Abstract
In this pilot study, a subset of CALERIE Phase 2 (No. NCT00427193, registered 25th Jan 2007) participants (n = 26) were evaluated for the effects of 2 years of 25% calorie restriction (CR) on N-glycosylation of IgG, plasma, and complement C3, as well as IgG-based biological age (GlycAge). Plasma samples were collected at baseline (BL), 12 (12mo), and 24 months (24mo). IgG galactosylation was higher at 24mo compared to BL (p = 0.051) and increased from 12mo to 24mo (p = 0.016); GlycAge decreased over the same period (p = 0.027). GlycAge was positively associated with TNF-α (p = 0.030) and ICAM-1 (p = 0.017). Between BL and 24mo, plasma high-branched glycans declined (p = 0.013), bisecting GlcNAcs increased in both plasma (p < 0.001) and IgG (p = 0.01), complement C3 protein (p < 0.001), C3-Man9 (p < 0.001), and C3-Man9Glc1C3 (p = 0.046) were reduced. The absence of a control group warrants cautious interpretation.
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Introduction
Calorie restriction (CR), a dietary intervention involving a reduction in energy intake below the amount consumed ad libitum, delays aging and increases maximum lifespan in various species1,2. Animal studies suggest that CR could extend lifespan and slow aging; however, there are limited data in human populations, and gaps and inconsistencies in our understanding of the mechanisms by which CR is geroprotective3,4,5,6. Thus, newer mechanisms influencing aging are important to investigate, as they might elucidate human-specific pathways at play6.
One potential contributing mechanism is protein N-glycosylation, a post-translational modification. Protein N-glycosylation is crucial to protein folding, stability, and function7. Further, with age, protein glycosylation is altered, and these alterations have been linked to various aging-related diseases, such that aberrant N-glycosylation may contribute to aging-related diseases via inflammaging and disruption of proteostasis8,9,10.
A key example of aging-related glycosylation is alterations in the glycosylation of IgG. IgG is the predominant antibody (or immunoglobulin) produced by B cells, key players in cellular immunity. With aging, both chronic antigen stimulation and oxidative stress11,12 upregulate inflammatory responses, leading to chronic low-grade inflammation, or inflammaging, a hallmark of aging13. With aging, IgG glycosylation changes include reductions in galactosylation and sialylation14,15,16,17,18,19 and increases in bisecting N-acetylglucosamines (GlcNAc)20,21,22. These aging-related IgG glycosylation alterations are all pro-inflammatory via structural changes that promote an inflammatory response14,15,16,17,18,19. Thus, IgG glycosylation alterations are a result of and accelerate the age-related onset of chronic inflammatory disease. Leveraging this, the IgG N-glycome composition and its changes have been used to develop a biological age marker/index23. Furthermore, mono and di-galactosylated IgG glycans (but not agalactosylated IgG) and reduced bisecting GlcNAc’s have been negatively associated with incident type 2 diabetes and cardiovascular disease24,25,26,27,28.
While not as extensively studied as IgG glycosylation, glycosylation of complement C3 has recently come to be associated with metabolic and autoimmune diseases29,30. C3 is a component of the complement cascade, a critical arm of the innate immune system. Greater concentrations of C3 have been noted in metabolic disorders such as adiposity, dyslipidemia, insulin resistance, liver dysfunction, and diabetes, suggesting C3 may be a cardiometabolic risk factor31,32. Alternatively, lower C3 plasma concentrations have been tied to longevity in centenarian populations33. Aging-related changes in complement C3 include greater amounts of mannose-rich (high-mannose) and high-mannose glucosylated (such as GlcNAc2Mannose9Glucose1) glycans. These specific changes are associated with the onset of type 1 diabetes and renal complications arising from type 1 diabetes, both of which are hypothesized to be triggered by Endoplasmic Reticulum (ER) stress30,34. It is important to note that GlcNAc2Mannose9Glucose1 is involved in the process of initiating the unfolded protein response (UPR), which can trigger ER stress35. Elevated ER stress signals loss of proteostasis, another hallmark of aging36,37, implicating a role for these specific glycoforms in the aging process.
In addition to immune-related glycoproteins like IgG and complement C3, total plasma protein glycome is also linked to age-related disease mechanisms14. Longitudinal investigations have identified the relative stability of the total plasma protein N-glycome over several years, while also identifying potential age-related changes38. Changes in plasma protein glycosylation patterns, such as appropriate sialylation and galactosylation, mark familial longevity and reflect healthy aging39. Moreover, several plasma, serum, and cell surface glycoprotein profiles in human epidemiological, animal model, and in vitro studies have identified agalactosylation and asialylation signatures, which have now come to be called “glycan” hallmarks of aging14.
This manuscript outlines results from a pilot investigation focused on identifying changes in glycomic biomarkers of aging and age-related disease onset in response to a 2y CR intervention in healthy adults with normal weight or mild-moderate overweight (by BMI) from the Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy (CALERIE™) trial. Specifically, IgG, C3, and total plasma protein N-glycosylation were evaluated. We further explored associations between CR-induced changes in a glycome-based biological age clock and epigenetic age clocks. Finally, we investigated muscle transcriptomic changes that are indicative of changes in glycosylation after the 2y CR intervention. Based on our understanding of glycomic changes resulting from aging17 and chronic metabolic disease onset14 we expected CR to favorably alter IgG and plasma protein N-glycosylation profiles with increased galactosylation and sialylation and reduced branching and bisecting GlcNAc. We expected CR to alter complement C3 glycosylation with fewer mannosylated and glucosylated C3 glycoforms. Furthermore, we anticipated that these alterations in the N-glycome would correlate with improvements in other biomarkers of aging, such as inflammation and epigenetic age clocks. Understanding the effect of CR on the glycome is crucial for advancing aging biology, and this study offers valuable data that could shape future investigations and interventions.
