GlycA Levels Independently Predict Coronary Artery Calcium Incidence and Progression in the ELSA-Brasil Cohort (Brazilian Longitudinal Study of Adult Health).

Atherosclerosis is an inflammatory disease. Coronary artery calcium (CAC) is a marker of atherosclerotic disease events and mortality risk. Increased GlycA, an emerging marker of inflammation, is associated with a higher risk for coronary artery disease (CAD). However, there is conflicting evidence on whether GlycA predicts subclinical CAD progression. We hypothesized that GlycA can predict subclinical CAC incidence/progression in healthy participants. We included 2,690 ELSA-Brasil cohort participants without cardiovascular/chronic inflammatory disease not receiving statin therapy who had GlycA levels measured and 2 interval CAC assessments between 2010 and 2018. Multivariable logistic and linear regression models were computed to evaluate GlycA as a predictor of CAC incidence and progression. CAC incidence required a baseline CAC of 0. CAC progression required a baseline CAC >0. The mean age of participants was 48.6 ± 7.7 years, 56.7% were women, and 54.6% and 16.1% (429 of 2,690) were White and Black, respectively. The mean CAC interscan period was 5.1 ± 0.9 years, the mean GlycA level was 414.7 ± 65 µmol/L, and the incidence of CAC was 13.1% (280 of 2,129). The GlycA level odds ratio for CAC incidence was 1.002 (95% confidence interval 1.0005 to 1.005, p = 0.016), adjusted for demographics, lifestyle, a family history of early CAD (≤60 years), lipids, and co-morbidities. The GlycA (≤p25 vs ≥p75) odds ratio for CAC progression (Berry definition) was 1.77 (95% confidence interval 1.07 to 2.96, p = 0.03) in a similar multivariable-adjusted model. Higher GlycA levels were associated with CAC incidence and progression in a healthy Brazilian cohort.

Combined Association of Novel and Traditional Inflammatory Biomarkers With Carotid Artery Plaque: GlycA Versus C-Reactive Protein (ELSA-Brasil).

Elevated levels of glycoprotein acetylation (GlycA) and C-reactive protein (CRP) have been associated with carotid artery plaque (CAP). However, it is not yet established if elevations in both inflammatory biomarkers provide incremental association with CAP. This study aimed evaluate the cross-sectional association of high CRP and GlycA with CAP at baseline participants from the ELSA-Brasil adult cohort. Participants with information on CRP, GlycA, and CAP with neither previous cardiovascular disease nor CRP >10 mg/L were included. High GlycA and CRP were defined as values within upper quintile and >3 mg/L, respectively. Participants were classified into 4 groups: 1. nonelevated CRP/GlycA (reference group); 2. elevated CRP alone; 3. elevated GlycA alone; and 4. both elevated. The analysis included 4,126 participants with median age of 50 years-old, being 54.2% of women. Prevalence of CAP was 36.1%. Participants with high CRP had the highest frequency of obesity, whereas participants with high GlycA presented higher cardiovascular risk factor burden and were more likely to have CAP than the reference group (odds ratio [OR] 1.39, 95% confidence interval [CI] 1.11 to 1.73), persisting after multivariable adjustment (OR 1.37, 95% CI 1.02 to 1.83). Participants with both elevated CRP and GlycA were more likely to have CAP in crude (OR 1.35, 95% CI 1.10 to 1.65) but not in adjusted models. The findings suggest potential different biologic pathways between inflammation and carotid atherosclerosis: high GlycA was associated with CAP whereas high CRP was more associated with obesity.

Accuracy of 23 Equations for Estimating LDL Cholesterol in a Clinical Laboratory Database of 5,051,467 Patients.

