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Associations of Objectively Measured Physical Activity and Abdominal Fat Distribution


Medicine & Science in Sports & Exercise: May 2015 - Volume 47 - Issue 5 - p 983–989
doi: 10.1249/MSS.0000000000000504

Introduction/Purpose Visceral adipose tissue (VAT) and physical activity are both independent predictors of Type 2 diabetes. Physical activity and overall obesity are inversely associated with each other. Yet the nature of the association between objectively measured dimensions of physical activity and abdominal fat distribution has not been well characterized. We aimed to do so in a middle-age to elderly population at high risk of diabetes.

Methods A cross-sectional analysis of 1134 participants of the ADDITION-PRO study. VAT and subcutaneous adipose tissue (SAT) were assessed one-dimensionally by ultrasonography and physical activity with combined accelerometry and HR monitoring. Linear regression of physical activity energy expenditure (PAEE) and time spent in different physical activity intensity levels on VAT and SAT was performed.

Results Median body mass index (BMI) was 26.6 kg·m−2 and PAEE was 28.1 kJ·kg−1·d−1, with 18.9 h·d−1 spent sedentary, 4.5 h·d−1 in light-intensity physical activity, and 0.4 h·d−1 in moderate-intensity physical activity. PAEE was significantly negatively associated with VAT, and in women, PAEE was also significantly negatively associated with SAT. The difference in VAT was −1.1 mm (95% confidence interval [CI] = −1.8 to −0.3) per 10-kJ·kg−1·d−1 increment, and the corresponding difference in SAT for women was −0.6 mm (95% CI = −1.2 to −0.04) in models adjusted for age, sex, and waist circumference. Exchanging 1 h of light physical activity with moderate physical activity was significantly associated with VAT (−4.5 mm, 95% CI = −7.6 to −1.5). Exchanging one sedentary hour with light physical activity was significantly associated with both VAT (−0.9 mm, 95% CI = −0.1 to −1.8) and SAT (−0.4 mm, 95% CI = −0.0 to −0.7).

Conclusions In this population with low physical activity levels, cross-sectional findings indicate that increasing overall physical activity and decreasing time spent sedentary is important to avoid the accumulation of metabolically deleterious VAT.

1Steno Diabetes Center A/S, Gentofte, DENMARK; 2Section of General Medical Practice, Department of Public Health, Aarhus University, Aarhus, DENMARK; 3MRC Epidemiology Unit, University of Cambridge, Cambridge, UNITED KINGDOM; 4Department of Internal Medicine F, Gentofte Hospital, Gentofte, DENMARK; 5Department of Endocrinology, Hospital of Southwest Denmark, Esbjerg, DENMARK; 6University Clinic in Nephrology and Hypertension, Department of Medical Research, Holstebro Hospital, Holstebro, DENMARK; 7Department of Public Health, Public Research Center for Health, Strassen, LUXEMBOURG

Address for correspondence: Dr. Annelotte Philipsen, Steno Diabetes Center A/S, Niels Steensensvej 2, NLD 2.06, Gentofte 2820, Denmark; E-mail:

Submitted for publication July 2014.

Accepted for publication August 2014.

Visceral adipose tissue (VAT) is an independent predictor of Type 2 diabetes and all-cause mortality above and beyond general obesity and overall abdominal obesity (14,30). The pathogenic nature of VAT is thought to be related to the higher degree of adverse metabolic activity seen in VAT (12). Conversely, increased physical activity (PA) is independently associated with decreased risk of Type 2 diabetes and mortality (6,27,31). Previous studies have firmly established an inverse relationship between PA and obesity, and there is also evidence that higher levels of PA reduce overall abdominal obesity (5). However, the nature of the association between PA and VAT has not been well characterized. Studies in this field typically have small samples sizes or use proxy measures for PA and abdominal fat distribution. Yet, in the context of the current obesity pandemic and concurrent rising levels of Type 2 diabetes disease worldwide, PA recommendations are a key component of obesity management. A detailed investigation of the association between these two risk factors is therefore warranted.

PA can be assessed in several dimensions. Current official PA recommendations focus both on total volume, often expressed as PA energy expenditure (PAEE), and on the separate entities duration, frequency, and intensity—including vigorous, moderate, and light PA (LPA), and typically advocate that adults should accumulate 30 min or more of moderate-to-vigorous PA (MVPA) daily in bouts of at least 10 min (9,33). During the last few years, studies have also shown that sedentary behavior (SED), defined as any waking behavior characterized by an energy expenditure ≤1.5 METs while in a sitting or reclining posture (21), is associated with detrimental health effects (10), and proposals to include minimizing sedentary behavior in official guidelines have been made (26).