Results
In the 26 participants included in this pilot study, % CR was ~18% in the BL-12mo interval, and went down to ~9% in the 12–24mo interval (Fig. 1). Body weight reduced significantly between BL and 12mo, and increased slightly from 12 to 24mo.
A A line chart with points depicts body weight at baseline, 12mo and 24mo. B A box and whisker plot, with a dotted line separating the individual 12mo intervals from the total study %CR. The plots depict %CR during each intervention period. Inset p-values in (A) were obtained from repeated-measures mixed models with time (months) as the repeated measure and participant as a random effect. Inset p-values in (B) were obtained from repeated-measures mixed models using time intervals as repeated measures and participant as a random effect.
CR effects on IgG GlycAge
A comparison of the chronological age of participants in this cohort (25.6–49.9 y at baseline - BL) and their calculated GlycAge is presented in Fig. 2. At 12mo, GlycAge was not different from chronological age, but at 24mo, GlycAge was lower than chronological age (Fig. 2A). A comparison of the slopes of a linear regression fit between GlycAge and chronological age against months in study (BL-12mo and 12–24mo) identified that the slope of GlycAge change between 12 and 24mo was significantly different from that of chronological age, but not between BL-12mo. Mean GlycAge (Fig. 2B) decreased between 12mo and 24mo (p = 0.027) but not between BL and 12mo or between baseline and 24mo. However, there was large inter-individual variability that warrants further investigation. During the BL-12mo interval (when the GlycAge change tracked chronological age), change in body weight was inversely associated with change in GlycAge; however, in the 12–24mo interval (when GlycAge was reduced in response to the intervention), there was no association between body weight change and GlycAge change.
A Chronological age compared to IgG-based biological age (GlycAge), with the slope (m) of each time interval (0–12mo and 12–24mo) inset, which were different (p = 0.009). However, a multiple comparison test (Tukey’s) comparing the two ages indicated that at 24mo the two ages showed a difference of moderate significance (p = 0.06). B GlycAge for all participants in study – each line represents each participant, connecting dots measured at BL, 12mo, and 24mo. C Biological age (GlycAge) percent change from baseline with significant p-value inset. Differences were evaluated using a mixed model using visit month as a repeated measure, participant as a random effect, sex, and baseline GlycAge as covariates, followed by FDR correction. If the FDR corrected p-values were significant, multiple comparison tests (Tukey’s) were conducted. Tukey’s tests indicated that the 12mo–24mo ages were different (p = 0.027). D, E Change from baseline visit to 12mo and 12–24mo of IgG-based biological age GlycAge and change in body weight. Bivariate regression plots are shown with the β-estimate and corresponding p-value, and Spearman’s correlation coefficient and corresponding p-values, inset evaluating the relationship between change in body weight and GlycAge by time interval (BL-12mo and 12–24mo).
In summary, GlycAge remained similar to chronological age at 12 months but was significantly lower at 24 months, with a notable decline between 12 and 24 months, independent of body weight change. Details of all outcomes from this report are tabulated in Supplementary Table 3, which includes Cohen’s f2, while Supplementary Table 4 has other effect size metrics for mixed models presented here.
CR effects on the IgG N-glycome
Compared to 12mo, IgG di-galactosylation was greater at 24mo (p = 0.039). Compared to BL, total galactosylation (which is the sum of glycan structures with single and double galactoses- Fig. 3C) was greater at 24mo (p = 0.051). While there was no significant change in agalactosylation (i.e., glycan structures with no galactose sugars, Fig. 3D, p = 0.073), bisecting GlcNAc was higher at both 12mo and 24mo compared to BL (p < 0.001 and 0.019, respectively, Fig. 3E).
A A representative CGE-LIF electropherogram of IgG N-glycome, with graphic representation of glycan structures corresponding to each glycan peak (P). B–E show the percentage change from baseline in the IgG glycome among a subset of CALERIE2 study participants (n = 26). Data were evaluated by mixed models using visit month as a repeated measure, participant as a random effect, baseline values for each parameter, sex, and age as covariates, followed by FDR correction. If the FDR corrected p-values were significant, multiple comparison tests (Tukey’s) were conducted. Significant p-values (inset in red) were obtained from Tukey’s tests.
In summary, IgG di-galactosylation and total galactosylation increased at 24 months, while bisecting GlcNAc was higher at both 12 and 24 months compared to baseline, though agalactosylation showed no significant change.
CR effects on total plasma N-glycome
Changes in select plasma glycome features before and after the 2y CR are presented in Fig. 4. Of particular interest are (a) highly sialylated and highly galactosylated glycans, both typically reduced with aging and chronic inflammation, (b) highly branched glycans, usually found at greater concentrations in chronic inflammatory diseases, and (c) glycans with bisecting GlcNAc, predominantly originating from immunoglobulins, which are also known to increase with biological age and chronic inflammation14. At 24mo, concentrations of tetra-sialylated glycans were reduced compared to BL (p < 0.001) and 12mo (p = 0.031). On the other hand, asialylated glycans were higher at 24mo compared to BL (p = 0.011) and 12mo (p = 0.036).
If the FDR corrected p-values were significant, multiple comparison tests (Tukey’s) were conducted, and significant p-values, if any, were highlighted in red. All figures show percent change from baseline. A, B Non-sialylated glycans were higher, whereas tetra-sialylated glycan species were lower at 24mo compared to BL. C, D Non-galactosylated species increased significantly, while tetra-galactosylated glycans reduced in the 12–24mo period of CR. E High-mannose glycans were significantly higher at 24mo compared to 12mo, but not compared to BL. F Glycans, with bisecting GlcNAc on plasma proteins, were higher at 12mo and 24mo compared to BL. G High-branched glycans were, however, lower at 24mo compared to BL.
