@article {7140, title = {Predicting Heart Failure With Preserved and Reduced Ejection Fraction: The International Collaboration on Heart Failure Subtypes.}, journal = {Circ Heart Fail}, volume = {9}, year = {2016}, month = {2016 Jun}, abstract = {

BACKGROUND: Heart failure (HF) is a prevalent and deadly disease, and preventive strategies focused on at-risk individuals are needed. Current HF prediction models have not examined HF subtypes. We sought to develop and validate risk prediction models for HF with preserved and reduced ejection fraction (HFpEF, HFrEF).

METHODS AND RESULTS: Of 28,820 participants from 4 community-based cohorts, 982 developed incident HFpEF and 909 HFrEF during a median follow-up of 12 years. Three cohorts were combined, and a 2:1 random split was used for derivation and internal validation, with the fourth cohort as external validation. Models accounted for multiple competing risks (death, other HF subtype, and unclassified HF). The HFpEF-specific model included age, sex, systolic blood pressure, body mass index, antihypertensive treatment, and previous myocardial infarction; it had good discrimination in derivation (c-statistic 0.80; 95\% confidence interval [CI], 0.78-0.82) and validation samples (internal: 0.79; 95\% CI, 0.77-0.82 and external: 0.76; 95\% CI: 0.71-0.80). The HFrEF-specific model additionally included smoking, left ventricular hypertrophy, left bundle branch block, and diabetes mellitus; it had good discrimination in derivation (c-statistic 0.82; 95\% CI, 0.80-0.84) and validation samples (internal: 0.80; 95\% CI, 0.78-0.83 and external: 0.76; 95\% CI, 0.71-0.80). Age was more strongly associated with HFpEF, and male sex, left ventricular hypertrophy, bundle branch block, previous myocardial infarction, and smoking with HFrEF (P value for each comparison <=0.02).

CONCLUSIONS: We describe and validate risk prediction models for HF subtypes and show good discrimination in a large sample. Some risk factors differed between HFpEF and HFrEF, supporting the notion of pathogenetic differences among HF subtypes.

}, issn = {1941-3297}, doi = {10.1161/CIRCHEARTFAILURE.115.003116}, author = {Ho, Jennifer E and Enserro, Danielle and Brouwers, Frank P and Kizer, Jorge R and Shah, Sanjiv J and Psaty, Bruce M and Bartz, Traci M and Santhanakrishnan, Rajalakshmi and Lee, Douglas S and Chan, Cheeling and Liu, Kiang and Blaha, Michael J and Hillege, Hans L and van der Harst, Pim and van Gilst, Wiek H and Kop, Willem J and Gansevoort, Ron T and Vasan, Ramachandran S and Gardin, Julius M and Levy, Daniel and Gottdiener, John S and de Boer, Rudolf A and Larson, Martin G} } @article {7549, title = {Predictors and outcomes of heart failure with mid-range ejection fraction.}, journal = {Eur J Heart Fail}, year = {2017}, month = {2017 Dec 11}, abstract = {

AIMS: While heart failure with preserved (HFpEF) and reduced ejection fraction (HFrEF) are well described, determinants and outcomes of heart failure with mid-range ejection fraction (HFmrEF) remain unclear. We sought to examine clinical and biochemical predictors of incident HFmrEF in the community.

METHODS AND RESULTS: We pooled data from four community-based longitudinal cohorts, with ascertainment of new heart failure (HF) classified into HFmrEF [ejection fraction (EF) 41-49\%], HFpEF (EF >=50\%), and HFrEF (EF <=40\%). Predictors of incident HF subtypes were assessed using multivariable Cox models. Among 28 820 participants free of HF followed for a median of 12 years, there were 200 new HFmrEF cases, compared with 811 HFpEF and 1048 HFrEF. Clinical predictors of HFmrEF included age, male sex, systolic blood pressure, diabetes mellitus, and prior myocardial infarction (multivariable adjusted P~<= 0.003 for all). Biomarkers that predicted HFmrEF included natriuretic peptides, cystatin-C, and high-sensitivity troponin (P~<= 0.0004 for all). Natriuretic peptides were stronger predictors of HFrEF [hazard ratio (HR) 2.00 per 1 standard deviation increase, 95\% confidence interval (CI) 1.81-2.20] than of HFmrEF (HR 1.51, 95\% CI 1.20-1.90, P~= 0.01 for difference), and did not differ in their association with incident HFmrEF and HFpEF (HR 1.56, 95\% CI 1.41-1.73, P~= 0.68 for difference). All-cause mortality following the onset of HFmrEF was worse than that of HFpEF (50 vs. 39 events per 1000 person-years, P~= 0.02), but comparable to that of HFrEF (46 events per 1000 person-years, P~= 0.78).

