A predictive paradigm for COVID-19 prognosis based on the longitudinal measure of biomarkers.

Novel coronavirus disease 2019 (COVID-19) is an emerging, rapidly evolving crisis, and the ability to predict prognosis for individual COVID-19 patient is important for guiding treatment. Laboratory examinations were repeatedly measured during hospitalization for COVID-19 patients, which provide the possibility for the individualized early prediction of prognosis. However, previous studies mainly focused on risk prediction based on laboratory measurements at one time point, ignoring disease progression and changes of biomarkers over time. By using historical regression trees (HTREEs), a novel machine learning method, and joint modeling technique, we modeled the longitudinal trajectories of laboratory biomarkers and made dynamically predictions on individual prognosis for 1997 COVID-19 patients. In the discovery phase, based on 358 COVID-19 patients admitted between 10 January and 18 February 2020 from Tongji Hospital, HTREE model identified a set of important variables including 14 prognostic biomarkers. With the trajectories of those biomarkers through 5-day, 10-day and 15-day, the joint model had a good performance in discriminating the survived and deceased COVID-19 patients (mean AUCs of 88.81, 84.81 and 85.62% for the discovery set). The predictive model was successfully validated in two independent datasets (mean AUCs of 87.61, 87.55 and 87.03% for validation the first dataset including 112 patients, 94.97, 95.78 and 94.63% for the second validation dataset including 1527 patients, respectively). In conclusion, our study identified important biomarkers associated with the prognosis of COVID-19 patients, characterized the time-to-event process and obtained dynamic predictions at the individual level.

Associations of genetic risk, BMI trajectories, and the risk of non-small cell lung cancer: a population-based cohort study.

BACKGROUND: Body mass index (BMI) has been found to be associated with a decreased risk of non-small cell lung cancer (NSCLC); however, the effect of BMI trajectories and potential interactions with genetic variants on NSCLC risk remain unknown. METHODS: Cox proportional hazards regression model was applied to assess the association between BMI trajectory and NSCLC risk in a cohort of 138,110 participants from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. One-sample Mendelian randomization (MR) analysis was further used to access the causality between BMI trajectories and NSCLC risk. Additionally, polygenic risk score (PRS) and genome-wide interaction analysis (GWIA) were used to evaluate the multiplicative interaction between BMI trajectories and genetic variants in NSCLC risk. RESULTS: Compared with individuals maintaining a stable normal BMI (n = 47,982, 34.74%), BMI trajectories from normal to overweight (n = 64,498, 46.70%), from normal to obese (n = 21,259, 15.39%), and from overweight to obese (n = 4,371, 3.16%) were associated with a decreased risk of NSCLC (hazard ratio [HR] for trend = 0.78, P < 2×10-16). An MR study using BMI trajectory associated with genetic variants revealed no significant association between BMI trajectories and NSCLC risk. Further analysis of PRS showed that a higher GWAS-identified PRS (PRSGWAS) was associated with an increased risk of NSCLC, while the interaction between BMI trajectories and PRSGWAS with the NSCLC risk was not significant (PsPRS= 0.863 and PwPRS= 0.704). In GWIA analysis, four independent susceptibility loci (P < 1×10-6) were found to be associated with BMI trajectories on NSCLC risk, including rs79297227 (12q14.1, located in SLC16A7, Pinteraction = 1.01×10-7), rs2336652 (3p22.3, near CLASP2, Pinteraction = 3.92×10-7), rs16018 (19p13.2, in CACNA1A, Pinteraction = 3.92×10-7), and rs4726760 (7q34, near BRAF, Pinteraction = 9.19×10-7). Functional annotation demonstrated that these loci may be involved in the development of NSCLC by regulating cell growth, differentiation, and inflammation. CONCLUSIONS: Our study has shown an association between BMI trajectories, genetic factors, and NSCLC risk. Interestingly, four novel genetic loci were identified to interact with BMI trajectories on NSCLC risk, providing more support for the aetiology research of NSCLC. TRIAL REGISTRATION: http://www. CLINICALTRIALS: gov , NCT01696968 .

