ResearchPad - diabetes-diagnosis-treatment-and-complications https://www.researchpad.co Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[SUN-624 Low Risk of Major Adverse Cardiovascular Events After Pancreas Transplantation Alone]]> https://www.researchpad.co/article/elastic_article_6360 INTRODUCTION: Type 1 Diabetes (T1D) patients have an increased risk for major adverse cardiovascular events (MACE). Pancreas Transplantation Alone (PTA) in patients with T1D achieves near normal glucose control for a prolonged period but limited data are available to date regarding MACE during a 10 year follow up period after the procedure.

OBJECTIVE: We studied incidence of MACE after PTA in T1D patients over a 10 year follow-up period.

METHODS: Retrospectively, we studied 113 T1D recipients of PTA at Mayo Clinic, Rochester with the procedure performed between January 1998 and August 2018 and follow up of at least 1 year. Data were collected before transplantation and up to 10 year follow up after the first PTA. MACE data were gathered until primary non function, re-transplantation, or complete loss of c-peptide (<0.01ng/ml). We report vascular risk factors including hypertension, hyperlipidemia, smoking and BMI along with MACE (defined as cardiac events as unstable angina, Myocardial Infarction (MI), need for re-vascularization, cardiac death, cerebral events as Transient ischemic attack (TIA), stroke, need for re-vascularization and peripheral arterial disease as need for re-vascularization, gangrene and amputation).

RESULTS: Eighteen subjects had pre-transplant MACE. A total of 14 subjects had graft failure within 24 to 36 hours due to thrombosis, with 3 in pre-transplant MACE cohort and 11 in no MACE cohort. Thus, we followed 99 subjects for the development of post-transplant MACE for a period of 6.3 ± 3.6 years. T1D subjects with MACE (n=15) had baseline characteristics: Age 48± 7.8 years, gender F/M 9/6,, duration of diabetes 33 ± 12 years, BMI 26± 3.1(Kg/m2), HbA1c 9.3 ± 1.5% and C-peptide 0.09 ng/ml. 84 T1D patients without MACE were age 42 ± 10.6 years, gender F/M 55/29, duration of diabetes 26.5 ± 10.7 years, BMI 26 ± 5.2(Kg/m2), HbA1c 6.7 ± 2.5 and C-peptide 0.09 ng/ml. There are a total of 584 person-years of follow up to first MACE event and 632 person-years of graft failure, death or last follow-up. Nine patients developed 11 MACE events post-PTA. Therefore, the event rate is 1.5 MACE events per 100 person-years for first MACE event and the total event rate is 1.7 MACE events per 100 person-years of follow-up. Age, smoking (yes), gender, duration of diabetes, HTN and Hyperlipidemia presence did not show any significant impact on post-transplant MACE outcome based on univariate Cox regression but the pre-transplant BMI (HR = 1.14; CI = (1.04, 1.26); p = 0.008) and pre-transplant HbA1c (HR = 1.26; CI = (1.06, 1.51); p = 0.01) showed statistically significant impact.

CONCLUSIONS: At our center, MACE is low in PTA recipients. There is no impact of presence of pre-transplant MACE on development of post-transplant MACE but pre-transplant BMI and HbA1c account for risk of MACE.

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<![CDATA[SUN-620 Gender Difference in the Outcome of Patients with Diabetes Admitted for Hyperosmolar Hyperglycemia. from the National Inpatient Sample]]> https://www.researchpad.co/article/elastic_article_6263 Objective: There is paucity of literature on the impact of gender on outcomes of hyperosmolar hyperglycemic state (HHS) among adult patients with diabetes. The aim of this study was to evaluate the effect of gender on the outcome of these patients. Methodology: The National Inpatient Sample (NIS) was queried for all patients who were admitted with a diagnosis of hyperosmolar hyperglycemic state (HHS) during the years 2005-2014. The primary outcomes of the study were all-cause mortality, acute myocardial infarction (MI), and acute stroke. The secondary outcomes were acute kidney injury (AKI), rhabdomyolysis, acute respiratory failure (ARF), need for mechanical ventilation (MV), length of stay (LOS), and total cost of stay. Results: Overall, 188,725 patients were admitted for HHS. Mean age of males was 53.7, standard error of the mean (SEM: 0.13), and of females was 58.5 (SEM: 0.15), p<0.001. Females were (43.9%), Caucasians were 37.4% while African Americans were 35.2%. Total mortality was 1.1%, MI was 1.3% and stroke was 1.1%. Most common secondary outcome was AKI seen in 31.3% followed by ARF seen in 2.9% of total. The mean cost was 7887 $ (SEM: 84.6) and mean LOS was 4.1 days (SEM: 0.03). Both males and females had equivalent rates of mortality, stroke, ARF and need for mechanical ventilation. Compared to males, females had significantly higher risk for MI 1.6% vs 1.1%, p<0.001, lower risk for AKI 29.3% vs 32.9%, p<0.001, lower risk for rhabdomyolysis 1.1% vs 2%, p<0.001 and higher LOS 4.3 vs 3.9 days, p<0..01 and higher total costs 8165.6 $ vs 7669.3 $, p < 0.001. On multivariable analysis, female gender was independently predictive for higher risk for MI with adjusted odds ratio (aOR) 1.34 [95%CI: 1.08-1.67] p=0.01 and lower risk for rhabdomyolysis with aOR 0.52 [95%CI: 0.42-0.63] p<0.001 and lower risk for AKI with aOR 0.74 [95%CI: 0.7-0.78] p<0.001. In addition, female gender correlated with higher cost and length of stay. Conclusion: Females with hyperosmolar hyperglycemic state are at higher risk for MI and lower risk for AKI and rhabdomyolysis.

