{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T04:07:47Z","timestamp":1790654867908,"version":"4.1.0"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"29","license":[{"start":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T00:00:00Z","timestamp":1760313600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T00:00:00Z","timestamp":1760313600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004281","name":"Narodowe Centrum Nauki","doi-asserted-by":"publisher","award":["UMO-2020\/39\/D\/ST4\/01604"],"award-info":[{"award-number":["UMO-2020\/39\/D\/ST4\/01604"]}],"id":[{"id":"10.13039\/501100004281","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Anal Bioanal Chem"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    In type 2 diabetes mellitus (T2DM), changes in glucose and lipid levels in serum are the first signals of disease progression. In this article, Raman spectroscopy supported with machine learning (ML) was used both to determine the level of imbalance between major metabolites caused by the disease and to investigate which metabolites were impacted the most. The principal component analysis separated the T2DM and control rats by PC1 and PC3. The bands that enabled that separation were assigned to all four main metabolite groups\u2014amino acids (745\u2013763\u00a0cm\n                    <jats:sup>\u22121<\/jats:sup>\n                    , 985\u20131027\u00a0cm\n                    <jats:sup>\u22121<\/jats:sup>\n                    ), amides (1199\u20131258\u00a0cm\n                    <jats:sup>\u22121<\/jats:sup>\n                    , 1574\u20131600\u00a0cm\n                    <jats:sup>\u22121<\/jats:sup>\n                    , 1653\u20131712\u00a0cm\n                    <jats:sup>\u22121<\/jats:sup>\n                    ), polysaccharides (1104\u20131192\u00a0cm\n                    <jats:sup>\u22121<\/jats:sup>\n                    ), and lipids (1442\u20131472\u00a0cm\n                    <jats:sup>\u22121<\/jats:sup>\n                    , 2831\u20133041\u00a0cm\n                    <jats:sup>\u22121<\/jats:sup>\n                    ). Moreover, diabetes induction caused major correlation changes between both proteins (amide) and the general amino acid band, with the organic matter band (CH band), which mainly arises due to lipid presence. Moreover, four different machine algorithms were employed to detect changes between the groups. AdaBoost showed the best performance in classifying the serum samples from diabetic and control rats (\n                    <jats:italic>F<\/jats:italic>\n                    <jats:sub>1<\/jats:sub>\n                    \u2009=\u20090.84), which was further refined when only the relevant bands were used for learning and classification (F\n                    <jats:sub>\n                      <jats:italic>1<\/jats:italic>\n                    <\/jats:sub>\n                    \u2009=\u20090.94). The bands responsible for the separation suggest that T2DM induces alterations in the protein profile, including changes in the levels of aromatic amino acids, as well as in both the quantity and composition of lipids, which is further confirmed by the ML classification.\n                  <\/jats:p>","DOI":"10.1007\/s00216-025-06156-9","type":"journal-article","created":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T05:22:57Z","timestamp":1760332977000},"page":"6655-6663","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Raman spectroscopy supported by machine learning reveals changes in balance of macromolecules in diabetic rat