Metabolites

BackgroundKnee osteoarthritis (OA) is a prevalent painful degenerative disease without effective disease-modifying drugs. The rising prevalence of comorbid obesity and knee OA underscores the urgent need for effective management to delay or prevent disease progression. In a recently completed randomized, placebo-controlled trial in adults with comorbid obesity (BMI >30 kg/m{superscript 2}) and unilateral or bilateral knee OA (Kellgren-Lawrence grade II-III), we were the first to demonstrate that a 6-month prebiotic intervention (16 g/day oligofructose-enriched inulin) significantly improved physical function and metabolic health. MethodsTo elucidate the underlying mechanisms, we incorporated metagenomics, metabolomics, and machine-learning-based multi-omics integration in 30 participants who completed baseline and at least one follow-up assessment and sample collection at months 3 and 6. ResultsPrebiotic supplementation reshaped gut microbial composition and function, increasing diet-derived carbohydrate availability, mitigating excessive host-glycan degradation and mucosal barrier disruption, reducing systemic inflammation and metabolic dysregulation, and ultimately improved physical performance and metabolic health. In a diet-induced obese rat model, prebiotic treatment reduced tibial cartilage degeneration and synovial membrane thickening, providing protection against OA onset and progression through a shared inflammatory pathway. ConclusionsOur findings provide mechanistic evidence supporting the therapeutic potential of prebiotic supplementation as a conservative management in humans and as a preventive approach for obesity-related knee OA in a preclinical rat model, mediated through the gut-joint axis. Trial registrationClinicaltrials.govNCT04172688

BackgroundRheumatoid arthritis (RA) is a chronic inflammatory disease characterized by metabolic dysregulation, including altered lipid metabolism. While polyunsaturated fatty acids have been studied, the plasma levels, endogenous synthesis, and relevance of monounsaturated fatty acids (MUFAs) in RA remain unclear. This study examined plasma MUFA levels in RA and their associations with disease activity, adiposity, and intake. MethodsIn this cross-sectional study, 59 individuals with rheumatoid arthritis (RA) and 33 non-RA controls frequency-matched on age, sex, and BMI were recruited between 2017 and 2022. Clinical assessments included disease activity (DAS28), body composition, and metabolic parameters. Dietary intake was assessed using a 4-day food journal, and plasma fatty acids were quantified by gas chromatography in 82 participants with available samples. The stearoyl-CoA desaturase-1 (SCD-1) index was used as a proxy for endogenous MUFA synthesis. Associations between MUFAs and clinical variables were evaluated using univariate and multivariable regression (p<0.05). ResultsRA participants had higher waist-to-hip ratio, fat mass, fasting triglycerides, and lower physical activity than controls. Plasma palmitoleic and oleic acids and the SCD-1 index were higher in RA, whereas linoleic and arachidonic acids were lower. Saturated and omega-3 fatty acids were similar. Higher oleic and gondoic acids were independently associated with greater disease activity; oleic acid was linked to central adiposity, and palmitoleic acid was higher in women, suggesting sex- and adiposity-specific regulation. ConclusionsHigher plasma MUFAs in RA are associated with disease activity, adiposity, and sex, highlighting altered MUFA metabolism as a feature of RA and a potential target for metabolic intervention. Key MessagesO_ST_ABSWhat is already known on this topicC_ST_ABSRheumatoid arthritis (RA) involves systemic inflammation and altered lipid metabolism. While polyunsaturated fatty acids have been studied extensively, the plasma levels, endogenous synthesis, and clinical relevance of monounsaturated fatty acids (MUFAs) in RA remain unclear. What this study addsPatients with RA have higher plasma MUFAs, including oleic and palmitoleic acids, and an elevated SCD-1 index, a marker of endogenous MUFA synthesis. Higher MUFAs are associated with disease activity, central adiposity, and sex-specific patterns, independent of dietary intake. How this study might affect research, practice or policyPlasma MUFAs could serve as potential biomarkers of RA disease activity and metabolic dysregulation. These findings suggest that altered MUFA metabolism contributes to inflammatory pathways, highlighting a potential target for future research, nutritional interventions, or therapeutic strategies.

