Metabolites, Healthy Lifestyle, and Polygenic Risk Score Associated with Upper Gastrointestinal Cancer: Findings from the UK Biobank Study.

Previous metabolomics studies have highlighted the predictive value of metabolites on upper gastrointestinal (UGI) cancer, while most of them ignored the potential effects of lifestyle and genetic risk on plasma metabolites. This study aimed to evaluate the role of lifestyle and genetic risk in the metabolic mechanism of UGI cancer. Differential metabolites of UGI cancer were identified using partial least-squares discriminant analysis and the Wilcoxon test. Then, we calculated the healthy lifestyle index (HLI) score and polygenic risk score (PRS) and divided them into three groups, respectively. A total of 15 metabolites were identified as UGI-cancer-related differential metabolites. The metabolite model (AUC = 0.699) exhibited superior discrimination ability compared to those of the HLI model (AUC = 0.615) and the PRS model (AUC = 0.593). Moreover, subgroup analysis revealed that the metabolite model showed higher discrimination ability for individuals with unhealthy lifestyles compared to that with healthy individuals (AUC = 0.783 vs 0.684). Furthermore, in the genetic risk subgroup analysis, individuals with a genetic predisposition to UGI cancer exhibited the best discriminative performance in the metabolite model (AUC = 0.770). These findings demonstrated the clinical significance of metabolic biomarkers in UGI cancer discrimination, especially in individuals with unhealthy lifestyles and a high genetic risk.

Brain extended and closed forms glutathione levels decrease with age and extended glutathione is associated with visuospatial memory.

During aging, the brain is subject to greater oxidative stress (OS), which is thought to play a critical role in cognitive impairment. Glutathione (GSH), as a major antioxidant in the brain, can be used to combat OS. However, how brain GSH levels vary with age and their associations with cognitive function is unclear. In this study, we combined point-resolved spectroscopy and edited spectroscopy sequences to investigate extended and closed forms GSH levels in the anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), and occipital cortex (OC) of 276 healthy participants (extended form, 166 females, age range 20-70 years) and 15 healthy participants (closed form, 7 females, age range 26-56 years), and examined their relationships with age and cognitive function. The results revealed decreased extended form GSH levels with age in the PCC among 276 participants. Notably, the timecourse of extended form GSH level changes in the PCC and ACC differed between males and females. Additionally, positive correlations were observed between extended form GSH levels in the PCC and OC and visuospatial memory. Additionally, a decreased trend of closed form GSH levels with age was also observed in the PCC among 15 participants. Taken together, these findings enhance our understanding of the brain both closed and extended form GSH time course during normal aging and associations with sex and memory, which is an essential first step for understanding the neurochemical underpinnings of healthy aging.

Trajectory patterns and cumulative burden of CEA during follow-up with non-small cell lung cancer outcomes: A retrospective longitudinal cohort study.

BACKGROUND: Previous studies of non-small cell lung cancer (NSCLC) focused on CEA measured at a single time point, ignoring serial CEA measurements. METHODS: This retrospective cohort included 2959 patients underwent surgery for stage I-III NSCLC. CEA trajectory patterns and long-term cumulative CEA burden were evaluated using the latent class growth mixture model. RESULTS: Four CEA trajectory groups were identified, named as low-stable, decreasing, early-rising and later-rising. Compared with the low-stable group, the adjusted hazard ratios associated with death were 1.27, 4.50, and 3.68 for the other groups. Cumulative CEA burden were positively associated with the risk of death in patients not belonging to the low-stable group. The 5-year overall survival (OS) rates decreased from 62.3% to 33.0% for the first and fourth quantile groups of cumulative CEA burden. Jointly, patients with decreasing CEA trajectory could be further divided into the decreasing & low and decreasing & high group, with 5-year OS rates to be 77.9% and 47.1%. Patients with rising CEA trajectory and high cumulative CEA were found to be more likely to develop bone metastasis. CONCLUSIONS: Longitudinal trajectory patterns and long-term cumulative burden of CEA were independent prognostic factors of NSCLC. We recommend CEA in postoperative surveillance of NSCLC.

Deciphering the omicron variant: integrated omics analysis reveals critical biomarkers and pathophysiological pathways.

