Lilian Fernandes Silva

Early predictors of heart failure (HF) remain poorly defined. We measured plasma metabolites in a population-based Finnish cohort to identify metabolites that independently predict incident HF. We also performed an exploratory analysis of whether selected metabolites improved discrimination beyond traditional clinical and laboratory factors. Untargeted LC–MS/MS quantified 1009 known metabolites in 10,115 men from the METSIM study. A total of 663 participants developed HF during 14.2-year follow-up. We applied Cox regression analysis to associate the clinical factors and metabolites alone and combined with the risk of HF. Genetic variants that were associated with metabolites and increased the risk of HF were identified. Forty-five metabolites (33 were novel) increased significantly the risk of HF, including markers of catecholamine turnover, polyamine catabolites, very-long-chain acyl-carnitines, and bile-acid sulfates. ROC analysis showed that adding 12 metabolites to the clinical model increased significantly the AUC from 0.775 to 0.795. The metabolites identified converged to the adrenergic/mitochondrial redox stress, nitrogen-polyamine flux, and membrane-lipid/bile-acid remodeling pathways. Ten metabolite-quantitative-trait loci included signaling, nitrogen handling, lipid remodeling, and nucleotide detoxification pathways to specific genes. We also showed that these metabolites add prognostic value to conventional risk factors and highlight pathways of early myocardial injury in patients with HF.

Genetic predisposition and alcohol consumption are risk factors for increased blood pressure (BP), but their interactions influencing BP remain understudied. We conducted population-specific and cross-population meta-analyses of genome-wide gene-alcohol (GxAlc) interactions affecting BP in >1.1M individuals from multiple populations. We identified 46 GxAlc interaction loci for BP, including 21 from one-degree-of-freedom interaction tests (PGxAlc<5x10-8; or <0.05/Meff, Meff independent BP associations at P<10-5), and 25 from two-degree-of-freedom tests of main and interaction effects (PGxAlc<0.05/M2df, M2df independent 2df-associations at P2df<5x10-8), including 7 novel and 39 known BP loci. The 12q24 locus highlights the genetic effect of BRAP-rs11066001 on BP, being ~6 times larger in current drinkers than in non-drinkers. Gene prioritization with 46 GxAlc loci identified 15 genes with ≥3 lines of evidence (location, literature, druggability, functional/regulatory annotation, or pathway analyses). Several loci showed sex- and population-specific effects and revealed biological pathways of alcohol’s influence on BP, suggesting mechanisms underlying alcohol-induced hypertension.

Aims/hypothesis: A recent study has suggested eight clusters of genetic variants associated with type 2 diabetes. We aimed to characterise metabolite associations for these eight clusters. Methods: We constructed type 2 diabetes overall and cluster-partitioned polygenic risk scores (PRSs) in 10,015 Finnish men with 979 named plasma metabolites measured in Metabolon HD4 mass spectrometry platform. We evaluated metabolite–PRS associations using linear regression. We also performed a mediation analysis to examine whether metabolites statistically accounted for part of the association between genetic risk and incident type 2 diabetes that developed in a mean of 13.6 years’ follow-up. Results: We identified 337 metabolites significantly associated with type 2 diabetes genetic risk, including 242 exclusive to cluster-partitioned PRSs. Of the significant metabolites, 26 exhibited significantly heterogeneous associations across clusters. We identified significant enrichment for 33 metabolic pathways among the cluster-associated metabolites. Notably, metabolites for the two pancreatic beta cell-related clusters exhibited enrichment in distinct pathways: the beta cell + proinsulin (PI) cluster in fructose, mannose and galactose metabolism; and the beta cell − PI cluster in branched-chain amino acid metabolism. Mediation analysis suggested that >50% of the associated metabolites showed patterns statistically consistent with a mediating role in the associations between PRSs and incident type 2 diabetes. Conclusions/interpretation: This study underscores the value of type 2 diabetes clustering and highlights metabolic heterogeneity across clusters. The findings have the potential to guide personalised interventions. Data availability: The datasets generated during and/or analysed in the current study are available in dbGaP (accession ID: phs000743.v4.p1 and phs004033.v1.p1).

Type 2 diabetes (T2D) and cardiovascular diseases (CVDs) are major public health challenges worldwide. Metabolomics, the exhaustive assessment of metabolites in biological systems, offers important insights regarding the metabolic disturbances related to these disorders. Recent advances toward the integration of metabolomics into clinical practice to facilitate the discovery of novel biomarkers that can improve the diagnosis, prognosis, and treatment of T2D and CVDs are discussed in this review. Metabolomics offers the potential to characterize the key metabolic alterations associated with disease pathophysiology and treatment. T2D is a heterogeneous disease that develops through diverse pathophysiological processes and molecular mechanisms; therefore, the disease-causing pathways of T2D are not completely understood. Recent studies have identified several robust clusters of T2D variants representing biologically meaningful, distinct pathways, such as the beta cell and proinsulin cluster related to pancreatic insulin secretion, obesity, lipodystrophy, the liver/lipid cluster, glycemia, and blood pressure, and metabolic syndrome clusters representing different pathways causing insulin resistance. Regarding CVDs, recent studies have allowed the metabolomic profile to delineate pathways that contribute to atherosclerosis and heart failure, as well as to the development of targeted therapy. This review also covers the role of metabolomics in integrated metabolic genomics and other omics platforms to better understand disease mechanisms, along with the transition toward precision medicine. This review further investigates the use of metabolomics in multi-metabolite modeling to enhance risk prediction models for predicting the first occurrence of major adverse cardiovascular events among individuals with T2D, highlighting the value of such approaches in optimizing the preventive and therapeutic models used in clinical practice.

