%0 Journal Article %1 doi:10.1126/sciadv.ade7692 %A Espinosa, Camilo A. %A Khan, Waqasuddin %A Khanam, Rasheda %A Das, Sayan %A Khalid, Javairia %A Pervin, Jesmin %A Kasaro, Margaret P. %A Contrepois, Kévin %A Chang, Alan L. %A Phongpreecha, Thanaphong %A Michael, Basil %A Ellenberger, Mathew %A Mehmood, Usma %A Hotwani, Aneeta %A Nizar, Ambreen %A Kabir, Furqan %A Wong, Ronald J. %A Becker, Martin %A Berson, Eloise %A Culos, Anthony %A Francesco, Davide De %A Mataraso, Samson %A Ravindra, Neal %A Thuraiappah, Melan %A Xenochristou, Maria %A Stelzer, Ina A. %A Marić, Ivana %A Dutta, Arup %A Raqib, Rubhana %A Ahmed, Salahuddin %A Rahman, Sayedur %A Hasan, A. S. M. Tarik %A Ali, Said M. %A Juma, Mohamed H. %A Rahman, Monjur %A Aktar, Shaki %A Deb, Saikat %A Price, Joan T. %A Wise, Paul H. %A Winn, Virginia D. %A Druzin, Maurice L. %A Gibbs, Ronald S. %A Darmstadt, Gary L. %A Murray, Jeffrey C. %A Stringer, Jeffrey S. A. %A Gaudilliere, Brice %A Snyder, Michael P. %A Angst, Martin S. %A Rahman, Anisur %A Baqui, Abdullah H. %A Jehan, Fyezah %A Nisar, Muhammad Imran %A Vwalika, Bellington %A Sazawal, Sunil %A Shaw, Gary M. %A Stevenson, David K. %A Aghaeepour, Nima %D 2023 %J Science Advances %K birth country ehr income lmic low maternal middle multiomics myown pregnancy preterm %N 21 %P eade7692 %R 10.1126/sciadv.ade7692 %T Multiomic signals associated with maternal epidemiological factors contributing to preterm birth in low- and middle-income countries %U https://www.science.org/doi/abs/10.1126/sciadv.ade7692 %V 9 %X Preterm birth (PTB) is the leading cause of death in children under five, yet comprehensive studies are hindered by its multiple complex etiologies. Epidemiological associations between PTB and maternal characteristics have been previously described. This work used multiomic profiling and multivariate modeling to investigate the biological signatures of these characteristics. Maternal covariates were collected during pregnancy from 13,841 pregnant women across five sites. Plasma samples from 231 participants were analyzed to generate proteomic, metabolomic, and lipidomic datasets. Machine learning models showed robust performance for the prediction of PTB (AUROC = 0.70), time-to-delivery (r = 0.65), maternal age (r = 0.59), gravidity (r = 0.56), and BMI (r = 0.81). Time-to-delivery biological correlates included fetal-associated proteins (e.g., ALPP, AFP, and PGF) and immune proteins (e.g., PD-L1, CCL28, and LIFR). Maternal age negatively correlated with collagen COL9A1, gravidity with endothelial NOS and inflammatory chemokine CXCL13, and BMI with leptin and structural protein FABP4. These results provide an integrated view of epidemiological factors associated with PTB and identify biological signatures of clinical covariates affecting this disease. Deep biological profiling and machine learning reveal insights into the epidemiology of preterm birth.

@article{doi:10.1126/sciadv.ade7692, abstract = {Preterm birth (PTB) is the leading cause of death in children under five, yet comprehensive studies are hindered by its multiple complex etiologies. Epidemiological associations between PTB and maternal characteristics have been previously described. This work used multiomic profiling and multivariate modeling to investigate the biological signatures of these characteristics. Maternal covariates were collected during pregnancy from 13,841 pregnant women across five sites. Plasma samples from 231 participants were analyzed to generate proteomic, metabolomic, and lipidomic datasets. Machine learning models showed robust performance for the prediction of PTB (AUROC = 0.70), time-to-delivery (r = 0.65), maternal age (r = 0.59), gravidity (r = 0.56), and BMI (r = 0.81). Time-to-delivery biological correlates included fetal-associated proteins (e.g., ALPP, AFP, and PGF) and immune proteins (e.g., PD-L1, CCL28, and LIFR). Maternal age negatively correlated with collagen COL9A1, gravidity with endothelial NOS and inflammatory chemokine CXCL13, and BMI with leptin and structural protein FABP4. These results provide an integrated view of epidemiological factors associated with PTB and identify biological signatures of clinical covariates affecting this disease. Deep biological profiling and machine learning reveal insights into the epidemiology of preterm birth.}, added-at = {2023-05-29T17:20:46.000+0200}, author = {Espinosa, Camilo A. and Khan, Waqasuddin and Khanam, Rasheda and Das, Sayan and Khalid, Javairia and Pervin, Jesmin and Kasaro, Margaret P. and Contrepois, Kévin and Chang, Alan L. and Phongpreecha, Thanaphong and Michael, Basil and Ellenberger, Mathew and Mehmood, Usma and Hotwani, Aneeta and Nizar, Ambreen and Kabir, Furqan and Wong, Ronald J. and Becker, Martin and Berson, Eloise and Culos, Anthony and Francesco, Davide De and Mataraso, Samson and Ravindra, Neal and Thuraiappah, Melan and Xenochristou, Maria and Stelzer, Ina A. and Marić, Ivana and Dutta, Arup and Raqib, Rubhana and Ahmed, Salahuddin and Rahman, Sayedur and Hasan, A. S. M. Tarik and Ali, Said M. and Juma, Mohamed H. and Rahman, Monjur and Aktar, Shaki and Deb, Saikat and Price, Joan T. and Wise, Paul H. and Winn, Virginia D. and Druzin, Maurice L. and Gibbs, Ronald S. and Darmstadt, Gary L. and Murray, Jeffrey C. and Stringer, Jeffrey S. A. and Gaudilliere, Brice and Snyder, Michael P. and Angst, Martin S. and Rahman, Anisur and Baqui, Abdullah H. and Jehan, Fyezah and Nisar, Muhammad Imran and Vwalika, Bellington and Sazawal, Sunil and Shaw, Gary M. and Stevenson, David K. and Aghaeepour, Nima}, biburl = {https://www.bibsonomy.org/bibtex/21b3630995b16cc681b9acc213344ec07/becker}, description = {impactfactor = {14.98},impactfactor-year = {2023},impactfactor-source = {https://academic-accelerator.com/Impact-of-Journal/Science-Advances}}, doi = {10.1126/sciadv.ade7692}, eprint = {https://www.science.org/doi/pdf/10.1126/sciadv.ade7692}, interhash = {625259f47c7f56b6f2cb4ffc07ae454e}, intrahash = {1b3630995b16cc681b9acc213344ec07}, journal = {Science Advances}, keywords = {birth country ehr income lmic low maternal middle multiomics myown pregnancy preterm}, number = 21, pages = {eade7692}, timestamp = {2023-10-05T04:53:48.000+0200}, title = {Multiomic signals associated with maternal epidemiological factors contributing to preterm birth in low- and middle-income countries}, url = {https://www.science.org/doi/abs/10.1126/sciadv.ade7692}, volume = 9, year = 2023 }