Asahi Kasei Corporation Research Articles

178-OR: Better Glycemic Control with Once-Weekly Insulin Icodec vs. Once-Daily Insulin Degludec in Insulin-Naïve Type 2 Diabetes (ONWARDS 3)

Efficacy and safety of once-weekly insulin icodec (icodec) vs once-daily insulin degludec (degludec) in insulin-naïve adults with T2D were examined in ONWARDS 3 (NCT04795531), a 26-week, randomized, double blind, double dummy, treat-to-target, phase 3a trial. Participants (n=588) were randomized 1:1 to icodec or degludec. Key outcomes are shown in the table. The primary endpoint was A1C change from baseline (BL) to week 26. Estimated mean A1C change was greater with icodec (−1.57%; BL: 8.55%) vs degludec (−1.36%; BL: 8.48%); estimated treatment difference −0.21%-points (95% CI −0.34, −0.08), confirming non-inferiority (p<0.0001) and superiority (p=0.0016). Level 2 or 3 hypoglycemia rates were numerically but not statistically significantly higher with icodec vs degludec in the on-treatment period. Odds of achieving A1C <7% without level 2 or 3 hypoglycemia were statistically significantly higher with icodec vs degludec. There were no significant differences in weekly insulin dose from week 24 to 26 or body weight change from BL to week 26 between arms. No unexpected safety findings were observed. Overall, once-weekly icodec demonstrated superior A1C reduction vs once-daily degludec with low level 2 or 3 hypoglycemia rates in both arms. Icodec may facilitate insulin treatment acceptance and adherence by reducing basal insulin injections from ≥365 to 52 per year. Disclosure I.Lingvay: Advisory Panel; Novo Nordisk A/S, Lilly Diabetes, Boehringer-Ingelheim, Sanofi, Consultant; Carmot Therapeutics, Inc., Merck Sharp & Dohme Corp., Janssen Scientific Affairs, LLC, Pfizer Inc., Intercept, Intarcia, Valeritas, TargetRWE, Shionogi, Zealand Pharma, Structure, Bayer, Research Support; Novo Nordisk A/S, Boehringer-Ingelheim. M.Asong: Employee; Novo Nordisk. C.Desouza: Advisory Panel; Novo Nordisk A/S, Bayer Inc., Consultant; Asahi Kasei Corporation, Research Support; Eli Lilly and Company. P.Gourdy: Advisory Panel; Novo Nordisk, Eli Lilly and Company, Sanofi, Bayer Inc., Merck Sharp & Dohme Corp., Novartis, Intercept Pharmaceuticals, Inc., Research Support; Novo Nordisk, Speaker's Bureau; Novo Nordisk, Eli Lilly and Company, Sanofi, Abbott, AstraZeneca, Boehringer-Ingelheim. S.Kar: None. A.G.Vianna: Advisory Panel; AstraZeneca, Board Member; Abbott Diabetes, Novo Nordisk, Medtronic, Research Support; Novo Nordisk, Anji Pharmaceuticals, Lilly, Speaker's Bureau; AstraZeneca, Novo Nordisk, Medtronic. T.Vilsbøll: Consultant; AstraZeneca, Boehringer Ingelheim Inc., Gilead Sciences, Inc., Eli Lilly and Company, Mundipharma, Merck & Co., Inc., Novo Nordisk A/S, Sanofi, Sun Pharmaceutical Industries Ltd., Bristol-Myers Squibb Company. S.M.Vinther: Employee; Novo Nordisk. Y.Mu: Speaker's Bureau; Abbott Diabetes, Boehringer Ingelheim Inc., Novo Nordisk (China), Sanifit. Funding Novo Nordisk A/S

Hydration and hypoxic-occlusion therapy (gel-wrap treatment) for hypertrophic scars and keloids

Keloids and hypertrophic scars are pathological fibroproliferative disorders characterized by excessive collagen deposition. The affected skin becomes raised and thickened, forming firm pink or red plaques. Common causes include trauma, burn, and surgery. Multiple studies on keloids and hypertrophic scars have been reported previously, leading to the development of various therapeutic approaches for them, most of which remain clinically unsatisfactory. In this study, three patients underwent the gel-wrap treatment: the gel (manufactured by ITO Co Ltd.) was applied to the lesion and the affected skin was wrapped with a polyvinylidene chloride film (Saran Wrap; Asahi Kasei Corporation). Satisfactory results were noted in all three cases. The gel-wrap treatment is more convenient and inexpensive than existing therapeutic approaches. Furthermore, reducing thymic stromal lymphopoietin seems crucial for the treatment of keloids and hypertrophic scars.

