Résumé

L’adiposopathie, un concept élaboré par Bays en 2003, s’intéresse à l’importance de l’état fonctionnel du tissu adipeux. Son étude peut être effectuée à l’aide du rapport de deux adipokines, adiponectine : leptine. Nous avons étudié la relation de ces adipokines, de leur rapport représentatif de l’adiposopathie, des marqueurs de l’inflammation,de la capacité aérobie (VO2max) et de l’adiposité avec la réponse à l’insuline. Notre cohorte est

composée d’hommes âgés entre 34-53 ans, répartis en 4 groupes en fonction de leur tolérance au glucose, de leur IMC et de leur niveau de « fitness ». Nos résultats ont montré qu’une faible adiposopathie est retrouvée dans la population qui a le meilleur profil métabolique et qu’elle est, après le VO2max, un prédicteur indépendant

5.2. Abstract

Adiposopathy, or sick fat, refers to the adipose tissue dysfunction. This dysfunction can lead to several complications such as dyslipidemia, insulin resistance and hyperglycemia, and can be assessed as a low plasma adiponectin/leptin ratio. The relative contribution of adiposopathy in predicting insulin resistance in relatively healthy populations remains unclear. We investigated the relationship between adiposopathy and body composition (hydrostatic weighing), insulin sensitivity (hyperinsulinemic- euglycemic clamp), inflammation, and fitness level (ergocycle VO2max) in 53 men (aged 34-53 yrs) from

4 groups: sedentary non-obese controls (body mass index [BMI] <25 kg/m2_{), sedentary obese (BMI>30}

kg/m2_{), sedentary obese glucose intolerant, and non-obese highly trained endurance active. The}

adiponectin/leptin ratio was highest in trained men (4.75±0.82) and lowest the obese intolerant subjects (0.27±0.06; ANOVA p<0.0001) indicating increased adiposopathy. The ratio was negatively associated with adiposity (e.g. waist circumference, r= -0.59, p<0.01), and positively associated with fitness (VO2max,r=0.67, p<0.01) and insulin sensitivity (M/I, r=0.73, p<0.01). Multiple regression analyses, with

or without athletes, reveal fitness level as the strongest independent predictor of insulin sensitivity (r2

range 0.64-0.65), but adiposopathy was also an independent and significant contributor (r2 _{range 0.044-}

0.096) while waist circumference was not retained in the model. A 16 week weight loss intervention led to an improvement in the ratio (+60%, p=0.039). We conclude that adiposopathy is a greater contributor to insulin resistance than abdominal obesity in non-diabetic men.

5.3. Introduction

Sedentary lifestyle, excess caloric intake, genetics and environmental factors contribute to obesity, insulin resistance and other metabolic diseases, and are major health concerns in our modern society. Adipose tissue is now known as a powerful secretory organ releasing adipokines and inflammatory cytokines having numerous actions on multiple target systems (Bays, Gonzalez-Campoy et al. 2008; Galic, Oakhill et al. 2010; Kwon and Pessin 2013). Notably, multiple secretory products from adipose tissue can have a significant effect on whole-body or tissue-specific insulin resistance (Kwon and Pessin 2013). In this context, it is of interest to investigate the secretory profile of adipose tissue as a predictor of insulin resistance, and perhaps as a potential diagnostic tool.

It is now well established that adipose tissue is not simply a fat storage organ, but also plays a major secretory role (Bays, Gonzalez-Campoy et al. 2008; Galic, Oakhill et al. 2010). This secretory function varies according to the anatomical location of the adipose tissue depot, generally classified as subcutaneous or visceral, but also by the type of adipocyte (white, brown or beige) found within each depot. Further, adipose tissue depots can also differ by a number of other characteristics, including their genetic origin and cellular composition (adipocyte type, stromal cells), to mention but a few (Lee, Wu et al. 2013). Each depot has the potential to influence different metabolic functions; for example, subcutaneous adiposity appears to better predict insulin sensitivity (Smith, Lovejoy et al. 2001), while visceral adiposity (often assessed as increased waist circumference) is a greater risk factor for metabolic syndrome and cardiovascular disease (Katzmarzyk, Janssen et al. 2006).

