Genetic Architecture and Clinical Outcomes of Combined Lipid Disturbances.

BACKGROUND: Dyslipoproteinemia often involves simultaneous derangements of multiple lipid traits. We aimed to evaluate the phenotypic and genetic characteristics of combined lipid disturbances in a general population-based cohort. METHODS: Among UK Biobank participants without prevalent coronary artery disease, we used blood lipid and apolipoprotein B concentrations to ascribe individuals into 1 of 6 reproducible and mutually exclusive dyslipoproteinemia subtypes. Incident coronary artery disease risk was estimated for each subtype using Cox proportional hazards models. Phenome-wide analyses and genome-wide association studies were performed for each subtype, followed by in silico causal gene prioritization and heritability analyses. Additionally, the prevalence of disruptive variants in causal genes for Mendelian lipid disorders was assessed using whole-exome sequence data. RESULTS: Among 450 636 UK Biobank participants: 63 (0.01%) had chylomicronemia; 40 005 (8.9%) had hypercholesterolemia; 94 785 (21.0%) had combined hyperlipidemia; 13 998 (3.1%) had remnant hypercholesterolemia; 110 389 (24.5%) had hypertriglyceridemia; and 49 (0.01%) had mixed hypertriglyceridemia and hypercholesterolemia. Over a median (interquartile range) follow-up of 11.1 (10.4-11.8) years, incident coronary artery disease risk varied across subtypes, with combined hyperlipidemia exhibiting the largest hazard (hazard ratio, 1.92 [95% CI, 1.84-2.01]; P=2×10-16), even when accounting for non-HDL-C (hazard ratio, 1.45 [95% CI, 1.30-1.60]; P=2.6×10-12). Genome-wide association studies revealed 250 loci significantly associated with dyslipoproteinemia subtypes, of which 72 (28.8%) were not detected in prior single lipid trait genome-wide association studies. Mendelian lipid variant carriers were rare (2.0%) among individuals with dyslipoproteinemia, but polygenic heritability was high, ranging from 23% for remnant hypercholesterolemia to 54% for combined hyperlipidemia. CONCLUSIONS: Simultaneous assessment of multiple lipid derangements revealed nuanced differences in coronary artery disease risk and genetic architectures across dyslipoproteinemia subtypes. These findings highlight the importance of looking beyond single lipid traits to better understand combined lipid and lipoprotein phenotypes and implications for disease risk.

Associations between lipoprotein(a), oxidized phospholipids, and extra-coronary vascular disease.

The roles of lipoprotein(a) [Lp(a)] and related oxidized phospholipids (OxPL) in the development and progression of coronary disease is known, but their influence on extra-coronary vascular disease is not well-established. We sought to evaluate associations between Lp(a), OxPL apolipoprotein B (OxPL-apoB), and apolipoprotein(a) (OxPL-apo(a)) with angiographic extra-coronary vascular disease and incident major adverse limb events (MALE). 446 participants who underwent coronary and/or peripheral angiography were followed up for a median of 3.7 years. Lp(a) and OxPLs were measured before angiography. Elevated Lp(a) was defined as ≥150 nmol/L. Elevated OxPL-apoB and OxPL-apo(a) were defined as greater than or equal to the 75th percentile (OxPL-apoB ≥8.2 nmol/L and OxPL-apo(a) ≥35.8 nmol/L, respectively). Elevated Lp(a) had a stronger association with the presence of extra-coronary vascular disease compared to OxPLs and was minimally improved with the addition of OxPLs in multivariable models. Compared to participants with normal Lp(a) and OxPL concentrations, participants with elevated Lp(a) levels were twice as likely to experience a MALE (OR 2.14 95% CI: 1.03, 4.44) and the strength of the association as well as the C statistic of 0.82 was largely unchanged with the addition of OxPL-apoB and OxPL-apo(a). Elevated Lp(a) and OxPLs are risk factors for progression and complications of extra-coronary vascular disease. However, the addition of OxPLs to Lp(a) does not provide additional information about risk of extra-coronary vascular disease. Therefore, Lp(a) alone captures the risk profile of Lp(a), OxPL-apoB, and OxPL-apo(a) in the development and progression of atherosclerotic plaque in peripheral arteries. These results lay a foundation in support of studying Lp(a) lowering medications and their effect on limb-related complications.

