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RationaleType 2 diabetes mellitus (T2D) is an established risk factor for cerebrovascular disease but the mechanisms underlying this association remain elusive. Disentangling the causal effects of T2D, hyperglycemia, and pre-diabetic phenotypes (insulin resistance, β-cell dysfunction) on major etiological stroke subtypes (ischemic stroke, intracerebral hemorrhage, ischemic stroke subtypes) could inform the development of preventive strategies.ObjectiveWe employed Mendelian randomization (MR) to explore the effects of genetic predisposition to T2D, hyperglycemia, insulin resistance, and β-cell dysfunction on risk of stroke subtypes and related cerebrovascular phenotypes.Methods and ResultsWe selected instruments for genetic predisposition to T2D, HbA1c levels, fasting glucose levels, insulin resistance, and β-cell dysfunction (proxied by pro-insulin levels) based on published genome-wide association studies (up to 898,130 individuals). Applying two-sample MR, we examined associations with ischemic stroke, intracerebral hemorrhage, and ischemic stroke subtypes (large artery, cardioembolic, small vessel stroke; up to 60,341 cases and 454,450 controls). We further explored associations with the related phenotypes of carotid atherosclerosis, imaging markers of cerebral white matter integrity, and brain atrophy. Genetic predisposition to T2D and elevated HbA1c levels in the pre-diabetic range were associated with higher risk of any ischemic stroke, large artery stroke, carotid plaque and small vessel stroke. Independently of HbA1c levels, we further found genetic predisposition to insulin resistance to be associated with large artery and small vessel stroke, whereas predisposition to β-cell dysfunction was associated with small vessel stroke. Predisposition to β-cell dysfunction was further associated with intracerebral hemorrhage, lower grey matter volume, and total brain volume.ConclusionsThis study supports causal effects of T2D and hyperglycemia on large artery and small vessel stroke. We show differential effects of genetically determined insulin resistance and β-cell dysfunction on large artery and small vessel stroke that might have implications for anti-diabetic treatments targeting these mechanisms.

Background: Attaining acceptable levels of LDL Cholesterol (LDL-C) significantly improves cardiovascular (CV) outcomes in patients with type 2 diabetes mellitus (T2DM). The LDL-C target attainment and the characteristics of patients attaining these targets were investigated in this study. Furthermore, the reasons for not choosing statins and the physicians’ attitudes on the treatment of diabetic dyslipidemia were also examined. Methods: A nationwide, cross-sectional survey was conducted in tertiary centers for diabetes management. Adult patients with T2DM, who were under follow-up for at least a year in outpatient clinics, were consecutively enrolled for the study. LDL-C goals were defined as below 70 mg/dL for patients with macrovascular complications or diabetic nephropathy, and below 100 mg/dL for other patients. Data about lipid-lowering medications were self-reported. Results: A total of 4504 patients (female: 58.6%) were enrolled for the study. The mean HbA1c and diabetes duration was 7.73 ± 1.74% and 10.9 ± 7.5 years, respectively. The need for statin treatment was 94.9% (n = 4262); however, only 42.4% (n = 1807) of these patients were under treatment, and only 24.8% (n = 448) of these patients achieved LDL-C targets. The main reason for statin discontinuation was negative media coverage (87.5%), while only a minority of patients (12.5%) mentioned side effects. Physicians initiated lipid-lowering therapy in only 20.3% of patients with high LDL-C levels. It was observed that the female gender was a significant independent predictor of not attaining LDL-C goals (OR: 0.70, 95% CI: 0.59–0.83). Conclusions: Less than 50 % of patients with T2DM who need statins were under treatment, and only a quarter of them attained their LDL-C targets. There exists a significant gap between the guideline recommendations and the real-world evidence in the treatment of dyslipidemia in T2DM. © 2020, The Author(s)., Ankara Universitesi; University of Health Sciences Lahore, UHS, The physicians and nurses in every TEMD study center who took role in recruitment of patients are acknowledged as the collaborators of TEMD Dyslipidemia