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Metabolomics and poor cardiovascular outcomes in patients with Heart Failure (HF): a systematic review and meta-analysis
Session:
Sessão de Posters 24 - Biomarcadores em Cardiologia
Speaker:
Leonel Sousa Neves
Congress:
CPC 2024
Topic:
J. Preventive Cardiology
Theme:
28. Risk Factors and Prevention
Subtheme:
28.14 Risk Factors and Prevention - Other
Session Type:
Cartazes
FP Number:
---
Authors:
Leonel Sousa Neves; Francisca Saraiva; Adelino Leite-Moreira; António S. Barros; Sílvia O. Diaz
Abstract
<p style="text-align:justify"><span style="font-size:12pt"><span style="font-family:Calibri,sans-serif"><span style="font-family:"Calibri",sans-serif"><span style="color:#212121"><strong>Introduction:</strong> While the association of plasma lipids and the risk of poor cardiovascular (CV) outcomes are known, other plasma circulating metabolites might add value to the prognosis of CV-associated events. Metabolomics may help uncover metabolic dysregulations underlying such associations.</span></span></span></span></p> <p style="text-align:justify"><span style="font-size:12pt"><span style="font-family:Calibri,sans-serif"><span style="font-family:"Calibri",sans-serif"><span style="color:#212121"><strong>Aim:</strong> To compile risk associations between metabolites and poor CV outcomes in patients with Heart Failure (HF) through a systematic review and meta-analysis.</span></span></span></span></p> <p style="text-align:justify"><span style="font-size:12pt"><span style="font-family:Calibri,sans-serif"><span style="font-family:"Calibri",sans-serif"><span style="color:#212121"><strong>Methods:</strong> We performed a systematic review using the PubMed database (last searched on 31/12/2022). Studies that used blood (plasma or serum) metabolomics, in HF patients to predict poor cardiovascular outcomes (death or hospitalization), irrespective of follow-up time, were included. Time-to-event outcomes were collected for each metabolite through adjusted Hazard Ratio (HR) along with its variance. Fixed and random effects models were used to compute statistical combined measures (HR) and 95% confidence intervals (CI) of individual metabolites.</span></span></span></span></p> <p style="text-align:justify"><span style="font-size:12pt"><span style="font-family:Calibri,sans-serif"><span style="font-family:"Calibri",sans-serif"><span style="color:#212121"><strong>Results:</strong> We identified 78 studies that used metabolomics in patients with HF. Of these, 4 articles, totalizing 1560 patients from 5 independent cohorts, computed and reported the HR of 83 metabolites and 37 metabolites ratios. Forty-two metabolites and three ratios, present in at least two cohorts, were assessed through meta-analysis. The mean/median follow-up period ranged from 1.0 to 6.3 years, and the rate of events ranged from 13-38% of the included sample (n varying from 136 and 479). Using random-effect models, we identified </span></span>7 metabolites and 1 metabolite ratio relevantly associated (<em>p </em>< 0.05 and <em>I<sup>2</sup></em> < 50%) with poor CV outcomes. Higher histidine (HR 0.74 95%CI [0.64-0.86]) and tryptophan (HR 0.82 [0.71-0.96]) seem to be protective of CV events, while higher symmetric dimethylarginine (SDMA) (HR 1.58 [1.30-1.93]), <em>N</em>-methyl-1-histidine (HR 1.56 [1.27-1.90]), SDMA/arginine (HR 1.38 [1.14-1.68]), putrescine (HR 1.31 [1.06-1.61]), methionine sulfoxide (HR 1.26 [1.03-1.52]) and 5-hydroxylysine (HR 1.25 [1.05-1.48]) associate with higher risk of CV events. Of these metabolites, tryptophan and histidine were reported in 3 cohorts, while the remaining metabolites were reported in only 2. </span></span></p> <p style="text-align:justify"><span style="font-size:12pt"><span style="font-family:Calibri,sans-serif"><span style="font-family:"Calibri",sans-serif"><span style="color:#212121"><strong>Conclusions:</strong> Despite the limited data available, we identified 7 metabolites and 1 ratio associated with CV events in HF patients. Our findings corroborate a derangement in the inflammatory response and in the NO synthesis pathways which need to be further explored. However, the lack of standardization in metabolomic studies and data reporting hampers the combination and comparison of different studies. In the long run, taking metabolomics into a clinical scenario could greatly benefit from harmonizing analytical analysis procedures.</span></span></span></span></p>
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