bluuebunny/crossref_metadata_2025_split huggingface dataset:
1990-LB: Associations of SARS-CoV-2 Infection with Incident Diabetes among U.S. Veterans in a Longitudinal Observational Cohort
Authors: HUANG LIUYE , SUGIMOTO JONATHAN D. , SHAH JAVEED A. , LEE JENNIFER , WANDER PANDORA 
Published: June 2024

Existing estimates of diabetes incidence after SARS-CoV-2 infection are derived from retrospective electronic health records (EHR) studies, which may lead to surveillance and misclassification bias given differences in receiving clinical care between the exposed and unexposed groups. We used data from the Veterans Health Administration (VHA)-based, longitudinal observational cohort study Epidemiology, Immunology and Clinical Characteristics of COVID-19 (EPIC3) and the VHA Corporate Data Warehouse to compare diabetes hazard rates over 12 months of follow-up (n=1245) in participants with and without a positive test for SARS-CoV-2. We fit Cox proportional hazard models, adjusted for age, sex, race/ethnicity, Charlson comorbidity index (CCI), and annual number of laboratory tests of any type except for SARS-CoV-2 related types.
Results: Compared to participants with a positive test for SARS-CoV-2, participants without a positive test were older and had a higher number of laboratory tests. They were more likely to have taken at least one dose of COVID19 vaccine, live in an urban area, being current smoker, and have a hospital stay within 30 days post enrollment. After adjustment, SARS-CoV-2 was not significantly associated with risk of incident diabetes overall (HR 0.71, 95%CI 0.33-1.51), in inpatients (HR 1.22, 95%CI 0.08-18.78), or in outpatients (HR 0.50, 95%CI 0.19-1.34).

Disclosure
L. Huang: None. J. Lee: None.

Funding
GR014814
1999-LB: GenomicKB—A Knowledge Graph for Human Genomic Data to Advance Understanding of Diabetes
Authors: FENG FAN , TANG FEITONG , GAO YIJIA , ZHU DONGYU , LI TIANJUN , YANG SHUYUAN , YAO YUAN , HUANG YUANHAO , SAUNDERS DIANE C. , PARKER STEPHEN C. , CARTAILLER JP , BRISSOVA MARCELA , LIU JIE 
Published: June 2024

With rapid advancements in technologies involving genetic testing, epigenomic and transcriptomic profiling, many landmark consortia have generated rich multimodal data for the human genome. Among those, the consortia focused on human pancreas and islet research have greatly enhanced our understanding of different forms of diabetes. Despite new insights provided at an unprecedented scale and depth, current islet and pancreas data are fragmented across different portals with lack of common metadata/ontology standards, varying maturity of quality control pipelines, and disconnected from clinical research communities. To overcome these challenges, we developed Genomic Knowledgebase (GenomicKB), a graph database that integrates human genomic data, allowing researchers to explore and investigate human genome, epigenome, transcriptome, and 4D nucleome. The database uses a knowledge graph to consolidate genomic datasets and annotations from over 30 consortia and portals, resulting in 347 million entities, 1.36 billion relations, and 3.9 billion properties, which covers extensive pancreas and diabetes-related data including GWAS, disease ontology, and eQTL. Compared with traditional tabular data stored in separate data portals, GenomicKB emphasizes the relations among genomic entities and intuitively connects isolated data entries. With GenomicKB, complicated analysis among multiple modalities is transformed into coding-free queries and facilitates data-driven discoveries. Our knowledge graph structure is machine learning-ready and can be coupled with evolving artificial intelligence algorithms. In the future, GenomicKB will include pancreas and islet imaging and physiology datasets to build a comprehensive resource, PanKGraph, that will accelerate progress towards understanding diabetes etiology and pathophysiology, contribute to new diagnostic tool development, and inform transformative changes in diabetes prevention and care.

Disclosure
F. Feng: None. F. Tang: None. Y. Gao: None. D. Zhu: None. T. Li: None. S. Yang: None. Y. Yao: None. Y. Huang: None. D.C. Saunders: None. S.C. Parker: Research Support; Pfizer Inc. J. Cartailler: None. M. Brissova: None. J. Liu: None.
2004-LB: Elucidating Causal Genes of Severe Insulin Resistance Diabetes through Polygenic Exploration of Type 2 Diabetes Heterogeneity
Authors: YOSHIJI SATOSHI , RUEBENACKER OLIVER A. , SMADBECK PATRICK , SAYICI AHMET , LLAMAS EDGAR A. , FLANNICK JASON 
Published: June 2024

