Abdominal obesity is known to predispose individuals to cardiovascular disease (CVD) and type 2 diabetes mellitus (T2DM), with regional fat deposits being postulated to be of greater importance than overall adiposity in causing metabolic and cardiovascular disturbance (5,31). Several studies have implicated pericardial and liver fat as particular pathogenic risk factors (23,26), with excess visceral adiposity also being associated with dyslipidemia, systemic inflammation, insulin resistance, T2DM, and all-cause mortality (1,7,15,18).
Despite the well-documented positive effects of moderate-to-vigorous physical activity (MVPA) on regional fat deposition (16), the associative role of sedentary behavior, independent of physical activity, is less well understood, and the available literature, equivocal.
Over the past decade, there has been an accumulation of epidemiological evidence from both cross-sectional and prospective observational studies indicating that sedentary behavior (best conceptualized as any nonexercise sitting time (30)) may be independently associated with several deleterious health outcomes, including T2DM, obesity, the metabolic syndrome, CVD, and cardiovascular mortality (8,33,36,37). However, previous cross-sectional and longitudinal studies conducted in the general population have shown no association between sedentary behavior and visceral fat accumulation in adults (20,25,29). Although associations between objectively measured sedentary time and pericardial fat have previously been observed (11,20), the relations were either attenuated after adjustment for MVPA (11) or MVPA was quantified using self-report (20), thus raising issues regarding response bias and poor levels of validity (27). It therefore remains unclear whether objectively measured sedentary behavior is associated with regional fat deposition, independent of MVPA or total physical activity. Moreover, to our knowledge, there are currently no reports examining the association between sedentary behavior and liver fat.
It is also necessary to establish the association between sedentary behavior and fat distribution in those at high risk of chronic disease. Both national and international recommendations and policies specify that chronic disease prevention strategies should include targeted interventions aimed at the identification and management of individuals at high risk (2). Moreover, sedentary time has been shown to be more strongly and adversely associated with cardiometabolic variables (including markers of adiposity) in individuals at high risk, (14) and those with established T2DM (3,4) after adjustment for MVPA and other important confounders. Given that associations between sedentary time and markers of adiposity (body mass index (BMI) and waist circumference) were weaker compared with other cardiometabolic variables (14), the association of sedentary behavior may extend beyond traditional measures of adiposity and may lie in the location of fat deposition, particularly within cells of nonadipose tissues that normally contain only small amounts of fat (ectopic fat). Such ectopic depositions result in excess lipids being driven into alternative, nonoxidative pathways, which in turn promote metabolically relevant cellular dysfunction (lipotoxicity).
The aim of this study, therefore, was to examine the association between objectively measured sedentary time and heart, liver, visceral, subcutaneous, and total body fat, independent of MVPA and whole-body fat in a population at high risk of T2DM.
The present study reports a baseline convenience subsample (n = 66) from the Walking Away from Type 2 Diabetes study (WA) and Project STAND (Sedentary Time and Diabetes). When combined, the full cohort for both studies included 1026 participants (WA, 833; Project STAND, 193). Both of these diabetes prevention studies were conducted by the same research group within the same geographical area (Leicestershire and South East Midlands, United Kingdom (UK)), and baseline data collection was undertaken during 2010. All measurements were performed by the same team of trained staff who followed identical standard operating procedures. A detailed description of both trial methods have been published elsewhere (38,39).
Participants (age 30–74 yr) were recruited from 10 primary care practices within the region of Leicestershire (city and county), UK. Individuals at high risk of impaired glucose regulation (IGR) (composite of impaired glucose tolerance (IGT) and/or impaired fasting glycemia (IFG)) or T2DM were identified using a modified version of the automated Leicester Risk Score, specifically designed to be administered in primary care (10). The Morbidity, Information Query, and Export Syntax program was used to assess medical records and rank individuals for diabetes risk using predefined weighted variables commonly held on practice databases (age, gender, BMI, family history of T2DM, and use of antihypertensive medication). Those scoring within the 90th percentile in each practice were invited to take part in the study. This approach has been shown to have good sensitivity and specificity for identifying participants at a high risk of IGR (10).
