bluuebunny/crossref_metadata_2025_split huggingface dataset:
476-P: Predicting the Risk of Chronic Kidney Disease in People with Diabetes (PWD) from Electronic Health Care Records (EHRs): A Validation Study
Authors: HUSCHTO TONY , KÖNIG HELENA , MARRIOTT CLAIRE E. , MILITZ DANIEL , RINGEMANN CHRISTIAN 
Published: June 2020

Chronic kidney disease (CKD) is one of the most frequent complications faced by people with diabetes mellitus.
The ability to accurately predict the onset of this disease at an early stage and adjust the individual therapy and lifestyle appropriately has the potential to prevent or at least slow down the progression of the disease significantly.
We have previously published a 3-year CKD risk engine based on longitudinal U.S. EHRs data (Indiana Network for Patient Care and IBM Explorys) from more than 600,000 PwDs and have demonstrated a superior performance compared to previously published risk algorithms developed solely on clinical trial data (AUC = 0.794 vs. AUC = 0.718 for the best benchmark algorithm). Nevertheless, our previous analysis was limited to healthcare data from the U.S. and the risk prediction was only tested for patients at the time point of the initial diabetes diagnosis. To prove that our algorithm has a broader applicability, we have independently validated our risk engine on a dataset of general practitioners from the UK (Clinical Practice Research Datalink) including longitudinal data from more than 140,000 PwDs, thereby showing the transferability of our algorithm into a European context (AUC = 0.763 +/- 0.005 vs. AUC = 0.594 +/- 0.006 for the best benchmark). Moreover, we used this analysis to apply our risk engine at variable time points after the initial diabetes diagnosis within the U.S. and European data and could thereby demonstrate that our algorithm is not limited to the initial time of diagnosis (average AUC = 0.79 irrespective of the time after diagnosis).
This validation analysis further corroborates the external validity of our approach to use Real-World-Data from EHRs in the development of CKD risk prediction algorithms and thereby supports a broader use of those algorithms in standard clinical practice.

Disclosure
T. Huschto: Employee; Self; Roche Diabetes Care. H. König: Employee; Self; Roche Diabetes Care. C.E. Marriott: Employee; Self; Roche Diabetes Care. D. Militz: Employee; Self; Roche Diabetes Care. C. Ringemann: Employee; Self; Roche Diabetes Care.
48-OR: Pancreatic ß Cell SERCA2 Deficiency Leads to Impaired Proinsulin Processing and Reduced ER to Golgi Protein Trafficking
Authors: KONO TATSUYOSHI , IIDA HITOSHI , JARVELA TIMOTHY S. , LEE CHIH-CHUN , TONG XIN , ARVAN PETER , LINDBERG IRIS , EVANS-MOLINA CARMELLA 
Published: June 2020

Impaired proinsulin processing is observed in both type 1 and type 2 diabetes. We have previously shown that reductions in endoplasmic reticulum (ER) calcium (Ca2+) in the pancreatic β cell arising from impaired activity of the Sarcoendoplasmic Reticulum Ca2+ ATPase (SERCA) pump are associated with increased proinsulin secretion. However, the mechanisms responsible for reduced proinsulin processing in the context of SERCA2 deficiency remain incompletely understood. To test this, we developed mice with β cell specific SERCA2 deletion (βS2KO mice) and S2KO INS1 cells. βS2KO mice exhibited age-dependent glucose intolerance and reduced glucose-stimulated insulin secretion without evidence of impaired insulin sensitivity. ER Ca2+ levels in islets from βS2KO mice were significantly reduced, while serum proinsulin/insulin (PI/I) ratios and whole pancreas PI/I content were elevated. Immunoblot analysis of βS2KO islets and S2KO INS-1 cells revealed reduced active forms of the proinsulin processing enzymes, PC1/3, PC2 and CPE. Restoration of SERCA2b via adenoviral transduction in S2KO INS1 cells was sufficient to restore PC1/3 and PC2 maturation and enzyme activity. Brefeldin A treatment in INS1 cells recapitulated the impairments in PC1/3 and PC2 maturation observed in S2KO cells, suggesting a disturbance in protein trafficking between the ER and Golgi. Consistent with this, trafficking assays were performed using a vesicular stomatitis virus G (VSVG) protein construct and revealed a significantly slower rate of VSVG movement from the ER to the Golgi in S2KO INS1 cells. Moreover, pancreas sections from βS2KO mice showed increased co-localization of proinsulin and ProPC2 in the early compartments of the secretory pathway. Taken together, these data suggest that loss of SERCA2 activity and ER Ca2+ loss in the pancreatic β cell leads to impaired proinsulin processing via reduced maturation and trafficking of proinsulin processing enzymes.

