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Maalmi, H.* ; Nguyen, B.H.P. ; Strom, A.* ; Zaharia, O.P.* ; Straßburger, K.* ; Bönhof, G.J.* ; Rathmann, W.* ; Trenkamp, S.* ; Burkart, V.* ; Szendrödi, J.* ; Menden, M.P. ; Ziegler, D.* ; Roden, M.* ; Herder, C.*

Prediction of polyneuropathy in recent-onset diabetes: A machine learning algorithm using blood-based protein biomarkers and standard demographic and clinical features.

Poster: (2023)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Background Blood-based protein biomarkers may be an attractive diagnostic tool to detect diabetic sensorimotor polyneuropathy (DSPN) in routine clinical examinations. We aimed to compare a protein-based model with a model using traditional risk factors in detecting the presence of DSPN in individuals recently diagnosed with diabetes from the German Diabetes Study. Methods A total of 135 inflammatory and neuronal protein biomarkers were measured using proximity extension assay in blood samples of 66 and 357 individuals with and without DSPN, respectively, based on the Toronto Consensus Criteria. We constructed (i) a protein-based prediction model using lasso logistic regression, (ii) an optimized traditional risk model with age, sex, waist circumference, and diabetes type as demographic/clinical attributes selected a priori, and (iii) a model combining both. The AUC (95% CI) and robust bootstrapping assessed predictive performances. Results Lasso logistic regression selected the neurofilament light chain (NFL) and fibroblast growth factor 19 (FGF-19) as the most predictive protein biomarkers for detecting DSPN in individuals with recent-onset diabetes. The proteomics model achieved an AUC of 0.66 (0.59, 0.74), while the demographic/clinical model had an AUC of 0.68 (0.62, 0.76). However, combined features boosted the model performance to an AUC of 0.75 (0.68, 0.82). Conclusion A model combining two objective blood-based biomarkers (NFL and FGF-19) and four standard demographic and clinical parameters (age, sex, waist circumference and diabetes type) has an acceptable performance for detecting early DSPN. This model could complement clinical and neurophysiological testing.
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Publikationstyp Konferenzposter
Korrespondenzautor
ISSN (print) / ISBN 1861-9002
e-ISSN 1861-9010
Zeitschrift Diabetologie und Stoffwechsel
Quellenangaben Band: 18, Heft: S 01, Seiten: , Artikelnummer: S36 Supplement: ,
Verlag Thieme
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)