Portal Visceral Hypothesis Statement

Abstract

Objective

Whether retroperitoneal fat should be included in the measurement of visceral fat remains controversial. We compared the relationships of fat areas in peritoneal, retroperitoneal, and subcutaneous compartments to metabolic syndrome, adipokines, and incident hypertension and diabetes.

Methods

We enrolled 432 adult participants (153 men and 279 women) in a community-based cohort study. Computed tomography at the umbilicus level was used to measure the fat areas.

Results

Retroperitoneal fat correlated significantly with metabolic syndrome (adjusted odds ratio (OR), 5.651, p<0.05) and the number of metabolic abnormalities (p<0.05). Retroperitoneal fat area was significantly associated with blood pressure, plasma glycemic indices, lipid profile, C-reactive protein, adiponectin (r = −0.244, P<0.05), and leptin (r = 0.323, p<0.05), but not plasma renin or aldosterone concentrations. During the 2.94±0.84 years of follow-up, 32 participants developed incident hypertension. Retroperitoneal fat area (hazard ration (HR) 1.62, p = 0.003) and peritoneal fat area (HR 1.62, p = 0.009), but not subcutaneous fat area (p = 0.14) were associated with incident hypertension. Neither retroperitoneal fat area, peritoneal fat area, nor subcutaneous fat areas was associated with incident diabetes after adjustment.

Conclusions

Retroperitoneal fat is similar to peritoneal fat, but differs from subcutaneous fat, in terms of its relationship with metabolic syndrome and incident hypertension. Retroperitoneal fat area should be included in the measurement of visceral fat for cardio-metabolic studies in human.

Citation: Hung C-S, Lee J-K, Yang C-Y, Hsieh H-R, Ma W-Y, Lin M-S, et al. (2014) Measurement of Visceral Fat: Should We Include Retroperitoneal Fat? PLoS ONE 9(11): e112355. https://doi.org/10.1371/journal.pone.0112355

Editor: Cordula M. Stover, University of Leicester, United Kingdom

Received: July 1, 2014; Accepted: September 18, 2014; Published: November 17, 2014

Copyright: © 2014 Hung et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The authors confirm that all data underlying the findings are fully available without restriction. All relevant data are within the paper and its Supporting Information files.

Funding: This work was supported in part by a grant (NSC 98-2314-B-002-024-MY3) from the National Science Council, Taiwan, and a grant (DAROC2012YPI-0001) from the Diabetes Association of the Republic of China, Taiwan. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Metabolic syndrome (MS) is a combination of risk factors mainly related to abdominal obesity and insulin resistance, according to the National Cholesterol Education Program’s Adult Treatment Panel III report (ATP III) [1]. Participants with MS have a higher risk of fatal and nonfatal cardiovascular diseases [2]. Abdominal or central obesity plays an important role in the development of MS. Indeed, central obesity is a requirement to meet the criteria of MS as defined by the International Diabetes Federation [3]. Abdominal adipose tissue is not only a fat storage site, but also acts as an endocrine organ that secretes various adipokines such as adiponectin and leptin as well as several inflammatory cytokines. In addition, the free fatty acid flux from abdominal adipose tissue to the liver results in disturbances in glucose and lipid metabolism [4], which contribute to the development of insulin resistance and dyslipidemia in participants with MS [5]. [6].

In the abdominal cavity, there are 3 different compartments of fat: omental, mesenteric, and retroperitoneal fat. Blood from fat tissue in the peritoneal region, including omental and mesenteric fat, is drained through the portal vein into the liver, while the blood from fat in the retroperitoneal region is drained into the kidney, pancreas, or directly to the vena cava [7]. Furthermore, the composition and amount of adipokines released from different compartments can differ, and along with their endocrine effects, these adipokines can have paracrine effects on adjacent organs. Therefore, peritoneal and retroperitoneal fats may play different roles in our metabolism. Two recent reports in animals have shown that peritoneal and retroperitoneal fat have different immuno-modulatory roles and respond differently to exercise training [8], [9]. However, most human studies using CT or MRI to assess the degree of visceral obesity have included the retroperitoneal fat area [10]–[12]. In the literature, retroperitoneal and peritoneal fat depots were quantified together as visceral fat, to study their relationships to cardiometabolic diseases. To the best of our knowledge, there is no report investigating the role of retroperitoneal fat, independent to other part of visceral fat, in metabolic abnormalities in humans. Therefore, we conducted this study to compare the relationships of retroperitoneal fat area, peritoneal fat area, and subcutaneous fat area to metabolic syndrome, adipocytokines, and incident hypertension and diabetes. Patterns of the relationships can help us to answer if we should include retroperitoneal fat when measuring visceral fat area in humans.

