The Association of Sedentary Behavior and Physical Activity with Different Measurements of Metabolic Syndrome: The Jackson Heart Study

Abstract

Purpose: Cross-sectional investigation of the association of sedentary behavior and physical activity with metabolic syndrome (MetS) among the African American participants in the Jackson Heart Study (JHS).

Methods: Prevalence, number of individual components, and MetS severity z-score (MetS-Z) were examined. MetS was classified using ATP-III thresholds. MetS-Z was calculated using sex-, race-, and ethnicity-specific formulas. Sedentary behavior and physical activity were calculated from the JHS Physical Activity Cohort survey (JPAC). Associations between sedentary behavior and physical activity with MetS were assessed by logistic, negative binomial, and ordinary least squares regressions.

Results: The mean participant age (N = 3370) was 61.7 ± 11.9 years and most were female (63.9%). Among all participants, 60.5% were classified with MetS. Overall MetS-Z was moderately high (.31 ± 1.07). Most waking hours were sedentary, with just under 40 daily minutes of self-reported physical activity. Physical activity was associated with lower prevalence of MetS, the number of individual components, and MetS-Z score (p < .05). Sedentary behavior was not associated with MetS in any fully adjusted models (p > .05).

Conclusions: Physical activity was associated with lower cardiometabolic risk, irrespective of sedentary behavior. Further studies are needed to better understand why no relation was found between sedentary behavior and cardiometabolic risk in this cohort of African American adults.

Keywords

Metabolic syndrome African Americans cardiometabolic risk movement behaviors

“Efforts should be made to maintain or increase physical activity at the expense of sedentary behavior to promote better cardiometabolic health.”

Introduction

Metabolic syndrome (MetS) is an indicator of cardiometabolic disease risk,¹ and is defined as a collection of anthropometric, metabolic, and inflammatory factors that contribute to and exacerbate risk for cardiovascular and renal diseases.^2-6 The prevalence of MetS among US and European adults ranges between 24–34.2%.^1,2,4,7 However, there is discordance between the prevalence of MetS, measured using the traditional dichotomous categories (Yes/No), and subsequent cardiovascular disease (CVD) among African American and White individuals.^1,8,9 In the United States (US), there is a lower prevalence of MetS among Black men (16.1%) compared with White men (29.3%);¹ however, African American individuals have a higher prevalence of select CVD risk factors associated with MetS, including hypertension and type 2 diabetes mellitus (T2DM).⁹ CVD mortality is higher among African American than White individuals and this discrepancy is expected to persist through and beyond 2030.¹⁰ The estimated prevalence of T2DM, both diagnosed and undiagnosed, among African American individuals is 77–93% higher than among their White peers.^8,11 The disconnect between the dichotomous classification of MetS and sequelae prompted the development of sex-, race-, and ethnicity-specific equations for continuous MetS severity z-scores (MetS-Z) to better identify the risk for cardiometabolic disease among African American individuals.¹

Structural inequities in society can lead to racial/ethnic disparities in the prevalence of and relation between many cardiometabolic risk factors, including sedentary behavior and physical activity.^12-14 The Centers for Disease Control and Prevention found that 30% or more of Non-Hispanic Black adults across 23 states and the District of Columbia, including Mississippi, reported no physical activity outside of their occupation; however, physical inactivity prevalence of at least 30% among Non-Hispanic White adults was observed in only 5 states.¹⁵ Researchers have found differences in physical activity by race across the lifespan accounts for differences in multimorbidity.¹⁶ Greater time spent in sedentary behavior is associated with an increased risk of developing MetS,^17,18 as well as exacerbation of individual components of MetS.¹⁹ Recently, researchers have implicated sedentary behavior and physical activity as independent behaviors associated with MetS prevalence.^17,18 While sedentary behavior and physical activity independently impact health, they are a part of the compositional 24-hours movement behavior paradigm, where each minute of sedentary behavior allows for the possibility of one less minute of physical activity.²⁰ Therefore, sedentary behavior and physical activity should be examined together. However, most studies investigating the associations between sedentary behavior, physical activity, and MetS have been conducted using predominately White cohorts.^17,18,21

The associations between sedentary behavior and physical activity may help to explain the paradoxical relation between lower MetS prevalence (dichotomous) but higher CVD risks among African American individuals. The purpose of this study was to investigate the association of sedentary behavior and physical activity with the prevalence of MetS (dichotomous), the number of individual MetS components, and the level of MetS-Z in a well-characterized cohort of African American adults.

Methods

Participants

The Jackson Heart Study (JHS) is a community-based, longitudinal observational study designed to investigate the risks for as well as the onset and progression of CVD among African American adults.²² Participants were recruited from the three-county metropolitan area of Jackson, Mississippi, which includes Hinds, Madison, and Rankin counties. Details about recruitment protocols have been published earlier.^22,23 Baseline data were collected from 2000–2004 and 5306 participants aged 21–84-years-old and self-reporting as African American or Black provided biological, anthropometric, and survey data during the initial study visit. The third wave of data were collected from 2009–2013 and 3819 of the JHS participants provided data during this follow-up exam. The present study used only data from the third exam period, due to data availability. Participants provided written informed consent at each study visit and the study protocol was approved by the institutional review boards of the University of Mississippi Medical Center, Jackson State University, and Tougaloo College.

