Abstract
Objective
Real-world-based population data about the optimal low-density lipoprotein cholesterol (LDL-C) level for preventing cardiovascular disease in very high-risk populations is scarce.
Methods
From 2009 to 2012, 26,922 people aged ≥ 40 years with type 2 diabetes mellitus (T2DM) who had a history of percutaneous coronary intervention (PCI) were analyzed. Data from the Korean National Health Insurance System were used. They were followed up to the date of a cardiovascular event or the time to death, or until December 31, 2018. Endpoints were recurrent PCI, newly stroke or heart failure, cardiovascular death, and all-cause death. Participants were divided into the following categories according to LDL-C level: <55 mg/dL, 55–69 mg/dL, 70–99 mg/dL, 100–129 mg/dL, 130–159 mg/dL, and ≥ 160 mg/dL.
Results
Compared to LDL-C < 55 mg/dL, the hazard ratios (HR) for re-PCI and stroke increased linearly with increasing LDL-C level in the population < 65 years. However, in ≥ 65 years old, HRs for re-PCI and stroke in LDL-C = 55–69 mg/dL were 0.97 (95% CI: 0.85–1.11) and 0.96 (95% CI: 0.79–2.23), respectively. The optimal range with the lowest HR for heart failure and all-cause mortality were LDL-C = 70–99 mg/dL and LDL-C = 55–69 mg/dL, respectively, in all age groups (HR: 0.99, 95% CI: 0.91–1.08 and HR: 0.91, 95% CI: 0.81–1.01).
Conclusion
LDL-C level below 55 mg/dL appears to be optimal in T2DM patients with established cardiovascular disease aged < 65 years, while an LDL-C level of 55–69 mg/dL may be optimal for preventing recurrent PCI and stroke in patients over 65 years old.
Introduction
Cardiovascular disease (CVD) is one of the most significant causes of death globally. Despite its critical fatality rate, CVD can be prevented by taking necessary precautions (1). Several studies have indicated that a high level of low-density lipoprotein cholesterol (LDL-C) is strongly associated with CVD. Hundreds of studies with millions of participants have suggested that LDL-C is a strong predictor of atherosclerosis that eventually leads to atherosclerotic cardiovascular disease (ASCVD). Therefore, several guidelines underscore the importance of intensive LDL-C lowering for primary and secondary prevention of CVD.
It is well known that patients with established CVD have a high risk of subsequent CVD events, including myocardial infarction, stroke, and death (1, 2). A recent study has estimated that the 10-year risk of recurrent vascular events in patients with pre-experienced coronary artery disease is 14% (2). Thus, secondary prevention for this population is crucial for reducing recurrent cardiovascular events (CVE). Since the Scandinavian Simvastatin Survival Study showed that lowering LDL-C could significantly decrease both cardiovascular mortality and all-cause mortality, several studies including meta-analyses have demonstrated that the relative reduction in CVD risk is proportional to the absolute reduction of LDL-C (3, 4, 5). Moreover, recent research studies on PCSK-9 inhibitors have shown the concept of ‘the lower, the better’ without an apparent lower threshold for LDL-C level for secondary prevention (6, 7, 8).
Type 2 diabetes mellitus (T2DM) is an independent risk factor for ASCVD. T2DM patients are at a two-fold to four-fold increased risk of developing CVD (9). As a result, CVD is the most important cause of morbidity and mortality in individuals with T2DM. T2DM patients are likely to have multiple ASCVD risk factors (including dyslipidemia and hypertension), each of which increases the risk of both ASCVD and non-ASCVD. As a result, several recent guidelines including the American Diabetes Association ‘Standards of Care in Diabetes 2023’ recommends an LDL-C goal of less than 55 mg/dL in people with diabetes and established ASCVD (10, 11, 12, 13).
However, real-world data about the optimal LDL-C target level for preventing CVE in this very high-risk population is insufficient. In particular, the debate over the clinical benefits of LDL-C lowering in older patients with diabetes for secondary prevention remains. Therefore, we conducted a close investigation on the association between LDL-C and the onset of CVE as well as the mortality in T2DM patients with established CVD by utilizing and examining the data from the Korean National Health Insurance System. In addition to this, an analysis was performed by age group classification. This big data study is expected to support the recommended LDL-C level in this population.
Materials and methods
Data sources
The National Health Insurance Service (NHIS) in South Korea covers about 97% of the Korean population and provides health screening examinations called the National Health Screening Program for all enrollees aged 40 years and older. The NHIS includes an eligibility database (age, sex, socioeconomic variables, type of eligibility, and so on), a medical treatment database (based on medical bills claimed by medical service providers for medical expenses), a health examination database (general health examinations and questionnaires on lifestyle and behavior), a medical care institution database, and death information. Dates of death were obtained from the database prepared by Statistics Korea.
Study population
From January 1, 2009, to December 31, 2012, a total of 2,554,830 people aged ≥ 40 years with T2DM underwent a health examination. Patients who had at least one service claim with a diagnosis of T2DM based on ICD-10 (E11–E14) and patients prescribed at least one antidiabetic drug at any time over 1 year were classified as having T2DM. For example, among those who underwent a health examination in 2009, we selected participants who had at least one service claim with a diagnosis of T2DM based on ICD-10 (E11–14), and those who were prescribed at least one antidiabetic drug at any time in 2009. The same criteria were used for 2010, 2011, and 2012. Duplicate individuals who underwent multiple health examinations in consecutive years were excluded. The index year was 2009–2012. We used the same method described by Kim et al. (14). To include subjects with established CVD, those who did not have a history of percutaneous coronary intervention (PCI) within 5 years before health examination were excluded (n = 2,416,428). People with missing data for at least one variable were also excluded (n = 2308). Those with TG levels of > 400 mg/dL were excluded (n = 88,147). To avoid confounding by preexisting diseases and to minimize possible effects of reverse causality, we also excluded those with histories of heart failure (HF) or stroke (n = 21,025) as indicated by their medical treatment and health examination data before the index year. Ultimately, a total of 26,922 people were eligible for this study. The flowchart for the selection of the study population is described in Fig. 1. This study was approved by the Institutional Review Board of the Catholic University of Korea (No. UC22ZISI0112). Anonymized and deidentified information were used for analyses; therefore, informed consent was not obtained.
Flowchart showing the selection of the study population.
