Effect of Maternal Pre-Pregnancy Body Mass Index on Longitudinal Fetal Growth and Mediating Role of Maternal Fasting Plasma Glucose: A Retrospective Cohort Study.

Purpose: To assess the impact of maternal pre-pregnancy body mass index (BMI) on longitudinal fetal growth, and the potential mediation effect of the maternal fasting plasma glucose in first trimester. Methods: In this retrospective cohort study, we collected pre-pregnancy BMI data and ultrasound measurements during pregnancy of 3879 singleton pregnant women who underwent antenatal examinations and delivered at Peking Union Medical College Hospital. Generalized estimation equations, linear regression, and logistic regression were used to examine the association between pre-pregnancy BMI with fetal growth and adverse neonatal outcomes. Mediation analyses were also used to examine the mediating role of maternal fasting plasma glucose (FPG) in first trimester. Results: A per 1 Kg/m² increase in pre-pregnancy BMI was associated with increase fetal body length Z-score (ß 0.010, 95% CI 0.001, 0.019) and fetal body weight (ß 0.017, 95% CI 0.008, 0.027). In mid pregnancy, pre-pregnancy BMI also correlated with an increase Z-score of fetal abdominal circumference, femur length (FL). Pre-pregnancy BMI was associated with an increased risk of large for gestational age and macrosomia. Mediation analysis indicated that the associations between pre-pregnancy BMI and fetal weight in mid and late pregnancy, and at birth were partially mediated by maternal FPG in first trimester (mediation proportion: 5.0%, 8.3%, 1.6%, respectively). Conclusion: Maternal pre-pregnancy BMI was associated with the longitudinal fetal growth, and the association was partly driven by maternal FPG in first trimester. The study emphasized the importance of identifying and managing mothers with higher pre-pregnancy BMI to prevent fetal overgrowth.

Investigating the shared genetic architecture between breast and ovarian cancers.

High heritability and strong correlation have been observed in breast and ovarian cancers. However, their shared genetic architecture remained unclear. Linkage disequilibrium score regression (LDSC) and heritability estimation from summary statistics (ρ-HESS) were applied to estimate heritability and genetic correlations. Bivariate causal mixture model (MiXeR) was used to qualify the polygenic overlap. Then, stratified-LDSC (S-LDSC) was used to identify tissue and cell type specificity. Meanwhile, the adaptive association test called MTaSPUsSet was performed to identify potential pleiotropic genes. The Single Nucleotide Polymorphisms (SNP) heritability was 13% for breast cancer and 5% for ovarian cancer. There was a significant genetic correlation between breast and ovarian cancers (rg=0.21). Breast and ovarian cancers exhibited polygenic overlap, sharing 0.4 K out 2.8 K of causal variants. Tissue and cell type specificity displayed significant enrichment in female breast mammary, uterus, kidney tissues, and adipose cell. Moreover, the 74 potential pleiotropic genes were identified between breast and ovarian cancers, which were related to the regulation of cell cycle and cell death. We quantified the shared genetic architecture between breast and ovarian cancers and shed light on the biological basis of the co-morbidity. Ultimately, these findings facilitated the understanding of disease etiology.

Causal association of rheumatoid arthritis with frailty and the mediation role of inflammatory cytokines: A Mendelian randomization study.

BACKGROUND: Previous observational studies have suggested the association between rheumatoid arthritis (RA) and frailty. However, it remains obscure whether this association is causal. This study aims to investigate the causal association of RA with frailty and the mediation effect of inflammatory cytokines using Mendelian randomization (MR) design. METHODS: Summary-level data for RA (N = 58,284), frailty index (FI) (N = 175,226), Fried frailty score (FFS) (N = 386,565), and 41 inflammatory cytokines (N = 8,293) were obtained from recent genome-wide association studies. Univariable and multivariable MR analyses were conducted to investigate and verify the causal association of RA with frailty. The potential mediation effects of inflammatory cytokines were estimated using two-step MR. RESULTS: Univariable inverse variance weighted MR analysis suggested that genetically determined RA was associated with increased FI (beta=0.021; 95 % CI: 0.012, 0.03; p = 2.2 × 10-6) and FFS (beta=0.011; 95 %CI: 0.007, 0.015; p = 8.811 × 10-8). The consistent results were observed in multivariable MR analysis after adjustment for asthma, smoking, BMI, physical activity, telomere length, and depression. Mediation analysis showed evidence of an indirect effect of RA on FI through monokine induced by interferon-gamma (MIG) with a mediated proportion of 9.8 % (95 %CI: 4.76 %, 19.05 %), on FFS via MIG and stromal cell-derived factor-1 alpha with a mediated proportion of 9.6 % (95 %CI: 0 %, 18.18 %) and 8.44 % (95 %CI: 0 %, 18.18 %), respectively. CONCLUSION: This study provided credible evidence that genetically predicted RA was associated with a higher risk of frailty. Additionally, inflammatory cytokines were involved in the mechanism of RA-induced frailty.