Metabolic Syndrome

MetS was examined using three different methodologies, all based on ATP-III criteria:² the traditional dichotomous categories, the number of positive criteria, and using MetS-Z. For the first method, the traditional dichotomous categories, using ATP-III criteria,² an individual must have a combination of at least three of the five risk factors to be classified with MetS. The threshold for each criterion is as follows: waist circumference, equal to or greater than 102 cm for men or 88 cm for women; triglycerides, equal to or greater than 150 mg⋅dL⁻¹ or medication for treatment; high-density lipoprotein cholesterol, less than 40 mg⋅dL⁻¹ for males or 50 mg⋅dL⁻¹ for females or medication for the treatment of lipid disorders; blood pressure, systolic blood pressure equal to or greater than 130 mmHg or diastolic blood pressure equal to or greater than 85 mmHg or medication for the treatment of hypertension; fasting blood glucose, equal to or greater than 100 mg⋅dL⁻¹ or medication for the treatment of diabetes.² Using the aforementioned criteria, the second method required summation of the total number of positive MetS risk factors, ranging from 0 to 5. The third method of examining MetS used sex-, race-, and ethnicity-specific formulas to calculate MetS-Z scores for all individuals following previously established methodology.¹ The six sex-, race-, and ethnicity-specific MetS-Z formulas were developed via confirmatory factor analysis of the MetS components to determine the weighted contribution of each MetS component to a latent MetS factor. The loading coefficients were then used to generate equations to calculate a standardized MetS-Z for each sex-, race-, and ethnicity-population.¹ The MetS-Z score has previously been utilized extensively within the JHS.^24-30

The JHS third exam was conducted at the Clinical Center, where participants underwent physical measurements and serology testing. Waist circumference was measured at the umbilicus in centimeters. Resting blood pressure was measured by trained technicians after a five-minute rest in a sitting position using a random-zero sphygmomanometer. Concentrations of fasting high-density lipoprotein cholesterol and triglycerides were assessed using Roche enzymatic methods via a Cobras centrifuge analyzer (Hoffman-La Roche, Inc., Nutley, New Jersey) in a laboratory certified by the Lipid Standardization Program of the Centers for Disease Control and Prevention and the National Heart, Lung, and Blood Institute. Fasting blood glucose was measured on a Vitros 950 or 250 analyzer (Ortho-Clinical Diagnostics, Raritan, New Jersey) following standard procedures.

Physical Activity

The physical activity measure for our analysis was derived from responses to items on the JHS Physical Activity Cohort survey (JPAC) that encompassed four domains—active living; occupational activities; home, family, yard, and garden; and sports and exercise.³¹ Three components are required to be able to calculate a continuous measure of the physical activity: the description of the specific physical activity being performed to assign the correct metabolic equivalent (MET) for the given intensity; the duration the physical activity was performed; and the frequency of the physical activity. MET values were identified in the most recent Compendium of Physical Activities.³² Any activity equal to or greater than 1.5 METs was considered physical activity and total time, across all four domains, was aggregated for a total daily physical activity time.

Sedentary Behavior

The second independent variable of interest in this study, sedentary behavior, involves activity in a sitting, reclining, or lying position equating to fewer than 1.5 METs.³³ Sedentary behavior was derived from self-reported physical activity and sleep data that was calculated using a three-component time model framework of a normal 24-hours day.^20,34 The JHS Sleep History Form inquires about the amount and quality of sleep. The question: “How much sleep do you usually get at night (or your main sleep period) on weekdays or workdays?” measures how much sleep, measured in hours, an individual achieves on a typical night. Hours of sleep were converted into minutes of sleep. Sedentary behavior, physical activity, and sleep are mutually exclusive movement behaviors, and knowing the total time of two of the three movement behaviors allows for the calculation of the third by summating the first two and subtracting from total minutes in a day (1440 minutes). Minutes per day of sedentary behavior was calculated as remaining time after subtracting physical activity and sleep from 1440 minutes.

Covariates

Smoking status was assessed with the question “Do you now smoke cigarettes?” and was answered dichotomously (Yes/No). Education level was assessed with the following question: “What is the highest degree or years of school you have completed, including trade or vocational school or college?” Response options were as follows: Some vocational or trade school, but no certificates; Vocational or trade certificate; Some college, but no degree; Associate degree, (junior college) (AA or AS); Bachelor’s degree (BA, BS, AB); Graduate or professional schools (MA, MS, Master’s Doctorate, MD, JD, DDS, DVM, etc.). Education was coded as 0 = Less than High School, 1 = High School Graduate/GED, and 2 = Attended Vocational School, Trade School, or College.^35,36

