Abstract

Hypertriglyceridemia (HTG) is an independent risk factor for atherosclerotic cardiovascular disease (ASCVD). One of the multiple origins of HTG alteration is impaired lipoprotein lipase (LPL) activity, which is an emerging target for HTG treatment. We hypothesised that early, even mild, alterations in LPL activity might result in an identifiable metabolomic signature. The aim of the present study was to assess whether a metabolic signature of altered LPL activity in a preclinical model can be identified in humans. A preclinical LPL-dependent model of HTG was developed using a single intraperitoneal injection of poloxamer 407 (P407) in male Wistar rats. A rat metabolomics signature was identified, which led to a predictive model developed using machine learning techniques. The predictive model was applied to 140 humans classified according to clinical guidelines as (1) normal, less than 1.7 mmol/L; (2) risk of HTG, above 1.7 mmol/L. Injection of P407 in rats induced HTG by effectively inhibiting plasma LPL activity. Significantly responsive metabolites (i.e. specific triacylglycerols, diacylglycerols, phosphatidylcholines, cholesterol esters and lysophospholipids) were used to generate a predictive model. Healthy human volunteers with the impaired predictive LPL signature had statistically higher levels of TG, TC, LDL and APOB than those without the impaired LPL signature. The application of predictive metabolomic models based on mechanistic preclinical research may be considered as a strategy to stratify subjects with HTG of different origins. This approach may be of interest for precision medicine and nutritional approaches.

Details

Title
Developing a model to predict the early risk of hypertriglyceridemia based on inhibiting lipoprotein lipase (LPL): a translational study
Author
Hernandez-Baixauli, Julia 1 ; Chomiciute, Gertruda 2 ; Alcaide-Hidalgo, Juan María 2 ; Crescenti, Anna 2 ; Baselga-Escudero, Laura 2 ; Palacios-Jordan, Hector 3 ; Foguet-Romero, Elisabet 3 ; Pedret, Anna 4 ; Valls, Rosa M. 4 ; Solà, Rosa 5 ; Mulero, Miquel 6 ; Del Bas, Josep M. 7 

 Unitat de Nutrició i Salut, Eurecat, Centre Tecnològic de Catalunya, Reus, Spain; Universitat Autònoma de Barcelona, Laboratory of Metabolism and Obesity, Vall d’Hebron-Institut de Recerca, Barcelona, Spain (GRID:grid.7080.f) (ISNI:0000 0001 2296 0625) 
 Unitat de Nutrició i Salut, Eurecat, Centre Tecnològic de Catalunya, Reus, Spain (GRID:grid.7080.f) 
 Centre for Omic Sciences (COS), Joint Unit Universitat Rovira i Virgili−EURECAT, Eurecat, Centre Tecnològic de Catalunya, Reus, Spain (GRID:grid.428412.9) 
 Universitat Rovira I Virgili, Functional Nutrition, Oxidation and Cardiovascular Diseases Group (NFOC-Salut), Facultat de Medicina i Ciències de la Salut, Reus, Spain (GRID:grid.410367.7) (ISNI:0000 0001 2284 9230) 
 Universitat Rovira I Virgili, Functional Nutrition, Oxidation and Cardiovascular Diseases Group (NFOC-Salut), Facultat de Medicina i Ciències de la Salut, Reus, Spain (GRID:grid.410367.7) (ISNI:0000 0001 2284 9230); Hospital Universitari Sant Joan de Reus, Internal Medicine Service, Reus, Spain (GRID:grid.411136.0) (ISNI:0000 0004 1765 529X) 
 Universitat Rovira i Virgili, Nutrigenomics Research Group, Department of Biochemistry and Biotechnology, Tarragona, Spain (GRID:grid.410367.7) (ISNI:0000 0001 2284 9230) 
 Eurecat, Centre Tecnològic de Catalunya, Reus, Spain (GRID:grid.410367.7) 
Pages
22646
Publication year
2023
Publication date
2023
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2903744716
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.