ResearchPad - clinical-and-translational-glucose-metabolism-and-diabetes Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[MON-619 Predictors of Complications with Fasting the Holy Month of Ramadan in Patients with Diabetes]]> Background: For a whole month, every year Muslims fast daily from dawn to sunset. Those with health conditions that put them at risk are exempted from fasting, yet most of patients with diabetes choose to fast. Clinical and metabolic complications of diabetes during this month are issues of concern for patients and their managing physicians. This study is designed to evaluate the impact of fasting Ramadan on safety of patients.

Methods: A multicentercross-sectional survey was conducted in four hospitals under the Ministry of National Guard Health Affairs; King Abdulaziz Hospital,Al-Ahsa, Imam Abdulrahman bin Faisal Hospital, Dammam, King Abdulaziz Medicalcities, Riyadh and Jeddah. All patients with diabetes followed in the diabetes clinics of all four centers who fulfilled the study inclusion and exclusion criteria were approached within three months post Ramadan and consented for participation in the survey, then filled a self-administered validated questionnaire that consisted of 15 items.

Results: Socio-demographic,clinical, and laboratory characteristics of 1438 patients with diabetes were analyzed. The majority 1207 (83.9%) had type II diabetes, and 828 (57.6%) were females.The mean age was 57.9 ± 14.9 years, and mean BMI 25.25 ± 5.39.The majority 1060 (73.7%) had concomitant diseases. 36 (2.5%)were on diet therapy alone, 147 (10.2%) on metformin monotherapy, and 261 (18.2%) on insulin therapy alone. The remaining 994 (69.1%) were on combination of insulin and oral agents. Health education was received on average by 688 (57.8%) of patients. Out of the 1191 (82.8%) who fasted the full month, 497 (41.7%) experienced acute glycemic complications. Multivariate analyses revealed that significant predictors for unsafe fasting were: type I diabetes [OR 1.8 (95%CI 1.2 - 2.8), p-value 0.007], insulin therapy [OR 1.8 (95% CI 1.4 - 2.3), p-value0.0001], previous history of breaking fast for glycemic reasons [OR 2.1 (95% CI1.5 - 2.9), p-value 0.0001], and not receiving health education [OR 1.6 (95% CI1.2 - 2.0), p-value 0.0006]. Blood sugar control, presence of concomitant diseases, and history of diabetes related hospitalization were not statistically significant predictors [(OR 1.25, 95% CI, 0.9 - 1.7, p-value 0.15),(1.3, 95% CI, 0.9 - 1.8, p-value 0.14), (1.1, 95% CI, 0.8 - 1.6, p-value 0.45)] respectively.

Conclusion: A significant proportion of patients with diabetes do not receive specific education pertinent to fasting Ramadan. Lack of health education, in addition to; type I diabetes, insulin therapy, and previous experience of complications are predictors for unsafe fasting. This highlights the need for better structured educational programs and further research in the field.

<![CDATA[MON-620 Ademolus Hypoglycemic Index]]> This article proposes ADEMOLUS HYPOGLYCEMIC INDEX (AHI) which is a mathematical representation of hypoglycemic episode (HE) in a patient with recurrent hypoglycemia over a consecutive three months period. It also apply it to clinical practice using diabetic patients in order to demonstrate and emphasize its relevance in present day medical practice worldwide.

This is a retrospective study of 65 HE occurring in 6 randomly selected diabetes mellitus patients from 86 case files studied who had attended the endocrinology unit. The data was analyzed by using Ademolus Classification of hypoglycemia (ACH) and the 2018 ADA/EASD Classification of hypoglycemia to define hypoglycemia. AHI was calculated from the HE using the proposed mathematical formula. SPSS version 23 was used for data analysis. All six patients had a series of hypoglycemic episodes occurring in three consecutive months. Patient 1 had an AHI of 0.53 using both ACH and 2018 ADA/EASD classification.By using the Pearson correlation statistics, AHI using ACH correlated well with AHI using 2018 ADA/EASD classification of hypoglycemia with a value of 0.993.Similarly the findings of AHI derived using ACH is significant with values derived using ADA/EASD 2018 classification of hypoglycemia with a p-value of 0.000 (correlation is significant at values of 0.01). One of the clinical implication of AHI is that the risk of developing reversible or irreversible neurological damage can be reduced clinically as patient with mild to moderate form of chronic hypoglycemia yet to develop irreversible neurological damage or neurological sequelae can be prevented early enough from progressing since a reduced AHI value out of a series will be a pointer towards progression to neurological damage if it has not occurred! In patient 1 her AHI was not in the severe range. In patient 3, in the last quarter of 2016, her AHI was 0.60, in the first quarter of 2017, her AHI was zero, then between June, July and August 2017 her AHI was 0.64. At this juncture in this patient management, it will be good to evaluate the etiological factors in this chronic kidney disease diabetic patient who once again has started having recurrent hypoglycemia as it was some 6 months earlier. Patient 6 had two consecutive reading of AHI for two consecutive quarters of a year.The result reveals that she is chronically deteriorating gradually and tending towards more severity in her development of HE as her AHI fell from 0.52 in the preceding quarter to 0.46., this connotes worsening chronic hypoglycemic state over time and a poorer prognosis. The lower the AHI, the poorer the prognosis.AHI is relevant for monitoring of chronic or long term recurrent HE in susceptible individuals whether diabetic or not.

