沙利度胺对β地中海贫血不同基因型的临床疗效。

IF 2.1 4区 医学 Q3 GENETICS & HEREDITY
Wei-Jia Yang, Qing-Ping Kang, Qian Zhou, Tao Lin, Xiao-Min Gong, Cui-Juan Huang, Min Dou, Ying Lin
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引用次数: 0

摘要

研究目的本研究的目的是调查沙利度胺对β地中海贫血不同基因型表现的疗效,以便及早筛查对沙利度胺敏感的地中海贫血病例,并了解铁超载对沙利度胺的影响:从最初的52例患者样本中,我们对48例β地中海贫血患者在服用沙利度胺两年后的情况进行了观察。其中包括 34 名输血依赖型地中海贫血(TDT)患者和 14 名非输血依赖型地中海贫血(NTDT)患者。我们记录了所有 48 例患者在基线期、入组后 1、3、6、12、18 和 24 个月的血红蛋白(Hb)、胎儿血红蛋白(HbF)和血清铁蛋白(SF)值,以及治疗前和治疗后的输血量。根据 6 个月观察期内 Hb 水平较基线的增加情况,沙利度胺的反应分为四个等级:主要反应(MaR)、次要反应(MiR)、缓慢反应(SLR)和无反应(NR)。与基线相比,血清铁蛋白水平的下降被视为铁过载的缓解。我们按以下方法计算总体反应率(ORR):ORR = MaR + MiR + SLR/观察病例数:ORR为91.7%(44/48例),72.9%显示出MaR(35/48例)。在 34 例 TDT 患者中,21 例患者(61.8%)无需输血,其余 13 例患者仍需输血,但其总输血量与基线相比减少了 31.3%。我们共发现 33 例患者有 10 种优势基因组合,其中 5 例为 βCD41-42/βCD17 基因组合,6 例为 βCD41-42/β-28 基因组合。根据观察组 48 例患者的治疗结果,MaR 组有 33 例,SLR/NR 组有 15 例。两组基线 HbF 存在差异(P = 0.041)。在 6 个月和 12 个月的时间点上,两组的 Hb 和 HbF 分别存在明显差异(P 结论:两组的 Hb 和 HbF 差异均为 0.041):在这项研究中,我们发现了 10 个对沙利度胺敏感的β地中海贫血基因组合。这些基因组合可用于初步筛选,并在临床实践中预测沙利度胺的治疗效果。我们研究了沙利度胺的治疗反应,发现沙利度胺与标准化除铁疗法联合使用更有利于减轻铁超载。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Clinical efficacy of thalidomide for various genotypes of beta thalassemia.

Objective: The objective of this study was to investigate the therapeutic efficacy of thalidomide across various genotype presentations of β-thalassemia so as to facilitate the early screening of thalidomide-sensitive thalassemia cases and to understand the impact of iron overload on thalidomide.

Methods: From our initial sample of 52 patients, we observed 48 patients with β-thalassemia for two years after administration of thalidomide. This cohort included 34 patients with transfusion-dependent thalassemia (TDT) and 14 patients with non-transfusion-dependent thalassemia (NTDT). We recorded the values of hemoglobin (Hb), fetal hemoglobin (HbF), and serum ferritin (SF) in the baseline period and at 1, 3, 6, 12, 18, and 24 months after enrollment, as well as the pre- and post-treatment blood transfusion volume in all 48 cases. According to the increase in Hb levels from baseline during the 6-month observation period, the response to thalidomide was divided into four levels: main response (MaR), minor response (MiR), slow response (SLR), and no response (NR). A decrease in serum ferritin levels compared to baseline was considered alleviation of iron overload. We calculated the overall response rate (ORR) as follows: ORR = MaR + MiR + SLR/number of observed cases.

Results: The ORR was 91.7% (44/48 cases), and 72.9% showed MaR (35/48 cases). Among the 34 patients with TDT, 21 patients (61.8%) were free of blood transfusion, and the remaining 13 patients still required blood transfusion, but their total blood transfusion volume reduced by 31.3% when compared to the baseline. We found a total of 33 cases with 10 combinations of advantageous genes, which included 5 cases with βCD41-42/βCD17 and 6 cases with βCD41-42/β-28. Based on the treatment outcomes among the 48 cases in the observation group, there were 33 cases in the MaR group and 15 cases in the SLR/NR group. There was a difference in HbF between the two groups at baseline (P = 0.041). There were significant differences between the two groups in Hb and HbF at the time points of 6 and 12 months, respectively (P < 0.001). Compared to the baseline measurement, there was a significant decrease in the level of SF at months 12 and 24 (P < 0.001).

Conclusion: In this study, we identified 10 β-thalassemia gene combinations that were sensitive to thalidomide. These gene combinations can be used for initial screening and to predict the therapeutic effect of thalidomide in clinical practice. We examined the therapeutic response to thalidomide and found that the administration of thalidomide in combination with standardized iron removal was more beneficial in reducing iron overload.

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来源期刊
BMC Medical Genomics
BMC Medical Genomics 医学-遗传学
CiteScore
3.90
自引率
0.00%
发文量
243
审稿时长
3.5 months
期刊介绍: BMC Medical Genomics is an open access journal publishing original peer-reviewed research articles in all aspects of functional genomics, genome structure, genome-scale population genetics, epigenomics, proteomics, systems analysis, and pharmacogenomics in relation to human health and disease.
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