Statistical Analysis

Participants were excluded from the analysis if their record was missing cardiometabolic data (systolic blood pressure = 5, fasting blood glucose = 348, high-density lipoprotein cholesterol = 1, and waist circumference = 60), typical sleep duration (29), or complete physical activity data (6) for a final sample size of 3370 (Figure 1). Descriptive characteristics were generated for the total sample and by sex. Continuous variables are reported with means and standard deviations while discrete variables are reported with frequencies. Associations between sedentary behavior, physical activity, and the presence of MetS were determined using logistic regression. Box-Tidwell tests were conducted to test for the linearity of sedentary behavior and physical activity. The assumption for linearity was met for sedentary behavior; however, the linearity assumption was violated for physical activity. Logistic regressions were conducted with both the non-transformed and transformed physical activity variable. Neither the direction nor the significance of the association of physical activity on MetS classification changed; therefore, we used the untransformed physical activity variable. Associations between sedentary behavior, physical activity, and the number of individual MetS components were determined using negative binomial regression. Associations between sedentary behavior, physical activity, and MetS-Z were determined via ordinary least squares regression. The first models were unadjusted and included only sedentary behavior or only physical activity. Then both sedentary behavior and physical activity were analyzed in the same model while controlling for age, smoking status, and educational level. All analyses were conducted with significance set a priori at p < .05 using StataSE (17.0, Stata Corp., College Station, Texas). The Bonferonni method was used to correct for multiple tests with significance at p ≤ .006.

Results

Descriptive Characteristics

The prevalence of MetS is summarized in Table 1. Of the 3370 participants, the prevalence of MetS was 60.50% (n = 2039). Males and females had a similar prevalence of MetS (61.33% and 60.04%, respectively). Overall, 56.23% of participants were categorized as obese with both mean waist circumference (102.96 ± 15.79 cm; 70.15%) and fasting blood glucose (105.22 ± 32.77 mg·dL⁻¹; 46.88%) were above the threshold for positive MetS classification. The three most prevalent number of MetS components among all participants was three (22.55%), two (21.07%), and four (19.76%), respectively. Among men, the three most prevalent number of MetS components was three (24.96%), four (22.74%), and two (17.98%). Among women, the three most prevalent number of MetS components were two (22.82%), three (21.19%), and five (20.72%).

Table 1.

Descriptive Characteristics—Total Sample and by Sex.

	Total Sample (n = 3370)	Female (n = 2152)	Male (n = 1218)
Age (years)	61.7 ± 11.9	62.2 ± 12.0	60.8 ± 11.8
Height (cm)	168.53 ± 9.32	163.54 ± 6.40	177.33 ± 6.81
Weight (kg)	91.10 ± 21.62	88.62 ± 21.16	95.49 ± 21.74
Body Mass Index (kg⋅m⁻²)	32.08 ± 7.24	33.10 ± 7.54	30.29 ± 6.29
Waist Circumference (cm)	102.96 ± 15.79	102.49 ± 15.94	103.80 ± 15.49
Systolic Blood Pressure (mmHg)	127.68 ± 18.63	127.11 ± 19.46	128.67 ± 17.02
Diastolic Blood Pressure (mmHg)	75.30 ± 10.72	74.62 ± 10.59	76.50 ± 10.85
Fasting Blood Glucose (mg⋅dL⁻¹)	105.22 ± 32.77	103.85 ± 31.88	107.64 ± 34.17
High-Density Lipoprotein (mg⋅dL⁻¹)	58.01 ± 16.10	61.14 ± 15.85	52.47 ± 15.02
Triglycerides (mg⋅dL⁻¹)	97.99 ± 55.80	93.43 ± 47.15	106.04 ± 67.73
Total cholesterol (mg⋅dL⁻¹)	197.00 ± 40.10	199.79 ± 39.73	192.07 ± 40.31
Metabolic syndrome positive	2039 (60.50%)	1,2932 (60.04%)	747 (61.33%)
Number of metabolic syndrome criteria
0	127 (3.77%)	69 (3.21%)	58 (4.76%)
1	494 (14.66%)	300 (13.94%)	194 (15.93%)
2	710 (21.07%)	491 (22.82%)	219 (17.98%)
3	760 (22.55%)	456 (21.19%)	304 (24.96%)
4	667 (19.76%)	390 (18.12%)	277 (22.74%)
5	612 (18.16%)	446 (20.72%)	166 (13.63%)
Metabolic Syndrome Severity z-score	.31 ± 1.07	.34 ± 1.08	.26 ± 1.05
Daily Sedentary Time (minutes⋅day⁻¹)	1018.76 ± 92.82	1018.93 ± 95.26	1018.45 ± 88.38
Daily Physical Activity Time (minutes⋅day⁻¹)	37.87 ± 29.67	35.06 ± 27.45	42.83 ± 32.65
Daily Physical Activity (MET⋅minutes⋅day⁻¹)	220.16 ± 181.96	204.29 ± 172.57	248.21 ± 194.38
Daily Sleep Time (minutes⋅day⁻¹)	383.38 ± 87.03	386.01 ± 90.86	378.72 ± 79.63

Continuous variables reported as mean ± standard deviation.

Discrete variables reported as frequencies.

MET = metabolic equivalent.

Sedentary Behavior are ≤1.5 METs.

Physical Activity are >1.5 METs.

Mean MetS-Z was moderate (.31 ± 1.07) with men lower than women. We calculated participants spent nearly 17 hours in sedentary behavior each day (1018.76 ± 92.82 minutes⋅day⁻¹). Participants reported engaging in slightly less than 40 minutes of total physical activity per day (37.87 ± 29.67 minutes⋅day⁻¹), translating to 220.16 ± 181.96 MET⋅minutes⋅day⁻¹.

