Penalized Regression Analysis Research Articles

Circulating ECM proteins decorin and alpha-L-iduronidase differentiate ATTRwt-CM from ATTRwt-negative HFpEF/HFmrEF.

Wild-type transthyretin cardiac amyloidosis (ATTRwt-CM) is an under-recognized aetiology of heart failure (HF), necessitating early detection for timely treatment. Our study aimed to differentiate patients with ATTRwt-CM from ATTRwt-negative HFpEF/HFmrEF patients by identifying and validating circulating protein biomarkers. In addition, we measured the same biomarkers in patients with cardiomyopathy due to light chain amyloidosis (AL)-CM to gain disease-specific insights. In this observational study, serum concentrations of 363 protein biomarkers were measured in a discovery cohort consisting of 73 ATTRwt-CM, 55 AL-CM, and 59 ATTRwt-negative HFpEF/HFmrEF patients, using multiplex proximity extension assays. Sparse partial least squares analyses showed overlapping ATTRwt-CM and AL-CM biomarker profiles with clear visual differentiation from ATTRwt-negative patients. Pathway analyses with g:Profiler revealed significantly up-regulated proteoglycans (PG) and cell adhesion pathways in both ATTRwt-CM and AL-CM. Penalized regression analysis revealed that the proteoglycan decorin (DCN), lysosomal hydrolase alpha-L-iduronidase (IDUA) and glycosyl hydrolase galactosidase β-1 (GLB-1) most effectively distinguished ATTRwt-CM from ATTRwt-negative patients (R2 = 0.71). In a prospective validation cohort of 35 ATTRwt-CM patients and 25 ATTRwt-negative patients, DCN and IDUA significantly predicted ATTRwt-CM in the initial analysis (DCN: OR 3.3, IDUA: OR 0.4). While DCN remained significant after correcting for echocardiographic parameters, IDUA did not. DCN showed moderate discriminative ability (AUC, 0.74; 95% CI, 0.61-0.87; sensitivity, 0.91; specificity, 0.52) as did IDUA (AUC, 0.78; 95% CI, 0.65-0.91; sensitivity, 0.91; specificity, 0.61). A model combining clinical factors (AUC 0.92) outperformed DCN but not IDUA, a combination of the biomarkers was not significantly better. Neither DCN nor IDUA correlated with established disease markers. ATTRwt-CM has a distinctly different biomarker profile compared with HFpEF/HFmrEF, while ATTRwt-CM patients share a similar biomarker profile with AL-CM patients characterized by up-regulation of proteoglycans and cell-adhesion pathways. The biomarkers DCN and IDUA show the potential to serve as an initial screening tool for ATTTRwt-CM. Further research is needed to determine the clinical usefulness of these and other extracellular matrix components in identifying ATTRwt-CM.

Associations of essential metals with the risk of aortic arch calcification: a cross-sectional study in a mid-aged and older population of Shenzhen, China.

Vascular calcification is a strong predictor of cardiovascular events. Essential metals play critical roles in maintaining human health. However, the association of essential metal levels with risk of aortic arch calcification (AoAC) remains unclear. We measured the plasma concentrations of nine essential metals in a cross-sectional population and evaluated their individual and combined effects on AoAC risk using multiple statistical methods. We also explored the mediating role of fasting glucose. In the logistic regression model, higher quartiles of magnesium and copper were associated with the decreased AoAC risk, while higher quartile of manganese was associated with higher AoAC risk. The least absolute shrinkage and selection operator penalized regression analysis identified magnesium, manganese, calcium, cobalt, and copper as key metals associated with AoAC risk. The weighted quantile sum regression suggested a combined effect of metal mixture. A linear and positive dose-response relationship was found between manganese and AoAC in males. Moreover, blood glucose might mediate a proportion of 9.38% of the association between manganese exposure and AoAC risk. In summary, five essential metal levels were associated with AoAC and showed combined effect. Fasting glucose might play a significant role in mediating manganese exposure-associated AoAC risk.

