Cord-Blood Lipidome in Progression to Islet Autoimmunity and Type 1 Diabetes

Previous studies suggest that children who progress to type 1 diabetes (T1D) later in life already have an altered serum lipid molecular profile at birth. Here, we compared cord blood lipidome across the three study groups: children who progressed to T1D (PT1D; n = 30), children who developed at least one islet autoantibody but did not progress to T1D during the follow-up (P1Ab; n = 33), and their age-matched controls (CTR; n = 38). We found that phospholipids, specifically sphingomyelins, were lower in T1D progressors when compared to P1Ab and the CTR. Cholesterol esters remained higher in PT1D when compared to other groups. A signature comprising five lipids was predictive of the risk of progression to T1D, with an area under the receiver operating characteristic curve (AUROC) of 0.83. Our findings provide further evidence that the lipidomic profiles of newborn infants who progress to T1D later in life are different from lipidomic profiles in P1Ab and CTR.Peer reviewe

Cord-Blood Lipidome in Progression to Islet Autoimmunity and Type 1 Diabetes

Previous studies suggest that children who progress to type 1 diabetes (T1D) later in life already have an altered serum lipid molecular profile at birth. Here, we compared cord blood lipidome across the three study groups: children who progressed to T1D (PT1D; n = 30), children who developed at least one islet autoantibody but did not progress to T1D during the follow-up (P1Ab; n = 33), and their age-matched controls (CTR; n = 38). We found that phospholipids, specifically sphingomyelins, were lower in T1D progressors when compared to P1Ab and the CTR. Cholesterol esters remained higher in PT1D when compared to other groups. A signature comprising five lipids was predictive of the risk of progression to T1D, with an area under the receiver operating characteristic curve (AUROC) of 0.83. Our findings provide further evidence that the lipidomic profiles of newborn infants who progress to T1D later in life are different from lipidomic profiles in P1Ab and CTR

Cord-Blood Lipidome in Progression to Islet Autoimmunity and Type 1 Diabetes

Previous studies suggest that children who progress to type 1 diabetes (T1D) later in life already have an altered serum lipid molecular profile at birth. Here, we compared cord blood lipidome across the three study groups: children who progressed to T1D (PT1D; n = 30), children who developed at least one islet autoantibody but did not progress to T1D during the follow-up (P1Ab; n = 33), and their age-matched controls (CTR; n = 38). We found that phospholipids, specifically sphingomyelins, were lower in T1D progressors when compared to P1Ab and the CTR. Cholesterol esters remained higher in PT1D when compared to other groups. A signature comprising five lipids was predictive of the risk of progression to T1D, with an area under the receiver operating characteristic curve (AUROC) of 0.83. Our findings provide further evidence that the lipidomic profiles of newborn infants who progress to T1D later in life are different from lipidomic profiles in P1Ab and CTR

Machine learning of plasma metabolome identifies biomarker panels for metabolic syndrome: Findings from the China Suboptimal Health Cohort

Background: Metabolic syndrome (MetS) has been proposed as a clinically identifiable high-risk state for the prediction and prevention of cardiovascular diseases and type 2 diabetes mellitus. As a promising “omics” technology, metabolomics provides an innovative strategy to gain a deeper understanding of the pathophysiology of MetS. The study aimed to systematically investigate the metabolic alterations in MetS and identify biomarker panels for the identification of MetS using machine learning methods. Methods: Nuclear magnetic resonance-based untargeted metabolomics analysis was performed on 1011 plasma samples (205 MetS patients and 806 healthy controls). Univariate and multivariate analyses were applied to identify metabolic biomarkers for MetS. Metabolic pathway enrichment analysis was performed to reveal the disturbed metabolic pathways related to MetS. Four machine learning algorithms, including support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), and logistic regression were used to build diagnostic models for MetS. Results: Thirteen significantly differential metabolites were identified and pathway enrichment revealed that arginine, proline, and glutathione metabolism are disturbed metabolic pathways related to MetS. The protein-metabolite-disease interaction network identified 38 proteins and 23 diseases are associated with 10 MetS-related metabolites. The areas under the receiver operating characteristic curve of the SVM, RF, KNN, and logistic regression models based on metabolic biomarkers were 0.887, 0.993, 0.914, and 0.755, respectively. Conclusions: The plasma metabolome provides a promising resource of biomarkers for the predictive diagnosis and targeted prevention of MetS. Alterations in amino acid metabolism play significant roles in the pathophysiology of MetS. The biomarker panels and metabolic pathways could be used as preventive targets in dealing with cardiometabolic diseases related to MetS

