bluuebunny/crossref_metadata_2025 huggingface dataset:
1254-P: Association between Hemoglobin A1c, Time in Range, and Dementia in Veterans with Diabetes Mellitus
Authors: UNDERWOOD PATRICIA C. , ZHANG LIBIN , MOHR DAVID , PRENTICE JULIA C. , BUDSON ANDREW E. , CONLIN PAUL R.
Published: June 2024

Introduction & Objective: Alzheimer’s Disease and related dementias (ADRD) are more common in individuals with diabetes. Traditional glycemic measures such as average hemoglobin A1c (A1c) may not capture a dynamic and complex pathophysiology in the relationship between diabetes and ADRD. We developed a measure of glycemic control, A1c time-in-range (TIR), that expresses A1c stability over time within specific ranges and may inform our understanding of the relationship between glucose levels over time and ADRD incidence. The aim of this study is to examine the relationship between A1c TIR and incidence of ADRD in older Veterans with diabetes mellitus.

Methods: 374,021 Veterans aged 65 years or older with diabetes during the study period January 1, 2005 to December 31, 2018 were included in this cohort analysis. Individuals with fewer than four A1c tests during a 3-year baseline period (2005-2017), dementia at baseline, or less than 4 years of follow up were excluded. A1c TIR was calculated as the percentage of days during baseline in which A1c was in individualized target ranges based on clinical characteristics and predicted life expectancy, with higher A1c TIR viewed as more favorable.

Results: Adjusted Cox proportional hazards models showed that lower A1c TIR was associated with increased risk of incident ADRD (A1c TIR: 0-<20% [HR=1.19;95% CI, 1.16-1.23]). Further, below range A1c levels was associated with incident ADRD. Having greater time below range (A1c TBR) (≥60%) was associated with significantly increased risk (HR 1.23; 95% CI, 1.19-1.27). Findings remained significant after excluding individuals with baseline use of medications associated with hypoglycemia risk (i.e., insulin, sulfonylureas) or with hypoglycemia events.

Conclusion: Among older adults with diabetes, increasing A1c stability within patient-specific target ranges is associated with a lower risk of ADRD. A1c time below range may identify patients at increased risk of ADRD.

Disclosure

P.C. Underwood: None. L. Zhang: None. D. Mohr: None. J.C. Prentice: None. A.E. Budson: Advisory Panel; Eli Lilly and Company. Research Support; Bristol-Myers Squibb Company. Advisory Panel; AbbVie Inc. Research Support; VoxNeuro. Other Relationship; Elsevier, Oxford University Press. P.R. Conlin: None.

Funding

This study was supported by the Department of Veterans Affairs, Veterans Health Administration, Office of Research and Development, Health Services Research and Development (IIR 15-116) and the National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases (R01 DK114098). Data were obtained with support from VA Information Resource Center, VA/CMS Data for Research Projects SDR 02-23 and 98-004.
1259-P: Relationship between Number of Multiple Risk Factors and Functional Disability (FD) in Those With and Without Diabetes Mellitus (DM)
Authors: SHIOZAKI HARUKA , FUJIHARA KAZUYA , TSURUOKA KAHORI , IKEDA IZUMI , KITAZAWA MASARU , MATSUBAYASHI YASUHIRO , YAMAMOTO MASAHIKO , YAMADA TAKAHO , KODAMA SATORU , SONE HIROHITO
Published: June 2024

Although control of multiple risk factors is essential to prevent FD with and without DM, longitudinal studies are scarce that directly and quantitatively compare effects of such control. Thus, we assessed whether reaching multifactorial targets for HbA1c, blood pressure, non-HDLC, and lifestyle risks such as smoking and no physical activity are associated with FD in those with and without DM.

Enrolled were 9652 Japanese aged 39-98 y. FD was defined based on the Japanese long-term care insurance system, and targets were based on ADA and Japanese guidelines.

Of 8,861 non-DM persons, 6.3, 25.2 and 49.1% were on target for 0-2, 3 and 4 factors, respectively. Of 791 persons with DM, 12.4, 68.0 and 19.3% were on target for 0-2, 3-4 and 5 factors, respectively.

Multivariate Cox analysis showed increased FD with decreased targets reached regardless of DM (Table, upper). Compared with people with non-DM who achieved 4 targets, non-DM persons who achieved 0 to 2 targets and DM persons who achieved 3 to 4 targets had nearly twice the incidence of FD (Table, lower). Moreover, people with DM who achieved only 0 to 2 targets had an approximately 4 times higher incidence of FD (Table, lower). Control of modifiable risk factors was effective regardless of DM. Achievement of all 5 targets among people with DM may reduce FD to the same level as in non-DM persons who achieved all 4 targets.

Disclosure

H. Shiozaki: None. K. Fujihara: None. K. Tsuruoka: None. I. Ikeda: None. M. Kitazawa: None. Y. Matsubayashi: None. M. Yamamoto: None. T. Yamada: None. S. Kodama: None. H. Sone: Research Support; Novo Nordisk, Astellas Pharma Inc., Kowa Company, Ltd., Kyowa Kirin Co., Ltd., Eisai Inc., Sumitomo Dainippon Pharma Co., Ltd.

Funding

the Japan Society for the Promotion of Science (21K11569); Daiwa Securities Health Foundation
126-OR: Differences of Three-Dimensional Pathology of Dorsal Root Ganglia in Neuropathy of Type 1 and Type 2 Diabetes Mellitus
Authors: MIZUKAMI HIROKI , SASAKI TAKANORI , KUSHIBIKI HANAE , RYUZAKI MASAKI , WANG ZHENCHAO
Published: June 2024

Introduction & Objective: Diabetic polyneuropathy (DPN) is the most frequent complication of diabetes mellitus. Reduction of axon number and atrophy of dorsal root ganglion (DRG) neurons are causative pathological changes in DPN, while a decrease in the number of neurons has been inconclusive in rodent models. Because the pathogenesis of DPN differs between type 1 and type 2 diabetes, the pathological changes in DRGs may be different. In this study, we quantified DRGs in streptozotocin (STZ)-treated C57BL/6 and db/db mice in three dimensions.

