bluuebunny/crossref_metadata_2025_split huggingface dataset:
1010-P: Diabetes Distress in Younger vs. Older Emerging Adults: Confirmation of Content Validity of the Problem Areas in Diabetes—Emerging Adult Survey (PAID-EA)
Authors: WENTZELL KATHERINE , VITALE REBECCA J. , LAFFEL LORI M. 
Published: May 2022

The developmental stage of emerging adulthood covers a wide age span (ages 18-30 years) with potential for differing emotional burdens for those living with type 1 diabetes (T1D) . To further confirm the content validity of the PAID-EA and to investigate differences between younger and older emerging adults, item responses on the newly developed, previously validated PAID-EA survey were examined by age group. Emerging adults (N=287) with T1D completed online measures of diabetes distress, depressive symptomology, and the PAID-EA. The PAID-EA has 25 items and uses a 5 point Likert-type response scale with options ranging from agree to disagree. The sample was split into 2 age groups: younger emerging adults (ages 18-24, N=143) and older emerging adults (ages 25-30, N=122) . Self-reported HbA1c was significantly worse in younger vs. older emerging adults (7.4% vs. 6.8%, p&lt;.001) . Other demographic variables and total scores on measures were similar between age groups. Only 3 items on the PAID-EA differed by age group with younger emerging adults endorsing more diabetes distress: (1) Item 6: “I worry about being able to socialize because of how alcohol affects my blood sugar” (p=.03) , (2) Item 7: “I worry that a new romantic partner will see my diabetes devices or supplies” (p=.04) , (3) Item 25: I feel like I am trying my hardest to take care of diabetes, but it never works” (p=.02) . Experimentation with alcohol and intimacy with new partners may be experienced differently for younger compared with older emerging adults. Intense feelings of burnout and failure may also differ; this finding requires further exploration. Though emerging adulthood spans more than a decade, perceptions of the emotional burden of living with T1D appear consistent for the younger and older age groups, confirming the content validity of the PAID-EA throughout emerging adulthood.

Disclosure
K.Wentzell: None. R.J.Vitale: None. L.M.Laffel: Advisory Panel; Medtronic, Roche Diabetes Care, Consultant; Boehringer Ingelheim International GmbH, Dexcom, Inc., Dompé, Insulet Corporation, Janssen Pharmaceuticals, Inc., Lilly Diabetes, Novo Nordisk, Provention Bio, Inc.

Funding
Joslin DRC: P30DK036836 NIH: T32DK007260, T32DK007529
1017-P: Relationship between Parent Fear of Hypoglycemia and Diabetes Family Conflict in Adolescents with Type 1 Diabetes
Authors: MANIS HANNAH , O'DONNELL HOLLY K. , ALONSO GUY T. , MAJIDI SHIDEH , SNELL-BERGEON JANET K. , WADWA R. PAUL , DRISCOLL KIMBERLY A. 
Published: May 2022

Few studies have examined the relationship between diabetes-related family conflict and parent fear of hypoglycemia (FOH) in adolescents, thus the current research evaluated this connection. This study specifically examined three subdimensions of FOH which parents of children with diabetes may experience: maintaining blood glucoses higher than medical recommendations, worrying about and/or feeling helplessness about preventing low blood glucoses, and worrying about social consequences associated with low blood glucoses.
At the baseline visit of an RCT aimed at improving health outcomes of youth with elevated A1C (9-12% in the last year) , caregivers completed the Diabetes Family Conflict Scale and the Parent Hypoglycemia Fear Survey. N=1child-caregivers dyads (child age=14.7±1.9 years, T1D duration=6.5±3.4 years, A1C=9.9±1.6%; 53% female; 77% continuous glucose monitor; 82% insulin pump) .
In three separate regression models, no child demographic or T1D clinical characteristics other than A1C were associated with family conflict. After controlling for A1C, only Worry/Helplessness About Low Blood Glucose (b=0.182; p=0.025) and Worry About Negative Social Consequences (b=0.532; p=0.008) significantly predicted parent-reported family conflict; Maintain High Blood Glucose (b=0.082; p=0.73) was not significant.
This is the first study to demonstrate that caregivers’ worry about their adolescents having low blood glucoses is associated with diabetes-specific family conflict- a common adherence barrier for adolescents with T1D and their families. This finding supports screening caregivers for fear of hypoglycemia and incorporating into treatment when providing intervention for families with high levels of conflict. Important next steps include dyadic data analysis to understand how fear of hypoglycemia symptoms among child-caregiver dyads affect family conflict.

