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Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgment
  8. Disclosure
  9. REFERENCES
  10. Supporting Information

Pericardial fat is emerging as a unique risk factor for coronary disease. We examined the relationship between objectively measured physical activity during free-living and pericardial fat. Participants were 446 healthy men and women (mean age = 66 ± 6 years), without history or objective signs of cardiovascular disease (CVD), drawn from the Whitehall II epidemiological cohort. Physical activity was objectively measured using accelerometers (Actigraph GT3X) worn around the hip during waking hours for 7 consecutive days (average daily wear time = 889 ± 68 min/day), and was classified as sedentary (<200 counts/min (cpm)), light (200–1,998 cpm), or moderate-vigorous physical activity (MVPA; ≥1,999 cpm). Pericardial fat volume was measured in each participant using electron beam computed tomography. Average daily cpm in men was 338.0 ± 145.0 and in women 303.8 ± 130.2. There was an inverse association between average cpm and pericardial fat (B = −0.070, 95% confidence interval (CI), −0.101, −0.040, P < 0.001), and this remained significant after adjusting for age, sex, registered wear time, BMI, lipids, glycemic control, blood pressure, smoking, statins, and social status. Both sedentary time (B = 0.081, 95% CI, 0.022, 0.14) and MVPA (B = −0.362, 95% CI, −0.527, −0.197) were also associated with pericardial fat, although associations for sedentary time did not remain significant after adjustment for MVPA. The inverse association between physical activity and pericardial fat was stronger among overweight and obese adults than in normal weight. Objectively assessed daily activity levels are related to pericardial fat in healthy participants, independently of BMI. This might be an important mechanism in explaining the association between physical activity and CVD prevention.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgment
  8. Disclosure
  9. REFERENCES
  10. Supporting Information

Physical activity is an important predictor of health in older adults (1,2,3) although the mechanisms remain poorly understood. Evidence from longitudinal studies has shown that habitual physical activity across the life course is associated with lower weight gain and reduced central adiposity in middle—late adulthood (4,5,6). Various training studies have shown that exercise reduces levels of visceral adiposity in the absence of weight loss (7,8), which might be a key mechanism in protection from cardiometabolic diseases. Indeed, regional visceral fat depots may be of greater importance than overall adiposity (9,10,11), and several studies have implicated pericardial fat as a unique pathogenic risk factor. Pericardial fat surrounds the coronary arteries and has been associated with cardiovascular risk factors and markers of subclinical atherosclerosis, independently of overall adiposity (12,13,14,15,16).

There are presently little data on the association between physical activity and pericardial fat. One recent study demonstrated an inverse association between cardiorespiratory fitness and pericardial fat (17). Also, a 12-week supervised exercise training program in obese men resulted in a 9% reduction in pericardial fat thickness (18). However, the association between physical activity during free-living and pericardial fat has not been investigated. Obtaining an accurate measure of physical activity in epidemiological studies using the traditional self-reported approach has several limitations (19). Therefore, in the present study we used 7-day accelerometry recordings in order to provide an accurate, objective assessment of free-living physical activity levels. The purpose of this study was to examine the relationship between pericardial fat and objectively measured physical activity and sedentary time measured over 7 days of free-living.

Methods and Procedures

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgment
  8. Disclosure
  9. REFERENCES
  10. Supporting Information

Participants

A subsample of participants was drawn from the Whitehall II epidemiological cohort (20) for this study. The criteria for entry into the study included no history or objective signs of cardiovascular disease (CVD), no previous diagnosis or treatment for hypertension, inflammatory diseases, or allergies. This information was confirmed by a telephone interview and verified from clinical data collected from the previous seven phases of the main Whitehall II study. Volunteers were of white European origin. Selection was stratified by grade of employment (current or most recent) to include higher and lower socioeconomic status participants. Participants were prohibited from using any antihistamine or anti-inflammatory medication 7 days before testing and were rescheduled if they reported colds or other infections on the day of testing. Participants gave full informed written consent to participate in the study and ethical approval was obtained from the University College London Hospital committee on the Ethics of Human Research.