serum"],"prefix":"10.1007","volume":"417","author":[{"given":"Adrianna","family":"Kryska","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joanna","family":"Depciuch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mikolaj","family":"Krysa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wieslaw","family":"Paja","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Agnieszka","family":"Wosiak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcin","family":"Nico\u015b","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Barbara","family":"Budzynska","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0739-3567","authenticated-orcid":false,"given":"Anna","family":"Sroka-Bartnicka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,13]]},"reference":[{"key":"6156_CR1","doi-asserted-by":"publisher","first-page":"782","DOI":"10.1038\/414782a","volume":"414","author":"P Zimmet","year":"2001","unstructured":"Zimmet P, Alberti KGMM, Shaw J. Global and societal implications of the diabetes epidemic. Nature. 2001;414:782\u20137.","journal-title":"Nature"},{"key":"6156_CR2","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1038\/s41574-022-00690-7","volume":"18","author":"D Tomic","year":"2022","unstructured":"Tomic D, Shaw JE, Magliano DJ. The burden and risks of emerging complications of diabetes mellitus. Nat Rev Endocrinol. 2022;18:525\u201339. https:\/\/doi.org\/10.1038\/s41574-022-00690-7.","journal-title":"Nat Rev Endocrinol"},{"key":"6156_CR3","unstructured":"World Health Organization (WHO). Classification of diabetes mellitus.\u00a0 World Health Organization; 2019."},{"key":"6156_CR4","doi-asserted-by":"publisher","unstructured":"Galicia-Garcia U, Benito-Vicente A, Jebari S, Larrea-Sebal A, Siddiqi H, Uribe KB, Ostolaza H, Mart\u00edn C, Biofisika Bizkaia F, Sarriena B. Pathophysiology of type 2 diabetes mellitus. Int J Mol Sci. 2020;21:6275.https:\/\/doi.org\/10.3390\/ijms21176275.","DOI":"10.3390\/ijms21176275"},{"key":"6156_CR5","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1038\/nrendo.2017.151","volume":"14","author":"Y Zheng","year":"2018","unstructured":"Zheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol. 2018;14:88\u201398. https:\/\/doi.org\/10.1038\/nrendo.2017.151.","journal-title":"Nat Rev Endocrinol"},{"key":"6156_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.clispe.2020.100004","volume":"2","author":"HJ Byrne","year":"2020","unstructured":"Byrne HJ, Bonnier F, McIntyre J, Parachalil DR. Quantitative analysis of human blood serum using vibrational spectroscopy. Clinical Spectroscopy. 2020;2: 100004. https:\/\/doi.org\/10.1016\/j.clispe.2020.100004.","journal-title":"Clinical Spectroscopy"},{"key":"6156_CR7","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1007\/s00216-019-02349-1","volume":"412","author":"DR Parachalil","year":"2020","unstructured":"Parachalil DR, McIntyre J, Byrne HJ. Potential of Raman spectroscopy for the analysis of plasma\/serum in the liquid state: recent advances. Anal Bioanal Chem. 2020;412:1993\u20132007. https:\/\/doi.org\/10.1007\/s00216-019-02349-1.","journal-title":"Anal Bioanal Chem"},{"key":"6156_CR8","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0256045","author":"R Staritzbichler","year":"2021","unstructured":"Staritzbichler R, Hunold P, Estrela-Lopis I, Hildebrand PW, Isermann B, Kaiser T. Raman spectroscopy on blood serum samples of patients with end-stage liver disease. PLoS ONE. 2021. https:\/\/doi.org\/10.1371\/journal.pone.0256045.","journal-title":"PLoS ONE"},{"key":"6156_CR9","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1021\/ACS.ACCOUNTS.6B00472\/ASSET\/IMAGES\/AR-2016-00472J_M005.GIF","volume":"50","author":"R Pandey","year":"2017","unstructured":"Pandey R, Paidi SK, Valdez TA, Zhang C, Spegazzini N, Dasari RR, Barman I. Noninvasive monitoring of blood glucose with Raman spectroscopy. Acc Chem Res. 2017;50:264\u201372. https:\/\/doi.org\/10.1021\/ACS.ACCOUNTS.6B00472\/ASSET\/IMAGES\/AR-2016-00472J_M005.GIF.","journal-title":"Acc