Deep serum proteomics reveal biomarkers and causal candidates for type 2 diabetes

Abstract

18The prevalence of type 2 diabetes mellitus (T2DM) is expected to increase rapidly in the next 19 decades, posing a major challenge to societies worldwide. The emerging era of precision 20 medicine calls for the discovery of biomarkers of clinical value for prediction of disease 21 onset, where causal biomarkers can furthermore provide actionable targets. Blood-based 22 factors like serum proteins are in contact with every organ in the body to mediate global 23 homeostasis and may thus directly regulate complex processes such as aging and the 24 development of common chronic diseases. We applied a data-driven proteomics approach 25 measuring serum levels of 4,137 proteins in 5,438 Icelanders to discover novel biomarkers for 26 incident T2DM and describe the serum protein profile of prevalent T2DM. We identified 536 27 proteins associated with incident or prevalent T2DM. Through LASSO penalized logistic 28 regression analysis combined with bootstrap resampling, a panel of 20 protein biomarkers that 29 accurately predicted incident T2DM was identified with a significant incremental 30 improvement over traditional risk factors. Finally, a Mendelian randomization analysis 31 provided support for a causal role of 48 proteins in the development of T2DM, which could 32 be of particular interest as novel therapeutic targets. 33 34 for age and sex, we identified 520 unique proteins that were significantly associated with 84 prevalent T2DM after Bonferroni correction for multiple hypothesis testing (P adj < 0.05), with 85 the strongest associations observed for ARFIP2, MXRA8 and CPM ( Fig. 1a, Table S2). In a 86 second model including adjustment for body mass index (BMI), 322 proteins remained 87 statistically significant ( Table S2). Many of the proteins were inter-correlated, with pairwise 88 Pearson's r ranging from -0.60 to 0.97 ( Fig. S2a). A pathway and gene ontology (GO) 89 enrichment analysis of all 520 proteins associated with prevalent T2DM revealed an 90 enrichment of proteins involved in extracellular matrix (ECM)-receptor interaction, 91 complement and coagulation cascades, metabolic processes and extracellular region (Fig. 92 S3a, Table S3). We furthermore found the genes encoding the 520 prevalent T2DM-93 associated proteins to be enriched for high expression in liver, followed by other tissues that 94 included kidney, gastrointestinal tract and pancreas ( Fig. S4a). Thus, the diabetic state is 95 reflected in a major shift in the serum proteome that is involved in metabolic, inflammatory 96 and ECM processes. 97 98 Serum protein profile of incident T2DM 99The serum protein profiles of T2DM patients observed in the cross-sectional analysis 100 described above may represent shifts that occurred either before or after the onset of the 101 disease. To identify serum protein signatures that preceded the onset of T2DM, we next 102 focused our analysis on the 2,940 non-diabetic AGES participants who participated in a 103 second study visit (AGESII) 5-years after the baseline visit, of which 112 develop...

Integrative analysis of the plasma proteome and polygenic risk of cardiometabolic diseases

“…Our findings were strongly consistent with a recent study of SomaLogic proteins in serum and incident T2D in the AGES-Reykjavik cohort 26 , providing support for both the T2D PRS associations and the temporality of polygenic associations with proteins which precede T2D incidence. Twenty-three of our 31 T2D PRS associated proteins (74%) were associated with prevalent T2D, and 17 (55%) were associated with 5-year risk of incident T2D (Table S8; Fisher's exact test P-value = 5×10 -19 ).…”

“…We found a strong and consistent inverse causal estimate for the known causal effect of decreased sex hormone binding globulin (SHBG) on increased T2D risk 24 (Figure 3, Table S10). Consistent with the Mendelian randomisation analysis in the AGES-Reykjavik cohort 26 , we found an inverse causal estimate for WFIKKN2 on T2D risk. We additionally found an inverse causal estimate for complement factor I (CFI) on T2D risk.…”

Effects of adiposity on the human plasma proteome: Observational and Mendelian randomization estimates

“…This association has been suggested in previous observational studies (46), but BMI-protein effect estimates from MR have been lacking. FABP4 has been implicated in cardiometabolic disease: a SNP which increases FABP4 was found to raise the odds of type II diabetes among adults (47), potentially through its contribution to higher insulin resistance (48). FABP has also been associated with higher risk of atherosclerosis among adults in an observational Chinese cohort study (49).…”

“…When evaluating the role of SHBG in disease, one MR analysis suggests that a SNP which is associated with an increase in SHBG contributes to a decrease in risk of cardioembolic stroke (54), thereby suggesting that the SHBG-lowering effect of higher BMI observed in this study would increase this risk. Another study has also implicated lower SHBG levels in increasing type II diabetes risk (47). SHBG has also been implicated in cardiometabolic disease in observational studies.…”