Background: Severe insulin resistance diabetes (SIRD) is a subgroup of type 2 diabetes (T2D) with particularly high risk of cardiovascular complications. A subset of T2D genome-wide association study (GWAS) associations are suggested to act through pathways underlying SIRD, but the number of known loci are limited (&lt;50), and the unavailability of large SIRD patient cohorts hampers directly conducting SIRD GWAS. We developed a novel model to computationally infer a full GWAS for SIRD, aiming to expand our understanding of its causal genes and relevant biological pathways.
Methods and Results: We developed a Bayesian model of latent SIRD association statistics conditional upon observed association statistics for 6 insulin resistance (IR)-related traits in up to 1 million individuals. We trained this model using 43 genetic variants within T2D GWAS loci suggested to be associated with SIRD, observing the expected “lipodystrophy” pattern such as positive effect sizes for insulin, TG, waist-hip-ratio, and negative for HDL and adiponectin. We then used the trained model to infer SIRD associations for over 2 million additional variants that were present in 6 IR-related GWAS. The resulting SIRD GWAS identified 429 independent variants at P &lt; 5×10-8, which were mapped to 316 putatively causal genes, including known genes IRS1, COBLL1, PPARG, as well as novel ones such as TGFB2. Notably, TGFB2 encodes TGF-β2, an adipokine linked to IR in mice, and our data suggest its potential as a therapeutic target for SIRD in humans. Pathway-enrichment analysis showed enrichment in multiple lipid-related pathways. Furthermore, a polygenic risk score built with the GWAS was strongly associated with coronary artery disease risk (P &lt; 1 × 10-10).
Conclusions: Using a novel Bayesian method, we inferred the first GWAS of SIRD, providing insights into molecular mechanisms of SIRD. The method is also applicable to other diabetes subtypes for which a training set of variants is available.

Disclosure
S. Yoshiji: None. P. Smadbeck: None. A. Sayici: Employee; Evotec International GmbH. J. Flannick: None.
2009-LB: Deep Learning Enhances Chromatin Accessibility Signals and Discovers Links with Genetic Variations in Skeletal Muscle Rare Cells
Authors: VU HA T.H. , VARSHNEY ARUSHI , ORCHARD PETER , LAAKSO MARKKU , TUOMILEHTO JAAKKO , LAKKA TIMO A. , MOHLKE KAREN L. , BOEHNKE MICHAEL , SCOTT LAURA , KOISTINEN HEIKKI A. , COLLINS FRANCIS S. , PARKER STEPHEN C. 
Published: June 2024

Chromatin accessibility quantitative trait loci (caQTL) discovery is vital for linking epigenomic features with genetic variations in Type 2 diabetes (T2D). However, caQTL scans require clear epigenomic signals, posing challenges in rare cell types from single nucleus (sn) studies. To address this, we proposed using deep learning (DL) to upscale chromatin accessibility signals e.g. snATAC-seq data in skeletal muscle (SM) to uncover new caQTLs in rare cells.
We used snATAC-seq data from a recent study representing ~260K nuclei across 287 human SM biopsies and a DL model to upscale profiles. The study identified 13 cell populations and performed caQTL scans in the five most prevalent types: muscle fibers (Type 1, 2a, 2x), endothelial cells (ECs) and fibro-adipogenic progenitors (FAPs). Their abundance ranged from 29% (Type 1) to 12% (ECs) and 5% (FAPs) of ATAC nuclei. We trained a model with two Type 1 fiber read sets: a sparse set with read count matching the median count across per-sample EC nuclei, and an abundant set 50x larger than the sparse set. The model was used to upscale ECs and FAPs’ snATAC signals for caQTL scans. CaQTLs identified with upscaled profiles were compared against those identified without upscaling (“base”).
With upscaled profiles, we found 4,753 and 8,722 significant caQTL peaks in ECs and FAPs, respectively, surpassing the base’s 3,412 EC and 4,762 FAP significant caQTL peaks (5% FDR). The upscaled and base scans showed high correlation (Spearman ρ = 0.96 and 0.95 for signed -log10(p-value); Spearman ρ = 1 and 0.99 for effect sizes in ECs and FAPs). Notably, DL upscaling additionally discovered 1,847 EC and 4,652 FAP caQTL peaks not detected in base scans.
DL is expected to help characterize rare cells’ genetic regulatory landscapes. We are working on colocalization between caQTLs from upscaling analysis and T2D and related trait GWAS signals to determine novel ones that may share common causal variants and nominate rare effector cell types in SM.

