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Abstract Background Protein biomarkers may contribute to the identification of vulnerable subgroups for premature mortality. This study aimed to investigate the association of plasma proteins with all-cause and cause-specific mortality among individuals with and without baseline type 2 diabetes (T2D) and evaluate their impact on the prediction of all-cause mortality in two prospective Cooperative Health Research in the Region of Augsburg (KORA) studies. Methods The discovery cohort comprised 1545 participants (median follow-up 15.6 years; 244 with T2D: 116 total, 62 cardiovascular, 31 cancer-related and 23 other-cause deaths; 1301 without T2D: 321 total, 114 cardiovascular, 120 cancer-related and 87 other-cause deaths). The validation cohort comprised 1031 participants (median follow-up 6.9 years; 203 with T2D: 76 total, 45 cardiovascular, 19 cancer-related and 12 other-cause deaths; 828 without T2D: 169 total, 74 cardiovascular, 39 cancer-related and 56 other-cause deaths). We used Cox regression to examine associations of 233 plasma proteins with all-cause and cause-specific mortality and Lasso regression to construct prediction models for all-cause mortality stratifying by baseline T2D. C-index, category-free net reclassification index (cfNRI), and integrated discrimination improvement (IDI) were conducted to evaluate the predictive performance of built prediction models. Results Thirty-five and 62 proteins, with 29 overlapping, were positively associated with all-cause mortality in the group with and without T2D, respectively. Out of these, in the group with T2D, 35, eight, and 26 were positively associated with cardiovascular, cancer-related, and other-cause mortality, while in the group without T2D, 55, 41, and 47 were positively associated with respective cause-specific outcomes in the pooled analysis of both cohorts. Regulation of insulin-like growth factor (IGF) transport and uptake by IGF-binding proteins emerged as a unique pathway enriched for all-cause and cardiovascular mortality in individuals with T2D. The combined model containing the selected proteins (five and 12 proteins, with four overlapping, in the group with and without T2D, respectively) and clinical risk factors improved the prediction of all-cause mortality by C-index, cfNRI, and IDI. Conclusions This study uncovered shared and unique mortality-related proteins in persons with and without T2D and emphasized the role of proteins in improving the prediction of mortality in different T2D subgroups.

Abstract Background The study aimed to explore the association between manganese concentration and all-cause, cardiovascular disease (CVD)-related, and cancer-related mortality in the general population of the United States. Methods We integrated the data from the National Health and Nutrition Examination Survey from 2011 to 2018. A total of 9,207 subjects were selected based on the inclusion and exclusion criteria. The relationship between manganese concentration and all-cause, CVD-related, and cancer-related mortality was analyzed by constructing a Cox proportional hazard regression model and a restricted cubic spline (RCS) plot. Additionally, subgroup analyses stratified by age, sex, race/ethnicity, hypertension, diabetes mellitus (DM), chronic heart disease, chronic heart failure, angina pectoris, heart attack, stroke, and BMI were further performed. Results In the full adjusted model, compared with the lowest quartile, the adjusted hazard ratios with 95% confidence intervals (CIs) for all-cause, CVD-related, and cancer-related mortality across manganese quartiles were (1.11 (0.87,1.41), 0.96 (0.74, 1.23), and 1.23 (0.96, 1.59); P-value for trend =0.041), (0.86 (0.54, 1.37), 0.87 (0.55, 1.40), and 1.07 (0.67, 1.72); P-value for trend =0.906), and (1.45 (0.92, 2.29), 1.14 (0.70, 1.88), and 1.26 (0.75, 2.11); P-value for trend =0.526), respectively. The RCS curve shown a U-shaped association between manganese concentration and all-cause mortality and CVD-related mortality (P-value for nonlinear

