Abstract
Background
Attaining guideline-recommended levels of physical activity is associated with substantially lower risk of cardiometabolic diseases.
Objectives
Although physical activity commonly follows a weekend warrior pattern, in which most moderate-to-vigorous physical activity is concentrated in 1 to 2 days rather than spread more evenly across the week (regular), the effects of activity pattern on imaging-based biomarkers of cardiometabolic health are unknown.
Methods
We analyzed 17,146 UK Biobank participants who wore accelerometers for 1 week, and later underwent cardiac magnetic resonance imaging. Activity was categorized as inactive, regular, or “weekend warrior”. Associations between activity pattern and magnetic resonance imaging–derived visceral adipose tissue (VAT) and epicardial and pericardial adipose tissue (EPAT) were assessed using multiple linear regression adjusted for confounding factors.
Results
Compared to inactive, VAT was progressively lower with weekend warrior (−0.71 L, 95% CI −0.78 to −0.64, P < 0.001) followed by regular activity (−0.96 L, 95% CI −1.04 to −0.88, P < 0.001). Observations were similar for EPAT (weekend warrior activity −2.84 cm2, 95% CI −3.20 to −2.49, P < 0.001; regular activity −3.62 cm2, 95% CI −4.03 to −3.20, P < 0.001). When compared directly, weekend warriors had modestly higher adipose tissue than regular activity (VAT difference 0.25 L, 95% CI 0.17-0.32, P < 0.001; EPAT 0.78 cm2, 95% CI 0.40-1.15, P < 0.001). No differences were observed after adjustment for total moderate-to-vigorous physical activity minutes (VAT 0.07 L, 95% CI −0.01 to 0.14, P = 0.09; EPAT 0.04 cm2, 95% CI −0.35 to 0.43, P = 0.84).
Conclusions
Guideline-adherent physical activity is associated with favorable quantitative measures of cardiometabolic health, with no differences based on activity pattern for a given activity volume.
Key words: accelerometry, cardiovascular disease, physical activity, prevention, visceral adipose tissue
Central Illustration
Physical activity is generally regarded as favorable to health and is consistently associated with lower risks of death and incident disease.1 Recent data suggest that concentrating most moderate-to-vigorous physical activity (MVPA) within 1 to 2 days of the week (“weekend warrior”) is common and may confer similar benefits as more regular activity for selected conditions,2 but effects on cardiometabolic health remain poorly defined.
Prior work focused on cardiometabolic disease has typically relied on diagnosis codes and/or self-reported survey data to ascertain outcomes, which can be imprecise, particularly for cardiometabolic conditions such as obesity or diabetes mellitus.3,4 Although mortality is likely less subject to misclassification, reliably attributing death to cardiometabolic causes is subject to similar limitations.5,6 Other studies focusing on quantitative cardiometabolic health parameters (eg, body mass index [BMI] or lipid levels7,8) have been conducted in relatively small and highly selected samples.
In contrast, cross-sectional imaging at scale now provides an opportunity to quantify associations between physical activity patterns with a particularly powerful and sensitive indicator of cardiometabolic health: adipose tissue deposition.9,10
Here, among over 17,000 participants of the UK Biobank study, we assessed associations between physical activity pattern measured prospectively using wrist-based accelerometers and 2 previously established machine learning–derived markers of cardiometabolic health11,12 quantified using magnetic resonance imaging (MRI): visceral adipose tissue (VAT) and epicardial and pericardial adipose tissue (EPAT).
Methods
Participants provided informed consent. The UK Biobank was approved by the UK Biobank Research Ethics Committee (reference# 11/NW/0382). UK Biobank data were used under application 17488.
