Adipose Tissue and Metabolic Alterations: Regional Differences in Fat Cell Size and Number Matter, But Differently: A Cross-Sectional Study

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1 JCEM ONLINE Hot Topics in Translational Endocrinology Endocrine Research Adipose Tissue and Metabolic Alterations: Regional Differences in Fat Cell Size and Number Matter, But Differently: A Cross-Sectional Study Mikael Rydén, Daniel P. Andersson, Ingrid B. Bergström, and Peter Arner Department of Medicine, Karolinska Institutet, Karolinska University Hospital, Huddinge, , Stockholm, Sweden Objective: White adipose tissue can expand by increasing the size and/or number of fat cells. Although increased sc and visceral fat cell size associates with an adverse metabolic profile, the relationship with fat cell number in either depot is unknown. We hypothesized that adipocyte number and size displayed different relationships with clinically relevant metabolic variables. Methods: This was a cross-sectional study of 204 patients scheduled for gastric bypass surgery. Fat cell size and number were determined in visceral and abdominal sc adipose tissue and related to insulin sensitivity (by hyperinsulinemic euglycemic clamp), fasting plasma levels of insulin, triglycerides and high-density lipoprotein (HDL) cholesterol. Results: Visceral and sc fat cell volumes were positively correlated with insulin and triglyceride levels and negatively with insulin sensitivity and HDL-cholesterol (P.0020 or better). In contrast, although visceral fat cell number did not associate with any metabolic parameter, sc adipocyte number displayed a positive association with insulin sensitivity and HDL-cholesterol and a negative relationship with insulin and triglyceride levels (P.0014 or better). All results were independent of body fat mass. Conclusions: Variations in fat cell size and number correlate differently with metabolic parameters in obesity. Increased fat cell size in visceral and sc depots associates with a pernicious metabolic profile, whereas increased sc, but not visceral, fat cell number correlates with a more beneficial phenotype. Whether determination of sc fat cell number, in addition to adipocyte size, may have a predictive value for the risk of type 2 diabetes needs to be demonstrated in prospective or mechanistic studies. (J Clin Endocrinol Metab 99: E1870 E1876, 2014) ISSN Print X ISSN Online Printed in U.S.A. Copyright 2014 by the Endocrine Society Received February 21, Accepted June 9, First Published Online June 17, 2014 Obesity is a major risk factor for metabolic disturbances, in particular dyslipidemia and type 2 diabetes mellitus (T2DM). Not surprisingly, the current worldwide increase in T2DM parallels the accelerated prevalence of overweight/obesity (1). Pronounced weight reduction is hard to achieve, and to date, the most effective long-term treatment of obesity and its metabolic complications is bariatric surgery (2, 3). However, large randomized controlled trials with long follow-up periods to confirm sustained improvements in metabolic complications and mortality are still lacking (4). Despite the weight reduction accomplished by bariatric surgery, T2DM is not reversed in all subjects, and in many cases, there is a relapse despite maintenance of reduced body weight status (5). Interestingly, longitudinal studies have shown that T2DM is rapidly reversed after bariatric surgery, long before maximum weight loss has been achieved (reviewed in Ref. 6). Furthermore, surgical removal of large amounts of abdominal sc adipose tissue from obese patients does not improve insulin resistance or other metabolic abnormalities (7, 8). Similarly, removal of visceral adipose tissue in connection with bariatric surgery does not improve insu- Abbreviations: BMI, body mass index; CT, computed tomography; DEXA, dual-energy x-ray absorptiometry; ESAT, estimated sc abdominal adipose tissue; EVAT, estimated visceral adipose tissue; HDL, high-density lipoprotein; LBM, lean body mass; T2DM, type 2 diabetes mellitus E1870 jcem.endojournals.org J Clin Endocrinol Metab, October 2014, 99(10):E1870 E1876 doi: /jc

