COVID-19 Severity is Tripled in the Diabetes Community: A Prospective Analysis of the Pandemic’s Impact in Type 1 and Type 2 Diabetes

10.2337/figshare.13235066.v1 ◽

2020 ◽

Author(s):

Justin M. Gregory ◽

James C. Slaughter ◽

Sara H. Duffus ◽

T. Jordan Smith ◽

Lauren M. LeStourgeon ◽

...

Keyword(s):

Type 2 Diabetes ◽

Type 1 Diabetes ◽

Technology Use ◽

Glycosylated Hemoglobin ◽

Illness Severity ◽

Health Insurance Status ◽

Diabetes Status ◽

Modifiable Factors

Objective: To quantify and contextualize the risk for COVID-19 related hospitalization and illness severity in type 1 diabetes. Research Design and Methods: We conducted a prospective cohort study to identify COVID-19 cases across a regional healthcare network of 137 service locations. Using an electronic health record query, chart review, and patient contact, we identified clinical factors influencing illness severity. Results: We identified COVID-19 in 6,138, 40, and 273 patients without diabetes and with type 1 and type 2 diabetes, respectively. Compared with not having diabetes, people with type 1 diabetes had adjusted odds ratios (ORs) of 3.90 (95% CI 1.75-8.69) for hospitalization and 3.35 (95% CI 1.53-7.33) for greater illness severity, which was similar to risk in type 2 diabetes. Among type 1 diabetes patients, glycosylated hemoglobin (HbA1c), hypertension, race, recent diabetic ketoacidosis (DKA), health insurance status, and less diabetes technology use were significantly associated with illness severity. <h2>Conclusions: Diabetes status, both type 1 and type 2, independently increases the adverse impacts of COVID-19. Potentially modifiable factors (e.g., HbA1c) had significant but modest impact compared to comparatively static factors (e.g. race, insurance) in type 1 diabetes indicating an urgent and continued need to mitigate SARS-CoV-2 infection risk in this community.</h2>

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Detection of Diabetes Status and Type in Youth using Electronic Health Records (EHRs): The SEARCH for Diabetes in Youth Study

10.2337/figshare.12558875.v1 ◽

2020 ◽

Author(s):

Brian J. Wells ◽

Kristin M. Lenoir ◽

Lynne E. Wagenknecht ◽

Elizabeth J. Mayer-Davis ◽

Jean M. Lawrence ◽

...

Keyword(s):

Type 2 Diabetes ◽

Type 1 Diabetes ◽

Multinomial Regression ◽

Rule Based ◽

Diabetes Status ◽

Icd 10 ◽

Youth Study ◽

Diabetes In Youth

Objective: Diabetes surveillance often requires manual medical chart reviews to confirm status and type. This project aimed to create an electronic health record (EHR)-based procedure for improving surveillance efficiency through automation of case identification. Research Design and Methods: Youth (< 20 years) with potential evidence of diabetes (N=8,682) were identified from EHRs at three children’s hospitals participating in the SEARCH for Diabetes in Youth Study. True diabetes status/type was determined by manual chart reviews. Multinomial regression was compared with an ICD-10 rule-based algorithm in the ability to correctly identify diabetes status and type. Subsequently, the investigators evaluated a scenario of combining the rule based algorithm with targeted chart reviews where the algorithm performed poorly. Results: The sample included 5308 true cases (89.2% type 1 diabetes). The rule-based algorithm outperformed regression for overall accuracy (0.955 vs 0.936). Type 1 diabetes was classified well by both methods: sensitivity (Se) (>0.95), specificity (Sp) (>0.96), and positive predictive value (PPV) (>0.97). In contrast, the PPVs for type 2 diabetes were 0.642 and 0.778 for the rule-based algorithm and the multinomial regression, respectively. Combining the rule-based method with chart reviews (n=695, 7.9%) of persons predicted to have non type 1 diabetes resulted in perfect PPV for the cases reviewed, while increasing overall accuracy (0.983). The sensitivity, specificity, and PPV for type 2 diabetes using the combined method were >=0.91. Conclusions: An ICD-10 algorithm combined with targeted chart reviews accurately identified diabetes status/type and could be an attractive option for diabetes surveillance in youth.