CONCLUSIONS: We found overlap in predictors of incident HFmrEF with other HF subtypes. In contrast, mortality risk after HFmrEF was worse than HFpEF, and similar to HFrEF.

}, issn = {1879-0844}, doi = {10.1002/ejhf.1091}, author = {Bhambhani, Vijeta and Kizer, Jorge R and Lima, Jo{\~a}o A C and van der Harst, Pim and Bahrami, Hossein and Nayor, Matthew and de Filippi, Christopher R and Enserro, Danielle and Blaha, Michael J and Cushman, Mary and Wang, Thomas J and Gansevoort, Ron T and Fox, Caroline S and Gaggin, Hanna K and Kop, Willem J and Liu, Kiang and Vasan, Ramachandran S and Psaty, Bruce M and Lee, Douglas S and Brouwers, Frank P and Hillege, Hans L and Bartz, Traci M and Benjamin, Emelia J and Chan, Cheeling and Allison, Matthew and Gardin, Julius M and Januzzi, James L and Levy, Daniel and Herrington, David M and van Gilst, Wiek H and Bertoni, Alain G and Larson, Martin G and de Boer, Rudolf A and Gottdiener, John S and Shah, Sanjiv J and Ho, Jennifer E} } @article {7603, title = {Association of Cardiovascular Biomarkers With Incident Heart Failure With Preserved and Reduced Ejection Fraction.}, journal = {JAMA Cardiol}, year = {2018}, month = {2018 Jan 10}, abstract = {

Importance: Nearly half of all patients with heart failure have preserved ejection fraction (HFpEF) as opposed to reduced ejection fraction (HFrEF), yet associations of biomarkers with future heart failure subtype are incompletely understood.

Objective: To evaluate the associations of 12 cardiovascular biomarkers with incident HFpEF vs HFrEF among adults from the general population.

Design, Setting, and Participants: This study included 4 longitudinal community-based cohorts: the Cardiovascular Health Study (1989-1990; 1992-1993 for supplemental African-American cohort), the Framingham Heart Study (1995-1998), the Multi-Ethnic Study of Atherosclerosis (2000-2002), and the Prevention of Renal and Vascular End-stage Disease study (1997-1998). Each cohort had prospective ascertainment of incident HFpEF and HFrEF. Data analysis was performed from June 25, 2015, to November 9, 2017.

Exposures: The following biomarkers were examined: N-terminal pro B-type natriuretic peptide or brain natriuretic peptide, high-sensitivity troponin T or I, C-reactive protein (CRP), urinary albumin to creatinine ratio (UACR), renin to aldosterone ratio, D-dimer, fibrinogen, soluble suppressor of tumorigenicity, galectin-3, cystatin C, plasminogen activator inhibitor 1, and interleukin 6.

Main Outcomes and Measures: Development of incident HFpEF and incident HFrEF.

