Metabolites

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.

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

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.

ContextPolycystic ovary syndrome (PCOS) is a heterogeneous endocrine disorder associated with reproductive dysfunction and long-term cardiometabolic risk. Traditional phenotype classifications based on diagnostic criteria may not fully capture the multidimensional biological variability underlying endocrine and metabolic risk profiles, particularly in young women. ObjectiveTo identify data-driven endocrine-metabolic phenotypes in young women with PCOS and evaluate their association with established cardiometabolic risk markers. Design and SettingCross-sectional study conducted at a tertiary Gynecological Endocrinology Clinic in Poland between January 2018 and May 2025. ParticipantsA total of 1300 young women diagnosed with PCOS according to Rotterdam criteria were included. The primary analytic cohort comprised 1032 participants aged 16-25 years with complete endocrine-metabolic biomarker data. Main Outcome MeasuresEndocrine-metabolic phenotypes were derived using principal component analysis followed by Gaussian mixture model clustering. Cardiometabolic risk endpoints included impaired glucose tolerance (2-hour plasma glucose during an oral glucose tolerance test [&ge;]140 mg/dL), an atherogenic lipid profile (triglycerides (TG)/high-density lipoproteins (HDL-C) ratio >3.50), elevated non-HDL cholesterol ([&ge;]130 mg/dL), and a composite outcome of any abnormality. ResultsPrincipal component analysis retained 10 components explaining 81.9% of total variance. Unsupervised clustering identified two stable phenotypes (silhouette = 0.392; ARI = 0.842). Cluster 0 (n=954; 92.4%) represented a mixed endocrine-metabolic profile, whereas cluster 1 (n=78; 7.6%) was enriched for thyroid/autoimmune features, with higher anti-thyroid peroxidase antibody levels and higher thyroid-stimulating hormone. Cluster 1 showed a higher prevalence of an atherogenic lipid profile compared with cluster 0, while differences in glucose intolerance and non-HDL cholesterol were modest. Logistic regression analyses suggested phenotype-specific variation in cardiometabolic risk markers. ConclusionsIn a large cohort of young women with PCOS, data-driven analysis identified two reproducible endocrine-metabolic phenotypes, including a distinct thyroid/autoimmune-enriched subgroup. These findings highlight clinically relevant heterogeneity beyond traditional diagnostic phenotypes and support the potential value of integrated endocrine-metabolic profiling for early risk stratification in PCOS.

The daily cortisol cycle is a critical indicator of hypothalamic-pituitary-adrenal (HPA) axis function. The current analytical approaches produce several outputs difficult to integrate into simple statistical models, clinical workflows, and ML/AI pipelines requiring single-value inputs. We developed the Cortisol Sine Score (CSS), a model-free scalar metric that quantifies daily cortisol exposure by computing a weighted sum of cortisol measurements across the day, using sine-transformed time-of-day weights. The CSS produces positive values for morning-dominant patterns, negative values for evening-shifted profiles, and near-zero values for flattened rhythms characteristic of chronic stress and circadian disruption. We validated the CSS performance in 3,006 samples from 501 pregnant women enrolled in the March of Dimes program, with cortisol values measured at 6 time points per day collected during the second trimester of pregnancy. The CSS showed strong correlations with observed and model-estimated amplitude and acrophase from Cosinor regression and JTK_CYCLE approaches, with excellent classifying performance (AUC=0.89, high versus low). The CSS successfully captured established associations between social disadvantage and cortisol dysregulation, and demonstrated utility in predicting gut microbiome composition in metagenomic analyses. Importantly, the CSS maintains excellent fidelity to the full 6-sample protocol with as few as 3-4 daily measurements. The 4-sample protocol achieves great performance (r = 0.952, MAE = 0.087) while reducing participant burden. The 06:00 time point was identified as essential for accurate CSS quantification. The CSS bridges the gap between circadian analysis and practical implementation by providing a simple, interpretable, and robust assessment of cortisol daily cycle in large-scale epidemiological studies, clinical screening, and biomedical sensors. HighlightsO_LICurrent state-of-the-art approaches estimating the daily cortisol exposures produce multi-output information difficult to implement in simple statistical analyses or ML/AI multi-omics approaches C_LIO_LICortisol Sine Score is a novel model-free scalar metric expressing cortisol daily exposure and rhythmicity (morning vs evening exposure) C_LIO_LICortisol Sine Score was validated using 3006 salivary samples from clinical data and golden standards in circadian analyses such as Cosinor and JTK_CYCLE C_LIO_LICortisol Sine Score was the top performer in our benchmarking approach predicting association with social disadvantage and gut microbiome composition C_LIO_LIReliable with 3-4 daily samples, reducing participant burden C_LIO_LIOpen-source R package CortSineScore democratizes cortisol cycle analysis C_LI