Yonghong Gao,1 Yan Wang,1 Dandan Zhang,2 Tingting Wu,1 Qingwei Li1 1Department of Cardiology, Shanghai Jinshan District Tinglin Hospital, Shanghai, 201505, People’s Republic of China; 2Department of General Practice, Shanghai Jinshan District Tinglin Hospital, Shanghai, 201505, People’s Republic of ChinaCorrespondence: Qingwei Li, Department of Cardiology, Shanghai Jinshan District Tinglin Hospital, Shanghai, 201505, People’s Republic of China, Email tel18930810663@163.comBackground: In the general population of the United States (U.S.), the relationship between dietary inflammatory index (DII) and mortality (all-cause, cardiovascular disease (CVD)-related, and cancer-related) is still unclear. Therefore, in this research, we examined the association of DII with mortality caused by all-cause, CVD-related, and cancer-related causes.Methods: Data from the National Health and Nutrition Examination Survey (NHANES) from 1999– 2018 was used for exploring the link between DII and all-cause, CVD-related, and cancer-related mortality through the use of Cox proportional hazard models and restricted cubic spline model. In addition, subgroup analyses were further performed.Results: The study included 29,013 individuals from the NHANES from 1999 to 2018. The DII scores were nominated as low (T1: − 5.281– 0.724), medium (T2: 0.725– 2.513), and high-grade inflammation (T3: 2.514– 5.795), with T1 serving as the reference group. The linear positive correlation between DII and all-cause and CVD-related mortality was studied using Cox regression analysis. In the full-adjusted model, as compared with the individuals with T1 DII scores, adjusted odds ratios with 95% confidence intervals for all-cause and CVD-related mortality were 1.149 (1.059, 1.247), and 1.186 (1.084, 1.297), as well as 1.197 (1.032, 1.387), and 1.198 (1.019, 1.409), respectively. However, there was no statistical significance between DII and cancer-related mortality. The RCS plot also showed a significant increase in all-cause and CVD-related mortality with increased DII. Nevertheless, as DII scores increased, cancer-related mortality first increased and then decreased.Conclusion: All-cause and CVD-related mortality are linked independently to high DII scores, independently. Further study of the association of DII scores with mortality caused by all-cause, CVD-related, and cancer-related causes is necessary to explore.Keywords: all-cause mortality, cancer-related mortality, cardiovascular disease-related mortality, dietary inflammation index, National Health and Nutrition Examination Survey

Background: The effect of flavonoid consumption on all-cause and special-cause mortality remains unclear among populations with hypertension. Methods: A total of 6110 people with hypertension from three NHANES survey cycles (2007–2008, 2009–2010, and 2017–2018) were enrolled in this study. Cox proportional hazard models were conducted to estimate the association between the intake of total flavonoids and flavonoid subclasses and all-cause, cancer-related, and cardiovascular disease (CVD)-related mortality. Nonlinear relationships were identified using restricted cubic splines (RCS). Results: During 43,977 person-years of follow-up, 1155 participants died from any cause, 282 participants died from CVD, and 265 participants died from cancer. After adjusting for relevant confounders, including demographic, lifestyle, and dietary intake, a higher intake of total flavonoids was significantly associated with lower all-cause mortality but not CVD-related and cancer-related mortality among the population with hypertension. Compared with extreme quartiles, the hazard ratio (HR) and 95% confidence interval (CI) were 0.74 (0.56–0.97) for all-cause mortality, 0.77 (0.40–1.46) for CVD-related mortality, and 0.62 (0.35–1.08) for cancer-related mortality. In terms of all-cause mortality, this inverse association was optimized at total flavonoid consumption of approximately 375 mg/day. In addition, the negative association between total flavonoid consumption and all-cause mortality was more pronounced in non-obese (BMI < 30 kg/m2) compared to obese (BMI ≥ 30 kg/m2) populations. Higher intakes of anthocyanidin, flavan-3-ol, flavonol, and isoflavone were significantly associated with lower all-cause mortality (HR (95%CI): 0.70 (0.55–0.89); 0.76 (0.59–0.96); 0.66 (0.46–0.94); 0.79 (0.67–0.93), respectively). Higher intakes of anthocyanidin, flavan-3-ol, and flavonol were significantly associated with lower cancer-related mortality (HR (95%CI): 0.55 (0.32–0.93); 0.51 (0.31–0.82); 0.52 (0.28–0.96), respectively). Conclusion: This study suggests that a heightened consumption of total flavonoids and some flavonoid subclasses was linked to lower mortality, which supports the proposal of increasing flavonoid intake as part of healthy diets in patients with hypertension.