Methods: STZ (160 mg/kg) was administered peritoneally to 8-week-old male C57BL/6 mice. At 8 and 16 weeks after administration, and in same-week-old db/db and db/m mice, lumbar L5 DRGs were dissected. Double immunofluorescent staining for the neuronal markers neurofilament (NF) and CGRP were applied to the tissue made transparent by the CUBIC method. Images were taken with a light sheet microscope. The captured 3D images were used to create training data, and a segmentation AI (DeepLab3+ modified) was trained. The trained AI processed the images and quantified them. The cells were divided into 4 groups as NF-low/high and CGRP+/- according to the median pixel value of each cell.

Results: Three-dimensional analysis showed that cell body volume and cell count decreased at both 8 and 16 weeks in the STZ group (p<0.05 vs. Control). Histograms of cell bodies showed a significant decrease in the range of 3000-6000 μm3 at both 8 and 16 weeks (p<0.05 vs. Control). When classified by NF/CGRP staining, the number and volume were reduced in the NF-low/CGRP- population (p<0.05). On the other hand, in db/db mice, although cell body volume was reduced (p<0.05 vs. Control), there was no significant difference in cell number. Also, there were no numerical changes in cell subpopulations.

Conclusions: Only DPN in type 1 diabetes showed a marked decrease in cell number of DRG neuron, which may lead to a new understanding of the pathogenesis of DPN.

Disclosure

H. Mizukami: None. T. Sasaki: None. H. Kushibiki: None. M. Ryuzaki: None. Z. Wang: None.
1273-P: Dysglycemia and Cardiac Autonomic Dysfunction—The Diabetes Prevention Program Outcomes Study (DPPOS)
Authors: ECHOUFFO TCHEUGUI JUSTIN , LIU XUN , GOLDBERG RONALD B. , BUDOFF MATTHEW , DAGOGO-JACK SAMUEL , GOLDEN SHERITA , IBEBUOGU UZOMA N. , KNOWLER WILLIAM C. , WHITE NEIL , TEMPROSA MARINELLA , RESEARCH GROUP DPP
Published: June 2024

Introduction: Limited data exist on links between glycemic markers and cardiac autonomic neuropathy (CAN) across the dysglycemia spectrum (including prediabetes and diabetes). We assessed associations of multiple glycemic markers with heart rate (HR) and CAN, measured via heart rate variability (HRV).

Methods: This cross-sectional analysis included 2,266 participants who attended a 14-year follow up DPPOS visit. We evaluated associations of glycemic markers (fasting glucose, HbA1c and HOMA-IR) with HR and HRV (standard deviation of all normal-to-normal intervals [SDNN], root mean square of successive differences between normal-to-normal intervals [rMSSD] and QT index [QTI]) measures, using linear regression. Similarly, the relations between glycemic markers and CAN (SDNN ≤17.13 ms, rMSSD ≤24.94 ms or QTI ≥112) were assessed using logistic regression.

Results: Participants had 65 years old on average, 68.2% were women, 53.4% were White, and 57.3% had diabetes. Glycemic measures were positively associated with HR and negatively associated with SDNN and rMSSD (Figure 1). Greater HbA1C, FPG and HOMA IR were associated with greater odds of CAN (OR [95% CI] per SD change: 1.34 [1.14, 1.62], 1.27 [1.08, 1.53] and 1.19 [1.02, 1.39], P<0.05).

Conclusions: Glucose metabolism is associated with heart rate and risk of cardiac autonomic dysfunction across the spectrum of dysglycemia.

Disclosure

J. Echouffo Tcheugui: None. X. Liu: None. R.B. Goldberg: None. M. Budoff: Speaker's Bureau; Boehringer-Ingelheim, Lilly Diabetes, AstraZeneca. S. Dagogo-Jack: Consultant; Merck Sharp & Dohme Corp., Medtronic, Bayer Inc. Stock/Shareholder; Aerami. S. Golden: None. U.N. Ibebuogu: None. W.C. Knowler: None. N. White: None. M. Temprosa: None. D. Research Group: None.

Funding

National Institute of Diabetes and Digestive and Kidney Diseases (UDK048489, UDK048339, UDK048377, UDK048349, UDK048381, UDK048468, UDK048434, UDK048485, UDK048375, UDK048514, UDK048437, UDK048413, UDK048411, UDK048406, UDK048380, UDK048397, UDK048412, UDK048404, UDK048387, UDK048407, UDK048443, UDK048400)
1280-P: Higher Triglyceride–Glucose Index Is Associated with a Higher Risk of Cardiovascular Disease in Patients with Type 1 Diabetes
Authors: OH ROSA , CHO SOHYUN , KIM SEOHYUN , YOON KIM JI , KIM GYURI , HYEON KIM JAE
Published: June 2024

The triglyceride-glucose (TyG) index has been linked to cardiovascular disease in diabetic or nondiabetic patients. However, it remains unclear whether elevated TyG index is associated with a high risk of cardiovascular disease in patients with type 1 diabetes. We enrolled 14,553 adults with type 1 diabetes with a mean follow-up of 7.52 years using the National Health Insurance Database in Korea between January 2009 and December 2015. We exclude patients who have other etiology of diabetes, cancer, and underlying cardiovascular disease. The TyG index was calculated as ln [fasting triglycerides (mg/dL) × fasting glucose (mg/dL)/2] and categorized into four quartiles. The primary outcome is the hospitalization due to cardiovascular disease. Cox proportional hazards models were used to estimate adjusted hazard ratios. We found that the TyG index is positively associated with cardiovascular disease and all-cause mortality. Compared to Q1, the aHRs(95%CI) for the hospitalization due to cardiovascular disease of Q2, Q3, Q4 were 1.26(1.13-1.41), 1.38(1.24-1.54), 1.84(1.65-2.04). Furthermore, compared to Q1, the aHRs (95%Cl) for the all-cause mortality of Q2,3,4 were 1.19(1.05-1.35), 1.28(1.13-1.45),1.64(1.45-1.85).

In conclusion, elevated TyG index was associated with a higher risk of cardiovascular disease and all-cause mortality in patients with type 1 diabetes.

