Daniel E Radford-Smith, Abi G Yates, Tereza Kacerova, Marjo Nylund, Marcus Sucksdorff, Markus Matilainen, Eline Willemse, Johanna Oechtering, Aleksandra Maleska Maceski, David Leppert, Jens Kuhle, Fay Probert, Daniel C Anthony, Laura Airas
{"title":"将TSPO-PET成像与代谢组学相结合,提高多发性硬化症的预后准确性。","authors":"Daniel E Radford-Smith, Abi G Yates, Tereza Kacerova, Marjo Nylund, Marcus Sucksdorff, Markus Matilainen, Eline Willemse, Johanna Oechtering, Aleksandra Maleska Maceski, David Leppert, Jens Kuhle, Fay Probert, Daniel C Anthony, Laura Airas","doi":"10.1136/bmjno-2025-001026","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>Blood samples were collected from 87 MS patients undergoing PET imaging with the TSPO-binding radioligand <sup>11</sup>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.</p><p><strong>Results: </strong>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).</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":52754,"journal":{"name":"BMJ Neurology Open","volume":"7 1","pages":"e001026"},"PeriodicalIF":2.1000,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12004482/pdf/","citationCount":"0","resultStr":"{\"title\":\"Integrating TSPO-PET imaging with metabolomics for enhanced prognostic accuracy in multiple sclerosis.\",\"authors\":\"Daniel E Radford-Smith, Abi G Yates, Tereza Kacerova, Marjo Nylund, Marcus Sucksdorff, Markus Matilainen, Eline Willemse, Johanna Oechtering, Aleksandra Maleska Maceski, David Leppert, Jens Kuhle, Fay Probert, Daniel C Anthony, Laura Airas\",\"doi\":\"10.1136/bmjno-2025-001026\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>Blood samples were collected from 87 MS patients undergoing PET imaging with the TSPO-binding radioligand <sup>11</sup>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.</p><p><strong>Results: </strong>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).</p><p><strong>Conclusion: </strong>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.</p>\",\"PeriodicalId\":52754,\"journal\":{\"name\":\"BMJ Neurology Open\",\"volume\":\"7 1\",\"pages\":\"e001026\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2025-04-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12004482/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BMJ Neurology Open\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1136/bmjno-2025-001026\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q3\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMJ Neurology Open","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1136/bmjno-2025-001026","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
Integrating TSPO-PET imaging with metabolomics for enhanced prognostic accuracy in multiple sclerosis.
Background: 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.
Methods: Blood samples were collected from 87 MS patients undergoing PET imaging with the TSPO-binding radioligand 11C-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.
Results: 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).
Conclusion: 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.