Psychopathy is a personality construct characterized by emotional detachment, impulsivity, and antisocial tendencies. Despite a clinical prevalence of 1%, the biological underpinnings of psychopathy remain poorly understood. Emerging evidence suggests a role for the microbiota-brain axis in personality and behavior, yet its relevance to psychopathy remains unexplored. This study investigated the relationship between gut and oral microbiota composition and psychopathic traits, while assessing metabolomic signatures that may contribute to microbiota-brain interactions. A cohort of 200 participants completed the Self-Report Psychopathy Scale Short Form, alongside measures of empathy (Affective and Cognitive Empathy Questionnaire; Interpersonal Reactivity Index) and anxiety (State-Trait Inventory for Cognitive and Somatic Anxiety). Microbiota composition was analyzed in fecal and oral samples, and targeted and untargeted metabolomics was performed in plasma and fecal samples. Distinct psychopathy-related profiles were identified via K-means clustering in an untargeted approach and exploratory analyses were done to assess associations with psychopathy scores. While gut and oral alpha and beta-diversity did not differ between psychopathy clusters, beta-diversity analyses revealed significant associations with psychopathy scores. Gut taxa Allisonella, Prevotella, Ruminococcaceae DTU089 and Cloacibacillus evryensis, as well as oral taxa Treponema vincentii exhibited differences in relative abundance between clusters. Allisonella, Prevotella, and C. evryensis showed significant positive associations with psychopathy scores, while T. vincentii was negatively associated. Fecal glucose and taurine levels differed between clusters, with both metabolites showing positive associations with psychopathy scores. These findings provide novel evidence linking gut and oral microbiota composition to subclinical psychopathic traits, highlighting potential pathways for understanding the biological basis of psychopathy.

Changes in the peripheral metabolome, particularly in the blood, may provide biomarkers for assessing lesion severity and predicting outcomes after spinal cord injury (SCI). Using principal component analysis (PCA) and Orthogonal Partial Least Squares Discriminatory Analysis (OPLS-DA), we sought to discover how SCI severity and location acutely affect the nuclear magnetic resonance-acquired metabolome of the blood, spinal cord, and liver at 6 h post-SCI in mice. Unsupervised PCA of the spinal cord metabolome separated mild (30 kdyne) and severe (70 kdyne) contusion injury groups but did not distinguish between lesion level. However, OPLS-DA could discriminate thoracic level T2 from T9 lesions in both blood plasma (accuracy 86 ± 6%) and liver (accuracy 89 ± 5%) samples. These differences were dependent on alterations in energy metabolites (lactate and glucose), lipoproteins, and lipids. Lactate was the most discriminatory between mild and severe injury at T2, whereas overlapping valine/proline resonances were most discriminatory between injury severities at T9. Plasma lactate correlated with blood-spinal cord barrier breakdown and plasma glucose with microglial density. We propose that peripheral biofluid metabolites can serve as biomarkers of SCI severity and associated pathology at the lesion site; their predictive value is most accurate when the injury level is also considered.

BACKGROUND:Reliable biomarkers for predicting disease progression in multiple sclerosis (MS) are crucial for advancing precision medicine and optimising treatment strategies. This study evaluates the predictive potential of serum nuclear magnetic resonance (NMR)-based metabolomics, individually and in combination with well-established biomarkers of neuroinflammation (serum glial fibrillary acidic protein, sGFAP) and axonal damage (neurofilament light chain, sNfL), in an extreme-phenotype subset of the Swiss Multiple Sclerosis Cohort (SMSC). METHODS:Serum samples were analysed using NMR-based metabolomics, along with quantification of sNfL and sGFAP. Supervised multivariate analysis was performed to differentiate MS phenotypes and identify future progressors. Multivariable receiver operating characteristic (ROC) analysis evaluated predictive performance, with key metabolite findings validated in an independent Oxford MS cohort. RESULTS:NMR-based metabolomics reliably distinguishes relapsing-remitting MS (RRMS) from secondary-progressive MS (SPMS) and predicts individual transitions. The identified predictive metabolites (lipoproteins, glutamine, alanine, valine, glucose) are also associated with progression independent of relapse activity (PIRA), a clinically relevant marker of sustained disability worsening. This demonstrates that the approach can both stage disease and forecast progression irrespective of stage. ROC analysis shows strong predictive performance (AUC = 0.81, p = 0.001), with external validation confirming robustness. Integration of NMR-metabolomics with sGFAP and sNfL further improves accuracy, yielding AUCs of 0.91 (p < 0.0001) and 0.87 (p = 0.0002), respectively, supported by independent validation. CONCLUSIONS:The integration of metabolic and protein biomarkers enables both accurate staging of RRMS versus SPMS and, critically, early prediction of progression irrespective of stage. This dual capability provides a clinically actionable, serum-based tool that can refine monitoring, improve therapeutic decision-making, and support a shift towards stage-agnostic, progression-focused care in MS.