BACKGROUND: The rapid emergence and global dissemination of the Omicron variant of SARS-CoV-2 have posed formidable challenges in public health. This scenario underscores the urgent need for an enhanced understanding of Omicron's pathophysiological mechanisms to guide clinical management and shape public health strategies. Our study is aimed at deciphering the intricate molecular mechanisms underlying Omicron infections, particularly focusing on the identification of specific biomarkers. METHODS: This investigation employed a robust and systematic approach, initially encompassing 15 Omicron-infected patients and an equal number of healthy controls, followed by a validation cohort of 20 individuals per group. The study's methodological framework included a comprehensive multi-omics analysis that integrated proteomics and metabolomics, augmented by extensive bioinformatics. Proteomic exploration was conducted via an advanced Ultra-High-Performance Liquid Chromatography (UHPLC) system linked with mass spectrometry. Concurrently, metabolomic profiling was executed using an Ultra-Performance Liquid Chromatography (UPLC) system. The bioinformatics component, fundamental to this research, entailed an exhaustive analysis of protein-protein interactions, pathway enrichment, and metabolic network dynamics, utilizing state-of-the-art tools such as the STRING database and Cytoscape software, ensuring a holistic interpretation of the data. RESULTS: Our proteomic inquiry identified eight notably dysregulated proteins (THBS1, ACTN1, ACTC1, POTEF, ACTB, TPM4, VCL, ICAM1) in individuals infected with the Omicron variant. These proteins play critical roles in essential physiological processes, especially within the coagulation cascade and hemostatic mechanisms, suggesting their significant involvement in the pathogenesis of Omicron infection. Complementing these proteomic insights, metabolomic analysis discerned 146 differentially expressed metabolites, intricately associated with pivotal metabolic pathways such as tryptophan metabolism, retinol metabolism, and steroid hormone biosynthesis. This comprehensive metabolic profiling sheds light on the systemic implications of Omicron infection, underscoring profound alterations in metabolic equilibrium. CONCLUSIONS: This study substantially enriches our comprehension of the physiological ramifications induced by the Omicron variant, with a particular emphasis on the pivotal roles of coagulation and platelet pathways in disease pathogenesis. The discovery of these specific biomarkers illuminates their potential as critical targets for diagnostic and therapeutic strategies, providing invaluable insights for the development of tailored treatments and enhancing patient care in the dynamic context of the ongoing pandemic.

Temporal changes in plasma metabolic signatures to predict immune response of antiretroviral therapy among people living with HIV.

Antiretroviral therapy (ART) is an effective treatment for people living with HIV (PLHIVs), requiring an extended period to achieve immune reconstitution. Metabolic alterations induced by ART are crucial for predicting long-term therapeutic responses, yet comprehensive investigation through large-scale clinical studies is still lacking. Here, we collected plasma samples from 108 PLHIVs to the untargeted plasma metabolomics study, based on the longitudinal metabolomics design. Cross-sectional analyzes were performed at pre- and post-ART to explore the metabolic transformation induced by the therapy. Subsequently, delta values between pre- and post-ART measurements were calculated to quantify metabolic alterations. Then, the optimal set of metabolic traits and clinical signatures were further identified and applied to construct random forest model for predicting the future therapeutic responses to ART. We found distinct ART-induced metabolic transformation among PLHIVs. After confounder-adjustments, five metabolites exhibited significant associations with future immune response: tetracosatetraenoic acid (24:4n-6) (pre-ART) (odds ratio [OR]: 0.978, 95% confidence interval [CI]: 0.955~0.997), 1-(3,4-dihydroxyphenyl)-5-hydroxy-3-decanone (pre-ART) (OR: 1.298, 95% CI: 1.061~1.727), beta-PC-M6 (change) (OR: 0.967, 95% CI: 0.938~0.993), d-Galactaro-1,4-lactone (change) (OR: 1.032, 95% CI: 1.007~1.063), Annuionone C (change) (OR: 1.100, 95% CI: 1.030~1.190). The addition of plasma metabolites to clinical markers accurately predicted immune response to ART with an area under curve of 0.91. Notably, most disrupted metabolites were significantly correlated with blood lipids, suggesting that metabolic transformation might contribute to dyslipidemia among PLHIVs. This study highlights the distinct metabolic transformation post-ART among PLHIVs and reveals the potential role of metabolic transformation as key determinants of ART efficacy.

