What is the effect of maternal obesity on the brain development of offspring? Obesity MaternalObesity BrainDevelopment UniofOxford
By Dr. Priyom Bose, Ph.D.Mar 22 2023Reviewed by Aimee Molineux During the perinatal period, the maternal diet and behavior affect the developmental trajectory of the offspring. Several studies have indicated maternal depression and obesity enhance the risk of neuropsychiatric disease in offspring.Obesity during pregnancy Obesity is a metabolic disorder that is prevalent globally. Nevertheless, this disorder can be prevented with lifestyle modification.
Maternal obesity was directly associated with attention deficit hyperactivity disorder , irrespective of birth weight. Pre-pregnancy obesity was linked with difficulties with emotional intensity in children. In addition, many of these children experience several behavioral issues, such as dissociative disorders, obsessive-compulsive disorder, and eating disorders.
Related StoriesEthological studies have indicated that Infant primates raised in absence of maternal care develop severe social deficits and get attached to inanimate objects that provide a sense of comfort. During the early postnatal period, maternal care or neglect plays a crucial role in the child’s neurodevelopment. Clinical studies have also shown the relationship between child maltreatment and their altered neurobiology and behavior.
More research is required to fully understand how obesity-induced changes in the maternal microbiome influence offspring neurodevelopment and behavior. In most obese mothers there is a deficiency of Bifidobacteria spp. or Lactobacillus spp, which reduce beneficial metabolites.
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Bayesian network modelling to identify on-ramps to childhood obesity - BMC MedicineBackground When tackling complex public health challenges such as childhood obesity, interventions focused on immediate causes, such as poor diet and physical inactivity, have had limited success, largely because upstream root causes remain unresolved. A priority is to develop new modelling frameworks to infer the causal structure of complex chronic disease networks, allowing disease “on-ramps” to be identified and targeted. Methods The system surrounding childhood obesity was modelled as a Bayesian network, using data from The Longitudinal Study of Australian Children. The existence and directions of the dependencies between factors represent possible causal pathways for childhood obesity and were encoded in directed acyclic graphs (DAGs). The posterior distribution of the DAGs was estimated using the Partition Markov chain Monte Carlo. Results We have implemented structure learning for each dataset at a single time point. For each wave and cohort, socio-economic status was central to the DAGs, implying that socio-economic status drives the system regarding childhood obesity. Furthermore, the causal pathway socio-economic status and/or parental high school levels → parental body mass index (BMI) → child’s BMI existed in over 99.99% of posterior DAG samples across all waves and cohorts. For children under the age of 8 years, the most influential proximate causal factors explaining child BMI were birth weight and parents’ BMI. After age 8 years, free time activity became an important driver of obesity, while the upstream factors influencing free time activity for boys compared with girls were different. Conclusions Childhood obesity is largely a function of socio-economic status, which is manifest through numerous downstream factors. Parental high school levels entangle with socio-economic status, and hence, are on-ramp to childhood obesity. The strong and independent causal relationship between birth weight and childhood BMI suggests a biological link. Our study imp
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