Physical activity assessment

Participants were asked to wear an accelerometer (Actigraph GT3X, FI; Actigraph, Pensacola, FL) around the hip that records movement on the vertical and horizontal axis, during waking hours for 7 consecutive days. The accelerometer provides a measure of the frequency, intensity, and duration of physical activity and allows classification of activity levels as sedentary, light, moderate and vigorous. The raw accelerometry data were processed using specialist software (MAHUffe; MRC Epidemiology Unit, Cambridge, UK) to produce a series of standardized outcome variables. All participants included in the present analysis recorded a minimum of 10 h/ day wear time for 6–7 days. The first and last days of data were excluded from the analysis and nonwear time was defined as intervals of at least 60 consecutive minutes of zero counts/minute (cpm). We used cutoff points previously used in an older sample of adults (21) to calculate daily times in each activity intensity band: sedentary (<1.5 MET): 0–199 cpm; light (1.5–3 MET) 200–1,998 cpm; moderate to vigorous physical activity (MVPA) (>3 MET): ≥1,999 cpm. Sensitivity analyses were also performed using a more conservative cut point of zero cpm to differentiate sedentary time from activity (22). All physical activity variables were converted to time (in minutes) per valid day.

Pericardial fat

The assessment of pericardial fat was performed on noncontrast coronary artery calcium scans obtained using an electron beam computed tomography scanner (GE-Imatron C-150; GE-Imatron, San Francisco, CA). In brief, 40 contiguous 3-mm slices were obtained during a single breath-hold starting at the carina and proceeding to the level of the diaphragm. Scan time was 100 ms/slice, synchronized to 40% of the R—R interval. The entire coronary artery calcium dataset was loaded onto the Volume analysis software on a Siemens multimodality workstation (Siemens, Forchheim, Germany) for evaluation of pericardial fat volume. Pericardial fat is defined as the adipose tissue contained by the visceral pericardium. Upper limit of the pericardial sac was identified at the level of bifurcation of the pulmonary artery and the lower limit was one slice below the posterior descending artery. The lower contours were adjusted manually where needed taking care to clearly distinguish between pericardial adiposity and fat tissue around the diaphragm. Regions of interest tracing the pericardium were drawn manually in axial slices of the coronary artery calcium dataset at 9-mm intervals. A smooth pericardial contour was obtained by interpolation. Volume of pericardial fat (in cm3) was calculated automatically based on threshold segmentation once the entire pericardial contour was highlighted. Within this outlined pericardial contour, contiguous voxels with attenuation values between −30 Hounsfield units and −190 Hounsfield units were defined as adipose tissue. Volumetric fat scores were calculated by two experienced investigators blinded to the physical activity and clinical data.

Covariates

Participants reported current smoking levels. Height and weight were recorded in light clothing for the calculation of BMI. Fasting blood samples were taken for analysis of total and high-density lipoprotein (HDL) cholesterol and triglycerides, which was measured within 72 h in serum stored at 4°C using enzymatic colorimetric methods. Low-density lipoprotein (LDL) cholesterol was derived using the Friedewald equation. Glucose homeostasis was assessed from glycated hemoglobin (HbA1c) concentration, assayed using boronate affinity chromatography, a combination of boronate affinity and liquid chromatography. Resting blood pressure was measured three times (using an automated UA-779 digital monitor) with participants in a seated position and a mean value was taken from the second and third readings.

Statistical analysis

General linear regression was used to examine the association between physical activity and pericardial fat. Various indices of physical activity from the Actigraph data were used, including cpm, sedentary time, and MVPA. All of these indices were normally distributed and used as continuous variables in all regression models. In order to model associations between physical activity and pericardial fat we included various covariates; model 1 adjusted for total registered Actigraph wear time, age and sex; model 2 adjusted for total registered Actigraph wear time, age, sex, and BMI; model 3 adjusted for total registered Actigraph wear time, age, sex, BMI, HDL, and LDL cholesterol, systolic blood pressure, HbA1c, smoking, statins use, and employment grade (as a proxy of social status). Since previous studies have indicated more pronounced effects of exercise on adiposity measures in obese participants, we ran additional general linear models stratifying participants by BMI (<25; 25–30; >30 kg/m2) and physical activity above and below the median split. All analyses were conducted using SPSS version 15 (SPSS, Chicago, IL).

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgment
  8. Disclosure
  9. REFERENCES
  10. Supporting Information

From the initial sample of 510 participants, 64 did not provide Actigraph data. Thus, the final analytic sample comprised 446 participants (characteristics are displayed in Table 1). Participants excluded from the analysis tended to be younger (60.9 vs. 63.0 years, P = 0.005) and have higher pericardial fat volume (141.1 vs. 118.7 cm3, P = 0.001) compared to those included.

