ADA Guidelines Research Articles

Prevalence of hyperuricemia in newly diagnosed type 2 diabetes mellitus patients

Background: The relationship between hyperuricemia and diabetes mellitus is proved to be associated with the risk of cardiovascular diseases, but it is unclear whether hyperuricemia is actually related to diabetes.Methods: A 50 patients with newly diagnosed diabetes according to ADA guidelines were selected. Uric acid level and HbA1C levels were measured. Results were calculated with the reference range of uric acid >7.0 mg/dl.Results: The mean serum uric acid level was higher in 72% of the newly diagnosed diabetic patients (36/50).Conclusions: Hyperuricemia seems to be associated with newly diagnosed diabetics. It can be used as a biomarker of deterioration of glucose metabolism.

Effect of diabetes on BMD and TBS values as determinants of bone health in the elderly: Bushehr Elderly Health program

Considering the aging population associated with higher osteoporotic fracture risk, high prevalence of diabetes and its effect on bone health along with lack of information on bone quality using common methods (BMD) the aim of present study was to determine the association between trabecular bone score (TBS) and diabetes in an elderly population participating in Bushehr Elderly Health (BEH) program. This cross-sectional study was performed on data collected during the BEH Program, stage II. Anthropometric indices were measured based on NHANES III protocol. Diabetes and pre-diabetes were defined according to ADA Guideline 2018. Bone density was measured using DXA method (DXA, Discovery WI, Hologic Inc., USA). A software installed on the same device (TBS iNsight® software) was applied to assess TBS values. Variables related to bone health were compared based on their glycemic status (participants with diabetes, participants with prediabetes, and normoglycemic) using analysis of variance. Univariate and multivariate linear and logistic regression models were used to determine the association between TBS values and bone density in different glycemic states. The data of 2263 participant aged 60years and over were analyzed. Mean TBS values were significantly different between participants with diabetes, participants with prediabetes, and normoglycemic groups (P = 0.004;, however, P trend was not significant (0.400)). Mean BMD values at femoral neck and lumbar spine were significantly higher in diabetics compared with those diagnosed with pre-diabetes; the latter also had higher bone density compared with normoglycemic individuals (both P ANOVA test and P trends for means were < 0.01]. In univariate linear regression model, TBS values were negatively associated with pre-diabetes (β = -0.070; P < 0.001) but not with diabetes (β = -0.002, P = 0.915). This significant relationship disappeared when the results were adjusted for BMI. In fully adjusted multivariate logistic regression models, odds ratio linking pre-diabetes and diabetes with spinal osteoporosis was 0.861 (CI 95% 0.670-1.105) and 0.525 (CI 95% 0.392-0.701), respectively. As for femoral osteoporosis, odds ratio was 0.615 (CI 95% 0.407-0.928) and 0.968 (CI 95% 0.629-1.489), correspondingly. Moreover, for cumulative osteoporosis, the odds were 0.843 (CI 95% 0.676-1.106) and 0.551 (CI 95% 0.415-0.732), respectively. Our findings suggest that subjects with pre-diabetes and diabetes have higher bone mineral density than normoglycemic subjects; the quality of bone, however, was not different between them. The discordance between BMD and TBS values in participants with diabetes suggest that although these patients have higher BMD values, their quality of bone microarchitecture may not be better than normoglycemic subjects.

Abstract P116: The Oral Glucose Tolerance Test is Highly Reproducible for the Diagnosis of Diabetes in Africans: The Africans in America Study

Introduction: As the diabetes epidemic spreads across Africa, the oral glucose tolerance test (OGTT) has the potential to provide important information on glucose tolerance category for the individual and across the population. However, for both patient care and the collection of prevalence statistics, there needs to be a high degree of certainty that the diagnosis made by a single OGTT is reproducible. There are no rigorous studies on the reproducibility of the OGTT in Africans. Objective: By performing two OGTT in African-born blacks living in the Washington DC area, our goal was to evaluate the reproducibility of: a) the glucose tolerance category b) time to glucose peak, c) shape of glucose curve, d) glucose concentration at 60 minutes. Methods: The participants were 90 African-born blacks (66% male (59/90), age 40±11y (mean±SD), BMI 27.6±4.6 kg/m 2 ), who self-identified as healthy and were enrolled in the Africans in America cohort. Two OGTT were performed 10±8 days apart. At each OGTT plasma samples were taken at -15, 0, 30, 60, 90 and 120 minutes. Diabetes (DM), prediabetes (pre-DM) and normal glucose tolerance (NGT)was defined by ADA guidelines for glucose. The shape of the glucose curve was characterized as monophasic, biphasic or indeterminate. During the OGTT, glucose at 60 minutes ≥ 155 mg/dL was defined as elevated. The κ-statistic was used to evaluate reproducibility (slight 0.00 to 0.20, fair 0.21 to 0.40; substantial 0.61 to 0.80, excellent 0.81 to 1.0). Results: At OGTT-1, DM, pre-DM and NGT occurred in 26% (23/90), 27% (24/90) and 48% (43/90), resp. At OGTT-2, DM, pre-DM and NGT occurred in 21% (19/90), 28% (25/90) and 51% (46/90) resp. Reproducibility for the diagnosis of glucose tolerance status (NGT, pre-DM, or DM) was just substantial (κ=0.66). However, reproducibility for the diagnosis of diabetes (DM or no DM) was excellent (κ=0.88). Furthermore, no one without DM at OGTT-1 was identified as having DM at OGTT-2. The reproducibility of time to glucose peak was only fair (κ=0.32), but 100% of participants with DM had peak glucose after 30 min. Similarly, the shape of the glucose curve had fair reproducibility (κ=0.21), but no one with DM had a biphasic curve. For elevated 60 min glucose, reproducibility was substantial (κ=0.62) and 96% (22/23) of the participants with DM had elevated glucose at 60 min. Conclusion: When duplicate OGTT were performed in Africans, the diagnosis of DM and the morphology of the glucose curves were highly reproducible.

