TY - GEN
T1 - Mid-pregnancy immune dysregulation and its association with maternal metabolic and genetic factors in those with preterm birth: Insights using artificial intelligence.
AU - L, Jelliffe-Pawlowski
AU - Piening, Brian D.
AU - comments, See full list of authors in
PY - 2022/5/1
Y1 - 2022/5/1
N2 - Introduction: Mid-pregnancy immune dysregulation, as indicated by maladaptive cytokine and immune cell signaling, is associated with an increased risk of preterm birth (PTB) (birth at < 37 weeks of completed gestation). While infection and immune-related disease (e.g. asthma, type I diabetes) explain some portion of observed immune dysregulation in pregnancies with PTB, in most instances this dysregulation is unexplained. Investigation of the relationship between immune dysregulation and other molecular factors with known links to PTB (e.g. metabolic and genetic factors) offers the opportunity for revealing novel causal pathways.
Methods: In a sample of pregnant women with PTB (n = 482,
Results: Using chi-square and parallelized grid search strategies, 28 metabolic, and 60 SNPs were found to be associated with PTB. Within this set, six metabolites (13-HODE + 9-HODE, 2-hydroxystearate, tryptophan betaine, palmitate (16:0), beta-hydroxyisovalerate, N1-methyladenosine), and five SNPs (2:216734070, 5:5181928, 7:151459336, 8:4481743, 9:132946644) were found to be related to immune dysregulation (significantly associated with five or more markers contained within established immune dysregulation models for PTB) (Figure). A number of cross metabolite, cross SNP, and metabolite by SNP associations were observed within the immune dysregulation network. For example, palmitate (16:0) was positively associated with 13-HODE + 9-HODE, 2-hydroxystearate, and beta-hydroxyisovalerate (Pearson’s Correlation Coefficients (PCCs) 0.16-0.53, p < .05) and negatively associated with N1-methyladenosine and 8:4481743 (PCCs -0.09 and -0.10 respectively, p < .05) (Figure).
Conclusions: Here we demonstrate a novel approach to investigating predictors and drivers of PTB and associated immune dysregulation. Our findings confirm the association of immune, metabolic, and genetic factors with PTB and identify new cross-pathway relationships that may be mechanistically important.
AB - Introduction: Mid-pregnancy immune dysregulation, as indicated by maladaptive cytokine and immune cell signaling, is associated with an increased risk of preterm birth (PTB) (birth at < 37 weeks of completed gestation). While infection and immune-related disease (e.g. asthma, type I diabetes) explain some portion of observed immune dysregulation in pregnancies with PTB, in most instances this dysregulation is unexplained. Investigation of the relationship between immune dysregulation and other molecular factors with known links to PTB (e.g. metabolic and genetic factors) offers the opportunity for revealing novel causal pathways.
Methods: In a sample of pregnant women with PTB (n = 482,
Results: Using chi-square and parallelized grid search strategies, 28 metabolic, and 60 SNPs were found to be associated with PTB. Within this set, six metabolites (13-HODE + 9-HODE, 2-hydroxystearate, tryptophan betaine, palmitate (16:0), beta-hydroxyisovalerate, N1-methyladenosine), and five SNPs (2:216734070, 5:5181928, 7:151459336, 8:4481743, 9:132946644) were found to be related to immune dysregulation (significantly associated with five or more markers contained within established immune dysregulation models for PTB) (Figure). A number of cross metabolite, cross SNP, and metabolite by SNP associations were observed within the immune dysregulation network. For example, palmitate (16:0) was positively associated with 13-HODE + 9-HODE, 2-hydroxystearate, and beta-hydroxyisovalerate (Pearson’s Correlation Coefficients (PCCs) 0.16-0.53, p < .05) and negatively associated with N1-methyladenosine and 8:4481743 (PCCs -0.09 and -0.10 respectively, p < .05) (Figure).
Conclusions: Here we demonstrate a novel approach to investigating predictors and drivers of PTB and associated immune dysregulation. Our findings confirm the association of immune, metabolic, and genetic factors with PTB and identify new cross-pathway relationships that may be mechanistically important.
M3 - Other contribution
T3 - Articles, Abstracts, and Reports
ER -