Cohort description
The UK Biobank is a prospective cohort of 502,629 participants enrolled 2006 to 2010.13 Briefly, 9.2 million individuals aged 40 to 69 years living within 25 miles of 22 assessment centers in the United Kingdom were invited, and 5.4% participated in the baseline assessment. Of these, 103,695 participants underwent 1 week of activity measurement using the Axivity AX3 wrist-based triaxial accelerometer several years after enrollment (range: 2013-2015) (Supplemental Figure 1).14 The sensor captured continuous acceleration at 100 Hz with dynamic range ±8g. As described previously, acceleration signals were calibrated to gravity.14 We excluded individuals whose wear-time was insufficient to support imputation (≥1 one-hour period of the 24-hour cycle where no wear time was ever observed over the course of the week), whose signals were insufficient for calibration or MVPA estimation, whose mean acceleration values were previously adjudicated as nonphysiologic,2,15 or who contributed less than a full week of activity data.
Activity patterns
MVPA was classified using a published machine learning–based method developed to classify a broad range of activities (eg, walking, jogging, stationary cycling, elliptical, and others) and validated in a UK-based sample.15 We defined active status at the guideline-based threshold (≥150 min/wk).16 Individuals were classified as inactive (below MVPA threshold), weekend warrior (at/above MVPA threshold and ≥50% of total MVPA over 1-2 days), and regular (at/above MVPA threshold but not weekend warrior). In sensitivity analyses, we used the sample median of MVPA in the UK Biobank as the threshold (240 min/wk). To account for the potential effects of differences based on sedentary behavior time, we fit additional models with further adjustment for the sedentary time over the accelerometer wear period.
Visceral and pericardial adipose tissue
This study focused on the subset of individuals in the accelerometer substudy who subsequently underwent protocolized MRI at the UK Biobank imaging visit.17 Cardiac MRI images were captured in a 20-minute study using a Siemens 1.5-T MAGNETOM Aera scanner (Siemens Healthineers). We used machine learning–derived measurements from previously published studies of VAT volume (L)11 and EPAT area (cm2).12
Statistical analysis
Baseline characteristics are presented as the mean ± SD or n (%), and VAT and EPAT as the median with 25th and 75th percentiles (Q1-Q3). We assessed associations between activity patterns (weekend warrior, regular, inactive); and 1) VAT and 2) EPAT, using multiple linear regression. Inspection of conventional diagnostics (eg, quantile-quantile plots, residual plots) supported the use of linear regression in the context of our analysis sample size. Regressions were performed separately with inactive and regular activity as references. Models were adjusted for age at acceleration monitoring, sex, ethnic background, tobacco use, Townsend Deprivation Index, alcohol intake, educational attainment, employment status, self-reported health, and diet quality. Detailed definitions of baseline clinical variables and covariates used for adjustment are provided in Supplemental Table 1. To assess whether differences in adipose tissue between weekend warrior and regular activity may be driven by baseline imbalances in total MVPA, we fit a second model including total MVPA (as a cubic spline) as an additional adjustment variable, with regular activity as the referent. The adjusted least squares mean difference (LSMD) in VAT and EPAT between groups with corresponding 95% CI served as the outcome of interest. A 2-sided P value <0.05 was considered statistically significant. All analyses were conducted using the R Statistical Software (version 4.2.1, R Core Team 2022).18
Results
Sample characteristics
The analysis sample comprised 17,146 individuals who underwent 1 week of activity measurement from June 8, 2013, to December 30, 2015, with protocolized MRI performed a mean 2.4 ± 1.8 years later (Figure 1). Due to temporal overlap between the accelerometer substudy and the imaging substudy, a minority of participants (n = 2,460, 14.3%) had accelerometer monitoring shortly after MRI (mean time from MRI to accelerometer: 0.61 ± 0.4 years) (Supplemental Figure 1).
Figure 1.
Study Overview
In 17,146 UK Biobank participants providing one week of accelerometer data and undergoing magnetic resonance imaging (MRI), we classified physical activity into 3 patterns: weekend warrior (WW), regular activity, and inactive, using the guideline-based threshold of ≥150 minutes of moderate-to-vigorous physical activity (MVPA)/week, and performed association testing with MRI-derived visceral adipose tissue (VAT) (N = 14,903) and epicardial and pericardial adipose tissue (EPAT) (N = 17,146). TDI = Townsend Deprivation Index.
