We used lipidomic analyses to investigate how individual free fatty acids (FFAs) behave differently in metabolic states altered by diet and by antibiotic treatment (ABX) that depletes gut bacteria. Wistar rats were fed either a low-fat or high-fat purified diet, or standard chow with or without antibiotics for two weeks (n = 8-10). Blood samples were then collected before and after meals. Individual FFAs were quantified and grouped based on distinct postprandial response patterns across dietary and treatment conditions. Eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), key ω-3 FFAs, exhibited postprandial shifts suggestive of suppressed adipocyte lipolysis following meals. Fatty acids in the high-fat diet (HFD) elevated postprandial FFA levels, masking the meal-induced suppression of lipolysis observed with chow or low-fat diet (LFD). Some FFAs, including medium-chain saturated species, remained unaffected by meals. We further evaluated the impact of diet and ABX on baseline (pre-meal) concentrations of FFAs. Certain FFAs were altered by purified diets compared to standard chow. Notably, EPA and DHA were selectively depleted under HFD conditions, likely due to enhanced catabolic activity. In conclusion, lipidomic profiling revealed divergent behaviors among individual FFAs, reflecting distinct metabolic processes and regulatory mechanisms under altered metabolic states.

Abstract Introduction: The potential for long-term exercise to affect the metabolome in healthy individuals is well established. Given the metabolic underpinnings of prostate cancer, it is important to investigate whether long-term exercise similarly alters the metabolome in disease-affected men. This study is among the first to characterize metabolic changes in individuals with prostate cancer, comparing those assigned to a home-based walking program to those assigned to printed materials with physical activity recommendations. Methods: Fifty-one men with prostate cancer on active surveillance were randomly allocated to the exercise or control intervention. Metabolomic profiling of primary metabolism, complex lipids, and biogenic amines was performed at the West Coast Metabolomics Center on serum samples collected at baseline and after the 16-week interventions; data were successfully generated for 22 participants in the exercise arm and 23 participants in the control arm. To identify intervention-related differences in metabolic changes, we performed hierarchical clustering and fit mixed-effects models including an arm x time interaction. Results: Hierarchical clustering indicated limited separation in metabolic profiles between the exercise and control groups at 16 weeks. Although none of the 1,220 named metabolites exhibited statistically significant differences in change between the two groups (q<0.10), 85 (7.0%) demonstrated nominal significance (p<0.05). Among the 15 metabolites with the smallest p-values, six (40%) were sphingolipids - specifically sphingomyelins - though sphingolipids comprised only 11% of all named metabolites. All six sphingomyelins decreased more over time in the exercise group than in the control group. Conclusions: Although metabolic profiles were not significantly altered overall, a walking intervention may promote the reduction of sphingomyelins, thereby shifting lipid signaling toward pathways that enhance mitochondrial function and reduce inflammation. Such changes are consistent with biologically plausible mechanisms through which exercise could favorably influence prostate cancer biology, even in the absence of broad metabolomic shifts. Citation Format: Rebecca E. Graff, Ritu Roy, Oliver Fiehn, Adam Olshen, Erin Van Blarigan, Stacey Kenfield, Jeffry P. Simko, Anthony Luke, Lee Jones, Matthew R. Cooperberg, Peter R. Carroll, June M. Chan. Effects of a home-based walking intervention on serum metabolomic profiles in men with prostate cancer on active surveillance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1237.

