bluuebunny/crossref_metadata_2025_split huggingface dataset:
1442-P: Validity of the American Diabetes Association Diabetes Risk Test in a Low-Income African-American Population
Authors: CHIMA CHARLES C. , ANIKPEZIE NNABUCHI , WADE BREANNA C. , PONGETTI LAUREN S. , POWELL THOMAS , BEECH BETTINA 
Published: June 2020

Objective: The use of self-reported data to assess diabetes risk in the general population is a key component of the National Diabetes Prevention Program. The American Diabetes Association Diabetes Risk Test (ADADRT) is a popular tool used for this purpose, yet there is paucity of data on its validity in real-world clinical settings. We assessed the performance of ADADRT in identifying undiagnosed prediabetes or diabetes at a Federally Qualified Health Center serving predominantly African Americans.
Methods: The ADADRT was administered to participants without pre-existing diagnosis of prediabetes or diabetes and standard validation measures were calculated using HbA1c as the gold standard. Findings are reported for participants who self-identified as being Black or African American (n = 151).
Results: HbA1c identified 25.8% of the sample as having prediabetes or diabetes. At the recommended cutoff for ADADRT (i.e., score ≥5) the following measures were observed: proportion of high-risk persons 47.7%, sensitivity 74.4%, specificity 61.6%, positive predictive value 40.3%, negative predictive value 87.3%, Youden Index 36, and area under the receiver-operating characteristic curve 0.68.
Conclusion: ADADRT reasonably identifies low income African Americans with high diabetes risk. Findings are comparable to the precursor to ADADRT as validated with nationally representative data.

Disclosure
C.C. Chima: None. N. Anikpezie: None. B.C. Wade: None. L.S. Pongetti: None. T. Powell: None. B. Beech: None.
1445-P: Metabolic Syndrome Subtypes Point to Distinct Origins of Glucose Intolerance
Authors: LUKOSE CHRISTY , CAMPBELL ERIN , CISTOLA DAVID P. 
Published: June 2020

Introduction: Metabolic syndrome (MetS) is a risk factor for type 2 diabetes and cardiovascular disease. The underlying pathophysiology encompasses insulin resistance (IR), abdominal obesity, ectopic lipid deposition and glucose intolerance. However, individual subjects may exhibit distinct MetS subtypes.
Hypothesis and Aim: We hypothesized that MetS has IR and non-IR subtypes. The specific aim was to identify and compare these subtypes, in human subjects without diabetes, using data from the 2015-2016 U.S. National Health and Nutrition Examination Survey.
Methods: The inclusion criterion was ages ≥ 12, and the exclusion criteria were diabetes, pregnancy, fasting &lt; 8 h or TG ≥ 885 mg/dL. The 2,325 subjects were categorized into four groups: no MetS and no hyperinsulinemia (NN; n=1,149), no MetS with hyperinsulinemia (NY; 497), MetS without hyperinsulinemia (YN; 249), and MetS with hyperinsulinemia (YY; 430); insulin cut point 11.25 µIU/mL. Parameter distributions were compared using non-parametric Kruskal-Wallis and Steel-Dwass tests, and 95% CI.
Results: Compared with YY, the YN subtype had significantly higher median age, blood pressure, HDL and smoking frequency, and lower waist circumference, TG and hs-CRP. Despite a large difference in median HOMA-IR for YY (2.33; 95% CI 2.26, 2.40) vs. YN (1.07; 95% CI 1.04, 1.14), the median fasting glucose and HbA1c were statistically identical for YY and YN. Most subjects in the YN group had impaired fasting glucose (median 104.7 mg/dL; 95% CI 103.2, 106.0). By contrast, median fasting glucose for the NN and NY groups were 95.8 (95% CI: 95.2, 96.3) and 97.6 mg/dL (95% CI: 96.8, 98.5), respectively.
Conclusion: In subjects without diabetes, the YN subtype of metabolic syndrome is characterized by glucose intolerance without insulin resistance. Thus, diabetes risk for YN subjects must arise from a loss of beta-cell function by IR-independent mechanisms. Therapeutic strategies designed to improve insulin sensitivity are unlikely to be effective in the YN subtype.