But adiposity, i.e. the quantity of adipose tissue, is in of itself insufficient to explain all of its metabolic effects. For example, although type 2 diabetes mellitus, hypertension or dyslipidemia are generally associated with obesity, these conditions can occur in non-obese individuals or differ in individuals with similar adiposity (Bays 2005). This has led to the concept that adipose tissue function independently from its quantity can influence the metabolic state of the organism, and this has been termed adiposopathy, or sick fat (Bays and Stein 2003). This describes a situation where perturbed adipocyte function, such as a modified secretory profile, contributes to metabolic disorders such as high blood glucose, high blood pressure and dyslipidemia (Bays, Abate et al. 2005; Bays 2011). In this context, the most often considered adipokines are adiponectin and leptin, both well known to have numerous metabolic actions, but in particular influencing insulin sensitivity (Caselli 2014; Khan and Joseph 2014). Adiponectin, an adipokine exclusively secreted by adipocytes, has several organ targets (including the lung, heart and skeletal muscle (Menzaghi, Trischitta et al. 2007)) and metabolic actions. Circulating

levels of this protein are negatively correlated with BMI and insulin resistance (Staiger, Tschritter et al. 2003). In terms of insulin sensitization, it increases glucose uptake and energy expenditure and decreases the accumulation of fat in the skeletal muscle (Caselli 2014). Leptin, also an adipokine secreted primarily by adipocytes, has a more central action on satiety centers and energy expenditure, but also influences peripheral tissues. It has similar actions as adiponectin on skeletal muscle, leading to increased storage of glucose as glycogen in myocytes. Yet, even if these peripheral effects are similar to those of adiponectin, leptin levels are positively correlated with BMI and insulin resistance, a result in part of the existence of a leptin resistance phenomenon (Vega and Grundy 2013; Khan and Joseph 2014). Given their similar effects on insulin sensitivity, but their inverse relationships with BMI and metabolic health, the adiponectin/leptin (A/L) ratio has been suggested and used as a marker of adiposopathy, i.e., a marker of the secretory “health” of adipocytes (Jung, Rhee et al. 2010; Vega and Grundy 2013).

Adipose tissue is also a major source of inflammatory cytokines that can impact the insulin response (Galic, Oakhill et al. 2010; Kwon and Pessin 2013; Bays 2014). With obesity there is an increased infiltration of white blood cells in adipose tissue, and both these cells and adipocytes can secrete anti- and pro-inflammatory molecules. Adiponectin is an example of the former, while IL-6, CRP, TNF-α, RANTES and MCP-1 are examples of the latter (Pradhan, Manson et al. 2001). TNF-α, secreted by stromal vascular cells and adipocytes, also has an impact on insulin sensitivity by diacylglycerol esterification (Katsuki, Sumida et al. 1998; Bruce and Dyck 2004). RANTES and MCP-1 are also increased with obesity, glucose intolerance and type 2 diabetes (Sell, Dietze-Schroeder et al. 2006; Kohno, Ueji et al. 2011).

Despite some work having studied the relationship between adiposopathy and the insulin response, many questions remain as to its relative contribution to insulin resistance when compared to other known contributors. The aim of this study was to investigate the relationship between inflammatory markers, adipokines, adiposopathy level and insulin resistance (evaluated by the euglycemic- hyperinsulinemic clamp), and to clarify how the fitness level (VO2max) and abdominal obesity could

influence these relations in a cohort of non-diabetic middle-aged men. We hypothesized that adiposopathy and an inflammatory state would independently increase skeletal muscle insulin resistance.

5.4. Materials and Methods

Fifty-three subjects aged 35 to 55 years were recruited in 4 groups in a cross sectional study: Sedentary (S; n=11), Obese (O; n=11), Obese glucose Intolerant (OI; n=16) and Athlete (A; n=15). The first three groups and testing methods were previously described in (Riou, Pigeon et al. 2009). In brief, sedentary subjects were defined by an absence of regular physical activities resulting in an energy expenditure of 8 METS or more, or activities lasting more than 30 min consecutively weekly, and had a BMI ≤ 25 kg·m-2 _{(Bouchard, Leon et al. 1995).}

Subjects in the obese group were sedentary and had a BMI > 30 kg·m-2 _{and normal glucose tolerance measured}

by OGTT (2 hr glucose ≤ 7.8 mmol/L). The obese glucose intolerant subjects differed from the obese by presenting impaired glucose tolerance measured by OGTT (2 hr glucose > 7.8 mmol/L). The athlete group consisted of non-obese subjects practicing high levels of regular aerobic physical activities. Exclusion and inclusion criteria are detailed in (Riou, Pigeon et al. 2009). Briefly, persons with diabetes, a ±2 kg body weight fluctuation in the previous 6 months, smokers, heavy alcohol consumers, asthmatics needing steroid therapy, or with any liver, renal or uncontrolled thyroid disorders were excluded. Subjects using medication with steroid hormones, α- or β-blockers, diuretics, or other lipid metabolism modulators (e.g. thiazolidinediones, statins, and insulin.) were also excluded. Individuals with a history or physical findings of coronary heart disease, peripheral vascular disease, hypertension or an inability to perform the exercise test were also excluded. The research protocol was approved by the Université Laval ethics committee and all subjects provided written informed consent.