Updated ADA guidelines question routine self-monitoring in type 2 diabetes

Updated ADA guidelines question routine self-monitoring in type 2 diabetes

Prescribing Pattern of Antidiabetic Drugs Amongst Pre-Obese Diabetic Patients in a Tertiary Care Hospital- An Observational Study

Background: In order to improve the prescription quality and rational prescription pattern promotion there is an inevitable need to investigate the factors that affect doctors' prescription patterns. Diabetes is a common non communicable disease. It leads to high morbidity and mortality due to the disease itself and its diverse complications like CAD, hypertension, renal complication, retinal damage, neurological disorders, generalised infections etc. With such multifactorial background of high prevalence, progressive nature of the disease, availability of multiple therapeutic regimens prescribed on trial and error basis, the treatment is individualized and neither complete nor satisfactory. Objectives: This study was undertaken to analyse the current prescribing pattern in pre-obese patients of type 2 diabetes mellitus with regard to drug/drugs precription, dose, and duration of treatment and frequency of change of drugs. Methods: This is a prospective, parallel group, comparitive observational study. The enrolled pre-obese patients were divided as a) New diabetic b) Old diabetic (&lt;3 years duration). Each category was further divided into four subgroups according to the treatment recieved a)Monotherapy-Metforminb)Combination therapy-Metformin+another antidiabetic groups, preferably sulfonylureas, alphaglucosidase inhibitors or DPP 4 inhibitors c)Triple therapy( Metformin+SU+Voglibose or Gliptins or Glitazones) d)Insulin with other oral hypoglycemic drugs. Results: In the study of prescribing pattern, it was observed that most prescriptions in this tertiary care hospital were found to be in compliance with the ADA guidelines. Metformin monotherapy was prescribed as initial treatment. Sulphonylureas/Gliptins/Alpha glucosidase inhibitors/thiazolidinediones were used as second line therapy mostly anyone, in addition to metformin or as monotherapy according to patient requirement, tolerability and cost.Conclusions: The antidiabetic medications prescribed in this hospital, were found to be in compliance with ADA guidelines

Should we assume accuracy of point of care glucose meters? an observation from a tertiary health care centre

Background: Self-monitoring of blood glucose is important in the management of patients with diabetes mellitus in the community as well as in the hospital. It has been used for calculation of insulin doses of individuals with dysglycaemia and monitoring of glucose control. Errors in the measurement of the blood glucose can lead poor management of a patient. There is therefore the need to ensure standardization of these meters in order to achieve accuracy and precision. Objectives: To evaluate the precision and accuracy of four glucose meters commonly used for self-monitoring of blood glucose (SMBG) in a tertiary health care Centre compared to the reference laboratory method. Materials and Methods: We analyzed blood glucose samples of 55 diabetic patients who came to diabetes clinic using 4 different glucose meters (Accucheck active, Novo max extra, One touch Ultra 2 and On call plus (accoson)). Capillary and Venous blood samples were taken simultaneously from each patient for analysis using four blood glucose meters and laboratory reference method respectively. The laboratory value was used as a tool for comparison. The accuracy and precision were evaluated by the ISO and ADA criteria. The results obtained were analysed using Bland Altman graphs, correlation coefficients, scatter plots and Clarke's error grid analysis. Results: We observed good correlation between two glucose meters (Accucheck TM and Novomax TM ) and laboratory analyzed values. Among the glucose meters Accucheck TM , Novomax TM , One touch Ultra TM and On call plus TM , the correlation coefficient was 0.97, 0.96, 0.88 and 0.69 respectively. The degree of agreement of the laboratory method and the Accucheck TM , Novomax TM , One touch ultra TM and On call plus TM glucose meters was 89.09, 80.00, 76.20 and 71.32% respectively. Accucheck and Novomax TM were within ±20% accuracy (14.5% and 16.1%). Conclusion: There is a need for adequate and appropriate evaluation of all glucose meters in our setting before that we deploy them for use. None of glucose meters met the ISO target. Only one glucose meter (Accucheck TM ) met the ADA guideline for accuracy. Keywords: Accuracy; glucose meters; diabetes mellitus; self-monitoring of blood glucose

Prevalence of abnormal glucose metabolism in pediatric acute, acute recurrent and chronic pancreatitis.