Modification of coronary artery disease clinical risk factors by coronary artery disease polygenic risk score.

BACKGROUND: The extent to which the relationships between clinical risk factors and coronary artery disease (CAD) are altered by CAD polygenic risk score (PRS) is not well understood. Here, we determine whether the interactions between clinical risk factors and CAD PRS further explain risk for incident CAD. METHODS: Participants were of European ancestry from the UK Biobank without prevalent CAD. An externally trained genome-wide CAD PRS was generated and then applied. Clinical risk factors were ascertained at baseline. Cox proportional hazards models were fitted to examine the incident CAD effects of CAD PRS, risk factors, and their interactions. Next, the PRS and risk factors were stratified to investigate the attributable risk of clinical risk factors. FINDINGS: A total of 357,144 individuals of European ancestry without prevalent CAD were included. During a median of 11.1 years of follow-up (interquartile range 10.4-14.1 years), CAD PRS was associated with 1.35-fold (95% confidence interval [CI] 1.332-1.368) risk per SD for incident CAD. The prognostic relevance of the following risk factors was relatively diminished for those with high CAD PRS on a continuous scale: type 2 diabetes (hazard ratio [HR]interaction 0.91, 95% CIinteraction 0.88-0.94), increased body mass index (HRinteraction 0.97, 95% CIinteraction 0.96-0.98), and increased C-reactive protein (HRinteraction 0.98, 95% CIinteraction 0.96-0.99). However, a high CAD PRS yielded joint risk increases with low-density lipoprotein cholesterol (HRinteraction 1.05, 95% CIinteraction 1.04-1.06) and total cholesterol (HRinteraction 1.05, 95% CIinteraction 1.03-1.06). CONCLUSION: The CAD PRS is associated with incident CAD, and its application improves the prognostic relevance of several clinical risk factors. FUNDING: P.N. (R01HL127564, R01HL151152, and U01HG011719) is supported by the National Institutes of Health.

Decoding Genetics, Ancestry, and Geospatial Context for Precision Health.

Mass General Brigham, an integrated healthcare system based in the Greater Boston area of Massachusetts, annually serves 1.5 million patients. We established the Mass General Brigham Biobank (MGBB), encompassing 142,238 participants, to unravel the intricate relationships among genomic profiles, environmental context, and disease manifestations within clinical practice. In this study, we highlight the impact of ancestral diversity in the MGBB by employing population genetics, geospatial assessment, and association analyses of rare and common genetic variants. The population structures captured by the genetics mirror the sequential immigration to the Greater Boston area throughout American history, highlighting communities tied to shared genetic and environmental factors. Our investigation underscores the potency of unbiased, large-scale analyses in a healthcare-affiliated biobank, elucidating the dynamic interplay across genetics, immigration, structural geospatial factors, and health outcomes in one of the earliest American sites of European colonization.

Dynamic Importance of Genomic and Clinical Risk for Coronary Artery Disease Over the Life Course.

Importance: Earlier identification of high coronary artery disease (CAD) risk individuals may enable more effective prevention strategies. However, existing 10-year risk frameworks are ineffective at earlier identification. Understanding the variable importance of genomic and clinical factors across life stages may significantly improve lifelong CAD event prediction. Objective: To assess the time-varying significance of genomic and clinical risk factors in CAD risk estimation across various age groups. Design Setting and Participants: A longitudinal study was performed using data from two cohort studies: the Framingham Offspring Study (FOS) with 3,588 participants aged 19-57 years and the UK Biobank (UKB) with 327,837 participants aged 40-70 years. A total of 134,765 and 3,831,734 person-time years were observed in FOS and UKB, respectively. Main Outcomes and Measures: Hazard ratios (HR) for CAD were calculated for polygenic risk scores (PRS) and clinical risk factors at each age of enrollment. The relative importance of PRS and Pooled Cohort Equations (PCE) in predicting CAD events was also evaluated by age groups. Results: The importance of CAD PRS diminished over the life course, with an HR of 3.58 (95% CI 1.39-9.19) at age 19 in FOS and an HR of 1.51 (95% CI 1.48-1.54) by age 70 in UKB. Clinical risk factors exhibited similar age-dependent trends. PRS significantly outperformed PCE in identifying subsequent CAD events in the 40-45-year age group, with 3.2-fold more appropriately identified events. The mean age of CAD events occurred 1.8 years earlier for those at high genomic risk but 9.6 years later for those at high clinical risk (p<0.001). Overall, adding PRS improved the area under the receiving operating curve of the PCE by an average of +5.1% (95% CI 4.9-5.2%) across all age groups; among individuals <55 years, PRS augmented the AUC-ROC of the PCE by 6.5% (95% CI 5.5-7.5%, p<0.001). Conclusions and Relevance: Genomic and clinical risk factors for CAD display time-varying importance across the lifespan. The study underscores the added value of CAD PRS, particularly among individuals younger than 55 years, for enhancing early risk prediction and prevention strategies.