Study (see supplementary data). The authors also appreciatively acknowledge the critical reviews and contributions of Professor Meral Kayikcioglu and Professor Ilker Tasci during the preparation of the manuscript. Consortium members and affiliations TEMD Study Group: Sibel Guldiken19, Semra Ayturk19, Murat Yilmaz20, Mehmet Asik21, Nevin Dinccag18, Ramazan Cakmak18, Fulya Turker18, Cemile Idiz18, Hulya Hacisahinogullari18, Elif Bagdemir18, Busra Yildiz18, Ozlem Haliloglu16, Seda Sancak22, Levent Ozsari23, Eylem Cagiltay23, Oguzhan Deyneli24, Eren Imre24, Sait Gonen25, S Nur Boysan19, Yuksel Altuntas26, Feyza Yener Ozturk26, Meral Mert27, Hamide Piskinpasa27, Hasan Aydin28, Sazi Imamoglu29, Ozen Oz Gul25, Sinem Kucuksarac Kiyici30, Berrin Cetinarslan31, Alev Selek31, Teoman Dogru32, Ali Kirik32, Belgin Efe14, Ahmet Kaya33, Ilker Cordan33, Suleyman Baldane34, Cem Onur Kirac34, Zehra Capa3, Mustafa Cesur35, Ilhan Yetkin36, Demet Corapcioglu37, Sule Canlar37, Okan Bulent Yildiz38, Suleyman Nahit Sendur38, Bekir Cakir9, Ahmet Corakci39, Mustafa Kutlu40, Neslihan Bascil Tutuncu41, Yusuf Bozkus41, Erman Cakal42, Berrin Demirbas43, Sibel Ertek44, Mustafa Altay45, Murat Dagdeviren45, Amir Hossein Abedi1, Sevki Cetinkalp46, Hatice Ozisik46, Guzide Gonca Oruk47, Serkan Yener48, Basak Ozgen Saydam48, Engin Guney49, Mustafa Unubol49, Guzin Fidan Yaylali50, Senay Topsakal50, Zeliha Hekimsoy51, Gulhan Akbaba52, Ibrahim Aslan53, Sefika Dalkiran13, Esen Akbay54, Kamile Gul55, Muge Ozsan Yilmaz6, Emre Bozkirli56, Seher Cetinkaya Altuntas12, Aysegul Atmaca57, Elif Tutku Durmuş57, Turkan Mete58, Faruk Kutluturk59, Ferit Kerim Kucukler60, Oguz Dikbas61, Safak Akin62, Irfan Nuhoglu63, Halil Onder Ersoz63, Taner Bayraktaroglu64, Pınar Sisman65, Ibrahim Sahin66, Sedat Cetin66, Ilyas Capoglu67, Emin Murat Akbas67, Rıfkı Ucler68, Mehmet Ali Eren4, Alpaslan Kemal Tuzcu69, Zafer Pekkolay69, Mesut Ozkaya70, Mustafa Araz71.19Trakya University, School of Medicine, Department of Endocrinology and Metabolism, Turkey.20Corlu REYAP Private Hospital, Department of Endocrinology and Metabolism, Turkey.21Canakkale 18 March University, School of Medicine, Department of Endocrinology and Metabolism, Turkey.22University of Health Sciences, School of Medicine, Fatih Sultan Mehmet Training and Research Hospital, Department of Endocrinology and Metabolism, Turkey.23University of Health Sciences, School of Medicine, Sultanabdulhamit Training and Research Hospital, Department of Endocrinology and Metabolism, Turkey.24Marmara University, School of Medicine, Department of Endocrinology and Metabolism, Turkey.25Istanbul Science University, School of Medicine, Department of Endocrinology and Metabolism, Turkey.26University of Health Sciences, School of Medicine, Sisli Hamidiye Etfal Training and Research Hospital, Department of Endocrinology and Metabolism, Turkey.27University of Health Sciences, School of Medicine, İstanbul Bakırkoy Dr. Sadi Konuk Training and Research Hospital, Department of Endocrinology and Metabolism, Turkey.28Yeditepe University, School of Medicine, Department of Endocrinology and Metabolism, Turkey.29Private Office.30University of Health Sciences, School of Medicine, Bursa Sevket Yılmaz Training and Research Hospital, Department of Endocrinology and Metabolism, Turkey.31Kocaeli University, School of Medicine, Department of Endocrinology and Metabolism, Turkey.32Balikesir University, School of Medicine, Department of Internal Medicine, Turkey.33Necmettin Erbakan University, School of Medicine, Department of Endocrinology and Metabolism, Turkey.34Selcuk University, School of Medicine, Department of Endocrinology and Metabolism, Turkey.35Private Guven Hospital, Department of Endocrinology and Metabolism, Turkey.36Gazi University, School of Medicine, Department of Endocrinology and Metabolism, Turkey.37Ankara University, School of Medicine, Department of Endocrinology and Metabolism, Turkey.38Hacettepe University, School of Medicine, Department of Endocrinology and Metabolism, Turkey.39Ufuk University, School of Medicine, Department of Endocrinology and Metabolism, Turkey.40Private Bayındır Hospital, Department of Endocrinology and Metabolism, Turkey.41Baskent University, School of Medicine, Department of Endocrinology and Metabolism, Turkey.42University of Health Sciences, School of Medicine, Diskapi Yildirim Beyazit Training and Research Hospital, Department of Endocrinology and