References: 1. Fonseca VA, Kirkman MS, Darsow T, Ratner RE (2012) The American Diabetes Association Diabetes Research Perspective. Diabetes Care 35(6): 1380–1387.

<![CDATA[MON-611 Using Machine Learning on Electronic Health Records to Predict Inpatient Glucose Levels and Physicians’ Insulin Dosing]]> The current optimal inpatient diabetes management schema involves administration of basal, prandial, and correctional insulin to maintain blood glucose (BG) within a target range. Nonetheless, practical management often fails to reach the ideal in both insulin dosing regimens and patients’ BG outcomes. Given the challenges of achieving adequate BG control for hospitalized patients using guidelines and expert knowledge alone, we attempted to use machine learning methods to predict (1) individual BGs, (2) average daily BGs, and (3) physician-ordered insulin doses based on data in an electronic health record-based repository between January 2014 and December 2018. We considered inpatients on subcutaneous insulin having a BG ≥ 200 mg/dL or ≤ 70 mg/dL or with an A1c percentage ≥ 6.5%. We excluded those missing critical data (such as weight), with fewer than five BG checks in 72 hours, or those on hemodialysis, resulting in a cohort of 3,461 patients with 175,934 BG checks among them. In this cohort, the average age was 61.4 years, the average A1c was 7.1%, and the average BG was 171.6 mg/dL, with approximately 25% of BGs ≥ 200 mg/dL and 1.7% of BGs < 70 mg/dL. Using linear regression, we identified features that contributed most to prediction of each of the outcomes. For all three outcomes, the average glucose in the past 24 hours was the most important feature. For prediction of glucose levels, previous BG, BG at the same time the previous day, A1c, BG variance, recent long-acting insulin dose, and glucocorticoid dose were all in the top 10 features. Similar features were important for predicting physician-ordered insulin doses. Surprisingly, neither weight nor creatinine were identified as top features for any outcome. Using these features in our predictive model, we found that individual BGs were highly erratic and could not be predicted precisely (R2 0.24). Similarly, and perhaps unsurprisingly, how physicians would order insulin for patients was also difficult to predict (R2 0.25). However, average daily glucose levels were predicted more reliably (R2 0.36), as was prediction of frank hyperglycemia (BG ≥ 200 mg/dL) in the next day (sensitivity 0.73, specificity 0.79). Given the typical practice pattern of a clinician evaluating the previous day’s insulin regimen performance and adjusting it by anticipating BGs for the next day, prediction of hyperglycemia in the next 24 hours can support decision-making for inpatient BG management.

<![CDATA[MON-615 Retrospective Analysis of the Contribution of Cannabis Usage to Diabetic Ketoacidosis at an Urban Teaching Hospital]]> Diabetic ketoacidosis (DKA) is an acute life-threatening complication of diabetes mellitus. It is responsible for greater than 100,000 hospital admissions per year in the US (1). There are few studies regarding the relationship between drug usage and acute diabetic complications (2). Since 2001, cannabis usage among US adults have more than doubled, as state legal restrictions have eased and attitudes towards cannabis have become more permissive. Cannabis is the most commonly used illicit drug in the US (3). Some studies suggested cannabis usage was associated with improvement in insulin sensitivity and pancreatic beta cell function. Other research demonstrated cannabis usage may contribute to diabetes-related hospitalizations.

A retrospective analysis was performed at an urban teaching hospital to examine the relationship between cannabis usage and risk for DKA upon presentation. From March 2017 to February 2019, all non-pregnant patients aged 18 years and older, and who met criteria for DKA admission upon medical records review, were included in the study. Demographics, vitals, biochemistry, and toxicology were evaluated. Overall, 188 admissions for DKA were identified in a total of 130 patients, and 43% (81/188) were readmissions by 23 patients.