Methods and Materials

Ethic statement

The study was approved by the Institutional Review Board of the National Taiwan University Hospital, and complied with the Helsinki Declaration. Written informed consent was obtained from each patient before enrollment.

Participants

From 2006 to 2012, residents from the Yunlin county, Taiwan, aged ≥18 years, who did not report the presence of diabetes during an interview, were invited to join this prospective study, named the Taiwan Lifestyle Study [13], [14]. There were 3 visits for this prospective study, separated by 1–3 years. Individuals underwent abdominal CT exam at the 2nd visit, which was defined as the baseline visit in the present study. The 3rd visit was defined as the follow-up visit. The new diabetes or hypertension was defined by comparing the status between baseline and follow-up visit. Written informed consent was obtained from every participant. The study was review and approved by the Institutional Review Board.

A questionnaire was administered by trained nurses in order to obtain data on the demographic characteristics, medical history, and health habits of the participants. Body height and weight were recorded to the nearest 0.5 cm and 0.1 kg, respectively. BMI was calculated from body weight in kilogram divided by the square of body height in meters. Waist circumferences were measured according to the method by the World Health Organization and the International Diabetes Federation to the nearest 0.1 cm [15]. Blood pressure was recorded using a mercury sphygmomanometer to the nearest 2 mmHg with the arm supported at the heart level after the subject sat calmly for 10 min; trained nurses took 3 separate readings at 1-min intervals, and the average of the second and the third readings was used for analysis. A standard 75-g oral glucose tolerance test (OGTT) was performed after fasting overnight for 8 h. All study participants were contacted by telephone, e-mail, or postal mail every 1 to 3 years after the initial visit, and follow-up visits were scheduled according to the respondent’s availability. Abdominal computer tomography was done at baseline visit to measure abdominal fat. Clinical questionnaires, physical examination, and blood tests including OGTT were repeated to know the development of incident hypertension or incident diabetes at the time of follow-up.

Plasma glucose and fasting serum total cholesterol, triglycerides, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol concentrations were measured using an automatic analyzer (Toshiba TBA 200 FR; Toshiba Medical Systems Co., Ltd., Tokyo, Japan). Plasma hemoglobin A1c (HbA1c) concentrations were measured using another automatic analyzer (HLC-723 G7 HPLC systems; Tosoh Corporation, Tokyo, Japan). Hepatitis B surface antigen and hepatitis C virus antibody were measured by the AxSYM System (Abbott Laboratories, North Chicago, IL). The laboratory attends and is qualified by an external quality assurance program by the Taiwan Society of Laboratory Medicine twice a year. The HbA1c assay was certified by the National Glycohemoglobin Standardization Program [16] and standardized to the Diabetes Control and Complications Trial reference assay.

Definitions

Diabetes was diagnosed according to the American Diabetes Association (ADA) recommendations in 2010 (HbA1c concentration ≥6.5% (48 mmol/mol), fasting plasma glucose (FPG) concentration ≥126 mg/dL, or OGTT 2h plasma glucose (OGTT-2h-PG) concentration ≥200 mg/dL). MS was diagnosed using the criteria defined in the ATP III, with a modification of waist circumference for Asians [17]. Participants were classified as having MS if they met 3 or more of the following 5 criteria: (1) high blood pressure: systolic and/or diastolic blood pressure ≥130/85 mmHg or receiving blood pressure-lowering medications; (2) hyperglycemia: fasting plasma glucose concentration ≥100 mg/dL (5.6 mmol/L) or receiving glucose-lowering medications; (3) hypertriglyceridemia: fasting plasma triglyceride concentration ≥150 mg/dL (1.69 mmol/L); (4) HDL cholesterol concentration <40 mg/dL in men and <50 mg/dL in woman; and (5) waist circumference ≥90 cm in men and ≥80 cm in women.

