Adipose Tissue Dysregulation in Patients with Metabolic Syndrome

Abstract

Context:

The metabolic syndrome (MetS) is associated with increased risk of diabetes and cardiovascular disease (CVD). Numerous groups have shown increased circulating biomarkers of inflammation in MetS. However, there are scanty data on the cellular sources contributing to this low-grade inflammation.

Objective:

The aim of this study was to determine the role of sc adipose tissue (SAT) biology in nascent MetS without concomitant diabetes or CVD.

Patients and Methods:

Subjects with MetS and controls were recruited after informed consent. Fasting blood was collected, and SAT was obtained by biopsy.

Results:

Circulating biomarkers of inflammation and insulin resistance, high-sensitivity C-reactive protein (hsCRP), IL-6, IL-1β, leptin, serum amyloid A, and retinol-binding protein-4 (RBP-4) concentrations were significantly higher in the MetS subjects than controls, whereas adiponectin concentrations were lower. In SAT, leptin, RBP-4, CRP, serum amyloid A, plasminogen activator inhibitor-1, IL-1, IL-6, IL-8, and monocyte chemotactic protein-1 (MCP-1) levels were significantly higher in MetS than controls. These differences except for RBP-4 persisted after adjusting for waist circumference. In addition, there were significantly increased numbers of macrophages infiltrating the SAT of MetS and increased numbers of crown-like structures compared with controls. hsCRP correlated positively with homeostasis model assessment and SAT MCP-1 and negatively with adiponectin. Homeostasis model assessment correlated positively with plasminogen activator inhibitor-1, RBP-4, and SAT MCP-1.

Conclusions:

We make the novel observation that SAT of MetS has increased macrophage recruitment with cardinal crown-like structure features and contributes to the increased cellular inflammation that produces increased levels of biomarkers that are correlated with both insulin resistance and low-grade inflammation. These aberrations could contribute to the progression of MetS and the increased risk for diabetes and CVD.

The metabolic syndrome (MetS) comprises a cluster of cardiometabolic risk markers with insulin resistance and adiposity as central features (1–4). Five diagnostic criteria for MetS have been identified [central obesity, dyslipidemia, high triglycerides (TG) and/or low high-density lipoprotein cholesterol (HDL-C), hypertension, and impaired fasting glucose] by the Adult Treatment Panel III criteria of the National Cholesterol and Education Program, and the presence of three features is considered sufficient to diagnose the syndrome (2, 4, 5). Using this definition, the National Health and Nutrition Examination Survey data show that currently approximately 35% of all U.S. adults have MetS (6). Furthermore, MetS confers an increased risk for diabetes and cardiovascular disease (CVD) (7–10). Although it appears that low-grade chronic inflammation could be a central feature to help explain the increased risks of CVD and diabetes in MetS, the precise mechanisms remain to be elucidated. The role of sc adipose tissue (SAT) dysregulation could be a potential mechanism.

Numerous investigators have shown increased circulating biomarkers of inflammation in MetS, thus providing support for the proinflammatory state of MetS (2, 4, 11). However, there are scant data on SAT biology in nascent MetS subjects without the confounding presence of diabetes and/or CVD. The relationship between inflammation and MetS is supported by several studies (2, 4, 12, 13), as is the relationship between increased visceral fat mass and MetS (14–16). However, the role of SAT in the pathogenesis of MetS has been debated given inconsistencies in the literature (17). The sc fat, which comprises approximately 80% of adipose tissue and the major source of fatty acids for the liver, is readily accessible to study and has been shown to be metabolically correlated to indices of insulin resistance as well as to visceral adipose tissue (VAT) (18–21). In addition to intraabdominal fat, Salmenniemi et al. (22) have shown that the amount of SAT in subjects with MetS positively correlates with increasing MetS factor scores and negatively correlates with circulating adiponectin levels. Carr et al. (23) have also reported that SAT is significantly associated with MetS and increases with increasing number of MetS features, independent of age and sex.

Furthermore, macrophage infiltration appears to be important in adipose tissue inflammation. Apovian et al. (24) examined abdominal SAT from obese subjects and reported that an inflamed adipose phenotype characterized by tissue macrophage accumulation in crown-like structures (CLS) was associated with systemic hyperinsulinemia and insulin resistance and impaired endothelium-dependent flow-mediated vasodilation. Macrophage retention in fat was also linked to up-regulated tissue CD68 and TNF-α mRNA expression in addition to increased plasma high-sensitivity C-reactive protein (hsCRP).