Illicit substance usage was addressed by history in 72% (135/188) of all admissions, among which 24% (33/135) reported cannabis usage. 36% (67/188) of all admissions, 73% (24/33) of the self-reported cannabis usage group, and 46% (37/81) of the readmissions, underwent general toxicology screening that did not include detection for cannabis. 11% (20/188) of all admissions, 24% (8/33) of the self-reported cannabis usage group, and 16% (13/81) of the readmissions, completed toxicology screening specifically for cannabis.

All of the self-reported cannabis usage admissions (33/33) and readmissions (81/81) presented with additional aggravating factors for DKA such as medication noncompliance, polysubstance abuse, and infection. Finally, 20 of the overall 130 patients admitted during this timeframe presented with new onset DKA, where none reported cannabis usage, 20% (4/20) completed general toxicology screening, and none underwent cannabis specific toxicology screening.

From the observational retrospective analysis at this hospital, there is a need for awareness about substance abuse screening, especially in adults with a history of recurrent hospital admissions for DKA. Knowledge among health care providers and patient education regarding the effect of cannabis usage on metabolic factors and its diabetes complications, including diabetes self-management at time of drug usage, can be further explored in prospective studies.

References: (1) Umpierrez (2006) Diabetes Care, 29(12), 2755-2757. (2) Brown et al., (2017) JAMA, 317(2), 207. (3) Haffajee et al., (2018) NEJM, 379(6), 501-504.

<![CDATA[MON-618 A Framework for Understanding and Managing ‘The Diabetes Syndrome’: A Unified Pathophysiologic Approaching the Context of the Beta-Cell Classification of Diabetes]]> We have previously presented a proposal for a new, beta-cell centric classification of diabetes based on a consilience of genetic, metabolic, and clinical research that have accrued since the current classification was instituted. It recognizes that the beta-cell is THE core defect in all patients with diabetes. Differences in the genetics (and epigenetics), insulin resistance, environment and inflammation/immune characteristics resulting in the damage to the beta-cell in each individual will determine the phenotypic presentation of hyperglycemia and allow for a patient-centric, precision-medicine therapeutic approach, part of which we labeled ‘the Egregious Eleven’.

We now recognize the same pathophysiologic mechanisms that account for damage to the beta-cells govern the susceptibility of the cells involved in the complications and other conditions ‘tied to’ diabetes to damage by the abnormal metabolic environment that typifies beta-cell dysfunction and ‘fuel excess’. This abnormal metabolic environment is typified by oxidative stress which alters metabolic pathways (a la Brownlee’s Hypothesis model), alterations in gene expression, epigenetics, and inflammation. This allows us to understand the varied risk of developing complications of diabetes, including malignancies, dementia, NASH, psoriasis with similar levels of glycemic control; how non-glycemic effects of some medications for diabetes result in marked complication risk modification; and the value treating co-morbidities of diabetes in modifying complication risk.

Principles we outlined in using ‘the Egregious Eleven’ model- use agents that preserve beta-cell function, treat with least number of agents that treat most number of mechanisms of hyperglycemia- can be extended to use those agents, in combination, that also engender weight loss, decrease CV outcomes and have real or potential benefits in cancers related to diabetes, dementia risk, NASH, psoriasis. This approach allows for a more accurate assessment of each patient’s disease and effecting true precision medicine

Schwartz, S, et al, Diabetes Care 2016, 39:179–186.

Schwartz SS, et al Trends Endocrinol Metab. 2017;28(9):645–655.

<![CDATA[MON-621 Prescription Analysis Shows High Metformin Use and Acceptance in a Diabetes Specialty Centre in Eastern India]]> Background and aims: Achieving glycemic goals is crucial in the overall management of diabetes. Selecting the right medication for the individual patient is of paramount importance in the present day’s patient centric glucose control. Metformin is the first line and gold standard antihyperglycemic agent that can be offered to type 2 diabetics. Addition of a second or third agent or insulin should be considered in those whose HbA1c remains high despite the up-regulated metformin dose or those who do not tolerate metformin. We aimed to find the pattern of metformin use in type 2 diabetic subjects in a diabetes specialty centre in coastal Odisha.

Materials and methods: This observational study was conducted in a diabetes setup in coastal Odisha in June 2018. After obtaining consent from patients, authors looked into the prescriptions of all type 2 diabetic adults. Subjects who were prescribed metformin (in any dose) were enrolled in the study. Those with established nephropathy, coronary artery disease, stroke or cancers were excluded.

