AI-Assisted Identification of Primary and Secondary Metabolomic Markers for Postoperative Delirium

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AI-Assisted Identification of Primary and Secondary Metabolomic Markers for Postoperative Delirium. / Ivanisenko, Vladimir A.; Rogachev, Artem D.; Makarova, Aelita-Luiza A. et al.

In: International Journal of Molecular Sciences, Vol. 25, No. 21, 11847, 11.2024.

Research output: Contribution to journal › Article › peer-review

BibTeX

@article{d830655591c94c13a46a6208128c8e6e,

title = "AI-Assisted Identification of Primary and Secondary Metabolomic Markers for Postoperative Delirium",

abstract = "Despite considerable investigative efforts, the molecular mechanisms of postoperative delirium (POD) remain unresolved. The present investigation employs innovative methodologies for identifying potential primary and secondary metabolic markers of POD by analyzing serum metabolomic profiles utilizing the genetic algorithm and artificial neural networks. The primary metabolomic markers constitute a combination of metabolites that optimally distinguish between POD and non-POD groups of patients. Our analysis revealed L-lactic acid, inositol, and methylcysteine as the most salient primary markers upon which the prediction accuracy of POD manifestation achieved AUC = 99%. The secondary metabolomic markers represent metabolites that exhibit perturbed correlational patterns within the POD group. We identified 54 metabolites as the secondary markers of POD, incorporating neurotransmitters such as gamma-aminobutyric acid (GABA) and serotonin. These findings imply a systemic disruption in metabolic processes in patients with POD. The deployment of gene network reconstruction techniques facilitated the postulation of hypotheses describing the role of established genomic POD markers in the molecular-genetic mechanisms of metabolic pathways dysregulation, and involving the identified primary and secondary metabolomic markers. This study not only expands the understanding of POD pathogenesis but also introduces a novel technology for the bioinformatic analysis of metabolomic data that could aid in uncovering potential primary and secondary markers in diverse research domains. ",

keywords = "Humans, Biomarkers, Metabolomics/methods, Delirium/metabolism, Female, Male, Postoperative Complications/metabolism, Aged, Middle Aged, Metabolome, Neural Networks, Computer, Lactic Acid/metabolism, Artificial Intelligence",

author = "Ivanisenko, {Vladimir A.} and Rogachev, {Artem D.} and Makarova, {Aelita-Luiza A.} and Basov, {Nikita V.} and Gaisler, {Evgeniy V.} and Kuzmicheva, {Irina N.} and Demenkov, {Pavel S.} and Venzel, {Artur S.} and Ivanisenko, {Timofey V.} and Antropova, {Evgenia A.} and Kolchanov, {Nikolay A.} and Plesko, {Victoria V.} and Moroz, {Gleb B.} and Lomivorotov, {Vladimir V.} and Pokrovsky, {Andrey G.}",

note = "This work was supported by a grant for research centers provided by the Analytical Center for the Government of the Russian Federation in accordance with the subsidy agreement (agreement identifier 000000D730324P540002) and the agreement with the Novosibirsk State University dated 27 December 2023 No. 70-2023-001318.",

year = "2024",

month = nov,

doi = "10.3390/ijms252111847",

language = "English",

volume = "25",

journal = "International Journal of Molecular Sciences",

issn = "1661-6596",

publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",

number = "21",

}

RIS

TY - JOUR

T1 - AI-Assisted Identification of Primary and Secondary Metabolomic Markers for Postoperative Delirium

AU - Ivanisenko, Vladimir A.

AU - Rogachev, Artem D.

AU - Makarova, Aelita-Luiza A.

AU - Basov, Nikita V.

AU - Gaisler, Evgeniy V.

AU - Kuzmicheva, Irina N.

AU - Demenkov, Pavel S.

AU - Venzel, Artur S.

AU - Ivanisenko, Timofey V.

AU - Antropova, Evgenia A.