Disclosure
H.Manis: Other Relationship; Abbott Diabetes. H.K.O'donnell: None. G.T.Alonso: None. S.Majidi: None. J.K.Snell-bergeon: Stock/Shareholder; GlaxoSmithKline plc. R.Wadwa: Advisory Panel; Dompé, Consultant; Beta Bionics, Inc., Other Relationship; Tandem Diabetes Care, Inc., Research Support; Dexcom, Inc., Eli Lilly and Company, Tandem Diabetes Care, Inc. K.A.Driscoll: None.

Funding
National Institute of Diabetes and Digestive and Kidney Diseases (DP3DK113363)
1019-P: Predictors of Response to Metformin in Youth with Type 1 Diabetes (T1D) and Increased BMI
Authors: WANG JOHNNY , BARUA SOUPTIK , TOSUR MUSTAFA , REDONDO MARIA J. , ISMAIL HEBA M. 
Published: May 2022

Metformin used as adjuvant therapy in youth with T1D and elevated BMI decreased total daily insulin dose (TDID) and insulin resistance, although hemoglobin A1c (A1c) did not change. Yet, the factors that are associated with greater benefit from metformin are not well known. Here, we tested whether baseline measures predict response to metformin in youth with T1D and increased BMI-z score.
We analyzed publicly available data from the metformin treatment arm of the T1D Exchange Metformin Study (NCT01881828) , with 61 adolescents (median age 15.3 yrs, range 12-18.9; 36% males; 72% non-Hispanic Whites, 98% overweight or obese; median diabetes duration 7.4 yrs, range 1.7-15) . We assessed baseline age, sex, diabetes duration, race/ethnicity, BMI z-score, waist circumference, body fat percentage (BF%) measured by Dual-energy X-ray Absorptiometry (DXA) scan and serum adipokines. Outcome measures included changes in A1c, TDID and BF% at 13 and 26 weeks (wks) . After adjusting for baseline values, multiple linear regression models were used to identify predictors for the outcomes. Decreases in TDID at 13 and 26 wks showed a significant negative association with baseline leptin (p=0.0008, p=0.004, respectively) . Decreases in BF% at 26 wks were positively associated with baseline BMI z-score (p=0.03) and negatively associated with baseline BF% (p=0.0002) . Increase in A1c at 26 wks trended towards a positive association with baseline BF% (p=0.07) and a negative association with baseline adiponectin (p=0.06) . Compared to females, males showed a significant reduction in BF% at 26 wks (p&lt;0.0001) and a trend towards a reduction in TDID at 13 wks (p=0.06) .In sum, in adolescents with T1D and elevated BMI treated with metformin as adjuvant to insulin, baseline leptin was the strongest predictor of decrease in TDID at 13 and 26 wks. Decrease in BF% was predicted by higher BMI z-score and lower BF% at baseline. These findings may aid a precision medicine approach in this population.

Disclosure
J.Wang: None. S.Barua: None. M.Tosur: Advisory Panel; Provention Bio, Inc. M.J.Redondo: Advisory Panel; Provention Bio, Inc. H.M.Ismail: n/a.
1021-P: Telehealth Benefits among Latinx Adolescents and Young Adults (AYA) with Type 1 Diabetes (T1D)
Authors: GARCIA JAQUELIN J. FLORES , REID MARK W. , PYATAK ELIZABETH , FOGEL JENNIFER L. , FOX D STEVEN , SALCEDO-RODRIGUEZ ELIZABETH , RAYMOND JENNIFER 
Published: May 2022

