Plasma Lipidomic Profiling Identifies Elevated Triglycerides as Potential Risk Factor in Chemotherapy-Induced Peripheral Neuropathy.

PURPOSE: Chemotherapy-induced peripheral neuropathy (CIPN) is a dose-limiting side effect of cytotoxic cancer treatment, often necessitating dose reduction (DR) or chemotherapy discontinuation (CD). Studies on peripheral neuropathy related to chemotherapy, obesity, and diabetes have implicated lipid metabolism. This study examined the association between circulating lipids and CIPN. METHODS: Lipidomic analysis was performed on plasma samples from 137 patients receiving taxane-based treatment. CIPN was graded using Total Neuropathy Score-clinical version (TNSc) and patient-reported outcome measure European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-CIPN (EORTC-QLQ-CIPN20). RESULTS: A significant proportion of elevated baseline lipids were associated with high-grade CIPN defined by TNSc and EORTC-QLQ-CIPN20 including triacylglycerols (TGs). Multivariable Cox regression on lipid species, adjusting for BMI, age, and diabetes, showed several elevated baseline TG associated with shorter time to DR/CD. Latent class analysis identified two baseline lipid profiles with differences in risk of CIPN (hazard ratio, 2.80 [95% CI, 1.50 to 5.23]; P = .0013). The higher risk lipid profile had several elevated TG species and was independently associated with DR/CD when modeled with other clinical factors (diabetes, age, BMI, or prior numbness/tingling). CONCLUSION: Elevated baseline plasma TG is associated with an increased risk of CIPN development and warrants further validation in other cohorts. Ultimately, this may enable therapeutic intervention.

Circulating Lipid Profiles Associated With Resistance to Androgen Deprivation Therapy in Localized Prostate Cancer.

PURPOSE: Intense androgen deprivation therapy (ADT) with androgen receptor pathway inhibitors (ARPIs) before radical prostatectomy (RP) produced favorable pathologic responses in approximately 20% of patients. The molecular reason for the low rate of response remains unclear. Lipid metabolism is known to influence androgen receptor signaling and ARPI efficacy. The aim of the study was to identify circulating lipid profiles associated with ADT/ARPI resistance in localized prostate cancer. MATERIALS AND METHODS: Two independent experimental approaches were used. Experiment 1: Post hoc analysis of the association between plasma lipidomic profiles and ADT/ARPI response was performed on patients (n = 104) from two phase II trials of neoadjuvant ADT/ARPI. Response to ADT/ARPI was defined by pathologic response. Experiment 2: Patient-derived tumor explants from RP (n = 105) were cultured in enzalutamide for 48 hours. Explant response to enzalutamide was evaluated against pre-RP plasma lipidomic profiles (n = 105) and prostate tissue lipidomic profiles (n = 36). Response was defined by Ki67 (cell proliferation marker) fold difference between enzalutamide and vehicle-treated explants. In both experiments, associations between lipid profiles and ADT/ARPI response were analyzed by latent class analysis. RESULTS: Pretreatment plasma lipid profiles classified each experimental cohort into two groups with differences in ADT/ARPI response rates. The response rates of the groups were 9.6% versus 29% in experiment 1 (chi-squared test P = .012) and 49% versus 70% in experiment 2 (chi-squared test P = .037). In both experiments, the group with a higher incidence of ADT/ARPI resistance had higher plasma levels of sphingomyelin, glycosylceramides, free fatty acids, acylcarnitines, cholesterol esters, and alkyl/alkenyl-phosphatidylcholine and lower plasma levels of triacylglycerols, diacylglycerols, and phosphoethanolamine (t-test P < .05). CONCLUSION: Pretreatment circulating lipid profiles are associated with ADT/ARPI resistance in localized cancer in both human cohorts and explant models.

Imputation of plasma lipid species to facilitate integration of lipidomic datasets.