Prediction of acute kidney injury after cardiac surgery: model development using a Chinese electronic health record dataset.

BACKGROUND: Acute kidney injury (AKI) is a major complication following cardiac surgery that substantially increases morbidity and mortality. Current diagnostic guidelines based on elevated serum creatinine and/or the presence of oliguria potentially delay its diagnosis. We presented a series of models for predicting AKI after cardiac surgery based on electronic health record data. METHODS: We enrolled 1457 adult patients who underwent cardiac surgery at Nanjing First Hospital from January 2017 to June 2019. 193 clinical features, including demographic characteristics, comorbidities and hospital evaluation, laboratory test, medication, and surgical information, were available for each patient. The number of important variables was determined using the sliding windows sequential forward feature selection technique (SWSFS). The following model development methods were introduced: extreme gradient boosting (XGBoost), random forest (RF), deep forest (DF), and logistic regression. Model performance was accessed using the area under the receiver operating characteristic curve (AUROC). We additionally applied SHapley Additive exPlanation (SHAP) values to explain the RF model. AKI was defined according to Kidney Disease Improving Global Outcomes guidelines. RESULTS: In the discovery set, SWSFS identified 16 important variables. The top 5 variables in the RF importance matrix plot were central venous pressure, intraoperative urine output, hemoglobin, serum potassium, and lactic dehydrogenase. In the validation set, the DF model exhibited the highest AUROC (0.881, 95% confidence interval [CI] 0.831-0.930), followed by RF (0.872, 95% CI 0.820-0.923) and XGBoost (0.857, 95% CI 0.802-0.912). A nomogram model was constructed based on intraoperative longitudinal features, achieving an AUROC of 0.824 (95% CI 0.763-0.885) in the validation set. The SHAP values successfully illustrated the positive or negative contribution of the 16 variables attributed to the output of the RF model and the individual variable's effect on model prediction. CONCLUSIONS: Our study identified 16 important predictors and provided a series of prediction models to enhance risk stratification of AKI after cardiac surgery. These novel predictors might aid in choosing proper preventive and therapeutic strategies in the perioperative management of AKI patients.

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.

Association of sleep traits with benign prostatic hyperplasia in middle-aged and elderly men: A prospective analysis in UK Biobank.

AIMS: The association of sleep traits (insomnia, sleep duration, chronotype, daytime sleepiness, and snoring) with benign prostatic hyperplasia (BPH) is unclear. This research aimed to examine the effects of sleep traits on BPH risk. METHODS: A total of 170 241 men aged 38 to 73 years from UK Biobank were included. An overall healthy sleep score was created based on five sleep traits. A Cox regression model was utilized to compute adjusted hazard ratios (HRs) and population attributable fractions (PAFs) with 95% confidence intervals (CIs) for BPH risk in relation to sleep traits. RESULTS: During a median of 12.0 years follow-up, 13 026 incident BPH cases occurred. We observed that sleep duration (7-8 h/d; HR 0.95; 95% CI 0.92-0.99), no frequent insomnia (HR 0.71; 95% CI 0.69-0.74), and no frequent daytime sleepiness (HR 0.86; 95% CI 0.79-0.93) were significantly related to reduced BPH risk. Each one-point increment of the healthy sleep score was related to a decreased BPH risk, with an adjusted HR of 0.90 (95% CI 0.89-0.92). The multivariable-adjusted HR in men adopting five versus zero to one low-risk sleep traits was 0.68 (95% CI 0.61-0.75) for BPH risk. Estimates of the PAF indicated that 9.1% (95% CI 5.8-12.5%) of BPH cases would be prevented if all individuals had adopted all five low-risk sleep traits, assuming causality. CONCLUSIONS: Our study indicates an association between a healthy sleep pattern and a lower risk of BPH, emphasizing the importance of adhering to such patterns for potentially reducing BPH risk. Geriatr Gerontol Int 2024; 24: 675-682.

