Author, as appears in the article.: Hernandez-Baixauli J; Chomiciute G; Alcaide-Hidalgo JM; Crescenti A; Baselga-Escudero L; Palacios-Jordan H; Foguet-Romero E; Pedret A; Valls RM; Solà R; Mulero M; Del Bas JM
Department: Medicina i Cirurgia Bioquímica i Biotecnologia
URV's Author/s: BASELGA ESCUDERO, LAURA / Mulero Abellán, Miguel / Pedret Figuerola, Anna / Solà Alberich, Rosa Maria / Valls Zamora, Rosa Maria
Keywords: Zero hunger
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.© 2023. The Author(s).
Thematic Areas: Zootecnia / recursos pesqueiros Saúde coletiva Química Psicología Odontología Nutrição Multidisciplinary sciences Multidisciplinary Medicina veterinaria Medicina iii Medicina ii Medicina i Materiais Matemática / probabilidade e estatística Letras / linguística Interdisciplinar Geografía Geociências Farmacia Engenharias iv Engenharias iii Engenharias ii Enfermagem Educação física Educação Economia Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências ambientais Ciências agrárias i Ciência de alimentos Ciência da computação Biotecnología Biodiversidade Astronomia / física
licence for use: https://creativecommons.org/licenses/by/3.0/es/
Author's mail: anna.pedret@urv.cat rosamaria.valls@urv.cat miquel.mulero@urv.cat rosa.sola@urv.cat
Author identifier: 0000-0002-5327-932X 0000-0002-3351-0942 0000-0002-8359-235X
Record's date: 2024-11-23
Papper version: info:eu-repo/semantics/publishedVersion
Link to the original source: https://www.nature.com/articles/s41598-023-49277-w
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Papper original source: Scientific Reports. 13 (1): 22646-22646
APA: Hernandez-Baixauli J; Chomiciute G; Alcaide-Hidalgo JM; Crescenti A; Baselga-Escudero L; Palacios-Jordan H; Foguet-Romero E; Pedret A; Valls RM; Solà (2023). Developing a model to predict the early risk of hypertriglyceridemia based on inhibiting lipoprotein lipase (LPL): a translational study. Scientific Reports, 13(1), 22646-22646. DOI: 10.1038/s41598-023-49277-w
Article's DOI: 10.1038/s41598-023-49277-w
Entity: Universitat Rovira i Virgili
Journal publication year: 2023
Publication Type: Journal Publications