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

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.

The world pandemic of diabetes

2011 ◽  
pp. 1795-1799
Author(s):  
Nigel Unwin
Keyword(s):  
Risk Factors ◽  
Type 2 Diabetes ◽  
Type 1 Diabetes ◽  
Geographical Area ◽  
Age Groups ◽  
The World ◽  
Level Of Economic Development ◽  
Increasing Trends

A pandemic refers to a disease that is rapidly increasing in frequency across many populations, over a wide geographical area (1). Put another way, it refers to the situation in which epidemics of the disease are occurring simultaneously in many countries. This is the case for diabetes, which has the dubious distinction of being one of the few chronic non-communicable diseases known to be increasing in all countries from which data are available, irrespective of the level of economic development (2). This is mirrored by a pandemic of people who are overweight or obese (3), the major risk factors for type 2 diabetes. This chapter focuses on diabetes in adults (aged 20 years old and above), of which 85% to more than 95%, depending on the population, have type 2 diabetes (2, 4), which is thus the main contributor to the growing burden of diabetes. However, it is worth noting that, in children (<15 years old), the incidence of type 1 diabetes is also increasing, particularly in the youngest age groups, across the vast majority of countries from which good data are available (5). The reasons for this increase are unclear, although various environmental risk factors have been implicated (5). This chapter aims to do the following: ◆ provide an overview of the prevalence and trends in diabetes in adults across the world and its contribution to mortality ◆ describe the broad determinants that underlie the increasing trends in diabetes in adults ◆ provide an introduction to variations by ethnicity in the prevalence of type 2 diabetes

scholarly journals Cohort profile: the Funen Diabetes Database—a population-based cohort of patients with diabetes in Denmark

BMJ Open ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. e035492
Author(s):  
Kasper Adelborg ◽  
Péter Szentkúti ◽  
Jan Erik Henriksen ◽  
Reimar Wernich Thomsen ◽  
Lars Pedersen ◽  
...  
Keyword(s):  
Risk Factors ◽  
Type 2 Diabetes ◽  
Type 1 Diabetes ◽  
Geographical Area ◽  
Population Based ◽  
Haemoglobin A1c ◽  
Patients With Diabetes

PurposeDetailed population-based data are essential to understanding the epidemiology of diabetes and its clinical course. This article describes the Funen Diabetes Database (FDDB). The purpose of the FDDB was to serve as a shared electronic medical record system for healthcare professionals treating patients with diabetes. The cohort can also be used for research.ParticipantsThe FDDB covers a geographical area of almost 500 000 Danish inhabitants. It currently includes 3691 patients with type 1 diabetes, 19 085 patients with type 2 diabetes, 292 patients with other types of diabetes and 5992 patients with an unknown type of diabetes. Patients have been continuously enrolled from general practitioners and endocrinology departments in the Funen area in Denmark since 2003. Patients undergo a clinical work-up at their first diabetes contact and during follow-up visits. The information collected includes type of diabetes contact, blood pressure, height, weight, lifestyle factors (smoking, exercise), laboratory records (eg, haemoglobin A1c and cholesterol levels), results from foot examinations (eg, pulse, cutaneous sensitivity and ankle brachial index), results from eye examinations (eg, degree of retinopathy assessed by retinal photo and eye examination), glucose-lowering drugs and diabetic complications.Findings to dateThe FDDB cohort was followed for a total of 212 234 person-years up to 2016. A cross-sectional study described the prevalence of diabetic retinopathy and its associated risk factors. The clinical outcomes of patients with type 1 diabetes, type 2 diabetes and latent autoimmune diabetes in adults have been assessed. Linkage to population-based medical registries with complete follow-up has enabled the collection of extensive continuous data on general practice contacts, diagnoses and procedures from hospital contacts, medication use and mortality.Future plansThe FDDB serves as a strong data resource that will be used in future studies of diabetes epidemiology with focus on occurrence, risk factors, treatment, complications and prognosis.

scholarly journals Evaluation of calcium level in patients with diabetes mellitus according to the examination in the mobile medical center (Diamodule)

2021 ◽  
Vol 23 (4) ◽  
pp. 382-388
Author(s):  
Anna V. Zheleznyakova ◽  
◽  
Victoriya L. Volodicheva ◽  
Olga K. Vikulova ◽  
Alexey A. Serkov ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Type 2 Diabetes ◽  
Type 1 Diabetes ◽  
Serum Calcium ◽  
Calcium Level ◽  
Medical Center ◽  
Patients With Diabetes

