bluuebunny/crossref_metadata_2025_split huggingface dataset:
190-OR: Development and Validation of a Social Risk Screening Tool for Adults with Type 2 Diabetes
Authors: WILLIAMS JONI S. , WALKER REBEKAH J. , EGEDE LEONARD E. 
Published: June 2024

Introduction & Objective: Social risk factors are the adverse social conditions associated with poor health including poor diabetes outcomes. Despite being important for assessing social needs and integrating social care into medical care, most measures that assess social determinants of health and social risk factors are lengthy and difficult to administer in clinical settings. Therefore, the objective of this study was to develop and validate a screening tool useful for identifying social risk factors and social needs among adults with type 2 diabetes.
Methods: Cross-sectional data from the comprehensive assessments of 615 adults with diabetes recruited from two primary care clinics were used for the analyses. The Kaiser Family Foundation Social Determinants of Health Framework was used as the theoretical basis for identifying and aligning measures across social risk domains of economic stability, neighborhood and physical environment, education, food, community and social context, and health care system. Each social risk factor was dichotomized in high and low risk. Factor analysis with orthogonal varimax rotation was used to identify latent variables underlying the social risk domains. Eigenvalue of 1 was used to identify number of factors. Factors loading at 0.5 and higher were included in the factor structure. Stata v16 was used for analyses.
Results: Twelve items were retained and loaded onto three domains that explained 100% of the variance: neighborhood factors (4 items: violence, crime, neighborhood rating, social isolation); psychosocial factors (5 items: food insecurity, depression, diabetes distress, psychological stress, perceived stress); and economic factors (3 items: financial hardship, high school education, literacy). Cronbach’s alpha of 0.7 indicated high reliability of the tool.
Conclusion: This screening tool will facilitate the identification of adults with diabetes, likely to have high social risks, to facilitate targeted interventions.

Disclosure
J.S. Williams: None. R.J. Walker: None. L.E. Egede: None.

Funding
NIH/NIDDK (R21DK123720, R01DK135838, R01DK118038, R01DK120861); NIH/NIMHD (R01MD013826, R01MD018012, R01MD017574)
1908-LB: A Machine-Learning Framework to Quantify Postprandial Glucose Responses in Gestational Diabetes
Authors: BARUA SOUPTIK , UPADHYAY DHAIRYA A. , SANGMO TENZIN , KHAN ARSALA , BERUBE LAUREN , LI LING-JUN , WILLIAMS SHAUNA , ROSEN TODD , RAWAL SHRISTI 
Published: June 2024

Objective: Continuous glucose monitoring (CGM) provides a novel approach to monitor postprandial glucose responses (PPGR) in persons with gestational diabetes mellitus (GDM). We sought to develop a machine learning (ML) framework to identify PPGRs automatically from CGM data to address challenges of manual meal logging.
Methods: Adults 18+ years diagnosed with GDM were enrolled at 24-32 gestational weeks and wore a blinded Freestyle Libre CGM and logged meals for up to 14 days each over 3 visits. A random forest ML algorithm was trained to identify the first meal of the day from raw daily CGM profiles. Comparing self-recorded and ML-predicted PPGRs, the primary outcome was difference in start time of PPGR while the secondary outcomes were the ratio of the corresponding 2-hr and 3-hr area under the PPGR curves.
Results: We analyzed data from 19 participants with CGM and self-recorded meal logs (35 ± 6 years, 47% Asian or Black/African-American, 37% Hispanic/Latino). The ML algorithm predicted start time of PPGRs to within a median 30 [19,45] minutes of self-logged meal time (Fig 1). The median ratio of the corresponding 2-hr PPGR AUCs was 1.0 [0.98,1.03] and 3-hr AUCs was 1.0 [1.00,1.01].
Conclusion: An ML algorithm showed promising performance in identifying PPGRs accurately from CGM data in persons with GDM, enabling a new automated approach to meal logging and analyzing postprandial glucose patterns.

Disclosure
S. Barua: None. T. Sangmo: None. A. Khan: None. L. Berube: None. L. Li: None. S. Williams: None. T. Rosen: None. S. Rawal: Stock/Shareholder; Merck & Co., Inc., LabCorp, Pfizer Inc. Research Support; National Institutes of Health. Stock/Shareholder; Bristol-Myers Squibb Company.

