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. Author manuscript; available in PMC: 2020 Nov 5.
Published in final edited form as: J Clin Lipidol. 2020 Aug 4;14(5):667–674. doi: 10.1016/j.jacl.2020.07.012

Associations of GlycA and High-Sensitivity C-Reactive Protein with Measures of Lipolysis in Adults with Obesity

Jordan A Levine 1, Jung Min Han 2, Anna Wolska 3, Sierra R Wilson 3, Tushar P Patel 1, Alan T Remaley 3, Vipul Periwal 2, Jack A Yanovski 1, Andrew P Demidowich 1,4,5
PMCID: PMC7642018  NIHMSID: NIHMS1616695  PMID: 32863171

Abstract

Background:

Obesity-associated inflammation promotes metabolic dysfunction. However, it is unclear how different inflammatory biomarkers predict dysregulation in specific tissues/organs, particularly adipose tissue.

Objective:

The aim of our study was to examine whether GlycA, a nuclear magnetic resonance-measured biomarker of inflammation, is a better predictor of insulin-suppressible lipolysis and other measures of metabolic dysfunction as compared to high-sensitivity C-reactive protein (hsCRP) in human obesity.

Methods:

This was a cross-sectional study of 58 non-diabetic adults with obesity (BMI: 39.8±7.0 kg/m2, age 46.5±12.2y, 67.2% female) who underwent a frequently sampled intravenous glucose tolerance test in the fasted state. Non-insulin-suppressible (l0), insulin-suppressible (l2), and maximal (l0+l2) lipolysis rates, as well as insulin sensitivity (SI) and acute insulin response to glucose (AIRG), were calculated by minimal model analysis. NMR was used to measure GlycA. Body composition was determined by dual-energy X-ray absorptiometry.

Results:

GlycA was strongly correlated with hsCRP (r=+0.46; p<0.001). GlycA and hsCRP were positively associated with l2, l0+l2, and fat mass (p’s<0.01). In linear regression models accounting for age, race, sex, and fat mass, GlycA remained significantly associated with l2 and l0+l2 (p’s<0.05), whereas hsCRP did not (p’s≥0.20). Neither GlycA nor hsCRP were associated with l0, SI or AIRG.

Conclusions:

GlycA was associated with elevated lipolysis, independent of adiposity, in adults with obesity. Our findings suggest that GlycA and hsCRP have distinct inflammation-mediated metabolic effects, with GlycA having a greater association with adipose tissue dysfunction. Further studies are warranted to investigate the mechanisms underlying these associations.

Keywords: Inflammation, GlycA, hsCRP, Lipolysis, Insulin Resistance, Adipose Tissue

Introduction:

Obesity-associated chronic inflammation contributes to the development of insulin resistance, adipose tissue dysfunction, and type 2 diabetes mellitus (T2D).[1–3] Inflammatory cytokines promote local insulin resistance in skeletal muscle, adipose, and hepatic tissue by inactivating insulin receptor substrate-1 (IRS-1), while pancreatic inflammation leads to impaired insulin secretion and beta cell death.[4, 5] The resultant insulin resistance manifests as the impaired suppression of free-fatty acid (FFA) release from adipocytes along with disrupted glucose and FFA uptake in liver and peripheral tissues.[6, 7]

However, obesity-induced inflammation is not a singular process, but rather encompasses multiple inflammatory pathways and mediators. For this reason, identifying inflammatory biomarkers that are associated with specific measures of impaired insulin regulation of glucose and FFA is key to developing a better understanding of the relationship between obesity, inflammation and the progression of T2D.

