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Introduction: Over six million U.S. adults have heart failure (HF). Evaluation of kidney function is critical to the care of patients with HF but may not be fully captured by traditional measures, such as estimated glomerular filtration rate (eGFR) or the Meta-Analysis Global Group in Chronic HF (MAGGIC) risk score. Identifying patients at highest risk of progressive chronic renal insufficiency (PCRI) may improve risk stratification in HF, beyond these traditional measures. High-throughput proteomics has allowed for the development of PCRI risk scores based on novel kidney function biomarkers. However, the prognostic value of a proteomic-based PCRI risk score in HF has not been explored. Hypothesis: Proteomic-based PCRI risk scores will improve risk stratification in HF. Methods: Clinical data and plasma were collected from 1,389 patients in a HF community cohort from Southeastern Minnesota (2003-2012). Results from the aptamer-based technology SomaScan® were used to derive PCRI risk scores using the SomaSignal™ Kidney Prognosis test, a protein-based algorithm developed to predict risk of PCRI within four years. Cox proportional hazard models were used to estimate the association between quintiles of PCRI and mortality, after adjustment for the MAGGIC score. Results: PCRI risk scores were available for 1,349 patients who were on average 75±13 years of age, 48% female, and had a median eGFR of 57 mL/min/1.73m 2 . There was a positive association with mortality across PCRI risk quintiles, after adjustment for the MAGGIC score. (Figure). Overall, the highest quintile was associated over a two-fold higher risk of mortality compared with the lowest quintile (HR 2.3, 95% CI 1.8,2.8). The higher risk of mortality remained in analyses stratified by patients with (HR 1.5, 95% CI 1.1,2.1) and without (HR 2.2, 95% CI 1.4,3.5) an eGFR <60 mL/min/1.73 m 2 . Conclusions: In this community HF cohort, proteomic-based PCRI risk scores improve risk stratification, independent of traditional measures of kidney function.

Background Larger within‐patient variability of lipid levels has been associated with increased risk of cardiovascular disease (CVD); however, measures of lipid variability require ≥3 measurements and are not currently used clinically. We investigated the feasibility of calculating lipid variability within a large electronic health record–based population cohort and assessed associations with incident CVD. Methods and Results We identified all individuals ≥40 years of age who resided in Olmsted County, MN, on January 1, 2006 (index date), without prior CVD, defined as myocardial infarction, coronary artery bypass graft surgery, percutaneous coronary intervention, or CVD death. Patients with ≥3 measurements of total cholesterol, low‐density lipoprotein cholesterol, high‐density lipoprotein cholesterol, or triglycerides during the 5 years before the index date were retained. Lipid variability was calculated using variability independent of the mean. Patients were followed through December 31, 2020 for incident CVD. We identified 19 652 individuals (mean age 61 years; 55% female), who were CVD‐free and had variability independent of the mean calculated for at least 1 lipid type. After adjustment, those with highest total cholesterol variability had a 20% increased risk of CVD (Q5 versus Q1 hazard ratio, 1.20 [95% CI, 1.06–1.37]). Results were similar for low‐density lipoprotein cholesterol and high‐density lipoprotein cholesterol. Conclusions In a large electronic health record–based population cohort, high variability in total cholesterol, high‐density lipoprotein cholesterol, and low‐density lipoprotein cholesterol was associated with an increased risk of CVD, independent of traditional risk factors, suggesting it may be a possible risk marker and target for intervention. Lipid variability can be calculated in the electronic health record environment, but more research is needed to determine its clinical utility.

Abstract Context Metformin is the first-line drug for treating diabetes but has a high failure rate. Objective To identify demographic and clinical factors available in the electronic health record (EHR) that predict metformin failure. Methods A cohort of patients with at least 1 abnormal diabetes screening test that initiated metformin was identified at 3 sites (Arizona, Mississippi, and Minnesota). We identified 22 047 metformin initiators (48% female, mean age of 57 ± 14 years) including 2141 African Americans, 440 Asians, 962 Other/Multiracial, 1539 Hispanics, and 16 764 non-Hispanic White people. We defined metformin failure as either the lack of a target glycated hemoglobin (HbA1c) (&lt;7%) within 18 months of index or the start of dual therapy. We used tree-based extreme gradient boosting (XGBoost) models to assess overall risk prediction performance and relative contribution of individual factors when using EHR data for risk of metformin failure. Results In this large diverse population, we observed a high rate of metformin failure (43%). The XGBoost model that included baseline HbA1c, age, sex, and race/ethnicity corresponded to high discrimination performance (C-index of 0.731; 95% CI 0.722, 0.740) for risk of metformin failure. Baseline HbA1c corresponded to the largest feature performance with higher levels associated with metformin failure. The addition of other clinical factors improved model performance (0.745; 95% CI 0.737, 0.754, P &lt; .0001). Conclusion Baseline HbA1c was the strongest predictor of metformin failure and additional factors substantially improved performance suggesting that routinely available clinical data could be used to identify patients at high risk of metformin failure who might benefit from closer monitoring and earlier treatment intensification.

