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Circulating Protein Signatures and Causal Candidates for Type 2 Diabetes

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Circulating Protein Signatures and Causal Candidates for Type 2 Diabetes Diabetes Volume 69, August 2020 1843 Circulating Protein Signatures and Causal Candidates for Type 2 Diabetes Valborg Gudmundsdottir,1,2 Shaza B. Zaghlool,3 Valur Emilsson,2,4 Thor Aspelund,1,2 Marjan Ilkov,2 Elias F. Gudmundsson,2 Stefan M. Jonsson,1 Nuno R. Zilhão,2 John R. Lamb,5 Karsten Suhre,3 Lori L. Jennings,6 and Vilmundur Gudnason1,2 Diabetes 2020;69:1843–1853 | https://doi.org/10.2337/db19-1070 GENETICS/GENOMES/PROTEOMICS/METABOLOMICS The increasing prevalence of type 2 diabetes poses More than 240 genetic loci have been associated with type a major challenge to societies worldwide. Blood-based 2 diabetes in genome-wide association studies (GWAS) factors like serum proteins are in contact with every (1–5), and blood-based biomarker candidates for type organ in the body to mediate global homeostasis and 2 diabetes have begun to emerge, perhaps most notably may thus directly regulate complex processes such as the branched-chain amino acids (BCAAs) (6,7), the catab- aging and the development of common chronic dis- olism of which has recently been proposed as a novel eases. We applied a data-driven proteomics approach, treatment target for obesity-associated insulin resistance measuring serum levels of 4,137 proteins in 5,438 elderly (8). However, only fragmentary data are available for Icelanders, and identified 536 proteins associated with serum protein links to type 2 diabetes (9). While few prevalent and/or incident type 2 diabetes. We validated biomarker candidates provide much improvement in a subset of the observed associations in an independent type 2 diabetes prediction over conventional measures case-control study of type 2 diabetes. These protein associations provide novel biological insights into the of glycemia and adiposity (9), they may provide insight molecular mechanisms that are dysregulated prior to into biological processes that are important in the disease and following the onset of type 2 diabetes and can be pathogenesis. detected in serum. A bidirectional two-sample Mendelian Proteins are the key functional units of biology and randomization analysis indicated that serum changes of disease; however, high-throughput detection and quanti- at least 23 proteins are downstream of the disease or fication of serum proteins in a large human population its genetic liability, while 15 proteins were supported as have been hampered by the limitations of available pro- having a causal role in type 2 diabetes. teomic profiling technologies. The Slow-Off rate Modified Aptamer (SOMAmer)-based technology has emerged as a powerful proteomic profiling platform in terms of sen- Type 2 diabetes is a progressive disease characterized by sitivity, dynamic range of detection, and multiplex capacity decreasing sensitivity of peripheral tissues to plasma in- (10–12). A custom-designed SOMAscan platform was re- sulin, accompanied by compensatory hyperinsulinemia, cently developed to measure 5,034 protein analytes in and a gradual failure of the pancreatic islet b-cells to a single serum sample, of which 4,782 SOMAmers bind maintain glucose homeostasis. In the past decade, the specifically to 4,137 distinct human proteins (13). We use of data-driven omics technologies has led to a signif- applied this platform to 5,457 subjects of the Age, icant advancement in the discovery of new biomarker Gene/Environment Susceptibility (AGES)-Reykjavik study candidates and biological insights for complex diseases. (13,14) and demonstrate novel serum protein associations 1Faculty of Medicine, University of Iceland, Reykjavik, Iceland This article contains supplementary material online at https://doi.org/10.2337/ 2Icelandic Heart Association, Holtasmari 1, Kopavogur, Iceland figshare.12249884. 3 Department of Biophysics and Physiology, Weill Cornell Medicine - Qatar, Doha, V.G., S.B.Z., and V.E. contributed equally as joint first authors. Qatar K.S., L.L.J., and V.G. contributed equally as joint senior authors. 4Faculty of Pharmaceutical Sciences, University of Iceland, Reykjavik, Iceland 5GNF Novartis, San Diego, CA © 2020 by the American Diabetes Association. Readers may use this article as 6Novartis Institutes for Biomedical Research, Cambridge, MA long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at https://www.diabetesjournals Corresponding author: Vilmundur Gudnason, [email protected] .org/content/license. Received 24 October 2019 and accepted 4 May 2020 1844 Proteomics Signatures of Type 2 Diabetes Diabetes Volume 69, August 2020 with prevalent and incident type 2 diabetes. A subset of proteins including cross-platform validation and detection these associations replicated in the Qatar Metabolomics of the cis-acting genetic effects (13), direct measures of the Study on Diabetes (QMDiab) with use of a different version SOMAmer specificity for 779 of the SOMAmers in complex of the SomaLogic platform. By applying a bidirectional biological samples were performed using tandem mass Mendelian