Causal effects of sleep traits on metabolic syndrome and its components: a Mendelian randomization study.

PURPOSE: Previous observational studies have suggested an association between sleep disturbance and metabolic syndrome (MetS). However, it remains unclear whether this association is causal. This study aims to investigate the causal effects of sleep-related traits on MetS using Mendelian randomization (MR). METHODS: Single-nucleotide polymorphisms strongly associated with daytime napping, insomnia, chronotype, short sleep, and long sleep were selected as genetic instruments from the corresponding genome-wide association studies (GWAS). Summary-level data for MetS were obtained from two independent GWAS datasets. Univariable and multivariable MR analyses were conducted to investigate and verify the causal effects of sleep traits on MetS. RESULTS: The univariable MR analysis demonstrated that genetically predicted daytime napping and insomnia were associated with increased risk of MetS in both discovery dataset (OR daytime napping = 1.630, 95% CI 1.273, 2.086; OR insomnia = 1.155, 95% CI 1.108, 1.204) and replication dataset (OR daytime napping = 1.325, 95% CI 1.131, 1.551; OR insomnia = 1.072, 95% CI 1.046, 1.099). For components, daytime napping was positively associated with triglycerides (beta = 0.383, 95% CI 0.160, 0.607) and waist circumference (beta = 0.383, 95% CI 0.184, 0.583). Insomnia was positively associated with hypertension (OR = 1.101, 95% CI 1.042, 1.162) and waist circumference (beta = 0.067, 95% CI 0.031, 0.104). The multivariable MR analysis indicated that the adverse effect of daytime napping and insomnia on MetS persisted after adjusting for BMI, smoking, drinking, and another sleep trait. CONCLUSION: Our study supported daytime napping and insomnia were potential causal factors for MetS characterized by central obesity, hypertension, or elevated triglycerides.

Associations between physical activity and all-cause and cardiovascular mortality in adults with type 2 diabetes mellitus: A prospective cohort study from NHANES 2007-2018.

AIMS: To investigate the dose-response association between physical activity and all-cause and cardiovascular mortality in adults with type 2 diabetes mellitus and the effects of replacing sedentary behavior with physical activity. METHODS: 4808 adults with type 2 diabetes mellitus were included in NHANES 2007-2018. Cox proportional hazards models were used to calculate hazard ratios and 95% confidence intervals. Isotemporal substitution analyses were further to determine the possible benefit of replacing sedentary time. RESULTS: During a median follow-up of 6.58 years, 902 deaths occurred, including 290 deaths from cardiovascular disease. Compared with the inactive group, the low-active and high-active groups were associated with declined risks of all-cause mortality [HRs (95% CIs) 0.64 (0.50, 0.83); 0.60 (0.50, 0.73), respectively] and cardiovascular mortality [0.50 (0.29, 0.88); 0.54 (0.39, 0.76)), respectively]. Dose-response analysis showed a significant U-shaped curve between physical activity and all-cause and cardiovascular mortality. Replacing 30 min/day of sedentary time with physical activity was substantially linked to a reduced risk of 8-32% mortality. CONCLUSION: A high level of PA of 40.52 and 31.66 MET-h/week was respectively related to the lowest risk of all-cause and cardiovascular mortality. Replacing sedentary time with physical activity could benefit the type 2 diabetes mellitus population.

