Atherosclerosis evaluation and cardiovascular risk estimation using coronary computed tomography angiography.

Clinical risk scores based on traditional risk factors of atherosclerosis correlate imprecisely to an individual's complex pathophysiological predisposition to atherosclerosis and provide limited accuracy for predicting major adverse cardiovascular events (MACE). Over the past two decades, computed tomography scanners and techniques for coronary computed tomography angiography (CCTA) analysis have substantially improved, enabling more precise atherosclerotic plaque quantification and characterization. The accuracy of CCTA for quantifying stenosis and atherosclerosis has been validated in numerous multicentre studies and has shown consistent incremental prognostic value for MACE over the clinical risk spectrum in different populations. Serial CCTA studies have advanced our understanding of vascular biology and atherosclerotic disease progression. The direct disease visualization of CCTA has the potential to be used synergistically with indirect markers of risk to significantly improve prevention of MACE, pending large-scale randomized evaluation.

Proteomics and lipidomics in atherosclerotic cardiovascular disease risk prediction.

Given the limited accuracy of clinically used risk scores such as the Systematic COronary Risk Evaluation 2 system and the Second Manifestations of ARTerial disease 2 risk scores, novel risk algorithms determining an individual's susceptibility of future incident or recurrent atherosclerotic cardiovascular disease (ASCVD) risk are urgently needed. Due to major improvements in assay techniques, multimarker proteomic and lipidomic panels hold the promise to be reliably assessed in a high-throughput routine. Novel machine learning-based approaches have facilitated the use of this high-dimensional data resulting from these analyses for ASCVD risk prediction. More than a dozen of large-scale retrospective studies using different sets of biomarkers and different statistical methods have consistently demonstrated the additive prognostic value of these panels over traditionally used clinical risk scores. Prospective studies are needed to determine the clinical utility of a biomarker panel in clinical ASCVD risk stratification. When combined with the genetic predisposition captured with polygenic risk scores and the actual ASCVD phenotype observed with coronary artery imaging, proteomics and lipidomics can advance understanding of the complex multifactorial causes underlying an individual's ASCVD risk.

Unexpected gaps in knowledge of familial hypercholesterolaemia among Dutch general practitioners.

BACKGROUND: Familial hypercholesterolaemia (FH) warrants early diagnosis to prevent premature atherosclerotic cardiovascular disease (CVD). However, underdiagnosis and undertreatment of FH persist. This study aimed to assess the knowledge and practice of FH care among general practitioners (GPs) in the Netherlands. METHODS: An internationally standardised, online questionnaire was sent to Dutch GPs between February 2021 and July 2022. The survey assessed knowledge and awareness of FH, encompassing general familiarity, awareness of management guidelines, inheritance, prevalence, CVD risk, and clinical practice related to FH. Comparative analysis was performed using data on primary care physicians from Western Australia, the Asia-Pacific region and the United Kingdom. RESULTS: Of the 221 participating GPs, 62.4% rated their familiarity with FH as above average (score > 4 on a 1-7 scale), with 91.4% considering themselves familiar with FH treatment and referral guidelines. Correct identification of the FH definition, typical lipid profile, inheritance pattern, prevalence and CVD risk was reported by 83.7%, 87.8%, 55.7%, 19.5%, and 13.6% of the respondents, respectively. Of the participants, 58.4% answered fewer than half of the 8 knowledge questions correctly. Dutch GPs reported greater FH familiarity and guideline awareness compared with their international counterparts but exhibited similar low performance on FH knowledge questions. CONCLUSION: Despite the Netherlands' relatively high FH detection rate, substantial knowledge gaps regarding FH persist among Dutch GPs, mirroring global trends. Enhanced FH education and awareness in primary care are imperative to improve FH detection and ensure adequate treatment. Targeting the global suboptimal understanding of FH might require international efforts.

Considerations for routinely testing for high lipoprotein(a).