Disclosure

R. Oh: None. S. Cho: None. S. Kim: None. J. Kim: None. G. Kim: None. J. Kim: None.
1282-P: Prediction of Coronary Artery Disease by Traditional Risk Factors according to Glucose Tolerance Status
Authors: FUJIHARA KAZUYA , MATSUBAYASHI YASUHIRO , YAMADA MAYUKO H. , YAMAMOTO MASAHIKO , OSAWA TAEKO , KITAZAWA MASARU , SATO TAKAAKI , SUZUKI HIROSHI , ISHIGURO HAJIME , YAMADA TAKAHO , YAGUCHI YUTA , KODAMA SATORU , SONE HIROHITO
Published: June 2024

Type 2 diabetes (T2D) and prediabetes are well known to be associated with an increased risk of coronary artery disease (CAD). However, it remains unclear whether the magnitude of effect of traditional risk factors for cardiovascular disease on the incidence of CAD in those with T2D and prediabetes differ from those in normoglycemic individuals. Thus, we compare the influence of traditional risk factors (age, sex, BMI, SBP, HDLC, LDLC, HbA1c, estimated GFR, smoking, physical activity habit, antihypertensive drugs, hypolipidemic drug) on the incidence of CAD according to glucose tolerance status. Using a nationwide claim-based database that included 512,901 people aged 18-75 y in Japan (512,901 with normal glucose tolerance; 225,403 with prediabetes; 43,624 with T2D). The areas under the receiver operation characteristic (ROC) curves (AUCs) were calculated for the detection of CAD. During a median follow-up period of 5.0 years, 3,836 cases of CAD occurred. ROC analysis showed that compared to those with normal glucose tolerance, the AUCs decreased in association with worsening of glucose tolerance (Fig). The AUCs in the ROC curve analyses of normal glucose tolerance status, prediabetes and T2D were 0.843 (0.833-0.853), 0.795 (0.785-0.806), and 0.713 (0.698-0.729), respectively). Results suggest that it is inadequate to predict CAD in people with T2D based on traditional risk factors alone.

Disclosure

K. Fujihara: None. Y. Matsubayashi: None. M.H. Yamada: None. M. Yamamoto: None. T. Osawa: None. M. Kitazawa: None. T. Sato: None. H. Suzuki: None. H. Ishiguro: None. T. Yamada: None. Y. Yaguchi: None. S. Kodama: None. H. Sone: Research Support; Novo Nordisk, Astellas Pharma Inc., Kowa Company, Ltd., Kyowa Kirin Co., Ltd., Eisai Inc., Sumitomo Dainippon Pharma Co., Ltd.
1285-P: Variability of Lipid Measures and Risk of Heart Failure Events—Findings from the ALLHAT Trial
Authors: NUYUJUKIAN DANIEL S. , ZHOU JIN , KOSKA JURAJ , REAVEN PETER
Published: June 2024

Introduction: The role of long-term variation in lipids in risk for cardiovascular events has been inadequately explored. We used data from the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack (ALLHAT) trial to explore the association between variability of plasma lipids and heart failure (HF).

Methods: Variability in lipid parameters was computed as coefficient of variation (i.e. CV-Chol, CV-LDL, CV-HDL, and CV-Trig). HF events were adjudicated by the ALLHAT endpoint committee. Participants with three or more fasting lipid readings were included. Time-dependent Cox regression models were used to compute hazard ratios (HR) assessing the associations between variability and HF.

Results: In ALLHAT, 4,189 participants reported three or more fasting lipid measures. HF was reported in 173 participants in this cohort. In unadjusted Cox models, variability of total cholesterol (CV-Chol) was associated with HF (HR = 1.23, p = 0.0006). This association was somewhat attenuated (HR = 1.22, p = 0.001) when adjusting for mean cholesterol levels. The association with total cholesterol was driven by a significant association with CV-LDL (p = 0.03) but not HDL. CV-Trig was not associated with HF in ALLHAT (p = 0.90).

Conclusions: Building on findings in some prior clinical trials, long-term variability in total cholesterol levels was linked to risk of HF in ALLHAT. These findings require further investigation in epidemiologic and real-world settings. Efforts to understand the mechanisms that link lipid variability to cardiovascular disease are also warranted.

Disclosure

D.S. Nuyujukian: None. J. Zhou: None. J. Koska: None. P. Reaven: Research Support; Dexcom, Inc.

Funding

American Diabetes Association (1-24-PDF-57); NIH/NHLBI (F32HL156626); NIH/NHLBI (R01HL138969)
1307-P: Natural Language Processing of Clinical Notes to Find Diabetes Type and Onset Year in Children and Young Adults
Authors: WONG ANTHONY , ZHONG VICTOR W. , ROSENMAN MARC
Published: June 2024

Studying diabetes (DM) poses challenges when relying on structured electronic health record (EHR) data. Unstructured clinical notes are underutilized but might augment the accuracy of EHR analyses. We developed and validated a rule-based Natural Language Processing (NLP) method for finding DM type and incident diagnosis year in clinical notes. In a single center, we used structured EHR data to identify a cohort age <45 with likely type 1 (T1D) or type 2 (T2D) DM in 2016-2019 records. We previously trained our NLP algorithm on 58,450 randomly selected clinical notes (1,465 patients). We required 3 distinct concepts at the sentence level to determine an incident DM diagnosis: DM, an onset attribute (e.g., “diagnosed in”), and a temporal component (e.g., 2008). We now added NLP rules to distinguish DM type (T1D, T2D, other, or no type mentioned) and enhanced the detection of onset year. The NLP for DM type uses that most found in the tripartite onset sentences. If it is not there, the NLP uses the plurality of mentions of T1D, T2D, or other across all notes. The NLP for DM onset year assigns the year with a plurality. We tested the handcrafted NLP rules against manual review in an independent set of 100 randomly selected patients from the cohort. Analysis was at the patient level. Our final test set had 22,791 clinical notes from 97 patients (3 had no notes). On manual review, 73 had T1D, 15 T2D, 5 other DM, and 4 no DM. NLP assigned DM type in 95 patients (83 via the onset sentence, plus 12 via the plurality method). The NLP type was correct in 65/73 (89%) for T1D (5 incorrect, 3 not found), 12/15 (80%) for T2D (3 incorrect), and 2/5 (40%) for Other. NLP assigned an onset year in 80 patients (79 with true DM). Manual review found an onset year in 84/93 with DM (9 had onset “a few years ago” or prevalent DM at first visit). The NLP onset year was correct in 77 (92%) of the 84 (2 incorrect, 5 no year found). NLP was beneficial in identifying DM type and onset timing. It may complement structured EHR data in DM research and surveillance.

