Utilization of at-home tests for coronavirus disease 2019 (COVID-19) among healthcare workers in Chicago.

Objective: To describe utilization of at-home coronavirus disease 2019 (COVID-19) testing among healthcare workers (HCW). Design: Serial cross-sectional study. Setting and participants: HCWs in the Chicago area. Methods: Serial surveys were conducted from the Northwestern Medicine (NM HCW SARS-CoV-2) Serology Cohort Study. In April 2022, participants reflected on the past 30 days to complete an online survey regarding COVID-19 home testing. Surveys were repeated in June and November 2022. The percentage of completed home tests and ever-positive tests were reported. Multivariable Poisson regression was used to calculate prevalence rate ratios (PRR) and univariate analysis was used for association between participant characteristics with home testing and positivity. Results: Overall, 2,226 (62.4%) of 3,569 responded to the survey in April. Home testing was reported by 26.6% of respondents and 5.9% reported having at least one positive home test. Testing was highest among those 30-39 years old (35.9%) and nurses (28.3%). A positive test was associated (P < .001) with exposure to people, other than patients with known or suspected COVID-19. Home testing increased in June to 36.4% (positivity 19.9%) and decreased to 25% (positivity 13.5%) by November. Conclusion: Our cohort findings show the overall increase in both home testing and ever positivity from April to November - a period where changes in variants of concern of SARS-CoV-2 were reported nationwide. Having an exposure to people, other than patients with known or suspected COVID-19 was significantly associated with both, higher home testing frequency and ever-test positivity.

Metabolic liability for weight gain in early adulthood.

While weight gain is associated with a host of chronic illnesses, efforts in obesity have relied on single "snapshots" of body mass index (BMI) to guide genetic and molecular discovery. Here, we study >2,000 young adults with metabolomics and proteomics to identify a metabolic liability to weight gain in early adulthood. Using longitudinal regression and penalized regression, we identify a metabolic signature for weight liability, associated with a 2.6% (2.0%-3.2%, p = 7.5 × 10-19) gain in BMI over ≈20 years per SD higher score, after comprehensive adjustment. Identified molecules specified mechanisms of weight gain, including hunger and appetite regulation, energy expenditure, gut microbial metabolism, and host interaction with external exposure. Integration of longitudinal and concurrent measures in regression with Mendelian randomization highlights the complexity of metabolic regulation of weight gain, suggesting caution in interpretation of epidemiologic or genetic effect estimates traditionally used in metabolic research.

Association Between Life's Essential 8 Cardiovascular Health Metrics With Cardiovascular Events in the Cardiovascular Disease Lifetime Risk Pooling Project.

BACKGROUND: The American Heart Association recently launched updated cardiovascular health metrics, termed Life's Essential 8 (LE8). Compared with Life's Simple 7 (LS7), the new approach added sleep health as an eighth metric and updated the remaining 7 health factors and behaviors. The association of the updated LE8 score with long-term cardiovascular disease (CVD) outcomes and death is unknown. METHODS: We pooled individual-level data from 6 contemporary US-based cohorts from the Cardiovascular Lifetime Risk Pooling Project. Total LE8 score (0-100 points), LE8 score without sleep (0-100 points), and prior LS7 scores (0-14 points) were calculated separately. We used multivariable-adjusted Cox models to evaluate the association of LE8 with CVD, CVD subtypes, and all-cause mortality among younger, middle, and older adult participants. Net reclassification improvement analysis was used to measure the improvement in CVD risk classification with the addition of LS7 and LE8 recategorization based on score quartile rankings. RESULTS: Our sample consisted of 32 896 US adults (7836 [23.8%] Black; 14 941 [45.4%] men) followed for 642 000 person-years, of whom 9391 developed CVD events. Each 10-point higher overall LE8 score was associated with lower risk by 22% to 40% for CVD, 24% to 43% for congenital heart disease, 17% to 34% for stroke, 23% to 38% for heart failure, and 17% to 21% for all causes of mortality events across age strata. LE8 score provided more granular differentiation of the related CVD risk than LS7. Overall, 19.5% and 15.5% of the study participants were recategorized upward and downward based on LE8 versus LS7 categories, respectively, and the recategorization was significantly associated with CVD risk in addition to LS7 score. The addition of recategorization between LE8 and LS7 categories improved CVD risk reclassification across age groups (clinical net reclassification improvement, 0.06-0.12; P<0.01). CONCLUSIONS: These findings support the improved utility of the LE8 algorithm for assessing overall cardiovascular health and future CVD risk.

