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Polymorphism Patterns and Socioeconomic Characteristics and Their Influence on the Risk of Preeclampsia.

Flavius George SocolMarius CrainaSimona-Alina Abu-AwwadIoana Denisa SocolSimona Sorina FarcasAhmed Abu-AwwadDenis SerbanAdina Ioana BucurElena Silvia BernadLioara BoscuLaura Claudia PopaNicoleta Ioana Andreescu
Published in: Medicina (Kaunas, Lithuania) (2024)
Background : Preeclampsia (PE) is a critical condition affecting pregnancies worldwide. Understanding its etiology, particularly the genetic factors, is vital. This study aims to investigate the association between ACE gene polymorphisms, specifically the ACE G2350A (rs4343) variant, and the predisposition to PE, offering insights into the genetic predisposition towards this complex condition. Methods : A case-control study was conducted with 140 participants without PE (Control Group) and 128 participants diagnosed with PE (PE Group). The study focused on comparing the prevalence of the rs4343 polymorphism between the groups. Results : The analysis identified a significantly reduced risk associated with the AG genotype and an insignificant increase in risk with the AA genotype. Statistically significant differences in demographic and clinical characteristics, such as BMI and marital status, were observed between the groups, suggesting a multifaceted risk profile for PE that includes genetic, environmental, and socio-economic factors. Conclusions : The study highlight the significant role of genetic variations, specifically the ACE G2350A (rs4343) polymorphism, in influencing PE predisposition. It highlights the intricate interplay between genetic predispositions and other risk factors in the development of PE. Further research is encouraged to expand on these findings and explore a wider range of genetic polymorphisms and their interactions with environmental factors.
Keyphrases
  • risk factors
  • genome wide
  • body mass index
  • dna methylation
  • gene expression
  • physical activity
  • angiotensin converting enzyme
  • weight loss
  • preterm birth
  • risk assessment
  • weight gain
  • data analysis