Microsoft Meeting

1. Microsoft Meeting John New Clinical Diabetologist

2. Role of Manchester / Salford • Socially deprived inner city community: • static population 220 000 • 98% white Caucasian • Long term electronic community wide data systems for diabetes since 1992 • Comprehensive EPR all people with CVD, renal, diabetes & stroke Dx (since 1995) • Genetic repository DM, renal disease, dermatology.. • Comprehensive EPR for all residents Sept 2008 • First wave eLab 2008/9 • Wirral (350k) & Stockport (250K)

3. Healthcare & IT in Salford

4. Integrating Care Hospital (2001) • Single EPR Demographics Admissions & procedures Laboratory data Letters • Linked to ONS Office National Statistics Exeter GPs, deaths, postcode

6. Integrating (Diabetes) Care 2004 HospEPR EMIS & Vision GP Master Patient Index EXETER Org Links GP XML (HL7v3) Central Data Repository GP Research & Clinical Audit XML Journal File Optometrist eye screening Community nurses Podiatry Anon Data Repository Web Server Audit Tool Excel Access SPSS Web view of Patient Record Electronic Web Forms

7. Salford Integrated EPR Record (2008) • All Practices in Salford • Integrates information between Hospital / GP • DM, CVS, Renal, Stroke • (all Salford residents 2007/8) • Demographics, Biochem • Observations (BP, BMI, waist circum) • All Drug Prescribing • Co morbidities i.e. psoriasis, CABG, arthritis • All hospital admissions (in and out patient)

12. Using the data • Routinely Collected Data • Hospital & Primary Care data • NHS Number common identifier • Pseudomonised for research dataset • Links to other data sources • i.e. Genetic repository (consented to link to EPR) • ONS, Health Episodes Statistics

13. Salford Integrated EPR Record:DM, kidney disease & anaemia • EPR contains all patients information • In Diabetes anaemia common in kidney disease • Anaemia not part of diabetes management • Used EPR to determine mortality according to kidney function and presence anaemia and diabetes

14. Use EPR: kidney disease & anaemia • Used EPR to • Calculate glomerular filtration rate (GFR) from creatinine, age and sex • Identify all haemoglobin measurements – Anaemia • Identify people with diabetes • All deaths (and NHS numbers to link to ONS mortality) • ONS data to determine cause mortality • Renal Registry data for renal replacement therapy • HES data all admissions to hospital (in & OP) • assess health resource utilisation / Health economics

15. CKD in Salford (All people) Up to date Easy to access data • Initial GFR 2000 • Follow up to August 2006 (mean 4.7 years) • Mortality, Anaemia & Diabetes • 59,915 people (56.4 years) • 7,018 GFR < 60 • 2,951 Anaemic • 6,519 DM • 10,309 died (17.3%) • 334 Renal replacement therapy RRT Large cohort patients Link to any biochem data Successful linking data To ONS, Renal Registry

16. Risk of death or RRT in 6 years

17. Using the Data • Linking routinely collected data to biological repository information • Genetic susceptibility to kidney disease in people with diabetes • Two cohorts identified • Those who develop / don’t develop kidney disease • All had normal GFR, albumin excretion rate 2000 • Those who had abnormal GFR or albuminuria 2004 • Look for susceptibility genes

18. Diabetic Nephropathy Genotype-dependent differences in median & mean serum ACR for 4 reno-protective IGFBP1 SNPs in T2DM Location of candidate SNPs in IGFBP1 gene locus

19. Genotyping of Polymorphic Diabetes/CKD Genes 658 SNPs in 57 candidate genes, in 4 x 384 well plates

20. Using the Data • Amazing that we found anything.... • This analysis was far too simplistic • Ignores • Metabolic control (cholesterol, glucose BP) • Interventions i.e. use ACE inhibition • Need to link genotype to phenotype • Also account for interventions over study duration, many years

21. Diabetic kidney disease • Commonest cause end stage renal disease UK, USA and western Europe • Factors associated with progression • Genetic • Age, duration of diabetes • Glucose control • Blood pressure control • Cholesterol control • Anaemia • Smoking

22. Diabetic kidney disease • Successful interventions to reduce development • Use of ACEI drugs (lower blood pressure) • Use of glucose lowering agents (some better?) • Use of statins to lower cholesterol • Use of antiplatelet drugs

23. Conventional group Aim: to modify CV risk factors to conventional targets: Systolic BP < 160 Diastolic BP < 95 HbA1c < 7.5% Fasting cholesterol 6.4mmol/L Fasting trigs 2.2mmol/L Aspirin for those with known ischaemia Intensive group Aim: to modify CV risk factors to strict targets: Systolic BP < 140 Diastolic BP < 85 HbA1c < 6.5% Fasting cholesterol 4.8 mmol/L Fasting trigs 1.7 mmol/L Aspirin for those with known ischaemia or PVD Automatic treatment with ACE inhibitor Steno-2 Interventions 160 people with Type 2 diabetes & microalbuminuria randomised to Intensive or conventional treatment for 8 years & Followed up for up to 13 years

24. Steno 2

25. Holistic Genetic Analysis / Data mining • Use standard demographics (age/sex/type DM) • Use data relating to metabolic control • Multiple risk factors • Time dependant analysis • Interactions between variables • Use data relating to pharmacological intervention • Use of ACEI / AT2 • Relate to metabolic effect and additional effects • High throughput SNP analysis • Additional genetic datasets (i.e. Renal, dermatology)

26. Holistic Genetic Analysis / Data mining • Identify SNPs associated with disease • Far more powerful to detect real SNPs • Accounts for modifiable risk factors • Identify pharmacological / intermediary metabolism effects • Identify • Holistic ‘real life’ analysis • New computational methods to mine medical datasets • Separate wheat from Chaff • Only possible where DNA, phenotypical, metabolic & pharmocological data aggregated over long time period • Manchester a valuable resource • Microsoft an invaluable partner

27. Thank you Any Questions?