The mean age of the sample at MRI was 64.2 ± 7.8 years and 7,959 (46.4%) were men. All individuals had available EPAT while 14,903 had available VAT. Stratified at the guideline-based threshold of ≥150 minutes MVPA/week, a total of 5,178 participants were in the inactive group (30.2%), 7,925 were in the weekend warrior group (≥50% of total MVPA within 1-2 days; 46.2%), and 4,043 in the regular group (no 1-2 day period with ≥50% of total MVPA; 23.6%). Average total weekly MVPA achieved was highest for the regular group (501 ± 268 minutes), followed by the weekend warrior group (363 ± 192 minutes) and the inactive group (mean 77 ± 44 minutes). Clinical characteristics were generally similar between the 2 active groups, with moderately higher BMI and comorbidity in the inactive group. Detailed baseline characteristics are displayed in Table 1. The distribution of total MVPA stratified by activity group is presented in Figure 2A.
Table 1.
Sample Characteristics
| Regular Activity (n = 4,043) | Weekend Warrior Activity (n = 7,925) | Inactive (n = 5,178) | Overall (N = 17,146) | |
|---|---|---|---|---|
| Age at accelerometer, y | 61.02 (7.59) | 61.85 (7.60) | 62.11 (7.67) | 61.73 (7.63) |
| Age at MRI, y | 63.65 (7.73) | 64.27 (7.78) | 64.37 (7.87) | 64.15 (7.80) |
| Male | 2,118 (52.4) | 4,018 (50.7) | 1,823 (35.2) | 7,959 (46.4) |
| Body mass index at enrollment, kg/m2a | 25.58 (3.76) | 25.99 (3.74) | 27.69 (4.82) | 26.41 (4.19) |
| Body mass index at MRI, kg/m2a | 25.29 (3.79) | 25.81 (3.85) | 27.75 (4.99) | 26.27 (4.33) |
| Race | ||||
| Asian | 40 (1.0) | 82 (1.0) | 71 (1.4) | 193 (1.1) |
| Black | 13 (0.3) | 33 (0.4) | 47 (0.9) | 93 (0.5) |
| Other | 47 (1.2) | 51 (0.6) | 43 (0.8) | 141 (0.8) |
| White | 3,943 (97.5) | 7,759 (97.9) | 5,017 (96.9) | 16,719 (97.5) |
| Tobacco use | ||||
| Current | 193 (4.8) | 330 (4.2) | 340 (6.6) | 863 (5.0) |
| Former | 1,377 (34.1) | 2,641 (33.3) | 1,792 (34.6) | 5,810 (33.9) |
| Never | 2,473 (61.2) | 4,954 (62.5) | 3,046 (58.8) | 10,473 (61.1) |
| Unemployed/retired | 1,319 (32.6) | 2,948 (37.2) | 1,965 (37.9) | 6,232 (36.3) |
| Alcohol intake, g/wk | 132.20 (131.08) | 130.91 (128.97) | 108.79 (125.14) | 124.53 (128.74) |
| Townsend Deprivation Index | −1.59 (2.85) | −2.05 (2.65) | −1.96 (2.68) | −1.92 (2.71) |
| Self-reported health | ||||
| Excellent | 1,211 (30.0) | 2,181 (27.5) | 935 (18.1) | 4,327 (25.2) |
| Fair | 416 (10.3) | 822 (10.4) | 937 (18.1) | 2,175 (12.7) |
| Good | 2,369 (58.6) | 4,825 (60.9) | 3,157 (61.0) | 10,351 (60.4) |
| Poor | 47 (1.2) | 97 (1.2) | 149 (2.9) | 293 (1.7) |
| Diet quality | ||||
| Good | 804 (19.9) | 1,443 (18.2) | 797 (15.4) | 3,044 (17.8) |
| Intermediate | 2,058 (50.9) | 3,912 (49.4) | 2,539 (49.0) | 8,509 (49.6) |
| Poor | 1,181 (29.2) | 2,570 (32.4) | 1,842 (35.6) | 5,593 (32.6) |
| Educational attainment, y | 16.39 (4.40) | 16.19 (4.42) | 15.03 (4.60) | 15.89 (4.51) |
| Diabetes mellitus | 98 (2.4) | 173 (2.2) | 276 (5.3) | 547 (3.2) |
| Heart failure | 21 (0.5) | 34 (0.4) | 35 (0.7) | 90 (0.5) |
| Atrial fibrillation | 89 (2.2) | 197 (2.5) | 160 (3.1) | 446 (2.6) |
| Myocardial infarction | 68 (1.7) | 159 (2.0) | 131 (2.5) | 358 (2.1) |
| Stroke | 43 (1.1) | 97 (1.2) | 79 (1.5) | 219 (1.3) |
| Total weekly MVPA, min | 501 (268) | 363 (192) | 77 (44) | 309 (246) |
Baseline characteristics are presented as the mean ± SD or count (N [percentage]).
