bluuebunny/crossref_metadata_2025_split huggingface dataset:
Socioeconomic Disparities in Outpatient Diabetes Care Patterns for Young Adults with Type 1 Diabetes
Authors: AGARWAL SHIVANI , VIROSLAV HANNAH L. , EIEL JACK N. , LONG JUDITH A. 
Published: June 2018

Background: Few young adults (YA) with type 1 diabetes (T1D) achieve national glycemic targets, and even fewer of low socioeconomic status (SES) meet these goals. Inconsistent clinic attendance is thought to contribute to this disparity in glycemic control but has not been studied.
Methods: We evaluated clinic attendance in YA with T1D, 18-25 years, seen in a young adult diabetes clinic. We extracted data from medical charts and used insurance status to identify low and higher SES YA, comparing individuals with federal/state-sponsored (public) insurance to commercial (private) insurance. Glycemic control was defined as the difference between the last documented A1c and the baseline first appointment A1c. There were 2 outcomes representing inconsistent attendance: 1) missing ≥1 scheduled quarterly visit per year and 2) gap in care &gt;6 months. We used multilevel mixed effects linear and logistic regression models to examine relationships between insurance status, inconsistent attendance, and glycemic control, adjusting for demographic, clinical, social, and health system factors.
Results: 312 YA were seen in the clinic (mean age 23.5 years) totaling 2376 care visits. Forty-six percent (n=147) had public insurance. Publicly insured YA were more frequently black (42% vs. 11%, p&lt;0.001) and in worse glycemic control (mean baseline A1c 9.9% vs. 8.1%, p&lt;0.001). In multivariate analyses, compared to privately insured YA, publicly insured were 50% more likely to miss a quarterly appointment (OR=1.56, p=0.005) and 96% more likely to have a gap in care &gt;6 months (OR 1.96, p=0.008). In addition, missed appointments worsened glycemic control from baseline (A1c +0.57%, p&lt;0.001).
Conclusions: Low SES YA with T1D are a particularly high-risk subset who are more likely to miss appointments and have gaps in care, which is associated with worse glycemic control. Our results highlight the need to refocus on improving follow-up rates in vulnerable populations to address disparities in glycemic outcomes.

Disclosure
S. Agarwal: None. H.L. Viroslav: None. J.N. Eiel: None. J.A. Long: None.
Effect of Significant Family Life Events in the First Year of Type 1 Diabetes (T1D)
Authors: STANEK KELLY R. , MAJIDI SHIDEH , YOUNGKIN ERIN M. , NOSER AMY , CLEMENTS MARK A. , PATTON SUSANA R. 
Published: June 2018

Objective: Examine the impact of stressful life events on families in the first year after diagnosis of T1D in a young child using a mixed methods design.
Methods: In a prospective study of 5-9 year-olds new to T1D (mean age 7.4±1.3 years, T1D duration 4.7±3.3 months), we measured A1c, changes in family health, marital status, coping, and income every 3 months up to 12 months post-baseline using surveys.
Results: Of 135 families, 40 reported job/income changes (11 promoted, 20 changed job, 7 quit job, 2 lost job), 4 marital changes (2 divorces, 1 separation, 1 spouse death), and 42 health changes (78.6% family member health, 21.4% child). Of those, 4 family members were also diagnosed with T1D and 3 youth were diagnosed with an additional autoimmune disorder in the 12 month follow-up. Income decrease at any time point in first 12 months correlated with health change (r=0.142, p=0.048) and marital status change (r=0.169, p=0.049) at any time point in the first 12 months, while baseline Active Avoidance Coping correlated with future income decrease (r=0.169, p=0.03). There was a cross-sectional association between A1c and income decrease at time point 4, corresponding to ∼1 year post-T1D diagnosis (Table).
Longitudinal HbA1c, incidence of life events, and correlation of income decrease and HbA1c in youth.Baseline3 Months6 Months9 Months12 MonthsMean HbA1c, % (SD)7.66 (±1.3)7.72 (±1.2)8.(±1.2)8.20 (±1.2)8.25 (±1.2)Health Change, n (%)14 (12.1%)11 (10.1%)8 (8.3%)9 (9.8%)Marital Status Change, n (%)75.7% married3 (2.6%)001 (1.1%)Income Decrease, n (%)5 (4.3%)9 (8.3%)4 (4.2%)5 (5.4%)Cross-sectional Correlation HbA1c To Income Decrease0.077-0.1080.260**0.083Sig. (1-tailed)0.2070.1310.0050.216N1351161099692
Conclusions: Families can experience significant concurrent life stressors during the first year of T1D. Future research should explore if these events have a direct effect on T1D management and long-term glycemic control.

