Quantifying the Relationship Between Physical Activity Energy Expenditure and Incident Type 2 Diabetes: A Prospective Cohort Study of Device-Measured Activity in 90,096 Adults

Abstract

Objective: To investigate the association between accelerometer-derived physical activity energy expenditure (PAEE) and incident type 2 diabetes (T2D) in a cohort of middle-aged adults and within subgroups.

Research design and methods: Data were from 90,096 UK Biobank participants without prevalent diabetes (mean 62 years of age; 57% women) who wore a wrist accelerometer for 7 days. PAEE was derived from wrist acceleration using a population-specific method validated against doubly labeled water. Logistic regressions were used to assess associations between PAEE, its underlying intensity, and incident T2D, ascertained using hospital episode and mortality data up to November 2020. Models were progressively adjusted for demographic, lifestyle factors, and BMI.

Results: The association between PAEE and T2D was approximately linear (n = 2,018 events). We observed 19% (95% CI 17-21) lower odds of T2D per 5 kJ · kg-1 · day-1 in PAEE without adjustment for BMI and 11% (9-13) with BMI adjustment. The association was stronger in men than women and weaker in those with obesity and higher genetic susceptibility to obesity. There was no evidence of effect modification by genetic susceptibility to T2D or insulin resistance. For a given level of PAEE, odds of T2D were lower among those engaging in more moderate-to-vigorous activity.

Conclusions: There was a strong linear relationship between PAEE and incident T2D. A difference in PAEE equivalent to an additional daily 20-min brisk walk was associated with 19% lower odds of T2D. The association was broadly similar across population subgroups, supporting physical activity for diabetes prevention in the whole population.

Graphical abstract

Figure 1

Cubic spline–modeled association between PAEE and incident T2D: UK Biobank (n = 90,096). Model 0 adjusted for age, sex, and season of accelerometry wear (using two orthogonal sine functions); model 1 additionally adjusted for ethnicity, Townsend index of deprivation, highest educational level achieved, employment status, parental history of diabetes, smoking status, alcohol drinking status, sleep duration, and fruit and vegetable intake. Model 2 additionally adjusted for BMI. Data presented for the observed range of PAEE amongst incident cases.

Figure 2

Odds ratios for incident T2D per 5 kJ · kg⁻¹ · day⁻¹ PAEE for the whole sample and in subgroups adjusted for BMI and other confounding factors (model 2): UK Biobank (n = 90,096). Model 2 adjusted for age, sex, season of accelerometry wear (using two orthogonal sine functions), ethnicity, Townsend index of deprivation, highest educational level achieved, employment status, parental history of diabetes, smoking status, alcohol drinking status, sleep duration, fruit and vegetable intake, and BMI. Genetic risk score–stratified analyses also adjusted for UK Biobank genotyping array and 10 genetic principal components but did not adjust for ethnicity as analyses were restricted to those of White European ancestry. P value for interaction between subgroups. CMI, cumulative incidence; GRS, genetic risk score.

Figure 3

The joint association of PAEE and %MVPA with the odds of incident T2D adjusted for BMI and other confounding factors (model 2): UK Biobank (n = 90,096). Model 2 adjusted for age, sex, season of accelerometry wear (using two orthogonal sine functions), ethnicity, Townsend index of deprivation, highest educational level achieved, employment status, parental history of diabetes, smoking status, alcohol drinking status, sleep duration, fruit and vegetable intake, and body mass index. Data presented for the observed range of PAEE amongst incident cases for a range around the %MVPA value (±5%, extending to respective end of distributions for 10% and 40%).