Circulating Branched Chain Amino Acids and Cardiometabolic Disease.

Branched chain amino acids (BCAAs) are essential for protein homeostasis, energy balance, and signaling pathways. Changes in BCAA homeostasis have emerged as pivotal contributors in the pathophysiology of several cardiometabolic diseases, including type 2 diabetes, obesity, hypertension, atherosclerotic cardiovascular disease, and heart failure. In this review, we provide a detailed overview of BCAA metabolism, focus on molecular mechanisms linking disrupted BCAA homeostasis with cardiometabolic disease, summarize the evidence from observational and interventional studies investigating associations between circulating BCAAs and cardiometabolic disease, and offer valuable insights into the potential for BCAA manipulation as a novel therapeutic strategy for cardiometabolic disease.

Targeted Proteomics Reveals Functional Targets for Early Diabetes Susceptibility in Young Adults.

BACKGROUND: The circulating proteome may encode early pathways of diabetes susceptibility in young adults for surveillance and intervention. Here, we define proteomic correlates of tissue phenotypes and diabetes in young adults. METHODS: We used penalized models and principal components analysis to generate parsimonious proteomic signatures of diabetes susceptibility based on phenotypes and on diabetes diagnosis across 184 proteins in >2000 young adults in the CARDIA (Coronary Artery Risk Development in Young Adults study; mean age, 32 years; 44% women; 43% Black; mean body mass index, 25.6±4.9 kg/m2), with validation against diabetes in >1800 individuals in the FHS (Framingham Heart Study) and WHI (Women's Health Initiative). RESULTS: In 184 proteins in >2000 young adults in CARDIA, we identified 2 proteotypes of diabetes susceptibility-a proinflammatory fat proteotype (visceral fat, liver fat, inflammatory biomarkers) and a muscularity proteotype (muscle mass), linked to diabetes in CARDIA and WHI/FHS. These proteotypes specified broad mechanisms of early diabetes pathogenesis, including transorgan communication, hepatic and skeletal muscle stress responses, vascular inflammation and hemostasis, fibrosis, and renal injury. Using human adipose tissue single cell/nuclear RNA-seq, we demonstrate expression at transcriptional level for implicated proteins across adipocytes and nonadipocyte cell types (eg, fibroadipogenic precursors, immune and vascular cells). Using functional assays in human adipose tissue, we demonstrate the association of expression of genes encoding these implicated proteins with adipose tissue metabolism, inflammation, and insulin resistance. CONCLUSIONS: A multifaceted discovery effort uniting proteomics, underlying clinical susceptibility phenotypes, and tissue expression patterns may uncover potentially novel functional biomarkers of early diabetes susceptibility in young adults for future mechanistic evaluation.

Incorporating longitudinal history of risk factors into atherosclerotic cardiovascular disease risk prediction using deep learning.

It is increasingly clear that longitudinal risk factor levels and trajectories are related to risk for atherosclerotic cardiovascular disease (ASCVD) above and beyond single measures. Currently used in clinical care, the Pooled Cohort Equations (PCE) are based on regression methods that predict ASCVD risk based on cross-sectional risk factor levels. Deep learning (DL) models have been developed to incorporate longitudinal data for risk prediction but its benefit for ASCVD risk prediction relative to the traditional Pooled Cohort Equations (PCE) remain unknown. Our study included 15,565 participants from four cardiovascular disease cohorts free of baseline ASCVD who were followed for adjudicated ASCVD. Ten-year ASCVD risk was calculated in the training set using our benchmark, the PCE, and a longitudinal DL model, Dynamic-DeepHit. Predictors included those incorporated in the PCE: sex, race, age, total cholesterol, high density lipid cholesterol, systolic and diastolic blood pressure, diabetes, hypertension treatment and smoking. The discrimination and calibration performance of the two models were evaluated in an overall hold-out testing dataset. Of the 15,565 participants in our dataset, 2170 (13.9%) developed ASCVD. The performance of the longitudinal DL model that incorporated 8 years of longitudinal risk factor data improved upon that of the PCE [AUROC: 0.815 (CI 0.782-0.844) vs 0.792 (CI 0.760-0.825)] and the net reclassification index was 0.385. The brier score for the DL model was 0.0514 compared with 0.0542 in the PCE. Incorporating longitudinal risk factors in ASCVD risk prediction using DL can improve model discrimination and calibration.

