Doha – May 19, 2022: Researchers at Weill Cornell Medicine-Qatar (WCM-Q) have identified metabolites that are associated with type 2 diabetes and its complications, mainly obesity, retinopathy and dyslipidemia.
The study, led by Dr. Noha Yousri, Assistant Professor of Research in Genetic Medicine at WCM-Q, analyzed 1,300 metabolites in samples from 996 Qatari adults (57 percent of whom had type 2 diabetes) and 1,159 metabolites from an independent cohort of 2,618 individuals from the Qatar BioBank (11 percent of whom had type 2 diabetes). It identified 373 metabolites associated with type 2 diabetes, obesity, retinopathy, dyslipidemia (measured through lipoprotein levels). A total of 161 of these metabolites were novel, meaning their association with the conditions has never previously been identified. The identified metabolites highlight perturbations in several biological pathways including oxidative stress, lipotoxicity and glucotoxicity, all of which cause dysfunction and damage at cellular level.
More interestingly, the researchers identified 15 patterns of what they termed “metabo-clinical signatures” based upon clusters of patients with type 2 diabetes who had similar metabolite levels and also shared two or more clinical characteristics, such as obesity, triglyceride, unhealthy HDL/LDL levels, or retinopathy.
Dr. Yousri explained: “The study of complex diseases as diabetes benefits from profiling metabolites from various metabolic pathways. Being affected by both genetics and the environment, metabolites are useful in deciphering the disease mechanisms. This study gives us new insights into shared metabolic pathways between diabetes and its complications. In addition, the large sample size allowed us to identify disease clusters of individuals with similar complications that are associated with broadly similar metabolic profiles. Identifying such metabo-clinical patterns, and future integration with other omics profiles will enhance personalized medicine approaches for type 2 diabetes, a prevalent disease that affects many people in our region and around the world.”
Type 2 diabetes is a complex disease with many different causes and a wide variety of clinical complications, which makes understanding the disease, and how to treat or prevent it, extremely challenging. To meet this challenge of complexity, WCM-Q uses a highly advanced precision testing platform and powerful computing technology. This allows researchers at WCM-Q to analyze vast quantities of complex data generated from biological samples drawn from very large numbers of individuals. Using this data to build up a ‘disease atlas’ of type 2 diabetes and its complications in this way could potentially provide targets for the development of new drugs and more effective precision medicine approaches.
Dr. Khalid Fakhro, Chief Research Officer and Director of the Precision Medicine Program at Sidra Medicine, and a senior author of the paper, added: “This study is yet another terrific demonstration of the close collaboration between Sidra Medicine and WCM-Q to characterize the Qatari population on a molecular level. Establishing mutli-omic reference databases based on metabolomics, transcriptomics, genomics and so on, of thousands of Qataris will be key to mapping markers for health and disease in the local population, and of great importance to the Arab world at large.”
The paper, entitled ‘Metabolic and Metabo-Clinical Signatures of Type 2 Diabetes, Obesity, Retinopathy, and Dyslipidemia,’ has been published in diabetes, a leading journal. Other researchers who majorly contributed to the paper include Dr. Ronald Crystal of Weill Cornell Medicine in New York, and Dr. Steven Hunt and Dr. Karsten Suhre, both of WCM-Q.
Dr. Khaled Machaca, Senior Associate Dean for Research, Innovations, and Commercialization at WCM-Q, said: “This important study takes a deep dive into the metabolomics of type 2 diabetes and its associated complications, which can be debilitating and distressing for patients and their families. At WCM-Q we are dedicated to using our advanced scientific capabilities to improve our understanding of diseases of importance to the Qatari and local populations in an effort to pave the way for advanced treatments.”
The study was supported by Qatar National Research Fund (a member of the Qatar Foundation) (NPRP11S-0114-180299, NPRP09-740-3-192, and NPRP09-741-3-793), and by Qatar Biobank. The work was also supported by the Bioinformatics Core and Biomedical Research Program in Weill Cornell Medicine-Qatar, funded by the Qatar Foundation.