Brahmbhatt S, Black G F, Carroll N M, Beyers N, Salker F, Kidd M, Lukey P T, Duncan K, van Helden P, Walzl G
Molecular Biology and Human Genetics, Department of Biomedical Sciences/MRC Centre for Molecular and Cellular Biology, University of Stellenbosch, South Africa.
Clin Exp Immunol. 2006 Nov;146(2):243-52. doi: 10.1111/j.1365-2249.2006.03211.x.
The development of a statistical model based on simple immunological markers which could predict the response to tuberculosis treatment would facilitate clinical trials of new anti-tuberculosis drugs. We have examined the ability of immunological biomarkers, measured at diagnosis and after 4 weeks of treatment, to predict sputum smear status at week 8. Eighteen tuberculosis patients with positive Ziehl-Nielsen (ZN)-stained sputum smears 8 weeks after initiation of treatment (slow response) were matched for age, gender, sputum smear grade and extent of disease on chest radiograph to 18 patients with negative sputum smears at week 8 (fast response). In addition to total white blood cell (WBC) counts and absolute lymphocyte, monocyte and neutrophil numbers, concentrations of six serum markers were measured by enzyme-linked immunosorbent assay (ELISA) in all patients (soluble interleukin-2 receptor alpha (sIL-2Ralpha), granzyme B, soluble tumour necrosis factor alpha receptors 1 and 2 (sTNF-R1 and -2), nitrotyrosine and interferon-gamma (IFN-gamma). At diagnosis, 4 biomarkers (sTNF-R1, total WBC, absolute monocyte and absolute neutrophil numbers) were significantly higher in slow response patients. At week 4, total WBC count and absolute monocyte and neutrophil numbers remained significantly higher in slow responders. Discriminant analysis of the diagnosis and week 4 data provided models for classification of slow response patients with 67% and 83% predictive accuracy. We suggest that treatment response phenotypes can be determined before the start of treatment. Reliable predictive models would allow targeted interventions for patients at risk for slow treatment response to standard tuberculosis therapy.
基于简单免疫标志物开发能够预测结核病治疗反应的统计模型，将有助于新型抗结核药物的临床试验。我们研究了在诊断时及治疗4周后测量的免疫生物标志物预测第8周痰涂片状况的能力。选取18例治疗开始8周后萋-尼（ZN）染色痰涂片阳性（反应缓慢）的结核病患者，按照年龄、性别、痰涂片分级及胸部X线片所示疾病范围，与18例第8周痰涂片阴性（反应快速）的患者进行匹配。除了全白细胞（WBC）计数以及淋巴细胞、单核细胞和中性粒细胞的绝对数量外，还采用酶联免疫吸附测定（ELISA）法检测了所有患者6种血清标志物的浓度（可溶性白细胞介素-2受体α（sIL-2Rα）、颗粒酶B、可溶性肿瘤坏死因子α受体1和2（sTNF-R1和-2）、硝基酪氨酸和干扰素-γ（IFN-γ））。诊断时，反应缓慢的患者中有4种生物标志物（sTNF-R1、全WBC、单核细胞绝对数量和中性粒细胞绝对数量）显著更高。在第4周时，反应缓慢者的全WBC计数以及单核细胞和中性粒细胞的绝对数量仍然显著更高。对诊断数据和第4周数据进行判别分析，得出了预测反应缓慢患者的模型，预测准确率分别为67%和83%。我们认为，在治疗开始前就能确定治疗反应表型。可靠的预测模型将使针对标准结核病治疗反应缓慢风险患者的靶向干预成为可能。
