The FASEB Journal article fj.14-263913. Published online November 19, 2014.
The FASEB Journal • Review
Molecular insights into the role of white adipose tissue in metabolically unhealthy normal weight and metabolically healthy obese individuals Flavia Badoud, Maude Perreault, Michael A. Zulyniak, and David M. Mutch1 Department of Human Health and Nutritional Sciences, University of Guelph, Guelph, Ontario, Canada Obesity is a risk factor for the development of type 2 diabetes and cardiovascular disease. However, it is now recognized that a subset of individuals have reduced cardiometabolic risk despite being obese. Paradoxically, a subset of lean individuals is reported to have high risk for cardiometabolic complications. These distinct subgroups of individuals are referred to as metabolically unhealthy normal weight (MUNW) and metabolically healthy obese (MHO). Although the clinical relevance of these subgroups remains debated, evidence shows a critical role for white adipose tissue (WAT) function in the development of these phenotypes. The goal of this review is to provide an overview of our current state of knowledge regarding the molecular and metabolic characteristics of WAT associated with MUNW and MHO. In particular, we discuss the link between different WAT depots, immune cell infiltration, and adipokine production with MUNW and MHO. Furthermore, we also highlight recent molecular insights made with genomic technologies showing that processes such as oxidative phosphorylation, branchedchain amino acid catabolism, and fatty acid b-oxidation differ between these phenotypes. This review provides evidence that WAT function is closely linked with cardiometabolic risk independent of obesity and thus contributes to the development of MUNW and MHO.— Badoud, F., Perreault, M., Zulyniak, M. A., and Mutch, D. M. Molecular insights into the role of white adipose tissue in metabolically unhealthy normal weight and metabolically healthy obese individuals. FASEB J. 29, 000–000 (2015). www.fasebj.org
ABSTRACT
Key Words: cardiometabolic risk • obesity • biomarkers OBESITY IS A HETEROGENEOUS condition influenced by numerous factors, including genetics, lifestyle habits (e.g., diet and physical activity), and behavior (1). Although central obesity is widely considered a primary cardiometabolic risk
Abbreviations: BCAA, branched-chain amino acids; BMI, body mass index; CVD, cardiovascular disease; ECM, extracellular matrix; HbA1c, glycosylated hemoglobin; HDL-c, HDL cholesterol; hsCRP, high sensitivity C-reactive protein; LDL-c, LDL cholesterol; LH, lean healthy; MHO, metabolically healthy obese; MUNW, metabolically unhealthy normal weight; MUO, metabolically unhealthy obese; SAT, subcutaneous adipose tissue; T2D, type 2 diabetes; TCA, tricarboxylic acid; TG, triglyceride; Th, T-helper; Total-c, total cholesterol; VAT, visceral adipose tissue; WAT, white adipose tissue
0892-6638/15/0029-0001 © FASEB
factor for the development of type 2 diabetes (T2D) and cardiovascular disease (CVD), empirical evidence has revealed that not all obese individuals develop T2D and CVD and not all lean individuals have a healthy cardiometabolic profile (2). This paradox highlights the notion that excess body weight is not the sole determinant of obesity-related complications (2). As research aims to improve our understanding regarding the basis for the variability in metabolic risk seen among individuals varying in body weight, distinct subgroups of people have been identified. Two subgroups that are now commonly discussed in the literature are referred to as metabolically unhealthy normal weight (MUNW) and metabolically healthy obese (MHO). To date, the majority of literature regarding MUNW and MHO has focused on demonstrating their prevalence in various populations, as well as the long-term health implications associated with each subgroup. Not surprisingly, there is considerable debate regarding the stability of these phenotypes over time, the health outcomes associated with these phenotypes, and whether their response to lifestyle interventions differs. Resolving these outstanding issues may have considerable impact on therapeutic strategies for these individuals. Although the clinical relevance of these phenotypes remains highly discussed, there is a notable paucity of information regarding the underlying physiologic and molecular basis of these distinct subgroups. One tissue that is widely recognized to influence an individual’s cardiometabolic health is white adipose tissue (WAT), in particular that located in the central regions of the body. Indeed, WAT is widely recognized to be a major regulator of whole-body lipid metabolism, glucose homeostasis, and inflammation (3). Therefore, exploring WAT function and morphology in MUNW and MHO individuals has the potential to help improve our understanding of these phenotypes, as well as better understand the links between WAT and cardiometabolic risk, independent of body weight. Thus, the goal of this review is to provide a timely overview of our current state of knowledge regarding the contribution of WAT to MUNW and MHO. In particular, we discuss research exploring different WAT depots, immune cell infiltration, and cytokine and adipokine production, as well as highlight novel insights made using genomic 1
Correspondence: Department of Human Health and Nutritional Sciences, University of Guelph, Guelph, ON, N1G 2W1, Canada; E-mail:
[email protected] doi: 10.1096/fj.14-263913
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technologies. Together, this review demonstrates that WAT has an important role in MUNW and MHO and reinforces the continued need to study these distinct subgroups of the population. BRIEF OVERVIEW OF MUNW AND MHO The current methods of classifying individuals as MUNW or MHO consider both a person’s adiposity [e.g., body mass index (BMI), percentage body fat, and waist circumference] and their cardiometabolic status (e.g., insulin sensitivity, blood lipid levels, and markers of inflammation). Using these parameters has enabled MUNW and MHO subgroups to be distinguished from lean healthy (LH) and metabolically unhealthy obese (MUO) individuals (Fig. 1). Although there is currently no global consensus regarding which parameters should be used to classify individuals into these various subgroups, we provide a general overview regarding the definition, prevalence, and primary cardiometabolic features of MUNW and MHO to set the context for the remainder of this article. MUNW individuals The term MUNW is used to describe individuals who have increased cardiometabolic risk despite having a BMI equivalent to LH individuals (i.e., ,25 kg/m2). The most common cardiometabolic abnormalities reported in MUNW individuals are reduced insulin sensitivity and
dyslipidemia (4); however, body composition analyses have revealed that MUNW individuals also have a higher percentage body fat compared with BMI-equivalent LH individuals (5, 6). Moreover, several studies have suggested that the increased percentage body fat in MUNW individuals may be the underlying cause for their hyperinsulinemia (7–10). MUNW individuals were also reported to have a blood lipid profile resembling that of MUO individuals (i.e., individuals with a BMI .30 kg/m2), with elevated triglycerides (TG) and LDL cholesterol (LDL-c) levels and reduced HDL cholesterol (HDL-c) compared with LH individuals (2, 5, 10). As such, MUNW individuals appear to have a cardiometabolic risk profile similar to that of MUO individuals, despite their significantly lower BMI. MUNW accounts for a non-negligible proportion of individuals with a BMI ,25 kg/m2, with various studies suggesting a prevalence ranging from ;10% to 37% depending on the ethnic population examined (2, 11, 12). Acquiring greater insight into the genetic, molecular, and physiologic factors, among others, that contribute to the development of MUNW is imperative given that epidemiologic studies indicate that these individuals, despite a normal BMI, run the same risk as MUO individuals for developing serious cardiometabolic complications (13). MHO individuals The term MHO is applied to individuals who have a BMI comparable to MUO individuals (i.e., .30 kg/m2), but present little to no sign of cardiometabolic complications
MHO
MUNW •
~10-27% of lean individuals
• ~13-29 % of obese individuals
•
BMI < 25 kg/m2
• BMI > 30 kg/m2
•
Insulin resistance
• Normal insulin sensitivity
•
Dyslipidemia (elevated TG and LDL-c, reduced HDL-c)
• Normal fasting glucose
•
Higher % body fat
• Normal blood lipid (reduced TG, total-cholesterol and LDL-c, elevated HDL-c)
Figure 1. Clinical features and prevalence of metabolically unhealthy normal weight and metabolically healthy obese individuals in the lean and obese population.