2 doi: /jc jcem.endojournals.org E1871 lin sensitivity or other metabolic abnormalities beyond weight loss itself (9 12). Taken together, these data strongly suggest that the amount or distribution of adipose tissue is not per se of major importance for the development of T2DM in obesity. Although factors not related to adipose tissue, eg, gut hormones and intestinal microbiota, may play a role in the development of T2DM and other metabolic complications, intrinsic adipose factors may also be of importance. One such aspect is adipose cellularity (13). Fat tissue can expand by primarily increasing either the size or number of its fat cells. It is well established that a phenotype characterized by large (hypertrophic) adipocytes, is associated with hyperinsulinemia, insulin resistance, and dyslipidemia (14 18). Several of these studies have shown that this association is independent of body fatness as measured by body mass index (BMI) (14, 15, 17, 18). Two prospective studies have confirmed that adipose hypertrophy is associated with increased risk for developing T2DM, independently of BMI (19, 20). However, the relationship between adipocyte number and insulin resistance as well as other metabolic variables associated with T2DM risk is unknown. We hypothesized that adipocyte number, in comparison with fat cell size, could display different relationships with clinical parameters and investigated the regional impact of adipose cellularity for insulin sensitivity and classical metabolic risk factors related to obesity/t2dm. In a cross-sectional cohort, fat cell size and number were determined in abdominal sc and visceral (greater omentum) adipose tissue of obese subjects and set in relation to insulin sensitivity (hyperinsulinemic-euglycemic clamp) and circulating levels of insulin, triglycerides, and high-density lipoprotein (HDL) cholesterol. Subjects and Methods Subjects Between May 2006 and September 2013, 204 patients referred to our surgical units (Ersta Hospital, Södertälje Hospital, Karolinska University Hospital Huddinge, and Danderyd Hospital in Stockholm, Sweden) for Roux-en-Y gastric bypass surgery for obesity were included in the study. Inclusion criteria were an age between 18 and 60 years and BMI 30 kg/m 2 with concomitant obesity-related complications or BMI 35 kg/m 2. Exclusion criteria were treatment with insulin or glitazones, oral or parenteral steroid treatment, complicated psychiatric disease, or warfarin use. To avoid possible influences of catabolism/ weight loss, none of the participants were put on preoperative caloric restriction, and subjects who reported a hypocaloric diet before surgery (ie, at their own initiative) were excluded. Subjects were weight stable (less than 2 kg weight change) for at least 1 year before the first visit. Twelve subjects were diagnosed with T2DM and treated with diet and metformin, whereas 57 had hypertension. An abdominal sc fat biopsy was obtained by needle aspiration under local anesthesia. A second fat biopsy was obtained from the greater omentum (visceral fat) at the beginning of general surgery. The study was approved by the local committee on ethics and explained to each participant. Written informed consent was obtained. Examinations The patients were investigated in the morning after an overnight fast. Height and weight were determined. A venous blood sample was obtained, and plasma levels of insulin, glucose, triglycerides, and HDL-cholesterol were measured as described (14). Lean body mass (LBM), total body fat, and android fat mass were measured by dual-energy x-ray absorptiometry (DEXA) using a GE Lunar idxa with the software EnCore (version ) with the CoreScan feature provided by the manufacturer (GE Medical Systems). CoreScan is an automated method for segmenting total adipose fat into sc fat and visceral fat within the android region. The estimation of visceral fat with this software has been approved for clinical use by the U.S. Food and Drug Administration (21). Automatic calibration checks of the DEXA were performed daily throughout the study, and thrice-weekly calibrations using a spine phantom (for bone mineral density, provided by the manufacturer) were performed. The coefficient of variation for the spine phantom testing was 1.5%. No hardware or software changes were made during the course of the trial. The subjects were scanned using standard imaging and positioning protocols and the same scan mode (set for obese subjects) was used throughout the study. CoreScan was used to calculate estimated visceral adipose tissue (EVAT) mass. The amount of estimated sc abdominal adipose tissue (ESAT) mass, which corresponds to the region for the sc fat biopsy, was calculated from the following formula: total adipose fat mass in the android region EVAT estimated sc adipose tissue in the android region ESAT, as previously described (21). Determination of EVAT with this method shows a strong correlation (r ) with measures using computed tomography (CT) (21). We subsequently