Poster presented at the First International Congress of CiiEM  - From Basic Sciences To Clinical Research. Egas Moniz, Caparica, Portugal, 27-28 November 2015.This work was supported by Egas Moniz grant # EMIB 2003-0

Barahona, Isabel

Mascarenhas, Paulo

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Meta-analysis and Cross-validation of a Hyperglycemic Status Predictor Model based on Salivary GlucosePaulo Mascarenhas and Isabel BarahonaLaboratório de Biologia Molecular, Centro de Investigação Interdisciplinar Egas Moniz, Instituto Superior de Ciências da Saúde Egas Moniz, Monte de Caparica, PortugalReviewEarly screening of type 2 diabetes mellitus (DM), a hyperglycemic status associated condition, is essential for preventive treatment and effective delay ofdiabetes clinical complications [1]. However, testing for hyperglycemic status often requires invasive and painful blood testing, limiting its large-scaleusefulness. To overcome this constraint salivary glucose assays have recently being studied with the purpose of screening hyperglycemia and type 2 DM[2]. Literature results regarding blood and salivary glucose relationship in diabetics or in healthy individuals are controversial. On the other hand, to thebest of our knowledge no validation has been done on type 2 DM / hyperglycaemia detection models based on salivary glucose. We have performed asystematic review and meta-analysis on those studies and cross-validated a type 2 DM / hyperglycemic status predictor model based on salivary glucose.Experimental Methods• On our global meta-analysis of standardized mean differences onsalivary glucose levels results show an overall large positive effect oftype 2 DM over salivary glucose (Hedge's g  > 1).• The logistic models showed a good accuracy on our sample predictingboth hyperglycemia (81,0%) and type 2 DM (84,5%), while in theexternal data cross-validation accuracy was lower (65,5% and 69,0%,respectively). While our prediction models are well fitted for ourPortuguese sample, cross-validation results using both Brazilian andIndian data showed a significant decrease on accuracy, suggesting thathyperglycemia status / type 2 DM prediction from salivary glucoseshould only be based on region specific models.ConclusionAcknowledgementsThis 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 theirsalivary glucose data. Drª Beatriz Vicoso, from UC Berkeley - USA, is thanked for his many useful discussions and contributionsto this work.We conducted a meta-analysis ofpeer-reviewed published articlesthat reported data regarding meansalivary glucose levels for type 2DM and non-diabetic individualscombined with our own results [3].Furthermore, we used our bloodand salivary glucose data fromtype 2 diabetics and healthyindividuals to build type 2 DM /hyperglycemia status predictionmodels. These logistic modelswere cross-validated againstexternal data (Brazil and India) andaccuracy was evaluated throughROC curve analysis.p2 + 2pq + q2 = 1Figure 2 - Subgroup forest plot of type 2 DM mean salivary glucose levels studies. Studies have been grouped according to the type 2 DM groupallocation: with or without subsets. Hedge's g (standardized mean difference) effect size estimates have been calculated with 95% confidenceintervals 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 lineboth point to the overall pooled estimate.Our results show that salivary glucose has potential to be used as type 2 DM biomarker. If associated with the development of sensitive portabletechnology to measure salivary glucose this will allow a less painful and invasive method for type 2 DM screening in large cohorts. Even if at present,due to several limitations, salivary glucose per se may not show enough cross-validation performance to be used as an global and autonomous DM type2 biomarker, our results suggest that it provides valuable information, and in the future may be combined with other salivary biomarkers to create aneffective high sensitivity/specificity DM type 2 large-scale screening system.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): e101706LaboratórioBiologia  MolecularFigure 1 - Flow of study selection for mean salivary glucose levels. *Studies were excluded unless contained salivary glucose data (means, standarddeviations and sample size) obtained from strictly diabetes mellitus type 2 patients and non-diabetic controls unstimulated whole saliva collected aftera 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 contactrequest or failed in giving back supplementary required data inexistent in the original article. Records containing data already published in otherarticle were also excluded.ResultsFigure 3 – A: ROC curve for hyperglycemia status prediction with a logistic model based on salivary glucose trained with the Portuguese sample set; B: ROC curve for type 2 DM status prediction with a logistic model based on salivary glucose trained with the Portuguese sample set.A B

Meta-analysis and cross-validation of a hyperglycemic status predictor model based on salivary glucose

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Meta-analysis and cross-validation of a hyperglycemic status predictor model based on salivary glucose

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