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A Data-Driven Review of the Genetic Factors of Pregnancy Complications.

ABSTRACT: Over the recent years, many advances have been made in the research of the genetic factors of pregnancy complications. In this work, we use publicly available data repositories, such as the National Human Genome Research Institute GWAS Catalog, HuGE Navigator, and the UK Biobank genetic and phenotypic dataset to gain insights into molecular pathways and individual genes behind a set of pregnancy-related traits, including the most studied ones-preeclampsia, gestational diabetes, preterm birth, and placental abruption. Using both HuGE and GWAS Catalog data, we confirm that immune system and, in particular, T-cell related pathways are one of the most important drivers of pregnancy-related traits. Pathway analysis of the data reveals that cell adhesion and matrisome-related genes are also commonly involved in pregnancy pathologies. We also find a large role of metabolic factors that affect not only gestational diabetes, but also the other traits. These shared metabolic genes include IGF2, PPARG, and NOS3. We further discover that the published genetic associations are poorly replicated in the independent UK Biobank cohort. Nevertheless, we find novel genome-wide associations with pregnancy-related traits for the FBLN7, STK32B, and ACTR3B genes, and replicate the effects of the KAZN and TLE1 genes, with the latter being the only gene identified across all data resources. Overall, our analysis highlights central molecular pathways for pregnancy-related traits, and suggests a need to use more accurate and sophisticated association analysis strategies to robustly identify genetic risk factors for pregnancy complications.

SUBMITTER: Barbitoff YA

PROVIDER: S-EPMC7246997 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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A Data-Driven Review of the Genetic Factors of Pregnancy Complications.

Barbitoff Yury A YA Tsarev Alexander A AA Vashukova Elena S ES Maksiutenko Evgeniia M EM Kovalenko Liudmila V LV Belotserkovtseva Larisa D LD Glotov Andrey S AS

International journal of molecular sciences 20200511 9

Over the recent years, many advances have been made in the research of the genetic factors of pregnancy complications. In this work, we use publicly available data repositories, such as the National Human Genome Research Institute GWAS Catalog, HuGE Navigator, and the UK Biobank genetic and phenotypic dataset to gain insights into molecular pathways and individual genes behind a set of pregnancy-related traits, including the most studied ones-preeclampsia, gestational diabetes, preterm birth, an ...[more]

PMID: 32403311

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Project description:BackgroundTo comprehensively assess evidence from published systematic review and meta-analyses (SRMAs) on the genetics of vascular diabetes complications.MethodsA systematic literature search conducted in Medline and Embase identified 63 non-overlapping SRMAs. We re-conducted meta-analyses to compare diabetes with and without complications using multiple genetic models; evaluated associations using Venice criteria and Bayesian false-discovery probability (BFDP); and graded as highly credible, credible, and not credible. We also contrasted highly credible and credible associations to recent genome-wide association studies (GWASs).ResultsHighly credible evidence was discovered for single nucleotide polymorphisms (SNPs) rs1024611 at MCP-1 gene and SNP rs3025039 at VEGF gene with diabetic retinopathy (DR) in type 2 diabetes; SNP rs2268388 at ACACB gene, insertion/deletion (Ins/Del) variant at ACE gene, SNP rs1801133 at MTHFR gene, and SNP rs7903146 at TCF7L2 gene with diabetic kidney disease (DKD) in type 2 diabetes; and SNP rs4880 at SOD2 gene with diabetic peripheral neuropathy (DPN) in type 1 diabetes. Combining type 1 and 2 diabetes, highly credible evidence was discovered for insertion/deletion variant at ACE gene, SNP rs759853 at AKR1B1 gene, SNP rs1044498 at ENPP1 gene and DKD, and SNP rs1617640 at EPO gene for the combined endpoint of DR and DKD. None of these associations was directly replicated in the latest GWASs for DR and DKD, however, another SNP, rs55853916 at TCF7L2 gene had been detected as a GWAS hit for DKD.ConclusionsThis umbrella review rigorously assessed evidence on the genetics of vascular diabetes complications, complemented findings in recent GWASs and yielded insight into the optimal selection of genetic models for the design of GWASs on vascular diabetes complications. Mechanistic or bioinformatic studies are warranted to further assess the role of these genes in the pathology of vascular diabetes complications and their potential as drug targets.RegistrationPROSPERO: CRD42022384423.

