Abstract
Purpose
We aimed to assess the relationship between the regional body fat distribution and insulin resistance and pancreas volume (PV) in type-2 diabetes (DM) patients.
Methods
Fifty-three consecutive type-2 diabetic and 51 non-diabetic patients matched by age, gender and body mass index (BMI) were enrolled. Subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), waist circumference, and PV were measured with computed tomography. Insulin resistance was assessed by the homeostasis model assessment of insulin resistance (HOMA-IR).
Results
Patients with type-2 DM had significantly lower PV than non-diabetic individuals. HOMA-IR ranged from 0.74 to 6.24; and from 0.37 to 3.26, in type-2 DM patients and non-diabetics, respectively. VAT was positively correlated with HOMA-IR in two groups. There were inverse correlations between PV and VAT and VAT/SAT but only in diabetics.
Conclusions
The VAT/SAT ratio may reflect the possible role of VAT to better understand the pathogenesis of obesity-related disorders in patients with type-2 DM.
Keywords: Insulin resistance; pancreas volume; visceral adipose tissue; subcutaneous adipose tissue, CT
INTRODUCTION
Type-2 DM results from a combination of genetic and acquired factors that cause varying degrees of insulin resistance and beta cell dysfunction. Insulin resistance is likely to be maximal already in the early stages of the disease. Throughout this period there is no hyperglycemia without beta cell dysfunction. With the decrease of compensatory insulin hyper-secretion, there is first impaired glucose tolerance and then overt diabetes (1, 2).
Obese individuals have varying degrees of decreased insulin-mediated glucose uptake (3).
In addition, obesity is associated with subclinical inflammation, hypertension, and cardiovascular disease (4). Adipose tissue expresses and secretes adipocytokines, which are known to regulate a host of physiological processes directly related to carbohydrate and fat metabolism. Some authors have attributed this variability of insulin resistance to differences in regional fat distribution in obese individuals (5-7).
The main components of body fat tissue are visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT). Traditionally, VAT has been considered a major culprit in the development of insulin resistance. Waist circumference and waist-to-hip ratio as anthropometric measurements of VAT have stronger associations with type-2 DM versus body mass index (BMI) (8, 9). Furthermore, there is a growing body of evidence supporting the role of SAT in the development of insulin resistance (10). Consequently the relative contributions of SAT and VAT to the pathophysiology of type-2 DM remain unclear (11, 12).
Pancreas volume (PV) is correlated with age, gender, ethnic origin, BMI, and body size (1, 13-15). In type-2 DM, PV is decreased and a correlation between pancreas volume and endocrine function has been reported (13-17). However, there are no studies highlighting the relationship between pancreas volume and regional fat distributions as a predictor of insulin resistance.
The aim of this study is to investigate the relationship between the regional fat distributions using VAT, SAT and VAT/SAT ratio assessed by computed tomography (CT), as well as insulin resistance and PV as an indicator of beta cell dysfunction in diabetic and non-diabetic patients.
MATERIALS AND METHODS
Patients
From March 5 to October 30 2015, all hospitalized type-2 DM patients and non-diabetics admitted to the Endocrinology Department who had abdominal CT examination within 3 months were enrolled. Sixty-four consecutive patients with type-2 diabetic and 62 non-diabetic patients were included into the study. All patients were evaluated according to the American Diabetes Association (ADA) criteria for type-2 DM (18). We excluded 2 non-diabetic patients who had any abdominal condition potentially affecting pancreas morphology (e.g., pancreatitis, pancreatic tumor and peritonitis), in 6 type-2 diabetic and 6 non-diabetic patients whose precise delineation of the pancreas from adjacent structures was not possible. Five type 2 diabetic and 3 non-diabetic patients whose weight changed more than 3 kg in the 3 months prior to analysis were also excluded. Finally, 53 consecutive patients with type-2 DM along with 51 non-diabetics matched by age, gender and BMI were enrolled. These patients had type 2 DM with a history of 10.31±7.35 years; 19 of them were treated by oral anti-diabetic medications and 34 by insulin therapy. Height and weight were measured using standard techniques. BMI was calculated as the weight in kilograms divided by the square of height in meters. This prospective study protocol conforms to the ethical guidelines of the Declaration of Helsinki as reflected in a priori approval by our institution’s human research committee. Informed written consent was obtained from all individuals.