1123-P: Racial Differences in Glycemia and A1C in the Vitamin D and Diabetes (D2d) Study

Understanding the association between glycemia and A1c by race can improve screening for prediabetes and type 2 diabetes (T2D) . This association was examined cross-sectionally in 1016 Black and 2658 White persons screened for D2d (mean age 60; fasting glucose [FG] 1mg/dL; A1c 5.9%) . Of these, 22with at least 2-of-3 ADA glycemic criteria for prediabetes (mean age 60, FG 1mg/dL, A1c 5.9%, 28% Black) were followed longitudinally for median 2.5 years with annual OGTT and semi-annual FG and A1c. Numerical integration methods were used for the estimation of area under the OGTT-derived glucose curve (AUC) . Mean A1c was estimated for a given AUC quintile using linear regression for Whites, Blacks, and Asians. Adjusting for covariates, the A1c was higher by 0.2-0.25% among Black vs. White participants with similar glycemia in all AUC quintiles. During follow-up, Black participants were more likely to be diagnosed with T2D based on A1c; White participants were more likely to be diagnosed by FG. The absolute increase in A1c over time according to baseline AUC-glucose quintiles differed by race. Compared to White participants, Black participants in lower baseline AUC quintiles had greater increases in A1c over time, while A1c increased similarly in White and Black persons in the two highest baseline AUC quintiles. In summary, Black participants had higher A1c than White participants for similar degree of glycemia defined by AUC; the glycemic criteria contributing to the diagnosis of T2D differed by race; and there were AUC by racial interactions in A1c increase over time. Disclosure E.S.Leblanc: n/a. S.Dagogo-jack: Consultant; AstraZeneca, Bayer AG, Boehringer Ingelheim International GmbH, Janssen Pharmaceuticals, Inc., Medtronic, Merck & Co., Inc., Sanofi. J.P.Nelson: None. D.S.Hsia: None. D2d research group: n/a. A.G.Pittas: None. R.Chatterjee montgomery: Research Support; Bristol-Myers Squibb Company, Epigenomics AG. N.Rasouli: Advisory Panel; Eli Lilly and Company, Novo Nordisk, Sanofi, Research Support; Allergan, Eli Lilly and Company, Novo Nordisk. M.K.Rhee: Research Support; Kowa Company, Ltd. R.E.Pratley: Other Relationship; Bayer AG, Corcept Therapeutics, Dexcom, Inc., Hanmi Pharm. Co., Ltd., Merck & Co., Inc., Metavention, Novo Nordisk, Pfizer Inc., Poxel SA, Sanofi, Scohia Pharma Inc., Sun Pharmaceutical Industries Ltd. C.Desouza: Advisory Panel; AstraZeneca, Bayer AG, Novo Nordisk A/S, Consultant; Asahi Kasei Corporation. L.M.Neff: Employee; Eli Lilly and Company, Research Support; Amryt Pharma Plc, Novo Nordisk, Stock/Shareholder; Eli Lilly and Company. A.L.Peters: Advisory Panel; Abbott Diabetes, AstraZeneca, Eli Lilly and Company, Novo Nordisk, Shouti, Vertex Pharmaceuticals Incorporated, Zealand Pharma A/S, Other Relationship; Omada Health, Inc., Research Support; Abbott Diabetes, Dexcom, Inc., Insulet Corporation, Leona M. and Harry B. Helmsley Charitable Trust, Stock/Shareholder; Teladoc Health. Funding American Diabetes Association (1-14-D2d-01) ; National Institutes of Health National Institute of Diabetes and Digestive and Kidney Diseases and Office of Dietary Supplements (U01DK098245)

143-OR: Mapping Genetic Determinants of Blood Glucose Control in the Action to Control Cardiovascular Risk in Diabetes Study Group (ACCORD)

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

418-P: Certain Patient Subgroups with Type 2 Diabetes May Benefit from Intensive Glycemic and Blood Pressure Control from Reductions in Major Adverse Cardiovascular Events (MACE) : A Machine Learning–Based Post Hoc Analysis of ACCORD Trial Data

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 <6.0% vs. A1c 7.0% to 7.9%) and SBP (<120 mmHg vs. <140 mmHg) control did not significantly reduce CVD risks. We reanalyzed data from 10,251 participants from the glucose trial and 4,733 from the SBP sub-trial separately. We used Major Adverse Cardiovascular Events (MACE) as our CVD outcome. We applied causal forest (CF) and causal tree (CT) models to identify participant characteristics that modulate the effectiveness of intensive glycemic and SBP intervention from a list of 48 candidate variables (demographics, disease histories, medications, and biomarkers) at the study baseline. Compared to controls, intensive SBP intervention reduced MACE in those with normal HDL (women >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 <8.5% (RR: 0.79, 95% CI: 0.67-0.93) and those with glomerular filtration rate >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.

