{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T02:44:03Z","timestamp":1773888243600,"version":"3.50.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,2,9]],"date-time":"2021-02-09T00:00:00Z","timestamp":1612828800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,2,9]],"date-time":"2021-02-09T00:00:00Z","timestamp":1612828800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Early differential diagnosis of several motor neuron diseases (MNDs) is extremely challenging due to the high number of overlapped symptoms. The routine clinical practice is based on clinical history and examination, usually accompanied by electrophysiological tests. However, although previous studies have demonstrated the involvement of altered metabolic pathways, biomarker-based monitoring tools are still far from being applied. In this study, we aim at characterizing and discriminating patients with involvement of both upper and lower motor neurons (i.e., amyotrophic lateral sclerosis (ALS) patients) from those with selective involvement of the lower motor neuron (LMND), by using blood data exclusively. To this end, in the last ten years, we built a database including 692 blood data and related clinical observations from 55 ALS and LMND patients. Each blood sample was described by 108 analytes. Starting from this outstanding number of features, we performed a characterization of the two groups of patients through statistical and classification analyses of blood data. Specifically, we implemented a support vector machine with recursive feature elimination (SVM-RFE) to automatically diagnose each patient into the ALS or LMND groups and to recognize whether they had a fast or slow disease progression. The classification strategy through the RFE algorithm also allowed us to reveal the most informative subset of blood analytes including novel potential biomarkers of MNDs. Our results show that we successfully devised subject-independent classifiers for the differential diagnosis and prognosis of ALS and LMND with remarkable average accuracy (up to 94%), using blood data exclusively.<\/jats:p>","DOI":"10.1038\/s41598-021-82940-8","type":"journal-article","created":{"date-parts":[[2021,2,9]],"date-time":"2021-02-09T21:23:32Z","timestamp":1612905812000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Using blood data for the differential diagnosis and prognosis of motor neuron diseases: a new dataset for machine learning applications"],"prefix":"10.1038","volume":"11","author":[{"given":"Alberto","family":"Greco","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maria Rosa","family":"Chiesa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ilaria","family":"Da Prato","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anna Maria","family":"Romanelli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cristina","family":"Dolciotti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gabriella","family":"Cavallini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Silvia Maria","family":"Masciandaro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Enzo Pasquale","family":"Scilingo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Renata","family":"Del Carratore","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paolo","family":"Bongioanni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,2,9]]},"reference":[{"key":"82940_CR1","doi-asserted-by":"publisher","first-page":"1688","DOI":"10.1056\/NEJM200105313442207","volume":"344","author":"LP Rowland","year":"2001","unstructured":"Rowland, L. P. & Shneider, N. A. Amyotrophic lateral sclerosis. N. Engl. J. Med. 344, 1688\u20131700 (2001).","journal-title":"N. Engl. J. Med."},{"key":"82940_CR2","doi-asserted-by":"publisher","first-page":"658","DOI":"10.1136\/bmj.39493.511759.BE","volume":"336","author":"CJ McDermott","year":"2008","unstructured":"McDermott, C. J. & Shaw, P. J. Diagnosis and management of motor neurone disease. BMJ 336, 658\u2013662 (2008).","journal-title":"BMJ"},{"key":"82940_CR3","doi-asserted-by":"publisher","first-page":"646","DOI":"10.1136\/jnnp.2009.197434","volume":"81","author":"C Douglass","year":"2010","unstructured":"Douglass, C., Kandler, R. H., Shaw, P. J. & Mcdermott, C. J. An evaluation of neurophysiological criteria used in the diagnosis of motor neuron disease. J. Neurol. Neurosurg. Psychiatry 81, 646\u2013649 (2010).","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"82940_CR4","doi-asserted-by":"publisher","first-page":"291","DOI":"10.3109\/21678421.2015.1049183","volume":"16","author":"A Ludolph","year":"2015","unstructured":"Ludolph, A. et al. A revision of the el escorial criteria-2015. Amyotroph Lateral Scler Frontotemp. Degener 16, 291\u2013292 (2015).","journal-title":"Amyotroph Lateral Scler Frontotemp. Degener"},{"key":"82940_CR5","first-page":"2269","volume":"130","author":"Q-Q Tao","year":"2017","unstructured":"Tao, Q.