Intramuscular Fat and Associations with Metabolic Risk Factors in the Framingham Heart Study (original) (raw)

• Journal List
• HHS Author Manuscripts
• PMC3696991

Arterioscler Thromb Vasc Biol. Author manuscript; available in PMC 2014 Apr 1.

Published in final edited form as:

PMCID: PMC3696991

NIHMSID: NIHMS446785

Kate E. Therkelsen

1Boston University School of Medicine, Boston, Massachusetts, 02118

2National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, MA 01702

Alison Pedley

2National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, MA 01702

Elizabeth K. Speliotes

3Department of Internal Medicine and Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan, 48109

Joseph M. Massaro

4Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, 02118

Joanne Murabito

2National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, MA 01702

5Section of General Internal Medicine, Boston University School of Medicine, Boston, Massachusetts, 02118

Udo Hoffmann

6Department of Medicine and Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, 02114

Caroline S. Fox

7Division of Endocrinology and Metabolism, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, 02115

8NHLBI Division of Intra-mural research and the Center for Population Studies, Framingham, MA

1Boston University School of Medicine, Boston, Massachusetts, 02118

2National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, MA 01702

3Department of Internal Medicine and Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan, 48109

4Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, 02118

5Section of General Internal Medicine, Boston University School of Medicine, Boston, Massachusetts, 02118

6Department of Medicine and Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, 02114

7Division of Endocrinology and Metabolism, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, 02115

8NHLBI Division of Intra-mural research and the Center for Population Studies, Framingham, MA

Corresponding Author Information: Caroline S. Fox, MD, MPH, 73 Mt Wayte Ave Suite #2, Framingham, MA 01702, (508) 935-3447 (phone), (508) 872-2678 (fax), vog.iblhn@acxof

Supplementary Materials

Supplement 1.

GUID: 4A8BB426-0F73-4203-B36E-D9EF883E7C34

Supplement 2.

GUID: 39FE576B-B059-4F94-8CDB-AAC6FBA26F8F

Abstract

Objective

Intramuscular fat accumulates between muscle fibers or within muscle cells. We investigated the association of intramuscular fat with other ectopic fat deposits and metabolic risk factors.

Approach and Results

Participants (n = 2945; 50.2% women; mean age 50.8 years) from the Framingham Heart Study underwent multidetector computed tomography scanning of the abdomen. Regions of interest were placed on the left and right paraspinous muscle and the muscle attenuation (MA) in Hounsfield units were averaged. We examined the association between MA and metabolic risk factors in multivariable models and additionally adjusted for BMI and visceral fat (VAT) in separate models. MA was associated with dysglycemia, dyslipidemia, and hypertension in both sexes. In women, per standard deviation decrease in MA, there was a 1.34 (95% CI 1.10–1.64) increase in the odds of diabetes, a 1.40 (95% CI 1.22 – 1.61) increase in the odds of high triglycerides, and a 1.29 (95% CI 1.12 – 1.48) increase in the odds of hypertension. However, none of these associations persisted after adjustment for BMI or VAT. In men, we observed similar patterns for most risk factors. The exception was metabolic syndrome, which retained association in women even after adjustment for BMI and VAT, and low HDL and high triglycerides in men, whose associations also persisted after adjustment for BMI and VAT.

Conclusions

MA was associated with metabolic risk factors, but most of these associations were lost after adjustment for BMI or VAT. However, a unique association remained for metabolic syndrome in women and lipids in men.

Keywords: Metabolism, obesity, intramuscular fat, epidemiology

Introduction

Body mass index (BMI) is often used to determine a patient’s risk for excess body fat related disease. However, BMI alone does not account for the heterogeneity of health outcomes from obesity.1 A body of literature exists to investigate the association of different ectopic fat depots in order to better characterize the variety of obesity-related health risks.2–7 Pericardial, perivascular, and renal sinus fat are associated with the hypothesized local effects of fat,4–6 whereas visceral adipose tissue (VAT), intrahepatic fat and intramuscular fat are associated with hypothesized systemic effects of adipose tissue.3;4;7;8

Intra-muscular fat is of particular interest due to the important role of muscle, particularly skeletal muscle, in insulin-mediated glucose uptake as well as in fat perioxidation.8 Due to skeletal muscle’s high insulin sensitivity and large percentage of body mass, fat accumulation and concomitant loss of insulin sensitivity potentially plays an important role in insulin resistance, obesity, and metabolic syndrome.8 Previous studies have shown that increased intramuscular fat is associated with decreased insulin sensitivity.9–14 The reason for this is not entirely understood, although studies have suggested that it may be due to altered action of mitochondrial proteins due to increased lipid peroxidation products.10

The current literature on the association between intramuscular fat and metabolic risk factors is limited by relatively small sample sizes (n = 32–173), samples enriched for adiposity,10;11;14 and often a limited panel of metabolic risk factors focusing on glucose-related factors.9–14 Finally, these studies generally did not account for potentially confounding factors, most notably BMI or VAT.9–14 Therefore, the goal of our research was to use a large, community-based cohort to examine the association between intramuscular fat and a comprehensive panel of metabolic risk factors while accounting for potentially confounding factors as well as for BMI and VAT.

