bluuebunny/crossref_metadata_2025_split huggingface dataset:
1192-P: Diabetes-Related Proteomic Alterations: The Atherosclerosis Risk in Communities (ARIC) Study
Authors: ECHOUFFO TCHEUGUI JUSTIN B. , CHEN JINGSHA , ROONEY MARY R. , TANG OLIVE , BALLANTYNE CHRISTIE M. , BOERWINKLE ERIC , NDUMELE CHIADI E. , PANKOW JAMES S. , DEMMER RYAN , LUTSEY PAMELA , GRAMS MORGAN , SELVIN ELIZABETH , CORESH JOSEF 
Published: May 2022

Introduction: There is a paucity of large-scale data from multiethnic populations on proteomic alterations related to type 2 diabetes, which could help identify prognostic mediators.
Method: We analyzed 4,955 plasma proteins using SomaLogic v4.0 on 10,478 ARIC Study participants (Visit 2, 1990-92, mean age: 57±6 years, 56% women, 22% Black adults, 14% with diabetes) . We used logistic regression to quantify proteins associations with prevalent diabetes in a 2/3 discovery and 1/3 validation subset, with Bonferroni correction of P-values.
Results: After adjustment for demographics, clinical, and lifestyle risk factors (Figure) , 642 proteins were significantly associated with diabetes in the discovery sample (p&lt;10−6) , with 3confirmed in the validation sample (p&lt;0.05/642) . The top five findings were semaphorin-6A, plexin-B2, inactive tyrosine-protein kinase 7, cartilage intermediate layer protein 2, and semaphorin-6B. Proteins with known diabetes associations were also identified : insulin receptor, insulin-like growth factor-binding protein 2, growth differentiation factor-15, and adiponectin.
Conclusions: A large-scale proteomics approach identified numerous diabetes-related alterations. Pathway analysis and comparison to proteomic risk factors for diabetes incidence and complications will shed light on plasma proteomic consequences of diabetes and their relation to future risk.

Disclosure
J.B.Echouffo tcheugui: None. P.Lutsey: None. M.Grams: None. E.Selvin: Other Relationship; Wolters Kluwer. J.Coresh: Consultant; Healthy.io Ltd, Research Support; National Institutes of Health. J.Chen: None. M.R.Rooney: None. O.Tang: None. C.M.Ballantyne: Consultant; Abbott Diagnostics, Althera Pharmaceuticals, Amarin Corporation, Amgen Inc., Arrowhead Pharmaceuticals, Inc., AstraZeneca, Corvidia Therapeutics, Denka Seiken, ESPERION Therapeutics, Inc., Genentech, Inc., Gilead Sciences, Inc., Illumina, Matinas BioPharma Inc, Merck & Co., Inc., New Amsterdam, Novartis Pharmaceuticals Corporation, Novo Nordisk, Pfizer Inc., Regeneron Pharmaceuticals Inc., Roche Diagnostics, Sanofi, Research Support; Abbott Diagnostics, Akcea Therapeutics, Amgen Inc., Arrowhead Pharmaceuticals, Inc., ESPERION Therapeutics, Inc., Ionis Pharmaceuticals, Novartis Pharmaceuticals Corporation, Regeneron Pharmaceuticals Inc., Roche Diagnostics. E.Boerwinkle: None. C.E.Ndumele: None. J.S.Pankow: None. R.Demmer: None.

Funding
National Institutes of Health (K23 HL153774)
1196-P: Sex Difference in the Progression of Metabolic Syndrome Severity from Normoglycemia, Prediabetes, to Diabetes
Authors: YOSHIDA YILIN , CHEN ZHIPENG , BAUDIER ROBIN L. , KROUSEL-WOOD MARIE , ANDERSON AMANDA H. , FONSECA VIVIAN , MAUVAIS-JARVIS FRANCK 
Published: May 2022

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 &lt;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
12-OR: Preventing ß-Cell Death under Glucotoxic Conditions: The Role of ChREBPb
Authors: KATZ LIORA S. , BRILL GABRIEL , HERMAN MARK A. , GARCIA-OCANA ADOLFO , SCOTT DONALD 
Published: May 2022