817-P: Identifying Modulators of Treatment Benefits of Albiglutide (GLP-1 RA) in Preventing Major Adverse Cardiovascular Events (MACE) among Individuals with Type 2 Diabetes in the HARMONY Trial Using Machine Learning

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.

174-OR: Identify Effect Modulators of Intensive Glucose Control in Preventing Cognitive Function Decline among Individuals with Type 2 Diabetes: A Revisit to the ACCORD MIND Trial

This study aimed to identify patient characteristics associated with the effectiveness of intensive glucose control (i.e., A1c<6.0% vs. A1c 7-8%) in preventing cognitive function decline (CFD) among individuals with type 2 diabetes (T2D) in the ACCORD trial. The Memory in Diabetes (MIND) study was sub trial of the ACCORD study, including 2,977 participants without a history of cognitive impairment or dementia. We applied the causal forest (CF) and causal tree (CT) algorithm to identify participant characteristics that modulate the effectiveness of intensive glucose control in preventing CFD from a list of 58 candidate variables (demographics, disease histories, medications, and biomarkers) at the study baseline. The study outcome was the change of cognitive function from the baseline to the 40th month, assessed through the Digit Symbol Substitution Test (DSST) , Rey Auditory Verbal Learning Test (RAVLT) , Mini-Mental State Examination (MMSE) , and Stroop test. A computer-driven approach identified a close relationship between renal function and the effectiveness of intensive glycemic control in preventing CFD: study participants with the urinary albumin less than 0.4 mg/dl (absolute function change (AFC) : 0.43 in MSSE) or urinary creatinine 120-200 mg/dl (AFC: 0.30 in RAVLT) had slower CFD associated with intensive glycemic control, while participants with GFR<60 (AFC:-0.51 in RVALT) , or GFR<90 (AFC:-1.7 in Stroop) had a faster CFD associated with intensive glycemic control. In addition, only participants with age below 60 years old benefited from the intensive glucose control (AFC:1.23 in DSST) in preventing CFD. All p<0.05. Our study tied renal function to the effectiveness of intensive glycemic control in preventing CFD. Clinicians may consider these individualized characteristics for better-targeted A1c goal setting, especially those at risk for CFD. 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.

12-LB: A Prediction Model on Incident End-Stage Kidney Disease (ESKD) among Individuals with Type 2 Diabetes (T2D) and Chronic Kidney Disease (CKD)

Diabetes is the leading cause of ESKD, a costly and disabling condition that often results in premature death. This study aimed to predict incident ESKD among individuals with T2D comorbid with CKD. The ACCORD clinical trial data were split into a training set and a validation set by a ratio of 7:3. A dynamic time-varying Cox model was fit to predict the development of incident ESKD. Significant predictors were identified from a list of candidate variables, including demographic characteristics, physical exam results, laboratory results, medical history, drug information, and healthcare utilization. Model performance was evaluated by Brier score and C statistics. Confidence intervals (CI) were calculated using a bootstrap method. Decomposition analysis was conducted to assess the variable importance. Patient-level data of HARMONY Outcome clinical trial was used for external validation. A total of 6,982 diabetes patients with CKD at baseline were used for model development, with a median follow-up of 4 years and 312 ESKD events. The significant predictors for the ESKD risk model were female, race, smoking status, age at T2D diagnosis, SBP, HR, HbA1c, estimated glomerular filtration rate (eGFR) , urine albumin-creatinine ratio (UACR) , retinopathy event happened in last year, antihypertensive drug use, and an interaction term between SBP and female. The model demonstrated good performance in discrimination (C-statistics 0.764 [95% CI 0.763-0.811]) and calibration (Brier Score 0.0083 [95% CI 0.0063-0.0108]) . The top 3 most important predictors in the prediction model were eGFR, retinopathy event, and UACR. There were 2,954 patients with CKD extracted from HARMONY Outcome with 545 ESKD events and a median follow-up of 2 years. Acceptable discrimination (C-statistics: 0.701 [95% CI 0.665-0.716]) and calibration (Brier Score: 0.0794 [95% CI 0.0733-0.1022]) were demonstrated in the external data. The dynamic risk prediction of incident ESKD among individuals with T2D can be used to support better disease management to lower the risk of developing ESKD. 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.

