bluuebunny/crossref_metadata_2025_split huggingface dataset:
1005-P: Patients with Overweight and Obesity at High-Risk for Type 2 Diabetes: A Predictive Model
Authors: TURCHIN ALEXANDER , MORRISON FRITHA J. , SHUBINA MARIA , SHINDE SHRADDHA , AHMAD NADIA , KAN HONG 
Published: June 2021

Obesity is the most common chronic disease in the U.S. Patients with obesity are at high risk for type 2 diabetes mellitus (T2DM); however, many of their risk factors for T2DM are modifiable. It is important to identify patients with obesity at high risk for T2DM to appropriately direct treatment and resources.
We conducted an observational study of EMR data of 399,711 adults with BMI ≥ 25 kg/m2 and without baseline T2DM between 2000 and 2019. We first identified a literature-based pool of risk factors for T2DM onset and conducted variable selection by applying LASSO penalized Cox regression with ten-fold cross-validation on an 80% training dataset. We estimated a regular Cox model on the training dataset with selected variables and evaluated model performance on the remaining 20% dataset.
Over a mean of 7.4 years of follow-up, 38,898 (9.8%) study patients developed T2DM. The predictive model achieved a Harrell’s C-statistic of 0.81. The greatest risk of T2DM was associated with gestational diabetes (HR 2.53; 95% CI 2.12-3.01), Asian and Hispanic race/ethnicity (HRs 2.38 and 1.94; 95% CIs 2.24-2.53 and 1.86-2.02, respectively), family history of DM (HR 2.00; 95% CI 1.95-2.04) and PCOS (HR 1.84; 95% CI 1.63-2.07). Female sex (HR 0.77; 95% CI 0.75-0.79), having a partner (HR 0.90; 95% CI 0.88-0.92) or commercial insurance (HR 0.86; 95% CI 0.84-0.88) were associated with a lower risk of T2DM. Characteristics found in &gt;5% of patients that carried additional risk included proteinuria (5.0% of patients; HR 1.53; 95% CI 1.47-1.59), hypertension (23.9%; HR 1.21; 95% CI 1.18-1.25), high cholesterol (11.9%; HR 1.19; 95% CI 1.15-1.23), and history of smoking (40.5%; HR 1.18; 95% CI 1.15-1.21). Each standard deviation increase in BMI (5.2 kg/m2) was associated with a 1.51-fold (95% CI 1.49-1.52) increase in T2DM onset.
It is feasible to use predictive modeling to identify patients with overweight and obesity at high risk of T2DM. This approach could be utilized to guide population management and clinical treatment decisions.

Disclosure
A. Turchin: Consultant; Self; Proteomics International, Research Support; Self; AstraZeneca, Eli Lilly and Company, Novo Nordisk, Pfizer Inc., Sanofi, Stock/Shareholder; Self; Brio Systems. F. J. Morrison: None. M. Shubina: None. S. Shinde: Employee; Self; Eli Lilly and Company, Stock/Shareholder; Self; Eli Lilly and Company. N. Ahmad: Employee; Self; Eli Lilly and Company. H. Kan: Employee; Self; Eli Lilly and Company, Stock/Shareholder; Self; Bristol-Myers Squibb Company, GlaxoSmithKline plc.

Funding
Eli Lilly and Company
1006-P: Progression to Type 2 Diabetes Despite a Baseline Fasting Plasma Glucose ≤ 80 mg/dl in a Population-Based Cohort
Authors: EGAN AOIFE M. , WOOD-WENTZ CHRISTINA , BAILEY KENT R. , VELLA ADRIAN 
Published: June 2021

