Plasma free hemoglobin is associated with LDH, AST, total bilirubin, reticulocyte count, and the hemolysis score in patients with sickle cell anemia

Plasma free hemoglobin is associated with LDH, AST, total bilirubin, reticulocyte count, and the hemolysis score in patients with sickle cell anemia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Plasma free hemoglobin is associated with LDH, AST, total bilirubin, reticulocyte count, and the hemolysis score in patients with sickle cell anemia Angela Liu, Charleen Jacobs-McFarlane, Paola Sebastiani, Jeffrey Glassberg, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4252554/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Feb, 2025 Read the published version in Annals of Hematology → Version 1 posted 10 You are reading this latest preprint version Abstract Plasma free hemoglobin (PFH) is a direct biomarker for hemolysis that has been associated with clinical complications such as pulmonary hypertension and death in patients with sickle cell disease (SCD). We sought to characterize the relationship between PFH and more clinically available hemolytic markers including lactate dehydrogenase (LDH), aspartate aminotransferase (AST), bilirubin, reticulocyte percentage and to derive a composite hemolysis score derived from principal component analysis (PCA) of these biomarkers. In 68 adult patients (median age 31 years old, IQR 25-39) with HbSS or HbSβ 0 -thalassemia enrolled in the IMPROVE II study, median PFH was elevated at 21.9 mg/dL (IQR 9.9-44.9 mg/dL). Using Pearson correlation analysis, PFH had a stronger relationship to LDH (R=0.699), AST (R=0.587), and total bilirubin (R=0.475), compared to reticulocyte count (R=0.316). The hemolysis score was significantly associated with PFH (R=0.677). When compared with other laboratory measures, PFH correlated with hemoglobin (R= -0.275) and HbS (R=0.277),but did not correlate with white blood cell count (WBC) or HbF. The hemolysis score was significantly associated with WBC (R=0.307), hemoglobin (R = -0.393), HbF (R=- 0.424), and HbS (R=0.423). This study confirms that the conventional hemolytic biomarkers LDH, AST, bilirubin, and reticulocyte percentage correlate with PFH. Additionally, the hemolysis score is a valid tool to measure hemolysis and that it may be a marker of global hemolysis as opposed to PFH, which quantifies intravascular hemolysis. Further studies will be needed to elucidate the role of PFH and intravascular hemolysis in the development of clinical complications of sickle cell disease. Sickle cell disease hemolysis plasma free hemoglobin hemolysis score Figures Figure 1 Figure 2 Figure 3 Introduction Sickle cell disease (SCD) is an inherited blood disorder characterized by vaso-occlusion, hemolysis, and chronic inflammation [1]. In patients with SCD, the degree of hemolysis is associated with complications of various organs including the lungs, brain, and kidneys [2,3]. Markers of hemolysis have been shown to increase during pain crises [4] and be improved in response to sickle cell therapies such as chronic transfusions [5]. Furthermore, sickle cell patients with more chronic hemolysis have been found to be at increased risk for devastating sequelae including pulmonary hypertension, priapism, leg ulcers, vaso-occlusive crises (VOC), and death [6-8]. Thus, it is important to characterize the degree of hemolysis to better risk stratify sickle cell patients and monitor for complications and response to therapy over time. Plasma free hemoglobin (PFH) is one of the most specific biomarkers of intravascular hemolysis [9]. Free hemoglobin is released when a red blood cell hemolyzes. Free heme can cause oxidative damage, contributing to the development of the complications above [6]. The downside of this test is that its turnaround time is on the order of days, limiting its clinical utility for prompt decision making. Lactate dehydrogenase (LDH), aspartate aminotransferase (AST), bilirubin, and reticulocyte percentage are other biomarkers that are indirect surrogates of hemolysis but are more readily available and often used to estimate hemolysis in patients with SCD [6]. The relationship between PFH and other hemolytic markers in SCD has not been well established. Kato et al [10] demonstrated that PFH is associated with LDH in SCD but there have been no other studies evaluating the relationship between PFH and AST, bilirubin, and reticulocyte percentage. It is important to confirm that these biomarkers are all correlated to PFH and the strength of these associations. A composite index called the hemolysis score, which is derived from principal component analysis of LDH, AST, total bilirubin, and reticulocyte percentage has been previously validated as a surrogate to estimate the degree of hemolysis in patients with SCD [11]. Because LDH, AST, bilirubin, and reticulocyte percentage are indirect markers of hemolysis that can be affected by other factors, the hemolysis score is a useful index that incorporates all four hemolytic biomarkers and accounts for as much of the variance as possible. There has been only one study to our knowledge to evaluate the relationship between PFH and the hemolysis score. Nouraie et al showed that PFH was significantly higher in the highest hemolysis score quartile when compared to that of the lowest quartile [2]. Although this paper established the differences in the highest and lowest quartiles of the hemolysis score, it did not describe the distribution and relationship to PFH in the other quartiles. Given the lack of data in this area, we sought to investigate PFH and its relationship with clinically available biomarkers (LDH, AST, total bilirubin, indirect bilirubin, reticulocyte percentage, and absolute reticulocyte count) and the hemolysis score derived from a combination of these biomarkers in patients with sickle cell. Methods Data from the IMPROVE II study [12] were used. IMPROVE II was a randomized, placebo-controlled, single-center interventional trial at our institution. Patients with SCD without asthma were randomized to receive either mometasone furoate 220 mcg or placebo inhaled powder for 48 weeks. Patients were adults (age ≥18 years old) confirmed to have either HbSS or HbSβ⁰-thalassemia. The protocol was approved by our center’s institutional review board. Baseline laboratory testing at the first visit prior to intervention was used in the current study. Pearson correlation analysis was used to evaluate the relationship between PFH and the variables AST, LDH, reticulocyte percent (retic), and total bilirubin (Tbili). Absolute reticulocyte count (ARC) and indirect bilirubin were also analyzed. The hemolysis score was derived from the first component of the principal component analysis (PCA) of the log-standardized values of AST, LDH, retic, and Tbili using the SPSS software package [13]. PCA of all four variables, as well as combinations of two or three variables, were derived. These hemolysis scores were then compared to PFH using Pearson correlation analysis. Pearson correlation coefficients were calculated to compare PFH and hemolysis to other laboratory data associated with disease severity including white blood cell (WBC) count, hemoglobin (Hgb), hemoglobin F (HbF) and hemoglobin S (HbS) percentages. PFH and the hemolysis score was stratified by hydroxyurea use. Medians were compared using the Mann-Whitney U test. Results There were 68 sickle cell patients with complete, steady-state laboratory data, 89.7% of whom were HbSS and 10.3% of whom were