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Aims: This study assessed the achievement rates of metabolic risk factor targets and their association with clinical characteristics and comorbidities among individuals with type 2 diabetes (T2D) treated in the primary care in Austria. Materials and Methods: A countrywide cross‐sectional study, the AUSTRO‐PROFIT, was conducted in Austria from 2021 to 2023 on 635 individuals with T2D. Metabolic risk factor targets were defined as the percentage of people achieving low‐density lipoprotein cholesterol (LDL‐C) <70 mg/dL (or < 55 mg/dL if cardiovascular or microvascular disease was present), glycated haemoglobin (HbA1c) <7% (53 mmol/mol) and blood pressure < 140/90 mmHg. Results: The mean age of the participants was 65.7 ± 11.2 years; the median duration of T2D was 8 (4–14) years; and 58.7% of the participants were male. The percentages of participants achieving LDL‐C, HbA1c, blood pressure and all targets were 44%, 53%, 57% and 13%, respectively. Older age, longer T2D duration, cardiovascular disease and microvascular complications were associated with suboptimal achievement of metabolic risk factor targets. Conclusions: The AUSTRO‐PROFIT study revealed notable variations in metabolic targets achievement with respect to clinical characteristics and comorbidities. These findings underscore the importance of establishing national diabetes registries and implementing multifactorial targeted and individualized interventions to further improve the quality of T2D care in primary care settings in Austria. [ABSTRACT FROM AUTHOR]

Background: Hyperferritinemia (HF) is a common finding and can be considered as metabolic HF (MHF) in combination with metabolic diseases. The definition of MHF was heterogenous until a consensus statement was published recently. Our aim was to apply the definition of MHF to provide data on the prevalence and characteristics of MHF in a Central-European cohort. Methods: This study was a retrospective analysis of the Paracelsus 10,000 study, a population-based cohort study from the region of Salzburg, Austria. We included 8408 participants, aged 40–77. Participants with HF were divided into three categories according to their level of HF and evaluated for metabolic co-morbidities defined by the proposed criteria for MHF. Results: HF was present in 13% (n = 1111) with a clear male preponderance (n = 771, 69% of HF). Within the HF group, 81% (n = 901) of subjects fulfilled the metabolic criteria and were defined as MHF, of which 75% (n = 674) were characterized by a major criterion. In the remaining HF cohort, 52% (n = 227 of 437) of subjects were classified as MHF after application of the minor criteria. Conclusion: HF is a common finding in the general middle-aged population and the majority of cases are classified as MHF. The new classification provides useful criteria for defining MHF. [ABSTRACT FROM AUTHOR]

Aims To examine the association of frozen shoulder (FS) with demographic and diabetes-related outcomes in individuals with type 1 (T1D) or type 2 (T2D) diabetes aged ≥30 years. Materials and methods Multivariable logistic regression models, adjusted for demographics were used to calculate the proportion of FS in association with age, gender, diabetes duration, body mass index (BMI), haemoglobin A1C (HbA1c) and diabetes treatment. Results The unadjusted percentage of FS was higher in T1D compared to T2D (0.22% vs. 0.06%). In T1D, adjusted regression models revealed higher prevalence of FS in women than men (0.26 [0.20–0.34] % vs. 0.15 [0.11–0.21] %, p =0.010). No significant relationship of age and BMI with FS was found in both diabetes types. Longer diabetes duration was associated with a higher proportion of FS in T1D (p <0.001) and T2D (p =0.004). In T1D, HbA1c >7% was related to a higher proportion of FS compared to HbA1c ≤7% (0.25 [0.19–0.32] vs. 0.12 [0.08–0.20] %, p =0.007), while an inverse relationship was found in T2D (HbA1c ≤7%: 0.08 [0.07–0.10] vs. HbA1c >7%: 0.05 [0.04–0.06] %, p =0.001). Conclusions Different associations of FS with gender and HbA1c were observed for T1D and T2D; however, longer diabetes duration increases the risk for FS independent of diabetes type. Musculoskeletal diseases are still underreported in individuals with diabetes and awareness should be raised for FS as a specific diabetes complication. [ABSTRACT FROM AUTHOR]

