Ruey Leng Loo

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.

Background
Alzheimer's disease (AD), the most common form of dementia, is marked by significant reductions in glucose metabolism. Such hypometabolism reflects underlying synaptic dysfunction, correlating with cognitive decline. Our study aimed to explore the impact of dietary patterns—specifically, the Western Diet and Prudent Diet—on change in glucose metabolism in brain regions associated with AD risk, [18F]Fluorodeoxyglucose positron emission tomography (FDG‐PET) imaging as a biomarker.

Method
Longitudinal data from 133 cognitively unimpaired older adults were analysed from the Western Australian Memory Study. Participants underwent dietary assessment using the Cancer Council of Victoria Food Frequency Questionnaire and completed FDG‐PET imaging up to three times over 43 months. Dietary patterns were identified through principal component analysis, yielding two patterns—named Western Diet and Prudent Diet. Pattern scores were computed by summing food group intakes weighted by their respective factor loadings. Linear mixed‐effect models evaluated the association between dietary adherence and brain glucose metabolism, including potential confounders. The cohort was stratified by apolipoprotein E (APOE) ε4 carrier status, a genetic risk factor for AD, to investigate potential differing effects.

Result
Adherence to a Western Diet, characterised by high sugars and saturated fats, was associated with a faster decline in glucose metabolism in the right fusiform gyrus among APOE ε4 carriers (β = ‐0.00012; SE = 0.00004; false discover rate adjusted p = .032), with no significant associations in APOE ε4 non‐carriers. Similarly, no significant associations were observed between the Prudent Diet, characterised by high intake of fruits, vegetables, and whole grains, and glucose metabolism, in both APOE ε4 carriers and non‐carriers.

Conclusion
Our study highlights the potential detrimental impact of a Western Diet on brain glucose metabolism, particularly for individuals at genetic risk for AD. The decline in glucose metabolism in the fusiform gyrus, a region essential for cognitive functions like facial recognition, emphasises the role of diet in brain health. Future research should examine the mechanisms linking diet to neurodegeneration and explore dietary interventions as preventive strategies against cognitive decline and dementia.

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.

The nutritional benefits of Extra Virgin Olive Oils (EVOOs) depend on their chemical composition. Currently, there is no simple way to compare the health benefits of different EVOOs. Samples from Australia, Greece, Italy, Spain and Tunisia (N = 423) were analyzed using proton nuclear magnetic resonance spectroscopy to screen against six quality parameters (free acidity, peroxides, K270, K232, Delta K, wax) and measure fat compositions. These fat compositions were compared against healthy eating guidelines to produce five binary descriptors, which were weighted by evidence to create an accessible Nutritional Quality Index (NQI). EVOOs were differentiated by saturated fat and balance between monounsaturated (MUFA) and polyunsaturated fat (PUFA). Most samples (56.4 %) showed poor SFA, poor PUFA and good MUFA (NQI = 56), 21 % had good SFA, poor PUFA and good MUFA (NQI = 81), and 19.4 % exhibited poor SFA, good PUFA and poor MUFA (NQI = 64). The NQI identifies EVOOs with superior nutritional value, enabling informed consumer choices.

Understanding individual variability to dietary interventions is emerging as an important consideration in dietary interventions. Prior research has demonstrated ranging success of interventions. For example, Gardner and colleagues (2007) compared 4 weight loss diets in participants over a 12-month period(1), noting the range of weight loss was between 3.1kg to 6.3kg depended on individual and diet. Song and colleagues (2023) examined post prandial glucose response (PPGR) to four different carbohydrate meals. Dependent on the meal, the PPGR varied significantly between individuals(2). As such, it is inappropriate to assume that there is one dietary pattern appropriate for all individuals. Understanding the driving factors behind individual variation to specific foods and dietary patterns will allow us to tailor interventions to create optimal health outcomes for each individual. The aim of our study is to examine individual responses to different diets promoted for health. In our study, we investigated the biological diversity in response to the same dietary inputs among 23 participants at risk of type 2 diabetes and chronic disease over a two-week period. All participants completed four days on three dietary interventions (Mediterranean, Australian and low carbohydrate diets). Urine, serum, plasma, and faecal samples were collected, alongside the use of continuous glucose monitoring data, to explore the metabolic and glycaemic responses. Our findings reveal significant individual differences in blood glucose levels and metabolic outcomes. When examining fasting blood glucose levels, the low carbohydrate and Australian diets were optimal for 8 participants each, while the Mediterranean diet was optimal for 7 participants. However, this did not always correlate with post prandial blood glucose level optimisation. While blood, urine and faecal samples are yet to be analysed, these are expected to provide further understanding of individual biological responses. These results underscore the limitations of a universal dietary approach for optimising glycaemic control and highlight the necessity of personalised dietary recommendations that consider individual metabolic profiles. Our study provides crucial insights for future advances in precision nutrition, suggesting that personalised nutrition plans could lead to more effective management and prevention of T2D.