Funding
Rutgers SHP Dean's Intramural Grant
1923-LB: FRONTIER—Flash Glucose Monitoring System Use in Ontario among Patients with DM in the ICES Database—Evidence from Real-World Practice
Authors: HARRIS STEWART B. , RABASA-LHORET RÉMI P. , RATZKI-LEEWING ALEXANDRIA , POON YEESHA 
Published: June 2024

Introduction & Objective: Sensor-based glucose monitoring technology may reduce risks of hypoglycemia and DKA by providing people with DM key insights into their glucose variability and trends. This study investigated HbA1c and healthcare resource utilization (HCRU) before and after adoption of flash glucose monitoring in people with DM.
Methods: This longitudinal retrospective cohort study used the IC/ES database of publicly funded administrative health data in Ontario, Canada. The analysis cohort comprised 45,523 people with DM in groups aged &lt; 66 or ≥ 66 years who had a first flash glucose monitoring claim between 16 Sep 2019 and 10 Apr 2021 (index date) and remained active on flash glucose monitoring for 24 months’ follow-up after the index date. ED visits and hospitalization were measured for 12 months before the index date and the last 12 months of follow-up. HbA1c data were taken from the last tests in each period.
Results: Mean HbA1c was significantly improved in both age cohorts (both p &lt;.0001; Table); reductions were seen for both T1DM and T2DM, regardless of treatment used. Mean ED visits and hospitalization rates (overall, for DKA, and for hypoglycemia) improved significantly in both age cohorts, with no change in mean resource intensity weight.
Conclusions: HbA1c levels and HCRU were reduced 12-24 months after sensor-based technology initiation in patients with DM.

Disclosure
S.B. Harris: Consultant; Abbott. Research Support; Boehringer-Ingelheim. Consultant; Dexcom, Inc. Advisory Panel; Eli Lilly and Company. Consultant; Eli Lilly and Company, Novo Nordisk, Sanofi. Research Support; Novartis AG. Consultant; Bayer Inc. R.P. Rabasa-Lhoret: Other Relationship; Abbott, AstraZeneca, Bayer Inc., Boehringer-Ingelheim, Dexcom, Inc. Research Support; Diabetes Canada. Other Relationship; Eli Lilly and Company. Research Support; Cystic Fibrosis Canada, Canadian Institutes of Health Research, FFRD - Fondation Francophone pour la Recherche du Diabète. Other Relationship; Janssen Pharmaceuticals, Inc. Research Support; Juvenile Diabetes Research Foundation (JDRF). Other Relationship; Novo Nordisk, GlaxoSmithKline plc. Consultant; HLS Therapeutics Inc., Insulet Corporation. Speaker's Bureau; CPD Networks. Other Relationship; Medtronic. Consultant; Pfizer Inc. Speaker's Bureau; Tandem Diabetes Care, Inc. Other Relationship; Sanofi. Speaker's Bureau; Vertex Pharmaceuticals Incorporated. Research Support; SFD - Société Francophone du Diabète. A. Ratzki-Leewing: Research Support; Sanofi. Advisory Panel; Dexcom, Inc. Consultant; Abbott. Advisory Panel; Sanofi-Aventis U.S. Other Relationship; American Diabetes Association. Consultant; Novo Nordisk, Eli Lilly and Company. Y. Poon: Employee; Abbott.
1925-LB: Sustained Reduction in Blood Glucose Levels Reflecting Diabetes Remission in People with Type 2 Diabetes Using a Digital Health Platform
Authors: FUNDOIANO-HERSHCOVITZ YIFAT , BREUER ASHER INBAR , MANEJWALA OMAR 
Published: June 2024