Table 1.  Characteristics of the study population (N = 446)
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The sample as a whole was relatively active, and 59.8% of men and 49.3% of women recorded at least 30 min/day of MVPA, although men were significantly more active than women (338.0 ± 145.0 vs. 303.8 ± 130.2 cpm, P = 0.009). In partial correlations controlling for age, sex, and wear time, average daily cpm was inversely related to BMI (r = −0.22, P <0.001), HbA1c (r = −0.10, P = 0.04), and positively with HDL cholesterol (r = 0.24, P <0.001). Sedentary time was related to BMI (r = 0.10, P = 0.03) and inversely with HDL cholesterol (r = −0.16, P = 0.001).

Pericardial fat volumes ranged from 29.1 to 287.6 cm3 and were higher in men (age adjusted β = 34.3, 95% confidence interval (CI), 25.9, 42.7), smokers (β = 24.3, 95% CI, 4.7, 44.0), and obese participants (β = 74.3, 95% CI, 62.7, 85.9). In basic models adjusted for age, sex and registered wear time, pericardial fat volume was inversely associated with physical activity (cpm and MVPA) and positively with sedentary time (see Table 2). For example, pericardial fat volume was 12.2 cm3 (95% CI, 3.7–20.7, P = 0.005) lower in participants that recorded at least 30 min/day of MVPA compared with those recording less than 30 min/day after accounting for age and sex. The association between sedentary time and pericardial fat did not remain significant (B = 0.033, 95% CI, −0.031, 0.096) after adjustment for MVPA. The inverse association between cpm and pericardial fat was maintained (P = 0.049) after making further adjustments for BMI, HDL and LDL cholesterol, systolic blood pressure, HbA1c, statins, and smoking. The results were not different when we employed a more conservative cut point of zero cpm for sedentary time.

Table 2.  Regression of accelerometry measures on pericardial fat (N = 446)
inline image

Since previous studies have indicated more pronounced effects of exercise on adiposity measures in obese participants, we performed additional analysis stratified by BMI categories, which is shown in Figure 1. In these analyses, physically active obese participants had lower pericardial fat compared with their nonactive counterparts (age adjusted β = −30.1, 95% CI, −54.7 to −5.4, P = 0.02); physically active overweight participants had lower pericardial fat compared with their nonactive counterparts (age adjusted β = −15.6, 95% CI, −27.4 to −3.7, P = 0.01); but there were no differences in physically active and nonactive normal weight individuals (age adjusted β = 4.0, 95% CI, −5.7 to 13.7, P = 0.42).

image

Figure 1. Objectively assessed physical activity and pericardial fat volume in relation to overall obesity. Filled and clear bars represent participants above and below the median split for total daily activity, respectively. Values are mean (SEM) adjusted for age (n = 446).

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Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgment
  8. Disclosure
  9. REFERENCES
  10. Supporting Information

The main results of this study demonstrate an inverse association between objectively assessed physical activity and pericardial fat, which was independent of overall adiposity (as indexed by BMI) and other risk factors. There was an association between sedentary time and pericardial fat although it did not remain significant after adjustment for MVPA, thus suggesting that any adverse effects of sedentary behavior were largely driven by a lack of exercise. To our knowledge, this is the first study to demonstrate an association between physical activity during free-living and pericardial fat. These findings are particularly relevant as several studies have implicated pericardial fat as a unique pathogenic risk factor. In particular, pericardial fat may play a more direct role in causing coronary atherosclerosis (12,13,14,15,16). It has been hypothesized that pericardial fat releases inflammatory signals that promote atherogenesis in coronary arteries (23). Compared with subcutaneous fat, pericardial fat has a higher rate of inflammatory cytokine secretion, such as monocyte chemoattractant protein 1, interleukin-6, and tumor necrosis factor-α (23), which might be even more detrimental to atherosclerosis development given the close proximity of pericardial fat to the coronary arteries. In addition, results from both human and animal studies suggest that atherosclerotic lesions are absent in the segments of coronary arteries lacking pericardial fat (24,25). Pericardial fat has also been related to mixed or noncalcified atherosclerotic plaques that are considered to be the most vulnerable to future rupture (16). Taken together, pericardial fat may therefore be an important mechanism in explaining the association between physical activity and CVD prevention, independently of overall adiposity.

