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OBJECTIVE To prospectively analyze the association of sedentary behavior time with type 2 diabetes (T2D) risk and perform the isotemporal substitution analyses to estimate the effect of substitution of sedentary behaviors by equal time of different types of daily-life physical activities and structured exercise. We also examined modifications by the genetic predisposition to T2D. RESEARCH DESIGN AND METHODS We included 475,502 participants free of T2D in the UK Biobank. Sedentary time was quantified by summing up the time spent on television watching, computer use, and driving. RESULTS During a median follow-up of 11 years, we documented 18,169 incident T2D cases. In comparison of the extreme categories (≥6 vs. CONCLUSIONS Our study indicates that sedentary behavior time is associated with an increased risk of T2D; replacing sedentary behavior with a short duration (30 min/day) of daily-life physical activities or structured exercise is related to a significant reduction in T2D risk. Furthermore, such association was stronger among those with a lower genetic risk of T2D.

Recommended A1C targets for people with type 2 diabetes are between 6.5 and 8%; however, real-world data suggest that an increasing proportion of people with diabetes have suboptimal control, and ∼15% have an A1C level >9%. People with A1C >9% are at increased risk for micro- and macrovascular complications and require treatment intensification to improve glycemic control as early as possible. In a series of short videos now available on the Clinical Diabetes website, the authors discuss the pathophysiological changes that occur during the progression of type 2 diabetes, with particular focus on the key role of declining β-cell function. The authors review clinical characteristics—long diabetes duration and A1C ≥9%—that are indicative of diminishing β-cell function, and they discuss the clinical data that support the use of available treatment options for these individuals, consistent with current diabetes treatment guidelines. This article is intended to briefly summarize those discussions. The videos described below are available in their entirety, along with short biographies of the authors, at https://clinical.diabetesjournals.org/content/combination-injectable-therapy. ### Need for Glycemic Control in Individuals With A1C ≥9% (Video 1) Video 1. Need for Glycemic Control in Individuals With A1C ≥9%. Available from https://bcove.video/3bda5FB. Current treatment guidelines for people with type 2 diabetes from the American Diabetes Association, the American Association of Clinical Endocrinologists, the American College of Endocrinology, and the American College of Physicians recommend A1C targets within the range of 6.5–8% (1–3). However, public health reports suggest that a significant proportion of U.S. adults with diabetes have an A1C >9% (4), which is defined as “poor glycemic control” by the Healthcare Effectiveness Data and Information Set (5). Furthermore, A1C levels >9% can negatively affect reimbursement (e.g., through a decrease in the star ratings used to assess quality and performance) (6). In this video, the authors discuss the deleterious health consequences for patients with high A1C levels, including …

Background: The Medicare Part D Senior Savings Model (SSM) took effect on January 1, 2021. This study estimated the number of beneficiaries who would benefit from SSM and the long-term health and economic consequences of implementing this new policy. Methods: Data for Medicare beneficiaries with diabetes treated with insulin were extracted from the 2018 Medical Expenditure Panel Survey. A validated diabetes microsimulation model estimated health and economic impacts of the new policy for the 5-year initial implementation period and a 20-year extended policy horizon. Costs were estimated from a health system perspective. Results: Of 4.2 million eligible Medicare beneficiaries, 1.6 million (38.3%) would benefit from the policy, and out-of-pocket costs per year per beneficiary would decrease by 61% or $500 on average. Compared with non-white subgroups, the white population subgroups would have a higher proportion of SSM enrollees (29.6% vs. 43.7%) and a higher annual out-of-pocket cost reduction (-$424 vs. -$531). Among the SSM enrollees, one-third (605,125) were predicted to have improved insulin adherence due to lower cost-sharing and improved health outcomes. In 5 years, the SSM would (1) avert 2,014 strokes, 935 heart attacks, 315 heart failure cases, and 344 end-stage renal disease cases; (2) gain 3,220 life-years and 3,381 QALYs; (3) increase insulin cost and total medical cost by $3.5 billion and $2.8 billion. In 20-years, the number of avoided clinical outcomes, life-years and QALYs gained, and the total and insulin cost would be larger. Conclusion: The Medicare SSM may reduce the out-of-pocket costs for approximately one-third of the Medicare beneficiaries treated with insulin, improving health outcomes via increased insulin adherence. However, the SSM will also increase overall Medicare spending for insulin and overall medical costs, which may impact future premiums and benefits. Our findings can inform policymakers about the potential impact of the new Medicare SSM.

This study aimed to identify patient characteristics associated with the effectiveness of intensive glucose control (i.e., A1c Disclosure H.Kianmehr: None. J.Guo: None. V.Fonseca: Consultant; Abbott, Asahi Kasei Corporation, Bayer AG, Novo Nordisk, Sanofi, Research Support; Fractyl Health, Inc., Jaguar Gene Therapy, Stock/Shareholder; Abbott, Amgen Inc., BRAVO4Health, Mellitus Health. L.Shi: None. H.Shao: Board Member; BRAVO4HEALTH, LLC.

CKD is highly prevalent and costly in individuals with diabetes. Clinicians need to have a tool for early identification and prediction of incident CKD in individuals with diabetes. This study aimed to predict incident CKD among individuals with diabetes. A time-varying Cox model was derived from the ACCORD clinical trial data to predict the risk of incident CKD, defined as eGFR30 mg/g lasting for 3 months. A list of candidate variables was chosen based on literature reviews and experts’ consultation, including demographic characteristics, physical exam, laboratory, medical history, drug use, and healthcare utilization. Predictors were selected from candidate variables using a stepwise algorithm. Data were split into training and validation set. Model performance was evaluated by Brier score (BS) and C statistics. Confidence intervals (CI) was calculated using a bootstrap method. Decomposition analysis was conducted to assess the predictor contribution. External validation was performed on patient-level data of HARMONY Outcome clinical trial. A total of 6,006 diabetes patients free of CKD at baseline were used for model development, with a median follow-up of 3 years and 2,257 new onset CKD events. The CKD risk model identified age at T2D diagnosed, smoking status, BMI, HDL, VLDL, ALT, eGFR, UACR, hypoglycemia event, retinopathy event, CHF event, CHD history, antihyperlipidemic drug use, antihypertensive drug use, and hospitalization. The model demonstrated good discrimination (C-statistics 0.828 [95% CI 0.812-0.845]) and calibration (BS 0.0552 [95% CI 0.0474-0.0593]) performance. UACR, eGFR, and CHF event were the top 3 factors that contributed most to the prediction. A total of 8,221 patients free of CKD was selected from HARMONY Outcome trial with 1,288 CKD events and median follow-up of 2 years. The model demonstrated acceptable discrimination (C-statistics: 0.772 [95% CI 0.767-0.805]) and calibration (BS: 0.0504 [95% CI 0.0477-0.0531]) in external data. A risk prediction of incident CKD among T2D patients was developed and validated for use in decision support of CKD prevention. Disclosure Y. Lin: None. H. Shao: Board Member; BRAVO4HEALTH, LLC. A. H. Anderson: None. V. Fonseca: Consultant; Abbott, Asahi Kasei Corporation, Bayer AG, Novo Nordisk, Sanofi, Research Support; Fractyl Health, Inc., Jaguar Gene Therapy, Stock/Shareholder; Abbott, Amgen Inc., BRAVO4Health, Mellitus Health. V. Batuman: None. L. Shi: None.

Time in range (TIR) referres to the percentage of time that blood glucose levels stay within a pre-determined range for patients with diabetes. This study used simulation methods to quantify the long-term health benefit and economic return associated with the improvement of TIR in individuals with T2D. Individuals with self-reported T2D were identified from the 2017-2018 National Health and Nutrition Examination Survey as the target population for the simulation. A Markov model with three states (i.e., diabetes, diabetes with a history of cardiovascular disease (CVD) , death) was developed to calculate the 20-year cost, quality-adjusted life-years (QALY) gained, and cardiovascular disease (CVD) risk reduction under four scenarios: TIR >85%, 71-85%, 51-70%, and ≤50%. The risk of CVD and mortality were extracted from literature. CVD and mortality risk reduction associated with TIR improvement, as well as cost and QALY associated with each health state were all extracted from the literature. Costs were standardized to the 2021 US Dollar, and a 3.0% annual discount rate was used. A willingness-to-pay threshold of $50,000/QALY was used to determine cost-effectiveness. Compared with having a TIR 85% was associated with an increase of 3.QALY and 1.97 life-years, and a 14.89% relative risk reduction in CVD. The annual spending on treatment to improve the TIR from 85% should be lower than $2,592, $3,082, and $4,120 respectively to make the treatment cost-effective. Improving TIR can potentially lead to a substantial health benefit. Our study qualified the maximum medical expenses allocated to improve the TIR while keeping the treatment cost-effective. Disclosure K.Alkhuzam: None. L.Shi: None. V.Fonseca: Consultant; Abbott, Asahi Kasei Corporation, Bayer AG, Novo Nordisk, Sanofi, Research Support; Fractyl Health, Inc., Jaguar Gene Therapy, Stock/Shareholder; Abbott, Amgen Inc., BRAVO4Health, Mellitus Health. Y.Zhang: None. J.Guo: None. H.Shao: Board Member; BRAVO4HEALTH, LLC.

Negative or neutral treatment effects reported in many clinical trials reflect average treatment effects in the trial population and may mask heterogeneous treatment effects across patient subgroups. Machine learning (ML) methods are valuable tools to study these treatment heterogeneities. Using ML methods and data collected in the ACCORD trial, we identified patient subgroups that might benefit from intensive glycemic and blood pressure intervention. The ACCORD found that intensive glucose (A1c 55 mg/dL, men >45 mg/dL; relative risk [RR] 0.51, 95% CI: 0.34-0.74%) and those with LDL >100 mg/dL (RR: 0.74, 95% CI: 0.59-0.94) , but had no significant effects in other patient groups. Intensive glycemic control reduced MACE in those with baseline A1c 1mL/min/1.73m2 (i.e., highest quartile; RR: 0.74, 95% CI: 0.55-0.99) ; reductions were not significant in other patient groups. Our findings may assist clinicians in developing a more precise approach to managing persons with type 2 diabetes at high risk of CVD events. Future clinical trials may be needed to confirm these findings. Disclosure H.Kianmehr: None. P.Zhang: None. N.Singh ospina: None. L.Shi: None. V.Fonseca: Consultant; Abbott, Asahi Kasei Corporation, Bayer AG, Novo Nordisk, Sanofi, Research Support; Fractyl Health, Inc., Jaguar Gene Therapy, Stock/Shareholder; Abbott, Amgen Inc., BRAVO4Health, Mellitus Health. J.Guo: None. H.Shao: Board Member; BRAVO4HEALTH, LLC.