Results: Among the 22 756 participants in these 4 cohorts (12 087 women and 10 669 men; mean [SD] age, 60 [13] years) in the study, during a median follow-up of 12 years, 633 participants developed incident HFpEF, and 841 developed HFrEF. In models adjusted for clinical risk factors of heart failure, 2 biomarkers were significantly associated with incident HFpEF: UACR (hazard ratio [HR], 1.33; 95\% CI, 1.20-1.48; P < .001) and natriuretic peptides (HR, 1.27; 95\% CI, 1.16-1.40; P < .001), with suggestive associations for high-sensitivity troponin (HR, 1.11; 95\% CI, 1.03-1.19; P = .008), plasminogen activator inhibitor 1 (HR, 1.22; 95\% CI, 1.03-1.45; P = .02), and fibrinogen (HR, 1.12; 95\% CI, 1.03-1.22; P = .01). By contrast, 6 biomarkers were associated with incident HFrEF: natriuretic peptides (HR, 1.54; 95\% CI, 1.41-1.68; P < .001), UACR (HR, 1.21; 95\% CI, 1.11-1.32; P < .001), high-sensitivity troponin (HR, 1.37; 95\% CI, 1.29-1.46; P < .001), cystatin C (HR, 1.19; 95\% CI, 1.11-1.27; P < .001), D-dimer (HR, 1.22; 95\% CI, 1.11-1.35; P < .001), and CRP (HR, 1.19; 95\% CI, 1.11-1.28; P < .001). When directly compared, natriuretic peptides, high-sensitivity troponin, and CRP were more strongly associated with HFrEF compared with HFpEF.

Conclusions and Relevance: Biomarkers of renal dysfunction, endothelial dysfunction, and inflammation were associated with incident HFrEF. By contrast, only natriuretic peptides and UACR were associated with HFpEF. These findings highlight the need for future studies focused on identifying novel biomarkers of the risk of HFpEF.

}, issn = {2380-6591}, doi = {10.1001/jamacardio.2017.4987}, author = {de Boer, Rudolf A and Nayor, Matthew and deFilippi, Christopher R and Enserro, Danielle and Bhambhani, Vijeta and Kizer, Jorge R and Blaha, Michael J and Brouwers, Frank P and Cushman, Mary and Lima, Jo{\~a}o A C and Bahrami, Hossein and van der Harst, Pim and Wang, Thomas J and Gansevoort, Ron T and Fox, Caroline S and Gaggin, Hanna K and Kop, Willem J and Liu, Kiang and Vasan, Ramachandran S and Psaty, Bruce M and Lee, Douglas S and Hillege, Hans L and Bartz, Traci M and Benjamin, Emelia J and Chan, Cheeling and Allison, Matthew and Gardin, Julius M and Januzzi, James L and Shah, Sanjiv J and Levy, Daniel and Herrington, David M and Larson, Martin G and van Gilst, Wiek H and Gottdiener, John S and Bertoni, Alain G and Ho, Jennifer E} } @article {7812, title = {The Association of Obesity and Cardiometabolic Traits With Incident~HFpEF and HFrEF.}, journal = {JACC Heart Fail}, volume = {6}, year = {2018}, month = {2018 Aug}, pages = {701-709}, abstract = {

OBJECTIVES: This study evaluated the associations of obesity and cardiometabolic traits with incident heart failure with preserved versus reduced ejection fraction (HFpEF vs. HFrEF). Given known sex differences in HF subtype, we examined men and women separately.

BACKGROUND: Recent studies suggest that obesity confers greater risk of HFpEF versus HFrEF. Contributions of associated metabolic traits to HFpEF are less clear.

METHODS: We studied 22,681 participants from 4 community-based cohorts followed for incident HFpEF versus HFrEF (ejection fraction >=50\% vs.~<50\%). We evaluated the association of body mass index (BMI) and cardiometabolic traits with incident HF subtype using Cox models.

RESULTS: The mean age was 60 {\textpm} 13 years, and 53\% were women. Over a median follow-up of 12 years, 628 developed incident HFpEF and 835 HFrEF. Greater BMI portended higher risk of HFpEF compared with HFrEF (hazard ratio [HR]: 1.34 per 1-SD increase in BMI; 95\% confidence interval [CI]: 1.24 to 1.45 vs. HR: 1.18; 95\% CI: 1.10 to 1.27). Similarly, insulin resistance (homeostatic model assessment of insulin resistance) was associated with HFpEF (HR: 1.20 per 1-SD; 95\% CI: 1.05 to 1.37), but not HFrEF (HR: 0.99; 95\% CI: 0.88 to 1.11; p~< 0.05 for difference HFpEF vs. HFrEF). We found that the differential association of BMI with HFpEF versus HFrEF was more pronounced among women (p for difference HFpEF vs. HFrEF~= 0.01) when compared with men (p~= 0.34).

