PPARγ-A Factor Linking Metabolically Unhealthy Obesity with Placental Pathologies.
Sebastian KwiatkowskiAnna KajdyKatarzyna A StefanskaMagdalena Bednarek-JędrzejekSylwia DzidekPiotr ToustyMałgorzata SokołowskaEwa KwiatkowskaPublished in: International journal of molecular sciences (2021)
Obesity is a known factor in the development of preeclampsia. This paper links adipose tissue pathologies with aberrant placental development and the resulting preeclampsia. PPARγ, a transcription factor from the ligand-activated nuclear hormone receptor family, appears to be one common aspect of both pathologies. It is the master regulator of adipogenesis in humans. At the same time, its aberrantly low activity has been observed in placental pathologies. Overweight and obesity are very serious health problems worldwide. They have negative effects on the overall mortality rate. Very importantly, they are also conducive to diseases linked to impaired placental development, including preeclampsia. More and more people in Europe are suffering from overweight (35.2%) and obesity (16%) (EUROSTAT 2021 data), some of them young women planning pregnancy. As a result, we will be increasingly encountering obese pregnant women with a considerable risk of placental development disorders, including preeclampsia. An appreciation of the mechanisms shared by these two conditions may assist in their prevention and treatment. Clearly, it should not be forgotten that health education concerning the need for a proper diet and physical activity is of utmost importance here.
Keyphrases
- insulin resistance
- adipose tissue
- physical activity
- weight loss
- transcription factor
- early onset
- healthcare
- metabolic syndrome
- mental health
- type diabetes
- public health
- pregnancy outcomes
- pregnant women
- high fat diet induced
- weight gain
- health information
- cardiovascular disease
- high fat diet
- risk assessment
- preterm birth
- cardiovascular events
- risk factors
- coronary artery disease
- fatty acid
- dna binding
- social media
- machine learning
- data analysis