Assessment of influencing factors of hospitalization expenses for Crohn's disease patients: Based on LASSO and linear mixed model.

Aims: Crohn's disease (CD) is a global disease that is dramatically increasing. This study aimed to identify the primary drivers of hospitalization expenses for CD patients to provide guidance on the allocation and control of health care costs. Methods: This study retrospectively collected the homepage data of the electronic medical records of CD patients in two tertiary hospitals in Zhejiang Province, China, from January 2016 to December 2021. The influencing factors of hospitalization expenses for CD were analyzed. A linear mixed model with least absolute shrinkage (LASSO-LMM) was used to develop a predictive model for hospitalization expenses for CD patients. Results: A total of 4,437 CD patients were analyzed in this study. CD patients' age, length of hospital stay, admission route, comorbidities, and main treatment were found to be statistically significant variables for CD patients' hospitalization expenses. The AIC and BIC of LASSO-LMM model were 319.033 and 306.241, respectively. Patients who were older, had a longer hospital stay, and had comorbidities had higher hospitalization expenses. The hospitalization expenses of outpatients were lower than those of emergency patients. The weight of surgical treatment was the highest among three treatments (0.602). Conclusions: Identifying and examining factors that influence hospitalization expenses for CD patients can help to control healthcare expenditures. Treatment mode was the most important impact on CD hospitalization expenses. Medical security departments can consider implement personalized and precise hospitalization expense compensation scheme base on LASSO-LMM prediction model in the future.

Association of Evaluated Glomerular Filtration Rate and Incident Diabetes Mellitus: A Secondary Retrospective Analysis Based on a Chinese Cohort Study.

BACKGROUND: Previous studies have revealed that chronic kidney disease (CKD) is a significant risk factor for insulin resistance and diabetes. However, few studies are on the association between estimated glomerular filtration rate (eGFR) and incident diabetes, especially in the Chinese population with eGFR>60 mL/min·1.73 m2. This study explored the relationship between eGFR and incident diabetes in a large cohort in the Chinese community. METHODS: This study was a retrospective cohort study. A total of 1,99,435 adults from Rich Healthcare Group in China were studied, including all medical records for participants who received a health check from 2010 to 2016. The target-independent and target-dependent variables were eGFR measured at baseline, and incident diabetes mellitus appeared during the follow-up. After testing the proportion hypothesis, Cox proportional hazards regression was used to investigate the association between eGFR and incident diabetes. A Cox proportional hazards regression with cubic spline functions and smooth curve fitting (the cubic spline smoothing) was used to identify non-linear relationships between eGFR and the risk of diabetes. Additionally, we also performed subgroup analysis and a series sensitivity analysis. It was stated that the data had been uploaded to the DATADRYAD website. RESULT: After adjusting gender, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting plasma glucose (FPG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglyceride (TG), alanine aminotransferase (ALT), aspartate aminotransferase (AST), smoking and drinking status, and family history of diabetes, the result showed that eGFR was negatively associated with incident diabetes [HR = 0.986, 95% CI (0.984, 0.988)]. A non-linear relationship was detected between eGFR and incident diabetes, with an inflection point of eGFR of 98.034 mL/min·1.73 m2. The effect sizes and the confidence intervals (Cis) on the left and right sides of the inflection point were 0.998 (0.993, 1.003) and 0.976 (0.972, 0.980), respectively. Subgroup analysis showed a stronger association in the population with FPG <6.1 mmol/L, BMI <24 kg/m2, SBP <140 mmHg, DBP <90 mmHg and family history without diabetes. The same trend was also seen in women and the population who never smoke. CONCLUSION: Estimated glomerular filtration rate is independently associated with incident diabetes. The relationship between eGFR and incident diabetes is also non-linear. eGFR is strongly related to incident diabetes when eGFR was above 98.034 mL/min·1.73 m2.

Epigenome-wide gene-age interaction analysis reveals reversed effects of PRODH DNA methylation on survival between young and elderly early-stage NSCLC patients.