CONCLUSIONS: Obesity and related cardiometabolic traits including insulin resistance are more strongly associated with risk of future HFpEF versus HFrEF. The differential risk of HFpEF with obesity seems particularly pronounced among~women and may underlie sex differences in HF subtypes.

}, issn = {2213-1787}, doi = {10.1016/j.jchf.2018.05.018}, author = {Savji, Nazir and Meijers, Wouter C and Bartz, Traci M and Bhambhani, Vijeta and Cushman, Mary and Nayor, Matthew and Kizer, Jorge R and Sarma, Amy and Blaha, Michael J and Gansevoort, Ron T and Gardin, Julius M and Hillege, Hans L and Ji, Fei and Kop, Willem J and Lau, Emily S and Lee, Douglas S and Sadreyev, Ruslan and van Gilst, Wiek H and Wang, Thomas J and Zanni, Markella V and Vasan, Ramachandran S and Allen, Norrina B and Psaty, Bruce M and van der Harst, Pim and Levy, Daniel and Larson, Martin and Shah, Sanjiv J and de Boer, Rudolf A and Gottdiener, John S and Ho, Jennifer E} } @article {7923, title = {Equalization of four cardiovascular risk algorithms after systematic recalibration: individual-participant meta-analysis of 86 prospective studies.}, journal = {Eur Heart J}, year = {2018}, month = {2018 Nov 22}, abstract = {

Aims: There is debate about the optimum algorithm for cardiovascular disease (CVD) risk estimation. We conducted head-to-head comparisons of four algorithms recommended by primary prevention guidelines, before and after {\textquoteright}recalibration{\textquoteright}, a method that adapts risk algorithms to take account of differences in the risk characteristics of the populations being studied.

Methods and results: Using individual-participant data on 360 737 participants without CVD at baseline in 86 prospective studies from 22 countries, we compared the Framingham risk score (FRS), Systematic COronary Risk Evaluation (SCORE), pooled cohort equations (PCE), and Reynolds risk score (RRS). We calculated measures of risk discrimination and calibration, and modelled clinical implications of initiating statin therapy in people judged to be at {\textquoteright}high{\textquoteright} 10 year CVD risk. Original risk algorithms were recalibrated using the risk factor profile and CVD incidence of target populations. The four algorithms had similar risk discrimination. Before recalibration, FRS, SCORE, and PCE over-predicted CVD risk on average by 10\%, 52\%, and 41\%, respectively, whereas RRS under-predicted by 10\%. Original versions of algorithms classified 29-39\% of individuals aged >=40 years as high risk. By contrast, recalibration reduced this proportion to 22-24\% for every algorithm. We estimated that to prevent one CVD event, it would be necessary to initiate statin therapy in 44-51 such individuals using original algorithms, in contrast to 37-39 individuals with recalibrated algorithms.

Conclusion: Before recalibration, the clinical performance of four widely used CVD risk algorithms varied substantially. By contrast, simple recalibration nearly equalized their performance and improved modelled targeting of preventive action to clinical need.

