Sex-specific-differences in cardiovascular risk in type-1-diabetes : a cross sectional study

Background: Little is known about the impact of sex-specific differences in the management of type 1 diabetes (T1DM). Thus, we evaluated the influence of gender on risk factors, complications, clinical care and adherence in patients with T1DM. Methods: In a cross-sectional study, sex-specific disparities in glycaemic control, cardiovascular risk factors, diabetic complications, concomitant medication use and adherence to treatment recommendations were evaluated in 225 consecutive patients (45.3% women) who were comparable with respect to age, diabetes duration, and body mass index. Results: Although women with T1DM had a higher total cholesterol than men, triglycerides were higher in obese men and males with HbA1c>7% than in their female counterparts. No sex differences were observed in glycaemic control and in micro- or macrovascular complications. However, the subgroup analysis showed that nephropathy was more common in obese men, hyperlipidaemic women and all hypertensive patients, whereas peripheral neuropathy was more common in hyperlipidaemic women. Retinopathy was found more frequently in women with HbA1c>7%, obese men and in both sexes with a long duration of diabetes. The multivariate analysis revealed that microvascular complications were associated with the duration of disease and BMI in both sexes and with hyperlipidaemia in males. The overall adherence to interventions according to the guidelines was higher in men than in women. This adherence was concerned particularly with co-medication in patients diagnosed with hypertension, aspirin prescription in elderly patients and the achievement of target lipid levels following the prescription of statins. Conclusions: Our data showed sex differences in lipids and overweight in patients with T1DM. Although glycaemic control and the frequency of diabetic complications were comparable between the sexes, the overall adherence to guidelines, particularly with respect to the prescription of statins and aspirin, was lower in women than in men

Sex‑Specifc Diferences in Mortality of Patients with a History of Bariatric Surgery: a Nation‑Wide Population‑Based Study

Purpose Bariatric surgery reduces mortality in patients with severe obesity and is predominantly performed in women. Therefore, an analysis of sex-specific differences after bariatric surgery in a population-based dataset from Austria was performed. The focus was on deceased patients after bariatric surgery. Materials and Methods The Austrian health insurance funds cover about 98% of the Austrian population. Medical health claims data of all Austrians who underwent bariatric surgery from 01/2010 to 12/2018 were analyzed. In total, 19,901 patients with 107,806 observed years postoperative were eligible for this analysis. Comorbidities based on International Classification of Diseases (ICD)-codes and drug intake documented by Anatomical Therapeutical Chemical (ATC)-codes were analyzed in patients deceased and grouped according to clinically relevant obesity-associated comorbidities: diabetes mellitus (DM), cardiovascular disease (CV), psychiatric disorder (PSY), and malignancy (M). Results In total, 367 deaths were observed (1.8%) within the observation period from 01/2010 to 04/2020. The overall mortality rate was 0.34% per year of observation and significantly higher in men compared to women (0.64 vs. 0.24%; p < 0.001(Chi-squared)). Moreover, the 30-day mortality was 0.19% and sixfold higher in men compared to women (0.48 vs. 0.08%; p < 0.001). CV (82%) and PSY (55%) were the most common comorbidities in deceased patients with no sex-specific differences. Diabetes (38%) was more common in men (43 vs. 33%; p = 0.034), whereas malignant diseases (36%) were more frequent in women (30 vs. 41%; p = 0.025). Conclusion After bariatric surgery, short-term mortality as well as long-term mortality was higher in men compared to women. In deceased patients, diabetes was more common in men, whereas malignant diseases were more common in women.publishedVersio

A polygenic predictor of treatment-resistant depression using whole exome sequencing and genome-wide genotyping

