Personalized prediction of glycemic responses to food in women with diet-treated gestational diabetes: the role of the gut microbiota
Abstract We developed a prediction model for postprandial glycemic response (PPGR) in pregnant women, including those with diet-treated gestational diabetes mellitus (GDM) and healthy women, and explored the role of gut microbiota in improving prediction accuracy. The study involved 105 pregnant wom...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | npj Biofilms and Microbiomes |
| Online Access: | https://doi.org/10.1038/s41522-025-00650-9 |
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| author | Polina V. Popova Artem O. Isakov Anastasiia N. Rusanova Stanislav I. Sitkin Anna D. Anopova Elena A. Vasukova Alexandra S. Tkachuk Irina S. Nemikina Elizaveta A. Stepanova Angelina I. Eriskovskaya Ekaterina A. Stepanova Evgenii A. Pustozerov Maria A. Kokina Elena Y. Vasilieva Lyudmila B. Vasilyeva Soha Zgairy Elad Rubin Carmel Even Sondra Turjeman Tatiana M. Pervunina Elena N. Grineva Omry Koren Evgeny V. Shlyakhto |
| author_facet | Polina V. Popova Artem O. Isakov Anastasiia N. Rusanova Stanislav I. Sitkin Anna D. Anopova Elena A. Vasukova Alexandra S. Tkachuk Irina S. Nemikina Elizaveta A. Stepanova Angelina I. Eriskovskaya Ekaterina A. Stepanova Evgenii A. Pustozerov Maria A. Kokina Elena Y. Vasilieva Lyudmila B. Vasilyeva Soha Zgairy Elad Rubin Carmel Even Sondra Turjeman Tatiana M. Pervunina Elena N. Grineva Omry Koren Evgeny V. Shlyakhto |
| author_sort | Polina V. Popova |
| collection | DOAJ |
| description | Abstract We developed a prediction model for postprandial glycemic response (PPGR) in pregnant women, including those with diet-treated gestational diabetes mellitus (GDM) and healthy women, and explored the role of gut microbiota in improving prediction accuracy. The study involved 105 pregnant women (77 with GDM, 28 healthy), who underwent continuous glucose monitoring (CGM) for 7 days, provided food diaries, and gave stool samples for microbiome analysis. Machine learning models were created using CGM data, meal content, lifestyle factors, biochemical parameters, and microbiota data (16S rRNA gene sequence analysis). Adding microbiome data increased the explained variance in peak glycemic levels (GLUmax) from 34 to 42% and in incremental area under the glycemic curve (iAUC120) from 50 to 52%. The final model showed better correlation with measured PPGRs than one based only on carbohydrate count (r = 0.72 vs. r = 0.51 for iAUC120). Although microbiome features were important, their contribution to model performance was modest. |
| format | Article |
| id | doaj-art-3e3bf2a53ad141879270fd290958177b |
| institution | Kabale University |
| issn | 2055-5008 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Biofilms and Microbiomes |
| spelling | doaj-art-3e3bf2a53ad141879270fd290958177b2025-02-09T12:15:20ZengNature Portfolionpj Biofilms and Microbiomes2055-50082025-02-0111111110.1038/s41522-025-00650-9Personalized prediction of glycemic responses to food in women with diet-treated gestational diabetes: the role of the gut microbiotaPolina V. Popova0Artem O. Isakov1Anastasiia N. Rusanova2Stanislav I. Sitkin3Anna D. Anopova4Elena A. Vasukova5Alexandra S. Tkachuk6Irina S. Nemikina7Elizaveta A. Stepanova8Angelina I. Eriskovskaya9Ekaterina A. Stepanova10Evgenii A. Pustozerov11Maria A. Kokina12Elena Y. Vasilieva13Lyudmila B. Vasilyeva14Soha Zgairy15Elad Rubin16Carmel Even17Sondra Turjeman18Tatiana M. Pervunina19Elena N. Grineva20Omry Koren21Evgeny V. Shlyakhto22World-Class Research Center for Personalized Medicine, Almazov National Medical Research CentreWorld-Class Research Center for Personalized Medicine, Almazov National Medical Research CentreWorld-Class Research Center for Personalized Medicine, Almazov National Medical Research CentreInstitute of Perinatology and Pediatrics, Almazov National Medical Research CenterWorld-Class Research Center for Personalized Medicine, Almazov National Medical Research CentreWorld-Class