bluuebunny/crossref_metadata_2025_split huggingface dataset:
569-P: Fear of Nighttime Hypoglycemia: Diabetes Providers Recognize Parents’ Concerns
Authors: MONZON ALEXANDRA , MCDONOUGH RYAN , CUSHING CHRISTOPHER C. , CLEMENTS MARK A. , PATTON SUSANA R. 
Published: June 2021

Objective: Pediatric diabetes healthcare providers and parents of youth with type 1 diabetes (T1D) described specific fears regarding nighttime hypoglycemia and behaviors parents engage in at night to avoid nighttime lows (i.e., blood glucose &lt; 70mg/dL).
Methods: We completed semi-structured interviews with 10 diabetes providers (i.e., physicians, nurses, educators) and 15 parents of youth with T1D (Mean parent age= 41.92 ± 5.72 years, Mean child age= 10.96 ± 2.37 years). All children used a continuous glucose monitor and 87% used an insulin pump. Providers described the fears and behaviors parents express in clinic related to their child’s overnight glucose management. Parents described their own fears and behaviors specific to nighttime lows. We transcribed interviews verbatim, and used Framework Matrix Analysis to identify common themes among responses.
Results: Provider and parent interviews converged on several main themes (provider percentages first, parent percentages second). Regarding specific fears, consensus among participants indicated that parents fear overnight lows will harm their child (i.e., death, seizures; 90%, 87%), parents worry they will not hear alarms or wake up to help their child at night (50%, 93%), and parents fear devices will malfunction at night (20%, 67%). Regarding avoidance behaviors, participants reported that parents provide snacks at bedtime to raise glucose levels (70%, 60%), parents set temporary (lower) basal levels (40%, 53%), under-correct at night 20%, 46%), set multiple alarms to wake up during the night to check on their child (80%, 87%), and do not allow their child to attend sleepovers (30%, 80%).
Conclusions: We identified several common fears and avoidance behaviors that may be specific to overnight lows in parents of youth with T1D. As a next step, we will use our qualitative data to inform a new tool to specifically assess parental fear of nighttime hypoglycemia, which will fill a current gap in both research and clinical care for youth with T1D.

Disclosure
A. Monzon: None. R. Mcdonough: None. C. C. Cushing: None. M. A. Clements: Consultant; Self; Eli Lilly and Company, Employee; Self; Glooko, Inc., Research Support; Self; Abbott Diabetes, Dexcom, Inc. S. R. Patton: None.
575-P: Serum Soluble Epoxide Hydrolase-Related Oxylipins Are Associated with Depression and Increased Symptom Severity in T2DM
Authors: ANITA NATASHA Z. , FORKAN NUBAIRA , KAMAL RADIA , NGUYEN MICHELLE M. , YU DI , MAJOR-ORFAO CHELSI , WONG SOPHIE K. , LANCTÔT KRISTA L. , HERRMANN NATHAN , OH PAUL , SHAH BAIJU R. , GILBERT JEREMY , ASSAL ANGELA , SWARDFAGER WALTER 
Published: June 2021

Depression in T2DM is associated with inflammation (e.g., interleukin-6 [IL-6]), but specific inflammatory pathways, and depressive symptoms involved, have not been identified. Cytochrome P450s (CYP450s) convert dietary fatty acids into pro-resolving epoxides but soluble epoxide hydrolase (sEH) metabolizes these into inactive diols, thereby limiting their anti-inflammatory benefits. We hypothesized that sEH oxylipins (diols and epoxides) will be associated with depression and increased symptom severity in T2DM. Patients with T2DM were diagnosed with depression (Structured Clinical Interview, DSM-5) and matched 1:1 to non-depressed patients for sex, HbA1c and BMI. Fasting serum oxylipins were quantified by ultra-high-performance liquid chromatography tandem mass spectrometry. All analyses controlled for IL-6, measured by ELISA. Diol/epoxide ratios were calculated to estimate sEH pathway flux. Among 20 depressed and 20 non-depressed patients (mean age 58.9±8.5, mean HbA1c 7.0±0.9%, 65% women), the 17,18 sEH derived eicosapentaenoic acid (EPA) diol was higher in depressed participants (F(1,37)=5.753, p=0.022). Somatic, negative affective, and anhedonic symptoms were screened (Center for Epidemiological Studies-Depression Scale). Three diol/epoxide oxylipin ratios were associated with higher somatic symptoms in the depressed group: the 16,17 docosahexaenoic acid (DHA) derived ratio (β=0.489, p=0.007), and both linoleic acid derived ratios (9,10 [β=0.549, p=0.017] and 12,13 [β=0.547, p=0.003]). The 14,15 arachidonic acid derived ratio was associated with higher negative affective symptoms (β=0.559, p=0.006). These associations were not found in non-depressed T2DM. This increased flux of omega-3 and omega-6 fatty acids through the sEH pathway associated with depression and symptom severity suggests the potential role of sEH inhibitors for T2DM-related depressive symptoms.