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(14). In other words, MHO individuals are typically insulin sensitive, and have a blood lipid profile and fasting glucose level in the normal healthy range (15–17). Indeed, the vast majority of studies have reported that MHO individuals are more insulin sensitive, have lower values of glycosylated hemoglobin (HbA1c), and reduced fasting glucose and insulin compared with MUO individuals (14, 18). Moreover, the use of hyperinsulinemic-euglycemic clamps demonstrated that MHO individuals have a higher glucose disposition index compared with MUO individuals (18, 19). Interestingly, MHO individuals also appear to have a blood lipid profile that falls intermediate to that of LH and MUO individuals (20). Compared with MUO individuals with a similar percentage body fat, MHO individuals were reported to have reduced TG and higher HDL-c levels, as well as reduced levels of total cholesterol (Total-c) and LDL-c (14, 18, 21–23). This suggests that MHO individuals have a reduced risk for cardiometabolic complications compared to their BMI-equivalent MUO counterparts. The MHO phenotype has an estimated prevalence ranging from ;13% to 29% in obese individuals, depending on the population studied (24–26). The range in prevalence estimates between different populations can be attributed in part to the various methods used to classify individuals as MHO but may also stem from differences in ethnicity, genetic makeup, and lifestyle factors. Interestingly, several studies explored whether the basis for MHO was solely related to an individual’s lifestyle habits (e.g., diet and physical activity) (27–29); however, this did not appear to be the case and suggests that genetic and molecular processes must also contribute to MHO. This notion is supported by intriguing data from Stefan et al., which intimated that specific polymorphisms in genes such as the adiponectin receptor 1 (ADIPOR1) and hepatic lipase (LIPC) may be associated with MHO (30). Although
promising, these findings stem from a small study population and must be replicated in larger cohorts. As such, this reinforces the need for continued investigation of this intriguing subgroup of obesity. WAT REMODELING IN OBESITY WAT plays an important role in the regulation of wholebody energy metabolism through its metabolic, cellular, and endocrine functions (3). The accumulation of fat, in particular in central regions of the body, is a primary risk factor for the development of obesity-related complications, such as CVD and T2D (3). In brief, WAT is characterized as a loose connective tissue, where each adipocyte is surrounded by a thick extracellular matrix (ECM). During chronic nutrient overload, the ECM undergoes constant remodeling to accommodate hypertrophic adipocytes; however, the accumulation of lipid-engorged adipocytes eventually leads to problems with WAT vascularization and innervation (31). Further, inefficient ECM remodeling, decreased adipogenesis, and impaired angiogenesis eventually leads to tissue hypoxia (3). This, in turn, increases the release of proinflammatory chemokines and cytokines that promote immune cell infiltration in WAT and cause phenotypic shifts in resident immune cells. This ultimately results in an inability to efficiently store excess energy in WAT. Indeed, dysfunctional WAT induces whole-body metabolic stress by releasing nonesterified fatty acids that are deposited ectopically (in skeletal muscle, liver, and pancreas), thereby disrupting key signaling pathways such as insulin signaling (Fig. 2) (32, 33). Together, the abovementioned interconnected processes that contribute to WAT dysfunction and propagate systemic inflammation increase an individual’s cardiometabolic risk.
Insulin Resistance Ectopic fat Immune cell infiltration
White adipose tissue -Visceral -Subcutaneous
y oph
ertr
Hyp
Altered adipose tissue functions Hypoxia
Caloric overload Hype
rplas
ia
Preserved adipose tissue functions Preserved Insulin Sensitivity
Figure 2. Interaction between WAT remodeling, inflammation, and insulin resistance. WAT (comprised of visceral and subcutaneous depots) undergoes hypertrophy (increased size of existing adipocytes) and hyperplasia (increased number of new adipocytes). In some instances, chronic calorie overload promotes adipocyte hypertrophy, which eventually causes WAT dysfunction by (1) placing a considerable strain on ECM remodeling machinery, (2) inducing adipose tissue hypoxia, and (3) increasing the secretion of proinflammatory proteins that recruit numerous proinflammatory immune cells. Together, the aforementioned processes contribute to the development of insulin resistance. In other instances, chronic calorie overload promotes hyperplasia, thus increasing the proportion of small adipocytes, which are associated with preserved WAT function and insulin sensitivity. Elucidating the mechanisms that control hyperplasia and hypertrophy will help improve our understanding of MHO and MUO phenotypes.
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Subcutaneous versus visceral adipose tissue WAT is separated into 2 primary fat depots: visceral fat (VAT; located around central organs) and subcutaneous fat (SAT; located beneath the skin). Because VAT is known to play a dominant role in the development of insulin resistance and inflammation (34), it was hypothesized that increased VAT in MUNW individuals may be a primary cause for their increased cardiometabolic risk (35). This notion may indeed have merit given that Ruderman et al. (8) reported that MUNW individuals had increased VAT compared with LH individuals. This was also observed by Dvorak et al. (36), who reported increased body fat percentage, as well as higher SAT and VAT abdominal adiposity, in MUNW women compared with LH women. Location of fat accumulation has also been postulated as 1 explanation for the reduced cardiometabolic risk of MHO individuals despite their increased body weight (37). Brochu et al. (21) identified a subgroup of obese postmenopausal women who were insulin sensitive despite high body fat and attributed this finding to lower amounts of VAT. Others have reached the same conclusion, suggesting that the preserved metabolic function of MHO individuals may be attributed to reduced amounts of VAT (21, 38). Furthermore, MHO individuals matched for percentage body fat with MUO individuals were reported to have ;50% less VAT (14, 21, 23, 39, 40). For example, Messier et al. (40) showed that postmenopausal women characterized as MHO who were equivalent for BMI, waist circumference, and fat mass compared with MUO women had significantly less VAT. In light of this evidence, it has been hypothesized that MHO individuals may preferentially store excess fat in SAT depots and that this could provide a partial explanation for their preserved insulin sensitivity (39, 41). However, this is not without controversy given that several studies have found no differences in SAT mass between MHO and MUO individuals (21, 23, 30, 38). WAT hypertrophy versus hyperplasia Another avenue of research has begun to examine whether adipocyte hypertrophy and hyperplasia in WAT may be associated with the different cardiometabolic risk profiles observed in MUNW, MHO, and MUO individuals. Evidence indicates that hypertrophic obesity (i.e., increased size of existing adipocytes), as opposed to hyperplasic obesity (i.e., increased number of adipocytes), is more likely to lead to insulin resistance and T2D due to increases in hypoxia, dysregulation in adipokine secretion, and inflammation (18, 22). Interestingly, Srdic et al. (42) found that MUNW individuals had increased adipocyte hypertrophy in VAT compared with LH subjects. In contrast, MHO individuals appear to have smaller adipocytes (;15% smaller) than their MUO counterparts (18, 43, 44). This finding, coupled with work by McLaughlin et al. (22) showing that MHO individuals had a 2- to 3-fold increase in the expression of genes related to adipocyte differentiation compared with MUO individuals, suggests that MHO may preserve WAT function by increasing adipocyte hyperplasia. Recently, Lackey et al. (45) studied a number of parameters related to ECM remodeling in obese individuals and reported that differences in WAT tensile strength, 4
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collagen, and proteolytic enzymes could discriminate MHO and MUO, thus highlighting that ECM remodeling is linked to metabolic health and may therefore be an important contributor to the MHO phenotype. As outlined above, efficient remodeling enables the healthy expansion of WAT; however, WAT expansion requires sufficient vasculature to support the increased demand for both oxygen and nutrients. Insufficient vasculature causes WAT to become hypoxic, leading to significant metabolic perturbations. Recently, Sun et al. (46) reported that the adipocytespecific overexpression of vascular endothelial growth factor-A promoted angiogenesis and preserved WAT function in diet-induced obese mice, indicating that maintaining adequate oxygen and nutrient delivery to WAT, even in an obese context, may be sufficient to preserve metabolic health. As such, future studies examining vasculature in various WAT depots from MUNW and MHO individuals may reveal that differences in angiogenesis are an underlying explanation for these phenotypes. Ectopic fat distribution The accumulation of ectopic fat in liver, skeletal muscle, and pancreas is known to impair normal tissue function and contribute to whole-body metabolic complications (47). As such, studying ectopic fat accumulation in MUNW, MHO, and MUO individuals can provide insight regarding the varying cardiometabolic risk of these distinct subgroups. Several studies have reported that ectopic fat accumulation in the liver and skeletal muscle is lower in MHO individuals compared with their MUO counterparts (14, 30). For example, Ogorodnikova et al. (48) reported that MHO women had intermediate levels of epicardial (i.e., fat tissue inside the pericardial sac from the heart), pericardial (i.e., fat tissue outside the pericardial sac from the heart), and hepatic fat compared with LH and MUO women. Moreover, these same authors showed that levels of hepatic fat were strongly associated with the MUO phenotype and insulin resistance (48). As such, the presence or absence of fatty liver has been positioned as a useful indicator of metabolic health (28). Interestingly, it was recently suggested that fat accumulation in the liver alters the release of hepatic proteins (i.e., hepatokines). For example, fetuin-A is a hepatokine that inhibits insulin signaling and induces proinflammatory cytokine expression in WAT by facilitating the activation of Toll-like receptors by saturated fatty acids (49, 50). MHO individuals were found to have lower blood levels of fetuin-A compared with MUO individuals, and this was associated with improved insulin sensitivity and glucose homeostasis (18, 51). This concept was recently extended by Stefan et al. (52), who showed that insulin-resistant individuals were characterized by high circulating levels of both fetuin-A and free fatty acids, thereby identifying a potential new molecular player linking lipids and inflammation. Although several studies support the notion that reduced ectopic fat accumulation may be associated with MHO, there is a noticeable lack of research investigating fat distribution in MUNW individuals. Nevertheless, the encouraging findings in MHO individuals suggest that these studies should be conducted in MUNW individuals as well.