Abstract Background Heart failure (HF) is a complex clinical syndrome that affects over 60 million persons worldwide.1 The 5-year survival rate of HF is less than 50% irrespective of ejection fraction (EF) and the limitations of EF in sufficiently phenotyping HF are recognized.2,3 High throughput proteomics assays are an attractive option for precision phenotyping.4 Purpose To explore the proteomic profiles and biomarker pathways associated with death in an HF community cohort. Methods We measured proteomics in plasma specimens collected from an HF cohort (2003–2012) of patients predominantly of European Ancestry (92%). The relative concentrations of circulating plasma proteins were measured using an aptamer-based assay containing 7335 human protein targets. Proteins significantly associated with mortality were selected while adjusting for age, sex, and eGFR. These proteins were employed in clustering analysis and subjected to 10-fold cross-validation. We quantified and corrected the clustering bias via bootstrapping and subsequently validated the results via semi-supervised clustering. Multivariable Cox regression was implemented for the cluster variable while adjusting for the Meta-analysis Global Group in Chronic Heart Failure (MAGGIC) risk score. Additionally, we performed enrichment and pathway analysis of the outcome-associated proteins considering significance thresholds of −log10(P-value) &gt;2, FDR corrected P&lt;0.05 (regulator analysis) and Z-scores of &gt;2 and &lt;−2 as thresholds for activation and inhibition of proteins/pathways. Results From a total of 1,388 patients (mean age 75±13; 52% male), 2 distinct clusters, (HR: 2.04; 95% CI [1.78–2.34]; p-value: &lt;0.0001 independent of MAGGIC) were identified, cluster 1; n=722, mean age 72±14; NYHA class III/IV: 41%/26%; EF: 47%±17 and cluster 2; n=666, mean age 79±11; NYHA class III/IV: 41%/31%, EF: 48%±16 based on the 447 proteins [354 positively and 93 negatively] significantly associated with mortality (1,158 events). Gene-set enrichment analysis of all 447 proteins revealed cell adhesion, extracellular matrix organization, enzyme-linked protein/tyrosine kinase signaling pathways, humoral immune response, tissue development, growth factor signaling, post-translational protein modification, and platelet degranulation as the dominant biological themes. Finally, we found 18 upstream regulators predicted to be driving the expressions of the proteins in the dataset including a predicted outcome-associated protein-Transforming Growth Factor Beta 1 – TGFB1 (p=7.46E-09), Interleukin 1B, IL1B (p=9.40E-08), and oncostatin M, OSM (p=6.61E-05) as the top 3 regulators (predicted activation) based on FDR-corrected statistical significance of P&lt;0.05 (Figure 1) Conclusion We identified distinct clusters based on proteomic profiles associated with HF mortality independently of clinical prognostic makers. Further, we delineated protein regulators driving the major pathways associated with HF survival. Funding Acknowledgement Type of funding sources: Public Institution(s). Main funding source(s): Department of Intramural Research, NIH/NHLBI

Abstract Background Risk factors for the pathogenesis of chronic rhinosinusitis (CRS) remain largely undetermined, which is likely due to the heterogeneity of the disease. White blood cell counts have been largely unexplored as a risk factor for CRS even though different types of white blood cells are involved in the inflammatory process of CRS. Objective To investigate causal associations between different types of white blood cells on risk of CRS utilizing a Mendelian randomization (MR) analysis. Methods A two-sample MR analysis was performed using respective GWAS summary statistics for the exposure traits (neutrophil count, eosinophil count, basophil count, lymphocyte count, and monocyte count) and outcome trait (CRS). For the exposure traits, the European Bioinformatics Institute database of complete GWAS summary data was used. For the outcome trait, summary statistics for CRS GWAS were obtained from FinnGen. Primary analysis for MR was performed using inverse-variance weighted two-sample MR. Sensitivity analyses included weighted median, MR-Egger, and MR-PRESSO (raw and outlier-corrected). Results Eosinophils were associated with CRS (OR = 1.55 [95%CI 1.38,1.73]; p = 4.3E-14). Eosinophil results were similar across additional MR methods. MR results did not demonstrate significant causal relationships between neutrophils, lymphocytes, monocytes, or basophils with CRS. No significant pleiotropic bias was observed. Conclusions In a two-sample MR analysis, a potential causal link between blood eosinophil counts and CRS has been demonstrated. In addition, causal relationships between blood counts among other white blood cell types and CRS were not found. Further studies involving genetic variation in CRS are needed to corroborate genetic causal effects for CRS.