HbSβ⁰-thalassemia. Demographics of the patients are in Table 1. The median age was 31 years old (IQR 25-39). The median PFH was 21.9 mg/dL (IQR 9.9-44.9 mg/dL). Median values of each variable AST, LDH, total bilirubin, indirect bilirubin, reticulocyte percentage, and absolute reticulocyte count were higher than the normal range (Table 1). To evaluate the relationship between PFH and other markers of hemolysis, each variable was compared to PFH (Table 2 and Figure 1). PFH was significantly associated with LDH (R=0.699, p<0.001), AST (R=0.587, p<0.001), total bilirubin (R=0.475, p<0.001), and reticulocyte count (R=0.316, p=0.010). Indirect bilirubin was evaluated and was also associated with PFH (R=0.454, p<0.001). ARC did not correlate with PFH (R=0.241, p=0.051). The hemolysis score was derived from the first component of PCA of the four variables AST, LDH, Tbili, and reticulocyte. This first component explained 60.0% of the total variance among the four variables in the sample. This hemolysis score was significantly associated with PFH with an R-value of 0.677, p<0.001. PCA of various combinations of three variables were evaluated. The hemolysis score that most strongly correlated with PFH was derived from two variables - AST and LDH (R = 0.709, p<0.001). The relationship between PFH or the hemolysis score and other laboratory features was examined (Table 3 and Figure 2). PFH correlated with hemoglobin (R= -0.275, p=0.023) and HbS (R=0.277, p=0.023), but did not correlate with WBC or HbF. The hemolysis score was associated with WBC (R=0.307, p=0.012), hemoglobin (R = -0.393, p=0.001), HbF (R= -0.424, p<0.001), and HbS (R=0.423, p<0.001). PFH and the hemolysis score were stratified by hydroxyurea status (Figure 3). Patients taking hydroxyurea had lower PFH compared to patients not taking hydroxyurea (17.4mg/dL vs 28.8mg/dL, respectively), however, this did not reach statistical significance (p=0.092). The hemolysis score was lower in the hydroxyurea group compared to the non-hydroxyurea group (-0.08 vs 0.81, respectively), however, this difference was statistically significant (p=0.003). Table 1. Baseline demographics and laboratory findings of included individuals Baseline demographics Value N 68 Age (y), median (IQR) 31 (25-39) Male sex (%) 52.9% (36/68) HbSS genotype 89.7% (61/68) Laboratory findings Median (IQR) Normal values Plasma free hemoglobin (mg/dL) 21.9 (9.9-44.9) 0.0-4.9 Aspartate aminotransferase (U/L) 35 (29-48) 1-35 Lactate Dehydrogenase (U/L) 416 (311-574) 100-220 Total bilirubin (mg/dL) 3.3 (1.9-4.1) 0.1-1.2 Bilirubin, indirect (mg/dL) 2.4 (1.3-3.5) 0.2-0.8 Reticulocyte (%) 7.4 (4.5-9.7) 0.7-2.8 Absolute reticulocyte count (cells/uL) 188.5 (123.4-248.9) 50-100 White blood cell count (x10 3 /uL) 8.9 (7.1, 11.2) 4.5 - 11.0 Hemoglobin (g/dL) 8.8 (7.8-9.6) 13.9 - 16.3 HbS (%) 80 (74-85) HbF (%) 13 (5-17) Table 2. Relationship between plasma free hemoglobin and indirect markers of hemolysis. Abbreviations: AST, aspartate aminotransferase; LDH, lactate dehydrogenase; Tbili, total bilirubin; ARC, absolute reticulocyte count Correlation coefficient p-value Individual variable AST 0.587 <0.001 LDH 0.699 <0.001 Total bilirubin 0.475 <0.001 Bilirubin, indirect 0.454 <0.001 Reticulocyte 0.316 0.010 Absolute reticulocyte count 0.241 0.051 Hemolytic component (variables used in PCA) AST, LDH, Tbili, Reticulocyte 0.677 <0.001 AST, LDH, Tbili 0.706 <0.001 AST, LDH 0.709 <0.001 LDH, Tbili 0.658 <0.001 Table 3. Association with PFH or Hemolysis score by Pearson correlation analysis PFH Hemolysis Score Correlation coefficient p Correlation coefficient p WBC 0.113 0.359 0.307 0.012 Hgb -0.275 0.023 -0.393 0.001 HbF -0.180 0.145 -0.424 <0.001 HbS 0.277 0.023 0.423 <0.001 Discussion We sought to evaluate the relationship between PFH and other, more easily and quickly obtainable markers of hemolysis. LDH is associated with PFH in patients with sickle cell disease [10], however, the relationship between PFH and the other biomarkers has not been established. Our study demonstrated that AST, LDH, total bilirubin, indirect bilirubin, and reticulocyte count correlated with PFH in patients with SCD. PFH is thought to be a direct biomarker of intravascular hemolysis as it is released from the cell during the process [9]. During intravascular hemolysis, a red blood cell releases hemoglobin, LDH, and AST [6]. Our findings support this model of intravascular hemolysis. It is important to note that there are limitations as AST, LDH, and bilirubin are indirect markers of hemolysis and can be affected by other events and conditions outside of hemolysis. LDH can be elevated in other conditions with tissue injury [14] and AST can be affected by muscle and liver injury [15]. Thus, both LDH and AST are not specific to hemolysis. Bilirubin is also a nonspecific surrogate marker for hemolysis as it is the converted form of free heme and can also be affected by liver or biliary disease [16,6]. Elevated PFH has been linked with sickle cell complications including pulmonary hypertension, cerebral vasculopathy, and increased risk of death [17,18,5], making it a clinically important biomarker to study. PFH is also easier to measure than other direct markers of hemolysis such as RBC survival [19]. To our knowledge, this is the first study quantifying the direct relationship between PFH and AST, bilirubin, and reticulocyte count in patients with SCD. Interestingly, reticulocyte count had a weaker correlation with PFH compared to the other hemolytic markers (AST, LDH, bilirubin). In SCD, both extravascular and intravascular hemolysis occur, and it is thought that one-third of hemolysis in patients with SCD is thought to be intravascular [20]. Reticulocyte count is considered one of the most robust markers of hemolysis as it is reflective of red cell production, which increases during intravascular as well as extravascular hemolysis [16]. PFH, which is a marker of only intravascular hemolysis, may not reflect the total extent of hemolysis [6]. This likely explains why PFH does not correlate as strongly to reticulocyte count as it does not reflect extravascular hemolysis. The hemolysis score derived in our study incorporated LDH, AST, bilirubin, and reticulocyte levels and thus may capture both intravascular as well as extravascular hemolysis. In research and clinical care, it can be useful to consolidate multiple, collinear markers of hemolysis into a single score. We found that the hemolysis score correlated with PFH. Our 4-variable hemolytic score PCA yielded a primary component which explained a similar proportion of the total variance to the hemolytic components in the literature (64.1% in our study vs 51-67% in other studies) [13,11,21,22,2,23,24]. The study done by Nouraie et al is the only paper to our knowledge to evaluate the relationship between PFH and hemolysis score (derived from AST, LDH, Tbili, reticulocyte count), which support our findings [2]. The authors showed that cell-free hemoglobin was higher in the highest quartile of their derived hemolytic score when compared to the lowest quartile. However, it was unclear how the hemolysis score compared to PFH in the other quartiles. Our study used all hemolysis score values and found that they correlated to PFH, which was not known previously. In evaluating the relationship between hemolysis and other laboratory features, we found that PFH was only associated with lower hemoglobin and higher HbS. Hydroxyurea use was associated with a lower PFH numerically but was not statistically significant. These findings