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Development of efficient bioconversion processes is limited by the ability to predictably improve metabolic flux. Here we deployed Bayesian Metabolic Control Analysis as a platform to integrate multi-omics data with metabolic modeling and evaluated its ability to predict genetic interventions that improve metabolic flux. Global Metabolomics and proteomics data was collected from 17 Aspergillus niger strains engineered to produce the platform biochemical 3-hydroxypropionic acid from which seven actional genetic interventions were predicted from significant flux control coefficients. Of the suggested genetic interventions, two were present within the intuitively designed strains used for training (malonic semialdehyde dehydrogenase and pyruvate carboxylase) while five predicted targets were present within non-intuitive areas of the metabolic network including 5-formyltetrahydrofolate deformylase and four mitochondrial enzymes, alcohol dehydrogenase, succinyl-CoA ligase, aspartate aminotransferase, and malate dehydrogenase. Six of the targets were validated in the highest performing 3-HP strain used for multi-omics data generation which contained a prior disruption of the highest scoring target malonic semialdehyde dehydrogenase. Predicted directional perturbation of five of the six tested targets significantly improved titer and rate of 3-HP production and two significantly improved yield. The greatest improvements were observed following disruption of the non-intuitive target succinyl-CoA ligase which increased titer by 39% and yield by 29% (to 20.4 g/L 3-HP and 0.31 g 3-HP/g glucose) over the strains used for training. This study demonstrates the utility of Bayesian Metabolic Control Analysis and highlights the ability to predict meaningful genetic targets in unexpected areas of metabolism to improve engineered strains for bioconversion.

Abstract Context Iron overload is a known risk factor for type 2 diabetes (T2D); however, iron overload and iron deficiency have both been associated with metabolic disorders in observational studies. Objective Using mendelian randomization (MR), we assessed how genetically predicted systemic iron status affected T2D risk. Methods A 2-sample MR analysis was used to obtain a causal estimate. We selected genetic variants strongly associated (P < 5 × 10−8) with 4 biomarkers of systemic iron status from a study involving 48 972 individuals performed by the Genetics of Iron Status consortium and applied these biomarkers to the T2D case-control study (74 124 cases and 824 006 controls) performed by the Diabetes Genetics Replication and Meta-analysis consortium. The simple median, weighted median, MR-Egger, MR analysis using mixture-model, weighted allele scores, and MR based on a Bayesian model averaging approaches were used for the sensitivity analysis. Results Genetically instrumented serum iron (odds ratio [OR]: 1.07; 95% CI, 1.02-1.12), ferritin (OR: 1.19; 95% CI, 1.08-1.32), and transferrin saturation (OR: 1.06; 95% CI, 1.02-1.09) were positively associated with T2D. In contrast, genetically instrumented transferrin, a marker of reduced iron status, was inversely associated with T2D (OR: 0.91; 95% CI, 0.87-0.96). Conclusion Genetic evidence supports a causal link between increased systemic iron status and increased T2D risk. Further studies involving various ethnic backgrounds based on individual-level data and studies regarding the underlying mechanism are warranted for reducing the risk of T2D.

Previous research suggests a protective effect of vegetable consumption against chronic disease, but the quality of evidence underlying those findings remains uncertain. We applied a Bayesian meta-regression tool to estimate the mean risk function and quantify the quality of evidence for associations between vegetable consumption and ischemic heart disease (IHD), ischemic stroke, hemorrhagic stroke, type 2 diabetes and esophageal cancer. Increasing from no vegetable consumption to the theoretical minimum risk exposure level (306–372 g daily) was associated with a 23.2% decline (95% uncertainty interval, including between-study heterogeneity: 16.4–29.4) in ischemic stroke risk; a 22.9% (13.6–31.3) decline in IHD risk; a 15.9% (1.7–28.1) decline in hemorrhagic stroke risk; a 28.5% (−0.02–51.4) decline in esophageal cancer risk; and a 26.1% (−3.6–48.3) decline in type 2 diabetes risk. We found statistically significant protective effects of vegetable consumption for ischemic stroke (three stars), IHD (two stars), hemorrhagic stroke (two stars) and esophageal cancer (two stars). Including between-study heterogeneity, we did not detect a significant association with type 2 diabetes, corresponding to a one-star rating. Although current evidence supports increased efforts and policies to promote vegetable consumption, remaining uncertainties suggest the need for continued research. A meta-analysis using the Burden of Proof function identified modest evidence supporting a protective role of vegetable consumption against ischemic heart disease, stroke and esophageal cancer but not type 2 diabetes.