Young adults who were at risk of developing T2DM were recruited from primary care practices located across Leicestershire and the South East Midlands regions. Practice databases were searched for participants meeting the following inclusion criteria: a) age 18–40 yr with a BMI in the obese range (≥30 kg·m−2 and ≥27.5 kg·m−2 for South Asians) or b) age 18–40 yr with a BMI in the overweight range ≥25 kg·m−2 (≥23 kg·m−2 for South Asians) plus one additional risk factor—a family history of T2DM or CVD, previous gestational diabetes, polycystic ovarian syndrome, HbA1c ≥ 5.8% or IGR (38).
Individuals were excluded from both studies if they were taking steroids or had previously diagnosed T2DM. A written informed consent was obtained from all eligible participants, and both studies gained full ethical and governance approval.
Information on current smoking status, family history of T2DM, medication status, and ethnicity (coded according to census criteria) was obtained after an interview-administered questionnaire with a health care professional. Waist circumference was measured over light clothing between the lower rib margin and the iliac crest. Height and weight (Tanita TBE 611; Tanita, West Drayton, UK) were obtained by trained staff according to standard operating procedures. The subsequent values were used to compute BMI (kg·m−2). Systolic and diastolic blood pressure measurements (mm Hg) were taken three times in succession, and the mean of the last two was used for analysis.
Social deprivation was determined by assigning an Index of Multiple Deprivation (IMD) score to the participant’s resident area (based on postal code) (32). IMD scores are publicly available continuous measures of compound social and material deprivation. Areas are ranked from least deprived to most deprived on the basis of several dimensions linked to health outcomes (including income, employment, education, living environment, and health).
Venous blood samples were obtained after an overnight fast, and all assays were measured in the same laboratory. Analysis was conducted by individuals blinded to the patients’ identity using stable methods standardized to external quality assurance values. HbA1c was analyzed using the Bio-Rad Variant II HPLC system (Bio-Rad Clinical Diagnostics, Hemel Hempstead, UK), and total cholesterol was measured using standard enzymatic techniques.
Quantification of sedentary time
All eligible participants were asked to wear a triaxial accelerometer at the baseline visit (ActiGraph GT3X; ActiGraph Corp., Pensacola, FL), for a minimum of seven consecutive days during waking hours. These accelerometers translate raw accelerations into activity counts. Freedson cut-points (34) were used to categorize an epoch as sedentary (<25 counts per 15 s) or MVPA (≥488 counts per 15 s). Total physical activity volume represented the summation of counts within each epoch.
Nonwear time was defined as a minimum of 60 min of continuous zero counts, and days with at least 600 min of wear time were considered valid (13,14). To be included in the analysis, participants were required to have a minimum of four valid days (35).
A data analysis tool (KineSoft version 3.3.76; Kinesoft, New Brunswick, Canada; www.kinesoft.org) was used to process the accelerometer data.
Measure of adiposity
Magnetic resonance imaging (MRI) was performed at Glenfield Hospital, Leicester, UK, where heart, liver, visceral, subcutaneous, and total body fat (includes liver, intraabdominal, subcutaneous, and visceral fat) were quantified. MRI is a reliable modality for the assessment of adipose tissue and is capable of measuring fat distribution with high spatial resolution (22).
Scanning was performed using either a 1.5-T Avanto (WA) or a 3.0-T Skyra system (STAND) (Siemens Medical, Erlangen, Germany). Flexible body array coils were applied to the thorax and abdomen for signal reception. For lipid volume quantification, a two-point Dixon gradient echo pulse sequence was used to separate tissue water signal from lipid signal and to create two separate image sets with signal intensity showing “fat” and “water” content (21). Three-dimensional images were acquired axially with 5-mm slice thickness and in-plane resolution of 1.56 mm, interpolated to 0.78 mm. The field of view was 500 (left to right) by 375 mm (anterior to posterior). Images were acquired in three contiguous blocks, covering the thoracic, abdominal, and pelvic regions, with each block acquired in a breath-hold at full inspiration to minimize motion-related artefacts and to negate changes in slice position. The acquisition time for each block was 18 s. All scans were performed by the same team of trained staff according to standardized procedures.
