Julien Wist

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.

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.

Accurate assignment of endogenous and exogenous constituents within complex biological matrices remains a central challenge in metabolomics. Phenylacetylglutamine (PAGln) and phenylacetylglycine (PAGly) are terminal metabolites of the host-microbial co metabolism of phenylalanine, predominantly observed in humans and rodents, respectively. Motivated by a pervasive misidentification within the literature, we demonstrate that PAGln in human urine has been erroneously identified as PAGly in 82 of 166 NMR-based studies, yielding an estimated misannotation rate of 49%. Herein, we provide definitive assignments for both PAGly and PAGln, and present a comprehensive statistical evaluation of the NMR resonances attributed to both species, alongside their quantitative determination via targeted line-fitting approaches in extensive human and rodent urine samples obtained from the INTERSALT cohort (N = 1589) and the historical COMET study (N = 4399) respectively. In agreement with canonical hepatic metabolic pathways, PAGln was found to be exclusively present in human urine, whereas PAGly was restricted to rodent (i.e., rat and mouse) urine. These findings underscore the need for systematic re-evaluation of prior metabolomic annotations involving these structurally related yet biochemically distinct metabolites.

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.

Rationale
Sarcoidosis is a complex inflammatory disease of unknown cause, with diagnosis often complicated by a lack of definitive biomarkers. This study employed Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Imaging (MALDI-MSI) to spatially profile lipids in sarcoid-like granulomatous tissues from animal and human samples.

Methods
ApoE-/- mice (n=23) were fed a cholate-containing high-fat diet for 16 weeks, inducing sarcoid-like granulomas. Granulomas were characterized through haematoxylin and eosin, Masson‘s Trichrome, and immunofluorescence staining, while lipidomic profiling of mouse hearts (n=10) and lymph nodes (n=10) was performed using MALDI-MSI. A comparative analysis was performed using a human sarcoid-like granulomatous lymph node.

Results
The mouse model exhibited granulomas, characterized by lipid-laden macrophages, fibrosis, and perivascular lymphocyte clusters. Human lymph nodes with sarcoid-like granulomas demonstrated hallmarks of sarcoidosis, including foamy histiocytes, non-necrotizing granulomas, and Langhans giant cells containing silicone and asteroid bodies. MALDI-MSI identified over 30 lipids that were consistently detected in murine heart and lymph node tissues. Of these, eight key lipids, belong to the Lysophosphatidylinositol (LPI), Phosphatidic acid (PA), Phosphatdylinositide (PI) and Phosphatidylserine (PS) classes, that were also detected in human lymph nodes.

Conclusions
To our knowledge, this is the first application of MALDI-MSI in spatial lipidomic profiling in sarcoid-like animal model and human sarcoid-like granulomatous tissue. MALDI-MSI revealed distinct yet shared lipidomic profiles in both sarcoid-like animal and human tissues. This finding provides a new perspective in sarcoidosis pathogenesis and warrants future mechanistic study and validation.

Understanding the distribution and variation in NMR-based inflammatory markers is crucial to 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 multicohort population study. A total of 5702 samples were studied to determine the signal variations in a range of chronic and acute inflammatory conditions. We found that while 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 = 2.9 × 10–10, p-value SPC3 = 2.2 × 10–3) and SPC1 (p-value = 0.29) being unchanged. SPC1 was decreased in acute inflammation, indicating a link to the immune response (p-value = 2.5 × 10–11). These findings confirm the independent biological relevance of all three SPC subregions and contraindicate the use of aggregate SPC values as general inflammatory markers.