Search
Close
Search
Advanced Search
Search Menu
AI Discovery Assistant

Abstract

Background

Individuals with obesity have higher concentrations of very low-density lipoprotein (VLDL) cholesterol and increased risk of myocardial infarction. We hypothesized that VLDL cholesterol explains a fraction of the excess myocardial infarction risk in individuals with obesity.

Methods

We included 29 010 individuals free of myocardial infarction at baseline, nested within 109 751 individuals from the Copenhagen General Population Study. During 10 years of follow-up, 2306 individuals developed myocardial infarction. Cholesterol content in large and small VLDLs, in intermediate-density lipoprotein (IDL), and in LDL was measured directly with nuclear magnetic resonance spectroscopy.

Results

Median concentrations of cholesterol in large and small VLDLs were 0.12 mmol/L (interquartile range [IQR], 0.07–0.20 mmol/L; 4.5 mg/dL [IQR, 2.6–6.9 mg/dL]) and 0.6 mmol/L (IQR, 0.5–0.8 mmol/L; 25 mg/dL [IQR, 20–30 mg/dL]) in individuals with obesity vs 0.06 mmol/L (IQR, 0.03–0.1 mmol/L; 2.2 mg/dL [IQR, 1.1–3.8 mg/dL]), and 0.5 mmol/L (IQR, 0.4–0.6 mmol/L; 20 mg/dL (IQR, 16–25 mg/dL]) in individuals with normal weight; in contrast, concentrations of IDL and LDL cholesterol were similar across body mass index (BMI) categories. Cholesterol in large and small VLDLs combined explained 40% (95% CI, 27%–53%) of the excess risk of myocardial infarction associated with higher BMI. In contrast, IDL and LDL cholesterol did not explain excess risk of myocardial infarction, whereas systolic blood pressure explained 17% (11%–23%) and diabetes mellitus explained 8.6% (3.2%–14%).

Conclusions

VLDL cholesterol explains a large fraction of excess myocardial infarction risk in individuals with obesity. These novel findings support a focus on cholesterol in VLDL for prevention of myocardial infarction and atherosclerotic cardiovascular disease in individuals with obesity.

Metabolomics, Stroke, Remnant cholesterol, Atherosclerotic cardiovascular disease, Epidemiology

Introduction

Individuals with obesity have higher concentrations of very low-density lipoprotein (VLDL) cholesterol (1, 2) and increased risk of myocardial infarction and atherosclerotic cardiovascular disease (3–6); however, the link between obesity and LDL cholesterol concentrations is weak. Previous studies reported that high plasma glucose, high systolic blood pressure, and high plasma total cholesterol may explain a fraction of the excess risk of atherosclerotic cardiovascular disease in individuals with obesity (7–10). Nevertheless, because high concentrations of cholesterol in VLDL, like high concentrations of cholesterol in intermediate-density lipoprotein (IDL) and LDL, represent a causal risk factor for myocardial infarction and atherosclerotic cardiovascular disease (11–17), it seems plausible that increased concentrations of cholesterol in VLDL could also explain part of the excess risk of myocardial infarction in individuals with obesity.

Only a few previous studies have examined head-to-head the extent of excess risk explained through high concentrations of remnant cholesterol or triglycerides (substitute markers for high VLDL cholesterol) and high concentrations of LDL cholesterol separately (18, 19), and none have used directly measured concentrations of both VLDL cholesterol and LDL cholesterol. Consequently, it is presently unknown to what extent directly measured cholesterol in large and small VLDLs, in IDL, and in LDL in head-to-head comparison each explain excess risk of myocardial infarction in individuals with obesity.

We hypothesized that VLDL cholesterol would explain a fraction of the excess risk of myocardial infarction in individuals with obesity. For this purpose, we measured cholesterol content in large VLDL, small VLDL, IDL, and LDL using nuclear magnetic resonance (NMR) spectroscopy in 29 010 individuals free of myocardial infarction at baseline, nested among 109 751 individuals from the Copenhagen General Population Study. During a mean of 10 years, 2306 individuals developed myocardial infarction. Within atherosclerotic cardiovascular disease, we studied myocardial infarction because this hard endpoint is extremely well captured in the Danish health registries.

Materials and Methods

This study was conducted according to the Declaration of Helsinki and was approved by a Danish ethics committee (H-KF-01-144/01) and by Herlev and Gentofte Hospital. All participants gave written informed consent. Individuals were White of Danish descent.

Participants

The Copenhagen General Population Study is a prospective cohort study of 109 751 Danish adults recruited between 2003 and 2015. Individuals aged 20–100 years were randomly selected to represent the general population. At examination, individuals had a physical examination, had blood samples drawn for biochemical analyses, and filled in a questionnaire.

We studied 2306 individuals with and 26 704 individuals without myocardial infarction during follow-up, using a nested study design. All were free of myocardial infarction at baseline.

Myocardial Infarction

A unique Civil Person Registration number is assigned to individuals living in Denmark, allowing follow-up of all individuals in Danish heath registries without losses to follow-up. Information on myocardial infarction was collected between April 1977 and December 2018 from the national Danish Patient Registry and the national Danish Causes of Death Registry. Myocardial infarction was defined according to the WHO International Classification of Diseases, Eighth Revision (ICD-8) code 410 and Tenth Revision (ICD-10) codes I21-22. Using Civil Person Registration numbers, exact dates of emigration (n = 74) or death (n = 9209) were obtained through the Danish Civil Registration System.

Laboratory Analyses

High-throughput NMR spectroscopy was used to measure cholesterol content of large VLDL, small VLDL, IDL, and LDL in nonfasting serum samples collected at the baseline examination. Serum samples were stored at −80 °C to maintain lipoprotein composition during long-term storage until NMR analysis. The NMR analyses were carried out using the Nightingale assay at the Metabolomics Core Facility at the University of Bristol (20). In addition, nonfasting plasma concentrations of total cholesterol, triglycerides, HDL cholesterol, glucose, and high-sensitivity C-reactive protein were analyzed using standard hospital assays.

Other Covariates at Baseline

At the date of examination, weight, height, and waist circumference were measured. Weight (in kg) divided by measured height squared (in m2) was used to calculate body mass index (BMI;  kg/m2). An automated blood pressure device was used to measure systolic blood pressure. Self-reported covariates included current smoking status, time since last meal (hours), alcohol intake (g/week), use of lipid-lowering therapy, and low education, defined as <3 years of education following mandatory primary school. Self-reported low physical activity in leisure time was defined as <4 h of low-intensity physical activity or <2 h of high-intensity physical activity per week. Type 2 diabetes mellitus was defined as ICD-8 code 250 and ICD-10 codes E11, E13, and E14, obtained from the national Danish Patient Registry; self-reported disease; use of antidiabetic medication; or nonfasting plasma glucose >11 mmol/L (180 mg/dL).

Statistical Analysis

We used STATA/SE 13.1 (StataCorp). Based on the WHO classification, individuals were divided into 4 BMI groups as underweight (<18.5), normal weight (18.5–24.9), overweight (25–29.9), and obese (≥30). Missing information (1.2% of all covariate information) on systolic blood pressure, alcohol intake, physical activity, educational level, and time since last meal was imputed based on sex and age, using multivariate imputation with chained equations. If only individuals with complete information were included, results were like those reported.