-Q. & Wu, Z.-Y. Amyotrophic lateral sclerosis: Precise diagnosis and individualized treatment. Chin. Med. J. 130, 2269 (2017).","journal-title":"Chin. Med. J."},{"key":"82940_CR6","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1007\/s12021-018-9406-9","volume":"17","author":"M Tang","year":"2019","unstructured":"Tang, M. et al. Model-based and model-free techniques for amyotrophic lateral sclerosis diagnostic prediction and patient clustering. Neuroinformatics 17, 407\u2013421 (2019).","journal-title":"Neuroinformatics"},{"key":"82940_CR7","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.nicl.2017.10.008","volume":"17","author":"H.-P M\u00fcller","year":"2018","unstructured":"M\u00fcller, H. .-P. et al. Fast progressive lower motor neuron disease is an ALS variant: A two-centre tract of interest-based MRI data analysis. NeuroImage Clin. 17, 145\u2013152 (2018).","journal-title":"NeuroImage Clin."},{"key":"82940_CR8","doi-asserted-by":"crossref","first-page":"3102","DOI":"10.1002\/hbm.24583","volume":"40","author":"EG Spinelli","year":"2019","unstructured":"Spinelli, E. G. et al. Brain MRI shows white matter sparing in Kennedy\u2019s disease and slow-progressing lower motor neuron disease. Hum. Brain Mapp. 40, 3102\u20133112 (2019).","journal-title":"Hum. Brain Mapp."},{"key":"82940_CR9","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1109\/JAS.2019.1911447","volume":"6","author":"H Liu","year":"2019","unstructured":"Liu, H., Zhou, M. & Liu, Q. An embedded feature selection method for imbalanced data classification. IEEE\/CAA J. Autom. Sin. 6, 703\u2013715 (2019).","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"82940_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-018-36873-4","volume":"9","author":"R Kueffner","year":"2019","unstructured":"Kueffner, R. et al. Stratification of amyotrophic lateral sclerosis patients: A crowdsourcing approach. Sci. Rep. 9, 1\u201314 (2019).","journal-title":"Sci. Rep."},{"key":"82940_CR11","unstructured":"Motor Neuron Diseases Fact Sheet. https:\/\/www.ninds.nih.gov\/Disorders\/Patient-Caregiver-Education\/Fact-Sheets\/Motor-Neuron-Diseases-Fact-Sheet (2019). Accessed: 2019-09-01."},{"key":"82940_CR12","doi-asserted-by":"crossref","unstructured":"Lacomis, D. & Gooch, C. Upper motor neuron assessment and early diagnosis in ALS: Getting it right the first time (2019).","DOI":"10.1212\/WNL.0000000000006867"},{"key":"82940_CR13","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1002\/humu.23697","volume":"40","author":"G Pampalakis","year":"2019","unstructured":"Pampalakis, G. et al. New molecular diagnostic trends and biomarkers for amyotrophic lateral sclerosis. Hum. Mutat. 40, 361\u2013373 (2019).","journal-title":"Hum. Mutat."},{"key":"82940_CR14","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/0022-510X(94)90191-0","volume":"124","author":"BR El Brooks","year":"1994","unstructured":"El Brooks, B. R. escorial world federation of neurology criteria for the diagnosis of amyotrophic lateral sclerosis. J. Neurol. Sci. 124, 96\u2013107 (1994).","journal-title":"J. Neurol. Sci."},{"key":"82940_CR15","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1080\/146608200300079536","volume":"1","author":"BR Brooks","year":"2000","unstructured":"Brooks, B. R., Miller, R. G., Swash, M. & El Munsat, T. L. El escorial revisited: Revised criteria for the diagnosis of amyotrophic lateral sclerosis. Amyotrophic Lateral Scler. Motor Neuron Disord. 1, 293\u2013299 (2000).","journal-title":"Amyotrophic Lateral Scler. Motor Neuron Disord."},{"key":"82940_CR16","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1016\/j.clinph.2007.09.143","volume":"119","author":"M De Carvalho","year":"2008","unstructured":"De Carvalho, M. et al. Electrodiagnostic criteria for diagnosis of ALS. Clin. Neurophysiol. 119, 497\u2013503 (2008).","journal-title":"Clin. Neurophysiol."},{"key":"82940_CR17","doi-asserted-by":"publisher","first-page":"66","DOI":"10.3389\/fnins.2019.00135","volume":"13","author":"V Grollemund","year":"2019","unstructured":"Grollemund, V. et al. Machine learning in amyotrophic lateral sclerosis: Achievements, pitfalls, and future directions. Front. Neurosci. 13, 66 (2019).","journal-title":"Front. Neurosci."},{"key":"82940_CR18","doi-asserted-by":"publisher","first-page":"383","DOI":"10.1111\/j.1600-0404.2008.01112.x","volume":"119","author":"D Keizman","year":"2009","unstructured":"Keizman, D. et al. Low-grade systemic inflammation in patients with amyotrophic lateral sclerosis. Acta Neurol. Scand. 