Prediction models of colorectal cancer prognosis incorporating perioperative longitudinal serum tumor markers: a retrospective longitudinal cohort study.

BACKGROUND: Current prognostic prediction models of colorectal cancer (CRC) include only the preoperative measurement of tumor markers, with their available repeated postoperative measurements underutilized. CRC prognostic prediction models were constructed in this study to clarify whether and to what extent the inclusion of perioperative longitudinal measurements of CEA, CA19-9, and CA125 can improve the model performance, and perform a dynamic prediction. METHODS: The training and validating cohort included 1453 and 444 CRC patients who underwent curative resection, with preoperative measurement and two or more measurements within 12 months after surgery, respectively. Prediction models to predict CRC overall survival were constructed with demographic and clinicopathological variables, by incorporating preoperative CEA, CA19-9, and CA125, as well as their perioperative longitudinal measurements. RESULTS: In internal validation, the model with preoperative CEA, CA19-9, and CA125 outperformed the model including CEA only, with the better area under the receiver operating characteristic curves (AUCs: 0.774 vs 0.716), brier scores (BSs: 0.057 vs 0.058), and net reclassification improvement (NRI = 33.5%, 95% CI: 12.3 ~ 54.8%) at 36 months after surgery. Furthermore, the prediction models, by incorporating longitudinal measurements of CEA, CA19-9, and CA125 within 12 months after surgery, had improved prediction accuracy, with higher AUC (0.849) and lower BS (0.049). Compared with preoperative models, the model incorporating longitudinal measurements of the three markers had significant NRI (40.8%, 95% CI: 19.6 to 62.1%) at 36 months after surgery. External validation showed similar results to internal validation. The proposed longitudinal prediction model can provide a personalized dynamic prediction for a new patient, with estimated survival probability updated when a new measurement is collected during 12 months after surgery. CONCLUSIONS: Prediction models including longitudinal measurements of CEA, CA19-9, and CA125 have improved accuracy in predicting the prognosis of CRC patients. We recommend repeated measurements of CEA, CA19-9, and CA125 in the surveillance of CRC prognosis.

Untargeted serum metabolomics reveals potential biomarkers and metabolic pathways associated with the progression of gastroesophageal cancer.

BACKGROUND: Previous metabolic studies in upper digestive cancer have mostly been limited to cross-sectional study designs, which hinders the ability to effectively predict outcomes in the early stage of cancer. This study aims to identify key metabolites and metabolic pathways associated with the multistage progression of epithelial cancer and to explore their predictive value for gastroesophageal cancer (GEC) formation and for the early screening of esophageal squamous cell carcinoma (ESCC). METHODS: A case-cohort study within the 7-year prospective Esophageal Cancer Screening Cohort of Shandong Province included 77 GEC cases and 77 sub-cohort individuals. Untargeted metabolic analysis was performed in serum samples. Metabolites, with FDR q value < 0.05 and variable importance in projection (VIP) > 1, were selected as differential metabolites to predict GEC formation using Random Forest (RF) models. Subsequently, we evaluated the predictive performance of these differential metabolites for the early screening of ESCC. RESULTS: We found a distinct metabolic profile alteration in GEC cases compared to the sub-cohort, and identified eight differential metabolites. Pathway analyses showed dysregulation in D-glutamine and D-glutamate metabolism, nitrogen metabolism, primary bile acid biosynthesis, and steroid hormone biosynthesis in GEC patients. A panel of eight differential metabolites showed good predictive performance for GEC formation, with an area under the receiver operating characteristic curve (AUC) of 0.893 (95% CI = 0.816-0.951). Furthermore, four of the GEC pathological progression-related metabolites were validated in the early screening of ESCC, with an AUC of 0.761 (95% CI = 0.716-0.805). CONCLUSIONS: These findings indicated a panel of metabolites might be an alternative approach to predict GEC formation, and therefore have the potential to mitigate the risk of cancer progression at the early stage of GEC.

Changes of serum metabolites levels during neoadjuvant chemoradiation and prediction of the pathological response in locally advanced rectal cancer.