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Comparative Study of the Effect of Various Forms of Quercetin on Experimental Diabetes

Romanian Journal of Diabetes Nutrition and Metabolic Diseases ◽

10.2478/rjdnmd-2018-0046 ◽

2018 ◽

Vol 25 (4) ◽

pp. 383-388

Author(s):

Kateryna Posokhova ◽

Iryna Stechyshyn ◽

Inna Krynytska ◽

Mariya Marushchak ◽

Inna Birchenko ◽

...

Keyword(s):

Diabetes Mellitus ◽

Type 2 Diabetes ◽

Type 1 Diabetes ◽

Comparative Study ◽

Experimental Diabetes ◽

Glycosylated Hemoglobin ◽

Water Soluble ◽

Soluble Form

Abstract Background and aims: Diabetes mellitus (DM) is a multifactorial metabolic disorder characterized by hyperglycaemia caused by insulin deficiency or insulin resistance. It is a global public health problem. This study aimed to determine specific pharmacological effect of quercetin in water soluble and liposomal preparations in experimental diabetes mellitus. Material and methods: We examined the effect of Corvitin and Lipoflavone (at the dose of 10 mg / kg body weight) in a comparative study in white rats with type 1 diabetes and type 2 diabetes coupled with obesity. To simulate the forms of diabetes mellitus most analogous to those in humans we used Streptozotocin at the doses of 30 mg / kg and 50 mg / kg. We tested the levels of glucose, glycosylated hemoglobin, C-reactive protein, and interleukins 6 and 4 in the blood. Results: In animals with type 1 and type 2 diabetes Lipoflavone significantly reduces glucose and glycosylated hemoglobin levels compared to the rats treated with Corvitin. When administered to animals with diabetes, the effect of quercetin in liposomal form on the concentrations of IL-6, IL-4 and Creactive protein is also larger compared to the water-soluble form. Conclusions: Water soluble quercetin preparation Corvitin and to a larger extent liposomal preparation of this flavonoid, Lipoflavone, show anti-inflammatory effect and restore key parameters of carbohydrate metabolism in experimental type 1 diabetes mellitus and type 2 diabetes coupled with obesity, reducing blood glucose and glycosylated hemoglobin levels.

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A Prospective Study: Highlights of Hippocampal Spectroscopy in Cognitive Impairment in Patients with Type 1 and Type 2 Diabetes

Journal of Personalized Medicine ◽

10.3390/jpm11020148 ◽

2021 ◽

Vol 11 (2) ◽

pp. 148

Author(s):

Julia Samoilova ◽

Mariia Matveeva ◽

Olga Tonkih ◽

Dmitry Kudlau ◽

Oxana Oleynik ◽

...

Keyword(s):

Risk Factors ◽

Type 2 Diabetes ◽

Type 1 Diabetes ◽

Cognitive Impairment ◽

Cognitive Dysfunction ◽

Cognitive Deficits ◽

Chronic Hyperglycemia ◽

Modifiable Factors

Diabetes mellitus type 1 and 2 is associated with cognitive impairment. Previous studies have reported a relationship between changes in cerebral metabolite levels and the variability of glycemia. However, the specific risk factors that affect the metabolic changes associated with type 1 and type 2 diabetes in cognitive dysfunction remain uncertain. The aim of the study was to evaluate the specificity of hippocampal spectroscopy in type 1 and type 2 diabetes and cognitive dysfunction. Materials and methods: 65 patients with type 1 diabetes with cognitive deficits and 20 patients without, 75 patients with type 2 diabetes with cognitive deficits and 20 patients without have participated in the study. The general clinical analysis and evaluation of risk factors of cognitive impairment were carried out. Neuropsychological testing included the Montreal Scale of Cognitive Dysfunction Assessment (MoCA test). Magnetic resonance spectroscopy (MRS) was performed in the hippocampal area, with the assessment of N-acetylaspartate (NAA), choline (Cho), creatine (Cr), and phosphocreatine (PCr) levels. Statistical processing was performed using the commercially available IBM SPSS software. Results: Changes in the content of NAA, choline Cho, phosphocreatine Cr2 and their ratios were observed in type 1 diabetes. More pronounced changes in hippocampal metabolism were observed in type 2 diabetes for all of the studied metabolites. Primary risk factors of neurometabolic changes in patients with type 1 diabetes were episodes of severe hypoglycemia in the history of the disease, diabetic ketoacidosis (DKA), chronic hyperglycemia, and increased body mass index (BMI). In type 2 diabetes, arterial hypertension (AH), BMI, and patient’s age are of greater importance, while the level of glycated hemoglobin (HbA1c), duration of the disease, level of education and insulin therapy are of lesser importance. Conclusion: Patients with diabetes have altered hippocampal metabolism, which may serve as an early predictive marker. The main modifiable factors have been identified, correction of which may slow down the progression of cognitive dysfunction.