Conveniently monitoring aldehyde changes in heated edible oils using miniaturized kapok fiber-supported liquid-phase extraction/in-situ derivatization coupled with liquid chromatography-tandem mass spectrometry.

Heating edible oils generates aldehydes, potentially leading to adverse health effects, making their analysis essential for quality control. This study presents a convenient miniaturized kapok fiber-supported liquid-phase extraction/in-situ derivatization method for the simultaneous extraction and derivatization of aldehydes in oils. The method involves placing 150 mg oil into a 1 mL pipette tip packed with 25 mg kapok fiber, adding 150 µL ACN with 1.5 mg mL-1 DNPH, and post 30-minute static extraction, retrieving the extractant with a pipettor for liquid chromatography-tandem mass spectrometry analysis. By optimizing critical parameters through a Box-Behnken design, the method exhibits good linearity (1-500 ng g-1, R2 ≥ 0.991), low detection limits (0.2-1.0 ng g-1), excellent accuracy (95.3-107.1%) and high precisions (relative standard deviation < 7.9%). This method simplifies sample preparation processes, cuts solvent use, and facilitates automation. It effectively identifies ten aldehyde variations in six heated oils, displaying distinct profiles consistent with prior research.

Integrated analysis of single-cell and bulk RNA-sequencing identifies a signature based on T-cell marker genes to predict prognosis and immunotherapy response in bladder cancer.

BACKGROUND: T cells have been proven to play important roles in anti-tumor and tumor microenvironment shaping, while these roles have not been explained in bladder cancer (BLCA). METHODS: Single-cell RNA-sequencing (scRNA-seq) data were downloaded from the gene expression omnibus (GEO) database to screen T-cell marker genes. Bulk RNA-sequencing data and clinical information from BLCA patients were downloaded from the cancer genome atlas (TCGA) database to develop a prognosis signature. We analyzed the association of different risk groups with survival analysis, gene set enrichment analysis (GSEA), tumor mutational burden (TMB), and immunotherapy response. RESULTS: Based on 192 T-cell marker genes identified by scRNA-seq analysis, we constructed a prognostic signature containing 7 genes in the training cohort, which was further validated in the testing cohort and GEO cohort. The areas under the receiver operating characteristic curve at 1-, 3-, and 5 years were 0.734, 0.742 and 0.726 in the training cohort, 0.697, 0.671 and 0.670 in the testing cohort, 0.702, 0.665 and 0.629 in the GEO cohort, respectively. In addition, we constructed a nomogram based on clinical factors and the risk score of the signature. The low-risk group exhibited higher immune-related pathways, immune cell infiltration and TMB levels. Importantly, immunophenotype score and immunotherapy cohort (IMvigor210) analyses showed that the low-risk group had better immunotherapy response and prognosis. CONCLUSIONS: Our study reveals a novel prognostic signature based on T-cell marker genes, which provides a new target and theoretical support for BLCA patients.

Coupling in-syringe kapok fiber-supported liquid-phase microextraction with flow injection-mass spectrometry for rapid and green biofluid analysis: Determination of antidepressants as an example.

Quantification of substances in biofluid samples (e.g., urine, blood, and cerebrospinal fluids) are useful for clinical diagnosis. In current study, a rapid and green strategy by coupling in-syringe kapok fiber-supported liquid-phase microextraction with flow-injection mass spectrometry was proposed. The natural kapok fiber was used as an oily extraction solvent (e.g., n-octanol) support material, and an in-syringe extraction device was conveniently constructed. The whole extraction processes, including sampling, washing and desorption, were conveniently conducted by simply pulling/pushing the syringe plunger, enabling rapid analyte enrichment and sample purification. The follow-up flow injection-mass spectrometry detection enabled rapid and high throughput analysis. As an example, the proposed method was applied to analyze antidepressants in plasma/urine, showing satisfied linearities (R2 ≥0.993) in ranges of 0.2-1000 ng/mL. By employing the in-syringe extraction method prior to flow injection-mass spectrometry detection, the LOQs in plasma and urine were reduced by 25-80 folds and 5-25 folds, respectively. Besides, by employing ethanol and 80% ethanol as the desorption solvent and carrier solvent, respectively, the analytical method showed excellent greenness. In general, the integrated method provides a promising choice for rapid and green biofluid analysis.

