Association of transportation noise with sleep during the first year of life: a longitudinal study

STUDY OBJECTIVES: During infancy, adequate sleep is crucial for physical and neurocognitive development. In adults and children, night-time noise exposure is associated with sleep disturbances. However, whether and to what extent infants' sleep is affected, is unknown. Thus, this study investigated the relationship between nocturnal transportation noise and actimetry-derived habitual sleep behavior across the first year of life. METHODS: In 144 healthy infants (63 girls), nocturnal (23:00-7:00) transportation noise (i.e., road, railway, and aircraft) was modelled at the infants' individual places of residence. Using actimetry, we recorded movement patterns for 11 days in a longitudinal design at 3, 6, and 12 months of age and derived the recently proposed core sleep composites of night-time sleep duration, activity, and variability. Using linear mixed-effects models, we determined associations between noise exposure and sleep composites. Sex, gestational age, parents' highest educational level, infants' age, and the existence of siblings served as control variables. RESULTS: In models without interactions, night-time transportation noise was unrelated to sleep composites across the first year of life (p > .16). Exploratory analyses of an interaction between noise and the existence of siblings yielded an association between night-time transportation noise and sleep duration in infants without siblings only (p = .004). CONCLUSION: In our study, sleep in infants during the first year of life was relatively robust against external perturbation by night-time transportation noise. However, particularly in children without siblings increasing night-time transportation noise reduced sleep duration. This suggests that the habitual noise environment may modulate individual susceptibility to adverse effects of noise on sleep

A 3-month period of electronic monitoring can provide important information to the healthcare team to assess adherence and improve asthma control

In children with difficult asthma, a single period of electronic monitoring can help to assess a patient's adherence and the possible impact of improved adherence on asthma control https://bit.ly/3c3Gj6

Pollen exposure is associated with risk of respiratory symptoms during the first year of life.

BACKGROUND Pollen exposure is associated with respiratory symptoms in children and adults. However, the association of pollen exposure with respiratory symptoms during infancy, a particularly vulnerable period, remains unclear. We examined whether pollen exposure is associated with respiratory symptoms in infants and if maternal atopy, infant's sex or air pollution modify this association. METHODS We investigated 14,874 observations from 401 healthy infants of a prospective birth cohort. The association between pollen exposure and respiratory symptoms, assessed in weekly telephone interviews, was evaluated using generalized additive mixed models (GAMM). Effect modification by maternal atopy, infant's sex and air pollution (NO2 , PM2.5 ) was assessed with interaction terms. RESULTS Per infant 37±2 (mean±SD) respiratory symptom scores were assessed during the analysis period (January through September). Pollen exposure was associated with increased respiratory symptoms during the daytime (RR [95% CI] per 10% pollen/m3 : combined 1.006 [1.002, 1.009]; tree 1.005 [1.002, 1.008]; grass 1.009 [1.000, 1.23]) and nighttime (combined 1.003 [0.999, 1.007]; tree 1.003 [0.999, 1.007]; grass 1.014 [1.004, 1.024]). While there was no effect modification by maternal atopy and infant's sex, a complex crossover interaction between combined pollen and PM2.5 was found (p-Value 0.002). CONCLUSION Even as early as during the first year of life, pollen exposure was associated with an increased risk of respiratory symptoms, independent of maternal atopy and infant's sex. Because infancy is a particularly vulnerable period for lung development, the identified adverse effect of pollen exposure may be relevant for the evolvement of chronic childhood asthma

Cardiopulmonary Exercise Testing Provides Additional Prognostic Information in Cystic Fibrosis

RATIONALE: The prognostic value of cardiopulmonary exercise testing (CPET) for survival in cystic fibrosis (CF) in the context of current clinical management, when controlling for other known prognostic factors, is unclear. OBJECTIVES: To determine the prognostic value of CPET-derived measures beyond peak oxygen uptake (V.o2peak) following rigorous adjustment for other predictors. METHODS: Data from 10 CF centers in Australia, Europe, and North America were collected retrospectively. A total of 510 patients completed a cycle CPET between January 2000 and December 2007, of which 433 fulfilled the criteria for a maximal effort. Time to death/lung transplantation was analyzed using Cox proportional hazards regression. In addition, phenotyping using hierarchical Ward clustering was performed to characterize high-risk subgroups. MEASUREMENTS AND MAIN RESULTS: Cox regression showed, even after adjustment for sex, FEV1% predicted, body mass index (z-score), age at CPET, Pseudomonas aeruginosa status, and CF-related diabetes as covariates in the model, that V.o2peak in % predicted (hazard ratio [HR], 0.964; 95% confidence interval [CI], 0.944–0.986), peak work rate (% predicted; HR, 0.969; 95% CI, 0.951–0.988), ventilatory equivalent for oxygen (HR, 1.085; 95% CI, 1.041–1.132), and carbon dioxide (HR, 1.060; 95% CI, 1.007–1.115) (all P < 0.05) were significant predictors of death or lung transplantation at 10-year follow-up. Phenotyping revealed that CPET-derived measures were important for clustering. We identified a high-risk cluster characterized by poor lung function, nutritional status, and exercise capacity. CONCLUSIONS: CPET provides additional prognostic information to established predictors of death/lung transplantation in CF. High-risk patients may especially benefit from regular monitoring of exercise capacity and exercise counseling

The neural signature of psychomotor disturbance in depression.

