Associations of dietary magnesium intake with the risk of atherosclerotic cardiovascular disease and mortality in individuals with and without type 2 diabetes: A prospective study in the UK Biobank.

BACKGROUND: The association between dietary magnesium (Mg) intake and the risk of atherosclerotic cardiovascular disease (ASCVD) remains uncertain. We aimed to examine the associations of dietary Mg intake with the risk of ASCVD events and mortality in individuals with and without type 2 diabetes. METHODS: A total of 149,929 participants (4603 with type 2 diabetes) from the UK Biobank were included in the analyses. The hazard ratios (HRs) and 95 % confidence intervals (CIs) were estimated using Cox proportional hazard models. Furthermore, interactions of dietary Mg intake with type 2 diabetes status were examined on multiplicative and additive scales. RESULTS: During a median follow-up of 12.0 and 12.1 years, 7811 incident ASCVD events and 5000 deaths (including 599 ASCVD deaths) were documented, respectively. There were significantly negative associations between sufficient dietary Mg intake (equal to or greater than the recommended daily intake) and the risk of ASCVD incidence (HR 0.63 [95 % CI 0.49;0.82]), ASCVD mortality (0.45 [0.24;0.87]), and all-cause mortality (0.71 [0.52;0.97]) in participants with type 2 diabetes, whereas no significant association was observed in participants without type 2 diabetes (1.01 [0.94;1.09] for ASCVD incidence; 1.25 [0.93;1.66] for ASCVD mortality; 0.97 [0.88;1.07] for all-cause mortality). Multiplicative and additive interactions of dietary Mg intake with type 2 diabetes status were both observed. CONCLUSION: Sufficient dietary Mg intake was significantly associated with lower risks of ASCVD events and mortality in individuals with type 2 diabetes but not in those without type 2 diabetes. Our findings provide insight into the importance of dietary Mg intake for reducing modifiable cardiovascular burden in individuals with type 2 diabetes, which may inform future personalized dietary guidelines.

Endoscopic Submucosal Dissection Criteria for Differentiated-type Early Gastric Cancer Are Applicable to Mixed-type Differentiated Predominant.

BACKGROUND: There is a lack of sufficient evidence on whether mixed-type differentiated predominant early gastric cancer (MD-EGC) can be treated endoscopically by referring to the criteria for differentiated-type early gastric cancer (EGC). This study aims to evaluate the efficacy of endoscopic submucosal dissection (ESD) in MD-EGC. METHODS: Patients with differentiated-type EGC treated with ESD first from January 2015 to June 2021 were reviewed, including MD-EGC and pure differentiated-type EGC (PD-EGC). Clinical data, including the clinicopathological characteristics, resection outcomes of ESD, and recurrence and survival time, were collected, and the difference between MD-EGC and PD-EGC was tested. RESULTS: A total of 48 patients (48 lesions) with MD-EGC and 850 patients (890 lesions) with PD-EGC were included. Compared with PD-EGC, MD-EGC had a higher submucosal invasion rate (37.5% vs. 13.7%, P<0.001) and lymphatic invasion rate (10.4% vs. 0.4%, P<0.001). The rates of complete resection (70.8% vs. 92.5%, P<0.001) and curative resection (54.2% vs. 87.4%, P<0.001) in MD-EGC were lower than those of PD-EGC. Multivariate analysis revealed that MD-EGC (OR 4.26, 95% CI, 2.22-8.17, P<0.001) was an independent risk factor for noncurative resection. However, when curative resection was achieved, there was no significant difference in the rates of recurrence (P=0.424) between the 2 groups, whether local or metachronous recurrence. Similarly, the rates of survival(P=0.168) were no significant difference. CONCLUSIONS: Despite the greater malignancy and lower endoscopic curative resection rate of MD-EGC, patients who met curative resection had a favorable long-term prognosis.

Beta spending function based on conditional power in group sequential design.