INTRODUCTION: Previous studies have explored prediction value of serum metabolites in neoadjuvant chemoradiation therapy (NCRT) response for rectal cancer. To date, limited literature is available for serum metabolome changes dynamically through NCRT. OBJECTIVES: This study aimed to explore temporal change pattern of serum metabolites during NCRT, and potential metabolic biomarkers to predict the pathological response to NCRT in locally advanced rectal cancer (LARC) patients. METHODS: Based on dynamic UHPLC-QTOF-MS untargeted metabolomics design, this study included 106 LARC patients treated with NCRT. Biological samples of the enrolled patients were collected in five consecutive time-points. Untargeted metabolomics was used to profile serum metabolic signatures from LARC patients. Then, we used fuzzy C-means clustering (FCM) to explore temporal change patterns in metabolites cluster and identify monotonously changing metabolites during NCRT. Repeated measure analysis of variance (RM-ANOVA) and multilevel partial least-squares discriminant analysis (ML-PLS-DA) were performed to select metabolic biomarkers. Finally, a panel of dynamic differential metabolites was used to build logistic regression prediction models. RESULTS: Metabolite profiles showed a clearly tendency of separation between different follow-up panels. We identified two clusters of 155 serum metabolites with monotonously changing patterns during NCRT (74 decreased metabolites and 81 increased metabolites). Using RM-ANOVA and ML-PLS-DA, 8 metabolites (L-Norleucine, Betaine, Hypoxanthine, Acetylcholine, 1-Hexadecanoyl-sn-glycero-3-phosphocholine, Glycerophosphocholine, Alpha-ketoisovaleric acid, N-Acetyl-L-alanine) were further identified as dynamic differential biomarkers for predicting NCRT sensitivity. The area under the ROC curve (AUC) of prediction model combined with the baseline measurement was 0.54 (95%CI = 0.43 ~ 0.65). By incorporating the variability indexes of 8 dynamic differential metabolites, the prediction model showed better discrimination performance than baseline measurement, with AUC = 0.67 (95%CI 0.57 ~ 0.77), 0.64 (0.53 ~ 0.75), 0.60 (0.50 ~ 0.71), and 0.56 (0.45 ~ 0.67) for the variability index of difference, linear slope, ratio, and standard deviation, respectively. CONCLUSION: This study identified eight metabolites as dynamic differential biomarkers to discriminate NCRT-sensitive and resistant patients. The changes of metabolite level during NCRT show better performance in predicting NCRT sensitivity. These findings highlight the clinical significance of metabolites variabilities in metabolomics analysis.

The mediation effect of serum metabolites on the relationship between long-term smoking exposure and esophageal squamous cell carcinoma.

BACKGROUND: Long-term smoking exposure will increase the risk of esophageal squamous cell carcinoma (ESCC), whereas the mechanism is still unclear. We conducted a cross-sectional study to explore whether serum metabolites mediate the occurrence of ESCC caused by cigarette smoking. METHODS: Serum metabolic profiles and lifestyle information of 464 participants were analyzed. Multiple logistic regression was used to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) of smoking exposure to ESCC risk. High-dimensional mediation analysis and univariate mediation analysis were performed to screen potential intermediate metabolites of smoking exposure for ESCC. RESULTS: Ever smoking was associated with a 3.11-fold increase of ESCC risk (OR = 3.11, 95% CI 1.63-6.05), and for each cigarette-years increase in smoking index, ESCC risk increased by 56% (OR = 1.56, 95% CI 1.18-2.13). A total of 5 metabolites were screened as mediators by high-dimensional mediation analysis. In addition, glutamine, histidine, and cholic acid were further proved existing mediation effects according to univariate mediation analysis. And the proportions of mediation of histidine and glutamine were 40.47 and 30.00%, respectively. The mediation effect of cholic acid was 8.98% according to the analysis of smoking index. CONCLUSIONS: Our findings suggest that cigarette smoking contributed to incident ESCC, which may be mediated by glutamine, histidine and cholic acid.

Water diversion induces more changes in bacterial and archaeal communities of river sediments than seasonality.