Disclosure
K.R. Stanek: None. S. Majidi: None. E.M. Youngkin: None. A. Noser: None. M.A. Clements: Speaker's Bureau; Self; Medtronic. Advisory Panel; Self; Glooko, Inc.. S.R. Patton: None.
Five-Year Experience with Telemedicine Clinics for Youth with Type 1 Diabetes (T1D)
Authors: WADWA R. PAUL , STACY JOHN , REZNICK-LIPINA TYLER , SLOVER ROBERT H. , THOMAS JOHN F. 
Published: June 2018

Poor access to specialty care is a problem for youth with T1D in rural America, where 79% of children live over 20 miles from a pediatric endocrinologist. Rural families are less likely to have adequate T1D follow-up per ADA guidelines. Telemedicine may mitigate such issues. Since 2012, the Barbara Davis Center for Diabetes telemedicine program has provided care for over 290 pediatric T1D patients in rural Wyoming and Colorado via real-time, encrypted videoconferencing. This study sought to determine how increased access to specialty care via telemedicine affects glycemic control over a 3 to 5 year follow-up period. Glycemic control was analyzed for 21 patients with at least 3 years follow-up since initial use of telemedicine. Data were analyzed by age group (&lt; 12 years (pre-pubertal, n=13) vs. ≥12 years (post-pubertal, n=8)) and treatment method at baseline (insulin pump, n=8 vs. multiple daily injections (MDI), n=13). Mean T1D duration at baseline was 4.1 ± 3.5 years and mean follow-up time was 4.1 ± 0.6 years. Mean change in A1c was 0.0 ± 2.0% (p=0.96 for baseline vs. follow-up A1c). Median (interquartile range (IQR)) A1c at baseline and at follow-up were 9.2 (8.2, 10.0)% and 9.0 (8.5, 10.1)% respectively. One third of patients had a clinically significant increase in A1c of more than 0.5%, a third had a decrease of more than 0.5% and a third had a change in A1c of less than 0.5%. The post-pubertal group showed worse change in glycemic control compared to the pre-pubertal group (mean A1c change 0.2 ± 3.0 vs. -0.1 ± 1.3% respectively (p=0.78)). Patients on MDI at baseline showed mean A1c change of -0.5 ± 1.4% compared to a mean A1c change for patients on insulin pumps of 0.9 ± 2.7% (p=0.21). Of 13 MDI patients, 7 transitioned to insulin pump use during follow-up. Overall, glycemic control was no worse and pump use increased with 5 years of telemedicine clinics for T1D youth. While further work is needed to improve overall glycemic control, use of telemedicine reduces barriers and improves access to T1D specialty care for rural families.

Disclosure
R. Wadwa: Advisory Panel; Self; Eli Lilly and Company. Research Support; Self; MannKind Corporation, Dexcom, Inc., Xeris Pharmaceuticals, Inc., Bigfoot Biomedical. J. Stacy: None. T. Reznick-Lipina: None. R.H. Slover: None. J.F. Thomas: None.
Longitudinal Assessment of Disordered Eating Behavior (DEB) Risk in Teens with Type 1 Diabetes (T1D)
Authors: CECILIA-COSTA RAQUEL , GUO ZIJING , HARRINGTON KARA R. , VOLKENING LISA , LAFFEL LORI M. 
Published: June 2018