Polygenic risk scores (PRS) have emerged as a promising tool for predicting disease risk and treatment outcomes using genomic data. Thousands of genome-wide association studies (GWAS), primarily involving populations of European ancestry, have supported the development of PRS models. However, these models have not been adequately evaluated in non-European populations, raising concerns about their clinical validity and predictive power across diverse groups. Addressing this issue requires developing novel risk prediction frameworks that leverage genetic characteristics across diverse populations, considering host-microbiome interactions and a broad range of health measures. One of the key aspects in evaluating PRS is understanding the strengths and limitations of various methods for constructing them. In this review, we analyze strengths and limitations of different methods for constructing PRS, including traditional weighted approaches and new methods such as Bayesian and Frequentist penalized regression approaches. Finally, we summarize recent advances in PRS calculation methods development, and highlight key areas for future research, including development of models robust across diverse populations by underlining the complex interplay between genetic variants across diverse ancestral backgrounds in disease risk as well as treatment response prediction. PRS hold great promise for improving disease risk prediction and personalized medicine; therefore, their implementation must be guided by careful consideration of their limitations, biases, and ethical implications to ensure that they are used in a fair, equitable, and responsible manner.

A scarcity of research assesses the effects of exposure to a combination of chemicals on lipid profiles as well as molecular mechanisms related to dyslipidemia. A cross-sectional study of 3692 adults aims to identify the association between chemical mixtures, including blood and urine 26 chemicals, and lipid profiles among Korean adults (aged ≥ 18) using linear regression models, weighted quantile sum (WQS) regression, quantile g-computation (qgcomp), and Bayesian kernel machine regression (BKMR). In silico toxicogenomic data-mining, we assessed molecular mechanisms linked with dyslipidemia, including genes, miRNAs, pathways, biological processes, and diseases. In the linear regression models, heavy metals, volatile organic compound metabolites, and phthalate metabolites were found to be related to HDL-C, triglycerides, LDL-C, total lipids, and total cholesterol, and significant trends were observed for these chemical quartiles ( p  < 0.01). The WQS index was significantly linked with HDL-C, triglycerides, LDL-C, total cholesterol, and total lipids. The qgcomp index also found a significant association between chemicals and HDL-C, triglycerides, and total lipids. In BKMR analysis, the overall effect of the chemical mixture was significantly associated with HDL-C, triglycerides, total cholesterol, and total lipids. We found that mixed chemicals interacted with the PPARA gene and were linked with dyslipidemia. Several pathways (“SREBF and miR33 in cholesterol,” “estrogen receptor pathway and lipid homeostasis,” and “regulation of PGC-1α”), “negative regulation of hepatocyte apoptotic process,” “negative regulation of sequestering of triglycerides,” “regulation of hepatocyte apoptotic process,” and “negative regulation of cholesterol storage,” and “abdominal obesity metabolic syndrome” were identified as key molecular mechanisms that may be affected by mixed chemicals and implicated in the development of dyslipidemia. The highest interaction and expression of miRNAs involved in the process of dyslipidemia were also described. Especially, the cutoff levels for chemical exposure levels related to lipid profiles were also provided.

