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Identification of clinical subgroups of Type 2 diabetes mellitus and application of pharmacogenetics in development of personalized antidiabetic therapy



Funding: European Regional Development Fund (ERDF) “On Implementation of Activity “Post-doctoral Research Aid” of the Specific Aid Objective 1.1.1 “To increase the research and innovative capacity of scientific institutions of Latvia and the ability to attract external financing, investing in human resources and infrastructure” of the Operational Programme “Growth and Employment”

Project Title: “Identification of clinical subgroups of Type 2 diabetes mellitus and application of pharmacogenetics in development of personalized antidiabetic therapy” 

Project Nr.

Period: 36 months (1st March 2019 – 28 February 2022)

Project costs: 133 806,00 EUR

Project implementer: Dr. biol. Linda Zaharenko

Project Summary:

The project proposes subtyping of Type 2 Diabetes mellitus (T2DM) patients and evaluation of effects of pharmacogenetic markers on patient-significant therapy outcomes and prevention of complications. The stratification of T2DM in a sample of ~ 3 800 patients will be assessed by using data-driven cluster analysis; pharmacogenetic research, investigating the association between clinical outcomes and T2DM susceptibility scores, will be investigated in the subgroup of ~ 1 000 patients with GWAS data. The acquired dataset will be analysed by applying classical statistical analysis and supervised machine learning methods (decision trees and simple neural networks). Identified significant clinical variables and genetic factors will be used to create a model for prediction of the treatment outcomes. Replication of findings will be performed in the GoDARTS dataset with 10 000 GWAS available. Project results will potentially facilitate the development of patient-centred treatment and identification of groups in need for early intensive therapy.

Information published 01.03.2019.


1 March 2019 – 31 May 2019

During the 01.03.2019.-31.05.2019. the spectrum of variables and metadata from ‘‘Register of patients with particular diseases, including patients with diabetes mellitus’’ and Genome Database of Latvian Population (LGDB) as well as from Death causes database of the Centre for Disease Prevention and Control were assessed and data analysis and storage methods chosen. The application for the approval of the project by The Central Medical Ethics Committee of Latvia was submitted. The preliminary application for additional retrieval data from UK Biobank data was prepared. The draft of a publication entitled "All-cause mortality risk in adults with and without type 2 diabetes: findings from the Genome Database of the Latvian Population" was prepared according to guidelines of BMC Public Health journal.

Information published 31.05.2019.


1 June 2019 – 31 August 2019

Database with relevant publications was created in reference program Mendeley and organised for research purposes. The clinical phenotypes and anthropometric measurements were investigated regarding susceptibility to complications and response to therapy. Possible associations were analysed in public databases The NHGRI-EBI GWAS Catalog and GWAS Central. Data were investigated regarding methods used in previous studies. R packages like ggplot2, dyplr, foreign, cluster and others were investigated for the purposes of the analysis of the data and visualization of the results.

Information published 30.08.2019.


1 September 2019 – 30 November 2019

In September the statistical analysis were finished and preparation for mobility started. Additional time was dedicated for learning to work with Python and LINUX. From 01.10.2019.-30.11.2019 resercher spent in Estonian Genome Center. During stay work started to learn analyse GWAs and work with data analysis within cluster from University of Tartu. Data analysis and reserch methods were compared regarding efficiency and quality.

Information published 29.11.2019.


1 December 2019 – 29 February 2020

From 01.12.2019.-29.02.2019 researcher spent in Estonian Genome Center. The data comparison was finished and list of phenotypes was revisited and additional tables and pictures were created. Additional time was dedicated to learn R and GWAs. The comparison revealed important phenotypes affecting development and progression of diabetes in the Latvian cohorts (T1DM and T2DM), as well as the methods learned in Estonia were applied for the connection of the datasets.

Information published 28.02.2020. 

Mājas lapas izstrādi finansēja ERAF aktivitātes projekts Nr. 2010/0196/2DP/ "Latvijas biomedicīnas pētījumu integrācija Eiropas zinātnes telpā".