DEBs in T1D are associated with poor glycemic control and development of diabetes complications. We assessed progression of risk for DEBs over 18 months in 169 teens, ages 13-17, with T1D. Teens completed the Diabetes Eating Problem Survey-Revised (DEPS) every 6 months. Teens were categorized by DEB risk: low, DEPS&lt;10; moderate, DEPS 10-19; high, DEPS≥20.
At baseline, teens (54% male) were 14.9±1.3 (M±SD) years old and had T1D for 7.4±3.7 years; 37% were overweight/obese. A1c was 8.5±1.0%; 68% were pump treated. Baseline DEB risk assessment included 60% at low risk, 27% at moderate risk, and 12% at high risk. Initial DEB risk was highly predictive of DEB risk after 18 months (Figure). Most teens (66%) stayed at their same risk level, 14% improved, and 20% progressed. In a multivariate mixed model predicting DEPS scores over 18 months, female sex (p=.0002), overweight/obesity (p=.0004), less frequent BG monitoring (p=.0008), and higher A1c (p=.003) significantly and independently predicted higher DEPS scores.
Without intervention, DEB risk appears to remain relatively stable in teens with T1D. Therefore, it is important to prevent onset of DEBs, potentially by targeting teens who are female and overweight/obese as well as teens who are less adherent (e.g., few BG checks/day) and have rising A1c levels. Identifying DEB risk may permit early intervention and prevention of progression to clinical eating disorders.

Disclosure
R. Cecilia-Costa: None. Z. Guo: None. K.R. Harrington: None. L. Volkening: None. L.M. Laffel: Consultant; Self; Eli Lilly and Company, Novo Nordisk Inc., Sanofi US, MannKind Corporation, Roche Diagnostics Corporation, Dexcom, Inc., Insulet Corporation, AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Johnson & Johnson Diabetes Institute, LLC..
Associations of Well-Being and Perceived Stress with Diabetes Control and Self-Care Behavior in Adolescents with Type 1 Diabetes
Authors: ADDALA ANANTA , CHAN RANDALL , WEIGENSBERG MARC 
Published: June 2018

Background: Psychosocial determinants of health are thought to play a significant role in type 1 diabetes (T1D) management. Higher rates of stress have also been shown to result in poorer health outcomes. The role that self-perceived well-being, stress, and barriers to diabetes care have in T1D outcomes is less well known.
Methods: At routine clinic visits, patients with known T1D completed a self-reported survey packet consisting of Perceived Stress Scale-4 (PSS), Diabetes Distress Scale-2 (DDS), and Global Well-Being Scale (GWB), as well as a novel barriers-to-care survey we developed. Inclusion criteria included all T1D patients older than 13 years of age (n=49); the instrument was administered to all participants recruited. Multivariate linear regression analysis assessed correlations between objective data (HgA1c and blood glucose checks per day) and the four self-reported measures.
Results: GWB scale was inversely correlated with HgA1C (R2=0.111; p=0.04) and positively correlated with glucose checks per day (R2=0.211; p=0.001). PSS scale was positively correlated with HgA1C (R2=0.109; p=0.04) and DDS was inversely correlated with glucose checks per day (R2=0.103; p=0.01). Increasing numbers of barriers trended toward positive correlation with HgA1C and inverse correlation with glucose checks per day but did not reach statistical significance.
Conclusion: Patient-reported markers of well-being and stress were predictive of home glucose monitoring frequency and glycemic control. The associations between HgA1C as well as glucose monitoring with well-being suggest that attention to assessing and optimizing positive psychological states may be valuable in achieving improved diabetes outcomes in adolescents with T1D.

Disclosure
A. Addala: None. R. Chan: None. M. Weigensberg: None.
Racial Ethnic Disparities in Diabetes Youth Participating in Diabetes Summer Camps
Authors: VALENZUELA JESSICA M. , MARTIN MEGAN T. , RECORDS SAM , O'NEAL KELLY , MUELLER KELLY , WOLF RISA M. 
Published: June 2018