Metabolomic profiles of sleep-disordered breathing are associated with hypertension and diabetes mellitus development

Sleep-disordered breathing (SDB) is a prevalent disorder characterized by recurrent episodic upper airway obstruction. Using data from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), we apply principal component analysis (PCA) to seven SDB-related measures. We estimate the associations of the top two SDB PCs with serum levels of 617 metabolites, in both single-metabolite analysis, and a joint penalized regression analysis. The discovery analysis includes 3299 individuals, with validation in a separate dataset of 1522 individuals. Five metabolite associations with SDB PCs are discovered and replicated. SDB PC1, characterized by frequent respiratory events common in older and male adults, is associated with pregnanolone and progesterone-related sulfated metabolites. SDB PC2, characterized by short respiratory event length and self-reported restless sleep, enriched in young adults, is associated with sphingomyelins. Metabolite risk scores (MRSs), representing metabolite signatures associated with the two SDB PCs, are associated with 6-year incident hypertension and diabetes. These MRSs have the potential to serve as biomarkers for SDB, guiding risk stratification and treatment decisions.

Defining a screening tool for post-traumatic stress disorder in East Africa: a penalized regression approach.

Scalable PTSD screening strategies must be brief, accurate and capable of administration by a non-specialized workforce. We used PTSD as determined by the structured clinical interview as our gold standard and considered predictors sets of (a) Posttraumatic Stress Checklist-5 (PCL-5), (b) Primary Care PTSD Screen for the DSM-5 (PC-PTSD) and, (c) PCL-5 and PC-PTSD questions to identify the optimal items for PTSD screening for public sector settings in Kenya. A logistic regression model using LASSO was fit by minimizing the average squared error in the validation data. Area under the receiver operating characteristic curve (AUROC) measured discrimination performance. Penalized regression analysis suggested a screening tool that sums the Likert scale values of two PCL-5 questions-intrusive thoughts of the stressful experience (#1) and insomnia (#21). This had an AUROC of 0.85 (using hold-out test data) for predicting PTSD as evaluated by the MINI, which outperformed the PC-PTSD. The AUROC was similar in subgroups defined by age, sex, and number of categories of trauma experienced (all AUROCs>0.83) except those with no trauma history- AUROC was 0.78. In some East African settings, a 2-item PTSD screening tool may outperform longer screeners and is easily scaled by a non-specialist workforce.

Predicting Outcomes of Antidepressant Treatment in Community Practice Settings.

The authors examined whether machine-learning models could be used to analyze data from electronic health records (EHRs) to predict patients' responses to antidepressant medications. EHR data from a Washington State health system identified patients ages ≥13 years who started an antidepressant medication in 2016 in a community practice setting and had a baseline Patient Health Questionnaire-9 (PHQ-9) score of ≥10 and at least one PHQ-9 score recorded 14-180 days later. Potential predictors of a response to antidepressants were extracted from the EHR and included demographic characteristics, psychiatric and substance use diagnoses, past psychiatric medication use, mental health service use, and past PHQ-9 scores. Random-forest and penalized regression analyses were used to build models predicting follow-up PHQ-9 score and a favorable treatment response (≥50% improvement in score). Among 2,469 patients starting antidepressant medication treatment, the mean±SD baseline PHQ-9 score was 17.3±4.5, and the mean lowest follow-up score was 9.2±5.9. Outcome data were available for 72% of the patients. About 48% of the patients had a favorable treatment response. The best-fitting random-forest models yielded a correlation between predicted and observed follow-up scores of 0.38 (95% CI=0.32-0.45) and an area under the receiver operating characteristic curve for a favorable response of 0.57 (95% CI=0.52-0.61). Results were similar for penalized regression models and for models predicting last PHQ-9 score during follow-up. Prediction models using EHR data were not accurate enough to inform recommendations for or against starting antidepressant medication. Personalization of depression treatment should instead rely on systematic assessment of early outcomes.

Associations of maternal exposure to multiple plasma metals with the risk of fetal congenital heart defects: a prospective nested case-control study

