To investigate the association between an almond-based low-carbohydrate diet (LCD) and continuous glucose monitoring (CGM) metrics in patients with type 2 diabetes mellitus (T2DM). This was a prospective randomized controlled study. Hospitalized patients with T2DM from Changshu No.1 People’s Hospital were recruited from January 2022 to December 2022. 33 patients using CGM were included and randomly divided into the low-fat diet (LFD) group or the LCD with almonds group based on the random number table. Primary CGM metrics including time in range (TIR), time in tight range (TITR), time above range (TAR), time below range (TBR) were compared between the two groups. Linear regression analysis was performed to assess the association between diet group and nocturnal glycemic metrics. Compared to LFD group, patients in the LCD with almonds group had higher nighttime TIR [98.51% (95.83, 99.88) vs. 91.67% (89.52, 98.07), P < 0.01] and TITR [94.14% (87.57, 98.41) vs. 77.58% (52.18, 90.58), P < 0.05]. Nighttime TAR [0.00% (0.00, 0.00) vs. 1.39% (0.00, 7.95), P < 0.01], HBGI [0.15 (0.01, 0.34) vs. 0.98 (0.08, 3.73), P < 0.05], and SD (15.57 ± 6.21 mg/dL vs. 23.62 ± 9.12 mg/dL, P < 0.05) were significantly lower in the LCD with almonds group. Multivariable regression demonstrated that LCD with almonds was independently associated with improvements in nocturnal TIR (β = 6.65%, P = 0.010) and TITR (β = 14.60%, P = 0.016) after adjustment for age, sex, and diabetes duration. The almond-based low-carbohydrate diet improved nocturnal glycemic control in patients with type 2 diabetes. These improvements in nocturnal TIR and TITR remained significant after multivariable adjustment for age, sex, and diabetes duration. The study was registered at clinicaltrials.gov (registration no. ChiCTR2600116495).

Listeria monocytogenes, Klebsiella pneumoniae, and Bacillus cereus are common foodborne pathogens widely distributed across various food products. However, studies on the contamination risk posed by these three pathogens in prepared meat products remain limited. This study aimed to investigate the prevalence of these pathogens in prepared meat products collected from Guangzhou, China, and to analyze their genetic diversity, virulence gene profiles, and antibiotic resistance. A total of 43 L. monocytogenes, 87 K. pneumoniae, and 21 B. cereus strains were isolated from 55 prepared meat product samples, with L. monocytogenes exhibiting the highest contamination rate. Multilocus sequence typing (MLST) analysis of 151 isolates identified 67 distinct sequence types (STs), including 23 novel STs, indicating high genetic diversity. Multiple virulence-associated genes were detected in isolates from all three genera. Antimicrobial susceptibility testing revealed that four isolates of L. monocytogenes exhibited resistance to all 12 antibiotics tested. K. pneumoniae isolates showed high resistance to ampicillin, and 23.8% of B. cereus isolates displayed resistance to four antibiotic classes. Overall, the high contamination rates, along with the genetic diversity and multidrug resistance observed among the isolates, indicate potential food safety risks, underscoring the need for enhanced microbiological surveillance of prepared meat products.

Type 2 diabetes (T2D) exhibits clinical heterogeneity, yet most existing classification models are derived from European populations and face challenges in clinical application. Here, we evaluate the generalizability of a tree-like graph structure from Scottish data to 32,501 newly diagnosed T2D patients from a multi-center Chinese cohort comprising over 8.6 million individuals. We observe similar distribution between the Scottish and Chinese individuals in heart and kidney outcomes, but diabetic retinopathy varies across ancestries even within similar phenotypes. To capture T2D Chinese-specific heterogeneity, we apply a variational autoencoder (VAE) framework to identify key clinical features and construct a tree structure using the Discriminative Dimensionality Reduction Tree (DDRTree) algorithm. This Chinese tree model is validated in two independent external cohorts and revealed longitudinal phenotypic shifts trending toward higher-risk branches. Our findings emphasize the need for population-specific classification frameworks to advance precision diabetology through individualized risk prediction and specialized treatment guidelines.

