Gavriel Olshansky

Traditional dietary pattern analyses commonly use predefined diet quality, which may not fully capture naturally occurring combinations of foods or their metabolic consequences. We applied an a posteriori approach to identify empirical dietary patterns and examine their lipidomic and health associations in two Australian cohorts: AusDiab (n=10,339) and MCCS (n=2,996). Factor analysis of 36 food groups identified two major patterns: a Western dietary pattern (WD) and a prudent dietary pattern (PD). The PD was associated with lipid species containing docosahexaenoic acid (22:6), whereas the WD was characterised by species enriched in 22:4 and other ω-6 fatty acids; these associations were replicated in MCCS. Ridge regression-derived metabolic signatures explained 35% and 18% of variability in the WD and PD, respectively. Participants in the highest quartile of the PD metabolic signature had a 27% lower risk of mortality (HR 0.73, 95% CI 0.65–0.83), while the FFQ-derived PD was not associated with mortality. Conversely, the highest quartiles of both the WD metabolic signature and FFQ-derived WD were associated with higher mortality risk. These findings demonstrate that empirically derived dietary patterns are reflected in the plasma lipidome and that lipidomic signatures may provide stronger indicators of long-term health risk than dietary assessment alone.

Diet is a key factor for many diseases, yet the underlying metabolic pathways involved remain poorly understood. We analyze data from 13,335 participants across two large Australian cohorts examining comprehensive lipidomic profiles in relation to diet. We also assess the link between metabolic signatures of dietary quality with cardiometabolic health and all-cause mortality. Here, using linear models and lipid set enrichment analysis, we report characteristic lipidomic profiles associated with dairy [sphingomyelins and lipids esterified with 14:0, 15:0, 17:0 or 17:1 fatty acid], red meat and poultry [alkyl- and alkenyl-phosphatidylcholine and phosphatidylethanolamine notably, with arachidonic acid], and fish intake [higher 22:6, and lower 22:4 fatty acids]. In a Cox proportional hazard regression, metabolic signatures of diet quality showed inverse associations with all–cause mortality with hazard ratios (95% confidence intervals) of 0.89 (0.84–0.93), 0.87 (0.83–0.92), and 0.88 (0.84–0.93) for the Australian Dietary Guideline Index (DGI), the Global Diet Quality Score (DGQS), and the Mediterranean-DASH (Dietary Approaches to Stop Hypertension) Intervention for Neurodegenerative Delay (MIND) score, respectively. Additionally, the intake of nuts (β = −0.07, p = 7.92 × 10⁻¹⁸) and the MIND score (β = −0.07, p = 1.51 × 10⁻¹⁸) were inversely associated with CVD risk. Our data provide new insights into how dietary exposure relates to lipid metabolism and metabolic health, opening avenues for future mechanistic studies and dietary interventions that can inform targeted strategies for the prevention of cardiometabolic diseases.

Diet is a key factor for many diseases, yet the underlying metabolic pathways involved remain poorly understood. We analyzed data from 13,335 participants across two large Australian cohorts examining comprehensive lipidomic profiles in relation to diet. We also assessed the link between metabolic signatures of dietary quality with cardiometabolic health and all-cause mortality. Here, we report characteristic lipidomic profiles linked to dairy [sphingomyelins and lipids esterified with 14:0, 15:0, 17:0 or 17:1 fatty acid], red meat and poultry [alkyl- and alkenyl-phosphatidylcholine and phosphatidylethanolamine notably, with arachidonic acid], and fish intake [higher 22:6, and lower 22:4 fatty acids]. The metabolic signatures of diet quality showed inverse associations with all–cause mortality with hazard ratios (95% confidence intervals) of 0.89 (0.84–0.93), 0.87 (0.83–0.92), and 0.88 (0.84–0.93) for the Australian Dietary Guideline Index (DGI), the Global Diet Quality Score (DGQS), and the Mediterranean-DASH (Dietary Approaches to Stop Hypertension) Intervention for Neurodegenerative Delay (MIND) score, respectively. Additionally, the intake of nuts (β = -0.07, p = 7.92 × 10⁻¹⁸) and the MIND score (β = -0.07, p = 1.51 × 10⁻¹⁸) were inversely associated with CVD risk. Our data highlight the critical role of diet in influencing lipid metabolism, thereby impacting metabolic health and mortality risk and provide a framework for the development of a precision health approach to dietary intervention.