Results

Study Sample Characteristics

Study sample characteristics can be found in Table 1. Our sample was middle-aged and half were women. On average, our sample was overweight. Five and a half percent of women had diabetes and 27.1% had metabolic syndrome. The distribution of muscle Hounsfield unit readings for our sample can be seen in Figure 1: the median MA in women was 56 Hounsfield units and for men it was 59 Hounsfield units.

Distribution of Muscle Hounsfield Units

Table 1

Study Sample Characteristics

Correlations between Intramuscular Fat and Cardiovascular Disease Risk Factors

Table 2 shows the sex-specific, age-adjusted Pearson correlation coefficients between MA and metabolic parameters. In women, MA was inversely correlated with all of the adiposity measures and metabolic risk factors we examined. That is, as muscle attenuation decreased (which corresponds with a higher level of intra-muscular fat), most metabolic risk factors were more adverse. For men, correlations between MA and metabolic parameters were generally similar to those of the women, but of a smaller magnitude. Among women, the correlation of MA with BMI is −0.26 (R2 = 6.6%), with VAT −0.48 (R2 = 23.2%), and with both, the R2 = 25.67%. For men, the correlation of MA with BMI is −0.19 (R2 = 3.73%), with VAT −0.38 (R2 = 14.7%), and with both, R2 = 15.72%.

Table 2

Age-adjusted Correlation Coefficients between Muscle Attenuation and Measures of Adiposity and Metabolic Risk Factors

Multivariable-adjusted Associations between MA and Metabolic Risk Factors

MA showed associations with glycemic, lipidemic, and blood-pressure related risk factors in both men and women. We examined these associations in Tables 3 & 4.

Table 3

Multivariable-adjusted Associations between Muscle Attenuation and Continuous Metabolic Risk Factors modeled per 1 SD decrease in muscle HU

Table 4

Multivariable-adjusted Associations of Metabolic Risk Factor Odds Ratios with Muscle Attenuation

Glycemic Risk Factors

In both men and women, MA was inversely associated with all of the glycemic risk factors that we investigated. For example, in women, per 1 standard deviation decrease in MA there was a 2.13 mg/dL increase in plasma glucose. This association persisted after adjustment for BMI (p = 0.014) but not after adjustment for VAT (p = 0.8). Generally, other glycemic associations in women did not persist after adjustment for BMI or VAT with the exception of metabolic syndrome (OR 1.50, p<0.0001 after BMI adjustment; OR 1.22, p=0.03 after VAT adjustment). This association of decreasing MA and increasing metabolic syndrome can even be seen after stratifying the data into VAT and SAT tertiles in Figures 2 and ​3, respectively. Figures 2 and ​3 examine the HU data within tertiles of VAT in order to examine the association between clinical risk factors and muscle HU within a narrower range of VAT. Results were generally similar but less striking in men.

Age-adjusted Metabolic Risk Factors by Muscle Tertiles within VAT Tertiles.

*p<0.05, **p<0.01, ***p<0.001

IR = insulin resistance, DM = diabetes, MetS = metabolic syndrome, High TG = high triglycerides, Low HDL = low high density lipoprotein, HTN = hypertension

Age-adjusted Metabolic Risk Factors by Muscle Tertiles within SAT Tertiles

*p<0.05, **p<0.01, ***p<0.001

IR = insulin resistance, DM = diabetes, MetS = metabolic syndrome, High TG = high triglycerides, Low HDL = low high density lipoprotein, HTN = hypertension

As MA decreased, insulin resistance in both women and men increased (women: OR 1.27, p=0.001; men: OR 1.04, p=0.5). However, upon adjustment for VAT, we noted that decreasing MA was associated with decreasing odds of insulin resistance (women: OR 0.73, p=0.001; men: OR 0.76, p=0.002). Displayed graphically in Figure 2, we saw a similar stepwise decrease of the prevalence of insulin resistance with decreasing MA within each VAT tertile.