Concordance of a High Lipoprotein(a) Concentration Among Relatives.

Importance: Lipoprotein(a) (Lp[a]) concentrations are a highly heritable and potential causal risk factor for atherosclerotic cardiovascular disease (ASCVD). Recent consensus statements by the European Atherosclerosis Society and American Heart Association recommend screening of relatives of individuals with high Lp(a) concentrations, but the expected yield of this approach has not been quantified in large populations. Objective: To measure the prevalence of high Lp(a) concentrations among first- and second-degree relatives of individuals with high Lp(a) concentrations compared with unrelated participants. Design, Setting, and Participants: In this cross-sectional analysis, pairs of first-degree (n = 19 899) and second-degree (n = 9715) relatives with measured Lp(a) levels from the UK Biobank study and random pairs of unrelated individuals (n = 184 764) were compared. Data for this study were collected from March 2006 to August 2010 and analyzed from December 2021 to August 2023. Exposure: Serum Lp(a) levels, with a high Lp(a) level defined as at least 125 nmol/L. Main Outcome and Measure: Concordance of clinically relevant high Lp(a) levels in first- and second-degree relatives of index participants with high Lp(a) levels. Results: A total of 52 418 participants were included in the analysis (mean [SD] age, 57.3 [8.0] years; 29 825 [56.9%] women). Levels of Lp(a) were correlated among pairs of first-degree (Spearman ρ = 0.45; P < .001) and second-degree (Spearman ρ = 0.22; P < .001) relatives. A total of 1607 of 3420 (47.0% [95% CI, 45.3%-48.7%]) first-degree and 514 of 1614 (31.8% [95% CI, 29.6%-34.2%]) second-degree relatives of index participants with high Lp(a) levels also had elevated concentrations compared with 4974 of 30 258 (16.4% [95% CI, 16.0%-16.9%]) pairs of unrelated individuals. The concordance in high Lp(a) levels was generally consistent among subgroups (eg, those with prior ASCVD, postmenopausal women, and statin users). The odds ratios for relatives to have high Lp(a) levels if their index relative had a high Lp(a) level compared with those whose index relatives did not have high Lp(a) levels were 7.4 (95% CI, 6.8-8.1) for first-degree relatives and 3.0 (95% CI, 2.7-3.4) for second-degree relatives. Conclusions and Relevance: The findings of this cross-sectional study suggest that the yield of cascade screening of first-degree relatives of individuals with high Lp(a) levels is over 40%. These findings support recent recommendations to use this approach to identify additional individuals at ASCVD risk based on Lp(a) concentrations.

Demonstrating paths for unlocking the value of cloud genomics through cross cohort analysis.

Recently, large scale genomic projects such as All of Us and the UK Biobank have introduced a new research paradigm where data are stored centrally in cloud-based Trusted Research Environments (TREs). To characterize the advantages and drawbacks of different TRE attributes in facilitating cross-cohort analysis, we conduct a Genome-Wide Association Study of standard lipid measures using two approaches: meta-analysis and pooled analysis. Comparison of full summary data from both approaches with an external study shows strong correlation of known loci with lipid levels (R2 ~ 83-97%). Importantly, 90 variants meet the significance threshold only in the meta-analysis and 64 variants are significant only in pooled analysis, with approximately 20% of variants in each of those groups being most prevalent in non-European, non-Asian ancestry individuals. These findings have important implications, as technical and policy choices lead to cross-cohort analyses generating similar, but not identical results, particularly for non-European ancestral populations.