Disclosure
N. Z. Anita: None. P. Oh: None. B. R. Shah: None. J. Gilbert: Speaker’s Bureau; Self; Abbott Diabetes, Amgen Canada, AstraZeneca, Boehringer Ingelheim (Canada) Ltd., Dexcom, Inc., Eli Lilly and Company, HLS Therapeutics Inc., Janssen Pharmaceuticals, Inc., Novo Nordisk Canada Inc., Sanofi. A. Assal: None. W. Swardfager: None. N. Forkan: None. R. Kamal: None. M. M. Nguyen: None. D. Yu: None. C. Major-orfao: None. S. K. Wong: None. K. L. Lanctôt: None. N. Herrmann: None.

Funding
Canadian Institutes of Health Research; Banting & Best Diabetes Centre; National Institute of Diabetes and Digestive and Kidney Diseases
577-P: Complexity of Antidiabetic Medication Regimen Is Associated with Increased Diabetes-Related Distress in Patients with Type 2 Diabetes
Authors: LUZURIAGA MARIA GRACIA , LEITE RAFAEL , AHMED HAMMAD , SAAB PATRICE G. , GARG RAJESH 
Published: June 2021

Objective: Diabetes-related distress is present in a high proportion of people with type 2 diabetes mellitus (T2DM). We hypothesized that complexity of the antidiabetic medication regimen is a factor that increases diabetes-related distress.
Research Design and Methods: We studied 74 consecutive patients with T2DM managed at a tertiary care center. Patients were screened for diabetes-related distress using the Diabetes Distress Scale-2 (DDS-2). A Diabetes Medication Complexity Scoring (DMCS) system was developed to objectively assess the diabetes medication complexity. Based on DMCS, participants were categorized into one of three groups: low (n = 26), moderate (n = 22) and high (n = 26) medication complexity.
Results: The prevalence of diabetes-related distress (DDS-2 ≥6) was 34.6% in the low, 36.4% in the moderate, and 69.2% in the high complexity groups (χ2(2) = 7.75, p= 0.021). Complexity groups were similar on sociodemographic characteristics, diabetes duration, BMI and blood pressure as well as the prevalence of hypertension, hyperlipidemia and hypoglycemic episodes over the last year (ps&gt;0.05). The prevalence of microvascular complications varied as a function of low (28%), moderate (45%), and high (76%) regimen complexity (χ2(2) = 11.8, p = 0.003). One-way ANOVA showed significant complexity group differences for HbA1c (F(2,70) = 5.47, p = 0.006) with higher HbA1c in the high and moderate complexity groups than in the low group. Analysis of covariance adjusting for HbA1c showed that DDS-2 significantly differed as a function of complexity group (F (2,69) = 3.93, p = .024). The model explained 16.2% of the variance in DDS-2 (R2 = 0.162). Post hoc analyses revealed that diabetes-related distress was significantly greater for the high (DDS-2 = 6.6) complexity group compared to the moderate complexity group (DDS-2 = 4.5) (p = .031).
Conclusions: A complex antidiabetic medication regimen is associated with high levels of diabetes distress.

Disclosure
M. Luzuriaga: None. R. Leite: None. H. Ahmed: None. P. G. Saab: None. R. Garg: None.
602-P: Validity of a Feeling-State Tag Accompanying Blood Glucose (BG) Measurements in a Digital Remote Diabetes Management Program (RDMP)
Authors: DZUBUR ELDIN , FAN LUDI , JAMES ROBERTA , LU WEI , MEADOWS ERIC , PEREZ-NIEVES MAGALY , SHAH BIMAL 
Published: June 2021