Background: Diabetes camps are beneficial for campers, including increases in diabetes knowledge, glycemic control and psychological functioning. Racial/ethnic minority youth are likely to have poorer disease management and glycemic control, more complications, and are often managed with less intensive, less technologically advanced regimens. We hypothesized that racial/ethnic minority youth with type 1 diabetes (T1D) would have reduced participation in diabetes summer camps.
Methods: We reviewed de-identified data from 5033 campers with T1D who participated in a network of 46 ADA-affiliated summer camps during the 2017 season. We performed t-tests and chi-square analysis to analyze the demographic characteristics of white vs. non-white campers.
Results: Campers with T1D came from all over the U.S. (49 states), 68.4% were return campers, 55% were female, ages ranging from 4-18 (m =11.7 ±2.8). Of campers, 73.2% were on insulin pump regimens. Camps cost $0 to $1400 (Day Camps M=$158.83 ± $103.69, Residential Camps M=$622.73 ± $274.26). Campers identified as caucasian (83.9%), black (4.8%), Hispanic (3.9%), Asian/Pacific Islander (2.7%), and Multiracial (3.9%). We compared ethnicity data to the 2009 SEARCH data from children and adolescents where T1D prevalence was 72% in caucasians, 9% black, 15% Hispanic and 2% Asian/Pacific Islander. Camper demographics were significantly different than the general population of children with T1D (p&lt;0.001). Racial/ethnic minority status was associated with type of camp attended (day vs. residential), and minority campers were less likely to be on insulin pump therapy.
Conclusions: Children of ethnic minorities with diabetes are underrepresented in diabetes camps nationwide compared to the prevalence of diabetes in each minority group as described by the SEARCH study. This highlights the need to assess barriers to camp attendance in these communities and to design outreach programs to increase minority youth participation in the diabetes camp experience.

Disclosure
J.M. Valenzuela: None. M.T. Martin: None. S. Records: None. K. O'NEal: None. K. Mueller: None. R.M. Wolf: None.
PKC Modulates sFlt1 Production by Human Placental Trophoblasts in Response to Oxidized LDL
Authors: CHOW REBECCA P. , ZHAO JIAWU , YU JEREMY 
Published: June 2018

Preeclampsia (PE) is a leading cause of maternal and perinatal mortality and mortality, and its prevalence is 4 fold higher in women with diabetes than those without: the underlying mechanism is unclear. The anti-angiogenic factor soluble fms-like tyrosine kinase-1 (sFlt1) plays a key role in PE pathogenesis. Evidence suggests that PKC activation, which is associated with the development of diabetic vascular complications, may mediate the enhanced sFlt1 release in non-trophoblast cells. We aimed to determine the role of PKC in the regulation of sFlt1 expression in placental trophoblasts, and whether diabetic conditions upregulate sFlt1 expression via the PKC pathway. Human trophoblasts HTR8/SVneo were treated in three separate experiments with PKC activator phorbol-12-myristate-13-acetate (PMA), or diabetes stimuli ’heavily oxidized, glycated’ low-density lipoproteins (HOG-LDL) vs. native LDL ± high glucose (30 mM), over 24h, with or without PKC inhibitor GF109203X (GX). Both sFlt1 mRNA expression (RT-PCR) and protein release (ELISA) were measured. Compared to the control, PMA (5 nM) increased sFlt1 mRNA expression (2.5 fold, p&lt;0.001) and protein release (383.8 ± 27.4 vs. 118.3 ± 21.3 pg/ml, p&lt;0.001) in trophoblasts; these effects were entirely abrogated by the pre-treatment with 5 uM GX for mRNA expression (by 54%, p&lt;0.001) and protein release (115.5 ± 20.9 pg/ml, p&lt;0.001) vs. PMA-treated cells. Similarly, HOG-LDL (50 µg/ml) increased sFlt1 mRNA expression (3.7 fold, p&lt;0.001) and protein release (126.4 ± 16.3 vs. 55.1 ± 2.4 pg/ml, p&lt;0.001), and the both effects were partially abolished by GX (by 33-37%, p&lt;0.05). High glucose did not significantly affect sFlt1 expression, or alter the effect of HOG-LDL. The data suggest that modified lipoproteins may promote PE development in women with diabetes, at least in part via PKC-mediated upregulation and release of sFlt1. These findings provide new insights into disease mechanisms, and may lead to new targets for prevention and treatment of PE.