Birth Weight Is Associated With Clonal Hematopoiesis of Indeterminate Potential and Cardiovascular Outcomes in Adulthood.

Background High and low birth weight are independently associated with increased cardiovascular disease risk in adulthood. Clonal hematopoiesis of indeterminate potential (CHIP), the age-related clonal expansion of hematopoietic cells with preleukemic somatic mutations, predicts incident cardiovascular disease independent of traditional cardiovascular risk factors. Whether birth weight predicts development of CHIP later in life is unknown. Methods and Results A total of 221 047 adults enrolled in the UK Biobank with whole exome sequences and self-reported birth weight were analyzed. Of those, 22 030 (11.5%) had low (<2.5 kg) and 29 292 (14.7%) high birth weight (>4.0 kg). CHIP prevalence was higher among participants with low (6.0%, P=0.049) and high (6.3%, P<0.001) versus normal birth weight (5.7%, ref.). Multivariable-adjusted logistic regression analyses demonstrated that each 1-kg increase in birth weight was associated with a 3% increased risk of CHIP (odds ratio, 1.03 [95% CI, 1.00-1.06]; P=0.04), driven by a stronger association observed between birth weight and DNMT3A CHIP (odds ratio, 1.04 per 1-kg increase [95% CI, 1.01-1.08]; P=0.02). Mendelian randomization analyses supported a causal relationship of longer gestational age at delivery with DNMT3A CHIP. Multivariable Cox regression demonstrated that CHIP was independently and additively associated with incident cardiovascular disease or death across birth weight groups, with highest absolute risks in those with CHIP plus high or low birth weight. Conclusions Higher birth weight is associated with increased risk of developing CHIP in midlife, especially DNMT3A CHIP. These findings identify a novel risk factor for CHIP and provide insights into the relationships among early-life environment, CHIP, cancer, and cardiovascular disease.

Genetic, sociodemographic, lifestyle, and clinical risk factors of recurrent coronary artery disease events: a population-based cohort study.

AIMS: Complications of coronary artery disease (CAD) represent the leading cause of death among adults globally. This study examined the associations and clinical utilities of genetic, sociodemographic, lifestyle, and clinical risk factors on CAD recurrence. METHODS AND RESULTS: Data were from 7024 UK Biobank middle-aged adults with established CAD at enrolment. Cox proportional hazards regressions modelled associations of age at enrolment, age at first CAD diagnosis, sex, cigarette smoking, physical activity, diet, sleep, Townsend Deprivation Index, body mass index, blood pressure, blood lipids, glucose, lipoprotein(a), C reactive protein, estimated glomerular filtration rate (eGFR), statin prescription, and CAD polygenic risk score (PRS) with first post-enrolment CAD recurrence. Over a median [interquartile range] follow-up of 11.6 [7.2-12.7] years, 2003 (28.5%) recurrent CAD events occurred. The hazard ratio (95% confidence interval [CI]) for CAD recurrence was the most pronounced with current smoking (1.35, 1.13-1.61) and per standard deviation increase in age at first CAD (0.74, 0.67-0.82). Additionally, age at enrolment, CAD PRS, C-reactive protein, lipoprotein(a), glucose, low-density lipoprotein cholesterol, deprivation, sleep quality, eGFR, and high-density lipoprotein (HDL) cholesterol also significantly associated with recurrence risk. Based on C indices (95% CI), the strongest predictors were CAD PRS (0.58, 0.57-0.59), HDL cholesterol (0.57, 0.57-0.58), and age at initial CAD event (0.57, 0.56-0.57). In addition to traditional risk factors, a comprehensive model improved the C index from 0.644 (0.632-0.654) to 0.676 (0.667-0.686). CONCLUSION: Sociodemographic, clinical, and laboratory factors are each associated with CAD recurrence with genetic risk, age at first CAD event, and HDL cholesterol concentration explaining the most.

Clonal Hematopoiesis of Indeterminate Potential Predicts Adverse Outcomes in Patients With Atherosclerotic Cardiovascular Disease.