PURPOSE OF REVIEW: Lipoprotein (a) [Lp(a)] is a likely causal risk factor for atherosclerotic cardiovascular disease (ASCVD) and aortic valve disease, confirmed by Mendelian randomization. With reliable assays, it has been established that Lp(a) is linearly associated with ASCVD. Current low-density lipoprotein cholesterol (LDL-C) lowering therapies do not or minimally lower Lp(a). This review focuses on the clinical importance and therapeutic consequences of Lp(a) measurement. RECENT FINDINGS: Development of RNA-based Lp(a) lowering therapeutics has positioned Lp(a) as one of the principal residual risk factors to target in the battle against lipid-driven ASCVD risk. Pelacarsen, which is a liver-specific antisense oligonucleotide, has shown Lp(a) reductions up to 90% and its phase 3 trial is currently underway. Olpasiran is a small interfering RNA targeting LPA messenger RNA, which is being investigated in phase 2 and has already shown dose-dependent Lp(a) reductions up to 90%. SUMMARY: Lp(a) should be measured in every patient at least once to identify patients with very high Lp(a) levels. These patients could benefit from Lp(a) lowering therapies when approved. In the meantime, therapy in high Lp(a) patients should focus on further reducing LDL-C and other ASCVD risk factors.

Atorvastatin lowers 68Ga-DOTATATE uptake in coronary arteries, bone marrow and spleen in individuals with type 2 diabetes.

AIMS/HYPOTHESIS: Inflammation is a core component of residual cardiovascular risk in type 2 diabetes. With new anti-inflammatory therapeutics entering the field, accurate markers to evaluate their effectiveness in reducing cardiovascular disease are paramount. Gallium-68-labelled DOTATATE (68Ga-DOTATATE) has recently been proposed as a more specific marker of arterial wall inflammation than 18F-fluorodeoxyglucose (18F-FDG). This study set out to investigate whether 68Ga-DOTATATE uptake is amenable to therapeutic intervention in individuals with type 2 diabetes. METHODS: Individuals aged >50 years with type 2 diabetes underwent 68Ga-DOTATATE positron emission tomography (PET)/computed tomography (CT) at baseline and after 3 months treatment with atorvastatin 40 mg once daily. Primary outcome was the difference in coronary 68Ga-DOTATATE uptake, expressed as target-to-background ratio (TBR). The secondary outcome was difference in bone marrow and splenic uptake, expressed as the standardised uptake value (SUV). RESULTS: Twenty-two individuals with type 2 diabetes (mean age 63.2±6.4 years, 82% male, LDL-cholesterol 3.42±0.81 mmol/l, HbA1c 55±12 mmol/mol [7.2%±3.2%]) completed both 68Ga-DOTATATE PET/CT scans. The maximum TBR was -31% (95% CI -50, -12) lower in the coronary arteries, and bone marrow and splenic 68Ga-DOTATATE uptake was also significantly lower post statin treatment, with a mean percentage reduction of -15% (95% CI -27, -4) and -17% (95% CI -32, -2), respectively. CONCLUSIONS/INTERPRETATION: 68Ga-DOTATATE uptake across the cardio-haematopoietic axis was lower after statin therapy in individuals with type 2 diabetes. Therefore, 68Ga-DOTATATE is promising as a metric for vascular and haematopoietic inflammation in intervention studies using anti-inflammatory therapeutics in individuals with type 2 diabetes. TRIAL REGISTRATION: ClinicalTrials.gov NCT05730634.

Targeted proteomics improves cardiovascular risk prediction in secondary prevention.