Results:There were 802 footfalls noted during the study period, of which 723 metformin taking participants (298 females, 41.2%) were considered for analysis (79 persons were excluded: not meeting inclusion criteria/ not willing to participate/ history of nephropathy/ CAD/ stroke). Mean age, diabetes duration, FPG, HbA1c, serum creatinine, eGFR of the study population were 51.6±10.6 years, 11.9±11.2 years, 138.7±51.7 mg/dl, 7.8±2.1%, 0.93±0.29 mg/dl and 96.5±11.1ml/min respectively. Patients were prescribed metformin in various doses, i.e., 500mg (42 patients, 5.8%), 850mg (47 patients, 6.5%), 1000mg (396 patients, 54.8%), 1500mg (13 patients, 1.8%), 1700mg (86 patients, 11.9%) and 2000mg (130 patients, 18.0%), and 2500mg (9 patients, 1.2%). Metformin was prescribed as monotherapy (n=34, 4.7%) or along with other OADs (n=589, 81.5%) or in combination with insulin (n=178, 24.6%). Retrospective analysis of the medical records and further questioning revealed that gastric intolerance was the commonest reason for withdrawal of metformin in otherwise eligible subjects.

Conclusion: Metformin was the most commonly prescribed antidiabetic drug and the daily dose of more than 85% of the metformin administered individuals was 1000mg or above.

<![CDATA[MON-630 Elevating the Patient Voice in Type 1 Diabetes Clinical Trials: A Comparison of In-Depth Exit Interviews and Diabetes-Specific Questionnaires]]> Healthcare decisions are more effective when the patient voice is included in clinical research, and the Food and Drug Administration encourages the patient’s voice in drug development and regulatory decision-making. Clinical trials should not only demonstrate the effect of a drug on clinical outcomes but should also demonstrate that these outcomes are important or meaningful to patients. Several qualitative and quantitative methods are available to collect patient experience data (e.g., traditional patient-reported outcome [PRO] measures, interviews with clinical trial participants). We aimed to understand if in-depth exit interviews were more effective assessments of the patient experience in recent type 1 diabetes (T1D) clinical trials than existing diabetes-specific PROs. In-depth qualitative interviews were conducted with 41 adults with T1D who had completed or withdrew from a phase 3 study of sotagliflozin, a dual inhibitor of SGLT1 and SGLT2. A targeted literature review was conducted to identify diabetes-specific PROs used in randomized controlled clinical trials of novel T1D medications reported over the past 5 years. Included trials had to investigate a pharmaceutical intervention for adults with T1D and report a diabetes-specific PRO. The concepts assessed in the PRO measures were mapped against those elicited during the 41 exit interviews. A total of 336 publications were identified in the literature search of which 26 were eligible for analysis. Eight diabetes-specific PROs were identified and reviewed from which 54 concepts related to the patient experience were identified. The patient exit interviews included 42/54 (78%) of the patient experience-related concepts identified across all 8 PROs from the literature review. Of the 8 PRO instruments, the Diabetes Quality of Life Measure (DQOL) covered the most concepts (18/54, 33%), followed closely by the Audit of the Diabetes-Dependent Quality of Life (ADDQoL; 16/54, 30%). Some of the most prominent concepts from both approaches were related to impact on life and family; fear of complications; and impact on physical activity, lifestyle and social perceptions. There were several concepts identified in the exit interviews that were not covered in any of the 8 PRO instruments (related to keeping blood sugars within a desired range, ability to manage changes in insulin use). Overall, the exit interviews appeared to provide a more comprehensive picture of patient experience domains. Although existing diabetes PRO measures cover a range of concepts and may adequately assess changes in certain outcomes, data from patient exit interviews provide more comprehensive insights into the patient experience. Exit interview data may provide a more detailed understanding of the disease burden and impact of treatment on improvements in well-being, daily functioning, and treatment satisfaction.

<![CDATA[MON-631 Quality of Life in a Pragmatic Trial of a Type 1 Diabetes Adolescent Transition Program]]> Introduction: Adolescents with type 1 diabetes (T1D) experience ongoing deterioration in their glycemic control as they transition to young adulthood.1 Several trials have evaluated possible transition interventions to ameliorate the care gap between pediatric and adult services in T1D care, although it remains unclear which are the most appropriate.2,3 In this pragmatic study, we sought to determine whether the change in quality of life pre- and post-transition was different between adolescents with T1D accessing a transition program versus those who did not.