BackgroundMetformin is the cornerstone therapy for type 2 diabetes, but gastrointestinal intolerance commonly limits dose escalation and long-term adherence. In the ProGasMet trial, multi-strain probiotic supplementation improved metformin tolerability. However, the underlying microbiome-metabolome mechanisms remain unclear. Methods and analysisWe performed an exploratory multi-omics analysis using Period 1 of a randomized, double-blind, placebo-controlled trial. Participants with metformin intolerance received a multi-strain probiotic or placebo for 12 weeks. Paired stool samples collected at baseline (Visit 2) and end of treatment (Visit 5) were available from 34 participants (68 samples). We integrated shotgun metagenomic species profiles, predicted gut metabolic modules, and untargeted faecal LC-MS metabolomics using multi-block sparse PLS (DIABLO), complemented by longitudinal feature-level analyses and associations with gastrointestinal symptom burden (QACSMI and a simplified GI score). ResultsMulti-omics integration showed moderate concordance across taxonomic, functional, and metabolomic blocks and separated probiotic from placebo profiles at 12 weeks. Bile acid-related metabolites were among the strongest contributors to group separation, with hyodeoxycholic acid and related compounds enriched in the probiotic arm. Global biodiversity and community-wide turnover did not differ materially between groups. Feature-level analyses suggested modest, directionally coherent changes in selected taxa, functional modules, and metabolites. Higher hyodeoxycholic acid concentrations at Visit 5 were associated with lower gastrointestinal symptom burden in probiotic-treated participants, a pattern not observed under placebo; statistical support was exploratory. ConclusionProbiotic supplementation may be associated with coordinated microbiome-metabolome shifts in metformin-intolerant type 2 diabetes, highlighting bile acid remodelling, particularly hyodeoxycholic acid, as a plausible mechanistic candidate for improved tolerability.

Gout is driven by an interleukin-1{beta}-mediated intense innate immune reaction to monosodium urate (MSU) crystals (MSUc). In cell culture models of inflammatory gout there is a synergistic effect of phagocytosis of MSUc and TLR2 and TLR4 activation by agonists such as free fatty acid and lipopolysaccharide (LPS) in NLRP3-inflammasome activation and IL-1{beta} secretion. A substantial number of gout patients do not report a dietary trigger, and observational studies associate airborne particulate matter with incident gout and flares. Airborne particulate matter contains LPS and airborne-derived particulate matter stimulates IL-1{beta} secretion in cell culture. We hypothesized that air-borne particulate matter could co-stimulate, with MSUc, IL-1{beta} secretion and inflammation. We tested the hypothesis using MSUc with extracted airborne PM4 in human cells (the THP-1 monocyte cell line, primary human monocytes and PBMCs) or carbon black particles with ozone (CB+O3) in a murine foot-pad injection model of gout. There was strong NLRP3-inflammasome-dependent co-stimulation of IL-1{beta} secretion in THP-1 cells with PM4+MSUc and a moderate additive effect in primary human PBMCs. However, there was no added effect on IL-1{beta} secretion of PM4 in isolated primary human monocytes. Inhalation of CB+O3 persistently exacerbated MSUc-induced murine paw inflammation, with an increase of alveolar/lavage macrophages that contained CB+O3 particles and increased lavage expression of IL-1{beta}. In conclusion, airborne-derived PM4 particulate matter enhanced MSUc-induced IL-1{beta} secretion in THP-1 cells and PBMCs. Combined with exacerbation of MSUc-induced inflammation by fine particulate matter in in vivo experiments, these data provide evidence that exposure to fine particulate matter may play a role in the etiology of gout.

Background Short-chain fatty acids (SCFAs) are widely used as functional readouts of gut microbial activity in vivo. The growing adoption of decentralised study designs and self-collection protocols has amplified the need for reliable room-temperature storage and shipment strategies. However, SCFAs volatility and the persistence of post-collection microbial metabolism raise concerns regarding pre-analytical stability and the interpretability of measured concentrations. Methods We assessed the temporal stability of fatty acids (FAs) across intestinal and systemic matrices under room-temperature storage. Untreated stool was compared with two nucleic acid stabilisation devices (eNAT and OMNIgene-GUT), while whole blood, plasma and dried blood spots (DBS) were evaluated as minimally invasive systemic sampling strategies. Profiles were quantified using complementary GC-MS and LC-MS/MS workflows. Results Untreated stool showed fermentation-driven increases in major SCFAs, whereas immediate freezing preserved baseline profiles. eNAT maintained faecal FA stability for up to 21 days, while OMNIgene-GUT exhibited baseline and time-dependent alterations. In systemic matrices, plasma and whole blood showed upward drift, whereas DBS declined initially before stabilising after approximately 14 days. Conclusions FA measurements are highly matrix- and device-dependent. Our findings provide practical guidance for the selection of sampling strategies in microbiome-associated FA studies and emphasise the need for controlled pre-analytical conditions in decentralised microbiome studies.