Project description:ImportanceGeneral trauma is the leading cause of nonobstetric maternal morbidity and mortality, affecting approximately 8% of all pregnancies. Pregnant women with traumatic brain injury (TBI) face high morbidity and mortality rates, requiring complex management due to physiological changes, teratogenic risks of treatments, and the need for fetal monitoring.ObjectivesTo assess the consequences of TBI during pregnancy on maternal and fetal outcomes and to evaluate management strategies to inform clinical decision-making.Evidence reviewA systematic literature search was conducted on January 12, 2024, in PubMed, Web of Science, and PsycInfo to identify articles published in English, German, or Spanish between January 1, 1990, and December 31, 2023, that included at least 1 pregnant individual with TBI. Peer-reviewed, human-based studies with original data on maternal and fetal outcomes were included. Reviews, meta-analyses, and nonhuman studies were excluded. Two independent reviewers screened abstracts and full-text articles. Study characteristics, pregnancy outcomes (maternal and fetal), management methods, and authors' conclusions were extracted. Risk of bias was assessed by 2 reviewers, with interrater agreement measured using Cohen κ. Disagreements were resolved through discussion. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guideline was followed.FindingsThis systematic review included 16 articles involving a total of 4112 individuals (mean maternal age, 26.9 years; range, 16-47 years) who experienced TBI during pregnancy (mean gestational age at injury, 24 weeks; range, 3-38 weeks). The articles comprised 10 case reports, 2 case series, and 4 cohort studies. Motor vehicle crashes were the most common cause of injury, reported in 12 articles. The average Glasgow Coma Scale score ranged from 3 to 15 across all individuals. Conservative management was reported in 7 case patients, whereas surgery was performed in 6 case patients. Maternal outcomes ranged from functional recovery to severe cognitive impairment, and fetal outcomes varied from stable to severe adverse outcomes, including stillbirth and death. Risk of bias assessment indicated moderate to good methodological validity overall, but most articles demonstrated poor quality of evidence.Conclusions and relevanceIn this review, no definitive association between TBI during pregnancy and maternal or fetal outcomes was found owing to conflicting findings, poor to moderate study quality, and limited evidence. Although some articles suggested increased risks such as placental abruption and cesarean delivery, the findings remained inconclusive. The findings of this review underscore the need for high-quality research, standardized reporting, and rigorous methodology to improve data reliability. Future research should focus on developing consensus-driven, multidisciplinary management strategies to improve maternal and fetal outcomes.

Project description:Introduction: Artificial intelligence is widely used in medical field, and machine learning has been increasingly used in health care, prediction, and diagnosis and as a method of determining priority. Machine learning methods have been features of several tools in the fields of obstetrics and childcare. This present review aims to summarize the machine learning techniques to predict perinatal complications. Objective: To identify the applicability and performance of machine learning methods used to identify pregnancy complications. Methods: A total of 98 articles were obtained with the keywords "machine learning," "deep learning," "artificial intelligence," and accordingly as they related to perinatal complications ("complications in pregnancy," "pregnancy complications") from three scientific databases: PubMed, Scopus, and Web of Science. These were managed on the Mendeley platform and classified using the PRISMA method. Results: A total of 31 articles were selected after elimination according to inclusion and exclusion criteria. The features used to predict perinatal complications were primarily electronic medical records (48%), medical images (29%), and biological markers (19%), while 4% were based on other types of features, such as sensors and fetal heart rate. The main perinatal complications considered in the application of machine learning thus far are pre-eclampsia and prematurity. In the 31 studies, a total of sixteen complications were predicted. The main precision metric used is the AUC. The machine learning methods with the best results were the prediction of prematurity from medical images using the support vector machine technique, with an accuracy of 95.7%, and the prediction of neonatal mortality with the XGBoost technique, with 99.7% accuracy. Conclusion: It is important to continue promoting this area of research and promote solutions with multicenter clinical applicability through machine learning to reduce perinatal complications. This systematic review contributes significantly to the specialized literature on artificial intelligence and women's health.

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A Data-Driven Review of the Genetic Factors of Pregnancy Complications.

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A Data-Driven Review of the Genetic Factors of Pregnancy Complications.

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