Aim: Evaluate if cardiovascular (CV) risk factors increase at a greater rate during the prediabetes (preDM) stage rather than the diabetes (DM) stage by sex. Methods: We pooled data from the Atherosclerosis Risk in Communities Study and the Multi-Ethnic Study of Atherosclerosis. The CV risk outcome was the metabolic syndrome (MetS) severity z-score derived from components of waist circumference, HDL, triglycerides, SBP, and fasting glucose accounting for the weight of each component. The score was calculated for participants at visits 1 to 4 (3-year interval between visits) . We used linear mixed regressions to model the MetS z-score over time in men and women by levels of glucose (normal Results: Men and women had a greater rate of change in MetS severity during the preDM than the DM stage. Women had a significantly greater increase of MetS severity than men during the preDM stage, while men had a significantly greater increase in the DM stage than women. Conclusion: A more rapid increase in MetS severity occurred in preDM than in DM. Women exhibited a faster increase in MetS severity compared to men during the preDM stage. Greater attention should be given to CV risk in women with preDM. Disclosure Y.Yoshida: None. Z.Chen: n/a. R.L.Baudier: None. M.Krousel-wood: None. A.H.Anderson: None. V.Fonseca: Consultant; Abbott, Asahi Kasei Corporation, Bayer AG, Novo Nordisk, Sanofi, Research Support; Fractyl Health, Inc., Jaguar Gene Therapy, Stock/Shareholder; Abbott, Amgen Inc., BRAVO4Health, Mellitus Health. F.Mauvais-jarvis: Consultant; Mithra Pharmaceutical. Funding NIH K12HD043451

The hemoglobin glycation index (HGI) is the difference between observed HbA1c and predicted HbA1c from FPG using linear regression. HGI is an important biomarker of clinical management/drug treatment outcomes and can identify individuals at high risk for multiple adverse events and outcomes before the appearance of clinical symptoms. Here, we sought to test if variation in HGI has genetic determinants in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) clinical trial using a genome-wide association approach (N = 7913) . We subsequently replicated the top hits (P < 1e-5) in the Atherosclerosis Risk in Communities Study (ARIC; N = 3741) . An intergenic SNP rs73407935 (7q11.22) was associated with HGI (P = 5.8e-10) with the locus replicating in ARIC. Further, multiple variants at suggestive genome-wide significance in discovery (P < 5e-6) replicated in ARIC including variants near or in ASAH1 (P = 2.4e-6) , a region previously associated with risk of CAD in T2D, LRRC4C (P = 2.4e-6) , SHLD1 (P = 3.7e-6) and FAM22B (P = 2.5e-6) . The top 2 replicated SNPs in FAM22B are characterized eQTLs for expression of multiple genes in cells from pancreas, brain, and the immune system, including TRAF4, PROCA1, and RPL23A. Many SNPs associated with HGI were distinct from those associated with FPG or HbA1c. In ACCORD, sex-specific HGI associations with SNPs in or near GALNT11 in females (P = 5e-9) and HECW2 (P = 1.5e-8) in males were observed. Further, analysis of 544 Hispanics revealed associations of a strong eQTL variant near USF1 (rs2516837; P = 1.5e-09) and SNPs near NXNL2/SPIN1 (rs141006133; P = 6.9e-9) with HGI. This work represents the first evaluation of the genetic etiology of HGI. We identified and replicated variants that merit further study in the development of precision medicine for treatment of T2D. The results of the stratified analyses highlight the potential importance of heterogeneity in these efforts. Disclosure J.S.House: None. D.M.Rotroff: Research Support; Novo Nordisk, Stock/Shareholder; Clarified Precision Medicine. V.Fonseca: Consultant; Abbott, Asahi Kasei Corporation, Bayer AG, Novo Nordisk, Sanofi, Research Support; Fractyl Health, Inc., Jaguar Gene Therapy, Stock/Shareholder; Abbott, Amgen Inc., BRAVO4Health, Mellitus Health. J.M.Hempe: None. J.Mychaleckyj: None. A.Doria: Research Support; Novo Nordisk Foundation. J.B.Buse: Consultant; Alkahest, Anji, AstraZeneca, Boehringer Ingelheim International GmbH, Cirius Therapeutics, Inc., Eli Lilly and Company, Fortress biotech, GentiBio, Glycadia, Glyscend, Janssen Pharmaceuticals, Inc., Mellitus Health, Moderna, Inc., Pendulum Therapeutics, Praetego, LLC, Stability Health, Valo, Zealand Pharma A/S, Other Relationship; Adocia, AstraZeneca, Eli Lilly and Company, Intarcia Therapeutics, Inc., MannKind Corporation, Novo Nordisk, Sanofi, Senseonics, vTv Therapeutics, Research Support; AstraZeneca, Dexcom, Inc., Eli Lilly and Company, Novo Nordisk, vTv Therapeutics, Stock/Shareholder; Glyscend, Mellitus Health, Pendulum Therapeutics, PhaseBio Pharmaceuticals, Inc., Praetego, LLC, Stability Health. A.Motsinger-reif: None. Funding NHLBI RO1HL110380NIDDK P30DK124723NCATS UL1TR002489

Several hypotheses have been proposed that SGLT2 inhibitors and GLP-1 receptor agonists may have additional mechanisms to further reduce risks of cardiovascular complications unexplained by traditional risk factors control. This study aimed to examine and quantify the magnitude of additional protective effects of these two drugs on cardiovascular outcomes in individuals with type 2 diabetes (T2D) . Patient characteristics were extracted from four cardiovascular outcomes trials (CVOTs) of SGLT2 inhibitors and four CVOTs of GLP-1 receptor agonists completed before Nov 2021. We used the BRAVO simulation model to translate risk factors reductions from the newer drugs into risk reductions in cardiovascular complications, assuming the simulated risk reductions are only driven by traditional risk factors (i.e., A1c, SBP, LDL, and BMI) . We then derived the relative risk (RR) between observed risk reductions and simulated risk-factor-driven risk reductions in study outcomes to determine whether additional cardiovascular benefit of the newer drugs exist that can be explained by improvement of traditional risk factor control. Controlling for effect from traditional risk factors, SGLT2 inhibitor was associated with an additional risk reduction of hospitalization for heart failure (HHF) (RR: 0.74, 95% confidence interval (CI) : 0.59-0.94) , and a meaningful but not significant reduction in all-cause mortality (RR: 0.90, 95% CI: 0.77-1.03) , but an increased risk of stroke (RR: 1.29, 95% CI: 1.03-1.62) . No additional benefit was observed in preventing MI or CVD mortality from SGLT2 inhibitors. No additional benefit in preventing any study outcomes was associated with GLP-1 receptor agonists. Except for the benefit on HHF by SGLT2i, improvement in traditional CVD risk factors largely account for all the benefit associated with GLP-1 and SGLT2i to reduce CVD. Disclosure S.Niu: None. D.Guan: None. N.Singh ospina: None. K.Cusi: Consultant; Altimmune, Arrowhead Pharmaceuticals, Inc., AstraZeneca, Boehringer Ingelheim International GmbH, Bristol-Myers Squibb Company, Eli Lilly and Company, Fractyl Health, Inc., Genentech, Inc., Hanmi Pharm. Co., Ltd., Intercept Pharmaceuticals, Inc., Novo Nordisk. V.Fonseca: Consultant; Abbott, Asahi Kasei Corporation, Bayer AG, Novo Nordisk, Sanofi, Research Support; Fractyl Health, Inc., Jaguar Gene Therapy, Stock/Shareholder; Abbott, Amgen Inc., BRAVO4Health, Mellitus Health. L.Shi: None. H.Shao: Board Member; BRAVO4HEALTH, LLC.

This study aimed to identify patient characteristics associated with the effectiveness of albiglutide in preventing MACE among patients with type 2 diabetes (T2D) in the HARMONY trial. The HARMONY trial was a randomized, double-blind, placebo-controlled trial to examine the effectiveness of albiglutide in reducing the risk of cardiovascular disease in 9,463 individuals with T2D. We used the causal forest (CF) and causal tree (CT) algorithm to identify participant characteristics that modulate the effectiveness of albiglutide in preventing MACE from a list of 16 candidate variables (demographics, disease histories, medications, and biomarkers) at the study baseline. A computer-driven approach identified that study participants with baseline A1c level above 8.0% (absolute risk change (ARC) : -2.77%, 95% confidence interval [-4.21% to -1.28%]; relative risk (RR) : 0.74 [0.62 to 0.87]) , or diabetes duration less than 15 years (ARC: -2.55% [ -3.85% to -1.04%]; RR: 0.70 [0.58 to 0.85]) , or DBP higher than 70 mmHg (ARC: -2.40% [-3.68% to -1.21%]; RR: 0.73 [0.62 to 0.86]) , or heart rate below 70 per hour (ARC: -2.88% [-4.76% to -1.04%]; RR: 0.70 [0.56 to 0.88]) , or ALP level below 70 U/L (ARC: -3.10% [-3.10% to -1.30%]; RR: 0.65 [0.52 to 0.81]) , or serum creatinine level below 90 umol/L (ARC: -1.99% [-3.33% to -0.81%]; RR: 0.74 [0.61 to 0.88]) benefited from albiglutide in reducing the risk of MACE. In contrast, their counterparts who did not have these characteristics did not receive statistically significant MACE risk reduction from albiglutide. Our study identified clinical features that may result in greater benefits of albiglutide on MACE than what would be expected from a population average. Clinicians may consider these individualized characteristics for better-targeted albiglutide treatment. However, future GLP-1 trials may help to confirm this approach to optimize outcomes. Disclosure H.Kianmehr: None. Y.Lin: None. L.Shi: None. V.Fonseca: Consultant; Abbott, Asahi Kasei Corporation, Bayer AG, Novo Nordisk, Sanofi, Research Support; Fractyl Health, Inc., Jaguar Gene Therapy, Stock/Shareholder; Abbott, Amgen Inc., BRAVO4Health, Mellitus Health. J.Guo: None. H.Shao: Board Member; BRAVO4HEALTH, LLC.

This study compared the 5-year risk of diabetes complications of tirzepatide (TZP) and semaglutide with insulin glargine in individuals with type 2 diabetes (T2Ds) , using the BRAVO diabetes model, which has been validated to have a high degree of accuracy. We used the BRAVO model to predict 5-year complications among users of TZP (5 mg, mg, or 15 mg) , semaglutide (1 mg) , and insulin glargine, based on short-tern efficacy data from the SURPASS-2 trial and SUSTAIN-4 trial, under two scenarios: the short-term drug efficacy lasted for 5 years (optimistic) , and the drug efficacy diminished in 5 years (conservative) . We found a 5-year risk reduction in major adverse cardiovascular events (MACE) (relative risk [RR] 63.7%, 95% CI: 61.4%-66.2%) with 15 mg TZP under an optimistic scenario, and MACE (RR 87.6%, 95% CI: 86.8%-88.6%) risk reduction with 15 mg TZP under a conservative scenario, when compared with insulin glargine (Figure) . Lower doses of TZP also had similar, albeit slightly smaller, benefits. The long-term use of TZP and semaglutide may lead to a substantial reduction in diabetes-related complications among individuals with T2Ds, compared to insulin. A high dose of TZP was also superior to semaglutide in this respect. Disclosure S.Niu: None. T.Jiao: None. Y.Guo: None. J.Bian: None. L.Shi: None. V.Fonseca: Consultant; Abbott, Asahi Kasei Corporation, Bayer AG, Novo Nordisk, Sanofi, Research Support; Fractyl Health, Inc., Jaguar Gene Therapy, Stock/Shareholder; Abbott, Amgen Inc., BRAVO4Health, Mellitus Health. H.Shao: Board Member; BRAVO4HEALTH, LLC.

