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Neglected tropical diseases (NTDs) comprise a group of conditions that disproportionately affect impoverished populations in tropical and subtropical regions, where limited access to healthcare and sanitation sustains disease transmission and health inequities. These conditions include parasitic, fungal, bacterial, and viral diseases, as well as snakebite envenoming (). According to the World Health Organization (WHO), more than 1.6 billion people require interventions for at least one NTD annually. NTDs contribute to disability, stigma, reduced quality of life, and persistent poverty, imposing substantial social and economic burdens.

Advances in omics sciences have transformed biomedical research by enabling comprehensive characterization of biological systems. Among these approaches, metabolomics investigates small-molecule metabolites that reflect the combined influence of genetic, environmental, and pathological factors. Metabolites provide a sensitive snapshot of physiological and disease states and can reveal disease-associated biochemical signatures. Metabolomics has been increasingly applied to infectious, cardiovascular, neurological, and neoplastic diseases, improving understanding of disease mechanisms, host-pathogen interactions, and treatment responses. By characterizing diverse metabolite classes, including amino acids, lipids, carbohydrates, and nucleotides, metabolomics has also emerged as a promising strategy for biomarker discovery, supporting the identification of diagnostic, prognostic, and treatment-monitoring indicators with potential applications in precision medicine - . Evidence regarding the application of metabolomics to NTDs remains fragmented across diseases, analytical platforms, and clinical contexts. This systematic review aimed to assess the available evidence on metabolomic profiles in NTDs and evaluate their potential applications as biomarkers for diagnosis, disease monitoring, prognosis, and clinical management.

Search strategies were developed using MeSH terms and keywords related to metabolomics and WHO-defined NTDs. A draft MEDLINE search strategy is provided in and was adapted for Web of Science and Scopus. Searches covered studies published up to March 2026 and combined (“metabolomic” OR “metabolomics”) with (“neglected tropical diseases”, “NTDs”, the name of each NTD, or its etiological agent). Only articles published in English were included, with no geographical restrictions. Reference lists of all included studies were also screened for additional relevant publications.

Methodological quality and risk of bias were assessed using a modified version of the QUADOMICS tool, developed for omics-based studies and derived from the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) framework. Six domains were evaluated and classified as presenting low, high, or unclear risk of bias according to predefined criteria: (1) participant selection; (2) sample description; (3) sample collection procedures and timing; (4) pre-analytical procedures and specimen handling; (5) execution of metabolomics analyses; and (6) risk of overfitting. Risk of bias was classified as low, high, or unclear when information was insufficient.

Data were extracted independently by two reviewers (JBM and VIM) using a standardized form, with disagreements resolved by consensus or consultation with a third reviewer (CF). Extracted information included study characteristics, participant demographics, biological samples, metabolomics methodologies, analytical platforms, metabolite identification strategies, statistical analyses, comparator groups, identified metabolites and metabolic pathways, diagnostic performance metrics (AUC - area under the curve, sensitivity, specificity, and accuracy), validation approaches, and overfitting assessments. Information regarding robustness, reproducibility, and clinical applicability, including validation in independent cohorts and feasibility of clinical implementation, was also recorded. When necessary, metabolite nomenclature and reported metrics were standardized to facilitate comparisons across studies.

Due to substantial heterogeneity across studies regarding NTDs, biological samples, metabolomics platforms, analytical methodologies, statistical approaches, and reported outcomes, quantitative synthesis was not considered appropriate. Therefore, findings were synthesized narratively and presented in text and tables. The synthesis focused on study characteristics, identified metabolites and metabolic pathways, methodological features, diagnostic performance metrics, and the clinical applicability of reported biomarkers. Methodological quality and risk-of-bias assessments obtained using the QUADOMICS tool were considered during data interpretation and discussed alongside the study findings.

summarizes the study identification and selection process. Our search strategy identified 2,673 citations. After removing duplicates and clearly ineligible records, 272 potentially eligible studies underwent full-text assessment, of which 198 were excluded according to the predefined criteria. Ultimately, 74 studies - met the inclusion criteria, highlighting the limited application of metabolomics to NTDs. The main characteristics of the included studies reporting potential biomarkers are presented in . shows the geographical distribution of studies. Most were conducted in China (13 studies) - , , , , , , , , and Brazil (12 studies) , , , , , , , , , , , , while the remaining studies were mainly carried out in Africa and Asia. Seven studies included populations from more than one country. shows the distribution of publication years. The included studies were published between 2010 and 2026, with the highest number of publications in 2023 (n=11).