Background: Alternatives to the Friedewald low-density lipoprotein cholesterol (LDL-C) equation have been proposed. Objective: To compare the accuracy of available LDL-C equations with ultracentrifugation measurement. Methods: We used the second harvest of the Very Large Database of Lipids (VLDbL), which is a population-representative convenience sample of adult and pediatric patients (N = 5,051,467) with clinical lipid measurements obtained via the vertical auto profile (VAP) ultracentrifugation method between October 1, 2015 and June 30, 2019. We performed a systematic literature review to identify available LDL-C equations and compared their accuracy according to guideline-based classification. We also compared the equations by their median error versus ultracentrifugation. We evaluated LDL-C equations overall and stratified by age, sex, fasting status, and triglyceride levels, as well as in patients with atherosclerotic cardiovascular disease, hypertension, diabetes, kidney disease, inflammation, and thyroid dysfunction. Results: Analyzing 23 identified LDL-C equations in 5,051,467 patients (mean±SD age, 56±16 years; 53.3% women), the Martin/Hopkins equation most accurately classified LDL-C to the correct category (89.6%), followed by the Sampson (86.3%), Chen (84.4%), Puavilai (84.1%), Delong (83.3%), and Friedewald (83.2%) equations. The other 17 equations were less accurate than Friedewald, with accuracy as low as 35.1%. The median error of equations ranged from -10.8 to 18.7 mg/dL, and was best optimized using the Martin/Hopkins equation (0.3, IQR-1.6 to 2.4 mg/dL). The Martin/Hopkins equation had the highest accuracy after stratifying by age, sex, fasting status, triglyceride levels, and clinical subgroups. In addition, one in five patients who had Friedewald LDL-C <70 mg/dL, and almost half of the patients with Friedewald LDL-C <70 mg/dL and triglyceride levels 150-399 mg/dL, had LDL-C correctly reclassified to >70 mg/dL by the Martin/Hopkins equation. Conclusions: Most proposed alternatives to the Friedewald equation worsen LDL-C accuracy, and their use could introduce unintended disparities in clinical care. The Martin/Hopkins equation demonstrated the highest LDL-C accuracy overall and across subgroups.

Associations of Adipokine Levels with Levels of Remnant Cholesterol: the Multi-Ethnic Study of Atherosclerosis (MESA).

Background: The metabolic syndrome phenotype of individuals with obesity is characterized by elevated levels of triglyceride (TG)-rich lipoproteins and remnant particles, which have been shown to be significantly atherogenic. Understanding the association between adipokines, endogenous hormones produced by adipose tissue, and remnant cholesterol (RC) would give insight into the link between obesity and atherosclerotic cardiovascular disease. Methods: We studied 1,791 MESA participants of an ancillary study on body composition who had adipokine levels measured (leptin, adiponectin, resistin) at either visit 2 or 3. RC was calculated as non-high density lipoprotein cholesterol minus low-density lipoprotein cholesterol (LDL-C), measured at the same visit as the adipokines, as well as subsequent visits 4 through 6. Multivariable-adjusted linear mixed effects models were used to assess the cross-sectional and longitudinal associations between adipokines and levels of RC. Results: Mean (SD) age was 64.5±9.6 years and for body mass index (BMI) was 29.9±5.0 kg/m2; 52.0% were women. In fully adjusted models that included BMI, LDL-C and lipid-lowering therapy, for each 1-unit increment in adiponectin, there was 14.4% (12.0, 16.8) lower RC. With each 1-unit increment in leptin and resistin, there was 4.5% (2.3, 6.6) and 5.1% (1.2, 9.2) higher RC, respectively. Lower adiponectin and higher leptin were also associated with longitudinal increases in RC levels over median follow-up of 5(4-8) years. Conclusions: Lower adiponectin and higher leptin levels were independently associated with higher levels of RC at baseline and longitudinal RC increase, even after accounting for BMI and LDL-C. CLINICAL PERSPECTIVE: What is new?: - Among individuals without history of cardiovascular disease, adiponectin is inversely associated with cross-sectional levels of remnant cholesterol, whereas leptin and resistin are directly associated.- Adiponectin had an inverse association with progression of remnant cholesterol levels over time.What are the clinical implications?: - Adiponectin levels were not associated with LDL-C levels but with levels of triglyceride-rich lipoproteins, particularly remnant cholesterol.-Incrementing adiponectin via lifestyle modification and/or pharmacological therapies (i.e. GLP-1 agonists) could be a mechanism to reduce remnant cholesterol levels and ultimately cardiovascular risk.

Lipoprotein(a) concentrations in acute myocardial infarction patients are not indicative of levels at six month follow-up.