Associations of Sedentary Behavior and Physical Activity with Metabolic Syndrome

The associations of sedentary behavior and physical activity with MetS are presented in Table 2. Physical activity was found to be significantly related to dichotomous MetS in the unadjusted (odds ratio .9991; 95% CI:0.9987, .9995; p < .001), and the adjusted model (.9995; 95% CI: .9991, .9999; p = .024). Sedentary behavior was not found to be significant in either model. We also noted age was significantly associated with dichotomous MetS in the adjusted model (1.0420; 95% CI: 1.0351, 1.0490; p < .001).

Table 2.

Association of Sedentary Behavior and Physical Activity with Metabolic Syndrome Presence.

	Model 1^a Odds Ratio (95% CI)	Model 1^b Odds Ratio (95% CI)	Model 2 Odds Ratio(95% CI)
Daily Sedentary Time (minutes⋅day⁻¹)	1.0002 (.9995, 1.0009)		1.0001 (.9992, 1.0009)
Daily Physical Activity (MET⋅minutes⋅day⁻¹)		.9991 (.9987, .9995)^c	.9995 (.9991, .9999)^c
Age (years)			1.0420 (1.0351, 1.0490)^c
Current Smoker
No			Reference
Yes			.9181 (.7332, 1.1495)
Education Attainment
Less Than High School			Reference
High School Graduate/GED			1.2089 (.9263, 1.5778)
Attended Vocational School, Trade School, or College			1.0864 (.8604, 1.3718)

^aOnly sedentary behavior.

^bOnly physical activity.

^cadjusted p≤ .006.

The associations of sedentary behavior and physical activity with the number of MetS criteria is presented in Table 3. In the unadjusted model, physical activity was associated with the number of MetS criteria (.9997; 95% CI: .9997, .9999; p < .001). Physical activity and age were significant in the adjusted model (.9998; 95% CI: .9997, 1.0000; p = .006 and 1.0111; 95% CI: 1.0093, 1.0129; p < .001, respectively). There was no association observed between sedentary behavior the number of MetS components in either unadjusted or adjusted models.

Table 3.

Association of Sedentary Behavior and Physical Activity with Number of Metabolic Syndrome Criteria.

	Model 1^a IRR (95% CI)	Model 1^b IRR (95% CI)	Model 2 IRR (95% CI)
Daily Sedentary Time (minutes⋅day⁻¹)	1.0001 (.9999, 1.0003)		1.0000 (.9998, 1.0002)
Daily Physical Activity (MET⋅minutes⋅day⁻¹)		.9997 (.9996, .9998)^c	.9998 (.9997, 1.0000)^c
Age (years)			1.0111 (1.0093, 1.0129)^c
Current Smoker
No			Reference
Yes			.9805 (.9198, 1.0452)
Education Attainment
Less Than High School			Reference
High School Graduate/GED			1.0477 (.9787, 1.1215)
Attended Vocational School, Trade School, or College			.9936 (.9354, 1.0554)

^aOnly sedentary behavior.

^bOnly physical activity.

^cadjusted p≤.006.

IRR = incidence rate ratio.

The associations of sedentary behavior and physical activity on MetS-Z are found in Table 4. Sedentary behavior was not associated with MetS-Z in either model. Again, physical activity was associated with MetS-Z in both the unadjusted (−.0008; 95% CI: −.0010, −.0006; p < .001) and adjusted model (−.0008; 95% CI: −.0010, −.0006; p < .001). While age was associated with MetS as a dichotomous variable and the number of MetS components, we did not find a significant association of age with the MetS-Z score.

Table 4.

Association of Sedentary Behavior and Physical Activity with Metabolic Syndrome Severity z-Score.

	Model 1^a Coefficient (95% CI)	Model 1^b Coefficient (95% CI)	Model 2 Coefficient (95% CI)
Daily Sedentary Time (minutes⋅day⁻¹)	.0005 (.0001, .0009)		−.0001 (−.0005, .0004)
Daily Physical Activity (MET⋅minutes⋅day⁻¹)		−.0008 (−.0010, −.0006)^c	−.0008 (−.0010, −.0006)^c
Age (years)			−.0018 (−.0051, .0014)
Current Smoker
No			Reference
Yes			−.1256 (−.2402, −.0111)
Education Attainment
Less Than High School			Reference
High School Graduate/GED			−.0054 (−.1346, .1239)
Attended Vocational School, Trade School, or College			−.0998 (−.2135, .0139)

^aOnly sedentary behavior.

^bOnly physical activity.

^cadjusted p ≤ .006.

Discussion

In this study examining a community-based cohort of African American adults participating in the JHS, physical activity had a significant association with decreasing risk of MetS while sedentary behavior had no association with MetS risk across all methods of assessing MetS. Sedentary behavior and physical activity are commonly noted to be independently associated with MetS prevalence. The present findings that physical activity was associated with decreasing risk of MetS are corroborated by previous researchers.^17,18,21 Different methods of examining MetS can illustrate the varying associations of sedentary behavior and physical activity with cardiometabolic risk.