may be limited by the small number of patients in our study, as a previous study showed that HbSS individuals on hydroxyurea therapy had significantly lower LDH and plasma free hemoglobin [10]. We found that the hemolysis score was associated with higher HbS percentage, and lower hemoglobin and HbF percentage. Hydroxyurea use was associated with a lower hemolysis score. These findings were also found in the Nouriae study [2]. This is the first study to assess the relationship between the hemolysis score and WBC count and we found a significant association. This may be the case as both a higher hemolysis score and leukocytosis are associated with sickle cell severity [25]. Our findings may suggest that the hemolysis score which incorporates four hemolytic biomarkers that span intravascular and extravascular hemolysis, in contrast to PFH which reflects only intravascular hemolysis, may be a more robust marker of overall ‘blood injury’ and disease severity. Future studies are indicated to investigate the predictive value of the hemolysis score and its validity as a marker of disease severity and response to therapy. Limitations of our study include a small sample size, though this is due to the prospective nature of the data. Future studies will be aimed at incorporating more participant data and trending PFH and hemolysis score over time. Conclusion Our study showed that PFH measured at baseline in patients with sickle cell disease was associated with other hemolytic markers LDH, AST, total bilirubin, and reticulocyte count. Intuitively, PFH was also associated with the hemolysis score which was derived from the first component of a principal component analysis of these hemolytic markers. PFH had a stronger relationship to LDH, AST, and bilirubin compared to reticulocyte percentage, suggesting that PFH may be more specific to intravascular hemolysis. These findings elucidate the relationship between PFH and other hemolytic biomarkers. Further studies will be needed to evaluate these relationships over time in both the acute and chronic settings, and in relation to clinical sequelae of sickle cell disease. Declarations The research leading to these results received funding from the National Heart, Lung, and Blood Institute (NHLBI) R01 HL142671 Grant. J.G. has served as a consultant for CSL Behring, Novartis, and Novo Nordisk synteract DSMB and is supported by NHLBI RO1HL159116, R01 HL142671, R01 ES030717, UG1 HL138645, UH3 HL143192, U01HL167036, and the Doris Duke Charitable Foundation Advancing Cures grant. S.C. has served as a consultant for Pfizer and is supported by the NHLBI 5K23HL151884 grant. A.L., C.J.M, P.S., and S.M. declare no conflicts and/or funding. References Kato GJ, Piel FB, Reid CD, Gaston MH, Ohene-Frempong K, Krishnamurti L, Smith WR, Panepinto JA, Weatherall DJ, Costa FF, Vichinsky EP (2018) Sickle cell disease. 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J.G. has served as a consultant for CSL Behring, Novartis, and Novo Nordisk synteract DSMB. S.C. has served as a consultant for Pfizer. A.L., C.J., P.S., S.M. declare no competing interests. Cite Share Download PDF Status: Published Journal Publication published 19 Feb, 2025 Read the published version in Annals of Hematology → Version 1 posted Editorial decision: Revision requested 12 Jan, 2025 Reviews received at journal 08 Jan, 2025 Reviewers agreed at journal 18 Dec, 2024 Reviewers agreed at journal 18 Dec, 2024 Reviews received at journal 23 Nov, 2024 Reviewers agreed at journal 25 Oct, 2024 Reviewers invited by journal 25 Oct, 2024 Submission checks completed at journal 25 Apr, 2024 Editor assigned by journal 25 Apr, 2024 First submitted to journal 11 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4252554","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":296064291,"identity":"861a4c04-1dd5-4c68-8f45-0111cdf41370","order_by":0,"name":"Angela Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYLCCBwwMMmwMzAeg3AQitADV8LAxsCUguERpASID4rTwz26/+CChhoGHTyLn68Yff7Yx8LPnGODVInHnTLFBwjGgwyRyt93mbbvNINnzBr8Whhs5aRIJbFAtjA23GQxuELBF/kZO+o+EfyAtOc9u/vhzm8GekBaDG+nHGBLbwFrYbvCwAW2RIKDF8EYOs0RinwQPG88zM5BfeCTOPCvAq0XuRvrDDx++2cjJtyeDHSbH3568Aa8WaHRIILgElIMA+wMiFI2CUTAKRsGIBgAvWEW/2bQ1AwAAAABJRU5ErkJggg==","orcid":"","institution":"Icahn School of Medicine at Mount Sinai","correspondingAuthor":true,"prefix":"","firstName":"Angela","middleName":"","lastName":"Liu","suffix":""},{"id":296064292,"identity":"eaff5983-404c-4134-a441-44cfe4a95a39","order_by":1,"name":"Charleen Jacobs-McFarlane","email":"","orcid":"","institution":"Icahn School of Medicine at Mount Sinai","correspondingAuthor":false,"prefix":"","firstName":"Charleen","middleName":"","lastName":"Jacobs-McFarlane","suffix":""},{"id":296064293,"identity":"95224972-66a4-4df9-81d4-8e7c4c275d60","order_by":2,"name":"Paola Sebastiani","email":"","orcid":"","institution":"Tufts Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Paola","middleName":"","lastName":"Sebastiani","suffix":""},{"id":296064294,"identity":"5f4184ee-57e5-4cc5-ac38-cc906fb83b3c","order_by":3,"name":"Jeffrey Glassberg","email":"","orcid":"","institution":"Icahn School of Medicine at Mount Sinai","correspondingAuthor":false,"prefix":"","firstName":"Jeffrey","middleName":"","lastName":"Glassberg","suffix":""},{"id":296064295,"identity":"34cce1aa-ff7d-4dea-b0e0-960c771ce336","order_by":4,"name":"Sarah McCuskee","email":"","orcid":"","institution":"Icahn School of Medicine at Mount Sinai","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"","lastName":"McCuskee","suffix":""},{"id":296064301,"identity":"e0019c7c-4ce3-4050-85c8-d92c9e4c99a1","order_by":5,"name":"Susanna Curtis","email":"","orcid":"","institution":"Icahn School of Medicine at Mount Sinai","correspondingAuthor":false,"prefix":"","firstName":"Susanna","middleName":"","lastName":"Curtis","suffix":""}],"badges":[],"createdAt":"2024-04-11 12:52:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4252554/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4252554/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00277-025-06253-w","type":"published","date":"2025-02-19T15:57:55+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":55761589,"identity":"8d7957c0-1fd3-494a-9de2-911b415ef7d0","added_by":"auto","created_at":"2024-05-02 19:02:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":786689,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between plasma free hemoglobin and indirect markers of hemolysis and hemolysis components.