Disclosure
C. Lukose: None. E. Campbell: None. D.P. Cistola: None.

Funding
National Institutes of Health (R21HL143030)
145-LB: Improving Continuous Glucose Monitoring (CGM) Use across Ten National Centers: Results from the T1D Exchange Quality Improvement Collaborative (T1DX-QI)
Authors: EBEKOZIEN OSAGIE , RIOLES NICOLE , DESALVO DANIEL , GALLAGHER KATHRYN , LEE JOYCE M. , MCDONOUGH RYAN , OBRYNBA KATHRYN , PRAHALAD PRIYA , THOMAS SARAH , WEINSTOCK RUTH S. , CORATHERS SARAH 
Published: June 2020

Many studies have demonstrated the clinical benefit of continuous glucose monitoring (CGM) in type 1 diabetes (T1D). Although favorable insurance policy changes have gradually increased the access of CGM nationally, widespread uptake has been slow. The T1DX-QI is a national type 1 diabetes learning collaborative coordinated by the T1D Exchange in Boston, MA.
This work describes how 10 diabetes centers in the T1DX-QI used QI principles including the Plan - Do - Study - Act (PDSA) cycles to test and expand different interventions to increase CGM use in their respective centers. Successful efforts include the redesign of relevant workflows, assessing and removing barriers to adoption, developing CGM patient education classes, and advocating for state Medicaid coverage of CGM. The centers piloted these changes in 12 - 26 year olds with T1D.
The coordinating center used statistical process control charts to evaluate the effectiveness of the interventions (Figure 1). Eight of 10 participating centers improved CGM use significantly. There was a 12% improvement from over 20 months across the entire cohort of centers (range 7-34%).
This demonstrates that by using QI principles to test interventions and cross-learning, the T1D community can increase use of CGM devices.
Figure 1.

Disclosure
O. Ebekozien: None. N. Rioles: None. D. DeSalvo: Consultant; Self; Dexcom, Inc., Insulet Corporation. K. Gallagher: None. J.M. Lee: Advisory Panel; Self; Goodrx. Consultant; Self; T1D Exchange. Research Support; Self; Lenovo. R. McDonough: None. K. Obrynba: None. P. Prahalad: None. S. Thomas: None. R.S. Weinstock: Board Member; Self; JDRF. Consultant; Self; Insulogic LLC. Research Support; Self; Boehringer Ingelheim International GmbH, Diasome Pharmaceuticals, Inc., Eli Lilly and Company, Insulet Corporation, Jaeb Center for Health Research, Kowa Research Institute, Inc., Medtronic, Tolerion, Inc. S. Corathers: None.

Funding
The Leona M. and Harry B. Helmsley Charitable Trust
1452-P: High-Altitude Polycythemia Is an Independent Risk Factor for Type 2 Diabetes
Authors: REN QIAN , LV XUEMEI , ZHOU LINGLI , LUO YINGYING , YANG LIHUI , JI LINONG 
Published: June 2020