Background: Historically disadvantaged, ethnically- and racially-diverse AYA experience significantly greater challenges with T1D management when compared to White peers. To address this inequity, the patient-centered CoYoT1 Care model was adapted and implemented at a T1D clinic serving primarily ethnically- and racially-diverse AYA.
Methods: In a randomized controlled 2x2 trial, 68 AYA received either CoYoT1 Care or Standard Care via in-person or Telehealth (TH) . Hemoglobin A1c (A1c) and T1D-related distress were measured at baseline and after four quarterly clinic visits, and analyzed via linear mixed models adjusting for age, sex, and attendance.
Results: At study end, participants in TH showed reduced A1c (p=0.01) and no changes in physician-related distress (p=0.04) , compared to increased A1c and distress in those randomized to in-person care. Secondary analyses revealed that these TH benefits were attributable to Latinx AYA subgroup. Compared to Latinx participants randomized to in-person care, those in TH showed significant reductions in A1c (p=0.003; Figure) and physician distress (p=0.008) . Among non-Latinx participants, no significant differences in A1c or distress were observed.
Conclusion: The TH CoYoT1 Care model engages underserved, ethnically-diverse AYA and leads to improved outcomes. Next steps include collaborating with stakeholders to adapt and implement the model nationwide.

Disclosure
J.J.Flores garcia: None. M.W.Reid: None. E.Pyatak: Research Support; Abbott Diabetes. J.L.Fogel: None. D.Fox: None. E.Salcedo-rodriguez: None. J.Raymond: None.
1025-P: Racial Disparities in Diabetes Distress and Technology Use in Adolescents with Type 1 Diabetes
Authors: WILCOX ANNABELLE , MENCHER SHANA , NALLY LAURA M. , WEINZIMER STUART A. 
Published: May 2022

Objective: Racial and ethnic minority (REM) youth with type 1 diabetes (T1D) are less likely to use diabetes technology and are at higher risk for adverse short-term outcomes. Diabetes distress (DD) , associated with less engagement with management and elevated hemoglobin A1c (HbA1c) , may be an additional mediator of this disparity. However, no studies have specifically evaluated DD in REM youth. This study aims to evaluate racial and ethnic disparities in DD and diabetes technology use in adolescents with T1D.
Method: We recruited adolescents with T1D (age 13-17) and their parent to complete an online survey. Adolescents were categorized by self-report into Non-Hispanic White (NHW) or REM groups.
Result: Of 45 adolescent surveys completed, 62% identified as NHW and 38% as REM. REM adolescents were less likely to use both a continuous glucose monitor and an insulin pump (p=0.001) and had higher HbA1c (p&lt;0.001) . There was a similarly high prevalence of DD (84%) in both groups of adolescents. Parents had a much lower rate of DD (14%, p&lt;0.001) , similar in both racial/ethnic groups.
Conclusion: We found disparities in technology use and HbA1c between NHW and REM adolescents. However, the similarly high rate of clinically significant DD confirms the need to address DD to decrease the burden of diabetes and improve outcomes in all racial/ethnic groups.

Disclosure
A.Wilcox: None. S.Mencher: None. L.M.Nally: Other Relationship; Novo Nordisk. S.A.Weinzimer: Consultant; Dompé, Research Support; Abbott Diabetes, Speaker's Bureau; Abbott Diabetes, Dexcom, Inc.
1028-P: Acute Kidney Injury in Youth with Diabetes Admitted with Isolated Diabetic Ketoacidosis, Isolated Hyperglycemic Hyperosmolar State, and Hyperosmolar Diabetic Ketoacidosis: A Single Center Experience
Authors: ZARSE ERICA , KNOLL MICHELLE , WILLIAMS DAVID D. , HALPIN KELSEE , KALLANAGOWDAR GOURI , TSAI SARAH 
Published: May 2022

