Diabetes-related complications: screening and treatment

Transcription

1 Diabetes-related complications: screening and treatment (focus on diabetic nephropathy) Denis Daneman University of Toronto The Hospital for Sick Children

2 Declared Conflicts of Interest Hvidore International Childhood Diabetes Study Group funded by NovoNordisk Pfizer Ltd. UK supplying medications and placebo for AdDIT

3 Pathogenesis of Complications Chronic hyperglycemia Hemodynamic alterations Advanced Glycation End Products (AGEs) Polyol pathway derangements Protein Kinase C (PKC) activation Growth factors and cytokines

4 Metabolic Pathway High Glucose AGEs, polyols, GLUTs, GAG Hemodynamic Pathway Ang ll (RAS), ET, VP, NEP, PG, TXA2 Growth factors and cytokines (GH/IGF-l, TGF-β, CTGF, VEGF, PDGF) Intracellular signalling molecules (PKC-α, PKC-β, NF-κB, MAPK) Structural EC matrix accumulation Functional Albuminuria Reduced GFR DIABETIC NEPHROPATHY Modified from Fukami et al, 2005

5 Metabolic Pathway High Glucose AGEs, polyols, GLUTs, GAG Dyslipidemia Hemodynamic Pathway Ang ll (RAS), ET, VP, NEP, PG, TXA2 Growth factors and cytokines (GH/IGF-l, TGF-β, CTGF, VEGF, PDGF) Intracellular signalling molecules (PKC-α, PKC-β, NF-κB, MAPK) Structural EC matrix accumulation Functional Albuminuria Reduced GFR DIABETIC NEPHROPATHY Modified from Fukami et al, 2005

6 Trajectories of DN AER Macroalbuminuria/ frank proteinuria Microalbuminuria d. persistent c. intermittent Onset First decade Second decade b. a. Duration of Diabetes

7 Risk factors: Modifiers and modifiables*: Early onset and long duration Glycemic control******** - metabolic memory Hypertension*** Family history candidate genes Autonomic neuropathy, retinopathy Smoking** Hyperlipidemia** - IR/Metabolic syndrome Diet* - protein intake, BMI* Exercise** Psychosocial dysfunction* (Cameron et al, Diab Care )

8 Age (years) CDA 2003: Glycemic Targets A1C target children and adolescents Preprandial glucose target (mmol/l) Considerations < 5 9% Extreme caution required to avoid severe hypoglycemia because of the risk of cognitive impairment in this age group % Targets should be graduated to the child s age % 6% Appropriate for most patients Consider for patients in whom it can be achieved safely.

9 Age (years) CDA 2003: Glycemic Targets A1C target children and adolescents Preprandial glucose target (mmol/l) Considerations < 5 9% 2008 <8.5% Extreme caution required to avoid severe hypoglycemia because of the risk of cognitive impairment in this age group Under intense debate: hyper vs hypo!! % Targets should be graduated to the child s age % 6% Appropriate for most patients Consider for patients in whom it can be achieved safely.

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11 Hvidore Study Group 2005: Institutional correlates of outcomes Centre differences significant but weak relationships with: Availability of a 24 hr hotline Clear (and lower) targets for glucose and A1c Frequency of visits with MD (inverse relationship with DNE/MHP) Balanced approach to parental involvement avoidance of extremes of neglect and over-dependence

12 Candidate genes: ACE gene: Boright et al (Diabetes 2005; 54: DCCT cohort) I/I confers lower risk for persisent MA and severe DN; also homozygosity for common TIC haplotype SOD 1/splicing factor serine alanine 15 variants from the DCCT/EDIC cohort 3q locus (3q23-q24) Moczulski (Diabetes 1998; 47:1164-9):?near AT1 (ang II type 1 receptor) Christiskov (DRCP 2004; 66:79-86): confirmed Moczulski Vionnet (Diabetes 2006; 55: ): 3q analysis:?dn associated with a variant in the promoter region of adiponectin gene Osterholm (KI 2007; 71:140-5): 3q likely has locus for DN susceptibility

