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

Abstract

Aims/hypothesisThe 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.MethodsIn 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.ResultsTwenty-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/interpretationThe 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 materialThe online version of this article (10.1007/s00125-019-4960-8) contains peer-reviewed but unedited supplementary material, which is available to authorised users.

Longitudinal plasma protein profiling of newly diagnosed type 2 diabetes

“…HNMT, SIT1, RTN4R, CDCP1, SIGLEC10, IFNLR1 and VSIG4) expand the knowledge about the biochemical manifestations of type 2 diabetes and provides a resource for new candidate biomarkers in this disease area. This study also confirms several previously published associations between key proteins and prevalent diabetes and/or diabetes progression, including PON3, HGF, CTSD, IL1RA, SIGLEC7, LPL, IL6, FGF21, ERBB2, ALDH1A1, GAL4, ADM and FABP4 [ 5 , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] ].…”

“…Many of these interactions are mediated through various circulating proteins, including hormones, growth factors, adipokines, cytokines and enzymes [4] . The recent advancements in high throughput technologies for measuring a large number of proteins in a single assay have enabled data-driven discoveries that may offer new insights into the dysregulated metabolic milieu of diabetes [ 5 , 6 ]. There is also a potential for comprehensive protein profiling in personalized medicine, by detecting early signs of disease development and providing simultaneous information on multiple cardiometabolic health indicators in individual patients [7] .…”

“…While efforts have been made to study protein alterations in diabetes [ 5 , 6 ], little is known about proteomic alterations at the very onset of the disease and before any diabetes treatment has been initiated. Patients at this stage of the disease can only be reached using screening programs since they lack classic symptoms of diabetes.…”

Integrating Genetics and the Plasma Proteome to Predict the Risk of Type 2 Diabetes

“…Only one of these studies showed a modest improvement in discrimination by adding 3 proteins (MASP, ApoE, and CRP) to the standard clinical model (AUC increased from 0.75 to 0.77). Up to 142 plasma proteins have been associated with prevalent T2D [ 59 •, 60 , 61 ] in cross-sectional studies, which aim to identify differences between T2D patients and well-matched control samples or associations with glycaemic parameters, providing a cost-effective and simple prioritisation strategy. However, these studies suffer from reverse confounding, i.e.…”

“…Systematic causal assessment for protein candidates on T2D had little success at first [ 56 , 60 , 87 ], and only recently, several proteins are suggested to be causally related to T2D (Table 2 ). WFIKKN2 is the only protein identified by more than one study with consistent effect directions.…”