Objective:

High visceral adipose tissue (VAT) and high liver fat (LF) are associated with the metabolic syndrome and diabetes. We studied changes in these two fat depots during weight loss and analyzed whether VAT and LF at baseline predict the response to lifestyle intervention.

Research Methods and Procedures:

One hundred twelve subjects (48 men and 64 women; age, 46 11 years; BMI, 29.2 4.4 kg/m²) were studied after a follow up-time of 264 60 (SD) days. Insulin sensitivity was estimated from the oral glucose tolerance test. Body fat depots were quantified using magnetic resonance imaging and spectroscopy.

Results:

Cross-sectionally high VAT (r = - 0.22, p = 0.02) and high LF (r = - 0.36, p < 0.0001) were independently associated with low insulin sensitivity. With intervention, BMI (- 3.0%), VAT (- 12.0%), and LF (- 33.0%) were reduced (all p < 0.001). Insulin sensitivity was improved (+17%, p < 0.01). The changes in BMI (r = - 0.41), VAT (r = - 0.28), and LF (r = - 0.39) were associated with the increase in insulin sensitivity (all p < 0.01). High VAT (r = - 0.28, p = 0.01) and high LF (r = - 0.38, p < 0.01) at baseline were associated with a lesser increase in insulin sensitivity.

Discussion:

Baseline values and changes in BMI, VAT, and LF are related to changes in insulin sensitivity during lifestyle intervention. Subjects with high VAT and LF have a lower chance of profiting from lifestyle intervention and may require intensified lifestyle prevention strategies or even pharmacological approaches to improve insulin sensitivity.

Introduction

Abdominal obesity has been shown to be a key determinant of the metabolic syndrome (1, 2, 3). In particular, reduced insulin sensitivity (4, 5, 6), dyslipidemia (7, 8), and impaired glucose tolerance (8) are associated with high visceral adipose tissue mass (VAT).¹ VAT is known to be metabolically more active compared with non-visceral adipose tissue (NVAT) (9) and to be a major source for free fatty acids (10, 11) and adipokine production (12). These findings point to a close relationship between visceral adiposity and insulin resistance.

In addition, VAT is closely related to liver fat (LF) content in healthy subjects (13, 14, 15) and patients with type 2 diabetes (16). The drainage of free fatty acids derived from VAT through the portal vein is likely to be a major determinant of LF content (17) and hepatic insulin sensitivity (18). Whether VAT is an independent predictor of whole body insulin sensitivity or exerts its effects on glucose metabolism only through increased LF content is less clear. We therefore performed a cross-sectional analysis to describe the relationships between abdominal and hepatic fat depots with insulin sensitivity in a healthy, non-diabetic population.

Both visceral fat (19, 20) and liver fat (19, 21) can be reduced by weight loss. The reduction in these fat depots is accompanied by an increase in insulin sensitivity (19, 20, 22). Furthermore, a reduction in visceral fat caused by surgical removal of adipose tissue is associated with increased insulin sensitivity (23, 24). In a longitudinal setting, pre-existing abdominal obesity per se is a risk factor for the development of impaired glucose tolerance (25) and type 2 diabetes (26). We hypothesized that pre-existing visceral adiposity and increased LF content are also important for the improvement in insulin sensitivity in a weight loss program. Therefore, we analyzed the effect of abdominal obesity and LF content at baseline and changes in these parameters on changes in insulin sensitivity in an intervention study.

Research Methods and Procedures

Subjects

A total of 112 non-diabetic subjects were included in the study. The population studied was at increased risk for type 2 diabetes because of one or more of the following risk factors: being overweight (BMI > 27 kg/m²), being a first-degree relative of a patient with type 2 diabetes, having impaired glucose tolerance, or having a history of gestational diabetes. After the baseline visits, including oral glucose tolerance test (OGTT), hyperinsulinemic euglycemic clamp, and magnetic resonance imaging, all subjects started an exercise and dietary lifestyle intervention (Tuebingen Lifestyle Intervention Study). This program includes the goals of the Diabetes Prevention Study (27): reduction of body weight by >5%, reduction of dietary intake of fat to <30% of calories, reduction of intake of saturated fatty acids to <10% of calories, increase of the daily amount of ingested fiber to >15 g/1000 kcal, and increase of the amount of weekly exercise to >3 h/wk. To achieve these goals, subjects were instructed by a team of dietitians. During the first 9 months, they had eight sessions with their lifestyle educators where they received individual advice. During each visit, participants had to present their 3-day food diary and discuss it with the dietitians. Nutrient intake was analyzed using a validated computer program (DGE-PC 3.0; Deutsche Gesellschaft für Ernährung, Bonn, Germany).

