Background: Maternal stress during pregnancy can affect fetal development during certain sensitive periods.
Objective: To longitudinally assess maternal hair cortisol levels during pregnancy, and the postpartum along with neonatal hair cortisol levels that could be associated with infant neurodevelopment at six months of age.
Methods: A sample of 41 pregnant women longitudinally assessed during the first, second, and third trimester and the postpartum, along with their 41 full-term neonates participated in this study. Hair cortisol levels were assessed from participants. Infant neurodevelopment was assessed by means of the Bayley Scale of Infants Development, Third Edition at age six months.
Results: Maternal hair cortisol levels in the first and second trimester accounted for 24% and 23%, respectively, of variance of infant gross motor development ( < 0.05). Maternal hair cortisol levels during the postpartum accounted for 31% of variance of infant cognitive development ( < 0.05), and 25% of variance of infant gross motor development ( < 0.05). Neonatal hair cortisol levels accounted for 28% of variance of infant gross motor development ( < 0.05).
Conclusions: The preconception and prenatal time are sensitive periods related to infant neurodevelopment along with the cortisol levels surrounding the fetus while in the womb. Pregnant women could be assessed for hair cortisol levels while attending a prenatal appointment.
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http://dx.doi.org/10.3390/jcm8112015 | DOI Listing |
Pediatr Infect Dis J
December 2024
From the Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
Introduction: Central nervous system (CNS) infections represent some of the most critical pediatric health challenges, characterized by high mortality rates and a notable risk of long-term complications. Despite their significance, standardized guidelines for endocrinological follow-up of CNS infection survivors are lacking, leading to reliance on the expertise of individual centers and clinicians.
Materials And Methods: Prospective monocentric observational study conducted at the Fondazione Policlinico Universitario Agostino Gemelli in Rome, Italy.
J Pediatr Endocrinol Metab
January 2025
Department of Pediatric Endocrinology, Ankara University School of Medicine, Ankara, Türkiye.
Objectives: Premature ovarian insufficiency (POI) affects 1 in 10,000 children, with its molecular causes largely unknown. Adult studies suggest that low androgen levels induce ovarian insufficiency, but data on about this in children is limited. This study aims to assess the prevalence of low androgen levels in childhood POI and its relationship with adrenal insufficiency.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Ageing Epidemiology Reseach Unit (AGE), School of Public Health, Imperial College London, London, UK.
Background: Several studies have investigated the link between sleep disturbances and allostatic load (AL), but the results are varied, and less is known about the associations in clinical samples. The goal of this study is to assess the associations between sleep disturbances and AL among memory clinic participants, and to examine differences according to sex, beta-amyloid status and history of burnout status.
Method: The study was based on 146 memory clinic participants diagnosed with either Mild Cognitive Impairment (MCI) or Subjective Cognitive Impairment (SCI) in the Cortisol and Stress in Alzheimer's Disease Study (Co-STAR) (Sweden).
Alzheimers Dement
December 2024
The University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
Background: Approximately 6.7 million people in the US are diagnosed with an Alzheimer's disease (AD), with greater incidence in women and minorities. Approximately 11 million family members provide uncompensated care to their family members with dementia, with more than 60% reporting high or very high levels of stress, a condition associated with increased risk for AD.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Background: Brain aging is a complex process influenced by various genetic, lifestyle, and environmental factors, as well as by age-related and often co-existing pathologies. MRI and AI methods have been instrumental in understanding neuroanatomical changes that occur during aging in large and diverse populations.
Method: We leveraged Surreal-GAN, a state-of-the-art deep representation learning method, to elucidate the heterogeneity of brain aging by deriving dominant dimensions (patterns) of brain changes and examining their associations with various clinical and genetic factors.
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