The general nuclear magnetic resonance analysis toolbox (GNAT) has established itself as a versatile free and open-source software suite for processing and analyzing NMR data. Here, we present a major expansion for comprehensive metabolomics analysis. The module implements a whole NMR metabolomics pipeline in a single piece of software. This expansion includes a new suite of powerful statistical tools designed for tasks like classification, discrimination, and correlation analysis, including principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), orthogonal projections to latent structures-discriminant analysis (OPLS-DA), and statistical total correlation analysis (STOCSY). A set of preprocessing tools for binning and variable selection (interval partial least squares regression, iPLS and backward interval partial least squares regression, biPLS) before metabolomics analysis is also available. Graphical tools for outlier detection allow researchers to identify and address potential data inconsistencies. Model validation and application to unknown samples are straightforward and supported by relevant analytical figures of merit. All analyses done in the toolbox can be exported as reports in various formats (including .txt, .xml, and .mat). The standard version of the GNAT is intended to be run within MATLAB, but standalone compiled versions are also available. The new functionalities are demonstrated using a test data set for the classification of edible oils.

Abstract Background Osteoarthritis is a leading cause of pain and disability, yet the biological processes linking peripheral joint pathology with central pain mechanisms and wider symptom burden remain poorly defined. Methods We performed an integrated metabolomic and inflammatory analysis of cerebrospinal fluid and serum obtained from patients with osteoarthritis ( n = 81) and healthy pain-free controls ( n = 70). Proton nuclear magnetic resonance spectroscopy was used for metabolomic profiling, alongside targeted protein assays for inflammatory mediators. Orthogonal partial least squares discriminant analysis was applied to assess separation between groups and to determine diagnostic accuracy. Associations between metabolites and clinical outcomes, including pain intensity, disability and sleep disturbance, were examined, with adjustment for age and BMI. Results Clear separation between osteoarthritis and healthy pain-free control participants was observed in both biofluids, with classification accuracies of 87% for serum and 89% for cerebrospinal fluid. Reduced serum histidine, glutamine, albumin (lysyl) and lysine distinguished osteoarthritis from healthy controls. In cerebrospinal fluid, osteoarthritis was characterised by higher lactate and glutamate and lower glucose and glutamine compared to controls. Combining metabolomic data with inflammatory proteins increased diagnostic accuracy to 90% and remained significant after matching for age and BMI. Reductions in serum histidine and glutamine were consistent across subgroups, including stratification by pain severity. These metabolites correlated inversely with pain intensity, disability, sleep disturbance and overall symptom impact, and were more markedly altered in women, who also reported greater symptom burden. Conclusions Osteoarthritis is associated with a distinct pattern of peripheral and central metabolic disturbance. Histidine and glutamine emerge as promising biomarkers related to pain and clinical severity, highlighting metabolic pathways as potential targets for improved stratification and intervention in osteoarthritis pain.