 Body composition

Body weight was obtained to the nearest 0.1 kg with a calibrated scale including a tension gauge (Intertechnology Inc., Don Mills, ON, Canada) and a Digital Panel Indicator (Beckman industrial series 600; Beckmann Coulter Canada Inc., Mississauga, ON, Canada). Height was measured to the nearest millimeter with a wall stadiometer. Waist circumference (WC) was taken in duplicate at the mid-distance between the iliac crest and last rib margin with a flexible steel metric tape to the nearest 0.1 cm. Body composition was obtained by hydrostatic weighing as described in (Bouchard, Leon et al. 1995) for precise assessment of fat and lean mass.

 Euglycemic-hyperinsulinemic clamp

Clamp conditions were previously described in (Pigeon, Riou et al. 2009). In brief, subjects fasted overnight (12 hours) (Oppert, Nadeau et al. 1997) and avoided exercise for three days preceding the clamp to minimize the acute effects of exercise on insulin sensitivity (Mikines, Sonne et al. 1988; Rice, Janssen et al. 1999). All subjects also consumed a standardized diet adjusted to their isocaloric needs the day preceding the clamp (White,

Bouchard et al. 1996). An antecubital arm vein was cannulated with a catheter for infusion of insulin and 20% glucose, and the contralateral arm vein was cannulated to allow sampling for determination of plasma insulin and glucose levels. Baseline fasting blood was drawn for measurements. A continuous infusion of insulin (Humulin; 40 mU/m2_{/min) was started, keeping arterial insulin in the upper physiologic range of approximately}

500 pM. Blood samples were taken every 5 minutes for measurements of plasma glucose, and insulin levels were measured every 10 minutes. Insulin sensitivity was expressed as M/I (glucose disposal rate/insulin concentration) calculated between 90 and 120 minutes of the clamp.

 Maximal aerobic power/ cardiorespiratory fitness (VO2 max)

The procedure was previously described in (Riou, Pigeon et al. 2009). In brief, the test was performed on a bicycle ergometer, a 5 min warm up between 50 and 75W followed by an incremental test starting between 100 and 150W with an increase of 25W every 2 minutes. Subjects were instructed to maintain a cadence of 70 rpm during the test. Direct measures of O2 and CO2 were obtained every 30 seconds. Perceived exertion (Borg

analogue scale) and blood pressure were measured every 2 minutes (Borg 1982). The end of the test occurred when the participants couldn’t maintain the required cadence or upon the appearance of a usual indicator for the determination of exercise testing (Fletcher, Balady et al. 2001).

 Diet induced weight loss

Eight volunteers from the glucose intolerant group followed a 16 week hypocaloric diet based on a weight loss protocol commonly used by our research group (Tremblay, Doucet et al. 1999; Berube-Parent, Prud'homme et al. 2001). In summary, subjects were instructed by a nutritionist to respect the following dietary guidelines: 1) decreased food intake of 700 kcal/day below isocaloric levels estimated from resting metabolic rate; 2) a gradual reduction in fat intake; 3) a slight increase in protein intake (+5-10% of total caloric intake); 4) decreased alcohol consumption; and 5) increased consumption of fibers and low glycemic index carbohydrates. Subjects were free to choose their foods, and a follow-up visit every 2 weeks with a nutritionist allowed us to verify that subjects were following guidelines and to perform anthropometric measurements. All tests and measures, including OGTT, clamp, and anthropometry were taken before and after the weight loss intervention.

 Blood analysis

An Elite Bayer glucometer (3903-E) was used to monitor glucose every 5 minutes during the clamp. Every 10 minutes, additional blood samples were collected, centrifuged to obtain plasma, and stored at −20°C for later analyses. Glucose was measured using the hexokinase method and plasma insulin using a radioimmunoassay with polyethylene glycol separation (Pigeon, Riou et al. 2009). Human high molecular weight (HMW) adiponectin was measured by ELISA (Millipore, Billerica, MA). Leptin and inflammatory molecules were measured by

Milliplex® Multiplex kits (Millipore, Billerica, MA) using Luminex® technology. The adiponectin/leptin ratio was used as an indicator of adipose tissue function, a higher ratio indicating good, healthy function, and a lower ratio indicating increased adiposopathy.

