High-coverage plasma lipidomics reveals novel sex-specific lipidomic fingerprints of age and BMI: Evidence from two large population cohort studies.

Obesity and related metabolic diseases show clear sex-related differences. The growing burden of these diseases calls for better understanding of the age- and sex-related metabolic consequences. High-throughput lipidomic analyses of population-based cohorts offer an opportunity to identify disease-risk-associated biomarkers and to improve our understanding of lipid metabolism and biology at a population level. Here, we comprehensively examined the relationship between lipid classes/subclasses and molecular species with age, sex, and body mass index (BMI). Furthermore, we evaluated sex specificity in the association of the plasma lipidome with age and BMI. Some 747 targeted lipid measures, representing 706 molecular lipid species across 36 classes/subclasses, were measured using a high-performance liquid chromatography coupled mass spectrometer on a total of 10,339 participants from the Australian Diabetes, Obesity and Lifestyle Study (AusDiab), with 563 lipid species being validated externally on 4,207 participants of the Busselton Health Study (BHS). Heat maps were constructed to visualise the relative differences in lipidomic profile between men and women. Multivariable linear regression analyses, including sex-interaction terms, were performed to assess the associations of lipid species with cardiometabolic phenotypes. Associations with age and sex were found for 472 (66.9%) and 583 (82.6%) lipid species, respectively. We further demonstrated that age-associated lipidomic fingerprints differed by sex. Specific classes of ether-phospholipids and lysophospholipids (calculated as the sum composition of the species within the class) were inversely associated with age in men only. In analyses with women alone, higher triacylglycerol and lower lysoalkylphosphatidylcholine species were observed among postmenopausal women compared with premenopausal women. We also identified sex-specific associations of lipid species with obesity. Lysophospholipids were negatively associated with BMI in both sexes (with a larger effect size in men), whilst acylcarnitine species showed opposing associations based on sex (positive association in women and negative association in men). Finally, by utilising specific lipid ratios as a proxy for enzymatic activity, we identified stearoyl CoA desaturase (SCD-1), fatty acid desaturase 3 (FADS3), and plasmanylethanolamine Δ1-desaturase activities, as well as the sphingolipid metabolic pathway, as constituent perturbations of cardiometabolic phenotypes. Our analyses elucidate the effect of age and sex on lipid metabolism by offering a comprehensive view of the lipidomic profiles associated with common cardiometabolic risk factors. These findings have implications for age- and sex-dependent lipid metabolism in health and disease and suggest the need for sex stratification during lipid biomarker discovery, establishing biological reference intervals for assessment of disease risk.

Correction: High-coverage plasma lipidomics reveals novel sex-specific lipidomic fingerprints of age and BMI: Evidence from two large population cohort studies.

[This corrects the article DOI: 10.1371/journal.pbio.3000870.].

Lipidomic profiling in the Strong Heart Study identified American Indians at risk of chronic kidney disease.

Dyslipidemia associates with and usually precedes the onset of chronic kidney disease (CKD), but a comprehensive assessment of molecular lipid species associated with risk of CKD is lacking. Here, we sought to identify fasting plasma lipids associated with risk of CKD among American Indians in the Strong Heart Family Study, a large-scale community-dwelling of individuals, followed by replication in Mexican Americans from the San Antonio Family Heart Study and Caucasians from the Australian Diabetes, Obesity and Lifestyle Study. We also performed repeated measurement analysis to examine the temporal relationship between the change in the lipidome and change in kidney function between baseline and follow-up of about five years apart. Network analysis was conducted to identify differential lipid classes associated with risk of CKD. In the discovery cohort, we found that higher baseline level of multiple lipid species, including glycerophospholipids, glycerolipids and sphingolipids, was significantly associated with increased risk of CKD, independent of age, sex, body mass index, diabetes and hypertension. Many lipid species were replicated in at least one external cohort at the individual lipid species and/or the class level. Longitudinal change in the plasma lipidome was significantly associated with change in the estimated glomerular filtration rate after adjusting for covariates, baseline lipids and the baseline rate. Network analysis identified distinct lipidomic signatures differentiating high from low-risk groups. Thus, our results demonstrated that disturbed lipid metabolism precedes the onset of CKD. These findings shed light on the mechanisms linking dyslipidemia to CKD and provide potential novel biomarkers for identifying individuals with early impaired kidney function at preclinical stages.

