







































_____________________________________________________________________________________________________ 
 
*Corresponding author: E-mail: calebangulu1@gmail.com; 
 
Cite as: Mamman, Godiya Peter, Caleb Ndako Angulu, Aminu, Ado, Mohammed Evuti Mahmud, Ruqayyatu Aliyu, Samuel 
Angulu, and Adeoye Daniel Owoyale. 2025. “Metabolomics Role in Health and Disease: Current Status and Future Directions”. 
Asian Journal of Immunology 8 (1):190-206. https://doi.org/10.9734/aji/2025/v8i1171. 
 

 
 

Asian Journal of Immunology 
 
Volume 8, Issue 1, Page 190-206, 2025; Article no.AJI.140476 
  

 
 

 

 

Metabolomics Role in Health and 
Disease: Current Status and  

Future Directions 
 

Godiya Peter Mamman a, Caleb Ndako Angulu b*,  

Aminu, Ado b, Mohammed Evuti Mahmud c,  

Ruqayyatu Aliyu a, Samuel Angulu d  

and Adeoye Daniel Owoyale e 
 

a Department of Biology Nigeria Army University, Biu Born State, Nigeria. 
b Department of Microbiology, Federal University Dutsin-Ma Katsina State, Nigeria. 

c Department of Natural and Applied Sciences, College of Nursing Sciences, Bida, Niger State, 
Nigeria. 

d Department of Basic and Natural Sciences, Niger State, College of Agriculture Mokwa,  
Niger State, Nigeria. 

e School of Life Sciences, Department of Microbiology, Federal University of Technology, 
Minna, Niger State, Nigeria. 

 
Authors’ contributions  

 
This work was carried out in collaboration among all authors. All authors read and approved the final 

manuscript. 
 

Article Information 

 
DOI: https://doi.org/10.9734/aji/2025/v8i1171  

 

Open Peer Review History: 

This journal follows the Advanced Open Peer Review policy. Identity of the Reviewers, Editor(s) and additional Reviewers,  
peer review comments, different versions of the manuscript, comments of the editors, etc are available here: 

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Received: 01/06/2025 
Published: 29/07/2025 

 
 

  

Review Article 

https://doi.org/10.9734/aji/2025/v8i1171
https://pr.sdiarticle5.com/review-history/140476


 
 
 
 

Mamman et al.; Asian J. Immunol., vol. 8, no. 1, pp. 190-206, 2025; Article no.AJI.140476 
 
 

 
191 

 

ABSTRACT 
 

The field of metabolomics involves the high-throughput identification and measurement of all 
endogenous and exogenous low-molecular-weight (<1kDa) small molecules or metabolites in a 
biological system by analyzing the metabolome in cells, biofluids, tissues, or organisms. 
Metabolome-wide association studies (MWAS), metabolic phenotyping, single-cell epidemiologic 
population studies, precision metabolomics, and in combination with other omics fields like 
integrative omics, biotechnology, and bioengineering are some of the applications of metabolomics 
in health and disease scenarios. Metabolomics and its potential to enhance human health, along 
with its developments and implications for pharmacometabolomics, lifespan, cancer, and the 
exposome, are the main focus of this review. Metabolomic profiles will facilitate the development 
and improvement of therapeutic strategies to treat human diseases. Future years will see a rise in 
the application of metabolomics to drug development, aging, and disease monitoring and 
diagnosis. Its potential extends to food science, and environmental research.   Through clinical 
metabolomics studies, which may also uncover diagnostic biomarkers that predict disease risk, 
cardiometabolic disorders can be better understood. Metabolomics is already being applied in 
biomedical settings to develop drugs by using acylcarnitines, phospholipids, genomes, and branch-
chain amino acids that are specific to monitoring the emergence of metabolic diseases such as 
obesity and diabetes. Indicators derived from metabolomics should be evaluated for therapeutic 
efficacy and adaptability, and their optimal application in large clinical settings should be the focus 
of future research. 

 

 
Keywords: Metabolomics; health; disease; metabolites; Biomarker. 
 

1. INTRODUCTION 
 

The rapidly developing field of metabolomics 
aims to accurately identify and measure all 
endogenous and exogenous low-molecular-
weight (<1kDa) small molecules or metabolites in 
a biological system in a high-throughput way. 
The proteome, genome, lifestyle, environment, 
drugs, and underlying disease all have an 
upstream influence on the composition of these 
endogenous compounds (Zhou and Zhong, 
2022). The study of metabolites, which are tiny 
chemical entities involved in biological systems' 
cellular processes, is known as metabolomics. It 
is used in molecular and personalized healthcare 
in clinical chemistry, transplant monitoring, 
newborn screening, pharmacology, and 
toxicology (Zhou and Zhong, 2022). The 
development of analytical techniques and 
bioinformatics has led to the emergence of 
metabolomics, a state-of-the-art omics 
technique. In order to identify and describe the 
metabolome, the metabolomics approach 
typically uses advanced analytical chemistry 
tools like nuclear magnetic resonance (NMR) 
and mass spectrometry (MS) in conjunction with 
different chromatographic techniques, such as 
gas chromatography (GC-MS) or liquid 
chromatography (LC-MS), with an emphasis on 
molecules smaller than 1500 Da found in cells, 
organs, tissues, or biofluids (Wishart, 2018; Sun 
et al., 2019).  