At baseline, all subjects performed a treadmill test to estimate individual aerobic capacity (28). Subsequently, they received an individual guidance on how to increase their level of physical activity. Endurance exercise such as walking, jogging, and swimming was recommended. The goal was to increase aerobic capacity. Exercise was performed in supervised group sessions and individually. Heart rate during exercise and amount of exercise was monitored using a pulse watch (Polar S610i; Polar Electro Oy, Kempele, Finland). Approximately 75% of participants achieved the goal of 3 hours of exercise per week.

The study design of the Tuebingen Lifestyle Intervention Study program includes a baseline visit (t0) and two follow-up visits after 9 (t1) and 24 months (t2). In this analysis, data from the first follow-up visit after 9 months are included [ mean follow-up time of 264 60 (SD) days; range, 151 to 465 days] . All subjects with available follow-up data of metabolic characterization by OGTT and magnetic resonance examinations were included. Food intake was estimated from dietary records of 15 1 representative days per subject. The mean intake of dietary fat was 31 5% of total caloric intake, the mean intake of saturated fatty acids was 12 4% of total caloric intake, and the mean daily amount of ingested fiber was 14 4 g/1000 kcal.

The local ethics committee approved all protocols. All subjects gave informed written consent.

OGTT

After a 10-hour overnight fast, the subjects ingested a solution containing 75 grams dextrose, and venous blood samples were obtained at 0, 30, 60, 90, and 120 minutes for determination of plasma glucose and plasma insulin. Insulin sensitivity during the OGTT was estimated by the formula of Matsuda and de Fronzo as previously described (29).

Analytical Procedures and Measurements

Serum insulin was determined with a microparticle enzyme immunoassay (Abbott, Wiesbaden, Germany). Venous plasma glucose was measured using a bedside glucose analyser (glucose oxidase method; Yellow Springs instruments, Yellow Springs, CO). Total body fat (%) was determined by bioimpedance analysis (BIA-101; RJL Systems, Clinton Twp., MI) following the guidelines of the user manual.

Magnetic Resonance Examinations for Determination of Adipose Tissue Depots

A whole body imaging protocol was applied for recording a set of 90 to 120 parallel transverse slices in all subjects as previously described (15). T1-weighted contrast was applied allowing semiautomatic quantitative assessment of fatty tissue and other tissue types in each cross-section. This approach enabled quantification of the different fat depots and calculation of visceral adipose tissue mass in relation to overall body weight.

Hepatic fat content was determined by localized proton-magnetic resonance spectroscopy (repetition time = 4 seconds, echo time = 10 ms, 32 scans) in the seventh segment of the liver. The lipid content was quantitatively assessed by analyzing the signal integral (methylene and methyl signals between 0.7 and 1.5 ppm), using the liver lipid and water signal integral as internal reference.

Statistical Analyses

All data are given as mean SE unless otherwise stated. Distribution was tested for normality using the Shapiro-Wilk W test. Non-normally distributed parameters were log-transformed to achieve normal distribution before statistical analyses. To adjust the effects of covariates and identify independent relationships, we performed multivariate linear regression analyses. A paired Student's t test was used to compare variables before and after lifestyle intervention. A p value <0.05 was considered to be statistically significant. The statistical software package JMP (SAS Institute, Cary, NC) was used.

Results

Cross-sectional Analysis

Anthropometrics of the study group are shown in Table 1. Mean VAT was 3.5 0.2% of body weight, mean NVAT was 25.5 0.8% of body weight, and LF was 6.5 0.7% signal. All three parameters were correlated to insulin sensitivity estimated from the OGTT (n = 112) in both men and women (Table 2). In multivariate regression analyses, VAT and LF were associated with insulin sensitivity independently of each other after adjusting for sex, age, and NVAT (Table 3).

Table 1. - Subject characteristics.

Full table

Table 2. - Univariate relationships between fat depots and insulin sensitivity.

Full table

Table 3. - Multivariate relationships between fat depots and insulin sensitivity (estimated from OGTT; n = 112).