AU - Kolchanov, Nikolay A.

AU - Plesko, Victoria V.

AU - Moroz, Gleb B.

AU - Lomivorotov, Vladimir V.

AU - Pokrovsky, Andrey G.

N1 - This work was supported by a grant for research centers provided by the Analytical Center for the Government of the Russian Federation in accordance with the subsidy agreement (agreement identifier 000000D730324P540002) and the agreement with the Novosibirsk State University dated 27 December 2023 No. 70-2023-001318.

PY - 2024/11

Y1 - 2024/11

N2 - Despite considerable investigative efforts, the molecular mechanisms of postoperative delirium (POD) remain unresolved. The present investigation employs innovative methodologies for identifying potential primary and secondary metabolic markers of POD by analyzing serum metabolomic profiles utilizing the genetic algorithm and artificial neural networks. The primary metabolomic markers constitute a combination of metabolites that optimally distinguish between POD and non-POD groups of patients. Our analysis revealed L-lactic acid, inositol, and methylcysteine as the most salient primary markers upon which the prediction accuracy of POD manifestation achieved AUC = 99%. The secondary metabolomic markers represent metabolites that exhibit perturbed correlational patterns within the POD group. We identified 54 metabolites as the secondary markers of POD, incorporating neurotransmitters such as gamma-aminobutyric acid (GABA) and serotonin. These findings imply a systemic disruption in metabolic processes in patients with POD. The deployment of gene network reconstruction techniques facilitated the postulation of hypotheses describing the role of established genomic POD markers in the molecular-genetic mechanisms of metabolic pathways dysregulation, and involving the identified primary and secondary metabolomic markers. This study not only expands the understanding of POD pathogenesis but also introduces a novel technology for the bioinformatic analysis of metabolomic data that could aid in uncovering potential primary and secondary markers in diverse research domains.

AB - Despite considerable investigative efforts, the molecular mechanisms of postoperative delirium (POD) remain unresolved. The present investigation employs innovative methodologies for identifying potential primary and secondary metabolic markers of POD by analyzing serum metabolomic profiles utilizing the genetic algorithm and artificial neural networks. The primary metabolomic markers constitute a combination of metabolites that optimally distinguish between POD and non-POD groups of patients. Our analysis revealed L-lactic acid, inositol, and methylcysteine as the most salient primary markers upon which the prediction accuracy of POD manifestation achieved AUC = 99%. The secondary metabolomic markers represent metabolites that exhibit perturbed correlational patterns within the POD group. We identified 54 metabolites as the secondary markers of POD, incorporating neurotransmitters such as gamma-aminobutyric acid (GABA) and serotonin. These findings imply a systemic disruption in metabolic processes in patients with POD. The deployment of gene network reconstruction techniques facilitated the postulation of hypotheses describing the role of established genomic POD markers in the molecular-genetic mechanisms of metabolic pathways dysregulation, and involving the identified primary and secondary metabolomic markers. This study not only expands the understanding of POD pathogenesis but also introduces a novel technology for the bioinformatic analysis of metabolomic data that could aid in uncovering potential primary and secondary markers in diverse research domains.

KW - Humans

KW - Biomarkers

KW - Metabolomics/methods

KW - Delirium/metabolism

KW - Female

KW - Male

KW - Postoperative Complications/metabolism

KW - Aged

KW - Middle Aged

KW - Metabolome

KW - Neural Networks, Computer

KW - Lactic Acid/metabolism

KW - Artificial Intelligence

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85208572453&origin=inward&txGid=c48ffea970a3ad13cbe19f08d6dd4063

U2 - 10.3390/ijms252111847

DO - 10.3390/ijms252111847

M3 - Article

C2 - 39519398

VL - 25

JO - International Journal of Molecular Sciences

JF - International Journal of Molecular Sciences

SN - 1661-6596

IS - 21

M1 - 11847

ER -

ID: 61106309