@article {9536, title = {Proteomic prediction of incident heart failure and its main subtypes.}, journal = {Eur J Heart Fail}, year = {2023}, month = {2023 Nov 08}, abstract = {

AIM: To examine the ability of serum proteins in predicting future heart failure (HF) events, including HF with reduced or preserved ejection fraction (HFrEF or HFpEF), in relation to event time, and with or without considering established HF-associated clinical variables.

METHODS AND RESULTS: In the prospective population-based Age, Gene/Environment Susceptibility Reykjavik Study (AGES-RS), 440 individuals developed HF after their first visit with a median follow-up of 5.45 years. Among them, 167 were diagnosed with HFrEF and 188 with HFpEF. A least absolute shrinkage and selection operator regression model with nonparametric bootstrap were used to select predictors from an analysis of 4782 serum proteins, and several pre-established clinical parameters linked to HF. A subset of 8-10 distinct or overlapping serum proteins predicted different future HF outcomes, and C-statistics were used to assess discrimination, revealing proteins combined with a C-index of 0.80 for all incident HF, 0.78 and 0.80 for incident HFpEF or HFrEF, respectively. In the AGES-RS, protein panels alone encompassed the risk contained in the clinical information and improved the performance characteristics of prediction models based on NT-proBNP and clinical risk factors. Finally, the protein predictors performed particularly well close to the time of an HF event, an outcome that was replicated in the Cardiovascular Health Study (CHS).

CONCLUSION: A small number of circulating proteins accurately predicted future HF in the AGES-RS cohort of older adults, and they alone encompass the risk information found in a collection of clinical data. Incident HF events were predicted up to eight years, with predictor performance significantly improving for events occurring less than one year ahead, a finding replicated in an external cohort study. This article is protected by copyright. All rights reserved.

}, issn = {1879-0844}, doi = {10.1002/ejhf.3086}, author = {Emilsson, Valur and Jonsson, Brynjolfur G and Austin, Thomas R and Gudmundsdottir, Valborg and Axelsson, Gisli T and Frick, Elisabet A and Jonmundsson, Thorarinn and Steindorsdottir, Anna E and Loureiro, Joseph and Brody, Jennifer A and Aspelund, Thor and Launer, Lenore J and Thorgeirsson, Gudmundur and Kortekaas, Kirsten A and Lindeman, Jan H and Orth, Anthony P and Lamb, John R and Psaty, Bruce M and Kizer, Jorge R and Jennings, Lori L and Gudnason, Vilmundur} } @article {9640, title = {Plasma proteomic signature of human longevity.}, journal = {Aging Cell}, year = {2024}, month = {2024 Mar 05}, pages = {e14136}, abstract = {

The identification of protein targets that exhibit anti-aging clinical potential could inform interventions to lengthen the human health span. Most previous proteomics research has been focused on chronological age instead of longevity. We leveraged two large population-based prospective cohorts with long follow-ups to evaluate the proteomic signature of longevity defined by survival to 90 years of age. Plasma proteomics was measured using a SOMAscan assay in 3067 participants from the Cardiovascular Health Study (discovery cohort) and 4690 participants from the Age Gene/Environment Susceptibility-Reykjavik Study (replication cohort). Logistic regression identified 211 significant proteins in the CHS cohort using a Bonferroni-adjusted threshold, of which 168 were available in the replication cohort and 105 were replicated (corrected p value <0.05). The most significant proteins were GDF-15 and N-terminal pro-BNP in both cohorts. A parsimonious protein-based prediction model was built using 33 proteins selected by LASSO with 10-fold cross-validation and validated using 27 available proteins in the validation cohort. This protein model outperformed a basic model using traditional factors (demographics, height, weight, and smoking) by improving the AUC from 0.658 to 0.748 in the discovery cohort and from 0.755 to 0.802 in the validation cohort. We also found that the associations of 169 out of 211 proteins were partially mediated by physical and/or cognitive function. These findings could contribute to the identification of biomarkers and pathways of aging and potential therapeutic targets to delay aging and age-related diseases.

}, issn = {1474-9726}, doi = {10.1111/acel.14136}, author = {Liu, Xiaojuan and Axelsson, Gisli Thor and Newman, Anne B and Psaty, Bruce M and Boudreau, Robert M and Wu, Chenkai and Arnold, Alice M and Aspelund, Thor and Austin, Thomas R and Gardin, Julius M and Siggeirsdottir, Kristin and Tracy, Russell P and Gerszten, Robert E and Launer, Lenore J and Jennings, Lori L and Gudnason, Vilmundur and Sanders, Jason L and Odden, Michelle C} } @article {9906, title = {Plasma Proteomic Assessment of Calcific Aortic Valve Disease in Older Adults.}, journal = {J Am Heart Assoc}, volume = {14}, year = {2025}, month = {2025 Mar 04}, pages = {e036336}, abstract = {

BACKGROUND: Calcific aortic valve disease (CAVD), and ensuing severe aortic stenosis (AS), is the foremost valvular disorder of aging, yet preventive therapies are lacking. A better understanding of the molecular underpinnings of aortic valve calcification (AVC) is necessary to develop pharmacologic interventions.

METHODS AND RESULTS: We undertook large-scale plasma proteomics in a cohort study of adults >=65 years old, the CHS (Cardiovascular Health Study), to identify individual proteins associated with echocardiographic AVC and incident moderate/severe AS. Proteomics measurements were performed with the aptamer-based SomaLogic platform of ~5000 proteins. Significant proteins were validated in a second cohort, the AGES-RS (Age, Gene/Environment Susceptibility-Reykjavik Study), which assessed AVC and AS by computed tomography. The potential causal associations of replicated proteins were tested in 2-sample Mendelian randomization using identified cis protein quantitative trait loci in consortia having computed tomography-quantified AVC or AS as outcomes. Six proteins showed Bonferroni-corrected significant relationships with AVC in CHS. Three of these, CXCL-12 (C-X-C chemokine ligand 12), KLKB1 (kallikrein), and leptin, replicated in AGES-RS, of which the former 2 are novel. Only 1 protein, CXCL6, which showed a near-significant association with AS in the replication cohort, was significantly (positively) associated with incident AS. Mendelian randomization analysis was conducted for KLKB1, CXCL12, and CXCL6, which supported a causal relationship for higher KLKB1 with lower AVC (beta=-0.25, =0.009).

CONCLUSIONS: This study of older adults newly identified and largely replicated associations of 3 circulating proteins with calcific aortic valve disease, of which the relationship of plasma KLKB1 may have a causal basis. Additional investigation is necessary to determine if KLKB1 could be harnessed for calcific aortic valve disease therapeutics.

}, keywords = {Age Factors, Aged, Aged, 80 and over, Aortic Valve, Aortic Valve Stenosis, Biomarkers, Blood Proteins, Calcinosis, Chemokine CXCL12, Echocardiography, Female, Humans, Male, Mendelian Randomization Analysis, Proteomics, Severity of Illness Index}, issn = {2047-9980}, doi = {10.1161/JAHA.124.036336}, author = {Bortnick, Anna E and Austin, Thomas R and Hamerton, Emily and Gudmundsdottir, Valborg and Emilsson, Valur and Jennings, Lori L and Gudnason, Vilmundur and Owens, David S and Massera, Daniele and Dufresne, Line and Yang, Ta-Yu and Engert, James C and Thanassoulis, George and Tracy, Russell P and Gerszten, Robert E and Psaty, Bruce M and Kizer, Jorge R} }