Measurements of abdominal adipose tissue

Abdominal adiposity was assessed using a 16-slice multidetector CT scanner (LightSpeed 16; GE Healthcare, Milwaukee, WI) in the supine position (120 kVp, 400 mAs, slice thickness of 5 mm). Image analysis software (ImageJ, version 1.44; National Institutes of Health, Bethesda, MD) was used to quantify the subcutaneous and visceral adipose fat areas, on one cross-sectional scan obtained at the level of umbilicus and expressed in millimeters squared. [11] After applying threshold with an attenuation range of –50 to –250 Hounsfield units, a fat-density mask was generated. A total fat mask was created after manual exclusion of non-adipose area (such as the CT table, air-object interface or fecal material) from the fat-density mask (Figure 1). The visceral fat area was determined by cutting areas other than visceral fat, and the subcutaneous fat area was calculated with the total fat area subtracted by the visceral fat area. The visceral fat area was further divided into peritoneal and retroperitoneal areas along the boundary comprised of posterior surface of small bowel, ascending colon, descending colon, mesenteric vessels and gonadal veins (Figure 1). The interfascial plane could be visualized as a thin line in some participants and this could help outline of the boundary.

Figure 1. Image demonstration of determining abdominal fat distribution on a CT scan.

Left, sample CT image obtained at the umbilicus level. Right, fat masks created for determining areas of subcutaneous fat (red, “S”), peritoneal fat (blue, “P”) and retroperitoneal fat (green, “R”) using methods described in the Materials and Methods section.

https://doi.org/10.1371/journal.pone.0112355.g001

Statistical analysis

The sample size estimation was based on the following assumptions that was modified from a previous study(18): a 2-sided α level of 0.05 and power of 95% and retroperitoneal fat area of 5000 mm2 in MS group and 4000 mm2 in non-MS group with a SD of 2500 mm2. The MS to non-MS group ratio was set at 1∶3 according to a prevalence of MS at around 25–30%. Accordingly, a sample size of 400 patients (100 patients in MS group and 300 patients in non-MS group) was calculated.

All variables are expressed as the mean and standard deviation (SD). Two-sample t-tests and Pearson’s chi-square tests were used to compare the demographic and metabolic parameters between participants with and without MS. The different fat areas were logarithmically transformed to approximate normal distributions. Logistic regression models were used to evaluate the relationship between MS and different fat areas using age, gender, and BMI as potential confounders. We also performed this analysis with standardized fat areas as follows: standardized fat area = (log fat area – mean of log fat area)/(SD of log fat area). Pearson’s correlation coefficients were used to assess the relationship between metabolic parameters and different fat areas. Partial correlation coefficients were calculated after adjusting for age and gender. Bootstrap resampling was used to compare the correlation coefficient of each metabolic variable and the retroperitoneal fat area with the correlation coefficient of each metabolic variable and the subcutaneous or peritoneal fat area. The differences in numbers of metabolic abnormalities were assessed by analysis of variance. The relationship between adipokines and the retroperitoneal fat area was expressed by Pearson’s correlation coefficients. Cox proportional hazard models were used to assess the relationship of abdominal fat areas and the development of incident hypertension or incident diabetes during follow-up, adjusted for age, sex, and family history of hypertension or diabetes. The fat areas in the Cox regression models were normalized by the standard deviation, to show the hazard ratios for every 1 standard deviation increase in fat areas. Kaplan-Meier failure curves were used to estimate the cumulative incidence of hypertension and diabetes in participants with fat areas above and below the median value. A two-tailed p value <0.05 was regarded as significant. Statistical analyses were performed with Stata/SE 11.0 for Windows (StataCorp LP, College Station, TX).

Results

A total of 432 participants (153 men and 279 women), with a mean age of 52.6±12.0 years, were enrolled in this study. Of the 432, 125 participants fulfilled the criteria for MS. The mean fat areas in men and women were as follows: 14,757 mm2 and 19,659 mm2, respectively, of subcutaneous fat area (p<0.001); 7,187 mm2 and 4,855 mm2, respectively, of peritoneal fat area (p<0.001); and 5,233 mm2 and 3,784 mm2, respectively, of retroperitoneal fat area (p<0.001). As shown in Table 1, participants with MS were older; more obese; more likely to have hypertension and diabetes; had higher plasma triglyceride, glutamyl pyruvic transaminase (GPT), leptin, and C-reactive protein (CRP) concentrations, had lower plasma adiponectin concentrations, and had higher subcutaneous, peritoneal, and retroperitoneal fat areas.

