Downregulated transferrin receptor in the blood predicts recurrent MDD in the elderly cohort: A fuzzy forests approach.

Aged Biomarkers Depressive Disorder, Major / genetics Humans Machine Learning Receptors, Transferrin Recurrence
Depression MDD Machine learning Random forests Transcriptome WGCNA

Journal

Journal of affective disorders
ISSN: 1573-2517
Titre abrégé: J Affect Disord
Pays: Netherlands
ID NLM: 7906073

Informations de publication

Date de publication:
15 04 2020
Historique:
received: 22 11 2019
revised: 28 01 2020
accepted: 01 02 2020
pubmed: 18 2 2020
medline: 16 2 2021
entrez: 18 2 2020
Statut: ppublish

Résumé

At present, no predictive markers for Major Depressive Disorder (MDD) exist. The search for such markers has been challenging due to clinical and molecular heterogeneity of MDD, the lack of statistical power in studies and suboptimal statistical tools applied to multidimensional data. Machine learning is a powerful approach to mitigate some of these limitations. We aimed to identify the predictive markers of recurrent MDD in the elderly using peripheral whole blood from the Sydney Memory and Aging Study (SMAS) (N = 521, aged over 65) and adopting machine learning methodology on transcriptome data. Fuzzy Forests is a Random Forests-based classification algorithm that takes advantage of the co-expression network structure between genes; it allows to alleviate the problem of p >> n via reducing the dimensionality of transcriptomic feature space. By adopting Fuzzy Forests on transcriptome data, we found that the downregulated TFRC (transferrin receptor) can predict recurrent MDD with an accuracy of 63%. Although we corrected our data for several important confounders, we were not able to account for the comorbidities and medication taken, which may be numerous in the elderly and might have affected the levels of gene transcription. We found that downregulated TFRC is predictive of recurrent MDD, which is consistent with the previous literature, indicating the role of the innate immune system in depression. This study is the first to successfully apply Fuzzy Forests methodology on psychiatric condition, opening, therefore, a methodological avenue that can lead to clinically useful predictive markers of complex traits.

Sections du résumé

BACKGROUND
At present, no predictive markers for Major Depressive Disorder (MDD) exist. The search for such markers has been challenging due to clinical and molecular heterogeneity of MDD, the lack of statistical power in studies and suboptimal statistical tools applied to multidimensional data. Machine learning is a powerful approach to mitigate some of these limitations.
METHODS
We aimed to identify the predictive markers of recurrent MDD in the elderly using peripheral whole blood from the Sydney Memory and Aging Study (SMAS) (N = 521, aged over 65) and adopting machine learning methodology on transcriptome data. Fuzzy Forests is a Random Forests-based classification algorithm that takes advantage of the co-expression network structure between genes; it allows to alleviate the problem of p >> n via reducing the dimensionality of transcriptomic feature space.
RESULTS
By adopting Fuzzy Forests on transcriptome data, we found that the downregulated TFRC (transferrin receptor) can predict recurrent MDD with an accuracy of 63%.
LIMITATIONS
Although we corrected our data for several important confounders, we were not able to account for the comorbidities and medication taken, which may be numerous in the elderly and might have affected the levels of gene transcription.
CONCLUSIONS
We found that downregulated TFRC is predictive of recurrent MDD, which is consistent with the previous literature, indicating the role of the innate immune system in depression. This study is the first to successfully apply Fuzzy Forests methodology on psychiatric condition, opening, therefore, a methodological avenue that can lead to clinically useful predictive markers of complex traits.

Identifiants

DOI: 10.1016/j.jad.2020.02.001 PMID: 32063571
pubmed: 32063571
pii: S0165-0327(19)33264-1
doi: 10.1016/j.jad.2020.02.001
pii:
doi:

Substances chimiques

Biomarkers 0
Receptors, Transferrin 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

42-48

Informations de copyright

Copyright © 2020 Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare no conflict of interest.

Auteurs

Liliana G Ciobanu (LG)

Discipline of Psychiatry, Adelaide Medical School, The University of Adelaide, South Australia, Australia; Quality Use of Medicines and Pharmacy Research Centre, School of Pharmacy and Medical Sciences, University of South Australia, Australia.

Perminder S Sachdev (PS)

Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, UNSW Sydney, New South Wales, Australia.

Julian N Trollor (JN)

Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, UNSW Sydney, New South Wales, Australia; Department of Developmental Disability Neuropsychiatry, School of Psychiatry, UNSW Sydney, New South Wales, Australia.

Simone Reppermund (S)

Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, UNSW Sydney, New South Wales, Australia; Department of Developmental Disability Neuropsychiatry, School of Psychiatry, UNSW Sydney, New South Wales, Australia.

Anbupalam Thalamuthu (A)

Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, UNSW Sydney, New South Wales, Australia.

Karen A Mather (KA)

Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, UNSW Sydney, New South Wales, Australia; Neuroscience Research Australia, Randwick, Australia.

Sarah Cohen-Woods (S)

School of Psychology, Flinders University, South Australia, Australia.

David Stacey (D)

MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK.

Catherine Toben (C)

Discipline of Psychiatry, Adelaide Medical School, The University of Adelaide, South Australia, Australia.

K Oliver Schubert (KO)

Discipline of Psychiatry, Adelaide Medical School, The University of Adelaide, South Australia, Australia; Northern Adelaide Local Health Network, Mental Health Services, Lyell McEwin Hospital, Elizabeth Vale, South Australia, Australia.

Bernhard T Baune (BT)

Department of Psychiatry and Psychotherapy, University of Münster, Münster, Germany; Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, Australia; The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia. Electronic address: bernhard.baune@ukmuenster.de.

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Classifications MeSH

questionsmedicales.fr › Personnes › Tranches d'âge › Adulte › Sujet âgé › Downregulated transferrin receptor in the...
questionsmedicales.fr › Facteurs biologiques › Marqueurs biologiques › Downregulated transferrin receptor in the...
questionsmedicales.fr › Troubles mentaux › Troubles de l'humeur › Trouble dépressif › Trouble dépressif majeur › Downregulated transferrin receptor in the...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Downregulated transferrin receptor in the...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Intelligence artificielle › Apprentissage machine › Downregulated transferrin receptor in the...
questionsmedicales.fr › Acides aminés, peptides et protéines › Protéines › Protéines membranaires › Récepteurs de surface cellulaire › Récepteurs à la transferrine › Downregulated transferrin receptor in the...
questionsmedicales.fr › États, signes et symptômes pathologiques › Processus pathologiques › Caractéristiques de la maladie › Récidive › Downregulated transferrin receptor in the...