EuCARE-POSTCOVID Study: a multicentre cohort study on long-term post-COVID-19 manifestations.

BACKGROUND: Post-COVID-19 condition refers to persistent or new onset symptoms occurring three months after acute COVID-19, which are unrelated to alternative diagnoses. Symptoms include fatigue, breathlessness, palpitations, pain, concentration difficulties ("brain fog"), sleep disorders, and anxiety/depression. The prevalence of post-COVID-19 condition ranges widely across studies, affecting 10-20% of patients and reaching 50-60% in certain cohorts, while the associated risk factors remain poorly understood. METHODS: This multicentre cohort study, both retrospective and prospective, aims to assess the incidence and risk factors of post-COVID-19 condition in a cohort of recovered patients. Secondary objectives include evaluating the association between circulating SARS-CoV-2 variants and the risk of post-COVID-19 condition, as well as assessing long-term residual organ damage (lung, heart, central nervous system, peripheral nervous system) in relation to patient characteristics and virology (variant and viral load during the acute phase). Participants will include hospitalised and outpatient COVID-19 patients diagnosed between 01/03/2020 and 01/02/2025 from 8 participating centres. A control group will consist of hospitalised patients with respiratory infections other than COVID-19 during the same period. Patients will be followed up at the post-COVID-19 clinic of each centre at 2-3, 6-9, and 12-15 months after clinical recovery. Routine blood exams will be conducted, and patients will complete questionnaires to assess persisting symptoms, fatigue, dyspnoea, quality of life, disability, anxiety and depression, and post-traumatic stress disorders. DISCUSSION: This study aims to understand post-COVID-19 syndrome's incidence and predictors by comparing pandemic waves, utilising retrospective and prospective data. Gender association, especially the potential higher prevalence in females, will be investigated. Symptom tracking via questionnaires and scales will monitor duration and evolution. Questionnaires will also collect data on vaccination, reinfections, and new health issues. Biological samples will enable future studies on post-COVID-19 sequelae mechanisms, including inflammation, immune dysregulation, and viral reservoirs. TRIAL REGISTRATION: This study has been registered with ClinicalTrials.gov under the identifier NCT05531773.

Does the delivery mode affect post-birth neonatal serum C-reactive protein levels? A causal effect analysis.

AIM: To determine if the delivery mode has a causal effect on neonatal serum C-reactive protein (CRP) levels. If such a causal effect exists, we aim to quantify its magnitude. METHODS: We investigated the causal effect of the delivery mode on serum CRP levels 6-8 h after delivery, with appropriate statistical tools for retrospective studies, combining classical and machine-learning methods. The statistical inference is followed by sensitivity analysis to quantify the magnitude of unobserved bias required in order to alter the study's conclusion. RESULTS: This retrospective study reviewed laboratory records of neonates after birth who underwent blood tests due to suspected sepsis. A total of 440 newborns were included, 324 of which underwent a vaginal delivery, 59 an urgent caesarean delivery, and 57 an elective caesarean delivery. Our results revealed that serum CRP values following elective caesarean deliveries were 50% less than those following a vaginal delivery (P = 0.030; -0.907; 95% CI [-1.545, -0.268] in log-CRP units). No significant effect was found for urgent caesarean deliveries compared to vaginal deliveries (P = 0.887). Those results were strengthened by (1) a sensitivity magnitude of 1.6 to unobserved bias and (2) non-significant effects when analysis is repeated on blood collected 12-24 h after birth. CONCLUSION: CRP concentrations in neonatal blood during the first 6-8 h of life are higher following vaginal deliveries compared to elective caesarean deliveries. Further studies with the intent of improving EONS detection should include information on the delivery mode.

Non-random length distribution of individual telomeres in immunodeficiency, centromeric instability and facial anomalies syndrome, type I.

