CSF Metabolomics of Tuberculous Meningitis: A Review

Untargeted metabolomic profiling reveals molecular signatures associated with type 2 diabetes in Nigerians

BACKGROUND

Type 2 diabetes (T2D) has reached epidemic proportions globally, including in Africa. However, molecular studies to understand the pathophysiology of T2D remain scarce outside Europe and North America. The aims of this study are to use an untargeted metabolomics approach to identify: (a) metabolites that are differentially expressed between individuals with and without T2D and (b) a metabolic signature associated with T2D in a population of Sub-Saharan Africa (SSA).

METHODS

A total of 580 adult Nigerians from the Africa America Diabetes Mellitus (AADM) study were studied. The discovery study included 310 individuals (210 without T2D, 100 with T2D). Metabolites in plasma were assessed by reverse phase, ultra-performance liquid chromatography and mass spectrometry (RP)/UPLC-MS/MS methods on the Metabolon Platform. Welch's two-sample t-test was used to identify differentially expressed metabolites (DEMs), followed by the construction of a biomarker panel using a random forest (RF) algorithm. The biomarker panel was evaluated in a replication sample of 270 individuals (110 without T2D and 160 with T2D) from the same study.

RESULTS

Untargeted metabolomic analyses revealed 280 DEMs between individuals with and without T2D. The DEMs predominantly belonged to the lipid (51%, 142/280), amino acid (21%, 59/280), xenobiotics (13%, 35/280), carbohydrate (4%, 10/280) and nucleotide (4%, 10/280) super pathways. At the sub-pathway level, glycolysis, free fatty acid, bile metabolism, and branched chain amino acid catabolism were altered in T2D individuals. A 10-metabolite biomarker panel including glucose, gluconate, mannose, mannonate, 1,5-anhydroglucitol, fructose, fructosyl-lysine, 1-carboxylethylleucine, metformin, and methyl-glucopyranoside predicted T2D with an area under the curve (AUC) of 0.924 (95% CI: 0.845-0.966) and a predicted accuracy of 89.3%. The panel was validated with a similar AUC (0.935, 95% CI 0.906-0.958) in the replication cohort. The 10 metabolites in the biomarker panel correlated significantly with several T2D-related glycemic indices, including Hba1C, insulin resistance (HOMA-IR), and diabetes duration.

CONCLUSIONS

We demonstrate that metabolomic dysregulation associated with T2D in Nigerians affects multiple processes, including glycolysis, free fatty acid and bile metabolism, and branched chain amino acid catabolism. Our study replicated previous findings in other populations and identified a metabolic signature that could be used as a biomarker panel of T2D risk and glycemic control thus enhancing our knowledge of molecular pathophysiologic changes in T2D. The metabolomics dataset generated in this study represents an invaluable addition to publicly available multi-omics data on understudied African ancestry populations.

First detection of bovine tuberculosis by Ziehl-Neelsen staining and polymerase chain reaction at dairy farms in the Lekok Sub-District, Pasuruan Regency, and Surabaya region, Indonesia

Background and Aim

Bovine tuberculosis (TB) is a zoonotic disease of great public health importance, particularly in Indonesia, where control measures are limited or are not implemented. This study aimed to detect the presence of Mycobacterium pathogens in milk samples from dairy cattle in Pasuruan regency and Surabaya City, East Java, using Ziehl-Neelsen acid-fast staining and polymerase chain reaction (PCR).

Materials and Methods

Milk samples were aseptically collected from 50 cattle in the Lekok Subdistrict, Pasuruan Regency, and 44 from dairy farms in the Lakarsantri Subdistrict, Wonocolo Subdistrict, Mulyorejo Subdistrict, and Kenjeran Subdistrict, Surabaya, East Java. To detect Mycobacteria at the species level, each sample was assessed by Ziehl-Neelsen staining and PCR using the RD1 and RD4 genes.

Results

The results of PCR assay from 50 samples in Lekok Subdistrict, Pasuruan Regency showed that 30 samples (60%) were positive for Mycobacterium tuberculosis and two samples (4%) were positive for Mycobacterium bovis, although Ziehl-Neelsen staining did not show the presence of Mycobacterium spp. In the Surabaya region, 31 samples (70.45%) were positive for M. tuberculosis and three samples (6.8%) were positive for M. bovis. Six samples (13.63%) from all PCR-positive samples could be detected microscopically with Ziehl-Neelsen.