Conditional power (CP) serves as a widely utilized approach for futility monitoring in group sequential designs. However, adopting the CP methods may lead to inadequate control of the type II error rate at the desired level. In this study, we introduce a flexible beta spending function tailored to regulate the type II error rate while employing CP based on a predetermined standardized effect size for futility monitoring (a so-called CP-beta spending function). This function delineates the expenditure of type II error rate across the entirety of the trial. Unlike other existing beta spending functions, the CP-beta spending function seamlessly incorporates beta spending concept into the CP framework, facilitating precise stagewise control of the type II error rate during futility monitoring. In addition, the stopping boundaries derived from the CP-beta spending function can be calculated via integration akin to other traditional beta spending function methods. Furthermore, the proposed CP-beta spending function accommodates various thresholds on the CP-scale at different stages of the trial, ensuring its adaptability across different information time scenarios. These attributes render the CP-beta spending function competitive among other forms of beta spending functions, making it applicable to any trials in group sequential designs with straightforward implementation. Both simulation study and example from an acute ischemic stroke trial demonstrate that the proposed method accurately captures expected power, even when the initially determined sample size does not consider futility stopping, and exhibits a good performance in maintaining overall type I error rates for evident futility.

Sample size estimation for stratified cluster randomization trial with survival endpoint.

Cluster randomization trials with survival endpoint are predominantly used in drug development and clinical care research when drug treatments or interventions are delivered at a group level. Unlike conventional cluster randomization design, stratified cluster randomization design is generally considered more effective in reducing the impacts of imbalanced baseline prognostic factors and varying cluster sizes between groups when these stratification factors are adopted in the design. Failure to account for stratification and cluster size variability may lead to underpowered analysis and inaccurate sample size estimation. Apart from the sample size estimation in unstratified cluster randomization trials, there are no development of an explicit sample size formula for survival endpoint when a stratified cluster randomization design is employed. In this article, we present a closed-form sample size formula based on the stratified cluster log-rank statistics for stratified cluster randomization trials with survival endpoint. It provides an integrated solution for sample size estimation that account for cluster size variation, baseline hazard heterogeneity, and the estimated intracluster correlation coefficient based on the preliminary data. Simulation studies show that the proposed formula provides the appropriate sample size for achieving the desired statistical power under various parameter configurations. A real example of a stratified cluster randomization trial in the population with stable coronary heart disease is presented to illustrate our method.

The heterogeneity effect of surveillance intervals on progression free survival.

Progression-free survival (PFS) is an increasingly important surrogate endpoint in cancer clinical trials. However, the true time of progression is typically unknown if the evaluation of progression status is only scheduled at given surveillance intervals. In addition, comparison between treatment arms under different surveillance schema is not uncommon. Our aim is to explore whether the heterogeneity of the surveillance intervals may interfere with the validity of the conclusion of efficacy based on PFS, and the extent to which the variation would bias the results. We conduct comprehensive simulation studies to explore the aforementioned goals in a two-arm randomized control trial. We introduce three steps to simulate survival data with predefined surveillance intervals under different censoring rate considerations. We report the estimated hazard ratios and examine false positive rate, power and bias under different surveillance intervals, given different baseline median PFS, hazard ratio and censoring rate settings. Results show that larger heterogeneous lengths of surveillance intervals lead to higher false positive rate and overestimate the power, and the effect of the heterogeneous surveillance intervals may depend upon both the life expectancy of the tumor prognoses and the censoring proportion of the survival data. We also demonstrate such heterogeneity effect of surveillance intervals on PFS in a phase III metastatic colorectal cancer trial. In our opinions, adherence to consistent surveillance intervals should be favored in designing the comparative trials. Otherwise, it needs to be appropriately taken into account when analyzing data.

Differential Diagnosis of Post Pancreatitis Diabetes Mellitus Based on Pancreatic and Gut Hormone Characteristics.