Traumatic brain injury (TBI) significantly contributes to morbidity and mortality worldwide, often leading to cognitive decline. Although there is a recognised link between TBI and the acceleration of Alzheimer's disease (AD), the precise biological mechanisms driving this relationship are not fully understood. While several studies have investigated TBI in AD mouse models, none have examined the role of systemic inflammation in this context. In this study, we investigated the inflammatory responses, both centrally and peripherally, in 1-year-old wild-type (WT) and J20 mice (Tg:PDGFB-APPSwInd), overexpressing human amyloid precursor protein with the Swedish and Indiana mutations. Following controlled cortical impact (CCI) at 0.5 mm depth to the left somatosensory cortex, we examined outcomes at 1 and 7 days post-injury. The J20 mice exhibited a persistent sensorimotor impairment post-TBI, as determined by the adhesive removal test. Although amyloid-β42 deposition progressively increased post-injury, this behavioural deficit was not associated with greater neuronal loss compared to WT mice. Using qPCR, it was revealed that the level of proinflammatory cytokine and chemokine expression in the brain was largely conserved between WT and J20 mice, though brain Cxcl10 expression increased by 28.6 % in J20 mice at 7d-post injury compared to WT. However, J20 mice exhibited an exaggerated acute phase response (APR) to the TBI in the liver and spleen at 7d. Accompanying the potentiated APR, 1H NMR revealed that plasma glucose was decreased in J20 mice compared to WT at 7d. Taken together, this suggests that the sustained sensorimotor deficit in J20 mice is associated with increased amyloid-β pathology, and a dysregulated and prolonged systemic inflammatory response, accompanied by hypoglycaemia. In general, TBI in the presence of AD pathology, results in extended systemic inflammatory and metabolic responses that are likely to underpin the extended cognitive impairment, and our findings emphasise the need for customised interventions that address central and systemic inflammation after TBI in individuals with neurodegenerative disease.

Untargeted metabolic profiling of plasma and serum by liquid chromatography-mass spectrometry (LC-MS) is becoming increasingly important in clinical and translational research; however, sample preparation protocols can have a significant impact on study outcomes, and there is currently a lack of standardized approaches. In this study we demonstrate that pretreatment of serum and plasma samples with 1% formic acid (FA, v/v) prior to acetonitrile (MeCN)-induced protein precipitation significantly enhances analytical performance in untargeted metabolomics using reversed-phase liquid chromatography (RPLC)-MS. We show an increase in sample preparation reproducibility and signal intensity across both positive and negative ionization modes. In two independent serum cohorts (OPTIMA and VITACOG), FA-based extraction improved multivariate modeling (orthogonal partial least-squares discriminant analysis, OPLS-DA), with consistently higher classification accuracy, sensitivity, and specificity, alongside reduced variability and increased fold-changes in discriminatory compound-features. We investigated factors potentially involved in the enhanced performance and observed outcomes consistent with the disruption of noncovalent protein-metabolite interactions and the stabilization of labile species. We found no correlation with either protein depletion or differential adduct formation. The results were also not attributable to lowering pH after metabolite extraction. In summary, we demonstrate that FA pretreatment of plasma and serum, prior to protein precipitation, significantly improves sample reproducibility and detection sensitivity in untargeted RPLC-MS metabolomics. This optimized sample preparation strategy offers clear advantages for clinical and translational metabolomics, with the potential to enhance biomarker discovery and metabolic phenotyping.

A, OPLS-DA plot showing separation of unwell patients with solid tumor diagnoses (blue, filled) from unwell patients with noncancer diagnoses (black, open). B, Sensitivity (dots), specificity (dashes), and F1 score (continuous) for solid tumors versus unwell patients without cancer (blue), or metastatic versus nonmetastatic cancers (green) at all possible thresholds of classification according to Component 1. Vertical dashed lines show optimal classification threshold for each model. C, ROC curves for classification between unwell with solid tumors versus unwell without cancer diagnoses (blue line; model in A), and metastatic versus nonmetastatic diagnoses (green line; model in C). Small, colored circles on lines indicate points closest to top-left corner, corresponding to dashed vertical lines in B. D, OPLS-DA plot showing separation of patients with nonmetastatic cancer diagnoses (red stars) or metastatic cancer diagnoses (green circles).

Study schematic showing patient recruitment into the study, exclusions, biofluid collection, and confirmed diagnoses.

A, Fold changes in key metabolites identified by multivariate analysis concentrations in unwell patients with solid tumors relative to the mean metabolite concentrations in unwell patients without cancer. B, Fold changes in key metabolite concentrations in patients with metastatic cancer, relative to the mean metabolite concentration in patients with nonmetastatic cancer. C, Venn diagram illustrating direction of metabolite concentration changes in metastatic and nonmetastatic cancers, relative to unwell patients without cancer. HDL, high-density lipoprotein. Note that “/” represents that the two metabolites overlap in the NMR data, and not a ratio of the two metabolite concentrations. Individual plots of metabolite concentrations are given in Supplementary Fig. S4.