AIMS: Current risk scores do not accurately identify patients at highest risk of recurrent atherosclerotic cardiovascular disease (ASCVD) in need of more intensive therapeutic interventions. Advances in high-throughput plasma proteomics, analysed with machine learning techniques, may offer new opportunities to further improve risk stratification in these patients. METHODS AND RESULTS: Targeted plasma proteomics was performed in two secondary prevention cohorts: the Second Manifestations of ARTerial disease (SMART) cohort (n = 870) and the Athero-Express cohort (n = 700). The primary outcome was recurrent ASCVD (acute myocardial infarction, ischaemic stroke, and cardiovascular death). Machine learning techniques with extreme gradient boosting were used to construct a protein model in the derivation cohort (SMART), which was validated in the Athero-Express cohort and compared with a clinical risk model. Pathway analysis was performed to identify specific pathways in high and low C-reactive protein (CRP) patient subsets. The protein model outperformed the clinical model in both the derivation cohort [area under the curve (AUC): 0.810 vs. 0.750; P < 0.001] and validation cohort (AUC: 0.801 vs. 0.765; P < 0.001), provided significant net reclassification improvement (0.173 in validation cohort) and was well calibrated. In contrast to a clear interleukin-6 signal in high CRP patients, neutrophil-signalling-related proteins were associated with recurrent ASCVD in low CRP patients. CONCLUSION: A proteome-based risk model is superior to a clinical risk model in predicting recurrent ASCVD events. Neutrophil-related pathways were found in low CRP patients, implying the presence of a residual inflammatory risk beyond traditional NLRP3 pathways. The observed net reclassification improvement illustrates the potential of proteomics when incorporated in a tailored therapeutic approach in secondary prevention patients.

Considerations for routinely testing for high Lp(a).

PURPOSE OF REVIEW: Lipoprotein(a) (Lp[a]) is a likely causal risk factor for atherosclerotic cardiovascular disease (ASCVD) and aortic valve disease, confirmed by Mendelian randomization. With reliable assays, it has been established that Lp(a) is linearly associated with ASCVD. Current low-density lipoprotein cholesterol (LDL-C) lowering therapies do not or minimally lower Lp(a). This review focuses on the clinical importance and therapeutic consequences of Lp(a) measurement. RECENT FINDINGS: Development of RNA-based Lp(a) lowering therapeutics has positioned Lp(a) as one of the principal residual risk factors to target in the battle against lipid-driven ASCVD risk. Pelacarsen, which is a liver-specific antisense oligonucleotide, has shown Lp(a) reductions up to 90% and its phase 3 trial is currently underway. Olpasiran is a small interfering RNA targeting LPA messenger RNA which is being investigated in phase 2 and has already shown dose-dependent Lp(a) reductions up to 90%. SUMMARY: Lp(a) should be measured in every patient at least once to identify patients with very high Lp(a) levels. These patients could benefit from Lp(a) lowering therapies when approved. In the meantime, therapy in high Lp(a) patients should focus on further reducing LDL-C and other ASCVD risk factors.

Lp(a): a New Pathway to Target?

PURPOSE OF REVIEW: Over the past decades, genetic and observational evidence has positioned lipoprotein(a) as novel important and independent risk factor for cardiovascular disease (ASCVD) and aortic valve stenosis. RECENT FINDINGS: As Lp(a) levels are determined genetically, lifestyle interventions have no effect on Lp(a)-mediated ASCVD risk. While traditional low-density lipoprotein cholesterol (LDL-C) can now be effectively lowered in the vast majority of patients, current lipid lowering therapies have no clinically relevant Lp(a) lowering effect. There are multiple Lp(a)-directed therapies in clinical development targeting LPA mRNA that have shown to lower Lp(a) plasma levels for up to 90%: pelacarsen, olpasiran, and SLN360. Pelacarsen is currently investigated in a phase 3 cardiovascular outcome trial expected to finish in 2024, while olpasiran is about to proceed to phase 3 and SLN360's phase 1 outcomes were recently published. If proven efficacious, Lp(a) will soon become the next pathway to target in ASCVD risk management.

Monocyte-Chemoattractant Protein-1 Levels in Human Atherosclerotic Lesions Associate With Plaque Vulnerability.