Bo Zhang,1 Huiping Shi,1 Hongtao Wang2 1Jinling Institute of Science and Technology, Nanjing City, Jiangsu Province, People’s Republic of China; 2School of Life Science, Tonghua Normal University, Tonghua City, Jilin Province, People’s Republic of ChinaCorrespondence: Bo Zhang, Jinling Institute of Science and Technology, No. 99, Hongjing Avenue, Jiangning District, Nanjing City, Jiangsu Province, 211169, People’s Republic of China, Email liuxing.2001@163.comAbstract: Cancer is a leading cause of morbidity and mortality worldwide. While progress has been made in the diagnosis, prognosis, and treatment of cancer patients, individualized and data-driven care remains a challenge. Artificial intelligence (AI), which is used to predict and automate many cancers, has emerged as a promising option for improving healthcare accuracy and patient outcomes. AI applications in oncology include risk assessment, early diagnosis, patient prognosis estimation, and treatment selection based on deep knowledge. Machine learning (ML), a subset of AI that enables computers to learn from training data, has been highly effective at predicting various types of cancer, including breast, brain, lung, liver, and prostate cancer. In fact, AI and ML have demonstrated greater accuracy in predicting cancer than clinicians. These technologies also have the potential to improve the diagnosis, prognosis, and quality of life of patients with various illnesses, not just cancer. Therefore, it is important to improve current AI and ML technologies and to develop new programs to benefit patients. This article examines the use of AI and ML algorithms in cancer prediction, including their current applications, limitations, and future prospects.Keywords: machine learning, artificial intelligence, treatment selection, cancer diagnosis, cancer-related mortality

Shengjue Xiao,1,&ast; Zhenwei Wang,1,&ast; Ronghua Zuo,2,&ast; Yufei Zhou,3 Yiqing Yang,1 Tian Chen,1 Naifeng Liu1 1Department of Cardiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, People’s Republic of China; 2Department of Anesthesiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, People’s Republic of China; 3Department of Cardiology, Shanghai Institute of Cardiovascular Diseases, Zhongshan Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Naifeng Liu, Department of Cardiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Dingjiaqiao, Nanjing, 210009, People’s Republic of China, Tel +86 025 8327 2002, Email liunf@seu.edu.cnObjective: Our research was designed to investigate the relationship between systemic immune inflammation (SII) index and all-cause, cardiovascular disease (CVD), and cancer-related mortality in patients with CVD.Methods: We used the National Health and Nutrition Examination Survey data from 1999 to 2018 to conduct this study. The association between SII index and all-cause, CVD, and cancer-related mortality in patients with CVD was examined using restricted cubic splines (RCS), Cox proportional hazard models, and subgroup analysis, respectively. CVD was defined as a composite of five outcomes of CVD, including coronary heart disease (CHD), congestive heart failure (CHF), angina pectoris, myocardial infarction, and stroke. Additionally, the link between SII index and all-cause, CVD, and cancer-related mortality in patients with a composite of five outcomes of CVD was also explored.Results: In total, 5329 participants were included. The RCS also showed a U-curve correlation between SII index and the all-cause, CVD, and cancer-related mortality in patients with CVD. As compared with the individuals with lowest quartile of SII index, hazard ratios with 95% confidence intervals for all-cause, CVD, and cancer-related mortality across the quartiles were (1.202 (0.981, 1.474), 1.184 (0.967, 1.450), and 1.365 (1.115, 1.672)), (1.116 (0.815, 1.527), 1.017 (0.740, 1.398), and 1.220 (0.891, 1.670)), and (1.202 (0.981, 1.474), 1.184 (0.967, 1.450), and 1.365 (1.115, 1.672)), respectively, in the full-adjusted model. The SII index also had a U-shaped relationship with all-cause, CVD, and cancer-related mortality in patients with CHD, angina, and myocardial infarction. Additionally, the U-shaped relationship between SII index and all-cause, and cancer-related mortality also exists in CHF, and stroke. However, there was a positive linear correlation between SII index and CVD mortality in patients with CHF, and stroke.Conclusion: In the United States general population, the correlation between SII index and all-cause, CVD, and cancer-related mortality showed a U-shaped curve in patients with CVD.Keywords: all-cause mortality, cross-sectional study, cardiovascular disease mortality, cancer-related mortality, systemic immune inflammatory index