Aims: Lipoprotein(a) [Lp(a)] levels are generally constant throughout an individual's lifetime, and current guidelines recommend that a single measurement is sufficient to assess the risk of coronary artery disease (CAD). However, it is unclear whether a single measurement of Lp(a) in individuals with acute myocardial infarction (MI) is indicative of the Lp(a) level six months following the event. Methods and results: Lp(a) levels were obtained from individuals with non-ST-elevation myocardial infarction (NSTEMI) or ST-elevation myocardial infarction (STEMI) (n = 99) within 24 h of hospital admission and after six months, who were enrolled in two randomized trials of evolocumab and placebo, and in individuals with NSTEMI or STEMI (n = 9) who enrolled in a small observation arm of the two protocols and did not receive study drug, but whose levels were obtained at the same time points. Median Lp(a) levels increased from 53.5 nmol/L (19, 165) during hospital admission to 58.0 nmol/L (14.8, 176.8) six months after the acute infarction (P = 0.02). Subgroup analysis demonstrated no difference in the baseline, six-month, or change between the baseline and six-month Lp(a) values between the STEMI and NSTEMI groups and between the group which received evolocumab and the group that did not. Conclusion: This study demonstrated that Lp(a) levels in individuals with acute MI are significantly higher six months after the initial event. Therefore, a single measurement of Lp(a) in the peri-infarction setting is not sufficient to predict the Lp(a)-associated CAD risk in the post-infarction period. Registration: Evolocumab in Acute Coronary Syndrome Trial [EVACS I] NCT03515304, Evolocumab in Patients with Acute Myocardial Infarction [EVACS II], NCT04082442.

The association between triglyceride-rich lipoproteins, circulating leukocytes, and low-grade inflammation: The Brazilian Longitudinal Study of Adult Health (ELSA-Brasil).

BACKGROUND: Experimental studies have linked triglyceride-rich lipoproteins (TRLs) to inflammation, but the extent of this phenomenon in vivo has not been completely elucidated. OBJECTIVE: We investigated the association between TRL subparticles and inflammatory markers (circulating leukocytes, plasma high-sensitivity C-reactive protein [hs-CRP], and GlycA) in the general population. METHODS: This was a cross-sectional analysis of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). TRLs (number of particles per unit volume) and GlycA were measured by nuclear magnetic resonance spectroscopy. The association between TRLs and inflammatory markers was determined by multiple linear regression models adjusted for demographic data, metabolic conditions, and lifestyle factors. Standardized regression coefficients (beta) with 95% confidence intervals are reported. RESULTS: The study population comprised 4,001 individuals (54% females, age 50 ± 9 years). TRLs, especially medium and large subparticles, were associated with GlycA (beta 0.202 [0.168, 0.235], p<0.001 for total TRLs). There was no association between TRLs and hs-CRP (beta 0.022 [-0.011, 0.056], p = 0.190). Medium, large, and very large TRLs were associated with leukocytes, with stronger connections with neutrophils and lymphocytes than monocytes. When TRL subclasses were analyzed as the proportion of the total pool of TRL particles, medium and large TRLs were positively related to leukocytes and GlycA, whereas smaller particles were inversely associated. CONCLUSIONS: There are different patterns of association between TRL subparticles and inflammatory markers. The findings support the hypothesis that TRLs (especially medium and larger subparticles) may induce a low-grade inflammatory environment that involves leukocyte activation and is captured by GlycA, but not hs-CRP.

Plasma Proprotein Convertase Subtilisin/kexin Type 9 (PCSK9) in the Acute Respiratory Distress Syndrome.

Background: Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a serine protease that is a mediator of the immune response to sepsis. PCSK9 is also highly expressed in pneumocytes and pulmonary endothelial cells. We hypothesized that serum PCSK9 levels would be associated with death and ICU outcomes in patients with ARDS. Methods: Using data and plasma samples from the NIH BioLINCC data repository, we assembled a cohort of 1,577 patients with the acute respiratory distress syndrome (ARDS) enrolled in two previously completed clinical trials, EDEN and SAILS. We measured PCSK9 levels in plasma within 24 h of intubation using commercially available ELISA kits (R&D Systems). We assessed the association of PCSK9 with mortality using Cox proportional hazard models. We also assessed clinical factors associated with PCSK9 level and the association of PCSK9 with the number of days free of mechanical ventilation and days free of ICU care. Results: In 1,577 ARDS patients, median age was 53 years (IQR 42-65 years) and median APACHE III score 91 (72-111) connoting moderate critical illness. PCSK9 levels were 339.3 ng/mL (IQR 248.0-481.0). In multivariable models, race, cause of ARDS, body mass index, pre-existing liver disease, body temperature, sodium, white blood cell count and platelet count were associated with PCSK9 level. Presence of sepsis, use of vasopressors and ventilator parameters were not associated with PCSK9 level. PCSK9 levels were not associated with in-hospital mortality (HR per IQR 0.96, 95% CI 0.84-1.08, P = 0.47). Higher PCSK9 levels were associated with fewer ICU and ventilator free days. Conclusions: Plasma PCSK9 is not associated with mortality in ARDS, however higher PCSK9 levels are associated with secondary outcomes of fewer ICU free and ventilator free days. Clinical factors associated with PCSK9 in ARDS are largely unmodifiable. Further research to define the mechanism of this association is warranted.