Biochemical analysis
A blood sample was withdrawn from all the participants after overnight fasting and serum was separated by centrifugation at 3000 rpm for 10 minutes. Plasma glucose was assayed by an automated glucose oxidase method. Fasting plasma insulin was measured by radioimmunoassay as reported previously (19). The homeostasis model assessment of insulin resistance (HOMA-IR) was calculated from fasting plasma glucose and insulin level by using the following formula: HOMA-IR = FPG (mg/dL) × fasting insulin (μU/mL)/405 (20). The patients with poor glycemic control were treated with insulin. Patients were switched to the treatment with sulfonylurea (glibenclamide 1.25 or 2.5 mg) instead of insulin on the night before the test to minimize the influence of insulin injected subcutaneously (21).
Computed tomography analysis
A multi-detector CT system (Activion 16-row CT scanner; Toshiba Medical Systems, Otawara, Japan) was used for CT imaging. The routine abdominal CT protocols were used for all patients including 120 kV, 144 effective mAs, a pitch factor of 0.938, a helical factor of 15.0, a rotation time of 0.75s and a reconstruction interval of 1 mm. Patients were administered oral contrast (EZ-Cat, barium sulphate suspension, EZ-Em, Westbury, NY). A total of 100 mL nonionic contrast agent (Ultravist 370; Bayer Schering Pharma, Berlin, Germany) was given at a rate of 2.0 mL/s via peripheral venous line.
VAT was defined as intra-abdominal fat bound by parietal peritoneum or the fascia transversalis excluding the vertebral column and the paraspinal muscles; SAT was defined as fat superficial to the parietal peritoneum including abdominal and back muscles. Both VAT and SAT were measured at one axial level (L4/L5 intervertebral disc), which was described as being the most accurate for approximating the VAT/SAT ratio of the entire abdomen (22, 23). The waist circumference was derived from the CT images at same axial level for VAT and SAT measurements (Fig. 1). The VAT and SAT were calculated by using a closed polygon tool with semiautomatic segmentation. The attenuation range was from -150 to -50 Hounsfield Units (HU) because it identifies adipose tissue (24). The VAT was isolated as the region of interest (ROI) by manually delineating the inner margin of the abdominal musculature surrounding the abdominal cavity using a cursor. The SAT was calculated by isolating the region superficial to the inner margin of the abdominal musculature using a cursor. The VAT and SAT pixels were identified as the pixels with this attenuation range within the ROI (Fig. 2). The normal pancreatic parenchyma was determined by 2-dimensional segmentation in the region. After tracing the pancreatic parenchyma in all slices, the pancreatic volume was determined via ROI volume measurements (Fig. 3).
Figure 1.
Area-based measurements performed at the level of L4/L5 (a). The waist circumference was measured using a closed polygon tool (b).
Figure 2.
A fixed attenuation range was used at the level of L4/L5 to delineate VAT and SAT with a closed polygon tool with semi-automatic segmentation (a) for the identification of SAT (b) and VAT (c).
Figure 3.
The calculation of pancreas volume. Pancreas areas were selected as regions of interest (ROI, green area) in each slice and the ROI volume was calculated using imaging software. Vessels, calcification, and dilated pancreatic duct were excluded from the selected pancreas areas.
For each patient, CT imaging analysis was measured by two independent radiologists with eight and six years of experience in abdomen imaging using image analysis software (OsiriX Foundation, Geneva, Switzerland) on a personal computer. The radiologists performing the measurements were blind to the clinical information. The reviewers could manipulate the images to optimize the visualization of the pancreas and adipose tissue. Intra-class correlation analysis shows the reproducibility estimate between two independent readers. The intra-class correlation coefficient (ICC) was excellent when measuring maximum VAT and SAT areas (0.990, %95 CI > 0.873-0.997 and 0.993, %95 CI > 0.708-0.998, respectively) and PV (0.983, %95 CI > 0.708-0.995).
Statistical analysis
Statistical analyses were performed using SPSS software (ver. 19.0 for Windows; SPSS Inc., Chicago, IL, USA). Descriptive statistics of continuous variables are given as the mean, standard deviation, median, minimum, and maximum values. Categorical variables are presented as frequencies and percentages. The Shapiro-Wilk test was used to test normality. The Mann-Whitney U-test was used for non-parametric two-group comparisons. Inter-rater reliability of the measurements of two independent observers was assessed with the intraclass correlation coefficient (ICC). Multiple regression analysis was performed to identify predictors of HOMA-IR. For all statistical comparisons, a p value < 0.05 was considered to indicate statistical significance.