Citation: Endocrine Connections 12, 11; 10.1530/EC-23-0142
Data collection
The covariates were based on data from the index year. They included age, sex, income level (low 25% or not), body mass index (BMI; kg/m2), smoking status (no, yes), alcohol consumption (no, yes), regular exercise, use of insulin (no, yes), duration of diabetes (year), and systolic/diastolic blood pressure (mmHg). The duration of diabetes was defined as the period time from January 1, 2002, to the time of the subject’s health examination, since the record from NHIS is only available from 2002. Regular exercise was defined as having 20 min or more of vigorous activity at least 3 days a week, or 30 min or more of moderate activity 5 or more days per week. Income level was dichotomized at the lowest 25%. We defined a statin user as a person who had a prescription of a statin within 1 year of health examination. Blood samples for measurements of serum glucose, creatinine, and lipid levels were drawn after an overnight fast. Blood samples for measurements of total cholesterol, high-density cholesterol (HDL-C), and triglyceride (TG) levels were obtained at the health examination after the participant had fasted for at least 8 h. LDL-C levels were calculated with the Friedewald formula: LDL-C = total cholesterol − HDL-C − (TG/5) (15).
Study endpoints and follow-up
Endpoints of this study were as follows: event of recurrent PCI, newly diagnosed stroke or HF, cardiovascular death, and all-cause death. The detailed definitions of outcomes are described in Supplementary Table 1 (see section on supplementary materials given at the end of this article). The study population was followed up from baseline to the date of a CVE or the time of the participant’s disqualification from receiving health services due to death or emigration, or until the end of the study period (December 31, 2018).
Statistical analysis
Baseline characteristics are presented as mean ± s.d., geometric mean (95% confidence interval (CI)), or n (%). Participants were divided into the following categories according to their LDL-C levels: < 55 mg/dL, 55–69 mg/dL, 70–99 mg/dL, 100–129 mg/dL, 130–159 mg/dL, and ≥ 160 mg/dL. The incidence rate (IR) of primary outcomes was calculated by dividing the number of incident cases by the total follow-up duration (person-years). The cumulative incidence of each CVE during follow-up according to LDL-C categories was assessed using Kaplan–Meier curves. The log-rank test was performed to evaluate differences among groups. Cox regression analyses were performed to estimate the risk of CVE and all-cause mortality for each LDL-C group using the group with LDL-C less than 55 mg/dL as the reference group. A multivariable-adjusted proportional hazards model was applied after it was adjusted for age, sex, BMI, smoking, alcohol consumption, regular exercise, household income, use of statins, fasting glucose levels, hypertension, use of insulin, and duration of diabetes. The potential effect modification by age was evaluated through stratified analysis and interaction testing using a likelihood ratio test. All statistical analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). A P < 0.05 was considered to indicate statistical significance.
Results
Baseline characteristics
Baseline characteristics of study subjects are presented in Table 1. Baseline characteristics according to gender are also shown in Supplementary Tables 2 and 3. For the cohort of 26,922 participants, the mean age was 63.0 ± 9.0 years and 19,425 (72.2%) participants were men. The mean LDL-C level was 81.0 ± 37.7 mg/dL. Patients in higher LDL-C categories were more likely to have higher fasting glucose levels. Conversely, patients with low LDL-C levels were more likely to have higher incomes, lower BMI, and engage in regular exercise. The baseline total cholesterol and triglyceride levels show significant differences according to LDL-C category.
Baseline characteristics of subjects according to the low-density lipoprotein cholesterol (LDL-C) levels.
| Total participants (N = 26,922) | LDL-C < 55 (N = 5203) | LDL-C < 70 (N = 5894) | LDL-C 70–99 (N = 9696) | LDL-C 100–129 (N = 4150) | LDL-C 130–159 (N = 1361) | LDL-C ≥160 (N = 618) | P | |
|---|---|---|---|---|---|---|---|---|
| Baseline LDL-C (mg/dL) | 81.0 ± 37.7 | 43.0 ± 9.7 | 62.2 ± 4.3 | 82.9 ± 8.4 | 112.0 ± 8.4 | 141.9 ± 8.4 | 206.6 ± 14.3 | <0.0001 |
| Age (years) | 63.0 ± 9.0 | 62.7 ± 8.9 | 63.1 ± 8.9 | 63.0 ± 9.0 | 63.1 ± 9.2 | 63.3 ± 9.3 | 62.2 ± 9.9 | 0.015 |
| Sex (male) | 19,425 (72.2) | 4090 (78.6) | 4386 (74.4) | 6885 (71.0) | 2791 (67.3) | 889 (65.3) | 384 (62.1) | <0.0001 |
| Body mass index (kg/m2) | 25.1 ± 3.0 | 25.0 ± 2.9 | 25.1 ± 3.0 | 25.2 ± 3.0 | 25.2 ± 3.1 | 25.2 ± 3.2 | 25.2 ± 3.3 | 0.002 |
| Fasting glucose (mg/dL) | 135.7 ± 44.5 | 133.7 ± 42.8 | 133.4 ± 42.6 | 134.9 ± 42.9 | 138.8 ± 47.8 | 142.4 ± 49.3 | 151.3 ± 58.4 | <0.0001 |
| eGFR (mL/min/1.73 m2) | 78.9 ± 34.7 | 80.1 ± 38.3 | 78.9 ± 31.1 | 78.9 ± 31.6 | 78.3 ± 40.6 | 77.4 ± 40.6 | 77.5 ± 26.9 | 0.056 |
| Baseline TC (mg/dL) | 155.5 ± 37.1 | 119.4 ± 17.8 | 135.2 ± 16.8 | 156.9 ± 18.0 | 188.7 ± 18.1 | 220.8 ± 19.6 | 266.4 ± 48.0 | <0.0001 |
| Baseline HDL-C (mg/dL) | 47.4 ± 17.1 | 46.6 ± 15.5 | 46.4 ± 13.6 | 47.4 ± 14.4 | 48.2 ± 17.1 | 48.6 ± 26.9 | 54.1 ± 45.7 | <0.0001 |
| Baseline TG (mg/dL) | 125.2 (124.4–125.9) | 130.1 (128.2–132.0) | 118.2 (116.7–119.7) | 120.6 (119.5–121.7) | 131.0 (129.2–132.8) | 142.6 (139.3–146.0) | 154.3 (149.1–159.7) | <0.0001 |
| Hypertension | 22,456 (83.4) | 4333 (83.3) | 4927 (83.6) | 8150 (84.1) | 3419 (82.4) | 1125 (82.7) | 502 (81.2) | 0.111 |
| Current smoker | 4955 (18.4) | 1038 (20.0) | 1033 (17.5) | 1690 (17.4) | 752 (18.1) | 302 (22.2) | 140 (22.7) | <0.0001 |
| Alcohol drinking | 8275 (30.7) | 1783 (34.3) | 1881 (31.9) | 2882 (29.7) | 1191 (28.7) | 376 (27.6) | 162 (26.2) | <0.0001 |
| Regular exercise | 6544 (24.3) | 1317 (25.3) | 1504 (25.5) | 2378 (24.5) | 940 (22.7) | 283 (20.8) | 122 (19.7) | <0.0001 |
| Income (lower 25%) | 5187 (19.3) | 986 (19.0) | 1122 (19.0) | 1832 (18.9) | 819 (19.7) | 278 (20.4) | 150 (24.3) | 0.022 |
| On insulin treatment | 6022 (22.4) | 1260 (24.2) | 1316 (22.3) | 2126 (21.9) | 872 (21.0) | 303 (22.3) | 145 (23.5) | 0.006 |
| On statin treatment | 2339 (86.9) | 4971 (95.5) | 5578 (94.6) | 8650 (89.2) | 3022 (72.8) | 799 (58.7) | 377 (61.0) | <0.0001 |
| Duration of diabetes (years) | 4.8 ± 3.5 | 5.2 ± 3.4 | 4.9 ± 3.4 | 4.7 ± 3.5 | 4.7 ± 3.5 | 4.6 ± 3.4 | 4.2 ± 3.4 | <0.0001 |
Data are expressed as the mean ± s.d., median (25–75%), or n (%).