DNA methylation changes during aging, but it remains unclear whether the effect of DNA methylation on lung cancer survival varies with age. Such an effect could decrease prediction accuracy and treatment efficacy. We performed a methylation-age interaction analysis using 1,230 early-stage lung adenocarcinoma patients from five cohorts. A Cox proportional hazards model was used to investigate lung adenocarcinoma and squamous cell carcinoma patients for methylation-age interactions, which were further confirmed in a validation phase. We identified one adenocarcinoma-specific CpG probe, cg14326354PRODH, with effects significantly modified by age (HRinteraction = 0.989; 95% CI: 0.986-0.994; P = 9.18×10-7). The effect of low methylation was reversed for young and elderly patients categorized by the boundary of 95% CI standard (HRyoung = 2.44; 95% CI: 1.26-4.72; P = 8.34×10-3; HRelderly = 0.58; 95% CI: 0.42-0.82; P = 1.67×10-3). Moreover, there was an antagonistic interaction between low cg14326354PRODH methylation and elderly age (HRinteraction = 0.21; 95% CI: 0.11-0.40; P = 2.20×10-6). In summary, low methylation of cg14326354PRODH might benefit survival of elderly lung adenocarcinoma patients, providing new insight to age-specific prediction and potential drug targeting.

Independent Validation of Early-Stage Non-Small Cell Lung Cancer Prognostic Scores Incorporating Epigenetic and Transcriptional Biomarkers With Gene-Gene Interactions and Main Effects.

BACKGROUND: DNA methylation and gene expression are promising biomarkers of various cancers, including non-small cell lung cancer (NSCLC). Besides the main effects of biomarkers, the progression of complex diseases is also influenced by gene-gene (G×G) interactions. RESEARCH QUESTION: Would screening the functional capacity of biomarkers on the basis of main effects or interactions, using multiomics data, improve the accuracy of cancer prognosis? STUDY DESIGN AND METHODS: Biomarker screening and model validation were used to construct and validate a prognostic prediction model. NSCLC prognosis-associated biomarkers were identified on the basis of either their main effects or interactions with two types of omics data. A prognostic score incorporating epigenetic and transcriptional biomarkers, as well as clinical information, was independently validated. RESULTS: Twenty-six pairs of biomarkers with G×G interactions and two biomarkers with main effects were significantly associated with NSCLC survival. Compared with a model using clinical information only, the accuracy of the epigenetic and transcriptional biomarker-based prognostic model, measured by area under the receiver operating characteristic curve (AUC), increased by 35.38% (95% CI, 27.09%-42.17%; P = 5.10 × 10-17) and 34.85% (95% CI, 26.33%-41.87%; P = 2.52 × 10-18) for 3- and 5-year survival, respectively, which exhibited a superior predictive ability for NSCLC survival (AUC3 year, 0.88 [95% CI, 0.83-0.93]; and AUC5 year, 0.89 [95% CI, 0.83-0.93]) in an independent Cancer Genome Atlas population. G×G interactions contributed a 65.2% and 91.3% increase in prediction accuracy for 3- and 5-year survival, respectively. INTERPRETATION: The integration of epigenetic and transcriptional biomarkers with main effects and G×G interactions significantly improves the accuracy of prognostic prediction of early-stage NSCLC survival.

Trans-omics biomarker model improves prognostic prediction accuracy for early-stage lung adenocarcinoma.

Limited studies have focused on developing prognostic models with trans-omics biomarkers for early-stage lung adenocarcinoma (LUAD). We performed integrative analysis of clinical information, DNA methylation, and gene expression data using 825 early-stage LUAD patients from 5 cohorts. Ranger algorithm was used to screen prognosis-associated biomarkers, which were confirmed with a validation phase. Clinical and biomarker information was fused using an iCluster plus algorithm, which significantly distinguished patients into high- and low-mortality risk groups (Pdiscovery = 0.01 and Pvalidation = 2.71×10-3). Further, potential functional DNA methylation-gene expression-overall survival pathways were evaluated by causal mediation analysis. The effect of DNA methylation level on LUAD survival was significantly mediated through gene expression level. By adding DNA methylation and gene expression biomarkers to a model of only clinical data, the AUCs of the trans-omics model improved by 18.3% (to 87.2%) and 16.4% (to 85.3%) in discovery and validation phases, respectively. Further, concordance index of the nomogram was 0.81 and 0.77 in discovery and validation phases, respectively. Based on systematic review of published literatures, our model was superior to all existing models for early-stage LUAD. In summary, our trans-omics model may help physicians accurately identify patients with high mortality risk.