RESULTS
Baseline characteristics of the study subjects are shown in Table 1. No statistically significant difference was found in age (p=0.227), gender (p=0.872) and BMI (p=0.814) between patients with type 2 DM and non-diabetic patients. In type 2 DM, HOMA-IR ranged from 0.74 to 6.24; in non-diabetics, it ranged from 0.37 to 3.26. There was a positive correlation between HOMA-IR and both VAT and SAT in type-2 diabetic patients (r = 0.292; p = 0.034 and r =0.373; p=0.006, respectively) and also in non-diabetics (r =0.541; p<0.001 and r =0.590; p<0.001, respectively). There was no correlation between VAT/SAT ratio and HOMA-IR (r = -0.076; p =0.588 and r =0.174; p=0.222, respectively).
Table 1.
Demographic characteristics of patients
| Type-2 Diabetes (n=53) | Non-Diabetes (n=51) | p Value | |
| Male (%) | 21 (39.6%) | 21 (41.2%) | 0.872 |
| Age (years) | 59 (28-85) | 58 (23-80) | 0.227 |
| BMI (kg/m2) | 28.72 (20.44-52.45) | 29.40 (19.95-41.73) | 0.814 |
| Fasting plasma glucose (mmol/L) | 136 (94-178) | 96 (75-110) | <0.001* |
| Fasting plasma insulin (mU/L) | 7.7 (3.2-15) | 6 (2-12) | <0.001* |
| HOMA-IR (µU/mol/L3) | 2.57 (0.74-6.24) | 1.44 (0.37-3.26) | <0.001* |
Abbreviations: BMI, body mass index; SAT, subcutaneous adipose tissue; VAT, visceral adipose tissue; HOMA-IR, homeostasis model assessment of insulin resistance.
The VAT, VAT/SAT ratio and waist circumference were significantly higher in type-2 diabetic patients versus non-diabetic patients (p=0.004, p=0.002, and p=0.021 respectively) (Table 2). However, there was no statistically significant difference in SAT between the groups (p=0.554).
Table 2.
CT measurements of body adipose tissue distribution and pancreas volume in type-2 diabetic and non-diabetic patients
| Type-2 Diabetes (n=53) | Non-Diabetes (n=51) | p-Value | |
| Waist circumference (cm) | 104.58 (74.25-138) | 98.32 (77.31-133) | 0.021* |
| SAT area (cm2) | 273.13 (32.51-571.91) | 242.13 (77.91-556.08) | 0.554 |
| VAT area (cm2) | 183.65 (17-350.14) | 122.58 (29.02-412.82) | 0.004* |
| VAT/SAT Ratio | 1.39 (0.58-4.91) | 1.80 (0.80-6.73) | 0,002* |
| Pancreas volume (cm3) | 49.14 (17.48-96.09) | 55.67 (27.96-105.21) | 0,043* |
Abbreviations: SAT, subcutaneous adipose tissue; VAT, visceral adipose tissue.
According to the multiple regression results, adjusted for age, HOMA-IR was statistically associated with only VAT and BMI (p=0.007 and p<0.001, respectively) among all variables including VAT, SAT, BMI and waist circumference in non-diabetic patients. 73.7% of the change in HOMA-IR is due to VAT and BMI. For these data, the best prediction equation is: HOMA-IR=-2.149 + 0.002 x VAT + 0.112 x BMI.
In type 2 DM, there was a statistically significant relationship between HOMA-IR and BMI among the same variables (p<0.001); 43.9% of the change in the HOMA-IR is explained by BMI. The multiple regression equation for the prediction of HOMA-IR by BMI is HOMA-IR=-1.464 + 0.147 x BMI.
PV was significantly lower in type-2 DM than non-diabetics (p=0.043) (Table 2). Considering the effects of the regional fat distributions on PV in both groups, VAT and VAT/SAT ratio were inversely correlated with PV (r = -0.355; p =0.009 and r = -0.278; p =0.044, respectively). There was no significant association between PV and SAT in type-2 diabetic patients (r =0.075; p =0.596). In non-diabetic patients, PV was not associated with all of VAT, SAT and VAT/SAT ratio (r = -0.095; p =0.507, r =0.070; p =0.628 and r = -0.252; p =0.074, respectively).