Background: Preeclampsia (PE) is a pregnancy-specific hypertensive disorder which affects up to 7% of all pregnancies. It is associated with increased risk for cardiovascular disease later in life. We have recently reported increased senescence and accelerated aging in women with PE. We hypothesize that senescence burden remains after PE. The aim of this study was to compare senescence profiles, and history of individual chronic conditions and multimorbidity, a marker of accelerated aging, in women with and without a history of PE. Methods: Forty women with histories of PE and 40 with histories of normotensive pregnancy confirmed by manual chart review were age- and parity-matched. None of the women had a history of CVD at the time of recruitment. Markers of senescence (p16, p21 and Klotho) and metabolic syndrome (leptin, adiponectin) were measured and compared between women with and without histories of preeclampsia using conditional logistic regression. The rates of chronic conditions and multimorbidity between the groups were also compared. Results: Urinary Klotho/creatinine levels were significantly lower (median [IQR]: 117 [87.4, 224] vs. 205 [101, 359], respectively, p=0.042), while leptin/adiponectin ratio was significantly higher (median [IQR]: 0.25 [0.13, 0.45] vs. 0.05 [0.02, 0.12], p=0.027) among women with histories of PE compared to controls. There were no significant differences observed in plasma p21 or p16 concentrations. Women with histories of PE experienced a higher prevalence of two or more co-morbidities compared to women with normotensive pregnancies [67.5% (27/40) vs. 22.5% (9/40), p<0.05]. Results were similar after removing hypertension as a comorbidity [45% (18/40) vs. 20.0% (8/40), p<0.05]. Conclusion: Women with a history of PE had altered markers of senescence, metabolic syndrome and multimorbidity decades after PE.

Background Hepatocyte growth factor (HGF) is a biomarker with potential for use in the diagnosis, treatment and prognostication of cardiovascular disease (CVD). Elevated HGF is associated with calcification in the coronary arteries. However, knowledge is limited on the role HGF may play in extracoronary calcification (ECC). This study examined whether HGF is associated with ECC in the aortic valve (AVC), mitral annulus (MAC), ascending thoracic aorta and descending thoracic aortic (DTAC). Methods At baseline, adults aged 45–84 years, free of CVD, in the Multi-Ethnic Study of Atherosclerosis had HGF and ECC measured by ELISA and cardiac CT scan, respectively. ECC measurements were repeated after an average of 2.4 years of follow-up. Prevalent ECC was defined as Agatston score >0 at baseline. Incident ECC was defined as Agatston score >0 at follow-up among participants with Agatston score=0 at baseline. We used Poisson and linear mixed-effects regression models to estimate the association between HGF and ECC, adjusted for sociodemographic and CVD risk factors. Results Of 6648 participants, 53% were women. Mean (SD) age was 62 (10) years. Median (IQR) of HGF was 905 (757-1087) pg/mL. After adjustment for CVD risk factors, the highest HGF levels (tertile 3) were associated with greater prevalence and extent of AVC, MAC and DTAC at baseline compared with the lowest tertile (tertile 1). Additionally, the risk of incident AVC and MAC increased by 62% and 45%, respectively, in demographic-adjusted models. However, the associations were not statistically significant in fully adjusted models. The highest HGF levels were also associated with 10% and 13% increase in MAC and DTAC progression, respectively, even after adjustment for CVD risk factors. Conclusion Higher HGF levels were significantly associated with a greater risk of calcification at some extracoronary sites, suggesting an alternate biological pathway that could be targeted to reduce CVD risk.

Introduction: The Framingham Heart Study Dementia Risk Score (FDRS) was developed in a general population of elderly individuals. It is unknown how the FDRS variables predict dementia in heart failure (HF) and atrial fibrillation (AF) populations. Thus, we aimed to evaluate the predictive ability of the FDRS variables in population-based cohorts of individuals with HF and AF and to determine whether the addition of other comorbidities and risk factors improves risk prediction for dementia. Methods: Residents aged ≥50 from 7 southeast MN counties with a first diagnosis of HF (ICD-9 428/ICD-10 I50) or AF (ICD-9 427.3/ICD-10 I48 with AF on electrocardiogram/Holter monitor) between 1/1/2013 and 12/31/2017 were identified. Patients with dementia (defined by ICD codes) prior to or within 6 months after their index event and patients who died within 6 months after index were excluded. For both cohorts, models were constructed to predict dementia ≥ 6 months post index including the variables in the FDRS (age, marital status, BMI, stroke/TIA, diabetes and cancer). Additional comorbidities and risk factors were added to the models. For all models c-statistics were calculated. Results: Among 3,291 patients with HF (mean age 76 years, 53% male), 626 cases of dementia occurred and among 4,107 patients with AF (mean age 74 years, 57% male), 736 cases of dementia occurred. Among patients with HF, the FDRS variables predicted dementia with a c-statistic of 0.68 (Table). Adding additonal comorbidities and risk factors impoved the c-statistic slightly to 0.69 (p=0.01). Among patients with AF, the FDRS variables also performed well (c-stastic: 0.73; Table) and adding other comorbidities and risk factors improved the performance (c-statistic: 0.75; p<0.01). Conclusions: The variables from the FDRS predict dementia well in both HF and AF populations. The addition of comorbidities and other CVD risk factors only modestly improved prediction, thus indicating that the FDRS variables are appropriate to predict dementia in patients with HF and AF.

