Association between protein signals and type 2 diabetes incidence

Abstract

Understanding early determinants of type 2 diabetes is essential for refining disease prevention strategies. Proteomic technology may provide a useful approach to identify novel protein patterns potentially related to pathophysiological changes that lead up to diabetes. In this study, we sought to identify protein signals that are associated with diabetes incidence in a middle-aged population. Serum samples from 519 participants in a nested case–control selection (167 cases and 352 age-, sex- and BMI-matched normoglycemic control subjects, median follow-up 14.0 years) within the Whitehall-II cohort were analyzed by linear matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). Nine protein peaks were found to be associated with incident diabetes. Rate ratios for high peak intensity ranged between 0.4 (95% CI, 0.2–0.8) and 4.0 (95% CI, 1.7–9.2) and were robust to adjustment for main potential confounders, including obesity, lipids and C-reactive protein. The proteins associated with these peaks may reflect diabetes pathogenesis. Our study exemplifies the utility of an approach that combines proteomic and epidemiological data.

Alzheimer’s disease and type 2 diabetes mellitus: A systematic review of proteomic studies

“…Following, the complete reading of each article resulted in 14 exclusions of studies on AD and seven on T2DM: a) AD – five studies did not identify the proteins (only the molecular mass); three evaluated only the protein glycosylation profile, or novel Aβ peptides species and one of them evaluated the lipidomic profile; two studies failed to include a control group or investigated only patients with mild cognitive impairment (MCI); one study evaluated the proteomics by machine learning without describing the relevant proteins; two studies applied the proteomic methodology in brain tissue or cerebrospinal fluid (CSF); and proteomic techniques were not used in the last study; b) T2DM – two studies failed to identify the proteins or to include a healthy control group; four investigated only the glycosylation profile or proteins turnover, or protein post‐translational modification; one study applied proteomic approaches in mononuclear peripheral cells. Thirty‐four of the 40 studies were classified as HQ by fulfilling at least 9/14 criteria of QUADOMICS (Abdulwahab, Alaiya, Shinwari, Allaith, & Giha, 2019; Chiu et al., 2018; Cocciolo et al, 2012; Craig‐Schapiro et al, 2010; Dayon et al., 2017; Dey et al., 2019; Dincer et al., 2009; Fania et al., 2017; Gómez‐Cardona et al., 2017; González‐Sánchez et al, 2008; Hu et al., 2012; Huth et al., 2019; Hye et al., 2006; IJsselstijn et al., 2011; Jensen et al., 2013; Kitamura et al., 2017; Li et al., 2008, 2018; Liu et al., 2006, 2009; Llano, Devanarayan, & Simon, 2013; Meng et al, 2017; Muenchhoff et al, 2017; Nazeri et al., 2014; Nowak et al., 2016; Park et al., 2019; Ray et al., 2007; Riaz, Alam, & Akhtar, 2010; Sattlecker et al., 2014; Shen et al., 2017; Soares et al., 2009; Zabel et al., 2012; Zhang et al., 2008; Zhao et al., 2015). The six remaining studies were classified as LQ, reaching less than 9/14 queries (Bennett et al., 2012; Johnstone, Milward, Berretta, Moscato, & Initiative, 2012; Kang et al., 2016; Long, Pan, Ifeachor, Belshaw, & Li, 2016; Yang, Lyutvinskiy, Soininen, & Zubarev, 2011; Yang ...…”

“…The Chiu et al, 2018;Cocciolo et al, 2012;Craig-Schapiro et al, 2010;Dayon et al, 2017;Dey et al, 2019;Dincer et al, 2009;Fania et al, 2017;Gómez-Cardona et al, 2017;González-Sánchez et al, 2008;Hu et al, 2012;Huth et al, 2019;Hye et al, 2006;IJsselstijn et al, 2011;Jensen et al, 2013;Kitamura et al, 2017;Li et al, 2008Li et al, , 2018Liu et al, 2006Liu et al, , 2009Llano, Devanarayan, & Simon, 2013;Meng et al, 2017;Muenchhoff et al, 2017;Nazeri et al, 2014;Nowak et al, 2016;Park et al, 2019;Ray et al, 2007;Riaz, Alam, & Akhtar, 2010;Sattlecker et al, 2014;Shen et al, 2017;Soares et al, 2009;Zabel et al, 2012;Zhang et al, 2008;Zhao et al, 2015).…”

The potential of novel biomarkers to improve risk prediction of type 2 diabetes

“…Six protein peaks were significantly associated with incident type 2 diabetes after adjustment for age, sex, obesity, lipids, C-reactive protein, fasting glucose and 2 h glucose, but no data on the potential improvement of prediction models by these proteins were provided [81]. However, this work can be seen as a proof-of-concept study suggesting that proteomic methods may be useful for the detection of blood proteins that play a role early in the development of type 2 diabetes.…”

Lessons and Applications of Omics Research in Diabetes Epidemiology

“…MS methodologies can be executed in a targeted or non-targeted modality, affording either high specificity in protein identification or simultaneous quantification of multiple analytes, even those present in low concentrations. Nonetheless, MS entails a laborious and temporally extensive workflow, necessitating the depletion of high-abundance plasma proteins, mechanical protein separation, trypsin digestion and subsequent verification via immunoassays or other confirmatory protocols [ 106 , 107 ].…”