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Circulating N-Linked Glycoprotein Acetyls and Longitudinal Mortality Risk

Originally published Research. 2016;118:1106–1115



Circulating glycoprotein N-acetyl glucosamine residues have recently been associated with incident cardiovascular disease and diabetes mellitus.


Using a plasma glycan biosignature (GlycA) to identify circulating N-acetyl glycan groups, we examined the longitudinal association between GlycA and mortality among initially healthy individuals.

Methods and Results:

We quantified GlycA by 400 MHz 1H nuclear magnetic resonance spectroscopy in 27 524 participants in the Women’s Health Study (NCT00000479). The primary outcome was all-cause mortality. We replicated the findings in an independent cohort of 12 527 individuals in the Justification for the Use of statins in Prevention: an Intervention Trial Evaluating Rosuvastatin (JUPITER) trial (NCT00239681). We also undertook secondary examination of cardiovascular disease and cancer mortality in the Women’s Health Study. In the Women’s Health Study, during 524 515 person-years of follow-up (median, 20.5 years), there were 3523 deaths. Risk factor–adjusted multivariable Cox proportional hazard ratio (95% confidence interval) per SD increment in GlycA for all-cause mortality was significantly increased at 5 years (1.21 [1.06–1.40]) and during maximal follow-up (1.14 [1.09–1.16]). Similar risk for all-cause mortality was observed in the replication cohort (1.33 [1.21–1.45]). In the Women’s Health Study, risk of cardiovascular disease mortality was increased at 5 years (1.43 [1.05–1.95]) and during maximal follow-up (1.15 [1.04–1.26]) and of cancer mortality at 5 years (1.23 [1.02–1.47]) and during maximal follow-up (1.08 [1.01–1.16]). Examination of correlations and mortality associations adjusted for high-sensitivity C-reactive protein, fibrinogen, and intercellular adhesion molecule-1, suggested that GlycA reflects summative risk related to multiple pathways of systemic inflammation.


Among initially healthy individuals, elevated baseline circulating glycoprotein N-acetyl methyl groups were associated with longitudinal risk of all-cause, cardiovascular, and cancer mortality.


Post-translational modification of secreted proteins via enzymatic glycosylation—the regulated enzymatic attachment of oligosaccharide (sugar) moieties—is important for regulating protein trafficking, function, folding and stability, and biosynthetic control,1,2 particularly of secreted inflammatory proteins and immune mediators.3 Quantifying and delineating the human glycome in health and disease has received increasing interest as a novel tool to identify markers of disease and potential mechanistic mediators of disease pathogenesis.47 Recently, we identified an association between a glycoprotein N-acetyl methyl group signature measured by proton nuclear magnetic resonance spectroscopy (1H NMR), termed GlycA, and incident cardiovascular disease (CVD) and diabetes mellitus.8,9 GlycA identifies the concentration of circulating protein-bound N-acetyl methyl groups of N-acetylglucosmine (GlcNAc) and N-acetylgalatosamine glycan moieties based on an 1H NMR signal arising at 2.00 (±0.01) ppm in the lipoprotein profile spectrum (Online Figure I).1012 In the current study, we sought to determine the potential divergence between health and disease associated with GlycA over an extended follow-up. To identify potentially common pathways of disease across several disease states, we looked at common causes of death, including all-cause mortality, CVD mortality, and cancer mortalities.

Because GlycA identifies aggregates of glycan residues on circulating glycoproteins, and given that the majority of circulating glycoproteins are acute phase reactants (APRs) and immunologic proteins, we hypothesized that a large proportion of the potential risk associated with elevated GlycA would relate to activation of systemic inflammatory pathways. To investigate what potential risk was predicted beyond known inflammatory pathways, we compared the mortality risks associated with GlycA with those of targeted circulating APRs. We also looked at risk associated with GlycA under conditions where other circulating APRs were not elevated. Finally, to adjust the risks associated with GlycA for the extent of systemic inflammation, we performed multivariable adjustment for high-sensitivity C-reactive protein (hsCRP). Overall, we hypothesized that GlycA would identify mortality risks that overlapped with those predicted by biomarkers of inflammation but also potentially attributable to additional pathways of risk, relating perhaps to alternate inflammatory pathways or protein glycosylation itself.


Study Population

The study population was derived from participants in the Women’s Health Study (WHS), a completed randomized controlled trial of aspirin, β-carotene, or vitamin E versus placebo in the prevention of CVD and cancer.1315 The study enrolled 39 876 women aged ≥45 years and free of self-reported CVD or cancer at study entry (1993–1996). Within the trial timeframe (ending in 2004), there was no significant effect of aspirin, β-carotene, or vitamin E on primary end points although some secondary end points were significant during the trial or extended follow-up.16 Baseline GlycA level was measured in 27 524 consenting WHS participants who agreed to noncompulsory blood sample collection. After trial completion, extended post-trial follow-up of participants remains on-going (although mailings, questionnaires, phone interviews, and national death index surveillance), with follow-up reported herein through 2014. Women could opt out of post-trial follow-up at the first post-trial follow-up questionnaire collected in 2005 to 2006 although 33 682 of the initial 39 876 participants (84.5%) agreed to participate in post-trial follow-up.