Abstract Background Congenital heart defects (CHDs) are the most common congenital anomalies with complex etiologies. There is knowledge gap with respect to the association between single and combined exposures to multiple heavy metals during pregnancy and the risk of CHDs in the fetus. Thus, we aimed to examine the association between maternal plasma heavy metal concentrations in mid-pregnancy and the risk of CHDs in the fetus. Methods A prospective nested case-control study was conducted in a cohort of 11,578 newborns. Exposure odds ratios (ORs) were compared between 164 CHD cases and 164 non-malformed controls delivered at the same hospital, individually matched by maternal age (±5 years) and parity. Concentrations of 21 metals were determined in maternal peripheral blood plasma via an ultra-performance liquid chromatography inductively coupled to mass spectrometry (ICP-MS). Single- and multiple-metal logistic regressions, the adaptive least absolute shrinkage and selection operator (LASSO) penalized regression analysis and restricted cubic spline (RCS) analysis were applied to explore the associations and dose-response relationships of plasma metals with CHD. We also applied Bayesian Kernel Machine Regression (BKMR) model to evaluate the cumulative effect of the exposure metals. Results Median (interquartile range, IQR) Vanadium (V) [0.3 (0.3-0.4) vs. 0.3 (0.3-0.4) μg/L, P = 0.027] and cadmium (Cd) [0.1 (0.0-0.1) vs. 0.1 (0.0-0.1) μg/L, P = 0.036] levels were different between the CHDs and control groups. Compared with the first quartile, the Directed acyclic graph(DAG) model-adjusted ORs (95% CI) were 3.12 (1.72-5.64) for kalium (K), 2.21 (1.23-3.94) for manganese (Mn), 0.55 (0.33-0.94) for cerium (Ce), 1.96 (1.06-3.64) for lead (Pb) in the second quartile, and ORs (95%CIs) were 1.88 (1.08-3.26) for V, 0.53 (0.30-0.93) for Cr, 1.92 (1.10-3.35) for copper (Cu), 2.09 (1.18-3.70) for Cd, 2.14 (1.24-3.70) for stannum (Sn) and 3.05 (1.69-5.49) for Pb in the third quartile. In the multi-metal model, compared to the lowest quartile, the ORs for Sn and Pb in the second quartile were 1.32 (95% CI: 0.69-2.55) and 1.94 (95% CI: 0.98-3.84), respectively, and the ORs for both metals in the third quartiles were 2.15 (95% CI: 1.16-3.98) and 3.32 (OR=0.66, 95% CI: 1.68-6.55), suggesting that Sn and Pb had relatively stable positive results with a linear dose progression. BKMR models with 2 cluster for 9 metals showed that the overall effect of metal mixture was not statistically significant at or below 35th percentile and at or above the 70th percentile, compared to their median levels. Pb was the major contributor to the combined effect. Conclusions Elevated level of plasma plumbum was associated with increased risk of CHDs. Further investigations in larger perspective cohorts are needed to confirm our findings.fig 1fig 2

Scoliosis school screening of 139,922 multi-ethnic children in Dali, southwestern China: A large epidemiological study

Idiopathic scoliosis (IS) primarily impacts adolescents and requires early intervention to prevent deformity. Early diagnosis and prediction of spine curvature in children could be aided by school scoliosis screening (SSS). In the Dali Bai Autonomous Prefecture, SSS, including 139,922 children from 18 ethnic groups in 8 counties ranging in age from 6 to 18, was carried out. A medical team conducted the screening with inspection, Adam's test, and angles of trunk rotation (ATR). The overall prevalence of suspected scoliosis was 2.37%, with girls (2.5%) more affected than boys (2.0%). Using penalized regression analysis of LASSO, the variable-selection process was conducted to determine the final regression model. The results showed that age, gender, height, BMI, altitude, latitude, ethnicity, and county were all influencing variables for suspected scoliosis, according to the adjusted final model of multi-factor regression analysis. These results provide substantial information and suggestions for preventative and person-centered healthcare interventions for IS.

Associations between Plasma Essential Metals Levels and the Risks of All-Cause Mortality and Cardiovascular Disease Mortality among Individuals with Type 2 Diabetes

Epidemiological evidence regarding the possible link between multiple essential metals levels and all-cause mortality and cardiovascular disease (CVD) mortality among type 2 diabetes (T2D) patients is sparse. Here, we aimed to evaluate the longitudinal associations between 11 essential metals levels in plasma and all-cause mortality and CVD mortality among T2D patients. Our study included 5278 T2D patients from the Dongfeng-Tongji cohort. LASSO penalized regression analysis was used to select the all-cause and CVD mortality-associated metals from 11 essential metals (iron, copper, zinc, selenium, manganese, molybdenum, vanadium, cobalt, chromium, nickel, and tin) measured in plasma. Cox proportional hazard models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Results: With a median follow-up of 9.8 years, 890 deaths were documented, including 312 deaths of CVD. LASSO regression models and the multiple-metals model revealed that plasma iron and selenium were negatively associated with all-cause mortality (HR: 0.83; 95%CI: 0.70, 0.98; HR: 0.60; 95%CI: 0.46, 0.77), whereas copper was positively associated with all-cause mortality (HR: 1.60; 95%CI: 1.30, 1.97). Only plasma iron has been significantly associated with decreased risk of CVD mortality (HR: 0.61; 95%CI: 0.49, 0.78). The dose-response curves for the association between copper levels and all-cause mortality followed a J shape (Pfor nonlinear = 0.01). Our study highlights the close relationships between essential metals elements (iron, selenium, and copper) and all-cause and CVD mortality among diabetic patients.

