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Individuals with bipolar disorders (BD) are at risk of premature death, mainly due to medical comorbidities. Childhood maltreatment might contribute to this medical morbidity, which remains underexplored in the literature.
We assessed 2891 outpatients with BD (according to DSM-IV criteria). Childhood maltreatment was assessed using the Childhood Trauma Questionnaire. Lifetime diagnoses for medical disorders were retrospectively assessed using a systematic interview and checked against medical notes. Medical morbidity was defined by the sum of medical disorders. We investigated associations between childhood maltreatment (neglect and abuse) and medical morbidity while adjusting for potential confounders.
One quarter of individuals had no medical comorbidities, while almost half of them had at least two. Multivariable regression showed that childhood maltreatment (mainly abuse, but also sexual abuse) was associated with a higher medical morbidity. Medical morbidity was also associated with sex, age, body mass index, sleep disturbances, lifetime anxiety disorders and lifetime density of mood episodes. Childhood maltreatment was associated with an increased prevalence of four (i.e. migraine/headache, drug eruption, duodenal ulcer, and thyroid diseases) of the fifteen most frequent medical disorders, however with no difference in terms of age at onset.
This large cross-sectional study confirmed a high medical morbidity in BD and its association with childhood maltreatment. The assessment of childhood maltreatment in individuals with BD should be systematically included in routine care and the potential impact on physical health of psycho-social interventions targeting childhood maltreatment and its consequences should be evaluated.
Converging evidence suggests that a subgroup of bipolar disorder (BD) with an early age at onset (AAO) may develop from aberrant neurodevelopment. However, the definition of early AAO remains unprecise. We thus tested which age cut-off for early AAO best corresponds to distinguishable neurodevelopmental pathways.
We analyzed data from the FondaMental Advanced Center of Expertise-Bipolar Disorder cohort, a naturalistic sample of 4421 patients. First, a supervised learning framework was applied in binary classification experiments using neurodevelopmental history to predict early AAO, defined either with Gaussian mixture models (GMM) clustering or with each of the different cut-offs in the range 14 to 25 years. Second, an unsupervised learning approach was used to find clusters based on neurodevelopmental factors and to examine the overlap between such data-driven groups and definitions of early AAO used for supervised learning.
A young cut-off, i.e. 14 up to 16 years, induced higher separability [mean nested cross-validation test AUROC = 0.7327 (± 0.0169) for ⩽16 years]. Predictive performance deteriorated increasing the cut-off or setting early AAO with GMM. Similarly, defining early AAO below 17 years was associated with a higher degree of overlap with data-driven clusters (Normalized Mutual Information = 0.41 for ⩽17 years) relatively to other definitions.
Early AAO best captures distinctive neurodevelopmental patterns when defined as ⩽17 years. GMM-based definition of early AAO falls short of mapping to highly distinguishable neurodevelopmental pathways. These results should be used to improve patients' stratification in future studies of BD pathophysiology and biomarkers.
Precision medicine in psychiatry is based on the identification of homogeneous subgroups of patients with the help of biosignatures—sets of biomarkers—in order to enhance diagnosis, stratification of patients, prognosis, evaluation, and prediction of treatment response. Within the broad domain of biomarker discovery, we propose retinal electrophysiology as a tool for identification of biosignatures. The retina is a window to the brain and provides an indirect access to brain functioning in psychiatric disorders. The retina is organized in layers of specialized neurons which share similar functional properties with brain neurons. The functioning of these neurons can be evaluated by electrophysiological techniques named electroretinogram (ERG). Since the study of retinal functioning gives a unique opportunity to have an indirect access to brain neurons, retinal dysfunctions observed in psychiatric disorders inform on brain abnormalities. Up to now, retinal dysfunctions observed in psychiatric disorders provide indicators for diagnosis, identification of subgroups of patients, prognosis, evaluation, and prediction of treatment response. The use of signal processing and machine learning applied on ERG data enhances retinal markers extraction, thus providing robust, reproducible, and reliable retinal electrophysiological markers to identify biosignatures in precision psychiatry. We propose that retinal electrophysiology may be considered as a new approach in the domain of electrophysiology and could now be added to the routine evaluations in psychiatric disorders. Retinal electrophysiology may provide, in combination with other approaches and techniques, sets of biomarkers to produce biosignatures in mental health.
Patients with psychiatric disorders are exposed to high risk of COVID-19 and increased mortality. In this study, we set out to assess the clinical features and outcomes of patients with current psychiatric disorders exposed to COVID-19.
This multi-center prospective study was conducted in 22 psychiatric wards dedicated to COVID-19 inpatients between 28 February and 30 May 2020. The main outcomes were the number of patients transferred to somatic care units, the number of deaths, and the number of patients developing a confusional state. The risk factors of confusional state and transfer to somatic care units were assessed by a multivariate logistic model. The risk of death was analyzed by a univariate analysis.
In total, 350 patients were included in the study. Overall, 24 (7%) were transferred to medicine units, 7 (2%) died, and 51 (15%) patients presented a confusional state. Severe respiratory symptoms predicted the transfer to a medicine unit [odds ratio (OR) 17.1; confidence interval (CI) 4.9–59.3]. Older age, an organic mental disorder, a confusional state, and severe respiratory symptoms predicted mortality in univariate analysis. Age >55 (OR 4.9; CI 2.1–11.4), an affective disorder (OR 4.1; CI 1.6–10.9), and severe respiratory symptoms (OR 4.6; CI 2.2–9.7) predicted a higher risk, whereas smoking (OR 0.3; CI 0.1–0.9) predicted a lower risk of a confusional state.
COVID-19 patients with severe psychiatric disorders have multiple somatic comorbidities and have a risk of developing a confusional state. These data underline the need for extreme caution given the risks of COVID-19 in patients hospitalized for psychiatric disorders.
The CAGE questionnaire is considered a useful screening and case-finding tool for alcohol use disorders in clinical populations. Our objectives were to validate the French version of the CAGE against DSM-IV criteria and to assess performance of each item of the scale.
Data were extracted from a hospital morbidity study conducted in central France. It concerned 5452 patients—48.5% men—in short and medium-stay units. Patients answered the CAGE questionnaire as a past-year assessment. The alcohol use disorders were diagnosed by the physicians using DSM-IV alcohol abuse or dependency criteria.
The CAGE questionnaire for a cut-off of 2 had a sensitivity of 77% and a specificity of 94%. The CAGE test was more sensitive for patients diagnosed as alcohol-dependent than for alcohol abusers (61% vs. 84%) with the same specificity (94%). These values are close to those for the English-language CAGE. The first three items (CAG) were very similar, with sensitivity 70% and specificity 94%. The eye-opening question (E) differentiated sharply between abuse and dependency, with sensitivities of 18% and 46%, respectively. A questionnaire comprising only the CAG questions of the CAGE had properties similar to the full questionnaire.
CAGE is a good screening tool for alcohol abuse or alcohol dependency. Given the frequent—and insufficiently diagnosed—alcohol problems among inpatients, CAGE is indicated as a first-line tool for screening for the most severe alcohol use disorders in hospital. It should ideally be used systematically. A positive reply to any of the first three items should alert the clinician and prompt further investigation.
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