Lipid Risk Factors

As MA decreased, there was an increase in log triglycerides (0.09 per standard deviation decrease in MA, p < 0.0001) and decrease in HDL (−1.54 mg/dL per standard deviation decrease in MA, p = 0.002) in women. After adjustment for BMI, the association with log triglycerides persisted (p = 0.0003), but did not persist after VAT adjustment (p=0.8). In men, we observed the opposite effect. Per standard deviation decrease in MA, there was a non-significant decrease in log triglycerides, even after adjustment for BMI (p=0.003) and VAT (p<0.0001). Displayed graphically in Figure 2, within each VAT tertile, as MA decreased, there was a stepwise decrease in the prevalence of high triglycerides, most prominently in the second VAT tertile. Similar trends were observed for low HDL in men. Both of these trends also persisted when the data was stratified by SAT tertiles instead of VAT tertiles (Figure 3).

Blood-Pressure Related Risk Factors

Blood-pressure related risk factors had less notable associations with MA. In the simple model, we observed associations between decreasing MA and increasing SBP in both women (1.70 mmHg per standard deviation decrease in MA, p = 0.0004) and men (0.97 mmHg per standard deviation decrease in MA, p = 0.03) and similar associations for women with DBP (0.58 mmHg per standard deviation decrease in MA, p = 0.04) and hypertension (1.29 odds per standard deviation decrease in MA, p = 0.0003). However, none of these associations persisted in the BMI or VAT adjusted models.

Framingham Risk Score

In women, per SD decrease in muscle HU, the Framingham Risk Score was higher, even after adjusting for BMI or VAT (Table 3). Similar observations were not observed in men.

Interaction Testing

We observed sex interactions for impaired fasting glucose, metabolic syndrome, log triglycerides, high triglycerides, SBP and DBP. In all cases, the association was stronger in women (Tables 3 and ​4). We also tested for age interactions (Supplemental Table I); we observed evidence for age interactions for DBP and insulin resistance, where associations were stronger in the younger group. When stratified by cohort, the results were not materially different (Supplemental Table II).

Discussion

Principal Findings

Our study shows that MA is associated with measures of adiposity including BMI, waist circumference, and VAT as well as metabolic risk factors. These associations were stronger in women, particularly for lipid and blood pressure traits. Overall, most of the associations between MA and metabolic risk factors did not persist after accounting for BMI or VAT, with the exception of metabolic syndrome in women, and HOMA-IR, triglycerides, and HDL in men.

In Context of Current Literature

Prior studies have found, using a diverse set of technology and techniques including NMR spectroscopy,9;10 that there is an association between increasing intramuscular fat and increasing insulin resistance (and related glycemic parameters).9–14 Our results without adjustment for BMI or VAT are consistent with this finding.

However, upon adjustment for BMI and VAT, our findings suggest an unexpected association between increasing intramuscular fat and decreasing insulin resistance. The reasons for this remain unclear. Given the correlation between intramuscular fat and generalized and central adiposity, the issue of collinearity is potentially of concern. However, this is unlikely to serve as the sole explanation for the present findings for the following reasons. First, we observed only a modest association between MA and VAT (r = 0.21 – 0.37), which is unlikely to result in significant collinearity. Second, when we considered the crude MA data stratified by tertiles of either VAT or SAT, we observed similar unexpected associations between decreasing MA and decreasing insulin resistance. One study has similarly adjusted for VAT and also observed a change in the direction of the association between MA and insulin resistance after accounting for VAT, although the ability to make inferences is limited by the modest sample size (n = 173).12 Other studies have also found an association between high intramuscular fat and high insulin sensitivity in endurance athletes.15;16 Exercise increases diacylglycerol acyltransferase, which catalyses triglyceride production from diacylglycerol and fatty acyl-CoA and protects against insulin resistance.17 However, given that our sample is drawn from a community-based study, it is unlikely that this mechanism can explain the present findings.

Similarly, in men after adjustment for VAT, we observed an unexpected directionality of association between decreasing MA and decreasing log triglycerides and increasing HDL. Triglyceride-to-HDL ratio has previously been found to be a useful proxy for insulin resistance in white samples such as ours.18 Thus, it is of particular interest that we identified paradoxical associations with triglycerides, HDL, and insulin resistance.

Fewer studies have investigated the association between MA and lipid or blood pressure parameters. In 79 men with a BMI ≥ 25 kg/m2, one study observed inverse correlations between MA and glucose, HOMA-IR, HDL, SBP and DBP, consistent with our findings.11 We extend the current literature by inclusion of a larger, community-based sample, which includes women in addition to men, excellent ascertainment of multiple important covariates, and a comprehensive panel of metabolic risk factors.