Intra-class correlation analysis shows the reproducibility estimate between two independent readers. Intra-class correlation coefficient (ICC) was excellent when measuring maximum VAT and SAT areas (0.990, %95 CI > 0.873-0.997 and 0.993, %95 CI > 0.708-0.998, respectively), and PV (0.983, %95 CI > 0.708-0.995).
DISCUSSION
In our study, VAT and VAT/SAT ratio were significantly higher in type-2 diabetic patients than in non-diabetics. There were no significant differences in SAT among groups. Additionally, both the VAT and SAT were associated with HOMA-IR in diabetic and non-diabetics. Several recent studies on the insulin resistance in obesity show that both VAT and SAT are associated with insulin resistance. Consequently, it is well recognized that overall obesity causes insulin resistance (9, 10, 25-28). McLaughlin et al. showed that increased VAT, after adjustment for SAT and BMI, was associated with elevated risk for insulin resistance; increased SAT decreases the risk of insulin resistance (4). In another study, it was shown that SAT, as a component of central adiposity, has an association with insulin resistance as strong as VAT (26).
Although VAT or SAT alone provides little information regarding the relative distribution of body fat, an increased VAT/SAT ratio is a marker of a higher propensity to store excess fat viscerally (29). Miyazaki and DeFronzo reported that the VAT/SAT ratio correlated with hepatic insulin resistance, whereas VAT or SAT alone did not in patients with type-2 DM. However, peripheral insulin resistance is more closely related to VAT and SAT, but not to the VAT/SAT ratio (30). Furthermore, Kaess et al. observed that, in a sample of 3,223 participants (48% women) from the Framingham Heart Study, the VAT/SAT ratio was associated with insulin resistance assessed by HOMA-IR, independent of BMI and VAT (29).
However, overall obesity leads to substantial insulin resistance. As a hormonally active component of total body fat, VAT can play a more important role than SAT in the development of insulin resistance - both VAT and SAT are important (11). There is a debate regarding whether VAT or SAT is more strongly associated with insulin resistance. The results of this study confirm prior suggestions that VAT or SAT are associated with insulin resistance. Higher VAT/SAT ratios in type-2 DM patients suggest that the VAT is more valuable than the SAT in determining insulin resistance.
At this point, the value of the VAT and the VAT/SAT ratio comes to the fore in determining the impact of adipose tissue in type-2 DM. We recommend that even a small increase in VAT in patients with type-2 DM while having slightly less SAT may result in dramatically different VAT/SAT ratios. Thus, the VAT/SAT ratio itself may reflect the possible role of VAT to better understand the pathogenesis of obesity-related disorders in patients with type-2 DM.
In our study, PV is decreased in type 2 DM patients versus non-diabetics. This is similar to other reports (13, 15, 31). Saisho et al. revealed that PV increases with adulthood and obesity. Thus, it begins to decline gradually at about 60 years of age (15). In this study, the parenchymal PV of type-2 diabetic patients was decreased relative to non-diabetics. However, there was no difference in fat volume between subjects with type-2 DM compared to non-diabetic subjects. Moreover, they showed that the relationship between PV and BMI was similar (15). Similarly, Burute et al. showed that type 2 diabetics had a lower PV than normoglycemic individuals according to MRI for PV measurement (13). However, Phillipe et al. have not seen a significant difference between type-1 and type-2 DM regarding PV(14). One study assessing PV in normoglycemic, type-1 and 2 diabetic patients showed that type-1 diabetics had a significantly smaller PV than normoglycemic individuals and Type 2 diabetics (31). The differences in disease processes between Type 1 and Type 2 DM may also participate in the emergence of various PV (14, 31-33).
We found that PV was inversely correlated with the VAT and VAT/SAT ratios in diabetic patients but not with SAT. On the other hand, no relationship was found between the PV and VAT, SAT and VAT/SAT ratio in the non-diabetic cohort. As mentioned before VAT as a hormonally active component of total body fat contributes to chronic inflammation state and is higher in type 2 diabetic patients (11). Chronic inflammation state and loss of the trophic effects of insulin may result in decreased pancreatic volume in type 2 diabetes. Hence, we consider that VAT can contribute to pancreatic volume loss via enhancing chronic inflammation state.
