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.
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.
The study includes a large sample of 177,860 patients with a mean follow-up of 6.1 years, providing robust data for analysis.
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.
The study utilizes electronic medical record data, reflecting a real-world clinical setting and enhancing the generalizability of the findings.
This medium-impact improvement would enhance the study's clinical relevance and impact on patient care guidelines. The Abstract section is the appropriate place for this clarification as it provides the first impression of the study's scope and limitations.
Implementation: Add a sentence explicitly stating that the study was unable to assess cause-specific mortality due to data limitations, and briefly mention the implications of this limitation. For example: "Due to the nature of the data, cause-specific mortality could not be assessed, limiting our ability to determine the impact of LDL-C on specific causes of death."
This high-impact improvement would strengthen the abstract's impact by directly addressing the clinical implications of the findings. The Abstract is the ideal location for this because it is often the only part of the paper that many readers will see, making it crucial for conveying the study's practical significance.
Implementation: Expand the conclusion to include a brief discussion on how the findings might influence clinical practice, particularly regarding patient counseling and treatment decisions. For example: "These findings suggest that current guidelines recommending aggressive LDL-C lowering in primary prevention may need reevaluation. Clinicians should consider a more holistic approach, focusing on overall cardiovascular risk factors rather than solely on LDL-C levels."
This low-impact improvement would enhance the abstract's transparency and allow for a more nuanced understanding of the presented results. While the full methods are detailed in the paper, briefly mentioning the key covariates adjusted for in the analysis would improve the abstract's stand-alone interpretability.
Implementation: Add a phrase to the results section indicating the key factors adjusted for in the analysis. For example: "Adjusted mortality HRs, controlling for age, sex, race, and other cardiovascular risk factors, as compared with the referent group..."
The Introduction effectively establishes the research problem by highlighting the conflict between prevailing beliefs about LDL-C and emerging evidence.
The section provides a thorough overview of relevant background information, including clinical guidelines, meta-analysis findings, and insights from life insurance medicine.
The Introduction clearly articulates the research gap by pointing out the need for a critical analysis of the LDL-C-mortality relationship in a real-world setting.
This medium-impact improvement would enhance the Introduction's justification for focusing on primary prevention. While the rationale is touched upon, explicitly connecting it to the broader debate surrounding LDL-C targets and statin use in primary prevention would further strengthen the study's relevance and potential impact.
Implementation: Add a paragraph that elaborates on the specific controversies and uncertainties surrounding LDL-C lowering in primary prevention, emphasizing the need for more evidence in this specific population. For example: "The debate surrounding optimal LDL-C targets is particularly relevant in primary prevention, where the benefits of aggressive lowering are less clear-cut compared to secondary prevention. Current guidelines vary, and the decision to initiate statin therapy often involves complex risk-benefit considerations. This study aims to contribute to this ongoing debate by providing real-world evidence on the association between LDL-C and mortality in a large cohort of primary prevention individuals."
This low-impact improvement would provide a more nuanced understanding of the existing literature. While the Introduction mentions conflicting evidence, briefly discussing the limitations of previous studies (e.g., sample size, study design, population characteristics) would further justify the need for the current study and highlight its unique contribution.
Implementation: Include a brief discussion of the limitations of the cited meta-analyses and observational studies. For example: "While these meta-analyses provide valuable insights, they are often limited by the heterogeneity of included studies, varying definitions of LDL-C categories, and potential residual confounding. Furthermore, many observational studies may not fully account for factors such as socioeconomic status and access to healthcare, which can influence both LDL-C levels and mortality risk. The present study aims to address some of these limitations by analyzing a large, well-defined cohort within a single healthcare system."
This medium-impact improvement would further highlight the study's contribution to the field. While the Introduction implies the study's novelty, explicitly stating what makes this study unique compared to previous research would further strengthen its impact and rationale.
Implementation: Add a sentence or two that explicitly state the novel aspects of the study. For example: "Unlike previous studies that have primarily relied on data from clinical trials or diverse populations, this study leverages a large, real-world dataset from a single healthcare system, allowing for a more detailed examination of the LDL-C-mortality relationship in a primary prevention setting. Additionally, the study's long-term follow-up and comprehensive data collection enable a robust assessment of potential confounders and effect modifiers."
Supplement Table 1. ASCVD 10-Year Risk Calculations for Primary Prevention* by Age, Race, and Sex
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
The study leverages a robust data infrastructure, integrating multiple clinical and administrative data sources within the UPMC system, enhancing data completeness and validity.
The study defines clear and specific eligibility criteria, ensuring a well-defined study population and reducing potential confounding.
