Abstract
Recent advances in proteomic technologies have made high-throughput profiling of low-abundance proteins in large epidemiological cohorts increasingly feasible. We investigated whether aptamer-based proteomic profiling could identify biomarkers associated with future development of type 2 diabetes (T2DM) beyond known risk factors. We identified dozens of markers with highly significant associations with future T2DM across 2 large longitudinal cohorts (n = 2839) followed for up to 16 years. We leveraged proteomic, metabolomic, genetic, and clinical data from humans to nominate 1 specific candidate to test for potential causal relationships in model systems. Our studies identified functional effects of aminoacylase 1 (ACY1), a top protein association with future T2DM risk, on amino acid metabolism and insulin homeostasis in vitro and in vivo. Furthermore, a loss-of-function variant associated with circulating levels of the biomarker WAP, Kazal, immunoglobulin, Kunitz, and NTR domain–containing protein 2 (WFIKKN2) was, in turn, associated with fasting glucose, hemoglobin A1c, and HOMA-IR measurements in humans. In addition to identifying potentially novel disease markers and pathways in T2DM, we provide publicly available data to be leveraged for insights about gene function and disease pathogenesis in the context of human metabolism.
Highlights
The burden of type 2 diabetes (T2DM) continues to increase, with an estimated 700 million cases worldwide by 2045 [1]
In pooled meta-analyses across Framingham Heart Study (FHS) and Malmö Diet and Cancer Study (MDCS), we identified 146 proteins that were associated with future risk of T2DM in age, sex, and batch-adjusted regression models (P < 3.83 × 10–5)
Using an aptamer-based proteomic profiling platform, we identified 146 plasma proteins with fasting baseline levels that were associated with the future development of T2DM in healthy, nondiabetic individuals up to 15 years prior to disease onset
Summary
The burden of type 2 diabetes (T2DM) continues to increase, with an estimated 700 million cases worldwide by 2045 [1]. Metabolic diseases such as diabetes are often present for years before becoming clinically apparent. While many studies have applied metabolite-profiling technologies toward the identification of T2DM biomarkers [5,6,7,8,9], proteomic analyses in large populations are still lacking. Additional studies have applied this technology to identify markers associated with coronary artery disease [13], muscular dystrophy [14], and Alzheimer’s disease [15] in patient cohorts. Proteomic technologies have been recently applied to diabetes but on a more limited scale [16,17,18,19,20,21]
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