bluuebunny/crossref_metadata_2025_split huggingface dataset:
1253-P: Differential Risk of Dementia in Older Adults with Diabetes by Race/Ethnicity, Gender, and Age—Health and Retirement Study 1992–2020
Authors: DAWSON APRILL Z. , EGEDE LEONARD E. 
Published: June 2024

While it is known that adults with diabetes have two times the risk of developing dementia compared to adults without diabetes, it is unknown whether the risk differs by race/ethnicity, gender, and age amongst older adults with diabetes. The aim of this analysis was to determine the risk of developing dementia in older adults with diabetes and to determine whether the risk differs by race/ethnicity, gender, and age. Data from the Health and Retirement Study(1992-2020) Harmonized Cognitive Assessment Protocol participants aged 50 and older were used (n=3496). Time to dementia was the dependent variable. The primary independent variable was diabetes (yes/no). Covariates included race/ethnicity, gender, age, education, employment, metropolitan statistical area, and region. Unadjusted and fully adjusted Cox proportional hazards models were run. Interactions between diabetes and race/ethnicity, gender, and age were tested. Models with significant interactions were stratified. Analyses were conducted using Stata v17.0, and statistical significance was p-value&lt;0.05.In unadjusted models, older adults with diabetes had 24% increased risk of developing dementia (HR 1.24; 95%CI 1.03,1.50) compared to those without diabetes. There was a significant interaction between diabetes and age. Fully adjusted, age stratified models showed adults with diabetes aged 50-64 (HR 1.73; 95%CI 1.01,2.95) and 65-74 years (HR 1.39; 95%CI: 1.02,1.88) had increased risk of developing dementia compared to those without diabetes; and there was no association for adults aged 75 and older. The effect of diabetes on dementia happens earlier in age, is more prominent in older adults aged 50 -74, and does not have an impact in older age groups (75 years and older). Targeted intervention is critical and may be more beneficial in older adults aged 50-74, and there is need for early recognition of cognitive decline in this population to mitigate adverse outcomes.

Disclosure
A.Z. Dawson: None. L.E. Egede: None.

Funding
American Diabetes Association (11-22-JDFHD-01); NIDDDK (1R21DK135965-01, PI:Dawson; R01DK118038, R01DK120861; PI: Egede)NIMHD (R01MD013826, PI: Egede/Walker; R01MD018012, R01MD017574, PI: Egede/Linde)
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-&lt;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&lt;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&lt;0.05 vs. Control). When classified by NF/CGRP staining, the number and volume were reduced in the NF-low/CGRP- population (p&lt;0.05). On the other hand, in db/db mice, although cell body volume was reduced (p&lt;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&lt;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 &lt;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 &lt;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.