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Introduction: Smoking significantly burdens human health, contributing to an increasing incidence of mortality and morbidity. This study aims to explore the prevalence of smoking, cessation, and the association between various risk factors and smoking intensity measured in pack-years among Chinese adults. Methods: During 2020-2021, the China Stroke High-risk Population Screening and Intervention Program (CSHPSIP) invited participants aged ≥ 40 from 31 provinces in mainland China. This cross-sectional study presented the standardized prevalence of smoking and cessation across various demographics, including age, sex, residence, income, education, BMI, and geographical region. Multivariable logistic regression was employed to examine the associations between smoking pack-years and related factors. Results: Among 524,741 participants (mean age: 61.9±10.9; 41.1% male; 58.9% female), standardized smoking prevalence was 19.3% (95% CI=19.2-19.4%), with men (37.2%; 95% CI=37.0-37.4%) displaying significantly higher rates than women (1.3%; 95% CI=1.2-1.3%). Smoking cessation rate stood at 11.2% (95% CI: 11.0-11.4%), with figures of 11.3% (95% CI: 11.1-11.5%) for men and 8.4% (95% CI: 7.5-9.2%) for women. Urban residents and those with advanced education had lower smoking rates and higher cessation rates. Additionally, the dose-response relationship indicated a more pronounced association between higher smoking pack-years and elevated health risks, including hypertension (aOR=1.30, 95% CI: 1.24-1.36), diabetes (aOR=1.26, 95% CI: 1.20-1.33), hyperlipidemia (aOR=1.22, 95% CI: 1.16-1.28), heart disease (aOR=1.40, 95% CI: 1.26-1.54), and stroke (aOR=1.23, 95% CI: 1.10-1.36). Conclusions: This comprehensive study emphasizes the profound impact of smoking on health in Chinese adults, indicating the critical need for tailored cessation programs, particularly for middle-aged individuals, men, rural residents, and those with lower educational backgrounds. [ABSTRACT FROM AUTHOR]

Background and Purpose. There are increasing evidences that show that the prognosis of patients with acute ischemic stroke (AIS) is closely related to the inflammatory response. In the inflammation caused by AIS, plasma C-reactive protein (CRP) will increase and is associated with prognosis in these patients; few studies have looked at the relationship between CRP and large artery atherosclerosis- (LAA-) type AIS. We aim to investigate the role of CRP in predicting the functional outcome of LAA-type AIS patients. Methods. We prospectively included 200 patients with LAA-type AIS and tested their CRP levels on admission. We followed these patients consecutively. The primary outcome was an adverse event, defined as a modified Rankin Scale score of 2-6 at months 3, 6, and 12 after discharge. A logistic regression model was used to analyze the relationship between CRP and the functional outcome of LAA stroke. Results. We divided 200 patients into 3 groups evenly based on CRP level. After adjustment for gender, age, smoking history, drinking history, history of hyperlipidemia, history of diabetes, lipid levels, and blood glucose levels, logistic regression showed that the incidence of LAA-type AIS poor outcome was positively associated with CRP level at admission, whether it was 3 months, 6 months, or 12 months after discharge, respectively (OR: 2.574, 95% CI: 1.213-5.463; OR: 2.806, 95% CI: 1.298-6.065; OR: 2.492, 95% CI: 1.167-5.321. In the highest tertile vs. the lowest tertile as a reference), and both were statistically different. Conclusions. High CRP level predicts poor functional outcome in LAA-type AIS patients, which provides a strong basis for clinicians to make treatment decisions for these patients. [ABSTRACT FROM AUTHOR]