However, there is a paucity of data examining SAT biology in subjects with MetS, specifically without diabetes and/or CVD compared with age- and gender-matched controls. Thus, the main objective of this study was to determine whether SAT biology in subjects with MetS is dysregulated and contributes to the syndrome's systemic low-grade inflammatory process.

Subjects and Methods

Subjects

Subjects were recruited from Sacramento County using procedures as described previously (25). Subjects were then classified as having MetS or not using the National Cholesterol and Education Program Adult Treatment Panel III criteria (5). Briefly, subjects classified as MetS had at least three risk factors to sustain the diagnosis, including central obesity, hypertension, dyslipidemia (low HDL-C, high TG), and/or hypertension or on antihypertensive medications. The control subject needed to have no more than two features of MetS and not be on blood pressure (BP) medications. Other exclusion criteria for controls were a fasting plasma glucose concentration higher than 100 mg/dl and a fasting TG concentration higher than 200 mg/dl. For both groups, other exclusion criteria were a previous diagnosis of diabetes, clinical atherosclerosis [coronary artery disease (CAD), peripheral vascular disease, CVD, etc.], a TG concentration higher than 400 mg/dl, a hsCRP concentration higher than 10 mg/liter, pregnancy, an abnormal complete blood count, alcohol consumption higher than 1 oz/d, consumption of N-3 polyunsaturated fatty acids, smoking, hypo- or hyperthyroidism, malabsorption, active wounds, recent surgery, inflammatory or malignant disease, anticoagulant therapy, steroid therapy, the current use of antiinflammatory drugs, statins and/or other hypolipidemic agents, hypoglycemic agents, angiotensin receptor blockers, oral contraceptives, and antioxidant supplements (in the previous six months), postmenopausal women on estrogen replacement therapy, and chronic high-intensity exercisers (exercise >100 min/wk). Informed consent was obtained from all participants in the study, and the study was approved by the Institutional Review Board at the University of California, Davis (UC Davis). After a history and physical examination, a fasting blood sample was obtained. A complete blood count, plasma lipid and lipoprotein profile, urea nitrogen, creatinine, aspartate aminotransferase, alanine aminotransferase, glucose and TSH were assayed by standard laboratory techniques in the Clinical Pathology Laboratory (UC Davis). Insulin levels were assayed by ELISA (Linco Biosystems, Lincoln, NE), and the homeostasis model assessment (HOMA) of insulin resistance was calculated from the fasting glucose and insulin levels (26).

Biomarkers of inflammation

Blood levels of hsCRP, adiponectin, and plasminogen activator inhibitor-1 (PAI-1) were assayed by ELISA. Also, IL-1β, TNF-α, IL-6, IL-8, IL-18, monocyte chemotactic protein-1 (MCP-1) were assayed by multiplex cytokine array (Bioplex, San Jose, CA).

SAT biopsy and fat analysis

SAT biopsies were performed on all subjects at the UC Davis Clinical and Translational Science Center's Clinical Research Center. Briefly, the gluteal region was sterilized, and 4 ml 2% lidocaine was injected to desensitize the area. A 14-gauge needle connected to a 20-ml syringe was then inserted at the site of lidocaine injection. Fat was aspirated as suction was applied while the syringe and needle were repeatedly rotated. Approximately 4–6 ml fat and fluid were aspirated, washed, and then placed in the transport medium. The area was cleaned and dressed. The aspirated SAT was weighed in a balance, then minced into fine (<10 mg) pieces and resuspended in 5 mm glucose culture medium. The SAT was then incubated in multiple wells for 24 h. The cell supernatants were collected and cells lysed in 0.1 n NaOH for protein measurement. All ELISA results are reported normalized to the amount of fat in grams and/or milligrams cell protein. Biomarkers that were examined included adiponectin, CRP, serum amyloid A (SAA), leptin, PAI-1, retinol-binding protein-4 (RBP-4), and chemokines (MCP-1 and IL-8) as well as cytokines (IL-1, TNF, IL-18, and IL-6). Adiponectin, SAA, RBP-4, and leptin levels were measured using reagents from Linco. CRP and PAI-1 were measured as previously described (27). IL-1β, TNF-α, IL-6, and IL-8 were measured using a multiplex cytokine/chemokine array. In addition, three to four fragments of SAT were fixed in 10% buffered formalin after cleaning and processed for immunohistochemistry in the Cytopathology Section of our department. The SAT sections were stained for CD68 (a macrophage marker) and T cells (CD3 and CD5) to assess macrophage/T cell infiltration into adipose tissue. The number of macrophages and CLS per high-power field (hpf) were counted in a blinded fashion. At least 10 fields were selected randomly and counted for analysis.