Fasting plasma glucose (FPG) concentration is a major predictor of a person’s risk of developing diabetes mellitus (DM). However, we sought to examine characteristics of individuals who develop DM despite a baseline FPG of ≤ 80mg/dL. The Rochester Epidemiology Project (REP) links the medical records of virtually all persons who have resided in Olmsted County, MN, USA between 1966 and present. Electronic laboratory data are available from 2005. Using REP, we identified individuals with ≥ 2 FPG tests from 2005-17, after excluding people who met criteria for DM on/before their first FPG. Those who developed DM (FPG &gt; 125mg/dL) were assigned an event date as the date of their first diagnostic value. Subjects who did not develop DM were censored at their last FPG value on/before Dec 31, 2017. The entire cohort consisted of 44,815 individuals, of whom 3689 (8.2%) developed DM. Although higher baseline FPG was strongly related to development of DM, 2315 had a baseline FPG of ≤ 80mg/dL with 130 (5.6%) developing DM. Within this subset, those who progressed were older at baseline (40 ± 11 vs. 36 ± 12 years, p&lt;0.02) and were more likely to be male (38 vs. 22%, p&lt;0.001). Those who progressed had no difference between BMI at baseline and time of diagnosis (28 ± 8 vs. 29 ± 8 kg/m2, p=0.07). Medical record review suggested that individuals who progressed had high medical complexity. Specifically, 28 (22%) were repeatedly exposed to high dose glucocorticoids during follow up, including 10 (8%) who received a solid organ transplant. Fifteen (12%) had a substance abuse disorder. Nineteen (15%) were treated with insulin at one year following diagnosis, four of whom were diagnosed with type 1 DM. Twenty nine (22%) had a first degree relative with DM. Additional longitudinal follow up revealed that 20 (15%) were deceased at a median time of 2.5 years following the DM diagnosis. While progression to DM in the context of a baseline FPG of ≤ 80mg/dL is uncommon, it typically occurs in the setting of significant medical comorbidity.

Disclosure
A. M. Egan: None. C. Wood-wentz: None. K. R. Bailey: None. A. Vella: Research Support; Self; Novo Nordisk.

Funding
National Institutes of Health (DK78646, DK116231, DK126206, K12HD065987)
1011-P: Major Depressive Disorder Is Associated with Higher Risk of Readmission following DKA
Authors: RAMIREZ MARCELO , MURTHI MUKUNTHAN , SHARMA BHAROSA , ATLURI RAMTEJ , SHAKA HAFEEZ , VELAZQUEZ GENARO 
Published: June 2021

Introduction: Diabetic ketoacidosis (DKA) is a medical emergency associated with significant patient mortality and morbidity. Studies have shown that the presence of Major Depressive Disorder (MDD) is associated with an increased incidence of DKA. We aimed to assess whether MDD was associated with increased risk of readmission following DKA.
Methods: We utilized the National Readmission Database for 2016 to identify hospitalized adult patients with a principal diagnosis of DKA and a secondary diagnosis of MDD from January 1 to November 30, 2016. We excluded patients with elective and traumatic admissions. We utilized a multivariate cox regression model to identify independent predictors of readmission.
Results: Of 15,981 patients with MDD and DKA who were discharged, 23.2%(n=3710) were readmitted within 30-days. The most common reason for readmission was DKA (63.9%, n=2371). In the multivariate cox regression model, MDD was associated with higher risk of readmission (HR: 1.17, 95% CI: 1.1 - 1.26, p=0.04) when adjusted for comorbidities and hospital characteristics. Of those MDD patients with DKA who were readmitted, type 1 DM (HR: 1.14, 95% CI: 1 - 1.3, p=0.04), CHF (HR: 1.40, 95% CI: 1.1 - 1.78, p=0.006), and CKD (HR: 1.44, 95% CI: 1.17 - 1.79, p=0.001) were associated with higher risk of readmission. Obesity (HR: 0.75, 95% CI: 0.59 - 0.97, p=0.025), private insurance (HR: 0.64, 95% CI: 0.53 - 0.79, p&lt;0.001), median patient household income between $54,000-$70,999 (HR: 0.73, 95% CI: 0.63 - 0.85, p&lt;0.001) and $71,000 or more (HR: 0.74, 95% CI: 0.62- 0.89, p=0.002) were associated with lower risk of readmission.
Conclusion: In this study, we found that patients with MDD admitted for DKA had a 17% increased risk of readmission within 30 days of discharge in comparison to those without MDD. This could be explained by their propensity for poor adherence to diet, exercise, and recommended pharmacological therapy. Further studies need to be done to assess and target the exact precipitants of readmission.

