Lower white matter volume in beta‐thalassemia associated with anemia and cognitive performance
Authors: Soyoung Choi, Richard M. Leahy, John C. Wood
Journal: American Journal of Hematology (2020)

We would like to thank Manara et al for their interest in our manuscript, "Anemia predicts lower white matter volume and cognitive performance in sickle and non-sickle cell anemia syndrome," that investigates the relationship between anemia, white matter volume and cognitive performance in chronically anemic and non-anemic subjects.1 Manara et al raised concerns about whether the relationship between anemia and white matter volume could be generalized outside of sickle cell disease to beta-thalassemia, as their own previous MR imaging studies failed to reveal abnormalities in patients with beta-thalassemia.2-4 They performed group comparisons between patients with beta-thalassemia and controls but showed minimal to no differences in T1-voxel intensity (white matter density; VBM) or Deformation Indices (white matter volume/Jacobian determinants; TBM). The discrepancies between our two analyses can be attributed to possible differences in study populations or in specific analytic methodologies. To test these possibilities, we first chose to restrict our previous analysis to patients with thalassemia and control subjects. This sub-cohort consisted of 16 thalassemia patients (age = 26.8 ± 8.2; F = 10, M = 6; hemoglobin = 10.2 ± 0.9 g/dL) and 40 controls (age = 27.7 ± 11.3; F = 28, M = 12; hemoglobin = 13.4 ± 1.3 g/dL). A detailed table of each patient's characteristics is provided in Table S1. Age and sex were not statistically different but hemoglobin count was statistically lower in our patient group (P < .001). We performed TBM analysis to search for group differences between beta-thalassemia patients and controls using Deformation Index (DI) maps as described in our manuscript (section 2.5). A group of 10 000 random permutations were run to determine a null distribution of t-statistics at each voxel. Note, 50 473 voxels showed a statistically significant difference at a False Discovery Rate of q = 0.10 using Benjamini and Hochberg's method (Figure 1A). While fewer voxels were found significant in this more restricted population, they were spatially consistent with the results in our manuscript. The largest clusters were observed in the prefrontal cortex where beta-thalassemia patients had lower DI than controls. Thalassemia patients had larger DI than controls in the clusters found in the occipital lobe. This is also consistent with the clusters found in our previous study where the severity of anemia correlated with higher DI (opposite from most other voxels where anemia correlated with lower DI). We additionally ran correlations between hemoglobin and DI using the same procedures as our previous study, but only including thalassemia patients and controls (Figure 1B). We found 2 804 significant voxels where most clusters overlapped with clusters found in the group comparison. Fewer significant voxels were observed in comparison to our findings in our manuscript but this was expected due to the limited patient sample size and lower range of hemoglobin values. Hemoglobin showed a significant positive correlation with mean DI across all significant voxels identified through both group testing (P < .001; r2 = 0.21) and correlation testing (P < .001; r2 = 0.40). Consistent with our previous study, females (n = 38) did not show significant relationships between mean DI and cognitive measures. In males (n = 18), mean DI from group testing was significantly correlated with Perceptual Reasoning Index (P = .037; r2 = 0.24), driven by Block Design (P = .045; r2 = 0.23), and Digit Span (P = .044; r2 = 0.23). Matrix Reasoning showed a trend towards significance (P = .094; r2 = 0.16). Mean DI obtained from correlation testing showed a significant positive association with Digit Span in males only (P = .106; r2 = 0.34). -Our observations here were largely consistent with the findings in our previous study. However, we did identify some key methodologic differences between the two studies that could contribute to the disparity with Manara's findings. We used non-parametric permutation testing that is robust to the underlying distribution while the parametric statistical method used by Manara et al assumed that calculated t-statistics followed a normal distribution. We used Benjamini and Hochberg False Discovery Rate (FDR-BH) for multiple comparison correction, instead of family-wise error rate (FWER) correction. The FWER is a more conservative approach than FDR which means that less voxels will survive correction. The FWER can be advantageous because it limits the risk of including false positives, but disadvantageous due to its lower power and sensitivity compared to FDR. We note that performing the same statistical analysis as Manara et al, 2044 significant voxels survived in our group comparisons (Figure 2). Lastly, we manually inspected and carefully edited masks at boundaries of the brain because we noted that imperfect skull stripping adversely impacted brain registration that is central for Deformation Index calculation; we cannot know if similar attention to detail was applied in the Manara analyses. We cannot exclude that physiological differences between the two study populations might also have contributed to the disparity. Our study population was younger and much more ethnically diverse, with more than half common from the Far East. To improve genotypic homogeneity, we excluded patients with alpha thalassemia syndromes from our sub analysis, but retained three patients with transfused hemoglobin E-Beta thalassemia. Since few of our patients have been followed at Children's Hospital Los Angeles for their entire lives, we did not always know how well their hemoglobin levels were maintained in early childhood. For example, subject 15 (who had 38 discrete white matter hyperintensities) was managed as a thalassemia intermedia patient for 25 years, before starting blood transfusions because of pulmonary hypertension. Thus, inadequate support of oxygen carrying capacity in childhood (when oxygen demand is the highest) could have been more prevalent in our mostly immigrant study population than in Italy where thalassemia is endemic, rapidly identified, and treated according to robust protocols. Thus, we are confident that our data is consistent with the hypothesis that anemia is harmful to white matter, but recognize that the critical severity and duration remain uncertain. Other co-morbidities such as traditional cardiovascular modulators (diet, exercise, sleep quality, smoking) are also likely to be important to brain health. Continued investigations will be invaluable to unravel the complex interactions among these variables. We thank Manara et al for their contributions and hope that our commentaries taken together can be insightful for those involved in the study and treatment of hemoglobinopathies and other chronic anemias. Table S1. Selective demographic information on beta-thalassemia patients. HgB, hemoglobin; WMH, white matter hyperintensity; FSIQ, Full Scale Intelligence Quotient; MR, Matrix Reasoning. Subj008 WMH count was not recorded due to severe motion in the image. 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