The Trajectory of Lipoprotein(a) During the Peri- and Early Postinfarction Period and the Impact of Proprotein Convertase Subtilisin/Kexin Type 9 Inhibition.

Lipoprotein(a), or Lp(a), levels and the effect of proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibition on Lp(a) during the peri-infarction and early postinfarction period are not well characterized. This study aimed to describe the trajectory of Lp(a), as well as the effect of PCSK9 inhibition on that trajectory during the peri-infarction and early postinfarction period. Lp(a) levels were obtained within 24 hours of hospital admission as well as within 24 hours of hospital discharge and at 30 days from 74 participants who presented with a NSTEMI (troponin I >5 ng/ml) or with a STEMI and were enrolled in 2 randomized, double-blind trials of evolocumab and placebo (Evolocumab in Acute Coronary Syndrome [EVACS I]; ClinicalTrials.gov, NCT03515304 and Evolocumab in Patients With STEMI [EVACS II]; ClinicalTrials.gov Identifier: NCT04082442). There was a significant increase from the pretreatment level in the placebo-treated patients, from 64 (41,187) nmol/L to 80 (47, 172) nmol/L at hospital discharge and to 82 (37, 265) at 30 days. This was primarily driven by the results from participants with high Lp(a) at hospital admission (>75 nmol/L) in whom the median increase was 28% as compared with a 10% increase in those with pretreatment Lp(a) of <75 nmol/L. In contrast, there was no significant change from the pretreatment level in the evolocumab-treated patients regardless of pretreatment Lp(a) levels. In conclusion, Lp(a) rises during the peri-infarction and early postinfarction period in patients with acute myocardial infarction. The increase was prevented by a single dose of subcutaneous evolocumab given within 24 hours of hospital admission.

Contemporary Management of Dyslipidemia.

The treatment of dyslipidemia continues to be a dynamic and controversial topic. Even the most appropriate therapeutic range for lipid levels-including that of triglycerides and low-density lipoprotein cholesterol-remain actively debated. Furthermore, with ever-increasing options and available treatment modalities, the management of dyslipidemia has progressed in both depth and complexity. An understanding of appropriate lipid-lowering therapy remains an essential topic of review for practitioners across medical specialties. The goal of this review is to provide an overview of recent research developments and recommendations for patients with dyslipidemia as a means of better informing the clinical practice of lipid management. By utilizing a guideline-directed approach, we provide a reference point on optimal lipid-lowering therapies across the spectrum of dyslipidemia. Special attention is paid to long-term adherence to lipid-lowering therapies, and the benefits derived from instituting appropriate medications in a structured manner alongside monitoring. Novel therapies and their impact on lipid lowering are discussed in detail, as well as potential avenues for research going forward. The prevention of cardiovascular disease remains paramount, and this review provides a roadmap for instituting appropriate therapies in cardiovascular disease prevention.

Quantifying the contribution of lipoprotein(a) to all apoB containing particles.

BACKGROUND: Elevated lipoprotein (a) [Lp(a)] is an independent risk factor for atherosclerotic cardiovascular disease (ASCVD). As clinical LDL cholesterol [LDL-C] incorporates cholesterol from Lp(a) [Lp(a)-C], there is interest in quantifying the contribution of Lp(a)-C to LDL-C given implications for risk assessment, diagnosis, and treatment. Estimating Lp(a)-C is subject to inaccuracies; measuring Lp(a) particle number [Lp(a)-P] is more accurate. OBJECTIVE: To capture how Lp(a) contributes to the concentration of atherogenic particles, we demonstrate a particle-based approach using readily available measures of Lp(a)-P and apolipoprotein B (apoB). METHODS: Using the Very Large Database of Lipids (VLDbL), we compared Lp(a)-P (nmol/L) with all apoB containing particles ("apoB-P"). apoB-P was calculated by converting apoB mass to molar concentration using the preserved molecular weight of apoB100 (512 kg/mol). We calculated the percentage of Lp(a)-P relative to apoB-P by Lp(a)-P deciles and stratified by triglycerides, LDL-C, and non-HDL-C. RESULTS: 158,260 patients from the VLDbL were included. The fraction Lp(a)-P/apoB-P increased with rising Lp(a)-P. Lp(a)-P comprised on average 3% of apoB containing particles among the study population and 15% at the highest Lp(a)-P decile. Lp(a)-P/apoB-P decreased at higher levels of triglycerides and LDL-C owing to larger contributions from VLDL and LDL. CONCLUSIONS: We demonstrate a particle-based approach to quantify the contribution of Lp(a) to all apoB-containing particles using validated and widely available clinical assays. This approach keeps in line with recommendations to move away from mass-based measurements of Lp(a) and prioritize more accurate particle-based measurements. Future research applying this method could define clinically meaningful thresholds and inform use in risk assessment and management.