Disclosure
M. Ramirez: None. M. Murthi: None. B. Sharma: None. R. Atluri: None. H. Shaka: None. G. Velazquez: None.
103-LB: Real-World Persistence, Adherence, Health Care Resource Utilization, and Costs in People with Type 2 Diabetes (T2D) Switching from Basal Insulin (BI) to Second-Generation BI (Insulin Glargine 300 U/mL [Gla-300]) vs. First-Generation BI (Insulin Glargine 1
Authors: WRIGHT EUGENE , GILL JASVINDER , HUSE SAMUEL , LI XUAN , REID TIMOTHY , ZHOU FANG L. 
Published: June 2021

This retrospective observational study using the Optum Clinformatics claims database included data from adults (≥18 y) with T2D who had received prior basal insulin (BI) (NPH, Gla-100, IDet) in the 6-month baseline period, and switched to either the second-generation (2nd-gen) BI, Gla-300 or a first-generation (1st-gen) BI (Gla-100, IDet) (treatment switch=index date) between April 1, 2015 and August 31, 2019. Participants were followed from index date for 12 months or plan disenrollment or death. Cohorts were propensity score matched (PSM) on baseline demographic/clinical characteristics. Outcomes were persistence (days on treatment without discontinuation), adherence (proportion days covered), healthcare resource utilization and costs. After PSM (n=3077/cohort; mean age 68 y, 52% female), cohorts were well balanced except for 1 variable (hospitalization) with standardized difference &gt;0.1, which was adjusted in models as a covariate. During the 12-month follow-up period, higher proportions of participants who received Gla-300 vs. 1st-gen BI were persistent (45.5 vs. 42.1%; adjusted p=.0001) and adherent (42.8 vs. 38.2%; adjusted p=.0006). All-cause healthcare resource utilization was lower for Gla-300 vs. 1st-gen BI (emergency room visits 111.9 vs. 148.8 events per 100 patient-years [PY]; inpatient hospitalizations 45.3 vs. 65.9 events per 100 PY; adjusted p&lt;.0001). Total healthcare costs were numerically lower for Gla-300 vs. 1st-gen BI ($41,255 vs. $45,316 per PY). Switching to Gla-300 was associated with significantly better persistence, adherence, and—despite these associated costs—lower all-cause healthcare resource utilization vs. switching to a 1st-gen BI, in people with T2D previously treated with BI therapy. This is the first study to assess these outcomes in tandem.

Disclosure
E. Wright: Advisory Panel; Self; Abbott Diabetes, Bayer Healthcare Pharmaceuticals Inc., Boehringer Ingelheim International GmbH, Sanofi, Consultant; Self; Abbott Diabetes, Boehringer Ingelheim Pharmaceuticals, Inc., Other Relationship; Self; American Diabetes Association, Speaker’s Bureau; Self; Bayer Healthcare Pharmaceuticals Inc., Sanofi. J. Gill: Employee; Self; Sanofi US, Other Relationship; Self; American Diabetes Association, Stock/Shareholder; Self; Sanofi US. S. Huse: Consultant; Self; Sanofi US. X. Li: Employee; Self; Sanofi US. T. Reid: Other Relationship; Self; Sanofi US. F. L. Zhou: Employee; Self; Sanofi, Stock/Shareholder; Self; Sanofi.

Funding
Sanofi US
1034-P: Association between Estimated Glomerular Filtration Rate (eGFR) and Proteinuria (UP) to Predict Risk of Start of Dialysis in People with and without Diabetes (DM)
Authors: OSAWA TAEKO , FUJIHARA KAZUYA , YAMADA MAYUKO H. , YAGUCHI YUTA , SATO TAKAAKI , KITAZAWA MASARU , MATSUBAYASHI YASUHIRO , YAMADA TAKAHO , SONE HIROHITO 
Published: June 2021