Disclosure
R.P. Chow: None. J. Zhao: None. J. Yu: None.
A Three-Way Accuracy Comparison of the Dexcom G5, Abbott Freestyle Libre Pro, and Senseonics Eversense CGM Devices in an Outpatient Study of Subjects with Type 1 Diabetes
Authors: JAFRI RABAB Z. , BALLIRO COURTNEY A. , EL-KHATIB FIRAS , MAHENO MICHELE , HILLARD MALLORY A. , O'DONOVAN ALEXANDER J. , SELAGAMSETTY RAJENDRANATH , ZHENG HUI , DAMIANO EDWARD , RUSSELL STEVEN J. 
Published: June 2018

There are no published studies directly comparing the accuracy of continuous glucose monitoring (CGM) devices in the outpatient setting. We tested the performance of the Dexcom G5, Abbot Freestyle Libre Pro, and Senseonics Eversense (an implantable CGM approved in Europe) during a 6-week, free-living, outpatient bionic pancreas study involving 23 subjects with type 1 diabetes who wore all 3 devices concomitantly. The primary outcome was the mean absolute relative difference (MARD) vs. plasma glucose (PG) values measured with the Nova Biomedical StatStrip Xpress meter that was also used for calibrations according to the manufacturer's instruction (except for Libre Pro that is not calibrated). We compared PG values with CGM readings when they were available from all 3 CGMS in the 5 minutes preceding the PG values (n=829 sets). Since the Libre Pro records readings every 15 minutes, we also did a two-way comparison between the G5 and the Eversense that allowed a higher number of comparisons (n=2277 sets). Statistical significance was determined using a repeated measurements model fitted with the generalized estimating equation method. All 3 CGM systems produced higher average MARDs than during in-clinic studies. However, since all three CGM systems were worn by the same individuals and used the same meter for calibration and as comparator, we were able to directly compare their performance under real-world conditions. In the 3-way comparison Eversense achieved the lowest nominal MARD (14.8%) followed by Dexcom G5 (16.3%) and Libre Pro (18.0%) (Eversense vs. Libre Pro p=0.004, other comparisons p=NS). In the 2-way comparison the MARD difference between Eversense (15.1%) and G5 (16.9%) was statistically significant (p=0.008). We found that the point accuracy of the Eversense was significantly better than two other CGM systems. The Eversense CGM system may be useful to provide glucose values to artificial pancreas devices.

Disclosure
R.Z. Jafri: None. C.A. Balliro: None. F. El-Khatib: Stock/Shareholder; Self; Beta Bionics. Employee; Self; Beta Bionics. M. Maheno: None. M.A. Hillard: None. A.J. O'Donovan: None. R. Selagamsetty: None. H. Zheng: None. E. Damiano: Other Relationship; Self; Beta Bionics. S.J. Russell: Other Relationship; Self; Beta Bionics, Novo Nordisk Inc.. Advisory Panel; Self; Companion Medical, Tandem Diabetes Care, Inc., Unomedical a/s. Research Support; Self; Beta Bionics, Zealand Pharma A/S, MITRE Corporation.
High Maternal DHA Levels in Hawaiian Women Decrease Insulin Resistance
Authors: ALVARADO FERNANDA , TSAI PAI-JONG , MINIUM JUDI , CATALANO PATRICK , O'TIERNEY-GINN PERRIE 
Published: June 2018