Attainment of remission from type 2 diabetes (T2D) is increasingly perceived as a treatment goal. Improvement in glycemic control can be attained through lifestyle intervention, potentially resulting in diabetes remission. Dario Health, a digital therapeutic platform, may assist in promoting lifestyle changes and support patients trying to achieve remission. A retrospective data analysis was performed on users with T2D who have reported not using insulin and were active on Dario platform &gt;6 months during 2018-2024, measured their BG in the first month and &gt;3 measurements in the last month. Monthly average blood glucose (AvgBG) and percentage of high readings (&gt;180 mg/dL) versus baseline (first month) were evaluated Paired sample t-test was used for testing differences in AvgBG. A linear mixed-effects model was applied to evaluate the changes in AvgBG and high-readings percentage over time. Finally, a simple slope analysis was used for the interpretation of the interaction of AvgBG and high-readings percentage with lifestyle activities. A group of 7240 users was evaluated (Age 53.8 (SD ±24.6); 41% females). A ratio of 44% (3169/7240) reduced their last monthly AvgBG to &lt;140 mg/dL (equivalent to estimated A1c of 6.5). A sustained AvgBG of &lt;140 mg/dL for at least three consecutive months was observed in 70% (2223/3169) of the users reflecting diabetes remission. The AvgBG was reduced by 15% vs. baseline (P&lt;.001) in this group. In addition, users at high-risk with a baseline AvgBG ≥183 mg/dL demonstrated significant reductions in AvgBG and high-readings percentage in their last month moderated by the number of monthly lifestyle activities (logging carbs, meals and physical activities) as revealed by the model (B=-.00, P=.02 and B=-.00, P=.002 respectively). This study exhibits the potential role of digital health technology to change individuals' behaviors during management of T2D and support patients to achieve remission.

Disclosure
Y. Fundoiano-Hershcovitz: Employee; DarioHealth Corp. I. Breuer Asher: None. O. Manejwala: Employee; DarioHealth Corp. Stock/Shareholder; DarioHealth Corp.
1952-LB: Impact of Automated Insulin Delivery Systems by Baseline HbA1c in Emerging Adults with Type 1 Diabetes
Authors: WATERMAN LAUREN A. , ALONSO GUY T. , SAKAMOTO CASEY , PYLE LAURA , FORLENZA GREGORY P. , SHAH VIRAL N. , COBRY ERIN C. 
Published: June 2024

Introduction & Objectives:Use of automated insulin delivery (AID) systems can improve HbA1c in people with type 1 diabetes (T1D). Data are limited on the impact in emerging adults with higher baseline HbA1cs.
Methods:Retrospective chart review performed on 14-30 year olds with T1D ≥ 1 year duration, started on advanced AID systems (Control IQ, Medtronic 770G, Omnipod 5) after 2018. HbA1c at baseline and at 3 month intervals (max 24 months) following AID initiation were collected. Mixed model was fit for HbA1c values and time, adjusting for age, sex, diabetes duration, race/ethnicity, and insurance. Contrasts generated to compare HbA1cs at each time point. Participants were categorized by baseline HbA1c (&lt;7%, 7-9% and &gt;9%).
Results:HbA1c values (N=3137) were collected from 613 emerging adults with T1D (mean age 21.1±4.2 years, diabetes duration 10.8±6.0 years, male 44.5%, non-Hispanic White 76.8%). Mean HbA1c changed from 11.2% to 9.2% (baseline and 24 months) for HbA1c &gt;9% (N=190); 8.3% to 7.9% for HbA1c 7-9% (N=314); and 6.5% to 6.8% for HbA1c &lt;7% (N=109) (all p&lt;0.001). The percent with HbA1c &lt;7% improved from 17.9% at baseline to 32.2% and 34.2% (3 and 24 months).
Conclusion:AID initiation is associated with sustained improvements for those not initially meeting glycemic targets, particularly those with the highest HbA1cs. As most remained above target, additional strategies for improvement are required.

Disclosure
L.A. Waterman: None. G.T. Alonso: Advisory Panel; MannKind Corporation. C. Sakamoto: None. L. Pyle: None. G.P. Forlenza: Research Support; Abbott, Dexcom, Inc. Consultant; Dexcom, Inc. Research Support; Insulet Corporation. Consultant; Insulet Corporation. Research Support; Medtronic. Advisory Panel; Medtronic. Research Support; Tandem Diabetes Care, Inc. Consultant; Tandem Diabetes Care, Inc. V.N. Shah: Consultant; Dexcom, Inc., Insulet Corporation. Research Support; Insulet Corporation. Advisory Panel; Novo Nordisk. Research Support; Novo Nordisk. Advisory Panel; Sanofi, Medscape. Consultant; embecta, Tandem Diabetes Care, Inc. E.C. Cobry: Advisory Panel; Dexcom, Inc. Other Relationship; Dexcom, Inc.

