Search

OBJECTIVECurrent guidelines recommend biennial diabetic retinopathy (DR) screening commencing at the age of 11 years and after 2–5 years’ duration of type 1 diabetes. Growing evidence suggests less frequent screening may be feasible.RESEARCH DESIGN AND METHODSProspective data were collected from 2,063 youth with type 1 diabetes who were screened two or more times between 1990 and 2019. Baseline (mean ± SD) age was 13.3 ± 1.8 years, HbA1c was 8.6 ± 1.3% (70.1 ± 14.7 mmol/mol), diabetes duration was 5.6 ± 2.8 years, and follow-up time was 4.8 ± 2.8 years. DR was manually graded from 7-field retinal photographs using the Early Treatment Diabetic Retinopathy Study (ETDRS) scale. Markov chain was used to calculate probabilities of DR change over time and hazard ratio (HR) of DR stage transition.RESULTSThe incidence of moderate nonproliferative DR (MNPDR) or worse was 8.6 per 1,000 patient-years. Probabilities of transition to this state after a 3-year interval were from no DR, 1.3%; from minimal DR, 5.1%; and from mild DR, 22.2%, respectively. HRs (95% CIs) for transition per 1% current HbA1c increase were 1.23 (1.16–1.31) from no DR to minimal NPDR, 1.12 (1.03–1.23) from minimal to mild NPDR, and 1.28 (1.13–1.46) from mild to MNPDR or worse. HbA1c alone explained 27% of the transitions between no retinopathy and MNPDR or worse. The addition of diabetes duration into the model increased this value to 31% (P = 0.03). Risk was also increased by female sex and higher attained age.CONCLUSIONSThese results support less frequent DR screening in youth with type 1 diabetes without DR and short duration. Although DR progression to advanced stages is generally slow, higher HbA1c greatly accelerates it.

Background: Current guidelines recommend biennial diabetic retinopathy (DR) screening commencing at age of 11years and after 2-5 years duration of type 1 diabetes. Growing evidence suggests less frequent screening may be feasible. Methods: Prospective data were collected from 2,063 youth with type 1 diabetes who were screened ≥2 times between 1990-2019. Baseline (mean±SD) age was 13.3±1.8 years, HbA1c 8.6±1.3% (70.1±14.7 mmol/mol), diabetes duration 5.6±2.8 years and follow-up time 4.8±2.8 years. DR was manually graded from 7-field retinal photographs using the ETDRS scale. Markov chain was used to calculate probabilities of DR change over time and hazard ratio (HR) of DR stage transition. Results: The incidence of moderate non-proliferative DR (MNPDR) or worse was 8.6 per 1000 patient years. Probabilities of transition to this state after 3- years interval were: from no-DR 1.3%; from minimal DR 5.1%; from mild DR 22.2% respectively. HRs (95%CI) for transition per 1% current HbA1c increase were: 1.23 (1.16-1.31) from no-DR to minimal NPDR, 1.12 (1.03-1.23) from minimal to mild NPDR, 1.28 (1.13-1.46) from mild to MNPDR or worse. HbA1c alone explained 27% of the transitions between no-retinopathy and MNPDR or worse. The addition of diabetes duration into the model increased this value to 31% (p=0.03). Risk was also increased by female sex, and higher attained age. Conclusions: These results support less frequent DR screening in youth with type 1 diabetes without DR and short duration. Although DR progression to advanced stages is generally slow, higher HbA1c greatly accelerates it.

