Recent large population cohort analysis demonstrated that a subset of individuals at high risk for developing type 2 diabetes or cardiovascular diseases can be predicted years before disease incidence. The study is published in Plos Biology.
It is well known that diet and lifestyle can influence the occurance of Type 2 diabetes (T2D) and cardiovascular disease (CVD) which burdens for most societies.
Physiological changes associated with T2D or CVD, such has high blood pressure and cholesterol and glucose levels in the blood, are often apparent prior to disease incidence.
Researchers integrated genetics, lipidomics, and standard clinical diagnostics to assess future T2D and CVD risk comprising 4,067 participants that were recruited from 1991 to 1994 and followed until 2015.
By training Ridge regression-based machine learning models on the measurements obtained at baseline when the individuals were healthy, Researchers computed several risk scores for T2D and CVD incidence during up to 23 years of follow up.
Using baseline blood samples, the concentrations of 184 lipids species or subspecies were assessed with high throughput and quantitative mass spectrometry. During the follow-up period, 13.8% of participants developed T2D, and 22% developed CVD.
Notably, lipidomic risk correlated only marginally with polygenic risk, indicating that the lipidome and genetic variants may constitute largely independent risk factors for T2D and CVD.
Risk stratification was further improved by adding standard clinical variables to the model, resulting in a case rate of 51.0% and 53.3% in the highest risk group for T2D and CVD, respectively.
The participants in the highest risk group showed significantly altered lipidome compositions affecting 167 and 157 lipid species for T2D and CVD, respectively.
Chris Lauber said that, our results demonstrated that a subset of individuals at high risk for developing T2D or CVD can be identified years before disease incidence. The lipidomic risk, which is derived from only one single mass spectrometric measurement that is cheap and fast, is informative and could extend traditional risk assessment based on clinical assays.