Educational Attainment and Lifetime Risk of Cardiovascular Disease.

Importance: Education is a social determinant of health. Quantifying its association with lifetime cardiovascular disease (CVD) risk has public health importance. Objective: To calculate lifetime risk estimates of incident CVD and CVD subtypes and estimate years lived with and without CVD by education. Design, Setting, and Participants: Included community-based cohort studies with adjudicated cardiovascular events used pooled individual-level data from 1985 to 2015 of 6 prospective cohort studies. The study team assessed the association between education and lifetime CVD risk with modified Kaplan-Meier and Cox models accounting for competing risk of noncardiovascular death. The study team estimated years lived with and without CVD by education with the Irwin restricted mean and the utility of adding educational attainment to CVD risk assessment. Participants (baseline 40 to 59 years old and 60 to 79 years old) were without CVD at baseline and had complete education, cardiovascular risk factors, and prospective CVD outcomes data. Data were analyzed from January 2022 to September 2022. Exposures: Educational attainment (less than high school, high school completion, some college, or college graduate). Main outcome and measures: Cardiovascular events (fatal and nonfatal coronary heart disease, heart failure, and stroke; CVD-related deaths; and total CVD encompassing any of these events). Results: There were 40 998 participants (23 305 female [56.2%]) with a mean (SD) age of 58.1 (9.7) years for males and 58.3 (9.9) years for females. Compared with college graduates, those with less than high school or high school completion had higher lifetime CVD risks. Among middle-aged men, the competing hazard ratios (HRs) for a CVD event were 1.58 (95% CI, 1.38-1.80), 1.30 (95% CI, 1.10-1.46), and 1.16 (95% CI, 1.00-1.34) in those with less than high school, high school, and some college, respectively, compared with those with college completion. Among women, these competing HRs were 1.70 (95% CI, 1.49-1.95), 1.19 (95% CI, 1.05-1.35), and 0.98 (95% CI, 0.83-1.15). Individuals with higher education had longer duration of life prior to incident CVD. Education provided limited contribution toward enhancing CVD risk prediction. Conclusions and relevance: Lower education was associated with lifetime CVD risk across adulthood; higher education translated to healthy longevity. Educational policy initiatives may associate with long-term health benefits.

The association of burnout with work absenteeism and the frequency of thoughts in leaving their job in a cohort of healthcare workers during the COVID-19 pandemic.

Introduction: During the coronavirus disease 2019 pandemic, high levels of burnout were reported among healthcare workers. This study examines the association of work absenteeism and frequency of thoughts in leaving current job with burnout among a cohort of healthcare workers during the COVID-19 pandemic. Methods: A cross-sectional survey of healthcare workers was conducted from April-May, 2022 on healthcare workers from 10 hospitals, 18 immediate care centers, and 325 outpatient practices in the Chicago area and surrounding Illinois suburbs. Logistic regression models were used to assess the association of burnout scores (Oldenburg Burnout Inventory-OLBI) and its sub-scores (exhaustion and disengagement scores) with work absenteeism and thoughts of leaving work. Results: One-fifth and 60% of respondents (n = 1,825) reported unplanned absenteeism and thoughts of leaving their job, respectively. After adjusting for covariates, higher burnout scores, especially exhaustion scores, were associated with increased odds of unplanned absenteeism (OR = 1.04, 95% CI: 1.01-1.08). Burnout scores and both sub-scores were also positively associated with the frequency of thoughts of leaving work, e.g., each unit increase in the OLBI burnout score was associated with 1.39 (95% CI: 1.34-1.43) times higher odds of thinking about leaving work "a lot/constantly" vs. "never". Discussion: Overall, this study cohort showed a positive association between burnout scores and unplanned work absenteeism (and frequency of thoughts in leaving job) during the COVID-19 pandemic. More research is needed to support healthcare worker well-being during times of stress and direct solutions to addressing unplanned absenteeism in the light of a pandemic.

Novel Size-Based High-Density Lipoprotein Subspecies and Incident Vascular Events.