C-reactive protein (CRP), a commonly used biomarker of inflammation, is an acute-phase protein released by the liver in response to IL-6 and other inflammatory cytokines.[8] GlycA, a recently described inflammatory biomarker measured by nuclear magnetic resonance (NMR) spectroscopy, reflects the increased glycan complexity and greater circulating acute phase protein concentrations in both acute and chronic inflammation, including the N-acetyl groups of mobile N-acetyl glucosamine and N-acetyl galactosamine components of protein glycans.[9] The GlycA signal is associated with concentrations of inflammatory biomarkers and effectors increased in obesity such as high-sensitivity CRP (hsCRP), serum amyloid A (SAA), interleukin (IL)-6, and tumor necrosis factor α (TNFα).[10–13]

In cross-sectional studies of adults with and without of obesity, GlycA and high-sensitivity assays for CRP (hsCRP) are positively correlated with body mass index (BMI), the insulin sensitivity index (SI), homeostatic model assessment of insulin resistance (HOMA-IR), and markers of metabolic syndrome (MetS).[14, 15] Moreover, previous clinical studies have shown that GlycA is associated with incident T2D and cardiovascular disease (CV) disease, even after adjusting for hsCRP and traditional risk factors.[16–20] These findings suggest that GlycA captures key components of chronic inflammation beyond hsCRP and may therefore better predict certain tissue-specific measures of metabolic dysregulation.

Herein, we investigate the cross-sectional associations of GlycA versus hsCRP with measures of insulin resistance, insulin secretion, and FFA kinetics, in adults with obesity. These analyses help highlight how different aspects of the obesity-induced inflammatory state may potentially influence specific metabolic processes in obesity.

Material and Methods:

Study Participants:

A convenience sample of healthy adult volunteers (age ≥ 18 years) with obesity (BMI ≥ 30 kg/m2) recruited for a clinical trial was examined at the NIH Clinical Center (CRC) in Bethesda, MD between 2014 and 2018. The study protocol was approved by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) Institutional Review Board and was registered at ClinicalTrials.gov (NCT02153983). The study was conducted in compliance with the principles of the Declaration of Helsinki. The study was overseen by a Data and Safety Monitoring Board convened by NICHD. All participants provided written informed consent prior to study participation. Individuals with significant chronic medical conditions including diabetes mellitus or taking medications affecting glycemia (e.g. metformin, insulin), body weight, inflammation (e.g. steroids, NSAIDs), or lipids/cholesterol (e.g. statins, fibrates) were excluded from the study.

Study Procedures:

This secondary analysis of data primarily obtained for a randomized clinical trial [21] includes 20 additional participants who attended baseline visits for the trial but were not randomized to receive study medication. Only subjects with complete data were included in this secondary analysis. Subjects were seen as outpatients at the NIH CRC and were asked to fast after 10 PM the previous evening. Body weight was measured using a calibrated digital scale and height using a calibrated stadiometer. Body composition was assessed by dual-energy x-ray absorptiometry (GE Lunar iDXA, GE Healthcare, Madison WI; software GE enCore 15 with CoreScan algorithm). GlycA concentrations were measured with a Vantera Clinical Analyzer using the LipoProfile-3 algorithm (LabCorp, Burlington, NC) as described previously.[22] Blood glucose and hsCRP were assessed by the NIH CRC clinical laboratory on a Roche Cobas 6000 analyzer (Roche Diagnostics, Indianapolis, IN). Plasma for glucose was collected in tubes containing the glycolytic inhibitor sodium fluoride. Concentrations of insulin (analytical sensitivity 0.2μU/mL; intra- and inter-assay CVs 1.1% and 4.3%) and free fatty acids (analytical sensitivity 0.01mEq/L; intra- and inter-assay CVs 1.6%, and 5.1%) were measured by an electrochemiluminescent immunoassay using a Roche Cobas e601analyzer (Roche Diagnostics, Indianapolis, IN). Fasting samples were used to estimate whole-body insulin resistance by the HOMA-IR index (insulin [μU/mL] x glucose [mg/dL]/405). The Adipo-IR index, a previously described surrogate measure of adipose insulin resistance (i.e. ability for insulin to suppress lipolysis), was calculated as insulin (μU/mL) x FFA (mEq/L).[23, 24] An insulin-modified 3-hour frequently sampled intravenous glucose tolerance test (FSIVGTT) was performed as described previously.[21] Briefly, a glucose load of 50% dextrose 0.3 g/kg given as a smooth bolus over 2 minutes was administered at time 0 and a bolus of 0.05 U/kg insulin was given just before minute 20. Blood samples were collected at times −15, −10, −5, −1 (averaged as the baseline), then at +2, 3, 4, 5, 6, 8, 10, 14, 19, 22, 25, 30, 40, 50, 70, 100, 140 and 180 minutes after glucose injection for the measurement of plasma glucose, plasma FFA, and serum insulin concentrations. Samples were collected on ice, centrifuged within 1 hour of collection, and measured at the NIH Department of Laboratory Medicine on the day of sample collection.

