A recent study introduced CosinorAge, a digital biomarker derived from accelerometer-based rest-activity rhythm data (UK Biobank, category 1008), to quantify circadian disruption. The study found that each one-year increase in CosinorAge was associated with an 8-12% rise in mortality (both all-cause and cause-specific) and a 3-14% higher risk of developing age-related diseases. Additionally, increased CosinorAge correlated with significant declines in resilience and physical functioning, including an 8-33% reduction in self-rated health and a 3-23% decrease in health-related quality of life, even after adjusting for covariates and multiple comparisons. These results underline the importance of CosinorAge as a valuable indicator of health and aging [1].
Building on these findings, our proposal aims to extend the utility of CosinorAge to mental health, cognition, and pain-domains closely linked to mental health conditions. We will leverage longitudinal UK Biobank data to evaluate whether CosinorAge predicts the onset of mental health disorders (category 100060), pain (category 100048), and related clinical diagnoses (ICD Chapter 4, Data-Field 41270). Cox regression models and Kaplan-Meier analyses will be employed to investigate long-term associations and to calculate hazard ratios. Following methods from [1], we will also externally validate our results using NHANES data to ensure robustness and generalizability.
Reference:
[1] Shim, J., Fleisch, E., & Barata, F. (2024). Circadian rhythm analysis using wearable-based accelerometry as a digital biomarker of aging and healthspan. NPJ Digital Medicine, 7(1), 146.