BackgroundWhile multi-omics approaches, incorporating polygenic risk scores (PRS), metabolomics, and proteomics have shown promise in predicting major adverse cardiovascular events (MACE), their added value beyond cardiovascular disease (CVD) risk factors remains underexplored. We aimed to assess whether integrating multi-omics biomarkers into the SCORE2 model improves the prediction of MACE in apparently healthy individuals. MethodsThis study included 24,042 UK Biobank participants without CVD or diabetes mellitus, aged 40-69 years. Multi-omics biomarkers were fitted in sex-specific models including the variables of SCORE2 and 9 metabolites, 12 proteins, and a PRS for CVD in males, as well as 7 metabolites, 11 proteins, and a PRS for CVD in females. The performance of the SCORE2 model and its multi-omics extensions was compared using Harrells C-index and the net reclassification index (NRI) in a training and test set (70% and 30% of study population). ResultsIn 10-year follow-up, 1,204 MACE events occurred. Integrating multi-omics biomarkers into SCORE2 significantly improved the predictive performance (C-index: 0.708 to 0.769, P<0.001; NRI=26.2%). In males, the C-index improved from 0.682 to 0.752 ({Delta}C-index=+0.070, P<0.001; NRI=12.4%), while in females, it increased from 0.724 to 0.782 ({Delta}C-index=+0.058, P<0.001; NRI=30.4%). However, full multi-omics measurements may not be needed because the combination of proteomics and PRS yielded comparable performance in males (C-index=0.749) and females (C-index=0.782). ConclusionsIntegrating a protein panel and a PRS significantly improves MACE risk prediction by the SCORE2 model, which includes HDL and total cholesterol. Adding further metabolites has limited additional predictive value.

ObjectivesTo characterize ultra-processed food (UPF) circulating metabolic signatures associated with Crohns disease (CD) and to localize key metabolic mediators linking UPF intake to CD risk. DesignProspective cohort study. SettingTwo large multi-center cohorts (UK Biobank [UKB] and Whitehall II [WHII] study) across the UK and an Eastern multi-center cohort ONE-IBD Study from China. ParticipantsUK Biobank discovery cohort (n=10,229) for signature derivation, internal validation cohort (n=91,306), external validation cohort Whitehall-II (n=7,893), and three additional cohorts (two Western and ONE-IBD) for validation of key metabolic drivers. Main outcome measuresPrimary outcomes were UPF-related circulating metabolic signatures and their associations with CD risk; secondary outcomes included evidence supporting causal roles of candidate metabolites and genetic pathways assessed by Mendelian randomization, colocalization, and gene-environment analysis. ResultsA UPF metabolic signature of 73 metabolites was constructed and validated across cohorts (Spearman {rho}: 0.20-0.25). More pronounced UPF metabolic signature was associated with increased CD risk (HRper SD=2.65, 95% CI 1.57-4.48). WGCNA revealed a cluster enriched in fatty acids. Within this cluster, docosahexaenoic acid (DHA) emerged as the strongest, which mediated 17.1% of the UPF-CD association. External validation in ONE-IBD supported DHA as the strongest associated metabolite with UPF and CD. Mendelian randomization supported a causal protective effect of DHA on CD (OR=0.72, 95% CI 0.61- 0.83; P<0.001), with colocalization implicating rs174546 in the FADS1 gene. ConclusionThe adverse effects of UPF on CD risk may be driven by a relative deficiency of protective metabolites such as DHA, apart from additive harm to metabolic depletion. This reframes UPF-related risk and highlighting potential targets for precision nutrition in CD prevention.