Background: Mitochondrial DNA (Mt-DNA) copy number (mtDNA-CN) has been proposed as a marker of diabetic kidney disease (DKD) . Most studies are in type 2 diabetes. We determined mtDNA-CN in adults with type 1 diabetes vs. non-diabetic subjects, including associations with complications and risk factors. Methods: Cross-sectional study: 178 T1D adults (mean (SD) age: 38 (14) yrs, 21 (14) yrs T1D, HbA1c: 8.0 (1.5) %, 36% with complications (CX+)) ; 132 non-diabetic adults (CON) (age: 37 (14) yrs, HbA1c: 5.1 (0.4) %) . mtDNA-CN by RT-PCR (ScienCell, Carlsbad, CA) in DNA from whole blood samples. Results: T1D vs. CON: Median (LQ, UQ) mtDNA-CN in T1D vs. CON: 270.8 (189.4, 348.1) vs. 320.5 (264.4, 410.5) ; p Conclusion: T1D per se is associated with lower mtDNA-CN than CON, particularly with DKD. Higher mtDNA-CN in T1D is associated with detectable C-peptide. Age and inflammation related factors are associated with mtDNA-CN. Longitudinal and intervention studies are merited. Disclosure A.S.Januszewski: None. L.Carroll: None. Y.W.Loh: None. D.N.O'neal: None. A.Jenkins: Advisory Panel; Abbott Diabetes, Amgen Inc., Board Member; Insulin for Life , International Diabetes Federation Western Pacific Region, Research Support; JDRF, Medtronic, Mylan N.V., NHMRC Australia.

Introduction: Excess adipose tissue, esp. with the metabolic syndrome (MetS) promotes development of type 2 diabetes (T2D) and cardiovascular disease (CVD). A major feature of MetS, reduced insulin sensitivity (IS), is associated with increased hepatic lipogenesis, a marker of which is a low SHBG. Features of T2D are increasingly common in T1D. Aim: In T1D adults to determine if SHBG is associated with surrogate measures of IS and MetS. Methods: Cross-sectional analysis of sera from 279 people (Table). Of those with T1D 41 had microvascular complications. SHBG was quantified by chemiluminescent immunoassay (Roche Diagnostics, Mannheim, Germany). IS in T1D was assessed by estimation of M/I by modified published formulae using sex, HDL-C, pulse pressure, T1D duration, waist/height, WHR, BMI and HbA1c. Results: SHBG levels were higher in T1D than in CON and also higher in F vs. M. SHBG levels correlated with age in CON and T1D. M/I in tertile 3 (sex and T1D adjusted) of SHBG was significantly higher vs. tertiles 1 and 2 (in CON and T1D). In T1D SHBG (age and sex adjusted) decreased with increasing number of MetS features. ROC curve for MetS diagnosis in T1D using SHBG had an AUC of 0.73. Conclusion: Taken together these data show that IS and MetS are associated with lower SHBG levels in T1D. We propose a role for SHBG in personalising insulin sensitiser treatment in T1D. Disclosure A. S. Januszewski: None. W. Rankin: None. D. N. O9neal: Advisory Panel; Self; Abbott Diabetes, Medtronic, Sanofi, Research Support; Self; Dexcom, Inc., GlySens Incorporated, Medtronic, Pacific Diabetes Technologies. G. A. Wittert: Advisory Panel; Self; Bayer AG, Research Support; Self; Bayer AG, Speaker9s Bureau; Self; Bayer AG. A. Jenkins: Advisory Panel; Self; Abbott Diabetes, Sanofi-Aventis, Other Relationship; Self; Amgen Inc., Research Support; Self; Abbott, International Diabetes Federation, JDRF, Medtronic, Mylan N. V.