Disclosure

A. Wong: None. V.W. Zhong: None. M. Rosenman: None.

Funding

CDC 1U18DP006693 and 1U18DP006694
1311-P: Waist Circumference Measures for Diabetes Screening in Individuals without Obesity from 61 Low- and Middle-Income Countries
Authors: SEIGLIE JACQUELINE A. , SEURING TILL , FLOOD DAVID , WOOLCOTT ORISON O. , MANNE-GOEHLER JENNIFER
Published: June 2024

Introduction & Objective: Waist circumference-based measures can improve assessment of diabetes risk beyond body mass index (BMI), but greater evidence is needed regarding their use in diabetes screening guidelines in low- and middle-income countries (LMICs). We examined the association of waist circumference (WC), relative fat mass (RFM), and BMI with diabetes across LMICs in individuals without obesity.

Methods: Pooled, cross-sectional analysis of nationally representative, individual-level surveys conducted in 61 LMICs, 2010-2019. We included participants ≥18 years with a BMI ≥18.5 and <30 kg/m2. We used multivariable Poisson regression analysis to assess the relationship between the anthropometric measures of interest and diabetes and compared their performance as classifiers of diabetes status, expressed as the area under the curve (AUC), using ROC analysis.

Results: Among 560,652 participants, diabetes prevalence was 5.8%. Risk of diabetes was greatest for men in East/Southeast Asia in the highest quintile of BMI (RR 5.84 [95% CI: 4.3, 7.9]) and WC (6.34 [4.2, 9.7]) and for women in Latin America & the Caribbean in the highest quintile of RFM (6.02 [3.3, 10.9]), compared to those in the lowest quintile of each anthropometric measure. AUC for WC and RFM were 0.70 and 0.71 for women and 0.69 and 0.70 for men, respectively, compared to 0.66 and 0.65 for BMI. There was substantial variation in the AUC thresholds for each measure by sex and across six world regions.

Conclusions: WC and RFM outperform BMI to assess diabetes risk in individuals without obesity, though there is substantial regional variation in optimal thresholds across LMICs.

Disclosure

J.A. Seiglie: Consultant; BDMT Global, Techspert.io. T. Seuring: None. D. Flood: None. O.O. Woolcott: Other Relationship; The Lancet Group. J. Manne-Goehler: None.
132-OR: Structured Medical Nutrition Therapy Improves Glycemia, Reduces Percentage Body Fat while Maintaining Fat-Free Mass in Individuals with Overweight/Obesity and Type 2 Diabetes
Authors: MOTTALIB ADHAM , HAMDY OSAMA
Published: June 2024

Recently approved long-acting GLP-1 based therapies are effective in glycemic control and weight loss. However, they reduce fat free mass (FFM), a surrogate for muscle mass. We previously showed that structured medical nutrition therapy (SMNT) also improves glycemia but its effect on FFM is unknown. This is a sub-analysis of a clinical trial where 108 individuals with type 2 diabetes (T2D) and overweight/obesity (age 60±10 years; 57% female; HbA1c 8.07±1.05%; weight 101.4±21.1kg and BMI 35.2±7.7kg/m2) were randomized to 3 groups. Group A met with a registered dietitian (RD) to develop individualized meal plans. Group B & C met with RD and followed an isocaloric SMNT plan that included specific menus and diabetes-specific nutrition formula targeting a total daily protein intake of 1-1.5 gm/kg of adjusted body weight. Group C received additional weekly RD phone support. Body composition was evaluated using bioelectrical impedance. After 16 weeks, A1C did not change in group A but decreased in groups B (-0.66%, 95% CI -1.03 to -0.30) and C (-0.61%, 95% CI -1.0 to -0.23), p <0.001 between groups over time. Weight change was not different between groups. Percentage body fat did not change in group A but decreased in groups B (−1.6%, 95% CI −2.4 to −0.8) and C (−1.2%, 95% CI −2 to−0.3), p <0.05 between groups over time. All groups had no change in FFM. While protein intake was similar between groups at baseline, only groups B & C increased their % energy intake from protein by 4.8% (95% CI 2.8 to 6.8) and 4.2% (95% CI 2.1 to 6.2) respectively, p <0.001 between groups over time. These data suggest that SMNT improves glycemia and reduces % body fat while maintaining FFM compared to individualized meal plans. These changes are likely attributed to increased % energy intake from protein. Further studies are needed to evaluate if SMNT mitigates FFM loss in individuals with overweight/obesity and T2D treated with GLP-1 based therapies.

Disclosure

A. Mottalib: None. O. Hamdy: Research Support; Eli Lilly and Company, Novo Nordisk. Consultant; Abbott Nutrition. Stock/Shareholder; Healthimation. Research Support; Gilead Sciences, Inc. Advisory Panel; Nemaura Medical, Twin Health, L-Nutra Inc.
1323-P: Machine Learning–Based Prediction Model for Neurodegenerative Disease in Patients with Type 2 Diabetes—Derivation and Validation in Two Independent Korean Cohorts
Authors: SANG HYUNJI , LEE MYEONGCHEOL , LEE HOJAE , PARK JAEYU , KIM SUNYOUNG , GEOL WOO HO , CHEOL HWANG YOU , SUN PARK TAE , LIM HYUNJUNG , KEON YON DONG , YOUL RHEE SANG
Published: June 2024

Objective: This study aimed to assess the validity and utility of a machine learning (ML) model for predicting the 3-year incidence of neurodegenerative diseases (ND) in patients with type 2 diabetes (T2DM).

Methods: We used data from two cohorts, the discovery cohort (one hospital; n=22,311) and the validation cohort (two hospitals; n=2,915) of patients with T2DM recruited between 2008-2022. The outcome of interest was the presence/absence of ND at 3 years. We selected different ML-based models with hyperparameter tuning in the discovery cohort and conducted an area under the receiver operating characteristic curve (AUROC) analysis in the validation cohort.

Results: ND was observed in 133 (0.6%) in the discovery cohort and 15 (0.5%) in the validation cohort. The AdaBoost model had a mean AUROC of 0.82 (95% CI, 0.79-0.85) in the discovery dataset. When this result was applied to the validation dataset, the AdaBoost model exhibited the best performance among the models, with an AUROC of 0.83 (accuracy of 78.6%, sensitivity of 78.6%, specificity of 78.6%, and balanced accuracy of 78.6%). The most influential factors in the AdaBoost model were age and cardiovascular disease.