eGFR, estimated glomerular filtration rate; HDL, high-density lipoprotein; LDL, low-density lipoprotein; TC, total cholesterol; TG, triglyceride.
LDL-C level and risk of CVE
Table 2 shows the incidence of re-PCI, stroke, HF, cardiovascular death, and all-cause mortality according to LDL-C level. The mean follow-up time was 7.6 ± 1.8 years for the total population. The final Model 3 was adjusted for age, sex, BMI, smoking, alcohol drinking, exercise, income status, hypertension, estimated glomerular filtration rate, fasting glucose levels, use of insulin, duration of diabetes, and use of statin. All models showed similar trends toward LDL–C levels. Using LDL-C level < 55 mg/dL as a reference, multivariable-adjusted hazard ratios (HR) and IR for re-PCI, stroke, and cardiovascular death increased linearly according to LDL-C level. HRs for re-PCI, stroke, and cardiovascular death in those with LDL-C ≥ 160 mg/dL were 2.12 (95% CI: 1.82–2.47), 1.68 (95% CI: 1.26–2.23), and 1.71 (95% CI: 1.17–2.48), respectively, in Model 3. However, the optimal LDL-C ranges with the lowest HR for HF and all-cause mortality were 70–99 mg/dL and 55–69 mg/dL, respectively (HR: 0.99, 95% CI: 0.91–1.08 and HR: 0.91, 95% CI: 0.81–1.01, respectively). In those with LDL-C ≥ 160 mg/dL, HRs for HF and all-cause mortality were 1.60 (95% CI 1.34–1.91), and 1.38 (95% CI 1.13–1.68) respectively. Figure 2 shows the cumulative incidence of re-PCI, stroke, and HF during follow-up. All the results in the figure showed a P < 0.001 (log-rank test). The incidence of outcomes according to gender is described in Supplementary Table 4. The results were similar to those for the general population.
Cumulative incidence of (A) recurrent percutaneous coronary intervention, (B) stroke, and (C) heart failure during follow-up. The log-rank test was used to evaluate differences between groups. All results in the figure showed a P < 0.001.
Citation: Endocrine Connections 12, 11; 10.1530/EC-23-0142
Risk of re-PCI, stroke, heart failure, cardiovascular death, and all-cause of mortality in patients with type 2 diabetes mellitus according to low-density lipoprotein cholesterol (LDL-C) category.
| LDL-C | N | Event | IR | Model 1 | Model 2 | Model 3 | |
|---|---|---|---|---|---|---|---|
| HR (95% CI)a | HR (95% CI)b | HR (95% CI)c | |||||
| Re-PCI | |||||||
| <55 | 5203 | 897 | 25.56 | 1 (ref.) | 1 (ref.) | 1 (ref.) | |
| 55–69 | 5894 | 1102 | 27.43 | 1.08 (0.99–1.18) | 1.10 (1.01–1.20) | 1.10 (1.00–1.20) | |
| 70–99 | 9696 | 2134 | 32.88 | 1.30 (1.20–1.40) | 1.32 (1.22–1.43) | 1.30 (1.20–1.40) | |
| 100–129 | 4150 | 1041 | 38.23 | 1.51 (1.38–1.65) | 1.54 (1.41–1.68) | 1.45 (1.32–1.59) | |
| 130–159 | 1361 | 403 | 47.16 | 1.85 (1.65–2.09) | 1.88 (1.67–2.11) | 1.71 (1.52–1.94 | |
| ≥160 | 618 | 216 | 58.57 | 2.29 (1.98–2.66) | 2.33 (2.01–2.70) | 2.12 (1.82–2.47) | |
| Stroke | |||||||
| <55 | 5203 | 280 | 7.31 | 1 (ref.) | 1 (ref.) | 1 (ref.) | |
| 55–69 | 5894 | 351 | 8.02 | 1.08 (0.92–1.26) | 1.09 (0.93–1.28) | 1.09 (0.93–1.27) | |
| 70–99 | 9696 | 574 | 7.95 | 1.08 (0.93–1.24) | 1.09 (0.95–1.26) | 1.07 (0.93–1.24) | |
| 100–129 | 4150 | 289 | 9.36 | 1.25 (1.06–1.47) | 1.26 (1.07–1.49) | 1.17 (0.99–1.39) | |
| 130–159 | 1361 | 119 | 12.01 | 1.57 (1.27–1.95) | 1.60 (1.29–1.98) | 1.43 (1.14–1.78) | |
| ≥160 | 618 | 61 | 13.88 | 1.89 (1.43–2.49) | 1.88 (1.43–2.49) | 1.68 (1.26–2.23) | |
| Heart failure | |||||||
| <55 | 5203 | 800 | 21.48 | 1 (ref.) | 1 (ref.) | 1 (ref.) | |
| 55–69 | 5894 | 979 | 22.97 | 1.04 (0.95–1.15) | 1.06 (0.97–1.16) | 1.06 (0.96–1.16) | |
| 70–99 | 9696 | 1528 | 21.73 | 0.98 (0.90–1.07) | 1.00 (0.92–1.09) | 0.99 (0.91–1.08) | |
| 100–129 | 4150 | 747 | 25.00 | 1.12 (1.01–1.23) | 1.13 (1.03–1.25) | 1.09 (0.98–1.20) | |
| 130–159 | 1361 | 292 | 30.24 | 1.32 (1.15–1.51) | 1.34 (1.17–1.54) | 1.26 (1.09–1.44) | |
| ≥160 | 618 | 158 | 37.79 | 1.72 (1.45–2.04) | 1.71 (1.44–2.03) | 1.60 (1.34–1.91) | |
| Cardiovascular death | |||||||
| <55 | 5203 | 150 | 3.84 | 1 (ref.) | 1 (ref.) | 1 (ref.) | |
| 55–69 | 5894 | 181 | 4.04 | 1.04 (0.84–1.29) | 1.05 (0.85–1.31) | 1.05 (0.84–1.30) | |
| 70–99 | 9696 | 353 | 4.78 | 1.25 (1.03–1.51) | 1.27 (1.05–1.54) | 1.22 (1.01–1.49) | |
