Impaired rest-activity rhythm characteristics predict higher risk for incident type 2 diabetes in UK Biobank participants

Objective

Circadian rhythms play a key role in metabolic health. Rest-activity rhythms, which are in part driven by circadian rhythms, may be associated with diabetes risk. There is a need for large prospective studies to comprehensively examine different rest-activity metrics to determine their relative strength in predicting risk for incident type 2 diabetes.

Research Design and Methods

In actigraphy data from 83,887 UK Biobank participants, we applied both parametric and nonparametric algorithms to derive 13 different metrics characterizing different aspects of the rest-activity rhythm. Diabetes cases were assessed by both self-reported data and health records. We used Cox proportional hazard models to assess associations between rest-activity parameters and type 2 diabetes risk and random forest models to determine the relative importance of these parameters in risk prediction.

Results

We found that multiple rest-activity characteristics were predictive of a higher risk of incident diabetes, including lower pseudo-F-statistic (hazard ratio (HR)Q1vsQ5: 1.27, 95% confidence interval (CI): 1.09-1.46, p-trend, <0.001), lower amplitude (2.56, 2.21-2.97, <0.001), lower mesor (2.59, 2.24-3.00, <0.001), lower RA (4.64, 3.74-5.76, <0.001), lower M10 (3.82, 3.20-4.55, <0.001), higher L5 (HR¬Q5vsQ1: 1.88, 1.62-2.19, <0.001), and later L5 start time (1.20, 1.04-1.38, 0.004). Random forest models ranked most of the rest-activity metrics as top predictors of diabetes incidence, when compared to traditional diabetes risk factors. The findings were consistent across subgroups of different age, sex, body mass index and shift work status.

Conclusions

Rest-activity rhythm characteristics measured from actigraphy data may serve as digital biomarkers for predicting type 2 diabetes risk.