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The pathophysiology of heart failure with preserved ejection fraction (HFpEF) is poorly understood and therapeutic strategies are lacking. This study aimed to identify plasma proteins with pathophysiological relevance in HFpEF and with respect to spironolactone-induced effects. We assessed 92 biomarkers in plasma samples from 386 HFpEF patients-belonging to the Aldo-DHF trial-before (baseline, BL) and after one-year treatment (follow up, FU) with spironolactone (verum) or a placebo. At BL, various biomarkers showed significant associations with the two Aldo-DHF primary end point parameters: 33 with E/e' and 20 with peak VO 2 . Ten proteins including adrenomedullin, FGF23 and inflammatory peptides (e.g., TNFRSF11A, TRAILR2) were significantly associated with both parameters, suggesting a role in the clinical HFpEF presentation. For 13 proteins, expression changes from BL to FU were significantly different between verum and placebo. Among them were renin, growth hormone, adrenomedullin and inflammatory proteins (e.g., TNFRSF11A, IL18 and IL4RA), indicating distinct spironolactone-mediated effects. BL levels of five proteins, e.g., inflammatory markers such as CCL17, IL4RA and IL1ra, showed significantly different effects on the instantaneous risk for hospitalization between verum and placebo. This study identified plasma proteins with different implications in HFpEF and following spironolactone treatment. Future studies need to define their precise mechanistic involvement.

The objective of the study was to estimate if altered levels of alpha-synuclein can be detected in tear fluid of patients with Parkinson's disease (PD). Therefore, tear fluid samples of 75 PD patients, 75 control subjects and 31 atypical Parkinsonian patients were collected and analyzed in triplicates using an ultra-sensitive single molecule array (SIMOA) system and applying a human alpha-synuclein immunoassay. In PD, levels of total soluble alpha-synuclein were significantly increased compared to control subjects (p = 0.03; AUC PD vs. controls 0.60). There was no difference comparing PD patients stratified by Hoehn & Yahr stages and atypical Parkinsonian syndromes stratified by tauopathies and non-PD-synucleinopathies against each other (p > 0.05). In conclusion, alpha-synuclein can be detected and quantified in tear fluid, revealing small but significant differences in total alpha-synuclein levels between PD and control subjects. Tear fluid can be collected non-invasively and risk-free, therefore presenting a promising source for further biomarker research.

Background: The diagnosis of Parkinson's disease (PD) is still challenging and biomarkers could contribute to an improved diagnostic accuracy. Tear fluid (TF) is an easily accessible body fluid reflecting pathophysiological changes in systemic and ocular diseases and is already used as a biomarker source for several ophthalmological disorders. Here, we analyzed the TF of patients with PD and controls (CTR) to describe disease-related changes in TF and identify putative biomarkers for the diagnosis of PD., Methods: Unstimulated TF samples of a pilot cohort with 36 PD patients and 18 CTR were collected via Schirmer tear test strips and then analyzed via a Bottom-up liquid chromatography electrospray ionization tandem mass spectrometry (BULCMS) workflow, followed by functional analysis encompassing protein-protein interaction as well as cellular component and pathway analysis., Results: BULCMS analysis lead to the identification of 571 tear proteins (false discovery rate, FDR < 1%), whereby 31 proteins were exclusively detected in the PD group and 7 only in the CTR group. Whereas 21 proteins were significantly increased in the PD versus CTR groups, 19 proteins were significantly decreased. Core networks of proteins involved in immune response, lipid metabolism and oxidative stress were distinctly altered in PD patients., Conclusions: To our best knowledge, this is the first description of TF proteome in PD patients. Tear protein level alterations suggest the contribution of different disease-related mechanisms in ocular pathology in PD and propose candidate proteins to be validated as potential biomarkers in larger cohorts., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

The diagnosis of Parkinson's disease (PD) still lacks objective diagnostic markers independent of clinical criteria. Cerebrospinal fluid (CSF) samples from 36 PD and 42 age-matched control patients were subjected to inductively coupled plasma-sector field mass spectrometry and a total of 28 different elements were quantified. Different machine learning algorithms were applied to the dataset to identify a discriminating set of elements yielding a novel biomarker signature. Using 19 stably detected elements, the extreme gradient tree boosting model showed the best performance in the discrimination of PD and control patients with high specificity and sensitivity (78.6% and 83.3%, respectively), re-classifying the training data to 100%. The 10 times 10-fold cross-validation yielded a good area under the receiver operating characteristic curve of 0.83. Arsenic, magnesium, and selenium all showed significantly higher mean CSF levels in the PD group compared to the control group (p = 0.01, p = 0.04, and p = 0.03). Reducing the number of elements to a discriminating minimum, we identified an elemental cluster (Se, Fe, As, Ni, Mg, Sr), which most importantly contributed to the sample discrimination. Selenium was identified as the element with the highest impact within this cluster directly followed by iron. After prospective validation, this elemental fingerprint in the CSF could have the potential to be used as independent biomarker for the diagnosis of PD. Next to their value as a biomarker, these data also argue for a prominent role of these highly discriminating six elements in the pathogenesis of PD., (© 2018 International Society for Neurochemistry.)

