The Relationship Between Moderate to Vigorous Physical Activity and Metabolic Syndrome: a Bayesian Measurement Error Approach

Overview

Journal J Appl Stat

Specialty Public Health

Date 2023 Jul 12

PMID 37434631

Authors

Daniel Ries

Alicia Carriquiry

Affiliations

Soon will be listed here.

Abstract

Metabolic Syndrome (MetS) is a serious condition that can be an early warning sign of heart disease and Type 2 diabetes. MetS is characterized by having elevated levels of blood pressure, cholesterol, waist circumference, and fasting glucose. There are many articles in the literature exploring the relationship between physical activity and MetS, but most do not consider the measurement error in the physical activity measurements nor the correlations among the MetS risk factors. Furthermore, previous work has generally treated MetS as binary, rather than directly modeling the risk factors on their measured, continuous space. Using data from the National Health and Nutrition Examination Survey (NHANES), we explore the relationship between minutes of moderate to vigorous physical activity (MVPA) and MetS risk factors. We construct a measurement error model for the accelerometry data, and then model its relationship between MetS risk factors with nonlinear seemingly unrelated regressions, incorporating dependence among MetS risk factors. The novel features of this model give the medical research community a new way to understand relationships between MVPA and MetS. The results of this approach present the field with a different modeling perspective than previously taken and suggest future avenues of scientific discovery.

References

Tucker J, Welk G, Beyler N, Kim Y . Associations Between Physical Activity and Metabolic Syndrome: Comparison Between Self-Report and Accelerometry. Am J Health Promot. 2015; 30(3):155-62. DOI: 10.4278/ajhp.121127-QUAN-576. View

Tooze J, Midthune D, Dodd K, Freedman L, Krebs-Smith S, Subar A . A new statistical method for estimating the usual intake of episodically consumed foods with application to their distribution. J Am Diet Assoc. 2006; 106(10):1575-87. PMC: 2517157. DOI: 10.1016/j.jada.2006.07.003. View

Huang J, Li R, Huang S, Sia H, Lee S, Wang W . Relationships between different types of physical activity and metabolic syndrome among Taiwanese workers. Sci Rep. 2017; 7(1):13735. PMC: 5653817. DOI: 10.1038/s41598-017-13872-5. View

Neelon B, OMalley A, Smith V . Modeling zero-modified count and semicontinuous data in health services research Part 1: background and overview. Stat Med. 2016; 35(27):5070-5093. DOI: 10.1002/sim.7050. View

Tooze J, Grunwald G, Jones R . Analysis of repeated measures data with clumping at zero. Stat Methods Med Res. 2002; 11(4):341-55. DOI: 10.1191/0962280202sm291ra. View

Smith V, Preisser J, Neelon B, Maciejewski M . A marginalized two-part model for semicontinuous data. Stat Med. 2014; 33(28):4891-903. DOI: 10.1002/sim.6263. View

Neelon B, OMalley A, Smith V . Modeling zero-modified count and semicontinuous data in health services research part 2: case studies. Stat Med. 2016; 35(27):5094-5112. DOI: 10.1002/sim.7063. View

Kipnis V, Freedman L, Carroll R, Midthune D . A bivariate measurement error model for semicontinuous and continuous variables: Application to nutritional epidemiology. Biometrics. 2015; 72(1):106-15. PMC: 4775438. DOI: 10.1111/biom.12377. View

Ford E, Kohl 3rd H, Mokdad A, Ajani U . Sedentary behavior, physical activity, and the metabolic syndrome among U.S. adults. Obes Res. 2005; 13(3):608-14. DOI: 10.1038/oby.2005.65. View

10.

Moore J, Chaudhary N, Akinyemiju T . Metabolic Syndrome Prevalence by Race/Ethnicity and Sex in the United States, National Health and Nutrition Examination Survey, 1988-2012. Prev Chronic Dis. 2017; 14:E24. PMC: 5364735. DOI: 10.5888/pcd14.160287. View

11.

Carpenter B, Gelman A, Hoffman M, Lee D, Goodrich B, Betancourt M . Stan: A Probabilistic Programming Language. J Stat Softw. 2022; 76. PMC: 9788645. DOI: 10.18637/jss.v076.i01. View

12.

Grundy S, Cleeman J, Daniels S, Donato K, Eckel R, Franklin B . Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation. 2005; 112(17):2735-52. DOI: 10.1161/CIRCULATIONAHA.105.169404. View

13.

Zhang S, Midthune D, Guenther P, Krebs-Smith S, Kipnis V, Dodd K . A NEW MULTIVARIATE MEASUREMENT ERROR MODEL WITH ZERO-INFLATED DIETARY DATA, AND ITS APPLICATION TO DIETARY ASSESSMENT. Ann Appl Stat. 2011; 5(2B):1456-1487. PMC: 3145332. DOI: 10.1214/10-AOAS446. View

14.

Kassi E, Pervanidou P, Kaltsas G, Chrousos G . Metabolic syndrome: definitions and controversies. BMC Med. 2011; 9:48. PMC: 3115896. DOI: 10.1186/1741-7015-9-48. View

15.

Zhang S, Krebs-Smith S, Midthune D, Perez A, Buckman D, Kipnis V . Fitting a bivariate measurement error model for episodically consumed dietary components. Int J Biostat. 2012; 7(1):1. PMC: 3406506. DOI: 10.2202/1557-4679.1267. View

16.

Kim J, Tanabe K, Yokoyama N, Zempo H, Kuno S . Association between physical activity and metabolic syndrome in middle-aged Japanese: a cross-sectional study. BMC Public Health. 2011; 11:624. PMC: 3199599. DOI: 10.1186/1471-2458-11-624. View

17.

Camhi S, Sisson S, Johnson W, Katzmarzyk P, Tudor-Locke C . Accelerometer-determined moderate intensity lifestyle activity and cardiometabolic health. Prev Med. 2011; 52(5):358-60. DOI: 10.1016/j.ypmed.2011.01.030. View

18.

Dalacorte R, Reichert C, Vieira J . Metabolic syndrome and physical activity in southern Brazilian community-dwelling elders: a population-based, cross-sectional study. BMC Public Health. 2009; 9:25. PMC: 2639578. DOI: 10.1186/1471-2458-9-25. View

19.

Sisson S, Camhi S, Church T, Tudor-Locke C, Johnson W, Katzmarzyk P . Accelerometer-determined steps/day and metabolic syndrome. Am J Prev Med. 2010; 38(6):575-82. DOI: 10.1016/j.amepre.2010.02.015. View

20.

Franks P, Ekelund U, Brage S, Wong M, Wareham N . Does the association of habitual physical activity with the metabolic syndrome differ by level of cardiorespiratory fitness?. Diabetes Care. 2004; 27(5):1187-93. DOI: 10.2337/diacare.27.5.1187. View