Assessing the Accuracy of Estimated Lipoprotein(a) Cholesterol and Lipoprotein(a)-Free Low-Density Lipoprotein Cholesterol.

Background Accurate measurement of the cholesterol within lipoprotein(a) (Lp[a]-C) and its contribution to low-density lipoprotein cholesterol (LDL-C) has important implications for risk assessment, diagnosis, and treatment of atherosclerotic cardiovascular disease, as well as in familial hypercholesterolemia. A method for estimating Lp(a)-C from particle number using fixed conversion factors has been proposed (Lp[a]-C from particle number divided by 2.4 for Lp(a) mass, multiplied by 30% for Lp[a]-C). The accuracy of this method, which theoretically can isolate "Lp(a)-free LDL-C," has not been validated. Methods and Results In 177 875 patients from the VLDbL (Very Large Database of Lipids), we compared estimated Lp(a)-C and Lp(a)-free LDL-C with measured values and quantified absolute and percent error. We compared findings with an analogous data set from the Mayo Clinic Laboratory. Error in estimated Lp(a)-C and Lp(a)-free LDL-C increased with higher Lp(a)-C values. Median error for estimated Lp(a)-C <10 mg/dL was -1.9 mg/dL (interquartile range, -4.0 to 0.2); this error increased linearly, overestimating by +30.8 mg/dL (interquartile range, 26.1-36.5) for estimated Lp(a)-C ≥50 mg/dL. This error relationship persisted after stratification by overall high-density lipoprotein cholesterol and high-density lipoprotein cholesterol subtypes. Similar findings were observed in the Mayo cohort. Absolute error for Lp(a)-free LDL-C was +2.4 (interquartile range, -0.6 to 5.3) for Lp(a)-C<10 mg/dL and -31.8 (interquartile range, -37.8 to -26.5) mg/dL for Lp(a)-C≥50 mg/dL. Conclusions Lp(a)-C estimations using fixed conversion factors overestimated Lp(a)-C and subsequently underestimated Lp(a)-free LDL-C, especially at clinically relevant Lp(a) values. Application of inaccurate Lp(a)-C estimations to correct LDL-C may lead to undertreatment of high-risk patients.

Coronary heart disease risk: Low-density lipoprotein and beyond.

Coronary heart disease (CHD) is the leading cause of morbidity and mortality world-wide and has been characterized as a chronic immunoinflammatory, fibroproliferative disease fueled by lipids. Great advances have been made in elucidating the complex mechanistic interactions among risk factors associated with CHD, yielding abundant success towards preventive measures and the development of pharmaceuticals to prevent and treat CHD via attenuation of lipoprotein-mediated risk. However, significant residual risk remains. Several potentially modifiable CHD risk factors ostensibly contributing to this residual risk have since come to the fore, including systemic inflammation, diabetes mellitus, high-density lipoprotein, plasma triglycerides (TG) and remnant lipoproteins (RLP), lipoprotein(a) (Lp[a]), and vascular endothelial dysfunction (ED). Herein, we summarize the body of evidence implicating each of these risk factors in residual CHD risk.

Comparison of Methods to Estimate Low-Density Lipoprotein Cholesterol in Patients With High Triglyceride Levels.