Chem Res"},{"key":"6156_CR10","doi-asserted-by":"publisher","first-page":"804","DOI":"10.1080\/05704928.2021.1946076","volume":"56","author":"M Paraskevaidi","year":"2021","unstructured":"Paraskevaidi M, Matthew BJ, Holly BJ, Hugh BJ, Thulya CPV, Loren C, StJohn C, Peter G, Callum G, Sergei KG, Kamila K, Maria K, K\u00e1ssio LMG, Pierre MHL, Evangelos P, Savithri P, John AA, Alexandra S, Marfran S, Josep SS, Gunjan T, Michael W, Bayden W. Clinical applications of infrared and Raman spectroscopy in the fields of cancer and infectious diseases. Appl Spectrosc Rev. 2021;56:804\u201368. https:\/\/doi.org\/10.1080\/05704928.2021.1946076.","journal-title":"Appl Spectrosc Rev"},{"key":"6156_CR11","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1631\/JZUS.B1700254","volume":"19","author":"X Guo","year":"2018","unstructured":"Guo X, Wang Y, Wang K, Ji B, Zhou F. Stability of a type 2 diabetes rat model induced by high-fat diet feeding with low-dose streptozotocin injection. J Zhejiang Univ Science B. 2018;19:559\u201369. https:\/\/doi.org\/10.1631\/JZUS.B1700254.","journal-title":"J Zhejiang Univ Science B"},{"key":"6156_CR12","doi-asserted-by":"publisher","first-page":"274","DOI":"10.17179\/EXCLI2022-5720","volume":"22","author":"A Ghasemi","year":"2023","unstructured":"Ghasemi A, Jeddi S. Streptozotocin as a tool for induction of rat models of diabetes: a practical guide. EXCLI J. 2023;22:274\u201394. https:\/\/doi.org\/10.17179\/EXCLI2022-5720.","journal-title":"EXCLI J"},{"key":"6156_CR13","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1016\/S0008-6215(00)82690-7","volume":"23","author":"PD Vasko","year":"1972","unstructured":"Vasko PD, Blackwell J, Koenig JL. Infrared and Raman spectroscopy of carbohydrates.\u202f: Part II: normal coordinate analysis of \u03b1-D-glucose. Carbohydrate Research. 1972;23:407\u201316. https:\/\/doi.org\/10.1016\/S0008-6215(00)82690-7.","journal-title":"Carbohydrate Research"},{"key":"6156_CR14","doi-asserted-by":"crossref","unstructured":"Carey PR. Biochemical applications of Raman and resonance Raman spectroscopies. Academic Press; 1982.","DOI":"10.1016\/B978-0-12-159650-7.50009-2"},{"issue":"5","key":"6156_CR15","doi-asserted-by":"publisher","first-page":"1219","DOI":"10.1562\/2005-02-24-RA-449","volume":"81","author":"Z Huang","year":"2005","unstructured":"Huang Z, Lui H, McLean DI, Korbelik M, Zeng H. Raman spectroscopy in combination with background near-infrared autofluorescence enhances the in vivo assessment of malignant tissues. Photochem Photobiol. 2005;81(5):1219\u201326. https:\/\/doi.org\/10.1562\/2005-02-24-RA-449.","journal-title":"Photochem Photobiol"},{"key":"6156_CR16","unstructured":"Socrates G. Infrared and Raman characteristic group frequencies, 3rd ed. John Wiley & Sons Ltd; 2001."},{"key":"6156_CR17","doi-asserted-by":"publisher","first-page":"7958","DOI":"10.1021\/ac0512599","volume":"77","author":"S Koljenovic","year":"2005","unstructured":"Koljenovic S, Schut TB, Vincent A, Kros JM, Puppels GJ. Detection of meningioma in dura mater by Raman spectroscopy. Anal Chem. 2005;77:7958\u201365. https:\/\/doi.org\/10.1021\/ac0512599.","journal-title":"Anal Chem"},{"key":"6156_CR18","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1038\/sj.bjc.6601059","volume":"89","author":"P Crow","year":"2003","unstructured":"Crow P, Stone N, Kendall CA, Uff JS, Farmer JAM, Barr H, Wright MPJ. The use of Raman spectroscopy to identify and grade prostatic adenocarcinoma in vitro. British J Cancer. 2003;89:106\u20138. https:\/\/doi.org\/10.1038\/sj.bjc.6601059.","journal-title":"British J Cancer"},{"key":"6156_CR19","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1098\/RSIF.2004.0008","volume":"1","author":"I Notingher","year":"2004","unstructured":"Notingher I, Green C, Dyer C, Perkins E, Hopkins N, Lindsay C, Hench LL. Discrimination between ricin and sulphur mustard toxicity in vitro using Raman spectroscopy. J R Soc Interface. 