BACKGROUND: Clonal hematopoiesis of indeterminate potential (CHIP)-the age-related clonal expansion of blood stem cells with leukemia-associated mutations-is a novel cardiovascular risk factor. Whether CHIP remains prognostic in individuals with established atherosclerotic cardiovascular disease (ASCVD) is less clear. OBJECTIVES: This study tested whether CHIP predicts adverse outcomes in individuals with established ASCVD. METHODS: Individuals aged 40 to 70 years from the UK Biobank with established ASCVD and available whole-exome sequences were analyzed. The primary outcome was a composite of ASCVD events and all-cause mortality. Associations of any CHIP (variant allele fraction ≥2%), large CHIP clones (variant allele fraction ≥10%), and the most commonly mutated driver genes (DNMT3A, TET2, ASXL1, JAK2, PPM1D/TP53 [DNA damage repair genes], and SF3B1/SRSF2/U2AF1 [spliceosome genes]) with incident outcomes were compared using unadjusted and multivariable-adjusted Cox regression. RESULTS: Of 13,129 individuals (median age: 63 years) included, 665 (5.1%) had CHIP. Over a median follow-up of 10.8 years, any CHIP and large CHIP at baseline were associated with adjusted HRs of 1.23 (95% CI: 1.10-1.38; P < 0.001) and 1.34 (95% CI: 1.17-1.53; P < 0.001), respectively, for the primary outcome. TET2 and spliceosome CHIP, especially large clones, were most strongly associated with adverse outcomes (large TET2 CHIP: HR: 1.89; 95% CI: 1.40-2.55; P <0.001; large spliceosome CHIP: HR: 3.02; 95% CI: 1.95-4.70; P < 0.001). CONCLUSIONS: CHIP is independently associated with adverse outcomes in individuals with established ASCVD, with especially high risks observed in TET2 and SF3B1/SRSF2/U2AF1 CHIP.

Lipoprotein(a), Oxidized Phospholipids, and Coronary Artery Disease Severity and Outcomes.

BACKGROUND: Lipoprotein(a) (Lp[a]) and oxidized phospholipids (OxPLs) are each independent risk factors for atherosclerotic cardiovascular disease. The extent to which Lp(a) and OxPLs predict coronary artery disease (CAD) severity and outcomes in a contemporary, statin-treated cohort is not well established. OBJECTIVES: This study sought to evaluate the relationships between Lp(a) particle concentration and OxPLs associated with apolipoprotein B (OxPL-apoB) or apolipoprotein(a) (OxPL-apo[a]) with angiographic CAD and cardiovascular outcomes. METHODS: Among 1,098 participants referred for coronary angiography in the CASABLANCA (Catheter Sampled Blood Archive in Cardiovascular Diseases) study, Lp(a), OxPL-apoB, and OxPL-apo(a) were measured. Logistic regression estimated the risk of multivessel coronary stenoses by Lp(a)-related biomarker level. Cox proportional hazards regression estimated the risk of major adverse cardiovascular events (MACEs) (coronary revascularization, nonfatal myocardial infarction, nonfatal stroke, and cardiovascular death) in follow-up. RESULTS: Median Lp(a) was 26.45 nmol/L (IQR: 11.39-89.49 nmol/L). Lp(a), OxPL-apoB, and OxPL-apo(a) were highly correlated (Spearman R ≥0.91 for all pairwise combinations). Lp(a) and OxPL-apoB were associated with multivessel CAD. Odds of multivessel CAD per doubling of Lp(a), OxPL-apoB, and OxPL-apo(a) were 1.10 (95% CI: 1.03-1.18; P = 0.006), 1.18 (95% CI: 1.03-1.34; P = 0.01), and 1.07 (95% CI: 0.99-1.16; P = 0.07), respectively. All biomarkers were associated with cardiovascular events. HRs for MACE per doubling of Lp(a), OxPL-apoB, and OxPL-apo(a) were 1.08 (95% CI: 1.03-1.14; P = 0.001), 1.15 (95% CI: 1.05-1.26; P = 0.004), and 1.07 (95% CI: 1.01-1.14; P = 0.02), respectively. CONCLUSIONS: In patients undergoing coronary angiography, Lp(a) and OxPL-apoB are associated with multivessel CAD. Lp(a), OxPL-apoB, and OxPL-apo(a) are associated with incident cardiovascular events. (Catheter Sampled Blood Archive in Cardiovascular Diseases [CASABLANCA]; NCT00842868).