CONTEXT: Distinguishing different types of diabetes is important in directing optimized treatment strategies and correlated epidemiological studies. OBJECTIVE: Through detailed analysis of hormone responses to mixed meal tolerance test (MMTT), we aimed to find representing characteristics of post-acute pancreatitis diabetes mellitus (PPDM-A) and post-chronic pancreatitis diabetes mellitus (PPDM-C). METHODS: Participants with PPDM-A, PPDM-C, type 1 diabetes, type 2 diabetes, and normal controls (NCs) underwent MMTT. Fasting and postprandial responses of serum glucose, C-peptide, insulin, glucagon, pancreatic polypeptide (PP), ghrelin, gastric inhibitory peptide (GIP), glucagon like peptide-1 (GLP-1), and peptide YY (PYY) were detected and compared among different groups. Focused analysis on calculated insulin sensitivity and secretion indices were performed to determine major causes of hyperglycemia in different conditions. RESULTS: Participants with PPDM-A were characterized by increased C-peptide, insulin, glucagon, and PP, but decreased ghrelin, GIP, and PYY compared with NCs. Patients with PPDM-C showed secretion insufficiency of C-peptide, insulin, ghrelin, and PYY, and higher postprandial responses of glucagon and PP than NCs. In particular, both fasting and postprandial levels of ghrelin in PPDM-C were significantly lower than other diabetes groups. PYY responses in patients with PPDM-A and PPDM-C were markedly reduced. Additionally, the insulin sensitivity of PPDM-A was decreased, and the insulin secretion for PPDM-C was decreased. CONCLUSION: Along with the continuum from acute to chronic pancreatitis, the pathological mechanism of PPDM changes from insulin resistance to insulin deficiency. Insufficient PYY secretion is a promising diagnostic marker for distinguishing PPDM from type 1 and type 2 diabetes. Absent ghrelin secretion to MMTT may help identify PPDM-C.

Efficacy and safety of a bolus of half-dose r-SAK prior to primary PCI in ST-elevation myocardial infarction: Rationale and design of the OPTIMA-6 trial.

BACKGROUND: Time to reperfusion is the key to the treatment of patients with ST-elevation myocardial infarction (STEMI). It is uncertain whether adjunctive thrombolytic therapy combined with contemporary antiplatelet agent ticagrelor improves outcomes as administered prior to primary percutaneous coronary intervention (PCI) expected to be performed within 120 minutes. METHODS: OPTIMA-6 is a multicenter, randomized, double-blind, placebo-controlled, and superiority trial to evaluate the efficacy of a bolus of half-dose recombinant staphylokinase (r-SAK) vs placebo prior to timely primary PCI in patients with STEMI. Enrollment began in April 2023 and is expected to enroll 2,260 patients at approximately 50 centers. Patients with acute STEMI presenting ≤12 hours of symptom onset and expected to undergo primary PCI within 120 minutes but more than 30 minutes are to be randomized to a bolus of half-dose r-SAK or placebo. All recruited patients will be mandatory to take aspirin and ticagrelor and receive a bolus of loading dose heparin before the thrombolytic therapy. The primary efficacy endpoint is major adverse cardiovascular events (MACE) within 90 days, and the MACE is defined as a composite of all-cause death, reinfarction, unplanned target vessel revascularization, heart failure or cardiogenic shock, and major ventricular arrhythmia. The primary safety endpoints are major bleeding events (BARC 3, 5) within 90 days. CONCLUSIONS: OPTIMA-6 will reveal the efficacy and safety of a contemporary facilitated PCI with a bolus of half-dose r-SAK in combination with ticagrelor in patients with STEMI.

Necroptosis-Related Modification Patterns Depict the Tumor Microenvironment, Redox Stress Landscape, and Prognosis of Ovarian Cancer.

Necroptosis is one of programmed cell death discovered recently, which involves in tumorigenesis, cancer metastasis, and immune reaction. We studied the necroptosis-related genes (NRGs) in ovarian cancer (OV) tissues using data from public databases, which separated into two NRGclusters. Patients in cluster A would have severe clinical characteristics, poor prognosis, and worse tumor microenvironment infiltration characteristics. The NRG score was achieved through the Cox analysis, along with a construction of a prognostic model. People with lower risk score would have better prognosis, lower expression of redox related genes, higher immunogenicity, and better effect on immunotherapy. In addition, the NRG score was closely related to cancer stem cell index, copy number variations, tumor mutation load, and chemosensitivity. We built a nomogram to enhance clinical application of the signature. These outcomes can help use know the function of NRGs in OV and provide new ideas for evaluating clinical outcome and developing more effective treatment protocols.

