Prediction of DM2 and CVD Development Risk Based on Lipidomic Risk Determination
The incidence of type 2 diabetes mellitus (DM2) and cardiovascular diseases (CVD) is strongly influenced by lifestyle and diet. Hypertension, hypercholesterolemia, and elevated blood glucose are often detectable before the full development of disease symptoms. Similarly, changes in the representation of other less noticeable markers—plasma lipids—can also be detected.
Changes in Lipid Spectrum as a Predictor of Disease Development
In recent years, extensive studies have examined individual differences in the genome, proteome, metabolome, and lipidome. Their results could help identify pathophysiological processes leading to disease development and determine to what extent these metabolic pathways differ among patient groups. Early and accurate identification of individuals at increased risk for DM2 and CVD, followed by dietary and lifestyle adjustments, could help reduce the incidence of these diseases.
Recent research suggests that the composition of plasma lipids quite accurately reflects the metabolic state of the organism. Plasma lipids are believed to form a certain depot to maintain the constant and functional composition of cell membranes. Their concentrations also significantly change during various pathological processes. Blood plasma, additionally, represents an easily accessible material for lipid spectrum analysis.
Methodology and Study Population
In the years 1991-1994, 4067 participants were selected for a prospective population cohort study in Malmö, Sweden, and were subsequently monitored until 2015. All participants underwent fasting peripheral blood sampling at the start of the research. Lipidomic analysis was then performed on these initial samples using mass spectrometry techniques, where molar concentrations of 184 plasma lipids were determined.
The aim of the presented analysis was to determine whether the lipidomic profile affects the incidence of DM2 and CVD. For monitoring the incidence of DM2, 3688 individuals were chosen (patients with pre-existing diabetes were excluded). During the follow-up, DM2 developed in 509 (13.8%) patients from the studied cohort. For the analysis of cardiovascular diseases, patients with existing CHD and stroke were excluded at baseline, resulting in a cohort of 3951 individuals for analysis. CHD or stroke subsequently manifested in 870 (22%) participants.
Results
Based on the concentration profiles of 184 plasma lipids, a lipidomic risk score was determined using regression models and machine learning, with the score value clearly increasing with the rising incidence of DM2 and CVD in the given cohort. After stratifying patients according to the lipidomic score, the incidence rate of DM2 in the highest risk group was 37.0%, whereas in the lowest risk group, it was 3.2%. Compared to the average incidence, the values corresponded to an odds ratio (OR) of 0.21 for patients at the lowest risk and an OR of 3.67 for patients at the highest risk.
For cardiovascular disease, the incidence rate after stratification by lipidomic score was 10.4% for patients at the lowest risk (OR 0.41) and 40.5% for patients in the highest risk group (OR 2.41).
Influence of Genetic Predisposition
Subsequently, the influence of genetic predisposition on the incidence of DM2 and CVD was examined. The polygenic risk score, calculated based on data from available genome-wide analyses, correlated with the incidence of DM2. However, its predictive value was considerably lower compared to the lipidomic score. The correlation of polygenic score with the incidence of cardiovascular disease was relatively small. Thus, lipidome and genetic variants could represent two independent risk factors for the future incidence of DM2 and CVD.
High-Risk Lipidomic Profile
To identify lipidome components specifically changed in high-risk individuals, a comparison of lipid concentrations in the highest-risk cohort (90-100% quantile according to the combination of predictive models) with lipid profiles of participants from other cohorts was conducted. For DM2, 91 lipids showed significantly higher concentrations, while 76 others were reduced. For cardiovascular disease, 67 lipids had increased concentrations, while 90 others had reduced concentrations compared to the control group.
Conclusion
The study results showed that at-risk individuals could be identified years before the onset of the disease. Changes in the lipidome form an independent and relatively accurate prognostic factor for the incidence of DM2 and CVD. Lipidomic risk can be easily, cheaply, and safely determined from a single spectrometric measurement, allowing for a relatively precise and informative refinement of risk estimate based on clinical examination.
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Source: Lauber C., Gerl M. J., Klose C. et al. Lipidomic risk scores are independent of polygenic risk scores and can predict incidence of diabetes and cardiovascular disease in a large population cohort. PLoS Biol 2022; 20 (3): e3001561, doi: 10.1371/journal.pbio.300156.
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