The association between BMI and directly measured cholesterol in large and small VLDL, in IDL, and in LDL were shown as median concentrations with interquartile ranges across BMI deciles. On a continuous scale, the association between BMI and directly measured cholesterol in the 4 lipoprotein fractions were graphed as smoothed values with 95% CIs from a Kernel-weighted local polynomial regression. Because of instability of relationships at outlier values of BMI, only individuals with BMI between 18.5 and 50 were included in most analyses; individuals with BMI <18.5 may have low BMI either naturally or because of disease, which can introduce bias. Individuals with BMI >50 were few and also could bias estimates because of instability of outlier values. Linear regression of continuous BMI vs directly measured cholesterol concentration in lipoprotein fractions was used to estimate the coefficient of determination R2 and P values.

The absolute 10-year risk of myocardial infarction as a function of concentration of cholesterol in large VLDL, small VLDL, IDL, and LDL, stratified by BMI categories (normal weight, overweight, and obesity) was estimated in 4 separate models for each lipoprotein fraction using Poisson regression adjusted for age, sex, smoking status, physical activity in leisure time, educational level, time since last meal, alcohol intake, and use of lipid-lowering therapy. Quartiles of cholesterol in lipoprotein fractions were calculated in 28 743 individuals (individuals with normal weight, overweight, or obesity combined, excluding 267 individuals with underweight) of whom 2289 developed myocardial infarction. Absolute risks were presented as estimated incidence rates (events per 10 years of follow-up) as percentages.

To identify possible explanatory factors from obesity to myocardial infarction, we used mediation analysis—a statistical method used to identify and explain possible mechanisms behind an observed association between 2 variables through a third variable. We hypothesized that part of the observed association between high BMI and risk of myocardial infarction may operate through the intermediate variables of cholesterol in large VLDL, cholesterol in small VLDL, cholesterol in IDL, cholesterol in LDL, systolic blood pressure, or diabetes mellitus. Before mediation analysis, we tested associations between BMI and myocardial infarction, between BMI and intermediate variables, and between intermediate variables and myocardial infarction. Analyses were done in 28 743 individuals (individuals with normal weight, overweight, or obesity combined, excluding 267 individuals with underweight) of whom 2289 developed myocardial infarction, adjusted for age, sex, smoking status, physical activity in leisure time, educational level, time since last meal, alcohol intake, and use of lipid-lowering therapy. Classical methods—for example, the method by Baron and Kenny (21)—use linear models to test for mediation, which are difficult to interpret for probit models. Therefore, we used the method by Karlson, Holm, and Breen (22) developed for application in nonlinear models to separately estimate the influence of explanatory factors for the increased risk of myocardial infarction associated with high BMI (i.e., overweight and obesity).

Results

Individuals with obesity vs normal weight were more likely to have lower levels of education (73% vs 59%) and physical activity (68% vs 51%), use lipid-lowering therapy (18% vs 8.2%), have higher plasma triglyceride (2.0 vs 1.2 mmol/L; 173 vs 105 mg/dL), have lower HDL cholesterol (1.3 vs 1.8 mmol/L; 50 vs 68 mg/dL), have higher systolic blood pressure (148 vs 137 mmHg), have higher C-reactive protein concentrations (2.6 vs 1.3 mg/L), have diabetes mellitus (13% vs 3.3%), and were less likely to be women (50% vs 62%) and current smokers (20% vs 27%) (Table 1). No major correlations between age and cholesterol in large VLDL, small VLDL, IDL, or LDL were observed (Supplemental Fig. 1).

Table 1
Open in new tab

Baseline characteristics by weight categories according to BMI.

Obese BMI ≥30Overweight BMI 25–29.9Normal weight BMI 18.5–24.9Underweight BMI <18.5
N536811 93011 445267
Potential confounders
 Age, years63 (53–71)63 (53–73)60 (49–72)63 (51–75)
 Sex, women2690 (50)5087 (43)7067 (62)230 (86)
 Current smoker1073 (20)2550 (21)3103 (27)128 (48)
 Low level of education3897 (73)7579 (64)6776 (59)169 (64)
 Low physical activity3585 (68)6394 (54)58 046 (51)167 (63)
 Lipid-lowering therapy943 (18)1577 (13)937 (8.2)17 (6.4)
 Alcohol, g/week96 (36–192)120 (48–216)108 (48–180)84 (24–144)
 Time since last meal, h2.5 (1.5–3.5)2.5 (1.5–3.5)2.5 (1.5–3.5)2.5 (1.5–3.5)
Lipid measurements
 Total cholesterol
  mmol/L5.7 (4.9–6.5)5.7 (5.0–6.5)5.5 (4.9–6.3)5.5 (4.8–6.3)
  mg/dL220 (189–251)220 (193–251)213 (189–244)213 (186–242)
 Total triglycerides
  mmol/L2.0 (1.4–2.8)1.6 (1.2–2.4)1.2 (0.9–1.7)1.0 (0.7–1.3)
  mg/dL173 (122–246)145 (102–209)105 (77–149)86 (65–116)
 HDL cholesterol
  mmol/L1.3 (1.1–1.6)1.5 (1.2–1.8)1.8 (1.4–2.1)2.0 (1.6–2.4)
  mg/dL50 (41–62)57 (46–70)68 (55–82)78 (63–94)
Within biological pathway
 Systolic blood pressure, mmHg148 (135–162)143 (130–158)137 (122–153)130 (119–150)
 C-reactive protein, mg/L2.6 (1.6–4.8)1.7 (1.1–3.0)1.3 (0.65–2.1)1.2 (0.51–2.1)
 Diabetes mellitus719 (13)779 (6.5)376 (3.3)7 (2.6)
Obese BMI ≥30Overweight BMI 25–29.9Normal weight BMI 18.5–24.9Underweight BMI <18.5
N536811 93011 445267
Potential confounders
 Age, years63 (53–71)63 (53–73)60 (49–72)63 (51–75)
 Sex, women2690 (50)5087 (43)7067 (62)230 (86)
 Current smoker1073 (20)2550 (21)3103 (27)128 (48)
 Low level of education3897 (73)7579 (64)6776 (59)169 (64)
 Low physical activity3585 (68)6394 (54)58 046 (51)167 (63)
 Lipid-lowering therapy943 (18)1577 (13)937 (8.2)17 (6.4)
 Alcohol, g/week96 (36–192)120 (48–216)108 (48–180)84 (24–144)
 Time since last meal, h2.5 (1.5–3.5)2.5 (1.5–3.5)2.5 (1.5–3.5)2.5 (1.5–3.5)
Lipid measurements
 Total cholesterol
  mmol/L5.7 (4.9–6.5)5.7 (5.0–6.5)5.5 (4.9–6.3)5.5 (4.8–6.3)
  mg/dL220 (189–251)220 (193–251)213 (189–244)213 (186–242)
 Total triglycerides
  mmol/L2.0 (1.4–2.8)1.6 (1.2–2.4)1.2 (0.9–1.7)1.0 (0.7–1.3)
  mg/dL173 (122–246)145 (102–209)105 (77–149)86 (65–116)
 HDL cholesterol
  mmol/L1.3 (1.1–1.6)1.5 (1.2–1.8)1.8 (1.4–2.1)2.0 (1.6–2.4)
  mg/dL50 (41–62)57 (46–70)68 (55–82)78 (63–94)
Within biological pathway
 Systolic blood pressure, mmHg148 (135–162)143 (130–158)137 (122–153)130 (119–150)
 C-reactive protein, mg/L2.6 (1.6–4.8)1.7 (1.1–3.0)1.3 (0.65–2.1)1.2 (0.51–2.1)
 Diabetes mellitus719 (13)779 (6.5)376 (3.3)7 (2.6)

Data are median (IQR) for continuous variables and number (percentage) for categorical variables. Baseline characteristics were measured at the date of examination. Low education was defined as <3 years following the mandatory primary school. Low physical activity in leisure time was defined as <4 h of low-intensity physical activity or <2 h of high-intensity physical activity per week. Type 2 diabetes mellitus was defined as ICD codes (ICD-8 250 and ICD-10 E11, E13, and E14) obtained from the national Danish Patient Registry, self-reported disease, use of antidiabetic medication, or nonfasting plasma glucose >11 mmol/L (198 mg/dL).