Mutations in the de novo DNA methyltransferase DNMT3B lead to Immunodeficiency, Centromeric Instability and Facial anomalies (ICF) syndrome, type I. This syndrome is characterized, among other hypomethylated genomic loci, by severe subtelomeric hypomethylation that is associated with abnormally short telomere length. While it was demonstrated that the mean telomere length is significantly shorter in ICF type I cells, it is unknown whether all telomeres are equally vulnerable to shortening. To study this question we determined by combined telomere-FISH and spectral karyotyping the relative length of each individual telomere in lymphoblastoid cell lines (LCLs) generated from multiple ICF syndrome patients and control individuals. Here we confirm the short telomere lengths, and demonstrate that telomere length variance in the ICF patient group is much larger than in the control group, suggesting that not all telomeres shorten in a uniform manner. We identified a subgroup of telomeres whose relatively short lengths can distinguish with a high degree of certainty between a control and an ICF metaphase, proposing that in ICF syndrome cells, certain individual telomeres are consistently at greater risk to shorten than others. The majority of these telomeres display high sequence identity at the distal 2 kb of their subtelomeres, suggesting that the attenuation in DNMT3B methylation capacity affects individual telomeres to different degrees based, at least in part, on the adjacent subtelomeric sequence composition.

Survival rates among Parkinson's disease patients who carry mutations in the LRRK2 and GBA genes.

BACKGROUND: The G2019S mutation in the LRRK2 gene generates a milder PD phenotype compared with GBA-PD; however, genetic based survival studies are lacking. OBJECTIVES: To compare mortality rates between LRRK2-PD, GBA-PD, and idiopathic PD patients (iPD). METHODS: Patients were screened for the G2019S mutation in the LRRK2 gene and the seven common GBA mutations among Ashkenazi Jews, classified as mild and severe (mGBA, sGBA). Motor symptoms onset and date of death were ascertained, with mortality rates calculated for each group of patients. RESULTS: Overall, 380 of 1,086 idiopathic PD patients, 49 of 159 LRRK2-PD, 56 of 148 mGBA-PD, and 13 of 49 sGBA-PD participants died by the time of analysis. LRRK2-PD tended to have longer survival compared to idiopathic PD whereas GBA status did not affect mortality. Genetic status did not predict mortality in a multivariate analysis. CONCLUSION: Survival of patients with PD does not seem to be related to GBA status, whereas LRRK2 might confer higher survival rates.

A novel performance scoring quantification framework for stress test set-ups.

Stress tests, e.g., the cardiac stress test, are standard clinical screening tools aimed to unmask clinical pathology. As such stress tests indirectly measure physiological reserves. The term reserve has been developed to account for the dis-junction, often observed, between pathology and clinical manifestation. It describes a physiological capacity that is utilized in demanding situations. However, developing a new and reliable stress test based screening tool is complex, prolonged, and relies extensively on domain knowledge. We propose a novel distributional-free machine-learning framework, the Stress Test Performance Scoring (STEPS) framework, to model expected performance in a stress test. A performance scoring function is trained with measures taken during the performance in a given task while exploiting information regarding the stress test set-up and subjects' medical state. Multiple ways of aggregating performance scores at different stress levels are suggested and are examined with an extensive simulation study. When applied to a real-world data example, an AUC of 84.35[95%CI: 70.68 - 95.13] was obtained for the STEPS framework to distinguish subjects with neurodegeneration from controls. In summary, STEPS improved screening by exploiting existing domain knowledge and state-of-the-art clinical measures. The STEPS framework can ease and speed up the production of new stress tests.

Novel Alzheimer's disease subtypes identified using a data and knowledge driven strategy.

The population of adults with Alzheimer's disease (AD) varies in needs and outcomes. The heterogeneity of current AD diagnostic subgroups impedes the use of data analytics in clinical trial design and translation of findings into improved care. The purpose of this project was to define more clinically-homogeneous groups of AD patients and link clinical characteristics with biological markers. We used an innovative big data analysis strategy, the 3C strategy, that incorporates medical knowledge into the data analysis process. A large set of preprocessed AD Neuroimaging Initiative (ADNI) data was analyzed with 3C. The data analysis yielded 6 new disease subtypes, which differ from the assigned diagnosis types and present different patterns of clinical measures and potential biomarkers. Two of the subtypes, "Anosognosia dementia" and "Insightful dementia", differentiate between severe participants based on clinical characteristics and biomarkers. The "Uncompensated mild cognitive impairment (MCI)" subtype, demonstrates clinical, demographic and imaging differences from the "Affective MCI" subtype. Differences were also observed between the "Worried Well" and "Healthy" clusters. The use of data-driven analysis yielded sub-phenotypic clinical clusters that go beyond current diagnoses and are associated with biomarkers. Such homogenous sub-groups can potentially form the basis for enhancement of brain medicine research.