Disclosure
T. Kono: None. H. Iida: None. T.S. Jarvela: None. C. Lee: None. X. tong: None. P. Arvan: None. I. Lindberg: None. C. Evans-Molina: Consultant; Self; Bristol-Myers Squibb.

Funding
National Institutes of Health (R01DK093954); U.S. Department of Veterans Affairs (2-I01BX001733)
485-P: T Lymphocytes Infiltration in Kidneys of People with Type 2 Diabetes
Authors: KONG LINGYUN , ANDRIKOPOULOS SOF , MACISAAC RICHARD , TORKAMANI NILOUFAR , COUGHLAN MELINDA , POWER DAVID , MARIN EVELYN C. , MACKAY LAURA K. , EKINCI ELIF I. 
Published: June 2020

Diabetic kidney disease (DKD) is a highly prevalent complication of diabetes and is the leading cause of ESKD. The role of T lymphocytes in DKD remains unclear. We aimed to determine the distribution of T lymphocytes in renal biopsies in people with T2DM with varying degrees of kidney function and albuminuria. Fresh renal biopsy samples were provided by the Victorian Cancer Biobank at the time of surgery from individuals undergoing partial/complete nephrectomy or from people with diabetes who had a renal biopsy for clinical indications at Austin Health. Participants were divided into the following groups: Diabetic kidney disease group (DKD was diagnosed by clinical and histologic characteristics), nondiabetic and intact renal function (Control) (eGFR &gt; 60ml/min/m2, no albuminuria) and nondiabetic kidney disease (NDKD) (eGFR &lt; 60ml/min/m2, no history of diabetes). DKD group was further stratified into normo-, micro-, or macroalbuminuria groups. Sections were immunostained with CD3 antibody. Results showed higher level of creatinine, systolic blood pressure, and reduced level of eGFR, poor glycemic control in people with DKD and macroalbuminuria as compared with people without diabetes. T lymphocytes numbers were significantly increased in the interstitial compartment of people with T2DM and macroalbuminuria compared to those without diabetes, suggesting the immunopathologic role of T lymphocytes in the development of DKD.

Disclosure
L. Kong: None. S. Andrikopoulos: None. R. MacIsaac: None. N. Torkamani: None. M. Coughlan: None. D. Power: None. E.C. Marin: None. L.K. Mackay: None. E.I. Ekinci: None.
487-P: AI Prediction of Chronic Kidney Disease Stage Progression Combinations for Type 2 Diabetes Mellitus
Authors: MIYAMOTO KOHTAROH , KOSEKI AKIRA , KUDO MICHIHARU , MAKINO MASAKI , SUZUKI ATSUSHI 
Published: June 2020

