Developmental programming by maternal obesity: Lessons from animal models
Abstract
The obesity epidemic has led to more women entering pregnancy overweight or obese. In addition to adverse short-term outcomes, maternal obesity and/or gestational diabetes predispose offspring to developing obesity, type 2 diabetes and cardiovascular disease in adulthood through developmental programming. Human epidemiological studies, although vital in identifying associations, are often unable to address causality and mechanistic studies can be limited by the lack of accessibility of key metabolic tissues. Furthermore, multi-generational studies take many years to complete. Integration of findings from human studies with those from animal models has therefore been critical in moving forward this field that has been termed the 'Developmental Origins of Health and Disease'. This review summarises the evidence from animal models and highlights how animal models provide valuable insight into the maternal factors responsible for developmental programming, potential critical developmental windows, sexual dimorphism, molecular mechanisms and age-related offspring outcomes throughout life. Moreover, we describe how animal models are vital to explore clinically relevant interventions to prevent adverse offspring outcomes in obese or glucose intolerant pregnancy, such as antioxidant supplementation, exercise and maternal metformin treatment.
Read the paper
Related studies
- Metformin as an adjuvant in breast cancer treatment
- Metformin Lysosomal Targeting: A Novel Aspect to Be Investigated for Metformin Repurposing in Neurodegenerative Diseases?
- Aberrant HSF1 signaling activation underlies metformin amelioration of myocardial infarction in mice
- Metformin induces ZFP36 by mTORC1 inhibition in cervical cancer-derived cell lines
- Protein Interaction Network for Identifying Vascular Response of Metformin (Oral Antidiabetic)
- Preventing Long COVID With Metformin
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.