[Figure: see text].

Improved cardiovascular risk prediction using targeted plasma proteomics in primary prevention.

AIMS: In the era of personalized medicine, it is of utmost importance to be able to identify subjects at the highest cardiovascular (CV) risk. To date, single biomarkers have failed to markedly improve the estimation of CV risk. Using novel technology, simultaneous assessment of large numbers of biomarkers may hold promise to improve prediction. In the present study, we compared a protein-based risk model with a model using traditional risk factors in predicting CV events in the primary prevention setting of the European Prospective Investigation (EPIC)-Norfolk study, followed by validation in the Progressione della Lesione Intimale Carotidea (PLIC) cohort. METHODS AND RESULTS: Using the proximity extension assay, 368 proteins were measured in a nested case-control sample of 822 individuals from the EPIC-Norfolk prospective cohort study and 702 individuals from the PLIC cohort. Using tree-based ensemble and boosting methods, we constructed a protein-based prediction model, an optimized clinical risk model, and a model combining both. In the derivation cohort (EPIC-Norfolk), we defined a panel of 50 proteins, which outperformed the clinical risk model in the prediction of myocardial infarction [area under the curve (AUC) 0.754 vs. 0.730; P < 0.001] during a median follow-up of 20 years. The clinically more relevant prediction of events occurring within 3 years showed an AUC of 0.732 using the clinical risk model and an AUC of 0.803 for the protein model (P < 0.001). The predictive value of the protein panel was confirmed to be superior to the clinical risk model in the validation cohort (AUC 0.705 vs. 0.609; P < 0.001). CONCLUSION: In a primary prevention setting, a proteome-based model outperforms a model comprising clinical risk factors in predicting the risk of CV events. Validation in a large prospective primary prevention cohort is required to address the value for future clinical implementation in CV prevention.

Assessment of atherosclerotic plaque burden: comparison of AI-QCT versus SIS, CAC, visual and CAD-RADS stenosis categories.

This study assesses the agreement of Artificial Intelligence-Quantitative Computed Tomography (AI-QCT) with qualitative approaches to atherosclerotic disease burden codified in the multisociety 2022 CAD-RADS 2.0 Expert Consensus. 105 patients who underwent cardiac computed tomography angiography (CCTA) for chest pain were evaluated by a blinded core laboratory through FDA-cleared software (Cleerly, Denver, CO) that performs AI-QCT through artificial intelligence, analyzing factors such as % stenosis, plaque volume, and plaque composition. AI-QCT plaque volume was then staged by recently validated prognostic thresholds, and compared with CAD-RADS 2.0 clinical methods of plaque evaluation (segment involvement score (SIS), coronary artery calcium score (CACS), visual assessment, and CAD-RADS percent (%) stenosis) by expert consensus blinded to the AI-QCT core lab reads. Average age of subjects were 59 ± 11 years; 44% women, with 50% of patients at CAD-RADS 1-2 and 21% at CAD-RADS 3 and above by expert consensus. AI-QCT quantitative plaque burden staging had excellent agreement of 93% (k = 0.87 95% CI: 0.79-0.96) with SIS. There was moderate agreement between AI-QCT quantitative plaque volume and categories of visual assessment (64.4%; k = 0.488 [0.38-0.60]), and CACS (66.3%; k = 0.488 [0.36-0.61]). Agreement between AI-QCT plaque volume stage and CAD-RADS % stenosis category was also moderate. There was discordance at small plaque volumes. With ongoing validation, these results demonstrate a potential for AI-QCT as a rapid, reproducible approach to quantify total plaque burden.

Enhanced identification of familial hypercholesterolemia using central laboratory algorithms.