Background High-density lipoprotein (HDL) particle concentration likely outperforms HDL cholesterol in predicting atherosclerotic cardiovascular events. Whether size-based HDL subspecies explain the atheroprotective associations of HDL particle concentration remains unknown. Our objective was to assess whether levels of specific size-based HDL subspecies associate with atherosclerotic cardiovascular disease in a multiethnic pooled cohort and improve risk prediction beyond traditional atherosclerotic cardiovascular disease risk factors. Methods and Results Seven HDL size-based subspecies were quantified by nuclear magnetic resonance (LP4 algorithm; H1=smallest; H7=largest) among participants without prior atherosclerotic cardiovascular disease in ARIC (Atherosclerosis Risk in Communities), MESA (Multi-Ethnic Study of Atherosclerosis), PREVEND (Prevention of Renal and Vascular Endstage Disease), and DHS (Dallas Heart Study) cohorts (n=15 371 people). Multivariable Cox proportional hazards models were used to evaluate the association between HDL subspecies and incident myocardial infarction (MI) or ischemic stroke at follow-up (average 8-10 years) adjusting for HDL cholesterol and risk factors. Improvement in risk prediction was assessed via discrimination and reclassification analysis. Within the pooled cohort (median age 57 years; female 54%; Black 22%) higher H1 (small) and H4 (medium) concentrations were inversely associated with incident MI (hazard ratio [HR]/SD, H1 0.88 [95% CI, 0.81-0.94]; H4 0.89 [95% CI, 0.82-0.97]). H4 but not H1 improved risk prediction indices for incident MI. Increasing H2 and H4 were inversely associated with improved risk prediction indices for composite end point of stroke, MI, and cardiovascular death (HR/SD, H2 0.94 [95% CI, 0.88-0.99]; H4 0.91 [95% CI, 0.85-0.98]). Levels of the large subspecies (H6 and H7) were not associated with any vascular end point. Conclusions Two of 7 HDL size-based subspecies modestly improved risk prediction for MI and composite vascular end points in a large multiethnic pooled cohort. These findings support assessment of precise HDL subspecies for future studies regarding clinical utility.

Incorporating longitudinal history of risk factors into atherosclerotic cardiovascular disease risk prediction using deep learning.

Background: It is increasingly clear that longitudinal risk factor levels and trajectories are related to risk for atherosclerotic cardiovascular disease (ASCVD) above and beyond single measures. Currently used in clinical care, the Pooled Cohort Equations (PCE) are based on regression methods that predict ASCVD risk based on cross-sectional risk factor levels. Deep learning (DL) models have been developed to incorporate longitudinal data for risk prediction but its benefit for ASCVD risk prediction relative to the traditional Pooled Cohort Equations (PCE) remain unknown. Objective: To develop a ASCVD risk prediction model that incorporates longitudinal risk factors using deep learning. Methods: Our study included 15,565 participants from four cardiovascular disease cohorts free of baseline ASCVD who were followed for adjudicated ASCVD. Ten-year ASCVD risk was calculated in the training set using our benchmark, the PCE, and a longitudinal DL model, Dynamic-DeepHit. Predictors included those incorporated in the PCE: sex, race, age, total cholesterol, high density lipid cholesterol, systolic and diastolic blood pressure, diabetes, hypertension treatment and smoking. The discrimination and calibration performance of the two models were evaluated in an overall hold-out testing dataset. Results: Of the 15,565 participants in our dataset, 2,170 (13.9%) developed ASCVD. The performance of the longitudinal DL model that incorporated 8 years of longitudinal risk factor data improved upon that of the PCE [AUROC: 0.815 (CI: 0.782-0.844) vs 0.792 (CI: 0.760-0.825)] and the net reclassification index was 0.385. The brier score for the DL model was 0.0514 compared with 0.0542 in the PCE. Conclusion: Incorporating longitudinal risk factors in ASCVD risk prediction using DL can improve model discrimination and calibration.

Metabolomic Profiling of Cholesterol Efflux Capacity in a Multiethnic Population: Insights From MESA.