Coexposure to multiple metals and renal tubular damage: a population-based cross-sectional study in China's rural regions.

Previous studies have indicated that exposure to a single toxic metal can cause renal tubular damage, while evidence about the effects of multimetal exposure on renal tubular damage is relatively limited. We aimed to evaluate the relationships of multimetal coexposure with renal tubular damage in adults in heavy metal-polluted rural regions of China. A cross-sectional study of 1918 adults in China's heavy metal-contaminated rural regions was conducted. Inductively coupled plasma-mass spectrometry (ICP-MS) was used to measure the plasma levels of 18 metals in participants, and immune turbidimetry was used to measure sensitive biological indicators, reflecting renal tubular damage (including retinol-binding protein and β2-microglobulin). Least absolute shrinkage and selection operator (LASSO) penalized regression analysis, logistic and linear regression analysis, restricted cubic spline (RCS) regression analysis and the Bayesian kernel machine regression (BKMR) method were used to explore associations of multimetal coexposure with renal tubular damage risk or renal tubular damage indicators. Plasma selenium, cadmium, arsenic, and iron were identified as the main plasma metals associated with renal tubular damage risk after dimensionality reduction. Multimetal regression models showed that selenium was positively associated, and iron was negatively associated with renal tubular damage risk or its biological indicators. Multimetal RCS analyses additionally revealed a non-linear relationship of selenium with renal tubular damage risk. The BKMR models showed that the metal mixtures were positively associated with biological indicators of renal tubular damage when the metal mixtures were above the 50th percentile of concentration. Our findings indicated that natural exposure to high levels of multimetal mixtures increases the risk of renal tubular damage. Under the conditions of multimetal exposure, selenium was positively associated, and iron was negatively associated with renal tubular damage risk or its biological indicators.

A Five-LLPS Gene Risk Score Prognostic Signature Predicts Survival in Hepatocellular Carcinoma.

Primary liver cancer, dominated by hepatocellular carcinoma (HCC), is one of the most common cancer types and the third leading cause of cancer death in 2020. Previous studies have shown that liquid-liquid phase separation (LLPS) plays an important role in the occurrence and development of cancer including HCC, but its influence on the patient prognosis is still unknown. It is necessary to explore the effect of LLPS genes on prognosis to accurately forecast the prognosis of HCC patients and identify relevant targeted therapeutic sites. Using The Cancer Genome Atlas dataset and PhaSepDB dataset, we identified LLPS genes linked to the overall survival (OS) of HCC patients. We applied Least Absolute Shrinkage and Selection Operator (LASSO) Cox penalized regression analysis to choose the best genes for the risk score prognostic signature. We then analysed the validation dataset and evaluated the effectiveness of the risk score prognostic signature. Finally, we performed quantitative real-time PCR experiments to validate the genes in the prognostic signature. We identified 43 differentially expressed LLPS genes that were associated with the OS of HCC patients. Five of these genes (BMX, FYN, KPNA2, PFKFB4, and SPP1) were selected to generate a prognostic risk score signature. Patients in the low-risk group were associated with better OS than those in the high-risk group in both the training dataset and the validation dataset. We found that BMX and FYN had lower expression levels in HCC tumour tissues, whereas KPNA2, PFKFB4, and SPP1 had higher expression levels in HCC tumour tissues. The validation demonstrated that the five-LLPS gene risk score signature has the capability of predicting the OS of HCC patients. Our study constructed a five-LLPS gene risk score signature that can be applied as an effective and convenient prognostic tool. These five genes might serve as potential targets for therapy and the treatment of HCC.

