Effects of plateau hypoxia on population pharmacokinetics and pharmacodynamics of metformin in patients with Type 2 diabetes. / 高原低氧对2åž‹ç³–å°¿ç— æ‚£è€ äºŒç”²åŒèƒç¾¤ä½“è¯åŠ¨å­¦å’Œè¯æ•ˆå­¦çš„å½±å“ï¼ˆè‹±æ–‡ï¼‰.

OBJECTIVES: Metformin is the basic drug for treating diabetes, and the plateau hypoxic environment is an important factor affecting the pharmacokinetics of metformin, but there have been no reports of metformin pharmacokinetic parameters in patients with diabetes mellitus type 2 (T2DM) in the high-altitude hypoxic environment. This study aims to investigate the effect of the hypoxic environment on the pharmacokinetics and assess the efficacy and safety of metformin administration in patients with Type 2 diabetes mellitus (T2DM). METHODS: A total of 85 patients with T2DM taking metformin tablets in the plateau group (n=32, altitude: 1 500 m) and control group (n=53, altitude: 3 800 m) were enrolled according to the inclusion and exclusion criteria, and 172 blood samples were collected in the plateau group and the control Group. A ultra-performance liquid chromatography/tandem mass spectrometry (UFLC-MS/MS) method was established to determine the blood concentration of metformin, and Phoenix NLME software was used to establish a model of pharmacokinetics of metformin in the Chinese T2DM population. The efficacy and serious adverse effects of metformin were compared between the 2 groups. RESULTS: The population pharmacokinetic modeling results showed that plateau hypoxia and age were the main covariates for model building, and the pharmacokinetic parameters were significantly different between the plateau and control groups (all P<0.05), including distribution volume (V), clearance (CL), elimination rate constant (Ke), half-life(T1/2), area under the curve (AUC), time to reach maximum concentration (Tmax). Compared with the control group, AUC was increased by 23.5%, Tmax and T1/2 were prolonged by 35.8% and 11.7%, respectively, and CL was decreased by 31.9% in the plateau group. The pharmacodynamic results showed that the hypoglycaemic effect of T2DM patients in the plateau group was similar to that in the control group, the concentration of lactic acid was higher in the plateau group than that in the control group, and the risk of lactic acidosis was increased after taking metformin in the plateau population. CONCLUSIONS: Metformin metabolism is slowed down in T2DM patients in the hypoxic environment of the plateau; the glucose-lowering effect of the plateau is similar, and the attainment rate is low, the possibility of having serious adverse effects of lactic acidosis is higher in T2DM patients on the plateau than on the control one. It is probably suggested that patients with T2DM on the plateau can achieve glucose lowering effect by extending the interval between medication doses and enhancing medication education to improve patient compliance.

Cohort Network: A Knowledge Graph toward Data Dissemination and Knowledge-Driven Discovery for Cohort Studies.

Contemporary environmental health sciences draw on large-scale longitudinal studies to understand the impact of environmental exposures and behavior factors on the risk of disease and identify potential underlying mechanisms. In such studies, cohorts of individuals are assembled and followed up over time. Each cohort generates hundreds of publications, which are typically neither coherently organized nor summarized, hence limiting knowledge-driven dissemination. Hence, we propose a Cohort Network, a multilayer knowledge graph approach to extract exposures, outcomes, and their connections. We applied the Cohort Network on 121 peer-reviewed papers published over the past 10 years from the Veterans Affairs (VA) Normative Aging Study (NAS). The Cohort Network visualized connections between exposures and outcomes across different publications and identified key exposures and outcomes, such as air pollution, DNA methylation, and lung function. We demonstrated the utility of the Cohort Network for new hypothesis generation, e.g., identification of potential mediators of exposure-outcome associations. The Cohort Network can be used by investigators to summarize the cohort's research and facilitate knowledge-driven discovery and dissemination.

Bacterial community assembly and antibiotic resistance genes in soils exposed to antibiotics at environmentally relevant concentrations.