Type 3C Diabetes, or diseases of the exocrine pancreas has been reported to occur in approximately 30% of adult patient with pancreatitis. The incidence of glucose abnormalities or risk factors that may predict the development of abnormal glucose in the pediatric pancreatitis population is not known. We performed a retrospective chart review from 1998–2016 for patients who carry the diagnosis of acute pancreatitis (AP), acute recurrent pancreatitis (ARP), and chronic pancreatitis (CP). We extracted glucose values, HbA1c%, and data from oral glucose tolerance and mixed meal testing with timing in relation to pancreatic exacerbations. Patient characteristic data such as age, gender, body proportions, family history of pancreatitis, exocrine function and genetic mutations were also assessed. Abnormal glucose was based on definitions put forth by the American Diabetes Society for pre-diabetes and diabetes. Fifty-two patients had AP and met criteria. Of those, 15 (29%) had glucose testing on or after the first attack, 21 (40%) were tested on or after the second attack (in ARP patients) and 16 (31%) were tested after a diagnosis of CP. Of the patients tested for glucose abnormalities, 25% (13/52) had abnormal glucose testing (testing indicating pre-DM or DM as defined by ADA guidelines. A significantly higher proportion of the abnormal glucose testing was seen in patients (85%, 11/13) with a BMI at or greater than the 85th percentile compared to the normal glucose patients (28%, 11/39) (p = 0.0007). A significantly higher proportion of the abnormal glucose patients (77%, 10/13) had SAP during the prior AP episode to testing compared to the 10% (4/39) of the normal glucose patients (p<0.0001). Older age at DM testing was associated with a higher prevalence of abnormal glucose testing (p = 0.04). In our patient population, a higher proportion of glucose abnormalities were after the second episode of pancreatitis, however 62% (8/13) with abnormalities was their first time tested. We identified obesity and having severe acute pancreatitis (SAP) during the prior AP episode to testing could be associated with abnormal glucose. We propose that systematic screening for abnormal glucose after the first episode of acute pancreatitis in order to better establish the timing of diabetes progression.

A9997 Hypertension and low estimated Glomerular Filtration rate (eGFR) - Screening for CKD Risk factor

Objectives: To study the prevalence of major CKD risk factors such as hypertension (HTN), Diabetes Mellitus (DM), obesity and also to find-out prevalence of low eGFR among subjects referred for health check-up in tertiary care hospital. Methods: Study was conducted among 3500 subjects. HTN was defined as per JNC-7 guidelines. SBP ≥ 140 mmHg and DBP ≥ 90 mmHg were used as cut-offs. Patients taking anti-hypertensive drugs were also included, even if they had BP values lower than cut-off. As per ADA guideline, a 12-hour fasting blood sugar level of ≥126 mg/dl was used as cutoff. As per KDIGO guideline low GFR defined as eGFR < 60 ml/min/1.73 m2 (by MDRD equation). Results: Out of 3500 subjects, data of 3460 subjects were used for analysis. More than half were male with age of 44.0 ± 12.3 yrs. Prevalence of HTN, DM and low GFR found to 34%, 18% and 2.3% respectively. More than half of treated patients had their BP under control. One fifth of population were newly diagnosed with HTN. Amongs HTN patients, prevalence of low eGFR were high (3.8%) as compared to 1.4% among normotensives. Two third of the subjects were obese (BMI ≥ 25 kg/m2). There were association of HTN, DM, age, obesity on the prevalence of low eGFR (Table). Conclusion: Prevalence of HTN and DM was high and low eGFR associated with HTN, age and obesity. There were significant numbers of newly diagnosed cases of CKD risk factors. Screening program is needed for primary prevention and management of CKD risk factors.

Type 1 Diabetes Transition Program at a Children’s Hospital—Lessons Learned and Future Directions

Background: Transitioning a child with type 1 diabetes (T1DM) poses many challenges. Structured programs decrease loss to follow-up and hospitalization rates. We created a transition program, modeled after the Sweet transition program (Queensland, Australia) and monitored the outcomes. Methods: The program was implemented in April 2015. Steps included: Assessing T1DM knowledge at age 12, education based on need/age, providing an adult provider list, sending providers a clinical summary and maintaining a referral registry. The program was revised in October 2016, now included follow-up by a coordinator and a patient survey evaluating the program. Results: There was increased attendance to education sessions. Transition to adult care within 6 months also improved. No one was lost to follow-up. We received useful feedback from patients about the program. Lessons learned: 1. The ADA guidelines need to be customized based on available resources. 2. Starting at age 12 helps improve transition. 3. A transition registry and coordinator are important. Future directions: 1. Provide subjective assessments for patients to gauge their T1DM knowledge. 2. Collaborate with adult providers via combined clinic. Table 1: Comparison of Program OutcomesApril 2015-16October 2016-17Total No. of Type 1 Patients910875High School and Beyond Education22 (8%)71 (30%)Transfer of Care Education 16 (8%)44 (29%)Referred Patients Seen within 6 months59%80%Referred Patients Seen &amp;gt;6 months17%20%Referred Patients Lost to Follow Up24%0% Disclosure H. Cornwell: None. S. Yigit: None. R. Purushothaman: None.