Although UP is a very important feature of diabetic nephropathy, decreased eGFR without UP has become phenotypic of renal complications of DM in the new definition of DKD. Although both UP and reduced eGFR are strong predictors of kidney disease, only a few studies have examined the association between these two variables and risk of dialysis in the same cohort that included people with and without DM. We analyzed data from a nationwide claims database involving 335,778 participants (DM 20,223, non-DM 315,555) during 2008-16. Multivariate Cox regression model identified risks of starting dialysis. HRs were compared among 8 groups of combinations of DM+/-, UP (-, ≥+) and eGFR (&lt;60, ≥60). Compared with eGFR ≥60, HRs for starting dialysis in those with eGFR &lt;60 in DM- and DM+ were 14.5 (10.3-20.6) and 15.7 (10.8-22.9), respectively. In comparison with UP (-), HRs for starting dialysis with UP (≥+) in DM- and DM+ were 8.00 (5.85-11.0) and 8.01 (5.45-11.8), respectively. In DM-, compared with UP(-)eGFR ≥60, the HRs for UP(≥+)eGFR ≥60, UP(-)eGFR &lt;60 and UP(≥+)eGFR &lt;60 were 1.19 (0.55-2.60), 4.21 (2.47-7.17) and 113.4 (77.9-165.0), respectively. In DM+, those values were 3.83 (2.15-6.83), 6.47 (3.33-12.6) and 94.4 (58.5-152.4), respectively. In comparison with DM(-)UP(-)eGFR ≥60, risks of starting dialysis for DM(+)UP(≥+)eGFR ≥60, DM(+)UP(-)eGFR &lt;60 and DM(+)UP(≥+)eGFR &lt;60 significantly increased 17.7 (10.6-29.7), 25.5 (13.8-47.1) and 358.1 times (239.1-536.5), respectively.
In summary, both eGFR&lt;60 and UP(≥+) were independent predictors for starting dialysis regardless of DM and they exhibited a synergistic risk of dialysis. Both eGFR&lt;60 and UP(+) present an extremely high risk for dialysis especially in DM. These results may be helpful in considering the relationship between the need for strict therapeutic interventions for eGFR and UP and the risk of dialysis, according to the presence or absence of DM.

Disclosure
T. Osawa: None. K. Fujihara: None. M. H. Yamada: None. Y. Yaguchi: None. T. Sato: None. M. Kitazawa: None. Y. Matsubayashi: None. T. Yamada: None. H. Sone: Research Support; Self; Astellas Pharma Inc., Eisai Co., Ltd., Kyowa Kirin Co., Ltd., Novo Nordisk, Ono Pharmaceutical Co., Ltd., Taisho Pharmaceutical Co., Ltd., Takeda Pharmaceutical Co.

Funding
Japan Society for the Promotion of Science (18K17897)
104-OR: Novel GIP/GLP-1/Glucagon Receptor Agonist LY3437943: A First-in-Human Dose Study in Healthy Subjects
Authors: URVA SHWETA , DU YU , THOMAS MELISSA K. , MILICEVIC ZVONKO , COSKUN TAMER , BENSON CHARLES , LOGHIN CORINA 
Published: June 2021

Multifunctional incretins are in clinical development for several metabolic conditions. Novel LY3437943 has potent agonist activity on glucose-dependent insulinotropic polypeptide (GIP), glucagon-like peptide-1 (GLP-1) and glucagon receptors. The primary objective of this randomized, double-blind, placebo (PBO)-controlled, Phase 1, first in human study was to assess safety and tolerability of single-ascending doses of LY3437943. Forty-five healthy subjects were randomized (6:2) to subcutaneous LY3437943 (6 rising dose levels) or PBO. Vital signs, ECGs, laboratory data and adverse events (AEs) were monitored to assess safety and tolerability. LY3437943 pharmacokinetics (PK) as well as change from baseline (BL) in fasting insulin, C-peptide and weight were measured. Appetite was assessed using a visual analog scale (VAS). The most common treatment-emergent AEs were gastrointestinal, including vomiting (with higher doses), abdominal distention and nausea, which were dose-dependent, mostly mild in severity, occurred within 4 days of dosing and resolved within a week of onset. Dose-dependent increases in heart rate and decreases in systolic blood pressure were observed, which returned to near BL by Day 29. Mean terminal half-life of LY3437943 ranged from 134-165 h (∼7 days) across the 6 doses, supporting once-weekly dosing. Dose-dependent increases in mean fasting insulin and C-peptide, with maximum levels observed at 24 and 48 h, returned to near BL by Day 15. Dose-dependent weight loss was statistically significant with the 3 highest doses vs. PBO (up to 3.5 kg at the highest dose). Weight loss was maintained up to Day 43 following single administration of the two highest doses. Overall VAS score significantly increased with higher doses vs. PBO, reflecting decreased appetite. Triple-agonist peptide LY3437943 had a safety and tolerability profile similar to other incretins in Phase 1 trials with PK and pharmacodynamic outcomes that support further clinical evaluation.