In addition, multiple regression analysis showed that BMI was a main predictor of insulin resistance in both groups. Unfortunately, BMI could not estimate the impact of obesity on PV in the two groups. Saisho et al. showed that BMI was correlated with PV in type-2 diabetic and non-diabetic subjects, however PV was decreased in subjects with type-2 DM than non-diabetic subjects (15). Based on this relationship, Kou et al. reported a positive correlation between BMI and PV in Japan (34). Considering the relationship between the parameters of body fat and both insulin resistance and PV, BMI remains the strongest predictor regarding insulin resistance even in normal subjects, but not in those with PV (15, 34, 35).
There are several limitations of this study. The first limitation is that we enrolled a relatively small number of patients from a single center. This could lead to selection bias. To minimize this selection bias, we tried to include all patients who met the inclusion criteria. The second limitation is that pancreatic volume was measured, not beta cell mass. New molecular imaging methods were used to assess pancreatic beta cell mass and function, but these are not yet clinically valid (36). The other limitation is the medication of type-2 diabetic patients that may have an influence on insulin resistance.
In conclusion, the results of our study demonstrated that VAT - unlike other parameters used for assessing body fat tissue - is associated with both insulin resistance and PV in type-2 DM. There is no consensus regarding which part of body fat has more important role in pathogenesis of obesity-related disorders. The measuring VAT and VAT/SAT ratio with a single abdominal CT image at L4/L5 intervertebral disc level which is the most accurate level for approximating the regional fat distribution of the entire abdomen would be useful in assessing obese individuals for predisposition to type-2 DM. In addition, BMI is still the easiest anthropometric measurement to predict insulin resistance. Comprehensive studies covering larger populations and long-term follow-up are needed to elucidate the role of VAT and SAT on type-2 DM.
Conflict of interest
We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome.
References
- 1.Marchetti P, Dotta F, Lauro D, Purrello F. An overview of pancreatic beta-cell defects in human type 2 diabetes: implications for treatment. Regul Pept. 2008;146:4–11. doi: 10.1016/j.regpep.2007.08.017. [DOI] [PubMed] [Google Scholar]
- 2.Lupi R, Del Prato S. Beta-cell apoptosis in type 2 diabetes: quantitative and functional consequences. Diabetes Metab. 2008;34(Suppl 2):S56–64. doi: 10.1016/S1262-3636(08)73396-2. [DOI] [PubMed] [Google Scholar]
- 3.McLaughlin T, Lamendola C, Liu A, Abbasi F. Preferential fat deposition in subcutaneous versus visceral depots is associated with insulin sensitivity. J Clin Endocrinol. Metab. 2011;96:E1756–E760. doi: 10.1210/jc.2011-0615. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Hussain A, Claussen B, Ramachandran A, Williams R. Prevention of type 2 diabetes: a review. Diabetes Res Clin Pract. 2007;76:317–326. doi: 10.1016/j.diabres.2006.09.020. [DOI] [PubMed] [Google Scholar]
- 5.Olefsky J, Reaven GM, Farquhar JW. Effects of weight reduction on obesity. Studies of lipid and carbohydrate metabolism in normal and hyperlipoproteinemic subjects. J Clin Invest. 1974;53:64–76. doi: 10.1172/JCI107560. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Pouliot MC, Després JP, Nadeau A, Moorjani S, Prud’Homme D, Lupien PJ, Tremblay A, Bouchard C, Visceral obesity in men Associations with glucose tolerance, plasma insulin, and lipoprotein levels. Diabetes. 1992;41:826–834. doi: 10.2337/diab.41.7.826. [DOI] [PubMed] [Google Scholar]