Proteome-wide Mendelian randomization identifies causal plasma proteins in lung cancer.

Plasma proteins are promising biomarkers and potential drug targets in lung cancer. To evaluate the causal association between plasma proteins and lung cancer, we performed proteome-wide Mendelian randomization meta-analysis (PW-MR-meta) based on lung cancer genome-wide association studies (GWASs), protein quantitative trait loci (pQTLs) of 4,719 plasma proteins in deCODE and 4,775 in Fenland. Further, causal-protein risk score (CPRS) was developed based on causal proteins and validated in the UK Biobank. 270 plasma proteins were identified using PW-MR meta-analysis, including 39 robust causal proteins (both FDR-q < 0.05) and 78 moderate causal proteins (FDR-q < 0.05 in one and p < 0.05 in another). The CPRS had satisfactory performance in risk stratification for lung cancer (top 10% CPRS:Hazard ratio (HR) (95%CI):4.33(2.65-7.06)). The CPRS [AUC (95%CI): 65.93 (62.91-68.78)] outperformed the traditional polygenic risk score (PRS) [AUC (95%CI): 55.71(52.67-58.59)]. Our findings offer further insight into the genetic architecture of plasma proteins for lung cancer susceptibility.

Spicy food consumption and risk of vascular disease: Evidence from a large-scale Chinese prospective cohort of 0.5 million people.

BACKGROUND: Spicy food consumption has been reported to be inversely associated with mortality from multiple diseases. However, the effect of spicy food intake on the incidence of vascular diseases in the Chinese population remains unclear. This study was conducted to explore this association. METHODS: This study was performed using the large-scale China Kadoorie Biobank (CKB) prospective cohort of 486,335 participants. The primary outcomes were vascular disease, ischemic heart disease (IHD), major coronary events (MCEs), cerebrovascular disease, stroke, and non-stroke cerebrovascular disease. A Cox proportional hazards regression model was used to assess the association between spicy food consumption and incident vascular diseases. Subgroup analysis was also performed to evaluate the heterogeneity of the association between spicy food consumption and the risk of vascular disease stratified by several basic characteristics. In addition, the joint effects of spicy food consumption and the healthy lifestyle score on the risk of vascular disease were also evaluated, and sensitivity analyses were performed to assess the reliability of the association results. RESULTS: During a median follow-up time of 12.1 years, a total of 136,125 patients with vascular disease, 46,689 patients with IHD, 10,097 patients with MCEs, 80,114 patients with cerebrovascular disease, 56,726 patients with stroke, and 40,098 patients with non-stroke cerebrovascular disease were identified. Participants who consumed spicy food 1-2 days/week (hazard ratio [HR] = 0.95, 95% confidence interval [95% CI] = [0.93, 0.97], P <0.001), 3-5 days/week (HR = 0.96, 95% CI = [0.94, 0.99], P = 0.003), and 6-7 days/week (HR = 0.97, 95% CI = [0.95, 0.99], P = 0.002) had a significantly lower risk of vascular disease than those who consumed spicy food less than once a week (Ptrend <0.001), especially in those who were younger and living in rural areas. Notably, the disease-based subgroup analysis indicated that the inverse associations remained in IHD (Ptrend = 0.011) and MCEs (Ptrend = 0.002) risk. Intriguingly, there was an interaction effect between spicy food consumption and the healthy lifestyle score on the risk of IHD (Pinteraction = 0.037). CONCLUSIONS: Our findings support an inverse association between spicy food consumption and vascular disease in the Chinese population, which may provide additional dietary guidance for the prevention of vascular diseases.

A trans-omics assessment of gene-gene interaction in early-stage NSCLC.