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CYTOKINES AND ADIPOKINES WAT endocrine function has an important role in the development of obesity-related complications by secreting cytokines that can regulate insulin sensitivity, inflammation, and lipid metabolism throughout the body (53, 54). Di Renzo et al. (55) reported that circulating levels of cytokines (IL-1a, IL-1b, IL-6, IL-10, IL-15, and TNF-a) in MUNW individuals were comparable to those measured in MUO individuals. This highlights the idea that inflammation, irrespective of body weight, is an independent contributor to cardiometabolic risk (55). The increased inflammatory profile of MUNW individuals compared with their lean counterparts was also observed by De Lorenzo et al. (56). MUNW individuals were also shown to have higher blood levels of leptin, TNF-a, and IL-6, and lower adiponectin levels compared with LH individuals (57). It is noteworthy that blood levels of high sensitivity C-reactive protein (hsCRP) have not been found to differ between MUNW and LH women (13, 56), suggesting that this common marker of whole-body inflammation may be insufficient to distinguish cardiometabolic risk in lean individuals. Low-grade inflammation has also been investigated in MHO individuals; however, not all studies have reached the same conclusion. Phillips et al. (58) recently showed that MHO and LH individuals have significantly lower concentrations of complement component 3, CRP, TNF-a, IL-6, and plasminogen activator inhibitor-1, as well as higher adiponectin levels, compared with their MUO and MUNW counterparts (Fig. 3). Some studies have shown that MHO individuals have higher hsCRP levels compared with LH (59) and MUNW subjects (60), but lower than MUO individuals (20, 38, 61, 62). In contrast, Messier et al. (40) found no significant differences in hsCRP between MHO and MUO individuals in their cohort. These conflicting results are particularly intriguing given that several of the study populations used in the aforementioned studies were similar to the one used by Messier et al. (40). In
contrast, we and others have reported that MHO individuals have lower plasma hsCRP and IL-6 levels compared with MUO individuals (20, 23). One can speculate that the reduced VAT in MHO individuals (relative to MUO individuals) could be an underlying basis for the lower circulating levels of proinflammatory adipokines (23). This notion aligns with the results of a recent study reporting that VAT from MUO individuals had a more inflammatory profile compared with MHO individuals, as assessed by the degree of macrophage infiltration and levels of proinflammatory proteins (caspase-1 and IL-1b) (63). Additional studies have also examined other adipokines in these distinct subgroups. Circulating progranulin, chemerin, and retinol-binding protein-4 were significantly reduced in MHO compared with MUO individuals (18). Several studies have reported that MHO individuals have circulating levels of total adiponectin (18, 64) or high molecular weight adiponectin (65) similar to LH individuals, and elevated compared with MUO individuals (Fig. 3). In a cohort of postmenopausal women classified as either MUNW or MHO, MUNW women had lower adiponectin levels compared with LH individuals, whereas MHO women had intermediate adipokine levels compared with LH and MUO individuals (66). This suggests that blood adiponectin levels could serve as a potential biomarker for cardiometabolic risk in MUNW, MHO, and MUO individuals that is independent of body weight. This is further supported by another study showing that adiponectin was positively correlated with HDL-c and inversely associated with TG levels, adiposity parameters, insulin resistance, and blood glucose (67). IMMUNE CELL INFILTRATION
Figure 3. Known differences in circulating blood markers between metabolically healthy obese and metabolically unhealthy obese individuals.
The presence of immune cells in WAT has an important role in the regulation of tissue function. Moreover, it is now recognized that the population of immune cells in WAT changes with obesity and metabolic dysfunction (68). First, resident macrophages experience a phenotypic switch from anti-inflammatory alternatively activated M2 macrophages to proinflammatory classically activated M1 macrophages. Second, the secretion of chemokines encourages the infiltration of additional immune cell types, such as T cells and B cells. Therefore, studying the immune cell population in different WAT depots will undoubtedly yield additional insights into the MUNW and MHO phenotypes. Dysfunctional WAT is typically characterized by increased infiltration of macrophages, which tend to accumulate in crown-like structures around necrotic adipocytes (69). To the best of our knowledge, macrophage infiltration has not been examined in MUNW to date. In contrast, MHO individuals were found to have reduced macrophage infiltration in VAT compared with MUO individuals, with no change observed in SAT macrophage infiltration (41). However, a separate study by Van Beek et al. (70) observed significantly fewer macrophages and crown-like structures in SAT from insulin-sensitive obese women compared with obese women with T2D, despite seeing no changes in VAT macrophage infiltration. Thus, although the results of these 2 studies do not agree with regard to the specific fat depot in which reduced macrophage infiltration is seen, both studies point to a reduced
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Circulating markers Inflammatory markers
IL-6, TNFα, complement component 3, C-reactive protein, plasminogen activator inhibitor-1
Adipokines
Leptin, chemerin, orosomucoid, resistin, progranulin, retinolbinding protein 4
MHO
MUO
Adiponectin Amino acid and derivatives
BCAA, acylcarnitines, aromatic amino acids
Organic acids
Uric acid
Vitamin
Vitamin D
infiltration in MHO individuals. Nevertheless, further work in the area is required before definitively concluding that reduced macrophage infiltration may confer an improved cardiometabolic profile. Furthermore, recent studies examining other immune cell types reinforce that studying the cell population in WAT will provide new insights regarding the varying cardiometabolic risk observed with MUNW, MHO, and MUO. For example, an increase in the number of CD4+ T cells that produce IL-22 [i.e., T-helper (Th)22] and IL-17 (i.e., Th17) were found in SAT of MUO individuals compared with MHO and LH individuals (71). This result aligns with that of Zhao et al., who showed that circulating Th22 cells were positively correlated with insulin resistance and T2D (72). In yet another study, the number of anti-inflammatory regulatory T cells was increased in VAT from MHO individuals to a level similar to that observed in LH individuals (63). Taken together, these results suggest that further investigation into the immune cell populations in both VAT and SAT depots in MUNW and MHO is warranted.
White AdiposeTissue Immune cell infiltration
MHO
MUO
Macrophages, Crownlike structures CD4+ T cells (Th22 andTh17) T regulatory (Tregs) cells
Biological Pathways
β-oxidation TCA cycle Oxidative phosphorylation BCAA catabolism Inflammation (IL-8,CCL5) Biosynthesis of unsaturated fatty acids
Amino acid and Aspartate derivatives
DISCOVERY OF CANDIDATE GENES AND BIOLOGIC PATHWAYS On the basis of family and twin research, it has been estimated that genetic factors comprising single nucleotide polymorphisms, copy number variant regions, gene expression, and epigenetics may explain up to 80% of the variability observed with BMI (73, 74). Furthermore, it is now recognized that gene-environment interactions also play important roles in the development of obesity and obesity-related complications (75). One area of genetic research that has proven highly adept at distinguishing subgroups of obese individuals pertains to the study of gene expression (i.e., transcriptomics) (76, 77); however, to the best of our knowledge only a couple of transcriptomic studies have been conducted in MHO individuals to date, and none have been reported in MUNW individuals. Nonetheless, as demonstrated by previous gene expression analyses in WAT during weight loss programs (76, 77), transcriptomics is an ideal approach to obtain a global perspective of the gene networks and biological pathways underlying distinct subgroups of individuals. Indeed, conducting gene expression studies in key metabolic tissues (WAT, liver, and muscle) will provide important insights into the various pathways associated with excess fat mass and/or metabolic abnormalities and will thus help to better understand the varying cardiometabolic risk of MUNW, MHO, and MUO individuals. To date, studies have either used a targeted (i.e., quantitative RT-PCR) (78) or global (i.e., whole transcriptome microarrays) (79) approach to study adipose tissue gene expression in MHO individuals compared with MUO individuals (Fig. 4). The goal of these studies has been to identify candidate genes and biological pathways that differ in distinct subgroups of obesity to generate new insights into the molecular mechanisms associated with MHO and MUO. A number of genes encoding chemokines, such as IL-8 and chemokine C-C motif ligand 5, were found to be more highly expressed in VAT from MUO individuals compared with MHO individuals, reinforcing the link between inflammation and cardiometabolic risk (78, 79). 6
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Figure 4. Known molecular differences between metabolically healthy obese and metabolically unhealthy obese individuals in WAT.