Disclosure
H.T.H. Vu: None. A. Varshney: None. P. Orchard: None. M. Laakso: None. J. Tuomilehto: Stock/Shareholder; Orion Pharma, Aktivolabs, Digostics. T.A. Lakka: None. K.L. Mohlke: None. M. Boehnke: None. L. Scott: None. H.A. Koistinen: Other Relationship; AstraZeneca, Novo Nordisk. F.S. Collins: None. S.C. Parker: Research Support; Pfizer Inc.

Funding
NIH NIDDK (U24DK138515)
201-OR: Fasting and Postprandial Triglycerides in Early Pregnancy Correlate with Mean Glucose and Time-in-Range by CGM at 28 Weeks of Gestation
Authors: BARBOUR LINDA A. , FOSDICK BAILEY K. , DUNN EMILY Z. , PIERCE STEPHANIE , ROLLOFF KRISTY P. , PHILLIPS ELIZABETH , MOSS LAURA K. , PHIPERS JOCELYN , EDWARDS RODNEY K. , FRIEDMAN JACOB E. , HERNANDEZ TERI L. 
Published: June 2024

Recent studies of obesity in pregnancy have demonstrated a link between higher maternal fasting and postprandial triglycerides (FTG, PPTG) and neonatal subcutaneous and liver fat. Maternal TG can be hydrolyzed by placental lipases to free fatty acids (FFA) for fetal fat accretion, which may be further stimulated by fetal hyperinsulinemia from maternal glucose exposure. We hypothesized that higher FTG and PPTG in early pregnancy are predictive of abnormal glucose metabolism later in pregnancy at 28 weeks gestation using a point-of-care (POC) TG meter and continuous glucose monitor (CGM) metrics that have been associated with fetal overgrowth. At 15.4±0.9 weeks gestation (Mean±SD; early pregnancy), 31 healthy pregnant participants who were overweight or obese (32±5 years; pre-pregnancy BMI 30.6±3.0 kg/m2; 81% White, 19% Hispanic/Latino) were provided an FDA-approved POC TG meter to measure FTG and 2-hr PPTG after each meal for 4 days on an ad libitum diet. At 22, 28, and 34 weeks, 4 days of FTG and PPTG measures were repeated; a CGM was worn at 15 and 28 weeks. In early pregnancy across 119 fasting samples, the average median FTG was 126 mg/dL (68 - 191) and across 329 postprandial samples, the 2-hr PPTG was 153 mg/dL (92 - 247) at 15.4 weeks. At 28 weeks, the mean 24-hr glucose was 100±9 mg/dL and % time-in-range (%TIR 63-140 mg/dl) was 94±6% by CGM. Early FTG were positively correlated with mean 24-hr glucose at 28 weeks (r=0.41; p=0.02) and negatively correlated with %TIR (r=-0.44; p=0.01) by CGM. Moreover, early PPTG correlated with mean 24-hr glucose at 28 weeks (r=0.38; p=0.03). By associating ~450 TG measures with CGM metrics, these data suggest that TG concentrations during early pregnancy in obesity may identify women at risk for higher glycemic measures later in pregnancy. Because both TG and glucose contribute to excess infant adiposity, the timing, interaction, and contribution of both exposures to excess fetal fat deposition warrant further investigation.

Disclosure
L.A. Barbour: None. B.K. Fosdick: None. E.Z. Dunn: None. S. Pierce: None. K.P. Rolloff: None. E. Phillips: None. L.K. Moss: None. J. Phipers: Research Support; Pfizer Inc., Aerogen, Cepheid, Natera, Myriad Genetics. R.K. Edwards: None. J.E. Friedman: None. T.L. Hernandez: None.

Funding
NIH (R01 HD102726); Harold Hamm Diabetes Center/Presbyterian Health Foundation Team Science Grant Pilot
2023-LB: Type 2 or Type 3c Diabetes Not an Exclusion Criteria for Total Pancreatectomy with Islet Auto Transplantation (TPIAT)
Authors: KANAK MAZHAR A. , BALAJI SARAVANAN PRATHAB , KALIVARATHAN JAGAN , MOHAMMED SHUJAUDDIN , KHAN AAMIR , LEVY MARLON F. 
Published: June 2024

