Blood-storage duration affects hematological and metabolic profiles in patients with sickle cell disease receiving transfusions

Abstract

BACKGROUNDPatients with sickle cell disease (SCD) frequently receive RBC units stored near the end of their permissible storage duration. We aimed to determine whether RBC storage duration influences recipient hematological, metabolic, and clinical chemistry parameters.METHODSIn a randomized, prospective, double-blind trial, 24 adults with SCD receiving chronic transfusion therapy were assigned to receive three consecutive outpatient transfusions with RBCs stored for either ≤10 days (short-stored; n = 13) or ≥30 days (long-stored; n = 11). Blood samples were collected from transfused units and from recipients at predefined time points for metabolomics, cytokine, and clinical laboratory analyses. The primary outcomes included post-transfusion hemoglobin and RBC count increments, metabolic markers of oxidative stress, iron metabolism, inflammation, and renal function.RESULTSTransfusion of short-stored RBCs was associated with significantly higher circulating 2,3-bisphosphoglycerate levels for up to 2 weeks after transfusion. Nadir RBC counts and hemoglobin A levels were higher in recipients of short-stored RBCs. In contrast, recipients of long-stored RBCs had higher transferrin saturation and plasma iron levels, elevated markers of oxidative stress and renal dysfunction, and increased proinflammatory cytokines and immunomodulatory metabolites. Metabolomics revealed storage age-dependent alterations in glycolysis, purine, and sphingolipid metabolism. Cytokine profiles and hematologic parameters corroborated the metabolic findings, indicating improved post-transfusion metabolic and inflammatory status with short-stored RBCs.CONCLUSIONTransfusion of short-stored RBCs yielded favorable metabolic and hematologic outcomes in adults with SCD, independent of immediate clinical endpoints.TRIAL REGISTRATIONClinicalTrials.gov NCT03704922FUNDINGNational Heart, Lung, and Blood Institute (NHLBI), NIH (K23HL136787, R01HL148151, R01HL146442, and R01HL149714).

Keywords: Clinical Research; Clinical practice; Clinical trials; Hematology; Metabolomics.

Figure 1. Blood units stored longer than 30 days are metabolically distinct from units stored less than 10 days.

(A) overview of the experimental design. Numbers indicate the total units transfused per transfusion event (Tx1–Tx3). (B) UMAP of metabolomics data for all blood units transfused at any of the 3 transfusion events for units stored for less than 10 days (short-stored) or longer than 30 days (long-stored). (C) As per the study design, the age of the blood, but not the transfusion sequence, was associated with significant metabolic changes (2-way ANOVA). The transfusion event sequence is shown merely to confirm the reproducibility of storage-age–related effects across events. (D) ROC curves for RBC and supernatant levels of the metabolic markers of the storage lesion (14) discriminant between short-stored and long-stored units. (E) Heatmap of the most significant metabolic changes in RBCs and supernatants as a function of the storage age of the unit (2-way ANOVA). (F) Summary overview of the RBC storage metabolic lesion. Illustration was created with BioRender.com.

Figure 2. Metabolic effect of transfusion on the recipients’ RBC metabolome.

(A) Twenty-six patients with SCD received 3 consecutive transfusions with short-stored (<10 days) or long-stored (≥30) RBCs. RBC samples were drawn for metabolomics analysis of plasma and RBCs from the recipient at baseline, before transfusion, and 2 or 24 hours after each one of the transfusion events. Two patients were excluded from the long-stored RBC study arm because some of the units they received did not match the age criteria of the study protocol. (B–D) UMAP (B), LDA (C), and heatmap (D) of significant RBC metabolites by time and storage age of blood by LDA. (E) Line plot of temporal changes in 2,3-BPG over multiple transfusions as a function of the storage age of the blood (light red and dark red for short-stored and long-stored units, respectively).

Figure 3. Effect of short-stored versus long-stored blood on glycolysis, the PPP, and redox homeostasis in the RBCs of transfusion recipients.

