3,168 research outputs found

    Anwendungen maschinellen Lernens für datengetriebene Prävention auf Populationsebene

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    Healthcare costs are systematically rising, and current therapy-focused healthcare systems are not sustainable in the long run. While disease prevention is a viable instrument for reducing costs and suffering, it requires risk modeling to stratify populations, identify high- risk individuals and enable personalized interventions. In current clinical practice, however, systematic risk stratification is limited: on the one hand, for the vast majority of endpoints, no risk models exist. On the other hand, available models focus on predicting a single disease at a time, rendering predictor collection burdensome. At the same time, the den- sity of individual patient data is constantly increasing. Especially complex data modalities, such as -omics measurements or images, may contain systemic information on future health trajectories relevant for multiple endpoints simultaneously. However, to date, this data is inaccessible for risk modeling as no dedicated methods exist to extract clinically relevant information. This study built on recent advances in machine learning to investigate the ap- plicability of four distinct data modalities not yet leveraged for risk modeling in primary prevention. For each data modality, a neural network-based survival model was developed to extract predictive information, scrutinize performance gains over commonly collected covariates, and pinpoint potential clinical utility. Notably, the developed methodology was able to integrate polygenic risk scores for cardiovascular prevention, outperforming existing approaches and identifying benefiting subpopulations. Investigating NMR metabolomics, the developed methodology allowed the prediction of future disease onset for many common diseases at once, indicating potential applicability as a drop-in replacement for commonly collected covariates. Extending the methodology to phenome-wide risk modeling, elec- tronic health records were found to be a general source of predictive information with high systemic relevance for thousands of endpoints. Assessing retinal fundus photographs, the developed methodology identified diseases where retinal information most impacted health trajectories. In summary, the results demonstrate the capability of neural survival models to integrate complex data modalities for multi-disease risk modeling in primary prevention and illustrate the tremendous potential of machine learning models to disrupt medical practice toward data-driven prevention at population scale.Die Kosten im Gesundheitswesen steigen systematisch und derzeitige therapieorientierte Gesundheitssysteme sind nicht nachhaltig. Angesichts vieler verhinderbarer Krankheiten stellt die Prävention ein veritables Instrument zur Verringerung von Kosten und Leiden dar. Risikostratifizierung ist die grundlegende Voraussetzung für ein präventionszentri- ertes Gesundheitswesen um Personen mit hohem Risiko zu identifizieren und Maßnah- men einzuleiten. Heute ist eine systematische Risikostratifizierung jedoch nur begrenzt möglich, da für die meisten Krankheiten keine Risikomodelle existieren und sich verfüg- bare Modelle auf einzelne Krankheiten beschränken. Weil für deren Berechnung jeweils spezielle Sets an Prädiktoren zu erheben sind werden in Praxis oft nur wenige Modelle angewandt. Gleichzeitig versprechen komplexe Datenmodalitäten, wie Bilder oder -omics- Messungen, systemische Informationen über zukünftige Gesundheitsverläufe, mit poten- tieller Relevanz für viele Endpunkte gleichzeitig. Da es an dedizierten Methoden zur Ex- traktion klinisch relevanter Informationen fehlt, sind diese Daten jedoch für die Risikomod- ellierung unzugänglich, und ihr Potenzial blieb bislang unbewertet. Diese Studie nutzt ma- chinelles Lernen, um die Anwendbarkeit von vier Datenmodalitäten in der Primärpräven- tion zu untersuchen: polygene Risikoscores für die kardiovaskuläre Prävention, NMR Meta- bolomicsdaten, elektronische Gesundheitsakten und Netzhautfundusfotos. Pro Datenmodal- ität wurde ein neuronales Risikomodell entwickelt, um relevante Informationen zu extra- hieren, additive Information gegenüber üblicherweise erfassten Kovariaten zu quantifizieren und den potenziellen klinischen Nutzen der Datenmodalität zu ermitteln. Die entwickelte Me-thodik konnte polygene Risikoscores für die kardiovaskuläre Prävention integrieren. Im Falle der NMR-Metabolomik erschloss die entwickelte Methodik wertvolle Informa- tionen über den zukünftigen Ausbruch von Krankheiten. Unter Einsatz einer phänomen- weiten Risikomodellierung erwiesen sich elektronische Gesundheitsakten als Quelle prädik- tiver Information mit hoher systemischer Relevanz. Bei der Analyse von Fundusfotografien der Netzhaut wurden Krankheiten identifiziert für deren Vorhersage Netzhautinformationen genutzt werden könnten. Zusammengefasst zeigten die Ergebnisse das Potential neuronaler Risikomodelle die medizinische Praxis in Richtung einer datengesteuerten, präventionsori- entierten Medizin zu verändern