Background: Women with diabetes are at higher risk of CVDs than men. We investigated the associations between the degree of risk factor control and CVD in women and men with diabetes. Methods: A total of 16,760 diabetes patients from UK Biobank were included. The exposure was the number of optimized risk factors including HbA1c level Results: During a mean follow-up of 8.4 years, 2000 incident CVD were documented, including 1685 IHD and 412 stroke. Number of risk-factor control was inversely associated with the risks of IHD, stroke and total CVD, and such associations were stronger in women than men (P for interaction with sex=0.012, 0.018 and 0.017, respectively) . The two most important risk factors for men were blood pressure and non-HDL-cholesterol, while for women were HbA1c and non-HDL-cholesterol. In addition, as compared with non-diabetic controls, the diabetes-related risk of CVD was theoretically eliminated when 2 or more risk-factors were under control in women, and 3 risk-factors were under control in men. Conclusion: Among diabetes patients, to control the risk factors within the target range benefit more in women than men in preventing subsequent risk of CVD. Our findings highlight the importance of raising awareness of control of risk factors and provide evidence for the sex-specific priority of control strategies. Disclosure X.Wang: None. H.Ma: None. V.Fonseca: Consultant; Abbott, Asahi Kasei Corporation, Bayer AG, Novo Nordisk, Sanofi, Research Support; Fractyl Health, Inc., Jaguar Gene Therapy, Stock/Shareholder; Abbott, Amgen Inc., BRAVO4Health, Mellitus Health. L.Qi: None. Funding National Heart, Lung, and Blood Institute (HL071981, HL034594, HL126024) , the National Institute of Diabetes and Digestive and Kidney Diseases (DK115679, DK091718, DK100383, DK078616)

The cost-effectiveness of SGLT2i can be modulated by patients’ characteristics, such as cardiovascular risks. We performed an individualized cost-effectiveness assessment of SGLT2i when added to metformin in people with inadequately controlled T2D to quantify the modulating effect of patients’ characteristics on the cost-effectiveness of SGLT2i. We identified survey participants with T2D receiving metformin monotherapy who had an A1c level above 7.0% from the National Health and Nutrition Examination Survey (2013-2018) . We applied a simulation-based smoothing method to populate the survey sample to its represented national population. We used the BRAVO diabetes model to simulate the lifetime cost and QALYs associated with SGLT2i or sulfonylureas. Treatment escalations were governed by the A1c level at 7% and 8% consequentially. Both costs and QALYs were discounted at 3% annually, and the incremental cost-effectiveness ratio (ICER) threshold was set at $100,000/QALYs. The study sample included 2.89 million individuals. Compared with sulfonylureas, the use of SGLT2i was associated with an average of $9,400 higher medical cost and 0.10 additional QALY, leading to an ICER of $94,000/QALY in the overall population. SGLT2i was cost-effective in people with a history of heart failure (ICER: $67,000/QALY) and myocardial infarction (ICER: $94,000/QALY) , and marginally cost-effective in stroke patients (ICER: $100,000/QALY) . Among individuals without CVD, SGLT2i was cost-effective only in subgroups with HbA1c > 7.5%, age > 60, and diabetes duration > 10 years. Overall, SGLT2i was only cost-effective in individuals with a baseline 10-year CVD risk over 16%, constituting 51.6% of the overall population. Our results align closely with the ADA guideline on the recommended population for SGLT2i use. Out-of-pocket payment subsidy or cost-sharing reduction of SGLT2i should be prioritized in patient subgroups in which SGLT2i has high economic values. Disclosure D. Guan: None. S. Niu: None. V. Fonseca: Consultant; Abbott, Asahi Kasei Corporation, Bayer AG, Novo Nordisk, Sanofi, Research Support; Fractyl Health, Inc., Jaguar Gene Therapy, Stock/Shareholder; Abbott, Amgen Inc., BRAVO4Health, Mellitus Health. L. Shi: None. N. Laiteerapong: None. J. Guo: None. H. Shao: Board Member; BRAVO4HEALTH, LLC.

We developed an individual-level discrete-time microsimulation model, namely the Building, Relating, Assessing, and Validating Outcomes (BRAVO) diabetes model. The BRAVO model was externally validated using cohort-level data in people with type 2 diabetes (T2D) . This study further externally validated the BRAVO diabetes model using patient-level data from the Exenatide Study of Cardiovascular Event Lowering Trial (EXSCEL) . Baseline characteristics and trajectories of key biomarkers, including A1c, LDL, SBP, and BMI, were retrospectively extracted from the EXSCEL study database. To protect patients’ private information, variables of age and BMI were masked in ranges from the EXSCEL study (e.g., BMI 35 coded as BMI>30) . To convert the ranges back into specific values to support the simulation, we used the National Health and Nutrition Examination Survey (NHANES) data for the data imputations. We used the median value for each population subgroup from NHANES to replace the ranges. The BRAVO diabetes model was applied to the EXSCEL baseline data to predict the 7-year EXSCEL study outcomes. We assessed the BRAVO model’s discrimination power using C-statistics and calibration using the Brier score. The model demonstrated good discrimination and calibration in predicting the first occurrence of non-fatal myocardial infarction (C-index 0.620 [95% CI 0.590-0.650] and Brier Score (BS) 0.080 [0.074-0.085]) , non-fatal stroke (C-index 0.696 [0.634-0.758] and BS 0.031 [0.028-0.034]) , heart failure (C-index 0.700 [0.666-0.733] and BS 0.037 [0.034-0.041]) , revascularization (C-index 0.664 [0.590-0.738] and BS 0.036 [0.033-0.039]) , and all-cause mortality (C-index 0.746 [0.711-0.781] and BS 0.102 [0.098-0.106]) , respectively. Although the majority of the data obtained from EXSCEL trial was obtained in range instead of specific values, the BRAVO model remained robust in prediction performance in diabetes complications and mortality. Disclosure Y.Shao: None. S.Niu: None. L.Shi: None. V.Fonseca: Consultant; Abbott, Asahi Kasei Corporation, Bayer AG, Novo Nordisk, Sanofi, Research Support; Fractyl Health, Inc., Jaguar Gene Therapy, Stock/Shareholder; Abbott, Amgen Inc., BRAVO4Health, Mellitus Health. H.Shao: Board Member; BRAVO4HEALTH, LLC. Funding Astrazeneca

Introduction: Finerenone reduced risk of cardiorenal outcomes in patients with CKD and T2D in the FIDELIO-DKD and FIGARO-DKD studies. In FIDELIO-DKD the effects of finerenone on kidney and CV outcomes were consistent irrespective of glucagon-like peptide-1 receptor agonist (GLP-1RA) use, but analyses were better powered to evaluate change in urine albumin-to-creatinine ratio (UACR) . Here, we extend these analyses to patients in both studies (FIDELITY analysis) , thus encompassing a larger population with broader inclusion criteria. Methods: Patients with T2D and CKD (UACR ≥30- Results: Of 13026 patients, 944 (7.2%) received GLP-1RAs at baseline. Results were consistent irrespective of GLP-1RA use at baseline for the CV composite outcome (hazard ratio [HR] 0.76; 95% CI 0.52-1.with GLP-1RA; HR 0.87, 95% CI 0.79-0.96 without GLP-1RA; p-interaction 0.63) , and the kidney composite outcome (HR 0.82; 95% CI 0.45-1.48 with GLP-1RA; HR 0.77, 95% CI 0.67-0.89 without GLP-1RA; p-interaction 0.79) . A greater reduction in UACR was observed with finerenone in patients taking GLP-1RA at baseline (placebo-corrected change -38% with GLP-1RA and -31% without GLP-1RA use; p-interaction 0.03) . Incidence of hyperkalemia was similarly increased with finerenone irrespective of GLP-1RA use at baseline. Conclusion: The benefits of finerenone on composite CV and kidney outcomes in patients with CKD and T2D are not modified by GLP-1RA use at baseline, with an increased effect observed for UACR reduction, suggesting a different mechanism of reduction in albuminuria. Disclosure P.Rossing: Consultant; Astellas Pharma Inc., AstraZeneca, Bayer AG, Gilead Sciences, Inc., Merck Sharp & Dohme Corp., Novo Nordisk A/S, Sanofi, Speaker's Bureau; Eli Lilly and Company. P.Viswanathan: Employee; Bayer AG. R.Lawatscheck: None. A.Joseph: Employee; Bayer AG. G.Bakris: Consultant; Alnylam Pharmaceuticals, Inc., AstraZeneca, DiaMedica Therapeutics, Inc., Horizon Therapeutics plc, Ionis Pharmaceuticals, Merck & Co., Inc., Other Relationship; Novo Nordisk. S.Anker: Consultant; Bayer AG, Boehringer Ingelheim International GmbH, Novartis AG, Novo Nordisk, Vifor Pharma Management Ltd., Research Support; Abbott. G.Filippatos: Other Relationship; Amgen Inc., Amgen Inc., Bayer AG, Boehringer Ingelheim International GmbH, Medtronic, Novartis AG, Servier Laboratories, Vifor Pharma Management Ltd. B.Pitt: Advisory Panel; Merck & Co., Inc., Consultant; AstraZeneca, Bayer AG, Boehringer Ingelheim International GmbH, Lexicon Pharmaceuticals, Inc., Vifor Pharma Management Ltd. L.M.Ruilope: Consultant; Bayer AG. V.Fonseca: Consultant; Abbott, Asahi Kasei Corporation, Bayer AG, Novo Nordisk, Sanofi, Research Support; Fractyl Health, Inc., Jaguar Gene Therapy, Stock/Shareholder; Abbott, Amgen Inc., BRAVO4Health, Mellitus Health. G.E.Umpierrez: Research Support; AstraZeneca, Dexcom, Inc., Novo Nordisk. L.Caramori: Advisory Panel; Bayer AG, Consultant; AstraZeneca, Boehringer Ingelheim International GmbH, Novo Nordisk, Research Support; Bayer AG, Novartis AG. M.Lambelet: Other Relationship; Bayer AG.

CKD progression among individuals with T2D is associated with poor outcomes and high costs. The risk factors for CKD progression in T2D have not been fully studied. This study aimed to predict CKD progression among individuals with T2D. Using the ACCORD clinical trial, a time-varying Cox model was developed to predict the risk of CKD progression among patients with CKD. CKD progression was defined as a 50% decline, or 25 mL/min/1.73 m2 decline in eGFR from baseline, doubling of the serum creatinine, or onset of ESKD. A list of candidate variables included demographic characteristics, physical exam, laboratory, medical history, drug use, and healthcare utilization. A stepwise algorithm was used for variable selection. Data was separated into training and validation set. Model performance was evaluated by Brier score (BS) and C statistics. Confidence intervals (CI) were calculated by bootstraping. Decomposition analysis was conducted to assess the predictor contribution. Generalizability was assessed on patient-level data of the HARMONY Outcome clinical trial. A total of 6,982 T2D patients with CKD were used for model development, with a median follow-up of 4 years and 3,346 CKD progression events. The predictors for CKD progression included female sex, age at T2D diagnosis, smoking status, BP, HR, HbA1c, ALT, eGFR, UACR, retinopathy event, hospitalization, interaction of SBP and smoking, and SBP and ALT. The model demonstrated good discrimination (C-statistics 0.745 [95% CI 0.723-0.763]) and calibration (BS 0.0923 [95% CI 0.0873-0.0965]) . The most contributing predictors for CKD progression were eGFR, HbA1c, and SBP. There were 2,954 patients with CKD extracted from HARMONY with 640 CKD progression events and median follow-up of 2 years. The model demonstrated acceptable discrimination (C-statistics: 0.742 [95% CI 0.721-0.7645]) and calibration (BS: 0.0988 [95% CI 0.0952-0.1022]) in the external data. For high-risk patients with both diabetes and CKD, the tool as a dynamic risk prediction of CKD progression may help develop novel strategies to lower the risk of CKD progression. Disclosure Y. Lin: None. H. Shao: Board Member; BRAVO4HEALTH, LLC. A. H. Anderson: None. V. Fonseca: Consultant; Abbott, Asahi Kasei Corporation, Bayer AG, Novo Nordisk, Sanofi, Research Support; Fractyl Health, Inc., Jaguar Gene Therapy, Stock/Shareholder; Abbott, Amgen Inc., BRAVO4Health, Mellitus Health. V. Batuman: None. L. Shi: None.

OBJECTIVE The effects of preventive interventions on cardiovascular autonomic neuropathy (CAN) remain unclear. We examined the effect of intensively treating traditional risk factors for CAN, including hyperglycemia, hypertension, and dyslipidemia, in individuals with type 2 diabetes (T2D) and high cardiovascular risk participating in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial. RESEARCH DESIGN AND METHODS CAN was defined as heart rate variability indices below the fifth percentile of the normal distribution. Of 10,251 ACCORD participants, 71% (n = 7,275) had a CAN evaluation at study entry and at least once after randomization. The effects of intensive interventions on CAN were analyzed among these subjects through generalized linear mixed models. RESULTS As compared with standard intervention, intensive glucose treatment reduced CAN risk by 16% (odds ratio [OR] 0.84, 95% CI 0.75–0.94, P = 0.003)—an effect driven by individuals without cardiovascular disease (CVD) at baseline (OR 0.73, 95% CI 0.63–0.85, P < 0.0001) rather than those with CVD (OR 1.10, 95% CI 0.91–1.34, P = 0.34) (Pinteraction = 0.001). Intensive blood pressure (BP) intervention decreased CAN risk by 25% (OR 0.75, 95% CI 0.63–0.89, P = 0.001), especially in patients ≥65 years old (OR 0.66, 95% CI 0.49–0.88, P = 0.005) (Pinteraction = 0.05). Fenofibrate did not have a significant effect on CAN (OR 0.91, 95% CI 0.78–1.07, P = 0.26). CONCLUSIONS These data confirm a beneficial effect of intensive glycemic therapy and demonstrate, for the first time, a similar benefit of intensive BP control on CAN in T2D. A negative CVD history identifies T2D patients who especially benefit from intensive glycemic control for CAN prevention.