Aims: Chronic heart failure (HF) is a common disease and one of the leading causes of death worldwide. Heart failure with preserved ejection fraction (HFpEF) and with reduced ejection fraction (HFrEF) are different diseases with distinct as well as comparable pathophysiologies and diverse responses to therapeutic agents. We aimed to identify possible pathobiochemical signalling pathways and biomarkers in HFpEF and HFrEF by using a broad proteomic approach. Methods and results: A total of 180 biomarkers in the plasma of a representative subgroup (71 years old) of HFpEF (70% female) with a left ventricular ejection fraction (LVEF) ≥ 50% and HFrEF (18% female) with an LVEF ≤ 40% patients (n = 127) from the Prevalence and Clinical Course of Diastolic Dysfunction and Diastolic Heart Failure (DIAST‐CHF) trial were examined and compared with a healthy control group (n = 40; 48% female). We were able to identify 35 proteins that were expressed significantly different in both HF groups compared with the control group. We determine 29 unique proteins expressed in HFpEF and 33 unique proteins in HFrEF. Significantly up‐regulated trefoil factor 3 (TFF3) and down‐regulated contactin‐1 could be identified as previously unknown biomarkers for HF. However, TFF3 is also a predictive factor for the occurrence of a cardiovascular event in HFpEF patients. In HFpEF, serine protease 27 was found at reduced levels for the first time, which could offer a new therapeutic target. Additionally, network analyses showed a special role of platelet‐derived growth factor subunit A, Dickkopf‐related protein 1, and tumour necrosis factor receptor superfamily member 6 in HFpEF patients, whereas perlecan and junctional adhesion molecule A stood out in the HFrEF group. Overall, signalling pathways of metabolic processes, cellular stress, and iron metabolism seemed to be important for HFrEF, whereas for HFpEF, oxygen stress, haemostasis, cell renewal, cell migration, and cell proliferation are in the foreground. Conclusions: The identified proteins and signalling pathways offer new therapeutic and diagnostic approaches for patients with chronic HF. [ABSTRACT FROM AUTHOR]

Background: Charcot-Marie-Tooth disease type 1A (CMT1A) is the most common inherited neuropathy, a debilitating disease without known cure. Among patients with CMT1A, disease manifestation, progression and severity are strikingly variable, which poses major challenges for the development of new therapies. Hence, there is a strong need for sensitive outcome measures such as disease and progression biomarkers, which would add powerful tools to monitor therapeutic effects in CMT1A.Methods: We established a pan-European and American consortium comprising nine clinical centres including 311 patients with CMT1A in total. From all patients, the CMT neuropathy score and secondary outcome measures were obtained and a skin biopsy collected. In order to assess and validate disease severity and progression biomarkers, we performed qPCR on a set of 16 animal model-derived potential biomarkers in skin biopsy mRNA extracts.Results: In 266 patients with CMT1A, a cluster of eight cutaneous transcripts differentiates disease severity with a sensitivity and specificity of 90% and 76.1%, respectively. In an additional cohort of 45 patients with CMT1A, from whom a second skin biopsy was taken after 2-3 years, the cutaneous mRNA expression of GSTT2, CTSA, PPARG, CDA, ENPP1 and NRG1-Iis changing over time and correlates with disease progression.Conclusions: In summary, we provide evidence that cutaneous transcripts in patients with CMT1A serve as disease severity and progression biomarkers and, if implemented into clinical trials, they could markedly accelerate the development of a therapy for CMT1A. [ABSTRACT FROM AUTHOR]

