Incidence and predictors of metabolic syndrome onset in individuals with bipolar disorders: A longitudinal study from the FACE‐BD cohort

Author:

Godin O.12,Olié E.13,Fond G.14ORCID,Aouizerate B.15,Aubin V.16,Bellivier F.178ORCID,Belzeaux R.19,Courtet P.13,Dubertret C.110,Haffen E.111,Lefrere A.112,Llorca P. M.113,Polosan M.114,Roux P.11516,Samalin L.113,Schwan R.117,Leboyer M.1218,Etain B.178ORCID,

Affiliation:

1. Fondation FondaMental Créteil France

2. INSERM U955, IMRB, Translational NeuroPsychiatry Laboratory, Université Paris Est Créteil Créteil France

3. Department of Emergency Psychiatry and Acute Care IGF, University of Montpellier, CNRS, INSERM, CHU Montpellier Montpellier France

4. AP‐HM, Academic Department of Psychiatry, Resistant Depression Expert Center (FondaMental Foundation), CHU La Conception Aix‐Marseille University Marseille France

5. Centre Hospitalier Charles Perrens, Pôle de Psychiatrie Générale et Universitaire, Laboratoire NutriNeuro (UMR INRAE 1286) Université de Bordeaux Bordeaux France

6. Pôle de Psychiatrie, Centre Hospitalier Princesse Grace Monaco France

7. INSERM UMR‐S 1144, Optimisation Thérapeutique en Neuropsychopharmacologie OTeN Université Paris Cité Paris France

8. Hôpital Fernand Widal, Département de Psychiatrie et de Médecine Addictologique AP‐HP, Groupe Hospitalo‐Universitaire AP‐HP Nord, DMU Neurosciences Paris France

9. University of Montpellier & Department of Psychiatry, CHU de Montpellier Montpellier France

10. AHPH, Departement de Psychiatrie Hopital Louis Mourier Colombes France

11. UR 481 LINC, Service de Psychiatrie de l'Adulte, CIC‐1431 INSERM, CHU de Besançon Université de Franche‐Comté Besançon France

12. Pôle de Psychiatrie, Assistance Publique Hôpitaux de Marseille, Marseille, France; INT‐UMR7289 CNRS Aix‐Marseille Université Marseille France

13. Department of Psychiatry, CHU Clermont‐Ferrand University of Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal (UMR 6602) Clermont‐Ferrand France

14. University of Grenoble Alpes, Inserm, U1216, CHU Grenoble Alpes, Grenoble Institut Neurosciences Grenoble France

15. Centre Hospitalier de Versailles Service Universitaire de Psychiatrie d'Adulte et d'Addictologie Le Chesnay France

16. Université Paris‐Saclay Université de Versailles Saint‐Quentin‐En‐Yvelines, DisAP‐DevPsy‐CESP, INSERM UMR1018 Villejuif France

17. Centre Psychothérapique de Nancy, Inserm U1254 Université de Lorraine Nancy France

18. AP‐HP, Hôpitaux Universitaires Henri Mondor, Département Médico‐Universitaire de Psychiatrie et d'Addictologie (DMUIMPACT) Fédération Hospitalo‐Universitaire de Médecine de Précision en Psychiatrie (FHU ADAPT) Créteil France

Abstract

AbstractIntroductionMetabolic syndrome (MetS) is a cluster of components including abdominal obesity, hyperglycemia, hypertension, and dyslipidemia. MetS is highly prevalent in individuals with bipolar disorders (BD) with an estimated global rate of 32.6%. Longitudinal data on incident MetS in BD are scarce and based on small sample size. The objectives of this study were to estimate the incidence of MetS in a large longitudinal cohort of 1521 individuals with BD and to identify clinical and biological predictors of incident MetS.MethodsParticipants were recruited from the FondaMental Advanced Center of Expertise for Bipolar Disorder (FACE‐BD) cohort and followed‐up for 3 years. MetS was defined according to the International Diabetes Federation criteria. Individuals without MetS at baseline but with MetS during follow‐up were considered as having incident MetS. A logistic regression model was performed to estimate the adjusted odds ratio and its corresponding 95% confidence interval (CI) for an association between each factor and incident MetS during follow‐up. We applied inverse probability‐of‐censoring weighting method to minimize selection bias due to loss during follow‐up.ResultsAmong individuals without MetS at baseline (n = 1521), 19.3% developed MetS during follow‐up. Multivariable analyses showed that incident MetS during follow‐up was significantly associated with male sex (OR = 2.2, 95% CI = 1.7–3.0, p < 0.0001), older age (OR = 2.14, 95% CI = 1.40–3.25, p = 0.0004), presence of a mood recurrence during follow‐up (OR = 1.91, 95% CI = 1.22–3.00, p = 0.0049), prolonged exposure to second‐generation antipsychotics (OR = 1.56, 95% CI = 0.99, 2.45, p = 0.0534), smoking status at baseline (OR = 1.30, 95% CI = 1.00–1.68), lifetime alcohol use disorders (OR = 1.33, 95% CI = 0.98–1.79), and baseline sleep disturbances (OR = 1.04, 95% CI = 1.00–1.08), independently of the associations observed for baseline MetS components.ConclusionWe observed a high incidence of MetS during a 3 years follow‐up (19.3%) in individuals with BD. Identification of predictive factors should help the development of early interventions to prevent or treat early MetS.

Funder

Fondation FondaMental

Assistance Publique - Hôpitaux de Paris

Publisher

Wiley

Subject

Psychiatry and Mental health

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