Nicola Gray Dr

Background and aims: Diabetic retinopathy (DR) is a major complication of diabetes mellitus and a leading cause of blindness globally. Early detection and intervention underpin optimal DR management and remain challenging. The underlying biological mechanisms are also poorly understood. This study examined comprehensive plasma lipid profiles using a targeted approach to detect potential DR biomarkers.

Materials and methods: We utilised data and samples from 762 adult participants with type 2 diabetes (mean age 64.5 years, 54.3% males, median diabetes duration 7.0 years) from the community-based longitudinal Fremantle Diabetes Study Phase II. All had DR status (none, mild non-proliferative diabetic retinopathy (NPDR), moderate NPDR, or severe NPDR or worse) assessed by colour fundus photography at baseline and at the Year 4 or 6 review. Ultra-performance liquid chromatography-tandem mass spectrometry was used for plasma lipid profiling using baseline samples. Multiple logistic regression was used to identify baseline associates of i) any new or worsening DR and ii) any incident DR. The likelihood ratio test (LRT) was used to evaluate the incremental contribution of potential new lipid biomarkers. The net reclassification improvement (NRI) was also calculated.

Results: Any new/worsening DR was observed in 121 participants (16%) and 35 of 495 without DR at baseline (7.0%) developed DR during follow-up. We detected five lipids independently associated with one or both DR outcomes, specifically cholesterol ester (20:4), fatty acid (20:3), lysophosphatidylglycerol (18:0), lysophosphatidylglycerol (18:1) and phosphatidylcholine (18:0_20:3). For any new/worsening DR, the inclusion of lipid parameters (cholesterol ester (20:4), fatty acid (20:3) and lysophosphatidylglycerol (18:1)) in addition to conventional risk factors (systolic hypertension, HbA1c, blood glucose-lowering treatment intensity and urinary albumin:creatinine) added significantly to the conventional model (LRT, P=0.00002). The NRI gain for at a moderate cut-off of 10% was 5.7% (SE 2.8%; P=0.043). For incident DR, inclusion of lipid parameters (fatty acid (20:3), lysophosphatidylglycerol (18:0) and phosphatidylcholine (18:0_20:3)) with HbA1c as the only conventional risk factor improved model performance (LRT, P=0.00001). The NRI gain at a moderate cut-off of 5% risk of incident DR was 28.3% (P=0.002).

Conclusion: These data demonstrate that disturbances in lipid metabolism are associated with DR progression in type 2 diabetes. The present five lipid biomarkers have not been identified as determinants of incident DR in limited previous longitudinal studies but have the potential to improve DR risk prediction and provide novel insights into the mechanistic pathways underlying the development and progression of DR.

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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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.

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.

Urban wetlands provide essential habitats for wildlife but face increasing contamination from anthropogenic sources, including but not limited to per- and polyfluoroalkyl substances (PFAS) and metal(loid)s. Accumulation of these contaminants are known to impair organism biological functions. This study quantified PFAS and metal(loid) concentrations in liver tissue of motorbike frogs (Ranoidea moorei) along a contamination gradient of conservation significant urban wetlands in Perth, Western Australia, and assessed relationships of liver concentrations with whole-organism health metrics and metabolomic profiles in four tissues to evaluate biological effects. Of 61 accepted PFAS in frog livers, 22 had at least one individual above reporting limit. Linear perfluorooctane sulfonate (PFOS) was the most abundant PFAS compound in frog livers and males had higher PFAS concentrations at larger body sizes. The total PFAS concentrations in motorbike frog livers (range: 1.3 - 145.7, outlier: 364.3 μg/kg) were relatively low compared to tertiary consumer species or those inhabiting heavily contaminated sites. Total PFAS concentrations, or interactions between total PFAS and metal(loid)s, were not significantly correlated with body condition, liver mass, or gross health indicators; however, there were subtle tissue-specific metabolic perturbations linked to PFAS exposure, particularly in males. Biological responses showed stronger associations with liver concentrations of Zn (range: 13.7 - 90 mg/kg), Se (range: <0.05 - 4.1 mg/kg), and Cu (range: 11.3 - 248.5 mg/kg). Zinc concentrations were negatively associated with body condition, and females showed greater muscle metabolic perturbation while males had more metabolic associations in fat tissue. Selenium concentrations exhibited a biphasic relationship with body condition, with males showing Se-associated metabolic changes in the liver and fat. Copper concentrations were inversely associated with liver mass, with females exhibiting higher Cu levels and greater metabolic disruption. This study integrates metabolomics with traditional health assessments to better understand how anthropogenic and naturally occurring contaminants influence amphibian health.

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.

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.

SARS-CoV-2 infections in children lead to symptoms from mild respiratory illness to severe postacute sequelae of COVID-19, including multisystem inflammatory syndrome in Children (MIS-C). We conducted a metabolic profiling of 147 children's serum samples, including acute COVID-19 patients, MIS-C patients, and healthy controls. Using nuclear magnetic resonance spectroscopy and liquid chromatography-mass spectrometry, we measured 1101 metabolites. The results revealed distinct metabolic profiles in acute COVID-19 and MIS-C patients, with significant alterations in lipid classes. Both conditions exhibited an elevated Apo-B100/Apo-A1 ratio and increased serum inflammatory markers. MIS-C patients showed unique disruptions, including increased triglycerides and altered lipoprotein composition. Despite milder clinical respiratory symptoms, children's metabolic disturbances mirrored those seen in severe adult COVID-19 patients, indicating a shared inflammatory response to SARS-CoV-2. This suggests potential long-term health impacts, underscoring the need for continued research into the metabolic consequences of COVID-19 in children.

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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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