Recently, biological pathways in SAT from MHO and MUO weight discordant monozygotic twins were examined by microarray (80). Similar to their lean healthy cotwin, the MHO cotwin showed an absence of SAT inflammation and increased mitochondrial biogenesis compared with MUO individuals. The authors specifically reported a decrease in the expression of SAT genes related to oxidative phosphorylation, branched-chain amino acid (BCAA) catabolism, and fatty acid b-oxidation in MUO individuals compared with their paired LH twin, whereas these same genes were not differentially expressed between MHO and LH twin pairs. This is intriguing given that mitochondrial dysfunction can impair adipocyte development, subsequently preventing its healthy expansion and leading to ectopic lipid deposition (81). Investigating WAT mitochondrial function and inflammatory gene expression in morbidly obese individuals who were either insulin sensitive or insulin resistant revealed that the expression of genes related to immune response were significantly upregulated in the insulin-resistant obese group compared with the insulin sensitive group, whereas genes related to b-oxidation and the tricarboxylic acid (TCA) cycle were down-regulated (82). Furthermore, this study showed that gene expression changes were occurring to a larger extent in VAT compared with SAT. These findings reinforce the idea that WAT inflammation increases cardiometabolic risk and that this can be independent of body weight. Another study reported an up-regulation of natural killer cellmediated cytotoxicity genes and a decreased expression of genes involved in the biosynthesis of unsaturated fatty acids in VAT from obese women with T2D compared with MHO women (83). The authors of this study proposed that VAT inflammation may promote the development of T2D by reducing the levels of unsaturated fatty acids. Our group also conducted a whole transcriptome analysis of SAT and observed that genes involved in the TCA cycle and BCAA
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catabolism were less compromised in MHO individuals compared with MUO individuals, thus suggesting a more favorable SAT function in MHO individuals (84). Taken together, these studies demonstrate that WAT gene expression will help unravel the complex and integrated pathways underlying the variable cardiometabolic risk in distinct subgroups of people. Furthermore, studying gene expression in other key metabolic tissues (e.g., liver and muscle) are also warranted and, collectively, will lead to a better understanding of the whole-body characteristics associated with the MUNW and MHO phenotypes. CIRCULATING METABOLITES AND NOVEL BIOMARKERS Metabolomics is a powerful approach with which to obtain a broad overview of the underlying metabolic changes associated with MUNW, MHO, and MUO. This is particularly relevant as metabolites reflect the end point of endogenous biochemical processes, as well as those stemming from environmental stresses (e.g., diet and smoking). Therefore, blood metabolite profiling is an attractive holistic approach with which to obtain a broad understanding of the molecular basis for these different groups of individuals. To date, few metabolomic studies have been conducted in individuals classified as MUNW, MHO, and MUO (85, 86). Batch et al. (87) used a targeted approach to measure 55 plasma metabolites, including amino acids and derivatives, total free fatty acids, glucose, and insulin, to identify clusters of metabolites associated with metabolic health. Using this strategy, the authors identified several metabolite clusters that were able to differentiate metabolic wellness, including a cluster comprised of BCAAs, a cluster comprised of acylcarnitines, and a cluster comprised of various amino acids. Additionally, they found that the cluster containing BCAAs could distinguish the metabolic health of individuals independent of BMI. Interestingly, the change in plasma BCAA levels aligns with the alterations in WAT BCAA catabolism noted in the previous section. Two recent studies have generated additional support for the work by Batch et al. (87). First, Wiklund et al. (86) found that a metabolite cluster comprising BCAAs, aromatic amino acids, and orosomucoid was associated with all clinical risk factors of metabolic syndrome (such as hypertension, dyslipidemia, and hyperglycemia) and that these associations occurred independent of body weight and fat mass. Second, a study from our group revealed that amino acids have the potential to distinguish LH, MHO, and MUO individuals (84). Specifically, relationships between BCAAs and various indices of insulin sensitivity (homeostatic model of assessment of insulin resistance and HbA1c values) were positively correlated with cardiometabolic risk. This agrees with findings by Newgard et al., who discovered an association between serum BCAA levels and the development of insulin resistance in obese individuals (87). Subsequent studies have suggested that WAT from obese individuals may be impaired in its ability to catabolize BCAAs (41, 76, 88), which would subsequently lead to increased circulating BCAA levels and contribute to the development of insulin resistance. Recently, a metabolomic study was conducted on in vitro ADIPOSE TISSUE FUNCTION IN MUNW AND MHO INDIVIDUALS
cultured human adipocytes and showed that intracellular aspartate levels were reduced by ;60% in adipocytes obtained from insulin-resistant obese individuals (MUO) compared with adipocytes obtained from MHO individuals, suggesting either a relative depletion of TCA cycle metabolites or a reduced aspartate uptake in MUO individuals (89). Together, these studies highlight an important link between amino acid homeostasis and cardiometabolic health. Several metabolomic studies have also revealed that organic acids, in particular uric acid, may be positively associated with cardiometabolic risk in lean individuals (90–92). For this reason, uric acid has been proposed as a potential biomarker for MUNW (8). Work by Matsuura et al. (93) suggested that circulating uric acid concentrations could be affected by body fat distribution, with VAT being more strongly associated with uric acid overproduction. Indeed, it was demonstrated that WAT is able to secrete uric acid and that its secretion is augmented with obesity (94). Another study in young and adult obese subjects also revealed that uric acid could distinguish MHO from MUO individuals with high specificity (95, 96). As such, metabolomics offers considerable promise to elucidate differences in metabolism that will help to further our understanding of MUNW, MHO, and MUO, as well as identify novel biomarkers that could be potentially used to better assess cardiometabolic risk independent of body weight (Fig. 3). PERSPECTIVES A burning question that remains unanswered is whether MHO subjects will stay healthy throughout their lifespan or whether MHO is simply a transitory state before becoming MUO. Similarly, it can be questioned if MUNW individuals will become MUO individuals over time. In a recent prospective study looking at cardiometabolic risk in MHO individuals, Appleton et al. reported that the MHO phenotype was stable for 67% of their study participants (n = 3743) over a 10 y period (97), suggesting that some individuals remain metabolically healthy over time despite their excess fat. As such, this implies that MHO individuals may have long-term protection or, at minimum, greater resistance to the development of obesity-related complications in comparison to MUO individuals (2, 98–101). In contrast, another study found that obese middle-age men with and without metabolic syndrome had an increased risk for CVD and all-cause mortality during the 30 y followup period compared with normal weight individuals, suggesting that excess body weight compromises long-term health (102). Interestingly, Schroeder et al. (103) reported that maintaining a healthy lifestyle (based on high leisuretime physical activity, high adherence to Mediterraneanlike diet, and nonsmoking behavior) can help maintain a favorable cardiometabolic profile and could reduce the risk of transitioning from MHO to MUO (assessed over a 9 y period). To confirm this intriguing concept, more longterm prospective studies must be conducted to assess the role of lifestyle on the stability of the MHO phenotype. Furthermore, it will be highly relevant to see how the various biological pathways discussed in the current review are modified over time and in relation to altered lifestyle habits. 7