- 7.Mathieu P, Poirier P, Pibarot P, Lemieux I, Després J-P. Visceral obesity: the link among inflammation, hypertension, and cardiovascular disease. Hypertension. 2009;53:577–584. doi: 10.1161/HYPERTENSIONAHA.108.110320. [DOI] [PubMed] [Google Scholar]
- 8.Hanley AJ, Wagenknecht LE, Norris JM, Bryer-Ash M, Chen YI, Anderson AM, Bergman R, Haffner SM. Insulin resistance, beta cell dysfunction and visceral adiposity as predictors of incident diabetes: the Insulin Resistance Atherosclerosis Study (IRAS) Family study. Diabetologia. 2009;52:2079–2086. doi: 10.1007/s00125-009-1464-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Gletsu-Miller N, Kahn HS, Gasevic D, Liang Z, Frediani JK, Torres WE, Ziegler TR, Phillips LS, Lin E. Sagittal abdominal diameter and visceral adiposity: correlates of beta-cell function and dysglycemia in severely obese women. Obesity surgery. 2013;23:874–881. doi: 10.1007/s11695-013-0874-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Patel P, Abate N. Body fat distribution and insulin resistance. Nutrients. 2013;5:2019–2027. doi: 10.3390/nu5062019. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Shuster A, Patlas M, Pinthus JH, Mourtzakis M. The clinical importance of visceral adiposity: a critical review of methods for visceral adipose tissue analysis. Br. J Radiol. 2012;85:1–10. doi: 10.1259/bjr/38447238. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Hayashi T, Boyko EJ, McNeely MJ, Leonetti DL, Kahn SE, Fujimoto WY. Visceral adiposity, not abdominal subcutaneous fat area, is associated with an increase in future insulin resistance in Japanese Americans. Diabetes. 2008;57:1269–1275. doi: 10.2337/db07-1378. [DOI] [PubMed] [Google Scholar]
- 13.Burute N, Nisenbaum R, Jenkins DJ, Mirrahimi A, Anthwal S, Colak E, Kirpalani A. Pancreas volume measurement in patients with Type 2 diabetes using magnetic resonance imaging-based planimetry. Pancreatology. 2014;14:268–274. doi: 10.1016/j.pan.2014.04.031. [DOI] [PubMed] [Google Scholar]
- 14.Philippe M-F, Benabadji S, Barbot-Trystram L, Vadrot D, Boitard C, Larger E. Pancreatic volume and endocrine and exocrine functions in patients with diabetes. Pancreas. 2011;40:359–363. doi: 10.1097/MPA.0b013e3182072032. [DOI] [PubMed] [Google Scholar]
- 15.Saisho Y, Butler AE, Meier JJ, Monchamp T, Allen-Auerbach M, Rizza RA, Butler PC. Pancreas volumes in humans from birth to age one hundred taking into account sex, obesity, and presence of type-2 diabetes. Clinical Anatomy. 2007;20:933–942. doi: 10.1002/ca.20543. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Macauley M, Percival K, Thelwall PE, Hollingsworth KG, Taylor R. Altered volume, morphology and composition of the pancreas in type 2 diabetes. PLoS One. 2015;10(5) doi: 10.1371/journal.pone.0126825. e0126825. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Lim S, Bae JH, Chun EJ, Kim H, Kim SY, Kim KM, Choi SH, Park KS, Florez JC, Jang HC. Differences in pancreatic volume, fat content, and fat density measured by multidetector-row computed tomography according to the duration of diabetes. Acta diabetologica. 2014;51:739–748. doi: 10.1007/s00592-014-0581-3. [DOI] [PubMed] [Google Scholar]
- 18.Diagnosis and classification of diabetes mellitus. Diabetes Care. 2010;33(Suppl 1):S62–69. doi: 10.2337/dc10-S062. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Boyko EJ, Fujimoto WY, Leonetti DL, Newell-Morris L. Visceral adiposity and risk of type 2 diabetes: a prospective study among Japanese Americans. Diabetes Care. 2000;23:465–471. doi: 10.2337/diacare.23.4.465. [DOI] [PubMed] [Google Scholar]
- 20.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. doi: 10.1007/BF00280883. [DOI] [PubMed] [Google Scholar]
- 21.Okita K, Iwahashi H, Kozawa J, Okauchi Y, Funahashi T, Imagawa A, Shimomura I. Homeostasis model assessment of insulin resistance for evaluating insulin sensitivity in patients with type 2 diabetes on insulin therapy. Endocr J. 2013;60:283–290. doi: 10.1507/endocrj.ej12-0320. [DOI] [PubMed] [Google Scholar]