Aims/hypothesisImmunotherapies such as Teplizumab can preserve residual beta cell function in individuals with newly diagnosed type 1 diabetes (T1D), but treatment response is variable. Currently, no biomarker exists to identify individuals most likely to benefit from immunotherapy. We believe that baseline serum metabolomic profiles can distinguish individuals who respond to treatment from nonresponders and predict therapeutic response. MethodsBaseline serum samples from 41 individuals newly diagnosed with T1D enrolled in the AbATE trial (NCT00129259) were analyzed to identify metabolic predictors of response to Teplizumab therapy in the AbATE trial. Responders to Teplizumab, as per study protocol, were defined as individuals who exhibited less than a 40% decline in baseline C-peptide levels at 2 years after start of treatment. We analyzed baseline serum samples using a semi-targeted metabolomics approach via liquid chromatography-high-resolution tandem mass spectrometry. Metabolites that were significantly different between responders and nonresponders were identified (P < 0.05), and the significant metabolites were used to train a supervised Random Forest model to predict treatment response. Model performance was evaluated using a 70/30 training/testing split, 5-fold cross-validation, bootstrap resampling (1,000 iterations), and permutation testing (1,000 permutations). ResultsWe identified 15 significantly different metabolites at baseline between responders and nonresponders (P < 0.05). These metabolites included amino acids and their derivatives, tricarboxylic acid (TCA) cycle intermediates, and microbially derived metabolites. At baseline, responders exhibited higher levels of TCA cycle metabolites, amino acid derivatives, and microbial metabolites, whereas nonresponders showed elevated levels of glutamate and acylcarnitines. The Random Forest classifier achieved an accuracy of 0.769 and an area under the receiver operating characteristic curve (AUC) of 0.881 in the test dataset. Cross-validation yielded a mean AUC of 0.856 (SD 0.156; 95% CI 0.719-0.992). Bootstrap analysis produced a test AUC 95% CI of 0.619-1.000, and permutation testing confirmed significance (p = 0.012). Conclusions/interpretationBaseline serum metabolomic signatures can predict responders to Teplizumab with high accuracy. This could potentially be applicable when considering other immunotherapies in preventative efforts in T1D. Trial registrationClinicalTrials.gov NCT00129259. Research in ContextO_ST_ABSWhat is already known about this subject?C_ST_ABSO_LITeplizumab can delay beta cell decline in individuals with newly diagnosed T1D, but treatment response varies. C_LIO_LINo validated biomarkers currently exist to predict which individuals will respond to immunotherapy. C_LIO_LIMetabolomic profiling has shown potential for identifying metabolic signatures associated with disease progression and immune activity in T1D. C_LI What is the key question?O_LICan baseline serum metabolomic profiles predict which individuals with newly diagnosed T1D will respond to Teplizumab therapy? C_LI What are the new findings?O_LIFifteen baseline metabolites differed significantly between responders and nonresponders, including amino acid derivatives, tricarboxylic acid cycle intermediates, and microbially derived metabolites. C_LIO_LIResponders exhibited metabolic signatures consistent with preserved beta cell function and enhanced mitochondrial and immune-regulatory activity. C_LIO_LIA Random Forest model developed using these metabolites accurately predicted treatment response (AUC 0.881), demonstrating strong predictive potential. C_LI How might this impact on clinical practice in the foreseeable future?O_LIBaseline metabolomic profiling could support personalized treatment strategies by identifying individuals most likely to benefit from treatment with Teplizumab or other immunotherapies. C_LI

Background and aimsThe optimal eating window for time-restricted eating (TRE) remains unclear. We investigated the effects of 8-hour TRE combined with usual care (UC, a Mediterranean diet-based education program), versus UC alone over 12 weeks on hepatic fat fraction, liver health markers, and fecal microbiota in adults with overweight or obesity. MethodsIn this multicenter randomized trial, participants (50% women) were assigned to UC (n=49), early TRE (n=49), late TRE (n=52), or self-selected TRE (n=47). Hepatic fat fraction was assessed by MRI; liver markers included elastography-based parameters, liver enzymes, and circulating biomarkers. Fecal microbiota was analyzed by 16S rRNA gene sequencing. ResultsHepatic fat fraction decreased significantly within the three TRE groups (all P[&le;]0.02), but no between-group differences were observed when comparing early TRE (mean difference [MD]: -0.4%; P=0.95), late TRE (MD: -1.5%; P=0.15), and self-selected TRE groups (MD: -0.7%; P=0.77) with the UC group, or among the TRE groups themselves (all P[&ge;]0.41). Similarly, no between-group differences were found in liver health markers and fecal microbiota. Participants with metabolic dysfunction-associated steatotic liver disease at baseline as well as those achieving [&ge;]5% weight loss had greater reductions in hepatic fat fraction than those who did not (MD: -2.7 and -2.6%; respectively, both P<0.001). A higher proportion of participants in the TRE groups achieved [&ge;]5% weight loss compared with UC (41-44% vs 16%; P=0.001). ConclusionThese findings suggest that the timing of the eating window in TRE may not impact liver fat or microbiota composition beyond the effects of weight loss, though the study was not powered for secondary outcomes. The study was registered on ClinicalTrials.gov (identifier: NCT05310721)