From BL to 24mo, there was an increase in agalactosylated glycans (p = 0.019), a reduction in tetra-galactosylated (p = 0.021) and highly branched glycans (p = 0.013). There was also an increase from BL to 12- and 24mo in bisecting GlcNAcs (p < 0.001, and p = 0.013, respectively). High-mannose glycans (HM) were also higher at 24mo compared to 12mo (p = 0.035).
In summary, following 2 y of caloric restriction, tetra-sialylated and highly branched glycans decreased, while asialylated and agalactosylated glycans increased; bisecting GlcNAcs rose at both 12 and 24 months, and high-mannose glycans were higher at 24 months.
CR effects on complement C3 N-glycome
Compared to baseline, at 12 and 24mo, complement C3 protein concentrations were significantly lower (p < 0.001, p < 0.001 respectively, Fig. 5). Among the C3-Asn85 glycoforms (glycosylated at the 85th Asparagine residue), compared to baseline, the Man5 (Asn85 N2H5) glycoform was reduced at 24mo (p = 0.002) and the Man6 (Asn85 N2H6) glycoform was elevated at 24mo (p < 0.001). The Man7 (Asn85 N2H7) glycoform was lower at 12mo compared to baseline (p < 0.001) and higher at 24mo compared to 12mo (p < 0.001). Among the C3-Asn-939 glycoforms (glycosylated at the 939th Asparagine residue), compared to baseline, at 12mo and 24mo, the Man8 glycoform (Asn-939 N2H8) was greater (p < 0.001, p = 0.034 respectively), while Man9 (Asn-939 N2H9) was reduced at 12mo (p < 0.001) and 24mo (p = 0.009). Compared to baseline, Man9Glc1 (Asn-939 N2H10: Glucose1Mannose9GlcNAc2) glycoforms were decreased at 12mo and 24mo (p = 0.015, p = 0.046, respectively).
Complement C3 concentration (A) and abundance of distinct C3 glycopeptides B–D glycosylated at asparagine (Asn)-85, and E–G glycosylated at Asn-939) before and after the 2y CR protocol in CALERIE2. All box plots show concentrations of total protein C3 and relative abundance of C3 glycopeptides at baseline, 12mo and 24mo of the CALERIE study in n = 26 participants. Data were evaluated using mixed models with visit month as a repeated measure, participant as a random effect, baseline values, sex, and age as covariates, followed by FDR correction. If the FDR corrected p-values were significant, multiple comparison tests (Tukey’s) were conducted, and significant p-values, if any, were highlighted in red.
In summary, complement C3 protein levels were significantly lower at 12 and 24 months, with distinct changes in its glycoforms: Man5 decreased and Man6 increased at 24 months, while Man7 fluctuated over time; at Asn-939, Man8 increased, Man9 decreased, and Man9Glc1 was reduced at both time points.
Association between GlycAge and other epigenetic clocks
Using this subset of n = 26, Fig. 6A, B depicts a comparison of chronological age to previously published epigenetic40 and non-epigenetic41 clock/biological age data from CALERIE2, along with the GlycAge index. Non-epigenetic clocks included KDM (Klemera–Doubal Method) Biological Age, PhenoAge, and HD (Homeostatic Dysregulation), three methods to quantify biological aging using blood chemistry and clinical data41. KDM Biological Age estimates the chronological age at which an individual’s physiology would be considered normal, and a KDM age that’s higher than chronological age indicates faster aging, and a lower than chronological age indicates slower biological aging. PhenoAge, on the other hand, predicts the chronological age at which an individual’s mortality risk would be typical in a reference population. PhenoAge higher than chronological age suggests greater/faster biological aging. Finally, Homeostatic Dysregulation (HD) measures how different an individual’s physiology is from a healthy reference group, with higher values indicating more advanced biological aging.
A Chronological age, IgG GlycAge, DNA methylation-based epigenetic clocks/ages (PC-GrimAge and DunedinPACE), and B non-epigenetic clocks (PhenoAge and HD score) at baseline, 12, and 24mo of the CR intervention. C A correlation matrix depicting repeated measures correlation coefficients between several epigenetic and non-epigenetic clock ages, IgG GlycAge, and chronological age at baseline, months 12 and 24. Any combinations of parameters not plotted (e.g., IgG GlycAge and HD.Clock), and all blank spaces did not have significant correlations; only r values with p < 0.05 are shown. Green indicates positive correlation, and red indicates negative correlation. PC – principal components.
While PC-GrimAge (an epigenetic clock) appears to increase with CR, DunedinPACE (also an epigenetic marker that measures pace of ageing) appears to decrease, like HD score and IgG GlycAge (these were not tested for significance). While all the epigenetic age clocks were positively correlated with each other (Fig. 6C) and with chronological age, IgG GlycAge, DunedinPace, and the non-epigenetic age clocks were not. Further, IgG GlycAge was inversely associated with both PC-GrimAge and PC-Skin and Blood Clocks, the latter both principal component-based versions of the original epigenetic clock ages – GrimAge42 and Skin and Blood Clocks43.
Associations between GlycAge and immune/metabolic parameters
Repeated measures correlation analysis revealed that GlycAge was significantly positively correlated with TGF-β, ICAM1, PDGF-AB, and TNF-α, while being inversely associated with HOMA-B (Fig. 7A–E). In mixed models where change in GlycAge was evaluated with the addition of inflammatory biomarkers as covariates, GlycAge sustained its significant difference between 12 and 24mo with TNFα as covariate (GlycAge p = 0.030, TNFα p = 0.606) and with ICAM1 as covariate (GlycAge p = 0.014, ICAM1 p = 0.016), but not with TFGβ as covariate (GlycAge p = 0.181, TFGβ p = 0.183).