Metabolism, Turkey.43TOBB University, School of Medicine, Department of Endocrinology and Metabolism, Turkey.44Private Memorial Hospital, Department of Endocrinology and Metabolism, Turkey.45University of Health Sciences, School of Medicine, Kecioren Training and Research Hospital, Department of Endocrinology and Metabolism, Turkey.46Ege University, School of Medicine, Department of Endocrinology and Metabolism, Turkey.47University of Health Sciences, School of Medicine, Izmir Ataturk Training and Research Hospital, Department of Endocrinology and Metabolism, Turkey.48Dokuz Eylul University, School of Medicine, Department of Endocrinology and Metabolism, Turkey.49Adnan Menderes University, School of Medicine, Department of Endocrinology and Metabolism, Turkey.50Pamukkale University, School of Medicine, Department of Endocrinology and Metabolism, Turkey.51Celal Bayar University, School of Medicine, Department of Endocrinology and Metabolism, Turkey.52Mugla University, School of Medicine, Department of Endocrinology and Metabolism, Turkey.53University of Health Sciences, School of Medicine, Antalya Training and Research Hospital, Department of Endocrinology and Metabolism, Turkey.54Mersin University, School of Medicine, Department of Endocrinology and Metabolism, Turkey.55Kahramanmaras Sutcu Imam University, School of Medicine, Department of Endocrinology and Metabolism, Turkey.56Baskent University, Adana Training Hospital, Department of Endocrinology and Metabolism, Turkey.5719 Mayıs University, School of Medicine, Department of Endocrinology and Metabolism, Turkey.58University of Health Sciences, School of Medicine, Samsun Training and Research Hospital, Department of Endocrinology and Metabolism, Turkey.59Gaziosmanpasa University, School of Medicine, Department of Endocrinology and Metabolism, Turkey.60Hitit University, School of Medicine, Department of Endocrinology and Metabolism, Turkey.61Giresun University, School of Medicine, Department of Endocrinology and Metabolism, Turkey.62Recep Tayyip Erdogan University, School of Medicine, Department of Endocrinology and Metabolism, Turkey.63Karadeniz Technical University, School of Medicine, Department of Endocrinology and Metabolism, Turkey.64Bulent Ecevit University, School of Medicine, Department of Endocrinology and Metabolism, Turkey.65Kars Harakani State Hospital, Department of Endocrinology and Metabolism, Turkey.66Inonu University, School of Medicine, Department of Endocrinology and Metabolism, Turkey.67Erzincan University, School of Medicine, Department of Endocrinology and Metabolism, Turkey.68Yuzuncu Yil University, School of Medicine, Department of Endocrinology and Metabolism, Turkey.69Dicle University, School of Medicine, Department of Endocrinology and Metabolism, Turkey.70University of Health Sciences, School of Medicine, Gaziantep Ersin Arslan Research and Training Hospital, Turkey.71Gaziantep University, School of Medicine, Department of Endocrinology and Metabolism, Turkey.

Introduction: Dapagliflozin is a sodium-glucose cotransporter 2 inhibitor that improves glycemic control in patients with type II diabetes mellitus which increasing urinary glucose excretion. With numerous controlled experimental studies of dapagliflozin, evaluation of real-life data after entry into clinical practice is an important condition. In our study, the effects of dapagliflozin (10 mg) on lipid profile were investigated retrospectively. Methods: A total of thirty-one type 2 diabetic patients with HbA1c level between 6,5% and 13%, aged 45 -80 years and whose body mass index higher than 20 kg/m(2) were enrolled to the study. Data before dapagliflozin treatment and three and six months results were recorded. Results: Dapagliflozin reduced HbA1c levels by 0,9% at 3 months and 0,79% at 6 months. Total cholesterol level decreased 17,6 mg/dl, LDL cholesterol level decreased 13,4 mg/dl and triglyceride level by 25.9mg/dl at the 6th months and it is observed that there is no serious side effect on the usage for 6 months. Conclusion: There are conflicting results about the effect of SGLT2 inhibitors on the lipid profile in the literature. According to our data, dapagliflozin has positive effects on lipid profile as weight and glycemic control and it is well tolerated. Therefore, dapagliflozin therapy is beneficial because of the positive change in lipid profile and weight loss in diabetic patients with overweight and hyperlipidemia.