    BET inhibitor trotabresib in heavily pretreated patients with solid tumors and diffuse large B-cell lymphomas

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    B-cell lymphoma; Cancer therapy; CNS cancerLimfoma de cèl·lules B; Teràpia del càncer; Càncer del SNCLinfoma de células B; Terapia del cáncer; Cáncer del SNCBromodomain and extraterminal proteins (BET) play key roles in regulation of gene expression, and may play a role in cancer-cell proliferation, survival, and oncogenic progression. CC-90010-ST-001 (NCT03220347) is an open-label phase I study of trotabresib, an oral BET inhibitor, in heavily pretreated patients with advanced solid tumors and relapsed/refractory diffuse large B-cell lymphoma (DLBCL). Primary endpoints were the safety, tolerability, maximum tolerated dose, and RP2D of trotabresib. Secondary endpoints were clinical benefit rate (complete response [CR] + partial response [PR] + stable disease [SD] of ≥4 months’ duration), objective response rate (CR + PR), duration of response or SD, progression-free survival, overall survival, and the pharmacokinetics (PK) of trotabresib. In addition, part C assessed the effects of food on the PK of trotabresib as a secondary endpoint. The dose escalation (part A) showed that trotabresib was well tolerated, had single-agent activity, and determined the recommended phase 2 dose (RP2D) and schedule for the expansion study. Here, we report long-term follow-up results from part A (N = 69) and data from patients treated with the RP2D of 45 mg/day 4 days on/24 days off or an alternate RP2D of 30 mg/day 3 days on/11 days off in the dose-expansion cohorts (parts B [N = 25] and C [N = 41]). Treatment-related adverse events (TRAEs) are reported in almost all patients. The most common severe TRAEs are hematological. Toxicities are generally manageable, allowing some patients to remain on treatment for ≥2 years, with two patients receiving ≥3 years of treatment. Trotabresib monotherapy shows antitumor activity, with an ORR of 13.0% (95% CI, 2.8–33.6) in patients with R/R DLBCL (part B) and an ORR of 0.0% (95% CI, 0.0–8.6) and a CBR of 31.7% (95% CI, 18.1–48.1) in patients with advanced solid tumors (part C). These results support further investigation of trotabresib in combination with other anticancer agents.This study was sponsored by Celgene, a Bristol Myers Squibb Company. The study sponsor was involved in the study design, analysis of data, and writing the manuscript. Medical writing and editorial assistance were provided by Bernard Kerr, PGDipSci, and Agata Shodeke, of Spark, funded by Bristol Myers Squibb

    Using population biobanks to understand complex traits, rare diseases, and their shared genetic architecture

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    The study of the role of genetic variability in common traits has led to a growing number of studies aimed at representing whole populations. These studies gather multiple layers of information on healthy and non-healthy individuals at large scales, constituting what is known as population biobanks.In this thesis I took advantage of the potential of these population biobanks to measure the influence of genetic variation in common and rare traits. I explored the mechanisms behind these by exploring their interaction with conditions, physiological measurements, and habits in general and healthy population. First, I used the Lifelines cohort, with genetic information of Dutch population. Here, my colleagues and I explored traits with different levels of genetic influence we uncovered associations between both Blood type and dairy consumption with human gut microbiome function and composition, and we identified a protective factor for a rare type of cardiomyopathy with potential use for diagnosis.Additionally, within a global collaboration across world-wide biobanks totaling > 2 million individuals, we demonstrated the robustness of the connections between genetic variation and 14 different diseases across the populations. We also provided methodological guidance for the combination of the effects of genetic variation to calculate the risk of disease in studies including biobanks with populations of different ethnic backgrounds.Overall, my PhD research contributed on identifying and validating which factors are relevant for potential clinical applications, and provided guidelines to be used in future genetic studies on common traits and diseases at a global scale