Many people do not comply with current recommendations for PA of this nature but instead accrue most of their PA as LPA and spend a substantial part of their day in sedentary behavior (10). Older age groups may not be able to perform MVPA and live up to the recommendation of 30 min of MVPA, making it relevant to focus on the role of LPA as well as the volume of PA. In this context, we aimed to study the association of objectively measured PAEE and time spent at different PA intensities in daily life with abdominal fat depots assessed by ultrasonography, in a large middle-age to elderly population at high risk of developing Type 2 diabetes.

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Study Population

We investigated participants who attended the 2009–2011 follow-up health examination of the ADDITION-PRO study, a cohort at high risk of developing Type 2 diabetes, identified through stepwise screening in Danish general practice (2001–2006), and performed a cross-sectional analysis. The details of the ADDITION-PRO study have been described elsewhere (13). In brief, 2082 people representing specific baseline diabetes risk groups took part in the 2009–2011 ADDITION-PRO examination. These groups were combined impaired fasting glycemia and impaired glucose tolerance, isolated impaired fasting glycemia, isolated impaired glucose tolerance, and high risk based on questionnaire data but normal glucose tolerance. The cohort also included a small group at low risk with normal glucose tolerance. The follow-up health assessment involved measurement of anthropometry, routine cardiovascular biochemistry, PA, and completion of validated questionnaires. Data were collected at four study centers in Denmark. The current article reports on a subgroup analysis of 1134 participants from two of the study centers, Steno Diabetes Center A/S and Aarhus University Hospital, where ultrasound assessments of abdominal fat distribution were performed. Participants attended after an overnight fast. Ethical approval was obtained from the scientific ethics committee in the Central Denmark Region (no. 20000183). Participants gave written informed consent to take part in the study and for linkage of their data with National Registers for the purposes of the ADDITION-PRO study.

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Measurement Methods

Physical activity.

PA was measured objectively using a combined accelerometer and HR monitor (Actiheart; CamNtech Ltd., Cambridge, UK). The method and data processing have been described in previous articles (3,8,23). Briefly, the participants performed a submaximal step test to ensure individual calibration of the HR to PA intensity. The monitor was then set up to record long-term PA, registering movement and HR every 60 s, and placed horizontally on the participant’s chest with two standard electrocardiogram electrodes (Maxensor, Alton, UK). Participants were asked to wear the monitor continuously for seven days and nights and to maintain their usual PA pattern throughout this period. The study was carried out continually during the period 2009–2011, such that the sample as a whole reflects the entire spectrum of seasonal variation in PA.

The relation between HR and PAEE was calibrated using data from an individually performed submaximal step test where available (n = 667) or based on a group calibration (n = 467) derived from the regression coefficients from the HR-to-PA intensity relationship in the 1046 participants from the whole ADDITION-PRO cohort who had a valid step test. This group calibration equation was semi-individualized using age, sex, sleeping HR, and β-blocker use. Accelerometry data were converted to energy expenditure using equations corresponding to walking and running (3). The PA measures were then derived by combining minute-by-minute HR and accelerometry measures using a branched equation model (2) to derive time series of intensity. Each individual record was then summarized as either PAEE (kJ·kg−1·d−1) or fraction of time spent in PA intensity groups, while minimizing diurnal information bias. Intensity was defined as multiples of a standard value of resting metabolic rate (1 MET = 3.5 mL O2·min−1·kg−1 (∼71 J·min−1·kg−1)). PA intensity groups were as follows: i) hours per day spent in sedentary behavior including sleep (SED; ≤1.5 METs) (h·d−1), ii) hours per day spent in LPA (>1.5 to 3.0 METs) (h·d−1), and iii) hours spent in MVPA (>3.0 METs) (h·d−1). For the analysis in the present article, only measures from participants with a minimum of 24 h of monitor wear time were considered valid.

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Obesity measures.

Height was measured without shoes to the nearest 0.1 cm with a stadiometer (Seca; Medical Scales and Measuring Systems, Hamburg, Germany). Weight was measured with the participants barefoot wearing light indoor clothing using a Tanita Body Composition Analyser (Tokyo, Japan). BMI was defined as weight (kg) divided by height (m) squared. Waist circumference was assessed as the average of two measurements to the nearest 0.1 cm at the midpoint between the iliac crest and the lower rib with the participant standing. A D-loop tape was used without applying pressure to the skin. Abdominal fat distribution was assessed by ultrasonography (Logiq 9 machine; GE Healthcare, Waukesha, WI) by trained sonographers according to a strict validated protocol with adequate reproducibility (4,17,18,24). Visceral fat was measured using a 4C abdominal convex transducer and subcutaneous fat with a 9-L small parts linear transducer. With the participant lying down, VAT thickness was defined as the depth (cm) from the peritoneum to the lumbar spine and SAT thickness was defined as the depth (cm) from the skin to the linea alba. Measurements were made at the point where the waist circumference crosses the midline. Coefficients of variation for intra- and interobserver variation were in the range 3.4%–6.1% except for interobserver variation for subcutaneous fat (9.5%) (17).