A1C Goal Attainment with Initial Dual Therapy with Metformin (MET)/Sitagliptin (SITA) Compared with Met Monotherapy—Impact of Baseline A1C and Target A1C

While MET is generally recommended as initial pharmacologic therapy for T2D, the substantial proportion of patients who do not achieve glycemic targets with monotherapy argues for initial use of dual therapy in patients with high A1C levels (e.g., &amp;gt;7.5% per AACE and &amp;gt;9.0% per ADA guidelines). The individualization of glycemic targets based on patient characteristics adds an additional complexity to treatment decision-making. This analysis was designed to identify the baseline A1C range from which patients benefitted the most with initial dual therapy (compared with monotherapy) across a range of A1C goals. Data from 3 randomized clinical trials containing treatment arms of both initial MET monotherapy (1700-2000 mg qd, n=778) and initial MET + SITA dual therapy (1700-2000 mg + 100 mg qd, n=789) were pooled. Patients in all 3 trials had an entry A1C of at least 7.5% (mean = 9.4% in both groups). Demographic and disease-related baseline characteristics were well balanced in both groups. Rates of achieving various A1C goals after 18-24 weeks of treatment were assessed across a range of baseline A1C, and odds ratios for goal attainment were calculated (Table). Initial combination therapy with MET + SITA was generally more effective than MET monotherapy in achieving treatment targets across a broad range of individualized glycemic goals and baseline A1C values. Post-treatment A1C goalGoal achieved, % (n/n) MET/SITA, N = 789Goal achieved, % (n/n) MET, N = 778Odds ratio (95% CI)Baseline A1C 7 - 7.5%&amp;lt; 6.5%74.1 (43/58)44.8 (26/58)1.9 (1.27, 2.78)&amp;lt; 7.0%91.4 (53/58)75.9 (44/58)1.8 (1.06, 3.18)Baseline A1C 7.5 - 8%&amp;lt; 6.5%60.7 (65/107)31.8 (41/129)1.8 (1.39, 2.38)&amp;lt; 7.0%82.2 (88/107)65.9 (85/129)1.5 (1.14, 2.11)&amp;lt; 7.5%90.7 (97/107)80.6 (104/129)1.5 (1.03, 2.26)Baseline A1C 8 - 8.5%&amp;lt; 6.5%43.2 (54/125)24.8 (27/109)1.5 (1.15, 2.01)&amp;lt; 7.0%69.6 (87/125)47.7 (52/109)1.6 (1.21, 2.07)&amp;lt; 7.5%81.6 (102/125 )66.1 (72/109)1.5 (1.12, 2.04)&amp;lt; 8.0%89.6 (112/125)80.7 (88/109)1.4 (0.99, 2.08)Baseline A1C 8.5 - 9%&amp;lt; 6.5%46.5 (47/101)17.4 (16/92)2.0 (1.46, 2.84)&amp;lt; 7.0%67.3 (68/101)41.3 (38/92)1.7 (1.28, 2.30)&amp;lt; 7.5%80.2 (81/101)60.9 (36/92)1.6 (1.17, 2.23)&amp;lt; 8.0%86.1 (87/101)77.2 (71/92)1.4 (0.93, 1.97)Baseline A1C 9 - 9.5%&amp;lt; 6.5%32.2 (29/90)15.9 (13/82)1.6 (1.10, 2.30)&amp;lt; 7.0%52.2 (47/90)32.9 (27/82)1.5 (1.09, 2.03)&amp;lt; 7.5%71.1 (64/90)58.5 (48/82)1.3 (0.96, 1.81)&amp;lt; 8.0%82.2 (74/90)75.6 (62/82)1.2 (0.84, 1.77)Baseline A1C 9.5- 10%&amp;lt; 6.5%32.8 (21/64)15.1 (11/73)1.7 (1.10, 2.51)&amp;lt; 7.0%53.1 (34/64)23.3 (17/73)1.9 (1.34, 2.79)&amp;lt; 7.5%60.9 (39/64)35.6 (26/73)1.7 (1.19, 2.38)&amp;lt; 8.0%68.8 (44/64)50.7 (37/73)1.5 (1.03, 2.08)Baseline A1C &amp;gt;10.0%&amp;lt; 6.5%19.0 (46/242)11.3 (26/230)1.4 (1.05, 1.76)&amp;lt; 7.0%32.6 (79/242)22.6 (52/230)1.3 (1.05, 1.58)&amp;lt; 7.5%44.6 (108/242)33.9 (78/230)1.3 (1.04, 1.51)&amp;lt; 8.0%56.6 (137/242)42.6 (98/230)1.3 (1.10, 1.59) Disclosure A. Raji: Employee; Self; Merck &amp; Co., Inc. M.C. Ellison: Employee; Self; Merck &amp; Co., Inc. R.L.H. Lam: Employee; Self; Merck &amp; Co., Inc. E.A. O'Neill: Employee; Self; Merck &amp; Co., Inc. S.S. Engel: Employee; Self; Merck &amp; Co., Inc.. Stock/Shareholder; Self; Merck &amp; Co., Inc..