BACKGROUND: Impaired cholesterol efflux capacity (CEC) is a novel lipid metabolism trait associated with atherosclerotic cardiovascular disease. Mechanisms underlying CEC variation are unknown. We evaluated associations of circulating metabolites with CEC to advance understanding of metabolic pathways involved in cholesterol efflux regulation. METHODS: Participants enrolled in the MESA (Multi-Ethnic Study of Atherosclerosis) who underwent nuclear magnetic resonance metabolome profiling and CEC measurement (N=3543) at baseline were included. Metabolite associations with CEC were evaluated using standard linear regression analyses. Repeated ElasticNet and multilayer perceptron regression were used to assess metabolite profile predictive performance for CEC. Features important for CEC prediction were identified using Shapley Additive Explanations values. RESULTS: Greater CEC was significantly associated with metabolite clusters composed of the largest-sized particle subclasses of VLDL (very-low-density lipoprotein) and HDL (high-density lipoprotein), as well as their constituent apo A1, apo A2, phospholipid, and cholesterol components (ß=0.072-0.081; P<0.001). Metabolite profiles had poor accuracy for predicting in vitro CEC in linear and nonlinear analyses (R2<0.02; Spearman ρ<0.18). The most important feature for CEC prediction was race, with Black participants having significantly lower CEC compared with other races. CONCLUSIONS: We identified independent associations among CEC, the largest-sized particle subclasses of VLDL and HDL, and their constituent apolipoproteins and lipids. A large proportion of variation in CEC remained unexplained by metabolites and traditional clinical risk factors, supporting further investigation into genomic, proteomic, and phospholipidomic determinants of CEC.

Preparative Electrophoresis for HDL Particle Size Separation and Intact-Mass Apolipoprotein Proteoform Analysis.

The most abundant proteins on high-density lipoproteins (HDLs), apolipoproteins A-I (APOA1) and A-II (APOA2), are determinants of HDL function with 15 and 9 proteoforms (chemical-structure variants), respectively. The relative abundance of these proteoforms in human serum is associated with HDL cholesterol efflux capacity, and cholesterol content. However, the association between proteoform concentrations and HDL size is unknown. We employed a novel native-gel electrophoresis technique, clear native gel-eluted liquid fraction entrapment electrophoresis (CN-GELFrEE) paired with mass spectrometry of intact proteins to investigate this association. Pooled serum was fractionated using acrylamide gels of lengths 8 and 25 cm. Western blotting determined molecular diameter and intact-mass spectrometry determined proteoform profiles of each fraction. The 8- and 25 cm experiments generated 19 and 36 differently sized HDL fractions, respectively. The proteoform distribution varied across size. Fatty-acylated APOA1 proteoforms were associated with larger HDL sizes (Pearson's R = 0.94, p = 4 × 10-7) and were approximately four times more abundant in particles larger than 9.6 nm than in total serum; HDL-unbound APOA1 was acylation-free and contained the pro-peptide proAPOA1. APOA2 proteoform abundance was similar across HDL sizes. Our results establish CN-GELFrEE as an effective lipid-particle separation technique and suggest that acylated proteoforms of APOA1 are associated with larger HDL particles.

Longitudinal trajectories of branched chain amino acids through young adulthood and diabetes in later life.

BACKGROUNDElevated circulating branched chain amino acids (BCAAs), measured at a single time point in middle life, are strongly associated with an increased risk of developing type 2 diabetes mellitus (DM). However, the longitudinal patterns of change in BCAAs through young adulthood and their association with DM in later life are unknown.METHODSWe serially measured BCAAs over 28 years in the Coronary Artery Risk Development in Young Adults (CARDIA) study, a prospective cohort of apparently healthy Black and White young adults at baseline. Trajectories of circulating BCAA concentrations from years 2-30 (for prevalent DM) or years 2-20 (for incident DM) were determined by latent class modeling.RESULTSAmong 3,081 apparently healthy young adults, trajectory analysis from years 2-30 revealed 3 distinct BCAA trajectory groups: low-stable (n = 1,427), moderate-stable (n = 1,384), and high-increasing (n = 270) groups. Male sex, higher body mass index, and higher atherogenic lipid fractions were more common in the moderate-stable and high-increasing groups. Higher risk of prevalent DM was associated with the moderate-stable (OR = 2.59, 95% CI: 1.90-3.55) and high-increasing (OR = 6.03, 95% CI: 3.86-9.43) BCAA trajectory groups in adjusted models. A separate trajectory group analysis from years 2-20 for incident DM after year 20 showed that moderate-stable and high-increasing trajectory groups were also significantly associated with higher risk of incident DM, after adjustment for clinical variables and glucose levels.CONCLUSIONBCAA levels track over a 28-year span in most young adults, but serial clinical metabolomic measurements identify subpopulations with rising levels associated with high risk of DM in later life.FUNDINGThis research was supported by the NIH, under grants R01 HL146844 (JTW) and T32 HL069771 (MRC). The CARDIA study is conducted and supported by the NIH National Heart, Lung, and Blood Institute in collaboration with the University of Alabama at Birmingham (HHSN268201800005I and HHSN268201800007I), Northwestern University (HHSN268201800003I), the University of Minnesota (HHSN268201800006I), and Kaiser Foundation Research Institute (HHSN268201800004I).