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Detection of Diabetes Status and Type in Youth using Electronic Health Records: The SEARCH for Diabetes in Youth Study

10.2337/figshare.12558875.v2 ◽

2020 ◽

Author(s):

Brian J. Wells ◽

Kristin M. Lenoir ◽

Lynne E. Wagenknecht ◽

Elizabeth J. Mayer-Davis ◽

Jean M. Lawrence ◽

...

Keyword(s):

Type 2 Diabetes ◽

Type 1 Diabetes ◽

Multinomial Regression ◽

Rule Based ◽

Diabetes Status ◽

Icd 10 ◽

Youth Study ◽

Diabetes In Youth

Objective: Diabetes surveillance often requires manual medical chart reviews to confirm status and type. This project aimed to create an electronic health record (EHR)-based procedure for improving surveillance efficiency through automation of case identification. Research Design and Methods: Youth (< 20 years) with potential evidence of diabetes (N=8,682) were identified from EHRs at three children’s hospitals participating in the SEARCH for Diabetes in Youth Study. True diabetes status/type was determined by manual chart reviews. Multinomial regression was compared with an ICD-10 rule-based algorithm in the ability to correctly identify diabetes status and type. Subsequently, the investigators evaluated a scenario of combining the rule based algorithm with targeted chart reviews where the algorithm performed poorly. Results: The sample included 5308 true cases (89.2% type 1 diabetes). The rule-based algorithm outperformed regression for overall accuracy (0.955 vs 0.936). Type 1 diabetes was classified well by both methods: sensitivity (Se) (>0.95), specificity (Sp) (>0.96), and positive predictive value (PPV) (>0.97). In contrast, the PPVs for type 2 diabetes were 0.642 and 0.778 for the rule-based algorithm and the multinomial regression, respectively. Combining the rule-based method with chart reviews (n=695, 7.9%) of persons predicted to have non type 1 diabetes resulted in perfect PPV for the cases reviewed, while increasing overall accuracy (0.983). The sensitivity, specificity, and PPV for type 2 diabetes using the combined method were >=0.91. Conclusions: An ICD-10 algorithm combined with targeted chart reviews accurately identified diabetes status/type and could be an attractive option for diabetes surveillance in youth.

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Sex, age, type of diabetes and incidence of atrial fibrillation in patients with diabetes mellitus: a nationwide analysis

Cardiovascular Diabetology ◽

10.1186/s12933-021-01216-7 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Arnaud Bisson ◽

Alexandre Bodin ◽

Grégoire Fauchier ◽

Julien Herbert ◽

Denis Angoulvant ◽

...

Keyword(s):

Diabetes Mellitus ◽

Atrial Fibrillation ◽

Type 2 Diabetes ◽

Type 1 Diabetes ◽

Incidence Rates ◽

Diabetic Patients ◽

Diabetes Status ◽

History Of

Abstract Background There remain uncertainties regarding diabetes mellitus and the incidence of atrial fibrillation (AF), in relation to type of diabetes, and the interactions with sex and age. We investigated whether diabetes confers higher relative rates of AF in women compared to men, and whether these sex-differences depend on type of diabetes and age. Methods All patients aged ≥ 18 seen in French hospitals in 2013 with at least 5 years of follow-up without a history of AF were identified and categorized by their diabetes status. We calculated overall and age-dependent incidence rates, hazard ratios, and women-to-men ratios for incidence of AF in patients with type 1 and type 2 diabetes (compared to no diabetes). Results In 2,921,407 patients with no history of AF (55% women), 45,389 had prevalent type 1 diabetes and 345,499 had prevalent type 2 diabetes. The incidence rates (IRs) of AF were higher in type 1 or type 2 diabetic patients than in non-diabetics, and increased with advancing age. Among individuals with diabetes, the absolute rate of AF was higher in men than in women. When comparing individuals with and without diabetes, women had a higher adjusted hazard ratio (HR) of AF than men: adjusted HR 1.32 (95% confidence interval 1.27–1.37) in women vs. 1.12(1.08–1.16) in men for type 1 diabetes, adjusted HR 1.17(1.16–1.19) in women vs. 1.10(1.09–1.12) in men for type 2 diabetes. Conclusion Although men have higher absolute rates for incidence of AF, the relative rates of incident AF associated with diabetes are higher in women than in men for both type 1 and type 2 diabetes.