Understanding how bacterial community assembly and antibiotic resistance genes (ARGs) respond to antibiotic exposure is essential to deciphering the ecological risk of anthropogenic antibiotic pollution in soils. In this study, three loam soils with different land management (unmanured golf course, dairy-manured pasture, and swine-manured cornfield) were spiked with a mixture of 11 antibiotics at the initial concentration of 100 and 1000 µg kg-1 for each antibiotic and incubated over 132 days, mimicking a scenario of pulse disturbance and recovery in soils, with unspiked soil samples as the control treatment. The Infer Community Assembly Mechanisms by Phylogenetic-bin-based null model (iCAMP) analysis demonstrated that drift and dispersal limitation contributed to 57%-65% and 16%-25%, and homogeneous selection 12%-16% of soil bacterial community assembly. Interestingly, antibiotic exposure to 1000 µg kg-1 level significantly increased the contribution of drift to community assembly, largely due to the positive response from Acidobacteria-6 in the golf course and pasture soils and from Chthoniobacteraceae in the cornfield soil to the antibiotic exposure. However, ARG abundance and diversity in the three soils exhibited antibiotics-independent temporal fluctuations, but were associated with the changes in soil bacterial communities over time. This study provides the first insight into the relative contributions of different bacterial community assembly processes in soils upon antibiotic exposure at environmentally relevant concentrations.

Air pollution and decreased bone mineral density among Women's Health Initiative participants.

Background: Osteoporosis heavily affects postmenopausal women and is influenced by environmental exposures. Determining the impact of criteria air pollutants and their mixtures on bone mineral density (BMD) in postmenopausal women is an urgent priority. Methods: We conducted a prospective observational study using data from the ethnically diverse Women's Health Initiative Study (WHI) (enrollment, September 1994-December 1998; data analysis, January 2020 to August 2022). We used log-normal, ordinary kriging to estimate daily mean concentrations of PM10, NO, NO2, and SO2 at participants' geocoded addresses (1-, 3-, and 5-year averages before BMD assessments). We measured whole-body, total hip, femoral neck, and lumbar spine BMD at enrollment and follow-up (Y1, Y3, Y6) via dual-energy X-ray absorptiometry. We estimated associations using multivariable linear and linear mixed-effects models and mixture effects using Bayesian kernel machine regression (BKMR) models. Findings: In cross-sectional and longitudinal analyses, mean PM10, NO, NO2, and SO2 averaged over 1, 3, and 5 years before the visit were negatively associated with whole-body, total hip, femoral neck, and lumbar spine BMD. For example, lumbar spine BMD decreased 0.026 (95% CI: 0.016, 0.036) g/cm2/year per a 10% increase in 3-year mean NO2 concentration. BKMR suggested that nitrogen oxides exposure was inversely associated with whole-body and lumbar spine BMD. Interpretation: In this cohort study, higher levels of air pollutants were associated with bone damage, particularly on lumbar spine, among postmenopausal women. These findings highlight nitrogen oxides exposure as a leading contributor to bone loss in postmenopausal women, expanding previous findings of air pollution-related bone damage. Funding: US National Institutes of Health.

Assessing the role of the gut microbiome at the interface between environmental chemical exposures and human health: Current knowledge and challenges.

The explosion of microbiome research over the past decade has shed light on the various ways that external factors interact with the human microbiome to drive health and disease. Each individual is exposed to more than 300 environmental chemicals every day. Accumulating evidence indicates that the microbiome is involved in the early response to environmental toxicants and biologically mediates their adverse effects on human health. However, few review articles to date provided a comprehensive framework for research and translation of the role of the gut microbiome in environmental health science. This review summarizes current evidence on environmental compounds and their effect on the gut microbiome, discusses the involved compound metabolic pathways, and covers environmental pollution-induced gut microbiota disorders and their long-term outcomes on host health. We conclude that the gut microbiota may crucially mediate and modify the disease-causing effects of environmental chemicals. Consequently, gut microbiota needs to be further studied to assess the complete toxicity of environmental exposures. Future research in this field is required to delineate the key interactions between intestinal microbiota and environmental pollutants and further to elucidate the long-term human health effects.

In Utero Exposure to Caffeine and Acetaminophen, the Gut Microbiome, and Neurodevelopmental Outcomes: A Prospective Birth Cohort Study.