The new field of medical genomics offers cutting-
edge technological instruments for identifying 
genetic susceptibilities to diseases. But 
metabolomics allows us to go further by linking 
disorders and defects in gene expression to the 
pathological phenotype. Although the concept of 
"clinical metabolomics" was first introduced in 
2008, the term was first used in the literature in 
2009 (Le Gouellec et al., 2023). The goal of 
clinical metabolomics is to identify metabolic 
signatures in bodily fluids (plasma, urine, saliva, 
cerebrospinal fluid, etc.) or tissues that are 
impacted by genetics, epigenetics, dietary 
patterns, environmental factors, and behavior in 
order to evaluate and forecast a subject's health 
and disease risk (Ceglarek et al., 2009; Mallu et 
al., 2021). A collection or combinations of 
impacted metabolites make up metabolic 
signatures (Wishart, 2018). 
 
Metabolomics' use in both health and disease 
has grown in importance. Based on a particular 
metabolic signature, metabolomics makes it 
possible to identify biomarkers for disease 
diagnosis, prognosis, and response to treatment 
(Cheng et al., 2012). The pathophysiology of 
complex diseases like cancer, 
neurodegenerative diseases, cardiovascular 
diseases, and metabolic syndrome can be 
largely explained by such metabolic profiles 
(Neergaard et al., 2017; Mallu et al., 2021; Vo 
and Trinh, 2024). Furthermore, metabolomics is 



 
 
 
 

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crucial to precision medicine because it allows 
for the identification of individual metabolic 
variations that can support tailored treatment 
approaches (Beger et al., 2016; Nielsen, 2017).  
According to that perspective, metabolomics is 
now a potent addition to personalized medicine, 
bringing fresh insights into the mechanisms 
underlying illnesses. Biomarkers are important in 
healthcare, particularly in diagnosis, prognosis, 
and treatment monitoring, since they are the 
quantifiable foundation of biological states and 
conditions (Califf, 2018). Biomarkers are crucial 
in diagnostics because they enable early and 
precise disease diagnosis, frequently before 
symptoms appear. This is especially true for 
diseases like cancer, where early intervention 
greatly enhances results. Particular metabolite 
profiles, for instance, can help distinguish 
between disease stages or subtypes in order to 
improve diagnostic accuracy and support patient 
stratification (Prat et al., 2021; Deng et al., 2022).  
 
Identifying markers of disease severity or 
recurrence can help healthcare providers predict 
disease trajectory and appropriately plan 
interventions, especially for chronic and complex 
diseases where metabolic biomarkers may 
indicate the risk for complications or rapid 
development of the disease process. Biomarkers 
help monitor treatments by providing real-time 
responses in patients, allowing for the 
examination of efficacy and possible treatment-
related side effects, allowing for modifications of 
therapeutic regimens for maximum efficacy with 
minimum side effects (Vo and Trinh, 2024). 
 
According to Vargas and Harris (2016), 
biomarkers play a significant role in advancing 
precision medicine by facilitating more 
individualized, proactive, and efficient 
approaches to healthcare delivery. New technical 
tools for identifying genetic susceptibilities to 
diseases have emerged with the recent rise of 
medical genomics. However, by linking the 
pathological phenotype to disorders and defects 
in gene expression, metabolomics allows us to 
go further. While the concept was first introduced 
in 2008, the term "clinical metabolomics" was 
first used in the literature in 2009 (Damiani et al., 
2020).  
 
In clinical metabolomics, metabolic signatures in 
bodily fluids (plasma, urine, saliva, cerebrospinal 
fluid, etc.) or tissues which are influenced by 
genetics, epigenetics, dietary patterns, 
environmental factors, and behavior are 
identified in order to evaluate and forecast a 

subject's health and disease risk (Cheng et al., 
2012). A group or combination of metabolites 
that are impacted make up metabolic signatures. 
In addition to being studied peripherally, as in 
biofluids like blood plasma, which contains a 
variety of metabolites that reflect organ metabolic 
activity and offer important pathophysiological 
information, the metabolome can also be studied 
intracellularly, exposing functional abnormalities 
at the cellular level (Vo and Trinh, 2024).  
 
Human plasma contains both endogenous and 
environmental metabolites. A number of factors, 
including dietary patterns, gut microbiota, and 
lifestyle choices (such as smoking or physical 
activity), greatly impact the metabolome's 
variability, which is partially inherited. Individual 
metabolic variability is mostly influenced by diet 
and microbiota, which makes each person's 
metabotype distinct (Vo and Trinh, 2024). 
Nonetheless, a person's metabotype stays 
largely constant over time if there are no notable 
changes in their health. 
 
The analysis of metabolites is predicated on 
two broad methods:  
 

1. Targeted metabolomics is the study of 
identifying a particular group of 
metabolites. 

2. Untargeted metabolomics compares and 
identifies as many metabolites as possible 
between samples, including unknown 
ones, using an objective method. The latter 
strategy allows for the routine detection of 
numerous metabolite features peaks that 
correspond to individual ions with unique 
mass-to-charge (m/z) ratios and retention 
times (RT) using LC/MS-based techniques 
(Vo and Trinh, 2024).The particular 
intended application will determine which 
of these methods is best.  