Background: According to previous researches, there were several risk factors of type 2 diabetes, including age, overweight or obesity, hypertension, dyslipidemia etc. However, it is not clear whether there are specific diabetes related risk factors in high altitude areas due to the unique low oxygen and low atmospheric pressure environment.
Methods: This study is a population-based, cross-sectional epidemiological survey. The permanent Tibetan residents in Lhasa and Shigatse were enrolled in this study, including 1 urban area (10 communities) and 7 rural areas (48 villages). OGTT, HbA1c, blood lipid and blood cell counts were measured in all the subjects. Diabetes was diagnosed according to the WHO criteria. The diagnostic standard of high altitude polycythemia (HAPC) is the universal Qinghai standard (male hemoglobin ≥ 210g/L, female ≥ 190g/L).
Results: A total of 1401 subjects were enrolled in this study. The average age was 44.3 ± 15.0 years old, 33.3% were male. The prevalence of diabetes in the study population was 3.6% (5.8% among men and 2.6% among women, p=0.003; 4.4% in rural vs. 2.4% in urban areas, p=0.033). The prevalence of diabetes increased with increasing age (1.9%, 4.7%, and 7.1% among persons who were 18 to 44, 45 to 59, and ≥60 years of age, respectively) and with increasing BMI (0, 2.3%, 4.7%, and 11.3% among persons with a BMI of &lt;18.5, 18.5 to 23.9, 24.0 to 27.9, and ≥28.0, respectively). The prevalence of HAPC was 6.4% (6.2% among men and 6.5% among women, p=0.453). The prevalence of diabetes was significantly higher in HAPC group than in non HAPC group (11.2% vs. 3.1%, P&lt;0.001). After quartile grouping of Hb, the prevalence of diabetes was 1.6%, 1.8%, 4.5% and 6.3% respectively (p=0.001). After adjusting for gender, age, BMI, hypertension, hyperuricemia and hypercholesterolemia, logistic regression analysis indicated that HAPC was an independent risk factor for diabetes (or = 3.514, 95% CI 1.606-7.689, P = 0.002).
Conclusion: HAPC is an independent risk factor of type 2 diabetes in Tibet, China.

Disclosure
Q. Ren: None. X. Lv: None. L. Zhou: None. Y. Luo: None. L. Yang: None. L. Ji: None.

Funding
Science and Technology Project of Tibet Autonomous Region (CGZH2017000085); National Key Research and Development Program (2016YFC1304901)
1455-P: Effect of Treatment of Anemia on HbA1c Levels
Authors: PENDELA VENKATA SATISH , VENUGOPAL SHARINI , KUDARAVALLI PUJITHA 
Published: June 2020

Introduction: While glucose remains the major determining factor of HbA1C, other conditions like hemolytic anemia, iron/vitamin B 12 deficiency anemia have also been shown to affect HbA1C.
Material and Methods: A prospective study was carried out in a tertiary care center, Kasturba hospital, India over a period of two years, that included patients in the age group of 18 to 70 years with anemia and no proven diabetes. We excluded all patients with diabetes, other forms of anemia, major organ dysfunction, hemoglobin &lt;5 g/dL and pregnancy.
Anemia was treated with iron and/or cobalamin. Hemoglobin was measured every month till normal values were attained. HbA1C was measured at the beginning and at the end of completion of 3 months of treatment. For comparing pre and post therapy HbA1C levels, paired t-testwas used. A p value of &lt;0.01 was considered statistically significant.
Results: We found a significant decrease in HbA1C levels among 70% of the iron deficient group, 72% of the B12 deficiency group and 83% of the dual deficiency group with the treatment of anemia. In our study, as many as 27 (38%) patients were labeled as prediabetic without considering their anemic status.
Conclusion: From the observations made in this study, it is evident that HbA1C could be influenced by the anemic state of an individual. Hence, it is advisable to treat patients with anemia before defining their diabetic status. Larger studies are required to know the degree of impact of anemia on HbA1C.

Disclosure
V. Pendela: None. S. Venugopal: None. P. Kudaravalli: None.
1459-P: NMR-Based Metabolomics for the Screening of Sleep-Disordered Breathing in Type 2 Diabetes
Authors: LERTDETKAJORN KHEMAPORN , POUNGSOMBAT PATCHA , KHOOMRUNG SAKDA , PHETCHARABURANIN JUTAROP , HONGTHONG SAKCHAI , KUHAKARN CHUTIMA , SIWASARANOND NANTAPORN , MANODPITIPONG AREESA , NINITPHONG HATAIKARN , SAETUNG SUNEE , ONGPHIPHADHANAKUL BOONSONG , REUTRAKUL VICHAI , REUTRAKUL SIRIMON 
Published: June 2020