GlycA Measurement

The identification and characterization of GlycA were recently described,10 building on previous 1H NMR observations.12 Briefly, the plasma 400 MHz 1H NMR signal at 2.00 ppm has been previously attributed to an amalgamation of resonances from a subset of mobile GlcNAc residues on the bi, tri, and tetra-antennary glycan branches of circulating glycoproteins (Online Figure I). This signal only arises from the GlcNAc units with β1→2 and β1→6 linkages on preceding mannose sugars.

This signal quantifies one of the more common glycosylation residues, and hence multiple N-GlcNAc–labeled proteins contribute to this resonance signal. The most abundant circulating glycoproteins are APRs and immunologic proteins (eg, immunoglobulins), and comparison of GlycA with the resonance spectra of targeted proteins identified the predominant contributors to the GlycA signal as α1-acid glycoprotein, haptoglobin, α1-antitrypsin, α1-antichymotrypsin, and transferrin.10,12 IgG and fibrinogen, although common in plasma, contain only structurally immobilized glycan chains, which will not give rise to narrow detectable NMR signals and hence do not contribute to the GlycA resonance spectrum or does α2-macroglobulin.10 Although other glycoproteins may contribute to a small extent, the detection sensitivity of 1H NMR under the conditions of measurement is ≈20 µmol/L, and hence circulating glycoproteins below this concentration (including hsCRP) are not detected by 1H NMR. Importantly, the balance and distribution of proteins contributing N-acetyl methyl resonances to GlycA are dynamic and reflect underlying physiological conditions—changes in GlycA composition and quantity depend on multiple protein regulatory mechanisms.

NMR LipoProfile spectra were acquired on EDTA plasma samples obtained during the trial and stored in vapor-phase liquid nitrogen (−170°C). The GlycA deconvolution algorithm was then applied to the stored NMR LipoProfile spectra to obtain GlycA concentrations for this study. At the time of initial NMR, a 90° pulse was used with a 1.0 s acquisition time and 3.0-s relaxation delay during which water presaturation was applied. The pulse repetition rate of 4.0 s was slightly <5T1; however, the T1 values of GlycA in different serum samples are similar, and thus this translates to minimal differential attenuation of the GlycA signal, and hence has a negligible effect on quantification. Separation of the GlycA signal from the neighboring lipoprotein shoulder (prominently reflecting the triglyceride concentration in very-low-density lipoprotein) was done through deconvolution. Cox models were also adjusted for triglyceride concentration to assure that we accounted for any potential signal overlap. The NMR analysis and deconvolution were performed by LipoScience (now LabCorp, Raleigh, NC), as an adjunct to the LipoProfile-3 assay. The data were provided to the investigators at no additional cost.

Other Laboratory Measurements

Measurement of hsCRP was performed by high-sensitivity immunoturbidimetric assay on the Hitachi 917 autoanalyzer (Roche Diagnostics, Indianapolis, IN), with reagents and calibrators from Denka Seiken, as described previously.17 Lipid measurements were performed in a laboratory certified by the Lipid Standardization Program of the National Heart, Lung, and Blood Institute/Centers for Disease Control and Prevention. Low-density lipoprotein cholesterol was measured by a homogenous direct method.18 High-density lipoprotein cholesterol was measured by direct enzymatic colorimetric assays. Triglycerides were measured enzymatically with correction for endogenous glycerol. Soluble intracellular adhesion molecule 1 (ICAM-1) was measured using an ELISA (R&D Systems, Minneapolis, MN), and fibrinogen was measured using an immunoturbidimetric assay (Kamiya Biomedical, Seattle, WA).19 Lp-PLA2 mass was measured by a latex particle–enhanced turbidimetric immunoassay run on the Roche P-modular analyzer (PLACTM test; diaDexus, San Francisco, CA).20

Mortality Outcomes

All-cause mortality in WHS was the primary outcome. All-cause mortality was ascertained through a combination of end points committee adjudication (53%), death certificate (16.4%), National Death Index (4.4%), and other (communication with family, employers, acquaintances; 26%). Cause-specific mortality in WHS was examined, including CVD and cancer (including subtypes) deaths, which were ascertained based on only end points committee adjudication and death certificate to limit potential for misclassification. Previous studies in similar cohorts have found a sensitivity of nosologists at accurately determining cause of death at 90%, with specificity >95%,21 hence death certificate–determined causes of death as classified were felt to be reliable. There no appreciable demographic differences among those with and without a known cause of mortality. CVD mortality was defined as death caused by myocardial infarction, stroke, peripheral vascular disease, valvular heart disease, or other CVD. Cancer mortality was further subdivided into mortality caused by colon cancer, breast cancer, pancreatic cancer, and lung cancer, as determined at the time of end points committee adjudication.