Treatment-resistant depression (TRD) occurs in ~30% of patients with major depressive disorder (MDD) but the genetics of TRD was previously poorly investigated. Whole exome sequencing and genome-wide genotyping were available in 1209 MDD patients after quality control. Antidepressant response was compared to non-response to one treatment and non-response to two or more treatments (TRD). Differences in the risk of carrying damaging variants were tested. A score expressing the burden of variants in genes and pathways was calculated weighting each variant for its functional (Eigen) score and frequency. Gene-based and pathway-based scores were used to develop predictive models of TRD and non-response using gradient boosting in 70% of the sample (training) which were tested in the remaining 30% (testing), evaluating also the addition of clinical predictors. Independent replication was tested in STAR*D and GENDEP using exome array-based data. TRD and non-responders did not show higher risk to carry damaging variants compared to responders. Genes/pathways associated with TRD included those modulating cell survival and proliferation, neurodegeneration, and immune response. Genetic models showed significant prediction of TRD vs. response and they were improved by the addition of clinical predictors, but they were not significantly better than clinical predictors alone. Replication results were driven by clinical factors, except for a model developed in subjects treated with serotonergic antidepressants, which showed a clear improvement in prediction at the extremes of the genetic score distribution in STAR*D. These results suggested relevant biological mechanisms implicated in TRD and a new methodological approach to the prediction of TRD.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Prediction of Autopsy Verified Neuropathological Change of Alzheimer’s Disease Using Machine Learning and MRI

Background: Alzheimer’s disease (AD) is the most common form of dementia. While neuropathological changes pathognomonic for AD have been defined, early detection of AD prior to cognitive impairment in the clinical setting is still lacking. Pioneer studies applying machine learning to magnetic-resonance imaging (MRI) data to predict mild cognitive impairment (MCI) or AD have yielded high accuracies, however, an algorithm predicting neuropathological change is still lacking. The objective of this study was to compute a prediction model supporting a more distinct diagnostic criterium for AD compared to clinical presentation, allowing identification of hallmark changes even before symptoms occur.Methods: Autopsy verified neuropathological changes attributed to AD, as described by a combined score for Aβ-peptides, neurofibrillary tangles and neuritic plaques issued by the National Institute on Aging – Alzheimer’s Association (NIAA), the ABC score for AD, were predicted from structural MRI data with RandomForest (RF). MRI scans were performed at least 2 years prior to death. All subjects derive from the prospective Vienna Trans-Danube Aging (VITA) study that targeted all 1750 inhabitants of the age of 75 in the starting year of 2000 in two districts of Vienna and included irregular follow-ups until death, irrespective of clinical symptoms or diagnoses. For 68 subjects MRI as well as neuropathological data were available and 49 subjects (mean age at death: 82.8 ± 2.9, 29 female) with sufficient MRI data quality were enrolled for further statistical analysis using nested cross-validation (CV). The decoding data of the inner loop was used for variable selection and parameter optimization with a fivefold CV design, the new data of the outer loop was used for model validation with optimal settings in a fivefold CV design. The whole procedure was performed ten times and average accuracies with standard deviations were reported.Results: The most informative ROIs included caudal and rostral anterior cingulate gyrus, entorhinal, fusiform and insular cortex and the subcortical ROIs anterior corpus callosum and the left vessel, a ROI comprising lacunar alterations in inferior putamen and pallidum. The resulting prediction models achieved an average accuracy for a three leveled NIAA AD score of 0.62 within the decoding sets and of 0.61 for validation sets. Higher accuracies of 0.77 for both sets, respectively, were achieved when predicting presence or absence of neuropathological change.Conclusion: Computer-aided prediction of neuropathological change according to the categorical NIAA score in AD, that currently can only be assessed post-mortem, may facilitate a more distinct and definite categorization of AD dementia. Reliable detection of neuropathological hallmarks of AD would enable risk stratification at an earlier level than prediction of MCI or clinical AD symptoms and advance precision medicine in neuropsychiatry

Profiling endogenous adrenal function during veno-venous ECMO support in COVID-19 ARDS: a descriptive analysis