\u003c/p\u003e\n\u003cp\u003eAbbreviations: AST, aspartate aminotransferase; Bilirubin, total bilirubin; LDH, lactate dehydrogenase; PFH, plasma free hemoglobin; Retic, reticulocyte count\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4252554/v1/29e1fea83f6a6a4855c60603.png"},{"id":55761590,"identity":"d0eaa01a-0bb1-4936-a288-2d671e9952a3","added_by":"auto","created_at":"2024-05-02 19:02:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":739423,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship of plasma free hemoglobin (PFH) and the hemolysis score with other laboratory features\u003c/p\u003e\n\u003cp\u003eAbbreviations: AST, aspartate aminotransferase; Bilirubin, total bilirubin; HbF, fetal hemoglobin; HbS, sickle hemoglobin; Hgb, hemoglobin; LDH, lactate dehydrogenase; PFH, plasma free hemoglobin; Retic, reticulocyte count; WBC, white blood cell count\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4252554/v1/4ee2557aaa34ad4af4b509d0.png"},{"id":55761591,"identity":"952d2a26-bb1b-4799-b2ef-8f0a2ac2de6a","added_by":"auto","created_at":"2024-05-02 19:02:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":145481,"visible":true,"origin":"","legend":"\u003cp\u003ePFH and the hemolysis score stratified by hydroxyurea use\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4252554/v1/ceb878acf11a9d6767a26420.png"},{"id":77052666,"identity":"9d0d940a-fcf6-4340-a72b-dc2d1fbfbfc6","added_by":"auto","created_at":"2025-02-24 16:22:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2373758,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4252554/v1/db8d2ea3-2c76-44e3-be10-6357353c4588.pdf"}],"financialInterests":"Competing interest reported. J.G. has served as a consultant for CSL Behring, Novartis, and Novo Nordisk synteract DSMB. S.C. has served as a consultant for Pfizer. A.L., C.J., P.S., S.M. declare no competing interests.","formattedTitle":"Plasma free hemoglobin is associated with LDH, AST, total bilirubin, reticulocyte count, and the hemolysis score in patients with sickle cell anemia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSickle cell disease (SCD) is an inherited blood disorder characterized by vaso-occlusion, hemolysis, and chronic inflammation\u0026nbsp;[1]. In patients with SCD, the degree of hemolysis is associated with complications of various organs including the lungs, brain, and kidneys\u0026nbsp;[2,3]. Markers of hemolysis have been shown to increase during pain crises\u0026nbsp;[4]\u0026nbsp;and be improved in response to sickle cell therapies such as chronic transfusions\u0026nbsp;[5]. Furthermore, sickle cell patients with more chronic hemolysis have been found to be at increased risk for devastating sequelae including pulmonary hypertension, priapism, leg ulcers, vaso-occlusive crises (VOC), and death\u0026nbsp;[6-8]. Thus, it is important to characterize the degree of hemolysis to better risk stratify sickle cell patients and monitor for complications and response to therapy over time. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePlasma free hemoglobin (PFH) is one of the most specific biomarkers of intravascular hemolysis\u0026nbsp;[9]. Free hemoglobin is released when a red blood cell hemolyzes. Free heme can cause oxidative damage, contributing to the development of the complications above\u0026nbsp;[6]. The downside of this test is that its turnaround time is on the order of days, limiting its clinical utility for prompt decision making. Lactate dehydrogenase (LDH), aspartate aminotransferase (AST), bilirubin, and reticulocyte percentage are other biomarkers that are indirect surrogates of hemolysis but are more readily available and often used to estimate hemolysis in patients with SCD\u0026nbsp;[6]. The relationship between PFH and other hemolytic markers in SCD has not been well established. Kato et al\u0026nbsp;[10]\u0026nbsp;demonstrated that PFH is associated with LDH in SCD but there have been no other studies evaluating the relationship between PFH and AST, bilirubin, and reticulocyte percentage. It is important to confirm that these biomarkers are all correlated to PFH and the strength of these associations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA composite index called the hemolysis score, which is derived from principal component analysis of LDH, AST, total bilirubin, and reticulocyte percentage has been previously validated as a surrogate to estimate the degree of hemolysis in patients with SCD\u0026nbsp;[11]. Because LDH, AST, bilirubin, and reticulocyte percentage are indirect markers of hemolysis that can be affected by other factors, the hemolysis score is a useful index that incorporates all four hemolytic biomarkers and accounts for as much of the variance as possible. There has been only one study to our knowledge to evaluate the relationship between PFH and the hemolysis score. Nouraie et al showed that PFH was significantly higher in the highest hemolysis score quartile when compared to that of the lowest quartile\u0026nbsp;[2]. Although this paper established the differences in the highest and lowest quartiles of the hemolysis score, it did not describe the distribution and relationship to PFH in the other quartiles. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGiven the lack of data in this area, we sought to investigate PFH and its relationship with clinically available biomarkers (LDH, AST, total bilirubin, indirect bilirubin, reticulocyte percentage, and absolute reticulocyte count) and the hemolysis score derived from a combination of these biomarkers in patients with sickle cell.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eData from the IMPROVE II study\u0026nbsp;[12]\u0026nbsp;were used. IMPROVE II was a randomized, placebo-controlled, single-center interventional trial at our institution. Patients with SCD without asthma were randomized to receive either mometasone furoate 220 mcg or placebo inhaled powder for 48 weeks. Patients were adults (age \u0026ge;18 years old) confirmed to have either HbSS or HbS\u0026beta;⁰-thalassemia. The protocol was approved by our center\u0026rsquo;s institutional review board.\u003c/p\u003e\n\u003cp\u003eBaseline laboratory testing at the first visit prior to intervention was used in the current study. Pearson correlation analysis was used to evaluate the relationship between PFH and the variables AST, LDH, reticulocyte percent (retic), and total bilirubin (Tbili). Absolute reticulocyte count (ARC) and indirect bilirubin were also analyzed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe hemolysis score was derived from the first component of the principal component analysis (PCA) of the log-standardized values of AST, LDH, retic, and Tbili using the SPSS software package\u0026nbsp;[13]. PCA of all four variables, as well as combinations of two or three variables, were derived. These hemolysis scores were then compared to PFH using Pearson correlation analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePearson correlation coefficients were calculated to compare PFH and hemolysis to other laboratory data associated with disease severity including white blood cell (WBC) count, hemoglobin (Hgb), hemoglobin F (HbF) and hemoglobin S (HbS) percentages.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePFH and the hemolysis score was stratified by hydroxyurea use. Medians were compared using the Mann-Whitney U test.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThere were 68 sickle cell patients with complete, steady-state laboratory data, 89.7% of whom were HbSS and 10.3% of whom were HbS\u0026beta;⁰-thalassemia. Demographics of the patients are in Table 1. The median age was 31 years old (IQR 25-39). The median PFH was 21.9 mg/dL (IQR 9.9-44.9 mg/dL). Median values of each variable AST, LDH, total bilirubin, indirect bilirubin, reticulocyte percentage, and absolute reticulocyte count were higher than the normal range (Table 1). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo evaluate the relationship between PFH and other markers of hemolysis, each variable was compared to PFH (Table 2 and Figure 1). PFH was significantly associated with LDH (R=0.699, p\u0026lt;0.001), AST (R=0.587, p\u0026lt;0.001), total bilirubin (R=0.475, p\u0026lt;0.001), and reticulocyte count (R=0.316, p=0.010). Indirect bilirubin was evaluated and was also associated with PFH (R=0.454, p\u0026lt;0.001). ARC did not correlate with PFH (R=0.241, p=0.051).