We used the recently developed MetS-Z, which was established to address the disparity between the prevalence of MetS and subsequent cardiometabolic disease among African American individuals.¹ In another cross-sectional study of the JHS, Cardell and colleagues²⁷ observed divergent results when investigating the association between perceived psychosocial stressors and MetS, both dichotomously and MetS-Z. Major life events and global stress were associated only with MetS-Z and not the dichotomous classification, possibly due to the lack of consideration of the influences of sex, race, and ethnicity on cardiometabolic disease risk.²⁷ However, a major benefit of the MetS-Z is the ability to track change in risk of MetS longitudinally.^26,28,37,38 DeBoer and colleagues²⁸ observed a positive association between MetS-Z and prevalence of estimated glomerular filtration rate (eGFR) in the lowest quartile (<85.1 mL·min⁻¹·1.73 m⁻²) at baseline among all JHS participants. When examining 8-year follow-up changes in eGFR, only female participants saw an association between MetS-Z and eGFR, as greater baseline MetS-Z was positively associated with eGFR and chronic kidney disease. Examining predictors of MetS-Z, Cardell and colleagues²⁶ observed US-Society subjective social status, measured with MacArthur Scales, to be associated with MetS-Z at 8-years follow-up, but only among female participants. While the present study is cross-sectional and cannot provide support for the longitudinal use of MetS-Z, there is existing evidence to support the longitudinal utility of MetS-Z at examining cardiometabolic disease risk. This evidence base is especially rooted within the JHS where the MetS-Z has been repeatedly used.^24-30

When examining movement behaviors, sedentary behavior was not significantly associated with MetS in any models. Physical activity maintained a significant independent association with two methodologies (i.e., number of components and MetS-Z) for measuring MetS in the adjusted models. The present observation of sedentary behavior not being associated with cardiometabolic health when also examining physical activity is met with mixed associations of extant findings, in part may be due to the difficulties of physical activity assessments have with capturing and quantifying workplace movement behaviors.^31,39 In line with the current results, Jones and colleagues³⁶ examined the association of self-reported occupational sitting with C-reactive protein (CRP) among participants of the JHS. Greater occupational sitting was associated with higher levels of CRP, but only among female participants and in the unadjusted model. However, in the fully adjusted model, which controlled for physical activity, occupational sitting was not associated with CRP levels for either sex. In contrast to the present findings, Greer and colleagues¹⁸ longitudinally examined the independent associations of sedentary behavior, physical activity, and cardiorespiratory fitness on incident MetS among primarily Non-Hispanic White men participating in the Aerobics Center Longitudinal Study (ACLS). Men who had engaged in the middle (12-19 hours·week⁻¹) and highest amounts (>19 hours·week⁻¹) of sedentary behavior had an increased risk of incidence MetS compared with the lowest group (≤12 hours·week⁻¹; 65% and 76%, respectively). The present results may be explained by the total volume of sedentary behavior and physical activity. Ekelund and colleagues⁴⁰ showed individuals with the greatest amounts of daily sedentary behavior to have worse hazard ratios for all-cause mortality. Both the JHS and the ACLS collected movement behavior data using questionnaires, the use of criterion-measured movement behavior data may assist in detangling the mixed associations observed with self-reported measures.⁴¹

In the present study, the ORs for physical activity are relatively small but must be interpreted in the context of the unit of analysis, MET-minutes per day. One MET is roughly the energy required to sit quietly at rest.³² Therefore, the risk of MetS can be reduced by engaging in increased duration of or increasing the MET requirement of the physical activity. Given the age of the participants in the present study, promoting activities of daily living (ADL) (e.g., cooking, grooming, etc.) can help to mitigate the risk of MetS. For example, cooking has a MET requirement of 3.5 METs,³² 2.5 METs above resting, and when engaged in for 15 minutes for each meal reduces MetS-Z by .09. Activities of daily living have been associated with increased life expectancy⁴² and physical activity.⁴³

We examined the association of two movement behaviors, sedentary behavior and physical activity, on the risk of MetS. However, the third movement behavior, sleep, has an important role in metabolic homeostasis and should be accounted for when examining movement behaviors.³⁴ We propose that future research examining the association of movement behaviors with MetS should utilize compositional data analysis. Compositional data analysis is a well-defined statistical field which has recently been identified as a tool when examining movement behaviors.^34,44,45 Using compositional data analysis, researchers can examine the effect of each movement behavior on MetS through compositional isotemporal substitution modeling. Isotemporal substitution modeling provides researchers the ability to model the asymmetrical change in an outcome based on a hypothetical substitution of time between two movement behaviors.⁴⁶ For example, researchers could examine how increasing physical activity by 10 minutes while reducing sedentary behavior by 10 minutes while maintaining time in sleep impacts MetS. Compositional isotemporal substitution allows researchers to better inform future interventions by modeling possible real-world exchanges of time between movement behaviors without actually increasing time in movement behaviors detrimental to health.

