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.  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. Background (1)

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

5.  G allele at position 276 genotype and plasma adiponectin levels, insulin resistance index and OR (Diabetes 51: 536, 2002) (Diabetes 51: 536, 2002) Background (2) Adiponectin Insulin resistance indexOdds ratio T/TG/T G/G 5 10 15 20 (μg/ml) T/TG/T G/G T/T G/G 0.4 0.8 1.2 1.6 000 0.5 1.0 1.5 2.0

6. Background (3)  UKPDS Risk Engine  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) (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) 0 100 3015 CHD year 10

7.  Subjectively thinking of multiple OR  Subjective probability (Baize Theorem) Background (4) Type 2 Diabetes PositiveNegative a causing factor Yesab Nocd Odds a/c (PO) b/d (NO) Odds Ratio (OR) PO/NO Age risk OR Sex risk ORBMI risk ORAdiponectin risk OR Total risk OR 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 with cut off value of odds ratio for accurate high risk group in type 2 diabetes. prediction of high risk group in type 2 diabetes.

9. Materials and Characteristics Subject TypeFor use Use Order NumberAgeBMI Reports in the Japan Diabetes Society 2007 Type 2 DiabeticDatabase11,791(500)56.1±9.724.6±0.8 Test, Retest2, 431259.1±4.925.8±2.6 Redatabase 3 1,893(500)57.0±8.625.6±2.3 Statistical national reports by Ministry of Health, Labor and Welfare 1997 and 2002 Diabetic or higher HbA1c 6.1% Test572662.5±2.324.4±0.5 Non-diabeticTest69,23151.4±1.722.8±0.5 Total: 11,850 subjects *(): for reference Data example (Number: 99, Age:63.1±8.8, Male, BMI:24.3±0.5, Diabetes)

10. 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 Subject 1 ( Age=a 1 ∩ Sex=s 1 ∩ BMI=b 1 ∩ Adiponectin=ad 1 ), total proportional risk to non-diabetic subject 0 is Total risk = OR(Age =a 1 )xOR(Sex =s 1 )xOR(BMI =b 1 )xOR(Adiponectin =ad 1 ) where Each OR’s cut off value = 1.5, and Total risk cut off value ≒ 5. 1 <= <=10 1 <= <=3 Total risk OR(Age) OR(Sex) OR(BMI) OR(Adiponectin) * Saved in Excel file which is read with Ajax * Displayed on the browser by using javascript Negative Positive low risk High risk same

11. Result (1)  Estimation of risk engine Sensitivity(%)Specificity(%)Positive hitting ratio (%)Negative (%)Accuracy(%) G/G genotype 100080080 T/T genotype 8875936085 Mean both genotype 9438866083 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. Result (2-2)  Risk data example for non-diabetic subject 4.973x1.6=7.957 1x1.6=1.6 → x1.6

14. Discussion (1)  User ankert (8 user, Mean age: 24.3, BMI: less than 22, (8 user, Mean age: 24.3, BMI: less than 22, Total risk: less than 5 with G/G genotype ) 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. Easy input or not Good reference or not Plain Visualization or not Will act by result or not

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