Considerable heterogeneity was observed in metabolomics research across WHO-defined NTDs. Dengue, onchocerciasis, schistosomiasis, leprosy, and Chagas disease accounted for the largest proportion of publications (B). In contrast, no eligible studies were identified for leishmaniasis, chromoblastomycosis and other deep mycoses, mycetoma, yaws, trachoma, scabies and other ectoparasites, podoconiosis, lymphatic filariasis, dracunculiasis, or noma (B). A total of 39 studies addressed parasitic helminth diseases. For echinococcosis, most studies (n=4) were conducted in China - . Among the six included studies - , four identified potential diagnostic biomarkers , - . Although six studies on foodborne trematode infections were included, none progressed to the identification of potential biomarkers. Regarding onchocerciasis, nine studies were included - , of which eight were conducted in African populations - , , mainly from Cameroon (n=5). Four studies identified potential biomarkers , , , . Ten studies investigated schistosomiasis - , - , , but only two reported potential biomarkers , . Study populations mainly originated from Africa , , - (n=5), while three studies were conducted in Brazil , , . Four studies evaluated soil-transmitted helminths, specifically A. lumbricoides , S. stercoralis , and T. trichiura ; however, only two identified potential biomarkers.

For protozoan diseases, nine studies were included. Six investigated Chagas disease - , , three of which identified biomarkers , , . Most were conducted in populations from South and Central America. Three studies evaluated human African trypanosomiasis - ; two were conducted in Uganda , and one in Angola, which was also the only study to identify disease-staging biomarkers. Eight studies investigated bacterial diseases. Seven focused on leprosy - , - , , with only one reporting prognostic biomarkers. Four were conducted in Brazil , , , and three in Asian populations , , . A total of 20 studies investigated viral diseases. Dengue accounted for the highest number of publications, with 17 studies - , - , - . Twelve were conducted in Asia - , - , , , and five in Latin America , , , , . Despite the larger number of studies, only six identified potential biomarkers , , , , , . Rabies was represented by three studies - , all involving predominantly North American populations, and only one identified disease-staging biomarkers. Taeniasis/cysticercosis, Buruli ulcer, and snakebite envenomation were each represented by a single study.

All studies that identified potential biomarkers included comparison groups. Healthy individuals were the most common controls , - , , , , , , , , . Other comparison groups included endemic and non-endemic controls , , disease-negative individuals , , , , patients with other diseases , , , and household or exposed but non-diseased contacts. The risk-of-bias assessment is summarized in . No study presented a low risk of bias across all six domains. Only one study fulfilled at least five of the seven criteria for low risk of bias. Thirty studies (40.5%) , , , , , - , , , , , , , , , , , , - , , , , , met at least four criteria, whereas another 30 studies (40.5%) - , , , , , , , , , , , , , - , , , , , - met three criteria. Thirteen studies , - , , , , , , , , , presented five or six methodological shortcomings. Lagatie et al. and Lagatie et al. were the only studies to validate biomarkers using independent sample sets from distinct populations. Lagatie et al. validated biomarkers in a validation cohort and a non-endemic Kenyan population, whereas Lagatie et al. used independent cohorts from Kenya, Indonesia, Ethiopia, and Belgium. These approaches reduced the likelihood of overfitting and strengthened reproducibility. However, neither study reported the anticoagulant used during sample collection, which may slightly limit reproducibility.