13 Data from 2007: mass screening FSX (Farsenoid X receptor nuclear hormone receptor superfamily) Jiang (Diabetes 2007, epub): FXR agonists modulate DN in animal model VEGF-A: Lindenmeyer (JASN 2007; 18: ): paradoxical findings ENTPD1: Friedman (Diabetes 2007; 56:2371-9): novel vascular protective factor in glomerular inflammation/thromboregulation Carnosinase (18q); ELMO1(7p14): Iyengar (Sem Nephr 2007; 27:208-22): preliminary data from genome-wide scans in large-scale studies 7q, 10p and 18q: Iyengar (Diabetes 2007;56: ) FIND Study 6p; 7q: Kankova (Diabetologia 2007; 50:990-9): SNP approach TGFB1 type 1 & 2 receptors: McKnight (BMC Med Genet 2007; 23:5) do not influence DN

14 Critical questions: How do we diagnose early complications, esp. diabetic nephropathy? Is early identification of at risk youth possible? Are there effective primary preventions or secondary interventions? Do we blithely follow the adult data? Exciting therapeutic agents in the pipeline

15 How do we diagnose early diabetic nephropathy?: Diagnostic levels of microalbuminuria are: Arbitrary: AER >15/20/30 µg/min; ACR? Based on prediction models in adults c1980s Not based on nondiabetic normal ranges: <7.2 or <7.5 µg/min (= 3 SDS above nondiabetic mean) ORPS database suggests trajectory within normal range

16 Predictive value of MA for development of DN: Early studies

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18 HR of Regression

19 Conclusions1: Microalbuminuria remains best predictor for progression of DN in adults BUT at a lower level Data not shown MA is an independent risk factor for CVD In adults, presence of MA indicates need for therapy (ACEi/ARB +/- statin) Case for intervention during adolescence: Based on limited data and declining rates of MA Extrapolated from adult data

20 ACEi in Adults: A Caveat Fiociello et al (Krolewski s group at Joslin). Determinants of progression from MA to Proteinuria in patients who have T1D and are treated with ACEi. Clin J AM Soc Nephrol 2: , Cohort of 373 with MA during 2 yr baseline fllwd X10 yr Patients treated with ACEi had higher BP, higher UAE, and longer duration of T1D

21 Fiociello2 Progression from MA to Proteinuria common: 6.3/100 person-years Determinants/predictors of progression: Poor glycemic control Elevated serum cholesterol

22 Fiociello3 Person-yr Progress to Prot (n) HbA1c <8.6% Inc rate (/100 pt-yr) P < % >9.6% Cholesterol < >

23 Incidence Rate per 100 p.yr No. of Predictors/determinants: HbA1c & Cholesterol

24 Should we screen for microalbuminuria prepuberty? How frequent is MA prepuberty? Lawson (1996, Toronto); Riihimaa (2000, Finland): Olsen (2004, Denmark): 0 Joner (1992, Norway): only 1/41 < 13 yrs Schultz et al (1999-, ORPS): Used ACR cut-offs of >3.5 & 4 for boys & girls respectively At 5 yr, 63/514 = 12.8% had MA NB: 21 regressed, 22 persisted and 20 no f/up Age at diagnosis: 5-11 appearance of MA after puberty onset <5 6 developed MA prepuberty but 3/3 with f/up regressed More rapid rate of MA increase in puberty cf prepuberty (80 vs 26%)

25 MA prepuberty an-other view Moore & Shield (2000, MIDAC) Used low ACR cut-off (>2) 5/131 (3.8%) of prepubertal children with MA Jones (Liverpool, 1998) Used 2.5 cut-off 13/34 (39%) prepuberty with persistent MA Donaghue (Sydney, 1997) 5/64 (8%) prepubertal children with AER >7.5 µg/min Only 1 >15 Also: Janner (Berne, 1994) Persistent MA linked to early puberty NB: Wessex Diabetes Nephropathy Project (2001) suggested ACR cut-offs of <4.5 and <5.2 mg/mmol Cr in males and females respectively.