| 100–129 | 4150 | 180 | 5.67 | 1.46 (1.17–1.81) | 1.48 (1.19–1.84) | 1.31 (1.04–1.64) | |
| 130–159 | 1361 | 65 | 6.31 | 1.58 (1.18–2.12) | 1.62 (1.21–2.18) | 1.35 (0.99–1.83) | |
| ≥160 | 618 | 36 | 7.82 | 2.06 (1.43–2.97) | 2.06 (1.43–2.97) | 1.71 (1.17–2.48) | |
| All-cause mortality | |||||||
| <55 | 5203 | 672 | 17.21 | 1 (ref.) | 1 (ref.) | 1 (ref.) | |
| 55–69 | 5894 | 695 | 15.50 | 0.90 (0.81–1.00) | 0.91 (0.82–1.01) | 0.91 (0.81–1.01) | |
| 70–99 | 9696 | 1307 | 17.69 | 1.05 (0.95–1.15) | 1.07 (0.97–1.17) | 1.04 (0.95–1.15) | |
| 100–129 | 4150 | 641 | 20.20 | 1.17 (1.05–1.30) | 1.19 (1.07–1.33) | 1.09 (0.98–1.22) | |
| 130–159 | 1361 | 227 | 22.02 | 1.25 (1.08–1.46) | 1.28 (1.10–1.49) | 1.13 (0.97–1.32) | |
| ≥160 | 618 | 119 | 25.85 | 1.56 (1.29–1.90) | 1.56 (1.28–1.89) | 1.38 (1.13–1.68) |
aAdjusted for age, sex, BMI, smoking, alcohol drinking, exercise, income status, hypertension, and estimated glomerular filtration rate.
bAdjusted for age, sex, BMI, smoking, alcohol drinking, exercise, income status, hypertension, estimated glomerular filtration rate, fasting glucose levels, use of insulin, and duration of diabetes.
cAdjusted for age, sex, BMI, smoking, alcohol drinking, exercise, income status, hypertension, estimated glomerular filtration rate, fasting glucose levels, use of insulin, duration of diabetes, and use of statin.
CI, confidence interval; HR, hazard ratio; IR, incidence ratio.
Risk of CVE according to LDL-C and age category
Table 3 shows HRs of re-PCI, stroke, HF, cardiovascular death, and all-cause mortality according to LDL-C level and age category in the adjusted model. Using LDL-C level < 55 mg/dL as reference, HRs for re-PCI and stroke increased linearly according to LDL-C level in the population under age 65. However, in the population aged ≥ 65 years, results were different. IR and HR for re-PCI and stroke were the lowest in those with LDL-C level 55–69 mg/dL. HRs for re-PCI and stroke in those with LDL-C level 55–69 mg/dL were 0.97 (95% CI: 0.85–1.11) and 0.96 (95% CI: 0.79–2.23), respectively, with LDL-C < 55 mg/dL as the reference. Trends in the incidence of HF, cardiovascular death, and all-cause mortality were similar and unrelated to the age groups.
Risk of re-PCI, stroke, heart failure, cardiovascular death, and all-cause of mortality in patients with type 2 diabetes mellitus according to low-density lipoprotein cholesterol (LDL-C) category and age.
| LDL-C | N | Event | IR | Model 3 HR (95% CI)a | ||
|---|---|---|---|---|---|---|
| Re-PCI | Age < 65 | |||||
| <55 | 2939 | 494 | 24.39 | 1 (ref.) | ||
| 55–69 | 3245 | 647 | 28.79 | 1.20 (1.07, 1.35) | ||
| 70–99 | 5403 | 1225 | 33.18 | 1.36 (1.23, 1.51) | ||
| 100–129 | 2267 | 602 | 39.70 | 1.57 (1.39, 1.77) | ||
| 130–159 | 741 | 246 | 51.95 | 1.97 (1.69, 2.30) | ||
| ≥160 | 353 | 136 | 64.44 | 2.42 (2.00, 2.94) | ||
| Age ≥ 65 | ||||||
| <55 | 2264 | 403 | 27.16 | 1 (ref.) | ||
| 55–69 | 2649 | 455 | 25.71 | 0.97 (0.85, 1.11) | ||
| 70–99 | 4293 | 909 | 32.48 | 1.21 (1.08, 1.37) | ||
| 100–129 | 1883 | 439 | 36.38 | 1.30 (1.14, 1.50) | ||
| 130–159 | 620 | 157 | 41.21 | 1.41 (1.17, 1.71) | ||
| ≥160 | 265 | 80 | 50.72 | 1.75 (1.37, 2.23) | ||
| Stroke | Age < 65 | |||||
| <55 | 2939 | 83 | 3.7175 | 1 (ref.) | ||
| 55–69 | 3245 | 130 | 5.2393 | 1.40 (1.07, 1.85) | ||
| 70–99 | 5403 | 204 | 4.9211 | 1.31 (1.02, 1.69) | ||
| 100–129 | 2267 | 105 | 6.0079 | 1.52 (1.14, 2.04) | ||
| 130–159 | 741 | 41 | 7.216 | 1.72 (1.18, 2.52) | ||
| ≥160 | 353 | 24 | 9.2976 | 2.25 (1.42, 3.57) | ||
| Age ≥ 65 | ||||||
| <55 | 2264 | 197 | 12.3339 | 1 (ref.) | ||
| 55–69 | 2649 | 221 | 11.6436 | 0.96 (0.79, 1.16) | ||
| 70–99 | 4293 | 370 | 12.0424 | 0.97 (0.81, 1.15) | ||
| 100–129 | 1883 | 184 | 13.7396 | 1.03 (0.84, 1.27) | ||
| 130–159 | 620 | 78 | 18.4596 | 1.30 (0.92, 1.71) | ||
| ≥160 | 265 | 37 | 20.3981 | 1.44 (1.01, 2.06) | ||
| Heart failure | Age < 65 | |||||
| <55 | 2939 | 358 | 16.5054 | 1 (ref.) | ||
| 55–69 | 3245 | 416 | 17.1374 | 1.02 (0.89, 1.18) | ||
| 70–99 | 5403 | 671 | 16.5722 | 0.98 (0.86, 1.12) | ||
| 100–129 | 2267 | 322 | 18.9874 | 1.08 (0.93, 1.26) | ||