Statistical analysis

Parametric data are expressed as mean ± sd, and nonparametric data are expressed as median ± interquartile ranges. Parametric data were compared using the Student's t test, whereas the nonparametric data were compared with the Wilcoxon signed rank tests. The Pearson and Spearman correlations were computed between variables of interest.

Results

Study cohort

The characteristics of the study population (including anthropometric measurements, BP, fasting glucose levels, fasting lipid levels, and circulating levels of adipocytokines and other biomarkers of inflammation) are shown in Table 1. There was no significant difference in the ages or male to female ratio between the controls and subjects with MetS. Waist circumference, body mass index, BP, fasting glucose concentrations, total cholesterol concentrations, TG concentrations, and HOMA were higher in the MetS subjects than in controls, whereas HDL-C concentrations were lower. In addition, hsCRP, IL-6, IL-1β, leptin, SAA, and RBP-4 concentrations were significantly higher in the MetS subjects than in controls, whereas the adiponectin concentrations were lower.

Table 1.

Subject characteristics

	Control (n = 26)	MetS (n = 39)	P value
Age (yr)	44 ± 10	49 ± 11	0.12
Waist (cm)	94 ± 17	108 ± 17	<0.001
Male to female ratio	10:16	14:25
BMI (kg/m2)	29 ± 7	34 ± 6	<0.01
Systolic BP (mm Hg)	118 ± 18	135 ± 18	<0.001
Diastolic BP (mm Hg)	77 ± 12	86 ± 17	<0.001
Fasting glucose (mg/dl)	88 ± 10	102 ± 12	<0.001
Total cholesterol (mg/dl)	187 ± 34	194 ± 38	<0.05
HDL-C (mg/dl)	68 ± 12	44 ± 19	<0.001
Non-HDL-C (mg/dl)	126 ± 22	148 ± 27	<0.01
TG (mg/dl)	78 (66–94)	129 (106–149)	<0.001
HOMA	1.1 (0.9–2.6)	2.1 (1.7–4.9)	<0.0001
hsCRP (mg/liter)	1.2 (0.5–2.8)	3.4 (1.6–5.2)	<0.01
Leptin (ng/ml)	34 (24–55)	79 (51–104)	<0.001
RBP-4 (μg/ml)	41 ± 13	51 ± 19	<0.02
Adiponectin (μg/ml)	7.8 (5.4–12.9)	5.4 (3.8–8.2)	<0.02
SAA (μg/ml)	6.5 ± 2.7	9.5 ± 3.2	<0.001
IL-1β (pg/ml)	9.7 (3.1–11.8)	20.6 (5.8–34.2)	<0.05
IL-6 (pg/ml)	1.2 (0.4–2.7)	2.9 (1.1–4.5)	<0.01
TNF (pg/ml)	1.7 (0.9–2.9)	2.3 (1.1–3.5)	>0.05

Data are expressed as mean ± sd or median (25–75th percentile) for skewed data. BMI, Body mass index.

SAT biomarkers

The concentrations of several biomarkers in SAT from controls and subjects with MetS are shown in Table 2. Expressed per gram of SAT, the amount of leptin, RBP-4, CRP, SAA, PAI-1, and MCP-1 were significantly higher in subjects with MetS than controls. In addition, release of IL-1β, IL-6, IL-8, and MCP-1, as expressed per milligram protein, was higher in SAT from subjects with MetS than controls.

Table 2.

SAT biomarker levels

	Controls	MetS	Adjusted P value
Adiponectin (ng/g)	4.2 (1.3–5.6)	3.7 (1.2–4.6)	0.077
Leptin (ng/g)	3.0 (2.1–6.2)	7.3 (3.8–18.6)a	<0.05
RBP-4 (ng/g)	11.1 (6.4–18.4)	29.1 (16.2–33.7)b	0.069
CRP (ng/g)	2.5 (2.3–7.9)	5.4 (3.4–19.1)a	<0.05
SAA (ng/g)	14.8 (5.1–34.2)	25.3 (14.5–55.7)a	<0.05
PAI-1 (ng/g)	3.2 (2.2–6.5)	5.6 (3.1–9.9)b	<0.001
MCP-1 (ng/g)	6.7 (4.3–9.1)	22.1 (11.8–33.5)b	<0.01
IL-1β (ng/mg protein)	31.1 (21.2–45.1)	39.7 (24.8–61.5)a	<0.05
TNF (ng/mg protein)	3.7 (1.9–4.6)	3.8 (2.9–5.3)	>0.05
IL-6 (ng/mg protein)	16.5 (10.6–24.5)	18.7 (12.7–33.2)a	<0.05
IL-8 (ng/mg protein)	10.9 (5.1–14.2)	17.4 (14.5–27.3)b	<0.02

Data are expressed as median (25–75th percentile). P values are compared with controls and adjusted for WC.