Up to 70% of patients with major depressive disorder present with psychomotor disturbance (PmD), but at the present time understanding of its pathophysiology is limited. In this study, we capitalized on a large sample of patients to examine the neural correlates of PmD in depression. This study included 820 healthy participants and 699 patients with remitted (n = 402) or current (n = 297) depression. Patients were further categorized as having psychomotor retardation, agitation, or no PmD. We compared resting-state functional connectivity (ROI-to-ROI) between nodes of the cerebral motor network between the groups, including primary motor cortex, supplementary motor area, sensory cortex, superior parietal lobe, caudate, putamen, pallidum, thalamus, and cerebellum. Additionally, we examined network topology of the motor network using graph theory. Among the currently depressed 55% had PmD (15% agitation, 29% retardation, and 11% concurrent agitation and retardation), while 16% of the remitted patients had PmD (8% retardation and 8% agitation). When compared with controls, currently depressed patients with PmD showed higher thalamo-cortical and pallido-cortical connectivity, but no network topology alterations. Currently depressed patients with retardation only had higher thalamo-cortical connectivity, while those with agitation had predominant higher pallido-cortical connectivity. Currently depressed patients without PmD showed higher thalamo-cortical, pallido-cortical, and cortico-cortical connectivity, as well as altered network topology compared to healthy controls. Remitted patients with PmD showed no differences in single connections but altered network topology, while remitted patients without PmD did not differ from healthy controls in any measure. We found evidence for compensatory increased cortico-cortical resting-state functional connectivity that may prevent psychomotor disturbance in current depression, but may perturb network topology. Agitation and retardation show specific connectivity signatures. Motor network topology is slightly altered in remitted patients arguing for persistent changes in depression. These alterations in functional connectivity may be addressed with non-invasive brain stimulation

Childhood trauma moderates schizotypy-related brain morphology: analyses of 1182 healthy individuals from the ENIGMA schizotypy working group.

BACKGROUND: Schizotypy represents an index of psychosis-proneness in the general population, often associated with childhood trauma exposure. Both schizotypy and childhood trauma are linked to structural brain alterations, and it is possible that trauma exposure moderates the extent of brain morphological differences associated with schizotypy. METHODS: We addressed this question using data from a total of 1182 healthy adults (age range: 18-65 years old, 647 females/535 males), pooled from nine sites worldwide, contributing to the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Schizotypy working group. All participants completed both the Schizotypal Personality Questionnaire Brief version (SPQ-B), and the Childhood Trauma Questionnaire (CTQ), and underwent a 3D T1-weighted brain MRI scan from which regional indices of subcortical gray matter volume and cortical thickness were determined. RESULTS: A series of multiple linear regressions revealed that differences in cortical thickness in four regions-of-interest were significantly associated with interactions between schizotypy and trauma; subsequent moderation analyses indicated that increasing levels of schizotypy were associated with thicker left caudal anterior cingulate gyrus, right middle temporal gyrus and insula, and thinner left caudal middle frontal gyrus, in people exposed to higher (but not low or average) levels of childhood trauma. This was found in the context of morphological changes directly associated with increasing levels of schizotypy or increasing levels of childhood trauma exposure. CONCLUSIONS: These results suggest that alterations in brain regions critical for higher cognitive and integrative processes that are associated with schizotypy may be enhanced in individuals exposed to high levels of trauma

Neurostructural subgroup in 4291 individuals with schizophrenia identified using the subtype and stage inference algorithm

Machine learning can be used to define subtypes of psychiatric conditions based on shared biological foundations of mental disorders. Here we analyzed cross-sectional brain images from 4,222 individuals with schizophrenia and 7038 healthy subjects pooled across 41 international cohorts from the ENIGMA, non-ENIGMA cohorts and public datasets. Using the Subtype and Stage Inference (SuStaIn) algorithm, we identify two distinct neurostructural subgroups by mapping the spatial and temporal ‘trajectory’ of gray matter change in schizophrenia. Subgroup 1 was characterized by an early cortical-predominant loss with enlarged striatum, whereas subgroup 2 displayed an early subcortical-predominant loss in the hippocampus, striatum and other subcortical regions. We confirmed the reproducibility of the two neurostructural subtypes across various sample sites, including Europe, North America and East Asia. This imaging-based taxonomy holds the potential to identify individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. Practitioner Points: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.</p