OBJECTIVE This study evaluated the ability of the Building, Relating, Assessing, and Validating Outcomes (BRAVO) risk engine to accurately project cardiovascular outcomes in three major clinical trials—BI 10773 (Empagliflozin) Cardiovascular Outcome Event Trial in Type 2 Diabetes Mellitus Patients (EMPA-REG OUTCOME), Canagliflozin Cardiovascular Assessment Study (CANVAS), and Dapagliflozin Effect on Cardiovascular Events–Thrombolysis in Myocardial Infarction (DECLARE-TIMI 58) trial—on sodium–glucose cotransporter 2 inhibitors (SGLT2is) to treat patients with type 2 diabetes. RESEARCH DESIGN AND METHODS Baseline data from the publications of the three trials were obtained and entered into the BRAVO model to predict cardiovascular outcomes. Projected benefits of reducing risk factors of interest (A1C, systolic blood pressure [SBP], LDL, or BMI) on cardiovascular events were evaluated, and simulated outcomes were compared with those observed in each trial. RESULTS BRAVO achieved the best prediction accuracy when simulating outcomes of the CANVAS and DECLARE-TIMI 58 trials. For EMPA-REG OUTCOME, a mild bias was observed (∼20%) in the prediction of mortality and angina. The effect of risk reduction on outcomes in treatment versus placebo groups predicted by the BRAVO model strongly correlated with the observed effect of risk reduction on the trial outcomes as published. Finally, the BRAVO engine revealed that most of the clinical benefits associated with SGLT2i treatment are through A1C control, although reductions in SBP and BMI explain a proportion of the observed decline in cardiovascular events. CONCLUSIONS The BRAVO risk engine was effective in predicting the benefits of SGLT2is on cardiovascular health through improvements in commonly measured risk factors, including A1C, SBP, and BMI. Since these benefits are individually small, the use of the complex, dynamic BRAVO model is ideal to explain the cardiovascular outcome trial results.

OBJECTIVE Early menopause may be associated with higher cardiovascular disease (CVD) risk. Type 2 diabetes mellitus (T2DM), coupled with early menopause, may result in even greater CVD risk in women. We examined CVD risk in women with early compared with normal-age menopause, with and without T2DM overall, and by race/ethnicity. RESEARCH DESIGN AND METHODS We pooled data from the Atherosclerosis Risk in Communities study, the Multi-Ethnic Study of Atherosclerosis, and the Jackson Heart Study. We included women with data on menopausal status, menopausal age, and T2DM, excluding pre- or perimenopausal women and those with prevalent CVD. Outcomes included incident coronary heart disease (CHD), stroke, heart failure (HF), and atherosclerotic cardiovascular disease (ASCVD) (CHD or stroke). We estimated the risk associated with early ( RESULTS We included 9,374 postmenopausal women for a median follow-up of 15 years. We observed 1,068 CHD, 659 stroke, 1,412 HF, and 1,567 ASCVD events. T2DM significantly modified the effect of early menopause on CVD risk. Adjusted hazard ratios for early menopause and the outcomes were greater in women with T2DM versus those without (CHD 1.15 [95% CI 1.00, 1.33] vs. 1.09 [1.03, 1.15]; stroke 1.21 [1.04, 1.40] vs. 1.10 [1.04, 1.16]; ASCVD 1.29 [1.09, 1.51] vs. 1.10 [1.04, 1.17]; HF 1.18 [1.00, 1.39] vs. 1.09 [1.03, 1.16]). The modifying effect of T2DM on the association between early menopause and ASCVD was only statistically significant in Black compared with White women. CONCLUSIONS Early menopause was associated with an increased risk for CVD in postmenopausal women. T2DM may further augment the risk, particularly in Black women.

OBJECTIVE To improve outcomes of patients with adult type 2 diabetes by decreasing HbA1c undertesting, reducing the proportion of patients with poor glycemic control, and lowering mean HbA1c levels using a quality improvement (QI) program. RESEARCH DESIGN AND METHODS Six years of outpatient electronic health record (EHR) data were analyzed for care gaps before and 2 years after implementing a QI initiative in an urban academic medical center. QI strategies included 1) individual provider and departmental outcome reports, 2) patient outreach programs to address timely follow-up care, 3) a patient awareness campaign to improve understanding of achieving clinical goals, 4) improving EHR data capture to improve population monitoring, and 5) professional education. RESULTS Analysis (January 2010 to May 2018) of 7,798 patients from Tulane Medical Center (mean age 61 years, 57% female, 62% black, 97% insured) with 136,004 visits showed target improvements. A Cox proportional hazards model controlling for age, sex, race, and HbA1c level showed a statistically significant reduction in HbA1c undertesting >6 months (hazard ratio 1.20 ± 0.07). Statistical process control charts showed 15.5% relative improvement in the patient proportion with HbA1c >9% (75 mmol/mol) from 13% to 11% (P < 10−6) following QI interventions and a 2.1% improvement of population mean HbA1c from 7.4% (57 mmol/mol) to 7.2% (55 mmol/mol) (P < 10−6). CONCLUSIONS Multidisciplinary QI teams using EHR data to design interventions for providers and patients produced statistically significant improvements in both care process and clinical outcome goals.

OBJECTIVE Although emerging evidence indicates that increased variability in cardiovascular risk factors (CVRFs) among populations at midlife or later is a reliable predictor of adverse health outcomes, it is unknown whether intraindividual CVRF variability during childhood or adolescence is an independent predictor of later-life diabetes. We aimed to examine the association of CVRF variability during childhood with diabetes in later life. RESEARCH DESIGN AND METHODS We included 1,718 participants who participated in the Bogalusa Heart Study and had measures at least four times during childhood (aged 4–19 years). The mean follow-up period was 20.5 years. Intraindividual CVRF variabilities during childhood were calculated using SD, coefficient of variation, deviation from age-predicted values, and residual SD based upon four to eight serial measurements in childhood. RESULTS Increased variability in BMI or HDL cholesterol (HDL-C) during childhood, irrespective of the indices used, was significantly positively associated with later-life diabetes risk independent of their respective mean levels in childhood and other possible confounding factors. In combined analysis, the magnitude of the association with diabetes risk was similar for high childhood BMI variability and high childhood HDL-C variability. After adjustments for potential confounding variables, other CVRF variabilities including systolic/diastolic blood pressure, total cholesterol, triglycerides, and LDL cholesterol were not significantly associated with diabetes. CONCLUSIONS Increased BMI and HDL-C variabilities during childhood were significant risk factors for the development of diabetes independently of diverse risk factors, which may offer new insights into the childhood origin of adult-onset diabetes.

CMS announced the Medicare Part D SSM to lower out-of-pocket costs for insulin. This study aimed to estimate the impacts of the SSM on clinical and economic outcomes among Medicare insulin users. We used the 2015-2016 National Health and Nutrition Examination Survey to generate a nationally representative simulation sample. We used the Building, Relating, Validating, Assessing, Outcomes (BRAVO™) Diabetes microsimulation model to simulate the impact of the SSM on diabetes-related complications, quality-adjusted life-years (QALYs), and medical costs. We used 30% rebate rate for Medicare insulin payment. 1.29 million insulin users covered by Medicare enhanced health plans are projected to be eligible for SSM in 2021. The SSM associated risk reductions in complications were reported in the Table. Over 5 years, the SSM is projected to increase $2.0 billion medical cost ($1,573/person) with a gain of 7,750 (0.01/person) QALYs (Incremental cost-effectiveness ratio (ICER): $157,300/QALY). In 20 years, the SSM will incur an additional $4.5 billion medical cost ($3474/person) with an increased QALYs of 72,095 (0.06/person, ICER=$57,900/QALY). Because the SSM was projected to increase medical costs for diabetes, this policy would have to be in place for longer than the 5-year period (as currently planned), or further reduce Medicare payment on insulin. Disclosure H. Shao: Research Support; Self; Sanofi. H. Kianmehr: None. L. Shi: Research Support; Self; AstraZeneca, Sanofi. V. Fonseca: Consultant; Self; Abbott Diabetes, Asahi Kasei Corporation, Bayer Inc., Boehringer Ingelheim Pharmaceuticals, Inc., Intarcia Therapeutics, Inc., Novo Nordisk, Pfizer Inc., Sanofi-Aventis, Stock/Shareholder; Self; Amgen Inc., Bravo4health, Mellitus Health. J. D. Brown: None.

Achieving treatment goals could prolong life in people with T2D, but this benefit has not been thoroughly examined. We quantified potential gains in LE from achieving different levels of A1c, systolic blood pressure (SBP), low-density lipoproteins (LDL), and body mass index (BMI). We previously developed and extensively validated the BRAVO diabetes miscrosimulation model. In current tudy, we calibrated the BRAVO diabetes model to a US representative cohort, using the 2011-2012 National Health and Nutrition Examination Survey and its linked mortality records from the National Death Index. We then used the BRAVO model to project LE by level of A1c, SBP, LDL, and BMI. We found that reducing BMI was associated with the largest gain in LE, followed by lowering SBP, and then LDL (Figure). A reduction in A1c from 4th (mean: 9.1%) to 3rd (mean: 7.2%) quartile was associated with a gain of about 2 years, but further A1c reduction was associated with little gains in LE. Our findings can be used to assist communications between clinicians and patients to motivate patients in achieving the recommended treatment goals, and to measure potential health benefits for interventions/programs to improve diabetes care in the US. Disclosure H. Kianmehr: None. P. Zhang: None. M. E. Pavkov: None. K. M. Bullard: None. V. Fonseca: Consultant; Self; Abbott Diabetes, Asahi Kasei Corporation, Bayer Inc., Boehringer Ingelheim Pharmaceuticals, Inc., Intarcia Therapeutics, Inc., Novo Nordisk, Pfizer Inc., Sanofi-Aventis, Stock/Shareholder; Self; Amgen Inc., Bravo4health, Mellitus Health. L. Shi: Research Support; Self; AstraZeneca, Sanofi. H. Shao: Research Support; Self; Sanofi.

Background: The difference between the costs of the newer and older glucose-lowering drugs has been steadily increasing since 2010. In 2018, newer drugs cost 8-12 times more than older drugs (except for insulin). This study aimed to understand how this change in drug cost influenced the cost-effectiveness (CE) of newer drugs compared with older drugs. Methods: We conducted a systematic literature review to identify US-based CE studies comparing newer (i.e., dipeptidyl peptidase-4 inhibitors (DPP4), glucagon-like peptide 1 receptor agonists (GLP1-RA), and sodium-glucose transport protein 2 inhibitors) with older glucose-lowering drugs. The identified 12 studies all reported the CE of newer drugs based on drug costs estimated before 2010. We have updated the corresponding CE of each study by replacing the old cost estimates with 2018 estimates from the 2018 IBM® MarketScan® Commercial Claims Databases. The time window and willingness to pay threshold were consistent with each original study. Results: Only 8% of the original studies suggested that the older drugs were cost-effective. However, 58% of studies were in favor of the older drugs after the cost update. Among the 4 studies comparing newer drugs with thiazolidinediones, all studies suggested thiazolidinedione to be cost-effective in the updated analysis, while the original results all favored newer drugs. For the 4 studies comparing newer drugs with sulfonylureas, 2 studies suggested the sulfonylureas to be cost-effective after the cost update. All 4 studies suggested newer drugs to be cost-effective when compared with insulin in the original study. Only 1 flipped its conclusion when 2018 costs were used. Our sensitivity analysis shows that our results are robust up to a 30% rebate. Conclusion: Significant changes in the cost of glucose-lowering drugs have impacted the economic value of different glucose-lowering agents substantially. More CE analyses are warranted to support the drug choice in T2DM management. Disclosure P. Li: None. R. S. Patel: None. S. M. Vouri: None. L. Shi: Research Support; Self; AstraZeneca, Sanofi. V. Fonseca: Consultant; Self; Abbott Diabetes, Asahi Kasei Corporation, Bayer Inc., Boehringer Ingelheim Pharmaceuticals, Inc., Intarcia Therapeutics, Inc., Novo Nordisk, Pfizer Inc., Sanofi-Aventis, Stock/Shareholder; Self; Amgen Inc., Bravo4health, Mellitus Health. H. Shao: Research Support; Self; Sanofi.