 
The general trends of recent discoveries and 
applications of metabolomics-based biomarkers, 
as well as their revolutionary effects on 
personalized medicine and precision health, are 
summarized in this review. It is becoming more 
feasible to find disease biomarkers, even for 
complex conditions like cancer, 
neurodegenerative diseases, cardiovascular 
diseases, and metabolic syndrome, thanks to 
technological advancements and metabolomics 
analysis capabilities. Although early diagnosis 
and classification are greatly enhanced by these 
biomarkers, they also enable a much more 
nuanced understanding of individual variability in 



 
 
 
 

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disease progression and response to treatment. 
This review's objective is to examine these new 
metabolomics biomarker discoveries with an 
emphasis on how they may help with healthcare 
personalization. The translational development of 
metabolomics for use in mainstream clinical 
practice is also covered, with particular emphasis 
on how metabolomics can transform healthcare 
from a one-size-fits-all model to a highly 
individualized one where any health risk can be 
promptly identified and treatment can be tailored 
for optimal efficacy. Methods for metabolomics 
research: There are two main methods for 
metabolomics research: 
 

1. Mass spectrometry (MS)-based 
metabolomics. 

2. Nuclear magnetic resonance (NMR) 
spectroscopy. 

 
By combining these methods, metabolome 
coverage can be improved by addressing many 
of their individual shortcomings (Wilkins and 
Trushina, 2018). While NMR is useful for 
identifying core metabolites in important 
metabolic pathways, MS-based metabolomics 
offers a broad metabolite coverage that includes 
polar metabolites as well as non-polar lipids and 
is particularly good at detecting low-abundance 
metabolites (Wilkins and Trushina, 2018). 
Additionally, NMR allows for the study of a large 
number of participants due to its low cost, high 
throughput, and analytical power (Surendran et 
al., 2022).  
 
Although the discovery of new biomarkers in 
metabolic pathways has contributed to the 
pathogenetic understanding of disease 
pathways, untargeted metabolomics has the 
potential to identify metabolites that may not be 
recognized due to their absence from the 
software library (Odom and Sutton, 2021). 
 

2.  BIOMARKERS OF METABOLIC AND 
CARDIOVASCULAR DISEASES USING 
METABOLOMICS 

 

2.1 Cardiovascular Biomarkers 
 

The metabolomics of cardiovascular disease 
have identified lipid biomarkers and other 
metabolites that describe key pathophysiological 
characteristics, such as inflammation, oxidative 
stress, and disrupted lipid metabolism 
(Upadhyay, 2015; Vona et al., 2019). In addition 
to helping to track the progression of the disease, 
these biomarkers have excellent diagnostic and 

prognostic values for identifying individuals who 
are at risk of cardiovascular diseases (CVD). By 
offering details on alterations in lipid metabolism 
that result in atherosclerosis and CVD, lipid 
biomarkers play a crucial role in determining the 
risk of CVD. Higher levels of ceramides have 
been linked to heart failure and atherosclerosis 
(Vona et al., 2019), indicating their involvement 
in inflammatory pathways and cellular apoptosis. 
More recently, certain lipid metabolites have 
emerged as novel biomarkers in atherosclerosis. 
CVD is linked to other non-lipid metabolites, 
including betaine (Millard et al., 2018), branched-
chain amino acids (BCAA) (Doestzada et al., 
2022), and trimethylamine N-oxide (TMAO) (Park 
et al., 2019). As a byproduct of the gut 
microbiota's metabolism of carnitine and choline, 
TMAO increases the amount of cholesterol that 
deposits in arterial walls, increasing the risk of 
atherosclerosis (Fig. 1) (Zhu et al., 2020). 
 
Cardiovascular problems are linked to elevated 
BCAA levels, which are known to cause insulin 
resistance and metabolic syndrome (Lynch and 
Adams, 2014). Betaine may help prevent heart 
disease because it is a metabolite that is 
involved in the metabolism of homocysteine, 
which is linked to improved endothelial function 
and decreased inflammation (Zhao et al., 2018). 
In addition to improving CVD risk prediction, 
these biomarkers offer potential targets for 
therapeutic intervention, which aids in the 
development of more individualized and 
preventive strategies for cardiovascular health. 
Clinicians can better risk-stratify patients, track 
the course of the disease, and improve their 
treatment plans by characterizing these particular 
lipid and metabolic biomarkers. 
 

2.2 Metabolic Syndrome and Diabetes  
 
In diabetes and metabolic syndrome, biomarkers 
based on metabolomics provide information 
about insulin resistance, obesity, and overall 
metabolic dysfunction that can be linked to     
either of these conditions (Park et al., 2015). 
Indeed, it has been suggested that biomarkers 
improve metabolic health management by 
enabling early detection, focused intervention, 
and therapeutic response monitoring. Insulin 
resistance and general metabolic dysfunction    
are modulated by amino acid biomarkers               
(Gar et al., 2018). High levels of the BCAAs 
leucine, isoleucine, and valine are frequently 
linked to insulin resistance and are observed                
in metabolic syndrome and type 2 diabetes     
(T2D) (Andersson-Hall et al., 2018). Because 



 
 
 
 

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BCAA interferes with insulin signaling, muscle 
cells are unable to absorb glucose as well                  

as lipids build up (Fig. 2) (Vanweert et al.,              
2022). 