Methods: Between 2016-2018, we recruited 68 adolescents with T1D at their last pediatric diabetes clinic visit from a tertiary diabetes center without a structured transition program (control group) and 33 from a community-based outreach clinic with a transition program (intervention group) consisting of a transition coordinator, joint transition clinics, and pediatric and adult clinics located in the same building. At the time of transition and at one-year post-transition, we conducted chart reviews and administered surveys, including the SF-8 quality of life questionnaire. Analysis included descriptive statistics and linear regression models.

Results: The control and intervention groups had the following characteristics, respectively: age at transition 18.4 years vs. 20.5 years (p<0.001); female 47% vs. 55% (p=0.49); average A1C at the time of transition 8.2% vs. 9.1% (p=0.0053). There was no statistically significant difference in the change in SF-8 scores for each of the eight domains (general health, physical functioning, role physical, bodily pain, vitality, social functioning, mental health, and role emotional) between the two groups. However, older age at transition was associated with an improvement in SF-8 vitality domain scores between the pre- and post-transition timepoints (p=0.034). Female sex was associated with a worsening in SF-8 vitality domain (p=0.004) and social functioning domain (p=0.015) scores between the pre- and post-transition timepoints. Finally, higher average A1C in the year prior to transition was associated with a worsening in SF-8 role physical domain scores between the pre- and post-transition timepoints (p=0.002).

Conclusions: Quality of life scores in the vitality domain improved in the first year post-transition amongst young adults with T1D who transitioned at an older age, suggesting that a later transition may benefit adolescents with T1D. Additionally, worsening quality of life scores amongst young women and in those with higher pre-transition A1Cs suggest that these populations may require more specialized care at the time of transition.

References: 1. Foster et al. Diabetes Technol Ther. 2019;21(2):66-72.

2. White et al. Lancet Child Adolesc Health. 2017;1(4):274-83.

3. Spaic et al. Diabetes Care. 2019;42(6):1018-26.

<![CDATA[MON-614 Degree of Diabetes Control Determines the Admission Severity of Diabetic Ketoacidosis]]> Objectives: To determine whether the degree of diabetes control correlates with the admission severity of diabetic ketoacidosis (DKA).

Methods: A Retrospective chart review was performed for patients admitted with DKA to the medical ICU at Abington Memorial Hospital between January 1, 2017 and January 1, 2018. Laboratory Data required to determine an acute physiology and chronic health evaluation (APACHE) score, hemoglobin A1C, length of hospital stay was recorded. The APACHE score was used to determine the severity of disease at admission. Patients were divided into two groups: low severity (APACHE <15) and high severity (APACHE >15).

Results: A total of 50 patients were included in the analysis. The mean age of the patients was 47 yrs (range 17-85 yrs). 52%(n=26) of the population were males. The overall mean APACHE II at admission was 15 (range 3-28). The low severity group (APACHE <=15) and high severity group (APACHE >15) were equally matched at 25 patients each. The mean APACHE scores were 9.9 and 18.7 for the low and high severity groups respectively. The mean hemoglobin A1C values for the low and high severity groups were 10.5 and 15 respectively. The average length of ICU/hospital stay was 1.6/3.65 and 1.54/3.61 days for the low and high severity groups respectively.

Conclusions: According to our study, a higher severity of DKA (higher APACHE) was associated with a higher hemoglobin A1C level. However, the study did not find any difference in the average length of ICU/hospital stay between the two groups.