Introduction and ObjectivesMetabolic dysfunction-associated steatotic liver disease (MASLD) affects about one-quarter of adults worldwide, and liver fibrosis is its strongest predictor of liver-related morbidity and mortality. Using combined in-silico screening and clinical validation, we aimed to identify circulating biomarkers associated with fibrosis progression. Fibulin-3 was identified, and its diagnostic performance was evaluated in biopsy-proven MASLD cohorts. Materials and MethodsThe GSE125251 RNA-seq dataset was reanalyzed to compare liver transcriptomes from MASLD subjects with minimal (F0-F1) versus moderate to advanced fibrosis (F2/F3-F4). Differentially expressed genes (DEGs) were filtered to retain plasma-secreted, protein-coding candidates. Top-ranked genes were evaluated in liver biopsies from a morbidly obese cohort (n = 65) stratified by fibrosis stage, and their plasma levels were measured via ELISA in an independent bariatric cohort (n = 225). ResultsAmong 106 DEGs, 22 encoded plasma-circulating proteins. Six top candidates (EFEMP1, LTBP2, LUM, DPT, CHI3L1, CCL20) were prioritized. EFEMP1 (Fibulin-3) showed the strongest association with fibrosis, with significantly higher hepatic mRNA and protein expression in F2/F3-F4 versus F0-F1 (p < 0.005). Plasma Fibulin-3 levels correlated with fibrosis stage ({rho} = 0.40, p < 0.0001), increasing from 9.4 ng/mL in F0-F1 to 21.7 ng/mL in F2/F3-F4. Its diagnostic performance for F [&ge;] 2 (AUROC = 0.78) exceeded that of APRI, FIB-4, NFS, and HSI. A combined index including Fibulin-3, HSI, platelets, and GGT increased the AUROC to 0.87 (CI: 0.79-0.92). ConclusionsPlasma Fibulin-3 is notably higher in individuals with advanced MASLD and represents a promising non-invasive biomarker for liver fibrosis stratification in metabolically unhealthy obese populations.

Mitochondrial dysfunction has long been associated with insulin resistance, yet the causal relationship in humans remains unresolved. Here, we leveraged individuals carrying the diabetogenic m.3243A>G mtDNA mutation as a human genetic model to probe the causal contribution of mitochondrial defects to insulin resistance and delineate the underlying molecular and bioenergetic mechanisms. In vivo metabolic phenotyping revealed selective skeletal muscle insulin resistance with preserved liver and adipose insulin sensitivity, accompanied by impaired glucose tolerance, {beta}-cell dysfunction, and elevated circulating levels of the mitochondrial stress-responsive cytokine GDF15. At the molecular level, this muscle-specific insulin-resistant phenotype featured preserved insulin-stimulated Akt-TBC1D4 signaling but blunted mTORC1 activation. Integrated muscle proteomic and bioenergetic profiling demonstrated reduced mitochondrial protein abundance and complex I-specific molecular and functional impairments, alongside downregulation of non-mitochondrial metabolic proteins including AMPK{gamma}2. In summary, our study establishes a mitochondrial basis for insulin resistance in humans, linking reduced mitochondrial content and complex I-related defects to disrupted mTORC1 signaling and impaired muscle insulin action. These findings highlight mitochondria-dependent molecular signatures of insulin resistance that may hold translational relevance for improving glucose regulation in common metabolic diseases. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/25342274v1_ufig1.gif" ALT="Figure 1"> View larger version (75K): org.highwire.dtl.DTLVardef@7b7f35org.highwire.dtl.DTLVardef@1dc7a0dorg.highwire.dtl.DTLVardef@1d121c7org.highwire.dtl.DTLVardef@10094da_HPS_FORMAT_FIGEXP M_FIG GRAPHICAL ABSTRACT C_FIG

BackgroundMetabolic dysfunction-associated steatotic liver disease (MASLD) is the fastest-growing etiology of hepatocellular carcinoma (HCC). A mechanistic understanding of the metabolic heterogeneity of MASLD-driven tumors is crucial to inform strategies for future treatment options. MethodsPaired tumor (n=8) and adjacent non-tumor tissue (n=8) were collected from patients with steatohepatitic HCC at the University of Kentucky Markey Cancer Center. Hematoxylin and eosin (H&E) staining was used for pathological determination of tumor and adjacent nontumor tissue by a board-certified pathologist. Lipidomic, metabolomic, and transcriptomic analyses were performed, and data were integrated across platforms to identify novel relationships across tumor and adjacent nontumor tissue. ResultsHistological analysis by H&E showed significant lipid vacuole accumulation and inflammatory foci in HCC tumors relative to nontumor tissue. Across omics platforms, we identified 1,679 genes, 1,696 metabolites, and 292 lipids that were significantly (padj<0.01) increased or decreased in tumors relative to nontumor tissue. We identified significant reductions in total ceramides and increases in fatty acyl chain saturation in tumor tissue. Furthermore, metabolites involved in amino acid and fatty acid metabolism were largely decreased in tumors relative to nontumor tissue. We also identified a total of 303 highly significant and novel transcript-metabolite associations (117 gene-metabolite; 186 gene-lipid) across tumor and nontumor tissue. ConclusionsTaken together, this integrative analysis reveals novel relationships between steady-state gene transcripts and specific metabolites in steatohepatitic tumors, thereby identifying new pharmacological targets that may be exploited for therapeutic benefit.