Five-Year Experience with Telemedicine Clinics for Youth with Type 1 Diabetes (T1D)

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 (&amp;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.

Characterizing Clinical Inertia in a Large, National Database

Objective: To characterize clinical inertia in the treatment of diabetes using a large, geographically diverse clinical database. Study Design: A retrospective descriptive analysis was conducted in a clinical database containing 22 million patient records across 22 health care organizations (HCOs). Population Studied: A total of 281,000 patients aged 18-75 were included during the 5.5-year study period (1/2012-6/2017). Patients had an outpatient visit in the last 12 months of the study period, an HbA1c in the last 24-30 months (index A1c), and a diagnosis of type 2 DM on a claim or EHR problem list at least 6 months prior to index A1c. A subset of 47,693 patients with an index A1c ≥8 and a prior A1c ≥8 or lack thereof, was observed for four 6-month follow-up periods for actions including a new class of diabetes medication prescribed or an A1c &amp;lt;8. The absence of observable action following index A1c suggests potential “clinical inertia.” Principal Findings: Six months following an index A1c≥8, 55% of patients received no observable clinical action ranging from 45-65% across HCOs and 18-96% across individual providers. A new diabetes prescription was observed in 35% of patients (7.5% moved into glycemic control, A1c&amp;lt;8) and 10% moved into glycemic control without a new prescription. Patient characteristics associated with increased clinical inertia, i.e., no observable action, during the 6002D and 24-month follow-up periods included black race, low-income insurance, normal body mass index, and being on bolus insulin (all P&amp;lt;.01). Within 24 months, clinical inertia was reduced to 19%, ranging from 13-28% across HCOs. Conclusions: Lack of clinical action in the 6 months following an A1c ≥8 suggests clinical inertia vs. ADA guidelines. Greater rates in low-income insurance and race/ethnic minority adults suggests potential populations to target to ensure adequate treatment for diabetes. The decline in clinical inertia within 24 months suggests either actions not seen in the data, or later interventions that were ultimately effective. Disclosure C.R. Rattelman: None. A. Arora: None. J.K. Cuddeback: Research Support; Self; Novo Nordisk Inc.. Other Relationship; Self; Johnson &amp; Johnson Diabetes Institute, LLC., Merck &amp; Co., Inc., Sanofi US, Boehringer Ingelheim Pharmaceuticals, Inc., Eli Lilly and Company. E.L. Ciemins: None.

Clinical Characteristics and Antibody Persistence over Time in Youth in the Pediatric Diabetes Consortium (PDC) Type 2 Diabetes (T2D) Cohort

Objective: To describe the characteristics of youth diagnosed with T2D who have tested positive for ≥1 islet autoantibody (IA) and to examine the persistence of IA over time. Methods: Youth enrolled in the PDC T2D Registry with ≥2 years follow-up after diagnosis and ≥1 positive IA at any time were identified. T2D was determined using ADA guidelines. IAs were measured at each PDC site according to local practice and included IAA, GADA, IA-2A, ZnT8A, and ICA. Positive IAA tests were excluded if measured after insulin initiation. Clinical characteristics, clinical presentation, and treatment over time were analyzed. Results: Positive IA is an exclusion for enrollment in the PDC T2D Registry; however, on review of the ∼1,300 youth in the PDC, 38 were found to have IA. A single IA was found in 35 and &amp;gt;1 IA in 3 participants. GADA was most common (N=32); 3 were persistently positive, 13 converted to negative, 8 became positive after a negative GADA, and 8 had only 1 IA determination. Positive IAA (N=5), IA-2A (N=4) and ZnT8 (N=1) were uncommon or rare, no positive ICA were found. At diagnosis, those IA positive were more likely to be treated with insulin (95% vs. 73%) but with similar HbA1c (10.4% vs. 10.0%). At last visit, those IA positive had longer diabetes duration (5.7 years. vs. 4.8 years.) and higher insulin use (71% vs. 59%) compared to the IA negative cohort, but HbA1c levels were similar (8.7% vs. 9.0%). Age at diagnosis, race/ethnicity, percent male, and BMI were not different between those with and without IA. Conclusion: Most IA positive youth with a T2D phenotype require insulin at diagnosis and more IA positive than IA negative youth require insulin during follow-up treatment. These findings suggest the importance of IA ascertainment at diagnosis and in those failing oral therapy. In T2D youth GADA is the most common IA; yield from ZnT8 and ICA determinations is low; fluctuations in IA positivity are not uncommon. Additional study of youth with T2D phenotype and IA is needed. Disclosure G.J. Klingensmith: Research Support; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Novo Nordisk Foundation. P. Cheng: None. R.L. Gal: Research Support; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Takeda Development Center Americas, Inc., Novo Nordisk Inc. L.C. Beaulieu: Research Support; Self; Novo Nordisk Inc., Boehringer Ingelheim Pharmaceuticals, Inc., Takeda Development Center Americas, Inc. W.V. Tamborlane: Consultant; Self; AstraZeneca, Boehringer Ingelheim GmbH, Eli Lilly and Company, Medtronic MiniMed, Inc., Novo Nordisk Inc., Sanofi, Takeda Pharmaceuticals U.S.A., Inc. F. Bacha: Research Support; Self; AstraZeneca, JAEB Center For Health Research, National Institutes of Health, Pediatric Diabetes Consortium. M.M. Kelsey: Other Relationship; Self; Daiichi Sankyo Company, Limited, Merck Sharp &amp; Dohme Corp. C. Kollman: Research Support; Self; JDRF, Bigfoot Biomedical, Dexcom, Inc., Tandem Diabetes Care, Inc., Medtronic MiniMed, Inc., Helmsley Charitable Trust.

