Personalized Prediction of Glycemic Responses to Food in Women with Gestational Diabetes: Gut Microbiota Matters DOI Creative Commons
Polina Popova, Artem Isakov,

Anastasia N. Rusanova

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 15, 2024

Abstract We aimed to develop a prediction model for postprandial glycemic response (PPGR) in pregnant women with gestational diabetes mellitus (GDM) and explore the influence of gut microbial data on accuracy. enrolled 105 (70 GDM 35 healthy). Participants underwent continuous glucose monitoring (CGM) 7 days provided detailed food diaries. Stool samples were collected at 28.8 ± 3.6 weeks, followed by 16S rRNA gene sequence analysis. developed machine learning algorithms predicting PPGR, incorporating CGM measurements, meal content, lifestyle factors, biochemical parameters, anthropometrics, microbiota data. The accuracy models without compared. PPGR created based 2,706 meals measured PPGRs. integration microbiome increased explained variance peak levels (GLUmax) from 34–42% incremental area under curve 120 minutes after start (iAUC120) 50–52%. final performed better than solely carbohydrate count terms correlation between predicted PPGRs (r = 0.72 vs r 0.51 iAUC120 0.66 0.35 GLUmax). After summing SHAP values associated features, emerged as fourth most impactful parameter GLUmax prediction, following composition, context. Microbiome features rank among top 5 parameters GDM.

Language: Английский

Personalized prediction of glycemic responses to food in women with diet-treated gestational diabetes: the role of the gut microbiota DOI Creative Commons
Polina Popova, Artem Isakov, Anastasiia Rusanova

et al.

npj Biofilms and Microbiomes, Journal Year: 2025, Volume and Issue: 11(1)

Published: Feb. 7, 2025

We developed a prediction model for postprandial glycemic response (PPGR) in pregnant women, including those with diet-treated gestational diabetes mellitus (GDM) and healthy explored the role of gut microbiota improving accuracy. The study involved 105 women (77 GDM, 28 healthy), who underwent continuous glucose monitoring (CGM) 7 days, provided food diaries, gave stool samples microbiome analysis. Machine learning models were created using CGM data, meal content, lifestyle factors, biochemical parameters, data (16S rRNA gene sequence analysis). Adding increased explained variance peak levels (GLUmax) from 34 to 42% incremental area under curve (iAUC120) 50 52%. final showed better correlation measured PPGRs than one based only on carbohydrate count (r = 0.72 vs. r 0.51 iAUC120). Although features important, their contribution performance was modest.

Language: Английский

Citations

0
Complete genome sequences of Sellimonas intestinalis JCM 30749 T , Sellimonas caecigallum JCM 35759 T , and Sellimonas catena JCM 35622 T and JCM 35623 DOI Open Access

Kazuyoshi Koike,

Kazutoshi Murotomi,

Mayu Hamajima

et al.

Microbiology Resource Announcements, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 25, 2025

ABSTRACT We obtained complete genome sequences of Sellimonas intestinalis JCM 30749 T , “ caecigallum ” 35759 catena 35622 and 35623. All four genomes consist a single circular chromosome, with lengths 2,758,996 to 3,825,976 base pairs G + C contents 45.12% 45.59%.

Language: Английский

Citations

0
The gut microbiota and diabetic nephropathy: an observational study review and bidirectional Mendelian randomization study DOI Creative Commons

Yi Han,

Yuzhuo Wang, Xing Yu Zhu

et al.

Trials, Journal Year: 2025, Volume and Issue: 26(1)

Published: March 24, 2025

Earlier studies have implicated a crucial link between diabetic nephropathy (DN) and the gut microbiota (GM) by considering gut-kidney axis; however, specific cause-and-effect connections these processes remain unclear. To compare changes in GM DN patients control subjects, review of observational was performed. The examination focused on phylum, family, genus, species/genus categories. delve deeper into cause–effect relationship, instrumental variables for 211 taxa (9 phyla, 16 classes, 20 orders, 35 families, 131 genera), which were eligible mbQTL (microbial quantitative trait locus) mapping analysis, collected from Genome Wide Association Study (GWAS). A Mendelian randomization investigation then conducted to gauge their impact susceptibility using data European Bioinformatics Institute (EBI) FinnGen consortium. included 1032 451,248 controls, while consortium consisted 3283 210,463 controls. Two-sample (TSMR) utilized determine DN. primary method analysis inverse variance weighted (IVW) approach. Moreover, reverse carried out, findings validated through sensitivity assessments. This examined 11 that satisfied inclusion exclusion criteria. There significant difference abundance 144 By employing MR technique, 13 bacteria pinpointed as having causal (including 3 unknown taxa). Even after Bonferroni correction, protective phylum Proteobacteria genus Dialister (Sequeira et al. Nat Microbiol. 5:304-313, 2020; Liu EBioMedicine. 90:104527, 2023) harmful Akkermansia, family Verrucomicrobiaceae, order Verrucomicrobia class Verrucomicrobiae remained significant. No noticeable heterogeneity or horizontal pleiotropy detected (IVs). However, investigations failed reveal any substantial relationship GM. Differences among healthy controls are explored studies. We verified possible connection certain genetically modified genera DN, thereby emphasizing "gut-kidney" axis new insights GM's role pathogenesis underlying Investigations this association necessary, novel biomarkers development targeted preventive strategies against needed.