    The pharmaco-epidemiology of loop diuretic dispensing and its relationship to the diagnosis of heart failure and to prognosis

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    Heart failure is a major and growing public health problem associated with poor patient outcomes, including reduced quality of life and high hospitalisation and mortality rates. It is a complex clinical syndrome rather than a single disease, which lacks a practical, universal, and standardised definition. Currently, the definition relies on the identification of symptoms and signs of cardiac dysfunction, such as ankle swelling and breathlessness, which are neither specific nor objective. Many patients are only diagnosed once their symptoms and signs are severe enough to require hospitalisation. Pathophysiologically, heart failure can be defined by the presence of salt and water retention, also known as congestion, associated with cardiac dysfunction. Within the United Kingdom, the pharmacological class of loop diuretics is used primarily for the treatment of congestion due to cardiac dysfunction. The aim of this thesis is to investigate the pharmacoepidemiology of loop diuretic dispensing and its relationship to the diagnosis of heart failure, with a particular focus on patient outcomes. The first analysis describes the prevalence of repeated loop diuretic dispensing and/or diagnosis of heart failure within the NHS Greater Glasgow & Clyde Health Board population on 1st January 2012, including patient outcomes over the following five years. This research is thought to be the first population-level investigation into the prevalence of repeated loop diuretic dispensing and its prognostic significance in patients with and without a diagnosis of heart failure. The analysis found that an estimated 3.2% of the population received repeated loop diuretic dispensing, while only 1.3% of the population had a diagnosis of heart failure. Hospitalisation rates were higher in those with a loop diuretic (0.99 admissions per patient-year at risk for those with only repeated loop diuretic dispensing and 1.51 admissions per patient-year at risk for those with both) than those with only a diagnosis of heart failure (0.93 admissions patient-year at risk). All-cause mortality followed a similar pattern; adjusting for age, sex, socioeconomic deprivation and comorbidity status, the 5-year hazard ratio and (95% confidence interval) were 1.8 (1.8 - 1.9) for those with those only repeated loop diuretic dispensing and 2.3 (2.2 - 2.4) for those with both, while only 1.2 (2.2 - 2.4) for those with only a diagnosis of heart failure, implying that the presence of repeated loop diuretic dispensing is a marker of serious disease. The second analysis stepped backwards in ‘patient-time’ to describe the pattern of hospitalisations in the year leading up to the initiation of loop diuretic dispensing or an incident diagnosis of heart failure using network graphs. While the precursors to heart failure are known, this research is thought to be the first to report the common patterns in events leading up to the initiation of loop diuretics. While there was little difference in comorbidity and medication levels 24 months prior, in the year leading up to the initiation, those who received a diagnosis of heart failure were more likely to be admitted for well-recognised contributors to the condition, including ischaemic heart disease in particular, but also atrial fibrillation/flutter and valve disease. In contrast, these patterns were not often seen in those who were only initiated on a loop diuretic, instead with a focus on admissions for non-specific symptoms and signs, most commonly unspecified chest pain. The third analysis starts where the second leaves off. It assesses the prognostic relationship between the initiation of loop diuretic and diagnosis of heart failure on mortality and whether the sequence of these events matters using semi-Markov multi-state modes, a flexible model for use on longitudinal time data where there is an event-related dependence on outcomes. Those on repeated loop diuretic dispensing without a diagnosis of heart failure were majority women (62%). Many with evidence of left atrial dilation (53%), while those with a diagnosis of heart failure without a repeat loop diuretic were majority men (63%). Many had a history of myocardial infarction (51%). Hospitalisations and mortality were higher in those with a repeat loop diuretic (within the first year per patient-year at risk: hospitalisation, 1.44; mortality, 0.20) compared to those with a diagnosis of heart failure without a repeat loop diuretic (within the first year per patient-year at risk: hospitalisation, 1.47; mortality, 0.14). Rates were higher still in those with both loop diuretic and heart failure (where both events occurred together within the first year per patient-year at risk: hospitalisation, 1.74; mortality, 0.16; or where the diagnosis of HF preceded the initiation of loop diuretic, within the first year per patient-year at risk: hospitalisation, 1.68; mortality, 0.20), with the highest being in those who initiated the loop diuretic in advance of receiving a diagnosis of heart failure (within the first year per patient-year at risk: hospitalisation, 2.26; mortality, 0.28). The fourth and final analysis subsets the population to investigate the mortality of the 24,921 patients with ischaemic heart disease according to whether or not they have had a repeat loop diuretic and/or diagnosis of heart failure; of whom, 3,806 had only repeat loop diuretic, 2,384 had only a diagnosis of heart failure, and 3,531 had both. This analysis found that after adjusting for age, sex, and other prognostic markers, mortality was associated with the repeat loop diuretic regardless of the patient’s heart failure status. Those with a repeat loop diuretic without a diagnosis of heart failure experienced substantially higher rates of cardiovascular (an estimated 15%) and all-cause mortality (47%) than those with a diagnosis of heart failure without a repeat loop diuretic (an estimated 8% cardiovascular and 19% all-cause mortality), while rates were highest for those with both (an estimated 25% cardiovascular and 57% all-cause mortality). In conclusion, these analyses found that many more patients are repeatedly treated with loop diuretic than ever receive a diagnosis of heart failure. These patients are at a high risk of hospitalisation and death, and based on their characteristics, many probably have undiagnosed heart failure. From a public health and epidemiological perspective, the current definition of heart failure likely underestimates the true burden on the healthcare system. From the patient’s perspective, with the efficacy of angiotensin receptor-neprilysin inhibitor, sodium-glucose co-transporter-2 inhibitors, and mineralocorticoid receptor antagonistss, a missed diagnosis means a missed opportunity to improve the patient’s outcome and quality of life, regardless of their heart failure phenotype. Even more alarming, if these patients are receiving the loop diuretic inappropriately, the loop diuretic is likely causing these increased hospitalisation and mortality rates. If the loop diuretic can be safely withdrawn, other medications with diuretic properties exist which have good safety profiles. Ultimately, further research is required to determine the optimal strategy for managing these patients