Pregnant individuals are exposed to acetaminophen and caffeine, but it is unknown how these exposures interact with the developing gut microbiome. We aimed to determine whether acetaminophen and/or caffeine relate to the childhood gut microbiome and whether features of the gut microbiome alter the relationship between acetaminophen/caffeine and neurodevelopment. Forty-nine and 85 participants provided meconium and stool samples at 6-7, respectively, for exposure and microbiome assessment. Fecal acetaminophen and caffeine concentrations were quantified, and fecal DNA underwent metagenomic sequencing. Caregivers and study staff assessed the participants' motor and cognitive development using standardized scales. Prenatal exposures had stronger associations with the childhood microbiome than concurrent exposures. Prenatal acetaminophen exposure was associated with a trend of lower gut bacterial diversity in childhood [ß = -0.17 Shannon Index, 95% CI: (-0.31, -0.04)] and was marginally associated with differences in the relative abundances of features of the gut microbiome at the phylum (Firmicutes, Actinobacteria) and gene pathway levels. Among the participants with a higher relative abundance of Proteobacteria, prenatal exposure to acetaminophen and caffeine was associated with lower scores on WISC-IV subscales. Acetaminophen during bacterial colonization of the naïve gut is associated with lasting alterations in childhood microbiome composition. Future studies may inform our understanding of downstream health effects.

Predicting pesticide dissipation half-life intervals in plants with machine learning models.

Pesticide dissipation half-life in plants is an important factor to assessing environmental fate of pesticides and establishing pre-harvest intervals critical to good agriculture practices. However, empirically measured pesticide dissipation half-lives are highly variable and the accurate prediction with models is challenging. This study utilized a dataset of pesticide dissipation half-lives containing 1363 datapoints, 311 pesticides, 10 plant types, and 4 plant component classes. Novel dissipation half-life intervals were proposed and predicted to account for high variations in empirical data. Four machine learning models (i.e., gradient boosting regression tree [GBRT], random forest [RF], supporting vector classifier [SVC], and logistic regression [LR]) were developed to predict dissipation half-life intervals using extended connectivity fingerprints (ECFP), temperature, plant type, and plant component class as model inputs. GBRT-ECFP had the best model performance with F1-microbinary score of 0.698 ± 0.010 for the binary classification compared with other machine learning models (e.g., LR-ECFP, F1-microbinary= 0.662 ± 0.009). Feature importance analysis of molecular structures in the binary classification identified aromatic rings, carbonyl group, organophosphate, =C-H, and N-containing heterocyclic groups as important substructures related to pesticide dissipation half-lives. This study suggests the utility of machine learning models in assessing the environmental fate of pesticides in agricultural crops.

Predicting chemical ecotoxicity by learning latent space chemical representations.

In silico prediction of chemical ecotoxicity (HC50) represents an important complement to improve in vivo and in vitro toxicological assessment of manufactured chemicals. Recent application of machine learning models to predict chemical HC50 yields variable prediction performance that depends on effectively learning chemical representations from high-dimension data. To improve HC50 prediction performance, we developed an autoencoder model by learning latent space chemical embeddings. This novel approach achieved state-of-the-art prediction performance of HC50 with R2 of 0.668 ± 0.003 and mean absolute error (MAE) of 0.572 ± 0.001, and outperformed other dimension reduction methods including principal component analysis (PCA) (R2 = 0.601 ± 0.031 and MAE = 0.629 ± 0.005), kernel PCA (R2 = 0.631 ± 0.008 and MAE = 0.625 ± 0.006), and uniform manifold approximation and projection dimensionality reduction (R2 = 0.400 ± 0.008 and MAE = 0.801 ± 0.002). A simple linear layer with chemical embeddings learned from the autoencoder model performed better than random forest (R2 = 0.663 ± 0.007 and MAE = 0.591 ± 0.008), fully connected neural network (R2 = 0.614 ± 0.016 and MAE = 0.610 ± 0.008), least absolute shrinkage and selection operator (R2 = 0.617 ± 0.037 and MAE = 0.619 ± 0.007), and ridge regression (R2 = 0.638 ± 0.007 and MAE = 0.613 ± 0.005) using unlearned raw input features. Our results highlighted the usefulness of learning latent chemical representations, and our autoencoder model provides an alternative approach for robust HC50 prediction.

Associations of Childhood and Perinatal Blood Metals with Children's Gut Microbiomes in a Canadian Gestation Cohort.