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Detection of Diabetes Status and Type in Youth using Electronic Health Records: The SEARCH for Diabetes in Youth Study

10.2337/figshare.12558875 ◽

2020 ◽

Author(s):

Brian J. Wells ◽

Kristin M. Lenoir ◽

Lynne E. Wagenknecht ◽

Elizabeth J. Mayer-Davis ◽

Jean M. Lawrence ◽

...

Keyword(s):

Type 2 Diabetes ◽

Type 1 Diabetes ◽

Multinomial Regression ◽

Rule Based ◽

Diabetes Status ◽

Icd 10 ◽

Youth Study ◽

Diabetes In Youth

Objective: Diabetes surveillance often requires manual medical chart reviews to confirm status and type. This project aimed to create an electronic health record (EHR)-based procedure for improving surveillance efficiency through automation of case identification. Research Design and Methods: Youth (< 20 years) with potential evidence of diabetes (N=8,682) were identified from EHRs at three children’s hospitals participating in the SEARCH for Diabetes in Youth Study. True diabetes status/type was determined by manual chart reviews. Multinomial regression was compared with an ICD-10 rule-based algorithm in the ability to correctly identify diabetes status and type. Subsequently, the investigators evaluated a scenario of combining the rule based algorithm with targeted chart reviews where the algorithm performed poorly. Results: The sample included 5308 true cases (89.2% type 1 diabetes). The rule-based algorithm outperformed regression for overall accuracy (0.955 vs 0.936). Type 1 diabetes was classified well by both methods: sensitivity (Se) (>0.95), specificity (Sp) (>0.96), and positive predictive value (PPV) (>0.97). In contrast, the PPVs for type 2 diabetes were 0.642 and 0.778 for the rule-based algorithm and the multinomial regression, respectively. Combining the rule-based method with chart reviews (n=695, 7.9%) of persons predicted to have non type 1 diabetes resulted in perfect PPV for the cases reviewed, while increasing overall accuracy (0.983). The sensitivity, specificity, and PPV for type 2 diabetes using the combined method were >=0.91. Conclusions: An ICD-10 algorithm combined with targeted chart reviews accurately identified diabetes status/type and could be an attractive option for diabetes surveillance in youth.

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Causes of diabetic ketoacidosis: type 1 diabetes versus type 2 diabetes

Endocrine Abstracts ◽

10.1530/endoabs.49.ep502 ◽

2017 ◽

Author(s):

Marwa Omri ◽

Rayene Ben Mohamed ◽

Imen Rezgani ◽

Sana Mhidhi ◽

Aroua Temessek ◽

...

Keyword(s):

Type 2 Diabetes ◽

Type 1 Diabetes ◽

Diabetic Ketoacidosis

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1746-P: Obese Youth with Type 1 Diabetes (T1D) Have Worse Hepatic Insulin Resistance (IR) Than Youth with Type 2 Diabetes (T2D)

Diabetes ◽

10.2337/db20-1746-p ◽

2020 ◽

Vol 69 (Supplement 1) ◽

pp. 1746-P

Author(s):

PATTARA WIROMRAT ◽

MELANIE CREE-GREEN ◽

BRYAN C. BERGMAN ◽

KALIE L. TOMMERDAHL ◽

AMY BAUMGARTNER ◽

...

Keyword(s):

Insulin Resistance ◽

Type 2 Diabetes ◽

Type 1 Diabetes ◽

Hepatic Insulin Resistance ◽

Obese Youth

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1264-P: Distinguishing between Obese Patients with Type 1 Diabetes (T1DMob) and Type 2 Diabetes in Adolescence (T2DMad) at Presentation

Diabetes ◽

10.2337/db20-1264-p ◽

2020 ◽

Vol 69 (Supplement 1) ◽

pp. 1264-P

Author(s):

FABRIZIO BARBETTI ◽

RICCARDO BONFANTI ◽

MAURIZIO DELVECCHIO ◽

DARIO IAFUSCO ◽

Keyword(s):

Type 2 Diabetes ◽

Type 1 Diabetes ◽

Obese Patients

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