Current nutritional rating systems, like the health star rating, help consumers understand the nutritional value of food and were designed in an effort to combat obesity. However, these systems have limitations, especially for edible oils, which vary widely in composition(1). Coupled with the lack of standardisation in ranking edible oils, there has been advocacy for the introduction of different nutritional scores for edible oils. This study aims to develop a simple and easy-to-use nutritional scoring index based on the composition of extra virgin olive oil (EVOO). The composition includes all fatty acid parameters and total polyphenol content, measurable by nuclear magnetic resonance (NMR) spectroscopy, thereby avoiding the need for multiple analytical platforms. The development of an EVOO nutritional score involved: i) establishing a unique consensus dietary reference index (DRIs) for each component and evaluating their impact on human health(2,3); and ii) computing Scoring Reference Values (SRVs) for each component, expressed as grams of component per 100 g of EVOO, based on the assumptions of a daily energy intake of 2000 kcal, with a fat intake of 35% of total caloric intake(2,3), and considering EVOO as the only source of fat. A nutritional score (0–100) was developed based on saturated fat (SFA), trans-unsaturated fat, oleic, linoleic, alpha-linolenic acids, and polyphenols. Components with more substantial effects/evidence on human health were given greater weight in the scoring. The developed index was subsequently applied to evaluate 314 EVOOs that passed the International Olive Oil Council (IOC) quality criteria. These oils were sourced from Australia (n = 94), Greece (n = 54), Italy (n = 54), Spain (n = 69), and Tunisia (n = 43) and analysed using 400 MHz NMR spectroscopy. Nutritional scores for all samples showed a mean of 62.3 (range 13 to 94), with Australian EVOOs exhibiting the highest mean score of 65, followed by Spain, Tunisia, Italy, and Greece. EVOOs were differentiated by their SFA content and the balance between polyunsaturated (PUFA) and monounsaturated fatty acids (MUFA). MUFA and PUFA were typically inversely related, except for two Australian oils that achieved high levels of both. This novel scoring index for EVOOs, grounded in health-related compositional parameters, facilitates the differentiation of EVOOs based on their nutritional value. This enables consumers to make informed choices regarding their oil selection. Given the rising prevalence of obesity and its associated morbidity, this tool is particularly significant. Additionally, the implementation of this nutritional index encourages producers to produce oils with superior nutritional profiles.

Diet is a key modifiable factor for improving suboptimal lipoprotein profiles and reducing cardiovascular disease (CVD) risk(1). Dietary patterns like the Dietary Approaches to Stop Hypertension (DASH) or the Mediterranean Diet, with varying macronutrient components, have shown positive effects on total cholesterol and low-density lipoproteins (LDL)(2). However, limited research exists on the impact of different healthy diets on lipoprotein subclass profiles, which are increasingly known to influence CVD risk. This study aims to compare the nuclear magnetic resonance (NMR)-measured 112 lipoprotein profiles across three healthy dietary patterns: a carbohydrate-rich diet (CARB), similar to the DASH diet; a protein-rich diet (PROT); and an unsaturated fat-rich diet (USFA), similar to the Mediterranean diet. Lipoprotein parameters were generated using the Bruker IVDr Lipoprotein Subclass Analysis (B.I.LISA) method(3). The lipoprotein subclasses included different molecular components of very low-density lipoprotein (VLDL, 0.950–1.006 kg/L), low-density lipoprotein (LDL, density 1.09–1.63 kg/L), intermediate-density lipoprotein (IDL, density 1.006–1.019 kg/L), and high-density lipoprotein (HDL, density 1.063–1.210 kg/L). The LDL subfraction was further divided into six density classes, and the HDL subfractions were divided into four different density classes. Plasma samples from a randomised cross-over intervention study involving 156 individuals who completed more than two dietary patterns were included for the NMR analysis (registered at www.clinicaltrials.gov as NCT00051350 and NCT03369535). The Friedman’s test with post-hoc analysis, corrected for multiple testing, showed that all healthy dietary patterns led to a reduction in overall lipoprotein subclasses known to be associated with atherogenic risk. This reduction included large and medium-sized LDL subclasses, all intermediate-density IDL subclasses, as well as total plasma cholesterol, triglycerides, apolipoprotein-B100, apo-B100/apo-A1 ratio, and LDL-cholesterol (p < 0.05). Additional variations in lipoprotein subclasses specific to each diet were also observed. The PROT diet showed a decrease in small-sized and dense LDL, large to medium VLDL subclasses, and large-sized HDL subclasses. Conversely, the CARB diet exhibited an increase in smaller-sized and denser LDL, along with a decrease in large-sized HDL and an increase in smaller-sized HDL subclasses. The USFA diet led to decreases in LDL and overall VLDL subclasses, while increasing LDL and HDL subclasses (p < 0.05). The impact of different healthy diets with differential effects on lipoproteins suggests the possibility of targeting the cholesterol status of individuals to optimise lipoprotein profiles and thereby reduce CVD risk. Preliminary exploratory analyses based on linear mixed-effect models coupled with a latent profile analysis, adjusted for cholesterol status, showed that individual lipoprotein responses to specific diets varied. Inter-individual variations in lipoprotein responses to healthy diets were evident. A small proportion of individuals only responded to specific diets, suggesting potential of personalised nutrition based on individual lipoprotein profiles. These observed variations highlight the complexity of individual responses to dietary interventions.