Background: Diabetes is thought to have a bi-directional relationship with mental health, including stress. Therefore, it is increasingly important to understand stress of patients with diabetes in real-time. Despite extensive use and success of RDMPs, there is limited psychometric research on the development of real-time measures of stress in the context of RDMPs. The objective of this study is to examine the validity of a stress tag among participants in an RDMP.
Methods: Participants in the study (ages 8-92) were enrolled in Livongo for Diabetes, an RDMP offering a glucometer that prompts user to endorse a feeling-tag to provide behavioral context to BG readings. Participants also received smartphone surveys at baseline, 6 months, and 12 months containing a two-item Diabetes Distress Scale SF. Repeated BG and stress data from the first 12 months of the program were aggregated to 3-month windows surrounding the smartphone surveys. Two-part generalized linear mixed models were used to test association between stress tags and DDS-SF.
Results: Participants (N=470) were, on average, 59.69 years old, 50% female, and 80% White, with 91% reporting type 2 diabetes and 36% reporting insulin usage. Two-part models revealed that each additional point on the DDS-SF scale was associated with a 46% increased likelihood (95% CI: 1.23-1.75) of any stress tag at that time point (z=4.25, p&lt;0.001). When stress tags were endorsed, each additional point on the DDS-SF scale was associated with a 14% (95% CI: 1.04-1.26) increase in the proportion of stress tags for that time period (z=2.72, p&lt;0.01).
Conclusions: The study found that a stress tag during glucometer use was associated with DDS-SF. In the context of an RDMP, a stress indicator allows for real-time data analysis, and thus a better understanding of a patient's experience with their disease. Future studies may seek to understand the extent to which such stress measures can be used to predict lapses in medication adherence.

Disclosure
E. Dzubur: Employee; Self; Livongo, Teladoc Health, Stock/Shareholder; Self; Livongo, Teladoc Health. L. Fan: Employee; Self; Eli Lilly and Company, Stock/Shareholder; Self; Eli Lilly and Company, Stock/Shareholder; Spouse/Partner; Eli Lilly and Company. R. James: Employee; Self; Livongo, Teladoc Health, Stock/Shareholder; Self; Livongo, Teladoc Health. W. Lu: Employee; Self; Teladoc Health. E. Meadows: Employee; Self; Eli Lilly and Company, Employee; Spouse/Partner; Eli Lilly and Company, Stock/Shareholder; Self; Eli Lilly and Company, Stock/Shareholder; Spouse/Partner; Eli Lilly and Company. M. Perez-nieves: Employee; Self; Eli Lilly and Company. B. Shah: Employee; Self; Teladoc Health, Stock/Shareholder; Self; Teladoc Health.
611-P: Users with High-Risk Type 2 Diabetes Experience a Change in Blood Glucose Levels Using Digital Therapeutic Platform
Authors: HERSHCOVITZ YIFAT , DAR SHARON , MANEJWALA OMAR , BACHER DROR 
Published: June 2021

Introduction: Digital therapeutic platforms are often used to assist users in maintaining target blood glucose levels and to prevent diabetes complications. Digital engagement can play a central role in supporting behavioral changes to manage diabetes. Such platforms allow evaluation of patient's blood glucose levels in real time and intervene based on actual behavioral and clinical indicators. The study examines the ratios of blood glucose levels over time based on real world data in one such platform.
Method: A retrospective data evaluation study was performed on the DarioTM data base. A population (“high-risk users”) of all users with type 2 diabetes activated during 2017-2020 who took measurements with Dario in the first 2 months and who started with an average blood glucose above 180 mg/dL was evaluated. The ratios assessed were target range (70-180 mg/dL) and high blood glucose (&gt;180 mg/dL) readings over a year. Socioeconomic status was matched by applying zip code data to census.gov data.
Results: For 11,101 users, the average ratio of target range readings (70-180 mg/dL) was significantly increased from 28.4% to 54.8% (P&lt;0.001). Average high events ratio (&gt;180 mg/dL) was significantly reduced from 71.3% to 44.4% over a full year usage (P&lt;0.001). The change appeared in the earliest months and was maintained over a year. Average number of days between measurements, i.e., “recency” was 3.3 days. A subset of Spanish language app users (N=169) was also evaluated, and comparable trends were observed. Matching Census.gov data on study population showed that 20% of users resided in low income zip codes, 70% in middle and 10% in upper income zip codes.
Conclusion: Digital therapeutic platforms may promote behavior modification in high-risk patients with type 2 diabetes to create sustainable outcomes. The study revealed that the digital diabetes platform has the potential to enhance self-care behaviors across socioeconomic statuses and among different language speakers.