Strengths of the present study include the use of a contemporary measure of cardiometabolic risk designed to address the paradox in MetS prevalence and subsequent cardiometabolic disease among African American individuals (i.e., MetS-Z).¹ This study also is novel in the assessment of movement behaviors. Calculating continuous physical activity from all four domains of the JPAC allowed for the estimation of sedentary behavior. Deriving sedentary behavior provides the ability to better examine the health of JHS participants in future studies. There are several limitations to the present study. Physical activity data were self-reported and may be incorrectly estimated by the participant; however, this is a universally acknowledged limitation of survey-based physical activity research.⁴¹ While the majority of studies using the JPAC have used responses as ordinal data,^47-50 some studies have calculated moderate-to-vigorous physical activity minutes per week from the sports and exercise domain,^47,48 or for some, but not all, domains.⁵¹ However, the JPAC provides the ability to calculate continuous measures of physical activity (MET⋅minutes⋅day⁻¹) for all four domains. Using the most conservative indication of time and intensity from the JPAC we attempted to not overestimate physical activity, which is a strength of the present study. Causality cannot be established, as only one exam period (Exam 3) of the Jackson Heart Study was used for the present cross-sectional study. The Jackson Heart Study is a single-site African American cohort in the southern United States and the present results may not be generalized to other regions or populations. MetS-Z is not the sole way of determining the risk of future cardiometabolic disease.⁴ Not all factors (e.g., diet) with established associations with metabolic syndrome were available at Exam 3 and therefore not controlled, which may influence the results. Future research using criterion measures of movement behaviors (i.e., accelerometry) is needed to increase comprehension of the impact of movement behaviors on MetS.

Conclusion

In the present study, we observed sedentary behavior to be associated only with MetS-Z (unadjusted), while physical activity was associated with each of the three ways of examining MetS (i.e., dichotomous, number of criteria, MetS-Z), even after adjustments. There is still little research examining the role of sedentary behavior and physical activity regarding cardiometabolic risk among African American adults. Examining indicators of cardiometabolic risk and the factors influencing such indices can begin to elucidate how modifiable structural inequities in society may lead to racial/ethnic disparities in health and where targeted strategies may benefit the most individuals. Efforts should be made to maintain or increase physical activity at the expense of sedentary behavior to promote better cardiometabolic health.

Footnotes

Declaration of Conflicting Interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The Jackson Heart Study (JHS) is supported and conducted in collaboration with Jackson State University (HHSN268201800013I), Tougaloo College (HHSN268201800014I), the Mississippi State Department of Health (HHSN268201800015I), and the University of Mississippi Medical Center (HHSN268201800010I, HHSN268201800011I, and HHSN268201800012I) contracts from the National Heart, Lung, and Blood Institute (NHLBI) and the National Institute on Minority Health and Health Disparities (NIMHD). The authors also wish to thank the staffs and participants of the JHS. RB is supported by NIH grant T32HL069771. KN is supported by NIH research grants UL1TR001881 and P30AG021684. RT is supported by NIH grant U54MD000214.

ORCID iD

Robert Booker

References

Gurka

Lilly

Oliver

DeBoer

. An examination of sex and racial/ethnic differences in the metabolic syndrome among adults: A confirmatory factor analysis and a resulting continuous severity score. Metabolism. 2014;63(2):218-225. doi:10.1016/j.metabol.2013.10.006

Alberti

Eckel

Grundy

, et al. Harmonizing the metabolic syndrome: A joint interim statement of the International diabetes federation task force on epidemiology and prevention; National heart, lung, and blood institute; American heart association; World heart federation; International atherosclerosis society; and International association for the study of obesity. Circulation. 2009;120(16):1640-1645. doi:10.1161/CIRCULATIONAHA.109.192644

Morrell

Byrd-Bredbenner

Quick

Olfert

Dent

Carey

. Metabolic syndrome: Comparison of prevalence in young adults at 3 land-grant universities. J Am Coll Health. 2014;62(1):1-9. doi:10.1080/07448481.2013.841703

Gurka

Filipp

Pearson

DeBoer

. Assessing baseline and temporal changes in cardiometabolic risk using metabolic syndrome severity and common Risk Scores. J Am Heart Assoc. 2018;7(16):e009754. doi:10.1161/JAHA.118.009754

Arnlov

Ingelsson

Sundstrom

Lind

. Impact of body mass index and the metabolic syndrome on the risk of cardiovascular disease and death in middle-aged men. Circulation . 2010;121(2):230-236. doi:10.1161/CIRCULATIONAHA.109.887521

Edwardson

Gorely

Davies

, et al. Association of sedentary behaviour with metabolic syndrome: A meta-analysis. PLoS One. 2012;7(4):e34916. doi:10.1371/journal.pone.0034916

Moore

Chaudhary

Akinyemiju

. 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. doi:10.5888/pcd14.160287

Menke

Casagrande

Geiss

Cowie

. Prevalence of and trends in diabetes among adults in the United States, 1988-2012. JAMA. 2015;314(10):1021-1029. doi:10.1001/jama.2015.10029

Benjamin

Virani

Callaway

, et al. Heart disease and stroke statistics-2018 update: A report from the American Heart Association. Circulation . 2018;137(12):e67-e492. doi:10.1161/CIR.0000000000000558

10.

Pearson-Stuttard

Guzman-Castillo

Penalvo

, et al. Modeling future cardiovascular disease mortality in the United States: National trends and racial and ethnic disparities. Circulation . 2016;133(10):967-978. doi:10.1161/CIRCULATIONAHA.115.019904

11.