Background: Unbalanced inflammatory response is a critical feature of sepsis, a life-threatening condition with significant global health burdens. Immune dysfunction, particularly that involving different immune cells in peripheral blood, plays a crucial pathophysiological role and shows early warning signs in sepsis. The objective is to explore the relationship between sepsis and immune subpopulations in peripheral blood, and to identify patients with a higher risk of 28-day mortality based on immunological subtypes with machine-learning (ML) model., Methods: Patients were enrolled according to the sepsis-3 criteria in this retrospective observational study, along with age- and sex-matched healthy controls (HCs). Data on clinical characteristics, laboratory tests, and lymphocyte immunophenotyping were collected. XGBoost and k-means clustering as ML approaches, were employed to analyze the immune profiles and stratify septic patients based on their immunological subtypes. Cox regression survival analysis was used to identify potential biomarkers and to assess their association with 28-day mortality. The accuracy of biomarkers for mortality was determined by the area under the receiver operating characteristic (ROC) curve (AUC) analysis., Results: The study enrolled 100 septic patients and 89 HCs, revealing distinct lymphocyte profiles between the two groups. The XGBoost model discriminated sepsis from HCs with an area under the receiver operating characteristic curve of 1.0 and 0.99 in the training and testing set, respectively. Within the model, the top three highest important contributions were the percentage of CD38 + CD8 + T cells, PD-1 + NK cells, HLA-DR + CD8 + T cells. Two clusters of peripheral immunophenotyping of septic patients by k-means clustering were conducted. Cluster 1 featured higher proportions of PD1 + NK cells, while cluster 2 featured higher proportions of naïve CD4 + T cells. Furthermore, the level of PD-1 + NK cells was significantly higher in the non-survivors than the survivors (15.1% vs 8.6%, P <0.01). Moreover, the levels of PD1 + NK cells combined with SOFA score showed good performance in predicting the 28-day mortality in sepsis (AUC=0.91,95%CI 0.82-0.99), which is superior to PD1 + NK cells only(AUC=0.69, sensitivity 0.74, specificity 0.64, cut-off value of 11.25%). In the multivariate Cox regression, high expression of PD1 + NK cells proportion was related to 28-day mortality (aHR=1.34, 95%CI 1.19 to 1.50; P <0.001)., Conclusion: The study provides novel insights into the association between PD1 + NK cell profiles and prognosis of sepsis. Peripheral immunophenotyping could potentially stratify the septic patients and identify those with a high risk of 28-day mortality., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Tang, Shang, Chang, Jiang, Xu, Zhang, Lu, Chen, Liu, Zeng, Cao and Li.)

Introduction: None of the studies of type 2 diabetes (T2D) subtyping to date have used linked population-level data for incident and prevalent T2D, incorporating a diverse set of variables, explainable methods for cluster characterization, or adhered to an established framework. We aimed to develop and validate machine learning (ML)-informed subtypes for type 2 diabetes mellitus (T2D) using nationally representative data., Research Design and Methods: In population-based electronic health records (2006-2020; Clinical Practice Research Datalink) in individuals ≥18 years with incident T2D (n=420 448), we included factors (n=3787), including demography, history, examination, biomarkers and medications. Using a published framework, we identified subtypes through nine unsupervised ML methods (K-means, K-means++, K-mode, K-prototype, mini-batch, agglomerative hierarchical clustering, Birch, Gaussian mixture models, and consensus clustering). We characterized clusters using intracluster distributions and explainable artificial intelligence (AI) techniques. We evaluated subtypes for (1) internal validity (within dataset; across methods); (2) prognostic validity (prediction for 5-year all-cause mortality, hospitalization and new chronic diseases); and (3) medication burden., Results: Development : We identified four T2D subtypes: metabolic, early onset, late onset and cardiometabolic. Internal validity : Subtypes were predicted with high accuracy (F1 score >0.98). Prognostic validity : 5-year all-cause mortality, hospitalization, new chronic disease incidence and medication burden differed across T2D subtypes. Compared with the metabolic subtype, 5-year risks of mortality and hospitalization in incident T2D were highest in late-onset subtype (HR 1.95, 1.85-2.05 and 1.66, 1.58-1.75) and lowest in early-onset subtype (1.18, 1.11-1.27 and 0.85, 0.80-0.90). Incidence of chronic diseases was highest in late-onset subtype and lowest in early-onset subtype. Medications : Compared with the metabolic subtype, after adjusting for age, sex, and pre-T2D medications, late-onset subtype (1.31, 1.28-1.35) and early-onset subtype (0.83, 0.81-0.85) were most and least likely, respectively, to be prescribed medications within 5 years following T2D onset., Conclusions: In the largest study using ML to date in incident T2D, we identified four distinct subtypes, with potential future implications for etiology, therapeutics, and risk prediction., Competing Interests: Competing interests: JBM, HG and TM are employed by AstraZeneca UK, a biopharmaceutical company, and declare stock and stock options. AB has received research funding from AstraZeneca. KK has acted as a consultant, speaker or received grants for investigator-initiated studies for AstraZeneca, Bayer, Novartis, Novo Nordisk, Sanofi-Aventis, Lilly and Merck Sharp & Dohme, Boehringer Ingelheim, Oramed Pharmaceuticals, Roche and Applied Therapeutics., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)