Background. Diabetes mellitus (DM) is characterized by multiple risk factors for the combined development of disorders of phosphorus-calcium metabolism, due to more frequent overweight, decreased renal filtration function and vitamin D deficiency in this category of patients. Aim. To assess the level of calcium in blood serum and its correlations with parameters of carbohydrate metabolism, body mass index (BMI) and renal function in patients with type 1 and type 2 diabetes. Materials and methods. The object of the study: adult patients with type 1 diabetes (T1DM) and type 2 diabetes (T2DM) ≥18 years old who underwent examination in Diamodule (n=294) in 2019 in the Voronezh region, who were additionally tested for serum calcium. The examination at the mobile medical center includes: assessment of anthropometric data (height, weight, BMI), biochemical data of blood lipids and creatinine levels with calculation of glomerular filtration rate (GFR-EPI), albuminuria and the albumin/creatinine in a single portion of urine, measurement of glycated hemoglobin (HbA1c), blood pressure, electrocardiogram, consultation with a cardiologist, ophthalmologist, specialist of the Diabetic Foot office, diabetologist. All biochemical parameters was carried out using commercial kits on a biochemical express analyzer Spotchem EZ ArkraySP-4430. Determination of HbA1c, albuminuria, albumin/creatinine was performed by immunochemical method on a DCA Vantage analyzer. Data analysis was carried out using the Statistica v.13.3 software package (TIBCO Software Inc., USA). The results are presented as medians and quartiles [25; 75], the level of reliability is taken as p<0.05. Results. Hypocalcemia (serum calcium level less 2.15 mmol/L) was detected in 88.3% of patients with T1DM and 73.6% of patients with T2DM. The level of serum calcium in T1DM was 1.96 [1.83; 2.07], in T2DM – 2.04 [1.97; 2.16] (p<0.001) with significant differences in GFR: in T1DM 69.6 [57.8; 82.5], with T2DM 50.5 [44.1; 59.9] (p<0.001). We observed correlation between GFR and calcium level r=-0.3 (p<0.05,). GFR<60 ml/min/1.73 m² was observed in 30.5% of T1DM patients, in 75.0% of T2DM. The HbA1c in T1DM was 8.7% [7.7; 9.8], in T2DM – 8.3% [6.8; 9.4] (p=0.01). Obesity was revealed in T1DM in 12.3%, in T2DM in 71.4%; the median BMI in T1DM was 25.5 kg/m2 [21.9; 28.4], in T2DM – 33.2 kg/m2 [29.7; 37.9]. There was correlation between BMI and calcium level r=0.26 (p<0.05). Conclusion. The results of the study revealed a high prevalence of hypocalcemia among patients with diabetes, the most pronounced in type 1 diabetes. In the presence of a correlation between calcium and GFR, there was a high incidence of hypocalcemia in patients with T1DM, even in the absence of a significant decrease in GFR, which may indicate to additional risk factors. Since the assessment of the level of calcium is not included in the list of standard clinical examination, it is recommended to include this parameter and conduct regular screening in risk groups, which include patients with diabetes. More extensive research is needed to analyze the factors.

scholarly journals Diagnosis of neuropathy and risk factors for corneal nerve loss in type 1 and type 2 diabetes: A corneal confocal microscopy study

2020 ◽  
Author(s):  
Maryam Ferdousi ◽  
Alise Kalteniece ◽  
Shazli Azmi ◽  
Ioannis N Petropoulos ◽  
Georgios Ponirakis ◽  
...  
Keyword(s):  
Risk Factors ◽  
Type 2 Diabetes ◽  
Type 1 Diabetes ◽  
Peripheral Neuropathy ◽  
Nerve Fibre ◽  
Fibre Length ◽  
Corneal Confocal Microscopy ◽  
Corneal Nerve