Effect of Number of Achieved Targets for Risk Factors on Coronary Artery Disease (CAD) in Those With and Without Diabetes Mellitus (DM)

Although control of multiple risk factors is essential to prevent CAD in persons with and without DM, longitudinal studies are scarce that directly and quantitatively compared effects of such control in DM and non-DM. Thus, we investigated effects on subsequent CAD of the number of controlled risk factors among blood pressure, LDL cholesterol and HbA1c using a nationwide claim-based database (median follow-up 4.8 y) in Japan. Targets were based on ADA and Japanese guidelines. Of 207,029 non-DM persons, 42.8% and 37.9% were at target for 1 and 2 factors, respectively. Of 13,471 persons with DM, 39.5%, 32.1%, and 10.0% were at target for 1, 2, and 3 factors, respectively. Multivariate Cox analysis showed reduced CAD risk with increased numbers of risk factor targets reached in DM compared to non-DM (Table, upper). However, in DM, although fulfillment of the target for only 1 risk factor significantly elevated the hazard ratio (HR) (i.e., 1.98 (1.28-3.07)) to non-significance (i.e., 1.03 (0.68-1.66)), no further significant HR reduction below reference (i.e., non-DM with no target achieved) was found (Table, lower). These findings show that composite control of modifiable risk factors has a larger effect in DM compared to non-DM, but the effect was not sufficient to bring CAD risk in DM below that for non-DM persons with none of 3 risk factors being at target based on current target levels. Disclosure K. Fujihara: None. Y. Matsubayashi: None. M. Kitazawa: None. M. Yamamoto: None. T. Osawa: None. M. Kaneko: None. N. Yamanaka: None. H. Seida: None. K. Kato: None. S. Kodama: None. H. Sone: Research Support; Self; Novo Nordisk Inc., Eli Lilly and Company, MSD K.K., Chugai Pharmaceutical Co., Ltd., Taisho Pharmaceutical Co., Ltd., Takeda Development Center Asia, Pte. Ltd., Daiichi Sankyo Company, Limited, Ono Pharmaceutical Co., Ltd., Kyowa Hakko Kirin Co., Ltd., Sanofi, Kowa Pharmaceuticals America, Inc., Eisai Inc..

Derivation of an EMR-Based Dysglycemia Risk Score

Few diabetes risk scores are derived from and designed for use in the electronic medical record (EMR). We developed an EMR-derived random blood glucose (RBG) diabetes risk score to detect dysglycemia (diabetes+prediabetes) and improve case identification. A RBG-risk score to detect prevalent dysglycemia (A1C≥5.7%) was derived from EMR data in an integrated safety-net health system. Primary care patients (age 18-64; resulted A1C in 2011-2014; ≥1 RBG 24 months prior to the A1C) were eligible. We excluded patients with diagnosed dysglycemia using ICD codes and lab results. Most recent RBG and EMR-extracted demographics, comorbidities, and diabetes risk factors on the date of the A1C were used to build logistic regression models predicting dysglycemia using backwards selection. Predictors retained in the final regression model were converted to a points-based risk score (Age 0-6; race 0-3, BMI 0-5; hypertension 0-1; RBG 0-13). We report dysglycemia risk, sensitivity, specificity, and PPV for clinically relevant cutpoints. The cohort included 11,387 patients (mean age 48, 65% female, 42% Hispanic, 36% black, mean BMI 32, 29% with hypertension), of whom 42% had dysglycemia. Screening criteria were met by 86% (ADA), 65% (USPSTF), and 40-60% of patients at clinically relevant Risk Scores (9-11). The Risk Score c-statistic was significantly better than both ADA (0.75 vs. 0.56; p&amp;lt;0.001) and USPSTF (0.75 vs. 0.60; p&amp;lt;0.001) guidelines. An example patient with a Risk Score of 9 would be: age 50, BMI 29, white, has hypertension and RBG 112. A score of 9 identified 61% of the sample as high risk (35%) for undiagnosed dysglycemia and yielded 80% sensitivity, 53% specificity, PPV 56%, and NPV 78%. Using a cutpoint of 9, if the Risk Score were run on 1000 patients, 610 would undergo screening, 335 new cases of dysglycemia would be detected, and 85 cases would be missed. An EMR-derived RBG-risk score capable of automation in the EMR outperforms screening guidelines to detect dysglycemia. Further validation studies are needed to determine clinical usefulness. Disclosure M.E. Bowen: None. J. Sanders: None. S. Zhang: None. N.O. Santini: None. I. Lingvay: Advisory Panel; Self; Novo Nordisk A/S, Eli Lilly and Company, AstraZeneca, Intarcia Therapeutics, Inc., Sanofi-Aventis. Research Support; Self; Novo Nordisk A/S, Merck Sharp &amp; Dohme Corp., Pfizer Inc., GI Dynamics Inc., Novartis AG. Other Relationship; Self; Sanofi, AstraZeneca, Boehringer Ingelheim GmbH, Novo Nordisk A/S. E. Halm: None.

