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Glycomic biomarkers are instrumental for suboptimal health status management in the context of predictive, preventive, and personalized medicine

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Abstract

Objectives

Suboptimal health status (SHS), a reversible borderline condition between optimal health status and disease, has been recognized as a main risk factor for non-communicable diseases (NCDs). From the standpoint of predictive, preventive, and personalized medicine (PPPM/3PM), the early detection of SHS provides a window of opportunity for targeted prevention and personalized treatment of NCDs. Considering that immunoglobulin G (IgG) N-glycosylation levels are associated with NCDs, it can be speculated that IgG N-glycomic alteration might occur at the SHS stage.

Methods

A case–control study was performed and it consisted of 124 SHS individuals and 124 age-, gender-, and body mass index–matched healthy controls. The IgG N-glycan profiles of 248 plasma samples were analyzed by ultra-performance liquid chromatography instrument.

Results

After adjustment for potential confounders (i.e., age, levels of education, physical activity, family income, depression score, fasting plasma glucose, and low-density lipoprotein cholesterol), SHS was significantly associated with 16 IgG N-glycan traits at 5% false discovery rate, reflecting decreased galactosylation and fucosylation with bisecting GlcNAc, as well as increased agalactosylation and fucosylation without bisecting GlcNAc. Canonical correlation analysis showed that glycan peak (GP) 20, GP9, and GP12 tended to be significantly associated with the 5 domains (fatigue, the cardiovascular system, the digestive system, the immune system, and mental status) of SHS. The logistic regression model including IgG N-glycans was of moderate performance in tenfold cross-validation, achieving an average area under the receiver operating characteristic curves of 0.703 (95% confidence interval: 0.637–0.768).

Conclusions

The present findings indicated that SHS-related alteration of IgG N-glycans could be identified at the early onset of SHS, suggesting that IgG N-glycan profiles might be potential biomarker of SHS. The altered SHS-related IgG N-glycans are instrumental for SHS management, which could provide a window opportunity for PPPM in advanced treatment of NCDs and shed light on future studies investigating the pathogenesis of progression from SHS to NCDs.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

ADCC:

Antibody-dependent cellular cytotoxicity

AUC:

Area under the receiver operating characteristic curve

BMI:

Body mass index

CCA:

Canonical correlation analysis

CI:

Confidence interval

COACS:

China suboptimal health cohort study

CVD:

Cardiovascular diseases

DBP:

Diastolic blood pressure

DG:

Derived glycan

Fc:

Fragment crystallizable

FcγRs:

Fragment crystallizable γ receptors

FcγRIIB:

Fragment crystallizable γ receptor IIB

FcγRIII:

Fragment crystallizable γ receptor III

FcγRIIIA:

Fragment crystallizable γ receptor IIIA

FcγRIIIB:

Fragment crystallizable γ receptor IIIB

FPG:

Fasting plasma glucose

FDR:

False discovery rate

GlcNAc:

N-Acetylglucosamine

GP:

Glycan peak

HDL-C:

High-density lipoprotein cholesterol

HILIC:

Hydrophilic interaction liquid chromatography

IgG:

Immunoglobulin G

IS:

Ischemic stroke

LASSO:

Least absolute shrinkage and selection operator

LDL-C:

Low-density lipoprotein cholesterol

NCDs:

Non-communicable diseases

3PM/PPPM:

Predictive, preventive, and personalized medicine

ROC:

Receiver operating characteristic

SBP:

Systolic blood pressure

SD:

Standard deviation

SHS:

Suboptimal health status

T2DM:

Type 2 diabetes mellitus

TC:

Total cholesterol

TG:

Triglycerides

UPLC:

Ultra-performance liquid chromatography

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Acknowledgements

The authors acknowledge the participants and their families who donated their time and effort in hel** to make this study possible

Funding

The study was supported by a grant from the “Bei**g Talents Project (2020A17)”.

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Authors and Affiliations

  1. Bei**g Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, 10 Youanmen, Fengtai District, Bei**g, 100069, China

    **aoni Meng, Biyan Wang, Wei Wang & Youxin Wang

  2. School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai’an, China

    **zhu Xu, Haifeng Hou & Wei Wang

  3. Centre for Precision Health, Edith Cowan University, Perth, Australia

    Manshu Song, Wei Wang & Youxin Wang

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  1. **aoni Meng
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  2. Biyan Wang
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  3. **zhu Xu
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  4. Manshu Song
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  5. Haifeng Hou
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  6. Wei Wang
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  7. Youxin Wang
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Contributions

Youxin Wang and Wei Wang contributed to the conception and design. Material preparation, data collection, and analysis were performed by **aoni Meng, Biyan Wang, **zhu Xu, Manshu Song, and Haifeng Hou. The first draft of the manuscript was written by **aoni Meng, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Youxin Wang and Wei Wang guarantee this work, have full access to all of the data and take responsibility for the integrity of the data.

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Correspondence to Youxin Wang.

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This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Capital Medical University, Bei**g, China (No.2009SY16).

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Abbreviations/explanations for all particular glycans can be found at the Supplementary Table 1.

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Meng, X., Wang, B., Xu, X. et al. Glycomic biomarkers are instrumental for suboptimal health status management in the context of predictive, preventive, and personalized medicine. EPMA Journal 13, 195–207 (2022). https://doi.org/10.1007/s13167-022-00278-1

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