 Statistical analysis

Statistical analyses were performed using JMP® 9.0.2 software (2010 SAS Institute Inc.). Values which were three standard deviations or more from the mean for normally distributed data were considered outliers and excluded from analyses. Group comparisons were performed with a one-way ANOVA followed by Tukey-Kramer HSD post-hoc tests for group differences with normally distributed data. For data that lacked normality, nonparametric Wilcoxon/Kruskal-Wallis (Rank Sums) tests and nonparametric Wilcoxon comparisons for each pair were performed. To assess the effects of diet in the weight-loss intervention group, repeated-measures t- tests were used. Data are presented as means ± SEM, and are considered statistically different when p<0.05.

5.5. Results

 Subjects characteristics

Subjects were generally well matched for age, although athletes were slightly older than obese intolerant subjects (Table 1). As expected, there were significant differences in body composition and anthropometric markers between obese and non-obese groups, the athletes having a smaller body mass, body fat, and waist circumference. Also as expected, the obese intolerant subjects had the highest fat free mass of any group. Insulin sensitivity and VO2max were significantly different between groups, being lowest in the obese intolerant

men, intermediary in the non-obese controls, and highest in the athletes. Tableau 5-1 : Physical characteristics, fitness, and insulin sensitivity of the subjects

Variable Sedentary Athletes Obese Obese glucose-

intolerant ANOVA P value

n 11 15 11 16 Age (years) 44.4±1.1ab _47.7±1.4a _44.9±2.0ab _41.5±1.3b _0.0220 Body mass (kg) 73.9±1.8a _74.0±2.2a _101.0±3.6b _108.0±3.4b _<0.0001 BMI (kg/m²) 23.9±0.5a _23.6±0.3a _32.1±1.0b _35.3±1.0c _<0.0001 Waist circumference (cm) 88.7±2.0 a _84.5±1.0a _111.0±3.0b _118.0±2.3b _<0.0001 Fat mass (kg) 14.7±0.9a _10.7±0.6a _33.0±2.6b _34.5±2.6b _<0.0001

Fat free mass (kg) 59.1±1.4a _63.3±1.8ab _68.0±1.6bc _74.1±1.6c _<0.0001

VO2max (ml/kg/min) 38.2±1.5a 54.0±1.1b 31.6±1.0c 29.8±1.3c <0.0001

Insulin sensitivity (M/I,

x1000) 11.0±1.46

a _{19.80 ±1.97}b _6.48±1.12ac _{4.56 ±0.71}c _<0.0001

Values are means± SEM. Values that share the same letter are not statistically different using the Tukey HSD post-hoc test.

 Plasma variables

Subject plasma glucose, insulin, inflammatory cytokines and adipokines levels are shown in Table 2. The glycemic profile shows no between groups differences for fasting glucose levels, as expected, whereas fasting insulin levels were lower in sedentary subjects and lowest in the athletes (2.5-fold less than the levels in the obese intolerant men). Obese intolerant subjects were the most metabolically deteriorated, their glucose levels almost two times higher and their insulin levels 5 times higher than athletes 2-hrs following glucose bolus ingestion during the OGTT.

No significant between group differences were seen for levels of inflammatory variables TNFα, MCP1 and RANTES. CRP levels were similar in the sedentary and athlete groups, but were significantly greater in obese

(2-fold higher) and glucose-intolerant subjects (4-fold higher). IL6 levels were similar in obese and obese intolerant subjects, sedentary subjects presenting intermediary values while athletes had the lowest levels (half those of the obese subject groups).

No significant differences were observed for adiponectin despite large differences in fat mass between groups. There was no difference between obese and obese glucose intolerant subjects for leptin levels, whereas sedentary subjects had levels 3-4 times lower and athletes 10 times lower than the obese subjects. The adiponectin/leptin ratio followed an inverse distribution pattern when compared to leptin, athletes having the highest (healthiest) value and obese intolerant subjects the lowest level (indicating increased adiposopathy). Tableau 5-2 : Plasma profile of subjects.