Gaillard

. The metabolic syndrome and its components in African-American women: Emerging trends and implications. Front Endocrinol. 2018;8:383. doi:10.3389/fendo.2017.00383

12.

Lee

Corneille

Hall

Yancu

Myers

. The stressors of being young and Black: Cardiovascular health and Black young adults. Psychol Health. 2016;31(5):578-591. doi:10.1080/08870446.2015.1127373

13.

Brewer

Cooper

. Race, discrimination, and cardiovascular disease. Virtual Mentor . 2014;16(6):455-460.

14.

Ray

. An intersectional analysis to explaining a lack of physical activity among middle class black women. Sociology Compass. 2014;8(6):780-791.

15.

Adult physical inactivity prevalence maps by race/ethnicity. 2022. https://www.cdc.gov/physicalactivity/data/inactivity-prevalence-maps/index.html

16.

Newsom

Denning

Elman

, et al. Physical activity as a mediator between race/ethnicity and changes in multimorbidity. J Gerontol: Series B. 2021.

17.

Ekblom

Ekblom-Bak

Rosengren

Hallsten

Bergstrom

Borjesson

. Cardiorespiratory fitness, sedentary behaviour and physical activity are independently associated with the metabolic syndrome, results from the SCAPIS pilot study. PLoS One. 2015;10(6):e0131586. doi:10.1371/journal.pone.0131586

18.

Greer

Sui

Maslow

Greer

Blair

. The effects of sedentary behavior on metabolic syndrome independent of physical activity and cardiorespiratory fitness. J Phys Act Health . 2015;12(1):68-73. doi:10.1123/jpah.2013-0186

19.

Powell

Herring

Dowd

Donnelly

Carson

. The cross-sectional associations between objectively measured sedentary time and cardiometabolic health markers in adults - a systematic review with meta-analysis component. Obes Rev . 2018;19(3):381-395. doi:10.1111/obr.12642

20.

Dumuid

Stanford

Martin-Fernandez

, et al. Compositional data analysis for physical activity, sedentary time and sleep research. Stat Methods Med Res . 2018;27(12):3726-3738. doi:10.1177/0962280217710835

21.

Knaeps

Lefevre

Wijtzes

Charlier

Mertens

Bourgois

. Independent associations between sedentary time, moderate-to-vigorous physical activity, cardiorespiratory fitness and cardio-metabolic health: A cross-sectional study. PLoS One. 2016;11(7):e0160166. doi:10.1371/journal.pone.0160166

22.

Taylor

Jr. Wilson

Jones

, et al. Toward resolution of cardiovascular health disparities in African Americans: Design and methods of the Jackson heart study. Ethn Dis . 2005;15(4 suppl 6):S6-S417.

23.

Taylor

Jr. Wilson

Jones

, et al. The Jackson heart study: An overview. Ethn Dis . 2005;15(4 suppl 6):S6-S13.

24.

Gurka

Golden

Musani

, et al. Independent associations between a metabolic syndrome severity score and future diabetes by sex and race: The atherosclerosis risk in communities study and Jackson heart study. Diabetologia. 2017;60(7):1261-1270. doi:10.1007/s00125-017-4267-6

25.

Cardel

Chi

Min

, et al. Experiences of discrimination are associated with worse metabolic syndrome severity among African Americans in the Jackson heart study. Ann Behav Med . 2021;55(3):266-279. doi:10.1093/abm/kaaa050

26.

Cardel

Guo

Sims

, et al. Objective and subjective socioeconomic status associated with metabolic syndrome severity among African American adults in Jackson Heart Study. Psychoneuroendocrinology. 2020;117:104686. doi:10.1016/j.psyneuen.2020.104686

27.

Cardel

Min

Sims

, et al. Association of psychosocial stressors with metabolic syndrome severity among African Americans in the Jackson Heart Study. Psychoneuroendocrinology. 2018;90:141-147. doi:10.1016/j.psyneuen.2018.02.014

28.

DeBoer

Filipp

Musani

Sims

Okusa

Gurka

. Metabolic syndrome severity and risk of CKD and worsened GFR: The Jackson heart study. Kidney Blood Press Res. 2018;43(2):555-567. doi:10.1159/000488829

29.

Guo

Musani

Sims

Pearson

DeBoer

Gurka

. Assessing the added predictive ability of a metabolic syndrome severity score in predicting incident cardiovascular disease and type 2 diabetes: The atherosclerosis risk in communities study and Jackson heart study. Diabetol Metab Syndr. 2018;10:42. doi:10.1186/s13098-018-0344-3

30.

Gurka

Vishnu

Okereke

Musani

Sims

DeBoer

. Depressive symptoms are associated with worsened severity of the metabolic syndrome in African American women independent of lifestyle factors: A consideration of mechanistic links from the Jackson heart study. Psychoneuroendocrinology . 2016;68:82-90. doi:10.1016/j.psyneuen.2016.02.030

31.

Smitherman

Dubbert

Grothe

, et al. Validation of the Jackson heart study physical activity survey in African Americans. J Phys Act Health. 2009;6(suppl 1):S124-S132. doi:10.1123/jpah.6.s1.s124

32.

