Literature-Grounded Simulation of Non-Invasive Glucose Monitoring Using NIR Wearable Sensor Archetypes: Accuracy Metrics, Candidate Confounders, and an Adaptive Calibration Framework.

Non-invasive continuous glucose monitoring remains an important technological challenge in diabetes management. This study was revised as a literature-grounded simulation and methodological proof-of-concept rather than an experimental clinical validation. The analysis used 320 independent synthetic reference-NIR glucose pairs generated from physiological ranges and performance distributions reported in the cited literature, together with an illustrative 576-point, 48-h synthetic time series. Three sensor archetypes were informed by published optical systems. Descriptive agreement was evaluated using mean absolute relative difference (MARD), Pearson correlation, Bland-Altman analysis, and the supplied Clarke Error Grid (CEG) categories. Candidate physiological and environmental factors were examined using Pearson correlations with 95% confidence intervals, raw p-values, and Holm correction. The synthetic dataset produced an overall MARD of 10.38% (SD = 7.67%), r = 0.958 (R2 = 0.918, p < 0.001), a Bland-Altman bias of 4.32 mg/dL, and 89.4%/10.3%/0.3% of records in CEG zones A/B/D, respectively. All candidate-factor correlations were small and non-significant after multiplicity correction; the largest was hematocrit (r = 0.090, 95% CI -0.019 to 0.198; Holm-adjusted p = 0.852). Differences in MARD among sensor archetypes were also non-significant (ANOVA p = 0.369; Kruskal-Wallis p = 0.543). Adaptive calibration is therefore presented as a literature-informed framework for future prospective validation, not as a performance improvement demonstrated by the present synthetic dataset. No FDA or ISO compliance or clinical applicability is claimed.