Potential Mechanisms

Several studies have investigated the physiologic mechanisms of association between intramuscular fat and insulin resistance.10;19–22 Increased lipid peroxidationmay cause mitochondrial protein modification as well as modification to other proteins along the insulin receptor pathway.10 This could decrease the sensitivity of the muscle to insulin and lead to insulin resistance.10 Obesity and diabetes are associated with increased transport of long chain fatty acids into skeletal muscle via an increase in the number of fatty acid translocases.21 The cause-effect relationship remains uncertain; however, one study did find that after high-fat feeding, mice developed both insulin resistance and increased intramuscular fat in their skeletal muscles.20 Comparatively, on the same high-fat diet, liver insulin resistance preceded increased intrahepatic fat.20 Therefore, intramuscular fat may be more closely associated with insulin resistance than other ectopic fats such as intrahepatic fat.

We have consistently observed stronger associations between ectopic fat depots and metabolic risk factors in women as compared to men.23 While the etiology of this remains uncertain, potential mechanisms include higher rates of delivery of hepatic free fatty acids in women as compared to men,24 gender differences in fat distribution whereby women have more subcutaneous but less visceral fat as compared to men,23 and a stronger familial contribution to the distribution of fat in women as compared to men.25 Whether this can account for the lower relative muscle attenuation in women as compared to men requires further inquiry.

Implications

Our findings emphasize the wide range of associations between MA and metabolic risk factors. Some of these associations persisted even after adjustment or stratification for VAT, an ectopic fat deposit strongly associated with metabolic risk factors.3;4 While MA is not a measure that could easily be used clinically, these associations have implications for future research. For example, it remains uncertain why there were unexpected trends in the associations of MA for insulin resistance, triglycerides and HDL after accounting for VAT in men. Therefore, further research into these associations and the mechanisms behind them could help to explain the heterogeneity of obesity-related health outcomes. If a proxy for intramuscular fat that is more appropriate for clinical use is later found, these associations could be used to help make metabolic prognoses.

Strengths and Limitations

Strengths of this study include its large sample size, which allowed us to investigate small but potentially significant associations between MA and metabolic risk factors. We identified and adjusted our findings for a number of well-measured potential confounders. Our sample was not drawn from a sample ascertained for obesity or metabolic disease; therefore, our findings are generalizable to a wide population. Our covariates and outcomes were well-documented. Some limitations warrant mention. We used MA as a proxy for intramuscular fat, which is not a direct measurement of intramuscular fat. While this may have lead to misclassification in our sample, it has likely biased our results towards the null, but is unlikely to account for our significant observations. Our sample is also primarily white; therefore the generalizability of our results to other ethnic groups is uncertain. While clinical and computed tomography data in the offspring was not collected contemporaneously; however, our stratified analyses do not suggest that this is an important explanation for our findings. In general, the associations between muscle HU and clinical risk factors are slightly lower as compared to larger fat depots such as visceral fat. Whether this is due to physiologic reasons of relative metabolic fat activity as compared to the use of a gold standard method of assessment warrants further study. Finally, due to our study’s observational and cross-sectional design, causality and temporality cannot be determined from our results.

Conclusions

Intramuscular fat, measured using MA on MDCT, was associated with a wide panel of metabolic risk factors. While some of these associations did not persist after adjustment for BMI and VAT, unique associations remained for metabolic syndrome in women and lipid-related parameters in men. Further investigation into this association may ultimately lead to a better understanding of the heterogeneity of obesity-related health outcomes.

Methods

Study Participants

Participants were derived from the Framingham Heart Study, a cohort study initiated in 1948 with 5209 participants.1 The offspring of the original participants as well as offspring spouses were recruited to the study in 19712 and the third generation and their spouses were recruited in 2002.3 Participants undergo regular examinations and health history updates.

This study was comprised of Framingham Heart Study offspring and third generation participants who had undergone multidetector computed tomography (MDCT) from 2002–2005. Men were required to be over 35 years of age and women over 40 years. Participants weighing >160 kg did not undergo MDCT due to weight restrictions. Of the 3529 participants who underwent CT scanning, 3127 had muscle attenuation (MA) measurements. Of the 3127, 2945 had a complete set of covariates and were included in the final analysis (1479 women, 1466 men). Of those final analysis participants without diabetes (1398 women, 1365 men), 1280 women and 1277 men had insulin data available and were included in analyses of HOMA-IR and insulin resistance.