 

 
 

Fig. 1. TMAO's function in the development and formation of atherosclerotic lesions. High 
levels of TMAO in the bloodstream are essential for the development of foam cells and 
endothelial dysfunction; TMAO can stimulate platelets and encourage the formation of 

thrombi, which increases the risk of rupture of the atherosclerotic plaque Copyright Wiley 
(2020) (Zhu et al., 2020) 

 

 
 

Fig. 2. Diagrammatic representation of the processes that connect insulin resistance and 
BCAA catabolism. GLUT4 glucose transporter type 4, IRS-1 insulin receptor substrate-1, PDH 

pyruvate dehydrogenase complex, S6K ribosomal S6 kinase, mTOR mammalian target of 
rapamycin complex, and BCAA branch-chain amino acids. (Vanweert et al., 2022). Copyright 

Nature Publishing Group (2022) 



 
 
 
 

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A higher risk of type 2 diabetes has also been 
linked to aromatic amino acids like phenylalanine 
and tyrosine, which are a sign of impaired amino 
acid metabolism in obesity and insulin resistance 
(Luo et al., 2020). Since impaired lipid 
metabolism is one of the factors contributing to 
insulin resistance and systemic inflammation, 
lipid biomarkers are essential in the development 
of diabetes and metabolic syndrome. High levels 
of obesity are associated with free fatty acids 
(FFAs), which exacerbate inflammation and 
insulin resistance (Marko et al., 2024). 
 
According to Roszczyc-Owsiejczuk et al. (2021), 
ceramides, a class of sphingolipids, are closely 
linked to lipid-induced cellular stress and 
apoptosis and are implicated in insulin 
resistance. Ceramides are a potential target for 
therapy because they are predictive of metabolic 
dysfunction and the onset of type 2 diabetes 
(Chaurasia et al., 2021). Metabolites of the 
tricarboxylic and glycolytic acid cycles are 
examples of additional biomarkers. The end 
products of glycolysis, lactate and pyruvate, have 
been found to be elevated in metabolic 
syndrome, indicating a disruption in the 
metabolism of glucose (Rabinowitz and 
Enerbäck, 2020). Insulin resistance-related 
mitochondrial dysfunction and elevated levels of 
tricarboxylic acid cycle intermediates, such as 
citrate and succinate signaling, have been linked 
to disruptions in energy metabolism in obese 
individuals (Ives et al., 2020).  
 

Additionally, biomarkers like these are crucial for 
the early detection of metabolic diseases and 
have bearing on individualized treatment plans. 
In addition to identifying individuals at high risk 
for diabetes and metabolic syndrome, such 
surveillance of particular metabolic signatures 
can give clinicians the ability to track the 
progression of the disease in real time, allowing 
them to adjust their intervention to maximize 
metabolic health (Ives et al., 2020). 
 

3. METABOLOMICS-BASED 
BIOMARKERS FOR CANCER  

 

3.1 Changes in Metabolism in Cancer 
 

Compared to normal cells, cancer cells exhibit 
significant metabolic reprogramming that enables 
them to invade, multiply, and survive quickly 
(Schiliro and Firestein, 2021). This type of 
metabolic change has been referred to as the 
"Warburg effect" (Liberti and Locasale, 2016; 
DeBerardinis and Chandel, 2020). Even when 

oxygen is present, aerobic glycolysis is preferred 
over oxidative phosphorylation. Due to this 
process, cancer cells generate massive amounts 
of lactate, which alters the tumor 
microenvironment's composition and encourages 
growth and immune evasion (Beloribi-Djefaflia et 
al., 2016). 
 

Furthermore, in order to maintain high cell 
division rates, cancer cells exhibit altered lipid 
metabolism, amino acid dependence (Lieu et al., 
2020), and increased nucleotide synthesis 
(Mullen and Singh, 2023). For identifying specific 
biomarkers linked to these specific cancer 
changes in metabolism, metabolomics has 
proven to be very helpful. According to Danzi et 
al. (2023), metabolomics profiling of metabolites 
in blood, tissue, and other biofluids reveals 
metabolic signatures linked to cancer that can 
differentiate cancerous cells from healthy cells. 
Some oncometabolites, like fumarate, succinate, 
and 2-hydroxyglutarate (2-HG) in gliomas, act as 
genetic mutations in metabolic enzymes in 
addition to being biomarkers for particular types 
of cancer (Sciacovelli and Frezza, 2016; Liu and 
Yang, 2021).  
 
Potential treatment targets are revealed by these 
tumor-specific metabolites, which provide insight 
into tumor metabolism. Additionally, the 
biomarkers obtained through metabolomics 
enable the advancement of personalized and 
precision oncology by facilitating early cancer 
diagnosis, treatment response, and disease 
surveillance (Mateo et al., 2022). Through 
metabolomics, this gives physicians the ability to 
see how cancer cells alter their metabolic activity 
in real time as a treatment progresses; as a 
result, these facts open the door to targeted 
interventions and individualized therapeutic 
approaches. 
 