Retroperitoneal fat area is associated with metabolic syndrome

As shown in Figure 2, participants with higher fat areas had more metabolic abnormalities in all compartments of abdominal fat (all p<0.05). In multivariate logistic regression analysis (Table 2), retroperitoneal and peritoneal fat areas, but not the subcutaneous fat area, were significant predictors for the presence of MS, independent of age, gender, and BMI (retroperitoneal fat: OR, 5.651; 95% CI, 2.707–11.79; p<0.05; peritoneal fat: OR, 3.991; 95% CI, 2.181–7.304; p<0.05; subcutaneous fat: OR, 2.569; 95% CI, 0.935–7.053; p>0.05). The odds ratios for every 1 SD increase in fat area, adjusted for age, gender, and BMI, were 1.562 (p>0.05) for the subcutaneous fat area, 3.489 (p<0.05) for the peritoneal fat area, and 2.849 (p<0.05) for the retroperitoneal fat area.

Figure 2. Clustering of metabolic abnormalities defined by MS in participants with body fat areas in the lowest, middle, or highest tertiles.

(A) Retroperitoneal fat, (B) peritoneal fat, and (C) subcutaneous fat. *p<0.05 vs. lowest tertile, #p<0.05 vs. middle tertile.

https://doi.org/10.1371/journal.pone.0112355.g002

Retroperitoneal fat area is associated with adipocytokines

Of the study population, 353 participants did not take any medication for hypertension, diabetes, or dyslipidemia. In these participants, there were significant correlations between metabolic variables and the retroperitoneal as well as peritoneal fat area (Table 3). The retroperitoneal fat area was significantly associated with blood pressure, BMI, waist circumference, plasma glycemic indices, lipid profile, CRP concentration s, GPT concentrations, leptin concentrations, and adiponectin concentrations, but not with plasma renin and aldosterone concentrations. The associations between retroperitoneal fat area and blood pressure, BMI, waist circumference, fasting glucose, total cholesterol, and LDL cholesterol were independent to peritoneal fat area. The retroperitoneal fat area correlated better with diastolic blood pressure and waist circumference as compare to the peritoneal fat area, whereas the peritoneal fat area correlated better with low HDL cholesterol, triglyceride, glutamyl oxaloacetic transaminase (GOT), and GPT concentrations. In contrast, the subcutaneous fat area was not significantly associated with blood pressure, fasting plasma glucose, HbA1c concentration, or lipid profile. The subcutaneous fat area correlated significantly with BMI, waist circumference, OGTT-2h-PG, plasma leptin concentration, adiponectin concentration, and CRP concentration. The retroperitoneal fat area correlated better with the systolic and diastolic blood pressure, waist circumference, plasma glycemic indices, lipid profile, CRP concentration, GOT concentration, GPT concentration, and leptin and adiponectin concentrations, as compared to the relationship with subcutaneous fat area. In participants with positive hepatitis B surface antigen or positive hepatitis C antibody (i.e., not hepatitis B carriers and did not have hepatitis C infection), the remaining 267 participants still showed a better correlation between the peritoneal fat area and GOT concentration (peritoneal fat vs. GOT, r = 0.1940, p<0.05; retroperitoneal fat vs. GOT, r = 0.0958, p>0.05 between 2 correlation coefficients) or GPT concentration (peritoneal fat vs. GPT, r = 0.2648, p<0.05; retroperitoneal fat vs. GPT, r = 0.2140, p<0.05 between 2 correlation coefficients).

Retroperitoneal and peritoneal fat areas were associated with incident hypertension

After a mean duration of 2.94±0.84 years, 297 (69%) of the 432 participants were followed successfully. Among the 199 participants who did not have hypertension at baseline and have been followed successfully, 32 participants developed incident hypertension. Among the 236 participants who did not have diabetes at baseline, 18 patients developed incident diabetes. Participants with retroperitoneal fat area above the median showed higher risk for incident hypertension, compared with participants with retroperitoneal fat area lower than the median (p = 0.004, Figure 3A). Similar trend can be found for peritoneal fat areas (Figure 3B and 3C), but not for subcutaneous fat areas. In Table 4, retroperitoneal and peritoneal fat areas were associated with the development of incident hypertension in adjusted models, which is different from subcutaneous fat areas (HR for retroperitoneal fat, 1.62 (1.18 2.22), p = 0.003, HR for peritoneal fat, 1.62 (1.12 2.34), p = 0.009, HR for subcutaneous fat, 1.38 (0.90 2.01), p = 0.14). Neither retroperitoneal fat area, peritoneal fat area, nor subcutaneous fat areas was associated with incident diabetes after the adjustment for age, gender and family history of diabetes, which may result from the limited event of incident diabetes in the present study.