Background: Discovery of biomarkers that are correlated with therapy response and thus with survival is an important goal of medical research on severe diseases, e.g. cancer. Frequently, microarray studies are performed to identify genes of which the expression levels in pretherapeutic tissue samples are correlated to survival times of patients. Typically, such a study can take several years until the full planned sample size is available. Therefore, interim analyses are desirable, offering the possibility of stopping the study earlier, or of performing additional laboratory experiments to validate the role of the detected genes. While many methods correcting the multiple testing bias introduced by interim analyses have been proposed for studies of one single feature, there are still open questions about interim analyses of multiple features, particularly of high-dimensional microarray data, where the number of features clearly exceeds the number of samples. Therefore, we examine false discovery rates and power rates in microarray experiments performed during interim analyses of survival studies. In addition, the early stopping based on interim results of such studies is evaluated. As stop criterion we employ the achieved average power rate, i.e. the proportion of detected true positives, for which a new estimator is derived and compared to existing estimators. Results: In a simulation study, pre-specified levels of the false discovery rate are maintained in each interim analysis, where reduced levels as used in classical group sequential designs of one single feature are not necessary. Average power rates increase with each interim analysis, and many studies can be stopped prior to their planned end when a certain pre-specified power rate is achieved. The new estimator for the power rate slightly deviates from the true power rate but is comparable to other estimators. Conclusions: Interim analyses of microarray experiments can provide evidence for early stopping of long-term survival studies. The developed simulation framework, which we also offer as a new R package 'SurvGenesInterim' available at http://survgenesinter.R-Forge.R-Project.org, can be used for sample size planning of the evaluated study design. [ABSTRACT FROM AUTHOR]

Simple Summary: Resistance to chemoradiotherapy represents a fundamental problem in modern oncology because it exposes patients to the potential negative side-effects of both radiation and chemotherapy without any clinical benefit. This study uncovers that the inflammatory signaling hub STAT3 conspires with the cell fate regulator NOTCH in rendering tumor cells refractory to chemoradiotherapy. The dichotomic signal alliance is based on a so-far unknown STAT3 target gene, RBPJ, providing the transcriptionally active partner of NOTCH intracellular domain. Unexpectedly, the latter is permanently produced by tonic proteolysis. Tumor mouse models and cancer patient cohorts demonstrate the usefulness of the STAT3/NOTCH axis as biomarker for patient stratification, and importantly, that STAT3 inhibition is a promising treatment option for re-sensitization of CRT-refractory tumors. Resistance of tumor cells to chemoradiotherapy represents a fundamental problem in clinical oncology. The underlying mechanisms are actively debated. Here we show that blocking inflammatory cytokine receptor signaling via STAT3 re-sensitized treatment-refractory cancer cells and abolished tumor growth in a xenograft mouse model when applied together with chemoradiotherapy. STAT3 executed treatment resistance by triggering the expression of RBPJ, the key transcriptional regulator of the NOTCH pathway. The mandatory RBPJ interaction partner, NOTCH intracellular domain, was provided by tumor cell-intrinsic expression of NOTCH ligands that caused tonic NOTCH proteolysis. In fact, NOTCH inhibition phenocopied the effect of blocking STAT3 signaling. Moreover, genetic profiling of rectal cancer patients revealed the importance of the STAT3/NOTCH axis as NOTCH expression correlated with clinical outcome. Our data uncovered an unprecedented signal alliance between inflammation and cellular development that orchestrated resistance to chemoradiotherapy. Clinically, our findings allow for biomarker-driven patient stratification and offer novel treatment options. [ABSTRACT FROM AUTHOR]

This study evaluates primary and secondary clinical outcome measures in Charcot-Marie-Tooth disease type 1A (CMT1A) with regard to their contribution towards discrimination of disease severity. The nine components of the composite Charcot-Marie-Tooth disease Neuropathy Score and six additional secondary clinical outcome measures were assessed in 479 adult patients with genetically proven CMT1A and 126 healthy controls. Using hierarchical clustering, we identified four significant clusters of patients according to clinical severity. We then tested the impact of each of the CMTNS components and of the secondary clinical parameters with regard to their power to differentiate these four clusters. The CMTNS components ulnar sensory nerve action potential (SNAP), pin sensibility, vibration and strength of arms did not increase the discriminant value of the remaining five CMTNS components (Ulnar compound motor action potential [CMAP], leg motor symptoms, arm motor symptoms, leg strength and sensory symptoms). However, three of the six additional clinical outcome measures – the 10 m-timed walking test (T10MW), 9 hole-peg test (9HPT), and foot dorsal flexion dynamometry – further improved discrimination between severely and mildly affected patients. From these findings, we identified three different composite measures as score hypotheses and compared their discriminant power with that of the CMTNS. A composite of eight components CMAP, Motor symptoms legs, Motor symptoms arms, Strength of Legs, Sensory symptoms), displayed the strongest power to discriminate between the clusters. As a conclusion, five items from the CMTNS and three secondary clinical outcome measures improve the clinical assessment of patients with CMT1A significantly and are beneficial for upcoming clinical and therapeutic trials. [ABSTRACT FROM AUTHOR]