Introduction: The “gold standard” measure of insulin sensitivity (IS), a euglycemic hyperinsulinemic clamp, is costly, time- and labour-intensive. Several formulae, developed using clamp data estimate insulin sensitivity, expressed as estimated glucose disposal rate (eGDR) or insulin sensitivity index (eM/I or elog10M/I). Due to clamp complexity and cost these formulae often lacks independent validation. Aim: To validate several formulae estimating IS using independent euglycaemic hyperinsulinemic clamp data. Methods: Euglycemic, hyperinsulinemic clamps were performed in 108 T1D adults (age (mean±SD) 34±7 yrs, T1D duration 10±4 yrs, HbA1c 7.7±1.5%; 33 with microvascular complications). Measured GDR (last 30min of the clamp) ranged 0.5 - 9.6 mg/kg/min (median (LQ, UQ) 4.45 (3.04, 6.5 mg/kg/min) were compared with eGDR and eLog10M/I calculated using simple formulae by (1) Williams, (2) Zheng, (3) Dabelea and (4) and (5) (Australia (AU)) authors, all derived from routine clinical chemistry and demographics. eGDR formula (Duca) was not assessed as it includes adiponectin levels. Results: Results are in Table 1. Conclusion: Authors (AU) formulae to estimate IS, including age, sex, HDL-C, BMI, HbA1c, pulse pressure and WHR had highest correlation with measured GDR and better performance (AUROC) in detecting low IS than other formulae. Disclosure A. S. Januszewski: None. P. Niedzwiecki: None. N. Sachithanandan: None. G. M. Ward: None. C. Karschimkus: None. D. N. O’neal: Advisory Panel; Self; Abbott Diabetes, Medtronic, Sanofi, Research Support; Self; Dexcom, Inc., GlySens Incorporated, Medtronic, Pacific Diabetes Technologies. D. Zozulinska-ziolkiewicz: None. A. Uruska: None. A. Jenkins: Advisory Panel; Self; Abbott Diabetes, Sanofi-Aventis, Other Relationship; Self; Amgen Inc., Research Support; Self; Abbott, International Diabetes Federation, JDRF, Medtronic, Mylan N. V.

Introduction: Current ISPAD guidelines recommend biennial diabetic retinopathy (DR) screening starting at age 11 yrs with type 1 diabetes (T1D) duration 2-5 yrs. Growing evidence suggesting less frequent screening. Methods: DR was ETDRS graded and progress assed by Markov chain model analysis. Retinal photos were from 2,169 patients (baseline (mean±SD): age 13.1±2.1 yrs, HbA1c 8.5±1.4%, 5.4±2.9 yrs T1D, follow-up 5.3±3.6 yrs) who attended the T1D clinic at least twice in 1990-2018. Results: In 11/2,169 patients DR progressed to severe non-proliferative (SNP) DR or above. See tables 1 and 2 below for detailed results. Conclusions: Our data support less frequent DR screening in T1D youth without any DR. Although DR progression is generally slow, poor glycaemic control greatly accelerates it. Disclosure A.S. Januszewski: None. V. Velayutham: None. P.Z. Benitez-Aguirre: None. M.E. Craig: None. G. Liew: None. Y. Cho: None. A. Jenkins: Advisory Panel; Self; Abbott, Medtronic. Research Support; Self; Abbott, GlySens Incorporated, Medtronic, Sanofi-Aventis. K.C. Donaghue: None.

Aim: Current guidelines recommend screening for retinopathy from 11 years in youth with type 1 diabetes (T1D) and 2-5 years T1D duration. We examined the longitudinal progression and regression of retinopathy in adolescents seen at our Diabetes Complications assessment service (DCAS) clinic. Methods: We analyzed digitized retinal photographs taken from 2000-2018 (n=814) in youth with >3 assessments. Retinopathy was graded using the modified Airlie house classification. Generalized estimating equations were used to explore the association between progression/regression and covariates (HbA1c, gender, and diabetes duration). Results: Mean age at baseline was 13.3+2.6 years, mean T1D duration 6.1+>3.4 years and mean follow- up 7.4 years. Retinopathy was present in 18.7% (n=152) at baseline assessment; with progression in 32.6% (n=266), regression in 7% (n=57) and persistence in 60.3% (n=491). Of those with progression, 185/266 (22.7%) had one step and 81 (9.9%) had > 2 step progression. Factors associated with retinopathy progression were HbA1c and T1D duration and for regression T1D duration (Table 1). Neither height nor SBP was significant for other outcomes. Conclusion: In adolescents with T1D, retinopathy persistence or progression was greater than of regression. Follow-up of into adulthood will inform the development of appropriate retinopathy screening schedules. Disclosure V. Velayutham: None. G. Liew: None. A.S. Januszewski: None. Y. Cho: None. P.Z. Benitez-Aguirre: None. M.E. Craig: None. A. Jenkins: Advisory Panel; Self; Abbott, Australian Diabetes Society, Medtronic. Research Support; Self; Abbott, GlySens Incorporated, Medtronic, Mylan. Speaker's Bureau; Self; Eli Lilly and Company, Novo Nordisk Inc. K.C. Donaghue: None.

