LOW SERUM CHOLESTEROL LEVEL AS A SIGNIFICANT PROGNOSTIC FACTOR IMPROVES CLL‐IPI IN CHRONIC LYMPHOCYTIC LEUKEMIA

Background: Hypocholesterolemia possibly due to increased demand and uptake is correlated with elevated cancer risk mortality. However, hardly any current prognostic factor or model for chronic lymphocytic leukemia (CLL) established on reprogramed lipid metabolism, which suggests the importance of nutritional status tumor metabolism during leukemogenesis has been neglected. Our study aims investigate association between CLL serum profile, evaluate value cholesterol levels in develop a nomogram incorporating facilitate stratification. Methods: In our study, 761 newly-diagnosed patients January 2007 2021 median follow-up 78 months were enrolled divided into derivation (n = 507) internal validation 254) cohorts (2:1). Prognostic was constructed based variables associated cancer-specific survival (CSS) multivariate Cox regression analyses. Predictive performance models evaluated using C-index, area under curve (AUC), calibration decision Results: Decreased total (TC), high-density lipoprotein (HDL-C) low-density (LDL-C) at diagnosis significantly worse time-to-first-treatment (TTFT) CSS (Figure 1A–D). Patient concurrently low HDL-C together LDL-C had remarkably TTFT compared those having only HDL-C, LDL-C, normal LDL-C. Multivariate analyses showed that synchronously an independent indicator both (HR 1.488; 95% CI: 1.187−1.865; p 0.001) 2.907; 1.848−4.572; < 0.001). who achieved complete remission partial significant increases post-chemotherapeutic TC, as baseline, these elevations favorable survival. Furthermore, we developed (ModelLipo-IPI) integrates all factors analyses, including age, stage, β2-MG level, TP53 IGHV status, profile 2E). ModelLipo-IPI augmenting CLL-international index (CLL-IPI) yielded better predictive accuracy discrimination capacity 3-year 5-year highest largest AUCs most desirable clinical net benefit reduced ModelCLL-IPI CLL-IPI alone 2F, G). The research funded by: National Natural Science Foundation China (grant number 82200887), Jiangsu Technology Department BK20220716) Postdoctoral 2022M711404). Keywords: diagnostic biomarkers, No conflicts interests pertinent abstract.