Previous studies have demonstrated that seasonal variation is often the most important factor affecting aquatic bacterial assemblages. Whether anthropogenic activities can dominate community dynamics remains unknown. Based on 16S rRNA high-throughput sequencing technology, this study revealed and compared the relative influence of water diversions and seasonality on bacterial and archaeal communities in river sediments from a region with obvious seasonality. The results indicate that the influence of water diversion on bacteria and archaea in water-receiving river sediments exceeded the influence of seasonal variation. Water diversion affected microbes by increasing EC, salinity, water flow rate, and organic matter carbon and nitrogen contents. Seasonal variations affected microbes by altering water temperature. Diversion responders but no season responders were classified by statistical methods in the microbial community. Diversion responder numbers were related to nitrogen concentrations, complex organic carbon contents and EC values, which were mainly affected by water diversion. With the joint impact of water diversion and seasonality, the correlations of bacterial and archaeal numbers with environmental factors were obviously weakened due to the increases in the ecological niche breadths of microorganisms. Natural seasonal changes in bacterial and archaeal communities were totally altered by changes in salinity, nutrients, and hydrological conditions induced by anthropogenic water diversions. These results highlight that human activity may be a stronger driver than natural seasonality in the alteration of bacterial and archaeal communities.

Multiple Compounds Recognition from The Tandem Mass Spectral Data Using Convolutional Neural Network.

Mixtures analysis can provide more information than individual components. It is important to detect the different compounds in the real complex samples. However, mixtures are often disturbed by impurities and noise to influence the accuracy. Purification and denoising will cost a lot of algorithm time. In this paper, we propose a model based on convolutional neural network (CNN) which can analyze the chemical peak information in the tandem mass spectrometry (MS/MS) data. Compared with traditional analyzing methods, CNN can reduce steps in data preprocessing. This model can extract features of different compounds and classify multi-label mass spectral data. When dealing with MS data of mixtures based on the Human Metabolome Database (HMDB), the accuracy can reach at 98%. In 600 MS test data, 451 MS data were fully detected (true positive), 142 MS data were partially found (false positive), and 7 MS data were falsely predicted (true negative). In comparison, the number of true positive test data for support vector machine (SVM) with principal component analysis (PCA), deep neural network (DNN), long short-term memory (LSTM), and XGBoost respectively are 282, 293, 270, and 402; the number of false positive test data for four models are 318, 284, 198, and 168; the number of true negative test data for four models are 0, 23, 7, 132, and 30. Compared with the model proposed in other literature, the accuracy and model performance of CNN improved considerably by separating the different compounds independent MS/MS data through three-channel architecture input. By inputting MS data from different instruments, adding more offset MS data will make CNN models have stronger universality in the future.

Habitual Iron Supplementation Associated with Elevated Risk of Chronic Kidney Disease in Individuals with Antihypertensive Medication.

The aim of this study was to examine the effects of habitual iron supplementation on the risk of CKD in individuals with different hypertensive statuses and antihypertension treatment statuses. We included a total of 427,939 participants in the UK Biobank study, who were free of CKD and with complete data on blood pressure at baseline. Cox proportional hazards regression models were used to examine the adjusted hazard ratios of habitual iron supplementation for CKD risk. After multivariable adjustment, habitual iron supplementation was found to be associated with a significantly higher risk of incident CKD in hypertensive participants (HR 1.12, 95% CI 1.02 to 1.22), particularly in those using antihypertensive medication (HR 1.21, 95% CI 1.08 to 1.35). In contrast, there was no significant association either in normotensive participants (HR 1.06, 95% CI 0.94 to 1.20) or in hypertensive participants without antihypertensive medication (HR 1.02, 95% CI 0.90 to 1.17). Consistently, significant multiplicative and additive interactions were observed between habitual iron supplementation and antihypertensive medication on the risk of incident CKD (p all interaction < 0.05). In conclusion, habitual iron supplementation was related to a higher risk of incident CKD among hypertensive patients, the association might be driven by the use of antihypertensive medication.

Relationship between dynamic changes of microorganisms in Qupi and the quality formation of Fengxiangxing Huairang Daqu.

