98%
921
2 minutes
20
Background: Epidemiologic evidence has linked refined grain intake to a higher risk of gestational diabetes (GDM), but the biological underpinnings remain unclear.
Objectives: We aimed to identify and validate refined grain-related metabolomic biomarkers for GDM risk.
Methods: In a metabolome-wide association study of 91 cases with GDM and 180 matched controls without GDM (discovery set) nested in the prospective Pregnancy Environment and Lifestyle Study (PETALS), refined grain intake during preconception and early pregnancy and serum untargeted metabolomics were assessed at gestational weeks 10-13. We identified refined grain-related metabolites using multivariable linear regression and examined their prospective associations with GDM risk using conditional logistic regression. We further examined the predictivity of refined grain-related metabolites selected by least absolute shrinkage and selection operator regression in the discovery set and validation set (a random PETALS subsample of 38 individuals with and 336 without GDM).
Results: Among 821 annotated serum (87.4% fasting) metabolites, 42 were associated with refined grain intake, of which 17 (70.6% in glycerolipids, glycerophospholipids, and sphingolipids clusters) were associated with subsequent GDM risk (all false discovery rate-adjusted P values <0.05). Adding 7 of 17 metabolites to a conventional risk factor-based prediction model increased the C-statistic for GDM risk in the discovery set from 0.71 (95% CI: 0.64, 0.77) to 0.77 (95% CI: 0.71, 0.83) and in the validation set from 0.77 (95% CI: 0.69, 0.86) to 0.81 (95% CI: 0.74, 0.89), both with P-for-difference <0.05.
Conclusions: Clusters of glycerolipids, glycerophospholipids, and sphingolipids may be implicated in the association between refined grain intake and GDM risk, as demonstrated by the significant associations of these metabolites with both refined grains and GDM risk and the incremental predictive value of these metabolites for GDM risk beyond the conventional risk factors. These findings provide evidence on the potential biological underpinnings linking refined grain intake to the risk of GDM and help identify novel disease-related dietary biomarkers to inform diet-related preventive strategies for GDM.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10273195 | PMC |
http://dx.doi.org/10.1016/j.ajcnut.2023.02.009 | DOI Listing |
J Med Imaging (Bellingham)
September 2025
Vanderbilt University, Data Science Institute, Nashville, Tennessee, United States.
Purpose: Recent developments in computational pathology have been driven by advances in vision foundation models (VFMs), particularly the Segment Anything Model (SAM). This model facilitates nuclei segmentation through two primary methods: prompt-based zero-shot segmentation and the use of cell-specific SAM models for direct segmentation. These approaches enable effective segmentation across a range of nuclei and cells.
View Article and Find Full Text PDFFront Artif Intell
August 2025
Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada.
Precision livestock farming increasingly relies on non-invasive, high-fidelity systems capable of monitoring cattle with minimal disruption to behavior or welfare. Conventional identification methods, such as ear tags and wearable sensors, often compromise animal comfort and produce inconsistent data under real-world farm conditions. This study introduces Dairy DigiD, a deep learning-based biometric classification framework that categorizes dairy cattle into four physiologically defineda groups-young, mature milking, pregnant, and dry cows-using high-resolution facial images.
View Article and Find Full Text PDFPLoS Comput Biol
September 2025
Department of Psychology and Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States of America.
Real-world decision-making often involves navigating large action spaces with state-dependent action values, taxing the limited cognitive resources at our disposal. While previous studies have explored cognitive constraints on generating action consideration sets or refining state-action mappings (policy complexity), their interplay remains underexplored. In this work, we present a resource-rational framework for policy compression that integrates both constraints, offering a unified perspective on decision-making under cognitive limitations.
View Article and Find Full Text PDFCrit Rev Food Sci Nutr
September 2025
Department of Food Science, Stocking Hall, Cornell University, Ithaca, NY, USA.
Sorghum () is an ancient grain and the fifth most produced cereal worldwide, and the most consumed cereal in the semi-arid regions of Africa and Asia, being a key grain for the diet of about 500 million people. It is rich in phenolic compounds (like flavonoids, 3-deoxyanthocyanidins, phenolic acids), resistant starch, and dietary fiber, which may beneficially influence intestinal health. This systematic review analyzed 22 studies to assess the effects of sorghum processing on bioactive compounds and their effects on intestinal health.
View Article and Find Full Text PDFIET Syst Biol
September 2025
School of Computer and Information Techonology, Xinyang Normal University, Xinyang, China.
Accurate polyp segmentation is crucial for computer-aided diagnosis and early detection of colorectal cancer. Whereas feature pyramid network (FPN) and its variants are widely used in polyp segmentation, inherent limitations existing in FPN include: (1) repeated upsampling degrades fine details, reducing small polyp segmentation accuracy and (2) naive feature fusion (e.g.
View Article and Find Full Text PDF