Disclosure
S. Urva: Employee; Self; Eli Lilly and Company. Y. Du: None. M. K. Thomas: Employee; Self; Eli Lilly and Company, Stock/Shareholder; Self; Eli Lilly and Company. Z. Milicevic: Employee; Self; Eli Lilly and Company. T. Coskun: Employee; Self; Eli Lilly and Company, Stock/Shareholder; Self; Eli Lilly and Company. C. Benson: Employee; Self; Eli Lilly and Company. C. Loghin: Employee; Self; Eli Lilly and Company.

Funding
Eli Lilly and Company
1046-P: Automated Early Flagging of Undiagnosed CKD Using Real-World Data
Authors: MALAGARRIGA DANIEL , CHITTAJALLU SIVA , GALLEY PAUL J. , SINGH INDERJIT , ODEGARD JOHN T. 
Published: June 2021

Aims: Early diagnosis and treatment of chronic kidney disease (CKD) may delay disease progression since 45% of patients are not diagnosed until stage 3. We examine the Kidney Disease Improving Global Outcomes (KDIGO) clinical guidelines that define rules based on the longitudinal progression of urine albumin creatinine ratio (UACR) and estimated glomerular filtration rate (eGFR) for flagging CKD in people with type 2 diabetes (T2-PwD).
Methods: EHR data from over 400,000 T2-PwDs in the Indiana Network for Patient Care (INPC) database were used for this analysis. The KDIGO clinical practice guidelines defines CKD as persistently elevated urine albumin excretion (&gt;=30 mg/g), or persistently reduced eGFR (&lt; 60 ml/min per 1.73m2), or both, for more than 3 months. Our automated system scanned each patient’s longitudinal record and identified those patients with abnormal UACR and eGFR values in the cases where sufficient values were available. A flag associated with either UACR or eGFR or both and the date of flagging is indicated by the system if abnormal marker values persist for more than 3 months. CKD is indicated if one or both flags are triggered.
Results: Out of 422,089 patients 35,573 (8%) were identified as diagnosed with CKD. Only 3% of these patients had at least 2 UACR readings and, therefore, the automated system considered primarily eGFR as the marker for flagging CKD. Out of 35,573 patients with CKD diagnosis, 16,342 (46%) had an eGFR warning flag that predated the CKD diagnosis date. The system flagged a potential identification as much as 599 days (median value) in advance of the diagnosis of CKD in the dataset.
Conclusion: Our results show that earlier identification of CKD onset can be automated to flag patients wherein a positive diagnosis of CKD is likely. This flag can then prompt a confirmatory diagnosis of CKD, allowing better treatment interventions and management of CKD, reducing the burden of disease, and potentially decreasing costs associated with treatments in later CKD stages.