<![CDATA[MON-623 Pattern of C-Peptide Response to Oral Glucose Tolerance Test: Interest and Cut-Off Values]]> Introduction: Oral glucose tolerance test (OGTT) allows classification of subjects in 3 groups, depending on glycaemia 120 minutes after 75g glucose ingestion: normal (glycaemia < 1.4 g/L), glucose intolerant (1.4-2 g/L) and diabetic (>2g/L). Five insulin profiles following OGTT associated with different incidence rates of diabetes over 10 years of follow-up have also previously been described (Kraft J et al, Laboratory Medicine, 1975; Hayashi T et al, Diabetes Care.2013). Insulin measurement is very sensible to hemolysis and can advantageously be replaced by C-peptide determination. However, little is known about C-peptide reference values and response to OGTT.Material and Methods: 128 patients were included to evaluate glyceamia (COBASe801® ROCHE Diagnostics, France), insulin and C-peptide (LiaisonXL®, Diasorin, France) responses to OGTT.Results: According to Hayashi classification, 23 (18%) patients of the whole cohort harbored a physiological insulin response corresponding to profile I (peak of insulin during OGTT at 30 min and higher insulin level at 60 vs. 120 min). Others presented 5 pathological profiles: 14 (11%) patients were classified in profile II (peak of insulin at 30 min and lower or equal insulin level at 60 vs. 120 min), 56 (44%) in profile III (peak of insulin at 60 min), 26 (20%) in profile IV (peak of insulin at 120 min and lower insulin level at 30 vs. 60 min), and finally 9 (7%) in profile V (peak of insulin at 120 min and higher or equal insulin level at 30 vs. 60 min). Only 4 different mean C-peptide profiles emerged from the subgroups previously defined by insulin profile, mean C-peptide profile being substantially similar to mean insulin profile. The only major difference relied on a similar C-peptide profile corresponding to a growing curve from T0 to T120 in both patients with insulin profile IV and V. Mean and 95% confidence interval of C-peptide value at the different times of OGTT were also calculated in the subgroup of patients with both normal glycemic and insulin (pattern I) responses to propose reference values: respectively T0: 0.53 (0.26-0.77); T30: 2.2 (1.24-3.29); T60: 2.26 (1.36-3.68); T120: 1.88 (0.84-2.62) nmol/L. Conclusion: C-peptide response to OGTT profile seems to give globally the same information as insulin profile and should therefore also be predictive of the risk type 2 diabetes in case of hemolyzed samples. The slight differences observed between insulin and C-peptide profiles can be explained by their different metabolic pathways, insulin being quickly degraded in the liver and C-peptide undergoing a longer renal elimination. This work also allows us to propose for the first-time reference values for C-peptide at the different times of OGTT using Liaison XL®.

<![CDATA[MON-634 Difference in Risk Factors Between Adults with Early Onset (&lt;40 Years Old) Versus Late Onset (≥40 Years Old) Diabetes Mellitus Type 2 at the University of Santo Tomas Hospital from January 2015-December 2017]]> INTRODUCTION: Diabetes will remain a threat to global health. The global burden of type 2 diabetes mellitus is significant and rising, with most of the increase occurring in the last two decades. While most of the rise in the prevalence of Type 2 diabetes mellitus occurs in the middle-aged and the elderly, it is becoming more common in younger patients. No longer just a disorder of mature age, there is now a well-recognized trend toward younger people presenting with the disease.

METHODS: This was a cross sectional study of medical records of adult patients at the University of Santo Tomas Hospital who met the inclusion criteria from January 2015 to December 2017. The subjects were divided into early onset (<40 years of age) and the late onset (≥40 years of age) group. Mean, standard deviation, counts and percentages were used to summarize data. The mean values of continuous variables between the two groups were analyzed using the independent sample t-test while categorical variables were analyzed using Chi square test. Logistic regression analysis was used to determine the association of age of onset and duration of diabetes to its complications.

RESULTS: The mean age for the early onset group was 34 years old, while that of the late onset group was 51 years old. No gender predilection was observed in both groups. The subjects of the early onset group were mostly obese as compared to the late onset group who were mostly overweight. Both groups were mostly smokers, and had a positive family history with an almost equal proportion of females having a history of gestational diabetes. The early onset group had higher hba1c and worse lipid profiles upon diagnosis. The most common comorbid illnesses observed in both groups include hypertension, dyslipidemia, fatty liver and metabolic syndrome. In terms of macrovascular complications, the frequency of myocardial infarction was higher in the late onset group. For the microvascular complications, the proportion of retinopathy was higher in the early onset group while the frequency of neuropathy was higher in the late onset group. Lastly, for both groups, the duration of diabetes was associated with microvascular complications such that for every year increase in the duration of diabetes, patients were more likely to develop retinopathy and neuropathy.

CONCLUSION: The mean age of Filipinos with early onset diabetes were at least 5 years younger as compared to Caucasians. Moreover, they were more obese, had worse lipid profiles and higher Hba1C levels. Among the macrovascular and microvascular complications, a higher proportion of the late onset group had peripheral neuropathy and had history of myocardial infarction while retinopathy was more prevalent in the early onset group. Lastly, for every year increase in the duration of diabetes, patients were more likely to develop retinopathy and neuropathy.

<![CDATA[MON-612 Vitamin D and Its Relation to Type II Diabetes Control and Complications]]> Vitamin D and its Relation to Type II Diabetes Control and Complications


Type II diabetes (T2D) prevalence in Saudi Arabia is among the highest in the MENA region according to the recent International Diabetes Federation published statistics. Recent study suggested that around 54% of the screened T2D patients have vitamin D deficiency. Our aim is to assess the prevalence of vitamin D deficiency and its relation to glycemic control and T2D related complications.