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe hemolysis score was derived from the first component of PCA of the four variables AST, LDH, Tbili, and reticulocyte. This first component explained 60.0% of the total variance among the four variables in the sample. This hemolysis score was significantly associated with PFH with an R-value of 0.677, p\u0026lt;0.001. PCA of various combinations of three variables were evaluated. The hemolysis score that most strongly correlated with PFH was derived from two variables - AST and LDH (R = 0.709, p\u0026lt;0.001). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe relationship between PFH or the hemolysis score and other laboratory features was examined (Table 3 and Figure 2). PFH correlated with hemoglobin (R= -0.275, p=0.023) and HbS (R=0.277, p=0.023), but did not correlate with WBC or HbF. The hemolysis score was associated with WBC (R=0.307, p=0.012), hemoglobin (R = -0.393, p=0.001),\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eHbF (R= -0.424, p\u0026lt;0.001), and HbS (R=0.423, p\u0026lt;0.001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePFH and the hemolysis score were stratified by hydroxyurea status (Figure 3). Patients taking hydroxyurea had lower PFH compared to patients not taking hydroxyurea (17.4mg/dL vs 28.8mg/dL, respectively), however, this did not reach statistical significance (p=0.092). The hemolysis score was lower in the hydroxyurea group compared to the non-hydroxyurea group (-0.08 vs 0.81, respectively), however, this difference was statistically significant (p=0.003).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Baseline demographics and laboratory findings of included individuals\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.34615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline demographics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eValue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.34615384615385%\" valign=\"top\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.34615384615385%\" valign=\"top\"\u003e\n \u003cp\u003eAge (y), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e31 (25-39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.34615384615385%\" valign=\"top\"\u003e\n \u003cp\u003eMale sex (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e52.9% (36/68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.34615384615385%\" valign=\"top\"\u003e\n \u003cp\u003eHbSS genotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e89.7% (61/68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.34615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.34615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLaboratory findings\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNormal values\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.34615384615385%\" valign=\"top\"\u003e\n \u003cp\u003ePlasma free hemoglobin (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e21.9 (9.9-44.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.0-4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.34615384615385%\" valign=\"top\"\u003e\n \u003cp\u003eAspartate aminotransferase (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e35 (29-48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e1-35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.34615384615385%\" valign=\"top\"\u003e\n \u003cp\u003eLactate Dehydrogenase (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e416 (311-574)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e100-220\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.34615384615385%\" valign=\"top\"\u003e\n \u003cp\u003eTotal bilirubin (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e3.3 (1.9-4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.1-1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.34615384615385%\" valign=\"top\"\u003e\n \u003cp\u003eBilirubin, indirect (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e2.4 (1.3-3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.2-0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.34615384615385%\" valign=\"top\"\u003e\n \u003cp\u003eReticulocyte (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e7.4 (4.5-9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.7-2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.34615384615385%\" valign=\"top\"\u003e\n \u003cp\u003eAbsolute reticulocyte count (cells/uL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e188.5 (123.4-248.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e50-100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.34615384615385%\" valign=\"top\"\u003e\n \u003cp\u003eWhite blood cell count (x10\u003csup\u003e3\u003c/sup\u003e/uL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e8.9 (7.1, 11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e4.5 - 11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.34615384615385%\" valign=\"top\"\u003e\n \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e8.8 (7.8-9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e13.9 - 16.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.34615384615385%\" valign=\"top\"\u003e\n \u003cp\u003eHbS (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e80 (74-85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.34615384615385%\" valign=\"top\"\u003e\n \u003cp\u003eHbF (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e13 (5-17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Relationship between plasma free hemoglobin and indirect markers of hemolysis. Abbreviations: AST, aspartate aminotransferase; LDH, lactate dehydrogenase; Tbili, total bilirubin; ARC, absolute reticulocyte count\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.57831325301205%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.710843373493976%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCorrelation coefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.710843373493976%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.57831325301205%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndividual variable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.710843373493976%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.710843373493976%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.57831325301205%\" valign=\"top\"\u003e\n \u003cp\u003eAST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.710843373493976%\" valign=\"top\"\u003e\n \u003cp\u003e0.587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.710843373493976%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.57831325301205%\" valign=\"top\"\u003e\n \u003cp\u003eLDH\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.710843373493976%\" valign=\"top\"\u003e\n \u003cp\u003e0.699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.710843373493976%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.57831325301205%\" valign=\"top\"\u003e\n \u003cp\u003eTotal bilirubin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.710843373493976%\" valign=\"top\"\u003e\n \u003cp\u003e0.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.710843373493976%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.57831325301205%\" valign=\"top\"\u003e\n \u003cp\u003eBilirubin, indirect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.710843373493976%\" valign=\"top\"\u003e\n \u003cp\u003e0.