Estimands in clinical trials of complex disease processes.

Clinical trials with random assignment of treatment provide evidence about causal effects of an experimental treatment compared to standard care. However, when disease processes involve multiple types of possibly semi-competing events, specification of target estimands and causal inferences can be challenging. Intercurrent events such as study withdrawal, the introduction of rescue medication, and death further complicate matters. There has been much discussion about these issues in recent years, but guidance remains ambiguous. Some recommended approaches are formulated in terms of hypothetical settings that have little bearing in the real world. We discuss issues in formulating estimands, beginning with intercurrent events in the context of a linear model and then move on to more complex disease history processes amenable to multistate modeling. We elucidate the meaning of estimands implicit in some recommended approaches for dealing with intercurrent events and highlight the disconnect between estimands formulated in terms of potential outcomes and the real world.

Incidence and Clinical Characteristics of COVID-19 Ophthalmopathy Following the Termination of Dynamic Zero COVID-19 Strategy in China.

Ocular involvement is not uncommon in patients with COVID-19. However, the incidence of COVID-19 ophthalmopathy in COVID-19 patients is still not clear. In this prospective case series study, we recruited 2445 consecutive cases presenting at Neuro-ophthalmology clinic of our Eye Center during the last resurgence of SARS-CoV-2 infection from 8 December 2022 to 15 March 2023 in China, 149 cases were diagnosed as COVID-19 ophthalmopathy, 87 cases were female, with a mean age of 43.2 years, and the mean follow-up time was 15.4 weeks. One hundred and twenty of 149 cases suffered from systemic symptoms mostly manifesting as fever, cough and muscle pain prior to or soon after ocular involvement. The most common COVID-19 ophthalmopathy was optic neuritis (51/149), followed by acute zonal occult outer retinopathy complex disease (31/149), uveitis (17/149), ocular mobility disorder-related (third, fourth, or sixth) cranial nerve neuritis (15/149), anterior ischaemic optic neuropathy (9/149), retinal artery occlusion (8/149), retinal microangiopathy including retinal haemorrhage and cotton wool spot (8/149), viral conjunctivitis (7/149), retinal vein occlusion (3/149), viral keratitis (2/149), ptosis (2/149), and other rare ocular diseases. Except 5 cases with central retinal artery occlusion, other 144 COVID-19 ophthalmopathy cases showed good response to steroid therapy. Our study revealed an incidence of 6.09% for COVID-19 ophthalmopathy in outpatients at our Neuro-ophthalmology clinic during last resurgence of COVID-19 in China, and demonstrated that SARS-CoV-2 infection could induce an initial onset or a relapse of ophthalmic diseases, and that ocular involvement might manifest as the initial or even the only presentation of COVID-19.

Constructing the biomolecular networks associated with diabetic nephropathy and dissecting the effects of biomolecule variation underlying pathogenesis.

Diabetic nephropathy (DN) is a common and serious complication of diabetes, contributing significantly to patient mortality. Complication of DN (CDN) ranks as the second leading cause of end-stage renal disease globally. To address this, understanding the genetic regulation underlying DN is crucial for personalized treatment strategies. In this study, we identified genes and lncRNAs associated with diabetes and diabetic nephropathy constructing a DN-related lncRNA-mRNA network (DNLMN). This network, characterized by scale-free biomolecular properties, generated through the study of topological properties, elucidates key regulatory interactions. Enrichment analysis of important network modules revealed critical biological processes and pathways involved in DN pathogenesis. In the second step, we investigated the differential expression and co-expression of hub nodes in diseased and normal individuals, identifying lncRNA-mRNA relationships implicated in disease regulation. Finally, we gathered DN-related single nucleotide polymorphisms (SNPs) and lncRNAs from the LincSNP 3.0 database. The DNLMN encompasses SNP-associated lncRNAs, and transcription factors (TFs) linked to differentially expressed lncRNAs between diseased and normal samples. These results underscore the significance of biomolecular networks in disease progression and highlighting the role of biomolecular variability contributes to personalized disease phenotyping and treatment.

Exploring the role of digital tools in rare disease management: An interview-based study.