Ainsworth

Haskell

Herrmann

, et al. Compendium of physical activities: A second update of codes and MET values. Med Sci Sports Exerc. 2011;43(8):1575-1581. doi:10.1249/MSS.0b013e31821ece12

33.

Tremblay

Aubert

Barnes

, et al. Sedentary behavior research network (SBRN) - terminology consensus project process and outcome. Int J Behav Nutr Phys Act. 2017;14(1):75. doi:10.1186/s12966-017-0525-8

34.

Chastin

Palarea-Albaladejo

Dontje

Skelton

. Combined effects of time spent in physical activity, sedentary behaviors and sleep on obesity and cardio-metabolic health markers: A novel compositional data analysis approach. PLoS One. 2015;10(10):e0139984. doi:10.1371/journal.pone.0139984

35.

Glover

Cain-Shields

Wyatt

Gebreab

Diez-Roux

Sims

. Life course socioeconomic status and hypertension in African American adults: The Jackson heart study. Am J Hypertens . 2020;33(1):84-91. doi:10.1093/ajh/hpz133

36.

Jones

Norris

McCoy

, et al. Association between occupational sitting with high sensitivity C-reactive protein: The Jackson heart study. Am J Lifestyle Med. 2021.

37.

DeBoer

Gurka

Woo

Morrison

. Severity of metabolic syndrome as a predictor of cardiovascular disease between childhood and adulthood: The Princeton lipid research cohort study. J Am Coll Cardiol. 2015;66(6):755-757. doi:10.1016/j.jacc.2015.05.061

38.

DeBoer

Gurka

Woo

Morrison

. Severity of the metabolic syndrome as a predictor of type 2 diabetes between childhood and adulthood: The Princeton lipid research cohort study. Diabetologia . 2015;58(12):2745-2752. doi:10.1007/s00125-015-3759-5

39.

Pereira

Folsom

McGovern

, et al. Physical activity and incident hypertension in black and white adults: The atherosclerosis risk in communities study. Prev Med . 1999;28(3):304-312. doi:10.1006/pmed.1998.0431

40.

Ekelund

Steene-Johannessen

Brown

, et al. Does physical activity attenuate, or even eliminate, the detrimental association of sitting time with mortality? A harmonised meta-analysis of data from more than 1 million men and women. Lancet. 2016;388(10051):1302-1310. doi:10.1016/S0140-6736(16)30370-1

41.

Sallis

Saelens

. Assessment of physical activity by self-report: status, limitations, and future directions. Res Q Exerc Sport . 2000;71(2 suppl l):S1-S14.

42.

Stineman

Xie

Pan

, et al. All-cause 1-5-and 10-year mortality in elderly people according to activities of daily living stage. J Am Geriatr Soc . 2012;60(3):485-492. doi:10.1111/j.1532-5415.2011.03867.x

43.

Chou

Hwang

. Effect of exercise on physical function, daily living activities, and quality of life in the frail older adults: A meta-analysis. Arch Phys Med Rehabil . 2012;93(2):237-244. doi:10.1016/j.apmr.2011.08.042

44.

Carson

Tremblay

Chaput

Chastin

. Associations between sleep duration, sedentary time, physical activity, and health indicators among Canadian children and youth using compositional analyses. Appl Physiol Nutr Metab . 2016;41(6 suppl 3):S294-S302. doi:10.1139/apnm-2016-0026

45.

Chastin

McGregor

Palarea-Albaladejo

, et al. Joint association between accelerometry-measured daily combination of time spent in physical activity, sedentary behaviour and sleep and all-cause mortality: A pooled analysis of six prospective cohorts using compositional analysis. Br J Sports Med. 2021;55:1277-1285. doi:10.1136/bjsports-2020-102345

46.

Biddle

GJH

Henson

Biddle

SJH

, et al. Modelling the reallocation of time spent sitting into physical activity: Isotemporal substitution vs. compositional isotemporal substitution. Int J Environ Res Public Health. 2021(12):18. doi:10.3390/ijerph18126210

47.

Diaz

Booth

3rd Seals

, et al. Sedentary behavior and subclinical atherosclerosis in African Americans: Cross-sectional analysis of the Jackson heart study. Int J Behav Nutr Phys Act. 2016;13:31. doi:10.1186/s12966-016-0349-y

48.

Diaz

Booth

3rd Seals

, et al. Physical activity and incident hypertension in African Americans: The Jackson heart study. Hypertension. 2017;69(3):421-427. doi:10.1161/HYPERTENSIONAHA.116.08398

49.

Tamura

Langerman

Orstad

, et al. Physical activity-mediated associations between perceived neighborhood social environment and depressive symptoms among Jackson heart study participants. Int J Behav Nutr Phys Act. 2020;17(1):91. doi:10.1186/s12966-020-00991-y

50.

Dubbert

Robinson

Sung

, et al. Physical activity and obesity in African Americans: The Jackson heart study. Ethn Dis . 2010;20(4):383-389.

51.

Kamimura

Cain-Shields

Clark

, et al. Physical activity, inflammation, coronary artery calcification, and incident coronary heart disease in African Americans: Insights from the Jackson heart study. Mayo Clin Proc . 2021;96(4):901-911. doi:10.1016/j.mayocp.2020.09.042