ECEESPE2025 Poster Presentations MTEabolism, Nutrition and Obesity (125 abstracts)
Serum inflammation based scores in prediction of liver fibrosis among cushing syndrome and MACS patients: data from ERCUSYN krakow database
Wiktoria Suchy 1 , Mari Minasyan 2 , Joanna Kokoszka 3 , Aleksandra Gamrat-Żmuda 2 , Alicja Hubalewska-Dydejczyk 2 , Elena Valassi 4 , 5 & Aleksandra Gilis-Januszewska 2
1Students’ Scientific Group of Endocrinology, Department of Endocrinology, Jagiellonian University Medical College, Kraków, Poland., Kraków, Poland; 2Department of Endocrinology, Jagiellonian University Medical College, Kraków, Poland; 3Department of Endocrinology, Oncological Endocrinology and Nuclear Medicine, University Hospital, Kraków, Poland; 4Endocrinology Department, Hospital Germans Trias i Pujol, Badalona (Barcelona), Spain, Barcelona, Spain; 5Universitat Internacional de Catalunya (UIC), Barcelona, Spain
JOINT3072
Introduction: Serum inflammation based scores (SIBS) are evolving biomarkers of chronic liver diseases. Multiple SIBS can be simply calculated using complete blood count (CBC) data. To the best of our knowledge, so far there are no studies investigating the role of SIBS in prediction of liver fibrosis (LF) in hypercortisolemia.
Objectives: To evaluate associations between selected SIBS and LF among newly diagnosed Cushing syndrome (CS) and mild autonomous cortisol secretion (MACS).
Materials and Methods: We analyzed retrospectively baseline data of adult patients from the ERCUSYN, Krakow database (N:184- 51% pituitary CS, 26% adrenal CS, 23% ectopic CS), MACS patients (N:121), control group (N:177 for CS [CS-CG] and 121 for MACS [MACS-CG], non-secretory adrenal incidentalomas matched to age, gender, weight, presence of hypercholesterolemia and diabetes mellitus). FIB4 (Age×AST/PLT×ALT1/2) was used as a LF predictor. The correlations between SIBS and FIB4 were assessed (RStudio version 4.2.2. P < 0.05).
| CS | CS-CG | MACS | MACS-CG | ||
| Male % | 23 | 26 | 29, 8 | 29, 8 | |
| Age (Q1-Q3) | 54 (38-65) | 62 (51-70) | 68 (60-72) | 67 (60-71) | |
| BMI kg/m2 (Q1-Q3) | 28, 73 (25, 67-33, 73) | 29, 03 (25, 3-32, 16) | 28, 52 (24, 98- 32, 41) | 28, 01 (24, 76-31, 14) | |
| Cortisol after 1 mg dexamethasone mg/dL (Q1-Q3) | 15, 86 (8, 99-23, 6) | 1, 19 (0, 98-1, 43) | 2, 52 (2, 09-4, 73) | 1, 3 (1, 04- 1, 55) | |
| DM % | 63 | 66, 1 | 57, 9 | 57, 9 | |
| Dyslipidemia % | 61, 4 | 67, 2 | 66, 9 | 70, 2 | |
| NLR (Q1-Q3) | 3, 67 (2, 49-6, 56) | 2, 4 (1, 8-3, 08) | 2, 46 (1, 76-3, 29) | 2, 33 (1, 75-2, 96) | |
| NPR (Q1-Q3) | 0, 026 (0, 019-0, 036) | 0, 017 (0, 013-0, 022) | 0, 018 (0, 014-0, 025) | 0, 016 (0, 013-0, 022) | |
| PLR (Q1-Q3) | 145, 52 (109, 57-196, 47) | 137, 73 (108, 3-187, 95) | 131, 3 (102, 5-184, 3) | 138, 89 (109, 55-176, 56) | |
| LMR (Q1-Q3) | 2, 5 (1, 75-3, 14) | 3, 51 (2, 74-4, 47) | 3, 36 (2, 52-3, 98) | 3, 56 (2, 79-4, 38) | |
| SII (Q1-Q3) | 942, 72 (568, 1-1456, 5) | 573, 87 (425, 1-793, 9) | 604, 1 (408, 1-891, 1) | 540 (395, 2-700, 2) | |
| Statistically significant SIBS correlations with FIB4: | |||||
| Neutrophil-Lymphocyte ratio (NLR) | r = 0, 177 | ||||
| Neutrophil-Platelet ratio (NPR) | r = 0, 331 | r = 0, 349 | r = 0, 343 | r = 0, 281 | |
| Platelet-Lymphocyte ratio (PLR) | r= -0, 17 | r= -0, 274 | r= -0, 44 | r= -0, 105 | |
| Lymphocyte-Monocyte ratio (LMR) | r= -0, 224 | ||||
| Systemic Inflammation Index (SII) | r= -0, 49 | r= -0, 211 |
Conclusion: There may be an association between FIB4 and SIBS in the following pattern: 1)positive with NLR in CS; 2)positive with NPR and negative with PLR in: CS, MACS and normocortisolemic population; 3)negative with SII in MACS and normocortisolemic population. Further studies are needed to estabilish SIBS role in LF prediction.
Volume 110
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