26 Effect of pubertal duration on prevalence of MA & kidney volume: (Lawson, Sochett et al, Diabetes 1996) STAGE Prepuberty Pubertal Postpub N Age (yr, SD) * A1c (%) T1DM duration Insulin (u/kg) * MA (%) >15<200 on 2/3 Kidney volume (/BSA) * *

27 MA in Adolescents: % of adolescents with persistent MA >15-20 µg/min after 5-8 yr Older studies: 7-27% Newer studies: 2-4% limited long-term studies - ORPS

28 DN in 27,805 children, adolescents and adults with T1D. Raile et al, Diabetes Care 30: , 2007 By 40 yr duration, 24.5% MA, <10% Macro/ESRD 26,605 = normal 919 = MA 78 = MacroAlbuminuria 203 = ESRD Determinants: Diabetes duration; A1c, LDL cholesterol; BP; male sex Childhood onset = protective young age at diagnosis reduced risk of MA corrected for duration & other independent covariates

29 Summary of 6 studies: Studies 6 % (range) Subjects 1443 MA initial (7-27) Regressors (31-65) Persistents (35-69) Follow-up 3-10 yr

30 Predictors of Regression/ Progression Shield: A1c, AER, age Rudberg: A1c, AER, age Gorman: A1c, AER Bojestig: A1c Schultz: A1c, females, age at diagnosis Steinke: Biopsy: inc GBM width and GFR NB: in these studies, neither disease duration nor clinic BP significantly contributed to MA status at follow-up

31 Does puberty initiate/accelerate DN? White, Krolewski, Kostraba: prepuberty contributes little to diabetes-related complications But many studies do not support this view: some retinopathy, microalbuminuria in prepubertal children? Some changes prepuberty, e.g. kidney size, GFR DCCT data affected by overall duration Important risk factors in adolescence: Deteriorating metabolic control Insulin resistance of puberty Changes in the GH/IGF-l axis

32 Control and Complications: Linear vs. Threshold effect?

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35 ORPS Database: Kaplan-Meier survival estimates for the risk of developing MA based on tertiles of albumin excretion phenotype (at yrs-corrected for gender, age at diagnosis, diabetes duration). RR 1.96 RR 4.03 Dunger et al. Diab Med 24:131-6,2007

36 Kaplan-Meier survival estimates for the risk of developing MA based on average HbA1c levels /> 9%. RR 0.26 Dunger et al. Diab Med 24:131-6,2007

37 Risk of developing MA comparing Group [1] subjects with an albumin excretion phenotype in upper tertile or in the middle tertile with HbA1c >9%: Group [2] - albumin excretion phenotype in the lowest tertile or in the middle tertile with an HbA1c 9%. RR 5.69 Dunger et al. Diab Med 24:131-6,2007

38 Conclusions2: Persistent microalbuminuria in the prepubertal years is sufficiently uncommon and likely to regress thereby making its routine screening costineffective Diabetic nephropathy risk prepuberty most closely linked to metabolic control and genetic risk

39 Conclusions3: From ORPS Prediction models for progression of DN possible using early ACR determinations adjusting for Age, gender, disease duration Glycemic exposure A1c In contrast to adults, few studies of intervention during adolescence DCCT glycemic intervention ACEi Cook (1990), Rudberg (1990), Yuksel (1998)

40 Lessons from the DCCT/EDIC Follow-up of DCCT cohort in EDIC since 1993: Glycemic Control METABOLIC MEMORY Search for complication susceptibility genes Novel analysis of data

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45 Copyright restrictions may apply. JAMA 2003;290: Prevalence and Cumulative Incidence of Microalbuminuria

46 Prevalence and Incidence of Albuminuria JAMA 2003;290: Copyright restrictions may apply.

47 Prevalence of Hypertension at Each Year of the EDIC Study Writing Team for the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Res Group, JAMA 2003;290: Copyright restrictions may apply. JAMA 2003;290:

48 Metabolic Memory The group treated intensively in the DCCT continues to maintain an advantage over the conventional treatment group >8 years later despite similar A1c levels (~8%) Metabolic memory may dissipate with time in adolescent cohort parallel cumulative incidence Concept that long periods of poor control prime the system for ongoing complication risk (Engerman in dogs in 1980s)