Analysis of the MRI was performed using image analysis software produced in-house (Java Image Manipulation, version 7). All analyses were undertaken by the same researcher who was blinded to the clinical, anthropometric, and physical activity data.
For analysis, the “fat” and “water” images were mathematically combined to create a “fat percentage” image. Fat-containing pixels were then defined as those with a pixel intensity between 51% and 99% (100% being due to image artefact). The images were reconstructed into 15-mm thick contiguous slices from the top of the pulmonary trunk extending to the bottom of the symphysis pubis. Volumes of interest for the whole body and heart were created by outlining the perimeter of the body and heart, respectively, on each relevant slice using a mouse-controlled pointer and excluding those pixels outside the structures. The region of interest surrounding the heart included myocardial, epicardial (pericardial), and immediate extrapericardial (thoracic) fat.
Visceral (and retroperitoneal) fat was further separated by outlining the abdominal and chest wall muscles and excluding the pixels for the subcutaneous fat. Fat volume was calculated automatically by multiplying the cross-sectional areas of the fat-containing pixels, summed over all slices on which the tissue was outlined, by the slice thickness. This created the following three fat volumes: total body fat, visceral fat from the top of the pulmonary trunk to the bottom of the symphysis pubis, and the heart fat volume. The liver fat percentage was also measured using a representative region of interest created in the right lobe of liver avoiding the main portal veins. Subcutaneous fat was calculated by subtracting visceral fat from total body fat.
IBM SPSS Statistics version 20.0 (Chicago, IL) was used to conduct all statistical analyses. Linear regression analysis was used on the combined study cohorts to examine the independent association of sedentary time (independent variable) with various markers of regional fat deposition (dependent variable). We display results per 30 min of sedentary time for ease of interpretation.
Model 1 was adjusted for age (continuous), gender, ethnicity (White European/South Asian/other), social deprivation (continuous), family history of T2DM (yes/no), smoking status (current smoker/former smoker/never smoked), total cholesterol, HbA1c, systolic blood pressure, blood pressure medication (angiotensin-converting-enzyme (ACE) inhibitors (yes/no)), beta-blockers (yes/no), lipid-lowering medication (yes/no), accelerometer wear time (average number of minutes per day), and MVPA. We also undertook the same model but adjusted for total physical activity volume (counts per day) rather than MVPA, given that others have suggested that this mediates significant associations between sedentary behavior and metabolic health (24). To examine the extent to which total adiposity attenuated these relations, model 2 was further adjusted for whole-body fat. Models were assessed for normality, and multicolinearity was assessed through the variance inflation factor. To further represent the strength of sedentary time with markers of adiposity, variables were also examined as tertiles using ANCOVA procedures.
Significant observations were followed up with interaction terms to assess associations between sedentary time and study, sex, level of MVPA, whole-body fat, and HbA1c. All interactions were adjusted for the covariates listed in model 1.
Two-tailed P values of 0.05 or less were considered statistically significant for main effects. A value of P < 0.1 was considered significant for interactions. To allow for direct comparisons across fat deposition markers, results of the generalized linear regression analysis are also presented as the standardized beta-coefficient (β) ± SE.
Table 1 displays the demographic, anthropometric, MRI-derived, and accelerometer characteristics of included participants. In total, 32 participants from Project STAND (age, 33.1 ± 6.0 yr; male, 34.4%) and 34 participants from WA (age, 61.9 ± 8.0 yr; male, 64.7%) had valid measures of objective activity and MRI data.
There were no statistical differences (P > 0.05) in anthropometric, metabolic, and social deprivation measures between participants who were included in this analysis versus those not included (did not undergo an MRI scan).
Model 1 illustrates the linear relation between each 30-min block of sedentary time and markers of regional fat deposition. After adjustment for various confounders, including HbA1c, and MVPA, 30 min of sedentary time was associated with 20.5 cm3 higher heart fat (95% confidence interval (CI), 5.4–35.6), 1.4% higher liver fat (0.3–2.5), and a 409.2 cm3 higher visceral fat (127.6–690.8). All significant associations seen in model 1 persisted after further adjustment for whole-body fat in model 2 (15.7 cm3 higher heart fat (95% CI, 0.5–30.8), 1.2% higher liver fat (0.3–2.3), and a 191.3 cm3 higher visceral fat (2.7–368.8).