Full table

Effects of Lifestyle Intervention

BMI decreased from 29.2 0.4 to 28.3 0.4 kg/m² (p < 0.0001). Body weight was reduced from 85.8 1.5 to 83.1 1.4 kg (- 3%, p < 0.0001). In addition, NVAT (from 25.5 0.1% to 24.4 0.1% body weight, p = 0.001), VAT (from 3.5 0.2% to 3.1 0.2% body weight, p < 0.0001), and LF (from 6.5 0.7% to 4.3 0.5% signal (p < 0.0001) were reduced (Figure 1). With these changes in body composition, insulin sensitivity increased from 13.2 0.7 to 15.5 0.8 AU (p < 0.01), and 2-hour glucose during OGTT decreased from 7.0 0.2 to 6.5 0.2 mM (p < 0.01). In addition, the change in BMI was correlated with the change in VAT (r = 0.54, p < 0.0001) and LF (r = 0.50, p < 0.0001). Interestingly, high LF at baseline predicted a more pronounced relative change in LF ( coefficient = –1.33 0.06) independent of sex, age, time of follow-up, and change in BMI.

Figure 1.

Effects of lifestyle intervention on NVAT, VAT, LF, and insulin sensitivity. With lifestyle intervention, NVAT was reduced (- 3%), whereas the relative reduction in VAT (- 12%) and LF (- 33%) was more pronounced. Insulin sensitivity was increased (+17%). AU, arbitrary units.

Full figure and legend (70K)

Effects of Change in NVAT, VAT, and LF on Insulin Sensitivity

The relative increase in insulin sensitivity after lifestyle intervention was predicted by the decrease in NVAT ( coefficient = –0.44 0.15, p < 0.01) after adjusting for sex, age, baseline NVAT, baseline insulin sensitivity, and time of follow-up. Furthermore, the relative increase in insulin sensitivity was predicted by the reduction in VAT ( coefficient = –0.71 0.20, p < 0.001) after adjusting for sex, age, baseline VAT, baseline insulin sensitivity, and time of follow-up. The same results were obtained when VAT was replaced by LF in the statistical model ( coefficient = –0.28 0.05, p < 0.0001). Figure 2 shows these findings. Subjects with decreasing VAT or LF showed an increased insulin sensitivity (p < 0.001), whereas in subjects with increasing VAT or LF, insulin sensitivity was unchanged.

Figure 2.

(Left) Scatterplot of change in VAT (x-axis) plotted against change in insulin sensitivity (y-axis) adjusted for sex, age, baseline insulin sensitivity, time of follow-up, and baseline VAT. (Right) Scatterplot of change in LF (x-axis) plotted against change in insulin sensitivity (y-axis) adjusted for sex, age, baseline insulin sensitivity, time of follow-up, and baseline LF. AU, arbitrary units.

Full figure and legend (116K)

Next, we included both the change in VAT and the change in LF into a multiple regression analysis to determine whether they are independently associated with the change in insulin sensitivity. Both the change in VAT ( coefficient = –0.52 0.22, p = 0.02) and the change in LF ( coefficient = –0.17 0.06, p < 0.01) were associated independently with the relative change in insulin sensitivity after adjusting for sex, age, baseline insulin sensitivity, NVAT, and time of follow-up.

Effects of Baseline VAT and LF on Insulin Sensitivity

The relative increase in insulin sensitivity after lifestyle intervention was predicted by VAT at baseline (p = 0.01) after adjusting for the independent effects of sex, age, change in VAT, baseline insulin sensitivity, and time of follow-up. A high amount of VAT was associated with a lesser increase in insulin sensitivity. When LF at baseline was entered into the model instead of VAT, the same results were obtained (p < 0.01). A high LF content was associated with a smaller increase in insulin sensitivity (Table 4).

Table 4. - Predictors of change in insulin sensitivity.

Full table

Discussion

In the cross-sectional part of this study, we confirmed that visceral adiposity and high LF are associated with reduced insulin sensitivity independent from each other using the technique of magnetic resonance imaging and spectroscopy. In accordance with this finding, increased intrahepatic lipids have been found to be associated with increased glucose production of the liver independent of visceral adiposity (30). It is well accepted that VAT is a major determinant of LF (17, 31) and that the flux of free fatty acids derived from visceral fat account for hepatic insulin resistance (18). In addition, the finding that LF and VAT are associated with insulin sensitivity independent from each other points to the possibility that both fat depots influence whole body insulin sensitivity through different mechanisms. This suggestion is in accordance with the observation that high LF is associated with increased hepatic glucose output independent of visceral adipose tissue mass (30). Other potential mechanisms how VAT and LF differentially influence whole body insulin sensitivity include different patterns of proteins associated with insulin resistance. For example, the adipocytokine adiponectin relates to visceral adiposity (32) and is closely associated with whole body insulin sensitivity, whereas 2-Heremans-Schmid glycoprotein/fetuin-A is produced in the liver and is closely associated with impaired insulin action and fat accumulation in the liver (33).