<b>Purpose: </b>To assess the diagnostic utility of corneal confocal microscopy (CCM) for diabetic peripheral neuropathy (DPN) and the risk factors for corneal nerve loss. <p><b>Methods: </b>490 participants including 72 healthy controls, 149 with type 1 diabetes and 269 with type 2 diabetes underwent detailed assessment of peripheral neuropathy and CCM in relation to risk factors.</p> <p><b>Results: </b>Corneal nerve fibre density (CNFD) (P<0.0001, P<0.0001), branch density (CNBD) (P<0.0001, P<0.0001) and length (CNFL) (P<0.0001, P=0.02) were significantly lower in patients with type 1 and type 2 diabetes, compared to controls. CNFD (P<0.0001), CNBD (P<0.0001) and CNFL (P<0.0001) were lower in type 1 diabetes compared to type 2 diabetes. Receiver operating characteristics (ROC) curve analysis for the diagnosis of DPN demonstrated a good area under the curve (AUC) for CNFD=0.81, CNBD=0.74 and CNFL=0.73. Multivariable regression analysis showed a significant association between reduced corneal nerve fibre length with age (β=-0.27, P=0.007), HbA1c (β=-1.1, P=0.01) and weight (β=-0.14, P=0.03) in patients with type 2 diabetes and with duration of diabetes (β=-0.13, P=0.02), LDL cholesterol (β=1.8, P=0.04), and triglycerides (β=-2.87, P=0.009) in patients with type 1 diabetes. </p> <b>Conclusion: </b>CCM identifies more severe corneal nerve loss in patients with type 1 compared to type 2 diabetes and shows good diagnostic accuracy for DPN. Furthermore, the risk factors for a reduction in corneal nerve fibre length differ between type 1 and type 2 diabetes.

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

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

<i>Objective: To quantify and contextualize the risk for COVID-19 related hospitalization and illness severity in type 1 diabetes.</i> <p> </p> <p><i>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. </i></p> <p> </p> <p><i>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.</i></p> <p> </p> <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>

scholarly journals Diagnosis of neuropathy and risk factors for corneal nerve loss in type 1 and type 2 diabetes: A corneal confocal microscopy study

2020 ◽  
Author(s):  
Maryam Ferdousi ◽  
Alise Kalteniece ◽  
Shazli Azmi ◽  
Ioannis N Petropoulos ◽  
Georgios Ponirakis ◽  
...  
Keyword(s):  
Risk Factors ◽  
Type 2 Diabetes ◽  
Type 1 Diabetes ◽  
Peripheral Neuropathy ◽  
Nerve Fibre ◽  
Fibre Length ◽  
Corneal Confocal Microscopy ◽  
Corneal Nerve

<b>Purpose: </b>To assess the diagnostic utility of corneal confocal microscopy (CCM) for diabetic peripheral neuropathy (DPN) and the risk factors for corneal nerve loss. <p><b>Methods: </b>490 participants including 72 healthy controls, 149 with type 1 diabetes and 269 with type 2 diabetes underwent detailed assessment of peripheral neuropathy and CCM in relation to risk factors.</p> <p><b>Results: </b>Corneal nerve fibre density (CNFD) (P<0.0001, P<0.0001), branch density (CNBD) (P<0.0001, P<0.0001) and length (CNFL) (P<0.0001, P=0.02) were significantly lower in patients with type 1 and type 2 diabetes, compared to controls. CNFD (P<0.0001), CNBD (P<0.0001) and CNFL (P<0.0001) were lower in type 1 diabetes compared to type 2 diabetes. Receiver operating characteristics (ROC) curve analysis for the diagnosis of DPN demonstrated a good area under the curve (AUC) for CNFD=0.81, CNBD=0.74 and CNFL=0.73. Multivariable regression analysis showed a significant association between reduced corneal nerve fibre length with age (β=-0.27, P=0.007), HbA1c (β=-1.1, P=0.01) and weight (β=-0.14, P=0.03) in patients with type 2 diabetes and with duration of diabetes (β=-0.13, P=0.02), LDL cholesterol (β=1.8, P=0.04), and triglycerides (β=-2.87, P=0.009) in patients with type 1 diabetes. </p> <b>Conclusion: </b>CCM identifies more severe corneal nerve loss in patients with type 1 compared to type 2 diabetes and shows good diagnostic accuracy for DPN. Furthermore, the risk factors for a reduction in corneal nerve fibre length differ between type 1 and type 2 diabetes.

scholarly journals Practical aspects of cardiovascular risk factors identify in patients with diabetes type 1 and 2

2011 ◽  
Vol 3 (1) ◽  
pp. 4-9
Author(s):  
Mehman N. Mamedov ◽  
Marina N. Kovrigina ◽  
Marina B. Buzurtanov
Keyword(s):  
Risk Factors ◽  
Blood Pressure ◽  
Type 2 Diabetes ◽  
Type 1 Diabetes ◽  
Cardiovascular Risk ◽  
Cardiovascular Risk Factors ◽  
Capillary Blood ◽  
The Difference