Conclusion: This study demonstrates the potential utility of ML in predicting ND incidence in patients with T2DM, highlighting its feasibility for patient screening.

Disclosure

H. Sang: None. M. Lee: None. H. Lee: None. J. Park: None. S. Kim: None. H. Woo: None. Y. Hwang: None. T. Park: None. H. Lim: None. D. Yon: None. S. Rhee: None.

Funding

National Institutes of Health Research Project of South Korea (No. 2022-ER1102-01).
1326-P: At-Risk MASH Diagnosis in Patients with Type 2 Diabetes Mellitus
Authors: MARTÍNEZ-ARRANZ IBON , ARRESE MARCO , MAYO REBECA , MINCHOLÉ ITZIAR , ENCARNACIÓN BLANCO MARÍA , ASPICHUETA PATRICIA , ALONSO CRISTINA , BANALES JESUS
Published: June 2024

Introduction/Objectives: The prevalence of metabolic dysfunction-associated steatohepatitis (MASH) in patients with type 2 diabetes mellitus (T2DM) in US is around 30% and about 20% of patients have liver fibrosis. This study aims to explore the effect of glycemic control in T2DM patients on the diagnosis of At-risk MASH (MASH + Fibrosis≥2) by means of three non-invasive tests (MASEF score, FIB-4 and Fibroscan).

Methods: The study included 275 biopsy-proven MASLD (Metabolic Dysfunction-Associated Steatotic Liver Disease) patients with T2DM. Patients were categorized as good glycemic control (GHb≤7%) and poor glycemic control (GHb>7%). A nested case-control study design was used, matching cohorts for age, BMI, gender and MASLD diagnosis. This process resulted in 97 patients in each group, with no significant differences in age (p=0.831), BMI (p=0.989), gender (p=0.991), and histology (p=0.714). MASEF score (non-invasive test based on metabolomics), FIB-4 and Fibroscan performance were compared to biopsy for the assessment of at-risk MASH.

Results: MASEF score (N=97/97), FIB-4 (N=83/87) and Fibroscan (N=54/54) performances in both cohorts were, respectively: GHb≤7%: AUC=0.76/0.77/0.75; GHb>7%: AUC=0.75/0.67/0.77. There were no significant differences among the three methods for the detection of at-risk MASH in patients with good and poor glycemic control, except for FIB-4 that showed a lower performance on GHb>7% cohort (p=0.01).

Conclusions: Glycemic control does not significantly influence the diagnosis of at-risk MASH in patients with T2DM when using MASEF score or Fibroscan as diagnostic tools.

Disclosure

I. Martínez-Arranz: None. M. Arrese: None. R. Mayo: None. I. Mincholé: None. M. Blanco: None. P. Aspichueta: None. C. Alonso: Employee; RUBIÓ METABOLOMICS. J. Banales: Advisory Panel; Owl-Rubio Metabolomics.
1330-P: Meta-analysis for Establishing Fasting Insulin Reference Ranges in Lean Healthy Asians across Different Ethnicities
Authors: HOR CHENG KOH , CHAN JAMES C.Y.
Published: June 2024

Diagnostic tests for early prediabetes screening using blood glucose usually suffer from poor sensitivity as these individuals often exhibit slight increase or even normoglycemic blood levels. Conversely, they manifest markedly elevated insulin to compensate for initial insulin resistance. This renders insulin a more sensitive biomarker for prediabetes detection. To date, there is no official guideline for a normal fasting insulin range demarcating healthy individuals from those with prediabetes.

We performed a meta-analysis to establish healthy fasting insulin reference ranges for different Asian ethnicities (Chinese, Malays, South Asians, Koreans and Japanese).

The mean (and 95% reference range) for lean Asians with BMI <25 kg/m2 is 6.12 µU/mL (1.53-24.53 µU/mL). When stratified by ethnicity, the values for Chinese, Malay, South Asians, Japanese and Korean are 6.21 µU/mL (1.55-24.95 µU/mL), 5.95 µU/mL (1.75-20.16 µU/mL), 8.27 µU/mL (1.45-47.19 µU/mL), 5.17 µU/mL (2.10-12.72 µU/mL) and 4.77 µU/mL (0.88-25.82 µU/mL) respectively. Mean and 95% reference range for Asians with BMI ≥ 25 kg/m2 were higher versus lean Asians at 11.23 µU/mL (3.06-41.15 µU/mL).

The fasting insulin ranges reported can serve as references for establishing official healthy fasting insulin ranges customized to specific ethnicities for assessing early insulin resistance for prediabetes diagnosis.

Disclosure

K. Hor Cheng: None. J.C.Y. Chan: None.
1339-P: Multiancestry Genome-Wide Association Study of Diabetic Ketoacidosis
Authors: RHEE MARY K. , DABBS-BROWN AMONAE , HUI QIN , RAGHAVAN SRIDHARAN , ZHOU JIN , REAVEN PETER , WILSON PETER W. , PHILLIPS LAWRENCE S. , SUN YAN
Published: June 2024

Diabetic ketoacidosis (DKA) is a life-threatening complication disproportionately affecting people with African (AFR) and Hispanic (HIS) more than European (EUR) ancestry. To examine genetic susceptibility to DKA, we performed multi-ancestry meta-analysis of genome-wide association studies (GWAS) of DKA, and phenome-wide association studies (PheWAS) of significant loci, in VA Million Veteran Program (MVP) participants with diabetes, with vs. without DKA, and adjusted for age, sex and top ten principal components. Among 5,036 DKA cases (2,843 EUR; 1,733 AFR; 460 HIS) and 152,119 controls (105,504 EUR; 33,037 AFR; 13,578 HIS), both had mean BMI 31 kg/m2 and were 94% male, but cases were younger than controls (60 vs. 65 yr). Of five genome-wide significant loci identified, four had similar effects across all ancestry groups: increased DKA risk associated with a chromosome (chr) 3 locus (rs1716165575, nearest gene SLC2A2, OR 1.2, 95% CI 1.2-1.3, p=1.25E-18) and two loci on chr 6 (rs3130396, HCG17, OR 1.1 [1.1-1.2], p=1.34E-08; and rs539981616, HLA-DQA1, OR 1.7 [1.6-1.8], p=7.82E-43), and decreased DKA risk with a chr 11 locus (rs3842752, INS-IGF2, OR 0.8 [0.8-0.9], p=8.74E-10). In contrast, for a chr 17 locus (rs113748381, SLC16A11, OR 1.5 [1.3-1.8], p=3.16E-20), HIS and AFR, but not EUR (p>0.05), individuals had higher risk for DKA (OR 1.7 [1.4-1.7] in HIS; OR 1.5 [1.4-1.6] in AFR), and higher effect allele frequencies (HIS 19% and AA 8%, vs. EUR 0.81%). In PheWAS analyses, all genetic loci, except chr 6 HCG17, were significantly associated with DKA (for both type 1 and type 2 diabetes), where SLC2A2, HLADQA1, and SLC16A11 were associated with increased risk of both, and INS-IGF2 was associated with decreased risk.