Background and AimMASLD affects 30-38% of Indian adults, yet the contribution of genetic risk variants to disease susceptibility and fibrosis progression remains poorly characterised. We investigated the association of 12 candidate SNPs with MASLD susceptibility and fibrosis severity in North Indian patients, benchmarking allele frequencies against IndiGenomes and global populations. MethodsSixty-nine MASLD patients (75.4% male; median BMI 29.8 kg/m{superscript 2}) from a tertiary care liver clinic in New Delhi were genotyped for 12 SNPs using Illumina custom BeadChip array and Sanger sequencing. Patients were stratified by liver stiffness measurement (LSM): significant fibrosis ([&ge;]8 kPa, n=38) versus no significant fibrosis (<8 kPa, n=31). Allele frequencies were compared with IndiGenomes ([~]1,020 Indian individuals) and 1000 Genomes populations. ResultsPNPLA3 rs738409 G allele was the strongest within-cohort predictor of significant fibrosis (allelic OR 2.89, 95% CI 1.35-6.19, P=0.006; dominant model OR 3.94, P=0.008), with carriers demonstrating higher LSM (median 15.6 vs. 7.5 kPa, P=0.005). SAMM50 rs3761472 (OR 2.12, P=0.065) and FTO rs9939609 (OR 2.08, P=0.089) showed non-significant trends. In the population-level comparison, APOC3 rs2854116 T allele was the only variant significantly enriched after Bonferroni correction (64.0% vs. 47.9%; OR 1.93, 95% CI 1.35-2.77, P<0.001), followed by PNPLA3 (33.3% vs. 24.1%, OR 1.57, P=0.019) and SAMM50 (31.2% vs. 22.6%, OR 1.55, P=0.028). Notably, APOC3 showed no association with fibrosis (OR 0.96, P=1.000), suggesting a role in susceptibility rather than progression. All SNPs were in Hardy-Weinberg equilibrium. ConclusionsThis study reveals a dissociation between genetic determinants of MASLD susceptibility and fibrosis progression in North Indian patients. APOC3 rs2854116 predisposes to MASLD at the population level, while PNPLA3 rs738409 drives fibrosis severity within established disease, underscoring the need for ancestry-specific genetic risk stratification. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/26347059v1_ufig1.gif" ALT="Figure 1"> View larger version (69K): org.highwire.dtl.DTLVardef@187f189org.highwire.dtl.DTLVardef@25d3borg.highwire.dtl.DTLVardef@13704e9org.highwire.dtl.DTLVardef@1238cce_HPS_FORMAT_FIGEXP M_FIG C_FIG

BackgroundThe effects of aspirin on hepatic steatosis and fibroinflammation are unclear. The study aimed to examine the association between aspirin use and liver MRI-derived liver fat and corrected T1 (cT1). MethodsWe used UK Biobank imaging cohort data. Aspirin use was self-reported at baseline and imaging assessment, and the main exposures were aspirin use at imaging assessment and longitudinal aspirin use patterns (never users, initiators, discontinuers, vs. persistent users). Outcomes were MRI-derived liver fat (%) and cT1 (ms). Multivariable adjustment analyses and inverse probability of treatment weighting (IPTW) analyses were performed, accounting for demographic, lifestyle and clinical factors. ResultsWe included 36413 participants (age 64.6 years, 51.4% female). Aspirin use at imaging assessment was associated with lower liver fat (-0.35 (95% CI: -0.51, - 0.20)) and slightly higher cT1 (5.13 (95% CI: 3.23, 7.03)). Analyses on longitudinal aspirin use pattern showed that compared to never users, initiators and persistent users showed lower liver fat (-0.48 (-0.69, -0.28) and (-0.24 (-0.45, -0.02)) and higher cT1 (2.94 (0.38, 5.49) and 8.31 (5.65, 10.97)). IPTW analyses showed consistent results. ConclusionIn this large population-based cohort, aspirin use was linked to reduced liver fat, but a small, clinically insignificant (i.e. <80ms) increase in cT1. These findings suggest aspirin may mitigate steatosis through metabolic pathways but does not necessarily rapidly reverse fibroinflammatory injury.