Improving the Recognition and Management of Elevated Blood Pressure among Youth with Type 1 Diabetes

Background: Studies show that youth with type 1 diabetes (T1D) harbor cardiovascular risk factors and exhibit clinical signs suggestive of cardiovascular disease. ADA and ISPAD guidelines promote regular blood pressure screening to reduce cardiovascular risk, and strategies should involve effective screening and management. The diabetes clinic previously had limited measures in place to manage T1D youth with elevated blood pressure. Objective: The aim of this quality improvement (QI) project was to increase provider recognition and follow-up of TID patients with elevated blood pressure readings ≥ 95%tile for age/gender/height, from 0% to 50%, by 12/31/17 and sustain through 12/31/2018. Methods: After the QI team clarified project goals, a Key Driver Diagram (KDD) and elevated blood pressure process map was established. This algorithm aided to clarify the care process to efficiently recognize and manage youth with T1D who presented with elevated blood pressure in the outpatient diabetes clinic. A “Best Practice Alert” (BPA) was created within the electronic medical record, which alerted the diabetes care team of the presence of elevated blood pressure among T1D patients in clinic. This allowed the provider to choose electronically tracked care options in the medical record to manage elevated blood pressure among T1D patients and coordinate care among subspecialties that manage elevated blood pressure. Data reports were compiled to track utilization of the blood pressure BPA from May 2017 (initiation date) to December 2017 with ongoing data collection. Results: Upon BPA creation, process mean of the utilization and management of elevated blood pressure BPA among T1D youth increased to 73% over a 7 month period. Conclusions: This project successfully improved screening and management of T1D youth with elevated blood pressure in the outpatient setting. This project builds on comprehensive diabetes care, with the hope of reducing future cardiovascular burden for youth with T1D. Disclosure K. Gandhi: None. D.A. Buckingham: None. J. Hehmeyer: None. A.M. Kramer: None. K. Obrynba: None. J.A. Indyk: None. M.K. Kamboj: None.

Rate of Diabetes in Maricopa County, AZ

The prevalence of type 2 diabetes mellitus (T2DM) is well documented at the national, state, and even county level. Results are captured in the Behavior Risk Factor Surveillance System (BRFSS), a U.S. survey that collects data on disease rates and health-related risk behavior. The 2015 BRFSS audit revealed Arizona has an overall adult T2DM prevalence of 9.1%. Further, in Maricopa County, the region which includes the greater Phoenix area, the T2DM prevalence is higher at 10.8%. Conversely, there is limited data available on prediabetes rates in Maricopa County; therefore, we examined the rate of diabetes and prediabetes during community-partnered events as part of an educational “A1C initiative.” Basic demographic data as well as a fingerstick blood sample were obtained. HbA1c was measured with a hand-held monitor. 794 participants (272 men) were screened at 15 events from April 2016 to Sept 2017. The majority of participants were of Hispanic descent (54%). HbA1c results were categorized according to the ADA guidelines; normal (&amp;lt; 5.7%), prediabetes (5.7-6.4%) and diabetes (≥6.5%) ranges. Mean HbA1c for all participants was 6.0% +1.2%, with 16% and 35% falling in the diabetes and prediabetes ranges respectively. Diabetes and prediabetes rates for males and females were similar. By ethnicity, prediabetes was highest among African Americans (AA) (41%), followed by white non-Hispanics (35%) then Hispanics (33%). Despite AA having a higher diabetes rate at 21%, HbA1c was significantly higher in Hispanics vs. AA diabetes only subjects (8.6% +2.0 vs. 7.4% +1.4, p&amp;lt;0.001), suggesting worse glycemic control for the Hispanic group. In line with the national average of prediabetes prevalence at 33.9%, the overall rate we observed was similar, yet higher in certain groups. Notably, the diabetes rate was nearly double than that of the BRFSS report. The initiative provided vital (pre)diabetes awareness to Maricopa County residents. These findings were shared with our community partners, to aid in developing strategies to best assist those at risk of developing T2DM. Disclosure S.H. Jaycox: None. S. Paglialunga: Employee; Self; Celerion.