    Ovarian hormones shape brain structure, function, and chemistry: A neuropsychiatric framework for female brain health

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    There are robust sex differences in brain anatomy, function, as well as neuropsychiatric and neurodegenerative disease risk (1-6), with women approximately twice as likely to suffer from a depressive illness as well as Alzheimer’s Disease. Disruptions in ovarian hormones likely play a role in such disproportionate disease prevalence, given that ovarian hormones serve as key regulators of brain functional and structural plasticity and undergo major fluctuations across the female lifespan (7-9). From a clinical perspective, there is a wellreported increase in depression susceptibility and initial evidence for cognitive impairment or decline during hormonal transition states, such as the postpartum period and perimenopause (9-14). What remains unknown, however, is the underlying mechanism of how fluctuations in ovarian hormones interact with other biological factors to influence brain structure, function, and chemistry. While this line of research has translational relevance for over half the population, neuroscience is notably guilty of female participant exclusion in research studies, with the male brain implicitly treated as the default model and only a minority of basic and clinical neuroscience studies including a female sample (15-18). Female underrepresentation in neuroscience directly limits opportunities for basic scientific discovery; and without basic knowledge of the biological underpinnings of sex differences, we cannot address critical sexdriven differences in pathology. Thus, my doctoral thesis aims to deliberately investigate the influence of sex and ovarian hormones on brain states in health as well as in vulnerability to depression and cognitive impairment:Table of Contents List of Abbreviations ..................................................................................................................... i List of Figures .............................................................................................................................. ii Acknowledgements .....................................................................................................................iii 1 INTRODUCTION .....................................................................................................................1 1.1 Lifespan approach: Sex, hormones, and metabolic risk factors for cognitive health .......3 1.2 Reproductive years: Healthy models of ovarian hormones, serotonin, and the brain ......4 1.2.1 Ovarian hormones and brain structure across the menstrual cycle ........................4 1.2.2 Serotonergic modulation and brain function in oral contraceptive users .................6 1.3 Neuropsychiatric risk models: Reproductive subtypes of depression ...............................8 1.3.1 Hormonal transition states and brain chemistry measured by PET imaging ...........8 1.3.2 Serotonin transporter binding across the menstrual cycle in PMDD patients .......10 2 PUBLICATIONS ....................................................................................................................12 2.1 Publication 1: Association of estradiol and visceral fat with structural brain networks and memory performance in adults .................................................................................13 2.2 Publication 2: Longitudinal 7T MRI reveals volumetric changes in subregions of human medial temporal lobe to sex hormone fluctuations ..............................................28 2.3 Publication 3: One-week escitalopram intake alters the excitation-inhibition balance in the healthy female brain ...............................................................................................51 2.4 Publication 4: Using positron emission tomography to investigate hormone-mediated neurochemical changes across the female lifespan: implications for depression ..........65 2.5 Publication 5: Increase in serotonin transporter binding across the menstrual cycle in patients with premenstrual dysphoric disorder: a case-control longitudinal neuro- receptor ligand PET imaging study ..................................................................................82 3 SUMMARY ...........................................................................................................................100 References ..............................................................................................................................107 Supplementary Publications ...................................................................................................114 Author Contributions to Publication 1 .....................................................................................184 Author Contributions to Publication 2 .....................................................................................186 Author Contributions to Publication 3 .....................................................................................188 Author Contributions to Publication 4 .....................................................................................190 Author Contributions to Publication 5 .....................................................................................191 Declaration of Authenticity ......................................................................................................193 Curriculum Vitae ......................................................................................................................194 List of Publications ................................................................................................................195 List of Talks and Posters ......................................................................................................19

