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We evaluated the impact of reimbursement for non-face-to-face chronic care management (NFFCCM) on healthcare utilization among Medicare beneficiaries with type 2 diabetes in Louisiana.We implemented group-based trajectory balancing and propensity score matching to obtain comparable treatment (with NFFCCM) and control (without NFFCCM) groups at baseline. Patients with diabetes with Medicare as their primary payer at baseline were extracted using electronic health records of 3 health systems from Research Action for Health Network, a Clinical Research Network. The study period is from 2013 to early 2020. Our outcomes include general healthcare utilization (outpatient, emergency department, and inpatient encounters) and health utilization related to diabetic complications. We tested each of these outcomes according to multiple treatment definitions and different subgroups.Receiving any NFFCCM was associated with an increase in outpatient visits of 657 (95% confidence interval [CI] 626-687; P.001) per 1000 patients per month, a decrease in inpatient admissions of 5 (95% CI 2-7; P.001) per 1000 patients per month, and a decrease in emergency department visits of 4 (95% CI 1-7; P = .005) per 1000 patients per month after 24-month follow-up from initial NFFCCM encounter. Both complex and noncomplex NFFCCM significantly increased visits to outpatient services and inpatient admissions per month. Receiving NFFCCM has a dose-response association with increasing outpatient visits per month.Patients with diabetes in Louisiana who received NFFCCM had more low-cost primary healthcare and less high-cost healthcare utilization in general. The cost savings of NFFCCM in diabetes management could be further explored in the future.

To explore details of the incidence and rates of daytime and nocturnal hypoglycaemia, levels of hypoglycaemia, and relationship to glycated haemoglobin (HbA1c), when comparing iGlarLixi versus premixed biphasic insulin aspart 30 (BIAsp 30) in the SoliMix randomized controlled trial.This exploratory analysis of SoliMix used logistic regression and negative binomial regression analyses to assess between-treatment differences in the incidence and rates of hypoglycaemia by time of day. A negative binomial model was used to derive estimated annualized hypoglycaemia rates as a function of HbA1c.iGlarLixi was associated with lower incidence and rates of American Diabetes Association Level 2 (54 mg/dL [3.0 mmol/L]) hypoglycaemia during both night and day versus BIAsp 30. Incidence and rates of Level 1 (70 to ≥54 mg/dL [3.9 to ≥3.0 mmol/L]) hypoglycaemia were also mostly shown to be reduced with iGlarLixi versus BIAsp 30. Severe (Level 3) events were too few for analysis (n = 3). iGlarLixi was associated with lower modelled event rates of Level 2 and Level 1 hypoglycaemia over a wide range of HbA1c levels versus BIAsp 30.These results show that the lower HbA1c levels and weight benefit seen with iGlarLixi versus premixed BIAsp 30 in people with type 2 diabetes advancing their basal insulin therapy in the SoliMix trial are also accompanied by a lower risk of hypoglycaemia at any time of day and across a broad range of HbA1c levels.

Background: Although the safety of mesenchymal stroma/stem cells (MSCs)-based therapies had already extensively demonstrated, non-clinical biodistribution studies are essential for predicting the safety and efficacy of these cells. Herein we assessed the biodistribution of human immature dental pulp stem cells (hIDPSCs), which has investigated as a candidate for the treatment of Huntington’s disease (HD). Method: For this, we intravenously transplanted hIDPSCs transfected with luciferase or labeled with magnetic nanoparticle in C57BL/6 mice and performed the bioluminescence image (BLI) or inductively coupled plasma mass spectrometer (ICP-MS) to quantity in vivo and ex vivo biodistribution after 4h, 24h, 3, 7, and 30 days of the hIDPSCs administration. Results: BLI’s results showed the presence of hIDPSCs in the chest, lungs, and head after 4h, 24 h, and 3 days of the cell transplantation. No bioluminescent signal was observed in the chest or head on days 7 and 30 days. The ICP-MS’s results showed that the hIDPSCs engraft into the liver, kidney, heart, and lungs. However, the number of hIDPSCs in these sites significantly reduced from the seventh day, being undetectable on the 30th day. By contrast, we observed that the hIDPSCs not only engrafted into the brain, but also remain in this organ for 30 days. Conclusion: These data provide evidence that the hIDPSCs successfully engraft and remain in the brain for until 30 days after the cell transplantation, demonstrating that these cells can migrate and homing to the brain, being a useful candidate for the treatment of neurodegenerative disorders, such as HD.