Table 1
Open in new tab

Baseline characteristics by weight categories according to BMI.

Obese BMI ≥30Overweight BMI 25–29.9Normal weight BMI 18.5–24.9Underweight BMI <18.5
N536811 93011 445267
Potential confounders
 Age, years63 (53–71)63 (53–73)60 (49–72)63 (51–75)
 Sex, women2690 (50)5087 (43)7067 (62)230 (86)
 Current smoker1073 (20)2550 (21)3103 (27)128 (48)
 Low level of education3897 (73)7579 (64)6776 (59)169 (64)
 Low physical activity3585 (68)6394 (54)58 046 (51)167 (63)
 Lipid-lowering therapy943 (18)1577 (13)937 (8.2)17 (6.4)
 Alcohol, g/week96 (36–192)120 (48–216)108 (48–180)84 (24–144)
 Time since last meal, h2.5 (1.5–3.5)2.5 (1.5–3.5)2.5 (1.5–3.5)2.5 (1.5–3.5)
Lipid measurements
 Total cholesterol
  mmol/L5.7 (4.9–6.5)5.7 (5.0–6.5)5.5 (4.9–6.3)5.5 (4.8–6.3)
  mg/dL220 (189–251)220 (193–251)213 (189–244)213 (186–242)
 Total triglycerides
  mmol/L2.0 (1.4–2.8)1.6 (1.2–2.4)1.2 (0.9–1.7)1.0 (0.7–1.3)
  mg/dL173 (122–246)145 (102–209)105 (77–149)86 (65–116)
 HDL cholesterol
  mmol/L1.3 (1.1–1.6)1.5 (1.2–1.8)1.8 (1.4–2.1)2.0 (1.6–2.4)
  mg/dL50 (41–62)57 (46–70)68 (55–82)78 (63–94)
Within biological pathway
 Systolic blood pressure, mmHg148 (135–162)143 (130–158)137 (122–153)130 (119–150)
 C-reactive protein, mg/L2.6 (1.6–4.8)1.7 (1.1–3.0)1.3 (0.65–2.1)1.2 (0.51–2.1)
 Diabetes mellitus719 (13)779 (6.5)376 (3.3)7 (2.6)
Obese BMI ≥30Overweight BMI 25–29.9Normal weight BMI 18.5–24.9Underweight BMI <18.5
N536811 93011 445267
Potential confounders
 Age, years63 (53–71)63 (53–73)60 (49–72)63 (51–75)
 Sex, women2690 (50)5087 (43)7067 (62)230 (86)
 Current smoker1073 (20)2550 (21)3103 (27)128 (48)
 Low level of education3897 (73)7579 (64)6776 (59)169 (64)
 Low physical activity3585 (68)6394 (54)58 046 (51)167 (63)
 Lipid-lowering therapy943 (18)1577 (13)937 (8.2)17 (6.4)
 Alcohol, g/week96 (36–192)120 (48–216)108 (48–180)84 (24–144)
 Time since last meal, h2.5 (1.5–3.5)2.5 (1.5–3.5)2.5 (1.5–3.5)2.5 (1.5–3.5)
Lipid measurements
 Total cholesterol
  mmol/L5.7 (4.9–6.5)5.7 (5.0–6.5)5.5 (4.9–6.3)5.5 (4.8–6.3)
  mg/dL220 (189–251)220 (193–251)213 (189–244)213 (186–242)
 Total triglycerides
  mmol/L2.0 (1.4–2.8)1.6 (1.2–2.4)1.2 (0.9–1.7)1.0 (0.7–1.3)
  mg/dL173 (122–246)145 (102–209)105 (77–149)86 (65–116)
 HDL cholesterol
  mmol/L1.3 (1.1–1.6)1.5 (1.2–1.8)1.8 (1.4–2.1)2.0 (1.6–2.4)
  mg/dL50 (41–62)57 (46–70)68 (55–82)78 (63–94)
Within biological pathway
 Systolic blood pressure, mmHg148 (135–162)143 (130–158)137 (122–153)130 (119–150)
 C-reactive protein, mg/L2.6 (1.6–4.8)1.7 (1.1–3.0)1.3 (0.65–2.1)1.2 (0.51–2.1)
 Diabetes mellitus719 (13)779 (6.5)376 (3.3)7 (2.6)

Data are median (IQR) for continuous variables and number (percentage) for categorical variables. Baseline characteristics were measured at the date of examination. Low education was defined as <3 years following the mandatory primary school. Low physical activity in leisure time was defined as <4 h of low-intensity physical activity or <2 h of high-intensity physical activity per week. Type 2 diabetes mellitus was defined as ICD codes (ICD-8 250 and ICD-10 E11, E13, and E14) obtained from the national Danish Patient Registry, self-reported disease, use of antidiabetic medication, or nonfasting plasma glucose >11 mmol/L (198 mg/dL).

The correlation between concentrations of VLDL cholesterol and LDL cholesterol was generally low, whereas correlation of cholesterol in large and small VLDL was high, as was the correlation between cholesterol in IDL and LDL (Table 2). Furthermore, coefficient of determination R2-values between VLDL cholesterol and LDL cholesterol were lowest in individuals with obesity compared with individuals with normal weight.

Table 2
Open in new tab

Intercorrelation shown as coefficient of determination R2 values between cholesterol content in large and small VLDL, IDL, and LDL in individuals with normal weight, overweight, or obesity.

Cholesterol contentLarge VLDLSmall VLDLIDLLDL
Normal weight
 Large VLDL1.0
 Small VLDL0.81.0
 IDL0.20.71.0
 LDL0.20.60.981.0
Overweight
 Large VLDL1.0
 Small VLDL0.81.0
 IDL0.20.71.0
 LDL0.10.60.971.0
Obese
 Large VLDL1.0
 Small VLDL0.81.0
 IDL0.10.61.0
 LDL0.030.50.971.0
Cholesterol contentLarge VLDLSmall VLDLIDLLDL
Normal weight
 Large VLDL1.0
 Small VLDL0.81.0
 IDL0.20.71.0
 LDL0.20.60.981.0
Overweight
 Large VLDL1.0
 Small VLDL0.81.0
 IDL0.20.71.0
 LDL0.10.60.971.0
Obese
 Large VLDL1.0
 Small VLDL0.81.0
 IDL0.10.61.0
 LDL0.030.50.971.0
Table 2
Open in new tab

Intercorrelation shown as coefficient of determination R2 values between cholesterol content in large and small VLDL, IDL, and LDL in individuals with normal weight, overweight, or obesity.

