Accelerometer-measured chronoactivity and type 2 diabetes risk: A prospective study in UK Biobank participants

Preventive Medicine, vol. 197, pp. 108312

Abstract

OBJECTIVE: We assessed whether timing of physical activity, independent from the total activity amount, - which we refer to as chronoactivity - is associated with type 2 diabetes (T2D) risk. METHODS: We included UK Biobank participants with valid accelerometry data (UK, exposure measurement: 2013-2015, follow-up till November 2023) and without diabetes mellitus at baseline (N = 89,439; mean age: 61.7 [SD:7.9] years). Relative hourly physical activity was calculated by dividing the average hourly clock time physical activity by the average hourly physical activity in a week. Participants were categorized into different chronoactivity clusters using k-means cluster analysis on relative hourly physical activity. We used multivariable-adjusted cox-proportional hazard regressions to examine associations between relative hourly physical activity, chronoactivity clusters and T2D, adjusted for potential confounders, including BMI as a potential mediator. RESULTS: Over 7.8 (interquartile range: 7.2 to 8.3) years of follow-up, 2240 participants developed T2D. Higher relative hourly activity amounts during late morning (8:00-10:59) and late afternoon (15:00-15:59, 17:00-17:59) were associated with approximately 5 %-10 % lower T2D risk. Four clusters of chronoactivity patterns were identified, notably: midday (reference), early morning peak, late morning peak, and evening peak. Compared with participants exhibiting a midday pattern, those with a late morning peak had a lower T2D risk (Hazards Ratio: 0.88, 95 %CI: 0.79, 0.98). Overall, all observations attenuated after additional BMI adjustment. CONCLUSIONS: Independent of the total amount of physical activity, specific timing of physical activity represents an additional dimension in T2D risk.

Authors 6

1. Qiuyu Feng corresponding Aachen Department of Human Genetics

   Leiden University Medical Center

   Affiliation as printed

   Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands. Electronic address: q.feng@lumc.nl

2. Gali Albalak Aachen Section of Gerontology and Geriatrics Department of Internal Medicine

   Leiden University Medical Center

   Affiliation as printed

   Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands

3. Ko Willems van Dijk Aachen

   Leiden University Medical Center

   Affiliation as printed

   Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands; Division of Endocrinology, Department of Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands; Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, the Netherlands

4. Jeroen H. P. M. van der Velde Aachen Department of Clinical Epidemiology

   Leiden University Medical Center

   Affiliation as printed

   Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands

5. Qian Xiao

   The University of Texas Health Science Center at Houston

   Affiliation as printed

   Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA; Center for Spatial-Temporal Modeling for Applications in Population Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA

6. Raymond Noordam Aachen

   Leiden University · Leiden University Medical Center

   Affiliation as printed

   Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands; Health Campus the Hague/Public Health and Primary Care, Leiden University Medical Center, The Hague, the Netherlands