Figure 3. Different fat compartments to predict the probability of incident hypertension.

Kaplan-Meier failure curves for the probability of developing hypertension in subgroups divided by the median of (A) retroperitoneal fat area, (B) peritoneal fat area, and (C) subcutaneous fat area. P values by log-rank tests are shown.

https://doi.org/10.1371/journal.pone.0112355.g003

Table 4. Hazard ratios (HRs) and 95% confidence interval (95% CI) of different fat components to predict the development of incident hypertension and incident diabetes during follow-up.

https://doi.org/10.1371/journal.pone.0112355.t004

Discussion

In the present study, we found that retroperitoneal fat area is associated with MS, plasma adipokines, systemic inflammation, and incident hypertension. Retroperitoneal fat is similar to peritoneal fat, but differs from subcutaneous fat, in its relationship with metabolic syndrome, adipokines, and incident hypertension. Our results indicate that the retroperitoneal fat area should be included as part of visceral fat assessment. Compared with peritoneal fat, retroperitoneal fat is more closely associated with blood pressure and waist circumference, whereas peritoneal fat correlates better with low HDL cholesterol concentration, high triglyceride concentration, and abnormal liver function.

In the literature, there is only one previous study investigating the independent role of retroperitoneal fat to other part of visceral fats in MS [18]. Supporting our findings, the authors of that study also reported a significant association between retroperitoneal fat and MS. However, since all study participants in that study had adrenal diseases (19 had adrenal-dependent Cushing’s syndrome, 12 had subclincal Cushing’s syndrome, and 30 had nonfunctional adrenal incidentaloma), it remained unknown whether the relationship only holds true for participants with adrenal diseases or if the relationship is a consequence of adrenal diseases. In the present study, we included generally healthy participants from the community and confirmed that there is a significant relationship between retroperitoneal fat and MS in the general population.

The mechanisms underlying how visceral adiposity induces metabolic abnormalities are not fully understood. Several hypotheses have been proposed, including increased portal free fatty acid, endocrine function of visceral adipose tissue (adipokines), and visceral obesity as a marker of dysfunctional adipose tissue leading to ectopic fat deposition [4]. For peritoneal and retroperitoneal fat, the blood supply and venous drainage systems are different. Peritoneal fat drains into the portal vein, which may result in increased free fatty acid flux to the liver. High concentration of free fatty acid to the liver leads to the development of fatty liver, insulin resistance, high triglyceride concentrations, and low HDL cholesterol concentrations [19], [20]. Indeed, when epididymal fat pads were transplanted into the mesenterium (portal drainage) or parietal peritoneum (systemic drainage) in mice, only mice with fat transplanted into the mesenterium developed impaired glucose tolerance and hepatic insulin resistance [21]. However, retroperitoneal fat can drain into the renal vein or directly into the inferior vena cava, which may lead to “fatty kidney.” Fatty kidney, as defined by higher renal sinus fat area on CT, has been shown to be associated with hypertension [22]. In addition, there are further differences between different compartments of abdominal fat. Mesenteric pre-adipocytes are found to be distinct from omental pre-adipocytes in their replication potential, mechanism of lipid accumulation, and response to tumor necrosis factor-alpha–induced apoptosis [23]. Retroperitoneal fat has been shown to contain increased amount of brown adipose tissue in humans, as reported by Betz MJ etal[24]. In mice, Cohen CA etal. found that intra-abdominal fat depots from different compartments showed distinct patterns in terms of leukocyte to monocyte ratio and gene expression profiles [8]. Indeed, we found different relationships of diastolic blood pressure and plasma HDL cholesterol, triglyceride, GOT, and GPT concentrations with peritoneal and retroperitoneal fat in humans. However, an even greater difference was noted between retroperitoneal fat and subcutaneous fat. The association trends between retroperitoneal fat, metabolic profiles, adipokine concentrations, CRP concentrations, and incident hypertension and diabetes were similar to those of peritoneal fat, but differed from those of subcutaneous fat in the present study.

