Consumo de substancias durante el embarazo y dimensiones de personalidad

Este estudio evalúa los patrones de consumo de substancias durante el embarazo y las dimensiones de personalidad asociadas, en una muestra multicéntrica de 1804 mujeres de población general. En el 2-3 día posparto, completaron una entrevista auto-administrada sobre el consumo de alcohol, tabaco, cafeína, cannabis, cocaína, opiáceos, drogas de diseño, además de variables socio-demográficas, obstétricas/reproductivas, historia psiquiátrica previa, apoyo social durante el embarazo y el cuestionario de personalidad de Eysenck (EPQ-RS). Se generaron modelos de regresión logística múltiple. La prevalencia del consumo fue del 50% (N=909): 40% cafeína, 21% tabaco, 3,5% alcohol, y 0,3 cannabis. Las puntuaciones T medias (DE) de personalidad fueron: extraversión 51,1 (9,6), psicoticismo 48 (8,9) y neuroticismo 43,6 (8,5). Las dimensiones de extraversión (p=0,029) y psicoticismo (p=0,009), fueron identificadas como factores de riesgo tras ajustar por edad, nivel educación, estatus laboral durante el embarazo, bajo apoyo social, e historia psiquiátrica previa. Para cada incremento de 10 unidades en sus puntuaciones, el odds de consumo de substancias durante el embarazo se incrementó un 12% y un 16% respectivamente. Menor educación, estar de baja, y antecedentes psiquiátricos fueron también factores independientes (p<0,05) asociados al consumo. Ser primípara fue factor protector (p=0,001). El modelo final mostró un ajuste satisfactorio (p=0,26). El cribaje de las mujeres con riesgo de consumo de substancias durante el embarazo debería incluir la personalidad además de variables psicosociales y antecedentes psiquiátricos. Identificar los factores de riesgo asociados es importante para prevenir y mejorar la salud materna y fetal/neonatal durante el embarazo y posparto.Peer ReviewedPostprint (published version

Data-Driven Phenotyping of Central Disorders of Hypersomnolence With Unsupervised Clustering.

BACKGROUND AND OBJECTIVES Recent studies fueled doubts as to whether all currently defined central disorders of hypersomnolence are stable entities, especially narcolepsy type 2 and idiopathic hypersomnia. New reliable biomarkers are needed and the question arises whether current diagnostic criteria of hypersomnolence disorders should be reassessed. The main aim of this data-driven observational study was to see if data-driven algorithms would segregate narcolepsy type 1 and identify more reliable subgrouping of individuals without cataplexy with new clinical biomarkers. METHODS We used agglomerative hierarchical clustering, an unsupervised machine learning algorithm, to identify distinct hypersomnolence clusters in the large-scale European Narcolepsy Network database. We included 97 variables, covering all aspects of central hypersomnolence disorders such as symptoms, demographics, objective and subjective sleep measures, and laboratory biomarkers. We specifically focused on subgrouping of patients without cataplexy. The number of clusters was chosen to be the minimal number for which patients without cataplexy were put in distinct groups. RESULTS We included 1078 unmedicated adolescents and adults. Seven clusters were identified, of which four clusters included predominantly individuals with cataplexy. The two most distinct clusters consisted of 158 and 157 patients respectively, were dominated by those without cataplexy and, amongst other variables, significantly differed in presence of sleep drunkenness, subjective difficulty awakening and weekend-week sleep length difference. Patients formally diagnosed as narcolepsy type 2 and idiopathic hypersomnia were evenly mixed in these two clusters. DISCUSSION Using a data-driven approach in the largest study on central disorders of hypersomnolence to date, our study identified distinct patient subgroups within the central disorders of hypersomnolence population. Our results contest inclusion of sleep-onset rapid eye moment periods (SOREMPs) in diagnostic criteria for people without cataplexy and provide promising new variables for reliable diagnostic categories that better resemble different patient phenotypes. Cluster-guided classification will result in a more solid hypersomnolence classification system that is less vulnerable to instability of single features

Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning

Narcolepsy is a rare life-long disease that exists in two forms, narcolepsy type-1 (NT1) or type-2 (NT2), but only NT1 is accepted as clearly defined entity. Both types of narcolepsies belong to the group of central hypersomnias (CH), a spectrum of poorly defined diseases with excessive daytime sleepiness as a core feature. Due to the considerable overlap of symptoms and the rarity of the diseases, it is difficult to identify distinct phenotypes of CH. Machine learning (ML) can help to identify phenotypes as it learns to recognize clinical features invisible for humans. Here we apply ML to data from the huge European Narcolepsy Network (EU-NN) that contains hundreds of mixed features of narcolepsy making it difficult to analyze with classical statistics. Stochastic gradient boosting, a supervised learning model with built-in feature selection, results in high performances in testing set. While cataplexy features are recognized as the most influential predictors, machine find additional features, e.g. mean rapid-eye-movement sleep latency of multiple sleep latency test contributes to classify NT1 and NT2 as confirmed by classical statistical analysis. Our results suggest ML can identify features of CH on machine scale from complex databases, thus providing 'ideas' and promising candidates for future diagnostic classifications.</p