Background: Diabetic ketoacidosis (DKA) and hyperglycemic hyperosmolar state (HHS) are hyperglycemic emergencies. The objective was to compare acute kidney injury (AKI) between youth with isolated DKA to those with hyperosmolarity and explore associations to risk of AKI.
Methods: Charts were reviewed from 01/20through 12/2020 for youth admitted with DKA (serum bicarbonate ≤16 mEq/L) and/or HHS (blood glucose ≥600 mg/dL and osmolality ≥320 mOsm/kg) . Isolated HHS youth were combined with hyperosmolar DKA for analysis. AKI was defined as elevated creatinine level for age. The total amount of isotonic fluids administered was collected, both given as bolus or continuously within the first 12 hours after DKA and/or HHS confirmation. Identification of patients on an angiotensin-converting enzyme inhibitor (ACEI) was a marker for microalbuminuria.
Results: A total of 369 admissions were included: 334 (90.5%) had isolated DKA, 32 (8.7%) had hyperosmolar DKA, and 3 (0.8%) had isolated HHS. Median age was 14 years (IQR=11.2,16.8) and 60% were female. Ninety-six percent had type 1 diabetes mellitus (DM) ; 4% had type 2 DM. New onset DM accounted for 31% of admissions. More hyperosmolar youth had AKI compared to isolated DKA (63% vs. 15%, p&lt;0.001) . Both received similar total volume of fluids; median was 33.6 ml/kg (IQR=28.0,39.5) for isolated DKA and 38.5 ml/kg (IQR=29.1,45.4) for hyperosmolar (p=0.09) . For each unit increase in serum osmolality, the odds of having AKI increase by a factor of 1. (p&lt;0.001) . The odds of AKI for new onset DM is 0.68 compared to known DM diagnosis (p=0.005) . Of the 17 patients (5%) taking an ACEI during admission, three had AKI.
Conclusions: Higher serum osmolality or new onset DM are associated with increased odds of having AKI. Our findings will be useful to identify youth at risk for AKI and guide interventional studies for those with AKI and hyperosmolarity.

Disclosure
E.Zarse: None. M.Knoll: None. D.D.Williams: None. K.Halpin: None. G.Kallanagowdar: None. S.Tsai: Stock/Shareholder; Dexcom, Inc.
1042-P: Large-for-Gestational-Age Birthweight in Gestational Glucose Intolerance
Authors: MAYA JACQUELINE , SELEN DARYL J. , THAWEETHAI TANAYOTT , HSU SARAH , YU CHU , JAMES KAITLYN , KAIMAL ANJALI , HIVERT MARIE-FRANCE , POWE CAMILLE E. 
Published: May 2022

Background: Treatment of gestational diabetes (GDM) reduces the risk of large for gestational age (LGA) birthweight and associated complications in infants. Pregnant people with gestational glucose intolerance (GGI, abnormal glucose loading test (GLT) without GDM) usually remain untreated. We examined the risk of LGA among women with GGI in a large cohort.
Methods: In a retrospective cohort study of 44,628 singleton pregnancies delivered at an academic center at &gt;28 weeks' gestation, pregnancies with a GLT &gt;140 mg/dl and either a normal 3-hour 100-gram oral glucose tolerance test (OGTT) or one abnormal OGTT value were classified as GGI, and those with 2 abnormal OGTT values as GDM. We used generalized estimating equations for logistic regression to examine the risk of LGA (birthweight &gt;90th percentile for gestational age) in GGI versus normal glucose tolerance (NGT, GLT &lt;140 mg/dl) pregnancies after adjustment for age, gender, 1st trimester BMI, parity, insurance, race/ethnicity, and marital status. A second model adjusted for gestational weight gain (GWG) .
Results: LGA was present in 7.7% of 36,964 pregnancies with NGT, 9.6% of 4357 with GGI and normal OGTT, 14.3% of 15with GGI and one abnormal OGTT value, and 14.5% of 1800 with GDM. The odds of LGA were higher in GGI than NGT pregnancies (adjusted OR 1.4 [1.3-1.5], p&lt;0.001) . When compared separately with NGT, both types of GGI had elevated odds of LGA: normal OGTT adjusted OR 1.2 [1.1-1.4], p&lt;0.001, one abnormal OGTT value adjusted OR 1.8 [1.5-2.1], p&lt;0.001. The latter was similar to that in GDM compared to NGT: adjusted OR 1.8 [1.5-2.0], p&lt;0.001. Increased odds of LGA were also present for GGI/GDM groups after GWG adjustment: normal OGTT OR 1.3 [1.2-1.5] p&lt;0.001, 1 abnormal OGTT value OR 1.8 [1.5-2.1] p&lt;0.0GDM OR 2.2 [1.9-2.5] p&lt;0.001.
Conclusion: Infants of untreated people with GGI have increased risk of LGA birthweight. The risk of LGA in GGI pregnancies with one abnormal OGTT value was similar to that in GDM in a clinical setting where only GDM was treated.