No significant associations were observed for whole-body and subcutaneous fat (Table 2). Supplementary Table 1 also displays the overall associations (presented as standardized β ± SE) in the combined cohort for total sedentary time with MRI-derived markers of regional fat deposition. (See Table, Supplemental Digital Content 1, Associations of total sedentary time with markers of MRI-derived regional fat distribution when adjusted for either MVPA or total physical activity volume, https://links.lww.com/MSS/A471.)
To provide a visual representation of reported associations, Figure 1 illustrates the associations between total sedentary time and heart fat, liver fat, and visceral fat when examined as tertiles after adjustment for the covariates listed previously. Compared with those in the lowest tertile of sedentary time, those in the highest tertile had, on average, 13.2 cm3 higher heart fat (P < 0.001), 1.6% higher liver fat (P < 0.001), and 556.3 cm3 higher visceral fat (P < 0.001).
Interaction analyses indicated a significant effect for study group with the older cohort (WA), demonstrating stronger associations of sedentary time with visceral fat (presented as unstandardized β (95% CI) (WA, 800.0 (345.3–1255.9), vs STAND, 69.4 (−297.8 to 436.6) (P for interaction = 0.010)) (Table 3). Sex interactions also indicated that sedentary time had a larger effect on visceral fat in males (male, 779.1 (171.4–1386.9), vs female, 133.4 (−269.0 to 544.8) (P for interaction = 0.049)) (Table 4). No other significant interactions for associations with measures of ectopic fat were observed for study group, sex, whole-body fat, MVPA, or HbA1c level (P > 0.1).
The findings previously discussed were unaffected if waist circumference or BMI rather than whole-body fat was used in model 2 (data not shown).
This study conducted in individuals at high risk of T2DM demonstrated that sedentary time was associated with heart, liver, and visceral fat, independent of measured confounders, including glycemia, whole-body fat, and MVPA. The findings from this study extend previous cross-sectional results observed in the general population by demonstrating the association of objectively measured sedentary behavior with markers of regional fat deposition. To our knowledge, this is the first study to show associations between sedentary time and liver, heart, and visceral fat in a population with a high risk of chronic disease.
The observation that sedentary time is associated with liver fat, independent of adiposity, is a novel finding and may suggest an independent association between sedentary time and liver fat accumulation. Nevertheless, the associations observed between sedentary time and heart and visceral fat are in contrast to most (11,25,29), but not all (20), previous literature, which has tended to show either weak or no associations. The discrepancy in findings between studies may be partially due to sedentary time having been previously quantified using self-report (29), which has high measurement error (27) or undertaken in generally healthy, low-risk populations compared with the present analysis, which specifically targeted individuals with a high risk of chronic disease and underlying metabolic dysfunction.
Visceral, hepatic, and cardiac adiposity, rather than obesity per se, have all been causally associated with glucose, insulin metabolism, and subsequent metabolic dysfunction (6). These mechanisms may induce multiple autocrine, paracrine, and endocrine influences, which include the proinflammatory cytokine response (28). Therefore, the associations observed for regional and ectopic fat in the present study may help partially explain the relatively strong association between sedentary time and glucose metabolism consistently reported in those with a high risk of, or diagnosed, T2DM (3,4,14). Although a causal link between sedentary behavior and differential regional and ectopic fat distribution has not been directly elucidated, there is some supporting evidence. Because this analysis and others have found only relatively weak associations between sedentary behavior and markers of overall adiposity (4,13,14), it is likely that potential mechanisms are beyond total energy balance. One possible candidate could be through the actions of lipoprotein lipase (LPL). Research using animal models of sedentary behavior has shown that muscle inactivity causes rapid and dramatic reductions in LPL activity (12). In turn, it has been suggested that reductions in LPL mass and activity may directly promote intraabdominal visceral fat accumulation (17). Therefore, if applicable to humans, it may be plausible that muscle inactivity induced by prolonged/chronic sitting-related sedentary behavior causes reductions in postural muscle LPL activity. This in turn may help promote the deposition of triglycerides into cells of nonadipose tissues, fueling the detrimental phenomenon of ectopic overaccumulation (31). However, this potential mechanism lacks confirmation in human research and thus remains suggestive rather than definitive. Our study supports the need for further experimental research in humans focusing on lipid metabolism and distribution.