Epigenome-wide gene-gene (G × G) interactions associated with non-small-cell lung cancer (NSCLC) survival may provide insights into molecular mechanisms and therapeutic targets. Hence, we proposed a three-step analytic strategy to identify significant and robust G × G interactions that are relevant to NSCLC survival. In the first step, among 49 billion pairs of DNA methylation probes, we identified 175 775 G × G interactions with PBonferroni ≤ 0.05 in the discovery phase of epigenomic analysis; among them, 15 534 were confirmed with P ≤ 0.05 in the validation phase. In the second step, we further performed a functional validation for these G × G interactions at the gene expression level by way of a two-phase (discovery and validation) transcriptomic analysis, and confirmed 25 significant G × G interactions enriched in the 6p21.33 and 6p22.1 regions. In the third step, we identified two G × G interactions using the trans-omics analysis, which had significant (P ≤ 0.05) epigenetic cis-regulation of transcription and robust G × G interactions at both the epigenetic and transcriptional levels. These interactions were cg14391855 × cg23937960 (ßinteraction  = 0.018, P = 1.87 × 10-12 ), which mapped to RELA × HLA-G (ßinteraction  = 0.218, P = 8.82 × 10-11 ) and cg08872738 × cg27077312 (ßinteraction  = -0.010, P = 1.16 × 10-11 ), which mapped to TUBA1B × TOMM40 (ßinteraction =-0.250, P = 3.83 × 10-10 ). A trans-omics mediation analysis revealed that 20.3% of epigenetic effects on NSCLC survival were significantly (P = 0.034) mediated through transcriptional expression. These statistically significant trans-omics G × G interactions can also discriminate patients with high risk of mortality. In summary, we identified two G × G interactions at both the epigenetic and transcriptional levels, and our findings may provide potential clues for precision treatment of NSCLC.

IPRS: Leveraging Gene-Environment Interaction to Reconstruct Polygenic Risk Score.

Background: Polygenic risk score (PRS) is widely regarded as a predictor of genetic susceptibility to disease, applied to individuals to predict the risk of disease occurrence. When the gene-environment (G×E) interaction is considered, the traditional PRS prediction model directly uses PRS to interact with the environment without considering the interactions between each variant and environment, which may lead to prediction performance and risk stratification of complex diseases are not promising. Methods: We developed a method called interaction PRS (iPRS), reconstructing PRS by leveraging G×E interactions. Two extensive simulations evaluated prediction performance, risk stratification, and calibration performance of the iPRS prediction model, and compared it with the traditional PRS prediction model. Real data analysis was performed using existing data from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial study to predict genetic susceptibility, pack-years of smoking history, and G×E interactions in patients with lung cancer. Results: Two extensive simulations indicated iPRS prediction model could improve the prediction performance of disease risk, the accuracy of risk stratification, and clinical calibration performance compared with the traditional PRS prediction model, especially when antagonism accounted for the majority of the interaction. PLCO real data analysis also suggested that the iPRS prediction model was superior to the PRS prediction model in predictive effect (p = 0.0205). Conclusion: IPRS prediction model could have a good application prospect in predicting disease risk, optimizing the screening of high-risk populations, and improving the clinical benefits of preventive interventions among populations.

Transmission dynamics model and the coronavirus disease 2019 epidemic: applications and challenges.

Since late 2019, the beginning of coronavirus disease 2019 (COVID-19) pandemic, transmission dynamics models have achieved great development and were widely used in predicting and policy making. Here, we provided an introduction to the history of disease transmission, summarized transmission dynamics models into three main types: compartment extension, parameter extension and population-stratified extension models, highlight the key contribution of transmission dynamics models in COVID-19 pandemic: estimating epidemiological parameters, predicting the future trend, evaluating the effectiveness of control measures and exploring different possibilities/scenarios. Finally, we pointed out the limitations and challenges lie ahead of transmission dynamics models.