MRI = magnetic resonance imaging; MVPA = moderate-to-vigorous physical activity.
Excludes missing body mass index in n = 22 (enrollment) and n = 587 (MRI).
Figure 2.
Distributions of Activity and Adipose Tissue
(A) The distribution of total MVPA minutes per activity category is shown in histograms relative to the total sample. (B) The distribution of visceral adipose tissue (upper panel) and epicardial and pericardial adipose tissue (lower panel) by activity group is depicted. MVPA = moderate-to-vigorous physical activity.
Salutary effects of physical activity on visceral fat
Median VAT was 3.1 L (Q1-Q3 1.9-4.9) and median EPAT was 22.9 cm2 (Q1-Q3 17.5-31.1). The distribution of the traits by activity group is displayed in Figure 2B. In multivariable-adjusted linear models, adipose tissue was lower with weekend warrior activity (LSMD for VAT −0.71 L or −0.32 SD, 95% CI −0.78 to −0.64, P < 0.001; EPAT −2.84 cm2 or −0.25 SD, 95% CI −3.20 to −2.49, P < 0.001) and regular activity (VAT −0.96 L or −0.43 SD, 95% CI −1.04 to −0.88, P < 0.001; EPAT −3.62 cm2 or −0.32 SD, 95% CI −4.03 to −3.20, P < 0.001) vs inactive (Table 2). In models additionally adjusted for sedentary behavior, associations remained robust although effect sizes were moderately smaller for weekend warrior activity (VAT −0.58 L, 95% CI −0.65 to −0.51, P < 0.001; EPAT −2.31 cm2, 95% CI −2.67 to −1.96, P < 0.001) and regular activity (VAT −0.78 L, 95% CI −0.86 to −0.70, P < 0.001; EPAT −2.89 cm2, 95% CI −3.31 to −2.47, P < 0.001).
Table 2.
Associations Between Activity Pattern and Adipose Tissue Mass
| Adjusted LSMD | 95% CI | P Value | |
|---|---|---|---|
| Visceral adipose tissue (L) | |||
| Adjusted for clinical factorsa | |||
| Regular activity vs inactive | −0.96 | −1.04 to −0.88 | <0.001 |
| Weekend warrior vs inactive | −0.71 | −0.78 to −0.64 | <0.001 |
| Weekend warrior vs regular activity | 0.25 | 0.17-0.32 | <0.001 |
| Adjusted for clinical factorsa and total MVPAb | |||
| Weekend warrior vs regular activity | 0.07 | −0.01 to 0.14 | 0.09 |
| Pericardial adipose tissue (cm2) | |||
| Adjusted for clinical factorsa | |||
| Regular activity vs inactive | −3.62 | −4.03 to −3.20 | <0.001 |
| Weekend warrior vs inactive | −2.84 | −3.20 to −2.49 | <0.001 |
| Weekend warrior vs regular activity | 0.78 | 0.40-1.15 | <0.001 |
| Adjusted for clinical factorsa and total MVPAb | |||
| Weekend warrior vs regular activity | 0.04 | −0.35 to 0.43 | 0.84 |
LMSD = least squares mean difference; other abbreviation as in Table 1.
Clinical factors include age at acceleration monitoring, sex, ethnic background, tobacco use, Townsend Deprivation Index, alcohol intake, educational attainment, employment status, self-reported health, and diet quality.
MVPA included as a natural cubic spline term.