^aP < 0.05.

^bP < 0.001.

SAT composition and architecture

Using CD3 and CD5 staining, no lymphocyte populations were observed in any of the SAT specimens. However, there were significantly increased numbers of macrophages infiltrating the SAT of MetS subjects compared with controls as demonstrated by positive CD68 staining (Fig. 1). Furthermore, there were significantly increased numbers of CLS in the SAT of MetS subjects than those from controls (3-fold more in MetS subjects compared with controls) (controls, five CLS per 10 hpf; MetS, 14 CLS/10 hpf; P < 0.001).

Fig. 1.

A, Number of infiltrating macrophages in adipose tissue of controls and subjects with MetS. The number of macrophages including CLS per 10 hpf were counted after immunohistochemistry and staining with CD68 as detailed in Subjects and Methods. *, P < 0.001 compared with control. B, Representative SAT biopsy specimens from controls and subjects with MetS (hematoxylin and eosin stain). Note the macrophages arranged adjacent to the adipocyte cell membrane forming a CLS in the SAT from the MetS subject. The images shown are at ×20 magnification; inset of the control is at ×40 magnification.

Adjusted analyses

Because the patients with MetS in our cohort had significantly greater waist circumferences (WC) than the controls, all the analytes reported above were repeated with WC as a covariate. Importantly, in these adjusted analyses, all reported differences between the two groups persisted except for RBP-4 (Table 2). Undertaking a subgroup analysis with a median WC of 42 in. revealed higher levels of only leptin and RBP-4 with WC over 42 vs. WC under 42 in. (for WC < 42 in., leptin was 71 (49–83) ng/ml vs. 82 (55–89) ng/ml for WC > 42 in.; for WC < 42 in., RBP-4 was 25 (11–31) ng/mg protein vs. 33 (17–37) ng/mg protein for WC > 42 in.; P < 0.05 compared with WC < 42 in.).

Correlations

Because both insulin resistance and low-grade inflammation are typical features of MetS, we used HOMA and hsCRP as the representative biomarkers for these conditions to probe relevant correlations. hsCRP correlated positively with HOMA (r = 0.39; P = 0.03) and adipose tissue MCP-1 (r = 0.46; P = 0.03) and negatively with adipose tissue adiponectin (r = −0.44; P = 0.01). Furthermore, there was a significant correlation between circulating and adipose tissue levels of CRP (r = 0.49; P = 0.01), IL-1β (r = 0.56; P = 0.002), and IL-6 (r = 0.76; P = 0.001). HOMA correlated positively with PAI-1 (r = 0.56; P = 0.020), RBP-4 (r = 0.49; P = 0.03), and SAT MCP-1 (r = 0.039; P = 0.040). Although there were significantly more CLS in patients with MetS, there were no significant correlations with biomarkers of inflammation.

Discussion

We demonstrate in this study, SAT biomarkers and architecture differ markedly in subjects with MetS compared with matched controls. We show that patients with MetS without the confounding conditions of diabetes and/or CVD have increased levels of adipocytokines and decreased adiponectin that are related to insulin resistance and inflammation. Furthermore, we show that this dysregulation of adipokines could not be accounted for simply by increased adiposity, suggesting that other aspects of MetS contribute to both the pro-inflammatory state and insulin resistance. More importantly, we document a significant increase in macrophages in SAT and abundant CLS that appear to surround a hypoxic environment triggered by adipocyte death (28–30). For reasons unclear to us, there were no significant correlations between CLS and biomarkers of inflammation. One can speculate that in humans, these CLS could be predominantly of the M2 macrophage phenotype participating in tissue remodeling. This will be the subject of a future study. Also, the chemokine MCP-1 was increased in SAT from MetS subjects and clearly facilitates homing of macrophages to such tissue depots. As such, the SAT may indeed be a key player in MetS and its associated comorbidities. Moreover, because SAT is easy to access and SAT biopsies can be performed in large-scale clinical studies, and the expression of inflammatory genes in SAT compares well with VAT (19, 20), the evaluation of SAT in subjects with and without MetS may provide novel insights into the syndrome's pathogenesis and serious sequelae.