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Statistical Methods

Linear regression analysis of PAEE and time spent at different PA intensity levels on VAT and SAT was performed with adjustment for age, sex, and smoking. We also present results with further adjustment for waist circumference. Interaction terms were included in all PA models to test for the presence of effect modification by sex. In the PAEE models, the presence of a nonlinear association was tested by inclusion of a quadratic. Given that PAEE and PA intensity variables are composite exposures and therefore not independent of each other, they were analyzed in separate models. As the three intensity variables are mutually exclusive and sum up to 24 h·d−1, they were analyzed by including SED and MVPA in the same model, leaving out LPA. In this manner, the regression coefficient for SED is the difference in VAT (or SAT) between individuals who differ 1 h in SED (and hence LPA) and do the same amount of MVPA. This can be thought of as the effect size on VAT (or SAT) of exchanging 1 h of LPA with 1 h of SED, assuming the association is causal. Similarly, the coefficient for MVPA corresponds to the effect of exchanging 1 h of LPA with 1 h of MVPA. All analyses were performed using SAS 9.2.

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Data on 1134 ADDITION-PRO participants were available for complete case analysis. Figure 1 shows the details on study sample selection, and Table 1 summarizes the characteristics of the study sample. This is a middle-age to elderly Caucasian population with a mean BMI of 26.9 kg·m−2. Of note, very little PA of vigorous intensity was undertaken (mean = 0.03 h). Therefore, the MVPA variable represents almost only moderate-intensity activity (MPA) and will be referred to as such in the rest of this article. Table 2 shows regression coefficients for the associations between abdominal fat depots and PA measures.







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We found a statistically significant negative association between PAEE and VAT, even after adjustment for waist circumference. There was no statistically significant sex interaction (P = 0.14). A 10-kJ·kg−1·d−1 higher PAEE was associated with 3.3-mm lower VAT. A sex interaction term was significant for SAT (P = 0.002). There was a negative association for both sexes, but it was only statistically significant for women. This association remained statistically significant after adjustment for waist circumference. Standardizing the coefficients by the population-specific SD for VAT and sex-specific for SAT showed that the strength of the associations was similar for VAT and SAT for women, whereas the effect in men for SAT was more than three times smaller.

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PA intensities.

Moderate PA was inversely statistically significantly associated with VAT. Exchanging 1 h·d−1 of LPA with moderate PA was associated with a 4.5-mm lower VAT. SED was also, less strongly, significantly and positively associated with VAT. Exchanging 1 h·d−1 of SED for LPA was associated with a 0.9-mm lower VAT distance. Neither association remained significant after further adjustment for waist circumference. For SAT, the same pattern of a negative association with moderate PA and a positive association with SED was seen. The coefficients found were smaller, especially for moderate PA, and only the association with SED was significant. For MPA, standardizing the coefficients showed the association with VAT to be approximately twice as strong as for SAT. Standardizing coefficients for SED showed that the strength of the associations was similar for VAT and SAT (SD for whole sample: VAT = 2.64 cm, SAT = 1.06 cm). For all the associations between PA measures and abdominal fat, at least approximately half of the magnitude of the association was captured by waist circumference. No quadratic terms were significant.

Some differences in characteristics between the participants with only group calibration of Actiheart data compared to those with individually calibrated data were found (group vs individual calibration): age (67.0 vs 65.3 yr, P ≤ 0.0001), BMI (27.4 vs 26.6 kg·m−2, P = 0.0019), waist circumference (96.1 vs 94.2 cm, P = 0.02), and PAEE (calculated using group calibration for all, 27.7 vs 32.4 kJ·kg−1·d−1, P ≤ 0.0001). There was no statistically significant difference for the two groups with regard to sex (52.4% vs 54.1%, P = 0.58) and smoking status (66.0% vs 61.3%, P= 0.11). For the participants with estimates of PAEE from both individual and group calibration, the mean difference between these two values and corresponding limits of agreement were 0.83 ± 14.4 kJ·kg−1·d−1. In a sensitivity analysis using only this subsample, we found similar results to the associations presented in Table 2. Regression coefficients were slightly numerically larger, that is, stronger associations, with wider confidence intervals and similar P values (data not shown).