119, 383\u2013389 (2009).","journal-title":"Acta Neurol. Scand."},{"key":"82940_CR19","doi-asserted-by":"publisher","first-page":"e45034","DOI":"10.1371\/journal.pone.0045034","volume":"7","author":"Y Nadjar","year":"2012","unstructured":"Nadjar, Y. et al. Elevated serum ferritin is associated with reduced survival in amyotrophic lateral sclerosis. PLoS ONE 7, e45034 (2012).","journal-title":"PLoS ONE"},{"key":"82940_CR20","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/S0304-3940(00)01177-0","volume":"287","author":"M Poloni","year":"2000","unstructured":"Poloni, M. et al. Circulating levels of tumour necrosis factor-$$\\alpha $$ and its soluble receptors are increased in the blood of patients with amyotrophic lateral sclerosis. Neurosci. Lett. 287, 211\u2013214 (2000).","journal-title":"Neurosci. Lett."},{"key":"82940_CR21","doi-asserted-by":"crossref","unstructured":"Berjaoui, S. et al. Complex inflammation MRNA-related response in ALS is region dependent. Neural Plast. 2015, (2015).","DOI":"10.1155\/2015\/573784"},{"key":"82940_CR22","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1002\/emmm.201201544","volume":"5","author":"JS Henkel","year":"2013","unstructured":"Henkel, J. S. et al. Regulatory t-lymphocytes mediate amyotrophic lateral sclerosis progression and survival. EMBO Mol. Med. 5, 64\u201379 (2013).","journal-title":"EMBO Mol. Med."},{"key":"82940_CR23","doi-asserted-by":"publisher","first-page":"e244","DOI":"10.1212\/NXI.0000000000000244","volume":"3","author":"C-H Lu","year":"2016","unstructured":"Lu, C.-H. et al. Systemic inflammatory response and neuromuscular involvement in amyotrophic lateral sclerosis. Neurol. Neuroimmunol. Neuroinflamm. 3, e244 (2016).","journal-title":"Neurol. Neuroimmunol. Neuroinflamm."},{"key":"82940_CR24","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1023\/A:1012487302797","volume":"46","author":"I Guyon","year":"2002","unstructured":"Guyon, I., Weston, J., Barnhill, S. & Vapnik, V. Gene selection for cancer classification using support vector machines. Mach. Learn. 46, 389\u2013422 (2002).","journal-title":"Mach. Learn."},{"key":"82940_CR25","first-page":"1357","volume":"3","author":"A Rakotomamonjy","year":"2003","unstructured":"Rakotomamonjy, A. Variable selection using SVM-based criteria. J. Mach. Learn. Res. 3, 1357\u20131370 (2003).","journal-title":"J. Mach. Learn. Res."},{"key":"82940_CR26","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/S0022-510X(99)00210-5","volume":"169","author":"JM Cedarbaum","year":"1999","unstructured":"Cedarbaum, J. M. et al. The alsfrs-r: A revised ALS functional rating scale that incorporates assessments of respiratory function. J. Neurol. Sci. 169, 13\u201321 (1999).","journal-title":"J. Neurol. Sci."},{"key":"82940_CR27","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1016\/j.snb.2015.02.025","volume":"212","author":"K Yan","year":"2015","unstructured":"Yan, K. & Zhang, D. Feature selection and analysis on correlated gas sensor data with recursive feature elimination. Sens. Actuat. B Chem. 212, 353\u2013363 (2015).","journal-title":"Sens. Actuat. B Chem."},{"key":"82940_CR28","doi-asserted-by":"publisher","first-page":"1305","DOI":"10.1016\/j.neunet.2009.06.003","volume":"22","author":"S Fazli","year":"2009","unstructured":"Fazli, S. et al. Subject-independent mental state classification in single trials. Neural Netw. 22, 1305\u20131312 (2009).","journal-title":"Neural Netw."},{"key":"82940_CR29","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1214\/09-SS054","volume":"4","author":"S Arlot","year":"2010","unstructured":"Arlot, S. et al. A survey of cross-validation procedures for model selection. Stat. Surv. 4, 40\u201379 (2010).","journal-title":"Stat. Surv."},{"key":"82940_CR30","doi-asserted-by":"publisher","first-page":"3003","DOI":"10.1214\/12-AOS1063","volume":"40","author":"G Xu","year":"2012","unstructured":"Xu, G. et al. Asymptotic optimality and efficient computation of the leave-subject-out cross-validation. Ann. Stat. 40, 3003\u20133030 (2012).","journal-title":"Ann. Stat."},{"key":"82940_CR31","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.neuroimage.2016.10.038","volume":"145","author":"G Varoquaux","year":"2017","unstructured":"Varoquaux, G. et al. Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines. NeuroImage 145, 166\u2013179 (2017).","journal-title":"NeuroImage"},{"key":"82940_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1961189.1961199","volume":"2","author":"C-C Chang","year":"2011","unstructured":"Chang, C.-C. & Lin, C.