Chronic Kidney Disease (CKD) for type 2 diabetes mellitus (T2DM) is an increasingly serious issue worldwide. CKD stage is known to progress in stages where 1 is normal and 5 is the worst case of hemodialysis. We have previously reported on the accuracy of AI technology to target CKD stage 1 patients and predict the progression to stages 2 and above. Such prediction is targeted to help identify patients who require intensive care. Such study showed maximum 0.75 AUC score. In this study, we have extended our study to investigate all other stage combinations. For example, we can set our goal to answer questions such as: "Can AI technology accurately predict stage 2 patients progressing into stage 5 (hemodialysis)?"
We have investigated the EMR (Electronic Medical Record) of 64,059 type-2 diabetes patients who have visited the hospital. We have extracted 36 features, where 12 sources (e.g., Urine protein, albuminuria and eGFR) were selectively chosen by extraction from known literature and 3 types of values (mean, latest, SD) were calculated for each source. Period of 180 days range was scanned within 20% range (i.e., 180 days * 0.2 = 36 days). We balanced the label transition of each stage evenly by the number of data for each good label (no progress from current target stage) and bad label (progress from target to worse stage). We created a new prediction model for each combination (i.e., 1 to 2, 1 to 3, 1 to 4, 1 to 5, etc.) and performed cross validation by Logistic Regression to calculate the accuracy, F-Score and AUC.
All the combinations showed relatively good results. The range and average of each measurements were:
Accuracy 0.653 through 0.753 Average: 0.708.
F-Score 0.673 through 0.754 Average:0.719.
AUC 0.705 through 0.826 Average 0.770.
We applied AI techniques to create new predictive model which can detect the progression for all combinations of type-2 diabetes CKD stage progression. This model may contribute by a more effective and accurate intervention to reduce hemodialysis and cardiovascular events.

Disclosure
K. Miyamoto: None. A. Koseki: Employee; Self; IBM. M. Kudo: Employee; Self; IBM. M. Makino: None. A. Suzuki: Research Support; Self; Chugai Pharmaceutical Co., Ltd., Kowa Company, Ltd., Ono Pharmaceutical Co., Ltd., Taisho Pharmaceutical Co., Ltd., Takeda Pharmaceutical Company Limited.
49-LB: “You Don’t Know When the Sugar’s Going to Kick In”: Experiences of People Managing Type 1 Diabetes during a Marathon
Authors: HUGHES ALLYSON , BISPHAM JEOFFREY , FULLER LILY , MCAULIFFE-FOGARTY ALICIA H. 
Published: June 2020

There are many benefits of regular exercise in diabetes, but people with type 1 diabetes (T1D) also have the risk of short-term complications. There is a lack of research and information about how individuals with T1D should prepare and recover from endurance exercise. The present study had two primary objectives: 1) Understand how previous runners experienced participating in a marathon and 2) Understand their experience managing their diabetes while training for and recovering from a marathon. The study included 10 participants who had previously participated in the New York City Marathon with the Beyond Type 1 Team (mean age 33.3 ± 12.9 years, duration of T1D 13.2 ± 14.8 years, A1c 6.59 ± .7%), 80% female, 90% on insulin pump and 100% on CGM. Interviews were transcribed and thematically analyzed. Themes included (but are not limited to): methods to maintain euglycemia, preparation to reduce glycemic variability during endurance activities, and managing blood glucose variability during the marathon and recovery period. Diabetes devices were identified as a key theme given how integral they were to marathon preparation and performance. Multiple participants discussed using specific CGM and pump site placement to prevent the devices from falling off. Participants described various strategies to prevent hypoglycemia during their runs (both for training and the marathon). Some participants described frequent disruptions to their training plan due to diabetes and setting a higher target glucose to accommodate for the exercise. This data provides initial insight into the decision-making that occurs for people with T1D who participate in endurance activities - including food preparation, planning ahead for supplies and insulin manipulation, in-the-moment management decisions, and recovery period glucose management.