Polycystic ovary syndrome (PCOS) severely affects women's fertility and accompanies serious metabolic disturbances, affecting 5%-20% of women of reproductive age globally. We previously found that exposure to toxic metals in the blood raised the risk of PCOS, but the association between exposure to toxic metals and the risk of PCOS in the follicular fluid, the microenvironment for oocyte growth and development in females, and its effect on metabolism has not been reported. This study aimed to evaluate the associations between the concentrations of cadmium (Cd), mercury (Hg), barium (Ba) and arsenic (As) in FF and the risk of PCOS, and to explore the mediating effect of metabolic markers in FF on the above relationship. We conducted a case–control study, including 557 women with PCOS and 651 controls. Ba, Cd, Hg and As levels in FF were measured by ICP-MS, metabolites levels in FF was measured by LC–MS/MS among 168 participants randomly selected from all the participants. Logistic regression models were used to assess the association of a single metal level with the PCOS risk, and linear regression models were used to assess the relationships of a single metal level with clinical phenotype parameters and metabolites levels. Combined effect of metals mixture levels on the risk of PCOS were assessed via weighted quantile sum (WQS) regression and bayesian kernel machine regression (BKMR). Medication analysis was performed to explore the role of metabolic markers on the relationship of toxic metals levels with the risk of PCOS. The exposure levels of Cd, Hg, Ba and As in FF were all positively and significantly associated with the PCOS risk (with respect to the highest vs. lowest tertile group: OR = 1.57, 95% CI = 1.17 ~ 2.12 for Cd, OR = 1.69, 95% CI = 1.22 ~ 2.34 for Hg, OR = 1.76, 95% CI = 1.32 ~ 2.34 for Ba, OR = 1.42, 95% CI = 1.05 ~ 1.91 for As). In addition, levels of metal mixture also significantly correlated with the risk of PCOS, Cd level contributed most to it. Moreover, we observed significant positive relationships between Cd level and LH (β = 0.048, 95% CI = 0.002 ~ 0.094), T (β = 0.077, 95% CI = 0.029 ~ 0.125) and HOMA-IR value (β = 0.060, 95% CI = 0.012 ~ 0.107), as well as Hg level with LH, FSH/LH ratio and TC. Furthermore, we revealed that estrone sulfate, LysoPE 22:6 and N-Undecanoylglycine were significantly and positively mediating the association between Cd level and the risk of PCOS (with mediated proportion of 0.39, 0.24 and 0.35, respectively), and between Hg level and the risk of PCOS (with mediated proportion of 0.29, 0.20 and 0.46, respectively). These highly expressed metabolites significantly enriched in the fatty acid oxidation, steroid hormone biosynthesis and glycerophospholipids metabolism, which may explain the reason why the levels of Cd and Hg in FF associated with the phenotype of PCOS. Ba and As in FF was not found the above phenomenon. Our results suggested that exposure to multiple toxic metals (Cd, Hg, Ba and As) in FF associated with the increased risk of PCOS, Cd was a major contributor. Levels of Cd and Hg in FF significantly associated with the phenotype of PCOS. The above association may result from that Cd and Hg in FF related with the disturbance of fatty acid oxidation, steroid hormone biosynthesis and the glycerophospholipids metabolism.

Background Metabolic disturbances adversely impact productive and reproductive performance of dairy cattle due to changes in endocrine status and immune function, which increase the risk of disease. This may occur in the post-partum phase, but also throughout lactation, with sub-clinical symptoms. Recently, increased attention has been directed towards improved health and resilience in dairy cattle, and genomic selection (GS) could be a helpful tool for selecting animals that are more resilient to metabolic disturbances throughout lactation. Hence, we evaluated the genomic prediction of serum biomarkers levels for metabolic distress in 1353 Holsteins genotyped with the 100K single nucleotide polymorphism (SNP) chip assay. The GS was evaluated using parametric models best linear unbiased prediction (GBLUP), Bayesian B (BayesB), elastic net (ENET), and nonparametric models, gradient boosting machine (GBM) and stacking ensemble (Stack), which combines ENET and GBM approaches. Results The results show that the Stack approach outperformed other methods with a relative difference (RD), calculated as an increment in prediction accuracy, of approximately 18.0% compared to GBLUP, 12.6% compared to BayesB, 8.7% compared to ENET, and 4.4% compared to GBM. The highest RD in prediction accuracy between other models with respect to GBLUP was observed for haptoglobin (hapto) from 17.7% for BayesB to 41.2% for Stack; for Zn from 9.8% (BayesB) to 29.3% (Stack); for ceruloplasmin (CuCp) from 9.3% (BayesB) to 27.9% (Stack); for ferric reducing antioxidant power (FRAP) from 8.0% (BayesB) to 40.0% (Stack); and for total protein (PROTt) from 5.7% (BayesB) to 22.9% (Stack). Using a subset of top SNPs (1.5k) selected from the GBM approach improved the accuracy for GBLUP from 1.8 to 76.5%. However, for the other models reductions in prediction accuracy of 4.8% for ENET (average of 10 traits), 5.9% for GBM (average of 21 traits), and 6.6% for Stack (average of 16 traits) were observed. Conclusions Our results indicate that the Stack approach was more accurate in predicting metabolic disturbances than GBLUP, BayesB, ENET, and GBM and seemed to be competitive for predicting complex phenotypes with various degrees of mode of inheritance, i.e. additive and non-additive effects. Selecting markers based on GBM improved accuracy of GBLUP.