Genetic and clinical factors underlying a self-reported family history of heart disease.

AIMS: To estimate how much information conveyed by self-reported family history of heart disease (FHHD) is already explained by clinical and genetic risk factors. METHODS AND RESULTS: Cross-sectional analysis of UK Biobank participants without pre-existing coronary artery disease using a multivariable model with self-reported FHHD as the outcome. Clinical (diabetes, hypertension, smoking, apolipoprotein B-to-apolipoprotein AI ratio, waist-to-hip ratio, high sensitivity C-reactive protein, lipoprotein(a), triglycerides) and genetic risk factors (polygenic risk score for coronary artery disease [PRSCAD], heterozygous familial hypercholesterolemia [HeFH]) were exposures. Models were adjusted for age, sex, and cholesterol-lowering medication use. Multiple logistic regression models were fitted to associate FHHD with risk factors, with continuous variables treated as quintiles. Population attributable risks (PAR) were subsequently calculated from the resultant odds ratios. Among 166 714 individuals, 72 052 (43.2%) participants reported an FHHD. In a multivariable model, genetic risk factors PRSCAD (OR 1.30, CI 1.27-1.33) and HeFH (OR 1.31, 1.11-1.54) were most strongly associated with FHHD. Clinical risk factors followed: hypertension (OR 1.18, CI 1.15-1.21), lipoprotein(a) (OR 1.17, CI 1.14-1.20), apolipoprotein B-to-apolipoprotein AI ratio (OR 1.13, 95% CI 1.10-1.16), and triglycerides (OR 1.07, CI 1.04-1.10). For the PAR analyses: 21.9% (CI 18.19-25.63) of the risk of reporting an FHHD is attributed to clinical factors, 22.2% (CI% 20.44-23.88) is attributed to genetic factors, and 36.0% (CI 33.31-38.68) is attributed to genetic and clinical factors combined. CONCLUSIONS: A combined model of clinical and genetic risk factors explains only 36% of the likelihood of FHHD, implying additional value in the family history.

With advances in genetics, it is tempting to assume that the 'family history' of a patient is an imperfect proxy for information we can already glean from genetics and laboratory tests. However, this study shows that much of the information contained in the self-reported family history of heart disease is not captured by currently available genetic and clinical biomarkers and highlights an important knowledge gap. Clinically used biomarkers explained only 21.9% of the likelihood of a patient reporting a family history of heart disease, while genetics explained 22.2%, and a combined model explained 36% of this likelihoodThe majority of the risk of reporting a family history went unexplained, implying that family history still has major relevance in clinical practice.

Genome-wide discovery for diabetes-dependent triglycerides-associated loci.

PURPOSE: We aimed to discover loci associated with triglyceride (TG) levels in the context of type 2 diabetes (T2D). We conducted a genome-wide association study (GWAS) in 424,120 genotyped participants of the UK Biobank (UKB) with T2D status and TG levels. METHODS: We stratified the cohort based on T2D status and conducted association analyses of TG levels for genetic variants with minor allele count (MAC) at least 20 in each stratum. Effect differences of genetic variants by T2D status were determined by Cochran's Q-test and we validated the significantly associated variants in the Mass General Brigham Biobank (MGBB). RESULTS: Among 21,176 T2D and 402,944 non-T2D samples from UKB, stratified GWAS identified 19 and 315 genomic risk loci significantly associated with TG levels, respectively. Only chr6p21.32 exhibited genome-wide significant heterogeneity (I2 = 98.4%; pheterogeneity = 2.1x10-15), with log(TG) effect estimates of -0.066 (95%CI: -0.082, -0.050) and 0.002 (95%CI: -0.002, 0.006) for T2D and non-T2D, respectively. The lead variant rs9274619:A (allele frequency 0.095) is located 2Kb upstream of the HLA-DQB1 gene, between HLA-DQB1 and HLA-DQA2 genes. We replicated this finding among 25,137 participants (6,951 T2D cases) of MGBB (pheterogeneity = 9.5x10-3). Phenome-wide interaction association analyses showed that the lead variant was strongly associated with a concomitant diagnosis of type 1 diabetes (T1D) as well as diabetes-associated complications. CONCLUSION: In conclusion, we identified an intergenic variant near HLA-DQB1/DQA2 significantly associates with decreased triglycerides only among those with T2D and highlights an immune overlap with T1D.