Disclosure
A. Hughes: None. J. Bispham: None. L. Fuller: None. A.H. Mcauliffe-Fogarty: None.
496-P: Risk Factors for Incident CKD in Prediabetes
Authors: HENNESSEY KELLY A. , DARATHA KENN B. , ALICIC RADICA Z. , JONES CAMI , NEUMILLER JOSHUA J. , DURU O. KENRIK , NICHOLAS SUSANNE , MCPHERSON STERLING M. , NORRIS KEITH C. , TUTTLE KATHERINE R. 
Published: June 2020

The magnitude of risk for chronic kidney disease (CKD) in prediabetes is unclear. The study aim was to determine CKD incidence and risk factors in patients with prediabetes from the CURE-CKD registry of electronic health record data from &gt;2.6 million patients with or at-risk for CKD.
Adults with prediabetes were identified by established criteria for %A1C, random and fasting blood glycemia, and administrative codes. CKD was defined by estimated glomerular filtration rate (eGFR) &lt;60 mL/min/1.73m2 or urine albumin-to-creatinine ratio &gt;30 mg/g based upon two or more measurements &gt;90 days apart and administrative codes. Cox proportional hazards regression was used to predict incident CKD.
From 2006 to 2017, there were 544,253 patients with prediabetes and no CKD. Baseline A1C was 5.9±0.3% (mean±SD) and eGFR was 90.7±16.8 mL/min/1.73 m2. Notable medication use was: NSAIDs n=110,964 (20%), PPIs n=49,916 (9%), and ACEi/ARB n=63,738 (12%). Incident CKD occurred (n=59,583) at a rate of 25.2/1,000 person-years with mean time to CKD of 2.9 years. Older age, female gender, Hispanic/Latinx, Black, Native Hawaiian and Other Pacific Islander race/ethnicity, lower eGFR, PPI and ACEi/ARB use were associated with higher hazards of CKD in prediabetes (Table 1).
Incident CKD is common in prediabetes with risk factors similar to those in diabetes. CKD screening and detection with a focus on kidney protection may offer novel opportunities to reduce CKD risk in prediabetes.

Disclosure
K.A. Hennessey: None. K.B. Daratha: None. R.Z. Alicic: None. C. Jones: None. J.J. Neumiller: None. O. Duru: None. S. Nicholas: Speaker’s Bureau; Self; Janssen Pharmaceuticals, Inc. Other Relationship; Self; Centers for Disease Control and Prevention, George Institute for Global Health, Goldfinch Bio, Inc., National Institutes of Health. S.M. McPherson: None. K.C. Norris: None. K.R. Tuttle: Consultant; Self; AstraZeneca, Bayer Inc., Boehringer Ingelheim Pharmaceuticals, Inc., Eli Lilly and Company, Gilead Sciences, Inc., Goldfinch Bio, Novo Nordisk Inc.
504-P: Albuminuria Affects Prevention of Diabetic Kidney Disease Progression by Sodium–Glucose Transporter 2 Inhibitor
Authors: KASHIMA KOJI , SHIMIZU HIROYUKI , YAMADA MASANOBU 
Published: June 2020

Objective: In type 2 diabetes(T2D), prevalence of chronic kidney disease (CKD) with normoalbuminuria is increasing. We assessed the annual eGFR decline rate (slope) in Japanese patients with T2D with CKD treated by sodium glucose transporter 2 inhibitor (SGLT2i).
Methods: A total of 75 patients with T2D with CKD (eGFR&lt;60ml/min/1.73m2) were recruited, whose initial eGFR slopes were calculated from 46±21 months period, and slopes after adding SGLT2 inhibitors (on empagliflozin 64% and others) were calculated from 21±8 months. Efficacy was compared between in patients with normo-/micro-alubuminuria (Norm/Micro) and with macroalbuminuria (Macro). Patients with Norm/Micro existed in 52% (39/75). At the time of recruitment, distribution in CKD stages are as follows, on stage 3A; eGFR 45-59 (21 vs. 8), stage 3B; eGFR 30-44, (13 vs. 15) and stage 4; eGFR&lt;30 (5 vs. 13) respectively. Average age was 70.7±12.2 years old and duration of diabetes was 15.0±9.3 years.
Results: 1) Average initial (first month) eGFR drop values are as follows, in Norm/Micro (51.2 to 47.9ml/min/1.73m2: P&lt;0.01), in Macro (40.7 to 39.0ml/min/1.73m2: P=0.01). 2) Annual mean eGFR slopes were improved in each group, in Norm/Micro (-4.3±4.0 to -1.2±4.1ml/min/1.73m2/year; P&lt;0.001), and in Macro (-8.9±12.2 to -2.3±3.6ml/min/1.73m2/year; P&lt;0.01). 3) Proportion of showing reduction of eGFR slopes after adding SGLT2i are 69.2% (27/39) and 75.0% (27/36), respectively. And even in stage 4, 80.0% (4/5) and 76.9% (10/13), respectively. 4) Rapid decliner of eGFR (&gt;10 ml/min/1.73m2/year) existed in 12.8% (5/39) and in 25.0% (9/36) respectively. Mean eGFR slopes in these rapid decliner were all improved and as follow, -12.2±2.1 to +1.2±3.4ml/min/1.73m2/year; P&lt;0.001 and -22.3±19.0 to -1.6±4.0ml/min/1.73m2/year; P&lt;0.01, respectively.
Conclusion: SGLT2i could still have a remarkable renal benefit in Norm/Micro diabetic kidney disease. Slope analysis might be useful for safety and detecting rapid eGFR decliner.

