Predictive Uncertainty Reads Insulin Resistance: A Latent-Dynamics Readout from Frozen CGM Forecasters and a Controlled Feedback System

Probabilistic time-series forecasters output uncertainty, but it is usually treated as an error bar rather than a signal. We ask whether predictive uncertainty carries information about a latent property of the data-generating process, using a frozen continuous glucose monitoring forecaster whose representation tracks gold-standard insulin resistance when read by a supervised probe. We define relative forecast uncertainty (RFU), the predictive scale normalized by recent within-person glucose variability, and find that it discriminates insulin resistance defined by the steady-state plasma glucose (SSPG) reference test in pooled gold-standard cohorts (AUROC 0.78, two-sided permutation p = 0.0009, n = 47); binary discrimination remains above chance after linear adjustment for glucose level, variability, and cohort (cross-fitted AUROC 0.70 [0.53, 0.85], nominally significant). The sign is clinically counterintuitive: more insulin-resistant subjects receive lower relative uncertainty, and the forecaster has greater realized skill relative to persistence in this group. The readout is sensitive to within-window time shuffling, overlaps substantially with long-range persistence, and is reproduced by four frozen forecasters, including a general-purpose time-series model applied zero-shot (subject rankings agree at 0.90 to 0.96), with protocol-dependent magnitude. RFU aligns with the representation-based readout it was derived to explain, and on a controlled feedback system with subject-level mean and variance matched it ranks a held-out latent gain (+0.90). RFU thus provides a scalar readout of SSPG-defined insulin-resistance status without label-based fitting; its physiological interpretation and the added value of the learned scale beyond normalization remain unresolved.