While digital tools, such as the Internet, smartphones, and social media, are an important part of modern society, little is known about the specific role they play in the healthcare management of individuals and caregivers affected by rare disease. Collectively, rare diseases directly affect up to 10% of the global population, suggesting that a significant number of individuals might benefit from the use of digital tools. The purpose of this qualitative interview-based study was to explore: (a) the ways in which digital tools help the rare disease community; (b) the healthcare gaps not addressed by current digital tools; and (c) recommended digital tool features. Individuals and caregivers affected by rare disease who were comfortable using a smartphone and at least 18 years old were eligible to participate. We recruited from rare disease organizations using purposive sampling in order to achieve a diverse and information rich sample. Interviews took place over Zoom and reflexive thematic analysis was utilized to conceptualize themes. Eight semistructured interviews took place with four individuals and four caregivers. Three themes were conceptualized which elucidated key aspects of how digital tools were utilized in disease management: (1) digital tools should lessen the burden of managing a rare disease condition; (2) digital tools should foster community building and promote trust; and (3) digital tools should provide trusted and personalized information to understand the condition and what the future may hold. These results suggest that digital tools play a central role in the lives of individuals with rare disease and their caregivers. Digital tools that centralize trustworthy information, and that bring the relevant community together to interact and promote trust are needed. Genetic counselors can consider these ideal attributes of digital tools when providing resources to individuals and caretakers of rare disease.

Edge-based relative entropy as a sensitive indicator of critical transitions in biological systems.

BACKGROUND: Disease progression in biosystems is not always a steady process but is occasionally abrupt. It is important but challenging to signal critical transitions in complex biosystems. METHODS: In this study, based on the theoretical framework of dynamic network biomarkers (DNBs), we propose a model-free method, edge-based relative entropy (ERE), to identify temporal key biomolecular associations/networks that may serve as DNBs and detect early-warning signals of the drastic state transition during disease progression in complex biological systems. Specifically, by combining gene‒gene interaction (edge) information with the relative entropy, the ERE method converts gene expression values into network entropy values, quantifying the dynamic change in a biomolecular network and indicating the qualitative shift in the system state. RESULTS: The proposed method was validated using simulated data and real biological datasets of complex diseases. The applications show that for certain diseases, the ERE method helps to reveal so-called "dark genes" that are non-differentially expressed but with high ERE values and of essential importance in both gene regulation and prognosis. CONCLUSIONS: The proposed method effectively identified the critical transition states of complex diseases at the network level. Our study not only identified the critical transition states of various cancers but also provided two types of new prognostic biomarkers, positive and negative edge biomarkers, for further practical application. The method in this study therefore has great potential in personalized disease diagnosis.

Integrating Genome Sequencing and Untargeted Metabolomics in Monozygotic Twins with a Rare Complex Neurological Disorder.

Multi-omics approaches, which integrate genomics, transcriptomics, proteomics, and metabolomics, have emerged as powerful tools in the diagnosis of rare diseases. We used untargeted metabolomics and whole-genome sequencing (WGS) to gain a more comprehensive understanding of a rare disease with a complex presentation affecting female twins from a consanguineous family. The sisters presented with polymicrogyria, a Dandy-Walker malformation, respiratory distress, and multiorgan dysfunctions. Through WGS, we identified two rare homozygous variants in both subjects, a pathogenic variant in ADGRG1(p.Arg565Trp) and a novel variant in CNTNAP1(p.Glu910Val). These genes have been previously associated with autosomal recessive polymicrogyria and hypomyelinating neuropathy with/without contractures, respectively. The twins exhibited symptoms that overlapped with both of these conditions. The results of the untargeted metabolomics analysis revealed significant metabolic perturbations relating to neurodevelopmental abnormalities, kidney dysfunction, and microbiome. The significant metabolites belong to essential pathways such as lipids and amino acid metabolism. The identification of variants in two genes, combined with the support of metabolic perturbation, demonstrates the rarity and complexity of this phenotype and provides valuable insights into its underlying mechanisms.

Modified peptides and organic metabolites of cyanobacterial origin with antiplasmodial properties.