Sex Differences in Temporal Trends of Cardiovascular Health in Young US Adults.

Background Favorable cardiovascular health (CVH) in young adulthood has been associated with lower future cardiovascular risk. We determined whether CVH and its sex differences in young adults have changed from 2007 to 2018. Methods and Results We identified 10 206 individuals, aged 20 to 39 years, from the National Health Examination and Nutrition Survey data. CVH was assessed on the basis of the American Heart Association's Life's Simple 7 metrics (of 7). Changes in the mean number of ideal CVH components and the ideal proportion of individual components were calculated using linear regression analysis. Changes in sex difference trends were assessed with an interaction term between sex and calendar year. The mean (SD) age of the study population was 29.3 (5.8) years, and 5260 (51.5%) individuals were women. The mean (SD) ideal CVH component remained unchanged for both women (4.40 [1.22] to 4.48 [1.15]; P=0.94) and men (3.97 [1.27] to 3.93 [1.24]; P=0.87), with stable sex differences (P for interaction=0.94). Nonetheless, sex differences in blood pressure widened as ideal blood pressure decreased in men (54.0% to 46.9%; P=0.03) but not in women (P for interaction <0.001). Concurrently, the proportion with ideal physical activity declined in women (57.3% to 49.4%; P=0.04) but remained stable in men (P for interaction=0.03). Nonsmoking increased to a greater extent in women (64.1% to 70.5%; P=0.05) than in men (P for interaction=0.01). Conclusions Sex disparities in CVH have persisted with exacerbated differences in blood pressure, physical activity, and smoking. These insights provide opportunities to promote equitable CVH.

Genome-wide pleiotropy analysis of coronary artery disease and pneumonia identifies shared immune pathways.

Coronary artery disease (CAD) remains the leading cause of death despite scientific advances. Elucidating shared CAD/pneumonia pathways may reveal novel insights regarding CAD pathways. We performed genome-wide pleiotropy analyses of CAD and pneumonia, examined the causal effects of the expression of genes near independently replicated SNPs and interacting genes with CAD and pneumonia, and tested interactions between disruptive coding mutations of each pleiotropic gene and smoking status on CAD and pneumonia risks. Identified pleiotropic SNPs were annotated to ADAMTS7 and IL6R. Increased ADAMTS7 expression across tissues consistently showed decreased risk for CAD and increased risk for pneumonia; increased IL6R expression showed increased risk for CAD and decreased risk for pneumonia. We similarly observed opposing CAD/pneumonia effects for NLRP3. Reduced ADAMTS7 expression conferred a reduced CAD risk without increased pneumonia risk only among never-smokers. Genetic immune-inflammatory axes of CAD linked to respiratory infections implicate ADAMTS7 and IL6R, and related genes.

Repeat Measures of Lipoprotein(a) Molar Concentration and Cardiovascular Risk.

BACKGROUND: When indicated, guidelines recommend measurement of lipoprotein(a) for cardiovascular risk assessment. However, temporal variability in lipoprotein(a) is not well understood, and it is unclear if repeat testing may help refine risk prediction of coronary artery disease (CAD). OBJECTIVES: The authors examined the stability of repeat lipoprotein(a) measurements and the association between instability in lipoprotein(a) molar concentration with incident CAD. METHODS: The authors assessed the correlation between baseline and first follow-up measurements of lipoprotein(a) in the UK Biobank (n = 16,017 unrelated individuals). The association between change in lipoprotein(a) molar concentration and incident CAD was assessed among 15,432 participants using Cox proportional hazards models. RESULTS: Baseline and follow-up lipoprotein(a) molar concentration were significantly correlated over a median of 4.42 years (IQR: 3.69-4.93 years; Spearman rho = 0.96; P < 0.0001). The correlation between baseline and follow-up lipoprotein(a) molar concentration were stable across time between measurements of <3 (rho = 0.96), 3-4 (rho = 0.97), 4-5 (rho = 0.96), and >5 years (rho = 0.96). Although there were negligible-to-modest associations between statin use and changes in lipoprotein(a) molar concentration, statin usage was associated with a significant increase in lipoprotein(a) among individuals with baseline levels ≥70 nmol/L. Follow-up lipoprotein(a) molar concentration was significantly associated with risk of incident CAD (HR per 120 nmol/L: 1.32 [95% CI: 1.16-1.50]; P = 0.0002). However, the delta between follow-up and baseline lipoprotein(a) molar concentration was not significantly associated with incident CAD independent of follow-up lipoprotein(a) (P = 0.98). CONCLUSIONS: These findings suggest that, in the absence of therapies substantially altering lipoprotein(a), a single accurate measurement of lipoprotein(a) molar concentration is an efficient method to inform CAD risk.

