<b>Baseline Serum Metabolites as Predictors of Teplizumab Response in Individuals with Type 1 Diabetes</b>

<jats:p>&lt;p dir="ltr"&gt;We analyzed baseline serum samples from 41 individuals newly diagnosed with type 1 diabetes (T1D) enrolled in the AbATE trial (NCT00129259) to identify metabolic predictors of Teplizumab response. Responders to Teplizumab were defined as individuals who exhibited &lt; 45% decline in baseline C-peptide levels at 2 years after start of treatment. We used a semi-targeted metabolomics approach via liquid chromatography–high-resolution tandem mass spectrometry. We identified fifteen significant (p&lt;0.05) metabolites, including amino acids and their derivatives, tricarboxylic acid (TCA) cycle intermediates, and microbially derived metabolites. Responders exhibited higher levels of TCA cycle metabolites, amino acid derivatives, and microbial metabolites, whereas nonresponders showed elevated glutamate and acylcarnitines. These metabolites were used to train a supervised Random Forest (RF) model to predict treatment response. Model performance was evaluated using a 70/30 training/testing split, 5-fold cross-validation, bootstrap resampling (1,000 iterations), and permutation testing (1,000 permutations). The RF classifier achieved an accuracy of 0.769 and an area under the receiver operating characteristic curve of 0.881 in the test dataset. These findings suggest baseline serum metabolomic signatures have the potential to predict responders to Teplizumab with accuracy. This could potentially be applicable to other immunotherapies in T1D preventative efforts. Further validation of our findings is needed.&lt;/p&gt;</jats:p>