Objective: Pregnancy causes major changes in metabolism, but body mass index (BMI) alone may not accurately reflect these changes. We examined whether a metabolome-derived BMI (metBMI) could better identify metabolic differences during pregnancy and improve the prediction of adverse maternal and neonatal outcomes. Methods: We studied 325 pregnant women from a prospective cohort with clinical, metabolomic, and lipidomic data. We compared how well different data types predicted key metabolic traits and then developed a metBMI score. We assessed its associations with metabolic features, pregnancy outcomes, neonatal outcomes, and overall metabolic patterns. Results: Clinical data predicted insulin resistance-related traits well, while glucose tolerance and gestational BMI change were poorly predicted across all data types. metBMI was strongly correlated with measured BMI, but it also identified important metabolic differences beyond body size alone. Women with higher metBMI had less favorable lipid profiles and greater insulin resistance. metBMI also added predictive value beyond BMI for several neonatal outcomes, including neonatal intensive care unit admission, congenital malformations, and infection. For maternal outcomes, metBMI and BMI showed largely similar associations. metBMI was not linked to broad BMI-independent shifts in the Metabolome, but it was associated with specific pathway changes and different metabolite-lipid relationship patterns. Conclusions: metBMI captures clinically relevant metabolic differences beyond conventional BMI during pregnancy and may improve risk assessment for selected adverse neonatal outcomes.

Bacillus cereus, an important pathogen responsible for causing foodborne diseases worldwide, releases pore-forming enterotoxins, which target host epithelial cells, leading to osmotic lysis and ultimately manifesting as diarrheal syndrome. Moreover, some B. cereus strains carry antimicrobial resistance genes that confer multidrug resistance against a spectrum of antibiotics. Characterizing the survival traits of multidrug-resistant (MDR) B. cereus strains in the intestinal microenvironment is essential for developing targeted strategies to effectively manage diarrheal foodborne diseases caused by this pathogen. This study used whole-genome sequencing (WGS) to evaluate the pre- and post-digestion toxigenic potential, antimicrobial resistance profiles, and genetic diversity of MDR B. cereus strains isolated from food samples in Guangdong Province, China. The four B. cereus isolates investigated in this study exhibited a genetic diversity, as determined by multilocus sequence typing analysis of WGS data. All four isolates produced the diarrheal toxins Hbl, Nhe, and CytK to varying levels, indicative of their potential to cause outbreaks of foodborne diseases. Each of the four isolates exhibited resistance to more than three classes of antibiotics, fulfilling the criterion for multidrug resistance. At an initial concentration of 9 log colony-forming units (CFU)/mL, the intestinal concentration of these four isolates crossed the threshold required to induce widespread diarrhea in the general population. Under rice slurry protection, all tested isolates maintained intestinal concentration beyond the threshold when the initial concentration was increased to ≥8 log CFU/mL. Moreover, the upregulations of genes associated with acid tolerance, bile tolerance and stress response were observed in the surviving MDR B. cereus isolates. Digestion markedly altered the antibiotic resistance profiles of the MDR B. cereus isolates. In the absence of a food matrix, the MDR isolates lost their resistance to imipenem, meropenem, amoxicillin-clavulanic acid, and trimethoprim-sulfamethoxazole post-digestion and was influenced by the initial concentration of the strains. In the presence of food matrix rice slurry, the effects of digestion on the antibiotic resistance of MDR B. cereus isolates can be mitigated, enabling them to maintain their antibiotic resistance to the greatest extent. Most remarkably, after digestion, the isolates Bce055 and Bce166 exhibited newly emergent resistance to cefotetan and trimethoprim-sulfamethoxazole, respectively. Our findings clarify the fate of MDR B. cereus isolates in the gastrointestinal tract and inform the development of prevention and control strategies for foodborne diseases caused by this pathogen.