ObjectiveTo introduce and evaluate the clinical utility of the "adipo-B index" as a novel metric of the adipose tissue-pancreatic beta cell axis. To our knowledge, no prior clinical metric has integrated adipose tissue insulin resistance and pancreatic beta-cell function into a single index applicable across therapeutic classes. MethodsTreatment-naive subjects with T2DM received monotherapy with modified traditional diet for diabetes (MJDD, n=61), canagliflozin (n=67), pioglitazone (n=54), or sitagliptin (n=63). Correlations between the baseline and changes in adipo-IR or adipo-B and clinical parameters were analyzed. This is a prospective, non-randomized observational study. ResultsAt baseline, among all the subjects, adipo-B significantly correlated with FBG, HbA1c, non-HDL-C and BMI, while adipo-IR did not. At 3 months, across all therapeutic strategies, significant negative correlations were observed between the changes in ({Delta})adipo-B and baseline adipo-B. By contrast, in MJDD, canagliflozin and pioglitazone, significant negative correlations were seen between {Delta}adipo-IR and baseline adipo-IR, while with sitagliptin, no correlations were noted. {Delta}adipo-B, but not {Delta}adipo-IR, correlated with the improvements of glycemic (FBG, HbA1c) and lipid (non-HDL-C) parameters across all these therapies. While significant correlations were seen between {Delta}adipo-B and {Delta}adipo-IR with MJDD, pioglitazone and sitagliptin, canagliflozin uniquely "decoupled" this axis. With sitagliptin and pioglitazone, adipo-B improved despite weight gain. ConclusionThe adipo-B index is a superior indicator of systemic metabolic status and therapeutic response and could serve as a useful tool for precision therapy for diabetes.

Background and AimFibromyalgia is an idiopathic chronic widespread pain syndrome affecting 2-4% of the general population globally. Besides widespread fibromyalgia pain, morning stiffness, associated neurologic as well as sleep problems are also reported. Disease is more prevalent in females of middle-age group with low socioeconomic status, thus deteriorating overall productivity and psychosocial health. There is no permanent cure of the disease. This study aimed to explore, validate and assess the effect of four weeks of supervised yogic intervention on pain status, quality of life, sleep, cortical excitability, flexibility and range of motion in fibromyalgia patients, as compared to standard therapy. MethodCase-control study, interventional study and assessor-blined randomized controlled trial, conducted in 120 fibromyalgia patients (60 yoga group: 60 waitlisted controls) and 60 age-matched healthy controls. Pain was assessed subjectively, using questionnaires and objectively, using quantitative sensory testing and ELISA. Sleep and quality of life were assessed using common and disease specific decsiptors. Flexibility and range of motion was assessed using sit and reach box, lateral goniometry and modified Schobers test. Transcranial magnetic stimulation on M1 was used to assess corticomotor excitability of participants. Study parameters were assessed at baseline and after four weeks of the intervention. ResultsA significantly poor sleep, flexibility and quality of life was reported in the fibromyalgia patients due to excruciating pain (VAS = 6.92{+/-}0.12); corticomotor function was also abnormal in the patients, which were restored after four weeks of yogic intervention. On subjective and objective assessment of pain, we found significant relief and improvement in pain status in the yoga group as compared to the waitlisted controls. Fibromyalgia impact, sleep, quality of life and flexibility were also found solely better in fibromyalgia patients undergoing yogic interventions. Cortical parameters, specifically RMT, MEPs and MEP recruitment curves showed a significant improvement in yoga group as compared to waitlisted controls. ConclusionFour weeks of regular and supervised yogic intervention may ameliorate pain, improve flexibility and range of motion and changes cortical plasticity in the Indian cohort of fibromyalgia patients, as compared to standard therapy. Yoga-based interventions can also improve overall quality of life and sleep impairmentsby reducing catastrophization and fibromyalgia impact.