Metabolomics is a rapidly growing multidisciplinary field with ever increasing demand and usability, which is attracting a surge of new researchers. While their varied skill sets, scientific questions, and approaches enrich the field with fresh perspectives and innovation, individual investigators also bring wide-ranging levels of metabolomics-specific experience and diverse areas of interest. These factors introduce considerable variability and inconsistency in both the methodology and reporting. A recent comparative literature review of nuclear magnetic resonance (NMR) metabolomics from studies published in 2010 and 2020 revealed significant shortcomings in the reporting of experimental details necessary for evaluating both the scientific rigor and the reproducibility of NMR-based metabolomics experiments. Each stage of metabolomics research contains multiple methodological choices and various optimization parameters, all of which can introduce experimental bias and alter the study results. This emphasizes the need for proper reporting to enhance reproducibility, data reusability, and study comparability. To address these concerns, the NMR Special Interest Group within the Metabolomics Association of North America presents reporting recommendations focused on fundamental aspects of NMR metabolomics research identified from the detailed literature review report. These include specifics with respect to study design, sample preparation, data acquisition, data processing and analysis, data accessibility, and comparability to previous studies. Also presented is a complementary list of seminal papers in the field to guide the study design and implementation of NMR metabolomics experiments. This initiative seeks to enhance the long-term impact of NMR metabolomics by supporting high-quality, reproducible, and impactful data collected from well-executed and thoroughly reported studies.

Background Predicting disease progression in multiple sclerosis (MS) remains challenging. PET imaging with 18 kDa translocator protein (TSPO) radioligands can detect microglial and astrocyte activation beyond MRI-visible lesions, which has been shown to be highly predictive of disease progression. We previously demonstrated that nuclear magnetic resonance (NMR)-based metabolomics could accurately distinguish between relapsing-remitting (RRMS) and secondary progressive MS (SPMS). This study investigates whether combining TSPO imaging with metabolomics enhances predictive accuracy in a similar setting.Methods Blood samples were collected from 87 MS patients undergoing PET imaging with the TSPO-binding radioligand 11C-PK11195 in Finland. Patient disability was assessed using the expanded disability status scale (EDSS) at baseline and 1 year later. Serum metabolomics was performed to identify biomarkers associated with TSPO binding and disease progression.Results Greater TSPO availability in the normal-appearing white matter and perilesional regions correlated with higher EDSS. Serum metabolites glutamate (p=0.02), glutamine (p=0.006), and glucose (p=0.008), detected by NMR, effectively distinguished future progressors. These three metabolites alone predicted progression with the same accuracy as TSPO-PET imaging (AUC 0.78; p=0.0001), validated in an independent cohort. Combining serum metabolite data with PET imaging significantly improved predictive power, achieving an AUC of 0.98 (p<0.0001).Conclusion Measuring three specific serum metabolites is as effective as TSPO imaging in predicting MS progression. However, integrating TSPO imaging with serum metabolite analysis substantially enhances predictive accuracy. Given the simplicity and affordability of NMR analysis, this approach could lead to more personalised, accessible treatment strategies and serve as a valuable tool for clinical trial stratification.

Background: Mass spectrometry (MS) and nuclear magnetic resonance (NMR) have emerged as pivotal tools in biofluid metabolomics, facilitating investigation of disease mechanisms and biomarker discovery. Despite complementary capabilities, these techniques are rarely combined, although their integration is often beneficial. Typically, different sample preparation approaches are used, and compatibility challenges potentially arise due to the requirement for deuterated buffered solvents in NMR but not MS techniques. Additionally, MS-based approaches necessitate protein removal from samples whilst in NMR proteins can be potentially useful biomarkers. In this study, we developed a blood serum preparation protocol enabling sequential NMR and multi-LC-MS untargeted metabolomics analysis using a single serum aliquot in a research discovery setting. Results: We analysed human serum samples using various untargeted NMR and multi-LC-MS platforms to assess the impact of deuterated solvents and buffers on detected compound-features. Employing multiple LC-MS profiling approaches, we observed no evidence of deuterium incorporation into metabolites following sample preparation with deuterated solvents. Furthermore, we demonstrated that buffers used in NMR were well tolerated by LC-MS. Protein removal, involving both solvent precipitation and molecular weight cut-off (MWCO) filtration, was identified as a primary factor influencing metabolite abundance. Our findings led to the development and validation of a serum sample preparation protocol enabling a combined NMR and multi-LC-MS analysis. Significance: Using a single clinical serum aliquot for simultaneous untargeted profiling via NMR and multi-LC-MS represents a highly efficient alternative to current methods. This approach reduces sample volume requirements and substantially expands the potential for broader metabolome coverage. Our study offers comprehensive insights into the impact of sample preparation on complex metabolic biofluid profiles, highlighting the compatibility and complementarity of LC-MS and NMR in metabolomics research.