The aim of this study was to investigate the characteristics of the risk factors and comparative analysis of different methods of identify of cardiovascular risk factors in outpatient with diabetes mellitus (DM) type 1 and 2. Methods. In cross-sectional clinical study included 244 persons with type 1 and type 2 mean age 46,4 yrs. All patients were interviewed using a questionnaire of WHO, they were measured blood pressure, heart rate, anthropometric data, including total fat mass with fat analyzer OMRON BF508. All patients were also measured venous and capillary blood glucose level, glycated hemoglobin (%), total cholesterol and triglycerides. Results. The difference between the two measurement glucose methods (in venous and capillary blood) was 6,7 0,6%. In patients with type 1 diabetes hypertension occurs 2.5 times in less compared with patients with type 2 diabetes. The difference between blood pressure measured by mechanical and automatic devices was less than 1%. The percentage of body fat, including visceral fat, both women and men with type 2 diabetes by age corresponds to the high deviation from the norm. Most men and women with type 1 and type 2 had hypercholesterolemia. Conclusion. Traditional cardiovascular risk factors are identified, not only among individuals with type 2 diabetes, but among patients with type 1 diabetes.

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

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

<i>Objective: To quantify and contextualize the risk for COVID-19 related hospitalization and illness severity in type 1 diabetes.</i> <p> </p> <p><i>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. </i></p> <p> </p> <p><i>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.</i></p> <p> </p> <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>

Periphere arterielle Verschlusskrankheit bei Diabetes mellitus Typ 1 und Typ 2: Unterscheiden sich die Risikofaktoren?

VASA ◽  
2002 ◽  
Vol 31 (4) ◽  
pp. 249-254 ◽  
Author(s):  
Zander ◽  
Heinke ◽  
Reindel ◽  
Kohnert ◽  
Kairies ◽  
...  
Keyword(s):  
Risk Factors ◽  
Type 2 Diabetes ◽  
Type 1 Diabetes ◽  
Multivariate Logistic Regression Analysis ◽  
Diabetes Duration ◽  
Diabetic Patients ◽  
Cross Sectional ◽  
The Impact

Background : Diabetic patients have increased prevalence of peripheral arterial disease (PAD). It is not clearly shown whether the prognostic factors are identical in relation to the type of diabetes. This study was done to compare the associations of PAD with risk factors and with micro-and macrovascular complications of inpatients with type 1 and type 2 diabetes. Methods: In a retrospective cross-sectional study 1087 patients with type 1 diabetes and 1060 patients with type 2 diabetes were examined. PAD was diagnosed when ankle-brachial-pressure-index (ABI) was < 1.0. In cases with incompressible arteries (mediasclerosis) pulse wave formes were analyzed. Multivariate logistic regression analysis was applied to evaluate the impact of different variables on PAD risk , after adjusting for different variables separately. Results: In both types of diabetes (type 1 vs. type 2) PAD risk (odds ratio; OR) was increased in the presence of coronary heart disease (OR 9.3 vs. 3.5), diabetic nephropathy (OR 3.0 vs.2.8), neuropathy (OR 7.9 vs. 1.8), foot ulceration (OR 8.9 vs. 5.5), increased daily insulin requirement > 0.6 m/kg b.w. (OR 5.2 vs. 2.9), diabetes duration of 20–29 years (OR 28.9) and > 30 years (OR 51.1) in type 1 diabetes, and diabetes duration of 10–19 years (OR 3.8) and > 20 years (OR 4.3) in type 2 diabetes. In type 2 diabetes, PAD risk was associated with microalbuminuria (OR 2.1), macroalbuminuria (OR 3.3), background retinopathy (OR 1.9), proliferative retinopathy (OR 2.8), increased triglycerides (TG) (OR 1.7) and decreased HDL-cholesterol (HDL-C > 0.90 mmol/l: OR 0.49). Conclusions: PAD risk factors and micro- and macrovascular comorbidity are very similar in type 1 and type 2 diabetes.

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

2017 ◽  
Author(s):  
Marwa Omri ◽  
Rayene Ben Mohamed ◽  
Imen Rezgani ◽  
Sana Mhidhi ◽  
Aroua Temessek ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Type 1 Diabetes ◽  
Diabetic Ketoacidosis
Sign in / Sign up

Export Citation Format

Share Document