BackgroundDigital health self-monitoring tools are widely used to support weight management and metabolic health. Higher engagement with these tools is often associated with better clinical outcomes; however, real-world engagement-outcome relationships for consumer metabolic monitoring devices remain incompletely characterized, particularly in heterogeneous user populations. ObjectiveTo evaluate whether engagement with a portable breath-based metabolic device (Lumen; Metaflow Ltd.) is associated with greater weight loss and reduction in body fat among real-world glucagon-like peptide-1 receptor agonist (GLP-1RA) users. The study also explores correlations between engagement and a device-specific measure of metabolic flexibility (FLEX score). MethodsWe retrospectively analyzed 2,296 adult Lumen users who self-reported GLP-1RA use over 24 weeks. Engagement was quantified as total engagement days over a 24-week period and ordered engagement consistency groups defined by weekly use frequency thresholds. Weight and body fat percentage data were collected by a combination of connected devices and manual user input in the Lumen smartphone application. Associations with weight loss and reduction in body fat percentage were evaluated using linear regression and ANCOVA adjusted for age, baseline BMI, and sex, with HC3 robust standard errors. Body fat percentage data were available for only 490 of the 2,296 subjects. In addition, similar associations were evaluated for FLEX score. GLP-1RA exposure was self-reported at onboarding and not verified longitudinally. ResultsAt 24 weeks, low/medium/high engagement users lost 3.2%, 4.6%, and 5.2% of body weight (trend p=2.36x10-11). Engagement days were associated with percent weight change (slope -0.0214% per day; P(HC3)=7.9x10- 18). Engagement days showed modest association with body fat percentage change (n=490; slope -0.0105% per day; P(HC3)=.010). The adjusted ANCOVA trend across engagement groups was not significant (P=.19). Engagement days and consistency both showed a highly significant trend in increase in FLEX score (slope +0.0185 per day; P(HC3)=2.0x10- 36). ConclusionsIn a real-world digital health dataset, higher engagement with a breath-based metabolic monitoring device and its smartphone application was associated with greater 24-week weight loss after adjustment for age, baseline BMI, and sex. The absolute difference between low and high engagement (2.0% body weight) is modest but clinically meaningful in real-world settings after 24 weeks of tracking. Associations with body fat percentage change were smaller and not consistently significant in adjusted analyses. Associations with metabolic flexibility were highly significant, but it remains unknown whether this parameter is predictive or reflective. Prospective controlled studies are needed to test causality and determine whether device-driven biofeedback and sustained engagement independently improve outcomes because GLP-1RA use was self-reported and unverified, and the present analysis was observational. These findings should be interpreted as engagement-outcome associations and reflect behavioral motivation and adherence rather than evidence of device efficacy.

Prediabetes and type 2 diabetes (T2D) are metabolic disorders characterized by insulin resistance and {beta}-cell dysfunction. To understand the molecular mechanisms driving the transition from prediabetes to T2D, we performed a longitudinal proteogenomic analysis on 458 participants from the Prediabetes Lifestyle Intervention Study (PLIS). We identified 185 plasma proteins to be differentially expressed between conditions, 36 of which predict future T2D-onset. Integrating genetic data from 321 individuals, we generated a genome-wide protein quantitative trait loci (pQTL) map, identifying 86 differential and 700 shared cis-pQTLs between prediabetes and T2D. Mediation analysis revealed 60 putative causal links connecting allele-driven plasma protein expression to clinical traits, identifying body fat distribution, insulin resistance, and {beta}-cell function as central drivers of pathogenesis. Collectively, these findings highlight specific proteins underlying disease progression and substantiate the view that prediabetes and T2D are not distinct conditions, but rather stages on a unified metabolic spectrum.

Anti-{beta}2-glycoprotein I (anti-{beta}2GPI) antibodies are central to the pathogenesis of antiphospholipid syndrome (APS), an autoimmune disease characterized by a strong predisposition to venous thromboembolism (VTE). In this study, we conducted a multi-ancestry genome-wide association study (GWAS) of quantitative total anti-{beta}2GPI levels in 5,969 participants enrolled in the Multi-Ethnic Study of Atherosclerosis (MESA) and identified a genome-wide significant association at the APOH locus. Paradoxically, genetically determined increases in anti-{beta}2GPI levels at this locus were associated with lower VTE risk. Fine-mapping and functional genomics prioritized the missense variant rs1801690 (W335S) in {beta}2GPI (apolipoprotein H, [APOH]) as the most likely causal variant. This variant has an allele frequency of 5-6% in European and East Asian ancestries but only 1% in African ancestries. Integrating prior experimental studies, molecular dynamics simulations and structure-based epitope prediction, we propose a dual-effect mechanism whereby W335S reduces thrombotic risk by disrupting phospholipid binding in Domain V, yet increases autoantibody production through conformational changes that enhance epitope exposure in Domains I and II. These findings mechanistically uncouple autoantibody formation from thrombotic risk in carriers of the W335S variant, and suggest that APOH genotype may represent a clinically relevant genetic biomarker with potential utility for thrombotic risk stratification in anti-{beta}2GPI-positive individuals.