Disclosure
K. Kashima: None. H. Shimizu: None. M. Yamada: None.
506-P: Hepatic Fibrosis but Not Steatosis Is Associated with Diabetic Kidney Disease in Nonobese Patients with Type 2 Diabetes
Authors: SEO DA HEA , CHO YONGIN , AHN SEONG HEE , HONG SEONGBIN , LEE YONG-HO , CHOI YOUNGJU , HUH BYOUNG WOOK , HUH KAP BUM , KIM SO HUN 
Published: June 2020

Background: Recent studies investigated the association between nonalcoholic fatty liver disease (NAFLD) and diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus (T2DM) but the results are inconclusive. Here we aimed to investigate the effect of obesity on the association between NAFLD and DKD in a large cohort of participants with T2DM.
Methods: In this cross-sectional study, a total of 3,439 patients (1,710 men and 1,729 women) with T2DM were recruited from the Seoul Metabolic Syndrome cohort between 2003 and 2016. NAFLD was defined by ultrasonographic detection of steatosis in the absence of other liver diseases. Advanced fibrosis was defined as FIB4 index ≥ 1.45. DKD was defined as an estimated glomerular filtration rate (eGFR) of &lt; 60 mL/min/1.73 m2. Obesity was defined as body mass index (BMI) ≥ 25 kg/m2.
Results: The mean age was 57.4 ± 10.3 years and duration of diabetes was 7.7 ± 7.1 years. Two hundred and fifty subjects (7.3%) had DKD and 1869 (54.3%) subjects had NAFLD. The prevalence of DKD were 7.0%, 4.5% and 11.9% in no-NAFLD group, liver steatosis only group and advanced fibrosis group respectively (p&lt;0.001). Advanced fibrosis was independently associated with an increased risk of DKD (odds ratio [OR] 1.75; 95% confidence interval [CI] 1.08-2.86; p = 0.034) compared to no NAFLD after adjustment for potential confounders in patients with BMI &lt; 25 kg/m2. However, the association between advanced fibrosis and DKD was not significant in those with BMI ≥ 25 kg/m2. There was no significant associated between hepatic steatosis without advanced fibrosis with DKD regardless of obesity status.
Conclusion: This study suggests that advanced liver fibrosis, a severe form of NAFLD, was independently associated with increased risk of DKD in non-obese patients with T2DM.