Measurement of Muscle Attenuation and Visceral and Subcutaneous Fat

Participants underwent abdominal CT imaging while in a supine position. As previously described, an eight-slice MDCT (LightSpeed Ultra, General Electric, Milwaukee, WI, USA) was used to image 25 contiguous 5-mm thick slices (120 kVp, 400 mA, 500 ms gantry rotation time, 3:1 table feed).4 One hundred and twenty-five millimeters above S1 level were covered by the scan.

A ~1 cm region of interest was placed on both the left and right paraspinous muscles at mid-abdomen level. The MA of these two areas were averaged in order to calculate mean MA; the correlation between the two reads is 0.86. The inter-reader correlation was 0.88. We focused on paraspinous muscles due to their slow-twitch characteristics, which tend to have more intramuscular fat than fast-twitch muscles.5;6 Skeletal MA on MDCT scans to evaluate intramuscular fat has been previously validated by muscle biopsy study.7 In vivo vastus lateralis MA via MDCT had a Pearson correlation coefficient of −0.43 (P<0.01, n=45) with vastus lateralis muscle fiber lipid content (% area) determined with histochemical staining of lipid after biopsy.7

Visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) volumes were determined via manual tracing of the abdominal muscular wall on 25 continuous 5 mm slices from an eight-slice MDCT scan of the abdomen. The window for fat was −195 to −45 Hounsfield units with a window center of −120 Hounsfield units. All fat outside of the manual tracing was defined as SAT while that inside of the tracing was defined as VAT. The inter-reader correlation for SAT was 0.997 and for VAT was 0.992.8

Cardiovascular Disease Risk Factor Assessment

Risk factors for this study were determined at the seventh offspring examination (1998– 2001) and the first third generation examination (2002–2005). Fasting glucose and lipids were measured after an overnight fast. Impaired fasting glucose (IFG) was defined as 100–125 mg/dL fasting plasma glucose for participants not on diabetes treatment. Diabetes (DM) was classified as fasting plasma glucose of ≥ 126 mg/dL or current use of insulin or a hypoglycemic treatment. The modified Adult Treatment Panel III criteria was used to classify metabolic syndrome.9 Insulin was calculated via radioimmunoassay (Offspring Cohort) and ELISA (Third Generation cohort). Third Generation cohort insulin values were standardized to the values of the Offspring Cohort in order to account for the different insulin calculation methods.10 HOMA-IR was determined by fasting glucose multiplied by fasting insulin divided by 22.5.11 Insulin resistance (IR) was classified as the top quarter of HOMA-IR in participants without diabetes.12 High TG was classified as ≥150 mg/dL or if the participant was on lipid-lowering medication. Low HDL was defined as HDL < 50 mg/dL for women and <40 mg/dL for men. Blood pressure was determined by averaging two reads taken at rest with a mercury column sphygmomanometer. Hypertension (HTN) was determined by systolic blood pressure (SBP) ≥ 140 mmHg, diastolic blood pressure (DBP) ≥ 90 mmHg or current use of hypertension treatment medication.

Measurement of Covariates

Participants were designated as current smokers if they had smoked ≥ 1 cigarette per day in the year prior to examination. Alcohol use was determined via a physician-administered questionnaire. Participants were split into two alcohol use groups based on whether they had > 7 drinks/week for women or > 14 drinks/week for men. Physical activity was calculated on the physical activity index (PAI) after a physician-administered questionnaire concerning the average number of hours of sleep and sedentary, slight, moderate and heavy activity the participant took part in per day. Women without menstrual bleeding for ≥ 1 year were considered postmenopausal. BMI was determined by participant weight in kilograms divided by the square of participant height in meters. Waist circumference was calculated at the level of the umbilicus.

Statistical Analysis

MA approximated the normal distribution for both women and men. Logs of triglycerides and HOMA-IR were performed to normalize their distributions. Sex-specific, age-adjusted correlations between MA and metabolic risk factors were calculated.

For continuous risk factors, multivariable-adjusted linear regression models were used to model the relative change in the outcome per one standard deviation decrease in MA (i.e., more intra-muscular fat). For dichotomous risk factors, a multivariable-adjusted logistic model was used. Three sex-specific models were fit separately for each of the following outcomes: plasma glucose, log HOMA-IR, insulin resistance, impaired fasting glucose, diabetes, metabolic syndrome, log triglycerides, high triglycerides, HDL, low HDL (<40 mg/dl in men and <50 mg/dl in women), SBP, DBP, and hypertension. In the first model, covariates included age, current smoking status, alcohol use, physical activity, treatment for hypertension (for SBP and DBP models), dyslipidemia (for triglyceride and HDL models), and diabetes (for glucose models) as well as menopausal status and hormone replacement therapy in models in women. The second model additionally adjusted for BMI. The third model additionally adjusted for VAT (but not for BMI).