Sedentary time in the current study was shown to have stronger associations with visceral fat in older adults compared to younger adults and in males compared to females. Although visceral fat is known to increase with age, clear sex dimorphisms also exist because of anatomical differences in adipose tissue deposition (6). For example, even after correcting for total body fat mass, women have been shown to have a lower ratio of visceral adipose tissue to total body fat mass compared with that of men (19). The underlying mechanisms driving these observations are largely unknown; it is likely to be a complex phenotype that includes sex hormones and adipose tissue storage dysfunction in several sites, including the heart and liver (6). Therefore, the preliminary findings from this study further highlight the importance of carefully considering the population under investigation in future experimental and epidemiological investigations.
The present study has several strengths, most notably the use of objective methodologies to estimate exposures and outcomes in a population with high risk of T2DM recruited through primary care. This is particularly important because our population is representative of those who are likely to be identified as being at high risk of T2DM within routine care and referred onto available prevention programs. Furthermore, all participants were from the same geographical location, with similar risk, metabolic, and physical activity profiles. All measurements (including MRI scans) were also performed by the same team of trained staff, following identical standard operating procedures.
However, the following limitations should be considered. Firstly, given the high-risk nature of the cohort, the results may have limited generalizability and the small sample size may restrict the external validity of our findings. Secondly, the cross-sectional design limits inference about the direction of causality between the sedentary variables and MRI markers; reverse causality remains a possibility, particularly as the relation between adiposity and sedentary time may be bidirectional (9). It is also plausible that unmeasured lifestyle variables (e.g., snacking, alcohol consumption) and preexisting comorbidities may have confounded the observed relations. Thirdly, cardiac images were ungated and we were unable to distinguish between pericardial, epicardial, and pericoronary fat. However, it could be argued that measuring whole heart fat reduces any potential bias particularly related to measurement in leaner individuals. Fourthly, accelerometers rely on categorizing movement (acceleration) as opposed to distinguishing between specific postures (sitting, lying, and standing behaviors), which may lead to an underestimation of the true association between sedentary time and markers of adiposity.
In conclusion, the present study provides new evidence suggesting that objectively measured sedentary behavior is associated with heart, liver, and visceral fat in individuals at a high risk of T2DM. Interestingly, because the associations remained after adjustment for whole-body fat and MVPA, it may suggest that sedentary behavior is linked to selective depositions of fat, which cannot be fully explained by an increase in overall adiposity and may act via an independent mechanism. However, given the limitations, more research is needed to determine the distinct pathological effects of each type of fat and how these end points might be associated with different behaviors, particularly sedentary time.
The research was supported by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care—Leicestershire, Northamptonshire, and Rutland, the University of Leicester Clinical Trials Unit, and the NIHR Leicester–Loughborough Diet, Lifestyle, and Physical Activity Biomedical Research Unit, which is a partnership among University Hospitals of Leicester National Health Service (NHS) Trust, Loughborough University, and the University of Leicester.
MRI scans (for the WA cohort only) were funded by Unilever Discover, UK. Project STAND was funded by the Medical Research Council and National Prevention Research Initiative funding partners (MRC project number 91409). Dr. G. McCann is funded by a postdoctoral NIHR fellowship.
The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health. The authors declare no conflict of interest.
The results of the present study do not constitute endorsement by the American College of Sports Medicine.
1. Calabro P, Yeh ET. Intra-abdominal adiposity, inflammation, and cardiovascular risk: new insight into global cardiometabolic risk. Curr Hypertens Rep
. 2008; 10 (1): 32–8.
2. Chatterton H, Younger T, Fischer A, Khunti K, Programme Development Group. Risk identification and interventions to prevent type 2 diabetes
in adults at high risk
: summary of NICE guidance. BMJ
. 2012; 345: e4624.