Left compared with right thoracic approach thoracotomy in esophageal cancer: a retrospective cohort study.

BACKGROUND: Esophagectomy is regarded as one of the optimal treatments for resectable esophageal cancer. However, the impact of surgical approach on the long-term prognosis of esophageal cancer remains controversial. This study attempted to compare the long-term survival outcomes of patients receiving left and right thoracic esophagectomy for esophageal cancer. METHODS: A total of 985 patients underwent esophagectomy (including 453 left and 532 right thoracic approach) for esophageal cancer in Henan Cancer Hospital from January 2015 to December 2016 were enrolled. Their 5 year overall survival (OS) and disease-free survival (DFS) were retrospectively collected. Cox regression was performed to compare OS and DFS in patients who underwent left and right thoracic esophagectomy. Propensity score matching (PSM) analysis was used to balance confounding factors. RESULTS: The 5 year OS rates were 60.21% in the left and 51.60% in the right thoracic esophagectomy, respectively (P = 0.67). The 5 year DFS rates were 56.73% in the left and 47.93% and in the right thoracic esophagectomy, respectively (P = 0.36). Cox regression analysis showed there was no significant difference in long-term survival between patients with left and right surgical access (OS: HR = 0.95, 95% CI 0.77-1.18; DFS: HR = 0.91, 95% CI 0.74-1.12). In the cohort of patients obtained by PSM, Cox regression analysis yielded the similar results. CONCLUSION: For patients with resectable esophageal cancer, the surgical treatment through left thoracic approach can achieve the same long-term survival outcomes as the right thoracic approach.

A fully green sample preparation method for synthetic antioxidants determination in edible oils based on natural feather fiber-supported liquid extraction.

The current study proposed a novel feather fiber-supported liquid extraction (FF-SLE) method for extracting analytes from oil samples. The natural feather fibers were used as the oil support material and directly loaded in the plastic tube of a disposable syringe to construct the low-cost extraction device (∼0.5 CNY). The edible oil without any pretreatment including dilution was added directly to the extraction device, followed by the addition of the green extraction solvent of ethanol. As an example, the proposed method was applied to extract nine synthetic antioxidants from edible oils. The optimized extraction conditions for processing 0.5 g of oil were obtained when the syringe dimension was 5 mL, the extraction solvent was 0.5 mL of ethanol, the amount of feather fibers was 200 mg of duck feather fibers and the static extraction time was 10 min. The applications to seven kinds of feathers and seven kinds of edible oils all indicated the excellent oil removal efficiencies (>98.0%). Combined with high-performance liquid chromatography-ultraviolet, a quantification method was validated with satisfied linearity (R2≥0.994), accuracy (95.8-114.6%) and precision (≤8.3%) with the limits of detection ranging from 50 to 100 ng/g. The proposed FF-SLE method was simple, effective, convenient, low-cost, green and environmental-friendly for the extraction of analytes from oil samples prior to instrument analysis.

Identification of exosomes-related lncRNAs in clear cell renal cell carcinoma based on Bayesian spike-and-slab lasso approach.

Exosomes-related long non-coding RNAs (lncRNAs) have been reported to play significant roles in clear cell renal cell carcinoma (ccRCC). However, there is little known about the relationship between exosomes-related lncRNAs and ccRCC. This study aimed to select optimal prognostic model based on exosomes-related lncRNAs to provide a methodological reference for high-dimensional data. Based on the Cancer Genome Atlas (TCGA) database of 515 ccRCC patients, two risk score models were generated underlying Bayesian spike-and-slab lasso and lasso regression. The optimal model was determined by calculating the area of time-dependent receiver-operating characteristic (ROC) curves in the TCGA and ArrayExpress databases. The immune patterns and sensitivity of immunotherapy between the high and low groups were further explored. Initially, we constructed two risk score models containing 11 and 7 exosomes-related lncRNAs according to Bayesian spike-and-slab lasso and lasso regression respectively. ROC curves revealed that the model constructed by Bayesian spike-and-slab lasso regression was more reliable in predicting survival at 1, 3, and 5 years, yielding an area under the curves (AUCs) of 0.796, 0.732, and 0.742, respectively. Kaplan-Meier (K-M) curves presented that prognosis was poorer in the high-risk score group (P < 0.001). Additionally, the high-risk score group patients were enriched in immune-activating phenotypes and more sensitive to immunotherapy. The exosomes-related lncRNAs model constructed with Bayesian spike-and-slab lasso regression has higher predictive power for ccRCC patients' prognosis, which provides methodological reference for the analysis of high-dimensional data in bioinformatics and guides the tailored treatment of ccRCC patients.

