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.

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.

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.

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.

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.

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.

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.

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.

Life-course blood pressure trajectories and incident diabetes: A longitudinal cohort in a Chinese population.

Background: Blood pressure levels are correlated with diabetes among middle-aged or older adults. However, longitudinal trajectories of blood pressure during young adulthood and their impact on diabetes have been insufficiently studied. Methods: The longitudinal cohort consisted of 4,625 adults who had blood pressure and body mass index (BMI) repeatedly measured five to nine times during 18-60 years of age. Distinct systolic blood pressure (SBP) trajectories were identified by a group-based trajectory model. Logistic regression analyses were used to investigate the association between trajectory patterns or quartiles of area under the curve values of SBP trajectories and incident diabetes, respectively. Results: Four distinct trajectory groups were identified for SBP: normotensive-stable (n = 761, 16.5%), prehypertension-stable (n = 2,381, 51.5%), stage I hypertension-increasing (n = 1,231, 26.6%), and stage II hypertension-increasing (n = 251, 5.4%). Compared with subjects who remained at SBP <120 mmHg in the normotensive-stable group, individuals in the prehypertension-stable trajectory exhibited a normal SBP range (<140 mmHg), and they still had a significantly higher risk of diabetes (adjusted OR = 1.82, p = 0.029). Individuals had a greater risk of diabetes in the stage I hypertension-increasing group (adjusted OR = 2.31, p = 0.006) and the highest risk in the stage II hypertension-increasing group (adjusted OR = 3.91, p < 0.001) relative to the normotensive-stable group. Furthermore, compared with the first quartile, adjusted ORs (95% CIs) of the fourth quartile of SBP incremental and total AUC were 2.50 (1.61-3.97) and 1.82 (1.15-2.94), respectively. Conclusions: Long-term SBP trajectory is a significant predictor for incident diabetes, which is independent of baseline SBP and body weight, attaching importance to maintaining optimal blood pressure levels and controlling changing slopes of SBP for preventing diabetes.

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.

Four-way decomposition of effect of cigarette smoking and body mass index on serum lipid profiles.

OBJECTIVE: Smoking and obesity are established risk factors of dyslipidemia, however, the interplay between them has not been well studied. This study aims to explore the joint effect of smoking and body mass index (BMI) on serum lipid profiles. METHODS: The study consisted of 9846 Chinese adults (mean age = 49.9 years, 47.6% males, 31.2% ever smokers), based on the China Health and Nutrition Survey. Serum lipid profiles included total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), apolipoprotein A (APO-A), and apolipoprotein B (Apo-B). The joint effect of smoking and BMI on serum lipids were examined by the four-way decomposition analysis and multivariate linear regression models. RESULTS: The four-way decomposition showed that the interplay between smoking and BMI was complicated. There was only indirect effect (the mediated effect) between smoking and BMI on TC, LDL-C and APO-B. The pure indirect effect was -0.023 for TC, -0.018 for LDL-C, and -0.009 for APO-B. For TG, HDL-C and APO-A, the interaction effect was dominant. The reference interaction (the interactive effect when the mediator is left to what it would be in the absence of exposure) was 0.474 (P < 0.001) for TG, -0.245 (P = 0.002) for HDL-C, and -0.222 (P < 0.001) for APO-A, respectively. The effect of BMI on TG, HDL-C and APO-A were significantly higher in smokers than in nonsmokers (TG: 0.151 in smokers versus 0.097 in nonsmokers, HDL-C: -0.037 versus -0.027, APO-A: -0.019 versus -0.009, P for difference < 0.001 for all). CONCLUSION: These findings illustrate the joint effects of smoking and BMI on serum lipid profiles. There were significant interaction effects of smoking and BMI on TG, HDL-C and APO-A, while BMI maybe a mediator for the association of smoking with TC, LDL-C and APO-B. The effects between them were rather complex. Smoking cessation is necessary, especially for those overweight.

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.

Temporal sequence of blood lipids and insulin resistance in perimenopausal women: the study of women's health across the nation.