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).

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 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.

Lipidomics Profiling and Risk of Coronary Artery Disease in the BioHEART-CT Discovery Cohort.

The current coronary artery disease (CAD) risk scores for predicting future cardiovascular events rely on well-recognized traditional cardiovascular risk factors derived from a population level but often fail individuals, with up to 25% of first-time heart attack patients having no risk factors. Non-invasive imaging technology can directly measure coronary artery plaque burden. With an advanced lipidomic measurement methodology, for the first time, we aim to identify lipidomic biomarkers to enable intervention before cardiovascular events. With 994 participants from BioHEART-CT Discovery Cohort, we collected clinical data and performed high-performance liquid chromatography with mass spectrometry to determine concentrations of 683 plasma lipid species. Statin-naive participants were selected based on subclinical CAD (sCAD) categories as the analytical cohort (n = 580), with sCAD+ (n = 243) compared to sCAD- (n = 337). Through a machine learning approach, we built a lipid risk score (LRS) and compared the performance of the existing Framingham Risk Score (FRS) in predicting sCAD+. We obtained individual classifiability scores and determined Body Mass Index (BMI) as the modifying variable. FRS and LRS models achieved similar areas under the receiver operating characteristic curve (AUC) in predicting the validation cohort. LRS enhanced the prediction of sCAD+ in the healthy-weight group (BMI < 25 kg/m2), where FRS performed poorly and identified individuals at risk that FRS missed. Lipid features have strong potential as biomarkers to predict CAD plaque burden and can identify residual risk not captured by traditional risk factors/scores. LRS compliments FRS in prediction and has the most significant benefit in healthy-weight individuals.

Metabolic phenotyping of BMI to characterize cardiometabolic risk: evidence from large population-based cohorts.

Obesity is a risk factor for type 2 diabetes and cardiovascular disease. However, a substantial proportion of patients with these conditions have a seemingly normal body mass index (BMI). Conversely, not all obese individuals present with metabolic disorders giving rise to the concept of "metabolically healthy obese". We use lipidomic-based models for BMI to calculate a metabolic BMI score (mBMI) as a measure of metabolic dysregulation associated with obesity. Using the difference between mBMI and BMI (mBMIΔ), we identify individuals with a similar BMI but differing in their metabolic health and disease risk profiles. Exercise and diet associate with mBMIΔ suggesting the ability to modify mBMI with lifestyle intervention. Our findings show that, the mBMI score captures information on metabolic dysregulation that is independent of the measured BMI and so provides an opportunity to assess metabolic health to identify "at risk" individuals for targeted intervention and monitoring.

Assessing the diagnostic performance of a novel RT-PCR fluorescence method for the detection of human plasmodium species.

BACKGROUND: Malaria elimination effort is hampered not only by the lack of effective medication but also due to the lack of sensitive diagnostic tools to detect infections with low levels of parasitemia. Therefore, more sensitive and specific high-throughput molecular diagnostic approaches are needed for accurate malaria diagnosis. METHODS: In the present study, the performance of a novel single-tube MC004 real-time polymerase chain reaction (PCR) assay (MRC-Holland, Amsterdam, the Netherlands) was assessed for the detection of infection and discrimination of Plasmodium species. Blood samples (n = 150) were collected from malaria suspected patients at Adama malaria diagnosis and treatment centre, Adama, central Ethiopia. The positive predictive value (PPV), negative predictive value (NPV), analytical sensitivity and specificity of the assay were assessed against the conventional microscopic method. RESULTS: Plasmodium species were detected in 59 (39.3%) of the samples by microscopy and in 62 (41.3%) by the novel MC004 RT-PCR. Plasmodium vivax, Plasmodium falciparum and mixed infections with Plasmodium falciparum & Plasmodium vivax accounted for 47.5%, 40.6% and 11.9% respectively as detected by microscopy. The MC004 RT-PCR assay identified 59.7% and 40.3% of the samples positive for Plasmodium vivax and Plasmodium falciparum respectively. The sensitivity, specificity, PPV, and NPV of the MC004 RT-PCR assay were 95.8%, 97.8%, 92%, and 98.9%, respectively. No mixed infections were detected using the MC004 assay. CONCLUSION: The MC004 RT-PCR assay is a useful tool for the early detection of malaria and identification of Plasmodium species with a high degree of sensitivity and specificity. Due to its high sensitivity, and simplicity (being a single-tube assay), the MC004 is suitable for use in clinical settings and epidemiological studies.

