Cargando…

In search of causal pathways in diabetes: a study using proteomics and genotyping data from a cross-sectional study

AIMS/HYPOTHESIS: The pathogenesis of type 2 diabetes is not fully understood. We investigated whether circulating levels of preselected proteins were associated with the outcome ‘diabetes’ and whether these associations were causal. METHODS: In 2467 individuals of the population-based, cross-section...

Descripción completa

Detalles Bibliográficos
Autores principales: Beijer, Kristina, Nowak, Christoph, Sundström, Johan, Ärnlöv, Johan, Fall, Tove, Lind, Lars
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6805963/
https://www.ncbi.nlm.nih.gov/pubmed/31446444
http://dx.doi.org/10.1007/s00125-019-4960-8
_version_ 1783461517678608384
author Beijer, Kristina
Nowak, Christoph
Sundström, Johan
Ärnlöv, Johan
Fall, Tove
Lind, Lars
author_facet Beijer, Kristina
Nowak, Christoph
Sundström, Johan
Ärnlöv, Johan
Fall, Tove
Lind, Lars
author_sort Beijer, Kristina
collection PubMed
description AIMS/HYPOTHESIS: The pathogenesis of type 2 diabetes is not fully understood. We investigated whether circulating levels of preselected proteins were associated with the outcome ‘diabetes’ and whether these associations were causal. METHODS: In 2467 individuals of the population-based, cross-sectional EpiHealth study (45–75 years, 50% women), 249 plasma proteins were analysed by the proximity extension assay technique. DNA was genotyped using the Illumina HumanCoreExome-12 v1.0 BeadChip. Diabetes was defined as taking glucose-lowering treatment or having a fasting plasma glucose of ≥7.0 mmol/l. The associations between proteins and diabetes were assessed using logistic regression. To investigate causal relationships between proteins and diabetes, a bidirectional two-sample Mendelian randomisation was performed based on large, genome-wide association studies belonging to the DIAGRAM and MAGIC consortia, and a genome-wide association study in the EpiHealth study. RESULTS: Twenty-six proteins were positively associated with diabetes, including cathepsin D, retinal dehydrogenase 1, α-l-iduronidase, hydroxyacid oxidase 1 and galectin-4 (top five findings). Three proteins, lipoprotein lipase, IGF-binding protein 2 and paraoxonase 3 (PON-3), were inversely associated with diabetes. Fourteen of the proteins are novel discoveries. The Mendelian randomisation study did not disclose any significant causal effects between the proteins and diabetes in either direction that were consistent with the relationships found between the protein levels and diabetes. CONCLUSIONS/INTERPRETATION: The 29 proteins associated with diabetes are involved in several physiological pathways, but given the power of the study no causal link was identified for those proteins tested in Mendelian randomisation. Therefore, the identified proteins are likely to be biomarkers for type 2 diabetes, rather than representing causal pathways. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00125-019-4960-8) contains peer-reviewed but unedited supplementary material, which is available to authorised users.
format Online
Article
Text
id pubmed-6805963
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher Springer Berlin Heidelberg
record_format MEDLINE/PubMed
spelling pubmed-68059632019-11-05 In search of causal pathways in diabetes: a study using proteomics and genotyping data from a cross-sectional study Beijer, Kristina Nowak, Christoph Sundström, Johan Ärnlöv, Johan Fall, Tove Lind, Lars Diabetologia Article AIMS/HYPOTHESIS: The pathogenesis of type 2 diabetes is not fully understood. We investigated whether circulating levels of preselected proteins were associated with the outcome ‘diabetes’ and whether these associations were causal. METHODS: In 2467 individuals of the population-based, cross-sectional EpiHealth study (45–75 years, 50% women), 249 plasma proteins were analysed by the proximity extension assay technique. DNA was genotyped using the Illumina HumanCoreExome-12 v1.0 BeadChip. Diabetes was defined as taking glucose-lowering treatment or having a fasting plasma glucose of ≥7.0 mmol/l. The associations between proteins and diabetes were assessed using logistic regression. To investigate causal relationships between proteins and diabetes, a bidirectional two-sample Mendelian randomisation was performed based on large, genome-wide association studies belonging to the DIAGRAM and MAGIC consortia, and a genome-wide association study in the EpiHealth study. RESULTS: Twenty-six proteins were positively associated with diabetes, including cathepsin D, retinal dehydrogenase 1, α-l-iduronidase, hydroxyacid oxidase 1 and galectin-4 (top five findings). Three proteins, lipoprotein lipase, IGF-binding protein 2 and paraoxonase 3 (PON-3), were inversely associated with diabetes. Fourteen of the proteins are novel discoveries. The Mendelian randomisation study did not disclose any significant causal effects between the proteins and diabetes in either direction that were consistent with the relationships found between the protein levels and diabetes. CONCLUSIONS/INTERPRETATION: The 29 proteins associated with diabetes are involved in several physiological pathways, but given the power of the study no causal link was identified for those proteins tested in Mendelian randomisation. Therefore, the identified proteins are likely to be biomarkers for type 2 diabetes, rather than representing causal pathways. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00125-019-4960-8) contains peer-reviewed but unedited supplementary material, which is available to authorised users. Springer Berlin Heidelberg 2019-08-24 2019 /pmc/articles/PMC6805963/ /pubmed/31446444 http://dx.doi.org/10.1007/s00125-019-4960-8 Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Article
Beijer, Kristina
Nowak, Christoph
Sundström, Johan
Ärnlöv, Johan
Fall, Tove
Lind, Lars
In search of causal pathways in diabetes: a study using proteomics and genotyping data from a cross-sectional study
title In search of causal pathways in diabetes: a study using proteomics and genotyping data from a cross-sectional study
title_full In search of causal pathways in diabetes: a study using proteomics and genotyping data from a cross-sectional study
title_fullStr In search of causal pathways in diabetes: a study using proteomics and genotyping data from a cross-sectional study
title_full_unstemmed In search of causal pathways in diabetes: a study using proteomics and genotyping data from a cross-sectional study
title_short In search of causal pathways in diabetes: a study using proteomics and genotyping data from a cross-sectional study
title_sort in search of causal pathways in diabetes: a study using proteomics and genotyping data from a cross-sectional study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6805963/
https://www.ncbi.nlm.nih.gov/pubmed/31446444
http://dx.doi.org/10.1007/s00125-019-4960-8
work_keys_str_mv AT beijerkristina insearchofcausalpathwaysindiabetesastudyusingproteomicsandgenotypingdatafromacrosssectionalstudy
AT nowakchristoph insearchofcausalpathwaysindiabetesastudyusingproteomicsandgenotypingdatafromacrosssectionalstudy
AT sundstromjohan insearchofcausalpathwaysindiabetesastudyusingproteomicsandgenotypingdatafromacrosssectionalstudy
AT arnlovjohan insearchofcausalpathwaysindiabetesastudyusingproteomicsandgenotypingdatafromacrosssectionalstudy
AT falltove insearchofcausalpathwaysindiabetesastudyusingproteomicsandgenotypingdatafromacrosssectionalstudy
AT lindlars insearchofcausalpathwaysindiabetesastudyusingproteomicsandgenotypingdatafromacrosssectionalstudy