Language: Английский

Citations

0
Echinacoside alleviates type 2 diabetes mellitus through inhibiting hepatic gluconeogenesis via gut bacterial-fungal trans-kingdom network reconstruction DOI
Li Fan, Jianjun Liu, Lin Li

et al.

Phytomedicine, Journal Year: 2025, Volume and Issue: unknown, P. 156802 - 156802

Published: April 1, 2025

Language: Английский

Citations

0
Gut microbiome links obesity to type 2 diabetes: insights from Mendelian randomization DOI Creative Commons
Li Fu, Ancha Baranova, Hongbao Cao

et al.

BMC Microbiology, Journal Year: 2025, Volume and Issue: 25(1)

Published: April 27, 2025

Research has established links between the gut microbiome (GM) and both obesity type 2 diabetes (T2D), which is much discussed, but underexplored. This study employed body mass index (BMI) as measurement of to delve deeper into correlations from a genetic perspective. We performed Mendelian randomization (MR) analysis examine causal effects GM on T2D BMI, vice versa. Genome-wide association (GWAS) summary datasets were utilized for analysis, including (N = 933,970), BMI 806,834), two international consortium MiBioGen (211 taxa, N 18,340) Dutch Microbiome Project (DMP) (207 7,738). These mainly cover European populations, with additional cohorts Asia other regions. To further explore potential mediating role in connections T2D, their interaction patterns summarized network. MR identified 9 taxa that showed protective properties against T2D. Seven species within Firmicutes Bacteroidales phyla DMP, (Odds Ratio (OR): 0.94-0.95). Conversely, components contributing abundance 12 associated increased risks (OR: 1.04-1.12). Furthermore, may elevate seven 1.03-1.08) reduce six 0.93-0.97). In influence component composition, affected 52 bacterial 28 decreasing 0.75-0.92) 24 increasing 1.08-1.27). Besides, abundances 25 negatively correlated 0.95-0.99), while positive detected 14 1.01-1.05). Notably, we uncovered 11 genetically formed an interactive Our findings provide evidence GM-mediated The identification relevant offers valuable insights these diseases.

Language: Английский

Citations

0
The characteristics of intestinal microbiota in patients with type 2 diabetes and the correlation with the percentage of T-helper cells DOI Creative Commons
Fan Yang, Jinyan Li,

Longqin Wei

et al.

Frontiers in Microbiology, Journal Year: 2024, Volume and Issue: 15

Published: Sept. 27, 2024

Background Type 2 diabetes (T2D) is related to intestinal microflora changes and immune inflammation. We aimed investigate the pattern of flora-systematic T helper (Th) cell linkage in T2D patients. Methods Participants with diagnosed by physicians healthy controls were enrolled study. The Th1, Th2, Th17 cells from peripheral blood assessed flow cytometry. feces collected. V3–V4 variable region 16S rRNA was sequenced analyzed using bioinformatics. Principal coordinate analysis (PCoA) non-metric multidimensional scaling (NMDS) performed assess beta diversity. linear discriminant (LDA) effect size (LEfSe) method applied identify amicrobial taxon specific T2D. Phylogenetic Investigation Communities Reconstruction Unobserved States (PICRUSt) conducted metabolic pathways. A network constructing a co-occurrence network. Results percentages Th1 higher patients than controls. Among top 30 genera microbiota, levels Lachnospiraceae _NK4A136_group, Ruminococcaceae _UCG002, Eubacterium_hallii _group lower In LEfSe analysis, it observed that families significantly different between Moreover, Th1/Th2 ratio positively correlated abundance Lachnoclostridium Ruminococcus_torques genera. ratio, _UCG-002, _NK4A136_group important nodes. Conclusion This study provided preliminary picture crosstalk microbiome systematic Th findings suggested relationship among metabolites, CD4+T lymphocyte immunity unbalanced T2D, which might have promoted development presents therapeutic opportunity modulate gut reaction then chronic inflammation manipulating microbiome-specific Th-cell response.

Language: Английский

Citations

2
Personalized Prediction of Glycemic Responses to Food in Women with Gestational Diabetes: Gut Microbiota Matters DOI Creative Commons
Polina Popova, Artem Isakov,

Anastasia N. Rusanova

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 15, 2024

Abstract We aimed to develop a prediction model for postprandial glycemic response (PPGR) in pregnant women with gestational diabetes mellitus (GDM) and explore the influence of gut microbial data on accuracy. enrolled 105 (70 GDM 35 healthy). Participants underwent continuous glucose monitoring (CGM) 7 days provided detailed food diaries. Stool samples were collected at 28.8 ± 3.6 weeks, followed by 16S rRNA gene sequence analysis. developed machine learning algorithms predicting PPGR, incorporating CGM measurements, meal content, lifestyle factors, biochemical parameters, anthropometrics, microbiota data. The accuracy models without compared. PPGR created based 2,706 meals measured PPGRs. integration microbiome increased explained variance peak levels (GLUmax) from 34–42% incremental area under curve 120 minutes after start (iAUC120) 50–52%. final performed better than solely carbohydrate count terms correlation between predicted PPGRs (r = 0.72 vs r 0.51 iAUC120 0.66 0.35 GLUmax). After summing SHAP values associated features, emerged as fourth most impactful parameter GLUmax prediction, following composition, context. Microbiome features rank among top 5 parameters GDM.

Language: Английский

Citations

0