Introduction: Fengxiangxing Huairang Daqu (FHD) is one of the major types of Daqu in China. However, the relationship between the microbial community structure at different stages, the changes in the sensory characteristics, fermentation characteristics, volatiles, the most critical process point, and the quality formation of FHD is not clear. Methods: Based on microscopic characterization, PacBio SMRT sequencing, and HS-SPME-GC-MS volatile metabolite analysis revealed the relationship between FHD quality formation and the dynamics of Qupi. Results: The results showed that the 12th day of the culture was the most critical process point, highlighting the most significant differences in microbial community structure, sensory characteristics, fermentation characteristics, and flavor substances. Bacillus licheniformis (43.25%), Saccharopolyspora rectivirgula (35.05%), Thermoascus aurantiacus (76.51%), Aspergillus amstelodami (10.81%), and Saccharomycopsis fibuligera (8.88%) were the dominant species in FHD. S. fibuligera, A. amstelodami, and T. aurantiacus were associated with the snow-white color of the FHD epidermis, the yellow color of the interior, and the gray-white color, respectively. The abundance of T. aurantiacus, A. amstelodami, B. licheniformis, and S. rectivirgula was positively associated with the esterifying power and liquefying power of FHD. The abundance of T. aurantiacus and A. amstelodami was positively correlated with the saccharifying power of FHD. The abundance of S. fibuligera was positively related to the fermenting power of FHD. A total of 248 volatiles were detected in Qupi, mainly including alcohols, esters, aldehydes, and ketones. Of them, eleven volatiles had a significant effect on the flavor of Qupi, such as 1-butanol-3-methyl-, hydrazinecarboxamide, ethanol, phenylethyl alcohol, ethyl acetate, 2-octanone, 1-octen-3-ol, formic acid-hexyl ester, (E)-2-octen-1-ol, ethyl hexanoate, and 2(3H)-furanone-dihydro-5-pentyl-. The abundance of B. licheniformis, S. rectivirgula, T. aurantiacus, and S. fibuligera was positively correlated with the alcohols, aromatic compounds, and phenols in FHD. The abundance of S. fibuligera was positively correlated with the acids, esters, and hydrocarbons in FHD. Discussion: These results indicate important theoretical basis and technical support for controllable adjustment of FHD microbial community structure, stable control of FHD quality, and precise, effective, and large-scale guidance of FHD production.

Investigation of the effect of ultrasonication on starch-fatty acid complexes and the stabilization mechanism.

The complexation of physically modified starch with fatty acids is favorable for the production of resistant starch. However, there is a lack of information on the effect of ultrasonication (UC) on the structure and properties of starch complexes and the molecular mechanism of the stabilization. Here, the multi-scale structure and in vitro digestive properties of starch-fatty acid complexes before and after UC were investigated, and the stabilization mechanisms of starch and fatty acids were explored. The results showed that the physicochemical properties and multi-scale structure of the starch-fatty acid complexes significantly changed with the type of fatty acids. The solubility and swelling power of the starch-fatty acid complexes were significantly decreased after UC (P < 0.05), which facilitated the binding of starch with fatty acids. The XRD results revealed that after the addition of fatty acids, the starch-fatty acid complexes showed typical V-shaped complexes. In addition, the starch-fatty acid complexes showed a significant increase in complexing index, improved short-range ordering and enhanced thermal stability. However, the differences in the structure and properties of the fatty acids themselves resulted in no significant improvement in the multi-scale structure of maize starch-palmitic acid by UC. In terms of digestibility, especially the complexes after UC were more compact in structure, which increased the difficulty of enzymatic digestion and thus slowed down the digestion process. DFT calculations and combined with FT-IR analysis showed that non-covalent interactions such as hydrogen bonding and hydrophobic interactions were the main driving force for the formation of the complexes, with binding energies (lauric acid, myristic acid and palmitic acid) of -30.50, -22.14 and -14.10 kcal/mol, respectively. Molecular dynamics simulations further confirmed the molecular mechanism of inclusion complex formation and stabilization. This study is important for the regulation of starchy foods by controlling processing conditions, and provides important information on the role of fatty acids in the regulation of starch complexes and the binding mechanism.

Mechanism of interaction between L-theanine and maize starch in ultrasonic field based on DFT calculations: Rheological properties, multi-scale structure and in vitro digestibility.

The digestibility of starch-based foods is receiving increased attention. To date, the full understanding of how including L-theanine (THE) can modify the structural and digestive properties of starch has not been fully achieved. Here, we investigated the multi-scale structure and digestibility of maize starch (MS) regulated by THE in ultrasound field and the molecular interactions. Ultrasound disrupted the structure of starch granules and opened the molecular chains of starch, promoting increased THE binding and producing more low-order or disordered crystal structures. In this case, the aggregation of starch molecules, especially amylose, was reduced, leading to increased mobility of the systems. As a result, the apparent viscosity, G', and G" were significantly decreased, which retarded the starch regeneration. Density functional theory calculations indicated that there were mainly non-covalent interactions between THE and MS, such as hydrogen bonding and van der Waals forces. These interactions were the main factors contributing to the decrease in the short-range ordering, the helical structure, and the enthalpy change (ΔH) of MS. Interestingly, the rapidly digestible starch (RDS) content of THE modified MS (MS-THE-30) decreased by 17.89 %, while the resistant starch increased to 26.65 %. These results provide new strategies for the safe production of resistant starch.