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<![CDATA[SUN-616 Poor Diagnostic Concordance Between Fasting Plasma Glucose and Glycosylated Hemoglobin in a Black South African Population]]> https://www.researchpad.co/article/elastic_article_6152 Background: While elevations in fasting plasma glucose (FPG) and glycosylated hemoglobin (HbA1c) are both recognized by the American Diabetes Association (ADA) as diagnostic of hyperglycemia, previous comparisons of these tests have demonstrated discordant individual classifications and population estimates. This may be due to additional postprandial glycemia reflected by HbA1c and, in African-descent populations, to non-glycemic factors that contribute to higher HbA1c at any given level of glycemia. We hypothesized that glycemic classifications based on FPG or HbA1c would differ in a Black South African population and investigated factors associated with discordance.

Methods: 889 Black adults with previously undiagnosed diabetes, aged 40-79 years, from the population-based Health and Ageing in Africa: a Longitudinal Study of an INDEPTH Community in South Africa (HAALSI) cohort were included. Concordance between ADA FPG (normoglycemia [NG] <100 mg/dl, prediabetes [pre-DM] 100-125 mg/dl, diabetes [DM] ≥ 126 mg/dl) and HbA1c (NG <5.7%, pre-DM 5.7-6.4%, DM ≥ 6.5%) classifications was assessed using Cohen’s kappa statistic and logistic regression models were used to identify predictors of discordance.

Results: Median age was 55 years (IQR 49-62) and 49.3% of the sample was male. Median glucose was 86.4 mg/dl and median HbA1c was 5.4%. Pre-DM, as defined by HbA1c, was present in 204 participants (22.9%), while FPG-defined pre-DM was present in 122 (13.7%). DM defined by HbA1c was present in 146 (16.4%), while FPG-defined DM was present in 36 (4.0%). Concordance between the two tests was poor (kappa statistic 0.18; 95%CI 0.13-0.24). Self-reported history of tuberculosis (OR 1.90, p=0.026) and higher HbA1c (OR 4.70, p<0.001) were associated with increased likelihood of discordance, whereas higher fasting glucose was associated with decreased likelihood of discordance (OR 0.58, p<0.001). There was no association between discordance and hemoglobin, HIV status, BMI, waist circumference or hip circumference.

Conclusion: FPG and HbA1c exhibit poor concordance in classifying hyperglycemia in this Black South African population, with HbA1c-based definitions identifying higher prevalences of pre-DM and DM. Further work is needed to confirm whether these discrepancies are due solely to elevations in postprandial glucose. In the interim, clinicians should consider confirming elevated HbA1c concentrations with oral glucose tolerance testing, particularly in those with a history of tuberculosis, prior to making a diagnosis of DM in this population.

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<![CDATA[SUN-625 The Impact of Age on Outcomes of Hyperosmolar Hyperglycemia Among Adult Patients with Diabetes. from the National Inpatient Sample]]> https://www.researchpad.co/article/elastic_article_5976 Objective: There is paucity of literature on the impact of age on outcomes hyperosmolar hyperglycemic state (HHS) among adult patients with diabetes. The aim of the study was to evaluate the effect of age on the outcome of patients admitted for the management of HHS. Methodology: The National Inpatient Sample (NIS) was queried for all patients who were admitted with a diagnosis of HHS during the years 2005-2014. The primary outcomes of the study were all-cause mortality, acute myocardial infarction (MI), and acute stroke. The secondary outcomes were acute kidney injury (AKI), rhabdomyolysis, acute respiratory failure (ARF), need for mechanical ventilation (MV) length of stay (LOS), and total cost of stay. Results: Overall, 188,725 patients were admitted for HHS. Mean age was 55.9, standard error of the mean (SEM): 0.1. Majority were of middle age. Females were (43.9%), Caucasians were 37.4% while African Americans were 35.2%. Total mortality was 1.1%, MI was 1.3% and stroke was 1.1%. Most common secondary outcome was AKI seen in 31.3% followed by ARF seen in 2.9% of total. The mean cost was 7887 $ (SEM: 84.6) and mean LOS was 4.1 days (SEM: 0.03). Young age was defined as age ≤ 35 years, middle age was > 35 and ≤ 65 years, old age was > 65 years. Mortality was 0.3 %, 0.6%, 2.5% in young, middle and older aged groups respectively. Similarly, higher age correlated with increased risk for MI, stroke and all secondary outcomes. On multivariable analysis, age was an independent predictor for all adverse outcomes. Compared to young patients, middle and older age groups had higher odds for mortality with adjusted odds ratio (aOR) 2.23 [95%CI:1.10-4.52], p=0.03 and aOR 7.35 [95%CI: 3.27-16.53], p<0.001 respectively, higher risk for stroke with aOR 9.32 [95%CI: 2.92-29.7], p<0.001 and aOR 17.46 [95%CI:5.23-58.3], p<0.001 and higher risk for MI aOR 5.18 [95%CI:2.15-12.51], p<0.00 and aOR 5.80 [95%CI:2.27-14.80], p<0.00 for middle and older age groups respectively. In addition, compared to the younge age group, the risk for rhabdomyolysis, AKI, ARF, MV, total cost and LOS was significantly higher among middle and older age groups respectively. Conclusion: Age is an important determinant for adverse outcomes among patients with hyperosmolar hyperglycemic sate.

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<![CDATA[SUN-618 Decision Analysis for Glucagon-Like Peptide Receptor Agonists vs. Sodium-Glucose cotransporter2 Inhibitors in Type 2 Diabetes Mellitus]]> https://www.researchpad.co/article/N46e66245-e466-4636-af9f-a27f93e44238 <![CDATA[SUN-622 The Risk of Hip and Non-Vertebral Fractures in Diabetes: A Systematic Review and Meta-Analysis Update]]> https://www.researchpad.co/article/Ndd9fdab4-1e73-4c79-9b65-8b999ab318b3 <![CDATA[SUN-LB114 Remission of Type 2 Diabetes (DMT2) in Hypogonadal Men Under Long-Term Testosterone Therapy]]> https://www.researchpad.co/article/N2e31356b-ba40-4d75-b21e-170b3f127284 94 cm) men aged 48 [33;57] with diagnosed DMT2 (IDF 2019 criteria), hypogonadism (ISSAM 2015 criteria) and vitamin D deficiency (Endocrine Society Guidelines 2014 criteria) were treated with testosterone undecanoate (1000 mg 4 mL injected i.m. every three months following an initial 6-week interval), cholecalciferol 5. 000-10.000 IU per day. 29 patients received 1000-2000 mg per day, 1 patient received liraglutide 1,8 mg a day + metformin 1500 mg a day, 1 patient received Sitagliptin 100 mg and metformin 1000 mg a day. 4 patients did not use anti-diabetic treatment ever. The duration of follow up was 36 months. Waist circumference (WC, cm), glucose (mmol/L), HbA1c (%), total testosterone (nmol/L) and vitamin D (25(OH)D3, ng/mL) were assessed at baseline and after 36 months of follow up. Statistical research was made using a software package statistics (StatSoft Inc. U.S., version 6.0). Quantitative data are presented as medians and quartile range. When comparing the quantitative data of two groups Wilcoxon test was used. Values were considered statistically significant if p <0.05. Results: All patients had no DMT2 diagnostic criteria after 24 months of treatment. WC changed from 107 [102;116] to 94 [88.5;97.5], HbA1c from 7,2 [6.9;7,7] to 5,7 [5.25;5.8], TT from 7.37 [6.2;9.18] to 24.65 [22.3;25.9], 25(OH)D3 from 20.2 [11.7;26.2] to 72 [65;88] at baseline and after 24 months of follow up, respectively. All changes were statistically significant. Anti-diabetic therapy was cancelled in all patients after 20,5 [15;24] months of follow up. All patients remained under further follow up for the period up to 36 months, no cases of DMT2 recurrence were registered.Conclusion: We conclude that correction of testosterone and vitamin D deficiency may work as a necessary stimulus for consequential facilitation of weight reduction and associated recovery, particularly in terms of a complete remission of diabetes. Long term data is needed. ]]> <![CDATA[SUN-LB112 Efficacy & Safety After Switchover to Remogliflozin in Indian T2DM Patients - a Real World Study]]> https://www.researchpad.co/article/Nf3dfed98-20c1-4ab2-8f44-541154c652f5 0.05) No events of hypoglycaemia, disturbance in electrolytes or any unusual adverse events were reported. Combined incidence of UTI & genital Mycotic infection was similar during 6 month observation period as compared to 6 months prior to index day. (8% vs 6%) CONCLUSION: In real world clinical practice, replacement of ongoing SGLT2i with Remogliflozin was observed to provide consistent glycaemic control without any tolerability issues. Hence, novel SGLT2i Remogliflozin can be considered as equivalent alternative for SGLT2i based regimen in management of Indian T2DM patients ]]> <![CDATA[SUN-614 Prediction of Hypertension, Diabetes and Fractures in Eucortisolemic Women by Measuring Parameters of Cortisol Milieu]]> https://www.researchpad.co/article/N73c0e05a-60a4-43a3-864b-60e9309f293b 0.9 μg/dL plus R-UFF/UFE >0.17 showed 82.1% specificity for predicting the presence of ≥1 comorbidities, while the simultaneous presence of F-1mgDST ≤0.9 μg/dL and R-UFF/UFE ≤0.17 showed 88% sensitivity for predicting the absence of comorbidities. The F-1mgDST >0.9 μg/dL or R-UFF/UFE >0.17 was associated with 2.8 and 2.1 fold increased risk of having ≥1 comorbidities, respectively. The F-1mgDST ≤0.9 μg/dL plus R-UFF/UFE ≤0.17 or F-1mgDST >0.9 μg/dL plus R-UFF/UFE >0.17 was associated with 2.8 fold reduced or 4.9 fold increased risk of having ≥1 comorbidities regardless of age, BMI and N363S-SNP. Conclusions. F-1mgDST >0.9 μg/dL and R-UFF/UFE >0.17 may be used for predicting the presence of ≥1 among HY, T2D and fragility FX. ]]> <![CDATA[SUN-615 Does Patient Location (Urban or Rural) Influence Risk Factors and Incidence Rate for 30-Day Readmission for Diabetic Ketoacidosis?]]> https://www.researchpad.co/article/Nb00358fc-0930-44b6-a50b-aeac6fe7388b = 18) admitted with a principal diagnosis of DKA and compare the risk factors for urban and rural patients. Methods We utilized Agency of Healthcare Research and Quality’s (AHRQ) 2014 Nationwide Readmission Database to identify admissions with a principal diagnosis of DKA related ICD-9 diagnosis (250.10, 250.11, 250.12, and 250.13) associated with both Type 1 and Type 2 Diabetes Mellitus. Applicable admissions were all adults (age >= 18) with an index hospitalization between January 1 to November 30, 2014. Patients who died during index admission and those with missing covariates were excluded. The 2013 NCHS Urban-Rural Classification System was used to classify if originating from an urban or rural location. All-cause readmission within 30-days of DKA were analyzed. Predictors for readmission were determined using logistic regression model. Results A total of 65,249 patients met criteria for inclusion. Of which, there was 12,561 readmissions (19.25 %) within 30-days of the index admission. Patients originating from urban locations had a readmission rate of 19.36% compared to 18.56 % for patients from rural locations (p=0.07). Multivariate analysis showed patients from either rural or urban location each had a higher likelihood of readmission if their disposition was home health or AMA, younger age (<65), female, Medicare as payer, LOS 7-14 days, absence of obesity, and presence of renal failure. In addition, disposition to short term hospital increased the odds for readmission from rural patients. Conclusion Almost 1 in 5 patients discharged with a principal diagnosis of DKA will be readmitted within 30 days. No difference was noted in rates of readmissions for patients originating from urban or rural locations. Risk factors are similar with further research needed to better understand the drivers of readmission. References: [1] CDC: National Diabetes Statistics Report (2017) [2] Ferdinand AO, et al. (2017). Diabetes-Related Hospital Mortality in Rural America: A Significant Cause for Concern. Policy Brief #3. Southwest Rural Health Research Center ]]> <![CDATA[SUN-LB115 Is the Stepping-Down Approach a Better Option Than Multiple Daily Injections in Patients With Chronic Poorly-Controlled Diabetes on Advanced Insulin Therapy?]]> https://www.researchpad.co/article/N68c91861-c7c6-43f4-9900-61c880b6b507 8% and eGFR >45. Patients were randomized to either intervention (Step-Down) or control (MDI) group. In the control group, the patient was advised to remain on MDI. In the intervention group, all prandial insulin injections were discontinued; but the patient remained on the basal insulin and metformin to which SGLT2i, Empagliflozin, and GLP1 RA, Dulaglutide, were added. They were followed up for 16 weeks. The primary outcome was A1c change and secondary outcomes were the change in fasting BG, weight, BP, HR, fasting lipids, serum Na and K, serum Cr, liver enzymes, CBC and Diabetes Medications Satisfaction (DM-SAT) scores at 16 weeks. Results: There was no difference in A1c between 2 groups (10.36% vs 9.69%;p=0.171) at baseline. However, A1c was significantly lower at 4 weeks (9.54% vs 7.31%; p=0.0088) and 16 weeks (9.7% vs 8.25%; p<0.001) in intervention group (n=10). Compared to baseline, in the control group (n=8), there was no significant change at secondary outcome variables except slightly higher SBP at 16 weeks. In intervention group, compared to baseline, there was a significant decrease in weight (-16.38 Lbs; p=0.003), BMI (-3.06; p<0.001), LDL cholesterol (-15.7 mg/d; p=0.0378), total cholesterol (-18.5 mg/dL; p=0.0386), total daily insulin dose (-57.3 units; p<0.001) and a significant improvement in DM-SAT patient satisfaction 0-100 scores - total score (+45.3; p <0.001) and subscale scores (Convenience +35.28, p =0.019; Lifestyle +35.8, p=0.0052; Medical control +51.3, p<0.001; Wellbeing +47.2,p=0.0091) at 16 weeks. Conclusions: De-escalating from AIT to the combined use of metformin, SGLT2i, GLP1 RA and basal insulin in obese patients with poorly-controlled T2DM by using the stepping-down approach results in the significant improvement in glycemic control, weight loss, and significantly higher patient satisfaction. This noble stepping-down approach may be a better option than continuing MDI in such patients. This pilot study result needs to be confirmed with a larger trial. ClinicalTrials ID: NCT02846233 ]]> <![CDATA[SUN-626 Survival of Patients with Gastroenteropancreatic Neuroendocrine Tumors and Diabetes Mellitus in a Seer-Medicare Cohort]]> https://www.researchpad.co/article/N49b4add3-6e6a-4e8e-9528-c2a8ccbd8ff8 <![CDATA[SUN-611 Impact of Existing Heart Failure on Outcomes of Hospitalization of Hyperosmolar Hyperglycemic State. From the National Inpatient Sample]]> https://www.researchpad.co/article/Neb63642c-9c8e-4c18-82cf-4ae313a91df4 <![CDATA[SUN-LB111 Comparison of Phenotype and Metabolic Abnormalities Among Familial Partial Lipodystrophy Due to LMNA or PPARG Variants]]> https://www.researchpad.co/article/Nd16db288-6ef0-45f0-9dc5-8b8182421435 <![CDATA[SUN-621 Body Weight and Body Composition in Patients with Chronic Pancreatitis Are Associated with Islet Function After Total Pancreatectomy and Islet Cell Transplantation]]> https://www.researchpad.co/article/Nedea2c98-ee8d-4416-9a3b-69770fc6d59d <![CDATA[SUN-LB110 The Nutrition Education Using a Health Care Application With Artificial Intelligence in Patients With Diabetes Mellitus]]> https://www.researchpad.co/article/N94eafb50-4e35-453e-8e6e-5644ca1eb6c9 <![CDATA[SUN-627 A Test for Variation in Insulin Concentrations at the Wilford Hall Ambulatory and Surgical Center]]> https://www.researchpad.co/article/N4f1c9098-98c2-4f03-b69b-d6b951e59bdd <![CDATA[SUN-613 Association of Electrocardiograph Parameters and Diabetic Peripheral Neuropathy]]> https://www.researchpad.co/article/Ne7818611-f0aa-4563-94ce-100865939146 <![CDATA[SUN-LB116 Improved Family Medicine Resident Diabetes Care Through Participation in a Diabetes Clinic]]> https://www.researchpad.co/article/N6d8cedd2-f5ed-4a38-a38b-dbc72285bb4d <![CDATA[SUN-LB113 A Continuous Remote Care Intervention Utilizing Carbohydrate Restriction Including Nutritional Ketosis Improves Markers of Metabolic Risk and Reduces Diabetes Medication Use in Patients With Type 2 Diabetes Over 3.5 Years]]> https://www.researchpad.co/article/Nf7d6e8c2-8a0a-4364-995f-441a6eb59ee7