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The current lack of efficient early diagnostic tools necessitates novel approaches to biomarker discovery. We propose an untargeted metabolomics approach using non-invasive exhaled breath analysis. Breath samples, collected from 73 PD patients, encompassing both genetic (\n                    <jats:italic>LRRK2<\/jats:italic>\n                    : n\u2009=\u200912,\n                    <jats:italic>GBA1<\/jats:italic>\n                    : n\u2009=\u200935,\n                    <jats:italic>PRKN<\/jats:italic>\n                    : n\u2009=\u20096) and idiopathic PD (n\u2009=\u200920), 4 unaffected\n                    <jats:italic>LRRK2<\/jats:italic>\n                    pathogenic variant carriers, and 90 controls underwent extreme-resolution FT-ICR-MS analysis. Findings were compared with metabolomics data from blood plasma. Biostatistical analyses identified discernible metabolic patterns in both biofluids, enabling differentiation of PD patients from healthy controls (OOB error &lt; 1%). Metabolomic breath profiling of PD patients yielded 7 significant metabolites putatively identified as tricosanoic acid, docosanamide, eicosanoic acid, homophytanic acid, nonadecyl-MG, stearic acid, and palmitic acid in PD patients, irrespective of the genetic status. Five of these metabolites were also found in unaffected carriers of pathogenic variants in\n                    <jats:italic>LRRK2<\/jats:italic>\n                    when compared to controls. Most of the proposed structures are intermediates in fatty acid metabolism, introducing new candidate biomarkers for breath analysis in PD, although their identities require MS\/MS confirmation. Breath analysis effectively distinguishes between PD patients and healthy controls and can identify metabolites that could serve as noninvasive biomarkers for PD, potentially including its presymptomatic stage.\n                  <\/jats:p>","DOI":"10.1038\/s41531-025-01255-x","type":"journal-article","created":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T13:54:39Z","timestamp":1768053279000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Metabolomic breath landscape analysis unravels lipid biomarker candidates in patients with genetic and idiopathic Parkinson\u2019s disease"],"prefix":"10.1038","volume":"12","author":[{"given":"Madiha","family":"Malik","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Norbert","family":"Br\u00fcggemann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tatiana","family":"Usnich","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Max","family":"Borsche","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tobias","family":"Demetrowitsch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bj\u00f6rn-Hergen","family":"Laabs","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karin","family":"Schwarz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Bauer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katja","family":"Lohmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christine","family":"Klein","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Kunze","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,10]]},"reference":[{"key":"1255_CR1","doi-asserted-by":"publisher","unstructured":"Luo, Y. et al. 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