Multi-domain potential biomarkers for post-traumatic stress disorder (PTSD) severity in recent trauma survivors.

Contemporary symptom-based diagnosis of post-traumatic stress disorder (PTSD) largely overlooks related neurobehavioral mechanisms and relies entirely on subjective interpersonal reporting. Previous studies associating biomarkers with PTSD have mostly used symptom-based diagnosis as the main outcome measure, disregarding the wide variability and richness of PTSD phenotypical features. Here, we aimed to computationally derive potential biomarkers that could efficiently differentiate PTSD subtypes among recent trauma survivors. A three-staged semi-unsupervised method ("3C") was used to firstly categorize individuals by current PTSD symptom severity, then derive clusters based on clinical features related to PTSD (e.g. anxiety and depression), and finally to classify participants' cluster membership using objective multi-domain features. A total of 256 features were extracted from psychometrics, cognitive functioning, and both structural and functional MRI data, obtained from 101 adult civilians (age = 34.80 ± 11.95; 51 females) evaluated within 1 month of trauma exposure. The features that best differentiated cluster membership were assessed by importance analysis, classification tree, and ANOVA. Results revealed that entorhinal and rostral anterior cingulate cortices volumes (structural MRI domain), in-task amygdala's functional connectivity with the insula and thalamus (functional MRI domain), executive function and cognitive flexibility (cognitive testing domain) best differentiated between two clusters associated with PTSD severity. Cross-validation established the results' robustness and consistency within this sample. The neural and cognitive potential biomarkers revealed by the 3C analytics offer objective classifiers of post-traumatic morbidity shortly following trauma. They also map onto previously documented neurobehavioral mechanisms associated with PTSD and demonstrate the usefulness of standardized and objective measurements as differentiating clinical sub-classes shortly after trauma.

Differential Associations Between Distinct Components of Cognitive Function and Mobility: Implications for Understanding Aging, Turning and Dual-Task Walking.

OBJECTIVE: Cognition and mobility are interrelated. However, this association can be impacted by the specific facets of cognition and mobility that are measured, and further by the different task conditions, e.g., single- versus dual-task walking, under which these associations are evaluated. Systematically studying the multiple facets of cognitive-mobility associations under both the task conditions is critical because both cognition and mobility change with age and pose significant risks associated with falls, morbidity, and disability. METHODS: Using a cross-sectional, prospective study design, data from 124 healthy adults [mean age (SD) = 61.51 (11.90); mean education (SD) = 15.94 (2.18)] were collected. A comprehensive battery of cognitive tests was administered, and gait was assessed using a small, lightweight, three-axis accelerometer with a gyroscope. ANALYTICAL PLAN: Data were transformed, and only relatively strong relationships survived after strict statistical criteria adjusting for multiple comparisons were applied. Spearman rho correlation coefficients were used to examine the matrix of correlations between the cognitive-motor variables while adjusting for age and gender. RESULTS: Executive functions, processing speed, and language were associated with distinct facets of variability, pace, and asymmetry, especially under the dual-task walking condition. Both turns and transitions were also associated with cognition during the Timed Up and Go Task. CONCLUSION: Our results extend converging evidence of the involvement of executive functions and processing speed in specific aspects of mobility, along with the role of language. The study has important implications for aging in terms of both assessment and rehabilitation of cognition and gait as well as for the emerging dual-tasking theories and the role of the neural pathways involved in mobility.

Current and Potential Approaches for Defining Disease Signatures: a Systematic Review.