An integrative systems biology study to understand immune aging in people living with HIV

Antiretroviral therapy (ART) reduces viral replication, restores T helper cells and improves the survival of people living with HIV (PWH), transforming a life-threatening disease into a manageable chronic infection. Nevertheless, PWH under ART shows aging-related diseases such as bone abnormalities, non-HIV-associated cancers, and cardiovascular and neurocognitive diseases. The complex immune metabolic dysregulation leading to these comorbidities is called immune aging. The main question raised by my thesis was, what are the complex mechanisms responsible for immune aging in HIV? Using advanced system biology and machine learning tools, I used multi-omics-based patient stratification to identify biologic perturbations associated with immune aging in PWH. First, we investigated PWH with Metabolic Syndrome (MetS), a relatively common agingrelated disease in HIV-1. In paper I, we identified the dysregulation of glutamate metabolism in PWH with MetS using plasma metabolomics and measure of cell transporters by flow cytrometry. Then, we investigated the mechanisms of differing PWH on long-term successful ART from HIV-negative controls (HC). In paper II, we identified the dysregulation of amino acids and, more specifically, glutaminolysis (i.e., lysis of glutamine to glutamate) in PWH compared to HC using metabolomics in two independent cohorts to avoid the potential cohort biases. We identified five neurosteroids to be lower in PWH and potentially create neurological impairments in PWH. The glutaminolysis inhibition in chronically infected HIV-1 promonocytic (U1) cells induced apoptosis and latency reversal which could clear HIV reservoirs. The first two papers universally clarified our knowledge about dysregulated metabolic traits following a prolonged ART in PWH. However, we observed heterogeneity among the clinically defined PWH. Therefore, we focused more on the multi-omics data-driven approaches to stratify the at-risk group who were either dysregulated metabolically atrisk PWH (paper III) or immunometabolic at-risk group (paper IV) and clarified the biological aging process by measuring transcriptomics age (paper V). In paper III, we found three groups of PWH based on multi-omics integration of lipidomics, metabolomics, and microbiome. The severe at-risk metabolic complications showed increased weight-related comorbidities and di- and triglycerides compared to the other clusters. At-risk and HC-like groups displayed similar metabolic profiles but were different from HC. An increase in Prevotella was linked to the overrepresentation of men having sex with men (MSM) in the at-risk group. The microbiome-associated metabolites (MAM) appeared dysregulated in all HIV groups compared to controls. We improved this clustering by adding transcriptomics and proteomics data for a refined immunometabolic at-risk-related clustering in PWH. In paper IV, immune-driven HC-like and at-risk groups were clustered based on metabolomics, transcriptomics, and proteomics. Several biomarkers from central carbon metabolism (CCM) and senescence-associated proteins were linked to the at-risk phenotype based on random forest, structural causal modeling, and co-expression networks. Senescent protein changes were associated with a deficiency in macrophage function based on single-cell data, cell profiling, flow cytometry, and proteomics from macrophage data and in vitro validation. We also developed personalized and group-level genome-scale metabolic models (GSMM) and confirmed the implication of metabolites from CCM and polyamides in at-risk phenotypes. Finally, we investigated the accelerated aging process (AAP) in PWH. In paper V, we calculated the biological age of PWH using transcriptomics data and grouped patients into aging groups; The decelerated aging process (DAP) group was linked with higher age, European origin, and a higher proportion of tenofovir disoproxil fumarate /alafenamide (TDF/TAF). AAP had a downregulation of metabolic pathways and an upregulation of inflammatory pathways. In conclusion, my thesis identifies underlying mechanisms of immune aging using system biology tools in three independent cohorts of PWH for mechanistic studies and to improve their care and achieve healthy aging