Background: This study assessed the predictive performance of inflammatory, hepatic, coagulation, and cardiac biomarkers in patients with prediabetes and diabetes mellitus hospitalized for COVID-19 in Austria. Methods: This was an analysis of a multicenter cohort study of 747 patients with diabetes mellitus or prediabetes hospitalized for COVID-19 in 11 hospitals in Austria. The primary outcome of this study was in-hospital mortality. The predictor variables included demographic characteristics, clinical parameters, comorbidities, use of medication, disease severity, and laboratory measurements of biomarkers. The association between biomarkers and in-hospital mortality was assessed using simple and multiple logistic regression analyses. The predictive performance of biomarkers was assessed using discrimination and calibration. Results: In our analysis, 70.8% had type 2 diabetes mellitus, 5.8% had type 1 diabetes mellitus, 14.9% had prediabetes, and 8.6% had other types of diabetes mellitus. The mean age was 70.3 ± 13.3 years, and 69.3% of patients were men. A total of 19.0% of patients died in the hospital. In multiple logistic regression analysis, LDH, CRP, IL-6, PCT, AST-ALT ratio, NT-proBNP, and Troponin T were significantly associated with in-hospital mortality. The discrimination of NT-proBNP was 74%, and that of Troponin T was 81%. The calibration of NT-proBNP was adequate (p = 0.302), while it was inadequate for Troponin T (p = 0.010). Conclusion: Troponin T showed excellent predictive performance, while NT-proBNP showed good predictive performance for assessing in-hospital mortality in patients with diabetes mellitus hospitalized with COVID-19. Therefore, these cardiac biomarkers may be used for prognostication of COVID-19 patients. [ABSTRACT FROM AUTHOR]

Objectives: We studied apolipoprotein C‐III (apoC‐III) in relation to diabetic kidney disease (DKD), cardiovascular outcomes, and mortality in type 1 diabetes. Methods: The cohort comprised 3966 participants from the prospective observational Finnish Diabetic Nephropathy Study. Progression of DKD was determined from medical records. A major adverse cardiac event (MACE) was defined as acute myocardial infarction, coronary revascularization, stroke, or cardiovascular mortality through 2017. Cardiovascular and mortality data were retrieved from national registries. Results: ApoC‐III predicted DKD progression independent of sex, diabetes duration, blood pressure, HbA1c, smoking, LDL‐cholesterol, lipid‐lowering medication, DKD category, and remnant cholesterol (hazard ratio [HR] 1.43 [95% confidence interval 1.05–1.94], p = 0.02). ApoC‐III also predicted the MACE in a multivariable regression analysis; however, it was not independent of remnant cholesterol (HR 1.05 [0.81–1.36, p = 0.71] with remnant cholesterol; 1.30 [1.03–1.64, p = 0.03] without). DKD‐specific analyses revealed that the association was driven by individuals with albuminuria, as no link between apoC‐III and the outcome was observed in the normal albumin excretion or kidney failure categories. The same was observed for mortality: Individuals with albuminuria had an adjusted HR of 1.49 (1.03–2.16, p = 0.03) for premature death, while no association was found in the other groups. The highest apoC‐III quartile displayed a markedly higher risk of MACE and death than the lower quartiles; however, this nonlinear relationship flattened after adjustment. Conclusions: The impact of apoC‐III on MACE risk and mortality is restricted to those with albuminuria among individuals with type 1 diabetes. This study also revealed that apoC‐III predicts DKD progression, independent of the initial DKD category. [ABSTRACT FROM AUTHOR]