A–E Repeated measures correlations (each color is one participant from n = 26) between GlycAge and immune parameters of interest at baseline, and 12 and 24mo of CR. Correlation coefficient r and corresponding p-values are shown, along with a repeated measures bivariate regression β-estimate and corresponding p-value.
Skeletal muscle transcriptomic changes in glycosylation processes due to CR
Figure 8 shows transcriptional changes in skeletal muscle glycogenes; CALERIE2 global DGE data were previously reported44. Compared to BL, at 24mo, muscle expression was greater for CHST3, a heparan sulfate processing enzyme (p < 0.01) and HSPA5 an ER resident Hsp70 chaperone45 involved in UPR (p < 0.05) (Fig. 8A). The largest number of differentially expressed genes in glycogene-related processes occurred in the ER and Golgi body, as expected, since the ER and the Golgi apparatus are responsible for most glycosylation processes (Fig. 8B). While N- and O-glycosylation processes were only ~1% of the total changes observed, glycosaminoglycans (GAGs) were ~9%.
A shows glycogenes that were differentially expressed over the two-year CR (linear) at p < 0.05 threshold (CHST3 is the exception with a p-value of <0.01). The standardized beta values plotted and listed are positive if their expression was higher in CR at 24mo compared to baseline, and inverse if lower at 24mo compared to baseline. B depicts the percent of differentially expressed secretory pathway-related genes that are associated with different glycosylation processes, which are overexpressed in CR based on beta values in (A). GAGs – glycosaminoglycans/proteoglycans, ER endoplasmic reticulum.
Discussion
In this pilot study of 26 participants who underwent 24 months of CR, detailed glycosylation profiling indicates CR increased IgG galactosylation, reduced high-branched and highly sialylated glycans in plasma, reduced complement C3 protein levels, and affected several high-mannose and glucosylated C3 glycans, along with a reduction in GlycAge. Furthermore, GlycAge changes were associated with a few other established inflammation- and aging-related biomarkers, demonstrating the potential of glycans in assessing the impact of CR on biological ageing (a summary is provided in Supplementary Table 5). In addition, it is possible to speculate that the skeletal muscle transcriptome changes could have resulted from CR's impact on the ER at the transcriptional level. Specifically, after 2 y of CR, skeletal muscle expression of HSPA5, the binding immunoglobulin protein coding gene (involved in UPR), was upregulated.
With CR, GlycAge, an IgG-glycan-based biological age clock, was reduced from 12mo to 24mo, during the period of weight stability. GlycAge was not associated with epigenetic clock ages. However, in this subset of 26 participants, none of the examined epigenetic clocks were reduced by CR intervention, except for DunedinPACE, which approximates a pace of aging40. Consistent with an inflammaging biomarker46, GlycAge was positively associated with various inflammatory biomarkers (TGFβ1, TNFα, PDGF-AB, and ICAM1), which are known to increase with age and aging-related diseases47. CR also led to an increase in IgG galactosylation, an IgG-glycan feature usually associated with younger age and IgG’s anti-inflammatory potential48. Surprisingly, an increase in IgG bisecting GlcNAc was also observed, a feature usually associated with increased pro-inflammatory IgG potential21, which increases with aging49. This suggests that while some aging-related changes in the glycome are affected by CR, others may not be, or perhaps need a greater percentage of CR.
Our findings are consistent with others, where extensive weight loss induced by a 3-week low-calorie diet, followed by bariatric surgery, has been shown to alter IgG glycosylation and reduce inflammageing50. In the DIOGENES study51 (n = 1850), the 8-week weight loss period reduced IgG agalactosylation, but did not affect galactosylation. However, at the end of the 6-month weight maintenance period, there was an increase in IgG di-galactosylation and an increase in bisecting GlcNAc. Similarly, in the CALERIE2 study, the initial baseline – 12mo period, when the weight loss was maximal (~−10kg), IgG di-galactosylation and GlycAge did not change significantly, but did change during the 12mo to 24mo period, when weight loss was minimal (~+0.5). While the %CR in the 12–24mo period recorded was lower, there was still substantial CR observed in the study population52 and in this cohort, as observed in Fig. 1B, which shows the average CR during the entire 24mo duration. As noted earlier, % CR decreased within the first 20 weeks (5 months) of intervention, and continued to drop till 60 weeks (15 months), when it plateaued. Body weight also continued to decline till about 60 weeks, and then plateaued, reaching a new energy balance equilibrium during the 12–24mo period52. Thus, it is likely that the changes we observe in di-galactosylation and the GlycAge during the 12–24mo period are a function of achieving and maintaining this new equilibrium in energy balance.
Glycosylation of total plasma protein, which includes several acute phase proteins, antithrombins, apolipoproteins, fibrinogens, and immunoglobulins53 showed a decrease in highly sialylated and galactosylated structures following CR intervention, with concomitant increases in asialylated glycans. In addition, there were decreased levels of high-branched (tri-and tetra-antennary) glycans in circulation following the intervention. While glycosylation is cell and tissue-type-specific54, plasma protein glycosylation patterns can provide overall insight into an individual’s immune and metabolic status and can differentiate healthy vs certain diseased states10,53. The reduced branched and highly sialylated glycans are a beneficial change in plasma protein glycome, since high branching and high sialylation of N-glycans are associated with increased inflammation and have been implicated in the development of metabolic diseases55,56,57. The increase in asialylation and agalactosylation, indicative of increased biological aging14 contrasts with the above findings. Hence, certain plasma glycome beneficial changes from the 2 y CR intervention were observed, while certain other biological aging-related processes were not.
Following the 2-year CR intervention, complement C3 concentrations decreased in the cohort we studied. Complement C3 is a critical component of innate and adaptive immunological processes and originates from hepatic and, occasionally, adipose tissue58. C3 concentrations are lower in naturally long-lived populations, e.g., centenarians, and higher in older individuals with metabolic syndrome and abdominal obesity33,59. Based on these prior reports, our results suggest that CR reductions of complement C3 are indicative of improvements in metabolic health and aging processes.