Cholesterol contentLarge VLDLSmall VLDLIDLLDL
Normal weight
 Large VLDL1.0
 Small VLDL0.81.0
 IDL0.20.71.0
 LDL0.20.60.981.0
Overweight
 Large VLDL1.0
 Small VLDL0.81.0
 IDL0.20.71.0
 LDL0.10.60.971.0
Obese
 Large VLDL1.0
 Small VLDL0.81.0
 IDL0.10.61.0
 LDL0.030.50.971.0
Cholesterol contentLarge VLDLSmall VLDLIDLLDL
Normal weight
 Large VLDL1.0
 Small VLDL0.81.0
 IDL0.20.71.0
 LDL0.20.60.981.0
Overweight
 Large VLDL1.0
 Small VLDL0.81.0
 IDL0.20.71.0
 LDL0.10.60.971.0
Obese
 Large VLDL1.0
 Small VLDL0.81.0
 IDL0.10.61.0
 LDL0.030.50.971.0

Distribution of Cholesterol in Lipoprotein Fractions According to BMI Categories

Distributions of cholesterol in large and small VLDL were skewed toward higher concentrations in individuals with overweight and obesity compared with those who were normal weight or underweight (Fig. 1). In contrast, distributions of cholesterol in IDL and LDL were similar for individuals who were underweight, normal weight, overweight, or obese.

Distribution of cholesterol content in the large VLDL, small VLDL, IDL, and LDL according to BMI categories. Density plots for directly measured cholesterol in large VLDL, small VLDL, IDL, and LDL in 29,010 individuals from the Copenhagen General Population Study divided into categories based on BMI. Blood samples were drawn at examination.
Fig. 1.

Distribution of cholesterol content in the large VLDL, small VLDL, IDL, and LDL according to BMI categories. Density plots for directly measured cholesterol in large VLDL, small VLDL, IDL, and LDL in 29,010 individuals from the Copenhagen General Population Study divided into categories based on BMI. Blood samples were drawn at examination.

Open in new tabDownload slide

Median cholesterol in large VLDL was 0.04 mmol/L (interquartile range [IQR], 0.02–0.06 mmol/L; 1.4 mg/dL [IQR, 0.7–2.3 mg/dL]) in individuals who were underweight, 0.06 mmol/L (IQR, 0.03–0.1 mmol/L; 2.2 mg/dL [IQR, 1.1–3.8 mg/dL]) in individuals with normal weight, 0.09 mmol/L (IQR, 0.05–0.1 mmol/L; 3.5 mg/dL [IQR, 1.9–5.8 mg/dL]) in individuals with overweight, and 0.12 mmol/L (IQR, 0.07–0.2 mmol/L; 4.5 mg/dL (IQR, 2.6–6.9 mg/dL]) in individuals with obesity. For cholesterol content in small VLDL, corresponding medians were 0.5 mmol/L (IQR, 0.4–0.6 mmol/L; 18 mg/dL (IQR, 15–22 mg/dL]) in individuals who were underweight, 0.5 mmol/L (IQR, 0.4–0.6 mmol/L; 20 mg/dL (IQR, 16–25 mg/dL]) in individuals with normal weight, 0.6 mmol/L (IQR, 0.5–0.7 mmol/L; 23 mg/dL (IQR, 19–29 mg/dL]) in individuals with overweight, and 0.6 mmol/L (IQR, 0.5–0.8 mmol/L; 25 mg/dL (IQR, 20–30 mg/dL]) in individuals with obesity (Fig. 1). In contrast, median cholesterol in IDL and in LDL was similar across BMI categories. Median cholesterol in LDL ranged from 1.5 mmol/L (IQR, 1.2–1.8 mmol/L; 58 mg/dL (IQR, 46–70 mg/dL]) in individuals who were underweight to 1.4 mmol/L (IQR, 1.1–1.7 mmol/L; 55 mg/dL (IQR, 44–67 mg/dL]) in individuals with obesity. Results were similar after exclusion of individuals using lipid-lowering therapy.

Correlation between BMI and Cholesterol in Lipoprotein Fractions

With higher BMI, concentrations of cholesterol were higher in large and small VLDL but slightly lower in IDL and LDL (Fig. 2). Concentrations of cholesterol in large and small VLDL were positively correlated with higher BMI, with a plateau at 0.14 mmol/L (5.4 mg/dL) for large VLDL cholesterol and 0.66 mmol/L (26 mg/dL) for small VLDL cholesterol at BMI above approximately 33 (Fig. 2, right-top section). In contrast, concentrations of cholesterol in IDL and LDL were stable up to BMI of approximately 26, followed by a slight downward trend (Fig. 2, right-bottom section).

Association between BMI and directly measured cholesterol in large VLDL, small VLDL, IDL, and LDL. Left section shows the association between BMI and directly measured cholesterol in large VLDL, small VLDL, IDL, and LDL by median concentrations with IQRs across BMI deciles. Bar color indicates BMI group, with green for normal weight, orange for overweight, and red for obese. Right section shows smoothed values with 95% CIs from a Kernel-weighted local polynomial regression of cholesterol in large and small VLDL, IDL and LDL vs BMI. Only individuals with BMI of 18.5–50 were included because few individuals were outside these limits, and thus very wide confidence intervals were at these extreme values. Linear regression was used to estimate coefficient of determination R2 and P values.
Fig. 2.

Association between BMI and directly measured cholesterol in large VLDL, small VLDL, IDL, and LDL. Left section shows the association between BMI and directly measured cholesterol in large VLDL, small VLDL, IDL, and LDL by median concentrations with IQRs across BMI deciles. Bar color indicates BMI group, with green for normal weight, orange for overweight, and red for obese. Right section shows smoothed values with 95% CIs from a Kernel-weighted local polynomial regression of cholesterol in large and small VLDL, IDL and LDL vs BMI. Only individuals with BMI of 18.5–50 were included because few individuals were outside these limits, and thus very wide confidence intervals were at these extreme values. Linear regression was used to estimate coefficient of determination R2 and P values.

Open in new tabDownload slide

Coefficient of determination R2 values from linear regression of BMI and directly measured cholesterol in lipoprotein fractions were 10% for cholesterol in large VLDL (P < 0.001), 8% for cholesterol in small VLDL (P < 0.001), 0.06% for cholesterol in IDL (P < 0.001), and 0.2% for cholesterol in LDL (P < 0.001) (Fig. 2, left).

Absolute Risk of Myocardial Infarction by BMI Categories and Quartiles of Cholesterol in Lipoprotein Fractions

The 10-year absolute risk of myocardial infarction was higher with increasing concentrations of directly measured cholesterol content in each of large VLDL, small VLDL, IDL, and LDL, regardless of BMI category (Fig. 3). Likewise, individuals with obesity had higher absolute risks of myocardial infarction compared with individuals with normal weight and overweight, regardless of lipoprotein concentrations. These results indicate that cholesterol content in all lipoprotein subfractions is related to risk of myocardial infarction; however, the risk varies according to BMI category.