Background: Haptoglobin (Hp) is a plasma protein with anti-oxidant, anti-inflammatory and pro-angiogenic properties. Based on the Hp gene alleles there are three Hp phenotypes (1-1, 2-1 and 2-2). In diabetes, Hp-2 is associated with substantially increased risk of cardiac and renal damage relative to Hp-1. AIMS: To evaluate 1) associations between Hp phenotype and levels; and 2) effects of fenofibrate vs. placebo on Hp levels. Methods: A substudy of 180 adults with type 2 diabetes (T2D) (50% females) from the FIELD trial. Average age was 60±6 years, BMI 30.4±5.1 kg/m2 and HbA1c 6.8±1.3%. Plasma (citrate) samples were evaluated at baseline, after 6-weeks daily 200 mg co-micronised fenofibrate (end of active run-in) and 2-years after randomisation to fenofibrate or placebo. Results: Hp phenotype 1-1 was detected in 19%, Hp 2-1 in 48% and Hp 2-2 in 33% subjects. Hp levels at baseline were 1.44±1.10, 1.31±0.49 and 0.99±0.46 mg/mL in type 1-1, 2-1 and 2-2 respectively (all p Conclusions: Hp levels differ by Hp phenotype in T2D adults. Fenofibrate lowers Hp levels in all phenotypes, however the effect is significantly attenuated in Hp 2-2. Disclosure A.S. Januszewski: None. H.K. Young: Other Relationship; Self; Medtronic. L. Li: None. R.L. OConnell: None. T. Lyons: None. C.B. Kelly: None. D. Sullivan: None. R. Scott: None. A.C. Keech: None. A. Jenkins: Advisory Panel; Self; Abbott, Australian Diabetes Society, Medtronic. Research Support; Self; Abbott, GlySens Incorporated, Medtronic, Mylan. Speaker’s Bureau; Self; Eli Lilly and Company, Novo Nordisk Inc. Funding Sydney Medical School Foundation; National Health and Medical Research Council of Australia

Background: Fenofibrate increases renal clearance of uric acid (UA). Relationships between baseline and short-term changes in plasma UA with fenofibrate and subsequent cardiovascular risk in patients with type 2 diabetes (T2D) are unknown. Method: Post hoc analyses of the FIELD trial explored the relationships between CVD events and 1) baseline UA level; and 2) short-term change in UA level over 6-week single-blind active fenofibrate run-in. Results: The FIELD trial showed 11% fewer cardiovascular events in patients with T2D assigned to fenofibrate compared to placebo over 5 years (HR = 0.89, 95% CI: 0.80-0.99, p = 0.035). Baseline UA (n=9622) ranged between 0.11 to 0.79 mmol/L (mean = 0.33, SD = 0.078). Each 0.1 mmol/L higher baseline UA increased CVD events by 21% (HR = 1.21 95% CI: 1.13-1.29, p < 0.001; Fig. 1A), and remained after adjusting for other classic risk factors (HR = 1.12, 95% CI: 1.04-1.21, p = 0.002). Fall in UA level with fenofibrate run-in also predicted lower CVD risk, adjusted for baseline UA and irrespective of trial treatment allocation (HR = 0.86, 95% CI: 0.76-0.97, p = 0.015; Fig. 1B). Discussion: Baseline and short-term fenofibrate-induced lowering of UA predict cardiovascular risk in patients with T2D, irrespective of long-term trial treatment allocation. The reduction in total CVD events with fenofibrate was not shown to be mediated through lowering of UA levels. Disclosure J.Y. Cao: None. B. Waldman: None. R.L. OConnell: None. D. Sullivan: None. V. Gebski: None. A.S. Januszewski: None. I. Marschner: None. R. Scott: None. M. Taskinen: Advisory Panel; Self; Novo Nordisk A/S. Consultant; Self; Akcea Therapeutics, Chiesi USA, Inc. Research Support; Self; Amgen Inc., Novo Nordisk A/S. Speaker’s Bureau; Self; Amgen Inc. R. Simes: None. N. McGill: None. A.J. Jenkins: Advisory Panel; Self; Abbott, Australian Diabetes Society, Medtronic. Research Support; Self; Abbott, GlySens Incorporated, Medtronic, Mylan. Speaker’s Bureau; Self; Eli Lilly and Company, Novo Nordisk Inc. A.C. Keech: None.