Functional near-infrared spectroscopy (fNIRS) and vagus somatosensory evoked potentials (VSEP) show deviant patterns in subjects with Alzheimer’s disease (AD) compared to healthy controls. We now aimed at testing the predictive value of these methods in the early diagnosis of AD. The Vogel study is a prospective, observational, long-term follow-up study with three time points of investigation within 6 years. Residents of the city of Wurzburg born between 1936 and 1941 were recruited. Every participant underwent physical, psychiatric, and laboratory examinations, and performed an intense neuropsychological testing as well as VSEP and NIRS according to the published procedures. 604 subjects were included. Mean age of the participants was 73.9 ± 1.55 years. The most frequent pathological physical and laboratory examination results were observed for blood pressure (62%), body weight (54%), HbA1c (16%), cholesterol (42%), and homocysteine (69%). Comprehensive analysis of cognitive testing showed mild cognitive impairment (MCI) in 12.3% of the patients. Concurrent major depression was found in 6.6% of the patients. We observed a high rate of MCI and somatic comorbidity in our cohort. The high rate of vascular risk factors and depressive symptoms, all of which are known risk factors of AD, is consistent with the notion that there are multiple options to prevent or postpone the onset of AD in a geriatric population like the one of the Vogel studies.

We investigated the associations of fasting (FG) and 2-h postload (2HG) plasma glucose from oral glucose tolerance test (OGTT) with gray (GMV) and white (WMV) matter volume.We analyzed data from 1330 subjects without known diabetes mellitus, aged 21 to 81, from the second cohort (SHIP-Trend-0) of the population-based Study of Health in Pomerania (SHIP). Following the OGTT, individuals were classified in five groups (according to the American Diabetes Association criteria): normal glucose tolerance (NGT), isolated impaired fasting glucose (i-IFG), isolated impaired glucose tolerance (i-IGT), combined IFG and IGT (IFG + IGT) and unknown type 2 diabetes mellitus (UDM). GMV and WMV were determined by magnetic resonance imaging. FG, 2HG and OGTT groups were associated with GMV and WMV by linear regression models adjusted for confounders. FG and 2HG were inversely associated with GMV. The adjusted mean GMV, when compared with the NGT group (584 ml [95% CI: 581 to 587]), was significantly lower in the groups i-IFG (578 ml [95% CI: 573 to 582]; p = 0.035) and UDM (562 ml [95% CI: 551 to 573]; p 0.001), but not different in the i-IGT (586 ml [95% CI: 576 to 596]; p = 0.688) and IFG + IGT (579 ml [95% CI: 571 to 586]; p = 0.209) groups. There were no associations of FG, 2HG and OGTT parameters with WMV.Our findings suggest that elevated FG levels, even within the prediabetic range, might already have some harmful effects on GMV.

Introduction: Glycosylated hemoglobin (HbA1c) concentrations measured in clinical chemistry laboratories show large differences between their interlaboratory reported values. Laboratory measurements of quality performance should be based on quantitative data. The sigma metrics model provides an objective method for the assessment of current HbA1c assays and is useful in quality management planning. The aim of our study was to evaluate the analytical performance of the MQ-2000 PT HbA1c analyzer test results in the context of our operating conditions on the sigma scale. Materials and methods: The coefficient of variation was determined from the calculated mean and standard deviation evaluated from internal quality control (QC) (N = 168 days) (Shanghai Huachen Biological Reagent Co. Ltd, China) data, and records of external quality data (KBUDEK, Istanbul, Turkey) measured in the period from May to November 2013 were used to determine the bias. The resulting data and total allowable error rate (TEA = 10%) from the Clinical Laboratory Improvement Amendments of 1988 (CLIA’88) were used to calculate the sigma level. Results: The calculated coefficient of variations (CVs) at the two levels, normal (QC1 = 36.6 ± 2.38 mmol/mol) and pathological (QC2 = 84.7 ± 2.68 mmol/mol), were 6.5% and 3.1%, respectively. The average bias between the external QC and MQ-2000 PT during the study period was 4.3%. The calculated average sigma value was 1.19. Conclusions: The MQ-2000 PT HbA1c is a new analyser in the market; there is need for improvement and the method should be controlled with greater attention to ensure quality.