A Hypoxia Molecular Signature-Based Prognostic Model for Endometrial Cancer Patients.

Endometrial cancer has the highest incidence of uterine corpus cancer, the sixth most typical cancer in women until 2020. High recurrence rate and frequent adverse events were reported in either standard chemotherapy or combined therapy. Hence, developing precise diagnostic and prognostic approaches for endometrial cancer was on demand. Four hypoxia-related genes were screened for the EC prognostic model by the univariate, LASSO, and multivariate Cox regression analysis from the TCGA dataset. QT-PCR and functional annotation analysis were performed. Associations between predicted risk and immunotherapy and chemotherapy responses were investigated by evaluating expressions of immune checkpoint inhibitors, infiltrated immune cells, m6a regulators, and drug sensitivity. The ROC curve and calibration plot indicated a fair predictability of our prognostic nomogram model. NR3C1 amplification, along with IL-6 and SRPX suppressions, were detected in tumor. High stromal score and enriched infiltrated aDCs and B cells in the high-risk group supported the hypothesis of immune-deserted tumor. Hypoxia-related molecular subtypes of EC were then identified via the gene signature. Cluster 2 patients showed a significant sensitivity to Vinblastine. In summary, our hypoxia signature model accurately predicted the survival outcome of EC patients and assessed translational and transcriptional dysregulations to explore targets for precise medical treatment.

Risk difference, relative risk, and odds ratio for non-inferiority clinical trials with risk rate endpoint.

Non-inferiority (NI) clinical trials are widely used to evaluate whether the new experimental treatment is not unacceptably worse than the current active-control treatment by more than a pre-specified non-inferiority margin (NI margin). However, choosing either an absolute difference [risk difference (RD)] or a relative difference [relative risk (RR) and odds ratio (OR)] to evaluate efficacy in NI clinical trials is still controversial. In this study, we aim to evaluate the performance of abovementioned three metrics for testing NI clinical trials with risk rate endpoint. Herein, extensive Monte Carlo simulations based on various parameter settings (NI margin as well as risk rates in the experimental group and active-control group) are conducted to compare the Type I error rate, statistical power, and the necessary sample size to achieve a desired power for testing NI using RD, RR, and OR. We show that testing NI using RD not only controls well the Type I error and achieves the highest statistical power but also requires the smallest sample size compared to RR and OR. In practice, however, the choice among three metrics still needs to be based upon clinical interpretations and regulatory perspectives.

Regional efficacy evaluation in multi-regional clinical trials using a discounting factor weighted Z test.

Multi-regional clinical trial (MRCT) is an efficient design to accelerate drug approval globally. Once the global efficacy of test drug is demonstrated, each local regulatory agency is required to prove effectiveness of test drug in their own population. Meanwhile, the ICH E5/E17 guideline recommends using data from other regions to help evaluate regional drug efficacy. However, one of the most challenges is how to manage to bridge data among multiple regions in an MRCT since various intrinsic and extrinsic factors exist among the participating regions. Furthermore, it is critical for a local agency to determine the proportion of information borrowing from other regions given the ethnic differences between target region and non-target regions. To address these issues, we propose a discounting factor weighted Z statistic to adaptively borrow information from non-target regions. In this weighted Z statistic, the weight is derived from a discounting factor in which the discounting factor denotes the proportion of information borrowing from non-target regions. We consider three ways to construct discounting factors based on the degree of congruency between target and non-target regions either using control group data, or treatment group data, or all data. We use the calibrated power prior to construct discounting factor based on scaled Kolmogorov-Smirnov statistic. Comprehensive simulation studies show that our method has desirable operating characteristics. Two examples are used to illustrate the applications of our proposed approach.

