NMR based CSF metabolomics in tuberculous meningitis: correlation with clinical and MRI findings

Parihar, Rashmi; Shukla, Ruchi; Baishya, Bikash; Kalita, Jayantee; Haldar, Rudrashish

doi:10.1007/s11011-021-00860-y

Cited by 5 publications

(2 citation statements)

References 46 publications

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“…As the disease advances, the patients with CNS infectious diseases often experience worsening motor dysfunction and cognitive impairments, and structural damage of the brain may be observed, such as basal meningeal enhancement, hydrocephalus, or cryptococcoma [ 46 ]. Moreover [ 47 ], a brain with structural damage may also be accompanied by severe metabolic disorders [ 46 , 48 ]. The normal central–peripheral regulatory network may be affected by brain damage, resulting in changes in the peripheral system [ 16 , 20 – 23 ].…”

Section: Discussionmentioning

confidence: 99%

A Retrospective Analysis of Central and Peripheral Metabolic Characteristics in Patients with Cryptococcal Meningitis

Qin,

Nong,

Zhu

et al. 2024

Neurol Ther

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Introduction Most current treatment strategies and investigations on cryptococcal meningitis (CM) focus primarily on the central nervous system (CNS), often overlooking the complex interplay between the CNS and the peripheral system. This study aims to explore the characteristics of central and peripheral metabolism in patients with CM. Methods Patients diagnosed with CM as per the hospital records of the Fourth People’s Hospital of Nanning were retrospectively analyzed. Patients were divided into two groups, non-structural damage of the brain (NSDB) and structural damage of the brain (SDB), according to the presence of brain lesions as detected with imaging. Based on the presence of enlarged cerebral ventricles, the cases in the SDB group were classified into non-ventriculomegaly (NVM) and ventriculomegaly (VM). Various parameters of cerebrospinal fluid (CSF) and peripheral blood (PB) were analyzed. Results A significant correlation was detected between CSF and PB parameters. The levels of CSF-adenosine dehydrogenase (ADA), CSF-protein, CSF-glucose, and CSF-chloride ions were significantly correlated with the levels of PB-aminotransferase, PB-bilirubin, PB-creatinine (Cr), PB-urea nitrogen, PB-electrolyte, PB-protein, and PB-lipid. Compared with NSDB, the levels of CSF-glucose were significantly decreased in the SDB group, while the levels of CSF-lactate dehydrogenase (LDH) and CSF-protein were significantly increased in the SDB group. In the SDB group, the levels of PB-potassium, PB-hemoglobin(Hb), and PB-albumin were significantly decreased in the patients with VM, while the level of PB-urea nitrogen was significantly increased in these patients. Conclusion Metabolic and structural alterations in the brain may be associated with peripheral metabolic changes.

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Section: Discussionmentioning

confidence: 99%

A Retrospective Analysis of Central and Peripheral Metabolic Characteristics in Patients with Cryptococcal Meningitis

Qin,

Nong,

Zhu

et al. 2024

Neurol Ther

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“…Lately, an NMR-based CSF metabolomics study ( Parihar et al, 2022 ) for cohorts of 36 TBM patients and 18 HCs found 11 differential metabolites that could distinguish TBM from HC, among which 7 (lactate, glutamate, alanine, arginine, 2-hydroxyisobutyrate, formate, and cis-aconitate) were upregulated, and 4 (glucose, fructose, glutamine, myo-inositol) downregulated in the TBM, compared to HCs. These differential metabolites were able to classify the 3 months treatment result with good sensitivity and specificity (AUC = 0.99).…”

Section: Biomarkers Based On Host Responsesmentioning

confidence: 99%

Biomarker discovery for tuberculosis using metabolomics

Jiang

2023

Front. Mol. Biosci.

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Tuberculosis (TB) is the leading cause of death among infectious diseases, and the ratio of cases in which its pathogen Mycobacterium tuberculosis (Mtb) is drug resistant has been increasing worldwide, whereas latent tuberculosis infection (LTBI) may develop into active TB. Thus it is important to understand the mechanism of drug resistance, find new drugs, and find biomarkers for TB diagnosis. The rapid progress of metabolomics has enabled quantitative metabolite profiling of both the host and the pathogen. In this context, we provide recent progress in the application of metabolomics toward biomarker discovery for tuberculosis. In particular, we first focus on biomarkers based on blood or other body fluids for diagnosing active TB, identifying LTBI and predicting the risk of developing active TB, as well as monitoring the effectiveness of anti-TB drugs. Then we discuss the pathogen-based biomarker research for identifying drug resistant TB. While there have been many reports of potential candidate biomarkers, validations and clinical testing as well as improved bioinformatics analysis are needed to further substantiate and select key biomarkers before they can be made clinically applicable.

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