Replication Cohort

We replicated the all-cause mortality associations in an independent cohort derived from an randomized clinical trial of rosuvastatin versus placebo (Justification for the Use of statins in Prevention: an Intervention Trial Evaluating Rosuvastatin, Justification for the Use of statins in Prevention: an Intervention Trial Evaluating Rosuvastatin [JUPITER]; NCT00239681) that enrolled 17 802 asymptomatic individuals (women ≥60 years, men ≥50 years) without prior history of coronary disease, stroke, or diabetes mellitus (2003–2006).22 The study allocated participants with low or normal low-density lipoprotein cholesterol (<130 mg/dL) but elevated levels of hsCRP (≥2.0 mg/L) to rosuvastatin versus placebo. There was a 44% relative risk reduction in the primary end point and a 20% relative risk reduction in all-cause mortality with rosuvastatin versus placebo. GlycA was measured with the same assay used in WHS in 12 527 JUPITER participants at baseline, of whom 10 060 also had 1-year measurements. Mortality was ascertained during the trial by end points committee adjudication through March 30, 2008.

Statistical Analyses

Testing for interactions between GlycA and randomization arms demonstrated no evidence of significant effect modification, nor did stratified analysis of mortality rates by randomization arm. Therefore, the treatment and placebo groups were pooled, and study drug adjusted as a covariable in regression models. Baseline descriptive clinical and biochemical characteristics were analyzed across quartiles of GlycA as medians (25th–75th percentiles) for quantitative variables and as percentages for qualitative variables. Comparisons across quartiles were assessed with the Kruskal–Wallis test for quantitative variables and with the χ2 test for qualitative variables. All P values were 2-tailed using α=0.05 experiment-wise error. Analyses were done using SAS version 9.3 (Cary, NC), except the all-cause mortality meta-analysis, which was done with STATA version 14 (College Station, TX).

The exposure time was calculated as the time from randomization to death or censoring. Cox proportional hazard models were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs). Regression analyses were presented in crude and adjusted form, based on GlycA SD increment or quartiles. Adjustment for potential confounders—selected based on prior clinical knowledge—was done sequentially by first including age, race, smoking (current or former), alcohol use (≥1 drink/d), history of hypertension, family history of myocardial infarction (in WHS) or coronary heart disease (in JUPITER), body mass index, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, glycated hemoglobin in a model, and a subsequent model which additionally included natural log-transformed hsCRP. Risk of all-cause mortality in the replication cohort (JUPITER) was modeled with the same regression models as in WHS. (Inclusion of sex in the JUPITER models did not appreciably alter the primary results and therefore this was not included for consistency.) P for nonlinear trend was calculated across quartiles. Risk over time was visually examined with Kaplan–Meier survival curves, and the Cox proportional hazard assumption was tested for by including a cross product term for GlycA and time as well as Schoenfeld residuals. Although longitudinal all-cause and cancer mortality risks associated with GlycA in WHS did not violate the proportional hazards assumptions (P=0.08 and P=0.16, respectively, for time interaction), there was evidence that the proportional hazard assumption was not met for CVD mortality over maximum follow-up (P=0.02 by time interaction) although not by Schoenfeld residuals (P=0.09). Based on these observations, and to mirror the 5-year maximum follow-up in the replication cohort (JUPITER), we performed a stratified mortality analysis looking at the 5-year mortality risks associated with GlycA in WHS, as well as over the entire duration of follow-up. Following replication in JUPITER, to analyze the data in aggregate, relative risks of adjusted mortality were compared per SD increment in GlycA with summary HRs (95% CIs) and visually using Forest plots, pooled using inverse variance weighting and random-effects meta-analysis models to create summary estimates. Although there was minimal evidence of between-study heterogeneity comparing outcomes during maximum follow-up (heterogeneity χ2P=0.083; I2=66.6%) and during 5-year follow-up (heterogeneity χ2P=0.937; I2=0.0%), random-effects models were used to better account for potential interstudy heterogeneity.

To understand what risk remained associated with GlycA beyond known inflammatory biomarkers, we performed several post hoc sensitivity analyses in addition to adjusting risk models for hsCRP. First, given that statins have anti-inflammatory effects,23 we compared all-cause mortality risks once on-treatment in JUPITER between GlycA and hsCRP in the rosuvastatin-allocated group. Second, we adjusted risk models for hsCRP, as well as the all-cause model for fibrinogen and ICAM-1. In addition, we examined mortality risk in WHS associated with GlycA when hsCRP was <25th percentile and conversely examined mortality risk associated with hsCRP when GlycA was <25th percentile. Finally, Spearman correlations were used to examine correlations of GlycA with hsCRP, fibrinogen, interleukin-6, tumor necrosis factor-αR2, ICAM-1, secreted phospholipase A2, and lipoprotein-associated phospholipase A2 mass.

Role of the Funding Source

The funding agencies had no role in the design and execution of the current study. All analyses herein were specified a priori by the investigators through a written detailed analysis plan, with the exception of several exploratory analyses, which were performed in response to the study findings (the on-statin risk comparisons in JUPITER and the hsCRP-GlycA discordance analysis in WHS).


Study Population (WHS)

WHS study participants included in the current study had a median [interquartile range] age of 53 [49–59] years at baseline. Stratification by GlycA quartile suggested that several comorbid conditions were more prevalent among those with higher levels of GlycA (Table 1). Similarly, levels of hsCRP were incrementally higher in higher quartiles of GlycA. Conversely, alcohol consumption (≥1 drink/d) showed an inverse relationship with GlycA levels.

