Oxygen Gradient Ektacytometry Is Associated with Markers of Hemolysis and Inflammation in a Large Sickle Cell Disease Cohort within the GenoMed4ALL Project
Bibliographic record
Abstract
Introduction: Sickle cell disease (SCD) is a hereditary disorder characterized by the production of structurally abnormal hemoglobin S (HbS) in red blood cells (RBCs). Under low oxygen saturation, HbS polymerizes, causing RBCs to deform, leading to hemolytic anemia, recurrent vaso-occlusive episodes (VOE) and organ damage. VOE are unpredictable and result in long-term morbidity and early mortality. RBC deformability and sickling tendency can be assessed ex vivo using oxygen gradient ektacytometry (oxygenscan). Key patient specific parameters are RBC deformability at normoxia (EImax), deformability upon deoxygenation (EImin), and pO2 at which sickling is initiated (PoS). In this study we developed two novel parameters: Slope that reflects how rapidly RBCs sickle during deoxygenation and the EI20 that is RBC deformability measured at a fixed pO2 of 20mmHg. Within GenoMed4ALL project clinical and laboratory data is integrated with oxygenscan parameters to enable early recognition of disease severity and individualize treatment options, addressing a critical need for precision medicine in SCD. Aim: The aim is to explore how 2 novel parameters (Slope, EI20) perform compared to key oxygenscan parameters (EImin, EImax, PoS) in a multi-national cohort study in regard to correlations with laboratory markers of SCD severity. Methods: Oxygenscan data was obtained for 807 SCD (HbSS, HbSβ0, HbSC, HbSβ+) patients at different EU health centres or consortia: 5 centers of SCORE consortium, The Netherlands (n=193), 8 Spanish health centers (n=169), Hospital Necker Paris (n=117), Hospital Henry Mondor (n=217) and University of Padova (n=111). All patients were older than 2 years and in steady state (no transfusions nor acute events <3 months) at the time of oxygenscan analysis. Oxygenscan measurements were performed in duplicates using the Laser-Optical Rotational Red Cell Analyzer (RR Mechatronics). Pearson's test was used to assess linear correlations between laboratory parameters and oxygenscan values and groups were compared using a t-test or ANOVA. Results: In this preliminary analysis, we evaluated oxygenscans from 350 patients with complete laboratory and clinical data. PoS was significantly lower in HbSβ0 (23,9, n=30) compared to HbSS (33,2, n=240, p<0.001). In contrast we found a lower EImax (0.41) compared to HbSS (0.46, p<0.05). The 2 novel parameters Slope and EI20 were significantly lower in patients with HbSS genotype (p<0.001) compared to other patients with HbSβ0, HbSC and HbSβ+ genotypes. Inflammatory markers (neutrophils and CRP) showed significant correlations solely in HbSβ0 patients with oxygenscan parameters. CRP positively correlated with the PoS (r=0.416, p<0.05) and neutrophils positively correlated with EImin, PoS, and EI20. Hemolysis markers such as bilirubin correlated with EImax (r=-0.403, p<0.05), LDH correlated with EImax (r=0.602, p<0.01), EI20 (r=0.512, p<0.01 and Slope (r=-0.389, p<0.05) in HbSβ0. In contrast, bilirubin and LDH correlated solely with Slope (r=0.201 and r=-0.219, p<0.01) and unconjugated bilirubin with EI20 (r=0.152, p<0.05) and Slope (r=0.201, p<0.001) in HbSS. All oxygenscan parameters were significantly correlated with hemoglobin (all p<0.001), reticulocyte count (all p<0.01), and fetal hemoglobin (all p<0.05) in HbSS patients which is in accordance with previous findings. Conclusion: Oxygenscan is a rapid and reproducible technique that quantifies sickling behaviour of RBCs. HbSβ0 patients are often grouped with HbSS patients due to the comparable clinical phenotype, however, our findings regarding RBC function suggest they might present specific different characteristics. We here introduce two novel parameters: slope and EI20. Slope that reflects heterogeneity of the RBC population and EI20 were found to be correlated to both hemolysis and inflammatory markers in HbSS and HbSβ0 highlighting the potential of these 2 parameters. Ongoing studies aim to explore how laboratory data and clinical complications of all patients within the GenoMed4ALL project are associated with oxygen gradient ektacytometry by implementing a generalized hierarchical linear mixed model. This approach will improve our understanding of SCD pathophysiology and enable personalized medicine in SCD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".