Minimal Models of Metabolic Dysregulation:

Acute insulin response to glucose (AIRG) was calculated as the insulin area under the curve using the trapezoidal rule obtained during the first 14 minutes. Insulin sensitivity (SI) was estimated using minimal model analysis (SAAM II, The Epsilon Group, Charlotte, VA). Because insulin effectively suppresses lipolysis, FFA entry into circulating plasma is typically maximal when insulin concentrations are lowest (e.g. in the fasted state). Robust endogenous insulin secretion or exogenous administration can suppress lipolysis to some miniscule but typically non-zero FFA rate of appearance; this residual lipolysis rate has previously been termed the “non-insulin suppressible lipolysis rate.” Therefore the difference between the maximal and non-insulin suppressible rates is termed the insulin-suppressible lipolysis rate.[25] Under the conditions of this study, FFA appearance in the circulation is largely considered to derive from intracellular lipolysis in adipocytes. Non-insulin-suppressible (l0), insulin-suppressible (l2), and maximal (l0+l2) lipolysis rates were calculated from the FSIVGTT via the minimal model as described previously.[25–27]

Statistical Analysis:

Differences between groups were assessed using Student’s t-test for continuous variables or chi-square analysis for categorical variables. Bivariate correlations between markers are reported using Pearson’s correlation coefficient. Multiple regression analyses were performed using dependent variables: Non-insulin-suppressible (l0), maximum (l0+l2) and insulin-suppressible (l2) lipolysis rates, insulin sensitivity (SI), acute insulin response to glucose (AIRG), HOMA-IR, and Adipo-IR, with independent variables: age, race (coded as non-Hispanic Black and Other), sex, and total fat mass in Model 1. Family history of T2D was defined as any first-degree relative being diagnosed with T2D. GlycA and hsCRP were separately included as independent variables in Model 2 and Model 3, respectively, to assess for their individual contributions. Data were transformed as necessary to maintain assumptions of normality. SPSS v25.0 (IBM Corp, Armonk, NY) was used for all statistical analyses.

Results:

The baseline characteristics of the 58 adult subjects studied (Mean ± SD: age 46.5 ± 12.2 y; BMI 39.8 ± 7.0 kg/m2, race: NHB 29.3%, NHW 39.7%, HISP 24.1%, Other 6.9%; sex: female 67.2%) are described in Table 1. The cohort was enriched, by design, for insulin resistance (HOMA-IR 5.2 ± 3.0) and elevated inflammation (hsCRP 5.9 ± 5.5 mg/L).

Table 1: Participant Characteristics (n=58).

Data are shown as the unadjusted mean±SD, unless otherwise indicated. FFA, free fatty acids; HOMA-IR, homeostatic model assessment of insulin resistance; Adipo-IR, adipose tissue insulin resistance; l0, non-insulin-suppressible lipolysis rate; l2, insulin-suppressible lipolysis rate; l0+l2, maximal lipolysis rate; SI, insulin sensitivity index; AIRG, acute insulin response to glucose; hsCRP, high-sensitivity C-reactive protein.

Characteristic Study Cohort
(n=58)
Age (y) 46.5±12.2
Race, n (%)
 Non-Hispanic Black 17 (29.3%)
 Non-Hispanic White 23 (39.7%)
 Hispanic 14 (24.1%)
 Other 4 (6.9%)
Sex, n (%)
 Female 39 (67.2%)
 Male 19 (32.8%)
T2D Family History, n (%) 22 (38%)
Insulin resistance*, n (%) 42 (72%)
BMI (kg/m2) 39.8±7.0
Fat Mass (kg) 52.2±14.9
Insulin (μU/mL) 20.8±11.3
Glucose (mg/dL) 101.2±8.4
FFA (mEq/L) 0.74±0.24
HOMA-IR 5.2±3.0
Adipo-IR (mU*mEq/L) 14.8±8.4
l0 (x10−3*mEq/L*min) 3.6±1.1
l2 (x10−3*mEq/L*min) 62.5±45.9
l0+l2 (x10−3*mEq/L*min) 66.1±45.8
SI (x10−4*min−1μU−1*ml) 1.25±0.88
AIRG (μU/mL/min) 1332.4±1814.4
GlycA (mmol/L) 416.5±52.6
hsCRP (mg/L) 5.9±5.5
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*

Insulin resistance defined as HOMA-IR ≥ 2.6.