Abstract Background To investigate whether metformin monotherapy or adjunctive therapy improves the prognosis in patients with any type of cancer compared to non-metformin users (age ≥18). Methods Databases (Medline, Embase, and the Cochrane Central Register of Controlled Trials) and clinical trial registries ( ClinicalTrials.gov ; the World Health Organization International Clinical Trials Registry Platform) were screened for randomized, controlled trials (RCT) reporting at least progression-free survival (PFS) and/or overall survival (OS). Main outcome measures included hazard ratios (HR), and combined HRs and 95% confidence intervals (CI) were calculated using random-effects models. Results Of the 8419 records screened, 22 RCTs comprising 5943 participants were included. Pooled HRs were not statistically significant in both PFS (HR 0.97, 95% CI 0.82–1.15, I 2 = 50%) and OS (HR 0.98, 95% CI 0.86–1.13, I 2 = 33%) for patients with cancer between the metformin and control groups. Subgroup analyses demonstrated that metformin treatment was associated with a marginally significant improvement in PFS in reproductive system cancers (HR 0.86, 95% CI 0.74–1.00) and a significantly worse PFS in digestive system cancers (HR 1.45, 95% CI 1.03–2.04). The PFS or OS was observed consistently across maintenance dose, diabetes exclusion, median follow-up, risk of bias, and combined antitumoral therapies. Conclusion Metformin treatment was not associated with cancer-related mortality in adults compared with placebo or no treatment. However, metformin implied beneficial effects in the PFS of the patients with reproductive system cancers but was related to a worse PFS in digestive system cancers. Systematic review registration PROSPERO registration number CRD42022324672.

Objective: To conduct a comprehensive systematic review and meta-analysis to compare mortality and other clinical outcomes associated with chili pepper (CP) consumption versus no/rare consumption of CP. Methods: A comprehensive search was performed using Ovid, Cochrane, Medline, EMBASE, and Scopus from inception till January 16, 2020. Observational studies and randomized controlled trials were included, while pediatric/animal studies, letters/case reports, reviews, abstracts, and book chapters were excluded. All-cause mortality was studied as the primary outcome. Cardiovascular mortality, cancer-related deaths and cerebrovascular accidents were studied as secondary outcomes. Results: From 4729 studies, four studies met the inclusion criteria. Random effects pooled analysis showed that all-cause mortality among CP consumers was lower, compared to rare/non-consumers, with a hazard ratio (HR) of 0.87 [95% CI: 0.85–0.90; p

Introduction: Colon cancer is one of the leading malignancies globally and continues to be one of the most typical causes of cancer-related mortality. The clinical outcome of the disease depends on the primary tumor stage, regional nodal involvement, and distant disease dissemination. It often presents with hematogenous spread to the liver at the time of diagnosis. Another factor for increased mortality is the presence of extramural venous invasion (EMVI). This is exceedingly important as it has significant prognostic significance and helps predict survival. Case Description: A middle-aged female with a recent history of cesarean delivery presented with abdominal pain and occasional constipation, which led to a series of investigations. Initial Computed Tomography (CT) scan showed proximal to mid transverse colonic tumoral thickening with locoregional lymphadenopathy and solitary distant metastasis in the left hepatic lobe. This was followed by extended right hemicolectomy and hepatic metastasectomy. The patient remained on follow-up and later presented with thrombus formation in the splenoportal circulation. Initially, this was considered a bland thrombus, and the patient was advised a close follow-up. However, the patient was lost to follow and later presented with extensive thrombosis of the portal and splenic veins. Practical Implications: Confident differentiation of the bland versus malignant thrombosis is crucial to ascertain disease stage and appropriate management. Invasive tissue sampling gives a confident diagnosis of benign versus malignant thrombus. However, using a noninvasive imaging modality, we can still distinguish between the two with reasonable certainty.