Abstract Introduction: Emerging studies link physical inactivity to early-onset colorectal cancer (EOCRC), but most rely on subjective measures of physical activity (PA). Metabolite signatures may offer an objective measure of PA that also captures the systemic metabolic response to activity. We assessed a previously validated PA metabolomic signature in patients with recently diagnosed colorectal cancer (CRC) and compared profile scores between patients with EOCRC (<50 yrs) vs. non-EOCRC (>50 yrs). Methods: We examined baseline (pre-surgery) data from 122 stage I-III patients with CRC in the ColoCare Study at Huntsman Cancer Institute (Utah) and Heidelberg University Hospital (Germany). PA for the previous year was measured with the International Physical Activity Questionnaire-Short Form. Untargeted serum metabolites and complex lipids were profiled at the West Coast Metabolomics Center. We calculated a 24-metabolite PA signature developed in >6,000 cancer-free individuals (Papadimitriou et al., CEBP, 2025) consisting of acylcarnitines, glycerophospholipids, monosaccharides, amino acids, and sphingolipids. Following metabolite pre-processing, normalization, and scaling, we performed multivariable linear regression on 20 metabolites available in our dataset, adjusting for age, sex, tumor stage, and body mass index (BMI). Metabolite scores were calculated for each participant by multiplying normalized metabolite concentrations by the previously developed PA metabolite signature coefficients and were then compared between EOCRC vs. non-EOCRC survivors. Results: Compared to patients with non-EOCRC (67±9 years, N=102), those with EOCRC (39±10 years, N=20) were diagnosed with higher stages (55% vs. 43% stage III), had lower prevalence of obese BMI (25% vs. 37%), and were more physically active (16±17 metabolic equivalent (MET) hrs/week vs. 11±17 MET hrs/week), p>0.05. Patients with EOCRC were more likely to meet PA guidelines (>150 min/week of moderate to vigorous PA; 60% vs. 38%) compared to those with non-EOCRC, p>0.05. Among the 20 metabolites investigated in our data, 15 showed a consistent direction of association with the previously developed PA signature. Patients with EOCRC had higher scores of the PA metabolite signature (0.04±0.09) than older patients (-0.01±0.10), t=2.3, p=0.03. This modest association remained significant after adjustment for stage, sex, and BMI (β=0.05, p=0.048). Conclusions: A PA metabolite signature showed comparable associations to questionnaire-derived PA measures in our CRC survivor cohort, consistent with findings in healthy individuals. Patients with EOCRC reported a higher level of PA compared to those with non-EOCRC and had a significantly higher PA metabolite signature score. The metabolite response to PA may clarify how physical inactivity influences EOCRC risk and outcomes. Citation Format: Victoria Maria Bandera, Tengda Lin, Patricia Erickson, Caroline Himbert, Aik Choon Tan, Mary C. Playdon, Alan Maschek, Paul Stewart, Sheetal Hardikar, Elaine M. Glenny, Jennifer Ose, Victoria Damerell, Christy A. Warby, Olena Aksonova, Oliver Fiehn, Kenneth Boucher, Peter Schirmacher, Ildiko Strehli, Megan Mclaws, Alejandro Sanchez, Jolanta Jedrzkiewicz, Lyen C. Huang, Vaia Florou, Jessica N. Cohan, Alexander Brobeil, Hans-Ulrich Kauczor, Christoph Kahlert, Meghana Karchi, Elizabeth H. Wood, Doratha A. Byrd, Erin M. Siegel, Adetunji T. Toriola, David Shibata, Christopher I. Li, Jane C. Figueiredo, Biljana Gigic, Jatin Roper, Stephen Hursting, Cornelia M. Ulrich. Metabolomic signatures of physical activity in treatment-naive patients with early-onset vs. late-onset colorectal cancer: Results from the ColoCare Study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 3627.

A self-driving metabolomics laboratory has long been envisioned but remains largely unrealized due to the complexity of analytical method design. As an initial step toward this goal, we developed BAGO, a self-optimizing framework for automated liquid chromatography (LC) gradient design in mass spectrometry-based untargeted metabolomics. BAGO aims to enhance global metabolite detection by improving the separation of all compounds, regardless of whether their identities are known or unknown. It operates through a data-driven Bayesian optimization process that iteratively learns from acquired MS data to propose improved gradients. To support this, we propose a global separation index that quantifies coelution among both annotated and unannotated features, enabling robust and structure-agnostic optimization across diverse sample types. Benchmarking across four metabolomics assays involving diverse sample matrices, column chemistries, and gradient durations, BAGO achieved substantial improvements within only 10 optimization iterations by balancing exploration and exploitation. The optimized gradients led to increased numbers of Gaussian-shaped peaks, higher MS/MS acquisition rates, and more annotated metabolites using both identity and analog search approaches. We further applied BAGO to a sex-differentiated metabolomics study of Drosophila abdominal carcasses, completing the workflow in parallel under both initial and optimized gradients. The optimized method resulted in a 41.9% increase in Gaussian-shaped peaks, a 36.8% increase in MS/MS-acquired peaks, and the identification of 18 additional biologically significant metabolites, including sex-associated compounds such as octopamine and pyroglutamic acid. BAGO (https://github.com/HuanLab/bago) is freely available as an open-source tool and represents a generalizable step toward fully automated, self-optimizing experimental workflows in untargeted metabolomics.