Pyroptosis-related gene expression patterns and corresponding tumor microenvironment infiltration characterization in ovarian cancer.

Pyroptosis, a form of inflammatory programmed cell death, is accompanied by inflammation and participate in the body's immune response. The expression of pyroptosis-related genes (PRGs) is associated with tumor prognosis in ovarian cancer (OC), but it is still unknown whether pyroptosis can affect tumor immune microenvironment (TME) of OC. Based on 30 PRGs, we comprehensively assessed the pyroptosis patterns by using PRGscore and correlated them with TME features in 474 OC patients. Finally, we identified three pyroptosis modification patterns and TME immune characteristics of these patterns were in response to three immune phenotypes (immune-desert, immune-inflamed, and immune-excluded phenotypes). PRGscore can predict patient survival, staging, grading, and immunotherapy efficacy. Low PRGscore was associated with better survival advantage and increased mutation burden. Low PRGscore patients showed significantly better therapeutic effects and clinical results in chemotherapy and immunotherapy. Besides, the capability of PRGscore in predicting prognosis and immunotherapy sensitivity was further verified in other three tumor cohorts. In conclusion, the comprehensive assessment of OC pyroptosis modifications can help enhancing our understanding of TME immune infiltration and provide better personalized treatment tactics for OC patients.

Identification of Specific Cervical Cancer Subtypes and Prognostic Gene Sets in Tumor and Nontumor Tissues Based on GSVA Analysis.

Background: Cervical cancer is the fourth common cancer among women. Its prognosis needs our more attention. Our purpose was to identity new prognostic gene sets to help other researchers develop more effective treatment for cervical cancer patients and improve the prognosis of patients. Methods: We used gene set variation analysis (GSVA) to calculate the enrichment scores of gene sets and identified three subtypes of cervical cancer through the Cox regression model, k-means clustering algorithm, and nonnegative matrix factorization method (NMF). Chi-square test was utilized to test whether a certain clinical characteristic is different among divided subtypes. We further screened the prognostic gene sets using differential analysis, univariate Cox regression analysis, and least absolute shrinkage and selection operator (LASSO) regression. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to analyze which pathways and function the genes from screened gene sets enriched. Search Tool for the Retrieval of Interacting Genes (STRING) was used to draw the protein-protein interaction network, and Cytoscape was used to visualize the hub genes of protein-protein interaction network. Results: We identified three novel subtypes of cervical cancer in The Cancer Genome Atlas (TCGA) samples and validated in Gene Expression Omnibus (GEO) samples. There were significant variations between the three subtypes in histological type, T stage, M stage, and N stage. T_GSE36888_UNTREATED_VS_IL2_TREATED_STAT5_AB_KNOCKIN_TCELL_2H_UP and N_HALLMARK_ANGIOGENESIS were screened prognostic gene sets. The prognostic model was as follows: riskScore = T_GSE36888_UNTREATED_VS_IL2_TREATED_STAT5_AB_KNOCKIN_TCELL_2H_UP∗ 2.617 + N_HALLMARK_ANGIOGENESIS∗ 4.860. Survival analysis presented that in these two gene sets, high enrichment scores were all significantly related to worse overall survival. The hub genes from T gene set included CXCL1, CXCL2, CXCL8, ALDOA, TALDO1, LDHA, CCL4, FCAR, FCER1G, SAMSN1, LILRB1, SH3PXD2B, PPM1N, PKM, and FKBP4. As for N gene sets, the hub genes included ITGAV, PTK2, SPP1, THBD, and APOH. Conclusions: Three novel subtypes and two prognostic gene sets were identified. 15 hub genes for T gene set and 5 hub genes for N gene set were discovered. Based on these findings, we can develop more and more effective treatments for cervical cancer patients. Based on the gene enriched pathways, we can development specific drugs targeting the pathways.

