Search

A set of gene knockouts as a resource for global lipidomic changes Aleksandra Spiegel, Chris Lauber, Mandy Bachmann, Anne-Kristin Heninger, Christian Klose, Kai Simons, Mihail Sarov, Mathias J. Gerl https://doi.org/10.1038/s41598-022-14690-0 Multiple tables on lipid identification and quality control. Tab "SwissLipids IDs": SwissLipids Identifiers for lipids in this study (Table S04). Tab "MS modes": List of analyzed lipid classes and detection mode. MSMS mode (i.e. the fragmentation of the lipid molecules) delivers additional confidence in lipid identification and additional structural information. Tab "Detection frequency histogram": A frequency histogram of the number of cell lines a lipid species has been detected in. Tab "Coefficients of Variation": Coefficients of Variation (CV) are provided for each lipid species and each gene knockout across replicates, clones, and methods. A separate CV is calculated for each growth state. In addition, CVs are calculated across all samples for the different growth states ("across all samples"). Summary statistics of CV values are provided across all lipid species with the minimal value (min), first quartile (Q1), median, third quartile (Q3), and the maximal value (max) for each knockout or group. Further, summary statistics of all available CVs calculated within each knockout are provided ("within each sample").

Type 2 diabetes (T2D) and cardiovascular disease (CVD) represent significant disease burdens for most societies and susceptibility to these diseases is strongly influenced by diet and lifestyle. 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. Here we integrated genetics, lipidomics, and standard clinical diagnostics to assess future T2D and CVD risk for 4,067 participants from a large prospective population-based cohort, the Malmö Diet and Cancer-Cardiovascular Cohort. By training Ridge regression-based machine learning models on the measurements obtained at baseline when the individuals were healthy, we computed several risk scores for T2D and CVD incidence during up to 23 years of follow-up. We used these scores to stratify the participants into risk groups and found that a lipidomics risk score based on the quantification of 184 plasma lipid concentrations resulted in a 168% and 84% increase of the incidence rate in the highest risk group and a 77% and 53% decrease of the incidence rate in lowest risk group for T2D and CVD, respectively, compared to the average case rates of 13.8% and 22.0%. 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. 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.

Supplemental Tables

Primer sequences for the real-time PCR

Supplemental Figures

Primer sequences for the real-time PCR

(A) Lipidomics data. Subject ID, cohort, diet, visit, time of day and lipid species measurements are shown. Lipid data are shown a pool/µL or µmol/L. (B) Lipid identifiers. Identifiers of lipids used in this study to the SwissLipids database (http://www.swisslipids.org) are provided.