Background and aimsMetabolic dysfunction-associated steatotic liver disease (MASLD) exhibits substantial heterogeneity in progression and cardiovascular outcomes. We aimed to identify reproducible MASLD subtypes in Chinese cohorts, characterize their distinct lipidomic profiles, and evaluate their associations with long-term cardiovascular outcomes.MethodsWe investigated the heterogeneity of MASLD using k-means clustering based on six simple clinical variables in a cohort of 5,329 individuals. The identified clusters were applied in an independent cohort of 1,432 participants. Cardiovascular outcomes were compared across clusters using Kaplan-Meier curves and log-rank tests. Untargeted lipidomic profiling was performed with partial least squares discriminant analysis and least absolute shrinkage and selection operator regression to identify discriminative lipids for XGBoost model. Feature importance was evaluated using SHapley Additive exPlanations.ResultsThree distinct MASLD subtypes were identified. Cluster A (Metabolic-Obesity subtype), characterized by the highest BMI and triglycerides with the lowest risk of cardiovascular outcomes. Cluster B (Dysglycemic subtype) distinguished by increased glycated hemoglobin. Cluster C (Aging-Hypertensive subtype) was primarily associated with age, blood pressure, and fibrosis markers, leading to higher risk of cardiovascular outcomes. Lipidomics identified 1061 metabolites, and ten lipids distinguishing the Cluster A and C were selected for modeling, yielding an area under the receiver operating characteristic curve of 0.900. Pathway enrichment analysis further revealed the Cluster C involved in sphingolipid metabolism.ConclusionsThree distinct MASLD subtypes with varying metabolic features and cardiovascular risks were identified in Chinese cohorts. The Aging-Hypertensive subtype showed the highest cardiovascular risk and was associated with dysregulated sphingolipid metabolism. These findings underscore the clinical heterogeneity of MASLD and highlight the need for risk classification and personalized intervention strategies.

Vibrio parahaemolyticus is a prevalent foodborne pathogen in aquatic products, for which rapid detection is crucial to ensure food safety. While magnetic separation enables efficient target enrichment, its detection phase remains dependent on exogenous labels, leading to cumbersome procedures and susceptibility to matrix interference. To address these limitations, this study developed an exogenous label-free thermomagnetic responsive aptasensing strategy termed ELTRAS, which leverages a charge-regulated thermomagnetic dual-responsive (FSPMS@BSCR-Apt) nanoprobe for efficient capture and rapid visual detection of V. parahaemolyticus. The FSPMS@BSCR-Apt probe was constructed via a layer-by-layer assembly strategy, featuring a superparamagnetic Fe3O4 core sequentially coated with a SiO2 interlayer and a thermo-responsive PNIPAm-co-MAA shell, followed by directional modification of a high negative charge-density aptamer via the streptavidin-biotin system, thereby integrating magnetic separation, temperature-regulated aggregation, and specific recognition. In the absence of the target bacteria, the probe remains dispersed due to strong electrostatic repulsion; upon bacterial presence, target binding induces charge shielding and thermally triggered aggregation, causing a visible solution transition from turbid to clear, which allows quantitative detection by monitoring the absorbance change at 420 nm. The assay could be completed within 22 min after sample introduction. This strategy exhibited a wide linear range from 2.2 & times; 101 to 2.2 & times; 108 CFU/mL (R2 = 0.99278), with a detection limit as low as 25.63 CFU/mL, and demonstrated high specificity against non-target bacteria along with excellent repeatability. Spike-and-recovery experiments in complex food matrices such as shrimp, crab, and seawater confirmed its strong anti-interference capability and accuracy. This exogenous label-free, rapid, and user-friendly aptasensing platform provides a promising tool for on-site food monitoring with potential extensibility to other foodborne pathogens.