In applying the magnetic resonance imaging technique in our intervention study, we found that the changes in body mass and the various fat depots of the body are disproportionate. A relatively minor loss of body weight (- 3%) was accompanied by a major reduction in visceral fat mass (- 12%) and in LF content (- 33%). This finding is in accordance with a previous study by Tiikkainen et al. (19), showing that the reduction in liver fat (approximately - 40%) is more pronounced than the reduction in body weight (approximately - 8%). In addition, Tiikkainen et al. (19) found that women with initially high LF lost more LF by similar weight loss than those with low LF at baseline. We confirmed this finding in our more heterogeneous group including men.

The finding that the smaller weight loss in our study also causes a pronounced reduction in LF is of interest because it has direct clinical implications. Participants of weight loss programs with and without type 2 diabetes should be encouraged to stick to the diet and exercise program, even if weight loss is relatively minor. According to our data, even a small reduction in body weight may have beneficial effects on body composition and may, therefore, result in favorable changes in metabolic parameters.

In addition, we found that changes in body weight, VAT mass, and LF content are closely related to the corresponding change in insulin sensitivity.

Interestingly, subjects with high visceral fat mass or high LF at baseline seemed to have a lower chance of improving insulin sensitivity. Baseline anthropometric parameters have previously been shown to influence the effect of lifestyle intervention: a higher baseline BMI was associated with a lower reduction in the incidence of type 2 diabetes in the Diabetes Prevention Program (34). Increased abdominal or hepatic fat deposition may predispose to resistance to lifestyle intervention, because even after weight loss, fat deposition in these subjects is above the average of the group. In other words, in some subjects, a reduction of LF from 20% to 10% does not make a metabolic difference, because an improvement in insulin action may require a reduction in ectopic fat deposition below a specific threshold. In addition, there is still the possibility that subjects with increased abdominal obesity and hepatic steatosis may simply have a specific genetic pattern that defines a different susceptibility to nutritional changes or exercise. This hypothesis is supported by the finding of our group that single nucleotide polymorphisms in genes relevant for hepatic lipid metabolism have been shown to be associated with hepatic fat content (35, 36). The well-established metabolic activity of the visceral fat compartment including adipokine production, and its genetic characteristics (9) are also likely to mediate the unfavorable effects of visceral adiposity in a lifestyle intervention program. In this context, it is of interest that VAT lipolysis accounts for an increasing proportion of free fatty acids delivered to the liver when visceral fat mass increases (11). This finding points to the possibility that increasing visceral fat mass is related to an increased metabolic activity of the visceral fat depot, resulting in a lower increase of insulin sensitivity after lifestyle intervention.

Our study includes only a small group of subjects with impaired glucose tolerance. Therefore, we cannot exclude that some of the associations described differ between normal glucose tolerant and impaired glucose tolerant subjects. All of the effects regarding the predictive value of baseline VAT and LF were statistically significant in the normal glucose tolerant group alone but failed to reach statistical significance in the small impaired glucose tolerant group. Therefore, inclusion of glucose tolerance status as an independent variable failed to reach statistical significance. Another limitation of the study is that not all subjects achieved the minimal goals of weight loss, nutrient intake, and physical exercise. We cannot exclude that the results of our study would have been different if the majority of participants effectively achieved these goals.

It is important to emphasize that our study is based on statistical analysis of metabolic variables and measures of body composition and does not include a control group. A randomized study design would allow us to study causative relationships. Therefore, we are unable to address the important and interesting question as to whether abdominal obesity is causally related to insulin resistance. For example, we cannot exclude that participants with high visceral adiposity and ectopic fat storage in the liver at baseline are those who are not able to follow the instructions of their nutritionists and trainers. In this setting, motivational and psychosocial skills may explain the findings of our study; consequently, future lifestyle intervention programs may require tasks aiming to improve these skills.

In conclusion, patients with abdominal obesity and increased LF content seem to be at increased risk for the insulin resistance syndrome at a given time-point. Furthermore, they also seem to have a lower chance of profiting from a lifestyle intervention program. They also have to achieve a greater reduction in their ectopic lipid stores to markedly improve their insulin sensitivity. Therefore, these individuals may require intensified lifestyle prevention strategies or even additional pharmacological approaches to improve their insulin sensitivity.

Notes

¹ Nonstandard abbreviations: VAT, visceral adipose tissue; NVAT, non-visceral adipose tissue; LF, liver fat; OGTT, oral glucose tolerance test.

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Acknowledgments

This study was supported by a research grant from the Deutsche Forschungsgemeinschaft (KFO 114/1). We thank Elke Maerker, Heike Luz, and Anna Bury for expert technical assistance and all of the research volunteers for participation in this study.