SIPA1L3 methylation modifies the benefit of smoking cessation on lung adenocarcinoma survival: an epigenomic-smoking interaction analysis.

Smoking cessation prolongs survival and decreases mortality of patients with non-small-cell lung cancer (NSCLC). In addition, epigenetic alterations of some genes are associated with survival. However, potential interactions between smoking cessation and epigenetics have not been assessed. Here, we conducted an epigenome-wide interaction analysis between DNA methylation and smoking cessation on NSCLC survival. We used a two-stage study design to identify DNA methylation-smoking cessation interactions that affect overall survival for early-stage NSCLC. The discovery phase contained NSCLC patients from Harvard, Spain, Norway, and Sweden. A histology-stratified Cox proportional hazards model adjusted for age, sex, clinical stage, and study center was used to test DNA methylation-smoking cessation interaction terms. Interactions with false discovery rate-q ≤ 0.05 were further confirmed in a validation phase using The Cancer Genome Atlas database. Histology-specific interactions were identified by stratification analysis in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) patients. We identified one CpG probe (cg02268510SIPA1L3 ) that significantly and exclusively modified the effect of smoking cessation on survival in LUAD patients [hazard ratio (HR)interaction  = 1.12; 95% confidence interval (CI): 1.07-1.16; P = 4.30 × 10-7 ]. Further, the effect of smoking cessation on early-stage LUAD survival varied across patients with different methylation levels of cg02268510SIPA1L3 . Smoking cessation only benefited LUAD patients with low methylation (HR = 0.53; 95% CI: 0.34-0.82; P = 4.61 × 10-3 ) rather than medium or high methylation (HR = 1.21; 95% CI: 0.86-1.70; P = 0.266) of cg02268510SIPA1L3 . Moreover, there was an antagonistic interaction between elevated methylation of cg02268510SIPA1L3 and smoking cessation (HRinteraction  = 2.1835; 95% CI: 1.27-3.74; P = 4.46 × 10-3 ). In summary, smoking cessation benefited survival of LUAD patients with low methylation at cg02268510SIPA1L3 . The results have implications for not only smoking cessation after diagnosis, but also possible methylation-specific drug targeting.

EGLN2 DNA methylation and expression interact with HIF1A to affect survival of early-stage NSCLC.

Hypoxia occurs frequently in human cancers and promotes stabilization and activation of hypoxia inducible factor (HIF). HIF-1α is specific for the hypoxia response, and its degradation mediated by three enzymes EGLN1, EGLN2 and EGLN3. Although EGLNs expression has been found to be related to prognosis of many cancers, few studies examined DNA methylation in EGLNs and its relationship to prognosis of early-stage non-small cell lung cancer (NSCLC). We analyzed EGLNs DNA methylation data from tumor tissue samples of 1,230 early-stage NSCLC patients, as well as gene expression data from The Cancer Genome Atlas. The sliding windows sequential forward feature selection method and weighted random forest were used to screen out the candidate CpG probes in lung adenocarcinomas (LUAD) and lung squamous cell carcinomas patients, respectively, in both discovery and validation phases. Then Cox regression was performed to evaluate the association between DNA methylation and overall survival. Among the 34 CpG probes in EGLNs, DNA methylation at cg25923056EGLN2 was identified to be significantly associated with LUAD survival (HR = 1.02, 95% CI: 1.01-1.03, P = 9.90 × 10-5), and correlated with EGLN2 expression (r = - 0.36, P = 1.52 × 10-11). Meanwhile, EGLN2 expression was negatively correlated with HIF1A expression in tumor tissues (r = - 0.30, P = 4.78 × 10-8) and significantly (P = 0.037) interacted with HIF1A expression on overall survival. Therefore, DNA methylation of EGLN2- HIF1A is a potential marker for LUAD prognosis and these genes are potential treatment targets for further development of HIF-1α inhibitors in lung cancer therapy.