Disclosure
D. Malagarriga: None. S. Chittajallu: Employee; Self; Roche Diabetes Care. P. J. Galley: Employee; Self; Roche Diabetes Care. I. Singh: Employee; Self; Roche Diabetes Care. J. T. Odegard: Employee; Self; Roche Diabetes Care.
1049-P: Finding Equity: Utilizing Artificial Intelligence to Identify Social and Infrastructural Predictors of Diabetes Mortality in Florida
Authors: CISTOLA ANDREW 
Published: June 2021

DM outcomes represent one of the largest avoidable cost burdens with opportunity for improvement in the U.S. health care system. Improving health equity in the context of DM will require targeted community improvements, infrastructure investments, and policy interventions that are designed to maximize the impact of resource allocation through the use of available data and computational resources. By using an Artificial Intelligence approach to evaluate over 2000 socio-economic and infrastructural predictors of DM mortality, this study used a specific series of modeling techniques to identify significant predictors without human selection and compare their predictive ability with all possible factors when passed through artificial neural networks. The final regression model using zip code and county level predictors had an R2 of 0.863. Significant predictors included: Population % White, Population % Householders, Population % Spanish spoken at home, Population % Divorced males, Population % With public health insurance coverage, Population % Employed with private health insurance coverage, Manufacturing-Dependent Designation, Low Education Designation, Population % Medicare Part A and B Female Beneficiaries, Number of Short Term General Hospitals with 50-99 Beds. Using a multi-layered perceptron to predict zip codes at risk the C-statistic for all 2000+ predictors was 0.7938 while the 13 selected predictors was 0.8232. This indicates that these factors are highly relevant for DM mortality in Florida. This process was completed without the need of human variable selection and indicates how AI can be used for informative precision public health analyses for targeted population health management efforts.

Disclosure
A. Cistola: None.
1052-P: Clinical Characteristics of Osteoporosis in Postmenopausal Women with Type 2 Diabetes Mellitus
Authors: WANG YAN , LINA ZHANG , GUO LIXIN 
Published: June 2021

Objective: To investigate clinical characteristics and risk factors of osteoporosis in postmenopausal women with type 2 diabetes mellitus.
Method: A total of 410 postmenopausal women with type 2 diabetes of natural menopause with no history of fracture were collected from outpatients of Beijing Hospital (Oct 2017 - Nov 2019). Bone mineral density (BMD) at lumbar spine L1-L4 and hip was measured using dual-energy X-ray absorptiometry. Biochemical markers of bone and glucose metabolism were evaluated. Patients were divided into three groups based on BMD: A (osteoporosis): T ≤-2.5 SD; B (osteopenia) -2.5 &lt; T ≤ -1 SD; C (normal bone mineral density) T &gt; -1 SD. Statistical differences of age, menopausal age, diabetes age, BMI and biochemical indexes were compared between groups.
Result: 1. Case number based on lumbar spine BMD: 189 in A (46.1%), 147 in B (35.9%), and 74 in C (18.0%). ANOVA analysis showed group A patients had the highest age (F 5.698, p 0.004), age of diabetes onset (F 7.596, p 0.001), ALP (F 3.231, p 0.041) and menopausal duration (F 7.596, p 0.001), but the lowest BMI (F 11.456, p 0.000). Logistic multivariate regression showed body mass index is an independent risk factor for osteoporosis in postmenopausal women with type 2 diabetes [A vs. C: OR1.224 (95% CI 1.106-1.355, p 0.000); A vs. B: OR1.172 (95% CI 1.079-1.274, p 0.000].2. Case number based on femoral neck BMD: 159 in A (38.8%), 188 in B (45.9%), 63 in C (15.3%). ANOVA analysis showed group A patients had the highest age (F 21.484, p 0.000), age of diabetes onset (F 10.125, p 0.000) and PTH (F3.691, 0.026), but the lowest BMI (F 11.456, p 0.000). Logistic multivariate regression showed body mass index is an independent risk factor for osteoporosis in postmenopausal women with type 2 diabetes [A vs. C: OR1.267 (95% CI 1.120-1.433, p 0.000); A vs. B: OR1.195 (95% CI1.099-1.301, p 0.000)].
Conclusion: Osteoporosis in postmenopausal women with type 2 diabetes is associated with older age, longer menopause duration, older age of diabetes onset and lower BMI.

Disclosure
Y. Wang: None. Z. Lina: None. L. Guo: Consultant; Self; AstraZeneca, Bayer Healthcare Pharmaceuticals Inc., BD, Eli Lilly and Company, Merck & Co., Inc., Merck Sharp & Dohme Corp., Novartis AG, Novo Nordisk, Sanofi China.