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Development of Type 2 Diabetes Risk Engine

Development of Type 2 Diabetes Risk Engine. Hiroko Ishida Wellbeing & Project Laboratory. Contents. Backgrounds/ Purpose Materials and Characteristics/ Methods Results/ Discussion Conclusion/ Acknowledgements. Background (1).

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Development of Type 2 Diabetes Risk Engine

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  1. Development of Type 2 Diabetes Risk Engine Hiroko Ishida Wellbeing & Project Laboratory

  2. Contents • Backgrounds/ Purpose • Materials and Characteristics/ Methods • Results/ Discussion • Conclusion/ Acknowledgements

  3. Background (1) • Type 2 Diabetes, which is one of lifestyle-related diseases associated with both environmental and genetic factors and caused by multiple factors, is recently very focused. • High risk group of diabetic also tend to have heart disease. • For Type 2 Diabetes, early and accurate diagnosis with genetic factors is very important.

  4. G G G G G G G G G G G G G T T T T T T T Low BMI High BMI Background (2) • G allele at position 276 genotype and plasma adiponectin levels, insulin resistance index and OR (Diabetes 51: 536, 2002)

  5. Insulin resistance index Odds ratio Adiponectin (μg/ml) 2.0 20 1.6 1.5 15 1.2 1.0 10 0.8 0.5 5 0.4 0 0 0 G/G G/G G/G T/T G/T T/T T/T G/T Background (2) • G allele at position 276 genotype and plasma adiponectin levels, insulin resistance index and OR (Diabetes 51: 536, 2002)

  6. Background (3) • UKPDS Risk Engine for the risk of CHD in Type II diabetes is an event probability calculated by UKPDS regression equation. (Clinical Science 101: 671, 2001) T,t: time d: duration of diagnosed Diabetes q: q=f(age)xf(sex)xf(smoke)xf(HBA1c)xf(other) 15 30 0 100 CHD year 10

  7. Age risk OR Sex risk OR BMI risk OR Adiponectin risk OR Total risk OR Background (4) • Subjectively thinking of multiple OR • Subjective probability (Baize Theorem) Can be applied to cross-sectional research because of just ratio

  8. Purpose Development of type 2 diabetes risk engine including risk of Adiponectin with cut off value of odds ratio for accurate prediction of high risk group in type 2 diabetes.

  9. Materials and Characteristics Data example (Number: 99, Age:63.1±8.8, Male, BMI:24.3±0.5, Diabetes) Total: 11,850 subjects *(): for reference

  10. 1 <= <=10 1 <= <=3 1 <= <=3 1 <= <=3 1 <= <=3 Total risk OR(Age) OR(Sex) OR(BMI) OR(Adiponectin) High risk same low risk Negative Positive Methods • Total risk is linear odds ratio of simultaneous and multiple risk factors: Age, Sex, BMI, and Adiponectin • Display range of indicators • Database and Display tool For Subject1(Age=a1 ∩ Sex=s1∩ BMI=b1 ∩ Adiponectin=ad1 ), total proportional risk to non-diabetic subject0 is Total risk = OR(Age=a1)xOR(Sex=s1)xOR(BMI=b1)xOR(Adiponectin=ad1) where Each OR’s cut off value = 1.5, and Total risk cut off value ≒ 5. * Saved in Excel file which is read with Ajax * Displayed on the browser by using javascript

  11. Result (1) • Estimation of risk engine Cf. Sensitivity: 72.3%(by blood glucose levels) , 78.3%(by plasma glucoalbumin )

  12. Result (2-1) • Risk data examples for non-diabetic subject

  13. → x1.6 Result (2-2) • Risk data example for non-diabetic subject 4.973x1.6=7.957 1x1.6=1.6

  14. Discussion (1) • User ankert (8 user, Mean age: 24.3, BMI: less than 22, Total risk: less than 5 with G/G genotype ) *As an action after using this risk engine, exercise was raised. *All conditions with G/G genotype were estimated as negative, so specificity may be still changed depending on actual data.

  15. Discussion (2) • Adiponectin and sex risk(1.6 around and 1.5 around) have to be evaluated with more actual data because these values are very high compared with BMI ratio(1.06) between the reality and ideal(22.9 →21.9) and to clear these risks must be tough challenge. • Adiponectin risk indicator has high detection ratio of high risk group to type 2 diabetes and it’s considered impactful for attention about involving type 2 diabetes. • Not only the simplest methods but also another methods should be considered for getting more information about each contribution of the risk in the cross-sectional or tracking study.

  16. Discussion (3) • In the future, it is important and needed that more data of both diabetic and non-diabetic is to collected for more accurate risk and clearer molecule affection style to calculate the risk.

  17. Conclusion • Through this study, Type 2 Diabetes Risk Engine was developed for plain use and evaluated highly for accurate prediction of high risk group in Type 2 Diabetes.

  18. Acknowdgements • Prof. Dr. Ryozo Nagai, The University of Tokyo Hospital　(Top Clinician in Japan as collaborator) • Prof. Dr. Takashi Kadowaki, The University of Tokyo Hospital (Top Clinician in Japan as collaborator) • Prof. Dr. Tsutomu Yamazaki, The University of Tokyo Hospital　(Top Clinician in Japan as collaborator) • Dr. Kazuo Hara, The University of Tokyo Hospital　(Top Clinician in Japan as collaborator)

  19. Thank you Very much For your attention

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