Purpose: Total pancreatectomy with islet auto-transplantation (TPIAT) is now a standard of care for patients with severe chronic pancreatitis. However, evaluation and patient selection is necessary to benefit from this difficult procedure. Several patients with CP also have underlying diabetes, Type 2 or type 3c. In this study, we performed a comparative analysis of the outcomes of the TPIAT procedure between our pre-transplant diabetic and non-diabetic patient cohorts.
Methods: A total of 100 Patients who underwent TPIAT at Virginia Commonwealth University from June 2016 to March 2024 have been included in this study. 26 out of 100 patients were diabetic before undergoing TPIAT and were either insulin-dependent or under other diabetic medications and with an HbA1c of more than 6.0%. Islet isolation outcomes were compared along with metabolic outcomes and pain management data at clinical time points post-transplantation. Maintenance of hypoglycemic awareness was also assessed in these patients.
Results: Islet yields in diabetic and non-diabetic patients undergoing transplantation were significantly different (Diabetic vs. Non-Diabetic; 158362±185600 vs. 345126±219809; P&lt;0.05). All Patients that were diabetic before TPIAT procedure continued to be insulin-dependent, whereas 40% of non-diabetic patients became insulin-independent at 1-year post-transplantation. Most interestingly, narcotic dependence was reduced in more than 50% at 1 year in patients with diabetes which was similar to patients who were non-diabetic. Similarly, pain scores were also reduced in patients equally regardless of their diabetic status prior to transplantation.
Conclusion: Pre-transplant diabetes should not be considered as a contra-indication for TPIAT, as these patients can maintain hypoglycemic awareness after transplantation of islets which is a major benefit along with relief from narcotic dependence.

Disclosure
M.A. Kanak: None. P. Saravanan: None. J. Kalivarathan: None. S. Mohammed: None. A. Khan: None. M.F. Levy: None.
2026-LB: Investigating the Role of NKX6.1 in Pancreatic Endocrine Cell Differentiation
Authors: LADD ANDREW D. , KRATZ AUSTIN T. , BARRA JESSIE , RUSS HOLGER A. 
Published: June 2024

Protocols to generate stem cell-derived beta cells have allowed novel investigations into the differentiation, specification and function of human beta cells. The transcription factor Nkx6.1 is critical to the specification of insulin-producing pancreatic β cells in mice. Knockout of Nkx6.1 in an inducible mouse model has been shown to demonstrated lack of β cell specification when Nkx6.1 is absent and instead a preferred differentiation to α cells. While Nkx6.1 is a highly conserved transcription factor between mice and humans, the role of NKX6.1 in human endocrine specification has not been studied in detail. Using CRISPR/CAS9 genome engineering, we generated an NKX6.1 knockout (KO) embryonic stem cell line to allow us to investigate NKX6.1 in the human context. Utilizing a directed differentiation protocol to produce stem-cell derived pancreatic β cells (sBCs), we hypothesize that the lack of NKX6.1 will induce a preference for α cell specification and eliminate β cell fate in NKX6.1 KO cells but not control wild-type (WT) cells, similar to what is observed in rodents. In addition, we will use an α-like cell differentiation protocol to test if α cell differentiation is altered between NKX6.1 KO and WT cells. Our experiments will provide genetic evidence that NKX6.1 is necessary for the specification of pancreatic β cells in humans and more fully elucidate the role of NKX6.1 in pancreatic endocrine cell fate.

Disclosure
A.D. Ladd: None. A.T. Kratz: None. J. Barra: None. H.A. Russ: Advisory Panel; Sigilon Therapeutics, Inc. Consultant; Minutia. Advisory Panel; Prellis Biologics. Consultant; Eli Lilly and Company.
2035-LB: Value of TyG Combined with Albumin in Predicting MAFLD in Mulam Type 2 Diabetes Mellitus Patients
Authors: QIU YAWEI , ZHU HENGLIANG , XU YANCHUN , SU GUOQIANG , JIANG SHU , YANG ZHIJUN 
Published: June 2024

Objective: There is no report on the study of T2DM combined with metabolic associated fatty liver disease(MAFLD)in Mulam nationality in China. This paper reports the value of triglyceride-glucose (TyG) combined with albumin in predicting MAFLD in Mulam T2DM patients.
Methods: A total of 105 Mulam T2DM inpatients in the Department of Endocrinology, Luocheng Mulam Autonomous County People's Hospital from July 2022 to August 2023 were enrolled and grouped according to whether they had MAFLD. 45 cases in the group with MAFLD and 60 cases in the control group without MAFLD.
Results: The proportion of newly diagnosed T2DM, waist circumference, hip circumference, weight, BMI, total protein, albumin, ALT, cholinesterase, TC, TG, LDL-C, non-HDL-C and TyG in MAFLD group were significantly higher than those in control group (P &lt; 0.05) (Table 1). Logistic regression analysis showed that TyG (OR=1.887, 95%CI: 1.124-3.357, P=0.021) was a risk factor for MAFLD in Mulam patients with T2DM. ROC curve analysis showed that the AUC of TyG in predicting MAFLD was 0.704 (95%CI: 0.597-0.812), the sensitivity and specificity were 0.865 and 0.481. The AUC of the diagnostic model established by TyG combined with albumin was 0.787 (95%CI: 0.692-0.882), the sensitivity and specificity was 0.730 and 0.788.
Conclusion: TyG is a risk factor for MAFLD in Mulam T2DM patients. TyG combined with albumin has a high predictive value for MAFLD in Mulam T2DM patients.

