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.

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.

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.

Background
The phenotyping of individuals using robust tools as lipidomics is crucial to implement preventive personalised medicine. Combining the findings from multiomics can result in broader CV risk-capturing, uncovering relevant molecules in highly prevalent syndromes conditions such as the Cardiovascular-Kidney-Metabolic (CKM).

Purpose
To discern the lipid metabolites with predictive power for the correct identification of patients at CKM Stage 4 and those who required advanced revascularization interventions, coronary artery bypass graft (CABG) and percutaneous coronary intervention (PCI).

Methods
Participants scheduled for a coronary angiogram in the CARDINOX agreed to provide blood. PBMCs were isolated for flow cytometry and plasma samples were processed for immunoassay and targeted lipidomics using liquid chromatography–mass spectrometry, spanning 1143 lipids from 20 different classes. Patients were assigned to stages using the AHA CKM definition. Statistical analyses were performed in R and GraphPad prism, using the LipidR package for analysis and multiple logistic regression for biomarker performance estimation.

Results
200 subjects were recruited, median age 67 years (IQR 58-74), 21% female, 42.5% had obesity, 42% had diabetes and 31% presented with an acute coronary syndrome (ACS). 39% were at CKM-Stage 3 and 20% at CKM-Stage 4; 31.5% required PCI and 13% of patients required CABG. The best predictive model for CKM-Stage 4 included NOX5 in PBMCs, Monocytes and plasma, two lysophosphatidylcholines (LPC) 18:1, LPC 20:0 and two phosphatidylcholines (PC) 14:0/18:2, PC 18:2/18:2, AUC=0.90, p<0.0001, NPV=91.0%, PPV=85.2%. The best model for those needing PCI included NOX5 in PBMCs and Monocytes, and five lipids: phosphatidylinositol (PI) 20:0/18:1, TG 54:3/16:0, TG 56:6/20:3, PC 18:1/18:3, and the diacylglycerol (DG) 16:0/20:5, AUC=0.92, p<0.0001, NPV=88.3%, PPV=85%. In the case of CABG, the best model included NOX5 in Monocytes and PBMCs, PC 18:1/18:3, PC 16:1/18:2, two triacylglycerols (TG) 54:3/16:0, TG 56:6/20:3 and the PI 18:0/20:3, AUC=0.96, p<0.0001, NPV=96.7%, PPV=90.9%. These models outperformed the predictive power of considering only the clinical or lipid parameters and achieved perfect discrimination when routine clinical variables were added.

Conclusions
Using a targeted lipidomic approach can substantially improve the classification of patients at advanced CV risk. When combining a few of the most differentially expressed lipids with other novel biomarkers such as NOX5, we obtained a clear distinction of patients that presented with CKM Stage 4 and those who required advanced revascularization (PCI and CABG). Additionally, these lipids hold the potential to inform biologically relevant pathways in CVD. When validated in prospective and external populations, NOX5 and lipidomic panels can be easily translated to the clinic for earlier identification of individuals at risk of adverse coronary outcomes.
[Table Omitted]

Nuclear magnetic resonance (NMR) spectroscopy is increasingly employed in research to quantify lipoprotein subfractions, offering potential utility in clinical diagnostics, particularly for cardiovascular risk assessment. However, the independent validation of proprietary NMR-based lipoprotein profiling methods is crucial for verifying clinical accuracy and reliability. This study presents a posthoc evaluation of concordance between the NMR-based B.I.LISA method and standard enzymatic assays for total cholesterol (TC), triglycerides (TGs), and high-density lipoprotein cholesterol (HDL-C), measured in 620 plasma samples from the OMNI-Heart study, focusing on their performance in evaluating the dietary intervention outcomes. Despite involving independently acquired data not designed for an intermethod validation, the comparison showed a high correlation between methods (R = 0.85–0.92), with median deviations of −4, −5, and −15% for HDL-C, TC, and TGs, respectively. The larger TG deviations are attributed to known issues arising from heterogeneity in high-TG samples, although intervention outcomes remained unaffected. Albumin was identified as a potential interfering factor affecting the TC and HDL-C measurements. HDL-C could also be affected by lipoprotein degradation, contributing to divergence in comparisons of marginal intervention outcomes. Extreme discrepancies were observed in atypical hypercholesterolemia samples. These findings highlight the reliability of the NMR approach despite revealing minor but significant deviations that warrant further research.

