Radford-Smith Daniel E, Yates Abi G, Kacerova Tereza, Nylund Marjo, Sucksdorff Marcus, Matilainen Markus, Willemse Eline, Oechtering Johanna, Maleska Maceski Aleksandra, Leppert David, Kuhle Jens, Probert Fay, Anthony Daniel C, Airas Laura
Department of Pharmacology, University of Oxford, Oxford, UK.
Department of Chemistry, University of Oxford, Oxford, UK.
BMJ Neurol Open. 2025 Apr 16;7(1):e001026. doi: 10.1136/bmjno-2025-001026. eCollection 2025.
Predicting disease progression in multiple sclerosis (MS) remains challenging. PET imaging with 18 kDa translocator protein (TSPO) radioligands can detect microglial and astrocyte activation beyond MRI-visible lesions, which has been shown to be highly predictive of disease progression. We previously demonstrated that nuclear magnetic resonance (NMR)-based metabolomics could accurately distinguish between relapsing-remitting (RRMS) and secondary progressive MS (SPMS). This study investigates whether combining TSPO imaging with metabolomics enhances predictive accuracy in a similar setting.
Blood samples were collected from 87 MS patients undergoing PET imaging with the TSPO-binding radioligand C-PK11195 in Finland. Patient disability was assessed using the expanded disability status scale (EDSS) at baseline and 1 year later. Serum metabolomics was performed to identify biomarkers associated with TSPO binding and disease progression.
Greater TSPO availability in the normal-appearing white matter and perilesional regions correlated with higher EDSS. Serum metabolites glutamate (p=0.02), glutamine (p=0.006), and glucose (p=0.008), detected by NMR, effectively distinguished future progressors. These three metabolites alone predicted progression with the same accuracy as TSPO-PET imaging (AUC 0.78; p=0.0001), validated in an independent cohort. Combining serum metabolite data with PET imaging significantly improved predictive power, achieving an AUC of 0.98 (p<0.0001).
Measuring three specific serum metabolites is as effective as TSPO imaging in predicting MS progression. However, integrating TSPO imaging with serum metabolite analysis substantially enhances predictive accuracy. Given the simplicity and affordability of NMR analysis, this approach could lead to more personalised, accessible treatment strategies and serve as a valuable tool for clinical trial stratification.
预测多发性硬化症（MS）的疾病进展仍然具有挑战性。使用18 kDa转运体蛋白（TSPO）放射性配体的PET成像可以检测到MRI可见病变之外的小胶质细胞和星形胶质细胞激活，这已被证明对疾病进展具有高度预测性。我们之前证明基于核磁共振（NMR）的代谢组学可以准确区分复发缓解型（RRMS）和继发进展型MS（SPMS）。本研究调查在类似情况下将TSPO成像与代谢组学相结合是否能提高预测准确性。
在芬兰，从87名接受TSPO结合放射性配体C-PK11195进行PET成像的MS患者中采集血样。在基线和1年后使用扩展残疾状态量表（EDSS）评估患者残疾情况。进行血清代谢组学以鉴定与TSPO结合和疾病进展相关的生物标志物。
在外观正常的白质和病灶周围区域，TSPO可用性越高与EDSS越高相关。通过NMR检测到的血清代谢物谷氨酸（p = 0.02）、谷氨酰胺（p = 0.006）和葡萄糖（p = 0.008）有效地区分了未来的病情进展者。仅这三种代谢物预测进展的准确性与TSPO-PET成像相同（曲线下面积[AUC] 0.78；p = 0.0001），在一个独立队列中得到验证。将血清代谢物数据与PET成像相结合显著提高了预测能力，AUC达到0.98（p < 0.0001）。
测量三种特定的血清代谢物在预测MS进展方面与TSPO成像一样有效。然而，将TSPO成像与血清代谢物分析相结合可显著提高预测准确性。鉴于NMR分析的简单性和可承受性，这种方法可能会带来更个性化、可及的治疗策略，并成为临床试验分层的有价值工具。