Importance: Low-density lipoprotein cholesterol (LDL-C) is typically estimated with the Friedewald or Martin/Hopkins equation; however, if triglyceride levels are 400 mg/dL or greater, laboratories reflexively perform direct LDL-C (dLDL-C) measurement. The use of direct chemical LDL-C assays and estimation of LDL-C via the National Institutes of Health Sampson equation are not well validated, and data on the accuracy of LDL-C estimation at higher triglyceride levels are limited. Objective: To compare an extended Martin/Hopkins equation for triglyceride values of 400 to 799 mg/dL with the Friedewald and Sampson equations. Design, Setting, and Participants: This cross-sectional study evaluated consecutive patients at clinical sites across the US with patient lipid distributions representative of the US population in the Very Large Database of Lipids from January 1, 2006, to December 31, 2015, with triglyceride levels of 400 to 799 mg/dL. Data analysis was performed from November 9, 2020, to March 23, 2021. Main Outcomes and Measures: Accuracy in LDL-C classification according to guideline-based categories and absolute errors between estimated LDL-C and dLDL-C levels. Patients were randomly assigned 2:1 to derivation and validation data sets. Levels of dLDL-C were measured by vertical spin-density gradient ultracentrifugation. The LDL-C levels were estimated using the Friedewald method, with a fixed ratio of triglycerides to very low-density lipoprotein cholesterol (VLDL-C ratio of 5:1), extended Martin/Hopkins equation with a flexible ratio, and Sampson equation with VLDL-C estimation by multiple least-squares regression. Results: A total of 111 939 patients (mean [SD] age, 52 [13] years; 65.0% male) with triglyceride levels of 400 to 799 mg/dL were included, representing 2.2% of 5 081 680 patients in the database. Across all individual guideline LDL-C classes (<40, 40-69, 70-99, 100-129, 130-159, 160-189, and ≥190), estimation of LDL-C by the extended Martin/Hopkins equation was most accurate (62.1%) compared with the Friedewald (19.3%) and Sampson (40.4%) equations. In classifying LDL-C levels less than 70 mg/dL across all triglyceride strata, the extended Martin/Hopkins equation was most accurate (67.3%) compared with Friedewald (5.1%) and Sampson (26.4%) equations. In addition, for classifying LDL-C levels less than 40 mg/dL across all triglyceride strata, the extended Martin/Hopkins equation was most accurate (57.2%) compared with the Friedewald (4.3%) and Sampson (14.4%) equations. However, considerable underclassification of LDL-C occurred. The magnitude of error between the Martin/Hopkins equation estimation and dLDL-C was also smaller: at LDL-C levels less than 40 mg/dL, 2.7% of patients had 30 mg/dL or greater differences between dLDL-C and estimated LDL-C using the Martin/Hopkins equation compared with the Friedewald (92.5%) and Sampson (38.7%) equations. Conclusions and Relevance: In this cross-sectional study, the extended Martin/Hopkins equation offered greater LDL-C accuracy compared with the Friedewald and Sampson equations in patients with triglyceride levels of 400 to 799 mg/dL. However, regardless of method used, caution is advised with LDL-C estimation in this triglyceride range.

Utility of non-HDL-C and apoB targets in the context of new more aggressive lipid guidelines.

OBJECTIVE: Major guidelines recommend the use of secondary targets, such as non-HDL-C and apoB, to further reduce cardiovascular risk. We aimed to evaluate the proportion at which newer, more aggressive secondary lipid targets are exceeded in patients with LDL-C < 70 mg/dL estimated by Friedewald (LDLf-C) and Martin/Hopkins equations (LDLm-C). METHODS: We analyzed patients from the Very Large Database of Lipids with fasting lipids and estimated LDL-C <70 mg/dL by the Friedewald equation and Martin/Hopkins algorithm. Patients were categorized into three groups: LDL-C <40, 40-54 and 55-69 mg/dL. We calculated the proportion of patients with non-HDL-C and apoB above high-risk targets (non-HDL-C ≥ 100 and apoB ≥ 80mg/dL) for those with LDL-C 55-69 mg/dL and very high-risk targets (non-HDL-C ≥ 85 and apoB ≥ 65mg/dL) for those with LDL-C < 40 mg/dL and 40-54 mg/dL. RESULTS: In patients with LDLf-C < 40 mg/dL, ~8 and ~4% did not meet high-risk secondary targets and ~21 and 25% did not meet very high-risk secondary targets for non-HDL-C and apoB, respectively. However, in patients with LDLm-C < 40 mg/dL <1% did not meet high-risk targets, while only 3% did not meet the very-high risk secondary target for apoB and none exceeded the very-high risk secondary target for non-HDL-C. Among individuals with LDL-C< 40 mg/dL, there were increasing proportions of individuals not meeting the very high-risk secondary apoB target at greater triglyceride levels, reaching up to ~19% using LDLm-C compared to ~60% using LDLf-C when triglyceride levels were 200-399 mg/dL. There were higher proportions of individuals not meeting high and very-high risk targets as triglyceride levels increased among those with LDL-C 40-54 and 55-69 mg/dL. CONCLUSION: In a large, US cross-sectional sample of individuals with LDL-C < 70 mg/dL, secondary non-HDL-C and apoB targets overall provide modest utility. However, attainment of very high-risk cutpoints for non-HDL-C and apoB is not achieved in a significant fraction of patients with triglycerides 200-399 mg/dL, even when using a more accurate calculation of LDL-C.