validated this in an independent cohort of obese women and men, comparing the weight of the greater omentum with visceral adipose tissue mass estimated using the presently used DEXA equipment/software and CT (22). Because only total android fat mass and EVAT are used to determine ESAT and both are valid measures, it follows that also the calculation of ESAT should be valid. Fat cell volume and weight were determined using a standard method in the field that has been described in detail previously (14, 22). In brief, fat cells were isolated, and the diameter of 100 cells was measured. The diameters were distributed in a unimodal way, and it has been shown in several independent studies that counting 100 or 300 cells by microscopy is a reliable estimate of fat cell size that is comparable with results obtained using either manual or automated image analysis software (23). Fat cell volume (expressed in picoliters) was calculated as (/6) where is the cell diameter in micrometers. As previously discussed (24, 25), the diameter (d) is a normally distributed variable but its cube (d 3 ) is skewed and the arithmetic mean of d 3 can therefore not be used to calculate mean fat cell volume. The average fat cell volume is instead much better approximated by using the formula (where is the mean diameter and is the standard deviation of the diameter). Assuming a mean density of fat cells equal to that of human adipose triglycerides, mean fat cell weight was obtained by multiplying

3 E1872 Rydén et al Adipocyte Size and Number J Clin Endocrinol Metab, October 2014, 99(10):E1870 E1876 the volume by (ie, the density in grams per milliliter of triolein). Estimated fat depot weight was then divided by mean fat cell weight in the corresponding biopsy to obtain fat cell number in the respective depot. After 45 minutes of rest, the subjects underwent a hyperinsulinemic-euglycemic clamp as described (12). In brief, after an iv bolus dose of insulin (1600 mu/m 2 body surface area, corresponding to 9600 pmol/m 2 when using the conversion factor 6.0 as described on insulin was infused iv at a rate of 120 mu/m 2/ min (720 pmol/m 2 /min) for 2 hours and a variable iv infusion of glucose (200 mg/ml) was used to maintain euglycemia between 81 and 99 mg/dl ( mmol/l). The infusion rate of glucose during the last 60 minutes of the clamp, when insulin levels are in a steady state, was used to calculate whole-body glucose disposal rates (M-value). The average values of plasma insulin at steady state during the clamp were mu/l ( pmol/l). Statistics Group values are mean and range. Adipose and metabolic parameters were compared using Spearman correlation, analysis of covariance, and multiple linear regression. In the multiple regression analyses, normal distribution of the residual error was determined using the Shapiro-Wilk W test and the constant variance of the errors was further checked by rando/mly distributed patterns in residual-by-predicted plots. A P-value 0.05 was considered to be statistically significant in all analyses. The Bonferroni test was used to correct for multiple comparison statistics in Tables 2 and 3. Statistical analyses were performed using the JMP 10.0 software (SAS Institute Inc, NC). Results A flowchart for inclusion in the study is depicted in Figure 1. In total, 204 subjects were included in the study where ESAT/EVAT values from 188 subjects could be obtained. In 14 patients, EVAT and ESAT values could not be measured because of the severe obesity. One subject was examined using different DEXA equipment, and in one individual, DEXA measurements were missing. The clinical characteristics of the cohort are described in Table 1. The results of Spearman correlation between metabolic variables and adipocyte size and number are summarized in Table 2. In visceral adipose tissue, fat cell volume, but not number, correlated significantly with insulin sensitivity and plasma levels of insulin, triglycerides, or HDL-cholesterol. In sc adipose tissue, similar correlations were observed regarding fat cell volume as for visceral adipose tissue (Table 2 and Figure 2). In contrast, although sc fat cell number correlated significantly with all variables, it did so in an opposite way compared with cell volume (Table 2 and Figure 2). Thus, there was a positive relationship with insulin sensitivity and HDL-cholesterol and a negative relationship with insulin and triglycerides. After Bonferroni correction of P values in Table 2 (16 comparisons allowing P.0031 to be significant), all significant correlations remained. Furthermore, numerically