Heterogeneity of Treatment Effects for Intensive Blood Pressure Therapy by Individual Components of FRS: An Unsupervised Data-Driven Subgroup Analysis in SPRINT and ACCORD.

BACKGROUND: Few studies have answered the guiding significance of individual components of the Framingham risk score (FRS) to the risk of cardiovascular disease (CVD) after antihypertensive treatment. This study on the systolic blood pressure intervention trial (SPRINT) and the Action to Control Cardiovascular Risk in Diabetes blood pressure trial (ACCORD-BP) aimed to reveal previously undetected association patterns between individual components of the FRS and heterogeneity of treatment effects (HTEs) of intensive blood pressure control. METHODS: A self-organizing map (SOM) methodology was applied to identify CVD-risk-specific subgroups in the SPRINT (n = 8,773), and the trained SOM was utilized directly in 4,495 patients from the ACCORD. The primary endpoints were myocardial infarction (MI), non-myocardial infarction acute coronary syndrome (non-MI ACS), stroke, heart failure (HF), death from CVD causes, and a primary composite cardiovascular outcome. Cox proportional hazards models were then used to explore the potential heterogeneous response to intensive SBP control. RESULTS: We identified four SOM-based subgroups with distinct individual components of FRS profiles and the CVD risk. For individuals with type 2 diabetes mellitus (T2DM) in the ACCORD or without diabetes in the SPRINT, subgroup I characterized by male with the lowest concentrations for total cholesterol (TC) and high-density lipoprotein (HDL) cholesterol measures, experienced the highest risk for major CVD. Conversely, subgroup III characterized by a female with the highest values for these measures represented as the lowest CVD risk. Furthermore, subgroup II, with the highest systolic blood pressure (SBP) and no antihypertensive agent use at baseline, had a significantly greater frequency of non-MI ACS under intensive BP control, the number needed to harm (NNH) was 84.24 to cause 1 non-MI ACS [absolute risk reduction (ARR) = -1.19%; 95% CI: -2.08, -0.29%] in the SPRINT [hazard ratio (HR) = 3.62; 95% CI: 1.33, 9.81; P = 0.012], and the NNH of was 43.19 to cause 1 non-MI ACS (ARR = -2.32%; 95% CI: -4.63, 0.00%) in the ACCORD (HR = 1.81; 95% CI: 1.01-3.25; P = 0.046). Finally, subgroup IV characterized by mostly younger patients with antihypertensive medication use and smoking history represented the lowest risk for stroke, HF, and relatively low risk for death from CVD causes and primary composite CVD outcome in SPRINT, however, except stroke, a low risk for others were not observed in ACCORD. CONCLUSION: Similar findings in patients with hypertensive with T2DM or without diabetes by multivariate subgrouping suggested that the individual components of the FRS could enrich or improve CVD risk assessment. Further research was required to clarify the potential mechanism.

A dynamic model for individualized prognosis prediction in patients with avian influenza A H7N9.

Background: Avian influenza A H7N9 progresses rapidly and has a high case fatality rate. However, few models are available to predict the survival of individual patients with H7N9 infection in real-time. This study set out to construct a dynamic model for individual prognosis prediction based on multiple longitudinal measurements taken during hospitalization. Methods: The clinical and laboratory characteristics of 96 patients with H7N9 who were admitted to hospitals in Jiangsu between January 2016 and May 2017 were retrospectively investigated. A random forest model was applied to longitudinal data to select the biomarkers associated with prognostic outcome. Finally, a multivariate joint model was used to describe the time-varying effects of the biomarkers and calculate individual survival probabilities. Results: The random forest selected a set of significant biomarkers that had the lowest classification error rates in the feature selection phase, including C-reactive protein (CRP), blood urea nitrogen (BUN), procalcitonin (PCT), base excess (BE), lymphocyte count (LYMPH), white blood cell count (WBC), and creatine phosphokinase (CPK). The multivariate joint model was used to describe the effects of these biomarkers and characterize the dynamic progression of the prognosis. Combined with the covariates, the joint model displayed a good performance in discriminating survival outcomes in patients within a fixed time window of 3 days. During hospitalization, the areas under the curve were stable at 0.75. Conclusions: Our study has established a novel model that is able to identify significant indicators associated with the prognostic outcomes of patients with H7N9, characterize the time-to-event process, and predict individual-level daily survival probabilities after admission.