Taken together, these data suggest that differences exist in the biologic functions of retroperitoneal fat and peritoneal fat. However, compared to subcutaneous fat, retroperitoneal fat and peritoneal fat behave more similarly. Therefore, when the visceral fat area is measured by CT or MRI in human studies for cardiovascular and metabolic diseases, the retroperitoneal fat area should be included.

To the best of our knowledge, the present study is the first comprehensive research to investigate if retroperitoneal fat should be included in the measurement of visceral fat in humans. We analyzed the relationship between retroperitoneal fat, MS, each metabolic abnormality, adipokines, a marker of systemic inflammation, and incident hypertension and diabetes. We also compared the pattern of these relationships among retroperitoneal fat, peritoneal fat, and subcutaneous fat. In addition, the use of CT to measure abdominal adiposity enabled us to compare different compartments of abdominal fat in detail. By contrast, the present study was limited in its follow-up rate of 69%. Although it is not likely that the relationships of retroperitoneal fat to incident hypertension and diabetes will be different in subjects who lost of follow-up, we cannot exclude the possibility completely.

Conclusion

Retroperitoneal fat is associated with MS, plasma adipokine concentrations, systemic inflammation, and incident hypertension. Retroperitoneal fat is similar to peritoneal fat, but differs from subcutaneous fat, in its relationship with metabolic syndrome, adipokines, and incident hypertension. Our results indicate that the retroperitoneal fat area should be included in the measurement of visceral fat area. In addition, since the relationships between these factors and different compartments of abdominal fat are different, further research is needed to explore the pathophysiologic implications of these findings.

Acknowledgments

H.-Y.L. had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. The authors would like to thank Ms. Chien-Yin Su, Ms. Kuan-Yi Wu, Ms. Ying-Jhu Liao, and the staff of the Eighth Core Lab, Department of Medical Research, and National Taiwan University Hospital, for their technical and computing assistance.

Author Contributions

Conceived and designed the experiments: CSH JKL MSL JML JNW HYL. Performed the experiments: CSH CYY WYM HYL SRS. Analyzed the data: CSH PHL HYL CYY JWL. Contributed reagents/materials/analysis tools: HRH HYL. Contributed to the writing of the manuscript: JML LMC MFC JNW.