-J. LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 1\u201327 (2011).","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"82940_CR33","doi-asserted-by":"publisher","first-page":"2507","DOI":"10.1093\/bioinformatics\/btm344","volume":"23","author":"Y Saeys","year":"2007","unstructured":"Saeys, Y., Inza, I. & Larra\u00f1aga, P. A review of feature selection techniques in bioinformatics. Bioinformatics 23, 2507\u20132517 (2007).","journal-title":"Bioinformatics"},{"key":"82940_CR34","doi-asserted-by":"publisher","first-page":"16334","DOI":"10.1038\/s41598-018-34642-x","volume":"8","author":"MS Bereman","year":"2018","unstructured":"Bereman, M. S., Beri, J., Enders, J. R. & Nash, T. Machine learning reveals protein signatures in CSF and plasma fluids of clinical value for ALS. Sci. Rep. 8, 16334 (2018).","journal-title":"Sci. Rep."},{"key":"82940_CR35","doi-asserted-by":"publisher","first-page":"e0182002","DOI":"10.1371\/journal.pone.0182002","volume":"12","author":"MP Gustafson","year":"2017","unstructured":"Gustafson, M. P. et al. Comprehensive immune profiling reveals substantial immune system alterations in a subset of patients with amyotrophic lateral sclerosis. PLoS ONE 12, e0182002 (2017).","journal-title":"PLoS ONE"},{"key":"82940_CR36","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1186\/s12859-019-2692-x","volume":"20","author":"A Zandon\u00e0","year":"2019","unstructured":"Zandon\u00e0, A., Vasta, R., Chi\u00f2, A. & Di Camillo, B. A dynamic Bayesian network model for the simulation of amyotrophic lateral sclerosis progression. BMC Bioinform. 20, 118 (2019).","journal-title":"BMC Bioinform."},{"key":"82940_CR37","first-page":"333","volume":"9","author":"J Yu","year":"2018","unstructured":"Yu, J. et al. Serum ferritin is a candidate biomarker of disease aggravation in amyotrophic lateral sclerosis. Biomed. Rep. 9, 333\u2013338 (2018).","journal-title":"Biomed. Rep."},{"key":"82940_CR38","doi-asserted-by":"publisher","first-page":"423","DOI":"10.3233\/CH-162066","volume":"63","author":"C Lima","year":"2016","unstructured":"Lima, C. et al. Identification of erythrocyte biomarkers in amyotrophic lateral sclerosis. Clin. Hemorheol. Microcircul. 63, 423\u2013437 (2016).","journal-title":"Clin. Hemorheol. Microcircul."},{"key":"82940_CR39","doi-asserted-by":"publisher","first-page":"669","DOI":"10.3389\/fneur.2017.00669","volume":"8","author":"S Peters","year":"2017","unstructured":"Peters, S. et al. The tgf-$$\\beta $$ system as a potential pathogenic player in disease modulation of amyotrophic lateral sclerosis. Front. Neurol. 8, 669 (2017).","journal-title":"Front. Neurol."},{"key":"82940_CR40","first-page":"67","volume":"213","author":"A Sobu\u015b","year":"2016","unstructured":"Sobu\u015b, A. & Machali\u0144ski, B. Trophic factors in the therapeutic challenge against ALS: Current research directions. Update Amyotrophic Lateral Scler. 213, 67 (2016).","journal-title":"Update Amyotrophic Lateral Scler."},{"key":"82940_CR41","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/j.nbd.2018.11.013","volume":"124","author":"KJ Spiller","year":"2019","unstructured":"Spiller, K. J. et al. Reduction of matrix metalloproteinase 9 (mmp-9) protects motor neurons from tdp-43-triggered death in rnls8 mice. Neurobiol. Dis. 124, 133\u2013140 (2019).","journal-title":"Neurobiol. Dis."},{"key":"82940_CR42","doi-asserted-by":"publisher","first-page":"124","DOI":"10.3390\/ph11040124","volume":"11","author":"B Chiou","year":"2018","unstructured":"Chiou, B. & Connor, J. Emerging and dynamic biomedical uses of ferritin. Pharmaceuticals 11, 124 (2018).","journal-title":"Pharmaceuticals"},{"key":"82940_CR43","doi-asserted-by":"publisher","first-page":"284","DOI":"10.3389\/fneur.2019.00284","volume":"10","author":"JDA Gonz\u00e1lez","year":"2019","unstructured":"Gonz\u00e1lez, J. D. A. Lipid biomarkers for amyotrophic lateral sclerosis. Front. Neurol. 10, 284\u2013284 (2019).","journal-title":"Front. Neurol."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-82940-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-82940-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-82940-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,20]],"date-time":"2023-10-20T21:32:30Z","timestamp":1697837550000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-82940-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,9]]},"references-count":43,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["82940"],"URL":"https:\/\/doi.org\/10.1038\/s41598-021-82940-8","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,9]]},"assertion":[{"value":"4 July 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 January 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 February 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"3371"}}