BACKGROUND: The gut microbiome is important in modulating health in childhood. Metal exposures affect multiple health outcomes, but their ability to modify bacterial communities in children is poorly understood. OBJECTIVES: We assessed the associations of childhood and perinatal blood metal levels with childhood gut microbiome diversity, structure, species, gene family-inferred species, and potential pathway alterations. METHODS: We assessed the gut microbiome using 16S rRNA gene amplicon sequencing and shotgun metagenomic sequencing in stools collected from 6- to 7-year-old children participating in the GESTation and Environment (GESTE) cohort study. We assessed blood metal concentrations [cadmium (Cd), manganese (Mn), mercury (Hg), lead (Pb), selenium (Se)] at two time points, namely, perinatal exposures at delivery (N=70) and childhood exposures at the 6- to 7-y follow-up (N=68). We used multiple covariate-adjusted statistical models to determine microbiome associations with continuous blood metal levels, including linear regression (Shannon and Pielou alpha diversity indexes), permutational multivariate analysis of variance (adonis; beta diversity distance matrices), and multivariable association model (MaAsLin2; phylum, family, species, gene family-inferred species, and pathways). RESULTS: Children's blood Mn and Se significantly associated with microbiome phylum [e.g., Verrucomicrobiota (coef=-0.305, q=0.031; coef=0.262, q=0.084, respectively)] and children's blood Mn significantly associated with family [e.g., Eggerthellaceae (coef=-0.228, q=0.052)]-level differences. Higher relative abundance of potential pathogens (e.g., Flavonifractor plautii), beneficial species (e.g., Bifidobacterium longum, Faecalibacterium prausnitzii), and both potentially pathogenic and beneficial species (e.g., Bacteriodes vulgatus, Eubacterium rectale) inferred from gene families were associated with higher childhood or perinatal blood Cd, Hg, and Pb (q<0.1). We found significant negative associations between childhood blood Pb and acetylene degradation pathway abundance (q<0.1). Finally, neither perinatal nor childhood metal concentrations were associated with children's gut microbial inter- and intrasubject diversity. DISCUSSION: Our findings suggest both long- and short-term associations between metal exposure and the childhood gut microbiome, with stronger associations observed with more recent exposure. Future epidemiologic analyses may elucidate whether the observed changes in the microbiome relate to children's health. https://doi.org/10.1289/EHP9674.

Predicting crop root concentration factors of organic contaminants with machine learning models.

Accurate prediction of uptake and accumulation of organic contaminants by crops from soils is essential to assessing human exposure via the food chain. However, traditional empirical or mechanistic models frequently show variable performance due to complex interactions among contaminants, soils, and plants. Thus, in this study different machine learning algorithms were compared and applied to predict root concentration factors (RCFs) based on a dataset comprising 57 chemicals and 11 crops, followed by comparison with a traditional linear regression model as the benchmark. The RCF patterns and predictions were investigated by unsupervised t-distributed stochastic neighbor embedding and four supervised machine learning models including Random Forest, Gradient Boosting Regression Tree, Fully Connected Neural Network, and Supporting Vector Regression based on 15 property descriptors. The Fully Connected Neural Network demonstrated superior prediction performance for RCFs (R2 =0.79, mean absolute error [MAE] = 0.22) over other machine learning models (R2 =0.68-0.76, MAE = 0.23-0.26). All four machine learning models performed better than the traditional linear regression model (R2 =0.62, MAE = 0.29). Four key property descriptors were identified in predicting RCFs. Specifically, increasing root lipid content and decreasing soil organic matter content increased RCFs, while increasing excess molar refractivity and molecular volume of contaminants decreased RCFs. These results show that machine learning models can improve prediction accuracy by learning nonlinear relationships between RCFs and properties of contaminants, soils, and plants.

Direct Prediction of Bioaccumulation of Organic Contaminants in Plant Roots from Soils with Machine Learning Models Based on Molecular Structures.