In bivariate analyses [Table 2], GlycA was positively associated with hsCRP (r =+0.46, p<0.001). HsCRP demonstrated a significant positive association with l2 (r=+0.38, p<0.01), l0+l2 (r=+0.38, p<0.01), BMI (r=+0.47, p<0.001), and fat mass (r=+0.55, p<0.0001) but not Adipo-IR (r=+0.13, p=0.34). GlycA showed a significant positive association with l2 (r=+0.39, p<0.01), l0+l2 (r=+0.39, p<0.01), BMI (r=+0.30, p=0.02), and fat mass (r=+0.34, p<0.01), and was also associated with Adipo-IR (r=+0.26, p<0.05). Neither hsCRP nor GlycA showed significant correlations with HOMA-IR, fasting glucose, fasting insulin, FFA, SI, AIRG, or l0 (p’s>0.05).

Table 2:

Bivariate correlation analyses.

GlycA hsCRP
r p r p
hsCRP +0.46 0.0003* - -
l0 −0.13 0.31 −0.13 0.32
l2 +0.39 0.003* +0.38 0.003*
l0+l2 +0.39 0.003* +0.38 0.003*
Adipo-IR +0.26 0.047* +0.13 0.34
HOMA-IR +0.16 0.23 +0.19 0.15
Glucose +0.23 0.08 −0.02 0.89
Insulin +0.13 0.32 +0.20 0.13
FFA +0.06 0.64 −0.12 0.36
SI −0.13 0.33 −0.04 0.76
AIRG +0.08 0.54 +0.08 0.53
Fat Mass +0.34 0.01* +0.55 <0.0001*
BMI +0.30 0.02* +0.47 0.0002*
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hsCRP, high-sensitivity C-reactive protein; l0, non-insulin-suppressible lipolysis rate; l2, insulin-suppressible lipolysis rate; l0+l2, maximal lipolysis rate; Adipo-IR, adipose tissue insulin resistance; HOMA-IR, homeostatic model assessment of insulin resistance; FFA, free fatty acid; SI, insulin sensitivity index; AIRG, acute insulin response to glucose; BMI, body mass index.

*

p<.05

Table 3 summarizes the multiple step regression analyses performed. Insulin-suppressible lipolysis rate (l2) was significantly associated with sex (β=+0.33, p=0.01) and fat mass (β=+0.27, p=0.047) in Model 1 (p=0.02). Model 2 (p=0.009) with the addition of GlycA as an independent variable revealed a positive association of GlycA with l2 (β=+0.27, p=0.04; Figure 1A). Adding hsCRP in place of GlycA in Model 3 (p=0.03) found no significant association of hsCRP with l2 (β=+0.17, p=0.23; Figure 1B).

Table 3:

Multiple linear regression analysis relating GlycA and hsCRP concentrations to insulin-suppressible (l2) and maximal lipolysis rates (l0+l2)