Association between soft drink consumption types and risk of lung cancer and all-cancer: A prospective study of PLCO data.

Diet/sugar-free soft drinks are considered to be healthier than regular soft drinks. However, few studies have examined the relationship between the types of soft drinks (regular and diet/sugar-free) and lung cancer (LC)/all-cancer (AC) risk. In this study, we comprehensively assessed the influence of the type of soft drink consumption on LC/AC risk based on the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. Multivariable Cox proportional hazards and competing risks Fine-Gray regression models adjusted for relevant confounders were used to estimate hazard ratios (HRs) and subdistribution HRs for different types of soft drink consumption. In the PLCO population, female subgroup, and the ever/current smoker subgroup, consumption of both regular and diet soft drinks was associated with a significantly reduced risk of LC compared with no soft drinks at all. For the non-lung cancer (NLC) risk, consumption of only diet soft drinks had a significant positive association for the total population and female subgroup. Based on our findings, it was suggested that partial replacement of regular soft drinks with diet soft drinks might be beneficial to LC prevention, especially for females and ever/current smokers. Additionally, completely replacing regular soft drinks with diet soft drinks might be detrimental to NLC prevention, especially for females.

Metabolic Obesity Phenotypes and Risk of Lung Cancer: A Prospective Cohort Study of 450,482 UK Biobank Participants.

(1) Background: The association between metabolic obesity phenotypes and incident lung cancer (LC) remains unclear. (2) Methods: Based on the combination of baseline BMI categories and metabolic health status, participants were categorized into eight groups: metabolically healthy underweight (MHUW), metabolically unhealthy underweight (MUUW), metabolically healthy normal (MHN), metabolically unhealthy normal (MUN), metabolically healthy overweight (MHOW), metabolically unhealthy overweight (MUOW), metabolically healthy obesity (MHO), and metabolically unhealthy obesity (MUO). The Cox proportional hazards model and Mendelian randomization (MR) were applied to assess the association between metabolic obesity phenotypes with LC risk. (3) Results: During a median follow-up of 9.1 years, 3654 incident LC patients were confirmed among 450,482 individuals. Compared with participants with MHN, those with MUUW had higher rates of incident LC (hazard ratio (HR) = 3.24, 95% confidence interval (CI) = 1.33-7.87, p = 0.009). MHO and MHOW individuals had a 24% and 18% lower risk of developing LC, respectively (MHO: HR = 0.76, 95% CI = 0.61-0.95, p = 0.02; MHO: HR = 0.82, 95% CI = 0.70-0.96, p = 0.02). No genetic association of metabolic obesity phenotypes and LC risk was observed in MR analysis. (4) Conclusions: In this prospective cohort study, individuals with MHOW and MHO phenotypes were at a lower risk and MUUW were at a higher risk of LC. However, MR failed to reveal any evidence that metabolic obesity phenotypes would be associated with a higher risk of LC.

Association of Hypokalemia Incidence and Better Treatment Response in NSCLC Patients: A Meta-Analysis and Systematic Review on Anti-EGFR Targeted Therapy Clinical Trials.