Table 1. Baseline Clinical and Biochemical Variables by GlycA Quartile in the Women’s Health Study

Quartiles of GlycA, μmol/L
≤326 (n=6996)>326 to 369 (n=6802)>369 to 416 (n=6875)>416 (n=6851)
Age, y51 (48, 57)53 (49, 59)54 (50, 60)54 (50, 60)
Race/ethnicity, %
BMI, kg/m223 (21, 25)24 (22, 27)26 (23, 29)28 (25, 32)
Hypertension, %14212739
Diabetes mellitus, %
HbA1c, %4.9 (4.8, 5.1)5.0 (4.8, 5.2)5.0 (4.9, 5.2)5.1 (4.9, 5.3)
hsCRP, mg/L0.7 (0.4, 1.5)1.5 (0.7, 2.9)2.6 (1.4, 4.6)5.1 (2.8, 8.3)
Family history MI, %13141416
LDL-C, mg/dL113 (94, 133)121 (101, 144)126 (104, 148)129 (106, 152)
HDL-C, mg/dL57 (48, 68)53 (45, 64)50 (42, 60)47 (39, 56)
Triglycerides, mg/dL86 (65, 116)111 (82, 154)133 (97, 186)168 (120, 236)
Fibrinogen, mg/dL314 (279, 350)341 (304, 384)362 (321, 409)402 (353, 457)
ICAM-1, ng/mL317 (281, 360)337 (297, 383)350 (308, 400)373 (328, 429)
Smoker,* %44485051
Alcohol, %1212107

Data presented as median (25th, 75th percentile) otherwise as percent where indicated. BMI indicates body mass index; HbA1c, hemoglobin A1c; HDL-C, high-density lipoprotein cholesterol; hsCRP, high-sensitivity C-reactive protein; ICAM-1, intercellular adhesion molecule 1; LDL-C, low-density lipoprotein cholesterol; and MI, myocardial infarction.

*Current or former.

Alcohol consumption ≥1 drink/d. All variables were P≤0.0001 across quartiles.

All-Cause Mortality

Among the 27 524 WHS participants, 3523 deaths occurred over a median (maximum) of 20.5 (21.7) years, representing 524 515 person-years of follow-up. The adjusted (for clinical risk factors) HR (95% CI) per SD (68.4 µmol/L) increment in GlycA for all-cause mortality was significantly increased, at 5 years (1.21 [1.06–1.40]; 255 events) and during maximal follow-up (1.14 [1.09–1.18]; Table 2; Figure 1). This risk remained significant after incrementally adjusting for hsCRP. Risk of all-cause mortality increased progressively at higher quartiles of GlycA (Figure 2A).

Table 2. Association of Baseline GlycA With Subsequent Mortality Among 27 524 Participants in the Women’s Health Study

MortalityEventsHazard Ratio Per SD* (95% Confidence Interval)
UnadjustedAdjusted for Clinical VariablesAdjusted for Clinical Variables and hsCRP
5-y follow-up
 All-cause2551.38 (1.23–1.55)§1.21 (1.06–1.40)1.23 (1.04–1.44)
 Cardiovascular461.80 (1.40–2.31)§1.43 (1.05–1.95)1.41 (0.99–2.01)
  All1491.31 (1.12–1.52)1.23 (1.02–1.47)1.23 (1.00–1.52)
  Breast111.07 (0.60–1.92)0.94 (0.46–1.91)0.77 (0.35–1.72)
  Lung401.48 (1.11–1.96)1.34 (0.95–1.89)1.10 (0.74–1.64)
  Colorectal201.56 (1.05–2.321.93 (1.21–3.06)2.64 (1.55–4.52)
  Pancreatic201.18 (0.77–1.80)0.79 (0.47–1.32)0.87 (0.49–1.53)
Complete follow-up
 All-cause35231.29 (1.25–1.33)§1.14 (1.09–1.18)§1.11 (1.06–1.16)§
 Cardiovascular5571.39 (1.29–1.51)§1.15 (1.04–1.26)1.12 (1.00–1.25)
  All11191.16 (1.10–1.23)§1.08 (1.01–1.16)1.02 (0.94–1.10)
  Breast1361.13 (0.96–1.34)1.16 (0.95–1.42)1.05 (0.83–1.32)
  Lung2731.33 (1.18–1.48)§1.28 (1.12–1.46)1.11 (0.95–1.29)
  Colorectal1011.36 (1.13–1.63)1.23 (0.98–1.54)1.27 (0.98–1.64)
  Pancreatic791.21 (0.98–1.50)1.08 (0.84–1.40)1.04 (0.78–1.40)

hsCRP indicates high-sensitivity C-reactive protein.

*SD GlycA in the Women’s Health Study=68.4 µmol/L.

Clinical variables include age, race, family history of myocardial infarction, hypertension, smoking, alcohol use, body mass index, hemoglobin A1c, low-density lipoprotein cholesterol, High-density lipoprotein cholesterol, log-transformed triglycerides, and randomization arm.

Natural log of hsCRP.

Significance of associations: §P<0.0001, ‖P≤0.05, otherwise P=nonsignificant.

Median [maximum] follow-up=20.5 (21.7) years.

Figure 1.

