk. An HR greater than 1.0 means the risk is higher in that category compared to the reference, while an HR less than 1.0 means the risk is lower. The reference text suggests that a lower T-C/HDL-C ratio (≤3.0) is associated with a lower risk of death.
• Confidence Interval (CI): The table likely also presents Confidence Intervals (CIs), usually 95% CIs, for each HR. The CI gives a range of values within which we are fairly certain the true HR lies. A narrower CI indicates a more precise estimate of the HR. If the CI includes 1.0, it suggests that there may not be a statistically significant difference in the risk of death between that category and the reference category.
• Baseline measurement: The term 'baseline' refers to the measurements taken at the start of the study. So, 'Total Cholesterol to HDL Cholesterol Ratio at Baseline' means the T-C/HDL-C ratio calculated from measurements taken at the beginning of the study.
• Adjusted HR: The reference text mentions an 'adjusted HR,' which means that the researchers have used statistical methods to account for other factors that could influence the risk of death, such as age, sex, and other health conditions. This helps to isolate the specific effect of the T-C/HDL-C ratio on mortality.

• Relevance of T-C/HDL-C ratio: The T-C/HDL-C ratio is a relevant measure of lipid profile that has been associated with cardiovascular risk in previous studies. It provides a combined assessment of total cholesterol and 'good' cholesterol levels.
• Appropriateness of statistical methods: The use of hazard ratios and confidence intervals is appropriate for assessing the risk of death associated with different T-C/HDL-C ratio categories. The validity of these measures depends on the correct application of the underlying statistical models (e.g., Cox proportional hazards regression).
• Adjustment for confounding factors: The reference text indicates that the HRs are adjusted for potential confounding factors. This is crucial for isolating the specific effect of the T-C/HDL-C ratio on mortality. The methods section should clearly state which covariates were included in the statistical models.
• Choice of T-C/HDL-C categories: The rationale for the specific T-C/HDL-C categories used in the table should be provided. The categories used appear to be clinically relevant, with the reference text highlighting the difference in risk between ratios ≤3.0 and >6.0.
• Consistency with other findings: The reference text mentions that the findings are consistent with those observed when stratifying by age (Figure 2), suggesting a robust association between the T-C/HDL-C ratio and mortality across different subgroups.

• Clarity of caption: The caption clearly states the content and purpose of the table, including the outcome being assessed (risks and HRs of death) and the variable used for grouping (T-C/HDL-C ratio at baseline).
• Organization and layout: The table is presumably well-organized, with clear headings for each column and row. The specific layout and formatting details are not provided in the text, but should be designed for optimal readability. The categories used for the T-C/HDL-C ratio should be clearly defined.
• Use of abbreviations: The table likely uses abbreviations (e.g., HR, CI, T-C, HDL-C). These abbreviations should be clearly defined in a footnote or legend accompanying the table.
• Accessibility to non-experts: The table presents complex statistical data that may be challenging for non-experts to fully understand. Providing a clear explanation of HRs, CIs, and the concept of T-C/HDL-C ratio in the results or discussion section could enhance comprehension for a broader audience.
• Completeness of information: The table should include the number of participants and the number of deaths in each T-C/HDL-C category to provide context for the risk estimates. The specific statistical methods used to calculate the HRs and CIs should be described in the methods section. The adjusted covariates should be listed either in the table footnote or the methods section.

Supplement Table 6. Risks and Hazard Ratios of Death by Triglycerides to HDL-C Ratio at Baseline