Deep Learning of the Retina Enables Phenome- and Genome-Wide Analyses of the Microvasculature.

BACKGROUND: The microvasculature, the smallest blood vessels in the body, has key roles in maintenance of organ health and tumorigenesis. The retinal fundus is a window for human in vivo noninvasive assessment of the microvasculature. Large-scale complementary machine learning-based assessment of the retinal vasculature with phenome-wide and genome-wide analyses may yield new insights into human health and disease. METHODS: We used 97 895 retinal fundus images from 54 813 UK Biobank participants. Using convolutional neural networks to segment the retinal microvasculature, we calculated vascular density and fractal dimension as a measure of vascular branching complexity. We associated these indices with 1866 incident International Classification of Diseases-based conditions (median 10-year follow-up) and 88 quantitative traits, adjusting for age, sex, smoking status, and ethnicity. RESULTS: Low retinal vascular fractal dimension and density were significantly associated with higher risks for incident mortality, hypertension, congestive heart failure, renal failure, type 2 diabetes, sleep apnea, anemia, and multiple ocular conditions, as well as corresponding quantitative traits. Genome-wide association of vascular fractal dimension and density identified 7 and 13 novel loci, respectively, that were enriched for pathways linked to angiogenesis (eg, vascular endothelial growth factor, platelet-derived growth factor receptor, angiopoietin, and WNT signaling pathways) and inflammation (eg, interleukin, cytokine signaling). CONCLUSIONS: Our results indicate that the retinal vasculature may serve as a biomarker for future cardiometabolic and ocular disease and provide insights into genes and biological pathways influencing microvascular indices. Moreover, such a framework highlights how deep learning of images can quantify an interpretable phenotype for integration with electronic health record, biomarker, and genetic data to inform risk prediction and risk modification.

Trends in cholesterol testing during the COVID-19 pandemic: COVID-19 and cholesterol testing.

OBJECTIVE: To characterize trends in cholesterol testing since the start of the COVID-19 pandemic. METHODS: We extracted testing for total cholesterol performed in adults ≥40 years old within the Mass General Brigham healthcare system between March and September 2020, as well those performed between March and September 2019 (reference period). Weekly cholesterol testing rates during the 2020 vs. 2019 study periods were compared using the paired samples t-test. Secondary analyses compared testing volumes and patient characteristics during the first vs. second half of the 2020 study period. RESULTS: The study sample included 296,599 tests for total cholesterol performed in 220,215 individuals. The mean (SD) weekly cholesterol tests performed were 6,361 (682) in 2019 vs. 3,867 (2,373) in 2020 (P = 2.6 × 10-5), representing an overall decline of 39.2%. However, weekly testing rates in 2020 were not uniform. Greatest reductions coincided with the "first wave" of the pandemic (March-May 2020), with up to 92% reductions in testing observed. In the first 14 weeks of each study period (March to mid-June), weekly testing rates were 71.8% lower in 2020. Among individuals tested in 2020, those tested between March and mid-June had substantially lower total cholesterol compared with individuals tested after mid-June (174.2 vs. 181.5 mg/dL, P<2.2 × 10-16). CONCLUSIONS: In a large integrated healthcare system, cholesterol testing rates were 39% lower between March-September 2020 compared with the same time period in 2019. Mechanisms for safely facilitating cholesterol testing and management for high-risk patients will be important as COVID-19 re-surges across the U.S. until widespread vaccination and population immunity allow resumption of routine preventive care.