Sleep-disordered breathing (SDB) is common in type 2 diabetes (T2D). This study aimed to develop a screening tool to detect SDB in T2D.
Method: SDB was screened in 95 patients by an overnight monitor and diagnosed if apnea-hypopnea index ≥ 5. Anthropometric and HbA1c data, and SDB risk by questionnaire were obtained. Quantitative NMR experiment was performed on a Bruker AVANCE 400 Ascend NMR spectrometer. Carr−Purcell−Meiboom−Gill (CPMG) pulse sequence at 310 K was applied to serum samples to enhance signal-to-noise ratio and hence detect low-molecular weight metabolites. Models based on six machine learning methods (e.g., learned vector quantization, stochastic gradient boosting model, support vector machines, decision tree, partial least squares, generalized linear model, and logistic regression) were constructed to predict SDB, tuned with the CARET R package, and evaluated using three repeats of 10-fold cross validation.
Results: Seventy-six patients (80.0%) had SDB. Compared with those without, those with SDB were more likely to be male, have SDB risk, higher BMI and neck circumferences, longer diabetes duration, and higher HbA1c. NMR-based metabolic profiling identified significantly lower levels of branched-chain amino acids (BCAAs) (e.g., L-leucine and L-isoleucine), lactate and threonine metabolites in those with SDB, while level of D-glucose-6 phosphatase was higher than those without SDB. Accuracies for predicting SBD using clinical data alone were ∼80% for each machine learning model. L-leucine and D-glucose-6-phosphatase were identified as being independently associated with SDB using a logistic regression, in addition to clinical factors, with an improved accuracy of 86.3%, and a Cohen’s kappa of 0.429.
Conclusions: Due to its high prevalence, clinical factors alone were not sufficient in predicting SDB in T2D. The discovery of metabolic profiles, including BCAAs and those involved in glucose metabolism, could facilitate a more precise and specific SDB prediction.

Disclosure
K. Lertdetkajorn: None. P. Poungsombat: None. S. Khoomrung: None. J. Phetcharaburanin: None. S. Hongthong: None. C. Kuhakarn: None. N. Siwasaranond: None. A. Manodpitipong: None. H. Ninitphong: Speaker’s Bureau; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Merck Sharp & Dohme Corp., Novo Nordisk Inc., Takeda Pharmaceutical Company Limited. S. Saetung: None. B. Ongphiphadhanakul: None. V. Reutrakul: None. S. Reutrakul: None.

Funding
Endocrine Society of Thailand; Ramathibodi Hospital; Mahidol University; Thailand Center of Excellence for Innovation in Chemistry
1460-P: Type 2 Diabetes Polygenic Score in Addition to Clinical Factors for Prediction of Diabetes Incidence in an Indigenous American Population
Authors: WEDEKIND LAUREN E. , KOBES SAYUKO , HSUEH WEN-CHI , BAIER LESLIE , KNOWLER WILLIAM C. , MAHAJAN ANUBHA , MCCARTHY MARK I. , HANSON ROBERT L. 
Published: June 2020

Background: Type 2 diabetes (T2D)-associated variants, derived from genome-wide association studies (GWAS), have largely been reproducible across populations, but there is limited information on how polygenic scores (PS) based on these variants add to clinical factors for predicting diabetes incidence, particularly in non-European populations.
Methods: A longitudinal study of diabetes was conducted in an Indigenous study population native to the Southwestern U.S. At each exam, a 75-g oral glucose tolerance test was administered with measurement of fasting and 2-hour plasma glucose (FPG, 2hPG). Genotypes were available from a GWAS for 2783 participants aged ≥ 20 years without diabetes at baseline, with imputed genotypes based on whole genome sequence data from 296 individuals from the population. PSs for participants were constructed and weighted using genome-wide significant variants in a T2D GWAS meta-analysis in European-descent populations (n=245; Mahajan 2018). Survival analyses were conducted to predict diabetes incidence in 2 models: (1) age, sex, BMI, FPG; (2) age, sex, BMI, FPG, T2D PS. Area under the receiver-operating characteristic curve (AUC) was calculated using a nonparametric method for survival analysis.
Results: 903 cases (32.5%) of diabetes occurred during 25,110 person-years of follow-up. The hazard ratio for T2D PS, controlled for age, sex, BMI and FPG, was 1.27 per SD (P=1.5×10-12). Cumulative incidence at 10 years was 0.076 in the lowest quartile for the prediction score based on clinical factors alone and 0.509 in the highest quartile. For the model including PS, corresponding values were 0.075 and 0.524. AUC was 0.710 for the model with clinical factors alone and 0.721 for the model including T2D PS.
Conclusion: Weighted T2D PSs based on 245 variants were informative in predicting T2D incidence in Indigenous Americans; however, the improvement in prediction beyond clinical factors alone was modest.