Root concentration factor (RCF) is an important characterization parameter to describe accumulation of organic contaminants in plants from soils in life cycle impact assessment (LCIA) and phytoremediation potential assessment. However, building robust predictive models remains challenging due to the complex interactions among chemical-soil-plant root systems. Here we developed end-to-end machine learning models to devolve the complex molecular structure relationship with RCF by training on a unified RCF data set with 341 data points covering 72 chemicals. We demonstrate the efficacy of the proposed gradient boosting regression tree (GBRT) model based on the extended connectivity fingerprints (ECFP) by predicting RCF values and achieved prediction performance with R-squared of 0.77 and mean absolute error (MAE) of 0.22 using 5-fold cross validation. In addition, our results reveal nonlinear relationships among properties of chemical, soil, and plant. Further in-depth analyses identify the key chemical topological substructures (e.g., -O, -Cl, aromatic rings and large conjugated π systems) related to RCF. Stemming from its simplicity and universality, the GBRT-ECFP model provides a valuable tool for LCIA and other environmental assessments to better characterize chemical risks to human health and ecosystems.

Assessing Consumer Buy and Pay Preferences for Labeled Food Products with Statistical and Machine Learning Methods.

ABSTRACT: Food labeling is one approach to encourage safe, healthy, and sustainable dietary practices. Consumer buy and pay preferences for specially labeled food products (e.g., U.S. Department of Agriculture organic, raised without antibiotics, and locally raised) may promote the adoption of associated production practices by food producers. Thus, it is important to understand how consumer buy and pay preferences for specially labeled products vary with their demographics, food-relevant habits, and foodborne disease perceptions. Using both conventional statistical and novel machine learning models, this study analyzed Michigan State University Environmental Science and Policy Program annual survey data (2019) to characterize consumer buy and pay preferences regarding eight labels related to food production practices. Older consumer age was significantly associated with lower consumer willingness to pay more for labeled products. Participants who prefer to shop in nonconventional grocery stores were more willing to buy and pay more for labeled products. Our machine learning models provide a new approach for analyzing food safety and labeling survey data and produced adequate average prediction accuracy scores for all eight labels. The label "raised without antibiotics" had the highest average prediction accuracy for consumer willingness to buy. Thus, the machine learning models may be used to analyze food survey data and help develop strategies for promoting healthy food production practices.

Bacterial community assembly and antibiotic resistance genes in the lettuce-soil system upon antibiotic exposure.

Bacteria and antibiotic resistance genes (ARGs) in vegetables may influence human gut microbiome and ultimately human health. However, little is known about how vegetable microbiomes and ARGs respond to exposure of anthropogenic antibiotics from crop irrigation water. This study investigated bacterial community assembly and ARG profiles in lettuce (Lactuca sativa) shoots and roots, rhizosphere soil, and bulk soil irrigated with antibiotics-containing water, using 16S rRNA amplicon sequencing and high throughput real-time qPCR, respectively. With antibiotic exposure alpha diversity values remained unchanged for the rhizosphere soil and lettuce roots, but were significantly decreased for the bulk soil and lettuce shoots (p < 0.05). Based on calculations of normalized stochastic ratio (NST), bacterial community assembly was more stochastic in the rhizosphere soil (83%-86%) and bulk soil (81%-84%) than in the lettuce roots (45%-48%). These results suggest a stronger deterministic control of plant roots in bacterial community assembly. Antibiotic exposure did not substantially change the stochasticity of the bacterial communities, despite the NST values were significantly increased by ~3% (p < 0.05) for the rhizosphere soil and lettuce roots and significantly decreased by ~3% (p < 0.05) for the bulk soil, when comparing treatments with and without antibiotics. The levels of Methylophilaceae and Beijerinckiaceae were significantly different between the antibiotic and antibiotics-free treatments. Antibiotic exposure consistently increased the abundance of mobile genetic elements (MGEs) in the rhizosphere soil, but not in other samples. No consistent changes in ARGs were observed with and without antibiotic exposure. Finally, the correlation network analysis revealed that the rhizosphere soil may be a hotspot for interactions between ARGs, MGEs, bacterial communities, and antibiotic residues.

Comparing root concentration factors of antibiotics for lettuce (Lactuca sativa) measured in rhizosphere and bulk soils.