N1-Methyladenosine-Related lncRNAs Are Potential Biomarkers for Predicting Prognosis and Immune Response in Uterine Corpus Endometrial Carcinoma.

Uterine corpus endometrial carcinoma (UCEC) is a malignant disease that, at present, has no well-characterised prognostic biomarker. In this study, two clusters were identified based on 28 N1-methyladenosine- (m1A-) related long noncoding RNAs (lncRNAs), of which cluster 1 was related to immune pathways according to the results of an enrichment analysis. We further observed better prognosis in patients with higher levels of immune cell infiltration, tumor mutation burden, microsatellite instability, and immune checkpoint gene expression. In addition, through Cox regression analysis and least absolute shrinkage and selection operator regression analysis, 10 m1A-related lncRNAs (mRLs) were employed to build a prognosis model. We found that people in higher risk categories had a poorer survival probability than those in lower risk. Low-risk samples were enriched with immune-related pathways, while the high-risk group was similar to the definition of the "immune desert" phenotype, which was associated with decreased immune infiltration, T cell failure, and decreased tumor mutation burden, while also being insensitive to immunotherapy and chemotherapy. This mRL-based model has the ability to accurately predict the prognosis of UCEC patients, and the mRLs could become promising therapeutic targets in enhancing the response of immunotherapy.

The m6A-Related Long Noncoding RNA Signature Predicts Prognosis and Indicates Tumor Immune Infiltration in Ovarian Cancer.

Background: OV is the most lethal gynecological malignancy. M6A and lncRNAs have a great impact on OV development and patient immunotherapy response. In this paper, we decided to establish a reliable signature of mRLs. Method: The lncRNAs associated with m6A in OV were analyzed and obtained by co-expression analysis of the TCGA-OV database. Univariate, LASSO and multivariate Cox regression analyses were employed to establish the model of mRLs. K-M analysis, PCA, GSEA and nomogram based on the TCGA-OV and GEO database were conducted to prove the predictive value and independence of the model. The underlying relationship between the model and TME and cancer stemness properties were further investigated through immune feature comparison, consensus clustering analysis and pan-cancer analysis. Results: A prognostic signature comprising four mRLs, WAC-AS1, LINC00997, DNM3OS and FOXN3-AS1, was constructed and verified for OV according to the TCGA and GEO database. The expressions of the four mRLs were confirmed by qRT-PCR in clinical samples. Applying this signature, one can identify patients more effectively. The samples were divided into two clusters, and the clusters had different overall survival rates, clinical features and tumor microenvironments. Finally, pan-cancer analysis further demonstrated that the four mRLs were significantly related to immune infiltration, TME and cancer stemness properties in various cancer types. Conclusions: This study provided an accurate prognostic signature for patients with OV and elucidated the potential mechanism of the mRLs in immune modulation and treatment response, giving new insights into identifying new therapeutic targets.

Crosstalk of Redox-Related Subtypes, Establishment of a Prognostic Model and Immune Responses in Endometrial Carcinoma.

Redox plays a central part in the pathogeneses and development of tumors. We comprehensively determined the expression patterns of redox-related genes (RRGs) in endometrial carcinoma (EC) cohorts from public databases and identified four different RRG-related clusters. The prognosis and the characteristics of TME cell infiltration of RRGcluster C patients were worse than those of other RRG clusters. When it comes to the gene cluster, there were great differences in clinicopathology traits and immunocyte infiltration. The RRG score was calculated by Cox analyses, and an RRG-based signature was developed. The risk score performed well in the EC cohort. Samples were separated into two risk subgroups with the standard of the value of the median risk score. Low-risk patients had a better prognosis and higher immunogenicity. In addition, RRG score was closely associated with immunophenoscore, microsatellite instability, tumor mutation burden, tumor stem cell index, copy number variation and chemotherapy sensitivity. The nomogram accurately predicted the prognosis of patients, and our model showed better performance than other published models. In conclusion, we built a prognostic model of RRGs which can help to evaluate clinical outcomes and guide more effective treatment.