Disclosure
L.E. Wedekind: None. S. Kobes: None. W. Hsueh: None. L. Baier: None. W.C. Knowler: None. A. Mahajan: None. M.I. McCarthy: Advisory Panel; Self; Illumina. Consultant; Self; Eli Lilly and Company, Novo Nordisk Inc., Zoe Global Ltd. Employee; Self; Genentech, Inc. Research Support; Self; AbbVie Inc., Merck & Co., Inc., Sanofi-Aventis, Servier, Takeda UK. R.L. Hanson: None.
1468-P: Assessment of Strategies for Identifying Undiagnosed Diabetes Cases in the General Population
Authors: DAURIZ MARCO , BONORA ENZO ,   THE VERONA & PADUA UNDIAGNOSED DIABETES STUDY GROUP
Published: June 2020

The rising tide of diabetes (DM) calls for strategies applicable at large scale to anticipate diagnosis at the earliest disease stages. However, no conclusive evidence exists on the most effective DM screening program. We tested whether random capillary blood glucose (RCBG), a validated DM risk questionnaire (FINDRISC), or both, may be applied as universal screening strategy in a 4-year (2015-18) multi-center study involving all individuals presenting to the information desks during the World Diabetes Day and similar initiatives in two metropolitan areas of Northern Italy. In case of high FINDRISC scores (&gt;14 points) and/or RCGB levels suggestive for DM (≥200 mg/dL within 2h after last meal/sugar drink;≥150 mg/dL within 2-5h; ≥125 mg/dL, &gt;5h), people were invited to the Diabetes Center (DC) for laboratory confirmation by fasting plasma glucose and A1c. The FINDRISC was also advertised on newspapers and flyers in squares, hospitals and pharmacies of both cities. Overall, 62,827 flyers were distributed and ∼400.000 inhabitants were exposed to the advertisements. Eventually, 154 individuals reporting high FINDRISC scores presented for a laboratory check and diagnosis was confirmed in 18 (11.7%). Among 8,563 individuals undergoing RCBG, 341 (4%) had glucose levels suggestive for DM; 86 had a laboratory check and DM was confirmed in 36 (41.9%). In addition to RCBG, 3,351 subjects also completed the FINDRISC and 480 (14.3%) reported high scores. Of them, 397 (82.7%) presented to the DC and 40 (10.1%) had diagnosis confirmed. In 30 individuals, both FINDRISC and RCBG were suggestive for DM and diagnosis was confirmed in 17/27 subjects eventually presented to the DC (63.0%). These data suggest that RCBG outperforms FINDRISC to identify unknown DM. The simultaneous presence of FINDRISC and RCBG suggestive for DM appears even more effective. Poor adherence to laboratory check in high-risk cases calls for initiatives to raise public awareness on the deleterious concequences of untreated DM.

Disclosure
M. Dauriz: None. E. Bonora: Advisory Panel; Self; Lilly Diabetes, Novartis AG, Sanofi. Speaker’s Bureau; Self; Abbott, Lilly Diabetes, Novo Nordisk A/S, Sanofi.

Funding
Italian Ministry of Health (RF-2011-02350193)
1469-P: OGTT-Based, Very Simple, Disposition Index (OGTT-DI) and Beta-Cell Function Index (OGTT-B) Proposed
Authors: SHINDE SURESH N. , SHINDE RITUPARNA S. , JOSHI ASHWINI S. 
Published: June 2020