Aim: To assess predictors of in‐hospital mortality in people with prediabetes and diabetes hospitalized for COVID‐19 infection and to develop a risk score for identifying those at the greatest risk of a fatal outcome. Materials and Methods: A combined prospective and retrospective, multicentre, cohort study was conducted at 10 sites in Austria in 247 people with diabetes or newly diagnosed prediabetes who were hospitalized with COVID‐19. The primary outcome was in‐hospital mortality and the predictor variables upon admission included clinical data, co‐morbidities of diabetes or laboratory data. Logistic regression analyses were performed to identify significant predictors and to develop a risk score for in‐hospital mortality. Results: The mean age of people hospitalized (n = 238) for COVID‐19 was 71.1 ± 12.9 years, 63.6% were males, 75.6% had type 2 diabetes, 4.6% had type 1 diabetes and 19.8% had prediabetes. The mean duration of hospital stay was 18 ± 16 days, 23.9% required ventilation therapy and 24.4% died in the hospital. The mortality rate in people with diabetes was numerically higher (26.7%) compared with those with prediabetes (14.9%) but without statistical significance (P =.128). A score including age, arterial occlusive disease, C‐reactive protein, estimated glomerular filtration rate and aspartate aminotransferase levels at admission predicted in‐hospital mortality with a C‐statistic of 0.889 (95% CI: 0.837‐0.941) and calibration of 1.000 (P =.909). Conclusions: The in‐hospital mortality for COVID‐19 was high in people with diabetes but not significantly different to the risk in people with prediabetes. A risk score using five routinely available patient variables showed excellent predictive performance for assessing in‐hospital mortality. [ABSTRACT FROM AUTHOR]

Background: It is a matter of debate whether diabetes alone or its associated comorbidities are responsible for severe COVID-19 outcomes. This study assessed the impact of diabetes on intensive care unit (ICU) admission and in-hospital mortality in hospitalized COVID-19 patients. Methods: A retrospective analysis was performed on a countrywide cohort of 40,632 COVID-19 patients hospitalized between March 2020 and March 2021. Data were provided by the Austrian data platform. The association of diabetes with outcomes was assessed using unmatched and propensity-score matched (PSM) logistic regression. Results: 12.2% of patients had diabetes, 14.5% were admitted to the ICU, and 16.2% died in the hospital. Unmatched logistic regression analysis showed a significant association of diabetes (odds ratio [OR]: 1.24, 95% confidence interval [CI]: 1.15–1.34, p < 0.001) with in-hospital mortality, whereas PSM analysis showed no significant association of diabetes with in-hospital mortality (OR: 1.08, 95%CI: 0.97–1.19, p = 0.146). Diabetes was associated with higher odds of ICU admissions in both unmatched (OR: 1.36, 95%CI: 1.25–1.47, p < 0.001) and PSM analysis (OR: 1.15, 95%CI: 1.04–1.28, p = 0.009). Conclusions: People with diabetes were more likely to be admitted to ICU compared to those without diabetes. However, advanced age and comorbidities rather than diabetes itself were associated with increased in-hospital mortality in COVID-19 patients. [ABSTRACT FROM AUTHOR]