Figure 1. Hazard ratio (HR; 95% confidence interval [CI]) for all-cause mortality (log scale) in the Women’s Health Study (WHS) per-SD increment higher GlycA at 5 y (red) and during maximum available follow-up (blue; median [maximum] follow-up = 20.5 [21.7] y), in unadjusted, clinically adjusted*, and clinically adjusted plus hsCRP models. *Adjusted for age, race, family history of myocardial infarction, hypertension, smoking, alcohol use, body mass index, glycated hemoglobin, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, log-transformed triglycerides, and randomization arm. hsCRP indicates high sensitivity C-reactive protein.

Figure 2.

Figure 2. Unadjusted Kaplan–Meier survival curves demonstrating total survival time (A) and survival free of cardiovascular (B) and cancer (C) mortality, by lowest (1) through highest (4) quartile of GlycA in the Women’s Health Study. (Note that the survival scale axis ends at 0.95 in B and C.) CVD indicates cardiovascular disease.

CVD Mortality

CVD mortality was associated with GlycA levels in WHS (Table 2; Figure 2B). Unadjusted 5-year CVD mortality risk in WHS (n=46 deaths) was increased per SD increment in GlycA (1.80 [1.40–2.31]), and this remained statistically significant after adjusting for clinical variables (1.43 [1.05–1.95]). With incremental adjustment for hsCRP the HR (95% CI) was 1.41 [0.99–2.01]). Risk during maximum follow-up in WHS (n=557 deaths) was attenuated but remained significant in all models, including unadjusted (1.39 [1.29–1.51]), clinically adjusted (1.15 [1.04–1.26]), and clinically adjusted plus hsCRP (1.12 [1.00–1.25]) models.

Cancer Mortality

Cancer mortality was associated with GlycA at 5-years (n = 149 events; HR [95% CI]) in unadjusted (1.31 [1.12, 1.52]), clinically-adjusted (1.23 [1.02, 1.47]), and clinically-adjusted plus hsCRP (1.23 [1.00, 1.52]) models (Table 2; Figure 2C). The results were similar but attenuated at maximal follow-up in WHS (1,119 events; Table 2). Cancer mortality appeared to be related to increases in mortality associated with colorectal cancer (clinically-adjusted model HR [95% CI] per SD increment: 1.93 [1.21, 3.06] at 5-years and 1.23 [0.98, 1.54] during maximal follow-up) and lung cancer (1.34 [0.95, 1.89] at 5-years and 1.28 [1.12, 1.46] during maximal follow-up). There was no association of GlycA with breast or pancreatic cancer mortality.

All-Cause Mortality Replication

Among 12 527 included JUPITER participants (Online Table I), 278 deaths occurred over a median (maximum) of 2.0 (4.9) years, representing 28 903 person-years of follow-up. Participants had a median [interquartile range] age of 66 [60–71] years and were 64% men. All-cause mortality risk in JUPITER was increased per SD (70.3 µmol/L) increment in GlycA in all models, with an HR adjusted for clinical variables of 1.33 (1.21–1.45), and an HR adjusted for clinical variables and hsCRP of 1.24 (1.10–1.39). This risk resembled the associations observed in WHS.


From random-effects meta-analysis of the all-cause mortality risks in the WHS and JUPITER cohorts, the pooled adjusted (for clinical variables and hsCRP) HR for all-cause mortality per SD increment in GlycA ≤5 years was 1.24 (95% CI, 1.12–1.36; Figure 3A), and during maximum available follow-up was 1.16 (1.04–1.29; Figure 3B).

Figure 3.

Figure 3. Forrest plots demonstrating the individual and pooled associations (adjusted [for clinical variables and high-sensitivity C-reactive protein] hazard ratio [95% confidence interval {CI}]) between GlycA and all-cause mortality in the Women’s Health Study (WHS; n=26 634) at 5 y (A) and during maximum available follow-up (median, 20.5 y [B]) and in Justification for the Use of statins in Prevention: an Intervention Trial Evaluating Rosuvastatin (JUPITER; n=12 249), per SD increment in GlycA. Data were pooled using inverse variance random-effects meta-analysis models.