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We found that PAEE was significantly inversely associated with VAT and that, for women, PAEE was also significantly inversely associated with SAT, even after adjustment for waist circumference. In the analysis of PA intensities, both time spent at moderate PA and SED were significantly associated (opposite directions) with VAT and time spent at SED was also significantly associated with SAT, although none of the associations found for either PA intensity variable remained significant after adjustment for waist circumference.

Our results thus suggest that volume of PA, expressed as PAEE, is important for limiting the accumulation of abdominal fat. We also found no evidence of a nonlinear relationship between PAEE and abdominal fat and hence of a certain minimum level of PAEE being necessary for an effect on VAT or SAT. The 10-kJ·kg−1·d−1 increase in PAEE associated with a 3.3-mm decrease in VAT is roughly a 30% increase in current – low – PA levels in our study population (Table 1). A PAEE difference of 10 kJ·kg−1·d−1 corresponds to 57 min of activity at an intensity above rest of 2.5 MET (gross = 3.5 METs), for example, moderate-paced walking at 2.8–3.2 mph, compared to spending those 57 min at rest (1). Whereas the effect size on VAT may be modest for the volume of PA expended by such an intensity activity, it may nonetheless be important from a public health perspective in addition to other risk-lowering behavior (19).

In the analysis of PA intensities, the significant association between moderate PA and VAT was the strongest. Given that more energy is expended at higher PA intensities, this result is to be expected in light of our finding that the volume of PA matters. Almost 0.5 cm of VAT is associated with exchanging 1 h of LPA with 1 h of an activity above 3 METs, even if the PA is not in the vigorous category.

Few other studies have investigated the association between objective measurements of PA and abdominal fat distribution in large samples. In a cross-sectional analysis of 253 young adults, Smith et al. (22) found that PA volume assessed as total accelerometer counts was not associated with VAT measured with computed tomography. They also did not find a significant association between MVPA and VAT in an analysis where all intensity categories are included in the same model, that is, an analysis similar to ours. However, that study may have been underpowered to detect the associations between PA and VAT we demonstrated. Furthermore, we derived PA measures from a combination of HR and torso acceleration, and there is evidence to suggest that this is a more precise measure (28). The combination compensates for some of the limitations that HR and accelerometer assessment each have when used alone, for example, the difficulty of accelerometer counts to accurately assess the PAEE of activities such as cycling, carrying heavy weights, or walking uphill. The study population of Smith et al. is also much younger than ours, and the length of exposure of particular PA patterns may matter for abdominal fat accumulation.

We found that the associations for SED were positive, but associations were no longer significant after adjustment for waist circumference. Exchanging 1 h of SED with 1 h of LPA was associated with a 0.9-mm decrease in VAT. This result is interesting in this population with low levels of activity and a presumed lower capacity for doing activities of higher intensities. It supports the health promotion message that “every minute counts” and that making a first move out of sedentary behavior into light activity has an effect. This finding is also in agreement with previous evidence that SED is positively associated with waist circumference independent of MVPA (10). A further study of 2056 European healthy middle-age adults found a positive association between SED measured using accelerometry and anthropometric measures including waist circumference (32), and Smith et al. (22) found a significant positive association between SED and VAT in women. Other studies did not find such associations, but these are characterized by smaller study samples and/or self-reported PA assessment (15,20).

A recent large study of objectively measured PA and anthropometry has suggested that there may be sex differences in such associations (32). One would perhaps expect sex differences in the association between PA and abdominal fat distribution, given the well-known difference in abdominal fat distribution between the sexes, although we had no specific a priori hypothesis regarding this. We found that PAEE is more strongly associated with SAT for women. This suggests that men may need to be more physically active than women to achieve a comparable reduction in SAT. There was also a pattern in our results to suggest that changing 1 h of SED with 1 h of LPA may be more strongly associated with both VAT and SAT for women. As mentioned, others have described a similar sex difference (22). However, the sex interaction terms in our analyses did not quite reach conventional statistical significance, so we are unable to make firm conclusions on this specific issue.

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Limitations and strengths.

Our study has a few limitations worth considering. First, we do not have information on caloric intake. SED may be associated with other factors, for example, snacking (7), which may influence VAT and SAT accumulation. Second, we were not able to distinguish sleep from sedentary behavior in the current analyses of PA intensity. Given that sleep may be inversely associated with obesity (16), sleep and SED may be associated with VAT and SAT in opposite directions, thus attenuating the associations found. However, others have found that excluding data recorded from midnight to 6 a.m. made little difference in a similar analysis of the association between sedentary time and anthropometrics (32).