BACKGROUND AND AIMS: Familial hypercholesterolemia (FH) is a highly prevalent genetic disorder resulting in markedly elevated LDL cholesterol levels and premature coronary artery disease. FH underdiagnosis and undertreatment require novel detection methods. This study evaluated the effectiveness of using an LDL cholesterol cut-off ≥99.5th percentile (sex- and age-adjusted) to identify clinical and genetic FH, and investigated underutilization of genetic testing and undertreatment in FH patients. METHODS: Individuals with at least one prior LDL cholesterol level ≥99.5th percentile were selected from a laboratory database containing lipid profiles of 590,067 individuals. The study comprised three phases: biochemical validation of hypercholesterolemia, clinical identification of FH, and genetic determination of FH. RESULTS: Of 5614 selected subjects, 2088 underwent lipid profile reassessment, of whom 1103 completed the questionnaire (mean age 64.2 ± 12.7 years, 48% male). In these 1103 subjects, mean LDL cholesterol was 4.0 ± 1.4 mmol/l and 722 (65%) received lipid-lowering therapy. FH clinical diagnostic criteria were met by 282 (26%) individuals, of whom 85% had not received guideline-recommended genetic testing and 97% failed to attain LDL cholesterol targets. Of 459 individuals consenting to genetic validation, 13% carried an FH-causing variant, which increased to 19% in clinically diagnosed FH patients. CONCLUSIONS: The identification of a substantial number of previously undiagnosed and un(der)treated clinical and genetic FH patients within a central laboratory database highlights the feasibility and clinical potential of this targeted screening strategy; both in identifying new FH patients and in improving treatment in this high-risk population.

Beyond early LDL cholesterol lowering to prevent coronary atherosclerosis in familial hypercholesterolaemia.

AIMS: Familial hypercholesterolaemia (FH) patients are subjected to a high lifetime exposure to low density lipoprotein cholesterol (LDL-C), despite use of lipid-lowering therapy (LLT). This study aimed to quantify the extent of subclinical atherosclerosis and to evaluate the association between lifetime cumulative LDL-C exposure and coronary atherosclerosis in young FH patients. METHODS AND RESULTS: Familial hypercholesterolaemia patients, divided into a subgroup of early treated (LLT initiated <25 years) and late treated (LLT initiated ≥25 years) patients, and an age- and sex-matched unaffected control group, underwent coronary CT angiography (CCTA) with artificial intelligence-guided analysis. Ninety genetically diagnosed FH patients and 45 unaffected volunteers (mean age 41 ± 3 years, 51 (38%) female) were included. Familial hypercholesterolaemia patients had higher cumulative LDL-C exposure (181 ± 54 vs. 105 ± 33 mmol/L ∗ years) and higher prevalence of coronary plaque compared with controls (46 [51%] vs. 10 [22%], OR 3.66 [95%CI 1.62-8.27]). Every 75 mmol/L ∗ years cumulative exposure to LDL-C was associated with a doubling in per cent atheroma volume (total plaque volume divided by total vessel volume). Early treated patients had a modestly lower cumulative LDL-C exposure compared with late treated FH patients (167 ± 41 vs. 194 ± 61 mmol/L ∗ years; P = 0.045), without significant difference in coronary atherosclerosis. Familial hypercholesterolaemia patients with above-median cumulative LDL-C exposure had significantly higher plaque prevalence (OR 3.62 [95%CI 1.62-8.27]; P = 0.001), compared with patients with below-median exposure. CONCLUSION: Lifetime exposure to LDL-C determines coronary plaque burden in FH, underlining the need of early as well as potent treatment initiation. Periodic CCTA may offer a unique opportunity to monitor coronary atherosclerosis and personalize treatment in FH.

This study reveals that young patients with familial hypercholesterolaemia (FH), as compared with individuals without FH, have a higher build-up of coronary artery plaque, linked directly to their increased lifetime exposure to LDL cholesterol. Genetically confirmed FH patients have a higher coronary plaque burden than those without FH, with every 75 mmol/L ∗ years increase in lifetime cumulative LDL cholesterol exposure resulting in a two-fold increase in total plaque volume. Early and potent LDL cholesterol lowering treatments are crucial for FH patients to prevent future cardiovascular diseases.