Trends of Incidence, Mortality, and Risk Factors for Lower Respiratory Infections among Children under 5 Years in China from 2000 to 2019.

BACKGROUND: Understanding the temporal trends in the burden of lower respiratory tract infections (LRI) and their attributable risk factors in children under 5 years is important for effective prevention strategies. METHODS: We used incidence, mortality, and attributable risk factors of LRI among children under 5 years from the Global Burden of Diseases database to analyze health patterns in 33 provincial administrative units in China from 2000 to 2019. Trends were examined using the annual average percentage change (AAPC) by the joinpoint regression method. RESULTS: The rates of incidence and mortality for under-5 LRI in China were 18.1 and 4134.3 per 100,000 children in 2019, with an AAPC decrease of 4.1% and 11.0% from 2000, respectively. In recent years, the under-5 LRI incidence rate has decreased significantly in 11 provinces (Guangdong, Guangxi, Guizhou, Hainan, Heilongjiang, Jiangxi, Qinghai, Sichuan, Xinjiang, Xizang, and Zhejiang) and remained stable in the other 22 provinces. The case fatality ratio was associated with the Human Development Index and the Health Resource Density Index. The largest decline in risk factors of deaths was household air pollution from solid fuels. CONCLUSIONS: The burden of under-5 LRI in China and the provinces has declined significantly, with variation across provinces. Further efforts are needed to promote child health through the development of measures to control major risk factors.

Prognostic Model for Clear-cell Renal Cell Carcinoma Based on Natural Killer Cell-related Genes.

BACKGROUND: Natural killer (NK) cells are a key factor affecting progression and immune surveillance of clear-cell renal cell carcinoma (ccRCC). This study sought to construct a natural killer cell-related prognostic signature (NKRPS) to predict the outcome of ccRCC patients and to furnish guidance for finding appropriate treatment strategies. METHODS: From the TCGA and ArrayExpress databases, transcriptomic profiles and relevant clinical information of ccRCC patients were downloaded for the TCGA cohort (n = 515) and the E-MTAB-1980 cohort (n = 101). With the univariate Cox analysis and LASSO-Cox regression algorithm, a NKRPS was built to evaluate patients' prognosis. Receiver operating characteristic (ROC) curves and calibration curves were drawn to estimate the predictive power of the prognostic model. Then, tumor microenvironment (TME), tumor mutational burden (TMB), sensitization to immune checkpoint inhibitors (ICIs) therapy and targeted drug treatment were analyzed in ccRCC patients. RESULTS: Nine genes (BID, CCL7, CSF2, IL23A, KNSTRN, RHBDD3, PIK3R3, RNF19B and VAV3) were identified to construct a NKRPS. High-risk group displayed undesirable survival compared to low-risk group (P < .05). Moreover, the area under the curve (AUC) of ROC at 1-, 3- and 5-year were 0.766, 0.755, and 0.757, respectively. High-risk group was characterized by superior immune infiltration, higher TMB, and higher expression of 5 ICI-related genes. Additionally, this model enabled to predict the sensitivity of patients to chemotherapy drugs. CONCLUSION: NKRPS had a strong predictive power on prognosis of ccRCC patients, which may facilitate individualized treatment and medical decision making.

Overlapping genetic susceptibility of seven autoimmune diseases:SPU tests based on genome-wide association summary statistics.