The link between the complement C3 glycome and aging/metabolic diseases is less understood. Here, CR reduced levels of Man9 and Man9Glc1 C3 glycoforms. Complement C3 glycans have been reported to be more “unprocessed” (i.e., with more mannose and glucose residues) in individuals with higher hemoglobin A1c and uncontrolled glycemia-associated vascular complications, compared to healthy counterparts30,34. With 12 months of CR, the Man9 and Man9Glc1 glycoforms were reduced, with reductions continuing over 24 months for the Man9 glycans. The concurrent, 24 months increase in skeletal muscle expression of HSPA5, which encodes BiP, a UPR-associated chaperone, may indicate a means by which CR impacts ER stress through transcriptional and glycosylation processes to improve aging-related processes. CR upregulates chaperone proteins like HSPA5, which assist in protein folding and prevent the accumulation of misfolded proteins, maintaining proteostasis60,61. HSPA5 regulates apoptosis-related proteins, protecting cells from ER stress-induced apoptosis, indicating its cytoprotective role in maintaining cell viability62. Thus, in response to CR, the upregulation of HSPA5 likely serves as a protective mechanism to ensure proper protein folding, prevent apoptosis, and promote cell survival. This, in conjunction with the reduced Man9 and Man9Glc1 C3 glycans, hints at a potential mechanism by which CR influences ER stress, and which is reflected in the complement C3 glycome. However, given the lack of a control group, the small sample size, and the fact that the C3 originates from the liver, whereas the HSPA5 changes are observed in the skeletal muscle, this will remain a speculation until further investigations can elucidate this more definitively.
In conclusion, this was a pilot study limited due to its small sample size consisting of 26 out of 188 completers of the CALERIE2 trial, with an average of >10% CR attainment at 12mo. Furthermore, we only studied the glycome of individuals in the CR group without comparison to the ad libitum group. This can introduce several biases including selection, compliance and attrition biases (we chose individuals with a high %CR, which may not represent the true distribution of CR maintained in the whole study, and does not account for attrition) and interpretation bias (lack of a control group can inflate positive findings in the intervention group, and potential confounding by time-varying factors that cannot be accounted for). The range-scaling step involved in the GlycAge calculation creates additional complexity compared to an ideal plug-and-play formula. Hence, future studies will aim to expand the cohort such that deeper insights with reduced bias can be obtained. Despite these limitations, our findings suggest that glycosylation biomarkers may play a key role in aging and inflammation, warranting further biochemical, genetic, cellular, and animal studies evaluation and validation. Such analyses might reveal diagnostic and therapeutic glycan targets that are useful in monitoring and manipulating biological age. Glyco-engineered IgG proteins, which have desired glycosylation profiles (i.e., more sialylated, more galactosylated, etc.), are being investigated as anti-inflammatory and anti-autoimmune therapeutics63. These might also have anti-aging benefits, and our ability to glyco-engineer therapeutics relies on being able to better understand the effect of CR on the glycome.
Methods
Study design
Details of the CALERIE Phase 2 trial were reported previously64 and experimental design and protocols are archived on the website of the CALERIE Biorepository (https://calerie.duke.edu/sites/default/files/2022-05/phase2_protocol.pdf). Briefly, this multi-center randomized controlled trial included 220 participants randomized in a 2:1 ratio in favor of CR or to an ad libitum (AL) control group64. CR group participants were provided with an intensive behavioral intervention and were prescribed an intake 25% below their total daily energy expenditure (which was equal to their energy intake at baseline). Participants’ daily energy expenditure was estimated using doubly labeled water for four weeks at baseline, and subsequent time points to estimate %CR65.
Participants
The study protocol (No. NCT00427193, registered 25th Jan 2007) received approval from the Institutional Review Boards of three clinical sites — Washington University Medical School (St. Louis, MO), Pennington Biomedical Research Center (Baton Rouge, LA), and Tufts University (Boston, MA) — as well as from the coordinating center at Duke University (Durham, NC). All study participants gave written informed consent. For this pilot investigation, we selected 26 participants from the CR group who completed the trial and who achieved the highest levels of CR (i.e., ~10–40% CR) on average.
Data and biospecimens from the biorepository
Plasma samples from the CALERIE2 study were obtained after approval from the CALERIE Steering Committee and the NIA Aging Research Biobank. De-identified plasma samples were recoded to blind the study personnel conducting analyses to the study time points. These samples are available from the NIA AgingResearchBiobank upon request (https://agingresearchbiobank.nia.nih.gov/studies/calerie/) and were obtained for baseline, 12mo, and 24mo. In addition, from the CALERIE Biorepository, we obtained anthropometrics (age, BMI, body weight, body composition), energy intake, energy expenditure and %CR, immune biomarkers (plasma TNF-α IL6, IL8, MCP1 (Monocyte chemoattractant protein-1), PDGF-AB (platelet-derived growth factor AB), TGFB1 (transforming growth factor – β1), CRP) and clinical variables (plasma HDL, LDL, triglycerides, total cholesterol, cortisol, fasting glucose and insulin, Homeostatic Model Assessment of Insulin resistance, HOMA-IR, Homeostasis Model Assessment of β-cell function (HOMA-β), Insulin-like growth factor binding protein-1 and -3 (IGFBP1 and IGFBP3)) that were collected as part of the study. In addition, DNA methylation-based epigenetic clock information40 and non-epigenetic clock/biological age measures41 previously published (available for all n = 26 participants) were analyzed to evaluate associations with glycomic biological age measures. Finally, skeletal muscle transcriptome data published earlier44 measured for n = 75 participants were also analyzed to identify glycosylation gene changes that were observed with the CR intervention.