The study compares acute complications among adults with type 1 diabetes (T1D) by glycemic control in a U.S. Electronic Health Record database (T1PCO) with the German/Austrian Prospective Diabetes Follow-up (DPV). T1PCO included 31,430 and DPV 5,190 individuals in the analysis (age ≥18 years, T1D duration ≥2 years). During baseline (12 months prior to index date), ≥1 HbA1c and ≥1 insulin prescription was required. Baseline demographics, severe hypoglycemia (SH) and diabetic ketoacidosis (DKA) were analyzed for overall populations, by age group, and glycemic control (HbA1c categories). In both databases, incidence of DKA was highest in those with HbA1c ≥9.0%. Additionally, the incidence of DKA (per year) increased with deteriorating control (HbA1c<8%: 1.3% vs. 1.7%, HbA1c≥10%: 19.9% vs. 22.9%; DPV and T1PCO respectively). In contrast, incidence of SH patterns differed between the databases. While in the T1PCO cohort SH increased with poorer glycemic control, the trend was reversed in DPV cohort (Figure). Real-world comparisons demonstrate the challenges in diabetes care across many countries around the world. Different patterns of incidence of SH by HbA1c may be related to differences in the capture of SH in databases, it may also reflect important differences in diabetes management including access to different types of insulin, CGM, pumps, education and type 1 savvy health care providers. Disclosure: F.L. Zhou: Employee; Self; Sanofi. Stock/Shareholder; Self; Sanofi. J. Hermann: None. J. Pettus: Consultant; Self; Diasome Pharmaceuticals, Inc., Lexicon Pharmaceuticals, Inc., MannKind Corporation, Novo Nordisk Inc., Sanofi. W. Kerner: None. K. Miller: None. L. Stechemesser: None. S. Edelman: Advisory Panel; Self; Brightsight, InPen, Lexicon Pharmaceuticals, Inc., Lilly Diabetes, Novo Nordisk A/S. Board Member; Self; Senseonics. Speaker's Bureau; Self; AstraZeneca, MannKind Corporation, Merck & Co., Inc., Sanofi. A.M. Ibald-Mulli: Employee; Self; Sanofi-Aventis. R.W. Holl: None. Funding: Sanofi [ABSTRACT FROM AUTHOR]

A small proportion of lean patients develop non-alcoholic fatty liver disease (NAFLD). We aimed to report the histological picture of lean NAFLD in comparison to overweight and obese NAFLD patients. Biopsy and clinical data from 466 patients diagnosed with NAFLD were stratified to groups according to body mass index (BMI): lean (BMI ≤ 25.0 kg/m², n confirmed to be appropriate = 74), overweight (BMI > 25.0 ≤ 30.0 kg/m², n = 242) and obese (BMI > 30.0 kg/m², n = 150). Lean NAFLD patients had a higher rate of lobular inflammation compared to overweight patients (12/74; 16.2% vs. 19/242; 7.9%; p = 0.011) but were similar to obese patients (25/150; 16.7%). Ballooning was observed in fewer overweight patients (38/242; 15.7%) compared to lean (19/74; 25.7%; p = 0.014) and obese patients (38/150; 25.3%; p = 0.006). Overweight patients had a lower rate of portal and periportal fibrosis (32/242; 13.2%) than lean (19/74; 25.7%; p = 0.019) and obese patients (37/150; 24.7%; p = 0.016). The rate of cirrhosis was higher in lean patients (6/74; 8.1%) compared to overweight (4/242; 1.7%; p = 0.010) and obese patients (3/150; 2.0% p = 0.027). In total, 60/466; 12.9% patients were diagnosed with non-alcoholic steatohepatitis (NASH). The rate of NASH was higher in lean (14/74; 18.9% p = 0.01) and obese (26/150; 17.3%; p = 0.007) compared to overweight patients (20/242; 8.3%)). Among lean patients, fasting glucose, INR and use of thyroid hormone replacement therapy were independent predictors of NASH in a multivariate model. Lean NAFLD patients were characterized by a severe histological picture similar to obese patients but are more progressed compared to overweight patients. Fasting glucose, international normalized ratio (INR) and the use of thyroid hormone replacement may serve as indicators for NASH in lean patients. [ABSTRACT FROM AUTHOR]

Background: Although diabetic kidney disease demonstrates both familial clustering and single nucleotide polymorphism heritability, the specific genetic factors influencing risk remain largely unknown., Methods: To identify genetic variants predisposing to diabetic kidney disease, we performed genome-wide association study (GWAS) analyses. Through collaboration with the Diabetes Nephropathy Collaborative Research Initiative, we assembled a large collection of type 1 diabetes cohorts with harmonized diabetic kidney disease phenotypes. We used a spectrum of ten diabetic kidney disease definitions based on albuminuria and renal function., Results: Our GWAS meta-analysis included association results for up to 19,406 individuals of European descent with type 1 diabetes. We identified 16 genome-wide significant risk loci. The variant with the strongest association (rs55703767) is a common missense mutation in the collagen type IV alpha 3 chain ( COL4A3) gene, which encodes a major structural component of the glomerular basement membrane (GBM). Mutations in COL4A3 are implicated in heritable nephropathies, including the progressive inherited nephropathy Alport syndrome. The rs55703767 minor allele (Asp326Tyr) is protective against several definitions of diabetic kidney disease, including albuminuria and ESKD, and demonstrated a significant association with GBM width; protective allele carriers had thinner GBM before any signs of kidney disease, and its effect was dependent on glycemia. Three other loci are in or near genes with known or suggestive involvement in this condition ( BMP7) or renal biology ( COLEC11 and DDR1 )., Conclusions: The 16 diabetic kidney disease-associated loci may provide novel insights into the pathogenesis of this condition and help identify potential biologic targets for prevention and treatment., (Copyright © 2019 by the American Society of Nephrology.)