COVID-19 vaccines are crucial in reducing SARS-CoV-2 transmission and severe health outcomes. Despite widespread administration, their long-term systemic effects on human metabolism remain inadequately understood. This longitudinal study aims to evaluate IgG responses, 34 cytokines, 112 lipoproteins, and 21 low-molecular-weight metabolites in 33 individuals receiving two to four COVID-19 vaccine doses. Changes in metabolic profiles for the first 16 days post each dose of vaccine, and up to 480 days post-initial dose, were compared to baseline (before vaccination). Additionally, metabolic profiles of vaccinated participants were compared to a reference cohort of unvaccinated individuals without prior exposure to SARS-CoV-2 infection (controls) and SARS-CoV-2 cases. Positive IgG responses were observed in 78.8% (N = 26) of participants after the first dose, reaching 100% with subsequent doses. The most common side effects were localized pain at the injection site and "flu-like" symptoms, reported by > 50% of participants. Systemic side effects, e.g., sore lymph nodes, fatigue, and brain fog, were reported but showed no significant correlations to IgG responses. Transient temporal changes were observed for cytokine IP10 (CXCL10) and glutamic acid around the third vaccine dose. Compared to the reference cohort, 497 vaccinated samples (95.0%) had profiles similar to the controls, while the remaining 26 samples with prior infection exposures were similar to mild cases of SARS-CooV-2 infection. In conclusion, COVID-19 vaccination did not induce lasting changes in inflammatory and metabolic responses, nor did it induce changes similar to mild cases of SARS-CoV-2 infection. This supports the metabolic safety of the vaccine and contributes to increased vaccine confidence. KEY MESSAGES: Minimal changes in inflammatory/metabolic markers up to 480 days post-vaccination. Transient increase in IP10 (CXCL10) and glutamic acid around the third dose. Post-vaccination IgG response did not alter metabolic profiles like SARS-CoV-2 cases. Our findings provide insights into the safety of repeated COVID-19 vaccinations.

Key messages
• Minimal changes in inflammatory/metabolic markers up to 480 days post-vaccination.
• Transient increase in IP10 (CXCL10) and glutamic acid around the third dose.
• Post-vaccination IgG response did not alter metabolic profiles like SARS-CoV-2 cases.
• Our findings provide insights into the safety of repeated COVID-19 vaccinations.

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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Inulin, a polysaccharide characterized by a β-2,1 fructosyl-fructose structure terminating in a glucosyl moiety, is naturally present in plant roots and tubers. Current methods provide average degrees of polymerization (DP) but lack information on the distribution and absolute concentration of each DP. To address this limitation, a reproducible (CV < 10 %) high throughput (<2 min/sample) MALDI-MRMS approach capable of characterizing and quantifying inulin molecules in plants using matched-matrix consisting of α-cyano-4-hydroxycinnamic acid butylamine salt (CHCA-BA), chicory inulin-12C and inulin-13C was developed. The method identified variation in chain lengths and concentration of inulin across various plant species. Globe artichoke hearts, yacón and elephant garlic yielded similar concentrations at 15.6 g/100 g dry weight (DW), 16.8 g/100 g DW and 17.7 g/100 g DW, respectively, for DP range between 9 and 22. In contrast, Jerusalem artichoke demonstrated the highest concentration (53.4 g/100 g DW) within the same DP ranges. Jerusalem artichoke (DPs 9–32) and globe artichoke (DPs 9–36) showed similar DP distributions, while yacón and elephant garlic displayed the narrowest and broadest DP ranges (DPs 9–19 and DPs 9–45, respectively). Additionally, qualitative measurement for all inulin across all plant samples was feasible using the peak intensities normalized to Inulin-13C, and showed that the ratio of yacón, elephant garlic and Jerusalem was approximately one, two and three times that of globe artichoke. This MALDI-MRMS approach provides comprehensive insights into the structure of inulin molecules, opening avenues for in-depth investigations into how DP and concentration of inulin influence gut health and the modulation of noncommunicable diseases, as well as shedding light on refining cultivation practices to elevate the beneficial health properties associated with specific DPs.
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