| 130–159 | 741 | 119 | 21.5214 | 1.17 (0.95, 1.45) | ||
| ≥160 | 353 | 76 | 30.6538 | 1.74 (1.35, 2.23) | ||
| Age ≥ 65 | ||||||
| <55 | 2264 | 442 | 28.4229 | 1 (ref.) | ||
| 55–69 | 2649 | 563 | 30.6932 | 1.08 (0.96, 1.23) | ||
| 70–99 | 4293 | 857 | 28.7265 | 0.99 (0.88, 1.11) | ||
| 100–129 | 1883 | 425 | 32.8882 | 1.09 (0.95, 1.25) | ||
| 130–159 | 620 | 173 | 41.9074 | 1.32 (1.10, 1.58) | ||
| ≥160 | 265 | 82 | 48.1888 | 1.50 (1.18, 1.90) | ||
| Cardiovascular death | Age < 65 | |||||
| <55 | 2939 | 46 | 2.0398 | 1 (ref.) | ||
| 55–69 | 3245 | 55 | 2.1802 | 1.05 (0.71, 1.56) | ||
| 70–99 | 5403 | 96 | 2.2802 | 1.10 (0.77, 1.56) | ||
| 100–129 | 2267 | 41 | 2.3031 | 1.03 (0.67, 1.57) | ||
| 130–159 | 741 | 21 | 3.6082 | 1.46 (0.87, 2.46) | ||
| ≥160 | 353 | 15 | 5.6034 | 2.34 (1.29, 4.21) | ||
| Age ≥ 65 | ||||||
| <55 | 2264 | 104 | 6.3083 | 1 (ref.) | ||
| 55–69 | 2649 | 126 | 6.4245 | 1.04 (0.81, 1.35) | ||
| 70–99 | 4293 | 257 | 8.0915 | 1.28 (1.02, 1.61) | ||
| 100–129 | 1883 | 139 | 9.9775 | 1.43 (1.10, 1.86) | ||
| 130–159 | 620 | 44 | 9.8054 | 1.30 (0.91, 1.88) | ||
| ≥160 | 265 | 21 | 10.9012 | 1.44 (0.89, 2.32) | ||
| All-cause mortality | Age < 65 | |||||
| <55 | 2939 | 196 | 8.6915 | 1 (ref.) | ||
| 55–69 | 3245 | 180 | 7.1353 | 0.81 (0.67, 1.00) | ||
| 70–99 | 5403 | 346 | 8.2182 | 0.95 (0.80, 1.13) | ||
| 100–129 | 2267 | 157 | 8.819 | 0.97 (0.78, 1.20) | ||
| 130–159 | 741 | 61 | 10.4808 | 1.05 (0.79, 1.41) | ||
| ≥160 | 353 | 39 | 14.569 | 1.53 (1.08, 2.16) | ||
| Age ≥ 65 | ||||||
| <55 | 2264 | 476 | 28.8728 | 1 (ref.) | ||
| 55–69 | 2649 | 515 | 26.2591 | 0.94 (0.83, 1.07) | ||
| 100–129 | 1883 | 961 | 30.2565 | 1.08 (0.97, 1.21) | ||
| 130–159 | 620 | 484 | 34.7418 | 1.15 (1.01, 1.31) | ||
| ≥160 | 265 | 166 | 36.9932 | 1.17 (0.97, 1.40) |
aAdjusted for age, sex, BMI, smoking, alcohol drinking, exercise, income status, hypertension, estimated glomerular filtration rate, fasting glucose levels, use of insulin, duration of diabetes, and use of statin.
CI, confidence interval; HR, hazard ratio; IR, incidence ratio.
Discussion
Our study highlights the inverse association between LDL-C level and CVE risk for secondary prevention in T2DM patients with preexisting CVD. Current guidelines recommend an LDL-C goal ≤ 55 mg/dL for patients with very high-risk ASCVD regardless of the patient’s age (10, 11, 12, 13). However, our data revealed that the optimal LDL-C level for preventing CVE was 55–70 mg/dL, not under 55 mg/dL, in the population aged above 65 years. Therefore, the idea of lowering LDL-C as much as possible in elderly patients should be reconsidered.
In the population under age 65, outcomes of re-PCI, stroke, and cardiovascular death were inversely correlated with LDL-C levels in a linear manner. As postulated, the risk of CVE was the lowest in the population with LDL less than 55 mg/dL. Similar to outcomes of FOURIER (6), ODYSSEY OUTCOMES (7), and SPIRE (16) clinical trials, the cardiovascular benefit of low LDL-C levels was reproduced in our study using real-world data. Likewise, a previous systematic review has revealed that each millimole per liter (40 mg/dL) decrease in LDL-C level is associated with a 4.6% lower 5-year major coronary event rate for secondary prevention (5). Plus, a meta-analysis has evaluated a total of 18,686 patients with diabetes (1466 type 1 and 17,220 type 2) for a mean of 4.3 years, and found that there is a significant 9% reduction in all-cause mortality and a 21% reduction in coronary death or myocardial infarction, coronary revascularization, and stroke for each mmol per liter (40 mg/dL) of LDL-C reduction (17).
However, the optimal range of LDL-C varied by age group in our study. For the prevention of re-PCI and stroke in the elderly, the optimal LDL-C range was 55–69 mg/dL, not less than 55 mg/dL. ‘The lower, the better’ was applicable only to the population under 65 years old. It is well known that lipid lowering is as effective in reducing CVE in elderly patients as young patients for secondary prevention (18, 19, 20, 21). However, more studies should be performed to determine the optimal LDL-C level for bringing an additional benefit to the elderly patients.