When compared directly, weekend warriors had modestly higher adipose tissue than regular activity (LSMD for VAT 0.25 L, 95% CI 0.17-0.32, P < 0.001; EPAT 0.78 cm2, 95% CI 0.40-1.15, P < 0.001), but differences were no longer observed after adjustment for total MVPA (VAT 0.07 L, 95% CI −0.01 to 0.14, P = 0.09; EPAT 0.04 cm2, 95% CI −0.35 to 0.43, P = 0.84), and both total MVPA and sedentary time (VAT 0.07 L, 95% CI −0.01 to 0.14, P = 0.08; EPAT 0.04 cm2, 95% CI −0.35 to 0.42, P = 0.86). Results were similar at the median threshold of 240 min/wk.
Discussion
Here, we leverage a unique resource of over 17,000 UK Biobank participants providing prospectively acquired wrist-based accelerometer and MRI data to characterize associations between activity pattern and 2 quantitative biomarkers of cardiometabolic health: VAT and EPAT. We observed strong associations between both weekend warrior and regular activity with lower adipose tissue. Differences were largest with regular activity, which appeared to be driven solely by a tendency for higher MVPA volumes, and consequently were no longer apparent after adjustment for total MVPA volume (Central Illustration).
Central Illustration.
“Weekend Warrior” Physical Activity and Adipose Tissue Deposition
Figure depicts the main findings of the study in a sample of 17,146 individuals who had accelerometer-derived physical activity measurement and quantification of MRI-derived visceral adipose tissue (VAT) (N = 14,903) and epicardial and pericardial adipose tissue (EPAT) (N = 17,146). MRI = magnetic resonance imaging.
Our findings support and extend previously reported positive effects of activity on surrogate measures of cardiometabolic health, such as blood-based lipid levels, glucose homeostasis, blood pressure, and waist circumference.7,8 By leveraging a comparably large sample of individuals with measured physical activity, we quantify associations between physical activity pattern and MRI-derived visceral and pericardial adipose tissue, 2 robust quantitative biomarkers for cardiometabolic health.11 Both machine learning–derived measures have been shown to be associated with higher risk of cardiometabolic conditions in the UK Biobank.11,12
Our findings yield 2 major implications. First, our results support the salutary effects of physical activity on cardiometabolic health. Our findings are particularly relevant in light of the obesity pandemic and recent positive studies of drugs targeting metabolic pathways (sodium-glucose cotransporter-2 inhibitors)19,20 or weight loss (glucagon-like peptide-1 receptor agonists),21,22 which may act synergistically with physical activity. Our studies compel future prospective interventions testing the effects of physical activity on cardiometabolic health parameters in addition to these newer therapies.
Second, efforts to optimize cardiometabolic health may be most effective if focused on total duration of MVPA rather than the specific pattern by which MVPA is obtained. Although we initially observed more prominent reductions in visceral and pericardial adipose tissue with regular activity as opposed to weekend warrior activity, differences were no longer observed after adjustment for a tendency for individuals partaking in regular activity to achieve higher total MVPA volumes. Elimination of such differences after adjustment for MVPA volume highlights the apparent dose-responsive nature of visceral and pericardial adipose tissue, in which the lowest values are associated with the highest levels of MVPA, which appear to be more commonly achievable through regular activity. Once a given level of MVPA is achieved, however, the pattern by which it is obtained does not appear to be associated with lower or higher adipose tissue.