similar and P values were obtained if insulin sensitivity was corrected for LBM (M/LBM, values not shown). The correlations between sc fat cell volume/number, visceral adipocyte volume, and insulin sensitivity (also as M/LBM, values not shown) or plasma levels of insulin, triglycerides, and HDL-cholesterol were analyzed using multiple linear regression (Table 3). All correlations remained statistically significant after correction for total body fat expressed in kilograms (or as percentage of total body weight, values not shown). The same was true if the Bonferroni correction was used (allowing P.0042 to be significant). We also analyzed the influence of Table 1. Clinical Characteristics a Figure 1. Flowchart for inclusion of subjects. Mean (range) Age, y (n 204) 43 (18 64) BMI, kg/m 2 (n 204) 41 ( ) Plasma insulin, mu/l (n 203) 16.2 ( ) Plasma HDL-cholesterol, mmol/l (n 204) 1.2 ( ) Plasma triglycerides, mmol/l (n 203) 1.4 ( ) M-value, mg/kg/min (n 177) 4.2 ( ) LBM, kg (n 201) 53.9 ( ) Total body fat, kg (n 202) 57.2 ( ) Android fat, kg (n 201) 5.9 ( ) EVAT, kg (n 188) 2.3 ( ) ESAT, kg (n 188) 3.5 ( ) EVAT fat cell volume, pl (n 203) 594 ( ) EVAT fat cell number 10 9 (n 188) 4.3 ( ) ESAT fat cell volume, pl (n 204) 923 ( ) ESAT fat cell number 10 9 (n 188) 4.3 ( ) a Subjects included 183 women and 21 men.

4 doi: /jc jcem.endojournals.org E1873 Table 2. Correlation Between Adipose and Metabolic Variables a Adipose Tissue Parameters Visceral Subcutaneous Fat Cell Number Fat Cell Volume Fat Cell Number Fat Cell Volume Metabolic Parameters P P P P Plasma insulin Insulin sensitivity (M) Plasma triglycerides Plasma HDL-cholesterol a Values were compared using Spearman s correlation, with and P values shown. Numerically similar and P values were obtained if M was corrected for total LBM (M/LBM, values not shown). gender, diagnosed T2DM, or hypertension on the data presented in Table 3 using analysis of covariance. All correlations remained significant with nominal P value.05 after adjustment for gender, diabetes, or hypertension. Figure 2. The relationship between sc fat cell volume or number and plasma insulin (P-insulin), insulin sensitivity (M-value), plasma triglycerides (TG), or plasma HDL-cholesterol. Discussion This study was performed to elucidate how fat cell number, in comparison with fat cell size, associates with insulin resistance and other obesity-linked metabolic abnormalities. It is well established that enlarged fat cell size is associated with several of the metabolic abnormalities observed in conditions with excess body fat (14 18). This was confirmed in the present study, demonstrating that individuals with large sc or visceral fat cells displayed unfavorable values for insulin sensitivity and plasma levels of insulin, triglycerides, and HDL-cholesterol. A novel and intriguing observation was the opposite findings with sc fat cell number. Thus, the number of fat cells in this depot was positively correlated with insulin sensitivity and HDL-cholesterol and negatively associated with fasting plasma insulin and triglyceride levels. These relationships, which were independent of total body fat mass, suggest that the size and number of fat cells in different adipose depots play different roles in obesity-associated metabolic complications. Large fat cells in both sc and visceral adipose tissue are associated with a more pernicious metabolic phenotype. In abdominal sc fat, more adipocytes is related to a more favorable state, whereas fat cell number in the visceral depot does not correlate with any of the studied metabolic parameters. Admittedly, although the correlations were highly significant, the fact that the values ranged from 0.5 to 0.5 suggest that it takes relatively large interindividual differences in adipocyte size/number to observe differences in the studied metabolic parameters. However, our assessments were performed in a rather homogeneous cohort of obese subjects consisting predominantly of women. It is possible that inclusion of a larger number of individuals, including normal-weight subjects and more men would result in different (and possibly stronger) correlations. More importantly, the predictive role and possible causal relationship between adipocyte size/number and metabolic outcome