Association between blood metal exposures and hyperuricemia in the U.S. general adult: A subgroup analysis from NHANES

BackgroundThe relationship between metal exposure and hyperuricemia is still controversial, especially in certain population groups. MethodsThis cross-sectional study included adults from NHANES 2011–2016. Seven metals, including Pb, Cd, Hg, Mn, Se, Cu, and Zn, were detected in blood by ICP‒MS. Multivariable logistic regression, LASSO, and BKMR models were used to assess the effects of single and mixed metal exposures and hyperuricemia. Moreover, this association was analyzed by sex, BMI, and age. ResultsA total of 4074 individuals were included in our final analysis, and 825 (20.3%) people were classified as having hyperuricemia. In logistic regression, Pb (Q4: OR [95% CI]: 1.50 [1.00, 2.24]) was associated with hyperuricemia after adjusting for all covariates. LASSO penalized regression analysis further indicated positive associations of lead and zinc with hyperuricemia. In BKMR analysis, U-shaped dose‒response curves were generated for Se, Mn, Cu, and Zn when fixing the other metals in the 50th percentile. A significant positive trend was found between mixed metal exposure and hyperuricemia, in which blood Se, Zn, and Cd had the highest PIP (PIP = 0.651, 0.549, and 0.530, respectively). In weighted logistic regression, subgroup analyses revealed a significant association between Pb and hyperuricemia among the non-overweight group. Significant positive trends between mixed metals and hyperuricemia were found in the male and non-overweight groups in the BKMR models. ConclusionOur study suggests that a higher concentration of mixed metal might increase the risk of hyperuricemia in U.S. adults. Males and people with a BMI under 25 are more likely to be affected.

HPGDS is a novel prognostic marker associated with lipid metabolism and aggressiveness in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most common respiratory globallywith a poor prognosis. Lipid metabolism is extremely important for the occurrence and development of cancer. However, the role of genes involved in lipid metabolism in LUAD development is unclear. We aimed to identify the abnormal lipid metabolism pathway of LUAD, construct a novel prognostic model of LUAD, and discover novel biomarkers involved in lipid metabolism inLUAD. Based on differentially expressed genes involved in lipid metabolism in LUAD samples from The Cancer Genome Atlas (TCGA), abnormal lipid metabolism pathways in LUAD were analyzed. The lasso penalized regression analysis was performed on the TCGA cohort (training set) to construct a risk score formula. The predictive ability of the risk score was validated in the Gene Expression Omnibus (GEO) dataset (validation set) using Kaplan-Meier analysis and ROC curves. Finally, based on CRISPR gene editing technology, hematopoietic prostaglandin D synthase (HPGDS) was knocked out in A549 cell lines, the changes in lipid metabolism-related markers were detected by western blotting, and the changes in cell migration were detected by transwellassay. Based on the differential genes between lung cancer tissue and normal tissue, we found that the arachidonic acid metabolism pathway is an abnormal lipid metabolism pathway in both lung adenocarcinoma and lung squamous cell carcinoma. Based on the sample information of TCGA and abnormally expressed lipid metabolism-related genes, a 9-gene prognostic risk score was successfully constructed and validated in the GEO dataset. Finally, we found that knockdown of HPGDS in A549 cell lines promoted lipid synthesis and is more invasive than in control cells. Rescue assays showed that ACSL1 knockdown reversed the pro-migration effects of HPGDS knockdown. The knockdown of HPGDS promoted migration response by upregulating the expression of the lipid metabolism key enzymes ACSL1 and ACC. The genes involved in lipid metabolism are associated with the occurrence and development of LUAD. HPGDS can be a therapeutic target of a potential lipid metabolism pathway in LUAD, and the therapeutic target of lipid metabolism genes in LUAD should be studied further.

Associations of clinical and circulating metabolic biomarkers with low physical fitness and function in adults with chronic lymphocytic leukemia.