Evidence of Risk Beyond Markers of Systemic Inflammation

We performed several sensitivity analyses to investigate the potential risk associated with GlycA beyond known markers of systemic inflammation. First, we observed correlations that ranged from weak to moderate for GlycA with various inflammatory biomarkers, with the strongest correlation (r=0.61) being with hsCRP (Table 3). Second, given that rosuvastatin 20 mg daily differentially reduced hsCRP (which was 37.1% lower at 1 year in the rosuvastatin arm than placebo in the overall JUPITER trial) compared with no appreciable change in GlycA (Online Figure II), we examined on-treatment mortality risk among those in the rosuvastatin-treated arm of JUPITER (n=4386 individuals) associated with GlycA versus hsCRP. Despite on-statin reductions in hsCRP, GlycA remained strongly associated with all-cause mortality (Online Table II). In models adjusting this GlycA–mortality association for hsCRP level, this risk remained largely unchanged, whereas adjusting the on-treatment mortality risk associated with hsCRP for GlycA level nullified the association between hsCRP and mortality. Third, we compared the all-cause mortality risk associated with GlycA directly with those associated with hsCRP, fibrinogen, and soluble ICAM-1. The magnitude of risk predicted by GlycA for those in the highest quartile was similar to that of hsCRP although it was stronger for fibrinogen and for ICAM-1 (Table 4). All-cause mortality risk during maximal available remained associated with GlycA after adjustment for clinical variables plus hsCRP or fibrinogen or ICAM-1, but was no longer significant in a model adjusting for clinical variables and all of these 3 inflammatory biomarkers, for those in the highest (relative to lowest) quartile (1.05 [0.92–1.19]). In JUPITER, adjustment of the GlycA risk for hsCRP only modestly attenuated risk (and this remained significant), whereas adjusting the hsCRP risk for GlycA had a larger attenuating effect. Finally, we compared mortality risk between GlycA and hsCRP among women with discordant biomarker levels (Online Table III). Specifically, we examined the mortality risk associated with GlycA in WHS when hsCRP was low (≤25th percentile), and compared this with risk associated with hsCRP when GlycA was low (≤25th percentile). Although the pattern of risk was similar, the magnitude of unadjusted risk was higher in the highest quartile for GlycA for all-cause mortality (HR [95% CI], 2.76 [2.05–3.73]), CVD mortality (2.89 [1.48–5.66]), and cancer mortality (1.87 [1.03–3.40]) than for hsCRP (1.81 [1.28–2.54], 1.79 [0.82–3.93], and 0.83 [0.36–1.88], respectively). In this smaller subgroup analysis, the adjusted risk ratios were no longer significant, except for the association between GlycA and all-cause mortality (P=0.013).

Table 3. Spearman Correlations Between GlycA and Plasma Inflammatory Cytokines and Acute Phase Reactants in WHS and JUPITER*

BiomarkerGlycA, µmol/L
hsCRP0.6127 4900.4612 527
Fibrinogen0.4727 336
ICAM-10.3027 325
sPLA20.3110 380
LpPLA2 mass−0.1310 505

hsCRP indicates high-sensitivity C-reactive protein; ICAM-1, soluble intracellular adhesion molecule 1; IL-6, interleukin 6; JUPITER, Justification for the Use of statins in Prevention: an Intervention Trial Evaluating Rosuvastatin; LpPLA2, lipoprotein-associated phospholipase A2 mass; n = number of individuals; r = correlation; sPLA2, secreted phospholipase A2; TNF-αR2, tumor necrosis factor-α-receptor II; and WHS, Women’s Health Study.

*All correlations P<0.0001 except TNF-αR2 where P=0.426.

Baseline correlation, in JUPITER; after 12-month rosuvastatin 20 mg daily, correlation between GlycA and hsCRP was r=0.50 for n=5473 individuals (P<0.0001).

Table 4. Comparison of the Association of GlycA and hsCRP With All-Cause Mortality in JUPITER and WHS, and Associations Between GlycA, hsCRP, Fibrinogen, and ICAM-1, and All-Cause Mortality in WHS,* and Association Between GlycA and hsCRP With All-Cause Mortality in the Replication Cohort

Hazard Ratio (95% Confidence Interval) All-Cause MortalityP for Trend
Women’s Health Study: maximum follow-up (no. of deaths 3459/27 284)
 UnadjustedRef.1.28 (1.15–1.41)1.37 (1.24–1.52)1.90 (1.73–2.10)<0.0001
 AdjustedRef.1.03 (0.93–1.15)0.99 (0.89–1.11)1.28 (1.14–1.43)<0.0001
 Adj.+hsCRPRef.1.01 (0.90–1.12)0.94 (0.84–1.05)1.17 (1.04–1.32)0.018
 Adj.+fibrinogenRef1.00 (0.90–1.12)0.93 (0.84–1.05)1.14 (1.01–1.28)0.055
 Adj.+ICAM-1Ref1.00 (0.89–1.11)0.95 (0.86–1.06)1.16 (1.04–1.30)0.011
 Adj.+hsCRP+fibrinogen+ICAM-1Ref0.97 (0.87–1.08)0.90 (0.81–1.01)1.05 (0.92–1.19)0.598
 UnadjustedRef.1.33 (1.19–1.47)1.59 (1.44–1.76)1.84 (1.67–2.03)<0.0001
 AdjustedRef.1.04 (0.93–1.16)1.11 (0.99–1.24)1.25 (1.11–1.40)<0.0001
 Adj.+GlycARef.1.01 (0.90–1.13)1.04 (0.93–1.16)1.11 (0.98–1.26)0.092
 UnadjustedRef.1.23 (1.10–1.37)1.63 (1.47–1.81)2.44 (2.21–2.69)<0.0001
 AdjustedRef.1.03 (0.92–1.15)1.10 (0.99–1.23)1.34 (1.20–1.49)<0.0001
 Adj.+GlycARef.1.01 (0.90–1.13)1.06 (0.95–1.18)1.23 (1.09–1.38)0.0001
 UnadjustedRef.1.25 (1.11–1.40)1.69 (1.52–1.88)2.98 (2.70–3.30)<0.0001
 AdjustedRef.0.97 (0.87–1.10)1.18 (1.06–1.33)1.73 (1.55–1.92)<0.0001
 Adj.+GlycARef.0.96 (0.85–1.08)1.16 (1.04–1.30)1.67 (1.50–1.86)<0.0001
JUPITER (no. of deaths 278/12 527)
 UnadjustedRef.1.03 (0.69–1.53)1.26 (0.86–1.83)2.77 (1.99–3.86)<0.0001
 AdjustedRef.1.15 (0.77–1.73)1.39 (0.94–2.04)2.42 (1.71–3.44)<0.0001
 Adj.+hsCRPRef.1.10 (0.74–1.65)1.26 (0.85–1.87)1.97 (1.33–2.91)0.0004
 UnadjustedRef.1.23 (0.84–1.79)1.28 (0.87–1.87)2.28 (1.62–3.23)<0.0001
 AdjustedRef.1.29 (0.88–1.89)1.30 (0.89–1.91)2.02 (1.42–2.87)<0.0001
 Adj.+GlycARef.1.21 (0.83–1.78)1.14 (0.78–1.69)1.45 (0.99–2.14)0.0813