Funding
National Institutes of Health (5T32DK063687)
1956-LB: Altered Gut Microbiota and Short-Chain Fatty Acids in Children with Diabetic Ketoacidosis at Onset of Type 1 Diabetes
Authors: NI YAN , LIU YARU , LIN HU , DONG GUANPING , FU JUNFEN 
Published: June 2024

Objective: Diabetic ketoacidosis (DKA) stands as the most common acute hyperglycaemic complication with relatively high mortality rate among children diagnosed with type 1 diabetes (T1D). Previous studies have shed light on gut dysbiosis in newly-onset T1D children, but research specifically targeting the gut microbiota and microbial metabolites in patients with DKA remains scarce.
Methods: Shotgun metagenomic analysis was conducted on fecal samples of 90 healthy children (HC) and 96 newly diagnosed T1D children, including 32 patients who presented with DKA upon admission. Additionally, short-chain fatty acids (SCFAs) were quantified using GC/MS.
Results: Gut microbiome indicated evident differences between T1D and healthy controls. At baseline, the phylum Bacteroidetes exhibited predominance in the DKA subgroup, while Firmicutes prevailed in the children without DKA. Compositional analysis unveiled increased levels of enteroviruses in the DKA group, encompassing Staphylococcus aureus and Streptococcus viridans, among others. Concurrently, various SCFA-producing microbiota (Anaerobutyricum, Dialister, Ruminococcus, Roseburia, Dorea, and Butyricicoccus) were significantly reduced in the DKA group. Correspondingly, targeted metabolomics analysis further corroborated significant reductions in the concentrations of SCFAs in children with DKA. Subsequent Spearman correlation analysis revealed the significant correlations between clinical parameters indicative of disease severity (bicarbonate, pH, urinary ketone, HbA1c), SCFAs, and SCFA-producing microbiota.
Conclusions: We reported a reduction of SCFAs and SCFA-producing bacteria in the fecal samples of T1D children experiencing DKA at onset. Our findings suggest a potential therapeutic avenue involving the modulation of gut microbiota and associated SCFA metabolism in the pathogenesis of T1D and onset DKA.

Disclosure
Y. Ni: None. H. Lin: None. G. Dong: None. J. Fu: None.

Funding
This study is funded by the National Key Research and Development Program of China (2021YFC2701901 and 2021YFC2701904) and Zhejiang Provincial Key Science and Technology Project (LGF21H070004).
1962-LB: Intrauterine Exposure to Gestational Diabetes and the Developing Brain
Authors: CHAKRAVARTTI SANDHYA P. , GAO XING , XIANG ANNY , PAGE KATHLEEN A. 
Published: June 2024

Intrauterine exposure to gestational diabetes mellitus (GDM) is associated with an increased risk for adverse metabolic and cognitive outcomes, but the mechanisms are not clear. We tested the hypothesis that exposure to GDM is associated with changes in adiposity and brain trajectories during childhood to adolescence. Participants were 204 children (110 GDM exposed; 94 unexposed; 61% female; age 7-11 at baseline). Children underwent brain MRIs up to 4 times over 6 years. Adiposity measurements were obtained yearly over 6 years. Adiposity measures and FreeSurfer-derived brain volumes were compared between GDM-exposed and unexposed groups using mixed-effect models. We observed significant non-linear trajectories of BMI, body fat %, and waist circumference by age (all adjusted for sex and pubertal stage, p&lt;0.0001) across all children. BMI (p=.058), body fat% (p&lt;.049), and waist circumference (p&lt;.015) trajectories varied by GDM exposure, with higher adiposity markers for GDM exposed vs unexposed. Compared to unexposed children, children exposed to GDM before 26 weeks had increased growth of total cortex (p&lt;.009) and grey matter volume (p&lt;.009), adjusted for age, sex, and pubertal stage, and multiple comparisons. Intrauterine exposure to GDM affects both adiposity and brain trajectories in transitioning from childhood to adolescence. These findings suggest a mechanistic link between GDM exposure and risk for adverse metabolic and neural outcomes.