AimsTo explore the modifying effect of glucagon-like peptide-1 receptor agonist (GLP-1RA) use on outcomes with finerenone across a wide spectrum of patients with chronic kidney disease (CKD) and type 2 diabetes (T2D) in the pooled analysis of FIDELIO-DKD and FIGARO-DKD.Materials and methodsPatients with T2D and CKD treated with optimized renin-angiotensin system blockade were randomized to finerenone or placebo. Effects of finerenone on a cardiovascular composite outcome (cardiovascular death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure) and a kidney composite outcome (kidney failure, sustained ≥57% estimated glomerular filtration rate [eGFR] decline, or renal death), change in urine albumin-to-creatinine ratio (UACR), and safety were analysed by GLP-1RA use.ResultsOf 13 026 patients, 944 (7.2%) used GLP-1RAs at baseline. Finerenone reduced the risk of the cardiovascular composite outcome (hazard ratio [HR] 0.76, 95% confidence interval [CI] 0.52–1.11 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) irrespective of baseline GLP-1RA use. Reduction in UACR with finerenone at Month 4 was –38% in patients with baseline GLP-1RA use compared with –31% in those without GLP-1RA use (P-interaction = 0.03). Overall safety and incidence of hyperkalaemia were similar, irrespective of GLP-1RA use.ConclusionsThe cardiorenal benefits of finerenone on composite cardiovascular and kidney outcomes and UACR reduction in patients with CKD and T2D appear to be maintained, regardless of GLP-1RA use. Subsequent studies are needed to investigate any potential benefit of this combination. Aims: To explore the modifying effect of glucagon-like peptide-1 receptor agonist (GLP-1RA) use on outcomes with finerenone across a wide spectrum of patients with chronic kidney disease (CKD) and type 2 diabetes (T2D) in the pooled analysis of FIDELIO-DKD and FIGARO-DKD. Materials and methods: Patients with T2D and CKD treated with optimized renin-angiotensin system blockade were randomized to finerenone or placebo. Effects of finerenone on a cardiovascular composite outcome (cardiovascular death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure) and a kidney composite outcome (kidney failure, sustained ≥57% estimated glomerular filtration rate [eGFR] decline, or renal death), change in urine albumin-to-creatinine ratio (UACR), and safety were analysed by GLP-1RA use. Results: Of 13 026 patients, 944 (7.2%) used GLP-1RAs at baseline. Finerenone reduced the risk of the cardiovascular composite outcome (hazard ratio [HR] 0.76, 95% confidence interval [CI] 0.52–1.11 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) irrespective of baseline GLP-1RA use. Reduction in UACR with finerenone at Month 4 was –38% in patients with baseline GLP-1RA use compared with –31% in those without GLP-1RA use (P-interaction = 0.03). Overall safety and incidence of hyperkalaemia were similar, irrespective of GLP-1RA use. Conclusions: The cardiorenal benefits of finerenone on composite cardiovascular and kidney outcomes and UACR reduction in patients with CKD and T2D appear to be maintained, regardless of GLP-1RA use. Subsequent studies are needed to investigate any potential benefit of this combination.

Introduction Although hyperkalemia and metabolic acidosis often co-occur in patients with chronic kidney disease (CKD), the prevalence of metabolic acidosis among patients with CKD and hyperkalemia is understudied. Therefore, we used medical record data from the Research Action for Health Network to estimate this prevalence. Methods Adult patients with CKD stage 3–5, ≥ 1 outpatient potassium value > 5.0 mEq/l, and ≥ 1 outpatient bicarbonate value available were identified. Patients with end stage kidney disease (ESKD) in the prior year were excluded. The prevalence of metabolic acidosis in each calendar year from 2014 to 2017 among patients with CKD and hyperkalemia was estimated using two definitions of hyperkalemia (potassium > 5.0 mEq/l and > 5.5 mEq/l) and metabolic acidosis (bicarbonate 5.0 mEq/l and ranged from 33 to 39% when hyperkalemia was defined by potassium > 5.5 mEq/l. Conclusion Results demonstrated that prevalence estimates of metabolic acidosis varied based on the definition of hyperkalemia and metabolic acidosis utilized.

