Structured multifrequency segmental bioimpedance descriptors for diabetes screening support

Background Blood-based measurements remain the standard for identifying diabetes mellitus (DM), but repeatable non-invasive approaches may have value for screening support. Bioelectrical impedance analysis (BIA) captures body-composition, hydration, and bioelectrical properties associated with metabolic health. However, previous studies have largely focused on analyzer-derived body-composition variables or individual parameters such as phase angle, whereas multifrequency segmental impedance responses have not been systematically characterized for screening-support applications. This study aimed to computationally characterize multifrequency segmental BIA responses using structured descriptors and to evaluate their potential for identifying previously diagnosed DM status within a proof-of-concept screening-support framework. Methods An ExtraTrees-based classification framework was developed to discriminate previously diagnosed DM status. Feature selection, input dimensionality determination, and model optimization were performed within a nested cross-validation framework, and performance was evaluated using pooled out-of-fold predictions. Alongside demographic and device-derived body-composition variables, we formulated 440 base BIA descriptors capturing amplitude and dispersion, phase angle, Cole and impedance geometry, curve shape and coupling, and regional-pattern information. We additionally developed frequency-interval and reliability-weighted interval descriptors to characterize interval-specific variations in multifrequency impedance-magnitude responses and their local agreement with the fitted frequency-response trend. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC) and average precision (AP), together with calibration and threshold-dependent metrics. Results Evaluated in 2,745 participants aged 47–90 years from a predominantly older-adult cohort, the ExtraTrees model achieved an AUROC of 0.8336 (95% CI, 0.8138–0.8523) and an AP of 0.6754 (95% CI, 0.6395–0.7092). The full descriptor configuration showed the highest overall discrimination, although the incremental gains associated with the newly developed frequency-interval and reliability-weighted descriptors were modest. These two descriptor groups together accounted for approximately 35.1% of the pooled out-of-fold SHAP contribution, consistent with a complementary rather than dominant predictive role. Conclusion This study demonstrates the feasibility of using structured multifrequency segmental BIA-derived descriptors to discriminate previously diagnosed DM status. The newly developed descriptors provided complementary predictive information, although their incremental improvement in discrimination was limited. These findings provide a methodological basis for future evaluation of BIA-assisted screening using contemporaneous blood-test-defined outcomes in appropriately recruited populations.