Conclusion: Both HLA and non-HLA chromosomal regions were associated with increased risk of DKA in a large multi-ancestry cohort. Variants at SLC16A11 were much more common in HIS and AFR compared to EUR individuals and might underlie racial/ethnic differences in the clinical presentation of DKA.

Disclosure

M.K. Rhee: Research Support; Kowa Pharmaceuticals America, Inc. A. Dabbs-Brown: None. Q. Hui: None. S. Raghavan: None. J. Zhou: None. P. Reaven: Research Support; Dexcom, Inc. P.W. Wilson: None. L.S. Phillips: Other Relationship; Diasyst, Inc. Research Support; Kowa Pharmaceuticals America, Inc., Janssen Pharmaceuticals, Inc., AbbVie Inc., Novo Nordisk, GlaxoSmithKline plc, Abbott, Sanofi-Aventis U.S., Pfizer Inc. Y. Sun: None.

Funding

VA Million Veterans Program (MVP 052)
1362-P: Influence of Sarcopenic Obesity on the Prognosis of Inpatients with Type 2 Diabetes—Results of the National MIDIA Registry
Authors: CARRETERO GOMEZ JUANA , ENA JAVIER , JAVIER CARRASCO-SÁNCHEZ FRANCISCO , CARLOS AREVALO LORIDO JOSE , GARCIA MARTINEZ LUIS , GONZÁLEZ GONZÁLEZ PATRICIA
Published: June 2024

Introduction & Objective: Obesity is characterized by an increase of fat mass and a reduced muscle mass. Sarcopenia is diagnosed based on low muscle strength, quantity, and physical performance. Muscle is the main peripheral glucose metabolizer and insulin resistance induces muscle mass lost. Sarcopenic Obesity (SO) may be defined as a clinical and functional condition characterized by the coexistence of obesity and sarcopenia. SO is associated with a poor prognosis in chronic diseases and poor metabolic control. We aimed to assess the relationship between SO and both, metabolic control and comorbidities in T2DM inpatients.

Methods: Over 1300 inpatients, we analyzed 376 with screening (SARC-F: strength; walking assistance; rising from a chair; climbing stairs) for sarcopenia. We compared patients with and without sarcopenia (SARC-F >4).

Results: 243 patients had sarcopenia. In table 1 we show the main characteristics of the groups. In the unadjusted logistic regression model, the risk of mortality among sarcopenics was OR 3.67 (95% CI, 1.22-15.85), p=0.039; After adjustment, the risk of mortality is related to age (p=0.001), comorbidity (p=0.02) and frailty (p=0.04).

Conclusion: patients with SO were older and had greater frailty and comorbidities. They had more glycemic variability and poor prognosis which depends on their own characteristics.

Disclosure

J. Carretero Gomez: None. J. Ena: None. F. Carrasco-Sánchez: None. J. Arevalo Lorido: None. L. Garcia Martinez: None. P. González González: None.
1375-P: Study of Clinical Profile and Comparison of Metabolic Parameter and Vitamin D Level in Patients with Young-Onset Diabetes Mellitus
Authors: JENA DEBARCHAN , SWAIN JAYSHREE , TELI BRIJ
Published: June 2024

Introduction: Metabolic phenotype of Indian population differs markedly from western population. Vitamin D deficiency has been associated with morbidities related to cardiovascular, inflammatory, autoimmune diseases and malignancy. Recent studies have linked vitamin D to the pathogenesis of diabetes and its complications.Objectives: To study the clinical profile and to correlate the metabolic parameters and Vitamin-D level among young onset diabetes patients.

Methodology: Total 148 Patients with young onset diabetes mellitus, presenting to the opd of Endocrinology department between July 2021 to August 2023 were included. Relevant sociodsemographic, metabolic and anthropometric parameters were obtained.

Results: Majority of patients were T1DM(64.2%), followed by T2DM(16.5%), FCPD(13.8%), MODY(2.8%) and Unclassified(2.7%.). Mean age was 15.6±8.9 years. Mean BMI was 17.27±5.1. BMI has significant(p<0.05) positive correlation with serum TG. Overall dyslipidaemia observed in 10.1% of patients. HbA1C level has significantly(p<0.05) negative correlation with TC and significant positive correlation with TG and significant(p<0.05) negative correlation with Vit-D level. Vit-D level was low in 48% of patients.

Conclusion: HbA1C was found to have negative correlation with Vitamin-D level and positive correlation with TG level among young diabetic patients. About one in two patients had low Vitamin-D level. We may conclude that Vit-D level and TG level may play important role in young onset diabetes mellites. However, further research is mandated to establish any significant association.

Disclosure

D. Jena: None. J. Swain: None. B. Teli: None.
1376-P: Association of Lung Function and Glycemic Control with All-Cause and Cardiovascular Mortality in Persons with Type 2 Diabetes
Authors: LI CHIA-ING , LIN CHENG-CHIEH , LI TSAI-CHUNG , LIU CHIU-SHONG , YANG CHUAN-WEI
Published: June 2024

Introduction & Objective: Hyperglycemia is associated with impaired pulmonary function, which can ultimately lead to all-cause and cardiovascular mortality. Few studies have examined the potential interaction effect of pulmonary function on the association between glycemic control and mortality. The aim of this study examined the association of pulmonary function parameters with mortality, and whether glycemic level modified the effect of pulmonary function on mortality in persons with type 2 diabetes (T2DM).