Model 1 Model 2 Model 3
β SE p β SE p β SE p
l2
Age −0.061 0.004 0.67 −0.088 0.004 0.53 −0.073 0.004 0.61
Race −0.067 0.089 0.60 −0.027 0.088 0.83 −0.06 0.089 0.64
Sex +0.333 0.089 0.01* +0.279 0.088 0.03* +0.307 0.09 0.02*
Fat Mass +0.274 0.003 0.047* +0.176 0.003 0.21 +0.19 0.003 0.21
GlycA - - - +0.273 0.001 0.04* - - -
hsCRP - - - - - - +0.171 0.008 0.23
p-value for model 0.02* 0.009* 0.03*
l0+l2
Age −0.055 0.003 0.70 −0.083 0.003 0.56 −0.068 0.003 0.64
Race −0.081 0.081 0.54 −0.039 0.079 0.76 −0.073 0.08 0.57
Sex +0.31 0.080 0.02* +0.254 0.079 0.056 +0.282 0.081 0.04*
Fat Mass +0.257 0.003 0.07 +0.155 0.003 0.28 +0.166 0.003 0.28
GlycA - - - +0.284 0.001 0.04* - - -
hsCRP - - - - - - +0.186 0.008 0.20
p-value for model 0.045* 0.01* 0.045*
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Model 1 includes: age (y), race (coded as non-Hispanic Black and Other), sex (coded as male and female), and fat mass (kg) as independent variables; Model 2 includes: age, race, sex, fat mass, and GlycA (mmol/L); Model 3 includes: age, race, sex, fat mass, and hsCRP (mg/dL). l2, insulin-suppressible lipolysis rate; l0+l2, maximal lipolysis rate; hsCRP, high-sensitivity C-reactive protein.

*

p<.05

Figure 1. Multiple linear regression analyses of the effects of GlycA and hsCRP on adjusted insulin-suppressible and maximal lipolysis rates.

Figure 1.

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(A) GlycA, but not (B) hsCRP, was significantly associated with insulin-suppressible lipolysis, after adjusting for age, race, sex, and fat mass. Similarly, (C) GlycA, but not (D) hsCRP, was significantly associated with maximal lipolysis, after adjusting for the same covariates. Data were transformed as necessary to maintain assumptions of normality for the purposes of the analyses.

Maximal lipolysis rate (l0+l2) was also significantly associated with sex (β=+0.31, p=0.02) and trended towards an association with fat mass (β=+0.26, p=0.07) in Model 1 (p=0.045). The addition of GlycA into the linear model (Model 2, p=0.01) showed a positive association of GlycA with l0+l2 (β=+0.28, p=0.04; Figure 1C). Adding hsCRP instead of GlycA (Model 3, p=0.045) revealed no significant association of hsCRP with l0+l2 (β=+0.19, p=0.20; Figure 1D).

HOMA-IR, a measure of whole-body (in the fasted state, primarily hepatic) insulin resistance and Adipo-IR, reflecting adipose insulin resistance, were both associated with fat mass (β=+0.33, p=0.02; and β=+0.39, p=0.006, respectively) in Model 1. The addition of GlycA or hsCRP in Models 2 and 3, respectively, did not significantly improve the predictive power of these models (p’s>0.05). Linear regression analyses with SI, AIRG, and non-insulin-suppressible lipolysis rate (l0) as independent variables similarly did not show any significant benefit from adding inflammatory factors to the models (p’s>0.05).

None of the models remained significant when the cohort was restricted to insulin resistant subjects only (n=42), as defined by HOMA-IR ≥ 2.6 (Supplemental Tables 1A–C). However, all the β values trended in the same direction as when the models were tested in the larger cohort. When family history of T2D was included as a covariate in the model for the entire cohort, Models 1–3 all remained significant for l2 and l0+l2 (Supplemental Tables 2A–C). For Model 2, GlycA trended towards, but did not reach, statistical significance for both l2 (β=+0.26, p=0.057) or l0+l2 (β=+0.27, p=0.051). In all models tested, family history of T2D was not significantly associated with l2 or l0+l2 (p’s≥0.16).

Discussion:

In this study, we examined the associations of inflammatory biomarkers, namely GlycA and hsCRP, with measures of adiposity, free-fatty acid (FFA) kinetics, insulin resistance and insulin secretion in adults with obesity but without diabetes. We report the novel finding that both GlycA and hsCRP showed positive associations with insulin-suppressible (l2) and maximum (l0+l2) lipolysis rates, quantitative markers of insulin regulation of lipolysis measured by FSIVGTT minimal model analysis. After adjusting for age, race, sex, and fat mass, the inflammatory marker GlycA, but not hsCRP, still significantly contributed to the models of l2 and l0+l2.