The plasma metabolome represents a valuable molecular readout of a persons physiological state, yet its relation to health, stress and lifestyle remains underexplored collectively. Here, we conducted an untargeted metabolomics analysis using 3804 paired samples from 1902 participants of the Netherlands Study of Depression and Anxiety at baseline and 6-year follow-up, quantifying 680 plasma metabolites. We characterised five metabolome profiles using principal component analysis, three with distinct biochemical enrichments related to transmembrane transport, sphingolipid and amino acid metabolism. Metabolite levels showed moderate intrapersonal correlation between baseline and 6-year follow-up (ICCmedian = 0.482), and only 22% of metabolites showed standardized mean differences greater than 0.2, indicating minimal 6-year population-level change. Multivariate linear modelling on 18 determinants across demographics, psychosocial environment, lifestyle, somatic and mental health explained a maximum of 35% of baseline metabolome profile variance and 12.2% in 6-year changes. Demographic (e.g., sex, age), somatic health (e.g., BMI, medication) and lifestyle factors (e.g., smoking, alcohol intake) demonstrated strong associations, while psychosocial factors and mental health contributed minor explained variance. Altogether, our study provides novel insights into the cross-sectional and longitudinal implications of health, stress and lifestyle exposures for the plasma metabolome, contributing to the understanding of metabolomic signatures in population studies.

BackgroundMetabolomic changes related to colorectal cancer (CRC) may serve as diagnostic markers to identify patients may develop or have developed CRC. MethodsUntargeted lipidomics were performed on serum from CRC cases and clean-colon controls from the Chicago Colorectal Cancer Consortium (CCCC) and the University of Arizona Cancer Center (UACC). ResultsUntargeted lipidomics in the CCCC CRC series revealed significant alterations in sphingolipids. Targeted lipidomics revealed a signature of five sphingomyelins (SMs) were significantly decreased in CRC patients in CCCC and UACC CRC series. Circulating SMs are degraded primarily by S-SMase and serum S-SMase activity was significantly higher in UACC cases as compared to controls. Serum S-SMase activity was also measured in two series of adenoma patients to determine if S-SMase may serve as a biomarker for development of colorectal neoplasia. While S-SMase activity was significantly higher in adenoma patients compared to controls in the mostly white UACC series, S-SMase activity in samples from the Chicago Black series (CCCC) were indistinguishable from each other and significantly higher than UACC controls. ConclusionsTogether, these studies suggest the potential for S-SMase activity to serve as a biomarker for colorectal neoplasia, with potential implications in some but perhaps not all populations.

Background/ObjectivesMetabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) is a global health crisis, but current diagnostics are limited. Liver biopsy is invasive, magnetic resonance imaging-proton density fat fraction (MRI-PDFF) is expensive, and quantitative ultrasound methods are low-accuracy, especially in patients with a high body mass index (BMI). This study introduces a novel thermo-acoustic (TA) method that generates ultrasound signals based on tissue electrical conductivity, where lean tissue (high in water and electrolytes) absorbs more radio-frequency (RF) energy than fatty tissue, providing a direct molecular contrast for fat. MethodsA prospective, cross-sectional feasibility study compared a new thermo-acoustic fat fraction (TAFF) score with the reference standard MRI-PDFF in 40 subjects with suspected fatty liver disease. Bland Altman analysis, Deming regression, and Binary classification performance were tested. To establish system stability, a dedicated Repeatability and Reproducibility (R&R) study (N = 14) evaluated inter-operator and intra-operator consistency using an Intraclass Correlation Coefficient (ICC) derived from a two-way random-effects ANOVA model. ResultsTAFF estimates demonstrated a substantial correlation (r =0.89) with MRI-PDFF and an average absolute error of 3.04% fat fraction. Classification performance was high, with an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.92 at the 12% fat fraction threshold and 0.99 at the 20% fat fraction threshold. The R&R study confirmed robust stability (intraclass correlation = 0.89) and a negligible mean inter-operator difference of 0.36%. Estimation errors showed no statistically significant correlation with BMI or other body habitus measurements. ConclusionsThese findings support thermoacoustics potential as an accurate, non-invasive, point-of-care solution that can serve as a new imaging biomarker. By providing predictive values closely aligned with MRI-PDFF across the full MASLD spectrum, TAFF can complement currently available ultrasound methods to address the cost and access constraints of MRI for the assessment, diagnosis, and monitoring of MASLD.