INTRODUCTION: To explore the temporal relationship between blood lipids and insulin resistance in perimenopausal women. RESEARCH DESIGN AND METHODS: The longitudinal cohort consisted of 1386 women (mean age 46.4 years at baseline) in the Study of Women's Health Across the Nation. Exploratory factor analysis was used to identify appropriate latent factors of lipids (total cholesterol (TC); triglyceride (TG); high-density lipoprotein cholesterol (HDL-C); low-density lipoprotein cholesterol (LDL-C); lipoprotein A-I (LpA-I); apolipoprotein A-I (ApoA-I); apolipoprotein B (ApoB)). Cross-lagged path analysis was used to explore the temporal sequence of blood lipids and homeostasis model assessment of insulin resistance (HOMA-IR). RESULTS: Three latent lipid factors were defined as: the TG factor, the cholesterol transport factor (CT), including TC, LDL-C, and ApoB; the reverse cholesterol transport factor (RCT), including HDL-C, LpA-I, and ApoA-I. The cumulative variance contribution rate of the three factors was 86.3%. The synchronous correlations between baseline TG, RCT, CT, and baseline HOMA-IR were 0.284, -0.174, and 0.112 (p<0.05 for all). After adjusting for age, race, smoking, drinking, body mass index, and follow-up years, the path coefficients of TG→HOMA-IR (0.073, p=0.004), and HOMA-IR→TG (0.057, p=0.006) suggested a bidirectional relationship between TG and HOMA-IR. The path coefficients of RCT→HOMA-IR (-0.091, P < 0.001) and HOMA-IR→RCT (-0.058, p=0.002) were also significant, but the path coefficients of CT→HOMA-IR (0.031, p=0.206) and HOMA-IR→CT (-0.028, p=0.113) were not. The sensitivity analyses showed consistent results. CONCLUSIONS: These findings provide evidence that TG and the reverse cholesterol transport-related lipids are related with insulin resistance bidirectionally, while there is no temporal relationship between the cholesterol transport factor and insulin resistance.

Four-Way Decomposition of Effect of Alcohol Consumption and Body Mass Index on Lipid Profile.

Background: Both obesity and alcohol consumption are strongly associated with dyslipidemia; however, it remains unclear whether their joint effect on lipid profiles is through mediation, interaction, or a combination of the two. Methods: In total, 9849 subjects were selected from the 2009 panel of China Health and Nutrition Survey (CHNS). A four-way decomposition method was used to validate the pathways of drinking and body mass index (BMI) on lipids (total cholesterol, TC; triglyceride, TG; low-density lipoprotein cholesterol, LDL-C; high-density lipoprotein cholesterol, HDL-C; apolipoprotein A, APO-A; and apolipoprotein B, APO-B). Results: According to four-way decomposition, the total effects of drinking on lipids were found to be statistically significant, except for LDL-C. The components due to reference interaction were 0.63, 0.48, 0.60, -0.39, -0.30, and 0.20 for TC, TG, LDL-C, HDL-C, APO-A and APO-B, respectively (p < 0.05 for all). The effect size of pure indirect effect and mediated interaction were 0.001~0.006 (p > 0.05 for all). Further, linear regression models were used to examine the effect of BMI on lipid profiles in drinkers and non-drinkers. The associations of BMI and lipids were higher in all drinkers than in non-drinkers (0.069 versus 0.048 for TC, 0.079 versus 0.059 for TG, 0.057 versus 0.037 for LDL-C, -0.045 versus -0.029 for HDL-C, -0.024 versus -0.011 for APO-A and 0.026 versus 0.019 for APO-B, p interaction <0.05 for all). Conclusions: The joint effect of alcohol consumption and obesity on lipid profiles is through interaction rather than mediation. Alcohol consumption amplifies the harmful effect of BMI on lipid profiles. Greater attention should be paid to lipid health and cardiovascular risk in obese individuals regarding alcohol consumption. For obese individuals, we do not recommend alcohol consumption.