    Prognosis of symptomatic patients with Brugada Syndrome through electrocardiogram biomarkers and machine learning

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    La Síndrome de Brugada (BrS) és un trastorn cardiovascular poc comú però greu que pot causar batecs perillosament ràpids i es caracteritza per presentar un conjunt particular de patrons d'electrocardiograma (ECG) als seus pacients. És una condició molt impredictible. Moltes persones no presenten cap símptoma, mentre que per altres, lamentablement, el primer símptoma és la mort. Per a pacients d'alt risc es recomana col•locar un desfibril•lador cardioversor implantable. Desafortunadament, això té greus riscos associats, com infeccions i descàrregues inadequades, per la qual cosa és clau identificar aquests pacients d'alt risc correctament. L'objectiu d'aquest projecte ha estat desenvolupar eines basades en aprenentatge automàtic que poguessin diferenciar els pacients amb Síndrome de Brugada simptomàtics dels quals no ho són. Es van considerar pacients simptomàtics aquells que s'havien recuperat de mort cardíaca, van patir un síncope arritmogènic o taquicàrdia sostinguda. Per fer-ho, després d'una investigació de l'estat de l'art dels temes pertinents, es van extreure diversos biomarcadors relacionats amb els patrons d'ECG de Brugada a partir de registres d'ECG de 24 hores de 45 pacients diferents, després d'haver estat processats per mitjà de promediat de senyal per reduir el soroll. Aquests biomarcadors, juntament amb algunes dades clíniques, es van separar de diferents maneres per entrenar i provar diferents models de classificació automatitzats basats en aprenentatge automàtic. Els resultats obtinguts dels models han estat molt pobres. Cap d'ells no ha pogut classificar de manera fiable els pacients amb BrS com es desitjava. Això no obstant, d'aquesta primera aproximació es poden extreure conclusions valuoses per assolir l'objectiu del projecte, i s’han desenvolupat eines útils que poden permetre un processament més ràpid de la base de dades utilitzada.El Síndrome de Brugada (BrS) es un trastorno cardiovascular poco común pero grave que puede causar latidos peligrosamente rápidos y se caracteriza por presentar un conjunto particular de patrones de electrocardiograma (ECG) en sus pacientes. Es una condición muy impredecible. Muchas personas no presentan ningún síntoma, mientras que para otras, lamentablemente, el primer síntoma es la muerte. Para pacientes de alto riesgo se recomienda la colocación de un desfibrilador cardioversor implantable. Desafortunadamente, eso tiene graves riesgos asociados, como infecciones y descargas inapropiadas, por lo que es clave identificar a esos pacientes de alto riesgo correctamente. El objetivo de este proyecto era desarrollar herramientas basadas en aprendizaje automático que puedan diferenciar a los pacientes con Síndrome de Brugada sintomáticos de aquellos que no lo son. Se consideraron pacientes sintomáticos aquellos que se habían recuperado de muerte cardiaca, sufrieron un síncope arritmogénico o una taquicardia sostenida. Para ello, tras una investigación del estado del arte de los temas relevantes, se extrajeron varios biomarcadores relacionados con los patrones de ECG de Brugada a partir de registros de ECG de 24h de 45 pacientes diferentes, después de haber sido procesados mediante promedio de señal para reducir su ruido. Estos biomarcadores, junto con algunos datos clínicos, se separaron de diferentes maneras para entrenar y probar diferentes modelos de clasificación automatizados basados en aprendizaje automático. Los resultados de los modelos obtenidos han sido muy pobres. Ninguno de ellos pudo clasificar de manera confiable a los pacientes con BrS como se deseaba. No obstante, de esta primera aproximación se pueden extraer valiosas conclusiones para continuar avanzando hacia el objetivo perseguido, y se desarrollaron herramientas útiles que permitirán un procesamiento más rápido de la base de datos utilizada.The Brugada Syndrome (BrS) is a rare but serious cardiovascular disorder that can cause dangerously fast heartbeats and is characterized by a particular set of electrocardiogram (ECG) patterns. It’s a very unpredictable condition. Many people don’t present symptoms at all, while for others, unfortunately, the first symptom is death. For high risk patients, having an implantable cardioverter-defibrillator placed is recommended. Unfortunately, that has severe risks associated, like infections and inappropriate shocks, so it’s key to identify those high risk patients. The objective of this project was to develop machine learning based tools that are able to tell symptomatic Brugada Syndrome patients apart from those who are not. Symptomatic patients were considered those who had recovered from cardiac death, suffered an arrhythmogenic syncope or sustained tachycardia. In order to do so, after an investigation of the state of the art of the relevant subjects, several biomarkers related with Brugada ECG patterns were extracted from 24h ECG recordings of 45 different patients, after having been processed by signal averaging in order to reduce their noise. Those biomarkers, alongside some clinical data, were then separated in different ways in order to train and test different machine learning based automated classifier models. The performances of those models were very poor. None of them was able to reliably classify BrS patients as desired. Nevertheless, valuable conclusions can be extracted from this first approach to pursue the intended goal further, and useful tools were developed that would allow for a faster processing of the database used