Branched-chain amino acids predict incident diabetes in the Brazilian Longitudinal Study of Adult Health - ELSA-Brasil.

AIMS: To evaluate the role of branch chain amino acid (BCAA) concentrations as a predictor for incident type 2 diabetes (DM). METHODS: Participants from ELSA-Brasil without diabetes at baseline and followed for 3.9 ± 0.6 years were included in the analysis. The determinations of BCAA (valine, leucine, isoleucine) were performed by proton nuclear magnetic resonance spectroscopy. Cardiometabolic profile and incidence of DM were evaluated according to quartiles of BCAA at baseline, stratified by sex. RESULTS: From 3,828 participants (56% female, 50.5 ± 8.7 years) 299 (8.5%) were diagnosed with DM. For both sexes, a worsening of cardiometabolic profile was observed across increasing BCAA quartiles. In survival analysis, incidence rates of DM for the entire period were highest in participants in the third and fourth quartile of BCAA (log Rank analysis < 0.001 for both sexes). In Cox regression analysis, for men, the HR (95%CI) for risk of DM was 2.24 (1.24-4.03) for those from the fourth quartile of BCAA, while in women it was 1.94 (1.07-3.50), comparing to first quartile of BCAA after adjustments for age, BMI, physical activity, family history of DM, pre-diabetes, blood pressure, total cholesterol and HOMA-IR. CONCLUSIONS: Higher levels of BCAA were independently predictors of DM.

Importance of the triglyceride level in identifying patients with a Type III Hyperlipoproteinemia phenotype using the ApoB algorithm.

BACKGROUND: Hyperlipoproteinemia Type III (HLP3), also known as dysbetalipoproteinemia, is defined by cholesterol and triglyceride (TG) enriched remnant lipoprotein particles (RLP). The gold standard for diagnosis requires demonstration of high remnant lipoprotein particle cholesterol (RLP-C) by serum ultracentrifugation (UC), which is not readily available in daily practice. The apoB algorithm can identify HLP3 using total cholesterol (TC), plasma triglyceride (TG), and apoB. However, the optimal TG cutoff is unknown. OBJECTIVE: We analyzed apoB algorithm defined HLP3 at different TG levels to optimize the TG cutoff for the algorithm. METHODS: 128,485 UC lipid profiles in the Very Large Database of Lipids (VLDbL) were analyzed. RLP-C was assessed at TG ≥ 133 mg/dL, ≥175 mg/dL, ≥200 mg/dL, and ≥ 250 mg/dL. Sensitivity (Sn), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), and prevalence adjusted and bias-adjusted kappa (PABAK) were calculated against UC Criterion (VLDL-C/TG ≥ 0.25) for HLP3. RESULTS: The median age (IQR) was 57 years (46-68). 45% were men, 20.1% had diabetes, and 25.5% had hypertension. The median RLP-C level for the TG cutoffs (mg/dL) of ≥ 133, ≥ 175, ≥ 200, and ≥ 250 were 34, 43, 50, and 62 mg/dL, respectively, compared to 67 mg/dL in UC defined HLP3. TG ≥ 133 mg/dL yielded optimal results (Sn 29.5%, Sp 98.5%, PABAK 0.96, PPV 13.6%, NPV 99.4%). CONCLUSION: TG ≥ 133 mg/dL allows for high sensitivity in screening for HLP3. Higher TG cutoffs may identify more severe HLP3 phenotypes, but with a large loss in sensitivity for HLP3.

Increased particle size of triacylglycerol-enriched remnant lipoproteins, but not their plasma concentration or lipid content, augments risk prediction of incident type 2 diabetes.