5 E1874 Rydén et al Adipocyte Size and Number J Clin Endocrinol Metab, October 2014, 99(10):E1870 E1876 Table 3. Relationship Between Adipose and Metabolic Variables After Correction for Total Body Fat a Adipose Variables Visceral Fat Cell Volume Subcutaneous Fat Cell Volume Subcutaneous Fat Cell Number Metabolic Variable Std P Std P Std P Plasma insulin b Insulin sensitivity (M) Plasma triglycerides b Plasma HDL-cholesterol b a Values were compared by multiple linear regression analysis using one adipose variable plus total body fat (in kilograms) together as independent variables. Standardized -values (Std ) and P values for the adipose variable is given. A negative Std indicates an inverse relationship between the adipose and the metabolic variable. Numerically similar Std and P values were obtained if M was corrected for total LBM (M/LBM) or if total fat mass was expressed in percentage of total body weight (values not shown). b Values were log 10 -transformed to obtain normally distributed residual errors. must be validated in prospective and/or mechanistic studies. It is important to note that calculation of fat cell number is based on the quotient between 2 independent parameters; the size of a specific fat depot and the mean weight of its adipocytes. Although variations in adipocyte size between different sc depots are rather small (26), fat cell volume (and thereby weight) can differ significantly between other adipose depots. This implies 1) that our determinations of adipose cellularity are only relevant for ESAT and EVAT and 2) that the number of fat cells is not a simple function of fat cell diameter. The latter notion was corroborated by the fact that despite the observed association with adipocyte volume in both regions, only sc fat cell number was related to the studied metabolic parameters. Which mechanisms could explain differences in adipose cellularity and their potential influence on hormonal and metabolic homeostasis? Although this study was not designed to answer this question, some speculations can be offered. Adipose inflammation has emerged as an important factor contributing to insulin resistance (27). Interestingly, adipocyte size correlates closely with the expression and secretion of the proinflammatory cytokine TNF in lean healthy women (28) and insulin resistance/hyperlipidemia in morbidly obese women (14). In these studies, subjects with many small sc fat cells displayed lower TNF secretion and better insulin sensitivity, respectively. Thus, although the mechanisms are not clear, increased fat cell size appears to alter several transcriptional regulatory networks resulting in increased inflammation, altered lipid metabolism, and insulin resistance. As discussed (29, 30), proinflammatory adipokines and lipids secreted from enlarged fat cells in visceral white adipose tissue may be of particular importance because this depot is drained via the portal vein into the liver, thereby impacting directly on hepatocyte function. A recent publication, combining clinical data and murine models, demonstrated that reduced activity of the transcription factor early B cell factor-1, possibly downregulated by increased local expression of TNF, could be an important causal factor promoting the development of adipose hypertrophy via altered expression of genes involved in adipogenesis and lipid turnover (31). These results suggest that early B cell factor-1 may constitute a causal link between TNF expression and fat cell size and number, a notion that needs to be validated in future studies. A related possibility explaining the association between adipose cellularity and metabolic changes may be that the capacity of adipose tissue to store lipids is limited (32). Consequently, when adipose expansion reaches a maximum, lipids are stored ectopically in skeletal muscle, liver, and pancreatic islets, which in turn leads to lipotoxic effects including abnormal lipoprotein metabolism, insulin dysfunction, and altered insulin secretion. The major contributor ( 95%) to adipocyte volume is the lipid droplet. As measured with the method adopted in the present study, fat cell volume in adult humans cannot become larger than 1400 pl (33). When this size limit is reached, the tissue must generate more fat cells to be able to expand further (13, 32). This is supported by the observation that total fat cell number is approximately doubled in obese compared with ageand gender-matched lean subjects (33). Interindividual differences in the weight of the greater omentum are determined by variations in fat cell number and less by adipocyte size (22), suggesting that visceral adipose expansion may primarily be determined by increased adipocyte number. It is not yet known how fat cell number and size contribute to variations in sc adipose tissue mass. Nevertheless, together with the data presented herein, it could be speculated that subjects who can increase their sc fat cell number may develop less ectopic lipid deposition and/or that newly formed sc adipocytes display a less pernicious functional profile. The fact that the sc depot is consider-