3. Cooper AJ, Brage S, Ekelund U, Wareham NJ, Griffin SJ, Simmons RK. Association between objectively assessed sedentary time and physical activity with metabolic risk factors among people with recently diagnosed type 2 diabetes
. 2014; 57 (1): 73–82.
4. Cooper AR, Sebire S, Montgomery AA, et al. Sedentary time, breaks in sedentary time and metabolic variables in people with newly diagnosed type 2 diabetes
. 2012; 55 (3): 589–99.
5. Despres JP. Excess visceral adipose tissue/ectopic fat the missing link in the obesity paradox? J Am Coll Cardiol
. 2011; 57 (19): 1887–9.
6. Despres JP, Lemieux I. Abdominal obesity and metabolic syndrome. Nature
. 2006; 444 (7121): 881–7.
7. Ebbert JO, Jensen MD. Fat depots, free fatty acids, and dyslipidemia. Nutrients
. 2013; 5 (2): 498–508.
8. Edwardson CL, Gorely T, Davies MJ, et al. Association of sedentary behaviour with metabolic syndrome: a meta-analysis. PLoS One
. 2012; 7 (4): e34916.
9. Golubic R, Wijndaele K, Sharp SJ, et al. Physical activity, sedentary time and gain in overall and central body fat: 7-year follow-up of the ProActive trial cohort. Int J Obes (Lond)
. 2015; 39: 142–8.
10. Gray LJ, Davies MJ, Hiles S, et al. Detection of impaired glucose regulation and/or type 2 diabetes
mellitus, using primary care
electronic data, in a multiethnic UK community setting. Diabetologia
. 2012; 55 (4): 959–66.
11. Hamer M, Venuraju SM, Urbanova L, Lahiri A, Steptoe A. Physical activity, sedentary time, and pericardial fat in healthy older adults. Obesity (Silver Spring)
. 2012; 20 (10): 2113–7.
12. Hamilton MT, Hamilton DG, Zderic TW. Exercise physiology versus inactivity physiology: an essential concept for understanding lipoprotein lipase regulation. Exerc Sport Sci Rev
. 2004; 32 (4): 161–6.
13. Healy GN, Matthews CE, Dunstan DW, Winkler EA, Owen N. Sedentary time and cardio-metabolic biomarkers in US adults: NHANES 2003–06. Eur Heart J
. 2011; 32 (5): 590–7.
14. Henson J, Yates T, Biddle SJ, et al. Associations of objectively measured sedentary behaviour and physical activity with markers of cardiometabolic health. Diabetologia
. 2013; 56 (5): 1012–20.
15. Hocking S, Samocha-Bonet D, Milner KL, Greenfield JR, Chisholm DJ. Adiposity and insulin resistance in humans: the role of the different tissue and cellular lipid depots. Endocr Rev
. 2013; 34 (4): 463–500.
16. Kay SJ, Fiatarone Singh MA. The influence of physical activity on abdominal fat: a systematic review of the literature. Obes Rev
. 2006; 7 (2): 183–200.
17. Kobayashi J, Tashiro J, Murano S, Morisaki N, Saito Y. Lipoprotein lipase mass and activity in post-heparin plasma from subjects with intra-abdominal visceral fat accumulation. Clin Endocrinol (Oxf)
. 1998; 48 (4): 515–20.
18. Kuk JL, Katzmarzyk PT, Nichaman MZ, Church TS, Blair SN, Ross R. Visceral fat is an independent predictor of all-cause mortality in men. Obesity (Silver Spring)
. 2006; 14 (2): 336–41.
19. Kuk JL, Lee S, Heymsfield SB, Ross R. Waist circumference and abdominal adipose tissue distribution: influence of age and sex. Am J Clin Nutr
. 2005; 81 (6): 1330–4.
20. Larsen BA, Allison MA, Kang E, et al. Associations of physical activity and sedentary behavior
with regional fat deposition. Med Sci Sports Exerc
. 2014; 46 (3): 520–8.
21. Le-Petross H, Kundra V, Szklaruk J, Wei W, Hortobagyi GN, Ma J. Fast three-dimensional dual echo dixon technique improves fat suppression in breast MRI
. J Magn Reson Imaging
. 2010; 31 (4): 889–94.