Human immune systems are highly variable, with most variation attributable to non-genetic sources. The gut microbiome crucially shapes the immune system; however, its relationship with the baseline immune states of healthy humans remains incompletely understood. Therefore, we performed multi-omic profiling of 110 healthy participants through the ImmunoMicrobiome study. A factor-based integrative approach identified coordinated variation, revealing that the interferon response was amongst the most variable immune features in healthy participants. Microbiome composition, pathways, and stool metabolites varied concomitantly with interferon response pathways. Longitudinal data spanning more than a year indicated the significant stability of these parameters within individuals over time. Our study provides extensive data to examine the relationship between the immune states and microbiomes of healthy individuals at steady state, which paves the way for delineating inter-individual differences relevant for disease susceptibility and responses to therapy.

Background & Aims Laterally spreading tumors (LST) are flat colorectal neoplasms with an accelerated risk of malignant transformation and interval colorectal cancer. Despite their clinical importance, the molecular and microbial mechanisms underlying LST's aggressive biology remain poorly understood. Thus, we aimed to characterize the transcriptomic and microbial landscape of LST in comparison with paired protruding lesions (PL) and adjacent normal colonic tissue. Methods Formalin-fixed, paraffin-embedded tissues from 36 samples were obtained from 15 adults and analyzed using RNA sequencing and 16S rRNA gene amplicon sequencing. Patterns of the differential gene expression were assessed using Gene Set Enrichment Analysis (GSEA) and Ingenuity Pathway Analysis (IPA). Microbial community composition and its predicted functional capacity were evaluated with ANCOM-BC in QIIME 2 and PICRUSt2, respectively. Results Compared with paired PL and normal tissue, LST exhibited a distinct pro-tumorigenic transcriptomic profile marked by activation of MYC, E2F, mTOR, DNA damage, and senescence-associated secretory phenotype pathways, as well as robust pro-inflammatory signaling driven by TNF, NF-κB, IL-1, and IL-17. LST tissue also demonstrated a permissive environment for genomic instability. Microbiome analysis revealed enrichment of Fusobacterium and depletion of beneficial taxa, including Lactococcus, accompanied by predicted suppression of carbohydrate fermentation and short-chain fatty acid production, as well as altered sulfur metabolism. Fusobacterium abundance correlated with increased TNF expression, supporting a microbiota-driven inflammatory niche in LST. Conclusions LST are characterized by a unique inflammatory/metabolic/senescence axis that distinguishes them from other paired colorectal tissue samples. This pro-carcinogenic signature is driven by a Fusobacterium-enriched and carbohydrate-fermentation-depleted microbial ecosystem. These findings highlight the gut microbial ecosystem as a critical cofactor in LST pathogenesis and further support that combined host/microbiota-targeted strategies may improve colorectal cancer prevention in this population. Given the exploratory nature and limited cohort size, these findings require validation in larger prospective cohorts with metagenomic and metabolomic integration.

Lipidomics, a rapidly evolving discipline at the interface of biology and analytical chemistry, seeks to comprehensively characterize the lipid composition of biological systems. Driven by advances in mass spectrometry, chromatography and computational analysis, lipidomics has enabled the high-resolution mapping of lipid networks and their functional dynamics across molecular, cellular and organismal scales. In biomedical research, lipidomics is emerging as a powerful platform for biomarker discovery, enabling early diagnosis, prognosis, and therapeutic monitoring of cancer, metabolic, and neurodegenerative diseases. The field is also reshaping drug discovery by uncovering lipid-mediated pathways, identifying novel therapeutic targets, and refining assessments of drug efficacy and safety. Beyond medicine, lipidomic analyses are redefining food and nutrition science by elucidating how dietary lipids influence metabolic health and disease risk. In parallel, environmental and ecological lipidomics are emerging as powerful frameworks for assessing ecosystem health, tracking the impact of pollutants and exploring the biological consequences of climate change. Such approaches are also informing the discovery of sustainable lipid resources and the development of novel biotechnological and agricultural innovations. With its rapidly expanding analytical repertoire and cross-disciplinary relevance, lipidomics is poised to make substantial contributions to both fundamental biology and applied science. This Perspective aims to synthesise the current state of the field, delineate major analytical and conceptual challenges, and outline future directions for translating lipidomic knowledge into tangible societal and environmental benefits.