- 22.Demerath EW, Shen W, Lee M, Choh AC, Czerwinski SA, Siervogel RM, Towne B. Approximation of total visceral adipose tissue with a single magnetic resonance image. Am J Clin Nutr. 2007;85:362–368. doi: 10.1093/ajcn/85.2.362. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Geraghty EM, Boone JM. Determination of height, weight, body mass index, and body surface area with a single abdominal CT image. Radiology. 2003;228:857–863. doi: 10.1148/radiol.2283020095. [DOI] [PubMed] [Google Scholar]
- 24.van der Kooy K, Seidell JC. Techniques for the measurement of visceral fat: a practical guide. International journal of obesity and related metabolic disorders: Journal of the International Association for the Study of Obesity. 1993;17:187–196. [PubMed] [Google Scholar]
- 25.Garg A. Regional adiposity and insulin resistance. J Clin Endocrinol Metab. 2004;89:4206–4210. doi: 10.1210/jc.2004-0631. [DOI] [PubMed] [Google Scholar]
- 26.Goodpaster BH, Thaete FL, Simoneau JA, Kelley DE. Subcutaneous abdominal fat and thigh muscle composition predict insulin sensitivity independently of visceral fat. Diabetes. 1997;46:1579–1585. doi: 10.2337/diacare.46.10.1579. [DOI] [PubMed] [Google Scholar]
- 27.Brochu M, Starling RD, Tchernof A, Matthews DE, Garcia-Rubi E, Poehlman ET. Visceral adipose tissue is an independent correlate of glucose disposal in older obese postmenopausal women. J Clin Endocrinol Metab. 2000;85:2378–2384. doi: 10.1210/jcem.85.7.6685. [DOI] [PubMed] [Google Scholar]
- 28.Després JP. Abdominal obesity as important component of insulin-resistance syndrome. Nutrition. 1993;9:452–459. [PubMed] [Google Scholar]
- 29.Kaess BM, Pedley A, Massaro JM, Murabito J, Hoffmann U, Fox CS. The ratio of visceral to subcutaneous fat, a metric of body fat distribution, is a unique correlate of cardiometabolic risk. Diabetologia. 2012;55:2622–2630. doi: 10.1007/s00125-012-2639-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Miyazaki Y, DeFronzo RA. Visceral fat dominant distribution in male type 2 diabetic patients is closely related to hepatic insulin resistance, irrespective of body type. Cardiovasc Diabetol. 2009;8:44. doi: 10.1186/1475-2840-8-44. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Goda K, Sasaki E, Nagata K, Fukai M, Ohsawa N, Hahafusa T. Pancreatic volume in type 1 and type 2 diabetes mellitus. Acta Diabetol. 2001;38:145–149. doi: 10.1007/s005920170012. [DOI] [PubMed] [Google Scholar]
- 32.Pap A. Effects of insulin and glucose metabolism on pancreatic exocrine function. Int J Diabetes Metab. 2004;12:30–34. [Google Scholar]
- 33.Donath MY, Schumann DM, Faulenbach M, Ellingsgaard H, Perren A, Ehses JA. Islet inflammation in type 2 diabetes: from metabolic stress to therapy. Diabetes Care. 2008;31(Suppl 2):S161–164. doi: 10.2337/dc08-s243. [DOI] [PubMed] [Google Scholar]
- 34.Kou K, Saisho Y, Jinzaki M, Itoh H. Relationship between Body Mass Index and Pancreas Volume in Japanese Adults. Journal of the Pancreas. 2014;15:626–627. doi: 10.6092/1590-8577/2858. [DOI] [PubMed] [Google Scholar]
- 35.Risérus U, Ärnlöv J, Berglund L. Long-Term Predictors of Insulin Resistance. Role of lifestyle and metabolic factors in middle-aged men. Diabetes Care. 2007;30:2928–2933. doi: 10.2337/dc07-0360. [DOI] [PubMed] [Google Scholar]
- 36.Normandin MD, Petersen KF, Ding YS, Lin SF, Naik S, Fowles K, Skovronsky DM, Herold KC, McCarthy TJ, Calle RA, Carson RE, Treadway JL, Cline GW. In vivo imaging of endogenous pancreatic beta-cell mass in healthy and type 1 diabetic subjects using 18F-fluoropropyl-dihydrotetrabenazine and PET. Journal of nuclear medicine: official publication, Society of Nuclear Medicine. 2012;53:908–916. doi: 10.2967/jnumed.111.100545. [DOI] [PMC free article] [PubMed] [Google Scholar]