ObjectivesCardiovascular risk excess in rheumatoid arthritis (RA) cannot be explained by traditional risk factors alone. Recent experimental data have identified ALDH4A1 as a mitochondrial self-antigen implicated in atherosclerosis, yet its clinical significance in human autoimmunity remains unexplored. We aimed to characterize ALDH4A1 and anti-ALDH4A1 antibody levels in early RA, and evaluate their associations with atherosclerosis burden and lipoprotein traits. MethodsALDH4A1 and anti-ALDH4A1 antibodies (IgM, IgG, IgA, and IgG subclasses) were measured in early RA (n=82), clinically suspect arthralgia (n=14), healthy controls (n=70), and a validation cohort of established RA. A prospective cohort (n=13) explored therapeutic modulation under TNF blockade. Associations with atherosclerosis burden, lipid/lipoprotein profiles, oxylipin signatures, proteomics, and cell-free DNA were assessed. ResultsALDH4A1 serum levels were associated with apoptotic-related proteomic pathways, cell-free DNA and lipidomic signatures in early RA. Reduced anti-ALDH4A1 antibodies were found, although divergent patters were noted across isotypes. These differences were confirmed in a validation cohort. IgG (predominantly IgG3) anti-ALDH4A1 correlated with favourable lipoprotein traits and cardiometabolic risk factors. Increased ALDH4A1 and reduced IgM/IgG anti-ALDH4A1 antibodies independently predicted atherosclerosis and improved risk stratification beyond mSCORE, most notably for IgG. ALDH4A1 tracked with TNF dynamics under TNF blockade, whereas increases in IgG antibodies occurred in responders and paralleled changes in lipoprotein features. ConclusionsThe ALDH4A1/anti-ALDH4A1 axis emerges as a novel player bridging lipid disturbances and atherosclerosis along the RA spectrum, hence highlighting the involvement of mitochondrial targets. These components hold promise as functional players, clinical tools and therapeutic targets.

IntroductionTacrolimus remains central to liver transplantation, yet its narrow therapeutic index and pharmacokinetic variability are associated with increased risk of post-transplant diabetes mellitus (PTDM). While polymorphisms in metabolizing enzymes modulate drug exposure and diabetogenic risk, these relationships have not been systematically integrated through targeted pharmacogenomic approaches. ObjectiveTo systematically evaluate genetic variants in tacrolimus-metabolizing genes and their associations with PTDM through integrated in silico pharmacogenomic analysis. MethodsAn in silico analysis was performed, integrating data from public repositories (PharmGKB), curated literature, and functional annotations of genetic variants. Machine learning models were developed using synthetic data generated from literature-derived effect sizes to demonstrate proof-of-concept feasibility. We prioritized genes (CYP3A5, CYP3A4, ABCB1) based on PharmGKB evidence levels, functional impact, and clinical associations with tacrolimus exposure and PTDM risk, incorporating genotype information, drug dosing, and metabolic outcomes. ResultsThe CYP3A5*1 allele emerged as a key determinant, consistently requiring 1.5- to 2.8-fold higher tacrolimus doses and conferring a significantly elevated risk of PTDM compared to non-expressers, an effect mediated by cumulative drug exposure. In the systematic review and synthetic modeling, carriers of functional CYP3A5 alleles expresser genotypes exhibited a significantly increased PTDM risk relative to non-expressers, demonstrating a clear dose-exposure-toxicity relationship. In contrast, CYP3A4 and ABCB1 showed only suggestive but heterogeneous, evidence of association. ConclusionThis in silico pharmacogenomic study demonstrates a clinically significant association between genetic variability in tacrolimus metabolism and the development of PTDM following liver transplantation. These findings support genotype-guided strategies to optimize immunosuppressive therapy and advance precision medicine in transplant care.

Uric acid (UA) is traditionally regarded as a metabolic risk marker; however, its dynamic behavior during glucose-lowering therapy remains incompletely understood. We compared UA responses to a modified traditional Japanese diet (MJDD) and the DPP-4 inhibitor alogliptin in patients with early-stage type 2 diabetes mellitus (T2DM). In this prospective observational study, drug-naive patients received MJDD (n=58) or alogliptin (n=52) monotherapy for 3 months. Changes ({Delta}) in serum UA were analyzed in relation to glycemic control, insulin resistance, adipose tissue insulin resistance (adipo-IR), and beta-cell function. Both interventions significantly reduced fasting blood glucose and HbA1c while paradoxically increasing serum UA and HOMA-B. Baseline UA was the primary determinant of {Delta}UA in both cohorts. MJDD significantly reduced body mass index, insulin, free fatty acids, HOMA-R, and adipo-IR, with effects most pronounced in subjects with baseline BMI >25. In contrast, alogliptin selectively reduced adipo-IR in leaner subjects (BMI <25). Across both treatments, {Delta}UA correlated positively with {Delta}HOMA-B and inversely with {Delta}HbA1c. Notably, during MJDD, {Delta}UA showed a paradoxical negative correlation with {Delta}BMI and {Delta}FBG, and a positive correlation with {Delta}FFA. Patients exhibiting the greatest UA increases demonstrated the most marked improvements in beta-cell function and, with MJDD, the greatest weight loss. These findings indicate that MJDD and alogliptin exert distinct metabolic effects in early T2DM, yet both link rising UA to enhanced beta-cell function, suggesting that UA may serve as a dynamic pharmacometabolic biomarker reflecting therapy-specific metabolic adaptation rather than metabolic deterioration.