Plasma metabolomics reveals the shared and distinct metabolic disturbances associated with cardiovascular events in coronary artery disease.

Risk prediction for subsequent cardiovascular events remains an unmet clinical issue in patients with coronary artery disease. We aimed to investigate prognostic metabolic biomarkers by considering both shared and distinct metabolic disturbance associated with the composite and individual cardiovascular events. Here, we conducted an untargeted metabolomics analysis for 333 incident cardiovascular events and 333 matched controls. The cardiovascular events were designated as cardiovascular death, myocardial infarction/stroke and heart failure. A total of 23 shared differential metabolites were associated with the composite of cardiovascular events. The majority were middle and long chain acylcarnitines. Distinct metabolic patterns for individual events were revealed, and glycerophospholipids alteration was specific to heart failure. Notably, the addition of metabolites to clinical markers significantly improved heart failure risk prediction. This study highlights the potential significance of plasma metabolites on tailed risk assessment of cardiovascular events, and strengthens the understanding of the heterogenic mechanisms across different events.

Prognostic model construction and immune microenvironment analysis of esophageal cancer based on gene expression data and microRNA target genes.

Background: Accumulating evidence suggests that microRNA-target genes are closely related to tumorigenesis and progression. This study aims to screen the intersection of differentially expressed mRNAs (DEmRNAs) and the target genes of differentially expressed microRNAs (DEmiRNAs), and to construct a prognostic gene model of esophageal cancer (EC). Methods: Gene expression, microRNA expression, somatic mutation, and clinical information data of EC from The Cancer Genome Atlas (TCGA) database were used. The intersection of DEmRNAs and the target genes of DEmiRNAs predicted by the Targetscan database and microRNA Data Integration Portal (mirDIP) database were screened. The screened genes were used to construct a prognostic model of EC. Then, the molecular and immune signatures of these genes were explored. Finally, the GSE53625 dataset from the Gene Expression Omnibus (GEO) database was further used as a validation cohort to confirm the prognostic value of the genes. Results: Six genes on the grounds of the intersection of DEmiRNAs target genes and DEmRNAs were identified as prognostic genes, including ARHGAP11A, H1.4, HMGB3, LRIG1, PRR11, and COL4A1. Based on the median risk score calculated for these genes, EC patients were divided into a high-risk group (n=72) and a low-risk group (n=72). Survival analysis showed that the high-risk group had a significantly shorter survival time than the low-risk group (TCGA and GEO, P<0.001). The nomogram evaluation showed high reliability in predicting the 1-year, 2-year, and 3-year survival probability of EC patients. Compared to low-risk group, higher expression level of M2 macrophages was found in high-risk group of EC patient (P<0.05), while STAT3 checkpoints showed attenuated expression level in high-risk group. Conclusions: A panel of differential genes was identified as potential EC prognostic biomarkers and showed great clinical significance in EC prognosis.

Relationship Between Left Ventricular Hypertrophy and Diabetes Is Likely Bidirectional: A Temporality Analysis.