Fetuin-A, also termed alpha2-Heremans-Schmid glycoprotein, is a 46kDa hepatocyte derived protein (hepatokine) and serves multifaceted functions., (© 2019 Elsevier Inc. All rights reserved.)

Objective: Elevated serum ferritin has been linked to type 2 diabetes (T2D) and adverse health outcomes in subjects with the Metabolic Syndrome (MetS). As the mechanisms underlying the negative impact of excess iron have so far remained elusive, we aimed to identify potential links between iron homeostasis and metabolic pathways., Methods: In a cross-sectional study, data were obtained from 163 patients, allocated to one of three groups: (1) lean, healthy controls (n = 53), (2) MetS without hyperferritinemia (n = 54) and (3) MetS with hyperferritinemia (n = 56). An additional phlebotomy study included 29 patients with biopsy-proven iron overload before and after iron removal. A detailed clinical and biochemical characterization was obtained and metabolomic profiling was performed via a targeted metabolomics approach., Results: Subjects with MetS and elevated ferritin had higher fasting glucose (p < 0.001), HbA1c (p = 0.035) and 1 h glucose in oral glucose tolerance test (p = 0.002) compared to MetS subjects without iron overload, whereas other clinical and biochemical features of the MetS were not different. The metabolomic study revealed significant differences between MetS with high and low ferritin in the serum concentrations of sarcosine, citrulline and particularly long-chain phosphatidylcholines. Methionine, glutamate, and long-chain phosphatidylcholines were significantly different before and after phlebotomy (p < 0.05 for all metabolites)., Conclusions: Our data suggest that high serum ferritin concentrations are linked to impaired glucose homeostasis in subjects with the MetS. Iron excess is associated to distinct changes in the serum concentrations of phosphatidylcholine subsets. A pathway involving sarcosine and citrulline also may be involved in iron-induced impairment of glucose metabolism.

Background: Imprecise carbohydrate counting as a measure to guide the treatment of diabetes may be a source of errors resulting in problems in glycemic control. Exact measurements can be tedious, leading most patients to estimate their carbohydrate intake. In the presented pilot study a smartphone application (BE(AR)), that guides the estimation of the amounts of carbohydrates, was used by a group of diabetic patients., Methods: Eight adult patients with diabetes mellitus type 1 were recruited for the study. At the beginning of the study patients were introduced to BE(AR) in sessions lasting 45 minutes per patient. Patients redraw the real food in 3D on the smartphone screen. Based on a selected food type and the 3D form created using BE(AR) an estimation of carbohydrate content is calculated. Patients were supplied with the application on their personal smartphone or a loaner device and were instructed to use the application in real-world context during the study period. For evaluation purpose a test measuring carbohydrate estimation quality was designed and performed at the beginning and the end of the study., Results: In 44% of the estimations performed at the end of the study the error reduced by at least 6 grams of carbohydrate. This improvement occurred albeit several problems with the usage of BE(AR) were reported., Conclusions: Despite user interaction problems in this group of patients the provided intervention resulted in a reduction in the absolute error of carbohydrate estimation. Intervention with smartphone applications to assist carbohydrate counting apparently results in more accurate estimations., (© 2015 Diabetes Technology Society.)