256-OR: Five-Year Simulation of Diabetes-Related Complications in Individuals Treated with Once-Weekly Tirzepatide and Semaglutide vs. Once-Daily Insulin Glargine

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.

125-LB: Individualized Cost-Effectiveness Assessment of Sodium–Glucose Cotransporter 2 Inhibitors (SGLT2i) vs. Sulfonylureas as Add-On Therapy in People with Inadequately Controlled Type 2 Diabetes (T2D) Under Metformin Monotherapy

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.

22-OR: Finerenone in Patients across the Spectrum of CKD and T2D by GLP-1RA Use

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-<300 mg/g and eGFR ≥25-≤90 mL/min/1.73 m2, or UACR ≥300-≤5000 mg/g and eGFR ≥25 mL/min/1.73 m2) , treated with optimized renin-angiotensin system blockade, were randomized to finerenone or placebo. The effects of finerenone on CV (CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure) and kidney (kidney failure, sustained ≥57% eGFR decline, or renal death) composite outcomes, and on UACR at month 4, were analyzed by GLP-1RA use. 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.

1196-P: Sex Difference in the Progression of Metabolic Syndrome Severity from Normoglycemia, Prediabetes, to Diabetes

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 <100, preDM 100-125, DM ≥126 mg/dL) , adjusting for age, race, education, smoking, medication & cohort. A sex interaction was added to assess if the magnitude of risk factor changes over time differs by sex in each glucose level. 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

1080-P: Sex-Specific Associations between Risk-Factor Control and Incident CVD in Diabetic Patients

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 <53 mol/mol (7%) , blood pressure <140/90 mm/Hg, and non-HDL-cholesterol <130 mg/dl. 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)

1326-P: Baseline OGTT as a Predictor of Type 2 Diabetes Progression in the GRADE Study

Type 2 diabetes (T2D) is progressive, often requiring additional medications to maintain glycemic control. We assessed baseline β-cell function and insulin sensitivity as predictors of worsening glycemia (HbA1c) in the GRADE Study. Adults with T2D <y and HbA1c 6.8-8.5% were randomized to glimepiride, sitagliptin, insulin glargine 100 U/mL or liraglutide added to metformin monotherapy. Complete baseline OGTT data were available for 4586 of 5047 participants (Mean±SD: Age 57±y, 64% male, 5.0±1.3 y f/up) . Insulin sensitivity (HOMA2S) and early (C-peptide index (CPI) : ΔCP/ΔG 0-30 min) and total (incAUC-CP/G 0-120 min) C-peptide responses were used in Cox proportional hazard models to predict time to 1° (HbA1c ≥7%) and 2° (HbA1c >7.5%) glycemic outcomes and tested for treatment interaction. Given the inverse relationship with insulin sensitivity, C-peptide responses were adjusted for HOMA2S. Values determined at baseline (mean±SD) were: CPI 0.8±.nmol/g, incAUC CP/G 1.0±0.6 nmol/mg, HOMA2S 35±68 %, fasting glucose 152±31 mg/dl. Higher C-peptide responses predicted lower risk of 1° (CPI: HR±SE per 1 unit change =0.74±0.03, p<0.001; incAUC-CP/G: HR=0.68±0.02, p<0.001) and 2° glycemic outcomes (CPI: HR=0.71±0.03, p<0.001; incAUC-CP/G: HR=0.60±0.03, p<0.001) . Risks did not differ by treatment. There was interaction for HOMA2S and treatment; a 5 unit change in HOMA2S predicted lower risk of 1° and 2° outcomes with glimepiride and sitagliptin (HR=0.97±0.for both 1° outcomes; HR=0.96±0.for 2° outcome with glimepiride, HR=0.95±0.for 2° outcome with sitagliptin; all p<0.05) . A mg/dL change in fasting glucose predicted greater risk of 1° outcomes for all treatments, lowest for insulin glargine (HR=1.05±0.01) and highest for sitagliptin (HR=1.11±0.01) . Conclusions: Impaired β-cell function based on an OGTT predicted higher risk of T2D progression regardless of assigned treatment. This approach may identify cases that may benefit from earlier more aggressive therapy. Disclosure K.Utzschneider: Consultant; Nevro Corp. T.A.Elasy: None. W.Valencia: None. C.Ma: None. N.Rasouli: Advisory Panel; Eli Lilly and Company, Novo Nordisk, Sanofi, Research Support; Allergan, Eli Lilly and Company, Novo Nordisk. S.E.Kahn: Advisory Panel; Bayer AG, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Intarcia Therapeutics, Inc., Merck & Co., Inc., Novo Nordisk, Pfizer Inc. W.Sivitz: n/a. Grade research group: n/a. N.Younes: None. N.M.Butera: None. A.Balasubramanyam: None. C.Desouza: Advisory Panel; AstraZeneca, Bayer AG, Novo Nordisk A/S, Consultant; Asahi Kasei Corporation. J.Krakoff: None. J.I.Barzilay: None. R.A.Defronzo: Advisory Panel; AstraZeneca, Boehringer Ingelheim International GmbH, Intarcia Therapeutics, Inc., Novo Nordisk, Research Support; AstraZeneca, Boehringer Ingelheim International GmbH, Merck & Co., Inc., Speaker’s Bureau; AstraZeneca. R.M.Bergenstal: Advisory Panel; Hygieia, Medtronic, Roche Diabetes Care, Zealand Pharma A/S, Consultant; Abbott Diabetes, Ascensia Diabetes Care, Bigfoot Biomedical, Inc., CeQur SA, Dexcom, Inc., Eli Lilly and Company, Novo Nordisk, Onduo LLC, Sanofi, United HealthCare Services, Inc., Research Support; Abbott Diabetes, Dexcom, Inc., Eli Lilly and Company, Insulet Corporation, Medtronic, Novo Nordisk, Sanofi. Funding National Institute of Diabetes and Digestive and Kidney Diseases (U01DK098246, U34-DK-088043)