Distress, Depression, or Both? Exploring Differences in the DDS-17 and the PHQ-9 in Diabetes Specialty Clinic

Diabetes-related distress (DRD) is distinctly different from depression and is more common than depression in people with diabetes. DRD has serious negative outcomes, including poor glycemic control. DRD has been identified in more than 40% of people with diabetes. Recent ADA Standards recommend assessing and addressing DRD; however, in clinical settings, confusion may exist concerning utility of screening for DRD. This study explores differences in our population between scores on DRD and depression screening measures. At the USAF Diabetes Center of Excellence (DCOE), a military specialty clinic, adult patients were administered the Patient Health Questionnaire (PHQ-9) and the Diabetes-related Distress Scale (DDS-17) as part of standard care from June 2015 through August 2016. Distinct areas of DRD are: 1) Emotional Burden (EB); 2) Physician-related Distress (PD); 3) Regimen-related Distress (RD); and 4) Interpersonal Distress (ID). DDS-17 was categorized as &amp;lt;2.0=little or no distress; 2.0-2.9=moderate distress; and ≥3=high distress. A score ≥ 10 was considered positive for depression on the PHQ-9. Patients (n=314) completed both the DDS-17 and the PHQ-9. Most patients had neither symptoms of depression nor DRD (70.4%). However, 23.9% scored high on the DDS-17 in at least one domain while 11.8% screened positive for depression. Overall, highest DRD was found in EB (17.0%) and RD (15.7%). Furthermore, 56 patients screened positive for DRD, but not for depression (17.8%). Nearly half of patients that screened positive for depression did not have DRD (5.7%). Our study reinforces ADA guidelines to screen and treat DRD as a distinct psychosocial issue. If a clinic does not screen for DRD, they are missing an opportunity to address this common experience. High distress was found in nearly one-quarter of our patients. Regular screening for distress related to demands of living with diabetes is crucial in identifying and preventing poor health outcomes associated with DRD. Disclosure J.L. Wardian: None. K.E. Kanzler: None. T.J. Sauerwein: Speaker's Bureau; Self; Merck &amp; Co., Inc., AstraZeneca. M.W. True: None. M.A. Glotfelter: None.

Interventional study to improve diabetic guidelines adherence using mobile health (m-Health) technology in Lahore, Pakistan

ObjectiveTo check if mobile health (m-Health) short message service (SMS) can improve the knowledge and practice of the American Diabetic Association preventive care guidelines (ADA guidelines) recommendations among physicians.MethodologyQuasi-experimental pre–post...

Associations between vitamin D deficiency, diet and physical activity and the development of gestational diabetes mellitus in Emirati women

Background: Gestational Diabetes Mellitus (GDM) affects 8-25% of Emirati women and its prevalence is progressively increasing in this population. Concurrently, vitamin D deficiency is a common problem among pregnant Emirati women. Existing literature suggests that vitamin D deficiency increases the risk of GDM. Objectives: to investigate the association between vitamin D deficiency, diet and physical activity and the development of GDM. Methods: A prospective cohort study was conducted on Emirati women (n=563) aged 18-45 years, who were free of GDM at baseline and underwent maternal serum screening in eight primary healthcare centers in Ras Al Khaimah, UAE. Data on demographics, physical activity (Global Physical Activity Questionnaire), diet intake (Food Frequency Questionnaire), anthropometrics and blood pressure were collected at baseline. Also, blood samples were drawn at baseline to measure the fasting blood glucose and the 25 OH D levels. GDM was screened and diagnosed by using the fasting and the75g 2-hour postprandial Oral Glucose Tolerance Test (OGTT) according to the ADA guidelines at follow up between 24th and 28th weeks of gestation. Vitamin D deficiency was diagnosed according to the NIH and NHS criteria (deficiency if 25(OH)D &lt; 50 nmol/l = 20 ng/ml). Results: Overall, 58% and 26% of pregnant women had vitamin D deficiency and insufficiency respectively. The incidence of GDM was 15.2 %. The adjusted odds ratio (aOR) for developing GDM was 1.27 (95% [CI]: 0.74-2.18, p: 0.37) in vitamin D deficient women versus non-deficient women. The adjusted odds ratio of GDM for sitting time without work was 1.04 (95% CI: 0.92-1.16, p: 0.50). The adjusted odds ratio of GDM was significant in those who ate red meat daily (OR: 6.54, 95% CI: 1.53-27.82, p=0.011). Family history of diabetes and obesity were significant risk factors for the development of GDM (OR: 1.9, 95% CI: 1.04-3.51, p: 0.03) and (OR: 2.62, 95%CI: 1.36-5.06, p=0.004) respectively. Conclusion: In this cohort study there was no statistically significant association between vitamin D deficiency in early pregnancy and the development of GDM.