Absolute 10-year risk of myocardial infarction by BMI categories according to quartiles of cholesterol content of large VLDL, small VLDL, IDL, and LDL. The absolute 10-year risk of myocardial infarction as a function of cholesterol content in large VLDL, small VLDL, IDL, and LDL divided into quartiles and stratified by BMI categories (normal weight, overweight, and obese) in 28,743 individuals, of whom 2289 individuals developed myocardial infarction. Numbers of individuals and events in each cell are presented in the right panel. Absolute risks are presented as estimated incidence rates (events per 10 years of follow-up) in percentage and based on quartiles of cholesterol in the specific lipoprotein subfraction calculated using all individuals with normal weight, overweight, and obesity combined.
Fig. 3.

Absolute 10-year risk of myocardial infarction by BMI categories according to quartiles of cholesterol content of large VLDL, small VLDL, IDL, and LDL. The absolute 10-year risk of myocardial infarction as a function of cholesterol content in large VLDL, small VLDL, IDL, and LDL divided into quartiles and stratified by BMI categories (normal weight, overweight, and obese) in 28,743 individuals, of whom 2289 individuals developed myocardial infarction. Numbers of individuals and events in each cell are presented in the right panel. Absolute risks are presented as estimated incidence rates (events per 10 years of follow-up) in percentage and based on quartiles of cholesterol in the specific lipoprotein subfraction calculated using all individuals with normal weight, overweight, and obesity combined.

Open in new tabDownload slide

In the highest quartile of large VLDL cholesterol, the 10-year absolute risk of myocardial infarction was 7.5% for individuals with normal weight, 8.8% for individuals with overweight, and 10.3% for individuals with obesity. Corresponding risks were 8.4%, 9.5%, and 11.2% for the highest quartile of small VLDL cholesterol; 7.7%, 9.4%, and 11.5% for the highest quartile of IDL cholesterol; and 7.8%, 9.5%, and 11.7% for the highest quartile of LDL cholesterol. Because the 10-year absolute risk estimates are based on multivariable adjusted regression models for each lipoprotein subfraction, the sum of the 10-year absolute risk for each BMI category differs across lipoprotein subfractions.

Explained Excess Risk in Obesity

We performed mediation analyses to assess the excess risk of BMI on myocardial infarction explained through cholesterol in large VLDL, small VLDL, IDL, LDL, systolic blood pressure, and diabetes mellitus. Directly measured cholesterol in large VLDL explained 35% (95% CI, 23%–47%) of the excess risk of myocardial infarction from higher BMI, and cholesterol in small VLDL explained 37% (95% CI, 26%–50%) (Fig. 4); however, these 2 fractions are highly correlated (Table 2), and cholesterol content in large and small VLDL combined explained 40% (95% CI, 27%–53%) of the excess risk. In contrast, cholesterol in IDL and LDL did not explain excess risk in the association between BMI and myocardial infarction. Systolic blood pressure explained 17% (95% CI, 11%–23%), and diabetes mellitus explained 8.6% (95% CI, 3.2%–14%) of the excess risk of myocardial infarction from obesity. Corresponding values after exclusion of individuals using lipid-lowering therapies were 39% (95% CI, 24%–55%) for cholesterol in large VLDL, 42% (95% CI, 27%–58%) for cholesterol in small VLDL, 46% (95% CI, 29%–63%) for cholesterol in large and small VLDL combined, 1.5% (95% CI, −0.2% to 3.2%) for cholesterol in IDL, −1.5% (95% CI, −3.4% to 0.3%) for cholesterol in LDL, 19% (95% CI, 11%–27%) for systolic blood pressure, and 9.3% (95% CI, 3.5%–15%) for diabetes mellitus.

Explained excess risk in obesity. Percentage of excess risk of myocardial infarction from obesity explained by intermediate variables in 28 743 individuals with normal weight, overweight, and obesity combined, of whom 2289 individuals developed myocardial infarction during a mean of 10 years of follow-up. The Karlson, Holm, and Breen method (22) was used for mediation analysis to separately estimate factors explaining the increased risk of myocardial infarction associated with higher BMI. As possible mediators, we used cholesterol content of large VLDL, small VLDL, IDL, LDL, systolic blood pressure, and diabetes mellitus.
Fig. 4.

Explained excess risk in obesity. Percentage of excess risk of myocardial infarction from obesity explained by intermediate variables in 28 743 individuals with normal weight, overweight, and obesity combined, of whom 2289 individuals developed myocardial infarction during a mean of 10 years of follow-up. The Karlson, Holm, and Breen method (22) was used for mediation analysis to separately estimate factors explaining the increased risk of myocardial infarction associated with higher BMI. As possible mediators, we used cholesterol content of large VLDL, small VLDL, IDL, LDL, systolic blood pressure, and diabetes mellitus.

Open in new tabDownload slide

Hazard ratios for myocardial infarction according to cholesterol in VLDL large and small combined and IDL and LDL combined, stratified by BMI categories, are shown in Supplemental Fig. 2.

Discussion

In 29 010 individuals selected for NMR spectroscopy measurements of cholesterol content of lipoprotein fractions from among 109 751 individuals from the Copenhagen General Population Study, we found that cholesterol in VLDL explained a large fraction of the excess risk of myocardial infarction in individuals with obesity. That said, individuals with obesity with the highest cholesterol concentrations in large VLDL, small VLDL, IDL, or LDL had similar 10-year absolute risk of myocardial infarction. These novel findings support a focus on cholesterol in VLDL for prevention of myocardial infarction and atherosclerotic cardiovascular disease in individuals with obesity.

Our findings seem biologically plausible because obesity is associated with an altered lipid profile, probably through an increased formation and decreased clearance of VLDL particles (1, 2). In this study, we confirmed that cholesterol in VLDL particles was increased in obesity. Such VLDL particles exchange triglycerides for cholesterol with LDL particles, forming VLDL particles enriched in cholesterol and altering the LDL composition with higher particle numbers and smaller LDL particle size (small dense LDL) (1, 2). As a result, the obesity-related lipid profile is characterized by higher concentrations of VLDL cholesterol and, to a lesser extent, higher concentrations of LDL cholesterol. The increased concentration of VLDL cholesterol likely leads to more of these particles penetrating into the arterial intima (23). Once VLDL particles have entered the arterial intima, they may get trapped (24, 25), causing local inflammation through triglyceride degradation, foam cell formation (26), and atherosclerotic plaque formation and rupture, which ultimately will lead to atherosclerotic cardiovascular disease and myocardial infarction (12, 27–29).

Consistent with previous findings from both observational and mendelian randomization (30) studies, we observed a positive correlation between BMI and cholesterol in large and small VLDL, whereas only minimal correlations between BMI and cholesterol in LDL and IDL were observed (18, 19, 31–33). However, in the present study, increased concentrations of cholesterol in all lipoprotein subfractions were associated with higher absolute risk of myocardial infarction regardless of BMI, indicating that the cholesterol contents in large and small VLDL, IDL, and LDL are all risk factors for myocardial infarction independent of overweight and obesity. This finding is in line with well-known causal associations of increased cholesterol in VLDL, IDL, and LDL with increased risk of myocardial infarction and atherosclerotic cardiovascular disease (11–17).