The Framingham Risk score was calculated as previously described13 and examined in association with muscle HU among participants free of clinical CVD.

Given previously observed interactions with sex, we tested the interactions between MA and sex; we also examined the interaction with age and metabolic risk factors. We also stratified our results by cohort to test whether the lack of temporality between the offspring clinical risk factor data and the computed tomography scans might impact the results.

Analyses were calculated with SAS version 9.2. P-values less than 0.05 were considered statistically significant for primary analyses. Because of the many interactions that we tested, we used a significance level of 0.01 to assess the significance of interaction terms.

​

Significance

Heterogeneity of obesity-related health risks is increasingly recognized. Body mass index (BMI) does not fully account for the differential associations of ectopic fat depots with health outcomes. Intramuscular fat accumulates between muscle fibers (interstitial fat) or within muscle cells (intramyocellular lipids). We investigated the association of intramuscular fat with other ectopic fat deposits and metabolic risk factors using participants from the Framingham Heart Study. We estimated intra-muscular fat by measuring muscle attenuation in the left and right paraspinous muscle, and examined its association with metabolic risk factors. We found that higher levels of intramuscular fat were associated with abnormal levels of glucose, lipids, and blood pressure. While most of these associations were due to levels of overall body fatness, the presence of the metabolic syndrome was associated with higher levels of intra-muscular fat even when we accounted for body mass index.

Supplementary Material

Supplement 1

Supplement 2

Acknowledgments

Sources of funding: Funding Acknowledgment: The Framingham Heart Study of the National Heart, Lung, and Blood Institute is supported by contract N01-HC-25195. Additionally, Dr. Speliotes is supported through K23 DK080145-01.

Selected Abbreviations

Footnotes

C) Disclosures: Alison Pedley is employed by Merck and Co, Inc.

Reference List

1. Cornier MA, Despres JP, Davis N, Grossniklaus DA, Klein S, Lamarche B, Lopez-Jimenez F, Rao G, St-Onge MP, Towfighi A, Poirier P. Assessing adiposity: a scientific statement from the american heart association. Circulation. 2011;124:1996–2019. [PubMed] [Google Scholar]

2. Britton KA, Fox CS. Ectopic fat depots and cardiovascular disease. Circulation. 2011;124:e837–e841. [PubMed] [Google Scholar]

3. Fox CS, Massaro JM, Hoffmann U, Pou KM, Maurovich-Horvat P, Liu CY, Vasan RS, Murabito JM, Meigs JB, Cupples LA, D’Agostino RB, Sr, O’Donnell CJ. Abdominal visceral and subcutaneous adipose tissue compartments: association with metabolic risk factors in the Framingham Heart Study. Circulation. 2007;116:39–48. [PubMed] [Google Scholar]

4. Rosito GA, Massaro JM, Hoffmann U, Ruberg FL, Mahabadi AA, Vasan RS, O’Donnell CJ, Fox CS. Pericardial fat, visceral abdominal fat, cardiovascular disease risk factors, and vascular calcification in a community-based sample: the Framingham Heart Study. Circulation. 2008;117:605–613. [PubMed] [Google Scholar]

5. Lehman SJ, Massaro JM, Schlett CL, O’Donnell CJ, Hoffmann U, Fox CS. Peri-aortic fat, cardiovascular disease risk factors, and aortic calcification: the Framingham Heart Study. Atherosclerosis. 2010;210:656–661. [PMC free article] [PubMed] [Google Scholar]

6. Foster MC, Hwang SJ, Porter SA, Massaro JM, Hoffmann U, Fox CS. Fatty kidney, hypertension, and chronic kidney disease: the Framingham Heart Study. Hypertension. 2011;58:784–790. [PMC free article] [PubMed] [Google Scholar]

7. Speliotes EK, Massaro JM, Hoffmann U, Vasan RS, Meigs JB, Sahani DV, Hirschhorn JN, O’Donnell CJ, Fox CS. Fatty liver is associated with dyslipidemia and dysglycemia independent of visceral fat: the Framingham Heart Study. Hepatology. 2010;51:1979–1987. [PMC free article] [PubMed] [Google Scholar]

8. Stump CS, Henriksen EJ, Wei Y, Sowers JR. The metabolic syndrome: role of skeletal muscle metabolism. Ann Med. 2006;38:389–402. [PubMed] [Google Scholar]

