Julien Wist

Background: Covid-19 and diabetes have a bidirectional relationship [1], with dyslipidaemia further aggravating disease severity [2]. This study investigates dyslipidaemia in diabetes with prior Covid-19 infection.

Methods: Nuclear Magnetic Resonance (NMR) and Liquid Chromatography-Mass Spectrometry (LC–MS) profiling of blood samples from 251 individuals yielded six data blocks with 1,110 metabolic variables, including 112 lipoproteins and 937 lipids, with 34 cytokines quantified by a multiplex immunoassay. A total 194 individuals with complete data were included and classified into four groups for analysis: control (n = 11), diabetes (n = 27), post-Covid (≥30 days since SARS-CoV-2 infection, n = 78) and diabetes with post-Covid (n = 78). Data were modelled using OnPLS for feature selection [3], followed by OPLS-DA to reveal metabolic alterations [4].

Results: OPLS-DA effectively discriminated diabetes from control (CV-AUROC = 0.63), identifying 102 altered metabolites, including 94 lipids (p < 0.05), and diabetes with post-Covid from post-Covdi (CV-AUROC = 0.83), identifying 614 altered metabolites, including 542 lipids and 54 lipoproteins (p < 0.05), revealing a broader dyslipidaemia in diabetes with post-Covid. Notably, increased small dense LDL cholesterol, phospholipids and their particle numbers, along with decreased HDL cholesterol, a marked increase in polyunsaturated triacylglycerols and elevated diacylglycerols and sphingolipids were prominently observed in diabetes with post-Covid, particularly in those with prior severe acute Covid-19 and persistent Covid-19 symptoms. These small-dense LDL particles were correlated with cytokines, such as TNF-alpha, IL-1beta and IL-17A (p < 0.05).

Conclusion: Long-term adverse effects of Covid-19 on people with diabetes are characterized by worsened atherogenic dyslipidaemia, associated with increased insulin resistance, chronic inflammation and risk of potentially debilitating and costly long-term organ complications, such as atherosclerotic cardiovascular disease.

Broad-spectrum viral biomarkers offer a promising approach to distinguishing viral from bacterial infections, thereby reducing inappropriate antibiotic use and improving diagnostic response during emerging infectious disease outbreaks. Among these, the deoxydidehydronucleoside (ddhN) class of nucleoside derivatives has emerged as a potential tool for early detection of viral infections in settings where pathogen-specific diagnostics are unavailable. To assess the clinical utility of these compounds, we investigated the metabolism and excretion rates of four principal ddhN metabolites, 3'-deoxy-3',4'-didehydrocytidine (ddhC), 3'-deoxy-3',4'-didehydrocytidine-5'-carboxylate (ddhC-5'CA), 3'-deoxy-3',4'-didehydrouridine (ddhU), and 3'-deoxy-3',4'-didehydrocytidine-5'-homocysteine (ddhC-5'Hcy), in the Sprague-Dawley rat model following a single intravenous dose. Time-resolved biological sampling was used to characterize urinary excretion and downstream biotransformation. All four metabolites exhibited rapid urinary clearance, ranging from approximately 3 to 8 h, consistent with a transient acute-phase profile. Notably, ddhC-5'Hcy underwent extensive biotransformation, with key metabolites produced via functionalization and conjugation identified following integration of nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) analyses. No adverse clinical signs were observed in any treatment group at any time point. These findings support further research into the ddhN series as markers of active viral infection for clinical application, particularly in critical care environments, where timely differentiation of infectious etiology is essential.

Methoxyacetic acid (MAA) is a testicular toxin that targets spermatocytes and round spermatids by disrupting mitochondrial function, leading to cellular energy depletion. Male Sprague-Dawley rats were given single oral doses of MAA (150 or 650 mg/kg), resulting in no mortality but transient toxicity signs and modest body weight effects, especially at the higher dose. Histopathology revealed dose- and time-dependent testicular damage, with selective germ cell necrosis by 48 h and extensive germ cell loss, spermatic giant cells, and epididymal inflammation observed in high-dose animals by 168 h. Metabolic analysis using high resolution 1H NMR spectroscopy and OPLS-DA identified elevated urinary excretion of N-butyryl glycine, a marker of mitochondrial dysfunction and impaired β-oxidation. The persistence of N-butyryl glycine and altered energy metabolites up to 168 h indicates sustained mitochondrial stress and disruption of ATP-dependent processes essential for spermatogenesis. Moreover, the close structural similarity between MAA and butyrate raises the possibility that MAA interacts directly with enzymes involved in butyryl-CoA turnover during the terminal steps of β-oxidation in rodents.