3.2 Important Biomarkers in Cancer 
 

In cancer diagnosis, prognosis, and treatment 
targeting, biomarkers particularly 
oncometabolites are important (Kes et al., 2020). 
Mostly caused by mutations in metabolic 
enzymes, oncometabolites are metabolic 
intermediates that, when present in abnormal 
amounts, cause neoplastic disease (Yong et al., 
2020). In general, these biomarkers are 
extremely valuable for predicting disease 
outcomes and treatment response in addition to 
being used to identify cancers. Mutations in the 
isocitrate dehydrogenase 1 (IDH1) and 2 (IDH2) 
enzymes in gliomas and acute myeloid leukemia 



 
 
 
 

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(AML) (Fig. 1) produce 2-HG, a well-known 
oncometabolite (Rakheja et al., 2013).  
 

Because 2-HG is a factor that damages cellular 
differentiation at high levels, which leads to 
tumorigenesis, high levels of 2-HG function as 
both a diagnostic and a prognostic biomarker 
(Wang et al., 2013). For example, Dinardo et al. 
2013 emphasize the value of 2-HG in AML for 
both diagnosis and prognosis (DiNardo et al., 
2013). This study showed that IDH1 and IDH2 
mutations were linked to significantly elevated 
serum 2-HG levels, and at a threshold of 700 
ng/mL, the diagnostic sensitivity and specificity 
were high, at 86.9% and 90.7%, respectively. 
Clinical outcomes were also linked to serum 2-
HG levels, with higher levels being associated 
with a worse prognosis and a greater tumor 
burden.  
 

The results suggest that 2-HG may be a 
promising non-invasive biomarker for detecting 
IDH mutations and tracking the progression of 
the disease and response to treatment in AML 
patients. Giving some methodological or 
standard information about the clinical validation 
and application of biomarkers would also be 
pertinent to the research on them. Furthermore, 
Miller et al. highlight the importance of IDH 
mutations and their metabolite, 2-HG, in the 
diagnosis and management of gliomas (Miller et 
al., 2023). A reliable biomarker for IDH-mutant 
gliomas, elevated 2-HG levels can be found 
using non-invasive techniques. Clinically 
speaking, IDH mutations differ from gliomas with 
wild-type IDH in that they offer higher survival 
rates than are otherwise observed. Targeted 
treatments for these mutations also have the 
potential to alter the tumor ecosystem in order to 
enhance therapeutics. These findings 
demonstrate how crucial IDH mutations are to 
improving glioma diagnosis and treatment 
accuracy. According to Dallas Pozza et al. 
(2020), succinate is another significant biomarker 
that builds up in some forms of paragangliomas 
and pheochromocytomas as a result of mutations 
in the enzyme succinate dehydrogenase (SDH). 
Similarly, hereditary leiomyomatosis and renal 
cell carcinoma cause fumarate to build up as a 
result of mutations in fumarate hydratase (FH) 
(Trpkov et al., 2016; Wang et al., 2024). 
 

Thus, these metabolites play a role in identifying 
hereditary cancer syndromes and assessing 
cancer risks (Collins et al., 2017; Di Gregorio et 
al., 2021) by acting as "oncometabolites" that 
drive DNA methylation (Lanzetti, 2024) and 
hypoxia-like responses (Fandrey et al., 2019). 

According to Vo and Trinh (2024), alterations in 
lipid metabolism metabolites, such as 
phosphocholine, are also being found to be 
biomarkers for prostate and breast cancers. 
Tumor growth is indicated by elevated levels of 
phosphocholine and other lipid-related 
metabolites, which can also provide diagnostic 
details about different cancer subtypes. 
 
In addition to supporting early diagnosis, these 
important biomarkers also offer valuable insights 
into tumor behavior and enable personalized 
prognosis, which leads to targeted therapies. As 
science continues to advance, the identification 
of biomarkers and their clinical applications 
continue to grow. According to Vo and Trinh 
(2024), these enhance precision oncology and 
enable therapies that are truly customized to a 
patient's metabolic profile.  
 

4. BIOMARKERS FOR MENTAL HEALTH 
AND NEURODEGENERATIVE DISEASE 

 

4.1 Neurodegenerative Diseases and 
Metabolomics  

 
A powerful tool in the hunt for neurodegenerative 
diseases is metabolomics, which makes it 
possible to identify biomarkers connected to the 
course of the illness and use them for diagnosis 
and treatment monitoring. In the early stages of 
detection and differential diagnosis, diseases like 
Alzheimer's disease (AD), Parkinson's disease 
(PD), and amyotrophic lateral sclerosis (ALS) are 
useful because they exhibit specific metabolic 
alterations linked to the underlying 
pathophysiological mechanisms in these 
conditions (Dubois et al., 2023). Glutamate, myo-
inositol, and phosphatidylcholine are biomarkers 
of Alzheimer's disease that are associated with 
inflammation, oxidative stress, and neuronal 
damage (Zhang et al., 2023; Reveglia et al., 
2023; Wang et al., 2024).  Low levels of acetyl-L-
carnitine are linked to mitochondrial dysfunction 
and cognitive decline (Pennisi et al., 2020), while 
elevated myoinositol levels have been linked to 
amyloid plaque deposition, one of the hallmark 
events of AD (Voevodskaya et al., 2016). Early 
intervention may be possible because other lipid-
related metabolites, which involve specific 
phospholipids, are being investigated as 
biomarkers of the preclinical stages of AD. 
Dopamine, uric acid, and homovanillic acid are 
the main metabolomics biomarkers that have 
been investigated in the context of Parkinson's 
disease (Kremer et al., 2021). Dopamine 
depletion brought on by dopaminergic neuron 



 
 
 
 

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degeneration is a key component of Parkinson's 
disease.  
 