}, issn = {1522-9645}, doi = {10.1093/eurheartj/ehy653}, author = {Pennells, Lisa and Kaptoge, Stephen and Wood, Angela and Sweeting, Mike and Zhao, Xiaohui and White, Ian and Burgess, Stephen and Willeit, Peter and Bolton, Thomas and Moons, Karel G M and van der Schouw, Yvonne T and Selmer, Randi and Khaw, Kay-Tee and Gudnason, Vilmundur and Assmann, Gerd and Amouyel, Philippe and Salomaa, Veikko and Kivimaki, Mika and Nordestgaard, B{\o}rge G and Blaha, Michael J and Kuller, Lewis H and Brenner, Hermann and Gillum, Richard F and Meisinger, Christa and Ford, Ian and Knuiman, Matthew W and Rosengren, Annika and Lawlor, Debbie A and V{\"o}lzke, Henry and Cooper, Cyrus and Mar{\'\i}n Iba{\~n}ez, Alejandro and Casiglia, Edoardo and Kauhanen, Jussi and Cooper, Jackie A and Rodriguez, Beatriz and Sundstr{\"o}m, Johan and Barrett-Connor, Elizabeth and Dankner, Rachel and Nietert, Paul J and Davidson, Karina W and Wallace, Robert B and Blazer, Dan G and Bj{\"o}rkelund, Cecilia and Donfrancesco, Chiara and Krumholz, Harlan M and Nissinen, Aulikki and Davis, Barry R and Coady, Sean and Whincup, Peter H and J{\o}rgensen, Torben and Ducimetiere, Pierre and Trevisan, Maurizio and Engstr{\"o}m, Gunnar and Crespo, Carlos J and Meade, Tom W and Visser, Marjolein and Kromhout, Daan and Kiechl, Stefan and Daimon, Makoto and Price, Jackie F and G{\'o}mez de la C{\'a}mara, Agustin and Wouter Jukema, J and Lamarche, Beno{\^\i}t and Onat, Altan and Simons, Leon A and Kavousi, Maryam and Ben-Shlomo, Yoav and Gallacher, John and Dekker, Jacqueline M and Arima, Hisatomi and Shara, Nawar and Tipping, Robert W and Roussel, Ronan and Brunner, Eric J and Koenig, Wolfgang and Sakurai, Masaru and Pavlovic, Jelena and Gansevoort, Ron T and Nagel, Dorothea and Goldbourt, Uri and Barr, Elizabeth L M and Palmieri, Luigi and Nj{\o}lstad, Inger and Sato, Shinichi and Monique Verschuren, W M and Varghese, Cherian V and Graham, Ian and Onuma, Oyere and Greenland, Philip and Woodward, Mark and Ezzati, Majid and Psaty, Bruce M and Sattar, Naveed and Jackson, Rod and Ridker, Paul M and Cook, Nancy R and D{\textquoteright}Agostino, Ralph B and Thompson, Simon G and Danesh, John and Di Angelantonio, Emanuele} } @article {8488, title = {Sex-Specific Associations of Cardiovascular Risk Factors and Biomarkers With Incident Heart~Failure.}, journal = {J Am Coll Cardiol}, volume = {76}, year = {2020}, month = {2020 Sep 22}, pages = {1455-1465}, abstract = {

BACKGROUND: Whether cardiovascular (CV) disease risk factors and biomarkers associate differentially with heart failure (HF) risk in men and women is unclear.

OBJECTIVES: The purpose of this study was to evaluate sex-specific associations of CV risk factors and biomarkers with incident HF.

METHODS: The analysis was performed using data from 4 community-based cohorts with 12.5 years of follow-up. Participants (recruited between 1989 and 2002) were free of HF at baseline. Biomarker measurements included natriuretic peptides, cardiac troponins, plasminogen activator inhibitor-1, D-dimer, fibrinogen, C-reactive protein, sST2, galectin-3, cystatin-C, and urinary albumin-to-creatinine ratio.

RESULTS: Among 22,756 participants (mean age 60 {\textpm} 13 years, 53\% women), HF occurred in 2,095 participants (47\% women). Age, smoking, type 2 diabetes mellitus, hypertension, body mass index, atrial fibrillation, myocardial infarction, left ventricular hypertrophy, and left bundle branch block were strongly associated with HF in both sexes (p~<~0.001), and the combined clinical model had good discrimination in men (C-statistic~=~0.80) and in women (C-statistic~=~0.83). The majority of biomarkers were strongly and similarly associated with HF in both sexes. The clinical model improved modestly after adding natriuretic peptides in men (ΔC-statistic~=~0.006; likelihood ratio chi-square~=~146; p~<~0.001), and after adding cardiac troponins in women (ΔC-statistic~=~0.003; likelihood ratio chi-square~=~73; p~<~0.001).

CONCLUSIONS: CV risk factors are strongly and similarly associated with incident HF in both sexes, highlighting the similar importance of risk factor control in reducing HF risk in the community. There are subtle sex-related differences in the predictive value of individual biomarkers, but the overall improvement in HF risk estimation when included in~a~clinical HF risk prediction model is limited in both sexes.