Example patient journeys showing how metabolomics and multidisciplinary diagnostic center workup provided different diagnoses.

BACKGROUND:The amino acid L-alanine, has been shown to be elevated in biofluids during major depression but its relevance remains unexplored. AIM:We have investigated the effects of repeated L-alanine administration on emotional behaviours and central gene expression in mice. METHODS:Mice received a daily, 2-week intraperitoneal injection of either saline or L-alanine at 100 or 200 mg/kg and were exposed to the open field, light-dark box and forced swim test. The expression of L-alanine transporters (asc-1, ASCT2), glycine receptor subunits (GlyRs), NMDA receptor subunits (GluNs) mRNAs were measured, together with western blots of the signalling protein mammalian target of rapamycin (mTOR). Since L-alanine modulates glucose homeostasis, peripheral and central metabolomes were evaluated with 1H-NMR. RESULTS:L-alanine administration at 100 mg/kg, but not at 200 mg/kg, to both male and female mice increased latency to float and reduced floating time in the forced swim test, but had no effect on anxious behaviour in the open field and light-dark box tests. There was a significant reduction in mRNAs encoding asc-1 and ASCT2 and GluN2B in the hippocampus of mice following 100 mg/kg L-alanine only. On western blots, hippocampal GluN2B immunoreactivity was reduced, but mTOR signalling was increased in the 100 mg/kg L-alanine group. 1H-NMR revealed gender-specific changes in the forebrain, plasma and liver metabolomes only at 200 mg/kg of L-alanine. CONCLUSIONS:Our data suggest that L-alanine may have antidepressant-like effect that may involve the modulation of glutamate neurotransmission independently of metabolism. In major depression, therefore, elevated L-alanine may be a homeostatic response to pathophysiological processes, though this will require further investigation.

The rapid development and worldwide distribution of COVID-19 vaccines is a remarkable achievement of biomedical research and logistical implementation. However, these developments are associated with the risk of a surge of substandard and falsified (SF) vaccines, as illustrated by the 184 incidents with SF and diverted COVID-19 vaccines which have been reported during the pandemic in 48 countries, with a paucity of methods for their detection in supply chains. In this context, matrix-assisted laser desorption ionisation-time of flight (MALDI-ToF) mass spectrometry (MS) is globally available for fast and accurate analysis of bacteria in patient samples, offering a potentially accessible solution to identify SF vaccines. We analysed the COVISHIELD™ COVID-19 vaccine; falsified versions of which were found in India, Myanmar and Uganda. We demonstrate for the first time that analysis of spectra from the vaccine vial label and its adhesive could be used as a novel approach to detect falsified vaccines. Vials tested by this approach could be retained in the supply chain since it is non-invasive. We also assessed whether MALDI-ToF MS could be used to distinguish the COVISHIELD™ vaccine from surrogates of falsified vaccines and the effect of temperature on vaccine stability. Both polysorbate 80 and L-histidine excipients of the genuine vaccine could be detected by the presence of a unique combination of MALDI-ToF MS peaks which allowed us to distinguish between the genuine vaccines and falsified vaccine surrogates. Furthermore, even if a falsified product contained polysorbate 80 at the same concentration as used in the genuine vaccine, the characteristic spectral profile of polysorbate 80 used in genuine products is a reliable internal marker for vaccine authenticity. Our findings demonstrate that MALDI-ToF MS analysis of extracts from vial labels and the vaccine excipients themselves can be used independently to detect falsified vaccines. This approach has the potential to be integrated into the national regulatory standards and WHO’s Prevent, Detect, and Respond strategy as a novel effective tool for detecting falsified vaccines.