The mechanisms underlying metabolic remodeling in metabolic dysfunction-associated steatotic liver disease (MASLD) remain unclear. Targeting the process of de novo lipogenesis (DNL) in the liver has the potential to mitigate MASLD. Here we show that interferon-related developmental regulator 1 (IFRD1) expression negatively correlates with MASLD/metabolic-associated steatohepatitis (MASH) progression in human liver tissues. In multiple mouse models, Ifrd1-/- mice exhibit an exacerbated MASLD phenotype, while hepatocyte-specific IFRD1 expression suppresses MASH progression. Mechanistically, IFRD1 promotes GLUD1's mitochondrial localization via direct interaction, stabilizing the enzyme's activity to enhance α-ketoglutarate (α-KG) production. α-KG reduces H3K36me3 level at lipogenic genes, thereby inhibiting DNL and ameliorating MASH. α-KG supplementation reverses MASH exacerbation in Ifrd1-CKO mice. Collectively, our research establishes the IFRD1-GLUD1-α-KG axis as a critical metabolic-epigenetic regulatory hub, providing novel targets for inhibiting hepatic DNL and developing therapeutic agents for MASLD/MASH.

Cereulide, a heat-stable toxin produced by Bacillus cereus, is recognized as a major foodborne risk. However, the metabolic features associated with cereulide production and their biological relevance remain insufficiently characterized. This study aimed to prioritize candidate metabolites associated with cereulide-related phenotypes and to explore their potential pathway-level relationships. An integrated analytical pipeline combining RF-CNN-assisted feature prioritization, pathway enrichment, time-series profiling, and temporal association analysis was developed to support both classification performance and biological interpretability. Using fivefold cross-validation, nine candidate metabolites were prioritized. Based on pathway enrichment and temporal analyses across three growth stages, histidinol, L-leucine, and biotin were highlighted as candidate metabolites associated with cereulide-related phenotypes. A putative working network describing their potential relationships was then proposed. In exogenous supplementation experiments, L-leucine and biotin were associated with reduced cereulide production, whereas histidine showed the opposite trend, providing phenotypic support for the biological relevance of the related pathways under the tested conditions. Overall, this study establishes a metabolomics-guided strategy for prioritizing candidate metabolites and provides biologically interpretable and testable hypotheses for exploring the potential metabolic basis of B. cereus-associated phenotypes.

Background:Metabolomic profiling via machine learning can reveal signatures of host metabolism and identify useful biomarkers. We aimed to investigate metabolomic profiles and biomarkers in adult patients with type 1 diabetes (T1D) via machine learning. Methods:We recruited 29 adult patients with T1D and matched them with 29 healthy controls on the basis of age, sex, and body mass index (BMI). We collected serum samples from both groups and performed nontargeted metabolomics with liquid chromatography‒mass spectrometry (LC‒MS). Four machine learning algorithms (logistic regression, support vector machine, Gaussian naive Bayes, and random forest) were used to screen potential T1D-related biomarkers. Results:We identified 328 differently abundant metabolites between the T1D group and the control group that were significantly enriched in three metabolic pathways (purine metabolism, ketone body synthesis and degradation, and methyl butyrate metabolism), with P values less than 0.05. Ten metabolites were identified as T1D-related indicators, including L-fucopyranose, hept-2-ulose, L-rhamnose, docosahexaenoic acid, pumiliotoxin 251d, 9,12-octadecadienal, oleamide, estrane, (e,e)-2,4-heptadienal, and hexadecanamide. The predictive value of the ten candidate metabolites, as measured by the area under the curve (AUC), ranged from 0.86 to 0.95. Conclusion:In this study, we identified purine metabolism, synthesis and degradation of ketone bodies, and impaired methyl butyrate metabolism as metabolic pathways that are altered in adult patients with T1D. Our findings present an extensive profile of metabolic changes in adult patients with T1D, and the identified biomarkers may have important clinical significance in the diagnosis of T1D and the monitoring of responses to therapeutic interventions.