An emerging avenue of interest regards the role of vitamin D. This is particularly relevant given the relationship between vitamin D and WAT. The possible regulatory role of vitamin D in adipose tissue development and function was discussed in a recent review (104). Although vitamin D is stored in WAT, Esteghamati et al. (105) reported that inflammation stemming from excess WAT and adipocyte hypertrophy may impair vitamin D function. Furthermore, vitamin D was shown to inhibit adipogenic transcription factors and prevent excess lipid accumulation, adipocyte hypertrophy, and subsequent inflammation (104, 105). As such, vitamin D may have an important role in regulating WAT function, thus influencing cardiometabolic health in MUNW, MHO, and MUO individuals. Finally, future work in this field should consider revisiting previous genome-wide association studies conducted in large cohorts, stratifying the cohort according to adiposity and cardiometabolic status, and examining whether specific loci are associated with these distinct subgroups of individuals. To the best of our knowledge, only a single study has examined whether obesity-related gene variants are also associated with metabolic health (106). Although intriguing observations were reported by the authors, additional studies in larger study cohorts are required to validate the relevance of these candidate genes as potential markers of cardiometabolic risk in lean and obese individuals that are independent of body weight. CONCLUSION MUNW and MHO constitute important subgroups of individuals and highlight that cardiometabolic risk is not solely related to body weight. This review has provided an up-to-date summary of our current state of knowledge regarding the contributions of WAT to the development of these 2 phenotypes. Although evidence supporting the contribution of WAT to these phenotypes continues to accumulate rapidly, it will be necessary to expand investigations into other key metabolic tissues to determine whether the changes in WAT function are the cause or consequence of MUNW and MHO phenotypes. As such, we anticipate that further molecular discoveries will facilitate and encourage discussions regarding the clinical relevance of these distinct subgroups of individuals and whether these subgroups would benefit from personalized health management strategies targeting WAT function. REFERENCES 1. Malik, V. S., Willett, W. C., and Hu, F. B. (2013) Global obesity: trends, risk factors and policy implications. Nat. Rev. Endocrinol. 9, 13–27 2. Karelis, A. D., St-Pierre, D. H., Conus, F., Rabasa-Lhoret, R., and Poehlman, E. T. (2004) Metabolic and body composition factors in subgroups of obesity: what do we know? J. Clin. Endocrinol. Metab. 89, 2569–2575 3. Rosen, E. D., and Spiegelman, B. M. (2014) What we talk about when we talk about fat. Cell 156, 20–44 4. Lee, S. H., Ha, H. S., Park, Y. J., Lee, J. H., Yim, H. W., Yoon, K. H., Kang, M. I., Lee, W. C., Son, H. Y., Park, Y. M., and Kwon, H. S. (2011) Identifying metabolically obese but normal-weight (MONW) individuals in a nondiabetic Korean population: the Chungju Metabolic disease Cohort (CMC) study. Clin. Endocrinol. (Oxf.) 75, 475–481
8
Vol. 29
March 2015
5. Marques-Vidal, P., P´ecoud, A., Hayoz, D., Paccaud, F., Mooser, V., Waeber, G., and Vollenweider, P. (2010) Normal weight obesity: relationship with lipids, glycaemic status, liver enzymes and inflammation. Nutr. Metab. Cardiovasc. Dis. 20, 669–675 6. Di Renzo, L., Del Gobbo, V., Bigioni, M., Premrov, M. G., Cianci, R., and De Lorenzo, A. (2006) Body composition analyses in normal weight obese women. Eur. Rev. Med. Pharmacol. Sci. 10, 191–196 7. Kahn, S. E., Hull, R. L., and Utzschneider, K. M. (2006) Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature 444, 840–846 8. Ruderman, N., Chisholm, D., Pi-Sunyer, X., and Schneider, S. (1998) The metabolically obese, normal-weight individual revisited. Diabetes 47, 699–713 9. Oliveros, E., Somers, V. K., Sochor, O., Goel, K., and LopezJimenez, F. (2014) The concept of normal weight obesity. Prog. Cardiovasc. Dis. 56, 426–433 10. Conus, F., Allison, D. B., Rabasa-Lhoret, R., St-Onge, M., St-Pierre, D. H., Tremblay-Lebeau, A., and Poehlman, E. T. (2004) Metabolic and behavioral characteristics of metabolically obese but normal-weight women. J. Clin. Endocrinol. Metab. 89, 5013–5020 11. Meigs, J. B., Wilson, P. W., Fox, C. S., Vasan, R. S., Nathan, D. M., Sullivan, L. M., and D’Agostino, R. B. (2006) Body mass index, metabolic syndrome, and risk of type 2 diabetes or cardiovascular disease. J. Clin. Endocrinol. Metab. 91, 2906–2912 12. Bednarek-Tupikowska, G., Stachowska, B., Miazgowski, T., ´ Krzy˙zanowska-Swiniarska, B., Katra, B., Jaworski, M., KuliczkowskaPłaksej, J., Jokiel- Rokita, A., Tupikowska, M., Bolanowski, M., Jedrejuk, D., and Milewicz, A. (2012) Evaluation of the prevalence of metabolic obesity and normal weight among the Polish population. Endokrynol. Pol. 63, 447–455 13. Kwon, B. J., Kim, D. W., Her, S. H., Kim, D. B., Jang, S. W., Cho, E. J., Ihm, S. H., Kim, H. Y., Youn, H. J., Seung, K. B., Kim, J. H., and Rho, T. H. (2013) Metabolically obese status with normal weight is associated with both the prevalence and severity of angiographic coronary artery disease. Metabolism 62, 952–960 14. Primeau, V., Coderre, L., Karelis, A. D., Brochu, M., Lavoie, M. E., Messier, V., Sladek, R., and Rabasa-Lhoret, R. (2011) Characterizing the profile of obese patients who are metabolically healthy. Int J Obes (Lond) 35, 971–981 15. Pataky, Z., Bobbioni-Harsch, E., and Golay, A. (2010) Open questions about metabolically normal obesity. Int J Obes (Lond) 34(Suppl 2), S18–S23 16. Aguilar-Salinas, C. A., Garc´ıa, E. G., Robles, L., Riaño, D., Ruiz-Gomez, D. G., Garc´ıa-Ulloa, A. C., Melgarejo, M. A., Zamora, M., Guillen-Pineda, L. E., Mehta, R., CanizalesQuinteros, S., Tusie Luna, M. T., and Gomez-Perez, F. J. (2008) High adiponectin concentrations are associated with the metabolically healthy obese phenotype. J. Clin. Endocrinol. Metab. 93, 4075–4079 17. Karelis, A. D., and Rabasa-Lhoret, R. (2008) Inclusion of Creactive protein in the identification of metabolically healthy but obese (MHO) individuals. Diabetes Metab. 34, 183–184 18. Kl¨oting, N., Fasshauer, M., Dietrich, A., Kovacs, P., Sch¨on, M. R., Kern, M., Stumvoll, M., and Bl¨uher, M. (2010) Insulin-sensitive obesity. Am. J. Physiol. Endocrinol. Metab. 299, E506–E515 19. Marini, M., Frontoni, S., Succurro, E., Arturi, F., Fiorentino, T., Sciacqua, A., Perticone, F., and Sesti, G. (2013) Differences in insulin clearance between metabolically healthy and unhealthy obese subjects. Acta Diabetol. 51, 1–5 20. Perreault, M., Zulyniak, M. A., Badoud, F., Stephenson, S., Badawi, A., Buchholz, A., and Mutch, D. M. (2014) A distinct fatty acid profile underlies the reduced inflammatory state of metabolically healthy obese individuals. PLoS ONE 9, e88539 21. Brochu, M., Tchernof, A., Dionne, I. J., Sites, C. K., Eltabbakh, G. H., Sims, E. A. H., and Poehlman, E. T. (2001) What are the physical characteristics associated with a normal metabolic profile despite a high level of obesity in postmenopausal women? J. Clin. Endocrinol. Metab. 86, 1020–1025 22. McLaughlin, T., Sherman, A., Tsao, P., Gonzalez, O., Yee, G., Lamendola, C., Reaven, G. M., and Cushman, S. W. (2007) Enhanced proportion of small adipose cells in insulin-resistant vs insulin-sensitive obese individuals implicates impaired adipogenesis. Diabetologia 50, 1707–1715 23. Shin, M. J., Hyun, Y. J., Kim, O. Y., Kim, J. Y., Jang, Y., and Lee, J. H. (2006) Weight loss effect on inflammation and LDL
The FASEB Journal x www.fasebj.org
BADOUD ET AL.
24.
25.
26.
27.
28. 29.
30.
31.
32. 33. 34. 35.
36.
37. 38.
39.
40.
41. 42.