A sub-analysis of the IMPROVE-IT trial with patients over 75 years old showed a reduction in CVE in the group with a mean LDL-C of 55.1 mg/dL compared to the one with a mean LDL of 68.7 mg/dL (19). These findings are in contrast with our results. The difference between IMPROVE-IT (19) and ours might have been caused to the difference in ethnicity. A meta-analysis report suggests that a target LDL-C level of less than 69 mg/dL is needed to reduce coronary atherosclerotic plaque in western populations, while a level of less than 84 mg/dL may be sufficient for Asians (22). Furthermore, there are differences in plaque morphology between East Asian and White patients (23). Our data indicate that a strict LDL-C lowering may not be as effective in elderly Asians as it is in White patients. To support these findings, a prospective prevention trial needs to be conducted.
Regarding the outcome for HF, ‘the lower, the better’ was not valid either. The optimal LDL-C level was 70–99 mg/dL in all age groups for preventing HF. Several previous studies have suggested that low total cholesterol levels are associated with increased mortality in patients with HF (24, 25, 26). In particular, Charach et al. (24) showed that low initial LDL-C level was a significant predictor of worse outcomes of both ischemic and nonischemic chronic HF in an elderly chronic HF cohort. Furthermore, a Korean adult study has demonstrated a U-curve association between LDL-C and HF mortality, with an optimal range of LDL-C at 130–159 mg/dL (25). Strict lowering of LDL-C might not be effective for the prevention or prognosis of HF, even for patients with established ASCVD. Several explanations can be offered for these findings. One explanation is that it might have a relation with the use of statins for CVD prevention. Coenzyme Q, an essential product of cardiac mitochondrial respiration, is reduced in congestive HF. Statin treatment can reduce coenzyme Q levels and it might be potentially harmful to patients with HF (27). Plus, lipoproteins are natural nonspecific buffers of endotoxin. Their binding to endotoxin can reduce lipopolysaccharide bioactivity and diminish immune activation known to impair chronic HF (26).
Regarding all-cause mortality, the optimal LDL-C level was 55–69 mg/dL in this very high-risk population. Several population-based studies have already shown a non-linear association between all-cause mortality and LDL-C levels (28, 29). A study on Koreans who were not taking statins has demonstrated a U-curve association between LDL-C and all-cause mortality, with an optimal LDL-C range at 140–159 mg/dL (25). Likewise, the Kangbuk Samsung Health Study on 347,971 individuals who were not taking statins highlighted that the group with an optimal LDL-C (130–159 mg/dL) had the lowest all-cause mortality (30). While the exact mechanism remains to be elucidated, several possibilities could explain this finding. It has been suggested that frailty, illnesses, and malnutrition are associated with lower cholesterol levels (31, 32). Higher LDL-C levels might reflect better nutritional and health status, which are likely related to better tolerance of acute medical stress (33, 34). Protective effects of LDL-C against cancer and infection have also been revealed (35, 36, 37). Malnutrition and frailty issues have more adverse impacts on the elderly. Therefore, we speculate that non-linear associations of LDL-C with all-cause mortality in this study might be due to the aforementioned cholesterol paradox.
The strength of this study is the use of the mass population. The NHIS database represents the entire Korean population. We analyzed nearly 27,000 people with T2DM with a history of prior PCI. With the aid of such big data, the present recommendation guideline for secondary prevention of lowering LDL-C to target has been proven valuable in the real-world setting for the population less than 65 years old. As proven in our data, an optimal LDL-C is crucial for survival benefits and quality of life.
This study has some limitations. First, the Korean NHIS database does not provide the assessment of LDL-C using blood samples. Therefore, LDL-C was calculated using the Friedewald formula. This calculation is only valid when the concentration of triglycerides is less than 400 mg/dL. It is not precise when LDL-C is very low (<50 mg/dL) (13). Although subjects with triglyceride greater than or equal to 400 mg/dL were excluded, those with very low LDL-C were included. In other words, although a lot of studies using the Korean NHIS database have published studies based on LDL-C using Friedewald formula, there might be an inconsistency between the calculated LDL-C and LDL-C assessed by blood samples.
Second, this study presumed that clinical factors used in this study were unchanged until the occurrence of the CVE or death. However, due to the nature of data sources, the LDL-C level was investigated in a single health screening examination. It is quite questionable that patients could sustain the observed cholesterol level and other indexes until the endpoint of this study. To conclusively demonstrate the benefit of optimal LDL-C levels in preventing further CVE, future studies are needed to show how reducing LDL-C levels to the optimal ranges compared to controls reduces its occurrence.
Third, this study was conducted only in South Korea. Results might not be applicable to other populations. Lastly, patients with severe disabilities might have difficulties undergoing health screening examinations. Therefore, they might not be included in the analysis.
Conclusion
T2DM patients with established ASCVD are at particularly high risk for recurrent CVE. LDL-C level less than 55 mg/dL was optimal for secondary prevention of CVE in the population aged less than 65 years old. However, the optimal level for preventing recurrent PCI and stroke was 55–69 mg/dL in patients over 65 years old.
Supplementary materials
This is linked to the online version of the paper at https://doi.org/10.1530/EC-23-0142.
Declaration of interest
Not applicable.
Funding
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors
Availability of data and materials
The data that support the findings of this study are available in Korean National Health Insurance Data Sharing Service at https://nhiss.nhis.or.kr/bd/ab/bdaba000eng.do.
