A new study shows that combining everyday wearable data with routine blood tests could help detect insulin resistance early, opening the door to more accessible screening for type 2 diabetes before it takes hold.
Research: Prediction of insulin resistance from wearable and routine blood biomarkers. Image credit: Black_Kira / Shutterstock
In a recent study published in the journal natureresearchers developed a method to predict insulin resistance (IR) using data from wearable devices, blood biomarkers, demographics, and other health information.
Currently, 537 million people worldwide have diabetes, and the majority (approximately 90%) have type 2 diabetes (T2D). The main problem with diabetes is the body’s inability to regulate blood sugar levels due to relative or absolute insulin deficiency. In type 1 diabetes (T1D), the immune system mistakenly destroys insulin-secreting pancreatic beta cells, leading to complete insulin deficiency.
In T2D, the body becomes insulin resistant and requires increased insulin production to achieve the same hypoglycemic effect. Over time, β-cells are no longer able to produce enough insulin to compensate for IR, leading to relative insulin deficiency and elevated blood glucose levels. The prevalence of IR is estimated to be 20%–40% in the general population and 84% in T2D.
IR is associated with fatty liver disease associated with cardiovascular disease and metabolic dysfunction. Early detection of IR can guide lifestyle interventions that can improve or reverse IR. Although several IR evaluation methods are available, they are not routinely implemented and remain expensive and inaccessible.
Study design and insulin resistance modeling
In this study, researchers developed a method to predict IR using signals obtained from wearable devices and blood biomarkers. Adults participated in the Wearables for Metabolic Health Study in the United States. The Google Health Studies application is configured to collect data from Google Pixel and Fitbit watches. Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) was used as a reference measure for model development, which is an alternative rather than the gold standard hyperinsulinemic-euglycemic clamp.
Participants were classified as having IR if their HOMA-IR was >2.9, insulin sensitivity (IS) if their HOMA-IR was <1.5, and impaired IS if their HOMA-IR was between 1.5 and 2.9. Overall, 1,165 participants with high-quality data participated in IR model development. These included 300 patients with IR, 459 patients with IS, and 406 patients with IS disorders.
Pearson correlation coefficients between HOMA-IR and lifestyle factors, demographics, glucose, lipids, electrolytes, liver and kidney function markers were calculated. HOMA-IR was significantly positively correlated with fasting blood glucose, glycated hemoglobin, BMI, resting heart rate, and triglycerides, and negatively correlated with daily step count, albumin/globulin ratio, high-density lipoprotein cholesterol, and heart rate variability.
These data suggested that HOMA-IR can be inferred from blood biomarkers and wearable measurements. We then trained a multimodal model using a combination of demographics, blood biomarkers, and wearable features for IR prediction. A regression model was trained to predict continuous HOMA-IR, and then a classification threshold was applied to determine IR status.
Incorporating wearables, blood biomarkers, and demographic data significantly improved predictive accuracy. A model based solely on demographics and wearable features predicted an IR with an area under the receiver operating characteristic curve (AUROC) of 0.7, specificity of 0.8, and sensitivity of 0.6. Including fasting glucose improved performance, yielding an AUROC of 0.78, specificity of 0.84, and sensitivity of 0.73.
The model using demographic data, wearable data, and blood biomarker data (metabolic and lipid panels) achieved an AUROC of 0.8, specificity of 0.84, and sensitivity of 0.76. Using each data source alone does not provide sufficient predictive power. The team also fine-tuned the Wearable Foundation Model (WFM), which was pre-trained on 40 million hours of sensor data, to improve analysis of time-series wearable data.
Verification results of wearable platform model
Using WFM feature embedding improved IR prediction. The model incorporating demographics and WFM-derived representations outperformed the demographics-only baseline. Incorporating WFM representations into models that included fasting blood glucose, lipid panel data, and demographics further improved predictive performance.
The IR model was validated in an independent cohort of 72 people with complete physiological biomarkers and wearable data. In this cohort, the model incorporating WFM representation along with demographics achieved an AUROC of 0.75, compared to 0.66 for the demographics-only baseline.
Integrating the WFM representation into a model that included lipid panel data, demographics, and fasting blood glucose levels improved predictive power (AUROC 0.88) compared to a model that did not include wearable data (AUROC 0.76). However, the validation cohort was small and not all biomarker combinations were externally validated.
The researchers also developed an IR Literacy and Understanding Agent (IR Agent) using a reason-and-act framework built on large-scale language models (LLMs), specifically Gemini 2.0 Flash.
IR agents combine language understanding with the ability to perform actions such as searching the web, accessing specialized tools, and using IR predictive models. Endocrinologists evaluated drug responses, and the results demonstrated high safety and overall factual accuracy, although performance varied by data type.
Conclusions and limitations of the study
To the authors’ knowledge, the proposed IR prediction framework represents the first deployable model using readily available data from routine blood biomarkers, wearables, and demographics. The model was trained using HOMA-IR, which has been validated in large-scale epidemiological studies. This study establishes a scalable and accessible framework for early screening of metabolic risk, allowing early identification and intervention of individuals at risk of progressing to T2D.
The authors noted several limitations. Only 25% of participants had complete data and were included in the analysis, potentially introducing selection bias. Additionally, all wearable data comes from Google and Fitbit devices, so extensive validation across other wearable ecosystems is required.