Variable Sedentary Athletes Obese Obese glucose-

intolerant ANOVA P value

Glycemic characteristics

Fasting glucose (mmol/l) 5.55±0.15 5.82±0.11 5.77±0.13 5.88±0.10 0.264

2-hr OGTT Glucose (mmol/l) 5.20±0.49a _6.11±0.47a _6.47±0.23ab _9.13±0.22b _<0.0001

Fasting insulin (mmol/l) 75.6±9.7a _63.4±7.9a _110.0±16.7ab _160.8±24.0b _0.0004

2-hr OGTT insulin (mmol/l) 526±93a _244±47b _767±134a _1284±178c _<0.0001

Inflammatory cytokines CRP (ng/mL) 943±326 a _980±247 a _2374±464 b _4403±922 b _0.0011 IL6 (pg/ml) 1.22±0.28 ab _0.94±0.29 a _2.04±0.32 b _1.91±0.20 b _0.0056 RANTES (pg/mL) 14591±3543 14518±3236 14578±4487 10243±2209 0.7892 MCP-1 (pg/mL) 226±17 212±11 251±16 229±18 0.3941 TNFα (pg/mL) 4.44±0.62 4.65±1.39 5.76±0.96 4.53±0.37 0.2016 Adipokines Adiponectin (ng/mL) 4361±1079 4416±473 4239±798 3173±722 0.2918 Leptin (pg/mL) 3176±506 a _1086±104 b _11227±1703 c _11193±1127 c _<0.0001 Adiponectin/Leptin 2.131±0.892 a _4.753±0.817b _0.435±0.085 c _0.267±0.057 c _<0.0001

Data are presented as mean ± SEM, (n= 42 to 53). Values that share the same letter are not statistically different using the Tukey HSD post-hoc test or nonparametric comparisons for each pair with the Wilcoxon Method.

 Relationships between inflammation, anthropometry, fitness, and insulin sensitivity

The results of Pearson correlation analyses between circulating inflammatory markers with anthropometry, fitness and clamp variables are presented in Table 3. CRP and IL6 showed moderate (r range 0.3-0.6) but statistically significant correlations with increasing body weight and adiposity, all positive except for a negative correlation between IL6 and lean body mass. The negative relationships between fitness and insulin sensitivity with CRP and IL6 were all moderate (r range -0.3 to -0.5) but statistically significant. No significant correlations were observed with RANTES, TNFα, or MCP1. Overall, this indicates that a deteriorated anthropometric, fitness and insulin sensitivity profile is associated with increased inflammation as measured as circulating IL6 and CRP. Tableau 5-3 : Pearson correlations (r values) between anthropometric measures, fitness, insulin sensitivity and plasma inflammatory markers. *, p<0.01 CRP (ng/mL) (pg/mL) IL6 RANTES (pg/mL) (pg/mL) TNFα (pg/mL) MCP1 Anthropometry Weight (kg) 0.568* 0.456* -0.122 0.022 0.014 BMI (kg/cm²) 0.639* 0.483* -0.152 0.009 0.024 Waist circumference (cm) 0.639* 0.468* -0.152 0.045 0.045 Lean mass (kg) 0.387* -0.341* -0.055 -0.032 -0.100 Fat mass (kg) 0.597* 0.452* -0.145 0.055 0.089

Fitness VO2max (mL/kg/min) -0.491* -0.483* 0.045 -0.032 -0.145

VO2max

(mL/kgMM/min) -0.456* -0.444* 0.007 0.006 -0.126

Insulin sensitivity M/I -0.474* -0.335* 0.004 0.155 -0.179

 Relationships between adipokines, anthropometry, fitness, and insulin sensitivity

Table 4 presents the results of Pearson correlation analyses between adipokines and the adiponectin/leptin ratio with anthropometry, fitness and clamp variables. Leptin levels were strongly positively correlated with markers of body weight and fat mass, and negatively correlated with lean body mass. Leptin levels were also moderately to strongly negatively correlated with fitness and insulin sensitivity. No significant correlations with any of the variables of interest were seen with circulating adiponectin levels. Correlations with the adiponectin/letpin ratio were the mirror image of those seen with leptin. Of note, the single strongest correlation with insulin sensitivity was observed with the adiponectin/leptin ratio.

