Articles producció científica> Medicina i Cirurgia

Developing a model to predict the early risk of hypertriglyceridemia based on inhibiting lipoprotein lipase (LPL): a translational study

  • Identification data

    Identifier: imarina:9333648
    Authors:
    Hernandez-Baixauli JChomiciute GAlcaide-Hidalgo JMCrescenti ABaselga-Escudero LPalacios-Jordan HFoguet-Romero EPedret AValls RMSolà RMulero MDel Bas JM
    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).
  • Others:

    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
    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: 2024-09-07
    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
  • Keywords:

    Multidisciplinary,Multidisciplinary Sciences
    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
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