Disclosure
S.P. Chakravartti: None. X. Gao: None. A. Xiang: None. K.A. Page: None.

Funding
American Diabetes Association (1-14-ACE 36); 1F31DK137584-01A1, R01DK116858, R01DK134079
1965-LB: Metabolomic and Transcriptomic Signatures of Gestational Diabetes Mellitus (GDM) Suggest Nutritional and Inflammatory Pathways in Intergenerational Transfer of Risk
Authors: THAKER VIDHU V. , FIRESTEIN MORGAN , MASSON CYNTHIA , LIU ZHONGHUA , REDDY UMA , GALLAGHER DYMPNA , LEDUC CHARLES A. 
Published: June 2024

Introduction & Objective: GDM impacts intergenerational transfer of metabolic risk. The underlying mechanisms could inform intervention strategies. The placenta nourishes the developing fetus and is influenced by the maternal milieu. We assessed placental transcriptome and cord blood metabolites to determine pathways perturbed by GDM.
Methods: Placental tissue and cord blood from 39 mother-baby dyads of gravidas with BMI &lt; 25 kg/m2 (controls, n= 13), obesity (BMI &gt; 30 kg/m2, n=14) and GDM (n=12) were used. RNA sequencing of placenta tissue and metabolite profiling of cord plasma were performed (LC-MS, HILIC-positive and C18 negative modes). Cord blood c-peptide and fructosamine concentrations were measured by ELISA.
Results: Pregnancy age was similar across the groups (34.0 [5.7] years, p =0.35); pre-gravida BMI (p &lt; 0.01) and gravidity (p = 0.01) were higher for obesity/GDM; but not offspring birthweight (p = 0.76). The GDM and obesity placental transcriptomic showed upregulation of intracellular protein transport, steroid hormone signaling, cellular cycle processing, histone modification and methylation, DNA alkylation and methylation compared to controls (FDR &lt; 0.05). Metabolite pathways related to biopterin, arachidonic acid, amino acids (tyrosine, histidine, alanine, aspartate, tryptophan), urea cycle and vitamins (B6, B1, B2 and biotin) were upregulated with obesity and GDM (FDR &lt; 0.05). Cord plasma fructosamine was elevated (p &lt;0.01); c-peptide was higher in obesity/GDM, but not statistically significant (p =0.17). INSR expression was higher in obesity/GDM.
Conclusions: Placental transcriptomic changes, nutrient transport abnormalities, and inflammatory processes, potentially driven by maternal hormones, may significantly impact methylation and other epigenetic changes. These changes provide a mechanistic hypothesis for the intergenerational inheritance of metabolic risk.

Disclosure
V.V. Thaker: None. M. Firestein: None. C. Masson: None. Z. Liu: None. U. Reddy: None. D. Gallagher: None. C.A. LeDuc: None.

Funding
Naomi Berrie Diabetes Research Center Pre-Translational Research Award, Columbia University to VVT and CAL
198-OR: Should We Improve or Replace the Antenatal Oral Glucose Tolerance Test for Diagnosis of Gestational Diabetes?
Authors: KUSINSKI LAURA C. , JONES DANIELLE , BARKER PETER , BURLING KEITH , HALSALL IAN , MEEK CLAIRE L. 
Published: June 2024