References

  1. 1. National Cholesterol Education Program Expert Panel on Detection E, Treatment of High Blood Cholesterol in A (2002) 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) final report. Circulation 106: 3143–3421.
  2. 2. Dekker JM, Girman C, Rhodes T, Nijpels G, Stehouwer CD, et al. (2005) Metabolic syndrome and 10-year cardiovascular disease risk in the Hoorn Study. Circulation 112: 666–673.
  3. 3. Alberti KG, Zimmet P, Shaw J (2006) Metabolic syndrome-a new world-wide definition. A Consensus Statement from the International Diabetes Federation. Diabet Med 23: 469–480.
  4. 4. Cornier MA, Despres JP, Davis N, Grossniklaus DA, Klein S, et al. (2011) Assessing adiposity: a scientific statement from the American Heart Association. Circulation 124: 1996–2019.
  5. 5. Despres JP, Lemieux I (2006) Abdominal obesity and metabolic syndrome. Nature 444: 881–887.
  6. 6. Ross R, Aru J, Freeman J, Hudson R, Janssen I (2002) Abdominal adiposity and insulin resistance in obese men. Am J Physiol Endocrinol Metab 282: E657–663.
  7. 7. Tchernof A, Despres JP (2013) Pathophysiology of human visceral obesity: an update. Physiol Rev 93: 359–404.
  8. 8. Cohen CA, Shea AA, Heffron CL, Schmelz EM, Roberts PC (2013) Intra-abdominal fat depots represent distinct immunomodulatory microenvironments: a murine model. PLoS One 8: e66477.
  9. 9. Chapados N, Collin P, Imbeault P, Corriveau P, Lavoie JM (2008) Exercise training decreases in vitro stimulated lipolysis in a visceral (mesenteric) but not in the retroperitoneal fat depot of high-fat-fed rats. Br J Nutr 100: 518–525.
  10. 10. Fox CS, Massaro JM, Hoffmann U, Pou KM, Maurovich-Horvat P, et al. (2007) Abdominal visceral and subcutaneous adipose tissue compartments: association with metabolic risk factors in the Framingham Heart Study. Circulation 116: 39–48.
  11. 11. Yoshizumi T, Nakamura T, Yamane M, Islam AH, Menju M, et al. (1999) Abdominal fat: standardized technique for measurement at CT. Radiology 211: 283–286.
  12. 12. Borel AL, Boulet G, Nazare JA, Smith J, Almeras N, et al. (2013) Improved plasma FFA/insulin homeostasis is independently associated with improved glucose tolerance after a 1-year lifestyle intervention in viscerally obese men. Diabetes Care 36: 3254–3261.
  13. 13. Hung CS, Lee PC, Li HY, Ma WY, Lin MS, et al. (2011) Haemoglobin A1c is associated with carotid intima-media thickness in a Chinese population. Clin Endocrinol (Oxf) 75: 780–785.
  14. 14. Li HY, Lin MS, Shih SR, Hua CH, Liu YL, et al. (2011) The performance of risk scores and hemoglobin A1c to find undiagnosed diabetes with isolated postload hyperglycemia. Endocr J 58: 441–448.
  15. 15. Ma WY, Yang CY, Shih SR, Hsieh HJ, Hung CS, et al. (2013) Measurement of Waist Circumference: midabdominal or iliac crest? Diabetes Care 36: 1660–1666.
  16. 16. Little RR, Rohlfing CL, Wiedmeyer HM, Myers GL, Sacks DB, et al. (2001) The national glycohemoglobin standardization program: a five-year progress report. Clin Chem 47: 1985–1992.
  17. 17. Tan CE, Ma S, Wai D, Chew SK, Tai ES (2004) Can we apply the National Cholesterol Education Program Adult Treatment Panel definition of the metabolic syndrome to Asians? Diabetes Care 27: 1182–1186.
  18. 18. Ersoz F, Erbil Y, Sari S, Salmaslioglu A, Issever H, et al. (2011) Predictive value of retroperitoneal fat area measurement for detecting metabolic syndrome in patients undergoing adrenalectomy. World J Surg 35: 986–994.
  19. 19. Yoshii H, Lam TK, Gupta N, Goh T, Haber CA, et al. (2006) Effects of portal free fatty acid elevation on insulin clearance and hepatic glucose flux. Am J Physiol Endocrinol Metab 290: E1089–1097.
  20. 20. Kabir M, Catalano KJ, Ananthnarayan S, Kim SP, Van Citters GW, et al. (2005) Molecular evidence supporting the portal theory: a causative link between visceral adiposity and hepatic insulin resistance. Am J Physiol Endocrinol Metab 288: E454–461.

1. Catalano KJ, Stefanovski D, Bergman RN: Critical role of the mesenteric depot versus other intra-abdominal adipose depots in the development of insulin resistance in young rats. Diabetes 2010;59:1416–1423 [PMC free article][PubMed]

2. Jensen MD: Role of body fat distribution and the metabolic complications of obesity. J Clin Endocrinol Metab 2008;93:S57–S63 [PMC free article][PubMed]

3. Kabir M, Catalano KJ, Ananthnarayan S, Kim SP, Van Citters GW, Dea MK, Bergman RN: Molecular evidence supporting the portal theory: a causative link between visceral adiposity and hepatic insulin resistance. Am J Physiol Endocrinol Metab 2005;288:E454–E461 [PubMed]

4. Klein S, Allison DB, Heymsfield SB, Kelley DE, Leibel RL, Nonas C, Kahn R: Waist circumference and cardiometabolic risk: a consensus statement from shaping America's health: Association for Weight Management and Obesity Prevention; NAASO, the Obesity Society; the American Society for Nutrition; and the American Diabetes Association. Diabetes Care 2007;30:1647–1652 [PubMed]

5. Konrad D, Rudich A, Schoenle EJ: Improved glucose tolerance in mice receiving intraperitoneal transplantation of normal fat tissue. Diabetologia 2007;50:833–839 [PubMed]

6. Tran TT, Yamamoto Y, Gesta S, Kahn CR: Beneficial effects of subcutaneous fat transplantation on metabolism. Cell Metab 2008;7:410–420 [PMC free article][PubMed]

7. Wueest S, Rapold RA, Schumann DM, Rytka JM, Schildknecht A, Nov O, Chervonsky AV, Rudich A, Schoenle EJ, Donath MY, Konrad D: Deletion of Fas in adipocytes relieves adipose tissue inflammation and hepatic manifestations of obesity in mice. J Clin Invest 2010;120:191–202 [PMC free article][PubMed]