Although it is widely accepted that physical exercise promotes weight loss, physical exercise alone had been found to result in only marginal weight loss compared to no treatment. Interestingly, both subjective and objective sleep duration have been shown to be negatively correlated to the body mass index (BMI). Despite this growing evidence of a relation between sleep duration and body weight, the role of habitual sleep duration in physical exercise-induced weight loss has not been studied so far. Twenty-two healthy elderly good sleepers aged 61–76 years (mean 68.36 years, 55 % female, BMI mean 25.15 kg/m2) either took part in a 12-week aerobic endurance training (3 × 30 min/week) or in a relaxation control (2 × 45 min/week). The BMI was assessed prior to and after intervention. Subjects maintained sleep logs every morning/evening during the training period, allowing for calculation of habitual sleep duration. Besides a significant main effect of the type of training, a significant interaction of type of training and habitual sleep duration was observed: while after treadmill training subjects who slept less than 7.5 h/night during intervention reduced their BMI by nearly 4 %, a comparable decrease in the BMI was found neither in subjects who slept more than 7.5 h nor after relaxation training independent of sleep duration. Sleep duration itself did not change in any group. Although results should be interpreted with caution due to the small sample size, this is the first study to indicate that physical exercise might compensate for disturbed body weight regulation associated with short sleep duration.

Type 2 diabetes mellitus (T2DM) is a risk factor of dementia. The effect of T2DM treatment quality on dementia risk, however, is unclear. 1,342 elderly individuals recruited via general practitioner registries (AgeCoDe cohort) were analyzed. This study analyzed the association between HbA1c level and the incidence of all-cause dementia (ACD) and of Alzheimer's disease dementia (referred to here as AD). HbA1c levels ≥6.5% were associated with 2.8-fold increased risk of incident ACD (p = 0.027) and for AD (p = 0.047). HbA1c levels ≥7% were associated with a five-fold increased risk of incident ACD (p = 0.001) and 4.7-fold increased risk of incident AD (p = 0.004). The T2DM diagnosis per se did not increase the risk of either ACD or AD. Higher levels of HbA1c are associated with increased risk of ACD and AD in an elderly population. T2DM diagnosis was not associated with increased risk if HbA1c levels were below 7%.

Background: Self-management is crucial in the daily management of type 2 diabetes. It has been suggested that mHealth may be an important method for enhancing self-management when delivered in combination with health counseling. Objective: The objective of this study was to test whether the use of a mobile phone–based self-management system used for 1 year, with or without telephone health counseling by a diabetes specialist nurse for the first 4 months, could improve glycated hemoglobin A1c (HbA1c) level, self-management, and health-related quality of life compared with usual care. Methods: We conducted a 3-arm prospective randomized controlled trial involving 2 intervention groups and 1 control group. Eligible participants were persons with type 2 diabetes with an HbA1c level ≥7.1% (≥54.1 mmol/mol) and aged ≥18 years. Both intervention groups received the mobile phone–based self-management system Few Touch Application (FTA). The FTA consisted of a blood glucose–measuring system with automatic wireless data transfer, diet manual, physical activity registration, and management of personal goals, all recorded and operated using a diabetes diary app on the mobile phone. In addition, one intervention group received health counseling based on behavior change theory and delivered by a diabetes specialist nurse for the first 4 months after randomization. All groups received usual care by their general practitioner. The primary outcome was HbA1c level. Secondary outcomes were self-management (heiQ), health-related quality of life (SF-36), depressive symptoms (CES-D), and lifestyle changes (dietary habits and physical activity). Data were analyzed using univariate methods (t test, ANOVA) and multivariate linear and logistic regression. Results: A total of 151 participants were randomized: 51 to the FTA group, 50 to the FTA-health counseling (FTA-HC) group, and 50 to the control group. Follow-up data after 1 year were available for 120 participants (79%). HbA1c level decreased in all groups, but did not differ between groups after 1 year. The mean change in the heiQ domain skills and technique acquisition was significantly greater in the FTA-HC group after adjusting for age, gender, and education (P=.04). Other secondary outcomes did not differ between groups after 1 year. In the FTA group, 39% were substantial users of the app; 34% of the FTA-HC group were substantial users. Those aged ≥63 years used the app more than their younger counterparts did (OR 2.7; 95% CI 1.02-7.12; P=.045). Conclusions: The change in HbA1c level did not differ between groups after the 1-year intervention. Secondary outcomes did not differ between groups except for an increase in the self-management domain of skill and technique acquisition in the FTA-HC group. Older participants used the app more than the younger participants did.