There is an unmet need to identify biomarkers of active thyroid eye disease (TED). scRNAseq revealed that orbital fibroblasts from orbital decompressions in people with TED express high levels of thyroid hormone receptors, growth factor receptors, including insulin-like growth factor 1 receptor (IGF1R), and extracellular matrix proteins including SPARC (osteonectin), whereas orbital fat endothelial cells expressed thyroid peroxidase (TPO). SPARC was significantly raised in the serum of people with thyroid disease compared to healthy controls. Furthermore, those with moderate, severe and sight threatening TED had higher SPARC levels than those with thyroid disease but free of TED or mild TED. Free-triiodothyronine (FT3) levels were positively correlated with SPARC in moderate-sight threatening TED. SPARC and IGF1 were positively correlated across people with thyroid disease alone, as well as TED. Thyroid stimulating hormone (TSH) levels were negatively correlated with SPARC in moderate-sight threatening TED. When participants were followed longitudinally, SPARC decreased after the active phase of TED. At the protein level, immunohistochemistry indicated that SPARC was heterogeneously expressed by fibroblasts in both control and TED orbital fat. SPARC is a key mediator of fibrosis and deposition of extracellular matrix and the correlation of SPARC serum levels to TED status and FT3 make it a promising biomarker of active TED.

Background and ObjectivesRecent large-scale studies have consistently linked healthy dietary patterns to improved cardiometabolic health; however, the underlying biological pathways remain largely unclear, especially in non-European populations. In this study, we leverage data from four population-based cohorts (UK Biobank, NEO study, GNHS, and 10K) to investigate both common and cohort-specific biological pathways linking healthy dietary patterns to cardiometabolic disease through multi-omics profiling. Material and methodsIn each cohort, we first assessed the associations between each of the five major dietary pattern scores (i.e., AMED, hPDI, DII, AHEI, and EDIH) and cardiometabolic disease risk using Cox or logistic regression models. To explore the potential mediating role, metabolomics and proteomics measurements were incorporated into the models. All models were adjusted for relevant confounders, and false discovery rate correction was applied to account for multiple testing. ResultsWith a total of 71,679 individuals without pre-existing cardiometabolic disease across four participating cohorts (UKB: 54,024, NEO: 4,838, GNHS: 3,201, and 10K: 9,616), we confirmed that adherence to healthy dietary patterns was associated with a 5-10% reduced risk of cardiometabolic disease. Three common biological pathways were identified: (1) mediation via large HDL particles and apolipoprotein F; (2) mediation via DNAJ/Hsp40 and triglyceride-rich lipoproteins; and (3) mediation via CRHBP-regulated HPA axis activity affecting triglyceride-rich lipoproteins. ConclusionsOur integrative multi-omics analysis across diverse populations identifies novel biomarkers that connect healthy dietary patterns with cardiometabolic risk. These findings deepen our understanding of the biological mechanisms underlying diet-related disease and hold promise for enhancing the development of precision nutrition interventions.

The biomarker MetaboHealth represents a novel indicator of overall health in middle age and may potentially be suitable as actionable health check in prevention strategies. MetaboHealth is a blood-based metabolomic composite score that predicts a wide range of age-related conditions and mortality in large European cohorts. Here, we investigated whether MetaboHealth can be personalised and limited to clinically validated metabolomic markers. Next, we assessed whether the updated MetaboHealth score predicts all-cause mortality and cardiometabolic disease incidence and can be improved by a lifestyle intervention. To personalise MetaboHealth, we scaled the metabolomic markers using a Dutch reference population (i.e. the Biobanking and BioMolecular Research Infrastructure Netherlands) and, in addition, based the score solely on clinically validated metabolic markers. The novel version of the score, Personal-MetaboHealth, retained predictive accuracy for all-cause mortality and showed an even stronger association with incident cardiometabolic disease in the Leiden Longevity Study (LLS) in which 2,404 participants were followed for up to 22 and 16 years for mortality and morbidity, respectively. The association of Personal-MetaboHealth with all-cause mortality remained robust after adjusting for smoking, alcohol use, and medication, while the cardiometabolic disease association was partially driven by smoking. Each standard deviation decrease in Personal-MetaboHealth was associated with a 11.7 year earlier onset of the first cardiometabolic disease in the LLS. Next we showed that Personal-MetaboHealth can be improved by a 3-month combined lifestyle intervention in middle aged individuals (Growing Old Together study), specifically in those at risk with an unhealthy score at baseline. Personal-MetaboHealth thus offers a potential actionable health check in middle age for early prevention and extension of healthy lifespan.