49 Metabolic Memory: Progression of incipient diabetic retinopathy during good glycemic control. Engerman and Kern, Diabetes dogs randomly divided into nondiabetic and 3 diabetic groups: poor control for 5 yr good control for 5 yr poor control for 2.5 yr followed by good control for 2.5 yr Retinal lesions developed during 60 mo of poor control but inhibited if good control was begun within 2 mo In group 3, retinopathy was absent/equivocal at 2.5 yr of poor control BUT, was found to develop subsequently despite good glycemic control

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51 Investigational targets for diabetic nephropathy Target 1: insulin resistance: PTP-1B inhibitors GSK-3 inhibitors Target 2: AGE accumulation: AGE formation inhibitors AGE crosslink breakers Soluble RAGE Target 3: Oxidative stress: Polyol pathway inhibitors Antioxidants RAS blockers GLUT inhibitors Target 5: Others: Glycosaminoglycans Vasopressin antagonists HMG-CoA reductase inhibitors Target 4: Fibrogenesis: RAS blockers Endothelin antagonists NEP inhibitors Cyclooxygenase -2 inhibitors Prostacycline analogue TXA-2 antagonists TGF-β antagonists CTGF antagonists VEGF antagonists PDGF antagonists *GH/IGF-1 inhibitors* PKC inhibitors NF-κB inhibitors MAPK inhibitors Modified from Fukami et al, 2005

52 Interventions in Adolescents: Step 1: Improve glycemia From diagnosis: A1c tracking DCCT suggests: Metabolic memory:?application to pubertal group Advantage of intensive insulin therapy Don t forget psychosocial factors! Step 2: If MA persistent/progressive, consider medication therapy ACEi/ARB/combination Addition of HMG Co-A reductase inhibitor may: Decrease MA Lower cardiovascular risk

53 What sort of RCT? Primary prevention: Introduction of renoprotection prior to identification of MA Problem: would need to treat enormous numbers for long time to show benefit in relatively few Secondary intervention: Renoprotection at first sign of risk: pma vs ORPS criteria Need: large scale screening and trajectory to identify at risk Choice of intervention: ACEi/ARB/combo + statin vs other Tertiary intervention: Treat advanced DN: fortunately too few in adolescence

54 AdDIT (UK, Australia, Canada) Secondary intervention: Eligibility according to ORPS criteria Large scale screening and trajectory to identify at risk yr olds treat through puberty?renoprotection Choice of intervention: Treatment Placebo ACEi Placebo ACEi Placebo Placebo Statin Statin

55 What about RASS? Primary prevention: Introduction of renoprotection prior to identification of MA year olds Biopsy pre and 5 yr post RCT Placebo vs ACEi/ARB No histologic advantage from so-called renoprotection at 5 yr (Mauer, ADA 2008)?significance of these results?

56 Take Home Messages Current treatment philosophy in T1D is informed by the outcomes of the DCCT/ EDIC Hyperglycemia is the single most important modifiable risk factor for the onset and/or progression of the micro- and macrovascular complications of T1D starting at disease onset. Tracking A1c from diagnosis most important indicator of risk trajectory But do not forget potentially important role of hyperlipidemia

57 Hypothesis since 1995: GLYCEMIA Obesity GLYCEMIA BP regulation Smoking Lipids/CV risk Diet Obesity/IR/MS Exercise prepuberty puberty GLYCEMIA Systemic Hypertension Smoking Lipids/CV risk factors Diet Obesity/IR/metabolic syndrome Exercise adulthood

58 Accumulating burden Glycemia Good start Risk factors: smoking, obesity, BP, lipids, genes Adolescent amplification Surveillance Psychosocial indicators In childhood, A1c = best indicator of DN risk

59 Acknowledgements SickKids: Etienne Sochett Jenny Cook (Couper) (Adelaide), Margaret Lawson (Ottawa), Beth Cummings (Halifax) Diabetes Team AdDIT: David Dunger (Cambridge), Tim Jones (Perth) Hvidore International Study Group Funders: JDRF/Diabetes UK/CDA/ Mclaughlin Chair