Acute insults ranging from blunt force trauma and thermal injury to pathogenic infection elicit systemic inflammatory cascades intended to limit further tissue damage. These responses are accompanied by metabolic disturbances that generate distinct biochemical signatures measurable through advanced analytical platforms, such as mass spectrometry and nuclear magnetic resonance spectroscopy (NMR). Although numerous studies have examined these metabolic alterations, findings remain fragmented across clinical and analytical disciplines, leaving it unclear whether the systemic metabolic response to acute insult is fundamentally conserved or insult-specific. In this comparative review, we consolidate evidence across diverse injury and infection contexts to identify shared metabolic patterns, context-dependent differences, and critical gaps in current understanding. Here, we focus on lipid and lipoprotein profiling of blood plasma and serum. We present exemplar case studies spanning traumatic brain injury, burn injury, and SARS-CoV-2 infection to illustrate how lipid and lipoprotein perturbations differ or converge across insult types. Notable observations include consistently elevated palmitic acid (16:0) and reduced phosphatidylcholine species across all three conditions, suggesting these features may represent cross-condition biomarkers and highlighting the value of comparative metabolic profiling. By integrating evidence across diverse contexts, we propose a framework describing the interplay between lipid metabolism, lipoprotein dynamics, and inflammatory activation. Finally, we discuss the translational potential of metabolic phenotyping in enhancing patient stratification, refining prognostic modelling, and improving patient outcomes.

Nuclear magnetic resonance (NMR) spectroscopy is widely adopted for assessing biochemical composition in agriculture. This study evaluated the feasibility of 400 MHz NMR to detect biochemical differences in Hass avocados grown under conventional (N = 101) and regenerative (N = 105) farming practices in Southwestern Australia. Phosphite, associated with Phytophthora root rot management, was a key discriminating feature (area under ROC curve = 0.96), being detected in 90% of conventional avocados (mean: 49 mg/kg) and 6 regenerative samples (mean: 24 mg/kg). To assess translational potential, water extracts of five samples were analyzed using 80 MHz benchtop NMR. Phosphite was detectable below the strictest maximum residue limit (25 mg/kg), demonstrating the potential of NMR as a sustainable and cost-effective solution for monitoring phosphite residues. This proof-of-concept benchtop NMR approach demonstrates analytical feasibility but requires further validation before application in field-based traceability or regulatory contexts, with a potential future relevance to environmental monitoring, sustainable agriculture, and other crop systems.

Understanding the distribution and variation in NMR-based inflammatory markers is crucial in the evaluation of their clinical utility in disease prognosis and diagnosis. We applied high resolution 1H NMR spectroscopy of blood plasma and serum to measure the acute phase reactive glycoprotein signals (GlycA and GlycB) and the subregions of the lipoprotein based Supramolecular Phospholipid Composite signals (SPC1, SPC2 and SPC3) in a large multi-cohort population study. A total of 5702 samples were measured to determine the signal variations in a range of chronic and acute inflammatory conditions. We found that while the GlycA and GlycB were increased in inflammation, the SPC regions behaved independently of Glyc signals, with SPC2 and SPC3 being reduced in chronic inflammation in comparison to healthy controls (p-value SPC2=2.9x10-10, p-value SPC3=2.2x10-3) and SPC1 (p-value=0.29) being unchanged. SPC1 was decreased in acute inflammation indicating a link to the immune response (p-value=2.5x10-11). These findings confirm the independent biological relevance of all 3 SPC subregions and contraindicate the use of aggregate SPC values as general inflammatory markers.

The Consortium for Metabonomic Toxicology (COMET) studies was designed to model metabolic responses to organ-and mechanism-specific toxins to predict acute drug toxicity in rats. A range of clinical chemical parameters were measured in 7-day toxicology studies for 86 toxins eliciting a range of organ-and mechanism-specific effects. Additionally, 21 surgical or physiological stressors were evaluated to identify physiological or metabolic responses that might confound the interpretation of observed toxicity profiles. From these studies on a total of 3473 rats measured at six pharmaceutical companies, we provide a set of 12 serum and 5 urine physical and clinical chemistry parameters. Samples were collected at 24 h, 48 h and 168 h post-dose for each animal and are presented as a downloadable database file. We also summarise the main observations based on the group response at the level of the individual toxin. We demonstrate that correlations between parameters, such as serum bilirubin and aspartate aminotransferase (AST), provide a more nuanced profile of organ-specific toxicity than consideration of individual parameters alone. In addition, we highlight the variability in the measured parameters across the dataset attributable to inter-laboratory differences, and the heterogeneity of metabolic responses to particular compounds or differences in temporal patterns of response. This clinical chemistry atlas of toxicity serves as a valuable reference tool for evaluating the potential toxicity of novel drug candidates.