Multimarkers of metabolic malnutrition and inflammation and their association with mortality risk in cardiac catheterisation patients: a prospective, longitudinal, observational, cohort study.

BACKGROUND: Complex and incompletely understood metabolic dysfunction associated with inflammation and protein-energy wasting contribute to the increased mortality risk of older patients and those with chronic organ diseases affected by cachexia, sarcopenia, malnutrition, and frailty. However, these wasting syndromes have uncertain relevance for patients with cardiovascular disease or people at lower risk. Studies are hampered by imperfect objective clinical assessment tools for these intertwined metabolic malnutrition and inflammation syndromes. We aimed to assess, in two independent cohorts of patients who underwent cardiac catheterisation, the mortality risk associated with the metabolic vulnerability index (MVX), a multimarker derived from six simultaneously measured serum biomarkers plausibly linked to these dysmetabolic syndromes. METHODS: In this prospective, longitudinal, observational study, we included patients aged ≥18 years recruited into the CATHGEN biorepository (Jan 2, 2001, to Dec 30, 2011) and the Intermountain Heart Collaborative Study (Sept 12, 2000, to Sept 21, 2006) who underwent coronary angiography and had clinical nuclear magnetic resonance metabolomic profiling done on frozen plasma obtained at catheterisation. We aggregated six mortality risk biomarkers (GlycA, small HDL, valine, leucine, isoleucine, and citrate concentrations) into sex-specific MVX multimarker scores using coefficients from predictive models for all-cause mortality in the CATHGEN cohort. We assessed associations of biomarkers and MVX with mortality in both cohorts using Cox proportional hazards models adjusted for 15 clinical covariates. FINDINGS: We included 5876 participants from the CATHGEN biorepository and 2888 from the Intermountain Heart study. Median follow-up was 6·2 years (IQR 4·4-8·9) in CATHGEN and 8·2 years (6·9-9·2) in the Intermountain Heart study. The six nuclear magnetic resonance biomarkers and MVX made strong, independent contributions to 5-year mortality risk prediction in both cohorts (hazard ratio 2·18 [95% CI 2·03-2·34] in the CATHGEN cohort and 1·67 [1·50-1·87] in the Intermountain Heart cohort). CATHGEN subgroup analyses showed similar MVX associations in men and women, older and younger individuals, for death from cardiovascular or non-cardiovascular causes, and in patients with or without multiple comorbidities. INTERPRETATION: MVX made a dominant contribution to mortality prediction in patients with cardiovascular disease and in low-risk subgroups without pre-existing disease, suggesting that metabolic malnutrition-inflammation syndromes might have a more universal role in survival than previously thought. FUNDING: Labcorp.

Secretory leukocyte protease inhibitor and risk of heart failure in the Multi-Ethnic Study of Atherosclerosis.

Circulating protease inhibitors are important regulators of inflammation that are implicated in the pathophysiology of heart failure (HF). Secretory leukocyte protease inhibitor (SLPI) is a serine protease inhibitor which protects pulmonary tissues against inflammatory damage; however, its role in HF is not well understood. We sought to evaluate associations of circulating SLPI and genetically-mediated serum SLPI with incident HF and its subtypes in a multi-ethnic cohort of adults using clinical and genetic epidemiological approaches. Among 2,297 participants in the Multi-Ethnic Study of Atherosclerosis (MESA), each doubling of serum SLPI was independently associated with incident HF (HR 1.77; 95% CI 1.02-3.02; P = 0.04), particularly incident HF with preserved ejection fraction (HFpEF; HR 2.44; 95% CI 1.23-4.84; P = 0.01) but not HF with reduced ejection fraction (HFrEF; HR 0.95; 95% CI 0.36-2.46; P = 0.91). Previously reported circulating SLPI protein quantitative trait loci (pQTLs) were not associated with serum SLPI levels or incident HF among MESA participants. In conclusion, baseline serum SLPI levels, but not genetically-determined serum SLPI, were significantly associated with incident HF and HFpEF over long-term follow-up in a multi-ethnic cohort. Serum circulating SLPI may be a correlate of inflammation that sheds insight on the pathobiology of HFpEF.