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Multivariate curve resolution-based data fusion approaches applied in 1H NMR metabolomic analysis of healthy cohorts.
Martínez Bilesio, Andrés R; Puig-Castellví, Francesc; Tauler, Romà; Sciara, Mariela; Fay, Fabián; Rasia, Rodolfo M; Burdisso, Paula; García-Reiriz, Alejandro G.
Afiliação
  • Martínez Bilesio AR; Instituto de Biología Molecular y Celular de Rosario, Consejo Nacional de Investigaciones Científicas y Técnicas (IBR-CONICET), Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario (UNR), Ocampo y Esmeralda, Rosario 2000, Argentina.
  • Puig-Castellví F; European Genomics Institute for Diabetes, INSERM U1283, CNRS UMR8199, Institut Pasteur de Lille, Lille University Hospital, University of Lille, Lille, France.
  • Tauler R; Department of Environmental Chemistry, IDAEA-CSIC, Jordi Girona 18-26, 08034, Barcelona, Spain.
  • Sciara M; Centro de Diagnóstico Médico de Alta Complejidad (CIBIC), Rosario, Argentina.
  • Fay F; Centro de Diagnóstico Médico de Alta Complejidad (CIBIC), Rosario, Argentina.
  • Rasia RM; Instituto de Biología Molecular y Celular de Rosario, Consejo Nacional de Investigaciones Científicas y Técnicas (IBR-CONICET), Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario (UNR), Ocampo y Esmeralda, Rosario 2000, Argentina; Plataforma Argentina de Biología Estru
  • Burdisso P; Instituto de Biología Molecular y Celular de Rosario, Consejo Nacional de Investigaciones Científicas y Técnicas (IBR-CONICET), Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario (UNR), Ocampo y Esmeralda, Rosario 2000, Argentina; Plataforma Argentina de Biología Estru
  • García-Reiriz AG; Instituto de Química Rosario (IQUIR-CONICET) Departamento de Química Analítica, Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario (UNR), Ocampo y Esmeralda, Rosario 2000, Argentina. Electronic address: garciareiriz@iquir-conicet.gov.ar.
Anal Chim Acta ; 1309: 342689, 2024 Jun 22.
Article em En | MEDLINE | ID: mdl-38772669
ABSTRACT

BACKGROUND:

Metabolomics plays a critical role in deciphering metabolic alterations within individuals, demanding the use of sophisticated analytical methodologies to navigate its intricate complexity. While many studies focus on single biofluid types, simultaneous analysis of multiple matrices enhances understanding of complex biological mechanisms. Consequently, the development of data fusion methods enabling multiblock analysis becomes essential for comprehensive insights into metabolic dynamics.

RESULTS:

This study introduces a novel guideline for jointly analyzing diverse metabolomic datasets (serum, urine, metadata) with a focus on metabolic differences between groups within a healthy cohort. The guideline presents two fusion strategies, 'Low-Level data fusion' (LLDF) and 'Mid-Level data fusion' (MLDF), employing a sequential application of Multivariate Curve Resolution with Alternating Least Squares (MCR-ALS), linking the outcomes of successive analyses. MCR-ALS is a versatile method for analyzing mixed data, adaptable at various stages of data processing-encompassing resonance integration, data compression, and exploratory analysis. The LLDF and MLDF strategies were applied to 1H NMR spectral data extracted from urine and serum samples, coupled with biochemical metadata sourced from 145 healthy volunteers.

SIGNIFICANCE:

Both methodologies effectively integrated and analysed multiblock datasets, unveiling the inherent data structure and variables associated with discernible factors among healthy cohorts. While both approaches successfully detected sex-related differences, the MLDF strategy uniquely revealed components linked to age. By applying this analysis, we aim to enhance the interpretation of intricate biological mechanisms and uncover variations that may not be easily discernible through individual data analysis.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Metabolômica Limite: Adult / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Metabolômica Limite: Adult / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2024 Tipo de documento: Article