Dried blood spot (DBS) sample collections can offer a minimally invasive, cost-effective alternative to traditional venepuncture for remote sampling and high-frequency metabolic profiling. We present an optimised protocol for DBS-based extraction and comprehensive untargeted 4D lipid profiling using ultrahigh-performance liquid chromatography coupled with high-resolution mass spectrometry, designed to support large-scale applications in population-wide lipidomics research. Inclusion of stable isotopically labelled internal standards allowed for semi-quantitative subclass-level correction for 10 μL DBS samples, enhancing the number of reproducible lipids within our curated target list (focussed on 432 unique rule-based lipid annotations out of 6845 features) across positive and negative heated electrospray ionisation modes. The reproducibility of unique lipid features detected in replicate DBS (n = 6) was assessed on both peak areas (351 lipids < 25 % CV) and calculated concentrations relative to internal standards (432 lipids < 25 % CV), underscoring the benefit of internal standard addition. Storage conditions for DBS were also evaluated to determine short-term lipid stability at different temperatures (-20 ˚C, 4 ˚C, room temperature, and 45 ˚C). The majority of lipid subclasses, excluding a minority of glycerophospholipids and oxylipins, were stable up to 1 week at -20 ˚C and 4 ˚C (log2-fold change < 30 % difference), which supports the short-term storage capacity for DBS in field and clinical settings. Similar stability was observed within a week at room temperature, excluding phosphatidylethanolamines and phosphatidylglycerols (log2-fold change > 30 % difference). Application of the optimised workflow to a microsampling device (n = 6) identified 432 lipid features (CV < 25 %) with three repeated samplings over an hour showing minimal impact on lipid profiles by principal component analysis, showing promise for high-frequency, longitudinal DBS monitoring in population health. This work represents a significant advance, highlighting the potential for reliable lipid analysis from DBS samples with short-term stability under various storage conditions, an important logistical benefit for remote or resource-limited settings.
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•Dried blood spots enable minimally invasive, cost-effective sampling in lipidomics•The developed untargeted 4D-lipidomic method annotates 432 lipids in 10 μL DBS•Majority of lipid subclasses are stable on DBS up to 1 week, ideal at -20°C and 4°C•Commercial microsampling devices suit remote, high-frequency lipid profiling

Pooled quality control (PQC) samples are the gold standard for data quality monitoring in metabolic phenotyping studies. Typically composed of equal parts from all study samples, PQCs can be challenging to generate in large cohorts or when sample volumes are low. As an alternative, externally sourced matrix-matched surrogate QCs (sQC) have been proposed. This study evaluates the performance of sQCs against PQCs for assessing analytical variation, data pre-processing, and downstream data analysis in a targeted lipidomics workflow.
Plasma samples (n = 701) from the Microbiome Understanding in Maternity Study, along with PQC (n = 80) and sQC (n = 80) samples, were analyzed using a lipidomics assay targeting 1162 lipids. QC samples were injected throughout acquisition, and data pre-processing was performed using each strategy. For simplicity, a subset (n = 381) of the study samples was used to assess differences in downstream statistical analyses.
Both QC approaches demonstrated high analytical repeatability. While PQC and sQC compositions differed, use of PQCs retained less than 4 % more lipid species during pre-processing. Univariate analysis identified more statistically significant lipids with PQC-based pre-processing, but multivariate model performance was similar between datasets.
This study provides a comprehensive comparison of QC strategies and emphasizes the importance of careful QC workflow selection. While PQCs offer advantages, sQCs serve as a suitable alternative for quality assessment and pre-processing. Their commercial availability also supports use as intra- and inter-laboratory long-term references, aiding data harmonization across studies and laboratories.
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•Comparison of two quality control workflows; pooled study and surrogate QC samples.•In-depth assessment of lipid composition, precision, and filtering.•OPLS-DA model predictive power maintained with both QC pre-processing strategies.•Surrogate QC samples are a robust alternative to a pooled QC in targeted lipidomics.

Background
Robust tissue pre-treatment and lipid extraction workflows are crucial to metabolic phenotyping studies and accurate interpretation of lipid profiles. Numerous methods for lipid extraction from tissues have been developed, and the choice of technique influences analysis. This study provides a comprehensive evaluation of six liquid-liquid extraction methods (three biphasic and three monophasic) used for lipidomic tissue analysis by liquid chromatography-mass spectrometry. Extraction methods were assessed for their suitability for comprehensive lipid profiling across diverse tissue types: adipose, liver, and heart. These techniques were compared using lyophilised and fresh frozen samples.

Results
The study revealed significant differences in the coverage and reliability of lipid species extracted using each technique, dependent on the tissue type. The optimal extraction method for adipose tissue was butanol:methanol (BUME) (3:1) which achieved the highest lipid coverage, yield and reproducibility (886 lipids with a coefficient of variation (CV) < 30 %); methyl tert-butyl ether (MTBE) with ammonium acetate was most effective for liver tissue (707 lipids CV < 30 %) and BUME (1:1) for heart tissue (311 lipids CV < 30 %). These findings showed that the most effective lipid extraction methods are highly tissue-specific, underscoring the critical need for bespoke protocols tailored to each tissue type. The optimised tissue-specific methods were validated using an intervention study in C57BL/6 mice to investigate diet-induced metabolic changes. The results demonstrated distinct discriminating lipid profiles unique to each tissue type, with 374 lipid species from 13 subclasses significantly different between high-fat diet (HFD) and normal diet (ND) in adipose tissue, while 485 lipid species from 17 subclasses were significantly different between HFD and ND in liver tissue.

Significance and novelty
This study presents a new approach to studying lipid profiles derived from diverse tissues that substantially improve comprehensive lipid species’ detection sensitivity and reliability. Our systematic evaluation provides evidence that tailored tissue-specific extraction protocols are highly valuable in comprehensive lipidomics studies, offering robust tools for reliably identifying lipid changes and facilitates a deeper understanding of tissue-specific metabolic processes in diverse research and clinical applications.
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