Adj. indicates adjusted; hsCRP, high-sensitivity C-reactive protein; ICAM-1, intercellular adhesion molecule 1; JUPITER, Justification for the Use of statins in Prevention: an Intervention Trial Evaluating Rosuvastatin; Ref., reference; and WHS, Women’s Health Study.

*Among the 27 284 WHS participants in whom all (GlycA, hsCRP, ICAM-1, and fibrinogen) markers were measured.

Model adjusted for age, race, family history of myocardial infarction (in WHS) or coronary heart disease (in JUPITER), hypertension, smoking, alcohol use, body mass index, hemoglobin A1c, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, log-transformed triglycerides, and randomization arm.


Our study was designed to investigate the longitudinal association between a biomarker of circulating N-acetyl glucosamine residues and total and cause-specific mortality among initially healthy individuals. Among 27 524 initially healthy individuals with over half a million years of cumulative patient follow-up, we found that GlycA, an 1H NMR-measured aggregate of circulating N-acetyl groups of N-acetylglucosmine and N-acetylgalatosamine glycan moieties, was significantly associated with mortality. Although individuals with elevated GlycA were more likely to have comorbid conditions, most associations remained significant after adjusting for clinical covariates. CVD and cancer mortality were both significantly associated with elevated levels of GlycA. Increased risks of colorectal and lung cancer mortality contributed to the elevated risk of cancer mortality. The 5-year risks in WHS declined but in most cases remained significant in the extended follow-up (>20 years). The all-cause mortality associations were replicated in an independent cohort of 12 527 individuals. GlycA was at-most moderately associated with established inflammatory biomarkers but remained an important predictor of mortality even after adjustment for inflammatory biomarkers, as well as in situations where these inflammatory biomarkers were low. Overall, these results identify a robust association with a measure of circulating N-linked glycoprotein acetyls (predominantly linked to APRs) and mortality among initially healthy individuals.

Co- and post-translational modification of secreted proteins via enzymatic glycosylation regulates protein trafficking, protein folding and stability, biosynthetic control, and function.1,2 The importance of glycosylation in immune regulation has also been clearly demonstrated,2,3,7 and an increasing number of studies have highlighted the importance of protein glycosylation in the pathogenesis of atherosclerosis24,25 and cancer.4,26 The commonality of inflammation and immune function to the pathogenesis of diverse diseases including CVD and some cancers has also been demonstrated.27,28 Because 1H NMR has previously been shown to identify N-acetyl glycan moieties on circulating blood glycoproteins,10,12 commonly APRs such as α1-acid glycoprotein, haptoglobin, α1-antitrypsin, α1-antichymotrypsin, and transferrin, we hypothesized that quantification of this glycan biosignature could facilitate identification of risk across a spectrum of diseases including CVD and cancer, to some extent relating to risks associated with systemic inflammation.

The magnitude and distribution of risk associated with GlycA was similar to that observed with established biomarkers of inflammation. Notably, the distribution of cancer mortality risk identified in our study (increased risk of mortality caused by colorectal and lung cancer) mirrors previous findings of cancer risk associated with inflammation in other populations.27,29 Based on these observations and knowledge of the chemical characteristics of GlycA, it appeared that a large component of the risk associated with GlycA related to systemic inflammation. However, correlations between GlycA and inflammatory cytokines and immune modulators (tumor necrosis factor-αR2, interleukin-6, ICAM-1, secreted phospholipase A2) were weak and were at most moderate with APRs (hsCRP and fibrinogen). These findings suggest incomplete overlap with the GlycA signal and these markers of systemic inflammation when assessed individually. Furthermore, GlycA remained associated with risk when hsCRP was low, and after statin-induced reductions in hsCRP (but not GlycA), risk remained significantly associated with GlycA and not with hsCRP. Differential changes in GlycA and hsCRP in response to statin therapy could relate to the summative nature of GlycA, which could be less prone to alteration in the setting of multiple parallel pathways contributing to its signal. Along these lines, the all-cause mortality risk during maximal available follow-up remained associated with GlycA after adjustment for clinical variables plus hsCRP or fibrinogen or soluble ICAM-1 individually, but no longer in a model adjusting for clinical variables plus all of these three inflammatory biomarkers. Taken together, these findings suggest that GlycA could reflect risk associated with a summation of multiple pathways of systemic inflammation. However, also as evidence by these weak-to-moderate individual correlations, persistence of risk associated with other inflammatory biomarkers—particularly ICAM-1—after adjustment for GlycA suggests that mortality risks associated with GlycA overlap, but does not fully encompass, those of the inflammatory biomarkers that it reflects. It remains uncertain whether alterations in other biological pathways could contribute to the GlycA signal.