Many patients with chronic lymphocytic leukemia (CLL) experience physical dysfunction and low overall fitness. It remains unknown what factors drive CLL physical dysfunction. We assessed physical function and metabolic lipoprotein panels in 106 patients with CLL. In univariate analyses of clinical factors, a longer time since diagnosis was associated with a higher likelihood of dysfunctional aerobic fitness (OR = 3.56, 95% CI: 1.37–9.22; p = 0.002) and physical performance (SPPB: OR = 2.03, 95% CI: 1.20–3.44; p = 0.004). Having received treatment was associated with a higher likelihood of dysfunctional aerobic fitness (OR = 1.57, 95% CI: 1.02–2.40; p = 0.036), SPPB (OR = 1.85, 95% CI: 1.13–3.03; p = 0.011) and grip strength (OR = 1.67, 95% CI: 1.10–2.55; p = 0.015). We found that several small HDL particle parameters, higher levels of citrate (OR = 2.01, 95% CI: 1.22–3.31; p = 0.030), and lower levels of hemoglobin (OR = 0.50, 95% CI: 0.31–0.82; p = 0.030) were associated with a higher likelihood of dysfunctional aerobic fitness. Multivariable least absolute shrinkage and selection operator (LASSO)-penalized regression analyses using variable importance measures (VIM) showed that 7.8-nm HDL particles (VIM = 1.000) and total HDL particle levels (VIM = 1.000) were more informative than clinical measures for the odds of dysfunctional aerobic fitness and 6-min walk functional fitness, respectively, while 10.3-nm HDL particles (VIM = 0.383) were more informative for grip strength. Time since diagnosis (VIM = 0.680) and having received treatment (VIM = 0.490) were more informative than lipoprotein measures for the odds of having dysfunctional SPPB. Taken together, we establish significant relationships between clinical and metabolic factors and physical characteristics that might prompt early use of ancillary support services.

Penalized regression analysis with individual-specific patterns of missing covariates

High-dimensional but incomplete data are common in many settings. With such data, regularized estimation and variable selection techniques developed for complete data fail to produce reliable results due to the missingness. To address this problem, we propose an iterative penalized least squares estimation (IPLSE) method, which customizes existing penalized regression techniques for data with high missing rates and individual-specific missing patterns. The proposed method simultaneously conducts missing value imputation, parameter estimation, and variable selection. Statistical properties are rigorously established, and a simulation demonstrates its competitive performance under various missing patterns, especially when there is missingness concerning important variables. An analysis of China’s provincial economic data further supports the merits of the proposed method.

A potential oral microbiome signature associated with coronary artery disease in Tunisia.

The coronary artery disease (CAD) is a chronic inflammatory disease involving genetic as well as environmental factors. Recent evidence suggests that the oral microbiome has a significant role in triggering atherosclerosis. The present study assessed the oral microbiome composition variation between coronary patients and healthy subjects in order to identify a potential pathogenic signature associated with CAD. We performed metagenomic profiling of salivary microbiomes by 16S ribosomal RNA (rRNA) next-generation sequencing. Oral microbiota profiling was performed for 30 individuals including 20 patients with CAD and ten healthy individuals without carotid plaques or previous stroke or myocardial infarction.We found that oral microbial communities in patients and healthy controls are represented by similar global core oral microbiome. The predominant taxa belonged to Firmicutes (genus Streptococcus, Veillonella, Granulicatella, Selenomonas), Proteobacteria (genus Neisseria, Haemophilus), Actinobacteria (genus Rothia), Bacteroidetes (genus Prevotella, Porphyromonas), and Fusobacteria (genus Fusobacterium, Leptotrichia). More than 60% relative abundance of each sample for both CAD patients and controls is represented by three major genera including Streptococcus (24.97 and 26.33%), Veillonella (21.43 and 19.91%), and Neisseria (14.23 and 15.33%).Using penalized regression analysis, the bacterial genus Eikenella was involved as the major discriminant genus for both status and Syntax score of CAD. We also reported a significant negative correlation between Syntax score and Eikenella abundance in coronary patients’ group (Spearman rho = −0.68, P=0.00094).In conclusion, the abundance of Eikenella in oral coronary patient samples compared with controls could be a prominent pathological indicator for the development of CAD.