AI-Guided Quantitative Plaque Staging Predicts Long-Term Cardiovascular Outcomes in Patients at Risk for Atherosclerotic CVD.

BACKGROUND: The recent development of artificial intelligence-guided quantitative coronary computed tomography angiography analysis (AI-QCT) has enabled rapid analysis of atherosclerotic plaque burden and characteristics. OBJECTIVES: This study set out to investigate the 10-year prognostic value of atherosclerotic burden derived from AI-QCT and to compare the spectrum of plaque to manually assessed coronary computed tomography angiography (CCTA), coronary artery calcium scoring (CACS), and clinical risk characteristics. METHODS: This was a long-term follow-up study of 536 patients referred for suspected coronary artery disease. CCTA scans were analyzed with AI-QCT and plaque burden was classified with a plaque staging system (stage 0: 0% percentage atheroma volume [PAV]; stage 1: >0%-5% PAV; stage 2: >5%-15% PAV; stage 3: >15% PAV). The primary major adverse cardiac event (MACE) outcome was a composite of nonfatal myocardial infarction, nonfatal stroke, coronary revascularization, and all-cause mortality. RESULTS: The mean age at baseline was 58.6 years and 297 patients (55%) were male. During a median follow-up of 10.3 years (IQR: 8.6-11.5 years), 114 patients (21%) experienced the primary outcome. Compared to stages 0 and 1, patients with stage 3 PAV and percentage of noncalcified plaque volume of >7.5% had a more than 3-fold (adjusted HR: 3.57; 95% CI 2.12-6.00; P < 0.001) and 4-fold (adjusted HR: 4.37; 95% CI: 2.51-7.62; P < 0.001) increased risk of MACE, respectively. Addition of AI-QCT improved a model with clinical risk factors and CACS at different time points during follow-up (10-year AUC: 0.82 [95% CI: 0.78-0.87] vs 0.73 [95% CI: 0.68-0.79]; P < 0.001; net reclassification improvement: 0.21 [95% CI: 0.09-0.38]). Furthermore, AI-QCT achieved an improved area under the curve compared to Coronary Artery Disease Reporting and Data System 2.0 (10-year AUC: 0.78; 95% CI: 0.73-0.83; P = 0.023) and manual QCT (10-year AUC: 0.78; 95% CI: 0.73-0.83; P = 0.040), although net reclassification improvement was modest (0.09 [95% CI: -0.02 to 0.29] and 0.04 [95% CI: -0.05 to 0.27], respectively). CONCLUSIONS: Through 10-year follow-up, AI-QCT plaque staging showed important prognostic value for MACE and showed additional discriminatory value over clinical risk factors, CACS, and manual guideline-recommended CCTA assessment.

Impact of atherosclerosis imaging-quantitative computed tomography on diagnostic certainty, downstream testing, coronary revascularization, and medical therapy: the CERTAIN study.