    The molecular genetics of familial cardiomyopathy

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    Introduction The cardiomyopathies are responsible for approximately 5.9 of 100,000 deaths in the general global population and in sub-Saharan Africa (SSA), these myocardial diseases are observed in 21.4% of patients with heart failure. The precise etiology of the cardiomyopathies is currently not well known and through our research we aim to contribute to the genetic landscape and bridge the gaps in knowledge for the different cardiomyopathies as SSA could provide some very important insights into the cardiomyopathies and identify other possible disease mechanisms. Methods Through next generation sequencing techniques such as whole exome sequencing and targeted resequencing we studied three South African families with severe cardiomyopathy. Clinical diagnosis and recruitment of cardiomyopathy patients into the study was done at Groote Schuur Hospital, Cape Town by a panel of experts. Next generation sequencing data was analysed and filtered through various stringent criteria and the final list of variants were validated through Sanger sequencing. Results In the first multi-generational family with severe dilated cardiomyopathy (DCM) (DCM 334), we identified a pathogenic DMPK c.1067C>T(p.P356L) variant in the proband and her affected father. We also screened a cohort of 542 cardiomyopathy probands though Sanger sequencing of the DMPK gene and identified the DMPK c.1477C>T(p.R493C) variant as a variant of unknown significance. We then investigated a three-generation family with four affected family members who were also affected with severe DCM (DCM343). We used whole exome sequencing and identified the pathogenic BAG3 c.925C>T (p.R309Ter) variant as the cause of disease within this family. Viral infection, anti-hypertensive medication and genetic modifiers in RYR1 and NEB contributed to the variable phenotype among the individuals with the BAG3 variant. Through targeted resequencing we also identified the same pathogenic BAG3 variant in 2 of the 634 cardiomyopathy probands screened. In the third family, we investigated a South African family affected with severe arrhythmogenic cardiomyopathy (ACM). We used whole exome sequencing and targeted resequencing in combination and identified the pathogenic PKP2 c.2197_2202InsGdelCACACC (p.H733Afs*8) as the cause of disease in the proband and his father. We also present evidence of the ALPK3 c.2701C>T(p.Q901Ter) variant modifying the phenotypic manifestation which correlates with the variable penetrance that is seen among ACM families. Conclusion Through this project, we have identified many firsts. To the best of our knowledge, we are the first to show that DMPK is associated with primary DCM in severely affected young patients. As a first for South Africa, we not only identified the pathogenic BAG3 variant in a family with severe DCM, but we also identified the same variant in two additional probands, raising the possibility of a founder effect. In the third and final family with ACM, we identified the pathogenic PKP2 variant as the cause of disease within this family with the novel ALPK3 variant acting as a possible modifier. Our research has added to what is currently known about the cardiomyopathies in Africa but there is still much work to be done as we believe we have just scratched the tip of the iceberg