The Birth of the Mammalian Sleep

Mammals evolved from small-sized reptiles that developed endothermic metabolism. This allowed filling the nocturnal niche. They traded-off visual acuity for sensitivity but became defenseless against the dangerous daylight. To avoid such danger, they rested with closed eyes in lightproof burrows during light-time. This was the birth of the mammalian sleep, the main finding of this report. Improved audition and olfaction counterweighed the visual impairments and facilitated the cortical development. This process is called “The Nocturnal Evolutionary Bottleneck”. Pre-mammals were nocturnal until the Cretacic-Paleogene extinction of dinosaurs. Some early mammals returned to diurnal activity, and this allowed the high variability in sleeping patterns observed today. The traits of Waking Idleness are almost identical to those of behavioral sleep, including homeostatic regulation. This is another important finding of this report. In summary, behavioral sleep seems to be an upgrade of Waking Idleness Indeed, the trait that never fails to show is quiescence. We conclude that the main function of sleep consists in guaranteeing it during a part of the daily cycle

Factor structure of the spanish version of the Edinburgh postnatal depression scale

INTRODUCTION: The Edinburgh Postnatal Depression Scale (EPDS) is considered the gold standard in screening for postpartum depression. Although the Spanish version has been widely used, its factorial structure has not yet been studied. METHODS: A total of 1,204 women completed the EPDS 32 weeks after delivery. To avoid multiple testing, we split the sample into two halves, randomly drawing two subsamples of 602 participants each. We conducted exploratory factor analysis (EFA), followed by an oblimin rotation with the first sub-sample. Confirmatory factor analysis (CFA) was conducted using a Weighted Least Squares Means and Variance (WLSMV) estimation of the data. We explored different solutions between two and four factors. We compared the factors between two groups with depression and non-depression (evaluated with the Diagnostic Interview for Genetic Studies (DIGS) for the DSM-IV). RESULTS: The EFA indicated a three-factor model consisting of anxiety, depression and anhedonia. The results of the CFA confirmed the three-factor model (&#967;2=99.203, p<0.001; RMSEA=0.06, 90% CI=0.04/0.07, CFI=0.87 and TLI=0.82). Women with depression in the first 32 weeks obtained higher scores for anxiety, depression and anhedonia dimensions (p<0.001). CONCLUSIONS: This is the first study of confirmatory analysis with the Spanish version of EPDS in a large sample of women without psychiatric care during pregnancy. A three-factor model consisting of anxiety, depression and anhedonia was used. Women with depression had a higher score in the three dimensions of the EPDS

Factor structure of the spanish version of the Edinburgh postnatal depression scale

INTRODUCTION: The Edinburgh Postnatal Depression Scale (EPDS) is considered the gold standard in screening for postpartum depression. Although the Spanish version has been widely used, its factorial structure has not yet been studied. METHODS: A total of 1,204 women completed the EPDS 32 weeks after delivery. To avoid multiple testing, we split the sample into two halves, randomly drawing two subsamples of 602 participants each. We conducted exploratory factor analysis (EFA), followed by an oblimin rotation with the first sub-sample. Confirmatory factor analysis (CFA) was conducted using a Weighted Least Squares Means and Variance (WLSMV) estimation of the data. We explored different solutions between two and four factors. We compared the factors between two groups with depression and non-depression (evaluated with the Diagnostic Interview for Genetic Studies (DIGS) for the DSM-IV). RESULTS: The EFA indicated a three-factor model consisting of anxiety, depression and anhedonia. The results of the CFA confirmed the three-factor model (&#967;2=99.203, p<0.001; RMSEA=0.06, 90% CI=0.04/0.07, CFI=0.87 and TLI=0.82). Women with depression in the first 32 weeks obtained higher scores for anxiety, depression and anhedonia dimensions (p<0.001). CONCLUSIONS: This is the first study of confirmatory analysis with the Spanish version of EPDS in a large sample of women without psychiatric care during pregnancy. A three-factor model consisting of anxiety, depression and anhedonia was used. Women with depression had a higher score in the three dimensions of the EPDS

Factor structure of the spanish version of the Edinburgh postnatal depression scale