2004;1:79\u201390. https:\/\/doi.org\/10.1098\/RSIF.2004.0008.","journal-title":"J R Soc Interface"},{"key":"6156_CR20","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1016\/j.saa.2014.02.080","volume":"128","author":"B Sj\u00f6berg","year":"2014","unstructured":"Sj\u00f6berg B, Foley S, Cardey B, Enescu M. An experimental and theoretical study of the amino acid side chain Raman bands in proteins. Spectrochim Acta A Mol Biomol Spectrosc. 2014;128:300\u201311. https:\/\/doi.org\/10.1016\/j.saa.2014.02.080.","journal-title":"Spectrochim Acta A Mol Biomol Spectrosc"},{"key":"6156_CR21","doi-asserted-by":"publisher","DOI":"10.1117\/1.1911847","volume":"10","author":"D Rohleder","year":"2005","unstructured":"Rohleder D, Kocherscheidt G, Gerber K, Kiefer W, K\u00f6hler W, M\u00f6cks J, Petrich W. Comparison of mid-infrared and Raman spectroscopy in the quantitative analysis of serum. J Biomed Opt. 2005;10: 031108. https:\/\/doi.org\/10.1117\/1.1911847.","journal-title":"J Biomed Opt"},{"key":"6156_CR22","doi-asserted-by":"publisher","DOI":"10.3892\/ETM.2021.10138","author":"S Shetty","year":"2021","unstructured":"Shetty S, Kumari S. Fatty acids and their role in type-2 diabetes (Review). Exp Ther Med. 2021. https:\/\/doi.org\/10.3892\/ETM.2021.10138.","journal-title":"Exp Ther Med"},{"key":"6156_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/J.JFF.2020.104222","volume":"75","author":"W Zhou","year":"2020","unstructured":"Zhou W, Chen G, Chen D, Ye H, Zeng X. The antidiabetic effect and potential mechanisms of natural polysaccharides based on the regulation of gut microbiota. J Funct Foods. 2020;75: 104222. https:\/\/doi.org\/10.1016\/J.JFF.2020.104222.","journal-title":"J Funct Foods"},{"key":"6156_CR24","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/S2213-8587(13)70112-8","volume":"2","author":"PZ Zimmet","year":"2014","unstructured":"Zimmet PZ, Magliano DJ, Herman WH, Shaw JE. Diabetes: a 21st century challenge. Lancet Diabetes Endocrinol. 2014;2:56\u201364. https:\/\/doi.org\/10.1016\/S2213-8587(13)70112-8.","journal-title":"Lancet Diabetes Endocrinol"},{"key":"6156_CR25","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1016\/BS.PMBTS.2019.01.004","volume":"162","author":"Q Zhang","year":"2019","unstructured":"Zhang Q, Zhao G, Yang N, Zhang L. Fasting blood glucose levels in patients with different types of diseases. Prog Mol Biol Transl Sci. 2019;162:277\u201392. https:\/\/doi.org\/10.1016\/BS.PMBTS.2019.01.004.","journal-title":"Prog Mol Biol Transl Sci"},{"key":"6156_CR26","doi-asserted-by":"publisher","first-page":"1433","DOI":"10.1016\/J.FCT.2005.04.004","volume":"43","author":"K Ravi","year":"2005","unstructured":"Ravi K, Rajasekaran S, Subramanian S. Antihyperlipidemic effect of Eugenia jambolana seed kernel on streptozotocin-induced diabetes in rats. Food Chem Toxicol. 2005;43:1433\u20139. https:\/\/doi.org\/10.1016\/J.FCT.2005.04.004.","journal-title":"Food Chem Toxicol"},{"key":"6156_CR27","doi-asserted-by":"publisher","unstructured":"Borges RdCF, Navarro RS, Giana HE, Tavares FG, Fernandes AB, Silveira L. Detecting alterations of glucose and lipid components in human serum by near-infrared Raman spectroscopy. Res Biomed Eng. 2015;31:160\u2013168. https:\/\/doi.org\/10.1590\/2446-4740.0593.","DOI":"10.1590\/2446-4740.0593"},{"key":"6156_CR28","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1586\/ERC.11.182","volume":"10","author":"D Farbstein","year":"2012","unstructured":"Farbstein D, Levy AP. HDL dysfunction in diabetes: causes and possible treatments. Expert Rev Cardiovasc Ther. 