that their cumulative expression profile reflects the specific activity of cells. Prote-
omics are an important tool for understanding the protein expression profile and
thus the diabetic phenotype. Sparre et al.,^163 analyzing proteomic approaches,
concluded that in T1DM,β-cells might be an active participant in their own
destruction and that the cumulative pattern of changes, rather than one protein,
may favor a transition from dynamic stability in the unperturbed-β-cell to dynamic
instability and eventually to cell destruction. Kim et al.^164 used proteomic approach
to analyze proteome of visceral adipose tissue in order to understand the link
between protein profile and early pathogenesis of T2DM. Proteomic researches of
diabetes pathophysiology and the effects of antidiabetic drugs were reviewed by
Sundstena and Ortsa ̈ter.^165 Authors pointed out not just the importance of obser-
vation of variations in protein abundance or localization but also the importance of
design of the protein interaction networks based on proteomic, genomic, and
transcriptomic research. Protein interactivity and stability provide a basis for an
integrated understanding of the pathology of diabetes. The molecular occurrences
based on interactivity (protein interactions with respect to transition, control by the
cell signaling system, and mRNA travel and transition, as well as disturbance of the
cell that causes pathologic changes) are recently presented in an impressive review
by Sarker et al.^166 Detailed network with assigned all protein isoforms, including
posttranslational modifications, is one of the prerequisites for personalized medi-
cine strategies to identify those individuals who are most at risk to develop DM and
its various complications and who are most likely to benefit from a specific
management strategy in order to apply proven measures to prevent or delay
progression to DM and subsequent complications.^167 Discovery of new biomarkers
currently attracts great attention since existing ones fail to offer early diagnose or
predict diabetes and its complications. Different biomarkers for DM were described
in plasma,^168 saliva,^169 urine,^170 and aqueous humor.^171 Using new microfluidic
platform, Mao and Wang^172 demonstrated high-throughput proteomic analysis of
most common biomarkers such as glycated hemoglobin from a drop of blood.
Application of modern MS-based proteomic approach—pressure cycling techno-
logy—sequential window acquisition of all theoretical fragment-ion workflow for
(^163) Sparre et al. ( 2005 ), pp. 441–457.
(^164) Kim et al. (2014b), pp. 811–822.
(^165) Sundstena and Ortsa ̈ter ( 2009 ), pp. 93–103.
(^166) Sarker et al. ( 2014 ), pp. 2–7.
(^167) Glauber et al. ( 2014 ), p. e0002.
(^168) Zhi et al. ( 2011 ), pp. 1–10; Kaur et al. ( 2012 ), pp. 5527–5539.
(^169) Cabras et al. ( 2010 ), pp. 2099–2108.
(^170) Caseiro et al. ( 2013 ), pp. 188–199.
(^171) Chiang et al. ( 2012 ), pp. 2950–2959.
(^172) Mao and Wang ( 2014 ), pp. 1560–1569.
206 D. Josic ́and U. Andjelkovic ́