Introduction: Therapeutic approaches to advancing treatment from basal insulin in T2D require assessment of the benefits and risks of any chosen intervention. Methods: SoliMix was a 26-week, open-label, multicenter study in which adults (N=887) with T2D and HbA1c ≥7.5-≤10.0 % (≥58-≤86 mmol/mol) using basal insulin + metformin ± sodium-glucose co-transporter-2 inhibitors were randomized to once-daily iGlarLixi (fixed-ratio combination of basal insulin glargine 100 U/mL + lixisenatide) or twice-daily premix insulin 30/70 analog (BiAsp 30). HbA1c improved and bodyweight changes were favorable with iGlarLixi vs. BiAsp 30 (primary outcomes), while hypoglycemia event rates were also lower with iGlarLixi vs. BiAsp 30. This subanalysis assessed estimated hypoglycemia event rates over the whole treatment period as a function of HbA1c at Week 26. Results: Modelled event profiles for clinically significant hypoglycemia (ADA Level 2 [ Conclusion: Lower rates of hypoglycemia with iGlarLixi across a broad range of HbA1c suggests that iGlarLixi facilitates greater HbA1c improvement vs. premix insulin 30/70. Disclosure R. J. Mccrimmon: Advisory Panel; Self; Novo Nordisk Inc., Sanofi-Aventis, Board Member; Self; Novo Nordisk Foundation, Research Support; Self; AstraZeneca. J. Rosenstock: Board Member; Self; Applied Therapeutics, Boehringer Ingelheim Pharmaceuticals, Inc., Eli Lilly and Company, Intarcia Therapeutics, Inc., Novo Nordisk, Oramed Pharmaceuticals, Inc., Sanofi, Consultant; Self; Applied Therapeutics, Boehringer Ingelheim Pharmaceuticals, Inc., Eli Lilly and Company, Intarcia Therapeutics, Inc., Novo Nordisk, Oramed Pharmaceuticals, Inc., Sanofi, Research Support; Self; Applied Therapeutics, AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Eli Lilly and Company, Genentech, Inc., Intarcia Therapeutics, Inc., Lexicon Pharmaceuticals, Inc., Novartis Pharmaceuticals Corporation, Novo Nordisk, Oramed Pharmaceuticals, Inc., Pfizer Inc., REMD Biotherapeutics, Sanofi. P. Home: Consultant; Self; American Diabetes Association, Kriya Technologies, Sanofi, Other Relationship; Self; Association of British Clinical Diabetologists, AstraZeneca, Novartis Malaysia, Sanofi, Research Support; Self; Sanofi. A. Y. Cheng: Advisory Panel; Self; Abbott, AstraZeneca, Bayer Inc., Boehringer Ingelheim Pharmaceuticals, Inc., Eli Lilly and Company, HLS Therapeutics Inc., Janssen Pharmaceuticals, Inc., Medtronic, Merck & Co., Inc., Merck Sharp & Dohme Corp., Novo Nordisk, Sanofi, Research Support; Self; Applied Therapeutics, The Medicine Company, Speaker’s Bureau; Self; Abbott, AstraZeneca, Bausch Health, Canada, Bayer Inc., Boehringer Ingelheim Pharmaceuticals, Inc., Eli Lilly and Company, HLS Therapeutics Inc., Janssen Pharmaceuticals, Inc., Medtronic, Merck & Co., Inc., Novartis Pharmaceuticals Corporation, Novo Nordisk, Sanofi. F. Giorgino: Consultant; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Lilly Diabetes, Novo Nordisk, Roche Diabetes Care, Sanofi, Research Support; Self; Lilly Diabetes, Roche Diabetes Care. V. Fonseca: Consultant; Self; Abbott Diabetes, Asahi Kasei Corporation, Bayer Inc., Boehringer Ingelheim Pharmaceuticals, Inc., Intarcia Therapeutics, Inc., Novo Nordisk, Pfizer Inc., Sanofi-Aventis, Stock/Shareholder; Self; Amgen Inc., Bravo4health, Mellitus Health. E. Souhami: Employee; Self; Sanofi, Stock/Shareholder; Self; Sanofi. A. Alvarez: Employee; Self; Sanofi. A. H. Boss: Employee; Self; Sanofi, Stock/Shareholder; Self; Novo Nordisk A/S. L. Melas-melt: None. Funding Sanofi

Background: Early menopause may be associated with higher cardiovascular disease (CVD) risk due to the early cessation of estrogens protection. The presence of diabetes (DM) coupled with early menopause may result in even greater CVD risk in postmenopausal women. We compared CVD risk in women with early vs. normal age menopause with and without DM. Methods: We pooled data from the Atherosclerosis Risk in Communities Study, the Multi-Ethnic Study of Atherosclerosis, and the Jackson Heart Study. Women with data on menopausal status, age at menopause and DM (yes/no) were included. We excluded pre- or peri-menopausal women, those with hysterectomy/oophorectomy or prevalent CVD. Outcomes were incident coronary heart disease (CHD), ischemic or hemorrhagic stroke, heart failure (HF), and a composite CVD outcome (CHD, stroke or HF). Cox models examined the risk associated with early ( Results: During a median follow-up of 15 yrs for 5964 postmenopausal women, 684 CHD, 434 stroke, 962 HF and 1535 CVD events occurred. In adjusted models, women with early menopause had elevated risk of CHD (Hazard ratio 1.12, 95% CI 1.03-1.22), stroke (1.09, 1.01-1.18) and HF (1.09, 1.00-1.19) compared to those with normal age menopause. DM was a significant effect modifier to the relationship between early menopause and CHD, stroke or CVD (P for interaction ≤0.05). HRs for early menopause and the outcomes were greater in diabetic than nondiabetic women (CHD 1.34, 1.02-1.78 vs. 1.10, 1.00-1.20; stroke 1.26, 0.98-1.61 vs. 1.07, 0.98-1.16; CVD 1.41, 1.01-1.95 vs. 1.04, 0.94-1.14). Conclusion: Early menopause increases risk for CVD in postmenopausal women; DM places this group at even higher CVD risk. Attention to CVD risk monitoring and management in women with early menopause, with and without diabetes, should be considered. Disclosure Y. Yoshida: None. Z. Chen: None. R. L. Baudier: None. M. Krousel-wood: None. A. H. Anderson: None. V. Fonseca: Consultant; Self; Abbott Diabetes, Asahi Kasei Corporation, Bayer Inc., Boehringer Ingelheim Pharmaceuticals, Inc., Intarcia Therapeutics, Inc., Novo Nordisk, Pfizer Inc., Sanofi-Aventis, Stock/Shareholder; Self; Amgen Inc., Bravo4health, Mellitus Health. F. Mauvais-jarvis: None. Funding National Institutes of Health (K12HD043451)

Objective: To examine patient characteristics that modulate the efficacy of risk factor reduction in preventing hospitalization for congestive heart failure (CHF) in diabetes management. Methods: The Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial tested the cardioprotective effect of achieving A1c< 6.0%, systolic blood pressure (SBP) Results: Individuals with baseline Alanine transaminase (ALT) at the lowest quartile (18 mg/dl and low-density lipoprotein > 110 mg/dl who were receiving ACE inhibitor at baseline also benefited substantially from SBP control (ARR: -1.6%, 95% CI: -2.8% to -0.3%). Optimizing lipid therapy with fenofibrate lowered CHF risk by -1.3% ~ -2.4% in individuals with normal baseline creatine phosphokinase (CPK) (< 200 mg/dl) and A1c < 8%. However, individuals with abnormal CPK at baseline were not associated with changes in CHF risk from fenofibrate. For glucose control, individuals with diastolic blood pressure (DBP) above 69 mmHg and very lower density lipoprotein below 25 mg/dl had an ARR of -0.8% (95% CI -1.6% to 0.0%), and individuals with DBP below 69 mm/Hg had no benefit. Conclusion: This novel approach demonstrates that ML can identify patients with characteristics that are more or less likely to reduce CHF risk from intensive therapy. Disclosure H. Kianmehr: None. J. Guo: None. L. Shi: Research Support; Self; AstraZeneca, Sanofi. V. Fonseca: Consultant; Self; Abbott Diabetes, Asahi Kasei Corporation, Bayer Inc., Boehringer Ingelheim Pharmaceuticals, Inc., Intarcia Therapeutics, Inc., Novo Nordisk, Pfizer Inc., Sanofi-Aventis, Stock/Shareholder; Self; Amgen Inc., Bravo4health, Mellitus Health. H. Shao: Research Support; Self; Sanofi.

This study assessed the 5-year risk of diabetes complications of iGlarLixi, a fixed-ratio combination of insulin glargine 100 U/ml and lixisenatide, in type 2 diabetes (T2Ds) using a simulation model. The six-month efficacy data of iGlarLixi and Standard of Care (SOC) were extracted from the LixiLan-O trial and ORIGIN trial, and patients’ baseline characteristics were standardized to the ACCORD trial. The BRAVO diabetes simulation model was used to project the 5-year complications based on patients’ baseline characteristics and treatment efficacy. An optimistic scenario where the six-month relative efficacy of iGlarLixi (i.e., iGlarlixi-SOC) lasted for 5 years, and a conservative scenario where the relative effect of iGlarLixi waned to none within 5 years were simulated. iGlarLixi was found to reduce HbA1c (-1.4%), SBP (-3.4 mmHg), and BMI (-0.6 kg/m2) in six-month, compared with SOC. We found a 5-year risk reduction in major adverse cardiovascular events (MACE) (relative risk [RR] 77.1%, 95% CI: 66.9%-88.2%), and all-cause mortality (RR 94.0%, 95% CI:92.4%-96.3%) under an optimistic scenario, and MACE (RR 85.5%, 95% CI: 74.7%-96.3%), and all-cause mortality (RR 95.7%, 95% CI:92.1%-98.1%) under a conservative scenario (See Table for other outcomes). The long-term use of iGlarLixi may lead to a substantial reduction in diabetes-related complications among people with T2D at elevated risk for CVD. Disclosure H. Shao: Research Support; Self; Sanofi. H. Kianmehr: None. P. Li: None. V. Fonseca: Consultant; Self; Abbott Diabetes, Asahi Kasei Corporation, Bayer Inc., Boehringer Ingelheim Pharmaceuticals, Inc., Intarcia Therapeutics, Inc., Novo Nordisk, Pfizer Inc., Sanofi-Aventis, Stock/Shareholder; Self; Amgen Inc., Bravo4health, Mellitus Health. L. Shi: Research Support; Self; AstraZeneca, Sanofi. Funding Sanofi (2020-1487)