Few other studies have characterized the clinical risk associated with GlycA. Our group recently described an association with GlycA and incident CVD9 and type II diabetes mellitus.8 The association with CVD mortality we observed corroborates these observations and broadens the potential spectrum of risk. Another recent study examined the association between 1H NMR-measured α-1-acid glycoprotein—one contributor to GlycA under most conditions—and mortality (cardiovascular and noncardiovascular) in a cohort of healthy Finish individuals.30 Similar to our study, these investigators identified increased mortality risk associated with α-1-acid glycoprotein; our results support and extend these findings.

Our results must be viewed in the context of the study design. First, because of the evidence that the mortality risks associated with GlycA in WHS were in some cases not constant over time, and given the shorter follow-up in the replication cohort (JUPITER), we examined the associations at 5 years in WHS, in addition to during maximal follow-up. Second, our glycosylation signal did not identify individual glycoproteins, but rather an aggregate, and this marker is not able to capture the complexities of the dynamic human glycome. However, that GlycA does measure an aggregate is also a strength, as this amalgam would be expected to be less sensitive to day-to-day variation in an individual’s glycoprotein milieu. Indeed, GlycA appears to have lower coefficients of variation than traditional biomarkers of inflammation,10 and among the placebo-allocated patients in JUPITER, baseline and 1-year levels of GlycA were more correlated than hsCRP (r=0.66 and 0.47, respectively). Third, we utilized multiple mechanisms of mortality ascertainment, which could potentially lead to misclassification bias. However, all CVD and cancer mortality events were either adjudicated by the WHS end point committee or ascertained through death certificates, a practice that has been shown to have high sensitivity and specificity in similar studies,21 and thus was felt to be unlikely to lead to misclassification bias.

In conclusion, among initially healthy individuals with >20 years of follow-up, elevated baseline levels of GlycA, a novel biomarker of circulating N-linked glycoprotein acetyls, predicted future total mortality, CVD mortality, and cancer mortality. A large proportion of this risk was attributable to systemic inflammation. It is possible that GlycA could provide broader profiling of systemic inflammation owing to the summative characteristics of this signal; future research is needed along these lines. Additional prospective studies will be valuable in further assessing the relationship between GlycA and clinical outcomes.

Nonstandard Abbreviations and Acronyms


acute phase reactants


cardiovascular disease


high sensitivity C-reactive protein


intercellular adhesion molecule 1


Justification for the Use of statins in Prevention: an Intervention Trial Evaluating Rosuvastatin trial


nuclear magnetic resonance spectroscopy


Women’s Health Study


In February 2016, the average time from submission to first decision for all original research papers submitted to Circulation Research was 14.68 days.

This article was sent to James T. Willerson, Consulting Editor, for review by expert referees, editorial decision, and final disposition.

The online-only Data Supplement is available with this article at

Correspondence to Samia Mora, MD, MHS, Center for Lipid Metabolomics, Division of Preventive Medicine, Brigham and Women’s Hospital, 900 Commonwealth Ave E, Boston, MA 02215. E-mail


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Novelty and Significance

What Is Known?

  • Proton nuclear magnetic resonance (1H NMR) spectroscopy can quantify metabolites circulating in the blood. Recently, a specific NMR signal (GlycA) quantifies the N-acetyl methyl groups of N-acetylglucosmine residues on the antennary branches of circulating glycoproteins, primarily from the acute phase reactants α1-acid glycoprotein, haptoglobin, α1-antitrypsin, α1-antichemotrypsin, and transferrin.

  • Profiling this GlycA signal in relation to diverse disease states could provide insight into a marker and potential mediator of disease risk and it has been recently associated with cardiovascular disease and diabetes mellitus.

What New Information Does This Article Contribute?

  • Our study demonstrated robust associations with all-cause mortality, including both cardiovascular and cancer mortality.

  • Furthermore, the risks related to this NMR glycan signal overlapped with those associated with several pathways of systemic inflammation, highlighting a common pathophysiologic link across diverse disease states.

Increased circulating circulating glycoprotein acetyls—part of the oligosaccharide chains post-translationally attached to glycosylated proteins—have been suggested to represent early markers of disease risk, including cardiovascular disease and diabetes mellitus. Using observations from >524 515 person-years of follow-up (median, 20.5 years), this study sought to understand the longitudinal relationship between a measure of circulating glycoprotein N-acetyl glucosamine residues and diverse disease processes by examining the association between an 1H NMR-measured plasma glycan biosignature and mortality among initially healthy individuals. Identifying a potentially common pathway of disease across several disease states, we demonstrated robust associations with mortality caused by cardiovascular disease and cancer. The overall mortality findings were replicated in an independent cohort. There was evidence that the risks captured by this NMR glycan signal overlapped with those associated with multiple pathways of systemic inflammation. Overall, these results provide important insight into a potential molecular beacon of longitudinal risk that can be easily and rapidly quantified using NMR spectroscopy on plasma obtained from healthy individuals.


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