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. 2015 May 14:10:39-45.
doi: 10.4137/BMI.S22177. eCollection 2015.

Literature-based discovery of salivary biomarkers for type 2 diabetes mellitus

Affiliations

Literature-based discovery of salivary biomarkers for type 2 diabetes mellitus

Mythily Srinivasan et al. Biomark Insights. .

Abstract

The alarming increase in type 2 diabetes mellitus (T2DM) underscores the need for efficient screening and preventive strategies. Select protein biomarker profiles emerge over time during T2DM development. Periodic evaluation of these markers will increase the predictive ability of diabetes risk scores. Noninvasive methods for frequent measurements of biomarkers are increasingly being investigated. Application of salivary diagnostics has gained importance with the establishment of significant similarities between the salivary and serum proteomes. The objective of this study is to identify T2DM-specific salivary biomarkers by literature-based discovery. A serial interrogation of the PubMed database was performed using MeSH terms of specific T2DM pathological processes in primary and secondary iterations to compile cohorts of T2DM-specific serum markers. Subsequent search consisted of mining for the identified serum markers in human saliva. More than 60% of T2DM-associated serum proteins have been measured in saliva. Nearly half of these proteins have been reported in diabetic saliva. Measurements of salivary lipids and oxidative stress markers that can exhibit correlated saliva plasma ratio could constitute reliable factors for T2DM risk assessment. We conclude that a high percentage of T2DM-associated serum proteins can be measured in saliva, which offers an attractive and economical strategy for T2DM screening.

Keywords: circulating biomarkers; diabetes mellitus; literature-based search; salivary proteins.

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Figures

Figure 1
Figure 1
T2DM-specific circulating protein markers. (A) Literature-based search of the PubMed database for identifying T2DM-specific serum proteins using the MeSH terms insulin resistance (P1) or glucose intolerance (P2), or insulin secreting cells (P3) and each of the 10 secondary sets is shown. [S1: lipids/blood, S2: obesity, S3: adipokines, S4: antibodies and globulins, S5: glycoproteins, S6: blood, coagulation factors, S7: inflammation mediators, S8: oxidative stress, S9: endothelium, and S10: iron-binding proteins]. The S1–10 × (P1 + P2 + P3) constitute the 10 common sets C1–C10. (B) Flow diagram of the search strategy for identification of T2DM-associated circulating protein biomarkers reported in saliva. The PubMed database was searched for saliva (MeSH) and blood proteins, the cohort being filtered for humans and English language. The articles in the retrieved cohort were searched for circulating biomarkers identified in (A) and manually screened for quantitation in saliva. The publication cohort of T2DM-specific circulating protein biomarkers reported in saliva was further divided into two groups based on articles reporting quantitation in T2DM saliva (diabetic saliva) or other conditions (nondiabetic saliva).
Figure 1
Figure 1
T2DM-specific circulating protein markers. (A) Literature-based search of the PubMed database for identifying T2DM-specific serum proteins using the MeSH terms insulin resistance (P1) or glucose intolerance (P2), or insulin secreting cells (P3) and each of the 10 secondary sets is shown. [S1: lipids/blood, S2: obesity, S3: adipokines, S4: antibodies and globulins, S5: glycoproteins, S6: blood, coagulation factors, S7: inflammation mediators, S8: oxidative stress, S9: endothelium, and S10: iron-binding proteins]. The S1–10 × (P1 + P2 + P3) constitute the 10 common sets C1–C10. (B) Flow diagram of the search strategy for identification of T2DM-associated circulating protein biomarkers reported in saliva. The PubMed database was searched for saliva (MeSH) and blood proteins, the cohort being filtered for humans and English language. The articles in the retrieved cohort were searched for circulating biomarkers identified in (A) and manually screened for quantitation in saliva. The publication cohort of T2DM-specific circulating protein biomarkers reported in saliva was further divided into two groups based on articles reporting quantitation in T2DM saliva (diabetic saliva) or other conditions (nondiabetic saliva).
Figure 2
Figure 2
(A) Functional classification of the T2DM-associated circulating protein markers reported in the PubMed database. The T2DM-specific circulating proteins identified as in Figure 1A were classified into six functional groups. Group I: lipids and enzymes, Group II: inflammatory mediators, Group III: adipokines, Group IV: vascular and coagulation factors, Group V: gut hormones and glucose homeostasis, and Group VI: oxidative stress. (A) shows the total number of markers reported in each group. (B, C) Classification of T2DM specific circulating protein markers reported in saliva. The PubMed database was searched for reports of blood proteins (see text) in saliva (MeSH) and filtered for humans and English language. After excluding review articles, the retrieved cohort was screened for each of the 214 T2DM-associated circulating markers. Each marker was counted once irrespective of the number of publications. (B) shows the percentage of T2DM markers reported to be measured in human saliva in each group. The publication cohort of T2DM-specific serum proteins reported in saliva was further divided into two groups based on measurement in T2DM saliva (diabetic saliva or DS) or other condition (nondiabetic saliva or NDS). (C) shows the number of markers of each group reported to be measured in DS and NDS.

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References

    1. Wild S, Roglic G, Green A, Sicree R, King H. Global prevalence of diabetes: estimates for the year 2000 and projections for 2030. Diabetes Care. 2004;27:1047–53. - PubMed
    1. Tuomilehto J, Lindstrom J, Hellmich M, et al. Development and validation of a risk-score model for subjects with impaired glucose tolerance for the assessment of the risk of type 2 diabetes mellitus-The STOP-NIDDM risk-score. Diabetes Res Clin Pract. 2010;87:267–74. - PubMed
    1. Kolberg JA, Jorgensen T, Gerwien RW, et al. Development of a type 2 diabetes risk model from a panel of serum biomarkers from the Inter99 cohort. Diabetes Care. 2009;32:1207–12. - PMC - PubMed
    1. Mook-Kanamori DO, El-Din Selim MM, Takiddin AH, et al. 1,5-anhydrog-lucitol in saliva is a non-invasive marker of short-term glycemic control. J Clin Endocrinol Metab. 2014;99(3):E479–83. - PubMed
    1. Carstensen M, Herder C, Kivimaki M, et al. Accelerated increase in serum interleukin-1 receptor antagonist starts 6 years before diagnosis of type 2 diabetes: Whitehall II prospective cohort study. Diabetes. 2010;59:1222–7. - PMC - PubMed

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