Methods: A retrospective cohort study identified 3846 persons with T2DM with pulmonary function testing in Taiwan between 2002 and 2020. Pulmonary function parameter was assessed by expiratory volume in 1 second (FEV1). Multivariate Cox proportional hazards model with product terms of pulmonary function parameters and glucose control were added to evaluate their interaction.

Results: A total of 733 deaths (19.06%) and 274 expanded CVD deaths (7.12%) were observed during an average follow-up of 7.83 years. The adjusted hazard ratios of all-cause mortality for persons with FEV1 Z-scores of < 0 to -1, <-1 to -2 and <-2 were 1.47 (1.20, 1.80), 2.48 (1.95, 3.14) and 3.07 (1.74, 5.44) compared to participants with Z-score ≥ 0, respectively (P for trend p<0.001). Subgroup analysis revealed that the association between FEV1 and all-cause mortality was stronger in persons with good glycemic control compared with poor glycemic control (FEV1-FPG interaction, P = 0.01; FEV1-HbA1c interaction, P = 0.03).

Conclusion: Our results showed that FEV1 is a strong predictor of mortality in persons with T2DM. Glycemic status had significantly modified the association between lung function parameters and mortality.

Disclosure

C. Li: None. C. Lin: None. T. Li: None. C. Liu: None. C. Yang: None.
1387-P: Prediabetes Subgroups, Type 2 Diabetes Risk, and Differential Intervention Effects
Authors: CASANOVA RAMON , STAFFORD JEANETTE M. , DEMESIE YITBAREK , JAEGER BYRON C. , WELLS BRIAN J. , BANCKS MICHAEL
Published: June 2024

Introduction and Objective: Many studies have subclassified T2D with various physiological factors to better understand disease heterogeneity but few have subclassified prediabetes, a period critical to prevention of T2D. We derived prediabetes subgroups and assessed risk for T2D.

Methods: We included 3043 participants from the Diabetes Prevention Program. We used baseline data for 35 clinical variables (table footnote) to compare two clustering methods: 1) kmeans using model statistics to identify the optimal cluster/subgroup number and 2) Uniform Manifold Approximation Projection data reduction with Hierarchical Density Based Spatial Clustering of Applications with Noise (HDBSCAN). We used Cox regression to assess 3-year T2D risk and intervention effects, restricting to 2549 participants randomized to the placebo, metformin, or lifestyle arms (with selection weighting).

Results: Both kmeans and HDBSCAN methods supported two subgroups as optimal. Despite general subgroup similarities between methods (high obesity in subgroup 1), the kmeans subgroups differed more notably in glucose/insulin measures and HDBSCAN subgroups differed in adiposity measures (table). Within clustering method, subgroups differed in both risk for T2D (32% and 88% higher risk, respectively) and intervention effects.

Conclusions: Prediabetes physiological subgrouping may inform T2D risk and targeted prevention strategies.

Disclosure

R. Casanova: None. J.M. Stafford: None. Y. Demesie: None. B.C. Jaeger: None. B.J. Wells: None. M. Bancks: None.

Funding

American Diabetes Association (11-22-ICTSPM-18)
14-PUB: The Effect of Structured Intensive Lifestyle Intervention on Muscle Mass in Patients with Type 2 Diabetes Receiving GLP-1 Receptor Agonists
Authors: AL-BADRI MARWA , ALMASIH BARBAR ASKAR ABD , KHATER ABDELRAHMAN , SALAH TAREQ , DHAVER SHILTON E. , AL-ROOMI FATIMAH , MOTTALIB ADHAM , HAMDY OSAMA
Published: June 2024

Glucagon-like-peptide-1 receptor agonists (GLP-1RAs) emerged as an important treatment option for type 2 diabetes (T2D) for their dual efficacy in improving glycemia and reducing body weight. However, they significantly reduce fat free mass (FFM), a surrogate for muscle mass, putting patients at a higher risk for sarcopenia and its sequelae. It is unknown whether structured intensive lifestyle intervention (sILI), that includes higher protein intake and strength exercises, can mitigate muscle mass loss with GLP-1RA use. We retrospectively evaluated 53 patients with T2D enrolled in a 12-week multidisciplinary sILI (mean age 58.17±11.3 years; 51% male; weight 104.3±19.7 kg; BMI 36.1±5.6 kg/m2, A1C 7.4±1.3%) and divided them into two groups: group 1, already taking GLP-1RA (n=32), and group 2, not on GLP-1RA (n=21). Both groups were instructed to follow a hypocaloric structured medical nutrition therapy plan with increased protein intake to 1-1.5 gm/kg of adjusted body weight. They were also instructed to exercise for 360 minutes/week with more emphasis on strength exercises. Body composition was analyzed by bioelectrical impedance. At 12 weeks, group 1 lost 7.4±4.5 kg (p<0.001) and group 2 lost 6.2±5.4 kg (p<0.001), and their A1C decreased by 0.84%±1.1% (p<0.001) and by 1%±1.06% (p<0.001) respectively, with no differences between groups over time. Group 1 had no change in FFM compared to baseline while group 2 lost 1.4±2.6 kg (p<0.05). There was no difference in FFM loss between groups over time (p=0.28). Our findings suggest that increasing protein intake and strength exercises, as part of sILI, may mitigate the deleterious effect of GLP-1RA on muscle mass in patients with T2D. These results support recommending an increase in protein intake and incorporation of strength exercises when GLP-1RA is prescribed for patients with T2D to minimize muscle mass loss. Further research is needed to validate these findings in a randomized prospective study.