We conducted a cross-sectional study at the Diabetes Center, Taif, Saudi Arabia for T2D patients whom age > 18 years and were seen in the clinic between Aug2015-Jan2017 and agreed to participate. We excluded those with gestational diabetes and type I diabetes. Baseline characteristics and measurement were obtained by the participated physician. Laboratory data was collected from the patient’s EMR. We considered those whom have vitamin D level below 30 (ng/ml) to be deficient.


A total of 228 patients with a mean age of 59.1 + 12.5 years, diabetes duration of 10.6 + 8.8 years, BMI of 33.4 + 6.2 kg/m2, heart rate 81.6 + 12.5, systolic BP 130.8 + 19.6, diastolic BP 75.4 + 9.9, fasting glucose 9.4 + 9.5 mmol/l, HbA1c 8.0 + 2.1%, vitamin D 23.2 + 12.3 ng/ml, serum creatinine 73.8 + 22.2, total cholesterol 4.5 + 1.1, LDL 2.7 + 0.9, triglyceride 1.6 + 0.8.

76.3% of the screened patients had vitamin D deficiency. Compared to those with vitamin D deficiency, those with normal vitamin D level less likely to have hypertension (P 0.521), to be male (P 0.028), to have microalbuminuria (P 0.331), and to be diagnosed with neuropathy and retinopathy (P 0.431 and 0.185 respectively). Also those with normal vitamin D were older (P 0.537),has shorter T2D duration (P 0.231), higher BMI (P 0.097), lower pulse rate (P 0.127), lower SBP (P 0.228), higher DBP (P 0.275), lower HbA1c (P 0.027), lower FBG (P 0.093), lower serum creatinine (P 0.039), and lower total cholesterol and LDL (P 0.497 and 0.404 respectively) when compared to those with vitamin D deficiency.

Adjusting for age, gender, diabetes duration, BMI, SBP, DBP, vitamin D supplements and dosage, there was non-significant correlation between HbA1c and vitamin D level.


Vitamin D deficiency is highly prevalent among our sample of T2D patients. In the non-adjusted modules, vitamin D deficiency were non-significantly associated with more microvascular complications and worse measured cardiovascular markers. Also, T2D patients with vitamin D deficiency had significantly higher HbA1c which were non-significant when adjusted for related variables.

<![CDATA[MON-613 The Expression of TBC1 Domain Family, Member 4 (TBC1D4) in Skeletal Muscles of Insulin-Resistant Mice in Response to Sulforaphane]]> 0.05).Furthermore, SFN treatment did not significantly affect the systemic insulin (1.84±0.74 vs 1.54±0.55 ng/ml, p=0.436), or adiponectin (11.96 ±2.29 vs 14.4±3.33 ug/ml, p=0.551) levels in SFN vs. vehicle-treated DIO mice, respectively. Conclusion: SFN treatment improves glucose disposal in DIO mice, which is not linked to the gene expression of GLUT4 and TBC1D4 and its mechanism of glucose disposal in skeletal muscles. Furthermore, SFN treatment did not improve insulin level, and the insulin sensitizer hormone adiponectin as potential players for enhancing insulin sensitivity.1.Axelsson AS, Tubbs E, Mecham B, Chacko S, Nenonen HA, Tang Y, et al. Sci Transl Med. 2017;9(394). ]]> <![CDATA[MON-622 Rapid Improvement in Glycated Albumin Before Educational Admission Predicts Fair Glycemic Control One Year After the Discharge of Patients with Type 2 Diabetes Mellitus]]> <![CDATA[MON-624 Effect of Whole Body Vibration on Glycemic Control in Adults with Type 2 Diabetes]]> <![CDATA[MON-625 Utility of Continuous Glucose Monitoring in Children with Type 1 Diabetes: Is HbA1c Enough?]]> 0.05) among the patients before and while using the CGM for gender (males and females), age groups (0-7 years, 8-14 years, and 15-24 years), and generations of DEXCOM used (G4, G5, and G6). Conclusion: As has been shown in other studies, we did not find a significant change in HbA1c after CGM use for 6 months in our patients. While HbA1C is a reflection of blood sugars over a 3-month period, it does not provide information about glycemic excursions. Metrics derived from CGM use, such as TIR, can provide actionable information which we did not address in our study. There have been reports of the association between TIR and long-term complications of diabetes. Most data comes from studies in adults and pediatric data is lacking. We propose that future studies must look into CGM metrics such as TIR to better define glycemic control in pediatric patients with diabetes mellitus. ]]> <![CDATA[MON-627 Number of Antihypertensives Required for Achieving BP Goals in Individuals with Diabetes and Hypertension-A Cross-Sectional Observational Study]]> <![CDATA[MON-632 The Effects of a High Intensity Glycemic Program on Weight and BMI]]> <![CDATA[MON-626 Frequency and Associated Factors with Multidrug-Resistant Organism Infection in Diabetic Foot Ulcers in a Peruvian Public Hospital]]>


: To determine the frequency and associated factors with multidrug-resistant organism (MDRO) infection among patients with diabetic foot ulcers in a Peruvian Public Hospital.