43.
oxidation in metabolically healthy but obese (MHO) individuals: low inflammation and LDL oxidation in MHO women. Int J Obes (Lond) 30, 1529–1534 van der A, D. L., Nooyens, A. C., van Duijnhoven, F. J., Verschuren, M. M., and Boer, J. M. (2014) All-cause mortality risk of metabolically healthy abdominal obese individuals: the EPIC-MORGEN study. Obesity (Silver Spring) 22, 557–564 You, T., Ryan, A. S., and Nicklas, B. J. (2004) The metabolic syndrome in obese postmenopausal women: relationship to body composition, visceral fat, and inflammation. J. Clin. Endocrinol. Metab. 89, 5517–5522 Yoo, H. K., Choi, E. Y., Park, E. W., Cheong, Y. S., and Bae, R. A. (2013) Comparison of metabolic characteristics of metabolically healthy but obese (MHO) middle-aged men according to different criteria. Korean J Fam Med 34, 19–26 Phillips, C. M., Dillon, C., Harrington, J. M., McCarthy, V. J., Kearney, P. M., Fitzgerald, A. P., and Perry, I. J. (2013) Defining metabolically healthy obesity: role of dietary and lifestyle factors. PLoS ONE 8, e76188 Stefan, N., H¨aring, H.-U., Hu, F. B., and Schulze, M. B. (2013) Metabolically healthy obesity: epidemiology, mechanisms, and clinical implications. Lancet Diabetes Endocrinol 1, 152–162 Camhi, S. M., Waring, M. E., Sisson, S. B., Hayman, L. L., and Must, A. (2013) Physical activity and screen time in metabolically healthy obese phenotypes in adolescents and adults. J. Obes. 2013, 984613 Stefan, N., Kantartzis, K., Machann, J., Schick, F., Thamer, C., Rittig, K., Balletshofer, B., Machicao, F., Fritsche, A., and H¨aring, H. U. (2008) Identification and characterization of metabolically benign obesity in humans. Arch. Intern. Med. 168, 1609–1616 Lemoine, A. Y., Ledoux, S., Qu´eguiner, I., Cald´erari, S., Mechler, C., Msika, S., Corvol, P., and Larger, E. (2012) Link between adipose tissue angiogenesis and fat accumulation in severely obese subjects. J. Clin. Endocrinol. Metab. 97, E775–E780 Cusi, K. (2010) The role of adipose tissue and lipotoxicity in the pathogenesis of type 2 diabetes. Curr. Diab. Rep. 10, 306–315 de Luca, C., and Olefsky, J. M. (2008) Inflammation and insulin resistance. FEBS Lett. 582, 97–105 Bastien, M., Poirier, P., Lemieux, I., and Despr´es, J.-P. (2014) Overview of epidemiology and contribution of obesity to cardiovascular disease. Prog. Cardiovasc. Dis. 56, 369–381 Katsuki, A., Sumida, Y., Urakawa, H., Gabazza, E. C., Murashima, S., Maruyama, N., Morioka, K., Nakatani, K., Yano, Y., and Adachi, Y. (2003) Increased visceral fat and serum levels of triglyceride are associated with insulin resistance in Japanese metabolically obese, normal weight subjects with normal glucose tolerance. Diabetes Care 26, 2341–2344 Dvorak, R. V., DeNino, W. F., Ades, P. A., and Poehlman, E. T. (1999) Phenotypic characteristics associated with insulin resistance in metabolically obese but normal-weight young women. Diabetes 48, 2210–2214 Tchernof, A., and Despr´es, J. P. (2013) Pathophysiology of human visceral obesity: an update. Physiol. Rev. 93, 359–404 Karelis, A. D., Faraj, M., Bastard, J. P., St-Pierre, D. H., Brochu, M., Prud’homme, D., and Rabasa-Lhoret, R. (2005) The metabolically healthy but obese individual presents a favorable inflammation profile. J. Clin. Endocrinol. Metab. 90, 4145–4150 Messier, V., Karelis, A. D., Prud’homme, D., Primeau, V., Brochu, M., and Rabasa-Lhoret, R. (2010) Identifying metabolically healthy but obese individuals in sedentary postmenopausal women. Obesity (Silver Spring) 18, 911–917 Messier, V., Karelis, A. D., Robillard, M. E., Bellefeuille, P., Brochu, M., Lavoie, J. M., and Rabasa-Lhoret, R. (2010) Metabolically healthy but obese individuals: relationship with hepatic enzymes. Metabolism 59, 20–24 Bl¨uher, M. (2010) The distinction of metabolically ‘healthy’ from ‘unhealthy’ obese individuals. Curr. Opin. Lipidol. 21, 38–43 Srdi´c, B., Stoki´c, E., Kora´c, A., Ukropina, M., Veliˇckovi´c, K., and Breberina, M. (2010) Morphological characteristics of abdominal adipose tissue in normal-weight and obese women of different metabolic profiles. Exp. Clin. Endocrinol. Diabetes 118, 713–718 Tynan, G. A., Hearnden, C. H., Oleszycka, E., Lyons, C. L., Coutts, G., O’Connell, J., Corrigan, M. A., Lynch, L., Campbell, M., Callanan, J. J., Mok, H. H., Geoghegan, J., O’Farrelly, C., Allan, S. M., Roche, H. M., O’Shea, D. B., and Lavelle, E. C.
ADIPOSE TISSUE FUNCTION IN MUNW AND MHO INDIVIDUALS
44.
45.
46.
47. 48.
49. 50.
51. 52. 53. 54. 55.
56. 57.
58. 59.
60.
61.
62.
(2014) Endogenous oils derived from human adipocytes are potent adjuvants that promote IL-1a-dependent inflammation. Diabetes 63, 2037–2050 O’Connell, J., Lynch, L., Cawood, T. J., Kwasnik, A., Nolan, N., Geoghegan, J., McCormick, A., O’Farrelly, C., and O’Shea, D. (2010) The relationship of omental and subcutaneous adipocyte size to metabolic disease in severe obesity. PLoS ONE 5, e9997 Lackey, D. E., Burk, D. H., Ali, M. R., Mostaedi, R., Smith, W. H., Park, J., Scherer, P. E., Seay, S. A., McCoin, C. S. B., Bonaldo, P., and Adams, S. H. (2014) Contributions of adipose tissue architectural and tensile properties toward defining healthy and unhealthy obesity. Am. J. Physiol. Endocrinol. Metab. 306, E233–E246 Sun, K., Wernstedt Asterholm, I., Kusminski, C. M., Bueno, A. C., Wang, Z. V., Pollard, J. W., Brekken, R. A., and Scherer, P. E. (2012) Dichotomous effects of VEGF-A on adipose tissue dysfunction. Proc. Natl. Acad. Sci. USA 109, 5874–5879 Thomas, E. L., Frost, G., Taylor-Robinson, S. D., and Bell, J. D. (2012) Excess body fat in obese and normal-weight subjects. Nutr. Res. Rev. 25, 150–161 Ogorodnikova, A. D., Khan, U. I., McGinn, A. P., Zeb, I., Budoff, M. J., Harman, S. M., Miller, V. M., Brinton, E. A., Manson, J. E., Hodis, H. N., Merriam, G. R., Cedars, M. I., Taylor, H. S., Naftolin, F., Lobo, R. A., Santoro, N., and Wildman, R. P. (2013) Ectopic fat and adipokines in metabolically benign overweight/ obese women: the Kronos Early Estrogen Prevention Study. Obesity (Silver Spring) 21, 1726–1733 Stefan, N., and H¨aring, H.-U. (2013) Circulating fetuin-A and free fatty acids interact to predict insulin resistance in humans. Nat. Med. 19, 394–395 Stefan, N., Artunc, F., Heyne, N., Machann, J., Schleicher, E. D., and H¨aring, H.-U. (2014) Obesity and renal disease: not all fat is created equal and not all obesity is harmful to the kidneys. Nephrol. Dial. Transplant. DOI: 10.1093/ndt/gfu081 Stefan, N., Fritsche, A., Weikert, C., Boeing, H., Joost, H.-G., H¨aring, H.-U., and Schulze, M. B. (2008) Plasma fetuin-A levels and the risk of type 2 diabetes. Diabetes 57, 2762–2767 Stefan, N., and H¨aring, H.