9. Ingram KH, Lara-Castro C, Gower BA, Makowsky R, Allison DB, Newcomer BR, Munoz AJ, Beasley TM, Lawrence JC, Lopez-Ben R, Rigsby DY, Garvey WT. Intramyocellular lipid and insulin resistance: differential relationships in European and African Americans. Obesity (Silver Spring) 2011;19:1469–1475. [PMC free article] [PubMed] [Google Scholar]

10. Ingram KH, Hill H, Moellering DR, Hill BG, Lara-Castro C, Newcomer B, Brandon LJ, Ingalls CP, Penumetcha M, Rupp JC, Garvey WT. Skeletal Muscle Lipid Peroxidation and Insulin Resistance in Humans. J Clin Endocrinol Metab. 2012 [PMC free article] [PubMed] [Google Scholar]

11. Komiya H, Mori Y, Yokose T, Kurokawa N, Horie N, Tajima N. Effect of intramuscular fat difference on glucose and insulin reaction in oral glucose tolerance test. J Atheroscler Thromb. 2006;13:136–142. [PubMed] [Google Scholar]

12. Pigeon E, Couillard E, Tremblay A, Bouchard C, Weisnagel SJ, Joanisse DR. Mid-thigh subcutaneous adipose tissue and glucose tolerance in the Quebec family study. Obes Facts. 2008;1:310–318. [PMC free article] [PubMed] [Google Scholar]

13. Torriani M, Hadigan C, Jensen ME, Grinspoon S. Psoas muscle attenuation measurement with computed tomography indicates intramuscular fat accumulation in patients with the HIV-lipodystrophy syndrome. J Appl Physiol. 2003;95:1005–1010. [PubMed] [Google Scholar]

14. Lee S, Guerra N, Arslanian S. Skeletal muscle lipid content and insulin sensitivity in black versus white obese adolescents: is there a race differential? J Clin Endocrinol Metab. 2010;95:2426–2432. [PMC free article] [PubMed] [Google Scholar]

15. Goodpaster BH, He J, Watkins S, Kelley DE. Skeletal muscle lipid content and insulin resistance: evidence for a paradox in endurance-trained athletes. J Clin Endocrinol Metab. 2001;86:5755–5761. [PubMed] [Google Scholar]

16. Bergman BC, Perreault L, Hunerdosse DM, Koehler MC, Samek AM, Eckel RH. Increased intramuscular lipid synthesis and low saturation relate to insulin sensitivity in endurance-trained athletes. J Appl Physiol. 2010;108:1134–1141. [PMC free article] [PubMed] [Google Scholar]

17. Liu L, Zhang Y, Chen N, Shi X, Tsang B, Yu YH. Upregulation of myocellular DGAT1 augments triglyceride synthesis in skeletal muscle and protects against fat-induced insulin resistance. J Clin Invest. 2007;117:1679–1689. [PMC free article] [PubMed] [Google Scholar]

18. Giannini C, Santoro N, Caprio S, Kim G, Lartaud D, Shaw M, Pierpont B, Weiss R. The triglyceride-to-HDL cholesterol ratio: association with insulin resistance in obese youths of different ethnic backgrounds. Diabetes Care. 2011;34:1869–1874. [PMC free article] [PubMed] [Google Scholar]

19. Sinha R, Dufour S, Petersen KF, LeBon V, Enoksson S, Ma YZ, Savoye M, Rothman DL, Shulman GI, Caprio S. Assessment of skeletal muscle triglyceride content by (1)H nuclear magnetic resonance spectroscopy in lean and obese adolescents: relationships to insulin sensitivity, total body fat, and central adiposity. Diabetes. 2002;51:1022–1027. [PubMed] [Google Scholar]

20. Park SY, Cho YR, Kim HJ, Higashimori T, Danton C, Lee MK, Dey A, Rothermel B, Kim YB, Kalinowski A, Russell KS, Kim JK. Unraveling the temporal pattern of diet-induced insulin resistance in individual organs and cardiac dysfunction in C57BL/6 mice. Diabetes. 2005;54:3530–3540. [PubMed] [Google Scholar]

21. Bonen A, Parolin ML, Steinberg GR, Calles-Escandon J, Tandon NN, Glatz JF, Luiken JJ, Heigenhauser GJ, Dyck DJ. Triacylglycerol accumulation in human obesity and type 2 diabetes is associated with increased rates of skeletal muscle fatty acid transport and increased sarcolemmal FAT/CD36. FASEB J. 2004;18:1144–1146. [PubMed] [Google Scholar]

22. Koves TR, Ussher JR, Noland RC, Slentz D, Mosedale M, Ilkayeva O, Bain J, Stevens R, Dyck JR, Newgard CB, Lopaschuk GD, Muoio DM. Mitochondrial overload and incomplete fatty acid oxidation contribute to skeletal muscle insulin resistance. Cell Metab. 2008;7:45–56. [PubMed] [Google Scholar]