As etiological agents of malaria disease, Plasmodium spp. parasites are responsible for one of the most severe global health problems occurring in tropical regions of the world. This work involved compiling marine cyanobacteria metabolites reported in the scientific literature that exhibit antiplasmodial activity. Out of the 111 compounds mined and 106 tested, two showed antiplasmodial activity at very low concentrations, with IC50 at 0.1 and 1.5 nM (peptides: dolastatin 10 and lyngbyabellin A, 1.9% of total tested). Examples of chemical derivatives generated from natural cyanobacterial compounds to enhance antiplasmodial activity and Plasmodium selectivity can be found in successful findings from nostocarboline, eudistomin, and carmaphycin derivatives, while bastimolide derivatives have not yet been found. Overall, 57% of the reviewed compounds are peptides with modified residues producing interesting active moieties, such as α- and ß-epoxyketone in camaphycins. The remaining compounds belong to diverse chemical groups such as alkaloids, macrolides, polycyclic compounds, and halogenated compounds. The Dolastatin 10 and lyngbyabellin A, compounds with antiplasmodial high activity, are cytoskeletal disruptors with different protein targets.

The Novel Tetra-Specific Drug C-192, Conjugated Using UniStac, Alleviates Non-Alcoholic Steatohepatitis in an MCD Diet-Induced Mouse Model.

Non-alcoholic steatohepatitis (NASH) is a complex disease resulting from chronic liver injury associated with obesity, type 2 diabetes, and inflammation. Recently, the importance of developing multi-target drugs as a strategy to address complex diseases such as NASH has been growing; however, their manufacturing processes remain time- and cost-intensive and inefficient. To overcome these limitations, we developed UniStac, a novel enzyme-mediated conjugation platform for multi-specific drug development. UniStac demonstrated high conjugation yields, optimal thermal stabilities, and robust biological activities. We designed a tetra-specific compound, C-192, targeting glucagon-like peptide 1 (GLP-1), glucagon (GCG), fibroblast growth factor 21 (FGF21), and interleukin-1 receptor antagonist (IL-1RA) simultaneously for the treatment of NASH using UniStac. The biological activity and treatment efficacy of C-192 were confirmed both in vitro and in vivo using a methionine-choline-deficient (MCD) diet-induced mouse model. C-192 exhibited profound therapeutic efficacies compared to conventional drugs, including liraglutide and dulaglutide. C-192 significantly improved alanine transaminase levels, triglyceride accumulation, and the non-alcoholic fatty liver disease activity score. In this study, we demonstrated the feasibility of UniStac in creating multi-specific drugs and confirmed the therapeutic potential of C-192, a drug that integrates multiple mechanisms into a single molecule for the treatment of NASH.

Effects of hypermobile Ehlers-Danlos syndrome patients on the workflow and professional satisfaction of genetic counselors.

The Ehlers-Danlos syndromes (EDS), a group of uncommon connective tissue disorders, are, paradoxically, an increasingly common referral to genetics specialists. Of the 13 types of EDS, the most common is hypermobile EDS (hEDS), which lacks a known genetic etiology and for which diagnosis is achieved via a robust set of clinical criteria. While previous investigations have characterized many clinical aspects of EDS as a syndrome and patients' lived experiences, a gap in the literature exists regarding clinicians' experience caring for these individuals. This study sought to understand the effects of hEDS patient referrals from genetic counselors' perspectives. To capture these novel views and values, we conducted semi-structured interviews with 15 participants who were members of the National Society of Genetic Counselors (NSGC) and had experience working with the hEDS patient population. Interview questions explored the frequency of hEDS referrals in their clinic, investigated their roles and responsibilities as genetic counselors when working with this population, analyzed their workflow for this indication, assessed the impacts on their professional satisfaction, and explored potential options for improving workflow and care for the hEDS patient population. Reflexive thematic analysis yielded four themes: (1) Referrals for hEDS have generally increased over time and many institutions have implemented new policies to control this influx, (2) genetic counselors' primary roles include education and addressing psychosocial matters for this population, (3) genetic counselors feel both rewarded and challenged by these referrals, and (4) genetic counselors call for more education and training on hEDS for all healthcare specialties. Our findings provide a better understanding of the goals of the hEDS patient referrals to genetics specialists and the opportunities and challenges those referrals present. Genetic counselors have specific training and skills in psychosocial counseling and communication, in some ways making them ideal care providers for this population. However, they are simultaneously a scarce resource and the complex medical issues presented by many patients with hEDS make multidisciplinary management essential. We conclude with potential avenues for improving interactions with this population.

Corrigendum: Antigen-specific cytokine profiles for pulmonary Mycobacterium avium complex disease stage diagnosis.

[This corrects the article DOI: 10.3389/fimmu.2023.1222428.].