FFA metabolism is a balance of FFA appearance in the circulation versus FFA clearance or disposal. FFA rate of appearance is typically maximal in a prolonged fasted state and is primarily the result of lipolysis from adipose tissue stores. The main enzyme catalyzing adipocyte triglyceride breakdown is hormone-sensitive lipase (HSL), which is readily inhibited by insulin. However, even with complete suppression of HSL, a minimal rate of FFA entry into the circulation persists (i.e. non-suppressible lipolysis rate).[28] The difference between the maximal and non-insulin-suppressible lipolysis rate has been previously termed the insulin-suppressible lipolysis rate.[25] Simultaneously, FFA is removed from circulation primarily for immediate consumption of energy (fatty acid oxidation) or for storage in peripheral tissues or the liver (e.g. triglyceride or VLDL synthesis), also known as non-oxidative disposal.[29]

Previous studies have shown that an imbalance between lipolysis and FFA clearance, leading to elevated circulating FFAs, may play a significant role in the pathogenesis of obesity-related insulin resistance and T2D.[30–33] Experimentally increasing plasma FFA concentrations in vivo has been shown to directly increase intramyocellular triglyceride content and thereby promote insulin resistance in skeletal muscle.[30] Moreover, insulin resistance at the level of the adipocyte, as evidenced by Adipo-IR, is associated with progressive decline in beta cell function and the progression of T2D.[5, 32] Taken together, the impaired insulin suppression of lipolysis observed in obesity both negatively influences insulin sensitivity in the periphery as well as insulin secretion from the pancreas.

The links between lipolysis rates and classic covariates, such as age, sex, race, and adiposity, in human beings are well-established. Previous studies using isotopically labeled tracers to measure FFA rate of release have shown that FFA flux is greater in subjects with obesity versus lean subjects, whereas insulin-mediated FFA disposal is greater among lean subjects.[34, 35] The total FFA rate of release into plasma increases linearly with increasing fat mass.[36] Lipolysis decreases with age presumably due to decreased sensitivity to catecholamines,[37, 38] while Black adults have been shown to have a significantly greater FFA rate of release as compared with White adults.[39] Adult women have also been found to have greater FFA release in plasma and greater non-oxidative FFA disposal and recycling versus men.[29] Moreover, a study conducted in adolescents with obesity found significantly greater insulin-suppressible (l2) and maximum (l0+l2) lipolysis rates in girls versus boys.[26] However, the relationship between chronic inflammation and dysregulated lipolysis in human obesity is not fully elucidated.

GlycA is a composite marker that integrates circulating glycosylated protein levels with their degree of enhanced glycosylation, while hsCRP reflects a single acute-phase protein. GlycA concentrations are associated with increased anti-microbial peptide production, circulating leukocytes, and neutrophil gene expression, key components of obesity-related chronic inflammation.[40] Because the GlycA measurement captures multiple inflammatory pathways at once, it may serve as a useful inflammatory biomarker for assessing different aspects of inflammation-induced metabolic dysfunction, as compared to hsCRP.

Although we replicated the finding that GlycA is positively associated with adiposity, we did not find any independent associations with measures of insulin sensitivity (SI) or beta cell function (AIRG). In a previous cross-sectional study consisting of adults both with and without diabetes, both GlycA and hsCRP were associated with insulin sensitivity, as measured by SI, but not with insulin secretion, as measured by AIRG. Notably hsCRP was more strongly associated with SI than was GlycA. A separate analysis of subjects without diabetes was not performed, so it is unclear if associations of GlycA and SI would still be demonstrable in this subgroup.[14]

In another cross-sectional study conducted in adults with varying degrees of glucose intolerance, 46% of whom met criteria for T2D, both GlycA and hsCRP were positively associated with the leptin/adiponectin ratio, a surrogate measure of adipocyte metabolic dysfunction, independent of HOMA-IR and BMI.[15] Interestingly, there were no significant associations of GlycA with insulin resistance or glucose tolerance, similar to our results. These findings suggest that GlycA may complement hsCRP as a useful biomarker in assessing inflammatory-mediated metabolic dysregulation. HsCRP likely better reflects glucose tolerance, while GlycA may better reflect the local inflammatory processes that are driving adipocyte dysfunction rather than insulin sensitivity in skeletal muscle or the liver. Interestingly, neither biomarker seems to be related to the inflammatory processes underlying beta cell dysfunction; further research needs to be done in this regard.