Plant uptake of antibiotics raises serious food safety concerns. Measurements and predictions of antibiotic uptake by plants are often based on root concentration factors (RCF) determined using antibiotic concentrations in bulk soil (RCFbs) rather than in rhizosphere soil (RCFrs) where root uptake actually occurs. This study investigated the fate and transport of nine antibiotics in the continuum of bulk soil, rhizosphere soil, roots and shoots of lettuce (Lactuca sativa) under soil-surface irrigation. Antibiotic concentrations in the lettuce shoots remained unchanged during 25-35 days after seedling transplantation. Compared with the RCFrs values, the RCFbs values were significantly greater for ciprofloxacin, lincomycin, oxytetracycline, sulfamethoxazole, and tetracycline (p < 0.05), similar for trimethoprim and tylosin, but significantly lower for monensin (p < 0.05). Ciprofloxacin, trimethoprim, and tylosin had the lowest translocation factors (TF) ranging between 0.03 and 0.05, suggesting their limited upward transport to the lettuce shoots. Oxytetracycline, monensin, and sulfamethoxazole had intermediate TF values of 0.36-0.64, whereas lincomycin had the highest TF value of 1.46. This study showed significant differences between RCFbs and RCFrs values, suggesting the need to reassess the utility of RCFbs in predicting the antibiotic root uptake in diverse soil-plant systems.

Pharmaceutical exposure changed antibiotic resistance genes and bacterial communities in soil-surface- and overhead-irrigated greenhouse lettuce.

New classes of emerging contaminants such as pharmaceuticals, antibiotic resistant bacteria (ARB), and antibiotic resistance genes (ARGs) have received increasing attention due to rapid increases of their abundance in agroecosystems. As food consumption is a direct exposure pathway of pharmaceuticals, ARB, and ARGs to humans, it is important to understand changes of bacterial communities and ARG profiles in food crops produced with contaminated soils and waters. This study examined the level and type of ARGs and bacterial community composition in soil, and lettuce shoots and roots under soil-surface or overhead irrigation with pharmaceuticals-contaminated water, using high throughput qPCR and 16S rRNA amplicon sequencing techniques, respectively. In total 52 ARG subtypes were detected in the soil, lettuce shoot and root samples, with mobile genetic elements (MGEs), and macrolide-lincosamide-streptogramin B (MLSB) and multidrug resistance (MDR) genes as dominant types. The overall abundance and diversity of ARGs and bacteria associated with lettuce shoots under soil-surface irrigation were lower than those under overhead irrigation, indicating soil-surface irrigation may have lower risks of producing food crops with high abundance of ARGs. ARG profiles and bacterial communities were sensitive to pharmaceutical exposure, but no consistent patterns of changes were observed. MGE intl1 was consistently more abundant with pharmaceutical exposure than in the absence of pharmaceuticals. Pharmaceutical exposure enriched Proteobacteria (specifically Methylophilaceae) and decreased bacterial alpha diversity. Finally, there were significant interplays among bacteria community, antibiotic concentrations, and ARG abundance possibly involving hotspots including Sphingomonadaceae, Pirellulaceae, and Chitinophagaceae, MGEs (intl1 and tnpA_1) and MDR genes (mexF and oprJ).

Analysis of transcriptome in hickory (Carya cathayensis), and uncover the dynamics in the hormonal signaling pathway during graft process.

BACKGROUND: Hickory (Carya cathayensis), a woody plant with high nutritional and economic value, is widely planted in China. Due to its long juvenile phase, grafting is a useful technique for large-scale cultivation of hickory. To reveal the molecular mechanism during the graft process, we sequenced the transcriptomes of graft union in hickory. RESULTS: In our study, six RNA-seq libraries yielded a total of 83,676,860 clean short reads comprising 4.19 Gb of sequence data. A large number of differentially expressed genes (DEGs) at three time points during the graft process were identified. In detail, 777 DEGs in the 7 d vs 0 d (day after grafting) comparison were classified into 11 enriched Gene Ontology (GO) categories, and 262 DEGs in the 14 d vs 0 d comparison were classified into 15 enriched GO categories. Furthermore, an overview of the PPI network was constructed by these DEGs. In addition, 20 genes related to the auxin-and cytokinin-signaling pathways were identified, and some were validated by qRT-PCR analysis. CONCLUSIONS: Our comprehensive analysis provides basic information on the candidate genes and hormone signaling pathways involved in the graft process in hickory and other woody plants.