{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T01:46:55Z","timestamp":1791424015750,"version":"4.3.3"},"reference-count":39,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T00:00:00Z","timestamp":1765843200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Andalusian Regional Ministry of Health","award":["AP-0009-2020-C1-F2"],"award-info":[{"award-number":["AP-0009-2020-C1-F2"]}]},{"name":"SAS 2024\u2014Reinforcement of Research Activity in Clinical Units of the Andalusian Health Service"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Wolfram syndrome (WS) is an ultrarare neuroendocrine disorder caused by pathogenic variants in WFS1, frequently leading to progressive neurological, autonomic, and cognitive impairment. Anticipating neurological trajectories remains challenging due to marked phenotypic variability and limited genotype\u2013phenotype data. Methods: Forty-five genetically confirmed patients with WS were evaluated between 1998 and 2024 in Spain. All WFS1 variants were systematically classified by exon, zygosity, protein-level functional impact, and predicted wolframin production (Classes 0\u20133). Machine learning models (Random Forests with engineered gene\u2013gene interaction terms) were applied to predict neurological manifestations and identify the strongest genetic determinants of symptom severity. Results: Neurological involvement was present in 93% of patients. The most prevalent manifestations were absence of gag reflex (67%), gait instability (64%), dysphagia (60%), and sialorrhea (60%), followed by dysmetria (56%), impaired tandem gait (53%), anosmia (44%), dysarthria (44%), and adiadochokinesia (42%). Most symptoms emerged in early adulthood (23\u201326 years), whereas cognitive decline occurred later (29.9 \u00b1 12.2 years). Homozygosity for truncating variants\u2014particularly c.409_424dup16 (Val142fsX110)\u2014and complete loss of wolframin production (Class 0; 67\u201383% across symptoms) were the strongest predictors of early and severe neurological involvement. Machine learning models achieved high discrimination for ataxia, gait instability, and absent gag reflex (AUC 0.63\u20130.86; calibrated AUC up to 0.97), identifying Mut1_Protein_Class and Mut2_Protein_Class as dominant predictors across all phenotypes, followed by coherent secondary effects from zygosity \u00d7 exon interaction terms (Prod_mgm). Conclusions: Integrating detailed genetic classification with machine learning methods enables accurate prediction of neurological outcomes in WS. Protein-level dysfunction and allele interaction structure are the principal drivers of neurological vulnerability. This framework enhances precision diagnosis and offers a foundation for individualized surveillance, clinical risk stratification, and future therapeutic trial design in WFS1-related disorders.<\/jats:p>","DOI":"10.3390\/diagnostics15243213","type":"journal-article","created":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T09:57:24Z","timestamp":1765879044000},"page":"3213","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Phenotype Correlations of Neurological Manifestations in Wolfram Syndrome: Predictive Modeling in a Spanish Cohort"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7554-9434","authenticated-orcid":false,"given":"Gema","family":"Esteban-Bueno","sequence":"first","affiliation":[{"name":"UGC La Ca\u00f1ada (Primary Care Management Unit), Almer\u00eda Periphery\u2013Almer\u00eda Health District, Andalusian Health Service (SAS), 04009 Almer\u00eda, Spain"},{"name":"Spanish Association for Research and Support to Wolfram Syndrome, 04120 Almer\u00eda, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luisa-Mar\u00eda","family":"Botella","sequence":"additional","affiliation":[{"name":"Centro de Investigaciones Biol\u00f3gicas Margarita Salas (CIB-CSIC), Consejo Superior de Investigaciones Cient\u00edficas (CSIC), CIBERER U707, 28040 Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4758-2832","authenticated-orcid":false,"given":"Juan Luis","family":"Fern\u00e1ndez-Mart\u00ednez","sequence":"additional","affiliation":[{"name":"Group of Inverse Problems, Optimization and Machine Learning, Department of Mathematics, Oviedo University, 33007 Oviedo, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1458","DOI":"10.1016\/S0140-6736(95)92473-6","article-title":"Neurodegeneration and diabetes: UK nationwide study of Wolfram (DIDMOAD) syndrome","volume":"346","author":"Barrett","year":"1995","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.1651-2227.1977.tb15069.x","article-title":"Juvenile diabetes mellitus, optic atrophy, hearing loss, diabetes insipidus, atonia of the urinary tract and bladder, and other abnormalities (Wolfram syndrome). 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