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Plasma Lipidomics in Patients with Heart Failure with Preserved Ejection Fraction
Session:
Comunicações Orais (Sessão 17) - Ciência Básica e Saúde Digital
Speaker:
Sílvia O. Diaz
Congress:
CPC 2022
Topic:
O. Basic Science
Theme:
36. Basic Science
Subtheme:
36.6 Basic Science - Other
Session Type:
Comunicações Orais
FP Number:
---
Authors:
Sílvia o. Diaz; António s. Barros; José mg Alvarenga; António Angélico-Gonçalves; Francisco Vasques-Nóvoa; Francisca Saraiva; José a. Belo; Otília v. Vieira; Adelino f. Leite-Moreira
Abstract
<p style="text-align:justify"><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif"><strong>Introduction:</strong> The importance of plasma lipids is well established in the context of cardiovascular diseases (CVD). Still, a comprehensive and detailed evaluation of plasma lipidome in HFpEF patients is missing and may provide important mechanistic clues with potential therapeutic impact. </span></span></p> <p style="text-align:justify"><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif"><strong>Aim:</strong> To study the lipidomic profile in patients with stable HFpEF compared to comorbidity-matched controls. </span></span></p> <p style="text-align:justify"><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif"><strong>Methods:</strong> Single-center and prospective cohort study including stable HFpEF patients (n = 60) and comorbidity-matched controls (n = 13). Plasma samples were analyzed using top-down shotgun lipidomics. The acquired MS shotgun signals were explored through orthogonal partial least squares discriminant analysis (OPLS-DA) to untangle and then identify lipid profiles distinctive of each group. Due to the unbalanced number of patients in both groups, which impacted the model's accuracy, specificity, sensitivity, and robustness, the SMOTE technique was previously applied. <em>SMOTE - </em><em><span style="background-color:white"><span style="color:black">Synthetic Minority Over-sampling Technique</span></span></em> <span style="background-color:white"><span style="color:black">– a kNN-nearest neighbour–based algorithm was used to generate partially synthetic data of the minority class, in this case, the controls samples. </span></span>The OPLS-DA model was internally validated using bootstrapping (resampling with 15% repetitions, recomputed 300 times).</span></span></p> <p style="text-align:justify"><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif"><strong>Results</strong>: The resulting model showed a group separation trend, as shown in the score plot (Fig 1a, controls shown in blue and HFpEF in red). Loading plots and Variable Importance to the Projection (VIP) revealed that 29 lipids (Fig 1 b-c) were harbouring this separation. Out of this panel, lipids increased in HFpEF predominantly belong to the family of phosphocholines, cholesterol esters and sphingomyelins (Fig 1b, grey connectors) and have, on average, a low number of unsaturations (Fig 1c, grey). On the other hand, lipids increased in controls mainly belong to the triacylglycerols, phosphoinositols, phosphoethanolamines, lyso-phosphocholines, some sphingomyelins, and cholesterol esters (Fig 1b, purple connectors) and have a higher number of unsaturations (Fig 1c, purple). These findings are widely consistent with previous work and support that polyunsaturated lipids are inversely associated with CVD. In contrast, sphingolipids and low unsaturated PC are directly associated with CVD.</span></span></p> <p style="text-align:justify"><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif"><strong>Conclusions: </strong>Despite the low and unbalanced sample numbers available, the use of appropriate data-analysis procedures, namely over-sampling and bootstrapping, enabled the development of robust multivariate models with interpretative value. These preliminary results showed that the lipidomic screening might help unravel the molecular mechanisms involved in HFpEF.</span></span></p> <p style="text-align:justify"> </p>
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