Disclosure
Y. Hershcovitz: Employee; Self; DarioHealth. S. Dar: Employee; Self; DarioHealth. O. Manejwala: Employee; Self; DarioHealth, Stock/Shareholder; Self; DarioHealth. D. Bacher: Employee; Self; DarioHealth.
623-P: Should We Continue Using HbA1c for Treatment Adjustment in Patients with Type 2 Diabetes?
Authors: TOTOMIROVA TZVETELINA , ARNAUDOVA MILA 
Published: June 2021

According current guidelines, treatment decision in patients with type 2 diabetes is based on HbA1c, reflecting approximately recent three months control. But there are many data showing that HbA1c is an insufficient control assessment tool omitting to present glucose fluctuations and variability. Continuous glucose monitoring (CGM) is used for insulin dose adjustment but is rarely used in type 2 diabetes patient treatment determination. In our study we compare the results derived from non-real time CGM and HbA1c to find out which could be more useful for drug prescription decision in patients with type 2 diabetes. We studied 85 patients with type 2 diabetes (35 women, 50 men; age 43.93±10.87 years, disease duration 21.91±6.07 years) receiving different treatment regimens (31 on oral therapy, 33 treated with premixed insulin, 21 on multiple insulin injections). Patients performed Non-Real Time Continuous Glucose Monitoring by using iProTM for seven days and HbA1c was measured at the end of this period. No difference in HbA1c level was found in three groups. HbA1c showed moderate negative correlation to percentage of time within the limits derived from CGM (r1= -0.654; r2= -0.674; r3= -0.521) with no dependence on therapeutic regimen. Moderate positive correlation was found between HbA1c and time spent above the limit and moderate negative correlation with time within the limit. CGM was more precise than HbA1c in defining patients with good control (p&lt;0.05). HbA1c correlates badly with overall glucose excursion (r1=-0.096, r2=0.200, r3=0,377). No difference was found between studied groups in regard to CGM percentage of time spent within the limits, above and below the limits, as well as to AUC above upper limit. We conclude that CGM is more precise than HbA1c in diabetes control assessment and thus in treatment adjustment. Much more information is received from CGM ant the latter gives opportunity to find the most appropriate drug and to achieve optimal results.

Disclosure
T. Totomirova: None. M. Arnaudova: None.
624-P: Sustained Effect in Diabetes Management Using a Digital Workflow and Decision Support System in Home Health Care
Authors: KOPANZ JULIA , SCHOLLE LARA K. , LICHTENEGGER KATHARINA M. , AMBROSCH GISELA , LIBISELLER ANGELA , ABERER FELIX , PANDIS MARLENE , DONSA KLAUS , TRUSKALLER THOMAS , PIEBER THOMAS , MADER JULIA K. 
Published: June 2021

Introduction: Basal insulin is the first choice for geriatric persons with type 2 diabetes (T2D) who require insulin therapy. A digital workflow and decision support system (GlucoTab@MobileCare) with an integrated basal or basal-plus insulin algorithm has demonstrated to be an effective and safe treatment in geriatric persons with T2D receiving home health care during a three months study period. The aim of this study was to investigate the long-term effect on diabetes management following treatment by GlucoTab@MobileCare.
Methods: Diabetes related aspects (glycemic control, acute hospital admissions, general practitioner (GP) consultations under routine conditions) were assessed in nine patients (5 female, age 77±10 years, BMI 27.7±4.7 kg/m2, HbA1c 60±13 mmol/mol [start of intervention] vs. 57±12 mmol/mol [end of intervention]) during the three months study intervention, six months before (n=9) and six months after (n=8, due to the death of one patient) study intervention. Data from electronic health records and diabetes documentation were analyzed.
Results: Mean fasting blood glucose (BG) was 212±72 mg/dL before, 156±33 mg/dL during and 162±42 mg/dL after intervention. During the study, no acute hospital admissions due to BG derailments were observed. Eight derailments before and four derailments after intervention occurred. Causes for diabetes-related emergency department (ED) admissions were hyperglycemia (n=10), hypoglycemia (n=1) and undefined (n=1). Nine of the twelve admissions resulted in hospitalizations. GPs were consulted for diabetes management 19-times before and 16-times after GlucoTab@MobileCare treatment. Reasons for consultations were routine check-up (n=12), therapy adjustment (n=22) and HbA1c measurement (n=1).
Conclusion: A sustained effect on glycemic control with reduced requirements for hospital admissions was observed six months after GlucoTab@MobileCare treatment, whereas GP consultations were not affected.