Clinical chemistry retains its position as a cornerstone of toxicological assessment, yet inter-laboratory variability in baseline values remains a challenge for the integration and interpretation of multisite datasets. This study leveraged a publicly available clinical chemistry database to assess the impact of inter-laboratory variability in response to hydrazine-induced steatosis. Seventeen clinical chemistry and physico-chemical parameters were evaluated in response to a single dose of hydrazine (at 30 mg/kg or 90 mg/kg) administered to Sprague-Dawley rats (n = 83) across five different pharmaceutical companies and compared with sham-dosed control animals. Hydrazine exposure produced a distinct and consistent biochemical signature at 48 h post-dose across the combined sample set from all laboratory sites, characterised by increased serum bilirubin and BUN and decreased serum protein concentrations, alongside atypical reductions in ALT and AST due to transaminase inhibition. Despite sizable inter-laboratory differences in response when considering single assays, multivariate analysis of the complete dataset was able to extract a core pathological response signature. Early changes at 24 h post-dose in AST, ALT, total protein, and calcium demonstrated strong predictive value for 48-h toxicity profiles (AUROC 0.98), underscoring the translational potential of early biomarkers. This study highlights both the robustness and contextual limitations of clinical chemistry data in toxicological studies. It underscores the importance of matched-control designs and multivariate approaches for multisite studies and advocates for the integration of early predictive modelling to optimise study design and align with the principles of the Replace, Reduce, and Refine initiative.

As part of a strategy for accommodating missing data in large heterogeneous datasets, two Random Forest-based (RF) imputation methods, missForest and MICE were evaluated along with several strategies to help navigate the inherently incomplete structure of the dataset. Background: A total of 3817 complete cases of clinical chemistry variables from a large-scale, multi-site preclinical longitudinal pathology study were used as an evaluation dataset. Three types of ‘missingness’ in various proportions were artificially introduced to compare imputation performance for different strategies including variable inclusion and stratification. Results: MissForest was found to outperform MICE, being robust and capable of automatic variable selection. Stratification had minimal effect on missForest but severely deteriorated the performance of MICE. Conclusion: In general, storing and sharing datasets prior to any correction is a good practise, so that imputation can be performed on merged data if necessary.

Background
Nuclear magnetic resonance (NMR) spectroscopy enables the characterisation of lipoprotein sub-particles, providing a more detailed lipid profile than the conventional lipid measurements, with potential clinical relevance, particularly in cardiovascular disease (CVD), which remains the leading cause of mortality worldwide. Nonetheless, for clinical implementation, it is essential to first determine the normal variation of lipoprotein parameters by age and sex.

Methods
This cross-sectional study analysed a large dataset of 31,275 serum or plasma samples from five different countries using the B.I.LISA™ NMR-based platform, quantifying 112 lipoprotein parameters, including subclass size and concentration. Lipoprotein parameters from specific cohorts were fitted to a Quantile Generalised Additive Model (QGAM) to calculate the different percentiles as a function of age and sex.

Findings
A sub-cohort of individuals belonging to non-oriented cohorts (27,470 individuals) showed that lipoprotein parameters exhibit distinct sex- and age-dependent patterns, with inflection points observed around 44 and 60 years in women and around 60 years in men, aligning with known ageing acceleration models. The sub-cohort of 3021 individuals showing cardiometabolic risk factors was used to evaluate the effect of obesity, hypertension and diabetes in the lipoprotein distribution. Finally, we analysed the lipoprotein parameters that align with SCORE2 (a well-known CVD risk predictor) in an age- and sex-dependent manner. Many NMR-derived parameters effectively distinguish between low and high/very high CVD risk profiles, with very low-density (VLDL)-associated parameters demonstrating the highest sensitivity across a broad age range.

Interpretation
Our findings provide reference values for NMR-derived lipoprotein parameters by age and sex, enabling their accurate interpretation in the context of cardiovascular disease risk stratification.

Funding
The specific funding of this article is provided in the acknowledgements section.