Glucotoxicity is a major mediator of β-cell dysfunction derived from prolonged hyperglycemia. A better understanding of glucotoxicity and preservation of β-cell mass are urgently needed. Carbohydrate response element binding protein (ChREBP) is a glucose-responsive, lipogenic transcription factor with two splice isoforms, ChREBPα and ChREBPβ. We previously showed ChREBPβ is induced by glucose and is required for glucose-stimulated β-cell proliferation in both rodent and human β-cells. In addition, we identified ChREBPβ as a marker for hyperglycemia in β-cells, with its nuclear expression reflecting the progression and severity of the diabetic state. Here, we demonstrate that β-cell death due to glucolipotoxicity (20 mM glucose + 1 mM palmitate) can be prevented by silencing ChREBPβ (6.5±3.1% TUNEL+/Insulin+ in ChREBPβ knock-out islets compared with 47.2±13.6% in control islets, N=7) , most likely via inhibition of lipogenesis and prevention of ceramide-mediated β-cell death. Furthermore, we generated mice with β-cell specific overexpression of ChREBPβ. These mice exhibited increased non-fasting blood glucose levels, impaired glucose tolerance and a marked decrease in β-cell mass, all in a gene dose-dependent manner (OE of one or two alleles of ChREBPβ, N=5) . ChREBPβ overexpression in human beta cells also mimicked glucotoxicity (93.6±3.1% TUNEL+/Insulin+, N=3) . Importantly, we show that ChREBPβ-mediated rodent and human β-cell death was rescued by co-expression with ChREBPα, or by pharmacological activation of Nrf2, a downstream effector of ChREBPα (6.6±1.9 and 2.8±0.9%, respectively, TUNEL+/Insulin+, N=3) . Altogether, our data demonstrate that ChREBPβ is a marker and a causative effector of glucotoxicity, and that downregulating ChREBPβ levels or upregulating ChREBPα levels or its downstream effector (NRF2) rescues β-cells from glucotoxicity. These results position ChREBPβ as a therapeutic target for the prevention of β-cell loss in T2D, an important unmet need in diabetes research.

Disclosure
L.S.Katz: None. G.Brill: None. M.A.Herman: Research Support; Eli Lilly and Company. A.Garcia-ocana: Consultant; Sun Pharmaceutical Industries Ltd. D.Scott: None.

Funding
NIH, R01DK130300NIH, R01DK114338NIH, R01DK108905
1202-P: Disparities in Incidence of Diagnosed Diabetes in Rural United States, 2011–2018
Authors: DUGANI SAGAR , LAHR BRIAN , BAILEY KENT R. , MIELKE MICHELLE M. , VELLA ADRIAN 
Published: May 2022

US rural, compared to non-rural, populations have diabetes health disparities. However, it is unknown if there are rural disparities in incident diabetes. We used CDC data on incidence of diagnosed diabetes in adults aged ≥20 years, available for 97.5% of US counties (n=3145/3226) . Trends in the annual age-adjusted incident diabetes rate per 1000 adults (AADR) were assessed by weighted least squares regression. Year was fitted nonlinearly with a spline function, and AADR fold-change was tested by a model-based contrast of 20vs. 2018. The average AADR of 8.7 (95% CI 8.6-8.7) ranged from 9.3 (in 2011) to 8.3 (in 2013) and 8.9 (in 2018) , and concealed subnational disparities based on rurality (Figure) . In 2018, compared with 2011, the AADR decreased for large central metro counties (most urban) with AADR fold-change of 0.92 (0.91-0.93) but increased for noncore counties (most rural) with AADR fold-change of 1. (1.01-1.08) (both P&lt;.01) . The AADR declined in counties with intermediate rurality except for micropolitan counties (no change; P=.56) . When stratified by region, the largest rural disparity in AADR was observed in the South, with smaller disparities in other regions.
In summary, from 2011-2018, rural counties had the highest overall AADR. The overall disparity in trends based on county rurality and the disparity in incidence in the rural South and rural West highlight areas for diabetes primary prevention interventions.

Disclosure
K.R. Bailey: None. M.M. Mielke: Consultant; Biogen, LabCorp. A. Vella: Advisory Panel; Crinetics Pharmaceuticals, Inc., Rezolute, Inc., vTv Therapeutics, Zealand Pharma A/S. Other Relationship; Novo Nordisk.

Funding
National Institute on Minority Health and Health Disparities (NIH K23 MD016230)
1203-P: Association of Bone Mineral Density (BMD) with Prediabetes in a Biracial Cohort
Authors: LIU ZHAO , PATEL AVNISHA A. , DAGOGO-JACK SAMUEL 
Published: May 2022