Our data are consistent with some but not all studies that have reported mRNA/gene expression profiles in SAT in subjects with MetS (31, 32). Specifically, Gormez et al. (31) reported increased mRNA levels of TNF-α and leptin but not adiponectin in SAT from subjects with MetS vs. controls, whereas our data showed only increased amounts of SAT secreted and circulating leptin. However, it needs to be emphasized that Gormez et al. (31) studied MetS patients with CAD with 88% of the subjects having concomitant diabetes and dyslipidemia. Thus, one cannot ascribe their findings to MetS alone, and the additional confounder of medications cannot be excluded. Furthermore, Sacks et al. (32) reported no changes with respect to IL-1β gene expression in SAT from 15 subjects with MetS with CAD vs. controls, whereas our data (using a much larger sample size) showed an increased amount of IL-1β released from SAT and in plasma. Thus, unlike the present report, these groups focused on a few selective biomediators/biomarkers. Also, these differences could easily be the result of posttranscriptional processes and highlight the problem with inferring that gene expression directly correlates with protein expression. Thus, the novelty of our study is that we report on patients with MetS but without diabetes and/or CVD and assayed both plasma and secreted levels of adipokines. We did not examine expression of these adipokines in stromal vascular vs. adipocyte fractions due to the confounding effects of collagenase treatment on inflammatory gene expression, and this could be perceived as a limitation of the study. However, we expressed levels of these biomediators per gram of fat mass as well as per milligram protein to examine whether any of these changes were per se due to changes in fat mass. As shown in Table 2, differences in adipokines were observed when expressed either per gram of fat or per milligram protein. It is the consensus that these circulating adipokines function as biomediators that contribute to the increased risk for both diabetes and CVD in MetS subjects (33–35). Also, it is important to note that CRP levels are significantly increased from adipose tissue of MetS subjects compared with controls. Although a large part of CRP appears to be produced in the liver, previous investigators have shown that there is increased CRP gene expression from adipose tissue (36) and vascular endothelium (37), a component of the stromal vascular fraction. These data support the idea that MetS is a proinflammatory state and that the adipose tissue of MetS contributes to the increased inflammation in these subjects.

The essential role of SAT in metabolic homeostasis has best been described in lipodystrophic syndromes, where its absence leads to ectopic fat accumulation in the liver and skeletal muscle with concomitant insulin resistance (38). And although a deficiency in SAT has been associated with MetS (39), our data demonstrate that the SAT may also be a key player in the pathogenesis of MetS, consistent with findings from the Framingham Heart Study showing that larger volumes of SAT were associated with more cardiometabolic risk factors (16).

Adipose tissue dysfunction has been shown to be a mediator in the development of obesity-associated complications (40), which include MetS. Thus, given that recent studies have shown that molecular adaptations in SAT are as discriminating as those in VAT in conditions of worsening metabolic status (41), we specifically chose to study SAT in subjects with and without MetS given its easy accessibility. Importantly, our study was designed not to address the levels of biomarker gene expression in subjects with MetS, as has been done before but suffers from the confounding of the comorbidities of diabetes and CVD (31, 32, 41), but instead to measure the amounts of biomarker proteins present in the SAT and secreted because it is the secreted proteins that mediate biological effects. This type of analysis thus bypasses the potential problems with assuming that gene expression correlates with protein expression and reveals the amount of protein that is actually present. Another important observation from our study is that MCP-1 appears to correlate well with insulin resistance and inflammation. Previous studies have shown, in mice, that knockout of the receptor for MCP-1, i.e. CCR2, results in decreased hepatic inflammation and steatosis (42) and also results in decreased adipose tissue macrophages (43), underscoring the importance of SAT MCP-1 in MetS subjects. The exact mechanisms by which MCP-1 contributes to both insulin resistance and increased inflammation in MetS need to be elucidated in future studies.

Our study also has some limitations. First, VAT samples were not obtained in our study population, so the correlation between SAT and VAT findings could not be corroborated. However, in future studies with our bariatric surgery colleagues, we intend to investigate this important tissue. Second, although we report on the comprehensive repertoire of biomarkers in MetS to date, the list is by no means exhaustive; however, most if not all of the biomarkers evaluated have biological plausibility and credence in the pathogenesis of MetS and or its sequelae. Also, we examined biomarkers in adipose tissue after 24 h incubation, whereas some investigators have examined these levels for up to 5 d after incubation. Nonetheless, we can be confident that our novel data support the notion that the dysregulation of SAT physiology/biology in MetS is important and could have implications for its pathogenesis and greater risk for diabetes and CVD. In future studies, we will focus on the role of the pivotal cell of innate immunity, the monocyte.

Conclusion

In this study, we make the novel observation that the SAT in subjects with MetS uncomplicated by the comorbidities of diabetes and/or CVD has increased macrophage recruitment with cardinal CLS in greater abundance. Furthermore, these cells in SAT conspire to produce increased levels of biomarkers that correlate with both insulin resistance and low-grade inflammation, potentially presaging the subsequent increased risk for diabetes and CVD.