Introduction: Advancing treatment from basal insulin (BI) to premix insulin may improve glycemic control, though at the expense of greater hypoglycemia risk, including nocturnal hypoglycemia. Methods: SoliMix was a 26-week open-label, multicenter, randomized trial comparing iGlarLixi vs. premix insulin analog (BiAsp 70/30) in adults with T2D and HbA1c ≥7.5-≤10 % on BI + oral antihyperglycemic drugs (OADs). Primary results showed that switching to once-daily iGlarLixi resulted in better glucose control vs. twice-daily BiAsp 70/30. This subanalysis assessed nocturnal hypoglycemia (defined as bedtime-waking or 00:00-06:00 h [any; any symptomatic; ADA Level 1, 2, 3]) during the 26-week study period. Results: Incidence and rates of ADA Level 2 and any symptomatic nocturnal hypoglycemia were lower with iGlarLixi vs. BiAsp 70/30 for both nocturnal time intervals (Table). Incidence and rates of any and Level 1 nocturnal hypoglycemia were lower with iGlarLixi vs. BiAsp 70/30 between 00:00 and 06:00. No nocturnal ADA Level 3 (severe) hypoglycemia occurred in either treatment arm. Conclusions: Switching to iGlarLixi resulted in better glucose control with less nocturnal (00:00-06:00) hypoglycemia vs. premix insulin analog. A reduction in ADA Level 2 and any symptomatic hypoglycemia was also observed for the more clinically relevant window of bedtime to waking. Disclosure R. J. Mccrimmon: Advisory Panel; Self; Novo Nordisk Inc., Sanofi-Aventis, Board Member; Self; Novo Nordisk Foundation, Research Support; Self; AstraZeneca. J. Rosenstock: Board Member; Self; Applied Therapeutics, Boehringer Ingelheim Pharmaceuticals, Inc., Eli Lilly and Company, Intarcia Therapeutics, Inc., Novo Nordisk, Oramed Pharmaceuticals, Inc., Sanofi, Consultant; Self; Applied Therapeutics, Boehringer Ingelheim Pharmaceuticals, Inc., Eli Lilly and Company, Intarcia Therapeutics, Inc., Novo Nordisk, Oramed Pharmaceuticals, Inc., Sanofi, Research Support; Self; Applied Therapeutics, AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Eli Lilly and Company, Genentech, Inc., Intarcia Therapeutics, Inc., Lexicon Pharmaceuticals, Inc., Novartis Pharmaceuticals Corporation, Novo Nordisk, Oramed Pharmaceuticals, Inc., Pfizer Inc., REMD Biotherapeutics, Sanofi. A. Y. Cheng: Advisory Panel; Self; Abbott, AstraZeneca, Bayer Inc., Boehringer Ingelheim Pharmaceuticals, Inc., Eli Lilly and Company, HLS Therapeutics Inc., Janssen Pharmaceuticals, Inc., Medtronic, Merck & Co., Inc., Merck Sharp & Dohme Corp., Novo Nordisk, Sanofi, Research Support; Self; Applied Therapeutics, The Medicine Company, Speaker’s Bureau; Self; Abbott, AstraZeneca, Bausch Health, Canada, Bayer Inc., Boehringer Ingelheim Pharmaceuticals, Inc., Eli Lilly and Company, HLS Therapeutics Inc., Janssen Pharmaceuticals, Inc., Medtronic, Merck & Co., Inc., Novartis Pharmaceuticals Corporation, Novo Nordisk, Sanofi. V. Fonseca: Consultant; Self; Abbott Diabetes, Asahi Kasei Corporation, Bayer Inc., Boehringer Ingelheim Pharmaceuticals, Inc., Intarcia Therapeutics, Inc., Novo Nordisk, Pfizer Inc., Sanofi-Aventis, Stock/Shareholder; Self; Amgen Inc., Bravo4health, Mellitus Health. R. Choza: None. T. Alessa: Advisory Panel; Self; AstraZeneca, Eli Lilly and Company, Novo Nordisk, Sanofi-Aventis, Research Support; Self; Servier Laboratories, Speaker’s Bureau; Self; AstraZeneca, Eli Lilly and Company, Novo Nordisk, Sanofi-Aventis, Servier Laboratories. A. Alvarez: Employee; Self; Sanofi. E. Souhami: Employee; Self; Sanofi, Stock/Shareholder; Self; Sanofi. A. H. Boss: Employee; Self; Sanofi, Stock/Shareholder; Self; Novo Nordisk A/S. P. Picard: None. Funding Sanofi (EudraCT 2017-003370-13)

Objectives: The effects of preventive interventions on cardiovascular autonomic neuropathy (CAN) remain unclear. We examined the effect of intensively treating traditional risk factors for CAN, including hyperglycemia, hypertension, and dyslipidemia, in persons with type 2 diabetes (T2D) and high cardiovascular risk participating in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial. Research Design and Methods: CAN was defined as heart rate variability indices below the 5th percentile of the normal distribution. Of 10,250 ACCORD participants, 71% (n=7,275) had a CAN evaluation at study entry and at least once after randomization. The effects of intensive interventions on CAN were analyzed among these subjects through generalized linear mixed models. Results: As compared to standard intervention, intensive glucose treatment reduced CAN risk by 16% (OR=0.84, 95%CI 0.75–0.94, p=0.003) – an effect driven by individuals without cardiovascular disease (CVD) at baseline (OR= 0.73, 95%CI 0.63–0.85, p Conclusions: These data confirm a beneficial effect of intensive glycemic therapy and demonstrate, for the first time, a similar benefit of intensive blood pressure control on CAN in T2D. A negative CVD history identifies T2D patients who especially benefit from intensive glycemic control for CAN prevention.

OBJECTIVE We successfully implemented the American Diabetes Association’s (ADA) Diabetes INSIDE (INspiring System Improvement with Data-Driven Excellence) quality improvement (QI) program at a university hospital and safety-net health system (Tulane and Parkland), focused on system-wide improvement in poorly controlled type 2 diabetes (HbA1c >8.0% [64 mmol/mol]). In this study, we estimated the 5-year risk reduction in complications and mortality associated with the QI program. RESEARCH DESIGN AND METHODS The QI implementation period was 1 year, followed by the postintervention period of 6 months to evaluate the impact of QI on clinical measures. We measured the differences between the baseline and postintervention clinical outcomes in 2,429 individuals with HbA1c >8% (64 mmol/mol) at baseline and used the Building, Relating, Assessing, and Validating Outcomes (BRAVO) diabetes model to project the 5-year risk reduction of diabetes-related complications under the assumption that intervention benefits persist over time. An alternative assumption that intervention benefits diminish by 30% every year was also tested. RESULTS The QI program was associated with reductions in HbA1c (−0.84%) and LDL cholesterol (LDL-C) (−5.94 mg/dL) among individuals with HbA1c level >8.0% (64 mmol/mol), with greater reduction in HbA1c (−1.67%) and LDL-C (−6.81 mg/dL) among those with HbA1c level >9.5% at baseline (all P < 0.05). The implementation of the Diabetes INSIDE QI program was associated with 5-year risk reductions in major adverse cardiovascular events (MACE) (relative risk [RR] 0.78 [95% CI 0.75–0.81]) and all-cause mortality (RR 0.83 [95% CI 0.82–0.85]) among individuals with baseline HbA1c level >8.0% (64 mmol/mol), and MACE (RR 0.60 [95% CI 0.56–0.65]) and all-cause mortality (RR 0.61 [95% CI 0.59–0.64]) among individuals with baseline HbA1c level >9.5% (80 mmol/mol). Sensitivity analysis also identified a substantially lower risk of diabetes-related complications and mortality associated with the QI program. CONCLUSIONS Our modeling results suggest that the ADA’s Diabetes INSIDE QI program would benefit the patients and population by substantially reducing the 5-year risk of complications and mortality in individuals with diabetes.

The American College of Physicians 2018 guidance statement recommended an A1c goal of 7%-8% for patients with type 2 diabetes because of a lack of statistically significant MACE risk reduction through intensive glycemic control in long-term clinical trials (e.g., ACCORDION trial and VADT). However, interpreting clinical trial results merely based on statical inference (p Disclosure H. Shao: None. S. Yang: None. V. Fonseca: Consultant; Self; Abbott, Asahi Kasei Corporation, AstraZeneca, Bayer Inc., Novo Nordisk Inc., Sanofi. Research Support; Self; Boehringer Ingelheim Pharmaceuticals, Inc. Stock/Shareholder; Self; Amgen, Bravo4health. J. Bian: None. L. Shi: None.

U.S. veterans have a high prevalence of post-traumatic stress disorder (PTSD), for which no effective psychotropic medications are indicated. Nearly 25% of veterans have diabetes, for which metformin is a first line treatment. Although metformin is associated with improved neurodegenerative outcomes, there is no evidence on the association between metformin and PTSD-related symptom relief. This study aimed to examine whether metformin was associated with a clinically meaningful reduction in PTSD symptoms (≥20 points reduction PTSD checklist for DSM-5 {PCL-5}) score)) among veterans with PTSD using data from Veterans Health Administration. The metformin cohort was defined as first metformin use after a recorded PTSD diagnosis with a PCL-5 score ≥33 (i.e., clinical cut-score). The control cohort (never used metformin) was selected by propensity score 1:1 matching on the likelihood of initiating metformin with the covariates of age, gender, race, durations of PTSD, diabetes, and depression, and baseline CCI, PCL-5, and PHQ-9 scores. Multivariable Cox proportional regression was used to assess the association between metformin exposure and improvement in the PCL-5 scores. A total of 7950 veterans (mean age: 51.9 ± 13.6 years, male: 85%, white: 61.4%, African American: 28.8%) with confirmed PTSD were comparable on the baseline demographics and medical conditions between metformin and control PSM-matched cohorts with a median follow-up 1.2 years in both cohorts. The baseline PCL-5 score was 56.2±11.6 and 56.5±11.7 in metformin and control cohort, respectively. The metformin-treated cohort was more likely to have a clinically meaningful reduction in PTSD symptoms (adjusted hazard ratio (aHR): 1.31, 95%CI: 1.16-1.48), compared the control cohort. Furthermore, patients on metformin ≥2 years were 22% more likely to reduce PTSD symptoms (aHR: 1.22, 95%CI: 1.04-1.43), compared with metformin exposure Disclosure S. Liu: None. A.M. Raines: None. Y. Yoshida: None. W. Tang: None. J.I. Constans: None. V. Fonseca: Consultant; Self; Abbott, Asahi Kasei Corporation, AstraZeneca, Bayer Inc., Novo Nordisk Inc., Sanofi. Research Support; Self; Boehringer Ingelheim Pharmaceuticals, Inc. Stock/Shareholder; Self; Amgen, Bravo4health. L. Shi: None.

Cardiovascular autonomic neuropathy (CAN) is a common complication that independently predicts cardiovascular (CV) morbidity and mortality in persons with type 2 diabetes (T2D). The effect of preventive interventions on CAN remains unclear. We examined the effects of intensively targeting hyperglycemia, hypertension, and dyslipidemia on CAN, in persons with T2D and high CV risk from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial. CAN was defined as heart rate variability indices below the 5th percentile of the normal distribution (standard deviation of all normal-to-normal R-R intervals [SDNN] Disclosure Y. Tang: None. H. Shah: None. C. Bueno Junior: None. X. Sun: None. J. Mitri: Consultant; Spouse/Partner; Janssen Pharmaceuticals, Inc., kymera. Consultant; Self; national dairy council/local dairy council. Research Support; Spouse/Partner; AbbVie Inc., beigene, Janssen Pharmaceuticals, Inc. Research Support; Self; Kowa Pharmaceuticals America, Inc., national dairy council. Research Support; Spouse/Partner; pharma cyclic, TG therapeutics. M. Sambataro: None. L. Sambado: None. H.C. Gerstein: Advisory Panel; Self; Abbott, AstraZeneca, Boehringer Ingelheim (Canada) Ltd., Eli Lilly and Company, Merck & Co., Inc., Novo Nordisk Inc., Sanofi. Consultant; Self; Kowa Pharmaceuticals America, Inc. Research Support; Self; AstraZeneca, Boehringer Ingelheim (Canada) Ltd., Eli Lilly and Company, Merck & Co., Inc., Novo Nordisk Inc., Sanofi. Other Relationship; Self; Boehringer Ingelheim (Canada) Ltd., Eli Lilly and Company, Sanofi. V. Fonseca: Consultant; Self; Abbott, Asahi Kasei Corporation, AstraZeneca, Bayer Inc., Novo Nordisk Inc., Sanofi. Research Support; Self; Boehringer Ingelheim Pharmaceuticals, Inc. Stock/Shareholder; Self; Amgen, Bravo4health. A. Doria: Research Support; Self; Sanofi. R. Pop-Busui: Advisory Panel; Self; Boehringer Ingelheim Pharmaceuticals, Inc. Consultant; Self; Bayer Healthcare Pharmaceuticals Inc., Novo Nordisk Inc. Research Support; Self; AstraZeneca. Other Relationship; Self; American Diabetes Association. Funding National Institutes of Health (N01HC95178, N01HC95179, N01HC95180, N01HC95181, N01HC95182, N01HC95183, N01HC95184, IAAY1HC-9035, IAAY1HC1010)