Disclosure
J.Maya: None. D.J.Selen: None. T.Thaweethai: None. S.Hsu: None. C.Yu: None. K.James: None. A.Kaimal: None. M.Hivert: Advisory Panel; American Heart Association, Research Support; American Diabetes Association. C.E.Powe: None.

Funding
MGH (Physician Scientist Development Award and Claflin Distinguished Scholar's Award)
107-OR: Randomization to a Higher–Complex Carbohydrate or Conventional Diet in GDM Results in Equivalent Glycemic Control by Continuous Glucose Monitoring
Authors: HERNANDEZ TERI L. , DUNN EMILY Z. , HIRSCH NICOLE , ROLLOFF KRISTY P. , CORBETT JOHN P. , FARABI SARAH S. , FRIEDMAN JACOB E. , BARBOUR LINDA (LYNN) A. 
Published: May 2022

Direct RCT evidence to support a range of nutrition options for treatment of GDM is lacking. We randomized women with diet-controlled GDM to either our CHOICE (60% complex carbohydrate (CHO) /25% fat) or a conventional diet (CONV; 40% CHO/45% fat) and previously reported equivalent newborn adiposity. We further tested the hypothesis that 6 wks on either diet would result in a similar 24-hr glucose (gluc) area-under-the-curve (AUC) by CGM. BMI-matched women were randomized to CHOICE or CONV by wk 31 (n=59; mean±SEM; BMI 32±1kg/m2) . All meals were provided, eucaloric, and simple sugars were controlled in both. A CGM was worn for 72 hrs in wk 31 and 36. Total gestational weight gain (GWG) (CHOICE vs. CONV; 9.8±0.9 vs. 10.8±1.2 kg) , GWG from 31 wks to delivery (2.0±0.4 vs. 1.8±0.3 kg) , physical activity, and days on diet were similar (48±2 vs. 54±2 days; p&gt;0.all) . At wk 31 (n=23 vs. 25/group) , fasting (89±3 vs. 87±2 mg/dL) , 1-hr (127±4 vs. 127±4) and 2-hr postprandial (PP) (112±3 vs. 110±3) gluc, and time in range (TIR; 70-140mg/dL) (89±1 vs. 86±1%) , were not different on CHOICE vs. CONV as were the mean 24-hr gluc, 24-hr AUC, nocturnal and daytime AUC (p&gt;0.5 all) . By wk 36, (n=22 vs. 21/group) , fasting (90±3 vs. 86±3) , 1-hr (117±4 vs. 119±3) , 2-hr PP (108±3 vs. 106±3) , 2-hr PP AUC, TIR (88±2 vs. 88±1%) , 24-hr AUC, nocturnal AUC and daytime AUC remained highly similar between diets (p&gt;0.all) . In paired analyses (n=19-21/group) , women on CHOICE showed an actual reduction in 1-hr PP gluc between 31-36 wks (124±5 vs. 115±4; p=0.02) . Surprisingly, the fasting, 2-hr PP gluc, 24-hr AUC, daytime and nocturnal AUC did not increase (nor did TIR decrease) , over time in either group with the expected rise in the insulin resistance of pregnancy. These data directly support for the first time in an RCT with all provided meals that excellent glycemic control by CGM was achieved with either a higher complex CHO versus low CHO diet in GDM, with similar glycemic parameters that did not increase over time.

Disclosure
T.L.Hernandez: None. E.Z.Dunn: None. N.Hirsch: None. K.P.Rolloff: None. J.P.Corbett: Employee; Tandem Diabetes Care, Inc., Other Relationship; Dexcom, Inc. S.S.Farabi: n/a. J.E.Friedman: None. L.A.Barbour: None.

Funding
National Institutes of Health (R01DK101659) CTSA UL1 TR002535 Janssen Research and Development
1106-P: Real-World Confirmation of the Nonlinear Relationship between Glucose and Glycated Hemoglobin
Authors: DUNN TIMOTHY , XU YONGJIN , SCHNEIDER SHARON , GAWEL SUSAN , GOPINATH RAJKAMAL , BERMAN MARVIN , ORZECHOWSKI ANTHONY P. 
Published: May 2022