Introduction: Heart failure (HF) is a clinical syndrome with high mortality. Left ventricular ejection fraction (EF) determines clinical phenotyping and HF is classified most often as preserved (HFpEF) or reduced (HFrEF) EF. The importance of expanding phenotyping beyond EF is recognized. We hypothesized that machine learning applied to echocardiography will provide information important for deep phenotyping. Methods: We collected clinical and echo data in a HF community cohort between 2013 and 2017. Unsupervised clustering was applied to 23 echo variables using partitioning around medoids (PAM), with Gower dissimilarity used to address the mixed variable types. Proportional hazards regression examined the association between cluster assignment and death. Results: We identified 4978 persons with HF [mean age 73.9 years (SD 13.9), 47% women] with echocardiography within 6 months of diagnosis. We identified 2 primary clusters using echo variables, as depicted using t-Distributed Stochastic Neighbor Embedding (t-SNE) applied to the dissimilarity matrix (Figure). Diastolic function variables exhibited the largest differences by clusters as shown by absolute standardized differences (Table). After 3.2 + 1.9 years of follow up, 2001 deaths occurred. Assignment to cluster 2 was associated with a near 20% excess risk of death independent of age, sex, comorbidity, and EF [HR 1.17 (1.07, 1.28) p<0.001]. Conclusions: Unsupervised clustering applied to echocardiography in a large community HF cohort identified 2 distinct clusters, which defined HF subgroups with different characteristics and were independently associated with risk of death.

Introduction: Hepatocyte growth factor (HGF) is released in response to endothelial injury, and higher levels of circulating HGF are associated with atherosclerosis. However, the association of HGF with atherosclerosis progression is unknown. Hypothesis: Circulating HGF is positively associated with atherosclerosis progression, measured as progression of 1) carotid artery plaque and 2) coronary artery calcium (CAC). Methods: The Multi-Ethnic Study of Atherosclerosis is a large, population-based cohort study of adults aged 45-84 and free of clinical cardiovascular disease. Participants had HGF measured in serum at baseline (2000-02), and were followed for progression of carotid plaque and CAC through 2012. We defined CAC and carotid plaque progression as dichotomous variables (progression, no progression). Carotid plaque progression is an increase in the number of carotid plaques. We used the Hokanson method to define CAC progression as an increase of ≥ 2.5 mm 3 in the square root-transformed calcium volume score. Relative risk regression quantified the association between HGF and carotid plaque progression. Cox regression models using inverse-probability-of-attrition weights estimated the association of HGF with progression of CAC. All effect estimates are adjusted for potential confounding variables. Results: The cohort included 6714 Americans with HGF measurements: 2593 non-Hispanic white, 797 Chinese, 1848 black, and 1476 Hispanic Americans. In 3363 participants with measurements for both carotid plaque and HGF, each standard deviation (259 pg/ml) increase in HGF was associated with a 4% greater risk (95% CI: 1.01-1.07) of carotid plaque progression (n=1875), which did not differ by race/ethnicity (interaction p = 0.3). Out of 6714 participants with measurements for both CAC and HGF, 2994 had CAC progression during follow-up. The association of HGF with CAC progression differed by race/ethnicity (interaction p = 0.01). Each standard deviation increase of HGF was associated with a 10% greater risk of CAC progression in non-Hispanic white (95% CI: 1.05-1.16) and black Americans (95% CI: 1.03-1.17), while HGF was not associated with CAC progression in Chinese (p = 0.5) nor Hispanic (p = 0.7) American participants. Conclusions: Circulating HGF was associated with a modestly increased risk of atherosclerosis progression in this large and diverse population, with the strongest associations between HGF and CAC progression in non-Hispanic white and black Americans. Our results suggest that HGF may have utility as a marker of atherosclerosis progression in some populations.