The lifetable approach to estimate the life-expectancy of T2DM patients used a weak assumption on the continuum risk profiles by age group. To improve transparency and accuracy of lifetable estimation, this study aimed to use a microsimulation modeling approach to produce the life-expectancy of T2DM patients. We used the National Health and Nutrition Examination Survey (2011-2012) and linked the National Death Index to calibrate the mortality equation of the Building, Relating, Assessing, and Validating Outcomes (BRAVO) diabetes model, a person-level discrete-time microsimulation model, which has been extensively validated against 18 international trials before. Life expectancy was estimated under two treatment targets: population average: A1c 7.5%, SBP 130 mmHg, LDL 100 mg/dl, and BMI 30 kg/m2; recommended level: A1c 6.5%, SBP 120 mmHg, LDL 90 mg/dl, and BMI 25 kg/m2. Table 1 presents the life-expectancies of patients with type 2 diabetes by subgroups. Females in general lives 4 years longer than males, while racial-ethnic difference in life-expectancy was not significant among T2DM patients, conditional on the trajectories of risk factors across groups. The recommended goal of biomarkers prolongs life expectancy by 1.3-1.8 years, compared with the current population average. The new approach may generate more accurate statistics for national diabetes surveillance. Disclosure H. Kianmehr: None. S. Yang: None. V. Fonseca: Consultant; Self; Abbott, Asahi Kasei Corporation, AstraZeneca, Bayer Inc., Novo Nordisk Inc., Sanofi. Research Support; Self; Boehringer Ingelheim Pharmaceuticals, Inc. Stock/Shareholder; Self; Amgen, Bravo4health. L. Shi: None. J.D. Brown: None. Y. Guo: None. H. Shao: None.

Injectable therapy initiation in T2D is often delayed. This retrospective cohort study evaluated the impact of the timing of initiating both basal insulin and GLP-1 RA in people with uncontrolled T2D (HbA1c ≥9 %) on OADs within 12 months (Optum Humedica database of electronic medical records and claims; 1/1/2011 to 6/30/2017). Study cohorts were defined by the days between initiating the first and second injectable: cohort A ≤30 (simultaneous); B 31-90; C 91-180; D 181-270; E 271-360 (sequential). Endpoints: proportion achieving HbA1c In summary, despite higher baseline HbA1c, addition of a second injectable agent within 90 days of the first led to significantly better glycemic control at 12 months than postponing addition to later in the year in uncontrolled T2D. Disclosure J. Rosenstock: Research Support; Self; AstraZeneca, Bristol-Myers Squibb, Genentech, Inc., GlaxoSmithKline plc., Lexicon Pharmaceuticals, Inc., Oramed Pharmaceuticals, PegBio Co., Ltd., Pfizer Inc., REMD Biotherapeutics. Other Relationship; Self; Applied Therapeutics, Boehringer Ingelheim Pharmaceuticals, Inc., Eli Lilly and Company, Intarcia Therapeutics, Janssen Pharmaceuticals, Inc., Novo Nordisk Inc., Sanofi. F. Ampudia Blasco: None. R. Lubwama: Employee; Self; Sanofi US. X.V. Peng: Employee; Self; Sanofi. A.H. Boss: Employee; Self; Sanofi. Stock/Shareholder; Self; Novo Nordisk Inc., Sanofi. L. Shi: None. V. Fonseca: Consultant; Self; Abbott, Asahi Kasei Corporation, AstraZeneca, Bayer Inc., Novo Nordisk Inc., Sanofi. Research Support; Self; Boehringer Ingelheim Pharmaceuticals, Inc. Stock/Shareholder; Self; Amgen, Bravo4health. Funding Sanofi

Glucagon-like peptide 1 (GLP-1) is known to suppress glucagon secretion, but the mechanism by which GLP-1 exerts this effect is unclear. In this study, we demonstrated GLP-1 receptor (GLP-1R) expression in α-cells using both antibody-dependent and antibody-independent strategies. A novel α-cell–specific GLP-1R knockout (αGLP-1R−/−) mouse model was created and used to investigate its effects on glucagon secretion and glucose metabolism. Male and female αGLP-1R−/− mice both showed higher nonfasting glucagon levels than their wild-type littermates, whereas insulin and GLP-1 levels remained similar. Female αGLP-1R−/− mice exhibited mild glucose intolerance after an intraperitoneal glucose administration and showed increased glucagon secretion in response to a glucose injection compared with the wild-type animals. Furthermore, using isolated islets, we confirmed that αGLP-1R deletion did not interfere with β-cell function but affected glucagon secretion in a glucose-dependent bidirectional manner: the αGLP-1R−/− islets failed to inhibit glucagon secretion at high glucose and failed to stimulate glucagon secretion at very low glucose condition. More interestingly, the same phenomenon was recapitulated in vivo under hypoglycemic and postprandial (fed) conditions. Taken together, this study demonstrates that GLP-1 (via GLP-1R in α-cells) plays a bidirectional role, either stimulatory or inhibitory, in glucagon secretion depending on glucose levels.

Diabetic retinopathy (DR), a microvascular complication of diabetes, is the leading cause of blindness among adults aged 20 to 74 years (1). Between 2010 and 2014, ∼4.2 million adults aged >40 years were diagnosed with DR (2,3). Of these, 655,000 suffered from advanced DR, which can cause blindness if left untreated (2). The American Diabetes Association recommends eye examinations for people with diabetes based on the evidence of DR present (i.e., if any level of DR is present, then exams should be repeated at least annually; if there is no evidence, then exams every 2 years may suffice) (3). Regardless of stipulations, it is evidenced that racial and ethnic minorities have been previously shown to have fewer exams than recommended (4). This study assesses the racial and ethnic disparities of eye examination rates among U.S. adults with diabetes after the increased access initiatives of the Affordable Care Act (ACA). Annual data from the 2014–2015 Medical Expenditure Panel Survey (MEPS) Household Component data files were analyzed. The study population included adults aged 18 years and older with a self-reported diagnosis of diabetes from a health care professional. The primary outcome was the annual eye examination rate, defined as …

We have previously described the utility and high predictive value of the Building, Relating, Assessing, and Validating Outcomes (BRAVO) model, which was developed from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial data. However, it uses diabetes duration as a key factor for modelling diabetes progression, and diabetes duration is not routinely collected in EMRs. This study use machine learning to estimate diabetes duration with EMR information. Among individuals (N=8,026) with complete records from the ACCORD trial, diabetes duration was estimated with an ordinary least squares (OLS) model, a least absolute shrinkage and selection operator (LASSO) model, a pruned decision tree model, and a random forest model using 37 baseline predictors such as biomarkers, demographics, disease history, and medications. Model performance was compared using root-mean-square Error (RMSE), mean-absolute error (MAS), and R-squared. All three indicators were calculated through 10-fold cross-validation with 1000 iterations in order to address over-fitting. Age, current HbA1c value, retinopathy, number of oral antidiabetic drugs, number of Insulin types, and use of sulfonylureas were identified as key predictors for patient-reported diabetes duration. The random forest model achieved the best performance on all three indicators (RMSE=6.16, MAE= 4.71, R2=0.30), significantly higher than the pruned decision tree model (RMSE=6.57, MAE= 5.03, R2=0.21) and LASSO model (RMSE=6.60, MAE= 5.08, R2=0.20). Diabetes duration can be predicted using fields routinely included in EMRs. Integration of the results of this prediction equation into predictive diabetes models (such as BRAVO) will allow patient-specific estimates of diabetes duration based solely on EMRs. This will increase precision when inferring risk level and using models to aid clinical decision making among patients with type 2 diabetes. Disclosure H. Shao: None. S. Yang: None. C. Stoecker: None. V. Fonseca: Board Member; Self; American Association of Clinical Endocrinologists. Consultant; Self; Abbott, Asahi Kasei Corporation, Novo Nordisk Inc., Sanofi US, Takeda Pharmaceutical Company Limited. Research Support; Self; Bayer US. Stock/Shareholder; Self; Amgen Inc., BRAVO4HEALTH, Mellitus Health. X. Cheng: None. L. Shi: None.

Objective: Several Cardiovascular Outcome Trials (CVOTs) have demonstrated benefits compared to placebo. However, the patients included were heterogeneous, including several with long duration diabetes, insulin treatment may be a surrogate for duration and severity of disease. We conducted a meta-analysis to evaluate the impact of concurrent insulin treatment on the odds of meeting the primary outcome in recent CVOTs. Methods: PubMed and google scholar were searched for CVOTs with new drugs for diabetes. We found 10 publications, of which 7 provided outcomes according to insulin use. We compared the risk ratio of the primary endpoint in the group taking insulin with those not taking insulin by extracting the number of events in both groups along with total number of patients. We also removed the 2 trials that used dipeptidyl peptidase 4 inhibitors, since none of the drugs had shown benefit in the trials. The I-square was used to assess between-study heterogeneity and all analyses were performed using STATA program version 15.1. Fixed effect models were used in these analyses due to low heterogeneity. This study was conducted in compliance with PRISMA guidelines. Results: When all 7 trials were included in the meta-analysis (EXSCEL, SUSTAIN6, SAVORTIMI, EMPAREG, LEADER, EXAMINE, and DECLARE), the risk ratio of the primary outcome was significantly increased indicating that patients using insulin with drug are more likely to have the primary cardiovascular outcome than the group of patients using drug without insulin. The RR was 1.43 (95% confidence interval: 1.32, 1.55). Conclusion: In recent CVOTs which have demonstrated a benefit of the study drug on the primary outcome compared to usual care, the concomitant use of Insulin along with the study drugs led to a higher risk of cardiovascular events than in patients who were on the drugs without insulin. The reason for this increased risk is unclear but may be related to the likely longer duration of diabetes and poorer antecedent control which led to the use of insulin. Disclosure J. Khatib: None. Y. Shao: None. L. Shi: None. V. Fonseca: Board Member; Self; American Association of Clinical Endocrinologists. Consultant; Self; Abbott, Asahi Kasei Corporation, Novo Nordisk Inc., Sanofi US, Takeda Pharmaceutical Company Limited. Research Support; Self; Bayer US. Stock/Shareholder; Self; Amgen Inc., BRAVO4HEALTH, Mellitus Health.