Research Center for Personalized Medicine, Almazov National Medical Research CentreInstitute of Endocrinology, Almazov National Medical Research CentreWorld-Class Research Center for Personalized Medicine, Almazov National Medical Research CentreInstitute of Endocrinology, Almazov National Medical Research CentreWorld-Class Research Center for Personalized Medicine, Almazov National Medical Research CentreInstitute of Endocrinology, Almazov National Medical Research CentreInstitute of Endocrinology, Almazov National Medical Research CentreWorld-Class Research Center for Personalized Medicine, Almazov National Medical Research CentreWorld-Class Research Center for Personalized Medicine, Almazov National Medical Research CentreInstitute of Molecular Biology and Genetics, Almazov National Medical Research CentreAzrieli Faculty of Medicine, Bar-Ilan UniversityAzrieli Faculty of Medicine, Bar-Ilan UniversityAzrieli Faculty of Medicine, Bar-Ilan UniversityAzrieli Faculty of Medicine, Bar-Ilan UniversityInstitute of Perinatology and Pediatrics, Almazov National Medical Research CenterWorld-Class Research Center for Personalized Medicine, Almazov National Medical Research CentreAzrieli Faculty of Medicine, Bar-Ilan UniversityWorld-Class Research Center for Personalized Medicine, Almazov National Medical Research CentreAbstract We developed a prediction model for postprandial glycemic response (PPGR) in pregnant women, including those with diet-treated gestational diabetes mellitus (GDM) and healthy women, and explored the role of gut microbiota in improving prediction accuracy. The study involved 105 pregnant women (77 with GDM, 28 healthy), who underwent continuous glucose monitoring (CGM) for 7 days, provided food diaries, and gave stool samples for microbiome analysis. Machine learning models were created using CGM data, meal content, lifestyle factors, biochemical parameters, and microbiota data (16S rRNA gene sequence analysis). Adding microbiome data increased the explained variance in peak glycemic levels (GLUmax) from 34 to 42% and in incremental area under the glycemic curve (iAUC120) from 50 to 52%. The final model showed better correlation with measured PPGRs than one based only on carbohydrate count (r = 0.72 vs. r = 0.51 for iAUC120). Although microbiome features were important, their contribution to model performance was modest.https://doi.org/10.1038/s41522-025-00650-9 |
| spellingShingle | Polina V. Popova Artem O. Isakov Anastasiia N. Rusanova Stanislav I. Sitkin Anna D. Anopova Elena A. Vasukova Alexandra S. Tkachuk Irina S. Nemikina Elizaveta A. Stepanova Angelina I. Eriskovskaya Ekaterina A. Stepanova Evgenii A. Pustozerov Maria A. Kokina Elena Y. Vasilieva Lyudmila B. Vasilyeva Soha Zgairy Elad Rubin Carmel Even Sondra Turjeman Tatiana M. Pervunina Elena N. Grineva Omry Koren Evgeny V. Shlyakhto Personalized prediction of glycemic responses to food in women with diet-treated gestational diabetes: the role of the gut microbiota npj Biofilms and Microbiomes |
| title | Personalized prediction of glycemic responses to food in women with diet-treated gestational diabetes: the role of the gut microbiota |
| title_full | Personalized prediction of glycemic responses to food in women with diet-treated gestational diabetes: the role of the gut microbiota |
| title_fullStr | Personalized prediction of glycemic responses to food in women with diet-treated gestational diabetes: the role of the gut microbiota |
| title_full_unstemmed | Personalized prediction of glycemic responses to food in women with diet-treated gestational diabetes: the role of the gut microbiota |
| title_short | Personalized prediction of glycemic responses to food in women with diet-treated gestational diabetes: the role of the gut microbiota |
| title_sort | personalized prediction of glycemic responses to food in women with diet treated gestational diabetes the role of the gut microbiota |
| url | https://doi.org/10.1038/s41522-025-00650-9 |
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