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Background: Sepsis remains a growing global health concern with soaring mortality and no direct anti-sepsis drug. Although smoking has distinct deleterious effects on chronic inflammatory illnesses and can impair immune function, a comprehensive analysis of the connection between sepsis and smoking is lacking. Methods: This large-scale longitudinal cohort study retrospectively assessed adults aged ≥ 20 years who underwent national health checkups under the Korean National Health Insurance Service between January and December 2009 (N = 4,234,415) and were followed up for 10 years. Sepsis was identified based on the International Classification of Diseases, 10th Revision codes, and smoking status, including accumulated amount, was collected through a self-administered questionnaire. The Cox proportional hazard regression model was used, adjusting for age, sex, household income, body mass index, drinking, exercise, diabetes, hypertension, dyslipidemia, and chronic renal disease. Results: After excluding cases with sepsis occurring before follow-up or after ≤ 1 year of follow-up, 3,881,958 participants, including non-smokers (N = 2,342,841), former smokers (N = 539,850), and active smokers (N = 999,267), were included. Compared to non-smokers, all active smokers (adjust hazard ratio: 1.41, 95% confidence interval 1.38–1.44) and former smokers (1.10, 1.07–1.14) with ≥ 20 pack-years exhibited a significantly higher risk of sepsis (p < 0.001). Smoking of ≥ 30 pack-years in former and active smokers groups significantly increased sepsis incidence (adjust hazard ratio [95% confidence interval] 1.34 [1.31–1.38], p < 0.001). Conclusions: Smoking is closely associated with the incidence of sepsis. Smoking cessation may help in the primary prevention of sepsis. [ABSTRACT FROM AUTHOR]

Inferring a person's smoking habit and history from blood is relevant for complementing or replacing self-reports in epidemiological and public health research, and for forensic applications. However, a finite DNA methylation marker set and a validated statistical model based on a large dataset are not yet available. Employing 14 epigenome-wide association studies for marker discovery, and using data from six population-based cohorts (N = 3764) for model building, we identified 13 CpGs most suitable for inferring smoking versus non-smoking status from blood with a cumulative Area Under the Curve (AUC) of 0.901. Internal fivefold cross-validation yielded an average AUC of 0.897 ± 0.137, while external model validation in an independent population-based cohort (N = 1608) achieved an AUC of 0.911. These 13 CpGs also provided accurate inference of current (average AUCcrossvalidation 0.925 ± 0.021, AUCexternalvalidation0.914), former (0.766 ± 0.023, 0.699) and never smoking (0.830 ± 0.019, 0.781) status, allowed inferring pack-years in current smokers (10 pack-years 0.800 ± 0.068, 0.796; 15 pack-years 0.767 ± 0.102, 0.752) and inferring smoking cessation time in former smokers (5 years 0.774 ± 0.024, 0.760; 10 years 0.766 ± 0.033, 0.764; 15 years 0.767 ± 0.020, 0.754). Model application to children revealed highly accurate inference of the true non-smoking status (6 years of age: accuracy 0.994, N = 355; 10 years: 0.994, N = 309), suggesting prenatal and passive smoking exposure having no impact on model applications in adults. The finite set of DNA methylation markers allow accurate inference of smoking habit, with comparable accuracy as plasma cotinine use, and smoking history from blood, which we envision becoming useful in epidemiology and public health research, and in medical and forensic applications. [ABSTRACT FROM AUTHOR]