-U. (2013) The role of hepatokines in metabolism. Nat. Rev. Endocrinol. 9, 144–152 Bl¨uher, M. (2013) Adipose tissue dysfunction contributes to obesity related metabolic diseases. Best Pract. Res. Clin. Endocrinol. Metab. 27, 163–177 Piya, M. K., McTernan, P. G., and Kumar, S. (2013) Adipokine inflammation and insulin resistance: the role of glucose, lipids and endotoxin. J. Endocrinol. 216, T1–T15 Di Renzo, L., Galvano, F., Orlandi, C., Bianchi, A., Di Giacomo, C., La Fauci, L., Acquaviva, R., and De Lorenzo, A. (2010) Oxidative stress in normal-weight obese syndrome. Obesity (Silver Spring) 18, 2125–2130 De Lorenzo, A., Del Gobbo, V., Premrov, M. G., Bigioni, M., Galvano, F., and Di Renzo, L. (2007) Normal-weight obese syndrome: early inflammation? Am. J. Clin. Nutr. 85, 40–45 Hyun, Y. J., Koh, S. J., Chae, J. S., Kim, J. Y., Kim, O. Y., Lim, H. H., Jang, Y., Park, S., Ordovas, J. M., and Lee, J. H. (2008) Atherogenecity of LDL and unfavorable adipokine profile in metabolically obese, normal-weight woman. Obesity (Silver Spring) 16, 784–789 Phillips, C. M., and Perry, I. J. (2013) Does inflammation determine metabolic health status in obese and nonobese adults? J. Clin. Endocrinol. Metab. 98, E1610–E1619 Shea, J. L., Randell, E. W., and Sun, G. (2011) The prevalence of metabolically healthy obese subjects defined by BMI and dual-energy X-ray absorptiometry. Obesity (Silver Spring) 19, 624–630 Kim, M., Paik, J. K., Kang, R., Kim, S. Y., Lee, S.-H., and Lee, J. H. (2013) Increased oxidative stress in normal-weight postmenopausal women with metabolic syndrome compared with metabolically healthy overweight/obese individuals. Metabolism 62, 554–560 Dai, X. P., Liu, Z. Q., Xu, L. Y., Gong, Z. C., Huang, Q., Dong, M., and Huang, X. (2012) Association of plasma epinephrine level with insulin sensitivity in metabolically healthy but obese individuals. Auton. Neurosci. 167, 66–69 Zhang, M., Tong, W., Chen, J., Zhang, Y., and Li, S. (2014) Metabolically healthy obesity and its associates in Mongolian Chinese adults. Metab. Syndr. Relat. Disord. 12, 185–190
9
63. Esser, N., L’homme, L., De Roover, A., Kohnen, L., Scheen, A. J., Moutschen, M., Piette, J., Legrand-Poels, S., and Paquot, N. (2013) Obesity phenotype is related to NLRP3 inflammasome activity and immunological profile of visceral adipose tissue. Diabetologia 56, 2487–2497 64. Doumatey, A. P., Bentley, A. R., Zhou, J., Huang, H., Adeyemo, A., and Rotimi, C. N. (2012) Paradoxical hyperadiponectinemia is associated with the metabolically healthy obese (MHO) phenotype in African Americans. J Endocrinol Metab 2, 51–65 65. Elisha, B., Karelis, A. D., Imbeault, P., and Rabasa-Lhoret, R. (2010) Effects of acute hyperinsulinaemia on total and highmolecular-weight adiponectin concentration in metabolically healthy but obese postmenopausal women: a Montreal-Ottawa New Emerging Team (MONET) study. Diabetes Metab. 36, 319–321 66. Khan, U. I., Ogorodnikova, A. D., Xu, L., Wang, D., Wassertheil-Smoller, S., Ho, G. Y. F., Sowers, M. F., Rajpathak, S. N., Allison, M. A., Mackey, R. H., Vitolins, M. Z., Manson, J. E., and Wildman, R. P. (2014) The adipokine profile of metabolically benign obese and at-risk normal weight postmenopausal women: the Women’s Health Initiative Observational Study. Obesity (Silver Spring) 22, 786–794 67. Eglit, T., Ringmets, I., and Lember, M. (2013) Obesity, highmolecular-weight (HMW) adiponectin, and metabolic risk factors: prevalence and gender-specific associations in Estonia. PLoS ONE 8, e73273 68. Chawla, A., Nguyen, K. D., and Goh, Y. P. S. (2011) Macrophage-mediated inflammation in metabolic disease. Nat. Rev. Immunol. 11, 738–749 69. Cancello, R., Tordjman, J., Poitou, C., Guilhem, G., Bouillot, J. L., Hugol, D., Coussieu, C., Basdevant, A., Bar Hen, A., Bedossa, P., Guerre-Millo, M., and Clement, K. (2006) Increased infiltration of macrophages in omental adipose tissue is associated with marked hepatic lesions in morbid human obesity. Diabetes 55, 1554–1561 70. van Beek, L., Lips, M. A., Visser, A., Pijl, H., Ioan-Facsinay, A., Toes, R., Berends, F. J., Willems van Dijk, K., Koning, F., and van Harmelen, V. (2014) Increased systemic and adipose tissue inflammation differentiates obese women with T2DM from obese women with normal glucose tolerance. Metabolism 63, 492–501 71. Fabbrini, E., Cella, M., McCartney, S. A., Fuchs, A., Abumrad, N. A., Pietka, T. A., Chen, Z., Finck, B. N., Han, D. H., Magkos, F., Conte, C., Bradley, D., Fraterrigo, G., Eagon, J. C., Patterson, B. W., Colonna, M., and Klein, S. (2013) Association between specific adipose tissue CD4+ T-cell populations and insulin resistance in obese individuals. Gastroenterology 145, 366–374 72. Zhao, R., Tang, D., Yi, S., Li, W., Wu, C., Lu, Y., Hou, X., Song, J., Lin, P., Chen, L., and Sun, L. (2014) Elevated peripheral frequencies of Th22 cells: a novel potent participant in obesity and type 2 diabetes. PLoS ONE 9, e85770 73. Hur, Y. M., Kaprio, J., Iacono, W. G., Boomsma, D. I., McGue, M., Silventoinen, K., Martin, N. G., Luciano, M., Visscher, P. M., Rose, R. J., He, M., Ando, J., Ooki, S., Nonaka, K., Lin, C. C., Lajunen, H. R., Cornes, B. K., Bartels, M., van Beijsterveldt, C. E., Cherny, S. S., and Mitchell, K. (2008) Genetic influences on the difference in variability of height, weight and body mass index between Caucasian and East Asian adolescent twins. Int J Obes (Lond) 32, 1455–1467 74. Moll, P. P., Burns, T. L., and Lauer, R. M. (1991) The genetic and environmental sources of body mass index variability: the Muscatine Ponderosity Family Study. Am. J. Hum. Genet. 49, 1243–1255 75. Heitmann, B. L., Westerterp, K. R., Loos, R. J. F., Sørensen, T. I. A., O’Dea, K., McLean, P., Jensen, T. K., Eisenmann, J., Speakman, J. R., Simpson, S. J., Reed, D. R., and Westerterp-Plantenga, M. S. (2012) Obesity: lessons from evolution and the environment. Obes. Rev. 13, 910–922 76. Mutch, D. M., Pers, T. H., Temanni, M. R., Pelloux, V., Marquez-Quiñones, A., Holst, C., Martinez, J. A., Babalis, D., van Baak, M. A., Handjieva-Darlenska, T., Walker, C. G., Astrup, A., Saris, W. H., Langin, D., Viguerie, N., Zucker, J. D., and Cl´ement, K.; DiOGenes Project. (2011) A distinct adipose tissue gene expression response to caloric restriction predicts 6-mo weight maintenance in obese subjects. Am. J. Clin. Nutr. 94, 1399–1409 77. Bouchard, L., Rabasa-Lhoret, R., Faraj, M., Lavoie, M. E., Mill, J., P´erusse, L., and Vohl, M. C. (2010) Differential epigenomic
10
Vol. 29 March 2015
78.
79.
80.
81.
82.
83.
84.
85.
86.
87.
88.
89.
90.
91. 92.