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.710843373493976%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.57831325301205%\" valign=\"top\"\u003e\n \u003cp\u003eReticulocyte\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.710843373493976%\" valign=\"top\"\u003e\n \u003cp\u003e0.316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.710843373493976%\" valign=\"top\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.57831325301205%\" valign=\"top\"\u003e\n \u003cp\u003eAbsolute reticulocyte count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.710843373493976%\" valign=\"top\"\u003e\n \u003cp\u003e0.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.710843373493976%\" valign=\"top\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.57831325301205%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.710843373493976%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.710843373493976%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.57831325301205%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHemolytic component (variables used in PCA)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.710843373493976%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.710843373493976%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.57831325301205%\" valign=\"top\"\u003e\n \u003cp\u003eAST, LDH, Tbili, Reticulocyte\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.710843373493976%\" valign=\"top\"\u003e\n \u003cp\u003e0.677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.710843373493976%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.57831325301205%\" valign=\"top\"\u003e\n \u003cp\u003eAST, LDH, Tbili\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.710843373493976%\" valign=\"top\"\u003e\n \u003cp\u003e0.706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.710843373493976%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.57831325301205%\" valign=\"top\"\u003e\n \u003cp\u003eAST, LDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.710843373493976%\" valign=\"top\"\u003e\n \u003cp\u003e0.709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.710843373493976%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.57831325301205%\" valign=\"top\"\u003e\n \u003cp\u003eLDH, Tbili\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.710843373493976%\" valign=\"top\"\u003e\n \u003cp\u003e0.658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.710843373493976%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eAssociation with PFH or Hemolysis score by Pearson correlation analysis\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.352564102564102%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.85897435897436%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePFH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.78846153846154%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHemolysis Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.385232744783305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74317817014446%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCorrelation coefficient\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.174959871589085%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.953451043338685%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCorrelation coefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74317817014446%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.385232744783305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWBC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74317817014446%\" valign=\"top\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.174959871589085%\" valign=\"top\"\u003e\n \u003cp\u003e0.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.953451043338685%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.307\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74317817014446%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.385232744783305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHgb\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74317817014446%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.275\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.174959871589085%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.953451043338685%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.393\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74317817014446%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.385232744783305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHbF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74317817014446%\" valign=\"top\"\u003e\n \u003cp\u003e-0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.174959871589085%\" valign=\"top\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.953451043338685%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.424\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74317817014446%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.385232744783305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHbS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74317817014446%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.277\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.174959871589085%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.953451043338685%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.423\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74317817014446%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe sought to evaluate the relationship between PFH and other, more easily and quickly obtainable markers of hemolysis. LDH is associated with PFH in patients with sickle cell disease\u0026nbsp;[10], however, the relationship between PFH and the other biomarkers has not been established.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur study demonstrated that AST, LDH, total bilirubin, indirect bilirubin, and reticulocyte count correlated with PFH in patients with SCD. PFH is thought to be a direct biomarker of intravascular hemolysis as it is released from the cell during the process\u0026nbsp;[9]. During intravascular hemolysis, a red blood cell releases hemoglobin, LDH, and AST\u0026nbsp;[6]. Our findings support this model of intravascular hemolysis. It is important to note that there are limitations as AST, LDH, and bilirubin are indirect markers of hemolysis and can be affected by other events and conditions outside of hemolysis. LDH can be elevated in other conditions with tissue injury\u0026nbsp;[14]\u0026nbsp;and AST can be affected by muscle and liver injury\u0026nbsp;[15]. Thus, both LDH and AST are not specific to hemolysis. Bilirubin is also a nonspecific surrogate marker for hemolysis as it is the converted form of free heme and can also be affected by liver or biliary disease\u0026nbsp;[16,6]. Elevated PFH has been linked with sickle cell complications including pulmonary hypertension, cerebral vasculopathy, and increased risk of death\u0026nbsp;[17,18,5], making it a clinically important biomarker to study. PFH is also easier to measure than other direct markers of hemolysis such as RBC survival\u0026nbsp;[19]. To our knowledge, this is the first study quantifying the direct relationship between PFH and AST, bilirubin, and reticulocyte count in patients with SCD. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInterestingly, reticulocyte count had a weaker correlation with PFH compared to the other hemolytic markers (AST, LDH, bilirubin). In SCD, both extravascular and intravascular hemolysis occur, and it is thought that one-third of hemolysis in patients with SCD is thought to be intravascular\u0026nbsp;[20]. Reticulocyte count is considered one of the most robust markers of hemolysis as it is reflective of red cell production, which increases during intravascular as well as extravascular hemolysis\u0026nbsp;[16]. PFH, which is a marker of only intravascular hemolysis, may not reflect the total extent of hemolysis\u0026nbsp;[6]. This likely explains why PFH does not correlate as strongly to reticulocyte count as it does not reflect extravascular hemolysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe hemolysis score derived in our study incorporated LDH, AST, bilirubin, and reticulocyte levels and thus may capture both intravascular as well as extravascular hemolysis. In research and clinical care, it can be useful to consolidate multiple, collinear markers of hemolysis into a single score. We found that the hemolysis score correlated with PFH. Our 4-variable hemolytic score PCA yielded a primary component which explained a similar proportion of the total variance to the hemolytic components in the literature (64.1% in our study vs 51-67% in other studies)\u0026nbsp;[13,11,21,22,2,23,24]. The study done by Nouraie et al is the only paper to our knowledge to evaluate the relationship between PFH and hemolysis score (derived from AST, LDH, Tbili, reticulocyte count), which support our findings\u0026nbsp;[2]. The authors showed that cell-free hemoglobin was higher in the highest quartile of their derived hemolytic score when compared to the lowest quartile. However, it was unclear how the hemolysis score compared to PFH in the other quartiles. Our study used all hemolysis score values and found that they correlated to PFH, which was not known previously.\u003c/p\u003e\n\u003cp\u003eIn evaluating the relationship between hemolysis and other laboratory features, we found that PFH was only associated with lower hemoglobin and higher HbS. Hydroxyurea use was associated with a lower PFH numerically but was not statistically significant. These findings may be limited by the small number of patients in our study, as a previous study showed that HbSS individuals on hydroxyurea therapy had significantly lower LDH and plasma free hemoglobin\u0026nbsp;[10].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe found that the hemolysis score was associated with higher HbS percentage, and lower hemoglobin and HbF percentage. Hydroxyurea use was associated with a lower hemolysis score. These findings were also found in the Nouriae study\u0026nbsp;[2]. This is the first study to assess the relationship between the hemolysis score and WBC count and we found a significant association. This may be the case as both a higher hemolysis score and leukocytosis are associated with sickle cell severity\u0026nbsp;[25].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur findings may suggest that the hemolysis score which incorporates four hemolytic biomarkers that span intravascular and extravascular hemolysis, in contrast to PFH which reflects only intravascular hemolysis, may be a more robust marker of overall \u0026lsquo;blood injury\u0026rsquo; and disease severity. Future studies are indicated to investigate the predictive value of the hemolysis score and its validity as a marker of disease severity and response to therapy. Limitations of our study include a small sample size, though this is due to the prospective nature of the data. Future studies will be aimed at incorporating more participant data and trending PFH and hemolysis score over time. \u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study showed that PFH measured at baseline in patients with sickle cell disease was associated with other hemolytic markers LDH, AST, total bilirubin, and reticulocyte count. Intuitively, PFH was also associated with the hemolysis score which was derived from the first component of a principal component analysis of these hemolytic markers. PFH had a stronger relationship to LDH, AST, and bilirubin compared to reticulocyte percentage, suggesting that PFH may be more specific to intravascular hemolysis. These findings elucidate the relationship between PFH and other hemolytic biomarkers. Further studies will be needed to evaluate these relationships over time in both the acute and chronic settings, and in relation to clinical sequelae of sickle cell disease. \u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe research leading to these results received funding from the National Heart, Lung, and Blood Institute (NHLBI) R01 HL142671 Grant. J.G. has served as a consultant for CSL Behring, Novartis, and Novo Nordisk synteract DSMB and is supported by NHLBI RO1HL159116, R01 HL142671, R01 ES030717, UG1 HL138645, UH3 HL143192, U01HL167036, and the Doris Duke Charitable Foundation Advancing Cures grant. S.C. has served as a consultant for Pfizer and is supported by the NHLBI 5K23HL151884 grant. A.L., C.J.M, P.S., and S.M. declare no conflicts and/or funding. \u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKato GJ, Piel FB, Reid CD, Gaston MH, Ohene-Frempong K, Krishnamurti L, Smith WR, Panepinto JA, Weatherall DJ, Costa FF, Vichinsky EP (2018) Sickle cell disease. Nat Rev Dis Primers 4:18010. doi:10.1038/nrdp.2018.10\u003c/li\u003e\n\u003cli\u003eNouraie M, Lee JS, Zhang Y, Kanias T, Zhao X, Xiong Z, Oriss TB, Zeng Q, Kato GJ, Gibbs JS, Hildesheim ME, Sachdev V, Barst RJ, Machado RF, Hassell KL, Little JA, Schraufnagel DE, Krishnamurti L, Novelli E, Girgis RE, Morris CR, Rosenzweig EB, Badesch DB, Lanzkron S, Castro OL, Goldsmith JC, Gordeuk VR, Gladwin MT, Walk PI, Patients (2013) The relationship between the severity of hemolysis, clinical manifestations and risk of death in 415 patients with sickle cell anemia in the US and Europe. 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Blood 126 (6):703-710. doi:10.1182/blood-2014-12-614370\u003c/li\u003e\n\u003cli\u003eKato GJ, Steinberg MH, Gladwin MT (2017) Intravascular hemolysis and the pathophysiology of sickle cell disease. J Clin Invest 127 (3):750-760. doi:10.1172/JCI89741\u003c/li\u003e\n\u003cli\u003eTaylor JGt, Nolan VG, Mendelsohn L, Kato GJ, Gladwin MT, Steinberg MH (2008) Chronic hyper-hemolysis in sickle cell anemia: association of vascular complications and mortality with less frequent vasoocclusive pain. PLoS One 3 (5):e2095. doi:10.1371/journal.pone.0002095\u003c/li\u003e\n\u003cli\u003eKato GJ, Gladwin MT, Steinberg MH (2007) Deconstructing sickle cell disease: reappraisal of the role of hemolysis in the development of clinical subphenotypes. Blood Rev 21 (1):37-47. doi:10.1016/j.blre.2006.07.001\u003c/li\u003e\n\u003cli\u003eGuarda CCD, Santiago RP, Fiuza LM, Aleluia MM, Ferreira JRD, Figueiredo CVB, Yahouedehou S, Oliveira RM, Lyra IM, Goncalves MS (2017) Heme-mediated cell activation: the inflammatory puzzle of sickle cell anemia. Expert Rev Hematol 10 (6):533-541. doi:10.1080/17474086.2017.1327809\u003c/li\u003e\n\u003cli\u003eKato GJ, McGowan V, Machado RF, Little JA, Taylor Jt, Morris CR, Nichols JS, Wang X, Poljakovic M, Morris SM, Jr., Gladwin MT (2006) Lactate dehydrogenase as a biomarker of hemolysis-associated nitric oxide resistance, priapism, leg ulceration, pulmonary hypertension, and death in patients with sickle cell disease. Blood 107 (6):2279-2285. doi:10.1182/blood-2005-06-2373\u003c/li\u003e\n\u003cli\u003eGordeuk VR, Campbell A, Rana S, Nouraie M, Niu X, Minniti CP, Sable C, Darbari D, Dham N, Onyekwere O, Ammosova T, Nekhai S, Kato GJ, Gladwin MT, Castro OL (2009) Relationship of erythropoietin, fetal hemoglobin, and hydroxyurea treatment to tricuspid regurgitation velocity in children with sickle cell disease. Blood 114 (21):4639-4644. doi:10.1182/blood-2009-04-218040\u003c/li\u003e\n\u003cli\u003eCurtis S, Stidham E, Cohen R, Liu A, Probst M, Mitchell T, Loo G, Bellis J, Ihiasota C, Glassberg J (2023) Clinical Results of a Randomized Placebo Controlled Trial of Inhaled Mometasone to Promote Reduction in Vaso-Occlusive Events (IMPROVE-II). 