Total plasma protein N-glycome analysis
For N-glycan release from total plasma proteins, each plasma sample (10 μL) was aliquoted into 1 mL 96-well collection plates (Waters, Milford, MA, USA) followed by a deglycosylation (release of glycans) procedure. Plasma proteins were denatured by the addition of 20 μl 2% (w/v) sodium dodecyl sulfate (SDS, Invitrogen, Carlsbad, CA, USA), and the sample was incubated at 65 °C for 10 min. After denaturation, 10 μl of 4% (v/v) Igepal-CA630 (Sigma–Aldrich, USA) was added to the samples, and the mixture was shaken for 15 min on a plate shaker (GFL, Germany). N-glycans were released by adding 1.2 U PNGase F (Promega, USA) and incubating overnight at 37 °C.
Fluorescent labeling and HILIC-SPE clean-up of released N-glycans
Released plasma N-glycans were labeled with 2-aminobenzamide (2-AB, Sigma–Aldrich). The labeling mixture consisted of 0.48 mg 2 AB and 1.12 mg 2-picoline borane (2-PB, Sigma–Aldrich) in 25 μl dimethyl sulfoxide (DMSO, Sigma–Aldrich) and glacial acetic acid (Merck, Darmstadt, Germany) (7:3, v/v) per sample. The labeling mixture was added to each sample, followed by incubation at 65 °C for 2 h. Excess reagents and proteins were removed from the samples using solid phase extraction by hydrophilic interaction liquid chromatography (HILIC-SPE) using 0.2 μm wwPTFE 96-well membrane filter plates (Pall, New York, NY, USA). The N-glycans were eluted with water and stored at −20 °C until use.
Hydrophilic interaction liquid chromatography of N-glycans
2AB-Fluorescently labeled glycans were separated on a Waters Glycan Premier BEH amide chromatography column, 150 × 2.1 mm × 1.7 μm BEH particles (Waters, USA), with 100 mM ammonium formate, pH 4.4 as solvent A and ACN as solvent B. A linear gradient of 30–47% ACN (Honeywell, USA) (v/v) at a flow rate of 0.561 ml/min was used for separation in a 32.5 min analytical run. The excitation and emission wavelengths were set to 250 nm and 428 nm, respectively. The chromatograms obtained were separated into 39 peaks (GP1-GP39) (Supplementary Fig. 1). The glycan peaks were analyzed by their elution positions and measured in glucose units, which were then compared to the reference values in the “GlycoStore” database (available at: https://glycostore.org/) for structure assignment66,67. The amount of glycans in each peak was expressed as a percentage of the total integrated area. For plasma glycans, 16 derived features were calculated in addition to 39 directly measured glycan features. (Supplementary Table 1).
Immunoglobulin G (IgG) N-glycome analysis
Samples were randomly distributed across three 96-well plates. To isolate IgG from a 25 μL plasma sample, the CIM® r-Protein G LLD 0.05 mL Monolithic 96-well plate (2 µm channels) (BIA Separations, Slovenia, Cat No. 120.1012-2) was used according to published protocols17. The vacuum-assisted setup utilized a manual system consisting of a multichannel pipette, a vacuum manifold (Pall Corporation, USA), and a vacuum pump (Pall Corporation, USA), with pressure reductions of approximately 5 mmHg during sample application and IgG elution. Following the isolation of IgG, the monolithic plate was stored in 20% (v/v) EtOH in 20 mM TRIS + 0.1 M NaCl, pH 7.4, at 4 °C. Subsequently, 20 μL of isolated IgG was aliquoted in a PCR plate (Thermo Scientific, UK) and dried in a vacuum centrifuge. For IgG N-glycan analysis by capillary gel electrophoresis with laser-induced fluorescence (CGE-LIF), deglycosylation, released N-glycan labeling, and clean-up of labeled IgG N-glycans were performed using modified protocols described elsewhere17,68.
IgG N-glycan release, labeling, and clean up
Isolated IgG samples were dried in a Savant SpeedVac vacuum concentrator, resuspended in 3 μL of 1.66 × PBS (w/v), and denatured in 4 μL 0.5% SDS (w/v) (Sigma–Aldrich, USA), followed by incubation at 65 °C for 10 min. Following incubation, 2 μL of 4% Igepal-CA630 (Sigma–Aldrich, USA) was added to the samples and incubated on a shaker for approximately 5 min. For glycan release, 1 μL of the enzyme mixture (1.2U of PNGase F (Promega, USA) in 1 μL 5× PBS) per sample was added and incubated for 3 h at 37 °C. After incubation, the deglycosylated IgG mix was dried in a vacuum concentrator for 1 h, diluted with 2 μL of ultrapure water, and left on a shaker for approximately 5 min to prepare the labeling mixture for labeling the released N-glycans. The labeling mixture was freshly prepared by combining 2 μL of 30 mM 8-aminopyrene-1,3,6-trisulfonic acid trisodium salt (APTS) (Synchem, DE) in 3.6 M citric acid (Sigma–Aldrich, USA) with 2 μL of 1,2 M 2-picoline borane in dimethylsulfoxide (Sigma–Aldrich, USA) per sample. Mixing was achieved by vortexing several times, followed by a 16 h incubation at 37 °C. The labeling reaction was stopped by adding 100 μL of cold 80% acetonitrile (Carlo Erba, Spain).
For the clean-up procedure, 200 μL of Bio-Gel P-10 slurry per well was added on a 0.2 μm wwPTFE AcroPrep filter plate (Pall Corporation, USA) and used as the stationary phase. The wells containing Bio-Gel P-10 were prewashed with 200 μL ultrapure water and 200 μL 80% cold ACN (v/v) (Carlo Erba, Spain), three times for each step. The samples were loaded into the wells and after a 5-min incubation on a shaker, samples were subsequently washed 5× with 5 × 200 μl 80% ACN containing 100 mM TEA, pH 8,5 (Carlo Erba, Spain/Millipore Sigma, USA) followed by 3 × 200 μl 80% ACN (Carlo Erba, Spain) after 2-min of incubation at room temperature. IgG N-glycans were eluted with a total of 500 μl of ultrapure water after 5-min incubation at room temperature after each step, and combined eluate fractions were stored at –20 °C until use.