Author contribution statement
Kyungdo Han and Hyuk-Sang Kwon contributed to the study concept and design, data extraction and assessment, data synthesis, statistical analysis, and interpretation of data. Chaiho Jeong contributed to study concept and design and writing, and interpretation of data. Bongseong Kim contributed to data extraction and assessment, and data synthesis. Jinyoung Kim, Hansang Baek, Mee Kyoung Kim, Tae-Seo Sohn, Ki-Hyun Baek, Ki-Ho Son, and Hyun-Shik Son contributed to study concept and design. All authors reviewed and edited the manuscript. Kyungdo Han and Hyuk-Sang Kwon are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
References
- 1↑
McGill HC, McMahan CA, & Gidding SS. Preventing heart disease in the 21st century: implications of the Pathobiological Determinants of Atherosclerosis in Youth (PDAY) study. Circulation 2008 117 1216–1227. (https://doi.org/10.1161/CIRCULATIONAHA.107.717033)
- 2↑
Kaasenbrood L, Boekholdt SM, van der Graaf Y, Ray KK, Peters RJ, Kastelein JJ, Amarenco P, LaRosa JC, Cramer MJ, Westerink J, et al. Distribution of estimated 10-year risk of recurrent vascular events and residual risk in a secondary prevention population. Circulation 2016 134 1419–1429. (https://doi.org/10.1161/CIRCULATIONAHA.116.021314)
- 3↑
Scandinavian Simvastatin Survival Study Group. Randomised trial of cholesterol lowering in 4444 patients with coronary heart disease: the Scandinavian simvastatin Survival Study (4S). Lancet 1994 344 1383–1389. (https://doi.org/10.1016/s0140-6736(9490566-5)
- 4↑
Cholesterol Treatment Trialists’ (CTT) Collaboration, Baigent C, Blackwell L, Emberson J, Holland LE, Reith C, Bhala N, Peto R, Barnes EH, Keech A, et al. Efficacy and safety of more intensive lowering of LDL cholesterol: a meta-analysis of data from 170,000 participants in 26 randomised trials. Lancet 2010 376 1670–1681. (https://doi.org/10.1016/S0140-6736(1061350-5)
- 5↑
Silverman MG, Ference BA, Im K, Wiviott SD, Giugliano RP, Grundy SM, Braunwald E, & Sabatine MS. Association between lowering LDL-C and cardiovascular risk reduction among different therapeutic interventions: a systematic review and meta-analysis. JAMA 2016 316 1289–1297. (https://doi.org/10.1001/jama.2016.13985)
- 6↑
Sabatine MS, Giugliano RP, Keech AC, Honarpour N, Wiviott SD, Murphy SA, Kuder JF, Wang H, Liu T, Wasserman SM, et al. Evolocumab and clinical outcomes in patients with cardiovascular disease. New England Journal of Medicine 2017 376 1713–1722. (https://doi.org/10.1056/NEJMoa1615664)
- 7↑
Schwartz GG, Steg PG, Szarek M, Bhatt DL, Bittner VA, Diaz R, Edelberg JM, Goodman SG, Hanotin C, Harrington RA, et al. Alirocumab and cardiovascular outcomes after acute coronary syndrome. New England Journal of Medicine 2018 379 2097–2107. (https://doi.org/10.1056/NEJMoa1801174)
- 8↑
Giugliano RP, Pedersen TR, Park JG, De Ferrari GM, Gaciong ZA, Ceska R, Toth K, Gouni-Berthold I, Lopez-Miranda J, Schiele F, et al. Clinical efficacy and safety of achieving very low LDL-cholesterol concentrations with the PCSK9 inhibitor evolocumab: a prespecified secondary analysis of the FOURIER trial. Lancet 2017 390 1962–1971. (https://doi.org/10.1016/S0140-6736(1732290-0)
- 9↑
Emerging Risk Factors Collaboration, Sarwar N, Gao P, Seshasai SR, Gobin R, Kaptoge S, Di Angelantonio E, Ingelsson E, Lawlor DA, Selvin E, et al. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet 2010 375 2215–2222. (https://doi.org/10.1016/S0140-6736(1060484-9)
- 10↑
ElSayed NA, Aleppo G, Aroda VR, Bannuru RR, Brown FM, Bruemmer D, Collins BS, Das SR, Hilliard ME, Isaacs D, et al. Cardiovascular disease and risk management: standards of care in Diabetes-2023. Diabetes Care 2023 46 S158–S190. (https://doi.org/10.2337/dc23-S010)
- 11↑
Blonde L, Umpierrez GE, Reddy SS, McGill JB, Berga SL, Bush M, Chandrasekaran S, DeFronzo RA, Einhorn D, Galindo RJ, et al. American Association of clinical endocrinology clinical practice guideline: developing a diabetes mellitus comprehensive care Plan-2022 update. Endocrine Practice 2022 28 923–1049. (https://doi.org/10.1016/j.eprac.2022.08.002)
- 12↑
Jellinger PS. American Association of Clinical Endocrinologists/American College of Endocrinology Management of Dyslipidemia and Prevention of Cardiovascular Disease Clinical Practice Guidelines. Diabetes Spectrum 2018 31 234–245. (https://doi.org/10.2337/ds18-0009)
- 13↑
Visseren FLJ, Mach F, Smulders YM, Carballo D, Koskinas KC, Bäck M, Benetos A, Biffi A, Boavida JM, Capodanno D, et al. 2021 ESC Guidelines on cardiovascular disease prevention in clinical practice. European Heart Journal 2021 42 3227–3337. (https://doi.org/10.1093/eurheartj/ehab484)
- 14↑
Kim MK, Han K, Joung HN, Baek KH, Song KH, & Kwon HS. Cholesterol levels and development of cardiovascular disease in Koreans with type 2 diabetes mellitus and without pre-existing cardiovascular disease. Cardiovascular Diabetology 2019 18 139. (https://doi.org/10.1186/s12933-019-0943-9)
- 15↑
Friedewald WT, Levy RI, & Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clinical Chemistry 1972 18 499–502. (https://doi.org/10.1093/clinchem/18.6.499)
- 16↑
Ridker PM, Revkin J, Amarenco P, Brunell R, Curto M, Civeira F, Flather M, Glynn RJ, Gregoire J, Jukema JW, et al. Cardiovascular efficacy and safety of bococizumab in high-risk patients. New England Journal of Medicine 2017 376 1527–1539. (https://doi.org/10.1056/NEJMoa1701488)
- 17↑
Cholesterol Treatment Trialists' (CTT) Collaborators, Kearney PM, Blackwell L, Collins R, Keech A, Simes J, Peto R, Armitage J, & Baigent C. Efficacy of cholesterol-lowering therapy in 18,686 people with diabetes in 14 randomised trials of statins: a meta-analysis. Lancet 2008 371 117–125. (https://doi.org/10.1016/S0140-6736(0860104-X)
