| Title: | Sex Differences in Risk Factors for Incident Sepsis Hospitalizations: A Prospective Cohort Study Using the UK Biobank |
| Journal: | The Journal of Infectious Diseases |
| Published: | 6 Mar 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40048641/ |
| DOI: | https://doi.org/10.1093/infdis/jiaf122 |
Publication 15875
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Abstract
BACKGROUND: Sepsis is a leading cause of death worldwide. The presence of most chronic conditions and other lifestyle-related risk factors increase sepsis risk. Whether there are sex differences in risk factors associated with sepsis hospitalization is unknown.</p>
METHODS: A prospective cohort study included participants aged 40-69 years recruited to the UK Biobank between 2006 and 2010. Sepsis was identified from hospitalization records. Poisson regression was used to estimate sex-specific incidence. Cox proportional hazards models were used to estimate hazard ratios (HRs) and the women-to-men ratio of HRs (RHR) with 95% confidence intervals (CIs) for risk factors associated with an incident sepsis hospitalization.</p>
RESULTS: Of 490 783 participants, 21 468 (47.6% women) experienced an incident sepsis hospitalization. Age-standardized risk was higher in men than in women (40.2 vs 31.2 per 10 000 person-years; HR, 1.26 [95% CI, 1.23-1.29]). Chronic obstructive pulmonary disease (COPD) conferred the highest risk for sepsis hospitalization, with excess risk in women (RHR, 1.23 [95% CI, 1.10-1.38]). Dyslipidemia (RHR, 1.08 [95% CI, 1.02-1.16]), myocardial infarction (1.22 [1.05-1.41]), and smoking (1.19 [1.09-1.29]) were associated with excess risk of sepsis hospitalization in women, compared with men. Dementia was associated with more than twice the risk of sepsis hospitalization in men (HR, 2.21 [95% CI, 1.37-3.55]).</p>
CONCLUSIONS: The risk of sepsis hospitalization was higher in the presence of most risk factors, with greater effects in women with a history of COPD, dyslipidemia, myocardial infarction, and smoking. Incorporating sex-specific risk factors in risk prediction modeling may facilitate targeted prevention efforts and support earlier recognition and treatment.</p>
4 Authors
- Gary K K Low
- Katie Harris
- Mark Woodward
- Kelly J Thompson
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