Conclusion

The presence of bovine TB in this study supports the importance of using a molecular tool alongside routine surveillance for a better understanding of the epidemiology of bovine TB in East Java.

The diagnosis of tuberculous meningitis: advancements in new technologies and machine learning algorithms

Tuberculous meningitis (TBM) poses a diagnostic challenge, particularly impacting vulnerable populations such as infants and those with untreated HIV. Given the diagnostic intricacies of TBM, there's a pressing need for rapid and reliable diagnostic tools. This review scrutinizes the efficacy of up-and-coming technologies like machine learning in transforming TBM diagnostics and management. Advanced diagnostic technologies like targeted gene sequencing, real-time polymerase chain reaction (RT-PCR), miRNA assays, and metagenomic next-generation sequencing (mNGS) offer promising avenues for early TBM detection. The capabilities of these technologies are further augmented when paired with mass spectrometry, metabolomics, and proteomics, enriching the pool of disease-specific biomarkers. Machine learning algorithms, adept at sifting through voluminous datasets like medical imaging, genomic profiles, and patient histories, are increasingly revealing nuanced disease pathways, thereby elevating diagnostic accuracy and guiding treatment strategies. While these burgeoning technologies offer hope for more precise TBM diagnosis, hurdles remain in terms of their clinical implementation. Future endeavors should zero in on the validation of these tools through prospective studies, critically evaluating their limitations, and outlining protocols for seamless incorporation into established healthcare frameworks. Through this review, we aim to present an exhaustive snapshot of emerging diagnostic modalities in TBM, the current standing of machine learning in meningitis diagnostics, and the challenges and future prospects of converging these domains.

Diagnosis and Management of Tuberculous Meningitis in Children- an Update

UPDATE ON THE DIAGNOSIS AND MANAGEMENT OF TUBERCULOUS MENINGITIS IN CHILDREN: Ronald van Toorn, Regan Solomons Seminars in Pediatric Neurology Volume 21, Issue 1, March 2014, Pages 12-18 Tuberculous meningitis (TBM), the most devastating manifestation of tuberculosis, is often missed or overlooked because of nonspecific symptoms and difficulties in diagnosis. It continues to be an important cause of neurologic handicap in resource-poor countries. Owing to the suboptimal performance of diagnostic tests of TBM, diagnosis relies on thorough history, clinical examination, and relevant investigations. The development of affordable, accurate diagnostic tests for TBM in resource-poor settings remains a priority. Short intensified treatment is safe and effective in both human immunodeficiency virus (HIV)-infected and HIV-uninfected children. Treatment of tuberculous hydrocephalus depends on the level of the cerebrospinal fluid obstruction. Corticosteroids reduce risk of neurodisability and death in HIV-uninfected children. Thalidomide should be considered in children compromised by tuberculosis abscesses and tuberculous-related optochiasmic arachnoiditis. In resource-poor countries, home-based TBM treatment after initial in-hospital stabilization is feasible in carefully selected patients. Early diagnosis and treatment of TBM is the single most important factor determining outcome.

Cerebrospinal fluid analysis: current diagnostic methods in central nervous system infectious diseases

Cerebrospinal fluid (CSF) analysis is an important diagnostic tool for many conditions affecting the central nervous system (CNS), especially CNS infectious diseases. Despite its low specificity, CSF white blood cell counts, CSF protein levels, CSF serum glucose ratio and CSF lactate measurement are useful in differentiating infections caused by distinct groups of pathogens. CSF direct examination and cultures can identify causative organisms and antibiotic sensitivities as well. Adjunctive tests such as latex agglutination, different immunological assays and molecular reactions have great specificities and increasing sensitivities. In this article, some recent diagnostic methods applied to CSF analysis for frequent CNS infections are presented.

Tuberculous Meningitis in Children: Reducing the Burden of Death and Disability

Tuberculous meningitis disproportionately affects young children. As the most devastating form of tuberculosis, it is associated with unacceptably high rates of mortality and morbidity even if treated. Challenging to diagnose and treat, tuberculous meningitis commonly causes long-term neurodisability in those who do survive. There remains an urgent need for strengthened surveillance, improved rapid diagnostics technology, optimised anti-tuberculosis drug therapy, investigation of new host-directed therapy, and further research on long-term functional and neurodevelopmental outcomes to allow targeted intervention. This review focuses on the neglected field of paediatric tuberculous meningitis and bridges current clinical gaps with research questions to improve outcomes from this crippling disease.