- 18↑
Gencer B, Marston NA, Im K, Cannon CP, Sever P, Keech A, Braunwald E, Giugliano RP, & Sabatine MS. Efficacy and safety of lowering LDL cholesterol in older patients: a systematic review and meta-analysis of randomised controlled trials. Lancet 2020 396 1637–1643. (https://doi.org/10.1016/S0140-6736(2032332-1)
- 19↑
Bach RG, Cannon CP, Giugliano RP, White JA, Lokhnygina Y, Bohula EA, Califf RM, Braunwald E, & Blazing MA. Effect of simvastatin-ezetimibe compared with simvastatin monotherapy after acute coronary syndrome among patients 75 years or older: a secondary analysis of a randomized clinical trial. JAMA Cardiology 2019 4 846–854. (https://doi.org/10.1001/jamacardio.2019.2306)
- 20↑
Sinnaeve PR, Schwartz GG, Wojdyla DM, Alings M, Bhatt DL, Bittner VA, Chiang CE, Correa Flores RM, Diaz R, Dorobantu M, et al. Effect of alirocumab on cardiovascular outcomes after acute coronary syndromes according to age: an Odyssey OUTCOMES trial analysis. European Heart Journal 2020 41 2248–2258. (https://doi.org/10.1093/eurheartj/ehz809)
- 21↑
Sever P, Gouni-Berthold I, Keech A, Giugliano R, Pedersen TR, Im K, Wang H, Knusel B, Sabatine MS, & O'Donoghue ML. LDL-cholesterol lowering with evolocumab, and outcomes according to age and sex in patients in the FOURIER Trial. European Journal of Preventive Cardiology 2021 28 805–812. (https://doi.org/10.1177/2047487320902750)
- 22↑
Li YF, Feng QZ, Gao WQ, Zhang XJ, Huang Y, & Chen YD. The difference between Asian and Western in the effect of LDL-C lowering therapy on coronary atherosclerotic plaque: a meta-analysis report. BMC Cardiovascular Disorders 2015 15 6. (https://doi.org/10.1186/1471-2261-15-6)
- 23↑
Bryniarski KL, Yamamoto E, Sugiyama T, Xing L, Lee H, & Jang IK. Differences in coronary plaque morphology between East Asian and Western White patients: an optical coherence tomography study. Coronary Artery Disease 2018 29 597–602. (https://doi.org/10.1097/MCA.0000000000000653)
- 24↑
Charach G, Rabinovich A, Ori A, Weksler D, Sheps D, Charach L, Weintraub M, & George J. Low levels of low-density lipoprotein cholesterol: a negative predictor of survival in elderly patients with advanced heart failure. Cardiology 2014 127 45–50. (https://doi.org/10.1159/000355164)
- 25↑
Yi SW, An SJ, Park HB, Yi JJ, & Ohrr H. Association between low-density lipoprotein cholesterol and cardiovascular mortality in statin non-users: a prospective cohort study in 14.9 million Korean adults. International Journal of Epidemiology 2022 51 1178–1189. (https://doi.org/10.1093/ije/dyac029)
- 26↑
Rauchhaus M, Clark AL, Doehner W, Davos C, Bolger A, Sharma R, Coats AJ, & Anker SD. The relationship between cholesterol and survival in patients with chronic heart failure. Journal of the American College of Cardiology 2003 42 1933–1940. (https://doi.org/10.1016/j.jacc.2003.07.016)
- 27↑
Banach M, Serban C, Ursoniu S, Rysz J, Muntner P, Toth PP, Jones SR, Rizzo M, Glasser SP, Watts GFet al. Statin therapy and plasma coenzyme Q10 concentrations—A systematic review and meta-analysis of placebo-controlled trials. Pharmacological Research 2015 99 329–336. (https://doi.org/10.1016/j.phrs.2015.07.008)
- 28↑
Liu Y, Liu F, Zhang L, Li J, Kang W, Cao M, Song F, & Song F. Association between low density lipoprotein cholesterol and all-cause mortality: results from the NHANES 1999–2014. Scientific Reports 2021 11 22111. (https://doi.org/10.1038/s41598-021-01738-w)
- 29↑
Kawamoto R, Kikuchi A, Akase T, Ninomiya D, & Kumagi T. Low density lipoprotein cholesterol and all-cause mortality rate: findings from a study on Japanese community-dwelling persons. Lipids in Health and Disease 2021 20 105. (https://doi.org/10.1186/s12944-021-01533-6)
- 30↑
Sung KC, Huh JH, Ryu S, Lee JY, Scorletti E, Byrne CD, Kim JY, Hyun DS, & Ko SB. Low levels of low-density lipoprotein cholesterol and mortality outcomes in non-statin users. Journal of Clinical Medicine 2019 8. (https://doi.org/10.3390/jcm8101571)
- 31↑
Ranieri P, Rozzini R, Franzoni S, Barbisoni P, & Trabucchi M. Serum cholesterol levels as a measure of frailty in elderly patients. Experimental Aging Research 1998 24 169–179. (https://doi.org/10.1080/036107398244300)
- 32↑
Liu Y, Coresh J, Eustace JA, Longenecker JC, Jaar B, Fink NE, Tracy RP, Powe NR, & Klag MJ. Association between cholesterol level and mortality in dialysis patients: role of inflammation and malnutrition. JAMA 2004 291 451–459. (https://doi.org/10.1001/jama.291.4.451)
- 33↑
Cho KH, Jeong MH, Ahn Y, Kim YJ, Chae SC, Hong TJ, Seong IW, Chae JK, Kim CJ, Cho MC, et al. Low-density lipoprotein cholesterol level in patients with acute myocardial infarction having percutaneous coronary intervention (the cholesterol paradox). American Journal of Cardiology 2010 106 1061–1068. (https://doi.org/10.1016/j.amjcard.2010.06.009)
- 34↑
Wang B, Liu J, Chen S, Ying M, Chen G, Liu L, Lun Z, Li H, Huang H, Li Q, et al. Malnutrition affects cholesterol paradox in coronary artery disease: a 41,229 Chinese cohort study. Lipids in Health and Disease 2021 20 36. (https://doi.org/10.1186/s12944-021-01460-6)
- 35↑
Ravnskov U, McCully KS, & Rosch PJ. The statin-low cholesterol-cancer conundrum. QJM 2012 105 383–388. (https://doi.org/10.1093/qjmed/hcr243)
- 36↑
Han R. Plasma lipoproteins are important components of the immune system. Microbiology and Immunology 2010 54 246–253. (https://doi.org/10.1111/j.1348-0421.2010.00203.x)
- 37↑
Kaysen GA, Ye X, Raimann JG, Wang Y, Topping A, Usvyat LA, Stuard S, Canaud B, van der Sande FM, Kooman JP, et al. Lipid levels are inversely associated with infectious and all-cause mortality: international MONDO study results. Journal of Lipid Research 2018 59 1519–1528. (https://doi.org/10.1194/jlr.P084277)