Metabolic syndrome is a chronic and complex disease characterized by environmental and genetic factors. However, the underlying mechanisms remain unclear. This study assessed the relationship between exposure to a mixture of environmental chemicals and metabolic syndrome (MetS) and further examined whether telomere length (TL) moderated these relationships. A total of 1265 adults aged > 20 years participated in the study. Data on multiple pollutants (polycyclic aromatic hydrocarbons, phthalates, and metals), MetS, leukocyte telomere length (LTL), and confounders were provided in the 2001–2002 National Health and Nutrition Examination Survey. The correlations between multi-pollutant exposure, TL, and MetS in the males and females were separately assessed using principal component analysis (PCA), logistic and extended linear regression models, Bayesian kernel machine regression (BKMR), and mediation analysis. Four factors were generated in PCA that accounted for 76.2% and 77.5% of the total environmental pollutants in males and females, respectively. The highest quantiles of PC2 and PC4 were associated with the risk of TL shortening ( P  < 0.05). We observed that the relationship between PC2, PC4, and MetS risk was significant in the participants with median TL levels ( P for trend = 0.04 for PC2, and P for trend = 0.01 for PC4). Furthermore, mediation analysis revealed that TL could explain 26.1% and 17.1% of the effects of PC2 and PC4 associated with MetS in males, respectively. The results of BKMR model revealed that these associations were mainly driven by 1-PYE (cPIP = 0.65) and Cd (cPIP = 0.29) in PC2. Meanwhile, TL could explain 17.7% of the mediation effects of PC2 associated with MetS in the females. However, the relationships between pollutants and MetS were sparse and inconsistent in the females. Our findings suggest that the effects of the risk of MetS associated with mixed exposure to multiple pollutants are mediated by TL, and this mediating effect in the males is more pronounced than that in the females.

Simultaneous analysis of genetic associations with multiple phenotypes may reveal shared genetic susceptibility across traits (pleiotropy). For a locus exhibiting overall pleiotropy, it is important to identify which specific traits underlie this association. We propose a Bayesian meta-analysis approach (termed CPBayes) that uses summary-level data across multiple phenotypes to simultaneously measure the evidence of aggregate-level pleiotropic association and estimate an optimal subset of traits associated with the risk locus. This method uses a unified Bayesian statistical framework based on a spike and slab prior. CPBayes performs a fully Bayesian analysis by employing the Markov Chain Monte Carlo (MCMC) technique Gibbs sampling. It takes into account heterogeneity in the size and direction of the genetic effects across traits. It can be applied to both cohort data and separate studies of multiple traits having overlapping or non-overlapping subjects. Simulations show that CPBayes can produce higher accuracy in the selection of associated traits underlying a pleiotropic signal than the subset-based meta-analysis ASSET. We used CPBayes to undertake a genome-wide pleiotropic association study of 22 traits in the large Kaiser GERA cohort and detected six independent pleiotropic loci associated with at least two phenotypes. This includes a locus at chromosomal region 1q24.2 which exhibits an association simultaneously with the risk of five different diseases: Dermatophytosis, Hemorrhoids, Iron Deficiency, Osteoporosis and Peripheral Vascular Disease. We provide an R-package 'CPBayes' implementing the proposed method.

Background Per- and polyfluoroalkyl substances (PFAS) have been implicated in metabolic dysregulation, yet their impact on integrated cardiometabolic risk remains unexplored. We aim to assess associations between serum PFAS and the cardiometabolic index (CMI) in U.S. adults. Methods We analyzed NHANES 2015–2020 data (N = 1371) using multivariable linear regression to estimate PFAS-CMI relationships across exposure quartile. Restricted cubic splines and threshold analyses characterized nonlinearity. Mixed-exposure effects were evaluated via Bayesian kernel machine regression (BKMR), weighted quantile sum (WQS), and quantile g-computation. Stratified models tested effect modification by covariates. Comparative Toxicogenomics Database (CTD) interrogation mapped PFAS targets and pathways. Results After full adjustment, PFDeA (β = − 0.06, 95% CI [− 0.11, − 0.01]) and PFUA (β = − 0.11, 95% CI [− 0.17, − 0.06]) remained inversely associated with CMI (P < 0.01). Spline models confirmed linear inverse trends for PFDeA and PFUA, whereas n-PFOS exhibited an inverted U-shape. The results of BKMR showed that the overall effect of PFAS on CMI was negative. WQS analyses consistently demonstrated a negative effect on CMI (β =  −0.16, 95% CI [− 0.25, − 0.07]), with PFUA carrying the greatest weight. Associations persisted across subgroups but were stronger in non-Hispanic Whites and modified by alcohol use and obesity. CTD network analysis identified PPARA, SREBF1, and CYP7A1 as central lipid-regulatory hubs for PFDeA and PFUA. Conclusion This is the first study to link individual and mixed PFAS exposures to CMI, leveraging robust mixture models and bioinformatic mechanistic mapping. Our findings reveal PFDeA and PFUA as key drivers of PFAS-related cardiometabolic risk and underscore the value of CMI as an integrative biomarker in environmental health research.