벌점화 회귀분석을 이용한 English Premier League의 승점 예측모형 개발 및 경기력 요인 탐색

Advancements in Information Communication Technology during the Fourth Industrial Revolution have led to rapid progress in soccer-related industries and research. Various countries have professional soccer leagues devoted to securing victory. These activities include studies that identify the performance factors that significantly influence the outcome of matches. Researchers often use linear regression analysis based on least-squares estimation for this process, but this method has several issues. As an alternative to this, penalized regression analysis that minimizes the penalty-pointed residual sum of squares has been developed as an alternative. However, there are few studies using the penalty point regression analysis to predict points in soccer. Therefore, in this study, theoretical contents were introduced to activate the penalty point regression analysis, and further examples of application were presented by empirically analyzing 2020-2021 EPL(EPL: The Football Association Premier League Limited) data. It was found to be a statistical predictive model. According to the results, the LASSO model and elastic net models were more suitable statistical prediction models from the perspective of the root mean square error (RMSE). Additionally, among the 19 variables used in the analysis, defensive factors such as tackles, clean sheets, and saves were identified as critical performance factors that impacted the score. Based on the results of this study, in order to enable more in-depth research in the future, the researchers hope that the EPL website will provide activity data, such as the total distance traveled, number of sprints, instantaneous speed, and distance between attackers and defenders.

Integrated analysis of single-cell RNA-seq dataset and bulk RNA-seq dataset constructs a prognostic model for predicting survival in human glioblastoma.

BackgroundGlioblastoma (GBM) is the most common primary malignant brain tumor in adults. For patients with GBM, the median overall survival (OS) is 14.6 months and the 5‐year survival rate is 7.2%. It is imperative to develop a reliable model to predict the survival probability in new GBM patients. To date, most prognostic models for predicting survival in GBM were constructed based on bulk RNA‐seq dataset, which failed to accurately reflect the difference between tumor cores and peripheral regions, and thus show low predictive capability. An effective prognostic model is desperately needed in clinical practice.MethodsWe studied single‐cell RNA‐seq dataset and The Cancer Genome Atlas‐glioblastoma multiforme (TCGA‐GBM) dataset to identify differentially expressed genes (DEGs) that impact the OS of GBM patients. We then applied the least absolute shrinkage and selection operator (LASSO) Cox penalized regression analysis to determine the optimal genes to be included in our risk score prognostic model. Then, we used another dataset to test the accuracy of our risk score prognostic model.ResultsWe identified 2128 DEGs from the single‐cell RNA‐seq dataset and 6461 DEGs from the bulk RNA‐seq dataset. In addition, 896 DEGs associated with the OS of GBM patients were obtained. Five of these genes (LITAF, MTHFD2, NRXN3, OSMR, and RUFY2) were selected to generate a risk score prognostic model. Using training and validation datasets, we found that patients in the low‐risk group showed better OS than those in the high‐risk group. We validated our risk score model with the training and validating datasets and demonstrated that it can effectively predict the OS of GBM patients.ConclusionWe constructed a novel prognostic model to predict survival in GBM patients by integrating a scRNA‐seq dataset and a bulk RNA‐seq dataset. Our findings may advance the development of new therapeutic targets and improve clinical outcomes for GBM patients.

Construction of a Personalized Insulin Resistance Risk Assessment Tool in Xinjiang Kazakhs Based on Lipid- and Obesity-Related Indices.

PurposeThis study aimed to explore the relationship between obesity- and lipid-related indices and insulin resistance (IR) and construct a personalized IR risk model for Xinjiang Kazakhs based on representative indices.MethodsThis cross-sectional study was performed from 2010 to 2012. A total of 2170 Kazakhs from Xinyuan County were selected as research subjects. IR was estimated using the homeostasis model assessment of insulin resistance. Multivariable logistic regression analysis, least absolute shrinkage and selection operator penalized regression analysis, and restricted cubic spline were applied to evaluate the association between lipid- and obesity-related indices and IR. The risk model was developed based on selected representative variables and presented using a nomogram. The model performance was assessed using the area under the ROC curve (AUC), the Hosmer–Lemeshow goodness-of-fit test, and decision curve analysis (DCA).ResultsAfter screening out 25 of the variables, the final risk model included four independent risk factors: smoking, sex, triglyceride-glucose (TyG) index, and body mass index (BMI). A linear dose–response relationship was observed for the BMI and TyG indices against IR risk. The AUC of the risk model was 0.720 based on an independent test and 0.716 based on a 10-fold cross-validation. Calibration curves showed good consistency between actual and predicted IR risks. The DCA demonstrated that the risk model was clinically effective.ConclusionThe TyG index and BMI had the strongest association with IR among all obesity- and lipid-related indices, and the developed model was useful for predicting IR risk among Kazakh individuals.

Identification of Immune-Related lncRNA Pairs and Construction and Validation of a New Prognostic Signature of Colon Cancer.