Disclosure

M. Al-Badri: None. A. Barbar Askar: None. A. Khater: None. T. Salah: None. S.E. Dhaver: None. F. Al-Roomi: None. A. Mottalib: None. O. Hamdy: Research Support; Eli Lilly and Company, Novo Nordisk. Consultant; Abbott Nutrition. Stock/Shareholder; Healthimation. Research Support; Gilead Sciences, Inc. Advisory Panel; Nemaura Medical, Twin Health, L-Nutra Inc.
140-OR: The Coronary Flow Reserve Increase after Dapagliflozin Treatment in Patients with Type 2 Diabetes Is Maintained after Four Years—A Four-Year DAPAHEART Follow-up Study
Authors: MORCIANO CASSANDRA , CINTI FRANCESCA , PIO SORICE GIAN , LECCISOTTI LUCIA , GUGLIANDOLO SHAWN , CAPECE UMBERTO , CAPPANNOLI LUIGI , D'AMARIO DOMENICO , MEZZA TERESA , DI GIUSEPPE GIANFRANCO , CICCARELLI GEA , SOLDOVIERI LAURA , GIACCARI ANDREA
Published: June 2024

Introduction & Objective: Cardiovascular (CV) outcome trials have shown that in patients with type 2 diabetes (T2D), treatment with sodium-glucose cotransporter-2 inhibitors (SGLT-2i) reduces CV mortality for heart failure. We demonstrated that 4-weeks-treatment with SGLT-2i leads to a 30% increase in coronary flow reserve (CFR) in T2D people with stable coronary artery disease (CAD) (DAPAHEART trial). The aim of this study is to evaluate the long-term effects of treatment with dapagliflozin on CFR in patients with T2D.

Methods: T2D patients with CAD already enrolled for a single-center, 4 weeks, prospective, randomized (1:1 dapagliflozin 10 mg or placebo), double-blind, controlled study have been followed up for 4 years. At the end of the trial all the patients in placebo group started therapy with dapagliflozin. After 4 years of treatment the CFR has been evaluated using 13N-ammonia PET-CT.

Results: During the follow-up, the antidiabetic treatment remained stable. The 2 groups were metabolically well balanced.In DAPA group (n=5), the increase of 30% in CFR was sustained over 4 years (p=ns). A comparison between pre and post-4-years follow-up in DAPA group showed a 35% increase in CFR (p=0.09). The placebo group (n=4), after 4 years of dapagliflozin treatment, exhibited a 28.6% increase in CFR (p=0.07). Overall, a 25% increase in CFR was observed across all patients after 4 years (p=0.008, n=9), along with a reduction in myocardial resting blood flow (p=0.002, n=9) as observed in the DAPAHEART trial.

Conclusions: This 4-years follow up trial demonstrates that the 30% increase of CFR obtained after 4 weeks treatment is maintained after 4 years, confirming the previous published results in a new population. This improvement in CFR may have important implications on the management and prognosis of patients with T2D and CAD.

Keywords: Coronary Flow Reserve, SGLT2i, Dapagliflozin, Type 2 Diabetes, Coronary Artery Disease, Long-Term.

Disclosure

C. Morciano: None. F. Cinti: None. G. Sorice: None. L. Leccisotti: None. S. Gugliandolo: None. U. Capece: None. L. Cappannoli: None. D. D'amario: None. T. Mezza: None. G. Di Giuseppe: None. G. Ciccarelli: None. L. Soldovieri: None. A. Giaccari: None.
1411-P: Predictive Utility of Single Point Insulin Sensitivity Estimator (SPISE) Index for Metabolic Syndrome (MetS) in U.S. Adults
Authors: CHO WONHEE , ROSENBERG JARED , YOUNG KIM JOON
Published: June 2024

Although SPISE index was recently introduced as an indicator of insulin resistance (IR), its predictive ability for MetS by race/ethnicity has not been tested. Here, we aimed to: 1) examine racial/ethnic differences in SPISE index among US adults, and 2) compare predictive power of SPISE index for MetS by race/ethnicity and against other well-known IR indices (TG/HDL-C, TyG index, HOMA-IR, and 1/Insulin). A total of 2,168 adults (814 white [W], 690 black [B], and 664 Hispanic [H]) from NHANES 2017-March 2020 was analyzed in this study. MetS was defined by the AHA/NCEP criteria. SPISE index and IR indices were calculated by using physical and cardiometabolic parameters (Image). SPISE index was lowested in H, followed by B and W, with no difference between W vs. B (Image). The area under the curve of receiver operating characteristics of SPISE index for predicting MetS was highested in W (88%), followed by H (86%) and B (82%) (P<0.05 vs. B), with optimal cutoffs of 5.03, 4.84, and 4.89, respectively. In the total cohort, the predictive power of SPISE index for MetS was 85%, higher than the other IR indices (Image; all P<0.05). SPISE index outperforms various IR indices for predicting MetS in US adults, signifying its potential in large-scale observational studies. Race/ethnicity should be stratified when using SPISE index as its predictive power and cutoffs for predicting MetS vary by race/ethnicity.

Disclosure

W. Cho: None. J. Rosenberg: None. J. Kim: None.
1417-P: Youth-Onset Diabetes—Validation of a Large-Scale Surveillance Approach to Identify Incident Cases from Electronic Medical Records
Authors: CRUME TESSA L. , BELLATORRE ANNA , BURGETT SHAWNA , CONWAY REBECCA B. , ANDERSON THERESA , SHIFERAW BETHELHEM A. , DABELEA DANA
Published: June 2024

Background: Surveillance of incident diabetes among youth is essential to identify health disparities and inform health care planning.

Objective: To evaluate the validity of a large-scale algorithmic approach to ascertaining diabetes incidence in Colorado youth < 20 years of age in 2018, by comparing it against manual chart review.

Methods: The algorithm for identifying incident diabetes status was based on diagnostic codes, medications, and laboratory data. Manual chart review was the gold standard. We calculated the sensitivity, sensitivity, positive predicted value (PPV) and negative predictive value (NPV).

Results:A total of 3779 youth were identified as youth onset diabetes cases by both methodologies between 2002 and 2018. To identify new onset (incident diabetes) the algorithmic approach was 96.4% sensitive, and 99.4% specific compared to chart review while the PPV and NPV were 95.1% and 99.6%, respectively. For incident type 1 diabetes, the algorithmic approach was 94.2% sensitive and 99.3% specific and the PPV and NPV were 93.7% and 99.4%, respectively. For incident type 2, the algorithmic approach was 52.9% sensitive and 99.9% specific and the PPV and NPV were 85.7% and 99.6%, respectively.

Conclusion: The algorithmic approach had high validity in identifying incident cases of type 1 diabetes among youth but performed poorly for type 2.

Disclosure

T.L. Crume: None. A. Bellatorre: None. S. Burgett: None. R.B. Conway: None. T. Anderson: None. B.A. Shiferaw: None. D. Dabelea: None.

Funding

Centers for Disease Control and Prevention (U18DP006517; U18DP006518)