Lipidomic Signatures of Changes in Adiposity: A Large Prospective Study of 5849 Adults from the Australian Diabetes, Obesity and Lifestyle Study.

Lipid metabolism is tightly linked to adiposity. Comprehensive lipidomic profiling offers new insights into the dysregulation of lipid metabolism in relation to weight gain. Here, we investigated the relationship of the human plasma lipidome and changes in waist circumference (WC) and body mass index (BMI). Adults (2653 men and 3196 women), 25-95 years old who attended the baseline survey of the Australian Diabetes, Obesity and Lifestyle Study (AusDiab) and the 5-year follow-up were enrolled. A targeted lipidomic approach was used to quantify 706 distinct molecular lipid species in the plasma samples. Multiple linear regression models were used to examine the relationship between the baseline lipidomic profile and changes in WC and BMI. Metabolic scores for change in WC were generated using a ridge regression model. Alkyl-diacylglycerol such as TG(O-50:2) [NL-18:1] displayed the strongest association with change in WC (ß-coefficient = 0.125 cm increment per SD increment in baseline lipid level, p = 2.78 × 10-11. Many lipid species containing linoleate (18:2) fatty acids were negatively associated with both WC and BMI gain. Compared to traditional models, multivariate models containing lipid species identify individuals at a greater risk of gaining WC: top quintile relative to bottom quintile (odds ratio, 95% CI = 5.4, 3.8-6.6 for women and 2.3, 1.7-3.0 for men). Our findings define metabolic profiles that characterize individuals at risk of weight gain or WC increase and provide important insight into the biological role of lipids in obesity.

Mapping the Associations of the Plasma Lipidome With Insulin Resistance and Response to an Oral Glucose Tolerance Test.

CONTEXT: Insulin resistance (IR) remains a global health challenge. Lipidomics offers an opportunity to identify biomarkers and better understand mechanisms of IR associated with abnormal lipid metabolism. OBJECTIVE: The objective of this article is to determine plasma lipid species associated with indices of IR and evaluate the lipidome response to an oral glucose tolerance test (OGTT). DESIGN AND SETTING: This study was community based and cross-sectional. PARTICIPANTS AND SAMPLE: Plasma samples (collected at 0 and 120 min during an OGTT) from nonobese, young adults age 18 to 34 years (n = 246) were analyzed using liquid chromatography-tandem mass spectrometry. MAIN OUTCOME MEASURES: The associations between indices of IR and lipid classes and species (with a sex interaction term), or changes in lipid levels during an OGTT, were tested using linear models (adjusted for age, sex, body mass index, total cholesterol, high-density lipoprotein cholesterol, and triglycerides). RESULTS: Some (213) and (199) lipid species were associated with the homeostatic model assessment of insulin resistance and insulin area under curve (AUC), respectively. Alkylphosphatidylcholine (10), alkenylphosphatidylcholine (23), and alkylphosphatidylethanolamine (6) species were associated with insulin AUC in men only. Species of phosphatidylcholine (7) and sphingomyelin (5) were associated in women only. In response to an OGTT, a perturbation in the plasma lipidome, particularly in acylcarnitine species, was observed; and the changes in many lipid species were associated with insulin AUC. CONCLUSIONS: The plasma lipidome and changes in lipid levels during an OGTT were associated with indices of IR. These findings underlie the involvement of molecular lipid species in the pathogenesis of IR and possibly crosstalk between IR and sex-specific regulation of lipid metabolism.