Metabolites are small molecules that are useful for estimating disease risk and elucidating disease biology. Here, we perform two-sample Mendelian randomization to systematically infer the potential causal effects of 1099 plasma metabolites measured in 6136 Finnish men from the METSIM study on risk of 2099 binary disease endpoints measured in 309,154 Finnish individuals from FinnGen. We find evidence for 282 putative causal effects of 70 metabolites on 183 disease endpoints. We also identify 25 metabolites with potential causal effects across multiple disease domains, including ascorbic acid 2-sulfate affecting 26 disease endpoints in 12 disease domains. Our study suggests that N-acetyl-2-aminooctanoate and glycocholenate sulfate affect risk of atrial fibrillation through two distinct metabolic pathways and that N-methylpipecolate may mediate the putative causal effect of N6,N6-dimethyllysine on anxious personality disorder.

Type 2 diabetes (T2D) and cardiovascular diseases (CVDs) are major public health challenges worldwide. Metabolomics, the exhaustive assessment of metabolites in biological systems, offers important understanding regarding metabolic disturbances related to these disorders. Recent advances towards integration of metabolomics into clinical practice to facilitate the discovery of novel biomarkers that can improve diagnosis, prognosis, and treatment of T2D and CVDs are discussed in this review. Metabolomics offers potential to characterize key metabolic alterations associated with disease pathophysiology and treatment. T2D is a heterogeneous disease that develops through diverse pathophysiological processes and molecular mechanisms, and therefore disease-causing pathways of T2D have not been completely understood. Recent studies have identified several robust clusters of T2D variants representing biologically meaningful distinct pathways, beta cell and proinsulin cluster related to pancreatic insulin secretion, obesity, lipodystrophy, liver/lipid cluster, glycemia, blood pressure and metabolic syndrome clusters representing different pathways causing insulin resistance. For CVDs, recent studies have allowed the metabolomic profile to delineate pathways that contribute to atherosclerosis and heart failure, as well as to the development of targeted therapy. It also covers the role of metabolomics in integrated metabolic genomics and other omics platforms to better understand disease mechanisms, and the transition towards precision medicine. This review further investigates the use of metabolomics in multi-metabolite modelling to enhance risk prediction models for the first occurrence of major adverse cardiovascular events among the individuals with T2D highlighting the value of such approaches in optimizing preventive and therapeutic models in clinical practice.

Abdominal aortic aneurysm represents a significant public health concern, particularly in men aged 55 to 64, where it occurs in about 1%. We investigated the metabolomics and genetics of AAA by analyzing a cohort including 76 patients with AAA and randomly selected 228 controls. Utilizing the Metabolon DiscoveryHD4 platform for non-targeted metabolomics profiling, we identified several novel metabolites significantly associated with AAA. These metabolites were primarily related to environmental and lifestyle factors, notably smoking and pesticide exposure, which underscores the influence of external factors on the progression of AAA. Additionally, several genetic variants were associated with xenobiotics, highlighting a genetic predisposition that may exacerbate the effects of these environmental exposures. The integration of metabolomic and genetic data provides compelling evidence that lifestyle, environmental, and genetic factors are intricately linked to the etiology of AAA. The results of our study not only deepen the understanding of the complex pathophysiology of AAA but also pave the way for the development of targeted therapeutic strategies.

Complete characterization of the genetic effects on gene expression is needed to elucidate tissue biology and the etiology of complex traits. In the present study, we analyzed 2,344 subcutaneous adipose tissue samples and identified 34,774 conditionally distinct expression quantitative trait locus (eQTL) signals at 18,476 genes. Over half of eQTL genes exhibited at least two eQTL signals. Compared with primary eQTL signals, nonprimary eQTL signals had lower effect sizes, lower minor allele frequencies and less promoter enrichment; they corresponded to genes with higher heritability and higher tolerance for loss of function. Colocalization of eQTLs with genome-wide association study (GWAS) signals for 28 cardiometabolic traits identified 1,835 genes. Inclusion of nonprimary eQTL signals increased discovery of colocalized GWAS–eQTL signals by 46%. Furthermore, 21 genes with ≥2 colocalized GWAS–eQTL signals showed a mediating gene dosage effect on the GWAS trait. Thus, expanded eQTL identification reveals more mechanisms underlying complex traits and improves understanding of the complexity of gene expression regulation.

Abdominal aortic aneurysm (AAA) represents a significant public health concern, particularly in men aged 55 to 64, where it occurs in about 1%. We investigated metabolomics and genetics of AAA by analyzing a cohort consisting of 76 patients diagnosed with AAA and 228 matched controls. Utilizing the Metabolon DiscoveryHD4 platform for non-targeted metabolomics profiling, we identified 11 novel metabolites that significantly increased the risk of AAA. These metabolites were primarily associated with environmental and lifestyle factors, notably smoking and pesticide exposure, which underscores the influence of external factors on the progression of AAA. Additionally, several genetic variants were associated with xenobiotics, highlighting a genetic predisposition that may exacerbate the effects of these environmental exposures. The integration of metabolomic and genetic data provides compelling evidence that lifestyle, environmental, and genetic factors are intricately linked to the etiology of AAA. The results of our study not only deepen the understanding of the complex pathophysiology of AAA but also pave the way for the development of targeted therapeutic strategies.