The serotonin-N-acetylserotonin–melatonin pathway as a biomarker for autism spectrum disorders

Elevated whole-blood serotonin and decreased plasma melatonin (a circadian synchronizer hormone that derives from serotonin) have been reported independently in patients with autism spectrum disorders (ASDs). Here, we explored, in parallel, serotonin, melatonin and the intermediate N-acetylserotonin (NAS) in a large cohort of patients with ASD and their relatives. We then investigated the clinical correlates of these biochemical parameters. Whole-blood serotonin, platelet NAS and plasma melatonin were assessed in 278 patients with ASD, their 506 first-degree relatives (129 unaffected siblings, 199 mothers and 178 fathers) and 416 sex- and age-matched controls. We confirmed the previously reported hyperserotonemia in ASD (40% (35–46%) of patients), as well as the deficit in melatonin (51% (45–57%)), taking as a threshold the 95th or 5th percentile of the control group, respectively. In addition, this study reveals an increase of NAS (47% (41–54%) of patients) in platelets, pointing to a disruption of the serotonin-NAS–melatonin pathway in ASD. Biochemical impairments were also observed in the first-degree relatives of patients. A score combining impairments of serotonin, NAS and melatonin distinguished between patients and controls with a sensitivity of 80% and a specificity of 85%. In patients the melatonin deficit was only significantly associated with insomnia. Impairments of melatonin synthesis in ASD may be linked with decreased 14-3-3 proteins. Although ASDs are highly heterogeneous, disruption of the serotonin-NAS–melatonin pathway is a very frequent trait in patients and may represent a useful biomarker for a large subgroup of individuals with ASD

Overlap and Mutual Distinctions between Clinical Recovery and Personal Recovery in People with Schizophrenia in a One-Year Study

Recovery is a multidimensional construct that can be defined either from a clinical perspective or from a consumer-focused one, as a self-broadening process aimed at living a meaningful life beyond mental illness. We aimed to longitudinally examine the overlap and mutual distinctions between clinical and personal recovery. Of 1239 people with schizophrenia consecutively recruited from the FondaMental Advanced Centers of Expertise for SZ network, the 507 present at one-year did not differ from those lost to follow-up. Clinical recovery was defined as the combination of clinical remission and functional remission. Personal recovery was defined as being in the rebuilding or in the growth stage of the Stages of Recovery Instrument (STORI). Full recovery was defined as the combination of clinical recovery and personal recovery. First, we examined the factors at baseline associated with each aspect of recovery. Then, we conducted multivariable models on the correlates of stable clinical recovery, stable personal recovery, and stable full recovery after one year. At baseline, clinical recovery and personal recovery were characterized by distinct patterns of outcome (i.e. better objective outcomes but no difference in subjective outcomes for clinical recovery, the opposite pattern for personal recovery, and better overall outcomes for full recovery). We found that clinical recovery and personal recovery predicted each other over time (baseline personal recovery for stable clinical recovery at one year; P =. 026, OR = 4.94 [1.30-23.0]; baseline clinical recovery for stable personal recovery at one year; P =. 016, OR = 3.64 [1.31-11.2]). In short, given the interaction but also the degree of difference between clinical recovery and personal recovery, psychosocial treatment should target, beyond clinical recovery, subjective aspects such as personal recovery and depression to reach full recovery. © 2021 The Author(s). Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved.Sorbonne Universités à Paris pour l'Enseignement et la RechercheFondaMental-Cohorte

Immuno-metabolic profile of patients with psychotic disorders and metabolic syndrome. Results from the FACE-SZ cohort

Background: Metabolic syndrome (MetS) is a highly prevalent and harmful medical disorder often comorbid with psychosis where it can contribute to cardiovascular complications. As immune dysfunction is a key shared component of both MetS and schizophrenia (SZ), this study investigated the relationship between immune alterations and MetS in patients with SZ, whilst controlling the impact of confounding clinical characteristics including psychiatric symptoms and comorbidities, history of childhood maltreatment and psychotropic treatments. Method: A total of 310 patients meeting DSM-IV criteria for SZ or schizoaffective disorders (SZA), with or without MetS, were systematically assessed and included in the FondaMental Advanced Centers of Expertise for Schizophrenia (FACE-SZ) cohort. Detailed clinical characteristics of patients, including psychotic symptomatology, psychiatric comorbidities and history of childhood maltreatment were recorded and the serum levels of 18 cytokines were measured. A penalized regression method was performed to analyze associations between inflammation and MetS, whilst controlling for confounding factors. Results: Of the total sample, 25% of patients had MetS. Eight cytokines were above the lower limit of detection (LLOD) in more than 90% of the samples and retained in downstream analysis. Using a conservative Variable Inclusion Probability (VIP) of 75%, we found that elevated levels of interleukin (IL)-6, IL-7, IL-12/23 p40 and IL-16 and lower levels of tumor necrosis factor (TNF)-α were associated with MetS. As for clinical variables, age, sex, body mass index (BMI), diagnosis of SZ (not SZA), age at the first episode of psychosis (FEP), alcohol abuse, current tobacco smoking, and treatment with antidepressants and anxiolytics were all associated with MetS. Conclusion: We have identified five cytokines associated with MetS in SZ suggesting that patients with psychotic disorders and MetS are characterized by a specific “immuno-metabolic” profile. This may help to design tailored treatments for this subgroup of patients with both psychotic disorders and MetS, taking one more step towards precision medicine in psychiatry. © 2022 The AuthorsImmuno-Génétique, Inflammation, retro-Virus, Environnement : de l'étiopathogénie des troubles psychotiques aux modèles animauxRéseau d'Innovation sur les Voies de Signalisation en Sciences de la Vi

Precision psychiatry with immunological and cognitive biomarkers: a multi-domain prediction for the diagnosis of bipolar disorder or schizophrenia using machine learning

Precision psychiatry is attracting increasing attention lately as a recognized priority. One of the goals of precision psychiatry is to develop tools capable of aiding a clinically informed psychiatric diagnosis objectively. Cognitive, inflammatory and immunological factors are altered in both bipolar disorder (BD) and schizophrenia (SZ), however, most of these alterations do not respect diagnostic boundaries from a phenomenological perspective and possess great variability in different individuals with the same phenotypic diagnosis and, consequently, none so far has proven to have the ability of reliably aiding in the differential diagnosis of BD and SZ. We developed a probabilistic multi-domain data integration model consisting of immune and inflammatory biomarkers in peripheral blood and cognitive biomarkers using machine learning to predict diagnosis of BD and SZ. A total of 416 participants, being 323, 372, and 279 subjects for blood, cognition and combined biomarkers analysis, respectively. Our multi-domain model performances for the BD vs. control (sensitivity 80% and specificity 71%) and for the SZ vs. control (sensitivity 84% and specificity 81%) pairs were high in general, however, our multi-domain model had only moderate performance for the differential diagnosis of BD and SZ (sensitivity 71% and specificity 73%). In conclusion, our results show that the diagnosis of BD and of SZ, and that the differential diagnosis of BD and SZ can be predicted with possible clinical utility by a computational machine learning algorithm employing blood and cognitive biomarkers, and that their integration in a multi-domain outperforms algorithms based in only one domain. Independent studies are needed to validate these findings

Key findings on bipolar disorders from the longitudinal FondaMental Advanced Center of Expertise-Bipolar Disorder (FACE-BD) cohort

International audienc

Key findings on bipolar disorders from the longitudinal FondaMental Advanced Center of Expertise-Bipolar Disorder (FACE-BD) cohort

International audienc