BACKGROUND: This meta-analysis was designed to explore the relationship between the level of serum potassium and the treatment effect of epidermal growth factor receptor (EGFR) antagonist in advanced non-small cell lung cancer (aNSCLC). METHODS: We searched phase II/III prospective clinical trials on treatment with EGFR antagonists for aNSCLC patients. The objective response rate (ORR) and/or the disease control rate (DCR) and the incidence of hypokalemia of high grade (equal to or greater than grade 3) were summarized from all eligible trials. Heterogeneity, which was evaluated by Cochran's Q-test and the I 2 statistics, was used to determine whether a random effects model or a fixed effects model will be used to calculate pooled proportions. Subgroup analysis was performed on different interventions, line types, phases, and drug numbers. RESULTS: From 666 potentially relevant articles, 36 clinical trials with a total of 9,761 participants were included in this meta-analysis. The pooled ORR was 16.25% (95%CI = 12.45-21.19) when the incidence of hypokalemia was 0%-5%, and it increased to 34.58% (95%CI = 24.09-45.07) when the incidence of hypokalemia was greater than 5%. The pooled DCR were 56.03% (95%CI = 45.03-67.03) and 64.38% (95%CI = 48.60-80.17) when the incidence rates of hypokalemia were 0%-5% and greater than 5%, respectively. The results of the subgroup analysis were consistent with the results of the whole population, except for not first-line treatment, which may have been confounded by malnutrition or poor quality of life in long-term survival. CONCLUSION: The efficacy of anti-EGFR targeted therapy was positively associated with the hypokalemia incidence rate. Treatment effects on the different serum potassium strata need to be considered in future clinical trials with targeted therapy.

Association between dietary sodium, potassium intake and lung cancer risk: evidence from the prostate, lung, colorectal and ovarian cancer screening trial and the Women's Health Initiative.

BACKGROUND: Epidemiological studies have reported that dietary mineral intake plays an important role on lung cancer risk, but the association of sodium, potassium intake is still unclear. METHODS: We determined the association between dietary sodium, potassium intake and lung cancer risk based on the Prostate, Lung, Colorectal and Ovarian (PLCO) cancer screening trial and the Women's Health Initiative (WHI). Totally 165,409 participants who completed the baseline questionnaire (BQ) and diet history questionnaire (DHQ) were included into the analytical dataset, including 92,984 (44,959 men and 48,025 women) from the PLCO trial and 72,425 (women only) from the WHI cohort. Multivariable Cox proportional hazards regression was used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) of incident lung cancer associated with dietary potassium and sodium intake. The dose-response relationship was also described using the spline smoothed curve after adjusting covariates. RESULTS: After the median follow-up of 8.55 and 18.56 years, 1,278 and 1,631 new cases of lung cancer were identified in the PLCO trial and WHI cohort, respectively. Intake of sodium was significantly associated with the incidence of lung cancer in the PLCO trial after multivariate adjustment for men (HR: 1.19, 95% CI: 1.05-1.35; P for linear trend =0.044). There was a suggestion that lung cancer risk had a quadratic curve correlation with the increase of potassium intake for women (third vs. lowest quintile: HR: 0.72, 95% CI: 0.54-0.96; P for quadratic trend =0.042). The similar results showing an inverse association between potassium intake and lung cancer risk were also observed in the WHI cohort for women (highest vs. lowest quintile: HR: 0.82, 95% CI: 0.70-0.97; P for linear trend =0.009). CONCLUSIONS: Appropriate intake of potassium has a protective effect against lung cancer, while high consumption of sodium is associated with an increased risk of lung cancer.

A multi-omics study links TNS3 and SEPT7 to long-term former smoking NSCLC survival.