Background The temporal relationship between type 2 diabetes (T2DM) and left ventricular hypertrophy (LVH) is not well established. This study aims to examine the temporal sequence between T2DM and LVH/cardiac geometry patterns in middle-aged adults. Methods and Results The longitudinal cohort consisted of 1000 adults (682 White individuals and 318 Black individuals; 41.1% men; mean age, 36.2 years at baseline) who had data on fasting glucose/T2DM, left ventricular mass index (LVMI), and relative wall thickness collected twice at baseline and follow-up over 9.4 years on average. The cross-lagged path analysis model in 905 adults who did not take antidiabetic medications and the longitudinal prediction model in 1000 adults were used to examine the temporal relationships of glucose/T2DM with LVMI, LVH, relative wall thickness, and remodeling patterns. After adjustment for age, race, sex, smoking, alcohol drinking, body mass index, heart rate, hypertension, and follow-up years, the path coefficient from baseline LVMI to follow-up glucose was 0.088 (P=0.005); the path from baseline glucose to follow-up LVMI was -0.009 (P=0.758). The 2 paths between glucose and relative wall thickness were not significant. The path analysis parameters did not differ significantly between race, sex, and follow-up duration subgroups. Incidence of T2DM was higher in the baseline LVH group than in the normal LVMI group (24.8% versus 8.8%; P=0.017 for difference). Incidence of LVH and concentric LVH was higher in the baseline T2DM group than in the group without T2DM (50.0% versus 18.2% for LVH [P=0.005 for difference]; 41.7% versus 12.6% for concentric LVH [P=0.004 for difference]), with adjustment for covariates. Conclusions This study suggests that the temporal relationship between T2DM and LVH is likely bidirectional. The path from LVMI/LVH to glucose/T2DM is stronger than the path from glucose/T2DM to LVMI/LVH.

Reduced brain glutathione levels during normal aging are associated with visuospatial memory.

During aging, the brain is subject to greater oxidative stress (OS), which is thought to play a critical role in cognitive impairment. Glutathione (GSH), as a major antioxidant in the brain, can be used to combatting OS. However, how brain GSH levels vary with age and their associations with cognitive function remain unclear. In this study, we combined point-resolved spectroscopy and edited spectroscopy sequences to investigate GSH levels in the anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), and occipital cortex (OC) of 276 healthy participants (166 females, age range 20-70 years) and examined their relationships with age and cognitive function. The results revealed decreased GSH levels with age in the PCC among all participants. Notably, the timecourse of GSH level changes in the PCC and ACC differed between males and females. Additionally, positive correlations were observed between GSH levels in the PCC and OC and visuospatial memory. Taken together, these findings enhance our understanding of the brain GSH timecourse during normal aging and associations with sex and memory, which is an essential first step for understanding the neurochemical underpinnings of OS-related diseases.

Joint trajectories of body mass index and waist circumference in early-life to mid-life adulthood and incident hypertension: the China Health and Nutrition Survey.

OBJECTIVE: This longitudinal study aims to identify distinct trajectories of body mass index (BMI) and waist circumference (WC) during 20-60 years old, and explore their joint effect on incident hypertension. DESIGN: A longitudinal cohort study. SETTING: China Health and Nutrition Survey, 1993-2011. PARTICIPANTS: The longitudinal cohort included 6571 participants (3063 men) who had BMI and WC repeatedly measured 3-7 times before incident hypertension or loss to follow-up. OUTCOMES: Hypertension was defined as systolic blood pressure/diastolic blood pressure>140/90 mm Hg or diagnosis by medical records or taking antihypertensive medication. RESULTS: Two distinct trajectories were characterised for both BMI and WC: low-increasing and high-increasing. Jointly, subjects were divided into four groups: normal (n=4963), WC-increasing (n=620), BMI-increasing (n=309) and BMI&WC-increasing (n=679). Compared with the normal group, the adjusted HRs and 95% CIs for hypertension were 1.43 (1.19 to 1.74), 1.51 (1.19 to 1.92) and 1.76 (1.45 to 2.14) for WC-increasing, BMI-increasing and BMI&WC-increasing groups, respectively. The model-estimated levels and slopes of BMI and WC were calculated at each age point in 1-year interval according to the model parameters and their first derivatives, respectively. The associations between model-estimated levels and hypertension increased with age, with adjusted ORs and 95% CIs ranging from 0.92 (0.86 to 0.98) to 1.57 (1.47 to 1.67) for BMI and 0.98 (0.92 to 1.05) to 1.44 (1.35 to 1.53) for WC. Conversely, the ORs (95% CIs) of level-adjusted linear slopes decreased with age, ranging from 1.47 (1.38 to 1.57) to 0.97 (0.92 to 1.03) for BMI and 1.36 (1.28 to 1.45) to 0.99 (0.93 to 1.06) for WC. CONCLUSIONS: Our study demonstrates that the joint trajectories of BMI and WC have significant effect on future hypertension risk, and the changing slopes of BMI and WC during young adulthood are independent risk factors. Both BMI and WC should be paid more attention to prevent hypertension, and young adulthood may be a crucial period for intervention.