Background: Understanding the long-term cardiovascular safety of type 2 diabetes (T2D) medications is a high-priority research area. We used electronic medical record (EMR) data in the National Patient Centered Clinical Research Network (PCORnet) to characterize “real-world” use of T2D medications. Methods and Results: We identified a retrospective cohort of patients with T2D using a validated computable phenotype. We queried 4 unique PCORnet sites for data regarding T2D medication use in the 90 days before and after cohort entry. We described patient demographics, comorbidities, non-T2D medication use, and clinical variables in the 730 days prior to cohort entry. We identified 49,654 patients with T2D across the 4 sites (range: 2,276-20,322). Use of T2D medications, characterized by pharmacologic class, and selected characteristics are presented in Table. Among patients prescribed a single class of T2D medication, metformin was the most common (52.5%). Among patients prescribed multiple classes of T2D agents, the most common combination was metformin and sulfonylureas (49%). Conclusions: Across 4 PCORnet sites, metformin-based regimens were very prevalent, and approximately 10% of regimens included newer agents. PCORnet will be a valuable resource for future observational studies of T2D medications and long-term cardiovascular outcomes in “real-world” populations. Disclosure K.N. Bachmann: Stock/Shareholder; Self; Medtronic. C. Roumie: None. A.D. Wiese: None. C.G. Grijalva: None. J.B. Buse: Consultant; Self; Neurimmune AG. Research Support; Self; AstraZeneca, National Center for Advancing Translational Sciences, National Institute of Diabetes and Digestive and Kidney Diseases, Novo Nordisk A/S, Sanofi, vTv Therapeutics. Stock/Shareholder; Self; Mellitus Health, PhaseBio Pharmaceuticals, Inc., Stability Health. Other Relationship; Self; ADOCIA, AstraZeneca, Dance Biopharm Holdings Inc., Eli Lilly and Company, MannKind Corporation, NovaTarg, Novo Nordisk A/S, Senseonics, vTv Therapeutics, Zafgen, Inc. R. Bradford: None. E.O. Zalimeni: None. P. Knoepp: None. H.L. Morris: None. W.T. Donahoo: None. N. Fanous: None. V. Fonseca: Board Member; Self; American Association of Clinical Endocrinologists. Consultant; Self; Abbott, Asahi Kasei Corporation, Novo Nordisk Inc., Sanofi US, Takeda Pharmaceutical Company Limited. Research Support; Self; Bayer US. Stock/Shareholder; Self; Amgen Inc., BRAVO4HEALTH, Mellitus Health. B. Katalenich: None. M.M. Cook: None. R.L. Rothman: Consultant; Self; Abbott, edlogics. R.J. Chakkalakal: None. Funding National Institutes of Health (UL1TR002489, UL1TR002489); U.S. Department of Veterans Affairs (IK2CX001678); Patient-Centered Outcomes Research Institute (CDRN-1306-04869)

This real-world retrospective observational analysis was conducted using data from REACHnet, a U.S. regional electronic medical records (EMR) database. Glycemic outcomes after simultaneous or sequential initiation of basal insulin (BI) and a glucagon-like peptide-1 receptor agonist (GLP-1 RA) were evaluated in patients with T2D uncontrolled (A1C ≥7.0%) on oral antidiabetes drugs (OADs). Three patient cohorts were defined: simultaneous BI and GLP-1 RA initiation (Cohort 1; n=109); initiation of BI followed by a GLP-1 RA with a gap of ≤90 days (Cohort 2; n=301); and initiation of BI followed by a GLP-1 RA with a gap of >90 days (Cohort 3; n=459). Baseline mean A1C was very similar across the cohorts (10.3%, 10.3%, and 10.2% in Cohorts 1, 2, and 3, respectively), as were comorbidities including dyslipidemia, hypertension, and obesity. Mean A1C decreased in all 3 cohorts in the 12 months from the date of first BI injection. The greatest mean A1C reductions occurred within 6 months of simultaneous administration (Cohort 1: -2.18%), followed by addition of a GLP-1 RA within 90 days (Cohort 2: -1.77%) and after 90 days (Cohort 3: -1.24%). Results at 12 months were Cohort 1: -1.66%; Cohort 2: -1.46%; and Cohort 3: -1.32%. Persistence (i.e., non-discontinuation of index treatments) at 12 months was 74.3%, 73.8%, and 80.0% in Cohorts 1, 2, and 3, respectively. Simultaneous initiation resulted in more patients achieving A1C In conclusion, this retrospective analysis of real-world EMR data in patients with T2D uncontrolled on OADs suggests that simultaneous initiation of BI and a GLP-1 RA resulted in significantly better glycemic control than sequential initiation with a gap of >90 days. Disclosure X.V. Peng: Employee; Self; Sanofi. Stock/Shareholder; Spouse/Partner; AbbVie Inc. Stock/Shareholder; Self; AbbVie Inc., Sanofi. R. Ayyagari: Consultant; Self; Sanofi US. Other Relationship; Self; Various pharmaceutical companies. R. Lubwama: Employee; Self; Merck & Co., Inc., Sanofi. M.J. Van Vleet: Employee; Self; Sanofi US. L. Shi: None. E.G. Price-Haywood: None. P. Hollander: Advisory Panel; Self; Novo Nordisk Inc. V. Fonseca: Board Member; Self; American Association of Clinical Endocrinologists. Consultant; Self; Abbott, Asahi Kasei Corporation, Novo Nordisk Inc., Sanofi US, Takeda Pharmaceutical Company Limited. Research Support; Self; Bayer US. Stock/Shareholder; Self; Amgen Inc., BRAVO4HEALTH, Mellitus Health. Funding Sanofi U.S.

We have successfully implemented the ADA’s Diabetes INSIDE® QI program at a University hospital and safety-net health system (Tulane and Parkland), focused on system-wide improvement in poorly controlled type 2 diabetes (A1c>8.0%). We extrapolated clinical outcomes of the QI program on a 5-year risk of complications, utilizing a validated risk prediction model: Building, Relating, Assessing, Validating of Outcomes (BRAVO) simulation model. We measured the differences between the baseline and post-intervention values of risk factors in 2,429 individuals with A1c >8% at baseline and used the BRAVO model to project the 5-year risk reduction of diabetes-related complications under the assumption that intervention benefits diminished with time. The intervention period was defined as one year and the post-intervention period as the subsequent six months. The QI program was associated with reductions in A1c (-0.84%) and LDL-C (-5.94 mg/dl) among individuals with A1c level >8.0%, with greater reduction in A1c (-1.67%) and LDL-C (-6.81 mg/dl) among those with A1c level > 9.5% at baseline (all p Disclosure H. Shao: None. L. Shi: None. R.E. Furman: None. L. Meneghini: Advisory Panel; Self; AstraZeneca, Novo Nordisk Inc., Sanofi-Aventis. Consultant; Self; Sanofi-Aventis. Other Relationship; Self; American Diabetes Association. T.S. Harlan: None. V. Fonseca: Board Member; Self; American Association of Clinical Endocrinologists. Consultant; Self; Abbott, Asahi Kasei Corporation, Novo Nordisk Inc., Sanofi US, Takeda Pharmaceutical Company Limited. Research Support; Self; Bayer US. Stock/Shareholder; Self; Amgen Inc., Bravo4health, Mellitus Health. Funding Lilly USA, LLC; Novo Nordisk; Sanofi

As a participant in the ADA’s Diabetes INSIDE program, a 19-member multidisciplinary QI team identified care quality gaps after reviewing 6 years (1/2010 to 5/2016) of EHR data on 3,136 T2DM outpatients seen by 190 PCPs in an urban academic medical center. The team set goals to increase HbA1c testing frequency (reduce 45% of patients with HbA1c intervals > 6 months) and to reduce HbA1c levels in both the overall population and patients with HbA1c > 9%. Implemented strategies included 1) a provider outreach program of departmental and individualized actionable data reports of HbA1c testing intervals and outcomes, 2) a patient outreach program to address individual timely follow-up care, 3) a patient awareness campaign to improve understanding of following and improving clinical outcomes, 4) improving structured EHR data capture to improve population monitoring, and 5) standardizing workflow across clinics for pre-visit planning, patient outreach, patient education, checking completion of appointment goals, and post-visit follow-up. Eight-years of EHR follow-up data (1/2010 to 5/2018) on 7,798 patients (mean age 61 years, 57% female, 62% black, 97% insured) with 136,004 visits showed target improvements. Population characteristics were stable over time. A department-stratified Cox proportional hazards model controlling for age, gender, race, and HbA1c level showed a statistically signification 20% reduction in HbA1c testing intervals > 6 months (HR = 1.20±0.07). Statistical process control charts (6 years pre- and 2 years post-intervention) showed statistically significant 15.5% relative improvement in the patient proportion with HbA1c > 9% from 13% to 11% following QI interventions and a significant 2.1% improvement of population mean HbA1c from 7.4% to 7.2%. Disclosure R.E. Furman: None. T.S. Harlan: None. L. LeBlanc: None. E. Furman: None. V. Fonseca: Board Member; Self; American Association of Clinical Endocrinologists. Consultant; Self; Abbott, Asahi Kasei Corporation, Novo Nordisk Inc., Sanofi US, Takeda Pharmaceutical Company Limited. Research Support; Self; Bayer US. Stock/Shareholder; Self; Amgen Inc., BRAVO4HEALTH, Mellitus Health. Funding Eli Lilly and Company; Novo Nordisk; Sanofi

OBJECTIVE In the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial, ∼4 years of intensive versus standard glycemic control in participants with type 2 diabetes and other cardiovascular risk factors had a neutral effect on the composite cardiovascular outcome, increased cardiovascular and total mortality, and reduced nonfatal myocardial infarction. Effects of the intervention during prolonged follow-up were analyzed. RESEARCH DESIGN AND METHODS All surviving ACCORD participants were invited to participate in the ACCORD Follow-on (ACCORDION) study, during which participants were treated according to their health care provider’s judgment. Cardiovascular and other health-related outcomes were prospectively collected and analyzed using an intention-to-treat approach according to the group to which participants were originally allocated. RESULTS A total of 8,601 people, representing 98% of those who did not suffer a primary outcome or death during the ACCORD trial, were monitored for a median of 8.8 years and a mean of 7.7 years from randomization. Intensive glucose lowering for a mean of 3.7 years had a neutral long-term effect on the primary composite outcome (nonfatal myocardial infarction, nonfatal stroke, or cardiovascular death), death from any cause, and an expanded composite outcome that included all-cause death. Moreover, the risk of cardiovascular mortality noted during the active phase (hazard ratio 1.49; 95% CI 1.19, 1.87; P < 0.0001) decreased (HR 1.20; 95% CI 1.03, 1.39; P = 0.02). CONCLUSIONS In high-risk people with type 2 diabetes monitored for 9 years, a mean of 3.7 years of intensive glycemic control had a neutral effect on death and nonfatal cardiovascular events but increased cardiovascular-related death.

We congratulate Segar et al. (1) on their prediction of hospitalization for heart failure (HHF) in type 2 diabetes, which confirms our previously published finding (2). Indeed, we have used a more sophisticated approach (machine learning, left truncation, time-varying risk factors, parametric proportional hazard) with a superior predictive capability (5-year c-statistic >0.80) for Action to Control Cardiovascular Risk in Diabetes (ACCORD) patients without heart failure at baseline. We have risk equations not just for HHF and other cardiovascular events, but for a wide range of complications of diabetes, including microvascular complications. …

Type 2 diabetes is one of the most common chronic diseases in the U.S.; however, few studies have comprehensively assessed the relations between its comorbidities and premature death. Prospective analyses were performed linking data from National Health Interview Surveys (1997 to 2009) to National Death Index records (2011) among 366,971 adults. Cox models were used to test the relations of diabetes and eleven comorbidities with all-cause, CVD, or cancer mortality. After a median follow-up of 8.0 years (2.9 million person-years), a total of 38,687 participants had died. Compared with common-conditions-free subjects, we ranked hazard ratios (HR[95% CI]) of eleven diabetic comorbidities, and the top three were chronic kidney disease (CKD: 5.3[4.9-5.7]), liver cirrhosis (5.1[4.6-5.8]), and blindness (4.4[3.8-5.1]) for all-cause mortality. Diabetics with CKD (5.9[5.0-6.9]) and blindness (5.0[3.8-6.5]) were at the highest risk for CVD deaths. Comorbid cancer (4.1[3.6-4.6]), liver cirrhosis (4.1[3.2-5.2]), and hepatitis (2.7[2.1-3.5]) ranked top three for cancer mortality. Diabetes comorbidities may substantially increase the risk of all-cause, CVD, or cancer mortality. Liver disease is more strongly related to cancer mortality, whereas CKD and blindness with CVD. Recognition of these associations may assist with more appropriate and effective screening programs that may help decrease associated mortality. Disclosure D. Sun: None. T. Zhou: None. X. Li: None. Y. Heianza: None. X. Shang: None. V. Fonseca: Consultant; Self; Abbott. Board Member; Self; American Association of Clinical Endocrinologists. Consultant; Self; Eli Lilly and Company. Stock/Shareholder; Self; Amgen Inc.. Consultant; Self; Asahi Kasei Corporation, AstraZeneca, Novo Nordisk Inc., ADOCIA, Intarcia Therapeutics, Inc., Sanofi-Aventis. L. Qi: None.