Insulin resistance prediction from wearables and routine blood biomarkers | Nature

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Subjects

- Machine learning

- Pre-diabetes

Abstract

Insulin resistance (IR), a primary precursor to type 2 diabetes, is characterized by impaired insulin action in tissues1. However, diagnostic methods remain expensive and inaccessible, which hinders early intervention2,3. Here we present the WEAR-ME study, a large, remotely conducted study of IR (n = 1,165 participants; median body mass index (BMI) = 28 kg m−2, median age = 45 years, median haemoglobin A1c (HbA1c) = 5.4%) 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) = 0.80, sensitivity = 76%, specificity = 84%) 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 million hours of sensor data. In an independent validation cohort (n = 72), a model integrating WFM-derived representations with demographic data surpassed a demographics-only baseline (AUROC = 0.75 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 = 0.88 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.

Main

At present, 537 million adults worldwide are living with diabetes, a figure that is estimated to increase to 643 million by 2030. Approximately 10% of people with diabetes have type 1 diabetes (T1D), and around 90% have type 2 diabetes (T2D)4. The rise in T2D is driven mainly by lifestyle factors5. In a healthy individual, insulin—a hormone secreted by pancreatic β-cells—helps to regulate blood glucose levels by facilitating the uptake of glucose from the blood into cells (including muscles, adipose and liver). In addition, incretin hormones, such as glucagon-like peptide 1 (GLP1) and gastric inhibitory polypeptide (GIP), can increase the secretion of insulin from pancreatic β-cells, leading to improved glycaemic control6. The fundamental problem in diabetes is the inability of the body to regulate blood glucose properly owing to absolute or relative insulin deficiency. In T1D, the body’s immune system mistakenly attacks and destroys the pancreatic β-cells, resulting in absolute insulin deficiency and high blood glucose7. By contrast, in most cases of T2D, the body becomes insulin resistant, meaning that higher amounts of insulin have to be produced by the pancreatic β-cells to achieve the same glucose-lowering effect. With time, the pancreatic β-cells can become unable to produce enough insulin to compensate for IR, leading to relative insulin deficiency and increased blood glucose levels. Figure 1a illustrates the complex nature of T2D and the intricate relationships between lifestyle choices, genetics and various metabolic subphenotypes and physiological processes that are involved in the development of the disease. Long-term complications of diabetes include damage to various organs and tissues over time, such as diabetic retinopathy, nephropathy and neuropathy8.

Fig. 1: Study design and data summary.

a, Overview of physiological factors and associated lifestyle factors leading to IR, prediabetes and diabetes. b, Our proposed modelling pipeline for predicting HOMA-IR and interpreting the results with the insulin resistance literacy and understanding agent (IR agent). c, Correlation of blood biomarkers and lifestyle features (continuous values) with HOMA-IR. d–f, Distribution of the top three features of wearables that are highly correlated with HOMA-IR (RHR (d), daily step counts (e) and HRV (f)) for stratified insulin sensitivity groups (IS, impaired-IS and IR). RMSSD, root mean square of successive differences. g–i, Distribution of the top three highly correlated blood biomarkers (triglycerides (g), HDL cholesterol (h) and albumin/globulin ratio (i)) for stratified insulin sensitivity groups. In the box plots in d–i, the centre line indicates the median, the bounds of box represent the 25th and 75th percentiles and the whiskers extend to 1.5 times the interquartile range. j, Scatter plot of BMI and HOMA-IR values, showing the relationship between higher BMI values and IR (measured through HOMA-IR). k, Confusion matrix showing the number of participants in each combination of IR status and diabetes status.

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The prevalence of IR in the general population is estimate