Australian healthcare professionals' perspectives on genetic counseling and genetic diagnosis in vascular anomalies.

Genomic technologies are now utilized for the genetic diagnosis of vascular anomalies. This provides the opportunity for genetic counselors to make a significant contribution to patient care for this complex disease. The aim of this study was to explore Australian healthcare professionals' perspectives on the relatively recent integration of molecular diagnostic testing for vascular anomalies, with or without genetic counseling support. Nine semi-structured interviews were conducted with Australian healthcare professionals involved in the provision of care for individuals with vascular anomalies. Thematic analysis identified six themes: (1) Molecular diagnosis is beneficial; (2) psychosocial needs can motivate families to pursue a molecular diagnosis; (3) molecular genetic testing for vascular anomalies is complex; (4) genetic service provision is not a one size fits all; (5) a client-centered approach for genetic service provision can go a long way; and (6) the value of genetic counselors. Based on our findings, implementation of a vascular anomalies genetic diagnostic program inclusive of genetic counseling may be challenging, yet such programs are likely to benefit both patients and their families, as well as healthcare professionals. As this paradigm shift unfolds, genetic counselors have an opportunity to contribute to the vascular anomaly field by educating healthcare professionals and patients, by participating in multidisciplinary clinics to support complex cases and by raising awareness regarding their practice and potential contributions.

Genotyping and population characteristics of the China Kadoorie Biobank.

The China Kadoorie Biobank (CKB) is a population-based prospective cohort of >512,000 adults recruited from 2004 to 2008 from 10 geographically diverse regions across China. Detailed data from questionnaires and physical measurements were collected at baseline, with additional measurements at three resurveys involving ∼5% of surviving participants. Analyses of genome-wide genotyping, for >100,000 participants using custom-designed Axiom arrays, reveal extensive relatedness, recent consanguinity, and signatures reflecting large-scale population movements from recent Chinese history. Systematic genome-wide association studies of incident disease, captured through electronic linkage to death and disease registries and to the national health insurance system, replicate established disease loci and identify 14 novel disease associations. Together with studies of candidate drug targets and disease risk factors and contributions to international genetics consortia, these demonstrate the breadth, depth, and quality of the CKB data. Ongoing high-throughput omics assays of collected biosamples and planned whole-genome sequencing will further enhance the scientific value of this biobank.

A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment.

Accurate diagnosis is the key to providing prompt and explicit treatment and disease management. The recognized biological method for the molecular diagnosis of infectious pathogens is polymerase chain reaction (PCR). Recently, deep learning approaches are playing a vital role in accurately identifying disease-related genes for diagnosis, prognosis, and treatment. The models reduce the time and cost used by wet-lab experimental procedures. Consequently, sophisticated computational approaches have been developed to facilitate the detection of cancer, a leading cause of death globally, and other complex diseases. In this review, we systematically evaluate the recent trends in multi-omics data analysis based on deep learning techniques and their application in disease prediction. We highlight the current challenges in the field and discuss how advances in deep learning methods and their optimization for application is vital in overcoming them. Ultimately, this review promotes the development of novel deep-learning methodologies for data integration, which is essential for disease detection and treatment.

Genetic insights into the age-specific biological mechanisms governing human ovarian aging.

There is currently little evidence that the genetic basis of human phenotype varies significantly across the lifespan. However, time-to-event phenotypes are understudied and can be thought of as reflecting an underlying hazard, which is unlikely to be constant through life when values take a broad range. Here, we find that 74% of 245 genome-wide significant genetic associations with age at natural menopause (ANM) in the UK Biobank show a form of age-specific effect. Nineteen of these replicated discoveries are identified only by our modeling framework, which determines the time dependency of DNA-variant age-at-onset associations without a significant multiple-testing burden. Across the range of early to late menopause, we find evidence for significantly different underlying biological pathways, changes in the signs of genetic correlations of ANM to health indicators and outcomes, and differences in inferred causal relationships. We find that DNA damage response processes only act to shape ovarian reserve and depletion for women of early ANM. Genetically mediated delays in ANM were associated with increased relative risk of breast cancer and leiomyoma at all ages and with high cholesterol and heart failure for late-ANM women. These findings suggest that a better understanding of the age dependency of genetic risk factor relationships among health indicators and outcomes is achievable through appropriate statistical modeling of large-scale biobank data.