Lipoprotein(a) [Lp(a)] is a genetically determined cardiovascular risk factor. Additionally, Lp(a) levels are affected by dietary saturated fat (SFA) reduction. We previously reported an Lp(a) increase in response to SFA reduction in both white and black cohorts. However, less is known whether diets impact Lp(a)’s oxidized phospholipids (OxPL) and lipid components. We assessed responses of Lp(a)-OxPL concentration, Lp(a)-OxPL subspecies abundance, and the Lp(a)-lipidome to SFA reduction [from 16% energy with the average American diet (AAD) to 6% energy with a DASH-type diet] in 166 African-Americans. Responses by variability in Lp(a) levels and apolipoprotein(a) [apo(a)] sizes were tested. Mean age was 35 years; 70% were women; mean BMI was 28 kg/m2. Median Lp(a)-OxPL total concentration did not differ although particle concentration of four OxPL subspecies, in particular ALDOPC, decreased from AAD to the DASH-type diet (P = 0.001). Of 440 Lp(a) lipids annotated, 87 species (20%) responded significantly to SFA reduction, with major changes in phosphatidylcholine, sphingomyelin, and alkylphosphatidylcholine classes. A marked Lp(a)-lipidome difference between diets was observed for participants with higher (≥50 mg/dl) but not with lower (<50 mg/dl) Lp(a) levels. The Lp(a)-lipidome response varied across apo(a) sizes; small apo(a) (≤22 Kringles) carriers exhibited a greater decrease (80%) than an increase (20%) in lipids, while the opposite was the case for carriers of apo(a) sizes >22 Kringles. In conclusion, dietary SFA reduction in African-Americans resulted in significant changes in the Lp(a)-lipidome with differences by Lp(a) levels and apo(a) sizes. The findings support a dynamic intraindividual nature of the Lp(a)-lipidome and an impact of metabolic regulation.

Standardized quality assurance and quality control (QA/QC) practices are essential for reproducible GC-MS metabolomics, yet systematic documentation of current laboratory practices has been lacking. Here, as part of the Metabolomic Quality Assurance and Quality Control Consortium (mQACC), we surveyed 85 laboratories from 27 countries to characterize QA/QC implementation and establish evidence-based recommendations. Respondents represented diverse applications, with 79% performing untargeted analysis, 60% conducting targeted analyses, and 44% conducting both. While single column chromatography is clearly the norm, 24% of the participants used multidimensional chromatography to improve the separation of complex mixtures. Electron ionization with autotuning dominated >95% of the respondents, but more than 30% of the laboratories at least occasionally also used chemical ionization. While most laboratories used low-resolution mass spectrometers, almost half of the laboratories also performed GC-MS analyses on high-resolution QTOF or Orbitrap instruments. A strong consensus emerged on critical QA/QC practices: >90% of laboratories use internal standards for quality control, perform regular leak checks, and maintain injector systems through routine component replacement, spanning column (exchange/cuts), liners, syringes, and septa. Routine monitoring (>50%) involves method blanks, peak shape assessments, and systematic evaluation of intensity drifts, carryovers, and contamination. Retention indices coupled with mass spectral library matching served as the primary annotation approach (60%). Overall, a consensus of best practices in QA/QC and reporting emerged, providing evidence-based recommendations for high-quality GC-MS metabolomics.