6 doi: /jc jcem.endojournals.org E1875 ably larger than the visceral, may explain why fat cell number associates with metabolic variables only in the former region. Our calculations of fat cell number rely on estimations of abdominal sc and visceral adipose depots using DEXA and the recently developed CoreScan tool. Although assessments with this method show very good correlations with values obtained using CT (21, 22), they remain indirect determinations that should warrant some caution in their interpretation. Admittedly, there have been concerns about the accuracies regarding DEXA measurements of adipose mass in obese individuals, but these pertain to previously used DEXA equipment and software (34). Nevertheless, a limitation of our study is that no visceral adipose tissue phantom was used for calibrations. However, the precision of the CoreScan tool to measure visceral adipose tissue mass has recently been estimated and shown to have relatively low error in both phantoms and obese women (35). We investigated only a limited set of relevant metabolic phenotypes linked to excess body fat, ie, insulin sensitivity and plasma insulin, triglycerides, and HDL-cholesterol. This approach was chosen first because these are the most commonly investigated parameters and second to avoid the inherent problems of using too many variables in multiple testing. Hence, we have not established how other obesity-associated metabolic variables such as lipoproteins and proinflammatory factors as well as other lipid and glucose measures are related to adipose cellularity. Moreover, our cohort included participants with non insulin-dependent diabetes (T2DM) and/or hypertension, and there was a clear imbalance in gender. This reflects that more than two-thirds of the subjects undergoing bariatric surgery in Sweden are women and many have developed obesity-associated metabolic complications upon acceptance for surgery. The subjects studied herein could therefore in theory not be representative for obese individuals in the general population. Nevertheless, when corrected for gender, diabetes, or hypertension, the observed correlations remained statistically significant with nominal P values.05. It has been suggested that the classical categorization of body fat status based on BMI and adipose distribution is insufficient (36). Up to one-third of overweight or obese subjects are metabolically healthy (37). Furthermore, the association between body fat distribution and insulin resistance/t2dm is controversial (38, 39). The present study suggest that classifying overweight/obese patients according to their fat cell size and number might be clinically useful to estimate the risk of T2DM and related metabolic complications and thereby determine treatment strategies, at least in specialized clinics. It is relatively easy to measure fat cell size in small sc needle biopsies (40), and fat mass in different regions can be assessed by DEXA, with the use of recently developed software. In conclusion, this study shows that fat cell size and number, independently of body fat mass, associate differently with metabolic parameters in obese subjects. Increased sc or visceral fat cell size correlates with an adverse insulin/lipid profile, whereas increased sc fat cell number is coupled to a less pernicious phenotype. Acknowledgments We thank research nurses Britt-Marie Leijonhufvud, Yvonne Widlund, and Katarina Hertel as well as Eva Sjölin, Kerstin Wåhlén, and Elisabeth Dungner from Karolinska Institutet for excellent technical assistance. Address all correspondence and requests for reprints to: Mikael Rydén, Professor, MD, PhD, Karolinska Institutet, Department of Medicine, Karolinska University Hospital Huddinge C2 94, The Clinic for Endocrinology, Metabolism, and Diabetes, SE Stockholm, Sweden. mikael.ryden@ki.se. This study was supported by grants from the Swedish Research Council, Novo Nordisk Foundation, Swedish Diabetes Association, EASD/Lilly Foundation, and Strategic Research Program in Diabetes at Karolinska Institutet. P.A. and M.R. designed the study and wrote the first version of the manuscript. All 3 authors contributed equally to data collection and analysis, writing of the manuscript, and approving the final version. 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