Genetic counseling in diabetes mellitus: A practice resource of the National Society of Genetic Counselors.

Diabetes mellitus is a group of diseases characterized by hyperglycemia and its consequences, affecting over 34 million individuals in the United States and 422 million worldwide. While most diabetes is polygenic and is classified as type 1 (T1D), type 2 (T2D), or gestational diabetes (GDM), at least 0.4% of all diabetes is monogenic in nature. Correct diagnosis of monogenic diabetes has important implications for glycemic management and genetic counseling. We provide this Practice Resource to familiarize the genetic counseling community with (1) the existence of monogenic diabetes, (2) how it differs from more common polygenic/complex diabetes types, (3) the advantage of a correct diagnosis, and (4) guidance for identifying, counseling, and testing patients and families with suspected monogenic diabetes. This document is intended for genetic counselors and other healthcare professionals providing clinical services in any setting, with the goal of maximizing the likelihood of a correct diagnosis of monogenic diabetes and access to related care.

Antigen-specific cytokine profiles for pulmonary Mycobacterium avium complex disease stage diagnosis.

Introduction: Controlling pulmonary Mycobacterium avium complex (MAC) disease is difficult because there is no way to know the clinical stage accurately. There have been few attempts to use cell-mediated immunity for diagnosing the stage. The objective of this study was to characterize cytokine profiles of CD4+T and CD19+B cells that recognize various Mycobacterium avium-associated antigens in different clinical stages of MAC. Methods: A total of 47 MAC patients at different stages based on clinical information (14 before-treatment, 16 on-treatment, and 17 after-treatment) and 17 healthy controls were recruited. Peripheral blood mononuclear cells were cultured with specific antigens (MAV0968, 1160, 1276, and 4925), and the cytokine profiles (IFN-γ, TNF-α, IL-2, IL-10, IL-13, and IL-17) of CD4+/CD3+ and CD19+ cells were analyzed by flow cytometry. Results: The response of Th1 cytokines such as IFN-γ and TNF-α against various antigens was significantly higher in both the on-treatment and after-treatment groups than in the before-treatment group and control (P < 0.01-0.0001 and P < 0.05-0.0001). An analysis of polyfunctional T cells suggested that the presence of IL-2 is closely related to the stage after the start of treatment (P = 0.0309-P < 0.0001) and is involved in memory function. Non-Th1 cytokines, such as IL-10 and IL-17, showed significantly higher responses in the before-treatment group (P < 0.0001 and P < 0.01-0.0001). These responses were not observed with purified protein derivative (PPD). CD19+B cells showed a response similar to that of CD4+T cells. Conclusion: There is a characteristic cytokine profile at each clinical stage of MAC.

MODILM: towards better complex diseases classification using a novel multi-omics data integration learning model.

BACKGROUND: Accurately classifying complex diseases is crucial for diagnosis and personalized treatment. Integrating multi-omics data has been demonstrated to enhance the accuracy of analyzing and classifying complex diseases. This can be attributed to the highly correlated nature of the data with various diseases, as well as the comprehensive and complementary information it provides. However, integrating multi-omics data for complex diseases is challenged by data characteristics such as high imbalance, scale variation, heterogeneity, and noise interference. These challenges further emphasize the importance of developing effective methods for multi-omics data integration. RESULTS: We proposed a novel multi-omics data learning model called MODILM, which integrates multiple omics data to improve the classification accuracy of complex diseases by obtaining more significant and complementary information from different single-omics data. Our approach includes four key steps: 1) constructing a similarity network for each omics data using the cosine similarity measure, 2) leveraging Graph Attention Networks to learn sample-specific and intra-association features from similarity networks for single-omics data, 3) using Multilayer Perceptron networks to map learned features to a new feature space, thereby strengthening and extracting high-level omics-specific features, and 4) fusing these high-level features using a View Correlation Discovery Network to learn cross-omics features in the label space, which results in unique class-level distinctiveness for complex diseases. To demonstrate the effectiveness of MODILM, we conducted experiments on six benchmark datasets consisting of miRNA expression, mRNA, and DNA methylation data. Our results show that MODILM outperforms state-of-the-art methods, effectively improving the accuracy of complex disease classification. CONCLUSIONS: Our MODILM provides a more competitive way to extract and integrate important and complementary information from multiple omics data, providing a very promising tool for supporting decision-making for clinical diagnosis.