A comprehensive prognostic and immune analysis of enhancer RNA identifies IGFBP7-AS1 as a novel prognostic biomarker in Uterine Corpus Endometrial Carcinoma.

BACKGROUND: Long non-coding RNAs (lncRNA) have been implicated in a hand of studies that supported an involvement and co-operation in Uterine Corpus Endometrial Carcinoma (UCEC). Enhancer RNAs (eRNA), a functional subtype of lncRNA, have a key role throughout the genome to guide protein production, thus potentially associated with diseases. METHODS: In this study, we mainly applied the Cancer Genome Atlas (TCGA) dataset to systematically discover crucial eRNAs involving UCEC. For the key eRNAs in UCEC, we employed RT-qPCR to compare eRNA expression levels in tumor tissues and paired normal adjacent tissues from UCEC patients for validation. Furthermore, the relationships between the key eRNAs and immune activities were measured from several aspects, including the analysis for tumor microenvironment, immune infiltration cells, immune check point genes, tumor mutation burden, and microsatellite instability, as well as m6A related genes. Finally, the key eRNAs were verified by a comprehensive pan-cancer analysis. RESULTS: IGFBP7 Antisense RNA 1 (IGFBP7-AS1) was identified as the key eRNA for its expression patterns of low levels in tumor tissues and favorable prognostic value in UCEC correlated with its target gene IGFBP7. In RT-qPCR analysis, IGFBP7-AS1 and IGFBP7 had down-regulated expression in tumor tissues, which was consistent with previous analysis. Moreover, IGFBP7-AS1 was found closely related with immune response in relevant immune analyses. Besides, IGFBP7-AS1 and its target gene IGFBP7 correlated with a multi-omics pan-cancer analysis. CONCLUSIONS: Finally, we suggested that IGFBP7-AS1 played a key role in impacting on clinical outcomes of UCEC patients for its possible influence on immune activity.

Association between groups of immunoglobulin G antibodies against periodontal microorganisms and diabetes-related mortality.

BACKGROUND: Immunoglobulin G (IgG) antibodies against periodontal microorganisms can be markers of periodontal infection because their levels rise following infection and remain elevated several years later. METHODS: We evaluated the relationship between groups of IgG antibodies against 19 periodontal microorganisms and diabetes-related mortality over 27 years among participants of the National Health and Nutrition Examination Survey III (1988 to 1992) aged ≥40 years at the time of examination (N = 8,153). RESULTS: Individuals in the highest versus lowest antibody tertiles were at 86% higher risk of dying due to diabetes-related causes in the Red-Green antibody cluster (T. forsythia, T. denticola, A. actinomycetemcomitans, E. corrodens, S. noxia, V. parvula, C. rectus) (hazard ratio [HR], 1.86; 95% CI, 1.09 to 3.20) and 55% lower in the Orange-Blue antibody cluster (E. nodatum, A. naeslundii) (HR, 0.45, 95% CI, 0.32 to 0.63) in multivariable models. In these models, individuals with diabetes at the time of examination had a 16-fold higher risk of dying due to diabetes-related causes (HR, 16.4; 95% CI, 11.0 to 24.7). CONCLUSION: As a subset of periodontal microorganisms are associated with adverse systemic outcomes, antibody profiles may help in prediction of diabetes-related mortality and identify subgroups of individuals among whom periodontal treatment may impact diabetes-related outcomes.

Association between dietary intake of polyunsaturated fatty acid and prevalence of hypertension in U.S. adults: A cross-sectional study using data from NHANES 2009-2016.

