#1265 Circulating neutrophils in diabetic kidney disease—the relationship with long-term cardiovascular outcomes

Abstract

Abstract Background and Aims Neutrophils play important roles in the pathogenesis of cardiovascular inflammation and affects cardiovascular disease (CVD) outcomes. The relationship of circulating neutrophil counts with cardiovascular disease (CVD) and all-cause mortality in patients with type 2 diabetes mellitus (T2DM) and diabetic kidney disease (DKD) remains unclear. Method We retrieved data from 44,494 participants in the National Health and Nutrition Examination Surveys (NHANES) from 2005 to 2020, and investigated the associations between circulating neutrophil counts, kidney functions and CVD mortality in T2DM and DKD patients. Clinical predictive models and risk scores for long-term mortality were constructed. Results Among the cohort of 44,332 patients (18% with T2DM and 82% without T2DM), a total of 2,220 patients were diagnosed with diabetic kidney disease (DKD) and 775 of them experienced mortality (31.5% related to CVD) during a follow-up of 6.18 (range: 5.94-6.42) years. The weighted mean of peripheral neutrophil count level (×109 /L) in DKD participants was 4.98. DKD patients in the highest quintile of neutrophil count exhibited a positive correlation with urine albumin-creatinine ratio (uACR) and a negative association with estimated glomerular filtration rate (eGFR), when compared to those in the lowest quintile. In the multivariate logistic regression analysis with RCS, circulating neutrophil counts showed a U-shaped association with CVD mortality. Neutrophil counts of 3.68 × 109 /L and 3.69 × 109 /L were associated with the lowest risk of CVD mortality and all-cause mortality respectively in DKD patients. Clinical predictive models showed good diagnostic performance for 5- and 10-year CVD mortality-free survival (all ROC AUC values >0.75). Conclusion In this study, higher peripheral neutrophil count was associated with an increased risk of cardiovascular mortality in DKD individuals. Our clinical predictive models may help identify at-risk patients for more aggressive CVD risk factors control.