Conversely, there is an increase in homovanillic 
acid, a dopamine metabolite that could indicate 
the severity of the illness (Kremer et al., 2021). 
Oxidative stress has been implicated in a higher 
risk of Parkinson's disease (PD), as evidenced 
by lower levels of the antioxidant uric acid (Seifar 
et al., 2022). In ALS, biomarkers like creatine, 
ascorbate, and different amino acids show 
abnormalities in energy metabolism, 
neuroinflammation, and oxidative stress (Kori et 
al., 2016; Lanznaster et al., 2018). While 
branched-chain amino acid changes reflect 
disruptions in the muscle metabolism of these 
vital nutrients in ALS patients, lower creatine 
levels signify deficiencies in cellular energy 
(Parvanovova et al., 2024).  
 

Profiling these biomarkers using metabolomics 
offers a better understanding of the mechanisms 
underlying neurodegenerative disease, which 
may lead to early diagnosis and help distinguish 
it from other conditions. Additionally, because 
metabolite levels can be tracked to evaluate 
treatment effectiveness and select individualized 
therapeutic approaches in neurodegenerative 
care, these biomarkers can provide insights into 
the development of targeted therapies 
(Parvanovova et al., 2024). 
 

4.2 The Biomarkers of Mental Health  
 

The field of mental health research has seen a 
lot of use of metabolomics, particularly in the 
hunt for biomarkers linked to a range of 
psychiatric conditions, such as anxiety, bipolar 
disorder, schizophrenia, and depression (Abi-
Dargham et al., 2023). The examination of 
neurotransmitter pathways and metabolites 
associated with the gut-brain axis may yield 
markers for the biochemical underpinnings of 
mental health disorders, enabling more precise 
diagnosis and customized treatment plans. 
Because they directly affect brain activity and 
mood regulation, neurotransmitter metabolites 
are significant in psychiatric disorders. For 
instance, serotonin and its metabolite 5-
hydroxyindoleacetic acid (5-HIAA) is the subject 
of much research in relation to mood disorders 
(Jayamohananan et al., 2019).  

 
Likewise, dopamine and homovanillic acid 
dysregulation is associated with schizophrenia 
and bipolar disorder (Wada et al., 2022), while 
depression is said to be associated with lower 
levels of serotonin and its metabolites (Moncrieff 
et al., 2023). A known inhibitory neurotransmitter, 
gamma-aminobutyric acid (GABA), has been 
linked to symptoms of anxiety and depression. 
Variations in GABA levels reflect shifts in mood 

 

 
 

Fig. 3. Potential pathways through which SCFAs influence gut–brain communication. 
Copyright Frontiers Media SA (2020) (Silva et al., 2020) 



 
 
 
 

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regulation and stress modulation (Prévot and 
Sibille, 2021). Furthermore, metabolites 
generated by the gut-brain axis are becoming 
more widely acknowledged for their role in 
mental health (Góralczyk-Bi´ nkowska et al., 
2022). 
 
Intestinal microbiota activity produces short-chain 
fatty acids (SCFAs), like butyrate and propionate, 
which impact neuroinflammation, stress 
reactions, and emotional regulation systems  
(Fig. 2) (Silva et al., 2020). 
 
Given their role in reducing inflammation and 
maintaining the integrity of the blood-brain 
barrier, low levels of SCFAs have been proposed 
to be linked to anxiety and depression (Silva et 
al., 2020). Tryptophan and kynurenine, two other 
gut-derived metabolites, have been linked to 
mood disorders and schizophrenia and are linked 
to immunological responses and 
neurotransmitter synthesis (Marx et al., 2021). 
Not only can these metabolomic biomarkers 
reveal the biochemical foundations of psychiatric 
disorders, but they will also enable a more 
individualized approach to mental health 
treatment. Continuous monitoring of these 
biomarkers would assist medical professionals in 
streamlining diagnostics, customizing treatments, 
and probably improving patient outcomes by 
providing more targeted interventions (Marx et 
al., 2021). 
 

4.3 Potential Early Diagnosis Biomarkers 
for Mental Health and 
Neurodegenerative Diseases  

 
In the diagnosis and treatment planning of 
neurological and psychiatric disorders, 
biomarkers based on metabolomics hold great 
promise (Török et al., 2020; Mallu et al., 2021). 
Through the identification of particular metabolic 
alterations that take place even prior to the 
manifestation of clinical symptoms, these 
biomarkers are anticipated to aid in early 
intervention, improving the prognosis by delaying 
the course of the disease (Sun et al., 2023).  
 