This study aimed to evaluate the association between dietary polyunsaturated fatty acid (PUFA) intake and the prevalence of hypertension in U.S. adults. Data from the National Health and Nutrition Examination Survey (NHANES) 2009-2016 were used. Total PUFAs and subtypes of PUFAs, including eicosapentaenoic acid (EPA), docosapentaenoic acid (DPA), docosahexaenoic acid (DHA), alpha-linolenic acid (ALA), linoleic acid (LA), and arachidonic acid (AA), were obtained through two 24 h recalls and adjusted by body weight. Hypertension was defined as the average of three measurements of blood pressure above 130/80 mmHg or taking antihypertensive medication. Weighted odds ratios (ORs) of hypertension and adjusted rate differences (ARDs) in prevalence, as well as their 95% confidence intervals (CIs), were estimated by using the logistic regression model of survey design. A total of 17,108 participants were included in this study. Dietary intake of PUFAs was significantly associated with a lower prevalence of hypertension for the highest versus lowest quartiles. The weighted ORs with 95% CIs of hypertension for total PUFA, omega-3 fatty acid, fish oil, ALA, omega-6 fatty acid, LA and AA were 0.47(0.40-0.55), 0.61(0.51-0.72), 0.85(0.74-0.97), 0.65(0.55-0.76), 0.49(0.42-0.58), 0.49(0.42-0.57) and 0.75(0.64-0.89), and the ARDs with 95% CIs were -18.06%(-22.54%, -13.58%), -12.06%(-16.68%, -7.44%), -4.13%(-8.25%, -0.01%), -10.54%(-15.31%, -5.78%), -17.03%(-21.49%, -12.58%), -17.23%(-21.76%, -12.69%) and -6.91%(-11.37%, -2.46%), respectively. Our study proposed that the intake of total PUFAs, omega-3 fatty acids, fish oil, ALA, omega-6 fatty acids, LA, and AA was associated with a lower prevalence of hypertension in the U.S. adults.

Pan-Cancer Analysis Revealed SRSF9 as a New Biomarker for Prognosis and Immunotherapy.

BACKGROUND: Serine/arginine-rich splicing factor 9 (SRSF9) is one of the members of SRSF gene family and related to the tumorigenesis and the progression of tumor. However, whether SRSF9 has a crucial role across pan-cancer is still unknown. METHODS: In this study, we used public databases, such as The Cancer Genome Atlas (TCGA), Cancer Cell Line Encyclopedia (CCLE), and Genotype-Tissue Expression (GTEx), to analyze SRSF9 expression level among tumor and normal cells. Survival analysis, K-M plotter, and PrognoScan were used to analyze the prognosis value of SRSF9, regarding to overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI). Moreover, we performed the correlation between SRSF9 and clinical characteristics (including the outcome of prognosis), as well as molecular events of tumor mutation burden (TMB), microsatellite instability (MSI), immune checkpoint gene, tumor microenvironment (TME), immune infiltrating cells, mismatch repair (MMR) genes, m6A genes, DNA methyltransferases, and neoantigen with bioinformatics methods and TISIDB, TIMER, and Sangerbox websites. RESULTS: In general, SRSF9 expression was upregulated in most cancers, such as BLCA, CHOL, and UCEC, which SRSF9 was associated with short survival and severe progression. In COAD, STAD, and UCEC, SRSF9 expression was positively related to both TMB and MSI. In BRCA, BLCA, ESCA, GBM, HNSC, LUSC, LUAD, OV, PRAD, TGCT, THCA, and UCEC, both immune score and stomal score showed a negative relationship with SRSF9 expression. Immune score showed a positive relationship with SRSF9 expression in LGG. SRSF9 expression had a significant and positive correlation with six types of immune infiltration cells in LGG, KIRC, LIHC, PCPG, PRAD, SKCM, THCA, and THYM, except in LUSC. In LIHC, SRSF9 was highly significant correlated with most immune checkpoint genes. For neoantigens, correlation between SRSF9 and the quantity of neoantigens was significantly positive in some cancer types. SRSF9 was also correlated with MMR genes, m6A genes, and DNA methyltransferases. In the 33 cancer types, gene set enrichment analysis (GSEA) demonstrated that SRSF9 was correlated with multiple functions and signaling pathways. CONCLUSION: These findings demonstrated that SRSF9 may be a new biomarker for the prognosis and immunotherapy in various cancers. As a result, it will be beneficial to provide new therapies for cancer patients, thereby improving the treatment and prognosis of cancer patients.