Disclosure
J. Kopanz: None. T. Pieber: Advisory Panel; Self; ADOCIA, Arecor, AstraZeneca, Eli Lilly and Company, Novo Nordisk A/S, Sanofi, Research Support; Self; AstraZeneca, Novo Nordisk A/S. J. K. Mader: Advisory Panel; Self; Becton, Dickinson and Company, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Medtronic, Roche Diabetes Care, Speaker’s Bureau; Self; Abbott Diabetes, Novo Nordisk A/S, Roche Diabetes Care, Sanofi, Stock/Shareholder; Self; decide Clinical Software GmbH. L. K. Scholle: None. K. M. Lichtenegger: None. G. Ambrosch: None. A. Libiseller: None. F. Aberer: None. M. Pandis: None. K. Donsa: Stock/Shareholder; Self; decide Clinical Software GmbH. T. Truskaller: None.
628-P: Accuracy of Continuous Glucose Monitoring (CGM) for Inpatient Diabetes Management
Authors: WRIGHT JORDAN J. , WILLIAMS ALEXANDER , WEAVER RITA , WILLIAMS JONATHAN M. , BAO SHICHUN 
Published: June 2021

In the hospitalized patient with diabetes (DM), adequate glucose control requires frequent measurement of point-of-care (POC) blood glucose. Use of inpatient continuous glucose monitoring (CGM) could reduce workload on the staff, increase ease and frequency of glucose assessment, provide continuous glucose trends, and decrease nurse exposure to infectious patients, but data on accuracy of CGM in the hospital setting is limited. We conducted an observational study of 44 adult patients with DM who were admitted to our hospital non-intensive care units from July-October 2020. After placement of Abbott 14-day Freestyle Libre CGM sensors, patients continued to receive standard POC glucose measurement using the Accu-Chek device. Insulin dosing was based on POC data. 44% percent of participants were female, and 69% had type 2 DM. Median weight was 100.1 kg (IQR 89.0 - 109.5) and median age was 56.6 years (IQR 47.9 - 63.5). Relative to POC glucose measurement, the CGM mean absolute relative difference (MARD), %15/15, and %20/20, three standard measures of CGM accuracy, were 21.4%, 32.6%, and 47.9% respectively. CGM paralleled POC glucose, but CGM readings were consistently lower compared to POC by 20.9% (IQR 12.4 - 28.6). The variation did not change with POC glucose levels or sensor wear-time, though most data (82%) were from the first three days of sensor wear. On multiple regression analysis, participant age, sex, weight, DM type, baseline renal function, and admission HbA1c did not contribute to variation in individual’s MARDs.
In conclusion, we report the accuracy of the Freestyle Libre CGM relative to POC measurements in hospitalized patients. CGM was lower than POC at all glucose levels, and accuracy did not change with other clinical variables measured. Future studies evaluating newer generations of CGMs relative to standardized plasma glucose measurements will further determine the safety and efficacy of using these new technologies for insulin dosing and glucose alerts in hospitalized patients.

Disclosure
J. J. Wright: None. A. Williams: None. R. Weaver: None. J. M. Williams: None. S. Bao: None.
633-P: Optimizing Workflow for Remote Diabetes Data Download: University of Pittsburgh Experience
Authors: KARSLIOGLU-FRENCH ESRA , SISTLA DIVYA , MEYER ANNA C. 
Published: June 2021

Background: As CMS started to reimburse videovisits during public health emergency, virtual diabetes care became routine for diabetes clinics. Since better diabetes care depends on review of data, at UPMC, we created a workflow to facilitate diabetes data download before virtual visits. Best practices for virtual diabetes data download is not known.
Objective: To analyze the diabetes technology data download workflow efficiency in our clinic for virtual visits.
Methods: At University of Pittsburgh endocrine clinic, staff reaches out to patients with diabetes who are scheduled for videovisits one week before their appointment with instructions on how to download glucometer, insulin pump and/or CGM data. We completed a retrospective chart review to understand the effectiveness of this workflow in obtaining data before visits.
Results: August 2020 to January 2020, our clinic staff reached out to 1197 patients with diabetes via secure portal before their videovisits to obtain diabetes data download. Patients used Tidepool software to download their pumps and glucometers or cloud technology for CGM data sharing. 164(14%) patients were on insulin pump(with or without CGM), 159(13%) patients had a CGM alone and 874(73%) patients had a glucometer alone. 190(15%) patients uploaded their data to Tidepool, 150 (12%) patients shared their CGM data via cloud using their cell phone apps.873(73%) patients did not download their blood sugar data before appointment, most of these patients had glucometer only. The barriers to sharing data virtually were reported by 104 patients. 25/104 (25%) of patients had no access to a computer. 6 patients (5%) reported having a glucometer that does not have compatibility with Tidepool software. 35/104 (32%) of patients reported not having cable to download their data.
Conclusion: The most common barrier for patients to share their diabetes technology data was lack of cables. Diabetes clinics should analyze their remote diabetes data workflows to troubleshoot barriers.

Disclosure
E. Karslioglu-french: None. D. Sistla: None. A. C. Meyer: None.