Imputation of missing values in lipidomic datasets.

Lipidomic data often exhibit missing data points, which can be categorized as missing completely at random (MCAR), missing at random, or missing not at random (MNAR). In order to utilize statistical methods that require complete datasets or to improve the identification of potential effects in statistical comparisons, imputation techniques can be employed. In this study, we investigate commonly used methods such as zero, half-minimum, mean, and median imputation, as well as more advanced techniques such as k-nearest neighbor and random forest imputation. We employ a combination of simulation-based approaches and application to real datasets to assess the performance and effectiveness of these methods. Shotgun lipidomics datasets exhibit high correlations and missing values, often due to low analyte abundance, characterized as MNAR. In this context, k-nearest neighbor approaches based on correlation and truncated normal distributions demonstrate best performance. Importantly, both methods can effectively impute missing values independent of the type of missingness, the determination of which is nearly impossible in practice. The imputation methods still control the type I error rate.

Neurotensin accelerates atherosclerosis and increases circulating levels of short-chain and saturated triglycerides.

BACKGROUND AND AIMS: Obesity and type 2 diabetes are significant risk factors for atherosclerotic cardiovascular disease (CVD) worldwide, but the underlying pathophysiological links are poorly understood. Neurotensin (NT), a 13-amino-acid hormone peptide, facilitates intestinal fat absorption and contributes to obesity in mice fed a high-fat diet. Elevated levels of pro-NT (a stable NT precursor produced in equimolar amounts relative to NT) are associated with obesity, type 2 diabetes, and CVD in humans. Whether NT is a causative factor in CVD is unknown. METHODS: Nt+/+ and Nt-/- mice were either injected with adeno-associated virus encoding PCSK9 mutants or crossed with Ldlr-/- mice and fed a Western diet. Atherosclerotic plaques were analyzed by en face analysis, Oil Red O and CD68 staining. In humans, we evaluated the association between baseline pro-NT and growth of carotid bulb thickness after 16.4 years. Lipidomic profiles were analyzed. RESULTS: Atherosclerotic plaque formation is attenuated in Nt-deficient mice through mechanisms that are independent of reductions in circulating cholesterol and triglycerides but associated with remodeling of the plasma triglyceride pool. An increasing plasma concentration of pro-NT predicts atherosclerotic events in coronary and cerebral arteries independent of all major traditional risk factors, indicating a strong link between NT and atherosclerosis. This plasma lipid profile analysis confirms the association of pro-NT with remodeling of the plasma triglyceride pool in atherosclerotic events. CONCLUSIONS: Our findings are the first to directly link NT to increased atherosclerosis and indicate the potential role for NT in preventive and therapeutic strategies for CVD.

Multi-omics subgroups associated with glycaemic deterioration in type 2 diabetes: an IMI-RHAPSODY Study.

Introduction: Type 2 diabetes (T2D) onset, progression and outcomes differ substantially between individuals. Multi-omics analyses may allow a deeper understanding of these differences and ultimately facilitate personalised treatments. Here, in an unsupervised "bottom-up" approach, we attempt to group T2D patients based solely on -omics data generated from plasma. Methods: Circulating plasma lipidomic and proteomic data from two independent clinical cohorts, Hoorn Diabetes Care System (DCS) and Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS), were analysed using Similarity Network Fusion. The resulting patient network was analysed with Logistic and Cox regression modelling to explore relationships between plasma -omic profiles and clinical characteristics. Results: From a total of 1,134 subjects in the two cohorts, levels of 180 circulating plasma lipids and 1195 proteins were used to separate patients into two subgroups. These differed in terms of glycaemic deterioration (Hazard Ratio=0.56;0.73), insulin sensitivity and secretion (C-peptide, p=3.7e-11;2.5e-06, DCS and GoDARTS, respectively; Homeostatic model assessment 2 (HOMA2)-B; -IR; -S, p=0.0008;4.2e-11;1.1e-09, only in DCS). The main molecular signatures separating the two groups included triacylglycerols, sphingomyelin, testican-1 and interleukin 18 receptor. Conclusions: Using an unsupervised network-based fusion method on plasma lipidomics and proteomics data from two independent cohorts, we were able to identify two subgroups of T2D patients differing in terms of disease severity. The molecular signatures identified within these subgroups provide insights into disease mechanisms and possibly new prognostic markers for T2D.