Background: Type 2 Diabetes (T2D) is associated with alterations in BMD, but association between prediabetes and BMD is less clear.
Subjects and Methods: We analyzed BMD in relation to demographic and metabolic parameters among the initially normoglycemic offspring of T2D parents in the POP-ABC (Pathobiology of Prediabetes in a Biracial Cohort) study. We further assessed the relationship between BMD and the risk of progression to prediabetes during 5 years of follow-up.
Results: A total of 376 participants underwent DEXA (Dual-energy X-ray absorptiometry) during Year 1 of POP-ABC and were followed quarterly for 5 years, the primary outcome being progression from normoglycemia to prediabetes. The mean age was 44.2 ± 10.6 years; BMI was 30.2 ± 7.23 kg/m2; 217 Black, 159 White, with 71% female. At baseline, the mean BMD was 1.176 ± 0.135 g/cm2 for the entire cohort; 1.230 ± 0.124 g/cm2 in men vs. 1.154 ± 0.134 g/cm2 in women (P&lt;0.0001) ; 1.203 ± 0.114 g/cm2 in Black vs. 1.146 ± 0.150 g/cm2 in White participants (P=0.0003) . There was an inverse relationship between BMD and age (r= −0.254, P&lt;0.0001) and a direct association with BMI (r=0.173, P=0.0028) and weight (r=0.315, P&lt;0.0001) . The BMD showed no significant correlation with insulin secretion or insulin sensitivity. However, the BMD z score, which compares BMD to the average values for a person of same age and gender, was significantly correlated with risk of progression to prediabetes (r = 0.173, P=0.0029) and showed positive association with total body fat mass (r=0.208, p=0.0003) and trunk fat mass (r= 0.178, P= 0.0021) . BMD z score was not significantly correlated with either CRP or adiponectin level.
Conclusion: Among POP-ABC participants, BMD had the expected relationships with age, sex, and race. Higher BMD z score was predictive of progression to prediabetes among initially normoglycemic offspring of T2D parents during 5-year follow-up, an association that requires further mechanistic insights.

Disclosure
Z.Liu: None. A.A.Patel: None. S.Dagogo-jack: Consultant; AstraZeneca, Bayer AG, Boehringer Ingelheim International GmbH, Janssen Pharmaceuticals, Inc., Medtronic, Merck & Co., Inc., Sanofi.

Funding
American Diabetes Association (7-07-MN-13) ; National Institutes of Health (RDK067269)
1204-P: Evaluation of Mediation via the Physical Activity Environment of Neighborhood, Socioeconomic Environment, and Type 2 Diabetes Associations
Authors: MOON KATHERINE A. , NORDBERG CARA M. , ZHU AOWEN , UDDIN JALAL , LOPEZ PRISCILLA M. , ORSTAD STEPHANIE L. , SCHWARTZ MARK D. , RYAN VICTORIA , HIRSCH ANNEMARIE G. , SCHWARTZ BRIAN S. , CARSON APRIL P. , LONG D LEANN , MEEKER MELISSA A. , BROWN JANENE R. , LOVASI GINA S. , ADHIKARI SAMRACHANA , KANCHI RANIA , AVRAMOVIC SANJA , SIEGEL KAREN R. , POULSEN MELISSA N. 
Published: May 2022

Inequitable access to neighborhood leisure-time physical activity (LTPA) resources may explain geographic disparities in type 2 diabetes (T2D) . We evaluated the LTPA environment as a potential mediating pathway through which the neighborhood socioeconomic environment (NSEE) affects T2D. We used harmonized measures and analytic approaches in three independent study samples in the Diabetes Location, Environmental Attributes, and Disparities Network: the national Veterans Administration Diabetes Risk (VADR) cohort, comprising electronic health records (EHR) of 4.1 million T2D-free veterans; Reasons for Geographic and Racial Differences in Stroke (REGARDS) , a national prospective cohort (n=11,2T2D-free) ; and a case-control study in the Geisinger EHR in Pennsylvania (15,888 T2D cases) . We measured NSEE with an index of census tract-level Census metrics (higher indicates disadvantage) . The LTPA environment was evaluated by PA facility density within street network buffers and population-weighted distance to parks. In models stratified by levels of community urbanicity, we estimated natural direct effects and indirect effects for each mediator. PA facility density mediated NSEE-T2D effects, but the magnitude and direction of the indirect effects differed by urbanicity and study sample. In rural communities, we found positive indirect effects via PA facility density (incidence rate difference [95% confidence interval] of NSEE quartile 4 vs. 1: 0.0[0.0038, 0.0099] in VADR; 1.53 [0.25, 3.05] in REGARDS) , representing a proportion mediated of 3% (VADR) and 51% (REGARDS) . We found small indirect effects for all other community types in VADR but none in the Geisinger sample. Distance to parks mediated NSEE-T2D effects only in VADR rural communities. Results suggest regional or population-specific differences in how and whether the LTPA environment mediates NSEE-T2D relations, which could support local T2D prevention efforts.