Recent advancements in plasma lipidomic profiling methodology have significantly increased specificity and accuracy of lipid measurements. This evolution, driven by improved chromatographic and mass spectrometric resolution of newer platforms, has made it challenging to align datasets created at different times, or on different platforms. Here we present a framework for harmonising such plasma lipidomic datasets with different levels of granularity in their lipid measurements. Our method utilises elastic-net prediction models, constructed from high-resolution lipidomics reference datasets, to predict unmeasured lipid species in lower-resolution studies. The approach involves (1) constructing composite lipid measures in the reference dataset that map to less resolved lipids in the target dataset, (2) addressing discrepancies between aligned lipid species, (3) generating prediction models, (4) assessing their transferability into the targe dataset, and (5) evaluating their prediction accuracy. To demonstrate our approach, we used the AusDiab population-based cohort (747 lipid species) as the reference to impute unmeasured lipid species into the LIPID study (342 lipid species). Furthermore, we compared measured and imputed lipids in terms of parameter estimation and predictive performance, and validated imputations in an independent study. Our method for harmonising plasma lipidomic datasets will facilitate model validation and data integration efforts.

A lipid atlas of human and mouse immune cells provides insights into ferroptosis susceptibility.

The cellular lipidome comprises thousands of unique lipid species. Here, using mass spectrometry-based targeted lipidomics, we characterize the lipid landscape of human and mouse immune cells ( www.cellularlipidatlas.com ). Using this resource, we show that immune cells have unique lipidomic signatures and that processes such as activation, maturation and development impact immune cell lipid composition. To demonstrate the potential of this resource to provide insights into immune cell biology, we determine how a cell-specific lipid trait-differences in the abundance of polyunsaturated fatty acid-containing glycerophospholipids (PUFA-PLs)-influences immune cell biology. First, we show that differences in PUFA-PL content underpin the differential susceptibility of immune cells to ferroptosis. Second, we show that low PUFA-PL content promotes resistance to ferroptosis in activated neutrophils. In summary, we show that the lipid landscape is a defining feature of immune cell identity and that cell-specific lipid phenotypes underpin aspects of immune cell physiology.

Development and validation of a plasmalogen score as an independent modifiable marker of metabolic health: population based observational studies and a placebo-controlled cross-over study.

BACKGROUND: Decreased levels of circulating ethanolamine plasmalogens [PE(P)], and a concurrent increase in phosphatidylethanolamine (PE) are consistently reported in various cardiometabolic conditions. Here we devised, a plasmalogen score (Pls Score) that mirrors a metabolic signal that encompasses the levels of PE(P) and PE and captures the natural variation in circulating plasmalogens and perturbations in their metabolism associated with disease, diet, and lifestyle. METHODS: We utilised, plasma lipidomes from the Australian Obesity, Diabetes and Lifestyle study (AusDiab; n = 10,339, 55% women) a nationwide cohort, to devise the Pls Score and validated this in the Busselton Health Study (BHS; n = 4,492, 56% women, serum lipidome) and in a placebo-controlled crossover trial involving Shark Liver Oil (SLO) supplementation (n = 10, 100% men). We examined the association of the Pls Score with cardiometabolic risk factors, type 2 diabetes mellitus (T2DM), cardiovascular disease and all-cause mortality (over 17 years). FINDINGS: In a model, adjusted for age, sex and BMI, individuals in the top quintile of the Pls Score (Q5) relative to Q1 had an OR of 0.31 (95% CI 0.21-0.43), 0.39 (95% CI 0.25-0.61) and 0.42 (95% CI 0.30-0.57) for prevalent T2DM, incident T2DM and prevalent cardiovascular disease respectively, and a 34% lower mortality risk (HR = 0.66; 95% CI 0.56-0.78). Significant associations between diet and lifestyle habits and Pls Score exist and these were validated through dietary supplementation of SLO that resulted in a marked change in the Pls Score. INTERPRETATION: The Pls Score as a measure that captures the natural variation in circulating plasmalogens, was not only inversely related to cardiometabolic risk and all-cause mortality but also associate with diet and lifestyle. Our results support the potential utility of the Pls Score as a biomarker for metabolic health and its responsiveness to dietary interventions. Further research is warranted to explore the underlying mechanisms and optimise the practical implementation of the Pls Score in clinical and population settings. FUNDING: National Health and Medical Research Council (NHMRC grant 233200), National Health and Medical Research Council of Australia (Project grant APP1101320), Health Promotion Foundation of Western Australia, and National Health and Medical Research Council of Australia Senior Research Fellowship (#1042095).