23. Fox CS, Massaro JM, Hoffmann U, Pou KM, Maurovich-Horvat P, Liu CY, Vasan RS, Murabito JM, Meigs JB, Cupples LA, D’Agostino RB, Sr, O’Donnell CJ. Abdominal visceral and subcutaneous adipose tissue compartments: association with metabolic risk factors in the Framingham Heart Study. Circulation. 2007;116:39–48. [PubMed] [Google Scholar]

24. Nielsen S, Guo Z, Johnson CM, Hensrud DD, Jensen MD. Splanchnic lipolysis in human obesity. J Clin Invest. 2004;113:1582–1588. [PMC free article] [PubMed] [Google Scholar]

25. Zillikens MC, Yazdanpanah M, Pardo LM, Rivadeneira F, Aulchenko YS, Oostra BA, Uitterlinden AG, Pols HA, van Duijn CM. Sex-specific genetic effects influence variation in body composition. Diabetologia. 2008;51:2233–2241. [PubMed] [Google Scholar]

Reference List

1. DAWBER TR, KANNEL WB, LYELL LP. An approach to longitudinal studies in a community: the Framingham Study. Ann N Y Acad Sci. 1963;107:539–556. [PubMed] [Google Scholar]

2. KANNEL WB, Feinleib M, McNamara PM, Garrison RJ, Castelli WP. An investigation of coronary heart disease in families. The Framingham offspring study. Am J Epidemiol. 1979;110:281–290. [PubMed] [Google Scholar]

3. Splansky GL, Corey D, Yang Q, Atwood LD, Cupples LA, Benjamin EJ, D’Agostino RB, Sr, Fox CS, Larson MG, Murabito JM, O’Donnell CJ, Vasan RS, Wolf PA, Levy D. The Third Generation Cohort of the National Heart, Lung, and Blood Institute’s Framingham Heart Study: design, recruitment, and initial examination. Am J Epidemiol. 2007;165:1328–1335. [PubMed] [Google Scholar]

4. Maurovich-Horvat P, Massaro J, Fox CS, Moselewski F, O’Donnell CJ, Hoffmann U. Comparison of anthropometric, area- and volume-based assessment of abdominal subcutaneous and visceral adipose tissue volumes using multi-detector computed tomography. Int J Obes (Lond) 2007;31:500–506. [PubMed] [Google Scholar]

5. Komiya H, Mori Y, Yokose T, Kurokawa N, Horie N, Tajima N. Effect of intramuscular fat difference on glucose and insulin reaction in oral glucose tolerance test. J Atheroscler Thromb. 2006;13:136–142. [PubMed] [Google Scholar]

6. Johnson MA, Polgar J, Weightman D, Appleton D. Data on the distribution of fibre types in thirty-six human muscles. An autopsy study. J Neurol Sci. 1973;18:111–129. [PubMed] [Google Scholar]

7. Goodpaster BH, Kelley DE, Thaete FL, He J, Ross R. Skeletal muscle attenuation determined by computed tomography is associated with skeletal muscle lipid content. J Appl Physiol. 2000;89:104–110. [PubMed] [Google Scholar]

8. Fox CS, Massaro JM, Hoffmann U, Pou KM, Maurovich-Horvat P, Liu CY, Vasan RS, Murabito JM, Meigs JB, Cupples LA, D’Agostino RB, Sr, O’Donnell CJ. Abdominal visceral and subcutaneous adipose tissue compartments: association with metabolic risk factors in the Framingham Heart Study. Circulation. 2007;116:39–48. [PubMed] [Google Scholar]

9. Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III) JAMA. 2001;285:2486–2497. [PubMed] [Google Scholar]

10. Preis SR, Massaro JM, Robins SJ, Hoffmann U, Vasan RS, Irlbeck T, Meigs JB, Sutherland P, D’Agostino RB, Sr, O’Donnell CJ, Fox CS. Abdominal subcutaneous and visceral adipose tissue and insulin resistance in the Framingham heart study. Obesity (Silver Spring) 2010;18:2191–2198. [PMC free article] [PubMed] [Google Scholar]

11. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28:412–419. [PubMed] [Google Scholar]

12. Balkau B, Charles MA. Comment on the provisional report from the WHO consultation. European Group for the Study of Insulin Resistance (EGIR) Diabet Med. 1999;16:442–443. [PubMed] [Google Scholar]

13. D’Agostino RB, Sr, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, Kannel WB. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008;117:743–753. [PubMed] [Google Scholar]