Cross-sectional survey was conducted from January 2017 -December 2018 at National Hospital in Lima Perú. Ulcers with clinical signs of infection (erythema, edema, pain, purulent exudate) according Infectious Diseases Society of America clinical practice guideline were included

. Wounds with only skin involvement were excluded. On admission, specimens for culture were obtained after cleansing and debriding of the wound. Samples were promptly sent to the microbiology laboratory for culture using appropriate transport media. Bacterial identification and antibiotic susceptibility testing were performed using the VITEK® 2 automated system (BioMérieux Laboratory, Argentina). Multidrug-resistant organisms were identified according to the recommendations of International Expert Proposal

. Prevalence ratios derived from bivariate analysis are given with their 95% CI, which was performed to study factors associated with the presence of multidrug-resistant bacteria; and a multivariate analysis with a lineal model to associated variables found in the bivariate analysis. This study has the approval of the Research Ethics Committee of the María Auxiliadora Hospital.

Among 153 selected subjects, 75% were male, with an average age of 59 yo, 70% had ≥10 years of diabetes duration and only 16% had HbA1C <7%. A frequency of 85% of patients with MDRO infection was found and was associated with minor amputation RP 1.18 (95% CI 1.01-1.44) and with hospitalization time of ≥ 28 days RP 1.21 (95% CI 1.03-1.30).

6 of 7 patients have MDRO infection among patients with diabetic foot ulcers and are associated with the occurrence of minor amputation and hospitalization time ≥ 28 days.


1. Lipsky BA,

. 2012 Infectious Diseases Society of America clinical practice guideline for the diagnosis and treatment of diabetic foot infections. Clin Infect Dis. 2012;54(12):e132-73.

2. Magiorakos AP,

. Multidrug-resistant, extensively drug-resistant and pandrugresistant bacteria: an international expert proposal for interim standard definitions for acquired resistance. Clin Microbiol Infect. 2012;18(3):268-81.

<![CDATA[MON-617 Improving Care Transitions: Optimizing Diabetes Medication Reconciliation]]>



Medication reconciliation (MR) is essential in caring for hospitalized patients with diabetes (DM). Inaccuracies prevail, affecting transitions of care. Factors contributing to errors are driven by providers, patients, and systems of practice and increase in transitions out of hospital. A study showed that MR errors occurred in about 38 % of admissions. Common discrepancies among DM subjects were related to DM and cardiovascular drugs. An intervention aiming to reduce MR discrepancies at discharge achieved 70% less errors due to communication between inpatient providers, primary care and patients within 24 hours. Systems-based interventions and multidisciplinary approaches show promise to improve processes. However, a comprehensive assessment of the elements of practice to target and how interventions may be more effective is lacking. Our purpose was to examine aspects of practice among personnel responsible for the MR steps, and within the hospital workflow in order to identify gaps in the process. We intend to recognize factors to be targeted to optimize MR for DM, and to provide a multipronged approach to inform changes in hospital processes.


We used quantitative and qualitative methods to investigate errors in the MR process as part of a hospital quality improvement program. We randomly included patients 18 years or older with type 1 or 2 DM evaluated by the endocrine team for 6 months. Chart reviews were conducted to assess type and frequency errors. Interviews of nurses, pharmacists, clinicians, and DM educators were sought to understand unique situations and the roles of health providers in the MR process.


Twenty-two subjects were identified with one or more of the following gaps in their MR pertaining to DM medications: a) missing, b) redundancy, and c) dosing error. Scenarios included ≥2 discrepancies (4 of 22); ≥1 medication inappropriately changed from home regimen (18 of 22); redundantly adding a medication (4 of 22); wrong dose of medication (1 of 22); incomplete prescription for DM supplies (8 of 22).


We identified deficits and their attribution to professionals and categorized errors in hospital workflows. Observations, providers’ insights and literature review enabled an assessment of MR failures. We developed a conceptual model where types of error, professionals’ roles, and solutions intersect Interface of prioritizing actions, hospital resources, use of checklists, interdisciplinary collaborations, and staff education is essential to advance adequate MR in the system of practice. Our future steps include a Plan-Do-Study-Act cycle to advance care.