Background and aims Iron deficiency (ID) and myocardial iron depletion (MID) are causally linked to heart failure (HF) in the general population and in preclinical models. ID is common amongst pregnant women, but its impact on cardiac adaptations to pregnancy is unknown. This study examines that impact, and its potential relevance to peripartum cardiomyopathy (PPCM). Methods. We provided female mice with iron-replete or iron-deficient diets, and monitored cardiac function and morphology longitudinally in pregnancy and postpartum. In women with no HF (n=64), we explored the associations between antenatal iron parameters and echocardiographic parameters in late pregnancy and at 6-12 months postpartum. We also performed a case (n=55), control (n=170) study comparing iron markers and assessing their association with PPCM risk. Results In mice, ID prevented postpartum reversal of pregnancy-induced hypertrophy, reduced postpartum LVEF, and caused profound MID. In women with no HF, low hepcidin, high transferrin and low serum iron were respectively associated with higher LVESV, lower LVEF and higher CMR T1-mapping (lower myocardial iron) in postpartum. In the PPCM study, serum iron, hepcidin and haemoglobin were significantly lower in cases than controls, and were independently associated with risk of PPCM. Mechanistically, myocardial proteomics revealed that ID caused sustained postpartum activation of pyruvate dehydrogenase kinase 4, a master cardiometabolic switch enzyme with a well-recognised role in HF. Conclusions This study links antenatal maternal ID to postpartum systolic dysfunction, and implicates MID and cardiometabolic switching as potential mechanisms. It suggests these links may potentially contribute to the pathophysiology of PPCM

Cushings disease is caused by the overproduction of cortisol. The effects of this disease are well known in a general population, including high blood pressure, diabetes, and weight gain. Cushings disease causes both obesity and metabolic related symptoms, and it can be difficult to discern the obesity-dependent from the obesity-independent mechanisms of Cushings disease. To identify patients with Cushings disease, we identified 476 Michigan Medicine patients between January 1st 2000-2025 along with propensity-matched control cases. We stratified our participants by obesity status and into a Cushings disease group and a control group. As expected, the Cushings group had an elevated BMI compared to the control group (34 kg/m2 vs 29 kg/m2). We found a higher proportion of females diagnosed with Cushings compared to males (287 vs 72). Cushings disease was associated with an increase in the fasting glucose levels in both non-obese and obese patients. In both the obese, and non-obese patients, there was an increase in ALT and AST levels regardless of Cushings disease status, but the increase due to Cushings disease was much greater in the patients with obesity (73.4 vs 35.1 mg/dL). Cushings disease also had a moderating effect on blood pressure, with participants a BMI under 30 kg/m2 increasing by 12.6 mmHg and participants with obesity increasing by only 7.9 mmHg. These findings highlight the need to consider obesity status when evaluating the effects of Cushings disease.

BackgroundFatal insulin intoxication remains difficult to diagnose because insulin undergoes rapid degradation after death, limiting the reliability of direct biochemical measurements. This creates diagnostic uncertainty when objective molecular confirmation of insulin excess are required. We hypothesised that insulin excess induces systemic metabolic alterations that persist beyond insulin degradation and can be captured using postmortem metabolomics in a forensic setting. MethodsHigh-resolution mass spectrometry (HRMS)-based metabolomics was applied to a national cohort comprising 51 fatal insulin intoxications. Orthogonal partial least squares-discriminant analysis (OPLS-DA) models were trained on cases collected between 2017-2022 to identify insulin-associated metabolite features using a shared-and-unique-structures approach. Performance was evaluated using two temporally distinct test sets (2023-2024): a matched validation cohort and a heterogeneous forensic cohort reflecting biological variability. ResultsHere we show that an insulin-associated metabolomic fingerprint comprising 91 features demonstrated reproducible discrimination across independent cohorts. In the matched cohort (n=59, including 14 insulin cases), insulin intoxication classification achieved 100% sensitivity and 73% specificity within the applicability domain. In the heterogeneous cohort (n=154, including 14 insulin cases), 100% sensitivity was maintained with a 72% specificity despite increased biological variability. Univariate analyses demonstrated significant alterations across multiple metabolite classes, including acylcarnitines, fatty acids/lipids, and purine/nucleoside metabolites, with moderate effect sizes, consistent with systemic effects of insulin-induced hypoglycaemia. ConclusionsFatal insulin intoxication is associated with a reproducible metabolomic fingerprint detectable after death. These findings demonstrate that postmortem metabolomics may serve as a complementary decision-support tool when conventional biomarkers are unreliable.