    International consensus statement on allergy and rhinology: Allergic rhinitis – 2023

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    Background In the 5 years that have passed since the publication of the 2018 International Consensus Statement on Allergy and Rhinology: Allergic Rhinitis (ICAR-Allergic Rhinitis 2018), the literature has expanded substantially. The ICAR-Allergic Rhinitis 2023 update presents 144 individual topics on allergic rhinitis (AR), expanded by over 40 topics from the 2018 document. Originally presented topics from 2018 have also been reviewed and updated. The executive summary highlights key evidence-based findings and recommendation from the full document. Methods ICAR-Allergic Rhinitis 2023 employed established evidence-based review with recommendation (EBRR) methodology to individually evaluate each topic. Stepwise iterative peer review and consensus was performed for each topic. The final document was then collated and includes the results of this work. Results ICAR-Allergic Rhinitis 2023 includes 10 major content areas and 144 individual topics related to AR. For a substantial proportion of topics included, an aggregate grade of evidence is presented, which is determined by collating the levels of evidence for each available study identified in the literature. For topics in which a diagnostic or therapeutic intervention is considered, a recommendation summary is presented, which considers the aggregate grade of evidence, benefit, harm, and cost. Conclusion The ICAR-Allergic Rhinitis 2023 update provides a comprehensive evaluation of AR and the currently available evidence. It is this evidence that contributes to our current knowledge base and recommendations for patient evaluation and treatment

    Arrhythmogenic cardiomyopathy - beyond monogenetic disease

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    Interpreting genetic variants, describing their associated clinical characteristics, and identifying new genetic loci involved in arrhythmogenic cardiomyopathy (ACM) is the focus of this thesis. By investigating various aspects of these genetic variants, we were able to correctly classify two variants occurring in the lamin A/C (LMNA) and titin (TTN) gene. We demonstrated that the reduced force generation seen in cardiomyocytes with the LMNA variant (LMNA c.992G>A) is due to remodelling within the cardiomyocytes and that patients with this specific variant have a milder phenotype compared to what is known from other pathogenic LMNA variants. By extensive phenotyping of carriers of a truncating TTN variant (TTN c.59926+1G>A) we were the first to show that (paroxysmal) atrial fibrillation is an important clinical feature in carriers of truncated TTN variants, even in the absence of dilated cardiomyopathy, atrial enlargement or generally accepted risk factors for atrial fibrillation. Thanks to extensive international collaboration it was possible to compile one of the largest cohorts of patients carrying truncating variants in desmoplakin (DSP). We showed that the location of such a genetic variant within the gene is associated with disease severity. Moreover, these studies show that enrichment of truncating genetic variants in specific regions of DSP variants in ACM patients, when compared to controls, facilitating interpretation of such variants. The multifactorial nature of ACM was underscored in a systematic analysis of the clinical outcome of patients from ACM cohorts carrying multiple variants in ACM related genes, showing that carrying multiple variants influences disease severity. Finally, by analysing genes encoding the sarcomere, the contractile unit of the heart muscle and the plectin (PLEC) gene for rare variants in ACM patients, we showed that these genes do not have a major role in the development of ACM
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