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Am J Hematol 96 (1):31-39. doi:10.1002/ajh.26003\u003c/li\u003e\n\u003cli\u003eGladwin MT, Barst RJ, Gibbs JS, Hildesheim M, Sachdev V, Nouraie M, Hassell KL, Little JA, Schraufnagel DE, Krishnamurti L, Novelli E, Girgis RE, Morris CR, Berman Rosenzweig E, Badesch DB, Lanzkron S, Castro OL, Taylor JGt, Goldsmith JC, Kato GJ, Gordeuk VR, Machado RF, walk PI, Patients (2014) Risk factors for death in 632 patients with sickle cell disease in the United States and United Kingdom. PLoS One 9 (7):e99489. doi:10.1371/journal.pone.0099489\u003c/li\u003e\n\u003cli\u003eQuinn CT, Smith EP, Arbabi S, Khera PK, Lindsell CJ, Niss O, Joiner CH, Franco RS, Cohen RM (2016) Biochemical surrogate markers of hemolysis do not correlate with directly measured erythrocyte survival in sickle cell anemia. Am J Hematol 91 (12):1195-1201. doi:10.1002/ajh.24562\u003c/li\u003e\n\u003cli\u003eBensinger TA, Gillette PN (1974) Hemolysis in sickle cell disease. Arch Intern Med 133 (4):624-631\u003c/li\u003e\n\u003cli\u003eMinniti CP, Sable C, Campbell A, Rana S, Ensing G, Dham N, Onyekwere O, Nouraie M, Kato GJ, Gladwin MT, Castro OL, Gordeuk VR (2009) Elevated tricuspid regurgitant jet velocity in children and adolescents with sickle cell disease: association with hemolysis and hemoglobin oxygen desaturation. Haematologica 94 (3):340-347. doi:10.3324/haematol.13812\u003c/li\u003e\n\u003cli\u003eNouraie M, Reading NS, Campbell A, Minniti CP, Rana SR, Luchtman-Jones L, Kato GJ, Gladwin MT, Castro OL, Prchal JT, Gordeuk VR (2010) Association of G6PD with lower haemoglobin concentration but not increased haemolysis in patients with sickle cell anaemia. Br J Haematol 150 (2):218-225. doi:10.1111/j.1365-2141.2010.08215.x\u003c/li\u003e\n\u003cli\u003eCita KC, Brureau L, Lemonne N, Billaud M, Connes P, Ferdinand S, Tressieres B, Tarer V, Etienne-Julan M, Blanchet P, Elion J, Romana M (2016) Men with Sickle Cell Anemia and Priapism Exhibit Increased Hemolytic Rate, Decreased Red Blood Cell Deformability and Increased Red Blood Cell Aggregate Strength. PLoS One 11 (5):e0154866. doi:10.1371/journal.pone.0154866\u003c/li\u003e\n\u003cli\u003eDavid S, Aguiar P, Antunes L, Dias A, Morais A, Sakuntabhai A, Lavinha J (2018) Variants in the non-coding region of the TLR2 gene associated with infectious subphenotypes in pediatric sickle cell anemia. Immunogenetics 70 (1):37-51. doi:10.1007/s00251-017-1013-7\u003c/li\u003e\n\u003cli\u003eOkpala I (2004) The intriguing contribution of white blood cells to sickle cell disease - a red cell disorder. Blood Rev 18 (1):65-73. doi:10.1016/s0268-960x(03)00037-7\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"annals-of-hematology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aohe","sideBox":"Learn more about [Annals of Hematology](http://link.springer.com/journal/277)","snPcode":"277","submissionUrl":"https://submission.nature.com/new-submission/277/3","title":"Annals of Hematology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Sickle cell disease, hemolysis, plasma free hemoglobin, hemolysis score","lastPublishedDoi":"10.21203/rs.3.rs-4252554/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4252554/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePlasma free hemoglobin (PFH) is a direct biomarker for hemolysis that has been associated with clinical complications such as pulmonary hypertension and death in patients with sickle cell disease (SCD). We sought to characterize the relationship between PFH and more clinically available hemolytic markers including lactate dehydrogenase (LDH), aspartate aminotransferase (AST), bilirubin, reticulocyte percentage and to derive a composite hemolysis score derived from principal component analysis (PCA) of these biomarkers. In 68 adult patients (median age 31 years old, IQR 25-39) with HbSS or HbSβ\u003csup\u003e0\u003c/sup\u003e-thalassemia enrolled in the IMPROVE II study, median PFH was elevated at 21.9 mg/dL (IQR 9.9-44.9 mg/dL). Using Pearson correlation analysis, PFH had a stronger relationship to LDH (R=0.699), AST (R=0.587), and total bilirubin (R=0.475), compared to reticulocyte count (R=0.316). The hemolysis score was significantly associated with PFH (R=0.677). When compared with other laboratory measures, PFH correlated with hemoglobin (R= -0.275) and HbS (R=0.277),but did not correlate with white blood cell count (WBC) or HbF. The hemolysis score was significantly associated with WBC (R=0.307), hemoglobin (R = -0.393), HbF (R=- 0.424), and HbS (R=0.423). This study confirms that the conventional hemolytic biomarkers LDH, AST, bilirubin, and reticulocyte percentage correlate with PFH. Additionally, the hemolysis score is a valid tool to measure hemolysis and that it may be a marker of global hemolysis as opposed to PFH, which quantifies intravascular hemolysis. Further studies will be needed to elucidate the role of PFH and intravascular hemolysis in the development of clinical complications of sickle cell disease. \u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Plasma free hemoglobin is associated with LDH, AST, total bilirubin, reticulocyte count, and the hemolysis score in patients with sickle cell anemia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-02 19:02:02","doi":"10.21203/rs.3.rs-4252554/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-01-13T04:24:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-08T17:12:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"80197544340571639521847232229797910436","date":"2024-12-18T21:24:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"192976006792699973229363440177095562616","date":"2024-12-18T06:50:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-23T19:34:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"49683767018364972707400540559498402695","date":"2024-10-25T23:34:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-25T23:16:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-25T15:01:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-25T15:01:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"Annals of Hematology","date":"2024-04-11T12:51:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"annals-of-hematology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aohe","sideBox":"Learn more about [Annals of Hematology](http://link.springer.com/journal/277)","snPcode":"277","submissionUrl":"https://submission.nature.com/new-submission/277/3","title":"Annals of Hematology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0df249d0-e07c-4d5c-a4cd-6f4c3ecc6301","owner":[],"postedDate":"May 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-24T16:03:50+00:00","versionOfRecord":{"articleIdentity":"rs-4252554","link":"https://doi.org/10.1007/s00277-025-06253-w","journal":{"identity":"annals-of-hematology","isVorOnly":false,"title":"Annals of Hematology"},"publishedOn":"2025-02-19 15:57:55","publishedOnDateReadable":"February 19th, 2025"},"versionCreatedAt":"2024-05-02 19:02:02","video":"","vorDoi":"10.1007/s00277-025-06253-w","vorDoiUrl":"https://doi.org/10.1007/s00277-025-06253-w","workflowStages":[]},"version":"v1","identity":"rs-4252554","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4252554","identity":"rs-4252554","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}