Prediction Model for Polyneuropathy in Recent-Onset Diabetes Based on Serum Neurofilament Light Chain, Fibroblast Growth Factor-19 and Standard Anthropometric and Clinical Variables.

ABSTRACT:

Background

Diabetic sensorimotor polyneuropathy (DSPN) is often asymptomatic and remains undiagnosed. The ability of clinical and anthropometric variables to identify individuals likely to have DSPN might be limited. Here, we aimed to integrate protein biomarkers for reliably predicting present DSPN.

Methods

Using the proximity extension assay, we measured 135 neurological and protein biomarkers of inflammation in blood samples of 423 individuals with recent-onset diabetes from the German Diabetes Study (GDS). DSPN was diagnosed based on the Toronto Consensus Criteria. We constructed (i) a protein-based prediction model using LASSO logistic regression, (ii) an optimised traditional risk model with age, sex, waist circumference, height and diabetes type and (iii) a model comb

SUBMITTER: Maalmi H 

PROVIDER: S-EPMC11601145 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature