scholarly journals Improving Microvascular Complications Screening Rates: Implementation of a Dedicated Diabetes Clinic (Diabetes 360) in an Underserved Area in Philadelphia, PA

2021 ◽  
pp. cd200072
Author(s):  
Lorenna Rodrigues Silva Sombra ◽  
Horacio David Hares ◽  
Matthew Behme ◽  
Karla M. Curet
2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Jonathan Trejo ◽  
Lyan Gondin Hernandez ◽  
Lucy M A Esteve ◽  
Libia Vasquez ◽  
Sheila Pinkson ◽  
...  

Abstract Recently, cluster analysis has been used to classify adult onset diabetes based on pathophysiologic profile. Using autoimmunity status, BMI, insulin resistance, and beta cell function, this classification system can predict diabetes associated complications. Individuals with primarily insulin resistant phenotype have been associated with increased incidence of nephropathy while those with insulin deficient phenotype are associated with retinopathy. Clinically, patients with severe insulin resistance can be defined as those who require high doses of insulin to achieve glycemic control, such as patients on U-500 insulin requiring more than 200 units of insulin a day. To characterize the clinical and metabolic phenotype of insulin-resistant patients from a South Texas VA diabetes clinic, we evaluated presence of macro or microvascular complications and beta-cell autoimmunity and function in this population. A retrospective cohort study was completed at the South Texas VA Diabetes Clinic. Charts were reviewed for anthropometric measurements, presence of macro and microvascular complications, anti-diabetic medication, lipid profile and HbA1c over 3 visits, autoimmunity (anti-GADab), and beta-cell function (fasting C-peptide). Patients with insulin doses >200 U/day or on U-500 insulin were categorized as “severe insulin-resistant”. Those with insulin doses < 0.5 U/kg/day were categorized as “mild insulin resistance” as a control group. Out of 120 patients, 30 met criteria for severe insulin resistance (n=30, M/F=29/1 age 61±1.6 years (yr), BMI 41±0.9 kg/m2, duration of diabetes 18.3±0.3 yr, HbA1c -8.4±0.2%, total daily insulin dose (TDD) 301±31U). 30 patients with insulin use <0.5 U/kg/day met criteria for mild insulin resistance (N=30, M/F: 28/2, age 62±2 yr, BMI 30±1 kg/m2, duration of diabetes 12±1.2 yr, HbA1c 7.2±0.2%, TDD 17±2U). Prevalence of nephropathy was higher in the insulin resistant group vs the mild insulin resistant group (76% vs 43%, p<0.05). There was no difference in prevalence of retinopathy (p=0.095) or CAD (p=0.6) between the groups. There was no difference in use of ACE-i or SGLT-2i between the groups. Insulin resistant subjects had a higher plasma triglyceride (325±0.3 vs 202±0.3 mg/dl, p=0.04). Prevalence of GAD ab was not different between the groups (3% vs 0%). Fasting C-peptide concentrations were similar in both groups (5.6±0.3 vs 5.2±0.25 ng/ml, p=0.3). HbA1c in the insulin resistant group improved between visits 1 and 3 (p<0.01). Weight increased over three visits in the severe insulin resistant group as opposed to mild weight loss in the mild insulin resistant group. Our results support the high prevalence of diabetic nephropathy in patients with severe insulin resistance, although it is unclear that insulin resistance is the etiology. Long-term follow up of these patients may provide insight into the underlying mechanisms of these complications.


2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Jonathan Trejo ◽  
Lyan Gondin Hernandez ◽  
Lucy M A Esteve ◽  
Libia Vasquez ◽  
Sheila Pinkson ◽  
...  

Abstract Recently a cluster-based classification of disease phenotypes has been developed as a tool to aid in improved characterization and management of diabetes. The majority of these studies have been completed in European populations, but it is unclear if these are applicable to other populations. Using these cohorts, we categorized patients in a South Texas VA diabetes clinic to evaluate if these phenotypes apply to that population. A retrospective cohort study was completed from August 2019 through October 2019, in which 120 patients’ records in the Audie Murphy VA Diabetes Clinic were reviewed for presence of macro and microvascular complications, type of anti-diabetic medication, lipid profile and HbA1c levels, and fasting C-peptide and GADab status. 86 patients who had anti-GADab and C-Peptide levels measured were then stratified into diabetic phenotype cohorts as defined by Ahlqvist et al. 2018, based on presence of diabetes associated autoantibodies, fasting C-peptide level, insulin use >200 U/day, BMI, and age >65. Six subjects belonged to the Severe Autoimmune Diabetes (SAID) cohort, with average GADab 713±301IU; 66% of the cohort had nephropathy, 33% had retinopathy. The Severe Insulin Deficiency (SIDD) cohort had 9 patients, with average fasting C-peptide of 0.58±0.08ng/ml, 44% of the cohort had retinopathy, nephropathy and CAD as complications. The Severe Insulin Resistant (SIRD) cohort had 26 patients; fasting C-peptide was 4.94±0.43ng/ml, 73% had nephropathy, 38% retinopathy and 46% CAD. The Mild Obesity Related (MOD) cohort had 35 patients with average BMI of 35±0.6 kg/m2 and average A1c 7.9±0.2%. Nephropathy was the most prevalent complication, present in 49% of the cohort. The Mild Age Related (MARD) cohort had 10 patients, with average age of 71±1.0 years, with nephropathy and CAD present in 66% of the cohort. The highest gross prevalence of nephropathy was in the SIRD cohort, whereas highest prevalence of retinopathy was in the SIDD cohort, both of which are concordant with the recently reported study, although not statistically significant (p=0.28 and 0.65, respectively). There was no difference in prevalence of CAD between the different categories of diabetes. These findings in a South Texas VA diabetes clinic population reflect agreement in diabetes associated complications in clusters of diabetes based on insulin resistance and insulin deficiency. Targeted intensification of therapy based on the major underlying pathophysiologic abnormalities may delay or prevent micro and macrovascular complications. 1. Ahlqvist E, et al. Novel Subgroups of Adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Endocrinology and Diabetes. 2018;6: 361-369.


Author(s):  
Hamed Taheri ◽  
Roya Rafaiee ◽  
Raheleh Rafaiee

Objective: Academic health services play an important role in the prevention and control of diabetes mellitus (DM) in Iran. This study aimed at determining the prevalence of DM-related complications and the associated risk factors among patients with DM in a university-affiliated outpatient diabetes clinic of a referral hospital in Southeast of Iran, Zahedan. Materials and Methods: This cross-sectional study was conducted from January to April 2019 in an academic diabetes clinic. A total of 334 patients with DM, whose characteristics (age, sex, family history of DM, and substance abuse), as well as laboratory and clinical information, were recorded in the baseline forms, were included. The relationship between variables were assessed by Pearson’s correlation coefficient at P-value< 0.05 and using SPSS version 20.0. Results: The mean age of the participants was 54.27 (±11.57) years. In these patients, DM type 2 was estimated at 99.1%, and the mean duration of the disease was 8.98 (±6.93) years. The findings showed that 77.2% of the patients had poor glycemic control. Also, 85.4% of the patients had fasting blood sugar (FBS) level >126 mg/dL. There was a significant relationship between insulin-dependent therapy and drug abuse (P-value <0.001). The prevalence of hyperlipidemia (68.9%), hypertension (50.6%), retinopathy (29.6%), nephropathy (11.7%), and neuropathy (12.3%) was also determined. Conclusion: The majority of the patients (77.2%) in this study had poor glycemic control, and 69.9% of them suffered from microvascular complications, macrovascular complications, or both. Therefore, frequent visits accompanied by patient education could help to better diabetes control


2019 ◽  
Vol 25 ◽  
pp. 113-114
Author(s):  
Nidhi Garg ◽  
Muralidhara Krishna ◽  
Madhumati S. Vaishnav ◽  
Vasanthi Nath ◽  
S. Chandraprabha ◽  
...  

2017 ◽  
Author(s):  
Yuliya Dydyshka ◽  
Alla Shepelkevich ◽  
Vladislav Yurkovets ◽  
Elena Brutskaya-Stempkovskaya ◽  
Marina Mantachik

2011 ◽  
Vol 4 (2) ◽  
pp. 12-14
Author(s):  
Dr Yash Patel ◽  
◽  
Dr Ashay Shingare ◽  
Dr Gautam Kalita ◽  
Dr Vinaya Bhandari

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