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Establishment of the early prediction models of low-birth-weight reveals influential genetic and environmental factors: a prospective cohort study.
Mizuno, Satoshi; Nagaie, Satoshi; Tamiya, Gen; Kuriyama, Shinichi; Obara, Taku; Ishikuro, Mami; Tanaka, Hiroshi; Kinoshita, Kengo; Sugawara, Junichi; Yamamoto, Masayuki; Yaegashi, Nobuo; Ogishima, Soichi.
Afiliación
  • Mizuno S; Department of Informatics for Genomic Medicine, Group of Integrated Database Systems, Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-Ku, Sendai, Miyagi, 980-8575, Japan.
  • Nagaie S; Department of Informatics for Genomic Medicine, Group of Integrated Database Systems, Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-Ku, Sendai, Miyagi, 980-8575, Japan.
  • Tamiya G; Department of Statistical Genetics and Genomics, Group of Disease Risk Prediction, Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan.
  • Kuriyama S; Department of Molecular Epidemiology, Group of the Birth and Three-Generation Cohort Study, Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan.
  • Obara T; Department of Molecular Epidemiology, Group of the Birth and Three-Generation Cohort Study, Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan.
  • Ishikuro M; Department of Molecular Epidemiology, Group of the Birth and Three-Generation Cohort Study, Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan.
  • Tanaka H; Medical Data Science Promotion, Tokyo Medical and Dental University, Tokyo, Japan.
  • Kinoshita K; Department of Statistical Genetics and Genomics, Group of Systems Bioinformatics, Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan.
  • Sugawara J; Department of Gynecology and Obstetrics, Tohoku University Graduate School of Medicine, Tohoku University, Miyagi, Japan.
  • Yamamoto M; Department of Feto-Maternal Medical Science, Group of Community Medical Supports, Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan.
  • Yaegashi N; Suzuki Memorial Hospital 3-5-5, Satonomori, Iwanumashi, Miyagi, 989-2481, Japan.
  • Ogishima S; Department of Medical Biochemistry, Graduate School of Medicine, Tohoku University, Sendai, Japan.
BMC Pregnancy Childbirth ; 23(1): 628, 2023 Aug 31.
Article en En | MEDLINE | ID: mdl-37653383
ABSTRACT

BACKGROUND:

Low birth weight (LBW) is a leading cause of neonatal morbidity and mortality, and increases various disease risks across life stages. Prediction models of LBW have been developed before, but have limitations including small sample sizes, absence of genetic factors and no stratification of neonate into preterm and term birth groups. In this study, we challenged the development of early prediction models of LBW based on environmental and genetic factors in preterm and term birth groups, and clarified influential variables for LBW prediction.

METHODS:

We selected 22,711 neonates, their 21,581 mothers and 8,593 fathers from the Tohoku Medical Megabank Project Birth and Three-Generation cohort study. To establish early prediction models of LBW for preterm birth and term birth groups, we trained AI-based models using genetic and environmental factors of lifestyles. We then clarified influential environmental and genetic factors for predicting LBW in the term and preterm groups.

RESULTS:

We identified 2,327 (10.22%) LBW neonates consisting of 1,077 preterm births and 1,248 term births. Our early prediction models archived the area under curve 0.96 and 0.95 for term LBW and preterm LBW models, respectively. We revealed that environmental factors regarding eating habits and genetic features related to fetal growth were influential for predicting LBW in the term LBW model. On the other hand, we identified that genomic features related to toll-like receptor regulations and infection reactions are influential genetic factors for prediction in the preterm LBW model.

CONCLUSIONS:

We developed precise early prediction models of LBW based on lifestyle factors in the term birth group and genetic factors in the preterm birth group. Because of its accuracy and generalisability, our prediction model could contribute to risk assessment of LBW in the early stage of pregnancy and control LBW risk in the term birth group. Our prediction model could also contribute to precise prediction of LBW based on genetic factors in the preterm birth group. We then identified parental genetic and maternal environmental factors during pregnancy influencing LBW prediction, which are major targets for understanding the LBW to address serious burdens on newborns' health throughout life.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Contexto en salud: 2_ODS3 / 7_ODS3_muertes_prevenibles_nacidos_ninos Problema de salud: 2_mortalidade_materna / 2_muertes_prevenibles / 7_environmental_health / 7_neonatal_care_health / 7_nutrition Asunto principal: Nacimiento Prematuro Tipo de estudio: Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Female / Humans / Newborn / Pregnancy Idioma: En Revista: BMC Pregnancy Childbirth Asunto de la revista: OBSTETRICIA Año: 2023 Tipo del documento: Article País de afiliación: Japón

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Contexto en salud: 2_ODS3 / 7_ODS3_muertes_prevenibles_nacidos_ninos Problema de salud: 2_mortalidade_materna / 2_muertes_prevenibles / 7_environmental_health / 7_neonatal_care_health / 7_nutrition Asunto principal: Nacimiento Prematuro Tipo de estudio: Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Female / Humans / Newborn / Pregnancy Idioma: En Revista: BMC Pregnancy Childbirth Asunto de la revista: OBSTETRICIA Año: 2023 Tipo del documento: Article País de afiliación: Japón
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