Introduction: Gestational diabetes, the most common medical complication of pregnancy, is diagnosed using an oral glucose tolerance test (OGTT), which has limited accuracy, reproducibility and practicality. Pre-analytical processing of glucose influences diagnosis rates, but the effect of this on a population level is unknown. We assessed how many cases of gestational diabetes were missed through suboptimal specimen processing, and if replacing the OGTT with a 28-week HbA1c was a suitable alternative for identification of hyperglycemia pregnancies.
Methods: We used data from a prospective cohort, the observational study of pregnancy hyperglycemia, endocrine causes, lipids, insulin and autoimmunity (OPHELIA), which included pregnant participants from nine UK centres recruited at the time of a 75g antenatal OGTT (criteria of the National Institute of Health and Care Excellence). Standard glucose processing (local procedures) was compared to enhanced glucose processing (storage on ice, rapid centrifugation, aliquoting and freezing &lt;2.5 hrs).
Results: We recruited 1308 participants of mean age 31.5 years (SD 5.0) and BMI 33.0kg/m2 (6.8) of 82.5% white ethnicity, representative of the UK population. Enhanced glucose processing resulted in glucose levels ~0.6 mmol/L (10.9 mg/dL) higher than standard processing, associated with an increase in gestational diabetes diagnosis from 9% to 22%. The 13% of women who had a missed diagnosis due to standard specimen processing were not representative of the whole cohort: they were more likely to be of white ethnicity with BMI &gt;30 kg/m2. HbA1c was not associated with most relevant pregnancy outcomes and receiver operator curves (ROC) demonstrated that the HbA1c was not a strong predictor of gestational diabetes diagnosis (AUROC 0.74; 95%CI 0.68 to 0.79).
Conclusions: Neither an oral glucose tolerance test with standard glucose processing nor 28-week HbA1c offers consistently accurate diagnosis of gestational diabetes.

Disclosure
L.C. Kusinski: None. D. Jones: None. P. Barker: None. K. Burling: None. I. Halsall: None. C.L. Meek: Research Support; Dexcom, Inc.

Funding
NIHR Cambridge BRC; Diabetes UK Harry Keen Intermediate Clinical Fellowship (17/0005712); EFSD-Novo Nordisk Foundation Future Leader's Award (NNF19SA058974)
1998-LB: Previously Frozen Beta-Cell Transcriptional Response to Cytokine Treatment Mirrors That of Fresh Beta Cells
Authors: ORCHARD PETER , WANG XIAOOU , YAN TINGFEN , NARISU NARISU , ROBERTSON CASSIE , ERDOS MICHAEL , MENG JERRY , COLLINS FRANCIS S. , CHEN SHUIBING , PARKER STEPHEN C. 
Published: June 2024

Introduction & Objective: Proinflammatory cytokines contribute to pancreatic beta cell death and dysfunction underlying Type I Diabetes (T1D) pathogenesis. We previously used 10X Genomics single-nucleus RNA-seq (snRNA-seq) and multiome sequencing (snRNA-seq and snATAC-seq from the same nucleus) to profile the transcriptional response of pancreatic islet cell types to cytokine treatment (IL-1B and TNF-a; N=6 treated and 6 untreated). Fresh islets were used; however, it would be convenient to use frozen islets to examine beta cell response to cytokines. In the current study, we sought to determine whether beta cells of frozen islets showed a similar transcriptional response to cytokines as those from fresh islets.
Methods: We selected frozen islet samples from three donors. After thawing, one sample from each donor was treated with cytokines as in our previous study and one sample from each donor was left untreated; samples were then re-frozen and underwent single-nucleus multiome profiling using a pooled design to minimize batch effects. We used Seurat to cluster nuclei and identify beta cells, and performed a differential gene expression analysis (DESeq2) on pseudobulk expression between the treated and untreated beta cells from each donor.
Results: 3,758 of 5,228 pass-QC nuclei were beta cell nuclei. 3,693 of 18,436 tested genes were differentially expressed (DE) between the conditions (5% FDR). There was significant overlap between DE genes in the current study and in the fresh islet study (two-sided Fisher’s Exact test p = 2.6x10-250, odds ratio = 14.5). Of 827 DE genes in the fresh islet study that were also tested in the current study, 794 (96.0%) showed the same direction-of-effect in the current study (regardless of significance; two-sided Fisher’s Exact test p = 3.4x10-173, odds ratio = 647.8).
Conclusion: Frozen islets show a similar transcriptional response to cytokines as fresh islets, suggesting they are appropriate to use in future research studies on this topic.

Disclosure
P. Orchard: None. X. Wang: None. T. Yan: None. N. Narisu: None. C. Robertson: None. M. Erdos: None. J. Meng: None. F.S. Collins: None. S. Chen: None. S.C. Parker: Research Support; Pfizer Inc.

Funding
National Institute of Diabetes and Digestive and Kidney Diseases (U01DK127777)