Results from the present study show that cholesterol in triglyceride-rich VLDL, systolic blood pressure, and diabetes mellitus explains a large proportion of the excess risk of myocardial infarction associated with obesity. Our findings are consistent with a previous study of 84 684 individuals (19) that found increased concentration of nonfasting remnant cholesterol and increased blood pressure partly explained the increased risk of ischemic heart disease associated with obesity. In addition, in a study of 188 577 individuals, increased triglyceride concentration (a substitute marker for VLDL cholesterol) and poor glycemic control appeared to explain a large proportion of the excess risk of coronary heart disease associated with BMI (18). Somewhat in contrast to our findings, Varbo et al. (19) found that LDL cholesterol explained 8% of the excess risk of ischemic heart disease associated with obesity; however, that study failed to find a causal association between BMI and LDL cholesterol, and thus results from their mediation analysis should be interpreted cautiously. Furthermore, Varbo et al. (19) used calculated remnant cholesterol (total cholesterol minus LDL cholesterol minus HDL cholesterol), which may also have influenced their findings, as calculated remnant cholesterol may be slightly higher than directly measured remnant cholesterol (32).

To date, no large clinical randomized trials have investigated whether lowering of VLDL cholesterol in general or in individuals with obesity specifically can lower the risk of atherosclerotic cardiovascular disease and myocardial infarction. However, the cholesterol content of VLDL is highly correlated with triglyceride concentration (11), and a recent systematic review and metaregression analysis of randomized controlled trials reported a relative risk for major atherosclerotic cardiovascular events of 0.84 (95% CI, 0.75–0.94) after a 1 mmol/L (88 mg/dL) reduction in plasma triglycerides (34). Only one of the included studies recruited individuals based on high triglyceride concentrations, the so-called REDUCE-IT trial (35). That study observed a relative risk for major atherosclerotic cardiovascular events of 0.75 (95% CI, 0.68–0.83) after a 0.44 mmol/L (39 mg/dL) reduction in plasma triglycerides, suggesting that individuals with high concentrations of triglyceride- and cholesterol-rich VLDL may benefit most from lowering of these particles.

Strengths of the present study include the large study population with ample statistical power due to the nested cohort study design, with 2306 individuals developing myocardial infarction during 10 years of follow-up. In addition, in contrast to previous studies, we were able to measure cholesterol in large and small VLDL, IDL, and LDL directly in all individuals using individual participant data. Finally, given the nature of the national Danish registries, which are complete for all practical purposes, we did not lose track of any individual during follow-up.

Possible limitations of our study include the fact that NMR spectroscopy measurement has not yet been validated because it is a novel method and thus has not yet entered standard laboratory practice. Therefore, absolute values of cholesterol content in large and small VLDL, IDL, and LDL are not standardized and should be interpreted with caution; the validation of the IDL cholesterol measurement has previously raised concerns, and comparison with previous validated methods such as β quantification should be done with caution. However, because all were measured directly using the same NMR spectroscopy measurement, comparison of the different lipoprotein fractions is valid. Another possible limitation is that samples were stored at −80 °C until analysis, which could affect measurements; however, comparison with measurements on fresh samples from 496 individuals indicated that storage at −80 °C did not affect measurement of cholesterol content in large and small VLDL, IDL, and LDL to any major degree. Another potential limitation is the availability and completeness of diagnostic information; however, information on myocardial infarction was collected from the national Danish health registers and is 99.5% correct when validated (36). Another potential limitation is that because we studied only White individuals of Danish descent, our results may not necessarily apply to other ethnic groups; however, we are not aware of any data suggesting that these results should not apply to other ethnicities. Finally, we were not able to include small dense LDL and lipoprotein(a) in the analysis; however, because small dense LDL and lipoprotein(a) are both related to LDL and not to VLDL, neither of these particles can explain our observation that cholesterol in large and small VLDL explains a large fraction of the increased myocardial infarction risk in individuals with obesity.

Future research could apply 2-stage and multivariable mendelian randomization methods (37, 38) to explore how adiposity influences different aspects of lipoprotein biology that, in turn, increase the risk of atherosclerotic cardiovascular disease and myocardial infarction. This approach could include investigation of the potential role of adiposity-responsive ApoB levels (39) as a rate-limiting step in the genesis of coronary disease (40).

Conclusions

VLDL cholesterol explains a large fraction of the excess myocardial infarction risk in individuals with obesity. These novel findings support focus on cholesterol in VLDL for prevention of myocardial infarction and atherosclerotic cardiovascular disease in individuals with obesity.

Supplemental Material

Supplemental material is available at Clinical Chemistry online.

Author Contributions: All authors confirmed they have contributed to the intellectual content of this paper and have met the following 4 requirements: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; (c) final approval of the published article; and (d) agreement to be accountable for all aspects of the article thus ensuring that questions related to the accuracy or integrity of any part of the article are appropriately investigated and resolved.

M.Ø. Johansen, statistical analysis; S.F. Nielsen, statistical analysis; B.G. Nordestgaard, financial support, provision of study material or patients.

Authors’ Disclosures or Potential Conflicts of Interest: Upon manuscript submission, all authors completed the author disclosure form. Disclosures and/or potential conflicts of interest:

Employment or Leadership: None declared.

Consultant or Advisory Role: B.G. Nordestgaard, AstraZeneca, Sanofi, Regeneron, Akcea, Amgen, Kowa, Denka Seiken, Amarin, Novartis, Novo Nordisk, and Silence Therapeutics.

Stock Ownership: None declared.

Honoraria: B.G. Nordestgaard, AstraZeneca, Sanofi, Regeneron, Akcea, Amgen, Kowa, Denka Seiken, Amarin, Novartis, Novo Nordisk, and Silence Therapeutics.

Research Funding: This work was supported by Innovation Fund Denmark (grant 9039-00360B to B.G. Nordestgaard) and Overlæge Johan Boserup og Lise Boserups legat (grant 20795-24 to M.Ø. Johansen). G.D. Smith was supported by the Medical Research Council Integrative Epidemiology Unit at the University of Bristol MC_UU_00011/1.

Expert Testimony: None declared.

Patents: None declared.

Role of Sponsor: The funding organizations played no role in the design of study, choice of enrolled patients, review and interpretation of data, preparation of manuscript, or final approval of manuscript.

Acknowledgments: The authors thank staff and participants of the Copenhagen General Population Study for their important work and contributions.

References

1

Franssen
R
,
Monajemi
H
,
Stroes
ESG
,
Kastelein
JJP.
Obesity and dyslipidemia
.
Med Clin North Am
2011
;
95
:
893
–
902
.

Google Scholar

Crossref
Search ADS
PubMed

 
2

Bays
HE
,
Toth
PP
,
Kris-Etherton
PM
,
Abate
N
,
Aronne
LJ
,
Brown
WV
, et al.
Obesity, adiposity, and dyslipidemia: a consensus statement from the National Lipid Association
.
J Clin Lipidol
2013
;
7
:
304
–
83
.

Google Scholar

Crossref
Search ADS
PubMed

 
3

Manson
JE
,
Colditz
GA
,
Stampfer
MJ
,
Willett
WC
,
Rosner
B
,
Monson
RR
, et al.
A prospective study of obesity and risk of coronary heart disease in women
.
N Engl J Med
1990
;
322
:
882
–
9
.