Despite increasing evidence supporting the promising anti-diabetic potential of natural polysaccharides, studies on structurally well-defined polysaccharides and their systemic mechanisms of action in type 2 diabetes mellitus (T2DM) remain limited. Here, we characterize GFP-Z, a bioactive α-glucan (1760.0 kDa) isolated from Grifola frondosa, which features an α-1,4-linked backbone with α-1,6, α-1,3 and α-1,2 branches. Pharmacological evaluation in db/db mice showed that GFP-Z administration significantly alleviated hyperglycemia and diabetic symptoms, with glucose-lowering effects comparable to metformin under the tested conditions, without causing apparent hepatorenal toxicity. Integrated multi-omics analyses, biochemical assays, and a preliminary pharmacological attenuation experiment further suggest that GFP-Z may exert its effects, at least in part, through immunomodulation-associated metabolic regulation, as reflected by reduced hepatic M1-type macrophage infiltration, altered circulating cytokines and hepatic immunometabolites, activation of the hepatic JAK/STAT-PI3K/AKT signaling axis, and attenuation of GFP-Z-associated immune and glucose-lowering responses by tofacitinib. Taken together, our findings suggest that GFP-Z may represent a promising bioactive polysaccharide capable of improving metabolic disorders in T2DM, potentially through systemic immunomodulation-associated metabolic regulation.

Introduction and Objective: Type 1 diabetes (T1D) exhibits substantial heterogeneity, yet the key clinical phenotypes remain unclear. We applied DDRTree machine learning to identify the key phenotype characterizing T1D heterogeneity and validate the existence of age-related endotypes. Methods: We performed DDRTree dimensionality reduction analysis using data on 13 phenotypes (age at onset, BMI, blood pressure, ALT, HbA1c, creatinine, triglycerides, HDL-C, total cholesterol, GADA, IA-2A, ZnT8A) from 879 patients with T1D to identify the most important clinical phenotype. We then evaluated the rationality of classifying patients into age-related endotypes (<7, 7-13, ≥13 years) and compared clinical, immunological, and genetic profiles across groups to confirm the presence of age-related endotypes. Finally, we validated the existence of these endotypes in an external UK Biobank cohort and investigated longitudinal cardiovascular outcomes associated with age-at-onset phenotypes. Results: Age at onset emerged as the most important phenotype for T1D heterogeneity, explaining 64.6% of the variance in the DDRTree dimensionality reduction. Classification into age-related endotypes showed significant differences in DDRTree dimensional coordinates (p = 1.61×10-54), supporting the rationality of this grouping. Autoantibody positivity (IA-2A, ZnT8A: p < 0.001), HLA genetic risk (p < 0.001), and metabolic characteristics differed significantly, further confirming age-related endotypes. Moreover, phenotypic heterogeneity by onset age attenuated with disease duration in both the internal and UK Biobank cohorts. Regarding long-term outcomes, older-onset T1D patients exhibited higher cardiovascular risk (sHR = 2.93, 95% CI: 1.49-5.78 for ≥13 vs. <7 years). Conclusion: DDRTree analysis identified age at onset as the key phenotype of T1D heterogeneity. Dimensionality reduction demonstrated excellent discriminative performance, supporting the existence of age-related endotypes. Disclosure S. Chang: None. H. Tan: None. T. Yue: None. Y. Ding: None. Z. Gu: None. Y. Shi: None. L. Pan: None. C. Guo: None. J. Weng: None. X. Zheng: None. Funding Foundation programs supporting included Noncommunicable Chronic Diseases-National Science and Technology Major Project (2023ZD0509100; 2023ZD0509102), the National Natural Science Foundation (8247087), and Program for Innovative Research Team of The First Affiliated Hospital of University of Science and Technology of China (CXGG02)

Virulence of foodborne pathogens, the ability to cause disease upon ingestion of contaminated food, depends on toxin production, host colonization, and gastrointestinal stress survival. Branched-chain amino acids (BCAAs), including leucine, isoleucine, and valine, function as key metabolic signals that coordinate virulence regulation. Fluctuations in BCAA availability reshape intracellular metabolic states and regulatory networks, influencing pathogenic behavior. This review summarizes current knowledge on how foodborne pathogens sense, acquire, and utilize BCAAs, covering transport systems, biosynthetic pathways and RNA-based mechanisms (T-box riboswitches and sRNAs) that regulate BCAAs utilization, alongside CodY-dependent circuits. We emphasize how these processes collectively modulate toxin production, stress adaptation, and biofilm formation. By integrating advances in BCAA-mediated metabolic signaling, this review highlights how metabolic cues shape virulence and explores targeting BCAA pathways for foodborne pathogen control.