The main reasons for high risk of bias were inadequate reporting of clinical characteristics, insufficient description of sample handling and pre-analytical procedures, and the lack of validation using independent sample sets or other approaches to address overfitting.

Despite the marked etiological diversity of NTDs, the metabolomic studies included in this review reveal remarkable convergence in the metabolic pathways affected. Alterations involving amino acid metabolism, lipid remodeling, energy metabolism, and inflammatory mediators were consistently reported across protozoan, helminth, bacterial, and viral infections.

Helminthiases represented the most heterogeneous group of NTDs evaluated, yet recurring metabolic patterns were consistently observed, including alterations in amino acids, lipids, energy metabolism, bile acids, and purines across echinococcosis, schistosomiasis, onchocerciasis, and intestinal helminthiases. Furthermore, changes in eicosanoids, sphingolipids, and fatty acids suggest that modulation of the host inflammatory response is a common feature of helminth infections.

Echinococcosis: Reported biomarkers were mainly associated with amino acid, lipid, and energy metabolism. Several carbohydrates, lipids, and amino acids distinguished cystic echinococcosis (CEC) patients from healthy controls. Urinary citrate and three plasma metabolites differentiated fertile from infertile cysts, suggesting utility for assessing cyst viability. Five metabolites have also been proposed as promising biomarkers for early diagnosis. More recently, Aimaiti et al. identified isopropamide, sphinganine, 15(S)-HETE-d8 (hydroxyeicosatetraenoic acid), allantoic acid, and Tyr-Arg-Leu (tyrosine-arginine-leucine) as candidate biomarkers. Additional studies reported elevated aromatic amino acids and glutamate, reduced branched-chain amino acids and creatine, and alterations in linoleic and arachidonic acid metabolism. Urinary succinate was also proposed as a marker distinguishing CEC patients from individuals without hydatid cysts.

Foodborne trematode infections : Although six studies were included, none identified validated diagnostic biomarkers. Nevertheless, metabolomics highlighted differentially expressed metabolite classes - and informed future biomarker discovery efforts. Bile metabolomics revealed reduced amino acids in Clonorchis sinensis infection and marked decreases in bile acids, including CDCA (chenodeoxycholic acid), UDCA (ursodeoxycholic acid), HDCA (hyodeoxycholic acid), and βCA (3β-cholic acid), although the invasive nature of bile collection limits clinical applicability. Urinary metabolomics identified alterations in purines, carnitines, bile acids, and steroid glucuronides associated with chronic Opisthorchis viverrini infection and cholangiocarcinoma , , while fecal metabolomics distinguished periductal fibrosis from cholangiocarcinoma. Serum metabolomics identified spermidine, LysoPA (16:0/0:0), 12(R)-HETE, L-4-hydroxyglutamate semialdehyde, and TDCA (taurodeoxycholic acid) as discriminatory metabolites in C. sinensis -associated hepatocellular carcinoma.

Onchocerciasis: Metabolomics studies identified several candidate biomarkers in urine, plasma, and serum. An initial study reported 10 candidate biomarkers, mainly fatty acids and related metabolites. N-acetyltyramine-O-β-glucuronide (NATOG) has been extensively investigated as a urinary marker of infection, disease progression, and treatment response , , although its diagnostic utility remains inconsistent. Lagatie et al. identified two plasma biomarkers associated with filarial infections. While elevated NATOG levels have been reported in mono- and co-infections with Loa loa and Mansonella perstans , other studies found limited discriminatory performance , . More recent evidence suggests that combining NATOG with additional metabolites may improve diagnostic accuracy. Elevated serum serotonin, hypoxanthine, pipecolic acid, and inosine, together with reduced glycerophosphocholine, choline, and adenine, have also been reported. Additionally, phosphatidylethanolamine lipid ethers detected in O. ochengi have been proposed as parasite-derived biomarkers.

