PMID- 38325523
OWN - NLM
STAT- MEDLINE
DCOM- 20240424
LR  - 20240424
IS  - 1097-6744 (Electronic)
IS  - 0002-8703 (Linking)
VI  - 271
DP  - 2024 May
TI  - Plasma proteomics for prediction of subclinical coronary artery calcifications in 
      primary prevention.
PG  - 55-67
LID - S0002-8703(24)00020-6 [pii]
LID - 10.1016/j.ahj.2024.01.011 [doi]
AB  - BACKGROUND AND AIMS: Recent developments in high-throughput proteomic 
      technologies enable the discovery of novel biomarkers of coronary 
      atherosclerosis. The aims of this study were to test if plasma protein subsets 
      could detect coronary artery calcifications (CAC) in asymptomatic individuals and 
      if they add predictive value beyond traditional risk factors. METHODS: Using 
      proximity extension assays, 1,342 plasma proteins were measured in 1,827 
      individuals from the Impaired Glucose Tolerance and Microbiota (IGTM) study and 
      883 individuals from the Swedish Cardiopulmonary BioImage Study (SCAPIS) aged 
      50-64 years without history of ischaemic heart disease and with CAC assessed by 
      computed tomography. After data-driven feature selection, extreme gradient 
      boosting machine learning models were trained on the IGTM cohort to predict the 
      presence of CAC using combinations of proteins and traditional risk factors. The 
      trained models were validated in SCAPIS. RESULTS: The best plasma protein subset 
      (44 proteins) predicted CAC with an area under the curve (AUC) of 0.691 in the 
      validation cohort. However, this was not better than prediction by traditional 
      risk factors alone (AUC = 0.710, P = .17). Adding proteins to traditional risk 
      factors did not improve the predictions (AUC = 0.705, P = .6). Most of these 44 
      proteins were highly correlated with traditional risk factors. CONCLUSIONS: A 
      plasma protein subset that could predict the presence of subclinical CAC was 
      identified but it did not outperform nor improve a model based on traditional 
      risk factors. Thus, support for this targeted proteomics platform to predict 
      subclinical CAC beyond traditional risk factors was not found.
CI  - Copyright (c) 2024 The Author(s). Published by Elsevier Inc. All rights reserved.
FAU - Royer, Patrick
AU  - Royer P
AD  - Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska 
      Academy, Gothenburg University, Gothenburg, Sweden; Region Vastra Gotaland, 
      Sahlgrenska University Hospital, Department of Clinical Physiology, Gothenburg, 
      Sweden; Department of Critical Care, University Hospital of Martinique, 
      Fort-de-France, France.
FAU - Bjornson, Elias
AU  - Bjornson E
AD  - Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska 
      Academy, Gothenburg University, Gothenburg, Sweden.
FAU - Adiels, Martin
AU  - Adiels M
AD  - Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska 
      Academy, Gothenburg University, Gothenburg, Sweden; School of Public Health and 
      Community Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, 
      Sweden.
FAU - Alvez, Maria Bueno
AU  - Alvez MB
AD  - Science for Life Laboratory, Department of Protein Science, KTH Royal Institute 
      of Technology, Stockholm, Sweden.
FAU - Fagerberg, Linn
AU  - Fagerberg L
AD  - Science for Life Laboratory, Department of Protein Science, KTH Royal Institute 
      of Technology, Stockholm, Sweden.
FAU - Backhed, Fredrik
AU  - Backhed F
AD  - Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska 
      Academy, Gothenburg University, Gothenburg, Sweden; Region Vastra Gotaland, 
      Sahlgrenska University Hospital, Department of Clinical Physiology, Gothenburg, 
      Sweden.
FAU - Uhlen, Mathias
AU  - Uhlen M
AD  - Science for Life Laboratory, Department of Protein Science, KTH Royal Institute 
      of Technology, Stockholm, Sweden; Department of Neuroscience, Karolinska 
      Institutet, Stockholm, Sweden.
FAU - Gummesson, Anders
AU  - Gummesson A
AD  - Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska 
      Academy, Gothenburg University, Gothenburg, Sweden; Region Vastra Gotaland, 
      Sahlgrenska University Hospital, Department of Clinical Genetics and Genomics, 
      Gothenburg, Sweden.
FAU - Bergstrom, Goran
AU  - Bergstrom G
AD  - Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska 
      Academy, Gothenburg University, Gothenburg, Sweden; Region Vastra Gotaland, 
      Sahlgrenska University Hospital, Department of Clinical Physiology, Gothenburg, 
      Sweden. Electronic address: goran.bergstrom@hjl.gu.se.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20240205
PL  - United States
TA  - Am Heart J
JT  - American heart journal
JID - 0370465
RN  - 0 (Biomarkers)
RN  - 0 (Blood Proteins)
SB  - IM
MH  - Humans
MH  - Middle Aged
MH  - *Coronary Artery Disease/blood/diagnostic imaging/diagnosis/epidemiology
MH  - Female
MH  - *Proteomics/methods
MH  - Male
MH  - *Vascular Calcification/blood/diagnostic imaging
MH  - *Biomarkers/blood
MH  - *Blood Proteins/analysis
MH  - *Primary Prevention/methods
MH  - Machine Learning
MH  - Risk Factors
MH  - Predictive Value of Tests
MH  - Tomography, X-Ray Computed/methods
MH  - Sweden/epidemiology
COIS- Conflict of interest None.
EDAT- 2024/02/08 00:42
MHDA- 2024/04/25 00:55
CRDT- 2024/02/07 19:14
PHST- 2024/01/30 00:00 [received]
PHST- 2024/01/30 00:00 [accepted]
PHST- 2024/04/25 00:55 [medline]
PHST- 2024/02/08 00:42 [pubmed]
PHST- 2024/02/07 19:14 [entrez]
AID - S0002-8703(24)00020-6 [pii]
AID - 10.1016/j.ahj.2024.01.011 [doi]
PST - ppublish
SO  - Am Heart J. 2024 May;271:55-67. doi: 10.1016/j.ahj.2024.01.011. Epub 2024 Feb 5.