Tableau 5-4 : Pearson correlations (r values) between anthropometric measures, fitness, and insulin sensitivity with plasma adipokines and the adiponectin/leptin ratio. *, p<0.01

Irisin

(ng/mL) Adiponectin (ng/mL) (pg/mL) Leptin Adiponectin/ leptin

Anthropometry Weight (kg) 0.397* -0.126 0.837* -0.539* BMI (kg/cm²) 0.378* -0.179 0.869* -0.566* Waist circumference (cm) 0.421* -0.167 0.889* -0.588* Lean mass (kg) -0.247* -0.234 -0.508* -0.388* Fat mass (kg) 0.430* -0.045 0.918* -0.554* Fitness VO2max (mL/kg/mn) -0.369* 0.170 -0.765* 0.670* VO2max (mL/kgMM/mn) -0.298* 0.148 -0.614* 0.614*

Insulin sensitivity M/I -0.355* 0.237 -0.677* 0.727*

 Multiple regression modelling of insulin sensitivity

In order to identify which variables independently predict insulin sensitivity, we chose the strongest single correlates from the above correlation analyses into stepwise multiple regression models (Table 5). Models were run with and without athletes, in order to exclude any specific confounding effect of regular training on the studied relationships.

In both models, fitness level and the adiponectin/leptin ratio were found to be the only significant and independent predictors of insulin sensitivity, waist circumference and CRP levels not being retained as significant predictors. Fitness was a very strong predictor in both models (65,1% and 63.6% predictive power with and without athletes, respectively). In the absence of the athletes, the adiponectin/leptin ratio became slightly more powerful as a predictor (4.4% with, 9.6% without athletes).

Tableau 5-5 : Multiple regression modelling of insulin sensitivity

Insulin sensitivity (M/I) With athletes Without athletes

Partial R2 _{P Partial R}2 _P

VO2max (mL/kg/min) 0.651 0.000 0.636 0.000

Adiponectin/leptin 0.044 0.021 0.096 0.003

Waist circumference (cm) 0.010 NS 0.016 NS

 Weight loss intervention

Diet-induced weight loss led to significant decreases in waist circumference (-6.78%) body weight (-7.4%) and fat mass (-18.4%), and a significant improvement of insulin sensitivity (+71.4%) with no significant change in fasting blood glucose and insulin (Table 6). Although significant changes to leptin levels (-39.5%), the adiponectin/leptin ratio (+148%) and 2h OGTT glucose (-24.4%) and insulin (-46.7%) were observed, no significant changes were seen to circulating inflammatory markers or adiponectin levels. No significant correlations were found between changes to these variables (Δ leptin, Δ adiponectin, Δ adiponectin/leptin, ΔCRP, Δ waist circumference) and changes to insulin sensitivity (ΔM/I).

Tableau 5-6 : Effects of weight loss in glucose-intolerant subjects.

Pre weight loss Post weight loss T-test p value Waist circumference (cm) 118.0±3.6 110.0±4.4 0.0039

Weight (kg) 108.7±4.8 100.6±5.2 <0.0001

Fat mass (kg) 33.4±3.7 28.2±4.2 0.0006

Fat free mass (kg) 75.3±1.9 72.4±1.7 0.0006

Fasting glucose (mmol/l) 5.87±0.10 5.79±0.22 0.327 2h OGTT glucose (mmol/l) 9.14±0.30 6.91±0.79 0.008

Fasting insulin (mmol/l) 137±28 131±39 0.326

2h OGTT insulin (mmol/l) 1123±310 599±176 0.010

Insulin sensitivity (M/I) (x1000) 5.32±1.29 9.12±1.89 0.008 CRP (ng/mL) 4532 ± 1586 3389 ± 1378 0.1348 IL6 (pg/mL) 1.60 ± 0.16 1.47 ± 0.15 0.1089 RANTES (pg/mL) 12 108 ± 3184 11 313 ± 3983 0.4585 TNFα (pg/mL) 4.36 ± 0.53 4.04 ± 0.61 0.2942 MCP1 (pg/mL) 211 ± 26 217 ± 27 0.3247 Leptin (pg/mL) 11423±1920 6906±1480 0.0019 Adiponectin (ng/mL) 2401±509 1989±346 0.1721 Adiponectin/leptin 0.23±0.06 0.58±0.31 0.0391

5.6. Discussion

Adipose tissue is highly heterogeneous, and possesses many functions beyond simple lipid storage (Bays, Gonzalez-Campoy et al. 2008; Galic, Oakhill et al. 2010). Depots can differ by cellular composition, endocrinology, immunology, blood flow, innervation and metabolic activity, to name but a few (Lee, Wu et al. 2013). Although often grossly classified as subcutaneous and visceral, the latter often described as more “pathologic” (Fox, Massaro et al. 2007) and the former as “protective” (Bays, Fox et al. 2010), the reality is that this distinction is often dubious as both increase the risks for deteriorated health (Bays 2014). Furthermore, differences in function can exist between depots of a given type (e.g., subcutaneous and omental visceral depots (Marette, Mauriege et al. 1997)), and the