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.

Systematic literature reviews are widely acknowledged to be at the top of most evidence hierarchies but are very time-consuming and labour-intensive. The advent of artificial intelligence (AI) offers possibilities to accelerate the review process. We propose a systematic literature review protocol that incorporates AI and evaluate the accuracy of AI against a human-constructed gold standard. Human researchers performed a systematic literature review on the PubMed metabolomic literature investigating Long COVID. OpenAI model o3-mini was then used via the application programming interface to carry out the three key stages of a systematic literature review: 1) screening articles for inclusion criteria, 2) evaluating articles' risk of bias (RoB) using a customised metabolomics tool based on well-established quality criteria, and 3) extracting data from articles. The performance of AI was evaluated against a manually created reference by the assessment of accuracy, negative predictive value, precision, sensitivity, specificity, F1 score, and Cohen's kappa. Out of the 320 identified articles, 33 met all inclusion criteria. AI performed screening, RoB assessment, and data extraction with mean accuracies of 92.7% +/- 4.9%, 91.9% +/- 5.9%, and 92.0% +/- 9.2%, respectively, in similar to 10 h and at a cost of similar to 15.35 USD, while human reviewers took similar to 116 h, equivalent to similar to 2000 USD at minimum wages, to complete these steps in duplicate. OpenAI model o3-mini can independently complete the three main stages of systematic literature reviews in the metabolomics domain, thus considerably reducing the time to complete such reviews, at minimal cost. Nonetheless, human reviewers should always verify the AI-generated output to ensure robustness.

Phenomic medicine is an emerging field that involves measurement and modelling of multidimensional phenotypic data to provide a systems-level framework to understand how health and disease evolve over time and inform decision making in a clinical context . Unlike genomic medicine, which is largely predictive of predisposition, phenomic medicine captures the dynamic biochemical, physiological, and metabolic changes that occur across the disease continuum. Metabolic profiling, imaging, digital health data, and electronic health records can be used to characterise these phenotypic shifts longitudinally by constructing temporal models that map disease trajectories at both individual and population-wide scales. Phenomic trajectories allow for the stratification of patients based on the evolving pathobiology arising from gene-environment interactions that underpin disease progression, resilience, or recovery. The modelling of these trajectories can identify early markers of transition from health to subclinical dysfunction, establish critical inflection points, and distinguish reversible versus irreversible stages of disease. This capability is particularly powerful in complex, multifactorial conditions such as cardiovascular disease, diabetes, or post-viral syndromes, where a simple panel of biomarkers established in cross-sectional cohort studies lacks the required specificity for prognostic precision. Ultimately, phenomic medicine offers a route to transforming care from a reactive to a proactive mode, and can support personalised health management across the life course.

Background
Obesity is associated with adverse alterations in lipoprotein profiles and increased cardiometabolic risk, yet standard clinical lipid measures provide limited resolution of the underlying lipoprotein structure. Here, we perform a population-level characterisation of the impact of obesity on the high-resolution blood lipoprotein subfraction profile.

Methods
Lipoprotein profiles were investigated in an Australian cohort (n = 1806) stratified by Body Mass Index (BMI) (healthy weight, overweight, obese). The composition and particle number of lipoproteins and their subfractions were quantified (n = 112 parameters) using 1H NMR spectroscopy. Associations between BMI, lipoprotein subfractions, and particle number ratios were evaluated. Key BMI-associated ratios were validated in a second Australian cohort (n = 1313)

Results
Participants with obesity had significantly higher particle concentrations of VLDL and IDL and lower HDLs, while total LDL concentration was similar between the BMI categories. Notably, obesity was associated with a marked redistribution of LDL subfractions, with a shift from larger, buoyant LDL1–3 particles to smaller, dense LDL4–6 particles, accompanied by a similar shift in HDL subfractions, with proportionally lower concentrations of HDL1–3 particles in the high BMI group. An inverse association between BMI was found for two LDL particle number ratios (LDL1–3:LDL4–6 and LDL2:LDL5) and one HDL ratio (HDL1:HDL4), which demonstrated good discriminatory power for obesity status (AUROCs 0.70–0.74).

Conclusions
The pattern of lipoprotein subfraction redistribution in participants with obesity was consistent across cohorts. These subfractional changes offer enhanced resolution compared with conventional lipid and lipoprotein measures and may contribute to improved cardiovascular risk stratification in individuals with obesity.

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.

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.

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.
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