and transcriptomic responses in subcutaneous adipose tissue between low and high responders to caloric restriction. Am. J. Clin. Nutr. 91, 309–320 Xu, X. J., Gauthier, M. S., Hess, D. T., Apovian, C. M., Cacicedo, J. M., Gokce, N., Farb, M., Valentine, R. J., and Ruderman, N. B. (2012) Insulin sensitive and resistant obesity in humans: AMPK activity, oxidative stress, and depot-specific changes in gene expression in adipose tissue. J. Lipid Res. 53, 792–801 Hardy, O. T., Perugini, R. A., Nicoloro, S. M., Gallagher-Dorval, K., Puri, V., Straubhaar, J., and Czech, M. P. (2011) Body mass index-independent inflammation in omental adipose tissue associated with insulin resistance in morbid obesity. Surg. Obes. Relat. Dis. 7, 60–67 Naukkarinen, J., Heinonen, S., Hakkarainen, A., Lundbom, J., Vuolteenaho, K., Saarinen, L., Hautaniemi, S., Rodriguez, A., Fr¨uhbeck, G., Pajunen, P., Hy¨otyl¨ainen, T., Oreˇsiˇc, M., Moilanen, E., Suomalainen, A., Lundbom, N., Kaprio, J., Rissanen, A., and Pietil¨ainen, K. H. (2014) Characterising metabolically healthy obesity in weight-discordant monozygotic twins. Diabetologia 57, 167–176 Samocha-Bonet, D., Dixit, V. D., Kahn, C. R., Leibel, R. L., Lin, X., Nieuwdorp, M., Pietil¨ainen, K. H., Rabasa-Lhoret, R., Roden, M., Scherer, P. E., Klein, S., and Ravussin, E. (2014) Metabolically healthy and unhealthy obese—the 2013 Stock Conference report. Obes. Rev. 15, 697–708 Qatanani, M., Tan, Y., Dobrin, R., Greenawalt, D. M., Hu, G., Zhao, W., Olefsky, J. M., Sears, D. D., Kaplan, L. M., and Kemp, D. M. (2013) Inverse regulation of inflammation and mitochondrial function in adipose tissue defines extreme insulin sensitivity in morbidly obese patients. Diabetes 62, 855–863 Mathur, S. K., Jain, P., Mathur, P., Punjabi, P., Agarwal, A., and Sharma, A. (2013) Transcriptomic analysis of visceral adipose from healthy and diabetic obese subjects. Indian J Endocrinol Metab 17, 446–450 Badoud, F., Lam, K. P., DiBattista, A., Perreault, M., Zulyniak, M. A., Cattrysse, B., Stephenson, S., Britz-McKibbin, P., and Mutch, D. M. (2014) Serum and adipose tissue amino acid homeostasis in the metabolically healthy obese. J. Proteome Res. 13, 3455–3466 Batch, B. C., Shah, S. H., Newgard, C. B., Turer, C. B., Haynes, C., Bain, J. R., Muehlbauer, M., Patel, M. J., Stevens, R. D., Appel, L. J., Newby, L. K., and Svetkey, L. P. (2013) Branched chain amino acids are novel biomarkers for discrimination of metabolic wellness. Metabolism 62, 961–969 Wiklund, P. K., Pekkala, S., Autio, R., Munukka, E., Xu, L., Saltevo, J., Cheng, S., Kujala, U. M., Alen, M., and Cheng, S. (2014) Serum metabolic profiles in overweight and obese women with and without metabolic syndrome. Diabetol Metab Syndr 6, 40 Newgard, C. B., An, J., Bain, J. R., Muehlbauer, M. J., Stevens, R. D., Lien, L. F., Haqq, A. M., Shah, S. H., Arlotto, M., Slentz, C. A., Rochon, J., Gallup, D., Ilkayeva, O., Wenner, B. R., Yancy, W. S., Jr, Eisenson, H., Musante, G., Surwit, R. S., Millington, D. S., Butler, M. D., and Svetkey, L. P. (2009) A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab. 9, 311–326 Wahl, S., Yu, Z., Kleber, M., Singmann, P., Holzapfel, C., He, Y., Mittelstrass, K., Polonikov, A., Prehn, C., R¨omisch-Margl, W., Adamski, J., Suhre, K., Grallert, H., Illig, T., Wang-Sattler, R., and Reinehr, T. (2012) Childhood obesity is associated with changes in the serum metabolite profile. Obes Facts 5, 660–670 B¨ohm, A., Halama, A., Meile, T., Zdichavsky, M., Lehmann, R., Weigert, C., Fritsche, A., Stefan, N., K¨onigsrainer, A., H¨aring, H.-U., de Angelis, M. H., Adamski, J., and Staiger, H. (2014) Metabolic signatures of cultured human adipocytes from metabolically healthy versus unhealthy obese individuals. PLoS ONE 9, e93148 Facchini, F., Chen, Y. D., Hollenbeck, C. B., and Reaven, G. M. (1991) Relationship between resistance to insulin-mediated glucose uptake, urinary uric acid clearance, and plasma uric acid concentration. JAMA 266, 3008–3011 Velho, S., Paccaud, F., Waeber, G., Vollenweider, P., and Marques-Vidal, P. (2010) Metabolically healthy obesity: different prevalences using different criteria. Eur. J. Clin. Nutr. 64, 1043–1051 Floegel, A., Stefan, N., Yu, Z., M¨uhlenbruch, K., Drogan, D., Joost, H.-G., Fritsche, A., H¨aring, H.-U., Hrabˇe de Angelis, M.,
The FASEB Journal x www.fasebj.org
BADOUD ET AL.
93.
94.
95.
96.
97.
98. 99.
Peters, A., Roden, M., Prehn, C., Wang-Sattler, R., Illig, T., Schulze, M. B., Adamski, J., Boeing, H., and Pischon, T. (2013) Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach. Diabetes 62, 639–648 Matsuura, F., Yamashita, S., Nakamura, T., Nishida, M., Nozaki, S., Funahashi, T., and Matsuzawa, Y. (1998) Effect of visceral fat accumulation on uric acid metabolism in male obese subjects: visceral fat obesity is linked more closely to overproduction of uric acid than subcutaneous fat obesity. Metabolism 47, 929–933 Tsushima, Y., Nishizawa, H., Tochino, Y., Nakatsuji, H., Sekimoto, R., Nagao, H., Shirakura, T., Kato, K., Imaizumi, K., Takahashi, H., Tamura, M., Maeda, N., Funahashi, T., and Shimomura, I. (2013) Uric acid secretion from adipose tissue and its increase in obesity. J. Biol. Chem. 288, 27138–27149 Mangge, H., Zelzer, S., Puerstner, P., Schnedl, W. J., Reeves, G., Postolache, T. T., and Weghuber, D. (2013) Uric acid best predicts metabolically unhealthy obesity with increased cardiovascular risk in youth and adults. Obesity (Silver Spring) 21, E71–E77 Weghuber, D., Zelzer, S., Stelzer, I., Paulmichl, K., Kammerhofer, D., Schnedl, W., Molnar, D., and Mangge, H. (2013) High risk vs. “metabolically healthy” phenotype in juvenile obesity - neck subcutaneous adipose tissue and serum uric acid are clinically relevant. Exp. Clin. Endocrinol. Diabetes 121, 384–390 Appleton, S. L., Seaborn, C. J., Visvanathan, R., Hill, C. L., Gill, T. K., Taylor, A. W., and Adams, R. J.; North West Adelaide Health Study Team. (2013) Diabetes and cardiovascular disease outcomes in the metabolically healthy obese phenotype: a cohort study. Diabetes Care 36, 2388–2394 Kuk, J. L., and Ardern, C. I. (2009) Are metabolically normal but obese individuals at lower risk for all-cause mortality? Diabetes Care 32, 2297–2299 Wildman, R. P., Kaplan, R., Manson, J. E., Rajkovic, A., Connelly, S. A., Mackey, R. H., Tinker, L. F., Curb, J. D., Eaton, C. B., and Wassertheil-Smoller, S. (2011) Body size
ADIPOSE TISSUE FUNCTION IN MUNW AND MHO INDIVIDUALS
100.
101.
102.
103.
104. 105.
106.
phenotypes and inflammation in the Women’s Health Initiative Observational Study. Obesity (Silver Spring) 19, 1482–1491 Khan, U. I., Wang, D., Thurston, R. C., Sowers, M., Sutton-Tyrrell, K., Matthews, K. A., Barinas-Mitchell, E., and Wildman, R. P. (2011) Burden of subclinical cardiovascular disease in “metabolically benign” and “at-risk” overweight and obese women: the Study of Women’s Health Across the Nation (SWAN). Atherosclerosis 217, 179–186 Ogorodnikova, A. D., Kim, M., McGinn, A. P., Muntner, P., Khan, U., and Wildman, R. P. (2012) Incident cardiovascular disease events in metabolically benign obese individuals. Obesity (Silver Spring) 20, 651–659 Arnl¨ov, J., Ingelsson, E., Sundstr¨om, J., and Lind, L. (2010) Impact of body mass index and the metabolic syndrome on the risk of cardiovascular disease and death in middle-aged men. Circulation 121, 230–236 Schr¨oder, H., Ramos, R., Baena-D´ıez, J., Mendez, M., Canal, D., F´ıto, M., Sala, J., and Elosua, R. (2013) Determinants of the transition from a cardiometabolic normal to abnormal overweight/obese phenotype in a Spanish population. Eur. J. Nutr. 53, 1345–1353 Ding, C., Gao, D., Wilding, J., Trayhurn, P., and Bing, C. (2012) Vitamin D signalling in adipose tissue. Br. J. Nutr. 108, 1915–1923 Esteghamati, A., Aryan, Z., Esteghamati, A., and Nakhjavani, M. (2014) Differences in vitamin D concentration between metabolically healthy and unhealthy obese adults: Associations with inflammatory and cardiometabolic markers in 4391 subjects. [E-pub ahead of print] Diabetes Metab. Burgdorf, K. S., Gjesing, A. P., Grarup, N., Justesen, J. M., Sandholt, C. H., Witte, D. R., Jørgensen, T., Madsbad, S., Hansen, T., and Pedersen, O. (2012) Association studies of novel obesity-related gene variants with quantitative metabolic phenotypes in a population-based sample of 6,039 Danish individuals. Diabetologia 55, 105–113 Received for publication September 17, 2014. Accepted for publication October 21, 2014.
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