BackgroundProlonged critical illness is often accompanied by an impairment of adrenal function, which has been frequently related to conditions complicating patient management. The presumed connection between hypoxia and the pathogenesis of this critical- illness- related corticosteroid insufficiency (CIRCI) might play an important role in patients with severe acute respiratory distress syndrome (ARDS). Since extracorporeal membrane oxygenation (ECMO) is frequently used in ARDS, but data on CIRCI during this condition are scarce, this study reports the behaviour of adrenal function parameters during oxygenation support with veno-venous (vv)ECMO in coronavirus disease 2019 (COVID-19) ARDS.MethodsA total of 11 patients undergoing vvECMO due to COVID-19 ARDS at the Medical University of Vienna, who received no concurrent corticosteroid therapy, were retrospectively included in this study. We analysed the concentrations of cortisol, aldosterone, and angiotensin (Ang) metabolites (Ang I–IV, Ang 1–7, and Ang 1–5) in serum via liquid chromatography/tandem mass spectrometry before, after 1 day, 1 week, and 2 weeks during vvECMO support and conducted correlation analyses between cortisol and parameters of disease severity.ResultsCortisol concentrations appeared to be lowest after initiation of ECMO and progressively increased throughout the study period. Higher concentrations were related to disease severity and correlated markedly with interleukin-6, procalcitonin, pH, base excess, and albumin during the first day of ECMO. Fair correlations during the first day could be observed with calcium, duration of critical illness, and ECMO gas flow. Angiotensin metabolite concentrations were available in a subset of patients and indicated a more homogenous aldosterone response to plasma renin activity after 1 week of ECMO support.ConclusionOxygenation support through vvECMO may lead to a partial recovery of adrenal function over time. In homogenous patient collectives, this novel approach might help to further determine the importance of adrenal stress response in ECMO and the influence of oxygenation support on CIRCI

Short Term Caloric Restriction and Biofeedback Enhance Psychological Wellbeing and Reduce Overweight in Healthy Women

Obesity is highly prevalent, causing substantial cardiovascular and mental health morbidity. Women show increased risk for mental health disorders, that is multiplied in obesity and related to cellular and psychological stress that can be targeted by non-pharmacological interventions. A total of 43 women underwent two weeks of caloric restriction, half of which also received 7 h of individualized clinical psychological intervention including psychoeducation, mindfulness, and heart-rate-variability biofeedback. Effects on body mass index (BMI), fatty liver index (FLI), bioimpedance measures, serum parameters, perceived stress (PSS), burn-out susceptibility (burn out diagnostic inventory) and dimensional psychiatric symptom load (brief symptom inventory, BSI) were analyzed with linear mixed effects models. Caloric restriction led to a reduction in BMI, body fat and FLI, decreased serum concentrations of leptin, PSS score, BSI dimensions and global severity index (all p ≤ 0.0001, withstanding Bonferroni–Holm correction). Benefits of add-on biofeedback were observed for BMI reduction (p = 0.041). Caloric restriction was effective in ameliorating both psychological wellbeing and metabolic functions following a BMI reduction. Biofeedback boosted effects on BMI reduction and the combinative therapy may be protective against common progression to mental health and cardiovascular disorders in overweight women while comparing favorably to pharmacological interventions in terms of side-effects and acceptability

Novel platforms toward C–C bond formation, stereocontrol, and expedited reaction generality

In the past decade and a half, photoredox catalysis has emerged as a powerful and widely adopted catalytic platform in the field of organic synthesis. In particular, the merger of photocatalysis with transition metal catalysis and biocatalysis has enabled the discovery of a range of synthetically valuable and hitherto elusive transformations. This has proven exceptionally attractive as highly tunable photocatalysts can selectively generate reactive intermediates from a cadre of abundant, commercially available, native functionalities under mild, biocompatible conditions in an orthogonal manner to traditional thermodynamic processes. For these reasons, this complementary approach has seen widespread adoption in the pharmaceutical, agrochemical, and fragrance industries. This dissertation describes novel applications of the merger of photocatalysis with biocatalysis and transition metal catalysis for the development of powerful synthetic platforms. The second chapter of this thesis outlines a copper metallaphotoredox platform to accomplish a decarboxylative trifluoromethylation of aliphatic carboxylic acids, thereby enabling the late-stage installation of the medicinally relevant moiety. In the third chapter, the development of a photoredox-, HAT-, and organocatalyzed racemization platform is described. Conceptually this demonstrates photocatalysis’ unique ability to generate prochiral radicals at stereocenters traditionally viewed as static. In a collaboration with the Hyster group, we demonstrate that the merger of this platform with enzyme catalysis enables an unprecedented stereoconvergent reaction. Finally, the fourth chapter of this thesis outlines a novel orthogonal approach to converting initial reaction hits into general reactions. This approach applies phenotypic screening techniques and high throughput experimentation to greatly improve the scope and functional group tolerance of the nickel and photoredox dual-catalyzed decarboxylative arylation