• Table purpose: This table examines the relationship between the ratio of triglycerides to HDL cholesterol and the risk of death. Triglycerides are a type of fat found 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 (triglycerides/HDL-C) provides an indication of cardiovascular risk. This table compares the chances of dying within a certain period for people grouped by their triglycerides/HDL-C ratio measured at the beginning of the study.
• Table structure: The table likely has columns representing different categories of triglycerides/HDL-C ratios, which are divided into quintiles. Quintiles are a way of dividing a group of data into five equal parts. Imagine you have 100 people; dividing them into quintiles would create five groups of 20 people each, based on their triglycerides/HDL-C ratio. The rows likely present information about the risk of death within each quintile, including the number of individuals in each category, the number of deaths, and statistical measures like cumulative incidence, hazard ratios, and confidence intervals.
• Quintiles used: The reference text mentions that the highest quintile represents a triglycerides/HDL-C ratio of 3.44 or higher, while the lowest quintile represents a ratio of 1.06 or lower. This means that the researchers have divided the participants into five groups based on their triglycerides/HDL-C ratio, with the lowest 20% having a ratio of 1.06 or less, and the highest 20% having a ratio of 3.44 or more.
• Hazard Ratio (HR): The table likely presents Hazard Ratios (HRs), which compare the risk of death in one triglycerides/HDL-C category to the risk in a reference category. An HR of 1.0 means there's no difference in risk. An HR greater than 1.0 means the risk is higher in that category compared to the reference, while an HR less than 1.0 means the risk is lower. The reference text indicates that patients in the lowest quintile had a 24% lower risk of mortality compared to those in the highest quintile (adjusted HR = 0.76).
• Confidence Interval (CI): The table likely also presents Confidence Intervals (CIs), usually 95% CIs, for each HR. The CI gives a range of values within which we are fairly certain the true HR lies. A narrower CI indicates a more precise estimate of the HR. If the CI includes 1.0, it suggests that there may not be a statistically significant difference in the risk of death between that category and the reference category.
• Baseline measurement: The term 'baseline' refers to the measurements taken at the start of the study. So, 'Triglycerides to HDL-C Ratio at Baseline' means the triglycerides/HDL-C ratio calculated from measurements taken at the beginning of the study.
• Adjusted HR: The reference text mentions an 'adjusted HR,' which means that the researchers have used statistical methods to account for other factors that could influence the risk of death, such as age, sex, and other health conditions. This helps to isolate the specific effect of the triglycerides/HDL-C ratio on mortality.

• Relevance of triglycerides/HDL-C ratio: The triglycerides/HDL-C ratio is a relevant measure of lipid profile that has been associated with cardiovascular risk and mortality in previous studies. It provides a combined assessment of triglyceride and 'good' cholesterol levels.
• Appropriateness of statistical methods: The use of hazard ratios and confidence intervals is appropriate for assessing the risk of death associated with different triglycerides/HDL-C ratio categories. The validity of these measures depends on the correct application of the underlying statistical models (e.g., Cox proportional hazards regression).
• Adjustment for confounding factors: The reference text indicates that the HRs are adjusted for potential confounding factors. This is crucial for isolating the specific effect of the triglycerides/HDL-C ratio on mortality. The methods section should clearly state which covariates were included in the statistical models.
• Choice of quintiles: Dividing the data into quintiles is a common way to categorize a continuous variable. However, the specific cut-off points for each quintile can influence the results. The rationale for using quintiles and the specific cut-off values (e.g., ≤1.06 and ≥3.44) should be justified.
• Comparison of extreme quintiles: Comparing the lowest and highest quintiles can highlight the potential impact of very low or very high triglycerides/HDL-C ratios. However, it is also important to examine the risk associated with the intermediate quintiles to get a more complete picture of the relationship.

• Clarity of caption: The caption clearly states the content and purpose of the table, including the outcome being assessed (risks and HRs of death) and the variable used for grouping (triglycerides/HDL-C ratio at baseline).
• Organization and layout: The table is presumably well-organized, with clear headings for each column and row. The specific layout and formatting details are not provided in the text, but should be designed for optimal readability. The quintiles used for the triglycerides/HDL-C ratio should be clearly defined, either in the table itself or in an accompanying footnote.
• Use of abbreviations: The table likely uses abbreviations (e.g., HR, CI, HDL-C). These abbreviations should be clearly defined in a footnote or legend accompanying the table.
• Accessibility to non-experts: The table presents complex statistical data that may be challenging for non-experts to fully understand. Providing a clear explanation of HRs, CIs, and the concept of triglycerides/HDL-C ratio in the results or discussion section could enhance comprehension for a broader audience.
• Completeness of information: The table should include the number of participants and the number of deaths in each triglycerides/HDL-C category to provide context for the risk estimates. The specific statistical methods used to calculate the HRs and CIs should be described in the methods section. The adjusted covariates should be listed either in the table footnote or the methods section. The specific values defining each quintile should also be clearly stated.