Glycated hemoglobin (A1C) is a key biomarker for glycemic control in diabetes and has an important relationship with glucose. The objective of this study was to evaluate the relationship of real-world laboratory results for patient samples with both glucose and A1C. 213,698 paired glucose and A1C measurements from 709 laboratories in 39 countries were collected January to October 2021. Values within the measuring interval 70 to 400 mg/dL for glucose and 4% to 13% (NGSP) for A1C were analyzed. The pairs were analyzed in twenty equal-sized groups by composite rank order. The summed absolute relative differences (SARD) were compared between best-fit nonlinear and linear models. The group mean values presented a clear nonlinear relationship, with an SARD seven times lower than the best-fit linear model. The nonlinear Michaelis constant for glucose and glucose transporter 1 (GLUT1) was 4mg/dL, which agrees with literature values. The 95% prediction intervals at the diabetes diagnosis levels of 6.5% A1C and 126 mg/dL glucose were narrower for the nonlinear model. This model-based formula allows better prediction of the relationship of glucose and A1C which can lead to improved personalized clinical decisions.
Figure: Nonlinear (left) and linear (right) best-fit models, N = 213,698. Each point represents the mean of approximately n=10,680 paired measurements. Dotted lines are 95% prediction intervals.

Disclosure
T.Dunn: Employee; Abbott. Y.Xu: Employee; Abbott Diabetes. S.Schneider: None. S.Gawel: Employee; Abbott Diagnostics. R.Gopinath: Employee; Abbott Diagnostics. M.Berman: None. A.P.Orzechowski: Employee; Abbott.
1110-P: Algorithmic Identification of Atypical Diabetes in Electronic Health Record (EHR) Systems
Authors: CHEN VICTORIA , CROMER SARA JANE , HAN CHRISTOPHER , MARSHALL WILLIAM G. , EMONGO SHEKINA , MAJARIAN TIM , FLOREZ JOSE C. , MERCADER JOSEP M. , UDLER MIRIAM 
Published: May 2022

Understanding atypical forms of diabetes may lead to personalized treatment regimens and discovery of novel pathophysiologic mechanisms of diabetes. We aimed to create a high-throughput method for identifying patients with atypical diabetes in large electronic health record (EHR) databases and validate this method in the Mass General Brigham (MGB) Biobank. Patients with likely type 2 diabetes (T2D) were identified using a validated machine-learning (ML) algorithm. “Typical” T2D was filtered out through a “base algorithm” that excluded individuals with body mass index (BMI) ever &gt;30 kg/m2, HDL values ever &lt; 50 mg/dL, and triglycerides ever &gt;150 mg/dL. To remove people with typical type 1 diabetes (T1D) , we tested six additional “branch algorithms,” relying on clinical characteristics, including autoantibodies, medication usage, and T1D diagnosis determined by ML algorithm, resulting in six overlapping cohorts. Charts were then manually reviewed, and diabetes type was classified by two endocrinologists into one of three categories: atypical, not atypical, and indeterminate due to missing information.
We identified 1potentially atypical cases, of whom 16 individuals were confirmed to have atypical diabetes after expert review, across the six branch algorithms. The branch algorithm which excluded T1D by removing patients who had ever used outpatient insulin had the highest percentage yield (13 of 27; 48.2%) of atypical diabetes . The 16 atypical cases had significantly lower BMI and higher HDL compared to an unselected group of individuals with T1D or T2D diagnosis by ML algorithm. Compared to the ML T1D group, the atypical group had a significantly higher T2D polygenic score and lower hemoglobin A1c.
In summary, we designed an algorithm to identify individuals with atypical diabetes within an EHR database with up to 48% yield which may shed light on the heterogeneity of T2D and help generate cohorts of atypical cases for studies, such as the Rare and Atypical Diabetes Network (RADIANT) .

Disclosure
V.Chen: Other Relationship; Draeger. S.Cromer: Other Relationship; Johnson & Johnson Medical Devices Companies, Research Support; National Institute of Diabetes and Digestive and Kidney Diseases. C.Han: None. W.G.Marshall: None. S.Emongo: None. T.Majarian: Employee; Vertex Pharmaceuticals Incorporated. J.C.Florez: Consultant; AstraZeneca, Goldfinch Bio, Inc., Other Relationship; AstraZeneca, Merck & Co., Inc., Novo Nordisk. J.M.Mercader: None. M.Udler: None.