This study investigated species composition, contamination sources, pathogenic potential, and antibiotic resistance of 175 Bacillus cereus group strains isolated from sufu samples, raw and auxiliary materials, production line, and processing environments using whole-genome sequencing and phenotypic analysis. Six species were identified, primarily B. cereus, Bacillus pacificus, and Bacillus paranthracis. Multilocus sequence typing identified 66 sequence types (STs), including 19 novel STs. Traceability analysis demonstrated that the production environment is a likely reservoir for B. pacificus ST90 contamination, while ST2567 in the production environment may cause B. paranthracis contamination. All strains harbored diarrheal virulence genes, whereas no emetic toxin genes were found. Moreover, B. pacificus and B. paranthracis almost lack hblCDA compared to B. cereus. Twelve antibiotic resistance genes were identified, mainly fosB, followed by BcII. Notably, 77.71% contained 3 or more distinct resistance gene classes. Thirty-seven isolates exhibited phenotypic resistance, primarily to tetracycline and chloramphenicol (CHL), including 3 multidrug-resistant isolates and 1 CHL-resistant B. pacificus from sufu. Genome-wide association studies and functional protein annotation indicated associations between resistance phenotypes and genotypes. This study provides the first systematic genomic analysis of the B. cereus group throughout sufu production, supporting risk assessment and targeted control of fermented soybean products.

Due to the rising global demand for natural food additives and their critical role in public health security, lactic acid bacteria (LAB) have garnered significant attention as natural biocontrol agents. This study utilized the Oxford cup method to screen LAB strains based on inhibition zone diameters against Salmonella, followed by comprehensive characterization of growth kinetics and probiotic potential. The results demonstrated that the selected strains exhibited gamma-hemolytic activity (non-pathogenic phenotype), susceptibility to seven clinically relevant antibiotics, robust environmental resilience (>85 % survival under simulated gastrointestinal stress), and potent antimicrobial efficacy. Notably, Pediococcus acidilactici OE-9 cell-free supernatant (CFS) showed exceptional performance, eliminating Salmonella within 6 h at 28 degrees C, with antibacterial effects persisting for 7 days, while also inhibiting biofilm formation on common food-contact surfaces in high-level contamination scenarios (10(6) CFU/mL Salmonella in eggshells), treatment with OE-9 CFS resulted in a 3-log reduction in pathogen viability. Based on these findings, the P. acidilactici OE-9 strain emerges as a novel LAB strain with remarkable antibacterial and bacteriostatic properties, holding significant potential for application in food preservation.

Large-for-gestational-age (LGA) births occur in many pregnancies with an apparently metabolically healthy phenotype, limiting risk identification based on conventional clinical characteristics alone. We explored the association between second-trimester maternal serum lipidomic profiles and LGA risk in this apparently healthy population using a nested case-control design within an ongoing prospective pregnancy cohort. The study included a derivation cohort of 135 participants and an independent temporal validation cohort of 66 participants. Lipidomic profiles were analyzed by using liquid chromatography and high-resolution mass spectrometry. Multivariate modeling identified 11 circulating lipid biomarkers (seven glycerophospholipids, two glycerolipids, and two sphingolipids) associated with LGA risk. Integrating these lipid biomarkers with routine clinical factors substantially improved predictive performance compared with clinical variables alone. These findings suggest that metabolic alterations are detectable before clinical manifestations become apparent and support serum lipidomic profiling as a complementary approach for early risk stratification in pregnancies traditionally considered low risk.