Background: In the FIELD trial, a 5-year randomized double-blind placebo-controlled trial of fenofibrate vs. placebo in 9,795 adults with type 2 diabetes (T2D), the only standard lipid parameter correlating with microvascular (renal) events was triglycerides. Given the high prevalence of diabetic microangiopathy, we explored associations between lipoprotein subfractions and nephropathy, retinopathy, microvascular amputations (MiAmp) and neuropathy. Methods: We performed ultracentrifugation using the vertical autoprofile (VAP, Atherotech) on plasma (baseline and after 6 weeks of fenofibrate). Analyses were performed using Cox proportional hazards and logistic regression for new on-study events. Results were adjusted for gender and fenofibrate or placebo allocation. Results: HDL related analytes (HDL-C, HDL3-C, apo A1, apoA2) correlated with reduced risk for neuropathy, nephropathy, and MiAmp. Buoyant LDL (LDL1-C) and densest LDL (LDL4-C) correlated negatively with neuropathy and positively for MiAmp, respectively. Triglycerides and apo C3 increased neuropathy and nephropathy risk. VLDL species correlated with increased MiAmp and retinopathy risk. Conclusions: VAP identifies multiple lipoprotein subclasses and VAP subclass/apoprotein ratios associated with diabetic microangiopathy. Many were improved with fenofibrate. Disclosure P.P. Toth: Consultant; Self; Amarin Corporation, Amgen Inc., AstraZeneca, Kowa Pharmaceutical Europe Co. Ltd., Novo Nordisk Inc. Speaker’s Bureau; Self; Amarin Corporation, Amgen Inc., Merck & Co., Inc., Novo Nordisk Inc., Regeneron Pharmaceuticals, Sanofi US. A.C. Keech: None. A.S. Januszewski: None. R.L. OConnell: None. L. Li: None. D. Sullivan: None. M. Taskinen: Advisory Panel; Self; Novo Nordisk A/S. Consultant; Self; Akcea Therapeutics, Chiesi USA, Inc. Research Support; Self; Amgen Inc., Novo Nordisk A/S. Speaker’s Bureau; Self; Amgen Inc. P.L. Drury: None. G.F. Watts: None. J.D. Best: Consultant; Self; Abbott. J. Ansquer: Research Support; Self; Abbott, Mylan. S.R. Jones: None. A.J. Jenkins: Advisory Panel; Self; Abbott, Australian Diabetes Society, Medtronic. Research Support; Self; Abbott, GlySens Incorporated, Medtronic, Mylan. Speaker’s Bureau; Self; Eli Lilly and Company, Novo Nordisk Inc.