Autoimmune diseases are a heterogeneous group of diseases with an unclear aetiology. Genome-wide association studies (GWAS) and meta-analysis are inefficient in identifying shared variants. This study aims to identify shared genetic susceptibility for seven autoimmune diseases using sum of powered score (SPU) tests. GWAS summary statistics datasets of seven autoimmune diseases were downloaded from Open Targets Genetics, Dryad and International Multiple Sclerosis Genetics Consortium (IMSGC). The MTaSPUs was applied to confirm common single-nucleotide polymorphisms (SNP), MTaSPUsSet and aSPUs were performed to identify potential shared genes, and MTaSPUsPath was conducted to explore biological functions based on Kyoto encyclopedia of genes and genomes (KEGG) biological pathways. The MTaSPUs test found 104 pleiotropic SNPs (P < 1.19 × 10-6) in our study. The 38 of these SNPs were associated with at least one trait in the original GWAS study. A total of 56 genes associated with at least one trait (P < 4.98 × 10-6) were recognized by aSPUs test. The 45 potential pleiotropic genes (P < 4.98 × 10-6) were significant in MTaSPUsSet test. By aggregating results of aSPUs test and MTaSPUsSet test, we ascertained 38 pleiotropic genes. The 10 of these 38 pleiotropic genes have been reported in previous studies, while the remaining 28 genes were newly discovered. These 38 genes were matched in 14 significant KEGG pathways. We found new variants linked with complicated illnesses derived from several GWAS datasets using the SPU testing technique. The discovery of pleiotropic genes and shared pathways may aid in the development of common treatment approaches for autoimmune disorders.

Genetically predicted blood pressure, antihypertensive drugs and risk of heart failure: a Mendelian randomization study.

BACKGROUND: Elevated blood pressure (BP) was associated with higher risk of heart failure, but the relationship between BP-lowering via antihypertensive drugs and diminution of heart failure was inconclusive. This study aimed to estimate the causal association of BP with heart failure, and explore the effects of BP-lowering through different antihypertensive drug classes on heart failure risk using Mendelian randomization analysis with genetic variants as instrument variables. METHODS: Genetic variants associated with BP were derived from UK Biobank ( n  = 317 754) and the genome-wide association study (GWAS) meta-analysis of UK Biobank and International Consortium of Blood Pressure ( n  = 757 601). Heart failure summary association data were contributed by HERMES Consortium (47 309 heart failure cases and 930 014 controls). Inverse variance weighted (IVW) was performed to estimate causality between exposure and outcome, and weighted median was utilized as sensitivity analysis, and Mendelian randomization-Egger regression was used to identify pleiotropy of instrument variables. Multivariable Mendelian randomization (MVMR) was applied to control for the confounders. RESULTS: Genetically predicted SBP and DBP were associated with heart failure [SBP: odds ratio (OR) = 1.355, 95% confidence interval (CI) 1.201-1.529; DBP: OR = 1.348, 95% CI 1.213-1.498] in UK Biobank. Likewise, in the GWAS meta-analysis of UK Biobank and International Consortium of Blood Pressure, the causal associations were observed between SBP, DBP and heart failure (SBP: OR = 1.237, 95% CI 1.188-1.289; DBP: OR = 1.337, 95% CI 1.245-1.437). Genetically determined ß-blockers and calcium channel blockers (CCBs) were associated with lower risk of heart failure (ß-blockers: OR = 0.617, 95% CI 0.453-0.839; CCBs: OR = 0.730, 95% CI 0.625-0.851). No association was found between angiotensin receptor blockers (ARBs) and heart failure (OR = 1.593, 95% CI 0.647-3.924). When adjusted for smoking, alcohol, physical activity, fruit and vegetable intake, the results were stable. CONCLUSION: Our study indicates causal associations between SBP, DBP, and heart failure, and suggests the preventive effects of heart failure by BP-lowering using ß-blockers and CCBs.

Identification of the shared genetic architecture underlying seven autoimmune diseases with GWAS summary statistics.