BackgroundOsteoarthritis (OA) is a leading cause of disability worldwide, yet no licensed therapies can prevent or slow its progression. We aimed to identify potential targets for disease-modifying OA drugs (DMOADs) by integrating genetic and differential protein expression (DPE) evidence. MethodsWe evaluated genetically predicted perturbations of plasma protein levels using cis-protein quantitative trait loci (cis-pQTLs) across three large European cohorts (UK Biobank Pharma Proteomics Project, deCODE, and Fenland) and outcome data from the Genetics of Osteoarthritis Consortium, covering 11 OA phenotypes. DPE analyses were performed in 44,789 UKB participants, comparing 2,920 protein measurements between OA cases and controls, supported by sensitivity analyses. Proteins identified through genetic and/or DPE approaches were further assessed in downstream analyses. FindingsIn total, 305 proteins showed evidence of association with OA through genetically predicted perturbations, with 81 supported by colocalisation across datasets. DPE analyses identified 605 proteins associated with at least one OA phenotype, of which 450 (74{middle dot}4%) remained robust after sensitivity testing. Several novel targets were identified, including PPP1R9B, PCSK7, and ITIH4. Integration of both approaches prioritised 5 proteins, 4 of which demonstrated druggable potential, including 3 high-confidence candidates DLK1, TNFRSF9, and OGN. Downstream analyses highlighted key biological pathways and candidate compounds with potential for repurposing. InterpretationThis large-scale study combines genetic and DPE evidence to prioritise candidate DMOAD targets. Findings reinforce established biology while revealing novel proteins and pathways, providing a foundation for therapeutic development in OA. FundingWL is supported by the Guangzhou Elite Project (project no. JY202314). SSZ is supported by The University of Manchester Deans Prize, Arthritis UK Career Development Fellowship (grant no. 23258). This work is supported by the NIHR Manchester Biomedical Research Centre (NIHR203308). Research in contextO_ST_ABSEvidence before this studyC_ST_ABSCirculating proteins have been linked to osteoarthritis (OA) in observational studies, supporting their potential as biomarkers and drug targets. However, differential protein expression analyses are vulnerable to confounding and reverse causation. Mendelian randomisation (MR) studies using proteomic GWAS instruments have suggested causal roles for several circulating proteins in OA-related traits and highlighted druggable candidates. However, many analyses relied on earlier OA GWAS data (e.g., Genetics of Osteoarthritis Consortium 1{middle dot}0) and smaller proteomic GWAS datasets, and typically did not integrate MR findings with large-scale differential protein expression. As a result, it remains unclear how well genetically predicted protein effects align with observed protein expression in OA, and how robust prioritised targets are when replicated across proteomic data from multiple cohorts. Added value of this studyThis study integrates large-scale proteomic MR and differential protein expression (DPE) analyses across multiple OA phenotypes using the largest datasets to date. By combining genetic evidence with observed protein dysregulation in population-based cohorts, we strengthen causal inference and improve robustness of target prioritisation. This approach allows us to distinguish proteins that are likely to play a causal role in OA from those that reflect downstream disease processes, and to highlight targets with greater translational relevance than identified by either method alone. Implications of all the available evidenceTaken together, our findings support a causal role for a subset of circulating proteins in OA and demonstrates the value of integrating genetic and observational proteomic data for target prioritisation. Proteins supported by both MR and DPE are more likely to represent biologically relevant drivers of disease and actionable therapeutic targets. This integrated framework reduces false positives arising from confounding or reverse causation and provides a more reliable basis for drug development, biomarker discovery, and patient stratification in OA.

Atherosclerosis develops over many years and its underlying mechanisms are still not fully understood. Plasma metabolomics across the different stages of development may help identify biomarkers that clarify disease pathways and improve early risk assessment. We performed untargeted plasma metabolomics using ultra-performance liquid chromatography-mass spectrometry in 8,146 participants without cardiovascular disease from the population-based SCAPIS cohort. Associations of 1,171 circulating metabolites with subclinical coronary atherosclerosis burden, assessed using coronary computed tomography angiography and quantified by segment involvement score, were assessed using multivariable models. Metabolites associated with coronary atherosclerosis were then evaluated in independent cohorts representing later stages of the atherosclerotic disease continuum: imminent myocardial infarction (MIMI, n=2,018), and coronary plaque burden and vulnerability in myocardial infarction survivors (PROSPECT II, n=898). Twelve metabolites, including phosphate, malate, sphingomyelins, amino acids, and one uncharacterized feature, were robustly associated with subclinical coronary atherosclerosis independent of traditional risk factors. Notably, sphingomyelins showed inverse associations with subclinical atherosclerosis, imminent myocardial infarction, and the presence of vulnerable plaques. Malate, N-acetyl-isoputreanine and an uncharacterized molecule (X-25790) were positively associated with subclinical coronary atherosclerosis and with imminent myocardial infarctions. These findings reveal a metabolomic signature across the atherosclerosis continuum, highlighting candidate biomarkers that may enhance understanding of disease mechanisms and aid risk stratification.