They are useful for early diagnosis because, for 
example, a preclinical stage of the disease has 
been noted in which AD patients exhibit elevated 
myo-inositol and alterations in phospholipid 
profiles (Ahanger et al., 2024). If these 
biomarkers are discovered early, prompt 
interventions that postpone cognitive decline will 
be possible. Metabolomics as a foundation for 
biomarker discovery for the early diagnosis of 

psychiatric and neurodegenerative disorders has 
produced very encouraging results in clinical 
studies. Alpha-synuclein aggregates and urates 
have been identified as interesting early 
indicators of Parkinson's disease (PD) due to 
their lower levels in CSF (Ganguly et al., 2021). 
According to Averina et al. (2024), depression 
and schizophrenia have been linked to 
alterations in serotonin and dopamine 
metabolism. 
 
Moreover, increased kynurenic acid levels are 
associated with oxidative stress and 
neuroinflammation, providing insight into the 
early phases of mood disorders and 
schizophrenia (O'Farrell and Harkin, 2017; Mor, 
et al., 2021). These gut-brain axis-derived altered 
SCFAs are emerging as promising biomarkers 
for mental health conditions, particularly 
depression (O'Riordan et al., 2022). 
Furthermore, ALS and MS can be accurately 
diagnosed early thanks to neurofilament light 
chain levels in ALS and sphingomyelin 
reductions in MS (Yang et al., 2022). According 
to Sacchet et al. (2024), elevated cortisol levels 
in anxiety disorders raise the risk and help make 
a diagnosis even before all symptoms appear.  
 
In addition to offering tools for early detection, 
these biomarkers also enable the application of 
tailored treatment to enhance prognosis and 
impede the progression of disease. Early 
detection of mental health conditions enables 
physicians to use treatment plans based on 
individual biochemical profiles that optimize an 
intervention's efficacy (Sacchet et al., 2024). It is 
hoped that the use of metabolomics-based 
biomarkers will eventually lead to a paradigm 
shift in how doctors choose treatments that are 
specific to each patient and diagnose illnesses 
early. This will improve patient outcomes by 
more effectively managing psychiatric and 
neurodegenerative conditions (Sacchet et al., 
2024). 
 

5. DISEASE AND HEALTH 
METABOLOMICS  

 
Applications of metabolomics in health and 
disease include single-cell, epidemiologic 
population studies, metabolic phenotyping, 
metabolome-wide association studies (MWAS), 
precision metabolomics, and integrative 
metabolomics, which is the use of metabolomics 
in conjunction with other omics disciplines 
(Dalamaga, 2024).Live single-cell mass 
spectrometry (LSCMS), LC-MS/MS, GC-MS/MS, 



 
 
 
 

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and single-cell metabolomics and lipidomics 
technologies enable high-dimensional 
characterizations of individual cells, disease 
heterogeneity and complexity, and the 
identification, expression, and abundance of 
disease-associated metabolites and small mole 
cules (Lempesis et al., 2022).  
 
Cell-cell interactions and tumor heterogeneity 
were discovered through imaging MS analysis of 
human breast cancer samples at the single-cell 
level (Lempesis et al., 2022). Metabolomics 
fingerprinting and footprinting techniques, 
MWAS, and clinical biomarkers and various 
metabotypes of disease severity correlated to 
exposures (Zhang et al., 2023) and biological 
outcomes (Sun et al., 2023) have been studied 
and identified in individuals and populations 
through metabolomics fingerprinting and 
footprinting techniques, which will facilitate 
precision medicine and public healthcare (Guo et 
al., 2023; Wang et al., 2023; Xu et al., 2023; Ou 
et al., 2024; Zhou et al., 2024; Xu, et, al., 2024).  
 
The transition from genome-wide association 
studies (GWAS) to metabolome-wide association 
studies (MWAS) was initially defined in 2008 as 
"an investigation of the relationships between 
phenotype variation and disease risk factors 
through environmental and genomic influences" 
(Xu et al., 2023; Ou et al., 2024). Exposures to 
single individual phenotypes and populations, 
epidemiologic research, disease risk, 
metabolome-wide association studies, and 
precision medicine were proposed by Rattray 
and colleagues (Ou et al., 2024). 
 

6. METABOLOMICS AND THE 
EXPOSOME 

 
Unique opportunities to identify food ingredients 
and comprehend their role in the dietary 
exposome and food quality are provided by 
metabolomics. Modern metabolomics techniques 
were employed in a study by Nikou et al. (2020) 
to find chemical biomarkers relevant to the 
production process, cultivation methods, and 
geographic origin of olive oil, a component of the 
Mediterranean diet that has been described as a 
nutritious dietary pattern. To examine intact oil 
and the associated polyphenols of extra virgin 
olive oils, metabolomic profiling using Flow 
Injection Analysis-Magnetical Resonance Mass 
Spectrometry (FIA-MRMS) and the LC-Orbitrap 
MS platform was employed. The approach may 
provide a means of combating food fraud and 
adulteration while also identifying elements of a 