The genetic architecture of non-small cell lung cancer (NSCLC) is relevant to smoking status. However, the genetic contribution of long-term smoking cessation to the prognosis of NSCLC patients remains largely unknown. We conducted a genome-wide association study primarily on the prognosis of 1299 NSCLC patients of long-term former smokers from independent discovery (n = 566) and validation (n = 733) sets, and used in-silico function prediction and multi-omics analysis to identify single nucleotide polymorphisms (SNPs) on prognostics with NSCLC. We further detected SNPs with at least moderate association strength on survival within each group of never, short-term former, long-term former, and current smokers, and compared their genetic similarity at the SNP, gene, expression quantitative trait loci (eQTL), enhancer, and pathway levels. We identified two SNPs, rs34211819TNS3 at 7p12.3 (P = 3.90 × 10-9) and rs1143149SEPT7 at 7p14.2 (P = 9.75 × 10-9), were significantly associated with survival of NSCLC patients who were long-term former smokers. Both SNPs had significant interaction effects with years of smoking cessation (rs34211819TNS3: Pinteraction = 8.0 × 10-4; rs1143149SEPT7: Pinteraction = 0.003). In addition, in silico function prediction and multi-omics analysis provided evidence that these QTLs were associated with survival. Moreover, comparison analysis found higher genetic similarity between long-term former smokers and never-smokers, compared to short-term former smokers or current smokers. Pathway enrichment analysis indicated a unique pattern among long-term former smokers that was related to immune pathways. This study provides important insights into the genetic architecture associated with long-term former smoking NSCLC.

Identification of genetic features associated with fine particulate matter (PM2.5) modulated DNA damage using improved random forest analysis.

Recent studies have found multiple single nucleotide variants (SNVs) associated with DNA damage. However, previous association analysis may ignore the potential interaction effects between SNVs. Therefore, we used an improved random forest (RF) analysis to identify the SNVs related to personal DNA damage in exon-focused genome-wide association study (GWAS). A total of 301 subjects from three independent centers (Zhuhai, Wuhan, and Tianjin) were retained for analysis. An improved RF procedure was used to systematically screen key SNVs associated with DNA damage. Furthermore, we used genetic risk score (GRS) and mediation analysis to investigate the integrative effect and potential mechanism of these genetic variants on DNA damage. Besides, gene set enrichment analysis was conducted to identify the pathways enriched by key SNVs using the Data-driven Expression Prioritized Integration for Complex Traits (DEPICT). Finally, a set of 24 SNVs with the lowest mean square errors (MSE) were identified by improved RF analysis. Both weighted and unweighted GRSs were associated with increased DNA damage levels (Pweight < 0.001 and Punweight < 0.001). Gene set enrichment analysis indicated that these loci were significantly enriched in several biological features associated with DNA damage. These findings suggested the role of SNVs in modifying DNA damage levels. It may be convincing that this improved RF analysis can effectively identify SNVs associated with DNA damage levels.

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.

Epigenetic-smoking interaction reveals histologically heterogeneous effects of TRIM27 DNA methylation on overall survival among early-stage NSCLC patients.

Tripartite motif containing 27 (TRIM27) is highly expressed in lung cancer, including non-small-cell lung cancer (NSCLC). Here, we profiled DNA methylation of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) tumours from 613 early-stage NSCLC patients and evaluated associations between CpG methylation of TRIM27 and overall survival. Significant CpG probes were confirmed in 617 samples from The Cancer Genome Atlas. The methylation of the CpG probe cg05293407TRIM27 was significantly associated with overall survival in patients with LUSC (HR = 1.65, 95% CI: 1.30-2.09, P = 4.52 × 10-5), but not in patients with LUAD (HR = 1.08, 95% CI: 0.87-1.33, P = 0.493). As incidence of LUSC is associated with higher smoking intensity compared to LUAD, we investigated whether smoking intensity impacted on the prognostic effect of cg05293407TRIM27 methylation in NSCLC. LUSC patients had a higher average pack-year of smoking (37.49LUAD vs 54.79LUSC, P = 1.03 × 10-19) and included a higher proportion of current smokers than LUAD patients (28.24%LUAD vs 34.09%LUSC, P = 0.037). cg05293407TRIM27 was significantly associated with overall survival only in NSCLC patients with medium-high pack-year of smoking (HR = 1.58, 95% CI: 1.26-1.96, P = 5.25 × 10-5). We conclude that cg05293407TRIM27 methylation is a potential predictor of LUSC prognosis, and smoking intensity may impact on its prognostic value across the various types of NSCLC.