BACKGROUND AND OBJECTIVES:Differentiating multiple sclerosis (MS) from antibody (Ab)-defined diseases, such as neuromyelitis optica spectrum disorders (NMOSDs), remains challenging, particularly as Ab levels decline. N-glycans play a key role in immunity, with changes in branching and fucosylation linked to T/B-cell function and MS onset while increased N-acetylglucosamine residues correlate with disease progression. Despite growing recognition of glycosylation in neuroinflammation, direct comparisons of the N-glycome between MS and Ab-defined diseases are lacking. This study aims to assess whether plasma N-glycome profiling can effectively differentiate these conditions and their subtypes. METHODS:This cohort study included 120 participants: 30 with relapsing-remitting MS (RRMS), 30 with secondary progressive MS (SPMS), 30 with myelin oligodendrocyte glycoprotein Ab-associated disease (MOGAD), and 30 with aquaporin-4 (AQP4)-Ab NMOSD, recruited from the John Radcliffe Hospital, Oxford University Hospitals National Health System (NHS) Trust. Plasma N-glycans were analyzed using ultra-high-performance (UHPLC) hydrophilic interaction liquid chromatography (HILIC) coupled with high-resolution mass spectrometry. Orthogonal partial least-squares discriminant analysis was applied to identify disease-specific glycomic patterns. RESULTS:Distinct N-glycome profiles were identified across diseases and phenotypes. Plasma N-glycans differentiated MS from Ab-defined diseases with 80.5% accuracy (±1.5%), MOGAD from AQP4-Ab NMOSD with 77.8% accuracy (±3.1%), and RRMS from SPMS with 75.2% accuracy (±3.6%). Key discriminatory features included increased monosialylation (S1; odds ratio [OR] = 2.57, p < 0.0001), trigalactosylation (G3; OR = 2.70, p < 0.0001), highly branched N-glycans (OR = 2.32, p = 0.0002), and antennary fucosylation (OR = 2.89, p < 0.0001), effectively distinguishing Ab-defined diseases from MS, independent of Ab serostatus at the time of sampling. DISCUSSION:These findings underscore the potential of plasma N-glycomics as a diagnostic tool for neuroinflammatory diseases. While further research is needed to clarify the mechanistic links between glycomic alterations and disease pathology, our results suggest that plasma N-glycan profiling could improve disease classification. Given its noninvasive and cost-effective nature, this approach holds promise as a complementary diagnostic tool for CNS demyelinating diseases in clinical practice.

INTRODUCTION:Elevated total homocysteine (tHcy) is a major predictor of brain atrophy, cognitive decline, and Alzheimer's disease (AD) progression. The VITACOG trial, a randomized, placebo-controlled study in mild cognitive impairment (MCI), previously showed that B vitamin supplementation lowered tHcy, slowing brain atrophy and cognitive decline; however, the underlying mechanisms remained unclear. METHODS:We used untargeted, multi-platform metabolomics, with nuclear magnetic resonance and liquid chromatography-mass spectrometry to analyze serum samples from 89 B vitamin-treated and 84 placebo-treated MCI participants over a 2 year follow-up period. RESULTS:Multivariate modeling distinguished treated from placebo groups with 91.2 ± 1.8% accuracy. B vitamin supplementation induced significant metabolic reprogramming, lowering quinolinic acid, α-ketoglutarate, α-ketobutyrate, glucose, and glutamate. DISCUSSION:These findings reveal that B vitamins influence metabolic pathways beyond tHcy reduction, particularly the tricarboxylic acid cycle and glutamine-glutamate cycling, critical for brain energy homeostasis and neurotransmission. This metabolic signature supports B vitamin supplementation as a strategy for slowing MCI progression. HIGHLIGHTS:Nuclear magnetic resonance and multi-platform liquid chromatography tandem mass spectrometry metabolomics were performed on serum samples from 89 B vitamin-treated and 84 placebo participants in the VITACOG trial. Multi-platform metabolomics revealed B vitamin-driven metabolic reprogramming, achieving 91% classification accuracy. B vitamin supplementation modulates key neuroprotective metabolic pathways. Regulation of energy metabolism and neurotransmission by B vitamins contributes to brain health in elderly individuals. B vitamins demonstrate potential as an adjunct therapy in mild cognitive impairment, potentially mitigating progression to Alzheimer's disease.

A, Mean NMR spectra for samples from unwell patients with either confirmed solid tumor diagnoses (red, n = 17) or confirmed noncancer diagnoses (black, n = 175). B, Difference spectrum showing regions that were increased in patients with solid tumors (red), decreased in patients with solid tumors (blue), or unchanged (gray). C, Insets showing magnified regions at points of significant difference between unwell with solid tumor spectra (red) and unwell without cancer spectra (black). NAC, N-acetylated glycoproteins.