Trajectories of Perioperative Serum Tumor Markers and Colorectal Cancer Outcomes: A Retrospective, Multicenter Longitudinal Cohort Study.

BACKGROUND: The dynamic monitoring of perioperative carcinoembryonic antigen (CEA) is recommended by current colorectal cancer (CRC) guidelines, while the benefits of additional measurements of carbohydrate antigen 19-9 (CA19-9) and carbohydrate antigen 125 (CA125) have remained controversial. METHODS: This retrospective longitudinal cohort included 3539 CRC patients who underwent curative resection. Distinct trajectory groups were identified by the latent class growth mixed model. Patients were grouped into subgroups jointly by CEA, CA19-9, and CA125 according to preoperative levels and longitudinal trajectories, respectively. The end points were overall survival (OS) and recurrence-free survival (RFS). FINDINGS: Three distinct trajectory groups were characterized for serum CEA, CA19-9, and CA125: low-stable, early-rising, and later-rising. Jointly, patients were grouped into six preoperative (trajectory) joint groups. Compared with the three-low group, the adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) associated with death were 1.87 (1.29-2.70), 3.82 (2.37-6.17), 1.87 (0.97-3.61), 2.81 (1.93-4.11), and 4.99 (2.80-8.86) for the CEA-high, CA19-9-high, CA125-high, two-high, and three-high group, respectively. And compared with the three-stable trajectory group, the corresponding HRs (95% CIs) were 1.59 (1.10-2.30), 1.55 (0.77-3.10), 6.25 (4.02-9.70), 4.05 (2.73-6.02), and 12.40 (5.77-26.70) for the five rising trajectory groups, respectively. Similar associations between joint groups and RFS were observed. Notably, the trajectory joint group still had prognostic significance after adjusting for preoperative levels. The CA19-9-high group (HR: 3.82, 95% CI: 2.37-6.17) was associated with higher risk of death than the two-high group (HR: 2.81, 95% CI: 1.93-4.11). Likewise, for the CA125-rising trajectory group and two-rising trajectory group, the HRs (95% CIs) were 6.13 (3.75-10.00) and 3.99 (2.63-6.05) for death, and 3.08 (2.07-4.58) and 2.10 (1.52-2.90) for recurrence. INTERPRETATION: In addition to CEA, the dynamic measurements of CA19-9 and CA125 are recommended to monitor the prognosis of CRC patients. FUNDING: National Natural Science Foundation of China [81973147, 82001986, 81960592, 82073569, 81660545].

Differential Lipidomic Characteristics of Children Born to Women with Polycystic Ovary Syndrome.

Objective: To describe the lipidomic characteristics of offspring born to polycystic ovary syndrome (PCOS) women (PCOS-off) and assess the associations between differential lipids and clinical phenotypes. Methods: Ultra performance liquid chromatography and mass spectrometry were performed on plasma samples from 70 PCOS-off and 71 healthy controls. The associations of differential metabolites with clinical phenotypes were examined by multiple linear regression. Results: Forty-four metabolites were significantly altered in PCOS-off, including 8 increased and 36 decreased. After stratification according to sex, 44 metabolites (13 increased and 31 decreased) were expressed differently in girls born to PCOS women (PCOS-g), most of which were glycerolipids. Furthermore, 46 metabolites (9 increased and 35 decreased) were expressed differently in boys born to PCOS women (PCOS-b), most of which were glycerophospholipids. Significant associations of metabolites with weight Z-score and high density lipoprotein cholesterol were found in PCOS-off. Triglycerides, low density lipoprotein cholesterol, and thyroid-stimulating hormone were separately correlated with some lipids in PCOS-g and PCOS-b. Conclusions: PCOS-off showed specific lipid profile alterations. The abnormal level of glycerophospholipids and sphingomyelin indicated the risk of glucose metabolism and cardiovascular diseases in PCOS-off. Some lipids, such as phosphatidylcholines, lysophosphatidylcholine and sphingomyelin, may be the potential markers. The results broadened our understanding of PCOS-offs' cardiometabolic status and emphasized more specific and detailed monitoring and management in this population.

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.