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Acta Psychiatr Scand. 2018 Oct;138(4):348-359. doi: 10.1111/acps.12901. Epub 2018 May 15.

Emotional hyper-reactivity and cardiometabolic risk in remitted bipolar patients: a machine learning approach.

Author information

1
Institut Pasteur, Unité Perception et Mémoire, Paris, France.
2
Centre National de la Recherche Scientifique, Unité Mixte de Recherche 3571, Paris, France.
3
Centre de Recherche Interdisciplinaire (CRI), Paris, France.
4
Institut Pasteur, Bioinformatics and Biostatistics Hub (C3BI), USR 3756 IP CNRS, Paris, France.
5
Fondation FondaMental, Fondation de Cooperation Scientifique, Créteil, France.
6
AP-HP, GH Saint-Louis - Lariboisière - Fernand Widal, Pôle Neurosciences Tête et Cou, INSERM UMRS 1144, University Paris Diderot, Paris, France.
7
Centre for Global Health, Institut Pasteur, Paris, France.
8
Département de Psychiatrie, Hôpital Sainte-Marguerite, Marseille, France.
9
Laboratoire de Psychologie, EA 4139, Centre Expert Troubles Bipolaires, Pôle 3-4-7, Hôpital Charles Perrens, Université Bordeaux, Bordeaux, France.
10
Université Grenoble Alpes, CHU de Grenoble et des Alpes, Grenoble Institut des Neurosciences (GIN) Inserm U 836, Grenoble, France.
11
Centre Hospitalier Universitaire de Nancy - Hôpitaux de Brabois, Université de Lorraine, Nancy, France.
12
Department of Adult Psychiatry, Versailles Hospital, Le Chesnay, France.
13
EA4047, University of Versailles Saint-Quentin-En-Yvelines, Montigny-le-Bretonneux, France.
14
Pôle de Psychiatrie, Centre Hospitalier Princesse Grace, Monaco, France.
15
Department of Emergency Psychiatry and Acute Care, CHU Montpellier, INSERM U1061, Montpellier University, Montpellier, France.
16
AP-HP, Pôle de psychiatrie, Hôpital H. Mondor - A. Chenevier, Créteil, France.
17
INSERM, U955, Université Paris-Est, Créteil, France.
18
Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada.

Abstract

OBJECTIVE:

Remitted bipolar disorder (BD) patients frequently present with chronic mood instability and emotional hyper-reactivity, associated with poor psychosocial functioning and low-grade inflammation. We investigated emotional hyper-reactivity as a dimension for characterization of remitted BD patients, and clinical and biological factors for identifying those with and without emotional hyper-reactivity.

METHOD:

A total of 635 adult remitted BD patients, evaluated in the French Network of Bipolar Expert Centers from 2010-2015, were assessed for emotional reactivity using the Multidimensional Assessment of Thymic States. Machine learning algorithms were used on clinical and biological variables to enhance characterization of patients.

RESULTS:

After adjustment, patients with emotional hyper-reactivity (n = 306) had significantly higher levels of systolic and diastolic blood pressure (P < 1.0 × 10-8 ), high-sensitivity C-reactive protein (P < 1.0 × 10-8 ), fasting glucose (P < 2.23 × 10-6 ), glycated hemoglobin (P = 0.0008) and suicide attempts (P = 1.4 × 10-8 ). Using models of combined clinical and biological factors for distinguishing BD patients with and without emotional hyper-reactivity, the strongest predictors were: systolic and diastolic blood pressure, fasting glucose, C-reactive protein and number of suicide attempts. This predictive model identified patients with emotional hyper-reactivity with 84.9% accuracy.

CONCLUSION:

The assessment of emotional hyper-reactivity in remitted BD patients is clinically relevant, particularly for identifying those at higher risk of cardiometabolic dysfunction, chronic inflammation, and suicide.

KEYWORDS:

C-reactive protein; bipolar disorder; cardiometabolic dysfunction; emotional hyper-reactivity; machine learning

PMID:
29766490
DOI:
10.1111/acps.12901
[Indexed for MEDLINE]

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