Poster apresentado no Ciência 2016 - Encontro com a Ciência e Tecnologia em Portugal, 4-6 Julho 2016, Centro de Congressos de Lisboa, PortugalN/

Bandarra, Susana

Barahona, Isabel

Mascarenhas, Paulo

Ribeiro, Ana Clara

English

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Printing:This poster is 48” wide by 36” high. It’s designed to be printed on a largeprinter.Customizing the Content:The placeholders in this formatted for you. the placeholders to add text, or click an icon to add a table, chart, SmartArt graphic, picture or multimedia file.Tremove bullet points from text, Hyperglycemia Phenotype as indicated by Salivary Glucose Biomarker Paulo Mascarenhas, Susana Bandarra, Ana Clara Ribeiro   and   Isabel Barahona   Laboratório de Biologia Molecular, Centro de Investigação Interdisciplinar Egas Moniz, CiiEM, Instituto Superior de Ciências da Saúde Egas Moniz, Monte de Caparica, Portugal Background Early screening of type 2 diabetes mellitus (DM), a hyperglycemia phenotype associated condition, is essential for preventive treatment and effective delay of diabetes clinical complications [1]. However, testing for hyperglycemia phenotype (fasting blood glucose > 110 mg/dl) often requires invasive and painful blood testing, limiting its large-scale usefulness. To overcome this constraint salivary glucose assays have recently being applied with the purpose of screening hyperglycemia and type 2 DM [2]. Literature results regarding glycemia and salivary glucose relationship in diabetics or in healthy individuals are controversial. On the other hand, to the best of our knowledge no cross-validation has been done on type 2 DM / hyperglycemia detection models based on salivary glucose. We have performed a systematic review and meta-analysis on previous studies and cross-validated a type 2 DM / hyperglycemia phenotype predictor model based on salivary glucose.   Experimental Methods   • Meta-analysis results show a significant increase of salivary glucose concentration  on type 2 DM individuals (Hedge's g  > 1).   • The fitted models show a good accuracy within the training sample for predicting both hyperglycemia (81,0%) and type 2 DM (84,5%), while in the external data cross-validation the prediction accuracy was lower: hyperglycemia (68,1%) and type 2 DM (65,5%).  Conclusions Acknowledgements         This work was supported by Egas Moniz grant # EMIB 2003-01 (http://www.egasmoniz.com.pt/pt-pt.aspx). We are grateful to Dr. Vagish Kumar, from Yenepoya University - India, and Dr. José Cortelli, from Taubaté University - Brazil, for making available their salivary glucose data. Drª Beatriz Vicoso, from UC Berkeley - USA, is thanked for his many useful discussions and contributions to this work. We conducted a meta-analysis of peer-reviewed published articles that reported data regarding mean salivary glucose levels for type 2 DM and non-diabetic individuals combined with our own results [3]. Furthermore, we used our blood and salivary glucose data from type 2 diabetics and healthy individuals to build type 2 DM / hyperglycemia phenotype logistic prediction models. These models were cross-validated against external data (Brazil and India) and accuracy was evaluated through ROC curve analysis.   Figure 2 - Subgroup forest plot of type 2 DM mean salivary glucose levels studies. Studies have been grouped according to the type 2 DM group allocation: with or without subsets. Hedge's g (standardized mean difference) effect size estimates have been calculated with 95% confidence intervals and are shown in the figure. Area of squares represents sample size, continuous horizontal lines and diamonds width represents 95% confidence interval. Yellow diamonds center indicates the subgroup pooled estimates while the blue diamond center and the vertical red dotted line both point to the overall pooled estimate.   •    Salivary glucose has potential to be used as a hyperglycemia phenotype biomarker combined with other salivary biomarkers and associated with sensitive portable technology.   •    Salivary glucose don't show enough cross-validation performance to be used as a hyperglycemia phenotype global biomarker and therefore predictions should be based on region specific models.  References • [1] Vashist P, Singh S, Gupta N, Saxena R (2011) Role of early screening for diabetic retinopathy in patients with diabetes mellitus: an overview. Indian J Community Med Oct 36(4): 247–52 • [2] Rathnayake N, Akerman S, Klinge B, Lundegren N, Jansson H, et al. (2013) Salivary biomarkers for detection of systemic diseases. PLoS One Apr 24 8(4): e61356 • [3] Mascarenhas P, Fatela B, Barahona I (2014) Effect of Diabetes Mellitus Type 2 on Salivary Glucose – A Systematic Review and Meta-Analysis of Observational Studies. PLoS ONE 9(7): e101706   Figure 1 - Flow of study selection for mean salivary glucose levels. *Studies were excluded unless contained salivary glucose data (means, standard deviations and sample size) obtained from strictly diabetes mellitus type 2 patients and non-diabetic controls unstimulated whole saliva collected after a minimum fast period of 2 hours. Were also excluded if the full-text article were not available and the author(s) failed in sending a copy after contact request or failed in giving back supplementary required data inexistent in the original article. Records containing data already published in other article were also excluded.    Results Table 1 – Predictive logistic models accuracy (%). * Portuguese training dataset; ** External cross-validation dataset (Brazil and India data)    Laboratório Biologia  Molecular 

Hyperglycemia phenotype as indicated by salivary glucose biomarker

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Hyperglycemia phenotype as indicated by salivary glucose biomarker

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