INTRODUCTION: The Edinburgh Postnatal Depression Scale (EPDS) is considered the gold standard in screening for postpartum depression. Although the Spanish version has been widely used, its factorial structure has not yet been studied. METHODS: A total of 1,204 women completed the EPDS 32 weeks after delivery. To avoid multiple testing, we split the sample into two halves, randomly drawing two subsamples of 602 participants each. We conducted exploratory factor analysis (EFA), followed by an oblimin rotation with the first sub-sample. Confirmatory factor analysis (CFA) was conducted using a Weighted Least Squares Means and Variance (WLSMV) estimation of the data. We explored different solutions between two and four factors. We compared the factors between two groups with depression and non-depression (evaluated with the Diagnostic Interview for Genetic Studies (DIGS) for the DSM-IV). RESULTS: The EFA indicated a three-factor model consisting of anxiety, depression and anhedonia. The results of the CFA confirmed the three-factor model (&#967;2=99.203, p<0.001; RMSEA=0.06, 90% CI=0.04/0.07, CFI=0.87 and TLI=0.82). Women with depression in the first 32 weeks obtained higher scores for anxiety, depression and anhedonia dimensions (p<0.001). CONCLUSIONS: This is the first study of confirmatory analysis with the Spanish version of EPDS in a large sample of women without psychiatric care during pregnancy. A three-factor model consisting of anxiety, depression and anhedonia was used. Women with depression had a higher score in the three dimensions of the EPDS

Data-Driven Phenotyping of Central Disorders of Hypersomnolence With Unsupervised Clustering

Background and ObjectivesRecent studies fueled doubts as to whether all currently defined central disorders of hypersomnolence are stable entities, especially narcolepsy type 2 and idiopathic hypersomnia. New reliable biomarkers are needed, and the question arises of whether current diagnostic criteria of hypersomnolence disorders should be reassessed. The main aim of this data-driven observational study was to see whether data-driven algorithms would segregate narcolepsy type 1 and identify more reliable subgrouping of individuals without cataplexy with new clinical biomarkers.MethodsWe used agglomerative hierarchical clustering, an unsupervised machine learning algorithm, to identify distinct hypersomnolence clusters in the large-scale European Narcolepsy Network database. We included 97 variables, covering all aspects of central hypersomnolence disorders such as symptoms, demographics, objective and subjective sleep measures, and laboratory biomarkers. We specifically focused on subgrouping of patients without cataplexy. The number of clusters was chosen to be the minimal number for which patients without cataplexy were put in distinct groups.ResultsWe included 1,078 unmedicated adolescents and adults. Seven clusters were identified, of which 4 clusters included predominantly individuals with cataplexy. The 2 most distinct clusters consisted of 158 and 157 patients, were dominated by those without cataplexy, and among other variables, significantly differed in presence of sleep drunkenness, subjective difficulty awakening, and weekend-week sleep length difference. Patients formally diagnosed as having narcolepsy type 2 and idiopathic hypersomnia were evenly mixed in these 2 clusters.DiscussionUsing a data-driven approach in the largest study on central disorders of hypersomnolence to date, our study identified distinct patient subgroups within the central disorders of hypersomnolence population. Our results contest inclusion of sleep-onset REM periods in diagnostic criteria for people without cataplexy and provide promising new variables for reliable diagnostic categories that better resemble different patient phenotypes. Cluster-guided classification will result in a more solid hypersomnolence classification system that is less vulnerable to instability of single features

Kleine-Levin syndrome is associated with birth difficulties and genetic variants in the TRANK1 gene loci

Kleine-Levin syndrome (KLS) is a rare disorder characterized by severe episodic hypersomnia, with cognitive impairment accompanied by apathy or disinhibition. Pathophysiology is unknown, although imaging studies indicate decreased activity in hypothalamic/thalamic areas during episodes. Familial occurrence is increased, and risk is associated with reports of a difficult birth. We conducted a worldwide case-control genome-wide association study in 673 KLS cases collected over 14 y, and ethnically matched 15,341 control individuals. We found a strong genome-wide significant association (rs71947865, Odds Ratio [OR] = 1.48, P = 8.6 * 10-9) within the 3'region of TRANK1 gene locus, previously associated with bipolar disorder and schizophrenia. Strikingly, KLS cases with rs71947865 variant had significantly increased reports of a difficult birth. As perinatal outcomes have dramatically improved over the last 40 y, we further stratified our sample by birth years and found that recent cases had a significantly reduced rs71947865 association. While the rs71947865 association did not replicate in the entire follow-up sample of 171 KLS cases, rs71947865 was significantly associated with KLS in the subset follow-up sample of 59 KLS cases who reported birth difficulties (OR = 1.54, P = 0.01). Genetic liability of KLS as explained by polygenic risk scores was increased (pseudo R 2 = 0.15; P &lt; 2.0 * 10-22 at P = 0.5 threshold) in the follow-up sample. Pathway analysis of genetic associations identified enrichment of circadian regulation pathway genes in KLS cases. Our results suggest links between KLS, circadian regulation, and bipolar disorder, and indicate that the TRANK1 polymorphisms in conjunction with reported birth difficulties may predispose to KLS