Two new indexes of DI and BCF, parallel to HOMA basal indexes, based on analysis of 1313 individuals subjected to 75 Gm OGTT are proposed. Glucose and insulin Area Under Curve (AUC) values were calculated using trapezoidal rule. OGTT Insulin Secretion (Isec) was calculated using ’AUCins/AUCglu’ and insulin sensitivity (Isen) by using ’(1/(AUCins * AUCglu)’ formula which was derived by omitting basal values from Matsuda Index. Calculation of Disposition Index (OGTT-DI) i.e., ISec*ISen, was found proportional to ’1/(AUCglu)^2’, as the ’AUCins’ values in denominator and numerator were cross-cancelled. Considering other non-insulin dependent glucose disposal variables as ’constant’, a new index of BCF function, OGTT-B, was defined and derived using ’(OGTT-DI Test/OGTT-DI Normal) * 100’ formula. This resulted in a final equation as ’[{(AUCglu normal) ^2/(AUCglu-test) ^2} * 100]’. Normal AUCglu, a population specific variable, was determined from 10 normoglycemic non-obese individuals and was ’241’. Fourteen categories of dysglycemia were defined based on glucose, insulin, HOMA-IR and Matsuda index. When compared, OGTT-DI showed more consistent linear relationship to evolving dysglycemia categories than other BCF indexes such as HOMA-DI, oral-DI and ISSI-2. (Figure below). Being independent of Insulin and BMI levels, OGTT-DI and OGTT-B are simple and low cost BCF indexes which will be useful in staging and managing dysglycemia.

Disclosure
S.N. Shinde: None. R.S. Shinde: None. A.S. Joshi: None.
1473-P: Evaluation of Insulin Resistance in Healthy Nonobese Asian Indians Using TyG (Triglyceride-Glucose) Index and Its Comparison with HOMA-IR
Authors: SHARMA CHARU , ARORA MANISHA , GUPTA DEEPAK , MAKKAR BRIJ M. 
Published: June 2020

Background: Insulin resistance(IR) is a key determinant of CV Risk and is highly prevalent in Asian Indians. However, there is limited data on IR in healthy non-obese individuals. Quantification of IR in clinical practice is a challenge and HOMA IR is the commonly used method. Triglyceride-Glucose(TyG) Index, a simple method for measuring IR, is not much used in clinical practice. This study assessed the prevalence of IR using TyG index in comparison to HOMA-IR in healthy non-obese Indians.
Aims and Objectives: 1. To assess the prevalence of IR using TyG index in healthy non-obese individuals 2. To assess TyG (Triglyceride-Glucose Index)Index as a measure of IR in comparison to HOMA IR.
Methodology: A total of 170 healthy non-obese individuals visiting for routine health checks were recruited in the study. Relevant anthropometric and laboratory parameters were recorded. IR was calculated using TyG index and HOMA IR using following formulae: TyG = ln (Triglycerides in mg/dL x Glucose in mg/dL/2)8 IRHOMA = (I0 × G0)/405 (I0 -fasting insulin in mIU/L, G0-fasting glucose in mg/dL)1 IR was defined as HOMA-IR &gt;2.8 or TyG index &gt;4.65. Prevalence of IR in the study population was calculated using both and compared.
Results: Mean age of study population was 43.45±15.72 years and 55% were males. Average BMI was 22.09± 2.29 Kg/m². Systolic and diastolic BP were 117.29±7.90 and 72.12±8.37 mmHg respectively. Mean fasting blood glucose was 92.79±6.92 mg/dL, mean triglycerides 107.48 ± 28.95 mg/dL and fasting serum insulin was 11.42 ± 8.53 μU/ml. Prevalence of IR defined by TyG index was 44% as compared to 34% by HOMA IR. There was a strong correlation between TyG index and HOMA IR(p=0.006). TyG index had a high negative predictive value (74%).
Conclusions: Prevalence of IR in healthy Indians is high and affects more than a third of healthy non-obese individuals. TyG index appears to be a better tool for evaluation of IR in clinical practice and has a strong correlation with HOMA IR.

Disclosure
C. Sharma: None. M. Arora: None. D. Gupta: None. B.M. Makkar: Advisory Panel; Self; Abbott, AstraZeneca, Biocon, Boehringer Ingelheim Pharmaceuticals, Inc., Eris Lifesciences, Novartis AG, Novo Nordisk Inc., Sanofi, USV Private Limited. Speaker’s Bureau; Self; Janssen Pharmaceuticals, Inc.