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.
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.
| Original language | English |
|---|---|
| Pages (from-to) | 1425–1433 |
| Number of pages | 9 |
| Journal | Diabetes Care |
| Volume | 48 |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - Aug 2025 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- circadian rhythm
- metabolic health
- Rest–activity rhythms
- Diabetes Risk
- type 2 diabetes
- biomarkers
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