Background: The common clinical symptoms and immunopathological mechanisms have been observed among multiple autoimmune diseases (ADs), but the shared genetic etiology remains unclear. Methods: GWAS summary statistics of seven ADs were downloaded from Open Targets Genetics and Dryad. Linkage disequilibrium score regression (LDSC) was applied to estimate overall genetic correlations, bivariate causal mixture model (MiXeR) was used to qualify the polygenic overlap, and stratified-LDSC partitioned heritability to reveal tissue and cell type specific enrichments. Ultimately, we conducted a novel adaptive association test called MTaSPUsSet for identifying pleiotropic genes. Results: The high heritability of seven ADs ranged from 0.1228 to 0.5972, and strong genetic correlations among certain phenotypes varied between 0.185 and 0.721. There was substantial polygenic overlap, with the number of shared SNPs approximately 0.03K to 0.21K. The specificity of SNP heritability was enriched in the immune/hematopoietic related tissue and cells. Furthermore, we identified 32 pleiotropic genes associated with seven ADs, 23 genes were considered as novel genes. These genes were involved in several cell regulation pathways and immunologic signatures. Conclusion: We comprehensively explored the shared genetic architecture across seven ADs. The findings progress the exploration of common molecular mechanisms and biological processes involved, and facilitate understanding of disease etiology.

Trends and influence factors in the prevalence, intervention, and control of metabolic syndrome among US adults, 1999-2018.

AIM: We aimed to describe the trends in the prevalence, intervention, and control of metabolic syndrome (MetS) among US adults through 1999-2018. Additionally, the influence factors of MetS and its control were further explored. METHODS: We included participants older than 20 using the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018 (n = 22,114). The rate of prevalence, intervention, and control of MetS were caculated by survey weights. Joinpoint regression and survey-weighted generalized linear models were used to analyze trends and influence factors, respectively. RESULTS: The prevalence of MetS increased from 28.23 to 37.09% during 1999-2018 (P for trend < 0.05). The former smoker (OR = 1.20, 95%CI: 1.07, 1.36) and current smoker (OR = 1.27, 95%CI: 1.11, 1.45) increased the prevalence of MetS. While vigorous activity (OR = 0.53, 95%CI: 0.47, 0.61) decreased it. Among MetS components, the prevalence of elevated blood-glucose (from 21.18 to 34.68%) and obesity (from 44.81 to 59.06%) raised (P for trend < 0.05), with an uptrend in the use of antidiabetic (from 9.87 to 28.63%) and a downtrend of vigorous activity (from 23.79 to 16.53%) (P for trend < 0.05). Decreased trends were observed in the control of Hb1Ac (< 7%) (from 87.13 to 84.06%) and BMI (<25 kg/m2) (from 11.36 to 7.49%). Among MetS underwent antidiabetic, 45-64 years old and male decreased the control of Hb1Ac (< 7%). The control of BMI (<25 kg/m2) among individuals with physical activity was reduced mainly in the population of younger (aged 20-44 years old), male, non-Hispanic black, middle income and smoker (former and current). CONCLUSIONS: The prevalence of MetS increased significantly through 1999-2018. Elevated blood glucose and obesity were the main causes of MetS burden. Quitting smoking and increasing physical activity may decrease the prevalence of MetS. In the control of blood-glucose and obesity, we should screen out the focus population to modify treatment and improve lifestyle.

Association between Dietary Total Antioxidant Capacity of Antioxidant Vitamins and the Risk of Stroke among US Adults.

The intake of antioxidant vitamins can scavenge free radicals and reduce oxidative stress, which may be beneficial for stroke. However, the relationship between total antioxidant capacity (TAC) of antioxidant vitamins and stroke is controversial. This study aims to investigate the association between dietary TAC and the risk of stroke in US adults. This study included participants over 20 years old from the 2001-2018 National Health and Nutrition Examination Survey (NHANES). Data from two 24 h dietary recalls were used to estimate the usual intake of antioxidant vitamins. TAC was calculated by the vitamin C equivalent antioxidant capacity reference values of individual antioxidant vitamins. Survey-weighted generalized linear models were performed to evaluate the relationship between TAC and the risk of stroke. A restricted cubic spline regression model was used to investigate the dose-response association. A total of 37,045 participants was involved, of whom 1391 suffered a stroke. Compared with the first tertile, the participants in the second tertile of TAC showed a lower risk of stroke (OR = 0.788, 95% CI: 0.662, 0.936) after adjusting for potential risk factors. The dose-response analysis showed a gradual increase in the risk of stroke as TAC decreases. Subgroups analyses indicated that this association was primarily in the population of those aged over 60 years old, who were female, consumed alcohol, were a former smoker and inactive. The sensitivity analysis presented consistent results. These results suggest that deficiency of dietary TAC was associated with an increased risk of stroke, particularly in populations with underlying oxidative stress injury.