2012;10:353. https:\/\/doi.org\/10.1586\/ERC.11.182.","journal-title":"Expert Rev Cardiovasc Ther"},{"key":"6156_CR29","doi-asserted-by":"publisher","first-page":"1090","DOI":"10.1134\/S1054660X12060023\/METRICS","volume":"22","author":"S Firdous","year":"2012","unstructured":"Firdous S, Nawaz M, Ahmed M, Anwar S, Rehman A, Rashid R, Mahmood A. Measurement of diabetic sugar concentration in human blood using Raman spectroscopy. Laser Phys. 2012;22:1090\u20134. https:\/\/doi.org\/10.1134\/S1054660X12060023\/METRICS.","journal-title":"Laser Phys"},{"issue":"10","key":"6156_CR30","doi-asserted-by":"publisher","first-page":"4998","DOI":"10.1364\/BOE.9.004998","volume":"9","author":"C Luevano-Contreras","year":"2018","unstructured":"Luevano-Contreras C, Gonz\u00e1lez FJ, Torres-Galv\u00e1n JC, Ram\u00edrez-El\u00edas MG, Guevara E. Use of raman spectroscopy to screen diabetes mellitus with machine learning tools. Biomed Opt Express. 2018;9(10):4998-5010 9. https:\/\/doi.org\/10.1364\/BOE.9.004998.","journal-title":"Biomed Opt Express"},{"key":"6156_CR31","doi-asserted-by":"publisher","unstructured":"Duc LA, Tung NT. Use of Raman spectroscopy to diagnose diabetes with SVM, Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST 473 LNICST 2023;79\u201387. https:\/\/doi.org\/10.1007\/978-3-031-28790-9_6.","DOI":"10.1007\/978-3-031-28790-9_6"},{"key":"6156_CR32","doi-asserted-by":"publisher","DOI":"10.1364\/boe.9.004998","volume":"9","author":"E Guevara","year":"2018","unstructured":"Guevara E, Torres-Galv\u00e1n JC, Ram\u00edrez-El\u00edas MG, Luevano-Contreras C, Gonz\u00e1lez FJ. Use of Raman spectroscopy to screen diabetes mellitus with machine learning tools. Biomed Opt Express. 2018;9: 4998. https:\/\/doi.org\/10.1364\/boe.9.004998.","journal-title":"Biomed Opt Express"},{"key":"6156_CR33","doi-asserted-by":"publisher","unstructured":"Kryska A, Depciuch J, Krysa M, Paja W, Wosiak A, Budzynska B, Sroka-Bartnicka A. Impact of Diabetes on balance of proteins, lipids, and polysaccharides in serum \u2013 Raman spectroscopy study supported with machine learning. 2025;2. https:\/\/doi.org\/10.17632\/T3W576YKDF.2.","DOI":"10.17632\/T3W576YKDF.2"}],"container-title":["Analytical and Bioanalytical Chemistry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00216-025-06156-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00216-025-06156-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00216-025-06156-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T18:06:30Z","timestamp":1764180390000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00216-025-06156-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,13]]},"references-count":33,"journal-issue":{"issue":"29","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["6156"],"URL":"https:\/\/doi.org\/10.1007\/s00216-025-06156-9","relation":{},"ISSN":["1618-2642","1618-2650"],"issn-type":[{"value":"1618-2642","type":"print"},{"value":"1618-2650","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,13]]},"assertion":[{"value":"3 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 September 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 September 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 October 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All experiments were carried out with the approval of the Local Ethics Committee in Lublin, Poland (Permission No: 14\/2022). The experiments were carried out in accordance with the National Institute of Health Guidelines for the Care and Use of Laboratory Animals and with the European Community Council Directive for the Care and Use of Laboratory Animals of 22 September 2010 (2010\/63\/EU).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics statement"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}