8. Fisher SJ, Kahn CR: Insulin signaling is required for insulin's direct and indirect action on hepatic glucose production. J Clin Invest 2003;111:463–468 [PMC free article][PubMed]

9. Kim JK, Michael MD, Previs SF, Peroni OD, Mauvais-Jarvis F, Neschen S, Kahn BB, Kahn CR, Shulman GI: Redistribution of substrates to adipose tissue promotes obesity in mice with selective insulin resistance in muscle. J Clin Invest 2000;105:1791–1797 [PMC free article][PubMed]

10. Saberi M, Bjelica D, Schenk S, Imamura T, Bandyopadhyay G, Li P, Vargeese C, Wang W, Bowman K, Zhang Y, Polisky B, Olefsky JM: Novel liver-specific TORC2 siRNA corrects hyperglycemia in rodent models of type 2 diabetes. Am J Physiol Endocrinol Metab 2009August25[Epub ahead of print] [PMC free article][PubMed]

11. Pfaffl MW: A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acid Res 2001;29:e45. [PMC free article][PubMed]

12. Huang W, Metlakunta A, Dedousis N, Zhang P, Sipula I, Dube JJ, Scott DK, O'Doherty RM: Depletion of liver Kupffer cells prevents the development of diet-induced hepatic steatosis and insulin resistance. Diabetes 2010;59:347–357 [PMC free article][PubMed]

13. Sabio G, Das M, Mora A, Zhang Z, Jun JY, Ko HJ, Barrett T, Kim JK, Davis RJ: A stress signaling pathway in adipose tissue regulates hepatic insulin resistance. Science 2008;322:1539–1543 [PMC free article][PubMed]

14. Senn JJ, Klover PJ, Nowak IA, Zimmers TA, Koniaris LG, Furlanetto RW, Mooney RA: Suppressor of cytokine signaling-3 (SOCS-3), a potential mediator of interleukin-6-dependent insulin resistance in hepatocytes. J Biol Chem 2003;278:13740–13746 [PubMed]

15. Weigert C, Hennige AM, Lehmann R, Brodbeck K, Baumgartner F, Schauble M, Haring HU, Schleicher ED: Direct cross-talk of interleukin-6 and insulin signal transduction via insulin receptor substrate-1 in skeletal muscle cells. J Biol Chem 2006;281:7060–7067 [PubMed]

16. Hocking SL, Chisholm DJ, James DE: Studies of regional adipose transplantation reveal a unique and beneficial interaction between subcutaneous adipose tissue and the intra-abdominal compartment. Diabetologia 2008;51:900–902 [PubMed]

17. Cancello R, Clement K: Is obesity an inflammatory illness? Role of low-grade inflammation and macrophage infiltration in human white adipose tissue. BJOG 2006;113:1141–1147 [PubMed]

18. Senn JJ, Klover PJ, Nowak IA, Mooney RA: Interleukin-6 induces cellular insulin resistance in hepatocytes. Diabetes 2002;51:3391–3399 [PubMed]

19. Carey AL, Febbraio MA: Interleukin-6 and insulin sensitivity: friend or foe? Diabetologia 2004;47:1135–1142 [PubMed]

20. Fontana L, Eagon JC, Trujillo ME, Scherer PE, Klein S: Visceral fat adipokine secretion is associated with systemic inflammation in obese humans. Diabetes 2007;56:1010–1013 [PubMed]

21. Shoelson SE, Lee J, Goldfine AB: Inflammation and insulin resistance. J Clin Invest 2006;116:1793–1801 [PMC free article][PubMed]

22. Rotter V, Nagaev I, Smith U: Interleukin-6 (IL-6) induces insulin resistance in 3T3–L1 adipocytes and is, like IL-8 and tumor necrosis factor-alpha, overexpressed in human fat cells from insulin-resistant subjects. J Biol Chem 2003;278:45777–45784 [PubMed]

23. Kanemaki T, Kitade H, Kaibori M, Sakitani K, Hiramatsu Y, Kamiyama Y, Ito S, Okumura T: Interleukin 1beta and interleukin 6, but not tumor necrosis factor alpha, inhibit insulin-stimulated glycogen synthesis in rat hepatocytes. Hepatology 1998;27:1296–1303 [PubMed]

0 thoughts on “Portal Visceral Hypothesis Statement

Leave a Reply

Your email address will not be published. Required fields are marked *