nutritious diet. Occupational exposures and 
patterns, such as shift work, are a significant 
component of the exposome. In recent years, 
shift work has been linked to a number of 
physiological changes and may be related to a 
number of diseases. Report by Borroni et al. 
(2023) assessed how night shift work affected 
the serum metabolome in a group of Italian 
female nurses who worked nights and female 
coworkers who did not. They found that there 
were variations in the levels of taurine, serotonin, 
aspartic acid, and certain lipids, all of which 
provide hints about the biological changes 
brought on by working nights.  According to 
Barupal et al. (2022) Reprocessing 
metabolomics datasets that have already been 
published and made publicly available can alter 
the number of metabolite identifications made 
and possibly reveal new biomarkers that were 
overlooked during the initial processing, as the 
authors of the paper by Barupal et al. (2022) 
showed. In contrast to the initial data pre-
processing method that employed the mass 
spectrometer manufacturer's software, the 
authors used MS-Dial, a publicly available 
program, for data pre-processing and applied 
less strict data processing thresholds. Different 
software and even small adjustments to pre-
processing parameters can affect the quality and 
quantity of data reported, as the manuscript 
illustrated. This presents both opportunities and 
challenges. Keski-Rahkonen et al. (2021) and 
Barupal et al. (2022) two additional comments on 
the manuscript sparked a heated discussion that 
added to the metabolomics community's broader 
conversations regarding data pre-processing 
techniques, software and parameters, reporting 
standards and formats, and intercomparability 
among data pre-processing methods. 
 

7. FUTURE DIRECTION OF 
METABOLOMICS 

 
Metabolomics technology will be integrated with 
other cutting-edge technologies to guarantee its 
expansion. MS-based metabolomics will be more 
precise, and a number of combined techniques, 
including GC/MS, LC/MS, and others, will 
provide a strong scientific basis for tackling the 
challenge of metabolite analysis and metabolic 
pathway discovery. Extended mass spectrometry 
holds a prominent position as a vital analytical 
tool in the field of metabolomics. The growth of 
this field has provided insight into the etiology of 
many diseases and assisted in the identification 
of numerous potential illness biomarkers (Xiong 
et al., 2020).  



 
 
 
 

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Some of the studies that have been assessed 
include research for recurrence testing, 
appropriate treatment, medication prognosis and 
prediction, and early disease diagnosis (Sun et 
al., 2019). LC, GC, or CE is used in most MS 
analytical procedures because biological 
mixtures are very complex. Rapidly growing 
applications using evolving separation 
mechanisms and protocols do present both 
opportunities and challenges, though. With the 
advent of chromatographic techniques, the 
capacity to separate metabolites and enhance 
the number of metabolites identified has surely 
improved (Li et al., 2017; Zhang et al., 2019).  
 

The inability to analyze and relate the results of 
the latter studies, which are carried out on 
comparable or identical samples gathered by 
multiple research institutions, is a major barrier. 
This is the biggest obstacle to progress in this 
field. Other factors, such as sample preparation, 
sample matrix, and residual effects, can also 
contribute to data variability. To overcome these 
challenges, it is essential to switch from 
calculating relative metabolite concentrations to 
more accurate absolute concentration 
measurements, regardless of the analytical 
platform, approach, and procedure. This 
approach is important even though MS finds it 
difficult. 
 

8. CONCLUSION  
 

A helpful method for identifying disease-related 
metabolites in biofluids or tissue as well as for 
classifying and/or characterizing molecular 
patterns linked to disease or treatment that are 
produced from metabolites is mass spectrometry 
(MS) based metabolomics/lipid omics. We 
continue to discover new biomarkers in 
metabolomics that redefine health and disease 
diagnostic precision and provide insight into the 
biochemical etiology of a wide range of diseases. 
This makes metabolomics a potentially useful 
platform for prognosis, early detection, and the 
application of tailored treatment plans founded 
on a thorough comprehension of metabolic 
changes brought on by illness and environmental 
influences. The metabolic profiling biomarkers 
have the potential to improve diagnosis and 
enable targeted interventions in metabolic 
syndromes, cancers, cardiovascular diseases, 
and neurodegenerative disorders. But before the 
biomarkers can truly reach the clinic, a lot more 
work needs to be done. To ensure that the 
biomarkers are accurate and repeatable across a 
range of populations, considerable attention must 

be paid to inter-individual variability, 
environmental factors, and data complexity                 
in metabolomics. Confounding factor 
considerations and a strict methodology in 
biomarker validation with large cohorts are 
necessary for that. Depending on the features of 
metabolic profiles, biomarker-focused research 
and translation to clinical practice are constantly 
pushing this field away from standard treatment 
and toward personalized therapies. In order to 
translate biomarkers from metabolomics into 
clinical practices for improved patient outcomes 
and to steer the trajectory of precision medicine, 
it will be necessary to overcome many of the 
current obstacles through ongoing research and 
collaboration among clinicians, researchers, and 
technologists. 

 
DISCLAIMER (ARTIFICIAL INTELLIGENCE) 

 
Author(s) hereby declare that NO generative AI 
technologies such as Large Language Models 
(ChatGPT, COPILOT, etc.) and text-to-image 
generators have been used during the writing or 
editing of this manuscript.  

 
CONSENT 
 
It is not applicable. 

 
ETHICAL APPROVAL 
 
It is not applicable. 

 
COMPETING INTERESTS 
 
Authors have declared that no competing 
interests exist. 

 
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