Disclosure
K.A. Moon: None. C.M. Nordberg: None. A. Zhu: Research Support; Amgen Inc. J. Uddin: None. P.M. Lopez: None. S.L. Orstad: None. M.D. Schwartz: None. V. Ryan: None. B.S. Schwartz: None. A.P. Carson: Research Support; Amgen Inc. D. Long: Research Support; Amgen Inc. J.R. Brown: None. G.S. Lovasi: None. S. Adhikari: Research Support; Johnson & Johnson. R. Kanchi: None. S. Avramovic: None. K.R. Siegel: None. M.N. Poulsen: None.

Funding
Centers for Disease Control (U01DP006293, Drexel University; U01DP006296, Geisinger-Johns Hopkins University; U01DP006299, New York University School of Medicine; U01DP006302, University of Alabama at Birmingham) .
1207-P: Hepatic Steatosis Index and Fibrosis-4 Scores in Relation to Cardiometabolic Risk in Healthy Offspring of Parents with Type 2 Diabetes
Authors: BARRETO BRIGIDA COLON , SIWAKOTI KRISHMITA , EDEOGA CHIMAROKE , EBENIBO SOTONTE , DAGOGO-JACK SAMUEL 
Published: May 2022

We explored the association between nonalcoholic fatty liver disease and cardiometabolic risk and the impact of race/ethnicity among healthy offspring of parents with type 2 diabetes (T2D) . These post hoc analyses were conducted on baseline data from the Pathobiology of Prediabetes in a Biracial Cohort (POP-ABC study) . Participants (N 376) were normoglycemic (NGT) White (N159) and Black (N 217) adults, aged 18-75 years, with at least 1 parent with T2D. The presence of hepatic steatosis was estimated by hepatic steatosis index (HSI; 8 × ALT/AST + BMI + 2 if female) ; and for hepatic fibrosis, fibrosis-4 score (FIB-4; [age × AST]/[platelets count × √ALT]) . We determined the association between HSI and Fib-4 vs. markers of the metabolic syndrome (MetS) , using a modified definition in our NGT cohort (waist≥100cm in men, ≥88 cm in women; BP ≥ 120/80mmHg, triglycerides ≥150mg/dl, HDL ≤40 in men, ≤50 in women) . HSI did not differ by sex or race, showed no correlation with age, but correlated significantly with MetS components: waist (r= 0.77, P&lt;0.0001; systolic BP (r= 0.246, P &lt;0.0001;) ; HDL (r= −0.224, P&lt;0.0001; triglycerides (r= 0.11, P= 0.0009) . HSI increased cumulatively with MetS markers. FIB-4 score showed no sex or ethnic disparities, and correlated with BMI (r= −0.17, P= 0.0015) , total fat mass (r= −0.13, P= 0.02) and HDL (r=0.11, P=0.044) but not waist, BP or triglycerides. Unlike HSI, Fib-4 showed no cumulative association with MetS markers. These findings were similar in Black and White participants. Our data from POPABC study indicate that HSI is an informative early marker of the link between hepatic steatosis and cardiometabolic risk in normoglycemic Black and White offspring of parents withT2D.

Disclosure
B.Colon barreto: None. K.Siwakoti: None. C.Edeoga: None. S.Ebenibo: None. S.Dagogo-jack: Consultant; AstraZeneca, Bayer AG, Boehringer Ingelheim International GmbH, Janssen Pharmaceuticals, Inc., Medtronic, Merck & Co., Inc., Sanofi.

Funding
American Diabetes Association (7-07-MN-13) ; National Institutes of Health (R01 DK067269)
1210-P: Associations between HbA1c, Area Level Social Vulnerability, and Poor COVID-Outcomes
Authors: HOWELL CARRIE R. , ZHANG LI , WILLIAMS SHANQUELA , YI NENGJUN , GARVEY W. TIMOTHY , CHERRINGTON ANDREA 
Published: May 2022