Background: Glucose variability (GV) may be reflected by variation in either the short-term glucose levels (assessed by continuous glucose monitoring (CGM)) or the long-term by HbA1c or intermediate by 1,5-anhydroglucitol (1,5-AG). In patients with T1DM management by continuous subcutaneous insulin infusion (CSII) is usually associated with improved glycaemia compared to multiple daily insulin injections (MDI). Serum 1,5-anhydroglucitol (1,5-AG) correlates with GV on CGM in adult and pediatric T1D groups. It is not known whether CSII therapy without CGM improves intermediate GV. Methods: Blood was obtained from children (mean age 14.1 +/- 1.3 years) within 3-months of T1D diagnosis. Children were treated with CSII (n=12) or MDI (n=15) and followed for a further 1 to 3 time points (median, LQ-UQ 535, 519-563 days follow-up). GV was assessed measuring 1,5-anhydroglucitol (1,5-AG) (GlycoMark, Inc., Winston-Salem, NC). The mean difference, standard deviation (SD) and coefficient of variation (CV) of 1,5-AG and HbA1c was compared between treatment arms. Results: There was no difference between treatment modalities in baseline HbA1c (median, LQ-UQ 11.8, 10.2-13.5 % (105, 87-124 mmol/mol) vs. 12.6, 10.3-14.0 % (114, 89-130 mmol/mol) CSII vs. MDI) nor baseline 1,5-AG (median, LQ-UQ 2.8, 1.8-4.0 μg/mL vs. 3.6, 2.1-12.1 μg/mL CSII vs. MDI). 1,5-AG improved more (increased) in the CSII vs. MDI group (3.1 +/- 4.1 vs. -2.2 +/- 7.0 μg/ml respectively, P=0.029). This difference persisted following adjustment for the duration of follow-up. There was no significant difference in the HbA1c improvement between the CSII and MDI groups. There was no significant difference in the SD or CV of 1,5-AG or HbA1c between groups. Conclusions: Early use of CSII post-T1D-diagnosis in a pediatric population is associated with a higher 1,5-AG than with MDI therapy implying reduced glycaemic variability. Disclosure E.S. Scott: None. A.S. Januszewski: None. G. Fulcher: Advisory Panel; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Janssen Research & Development, Merck Sharp & Dohme Corp., Novo Nordisk Inc. Consultant; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Janssen Research & Development, Merck Sharp & Dohme Corp., Novo Nordisk Inc. Research Support; Self; Novo Nordisk Inc. T. Jones: Research Support; Self; Dexcom, Inc., Medtronic MiniMed, Inc. Speaker's Bureau; Self; Eli Lilly and Company, Roche Diabetes Care. E.A. Davis: None. A.J. Jenkins: Advisory Panel; Self; Abbott, Australian Diabetes Society, Medtronic. Research Support; Self; Abbott, GlySens Incorporated, Medtronic, Mylan. Speaker’s Bureau; Self; Eli Lilly and Company, Novo Nordisk Inc.

Background: The FIELD study, a 5-year randomized double-blind placebo-controlled trial of fenofibrate vs. placebo in 9,795 adults with type 2 diabetes (T2D), was designed to evaluate effects on coronary and cardiovascular (CV) events. In this analysis we explored associations between lipoprotein subfractions and on-study CV events and death. Methods: We performed ultracentrifugation using the vertical auto profile (VAP, Atherotech) on plasma at baseline and after 6 weeks of fenofibrate (active run-in) exposure. Analyses were performed using Cox proportional hazards and logistic regression analysis. Results were adjusted for gender, with log transformation as necessary. Results: Total CVD and CV death correlated negatively with HDL-C and its subfractions, as well as apoA1 and apoA2. In addition to LDL-C, small, dense LDL (LDL4-C), non-HDL-C, apo B, triglycerides, and apoC3 increased risk for total CVD. For CVD death, HDL-C and its subfractions were protective, while apo B, triglycerides, apo C3, and various VLDL-C species were associated with increased risk. Conclusions: Detailed lipoprotein analysis identifies multiple lipoprotein subclasses and apolipoproteins and their ratios associated with acute cardiovascular events and death. Serum levels of a number of these lipoprotein and apoprotein species were improved by fenofibrate therapy. Disclosure S.R. Jones: None. A.J. Jenkins: Advisory Panel; Self; Abbott, Australian Diabetes Society, Medtronic. Research Support; Self; Abbott, GlySens Incorporated, Medtronic, Mylan. Speaker’s Bureau; Self; Eli Lilly and Company, Novo Nordisk Inc. A.S. Januszewski: None. R.L. OConnell: None. L. Li: None. D. Sullivan: None. M. Taskinen: Advisory Panel; Self; Novo Nordisk A/S. Consultant; Self; Akcea Therapeutics, Chiesi USA, Inc. Research Support; Self; Amgen Inc., Novo Nordisk A/S. Speaker’s Bureau; Self; Amgen Inc. R. Scott: None. G.F. Watts: None. P.J. Barter: Advisory Panel; Self; Merck & Co., Inc., Pfizer Inc. R. Simes: None. P.P. Toth: Consultant; Self; Amarin Corporation, Amgen Inc., AstraZeneca, Kowa Pharmaceutical Europe Co. Ltd., Novo Nordisk Inc. Speaker’s Bureau; Self; Amarin Corporation, Amgen Inc., Merck & Co., Inc., Novo Nordisk Inc., Regeneron Pharmaceuticals, Sanofi US. A.C. Keech: None.