Schistosomiasis: Metabolomics studies have identified metabolic alterations associated with disease progression and host-parasite interactions. Catechol estrogens distinguished uncomplicated S. haematobium infection from advanced bladder pathology, while three bile acid-based biomarkers were proposed for chronic and advanced S. japonicum infection. Additional studies reported alterations in lipid metabolism , , particularly involving choline and sphingolipid pathways. 1 H-NMR studies identified higher alanine, glycolaldehyde, and N-acetylglucosamines in severe periportal fibrosis and lower valine and carbohydrates in mild disease. Lactate was elevated in S. mansoni patients coinfected with hepatitis B or C viruses, whereas HDL signals were more intense in schistosomiasis alone. Additional metabolites related to gut microbiota, energy metabolism, and liver function have also been described, supporting the potential of metabolomic signatures for disease staging and monitoring.

Soil-transmitted helminthiasis: In Ascaris lumbricoides , urinary 2-methyl-pentanoyl-carnitine showed 85.7% accuracy for infection detection and 90.5% accuracy for assessing infection intensity, with levels declining after albendazole treatment. For Strongyloides stercoralis , 28 amino acid- and lipid-related metabolites differentiated infected, uninfected, and post-treatment individuals, while five candidate biomarkers achieved accuracies above 89%. In Trichuris trichiura infection, myristic acid and DL-dipalmitoylphosphatidylcholine were strongly associated with infection intensity. Overall, metabolomics may support diagnosis, treatment monitoring, and investigation of host-parasite interactions - , . Taeniasis/cysticercosis: Neurocysticercosis (NCC) is primarily diagnosed by neuroimaging, which is often unavailable in endemic rural settings. An exploratory 1 H NMR study demonstrated moderate discrimination between NCC patients and healthy controls using urinary metabolic profiles, although the responsible metabolites were not identified. Despite its exploratory nature, the study highlights the potential of non-invasive metabolomics for biomarker discovery in NCC.

Chagas disease (CD) and African trypanosomiasis (HAT) showed convergent alterations in energy, amino acid, and lipid metabolism, including changes in aromatic and branched-chain amino acids, tricarboxylic acid cycle intermediates, acylcarnitines, and phospholipids, reflecting mitochondrial dysfunction, altered energy metabolism, and systemic inflammation. While these metabolic changes are associated with chronic cardiomyopathy in CD and central nervous system involvement in HAT, they collectively suggest shared host metabolic responses despite disease-specific biomarkers.

Chagas disease: The absence of reliable biomarkers for disease progression, treatment response, and cure remains a major challenge in CD. Metabolomics studies have consistently identified molecular signatures in chronic chagasic cardiomyopathy (CCC) - , including amino acids, acylcarnitines, sphingomyelins, lysophosphatidylcholines, phosphatidylcholines, and metabolites of the tryptophan pathway such as serotonin, tryptamine, kynurenine, and N-acetylserotonin. Integrated metabolomics identified 10-hydroxydecanoic acid and the phosphatidylethanolamines PE(18:0/20:4) and PE(18:1/20:4) as promising biomarkers for distinguishing symptomatic from asymptomatic Trypanosoma cruzi infection. Several sphingolipids normalized after antiparasitic therapy, suggesting utility for treatment monitoring. Additional studies reported alterations in tryptophan, methionine, urea, myo-inositol, branched-chain amino acids, acylcarnitines, fatty acid metabolism, the tricarboxylic acid cycle, and antioxidant defenses. In CCC patients, elevated progesterone and one peptide, together with reduced amino acid levels, were observed before treatment, with most metabolites normalizing after therapy. Validation through targeted metabolomics and comparison with other cardiomyopathies remain necessary - , .

African trypanosomiasis: Metabolomics studies have investigated metabolic alterations associated with HAT and disease progression - . Lamour et al. reported elevated plasma levels of phenylalanine, formate, creatinine, N-acetylated glycoproteins, and triglycerides in infected individuals compared with controls. Although no stage-specific metabolic signatures were identified, increased 3-hydroxybutyrate and alanine, together with reduced mannose and urea, were associated with neurological manifestations such as somnolence and gait ataxia, suggesting potential utility as disease-stage biomarkers. Using mass spectrometry, Vincent et al. identified CSF metabolites, but not urinary markers, capable of discriminating disease stages, with 100% sensitivity and specificity for distinguishing stage 1 from stage 2 disease.

