Impaired Rest–Activity Rhythm Characteristics Predict Higher Risk of Incident Type 2 Diabetes in UK Biobank Participants

Abstract

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 of 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 rest–activity rhythm. Diabetes cases were identified using both self-reported data and health records. We used Cox proportional hazards 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] of quintile 1 ([Q1] vs. Q5 1.27; 95% CI 1.09–1.46; Ptrend < 0.001), lower amplitude (HRQ1 vs. Q5 2.56; 95% CI 2.21–2.97; Ptrend < 0.001), lower midline estimating statistic of rhythm (HRQ1 vs. Q5 2.59; 95% CI 2.24–3.00; Ptrend < 0.001), lower relative amplitude (HRQ1 vs. Q5 4.64; 95% CI 3.74–5.76; Ptrend < 0.001), lower M10 (HRQ1 vs. Q5 3.82; 95% CI 3.20–4.55; Ptrend < 0.001), higher L5 (HRQ5 vs. Q1 1.88; 95% CI 1.62–2.19; Ptrend < 0.001), and later L5 start time (HRQ5 vs. Q1 1.20; 95% CI 1.04–1.38; Ptrend = 0.004). Random forest models ranked most of the rest–activity metrics as top predictors of diabetes incidence, when compared with traditional diabetes risk factors. The findings were consistent across subgroups of age, sex, BMI, and shift work status. CONCLUSIONS Rest–activity rhythm characteristics measured from actigraphy data may serve as digital biomarkers for predicting type 2 diabetes risk.