Electrospray ionization (ESI) is a foundational technique in mass spectrometry (MS) widely applied to measure nonvolatile molecules in diverse chemical and biological samples. Yet variation in ESI-MS instrumentation, operating settings, and parameters such as solvent composition creates specific, local ionization environments that drive the formation of select ion species for individual analytes. As ESI continues to advance analytical discovery, understanding the extent to which variation in ion species formation impacts intra- and inter-experimental results is essential. Here, we assessed ion species formation by analyzing an internal retention time standard (IRTS) mixture across ten laboratories employing high-resolution ESI-MS instrumentation from four vendors (Agilent Technologies, ThermoFisher Scientific, Shimadzu Corporation, and Waters Corporation). Instrument vendors were considered not as a benchmark of performance, but as a practical framework to capture differences in source design, ion optics, and analyzer/detectors that are inherently coupled to commercial platforms. Despite the use of standardized extraction and chromatographic protocols, differences in instrument configuration, source conditions, and method execution resulted in variation in ion species formation across vendors, among laboratories using instruments from the same vendor, and even within individual laboratories. These findings demonstrate that, even with standardized methods, the collective influence of local ionization environments on ion species formation remains a critical obstacle for interpreting LC-MS small molecule data and improving reproducibility and comparability across studies.

Abstract Increases in body mass are associated with the development of metabolic dysfunction‐associated steatotic liver disease (MASLD). Caloric restriction (CR) is the primary non‐pharmacological defense against MASLD; however, poor CR maintenance induces partial body mass regain (PR), leading to MASLD progression. We conducted an in vivo study to investigate the effects of PR following 2 weeks of CR on the hepatic lipidome during Metabolic Syndrome (MetS) using the male Otsuka Long Evans Tokushima Fatty (OLETF) rat and the Long Evans Tokushima Otsuka (LETO) rat as strain control (Ctrl). PR reduced hepatic acyl‐CoA oxidase 1 (ACOX1) and carnitine palmitoyl transferase II (CPT2) protein expression compared to OLETF CR. The lipidome indicated that PR maintained reduced total TAGs primarily MUFA and PUFA TAGs, while SFA‐TAGs were elevated. Additionally, OLETF PR demonstrated a negative correlation with cholesteryl ester and an elevation in HDL levels vs. OLETF CR. These findings suggest that PR may promote hepatic steatosis by reducing fatty acid utilization and potentially associated with dysfunctional HDL, which can induce hepatic inflammation and injury. This study highlights the detriments of PR during MetS, suggesting that careful health monitoring is necessary for individuals with compromised metabolism when using dietary interventions to ameliorate MASLD.

Long term hypoxic stress causes structural and functional adaptations to the carotid artery, which leads to dysregulation in cerebral blood flow. While the physiological impact of hypoxia on cerebral vascular function is well-known, the dysregulation that occurs at the cellular and molecular level is not yet completely understood for adult carotid arteries. Exploring the effects of long-term hypoxia (LTH) using a metabolomic approach is advantageous in deciphering the etiology associated with the development of disease. LTH is well regarded for inducing oxidative stress and causing inflammation. A number of possible regulators associated with LTH stress include oxylipins and endocannabinoids, products of polyunsaturated fatty acid (PUFA) oxidation. We hypothesized that high-altitude LTH would reduce the levels of key oxylipins and endocannabinoids in carotid arteries of adult sheep and give rise to an increase in reactive oxygen species (ROS) generation. To investigate this, we obtained carotid arteries from adult normoxic and hypoxic sheep that resided at 3,800 meters above sea level for ~ 110 days. Metabolite levels were quantified using ultra performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). Chemical similarity enrichment analyses and visualization by complex pathway analyses was performed on the datasets. Enrichment analysis revealed changes in key metabolic pathways such as glycolysis, purine metabolism, polyol metabolism, and lipid metabolism. Crucial anti-inflammatory oxylipins known for being downstream of inflammatory molecules such as 12(13)-EpOME experienced downregulation under hypoxic stress. A glycolytic shift was also observed, indicative that aerobic respiration was impacted due to the reduced oxygen availability at altitude. These included metabolites involved in glycolysis and polyol metabolism, with mannitol, lyxitol, glycerol-alpha-phosphate all being increased under hypoxic stress conditions. Our results support the hypothesis that chronic hypoxia initiates redox stress-mediated metabolic reprogramming, leading to impaired glycolytic flux and subsequent carotid vascular dysfunction, a mechanism that warrants continued investigation. This abstract was presented at the American Physiology Summit 2026 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.