22. Machann J, Thamer C, Stefan N, et al. Follow-up whole-body assessment of adipose tissue compartments during a lifestyle intervention in a large cohort at increased risk for type 2 diabetes
. 2010; 257 (2): 353–63.
23. Mahabadi AA, Massaro JM, Rosito GA, et al. Association of pericardial fat, intrathoracic fat, and visceral abdominal fat with cardiovascular disease burden: the Framingham Heart Study. Eur Heart J
. 2009; 30 (7): 850–6.
24. Maher C, Olds T, Mire E, Katzmarzyk PT. Reconsidering the sedentary behaviour paradigm. PLoS One
. 2014; 9 (1): e86403.
25. McGuire KA, Ross R. Incidental physical activity and sedentary behavior
are not associated with abdominal adipose tissue in inactive adults. Obesity (Silver Spring)
. 2012; 20 (3): 576–82.
26. Nazare JA, Smith JD, Borel AL, et al. Ethnic influences on the relations between abdominal subcutaneous and visceral adiposity, liver fat, and cardiometabolic risk profile: the International Study of Prediction of Intra-Abdominal Adiposity and Its Relationship With Cardiometabolic Risk/Intra-Abdominal Adiposity. Am J Clin Nutr
. 2012; 96 (4): 714–26.
27. Neilson HK, Robson PJ, Friedenreich CM, Csizmadi I. Estimating activity energy expenditure: how valid are physical activity questionnaires? Am J Clin Nutr
. 2008; 87 (2): 279–91.
28. Richardson VR, Smith KA, Carter AM. Adipose tissue inflammation: feeding the development of type 2 diabetes
. 2013; 218 (12): 1497–504.
29. Saunders TJ, Tremblay MS, Despres JP, Bouchard C, Tremblay A, Chaput JP. Sedentary behaviour, visceral fat accumulation and cardiometabolic risk in adults: a 6-year longitudinal study from the Quebec Family Study. PLoS One
. 2013; 8 (1): e54225.
30. Sedentary Behaviour Research Network. Letter to the editor: standardized use of the terms “sedentary” and “sedentary behaviours”. Appl Physiol Nutr Metab
. 2012; 37 (3): 540–2.
31. Snel M, Jonker JT, Schoones J, et al. Ectopic fat and insulin resistance: pathophysiology and effect of diet and lifestyle interventions. Int J Endocrinol
. 2012; 2012: 983814.
33. Thorp AA, Owen N, Neuhaus M, Dunstan DW. Sedentary behaviors and subsequent health outcomes in adults a systematic review of longitudinal studies, 1996–2011. Am J Prev Med
. 2011; 41 (2): 207–15.
34. Freedson PS, Melanson E, Sirard J. Calibration of the Computer Science and Applications, Inc. accelerometer. Med Sci Sports Exerc
. 1998; 30 (5): 777–81.
35. Trost SG, McIver KL, Pate RR. Conducting accelerometer-based activity assessments in field-based research. Med Sci Sports Exerc
. 2005; 37 (11 Suppl): S531–43.
36. Wijndaele K, Orrow G, Ekelund U, et al. Increasing objectively measured sedentary time increases clustered cardiometabolic risk: a 6 year analysis of the ProActive study. Diabetologia
. 2014; 57 (2): 305–12.
37. Wilmot EG, Edwardson CL, Achana FA, et al. Sedentary time in adults and the association with diabetes, cardiovascular disease and death: systematic review and meta-analysis. Diabetologia
. 2012; 55 (11): 2895–905.
38. Wilmot EG, Edwardson CL, Biddle SJ, et al. Prevalence of diabetes and impaired glucose metabolism in younger ‘at risk’ UK adults: insights from the STAND programme of research. Diabet Med
. 2013; 30 (6): 671–5.
39. Yates T, Davies MJ, Henson J, et al. Walking away from type 2 diabetes
: trial protocol of a cluster randomized controlled trial evaluating a structured education programme in those at high risk
of developing type 2 diabetes
. BMC Fam Pract
. 2012; 13: 46.