Introduction: Reviewing rCGM patterns may prompt lifestyle or therapeutic changes to achieve glycaemic targets but evidence for rCGM use in primary care management of T2D is limited. Methods: Two-arm RCT. Participants: Adults with T2D, HbA1c ≥0.5% above target, prescribed ≥2 non-insulin glycemia medications or insulin. Intervention: 1-hour diabetes education and (at 0/3/6/9/12 months): HbA1c assessment + wearing FreeStyle Libre Pro (Abbott) for up to 14 days prior to being discussed at a clinic visit. Physicians were trained in interpreting ambulatory glucose profiles. Control: 3-monthly ‘usual care’ clinic visits + r-CGM device worn (blinded, research data) at 0 and 12 months. Primary outcome: difference in mean HbA1c at 12 months. Secondary outcomes: mean differences in time in range (TIR: 4-10 mmol/L) and diabetes-specific distress (PAID) at 12 months, and HbA1c at 6 months. ITT analysis. Results: In 25 primary care practices, participants were: 299 adults with T2D, aged (mean(SD)) 60(10) years, HbA1c: 8.9(1.2)%, diabetes duration (median(IQR)) of 12(8,20) years. At 12 months, the between-group difference in mean HbA1c was 0.2% (p=0.112). The estimated mean percentage TIR was 8.4% higher in the intervention than the control arm (p=0.004). Diabetes-specific distress did not differ between arms (0.5; p=0.71). At 6 months, HbA1c was significantly lower in the intervention arm (0.5%; p Discussion: Use of 3-monthly rCGM in adults with T2D in primary care does not improve HbA1c at 12 months. We showed a statistically and clinically significant reduction in HbA1c at 6 months in the intervention group, as well as significant improvements in TIR at 12 months, with no change in diabetes distress. Our findings suggest the primary impact of r-CGM use on HbA1c in this setting is short term, and not associated with increase in the number of diabetes medications. Disclosure J. Furler: Research Support; Self; Abbott, Sanofi. D.N. O’Neal: None. J. Speight: Research Support; Self; AstraZeneca, Medtronic, Sanofi. Speaker’s Bureau; Self; Novo Nordisk A/S, Roche Diabetes Care. J.E. Manski-Nankervis: Research Support; Self; Australian National Health and Medical Research Council, Boehringer Ingelheim International GmbH, Diabetes Australia, Eli Lilly and Company. Speaker’s Bureau; Self; RACGP. S. Thuraisingam: None. E. Holmes-Truscott: Research Support; Self; Sanofi. Speaker’s Bureau; Self; Novo Nordisk Inc. K.R. De La Rue: None. L.E. Ginnivan: None. R.C. Doyle: None. K. Khunti: Advisory Panel; Self; Amgen Inc., AstraZeneca, Eli Lilly and Company, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Sanofi. Consultant; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Merck Sharp & Dohme Corp., Novartis AG, Novo Nordisk A/S, Pfizer Inc., Sanofi-Aventis, Servier, Takeda Pharmaceutical Company Limited. Research Support; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Merck Sharp & Dohme Corp., Novartis AG, Novo Nordisk A/S, Pfizer Inc., Sanofi-Aventis. Speaker’s Bureau; Self; AstraZeneca, Berlin-Chemie AG, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Menarini Group, Merck Sharp & Dohme Corp., Novartis AG, Novo Nordisk A/S, Roche Pharma, Sanofi, Servier, Takeda Pharmaceutical Company Limited. M. Catchpool: None. K. Dalziel: None. J.I. Chiang: None. I. Blackberry: None. R. Audehm: Advisory Panel; Self; AstraZeneca, Novartis Pharmaceuticals Corporation. M. Kennedy: Advisory Panel; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Merck Sharp & Dohme Corp., Novo Nordisk Inc., Sanofi. M.J. Clark: None. A.J. Jenkins: Advisory Panel; Self; Abbott, Australian Diabetes Society, Medtronic. Research Support; Self; Abbott, GlySens Incorporated, Medtronic, Mylan. Speaker’s Bureau; Self; Eli Lilly and Company, Novo Nordisk Inc. A.S. Januszewski: None. D. Liew: Advisory Panel; Self; AstraZeneca, Bayer AG. Research Support; Self; AbbVie Inc., AstraZeneca, Bristol-Myers Squibb Company, CSL Behring, Pfizer Inc. P.M. Clarke: None. J.D. Best: Consultant; Self; Abbott. Funding National Health and Medical Research Council of Australia (APP1104241); Sanofi Australia; Abbott Diabetes Care