BACKGROUND:Suboptimal early-life growth trajectories are predictors of later cardiometabolic risk. Although mid- to late-pregnancy maternal metabolites have been linked to fetal and birth outcomes, the role of early pregnancy metabolomic profiles in child growth trajectories is underexplored. OBJECTIVES:We examined associations between early pregnancy metabolites and child growth acceleration and deceleration from birth to age 1 and 2 y. METHODS:Among 1152 mother-child dyads from the prospective Pregnancy Environment and Lifestyle Study, maternal untargeted metabolomics were measured by gas chromatography/time-of-flight mass spectrometry, reversed phase-, and hydrophilic interaction liquid chromatography/quadrupole time-of-flight mass spectrometry using fasting serum collected at gestational weeks 10 to 13. Of this sample, 813 had child anthropometric data at age 1 y and 770 at age 2 y. Growth trajectories were defined by changes in weight-for-length z-scores from birth to age 1 or 2 y: growth acceleration [≥0.67 standard deviation (SD)], growth deceleration (≤-0.67 SD), extreme growth acceleration (≥1.34 SD), and extreme growth deceleration (≤-1.34 SD), with normal growth (-0.67 to 0.67 SD) as reference. Poisson regression examined associations between metabolites and child growth trajectories. Chemical enrichment analysis identified metabolite clusters associated with child growth trajectories, adjusted for false discovery rate (FDR). RESULTS:A total of 431 annotated metabolites were identified. From birth to age 1 y, the polyunsaturated plasmalogen phosphatidylcholine (PC) cluster was associated with growth acceleration, whereas unsaturated fatty acids and polyunsaturated fatty acids were associated with growth deceleration (all FDR-corrected P < 0.05). Polyunsaturated plasmalogen PC and unsaturated PC clusters were associated with extreme growth acceleration from birth to 1 y, dicarboxylic acids with extreme growth acceleration from birth to 2 y, and branched-chain amino acids with extreme growth deceleration from birth to 2 y (all FDR-corrected P < 0.05). CONCLUSIONS:Early pregnancy lipids are positively associated with early-life growth acceleration and extreme growth acceleration. Findings may provide insights into how early pregnancy metabolomic changes shape development.

The aim of metabolic phenotyping (metabotyping) is to discover and identify metabolites (including lipids) that can be used to characterize biological samples and differentiate between different physiological states. The identification of the metabolites responsible for this differentiation is essential if mechanistic understanding is to be obtained. Confident metabolite identification arguably represents the most important outcome of untargeted metabolomics studies but currently the standards used for metabolite identification reported in many publications do not strictly follow the various published guidelines and thus these identifications lack sufficient proof. In this perspective we define problems that currently plague the field of metabolite identification using MS-based techniques, particularly LC-MS, in untargeted metabolic phenotyping. Despite considerable efforts by the community (researchers, instrument manufacturers, software, and database developers) this continues to be a contentious and error-prone step in the metabolomics workflow. The majority of publications provide only sparse data on the evidence for metabolic markers “identified” and we have observed an alarming increase in the frequency of erroneous metabolite identifications. Here, we describe the problem and provide several illustrative case studies. Our goal is to raise awareness and highlight the issue of poor metabolite identification, since it is also increasingly apparent that these errors are not always recognised during the reviewing process, such that papers with potentially erroneous metabolite identities reach publication. Poor metabolite identification potentially represents an existential threat to the credibility of untargeted “discovery” metabolomics and can pollute the literature. Here we describe the aetiology of the problem and explain how and why this issue affects the field. We argue that coordinated action is required by researchers, database managers, scientific societies and the reviewers, editors and publishers of scientific journals to both acknowledge and address this important problem.