Mining of immunological and prognostic-related biomarker for cervical cancer based on immune cell signatures.

Background: Immunotherapy has changed the therapeutic landscape of cervical cancer (CC), but has durable anti-tumor activity only in a subset of patients. This study aims to comprehensively analyze the tumor immune microenvironment (TIME) of CC and to mine biomarkers related to immunotherapy and prognosis. Methods: The Cancer Genome Atlas (TCGA) data was utilized to identify heterogeneous immune subtypes based on survival-related immune cell signatures (ICSs). ICSs prognostic model was constructed by Cox regression analyses, and immunohistochemistry was conducted to verify the gene with the largest weight coefficient in the model. Meanwhile, the tumor immune infiltration landscape was comprehensively characterized by ESTIMATE, CIBERSORT and MCPcounter algorithms. In addition, we also analyzed the differences in immunotherapy-related biomarkers between high and low-risk groups. IMvigor210 and two gynecologic tumor cohorts were used to validate the reliability and scalability of the Risk score. Results: A total of 291 TCGA-CC samples were divided into two ICSs clusters with significant differences in immune infiltration landscape and prognosis. ICSs prognostic model was constructed based on eight immune-related genes (IRGs), which showed higher overall survival (OS) rate in the low-risk group (P< 0.001). In the total population, time-dependent receiver operating characteristic (ROC) curves displayed area under the curve (AUC) of 0.870, 0.785 and 0.774 at 1-, 3- and 5-years. Immunohistochemical results showed that the expression of the oncogene (FKBP10) was negatively correlated with the degree of differentiation and positively correlated with tumor stage, while the expression of tumor suppressor genes (S1PR4) was the opposite. In addition, the low-risk group had more favorable immune activation phenotype and higher enrichment of immunotherapy-related biomarkers. The Imvigor210 and two gynecologic tumor cohorts validated a better survival advantage and immune efficacy in the low-risk group. Conclusion: This study comprehensively assessed the TIME of CC and constructed an ICSs prognostic model, which provides an effective tool for predicting patient's prognosis and accurate immunotherapy.

Prognostic characteristics of immune subtypes associated with acute myeloid leukemia and their identification in cell subsets based on single-cell sequencing analysis.

Immune genes play an important role in the development and progression of acute myeloid leukemia (AML). However, the role of immune genes in the prognosis and microenvironment of AML remains unclear. In this study, we analyzed 151 AML patients in the TCGA database for relevant immune cell infiltration. AML patients were divided into high and low immune cell infiltration clusters based on ssGSEA results. Immune-related pathways, AML pathways and glucose metabolism pathways were enriched in the high immune cell infiltration cluster. Then we screened the differential immune genes between the two immune cell infiltration clusters. Nine prognostic immune genes were finally identified in the train set by LASSO-Cox regression. We constructed a model in the train set based on the nine prognostic immune genes and validated the predictive capability in the test set. The areas under the ROC curve of the train set and the test set for ROC at 1, 3, 5 years were 0.807, 0.813, 0.815, and 0.731, 0.745, 0.830, respectively. The areas under ROC curve of external validation set in 1, 3, and 5 years were 0.564, 0.619, and 0.614, respectively. People with high risk scores accompanied by high TMB had been detected with the worst prognosis. Single-cell sequencing analysis revealed the expression of prognostic genes in AML cell subsets and pseudo-time analysis described the differentiation trajectory of cell subsets. In conclusion, our results reveal the characteristics of immune microenvironment and cell subsets of AML, while it still needs to be confirmed in larger samples studies. The prognosis model constructed with nine key immune genes can provide a new method to assess the prognosis of AML patients.