Study limitations
First, we acknowledge that our findings are purely observational, and randomized studies are needed to quantify the degree to which physical activity may causally affect adiposity and cardiometabolic disease. Second, since MRI was only performed at the imaging visit, we are unable to ascertain baseline adipose tissue volume prior to assessment of the physical activity exposure. Consistent with expectations, however, baseline BMI was moderately higher among inactive individuals, and although BMI or adiposity may act as a confounder by affecting activity levels, we submit that accelerometer-measured physical activity is a surrogate for longitudinal habitual physical activity,23 and therefore even baseline differences in BMI may be attributable to longitudinal physical activity patterns captured during the monitoring period. By the same rationale, we included a small subset of individuals (14.3%) who had MRI shortly before accelerometry. Third, although 1 week of physical activity monitoring with accelerometers is a common standard and has been shown to serve as a surrogate for habitual activity,23,24 a longer monitoring period would have permitted more accurate classification of activity patterns. Future studies providing longer or repeated measures of physical activity coupled with advanced imaging are warranted to better understand associations between activity patterns and cardiometabolic disease. Fourth, our methodology relied on a previously validated MVPA classification method encompassing various activities like walking, jogging, stationary cycling, elliptical, and others,14,15 yet the accuracy of MVPA classification may vary depending on the type of activity. Fifth, while optimal MVPA thresholds using wrist-based accelerometers remain uncertain, our results remained consistent when utilizing both the 150 min/wk threshold endorsed in consensus guidelines, as well as the sample median threshold of 240 min/wk. Sixth, the UK Biobank primarily consists of individuals who self-describe as White, which may limit the generalizability of our findings to other ethnic or racial groups. Seventh, many covariates were assessed several years before accelerometry, introducing the possibility of misclassification. Finally, on account of survivorship, participants of the UK Biobank imaging substudy are likely healthier than the overall UK Biobank cohort.17
Conclusions
In a cohort of over 17,000 individuals with prospective wrist-based activity tracking and adipose tissue quantification by MRI, we found that achievement of guideline-adherent physical activity levels was associated with substantially lower adipose tissue mass. Once a given level of activity was achieved, reductions in adipose were similar regardless of whether activity was concentrated within 1 to 2 days or more regular. Future studies are warranted to assess the potential value of concentrated physical activity interventions to improve public health.
Perspectives.
COMPETENCY IN MEDICAL KNOWLEDGE: Compared to the inactive state, we found that both weekend warrior and regular physical activity are associated with lower visceral and epi-/pericardial adipose tissue volume. After adjusting for total physical activity minutes, both activity patterns were associated with similar volumes of adipose tissue.
TRANSLATIONAL OUTLOOK: Physical activity, regardless of how it is achieved, is associated with lower visceral and epi-/pericardial adipose tissue, which can be associated with better cardiometabolic health. Prospective studies are needed to further quantify and understand cardiometabolic response to different physical activity patterns.
Funding support and author disclosures
Dr Kany is supported by the Walter Benjamin Fellowship from the Deutsche Forschungsgemeinschaft (521832260). Dr Rämö is supported by a research fellowship from the Sigrid Jusélius Foundation. Dr Pirruccello is supported by the NIH (K08HL159346). Dr Churchill is supported by the National Institutes of Health (K23HL15926201A1). Dr Guseh is supported by the American Heart Association (19AMFDP34990046) and the President and Fellows of Harvard College (5KL2TR002542-04). Dr Ellinor is supported by grants from the National Institutes of Health (1RO1HL092577, 1R01HL157635, 5R01HL139731), from the American Heart Association (18SFRN34230127, 961045), and from the European Union (MAESTRIA 965286). Dr Lubitz previously received support from NIH grants R01HL139731 and R01HL157635, and American Heart Association 18SFRN34250007. Dr Al-Alusi is supported by the NIH (T32-HL007208). Dr Khurshid is supported by the NIH (K23HL169839-01) and the American Heart Association (2023CDA1050571). Dr Ellinor receives sponsored research support from Bayer AG, IBM Research, Bristol Myers Squibb, Pfizer, and Novo Nordisk; he has also served on advisory boards or consulted for MyoKardia and Bayer AG. Dr Lubitz is an employee of Novartis as of July 2022; has received sponsored research support from Bristol Myers Squibb, Pfizer, Boehringer Ingelheim, Fitbit, Medtronic, Premier, and IBM; and has consulted for Bristol Myers Squibb, Pfizer, Blackstone Life Sciences, and Invitae. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
Footnotes
The authors attest they are in compliance with human studies committees and animal welfare regulations of the authors’ institutions and Food and Drug Administration guidelines, including patient consent where appropriate. For more information, visit the Author Center.
Appendix
For a supplemental table and figure, please see the online version of this paper.
Supplementary data
References
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