AIMS: The incremental impact of atherosclerosis imaging-quantitative computed tomography (AI-QCT) on diagnostic certainty and downstream patient management is not yet known. The aim of this study was to compare the clinical utility of the routine implementation of AI-QCT versus conventional visual coronary CT angiography (CCTA) interpretation. METHODS AND RESULTS: In this multi-centre cross-over study in 5 expert CCTA sites, 750 consecutive adult patients referred for CCTA were prospectively recruited. Blinded to the AI-QCT analysis, site physicians established patient diagnoses and plans for downstream non-invasive testing, coronary intervention, and medication management based on the conventional site assessment. Next, physicians were asked to repeat their assessments based upon AI-QCT results. The included patients had an age of 63.8 ± 12.2 years; 433 (57.7%) were male. Compared with the conventional site CCTA evaluation, AI-QCT analysis improved physician's confidence two- to five-fold at every step of the care pathway and was associated with change in diagnosis or management in the majority of patients (428; 57.1%; P < 0.001), including for measures such as Coronary Artery Disease-Reporting and Data System (CAD-RADS) (295; 39.3%; P < 0.001) and plaque burden (197; 26.3%; P < 0.001). After AI-QCT including ischaemia assessment, the need for downstream non-invasive and invasive testing was reduced by 37.1% (P < 0.001), compared with the conventional site CCTA evaluation. Incremental to the site CCTA evaluation alone, AI-QCT resulted in statin initiation/increase an aspirin initiation in an additional 28.1% (P < 0.001) and 23.0% (P < 0.001) of patients, respectively. CONCLUSION: The use of AI-QCT improves diagnostic certainty and may result in reduced downstream need for non-invasive testing and increased rates of preventive medical therapy.

Development and Validation of a Quantitative Coronary CT Angiography Model for Diagnosis of Vessel-Specific Coronary Ischemia.

BACKGROUND: Noninvasive stress testing is commonly used for detection of coronary ischemia but possesses variable accuracy and may result in excessive health care costs. OBJECTIVES: This study aimed to derive and validate an artificial intelligence-guided quantitative coronary computed tomography angiography (AI-QCT) model for the diagnosis of coronary ischemia that integrates atherosclerosis and vascular morphology measures (AI-QCTISCHEMIA) and to evaluate its prognostic utility for major adverse cardiovascular events (MACE). METHODS: A post hoc analysis of the CREDENCE (Computed Tomographic Evaluation of Atherosclerotic Determinants of Myocardial Ischemia) and PACIFIC-1 (Comparison of Coronary Computed Tomography Angiography, Single Photon Emission Computed Tomography [SPECT], Positron Emission Tomography [PET], and Hybrid Imaging for Diagnosis of Ischemic Heart Disease Determined by Fractional Flow Reserve) studies was performed. In both studies, symptomatic patients with suspected stable coronary artery disease had prospectively undergone coronary computed tomography angiography (CTA), myocardial perfusion imaging (MPI), SPECT, or PET, fractional flow reserve by CT (FFRCT), and invasive coronary angiography in conjunction with invasive FFR measurements. The AI-QCTISCHEMIA model was developed in the derivation cohort of the CREDENCE study, and its diagnostic performance for coronary ischemia (FFR ≤0.80) was evaluated in the CREDENCE validation cohort and PACIFIC-1. Its prognostic value was investigated in PACIFIC-1. RESULTS: In CREDENCE validation (n = 305, age 64.4 ± 9.8 years, 210 [69%] male), the diagnostic performance by area under the receiver-operating characteristics curve (AUC) on per-patient level was 0.80 (95% CI: 0.75-0.85) for AI-QCTISCHEMIA, 0.69 (95% CI: 0.63-0.74; P < 0.001) for FFRCT, and 0.65 (95% CI: 0.59-0.71; P < 0.001) for MPI. In PACIFIC-1 (n = 208, age 58.1 ± 8.7 years, 132 [63%] male), the AUCs were 0.85 (95% CI: 0.79-0.91) for AI-QCTISCHEMIA, 0.78 (95% CI: 0.72-0.84; P = 0.037) for FFRCT, 0.89 (95% CI: 0.84-0.93; P = 0.262) for PET, and 0.72 (95% CI: 0.67-0.78; P < 0.001) for SPECT. Adjusted for clinical risk factors and coronary CTA-determined obstructive stenosis, a positive AI-QCTISCHEMIA test was associated with an HR of 7.6 (95% CI: 1.2-47.0; P = 0.030) for MACE. CONCLUSIONS: This newly developed coronary CTA-based ischemia model using coronary atherosclerosis and vascular morphology characteristics accurately diagnoses coronary ischemia by invasive FFR and provides robust prognostic utility for MACE beyond presence of stenosis.