It is important to note that in our study, GlycA and hsCRP were not significantly associated with non-insulin-suppressible lipolysis rate (l0). However, GlycA was significantly associated with both insulin-suppressible (l2) and maximum (l0+l2) lipolysis rates after correction for other covariates, suggesting that GlycA more closely relates to the impaired insulin suppression of lipolysis, a key feature of obesity-induced insulin resistance specific to adipose tissue inflammation, rather than the intracellular adipocyte lipolysis machinery itself.

By bivariate analysis GlycA, but not hsCRP, was also significantly associated with Adipo-IR, another surrogate measure of adipose insulin resistance. This association was no longer significant in the linear regression model after accounting for other covariates, although this may simply be due to lack of power rather than an actual lack of association.

Although GlycA is a relatively new inflammatory marker, clinicians can now readily order GlycA testing through LabCorp (https://www.labcorp.com/assets/17167). As described above, previous studies have demonstrated the clinical utility of GlycA for predicting incident T2D and CVD, as well as mortality, independent of hsCRP levels. Therefore, an otherwise “healthy” individual with an elevated GlycA may warrant more intense lifestyle/dietary counselling and earlier initiation of primary prevention strategies. The results from this article further add to the growing literature that GlycA complements hsCRP as an inflammatory marker in metabolic health risk stratification. Moreover, our study raises interesting new clinical research questions, such as would patients with pre-diabetes and elevated GlycA levels derive particular benefit from Metformin, an insulin sensitizer known to lower lipolysis rates, as compared to patients with similar levels of glycemia but low GlycA concentrations?

A strength of our study was a racially diverse cohort of adults with a broad range of ages and degree of obesity. Another important feature of this study was that individuals were not on any glucose-lowering, lipid-lowering (e.g. statins), or anti-inflammatory medications, as these medications could significantly skew metabolic and inflammatory measurements and their associations. Additionally we used fat mass, rather than BMI, as a covariate in our multiple linear regression analyses. A limitation of our study is the use of measures of FFA flux by mathematically modeling data from an insulin-modified FSIVGTT instead of using tracers during a hyperinsulinemic-euglycemic clamp. Additionally, our relatively small sample size may have impaired our ability to uncover significant associations. Furthermore, including five to six covariates in the multiple linear regression models with this sample size may have caused the problem of overfitting. As our cohort included only otherwise healthy individuals, our results are likely generalizable to those “early” in the development of metabolic dysregulation, but it is unclear whether our findings are applicable to people with advanced age or significant comorbid conditions (e.g. diabetes or cardiovascular disease). Another limitation is the measurement of hsCRP only once in all subjects on the day of the FSIVGTT, not multiple times as recommended by current guidelines.

Conclusion:

We found independent associations of GlycA, but not hsCRP, with measures of FFA flux in adults with obesity who did not have diabetes. Our findings suggest that GlycA and hsCRP reflect different inflammatory-mediated metabolic processes, with GlycA having a greater association with adipose tissue dysfunction. Further studies are warranted to examine the biological mechanisms underlying the relationship between different inflammatory pathways and adipose tissue dysfunction.

Supplementary Material

1

Acknowledgements:

We thank the participants, the nursing staff of the NIH Clinical Center, and Sheila Brady, CRNP for their help collecting these data.

Funding Source: This research was supported by the Intramural Research Program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health, 1ZIAHD000641 (to JAY), with supplemental funding from an NICHD Division of Intramural Research Director’s Award.

Disclosures: Dr. Jack A. Yanovski receives grant support for unrelated studies sponsored by Rhythm Pharmaceuticals Inc., and by Soleno Therapeutics Inc. JAL, JMH, AW, SRW, TPP, ATR, VP, and APD have no conflicts of interest to declare.

Footnotes

Clinical Trial Registration: www.clinicaltrials.gov (NCT02153983, registered May 31, 2014)

Data sharing statement: The individual participant data that underlie the results reported in this article, after deidentification (text, tables) will be made available upon request to one of the Corresponding Authors immediately after publication, to researchers who provide a methodologically sound proposal for any purpose. To gain access, data requestors will need to sign a data access agreement.

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