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Metabolites
Special Issues
Metabolic Dysfunction in Diabetic Neuropathy
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Metabolic Dysfunction in Diabetic Neuropathy

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Endocrinology and Clinical Metabolic Research".

Deadline for manuscript submissions: 15 September 2026 | Viewed by 893

Special Issue Editor

Dr. Tatsuya Fukuda

E-Mail Website
Guest Editor
Department of Endocrinology and Metabolism, Tokyo Metropolitan Okubo Hospital, Tokyo 160-8488, Japan
Interests: diabetes; metabolism-related diseases; endocrinology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to provide a comprehensive and clinically relevant overview of metabolic dysfunction underlying diabetic neuropathy by integrating evidence from basic, translational, and clinical research. We welcome studies exploring key mechanisms such as insulin resistance, dysregulated lipid metabolism, chronic inflammation, mitochondrial dysfunction, and muscle–nerve interactions that contribute to the onset and progression of neuropathy. Particular emphasis will be placed on clinically oriented research, including real-world data analyses, cohort studies, and investigations of metabolic biomarkers with diagnostic or prognostic value. Both original research articles and state-of-the-art reviews are encouraged. Through this Special Issue, we aim to promote collaboration between researchers and clinicians and to advance clinically meaningful insights that may improve the understanding, risk stratification, and management of diabetic neuropathy.

Dr. Tatsuya Fukuda
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • diabetic neuropathy
  • metabolic dysfunction
  • type 2 diabetes
  • peripheral nerve complications
  • insulin resistance
  • sarcopenia
  • metabolic biomarkers
  • real-world clinical data

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Published Papers (1 paper)

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Research

21 pages, 1195 KB  
Article
Interpretable Machine Learning to Predict Metformin-Induced Vitamin B12 Deficiency: Association with Glycemic Control and Neuropathic Symptoms
by Yasmine Salhi, Meriem Yazidi, Amine Dhraief, Elyes Kamoun, Melika Chihaoui, Tamim Alsuliman and Layth Sliman
Metabolites 2026, 16(4), 227; https://doi.org/10.3390/metabo16040227 - 30 Mar 2026
Viewed by 649
Abstract
Background/Objectives: Vitamin B12 deficiency is a common but often underdiagnosed complication in patients with type 2 diabetes (T2D) undergoing long-term metformin therapy. Accurate early prediction could enable targeted screening and timely intervention. This study aimed to develop and interpret a machine learning model [...] Read more.
Background/Objectives: Vitamin B12 deficiency is a common but often underdiagnosed complication in patients with type 2 diabetes (T2D) undergoing long-term metformin therapy. Accurate early prediction could enable targeted screening and timely intervention. This study aimed to develop and interpret a machine learning model for predicting vitamin B12 deficiency in metformin-treated patients with T2D, using eXtreme Gradient Boosting (XGBoost). Methods: A retrospective cross-sectional study was conducted at a single endocrinology centre (La Rabta University Hospital, Tunis, Tunisia). Patients with T2D treated with metformin for at least three years were included (n = 257); those with conditions independently affecting vitamin B12 metabolism were excluded. Vitamin B12 deficiency was defined as a serum B12 level below 150 pmol/L or a borderline level (150–221 pmol/L) with concurrent hyperhomocysteinemia (>15 μmol/L). XGBoost was selected after comparison with Logistic Regression (L2), Random Forest, and Support Vector Machine on the same 5-fold stratified cross-validated pipeline. Hyperparameters were optimized via Bayesian search (100 iterations × 5-fold stratified cross-validation), with the Matthews correlation coefficient (MCC) as the primary optimization metric to account for class imbalance. Model interpretability was achieved using SHapley Additive exPlanations (SHAP). Discrimination and calibration were assessed on an independent test set using bootstrap 95% confidence intervals (2000 resamples). Results: Of 257 patients, 95 (37.0%) presented with vitamin B12 deficiency. On the independent test set (n = 52), the optimized XGBoost model achieved an ROC-AUC of 0.671 [95% CI: 0.514–0.818], sensitivity of 0.737 [95% CI: 0.533–0.938], specificity of 0.545 [95% CI: 0.375–0.710], MCC of 0.273 [95% CI: 0.018–0.517], and a Brier Score of 0.259. SHAP analysis identified HbA1c, microalbuminuria, autonomic neuropathy, BMI, DN4 score, and fasting glucose as the most influential predictors. Nonlinear SHAP interaction plots revealed an increased predicted risk in patients with low HbA1c combined with a high cumulative metformin dose. Conclusions: The XGBoost–SHAP framework provided interpretable predictions of vitamin B12 deficiency in patients with T2D on metformin, identifying key clinical profiles for targeted screening. External multi-centre validation is required before clinical deployment. Full article
(This article belongs to the Special Issue Metabolic Dysfunction in Diabetic Neuropathy)
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