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Clinical metabolomics by NMR revealed serum metabolic signatures for differentiating sarcoidosis from tuberculosis

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Abstract

Background

Pulmonary sarcoidosis (SAR) and tuberculosis (TB) are two granulomatous lung-diseases and often pose a diagnostic challenge to a treating physicians.

Objective

The present study aims to explore the diagnostic potential of NMR based serum metabolomics approach to differentiate SAR from TB.

Materials and Method

The blood samples were obtained from three study groups: SAR (N = 35), TB (N = 28) and healthy normal subjects (NC, N = 56) and their serum metabolic profiles were measured using 1D 1H CPMG (Carr-Purcell-Meiboom-Gill) NMR spectra recorded at 800 MHz NMR spectrometer. The quantitative metabolic profiles were compared employing a combination of univariate and multivariate statistical analysis methods and evaluated for their diagnostic potential using receiver operating characteristic (ROC) curve analysis.

Results

Compared to SAR, the sera of TB patients were characterized by (a) elevated levels of lactate, acetate, 3-hydroxybutyrate (3HB), glutamate and succinate (b) decreased levels of glucose, citrate, pyruvate, glutamine, and several lipid and membrane metabolites (such as very-low/low density lipoproteins (VLDL/LDL), polyunsaturated fatty acids, etc.).

Conclusion

The metabolic disturbances not only found to be well in concordance with various previous reports, these further demonstrated very high sensitivity and specificity to distinguish SAR from TB patients suggesting serum metabolomics analysis can serve as surrogate method in the diagnosis and clinical management of SAR.

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Data availability

The data that support the findings of this study has been uploaded on ZENODO (https://zenodo.org/record/ 5,593,941 | https://doi.org/10.5281/zenodo.5593941) and is available without undue reservation for further studies on request to the corresponding authors.

Abbreviations

1D/2D:

One/Two dimensional

3HB:

3-Hydroxy butyrate

ANOVA:

One-way analysis of variance

ATB:

Active Tuberculosis

AUROC:

Area under ROC curve

BCAAs:

Branched‐chain amino acids

BMRB:

Biological Magnetic Resonance Data Bank

CI:

Confidence interval

CPMG:

Carr–Purcell–Meiboom–Gill

CT:

Computerized tomography

ELISA:

Enzyme-linked Immunosorbent Assay

ESM:

Electronic Supplementary Material

FID:

Free induction decay

FT:

Fourier Transformation

HMDB:

The Human Metabolome Database

HSQC:

Heteronuclear Single Quantum Correlation

IGRA:

Interferon-gamma Release Assay

IL:

Interleukin

IQR:

Interquartile range

LDL:

Low-density lipoproteins

LTBI:

Latent Tuberculosis infection

MTB:

Mycobacterium tuberculosis

MVA:

Multivariate analysis

MW:

Molecular Weight

NC:

Normal control

NMR:

Nuclear Magnetic Resonance

NSAID:

Non-steroidal anti-inflammatory drugs

OPLS-DA:

PLS-DA with Orthogonal Signal Correction (OSC)

PCA:

Principal component analysis

PLS-DA:

Projection to least-squares discriminant analysis

POC:

Point-of-care

PTR:

Phenylalanine-to-tyrosine ratio

RCF:

Relative centrifugal force

ROC:

Receiver operating characteristic curve

RPM:

Revolutions per minute

SAR:

Sarcoidosis

SD:

Standard deviation

TB:

Tuberculosis

TCA:

Tricarboxylic acid cycle

TMAO:

Trimethylamine-N-oxide

TOCSY:

Total Correlation Spectroscopy

TST:

Tuberculin skin test

VIP:

Variable importance in projection

VLDL:

Very Low-density lipoproteins

WHO:

World Health Organization

μL/ml:

Microliter/Milliliter

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Acknowledgements

DK acknowledges the Department of Medical Education, Govt. of Uttar Pradesh for supporting the High Field NMR Facility at Centre of Biomedical Research, Lucknow, India. RR acknowledges the receipt of a SRF fellowship from CSIR, INDIA.The manuscript communication number is IU/R&D/2023-MCN0002213.

Funding

This research received intramural funding from the Centre of Biomedical Research (CBMR), Lucknow (Project No. CBMR/IMR/0010/2021 | PI: Dr. Dinesh Kumar).

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Author notes

  1. Saurabh Chaturvedi

    Present address: Department of Medical Laboratory Technology, School of Allied Health Sciences, Delhi Pharmaceutical Sciences and Research University, Sector III, Pushp Vihar, M.B. Road, New Delhi, 110017, India

  2. Avinash Jain

    Present address: Department of Clinical Immunology and Rheumatology, SMS Medical College, Jaipur, India

  3. Mohit Kumar Rai and Sachin Yadav have contributed equally.

Authors and Affiliations

  1. Department of Clinical Immunology and Rheumatology, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, UP, 226014, India

    Mohit Kumar Rai, Sachin Yadav, Avinash Jain, kritika Singh, Durgesh Dubey, Harshit Singh, Saurabh Chaturvedi, Durga Prasanna Misra & Vikas Agarwal

  2. Department of Chemistry, Integral University, Lucknow, UP, 226026, India

    Sachin Yadav & Abdul Rahman Khan

  3. Centre of Biomedical Research (CBMR), Lucknow, UP, 226014, India

    Amit Kumar, Ritu Raj, Durgesh Dubey, Anupam Guleria & Dinesh Kumar

  4. Department of Pulmonary Medicine, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, UP, 226014, India

    Alok Nath

  5. Immuno Biology Lab, Translational Health Science and Technology Institute, Faridabad, HR, 121001, India

    Harshit Singh

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  1. Mohit Kumar Rai
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Contributions

AJ and VA conceptualized the idea; AJ, MKR, HS, KS, SC, AN, DPM, VA identified the patients and collected the samples. AK, RR, DD, SY, and ARK prepared the NMR samples and performed the NMR experiments; MKR, SY, ARK and DK: NMR data analysis and multivariate/univariate statistics. DK and AG wrote the first draft. AJ, VA and DK critically reviewed and revised the draft. All authors read and verified the manuscript.

Corresponding authors

Correspondence to Avinash Jain, Vikas Agarwal or Dinesh Kumar.

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Supplementary Information

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11306_2023_2052_MOESM1_ESM.doc

Supplementary file1 Figure S1: Multivariate exploratory ROC analysis based on eighteen discriminatory metabolites of diagnostic potential. Figure S2: The box-cum-whisker plots showing quantitative differences for eighteen discriminatory serum metabolic entities between SAR and TB. Table S1: The serum metabolic features indexed top to bottom based on variable importance in projection (VIP) score values derived from the PLS-DA analysis of complete and pruned CPMG data matrix. Table S2: The spectral features strategically selected from the 0.02 ppm binned CPMG data matrix for univariate statistical and ROC curve analysis. Table S3: Serum metabolic features identified for statistically significant difference between the study-groups using ANOVA statistics. (DOC 8617 KB)

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Rai, M.K., Yadav, S., Jain, A. et al. Clinical metabolomics by NMR revealed serum metabolic signatures for differentiating sarcoidosis from tuberculosis. Metabolomics 19, 92 (2023). https://doi.org/10.1007/s11306-023-02052-4

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