Development of Type 2 Diabetes Risk Development of Type 2 Diabetes Risk EngineEngine

Hiroko IshidaHiroko IshidaWellbeing & Project LaboratoryWellbeing & Project Laboratory

ContentsContents

Backgrounds/ PurposeBackgrounds/ Purpose

Materials and Characteristics/ MethodsMaterials and Characteristics/ Methods

Results/ DiscussionResults/ Discussion

Conclusion/ AcknowledgementsConclusion/ Acknowledgements

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)Background (1)

G allele at position 276 genotype and plasma adiponectin G allele at position 276 genotype and plasma adiponectin levels, insulin resistance index and ORlevels, insulin resistance index and OR

(Diabetes 51: 536, 2002)(Diabetes 51: 536, 2002)

Background (2)Background (2)

G TLow BMI T

T

TTG

G

GG

High BMIG

GG

G

GG

GG

TT

G allele at position 276 genotype and plasma adiponectin G allele at position 276 genotype and plasma adiponectin levels, insulin resistance index and ORlevels, insulin resistance index and OR

(Diabetes 51: 536, 2002)(Diabetes 51: 536, 2002)

Background (2)Background (2)

Adiponectin Insulin resistance index Odds ratio

T/T G/T G/G

5

10

15

20(μg

/ml)

T/T G/T G/G T/T G/G

0.4

0.8

1.2

1.6

0 0 0

0.5

1.0

1.5

2.0

Background (3)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: timed: duration of diagnosed Diabetesq: q=f(age)xf(sex)xf(smoke)xf(HBA1c)xf(other)

0 1003015

CHDyear 10

Subjectively thinking of multiple ORSubjectively thinking of multiple OR

Subjective probability (Baize Theorem)

Background (4)Background (4)

Type Type 2 Diabetes2 Diabetes

PositiPositiveve

NegatNegativeive

a a causing causing factorfactor

YesYes aa bb

NoNo cc dd

OddsOdds a/c a/c (PO)(PO)

b/d b/d (NO)(NO)

Odds Odds Ratio (OR)Ratio (OR)

PO/PO/NONO

Age risk ORSex risk ORBMI risk ORAdiponectin risk ORTotal risk OR

Can be applied to cross-sectional research because of just ratio

PurposePurpose

Development of type 2 diabetes risk engine Development of type 2 diabetes risk engine

including risk of Adiponectin including risk of Adiponectin

with cut off value of odds ratio for with cut off value of odds ratio for accurateprediction of high risk group in type 2 diabetes.high risk group in type 2 diabetes.

Materials Materials and Characteristicsand Characteristics

Subject Type For use

Use

Order

Number

Age BMI

Reports in the Japan Diabetes Society 2007

　 　 　 　

Type 2 Diabetic Database 11,791(5

00)56.1±

9.724.6±0.

8

　 Test, Retest2, 4

31259.1±

4.925.8±2.

6

　 Redatabase　

31,893(5

00)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%

Test 5 72662.5±

2.324.4±0.

5

Non-diabetic Test 6 9,23151.4±

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)

Methods Methods Total risk is linear odds ratio of simultaneous and Total risk is linear odds ratio of simultaneous and multiple risk factors: multiple risk factors: Age, Sex, BMI, and AdiponectinAge, Sex, BMI, and Adiponectin

Display range of indicatorsDisplay range of indicators

Database and Display toolDatabase 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.≒

1 <= <=101 <= <=31 <= <=31 <= <=31 <= <=3

Total riskOR(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 risksame

Result (1)Result (1)Estimation of risk engineEstimation of risk engine

SensitiSensitivityvity

(%)(%)

SpecifiSpecificitycity

(%)(%)

Positive Positive

hitting hitting ratioratio

(%)(%)

NegativNegative e

hitting hitting ratioratio

(%)(%)

AccuraAccuracycy

(%)(%)

G/G G/G genotypegenotype

100100 00 8080 00 8080

T/T T/T genotypegenotype

8888 7575 9393 6060 8585

Mean both Mean both genotypegenotype

9494 3838 8686 6060 8383Cf. Sensitivity: 72.3%(by blood glucose levels) , 78.3%(by plasma glucoalbumin )

Result (2-1)Result (2-1)Risk data examples for non-diabetic subjectRisk data examples for non-diabetic subject

Result (2-2)Result (2-2)Risk data example for non-diabetic subjectRisk data example for non-diabetic subject

4.973x1.6=7.957

1x1.6=1.6

→ x1.6

Discussion (1)Discussion (1)

User ankertUser 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.*As an action after using this risk engine, exercise was raised.*All conditions with G/G genotype were estimated as negative, so *All conditions with G/G genotype were estimated as negative, so specificity may be still changed depending on actual data.specificity may be still changed depending on actual data.

Easy inputEasy input or notor not

Good referenceGood reference or notor not

Plain VisualizationPlain Visualization or notor not

Will act by resultWill act by result or or notnot

Discussion (2)Discussion (2)Adiponectin and sex risk(1.6 around and 1.5 around) Adiponectin and sex risk(1.6 around and 1.5 around) have to be evaluated with more actual data because have to be evaluated with more actual data because these values are very high compared with BMI ratio(1.06) these values are very high compared with BMI ratio(1.06) between the reality and ideal(22.9 →21.9) and to clear between the reality and ideal(22.9 →21.9) and to clear these risks must be tough challenge.these risks must be tough challenge.

Adiponectin risk indicator has high detection ratio of Adiponectin risk indicator has high detection ratio of high risk group to type 2 diabetes and it’s considered high risk group to type 2 diabetes and it’s considered impactful for attention about involving type 2 diabetes.impactful for attention about involving type 2 diabetes.

Not only the simplest methods but also another Not only the simplest methods but also another methods should be considered for getting more methods should be considered for getting more information about each contribution of the risk in the information about each contribution of the risk in the cross-sectional or tracking study.cross-sectional or tracking study.

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

ConclusionConclusion

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

AcknowdgementsAcknowdgements

Prof. Dr. Ryozo Nagai, The University of Prof. Dr. Ryozo Nagai, The University of Tokyo HospitalTokyo Hospital 　　 (Top Clinician in Japan as collaborator(Top Clinician in Japan as collaborator ))

Prof. Dr. Takashi Kadowaki, The University Prof. Dr. Takashi Kadowaki, The University of Tokyo Hospital of Tokyo Hospital (Top Clinician in Japan as collaborator(Top Clinician in Japan as collaborator ))

Prof. Dr. Tsutomu Yamazaki, The University Prof. Dr. Tsutomu Yamazaki, The University of Tokyo Hospitalof Tokyo Hospital 　　 (Top Clinician in Japan as collaborator(Top Clinician in Japan as collaborator ))

Dr. Kazuo Hara, The University of Tokyo Dr. Kazuo Hara, The University of Tokyo HospitalHospital 　　 (Top Clinician in Japan as collaborator(Top Clinician in Japan as collaborator ))

Thank youThank youVery muchVery much

For your attentionFor your attention