}, issn = {1558-3597}, doi = {10.1016/j.jacc.2020.07.044}, author = {Suthahar, Navin and Lau, Emily S and Blaha, Michael J and Paniagua, Samantha M and Larson, Martin G and Psaty, Bruce M and Benjamin, Emelia J and Allison, Matthew A and Bartz, Traci M and Januzzi, James L and Levy, Daniel and Meems, Laura M G and Bakker, Stephan J L and Lima, Jo{\~a}o A C and Cushman, Mary and Lee, Douglas S and Wang, Thomas J and deFilippi, Christopher R and Herrington, David M and Nayor, Matthew and Vasan, Ramachandran S and Gardin, Julius M and Kizer, Jorge R and Bertoni, Alain G and Allen, Norrina B and Gansevoort, Ron T and Shah, Sanjiv J and Gottdiener, John S and Ho, Jennifer E and de Boer, Rudolf A} } @article {9637, title = {Development and Validation of the American Heart Association{\textquoteright}s PREVENT Equations.}, journal = {Circulation}, volume = {149}, year = {2024}, month = {2024 Feb 06}, pages = {430-449}, abstract = {

BACKGROUND: Multivariable equations are recommended by primary prevention guidelines to assess absolute risk of cardiovascular disease (CVD). However, current equations have several limitations. Therefore, we developed and validated the American Heart Association Predicting Risk of CVD EVENTs (PREVENT) equations among US adults 30 to 79 years of age without known CVD.

METHODS: The derivation sample included individual-level participant data from 25 data sets (N=3 281 919) between 1992 and 2017. The primary outcome was CVD (atherosclerotic CVD and heart failure). Predictors included traditional risk factors (smoking status, systolic blood pressure, cholesterol, antihypertensive or statin use, and diabetes) and estimated glomerular filtration rate. Models were sex-specific, race-free, developed on the age scale, and adjusted for competing risk of non-CVD death. Analyses were conducted in each data set and meta-analyzed. Discrimination was assessed using the Harrell C-statistic. Calibration was calculated as the slope of the observed versus predicted risk by decile. Additional equations to predict each CVD subtype (atherosclerotic CVD and heart failure) and include optional predictors (urine albumin-to-creatinine ratio and hemoglobin A1c), and social deprivation index were also developed. External validation was performed in 3 330 085 participants from 21 additional data sets.

RESULTS: Among 6 612 004 adults included, mean{\textpm}SD age was 53{\textpm}12 years, and 56\% were women. Over a mean{\textpm}SD follow-up of 4.8{\textpm}3.1 years, there were 211 515 incident total CVD events. The median C-statistics in external validation for CVD were 0.794 (interquartile interval, 0.763-0.809) in female and 0.757 (0.727-0.778) in male participants. The calibration slopes were 1.03 (interquartile interval, 0.81-1.16) and 0.94 (0.81-1.13) among female and male participants, respectively. Similar estimates for discrimination and calibration were observed for atherosclerotic CVD- and heart failure-specific models. The improvement in discrimination was small but statistically significant when urine albumin-to-creatinine ratio, hemoglobin A1c, and social deprivation index were added together to the base model to total CVD (ΔC-statistic [interquartile interval] 0.004 [0.004-0.005] and 0.005 [0.004-0.007] among female and male participants, respectively). Calibration improved significantly when the urine albumin-to-creatinine ratio was added to the base model among those with marked albuminuria (>300 mg/g; 1.05 [0.84-1.20] versus 1.39 [1.14-1.65]; =0.01).

CONCLUSIONS: PREVENT equations accurately and precisely predicted risk for incident CVD and CVD subtypes in a large, diverse, and contemporary sample of US adults by using routinely available clinical variables.