Capillary gel electrophoresis N-glycan analysis and data processing
The IgG N-glycoprofiling by capillary gel electrophoresis with laser-induced fluorescence (CGE-LIF) was performed using an Applied Biosystems 3500 Genetic Analyser (Thermo-Fischer Scientific, USA), equipped with a 50-cm-long 8-capillary array (Thermo-Fischer Scientific, USA). Polymer POP-7 (Thermo-Fischer Scientific, USA) was used as a separation matrix in capillaries. In a 96-well MicroAmp Optical 96-Well reaction plate, 2 μl of APTS-labeled N-glycans were combined with 8 μl of HiDi formamide, ensuring a thorough and uniform resuspension. The instrumental method was created by setting the operating parameters as follows: injection time: 9 s; injection voltage: 15 kV; run voltage: 19.5 kV; oven temperature: 60 °C; and working time: 1000 s. The obtained electropherograms were integrated in the same manner into 27 peaks using Waters Empower 3 software. The Glycan composition of each peak has been previously determined17. For IgG glycans, 12 derived features were calculated in addition to 27 directly measured glycan peaks (Supplementary Table 2).
IgG-glycan-based biological age calculation
The IgG-glycan-based biological age index was developed previously by analyzing plasma glycome from ~5100 individuals from diverse populations using a linear gradient separation method23. In total, 906 individuals from the Croatian island Vis (377 men, 529 women), 915 individuals from the Croatian island Korcula (320 men, 595 women), 2035 individuals from the Orkney Islands in Scotland (797 men, 1238 women), and 1261 female twins from the TwinsUK cohort were measured. Their age ranges varied across cohorts, 16–100 years in Orkney, 18–98 in Korcula, 18–93 in Vis, and 27–83 in the TwinsUK cohort. The biological age index was developed as a function of specific galactosylated glycans (GP6, GP14, and GP15 – sialylated, mono and di-galactosylated low-branched glycans) and was found to explain a greater proportion of variance in biological age than telomere length measures. In particular, the lesser galactosylated IgGs were identified to be a strong predictor of biological and chronological age, and inflammaging, while the more galactosylated versions reflected the opposite. These findings establish the IgG glycome as a marker of both inflammaging and aging-related diseases. Since its development, either the IgG-glycan-based biological age index or its chromatogram components (GPs) have been evaluated in various conditions, including diabetes69, cardiovascular disease70, rheumatoid arthritis71, and neurodegenerative diseases72, and demonstrated strong disease predictive capability. Using this23,73, an IgG-glycan-based biological age (GlycAge) was calculated. Since the original formula was published, the IgG-based index has been made into a commercial product called GlycanAge, which uses a proprietary algorithm to calculate it74. As an alternative to the proprietary approach, we used a range-scaling step following the use of the formula, since the use of the formula alone returned values that did not resemble the age span for humans. The following formula (see Equation 1 below) was used as the final step in our analysis to ensure that GlycAge was within the range of the participants in the CALERIE2 study. While range-scaling is commonly used to scale data to a range between 0 and 1, it can also be applied to transform data into any other specified range. This is done by adjusting the data based on its minimum and maximum values, allowing it to be rescaled to a biologically relevant range or any other predefined limits.
Complement component (C3) N-glycome analysis
Complement component C3 was enriched from human plasma following the published method34 with minor adjustments. Briefly, Concanavalin A (Con A)-Sepharose 4B resin (7.5 µL) was preconditioned with a binding buffer (20 mM Tris-HCl, 0.5 M NaCl, pH 7.4) on a 96-well polypropylene filter plate (Orochem Technologies Inc.). Plasma samples (10 µL) diluted with the binding buffer in a ratio of 1:9 were loaded on conditioned resin and incubated for 2 h at 4 °C, while shaking. Matrix was washed with 250 µL of binding buffer three times and incubated in 200 µL of elution buffer for 45 min before glycoproteins were eluted and dried in the vacuum concentrator.
C3 digestion and glycopeptide enrichment
Dried samples were resuspended in 78 µL of 15% (v/v) 2-propanol in 0.1 M ammonium acetate and denatured at 60 °C for 10 min. Subsequently, 2 µL of endoproteinase Glu-C (0.5 U/ µL) was added to samples for incubation overnight at 37 °C. Hydrophilic interaction chromatography-based solid phase extraction (HILIC-SPE) enrichment of glycopeptides was done as previously described34 using 85% acetonitrile (ACN) instead of 90%.
Nano-LC-ESI-MS analysis
Enriched C3 glycopeptides were analyzed using an Ultimate 3000 RSLC nano system (Dionex/Thermo Fisher Scientific) coupled to an Orbitrap Exploris 240 (Thermo Fisher Scientific) using an EASY-Spray source. Samples (1 µL) were loaded onto an Acclaim PepMap C18 trap column (5 × 0.3 mm, 5 μm, 100 Å, Thermo Fisher Scientific) at a flow of 80 µL/min and washed for 3 min. Separation was performed on EASY-Spray PepMap™ RSLC C18 column (75 µm × 150 mm, particle size 3 µm, pore size 100 Å, Thermo Scientific) at a flow of 0.5 µL/min with a linear gradient. The column was maintained at 35 °C. The MS instrument was operated in a data-dependent mode. Samples were ionized at a spray voltage of 2250 V, and full MS1 scans were acquired within m/z 600–2000 with a 60,000 resolution and standard AGC target. The RF level was set to 90% and the maximum injection time was set to automatic. Previously annotated glycopeptides were separated based on the peptide backbone into two clusters with three glycoforms each (Supplementary Fig. 2). Data were extracted and quantified using LacyTools software75.