Background The difference between the costs of the newer and older glucose-lowering drugs (GLMs) 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 the cost change influenced the cost-effectiveness of the newer GLMs. Methods Based on our previous literature review on US-based cost-effectiveness 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 GLMs, we identified 12 studies that reported the cost-effectiveness of newer drugs based on drug costs estimated before 2010. We updated the corresponding cost-effectiveness 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 the original studies. 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 four studies comparing newer drugs with thiazolidinediones, all the original results favored newer drugs. However, all studies suggested thiazolidinedione to be cost-effective in the updated analysis. For the four studies comparing newer drugs with sulfonylureas, two studies suggested the sulfonylureas to be cost-effective after the cost update. All four 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 under a 30% rebate. Conclusion Significant changes in the cost of GLMs have impacted the economic value of different GLM classes substantially. More cost-effectiveness analyses are warranted to support the drug choice in T2DM management.

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 …

OBJECTIVES Heart disease is the most common cause of death in patients with nonalcoholic fatty liver disease (NAFLD). Emerging data have shown that NAFLD may affect subclinical myocardial remodeling, mainly left ventricular hypertrophy; however, evidence from the prospective studies is still lacking. METHODS Prospective analyses were performed to investigate the association of fatty liver index (FLI) with left ventricular mass (LVM) among 1962 participants from the Bogalusa Heart Study (BHS, 1995-2010) and 1547 participants from the Cardiovascular Risk in Young Finns Study (YFS, 2001-2011) free of cardiovascular diseases (CVD) at baseline. LVM was assessed by two-dimensional guided M-mode echocardiography and indexed (LVMI) to body height (m2.7). Multivariable regression models were applied after adjustment for traditional CVD risk factors. RESULTS In both cohorts, we observed significant and positive associations between FLI and LVM (BHS: β=0.59, P

Whether lowering the hemoglobin A1c to6.0% in patients with type 2 diabetes can reduce the risk of cardiovascular disease (CVD) remains under debate. The ACCORDION and the VADT studies both found reductions in the primary CVD composite associated with intensive glycemic control, though the difference is not statistically significant. However, the lack of significance is often overinterpreted as non-effective: a

Management of diabetes requires a multifaceted approach of risk factor reduction; through management of risk factors such as glucose, blood pressure and cholesterol. Goals for these risk factors often vary and guidelines suggest that this is based on patient characteristics and need to be individualized. Evaluating risk is therefore critically important to determine goals and choose appropriate treatments. A risk engine is an analytic tool that collects a large amount of population data allowing the simulation of the progression of diabetes with set equations over a period of time. Recently, a number of data cohorts have become available, leading to the development of newer risk engines that are more dynamic and generalizable. An example is the Building, Relating, Assessing, and Validating Outcomes in (BRAVO) diabetes model which was built on the ACCORD trial database. It is capable of accurately predicting diabetes comorbidities in an international population based on calibration with international clinical trial data. It has potential uses in risk stratification of patients, evaluation of interventions and calculation of their long term cost effectiveness. Recently, it has been used to simulate long term outcomes based on short term data, using difference modelling scenarios.

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.

Many people with type 2 diabetes mellitus (T2DM) experience suboptimal glycemic control and require therapy advancement. This cost-effectiveness analysis was conducted to compare iGlarLixi (insulin glargine 100 U/mL plus lixisenatide) versus BIAsp 30 (biphasic insulin aspart 30) in people with T2DM suboptimally controlled with basal insulin.The IQVIA Core Diabetes Model was used to estimate lifetime costs and outcomes for people with T2DM from a US healthcare payer perspective. Initial clinical data were based on the phase 3 randomized, open-label, active-controlled SoliMix clinical study, which compared the efficacy and safety of once-daily iGlarLixi with twice-daily BIAsp 30. Lifetime costs (US$) and quality-adjusted life-years (QALYs) were predicted, and the incremental cost-effectiveness ratio (ICER) for iGlarLixi versus BIAsp 30 was estimated; the willingness-to-pay threshold was considered to be $50,000. A subgroup analysis considered people with T2DM aged ≥ 65 years.Estimated QALYs gained were slightly higher with iGlarLixi compared with BIAsp 30 (9.3 vs. 9.2), with lower costs for iGlarLixi ($117,854 vs. $120,109); the ICER for iGlarLixi was therefore considered dominant over BIAsp 30 in the base case. Key drivers for cost savings were the higher dose and twice-daily administration for BIAsp 30 versus once-daily administration for iGlarLixi. The robustness of the base-case results was confirmed by sensitivity and scenario analyses. Results were similar in a subgroup of people with T2DM aged ≥ 65 years.In people with T2DM with suboptimal glycemic control on basal insulin, iGlarLixi confers improved QALYs and reduced costs compared with BIAsp 30.