\u22122<\/sup>, median\u00a0age\u2009=\u200945 years, median\u00a0haemoglobin A1c (HbA1c)\u2009=\u20095.4%) that uses time-series data from wearable devices and routine blood biomarkers to train deep neural networks against a ground-truth measure of IR (homeostatic model assessment of IR; HOMA-IR). Using a HOMA-IR cut-off of 2.9, our multimodal model achieved robust performance (area under the receiver operating characteristic curve (AUROC)\u2009=\u20090.80, sensitivity\u2009=\u200976%, specificity\u2009=\u200984%) with data from wearable devices, together with demographic and routine blood biomarker data. To enhance the use of time-series data from wearables, we fine-tuned a wearable foundation model (WFM) pretrained on 40\u2009million hours of sensor data. In an independent validation cohort (n<\/i>\u2009=\u200972), a model integrating WFM-derived representations with demographic data surpassed a demographics-only baseline (AUROC\u2009=\u20090.75 versus 0.66). Moreover, adding WFM-derived representations to a model with demographics, fasting glucose and a lipid panel substantially improved performance, compared with an identical model without data from wearables (AUROC\u2009=\u20090.88 versus 0.76). We integrate IR prediction into a large language model to contextualize the results and facilitate personalized recommendations. This work establishes a scalable, accessible framework for the early detection of metabolic risk, which could enable timely lifestyle interventions to prevent progression to type 2 diabetes.<\/p>\n","protected":false},"excerpt":{"rendered":"In this talk, we discuss the paper \u201cInsulin resistance prediction from wearables and routine blood biomarkers\u201d by Ahmed A. Metwally et al., Nature, 2026. Abstract: Insulin resistance (IR), a … <\/p>\n
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