Disclosure
Y. Qiu: None. H. Zhu: None. Y. Xu: None. G. Su: None. S. Jiang: None. Z. Yang: None.
2039-LB: Insulin Sensitivity Is Impaired Independent of Changes in Body Weight in Liver-Specific GDF15 Knockout Mice
Authors: KASTROLL JAKE , JURCZAK MICHAEL J. , ALDER JONATHAN , VANDEVENDER AMBER , DEDOUSIS NIKOLAOS , SIPULA IAN J. 
Published: June 2024

Introduction & Objective: Growth differentiation factor 15 (GDF15), a stress-induced protein, constrains body weight gain in models of obesity by reducing feeding and potentially increasing energy expenditure, leading to improved insulin sensitivity. Whether GDF15 affects insulin sensitivity independent of changes in body weight remains an open question.
Methods: To address this question directly, we generated liver-specific GDF15 knockout mice (Gdf15fl/fl x Albcre-/+ to yield Gdf15fl/fl/Albcre-/+ LKO or Gdf15fl/fl/Albcre-/- WT mice). GDF15 LKO mice were fed either standard chow or high-fat diet (HFD) for 4 and 12 weeks. After the dietary challenge, we performed comprehensive metabolic phenotyping.
Results: Chow-fed GDF15 LKO mice were similar to WT mice and there were no differences in body weight, feeding, circulating GDF15 levels or glucose homeostasis. After 12 weeks HFD, circulating GDF15 was 50% less in LKO mice, which was associated with a modest increase in body weight and significantly increased plasma insulin levels during glucose tolerance tests, indicative of insulin resistance. To avoid the confounding influence of body weight on comparisons of insulin resistance, we fed LKO mice HFD for 4 weeks. Circulating GDF15 was 20% less in LKO mice and there were no differences in body weight or feeding compared to WT. Hyperinsulinemic euglycemic clamps demonstrated impaired whole-body insulin sensitivity in LKO mice that was due primarily to impaired suppression of hepatic glucose output by insulin, indicative of liver insulin resistance.
Conclusion: These data demonstrate body weight-independent effects of hepatocyte GDF15 on insulin sensitivity and suggest the potential for local, GFRAL-independent effects of hepatocyte-derived GDF15 on liver insulin signaling.

Disclosure
J. Kastroll: None. M.J. Jurczak: None. J. Alder: None. A. Vandevender: None. N. Dedousis: None. I.J. Sipula: None.
2040-LB: Antidiabetic Effects and Mechanisms of Saccharum sinense Roxb. Polysaccharides
Authors: XIE CEN , LIU YAMENG 
Published: June 2024

Introduction & Objective: Diabetes is a chronic metabolic disease characterized by insulin resistance or insufficient insulin secretion, resulting in hyperglycemia. The global prevalence of diabetes is on the rise, especially among younger age groups, presenting significant public health challenges. China has the most diabetes patients worldwide, with nearly 61 million new cases in the past decade. Therefore, there is an urgent need for safer, more effective, and cost-effective antidiabetic medications. This study aims to investigate the potential of Saccharum sinense Roxb. polysaccharides as a novel herbal medicine for diabetes treatment.
Methods: We administered a single dose of Saccharum sinense Roxb. polysaccharides to C57BL/6 mice to assess its impact on peak blood glucose levels during oral glucose tolerance tests. Additionally, we utilized diabetic mouse models for drug screening, using metformin as the positive control, to identify Z300 as a potential component.
Results: Our findings revealed that Z300 effectively improved disturbances in glucose metabolism, kidney injury, and lipid metabolism. It regulated body weight gain, prevented fatty liver, and exhibited actions such as promoting insulin secretion and inhibiting α-glucosidase activity. Cell and organoid experiments validated the beneficial effects of Z300 on insulin and GLP-1 secretion. Mechanistic studies indicated that Z300 influenced the expression of various metabolism-sensing-related proteins such as p-AMPK, p-AKT, and p-GSK3β, highlighting its broad therapeutic applications.
Conclusion: This research emphasizes the potential of Saccharum sinense Roxb. polysaccharides, particularly Z300, as a promising herbal medicine for diabetes management. These results support the efficacy of Z300 in improving various aspects of glucose metabolism and insulin regulation, offering a valuable avenue for further exploration and development of novel antidiabetic therapies.

Disclosure
C. Xie: None. Y. Liu: None.