Disclosure
D. Seo: None. Y. Cho: None. S. Ahn: None. S. Hong: None. Y. Lee: None. Y. Choi: None. B. Huh: None. K. Huh: None. S. Kim: None.
508-P: Single and Joint Impact of Type 2 Diabetes and of Congestive Heart Failure on Albuminuria
Authors: MAECHLER MAXIMILIAN , VONBANK ALEXANDER , LARCHER BARBARA , MADER ARTHUR , SPRENGER LUKAS , LEIHERER ANDREAS , PURIN DANIELA , MUENDLEIN AXEL , DREXEL HEINZ , SAELY CHRISTOPH H. 
Published: June 2020

Albuminuria is a characteristic feature of diabetic nephropathy, and urine albumin excretion is also increased in patients with congestive heart failure (CHF). However, no data are available on the single and joint associations of type 2 diabetes (T2DM) and CHF with albuminuria. This issue therefore was addressed in the present study. We investigated 181 patients with CHF, of whom 84 had T2DM (CHF+/T2DM+) and 97 did not have diabetes (CHF+/T2DM-) and 413 controls without CHF, of whom 79 had T2DM (CHF-/T2DM+) and 334 did not have diabetes (CHF-/T2DM-). The albumin/creatinine ratio (ACR) was 34±117 in CHF-/T2DM- patients. Compared to this group it was higher in CHF-/T2DM+ patients (56±110; p=0,002), in CHF+/T2DM- patients (98±311; p&lt;0,001) and in CHF+/T2DM+ patients (286±779; p&lt;0,001), in whom in turn it was higher than in CHF-/T2DM+ (p&lt;0,001) or in CHF+/T2DM- (p=0,001) patients. The ACR did not differ significantly between CHF-/T2DM+ and CHF+/T2DM- patients (p=0,345). In multivariate analysis of covariance, T2DM and CHF proved to be independent predictors of the ACR after adjustment for age, sex, body mass index, LDL-C, smoking and hypertension (F=6,392; p=0,012 and F=13,14; p=&lt;0,001, respectively). We conclude that T2DM and CHF are mutually independent determinants of albuminuria.

Disclosure
M. Maechler: None. A. Vonbank: None. B. Larcher: None. A. Mader: None. L. Sprenger: None. A. Leiherer: None. D. Purin: None. A. Muendlein: None. H. Drexel: None. C.H. Saely: None.
51-LB: Young Adults with T1DM: Is Fear a Mediator between Self-Efficacy and Quality of Life?
Authors: KENT DENISE A. , PARK CHANG G. , FRITSCHI CYNTHIA , QUINN LAURIE T. 
Published: June 2020

It has been reported that the many demands of young adulthood in addition to the complex treatment regimen required to manage T1DM impacts quality of life (QoL). It has also been stated in the literature that self-efficacy (SE), fear of complications (FOC) and fear of hypoglycemia (FOH) are predictive of quality of life. However, is fear a mediator in the relationship between SE and QoL? The literature to no extent has reported on whether FOC and FOH mediate the relationship between SE and QoL. Therefore, the aim of this study was to determine if fear mediates the relationship between SE and QoL in young adults with T1DM. This descriptive, cross-sectional study examined SE among young adults with T1DM. The subjects included 201 young adults (age: 27.4 ± 5; gender: 163(F) 38 (M); A1C: 7.4 ± 1.5%; age of diagnosis: 14 ± 8 years). The subjects completed a general health survey and six self-report instruments accessed via the web including the Diabetes Knowledge Test (DKT), Hypoglycemia Fear Scale (HFS), the Fear of Complications Questionnaire (FCQ), Self-Efficacy for Diabetes Self-Management (SEDM), the Diabetes Behavior Rating Scale (DBRS), the Diabetes Quality of Life Measure (DQoL). This study explored the relationship between SE and QoL and found the FOC and FOH significantly mediated the relationship between SE and QoL (p&lt;.000). This study suggests that although the higher ones SE is, the higher the reported QoL is, FOC and FOH impact this relationship when present. The young adults in this study reported a high level of fear centered around complications from their diabetes as well as fear of hypoglycemia in trying to maintain tight glucose control. Therefore, it is important for researchers and clinicians working to improve QoL in young adults to consider mediating factors such as FOC and FOH when designing interventions aimed to impact QoL.

Disclosure
D.A. Kent: None. C.G. Park: None. C. Fritschi: None. L.T. Quinn: None.