Insulin as Monotherapy and in Combination with Other Glucose-Lowering Drugs Is Related to Increased Risk of Diagnosis of Pneumonia: A Longitudinal Assessment over Two Years

Objective: Patients with type 2 diabetes mellitus (T2DM) are at an increased risk of developing infectious diseases such as pneumonia. Hitherto, there has been uncertainty as to whether there is a relationship between different antidiabetic drug combinations and development of pneumonia in this specific cohort. Research Design and Methods: In this longitudinal retrospective study we used multiple logistic regression analysis to assess the odds ratios (ORs) of pneumonia during an observational period of 2 years in 31,397 patients with T2DM under previously prescribed stable antidiabetic drug combinations over a duration of 4 years in comparison to 6568 T2DM patients without drug therapy over 4 years adjusted for age, sex and hospitalization duration. Results: Of the 37,965 patients with T2DM, 3720 patients underwent stable monotherapy treatment with insulin (mean age: 66.57 ± 9.72 years), 2939 individuals (mean age: 70.62 ± 8.95 y) received stable statin and insulin therapy, and 1596 patients were treated with a stable combination therapy of metformin, insulin and statins (mean age: 68.27 ± 8.86 y). In comparison to the control group without antidiabetic drugs (mean age: 72.83 ± 9.96 y), individuals undergoing insulin monotherapy (OR: 2.07, CI: 1.54–2.79, p &lt; 0.001); insulin and statin combination therapy (OR: 2.24, CI: 1.68–3.00, p &lt; 0.001); metformin, insulin and statin combination therapy (OR: 2.27, CI: 1.55–3.31, p &lt; 0.001); statin, insulin and dipeptidyl peptidase-4 inhibitor (DPP-IV inhibitor) combination therapy (OR: 4.31, CI: 1.80–10.33, p = 0.001); as well as individuals treated with metformin and sulfonylureas (OR: 1.70, CI: 1.08–2.69, p = 0.02) were at increased risk of receiving a diagnosis of pneumonia. Conclusions: Stable monotherapy with insulin, but also in combination with other antidiabetic drugs, is related to an increased risk of being diagnosed with pneumonia during hospital stays in patients with type 2 diabetes mellitus compared to untreated controls

Unraveling cradle-to-grave disease trajectories from multilayer comorbidity networks

Abstract We aim to comprehensively identify typical life-spanning trajectories and critical events that impact patients’ hospital utilization and mortality. We use a unique dataset containing 44 million records of almost all inpatient stays from 2003 to 2014 in Austria to investigate disease trajectories. We develop a new, multilayer disease network approach to quantitatively analyze how cooccurrences of two or more diagnoses form and evolve over the life course of patients. Nodes represent diagnoses in age groups of ten years; each age group makes up a layer of the comorbidity multilayer network. Inter-layer links encode a significant correlation between diagnoses (p  1.5), while intra-layers links encode correlations between diagnoses across different age groups. We use an unsupervised clustering algorithm for detecting typical disease trajectories as overlapping clusters in the multilayer comorbidity network. We identify critical events in a patient’s career as points where initially overlapping trajectories start to diverge towards different states. We identified 1260 distinct disease trajectories (618 for females, 642 for males) that on average contain 9 (IQR 2–6) different diagnoses that cover over up to 70 years (mean 23 years). We found 70 pairs of diverging trajectories that share some diagnoses at younger ages but develop into markedly different groups of diagnoses at older ages. The disease trajectory framework can help us to identify critical events as specific combinations of risk factors that put patients at high risk for different diagnoses decades later. Our findings enable a data-driven integration of personalized life-course perspectives into clinical decision-making