The association between physical activity and pericardial fat was particularly evident in overweight and obese participants, which is consistent with prior evidence showing that exercise induced loss of visceral adipose tissue is greatest in obese adults compared with the lean (7). A 12-week supervised exercise training program in obese men has also demonstrated a 9% reduction in pericardial fat thickness (18). Another study in obese postmenopausal women using exercise and calorie restriction interventions of equal energy deficit showed an average loss in pericardial fat of 17% regardless of whether the energy deficit was due to calorie restriction alone or in combination with aerobic exercise (26). In the present study we observed the most robust associations with crude counts per minute, which might be because this reflects the most complete measure of physical activity, whereas other measures depend on arbitrary cutpoints. Moderate to vigorous activity was associated with pericardial fat in minimally adjusted models, although light activity was not (coefficient = −0.044, 95% CI, −0.109, 0.020), which suggest the relationship might be dose dependent. Since only a small fraction of the sample (5.2%) recorded any vigorous activity (≥4,000 cpm) we made the decision to combine moderate and vigorous activity into one variable. However, when vigorous activity was treated as a separate variable we did observe a significant inverse association with pericardial fat (B coefficient = −0.53, 95% CI, −0.78 to −0.28) after adjustment for age, sex and wear time, which became attenuated after full adjustments (B coefficient = −0.16, 95% CI, −0.37 to 0.06). These data support previous evidence that have demonstrated profound cardiovascular and metabolic benefits from short term intense forms of training (27).

The mechanisms through which physical activity lowers the risk of CVD are incompletely understood. Exercise training studies demonstrate modest but consistent improvements in various risk factors such as blood pressure and vascular function (28,29), HDL cholesterol (30), and inflammatory markers (31). Recent epidemiological evidence suggests that inflammatory and haemostatic risk markers and blood pressure made the largest contribution to the inverse association between physical activity and CVD events, although the contribution of BMI was negligible (32,33). However, regional visceral fat depots may be of greater importance than crude measures of overall adiposity such as BMI. Given that the association between physical activity and pericardial fat was minimally attenuated after adjustment for other established risk factors, this suggests that it is an independent mechanism.

In the present study, we found evidence for a link between sedentary time with BMI and pericardial fat, although the associations did not persist after controlling for MVPA. Some previous studies using accelerometry based measures have observed detrimental, linear associations of sedentary time with waist circumference and other metabolic risk factors after controlling for physical activity (34,35), although in other studies the associations do not persist after adjusting for physical activity (36,37,38). Thus, there appears to be some evidence to suggest distinct yet overlapping pathways involved in the adverse health outcomes associated with sedentary time beyond that which is due to lack of exercise per se. Using a different objective technique to assess sedentary behavior that involved individually calibrated minute-by-minute heart rate monitoring, Helmerhorst and colleagues (39) demonstrated an association between time spent sedentary and higher levels of fasting insulin over 5.6 years follow-up. However, the association between physical activity, sedentary time, and adiposity might be bidirectional. For example, in a sample of healthy adults, BMI and fat mass at baseline predicted sedentary behavior at 5.6 years follow-up, although the reverse association was not evident (40). The discrepancy in these findings might be explained by the precision of different techniques to capture sedentary behavior. Indeed, the accelerometry device used in the present study could not distinguish between sitting and standing, thus more precise objective techniques to capture prolonged periods of sitting may improve the consistency of overall findings.

Limitations

The present study was cross-sectional thus we are unable to infer causality. We were unable to account for dietary intake as a possible confounder, which might be relevant given the importance of overall energy balance. The participants in this study were of white European origin, healthy, and demonstrated higher activity levels compared with similar aged British cohorts (21). Therefore, our results might not be representative of the wider community. Some biases were possibly introduced as participants that did not complete Actigraph measures had higher levels of pericardial fat. It is likely that participants declining to complete the Actigraph measures were less active, thus our results might have underestimated the true effects.

In summary, our results show that in healthy, older adults objectively assessed daily activity levels are related to pericardial fat independently of BMI and other risk factors.

Acknowledgment

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgment
  8. Disclosure
  9. REFERENCES
  10. Supporting Information

We acknowledge the contributions of Sir Prof Michael Marmot, MD, to the study design, Bev Murray to data collection, Amanda Rossi to Actigraph data processing, and Satvir Atwal for assistance in EBCT scans. M.H. had full access to the data, and takes responsibility for the integrity of the data and accuracy of the data analyses. All authors contributed to the concept and design of study, drafting, and critical revision of the manuscript. This research was supported by the British Heart Foundation and the BUPA Foundation, UK. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Disclosure

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgment
  8. Disclosure
  9. REFERENCES
  10. Supporting Information

The authors declared no conflict of interest.

See the online http:obyjournalvaopncurrentformsoby201261_coi.pdfICMJE Conflict of Interest Forms for this article.

REFERENCES

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgment
  8. Disclosure
  9. REFERENCES
  10. Supporting Information

Supporting Information

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgment
  8. Disclosure
  9. REFERENCES
  10. Supporting Information

Supporting Information

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oby_2645_sm_coi.pdf1230KSupporting info item

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