randomization (MR) analysis, we identify a sub- spectrometry (13). Hybridization controls were used to set of proteins that may be causally related to type 2 di- correct for systematic variability in detection, and calibra- abetes and another set of proteins that may be affected by tor samples of three dilution sets (40%, 1%, and 0.005%) the disease itself or its genetic liability. were included so that the degree of fluorescence was a quantitative reflection of protein concentration. In the RESEARCH DESIGN AND METHODS QMDiab cohort, the kit-based SOMAscan platform run by Study Populations the Weill Cornell Medicine - Qatar (WCM-Q) proteomics An overview of the cohort and study workflow is shown core was used to quantify a total of 1,129 protein measure- in Supplementary Fig. 1. Cohort participants aged 66–96 ments in 356 plasma samples from QMDiab (19), with years were included from the AGES-Reykjavik Study (14), median intra- and interassay CVs ,7%. Here, 1,079 a prospective study of deeply phenotyped subjects (North- SOMAmers targeting 1,050 proteins were shared with ern Europeans). After exclusion of individuals without the platform used in AGES-Reykjavik. Protocols and in- a fasting glucose measurement or with established type strumentation were provided and certified using reference 1 diabetes, 5,438 individuals remained for analysis in the samples by SomaLogic. Experiments were conducted under current study (mean age 76.6 6 5.6 years). Type 2 diabetes supervision of SomaLogic personnel. No samples or probes was defined from self-reported diabetes, diabetes medica- were excluded. tion use, or fasting plasma glucose $7 mmol/L (15). Of 4,784 AGES-Reykjavik participants free of diabetes at Genotyping and Imputation baseline, 2,940 attended a 5-year follow-up visit (AGESII). Genetic data were available for 3,219 AGES-Reykjavik par- Manifest type 2 diabetes at the follow-up visit was clas- ticipants. Genotyping was performed using the Illumina sified as an incident case, using the same criteria as for the 370CNV BeadChip array, and genotype calling was per- baseline visit. Blood samples were collected at the baseline formed using the Illumina BeadStudio. Samples were ex- cluded based on sample failure, genotype mismatch with visit after overnight fasting and serum lipids, glucose, HbA1c, and insulin measured using standard protocols. Serum reference panel, and sex mismatch on genotypes (20). Im- creatinine was measured with the Roche Hitachi 912 instru- putation (1000 Genomes Phase 1 v3 reference panel) was ment and estimated glomerular filtration rate (eGFR) de- performed using MaCH (version 1.0.16), and the following fi rived with the four-variable MDRD study equation (16). quality control ltering was applied at the variant level: call , P , 3 26 External validation analysis was performed in the rate ( 95%), Hardy Weinberg equilibrium ( 1 10 ), QMDiab study, which is a cross-sectional case-control PLINK mishap haplotype-based test for nonrandom missing P , 3 29 study for type 2 diabetes that was carried out in 2012 at genotype data ( 1 10 ), and mismatched positions the Dermatology Department in Hamad Medical Corpo- between Illumina, dbSNP, and/or HapMap. ration (HMC) (Doha, Qatar). This study has previously been described and comprises 388 study participants from Statistical Analysis Arab and Asian ethnicities (17). A subset of 356 partic- Box-Cox transformation was applied on the protein data ipants with proteomics data were used in this study (21). Extreme outlier values were excluded, defined as (ncases 5 179). values above the 99.5th percentile of the distribution of 99th percentile cutoffs across all proteins after scaling, Protein Profiling resulting in the removal of an average 11 samples per Each protein has its own detection reagent selected SOMAmer. Associations between serum protein levels and from chemically modified DNA libraries, referred to as prevalent or incident type 2 diabetes were determined SOMAmers (18). The custom version of the SOMApanel using a logistic regression adjusted for age and sex (base platform included proteins known or predicted to be found model), where Bonferroni-corrected P , 0.05/4,782 2 in the extracellular milieu (18). Serum levels of 4,137 SOMAmers 5 1.1 3 10 5 was considered statistically human proteins, targeted by 4,782 SOMAmers, were de- significant. In subsequent models, the following covariates termined at SomaLogic (Boulder, CO) in samples from were included: fasting glucose (only for incident type 5,457 AGES-Reykjavik participants as previously described 2 diabetes), BMI, fasting insulin, HDL, triglycerides (13). Sample collection and processing for protein mea- (TG), eGFR, systolic blood pressure (SBP), abdominal surements were randomized and all samples run as a single circumference, and parental history of diabetes. These set. The SOMAmers that passed quality control had me- covariates include the components of the Framingham dian intra-assay and interassay coefficients of variation Offspring Risk Score (FORS) clinical prediction model (CV) ,5%, similar to that reported on variability in the (22) in addition to fasting insulin and eGFR.
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