Google Scholar

Crossref
Search ADS
PubMed

 
4

Prospective Studies Collaboration.

Body-mass index and cause-specific mortality in 900 000 adults: collaborative analyses of 57 prospective studies
.
Lancet
2009
;
373
:
1083
–
96
.

Crossref
Search ADS
PubMed

 
5

Nordestgaard
BG
,
Palmer
TM
,
Benn
M
,
Zacho
J
,
Tybjærg-Hansen
A
,
Davey Smith
G
, et al.
The effect of elevated body mass index on ischemic heart disease risk: causal estimates from a mendelian randomisation approach
.
PLoS Med
2012
;
9
:
e1001212
.

Google Scholar

Crossref
Search ADS
PubMed

 
6

Dale
CE
,
Fatemifar
G
,
Palmer
TM
,
White
J
,
Prieto-Merino
D
,
Zabaneh
D
, et al.
Causal associations of adiposity and body fat distribution with coronary heart disease, stroke subtypes, and type 2 diabetes mellitus: a mendelian randomization analysis
.
Circulation
2017
;
135
:
2373
–
88
.

Google Scholar

Crossref
Search ADS
PubMed

 
7

Bogers
RP
,
Bemelmans
WJE
,
Hoogenveen
RT
,
Boshuizen
HC
,
Woodward
M
,
Knekt
P
, et al.
Association of overweight with increased risk of coronary heart disease partly independent of blood pressure and cholesterol levels. a meta-analysis of 21 cohort studies including more than 300 000 persons
.
Arch Intern Med
2007
;
167
:
1720
.

Google Scholar

Crossref
Search ADS
PubMed

 
8

Lu
Y
,
Hajifathalian
K
,
Ezzati
M
,
Woodward
M
,
Rimm
EB
,
Danaei
G
, et al.
Metabolic mediators of the effects of body-mass index, overweight, and obesity on coronary heart disease and stroke: a pooled analysis of 97 prospective cohorts with 1·8 million participants
.
Lancet
2014
;
383
:
970
–
83
.

Google Scholar

Crossref
Search ADS
PubMed

 
9

Bakhtiyari
M
,
Schmidt
N
,
Hadaegh
F
,
Khalili
D
,
Mansournia
N
,
Asgari
S
, et al.
Direct and indirect effects of central and general adiposity on cardiovascular diseases: the Tehran Lipid and Glucose Study
.
Eur J Prev Cardiolog
2018
;
25
:
1170
–
81
.

Google Scholar

Crossref
Search ADS

 
10

Boloukat
RR
,
Ramezankhani
A
,
Hasheminia
M
,
Tasdighi
E
,
Azizi
F
,
Hadaegh
F.
Impact of blood pressure, cholesterol and glucose in the association between adiposity measures and coronary heart disease and stroke among Iranian population
.
Clin Nutr
2018
;
37
:
2060
–
7
.

Google Scholar

Crossref
Search ADS
PubMed

 
11

Varbo
A
,
Benn
M
,
Tybjærg-Hansen
A
,
Jørgensen
AB
,
Frikke-Schmidt
R
,
Nordestgaard
BG.
Remnant cholesterol as a causal risk factor for ischemic heart disease
.
J Am Coll Cardiol
2013
;
61
:
427
–
36
.

Google Scholar

Crossref
Search ADS
PubMed

 
12

Varbo
A
,
Benn
M
,
Tybjærg-Hansen
A
,
Nordestgaard
BG.
Elevated remnant cholesterol causes both low-grade inflammation and ischemic heart disease, whereas elevated low-density lipoprotein cholesterol causes ischemic heart disease without inflammation
.
Circulation
2013
;
128
:
1298
–
309
.

Google Scholar

Crossref
Search ADS
PubMed

 
13

Jørgensen
AB
,
Frikke-Schmidt
R
,
West
AS
,
Grande
P
,
Nordestgaard
BG
,
Tybjærg-Hansen
A.
Genetically elevated non-fasting triglycerides and calculated remnant cholesterol as causal risk factors for myocardial infarction
.
Eur Heart J
2013
;
34
:
1826
–
33
.

Google Scholar

Crossref
Search ADS
PubMed

 
14

Crosby
J
,
Peloso
GM
,
Auer
PL
,
Crosslin
DR
,
Stitziel
NO
,
Lange
LA
, et al.
Loss-of-function mutations in APOC3, triglycerides, and coronary disease
.
N Engl J Med
2014
;
371
:
22
–
31
.

Google Scholar

Crossref
Search ADS
PubMed

 
15

Jørgensen
AB
,
Frikke-Schmidt
R
,
Nordestgaard
BG
,
Tybjærg-Hansen
A.
Loss-of-function mutations in APOC3 and risk of ischemic vascular disease
.
N Engl J Med
2014
;
371
:
32
–
41
.

Google Scholar

Crossref
Search ADS
PubMed

 
16

Helgadottir
A
,
Gretarsdottir
S
,
Thorleifsson
G
,
Hjartarson
E
,
Sigurdsson
A
,
Magnusdottir
A
, et al.
Variants with large effects on blood lipids and the role of cholesterol and triglycerides in coronary disease
.
Nat Genet
2016
;
48
:
634
–
9
.

Google Scholar

Crossref
Search ADS
PubMed

 
17

Khera
AV
,
Won
H-H
,
Peloso
GM
,
O’Dushlaine
C
,
Liu
D
,
Stitziel
NO
, et al.
Association of rare and common variation in the lipoprotein lipase gene with coronary artery disease
.
JAMA
2017
;
317
:
937
.

Google Scholar

Crossref
Search ADS
PubMed

 
18

Xu
L
,
Borges
MC
,
Hemani
G
,
Lawlor
DA.
The role of glycaemic and lipid risk factors in mediating the effect of BMI on coronary heart disease: a two-step, two-sample mendelian randomisation study
.
Diabetologia
2017
;
60
:
2210
–
20
.

Google Scholar

Crossref
Search ADS
PubMed

 
19

Varbo
A
,
Benn
M
,
Smith
GD
,
Timpson
NJ
,
Tybjærg-Hansen
A
,
Nordestgaard
BG.
Remnant cholesterol, low-density lipoprotein cholesterol, and blood pressure as mediators from obesity to ischemic heart disease
.
Circ Res
2015
;
116
:
665
–
73
.

Google Scholar

Crossref
Search ADS
PubMed

 
20

Würtz
P
,
Kangas
AJ
,
Soininen
P
,
Lawlor
DA
,
Davey Smith
G
,
Ala-Korpela
M.
Quantitative serum nuclear magnetic resonance metabolomics in large-scale epidemiology: a primer on -omic technologies
.
Am J Epidemiol
2017
;
186
:
1084
–
96
.

Google Scholar

Crossref
Search ADS
PubMed

 
21

Baron
RM
,
Kenny
DA.
The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations
.
J Pers Soc Psychol
1986
;
51
:
1173
–
82
.

Google Scholar

Crossref
Search ADS
PubMed

 
22

Karlson
KB
,
Holm
A
,
Breen
R.
Comparing regression coefficients between same-sample nested models using logit and probit: a new method
.
Soc Methodol
2012
;
42
:
286
–
313
.

