Variable Odds 95% CI p-value

1 year post diagnosis* 0.73 [0.59, 0.90] 0.00282 years post diagnosis* 0.67 [0.52, 0.85] 0.00123 years post diagnosis* 0.50 [0.38, 0.67] <.00014 years post diagnosis* 0.48 [0.35, 0.65] <.0001Health Region A (FH)þ 0.89 [0.57, 1.37] 0.5950Health Region B (IH)þ 0.81 [0.47, 1.39] 0.4438Health Region C (NH)þ 0.87 [0.49, 1.53] 0.6234Health Region D (VCH)þ 1.25 [0.80, 1.94] 0.3208Gender, female

ˇ

0.78 [0.59, 1.03] 0.0760Age at diagnosis 0.95 [0.90, 1.00] 0.0360MSP Premium Subsidy Status 1.14 [0.86, 1.52] 0.3451

* Reference ¼ year of diagnosis.þ Reference ¼ health region E (VIH).

ˇ

Reference ¼ male.

Abstracts / Can J Diabetes 36 (2012) S24eS76S70

patients newly diagnosed with T2DM in Canada. We useda validated simulation tool, the Economic and Health Outcomes(ECHO)-T2DM model to compare the development of micro- andmacrovascular diabetic complications, mortality and quality of life(QoL) over 25 years between patients who lose 5 percent (%) andmaintain over time their body weight and patients who do not loseweight. Treatment for hyperglycemia, hyperlipidemia and highblood pressure was simulated according to the 2008 CanadianDiabetes Association guidelines. The effect of weight on complica-tions was modeled using the seminal risk equations from the UKProspective Diabetes Study, wherein weight is only the directdeterminant of congestive heart failure (CHF). Weight changeaffects the risks of stroke and myocardial infarction indirectly viatheir linkages with CHF and the risk of mortality indirectly viamacrovascular event history. Weight change was also assumed toaffect patient QoL. Five percent weight loss resulted in improvedhealth outcomes, with the largest benefit observed in CHF (relativerisk reduction of 7.0%). Expected survival increased marginally,while quality adjusted life years increased by 20 days. As thissimulation conservatively excluded a number of possible benefits ofweight loss (e.g., improved lipids, blood pressure and obesity-related illnesses), the true impact of weight loss in themanagementof T2DM is likely greater.

265

The Brain Expresses the Insulin and Ins2+/- Mice DisplayIncreased High-fat Food IntakeARYA E. MEHRAN, XIAOKE HU, G. STEFANO BRIGIDI,BRAD G. HOFFMAN, SHERNAZ X. BAMJI, SUSANNE M. CLEE,JAMES D. JOHNSONVancouver, BC

It is well established that insulin can act in the brain as a satietyfactor. However, the source of insulin acting in the brain has beensubject of much debate. Though, insulin can cross the blood-brainbarrier from the circulation, we confirmed previous reports of localIns2 production in the brain using Ins2-/- negative controls,Ins2bgal knock-in alleles, and a number of complementary tech-niques. Taqman RT-qPCR, deep sequencing, histone methylationenhancer analysis, immunohistochemistry all demonstrated thatIns2, but not Ins1, is expressed in several brain regions, includingthe hippocampus, cerebral cortex, olfactory nucleus and cere-bellum. Since these regions are known to control and project tofeeding, reward and memory centers, we hypothesized that centralIns2 regulates food intake or food preference. Indeed, mice withreduced Ins2 gene dosage (Ins2+/-) exhibited weight gain andincreased food intake on the high-fat diet, but not a control diet. Nosignificant differences were observed in circulating insulin levels,glucose homeostasis, insulin sensitivity, or energy expenditure.These data support a model whereby insulin expressed locally inthe brain, at levels too low to affect circulating insulin, playsa critical role as a diet-specific satiety factor.

243

Adherence to Clinical Practice Guidelines Among Youth withType 2 Diabetes in British Columbia, CanadaSHAZHAN AMED*, BEHRAD BESHARATIAN, KIM REIMER,DIETER AYERS, SEMA AYDEDE, JEAN-PAUL COLLET,KIMBERLEY NUERNBERGERVancouver, BC; Victoria, BC

This study describes adherence to clinical practice guidelines(ACPG) in youth with T2D living in British Columbia, Canada.

All youth diagnosed with T2D at<20 years of age between 1996-2008 were identified within linked health administrative datasetsusing a validated diabetes case-finding algorithm and diabetes-differentiating algorithm. Incident cases were followed for

a variable period of 2-5 years (N¼488 individuals; 2111 person-years). Two measures were developed using Canadian clinicalpractice guidelines: (i) an ACPG score ranging from optimal care¼4,good care¼3, minimal care¼2, and poor care¼1; and (ii) a cate-gorical variable where individuals with optimal or good care wereclassified as being “at goal.” Outcome variables included meanannual ACPG score and proportion of individuals “at goal.”Descriptive statistics and logistic regression were used to describeassociations between key variables and ACPG.

Overall, 68.1% [95% CI: 66.1%-70.1%] of youth with T2D had poorACPG (<2 T2D related physician visits and hemoglobin A1c testsand no screening tests in a 1-yr period). Mean annual ACPG scoredecreased from year of diagnosis to 4-years post-diagnosis (1.85[95% CI: 1.76-1.94] versus 1.49 [95% CI: 1.40-1.59]) and as youthaged from 10-14 to 20-24 years (1.84 [95% CI: 1.74-1.94] versus 1.53[95% CI: 1.46-1.60]). Males (N¼218) had a highermean annual ACPGscore compared to females (N¼270) (1.74 [95% CI: 1.65-1.84] versus1.59 [95% CI: 1.51-1.67]), however, this difference disappeared inthe 20-24 year age group.

This study supports existing evidence of sub-optimal ACPG foryouth-onset T2D and suggests that youth transitioning into adult-hood are a particularly vulnerable population.

244

Impact of the Social Determinants of Health on GlycemicControl in Pediatric Type 1 Diabetes and the Modifying Effect ofInsulin Pump TherapyCAROLINE S. ZUIJDWIJK*, FARID H. MAHMUDToronto, ON

Objective: To evaluate the relationship between glycemic controland the social determinants of health, as measured by deprivationindices (DI), in a large urban pediatric type 1 diabetes (T1D)population.Methods: The INSPQ Material and Social Deprivation Indices andthe ON-Marginalization Ethnic Concentration Index were used toascertain population-level measures of socioeconomic status,family structure, and ethnicity in T1D patients followed at TheHospital for Sick Children August 2010-2011. De-identified patientpostal codes were used to determine dissemination areas, whichwere then linked to the data sets of the DIs. This yielded 3 DIquintile scores for each patient, which were related to ourprimary outcome measure of mean A1C. Mean A1C levels werecompared between the most deprived and the least deprivedquintiles for each DI. These associations were also estimatedcontrolling for age and gender. Finally, the differences in meanA1C between quintiles were assessed for each insulin pumpgroup (on/off).Results: A1C levels were higher in patients with the greatestdegree of deprivation (5th vs. 1st quintile) on all three DIs: Material