}, keywords = {Adult, Aged, Albumins, American Heart Association, Atherosclerosis, Cardiovascular Diseases, Creatinine, Female, Glycated Hemoglobin, Heart Failure, Humans, Male, Middle Aged, Risk Assessment, Risk Factors}, issn = {1524-4539}, doi = {10.1161/CIRCULATIONAHA.123.067626}, author = {Khan, Sadiya S and Matsushita, Kunihiro and Sang, Yingying and Ballew, Shoshana H and Grams, Morgan E and Surapaneni, Aditya and Blaha, Michael J and Carson, April P and Chang, Alexander R and Ciemins, Elizabeth and Go, Alan S and Gutierrez, Orlando M and Hwang, Shih-Jen and Jassal, Simerjot K and Kovesdy, Csaba P and Lloyd-Jones, Donald M and Shlipak, Michael G and Palaniappan, Latha P and Sperling, Laurence and Virani, Salim S and Tuttle, Katherine and Neeland, Ian J and Chow, Sheryl L and Rangaswami, Janani and Pencina, Michael J and Ndumele, Chiadi E and Coresh, Josef} } @article {9649, title = {Large-Scale Proteomics Identifies Novel Biomarkers and Circulating Risk Factors for Aortic Stenosis.}, journal = {J Am Coll Cardiol}, volume = {83}, year = {2024}, month = {2024 Feb 06}, pages = {577-591}, abstract = {

BACKGROUND: Limited data exist regarding risk factors for aortic stenosis (AS). The plasma proteome is a promising phenotype for discovery of novel biomarkers and potentially causative mechanisms.

OBJECTIVES: The aim of this study was to discover novel biomarkers with potentially causal associations with AS.

METHODS: We measured 4,877 plasma proteins (SomaScan aptamer-affinity assay) among ARIC (Atherosclerosis Risk In Communities) study participants in mid-life (visit 3 [V3]; n~=~11,430; age 60 {\textpm} 6 years) and in late-life (V5; n~=~4,899; age 76 {\textpm} 5 years). We identified proteins cross-sectionally associated with aortic valve (AV) peak velocity (AVmax) and dimensionless index by echocardiography at V5 and with incident AV-related hospitalization after V3 with the use of multivariable linear and Cox proportional hazard regression. We assessed associations of candidate proteins with changes in AVmax over 6 years and with AV calcification with the use of cardiac computed tomography, replicated analysis in an independent sample, performed Mendelian randomization, and evaluated gene expression in explanted human AV tissue.

RESULTS: Fifty-two proteins cross-sectionally were associated with AVmax and dimensionless index at V5 and with risk of incident AV-related hospitalization after V3. Among 3,413 participants in the Cardiovascular Health Study, 6 of those proteins were significantly associated with adjudicated moderate or severe AS, including matrix metalloproteinase 12 (MMP12), complement C1q tumor necrosis factor-related protein 1 (C1QTNF1), and growth differentiation factor-15. MMP12 was also associated with greater increase in AVmax over 6 years, greater degree of AV calcification, and greater expression in calcific compared with normal or fibrotic AV tissue. C1QTNF1 had consistent potential causal effects on both AS and AVmax according to Mendelian randomization analysis.

CONCLUSIONS: These findings identify MMP12 as a potential novel circulating biomarker of AS risk and C1QTNF1 as a new putative target to prevent AS progression.

}, keywords = {Aged, Aged, 80 and over, Aortic Valve, Aortic Valve Stenosis, Biomarkers, Calcinosis, Humans, Matrix Metalloproteinase 12, Middle Aged, Proteomics, Risk Factors}, issn = {1558-3597}, doi = {10.1016/j.jacc.2023.11.021}, author = {Shelbaya, Khaled and Arthur, Victoria and Yang, Yimin and Dorbala, Pranav and Buckley, Leo and Claggett, Brian and Skali, Hicham and Dufresne, Line and Yang, Ta-Yu and Engert, James C and Thanassoulis, George and Floyd, James and Austin, Thomas R and Bortnick, Anna and Kizer, Jorge and Freitas, Renata C C and Singh, Sasha A and Aikawa, Elena and Hoogeveen, Ron C and Ballantyne, Christie and Yu, Bing and Coresh, Josef and Blaha, Michael J and Matsushita, Kunihiro and Shah, Amil M} }