Background: HbA1c has been associated with COVID-poor outcomes in diabetic and non-diabetic populations, while patients living in census tracts with high levels of social vulnerability [measured using the CDC’s Social Vulnerability Index (SVI) ] have experienced poorer outcomes.
Objective: To examine associations between HbA1c, area level social vulnerability and poor COVID outcomes in patients who tested positive for COVID and were captured in the electronic medical record (EMR) at a large academic institution in the Southeastern United States.
Methods: HbA1c and SVI, collected up to 3 years prior to a positive COVID test, were extracted from the EMR. HbA1c and SVI were compared by poor outcome status [hospitalization, intensive care unit (ICU) admission, and mortality]. Bayesian logistic regression was used to examine associations between HbA1c [≤5.7%; 5.7%-&lt;6.5%; and ≥6.5%], SVI [living in high SVI census tract vs low SVI tract] and each COVID outcome separately. Multivariable models were adjusted for sex, age, race, body mass index and diabetes status. Results presented as odds ratios (OR) and 95% confidence intervals (95% CI) .
Results: N=3,7patients were identified [mean age: 54 years (SD 16.3) , 60% female, 47% Black]. Patients with HbA1c ≥ 6.5% and those living in a high SVI census tract were more likely to experience a poor COVID outcome (p’s&lt;0.0001) . In multivariable models, patients with HbA1c ≥ 6.5% had higher odds of hospitalization (OR 1.79, 95% CI 1.44-2.22) ; ICU admission (OR 2.13, 95% CI 1.78-2.55) ; and mortality (OR 1.60, 95% CI 1.12-2.28) . Patients living in a high SVI census tract had higher odds of hospitalization (OR 2.47, 95% CI 1.94-3.15) ; ICU admission (OR 2.58, 95% CI 2.12-3.14) ; and mortality (OR 2.07, 95% CI 1.39-3.09) .
Conclusion: HbA1c ≥6.5% and living in a census tract with high social vulnerability were independently associated with poor COVID-outcomes. Findings highlight the need to assess HbA1c and area level social determinants in the context of COVID.

Disclosure
C.R.Howell: None. L.Zhang: None. S.Williams: None. N.Yi: None. W.Garvey: Other Relationship; Boehringer Ingelheim International GmbH, Eli Lilly and Company, Epitomee, JAZZ Pharmaceuticals, Novo Nordisk, Novo Nordisk, Pfizer Inc., Pfizer Inc. A.Cherrington: Consultant; Bayer AG, Other Relationship; Novo Nordisk.

Funding
This work was supported by funding from the National Institute on Minority Health and Health Disparities (U54MD008176) .
1215-P: Evaluating Alternate Bias Correction Methods in Estimating Diabetes Prevalence with Electronic Health Records
Authors: CONDERINO SARAH , ANTHOPOLOS REBECCCA , THORPE LORNA , CAI BO , SHAO HUI , ONG TOAN C. , CRUME TESSA L. , SCHWARTZ BRIAN S. , KIRCHNER H. LESTER , ROSENMAN MARC , ZHONG VICTOR W. , REYNOLDS KRISTI , PARK SEHO , UTIDJIAN LEVON H. , DIVERS JASMIN 
Published: May 2022

Background: The Assessing the Burden of Diabetes by Type in Children, Adolescents and Young Adults (DiCAYA) Network aims to provide timely estimates of diabetes prevalence and incidence using large scale electronic health record (EHR) data. However, patients who self-select into health systems are likely to differ demographically and by health status than the overall source population of interest, which could limit the generalizability of surveillance estimates.
Methods: Using simulations, we evaluated the potential of established bias correction methods (inverse probability weighting, post stratification, raking and multilevel regression with post stratification (MRP)) to mitigate selection biases within EHR data. The EHR sample was assumed to have overrepresentation of younger, higher socioeconomic status (SES) and white individuals than the source. Simulations varied the selection processes (e.g. dependent on diabetes status) and misspecification in the adjustment methods. The performance of each method was assessed as the absolute difference between the estimated prevalence in the sample and true prevalence in the simulated source.
Results: When selection was dependent on demographics alone, the mean bias in the crude prevalence estimate was -0.7 percentage points (pp) . Each method performed well in removing this bias when demographics that affected selection were adequately accounted for. Biases remained when SES was misclassified in the adjustment process, with best performance seen for MRP (-0.5 pp) . When selection was dependent on diabetes status, the mean bias in the crude prevalence estimate was 1.3 pp and each method similarly worsened this bias (2.3 pp) .
Conclusions: These methods are easy to implement and can provide reliable surveillance estimates if factors that are associated with selection into the EHR can be accounted for. However, novel methods may be needed when selection is dependent on diabetes status or other unmeasured factors.

Disclosure
S.Conderino: None. M.Rosenman: None. V.W.Zhong: None. K.Reynolds: Research Support; Amgen Inc., Merck & Co., Inc., Novartis Pharmaceuticals Corporation. S.Park: None. L.H.Utidjian: None. J.Divers: None. R.Anthopolos: None. L.Thorpe: None. B.Cai: None. H.Shao: Board Member; BRAVO4HEALTH, LLC. T.C.Ong: None. T.L.Crume: None. B.S.Schwartz: None. H.Kirchner: None.

Funding
Centers for Disease Control and Prevention (1 U18DP006633-01-00)