The incidence of diabetes is increasing globally, and in spite of modern treatments for glucose, blood pressure, and lipids, the vascular and neurologic complications of diabetes still occur at unacceptably high rates. The personal and economic burdens are enormous. Diabetes is a common cause for vision loss (diabetic retinopathy [DR]), renal impairment (nephropathy), peripheral and autonomic neuropathy and accelerated atherosclerosis leading to cardiovascular disease, cerebrovascular and peripheral vascular disease, and premature death (1). People with diabetes and with early renal damage are at particularly high risk of DR, renal failure, premature cardiovascular disease, and death. Moreover, people with DR are at increased risk of other micro- and macrovascular complications of diabetes (2). This may relate to common risk factors and mechanisms, such as damage to the vasculature, a chronic and clinically silent process. Vascular endothelial cells are the interface between blood and surrounding tissues, with key roles in regulation of inflammation, thrombosis, and angiogenesis and therefore a popular choice as a model for assessing endothelial damage and metabolic stress (3,4). Vision loss due to DR is often preventable. However, for many people with diabetes it is often difficult to predict the clinical course of disease. Furthermore, not all people take or respond well to proven primary and secondary treatments for DR, including risk factor control, renin-angiotensin system drugs, fenofibrate, and late-stage treatments, such as ocular laser, corticosteroids, and anti–vascular endothelial growth factor agents. This highlights the importance of improving our understanding of the pathogenesis of DR and developing …

We examined the ability of pyridoxamine (PM), an inhibitor of formation of advanced glycation end products (AGEs) and lipoxidation end products (ALEs), to protect against diabetes-induced retinal vascular lesions. The effects of PM were compared with the antioxidants vitamin E (VE) and R-alpha-lipoic acid (LA) in streptozotocin-induced diabetic rats. Animals were given either PM (1 g/l drinking water), VE (2,000 IU/kg diet), or LA (0.05%/kg diet). After 29 weeks of diabetes, retinas were examined for pathogenic changes, alterations in extracellular matrix (ECM) gene expression, and accumulation of the immunoreactive AGE/ALE N( epsilon )-(carboxymethyl)lysine (CML). Acellular capillaries were increased more than threefold, accompanied by significant upregulation of laminin immunoreactivity in the retinal microvasculature. Diabetes also increased mRNA expression for fibronectin (2-fold), collagen IV (1.6-fold), and laminin beta chain (2.6-fold) in untreated diabetic rats compared with nondiabetic rats. PM treatment protected against capillary drop-out and limited laminin protein upregulation and ECM mRNA expression and the increase in CML in the retinal vasculature. VE and LA failed to protect against retinal capillary closure and had inconsistent effects on diabetes-related upregulation of ECM mRNAs. These results indicate that the AGE/ALE inhibitor PM protected against a range of pathological changes in the diabetic retina and may be useful for treating diabetic retinopathy.