Identifying disease signatures in order to facilitate accurate diagnosis/treatment has been the focus of research efforts in the last decade. However, the term "disease signature" has not been properly defined, resulting in inconsistencies between studies, as well as limited ability to fully utilize the tools/information available in the evolving field of healthcare big data. Research was conducted according to the Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA) guidelines. The search (in PubMed, Cochrane, and Web of Science) was limited to English articles published up to 31/12/2016. The search string was "disease signature" OR "disease signatures" OR "disease fingerprint" OR "disease fingerprints" OR "subtype signature" OR "subtype signatures" OR "subgroup signature" OR "subgroup signatures." The full text of the articles was reviewed to determine the meaning of the phrase "disease signature" as well as the context of its use. Of 285 articles identified in the search, 129 were included in the final analysis. The term disease signature was first found in an article from 2001. In the last 10 years, the use of the term increased by approximately ninefold, which is double the general increase in the number of published articles. Only one article attempted to define the term. The two major medical fields where the term was used were oncology (31%) and neurology (20%); 71% of the identified articles used a single biomarker to define the term, 13% of the articles used a pair of biomarkers, and 16% used signatures with multiple biomarker; in 42% of the identified articles, genomic biomarkers were used for the signature, in 17% measurements of biochemical compounds in body fluids, and in 10%, changes in imaging studies were used for the signature. Our findings identified a lack of consistency in defining the term disease signature. We suggest a novel hierarchical multidimensional concept for this term that would combine both current approaches for identifying diseases (one focusing on undesired effects of the disease and the other on its causes). This model can improve disease signature definition consistency which will enable to generalize and classify diseases, resulting in more precise treatments and better outcomes. Ultimately, this model could lead to developing a statistical confidence in a disease signature that would allow physicians/patients to estimate the precision of the diagnosis, which, in turn, may have important implications on patients' prognosis and treatment.

Hierarchical Data-Driven Analysis of Clinical Symptoms Among Patients With Parkinson's Disease.

Mutations in the LRRK2 and GBA genes are the most common inherited causes of Parkinson's disease (PD). Studies exploring phenotypic differences based on genetic status used hypothesis-driven data-gathering and statistical-analyses focusing on specific symptoms, which may influence the validity of the results. We aimed to explore phenotypic expression in idiopathic PD (iPD) patients, G2019S-LRRK2-PD, and GBA-PD using a data-driven approach, allowing screening of large numbers of features while controlling selection bias. Data was collected from 1525 Ashkenazi Jews diagnosed with PD from the Tel-Aviv Medical center; 161 G2019S-LRRK2-PD, 222 GBA-PD, and 1142 iPD (no G2019S-LRRK2 or any of the 7 AJ GBA mutations tested). Data included 771 measures: demographics, cognitive, physical and neurological functions, performance-based measures, and non-motor symptoms. The association of the genotypes with each of the measures was tested while accounting for age at motor symptoms onset, gender, and disease duration; p-values were reported and corrected in a hierarchical approach for an average over the selected measures false discovery rate control, resulting in 32 measures. GBA-PD presented with more severe symptoms expression while LRRK2-PD had more benign symptoms compared to iPD. GBA-PD presented greater cognitive and autonomic involvement, more frequent hyposmia and REM sleep behavior symptoms while these were less frequent among LRRK2-PD compared to iPD. Using a data-driven analytical approach strengthens earlier studies and extends them to portray a possible unique disease phenotype based on genotype among AJ PD. Such findings could help direct a more personalized therapeutic approach.

The Importance of Nonlinear Transformations Use in Medical Data Analysis.

BACKGROUND: The accumulation of data and its accessibility through easier-to-use platforms will allow data scientists and practitioners who are less sophisticated data analysts to get answers by using big data for many purposes in multiple ways. Data scientists working with medical data are aware of the importance of preprocessing, yet in many cases, the potential benefits of using nonlinear transformations is overlooked. OBJECTIVE: Our aim is to present a semi-automated approach of symmetry-aiming transformations tailored for medical data analysis and its advantages. METHODS: We describe 10 commonly encountered data types used in the medical field and the relevant transformations for each data type. Data from the Alzheimer's Disease Neuroimaging Initiative study, Parkinson's disease hospital cohort, and disease-simulating data were used to demonstrate the approach and its benefits. RESULTS: Symmetry-targeted monotone transformations were applied, and the advantages gained in variance, stability, linearity, and clustering are demonstrated. An open source application implementing the described methods was developed. Both linearity of relationships and increase of stability of variability improved after applying proper nonlinear transformation. Clustering simulated nonsymmetric data gave low agreement to the generating clusters (Rand value=0.681), while capturing the original structure after applying nonlinear transformation to symmetry (Rand value=0.986). CONCLUSIONS: This work presents the use of nonlinear transformations for medical data and the importance of their semi-automated choice. Using the described approach, the data analyst increases the ability to create simpler, more robust and translational models, thereby facilitating the interpretation and implementation of the analysis by medical practitioners. Applying nonlinear transformations as part of the preprocessing is essential to the quality and interpretability of results.