869-P: Reductions in Traditional Risk Factors Explain Most of the Cardiovascular Benefit of SGLT2 Inhibitors and GLP-1 Receptor Agonists: An Analysis Using the BRAVO Risk Engine

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.

659-P: Long-Term Health Benefit and Economic Return of Time in Range (TIR) Improvement in Individuals with Type 2 Diabetes

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 <50%, improving the TIR to 51-70% was associated with an increase of 1.14 QALY and 1.20 life years, and a 5.86% relative risk reduction in CVD; improving the TIR to 71-85% was associated with an increase of 1.69 QALY and 1.46 life-years, and a 8.05% relative risk reduction in CVD; improving the TIR to >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 <50% to 51-70%, 71-85%, and >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.

11-LB: A Prediction Model on Incident CKD among Individuals with T2D in the United States

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 eGFR<60 mL/min/1.73 m2 or UACR>30 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.

895-P: External Validation of the BRAVO Diabetes Model Using Data from the EXSCEL Study

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

1494-PUB: Variable Responses to GLP-1 Receptor Agonists

Background: GLP1s are ineffective for a subset of patients. Differentiating between responders and non-responders based on baseline variables would allow for an individualized approach to managing diabetes. Methods: A retrospective analysis of 324 veterans at the Omaha VA with type 2 diabetes on a GLP1 agonist to evaluate effectiveness quantified by reductions in HbA1c (>/= 0.5%) body weight (>/= 3%) and insulin doses (>/= 10% decrease or off insulin) relative to baseline was done with the outcome being a complete response in all three. Associations between categorical variables of interest and the dichotomous outcome were assessed using Chi-Square and t-tests. A logistic regression was run for the dichotomous outcome and included variables with bivariate p-values less than 0.20. Results: In terms of, response to one, two or all outcome variables, Dulaglutide had, 22 (38.6%) , 21 (36.8%) and 9 (15.8%) ; Liraglutide had 45 (34.6%) , 44 (33%) and 33 (25.3%) ; Semaglutide had 24 (17.5%) , 64 (46.7%) and 43 (31.4%) . Patients on Semaglutide trended towards having higher odds of a complete response (odds 2.45 95% CI 0.93, 6.48) compared to Dulaglutide. Older patients had a higher odds of a complete response (odds 1.95% CI 1.01, 1.p=0.02 Table 1) . Variables including baseline A1c, insulin dose, BMI, Metformin, and eGFR were not significantly associated with a complete response to GLP1. Conclusion: Age and the type of GLP1 are variables that determine effectiveness. Disclosure S.Kunwar: None. K.Samson: None. C.Davis: None. V.Shivaswamy: Research Support; Eli Lilly and Company, Kowa Company, Ltd., Novo Nordisk. C.Desouza: Advisory Panel; AstraZeneca, Bayer AG, Novo Nordisk A/S, Consultant; Asahi Kasei Corporation.

13-LB: A Prediction Model of Chronic Kidney Disease Progression among Individuals with Type 2 Diabetes in the United States

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.