Metformin is the most widely prescribed antidiabetic drug, yet adherence remains difficult to objectively assess. Using untargeted metabolomics and lipidomics, we analyzed plasma from 637 patients with type 2 diabetes (T2D) with confirmed metformin use and 143 nondiabetic controls, annotating 614 metabolites. Patients were stratified by plasma metformin into sub-therapeutic, therapeutic, and supra-therapeutic groups, and associations were evaluated by multiple linear regression and composite metabolite ranking. Five previously unannotated features were structurally identified as N-lactoyl-amino acids, whose levels correlated strongly with plasma metformin (ρ = 0.42-0.55, P < 0.0001) and increased up to 7.2-fold in the supra-therapeutic group (> 2000 ng/mL). While N-lactoyl-amino acids were consistently detected in the nanomolar range, they still displayed robust and dose-dependent associations with metformin. Broader metabolic changes in T2D included elevated lactate, organic acids, and branched-chain amino acids, together with reduced urea cycle metabolites. Lipidomics showed increases in saturated triacylglycerols and diacylglycerols and decreases in cholesteryl esters, sphingomyelins, and phospholipids. These findings establish N-lactoyl-amino acids as robust, dose-responsive plasma biomarkers of metformin exposure. Despite being up to four orders of magnitude less abundant than their amino acid precursors, they sensitively reflect mitochondrial lactate overflow and pharmacodynamic adaptation, offering objective assessment of adherence.

BACKGROUND:Genetic mechanisms that predispose people to type 2 diabetes (T2D) and cardiovascular disease (CVD) remain poorly understood, partly because of a lack of sufficient data on non-European ethnic groups. Extending these evaluations to diverse cohorts is essential for gaining insights into the molecular pathways involved in disease development among human populations. In this study, we aimed to evaluate the genetic connection between the human lipidome and cardiometabolic disorders. We conducted a metabolite genome-wide association study (mGWAS) in a Punjabi population from India, along with multi-layer replication studies using the UK Biobank and other independent European and non-European cohorts. METHODS AND FINDINGS:We performed mGWAS using 516 lipid metabolites in 3,000 Punjabi Sikh individuals, and validation was performed in 1.13M Europeans and 15K individuals from Asian Indian ancestry using independent cohorts of the UK Biobank, GeneRISK, DIAMANT, PROMIS, and other studies. We identified 609 SNP-metabolite associations representing 236 SNP-metabolite pairs that attained genome-wide significance (p </= 5 × 10-8). Of the 36 SNP-lipid metabolite signals that survived multiple testing correction (p </= 1.92 × 10-10), 33 associations were not reported before, and 3 associations were confirmed to be ancestry-specific. Using colocalization analysis, polygenic risk scores, and Mendelian randomization approaches, we identified a causal association of LPC O-16:0 with T2D, represented by a lead variant in CD45, a key regulator of T- and B-cell antigen receptor signaling, and is already used as a therapeutic target. Another possible causal relationship of PC 38:4 (C) in protecting against coronary artery disease risk in Asian Indians, attributed to a variant in the untranslated region in the FADS1/2 genes, may be specific to ancestry and/or could not be confirmed in Europeans because of extensive pleiotropy in this region. The main limitation of this study was the absence of an independent validation cohort of Asian Indians from India. CONCLUSIONS:The mGWAS of Asian Indians offers new insights into the diverse molecular origins of cardiometabolic diseases and suggests potential pathways for innovative treatments. Our findings highlight the need for additional research on human lipidomics to better understand the downstream effects of the genome and its impact on cardiometabolic health.

The pace of aging can be delayed by mutations, dietary manipulations, and drugs, yet the metabolic mechanisms underlying longevity interventions remain poorly understood. Here we present a multi-tissue metabolomic analysis of male UM-HET3 mice treated from 4 to 12 months of age with five validated longevity interventions: rapamycin, acarbose, 17α-estradiol, canagliflozin, or caloric restriction. Using a feature-stabilized XGBoost pipeline applied to seven tissues, we show that metabolomic profiles can identify treated mice as likely recipients of a lifespan-extending intervention well before survival differences emerge. A leave-one-intervention-out procedure confirmed that models trained on any four interventions successfully classified mice from a fifth, unseen intervention, implying shared metabolic alterations across mechanistically distinct treatments. The most influential metabolites - defined as the minimum set explaining 50% of cumulative model gain - differed substantially across tissues. Only ergothioneine, a dietary antioxidant, ranked highly in more than two tissues: it was elevated by all five interventions in plasma and brain, and by four of five in muscle. Enrichment analyses further identified coordinated remodeling of lipid classes in plasma, perigonadal fat, and kidney. These findings reveal tissue-specific metabolic reprogramming shared across mechanistically distinct longevity interventions and, pending validation against interventions that do not extend lifespan, suggest a path toward metabolomic screening of candidate anti-aging drugs.