Elaine Holmes

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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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.

Interplay between the maternal diet and gut microbiome may impact fetal immune development and allergic disease risk. This study investigated associations between maternal prenatal urinary metabolites and infant food allergy and then extended to potentially relevant dietary and microbial precursors.
We investigated 599 mother-infant dyads from an Australian population-derived prebirth cohort. Maternal dietary data and fecal and urine samples were collected in the third trimester. NMR was used to measure prenatal urinary metabolites. Infant food allergy status was determined at 1 year by skin prick allergy testing and food challenge. Regression techniques were used to investigate associations and adjust for pre-specific confounding factors.
Higher concentration of hippuric acid in maternal urine, an end-product of dietary polyphenol metabolism, was associated with a lower risk of infant food allergy (odds ratio (OR) 0.62 (95% CI 0.42, 0.93)). Consistent with this, dietary proanthocyanidins, a polyphenol, were positively associated with both higher urinary hippuric acid concentration (0.11 log units, CI 0.01, 0.22) and lower risk of infant food allergy (OR 0.58 (CI 0.36, 0.96)). Maternal carriage of the gut commensal Prevotella copri, previously associated with protection against infant allergic disease, was associated with 21% higher urinary hippuric acid concentrations (CI 4%, 40%, corresponding to 0.19 log units CI 0.04, 0.34); however there was no evidence of mediation.
Further studies are required to confirm whether higher dietary intake of proanthocyanidins during pregnancy is associated with protection against allergic disease in the infant via gut microbiome production of hippuric precursors and other immune-active metabolites.

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.

Microbiome engineering has emerged as a promising strategy to drive biotechnological developments across diverse fields. Microbiome-based fertilizers could significantly contribute to the gradual replacement of synthetic chemical fertilizers, potentially leading to substantial environmental and economic impacts. This study employed microbiome engineering to develop a self-assembled nitrogen-fixing microbial community utilizing carbon compounds from animal waste. This was achieved by enriching soil samples in bioreactors supplied with nitrogen via air pumping and fed with volatile fatty acids (VFAs) as the only carbon source. VFAs are the most common by-products of anaerobic waste fermentation. Results show a self-assembled community, dominated by Sinirhodobacter spp. (44.4%), Aureimonas spp. (17.7%), and Taibaiella spp. (12.4%), capable of fixing 2.7 times more nitrogen than the initial microbiome. During cultivation, inorganic nitrogen forms were detected in the supernatant at concentrations of up to 12.7 mg·L −1 . Once the self-assembled community was inoculated in tomato plants, Pseudomonas spp. and Exiguobacterium spp. became the most abundant and significantly enhanced tomato plant growth in both hydroponic and soil-based systems. Plant height and yield were comparable to those achieved with conventional synthetic nitrogen fertilizers. This study shows the potential of this methodology for developing effective biofertilizers while promoting a circular economy strategy that transforms waste into high-value bioproducts. This approach, combined with the simplicity of the bioreactor system, offers a viable and sustainable solution for developing countries with limited technological resources, and materializes the One Health vision while simultaneously protecting the health of people, crops, and animals.

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.

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.

Advanced phenotyping using lipidomics enables personalized risk stratification in cardiometabolic disease. Integrating multi-omics approaches may improve detection of high-risk individuals in the Cardiovascular-Kidney-Metabolic (CKM) spectrum.

Objective: To identify lipid metabolites and immune signatures predictive of CKM Stage 4 and the need for advanced coronary interventions: PCI and CABG.

Methods: Patients undergoing coronary angiography provided blood samples for PBMC isolation (flow cytometry) and plasma analyses (immunoassay, targeted lipidomics via LC-MS). A total of 1,143 lipids across 20 classes were profiled. CKM staging followed AHA guidelines. Data were analyzed in R and GraphPad using LipidR and logistic regression models.

Results: Among 200 participants (median age 67, 21% female), 42.5% had obesity, 42% diabetes, and 31% ACS. CKM Stage 3 and 4 were identified in 39% and 20%, respectively; 31.5% underwent PCI, 13% CABG.

The model predicting CKM Stage 4 included NOX5 (PBMCs and monocytes), two lysophosphatidylcholines LPC(18:1), LPC(20:0) and two phosphatidylcholines PC(14:0_18:2) and PC(18:2_18:2), achieving AUC=0.90, NPV=91.0%, PPV=85.2%. PCI prediction included NOX5 and five lipids: phosphatidylinositol PI(20:0_18:1), triacylglycerols TG(54:3_FA(16:0)) and TG(56:6_FA(20:3)), PC(18:1_18:3), and the diacylglycerol DG(16:0_20:5), AUC=0.92, NPV=88.3%, PPV=85%. The CABG model featured NOX5 and PC(18:1_18:3) and PC(16:1_18:2), two TG(54:3_FA(16:0)), TG(56:6_FA(20:3)) and PI(18:0_20:3), with AUC=0.96, NPV=96.7%, PPV=90.9%. All models outperformed clinical variables alone (p<0.0001) and achieved perfect discrimination when clinical variables were integrated (AUC=1.0) Table 1, Figure 1.

Conclusion: Targeted lipidomics combined with immune markers, particularly NOX5, enables accurate classification of patients at advanced cardiometabolic risk. These signatures offer strong translational potential for early detection and intervention in CKM and coronary artery disease.