Obesity prevalence during pregnancy (PG), a significant risk factor for metabolic inflammation and subsequent increase in insulin resistance (IR), has increased ∼ 35% in the last 20 years. Offspring of obese (OB) women are at a higher risk of metabolic dysfunction. Omega-3 fatty acids such as DHA have been shown to decrease IR in animals, though the effects of DHA in PG women are unclear. We hypothesize that high plasma DHA levels are associated with lower IR in PG women in an environment of high omega-3 intake (Hawaii), but differ in metabolic effect in OB vs. lean (LN) women. We evaluated 54 healthy, normal glucose tolerant women and their offspring at term elective C-section in Honolulu. We grouped subjects according to their pre-PG BMI into LN and OB. We measured maternal plasma insulin, glucose, C-reactive protein (CRP), and DHA concentration (gas chromatography/mass spectrophotometry) and calculated the homeostatic model assessment for IR (HOMA-IR). As expected, IR was higher in OB women (Table 1). Maternal DHA was negatively correlated with maternal HOMA-IR (R2= 0.25 p = 0.001), and CRP (R2=0.15 p= 0.004) among all women. These correlations persisted for OB women: HOMA-IR (R2=38 p=0.0007), CRP (R2=0.20 p=0.01); but not for LN women. High maternal DHA is associated with lower inflammation and IR in OB PG women at term. We speculate that high pre-PG omega-3 intake has a significant metabolic impact on insulin sensitivity, especially in OB women.Table 1Lean (BMI: 16-24.9 kg/m2 n=29)Obese (BMI&gt;30, n=26)P.valueGestational Age at delivery (wks)38.8 ± 0.639.1 ± 0.40.088Pre-Pregnancy BMI (kg/m2)21.1 ± 2.236.8 ± 6.9&lt;0.0001DHA (µmol/L)453 ± 187347 ± 1200.02HOMA IR1.1 ± 0.82.4 ± 1.4&lt;0.0001CRP (ng/mL)5180 ± 2688 7859 ± 23180.0005Birth Weight (kg)3.3 ± 0.43.5 ± 0.40.04

Disclosure
F. Alvarado: None. P. Tsai: None. J. Minium: None. P. Catalano: None. P. O'Tierney-Ginn: None.
Is Gestational Weight Gain Associated with Change in Ectopic Lipid Stores?
Authors: VESCO KIMBERLY , LEO MICHAEL C. , FRANCISCO MELANIE , BAETSCHER ERIC , ROONEY WILLIAM , MARSHALL NICOLE E. , PURNELL JONATHAN Q. 
Published: June 2018

Background: In nonpregnant adults, increased ectopic lipid stores [intrahepatic (IHL), intramyocellular (IMCL)] are associated with increased risk of diabetes mellitus and nonalcoholic fatty liver disease. While the amount of weight gained during pregnancy is significantly associated with the amount of weight retained after delivery, it is unclear whether weight gain during pregnancy is correlated with increased IHL or IMCL stores; thus, predisposing women with postpartum weight retention to increased risk of diabetes and NAFLD.
Methods: We evaluated 36 women with an enrollment BMI between 18.5 and 38 kg/m2 at a mean (min-max) of 15.6 weeks (12.9-17.3) gestation and again at 34.2 (32.1-37.7) weeks gestation. Women with pregestational diabetes and multifetal pregnancies were excluded. Participants underwent 1H magnetic resonance spectroscopy to assess IHL and IMCL content, and air displacement plethysmography to assess body weight, percent (%) body fat, fat mass, and lean mass.
Results: The participants were primarily white (89%) with mean age of 30.4 years. Table shows mean, standard deviation (SD), and Pearson’s correlation coefficients for all measures.
Conclusion: Gestational weight gain tracks closely with increase in fat mass during pregnancy, but is not significantly correlated with change in IHL or IMCL stores.able. Means, standard deviations, and Pearson’s correlation coefficients for weight and total and ectopic fat measures at early pregnancy and from early to late pregnancy.MeasureEarly pregnancy Mean (SD)Mean change (SD) from early to late pregnancyCorrelation of change with weight changeWeight (kg)72.2 (10.3)10.4 (3.2)--BMI (kg/m2)25.2 (3.4)3.6 (1.1)--Body fat (%)33.6 (6.5)-0.6 (3.0).38*Fat mass (kg)24.8 (7.6)3.1 (3.1).70*Lean mass (kg)47.5 (5.1)7.4 (2.4).40*IHL (% water peak)0.5 (0.2)0.1 (0.2)-.12IMCL (% water peak)0.9 (0.4)0.2 (0.5)-.25*p&lt;.05

Disclosure
K. Vesco: None. M.C. Leo: None. M. Francisco: None. E. Baetscher: None. W. Rooney: None. N.E. Marshall: None. J.Q. Purnell: Advisory Panel; Self; Novo Nordisk Inc..