AIMS/HYPOTHESIS: Type 2 diabetes prevention requires the accurate identification of those at high risk. Beyond the association of fasting serum triacylglycerols with diabetes, triacylglycerol-enriched remnant lipoproteins (TRLs) more accurately reflect pathophysiological changes that underlie progression to diabetes, such as hepatic insulin resistance, pancreatic steatosis and systemic inflammation. We hypothesised that TRL-related factors could improve risk prediction for incident diabetes. METHODS: We included individuals from the Brazilian Longitudinal Study of Adult Health cohort. We trained a logistic regression model for the risk of incident diabetes in 80% of the cohort using tenfold cross-validation, and tested the model in the remaining 20% of the cohort (test set). Variables included medical history and traits of the metabolic syndrome, followed by TRL-related measurements (plasma concentration, TRL particle diameter, cholesterol and triacylglycerol content). TRL features were measured using NMR spectroscopy. Discrimination was assessed using the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). RESULTS: Among 4463 at-risk individuals, there were 366 new cases of diabetes after a mean (±SD) of 3.7 (±0.63) years of follow-up. We derived an 18-variable model with a global AUROC of 0.846 (95% CI: 0.829, 0.869). Overall TRL-related markers were not associated with diabetes. However, TRL particle diameter increased the AUROC, particularly in individuals with HbA1c <39 mmol/mol (5.7%) (hold-out test set [n = 659]; training-validation set [n = 2638]), but not in individuals with baseline HbA1c 39-46 mmol/mol (5.7-6.4%) (hold-out test set [n = 233]; training-validation set [n = 933]). In the subgroup with baseline HbA1c <39 mmol/mol (5.7%), AUROC in the test set increased from 0.717 (95% CI 0.603, 0.818) to 0.794 (95% CI 0.731, 0.862), and AUPRC in the test set rose from 0.582 to 0.701 when using the baseline model and the baseline model plus TRL particle diameter, respectively. TRL particle diameter was highly correlated with obesity, insulin resistance and inflammation in those with impaired fasting glucose at baseline, but less so in those with HbA1c <39 mmol/mol (5.7%). CONCLUSIONS/INTERPRETATION: TRL particle diameter improves the prediction of diabetes, but only in individuals with HbA1c <39 mmol/mol (5.7%) at baseline. These data support TRL particle diameter as a risk factor that is changed early in the course of the pathophysiological processes that lead to the development of type 2 diabetes, even before glucose abnormalities are established. Graphical abstract.

Modern prevalence of the Fredrickson-Levy-Lees dyslipidemias: findings from the Very Large Database of Lipids and National Health and Nutrition Examination Survey.

INTRODUCTION: Five decades ago, Fredrickson, Levy, and Lees (FLL) qualitatively characterized clinical dyslipidemias with specific implications for cardiovascular and non-cardiovascular morbidity and mortality. They separated disorders of elevated cholesterol and triglycerides into five phenotypes (types I-V) based on their lipoprotein profile. Although clinicians generally consider them rare entities, modern FLL prevalence may be greater than previously reported. MATERIAL AND METHODS: We performed a cross-sectional analysis in 5,272 participants from the 2011-2014 National Health and Nutrition Examination Survey and 128,506 participants from the Very Large Database of Lipids study with complete, fasting lipid profiles. We used a validated algorithm to define FLL phenotypes employing apolipoprotein B, total cholesterol, and triglycerides. RESULTS: Overall prevalence of FLL phenotypes was 33.9%. FLL prevalence in the general population versus clinical lipid database was: type I (0.05 vs. 0.02%), type IIa (3.2 vs. 3.9%), type IIb (8.0 vs. 10.3%), type III (2.0 vs. 1.7%), type IV (20.5 vs. 24.1%), and type V (0.15 vs. 0.13%). Those aged 40-74 years had a higher overall prevalence compared to other age groups (p < 0.001) and men had overall higher prevalence than women (p < 0.001). Those with diabetes (51.6%) or obese BMI (49.0%) had higher prevalence of FLL phenotypes compared to those without diabetes (31.3%; p < 0.001) and normal BMI (18.3%; p < 0.001). CONCLUSIONS: FLL phenotypes are likely far more prevalent than appreciated in clinical practice, in part due to diabetes and obesity epidemics. Given the prognostic and therapeutic importance of these phenotypes, their identification becomes increasingly important in the era of precision medicine.