Google Scholar

Crossref
Search ADS

 
23

Nordestgaard
BG
,
Tybjaerg-Hansen
A
,
Lewis
B.
Influx in vivo of low density, intermediate density, and very low density lipoproteins into aortic intimas of genetically hyperlipidemic rabbits. Roles of plasma concentrations, extent of aortic lesion, and lipoprotein particle size as determinants
.
Arterioscler Thromb
1992
;
12
:
6
–
18
.

Google Scholar

Crossref
Search ADS
PubMed

 
24

Shaikh
M
,
Wootton
R
,
Nordestgaard
BG
,
Baskerville
P
,
Lumley
JS
,
La Ville
AE
, et al.
Quantitative studies of transfer in vivo of low density, Sf 12-60, and Sf 60-400 lipoproteins between plasma and arterial intima in humans
.
Arterioscler Thromb
1991
;
11
:
569
–
77
.

Google Scholar

Crossref
Search ADS
PubMed

 
25

Nordestgaard
BG
,
Wootton
R
,
Lewis
B.
Selective retention of VLDL, IDL, and LDL in the arterial intima of genetically hyperlipidemic rabbits in vivo: molecular size as a determinant of fractional loss from the intima–inner media
.
Arterioscler Thromb Vasc Biol
1995
;
15
:
534
–
42
.

Google Scholar

Crossref
Search ADS
PubMed

 
26

Nakashima
Y
,
Fujii
H
,
Sumiyoshi
S
,
Wight
TN
,
Sueishi
K.
Early human atherosclerosis: accumulation of lipid and proteoglycans in intimal thickenings followed by macrophage infiltration
.
Arterioscler Thromb Vasc Biol
2007
;
27
:
1159
–
65
.

Google Scholar

Crossref
Search ADS
PubMed

 
27

Nordestgaard
BG
,
Varbo
A.
Triglycerides and cardiovascular disease
.
Lancet
2014
;
384
:
626
–
35
.

Google Scholar

Crossref
Search ADS
PubMed

 
28

Nordestgaard
BG.
Triglyceride-rich lipoproteins and atherosclerotic cardiovascular disease: new insights from epidemiology, genetics, and biology
.
Circ Res
2016
;
118
:
547
–
63
.

Google Scholar

Crossref
Search ADS
PubMed

 
29

Hansen
SEJ
,
Madsen
CM
,
Varbo
A
,
Nordestgaard
BG.
Low-grade inflammation in the association between mild-to-moderate hypertriglyceridemia and risk of acute pancreatitis: a study of more than 115000 individuals from the general population
.
Clin Chem
2019
;
65
:
321
–
32
.

Google Scholar

Crossref
Search ADS
PubMed

 
30

Davey Smith
G
,
Ebrahim
S.
 “
Mendelian randomization”: can genetic epidemiology contribute to understanding environmental determinants of disease?
Int J Epidemiol
2003
;
32
:
1
–
22
.

Google Scholar

Crossref
Search ADS
PubMed

 
31

Würtz
P
,
Wang
Q
,
Kangas
AJ
,
Richmond
RC
,
Skarp
J
,
Tiainen
M
, et al.
Metabolic signatures of adiposity in young adults: mendelian randomization analysis and effects of weight change
.
PLoS Med
2014
;
11
:
e1001765
.

Google Scholar

Crossref
Search ADS
PubMed

 
32

Varbo
A
,
Freiberg
JJ
,
Nordestgaard
BG.
Remnant cholesterol and myocardial infarction in normal weight, overweight, and obese individuals from the copenhagen general population study
.
Clin Chem
2018
;
64
:
219
–
30
.

Google Scholar

Crossref
Search ADS
PubMed

 
33

Laclaustra
M
,
Lopez-Garcia
E
,
Civeira
F
,
Garcia-Esquinas
E
,
Graciani
A
,
Guallar-Castillon
P
, et al.
LDL cholesterol rises with BMI only in lean individuals: cross-sectional US and Spanish representative data
.
Diabetes Care
2018
;
41
:
2195
–
201
.

Google Scholar

Crossref
Search ADS
PubMed

 
34

Marston
NA
,
Giugliano
RP
,
Im
K
,
Silverman
MG
,
O’Donoghue
ML
,
Wiviott
SD
, et al.
Association between triglyceride lowering and reduction of cardiovascular risk across multiple lipid-lowering therapeutic classes: a systematic review and meta-regression analysis of randomized controlled trials
.
Circulation
2019
;
140
:
1308
–
17
.

Google Scholar

Crossref
Search ADS
PubMed

 
35

Bhatt
DL
,
Steg
PG
,
Miller
M
,
Brinton
EA
,
Jacobson
TA
,
Ketchum
SB
, et al.
Cardiovascular risk reduction with icosapent ethyl for hypertriglyceridemia
.
N Engl J Med
2019
;
380
:
11
–
22
.

Google Scholar

Crossref
Search ADS
PubMed

 
36

Kamstrup
PR
,
Tybjaerg-Hansen
A
,
Steffensen
R
,
Nordestgaard
BG.
Genetically elevated lipoprotein(a) and increased risk of myocardial infarction
.
JAMA
2009
;
301
:
2331
.

Google Scholar

Crossref
Search ADS
PubMed

 
37

Relton
CL
,
Davey Smith
G.
Two-step epigenetic mendelian randomization: a strategy for establishing the causal role of epigenetic processes in pathways to disease
.
Int J Epidemiol
2012
;
41
:
161
–
76
.

Google Scholar

Crossref
Search ADS
PubMed

 
38

Sanderson
E
,
Davey Smith
G
,
Windmeijer
F
,
Bowden
J.
An examination of multivariable mendelian randomization in the single-sample and two-sample summary data settings
.
Int J Epidemiol
2019
;
48
:
713
–
27
.

Google Scholar

Crossref
Search ADS
PubMed

 
39

Dansinger
ML
,
Williams
PT
,
Superko
HR
,
Schaefer
EJ.
Effects of weight change on apolipoprotein B-containing emerging atherosclerotic cardiovascular disease (ASCVD) risk factors
.
Lipids Health Dis
2019
;
18
:
154
.

Google Scholar

Crossref
Search ADS
PubMed

 
40

Richardson
TG
,
Sanderson
E
,
Palmer
TM
,
Ala-Korpela
M
,
Ference
BA
,
Davey Smith
G
, et al.
Evaluating the relationship between circulating lipoprotein lipids and apolipoproteins with risk of coronary heart disease: a multivariable mendelian randomisation analysis
.
PLoS Med
2020
;
17
:
e1003062
.

Google Scholar

Crossref
Search ADS
PubMed

 

Nonstandard Abbreviations:

     
  • IDL

    intermediate-density lipoprotein

    •  
  • NMR

    nuclear magnetic resonance

    •  
  • ICD-8

    International Classification of Diseases, Eighth Revision

    •  
  • ICD-10

    International Classification of Diseases, Tenth Revision

    •  
  • BMI

    body mass index

    •  
  • IQR

    interquartile range

© American Association for Clinical Chemistry 2021. All rights reserved. For permissions, please email: [email protected].
This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://dbpia.nl.go.kr/journals/pages/open_access/funder_policies/chorus/standard_publication_model)
Issue Section:
Articles > Lipids, Lipoproteins, and Cardiovascular Risk Factors
Download all slides
  • Supplementary data

    • Supplementary data

    hvaa290_Supplementary_Data - docx file