Cluster Analysis Identified Clinically Relevant Metabolic Syndrome Endophenotypes

Abstract

AbstractAims/hypothesisMetabolic syndrome (MetS) is a collection of cardiovascular risk factors; however, the high prevalence and heterogeneity impede proper and effective clinical management of MetS. In order for precision medicine to work for MetS, we aimed to identify clinically relevant MetS sub-phenotypes.MethodsWe conducted cluster analysis on individuals from UK Biobank based on MetS criteria to reveal endophenotypes, identified the corresponding cardiometabolic traits and established the association across 21 clinical outcomes. Genome-wide association studies were conducted to identify associated genotypic traits. We further compared the genotypic traits to reveal endophenotypes-specific genotypic traits. Lastly, potential drug targets were identified for the different endophenotypes.ResultsFive MetS subgroups were identified which were Cluster 1 (C1): non-descriptive, Cluster 2 (C2): hypertensive, Cluster 3 (C3): obese, Cluster 4 (C4): lipodystrophy-like, and Cluster 5 (C5): hyperglycaemic. Some MetS clusters had higher CVD risks such as C1 (OR=6·765) and C5 (OR=9·486). Despite being non-descriptive across all cardiometabolic traits, C1 had higher risks for most clinical outcomes. MetS clusters also had different risks to various types of cancers. GWAS of each MetS clusters revealed different genotypic traits.LPCAT2was associated with all clusters except C2 and expression is specific to immune cells. C1 GWAS revealed novel findings ofTRIM63, MYBPC3, MYLPF, andRAPSN. Intriguingly, C1, C3, and C4 were associated with genes highly expressed in brain tissues:CN1H2, TMEM151A, MT3, andC1QTNF4. The cluster-specific genotypic traits also revealed potential drug repurposing targets specific to the endophenotypes.Conclusion/interpretationMetS is highly heterogeneous with endophenotypes that are different in terms of phenotypic and genotypic traits. GWAS of subgroups revealed novel cardiometabolic genotypes which were masked by heterogeneity of MetS.Research in contextEvidence before this studyWe searched PubMed, Science Direct and Scopus from 1stJanuary 2012 to 30thSeptember 2022 for “unsupervised learning” or “clustering” or “endophenotype” or “subclassifications” or “sub-phenotype” and “metabolic syndrome” or “complex diseases”. Google Scholar, UK Biobank published work and approved research were also searched for similar study. This search only revealed published work in other complex diseases such as T2D (which is heavily referenced in our manuscript), Alzheimer’s diseases, psychiatric diseases, and asthma. None of the previously published work applied the combination of unsupervised learning and GWAS for identification of clinically relevant endophenotypes in metabolic syndrome or any complex diseases.Added value of this studyMetabolic syndrome (MetS) is a known cardiovascular disease risk factor, however the constantly changing MetS criteria and high prevalence of MetS impede proper clinical management of individuals with MetS. Through clustering, we identified MetS endophenotypes with semi-distinctive cardiometabolic traits. Some of the MetS endophenotypes correspond with T2D subgroups discovered by other research groups. However, our endophenotypes are more clinically relevant, due to the differing clinical risks across 21 clinical outcomes. We also identified a non-descriptive MetS subgroup with strikingly high cardiovascular risk which likely to be overlooked in clinical settings. Through genome-wide association studies, our endophenotypes also revealed interesting insights into the genetic causes and biological pathways of MetS. We were able to identified genotypic traits that are unique to each MetS endophenotypes and shared genotypic traits which highlights the common pathophysiology underlying MetS. Lastly, we were also able to reveal potential drug targets for drug repurposing, some drug targets are unique to specific endophenotypes.Implications of all the available evidenceOur study attempted to resolve the issue of MetS heterogeneity, by revealing clinically relevant endophenotypes which might respond to different pharmacotherapy. Furthermore, our findings challenge the “one size fits all” step-wise approach in managing complex diseases, emphasizing tailored treatment for different subgroups of patients, a key step towards precision medicine in clinical practice.Graphical Abstract