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.

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”. Using comprehensive lipidomic datasets from two large independent population cohorts in Australia (n = 14,831), we developed models that predicted BMI and calculated a metabolic BMI score (mBMI) as a measure of metabolic dysregulation associated with obesity. We postulated that the mBMI score would be an independent metric for defining obesity and help identify a hidden risk for metabolic disorders regardless of the measured BMI. Based on the difference between mBMI and BMI (mBMI delta; “mBMIΔ”), we identified individuals with a similar BMI but differing in their metabolic health profiles. Participants in the top quintile of mBMIΔ (Q5) were more than four times more likely to be newly diagnosed with T2DM (OR = 4.5; 95% CI = 3.1–6.6), more than two times more likely to develop T2DM over a five year follow up period (OR = 2.5; CI = 1.5–4.1) and had higher odds of cardiovascular disease (heart attack or stroke) (OR = 2.1; 95% CI = 1.5–3.1) relative to those in the bottom quintile (Q1). Exercise and diet were associated with mBMIΔ suggesting the ability to modify mBMI with lifestyle intervention. In conclusion, 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 individuals at risk for targeted intervention and monitoring.

We integrated lipidomics and genomics to unravel the genetic architecture of lipid metabolism and identify genetic variants associated with lipid species putatively in the mechanistic pathway for coronary artery disease (CAD). We quantified 596 lipid species in serum from 4,492 individuals from the Busselton Health Study. The discovery GWAS identified 3,361 independent lipid-loci associations, involving 667 genomic regions (479 previously unreported), with validation in two independent cohorts. A meta-analysis revealed an additional 70 independent genomic regions associated with lipid species. We identified 134 lipid endophenotypes for CAD associated with 186 genomic loci. Associations between independent lipid-loci with coronary atherosclerosis were assessed in ∼456,000 individuals from the UK Biobank. Of the 53 lipid-loci that showed evidence of association (P < 1 × 10⁻³), 43 loci were associated with at least one lipid endophenotype. These findings illustrate the value of integrative biology to investigate the aetiology of atherosclerosis and CAD, with implications for other complex diseases.

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.

Changes to lipid metabolism are tightly associated with the onset and pathology of Alzheimer’s disease (AD). Lipids are complex molecules comprising of many isomeric and isobaric species, necessitating detailed analysis to enable interpretation of biological significance. Our expanded targeted lipidomics platform (569 lipid species across 32 lipid (sub)classes) allows for detailed isomeric and isobaric lipid separation. We applied the methodology to examine plasma samples from the Australian Imaging, Biomarkers and Lifestyle flagship study of aging (AIBL, n = 1112) and serum from the Alzheimer’s Disease Neuroimaging Initiative (ADNI, n = 800) studies. Cross sectional analysis using both cohorts identified concordant unique peripheral signatures associated with AD. Specific pathways include; sphingolipids, including G _M3 gangliosides, where their acyl composition drove the major associations, and lipids previously associated with dysfunctional lipid metabolism in cardiometabolic disease including the phosphatidylethanolamine and triglyceride classes. Infomation derived from improved isomeric seperation highlighted pathway-specific changes with ether lipids including plasmalogens implicating perixosmal dysfunction in disease pathology. Longitudinal analysis revealed similar lipid signitures in both AIBL and ADNI cohorts with future disease onset. We utilised the two independent studies to train and validate multivariate lipid models that significantly improved disease classification and prediction. Together our results provide a holistic view of the lipidome and its relationship with AD using a comprehensive lipidomics approach, providing targets for further mechanistic investigation.