Background More and more evidence has shown that immune-related long noncoding ribonucleic acid (irlncRNAs) is a potential prognostic factor for colon cancer. The relevant gene pair pattern can improve the sensitivity of the prognostic model. Therefore, our present study aimed to identify irlncRNA Pairs and construct and validate a new prognostic signature in colon cancer. Methods We downloaded the expression matrix of mRNA and lncRNA of patients with colon cancer and their clinical information from the public TCGA database. We obtained immune genes from the ImmPort database. Coexpression analysis was performed to identify irlncRNAs. We built an irlncRNA pair matrix by comparing the expression levels of each lncRNA pair in a cycle. Univariate Cox regression analysis, LASSO penalized regression analysis, and multivariate Cox regression analysis were performed to determine the final variables to construct the prognostic risk score model (a new signature). We draw the receiver operating characteristic (ROC) curves of the signature and clinical characteristics and determine the optimal cutoff value by the optimal Akaike Information Criterion (AIC) value. Based on the optimal cutoff value of the ROC curve of the signature, colon cancer patients were divided into the high- and low-risk groups. Then, the signature was evaluated by clinicopathological features, tumor-infiltrating immune cells, checkpoint-related biomarkers, targeted therapy, and chemotherapy. Results We identified 8 lncRNA pairs including AC103740.1|LEF1-AS1, LINC02391|AC053503.5, WWC2-AS2|AL355916.2, AC104090.1|NEURL1-AS1, AC099524.1|AL161908.1, AC074011.1|AL078601.2, AL355916.2|LINC01723, and AP003392.4|LINC00598 from 71 differently expressed irlncRNAs. We constructed a prognostic risk score model (a new signature) using these optimal eight irlncRNA pairs. ROC curve analysis revealed that the highest AUC value of the signature was 0.776 at 1 year, with the optimal cutoff value of 1.283. Our present study also showed that the constructed signature could accurately identify adverse survival outcomes, prognostic clinicopathological features, and specify tumor invasion status. The expression of immune checkpoint-related genes and chemical drug sensitivity were related to different risk groups. Conclusion In our present study, we constructed a new irlncRNA signature of colon cancer based on the irlncRNA pairs instead of the special expression level of lncRNA. We found this signature had not only good prognostic value but also certain clinical value, which might provide a new insight into the treatment and prognosis of colon cancer.

Associations of multiple plasma metals with the risk of metabolic syndrome: A cross-sectional study in the mid-aged and older population of China

BackgroundMetal exposures have been reported to be related to the progress of metabolic syndrome (MetS), however, the currents results were still controversial, and the evidence about the effect of multi-metal exposure on MetS were limited. In this study, we intended to evaluate the relationships between metal mixture exposure and the prevalence of MetS in a mid-aged and older population of China. MethodsThe plasma levels of 13 metals (aluminum, magnesium, calcium, iron, manganese, cobalt, copper, arsenic, zinc, selenium, cadmium, molybdenum and thallium) were detected by inductively coupled plasma mass spectrometry (ICP-MS) in 1277 adults recruited from the Eighth Affiliated Hospital of Sun Yat-Sen University (Shenzhen, China). Logistic regression, the adaptive least absolute shrinkage and selectionator operator (LASSO) penalized regression analysis and restricted cubic spline (RCS) analysis were used to explore the associations and dose-response relationships of plasma metals with MetS. To evaluate the cumulative effect of metals, the Bayesian Kernel Machine Regression (BKMR) model was applied. ResultsThe concentrations of magnesium and molybdenum were lower in the MetS group (p < 0.05). In the single-metal model, the adjusted ORs (95%CI) in the highest quartiles were 0.44 (0.35, 0.76) for magnesium and 0.30 (0.17, 0.51) for molybdenum compared with the lowest quartile. The negative associations and dose-dependent relationships of magnesium and molybdenum with MetS were further validated by the stepwise model, adaptive LASSO penalized regression and RCS analysis. The BKMR models showed that the metal mixture were associated with decreased MetS when the chemical mixtures were≥ 25th percentile compared to their medians, and Mg, Mo were the major contributors to the combined effect. Moreover, concentrations of magnesium were significantly related to blood glucose, and molybdenum was related with BMI, blood glucose and blood pressure. ConclusionsElevated levels of plasma magnesium and molybdenum were associated with decreased prevalence of MetS. Further investigations in larger perspective cohorts are needed to confirm our findings.