Immunoturbidimetric C3 assay
The plasma concentration of complement C3 was measured using an immunoturbidimetric method with a commercial reagent kit (Beckman Coulter Inc., Brea, CA, USA) on the AU5800 Series Clinical Chemistry Analyzer (Beckman Coulter Inc., Tokyo, Japan) according to the Manufacturer’s instructions. C3 from the sample reacts with the specific antibody from the reagent and forms insoluble complexes. The quantity of formed complexes is monitored spectrophotometrically, and the absorbance of the complex is proportional to the C3 concentration in the sample. The reference range was 0.79–1.52 g/L.
Statistical analysis
Data analyses were done using JMP Pro 17.2.0 (SAS Institute, Cary, NC) and R Statistical Software (R Studio version 2023.06.1, R version 4.3.1)76. All data were evaluated for normality and missingness. Data were transformed to log, cube root, or Johnson family of distributions (after evaluating several types of transformations) to achieve normality prior to analysis, evaluated using Q–Q plots (to evaluate linearity) and Shapiro–Wilk tests (p > 0.05). For anthropometric parameters, repeated measures mixed model analysis of covariance was used to evaluate the differences between baseline (BL), mid-intervention (12mo), and end of intervention (24mo) using participant as a random effect (Model 1). For all glycan parameters, repeated measures mixed model analysis of covariance was used to evaluate the differences in glycan parameters between baseline, 12mo, and 24mo. using participant as a random effect, baseline, age, and sex as covariates (Model 2), followed by FDR adjustment for multiple comparison correction. Where appropriate, family-wise multiple comparison tests (Tukey’s) were performed to obtain individual time point differences. Bivariate regression with repeated measures, and repeated measures correlation (package rmcorr in R77) were used to evaluate relationships between glycan parameters and other features (epigenetic clocks from Waziry et al.ref. 40, non-epigenetic clocks from Kwon and Belsky41 and immune and metabolic parameters) to account for repeated measures from the same individual at multiple time points. There was missing data in immune and metabolic parameters at baseline, and analyses in these instances were done using only available time points. To evaluate if the change in IgG GlycAge was independent of traditional inflammatory biomarkers, Model 1 was used, with the addition of inflammatory biomarkers as covariates one at a time (TNFα, TFGβ, ICAM1). Skeletal muscle differential gene expression (DGE) analysis was reported by Das et al., comparing 57 CR and 33 AL group individuals from the CALERIE2 trial. The DGE identified the top 1003 genes whose expression changed differentially between BL and 24mo at p < 0.05. Here, we further identified mammalian glycosylation genes (glycogenes) encoding glycosylation machinery enzymes as listed in the Glycoenzyme repository (https://glycoenzymes.ccrc.uga.edu/) that were among the 1003 identified genes from the DGE. Functional enrichment analysis using only the glycogenes was done with g:Profiler, which identified specific glycosylation processes that were different between the CR and AL groups.
Data availability
All data will be available from the AgingResearchBiobank (NIA) and the CALERIE2 repository at Duke.
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Acknowledgements
This research was supported by US National Institute on Aging grants U01AG020487, R33AG070455, and R01AG061378. S.K. was supported by a training grant NHLBIK12HL141956 from the National Institutes of Health. N.E.L. was supported by NIGMH (R35GM119850) and the Novo Nordisk Foundation (NNF20SA0066621), and V.B.K. was funded by R01AG054840 and by P30 AG028716. This work was supported in part by the National Institute on Aging Intramural Research Program (J.D. and L.F.). S.K.D. is supported by the USDA Agricultural Research Service Cooperative Agreement # 8050-10700-002. D.W.B., K.M.H., W.E.K., S.K.D., S.B.R., and C.P. are supported by NIH R01: AG071717; G.L. is supported by the European Research Council (ERC) Synergy grant “GlycanSwitch” (contract no. 101071386), and the European Union’s Horizon Europe research and innovation programme grant “SYNHEALTH” (contract no. 101159018). The content in this article is the sole responsibility of the author and does not necessarily represent the official views of the USDA.
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S.K., G.L. and N.E.L. conceived the study hypothesis, S.K. obtained regulatory approvals and samples from NIA; L.D. generated Complement C3 data; G.L., T.P., H.D., J.S., T.S., and A.F.H. generated all glycomic data; J.K.D., N.B., and L.F. generated transcriptomic data, D.W.B. generated DNA methylation and epigenetic age data; S.K., T.P., and A.F.H. conducted glycomic data analysis, J.K.D. and S.K. conducted transcriptomic data analysis of glycogenes; C.P. had significant input into statistical approaches utilized; S.K. conducted comparative analyses between epigenetic and glycomic data; M.O., K.M.H., W.E.K., V.B.K., S.K.D., S.B.R., and C.P. were involved in conceiving, designing and conducting the original CALERIE2 intervention and data analysis from that study; S.K. wrote the first draft, all authors read, offered critical input and reviewed the final version.
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T.P., H.D., J.S., T.S., A.F.H., and G.L. are working for or have stakes in Genos Ltd. D.W.B. is listed as an inventor on a Duke University and University of Otago invention, DunedinPACE, that was licensed to a commercial entity. S.B.R. serves on the Scientific Advisory Board for Pritikin Intensive Cardiac Rehab.
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Pribić, T., Das, J.K., Đerek, L. et al. A 2-year calorie restriction intervention may reduce glycomic biological age biomarkers – a pilot study. npj Aging 11, 71 (2025). https://doi.org/10.1038/s41514-025-00254-9
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DOI: https://doi.org/10.1038/s41514-025-00254-9