We chose to report PA estimates using individual calibration where possible rather than apply group calibration for the whole sample. This may introduce bias. Participants without a valid step test included those excluded from the test because of protocol exclusion criteria, participants not able to perform the test because of station shut down, and those with invalid test results because of bad signal and download problems. These participants were older with larger BMI than those with valid step tests. However, using a group calibration for all participants does not eliminate the issue of bias. In doing so we would assume that every HR above resting value is worth the same for all individuals. This is not the case because the relationship of HR to physical intensity varies between individuals. Therefore, we sought to generate more accurate PA estimates by calibrating using dynamic data for each individual where available. Furthermore, for the participants with estimates of PAEE from both individual and group calibration, the mean difference between these two values was small, suggesting that the bias introduced from not choosing to use group calibration for all participants is limited.

None of our participants undertook vigorous activity, focusing our analysis on the lower end of the PA intensity spectrum. At the same time, this highlights the need to tailor recommendations to older age groups, who do not necessarily engage in vigorous activity. Indeed, it is a strength that our study examined real-life PA levels, showing the potential size of realistically achievable changes in PA levels in predominantly sedentary populations such as ours.

Our study is a cross-sectional analysis. It does not tell us what the effect of PA over time is on abdominal fat distribution, although usual PA levels tend to track over the adult life (25), and this single week of measurement thus can be assumed to be representative of usual PA levels. Two recent meta-analyses have reviewed the effect of short-term aerobic and resistance exercise intervention on VAT, assessed by computed tomography or MRI. One meta-analysis (35 randomized controlled studies, 2145 participants) investigated the independent and combined effects of aerobic and resistance exercise training intensity on VAT (11). Studies with restrictions on diet were included only if the diet was the same in the intervention and control groups. It found that only aerobic exercise compared to control is effective in reducing VAT. The intensity of the prescribed aerobic exercise was mostly moderate. No evidence was found to suggest a relationship between either volume of aerobic exercise and VAT reduction or mean intensity and VAT reduction. The latter finding is perhaps due to the heterogeneity of the exercise interventions making it difficult to pool results. A similar meta-analysis (15 studies, 852 participants) with no caloric restriction concluded that aerobic exercise of at least moderate intensity had the highest potential to reduce VAT, although no firm conclusions were made regarding the effect of PA volume (29).

It is a major strength of our study that we have a large sample size and used objective assessment of PA with combined HR and accelerometry and VAT/SAT depots with ultrasonography. To the best of our knowledge, our results add new information to the field in combining these three aspects. A further strength is the type of population we examined. In daily practice, clinicians are often in doubt as to how to target prevention efforts for people with moderately elevated levels of risk factors for metabolic disease. We have shown that, even in people with this risk profile and age range, a difference in PAEE and SED may reduce abdominal fat.

In conclusion, an understanding of the association between dimensions of PA and abdominal fat distribution is necessary to provide detailed PA recommendations aiming to reduce metabolically detrimental VAT. We have shown that increasing PAEE, increasing PA intensity, and reducing sedentary behavior matter in this respect. PAEE can be increased through both LPA and moderate PA, but moderate PA is a more time-efficient way of doing so. In this sense, our results support current official recommendations on PA in their focus on MVPA but this does not preclude a role for LPA. We have also shown that a lack of an effect on waist circumference may overlook an advantageous effect on VAT. The potential effect of exchanging sedentary behavior for LPA is important because it represents a realistic target in a predominantly sedentary middle-age to elderly population. Future research should prospectively assess the associations between PA dimensions and changes in abdominal fat depots and also focus on further exploring possible sex differences.

The authors thank the participants of the ADDITION-PRO study and their general practitioners for their contribution to the study. The authors also acknowledge the four ADDITION-PRO study centers: Jens Sandahl Christiansen and his team at Aarhus University Hospital, the Clinical Research Center at Steno Diabetes Center led by Lise Tarnow, and the clinical research teams at Holstebro Hospital and the Hospital of South West Jutland, Esbjerg. The ADDITION-PRO study was funded by an unrestricted grant from the European Foundation for the Study of Diabetes/Pfizer for Research into Cardiovascular Disease Risk Reduction in Patients with Diabetes (74550801), by the Danish Council for Strategic Research, and by internal research and equipment funds from Steno Diabetes Center A/S. The funding bodies had no role in the design, collection, analysis, and interpretation of the data. The results of the present study do not constitute endorsement by the American College of Sports Medicine.

Conflicts of interest statement: The authors declare no conflict of interest.

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