Bacterial diseases, particularly leprosy, were mainly characterized by alterations in lipid immunometabolism, including eicosanoids, resolvins, prostaglandins, and arachidonic acid pathways, highlighting the role of inflammatory resolution in disease progression. Although only one Buruli ulcer study was included, it also identified disturbances in energy and lipid metabolism, suggesting shared metabolic mechanisms among neglected mycobacterial diseases. Buruli ulcer: A single metabolomics study investigated Buruli ulcer and identified significant alterations in metabolites related to hexose metabolism, steroid hormones, acylcarnitines, purines, heme, bile acids, riboflavin, and lysolipids. Altered hexose metabolism was associated with impaired glucose homeostasis, suggesting potential utility for disease monitoring.

Leprosy: Metabolomics studies in leprosy have consistently highlighted alterations in lipid mediators involved in inflammatory pathways. Elevated levels of polyunsaturated fatty acids, including arachidonic acid, eicosapentaenoic acid, and docosahexaenoic acid, together with omega-3- and omega-6-derived pro-resolving mediators, were reported by Al-Mubarak et al. and Amaral et al.. Additional alterations involved prostaglandin, linoleate, arachidonic acid, glycerophospholipid, and carnitine shuttle pathways. Similarly, Silva et al. , identified increased arachidonic acid, leukotriene B4, prostaglandin D2, and lipoxin A4 in household contacts who subsequently developed leprosy, proposing resolvins and one prostaglandin as potential early biomarkers. Other studies reported metabolic alterations in amino acid and lipid pathways in ulcerated skin and identified urinary metabolite profiles capable of distinguishing controls from patients experiencing inflammatory reactions, suggesting potential utility for early detection of acute inflammation and prevention of tissue damage.

Viral diseases, particularly dengue, were characterized by alterations in lipid metabolism, including lysophospholipids, sphingolipids, polyunsaturated fatty acids, and acylcarnitines, as well as changes in serotonin, kynurenine, and energy metabolism. These alterations were associated with inflammation, platelet activation, and disease severity, making most proposed biomarkers more suitable for prognosis than diagnosis. In contrast, rabies was predominantly associated with disruptions in cerebral energy metabolism, the tricarboxylic acid cycle, ketogenesis, and neurotransmitter pathways, consistent with central nervous system involvement.

Dengue: Metabolomics studies consistently demonstrate alterations in lipid metabolism, particularly involving glycerophospholipids and sphingolipids, which are associated with disease progression and changes in lymphocyte and platelet counts , , . Potential biomarkers include platelet-activating factor precursors, phosphatidylcholine derivatives, and triglycerides, while serotonin and kynurenine have been linked to platelet aggregation and immune regulation. Increased bile acids were associated with dengue hemorrhagic fever, whereas comparative studies identified reduced serotonin and bile acids in dengue compared with influenza. Additional metabolites related to viral replication, immune responses, disease severity, oxidative stress, and inflammation have also been reported - , , . Several biomarkers demonstrated promising diagnostic performance. An unidentified metabolite feature (m/z 246.2655) showed good accuracy in Ecuadorian children and adolescents, while trans-cinnamic acid, L-acetylcarnitine, sphingomyelin SM(d17:1/17:0), physalactone, S-japonin, and 9-tridecynoic acid achieved good discriminatory performance in a Chinese cohort. Other studies identified alterations in lipid, amino acid, and energy metabolism associated with disease progression and severe dengue, as well as potential biomarkers including fatty acids, phospholipids, vitamins, amino acids, and organic acids , , , , . Reduced lipoproteins, glutamine, acetate, and creatine distinguished infected patients from controls, while urinary metabolomics revealed additional changes in amino acids, organic acids, and betaine.