Do physical activity, commuting mode, cardiorespiratory fitness and sedentary behaviours modify the genetic predisposition to higher BMI? Findings from a UK Biobank study

Celis-Morales, Carlos; Lyall, Donald M.; Petermann, Fanny; Anderson, Jana; Iliodromiti, Stamatina; Mackay, Daniel; Welsh, Paul; Bailey, Mark E.S.; Pell, Jill P.; Sattar, Naveed; Gill, Jason M.R.; Gray, Stuart R.

doi:10.1038/s41366-019-0381-5

Cited by 14 publications

(13 citation statements)

References 54 publications

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“…Recently, the Sedentary Behavior Research Network (SBRN; a network connecting sedentary behavior researchers and health professionals from around the world) updated its definitions on, among others, sedentary behavior and physical inactivity and thereby supported that an insufficient physical activity level is not the same as sedentary behavior 1 . This idea is supported by the modest inverse correlations detected between objective sedentary time and MVPA in this study, matching those observed in earlier studies 16‐18 . This confirms that sedentary time is not simply the inverse of MVPA.…”

Section: Discussionsupporting

confidence: 85%

“…Finally, there is ongoing debate on whether the risks of sedentary behavior may not simply converge with the risks of not meeting physical activity recommendations (referred to as physical inactivity), as both sedentary behavior and physical inactivity contribute to the burden of chronic diseases 8 . The current literature seems to argue against this idea because there is only a modest relationship between MVPA and sedentary behavior 16‐18 . However, lower levels of MVPA, like higher levels of sedentary behavior, have shown to increase the risk of cardiovascular disease, type 2 diabetes, and certain types of cancer 19,20 .…”

Section: Introductionmentioning

confidence: 99%

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Heritability of objectively assessed and self‐reported sedentary behavior

Schutte

Huppertz

Doornweerd

et al. 2020

Scandinavian Med Sci Sports

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Understanding the sources of the large individual differences in sedentary behavior is of great importance as this behavior is associated with pre‐mature mortality and non‐communicable diseases. Here, we report on the contribution of genetic and environmental factors to the variation in objectively assessed (accelerometer) sedentary behavior and self‐reported sitting and their shared genetic basis. In addition, the overlap of the genetic risk factors influencing sedentary time and moderate‐to‐vigorous physical activity (MVPA) was estimated. A sample of 800 individuals (twins and their siblings) was equipped with an Actigraph accelerometer for 7 days and reported on their sitting time and time spent on MVPA on those days using the IPAQ‐SF. Genetic factors explained 56% (CI: 44%, 65%) of the individual differences in objective sedentary behavior (Actigraph) and 26% (CI: 0%, 51%) of the individual differences in self‐reported sedentary behavior (IPAQ‐SF). A modest correlation (0.33) was found between these measures, which was for 45% accounted for by genetic influences. The genetic correlation was 0.49 reflecting a partly overlapping set of genes that influenced both measurements. A modest correlation (−0.27) between Actigraph‐derived sedentary time and MVPA was found, which was 13% accounted for by genetic effects. The genetic correlation was −0.31, indicating that there are overlapping genetic variants that increase sedentary time and decrease MVPA or vice versa. To conclude, more than half of the individual differences in objective sedentary time could be attributed to genetic differences, while for self‐reported sitting this was much lower. In addition, using objective measurements, this study confirms that sedentary time is not simply the inverse of MVPA. Future studies are needed to understand the pathways translating genomic variation into variation in these behaviors and how this knowledge might feed into the development of health promotion interventions.

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Section: Discussionsupporting

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Heritability of objectively assessed and self‐reported sedentary behavior

Schutte

Huppertz

Doornweerd

et al. 2020

Scandinavian Med Sci Sports

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“…Other approaches also use simple linear regression, but establish the HR-to-WR relationship by relating resting HR to only a single measurement of HR during the test 20,43,44,22,18,21,45,46,17 . HR measurement noise will greatly decrease precision in this approach, and the CRF estimates are still subject to bias, the extent of which may differ by protocol.…”

Section: Discussionmentioning

confidence: 99%

The UK Biobank submaximal cycle ergometer test for assessment of cardiorespiratory fitness: Validity, reliability, and association with disease outcomes

Gonzales

Westgate

Strain

et al. 2020

Preprint

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Background: Cardiorespiratory fitness (CRF) was assessed in UK Biobank (UKB) using heart rate response to a submaximal ramped cycle ergometer test that was individualised for participant characteristics including cardiovascular disease risk. Studies have since explored health associations with CRF by estimating maximal oxygen consumption (VO2max) from UKB test data using interpretation methods that have not accounted for this individualisation procedure. Thus, dose-response relationships reported in these studies may be inaccurate. We developed and validated a novel VO2max estimation approach that accounts for the UKB test individualisation procedure and compared dose-response relationships with health outcomes between the novel and previous methods. Methods: In a cross-over study (n=189), participants completed several UKB tests and VO2max was measured. A multilevel modelling framework was developed that combines heart rate response features from the UKB test to estimate VO 2 max. Estimates were compared within participants across UKB test protocols, and with directly measured VO2max. Short-term test-retest reliability was assessed in a subsample of participants (n=87). In UKB, we examined associations between estimated CRF and disease endpoints (n=80,259) and compared associations obtained with an unvalidated method. Long-term test-retest reliability was examined (n=2877). Results: Estimated and directly measured VO2max were strongly correlated (Pearsons r range: 0.68 to 0.74) with no mean bias (women bias: -0.8 to 0.4; men bias range: -0.3 to 0.3), outperforming a previous approach for interpreting UKB test data. Agreement between estimated VO2max across different test protocols was strong (Pearsons r range: 0.94 to 0.99). Short- and long-term reliability was also high (lambda=0.91 and 0.80, respectively). All-cause mortality was 7 (95%CI 4-10%, 2686 deaths) lower and CVD mortality 9% (95%CI 3-14%, 858 deaths) lower for every 1-MET difference in fitness, associations twice as strong as determined by previous methods. Conclusions: We present a valid and reliable method for estimating CRF in UKB and demonstrate its utility in characterising dose-response relationships with health outcomes. Accounting for the individualisation procedure strengthens observed relationships between CRF and disease and enhances the case for promoting improved fitness in the general population.

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“…A pesar de esto, es importante destacar que los mencionados no son los únicos actores de este ambiente, ya que el consumo excesivo de calorías -independiente del alimento que provengan-tendrá un efecto similar sobre nuestros genes. Cabe destacar el rol de la inactividad fisca, el tiempo que destinamos a estar sentados y otros factores de riesgo emergentes como lo es el tiempo que destinamos a dormir, los cuales también son actores fundamentales de este ambiente de riesgo obesogénico 3,5,6 . Por consiguiente, la promoción e implementación de políticas públicas que permitan reducir estos factores de riesgo son esenciales para ganar la batalla contra la obesidad,…”

Section: Sr Editorunclassified