Line plots show temporal changes after transfusions (Tx) 1, 2, and 3 (light red and dark red represent the median ± IQRs for short-stored and long-stored units, respectively). Vignettes were created with BioRender.com.

Figure 4. Metabolic effect of transfusion on the recipients’ plasma metabolome and cytokines.

(A–C) UMAP, LDA, and heatmap of significant plasma metabolites by time and storage age of blood, as assessed by LDA. (D) Line plots of temporal changes in the most significantly affected plasma metabolites (as assessed by LDA) over multiple transfusions as a function of the storage age of the blood (light and dark red for short-stored and long-stored units, respectively). (E) Volcano plot comparing changes in circulating levels of cytokines in recipients of long- or short-stored pRBCs shows significant effects (2-tailed t test, adjusted) on anti- and proinflammatory cytokines (e.g., IL-10 and IL-6, respectively) at all tested time points. (F) Violin plots for representative antiinflammatory IL-10 and proinflammatory IL-6 being higher and lower in recipients of short-stored RBCs (Young) compared with recipients of long-stored RBCs (Old). (G) Line plots are shown for the time course effects for the most significantly affected pro- and antiinflammatory cytokines (2-way ANOVA). *P < 0.05, **P < 0.01.

Figure 5. Effect of transfusion of short-stored versus long-stored blood on the recipients’ clinical chemistry panels and complete blood counts.

(A and B) LDA (A) and heatmap (B) of significant clinical chemistry and hematological parameters affected by time and storage age of blood. (C and D) UMAPs (2D and 3D with temporal trajectories). (E) Line plots of temporal changes in the most significantly affected (2-way ANOVA in F) clinical chemistry and hematological parameters over multiple transfusions as a function of the storage age of the blood (light and dark red for short-stored and long-stored units, respectively). (F) Summary statistics of significant variables by study arm or time after transfusion and the interaction between the 2 factors. (G) LDA of long-stored versus short-stored blood at the 24-hour versus pre-transfusion time point for each transfusion.

Figure 6. Correlation analysis of plasma versus RBC metabolic phenotypes after all transfusion events as a function of the storage age of transfused RBCs.

(A) 3D map of Spearman’s rho correlations (z axis) of transfusion recipient plasma versus RBC metabolites (x and y axes). (B) Volcano plots of these correlations, with the top 10 most significant correlations highlighted for short-stored and long-stored blood units for plasma (light red) and RBCs (dark red). (C and D) Network and heatmap view of the correlation matrix (top 25% significant same-matrix correlations are shown). (E) Top 20 metabolite-metabolite correlations affected by transfusions of short-stored versus long-stored blood units. (F and G) Volcano plot (F) and scatter plots (G) of the most significantly positive and negative correlations for the same metabolite in plasma versus RBCs.

Figure 7. Metabolic correlates to clinical chemistry and hematological parameters.

(A) Correlation matrix (Spearman’s rho) between metabolites and clinical chemistry and complete blood count parameters in transfusion recipients. (B) Same as in A, with the z axis representing Spearman’s rho positive versus negative values, and colors proportional to the –log₁₀ P value of the correlation’s significance. (C and D) DSPC Networks 1 and 2 of the top metabolite-metabolite and metabolite-clinical covariates in this study. (E–G) Volcano plots (Spearman’s rho vs. –log₁₀ P value for x and y axes, respectively) for clinical chemistry measurements of creatinine, hemoglobin (g/dL), and bilirubin in transfusion recipients. (H) Hive plot summarizing correlations (module of Spearman’s rho ≥0.85) for cytokines versus metabolites in RBC units, recipients’ RBCs, or plasma or clinical labs identifies a stronger association between proinflammatory cytokines and metabolites levels in patients with SCD who received units stored longer than 30 days. (I) Volcano plot of Spearman correlations to IL-6 levels in the recipient shows strong positive correlations among proinflammatory cytokines and circulating levels of kynurenine.