Use of Lipoprotein(a) to improve diagnosis and management in clinical familial hypercholesterolemia.

BACKGROUND AND AIMS: Lipoprotein(a) (Lp(a)) is an LDL-like particle whose plasma levels are largely genetically determined. The impact of measuring Lp(a) in patients with clinical familial hypercholesterolemia (FH) referred for genetic testing is largely unknown. We set out to evaluate the contribution of (genetically estimated) Lp(a) in a large nation-wide referral population of clinical FH. METHODS: In 1504 patients referred for FH genotyping, we used an LPA genetic instrument (rs10455872 and rs3798220) as a proxy for plasma Lp(a) levels. The genetic Lp(a) proxy was used to correct LDL-cholesterol and reclassify patients with clinical FH based on Dutch Lipid Criteria Network (DLCN) scoring. Finally, we used estimated Lp(a) levels to reclassify ASCVD risk using the SCORE and SMART risk scores. RESULTS: LPA SNPs were more prevalent among mutation-negative compared with mutation-positive patients (296/1280 (23.1%) vs 35/224 (15.6%), p = 0.016). Among patients with genetically defined high Lp(a) levels, 9% were reclassified to the DLCN category 'unlikely FH' using Lp(a)-corrected LDL-cholesterol (LDL-Ccor) and all but one of these patients indeed carried no FH variant. Furthermore, elevated Lp(a) reclassified predicted ASCVD risk into a higher category in up to 18% of patients. CONCLUSIONS: In patients referred for FH molecular testing, we show that taking into account (genetically estimated) Lp(a) levels not only results in reclassification of probability of genetic FH, but also has an impact on individual cardiovascular risk evaluation. However, to avoid missing the diagnosis of an FH variant, clear thresholds for the use of Lp(a)-cholesterol adjusted LDL-cholesterol levels in patients referred for genetic testing of FH must be established.

ANGPTL3 (Angiopoietin-Like 3) Preferentially Resides on High-Density Lipoprotein in the Human Circulation, Affecting Its Activity.

Background ANGPTL3 (angiopoietin-like protein 3) is an acknowledged crucial regulator of lipid metabolism by virtue of its inhibitory effect on lipoprotein lipase and endothelial lipase. It is currently unknown whether and to which lipoproteins ANGPTL3 is bound and whether the ability of ANGPTL3 to inhibit lipase activity is affected by binding to lipoproteins. Methods and Results Incubation of ultracentrifugation-isolated low-density lipoprotein (LDL) and high-density lipoprotein (HDL) fractions from healthy volunteers with recombinant ANGPTL3 revealed that ANGPTL3 associates with both HDL and LDL particles ex vivo. Plasma from healthy volunteers and a patient deficient in HDL was fractionated by fast protein liquid chromatography, and ANGPTL3 distribution among lipoprotein fractions was measured. In healthy volunteers, ≈75% of lipoprotein-associated ANGPTL3 resides in HDL fractions, whereas ANGPTL3 was largely bound to LDL in the patient deficient in HDL. ANGPTL3 activity was studied by measuring lipolysis and uptake of 3H-trioleate by brown adipocyte T37i cells. Unbound ANGPTL3 did not suppress lipase activity, but when given with HDL or LDL, ANGPTL3 suppressed lipase activity by 21.4±16.4% (P=0.03) and 25.4±8.2% (P=0.006), respectively. Finally, in a subset of the EPIC (European Prospective Investigation into Cancer) Norfolk study, plasma HDL cholesterol and amount of large HDL particles were both positively associated with plasma ANGPTL3 concentrations. Moreover, plasma ANGPTL3 concentrations showed a positive association with incident coronary artery disease (odds ratio, 1.25 [95% CI, 1.01-1.55], P=0.04). Conclusions Although ANGPTL3 preferentially resides on HDL, its activity was highest once bound to LDL particles.