The use of Kaplan-Meier and Cox regression analyses is appropriate for time-to-event data, and the use of non-parametric models provides flexibility in exploring the relationship between lipid parameters and mortality.
The study design explicitly addresses potential reverse causation by excluding patients who died within one year of baseline and those with very low cholesterol levels.
This medium-impact improvement would enhance the transparency of the statistical analysis and allow readers to better understand the potential confounders considered. The Methods section is the appropriate place for this clarification as it provides the details of the study's analytical approach.
Implementation: Provide a list of the specific covariates included in the adjusted Cox regression model. For example: "The adjusted model included the following covariates: age, sex, race, body mass index, smoking status, history of hypertension, history of atrial fibrillation, history of heart failure, history of cancer, history of chronic obstructive pulmonary disease, history of chronic kidney disease, baseline systolic blood pressure, baseline diastolic blood pressure, baseline glucose level, use of ACE inhibitors, use of beta-blockers, use of calcium channel blockers, use of diuretics, use of aspirin, use of direct oral anticoagulants, use of antidepressants, use of opioids, and statin initiation after one year of follow-up."
This low-impact improvement would provide a more complete picture of the data quality and potential limitations. While the requirement for non-missing lipid values is mentioned, a brief discussion of how other missing data were handled would further strengthen the methodological rigor.
Implementation: Add a sentence or two describing the approach to missing data for covariates other than the primary lipid measurements. For example: "Patients with missing data for any of the covariates included in the adjusted model were excluded from that specific analysis. Sensitivity analyses were conducted to assess the potential impact of missing data on the main findings."
This medium-impact improvement would provide a clearer rationale for the selection of the LDL-C 80-99 mg/dL category as the referent group. While the reason is mentioned (to mitigate potential bias), further elaborating on this choice would enhance the reader's understanding of the study's design.
Implementation: Expand the explanation for choosing the LDL-C 80-99 mg/dL category as the referent group. For example: "The LDL-C category of 80-99 mg/dL was chosen as the referent group because it represents a commonly observed range in the general population and is less likely to be influenced by underlying health conditions that might lead to very low LDL-C levels. This approach helps to minimize potential bias due to reverse causation, where low LDL-C could be a marker of poor health rather than a cause of increased mortality."
The Results section effectively presents the baseline characteristics of the study population, stratified by LDL-C categories, providing a clear understanding of the cohort's composition and potential confounders.
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.
The inclusion of secondary outcomes, such as ASCVD, and analyses of secondary lipid measures adds depth to the study and provides a more holistic view of the relationship between lipid profiles and cardiovascular health.
The study explicitly addresses the potential for reverse causation by excluding early deaths and providing a rationale for the choice of the referent LDL-C group, demonstrating a thoughtful approach to study design and analysis.
This medium-impact improvement would enhance the transparency and interpretability of the results. While the Methods section mentions covariate adjustment, explicitly stating the adjusted covariates in the Results section when presenting the hazard ratios would allow readers to immediately understand the factors accounted for in the analysis.
Implementation: When presenting the adjusted hazard ratios in the Results section (e.g., in Table 2's footnote or within the text), list the specific covariates included in the adjustment model. For example: "Adjusted hazard ratios were calculated after controlling for age, sex, race, BMI, smoking status, history of hypertension, history of diabetes, and use of antihypertensive medications."
This low-impact improvement would provide a more complete understanding of the ASCVD findings. While the Results section presents the U-shaped relationship between LDL-C and ASCVD, briefly discussing the clinical significance of these findings and how they relate to the primary mortality outcome would enhance the reader's interpretation.
Implementation: Add a sentence or two after presenting the ASCVD results, explaining their clinical implications. For example: "Although a U-shaped relationship was observed between LDL-C and ASCVD, the absolute differences in ASCVD rates across the LDL-C categories were relatively small. This suggests that while LDL-C may play a role in ASCVD development, other factors likely contribute significantly to overall cardiovascular risk in this primary prevention population. These findings further support the primary mortality results, highlighting the limited utility of LDL-C as an isolated predictor of adverse outcomes."
This medium-impact improvement would strengthen the Results section by providing a clear justification for the choice of the referent group within the section itself. While the Methods section explains this choice, reiterating the rationale briefly in the Results section would enhance the reader's understanding of the presented comparisons and reinforce the study's methodological rigor.
Implementation: When first introducing the referent group in the Results section, add a brief phrase explaining the rationale. For example: "As described in the Methods, the LDL-C category of 80-99 mg/dL was selected as the referent group to mitigate potential bias due to reverse causation. Therefore, adjusted mortality HRs are presented in comparison to this group."
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 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.
Table 1 Baseline characteristics of study population by baseline LDL cholesterol value