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An integrated signature of extracellular matrix proteins and a diastolic function imaging parameter predicts post-MI long-term outcomes.
Koh, Hiromi W L; Pilbrow, Anna P; Tan, Sock Hwee; Zhao, Qing; Benke, Peter I; Burla, Bo; Torta, Federico; Pickering, John W; Troughton, Richard; Pemberton, Christopher; Soo, Wern-Miin; Ling, Lieng Hsi; Doughty, Robert N; Choi, Hyungwon; Wenk, Markus R; Richards, A Mark; Chan, Mark Y.
Affiliation
  • Koh HWL; Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
  • Pilbrow AP; Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (ASTAR), Singapore, Singapore.
  • Tan SH; Department of Medicine, Christchurch Heart Institute, University of Otago, Christchurch, New Zealand.
  • Zhao Q; Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
  • Benke PI; National University Heart Centre, National University Health System, Singapore, Singapore.
  • Burla B; Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
  • Torta F; Singapore Lipidomics Incubator (SLING), Life Sciences Institute, National University of Singapore, Singapore, Singapore.
  • Pickering JW; Singapore Lipidomics Incubator (SLING), Life Sciences Institute, National University of Singapore, Singapore, Singapore.
  • Troughton R; Singapore Lipidomics Incubator (SLING), Life Sciences Institute, National University of Singapore, Singapore, Singapore.
  • Pemberton C; Precision Medicine Translational Research Programme and Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
  • Soo WM; Department of Medicine, Christchurch Heart Institute, University of Otago, Christchurch, New Zealand.
  • Ling LH; Department of Medicine, Christchurch Heart Institute, University of Otago, Christchurch, New Zealand.
  • Doughty RN; Department of Medicine, Christchurch Heart Institute, University of Otago, Christchurch, New Zealand.
  • Choi H; Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
  • Wenk MR; National University Heart Centre, National University Health System, Singapore, Singapore.
  • Richards AM; Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
  • Chan MY; National University Heart Centre, National University Health System, Singapore, Singapore.
Front Cardiovasc Med ; 10: 1123682, 2023.
Article in En | MEDLINE | ID: mdl-37123479
ABSTRACT

Background:

Patients suffering from acute myocardial infarction (AMI) are at risk of secondary outcomes including major adverse cardiovascular events (MACE) and heart failure (HF). Comprehensive molecular phenotyping and cardiac imaging during the post-discharge time window may provide cues for risk stratification for the outcomes. Materials and

methods:

In a prospective AMI cohort in New Zealand (N = 464), we measured plasma proteins and lipids 30 days after hospital discharge and inferred a unified partial correlation network with echocardiographic variables and established clinical biomarkers (creatinine, c-reactive protein, cardiac troponin I and natriuretic peptides). Using a network-based data integration approach (iOmicsPASS+), we identified predictive signatures of long-term secondary outcomes based on plasma protein, lipid, imaging markers and clinical biomarkers and assessed the prognostic potential in an independent cohort from Singapore (N = 190).

Results:

The post-discharge levels of plasma proteins and lipids showed strong correlations within each molecular type, reflecting concerted homeostatic regulation after primary MI events. However, the two molecular types were largely independent with distinct correlation structures with established prognostic imaging parameters and clinical biomarkers. To deal with massively correlated predictive features, we used iOmicsPASS + to identify subnetwork signatures of 211 and 189 data features (nodes) predictive of MACE and HF events, respectively (160 overlapping). The predictive features were primarily imaging parameters, including left ventricular and atrial parameters, tissue Doppler parameters, and proteins involved in extracellular matrix (ECM) organization, cell differentiation, chemotaxis, and inflammation. The network signatures contained plasma protein pairs with area-under-the-curve (AUC) values up to 0.74 for HF prediction in the validation cohort, but the pair of NT-proBNP and fibulin-3 (EFEMP1) was the best predictor (AUC = 0.80). This suggests that there were a handful of plasma proteins with mechanistic and functional roles in predisposing patients to the secondary outcomes, although they may be weaker prognostic markers than natriuretic peptides individually. Among those, the diastolic function parameter (E/e' - an indicator of left ventricular filling pressure) and two ECM proteins, EFEMP1 and follistatin-like 3 (FSTL3) showed comparable performance to NT-proBNP and outperformed left ventricular measures as benchmark prognostic factors for post-MI HF.

Conclusion:

Post-discharge levels of E/e', EFEMP1 and FSTL3 are promising complementary markers of secondary adverse outcomes in AMI patients.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: Front Cardiovasc Med Year: 2023 Document type: Article Affiliation country: Singapore

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: Front Cardiovasc Med Year: 2023 Document type: Article Affiliation country: Singapore