A lipidomic based metabolic age score captures cardiometabolic risk independent of chronological age.

BACKGROUND: Metabolic ageing biomarkers may capture the age-related shifts in metabolism, offering a precise representation of an individual's overall metabolic health. METHODS: Utilising comprehensive lipidomic datasets from two large independent population cohorts in Australia (n = 14,833, including 6630 males, 8203 females), we employed different machine learning models, to predict age, and calculated metabolic age scores (mAge). Furthermore, we defined the difference between mAge and age, termed mAgeΔ, which allow us to identify individuals sharing similar age but differing in their metabolic health status. FINDINGS: Upon stratification of the population into quintiles by mAgeΔ, we observed that participants in the top quintile group (Q5) were more likely to have cardiovascular disease (OR = 2.13, 95% CI = 1.62-2.83), had a 2.01-fold increased risk of 12-year incident cardiovascular events (HR = 2.01, 95% CI = 1.45-2.57), and a 1.56-fold increased risk of 17-year all-cause mortality (HR = 1.56, 95% CI = 1.34-1.79), relative to the individuals in the bottom quintile group (Q1). Survival analysis further revealed that men in the Q5 group faced the challenge of reaching a median survival rate due to cardiovascular events more than six years earlier and reaching a median survival rate due to all-cause mortality more than four years earlier than men in the Q1 group. INTERPRETATION: Our findings demonstrate that the mAge score captures age-related metabolic changes, predicts health outcomes, and has the potential to identify individuals at increased risk of metabolic diseases. FUNDING: The specific funding of this article is provided in the acknowledgements section.

Lipidomic Risk Score to Enhance Cardiovascular Risk Stratification for Primary Prevention.

BACKGROUND: Accurate risk stratification is vital for primary prevention of cardiovascular disease (CVD). However, traditional tools such as the Framingham Risk Score (FRS) may underperform within the diverse intermediate-risk group, which includes individuals requiring distinct management strategies. OBJECTIVES: This study aimed to develop a lipidomic-enhanced risk score (LRS), specifically targeting risk prediction and reclassification within the intermediate group, benchmarked against the FRS. METHODS: The LRS was developed via a machine learning workflow using ridge regression on the Australian Diabetes, Obesity, and Lifestyle Study (AusDiab; n = 10,339). It was externally validated with the Busselton Health Study (n = 4,492), and its predictive utility for coronary artery calcium scoring (CACS)-based outcomes was independently validated in the BioHEART cohort (n = 994). RESULTS: LRS significantly improved discrimination metrics for the intermediate-risk group in both AusDiab and Busselton Health Study cohorts (all P < 0.001), increasing the area under the curve for CVD events by 0.114 (95% CI: 0.1123-0.1157) and 0.077 (95% CI: 0.0755-0.0785), with a net reclassification improvement of 0.36 (95% CI: 0.21-0.51) and 0.33 (95% CI: 0.15-0.49), respectively. For CACS-based outcomes in BioHEART, LRS achieved a significant area under the curve improvement of 0.02 over the FRS (0.76 vs 0.74; P < 1.0 × 10-5). A simplified, clinically applicable version of LRS was also created that had comparable performance to the original LRS. CONCLUSIONS: LRS, augmenting the FRS, presents potential to improve intermediate-risk stratification and to predict atherosclerotic markers using a simple blood test, suitable for clinical application. This could facilitate the triage of individuals for noninvasive imaging such as CACS, fostering precision medicine in CVD prevention and management.